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Efficient protein turnover is essential for cellular homeostasis and organ function. Loss of proteostasis is a hallmark of aging culminating in severe dysfunction of protein turnover. To investigate protein turnover dynamics as a function of age, we performed continuous in vivo metabolic stable isotope labeling in mice along the aging continuum. First, we discovered that the brain proteome uniquely undergoes dynamic turnover fluctuations during aging compared to heart and liver tissue. Second, trends in protein turnover in the brain proteome during aging showed sex-specific differences that were tightly tied to cellular compartments. Next, parallel analyses of the insoluble proteome revealed that several cellular compartments experience hampered turnover, in part due to misfolding. Finally, we found that age-associated fluctuations in proteasome activity were associated with the turnover of core proteolytic subunits, which was recapitulated by pharmacological suppression of proteasome activity. Taken together, our study provides a proteome-wide atlas of protein turnover across the aging continuum and reveals a link between the turnover of individual proteasome subunits and the age-associated decline in proteasome activity. Synopsis: Continuous pulse-step metabolic labeling with quantitative proteomics in mice reveals sex-specific differences in protein turnover linked to subcellular compartments. Age-related changes in protein turnover result in part from altered proteasome activity and subunit turnover. Brain protein turnover trends during aging display sex-specific differences tied to subcellular compartments. Misfolded proteins contribute to differential turnover trends in the brain. Reduced proteasome activity is associated with slowed turnover of proteasome subunits during aging. Continuous pulse-step metabolic labeling with quantitative proteomics in mice reveals sex-specific differences in protein turnover linked to subcellular compartments. Age-related changes in protein turnover result in part from altered proteasome activity and subunit turnover. [ABSTRACT FROM AUTHOR]

The article features comments on EGFR signaling. By using protein microarray and quantitative proteomics technologies, a group has recently identified proteins associated with the cytosolic domains of the human EGFR family. In a related project, others have used protein microarrays to identify ErbB interactors. It is commented that the dramatic differences are due to the fact that the two groups looked at very different things. The paper indicates that systems biology is in its nascent stage.

Abstract: Quantitative mass spectrometry enables to monitor the abundance of thousands of proteins across biological conditions. Currently, most data analysis approaches rely on the assumption that the majority of the observed proteins remain unchanged across compared samples. Thus, gross morphological differences between cell states, deriving from, e.g., differences in size or number of organelles, are often not taken into account. Here, we analyzed multiple published datasets and frequently observed that proteins associated with a particular cellular compartment collectively increase or decrease in their abundance between conditions tested. We show that such effects, arising from underlying morphological differences, can skew the outcome of differential expression analysis. We propose a method to detect and normalize morphological effects underlying proteomics data. We demonstrate the applicability of our method to different datasets and biological questions including the analysis of sub‐cellular proteomes in the context of Caenorhabditis elegans aging. Our method provides a complementary perspective to classical differential expression analysis and enables to uncouple overall abundance changes from stoichiometric variations within defined group of proteins. [ABSTRACT FROM AUTHOR]

Why are neighbouring genes co-expressed at the RNA level? While it is tempting to think that this is to enable coordination of functionally related proteins, analysis of human proteomics data by Rappsilber and colleagues (Kustatscher et al, ) suggests this is the exception not the rule. Rather it might either be just something that happens or, in some instances, an epiphenomenon of coordination of expression to enable reduced gene expression noise. [ABSTRACT FROM AUTHOR]