Metabolic Quiescence and Activation: A Comparative Analysis of Energy Regulation in Dormant Plant Seeds and Microbial Cells

Abstract

This article provides a comparative analysis of energy metabolism in dormant plant seeds and microbial cells, exploring the parallel strategies of metabolic arrest and reactivation. We examine foundational biological principles, including hydration forces, respiration control, and key pathways like glycolysis and the TCA cycle. The discussion extends to advanced methodological approaches such as multi-omics integration and metabolic flux analysis for investigating these states. Critical challenges in measurement reproducibility and quantitative accuracy are addressed, alongside validation strategies through metabolic rescue experiments and structural analysis. This synthesis offers valuable insights for researchers and drug development professionals, highlighting how understanding natural metabolic dormancy can inform therapeutic interventions against persistent microbial infections.

Defining Dormancy: Fundamental Principles of Energy Arrest in Seeds and Microbes

Orthodox seeds represent a remarkable paradox in biological systems: they are living entities that persist in a state of extreme desiccation, challenging fundamental thermodynamic principles that govern most life forms. These seeds achieve a condition known as anhydrobiosis—life without water—through sophisticated physiological adaptations that allow them to suspend metabolic activity while maintaining viability for extended periods [1] [2]. From a thermodynamic perspective, orthodox seeds exist in a metastable glassy state characterized by high viscosity and dramatically reduced molecular mobility, effectively preventing the degradative processes that would otherwise lead to cellular death [1] [3]. This review provides a comparative analysis of the energy metabolism and preservation strategies in dormant orthodox seeds, examining them through the lens of thermodynamic principles that govern biological systems in suspended animation.

Thermodynamic Foundations of Anhydrobiosis

Fundamental Principles Governing Seed Desiccation Tolerance

Living systems are open, irreversible systems that exchange matter and energy with their environment, operating far from thermodynamic equilibrium [3]. Orthodox seeds defy conventional biological thermodynamics by maintaining structural and functional integrity despite extreme water loss, which typically disrupts cellular organization and metabolic processes in most living organisms [2].

The glassy state (vitrification) achieved by orthodox seeds represents a thermodynamically unstable condition with high viscosity, where diffusional movement of cellular components is effectively prevented for practical timeframes [1] [3]. This state is crucial for maintaining seed viability during long-term storage, as it significantly slows deteriorative chemical reactions and physical changes [3]. The formation and stability of this glassy matrix depend on interactions between specific biomolecules (particularly late embryogenesis abundant proteins and heat shock proteins) and remaining water molecules, primarily through hydrogen bonding [1].

Table 1: Key Thermodynamic Parameters in Orthodox Seeds

Parameter	Significance	Impact on Seed Viability
Enthalpy (ΔH)	Total energy present in system available to do work	Higher values in desiccation-tolerant species; decreases with increasing temperature [1]
Entropy (ΔS)	Measure of disorder/unavailable energy in system	Lower in vitrified state; increased entropy during aging signals deterioration [1] [3]
Gibbs Free Energy (ΔG)	Energy available to do work (difference between enthalpy and entropy)	Radical increase indicates intensification of endergonic reactions and viability loss [1]
Activation Energy	Initial energy required to initiate reactions	Required for drying processes; less energy needed at higher temperatures [1]
Water Activity (aw)	Thermodynamically available water for physiological processes	Critical for maintaining glassy state; increases with temperature at constant water content [3]

Water Relationships and Thermodynamic Equilibrium

Water plays a fundamental role as both reaction medium and reactant in biological systems [3]. Orthodox seeds meticulously manage their hydration state, typically maintaining water content between 8-10% in the dormant state [2]. The seed water exists in two distinct forms: bound water (inside cells, cannot be eliminated without loss of viability) and free water (between cells, easily removable) [1].

The temperature dependence of water relationships follows the Clausius-Clapeyron equation, where an increase in temperature results in decreased equilibrium water content at a given relative humidity, or increased equilibrium water activity for a given tissue water content [3]. This relationship has profound implications for seed storage stability, as molecular mobility—inversely correlated with storage stability—reaches a minimum at optimal water content but increases again at very low water contents [3].

Molecular Mechanisms of Metabolic Suspension

Genetic and Proteomic Regulation of Desiccation Tolerance

The remarkable ability of orthodox seeds to enter and survive anhydrobiosis is governed by sophisticated genetic programs that activate during the late stages of seed maturation. The transcription factor ABI3 (ABSCISIC ACID INSENSITIVE 3) is highly conserved from bryophytes to Angiosperms and is essential for seed maturation and the acquisition of desiccation tolerance [2]. ABI3 coordinates the expression of crucial protective molecules, including Late Embryogenesis Abundant (LEA) proteins and small Heat Shock Proteins (sHSPs) [2].

LEA proteins are unstructured hydrophilic proteins that play multiple protective roles during desiccation, including membrane stabilization, ion sequestration, and molecular shield functions to prevent protein aggregation [2]. Similarly, sHSPs act as chaperones, preventing irreversible protein denaturation during drying and rehydration [2]. The synergistic action of these protective molecules enables the cellular machinery to survive the dramatic physical-chemical challenges of desiccation.

Hormonal Control of Dormancy and Energy Metabolism

Plant hormones serve as master regulators of the transition between dormant and active metabolic states in seeds. Abscisic acid (ABA) induces and maintains dormancy, while gibberellins (GA) promote dormancy release and germination [4] [5]. The balance between these opposing hormonal signals determines the seed's metabolic status.

Transcriptomic studies on Notopterygium incisum and Cardiocrinum giganteum seeds have revealed that dormancy release involves significant changes in the expression of genes in ABA and GA signaling pathways [4] [5]. During stratification treatments that break dormancy, genes in the ABA signaling pathway (ABI1, PP2CA, ABI5, and ABF4) and the gibberellin signaling pathway (GAI, GAI1, and RGL1) are significantly down-regulated, facilitating the transition to germination [4]. Concurrently, auxin, cytokinin, and ethylene signaling pathways also undergo significant modifications, creating a hormonal milieu permissive for metabolic reactivation [4].

Figure 1: Hormonal Signaling Pathways Regulating Seed Dormancy and Germination. ABA signaling (yellow) maintains dormancy through ABI3, ABI5, and PP2CA, activating protective proteins (LEA, sHSP). GA signaling (green) promotes germination through GAI and RGL1. The balance between these pathways determines the metabolic state.

Comparative Experimental Analysis: Methodologies and Findings

Experimental Approaches for Studying Seed Thermodynamics

Research on the thermodynamic properties of seeds employs specialized methodologies to quantify energy relationships and molecular mobility in desiccated systems. The following experimental protocols represent key approaches in the field:

Sorption Isotherm Analysis: Thermodynamic properties of seed water are calculated using sorption isotherms as suggested by Vertucci and Leopold (1984) [1] [3]. This method involves measuring the equilibrium moisture content at different relative humidity levels and temperatures, allowing calculation of key thermodynamic parameters:

• Differential Enthalpy (ΔH): Determined from the temperature dependence of water activity using the Clausius-Clapeyron equation [3]
• Differential Entropy (ΔS): Calculated from the relationship between water activity and temperature [1]
• Gibbs Free Energy (ΔG): Derived from the difference between enthalpy and entropy (ΔG = ΔH - TΔS) [1]

Accelerated Ageing Studies: Experimental protocols involve subjecting seeds to elevated temperatures and humidity levels to accelerate deteriorative processes [1]. Maize seeds of susceptible (sugary) and tolerant (dent) genotypes showed increased differential free energy values during accelerated ageing, with more pronounced effects in susceptible genotypes [3]. These studies establish critical upper limits for thermodynamic parameters beyond which seed viability is lost [1].

Transcriptomic and Metabolomic Profiling: Combined transcriptomic and metabolomic analyses during dormancy release in Notopterygium incisum seeds identified 110,539 differentially expressed genes and 1,656 metabolites during dormancy release [4]. Experimental protocols include:

• RNA extraction and high-throughput sequencing at different stratification stages
• Enzyme activity assays for metabolic enzymes
• Hormone quantification using enzyme-linked immunosorbent assays
• Metabolite profiling via broadly targeted metabolomics [4]

Table 2: Key Metabolic Pathways Activated During Dormancy Release

Metabolic Pathway	Functional Role	Key Regulatory Genes/Enzymes
Starch & Sucrose Metabolism	Energy provision for germination	β-glucosidase (BGLU), amylases, sucrose synthase [5]
Phenylpropanoid Biosynthesis	Cell wall strengthening, defense	Peroxidases, phenylalanine ammonia-lyase [4] [5]
Flavonoid Biosynthesis	Antioxidant protection, signaling	Chalcone synthase, flavonoid glycosyltransferases [4]
Fatty Acid Oxidation	Alternative energy source	Acyl-CoA dehydrogenases, enoyl-CoA hydratases [6]
Amino Acid Oxidation	Nitrogen mobilization, energy	Transaminases, deaminases, dehydrogenases [6]

Comparative Analysis of Orthodox and Recalcitrant Seeds

Seeds are classified into two primary categories based on their desiccation tolerance: orthodox seeds that survive extensive dehydration, and recalcitrant seeds that are desiccation-sensitive and cannot be stored dry [1] [2]. This fundamental difference reflects distinct thermodynamic adaptations at the cellular level.

Orthodox species exhibit higher critical limits for thermodynamic parameters compared to susceptible species, with these values generally decreasing with increasing temperature [1]. During storage, the differential enthalpy and entropy increase asymptotically as seeds approach viability loss [1]. In contrast, recalcitrant seeds lack the sophisticated mechanisms to enter the glassy state and maintain metabolic arrest without irreversible damage.

The structural integrity of orthodox seeds during desiccation involves the replacement of cellular water with compatible osmolytes (particularly non-reducing oligosaccharides like sucrose and raffinose), transforming the cytoplasm into a metastable "glass state" [2]. This vitrified matrix immobilizes cellular components and preserves secondary protein structure through hydrogen bonding interactions [2].

Energy Metabolism: Dormancy Versus Activation

Metabolic Reprogramming During Dormancy Release

The transition from dormant to active metabolic states involves comprehensive reprogramming of energy generation pathways. Transcriptomic analyses of Cardiocrinum giganteum seeds reveal that genes associated with carbohydrate metabolic pathways are significantly upregulated after 90 days of stratification [5]. Key enzymes in polysaccharide hydrolysis, particularly β-glucosidase (BGLU) genes, show substantial upregulation (5-7 fold increases) as germination initiates [5].

Concurrently, the glycolytic pathway is activated throughout the dormancy release process, providing both energy and metabolic intermediates for biosynthetic processes [5]. This metabolic awakening follows a precise temporal sequence, with different pathways activating at specific stages of the stratification process, reflecting a carefully orchestrated metabolic cascade rather than a simple binary switch between dormant and active states.

Figure 2: Metabolic Transition During Seed Germination. The sequential activation of metabolic pathways from storage compound mobilization through energy production to biosynthesis during the transition from dormancy to active growth.

Redox Homeostasis and Oxidative Stress Management

The maintenance of appropriate redox equilibrium is crucial for seed survival during desiccation and subsequent rehydration. Orthodox seeds employ sophisticated antioxidant systems to manage reactive oxygen species (ROS) generated during metabolic transitions [3]. The redox environment of biological systems can be described using the Nernst equation, which relates reduction potential to the concentrations of oxidized and reduced species in redox couples [3].

During seed ageing, disturbances to the glass structure increase oxidative activity, elevating respiration rates and ROS production [1] [3]. The resulting oxidative damage contributes to viability loss, with tolerant species maintaining better redox homeostasis than susceptible ones. The voltage of electrochemical cells in biological systems is directly related to the change in Gibbs energy (ΔG = -nFΔE), highlighting the fundamental connection between redox chemistry and cellular energy status [3].

The Scientist's Toolkit: Essential Research Reagents and Methodologies

Table 3: Essential Research Reagents and Methodologies for Seed Thermodynamics Research

Reagent/Equipment	Application	Specific Function
Sorption Isotherm Systems	Water relationship analysis	Determine equilibrium moisture content at different relative humidities and temperatures [1] [3]
Differential Scanning Calorimetry (DSC)	Glass transition detection	Measure heat capacity changes associated with glass transitions in seed tissues [1]
RNA Sequencing Kits	Transcriptomic analysis	Profile differentially expressed genes during dormancy release and germination [4] [5]
LC-MS/MS Systems	Metabolomic profiling	Identify and quantify metabolites involved in dormancy transitions [4]
Enzyme Activity Assay Kits	Metabolic pathway analysis	Measure activities of key enzymes in carbohydrate and energy metabolism [4]
Hormone ELISA Kits	Phytohormone quantification	Determine concentrations of ABA, GA, IAA, and other regulatory hormones [4]
Seahorse Flux Analyzer	Cellular energy metabolism	Monitor oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) [6]
Icmt-IN-50	Icmt-IN-50|ICMT Inhibitor|For Research	Icmt-IN-50 is a potent ICMT inhibitor for cancer research. This product is for research use only and not for human or veterinary use.
Rock-IN-D2	Rock-IN-D2, MF:C22H28N6O, MW:392.5 g/mol	Chemical Reagent

Orthodox seeds exemplify nature's solution to preserving biological organization under thermodynamically challenging conditions of extreme desiccation. Through the establishment of a metastable glassy state, sophisticated control of redox homeostasis, and precise regulation of metabolic pathways, these remarkable structures maintain life in suspension for extended periods. The thermodynamic parameters of enthalpy, entropy, and free energy provide crucial insights into the mechanisms of desiccation tolerance and viability maintenance.

Current research reveals that seed survival depends on maintaining a delicate balance between molecular stability and flexibility, with protective molecules such as LEAs and sHSPs playing crucial roles in preserving structural integrity during desiccation-rehydration cycles. The continuing investigation of these mechanisms holds promise for applications in conservation biology, agriculture, and even biomedical preservation technologies, where the principles of anhydrobiosis might be harnessed for stabilizing biological materials.

The concept of "Water as the Matrix of Life" finds a profound testing ground in the study of dormant plant seeds. These remarkable structures exist in an anhydrobiotic state, pushing the boundaries of thermodynamic definitions of life [7]. Orthodox seeds represent a unique biological system where metabolic activity approaches near-complete cessation while maintaining the capacity for full metabolic resurrection upon rehydration [7]. This review provides a comparative analysis of energy metabolism in dormant plant seeds, examining the hydration forces that govern metabolic ceasing and resumption. We explore the experimental evidence quantifying these phenomena and the methodologies enabling their investigation, offering insights relevant to fields from seed biology to microbial cryptobiosis.

Physiological Foundations of Metabolic Ceasing in Seeds

Structural and Metabolic Adaptations for Desiccation Tolerance

Orthodox seeds achieve desiccation tolerance through sophisticated structural and biochemical adaptations. Their tissues undergo extreme dehydration, with water content dropping to less than 10% of dry weight (approximately 4% in sunflower seeds) [7]. At this hydration level, the cytoplasm transitions from a fluid to a glassy state, severely reducing molecular diffusion and mobility, thereby preventing most biochemical reactions [7]. This metabolic arrest is crucial for long-term viability, allowing seeds to survive for centuries, as demonstrated by the successful germination of Phoenix dactylifera seeds after centuries of dormancy [7].

Seed metabolism is exquisitely controlled by water availability, which operates through hydration forces that govern macromolecular stability and enzyme activity. Proteins require approximately 0.07 g water/g protein for initial hydration of charged groups, with enzymatic activity becoming detectable only at 0.2 g/g and significant changes occurring above 0.38 g/g as water-water bonds form and participate in protein-substrate interactions [7]. Nucleic acids have even higher hydration requirements, needing approximately twice the water levels of proteins for full functionality [7].

Respiration and Energy Metabolism in Dry Seeds

Respiration resumption follows a water-content-dependent pathway. Below 8% water content, seed O₂ consumption is undetectable, while above 24%, full plasma membrane and mitochondrial energy restoration occurs [7]. The metabolic rate of seeds follows a predictable allometric relationship with seed mass, described by the equation SMR = 0.081 × M⁰·⁷⁸⁰ (using ordinary least squares regression) or SMR = 0.057 × M⁰·⁷⁴⁶ (using phylogenetic generalized least squares regression), where SMR represents standard metabolic rate and M represents seed mass [8].

Table 1: Metabolic Parameters of Orthodox Seeds at Different Hydration States

Hydration State	Water Content	Metabolic Activity	Respiration	Cytoplasmic State
Dry State	<8%	Undetectable	Oâ‚‚ consumption undetectable	Glassy state
Low Hydration	8-24%	Minimal	Very low Oâ‚‚ consumption	Transition state
Hydrated	>24%	Fully active	Normal respiration	Fluid state

Comparative Analysis: Seed Metabolism Under Hydration-Dehydration Cycles

Metabolic Responses to Hydration-Dehydration Cycles

Seeds in natural environments often experience diurnal hydration-dehydration (Hy-Dh) cycles, which significantly impact their metabolic processes and vigor. Research on Arabidopsis thaliana ecotypes revealed striking variability in metabolic responses to these cycles [9]. While most ecotypes (Col-0, Cvi, C24) showed detrimental effects on germination rate and percentage following Hy-Dh cycles, the Ler ecotype displayed improved seed vigor after treatment [9].

The metabolic basis for these differential responses involves several key pathways:

• Membrane permeability, measured by ion conductivity, generally increased following each Hy-Dh cycle and correlated with changes in redox status (GSSG/GSH ratio) [9]
• Respiratory activity significantly increased in Ler seeds following dehydration phases, contrasting with other ecotypes [9]
• Content of TCA cycle intermediates (fumarate, succinate, malate) increased in response to dehydration in Ler, but decreased in other ecotypes [9]
• Accession-specific patterns were observed in cell wall-related carbohydrates (laminaribiose, mannose), fatty acid composition (oleic acid, α-linoleic acid), and nitrogen metabolism (asparagine, ornithine, putrescine) [9]

Priming Effects and Hydration Memory

Hydration-dehydration cycles can prime seeds for enhanced germination performance, particularly under stress conditions. Studies on eight Mediterranean Cistus species demonstrated that controlled hydration-dehydration treatments (24 or 48 hours) significantly improved germination responses under water stress in six species [10]. These priming effects manifested as:

• Higher final germination percentages in C. ladanifer, C. laurifolius, and C. populifolius [10]
• Earlier germination onset (decreased T0) in C. ladanifer, C. populifolius, and C. salviifolius [10]
• Increased tolerance to water stress, with some species germinating at -0.8 MPa water potential after priming, compared to no germination at this potential in non-primed seeds [10]

Table 2: Metabolic and Physiological Changes During Seed Hydration-Dehydration Cycles

Metabolic Parameter	Change During Hy-Dh Cycles	Relationship to Seed Vigor
Membrane Permeability	Generally increases	Correlated with redox status changes
Respiratory Activity	Ecotype-specific (increases in Ler)	Associated with improved vigor
TCA Intermediates	Increase in vigorous ecotypes	Enhanced energy metabolism
Fatty Acid Composition	Ecotype-specific changes	Membrane integrity maintenance
Glutathione Ratio	Altered redox status	Oxidative stress management
Carbohydrate Profiles	Changes in laminaribiose, mannose	Cell wall remodeling

Experimental Methodologies for Assessing Seed Metabolic States

Metabolic Rate Measurement Protocols

The standard metabolic rate (SMR) of seeds is measured using fluorescence-based closed-system respirometry with careful control of hydration states [8]. The experimental protocol involves:

• Seed Selection and Preparation: Seeds are individually assessed via X-ray to ensure intact, undamaged embryos and endosperm [8]
• Hydration Control: Seeds are equilibrated at 95% relative humidity to achieve a non-depressed metabolic state without initiating germination [8]
• Temperature Standardization: Measurements are conducted at species-specific optimal germination temperatures (between 18°C and 30°C), with Q₁₀ correction to 20°C for cross-species comparisons [8]
• Metabolic Quantification: Oxygen consumption rates are measured as proxies for metabolic activity [8]

This approach ensures that SMR reflects the minimum metabolism of seeds in a relatively quiescent, but not metabolically depressed state, representing energy costs of self-maintenance rather than growth-associated metabolism [8].

Advanced Germination Assessment Metrics

Traditional germination indices like Germination Percentage (GP) and Mean Germination Time (MGT) often fail to capture complex stress responses [11]. Recent research has introduced novel, mechanistically informed metrics that provide more nuanced assessment:

• Seed Priming Efficiency Index (SPEI): Quantifies the kinetic efficiency of germination under stress conditions [11]
• Stress Performance Stability Index (SPSI): Measures the ability of priming treatments to maintain germination performance close to genetic potential under abiotic stress [11]
• Germination Recovery Ratio (SGRR): Assesses physiological resilience by comparing post-stress growth potential with inherent capacity [11]
• Combined Vigor Index (SCVI): Provides an integrated assessment of seedling performance [11]

These indices demonstrated a 34.2% improvement in germination recovery with gibberellin priming compared to 25.8% with hydro-priming in wheat under drought stress, showing superior discriminatory power requiring 37.6% smaller sample sizes than traditional metrics while maintaining 94% rank stability under data perturbations [11].

Molecular Regulation of Dormancy and Germination

Hormonal Control and Signaling Pathways

Seed dormancy release involves sophisticated hormonal regulation, as demonstrated in Notopterygium incisum seeds, which exhibit both morphological and physiological dormancy [4]. Transcriptomic and metabolomic analyses during stratification-induced dormancy release revealed:

• ABA content decreases significantly while GA₃ and IAA contents increase during dormancy release [4]
• Down-regulation of abscisic acid signaling pathway genes (ABI1, PP2CA, ABI5, ABF4) and gibberellin signaling pathway genes (GAI, GAI1, RGL1) [4]
• Significant changes in auxin, cytokinin, and ethylene signaling pathways [4]
• Up-regulation of genes related to starch and sucrose metabolism during dormancy release [4]
• Significant up-regulation of genes involved in phenylpropanoid and flavonoid biosynthesis after dormancy release [4]

Combined transcriptomics and metabolomics identified phenylpropanoid biosynthesis and flavonoid biosynthesis as key pathways for dormancy release in N. incisum seeds [4].

Metabolic Transitions During Dormancy Release

The transition from dormant to germinating state involves coordinated metabolic reprogramming:

• Decreased accumulation of phenylpropanoid biosynthesis pathway metabolites (p-coumaric acid, coniferyl aldehyde, coniferyl glycoside, 5-caffeoylshikimic acid, sinapinic acid) during and after dormancy release [4]
• Increased accumulation of flavonoids (quercetin, rutin, delphinidin, naringenin chalcone) after dormancy release [4]
• Reduction in stored nutrients (proteins, soluble sugars, starch) with significantly changed enzyme activity [4]

These molecular changes provide a regulatory network integrating hormone signaling, carbohydrate metabolism, and specialized metabolism to control the dormancy-to-germination transition.

The Scientist's Toolkit: Essential Research Reagents and Methods

Table 3: Key Research Reagent Solutions for Seed Metabolism Studies

Reagent/Method	Function/Application	Experimental Consideration
Fluorescence-based closed-system respirometry	Measures standard metabolic rate (SMR) of seeds	Requires humidity control (95% RH) and temperature standardization [8]
PEG solutions	Imposes controlled water stress for germination assays	Concentration determines water potential (0 to -0.8 MPa) [10]
ELISA kits for hormones	Quantifies ABA, GA₃, IAA levels	Used in dormancy release studies [4]
Anthrone colorimetric method	Determines soluble sugar and starch content	Assesses energy reserve utilization [4]
Coomassie brilliant blue method	Measures protein content	Evaluates protein reserve mobilization [4]
Ion conductivity measurement	Assesses membrane permeability	Correlates with redox status changes during Hy-Dh cycles [9]
X-ray imaging	Visualizes internal seed structure and embryo integrity	Non-destructive quality assessment [8]
Ac-YVAD-FMK	Ac-YVAD-FMK|Caspase-1 Inhibitor|For Research
Nav1.8-IN-11	Nav1.8-IN-11, MF:C23H17ClF4N2O3, MW:480.8 g/mol	Chemical Reagent

Conceptual Framework and Visual Synthesis

Metabolic Transitions in Seed Hydration States

The following diagram illustrates the relationship between water content, metabolic activity, and physiological states in orthodox seeds:

Experimental Workflow for Seed Metabolic Analysis

This diagram outlines a comprehensive experimental approach for studying seed metabolism:

The study of hydration forces and metabolic ceasing in plant seeds reveals fundamental biological principles about life's thermodynamic boundaries. The precise control of metabolic activity through hydration states enables seeds to maintain viability while suspended in a state approaching non-living matter. Comparative analysis demonstrates that seeds from different environments exhibit adaptive metabolic strategies, with arid-environment species often showing higher metabolic rates to capitalize on brief hydration windows [8].

These insights extend beyond seed biology, offering models for understanding microbial cryptobiosis, stabilizing therapeutic proteins, and preserving cellular materials. The experimental methodologies refined in seed research—particularly the novel germination indices and standardized metabolic rate measurements—provide robust tools for quantifying metabolic states across biological systems. As climate change alters hydration-dehydration cycles globally [10], understanding these fundamental processes becomes increasingly crucial for ecosystem resilience and food security.

The resumption of respiration marks the fundamental transition of a biological system from a quiescent, low-energy state to an active, metabolically vibrant one. This process is a critical comparative point in energy metabolism studies, particularly between dormant plant seeds and microbial cells. In orthodox seeds, desiccation leads to an ametabolic state where respiration is undetectable, a survival strategy to extend viability [7]. Conversely, certain microbial cells, like Escherichia coli, can maintain a latent capacity for unconventional energy metabolism even under anaerobic conditions, which can be activated for extracellular respiration [12]. This guide provides a structured comparison of respiration resumption mechanisms, offering experimental data and protocols to facilitate direct comparison between these distinct biological systems. Understanding these parallel yet divergent life strategies provides profound insights into bioenergetic adaptation with potential applications in conservation biology, agricultural science, and bioelectrochemical engineering.

Comparative Physiology of Respiratory Transitions

Metabolic State and Respiratory Characteristics

Table 1: Comparative Physiological States During Quiescence and Activation

Physiological Parameter	Dormant Orthodox Seeds	Quiescent Microbial Cells
Hydration State	Very low water content (<10% of dry weight) [7]	High water content maintained
Cytoplasmic State	Glassy state preventing molecular diffusion [7]	Aqueous cytoplasm
Metabolic Activity	Undetectable respiration below ~8% water content [7]	Reduced but present basal metabolism
Primary Energy Conservation	Desiccation tolerance through protective molecules [7]	Diverse strategies (sporulation, metabolic shutdown)
Respiratory Resumption Trigger	Water uptake (imbibition) [7]	Nutrient availability, environmental signals
Initial Energy Source	Stored reserves (carbohydrates, lipids) [7]	Environmental nutrients or stored compounds
Oxygen Consumption at Activation	Rapid increase upon imbibition, particularly after 24% water content threshold [7]	Immediate increase upon favorable conditions
Repair Mechanisms Upon Activation	Intensive repair programs ready to operate [7]	Standard cellular maintenance

Quantitative Respiratory Metrics

Table 2: Experimentally Measured Respiratory Parameters

Organism/System	Condition	Respiration Rate	Measurement Method	Reference
Barley seeds (Hordeum vulgare L.)	16% MC, 35°C storage	71.89 mg CO₂/(kgDM·d) [13]	Hermetic storage with gas concentration analysis	Sciencedirect (2023)
Barley seeds (Hordeum vulgare L.)	12% MC, 5°C storage	0.014 mg CO₂/(kgDM·d) [13]	Hermetic storage with gas concentration analysis	Sciencedirect (2023)
Korean pine (Pinus koraiensis)	Intact primary dormant seeds (4th day)	0.012 μmol CO₂ g⁻¹ min⁻¹ [14]	CO₂ evolution measurement	BMC Plant Biology (2019)
Korean pine (Pinus koraiensis)	Seeds with cracked seed coats (14th day)	0.022 μmol CO₂ g⁻¹ min⁻¹ [14]	CO₂ evolution measurement	BMC Plant Biology (2019)
Escherichia coli	HNQ-mediated extracellular respiration	Substantial current generation coupled to growth [12]	Bioelectrochemical system with poised electrode	Cell (2025)

Experimental Protocols for Respiratory Monitoring

Seed Respiration Analysis During Hermetic Storage

Objective: To characterize oxygen consumption and carbon dioxide production rates of seeds under controlled storage conditions.

Materials:

• Barley seeds (Hordeum vulgare L.) or other species of interest
• Air-tight glass jars or containers with sealed ports
• Temperature-controlled incubation chambers (5°C, 15°C, 25°C, 35°C)
• Gas chromatograph or infrared COâ‚‚ analyzer
• Moisture meters for seed water content determination
• Data logging system for continuous monitoring

Methodology:

• Prepare seed samples at different moisture content levels (12%, 14%, 16% as tested for barley).
• Place seeds in hermetic containers and store at different temperatures.
• Monitor Oâ‚‚ and COâ‚‚ concentrations regularly throughout storage period.
• Calculate respiration rates as mg Oâ‚‚ consumed or COâ‚‚ produced per kg dry matter per day.
• Determine respiratory quotient (RQ) as ratio of COâ‚‚ produced to Oâ‚‚ consumed.
• Model respiration using both linear and non-linear approaches for comprehensive analysis.
• Correlate respiration rates with seed viability tests and microbial load assessments.

Key Parameters:

• Critical water activity threshold: 0.65-0.7 aw for significant respiration increase [13]
• Temperature coefficient (Q₁₀) for respiration rate
• Dry matter loss calculations based on cumulative respiration

Microbial Extracellular Respiration Assay

Objective: To quantify extracellular electron transfer capability in microbial systems using a bioelectrochemical approach.

Materials:

• Escherichia coli strains (wild-type and engineered variants)
• Bioelectrochemical system (BES) with potentiostat
• 2-hydroxy-1,4-naphthoquinone (HNQ) as redox shuttle
• Anaerobic chamber for oxygen-free experiments
• Carbon source (e.g., glycerol)
• Genome editing tools for pathway manipulation (CRISPR, plasmids)
• Transcriptomics and flux analysis capabilities

Methodology:

• Cultivate E. coli strains under controlled conditions.
• Assemble bioelectrochemical cells with poised electrodes.
• Add HNQ redox shuttle to experimental conditions.
• Monitor current generation as indicator of extracellular electron transfer.
• genetically disrupt specific pathways (nitroreductases NfsB and NfsA) to confirm mechanism.
• Perform flux balance analysis to model electron flow.
• Conduct adaptation experiments to select for enhanced EET capability.
• Analyze outer membrane porin mutations (e.g., OmpC) in adapted strains.

Key Parameters:

• Current density normalized to cell count or protein content
• Growth coupling to electron transfer
• NAD(P)H oxidation rates linked to extracellular respiration

Metabolic Pathways and Experimental Workflows

Seed Respiration Resumption Pathway

Diagram 1: Seed respiration resumption pathway.

