Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Flux balance analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed2026

Dynamic metabolic modelling of ATP allocation during viral infection.

Viral pathogens, like SARS-CoV-2, hijack the host's macromolecular production machinery, imposing an energetic burden that is distributed across cellular metabolism. To explore the dynamic metabolic tension between the host's survival and viral replication, we developed a computational framework that uses genome-scale models to perform dynamic flux balance analysis of human cell metabolism during virus infections. Relative to previous models, our framework addresses the physiology of viral infections of non-proliferating host cells through two new features. First, by incorporating the lipid content of SARS-CoV-2 biomass, we discovered activation of previously overlooked pathways giving rise to new predictions of possible drug targets. Furthermore, we introduce a dynamic model that simulates the partitioning of resources between the virus and the host cell, capturing the extent to which the competition depletes the human cells from essential ATP. By incorporating viral dynamics into our COMETS framework for spatio-temporal modelling of metabolism, we provide a mechanistic, dynamic and generalizable starting point for bridging systems biology modelling with viral pathogenesis. This framework could be extended to broadly incorporate phage dynamics in microbial systems and ecosystems.

Humans↗
PubMed2026

dAMN: a genome-scale neural-mechanistic hybrid model to predict bacterial growth dynamics.

SUMMARY: This study presents dAMN, a genome-scale neural-mechanistic hybrid model that combines neural networks with dynamic flux balance analysis to predict bacterial growth dynamics across diverse nutrient environments. Using a residual network architecture, dAMN predicts reaction fluxes and lag-phase parameters from initial medium composition, then integrates these predictions under stoichiometric constraints derived from genome-scale metabolic models. Trained on Escherichia coli and Pseudomonas putida growth datasets across combinatorial media, dAMN accurately forecasts temporal growth dynamics and generalizes to unseen media conditions, with mean R² ≥ 0.9. The model also reproduces biologically relevant behaviors including substrate depletion, acetate overflow, and diauxic shifts, while explicitly modeling lag phases usually absent from standard dFBA. AVAILABILITY AND IMPLEMENTATION: The dAMN software, associated models, and datasets are available at https://github.com/brsynth/dAMN-main-release and via Zenodo DOI: 10.5281/zenodo.17908125.

Escherichia coli↗
PubMed2026

Membrane and proteome allocation constraints in Escherichia coli models during overflow metabolism.

The allocation of finite cellular resources is a fundamental principle that dictates microbial metabolic strategies and gives rise to complex phenomena, such as overflow metabolism, characterized by the production of respiro-fermentative by-products, including acetate, during rapid growth. Although proteome-constrained models have successfully predicted overflow metabolism in Escherichia coli, they often overlook the distinct biophysical and energetic costs associated with protein localization. The cellular membrane, in particular, represents a critical and constrained compartment where competition for space and synthesis machinery can create significant metabolic bottlenecks. To investigate this, we developed the membrane-associated constrained flux balance analysis (MAFBA), a scalable, genome-scale metabolic model that introduces a tunable constraint on the total protein mass allocated to the cellular membrane. Our model demonstrates that the overall and membrane-associated proteome allocation constraints interact to improve the accuracy of predicting the onset of overflow metabolism. It mechanistically reveals that at high growth rates, competition for limited membrane allocation forces a trade-off between growth-essential functions and respiratory capacity, leading to acetate production. Furthermore, MAFBA quantitatively explains the widely observed experimental phenomenon that expressing heterologous membrane proteins imposes a significantly higher metabolic burden than expressing cytosolic proteins. This study establishes membrane resource allocation as a key constraint governing bacterial physiology, acting in concert with overall proteome limitations. The resulting MAFBA framework provides a powerful and accessible tool for synthetic biology and metabolic engineering, enabling the prediction of metabolic costs associated with expressing membrane-bound proteins and guiding strain design strategies, holding promise for applications in bioproduction and metabolic engineering.

Escherichia coli↗
PubMed2025

Seed2LP: seed inference in metabolic networks for reverse ecology applications.

