Empirical evidence for metabolic drift in plant and algal lipid biosynthesis pathways.

Metabolic pathway drift has been formulated as a general principle to help in the interpretation of comparative analyses between biosynthesis pathways. Indeed, such analyses often indicate substantial differences, even in widespread pathways that are sometimes believed to be conserved. Here, our purpose is to check how much this interpretation fits to empirical data gathered in the field of plant and algal biosynthesis pathways. After examining several examples representative of the diversity of lipid biosynthesis pathways, we explain why it is important to compare closely related species to gain a better understanding of this phenomenon. Furthermore, this comparative approach brings us to the question of how much biotic interactions are responsible for shaping this metabolic plasticity. We end up introducing some model systems that may be promising for further exploration of this question.

Regulus infers signed regulatory relations from few samples' information using discretization and likelihood constraints.

MOTIVATION: Transcriptional regulation is performed by transcription factors (TF) binding to DNA in context-dependent regulatory regions and determines the activation or inhibition of gene expression. Current methods of transcriptional regulatory circuits inference, based on one or all of TF, regions and genes activity measurements require a large number of samples for ranking the candidate TF-gene regulation relations and rarely predict whether they are activations or inhibitions. We hypothesize that transcriptional regulatory circuits can be inferred from fewer samples by (1) fully integrating information on TF binding, gene expression and regulatory regions accessibility, (2) reducing data complexity and (3) using biology-based likelihood constraints to determine the global consistency between a candidate TF-gene relation and patterns of genes expressions and region activations, as well as qualify regulations as activations or inhibitions. RESULTS: We introduce Regulus, a method which computes TF-gene relations from gene expressions, regulatory region activities and TF binding sites data, together with the genomic locations of all entities. After aggregating gene expressions and region activities into patterns, data are integrated into a RDF (Resource Description Framework) endpoint. A dedicated SPARQL (SPARQL Protocol and RDF Query Language) query retrieves all potential relations between expressed TF and genes involving active regulatory regions. These TF-region-gene relations are then filtered using biological likelihood constraints allowing to qualify them as activation or inhibition. Regulus provides signed relations consistent with public databases and, when applied to biological data, identifies both known and potential new regulators. Regulus is devoted to context-specific transcriptional circuits inference in human settings where samples are scarce and cell populations are closely related, using discretization into patterns and likelihood reasoning to decipher the most robust regulatory relations.

Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe.

Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.

Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85.

Fibrobacter succinogenes is a cellulolytic bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. It converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate, and formate. We developed dynamic models of F. succinogenes S85 metabolism on glucose, cellobiose, and cellulose on the basis of a network reconstruction done with the automatic reconstruction of metabolic model workspace. The reconstruction was based on genome annotation, five template-based orthology methods, gap filling, and manual curation. The metabolic network of F. succinogenes S85 comprises 1,565 reactions with 77% linked to 1,317 genes, 1,586 unique metabolites, and 931 pathways. The network was reduced using the NetRed algorithm and analyzed for the computation of elementary flux modes. A yield analysis was further performed to select a minimal set of macroscopic reactions for each substrate. The accuracy of the models was acceptable in simulating F. succinogenes carbohydrate metabolism with an average coefficient of variation of the root mean squared error of 19%. The resulting models are useful resources for investigating the metabolic capabilities of F. succinogenes S85, including the dynamics of metabolite production. Such an approach is a key step toward the integration of omics microbial information into predictive models of rumen metabolism. IMPORTANCE F. succinogenes S85 is a cellulose-degrading and succinate-producing bacterium. Such functions are central for the rumen ecosystem and are of special interest for several industrial applications. This work illustrates how information of the genome of F. succinogenes can be translated to develop predictive dynamic models of rumen fermentation processes. We expect this approach can be applied to other rumen microbes for producing a model of rumen microbiome that can be used for studying microbial manipulation strategies aimed at enhancing feed utilization and mitigating enteric emissions.

Insights into the potential for mutualistic and harmful host-microbe interactions affecting brown alga freshwater acclimation.