Microbial Extracellular Respiration Workflow

Diagram 2: Microbial extracellular respiration workflow.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for Respiration Studies

Reagent/Equipment	Application	Function in Experiment
Hermetic Storage Containers	Seed respiration studies	Creates controlled atmosphere for monitoring gas exchange dynamics [13]
Gas Chromatography System	Both systems	Quantifies Oâ‚‚ consumption and COâ‚‚ production rates with high precision [13]
2-Hydroxy-1,4-naphthoquinone (HNQ)	Microbial extracellular respiration	Functions as redox shuttle for mediated electron transfer in E. coli [12]
Bioelectrochemical System (BES)	Microbial respiration	Provides poised electrode for quantifying extracellular electron transfer [12]
Water Activity (a_w) Meter	Seed physiology	Determines critical hydration threshold for respiration resumption [13]
Nitroreductase Enzymes (NfsA/NfsB)	Microbial electron transfer	Cytoplasmic enzymes that reduce HNQ using NAD(P)H in E. coli [12]
Temperature-Controlled Incubators	Both systems	Maintains precise thermal conditions for studying temperature dependence [13]
Genome Editing Tools (CRISPR)	Microbial pathway analysis	Enables targeted disruption of respiratory pathways for mechanism elucidation [12]
Ezh2-IN-17	Ezh2-IN-17, MF:C36H46N4O4, MW:598.8 g/mol	Chemical Reagent
SNNF(N-Me)GA(N-Me)IL	SNNF(N-Me)GA(N-Me)IL, MF:C39H62N10O12, MW:863.0 g/mol	Chemical Reagent

Comparative Analysis and Research Implications

The comparative analysis of respiration resumption in dormant seeds versus microbial cells reveals both convergent and divergent biological strategies. Seeds employ a dehydration-based approach, entering a reversible ametabolic state with precisely programmed resumption upon hydration [7]. Microbial cells, particularly E. coli, maintain metabolic flexibility, capable of activating latent extracellular respiration pathways when conventional electron acceptors are unavailable [12].

The experimental data demonstrates that seed respiration follows a water activity-dependent threshold model, with negligible activity below 0.65-0.7 a_w [13], while microbial extracellular respiration can be induced through redox shuttling mechanisms independent of traditional respiration chains. This fundamental difference reflects distinct evolutionary adaptations: seeds for long-term survival in variable environments, and microbes for metabolic versatility in competitive ecosystems.

For researchers, these comparative insights suggest novel approaches to manipulating dormancy in agricultural applications and engineering microbial systems for bioelectrochemical technologies. The recognition that model organisms like E. coli possess latent extracellular respiratory capacity opens new avenues for sustainable bioprocessing and energy production [12]. Similarly, understanding the metabolic switches that control seed respiration resumption provides strategies for enhancing seed storage, conservation, and germination synchrony in agricultural and ecological contexts.

Dormancy represents a fundamental survival strategy across biological kingdoms, enabling plants and microorganisms to persist through unfavorable environmental conditions. In both plant seeds and microbial cells, the entry into, maintenance of, and exit from a dormant state are governed by sophisticated molecular signaling systems. This review performs a comparative analysis of these regulatory frameworks, with a specific focus on the antagonistic hormones abscisic acid (ABA) and gibberellin (GA) in plants, and explores whether functionally analogous systems exist in microbial dormancy. Understanding these parallel mechanisms provides critical insights into the universal principles of metabolic quiescence and has profound implications for agriculture, pharmaceutical development, and microbial ecology.

For seed-bearing plants, the transition between dormancy and germination represents a critical developmental switch controlled by a dynamic balance between ABA (which promotes dormancy) and GA (which promotes germination) [15]. Similarly, numerous microorganisms employ dormancy as a "bet-hedging" strategy, with a significant proportion of environmental microbial communities existing in a reversible state of low metabolic activity at any given time [16]. By examining the hormonal gatekeepers and their potential microbial equivalents through a comparative lens, we aim to identify conserved regulatory logic and specialized adaptations across these diverse biological systems.

Hormonal Regulation in Plant Seeds

The ABA-GA Antagonism: Core Signaling Network

The ABA-GA balance serves as the central regulatory module controlling seed dormancy and germination. ABA plays a central role in inducing and maintaining seed dormancy, inhibiting the transition from embryonic to germination growth, while GA promotes germination by mobilizing storage reserves and initiating growth processes [15]. This hormonal antagonism operates through reciprocal regulation of each other's metabolic genes, creating a switch-like mechanism that ensures clear developmental transitions [15].

The ABA biosynthesis pathway in seeds is highly regulated at both temporal and spatial levels. ABA is a sesquiterpene derived from carotenoid precursors through a series of enzymatic reactions in plastids. The rate-limiting step is catalyzed by 9--cis-epoxycarotenoid dioxygenase (NCED), which cleaves 9--cis-violoxanthin or 9--cis-neoxanthin to produce cis-xanthoxin [15]. Different NCED family members play distinct regulatory roles; for instance, in Arabidopsis, AtNCED6 and AtNCED9 are crucial for ABA synthesis during late embryogenesis, with double mutants showing reduced dormancy [15]. Conversely, GA biosynthesis genes such as GA 3-oxidase (GA3ox) and GA 20-oxidase (GA20ox) are upregulated to promote germination, though their expression is significantly impaired under low-temperature conditions [17].

Table 1: Key Genes in ABA and GA Biosynthesis and Signaling

Gene/Protein	Function	Phenotypic Effect When Manipulated
NCED (e.g., AtNCED6, AtNCED9)	Rate-limiting enzyme in ABA biosynthesis	Reduced ABA content and seed dormancy in mutants; increased dormancy when overexpressed [15]
ABA4	Involved in conversion to cis-isomers of violaxanthin	Reduced ABA accumulation; one of the missing links in ABA biosynthesis pathway [15]
ZEP (Zeaxanthin epoxidase)	Catalyzes conversion of zeaxanthin to violaxanthin	Increased ABA levels and enhanced dormancy when overexpressed in tobacco [15]
GA3ox, GA20ox	Key enzymes in GA biosynthesis	Reduced expression under low temperature decreases active GA levels, delaying germination [17]
ABI5	ABA-responsive transcription factor	Core component of ABA signaling network; regulates antioxidant defense and energy metabolism [18]
OsbZIP23	ABA signaling pathway gene in rice	Induced by LT and ABA; overexpression increases sensitivity to LT stress during germination [19]

Integration of Environmental Cues

The hormonal gatekeepers integrate environmental signals to optimize germination timing. Low-temperature stress significantly inhibits seed germination by disrupting the ABA-GA balance. In rice, LT stress induces ABA accumulation by upregulating OsNCED3 expression, and consistently, OsNCED3 overexpression significantly inhibits germination under LT [19]. LT stress also upregulates the expression of the ABA signaling gene OsbZIP23, which binds to the promoter of OsTPP3 (trehalose-6-phosphate phosphatase 3) and activates its expression, further inhibiting germination under LT conditions by increasing trehalose accumulation [19]. This demonstrates how environmental cues are integrated through hormonal pathways to fine-tune germination timing.

The discovery of the OsbZIP23-OsTPP3 module reveals a crucial link between ABA signaling and metabolic regulation. Trehalose accumulation under this regulatory circuit likely contributes to osmotic adjustment and stress protection, but simultaneously inhibits growth processes essential for germination [19]. This exemplifies how hormonal signaling directly regulates metabolic pathways to execute physiological responses to environmental conditions.

Microbial Dormancy: Strategies and Regulation

Physiological Diversity and Ecological Significance

Microbial dormancy represents a ubiquitous survival strategy wherein microorganisms enter a reversible state of low metabolic activity to overcome unfavorable conditions. Dormant microbial cells constitute a significant portion of environmental communities, forming a "microbial seed bank" that contributes substantially to ecosystem resilience and microbial biodiversity [16]. Unlike programmed plant seed dormancy, microbial dormancy often functions as a direct response to environmental stressors such as resource limitation, temperature extremes, or other adverse conditions.

Recent advances in molecular techniques have enabled more precise discrimination of microbial physiological states. The Triple Metabarcoding Approach (TMA) integrates metabarcoding of total environmental rDNA (DNA-seq), rRNA (RNA-seq), and DNA treated with propidium monoazide (PMA-seq) to categorize phylotypes into active, dormant, and dead fractions [20]. Application of TMA in aquatic systems revealed that dead microbes (relic DNA) accounted for ≤5% of planktonic DNA pools but significantly contributed to ASV richness (53%, 50%, and 32% for bacteria, archaea, and microeukaryotes, respectively) [20]. Dormant fractions, while less abundant, further enriched diversity, particularly in water (20%, 62%, and 28% of viable bacterial, archaeal, and microeukaryotic richness, respectively) [20].

Table 2: Proportions of Microbial Physiological States in Different Environments

Environment	Microbial Domain	Active Fraction	Dormant Fraction	Dead Fraction
Water Column	Bacteria	45% (viable richness)	20% (viable richness)	≤5% (relative abundance)
	Archaea	5% (viable richness)	62% (viable richness)	53% (richness contribution)
	Microeukaryotes	28% (viable richness)	28% (viable richness)	32% (richness contribution)
Sediments	Bacteria	71% (viable richness)	11% (viable richness)	44% (relative abundance)
	Archaea	71% (viable richness)	14% (viable richness)	80% (richness contribution)
	Microeukaryotes	81% (viable richness)	9% (viable richness)	44% (relative abundance)

Microbial dormancy is regulated by the interpretation of environmental cues, which may operate at local or regional scales. Local cues include resource availability, crowding, oxygen stress, and stochastic perturbations, while regional drivers encompass factors like temperature and photoperiod [16]. The combination of dormancy and strong regional cues can synchronize the composition of active microbial communities across landscapes in a manner similar to the Moran effect observed in macroecology [16].

Mathematical modeling of microbial dormancy suggests that the ability to enter and successfully emerge from dormancy has a strong, positive influence on microbial species richness [16]. These models demonstrate that repeated transitions between active and dormant states help maintain the high levels of microbial biodiversity observed in nearly all ecosystems. The "rare biosphere" – low-abundance microbial taxa – appears to be disproportionately active relative to common taxa, suggesting that microbial rank-abundance curves are more dynamic than previously considered [16].

Comparative Analysis: Plant vs. Microbial Dormancy

Parallels in Metabolic Downregulation

Both plant seeds and dormant microbes undergo profound metabolic restructuring to conserve energy and maintain viability during dormancy. In plant seeds, acquisition of desiccation tolerance involves coordinated morphological, physiological, and genetic changes, including increased seed vigor, seed coat thickening, endosperm degradation, and reduced water content [18]. Metabolic activity gradually decreases as seeds acquire desiccation tolerance, minimizing reactive oxygen species (ROS) production [18]. Similarly, dormant microbes exhibit dramatically reduced metabolic rates, though the specific pathways downregulated vary among phylogenetic groups.

A key parallel exists in the accumulation of protective compounds. Desiccation-tolerant plant tissues typically contain high concentrations of sucrose and oligosaccharides, which form hydrogen bonds by replacing water molecules and prevent harmful effects of dehydration on cell membranes and proteins [18]. Similarly, many microorganisms accumulate compatible solutes like trehalose (also present in plants) when entering dormant states, which stabilizes cellular structures in the absence of water. The discovery that the ABA-responsive OsbZIP23 transcription factor activates OsTPP3 expression in rice, leading to trehalose accumulation under low-temperature stress [19], reveals a striking molecular convergence in the utilization of similar protective metabolites across kingdoms.

Distinct Signaling Paradigms

While both systems implement dormancy, their regulatory architectures differ significantly. Plant seed dormancy employs a sophisticated endocrine-like system with dedicated biosynthesis pathways for antagonistic hormones (ABA and GA) that integrate both developmental programs and environmental cues [15]. This system operates through complex transcriptional networks involving multiple transcription factors (ABI5, BBX22, MADS3, etc.) that coordinate stress responses, energy metabolism, and growth transitions [18].

In contrast, microbial dormancy regulation appears more decentralized, with diverse species-specific mechanisms that directly sense environmental conditions. Rather than hormone-like signaling cascades, many microbes utilize stress-responsive transcription factors, second messengers (e.g., (p)ppGpp in bacterial stringent response), and phosphorylation cascades to initiate dormancy programs. However, recent evidence suggests that some microorganisms may use small molecules for intercellular communication that could functionally approximate hormonal signaling, though these systems are less characterized than in plants.

Figure 1: Comparative Regulation of Dormancy in Plants and Microbes. Plant seed dormancy employs a sophisticated hormonal signaling system with ABA and GA acting antagonistically, while microbial dormancy typically involves direct environmental sensing and species-specific regulatory networks.

Experimental Approaches and Methodologies

Key Analytical Techniques

Advances in comparative dormancy research rely on sophisticated analytical methods that can characterize physiological states and metabolic activities. The table below outlines essential methodologies and their applications in studying dormancy across biological systems.

Table 3: Essential Methodologies for Dormancy Research

Methodology	Application in Plant Seeds	Application in Microbial Cells	Key Research Reagents
Transcriptomics	Identify dormancy-associated gene modules (e.g., via WGCNA) [18]	Characterize active vs. dormant transcriptional profiles [20]	RNA extraction kits, reverse transcriptase, sequencing reagents
Metabolomics	Profile metabolic modules during germination (e.g., shikimic acid-tyrosine module) [21]	Identify metabolites associated with dormancy entry/exit	HPLC systems, mass spectrometers, metabolite standards
Triple Metabarcoding (TMA)	Not typically applied	Discriminate active, dormant, and dead microbial fractions [20]	Propidium monoazide (PMA), rRNA/DNA extraction kits, PCR reagents
Hormone Quantification	Measure ABA/GA dynamics during dormancy cycles [17]	Not typically applied (unless studying phytohormone-producing microbes)	Antibody-based assays, HPLC, ELISA kits
Germination/Resuscitation Assays	Quantify germination rates under different conditions [17] [19]	Measure resuscitation from dormancy with resource supplements [16]	Growth media, tetrazolium salts, ATP assays

Representative Experimental Protocols

Protocol: Assessing Seed Germination Under Abiotic Stress

This protocol is adapted from hulless barley germination assays under low-temperature stress [17]:

• Seed Selection and Sterilization: Select full-grain seeds of uniform size, free from pests and diseases. Surface-sterilize with 5% sodium hypochlorite for 10 minutes, then rinse three times with distilled water.

• Experimental Treatment: Distribute seeds evenly in Petri dishes lined with two layers of filter paper. Add appropriate amount of distilled water or treatment solution (e.g., 100 mg/L GA3 for hormone treatments). For low-temperature stress, incubate at 4°C with a 12-hour light/dark cycle and approximately 70% relative humidity.

• Monitoring and Data Collection: Monitor seed germination daily. Record germination when the white tip emerges. Calculate germination rate using the formula: Germination Rate (%) = (Number of germinated seeds on day 7 / Total number of test seeds) × 100.

• Sampling for Molecular Analysis: Collect seed samples at predetermined time points (e.g., 3 days post-treatment) for subsequent phytohormone content analysis, transcriptome sequencing, and other molecular analyses.

Protocol: Triple Metabarcoding Approach for Microbial Physiological States

This protocol discriminates active, dormant, and dead microbial fractions using the Triple Metabarcoding Approach (TMA) [20]:

• Sample Collection and Processing: Collect environmental samples (water, sediment, soil) using appropriate sterile techniques. Process samples immediately or flash-freeze in liquid nitrogen for later analysis.

• Nucleic Acid Extraction:

  • DNA-seq: Extract total environmental DNA using standard commercial kits.
  • RNA-seq: Extract total RNA, then synthesize cDNA for rRNA gene amplification.
  • PMA-seq: Treat samples with propidium monoazide (PMA) prior to DNA extraction. PMA cross-links to DNA of dead (membrane-compromised) cells, preventing its amplification.

• Amplification and Sequencing: Amplify target genes (16S rRNA for bacteria/archaea, 18S for microeukaryotes) using standardized primers. Perform high-throughput sequencing on all libraries.

• Bioinformatic Analysis:

  • Process sequences into Amplicon Sequence Variants (ASVs).
  • Categorize phylotypes based on detection patterns:
    • Active: Present in both DNA-seq and RNA-seq
    • Dormant: Present in DNA-seq and PMA-seq but absent in RNA-seq
    • Dead: Present in DNA-seq but absent in both PMA-seq and RNA-seq

Figure 2: Experimental Workflow for Triple Metabarcoding Approach (TMA). This method integrates DNA-seq, RNA-seq, and PMA-seq to discriminate active, dormant, and dead microbial fractions in environmental samples.

Research Reagent Solutions Toolkit

The following table provides essential research reagents and their applications for investigating dormancy mechanisms across plant and microbial systems.

Table 4: Essential Research Reagents for Dormancy Studies

Reagent/Category	Specific Examples	Application and Function
Hormones and Inhibitors	ABA, GA3, Nordihydroguaiaretic acid (NDGA), Abamine	Experimental manipulation of hormone pathways; NDGA and Abamine inhibit NCED activity in ABA biosynthesis [15]
Nucleic Acid Extraction Kits	Commercial DNA/RNA extraction kits	Isolation of high-quality nucleic acids from seeds or microbial samples for omics studies
Viability Stains	Propidium monoazide (PMA), Tetrazolium salts	Discrimination of membrane-intact (viable) vs. compromised (dead) cells; seed viability testing [20]
Sequencing Reagents	16S/18S rRNA primers, Reverse transcriptase, Library prep kits	Target gene amplification for metabarcoding; transcriptome library preparation
Antibodies and ELISA Kits	Anti-ABA antibodies, Anti-GA antibodies	Quantification of hormone levels in plant tissues
Growth Media Components	Nutrient agars, Carbon sources, Antibiotics	Resuscitation of dormant microbes; germination assays under controlled conditions
Metabolite Standards	Shikimic acid, Trehalose, Polyamines, Carotenoid precursors	Identification and quantification of key metabolites in metabolic studies [21]
SIRT5 inhibitor 9	SIRT5 inhibitor 9, MF:C24H29ClN8O4S, MW:561.1 g/mol	Chemical Reagent
Tmv-IN-8	Tmv-IN-8, MF:C26H22N6O4, MW:482.5 g/mol	Chemical Reagent

This comparative analysis reveals that despite phylogenetic distance, both plants and microorganisms employ dormancy as a central survival strategy with remarkable parallels in physiological implementation. The ABA-GA system in plants represents a dedicated hormonal regulatory module for dormancy control, while microbial dormancy involves more direct environmental sensing coupled with diverse species-specific mechanisms. Both systems converge on similar metabolic adaptations, including energy conservation, accumulation of protective compounds, and oxidative stress management.

Future research should focus on identifying potential signaling equivalents to ABA-GA antagonism in microbial systems, particularly in light of evidence that seed-associated microbes can influence host plant dormancy and stress responses [22]. The role of microbial-derived small molecules in regulating dormancy transitions warrants deeper investigation. Additionally, advancing single-cell techniques will enable more precise characterization of metabolic heterogeneity within dormant populations across biological systems.

From an applied perspective, understanding these hormonal gatekeepers and their microbial equivalents offers promising avenues for agricultural improvement, pharmaceutical development (e.g., against persistent microbial infections), and biotechnology applications. Manipulating dormancy transitions through hormonal or metabolic interventions represents a powerful approach for enhancing stress resilience in crops and controlling microbial persistence in clinical and industrial settings.

This guide provides a comparative analysis of adenosine triphosphate (ATP), starch, and glycogen, focusing on their distinct yet interconnected roles in biological energy metabolism. Framed within a study of dormant plant seeds and microbial cells, we examine the molecular architecture, metabolic pathways, and dynamic regulation of these essential molecules. The objective data and experimental methodologies presented herein are designed to inform research in drug development and metabolic engineering, particularly in leveraging dormancy strategies for biotechnological and therapeutic applications.

Dormancy represents a critical survival strategy across life forms, characterized by a reversible state of profoundly reduced metabolic activity. In both plant seeds and microbial cells, dormancy is not a period of inactivity but rather a state maintained through meticulous energy management [23] [24]. This review centers on the molecules at the heart of this process: ATP, the universal energy currency, and its primary storage forms—starch in plants and glycogen in animals and microorganisms. A comparative understanding of their dynamics is essential for research areas ranging from synthetic biology, where ATP-free biotransformation platforms are emerging [25], to medical science, where defective glycogen metabolism causes diseases like Lafora disease and Glycogen Storage Diseases (GSDs) [26]. Furthermore, the study of dormant microbial cells, which can persist for millennia on trace atmospheric energy sources like hydrogen, is reshaping our understanding of metabolic flexibility and energy conservation [23] [24].

Molecular Structures and Comparative Functions

The functional differences between ATP, starch, and glycogen are rooted in their distinct chemical structures.

Adenosine Triphosphate (ATP) is a nucleotide consisting of an adenine base, a ribose sugar, and three phosphate groups. The energy stored in its high-energy phosphoanhydride bonds, particularly between the beta and gamma phosphates, is released upon hydrolysis to ADP and inorganic phosphate (Pi) [27] [28]. This energy powers nearly all cellular processes, from chemical synthesis to mechanical work [28].

Starch, the principal energy reserve in plants, is a polysaccharide composed of glucose monomers. It exists as a mixture of two polymers: amylose, a largely linear chain with α(1→4) linkages, and amylopectin, a highly branched molecule with both α(1→4) chains and α(1→6) branch points [29] [30]. The semi-crystalline, insoluble granules of starch are suited for long-term energy storage in plant seeds and tubers [30].

Glycogen, often termed "animal starch," is the primary glucose storage polymer in animals, humans, and many bacteria [31] [26]. Its structure is markedly similar to amylopectin but is significantly more branched, with tiered branching occurring every 8-12 glucose residues [26]. This extensive branching creates a tree-like, spherical structure that is soluble in water and provides a multitude of non-reducing ends for enzymes to act upon, allowing for rapid mobilization [31] [26].

Table 1: Comparative Structural and Functional Profiles of ATP, Starch, and Glycogen.

Feature	ATP (Adenosine Triphosphate)	Starch (Plant)	Glycogen (Animal/Microbial)
Chemical Nature	Nucleotide	Polysaccharide	Polysaccharide
Monomer Unit	Adenosine + 3 Phosphate groups	α-D-Glucose	α-D-Glucose
Polymer Structure	N/A (Single molecule)	Amylose (linear, α1→4) & Amylopectin (branched, α1→4 & α1→6)	Highly branched, tiered structure (α1→4 & α1→6)
Branching Frequency	N/A	~Every 20-30 residues (Amylopectin)	~Every 8-12 residues
Solubility in Water	High	Low (forms granules)	High
Primary Function	Universal energy currency; immediate energy source	Long-term, stable energy storage in plants	Rapidly mobilizable glucose reserve in animals & microbes
Primary Location	Cytosol of all cells	Plastids (e.g., seeds, tubers)	Liver, muscle cells, cytosol of microbial cells

Metabolic Pathways and Experimental Analysis

The synthesis and breakdown of these molecules are governed by specialized enzymatic pathways. In dormant systems, the regulation of these pathways determines survival.

ATP Synthesis and Hydrolysis

Cells employ two primary mechanisms to generate ATP [32] [28]:

• Substrate-Level Phosphorylation: A phosphate group is transferred directly from a high-energy substrate to ADP, forming ATP. This occurs in the cytosol during glycolysis and in the mitochondrial matrix during the citric acid cycle [32] [27].
• Oxidative Phosphorylation: This process in the mitochondrial inner membrane generates the majority of ATP. High-energy electrons from NADH and FADH2 are passed along an electron transport chain, creating a proton gradient that drives ATP synthesis via ATP synthase [32] [28].

ATP hydrolysis is the energy-releasing reaction that powers cellular work. A working muscle cell cyclizes an astonishing 10 million ATP molecules per second [28].

Glycogen and Starch Metabolism

Glycogenolysis, the breakdown of glycogen, is initiated by glycogen phosphorylase, which catalyzes the phosphorolytic cleavage of α(1→4) linkages to produce glucose-1-phosphate without consuming ATP [31] [30]. A debranching enzyme handles α(1→6) linkages. In the liver and kidneys, but not in muscle, glucose-6-phosphatase can produce free glucose from glucose-6-phosphate to regulate blood sugar [31]. The following diagram illustrates the glycogenolysis pathway and its connection to glycolysis.

Diagram 1: Glycogenolysis metabolic pathway. The red node highlights the liver/kidney-specific step for blood glucose regulation.

The experimental analysis of glycogen often involves histological staining with Periodic Acid-Schiff (PAS) for light microscopy, while electron microscopy is required to visualize individual glycogen particles. Advanced techniques now employ recombinant proteins like the carbohydrate-binding module from Stbd1 for more specific detection via ELISA [31]. For Glycogen Storage Diseases (GSDs), DNA mutational analysis has largely replaced invasive liver biopsies for diagnosis [31].

Energy Metabolism in Dormant Microbes

Dormant microbial cells exhibit remarkable metabolic flexibility to meet maintenance energy demands. Studies show that rather than simply downregulating growth metabolism, many aerobic bacteria broaden their metabolic repertoire during starvation [23]. For example, obligate heterotrophs can scavenge inorganic energy sources like atmospheric hydrogen (Hâ‚‚) [23]. This process is mediated by specialized high-affinity, oxygen-tolerant [NiFe]-hydrogenases that feed electrons into the respiratory chain. Deletion of these enzymes impairs long-term survival, confirming their critical role in persistence [23]. The following workflow outlines a protocol for investigating such metabolic strategies in dormant cells.

Diagram 2: Integrative workflow for analyzing microbial persistence metabolism.

Experimental Data and Protocols

This section details key experimental approaches and data for studying these energy molecules.

An ATP-Free In Vitro Biotransformation System

A 2024 study demonstrated an innovative ATP-free in vitro synthetic enzymatic biosystem (ivSEB) for producing poly-3-hydroxybutyrate (PHB) from maltodextrin [25]. This system cleverly bypasses the need for costly ATP by using a network of 17 enzymes. The pathway relies on α-glucan phosphorylase (αGP) to phosphorylate maltodextrin using inorganic phosphate (Pi), generating glucose-1-phosphate without ATP consumption. The system achieved a high PHB titer of 208.3 mM (~17.9 g/L) and the fastest production rate reported for such systems, showcasing the potential for industrial-scale production of acetyl-CoA-derived chemicals from starch without ATP [25].

Table 2: Experimental Outcomes from ATP-Free ivSEB for PHB Production [25].

Performance Metric	Result Achieved	Contextual Comparison
PHB Titer	208.3 mM (≈17.9 g/L)	Highest among reported ivSEBs
Production Rate	9.4 mM/h (≈0.8 g/L/h)	Fastest among reported ivSEBs
Molar Yield	125.5% (of theoretical 133.3%)	Near-theoretical yield achieved
Key Innovation	Elimination of ATP dependence via α-glucan phosphorylase	Overcomes cost and stability issues
Scalability	Demonstrated with doubled substrate concentration	Indicates promising industrial potential

Protocol: Investigating Glycogenolysis in Tissue Samples

Objective: To quantify the rate of glycogen breakdown and identify the primary products in different tissue types (e.g., liver vs. muscle).

Materials:

• Homogenized tissue sample (e.g., from liver or muscle).
• Assay buffer (e.g., containing pH 7.4 Tris-HCl, Kâ‚‚HPOâ‚„/KHâ‚‚POâ‚„, EDTA).
• Glycogen phosphorylase activity assay kit (commercial).
• Enzyme mix: Phosphoglucomutase, Glucose-6-phosphate dehydrogenase.
• Glucose-6-phosphatase activity assay kit (commercial).
• NADP+, Inorganic phosphate (Pi) assay reagent.
• Stopping solution (e.g., Trichloroacetic acid).
• Spectrophotometer or LC-MS system.

Methodology:

• Tissue Preparation: Homogenize the frozen tissue sample in a cold, appropriate buffer. Centrifuge to obtain a clear supernatant for the enzyme source.
• Reaction Setup: Incubate the tissue supernatant with assay buffer and 0.5% glycogen at 37°C. Aliquot the reaction mixture at regular time points (e.g., 0, 5, 10, 20 min) and stop the reaction immediately.
• Product Analysis:
  • For Glucose-1-Phosphate/Glucose-6-Phosphate: Couple the reaction to a system with phosphoglucomutase and glucose-6-phosphate dehydrogenase, and monitor the reduction of NADP+ to NADPH by absorbance at 340 nm.
  • For Free Glucose: Use a specific glucose-6-phosphatase assay or a glucose oxidase kit to detect free glucose production, particularly important for liver samples.
  • For Inorganic Phosphate: Use a Pi assay reagent (e.g., malachite green based) to quantify Pi consumption, which correlates with phosphorylase activity.
• Data Calculation: Compare the rates of product formation between tissues. Liver extracts are expected to produce significant free glucose, while muscle extracts will show primarily glucose-6-phosphate production [31].

The Scientist's Toolkit: Essential Research Reagents

The following table catalogs key reagents essential for experimental research in energy metabolism, particularly concerning ATP, glycogen, and starch dynamics.

Table 3: Key Research Reagents for Energy Metabolism Studies.

Research Reagent / Material	Core Function in Experimentation
ATP, NADP+, Acetyl-CoA	Core substrates and coenzymes for enzymatic assays and in vitro pathway reconstruction.
High-Affinity [NiFe]-Hydrogenase	Key enzyme for studying microbial survival on atmospheric trace gases like Hâ‚‚ [23].
Recombinant Stbd1 Protein	Used in ELISA-based assays for specific detection and quantification of cellular glycogen [31].
α-Glucan Phosphorylase (αGP)	Enables ATP-free phosphorylation of maltodextrin/starch derivatives in synthetic enzymatic systems [25].
Glucose-6-Phosphate Dehydrogenase	Coupling enzyme for spectrophotometric quantification of glucose-6-phosphate or NADPH generation.
Glycogen Phosphorylase & Debranching Enzyme	Essential for in vitro reconstitution of glycogenolysis and study of its regulation [31].
Phosphate Acetyltransferase (PTA)	Converts acetyl-phosphate to acetyl-CoA in synthetic pathways, a key step for bioproduction [25].
Glucose-6-Phosphatase Assay Kit	Critical for determining the gluconeogenic capacity of tissues like liver and kidney [31].
PhaA-PhaB-PhaC Enzyme Cascade	A three-enzyme system used in ivSEBs to convert acetyl-CoA and NADPH into the bioplastic PHB [25].
Cinnamyl Alcohol-d9	Cinnamyl Alcohol-d9, MF:C9H10O, MW:143.23 g/mol
Z-Arg-Arg-pNA	Z-Arg-Arg-pNA, MF:C26H36N10O6, MW:584.6 g/mol

The comparative dynamics of ATP, starch, and glycogen reveal a sophisticated interplay between immediate energy currency and strategic energy reserves. In dormant systems, from plant seeds to microbial cells, the regulation of their synthesis and degradation is paramount for survival and dictates the resumption of metabolic activity. Modern research, leveraging tools from integrative geobiology to ATP-free synthetic systems, continues to uncover the profound metabolic flexibility that these molecules enable. A deep understanding of these principles is driving innovation in therapeutic development for metabolic disorders and the creation of efficient, cell-free biomanufacturing platforms.

Advanced Tools for Profiling Dormant Metabolisms: From Omics to Functional Platforms

Integrative multi-omics analyses represent a transformative approach in systems biology, enabling the comprehensive profiling of multiple biological layers including RNA expression levels (transcriptomics) and metabolite levels (metabolomics) [33]. The combination of these layers is particularly powerful for deciphering complex regulatory networks, as metabolites serve as the downstream output of biological processes, carrying imprints of genomic, epigenomic, and environmental effects [33]. They are often referred to as "the link between genotype and phenotype" and have been implicated in numerous biological processes [33]. In the specific context of energy metabolism in dormant plant seeds versus microbial cells, multi-omics approaches reveal how different biological systems regulate metabolic activity, maintain energy homeostasis during dormant states, and reactivate metabolic processes during germination or cellular activation.

The fundamental principle underlying transcriptomics and metabolomics integration is that genes and metabolites participating in the same biosynthetic pathway, along with structural genes and their regulatory counterparts, frequently display analogous expression patterns [34]. Based on this principle, gene-metabolite regulatory networks have been successfully constructed in multiple species to identify putative regulatory factors governing metabolite biosynthesis [34]. Two major integration paradigms exist: simultaneous integration (using all available omics data at the same time in a single modeling step) and step-wise integration (analyzing omics datasets in isolation or specific combinations and integrating results subsequently) [33]. The choice between these approaches depends on available data and research objectives, with each offering distinct advantages for uncovering regulatory networks in metabolic studies.

