Metabolic Reconstruction Research Articles

Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathogens

Bacterial pathogens pose a major risk to human health, leading to tens of millions of deaths annually and significant global economic losses. While bacterial infections are typically treated with antibiotic regimens, there has been a rapid emergence of antimicrobial resistant (AMR) bacterial strains due to antibiotic overuse. Because of this, treatment of infections with traditional antimicrobials has become increasingly difficult, necessitating the development of innovative approaches for deeply understanding pathogen function. To combat issues presented by broad- spectrum antibiotics, the idea of narrow-spectrum antibiotics has been previously proposed and explored. Rather than interrupting universal bacterial cellular processes, narrow-spectrum antibiotics work by targeting specific functions or essential genes in certain species or subgroups of bacteria. Here, we generate a collection of genome-scale metabolic network reconstructions (GENREs) of pathogens through an automated computational pipeline. We used these GENREs to identify subgroups of pathogens that share unique metabolic phenotypes and determined that pathogen physiological niche plays a role in the development of unique metabolic function. For example, we identified several unique metabolic phenotypes specific to stomach pathogens. We identified essential genes unique to stomach pathogens in silico and a corresponding inhibitory compound for a uniquely essential gene. We then validated our in silico predictions with an in vitro microbial growth assay. We demonstrated that the inhibition of a uniquely essential gene, thyX, inhibited growth of stomach-specific pathogens exclusively, indicating possible physiological location-specific targeting. This pioneering computational approach could lead to the identification of unique metabolic signatures to inform future targeted, physiological location-specific, antimicrobial therapies, reducing the need for broad-spectrum antibiotics.

Effect of plant-derived microbial soil legacy in a grafting system-a turn for the better.

Plant-soil feedback arises from microbial legacies left by plants in the soil. Grafting is a common technique used to prevent yield declines in monocultures. Yet, our understanding of how grafting alters the composition of soil microbiota and how these changes affect subsequent crop performance remains limited. Our experiment involved monoculturing ungrafted and grafted watermelons to obtain conditioned soils, followed by growing the watermelons on the conditioned soils to investigate plant-soil feedback effects. Ungrafted plants grew better in soil previously conditioned by a different plant (heterospecific soil) while grafted plants grew better in soil conditioned by the same plant (conspecific soil). We demonstrated experimentally that these differences in growth were linked to changes in microorganisms. Using a supervised machine learning algorithm, we showed that differences in the relative abundance of certain genera, such as Rhizobium, Chryseobacterium, Fusarium, and Aspergillus, significantly influenced the conspecific plant-soil feedback. Metabolomic analyses revealed that ungrafted plants in heterospecific soil enriched arginine biosynthesis, whereas grafted plants in conspecific soil increased sphingolipid metabolism. Elsewhere, the metagenome-assembled genomes (MAGs) of ungrafted plants identified in heterospecific soil include Chryseobacterium and Lysobacter, microorganisms having been prominently identified in earlier research as contributors to plant growth. Metabolic reconstruction revealed the putative ability of Chryseobacterium to convert D-glucono-1,5-lactone to gluconic acid, pointing to distinct disease-suppressive mechanisms and hence distinct microbial functional legacies between grafted and ungrafted plants. Our findings show a deep impact of the soil microbial reservoir on plant growth and suggest the necessity to protect and improve this microbial community in agricultural soils. The work also suggests possibilities of optimizing microbiota-mediated benefits through grafting herein, a way that "engineered" soil microbial communities for better plant growth. Video Abstract.

GutMGene v2.0: an updated comprehensive database for target genes of gut microbes and microbial metabolites.

The gut microbiota is essential for various physiological functions in the host, primarily through the metabolites it produces. To support researchers in uncovering how gut microbiota contributes to host homeostasis, we launched the gutMGene database in 2022. In this updated version, we conducted an extensive review of previous papers and incorporated new papers to extract associations among gut microbes, their metabolites, and host genes, carefully classifying these as causal or correlational. Additionally, we performed metabolic reconstructions for representative gut microbial genomes from both human and mouse. gutMGene v2.0 features an upgraded web interface, providing users with improved accessibility and functionality. This upgraded version is freely available at http://bio-computing.hrbmu.edu.cn/gutmgene. We believe that this new version will greatly advance research in the gut microbiota field by offering a comprehensive resource.

