Background: Hypertrophic cardiomyopathy (HCM) is the most common genetic heart disease. While ≈50% of patients with HCM carry a sarcomere gene mutation (sarcomere mutation-positive, HCM _SMP ), the genetic background is unknown in the other half of the patients (sarcomere mutation-negative, HCM _SMN ). Genotype-specific differences have been reported in cardiac function. Moreover, HCM _SMN patients have later disease onset and a better prognosis than HCM _SMP patients. To define if genotype-specific derailments at the protein level may explain the heterogeneity in disease development, we performed a proteomic analysis in cardiac tissue from a clinically well-phenotyped HCM patient group. Methods: A proteomics screen was performed in cardiac tissue from 39 HCM _SMP patients, 11HCM _SMN patients, and 8 nonfailing controls. Patients with HCM had obstructive cardiomyopathy with left ventricular outflow tract obstruction and diastolic dysfunction. A novel MYBPC3 _2373insG mouse model was used to confirm functional relevance of our proteomic findings. Results: In all HCM patient samples, we found lower levels of metabolic pathway proteins and higher levels of extracellular matrix proteins. Levels of total and detyrosinated α-tubulin were markedly higher in HCM _SMP than in HCM _SMN and controls. Higher tubulin detyrosination was also found in 2 unrelated MYBPC3 mouse models and its inhibition with parthenolide normalized contraction and relaxation time of isolated cardiomyocytes. Conclusions: Our findings indicate that microtubules and especially its detyrosination contribute to the pathomechanism of patients with HCM _SMP . This is of clinical importance since it represents a potential treatment target to improve cardiac function in patients with HCM _SMP , whereas a beneficial effect may be limited in patients with HCM _SMN .

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

Extracellular vesicles (EVs) secreted by prostate cancer (PCa) cells contain specific biomarkers and can be isolated from urine. Collection of urine is not invasive, and therefore urinary EVs represent a liquid biopsy for diagnostic and prognostic testing for PCa. In this study, we optimised urinary EV isolation using a method based on heat shock proteins and compared it to gold‐standard ultracentrifugation. The urinary EV isolation protocol using the Vn96‐peptide is easier, time convenient (≈1.5 h) and no special equipment is needed, in contrast to ultracentrifugation protocol (>3.5 h), making this protocol clinically feasible. We compared the isolated vesicles of both ultracentrifugation and Vn96‐peptide by proteome profiling using mass spectrometry‐based proteomics (n = 4 per method). We reached a depth of >3000 proteins, with 2400 proteins that were commonly detected in urinary EVs from different donors. We show a large overlap (>85%) between proteins identified in EVs isolated by ultracentrifugation and Vn96‐peptide. Addition of the detergent NP40 to Vn96‐peptide EV isolations reduced levels of background proteins and highly increased the levels of the EV‐markers TSG101 and PDCD6IP, indicative of an increased EV yield. Thus, the Vn96‐peptide‐based EV isolation procedure is clinically feasibly and allows large‐scale protein profiling of urinary EV biomarkers.

Most patients with advanced colorectal cancer (CRC) eventually develop resistance to systemic combination therapy. miR-195-5p and miR-497-5p are downregulated in CRC tissues and associated with drug resistance. Sensitization to 5-FU, oxaliplatin, and irinotecan by transfection with miR-195-5p and miR-497-5p mimics was studied using cell viability and clonogenic assays in cell lines HCT116, RKO, DLD-1, and SW480. In addition, proteomic analysis of transfected cells was implemented to identify potential targets. Significantly altered proteins were subjected to STRING (protein-protein interaction networks) database analysis to study the potential mechanisms of drug resistance. Cell viability analysis of transfected cells revealed increased sensitivity to oxaliplatin in microsatellite instable (MSI)/P53 wild-type HCT116 and RKO cells. HCT116 transfected cells formed significantly fewer colonies when treated with oxaliplatin. In sensitized cells, proteomic analysis showed 158 and 202 proteins with significantly altered expression after transfection with miR-195-5p and miR-497-5p mimics respectively, of which CHUK and LUZP1 proved to be coinciding downregulated proteins. Resistance mechanisms of these proteins may be associated with nuclear factor kappa-B signaling and G1 cell-cycle arrest. In conclusion, miR-195-5p and miR-497-5p replacement enhanced sensitivity to oxaliplatin in treatment naïve MSI/P53 wild-type CRC cells. Proteomic analysis revealed potential miRNA targets associated with the cell-cycle which possibly bare a relation with chemotherapy sensitivity.

To answer the increasing need for detecting and validating protein biomarkers in clinical specimens, proteomic techniques are required that support the fast, reproducible and quantitative analysis of large clinical sample cohorts. Targeted mass spectrometry techniques, specifically SRM, PRM and the massively parallel SWATH/DIA technique have emerged as a powerful method for biomarker research. For optimal performance, they require prior knowledge about the fragment ion spectra of targeted peptides. In this report, we describe a mass spectrometric (MS) pipeline and spectral resource to support data-independent acquisition (DIA) and parallel reaction monitoring (PRM) based biomarker studies. To build the spectral resource we integrated common open-source MS computational tools to assemble an open source computational workflow based on Docker. It was then applied to generate a comprehensive DIA pan-human library (DPHL) from 1,096 data dependent acquisition (DDA) MS raw files, and it comprises 242,476 unique peptide sequences from 14,782 protein groups and 10,943 SwissProt-annotated proteins expressed in 16 types of cancer samples. In particular, tissue specimens from patients with prostate cancer, cervical cancer, colorectal cancer, hepatocellular carcinoma, gastric cancer, lung adenocarcinoma, squamous cell lung carcinoma, diseased thyroid, glioblastoma multiforme, sarcoma and diffuse large B-cell lymphoma (DLBCL), as well as plasma samples from a range of hematologic malignancies were collected from multiple clinics in China, the Netherlands and Singapore and included in the resource. This extensive spectral resource was then applied to a prostate cancer cohort of 17 patients, consisting of 8 patients with prostate cancer (PCa) and 9 with benign prostate hyperplasia (BPH), respectively. Data analysis of DIA data from these samples identified differential expressions of FASN, TPP1 and SPON2 in prostate tumors. Thereafter, PRM validation was applied to a larger PCa cohort of 57 patients and the differential expressions of FASN, TPP1 and SPON2 in prostate tumors were validated. As a second application, the DPHL spectral resource was applied to a patient cohort consisting of samples from 19 DLBCL patients and 18 healthy individuals. Differential expressions of CRP, CD44 and SAA1 between DLBCL cases and healthy controls were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supported that DIA-PRM MS pipeline enables robust protein biomarker discoveries.