Explore the vast range of titles available.

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV‐R) and germ‐free (GF) mice using gene expression data and tissue‐specific genome‐scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue‐specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV‐R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N‐acetylated AAs in the hepatic portal vein of CONV‐R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV‐R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. Synopsis Tissue‐specific genome‐scale metabolic models (GEMs), transcriptomic and metabolomic analyses reveal global metabolic differences between conventionally raised and germ‐free mice and show that the gut microbiota affects host amino acid and glutathione metabolism. A generic Mouse Metabolic Reaction GEM (MMR) is created using the mouse orthologs of human genes in HMR2. Tissue‐specific GEMs for 28 mouse tissues are reconstructed and applied for the analysis of global gene expression data. Microbial‐induced metabolic differences in the small intestine are simulated using the relative metabolic differences (RMetD) method. The model predictions are validated by measuring amino acid levels in the portal vein. Graphical Abstract Tissue‐specific genome‐scale metabolic models (GEMs), transcriptomic and metabolomic analyses reveal global metabolic differences between conventionally raised and germ‐free mice and show that the gut microbiota affects host amino acid and glutathione metabolism.

Genome‐scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype–phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task‐driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type‐specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty‐two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line. Synopsis Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task‐driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients. The presence of proteins encoded by 15,841 genes in tumors from 27 HCC patients is evaluated by immunohistochemistry. Personalized GEMs for six HCC patients and GEMs for 83 healthy cell types are reconstructed based on HMR 2.0 and the tINIT algorithm for task‐driven model reconstruction. 101 antimetabolites are predicted to inhibit tumor growth in all patients. Antimetabolite toxicity is tested using the 83 cell type‐specific GEMs. An l ‐carnitine analog inhibits the proliferation of HepG2 cells. Graphical Abstract Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task‐driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients.

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome‐scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence‐level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations. Synopsis A large‐scale systematic evolution analysis, metabolic model reconstruction and simulation are used to examine the evolutionary mechanism underlying the emergence of diverse traits across 332 different yeast species. Reconstruction and comparative analysis of 332 yeast species‐specific genome‐scale metabolic models allows refining gene function annotation and characterizing unclear substrate utilization pathways. Metabolic network expansion through gene duplication and enzyme promiscuity drives trait gains in substrate utilization. Gaps in downstream pathways can result in trait losses. Integrative analyses show that positive selection of genes in amino acid and protein synthesis sub‐pathways is relevant for the emergence of thermotolerance in yeast. Graphical Abstract A large‐scale systematic evolution analysis, metabolic model reconstruction and simulation are used to examine the evolutionary mechanism underlying the emergence of diverse traits across 332 different yeast species.

Quantifying the differential expression of genes in various human organs, tissues, and cell types is vital to understand human physiology and disease. Recently, several large‐scale transcriptomics studies have analyzed the expression of protein‐coding genes across tissues. These datasets provide a framework for defining the molecular constituents of the human body as well as for generating comprehensive lists of proteins expressed across tissues or in a tissue‐restricted manner. Here, we review publicly available human transcriptome resources and discuss body‐wide data from independent genome‐wide transcriptome analyses of different tissues. Gene expression measurements from these independent datasets, generated using samples from fresh frozen surgical specimens and postmortem tissues, are consistent. Overall, the different genome‐wide analyses support a distribution in which many proteins are found in all tissues and relatively few in a tissue‐restricted manner. Moreover, we discuss the applications of publicly available omics data for building genome‐scale metabolic models, used for analyzing cell and tissue functions both in physiological and in disease contexts. Graphical Abstract Quantifying gene expression in human organs, tissues and cell types is vital to understand physiology and disease. Here we discuss publically available human transcriptome resources and their applications in combination with genome‐scale metabolic models.