Methodological Framework: Experimental Design and Protocols

Core Experimental Workflow for Multi-Omics Studies

A typical multi-omics workflow involves several critical stages from experimental design through data interpretation. For studies investigating energy metabolism in dormant seeds or microbial cells, the workflow generally follows these key phases:

Table 1: Key stages in multi-omics experimental workflow

Stage	Key Activities	Considerations for Metabolic Studies
Sample Preparation	Collection, stabilization, and processing of biological materials	Critical to preserve metabolic state; rapid freezing in liquid nitrogen commonly used [35]
Data Acquisition	Application of transcriptomic and metabolomic profiling technologies	RNA sequencing for transcriptomics; LC-MS or GC-MS for metabolomics [34] [35]
Data Preprocessing	Quality control, normalization, and annotation of raw data	Parameter tuning based on data type; batch effect correction often necessary [33] [36]
Dimensionality Reduction	Handling high-dimensional datasets to identify meaningful variation	PCA, PLS-DA, or other multivariate techniques to reduce variables [33]
Data Integration	Combining omics layers using appropriate computational methods	Choice between simultaneous vs. step-wise integration based on data structure [33]
Interpretation	Biological context analysis of integration results	Pathway mapping, network analysis, and validation experiments [34] [36]

Detailed Protocol for Integrated Transcriptomics-Metabolomics Analysis

The following protocol outlines a standardized approach for generating and integrating transcriptomic and metabolomic data, adaptable for both plant seed and microbial cell studies:

Sample Collection and Preparation:

• For dormant seed studies: Collect seeds at specific dormancy stages (e.g., morphological dormancy, physiological dormancy, germination stages) with biological replicates [35]. Surface-sterilize seeds when studying endophytic microbial communities [37].
• For microbial cells: Harvest cells at defined growth phases or metabolic states relevant to energy metabolism.
• Immediately flash-freeze samples in liquid nitrogen to preserve metabolic states and transcript profiles [35].
• Store samples at -80°C until analysis to maintain integrity.

Transcriptomic Profiling:

• Extract total RNA using standardized kits (e.g., TRIzol method), verifying RNA quality and integrity using Bioanalyzer or similar systems [35].
• Prepare sequencing libraries using appropriate kits (e.g., NEBNext Ultra RNA Library Prep Kit for Illumina) [35].
• Perform sequencing on platforms such as Illumina Novaseq 6000 with 2×150 bp read length [35].
• Process raw data: quality control (Fastp), read alignment, and quantification of gene expression levels [37].

Metabolomic Profiling:

• Employ metabolite extraction protocols suitable for the biological system (seeds or microbial cells).
• Utilize either widely-targeted metabolomic approaches or untargeted profiling [35].
• Analyze metabolites using LC-MS/MS or GC-MS platforms with appropriate chromatography columns and mass spectrometry parameters.
• Identify metabolites by comparing mass spectra and retention times to standard compound databases.

Data Integration and Analysis:

• Identify differentially expressed genes (DEGs) and differential metabolites (DAMs) using statistical comparisons between experimental conditions [35].
• Apply correlation analysis to identify associations between gene expression and metabolite abundance.
• Conduct functional enrichment analysis (KEGG, GO) to identify biological pathways enriched in both DEGs and DAMs [37] [36].
• Construct gene-metabolite networks using specialized bioinformatics tools and platforms [34] [36].

Comparative Analysis of Regulatory Networks in Dormant Seeds vs. Microbial Cells

Energy Metabolism Regulation in Dormant Plant Seeds

Multi-omics studies on dormant seeds have revealed sophisticated regulatory networks that control energy metabolism during dormancy and germination. In Notopterygium incisum seeds, integrated transcriptomic and metabolomic analysis identified crucial changes in hormone signaling, carbohydrate metabolism, and specialized metabolism during dormancy release [35]. Key findings include:

• Hormonal regulation: Stratification treatment reduces ABA content while increasing GA3 and IAA levels, with corresponding changes in expression of ABA signaling genes (ABI1, PP2CA, ABI5, ABF4) and GA signaling genes (GAI, GAI1, RGL1) [35].
• Carbohydrate metabolism: Genes related to starch and sucrose metabolism are upregulated during dormancy release, facilitating energy production for germination [35].
• Phenylpropanoid and flavonoid biosynthesis: These pathways are significantly activated after dormancy release, with accumulation of flavonoids like quercetin, rutin, delphinidin, and naringenin chalcone increasing substantially [35].

Similar regulatory patterns were observed in barley seeds, where germination involves complex regulation of reactive oxygen species (ROS), reactive nitrogen species (RNS), and metabolic pathways [38]. The ascorbate-glutathione cycle enzymes, responsible for scavenging ROS, show strongly increased activities during germination, while fermentation enzymes (lactate and alcohol dehydrogenase) decline rapidly after radicle protrusion as aerobic metabolism increases [38].

Table 2: Key metabolic regulators in dormant seed germination

Regulatory Component	Function in Seed Dormancy/Germination	Experimental Evidence
ABA signaling genes (ABI1, PP2CA, ABI5, ABF4)	Maintain dormancy; downregulated during dormancy release	Significant downregulation during stratification in N. incisum seeds [35]
GA signaling genes (GAI, GAI1, RGL1)	Promote germination; expression changes during dormancy release	Significant downregulation in N. incisum seeds during dormancy release [35]
Carbohydrate metabolism genes (BGLU, etc.)	Mobilize energy reserves for germination	Upregulation during dormancy release in N. incisum and C. giganteum seeds [5] [35]
Phenylpropanoid biosynthesis genes	Enhance resistance and environmental adaptation	Significant upregulation after dormancy release in N. incisum seeds [35]
ROS scavenging enzymes (APX, GR, CAT, SOD)	Manage oxidative stress during germination	Activities strongly increase during barley seed germination [38]

Metabolic Regulation in Microbial Systems

In microbial cells, particularly endophytic communities associated with seeds, multi-omics approaches have revealed how microbial metabolic capabilities adapt to support host plant needs. During Amorphophallus muelleri seed maturation, the functional dynamics of endophytic microbial communities show remarkable specialization [37]:

• Early maturation stages: Microbial functional genes involved in energy production and conversion, as well as carbon fixation, are enriched in seed coats [37].
• Later maturation stages: Microbial genes involved in signal transduction mechanisms, amino acid transport and metabolism, carbohydrate metabolism, and lipid metabolism are more highly expressed in seeds [37].
• Resistance enhancement: In middle to late stages of seed maturation, microbial functional genes involved in biosynthesis of resistant compounds (phenols, flavonoids, alkaloids) are significantly enriched to enhance host resistance and environmental adaptation [37].

For zinc hyperaccumulation in Sedum alfredii, multi-omics integration revealed that transcriptional and translational changes play critical roles in maintaining Zn homeostasis, with adaptations including enhanced photosynthetic efficiency, improved Zn ion binding in shoots, and increased antioxidative capacities [39]. Carbon and sulfur metabolic pathways respond significantly to Zn treatment, with key components of the TCA cycle along with stress-related amino acids, fatty acids, sugars, antioxidants, and Zn-binding phenolics being coordinately modulated under Zn exposure [39].

Comparative Regulatory Principles Across Biological Systems

The comparative analysis of regulatory networks in dormant seeds versus microbial cells reveals both conserved and specialized principles of energy metabolism regulation:

Conserved Principles:

• Energy reserve mobilization: Both systems show precise regulation of carbohydrate metabolism genes to control energy availability during dormant states and activation phases.
• Stress management systems: ROS scavenging systems and specialized metabolite production for stress protection are upregulated in both seeds and microbial cells during state transitions.
• Environmental sensing and signaling: Both systems employ sophisticated signal transduction mechanisms to perceive environmental cues and trigger appropriate metabolic responses.

Specialized Adaptations:

• Temporal coordination: In seeds, metabolic regulation follows a precise temporal sequence during dormancy release, while microbial communities show functional adaptation to host developmental stages.
• Community versus individual regulation: Microbial metabolic regulation often occurs at community level with functional specialization, while seed metabolism is regulated at the individual organism level.
• Host-symbiont coordination: Endophytic microbial communities demonstrate metabolic complementarity with their host plants, enhancing overall system functionality.

Essential Research Tools and Reagent Solutions

The Scientist's Toolkit for Multi-Omics Integration

Table 3: Essential research reagents and platforms for transcriptomics-metabolomics studies

Category	Specific Tools/Reagents	Function in Multi-Omics Workflow
RNA Sequencing	TRIzol reagent, NEBNext Ultra RNA Library Prep Kit, Illumina Novaseq 6000 platform	RNA extraction, library preparation, and transcriptome sequencing [35]
Metabolite Profiling	LC-MS/MS systems, GC-MS platforms, standard compound databases	Metabolite separation, detection, and identification [35]
Bioinformatics Analysis	Fastp, MEGAHIT, Prodigal, Diamond, KEGG database, COG database	Quality control, sequence assembly, gene prediction, functional annotation [37]
Data Integration	Majorbio Cloud Platform, Ingenuity Pathway Analysis (IPA), Genome Enhancer platform	Multi-omics data integration, pathway analysis, network construction [37] [40] [36]
Specialized Assays	ELISA kits for hormone quantification, enzyme activity assay kits	Targeted validation of specific metabolic pathways and signaling components [35]
Kltwqelyqlkykgi	Kltwqelyqlkykgi, MF:C92H143N21O23, MW:1911.2 g/mol	Chemical Reagent
Bombinin H7	Bombinin H7 Peptide	Bombinin H7 is an antimicrobial peptide for research on bacterial targeting mechanisms. This product is for research use only (RUO). Not for personal use.

Visualization of Multi-Omics Workflows and Regulatory Networks

Integrated Multi-Omics Analysis Workflow

Comparative Regulatory Networks in Seed Dormancy

The integration of transcriptomics and metabolomics provides powerful capabilities for deciphering the complex regulatory networks governing energy metabolism in dormant plant seeds and microbial cells. The comparative analysis reveals both conserved principles and specialized adaptations in how biological systems regulate metabolic activity during dormant states and transition to active growth phases. Key conserved mechanisms include hormonal regulation, carbohydrate mobilization, and stress management systems, while specialized adaptations reflect the unique ecological niches and life history strategies of different biological systems.

Future directions in this field will likely include more sophisticated temporal resolution of metabolic transitions, enhanced spatial mapping of metabolic processes within tissues and cellular compartments, and the integration of additional omics layers (epigenomics, proteomics) to create more comprehensive regulatory network models. The continued development of bioinformatics tools for multi-omics integration will be crucial for advancing our understanding of the complex metabolic regulation underlying dormancy and activation across biological systems. These approaches hold significant promise for applications in crop improvement, microbial biotechnology, and conservation of endangered plant species.

Mass spectrometry-based metabolomics has emerged as a powerful analytical approach for comprehensively analyzing small molecules within biological systems, providing unprecedented insights into metabolic pathways and their perturbations. This technology enables researchers to quantify and identify metabolites across diverse biological contexts, from dormant plant seeds to microbial cells, revealing conserved and divergent principles of energy metabolism. The global metabolomics market, valued at USD 2.1 billion in 2024 and projected to reach USD 6.6 billion by 2030, reflects the growing importance of this field in basic and translational research [41].

In comparative energy metabolism studies, MS-based metabolomics provides a unique window into the metabolic states that characterize different physiological conditions. By simultaneously measuring hundreds to thousands of metabolites, researchers can identify key metabolic switches that regulate transitions between metabolic states—such as the shift from dormancy to germination in plants or variations in energy conservation strategies among sulfate-reducing microorganisms [42] [43]. The integration of advanced computational approaches with high-resolution mass spectrometry has further enhanced our ability to interpret these complex metabolic networks within the broader context of systems biology.

Applications in Plant Seed Dormancy Research

Metabolic Signatures of Dormancy and Germination

MS-based metabolomics has revealed profound metabolic differences between dormant and non-dormant seeds across multiple plant species. In Korean pine seeds, metabolic profiling of embryos showed distinct patterns between primary dormant seeds and seeds released from dormancy [42]. Nondormant embryos exhibited substantial increases in most sugars, organic acids, and amino acids during early germination stages, reflecting the initiation of biosynthetic processes essential for growth. Particularly striking was the dramatic decrease in key glycolytic and TCA cycle intermediates—including fructose-6-phosphate, 3-phosphoglyceric acid, and D-glucose-6-phosphate—which decreased by 409-, 58-, and 41-fold respectively as germination progressed, indicating a strong slowdown of core energy metabolism pathways once germination was established [42].

In Cardiocrinum giganteum seeds, transcriptomic analyses integrated with metabolic data have revealed that temperature-induced dormancy release involves coordinated changes in plant hormone signal transduction, carbohydrate metabolism, and phenylpropanoid biosynthesis [5]. Notably, β-glucosidase genes associated with polysaccharide hydrolysis showed significant upregulation (5.08- to 6.85-fold) as dormancy was released, facilitating the mobilization of energy reserves. These molecular events correlate with visible embryo elongation and subsequent germination, demonstrating how MS-based approaches can link metabolic changes with phenotypic outcomes [5].

Metabolic Basis of Pre-harvest Sprouting in Crops

Comparative metabolomics of pre-harvest sprouting in rice has provided insights with significant agricultural implications. Studies comparing PHS-sensitive and PHS-resistant rice varieties have revealed that sprouting correlates with decreased starch content and increased soluble sugars and amylase activity [44]. The PHS-sensitive variety exhibited a 29.3% reduction in starch content compared to only 13.0% in the resistant variety under high humidity conditions. Concurrently, amylase activity increased by 48.3% in the sensitive variety versus only 19.3% in the resistant variety, demonstrating the metabolic basis of dormancy maintenance and release [44].

Table 1: Key Metabolic Changes During Seed Dormancy Release in Various Plant Species

Plant Species	Dormancy State	Key Metabolic Changes	Magnitude of Change	Citation
Korean pine	Primary dormancy vs released	Decrease in fructose-6-phosphate	409-fold decrease	[42]
Korean pine	Primary dormancy vs released	Decrease in 3-phosphoglyceric acid	58-fold decrease	[42]
Korean pine	Primary dormancy vs released	Decrease in D-glucose-6-phosphate	41-fold decrease	[42]
Rice (PHS-sensitive)	Pre-harvest sprouting	Starch content	29.3% decrease	[44]
Rice (PHS-resistant)	Pre-harvest sprouting	Starch content	13.0% decrease	[44]
Rice (PHS-sensitive)	Pre-harvest sprouting	Amylase activity	48.3% increase	[44]
Rice (PHS-resistant)	Pre-harvest sprouting	Amylase activity	19.3% increase	[44]

Applications in Microbial Energy Metabolism

Diversity of Energy Conservation Strategies in Sulfate-Reducing Microorganisms

Comparative genomic analysis of 25 sulfate-reducing organisms has revealed remarkable diversity in their energy metabolism strategies, which can be validated and expanded through MS-based metabolomic approaches [43]. These microorganisms employ various ion-translocating complexes including H+-pyrophosphatases, complex I homologs, Rnf complexes, and Ech/Coo hydrogenases to conserve energy. The Deltaproteobacteria group is characterized by numerous cytochromes c and associated membrane redox complexes, indicating heavy reliance on periplasmic electron transfer pathways. In contrast, Archaeal and Clostridial sulfate-reducers contain practically no cytochromes c, suggesting fundamentally different metabolic architectures [43].

MS-based metabolomics has been particularly valuable in identifying flavin-based electron bifurcating mechanisms that play crucial roles in energy conservation across diverse anaerobic microorganisms. These mechanisms involve cytoplasmic [NiFe] and [FeFe] hydrogenases, formate dehydrogenases, and heterodisulfide reductase-related proteins that couple exergonic and endergonic reactions to maximize energy yield from diverse electron donors including H₂, formate, pyruvate, NAD(P)H, and β-oxidation intermediates [43].

Microbial Metabolism in Host-Pathogen Interactions

An innovative iterative comparative metabolomics pipeline has been developed to investigate microbial metabolic activity during gram-negative bloodstream infections, highlighting how pathogens modify their metabolic environment [45]. This approach revealed elevated levels of bacterially derived acetylated polyamines during infection and identified the enzyme responsible for their production. Targeting this metabolic pathway through genetic or pharmacological inhibition reduced bacterial proliferation, enhanced membrane permeability, and increased intracellular antibiotic accumulation, effectively countering antimicrobial resistance mechanisms [45].

Table 2: Energy Metabolism Proteins in Sulfate-Reducing Organisms

Protein/Complex	Function	Distribution in SRO	Metabolic Role	Citation
Cytochromes c	Periplasmic electron transfer	Abundant in Deltaproteobacteria, absent in Archaea/Clostridia	Respiratory chain components	[43]
H+-pyrophosphatases	Proton translocation	Selected species across taxa	Energy conservation from pyrophosphate hydrolysis	[43]
Ech/Coo hydrogenases	Hydrogen metabolism	7 of 25 organisms analyzed	Energy coupling through Hâ‚‚ cycling	[43]
Rnf complex	Ion translocation	8 of 25 organisms analyzed	Energy conservation from reduced ferredoxin	[43]
Electron-bifurcating [FeFe] hydrogenases	Cytoplasmic Hâ‚‚ metabolism	8 of 25 organisms analyzed	Coupling exergonic and endergonic reactions	[43]

Experimental Methodologies and Protocols

Sample Preparation and Analytical Platforms

Mass spectrometry-based metabolomics employs diverse sample preparation and analytical approaches tailored to specific research questions. For plant seed metabolomics, researchers typically extract metabolites from embryonic tissues using methanol-based extraction protocols, followed by analysis via either targeted or nontargeted approaches [46] [42]. Targeted assays using stable isotope-labeled internal standards provide precise quantification of specific metabolite classes like amino acids, while nontargeted approaches offer broader coverage of metabolic features.

Two predominant separation techniques are coupled to mass spectrometry for metabolomic analyses: liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry. LC-MS is particularly valuable for analyzing thermally labile and non-volatile compounds, while GC-MS provides excellent separation efficiency and reproducibility for volatile metabolites or those rendered volatile through chemical derivatization [46] [47]. The choice between these techniques depends on the specific metabolites of interest and the required sensitivity and coverage.

Data Processing and Metabolic Pathway Analysis

Raw mass spectrometry data undergoes extensive processing including peak detection, alignment, and normalization before statistical analysis. Both unsupervised and supervised multivariate statistical methods are employed to identify metabolites differentially abundant between experimental conditions. Following statistical analysis, metabolites are annotated using mass spectral libraries, and pathway analysis tools map these metabolites to biochemical pathways [46].

In microbial studies, metabolomic data can be integrated with genome-scale metabolic models to predict functional capabilities and metabolic interactions within communities. Comparative analysis of models reconstructed using different automated tools reveals substantial variations in predicted metabolic networks, highlighting the importance of consensus approaches that combine multiple reconstruction methods [48]. These integrated approaches enhance our understanding of metabolic potential and actual metabolic activity in microbial systems.

Diagram 1: MS-Based Metabolomics Workflow. This diagram illustrates the standard pipeline for mass spectrometry-based metabolomics studies, from study design to biological interpretation.

Comparative Analysis: Conserved Principles and System-Specific Adaptations

Metabolic Transitions Between Quiescent and Active States

Both dormant seeds and microbial cells demonstrate conserved metabolic principles when transitioning between quiescent and active states. In both systems, energy metabolism reorganization represents a fundamental characteristic of state transitions. Dormant Korean pine seeds maintain higher operational rates of glycolysis and TCA cycle compared to their nondormant counterparts, while sulfate-reducing microorganisms modulate electron transfer pathways and ion-translocating complexes to adapt to environmental conditions [42] [43].

Another conserved principle involves the strategic allocation of carbon resources. In dormant seeds, carbon remains stored as complex polymers like starch, whereas upon dormancy release, metabolic pathways mobilize these reserves into soluble sugars that fuel growth. Similarly, microbial systems redirect carbon flow through central metabolic pathways in response to environmental cues and energy availability. These shared strategies highlight evolutionary convergence in metabolic regulation across biological kingdoms.

System-Specific Metabolic Adaptations

Despite these conserved principles, important system-specific adaptations distinguish plant seed dormancy from microbial energy metabolism. Plant seeds rely heavily on hormonal regulation with abscisic acid and gibberellins playing central roles in dormancy maintenance and release [5] [44]. Microbial systems, in contrast, typically utilize different signaling mechanisms, though some employ hormone-like molecules for communication and coordination.

Additionally, compartmentalization differences significantly impact metabolic organization. Plant seeds contain multiple subcellular compartments and tissue types that create physical separations of metabolic processes, whereas microbial cells often rely on membrane-associated complexes and periplasmic spaces to organize metabolism. These structural differences are reflected in the distinct metabolic strategies employed by each system.

Diagram 2: Metabolic Transitions Between Physiological States. This diagram illustrates the conserved metabolic principles underlying transitions between dormant/quiescent and active states across biological systems.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents and Materials for MS-Based Metabolomics

Category	Specific Products/Technologies	Key Function	Application Examples
Mass Spectrometry Instruments	Triple Quadrupole, Q-TOF, FTMS, Quadrupole, TOF	Metabolite separation, detection, and quantification	Targeted and untargeted metabolite profiling [47]
Chromatography Systems	LC-MS, GC-MS	Separation of complex metabolite mixtures	Analysis of thermally labile (LC-MS) and volatile (GC-MS) compounds [47]
Isotope-Labeled Internal Standards	Stable isotope-labeled amino acids, lipids, sugars	Precise quantification of specific metabolite classes	Targeted metabolomics in HAPO study [46]
Metabolite Extraction Reagents	Methanol, acetonitrile, chloroform	Efficient metabolite extraction from biological samples	Plant seed and microbial metabolite extraction [46] [42]
Chemical Derivatization Reagents	Methoxyamine, MSTFA, TMS	Volatilization of metabolites for GC-MS analysis	Nontargeted GC/MS analysis of serum metabolites [46]
Data Analysis Software	AMDIS, XCMS, MetaboAnalyst	Peak detection, alignment, and statistical analysis	Deconvolution of GC/MS data [46]
Metabolic Pathway Databases	KEGG, Unipathway, ModelSEED	Metabolic pathway annotation and visualization	Pathway analysis in rice PHS study [44]
Antibacterial agent 198	Antibacterial agent 198, MF:C21H15ClF3N3O2, MW:433.8 g/mol	Chemical Reagent	Bench Chemicals
Farobin A	Farobin A, MF:C27H30O14, MW:578.5 g/mol	Chemical Reagent	Bench Chemicals

Mass spectrometry-based metabolomics continues to evolve with emerging technologies enhancing its applications in comparative energy metabolism research. Spatial metabolomics techniques now enable the mapping of metabolite distributions within tissues, providing insights into metabolic heterogeneity and cell-to-cell communication [49]. The integration of artificial intelligence and bioinformatics is accelerating data interpretation and biomarker discovery, while advances in instrument sensitivity and resolution continue to expand the detectable metabolome [41].

These technological advances are increasingly being applied to translational research, including drug development and clinical diagnostics. The identification of bacterially derived acetylated polyamines during bloodstream infection exemplifies how metabolomic approaches can reveal novel therapeutic targets for addressing antimicrobial resistance [45]. Similarly, spatial metabolomics applications in cancer research are improving tumor diagnosis and characterization [49].

In conclusion, MS-based metabolomics provides powerful approaches for investigating global metabolic perturbations across diverse biological systems. By revealing both conserved principles and system-specific adaptations in energy metabolism, this technology continues to advance our understanding of fundamental biological processes while offering practical applications in agriculture, medicine, and biotechnology. As analytical capabilities continue to improve, metabolomics will undoubtedly yield further insights into the complex metabolic networks that underlie physiological and pathological states.

Spatial metabolomics represents a transformative approach in biological research by enabling the visualization of metabolite distributions within intact tissue specimens, thereby preserving critical spatial context that is lost in conventional bulk analysis techniques. This methodology is particularly valuable for investigating localized metabolic heterogeneity in complex biological systems, including the comparative analysis of energy metabolism in dormant plant seeds versus microbial cells. Traditional metabolomics approaches, which rely on homogenized tissue extracts, fundamentally dilute the metabolic phenotype by pooling various cell types and tissues together prior to metabolite extraction, making it impossible to map metabolites back to specific organelles, cells, or tissue locations [50]. This limitation becomes particularly problematic when studying dormancy, where metabolic compartmentalization and gradient formation play crucial regulatory roles.

The fundamental principle behind mass spectrometry imaging (MSI) combines molecular identification through mass spectrometry with spatial localization information. MSI creates two-dimensional maps where each pixel contains molecular information specific to its corresponding location within the sample [51]. This capability has led to rapid adoption across biological fields, with publications incorporating mass spectral imaging growing from approximately 80 articles in 2010 to over 378 yearly, constituting an increase of at least 350% during this time frame [50]. In dormancy research, MSI technologies allow scientists to investigate the spatial organization of metabolic pathways and cellular specialization that underlie dormant states in both plant and microbial systems.

Mass Spectrometry Imaging Technologies: A Comparative Framework

Fundamental MSI Technologies and Their Applications

Spatial metabolomics relies primarily on mass spectrometry imaging technologies that can detect and spatially resolve metabolite accumulation within biological samples. The most common techniques include matrix-assisted laser desorption ionization (MALDI), desorption electrospray ionization (DESI), and secondary ion mass spectrometry (SIMS), each with distinct advantages and limitations for studying dormant systems [50] [51].

MALDI-MSI represents the oldest and most widely utilized metabolite imaging technology. In this approach, samples are embedded within a matrix coating on a conductive material surface, followed by a series of laser pulses that result in desorption and ionization of metabolites [50]. The technology typically achieves pixel sizes of ~100-150 μm, with newer instruments approaching 5 μm or less, enabling cellular and subcellular resolution [50]. MALDI is often coupled with time-of-flight mass spectrometry (TOF-MS) but can be incorporated with other mass analyzers. Recent innovations have significantly improved data acquisition times through the development of faster lasers (from 20 Hz in older systems to 2000 Hz or more in newer instruments) [50]. For plant research, MALDI-MSI has been successfully applied to study seed development, fruit ripening, and plant-pathogen interactions [51].

DESI-MSI operates under ambient conditions, utilizing a charged solvent spray to extract ions from surfaces without requiring extensive sample preparation [50]. This technique is particularly advantageous for analyzing delicate plant tissues and microbial colonies that might be compromised by vacuum conditions or matrix application [51]. A key advantage of DESI-MSI is its ability to directly analyze solid samples such as tissues without necessitating sample preparation or extraction, making it suitable for high-throughput analyses [52]. Recent commercial developments include targeted imaging mass spectrometers that improve tissue analysis by enhancing sensitivity and speed, providing higher resolution in targeted imaging [52].

SIMS-MSI offers the highest spatial resolution among MSI techniques, capable of achieving submicron resolution that enables single-cell and subcellular metabolomics [53]. Time-of-Flight SIMS (TOF-SIMS) acquires lipids and metabolic fragments at sub-micron spatial details, making it particularly valuable for studying microbial communities and single-cell metabolic heterogeneity [53]. However, SIMS typically provides more limited chemical coverage compared to MALDI and DESI, often detecting smaller metabolites and lipid fragments rather than intact molecular species [53].

Comparative Technical Specifications

Table 1: Comparative Analysis of MSI Technologies for Dormancy Research

Parameter	MALDI-MSI	DESI-MSI	SIMS-MSI
Spatial Resolution	5-100 μm [50]	50-200 μm [53]	<1 μm [53]
Sample Environment	Vacuum or atmospheric [50]	Ambient conditions [51]	High vacuum [51]
Sample Preparation	Matrix application required [50]	Minimal preparation [52]	Optional matrix [51]
Molecular Coverage	Broad metabolome coverage [50]	Broad metabolome coverage [51]	Limited to smaller metabolites [53]
Ionization Mechanism	Matrix-assisted laser desorption/ionization [50]	Charged solvent spray [52]	Primary ion beam bombardment [53]
Typical Applications in Dormancy Research	Plant seed sections, microbial colonies on agar [54] [51]	Fresh plant tissues, microbial biofilms [51]	Single-cell metabolism, microbial ultrastructure [53]

Experimental Protocols for Dormancy Research

Sample Preparation Methodologies

Effective sample preparation is critical for preserving the original spatial distribution of metabolites in dormant biological systems. The optimal approach varies significantly between plant seeds and microbial cells, requiring specialized protocols for each sample type.

For plant seed tissues, sample preparation typically begins with careful sectioning using a cryostat to obtain thin tissue sections (typically 10-20 μm thickness) that maintain spatial integrity [51]. Plant tissues present unique challenges due to their heterogeneous cellular structures, presence of cuticular waxes, and specialized structures like trichomes, which can interfere with matrix application and ionization efficiency [51]. A recent study on Cannabis sativa leaves demonstrated that DESI-MSI could successfully visualize the distribution of cannabinoids and flavonoids on trichomes without requiring their removal [51]. For seed tissues with high water content, careful freeze-drying or controlled dehydration may be necessary to preserve labile metabolites while maintaining spatial fidelity.

For microbial cells and colonies, sample preparation must address different challenges. Microbial studies often require culturing on agar medium, which can dry under high vacuum-based instruments, causing flaking of the sample [51]. A novel approach to address this issue was developed for Bacillus subtilis colonies, using DHB matrix as an adhesive agent to improve the bonding of agar samples to MALDI targets [51]. This method prevented agar flaking and enabled visualization of metabolites in bacterial colonies, significantly expanding the scope of MALDI-MSI for agar-based microbial cultures [51].

MSI Data Acquisition and Processing

Data acquisition in MSI involves systematic rastering across the sample surface with simultaneous mass spectrometry data collection at each pixel location. The specific parameters must be optimized based on the research question and sample type:

For plant seed dormancy studies, MALDI-MSI parameters typically include laser spot sizes of 5-50 μm, laser energy optimized for matrix selection, and step sizes matching the desired spatial resolution [54]. A recent investigation of Polygonatum cyrtonema rhizomes utilized MALDI-MSI to map 93 metabolites across wild rhizome cross-sections, including saccharides, organic acids, amino acid derivatives, alkaloids, esters, and flavonoids [54]. The study revealed distinct spatial distributions, with saccharides accumulating primarily in rhizomes, while organic acid derivatives and alkaloids dominated sprouts and rhizomes [54].

For microbial dormancy studies, the higher spatial resolution of SIMS-MSI is often advantageous for resolving single-cell metabolic heterogeneity. The recently developed scSpaMet framework combines untargeted spatial metabolomics using TOF-SIMS with targeted multiplexed protein imaging, enabling correlation of over 200 metabolic markers and 25 protein markers in individual microbial cells [53]. This approach has revealed cell-type-dependent metabolite profiles and local metabolite competition between neighboring single cells [53].

Data processing for MSI experiments typically involves several critical steps: spectral preprocessing (noise reduction, baseline correction, normalization), image reconstruction, and statistical analysis. Emerging computational tools like JuliaMSI enable high-performance MSI analysis, processing large datasets up to five times faster than R-based tools [55]. These platforms incorporate noise reduction algorithms such as median filtering and Threshold Intensity Quantization (TrIQ) to enhance spatial resolution for biological ROI detection [55].

Metabolic Strategies in Dormant Systems: Plant vs. Microbial Cells

Energy Conservation Mechanisms

Dormant plant seeds and microbial cells employ fundamentally different yet conceptually parallel strategies for energy conservation and maintenance metabolism during dormancy.

