Improved determination of protein turnover rate with heavy water labeling by mass isotopomer ratio selection.

Unlabelled: The synthesis and degradation rates of proteins form an essential component of gene expression control. Heavy water labeling has been used in conjunction with mass spectrometry to measure protein turnover rates, but the optimal analytical approaches to derive turnover rates from the isotopomer patterns of deuterium labeled peptides continue to be a subject of research. Here we describe a method, which comprises a reverse lookup of numerically approximated peptide isotope envelopes, coupled to the selection of optimal isotopomer pairs based on peptide sequence, to calculate the molar fraction of new peptide synthesis in heavy water labeling mass spectrometry experiments.

Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions.

The spatial and temporal distributions of proteins are critical to protein function, but cannot be directly assessed by measuring protein bundance. Here we describe a mass spectrometry-based proteomics strategy, Simultaneous Proteome Localization and Turnover (SPLAT), to measure concurrently protein turnover rates and subcellular localization in the same experiment. Applying the method, we find that unfolded protein response (UPR) has different effects on protein turnover dependent on their subcellular location in human AC16 cells, with proteome-wide slowdown but acceleration among stress response proteins in the ER and Golgi.

Co-culture with normal and senescent AC16 cardiomyocytes modulates fibrotic response in primary human ventricular fibroblasts.

Transwell co-culture with human AC16 cardiomyocyte-like cells modifies the response of primary human ventricular fibroblasts to TGF-β stimulation. Fibrotic response markers including collagen I (COL1A1) and ɑ-smooth muscle actin (ACTA2) are amplified in the presence of AC16 cells, whereas others including periostin (POSTN) and fibronectin (FN1) are suppressed. Similar modulation is observed when the ventricular fibroblasts are co-cultured with AC16 cells under baseline and induced senescence conditions.

Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions.

The functions of proteins depend on their spatial and temporal distributions, which are not directly measured by static protein abundance. Under endoplasmic reticulum (ER) stress, the unfolded protein response (UPR) pathway remediates proteostasis in part by altering the turnover kinetics and spatial distribution of proteins. A global view of these spatiotemporal changes has yet to emerge and it is unknown how they affect different cellular compartments and pathways.

Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners.

Protein and mRNA levels correlate only moderately. The availability of proteogenomics data sets with protein and transcript measurements from matching samples is providing new opportunities to assess the degree to which protein levels in a system can be predicted from mRNA information. Here we examined the contributions of input features in protein abundance prediction models.