Evidence map›Paper›PMID 25339987›Full record

ArticleF1000Research2014

How do eubacterial organisms manage aggregation-prone proteome?

Rishi Das Roy, Manju Bhardwaj, Vasudha Bhatnagar, Kausik Chakraborty, Debasis Dash

Abstract read
In one paragraph

Article in F1000Research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Rishi Das RoyGNR Knowledge Centre for Genome Informatics, Institute of Genomics and Integrative Biology, Council of Scientific and Industrial Research, Delhi, 110007, India ; Department of Biotechnology, University of Pune, Pune, 411007, India.
Manju BhardwajDepartment of Computer Science, Maitreyi College, Chanakyapuri, Delhi, 110021, India.
Vasudha BhatnagarDepartment of Computer Science, Faculty of Mathematical Sciences, University of Delhi, Delhi, 110007, India.
Kausik ChakrabortyGNR Knowledge Centre for Genome Informatics, Institute of Genomics and Integrative Biology, Council of Scientific and Industrial Research, Delhi, 110007, India.
Debasis DashGNR Knowledge Centre for Genome Informatics, Institute of Genomics and Integrative Biology, Council of Scientific and Industrial Research, Delhi, 110007, India ; Department of Biotechnology, University of Pune, Pune, 411007, India.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Eubacterial genomes vary considerably in their nucleotide composition. The percentage of genetic material constituted by guanosine and cytosine (GC) nucleotides ranges from 20% to 70%.  It has been posited that GC-poor organisms are more dependent on protein folding machinery. Previous studies have ascribed this to the accumulation of mildly deleterious mutations in these organisms due to population bottlenecks. This phenomenon has been supported by protein folding simulations, which showed that proteins encoded by GC-poor organisms are more prone to aggregation than proteins encoded by GC-rich organisms. To test this proposition using a genome-wide approach, we classified different eubacterial proteomes in terms of their aggregation propensity and chaperone-dependence using multiple machine learning models. In contrast to the expected decrease in protein aggregation with an increase in GC richness, we found that the aggregation propensity of proteomes increases with GC content. A similar and even more significant correlation was obtained with the GroEL-dependence of proteomes: GC-poor proteomes have evolved to be less dependent on GroEL than GC-rich proteomes. We thus propose that a decrease in eubacterial GC content may have been selected in organisms facing proteostasis problems.

Identifiers

PMID25339987
PMCPMC4193397

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