Plasma proteomics identify potential severity biomarkers from COVID-19 associated network.

PURPOSE: Coronavirus disease 2019 (COVID-19) continues to threaten public health globally. Severe acute respiratory coronavirus type 2 (SARS-CoV-2) infection-dependent alterations in the host cell signaling network may unveil potential target proteins and pathways for therapeutic strategies. In this study, we aim to define early severity biomarkers and monitor altered pathways in the course of SARS-CoV-2 infection. EXPERIMENTAL DESIGN: We systematically analyzed plasma proteomes of COVID-19 patients from Turkey by using mass spectrometry. Different severity grades (moderate, severe, and critical) and periods of disease (early, inflammatory, and recovery) are monitored. Significant alterations in protein expressions are used to reconstruct the COVID-19 associated network that was further extended to connect viral and host proteins. RESULTS: Across all COVID-19 patients, 111 differentially expressed proteins were found, of which 28 proteins were unique to our study mainly enriching in immunoglobulin production. By monitoring different severity grades and periods of disease, CLEC3B, MST1, and ITIH2 were identified as potential early predictors of COVID-19 severity. Most importantly, we extended the COVID-19 associated network with viral proteins and showed the connectedness of viral proteins with human proteins. The most connected viral protein ORF8, which has a role in immune evasion, targets many host proteins tightly connected to the deregulated human plasma proteins. CONCLUSIONS AND CLINICAL RELEVANCE: Plasma proteomes from critical patients are intrinsically clustered in a distinct group than severe and moderate patients. Importantly, we did not recover any grouping based on the infection period, suggesting their distinct proteome even in the recovery phase. The new potential early severity markers can be further studied for their value in the clinics to monitor COVID-19 prognosis. Beyond the list of plasma proteins, our disease-associated network unravels altered pathways, and the possible therapeutic targets in SARS-CoV-2 infection by connecting human and viral proteins. Follow-up studies on the disease associated network that we propose here will be useful to determine molecular details of viral perturbation and to address how the infection affects human physiology.

Quantitative Phosphoproteomics Analysis Uncovers PAK2- and CDK1-Mediated Malignant Signaling Pathways in Clear Cell Renal Cell Carcinoma.

Clear cell Renal Cell Carcinoma (ccRCC) is among the 10 most common cancers in both men and women and causes more than 140,000 deaths worldwide every year. In order to elucidate the underlying molecular mechanisms orchestrated by phosphorylation modifications, we performed a comprehensive quantitative phosphoproteomics characterization of ccRCC tumor and normal adjacent tissues. Here, we identified 16,253 phosphopeptides, of which more than 9000 were singly quantified. Our in-depth analysis revealed 600 phosphopeptides to be significantly differentially regulated between tumor and normal tissues. Moreover, our data revealed that significantly up-regulated phosphoproteins are associated with protein synthesis and cytoskeletal re-organization which suggests proliferative and migratory behavior of renal tumors. This is supported by a mesenchymal profile of ccRCC phosphorylation events. Our rigorous characterization of the renal phosphoproteome also suggests that both epidermal growth factor receptor and vascular endothelial growth factor receptor are important mediators of phospho signaling in RCC pathogenesis. Furthermore, we determined the kinases p21-activated kinase 2, cyclin-dependent kinase 1 and c-Jun N-terminal kinase 1 to be master kinases that are responsible for phosphorylation of many substrates associated with cell proliferation, inflammation and migration. Moreover, high expression of p21-activated kinase 2 is associated with worse survival outcome of ccRCC patients. These master kinases are targetable by inhibitory drugs such as fostamatinib, minocycline, tamoxifen and bosutinib which can serve as novel therapeutic agents for ccRCC treatment.

Quantitative Proteomics Identifies Secreted Diagnostic Biomarkers as well as Tumor-Dependent Prognostic Targets for Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the third most common and most malignant urological cancer, with a 5-year survival rate of 10% for patients with advanced tumors. Here, we identified 10,160 unique proteins by in-depth quantitative proteomics, of which 955 proteins were significantly regulated between tumor and normal adjacent tissues. We verified four putatively secreted biomarker candidates, namely, PLOD2, FERMT3, SPARC, and SIRPα, as highly expressed proteins that are not affected by intratumor and intertumor heterogeneity. Moreover, SPARC displayed a significant increase in urine samples of patients with ccRCC, making it a promising marker for the detection of the disease in body fluids. Furthermore, based on molecular expression profiles, we propose a biomarker panel for the robust classification of ccRCC tumors into two main clusters, which significantly differed in patient outcome with an almost three times higher risk of death for cluster 1 tumors compared with cluster 2 tumors. Moreover, among the most significant clustering proteins, 13 were targets of repurposed inhibitory FDA-approved drugs. Our rigorous proteomics approach identified promising diagnostic and tumor-discriminative biomarker candidates which can serve as therapeutic targets for the treatment of ccRCC. IMPLICATIONS: Our in-depth quantitative proteomics analysis of ccRCC tissues identifies the putatively secreted protein SPARC as a promising urine biomarker and reveals two molecular tumor phenotypes.

Proteomics in Cell Division.

Cell division requires a coordinated action of the cell cycle machinery, cytoskeletal elements, chromosomes, and membranes. Cell division studies have greatly benefitted from the mass spectrometry (MS)-based proteomic approaches for probing the biochemistry of highly dynamic complexes and their coordination with each other as a cell progresses into division. In this review, the authors first summarize a wide-range of proteomic studies that focus on the identification of sub-cellular components/protein complexes of the cell division machinery including kinetochores, mitotic spindle, midzone, and centrosomes. The authors also highlight MS-based large-scale analyses of the cellular components that are largely understudied during cell division such as the cell surface and lipids. Then, the authors focus on posttranslational modification analyses, especially phosphorylation and the resulting crosstalk with other modifications as a cell undergoes cell division. Combining proteomic approaches that probe the biochemistry of cell division components with functional genomic assays will lead to breakthroughs toward a systems-level understanding of cell division.