PROTEOMICS – Clinical Applications最新文献

Purpose: Delirium presents a significant healthcare burden. It complicates post-operative care in up to 50% of cardiac surgical patients with worse outcomes, longer hospital stays and higher cost of care. Moreover, the nature of delirium following cardiac surgery with cardiopulmonary bypass (CPB) remains unclear, the underlying pathobiology is poorly understood, status quo diagnostic methods are subjective, and diagnostic biomarkers are currently lacking.

Objective: To identify diagnostic biomarkers of delirium and for insights into possible neuronal pathomechanisms.

Experimental design: Comparative proteomic analyses were performed on plasma samples from a nested matched cohort of patients who underwent cardiac surgery. Validation by targeted proteomics was performed in an independent set of samples. Biomarkers were assessed for biological functions and diagnostic accuracy.

Results: Forty-seven percent of subjects demonstrated delirium. Of 3803 proteins identified from patient samples by multiplexed quantitative proteomics, 16 were identified as signatures of exposure to CPB, and 11 biomarkers distinguished delirium cases from non-cases (AuROC = 93%). Notable among these biomarkers are C-reactive protein, serum amyloid A-1 and cathepsin-B.

Conclusions and clinical relevance: The interplay of systemic and central inflammatory markers sheds new light on delirium pathogenesis. This work suggests that accurate identification of cases may be achievable using panels of biomarkers.

Introduction: Kidney transplant recipients (KTRs) are at an increased risk of fractures. Total urinary hydroxyproline excretion served as marker for bone resorption (BR) but was replaced by β-CrossLaps (CTX), a C-terminal collagen α-1(I) chain (COL1A1) telopeptide. We investigated the low-molecular-weight urinary proteome for peptides associated with changes in bone metabolism after kidney transplantation.

Methods: Clinical and laboratory data including serum levels of CTX in 96 KTR from two nephrology centers were correlated with signal intensities of urinary peptides identified by capillary electrophoresis mass spectrometry.

Results: Eighty-two urinary peptides were significantly correlated with serum CTX levels. COL1A1 was the predominant peptide source. Oral bisphosphonates were administered for decreased bone density in an independent group of 11 KTR and their effect was evaluated on the aforementioned peptides. Study of the peptides cleavage sites revealed a signature of Cathepsin K and MMP9. Seventeen of these peptides were significantly associated with bisphosphonate treatment, all showing a marked reduction in their excretion levels compared to baseline.

Discussion: This study provides strong evidence for the presence of collagen peptides in the urine of KTR that are associated with BR and that are sensitive to bisphosphonate treatment. Their assessment might become a valuable tool to monitor bone status in KTR.

Purpose: Extracellular vesicles (EVs) have become promising biomarkers for cancer management. Particularly, the molecular cargo such as proteins carried by EVs are similar to their cells of origin, providing important information that can be used for cancer diagnostics, prognosis, and treatment monitoring. However, to date, molecular analysis on EVs is still challenging, limited by the availability of efficient analytical technologies, largely due to the small size of EVs. In this work, we developed a computational workflow for in silico identification of potential EV protein markers from genomic and proteomic databases, and applied it for the discovery of osteosarcoma (OS) EV protein markers.

Experimental design: Both mRNA and protein data were computed and compared from publicly accessible databases, and top markers with high differential expression levels were selected.

Results: Thirty nine markers were identified overexpressed and seven found to be downregulated. These identified markers have been found to be associated with OS on different aspects in literature, demonstrating the usability of this workflow.

Conclusions and clinical relevance: This work provides a list of potential EV protein markers that are either overexpressed or downregulated in OS for further experimental validation for improved clinical management of OS.

Purpose: This study aimed to identify serum glycoprotein biomarkers for early detection of high-grade serous ovarian cancer (HGSOC), the most common and aggressive histotype of ovarian cancer.

Experimental design: The glycoproteomics pipeline lectin magnetic bead array (LeMBA)-mass spectrometry (MS) was used in age-matched case-control serum samples. Clinical samples collected at diagnosis were divided into discovery (n = 30) and validation (n = 98) sets. We also analysed a set of preclinical sera (n = 30) collected prior to HGSOC diagnosis in the UK Collaborative Trial of Ovarian Cancer Screening.

Results: A 7-lectin LeMBA-MS/MS discovery screen shortlisted 59 candidate proteins and three lectins. Validation analysis using 3-lectin LeMBA-multiple reaction monitoring (MRM) confirmed elevated A1AT, AACT, CO9, HPT and ITIH3 and reduced A2MG, ALS, IBP3 and PON1 glycoforms in HGSOC. The best performing multimarker signature had 87.7% area under the receiver operating curve, 90.7% specificity and 70.4% sensitivity for distinguishing HGSOC from benign and healthy groups. In the preclinical set, CO9, ITIH3 and A2MG glycoforms were altered in samples collected 11.1 ± 5.1 months prior to HGSOC diagnosis, suggesting potential for early detection.