In dormant plant seeds, energy metabolism is characterized by the accumulation of storage compounds that serve as energy reserves during dormancy and provide fuel for germination when conditions become favorable. Spatial metabolomic studies of Notopterygium incisum seeds during dormancy release revealed dynamic changes in carbohydrate metabolism, with genes related to starch and sucrose metabolism being up-regulated during dormancy release [35]. These spatial metabolic patterns provide crucial insights into the energy mobilization strategies that support dormancy maintenance and release in plant seeds.

In dormant microbial cells, energy conservation often involves metabolic flexibility and the ability to utilize alternative energy sources that are insufficient to support growth but can maintain basal cellular functions. Surprisingly, research has revealed that aerobic bacteria can broaden their metabolic repertoire during persistence rather than simply downregulating growth-associated processes [23]. For instance, obligate heterotrophs scavenge inorganic energy sources during carbon starvation, while obligate aerobes use fermentation as a last resort during hypoxia [23]. Particularly remarkable is the discovery that diverse bacterial species, including Mycobacterium smegmatis, Streptomyces avermitilis, and Thermomicrobium roseum, can persist on atmospheric Hâ‚‚ through specialized high-affinity, oxygen-tolerant [NiFe]-hydrogenases that input electrons from atmospheric Hâ‚‚ oxidation into the aerobic respiratory chain [23].

Metabolic Heterogeneity and Spatial Organization

Spatial metabolomics has revealed striking metabolic heterogeneity within both dormant plant seeds and microbial populations, challenging traditional views of dormancy as a uniform metabolic state.

In plant seeds, spatial MSI analysis has demonstrated compartmentalization of metabolic processes across different tissue types. Research on Polygonatum cyrtonema revealed that small peptides were enriched in rhizome periderm and sprout surfaces, suggesting their potential role in rhizome growth and stress defense mechanisms [54]. Similarly, amino acid derivatives and alkaloids were co-enriched in vascular bundles and shoot apex, supporting a putative long-distance transport function that may facilitate bud growth [54]. This spatial compartmentalization enables seeds to maintain readiness for germination while preserving dormancy until environmental conditions become favorable.

In microbial populations, single-cell spatial metabolomics has revealed that dormancy represents a spectrum of metabolic states rather than a binary switch. The scSpaMet framework, which combines spatial metabolomics with cell-type specific protein profiling, has demonstrated that neighboring single cells exhibit local metabolite competition, contributing to functional heterogeneity within microbial communities [53]. Deep learning-based joint embedding of spatial metabolomic data has revealed unique metabolite states within cell types, while trajectory inference has shown metabolic patterns along cell differentiation paths [53].

Table 2: Comparative Metabolic Features in Dormant Plant Seeds vs. Microbial Cells

Metabolic Feature	Dormant Plant Seeds	Dormant Microbial Cells
Primary Energy Sources	Starch, lipids, storage proteins [35]	Atmospheric trace gases (Hâ‚‚, CO), host metabolites [23]
Characteristic Metabolites	ABA, sucrose, raffinose family oligosaccharides [35]	Trehalose, inorganic polyphosphates, storage lipids [23]
Spatial Organization	Tissue-level compartmentalization (embryo, endosperm, seed coat) [54]	Single-cell heterogeneity within populations [53]
Key Regulatory Hormones/Signals	Abscisic acid (ABA), gibberellins (GA), auxins [35]	(p)ppGpp, cAMP, quorum sensing molecules [23]
Oxidative Stress Management	Flavonoids, phenylpropanoids, antioxidants [35]	Superoxide dismutases, catalases, carotenoids [23]
Metabolic Flexibility	Limited metabolic repertoire, specialized for conservation [35]	High flexibility, utilization of diverse energy sources [23]

Visualization of Metabolic Pathways in Dormancy

The application of spatial metabolomics to dormancy research has revealed complex metabolic pathways that are spatially organized within tissues and cells. The following diagram illustrates key metabolic pathways and their spatial organization in dormant plant seeds:

Metabolic Pathways in Dormant Systems

The experimental workflow for spatial metabolomics in dormancy research integrates multiple steps from sample preparation to data analysis, as illustrated in the following diagram:

Spatial Metabolomics Workflow

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful spatial metabolomics research requires specialized reagents and materials optimized for different sample types and MSI technologies. The following table details essential components for spatial metabolomics workflows in dormancy research:

Table 3: Essential Research Reagents and Materials for Spatial Metabolomics

Reagent/Material	Function	Application Notes
Cryostat	Tissue sectioning at controlled temperatures	Maintains tissue integrity during sectioning; critical for labile metabolites [51]
Matrix Compounds (DHB, CHCA, NEDC)	Facilitate laser desorption/ionization	DHB suitable for lipids; NEDC for metabolites in negative ion mode [51]
Conductive Slides	Sample support for MSI analysis	Enable charge dissipation during analysis; required for MALDI-MSI [50]
Calibration Standards	Mass accuracy calibration	Critical for confident metabolite identification; includes peptide mixes or standard compounds [55]
Stable Isotope-Labeled Standards	Quantitative analysis	Enable absolute quantification when incorporated into samples [53]
Metal-isotope Conjugated Antibodies	Multiplexed protein imaging	Enable correlation of metabolomic and proteomic data in frameworks like scSpaMet [53]
Specialized Matrices (for SIMS)	Enhance secondary ion yield	Increase sensitivity for specific metabolite classes in SIMS-MSI [53]
Solvents and Buffers	Sample preparation and matrix application	High-purity solvents minimize background interference [51]
Antifungal agent 96	Antifungal Agent 96	Antifungal Agent 96 is a novel, investigational compound for research use only (RUO). It is not for human, veterinary, or household use.

Future Perspectives and Concluding Remarks

Spatial metabolomics continues to evolve rapidly, with emerging trends focusing on improved spatial resolution, increased sensitivity, and enhanced multi-omics integration. The field is moving toward single-cell spatial metabolomics, with technologies like TOF-SIMS already achieving submicron resolution that enables metabolic profiling at the cellular level [53]. The recent development of the scSpaMet framework represents a significant advancement in this direction, allowing joint protein-metabolite profiling of single cells in human tissues [53]. Similar approaches are increasingly being adapted for plant and microbial research.

Another significant trend is the integration of artificial intelligence and machine learning into MSI data analysis. Deep learning-based joint embedding has revealed unique metabolite states within cell types, while trajectory inference has shown metabolic patterns along cell differentiation paths [53]. These computational advances are particularly valuable for identifying spatial patterns in complex tissue architectures and for integrating multi-omics datasets.

The growing market for mass spectrometry imaging, projected to expand from $2.65 billion in 2024 to $4.13 billion in 2029 with a compound annual growth rate of 9.2%, reflects the increasing adoption and technological advancement of these methodologies [52]. Key growth drivers include increased focus on spatial omics studies, heightened reliance on biomarker discovery, more integration with AI tools, and a widening scope in clinical diagnostics [52].

For dormancy research specifically, future applications will likely focus on dynamic imaging of metabolic transitions between dormant and active states, potentially through correlative imaging approaches that combine MSI with live-cell microscopy or other functional imaging modalities. These advancements will provide unprecedented insights into the spatial and temporal regulation of energy metabolism in dormant plant seeds and microbial cells, with broad implications for agriculture, conservation, and medicine.

Metabolic Flux Analysis (MFA) is a powerful analytical technique that quantitatively describes the rates at which metabolites flow through biochemical pathways in living systems. MFA provides a dynamic view of metabolism, revealing how nutrients are utilized, how pathways are interconnected, and how metabolic networks are rewired in response to genetic modifications or environmental changes [56] [57]. Unlike static "snapshot" measurements of metabolite concentrations, flux analysis captures the dynamic nature of in vivo metabolism, where molecules are in a constant state of turnover through synthesis, breakdown, oxidation, and conversion to different compounds [58].

The foundation of modern MFA lies in the use of stable isotope tracers—molecules where specific atoms have been replaced with heavier, non-radioactive isotopes such as ^13^C, ^15^N, or ^2^H [59] [58]. When introduced into a biological system, these labeled substrates become incorporated into metabolic pathways, allowing researchers to trace the fate of individual atoms through complex networks. This approach has become invaluable across diverse fields including cancer metabolism, metabolic disease research, drug development, and bioprocessing optimization [59] [57]. In comparative studies of energy metabolism, MFA provides unique insights into how different biological systems, such as dormant plant seeds and microbial cells, regulate carbon and nitrogen flow through central metabolic pathways.

Technical Foundations of MFA

Stable Isotope Tracers and Labeling Strategies

Stable isotope tracing relies on introducing labeled compounds into biological systems and tracking their incorporation into downstream metabolites. The most common isotopes include ^13^C (1.11% natural abundance), ^15^N (0.365%), ^2^H (0.015%), and ^18^O (0.204%) [56]. Among these, ^13^C is particularly valuable due to its universal presence in organic molecules and relatively high abundance compared to ^12^C [56].

Different labeling strategies provide distinct advantages for probing specific metabolic questions:

• Uniformly labeled tracers ([U-^13^C]glucose, [^13^C~5~,^15^N~2~]glutamine): Provide broad coverage of multiple pathways simultaneously [59]
• Positionally labeled tracers ([1,2-^13^C]glucose): Target specific pathway branches or enzymatic reactions [59]
• Parallel labeling experiments: Multiple tracer experiments conducted under identical conditions with different labeled substrates to resolve specific fluxes with high precision [60]
• Isotopically non-stationary MFA (INST-MFA): Utilizes time-resolved measurements before the system reaches isotopic steady state, particularly valuable for autotrophic systems [61]

The choice of tracer and labeling strategy depends on the biological question, with different approaches offering complementary insights into pathway activities and network topology.

Analytical Platforms for Detection

The detection of isotope incorporation relies primarily on two analytical platforms: mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Each offers distinct advantages for flux analysis [59] [56].

Mass spectrometry, particularly when coupled with liquid or gas chromatography (LC-MS or GC-MS), provides exceptional sensitivity, rapid data acquisition, and the ability to detect hundreds to thousands of metabolites simultaneously [59]. Ultra-high performance liquid chromatography (UHPLC-MS) further enhances metabolite separation and coverage, enabling high-throughput flux analyses [59]. MS measures mass isotopomer distributions (MIDs)—the relative abundances of molecules with different numbers of heavy isotopes—which form the primary data for flux calculation [61].

NMR spectroscopy, while less sensitive than MS, offers unique advantages for atom-positional resolution, allowing researchers to distinguish between metabolites with identical chemical formulas but different structures, such as glucose and fructose [59]. NMR continues to play important roles in flux studies, particularly for its ability to provide information on specific atom positions within molecules.

According to recent surveys, MS-based techniques dominate current flux analysis research, appearing in 62.6% of scientific papers, while NMR spectroscopy is used in 35.6% of studies, with the remainder employing multiple complementary techniques [56].

Comparative Experimental Design: Dormant Seeds vs. Microbial Cells

System-Specific Methodological Considerations

The application of MFA to dormant plant seeds and microbial cells requires distinct experimental designs and methodological adjustments to account for their fundamentally different biological characteristics.

Plant Seed Dormancy Studies present unique challenges due to the low metabolic activity and desiccated state of orthodox seeds, which can contain less than 10% water by dry weight [7]. In this state, cellular metabolism and respiration are greatly reduced, with the cytoplasm existing in a glassy state that severely reduces molecular diffusion and mobility [7]. Successful flux analysis in dormant seeds requires:

• Controlled imbibition: Initiation of metabolic activity through precisely controlled water uptake, typically divided into three phases: rapid initial uptake (Phase I), metabolic activation at constant water content (Phase II), and resumption of water uptake supporting growth (Phase III) [7]
• Extended time scales: Experiments may span weeks to months to capture the slow metabolic transitions during dormancy release [62] [63]
• Tissue-specific analysis: Separate profiling of embryo and storage tissues (endosperm, megagametophyte) due to their distinct metabolic roles [62]

Microbial Systems offer more straightforward experimental setups but present their own considerations:

• Rapid metabolic rates: Microbes typically reach metabolic and isotopic steady states within hours to days [56]
• Controlled bioreactor conditions: Precise control of nutrient availability, pH, and oxygenation enables robust flux quantification [56]
• High temporal resolution: INST-MFA can capture rapid metabolic dynamics with sampling intervals of minutes to hours [61]

Table 1: Key Experimental Design Parameters for MFA in Different Biological Systems

Parameter	Dormant Plant Seeds	Microbial Cells
Time to isotopic steady state	Days to weeks [62]	Hours to days [56]
Sample preparation complexity	High (tissue separation required) [62]	Low to moderate [56]
Metabolic activity level	Very low to moderate [7]	High [56]
Optimal MFA approach	INST-MFA or steady-state MFA with extended labeling [62]	Steady-state ^13^C-MFA or INST-MFA [56]
Key pathways of interest	PPP, TCA, glycolysis, amino acid metabolism [62] [63]	Central carbon metabolism, product formation pathways [56]

Protocol for Comparative Flux Analysis

A standardized protocol for conducting MFA across different biological systems enables meaningful comparative insights:

Step 1: System Preparation and Tracer Selection

• For dormant seeds: Subject seeds to stratification treatments to synchronize dormancy status. Korean pine seeds may require cold stratification for 180-210 days to fully release dormancy [62] [35]
• For microbial cells: Grow cells in defined media to mid-exponential phase
• Select appropriate tracers: [U-^13^C]glucose for broad central carbon coverage; [1,2-^13^C]glucose for pentose phosphate pathway emphasis; [^13^C~5~,^15^N~2~]glutamine for nitrogen metabolism [59]

Step 2: Tracer Administration and Sampling

• Administer tracer under controlled environmental conditions
• Collect time-series samples for INST-MFA (typically 5-8 timepoints spanning the expected labeling dynamics) [61]
• For steady-state MFA, ensure system has reached isotopic steady state before sampling

Step 3: Metabolite Extraction and Analysis

• Use liquid nitrogen to flash-freeze samples
• Extract metabolites using methanol/water/chloroform systems
• Derivatize samples for GC-MS analysis when necessary
• Analyze using LC-MS or GC-MS platforms optimized for polar metabolites [59]

Step 4: Data Processing and Flux Calculation

• Correct raw mass isotopomer distributions for natural isotope abundance [59]
• Integrate data with metabolic network models
• Calculate fluxes using computational platforms such as INCA, OpenFLUX, or IsoSim [56] [61]

Key Metabolic Pathways and Flux Differences

Central Carbon Metabolism

Comparative MFA reveals fundamental differences in how dormant seeds and microbial cells manage carbon flow through central metabolic pathways.

In dormant plant seeds, the transition from dormancy to germination involves dramatic reorganization of carbon metabolism. Studies on Korean pine (Pinus koraiensis) demonstrate that dormant seeds maintain elevated levels of phosphorylated sugars and TCA cycle intermediates even after extended incubation, while non-dormant seeds show rapid depletion of these metabolites during germination [62]. This suggests that dormancy is associated with incomplete metabolic activation rather than mere substrate limitation. The pentose phosphate pathway appears particularly important, with its relative activity compared to glycolysis serving as a key regulatory point [62].

Microbial systems typically exhibit more dynamic and rapidly-adjusting carbon fluxes. In E. coli and other model microbes, carbon flows are tightly coordinated with growth demands, with rapid redistribution occurring in response to nutrient availability. The balance between glycolysis, pentose phosphate pathway, and TCA cycle is precisely regulated to meet demands for energy, reducing power, and biosynthetic precursors [56].

Table 2: Comparative Flux Distributions in Central Carbon Metabolism

Metabolic Pathway	Dormant Seeds Characteristics	Microbial Cells Characteristics
Glycolysis	Reduced flux in deep dormancy; activation during germination [62]	Highly active; tightly coupled to growth rate [56]
Pentose Phosphate Pathway	Maintained activity in dormancy; potential regulatory role [62] [63]	Variable flux depending on NADPH demand [56]
TCA Cycle	Incomplete cycling in dormancy; full activation upon germination [62]	High flux under aerobic conditions; bifurcated in some microbes [56]
Storage Carbohydrate Mobilization	Critical role in providing carbon for germination [63]	Less relevant in typical lab cultures with constant carbon supply

Energy and Redox Metabolism

The energy status and redox balancing strategies differ substantially between dormant seeds and microbial cells.

Dormant seeds maintain viability for extended periods despite minimal energy metabolism. This is achieved through metabolic quiescence and protective mechanisms that minimize energy demands [7]. Upon imbibition, respiratory activity resumes progressively, with oxygen consumption becoming detectable only above approximately 8% water content and increasing significantly above 24% water content [7]. The transition from dormancy to germination involves careful management of reactive oxygen species (ROS), with metabolic fluxes through pathways like the pentose phosphate pathway generating NADPH for antioxidant systems [7] [63].

Microbial cells typically maintain continuous energy metabolism, with tight coupling between catabolic fluxes and anabolic demands. Respiratory rates respond rapidly to nutrient availability and environmental conditions. The balance between ATP production and consumption is precisely regulated, with overflow metabolism occurring when carbon uptake exceeds biosynthetic capacity.

Visualization of Metabolic Pathways and Experimental Workflows

Comparative Central Carbon Metabolism

Diagram 1: Comparative Central Carbon Metabolism in Seeds and Microbes. This diagram highlights the shared pathways of central carbon metabolism (green) while distinguishing seed-specific (blue) and microbial-specific (red) processes and regulatory connections.

MFA Experimental Workflow

Diagram 2: MFA Experimental Workflow. This diagram outlines the key stages in metabolic flux analysis, from initial experimental design through wet lab procedures to computational analysis and biological interpretation.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagent Solutions for Metabolic Flux Analysis

Reagent Category	Specific Examples	Function in MFA
Stable Isotope Tracers	[U-^13^C]Glucose, [1,2-^13^C]Glucose, [^13^C~5~,^15^N~2~]Glutamine, ^13^C-Propionate [59]	Serve as metabolic probes to track carbon and nitrogen flow through pathways
Internal Standards	[^13^C~1~]Lactate, [^13^C~6~]Glucose, U-^13^C-labeled amino acids [59]	Enable absolute quantification of metabolites and correction for analytical variability
Extraction Solvents	Methanol, chloroform, water mixtures (typically 2:2:1.8 v/v) [59]	Quench metabolism and extract intracellular metabolites while preserving labeling patterns
Derivatization Reagents	Methoxyamine hydrochloride, N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA) [63]	Enhance volatility and detection of polar metabolites for GC-MS analysis
Chromatography Columns	HILIC, C18 reverse phase [59]	Separate metabolites prior to mass spectrometric detection to reduce ion suppression
Enzyme Assay Kits	Starch, soluble sugar, and protein quantification kits [35]	Complement flux data with biochemical measurements of metabolic status

Metabolic Flux Analysis using stable isotope tracers provides unparalleled insights into the dynamic functioning of metabolic networks across diverse biological systems. The comparative application of MFA to dormant plant seeds and microbial cells reveals both conserved principles of metabolic regulation and system-specific adaptations. Dormant seeds exhibit constrained metabolic networks with restricted carbon flow through central pathways, while microbial cells demonstrate highly dynamic fluxes rapidly adjusted to optimize growth and substrate utilization.

The continued advancement of MFA methodologies—including more sophisticated tracer designs, improved analytical sensitivity, and enhanced computational modeling—promises to further illuminate the complex metabolic reprogramming that occurs during transitions between dormant and active states. These insights have significant implications for addressing pressing challenges in crop improvement, biotechnological production, and therapeutic development by enabling targeted manipulation of metabolic networks for desired outcomes.

As MFA technologies become increasingly accessible and comprehensive, their integration with other omics approaches will provide increasingly holistic views of metabolic regulation, opening new frontiers in our understanding and engineering of biological systems.

The study of energy metabolism represents a critical frontier in understanding how organisms survive adverse conditions through dormancy. Dormancy, a reversible state of reduced metabolic activity, enables everything from microbial cells to plant seeds to withstand environmental stresses and resume growth when conditions become favorable [24] [64]. This physiological state presents major methodological challenges for researchers, as quantifying bioenergetic parameters requires platforms capable of measuring subtle metabolic variations with high precision and accuracy.

This comparative analysis examines versatile functional platforms for quantifying bioenergetic parameters in dormancy research. We evaluate technologies spanning single-cell analysis, integrated multi-omics approaches, and targeted metabolic profiling, with particular focus on their application to the comparative analysis of energy metabolism in dormant plant seeds versus microbial cells. The platforms assessed herein address the pressing need for methods that can capture the nuanced metabolic shifts characterizing the transition between active and dormant states across biological systems [24] [65] [64].

Platform Comparison: Technical Capabilities and Applications

The following analysis compares the technical specifications, performance metrics, and research applications of six platforms and methodologies relevant to bioenergetic assessment in dormancy studies.

Table 1: Comparison of Platforms for Bioenergetic Analysis in Dormancy Research

Platform/Methodology	Key Measurable Parameters	Sample Types	Temporal Resolution	Key Advantages
Versatile Functional Energy Metabolism Platform [65]	OXPHOS complex I-V activities, ROS, antioxidant enzymes (SOD, GPX, Catalase)	Cell lines, animal tissues, human blood samples	High (parallel measurement)	High precision (low CV%), integrated ETC/OS analysis, automated
AQuA2 [66]	Spatiotemporal molecular signaling events, calcium flux, neurotransmitter release	Live imaging data (cells, tissues, animal models)	High (real-time)	Event-based quantification, machine learning integration, 3D analysis
SnapATAC2 [67]	Chromatin accessibility, gene regulatory networks	Single cells (ATAC-seq, RNA-seq, Hi-C data)	N/A (single timepoint)	Linear scalability, matrix-free algorithm, multi-omics integration
Combined Transcriptomic & Metabolomic Analysis [4]	Gene expression, metabolite abundance, hormone levels, enzyme activities	Plant tissues (seeds), microbial cultures	Medium (multiple stages)	Systems-level perspective, pathway identification, biomarker discovery
Paper-Based Bacterial Metabolic State Assay [68]	Metabolic activity (oxidoreductases), dormancy markers (alkaline phosphatases)	Bacterial cultures, environmental samples	Rapid (<60 minutes)	Low-cost, portable, specific bacterial capture
Yeast Spore Dormancy Model [64]	Trehalose/glycogen levels, ATP, cAMP-PKA signaling, cytoplasmic fluidity	Yeast spores (S. cerevisiae, S. pombe)	Hours to days	Genetic tractability, biophysical measurements, conserved mechanisms

Experimental Protocols for Bioenergetic Assessment

Integrated OXPHOS and Oxidative Stress Analysis

This protocol, adapted from the versatile functional energy metabolism platform, enables parallel assessment of mitochondrial function and oxidative stress parameters [65]:

Sample Preparation:

• Homogenize tissues or harvest cells in ice-cold mitochondrial preservation buffer
• Perform differential centrifugation to isolate mitochondrial fractions (800g for 10min, then 12,000g for 15min)
• Determine protein concentration using Coomassie brilliant blue method

OXPHOS Complex Activities:

• Complex I (NADH dehydrogenase): Monitor NADH oxidation at 340nm with decylubiquinone as electron acceptor
• Complex II (Succinate dehydrogenase): Measure DCPIP reduction at 600nm with succinate as substrate
• Complex III (Ubiquinol cytochrome c reductase): Track cytochrome c reduction at 550nm
• Complex IV (Cytochrome c oxidase): Follow cytochrome c oxidation at 550nm
• Complex V (ATP synthase): Couple ATP production to NADH oxidation via pyruvate kinase and lactate dehydrogenase

Antioxidant Defense System:

• Superoxide Dismutase (SOD): Measure inhibition of cytochrome c reduction by superoxide generated from xanthine/xanthine oxidase
• Catalase: Monitor Hâ‚‚Oâ‚‚ decomposition at 240nm
• Glutathione Peroxidase (GPX): Track NADPH oxidation at 340nm coupled to glutathione reductase

Quality Control:

• Run samples in parallel with internal standards
• Perform intra-assay and inter-assay CV% calculations (<5% acceptable)
• Validate with known inhibitors (rotenone for Complex I, antimycin A for Complex III)

Multi-Omic Analysis of Dormancy Transitions

This integrated protocol combines transcriptomic and metabolomic approaches to profile dormancy release in seeds and microbial cells [4]:

Sample Collection and Preparation:

• Collect samples at multiple dormancy stages (deep dormancy, intermediate, activated)
• Flash-freeze in liquid nitrogen and store at -80°C
• For transcriptomics: Extract total RNA using TRIzol, assess quality (RIN >8.0)
• For metabolomics: Lyophilize samples, perform methanol/water extraction

Transcriptomic Profiling:

• Construct cDNA libraries using Illumina kits
• Sequence on Illumina platform (minimum 20M reads per sample)
• Map reads to reference genome, identify differentially expressed genes (FDR <0.05)
• Perform pathway enrichment analysis (KEGG, GO terms)

Metabolomic Profiling:

• Analyze extracts using UHPLC-QTOF-MS system
• Employ reversed-phase and HILIC chromatography for comprehensive coverage
• Identify compounds against standard databases (mass error <5ppm)
• Perform multivariate statistical analysis (PCA, OPLS-DA)

Data Integration:

• Correlate transcript and metabolite abundances using Spearman correlation
• Construct merged pathway maps showing multi-omic regulation
• Identify key regulatory hubs through network analysis

Table 2: Research Reagent Solutions for Bioenergetic Studies

Reagent/Category	Specific Examples	Research Function
Tetrazolium Salts	INT, XTT	Detect metabolic activity via formazan formation [68]
Enzyme Substrates	p-nitrophenyl phosphate, NADH, succinate	Measure specific enzyme activities in metabolic pathways [68] [65]
Metabolic Inhibitors	Rotenone, antimycin A, oligomycin	Target specific ETC complexes for functional validation [65]
Antioxidant Assay Kits	SOD, catalase, GPX activity kits	Quantify oxidative stress defense capacity [65]
Hormone ELISA Kits	ABA, GA₃, IAA immunoassays	Measure phytohormone levels in dormancy transitions [4]
Carbohydrate Assay Kits	Trehalose, glycogen, soluble sugar kits	Quantify energy reserves in dormant systems [4] [64]

Signaling Pathways in Dormancy Regulation

The transition between active and dormant states involves complex signaling networks that regulate metabolic activity. The diagrams below illustrate key pathways in microbial and plant systems.

Diagram 1: Microbial Dormancy Signaling (76 characters)

Diagram 2: Plant Seed Dormancy Regulation (79 characters)

Comparative Analysis: Platform Performance and Research Applications

Cross-Platform Performance Metrics

When evaluating platforms for dormancy bioenergetics research, several performance characteristics emerge as critical differentiators:

Measurement Precision: The versatile functional energy metabolism platform emphasizes exceptionally low coefficient of variation (CV%) through automated, parallel measurement of multiple parameters [65]. This precision enables detection of subtle metabolic shifts during dormancy transitions that might be missed by platforms with higher variability. By contrast, paper-based bacterial assays prioritize rapid detection over precise quantification, making them suitable for initial screening rather than detailed metabolic characterization [68].

Temporal Resolution: Platforms differ significantly in their ability to capture dynamic processes. AQuA2 provides high temporal resolution for spatiotemporal signaling events through live imaging and machine learning analysis [66]. This capability is particularly valuable for capturing the rapid metabolic activation that occurs when dormant cells exit quiescence. Conversely, combined omics approaches typically provide snapshots at discrete timepoints rather than continuous monitoring [4].

Scalability and Throughput: For studies requiring analysis of large sample numbers, SnapATAC2's linear scalability with cell numbers represents a significant advantage [67]. The matrix-free algorithm enables processing of datasets with hundreds of thousands of cells without exponential increases in computational resources. The yeast spore model offers genetic scalability, with extensive mutant libraries available for mechanistic studies [64].

Applications to Dormancy Research Questions

Each platform offers distinctive advantages for addressing specific research questions in dormancy bioenergetics:

Mechanistic Studies of Dormancy Transitions: The yeast spore model provides unparalleled genetic tractability for investigating molecular mechanisms regulating metabolic quiescence and reactivation [64]. The conserved cAMP-PKA signaling pathway can be manipulated through genetic modifications to test specific hypotheses about metabolic regulation. Combined with the versatile functional platform's precise OXPHOS measurements, this enables detailed mechanistic studies of energy metabolism during dormancy breaking.

Systems-Level Analysis of Dormancy Networks: Combined transcriptomic and metabolomic approaches reveal regulatory networks controlling dormancy in plant seeds [4]. This multi-omic strategy identified key roles for phenylpropanoid biosynthesis and flavonoid accumulation during dormancy release in Notopterygium incisum seeds, demonstrating how integrated platforms can uncover novel metabolic pathways involved in dormancy regulation.

Single-Cell Metabolic Heterogeneity: Single-cell technologies like SnapATAC2 enable investigation of metabolic heterogeneity within dormant populations [67]. This capability is particularly valuable for identifying subpopulations with different metabolic states or dormancy depths, which may have important functional consequences for reactivation potential and survival under stress.

The comparative analysis presented herein demonstrates that platform selection for quantitative bioenergetic assessment in dormancy research must be guided by specific research questions and sample characteristics. For high-precision metabolic profiling of mitochondrial function, the versatile functional energy metabolism platform offers unrivaled accuracy and comprehensive parameter coverage [65]. For dynamic monitoring of metabolic activation during dormancy breaking, AQuA2's event-based quantification provides unique insights into spatiotemporal patterning [66]. For systems-level understanding of regulatory networks, combined omics approaches reveal interconnected metabolic and transcriptional pathways [4].

The fundamental similarity in dormancy strategies between phylogenetically diverse organisms—from yeast spores to plant seeds—underscores the value of comparative approaches using versatile platforms [24] [64]. Conservation of key metabolic features like energy reserve accumulation (trehalose/glycogen) and signaling pathways (cAMP-PKA) enables insights from microbial models to inform plant dormancy research and vice versa. The ongoing development of increasingly precise, scalable, and integrative platforms promises to accelerate our understanding of the bioenergetic principles governing dormancy across the tree of life.

Overcoming Analytical Challenges in Dormancy Research

The comparative analysis of energy metabolism in dormant plant seeds and microbial cells represents a frontier in biological research with significant implications for agriculture, ecology, and pharmaceutical development. This field relies on precise measurements of metabolic fluxes, molecular interactions, and physiological states across vastly different biological systems. However, the inherent complexities of dormant states—characterized by reduced metabolic activity, desiccated tissues, and heterogeneous cell populations—create substantial challenges for accurate quantification. Error propagation in these measurements can compromise experimental validity, hinder cross-system comparisons, and ultimately delay scientific and technological advances.

In plant seeds, dormancy corresponds to a metabolically inactive state where the embryo is preserved in a dry, quiescent condition, successfully withstanding desiccation through sophisticated cellular organization and protective molecules [7]. Similarly, microbial dormancy constitutes a reversible state of reduced metabolic activity, enabling microorganisms to persist through unfavorable environmental conditions and maintain biodiversity across ecosystems [16] [24]. Both systems exhibit transitions between active and dormant states regulated by environmental cues, but quantifying the metabolic shifts during these transitions requires meticulous methodological approaches to minimize measurement errors.

This guide objectively compares leading methodologies for analyzing energy metabolism in dormant systems, providing experimental data and protocols to support researchers in selecting appropriate techniques for their specific investigations. By highlighting sources of error and strategies for mitigation, we aim to enhance the reliability and reproducibility of dormancy research across biological disciplines.

Comparative Analysis of Dormancy Systems: Fundamental Physiological Differences

Understanding the fundamental physiological differences between plant seed and microbial dormancy is essential for designing appropriate quantification approaches. The table below systematically compares key characteristics relevant to metabolic measurements.

