Phosphoproteomics Reveals Resveratrol-Dependent Inhibition of Akt/mTORC1/S6K1 Signaling

Resveratrol, a plant-derived polyphenol, regulates many cellular processes, including cell proliferation, aging and autophagy. However, the molecular mechanisms of resveratrol action in cells are not completely understood. Intriguingly, resveratrol treatment of cells growing in nutrient-rich conditions induces autophagy, while acute resveratrol treatment of cells in a serum-deprived state inhibits autophagy. In this study, we performed a phosphoproteomic analysis after applying resveratrol to serum-starved cells with the goal of identifying the acute signaling events initiated by resveratrol in a serum-deprived state. We determined that resveratrol in serum-starved conditions reduces the phosphorylation of several proteins belonging to the mTORC1 signaling pathway, most significantly, PRAS40 at T246 and S183. Under these same conditions, we also found that resveratrol altered the phosphorylation of several proteins involved in various biological processes, most notably transcriptional modulators, represented by p53, FOXA1, and AATF. Together these data provide a more comprehensive view of both the spectrum of phosphoproteins upon which resveratrol acts as well as the potential mechanisms by which it inhibits autophagy in serum-deprived cells

Quantitation and Identification of Thousands of Human Proteoforms below 30 kDa

Top-down proteomics is capable of identifying and quantitating unique proteoforms through the analysis of intact proteins. We extended the coverage of the label-free technique, achieving differential analysis of whole proteins <30 kDa from the proteomes of growing and senescent human fibroblasts. By integrating improved control software with more instrument time allocated for quantitation of intact ions, we were able to collect protein data between the two cell states, confidently comparing 1577 proteoform levels. To then identify and characterize proteoforms, our advanced acquisition software, named Autopilot, employed enhanced identification efficiency in identifying 1180 unique Swiss-Prot accession numbers at 1% false-discovery rate. This coverage of the low mass proteome is equivalent to the largest previously reported but was accomplished in 23% of the total acquisition time. By maximizing both the number of quantified proteoforms and their identification rate in an integrated software environment, this work significantly advances proteoform-resolved analyses of complex systems

Quantitation and Identification of Thousands of Human Proteoforms below 30 kDa

Top-down proteomics is capable of identifying and quantitating unique proteoforms through the analysis of intact proteins. We extended the coverage of the label-free technique, achieving differential analysis of whole proteins <30 kDa from the proteomes of growing and senescent human fibroblasts. By integrating improved control software with more instrument time allocated for quantitation of intact ions, we were able to collect protein data between the two cell states, confidently comparing 1577 proteoform levels. To then identify and characterize proteoforms, our advanced acquisition software, named Autopilot, employed enhanced identification efficiency in identifying 1180 unique Swiss-Prot accession numbers at 1% false-discovery rate. This coverage of the low mass proteome is equivalent to the largest previously reported but was accomplished in 23% of the total acquisition time. By maximizing both the number of quantified proteoforms and their identification rate in an integrated software environment, this work significantly advances proteoform-resolved analyses of complex systems

Quantitation and Identification of Thousands of Human Proteoforms below 30 kDa

Top-down proteomics is capable of identifying and quantitating unique proteoforms through the analysis of intact proteins. We extended the coverage of the label-free technique, achieving differential analysis of whole proteins <30 kDa from the proteomes of growing and senescent human fibroblasts. By integrating improved control software with more instrument time allocated for quantitation of intact ions, we were able to collect protein data between the two cell states, confidently comparing 1577 proteoform levels. To then identify and characterize proteoforms, our advanced acquisition software, named Autopilot, employed enhanced identification efficiency in identifying 1180 unique Swiss-Prot accession numbers at 1% false-discovery rate. This coverage of the low mass proteome is equivalent to the largest previously reported but was accomplished in 23% of the total acquisition time. By maximizing both the number of quantified proteoforms and their identification rate in an integrated software environment, this work significantly advances proteoform-resolved analyses of complex systems