Conclusions and clinical relevance: Our findings provide evidence of candidate early HGSOC serum glycoprotein biomarkers, laying the foundation for further study in larger cohorts.

Purpose: Helcococcus kunzii is a skin commensal, Gram-positive bacterium, mostly isolated from infected chronic wounds. This opportunistic pathogen is usually co-isolated with Staphylococcus aureus. The present dataset explores the production and secretion of H. kunzii bacterial virulence interacting proteins in a growth medium mimicking chronic wounds in exponential and stationary growth phases.

Experimental design: The H. kunzii cellular proteome and exoproteome were assessed by analyzing three biological replicates per condition tested. Samples were analyzed using a Q-Exactive HF mass spectrometer. Comparative and functional analyses were performed to profile the identified protein set.

Results: The H. kunzii's cellular proteome encompassed 969 proteins, among which 64 and 53 were specifically identified in the exponential and stationary phase of growth, respectively. Its exoproteome comprised 58 proteins, among which 16 and 14 were characteristic of each growth stage. Metabolic differences between the two phases of growth are discussed. Besides, the production of previously shortlisted and novel putative H. kunzii targets involved in modulating the virulence of S. aureus is investigated.

Conclusion and clinical relevance: This work, pioneering the study of H. kunzii physiology in a chronic wound-like environment, should assist future research on this opportunistic pathogen and the search for innovative approaches for wound management.

Purpose: To characterize and analytically validate the MSDA Test, a multi-protein, serum-based biomarker assay developed using Olink^® PEA methodology.

Experimental design: Two lots of the MSDA Test panel were manufactured and subjected to a comprehensive analytical characterization and validation protocol to detect biomarkers present in the serum of patients with multiple sclerosis (MS). Biomarker concentrations were incorporated into a final algorithm used for calculating four Disease Pathway scores (Immunomodulation, Neuroinflammation, Myelin Biology, and Neuroaxonal Integrity) and an overall Disease Activity score.

Results: Analytical characterization demonstrated that the multi-protein panel satisfied the criteria necessary for a fit-for-purpose validation considering the assay's intended clinical use. This panel met acceptability criteria for 18 biomarkers included in the final algorithm out of 21 biomarkers evaluated. VCAN was omitted based on factors outside of analytical validation; COL4A1 and GH were excluded based on imprecision and diurnal variability, respectively. Performance of the four Disease Pathway and overall Disease Activity scores met the established acceptability criteria.

Conclusions and clinical relevance: Analytical validation of this multi-protein, serum-based assay is the first step in establishing its potential utility as a quantitative, minimally invasive, and scalable biomarker panel to enhance the standard of care for patients with MS.

Background: Lipidomics and metabolomics are closely related to tumor phenotypes, and serum lipoprotein subclasses and small-molecule metabolites are considered as promising biomarkers for breast cancer (BC) diagnosis. This study aimed to explore potential biomarker models based on lipidomic and metabolomic analysis that could distinguish BC from healthy controls (HCs) and triple-negative BC (TNBC) from non-TNBC.

Methods: Blood samples were collected from 114 patients with BC and 75 HCs. A total of 112 types of lipoprotein subclasses and 30 types of small-molecule metabolites in the serum were detected by ¹ H-NMR. All lipoprotein subclasses and small-molecule metabolites were subjected to a three-step screening process in the order of significance (p < 0.05), univariate regression (p < 0.1), and lasso regression (nonzero coefficient). Discriminant models of BC versus HCs and TNBC versus non-TNBC were established using binary logistic regression.

Results: We developed a valid discriminant model based on three-biomarker panel (formic acid, TPA2, and L6TG) that could distinguish patients with BC from HCs. The area under the receiver operating characteristic curve (AUC) was 0.999 (95% confidence interval [CI]: 0.995-1.000) and 0.990 (95% CI: 0.959-1.000) in the training and validation sets, respectively. Based on the panel (D-dimer, CA15-3, CEA, L5CH, glutamine, and ornithine), a discriminant model was established to differentiate between TNBC and non-TNBC, with AUC of 0.892 (95% CI: 0.778-0.967) and 0.905 (95% CI: 0.754-0.987) in the training and validation sets, respectively.

Conclusion: This study revealed lipidomic and metabolomic differences between BC versus HCs and TNBC versus non-TNBC. Two validated discriminatory models established against lipidomic and metabolomic differences can accurately distinguish BC from HCs and TNBC from non-TNBC.

Impact: Two validated discriminatory models can be used for early BC screening and help BC patients avoid time-consuming, expensive, and dangerous BC screening.