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Title: A taxonomy of bacterial microcompartment loci constructed by a novel scoring method

Bacterial microcompartments (BMCs) are proteinaceous organelles involved in both autotrophic and heterotrophic metabolism. All BMCs share homologous shell proteins but differ in their complement of enzymes; these are typically encoded adjacent to shell protein genes in genetic loci, or operons. To enable the identification and prediction of functional (sub)types of BMCs, we developed LoClass, an algorithm that finds putative BMC loci and inventories, weights, and compares their constituent pfam domains to construct a locus similarity network and predict locus (sub)types. In addition to using LoClass to analyze sequences in the Non-redundant Protein Database, we compared predicted BMC loci found in seven candidate bacterial phyla (six from single-cell genomic studies) to the LoClass taxonomy. Together, these analyses resulted in the identification of 23 different types of BMCs encoded in 30 distinct locus (sub)types found in 23 bacterial phyla. These include the two carboxysome types and a divergent set of metabolosomes, BMCs that share a common catalytic core and process distinct substrates via specific signature enzymes. Furthermore, many Candidate BMCs were found that lack one or more core metabolosome components, including one that is predicted to represent an entirely new paradigm for BMC-associated metabolism, joining the carboxysome and metabolosome. By placing thesemore » results in a phylogenetic context, we provide a framework for understanding the horizontal transfer of these loci, a starting point for studies aimed at understanding the evolution of BMCs. This comprehensive taxonomy of BMC loci, based on their constituent protein domains, foregrounds the functional diversity of BMCs and provides a reference for interpreting the role of BMC gene clusters encoded in isolate, single cell, and metagenomic data. Many loci encode ancillary functions such as transporters or genes for cofactor assembly; this expanded vocabulary of BMC-related functions should be useful for design of genetic modules for introducing BMCs in bioengineering applications.« less
 [1] ;  [2] ;  [3] ;  [4]
  1. DOE Joint Genome Institute, Walnut Creek, CA (United States)
  2. Univ. of California Berkeley, Berkeley, CA (United States). Dept. of Plant and Microbial BIology.
  3. Univ. of California Berkeley, Berkeley, CA (United States). Dept. of Plant and Microbial BIology; Michigan State University; East Lansing, MI (United States). DOE Plant Research Lab.; Lawrence Berkeley National Lab., Berkeley, CA (United States). Physical Biosciences Div.; Berkeley Synthetic Biology Inst., Berkeley, CA (United States)
  4. University of New South Wales (Australia)
Publication Date:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 10; Journal Issue: 10; Journal ID: ISSN 1553-7358
Public Library of Science
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE; National Science Foundation (NSF)
Country of Publication:
United States
59 BASIC BIOLOGICAL SCIENCES; genetic loci; enzyme metabolism; enzymes; protein metabolism; protein domains; genomic databases; sequence alignment; cofactors (biochemistry)