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PubMed2026

Carbon metabolic homogenization is linked to microbial competition and antimicrobial resistance in soils under forest-to-cropland conversion.

Global agricultural expansion by converting natural forests into croplands often leads to soil functional homogenization and antimicrobial resistance enhancement, threatening ecosystem services. However, the associations between microbial carbon metabolic homogenization and antimicrobial resistance remain largely unknown. Here, we collected 240 paired forest and cropland soil samples from the most intensively farmed Yangtze River Basin in China, and constructed a novel framework based on microbial functional traits to decipher the role of carbon metabolic homogenization on antimicrobial resistance via microbial competition for metabolites. Using genome-scale metabolic models, we found that carbon metabolic homogenization was associated with a shift in microbial interactions from cooperation toward competition, with a 45.6% increase in competitive interactions that coincided with a 35.6% higher antimicrobial resistance gene (ARG) diversity. This shift was accompanied by smaller genome sizes and higher 16S rRNA copy numbers, indicating fast-growing, resource-acquisitive microbial strategies. Metabolic transfer analyses further revealed less cooperation relationships among microbial communities in cropland soils than in forest soils, indicating an intensified battle for communal metabolites and an attenuated exchange for complementary metabolites. Together, these findings provide a new framework to understand the association between carbon metabolic homogenization and soil antimicrobial resistance risks from the perspective of microbial traits and interactions under land use change.

Soil Microbiology↗
PubMed2025

Function-based selection of synthetic communities enables mechanistic microbiome studies.

Understanding the complex interactions between microbes and their environment requires robust model systems such as synthetic communities (SynComs). We developed a functionally directed approach to generate SynComs by selecting strains that encode key functions identified in metagenomes. This approach enables the rapid construction of SynComs tailored to any ecosystem. To optimize community design, we implemented genome-scale metabolic models, providing in silico evidence for cooperative strain coexistence prior to experimental validation. Using this strategy, we designed multiple host-specific SynComs, including those for the rumen, mouse, and human microbiomes. By weighting functions differentially enriched in diseased versus healthy individuals, we constructed SynComs that capture complex host-microbe interactions. We designed an inflammatory bowel disease SynCom of 10 members that successfully induced colitis in gnotobiotic IL10-/- mice, demonstrating the potential of this method to model disease-associated microbiomes. Our study establishes a framework for designing functionally representative SynComs of any microbial ecosystem, facilitating mechanistic study.

Animals↗
PubMed2026

Patient-specific modeling identifies metabolic interventions for reversing glucose use reprogramming in alcohol-associated hepatitis.

Alcoholic hepatitis (AH) is an acute form of alcohol-associated liver disease with very few treatment options. Recent studies highlighted liver metabolic reprogramming in AH as an indicator of severity. We aim at identifying new intervention points to reverse liver metabolic dysregulation across varying degrees of AH. We develop 89 personalized genome-scale metabolic models by integrating a generic human cellular metabolic model with liver transcriptomics data from AH patients with varying disease severity and healthy controls. We grade the AH patients based on the model-predicted level of glycolysis reprogramming and validate the results using published metabolomics data. We test in silico gene knockdown interventions to reverse the aberrant metabolic reprogramming in AH. Knockdown of two glycolytic genes, Hkdc1 and Pkm, significantly rebalance the metabolic fluxes toward a healthy liver metabolic phenotype. We use machine learning on the glycolysis fluxes to develop a quantitative glucose use reprogramming score, which correlates with AH severity and patient-specific responses to in silico gene knockdown interventions. The score was independently validated using a published AH liver transcriptomics dataset. We propose a cellular metabolism-based therapy targeting Hkdc1 and Pkm in the glycolysis pathway as a potential treatment for reversing the aberrant glucose metabolism in AH.

Humans↗
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

Reducing redundancy and enhancing accuracy through a phylogenetically-informed microbial community metabolic modeling approach.

