Abstract

To comprehensively study extracellular small RNAs (sRNA) by sequencing (sRNA-seq), we developed a novel pipeline to overcome current limitations in analysis entitled, “Tools for Integrative Genome analysis of Extracellular sRNAs (TIGER)”. To demonstrate the power of this tool, sRNA-seq was performed on mouse lipoproteins, bile, urine and livers. A key advance for the TIGER pipeline is the ability to analyse both host and non-host sRNAs at genomic, parent RNA and individual fragment levels. TIGER was able to identify approximately 60% of sRNAs on lipoproteins and >85% of sRNAs in liver, bile and urine, a significant advance compared to existing software. Moreover, TIGER facilitated the comparison of lipoprotein sRNA signatures to disparate sample types at each level using hierarchical clustering, correlations, beta-dispersions, principal coordinate analysis and permutational multivariate analysis of variance. TIGER analysis was also used to quantify distinct features of exRNAs, including 5ʹ miRNA variants, 3ʹ miRNA non-templated additions and parent RNA positional coverage. Results suggest that the majority of sRNAs on lipoproteins are non-host sRNAs derived from bacterial sources in the microbiome and environment, specifically rRNA-derived sRNAs from Proteobacteria. Collectively, TIGER facilitated novel discoveries of lipoprotein and biofluid sRNAs and has tremendous applicability for the field of extracellular RNA.

Document Type

Article

Publication Date

8-13-2018

Notes/Citation Information

Published in Journal of Extracellular Vesicles, v. 7, issue 1, 1506198, p. 1-25.

© 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of The International Society for Extracellular Vesicles.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Digital Object Identifier (DOI)

https://doi.org/10.1080/20013078.2018.1506198

Funding Information

This work was supported by the National Institutes of Health [HL128996 to K.C.V., HL113039 to K.C.V., HL127173 to K.C.V. and M.F.L., HL116263 to M.F.L., DK113625 to G.A.G., RR021954 to G.A.G., GM103527 to G.A.G., TR000117 to G.A.G., CA179514 to K.C.V.]; and the American Heart Association [CSA2066001 to K.C.V. and P.S., POST25710170 to R.M.A., POST26630003 to D.L.M.].

Related Content

TIGER is an open source collaborative initiative available in the GitHub repository (https://github.com/shengqh/TIGER).

The datasets generated and/or analysed in this study are available in the Gene Expression Omnibus (GEO) repository (www.ncbi.nlm.nih.gov/geo), study GSE109655.

Supplementary data for this article can be accessed at: https://doi.org/10.1080/20013078.2018.1506198

Allen_TIGER_2018_JEVRevision_Supplementary_Figures_Final.pdf (18731 kB)
Supplemental Material: Supplementary Figures 1-33

Allen_TIGER_2018_JEV_Revision_SuppTableFigureLegnds.pdf (135 kB)
Supplemental Material: Supplementary Tables and Figures Legend

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