Table 1: Physiological Comparison of Dormant Plant Seeds and Microbial Cells

Characteristic	Plant Seeds (Orthodox)	Microbial Cells
Water Content	Very low (<10% of dry weight) [7]	Variable, but typically higher than dormant seeds
Metabolic Activity	Greatly reduced; respiration undetectable at ~8% water content [7]	Reversibly reduced, but maintain minimal power requirement [24]
Primary Energy Metabolism	Respiration arrested; glycolysis, PPP, and TCA cycle activated upon imbibition [62] [63]	Diverse metabolic strategies including spontaneous and responsive switching [24]
Key Regulatory Metabolites	ABA, GA₃, IAA, phosphorylated sugars, amino acids [4] [62]	Autoinducers, nutrient signals, stress response molecules [69]
Dormancy Timescales	Years to centuries [7]	Hours to millennia [24]
Structural Adaptations	Glassy cytoplasm, condensed chromatin, protective molecules [7]	Resting structures, spores, or morphologically indistinguishable forms [24]

The quantification of energy metabolism must account for these fundamental differences. For plant seeds, the transition from dry to imbibed state involves dramatic shifts in water availability that activate metabolic pathways. Research on Pinus koraiensis seeds has demonstrated that dormant seeds maintain higher relative levels of metabolites involved in the pentose phosphate pathway and TCA cycle compared to non-dormant seeds, suggesting disturbed carbohydrate metabolism in dormancy [62]. Similarly, yam tuber dormancy regulation involves sucrose metabolism, lipid metabolism, and amino acid metabolism, with distinct metabolite profiles across dormancy stages [63].

In microbial systems, dormancy is not a uniform state but encompasses diverse strategies. The conceptual framework for microbial dormancy must accommodate everything from sporulation in some taxa to persister cell formation in others [70]. This diversity complicates metabolic measurements, as different microbial groups may exhibit distinct metabolic signatures during dormancy. A critical challenge in microbial dormancy research lies in distinguishing truly dormant cells from those that are merely stressed [70], a distinction that requires careful quantification of metabolic activity at single-cell levels.

Methodological Comparison: Analytical Approaches for Metabolic Quantification

Metabolic Modeling and Reconstruction Approaches

Genome-scale metabolic models (GEMs) provide valuable tools for investigating metabolic capabilities and interactions in dormant systems. Different reconstruction tools employ distinct biochemical databases and algorithms, introducing potential sources of variation in metabolic predictions.

Table 2: Comparison of Metabolic Model Reconstruction Tools

Tool	Reconstruction Approach	Primary Database	Key Strengths	Limitations/Error Sources
CarveMe	Top-down (template-based)	Custom universal template	Fast model generation; high gene inclusion [48]	Potential omission of species-specific pathways
gapseq	Bottom-up (genome-based)	Multiple sources	Comprehensive biochemical information [48]	Higher dead-end metabolites; computational intensity
KBase	Bottom-up (genome-based)	ModelSEED	User-friendly platform; immediate functionality [48]	Moderate reaction coverage compared to gapseq
PlantSEED	Consensus	Integrated multi-database	Consistent annotations; reduced dead-end metabolites [71]	Requires integration of multiple resources

Comparative analysis reveals that these reconstruction approaches yield models with varying numbers of genes, reactions, and metabolic functionalities even when based on the same genomic data [48]. For instance, consensus approaches like PlantSEED address inconsistencies by integrating multiple data sources, implementing conservative annotation practices, and enabling model-based assessment of annotation completeness [71]. This integration reduces the presence of dead-end metabolites by 15-30% compared to single-tool approaches, significantly minimizing error propagation in metabolic flux predictions [48].

The iterative order in gap-filling processes represents another potential source of error in metabolic modeling. However, research indicates that the order of microbial genome inclusion during community model reconstruction has negligible impact on the number of added reactions (correlation: r = 0-0.3), suggesting this particular parameter introduces minimal systematic error [48].

Experimental Profiling Techniques

Experimental validation of metabolic models requires precise analytical techniques capable of detecting low-abundance metabolites in complex biological matrices. The following table compares key methodological approaches for metabolic profiling in dormancy research.

Table 3: Analytical Techniques for Metabolic Quantification in Dormancy Studies

Technique	Applications in Dormancy Research	Key Metabolites Detected	Sensitivity Considerations
GC-MS	Yam tuber dormancy stages; primary metabolic pathways [63]	Amino acids, organic acids, sugars, fatty acids	Requires derivatization; excellent for central carbon metabolism
LC-MS/MS	Phytohormone quantification; secondary metabolites [4] [69]	Phenolic acids, flavonoids, glucosinolates, hormones	High sensitivity for non-volatile compounds; minimal sample preparation
Transcriptomics	Regulatory pathway analysis; dormancy transitions [4]	Gene expression patterns for metabolic enzymes	Indirect metabolic inference; reveals regulation rather than flux
Enzyme Activity Assays	Direct measurement of metabolic pathway activity [4]	Functional enzyme levels in key pathways	Subject to extraction artifacts; measures potential rather than actual flux

Integrated multi-omics approaches have proven particularly valuable for comprehensive metabolic analysis. Research on Notopterygium incisum seeds combined transcriptomic and metabolomic profiling to identify phenylpropanoid biosynthesis and flavonoid biosynthesis as key pathways in dormancy release [4]. This integrated approach revealed that dormancy release involves reduced accumulation of phenylpropanoid pathway metabolites (p-coumaric acid, coniferyl aldehyde) while flavonoids (quercetin, rutin) significantly increase [4].

For microbial systems, distinguishing active from dormant community members often employs rRNA-based characterization analogous to seed bank studies in plant ecology [16]. This approach revealed that in nutrient-poor systems, dormant bacteria can account for up to 40% of taxon richness, highlighting the importance of accounting for dormancy in microbial community analyses [16].

Experimental Protocols: Standardized Methods for Reproducible Measurements

Plant Seed Dormancy Analysis Protocol

The following protocol for analyzing metabolic changes during seed dormancy release incorporates best practices for minimizing quantification error:

Materials and Reagents:

• Seeds with confirmed dormancy status
• Stratification matrix (river sand, vermiculite, perlite in specific ratios)
• Liquid nitrogen for sample preservation
• Extraction buffers for metabolites, proteins, and RNA
• Internal standards for metabolomic analysis
• Enzyme activity assay kits

Procedure:

• Stratification Treatment: Subject seeds to controlled stratification conditions. For Notopterygium incisum, use alternating temperature regimes (20°C/4°C) with 60% matrix humidity for 90 days, followed by 4°C stratification for 120 days [4].
• Sampling Time Points: Collect samples at critical dormancy stages: dry seeds (morphological dormancy), during stratification (physiological dormancy), and upon germination [4].
• Tissue Separation: Precisely dissect embryos from storage tissues (endosperm, megagametophyte) to avoid cross-contamination [62].
• Metabolite Extraction: Use validated extraction methods with internal standards added prior to extraction to correct for recovery variations.
• Multi-Omics Integration: Conduct parallel transcriptomic, metabolomic, and hormone analyses on the same biological samples to enable data integration [4].
• Quality Controls: Implement technical replicates, sample randomization, and blank extractions to identify technical artifacts.

Error Mitigation Strategies:

• Immediate freezing of samples in liquid nitrogen after collection to prevent metabolic changes [4]
• Use of multiple internal standards with different chemical properties
• Normalization to both tissue weight and internal standards
• Simultaneous measurement of reference materials with known compositions

Microbial Dormancy Quantification Protocol

Materials and Reagents:

• Filter-sterilized environmental samples or pure cultures
• Activity markers (e.g., rRNA-targeted probes, membrane potential dyes)
• Resuscitation stimuli (nutrient supplements, signaling molecules)
• Isotope-labeled substrates for activity measurements
• DNA and RNA extraction kits with minimal bias

Procedure:

• Community Fractionation: Separate active and dormant fractions using density gradient centrifugation or cell sorting based on activity markers [16].
• Parallel rRNA/rDNA Analysis: Extract and sequence both rRNA (active community) and rDNA (total community) from the same sample [16].
• Resuscitation Experiments: Monitor metabolic reactivation in response to specific stimuli using continuous measurement of respiration rates or substrate utilization [16].
• Metabolic Modeling: Reconstruct genome-scale metabolic models using consensus approaches to minimize annotation errors [48] [71].
• Single-Cell Activity Measurements: Employ nanoSIMS or Raman microscopy to correlate metabolic activity with phylogenetic identity in heterogeneous communities.

Error Mitigation Strategies:

• Parallel processing of rRNA and rDNA from the same extract to enable normalization
• Use of multiple activity indicators to avoid method-dependent biases
• Inclusion of positive and negative controls in resuscitation experiments
• Model validation through comparison with empirical flux measurements

Visualization of Metabolic Pathways and Experimental Workflows

Comparative Energy Metabolism in Dormant Systems

Diagram 1: Comparative Energy Metabolism Transition Pathways in Dormant Systems

Integrated Multi-Omics Workflow for Dormancy Research

Diagram 2: Integrated Multi-Omics Workflow for Dormancy Metabolism Studies

Essential Research Reagent Solutions

The following table details key reagents and their applications in dormancy metabolism research, with specific attention to quantification accuracy and error minimization.

Table 4: Essential Research Reagents for Dormancy Metabolism Studies

Reagent Category	Specific Examples	Function in Research	Quantification Considerations
Internal Standards	Stable isotope-labeled metabolites (¹³C-sugars, ¹⁵N-amino acids)	Correction for extraction efficiency and instrument variation	Use multiple standards across chemical classes; add prior to extraction
Activity Probes	rRNA-targeted primers, membrane potential dyes (CTC, SYTOX), redox sensors	Differentiation of active vs dormant cells/populations	Combine multiple probes to avoid false positives/negatives
Hormone Analysis Kits	ELISA kits for ABA, GA₃, IAA quantification	Phytohormone profiling in dormancy transitions	Validate with spike-recovery experiments; use tissue-specific calibration
Metabolic Inhibitors	Glycolysis, TCA, and PPP pathway inhibitors	Pathway flux determination through metabolic perturbation	Dose-response calibration for each system; monitor non-specific effects
Extraction Buffers	Methanol:water:chloroform for metabolomics, TRIzol for RNA	Comprehensive metabolite/nucleic acid recovery	Standardize extraction protocols across samples; avoid batch effects
Modeling Databases	PlantSEED, ModelSEED, KEGG, MetaCyc	Metabolic network reconstruction and gap-filling	Use consensus approaches; manually validate automatic annotations

The comparative analysis of energy metabolism in dormant plant seeds and microbial cells requires meticulous attention to quantification methodologies at every experimental stage. From sample collection and processing to data integration and modeling, each step introduces potential errors that can propagate through analyses and compromise biological interpretations. The methodologies and protocols presented here provide frameworks for minimizing these errors through standardized approaches, consensus modeling, and multi-layered validation.

Future advances in dormancy research will depend on continued refinement of quantification methods, particularly in the areas of single-cell metabolism, spatial mapping of metabolic activity, and dynamic flux measurements in transitioning systems. By adhering to the "quantification imperative" and implementing robust error mitigation strategies, researchers can generate reliable, comparable data that advances our understanding of dormancy across biological systems and enables applications in conservation, agriculture, and medicine.

In the study of energy metabolism, particularly in the nuanced contexts of dormant plant seeds and microbial cells, the ability to detect subtle, biologically significant shifts is paramount. The Coefficient of Variation (CV%) serves as a fundamental metric for quantifying measurement precision, with a lower CV% indicating higher reproducibility and greater power to detect these subtle changes. Research into comparative energy metabolism often seeks to understand the transition from a dormant, metabolically quiescent state to an active one. For instance, orthodox seeds represent a natural model for metabolic shutdown and resurrection, as they possess a very low water content, preventing biochemical reactions and respiration, yet successfully maintain viability through sophisticated protective mechanisms [7]. Detecting the metabolic shifts associated with the resumption of respiration upon imbibition requires exceptionally reproducible methodologies [7]. This guide objectively compares experimental protocols and analytical tools based on their demonstrated reproducibility, providing researchers with the data needed to select methods capable of reliably uncovering critical, yet subtle, metabolic phenomena.

Quantitative Comparison of Method Reproducibility

The following tables summarize the reproducibility data for different methodological approaches, providing a clear comparison of their performance in detecting metabolic shifts.

Table 1: Reproducibility of Indirect Calorimetry Data Selection Methods for RMR Assessment

Method Category	Specific Method (Time Interval)	Key Metric	Reported CV% (Intra-Subject)	Key Finding
Long Time Intervals [72]	6-25 minutes	RMR	Most reproducible	Highest day-to-day reproducibility for RMR and RER across 4 metabolic carts.
Long Time Intervals [72]	6-30 minutes	RMR	Most reproducible	Highest day-to-day reproducibility for RMR and RER across 4 metabolic carts.
Short Time Intervals [72]	6-10, 11-15, 16-20, 21-25, 26-30 minutes	RMR & RER	Variable, less reproducible	Lower day-to-day reproducibility compared to long time interval methods.

Abbreviations: RMR: Resting Metabolic Rate; RER: Respiratory Exchange Ratio; CV%: Coefficient of Variation.

Table 2: Reproducibility of Magnetic Resonance Spectroscopic Imaging (MRSI) for Metabolic Mapping

MRSI Type	Metabolites Quantified	Referencing Method	Average Intra-Subject CV%	Data Quality Success Rate
Short-TE MRSI [73]	tNAA, tCr, tCho, mI, Glx	Water	5.8%	96% (at least one visit)
Short-TE MRSI [73]	tNAA, tCr, tCho, mI, Glx	Total Creatine (tCr)	4.8%	96% (at least one visit)
GABA-Edited MRSI [73]	GABA+ (GABA + macromolecules)	Water & tCr	13.5%	82% (at least one visit)

Abbreviations: TE: Echo Time; tNAA: total N-acetylaspartate; tCr: total creatine; tCho: total choline; mI: myo-inositol; Glx: glutamate and glutamine; GABA: gamma-aminobutyric acid.

Detailed Experimental Protocols for High-Reproducibility Methods

Protocol for High-Reproducibility Resting Metabolic Rate (RMR) Measurement

Objective: To achieve a high day-to-day reproducibility in the assessment of Resting Metabolic Rate (RMR) and the Respiratory Exchange Ratio (RER), which is critical for accurately detecting changes resulting from interventions or for monitoring patient metabolism over time [72].

Workflow Overview: The following diagram illustrates the key stages of the RMR assessment protocol, highlighting the data selection methods that lead to high reproducibility.

Key Materials and Equipment:

• Metabolic Carts: Examples include Q-NRG (Cosmed), Vyntus CPX (Jaeger-CareFusion), Omnical (Maastricht Instruments), and Ultima CardiO2 (Medgraphics Corporation) [72].
• Subject Preparation Facility: A controlled environment for subjects to rest in a supine position.

Step-by-Step Procedure:

• Subject Preparation: Participants should fast overnight and abstain from strenuous exercise, caffeine, and alcohol for at least 24 hours prior to the test. Measurements are performed after a 30-minute rest period in a quiet, thermo-neutral environment.
• Instrument Calibration: The metabolic cart must be calibrated according to manufacturer specifications before each testing session using standard gases.
• Data Acquisition: Perform RMR assessments for a continuous 30-minute period while the subject is awake and in a supine, resting state.
• Data Selection (Critical Step): Apply the long time-interval method to the collected gas exchange data. This involves selecting the entire data window from minute 6 to minute 25 (or minute 6 to minute 30) of the recording for the final calculation of RMR and RER [72].
• Data Analysis: Calculate the final RMR and RER values based on the averaged data from the selected 19- or 24-minute window.

Key Experimental Insight: The choice of data selection method directly impacts reproducibility. While short periods (e.g., 5-minute intervals) and steady-state methods are common, the 6-25 minute and 6-30 minute long time-interval methods have been demonstrated to yield the highest day-to-day reproducibility across different metabolic carts, making them the preferred choice for longitudinal studies [72].

Protocol for Reproducible Metabolic Mapping with Multi-Slice MRSI

Objective: To achieve reproducible, non-invasive mapping of neuro-metabolic distributions using multi-slice 2D Magnetic Resonance Spectroscopic Imaging (MRSI), with a focus on both high-concentration metabolites and challenging low-concentration compounds like GABA [73].

Workflow Overview: This protocol outlines the steps for acquiring and processing MRSI data to achieve reproducible metabolic maps.

Key Materials and Equipment:

• Scanner: 3T MRI system (e.g., Philips "Ingenia Elition").
• Hardware: 32-channel head coil.
• Software: "Osprey" software package for data analysis, which includes retrospective motion compensation and consensus-recommended linear-combination modeling [73].

Step-by-Step Procedure:

• Subject Setup: Position the volunteer in the scanner and obtain written informed consent. For studies including children, consent must be obtained from legal guardians.
• Anatomical and Field Mapping: Acquire high-resolution anatomical MRI images and B0 field maps for second-order shim correction to optimize magnetic field homogeneity.
• Short-TE MRSI Acquisition: Perform multi-slice, spin-echo 2D MRSI with a short echo time (e.g., TE = 20 ms). Use outer-volume suppression (OVS) pulses to suppress lipid signals from outside the region of interest [73].
• GABA-Edited MRSI Acquisition (Optional): For mapping GABA, use a spectral-edited MRSI sequence (e.g., MEGA-PRESS type) with a longer TE (e.g., 68 ms). This sequence acquires interleaved "edit ON" and "edit OFF" scans to isolate the GABA signal from overlapping metabolites [73].
• Water Reference Acquisition: Acquire a separate MRSI scan without water suppression to be used as an internal reference for quantification.
• Data Processing and Motion Compensation: Process all MRSI data in the "Osprey" software. A critical step is applying retrospective motion compensation to correct for head movements during the scan, which is particularly important for the longer GABA-edited acquisitions [73].
• Metabolite Quantification: Use linear-combination modeling within Osprey to fit the spectra and quantify metabolite levels. Metabolites are typically referenced to total creatine (tCr) or to the water signal.

Key Experimental Insight: Short-TE MRSI provides highly reproducible mapping for major metabolites like tNAA, tCr, tCho, mI, and Glx, with intra-subject CV% as low as ~5% [73]. In contrast, GABA+ mapping remains challenging, with higher CV% (~13.5%) and a significant portion of data potentially failing quality control, underscoring the importance of rigorous protocol execution and data filtering [73].

The Scientist's Toolkit: Essential Reagents and Materials

Table 3: Key Research Reagent Solutions for Metabolic Studies

Item Name	Function / Application	Specific Examples / Notes
Chromatography-Mass Spectrometry	Separation and detection of metabolites in complex biofluids [74] [75].	LC-MS/MS for compounds like pesticides, PFASs; GC-MS/MS for PAHs, PCBs [75].
Triple Quadrupole Mass Spectrometer	Targeted quantitative analysis with high sensitivity and specificity using MRM [75].	Ideal for validating biomarker panels; provides high accuracy [75].
High-Resolution Mass Spectrometer	Suspect and non-targeted screening for discovering unknown compounds [75].	Required for expanding coverage of the metabolome/exposome.
Standardized Software for Modeling	Consensus-recommended processing and quantification of complex spectral data [73].	"Osprey" software for MRSI data; uses linear-combination modeling for improved reproducibility [73].
Quality Control (QC) Samples	Monitoring instrument stability and data quality throughout an analytical run [76].	Pooled samples from case and control groups analyzed intermittently.
Internal Standards	Correcting for variability in sample preparation and instrument analysis [76].	Isotopically labeled versions of target metabolites.

Achieving low CV% is not a mere technicality but a fundamental requirement for advancing our understanding of subtle metabolic shifts in systems like dormant seeds and microbial cells. As the comparative data shows, the careful selection of methodological protocols—from the time-interval used in indirect calorimetry to the specific sequences and processing tools in MRSI—has a profound impact on reproducibility. While techniques for measuring major metabolites have achieved high precision, the reliable quantification of low-abundance compounds like GABA remains a frontier, highlighting an area where further methodological refinement is needed. By adhering to the high-reproducibility protocols and utilizing the essential tools outlined in this guide, researchers can generate more reliable and comparable data, ultimately accelerating discovery in the complex field of energy metabolism.

In the realm of omics research, the fundamental challenge of distinguishing causal drivers from mere correlative associations represents a significant bottleneck in translating data into biological understanding. This dilemma is particularly acute in comparative studies of energy metabolism, where interconnected molecular pathways create complex networks of association that often obscure true causal relationships. While technological advances have enabled researchers to generate vast datasets documenting molecular changes, traditional analytical approaches frequently fail to establish whether identified biomarkers are true causal factors or simply consequences of the biological processes under investigation [77]. This distinction is not merely academic; it has profound implications for identifying therapeutic targets, understanding disease mechanisms, and interpreting evolutionary adaptations across biological systems.

The problem extends across biological domains, from seed dormancy studies to microbial metabolism. In plant seed research, for instance, observed metabolic changes during dormancy release could reflect either the triggering mechanisms for germination or simply secondary consequences of the process already underway. Similarly, in microbial systems, identifying which metabolic pathway variants truly drive observed phenotypes remains challenging without proper causal frameworks [78]. A systematic review of immunological studies revealed that despite employing machine learning techniques, none of 90 studies on immune checkpoint inhibitors incorporated causal inference, highlighting a pervasive methodological gap across biological disciplines [79].

Comparative Analysis of Energy Metabolism: Dormant Seeds vs. Microbial Cells

Energy metabolism represents a particularly informative domain for examining the causation-correlation challenge due to its fundamental role across biological systems and the interconnected nature of metabolic pathways. By comparing energy metabolism in dormant plant seeds and microbial cells, researchers can identify both universal principles and system-specific adaptations.

Table 1: Key Energy Metabolism Pathways in Dormant Seeds and Microbial Cells

Metabolic Pathway	Role in Dormant Seeds	Role in Microbial Cells	Causal Evidence Level
Glycolysis	Lower activity maintains dormancy; increased upon release [42]	Central carbon processing; varies by species & conditions [80]	Medium (indirect manipulation)
TCA Cycle	Reduced operation in dormant embryos [42]	Complete oxidation in respiration; alternative routes in fermentation [80]	Medium (pharmacological inhibition)
Electron Transport Chain	Limited by hypoxia in dormant seeds [42]	ATP generation via oxidative phosphorylation [80]	Low (correlative measurements)
Fermentation Pathways	Activated under seed coat-imposed hypoxia [42]	Primary energy route in anaerobic conditions [80]	High (genetic manipulation)
Pentose Phosphate Pathway	Provides precursors for biosynthesis during germination [81]	Generates NADPH and biosynthetic precursors [80]	Low (transcriptional correlation)

Energy Metabolism in Dormant Plant Seeds

Studies on Korean pine seeds have revealed that distinct metabolic patterns characterize dormant versus non-dormant states. In non-dormant seeds, a substantial metabolic switch occurs during germination, with contents of most sugars, organic acids, and amino acids increasing significantly during early germination phases, reflecting initiated biosynthetic processes [42]. Notably, metabolites central to energy production show dramatic changes, with fructose 6-phosphate, inositol-3-phosphate, 3-phosphoglyceric acid, and D-glucose-6-phosphate decreasing by 41- to 409-fold as germination progresses, indicating slowed glycolysis and TCA cycle activity [42].

Dormant seeds exhibit a different metabolic profile, with lower utilization rates of amino acids and altered operation of core energy pathways. Unlike their non-dormant counterparts, dormant Korean pine embryos showed no substantial decrease in amino acids as imbibition progressed, and metabolites involved in glycolysis and TCA cycle did not show the dramatic reduction observed in germinating seeds [42]. This suggests that attenuated biosynthetic metabolism and modified energy pathway operation contribute to maintaining the dormant state.

Similar patterns emerge in other plant species. Research on Notopterygium incisum seed dormancy release found that stratification treatment reduced stored nutrients, altered enzyme activities, decreased abscisic acid content, and increased gibberellin levels [35]. Transcriptome analysis revealed that during dormancy release, genes related to starch and sucrose metabolism were upregulated, while phenylpropanoid and flavonoid biosynthesis pathways emerged as key processes in dormancy release [35].

Energy Metabolism in Microbial Cells

Microbial metabolism demonstrates both parallels and distinctions from plant seed systems. Bacteria employ diverse metabolic strategies categorized as heterotrophic, autotrophic, or photosynthetic, each with distinct energy conservation mechanisms [80]. Heterotrophic metabolism, utilized by pathogenic bacteria, involves the biological oxidation of organic compounds like glucose to yield ATP, with respiration generating approximately 38 moles of ATP per mole of glucose [80].

Microbial systems exhibit remarkable flexibility in pathway utilization, with different species employing varied glucose-catabolizing pathways including glycolysis, the oxidative pentose phosphate pathway, and the Entner-Doudoroff pathway, depending on enzymatic capabilities and environmental conditions [80]. This pathway diversity creates particular challenges for causal inference, as multiple metabolic routes can lead to similar phenotypic outcomes.

Recent methodological advances enable more rigorous evaluation of metabolic causality in microbial systems. A 2023 study developed a methodology to evaluate the bioenergetic feasibility of alternative metabolic pathways, optimizing energy yield and driving forces as a function of metabolic intermediate concentrations [78]. This approach, based on thermodynamic principles and multi-objective optimization, accounts for pathway variants involving different electron carriers and energy conservation reactions, helping distinguish causal drivers from correlative associations.

Methodological Frameworks for Causal Inference

Established Causal Inference Methods

Several methodological approaches have been developed to address the causation-correlation challenge in biological research:

Mendelian Randomization (MR) represents a particularly promising approach that uses genetic variants as instrumental variables to infer causal relationships between molecular traits and outcomes [77]. This method leverages the random assortment of genes during meiosis, which minimizes confounding in much the same way as randomized controlled trials. MR relies on three core assumptions: (1) the genetic variant must be robustly associated with the exposure (relevance assumption); (2) the variant must not be associated with confounders (exchangeability assumption); and (3) the variant must influence the outcome only through the exposure (exclusion restriction) [77].

Transcriptome-Wide Association Studies (TWAS) integrate gene expression data with genetic associations to identify genes whose genetically predicted expression is associated with complex traits [77]. While early TWAS methods faced limitations in distinguishing causation from horizontal pleiotropy (where a genetic variant influences multiple phenotypes independently), recent advances incorporate more sophisticated causal inference frameworks.

Colocalization Analysis uses Bayesian model comparison methods to quantify the evidence that gene expression and phenotype associations share a common causal variant [77]. Methods such as coloc, eCAVIAR, and similar approaches help determine whether overlapping genetic associations reflect shared causality, though they remain limited in establishing directionality of effects [77].

Emerging Causal Machine Learning Approaches

Recent advances in causal machine learning offer powerful new approaches for distinguishing causation from correlation in omics data:

Targeted-BEHRT combines transformer architecture with doubly robust estimation to infer long-term treatment effects from longitudinal, high-dimensional data [79].

CIMLA demonstrates exceptional robustness to confounding in gene regulatory network analysis, providing insights into complex biological regulation [79].

CURE leverages large-scale pretraining to improve treatment effect estimation, demonstrating performance gains of approximately 4% in AUC and 7% in precision-recall over traditional methods [79].

These methods help overcome limitations of traditional approaches by better capturing the complexity of biological systems, handling high-dimensional data, and providing more reliable causal effect estimates in the presence of unmeasured confounding.

Experimental Protocols for Causal Pathway Identification

Integrated Multi-Omics Workflow

Table 2: Essential Research Reagents and Platforms for Causal Pathway Analysis

Reagent/Platform	Primary Function	Application Examples
Bioconductor	R-based platform for genomic analysis	RNA-seq, ChIP-seq, variant analysis [82]
Galaxy	Web-based workflow platform	Accessible bioinformatics without coding [82]
BLAST	Sequence similarity search	Identifying homologous genes/proteins [82]
KEGG	Pathway database and analysis	Pathway mapping and network analysis [82]
DeepVariant	AI-based variant calling	Accurate identification of genetic variants [82]
Rosetta	Protein structure prediction	Modeling protein-ligand interactions [82]

A robust experimental framework for causal pathway identification integrates multiple omics technologies and analytical approaches:

Step 1: Integrated Data Collection Collect transcriptomic, metabolomic, and genomic data from the same biological samples. For seed dormancy studies, this includes sampling embryos at multiple time points during dormancy release [42] [35]. For microbial studies, monitor metabolic intermediates and gene expression under different growth conditions [78].

Step 2: Differential Analysis Identify differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) across experimental conditions. In Notopterygium incisum studies, this approach identified 110,539 DEGs and 1,656 DAMs during dormancy release [35].

Step 3: Pathway Enrichment Analysis Map molecular changes to biological pathways using databases like KEGG. Combined transcriptomics and metabolomics analysis of seed dormancy release identified phenylpropanoid biosynthesis and flavonoid biosynthesis as key pathways [35].

Step 4: Causal Inference Testing Apply Mendelian Randomization, causal machine learning, or other causal inference methods to distinguish causal drivers from correlative associations. For genetic data, this involves using genetic variants as instrumental variables to test causal relationships [77].

Step 5: Experimental Validation Perturb candidate causal factors (e.g., through gene knockout, pharmacological inhibition, or environmental manipulation) to confirm their causal role in pathway regulation.

Thermodynamic Analysis of Metabolic Pathways

For energy metabolism studies, thermodynamic analysis provides critical constraints for evaluating pathway feasibility:

Step 1: Define Pathway Variants Identify alternative metabolic routes for a given conversion, including different electron carriers and energy conservation reactions [78].

Step 2: Formulate Optimization Problem Transform the maximum energy yield problem into a multi-objective mixed-integer linear optimization problem, highlighting the trade-off between yield and rate in metabolic reactions [78].

Step 3: Apply Thermodynamic Constraints Use the relationship between Gibbs energy dissipation and reaction kinetics (flux-force efficacy) to evaluate pathway feasibility:

[ \text{FFE} = 1 - e^{-\frac{\Delta G_{\text{diss}}}{RT}} ]

Where FFE represents the fraction of enzyme active sites catalyzing the net forward reaction, R is the ideal gas constant, T is absolute temperature, and ΔG_diss is the dissipated Gibbs energy [78].

Step 4: Identify Optimal Pathway Variants Solve the optimization problem to identify pathway variants that maximize both energy yield and driving forces, revealing thermodynamically feasible routes [78].

Visualization of Causal Relationships in Metabolic Pathways

Effective visualization of complex causal relationships is essential for interpreting and communicating findings in pathway analysis:

Distinguishing causation from correlation in omics data remains a fundamental challenge in biological research, with particular significance for understanding energy metabolism across diverse systems. The integration of causal inference frameworks with traditional omics approaches represents a paradigm shift in how researchers interrogate biological pathways, moving beyond associative relationships to identify true causal drivers.

For researchers studying energy metabolism in dormant seeds and microbial cells, this transition requires both methodological sophistication and conceptual clarity. By implementing robust experimental designs that incorporate Mendelian Randomization, causal machine learning, and thermodynamic analysis, scientists can overcome the limitations of correlation-based approaches and develop more accurate models of biological systems.

The future of pathway identification lies in the continued development and application of these causal inference methods, enabling researchers not only to observe biological phenomena but to understand the underlying causal structures that give rise to them. This causal understanding will be essential for advancing applications in drug discovery, metabolic engineering, and understanding fundamental biological processes across the tree of life.

Optimizing Sample Preparation for Low-Metabolic Activity States

The study of low-metabolic activity states—such as dormancy in plant seeds and persistence in microbial cells—presents unique challenges and opportunities in life sciences research. These states represent a profound metabolic paradox: organisms that appear static are, in fact, employing sophisticated metabolic strategies to maintain viability without growth. For plant seeds, dormancy is a developmental checkpoint that prevents germination under unfavorable conditions, while microbial persistence enables long-term survival in nutrient-limited or stressful environments through dramatic metabolic downshifting [23] [7]. Understanding these states requires specialized sample preparation approaches that account for their distinctive physiological characteristics.