Microbes can modify their hosts' stress tolerance, thus potentially enhancing their ecological range. An example of such interactions is Ectocarpus subulatus, one of the few freshwater-tolerant brown algae. This tolerance is partially due to its (un)cultivated microbiome. We investigated this phenomenon by modifying the microbiome of laboratory-grown E. subulatus using mild antibiotic treatments, which affected its ability to grow in low salinity. Low salinity acclimation of these algal-bacterial associations was then compared. Salinity significantly impacted bacterial and viral gene expression, albeit in different ways across algal-bacterial communities. In contrast, gene expression of the host and metabolite profiles were affected almost exclusively in the freshwater-intolerant algal-bacterial communities. We found no evidence of bacterial protein production that would directly improve algal stress tolerance. However, vitamin K synthesis is one possible bacterial service missing specifically in freshwater-intolerant cultures in low salinity. In this condition, we also observed a relative increase in bacterial transcriptomic activity and the induction of microbial genes involved in the biosynthesis of the autoinducer AI-1, a quorum-sensing regulator. This could have resulted in dysbiosis by causing a shift in bacterial behaviour in the intolerant algal-bacterial community. Together, these results provide two promising hypotheses to be examined by future targeted experiments. Although they apply only to the specific study system, they offer an example of how bacteria may impact their host's stress response.

MERRIN: MEtabolic regulation rule INference from time series data.

MOTIVATION: Many techniques have been developed to infer Boolean regulations from a prior knowledge network (PKN) and experimental data. Existing methods are able to reverse-engineer Boolean regulations for transcriptional and signaling networks, but they fail to infer regulations that control metabolic networks. RESULTS: We present a novel approach to infer Boolean rules for metabolic regulation from time-series data and a PKN. Our method is based on a combination of answer set programming and linear programming. By solving both combinatorial and linear arithmetic constraints, we generate candidate Boolean regulations that can reproduce the given data when coupled to the metabolic network. We evaluate our approach on a core regulated metabolic network and show how the quality of the predictions depends on the available kinetic, fluxomics or transcriptomics time-series data. AVAILABILITY AND IMPLEMENTATION: Software available at https://github.com/bioasp/merrin. SUPPLEMENTARY INFORMATION: Supplementary data are available at https://doi.org/10.5281/zenodo.6670164.

Discrete modeling for integration and analysis of large-scale signaling networks.

Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks. Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files. The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states.

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology.

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

Hypertensive Disorders of Pregnancy: Common Clinical Conundrums.

Importance: Hypertensive complications of pregnancy comprise 16% of maternal deaths in developed countries and 7.4% of deaths in the United States. Rates of preeclampsia increased 25% from 1987 to 2004, and rates of severe preeclampsia have increased 6.7-fold between 1980 and 2003. Objective: The aim of this study was to review current and available evidence for common clinical questions regarding the management of hypertensive disorders of pregnancy. Evidence Acquisition: Original research articles, review articles, and guidelines on hypertension in pregnancy were reviewed. Results: Severe gestational hypertension should be managed as preeclampsia with severe features. Serum uric acid levels can be useful in predicting development of superimposed preeclampsia for women with chronic hypertension. When presenting with preeclampsia with severe features before 34 weeks, expectant management should be considered only when both maternal and fetal conditions are stable. In the setting of hypertensive disorders of pregnancy, oral antihypertensive medications should be initiated when systolic blood pressure is greater than 160 mm Hg or when diastolic blood pressure is greater than 110 mm Hg, with the most ideal agents being labetalol or nifedipine. Furthermore, although risk of preeclampsia recurrence in future pregnancy is low, women with a history of preeclampsia should be managed with 81 mg aspirin daily for preeclampsia prevention. Conclusions and Relevance: Despite the frequency with which hypertensive disorders of pregnancy are encountered clinically, situations arise frequently with limited evidence to guide providers in their management. An urgent need exists to better understand this disease to optimize outcomes for impacted patients.

Improving reusability along the data life cycle: a regulatory circuits case study.

