Dataset Information

Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans.

ABSTRACT: Elucidating the consequences of genetic differences between humans is essential for understanding phenotypic diversity and personalized medicine. Although variation in RNA levels, transcription factor binding, and chromatin have been explored, little is known about global variation in translation and its genetic determinants. We used ribosome profiling, RNA sequencing, and mass spectrometry to perform an integrated analysis in lymphoblastoid cell lines from a diverse group of individuals. We find significant differences in RNA, translation, and protein levels suggesting diverse mechanisms of personalized gene expression control. Combined analysis of RNA expression and ribosome occupancy improves the identification of individual protein level differences. Finally, we identify genetic differences that specifically modulate ribosome occupancy--many of these differences lie close to start codons and upstream ORFs. Our results reveal a new level of gene expression variation among humans and indicate that genetic variants can cause changes in protein levels through effects on translation.

SUBMITTER: Cenik C

PROVIDER: S-EPMC4617958 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans.

Cenik Can C Cenik Elif Sarinay ES Byeon Gun W GW Grubert Fabian F Candille Sophie I SI Spacek Damek D Alsallakh Bilal B Tilgner Hagen H Araya Carlos L CL Tang Hua H Ricci Emiliano E Snyder Michael P MP

Genome research 20150821 11

Elucidating the consequences of genetic differences between humans is essential for understanding phenotypic diversity and personalized medicine. Although variation in RNA levels, transcription factor binding, and chromatin have been explored, little is known about global variation in translation and its genetic determinants. We used ribosome profiling, RNA sequencing, and mass spectrometry to perform an integrated analysis in lymphoblastoid cell lines from a diverse group of individuals. We fin ...[more]

PMID: 26297486

Similar Datasets

Project description:Transcription signatures have been used to stratify breast cancer patients into clinically distinct subgroups. However, transcription alone does not determine protein expression. Of potentially equal importance for determining the tumor phenotype is the rate at which transcripts are translated to form protein. Protein translation is controlled to a major degree by miRNA, and cancer cells may deregulate the expression of key genes by altering the activity of relevant miRNAs. The importance of miRNA deregulation and the extent to which multiple miRNAs coordinately deregulate key proteins in breast cancer is only partly understood. To gain such insight, we analyzed genome-wide miRNA expression and mRNA/protein expression for a panel of 105 selected cancer related genes in breast carcinomas from 283 patients. The miRNA-mRNA-protein interactome for the selected genes was constructed by modeling protein expression as a joint function of mRNA and miRNA expression, considering the effect of both one miRNA at a time, and all studied miRNAs simultaneously. The interactome represents a map of the global effects of miRNAs on protein expression, capturing direct as well as indirect effects. The results reveal extensive association between miRNA and protein expression as well as coordinated effects of multiple miRNAs on individual proteins. Applying the model onto two other independent primary breast cancer cohorts confirmed the generalizability of key aspects of the interactome map. The mRNA expression profiling of 283 breast cancer samples was performed using the SurePrint G3 Human GE 8x60K one-color microarrays from Agilent (Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer’s protocol (One-Color Microarray-Based Gene Expression Analysis, Low Input Quick Amp Labeling, v.6.5, May 2010). For each sample, 100 ng of RNA was amplified and hybridized on the array. Scanning was performed with Agilent Scanner G2565A, using AgilentG3_GX_1Color as profile. Signals were extracted using FE v.10.7.3.1 and protocol GE1_107_Sep09 (Agilent Technologies). Arrays were log2-transformed, quantile normalized and hospital-adjusted by subtracting from each probe value the mean probe value among samples from the same hospital. The Oslo Breast Cancer Consortium (OSBREAC)

Dataset Information

Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans.

Publications

Integrative analysis of RNA, translation, and protein levels reveals distinct regulatory variation across humans.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets