Automated reconstruction and comparison of metabolic models for diverse fungal genomes

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Reconstruction and Analysis of Central Metabolism in Microbes

Genome-scale metabolic models (GEMs) generated from automated reconstruction pipelines often lack accuracy due to the need for extensive gapfilling and the inference of periphery metabolic pathways based on lower-confidence annotations. The central carbon pathways and electron transport chains are among the most well-understood regions of microbial metabolism, and these pathways contribute significantly toward defining cellular behavior and growth conditions. Thus, it is often useful to construct a simplified core metabolic model (CMM) that is comprised of only the high-confidence central pathways. In this chapter, we discuss methods for producing core metabolic models (CMM) based on genome annotations. With its reduced scope compared to GEMs, CMM reconstruction focuses on accurate representation of the central metabolic pathways related to energy biosynthesis and accurate energy yield predictions. We demonstrate the reconstruction and analysis of CMMs using the DOE Systems Biology Knowledgebase (KBase). The complete workflow is available at http://kbase.us/core-models/

Draft Genome Sequence of Rhodococcus sp. Strain ATCC 49988, a Quinoline-Degrading Bacterium.

We report here the 4.9-Mb genome sequence of a quinoline-degrading bacterium, Rhodococcus sp. strain ATCC 49988. The draft genome data will enable the identification of genes and future genetic modification to enhance traits relevant to heteroaromatic compound degradation

Computing and applying atomic regulons to understand gene expression and regulation

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb.2016.01819/full#supplementary-materialUnderstanding gene function and regulation is essential for the interpretation prediction and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets Atomic Regulons ARs represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here we describe an approach for inferring ARs that leverages large-scale expression data sets gene context and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness CLR analysis finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms we computed ARs for Shewanella oneidensis Pseudomonas aeruginosa Thermus thermophilus and Staphylococcus aureus each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.JF acknowledges funding from [SFRH/BD/70824/2010] of the FCT (Portuguese Foundation for Science and Technology) PhD program. CH and PW were supported by the National Science Foundation under grant number EFRI-MIKS-1137089. RT was supported by the Genomic Science Program (GSP), Office of Biological and Environmental Research (OBER), U.S. Department of Energy(DOE),and his work is a contribution of the Pacific North west National Laboratory (PNNL) Foundational Scientific Focus Area. This work was partially supported by an award from the National Science Foundation to MD, AB, NT, and RO (NSFABI-0850546). This work was also supported by the United States National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Service [Contract No. HHSN272201400027C]

Evolution of substrate specificity in a retained enzyme driven by gene loss.

The connection between gene loss and the functional adaptation of retained proteins is still poorly understood. We apply phylogenomics and metabolic modeling to detect bacterial species that are evolving by gene loss, with the finding that Actinomycetaceae genomes from human cavities are undergoing sizable reductions, including loss of L-histidine and L-tryptophan biosynthesis. We observe that the dual-substrate phosphoribosyl isomerase A or priA gene, at which these pathways converge, appears to coevolve with the occurrence of trp and his genes. Characterization of a dozen PriA homologs shows that these enzymes adapt from bifunctionality in the largest genomes, to a monofunctional, yet not necessarily specialized, inefficient form in genomes undergoing reduction. These functional changes are accomplished via mutations, which result from relaxation of purifying selection, in residues structurally mapped after sequence and X-ray structural analyses. Our results show how gene loss can drive the evolution of substrate specificity from retained enzymes

Improving automated model reconstruction across phylogenetically diverse genome-scale metabolic models

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Automated pathway curation and improving metabolic model reconstruction based on phylogenetic analysis of pathway conservation

ICSB 2017 - 18th International Conference on Systems BiologyMetabolic models generated by automated reconstruction pipelines are widely used for high-throughput prediction of microbial phenotypes. However, the generation of accurate in-silico phenotype predictions based solely on genomic data continues to be a challenge as metabolic models often require extensive gapfilling in order to produce biomass. As a result, the true physiological profile of an organism can be altered by the addition of non-native biochemical pathways or reactions during the gapfilling process. In this study, we constructed draft genome-scale metabolic models for ~1000 diverse set of reference microbial genomes currently available in GenBank, and we decomposed these models into a set of classical biochemical pathways. We then determine the extent to which each pathway is either consistently present or absent in each region of the phylogenetic tree, and we study the degree of conservation in the specific steps where gaps exist in each pathway across a phylogenetic neighborhood. Based on this analysis, we improved the reliability of our gapfilling algorithms, which in turn, improved the reliability of our models in predicting auxotrophy. This also resulted in improvements to the genome annotations underlying our models. We validated our improved auxotrophy predictions using growth condition data collected for a diverse set of organisms. Our improved gapfilling algorithm will be available for use within the DOE Knowledgebase (KBase) platform (https://kbase.us).info:eu-repo/semantics/publishedVersio

kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase

Microbial genome annotation is the process of identifying structural and functional elements in DNA sequences and subsequently attaching biological information to those elements. DRAM is a tool developed to annotate bacterial, archaeal, and viral genomes derived from pure cultures or metagenomes. DRAM goes beyond traditional annotation tools by distilling multiple gene annotations to genome level summaries of functional potential. Despite these benefits, a downside of DRAM is the requirement of large computational resources, which limits its accessibility. Further, it did not integrate with downstream metabolic modeling tools that require genome annotation. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated with the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point-and-click interface, as well as generate genome-scale metabolic models from DRAM annotations.Availability and implementationFor kb_DRAM users, the kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. For kb_DRAM users, a tutorial workflow with all documentation is available at https://narrative.kbase.us/narrative/129480. For kb_DRAM developers, software is available at https://github.com/shafferm/kb_DRAM

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

In the Abstract, the url https://modelseed.org has been replaced with https://modelseed.org/biochem

Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host–microbiome metabolic interactions