BACKGROUND: In life sciences, there has been a long-standing effort of standardization and integration of reference datasets and databases. Despite these efforts, many studies data are provided using specific and non-standard formats. This hampers the capacity to reuse the studies data in other pipelines, the capacity to reuse the pipelines results in other studies, and the capacity to enrich the data with additional information. The Regulatory Circuits project is one of the largest efforts for integrating human cell genomics data to predict tissue-specific transcription factor-genes interaction networks. In spite of its success, it exhibits the usual shortcomings limiting its update, its reuse (as a whole or partially), and its extension with new data samples. To address these limitations, the resource has previously been integrated in an RDF triplestore so that TF-gene interaction networks could be generated with two SPARQL queries. However, this triplestore did not store the computed networks and did not integrate metadata about tissues and samples, therefore limiting the reuse of this dataset. In particular, it does not enable to reuse only a portion of Regulatory Circuits if a study focuses on a subset of the tissues, nor to combine the samples described in the datasets with samples from other studies. Overall, these limitations advocate for the design of a complete, flexible and reusable representation of the Regulatory Circuits dataset based on Semantic Web technologies. RESULTS: We provide a modular RDF representation of the Regulatory Circuits, called Linked Extended Regulatory Circuits (LERC). It consists in (i) descriptions of biological and experimental context mapped to the references databases, (ii) annotations about TF-gene interactions at the sample level for 808 samples, (iii) annotations about TF-gene interactions at the tissue level for 394 tissues, (iv) metadata connecting the knowledge graphs cited above. LERC is based on a modular organisation into 1,205 RDF named graphs for representing the biological data, the sample-specific and the tissue-specific networks, and the corresponding metadata. In total it contains 3,910,794,050 triples and is available as a SPARQL endpoint. CONCLUSION: The flexible and modular architecture of LERC supports biologically-relevant SPARQL queries. It allows an easy and fast querying of the resources related to the initial Regulatory Circuits datasets and facilitates its reuse in other studies. ASSOCIATED WEBSITE: https://regulatorycircuits-lod.genouest.org.

The impact of excessive gestational weight gain timing on neonatal outcomes in women with class III obesity.

OBJECTIVE: This study evaluated the influence of early gestational weight gain (GWG) on neonatal outcomes among women with class III obesity. STUDY DESIGN: Retrospective cohort of women with class III obesity who gained more than the Institute of Medicine (IOM) guidelines (>20lbs). Women gaining ≥75% of total gestational weight prior to 28 weeks (EWG) were compared to women gaining <75% of their total weight prior to 28 weeks (SWG). The primary outcome was a neonatal composite morbidity and mortality. Secondary outcomes included individual components of composite and LGA. RESULTS: Of 144 women identified, 42 (29.2%) had EWG and 102 (70.8%) had SWG. Though 11% of the total population had composite neonatal morbidity, this did not differ between groups (p = .4). LGA was nearly twice as common in the SWG group (41% vs 26%, p = .13). EWG was associated with decreased risk of LGA (AOR 0.25 95% CI 0.08, 0.78) and lower median birth weight (AOR -312 g 95% CI -534.7, -90.2). CONCLUSION: Though adverse neonatal outcomes were common in this population, timing of gestational weight gain was not correlated. Increased rates of LGA and higher median birth weight in the SWG group suggests excessive GWG continuing in the third trimester of pregnancy may be of import for neonatal size.

Pregnancy outcomes among women with class III obesity with normal early glucola.

BACKGROUND: Because obese women are at increased risk for insulin resistance and development gestational diabetes (GDM), the American College of Obstetricians and Gynecologists (ACOG) recommends early GDM screening in this population. For obese women with a normal early 1-hour 50 g oral glucose challenge test (eGCT), the risk of developing GDM later in the pregnancy is unknown. Thus, we aimed to assess the risk of developing gestational diabetes based on the value of a normal eGCT. STUDY DESIGN: Retrospective cohort of non-anomalous singleton pregnancies with maternal body mass index (BMI) ≥40 at the time of entry to prenatal care at a single institution from 2013 to 2017. Pregnancies with abnormal early 1-hour 50 g glucose challenge test (eGCT), multiple gestation, late entry to care, type 1 or 2 diabetes, and missing diabetes-screening information are excluded. Primary outcome was development of GDM. Secondary outcomes include fetal growth restriction, macrosomia, gestational age at delivery, large for gestational age, delivery BMI, total weight gain in pregnancy, induction of labor, shoulder dystocia, and cesarean delivery. Bivariate statistics compare demographics, pregnancy complications and delivery characteristics of women who had an eGCT≤ 100 mg/dL (low-normal eGCT) and women who had an eGCT of 101-134 mg/dL (high-normal eGCT). Regression models used to estimate odds of primary outcome. RESULTS: Of 169 women, 66(39%) had a low-normal eGCT, and 103(61%) had a high-normal eGCT. Women in the low-normal eGCT group were more likely to use recreational drugs (p = 0.03), other baseline demographics did not differ. The rate of GDM was low in this population (5.3%), with no difference in the rate of GDM between with a low-normal eGCT (1.5%) and high-normal eGCT (7.7%) (p = 0.09). The median neonatal birthweight was higher in the high-normal GCT group (3405 g) as compared to the low-no GCT (3285 g) (p = 0.03). CONCLUSIONS: Among women with class 3 obesity, the specific value of an early normal GCT was not associated with developing gestational diabetes mellitus later in the pregnancy. Larger studies are needed confirm these findings.

