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MetaRibo-Seq measures translation in microbiomes.
Nat Commun. 2020 06 29; 11(1):3268.NC

Abstract

No method exists to measure large-scale translation of genes in uncultured organisms in microbiomes. To overcome this limitation, we develop MetaRibo-Seq, a method for simultaneous ribosome profiling of tens to hundreds of organisms in microbiome samples. MetaRibo-Seq was benchmarked against gold-standard Ribo-Seq in a mock microbial community and applied to five different human fecal samples. Unlike RNA-Seq, Ribo-Seq signal of a predicted gene suggests it encodes a translated protein. We demonstrate two applications of this technique: First, MetaRibo-Seq identifies small genes, whose identification until now has been challenging. For example, MetaRibo-Seq identifies 2,091 translated, previously unannotated small protein families from five fecal samples, more than doubling the number of small proteins predicted to exist in this niche. Second, the combined application of RNA-Seq and MetaRibo-Seq identifies differences in the translation of transcripts. In summary, MetaRibo-Seq enables comprehensive translational profiling in microbiomes and identifies previously unannotated small proteins.

Authors+Show Affiliations

Department of Genetics, Stanford University, Stanford, CA, 94305, USA.Department of Genetics, Stanford University, Stanford, CA, 94305, USA. Department of Medicine (Hematology & Blood and Marrow Transplantation), Stanford University, Stanford, CA, 94305, USA.Department of Genetics, Stanford University, Stanford, CA, 94305, USA. asbhatt@stanford.edu. Department of Medicine (Hematology & Blood and Marrow Transplantation), Stanford University, Stanford, CA, 94305, USA. asbhatt@stanford.edu.

Pub Type(s)

Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32601270

Citation

Fremin, Brayon J., et al. "MetaRibo-Seq Measures Translation in Microbiomes." Nature Communications, vol. 11, no. 1, 2020, p. 3268.
Fremin BJ, Sberro H, Bhatt AS. MetaRibo-Seq measures translation in microbiomes. Nat Commun. 2020;11(1):3268.
Fremin, B. J., Sberro, H., & Bhatt, A. S. (2020). MetaRibo-Seq measures translation in microbiomes. Nature Communications, 11(1), 3268. https://doi.org/10.1038/s41467-020-17081-z
Fremin BJ, Sberro H, Bhatt AS. MetaRibo-Seq Measures Translation in Microbiomes. Nat Commun. 2020 06 29;11(1):3268. PubMed PMID: 32601270.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - MetaRibo-Seq measures translation in microbiomes. AU - Fremin,Brayon J, AU - Sberro,Hila, AU - Bhatt,Ami S, Y1 - 2020/06/29/ PY - 2020/03/10/received PY - 2020/06/11/accepted PY - 2020/7/1/entrez PY - 2020/7/1/pubmed PY - 2020/7/1/medline SP - 3268 EP - 3268 JF - Nature communications JO - Nat Commun VL - 11 IS - 1 N2 - No method exists to measure large-scale translation of genes in uncultured organisms in microbiomes. To overcome this limitation, we develop MetaRibo-Seq, a method for simultaneous ribosome profiling of tens to hundreds of organisms in microbiome samples. MetaRibo-Seq was benchmarked against gold-standard Ribo-Seq in a mock microbial community and applied to five different human fecal samples. Unlike RNA-Seq, Ribo-Seq signal of a predicted gene suggests it encodes a translated protein. We demonstrate two applications of this technique: First, MetaRibo-Seq identifies small genes, whose identification until now has been challenging. For example, MetaRibo-Seq identifies 2,091 translated, previously unannotated small protein families from five fecal samples, more than doubling the number of small proteins predicted to exist in this niche. Second, the combined application of RNA-Seq and MetaRibo-Seq identifies differences in the translation of transcripts. In summary, MetaRibo-Seq enables comprehensive translational profiling in microbiomes and identifies previously unannotated small proteins. SN - 2041-1723 UR - https://www.unboundmedicine.com/medline/citation/32601270/MetaRibo-Seq_measures_translation_in_microbiomes L2 - http://dx.doi.org/10.1038/s41467-020-17081-z DB - PRIME DP - Unbound Medicine ER -
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