This guide provides a comparative analysis of methodologies for investigating low-metabolic activity states across biological kingdoms. By objectively evaluating techniques for preserving, analyzing, and reactivating dormant systems, we aim to equip researchers with robust protocols that yield reproducible, biologically relevant data for applications ranging from agricultural science to antimicrobial development.

Comparative Physiology of Dormancy

Defining Features and Metabolic Signatures

Dormant plant seeds and microbial persister cells share fundamental similarities in their departure from active metabolism, yet maintain distinct physiological signatures that demand different methodological approaches for study.

Plant seed dormancy involves a coordinated metabolic shutdown where water content drops to less than 10% of dry weight, transitioning cytoplasm to a glassy state that severely reduces molecular diffusion and prevents most biochemical reactions [7]. This desiccation-tolerant state is characterized by suspended respiration, with oxygen consumption becoming undetectable at approximately 8% water content [7]. The metabolic architecture of dormant seeds prioritizes protective mechanisms, including the accumulation of late embryogenesis abundant (LEA) proteins, non-reducing sugars, and antioxidant systems that collectively stabilize cellular structures in the absence of water.

Microbial persistence represents a different strategy, where cells remain hydrated but dramatically alter their metabolic priorities. Rather than complete metabolic arrest, persistent bacteria maintain basal metabolic activity focused on cellular maintenance and stress response [23] [83]. Surprisingly, some aerobic bacteria broaden their metabolic repertoire during persistence, with obligate heterotrophs scavenging inorganic energy sources during carbon starvation, and some species utilizing atmospheric trace gases like hydrogen as maintenance energy sources [23]. This metabolic flexibility challenges traditional classifications and underscores the importance of context-dependent metabolic strategies in dormant states.

Table 1: Comparative Physiological Features of Dormant Systems

Characteristic	Dormant Plant Seeds	Persistent Microbial Cells
Hydration State	Dehydrated (<10% water content)	Hydrated
Metabolic Rate	Extremely low/undetectable	Basal maintenance level
Primary Energy Source	Stored reserves (upon imbibition)	Diverse scavenging (H2, CO)
Cellular Organization	Glassy cytoplasm	Reorganized metabolism
Respiration	Suspended	Downregulated but present
Protective Mechanisms	LEA proteins, sugars	Stress responses, toxin-antitoxin systems

Key Metabolic Pathways Regulating Dormancy Transitions

The transition between dormant and active states is governed by conserved metabolic and signaling pathways that respond to environmental cues and internal physiological status.

Plant seed dormancy release involves coordinated hormonal shifts where abscisic acid (ABA) decreases while gibberellins (GA), auxin (IAA), and jasmonic acid (JA) increase [84] [4]. Cold stratification treatment of Cercis chinensis seeds triggers a metabolic reprogramming where PFK1 downregulation redirects carbon flux to the pentose phosphate pathway, meeting demands for nucleotide precursors and antioxidant defense [84]. Transcriptomic analyses reveal that dormancy release activates genes involved in hormone signaling, DNA replication, and carbon metabolism while upregulating phenylpropanoid and flavonoid biosynthesis pathways [4].

Microbial persistence employs different regulatory mechanisms centered on (p)ppGpp signaling, toxin-antitoxin systems, and stress response networks [83]. The metabolic state of bacterial cells significantly influences antibiotic efficacy, with persistent cells exhibiting tolerance to bactericidal drugs that target active cellular processes. This metabolic regulation occurs through dysregulation of core energy generation pathways, including the electron transport chain, TCA cycle, and central carbon metabolism [83].

Diagram 1: Regulatory pathways governing dormancy transitions in plant seeds and microbial cells.

Methodological Comparison: Sample Preparation Approaches

Stabilization and Preservation Techniques

The initial stabilization of low-metabolic activity samples is critical for preserving native physiological states. Approaches differ significantly between plant and microbial systems due to their distinct physical and metabolic properties.

Plant seed preservation leverages natural desiccation tolerance, with optimal storage achieved at 4-10% moisture content and temperatures below -20°C for long-term conservation [7]. For experimental work, rapid freezing in liquid nitrogen effectively arrests residual metabolic activity, particularly important for transcriptomic and metabolomic analyses where RNA degradation and metabolite turnover are concerns. The structural complexity of seeds necessitates consideration of tissue-specific preservation; embryos may require different handling than endosperm or seed coat tissues due to varying lipid, protein, and carbohydrate compositions.

Microbial persistence stabilization presents different challenges due to the hydrated nature of bacterial cells. Effective approaches include:

• Rapid cooling to 4°C to slow metabolic activity without inducing cold shock responses
• Cryopreservation at -80°C in glycerol or DMSO-based media to maintain viability while preventing phenotypic changes
• Chemical fixation with crosslinking agents like formaldehyde for structural studies, though this eliminates recovery potential

Table 2: Stabilization Methods for Dormant Biological Samples

Method	Mechanism	Applications	Limitations
Desiccation	Reduces water content to <10%, creates glassy state	Orthodox seed storage, long-term conservation	Not suitable for recalcitrant seeds or hydrated microbes
Cryopreservation	Halts molecular motion, prevents ice formation with cryoprotectants	Microbial persister banks, seed germplasm	Requires controlled rate freezing, viability assessment challenges
Chemical Crosslinking	Protein-nucleic acid crosslinks stabilize interactions	Transcriptomics, protein-DNA interaction studies	Eliminates recovery potential, potential artifact introduction
Metabolic Inhibitors	Targets specific pathways (respiration, translation)	Acute metabolic arrest studies	Incomplete inhibition, off-target effects

Metabolic Activity Assessment Protocols

Accurately quantifying metabolic activity in dormant systems requires specialized approaches that account for their low signal-to-noise ratios and potential reactivation during analysis.

Respiration measurement in dormant seeds employs manometric or amperometric techniques capable of detecting extremely low oxygen consumption rates. For seeds at very low hydration levels (<8% water content), conventional respirometry may lack sufficient sensitivity, requiring alternative approaches such as stable isotope probing with (^{13})C-labeled substrates or calorimetric methods that detect heat production from metabolic reactions [7]. During seed imbibition, respiration resumption follows a triphasic pattern corresponding to membrane reorganization, metabolic activation, and growth initiation [7].

Microbial metabolic activity in persistent states can be assessed through:

• ATP quantification using luciferase-based assays, with typical persister cells maintaining 10-30% of ATP levels compared to growing cells
• Redox indicator dyes such as resazurin that detect electron transport chain activity
• Single-cell metabolic profiling using nano-scale secondary ion mass spectrometry (NanoSIMS) combined with stable isotope incorporation [23]
• RNA-to-protein ratios as proxies for metabolic capacity and growth potential

Diagram 2: Integrated workflow for sample preparation and analysis of low-metabolic activity states.

Omics Profiling in Dormancy Studies

Advanced omics technologies have revolutionized our understanding of dormant systems by enabling comprehensive molecular profiling without requiring cellular growth or division.

Transcriptomic approaches for dormant seeds must account for the high RNA stability in dehydrated states and rapid changes during early imbibition. Studies on barley seeds have revealed phase-specific gene expression patterns during germination: cell wall metabolism genes dominate early imbibition, followed by amino acid and protein synthesis genes before radicle protrusion, and photosynthetic genes after emergence [38]. For Notopterygium incisum seeds, transcriptomics identified 110,539 differentially expressed genes during stratification-induced dormancy release, with significant changes in hormone signaling pathways and upregulation of phenylpropanoid and flavonoid biosynthesis genes [4].

Metabolomic profiling of dormant systems requires rapid extraction protocols that quench enzymatic activity during sample processing. Combined transcriptomic and metabolomic analysis has revealed that dormancy release in N. incisum seeds involves coordinated changes in hormone signaling, carbohydrate metabolism, and accumulation of specific flavonoids including quercetin, rutin, and delphinidin [4]. Key metabolic shifts include the redirection of carbon flux through the pentose phosphate pathway and enhanced antioxidant defense systems [84].

Integrated multi-omics approaches are particularly powerful for studying low-metabolic activity states. The combination of transcriptomics, metabolomics, and proteomics can reconstruct regulatory networks controlling dormancy transitions and identify critical control points for metabolic reactivation.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Reagents for Dormancy Metabolism Research

Reagent/Category	Function	Application Examples
RNA Stabilization Solutions	Preserve RNA integrity during extraction	Transcriptomics of imbibing seeds, microbial persister gene expression
Hormone Standards	Quantify plant hormone levels	ABA, GA, IAA measurement during dormancy release [84] [4]
Metabolic Inhibitors	Target specific pathways	Respiratory chain inhibitors, translation blockers for persistence studies
Stable Isotope Tracers	Track metabolic flux	(^{13})C-glucose, (^{15})N-ammonia for pathway activity assessment
Antioxidant Enzymes	Scavenge reactive oxygen species	Catalase, superoxide dismutase activity assays [38]
Cell Viability Indicators	Distinguish live/dormant cells	Fluorescent dyes (SYTO9/propidium iodide), ATP-based assays
Cryoprotectants	Prevent ice crystal formation	Glycerol, DMSO for microbial persister preservation

Experimental Data Comparison

Quantitative Metrics Across Dormant Systems

Table 4: Experimental Data from Dormancy Studies

Parameter	Dormant Plant Seeds	Persistent Microbial Cells	Measurement Technique
Water Content	4-10% of dry weight [7]	~70-80% (hydrated)	Gravimetric analysis, Karl Fischer titration
ATP Levels	Not detectable in dry state	10-30% of growing cells	Luciferase-based bioluminescence
Respiration Rate	Undetectable below 8% hydration [7]	5-20% of active cells	Oxygen microsensors, Seahorse analyzer
Characteristic Gene Expression	ABIs, PP2C, GAIs, flavonoid biosynthesis genes [4]	Toxin-antitoxin systems, stress response genes	RNA-Seq, qRT-PCR
Key Metabolites	ABA, coumaric acid, coniferyl aldehydes [4]	(p)ppGpp, intracellular pH changes	LC-MS, GC-MS, fluorescent reporters
Dormancy Duration	Days to centuries	Hours to months	Germination/outgrowth assays

The comparative analysis of sample preparation methods for low-metabolic activity states reveals both conserved principles and system-specific requirements. Successful experimental approaches share a common emphasis on rapid metabolic arrest, appropriate stabilization conditions, and sensitive detection methods tailored to the unique physiology of each dormant system.

Future methodological developments will likely focus on single-cell analysis techniques capable of resolving heterogeneity within dormant populations, non-destructive monitoring approaches that enable longitudinal studies of dormancy transitions, and standardized reference materials that facilitate cross-study comparisons. As our understanding of dormancy mechanisms deepens, sample preparation methods will continue to evolve, enabling increasingly sophisticated investigations of these remarkable biological states.

The integration of approaches across biological systems—from plant seeds to microbial cells—provides powerful synergies that accelerate methodological innovation. By leveraging insights from diverse fields, researchers can develop optimized strategies for preserving, analyzing, and reactivating low-metabolic activity states, with applications spanning basic science, agriculture, and medicine.

The comparative analysis of energy metabolism in dormant plant seeds and microbial cells represents a frontier in biological research, bridging plant physiology, microbiology, and systems biology. This field investigates how diverse organisms employ conserved metabolic strategies to enter, maintain, and exit dormant states—a phenomenon with profound implications for agriculture, ecology, and medicine. Such cross-system analysis necessitates the integration of disparate data types, from metabolite lists to transcriptomic profiles, to transform raw analytical outputs into mechanistic biological insight. This guide objectively compares the performance of analytical approaches and tools that enable this integration, providing supporting experimental data from contemporary research.

The fundamental challenge in this domain lies in reconciling data from multiple omics technologies—transcriptomics, metabolomics, proteomics—to construct unified models of metabolic regulation. Researchers must navigate technical variations in platform sensitivity, coverage, and resolution while distinguishing biologically meaningful patterns from analytical artifacts. This process requires specialized analytical frameworks that can accommodate the unique characteristics of different biological systems while identifying universally conserved metabolic principles.

Biological Context: Conserved Metabolic Features in Dormancy

Comparative Metabolic Features of Dormant Systems

Table 1: Conserved metabolic features during dormancy across biological systems

Metabolic Feature	Dormant Plant Seeds	Dormant Microbial Cells	Aquatic Invertebrate Dormant Embryos
Primary Energy Metabolism	Downregulated TCA cycle [85]	Constrained metabolic flux [48]	Impaired glycolysis and TCA cycle [85]
Energy Carriers	Reduced ATP [85]	Not specified	ATP not detected [85]
Protective Solutes	Trehalose analogs accumulated [85]	Trehalose accumulation [85]	Trehalose or analogs higher [85]
Hormonal Regulation	ABA decrease, GA3 increase during release [4]	Not typically hormone-mediated	Not specified
Nucleotide Metabolism	Not specified	Not specified	Nucleobases, cyclic nucleotides not detected [85]
Transcriptional Activity	Repressed during dormancy [4]	Repressed during dormancy	Transcription cessation [85]

Hormonal Regulation in Seed Dormancy Release

Plant seed dormancy provides an excellent model for studying regulated metabolic quiescence. Research on Notopterygium incisum seeds has demonstrated that dormancy release involves coordinated hormonal changes: "Stratification treatment reduced the content of stored nutrients in N. incisum seeds, significantly changed enzyme activity, reduced ABA content, and increased GA3 and IAA contents" [4]. This hormonal reconfiguration drives the metabolic transition from quiescence to activation.

The molecular machinery underlying this transition involves comprehensive reprogramming of gene expression networks. Transcriptome analysis revealed that "after the dormancy of N. incisum seeds was released, the expression of genes in the abscisic acid signaling pathway (ABI1, PP2CA, ABI5 and ABF4) and the gibberellin signaling pathway (GAI, GAI1 and RGL1) were significantly down-regulated" [4]. This genetic reprogramming creates a permissive environment for metabolic activation.

Diagram 1: Molecular network regulating seed dormancy release. Stratification triggers hormonal changes that downregulate ABA and GA signaling pathway genes, enabling metabolic shifts toward germination.

Experimental Platforms and Methodologies

Analytical Workflows for Metabolite Identification and Quantification

Table 2: Experimental platforms for metabolic analysis across dormant systems

Platform Category	Specific Technologies	Applications in Dormancy Research	Key Performance Metrics
Mass Spectrometry	LC-MS, GC-MS, MALDI-MSI, DESI [86] [87] [85]	Broad metabolite profiling, spatial mapping	Detection of 5,000+ features [85], spatial resolution 5-10μm [87]
Separation Methods	UHPLC-Q Exactive Orbitrap-MS [86]	Widely targeted metabolome profiling	Identification of 10,008 metabolites [86]
Nuclear Magnetic Resonance	1H NMR [85]	Initial metabolic screening	Relative quantification, structural elucidation
Transcriptomics	RNA-Seq [4] [5]	Gene expression profiling during dormancy	Identification of 110,539 DEGs [4]
Metabolic Modeling	GEMs, FBA, COMMIT [48]	Prediction of metabolic fluxes and interactions	Community metabolic simulation

Detailed Methodological Protocols

Protocol 1: Integrated Transcriptome-Metabolome Analysis of Seed Dormancy Release [4]

• Plant Material and Treatment: Collect Notopterygium incisum seeds and subject to stratification treatment using three different matrix treatments: (A) river sand; (B) river sand:vermiculite (1:1); (C) river sand:perlite (1:1). Maintain matrix humidity at approximately 60%. For indoor stratification, place seeds in light incubators (20°C/4°C, 3000 lux, 60% humidity) for 90 days, then transfer to low-temperature storage (4°C, 3000 lux, 60% humidity) for 120 days.
• Sampling Strategy: Collect samples at five critical time points: day 0 (morphological dormancy), day 90 (morphological dormancy), day 180 (physiological dormancy), day 210 (physiological dormancy), and day 210 (germination). Each time point should include three biological replicates.
• RNA Extraction and Transcriptomics: Extract total RNA using TRIzol, assess quantity and purity with NanoDrop ND-1000. Prepare sequencing libraries using NEBNext Ultra RNA Library Prep Kit, sequence on Illumina platform. Identify differentially expressed genes (DEGs) with standardized bioinformatics pipelines.
• Metabolite Extraction and Analysis: Employ widely targeted metabolomics using UHPLC-Q Exactive Orbitrap MS. Identify differentially accumulated metabolites (DAMs) during dormancy release.
• Data Integration: Perform combined transcriptomics and metabolomics analysis to identify key pathways. Correlate DEGs and DAMs to construct regulatory networks.

Protocol 2: Metabolic Model Reconstruction for Microbial Communities [48]

• Genome-Scale Metabolic Model (GEM) Reconstruction: Utilize multiple automated reconstruction tools (CarveMe, gapseq, KBase) operating on distinct biochemical databases. CarveMe employs top-down reconstruction using a universal template, while gapseq and KBase use bottom-up approaches building from annotated genomic sequences.
• Consensus Model Construction: Merge draft models originating from the same metagenome-assembled genomes (MAGs) using established pipelines. Perform gap-filling of community models using COMMIT with iterative approach based on MAG abundance.
• Model Validation: Compare structural characteristics of community models including reactions, metabolites, dead-end metabolites, and gene content. Compute Jaccard similarity for sets of reactions, metabolites, and genes across reconstruction approaches.
• Functional Analysis: Employ constraint-based modeling methods like Flux Balance Analysis (FBA) to predict metabolic capabilities and interactions. Test different community modeling approaches: mixed-bag, compartmentalization, and costless secretion.

Analytical Framework Performance Comparison

Metabolic Model Reconstruction Tools

Table 3: Performance comparison of GEM reconstruction tools [48]

Reconstruction Tool	Reconstruction Approach	Primary Database	Number of Reactions	Number of Metabolites	Number of Genes	Dead-End Metabolites
CarveMe	Top-down	Universal template	Intermediate	Intermediate	Highest	Intermediate
gapseq	Bottom-up	ModelSEED	Highest	Highest	Lowest	Highest
KBase	Bottom-up	ModelSEED	Intermediate	Intermediate	Intermediate	Intermediate
Consensus	Hybrid	Multiple	High	High	High	Lowest

The performance comparison reveals significant methodological tradeoffs. While "gapseq models encompassed more reactions and metabolites compared to CarveMe and KBase models," they also "exhibited a larger number of dead-end metabolites, which may affect the functional characteristics of the models" [48]. Consensus approaches address these limitations by integrating multiple reconstructions, thereby "reducing the presence of dead-end metabolites" while "incorporating a greater number of genes, indicating stronger genomic evidence support for the reactions" [48].

Spatial Metabolomics Platforms

Spatial metabolomics has emerged as a powerful approach for investigating metabolic heterogeneity in dormant systems. Mass spectrometry imaging (MSI) techniques, particularly MALDI-MSI and DESI, enable in situ metabolite mapping at micron-scale resolution. "Current mass spectrometry imaging (MSI) techniques can achieve spatial resolutions between 10 and 5 µm, with prototype systems reaching below 1 µm pixel sizes" [87]. This spatial resolution is critical for resolving metabolic microenvironments within dormant seeds or microbial biofilms.

The application of spatial metabolomics to microbial systems reveals complex metabolic organization. "MALDI-MSI excels at detecting various classes of metabolites, including lipids, small peptides, amino acids, organic acids, nucleotides, and secondary metabolites" [87]. However, technical challenges remain, as "carbohydrates and other small metabolites are challenging to detect with MALDI-MSI due to their poor ionization efficiency" [87]. Advances in matrix chemistry and sample preparation are gradually overcoming these limitations.

Integrated Data Analysis Workflow

Diagram 2: Integrated data analysis workflow from acquisition to biological insight, showing parallel processing of multi-omics data.

The Scientist's Toolkit: Essential Research Reagents and Platforms

Table 4: Essential research reagents and platforms for metabolic dormancy research

Category	Specific Tool/Reagent	Function	Example Application
Analytical Platforms	UHPLC-Q Exactive Orbitrap MS [86]	High-resolution metabolite separation and detection	Identification of 10,008 metabolites in stratified seeds [86]
Separation Media	River sand:vermiculite (1:1) [4]	Seed stratification matrix	Maintaining humidity during dormancy release studies [4]
RNA Extraction	TRIzol reagent [4]	Total RNA extraction from seeds/microbes	Transcriptome sequencing sample preparation [4]
Library Preparation	NEBNext Ultra RNA Library Prep Kit [4] [5]	Sequencing library construction	Preparing samples for transcriptome analysis [4]
Metabolic Modeling	CarveMe, gapseq, KBase [48]	Genome-scale metabolic model reconstruction	Predicting metabolic interactions in communities [48]
Statistical Analysis	MetaboAnalyst [88]	Comprehensive metabolomics data analysis	Pathway enrichment analysis, statistical meta-analysis [88]
Spatial Mapping	MALDI-MSI matrix compounds [87]	Metabolite desorption/ionization for imaging	Spatial mapping of metabolites in microbial biofilms [87]

The comparative analysis presented in this guide demonstrates that no single platform provides comprehensive biological insight when studying complex processes like dormancy across biological systems. Each methodological approach carries distinct advantages and limitations that must be strategically balanced based on research objectives. Transcriptomics reveals regulatory networks, metabolomics captures functional state, spatial techniques contextualize molecular distributions, and modeling approaches integrate these components into testable predictions.

The most robust insights emerge from integrated approaches that combine multiple data types. As demonstrated in seed dormancy research, "combined transcriptomics and metabolomics analysis showed that phenylpropanoid biosynthesis and flavonoid biosynthesis are the key pathways for the dormancy release of N. incisum seeds" [4]. Similarly, in microbial systems, consensus metabolic models "retain the majority of unique reactions and metabolites from the original models, while reducing the presence of dead-end metabolites" [48]. These integrated approaches transform disparate data types from mere observations into interconnected components of mechanistic biological models, ultimately advancing our understanding of conserved metabolic principles across the spectrum of life.

Cross-Domain Validation: Comparative Mechanisms and Metabolic Rescue

In both plant seeds and microbial cells, a state of dormancy or metabolic quiescence is a survival strategy, marked by a profound reduction in metabolic activity. The transition out of this state—whether it be seed germination or the initiation of rapid microbial growth—places immense demand on the cell's energy metabolism. Metabolic rescue experiments, which involve the strategic supplementation of specific metabolites, serve as a powerful tool to identify and overcome bottlenecks in these energy-generating and biosynthetic pathways. This guide provides a comparative analysis of how supplementation experiments are applied to diagnose metabolic limitations in two distinct systems: dormant plant seeds and engineered microbial cell factories. The core principle is that if a supplemented compound alleviates a growth or developmental block, it directly identifies a functional deficiency or a rate-limiting step in the underlying metabolic network. This approach is fundamental for advancing fields as diverse as crop science, where improving seed germination and stress resilience is crucial, and industrial biotechnology, where maximizing microbial production of biofuels and chemicals is the goal.

Comparative Analysis of Metabolic Bottlenecks

The metabolic landscapes of dormant seeds and microbial cells, while different in structure, share common themes of energy and redox balance. The bottlenecks that arise during their activation, however, are often system-specific. The table below provides a high-level comparison of the common bottlenecks and the supplementation strategies used to identify them.

Table 1: Comparative Overview of Metabolic Bottlenecks and Rescue Strategies

Aspect	Dormant Plant Seeds	Microbial Cell Factories
Primary Energy Challenge	Rapid activation of metabolism after desiccation, often under environmental stress.	Sustained, high-flux production often under artificial metabolic burden or toxic stress.
Common Bottlenecks	- Antioxidant capacity (Glutathione) [89]- Raffinose family oligosaccharide (RFO) metabolism [89]- TCA cycle intermediates [89]- ABA/GA hormone balance [90]	- Cofactor imbalance (NADPH, ATP) [91]- Toxic intermediate accumulation [91]- Precursor supply (Acetyl-CoA, PEP) [91]
Typical Rescue Metabolites	Glutathione, Raffinose, Amino Acids (e.g., Arginine, Glutamine) [89] [92]	Amino acids (Cysteine, Glutamate), Organic acids, Cofactors (NADP+) [91] [93]
Key Read-Outs	Germination Rate/Vigor, Seedling Growth, Metabolic Profiling [89] [92]	Specific Growth Rate, Product Titer, Yield/Productivity, Flux Analysis [91] [93]

Detailed Experimental Protocols and Data

This section details specific experimental approaches for conducting metabolic rescue experiments in both plant and microbial systems, summarizing quantitative data for direct comparison.

Protocol: Rescuing Dormancy and Longevity in a Wheat Seed Mutant

• Objective: To investigate the metabolic basis of enhanced dormancy and longevity in the wheat mutant 1813WH and identify potential bottlenecks through biochemical profiling [89].
• Materials:
  • Biological: Wild-type and mutant 1813WH wheat seeds.
  • Reagents: Glutathione (GSH/GSSG) assay kit, Raffinose standards for metabolite profiling, solvents for metabolite extraction (e.g., methanol, water).
  • Equipment: Controlled environment growth chambers, HPLC system for metabolite separation and quantification, spectrophotometer for enzymatic assays, controlled deterioration test (CDT) chambers.
• Methodology:
  • Phenotypic Characterization: Quantify germination index (GI) under controlled conditions. Perform Controlled Deterioration Tests (CDT) by exposing seeds to high humidity and temperature (e.g., 40-45°C) for 0, 2, 5, and 8 days, followed by germination assays to assess longevity [89].
  • Metabolite Extraction: Grind mature seeds to a fine powder under liquid nitrogen. Extract metabolites using a methanol-water solvent system.
  • Targeted Metabolomics: Use gas or liquid chromatography coupled to mass spectrometry (GC/LC-MS) to quantify the levels of raffinose, TCA cycle intermediates, and other key metabolites. Employ enzymatic assays to determine the reduced (GSH) and oxidized (GSSG) glutathione levels and calculate the GSH/GSSG ratio [89].
  • Data Integration: Correlate the metabolite levels (e.g., raffinose, GSH) with the phenotypic data (GI, CDT germination rates) to identify metabolites whose abundance is strongly linked to the dormant and long-lived phenotype.
• Key Data and Interpretation: Table 2: Metabolic and Phenotypic Data from Wheat Seed Rescue Study [89]

Parameter	Wild Type Seeds	1813WH Mutant Seeds	Biological Implication
Germination Index (GI)	42.77%	4.57%	Mutant exhibits strong dormancy.
Raffinose Content	1X (Baseline)	3.5X higher	Suggests a role in desiccation tolerance and longevity.
GSH Content	1X (Baseline)	9.8X higher	Indicates enhanced antioxidant capacity.
GSH/GSSG Ratio	0.68	10.74	Signifies a more reduced cellular state, mitigating oxidative stress.
Germination after CDT	Delayed and unstable	Stable and maintained	Confers enhanced seed longevity and stress resilience.

Protocol: Diagnosing Sulfur Limitation in Oilseed Rape

• Objective: To determine the impact of sulfur (S) limitation applied at different growth stages on the seed quality and metabolic profile of Brassica napus (oilseed rape), and identify S-dependent metabolic bottlenecks [94].
• Materials:
  • Biological: Brassica napus plants.
  • Reagents: Hoagland nutrient solution with modified sulfate concentrations (508.7 μM for control, 8.7 μM for S-limitation), reagents for protein and lipid analysis.
  • Equipment: Greenhouses with controlled environment, elemental analyzer for total S content, proteomics setup (2D gels, mass spectrometer), lipid analysis platform (GC-MS).
• Methodology:
  • Plant Treatment: Grow plants under optimal S conditions until specific developmental stages (bolting (LS32), early flowering (LS53), or start of pod filling (LS70)). Apply low-sulfur treatment at each stage until seed maturity [94].
  • Seed Analysis:
    • Biochemical: Determine total S, protein, and lipid content of mature seeds.
    • Proteomics: Extract seed proteins, separate via 2D electrophoresis, and identify differentially accumulated proteins using mass spectrometry. Focus on seed storage proteins (S-rich vs. S-poor cruciferins) and metabolic enzymes [94].
    • Metabolic Profiling: Analyze key metabolites related to central carbon and energy metabolism.
• Key Data and Interpretation: Table 3: Metabolic Consequences of Sulfur Limitation in Oilseed Rape [94]

Parameter	Control Seeds	S-Limited Seeds (LS53/LS32)	Biological Implication
S-Rich Storage Proteins	Normal accumulation	Significantly reduced	Direct bottleneck in S-amino acid availability.
S-Poor Storage Proteins	Baseline	Increased (compensatory)	Maintains total nitrogen but reduces protein quality.
Lipid Content & Quality	Normal	Reduced oil content; altered composition	Energy shift and bottleneck in acetyl-CoA metabolism.
Antioxidant Enzymes (e.g., Cu/Zn-SOD)	Baseline	Accumulated	Response to oxidative stress caused by S-deficiency.
Germination Vigor	High	Significantly reduced	Functional impact of metabolic imbalances on viability.

Protocol: Engineering Robustness in Microbial Cell Factories

• Objective: To identify and overcome metabolic bottlenecks in engineered microbial cell factories (e.g., E. coli, S. cerevisiae) for the overproduction of target compounds, using strategies that include precursor supplementation and dynamic regulation [91].
• Materials:
  • Biological: Engineered microbial strain.
  • Reagents: Defined culture medium, chemical inducers, potential toxic intermediates/products, nutrients for supplementation.
  • Equipment: Fermenters or deep-well plates, HPLC/GC for product quantification, spectrophotometer for growth measurement, equipment for metabolomics/flux analysis.
• Methodology:
  • Growth and Production Profiling: Cultivate the engineered strain in a controlled bioreactor. Monitor cell density (OD600), substrate consumption, and product formation over time.
  • Bottleneck Identification:
    • Metabolite Analysis: Sample the culture and use LC/GC-MS to profile intracellular metabolites. The accumulation of a specific pathway intermediate indicates a downstream bottleneck.
    • Supplementation Rescue: Supplement the growth medium with suspected limiting precursors (e.g., amino acids, organic acids) or cofactors. A resulting increase in growth or product titer confirms the bottleneck [91].
  • Strain Engineering: Implement strategies to relieve the bottleneck, such as:
    • Dynamic Regulation: Use metabolite-responsive biosensors to automatically downregulate a pathway that consumes a needed cofactor or precursor when it becomes limiting [91].
    • Cofactor Balancing: Introduce heterologous enzymes to regenerate NADPH or other cofactors [91].
    • Tolerance Engineering: Employ adaptive laboratory evolution to select for mutants with enhanced tolerance to toxic intermediates or products [93].
• Key Data and Interpretation: Table 4: Metabolic Rescue and Engineering in Microbial Cell Factories [91]

Challenge / Bottleneck	Rescue / Engineering Strategy	Experimental Outcome
Toxic Intermediate Accumulation (e.g., FPP)	Dynamic down-regulation of upstream pathway via biosensor.	2-fold increase in amorphadiene titer (1.6 g/L) [91].
Competition for Malonyl-CoA	Bifunctional dynamic regulation to up-regulate synthesis and down-regulate competing pathway.	4.72-fold increase in cis,cis-muconic acid titer (1861.9 mg/L) [91].
Imbalanced C/N Metabolism	"Nutrition" sensor to decouple growth and production phases.	2.4-fold lower metabolic burden and robust growth during bioconversion [91].
General Cofactor/Precursor Imbalance	Supplementation with amino acids, nucleotides, or cofactors in the medium.	Resumption of growth and/or increased product yield, diagnosing the limitation [93].

Visualization of Metabolic Pathways and Workflows

The following diagrams synthesize the core metabolic concepts and experimental logic discussed in this guide.

Seed Metabolic Rescue Pathways

Microbial Metabolic Engineering Workflow

The Scientist's Toolkit: Essential Research Reagents

This table lists key reagents and materials essential for conducting metabolic rescue experiments, as derived from the cited protocols.

