A functional microbiome catalogue crowdsourced from North American rivers.

Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires knowledge of the spatial drivers of river microbiomes. However, understanding of the core microbial processes governing river biogeochemistry is hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we used a community science effort to accelerate the sampling, sequencing and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb).

A functional microbiome catalog crowdsourced from North American rivers.

Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires understanding the spatial drivers of river microbiomes. However, the unifying microbial processes governing river biogeochemistry are hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we employed a community science effort to accelerate the sampling, sequencing, and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb).

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

Unlabelled: 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.

Application of the Metabolic Modeling Pipeline in KBase to Categorize Reactions, Predict Essential Genes, and Predict Pathways in an Isolate Genome.

The DOE Systems Biology Knowledgebase (KBase) platform offers a range of powerful tools for the reconstruction, refinement, and analysis of genome-scale metabolic models built from microbial isolate genomes. In this chapter, we describe and demonstrate these tools in action with an analysis of isoprene production in the Bacillus subtilis DSM genome. Two different methods are applied to build initial metabolic models for the DSM genome, then the models are gapfilled in three different growth conditions.

A genomic catalog of Earth's microbiomes.

The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth's continents and oceans, including metagenomes from human and animal hosts, engineered environments, and natural and agricultural soils, to capture extant microbial, metabolic and functional potential. This comprehensive catalog includes 52,515 metagenome-assembled genomes representing 12,556 novel candidate species-level operational taxonomic units spanning 135 phyla.

Draft Genome Sequence of sp. Strain ATCC 49442 (Formerly Micrococcus luteus), a Pyridine-Degrading Bacterium.

We present here the draft genome sequence of a pyridine-degrading bacterium, ATCC 49442, which was reclassified as sp. strain ATCC 49442 based on its draft genome sequence. Its genome length is 4.

Draft Genome Sequence of 2-Methylpyridine-, 2-Ethylpyridine-, and 2-Hydroxypyridine-Degrading sp. Strain ATCC 49987.

Here, we report the draft genome sequence of sp. strain ATCC 49987, consisting of three contigs with a total length of 4.4 Mbp.

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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

We report here the 4.9-Mb genome sequence of a quinoline-degrading bacterium, sp. strain ATCC 49988.

Draft Genome Sequence of sp. Strain A8, a Carbazole-Degrading Bacterium.

We present here the draft genome sequence of a carbazole-degrading species. The draft genome sequence will provide insight into various genes involved in the degradation of carbazole and other related aromatic compounds.

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.

Metabolic Reconstruction and Modeling Microbial Electrosynthesis.

Microbial electrosynthesis is a renewable energy and chemical production platform that relies on microbial cells to capture electrons from a cathode and fix carbon. Yet despite the promise of this technology, the metabolic capacity of the microbes that inhabit the electrode surface and catalyze electron transfer in these systems remains largely unknown. We assembled thirteen draft genomes from a microbial electrosynthesis system producing primarily acetate from carbon dioxide, and their transcriptional activity was mapped to genomes from cells on the electrode surface and in the supernatant.

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 gene, at which these pathways converge, appears to coevolve with the occurrence of and genes.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation.

Understanding 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, (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.

Modeling central metabolism and energy biosynthesis across microbial life.

Background: Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles.

Results: To overcome this challenge, we developed methods and tools ( http://coremodels.

Enabling comparative modeling of closely related genomes: example genus Brucella.

For many scientific applications, it is highly desirable to be able to compare metabolic models of closely related genomes. In this short report, we attempt to raise awareness to the fact that taking annotated genomes from public repositories and using them for metabolic model reconstructions is far from being trivial due to annotation inconsistencies. We are proposing a protocol for comparative analysis of metabolic models on closely related genomes, using fifteen strains of genus Brucella, which contains pathogens of both humans and livestock.

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction.

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available 'omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process.

Comparative genomics of cultured and uncultured strains suggests genes essential for free-living growth of Liberibacter.

The full genomes of two uncultured plant pathogenic Liberibacter, Ca. Liberibacter asiaticus and Ca. Liberibacter solanacearum, are publicly available.