MOTIVATION: A challenging problem in microbiology is to determine nutritional requirements of microorganisms and culture them, especially for the microbial dark matter detected solely with culture-independent methods. The latter foster an increasing amount of genomic sequences that can be explored with reverse ecology approaches to raise hypotheses on the corresponding populations. Building upon genome-scale metabolic networks (GSMNs) obtained from genome annotations, metabolic models predict contextualized phenotypes using nutrient information. RESULTS: We developed the tool Seed2LP, addressing the inverse problem of predicting source nutrients, or seeds, from a GSMN and a metabolic objective. The originality of Seed2LP is its hybrid model, combining a scalable and discrete Boolean approximation of metabolic activity, with the numerically accurate flux balance analysis (FBA). Seed inference is highly customizable, with multiple search and solving modes, exploring the search space of external and internal metabolites combinations. Application to a benchmark of 107 curated GSMNs highlights the usefulness of a logic modelling method over a graph-based approach to predict seeds, and the relevance of hybrid solving to satisfy FBA constraints. Focusing on the dependency between metabolism and environment, Seed2LP is a computational support contributing to address the multifactorial challenge of culturing possibly uncultured microorganisms. AVAILABILITY AND IMPLEMENTATION: Seed2LP is available on https://github.com/bioasp/seed2lp.

Metabolic Networks and Pathways↗
PubMed2026

BioEMMA: Automated Generation of Model-Specific Escher-Compatible Maps from KEGG Pathways.

Genome-scale metabolic models are widely used to investigate cellular metabolism, but their interpretation and comparison are limited by the lack of reproducible pathway-level visualizations with a common spatial organization. This study presents BioEMMA, a Python-based tool for the automated generation of model-specific metabolic pathway maps in the Escher JSON format using coordinate information from curated KEGG pathway maps. BioEMMA parses KGML files, map reaction and metabolite identifiers to model database namespaces, filters pathway elements according to an input SBML model, adds non-primary metabolites, reconstructs Escher-compatible layouts, and supports flux visualization. The tool was integrated into a reproducible BioUML workflow for metabolic model reconstruction. BioEMMA was evaluated using the e_coli_core model and the KEGG glycolysis/gluconeogenesis pathway while generating a model-specific map with overlaid FBA fluxes. It was then applied to compare E. coli reconstructions generated by gapseq, ModelSEEDpy, and Reconstructor across three central carbon metabolism pathways. To broaden the evaluation, BioEMMA was applied using 87 prokaryotic BiGG models and three eukaryotic models. The analysis revealed pathway-specific differences in reaction coverage, shared and model-specific reactions, and predicted flux activity. BioEMMA therefore provides a reproducible framework for pathway-level visualization and comparison of genome-scale metabolic reconstructions within a common spatial coordinate system.

Escher maps↗
PubMed2026

Adaptive laboratory evolution enables carbon-negative mixotrophic fermentation and enhanced chain elongation in Clostridium sp. JS66.

Improving carbon recovery during sugar fermentation remains a major challenge because a substantial fraction of substrate carbon is lost as CO2 during central metabolism. To overcome this limitation, Clostridium sp. JS66 (JS66), an acetogen producing hexanoic acid from glucose, was subjected to adaptive laboratory evolution under autotrophic CO2/H2 conditions to enhance H2-assisted CO2 reassimilation during glucose fermentation. The evolved strain, ALECO2, exhibited CO2 consumption without a lag phase under autotrophic conditions and reached a 9.5-fold higher CO2 uptake rate than JS66. Under fed-batch mixotrophic conditions, glucose-only fermentation yielded a carbon molar yield (Cmetabolite/Csugar, CM/CS) of 0.60, whereas H2 supplementation increased CM/CS to 0.91 and redirected carbon flux toward C6 products (hexanoic acid and hexanol), which accounted for 49% of total C_output. With additional CO2 supplementation, ALECO2 further assimilated externally supplied CO2, increasing the CM/CS to 1.10 and demonstrating carbon-negative fermentation. Assimilation of externally supplied CO2 further redirected carbon flux toward chain elongation, producing 7.14 g/L hexanoic acid and increasing the C6 carbon fraction to 57% of total C_output. Constraint-based flux analysis supported increased acetyl-CoA formation through the Wood-Ljungdahl pathway and enhanced flux toward reverse β-oxidation under H2- and CO2/H2-supplemented conditions. Genome analysis identified mutations including genes encoding a putative HytB homolog and a LysR-type transcriptional regulator. These results establish ALECO2 as a promising evolved anaerobic non-photosynthetic (ANP) mixotrophy platform that links CO2 reassimilation and external CO2 assimilation with chain elongation, enabling carbon-neutral and carbon-negative production of value-added C6 products from glucose.

