Genome-scale Metabolic Models Research Articles

Identifying Hub Genes and Metabolic Pathways in Collagen VI-Related Dystrophies: A Roadmap to Therapeutic Intervention

Collagen VI-related dystrophies (COL6RD) are a group of rare muscle disorders caused by mutations in specific genes responsible for type VI collagen production. It affects muscles, joints, and connective tissues, leading to weakness, joint problems, and structural issues. Currently, there is no effective treatment for COL6RD; its management typically addresses symptoms and complications. Therefore, it is essential to decipher the disease’s molecular mechanisms, identify drug targets, and develop effective treatment strategies to treat COL6RD. In this study, we employed differential gene expression analysis, weighted gene co-expression network analysis, and genome-scale metabolic modeling to investigate gene expression patterns in COL6RD patients, uncovering key genes, significant metabolites, and disease-related pathophysiological pathways. First, we performed differential gene expression and weighted gene co-expression network analyses, which led to the identification of 12 genes (CHCHD10, MRPS24, TRIP10, RNF123, MRPS15, NDUFB4, COX10, FUNDC2, MDH2, RPL3L, NDUFB11, PARVB) as potential hub genes involved in the disease. Second, we utilized a drug repurposing strategy to identify pharmaceutical candidates that could potentially modulate these genes and be effective in the treatment. Next, we utilized context-specific genome-scale metabolic models to compare metabolic variations between healthy individuals and COL6RD patients. Finally, we conducted reporter metabolite analysis to identify reporter metabolites (e.g., phosphatidates, nicotinate ribonucleotide, ubiquinol, ferricytochrome C). In summary, our analysis revealed critical genes and pathways associated with COL6RD and identified potential targets, reporter metabolites, and candidate drugs for therapeutic interventions.

Pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes

The accurate reconstruction of genome-scale metabolic models (GEMs) for unculturable species poses challenges due to the incomplete and fragmented genetic information typical of metagenome-assembled genomes (MAGs). While existing tools leverage sequence homology from single genomes, this study introduces pan-Draft, a pan-reactome-based approach exploiting recurrent genetic evidence to determine the solid core structure of species-level GEMs. By comparing MAGs clustered at the species-level, pan-Draft addresses the issues due to the incompleteness and contamination of individual genomes, providing high-quality draft models and an accessory reactions catalog supporting the gapfilling step. This approach will improve our comprehension of metabolic functions of uncultured species.

A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks

Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and they can affect the predictivity of analysis. In this study, we aimed to compare five different RNA-seq data normalization methods (TPM, FPKM, TMM, GeTMM, and RLE) and covariate adjusted versions of the normalized data by mapping them on a human GEM using the iMAT and INIT algorithms to generate personalized metabolic models. We used RNA-seq data for Alzheimer’s disease (AD) and lung adenocarcinoma (LUAD) patients. The results demonstrated that RNA-seq data normalized by the RLE, TMM, or GeTMM methods enabled the production of condition-specific metabolic models with considerably low variability in terms of the number of active reactions compared to the within-sample normalization methods (FPKM, TPM). Using these models, we could more accurately capture the disease-associated genes (average accuracy of ~0.80 for AD and ~0.67 for LUAD) for the RLE, TMM, and GeTMM normalization methods. An increase in the accuracies was observed for all the methods when covariate adjustment was applied. We found a similar accuracy trend when we compared the metabolites of perturbed reactions to metabolome data for AD. Together, our benchmark study shows that the between-sample RNA-seq normalization methods reduce false positive predictions at the expense of missing some true positive genes when mapped on GEMs.

MicrobiomeNet: exploring microbial associations and metabolic profiles for mechanistic insights.

The growing volumes of microbiome studies over the past decade have revealed a wide repertoire of microbial associations under diverse conditions. Microbes produce small molecules to interact with each other as well as to modulate their environments. Their metabolic profiles hold the key to understanding these association patterns for translational applications. Based on this concept, we developed MicrobiomeNet, a comprehensive database that integrates microbial associations with their metabolic profiles for mechanistic insights. It currently contains a total of ∼5.8 million known microbial associations, coupled with>12 400 genome-scale metabolic models (GEMs) covering ∼6000 microbial species. Users can intuitively explore microbial associations and compare their corresponding metabolic profiles. Our case studies show that MicrobiomeNet can provide mechanistic insights that are consistent with the literature. MicrobiomeNet is freely available at https://www.microbiomenet.com/.

