In-depth proteomic analyses of direct expressed prostatic secretions.

It is expected that clinically obtainable fluids that are proximal to organs contain a repertoire of secreted proteins and shed cells reflective of the physiological state of that tissue and thus represent potential sources for biomarker discovery, investigation of tissue-specific biology, and assay development. The prostate gland secretes many proteins into a prostatic fluid that combines with seminal vesicle fluids to promote sperm activation and function. Proximal fluids of the prostate that can be collected clinically are seminal plasma and expressed prostatic secretion (EPS) fluids. In the current study, MudPIT-based proteomics was applied to EPS obtained from nine men with prostate cancer and resulted in the confident identification of 916 unique proteins. Systematic bioinformatics analyses using publicly available microarray data of 21 human tissues (Human Gene Atlas), the Human Protein Atlas database, and other published proteomics data of shed/secreted proteins were performed to systematically analyze this comprehensive proteome. Therefore, we believe this data will be a valuable resource for the research community to study prostate biology and potentially assist in the identification of novel prostate cancer biomarkers. To further streamline this process, the entire data set was deposited to the Tranche repository for use by other researchers.

Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma.

Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of VHL that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC. Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant. These 344 differentially abundant proteins were enriched in metabolic pathways and further examination revealed metabolic dysfunction consistent with the Warburg effect. Additionally, the protein data indicated activation of ESRRA and ESRRG, and HIF1A, as well as inhibition of FOXA1, MAPK1 and WISP2. A subset analysis of complementary gene expression array data on 47 pairs of these same tissues indicated similar upstream changes, such as increased HIF1A activation with stage, though ESRRA and ESRRG activation and FOXA1 inhibition were not predicted from the transcriptomic data. The activation of ESRRA and ESRRG implied that HIF2A may also be activated during later stages of ccRCC, which was confirmed in the transcriptional analysis. This combined analysis highlights the importance of HIF1A and HIF2A in developing the ccRCC molecular phenotype as well as the potential involvement of ESRRA and ESRRG in driving these changes. In addition, cofilin-1, profilin-1, nicotinamide N-methyltransferase, and fructose-bisphosphate aldolase A were identified as candidate markers of late stage ccRCC. Utilization of data collected from heterogeneous biological domains strengthened the findings from each domain, demonstrating the complementary nature of such an analysis. Together these results highlight the importance of the VHL/HIF1A/HIF2A axis and provide a foundation and therapeutic targets for future studies. (Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.).

Proteomic expression profiling and identification of serum proteins using immobilized trypsin beads with MALDI-TOF/TOF.

MALDI-TOF mass spectrometry is a widely used technique for serum protein expression profiling and biomarker discovery. Many profiling strategies typically employ chemical affinity beads or surfaces to decrease sample complexity of dynamic fluids such as serum or plasma. However, many of the proteins captured on a particular surface or bead are not resolved in the lower mass ranges where time-of-flight mass spectrometers are most effective. Thus, a majority of reported protein expression profiling studies primarily interrogate the native low molecular mass constituents of the target sample. We report an expression profiling workflow that utilizes immobilized trypsin paramagnetic beads following an initial affinity bead fractionation step, thereby reducing large mass proteins to peptides that are better suited to analysis and sequencing determinations. Our bead-based trypsin approach resulted in more efficient digestion of complex serum protein extracts at short incubation times. This method was reproducible and readily adaptable to robotic sample handling and may be combined in tandem with other bead fractionation surfaces. When weak cationic and weak anionic bead surfaces were used, experimental conditions were optimized for tandem combinations of these beads with the immobilized trypsin step to produce an efficient serum fractionation strategy. A proof-of-concept pilot experiment using pooled human serum samples demonstrating reproducibility is presented, along with the sequence determination of selected tryptic peptides of serum proteins.