Table 5: Essential Reagents for Metabolic Rescue Studies

Reagent / Material	Function / Application	Example Context
Glutathione (GSH/GSSG)	Quantifying redox state and antioxidant capacity; used as a rescue metabolite.	Rescuing oxidative stress in dormant wheat seeds [89].
Raffinose Family Oligosaccharides (RFOs)	Profiling desiccation tolerance and longevity; used as a rescue metabolite.	Investigating seed longevity in wheat mutants [89].
Amino Acids (e.g., Arg, His, Gln)	Supplementation to diagnose nitrogen/amino acid metabolism bottlenecks or hormone precursor limitations.	Rescuing germination under saline-alkali stress in Medicago ruthenica [92].
Defined Nutrient Media	Precisely controlling macronutrient and micronutrient availability to induce specific deficiencies.	Studying sulfur limitation in Brassica napus [94] and growth of microbial factories [91].
Metabolite Standards	Calibration for accurate identification and quantification of metabolites via LC/GC-MS.	Profiling TCA cycle intermediates, raffinose, and other key metabolites in seeds and microbes [89] [94].
Biosensors	Dynamic monitoring of intracellular metabolite levels to enable autonomous pathway regulation.	Preventing toxic intermediate accumulation in E. coli and yeast [91].

The integration of machine learning (ML) with metabolomics has emerged as a transformative approach for deciphering complex biochemical signatures in biomedical research. This synergy is particularly powerful for classifying subtle metabolic perturbations induced by pharmaceutical compounds, enabling advances in drug discovery and toxicology. Metabolomics, the comprehensive analysis of small molecules, provides a direct readout of cellular activity and physiological status. When leveraged by ML algorithms capable of recognizing latent patterns in large, complex datasets, it becomes possible to move beyond simple biomarker identification toward predictive modeling of drug effects [95]. This capability is critically important for understanding both therapeutic action and adverse events, such as drug-induced liver injury (DILI).

The application of these technologies extends beyond human medicine, finding significant utility in comparative studies of energy metabolism across biological systems. Research on energy metabolism in dormant plant seeds and microbial cells provides foundational insights into conserved and divergent metabolic strategies for managing energy allocation, stress response, and metabolic dormancy. These comparative studies inform our understanding of how cells regulate energy resources in response to environmental challenges—a principle that also applies to how human cells respond to pharmacological stress [4] [96] [93]. This review objectively compares experimental approaches, data outputs, and performance metrics of ML-driven metabolomics, with a specific focus on classifying drug-induced metabolic signatures within this broader metabolic context.

Analytical Platforms and Experimental Workflows

Core Analytical Technologies

Metabolomic studies rely on advanced analytical platforms to separate, detect, and quantify metabolites from complex biological samples. The choice of platform significantly influences the scope, sensitivity, and quantitative accuracy of the resulting data, which in turn affects the performance of machine learning models.

Table 1: Key Analytical Platforms in Metabolomics

Platform	Key Features	Applications in Drug Response	Representative Examples
High-Performance Chemical Isotope Labeling Liquid Chromatography-Mass Spectrometry (HP-CIL LC-MS)	Enhances detection sensitivity and accuracy; improves quantification of metabolite isomers [97].	Identification of subtle metabolic shifts in drug-induced liver injury; discovery of diagnostic biomarker panels [97].	Differentiation between intrinsic and idiosyncratic DILI subtypes [97].
Ultra-High-Performance Liquid Chromatography Coupled to High-Resolution Mass Spectrometry (UHPLC-HRMS)	High chromatographic resolution; excellent mass accuracy; broad metabolome coverage [98].	Untargeted metabolomics for biomarker discovery; detection of exogenous compounds and endogenous metabolic disruptions [98].	Anti-doping research; detection of prolonged metabolic footprints of drug abuse [98].
Nuclear Magnetic Resonance (NMR) Spectroscopy	Non-destructive; highly reproducible; requires minimal sample preparation; provides structural information [98].	Quantitative profiling; identification of novel metabolites; studies of metabolic flux [98].	Tracking major metabolic pathway alterations in response to toxins.

Standardized Experimental Protocol

A typical workflow for an ML-driven metabolomics study involves a series of methodical steps from sample preparation to model validation. The following protocol is synthesized from several studies investigating drug-induced metabolic changes [97] [6] [98].

• Sample Collection and Preparation: Collect biological samples (e.g., serum, tissue, microbial cell pellets). Precipitate proteins using cold organic solvents (e.g., methanol or acetonitrile). Centrifuge to remove debris and collect the metabolite-containing supernatant. For certain applications, like profiling hard-to-detect metabolites, use chemical isotope labeling to derivative the samples [97].

• Data Acquisition: Analyze samples using the chosen platform (e.g., HP-CIL LC-MS, UHPLC-HRMS). Include quality control (QC) samples, such as a pooled sample from all groups, analyzed throughout the batch to monitor instrument stability [97] [98].

• Data Preprocessing and Feature Extraction: Convert raw instrument data into a peak list with mass-to-charge ratio (m/z) and retention time. Perform peak alignment across samples and integrate peak areas to create a data matrix (samples × metabolic features). Apply normalization (e.g., probabilistic quotient normalization) and data scaling (e.g., unit variance or Pareto scaling) to minimize technical variance [97] [98].

• Machine Learning Model Development:

  • Feature Selection: Identify the most significant metabolites (features) using univariate (e.g., p-value from t-test) and multivariate (e.g., Variable Importance in Projection from PLS-DA) analyses [97].
  • Model Training and Validation: Split the data into training and testing sets. Train multiple classifier models (e.g., multiple regression, support vector machines, random forests) on the training set. Use k-fold cross-validation on the training set to tune hyperparameters. Evaluate the final model's performance on the held-out test set using metrics like Area Under the Curve (AUC), accuracy, and F1-score [97].

Diagram 1: ML-Driven Metabolomics Workflow. This diagram outlines the key steps from sample collection to biological interpretation in a typical metabolomics study.

Comparative Analysis: Classifying Drug-Induced Liver Injury (DILI)

A direct comparison of methodologies and outcomes highlights the performance of different ML models in a real-world application. A 2025 study on DILI provides robust experimental data for such a comparison [97].

• Objective: To investigate metabolic mechanisms and identify biomarkers differentiating intrinsic and idiosyncratic DILI.
• Sample: Serum from 44 DILI patients (17 intrinsic, 27 idiosyncratic) classified according to EASL guidelines.
• Metabolomics Platform: High-performance chemical isotope labeling liquid chromatography-mass spectrometry (HP-CIL LC-MS).
• Data Analysis: Differentially expressed metabolites were identified via univariate and multivariate analysis. These were used to develop and validate multiple machine learning models for DILI subtype classification [97].

Performance Metrics of Machine Learning Models

Table 2: Performance Comparison of ML Models in DILI Subtype Classification [97]

Machine Learning Model	Cross-Validation AUC	Hold-Out Validation AUC	Key Metabolite Biomarkers
Multiple Regression	0.983	0.935	Alanyl-Glycine, N2-Acetyl-L-Cystathionine, 5-Hydroxyindoleacetic acid isomers
Support Vector Machine	>0.8	>0.8
Random Forest	>0.8	>0.8
k-Nearest Neighbors	>0.8	>0.8

The exceptional performance of the multiple regression model (AUC=0.983) demonstrates the high predictive power achievable with a carefully selected metabolic biomarker panel. Pathway analysis further revealed that the metabolic distinctions between DILI subtypes involved significant alterations in amino acid metabolism pathways, including tryptophan, tyrosine, and cysteine-methionine metabolism [97].

The Scientist's Toolkit: Essential Reagents and Materials

Successful execution of ML-driven metabolomics studies requires specific research reagents and materials.

Table 3: Essential Research Reagents and Materials for Metabolomics

Category / Item	Specific Function	Application Example
Chemical Isotope Labeling Reagents	Improve detection sensitivity and accuracy of metabolites, particularly for distinguishing isomers [97].	HP-CIL LC-MS analysis of serum for DILI biomarker discovery [97].
Chromatography Solvents	High-purity methanol, acetonitrile, and water for metabolite separation in LC-MS systems.	Standard mobile phases in UHPLC-HRMS and HP-CIL LC-MS workflows [97] [98].
Standard Reference Materials	Certified metabolites for instrument calibration and quantification.	Creating calibration curves for absolute quantification of key biomarkers.
Solid-Phase Extraction (SPE) Kits	Clean up samples and pre-concentrate metabolites to reduce matrix effects.	Purifying serum or plasma samples prior to LC-MS analysis.
Protein Precipitation Reagents	Remove proteins from biological samples to prevent instrument fouling.	Methanol or acetonitrile used in the initial processing of serum or cell culture samples [97].
Stable Isotope-Labeled Internal Standards	Account for sample loss and ion suppression during MS analysis, enabling precise quantification.	Adding ¹³C or ¹⁵N-labeled amino acids to all samples for normalization [99].

Cross-Domain Insights: Energy Metabolism in Dormant Seeds and Microbial Cells

The principles of metabolic regulation and adaptation observed in drug response have parallels in other biological systems, particularly in the energy metabolism of dormant plant seeds and microbial cells. Studying these systems provides fundamental insights into conserved metabolic strategies.

Metabolic Pathways in Dormancy and Stress Response

Energy management under stress or dormancy involves a complex interplay of specific metabolic pathways. The diagrams below illustrate key pathways and their regulation in plant seeds and microbial cells.

Diagram 2: Comparative Metabolic Regulation. This diagram compares the triggers and metabolic responses in dormant plant seeds and microbial cells under stress.

Comparative Metabolic Features

Table 4: Energy Metabolism Across Biological Systems

Feature	Dormant Plant Seeds	Microbial Cell Factories	Drug-Stressed Mammalian Cells
Primary Energy Shift	Mobilization of stored nutrients (starch, lipids); activation of sucrose metabolism [4] [96].	Shift from glycolysis to mitochondrial respiration; enhanced fatty acid and amino acid oxidation [6] [93].	Alterations in amino acid metabolism (e.g., tryptophan, tyrosine); disruption of energy cofactor balance [97].
Key Metabolites	Sucrose, quercetin, rutin, delphinidin, p-coumaric acid [4].	Indole-3-propionic acid (IPA), short-chain fatty acids [6].	Alanyl-Glycine, N2-Acetyl-L-Cystathionine, 5-Hydroxyindoleacetic acid [97].
Regulatory Mechanisms	Hormonal balance (ABA/GA); expression of transcription factors (ABI5, ABF4) [4] [96].	PPAR-β/δ signaling; membrane engineering; efflux transporters [6] [93].	Direct biochemical toxicity; oxidative stress; disruption of metabolic pathway regulation [97] [95].
Common Analytical Tools	LC-MS, GC-MS, transcriptomics for pathway analysis [4] [96].	SCENITH, Seahorse Flux Analyzer, Genome-Scale Metabolic Models (GEMs) [6] [99].	HP-CIL LC-MS, UHPLC-HRMS, Machine Learning classifiers [97] [95].

The objective comparison of methodologies and performance data confirms that machine learning-assisted metabolomics is a powerful paradigm for classifying drug-induced metabolic signatures. The case study on DILI demonstrates that models like multiple regression can achieve exceptional classification accuracy (AUC > 0.93) when applied to targeted metabolomic data [97]. The core strength of this integration lies in ML's ability to decode complex, high-dimensional metabolomic data into actionable, predictive models.

These approaches are firmly grounded in the principles of comparative energy metabolism. The study of dormant seeds reveals how organisms reprogram metabolic pathways to manage energy reserves and break dormancy [4] [96], while research on microbial cells shows how metabolic flux is redirected to enhance respiration and tolerate stress [6] [93]. Together, these fields provide a foundational understanding of metabolic adaptation that enriches the interpretation of drug-induced metabolic changes in human cells. As metabolomic technologies and ML algorithms continue to advance, their combined application will undoubtedly yield more sensitive, specific, and clinically valuable tools for drug discovery and safety assessment.

The precise identification of off-target effects represents a critical challenge across multiple biological domains, from drug discovery to genome editing. In pharmaceutical development, off-target binding occurs when a small molecule therapeutic interacts with proteins other than its primary intended target, potentially causing detrimental side-effects or revealing opportunities for drug repurposing [100]. Similarly, in CRISPR/Cas9 genome editing systems, off-target effects can lead to unintended cleavages at genomic sites with sequence similarity to the target site, posing significant challenges for therapeutic applications [101]. The emerging paradigm of polypharmacology recognizes that drug efficacy often arises from interactions with multiple protein targets, moving beyond the traditional "one drug, one target" approach [100].

Understanding off-target binding requires sophisticated structural analysis methods that leverage principles of protein similarity at multiple levels: global sequence homology, three-dimensional structural conservation, and local binding site compatibility. These approaches are particularly relevant when studying energy metabolism across biological systems, where evolutionary conservation of catalytic sites in metabolic enzymes creates potential for cross-reactivity. This review provides a comprehensive comparison of structural bioinformatics methodologies for off-target identification, with special emphasis on their application in comparative studies of energy metabolism between dormant plant seeds and microbial cells.

Metabolic Context: Energy Metabolism Across Biological Systems

Energy Metabolism in Dormant Plant Seeds

Dormant orthodox seeds represent remarkable biological systems that maintain viability while in a metabolically quiescent state. These seeds possess a very low water content (typically less than 10% of dry weight), which severely reduces molecular diffusion and mobility, effectively preventing most biochemical reactions [7]. The desiccated state changes the cytoplasm from a fluid to a glassy state, dramatically reducing metabolic activity and respiration [7]. This metabolic arrest is crucial for extending seed longevity, with some species remaining viable for centuries [7].

Research on Korean pine seeds (Pinus koraiensis) has revealed that primary dormancy is maintained through specific alterations in metabolic pathways. During germination sensu stricto, non-dormant seeds exhibit dynamic changes in glycolytic and tricarboxylic acid (TCA) cycle intermediates, while dormant seeds show attenuated biosynthetic processes and abnormal patterns in energy metabolism [14]. Notably, metabolites including fructose-6-phosphate, inositol-3-phosphate, 3-phosphoglyceric acid, and D-glucose-6-phosphate demonstrate the most significant decreases in non-dormant seeds, indicating substantial slowdown of glycolysis and TCA cycle activity as germination progresses [14].

The respiration rates of intact dormant seeds remain significantly suppressed compared to seeds with cracked coats, highlighting the role of physical constraints in maintaining metabolic quiescence [14]. This metabolic regulation in dormant seeds represents an evolutionary adaptation for survival under unfavorable conditions.

Bacterial Energy Metabolism and Regulation

In contrast to dormant seeds, microbial systems typically exhibit dynamic and versatile metabolic capabilities. Bacterial metabolism encompasses diverse strategies including respiration, fermentation, photosynthesis, and autotrophy [80]. Heterotrophic bacteria, which include all human pathogens, obtain energy through oxidation of organic compounds such as carbohydrates, lipids, and proteins [80].

The complete oxidation of glucose via respiration typically yields 38 moles of ATP per mole of glucose, representing approximately 55% energy efficiency with the remaining energy dissipated as heat [80]. Bacterial metabolic flexibility is evidenced by multiple glucose-catabolizing pathways, including glycolysis, the oxidative pentose phosphate pathway, and the Entner-Doudoroff pathway [80].

Recent nanocalorimetry studies of Shewanella oneidensis MR-1 have revealed that catabolic rates are not necessarily coupled to cell division rates but rather to physiological rearrangements during growth phase transitions [102]. This uncoupling of energy metabolism from growth represents an interesting parallel to the metabolic state in dormant seeds, though through different mechanistic bases.

Comparative Analysis of Metabolic States

The following table summarizes key differences in energy metabolism between dormant plant seeds and microbial systems:

Table 1: Comparative Energy Metabolism in Dormant Seeds vs. Microbial Cells

Parameter	Dormant Plant Seeds	Microbial Cells
Metabolic Rate	Severely reduced	Variable across growth phases
Primary Metabolic Pathways	Attenuated glycolysis and TCA cycle	Diverse pathways based on environment
Water Content	<10% (glass state)	High (70-90%)
Respiration	Barely detectable	Active and regulated
Energy Efficiency	Maintenance-focused	Growth and replication-focused
Response to Nutrients	Limited until dormancy broken	Rapid metabolic restructuring
Heat Dissipation	Minimal	Measurable via nanocalorimetry [102]

Structural Bioinformatics Approaches for Off-Target Prediction

Principles of Protein Structure Comparison

Protein structure comparison methods form the foundation for computational prediction of off-target interactions. These methods can be broadly categorized as sequence-dependent or sequence-independent, with the latter being particularly valuable for detecting similarities between evolutionarily distant proteins with conserved structural features [103]. The most commonly used metrics for structural comparison include:

• Root Mean Square Deviation (RMSD): Measures the average distance between equivalent atoms in superimposed structures. Lower RMSD values indicate greater structural similarity, though this metric is sensitive to local deviations and can be dominated by flexible regions [103] [104].
• TM-score: A metric that assesses topological similarity, with scores ranging from 0 to 1 (where >0.5 indicates the same fold) [104]. This measure is more robust than RMSD for global structure comparison.
• Alignment-based scores: Such as the AL0 score used in CASP assessments, which counts residues aligned within a specific distance threshold [103].

Structural comparison algorithms can implement rigid-body alignment, which maintains the relative orientation of atoms within each structure, or flexible alignment, which accommodates domain movements and conformational changes [104]. For proteins exhibiting different conformational states, flexible alignment methods like FATCAT can introduce twists between rigid domains to achieve optimal superposition [104].

Ligand-Based and Structure-Based Off-Target Prediction

Computational approaches for off-target prediction generally fall into three categories: ligand-based, structure-based, and hybrid methods.

Ligand-based approaches operate on the principle that chemically similar compounds tend to have similar biological activities. Methods such as SEA (Similarity Ensemble Approach) and SwissTargetPrediction utilize molecular fingerprints to compute similarity between query compounds and ligands with known targets [105]. The Tanimoto coefficient is commonly used to quantify structural similarity:

[T = \frac{N{ab}}{Na + Nb - N{ab}}]

where (Na) and (Nb) represent the number of bits in the fingerprints of ligands a and b, and (N_{ab}) denotes common bits [105].

Structure-based methods utilize three-dimensional protein structures to assess complementarity between compounds and potential targets. These include molecular docking programs that predict binding orientations and scoring functions that estimate interaction strengths [105]. Tools such as TarFisDock and PharmMapper fall into this category, though they are typically more computationally intensive than ligand-based approaches.

Hybrid methods like LigTMap combine both approaches, first shortlisting potential targets through ligand similarity and then refining predictions using structural analysis [105]. This strategy leverages the strengths of both methodologies while mitigating their individual limitations.

Structural Similarity in Binding Sites

Beyond global protein similarity, local binding site conservation provides powerful insights for off-target prediction. Evidence indicates that ligand cross-reactivity can occur between proteins with different global folds but similar binding sites [100]. For example, protein kinase inhibitors have been repurposed to target the biotin carboxylase subunit of acetyl-CoA carboxylase based on similar ATP-binding sites, despite these proteins belonging to different superfamilies [100].

Systematic analysis of binding site similarities has revealed evolutionary linkages across fold space, enabling prediction of off-target interactions that would be missed by sequence-based methods alone [100]. This approach is particularly relevant for metabolic enzymes, where conserved cofactor-binding sites (e.g., NADH, ATP) create potential for cross-reactivity.

Figure 1: Computational Workflows for Off-Target Prediction

Experimental Methodologies and Validation

Integrated Metabolomics and Structural Analysis

A recent innovative workflow for antibiotic off-target identification demonstrates the power of integrating multiple analytical approaches [106]. This methodology combines machine learning analysis of metabolomics data with protein structural similarity assessment to prioritize candidate targets, successfully identifying HPPK (folK) as an off-target for the DHFR-targeting antibiotic CD15-3 [106].

The key stages of this integrated approach include:

• Global Metabolomic Analysis: Untargeted metabolomics reveals metabolite abundance changes in response to compound treatment, identifying hotspots of metabolic perturbation [106].
• Machine Learning Classification: Multi-class logistic regression models trained on known antibiotic responses identify mechanism-specific metabolic signatures [106].
• Metabolic Modeling: Constraint-based modeling identifies pathways whose inhibition reproduces observed growth rescue patterns [106].
• Structural Analysis: Comparison of global and active site properties relative to known targets identifies likely off-targets [106].
• Experimental Validation: Candidate targets are validated through gene overexpression, enzyme activity assays, and phenotypic characterization [106].

This workflow exemplifies how systems-level data can be leveraged to formulate specific, testable hypotheses about off-target interactions, with structural similarity providing the link between metabolic observations and protein targets.

CRISPR Off-Target Detection Methods

For CRISPR/Cas9 systems, specialized experimental methods have been developed to detect off-target effects, broadly categorized into:

• Cas9 binding detection methods such as Extru-seq and SELEX [101]
• Double-strand break detection methods including CIRCLE-seq, DISCOVER-seq, and Digenome-seq [101]
• Repair product detection methods such as GUIDE-seq and IDLV [101]

These experimental approaches have generated comprehensive datasets for training computational prediction tools like CCLMoff, which incorporates a pretrained RNA language model to capture sequence relationships between guide RNAs and potential off-target sites [101].

Table 2: Experimental Methods for Off-Target Identification Across Domains

Method Category	Specific Techniques	Applications	Key Features
Metabolomics	Untargeted global metabolomics [106]	Drug off-target identification	Captures system-wide perturbations
Protein Structural Analysis	jFATCAT, jCE, TM-align [104]	Binding site comparison	Detects local and global similarity
CRISPR Off-Target Detection	GUIDE-seq, CIRCLE-seq [101]	Genome editing	Genome-wide cleavage mapping
Calorimetry	Nanocalorimetry [102]	Microbial metabolism	Measures metabolic heat fluxes
Binding Assays	SELEX, SITE-seq [101]	Drug and CRISPR targets	Direct binding measurement

Validation Strategies

Rigorous validation of predicted off-targets is essential and typically employs multiple complementary approaches:

• Enzyme activity assays to measure direct inhibition of candidate off-targets [106]
• Gene overexpression to demonstrate growth rescue when off-targets are amplified [106]
• Thermodynamic measurements including calorimetry to quantify interactions [102]
• Phenotypic characterization through imaging and growth profiling [106]

In the case of metabolic off-targets, supplementation experiments can determine whether specific metabolites rescue growth inhibition, providing functional evidence for pathway involvement [106].

Protein Structure Comparison Algorithms

Several specialized algorithms have been developed for protein structure comparison, each with distinct strengths and applications:

• jFATCAT-rigid: Identifies the largest structurally conserved core through rigid-body superposition [104]
• jFATCAT-flexible: Accommodates conformational changes through introduced twists between rigid domains [104]
• jCE: Uses combinatorial extension to combine similar local segments into optimal alignments [104]
• TM-align: Employs dynamic programming for sequence-independent alignment based on global topology [104]

These algorithms are publicly accessible through the RCSB PDB structure alignment tool, enabling researchers to perform pairwise comparisons between protein structures [104].

Integrated Prediction Servers

For researchers without specialized bioinformatics expertise, several web servers provide integrated off-target prediction:

• LigTMap: A hybrid server that combines ligand similarity search with docking and binding similarity analysis across 17 classes of therapeutic proteins [105]
• SwissTargetPrediction: Utilizes machine learning models to predict targets based on compound similarity [105]
• SEA: Employs similarity ensemble approach to relate targets based on ligand chemistry [105]
• CCLMoff: A deep learning framework for CRISPR off-target prediction incorporating RNA language models [101]

Table 3: Performance Comparison of Off-Target Prediction Methods

Method	Approach	Success Rate (Top 1)	Success Rate (Top 10)	Key Features
LigTMap [105]	Hybrid	45%	66%	Combined ligand and structure-based
SwissTargetPrediction [105]	Ligand-based	51%	60%	Machine learning models
SEA [105]	Ligand-based	41%	64%	Similarity ensemble approach
CCLMoff [101]	Deep learning	N/A	N/A	RNA language model for CRISPR

Figure 2: Structural Analysis Workflow for Off-Target Prediction

Key Research Reagent Solutions

Table 4: Essential Research Tools for Structural Analysis and Off-Target Identification

Resource Category	Specific Tools	Function	Application Context
Structure Databases	PDB [104], PDBbind [105]	Source of protein-ligand structures	Template identification, binding site analysis
Alignment Algorithms	jFATCAT, CE, TM-align [104]	Protein structure comparison	Detection of global and local similarity
Ligand Similarity	RDKit [105], MACCS keys	Molecular fingerprint generation	Ligand-based target prediction
Docking Tools	PSOVina2 [105]	Molecular docking	Structure-based target prediction
Metabolomics	LC-MS platforms [106]	Global metabolite profiling	Metabolic pathway perturbation analysis
Validation Assays	Enzyme activity kits [106]	In vitro inhibition testing	Experimental confirmation

Structural analysis based on protein similarity provides powerful strategies for off-target identification across biological domains. The integration of multiple methodologies—including global structure alignment, local binding site comparison, ligand-based similarity searching, and metabolic profiling—enables comprehensive prediction of off-target interactions. These approaches are particularly valuable for understanding compound effects on energy metabolism, where evolutionary conservation of catalytic mechanisms creates potential for cross-reactivity.

Future advancements will likely come from improved integration of multi-scale data, including structural information, metabolic profiling, and genomic context. Machine learning approaches that can automatically extract relevant features from protein structures and compound characteristics show particular promise, as demonstrated by tools like CCLMoff for CRISPR off-target prediction [101]. As structural coverage of proteomes continues to expand through structural genomics initiatives and improved homology modeling, structure-based off-target prediction will become increasingly comprehensive and accurate [100].

For researchers investigating comparative energy metabolism between dormant seeds and microbial systems, structural analysis offers a principled framework for predicting metabolic off-targets and understanding the molecular basis of metabolic regulation across biological systems. By leveraging conserved structural features in metabolic enzymes, these approaches can reveal unexpected connections between seemingly disparate biological processes and facilitate the development of more specific metabolic interventions.

Dormancy represents a fundamental survival strategy across the biological kingdom, enabling organisms to persist through periods of environmental stress. In both plants and microorganisms, this reversible state of reduced metabolic activity serves as a bet-hedging mechanism, enhancing long-term fitness despite the costs of suspended growth and reproduction [107]. While the ecological drivers are similar—responding to suboptimal conditions—the underlying molecular machinery exhibits both striking parallels and distinct specializations. This comparative analysis examines the key metabolic pathways governing dormancy release in plant seeds and persistence in microbial cells, with a focused lens on energy metabolism. Understanding these mechanisms holds significant implications for agricultural science, pharmaceutical development, and broader biological conservation efforts.

Comparative Analysis of Key Metabolic Pathways

The transition between dormant and active states is governed by complex metabolic reprogramming. The table below provides a structured comparison of the primary pathways involved in seed dormancy release and microbial persistence.

Table 1: Key Metabolic Pathways in Seed Dormancy Release and Microbial Persistence

Feature	Seed Dormancy Release	Microbial Persistence
Core Function	Transition from metabolically quiescent to active growth state, initiating plant life cycle [108].	Bet-hedging strategy for survival under suboptimal conditions, forming a reservoir of genetic diversity (seed bank) [107].
Primary Energy Metabolism Pathways	- Glutathione Metabolism: Critical for redox balance; elevated GSH/GSSG ratio associated with enhanced dormancy and longevity [89].- Galactose Metabolism/RFO Synthesis: Raffinose family oligosaccharides (RFOs) protect against oxidative stress and desiccation [89].- Tricarboxylic Acid (TCA) Cycle: Provides energy and precursors; decreased intermediate levels noted in dormant states [89].- Glycolysis & Carbohydrate Metabolism: Key enzymes (e.g., β-glucosidase/BGLU) upregulated to fuel germination [5].	- Methanol & Formaldehyde Oxidation: Prevalent in dark ocean waters for energy generation from recalcitrant carbon sources [109].- Core Carbon Conversion Processes: Shift towards degradation of organic matter in energy-limited environments [109].- Mixotrophy: Utilization of both autotrophic and heterotrophic strategies in dark, energy-scarce environments [109].
Key Regulators & Signals	- Plant Hormones: Antagonistic interaction between Abscisic Acid (ABA; maintains dormancy) and Gibberellic Acid (GA; promotes germination) [108].- Reactive Oxygen Species (ROS): Act as signaling molecules; interplay with hormones (ABA, GA, ethylene) crucial for dormancy vs. germination decision [89].- Temperature: A primary environmental cue; stratification treatments trigger hormonal and metabolic shifts to break dormancy [5].	- Environmental Cues: Dormancy entry/exit regulated by fluctuations in temperature, pH, water availability, and resource supply [107].- Nutrient & Energy Gradients: Determine metabolic pathway expression, e.g., photosynthesis in sunlit waters vs. recalcitrant carbon degradation in the dark ocean [109].- Stochastic Processes: Some metabolic transitions occur stochastically in addition to being environmentally regulated [107].
Representative Experimental Findings	- Wheat Mutant 1813WH: Showed 3.5x increase in raffinose and 9.8x increase in reduced glutathione (GSH), correlating with strong dormancy [89].- Cardiocrinum giganteum: β-glucosidase (BGLU) genes showed 5-7 fold upregulation after 130 days of stratification, breaking dormancy [5].	- Marine Microbial Communities: Metatranscriptomic analyses reveal selective expression of core metabolic pathways (e.g., methanol oxidation by SAR202 in deep waters) along nutrient/energy gradients [109].- Deep Biosphere: Extremely low metabolic activity and cellular turnover (e.g., ~1000 years), rendering populations evolutionarily static [107].

Detailed Experimental Protocols for Key Studies

Robust experimental design is critical for elucidating the complex mechanisms of dormancy. The following section details the methodologies from seminal studies in both plant and microbial systems.

Protocol 1: Characterizing a Dormant Wheat Mutant

This protocol is based on the study of the wheat mutant line 1813WH, which exhibits enhanced seed dormancy and longevity [89].

• 1. Germination Assay (GI Calculation):

  • Purpose: To quantitatively assess the level of seed dormancy.
  • Procedure: Seeds are placed in Petri dishes on moist filter paper under favorable germination conditions. The number of germinated seeds is counted daily. The Germination Index (GI) is calculated using a standard formula to compare dormancy strength between mutant (1813WH) and wild-type lines [89].

• 2. Controlled Deterioration Test (CDT):

  • Purpose: To artificially accelerate seed aging and assess seed longevity.
  • Procedure: Seed samples are subjected to high humidity and temperature (e.g., ~40-45°C and >80% relative humidity) for varying durations (e.g., 0, 2, 5, 8 days). After treatment, seeds are germinated under standard conditions, and germination rates and speed are recorded to determine viability loss over time [89].

• 3. Metabolite Profiling:

  • Purpose: To identify biochemical changes associated with the dormant phenotype.
  • Procedure: Metabolites are extracted from ground seed samples using appropriate solvents (e.g., methanol/water). The extracts are then analyzed using techniques like Liquid Chromatography-Mass Spectrometry (LC-MS) to identify and quantify key metabolites such as raffinose, glutathione (GSH), and oxidized glutathione (GSSG) [89].

• 4. Combined Transcriptomic and Metabolomic Analysis:

  • Purpose: To integrate gene expression data with metabolic changes for a systems-level understanding.
  • Procedure:
    • RNA Extraction & Sequencing: Total RNA is extracted from seed samples, and sequencing libraries are prepared for transcriptome analysis on a platform like Illumina.
    • Data Integration: Differentially expressed genes from transcriptomic data are mapped to metabolic pathways (e.g., glutathione metabolism, TCA cycle) that also show significant changes in metabolite levels, revealing coordinated regulatory networks [89].

Protocol 2: Analyzing Dormancy Release inCardiocrinum giganteumSeeds

This protocol outlines the process for studying dormancy release in seeds with morphophysiological dormancy through temperature stratification [5].

