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Small RNA sequencing on liver samples of DO mice

ABSTRACT: Genetic variation, in addition to environmental influences like diet, can govern the expression levels of microRNAs (miRNAs). MiRNAs are commonly found to operate cooperatively in groups to regulate gene expression. To investigate this, we combined small RNA sequencing, clinical phenotypes, and microarray data measuring gene expression from an outbred mouse model, the Diversity Outbred population. In the DO population, each individual has a distinct genome that is a mosaic of 8 inbred founder strains. We used these data to identify co-regulated modules of miRNAs and genes that are influenced by genetics and diet, and identify relationships between the modules and phenotypes in over 200 DO mice.

ORGANISM(S): Mus musculus

PROVIDER: GSE99560 | GEO | 2017/09/26

SECONDARY ACCESSION(S): PRJNA388959

REPOSITORIES: GEO

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Project description:Purpose: To investigate the diet- or strain-dependence of liver transcriptome in eight Diversity Outbred (DO) founder strains [A/J, C57BL/6J (B6), 129S1/SvImJ (129), NOD/ShiLtJ (NOD), NZO/HILtJ (NZO), and three wild-derived strains CAST/EiJ (CAST), PWK/PhJ (PWK), and WSB/EiJ (WSB)] Methods: Total RNA from eight DO founder strains was extracted from snap frozen liver using Maxwell® 16 LEV simplyRNA Tissue Kit (Promega) according to the manufacturer’s protocol. The quality and amount of liver RNA was evaluated using a qubit RNA assay (Invitrogen R11490) and run on an Biorad Bioanalyzer Chip to verify RNA integrity. RNASeq library was prepared by using Illumina TruSeq RNA library construction protocol. RNA samples from 24 mice-fed AIN-93 M diet and 24 mice-fed HFCA diet were submitted to the David H. Murdock Research Institute (DHMRI) for sequencing. The RNA-seq libraries were constructed from 1 µg total RNA after poly-A library preparation. The pooled libraries were sequenced on two lanes of the Illumina HiSeq2500 Instrument. Results: Our liver transcriptomic analysis of eight mouse strains shows that diet and genetic background have a strong effect on the liver transcriptome, which may be related to differences in cardo-metabolic traits. We calculated a gene module consisting of highly correlated transcripts that enriched the biological pathway and provided a searchable database of liver transcript profiles. To further investigate the association of hepatic gene modules with cardio-metabolic traits, Fmo3, a key enzyme in the production of trimethylamine N-oxide (TMAO), and Nox4, which showed strong association with plasma TMAO and liver triglyceride, were identified as the highest expressed in the laboratory-like inbred strains (B6, 129 and NZO) and the lowest expressed transcripts in the CAST strain. We found differences in the production of TMAO and liver triglyceride, which contributes to metabolic syndrome, depending on genetic backgrounds. Conclusions: Our study provide a valuable data resource to the research community and show that liver transcriptomic analysis identified diet- or strain-specific pathways to pathogenesis of metabolic syndrome.

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Small RNA sequencing on liver samples of DO mice

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