MOTIVATION: Metabolic modeling has emerged as a powerful tool for predicting community functions. However, current modeling approaches face significant challenges in balancing the metabolic trade-offs between individual and community-level growth. In this study, we investigated the effect of metabolic relatedness among taxa on growth rate calculations by merging related taxa based on their metabolic similarity, introducing this approach as PhyloCOBRA. RESULTS: This approach enhanced the accuracy and efficiency of microbial community simulations by combining genome-scale metabolic models (GEMs) of closely related organisms, aligning with the concepts of niche differentiation and nestedness theory. To validate our approach, we implemented PhyloCOBRA within the MICOM and OptCom package (creating PhyloMICOM and PhyloOptCom, respectively), and applied it to metagenomic data from 186 individuals and four-species synthetic community (SynCom). Our results demonstrated significant improvement in the accuracy and reliability of growth rate predictions compared to the standard methods. Sensitivity analysis revealed that PhyloMICOM models were more robust to random noise, while Jaccard index calculations showed a reduction in redundancy, highlighting the enhanced specificity of the generated community models. Furthermore, PhyloMICOM reduced the computational complexity, addressing a key concern in microbial community simulations. This approach marks a significant advancement in community-scale metabolic modeling, offering a more stable, efficient, and ecologically relevant tool for simulating and understanding the intricate dynamics of microbial ecosystems. AVAILABILITY AND IMPLEMENTATION: PhyloCOBRA implementations are available as extensions to the MICOM packages and can be accessed at https://github.com/sepideh-mofidifar/PhyloCOBRA.

Phylogeny↗
PubMed2004

In Silico Metabolic Model and Protein Expression of Haemophilus influenzae Strain Rd KW20 in Rich Medium.

The intermediary metabolism of Haemophilus influenzae strain Rd KW20 was studied by a combination of protein expression analysis using a recently developed direct proteomics approach, mutational analysis, and mathematical modeling. Special emphasis was placed on carbon utilization, sugar fermentation, TCA cycle, and electron transport of H. influenzae cells grown microaerobically and anaerobically in a rich medium. The data indicate that several H. influenzae metabolic proteins similar to Escherichia coli proteins, known to be regulated by low concentrations of oxygen, were well expressed in both growth conditions in H. influenzae. An in silico model of the H. influenzae metabolic network was used to study the effects of selective deletion of certain enzymatic steps. This allowed us to define proteins predicted to be essential or non-essential for cell growth and to address numerous unresolved questions about intermediary metabolism of H. influenzae. Comparison of data from in vivo protein expression with the protein list associated with a genome-scale metabolic model showed significant coverage of the known metabolic proteome. This study demonstrates the significance of an integrated approach to the characterization of H. influenzae metabolism.

Biochemistry↗
PubMed2026

Genomic features, metabolism, and biotechnological applications of Candida tropicalis and other non-albicans Candida species.

The production of bio-based products by yeasts from agroindustrial byproducts is a key strategy for advancing circular bioeconomy. While Saccharomyces species remain the predominant industrial yeasts, their limited ability to assimilate lactose, pentoses, and glycerol, as well as their sensitivity to lignocellulose-derived inhibitors, restricts their efficient application in bioprocesses based on using industrial byproducts as fermentation media. In contrast, several non-albicans Candida species exhibit broad substrate utilization capacities and enhanced tolerance to industrial stresses, making them attractive candidates for the bioconversion of agroindustrial residues. This review critically examines recent advances in the genomic, metabolic, and physiological characterization of promising non-albicans Candida species, including Candida tropicalis, Candida parapsilosis, Candida viswanathii, Candida sojae, and Candida maltosa. Emphasis is given to genome-scale metabolic models, carbon assimilation pathways, stress-response mechanisms, and metabolic engineering approaches aiming at the production of value-added compounds. By identifying current achievements, knowledge gaps, and biotechnological bottlenecks, this review highlights the potential of these yeasts as emerging platforms for sustainable bioprocesses within a circular bioeconomy framework.

Biotechnology↗
PubMed2005

Genome-scale analysis of Streptomyces coelicolor A3(2) metabolism.