Understanding disease-associated metabolic changes in human colon epithelial cells using i ColonEpithelium metabolic reconstruction.

The colon epithelium plays a key role in the host-microbiome interactions, allowing uptake of various nutrients and driving important metabolic processes. To unravel detailed metabolic activities in the human colon epithelium, our present study focuses on the generation of the first cell-type specific genome-scale metabolic model (GEM) of human colonic epithelial cells, named iColonEpithelium. GEMs are powerful tools for exploring reactions and metabolites at systems level and predicting the flux distributions at steady state. Our cell-type-specific iColonEpithelium metabolic reconstruction captures genes specifically expressed in the human colonic epithelial cells. The iColonEpithelium is also capable of performing metabolic tasks specific to the cell type. A unique transport reaction compartment has been included to allow simulation of metabolic interactions with the gut microbiome. We used iColonEpithelium to identify metabolic signatures associated with inflammatory bowel disease. We integrated single-cell RNA sequencing data from Crohn's Diseases (CD) and ulcerative colitis (UC) samples with the iColonEpithelium metabolic network to predict metabolic signatures of colonocytes between CD and UC compared to healthy samples. We identified reactions in nucleotide interconversion, fatty acid synthesis and tryptophan metabolism were differentially regulated in CD and UC conditions, which were in accordance with experimental results. The iColonEpithelium metabolic network can be used to identify mechanisms at the cellular level, and our network has the potential to be integrated with gut microbiome models to explore the metabolic interactions between host and gut microbiota under various conditions.

Seasonal variations of microbial communities and viral diversity in fishery-enhanced marine ranching sediments: insights into metabolic potentials and ecological interactions

BackgroundThe ecosystems of marine ranching have enhanced marine biodiversity and ecological balance and have promoted the natural recovery and enhancement of fishery resources. The microbial communities of these ecosystems, including bacteria, fungi, protists, and viruses, are the drivers of biogeochemical cycles. Although seasonal changes in microbial communities are critical for ecosystem functioning, the current understanding of microbial-driven metabolic properties and their viral communities in marine sediments remains limited. Here, we employed amplicon (16S and 18S) and metagenomic approaches aiming to reveal the seasonal patterns of microbial communities, bacterial-eukaryotic interactions, whole metabolic potential, and their coupling mechanisms with carbon (C), nitrogen (N), and sulfur (S) cycling in marine ranching sediments. Additionally, the characterization and diversity of viral communities in different seasons were explored in marine ranching sediments.ResultsThe current study demonstrated that seasonal variations dramatically affected the diversity of microbial communities in marine ranching sediments and the bacterial-eukaryotic interkingdom co-occurrence networks. Metabolic reconstruction of the 113 medium to high-quality metagenome-assembled genomes (MAGs) was conducted, and a total of 8 MAGs involved in key metabolic genes and pathways (methane oxidation - denitrification - S oxidation), suggesting a possible coupling effect between the C, N, and S cycles. In total, 338 viral operational taxonomic units (vOTUs) were identified, all possessing specific ecological characteristics in different seasons and primarily belonging to Caudoviricetes, revealing their widespread distribution and variety in marine sediment ecosystems. In addition, predicted virus-host linkages showed that high host specificity was observed, with few viruses associated with specific hosts.ConclusionsThis finding deepens our knowledge of element cycling and viral diversity in fisheries enrichment ecosystems, providing insights into microbial-virus interactions in marine sediments and their effects on biogeochemical cycling. These findings have potential applications in marine ranching management and ecological conservation.DRyDF7ykR-XALqAtu34uZtVideo

An automated network-based tool to search for metabolic vulnerabilities in cancer

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gmctool, a freely accessible online tool that allows us to accomplish this task in a simple, efficient and intuitive environment. gmctool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. gmctool introduces qualitative and quantitative improvements over our previously developed algorithms to predict, visualize and analyze metabolic vulnerabilities in cancer, demonstrating a superior performance than competing algorithms. A detailed illustration of gmctool is presented for multiple myeloma (MM), an incurable hematological malignancy. We provide in vitro experimental evidence for the essentiality of CTPS1 (CTPS synthase) and UAP1 (UDP-N-Acetylglucosamine Pyrophosphorylase 1) in specific MM patient subgroups.