Anaerobic non-photosyntheticmixotrophy (ANP)↗
PubMed2026

Diet modulates cardiac metabolic stress during anthracycline treatment.

Diet is a modifiable determinant of cardiovascular risk and may influence tolerance to cancer therapies. The mechanisms by which specific dietary components affect cardiac metabolism during anthracycline treatment remain poorly defined, limiting the incorporation of dietary recommendations into treatment guidelines. Here, we integrated heart proteomics data from patients treated with or without anthracyclines with a genome-scale reconstruction of human cardiac metabolism (CardioNet). Using constraint-based flux analysis, we conducted >30,000 in silico simulations of diet scenarios generated from chemical profiles of ∼500 foods curated in the Periodic Table of Food Initiative. These simulations revealed that diets enriched in rapidly absorbable sugars and depleted of essential fatty acids impair cardiac metabolic efficiency, increasing reactive oxygen species production and the demand for purine salvage fluxes. These predicted metabolic patterns were consistent with plasma metabolomics from patients treated with anthracyclines, validating our findings. Computational modeling of 39 recipes across six cuisines revealed cardiometabolic effects of omnivorous versus vegan diets in patients. Modeling of a healthy vegan diet increased cardiometabolic efficiency compared with a healthy omnivorous diet in patients treated with anthracyclines, independent of the culinary background. Our approach demonstrates that integrating the molecular composition of food with genome-scale metabolic models enables systematic analysis of diet patterns for translational testing. Ultimately, these in silico studies provide a framework for trials and may inform dietary recommendations for improving cardiometabolic health.NEW & NOTEWORTHY We developed a systems biology framework to predict how diet influences cardiac metabolism during cancer therapy. Across >30,000 in silico diet simulations, we identified nutrient patterns that either exacerbate or mitigate anthracycline-induced metabolic stress. These findings demonstrate how computational modeling can uncover diet-metabolism interactions driving cardiotoxicity and guide dietary interventions.

Humans↗
PubMed2025

Biophysical metabolic modeling of complex bacterial colony morphology.

Microbial colony growth is shaped by the physics of biomass propagation and nutrient diffusion and by the metabolic reactions that organisms activate as a function of the surrounding environment. While microbial colonies have been explored using minimal models of growth and motility, full integration of biomass propagation and metabolism is still lacking. Here, building upon our framework for computation of microbial ecosystems in time and space (COMETS), we combine dynamic flux balance modeling of metabolism with collective biomass propagation and demographic fluctuations to provide nuanced simulations of E. coli colonies. Simulations produced realistic colony morphology, consistent with our experiments. They characterize the transition between smooth and furcated colonies and the decay of genetic diversity. Furthermore, we demonstrate that under certain conditions, biomass can accumulate along "metabolic rings" that are reminiscent of coffee-stain rings but have a completely different origin. Our approach is a key step toward predictive microbial ecosystems modeling. A record of this paper's transparent peer review process is included in the supplemental information.

Models, Biological↗
PubMed2026

Engineering Bacillus Subtilis for Efficient Biosynthesis of Riboflavin: Current Knowledge and Future Perspectives.

Riboflavin is an essential water-soluble vitamin that serves as a precursor for the biosynthesis of the flavin cofactors FMN and FAD, which play pivotal roles in numerous redox and energy metabolism reactions. With the growing global demand for sustainable vitamin production, microbial fermentation has become an attractive alternative to chemical synthesis due to its environmental and economic advantages. Among microbial hosts, Bacillus subtilis has emerged as a leading cell factory for riboflavin production owing to its GRAS status, well-characterized genetics, and efficient protein secretion system. This review provides a comprehensive overview of recent advances in metabolic engineering strategies to enhance riboflavin biosynthesis in B. subtilis. Key topics include strengthening biosynthetic and precursor pathways, relieving feedback inhibition, balancing metabolic flux and cell growth, employing adaptive laboratory evolution, and utilizing omics-guided optimization and 13C metabolic flux analysis. Moreover, the integration of synthetic biology tools such as riboswitch engineering, regulatory element design, and high-throughput screening has significantly accelerated strain improvement. Despite remarkable progress, challenges remain in achieving precise regulatory control, optimizing multi-gene expression, and enhancing genome integration efficiency. Future research combining multi-omics data, synthetic regulatory design, and machine learning-driven predictive modeling is expected to further advance the development of intelligent B. subtilis cell factories. However, the practical implementation of these systems remains constrained by the metabolic burden of overproduction and the lack of universal regulatory models that can predict strain performance across varying industrial scales.