Pregnancy Outcomes among Women with Class III Obesity with Pre-diabetic Early Hemoglobin A1C.

OBJECTIVE: We aimed to assess the risk of developing gestational diabetes mellitus (GDM) in women with a normal A1C (<5.7) compared with those with an A1C in the pre-diabetic range (5.7-6.4). STUDY DESIGN: This study comprises of a retrospective cohort of non-anomalous singleton pregnancies with maternal body mass index (BMI) ≥40 at a single institution from 2013 to 2017. Pregnancies with multiple gestation, late entry to care, type 1 or 2 diabetes, and missing diabetes-screening information were excluded. The primary outcome was development of GDM. Secondary outcomes included fetal growth restriction, macrosomia, gestational age at delivery, large for gestational age, delivery BMI at delivery, total weight gain in pregnancy, induction of labor, shoulder dystocia, and cesarean delivery. Bivariate statistics were used to compare demographics, pregnancy complications, and delivery characteristics of women who had an early A1C < 5.7 and A1C 5.7 to 6.4. Multivariable analyses were used to estimate the odds of the primary outcome. RESULTS: Eighty women (68%) had an early A1C <5.7 and 38 (32%) had a A1C 5.7 to 6.4. Women in the lower A1C group were less likely to be Black (45 vs. 74%, p = 0.01). No differences in other baseline demographics were observed. The median A1C was 5.3 for women with A1C < 5.7 and 5.8 for women with A1C 5.7 to 6.4 (p < 0.001). GDM was significantly more common in women with A1C 5.7 to 6.4 (3.8 vs. 24%, p = 0.002). Women with pre-diabetic range A1C had an odd ratio of 11.1 (95% CI 2.49-48.8) for GDM compared with women with a normal A1C. CONCLUSION: Women with class III obesity and a pre-diabetic range A1C are at an increased risk for gestational diabetes when compared with those with a normal A1C in early pregnancy. KEY POINTS: · One in 3 women with class III obesity had a pre-diabetic range early A1C.. · Class III obese women who have a pre-diabetic A1C have a higher risk of gestational diabetes.. · In this high-risk population, early A1C results in the pre-diabetic range are associated with higher rates of gestational diabetes..

Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA.

BACKGROUND: The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A[Formula: see text]C). RESULTS: We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures. CONCLUSIONS: Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.

PAX2GRAPHML: a python library for large-scale regulation network analysis using BioPAX.

SUMMARY: PAX2GRAPHML is an open-source Python library that allows to easily manipulate BioPAX source files as regulated reaction graphs described in.graphml format. The concept of regulated reactions, which allows connecting regulatory, signaling and metabolic levels, has been used. Biochemical reactions and regulatory interactions are homogeneously described by regulated reactions involving substrates, products, activators and inhibitors as elements. PAX2GRAPHML is highly flexible and allows generating graphs of regulated reactions from a single BioPAX source or by combining and filtering BioPAX sources. Supported by the graph exchange format .graphml, the large-scale graphs produced from one or more data sources can be further analyzed with PAX2GRAPHML or standard Python and R graph libraries. AVAILABILITY AND IMPLEMENTATION: https://pax2graphml.genouest.org.

Semi-Quantitative Targeted Gas Chromatography-Mass Spectrometry Profiling Supports a Late Side-Chain Reductase Cycloartenol-to-Cholesterol Biosynthesis Pathway in Brown Algae.

Sterols are biologically important molecules that serve as membrane fluidity regulators and precursors of signaling molecules, either endogenous or involved in biotic interactions. There is currently no model of their biosynthesis pathways in brown algae. Here, we benefit from the availability of genome data and gas chromatography-mass spectrometry (GC-MS) sterol profiling using a database of internal standards to build such a model. We expand the set of identified sterols in 11 species of red, brown, and green macroalgae and integrate these new data with genomic data. Our analyses suggest that some metabolic reactions may be conserved despite the loss of canonical eukaryotic enzymes, like the sterol side-chain reductase (SSR). Our findings are consistent with the principle of metabolic pathway drift through enzymatic replacement and show that cholesterol synthesis from cycloartenol may be a widespread but variable pathway among chlorophyllian eukaryotes. Among the factors contributing to this variability, one could be the recruitment of cholesterol biosynthetic intermediates to make signaling molecules, such as the mozukulins. These compounds were found in some brown algae belonging to Ectocarpales, and we here provide a first mozukulin biosynthetic model. Our results demonstrate that integrative approaches can already be used to infer experimentally testable models, which will be useful to further investigate the biological roles of those newly identified algal pathways.

Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines.

Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines (Prokka, MaGe, IMG, DFAST, RAST) from predicted annotation features (coding sequences, Enzyme Commission numbers, hypothetical proteins) to the metabolic network-based analysis of symbiotic communities (biochemical reactions, producible compounds, and selection of minimal complementary bacterial communities). While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases.

Cyanorak v2.1: a scalable information system dedicated to the visualization and expert curation of marine and brackish picocyanobacteria genomes.

Cyanorak v2.1 (http://www.sb-roscoff.fr/cyanorak) is an information system dedicated to visualizing, comparing and curating the genomes of Prochlorococcus, Synechococcus and Cyanobium, the most abundant photosynthetic microorganisms on Earth. The database encompasses sequences from 97 genomes, covering most of the wide genetic diversity known so far within these groups, and which were split into 25,834 clusters of likely orthologous groups (CLOGs). The user interface gives access to genomic characteristics, accession numbers as well as an interactive map showing strain isolation sites. The main entry to the database is through search for a term (gene name, product, etc.), resulting in a list of CLOGs and individual genes. Each CLOG benefits from a rich functional annotation including EggNOG, EC/K numbers, GO terms, TIGR Roles, custom-designed Cyanorak Roles as well as several protein motif predictions. Cyanorak also displays a phyletic profile, indicating the genotype and pigment type for each CLOG, and a genome viewer (Jbrowse) to visualize additional data on each genome such as predicted operons, genomic islands or transcriptomic data, when available. This information system also includes a BLAST search tool, comparative genomic context as well as various data export options. Altogether, Cyanorak v2.1 constitutes an invaluable, scalable tool for comparative genomics of ecologically relevant marine microorganisms.

Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species.

To capture the functional diversity of microbiota, one must identify metabolic functions and species of interest within hundreds or thousands of microorganisms. We present Metage2Metabo (M2M) a resource that meets the need for de novo functional screening of genome-scale metabolic networks (GSMNs) at the scale of a metagenome, and the identification of critical species with respect to metabolic cooperation. M2M comprises a flexible pipeline for the characterisation of individual metabolisms and collective metabolic complementarity. In addition, M2M identifies key species, that are meaningful members of the community for functions of interest. We demonstrate that M2M is applicable to collections of genomes as well as metagenome-assembled genomes, permits an efficient GSMN reconstruction with Pathway Tools, and assesses the cooperation potential between species. M2M identifies key organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses.

All the microbes that live in a specific environment, for example an organ, are collectively called the microbiota. In humans, the microbiota of the gut has been extensively studied by sequencing the DNA of the different microbes to identify them and determine the roles they play in health and disease. The DNA sequences of all the members of the microbiota is called the metagenome. The chemical reactions that the gut microbiota perform to produce energy and make the biomolecules they need to survive are collectively referred to as the metabolism of these microbes. Studying the metabolism of the gut microbiota can help researchers understand the roles of the different microbes. However, the large variety of species in the gut microbiota and gaps in the information about them render these studies difficult, despite technology improving quickly. To tackle this issue, Belcour, Frioux et al developed a new piece of software called Metage2Metabo (M2M) that simulates the metabolism of the gut microbiota and describes the metabolic relationships between the different microbes. Metage2Metabo analyses the roles of the metabolic genes of a large number of microbe species to establish how they complement each other metabolically. Then, it can calculate the minimum number of species needed to perform a metabolic role of interest within that microbiota, and which key species are associated with that role. To test the new software, Belcour, Frioux et al. used Metage2Metabo to analyse genomes from the human gut microbiota and from the cow rumen (one of the cow's stomachs). They showed that even if the metagenome was incomplete, the software was able to make stable predictions of key species involved in metabolic complementarity. Additionally, they also illustrated how the method can be used to study the gut microbiota of individuals. This work presents a new method for determining the metabolic relationships between species within a microbiota. The software is highly flexible and could be adapted to identify key members within different communities. In the context of the gut microbiota, the predictions of Metage2Metabo could shed lights on the interactions between the host and the microbes and contribute to a better understanding of microbe environments.