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Piehowski, P.

Publications and source records attributed to Piehowski, P..

2 recordsLinked to original sources

Proteomic Dissection of a Giant Cell

Many individual proteins have been identified as having defined positions relative to cell polarity axes, raising the question of what fraction of all proteins may have polarized localizations. We took advantage of the giant ciliate Stentor coeruleus to quantify the extent of polarized localization proteome-wide. This trumpet-shaped unicellular organism shows a clear morphological anterior-posterior axis defined by a circular array of cilia known as a membranellar band at one end, and a holdfast at the other end. Because individual Stentor cells are over a millimeter in length, we were able to cut the cells into three pieces along the anterior-posterior axis, followed by proteomic analysis of proteins enriched in each piece. We find that approximately 30% of all detected proteins show a polarized location relative to the anterior-posterior cell axis. Proteins with polarized enrichment include centrin-like proteins, calcium-regulated kinases, orthologs of SFI1 and GAS2, and proteases. At the organelle level, nuclear and mitochondrial proteins are enriched in the anterior half of the cell body, but not in the membranellar band itself, while ribosome related proteins are apparently uniformly distributed. RNAi of signaling proteins enriched in the membranellar band, which is the anterior-most structure in the cell, revealed a protein phosphatase 2 subunit b ortholog required for closure of the membranellar band into the ring shape characteristic of Stentor. These results suggest that a large fraction of the Stentor proteome has a polarized localization, and provide a protein-level framework for future analysis of pattern formation and regeneration in Stentor as well as defining a general strategy for subcellular spatial proteomics based on physical dissection of cells.

cell biology↗

Utilizing proteomics and phosphoproteomics to predict ex vivo drug sensitivity across genetically diverse AML patients

Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemia. This genetic heterogeneity is difficult to treat using conventional therapies that are generally based on the detection of a single driving mutation. Thus, the use of molecular signatures, consisting of multiple functionally related transcripts or proteins, in making treatment decisions may overcome this hurdle and provide a more effective way to inform drug treatment protocols. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify signatures that could predict patient-specific drug responses. The Clinical Proteomic Tumor Analysis Consortium is in the process of extending this cohort to collect proteomic and phosphoproteomic measurements from a subset of these patient samples to evaluate the hypothesis that proteomic signatures can robustly predict drug response in AML patients. We sought to examine this hypothesis on a sub-cohort of 38 patient samples from Beat AML with proteomic and drug response data and evaluate our ability to identify proteomic signatures that predict drug response with high accuracy. For this initial analysis we built predictive models of patient drug responses across 26 drugs of interest using the proteomics and phosphproteomics data. We found that proteomics-derived signatures provide an accurate and robust signature of drug response in the AML ex vivo samples, as well as related cell lines, with better performance than those signatures derived from mutations or mRNA expression. Furthermore, we found that in specific drug-resistant cell lines, the proteins in our prognostic signatures represented dysregulated signaling pathways compared to parental cell lines, confirming the role of the proteins in the signatures in drug resistance. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to predict drug sensitivity in AML.

bioinformatics↗