Bacillus subtilis↗
PubMed2025

MiNEApy: enhancing enrichment network analysis in metabolic networks.

MOTIVATION: Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS: Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION: MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.

Metabolic Networks and Pathways↗
PubMed1979

Spontaneous secretion from the dog small intestine in vivo.

Forty loops of small intestine in the dog were perfused under control conditions with a balanced electrolyte solution. Most of the loops absorbed sodium and water, but 10 loops were in a state of spontaneous intestinal secretion. Compared to absorbing loops, spontaneously secreting loops exhibited reduced values for lumen-to-plasma fluxes of sodium and chloride and increased values for the plasma-to-lumen fluxes of these ions. Analysis of flux ratios suggested that sodium and chloride were actively scecreted during spontaneous intestinal secretion in the dog. Spontaneous secretion was similar to the secretion induced by CT or VIP, except that the latter were associated with a change in PD whereas the PD in the spontaneously secreting loops was the same as in the spontaneously absorbing loops.

Animals↗
PubMed2026

Global Patterns of Net Ecosystem Exchange in peatlands: A Systematic Review and Meta-analysis of Drivers Across Land Use and Environmental Gradients.

Peatlands play an essential role in the global carbon cycle, storing approximately one-third of the world's soil carbon despite covering less than 3% of the land surface. Peatland degradation from anthropogenic activities and climate change can convert peatlands from net carbon sinks to sources by altering carbon cycling. Net Ecosystem Exchange (NEE), the balance between CO2 uptake and emission, is a critical indicator for assessing peatland condition and restoration efforts. We conducted a systematic quantitative literature review to investigate global patterns of NEE in peatlands and identify key environmental and anthropogenic drivers of CO2 flux variability. Annual NEE values from 120 globally distributed sites reported in peer-reviewed literature were analyzed in relation to climatic zone, land use, vegetation type, peatland condition, and water table depth. Our synthesis revealed significant geographic gaps, with peatland NEE studies substantially underrepresented in the Tropics, Africa, and Oceania. Agricultural peatlands emitted significantly more CO2 than sites under natural land uses or peat extraction, while degraded peatlands were significantly greater net CO2 sources than intact and restored systems. Restored peatlands remained net CO2 sources on average, emphasizing the importance of long-term monitoring and adaptive management following restoration interventions. Water table depth significantly affected NEE variability, with CO2 emissions increasing approximately 7.2 gCO2-C m-2yr-1 for every centimeter of water table drawdown. A substantial variability in measurement methods, data processing software, and protocols highlighted the critical need for methodological standardization. Our findings provide evidence-based targets for peatland conservation and restoration monitoring as nature-based climate solutions.

Ecosystem↗
PubMed2026

CRISPR-Enabled functional genomics in hPSCs-derived neural models for autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a genetically heterogeneous neurodevelopmental condition in which hundreds of individually rare risk variants converge on a small number of shared biological pathways, including synaptic scaffolding, chromatin remodeling, excitation-inhibition balance, and cellular energy metabolism. Translating this genetic heterogeneity into mechanistic insight requires experimental systems capable of interrogating individual gene functions in human-relevant neural contexts at scale. CRISPR-enabled functional genomics in human pluripotent stem cell (hPSC)-derived neural models, spanning neural progenitors, cortical and inhibitory neurons, astrocytes, microglia, and brain organoids, provides precisely this capability. By integrating pooled perturbation screens with multimodal readouts including single-cell and spatial transcriptomics, chromatin accessibility profiling, proximity labeling proteomics, multi-electrode array electrophysiology, and metabolic flux analysis, these platforms enable systematic, causal mapping of ASD gene function at system resolution. Early applications have already revealed convergent mechanisms: BAF complex disruption expands the ventral progenitor pool and biases its fate toward oligodendrocyte and interneuron lineages; ADNP loss impairs microglial synaptic pruning through altered endocytic trafficking; and mTOR pathway dysregulation in PTEN- and TSC2-perturbed models links genetic risk directly to metabolic and mitochondrial dysfunction. Computational frameworks including MIMOSCA and SCEPTRE enable causal network reconstruction and pseudotime inference from these datasets, moving the field from gene lists toward pathway-level models of ASD pathobiology. Translational applications leverage isogenic iPSC panels and variant-level base and prime editing to stratify ASD variants by functional impact, informing gene therapy design for haploinsufficient targets such as CHD8 and SCN2A via AAV or antisense oligonucleotide delivery. Remaining challenges, including model developmental immaturity, batch variability, and the difficulty of modeling polygenic risk, are addressed by a roadmap integrating spatial perturbomics, AI-driven causal inference, and population-scale standardized biobanks. This review synthesizes the current state of CRISPR-based functional genomics in human stem cell neural models as a coherent experimental framework for converting ASD genetic associations into mechanistic understanding and therapeutic opportunity.

