Assistant Professor at University of Colorado School of Medicine. Our lab studies protein homeostasis and turnover using proteomics, bioinformatics & iPSC. Previously Stanford CVI. #TeamMassSpec
We are hiring graduate students and post-doctoral fellows! Visit our website at https://laulab.net and follow us on @edwardlau and on https://post.news/edwardlau!
Graduate student researcher in our lab Jordan Currie will be giving an oral presentation on his work at #USHUPO2023. Jordan has some interesting new data on proteome dynamics that we are excited to share. Looking forward to the #USHUPO meeting in Chicago in March!
Share your work on how aging affects the cardiovascular system! Check out the AJP special issue on the Impact of Aging on the Cardiovascular System for details: http://ow.ly/T35E50Lt7B8
A new HUPOST our lab co-wrote with @astacus@twitter.com where we discussed recent work on measuring protein turnover in intact animals https://hupo.org/News/13001621
@cosminribo Interesting points, we haven't quite looked at homomultimers complexes or which complexes are co-translationally assembled. We do know that poor mRNA/protein correlation genes (r < 0.3) are enriched in quite a few large complexes including the ribosomes, proteasomes, and mediators.
@bensb Interesting, thanks for sharing! There was a 2017 paper that showed CNV being buffered at the protein level in CPTAC data ... would be interesting to see how that relates to gene coordinates https://www.sciencedirect.com/science/article/pii/S240547121730385X
The paper “widespread post-transcriptional regulation of protein abundance by interacting partners” is on PLOS Comp Biol. Thanks to Himangi Srivastava, Mike Lippincott, Jordan Currie, and the Maggie Lam Lab for this collaboration, and the reviewers and editors for their constructive comments!
Prior selection of mRNA features not only improved protein predictions, but may also help find new protein-level driver genes. E.g., using a directed graph model, we predict that the LACTB mRNA may have an outsized effect on mitochondrial ribosome protein abundance.
The data suggests degradation of supernumerary interactors is a driver of protein levels. While this was known for large complexes, this phenomenon is widespread and affects many small stable complexes incl. propionyl-CoA carboxylase, mito. calcium uniporter, calcineurin, etc.
Inspecting further, we saw many proteins show very poor correlation with their cognate mRNA but instead a strong correlation with another transcript, which are usually but not always their known protein-protein interaction partners.
Building on prior work, we trained machine learning models to predict the across-sample protein variance from RNA-seq data. We saw huge gene-wise differences in predictability. We found that up to 1/3 of proteins are poorly predicted by mRNA.
When does mRNA level not predict protein level? A new paper from our lab revisited the question of how well mRNA levels reflect protein variances across different tumors and normal tissues using CPTAC data.
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010702
