Isoform‐resolved correlation analysis between mRNA abundance regulation and protein level degradation

Abstract

Profiling of biological relationships between different molecular layers dissects regulatory mechanisms that ultimately determine cellular function. To thoroughly assess the role of protein post‐translational turnover, we devised a strategy combining pulse stable isotope‐labeled amino acids in cells (pSILAC), data‐independent acquisition mass spectrometry (DIA‐MS), and a novel data analysis framework that resolves protein degradation rate on the level of mRNA alternative splicing isoforms and isoform groups. We demonstrated our approach by the genome‐wide correlation analysis between mRNA amounts and protein degradation across different strains of HeLa cells that harbor a high grade of gene dosage variation. The dataset revealed that specific biological processes, cellular organelles, spatial compartments of organelles, and individual protein isoforms of the same genes could have distinctive degradation rate. The protein degradation diversity thus dissects the corresponding buffering or concerting protein turnover control across cancer cell lines. The data further indicate that specific mRNA splicing events such as intron retention significantly impact the protein abundance levels. Our findings support the tight association between transcriptome variability and proteostasis and provide a methodological foundation for studying functional protein degradation.

Data-independent Acquisition-based Proteome and Phosphoproteome Profiling across Six Melanoma Cell Lines Reveals Determinants of Proteotypes

“…While the absolute mRNA~protein correlation analysis (normally performed within one sample) could be affected by the across-gene variation, the relative mRNA~protein correlation (performed between samples and conditions) can remove this confounding factor for inferring the significance of the post-transcriptional regulation 41,42,46,47 . The perspective of absolute and relative relationships may also provide insights for analyzing regulations at other molecular layers 48 . Herein, our well-matched mRNA~protein and protein~phosphosite datasets allow for a rigorous assessment of the variability of protein expression and phosphorylation in the genomic context of steady-state melanoma cell lines.…”

BoxCarmax: a high-selectivity data-independent acquisition mass spectrometry method for the analysis of protein turnover and complex samples

“…This diversity emphasizes the different extent of protein translation and degradation contributing to the conventional abundance-centric analysis. Last but not the least, we found certain organelle sub-structures 24, 44 could be regulated selectively or distinctively by protein turnover and post-transcriptional process ( Figure S6 ). For example, although mitochondrial proteome had a relatively faster protein degradation in the starving cells, the respiratory chain complex I exceptionally reduced protein degradation process, suggesting the post-translational buffering role of protein degradation for complex I to cope with starvation.…”

“…The major cellular degradative mechanisms during nutritional deprivation present one of the most fundamental questions in cell biology [39][40][41]44 . Figure 4C illustrated that BoxCarmax improved the overall quantification accuracy in pSILAC experiment especially on early time points, which are essential for robust and accurate protein turnover calculation 24,26 . Therefore, we applied BoxCarmax to measure the rate of protein degradation change during the serum starvation time intervals in PC12 cells labeled by pSILAC.…”

“…The comprehensive samplespecific libraries were generated based on previous publications and datasets in the present study ( Supplementary Experimental Section) . To perform SILAC and pSILAC targeted data extraction, the Inverted Spike-In workflow was used 24 . The PECA pS model in the PECAplus Perseus plugin 32 (https://github.com/PECAplus/Perseus-PluginPECA) was used to estimate the degradation rate parameters from pulsed proteomics data per gene per measurement interval.…”

“…In SILAC analysis, peptide pairs normally ending with heavy (H) and light (L) isotopes are always co-eluted and are, in most cases, measured in the same DIA window 15 . Despite of this difficulty, others and we have previously demonstrated that DIA-MS combined with SILAC 15,19–21 and pulse SILAC (pSILAC) experiments 22–26 impressively retains the quantitative performance of DIA, as compared to MS 1 −based SILAC approaches 20, 24 Recently, we optimized a computational framework to retrieve more H/L features in early time points during pSILAC labeling, for the purpose of quantifying the proteome-wide protein turnover rates 24 . In the present study, we aim to further optimize a particular DIA-MS strategy that supports the analysis of complex samples, such as those generated from SILAC and pSILAC experiments .…”