Humans↗
PubMed2026

Progressive salinity drives flavonoid branch reprogramming in Anoectochilus roxburghii.

Flavonoids play critical roles in plant adaptation to abiotic stress; however, how salt stress modulates metabolic flux distribution within flavonoid branches remains poorly understood, particularly in non-model medicinal plants. Here, we integrated targeted metabolomics, transcriptomics, and proteomics to examine flavonoid regulation in Anoectochilus roxburghii under 0, 50, 100, and 200 mmol·L- 1 NaCl. Metabolite profiling showed that salinity reshaped flavonoid composition rather than uniformly increasing flavonoid abundance. A metabolite-derived branch bias index (MI), representing the balance between reductive branch metabolites and flavonol products, increased under salt treatment, peaked at 100 mmol·L- 1 NaCl, and declined at 200 mmol·L- 1, indicating maximal branch bias under moderate stress followed by partial rebalancing under severe stress. Transcriptomic analysis showed induction of upstream phenylpropanoid and flavonoid entry genes, including PAL, 4CL, and CHS, whereas F3H was suppressed and FLS showed no induction. Furthermore, several short-chain dehydrogenase/reductase homologs (IFR-like SDR homologs) were upregulated, and the transcript-derived reductive branch index (EI) increased progressively across the salt gradient. EI was positively associated with MI, although the relationship was not strictly proportional under severe stress (200 mmol·L- 1 NaCl). Proteomic profiling further provided supportive evidence for sustained activation of upstream flavonoid biosynthesis, such as salt-induced accumulation of chalcone synthase (CHS) protein, complementing the transcriptomic and metabolomic datasets. Together, these results indicate that salt stress reorganizes flavonoid metabolism in A. roxburghii through persistent upstream activation and branch-specific regulation, favoring the reductive branch under moderate salinity.

Orchidaceae↗
PubMed2026

Antarctic Peninsula soil carbon stock and efflux: A complex interplay of soil properties and heavy metals.

This study establishes a quantitative framework for understanding surface soil carbon dynamics and ecosystem connectivity in Fildes Peninsula and Ardley Island, King George Island, South Shetland Islands, Antarctic Peninsula. The mean soil organic carbon (SOC) stock across all study sites was 1.10 ± 1.93 kg C/m². Restricting net carbon balance analysis to Fildes Peninsula, where soil respiration (Rs) data were available, yielded a site-specific SOC stock of 0.45 ± 0.45 kg C/m². Scaling Rs to a realistic 120-day active season and assuming stable SOC stocks resulted in estimated annual carbon loss of 15 g C/(m2·yr), equivalent to 3.3 % of standing SOC. Comprehensive sensitivity analyses spanning plausible winter respiration (0 %-20 % of summer rates) and annual change in SOC stocks (-1 %-2 %) consistently supported a net carbon sink, with turnover rates constrained to 3.3 %/yr-4.7 %/yr. Principal component analysis showed that ornithogenic processes as the dominant control on SOC, total nitrogen (TN), zinc (Zn), copper (Cu), and cadmium (Cd) provide a clear multivariate signature of marine-derived nutrient, while Pb was decoupled from this gradient and associated instead with fine-particle size controls. These results reveal dual but independent drivers of soil metal enrichment in this region. Despite their limited spatial extent, ornithogenic soils store disproportionately large carbon pools. Overall, this integrated analysis reveals how marine-terrestrial subsidies regulate Antarctic carbon cycling and provides a quantitative and reproducible framework for assessing carbon dynamics under ongoing climate change.

Antarctic Regions↗
PubMed1978

Intestinal secretion induced by vasoactive intestinal polypeptide. A comparison with cholera toxin in the canine jejunum in vivo.