Resource sharing of an infant gut microbiota synthetic community in combinations of human milk oligosaccharides.

Quickly after birth, the gut microbiota is shaped via species acquisition and resource pressure. Breastmilk, and more specifically, human milk oligosaccharides are a determining factor in the formation of microbial communities and the interactions between bacteria. Prominent human milk oligosaccharide degraders have been rigorously characterized, but it is not known how the gut microbiota is shaped as a complex community. Here, we designed BIG-Syc, a synthetic community of 13 strains from the gut of vaginally born, breastfed infants. BIG-Syc replicated key compositional, metabolic, and proteomic characteristics of the gut microbiota of infants. Upon fermentation of a 4 and 5 human milk oligosaccharide mix, BIG-Syc demonstrated different compositional and proteomic profiles, with Bifidobacterium infantis and Bifidobacterium bifidum suppressing one another. The mix of 5 human milk oligosaccharides resulted in a more diverse composition with dominance of B. bifidum, whereas that with 4 human milk oligosaccharides supported the dominance of B. infantis, in 4 of 6 replicates. Reintroduction of bifidobacteria to BIG-Syc led to their engraftment and establishment of their niche. Based on proteomics and genome-scale metabolic models, we reconstructed the carbon source utilization and metabolite and gas production per strain. BIG-Syc demonstrated teamwork as cross-feeders utilized simpler carbohydrates, organic acids, and gases released from human milk oligosaccharide degraders. Collectively, our results showed that human milk oligosaccharides prompt resource-sharing for their complete degradation while leading to a different compositional and functional profile in the community. At the same time, BIG-Syc proved to be an accurate model for the representation of intra-microbe interactions.

A metabolic modeling-based framework for predicting trophic dependencies in native rhizobiomes of crop plants.

The exchange of metabolites (i.e., metabolic interactions) between bacteria in the rhizosphere determines various plant-associated functions. Systematically understanding the metabolic interactions in the rhizosphere, as well as in other types of microbial communities, would open the door to the optimization of specific predefined functions of interest, and therefore to the harnessing of the functionality of various types of microbiomes. However, mechanistic knowledge regarding the gathering and interpretation of these interactions is limited. Here, we present a framework utilizing genomics and constraint-based modeling approaches, aiming to interpret the hierarchical trophic interactions in the soil environment. 243 genome scale metabolic models of bacteria associated with a specific disease-suppressive vs disease-conducive apple rhizospheres were drafted based on genome-resolved metagenomes, comprising an in silico native microbial community. Iteratively simulating microbial community members' growth in a metabolomics-based apple root-like environment produced novel data on potential trophic successions, used to form a network of communal trophic dependencies. Network-based analyses have characterized interactions associated with beneficial vs non-beneficial microbiome functioning, pinpointing specific compounds and microbial species as potential disease supporting and suppressing agents. This framework provides a means for capturing trophic interactions and formulating a range of testable hypotheses regarding the metabolic capabilities of microbial communities within their natural environment. Essentially, it can be applied to different environments and biological landscapes, elucidating the conditions for the targeted manipulation of various microbiomes, and the execution of countless predefined functions.

Metabolic cross-feeding interactions modulate the dynamic community structure in microbial fuel cell under variable organic loading wastewaters.

The efficiency of microbial fuel cells (MFCs) in industrial wastewater treatment is profoundly influenced by the microbial community, which can be disrupted by variable industrial operations. Although microbial guilds linked to MFC performance under specific conditions have been identified, comprehensive knowledge of the convergent community structure and pathways of adaptation is lacking. Here, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) based on metabolic cross-feeding to study the adaptation of microbial communities in MFCs treating sulfide-containing wastewater from a canned-pineapple factory. The metabolic model encompassed three major microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfide-oxidizing bacteria (SOB). Our findings revealed a shift from an SOB-dominant to MET-dominant community as organic loading rates (OLRs) increased, along with a decline in MFC performance. The mmGEM accurately predicted microbial relative abundance at low OLRs (L-OLRs) and adaptation to high OLRs (H-OLRs). The simulations revealed constraints on SOB growth under H-OLRs due to reduced sulfate-sulfide (S) cycling and acetate cross-feeding with SRB. More cross-fed metabolites from SRB were diverted to MET, facilitating their competitive dominance. Assessing cross-feeding dynamics under varying OLRs enabled the execution of practical scenario-based simulations to explore the potential impact of elevated acidity levels on SOB growth and MFC performance. This work highlights the role of metabolic cross-feeding in shaping microbial community structure in response to high OLRs. The insights gained will inform the development of effective strategies for implementing MFC technology in real-world industrial environments.