Intratumoral Heterogeneity and Metabolic Cross-Feeding in a Three-Dimensional Breast Cancer Culture: An In Silico Perspective.

Today, the intratumoral composition is a relevant factor associated with the progression and aggression of cancer. Although it suggests a metabolic interdependence among the subpopulations inside the tumor, a detailed map of how this interdependence contributes to the malignant phenotype is still lacking. To address this issue, we developed a systems biology approach integrating single-cell RNASeq and genome-scale metabolic reconstruction to map the metabolic cross-feeding among the subpopulations previously identified in the spheroids of MCF7 breast cancer. By calibrating our model with expression profiles and the experimental growth rate, we concluded that the reverse Warburg effect emerges as a mechanism to optimize community growth. Furthermore, through an in silico analysis, we identified lactate, alpha-ketoglutarate, and some amino acids as key metabolites whose disponibility alters the growth rate of the spheroid. Altogether, this work provides a strategy for assessing how space and intratumoral heterogeneity influence the metabolic robustness of cancer, issues suggesting that computational strategies should move toward the design of optimized treatments.

Fulvic Acid Enhances Nitrogen Fixation and Retention in Paddy Soils through Microbial-Coupled Carbon and Nitrogen Cycling.

Fulvic acid, the most soluble and active humic substance, is widely used as an agent to remediate contaminated soils and improve soil fertility. However, the influence of fulvic acid (FA), as a microbial carbon source, on carbon and nitrogen cycles in paddy soils remains elusive. Therefore, to investigate it, an incubation experiment was conducted. Gas analyses indicated that the carbon dioxide and methane emissions were enhanced in FA treatment, which increased up to 94.08-fold and 5.06-fold, respectively. 15N-labeling experiments revealed that nitrogen fixation capability was promoted (1.2-fold) to reduce the carbon and nitrogen imbalance due to fulvic acid amendment. Metagenomic analysis further revealed that gene abundances of degradation of lignin-like compounds, gallate degradation, methanogenesis, nitrogen fixation, and urea hydrolysis increased, while the bacterial ammonia oxidation and anaerobic ammonium oxidation decreased, caused by FA application. Metabolic reconstruction of metagenome-assembled genomes revealed that Azospirillaceae, Methanosarcinaceae, and Bathyarchaeota, with higher abundance in FA treatment, were the key microorganisms to maintain the carbon and nitrogen balance. The metabolic pathways of fulvic acid degradation and coupled nitrogen fixation and retention were constructed. Collectively, our results provided novel insights into the theoretical basis of the use of humic substances for reducing nitrogen fertilization and climate change.

Research progress in bioconversion of C1 gases into oleochemicals

The utilization of C1 gases (CH4, CO2, and CO) for the production of oleochemicals applied in the energy and platform chemicals through microbial engineering has emerged as a promising approach to reduce greenhouse gas emissions and decrease dependence on fossil fuel. C1 gas-utilizing microorganisms, such as methanotrophs, microalgae, and acetogens, are capable of converting C1 gases as the sole substrates for cell growth and oleochemical synthesis with different carbon-chain lengths, garnering considerable attention from both scientific community and industry field for sustainable biomanufacturing. This paper comprehensively reviews recent advancements in the development of engineered cell factories utilizing C1 gases for the production of oleochemicals, elucidating the key metabolic pathways of biosynthesis. Furthermore, this paper highlights the research progress and prospects in optimizing gene expression, metabolic pathway reconstruction, and fermentation conditions for efficient oleochemical production from C1 gases. This review provides valuable insights and guidance for the efficient utilization of C1 gases and the development of carbon cycling-based bioeconomy.

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.