Genome-scale metabolic models bridge the gap between genome-derived biochemical information and metabolic phenotypes in a principled manner, providing a solid interpretative framework for experimental data related to metabolic states, and enabling simple in silico experiments with whole-cell metabolism. Models have been reconstructed for almost 20 bacterial species, so far mainly through expert curation efforts integrating information from the literature with genome annotation. A wide variety of computational methods exploiting metabolic models have been developed and applied to bacteria, yielding valuable insights into bacterial metabolism and evolution, and providing a sound basis for computer-assisted design in metabolic engineering. Recent advances in computational systems biology and high-throughput experimental technologies pave the way for the systematic reconstruction of metabolic models from genomes of new species, and a corresponding expansion of the scope of their applications. In this review, we provide an introduction to the key ideas of metabolic modeling, survey the methods, and resources that enable model reconstruction and refinement, and chart applications to the investigation of global properties of metabolic systems, the interpretation of experimental results, and the re-engineering of their biochemical capabilities.

The Escherichia coli genome‐scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision–recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene‐protein‐reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high‐throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond. Synopsis Escherichia coli genome‐scale metabolic model flux balance analysis (FBA) growth prediction accuracy is quantified using published experimental data assaying mutant fitness across different carbon sources, revealing improved practices for model accuracy evaluation and sources of prediction inaccuracy. The area under a precision–recall curve was highlighted as a reliable metric for model accuracy quantification. Adding vitamins/cofactors to the model environment improved correspondence between model predictions and experimental data. Isoenzyme gene‐protein‐reaction mapping was identified as a prominent source of model inaccuracy. Machine learning revealed hydrogen ion exchange and central metabolism branch points as important features in the determination of model accuracy. Graphical Abstract Escherichia coli genome‐scale metabolic model flux balance analysis (FBA) growth prediction accuracy is quantified using published experimental data assaying mutant fitness across different carbon sources, revealing improved practices for model accuracy evaluation and sources of prediction inaccuracy.

This study aimed to assess the potential of Lentilactobacillus hilgardii as a novel candidate for malolactic fermentation (MLF) in winemaking, through comparative genomics and experimental validation, in direct comparison with Oenococcus oeni. We performed a pangenome analysis on 16 L. hilgardii and 7 O. oeni strains to explore their genetic diversity, focusing on wine‐related traits. Functional predictions were generated using genome‐scale metabolic models (ModelSEED/KBase), including in silico co‐inoculation with Saccharomyces cerevisiae EC1118 and post‐alcoholic fermentation simulations. The reference strains L. hilgardii DSM 20176 and O. oeni DSM 20252 were experimentally tested for MLF performance in a synthetic wine‐like medium at 25°C and 10°C. Core‐genome comparison revealed that 67.9% of the malolactic enzyme sequence is conserved between the two species, with comparable docking affinity to L‐malic acid. L. hilgardii harboured unique enzymes with potential oenological interest (phenolic acid decarboxylase, mannitol dehydrogenase, glucosidase) and distinctive stress‐related proteins (YaaA, HrcA, ASP23), suggesting improved tolerance to oxidative, temperature, and alkaline stresses. Notably, L. hilgardii showed genomic potential to degrade putrescine, arginine, and ornithine, precursors of ethyl carbamate. Experimentally, L. hilgardii reduced L‐malic acid from 2.5 g/L to < 0.1 g/L within 12 days at 10°C, while O. oeni showed no MLF activity at this temperature. At 25°C, both strains completed MLF within 6–7 days. L. hilgardii also consumed > 80% of residual fructose at 10°C, whereas O. oeni showed minimal utilisation. Our results demonstrate that L. hilgardii combines a favourable genomic repertoire for wine adaptation with superior MLF performance at low temperature, suggesting its potential as an alternative to O. oeni in cool‐climate winemaking. This work provides the first genome‐scale comparative and functional evaluation of L. hilgardii in the winemaking context, highlighting its technological promise to improve fermentation reliability, reduce spoilage risk, and expand the biodiversity of malolactic starters. This study explores the metabolic potential of Lentilactobacillus hilgardii for malolactic fermentation in winemaking. Genomic and in silico analyses, combined with experimental validation, reveal its resilience at low temperatures and unique enzymatic traits. Comparative insights with Oenococcus oeni highlight L. hilgardii as a promising alternative malolactic starter under challenging wine conditions.

Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade‐off with cell growth. Here, we utilize genome‐scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth‐secretion trade‐off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model‐designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux‐rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model‐designed selection environments open new opportunities for predictive evolution. Synopsis EvolveX, a new algorithm enabling model‐guided design of chemical environments for targeted adaptive evolution, is applied to evolve a wine yeast strain for increased aroma secretion. EvolveX predicts environment‐dependence of trait‐fitness correlations using genome‐scale metabolic models. Multi‐omics analysis shows agreement with the model‐predicted metabolic changes. EvolveX enables devising adaptive evolution strategies for improving traits uncorrelated with cell fitness. Graphical Abstract EvolveX, a new algorithm enabling model‐guided design of chemical environments for targeted adaptive evolution, is applied to evolve a wine yeast strain for increased aroma secretion.

We evaluated the presence/absence of proteins encoded by 14 077 genes in adipocytes obtained from different tissue samples using immunohistochemistry. By combining this with previously published adipocyte‐specific proteome data, we identified proteins associated with 7340 genes in human adipocytes. This information was used to reconstruct a comprehensive and functional genome‐scale metabolic model of adipocyte metabolism. The resulting metabolic model, iAdipocytes1809 , enables mechanistic insights into adipocyte metabolism on a genome‐wide level, and can serve as a scaffold for integration of omics data to understand the genotype–phenotype relationship in obese subjects. By integrating human transcriptome and fluxome data, we found an increase in the metabolic activity around androsterone, ganglioside GM2 and degradation products of heparan sulfate and keratan sulfate, and a decrease in mitochondrial metabolic activities in obese subjects compared with lean subjects. Our study hereby shows a path to identify new therapeutic targets for treating obesity through combination of high throughput patient data and metabolic modeling. Combining large‐scale immunohistochemical analysis and proteomics data, 7340 gene products are identified in human adipocytes. Based on this data, a genome‐scale metabolic model is reconstructed and used to integrate clinical and transcriptome data from lean and obese subjects. Synopsis Combining large‐scale immunohistochemical analysis and proteomics data, 7340 gene products are identified in human adipocytes. Based on this data, a genome‐scale metabolic model is reconstructed and used to integrate clinical and transcriptome data from lean and obese subjects. We simulated the metabolic differences between the individuals with different body mass indexes (BMIs) using transcriptome and fluxome data. An increase in the metabolic activity around androsterone, ganglioside GM2 and degradation products of heparan sulfate and keratan sulfate, and a decrease in mitochondrial metabolic activities are found in obese subjects compared with lean subjects. We simulated the change in lipid droplet (LD) size and found that lean subjects have large dynamic changes in LD formation compared with obese subjects. Besides enabling patient stratification, our study allows the identification of novel therapeutic targets for obesity.

Background Limosilactobacillus reuteri (earlier known as Lactobacillus reuteri ) is a well-studied lactic acid bacterium, with some specific strains used as probiotics, that exists in different hosts such as human, pig, goat, mouse and rat, with multiple body sites such as the gastrointestinal tract, breast milk and mouth. Numerous studies have confirmed the beneficial effects of orally administered specific L. reuteri strains, such as preventing bone loss and promoting regulatory immune system development. L. reuteri ATCC PTA 6475 is a widely used strain that has been applied in the market as a probiotic due to its positive effects on the human host. Its health benefits may be due, in part, to the production of beneficial metabolites. Considering the strain-specific effects and genetic diversity of L. reuteri strains, we were interested to study the metabolic versatility of these strains. Results In this study, we aimed to systematically investigate the metabolic features and diversities of L. reuteri strains by using genome-scale metabolic models (GEMs). The GEM of L. reuteri ATCC PTA 6475 was reconstructed with a template-based method and curated manually. The final GEM i HL622 of L. reuteri ATCC PTA 6475 contains 894 reactions and 726 metabolites linked to 622 metabolic genes, which can be used to simulate growth and amino acids utilization. Furthermore, we built GEMs for the other 35  L. reuteri strains from three types of hosts. The comparison of the L. reuteri GEMs identified potential metabolic products linked to the adaptation to the host. Conclusions The GEM of L. reuteri ATCC PTA 6475 can be used to simulate metabolic capabilities and growth. The core and pan model of 35  L. reuteri strains shows metabolic capacity differences both between and within the host groups. The GEMs provide a reliable basis to investigate the metabolism of L. reuteri in detail and their potential benefits on the host.