Streptomyces are filamentous soil bacteria that produce more than half of the known microbial antibiotics. We present the first genome-scale metabolic model of a representative of this group--Streptomyces coelicolor A3(2). The metabolism reconstruction was based on annotated genes, physiological and biochemical information. The stoichiometric model includes 819 biochemical conversions and 152 transport reactions, accounting for a total of 971 reactions. Of the reactions in the network, 700 are unique, while the rest are iso-reactions. The network comprises 500 metabolites. A total of 711 open reading frames (ORFs) were included in the model, which corresponds to 13% of the ORFs with assigned function in the S. coelicolor A3(2) genome. In a comparative analysis with the Streptomyces avermitilis genome, we showed that the metabolic genes are highly conserved between these species and therefore the model is suitable for use with other Streptomycetes. Flux balance analysis was applied for studies of the reconstructed metabolic network and to assess its metabolic capabilities for growth and polyketides production. The model predictions of wild-type and mutants' growth on different carbon and nitrogen sources agreed with the experimental data in most cases. We estimated the impact of each reaction knockout on the growth of the in silico strain on 62 carbon sources and two nitrogen sources, thereby identifying the "core" of the essential reactions. We also illustrated how reconstruction of a metabolic network at the genome level can be used to fill gaps in genome annotation.

Bacterial Proteins↗
PubMed2006

FAMS complex: a fully automated homology modeling system for protein complex structures.

The formation of a protein-protein complex is responsible for many biological functions; therefore, three-dimensional structures of protein complexes are essential for deeper understandings of protein functions and the mechanisms of diseases at the atomic level. However, compared with individual proteins, complex structures are difficult to solve experimentally because of technical limitations. Thus a method that can predict protein complex structures would be invaluable. In this study, we developed new software, FAMS Complex; a fully automated homology modeling system for protein complex structures consisting of two or more molecules. FAMS Complex requires only sequences and alignments of the target protein as input and constructs all molecules simultaneously and automatically. FAMS Complex is likely to become an essential tool for structure-based drug design, such as in silico screening to accelerate drug discovery before an experimental structure is solved. Moreover, in this post-genomic era when huge amounts of protein sequence information are available, a major goal is the determination of protein-protein interaction networks on a genomic scale. FAMS Complex will contribute to this goal, because its procedure is fully automated and so is suited for large-scale genome wide modeling.

Automation↗
PubMed2006

Integration of metabolome data with metabolic networks reveals reporter reactions.

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through combination of metabolome and transcriptome data.

Algorithms↗
PubMed2005

In silico design and adaptive evolution of Escherichia coli for production of lactic acid.

The development and validation of new methods to help direct rational strain design for metabolite overproduction remains an important problem in metabolic engineering. Here we show that computationally predicted E. coli strain designs, calculated from a genome-scale metabolic model, can lead to successful production strains and that adaptive evolution of the engineered strains can lead to improved production capabilities. Three strain designs for lactate production were implemented yielding a total of 11 evolved production strains that were used to demonstrate the utility of this integrated approach. Strains grown on 2 g/L glucose at 37 degrees C showed lactate titers ranging from 0.87 to 1.75 g/L and secretion rates that were directly coupled to growth rates.

Adaptation, Physiological↗
PubMed2025

A global survey of taxa-metabolic associations across mouse microbiome communities.

Host-microbiota mutualism is rooted in the exchange of dietary and metabolic molecules. Microbial diversity broadens the metabolite pool, with each taxon contributing distinct compounds in varying proportions. In the human microbiome, high variability in consortial composition is largely compensated by similar metabolic functions across different taxa. However, the extent of compensation in lower diversity mouse models, and whether vivaria are metabolically equivalent, is unknown. We provide a searchable resource of microbiome composition variability across 51 murine vivaria and 12 wild mouse colonies worldwide, with vivarium-specific variants mapped according to predicted 3D structures for each microbial species. Our matched metabolomics data show that realized metabolic potential has relatively low variability, providing functional evidence for metabolic compensation. Additionally, variability is related to taxonomic composition rather than vivarium, revealing taxa-metabolite associations that are potentially relevant to phenotypic differences between vivaria. Collectively, this resource offers tools to strengthen microbiome studies and collaborative science.

