Uncovering Hidden Layers of Cell Cycle Regulation through Integrative Multi-omic Analysis

Abstract

Studying the complex relationship between transcription, translation and protein degradation is essential to our understanding of biological processes in health and disease. The limited correlations observed between mRNA and protein abundance suggest pervasive regulation of post-transcriptional steps and support the importance of profiling mRNA levels in parallel to protein synthesis and degradation rates. In this work, we applied an integrative multi-omic approach to study gene expression along the mammalian cell cycle through side-by-side analysis of mRNA, translation and protein levels. Our analysis sheds new light on the significant contribution of both protein synthesis and degradation to the variance in protein expression. Furthermore, we find that translation regulation plays an important role at S-phase, while progression through mitosis is predominantly controlled by changes in either mRNA levels or protein stability. Specific molecular functions are found to be co-regulated and share similar patterns of mRNA, translation and protein expression along the cell cycle. Notably, these include genes and entire pathways not previously implicated in cell cycle progression, demonstrating the potential of this approach to identify novel regulatory mechanisms beyond those revealed by traditional expression profiling. Through this three-level analysis, we characterize different mechanisms of gene expression, discover new cycling gene products and highlight the importance and utility of combining datasets generated using different techniques that monitor distinct steps of gene expression.

Integrated analysis of the aging brain transcriptome and proteome in tauopathy

“…Remarkably, 42% of Tau-induced expression changes were discordant, with transcript and protein changing in opposite directions. This result is largely consistent with other emerging findings of surprisingly poor correlation between mRNA and protein levels among a variety of experimental systems [13,17,[53][54][55], including analyses of human postmortem brain tissue [18,19,56]. In one notable study relevant to AD, a similar fraction (40%) of differentially expressed transcripts in the 5XFAD amyloid precursor protein transgenic mouse showed discordant changes in the proteome [57].…”

The Perseus computational platform for comprehensive analysis of (prote)omics data

“…The reads can represent RNA-seq or ribosome-profiling data 50 , which are then converted to quantitative expression profiles by, for instance, calculating reads per kilobase of transcript per million mapped reads (RPKM) values 51 . To investigate the relationship between transcription and translation, this matrix can then be matched to another matrix containing protein expression values such as iBAQ values, which are estimates of absolute protein abundances 52,53 . This enables correlation analysis between the two quantitative omics dimensions (Supplementary Fig.…”

Poïkilodermie héréditaire fibrosante, myopathie rétractile et fibrose pulmonaire (POIKTMP)

“…La fonction de FAM111B est encore très mal connue. Un rôle dans la réplication de l'ADN est suggéré [5], ce qui serait concordant avec le rôle de RECQL4, hélicase impliquée dans le principal diagnostic différentiel, le syndrome de Rothmund-Thomson. L'atteinte musculaire semble en revanche spécifique du syndrome POIKTMP.…”