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

ABSTRACT: 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 these 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.

SUBMITTER: Axen SD

PROVIDER: S-EPMC4207490 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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

Axen Seth D SD Erbilgin Onur O Kerfeld Cheryl A CA

PLoS computational biology 20141023 10

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 ...[more]

PMID: 25340524

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Project description:Classifying scientific publications according to Field-of-Science taxonomies is of crucial importance, powering a wealth of relevant applications including Search Engines, Tools for Scientific Literature, Recommendation Systems, and Science Monitoring. Furthermore, it allows funders, publishers, scholars, companies, and other stakeholders to organize scientific literature more effectively, calculate impact indicators along Science Impact pathways and identify emerging topics that can also facilitate Science, Technology, and Innovation policy-making. As a result, existing classification schemes for scientific publications underpin a large area of research evaluation with several classification schemes currently in use. However, many existing schemes are domain-specific, comprised of few levels of granularity, and require continuous manual work, making it hard to follow the rapidly evolving landscape of science as new research topics emerge. Based on our previous work of scinobo, which incorporates metadata and graph-based publication bibliometric information to assign Field-of-Science fields to scientific publications, we propose a novel hybrid approach by further employing Neural Topic Modeling and Community Detection techniques to dynamically construct a Field-of-Science taxonomy used as the backbone in automatic publication-level Field-of-Science classifiers. Our proposed Field-of-Science taxonomy is based on the OECD fields of research and development (FORD) classification, developed in the framework of the Frascati Manual containing knowledge domains in broad (first level(L1), one-digit) and narrower (second level(L2), two-digit) levels. We create a 3-level hierarchical taxonomy by manually linking Field-of-Science fields of the sciencemetrix Journal classification to the OECD/FORD level-2 fields. To facilitate a more fine-grained analysis, we extend the aforementioned Field-of-Science taxonomy to level-4 and level-5 fields by employing a pipeline of AI techniques. We evaluate the coherence and the coverage of the Field-of-Science fields for the two additional levels based on synthesis scientific publications in two case studies, in the knowledge domains of Energy and Artificial Intelligence. Our results showcase that the proposed automatically generated Field-of-Science taxonomy captures the dynamics of the two research areas encompassing the underlying structure and the emerging scientific developments.

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

Publications

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

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