Proteogenomic reconstruction of organ-specific metabolic networks in an environmental sentinel species, the amphipod Gammarus fossarum

Metabolic pathways are affected by the impacts of environmental contaminants underlying a large variability of toxic effects across different species. However, the systematic reconstruction of metabolic pathways remains limited in environmental sentinel species due to the lack of available genomic data in many taxa of animal diversity. In this study we used a multi-omics approach to reconstruct the most comprehensive map of metabolic pathways for a crustacean model in biomonitoring, the amphipod Gammarus fossarum in order to improve the knowledge of the metabolism of this sentinel species.We revisited the assembly of RNA-seq data by de novo approaches to reduce RNA contaminants and transcript redundancy. We also acquired extensive mass spectrometry shotgun proteomic data on several organs from a reference population of G. fossarum males and females to identify organ-specific metabolic profiles.The G. fossarum metabolic pathway reconstruction (available through the metabolic database GamfoCyc) was performed by adapting the genomic tool CycADS and we identified 377 pathways representing 7630 annotated enzymes, 2610 enzymatic reactions and the expression of 858 enzymes was experimentally validated by proteomics. To our knowledge, our analysis provides for the first time a systematic metabolic pathway reconstruction and the proteome profiles of these pathways at the organ level in this sentinel species. As an example, we show an elevated abundance in enzymes involved in ATP biosynthesis and fatty acid beta-oxidation indicative of the high-energy requirement of the gills, or the key anabolic and detoxification role of the hepatopancreatic caeca, as exemplified by the specific expression of the retinoid biosynthetic pathways and glutathione synthesis.In conclusion, the multi-omics data integration performed in this study provides new resources to investigate metabolic processes in crustacean amphipods and their role in mediating the effects of environmental contaminant exposures in sentinel species. SynopsisThis study provide the first evidence that it is possible to combine multiple omics data to exhaustively describe the metabolic network of a model species in ecotoxicology, Gammarus fossarum, for which a reference genome is not yet available.

Characterizing flavor development in low-salt Chinese horse bean-chili paste through integrated metabolomics and metagenomics

This study utilized metabolomics and metagenomics to investigate the microbial composition and functions in low- and high-salt Chinese horse bean-chili pastes (CHCPs). The results showed that 25 key metabolites were identified to distinguish the flavor attributes between the two samples. Leuconostoc was identified as the dominant microbiota in low-salt CHCP, while Pantoea prevailed in the high-salt CHCP. Compared to traditional high-salt fermentation, low-salt and inoculated fermentation promoted the increase in the relative abundances of Companionlactobacillus, Levilactobacillus, Tetragenococcus, Zygosaccharomyces and Wickerhamiella as well as the enrichment of carbohydrate and amino acid metabolic pathways, which contributed to the enhancement of characteristic flavor compounds. Further metabolic pathway reconstruction elucidated 21 potential microbial genera associated with the formation of key metabolites, such as Leuconostoc, Levilactobacillus, Pantoea, and Pectobacterium. This study may provide insights for optimizing the fermentation process and improving the flavor quality of low-salt CHCP and similar fermentation products. KeywordsLow-salt fermentationHight-salt fermentationChinese horse-bean chili pasteFlavor formationMetabolomicsMetagenomics

Transcriptomic analysis of the metabolic characteristics of Aspergillus oryzae during the fermentation of doenjang-meju, a traditional Korean fermented soybean brick

To investigate the fermentative traits of Aspergillus oryzae in doenjang-meju, a traditional Korean fermented soybean brick, we prepared doenjang-meju bricks inoculated with both A. oryzae and Bacillus velezensis––recognized as crucial microbes in doenjang-meju fermentation, and then the transcriptomes of A. oryzae were analyzed throughout the entire fermentation period. The transcriptomic analysis of carbohydrate-active enzyme (CAZy) genes revealed that A. oryzae exhibited robust expression of CAZy genes involved in the decomposition of various carbohydrates, particularly cellulose, starch, and hemicellulose, indicating the active breakdown of soybean carbohydrates by A. oryzae during fermentation. Furthermore, KEGG analysis, metabolic pathway reconstruction, and their transcriptomic analysis provided a comprehensive overview of the decomposition patterns of various carbohydrates, lipids, and proteins and the metabolic features of various carbon compounds during fermentation. Additionally, the transcriptomic analysis of biosynthetic gene clusters indicated the potential production of various secondary metabolites by A. oryzae during fermentation. Genes related to carbohydrate and carbon compound metabolisms and secondary metabolite biosynthesis displayed unique and different transcriptional expression patterns depending on the specific gene and fermentation stage. Overall, our findings provide comprehensive insights into the metabolic characteristics of A. oryzae, thus contributing to our understanding of soybean fermentation.

