The Urinary Transcriptome as a Source of Biomarkers for Prostate Cancer.

Prostate cancer (PCa) is the second most common cancer of men and is typically slow-growing and asymptomatic. The use of blood PSA as a screening method has greatly improved PCa diagnosis, but high levels of false positives has raised much interest in alternative biomarkers. We used next-generation sequencing (NGS) to elucidate the urinary transcriptome of whole urine collected from high-stage and low-stage PCa patients as well as from patients with the confounding diagnosis of benign hyperplasia (BPH). We identified and validated five differentially expressed protein-coding genes (FTH1 BRPF1, OSBP, PHC3, and UACA) in an independent validation cohort of small-volume (1 mL) centrifuged urine (n = 94) and non-centrifuged urine (n = 84) by droplet digital (dd)PCR. These biomarkers were able to discriminate between BPH and PCa patients and healthy controls using either centrifuged or non-centrifuged whole urine samples, suggesting that the urinary transcriptome is a valuable source of non-invasive biomarkers for PCa that warrants further investigation.

The Circulating Transcriptome as a Source of Biomarkers for Melanoma.

The circulating transcriptome is a valuable source of cancer biomarkers, which, with the exception of microRNAs (miRNAs), remains relatively unexplored. To elucidate which RNAs are present in plasma from melanoma patients and which could be used to distinguish cancer patients from healthy individuals, we used next generation sequencing (NGS), and validation was carried out by qPCR and/or ddPCR. We identified 442 different microRNAs in samples, eleven of which were differentially expressed (p < 0.05). Levels of miR-134-5p and miR-320a-3p were significantly down-regulated (p < 0.001) in melanoma samples (n = 96) compared to healthy controls (n = 28). Differentially expressed protein-encoding mRNA 5'-fragments were enriched for the angiopoietin, p21-activated kinase (PAK), and EIF2 pathways. Levels of ATM1, AMFR, SOS1, and CD109 gene fragments were up-regulated (p < 0.001) in melanoma samples (n = 144) compared to healthy controls (n = 41) (AUC = 0.825). Over 40% of mapped reads were YRNAs, a class of non-coding RNAs that to date has been little explored. Expression levels of RNY3P1, RNY4P1, and RNY4P25 were significantly higher in patients with stage 0 disease than either healthy controls or more advanced stage disease (p < 0.001). In conclusion, we have identified a number of novel RNA biomarkers, which, most importantly, we validated in multi-center retrospective and prospective cohorts, suggesting potential diagnostic use of these RNA species.

Pervasive Protein Thermal Stability Variation during the Cell Cycle.

Quantitative mass spectrometry has established proteome-wide regulation of protein abundance and post-translational modifications in various biological processes. Here, we used quantitative mass spectrometry to systematically analyze the thermal stability and solubility of proteins on a proteome-wide scale during the eukaryotic cell cycle. We demonstrate pervasive variation of these biophysical parameters with most changes occurring in mitosis and G1. Various cellular pathways and components vary in thermal stability, such as cell-cycle factors, polymerases, and chromatin remodelers. We demonstrate that protein thermal stability serves as a proxy for enzyme activity, DNA binding, and complex formation in situ. Strikingly, a large cohort of intrinsically disordered and mitotically phosphorylated proteins is stabilized and solubilized in mitosis, suggesting a fundamental remodeling of the biophysical environment of the mitotic cell. Our data represent a rich resource for cell, structural, and systems biologists interested in proteome regulation during biological transitions.

Systematic analysis of protein turnover in primary cells.

A better understanding of proteostasis in health and disease requires robust methods to determine protein half-lives. Here we improve the precision and accuracy of peptide ion intensity-based quantification, enabling more accurate protein turnover determination in non-dividing cells by dynamic SILAC-based proteomics. This approach allows exact determination of protein half-lives ranging from 10 to >1000 h. We identified 4000-6000 proteins in several non-dividing cell types, corresponding to 9699 unique protein identifications over the entire data set. We observed similar protein half-lives in B-cells, natural killer cells and monocytes, whereas hepatocytes and mouse embryonic neurons show substantial differences. Our data set extends and statistically validates the previous observation that subunits of protein complexes tend to have coherent turnover. Moreover, analysis of different proteasome and nuclear pore complex assemblies suggests that their turnover rate is architecture dependent. These results illustrate that our approach allows investigating protein turnover and its implications in various cell types.