Genomic and functional characterization of the Atlantic salmon gut microbiome in relation to nutrition and health.

To ensure sustainable aquaculture, it is essential to understand the path 'from feed to fish', whereby the gut microbiome plays an important role in digestion and metabolism, ultimately influencing host health and growth. Previous work has reported the taxonomic composition of the Atlantic salmon (Salmo salar) gut microbiome; however, functional insights are lacking. Here we present the Salmon Microbial Genome Atlas consisting of 211 high-quality bacterial genomes, recovered by cultivation (n = 131) and gut metagenomics (n = 80) from wild and farmed fish both in freshwater and seawater. Bacterial genomes were taxonomically assigned to 14 different orders, including 35 distinctive genera and 29 previously undescribed species. Using metatranscriptomics, we functionally characterized key bacterial populations, across five phyla, in the salmon gut. This included the ability to degrade diet-derived fibres and release vitamins and other exometabolites with known beneficial effects, which was supported by genome-scale metabolic modelling and in vitro cultivation of selected bacterial species coupled with untargeted metabolomic studies. Together, the Salmon Microbial Genome Atlas provides a genomic and functional resource to enable future studies on salmon nutrition and health.

Genome-Scale Metabolic Models in Fungal Pathogens: Past, Present, and Future.

Fungi are diverse organisms with various characteristics and functions. Some play a role in recycling essential elements, such as nitrogen and carbon, while others are utilized in the food and drink production industry. Some others are known to cause diseases in various organisms, including humans. Fungal pathogens cause superficial, subcutaneous, and systemic infections. Consequently, many scientists have focused on studying the factors contributing to the development of human diseases. Therefore, multiple approaches have been assessed to examine the biology of these intriguing organisms. The genome-scale metabolic models (GEMs) have demonstrated many advantages to microbial metabolism studies and the ability to propose novel therapeutic alternatives. Despite significant advancements, much remains to be elucidated regarding the use of this tool for investigating fungal metabolism. This review aims to compile the data provided by the published GEMs of human fungal pathogens. It gives specific examples of the most significant contributions made by these models, examines the advantages and difficulties associated with using such models, and explores the novel approaches suggested to enhance and refine their development.

Navigating the microalgal maze: a comprehensive review of recent advances and future perspectives in biological networks.

PPI analysis deepens our knowledge in critical processes like carbon fixation and nutrient sensing. Moreover, signaling networks, including pathways like MAPK/ERK and TOR, provide valuable information in how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. This review examines recent advancements in the study of biological networks within microalgae, with a focus on the intricate interactions that define these organisms. It emphasizes how network biology, an interdisciplinary field, offers valuable insights into microalgae functions through various methodologies. Crucial approaches, such as protein-protein interaction (PPI) mapping utilizing yeast two-hybrid screening and mass spectrometry, are essential for comprehending cellular processes and optimizing functions, such as photosynthesis and fatty acid biosynthesis. The application of advanced computational methods and information mining has significantly improved PPI analysis, revealing networks involved in critical processes like carbon fixation and nutrient sensing. The review also encompasses transcriptional networks, which play a role in gene regulation and stress responses, as well as metabolic networks represented by genome-scale metabolic models (GEMs), which aid in strain optimization and the prediction of metabolic outcomes. Furthermore, signaling networks, including pathways like MAPK/ERK and TOR, are crucial for understanding how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. The integration of these network biology approaches has deepened our understanding of microalgal interactions, paving the way for more efficient cultivation and new industrial applications.

Pan-genome-scale metabolic modeling of Bacillus subtilis reveals functionally distinct groups.