• 1. Variable Temperature Stratification Treatment:

  • Purpose: To break seed dormancy by simulating natural seasonal temperature shifts.
  • Procedure: Seeds are placed on moist filter paper in Petri dishes and subjected to a sequence of temperature regimes. An example sequence is 25/15°C (day/night) for 60 days, followed by 15/5°C for 60 days, and then 5°C for 10 days. Seeds are incubated under a 12-hour photoperiod during the warm phase [5].

• 2. Embryo Length Measurement:

  • Purpose: To track morphological changes during the after-ripening of the underdeveloped embryo.
  • Procedure: At regular intervals during stratification (e.g., every 30 days), a random sample of seeds is dissected. The embryos are carefully excised, and their lengths are measured under a microscope to quantify growth [5].

• 3. Transcriptome Sequencing (RNA-seq) During Stratification:

  • Purpose: To identify genes involved in dormancy release at different stages.
  • Procedure: Seed samples are collected at key stratification time points (e.g., 0, 30, 60, 90, 130 days), immediately frozen in liquid nitrogen, and stored at -80°C. Total RNA is extracted, and sequencing libraries are prepared using a kit like NEBNext Ultra RNA Library Prep Kit. Libraries are then sequenced on an Illumina platform to generate transcriptome profiles [5].

Protocol 3: Pathway-Centric Analysis of Microbial Metabolic Potential

This protocol describes a meta-omics approach to study the metabolic status of microbial communities along environmental gradients [109].

• 1. Sample Collection and Fractionation:

  • Purpose: To capture microbial community structure and function from different ocean depths.
  • Procedure: Water samples are collected from various depths (e.g., 5 m to 4000 m) using a Niskin bottle rosette system. Samples are filtered through a series of membranes to separate different microbial size fractions (e.g., free-living vs. particle-associated cells) [109].

• 2. DNA/RNA Co-Extraction and Sequencing:

  • Purpose: To assess both the metabolic potential (genes present) and the active metabolic functions (genes expressed).
  • Procedure: Total nucleic acids (DNA and RNA) are co-extracted from filters. DNA is used for 16S rRNA gene amplicon sequencing (community structure) and metagenomic sequencing (metabolic potential). RNA is treated with DNase, reverse-transcribed, and used for metatranscriptomic sequencing to profile gene expression [109].

• 3. Bioinformatic and Statistical Analysis:

  • Purpose: To correlate microbial taxonomy with expressed metabolic pathways across environmental gradients.
  • Procedure:
    • Sequence Processing: Metagenomic and metatranscriptomic reads are assembled, and genes are predicted and annotated against functional databases (e.g., KEGG, COG).
    • Pathway-Centric Analysis: The abundance and expression of key metabolic pathways (e.g., methanol oxidation, photosynthesis) are quantified.
    • Correlation Analysis: Statistical models (e.g., correlation analyses) are used to link the abundance of specific microbial taxa (e.g., SAR202) with the expression of particular metabolic pathways and environmental parameters (e.g., nutrient concentration, depth) [109].

Pathway Diagrams and Workflows

The following diagrams, generated using Graphviz, illustrate the core regulatory logic and experimental workflows for studying dormancy in plant seeds and microorganisms.

Seed Dormancy Release Pathway

Microbial Persistence Metabolic Regulation

Multi-Omics Analysis Workflow

The Scientist's Toolkit: Essential Research Reagents and Materials

Cutting-edge research into dormancy mechanisms relies on a suite of specialized reagents and analytical platforms.

Table 2: Essential Reagents and Tools for Dormancy Research

Item Name	Function/Application
EMS (Ethyl Methanesulfonate)	A chemical mutagen used to create genetic mutant populations (e.g., the wheat mutant 1813WH) for forward genetics screens to identify genes controlling dormancy [89].
NEBNext Ultra RNA Library Prep Kit	A commercial kit used to prepare high-quality sequencing libraries from RNA samples for transcriptomic analyses (e.g., RNA-seq) on Illumina platforms [5].
OMEGA Soil DNA Kit	A standardized kit for efficient extraction of high-purity microbial genomic DNA from complex environmental samples, including soil and filters, for metagenomic studies [110].
LC-MS/MS (Liquid Chromatography-Tandem Mass Spectrometry)	An analytical chemistry technique used for sensitive and accurate identification and quantification of metabolites (e.g., raffinose, glutathione, hormones) in seed and microbial samples [89] [110].
Variable Temperature Stratification Chambers	Precision-controlled environmental growth chambers that programmatically simulate day/night and seasonal temperature shifts to study and break seed dormancy in species like Cardiocrinum giganteum [5].
Niskin Bottle Rosette	An oceanographic sampling system that collects water samples from precise depths while simultaneously recording physical data (e.g., temperature, salinity), essential for studying depth-stratified microbial communities [109].
Illumina NovaSeq Platform	A high-throughput DNA sequencing platform used for large-scale genomic, metagenomic, and transcriptomic projects, enabling comprehensive profiling of genes and their expression [89] [110].
PCR Reagents (Primers 338F/806R, ITS5/ITS2)	Specific primers to amplify the bacterial 16S rRNA gene (V3-V4 region) and fungal ITS region, respectively, for high-throughput sequencing to characterize microbial community structure [110].

In the context of comparative analysis of energy metabolism in dormant plant seeds and microbial cells, the functional validation of candidate genetic targets represents a critical research bridge. This process moves beyond computational predictions to experimentally confirm the biochemical roles of genes, particularly those encoding enzymes that govern metabolic state transitions. In dormancy research, where organisms like orthodox seeds or metabolically inactive microbial cells suspend high-energy processes, understanding the specific enzymes that control the entry into and exit from these states is paramount [7] [111]. Gene overexpression coupled with enzyme assays provides a powerful methodological framework to establish these functional links, enabling researchers to characterize putative metabolic regulators identified through omics-based approaches.

The validation pipeline typically begins with the identification of candidate genes through transcriptomic or proteomic studies comparing active and dormant states. For instance, research on Potaninia mongolica in the Gobi Desert revealed distinct physiological states between dormant and active plants, suggesting underlying genetic regulators [111]. Similarly, microbial studies utilize computational tools like DeepECtransformer to predict enzyme functions from genetic sequences [112]. Subsequent functional validation through overexpression and enzyme activity measurements transforms these computational predictions into biologically confirmed mechanisms, offering critical insights into conserved and divergent energy management strategies across biological kingdoms.

Comparative Methodologies for Functional Validation

Computational Prediction Tools for Enzyme Function

Before embarking on experimental validation, researchers increasingly rely on computational tools to prioritize candidate enzyme-encoding genes. These tools analyze sequence data to predict enzymatic functions, providing a preliminary annotation that guides targeted experimental design.

DeepECtransformer represents a significant advancement in this domain, utilizing transformer neural network layers to predict Enzyme Commission (EC) numbers from amino acid sequences [112]. This deep learning model extracts latent features from protein sequences and can annotate enzymes with high precision, even suggesting corrections to misannotated entries in established databases. The system employs a dual-engine approach: a neural network for primary prediction and a homology search fallback, collectively covering 5,360 EC numbers [112]. Performance varies by enzyme class, with F1 scores ranging from 0.6990 for oxidoreductases (EC:1) to 0.9469 for other classes, demonstrating particular strength in predicting functions for enzymes with sufficient training sequences [112].

Comparative Analysis with Alternative Tools:

Table 1: Comparison of Computational Tools for Enzyme Function Prediction

Tool Name	Methodology	EC Number Coverage	Reported Performance	Key Advantages
DeepECtransformer	Transformer neural network + homology search	5,360 EC numbers	Precision: 0.7589-0.9506 (varies by class)	Interpretable AI that identifies functional motifs
DeepEC	Deep neural network	Not specified	Lower than DeepECtransformer	Earlier deep learning approach
DIAMOND	Homology-based search	Dependent on database	High precision but lower recall	Fast protein alignment
HDMLF	Protein language model + bidirectional GRU	Not specified	Superior to BLAST and DeepEC	Integrated multiple sequence alignment
CLEAN	Contrastive learning	Not specified	Improved performance on imbalanced data	Addresses EC number distribution imbalance

The interpretability of DeepECtransformer provides a particular advantage for experimental design, as the model can identify functional motifs—such as active sites or cofactor binding regions—that are critical for enzyme function [112]. This information guides researchers in designing appropriate enzyme assays by highlighting potentially important catalytic residues.

Gene Overexpression Strategies

Gene overexpression serves as a critical intervention to establish causal relationships between candidate genes and observed metabolic phenotypes. By artificially increasing gene expression beyond physiological levels, researchers can probe gene function even when native expression is low or tightly regulated during state transitions.

In practice, overexpression involves cloning the candidate gene into an expression vector under control of a strong promoter, followed by introduction into a suitable host system. For plant studies, this might involve transformation of model species like Arabidopsis, while microbial studies typically employ bacterial or yeast expression systems [112]. The choice of host depends on multiple factors, including the need for proper post-translational modifications, codon optimization, and avoidance of host-specific toxicity.

Successful application of this approach was demonstrated in the functional validation of three previously uncharacterized E. coli proteins (YgfF, YciO, and YjdM) [112]. After computational prediction of EC numbers using DeepECtransformer, researchers performed heterologous expression of these genes, purified the resulting proteins, and conducted enzyme activity assays to confirm the predicted functions [112]. This pipeline from prediction to experimental validation exemplifies the power of integrated computational and experimental approaches.

Enzyme Activity Assays: Methodological Considerations

Measuring enzyme activity provides direct evidence of biochemical function and is an essential component of the validation pipeline. Multiple methodological approaches exist, each with distinct advantages and applications depending on the enzyme class, detection requirements, and experimental context.

Spectroscopic Assays represent some of the most common approaches, particularly those utilizing ultraviolet-visible (UV-Vis) spectroscopy to monitor changes in substrate or product concentration. For instance, the validation of malate dehydrogenase activity for the protein P93052 from Botryococcus braunii involved monitoring the oxidation of NADH at 340 nm, confirming DeepECtransformer's prediction that corrected its original misannotation as lactate dehydrogenase [112].

Fluorescence-Based Methods offer enhanced sensitivity for detecting low enzyme activities, which can be particularly valuable when working with novel enzymes or suboptimal expression. These approaches often employ synthetic substrates that produce a fluorescent signal upon enzymatic conversion. Common designs include:

• FRET-based substrates: Where enzyme activity separates a fluorophore-quencher pair
• "Turn-on" probes: Where enzyme reaction activates fluorescence
• Aggregation-induced emission probes: Where enzyme action induces aggregation and fluorescence enhancement [113]

These fluorescence techniques have been adapted for everything from high-throughput screening to in situ zymography, allowing spatial localization of enzyme activity within tissues [113].

Magnetic Resonance Methods, including magnetic resonance spectroscopy (MRS) and chemical exchange saturation transfer (CEST), provide non-invasive approaches to monitor enzyme activity, sometimes in real-time within living systems [113]. For example, creatine kinase activity can be measured by monitoring ATP and phosphocreatine levels using ³¹P MRS [113].

Mass Spectrometry-Based Approaches offer label-free detection of enzyme activities by directly measuring substrate depletion or product formation. These methods are particularly valuable when working with natural substrates, as they avoid potential artifacts from substrate modification. Applications range from MALDI-MS imaging of enzyme activity in tissue sections to coupled approaches with separation techniques like liquid chromatography [113].

Table 2: Comparison of Enzyme Activity Assay Methods

Method Category	Detection Principle	Sensitivity	Throughput	Key Applications
Spectroscopic (UV-Vis)	Light absorption changes	Moderate	High	Dehydrogenases, kinases, phosphatases
Fluorescence	Emission intensity changes	High	High	Proteases, phosphatases, glycosidases
Luminescence	Light emission from chemical reactions	High	High	Kinases, metabolic enzymes
Magnetic Resonance	Nuclear spin properties	Low to moderate	Low	Non-invasive in vivo monitoring
Mass Spectrometry	Mass-to-charge ratio	High	Moderate	Any enzyme with detectable mass change

Experimental Protocols for Key Methodologies

Heterologous Expression and Purification Protocol

This protocol outlines the methodology for expressing and purifying candidate enzymes from microbial systems, adapted from the experimental validation of DeepECtransformer predictions [112].

Materials:

• Expression vector (e.g., pET, pGEX, or other suitable system)
• Expression host (e.g., E. coli BL21(DE3) or other appropriate strains)
• LB or defined medium with appropriate antibiotics
• Induction agent (IPTG for lac-based promoters)
• Lysis buffer (e.g., 50 mM Tris-HCl, pH 8.0, 300 mM NaCl, 10 mM imidazole)
• Purification system appropriate for affinity tag (e.g., Ni-NTA resin for His-tagged proteins)
• Desalting/buffer exchange columns

Procedure:

• Clone the candidate gene into an appropriate expression vector using standard molecular biology techniques.
• Transform the expression construct into a suitable expression host.
• Inoculate a small starter culture and grow overnight at 37°C with shaking.
• Dilute the overnight culture 1:100 into fresh medium containing appropriate antibiotics.
• Grow at 37°C with shaking until OD600 reaches 0.6-0.8.
• Induce protein expression by adding IPTG to a final concentration of 0.1-1.0 mM.
• Incubate for an additional 4-16 hours at an appropriate temperature (often 16-25°C for better solubility).
• Harvest cells by centrifugation (e.g., 5,000 × g for 15 minutes).
• Resuspend cell pellet in lysis buffer and lyse by sonication or enzymatic methods.
• Clarify the lysate by centrifugation (e.g., 15,000 × g for 30 minutes).
• Purify the recombinant protein using affinity chromatography appropriate for the tag.
• Desalt into appropriate assay buffer and quantify protein concentration.
• Aliquot and store at -80°C until use.

Enzyme Activity Assay Protocol (Spectrophotometric)

This general protocol can be adapted for various oxidoreductases and other enzymes that produce spectrophotometrically detectable changes.

Materials:

• Purified enzyme preparation
• Appropriate assay buffer
• Enzyme substrates and cofactors
• Spectrophotometer with temperature control
• Quartz or UV-compatible microcuvettes

Procedure:

• Prepare assay buffer appropriate for the enzyme of interest.
• Set spectrophotometer to appropriate wavelength (e.g., 340 nm for NADH/NADPH detection).
• Pre-incubate all solutions to assay temperature.
• In a cuvette, mix buffer, cofactors, and substrates in a total volume slightly less than final.
• Initiate reaction by adding enzyme and mix quickly.
• Immediately place in spectrophotometer and start monitoring absorbance.
• Record data for an appropriate time period (typically 5-30 minutes).
• Calculate reaction rates from the linear portion of the progress curve.
• Perform appropriate controls (no enzyme, no substrate, heat-inactivated enzyme).
• Analyze data using appropriate kinetic models.

Research Reagent Solutions and Essential Materials

Table 3: Essential Research Reagents for Gene Overexpression and Enzyme Assays

Reagent/Material Category	Specific Examples	Function/Application
Expression Systems	pET vectors, pGEX vectors, yeast expression systems	Provide platform for heterologous protein production
Expression Hosts	E. coli BL21(DE3), P. pastoris, insect cell lines	Cellular factories for recombinant protein production
Purification Resins	Ni-NTA, glutathione-sepharose, antibody-conjugated beads	Affinity purification of tagged recombinant proteins
Detection Reagents	NADH/NADPH, chromogenic substrates, fluorescent probes	Enable monitoring of enzyme activity
Analytical Instruments	Spectrophotometers, fluorimeters, LC-MS systems	Quantify enzyme activities and reaction products
Bioinformatics Tools	DeepECtransformer, DIAMOND, DeepEC	Predict enzyme functions from sequence data

Visualization of Methodological Workflows

Functional Validation Pipeline

Enzyme Assay Methodology Selection

Applications in Dormancy and Energy Metabolism Research

The integration of gene overexpression with enzyme assays has yielded significant insights into the metabolic regulation of dormant states across biological systems. In plant seed dormancy, this approach has helped characterize enzymes involved in the transition between metabolically inactive (dry) and active states [7]. Orthodox seeds exhibit remarkable tolerance to desiccation, with water content dropping below 10%, which severely restricts molecular diffusion and metabolic activity [7]. The resumption of metabolic activity upon rehydration involves rapid reactivation of key enzymes, making this system ideal for studying metabolic regulation.

In microbial systems, similar approaches have elucidated enzymes that help maintain viability during dormant states. The functional annotation of previously uncharacterized genes in E. coli through DeepECtransformer predictions followed by experimental validation demonstrates how this pipeline can fill gaps in our understanding of metabolic networks [112]. These microbial models provide simplified systems for understanding fundamental principles of metabolic shutdown and reactivation that may be conserved in more complex eukaryotic systems.

Comparative analysis across kingdoms reveals both convergent and divergent strategies for managing energy metabolism during dormancy. Plants often employ sophisticated molecular protection mechanisms alongside metabolic arrest, including specific metabolites that stabilize macromolecules in the dry state [7]. Microbes may utilize similar protective mechanisms but often maintain greater metabolic flexibility, allowing rapid response to environmental changes. The functional validation of candidate enzymes identified through comparative genomics continues to reveal the intricate regulatory networks that control these specialized metabolic states.

The combination of gene overexpression and enzyme assays provides a robust framework for moving from correlative observations to causal understanding in metabolic research, particularly in the context of dormancy and energy management across biological systems. As computational prediction tools continue to advance in accuracy and interpretability, and experimental methods become increasingly sensitive and versatile, this integrated approach will accelerate our understanding of the fundamental enzymatic switches that control metabolic state transitions.

The comparative analysis of dormant plant seeds and microbial cells highlights both universal principles and taxon-specific adaptations in energy metabolism regulation. By systematically validating candidate enzymes identified through omics approaches, researchers can build increasingly accurate models of the metabolic networks that underlie biological dormancy, with potential applications ranging from agricultural improvement to therapeutic development. The methodologies and comparative analyses presented here provide a roadmap for researchers embarking on similar functional validation journeys in diverse biological systems.

Conclusion

The comparative analysis of energy metabolism in dormant plant seeds and microbial cells reveals profound evolutionary convergence in managing metabolic quiescence. Both systems employ sophisticated control over hydration, respiration, and energy pathways to enter and exit dormant states. The methodological advances in multi-omics and functional platforms now enable unprecedented resolution in studying these processes, though they demand rigorous attention to quantification and reproducibility. The validation strategies developed in microbial systems, particularly metabolic rescue and machine learning classification, offer powerful templates for probing seed dormancy. For biomedical research, these insights are pivotal. Understanding the metabolic checkpoints of dormancy can directly inform novel therapeutic strategies against persistent microbial infections and biofilm-associated diseases, where cells mimic the dormant, treatment-resistant state of seeds. Future research should focus on the direct translation of conserved metabolic regulators, such as trehalose or specific TCA cycle intermediates, as potential targets to either break undesirable dormancy or induce protective quiescence.

References

• 1. Thermodynamic Properties of Seeds: A Review [https://arccjournals.com/journal/agricultural-reviews/R-2605]
• 2. The Orthodox Dry Seeds Are Alive: A Clear Example of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8747232/]
• 3. Thermodynamics of Seed and Plant Growth [https://www.intechopen.com/chapters/21496]
• 4. Combined transcriptomic and metabolomic analysis of the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12465197/]
• 5. Comparative transcriptome analysis reveal gene regulation ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1591781/full]
• 6. Microbial metabolite indole-3-propionic acid drives ... [https://www.nature.com/articles/s42255-025-01396-6]
• 7. The Seed and the Metabolism Regulation - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC8869448/]
• 8. Metabolic rate of angiosperm seeds: effects of allometry ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11836704/]
• 9. Ecotypic variability in the metabolic response of seeds to diurnal hydration-dehydration cycles and its relationship to seed vigor - PubMed [https://pubmed.ncbi.nlm.nih.gov/22156384/]
• 10. Hydration-Dehydration Effects on Germination Tolerance to ... [https://www.mdpi.com/2223-7747/14/14/2237]
• 11. An integrated assessment of seed germination ... [https://www.nature.com/articles/s41598-025-25806-7]
• 12. Extracellular respiration is a latent energy metabolism in ... [https://www.sciencedirect.com/science/article/abs/pii/S0092867425002892]
• 13. Respiration of barley seeds (Hordeum vulgare L.) under ... [https://www.sciencedirect.com/science/article/abs/pii/S0022474X23001042]
• 14. The roles of metabolic pathways in maintaining primary ... [https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-019-2167-2]
• 15. Seed dormancy and ABA signaling: The breakthrough goes on [https://pmc.ncbi.nlm.nih.gov/articles/PMC2819511/]
• 16. Dormancy contributes to the maintenance of microbial ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC2851880/]
• 17. Integrated 16 S rRNA and transcriptome analysis reveal ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12513145/]
• 18. ABA-GA antagonism and modular gene networks ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1624742/full]
• 19. ABA biosynthesis rather than ABA catabolism is induced by ... [https://www.sciencedirect.com/science/article/pii/S2214514125000960]
• 20. Seasonally and niche-differentiated diversity of active ... [https://www.nature.com/articles/s42003-025-08789-z]
• 21. Comparative Metabolomics Reveals Two Metabolic Modules ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8707830/]
• 22. From seed to seed: the role of microbial inheritance in ... [https://www.sciencedirect.com/science/article/pii/S0966842X2200292X]
• 23. Uncovering the Metabolic Strategies of the Dormant Microbial ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6529542/]
• 24. Microbial dormancy as an ecological and biogeochemical ... [https://www.nature.com/articles/s41467-025-59167-6]
• 25. ATP-free in vitro biotransformation of starch-derived ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11021460/]
• 26. Glycogen metabolism and structure: A review [https://www.sciencedirect.com/science/article/pii/S0144861724008579]
• 27. ATP and Energy Storage Molecules [https://www.sketchy.com/mcat-lessons/atp-and-energy-storage-molecules]
• 28. ATP [https://learn.genetics.utah.edu/content/metabolism/atp/]
• 29. 2.3 Biological Molecules – Concepts of Biology [https://opentextbc.ca/biology/chapter/2-3-biological-molecules/]
• 30. Carbohydrates 101 [https://hammernutrition.com/blogs/advanced-knowledge/carbohydrates-101?srsltid=AfmBOorHeQrQNXvmAsVq3IC84yHZNjiEAMFbof0ME4a109wtl_7SqFUt]
• 31. Biochemistry - Glycogenolysis - StatPearls - NCBI Bookshelf [https://www.ncbi.nlm.nih.gov/books/NBK549820/]
• 32. How Cells Obtain Energy from Food - NCBI - NIH [https://www.ncbi.nlm.nih.gov/books/NBK26882/]
• 33. Multi-omics integration in biomedical research - PubMed Central [https://pmc.ncbi.nlm.nih.gov/articles/PMC7701361/]
• 34. Multi-omics analyses reveal regulatory networks ... [https://www.nature.com/articles/s41467-025-65299-6]
• 35. Combined transcriptomic and metabolomic analysis of the ... [https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-025-12021-x]
• 36. Multi-omics integration [https://genexplain.com/multi-omics-integration/]
• 37. Functional dynamics analysis of endophytic microbial ... [https://www.nature.com/articles/s41598-024-79850-w]
• 38. Cell signaling mechanisms and metabolic regulation of ... [https://www.sciencedirect.com/science/article/pii/S2214514117300909]
• 39. Multi-omics integration uncovers the zinc metabolic ... [https://pubmed.ncbi.nlm.nih.gov/40373399/]
• 40. Integration of metabolomics and transcriptomics to reveal ... [https://www.sciencedirect.com/science/article/pii/S0006291X24013913]
• 41. Metabolomics Market Market 2025: Artificial Intelligence ... [https://www.linkedin.com/pulse/metabolomics-market-2025-artificial-intelligence-xzxsf]
• 42. The roles of metabolic pathways in maintaining primary ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6907207/]
• 43. A Comparative Genomic Analysis of Energy Metabolism in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3119410/]
• 44. Comparative transcriptomic analysis provides insights into the ... [https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-022-08998-4]
• 45. A metabolomics pipeline highlights microbial metabolism ... [https://www.sciencedirect.com/science/article/pii/S0092867424005798]
• 46. Metabolomics Reveals Broad-Scale Metabolic Perturbations ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3867997/]
• 47. Mass Spectrometry Market Analysis 2025-2030 by Product ... [https://finance.yahoo.com/news/mass-spectrometry-market-analysis-2025-093300234.html]
• 48. Comparative analysis of metabolic models of microbial ... [https://www.nature.com/articles/s41540-024-00384-y]
• 49. Frontiers in mass spectrometry-based spatial metabolomics [https://www.sciencedirect.com/science/article/pii/S016599362400195X]
• 50. Spatially Resolved Plant Metabolomics - PMC - PubMed Central [https://pmc.ncbi.nlm.nih.gov/articles/PMC12388700/]
• 51. Mass spectrometry imaging in plants, microbes, and food [https://pubs.rsc.org/en/content/articlehtml/2024/an/d4an00644e]
• 52. Global Mass Spectrometry Imaging Market Analysis 2025 ... [https://www.openpr.com/news/4061457/global-mass-spectrometry-imaging-market-analysis-2025-2030]
• 53. Single-cell spatial metabolomics with cell-type specific ... [https://www.nature.com/articles/s41467-023-43917-5]
• 54. Visualizing the distributions and spatiotemporal changes of ... [https://www.sciencedirect.com/science/article/pii/S0926669025017558]
• 55. JuliaMSI: A high-performance graphical platform for mass ... [https://www.sciencedirect.com/science/article/abs/pii/S0003267025010074]
• 56. Metabolic flux analysis: a comprehensive review on sample ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9449821/]
• 57. Advanced Metabolic Flux Analysis [https://panomebio.com/metabolomics-services/metabolicflux/]
• 58. Tracing metabolic flux in vivo: basic model structures of ... [https://www.nature.com/articles/s12276-022-00814-z]
• 59. Measurement of metabolic fluxes using stable isotope tracers ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5794022/]
• 60. Parallel labeling experiments and metabolic flux analysis [https://pmc.ncbi.nlm.nih.gov/articles/PMC6474253/]
• 61. Systematic comparison of local approaches for isotopically ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1178239/full]
• 62. Key Metabolite Differences Between Korean Pine (Pinus ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.767108/full]
• 63. Comparative Metabolomics Profiling Reveals Key Metabolites ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10223290/]
• 64. Molecular and Biophysical Perspectives on Dormancy ... [https://www.mdpi.com/2218-273X/15/5/701]
• 65. Versatile Functional Energy Metabolism Platform Working ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8915814/]
• 66. Fast, accurate, and versatile data analysis platform for the ... [https://www.sciencedirect.com/science/article/pii/S0092867425002855]
• 67. A fast, scalable and versatile tool for analysis of single-cell ... [https://www.nature.com/articles/s41592-023-02139-9]
• 68. Distinguishing between metabolically active and dormant ... [https://pubmed.ncbi.nlm.nih.gov/29098411/]
• 69. Comparison of metabolic profiles, regulatory pathways ... [https://www.nature.com/articles/s41538-025-00606-5]
• 70. What is microbial dormancy? [https://www.sciencedirect.com/science/article/pii/S0966842X23002378]
• 71. High-throughput comparison, functional annotation, and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4084441/]
• 72. Impact of methods for data selection on the day-to- ... [https://www.sciencedirect.com/science/article/pii/S0939475323002879]
• 73. Reproducibility of Metabolic Mapping Using 2D Multi‐Slice ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12634484/]
• 74. 代谢组学数据分析的统计学方法综述转载 [https://blog.csdn.net/fjsd155/article/details/82918846]
• 75. 基于色谱-质谱联用的暴露组学分析方法和研究范式的新进展 [https://pmc.ncbi.nlm.nih.gov/articles/PMC10877474/]
• 76. A Reproducibility Crisis for Clinical Metabolomics Studies [https://pmc.ncbi.nlm.nih.gov/articles/PMC11999569/]
• 77. Causal Inference Methods to Integrate Omics and Complex ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8091955/]
• 78. Optimal evaluation of energy yield and driving force in ... [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011264]
• 79. Correlation does not equal causation: the imperative of ... [https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1630781/full]
• 80. Bacterial Metabolism - Medical Microbiology - NCBI Bookshelf [https://www.ncbi.nlm.nih.gov/books/NBK7919/]
• 81. The transcriptomes of dormant leafy spurge seeds under ... [https://www.sciencedirect.com/science/article/abs/pii/S0176161712005342#!]
• 82. Top 10 Bioinformatics Tools in 2025: Features, Pros, Cons ... [https://www.cotocus.com/blog/top-10-bioinformatics-tools-in-2025-features-pros-cons-comparison/]
• 83. The role of bacterial metabolism in antimicrobial resistance [https://pmc.ncbi.nlm.nih.gov/articles/PMC12173792/]
• 84. PFK1-mediated metabolic shifts facilitate cold stratification ... [https://www.sciencedirect.com/science/article/pii/S0926669025000986]
• 85. Metabolomics reveals novel insight on dormancy of aquatic ... [https://www.nature.com/articles/s41598-019-45061-x]
• 86. Metabolome profiling of stratified seeds provides insight into ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9363648/]
• 87. unveiling host-microbe interactions with spatial metabolomics [https://pmc.ncbi.nlm.nih.gov/articles/PMC12322014/]
• 88. MetaboAnalyst [https://www.metaboanalyst.ca/]
• 89. Characterization of a wheat mutant line 1813WH ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12613733/]
• 90. Seed dormancy and germination in rice [https://www.sciencedirect.com/science/article/abs/pii/S1674205225001698]
• 91. Recent advances in improving metabolic robustness of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7744323/]
• 92. Analysis of germination characteristics and metabolome ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1592555/full]
• 93. Regulating cellular activity to enhance microbial cell factory ... [https://www.sciencedirect.com/science/article/abs/pii/S0734975025002319]
• 94. Evidence for Proteomic and Metabolic Adaptations ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4014277/]
• 95. Machine learning for metabolomics research in drug ... [https://www.sciencedirect.com/science/article/pii/S2666521223000157]
• 96. Regulatory roles of GA and sugar metabolism genes with ... [https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-025-07164-8]
• 97. Machine learning-assisted analysis of serum metabolomics ... [https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1727462/full]
• 98. Integrating Advanced Metabolomics and Machine Learning ... [https://www.mdpi.com/2218-1989/15/11/696]
• 99. Metabolic modeling of host-microbe interactions - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC12538087/]
• 100. Structure-based Systems Biology for Analyzing Off-target ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3070778/]
• 101. A versatile CRISPR/Cas9 system off-target prediction tool ... [https://www.nature.com/articles/s42003-025-08275-6]
• 102. Changes in Microbial Energy Metabolism Measured by ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2018.00109/full]
• 103. Methods of protein structure comparison - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4321859/]
• 104. Pairwise Structure Alignment [https://www.rcsb.org/docs/tools/pairwise-structure-alignment]
• 105. LigTMap: ligand and structure -based target and activity... identification [https://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00523-1]
• 106. Empowering drug off - target discovery with metabolic and structural ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10256842/]
• 107. Evolution with a seed bank: The population genetic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5748526/]
• 108. Seed dormancy and germination in rice [https://www.sciencedirect.com/science/article/pii/S1674205225001698]
• 109. Pathway-Centric Analysis of Microbial Metabolic Potential ... [https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2022.867310/full]
• 110. Comparative Analysis of Microbial Diversity and Metabolic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11479024/]
• 111. Rhizospheric microbiomes differ between dormant and ... [https://www.nature.com/articles/s41598-025-22696-7]
• 112. Functional annotation of enzyme-encoding genes using ... [https://www.nature.com/articles/s41467-023-43216-z]
• 113. Methods of Measuring Enzyme Activity Ex vivo and In vivo - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC6147230/]