The effect of vasoactive intestinal polypeptide (VIP) on intestinal water and electrolyte transport and transmucosal potential difference was investigated in the dog jejunum in vivo and compared to secretion induced by cholera toxin. Isolated jejunal loops were perfused with a plasma-like electrolyte solution. VIP (0.08 mug/kg per min) was administered directly into the superior mesenteric artery by continuous infusion over 1 h. From a dye dilution method, it was estimated that a mean plasma VIP concentration of 12,460 pg/ml reached the loops. VIP caused secretion of water and electrolytes; for example, chloride: control, 8 mueq/cm per h absorption; VIP, 92 mueq/cm per h secretion. A marked increase in transmucosal potential difference (control, -1.0 mV; VIP, -5.9 mV, lumen negative) occurred within 1 min after starting VIP infusion. Analysis of unidirectional fluxes showed increased plasma-to-lumen flux of sodium and chloride and decreased lumen-to-plasma flux of sodium. Chloride and bicarbonate were actively secreted against an electrochemical gradient. Although sodium secretion occurred down an electrochemical gradient, flux ratio analysis suggested a component of active sodium secretion. VIP caused a slight increase in protein output into the loops; light microscopy revealed capillary dilatation and closed intercellular spaces. The effect of VIP was readily reversible. Except for the delayed onset of secretion, the effect of cholera toxin was qualitatively similar to VIP; however, capillary dilatation and increased protein output were not noted with cholera toxin.

Animals↗
PubMed2026

The anaerobic fungus Caecomyces churrovis produces H2 via a non-bifurcating NADH-dependent enzyme complex.

UNLABELLED: Hydrogenosomes are mitochondrion-derived organelles that produce ATP and H2 to support energy metabolism in anaerobic eukaryotes. H2 production allows reoxidation of reduced cofactors generated during fermentative metabolism; however, the metabolic mechanisms for H2 production in anaerobic eukaryotes remain incompletely understood. In particular, it remains unclear whether anaerobic fungi (AF) hydrogenosomes use a ferredoxin-dependent pathway or a distinct mechanism to regenerate NAD(P)+ and link electron transfer to H2 formation. Here, by combining genomic search, proteomic analysis, and enzymology, we reveal the molecular mechanism for H2 production in the AF Caecomyces churrovis. Our enzyme assays on the organelle fraction of C. churrovis revealed the activity of H2:NAD+ oxidoreductase but not pyruvate:ferredoxin oxidoreductase, which is usually linked to H2 formation. We identified genes encoding [FeFe] hydrogenase (Hyd) and NADH dehydrogenase subunits E and F (NuoE and NuoF) in C. churrovis and confirmed their expression in the isolated hydrogenosomal fractions by proteomic analysis. Combining the individually purified enzymes, we found Hyd and NuoEF proteins formed H2 directly from NADH independently of ferredoxin, functioning as a non-bifurcating NADH-dependent enzyme rather than an electron-bifurcating enzyme known from anaerobic prokaryotes. We identified homologs of hydrogenosomal NuoE, NuoF, and Hyd in many other AF, indicating this pathway is commonly shared among the AF. This work demonstrates the existence of a non-bifurcating NADH-dependent enzyme complex for H2 production in eukaryotes. Moreover, this complex could potentially be exploited as a target for controlling AF H2 production and altering fungal metabolism. IMPORTANCE: H2 production is a prominent feature of anaerobic energy metabolism, yet our understanding of eukaryotic mechanisms remains limited. Anaerobic fungi (AF) are key decomposers of lignocellulose and contribute to hydrogen flux in anaerobic environments. Although it has been more than 40 years since the H2 production in Neocallimastix was first reported, the molecular mechanism for hydrogenosomal H2 production and redox balance remains unclear. We demonstrate that AF produce H2 from NADH utilizing a non-bifurcating NADH-dependent enzyme complex rather than an electron-bifurcating, ferredoxin-dependent variant. We show that this enzyme complex is conserved across multiple AF lineages and thus demonstrate the occurrence of a non-bifurcating NADH-dependent enzyme in eukaryotes. This discovery expands our understanding of eukaryotic hydrogenosomal metabolism, reveals a previously unknown strategy for redox balancing, and highlights potential targets for manipulating H2 production. These insights have broad implications for microbial energy metabolism, anaerobic ecosystems, and bioengineering of H2-producing systems.

Hydrogen↗