Dataset Information

EMpire: Deep isoform level integration of the transcriptome, translatome, and proteome

ABSTRACT: In this study, we make used of mRNA-seq and its ability to reliably quantify isoforms, integrating this data with ribosome profiling and LC-MS/MS, to assign ribosome footprints and peptides at the isoform level. We leverage the principle that most cell types, and even tissues, predominantly express a single principal isoform to set isoform-level mRNA-seq quantifications as priors to guide and improve allocation of footprints or peptides to isoforms. Through tightly integrated mRNAseq, ribosome footprinting and/or LC-MS/MS proteomics we demonstrate that a principal isoform can be identified in over 80% of gene products in homogenous HEK293 cell culture and over 70% of proteins detected in complex human brain tissue. Defining isoforms in experiments with matched RNA-seq and translatomic/proteomic data increases the functional relevance of such datasets and will further broaden our understanding of multi-level control of gene expression. In this PRIDE submission you will find the raw files for the HEK293 cell proteomics. Files for the human brain proteomics can be found at PXD005445. We have also uploaded a zip file that contains the input files for our HEK293 cell analysis, and the isoform level output files – there is a separate folder within the zip files for these. The data used to create the manuscript figures is in the Rdata file. Code for assigning peptides and footprints to isoforms can be found on Github here: https://github.com/rkitchen/EMpire

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: TuKiet Lam

LAB HEAD: TuKiet Tan Lam

PROVIDER: PXD008693 | Pride | 2018-08-21

REPOSITORIES: Pride

ACCESS DATA

Publications

Isoform-Level Interpretation of High-Throughput Proteomics Data Enabled by Deep Integration with RNA-seq.

Carlyle Becky C BC Kitchen Robert R RR Zhang Jing J Wilson Rashaun S RS Lam Tukiet T TT Rozowsky Joel S JS Williams Kenneth R KR Sestan Nenad N Gerstein Mark B MB Nairn Angus C AC

Journal of proteome research 20180906 10

Cellular control of gene expression is a complex process that is subject to multiple levels of regulation, but ultimately it is the protein produced that determines the biosynthetic state of the cell. One way that a cell can regulate the protein output from each gene is by expressing alternate isoforms with distinct amino acid sequences. These isoforms may exhibit differences in localization and binding interactions that can have profound functional implications. High-throughput liquid chromatog ...[more]

PMID: 30125121

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Project description:Abstract/Project overview Tumour recurrence can be influenced by the mutational status of a tumour but also how the tumour responds to therapy. Whilst activation of Type 1 interferon signalling is a hallmark of how cells respond to viral infection, in cancer cells, multiple stresses are known to activate this same response. In this study we have evaluated for the first time the changes in the interferon response induced by culturing prostate cancer cells under sphere-forming conditions and following irradiation. We identify that both stresses induced a selection/reprogramming towards a tumour-initiating/stem-like cell population That are known to be highly adaptable and have been shown to be resistant to multiple therapies. We report a conserved upregulated transcript profile for both conditions that is tightly associated with therapeutic resistance and cell survival in vitro and in vivo. The profile includes and is regulated by the Type 1 interferon master regulator IRF7, which when targeted delays tumour re-growth following irradiation. Understanding the impact of radiotherapy on the evolution of treatment resistant prostate cancer is critical for selecting effective treatment combinations. Our first-in-field data define irradiation as a selection pressure for a multi-treatment resistant, interferon response-dependent biology and further show that these cells acquire enhanced sensitivity to a combination of IKKε/TBK1 and MEK inhibition. Design of the sequencing experiment/Methods The RNA-seq experiment within this study was designed to identify radiation-responsive and IRF7-responsive genes in a prostate cancer cell-line, PC3. To do so we generated stable IRF7 knockdown derivatives using shRNA (referred to as 'IRF7' in the files) and scrambled controls (referred to as 'scr'). We subjected 5 million cells per condition to 1,2 or 3 doses of irradiation or mock irradiation with 24 hour or 72 hour recovery periods interspersed (annotated for example as '24-1', one dose with a 24-hour recovery period). At 24 hours or 72 hours after the final treatment we harvested cells and extracted total RNA using Trizol. To assess the yield and integrity of mRNA, 2ul of each sample was taken for analysis on a Fragment Analyzer Automated CE System (Advanced Analytical) and smear analysis run using a DNF-473 Standard Sensitivity NGS Fragment Analysis Kit (Advanced Analytical). Total RNA (1ug) was processed to first-stranded cDNA using the TruSeq Stranded mRNA Library Prep Kit (Illumina), following the manufacturer’s instructions for the low-throughput protocol. First-stranded cDNA samples were processed to barcoded cDNA libraries using a Diagenode IP-Star, according to the manufacturer’s instructions for Illumina TruSeq library prep. Resulting libraries were PCR-amplified to incorporate unique indices and cleanup performed using AMPure XP Beads (Becman Coulter) according to Truseq LT protocol. Resulting libraries were quantified, pooled and sequenced on a Nextseq 500 paired end 2x75Bp run. FASTQ files generated were de-multiplexed and adapter trimmed in the Illumina basespace and aligned to hg19 using Bowtie2 with default settings in the Basespace RNA-seq Express app. Resulting bam alignments files were exported and aligned to Refseq transcriptome annotation in Partek Genomic Suite and annotated transcripts with mapped sequence reads were quantified for relative expression in each sample and gene level RPKM (Reads Per Kilobase of transcript per Million mapped reads) counts generated for downstream analysis.

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Dataset Information

EMpire: Deep isoform level integration of the transcriptome, translatome, and proteome

Publications

Isoform-Level Interpretation of High-Throughput Proteomics Data Enabled by Deep Integration with RNA-seq.

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