Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA297338 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR2536830.fastq.gz | Fastqsanger.gz | |||
| SRR2536840.fastq.gz | Fastqsanger.gz | |||
| SRR2536850.fastq.gz | Fastqsanger.gz | |||
| SRR2536860.fastq.gz | Fastqsanger.gz | |||
| SRR2536831.fastq.gz | Fastqsanger.gz |
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Project description:Two novel approaches were recently suggested for genome-wide identification of protein aspects synthesized at a given time. Ribo-Seq is based on sequencing all the ribosome protected mRNA fragments in a cell, while PUNCH-P is based on mass-spectrometric analysis of only newly synthesized proteins. Here we describe the first Ribo-Seq/PUNCH-P comparison via the analysis of mammalian cells during the cell-cycle for detecting relevant differentially expressed genes between G1 and M phase. Our analyses suggest that the two approaches significantly overlap with each other. However, we demonstrate that there are biologically meaningful proteins/genes that can be detected to be post-transcriptionally regulated during the mammalian cell cycle only by each of the approaches, or their consolidation. Such gene sets are enriched with proteins known to be related to intra-cellular signalling pathways such as central cell cycle processes, central gene expression regulation processes, processes related to chromosome segregation, DNA damage, and replication, that are post-transcriptionally regulated during the mammalian cell cycle. Moreover, we show that combining the approaches better predicts steady state changes in protein abundance. The results reported here support the conjecture that for gaining a full post-transcriptional regulation picture one should integrate the two approaches.
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Project description:Genetic variants that impact gene regulation are important contributors to human phenotypic variation. For this reason, considerable efforts have been made to identify genetic associations with differences in mRNA levels of nearby genes, namely, cis expression quantitative trait loci (eQTLs). The phenotypic consequences of eQTLs are presumably due, in most cases, to their ultimate effects on protein expression levels. Yet, only few studies have quantified the impact of genetic variation on proteins levels directly. It remains unclear how faithfully eQTLs are reflected at the protein level, and whether there is a significant layer of cis regulatory variation acting primarily on translation or steady state protein levels. To address these questions, we measured ribosome occupancy by high-throughput sequencing, and relative protein levels by high-resolution quantitative mass spectrometry, in a panel of lymphoblastoid cell lines (LCLs) in which we had previously measured transcript expression using RNA sequencing. We then mapped genetic variants that are associated with changes in transcript expression (eQTLs), ribosome occupancy (rQTLs), or protein abundance (pQTLs). Most of the QTLs we detected are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, we found that eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we confirmed the presence of a class of cis QTLs that specifically affect protein abundance with little or no effect on mRNA levels; most of these QTLs have little effect on ribosome occupancy, and hence may arise from differences in post-translational regulation. We measured level of translation transcriptome-wide in lymphoblastoid cell lines derived from 72 HapMap Yoruba individuals using ribosome profiling assay, for which we have transcript level, protein level (62 out of 72) and genotype information collected.
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Project description:Accurate annotations of protein coding regions are essential for understanding how genetic information is translated into biological functions. The recent development of ribosome footprint profiling provides an important new tool for measuring translation. Here we describe riboHMM, a new method that uses ribosome footprint data along with gene expression and sequence information to accurately infer translated sequences. We applied our method to human lymphoblastoid cell lines and identified 7,863 previously unannotated coding sequences, including 445 translated sequences in pseudogenes and 2,442 translated upstream open reading frames. We observed an enrichment of harringtonine-treated ribosome footprints at the inferred initiation sites, validating many of the novel coding sequences. In aggregate, the novel sequences exhibit significant signatures of purifying selection indicative of protein-coding function, suggesting that many of the novel sequences are functional. We observed that nearly 40% of bicistronic transcripts showed significant negative correlation in the levels of translation of their two coding sequences, suggesting a key regulatory role for these novel translated sequences. Despite evidence for their functional importance, the novel peptide sequences were detected by mass spectrometry at a lower rate than predicted based on data from annotated proteins, thus suggesting that many of the novel peptide products may be relatively short-lived. Our work illustrates the value of ribosome profiling for improving coding annotations, and significantly expands the set of known coding regions.
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