Animals↗
PubMed2006

Global transcriptional and physiological responses of Saccharomyces cerevisiae to ammonium, L-alanine, or L-glutamine limitation.

The yeast Saccharomyces cerevisiae encounters a range of nitrogen sources at various concentrations in its environment. The impact of these two parameters on transcription and metabolism was studied by growing S. cerevisiae in chemostat cultures with l-glutamine, l-alanine, or l-ammonium in limitation and by growing cells in an excess of ammonium. Cells grown in l-alanine-limited cultures had higher biomass yield per nitrogen mole (19%) than those from ammonium-limited cultures. Whole-genome transcript profiles were analyzed with a genome-scale metabolic model that suggested increased anabolic activity in l-alanine-limited cells. The changes in these cells were found to be focused around pyruvate, acetyl coenzyme A, glyoxylate, and alpha-ketoglutarate via increased levels of ALT1, DAL7, PYC1, GDH2, and ADH5 and decreased levels of GDH3, CIT2, and ACS1 transcripts. The transcript profiles were then clustered. Approximately 1,400 transcripts showed altered levels when amino acid-grown cells were compared to those from ammonium. Another 400 genes had low transcript levels when ammonium was in excess. Overrepresentation of the GATAAG element in their promoters suggests that nitrogen catabolite repression (NCR) may be responsible for this regulation. Ninety-one genes had transcript levels on both l-glutamine and ammonium that were decreased compared to those on l-alanine, independent of the concentration. The GATAAG element in these genes suggests two groups of NCR-responsive genes, those that respond to high levels of nitrogen and those that respond to levels below 30 muM. In conclusion, our results reveal that the nitrogen source has substantial influence on the transcriptome of yeasts and that transcriptional changes may be correlated to physiology via a metabolic model.

Alanine↗
PubMed2007

A systems biology approach to study glucose repression in the yeast Saccharomyces cerevisiae.

Glucose repression in the yeast Saccharomyces cerevisiae has evolved as a complex regulatory system involving several different pathways. There are two main pathways involved in signal transduction. One has a role in glucose sensing and regulation of glucose transport, while another takes part in repression of a wide range of genes involved in utilization of alternative carbon sources. In this work, we applied a systems biology approach to study the interaction between these two pathways. Through genome-wide transcription analysis of strains with disruption of HXK2, GRR1, MIG1, the combination of MIG1 and MIG2, and the parental strain, we identified 393 genes to have significantly changed expression levels. To identify co-regulation patterns in the different strains we applied principal component analysis. Disruption of either GRR1 or HXK2 were both found to have profound effects on transcription of genes related to TCA cycle and respiration, as well as ATP synthesis coupled proton transport, all displaying an increased expression. The hxk2Delta strain showed reduced overflow metabolism towards ethanol relative to the parental strain. We also used a genome-scale metabolic model to identify reporter metabolites, and found that there is a high degree of consistency between the identified reporter metabolites and the physiological effects observed in the different mutants. Our systems biology approach points to close interaction between the two pathways, and our metabolism driven analysis of transcription data may find a wider application for analysis of cross-talk between different pathways involved in regulation of metabolism.

Gene Expression Profiling↗
PubMed2003

cDNA array technology in melanoma: an overview.

Genetic aberrations, mostly resulting in changes in gene expression, are critical events in cancer onset and progression. The advent of the cDNA array technology allows the screening and the efficient measurement of expression of thousands genes simultaneously in a wide spectrum of experimental and clinical models. This genomic scale approach is being currently used to obtain global views of human cancer gene expression and to identify genetic markers that might be important for diagnosis, prognosis, and therapy. This review discusses some recent findings obtained by means of cDNA arrays investigating the human melanoma.

Gene Expression Profiling↗
PubMed2004

Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes.

