The functions of proteins depend on their spatial and temporal distributions, which are not directly measured by static protein abundance. Under protein misfolding stress, the unfolded protein response (UPR) pathway remediates proteostasis in part by altering the turnover kinetics and spatial distribution of proteins, yet a global view of these spatiotemporal changes has yet to emerge and it is unknown how they affect different cellular compartments and pathways. Here we describe a mass spectrometry-based proteomics strategy and data analysis pipeline, named Simultaneous Proteome Localization and Turnover (SPLAT), to measure concurrently the changes in protein turnover and subcellular distribution in the same experiment. Investigating two common UPR models of thapsigargin and tunicamycin challenge, we find that the global suppression of protein synthesis during UPR is dependent on subcellular localization, with more severe slowdown in lysosome vs. endoplasmic reticulum (ER) protein turnover. Most candidate translocation events affect pre-existing proteins and likely involve vesicular transport across endomembrane fractions including an expansion of an ER-derived vesicle (ERV) compartment containing RNA binding proteins and stress response proteins. In parallel, we observed specific translocations involving only newly synthesized protein pools that are indicative of endomembrane stalling. The translocation of a subclass of cell surface proteins to the endomembrane including EGFR and ITGAV upon UPR affects only heavy labeled proteins, which suggest their internalization is driven by nascent protein trafficking rather than ligand dependent endocytosis. The approach described here may be broadly useful for inferring the coordinations between spatial and temporal proteome regulations in normal and stressed cells.

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. Using large proteogenomics data from 8 cancer types within the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set, we trained models to predict the abundance of over 13,000 proteins using matching transcriptome data from up to 958 tumor or normal adjacent tissue samples each, and compared predictive performances across algorithms, data set sizes, and input features. Over one-third of proteins (4,648) showed relatively poor predictability (elastic net r ≤ 0.3) from their cognate transcripts. Moreover, we found widespread occurrences where the abundance of a protein is considerably less well explained by its own cognate transcript level than that of one or more trans locus transcripts. The incorporation of additional trans-locus transcript abundance data as input features increasingly improved the ability to predict sample protein abundance. Transcripts that contribute to non-cognate protein abundance primarily involve those encoding known or predicted interaction partners of the protein of interest, including not only large multi-protein complexes as previously shown, but also small stable complexes in the proteome with only one or few stable interacting partners. Network analysis further shows a complex proteome-wide interdependency of protein abundance on the transcript levels of multiple interacting partners. The predictive model analysis here therefore supports that protein-protein interaction including in small protein complexes exert post-transcriptional influence on proteome compositions more broadly than previously recognized. Moreover, the results suggest mRNA and protein co-expression analysis may have utility for finding gene interactions and predicting expression changes in biological systems.