Deciphering the behind blackspot exocarp disorder in avocado cv. Hass through a hormonal, transcriptional and metabolic integration approach

Avocado cv. Hass is an important sub-tropical crop with an increasing global demand. However, the avocado supply chain experiences significant fruit losses, particularly during the postharvest stage due to diseases and disorders that manifest after prolonged cold storage or the ready-to-eat stage. The blackspot exocarp disorder, which appears as brown or black blotches only after extended cold storage conditions, leads to substantial commercial losses for exported avocados. This research aimed to identify transcriptomic, metabolomic, and hormonal changes in avocado fruits affected by blackspot disorder, differentiating between the green and black exocarp tissues directly impacted by this physiological disorder. The results showed a correlation between the black-colored exocarp of blackspot affected fruits with high levels of gibberellins, cytokinins, jasmonic acid and salicylic acid hormones. Metabolically, these changes were accompanied by a high fatty acid content of oleate, palmitate and linoleate. Using a metabolic pathway reconstruction analysis, we integrated hormonal and metabolic data with transcriptomic information. This approach identified several genes involved in central carbon metabolism, long-chain fatty acid elongation, and jasmonate/salicylate biosynthesis pathways, as well as a possible accumulation of lignins due to a high expression of genes associated with the phenylpropanoid pathway in the black exocarp of blackspot-affected fruits. These findings suggest that blackspot disorder results from a combination of plant defense mechanisms triggered to strengthen the fruit exocarp tissue.

Insights into the evolutionary and ecological adaption strategies of nirS- and nirK-type denitrifying communities.

Denitrification is a crucial process in the global nitrogen cycle, in which two functionally equivalent genes, nirS and nirK, catalyse the critical reaction and are usually used as marker genes. The nirK gene can function independently, whereas nirS requires additional genes to encode nitrite reductase and is more sensitive to environmental factors than nirK. However, the ecological differentiation mechanisms of those denitrifying microbial communities and their adaptation strategies to environmental stresses remain unclear. Here, we conducted metagenomic analysis for sediments and bioreactor samples from Lake Donghu, China. We found that nirS-type denitrifying communities had a significantly lower horizontal gene transfer frequency than that of nirK-type denitrifying communities, and nirS gene phylogeny was more congruent with taxonomy than that of nirK gene. Metabolic reconstruction of metagenome-assembled genomes further revealed that nirS-type denitrifying communities have robust metabolic systems for energy conservation, enabling them to survive under environmental stresses. Nevertheless, nirK-type denitrifying communities seemed to adapt to oxygen-limited environments with the ability to utilize various carbon and nitrogen compounds. Thus, this study provides novel insights into the ecological differentiation mechanism of nirS and nirK-type denitrifying communities, as well as the regulation of the global nitrogen cycle and greenhouse gas emissions.

Metabolic Network Analysis Reveals Human Impact on Urban Nitrogen Cycles

Human interactions have led to the emergence of a higher complexity of urban metabolic networks; hence, traditional natural- or agriculture-oriented biogeochemical models might not be transferred well to urban environments. Increasingly serious environmental problems require the development of new concepts and models. Here, we propose a basic paradigm for urban–rural complex nitrogen (N) metabolic network reconstruction (NMNR) by introducing new concepts and methodologies from systems biology at the molecular scale, analyzing both local and global structural properties and exploring optimization and regulation methods. Using the Great Hangzhou Areas System (GHA) as a case study, we revealed that pathway fluxes follow a power law distribution, which indicates that human-dominated pathways constitute the principal part of the functions of the whole network. However, only 1.16% of the effective cycling pathways and an average hamming distance of only 5.23 between the main pathways indicate that the network lacks diverse pathways and feedback loops, which could lead to low robustness. Furthermore, more than half of the N fluxes did not pass through core metabolism, causing waste and pollution. We also provided strategies to design network structures and regulate system function: improving robustness and reducing pollution by referring to the characteristics of biochemical metabolic networks (e.g., the bow-tie structure). This method can be used to replace the trial-and-error method in system regulation and design. By decomposing the GHA N metabolic network into 4398 metabolic pathways and the corresponding fluxes with a power law distribution, NMNR helps us quantify the vulnerability in the current urban nitrogen cycle. The basic ideas and methodology in NMNR can be applied to coupled human and natural systems to advance global sustainable development studies, and they can also extend systems biology from the molecule to complex ecosystems and lead to the development of multi-scale unified theory in systems biology.