Genome-scale metabolic models have a promising ability to describe cellular phenotypes accurately. Here we show that strains of Escherichia coli carrying a deletion of a single metabolic gene increase their growth rates (by 87% on average) during adaptive evolution and that the endpoint growth rates can be predicted computationally in 39 of 50 (78%) strains tested. These results show that computational models can be used to predict the eventual effects of genetic modifications.

Directed Molecular Evolution↗
PubMed2026

Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.

The escalating generation of global vegetable waste represents a critical loss of bioactive resources, necessitating a paradigm shift from passive disposal to active nutrient upcycling. However, the industrial conversion of this heterogeneous biomass into standardized functional food ingredients is currently impeded by significant techno-economic barriers, primarily structural recalcitrance, compositional inconsistency, and the presence of toxic fermentation inhibitors. This review provides a comprehensive analysis of the synergistic application of microbial engineering and artificial intelligence (AI) to resolve these bioprocessing bottlenecks within a food-to-food closed-loop framework (as shown in the graphical abstract). We evaluate recent advances in engineering food-grade microbial chassis (e.g., Saccharomyces cerevisiae and Escherichia coli) to enhance lignocellulose degradation and stress tolerance. Concurrently, we examine the integration of AI across the entire value chain, covering deep learning-based rational enzyme design, genome-scale metabolic modeling, and intelligent process control for precision fermentation. Current evidence demonstrates that the hardware-software coupling of engineered strains and AI algorithms significantly enhances conversion efficiency and process robustness. Key findings highlight that AI-driven Design-Build-Test-Learn cycles facilitate the de novo creation of enzymes with superior kinetics and strains with adaptive stress response capabilities against toxins. Moreover, dynamic digital twin models effectively mitigate the impact of substrate variability, ensuring the batch-to-batch consistency required for food applications. We conclude that this data-driven synergistic paradigm is pivotal for establishing a resilient circular bioeconomy, enabling the reliable bioconversion of waste into high-value single-cell proteins, natural flavor additives, and sustainable packaging materials.

Artificial Intelligence↗
PubMed2025

Spatiotemporal diversity in molecular and functional abnormalities in the mdx dystrophic brain.

Duchenne muscular dystrophy (DMD) is characterized by progressive muscle degeneration and neuropsychiatric abnormalities. Loss of full-length dystrophins is both necessary and sufficient to initiate DMD. These isoforms are expressed in the hippocampus, cerebral cortex (Dp427c), and cerebellar Purkinje cells (Dp427p). However, our understanding of the consequences of their absence, which is crucial for developing targeted interventions, remains inadequate. We combined RNA sequencing with genome-scale metabolic modelling (GSMM), immunodetection, and mitochondrial assays to investigate dystrophic alterations in the brains of the mdx mouse model of DMD. The cerebra and cerebella were analysed separately to discern the roles of Dp427c and Dp427p, respectively. Investigating these regions at 10 days (10d) and 10 weeks (10w) followed the evolution of abnormalities from development to early adulthood. These time points also encompass periods before onset and during muscle inflammation, enabling assessment of the potential damage caused by inflammatory mediators crossing the dystrophic blood-brain barrier. For the first time, we demonstrated that transcriptomic and functional dystrophic alterations are unique to the cerebra and cerebella and vary substantially between 10d and 10w. The common anomalies involved altered numbers of retained introns and spliced exons across mdx transcripts, corresponding with alterations in the mRNA processing pathways. Abnormalities in the cerebra were significantly more pronounced in younger mice. The top enriched pathways included those related to metabolism, mRNA processing, and neuronal development. GSMM indicated dysregulation of glucose metabolism, which corresponded with GLUT1 protein downregulation. The cerebellar dystrophic transcriptome, while significantly altered, showed an opposite trajectory to that of the cerebra, with few changes identified at 10 days. These late defects are specific and indicate an impact on the functional maturation of the cerebella that occurs postnatally. Although no classical neuroinflammation markers or microglial activation were detected at 10 weeks, specific differences indicate that inflammation impacts DMD brains. Importantly, some dystrophic alterations occur late and may therefore be amenable to therapeutic intervention, offering potential avenues for mitigating DMD-related neuropsychiatric defects.

Animals↗