A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data

MicrobeMASST, a taxonomically-informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbial-derived metabolites and relative producers, without a priori knowledge, will vastly enhance the understanding of microorganisms’ role in ecology and human health

Rumen metaproteomics: Closer to linking rumen microbial function to animal productivity traits

publishedVersio

Integration of absolute multi-omics reveals dynamic protein-to-RNA ratios and metabolic interplay within mixed-domain microbiomes

publishedVersio

Rumen metaproteomics: Closer to linking rumen microbial function to animal productivity traits

publishedVersio

Structural insights of the enzymes from the chitin utilization locus of Flavobacterium johnsoniae

Chitin is one of the most abundant renewable organic materials found on earth. The chitin utilization locus in Flavobacterium johnsoniae, which encodes necessary proteins for complete enzymatic depolymerization of crystalline chitin, has recently been characterized but no detailed structural information on the enzymes was provided. Here we present protein structures of the F. johnsoniae chitobiase (FjGH20) and chitinase B (FjChiB). FjGH20 is a multi-domain enzyme with a helical domain not before observed in other chitobiases and a domain organization reminiscent of GH84 (β-N-acetylglucosaminidase) family members. The structure of FjChiB reveals that the protein lacks loops and regions associated with exo-acting activity in other chitinases and instead has a more solvent accessible substrate binding cleft, which is consistent with its endo-chitinase activity. Additionally, small angle X-ray scattering data were collected for the internal 70 kDa region that connects the N- and C-terminal chitinase domains of the unique 158 kDa multi-domain chitinase A (FjChiA). The resulting model of the molecular envelope supports bioinformatic predictions of the region comprising six domains, each with similarities to either Fn3-like or Ig-like domains. Taken together, the results provide insights into chitin utilization by F. johnsoniae and reveal structural diversity in bacterial chitin metabolism

Integration of absolute multi-omics reveals dynamic protein-to-RNA ratios and metabolic interplay within mixed-domain microbiomes

publishedVersio

Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber

publishedVersio

DRAM for distilling microbial metabolism to automate the curation of microbiome function

Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.publishedVersio

DRAM for distilling microbial metabolism to automate the curation of microbiome function

Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.publishedVersio