As the volume of genomic data and computational power have increased, so has the number of genome-scale metabolic models. These models encapsulate the totality of metabolic functions for a given organism. Bacillus subtilis strain 168 is one of the first bacteria for which a metabolic network was reconstructed. Since then, several updated reconstructions have been generated for this model microorganism. Here, we expand the metabolic model for a single strain into a pan-genome-scale model, which consists of individual models for 481 B. subtilis strains. By evaluating differences between these strains, we identified five distinct groups of strains, allowing for the rapid classification of any particular strain. Furthermore, this classification into five groups aids the rapid identification of suitable strains for any application.

Methanothermobacter thermautotrophicus and Alternative Methanogens: Archaea-Based Production.

Methanogenic archaea convert bacterial fermentation intermediates from the decomposition of organic material into methane. This process has relevance in the global carbon cycle and finds application in anthropogenic processes, such as wastewater treatment and anaerobic digestion. Furthermore, methanogenic archaea that utilize hydrogen and carbon dioxide as substrates are being employed as biocatalysts for the biomethanation step of power-to-gas technology. This technology converts hydrogen from water electrolysis and carbon dioxide into renewable natural gas (i.e., methane). The application of methanogenic archaea in bioproduction beyond methane has been demonstrated in only a few instances and is limited to mesophilic species for which genetic engineering tools are available. In this chapter, we discuss recent developments for those existing genetically tractable systems and the inclusion of novel genetic tools for thermophilic methanogenic species. We then give an overview of recombinant bioproduction with mesophilic methanogenic archaea and thermophilic non-methanogenic microbes. This is the basis for discussing putative products with thermophilic methanogenic archaea, specifically the species Methanothermobacter thermautotrophicus. We give estimates of potential conversion efficiencies for those putative products based on a genome-scale metabolic model for M. thermautotrophicus.

A diel multi-tissue genome-scale metabolic model of Vitis vinifera.

Vitis vinifera, also known as grapevine, is widely cultivated and commercialized, particularly to produce wine. As wine quality is directly linked to fruit quality, studying grapevine metabolism is important to understand the processes underlying grape composition. Genome-scale metabolic models (GSMMs) have been used for the study of plant metabolism and advances have been made, allowing the integration of omics datasets with GSMMs. On the other hand, Machine learning (ML) has been used to analyze and integrate omics data, and while the combination of ML with GSMMs has shown promising results, it is still scarcely used to study plants. Here, the first GSSM of V. vinifera was reconstructed and validated, comprising 7199 genes, 5399 reactions, and 5141 metabolites across 8 compartments. Tissue-specific models for the stem, leaf, and berry of the Cabernet Sauvignon cultivar were generated from the original model, through the integration of RNA-Seq data. These models have been merged into diel multi-tissue models to study the interactions between tissues at light and dark phases. The potential of combining ML with GSMMs was explored by using ML to analyze the fluxomics data generated by green and mature grape GSMMs and provide insights regarding the metabolism of grapes at different developmental stages. Therefore, the models developed in this work are useful tools to explore different aspects of grapevine metabolism and understand the factors influencing grape quality.

Genome-Scale Metabolic Modeling of Halomonas elongata 153B Explains Polyhydroxyalkanoate and Ectoine Biosynthesis in Hypersaline Environments.

Halomonas elongata thrives in hypersaline environments producing polyhydroxyalkanoates (PHAs) and osmoprotectants such as ectoine. Despite its biotechnological importance, several aspects of the dynamics of its metabolism remain elusive. Here, we construct and validate a genome-scale metabolic network model for H. elongata 153B. Then, we investigate the flux distribution dynamics during optimal growth, ectoine, and PHA biosynthesis using statistical methods, and a pipeline based on shadow prices. Lastly, we use optimization algorithms to uncover novel engineering targets to increase PHA production. The resulting model (iEB1239) includes 1534 metabolites, 2314 reactions, and 1239 genes. iEB1239 can reproduce growth on several carbon sources and predict growth on previously unreported ones. It also reproduces biochemical phenotypes related to Oad and Ppc gene functions in ectoine biosynthesis. A flux distribution analysis during optimal ectoine and PHA biosynthesis shows decreased energy production through oxidative phosphorylation. Furthermore, our analysis unveils a diverse spectrum of metabolic alterations that extend beyond mere flux changes to encompass heightened precursor production for ectoine and PHA synthesis. Crucially, these findings capture other metabolic changes linked to adaptation in hypersaline environments. Bottlenecks in the glycolysis and fatty acid metabolism pathways are identified, in addition to PhaC, which has been shown to increase PHA production when overexpressed. Overall, our pipeline demonstrates the potential of genome-scale metabolic models in combination with statistical approaches to obtain insights into the metabolism of H. elongata. Our platform can be exploited for researching environmental adaptation, and for designing and optimizing metabolic engineering strategies for bioproduct synthesis.