Genome-scale community modelling elucidates the metabolic interaction in Indian type-2 diabetic gut microbiota

Type-2 diabetes (T2D) is a rapidly growing multifactorial metabolic disorder that induces the onset of various diseases in the human body. The compositional and metabolic shift of the gut microbiota is a crucial factor behind T2D. Hence, gaining insight into the metabolic profile of the gut microbiota is essential for revealing their role in regulating the metabolism of T2D patients. Here, we have focused on the genome-scale community metabolic model reconstruction of crucial T2D-associated gut microbes. The model-based analysis of biochemical flux in T2D and healthy gut conditions showed distinct biochemical signatures and diverse metabolic interactions in the microbial community. The metabolic interactions encompass cross-feeding of short-chain fatty acids, amino acids, and vitamins among individual microbes within the community. In T2D conditions, a reduction in the metabolic flux of acetate, butyrate, vitamin B5, and bicarbonate was observed in the microbial community model, which can impact carbohydrate metabolism. The decline in butyrate levels is correlated with both insulin resistance and diminished glucose metabolism in T2D patients. Compared to the healthy gut, an overall reduction in glucose consumption and SCFA production flux was estimated in the T2D gut environment. Moreover, the decreased consumption profiles of branch chain amino acids (BCAAs) and aromatic amino acids (AAAs) in the T2D gut microbiota can be a distinct biomarker for T2D. Hence, the flux-level analysis of the microbial community model can provide insights into the metabolic reprogramming in diabetic gut microbiomes, which may be helpful in personalized therapeutics and diet design against T2D.

Transporter annotations are holding up progress in metabolic modeling

Mechanistic, constraint-based models of microbial isolates or communities are a staple in the metabolic analysis toolbox, but predictions about microbe-microbe and microbe-environment interactions are only as good as the accuracy of transporter annotations. A number of hurdles stand in the way of comprehensive functional assignments for membrane transporters. These include general or non-specific substrate assignments, ambiguity in the localization, directionality and reversibility of a transporter, and the many-to-many mapping of substrates, transporters and genes. In this perspective, we summarize progress in both experimental and computational approaches used to determine the function of transporters and consider paths forward that integrate both. Investment in accurate, high-throughput functional characterization is needed to train the next-generation of predictive tools toward genome-scale metabolic network reconstructions that better predict phenotypes and interactions. More reliable predictions in this domain will benefit fields ranging from personalized medicine to metabolic engineering to microbial ecology.

Q2-metnet: QIIME2 package to analyse 16S rRNA data via high-quality metabolic reconstructions of the human gut microbiota.

16S rRNA gene sequencing is the most frequent approach for the characterization of the human gut microbiota. Despite different efforts in the literature, the inference of functional and metabolic interpretations from 16S rRNA gene sequencing data is still a challenging task. High-quality metabolic reconstructions of the human gut microbiota, such as AGORA and AGREDA, constitute a curated resource to improve functional inference from 16S rRNA data, but they are not typically integrated into standard bioinformatics tools. Here, we present q2-metnet, a QIIME2 plugin that enables the contextualization of 16S rRNA gene sequencing data into AGORA and AGREDA. In particular, based on relative abundances of taxa, q2-metnet determines normalized activity scores for the reactions and subsystems involved in the selected metabolic reconstruction. Using these scores, q2-metnet allows the user to conduct differential activity analysis for reactions and subsystems, as well as exploratory analysis using PCA and hierarchical clustering. We apply q2-metnet to a dataset from our group that involves 16S rRNA data from stool samples from lean, allergic to cow's milk, obese and celiac children, and the Belgian Flemish Gut Flora Project cohort, which includes faecal 16S rRNA data from obese and normal-weight adult individuals. In the first case, q2-metnet outperforms existing algorithms in separating different clinical conditions based on predicted pathway abundances and subsystem scores. In the second case, q2-metnet complements competing approaches in predicting functional alterations in the gut microbiota of obese individuals. Overall, q2-metnet constitutes a powerful bioinformatics tool to provide metabolic context to 16S rRNA data from the human gut microbiota. Python code of q2-metnet is available in https://github.com/PlanesLab/q2-metnet and https://figshare.com/articles/dataset/q2-metnet_package/26180446.