Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

/pdf/a-community-driven-global-reconstruction-of-human-metabolism-13sqd5jswu.pdf

A community-driven global reconstruction of human metabolism

COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software. Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes. COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. http://opencobra.sourceforge.net/

https://link.springer.com/content/pdf/10.1186%2F1752-0509-7-74.pdf

COBRApy: COnstraints-Based Reconstruction and Analysis for Python.

A large set of microbial metabolic models (AGORA) could be applied to better understand the functions of the human gut microbiome. Genome-scale metabolic models derived from human gut metagenomic data can be used as a framework to elucidate how microbial communities modulate human metabolism and health. We present AGORA (assembly of gut organisms through reconstruction and analysis), a resource of genome-scale metabolic reconstructions semi-automatically generated for 773 human gut bacteria. Using this resource, we identified a defined growth medium for Bacteroides caccae ATCC 34185. We also showed that interactions among modeled species depend on both the metabolic potential of each species and the nutrients available. AGORA reconstructions can integrate either metagenomic or 16S rRNA sequencing data sets to infer the metabolic diversity of microbial communities. AGORA reconstructions could provide a starting point for the generation of high-quality, manually curated metabolic reconstructions. AGORA is fully compatible with Recon 2, a comprehensive metabolic reconstruction of human metabolism, which will facilitate studies of host–microbiome interactions.

/pdf/generation-of-genome-scale-metabolic-reconstructions-for-773-3aeeffm3ph.pdf

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

Genome-scale models (GEMs) of metabolism were constructed for 55 fully sequenced Escherichia coli and Shigella strains. The GEMs enable a systems approach to characterizing the pan and core metabolic capabilities of the E. coli species. The majority of pan metabolic content was found to consist of alternate catabolic pathways for unique nutrient sources. The GEMs were then used to systematically analyze growth capabilities in more than 650 different growth-supporting environments. The results show that unique strain-specific metabolic capabilities correspond to pathotypes and environmental niches. Twelve of the GEMs were used to predict growth on six differentiating nutrients, and the predictions were found to agree with 80% of experimental outcomes. Additionally, GEMs were used to predict strain-specific auxotrophies. Twelve of the strains modeled were predicted to be auxotrophic for vitamins niacin (vitamin B3), thiamin (vitamin B1), or folate (vitamin B9). Six of the strains modeled have lost biosynthetic pathways for essential amino acids methionine, tryptophan, or leucine. Genome-scale analysis of multiple strains of a species can thus be used to define the metabolic essence of a microbial species and delineate growth differences that shed light on the adaptation process to a particular microenvironment.

https://www.pnas.org/content/pnas/110/50/20338.full.pdf

Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments

Enteric pathogens such as Salmonella enterica cause significant morbidity and mortality. S. enterica serovars are a diverse group of pathogens that have evolved to survive in a wide range of environments and across multiple hosts. S. enterica serovars such as S. Typhi, S. Dublin, and S. Gallinarum have a restricted host range, in which they are typically associated with one or a few host species, while S. Enteritidis and S. Typhimurium have broad host ranges. This review examines how S. enterica has evolved through adaptation to different host environments, especially as related to the chicken host, and continues to be an important human pathogen. Several factors impact host range, and these include the acquisition of genes via horizontal gene transfer with plasmids, transposons, and phages, which can potentially expand host range, and the loss of genes or their function, which would reduce the range of hosts that the organism can infect. S. Gallinarum, with a limited host range, has a large number of pseudogenes in its genome compared to broader-host-range serovars. S. enterica serovars such as S. Kentucky and S. Heidelberg also often have plasmids that may help them colonize poultry more efficiently. The ability to colonize different hosts also involves interactions with the host's immune system and commensal organisms that are present. Thus, the factors that impact the ability of Salmonella to colonize a particular host species, such as chickens, are complex and multifactorial, involving the host, the pathogen, and extrinsic pressures. It is the interplay of these factors which leads to the differences in host ranges that we observe today.

Salmonella Pathogenicity and Host Adaptation in Chicken-Associated Serovars

Background: Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem. Results: Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches. Conclusion: Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.

https://biblio.ugent.be/publication/3186517/file/3186557.pdf

Suresh Sudarsan

Papers

A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2