Unveiling the Molecular Mechanisms of Glioblastoma through an Integrated Network-Based Approach.

Background/Objectives: Despite current treatments extending the lifespan of Glioblastoma (GBM) patients, the average survival time is around 15-18 months, underscoring the fatality of GBM. This study aims to investigate the impact of sample heterogeneity on gene expression in GBM, identify key metabolic pathways and gene modules, and explore potential therapeutic targets. Methods: In this study, we analysed GBM transcriptome data derived from The Cancer Genome Atlas (TCGA) using genome-scale metabolic models (GEMs) and co-expression networks. We examine transcriptome data incorporating tumour purity scores (TPSs), allowing us to assess the impact of sample heterogeneity on gene expression profiles. We analysed the metabolic profile of GBM by generating condition-specific GEMs based on the TPS group. Results: Our findings revealed that over 90% of genes showing brain and glioma specificity in RNA expression demonstrate a high positive correlation, underscoring their expression is dominated by glioma cells. Conversely, negatively correlated genes are strongly associated with immune responses, indicating a complex interaction between glioma and immune pathways and non-tumorigenic cell dominance on gene expression. TPS-based metabolic profile analysis was supported by reporter metabolite analysis, highlighting several metabolic pathways, including arachidonic acid, kynurenine and NAD pathway. Through co-expression network analysis, we identified modules that significantly overlap with TPS-correlated genes. Notably, SOX11 and GSX1 are upregulated in High TPS, show a high correlation with TPS, and emerged as promising therapeutic targets. Additionally, NCAM1 exhibits a high centrality score within the co-expression module, which shows a positive correlation with TPS. Moreover, LILRB4, an immune-related gene expressed in the brain, showed a negative correlation and upregulated in Low TPS, highlighting the importance of modulating immune responses in the GBM mechanism. Conclusions: Our study uncovers sample heterogeneity's impact on gene expression and the molecular mechanisms driving GBM, and it identifies potential therapeutic targets for developing effective treatments for GBM patients.

Core and accessory genomic traits of Vibrio cholerae O1 drive lineage transmission and disease severity

In Bangladesh, Vibrio cholerae lineages are undergoing genomic evolution, with increased virulence and spreading ability. However, our understanding of the genomic determinants influencing lineage transmission and disease severity remains incomplete. Here, we developed a computational framework using machine-learning, genome scale metabolic modelling (GSSM) and 3D structural analysis, to identify V. cholerae genomic traits linked to lineage transmission and disease severity. We analysed in-patients isolates from six Bangladeshi regions (2015-2021), and uncovered accessory genes and core SNPs unique to the most recent dominant lineage, with virulence, motility and bacteriophage resistance functions. We also found a strong correlation between V. cholerae genomic traits and disease severity, with some traits overlapping those driving lineage transmission. GSMM and 3D structure analysis unveiled a complex interplay between transcription regulation, protein interaction and stability, and metabolic networks, associated to lifestyle adaptation, intestinal colonization, acid tolerance and symptom severity. Our findings support advancing therapeutics and targeted interventions to mitigate cholera spread.

INAP 2.0: Harnessing metabolic complementarity in microbial network analysis.

With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co-occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four-module process for metabolic interaction analysis, namely: (I) Prepare genome-scale metabolic models; (II) Infer pairwise interactions of genome-scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome-assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance-adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross-feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome-assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.

Exploring the metabolic profile of A. baumannii for antimicrobial development using genome-scale modeling.

With the emergence of multidrug-resistant bacteria, the World Health Organization published a catalog of microorganisms urgently needing new antibiotics, with the carbapenem-resistant Acinetobacter baumannii designated as "critical". Such isolates, frequently detected in healthcare settings, pose a global pandemic threat. One way to facilitate a systemic view of bacterial metabolism and allow the development of new therapeutics is to apply constraint-based modeling. Here, we developed a versatile workflow to build high-quality and simulation-ready genome-scale metabolic models. We applied our workflow to create a metabolic model for A. baumannii and validated its predictive capabilities using experimental nutrient utilization and gene essentiality data. Our analysis showed that our model iACB23LX could recapitulate cellular metabolic phenotypes observed during in vitro experiments, while positive biomass production rates were observed and experimentally validated in various growth media. We further defined a minimal set of compounds that increase A. baumannii's cellular biomass and identified putative essential genes with no human counterparts, offering new candidates for future antimicrobial development. Finally, we assembled and curated the first collection of metabolic reconstructions for distinct A. baumannii strains and analyzed their growth characteristics. The presented models are in a standardized and well-curated format, enhancing their usability for multi-strain network reconstruction.

Genome-Scale Metabolic Model Reconstruction and Investigation into the Fluxome of the Fast-Growing Cyanobacterium Synechococcus sp. PCC 11901.

The ability to convert atmospheric CO2 and light into biomass and value-added chemicals makes cyanobacteria a promising resource microbial host for biotechnological applications. A newly discovered fastest-growing cyanobacterial strain, Synechococcus sp. PCC 11901, has been reported to have the highest biomass accumulation rate, making it a preferred target host for producing renewable fuels, value-added biochemicals, and natural products. System-level knowledge of an organism is imperative to understand the metabolic potential of the strain, which can be attained by developing genome-scale metabolic models (GEMs). We present the first genome-scale metabolic model of Synechococcus sp. PCC 11901 (iRS840), which contains 840 genes, 1001 reactions, and 944 metabolites. The model has been optimized and validated under different trophic modes, i.e., autotrophic and mixotrophic, by conducting an in vivo growth experiment. The robustness of the metabolic network was evaluated by changing the biomass coefficient of the model, which showed a higher sensitivity toward pigments under the photoautotrophic condition, whereas under the heterotrophic condition, amino acids were found to be more influential. Furthermore, it was discovered that PCC 11901 synthesizes succinyl-CoA via succinic semialdehyde due to its imperfect TCA cycle. Subsequent flux balance analysis (FBA) revealed a quantum yield of 0.16 in silico, which is higher compared to that of PCC 6803. Under mixotrophic conditions (with glycerol and carbon dioxide), the flux through the Calvin cycle increased compared to autotrophic conditions. This model will be useful for gaining insights into the metabolic potential of PCC 11901 and developing effective metabolic engineering strategies for product development.

Refining microbial community metabolic models derived from metagenomics using reference-based taxonomic profiling.

Characterization of microbial community metabolic output is crucial to understanding their functions. Construction of genome-scale metabolic models from metagenome-assembled genomes (MAG) has enabled prediction of metabolite production by microbial communities, yet little is known about their accuracy. Here, we examined the performance of two approaches for metabolite prediction from metagenomes, one that is MAG-guided and another that is taxonomic reference-guided. We applied both on shotgun metagenomics data from human and environmental samples, and validated findings in the human samples using untargeted metabolomics. We found that in human samples, where taxonomic profiling is optimized and reference genomes are readily available, when number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach. The two approaches showed significant overlap but each identified metabolites not predicted in the other. Pathway enrichment analyses identified significant differences in inferences derived from data based on the approach, highlighting the need for caution in interpretation. In environmental samples, when the number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach for total metabolites in both sample types and non-redundant metabolites in seawater samples. Nonetheless, as was observed for the human samples, the approaches overlapped substantially but also predicted metabolites not observed in the other. Our findings report on utility of a complementary input to genome-scale metabolic model construction that is less computationally intensive forgoing MAG assembly and refinement, and that can be applied on shallow shotgun sequencing where MAGs cannot be generated.IMPORTANCELittle is known about the accuracy of genome-scale metabolic models (GEMs) of microbial communities despite their influence on inferring community metabolic outputs and culture conditions. The performance of GEMs for metabolite prediction from metagenomes was assessed by applying two approaches on shotgun metagenomics data from human and environmental samples, and validating findings in the human samples using untargeted metabolomics. The performance of the approach was found to be dependent on sample type, but collectively, the reference-guided approach predicted more metabolites than the MAG-guided approach. Despite the differences, the predictions from the approaches overlapped substantially but each identified metabolites not predicted in the other. We found significant differences in biological inferences based on the approach, with some examples of uniquely enriched pathways in one group being invalidated when using the alternative approach, highlighting the need for caution in interpretation of GEMs.