Population-scale proteome variation in human induced pluripotent stem cells
- European Bioinformatics Institute, United Kingdom
- University of Dundee, United Kingdom
- University College London, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, United Kingdom
Abstract
Human disease phenotypes are ultimately driven primarily by alterations in protein expression and/or function. To date, relatively little is known about the variability of the human proteome in populations and how this relates to variability in mRNA expression and to disease loci. Here, we present the first comprehensive proteomic analysis of human induced pluripotent stem cells (iPSC), a key cell type for disease modelling, analysing 202 iPSC lines derived from 151 donors, with integrated transcriptome and genomic sequence data from the same lines. We characterised the major genetic and non-genetic determinants of proteome variation across iPSC lines and assessed key regulatory mechanisms affecting variation in protein abundance. We identified 654 protein quantitative trait loci (pQTLs) in iPSCs, including disease-linked variants in protein coding sequences and variants with trans regulatory effects. These include pQTL linked to GWAS variants that cannot be detected at the mRNA level, highlighting the utility of dissecting pQTL at peptide level resolution.
Data availability
RNA-Seq data for 331 samples are available on the European Nucleotide Archive (ENA): study PRJEB7388; accession ERP007111. Proteomics quantifications (protein group and peptide resolution; MaxQuant output), and run parameters are available on the PRIDE Archive PRIDE (PXD010557). Analysed data is included in the supplementary external files.
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HipSci: the iPSC proteomic compendiumPRIDE Archive, PXD010557.
Article and author information
Author details
Funding
Wellcome Trust Strategic Award and UK Medical Research Council (WT098503)
- Bogdan Andrei Mirauta
- Daniel D Seaton
- Dalila Bensaddek
Wellcome Trust Strategic Award (105024/Z/14/Z)
- Bogdan Andrei Mirauta
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Mirauta et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Population-scale proteome variation in human induced pluripotent stem cells
eLife 9:e57390.
https://doi.org/10.7554/eLife.57390
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Enhancers and promoters are classically considered to be bound by a small set of transcription factors (TFs) in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected five distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.
