PROTEOMICS – Clinical Applications最新文献

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: 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: 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.

Purpose: Apolipoprotein monitoring is useful for diagnosing cardiovascular diseases, as they are risk factors of arteriosclerosis and other neutral fat-related diseases. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is advantageous for simultaneous apolipoprotein quantification, differentiation, and standardization including their isoforms. However, fast and straightforward sample preparation that retains quantification accuracy remains challenging in clinical MS.

Experimental design: We developed a simultaneous assay for serum apolipoprotein A-I (ApoA-I), apolipoprotein B100 family, and apolipoprotein C-III (ApoC-III) using a high-throughput LC-MS/MS platform coupled with a BRAVO system. The assay was simplified by using sodium deoxycholate and trypsin/lys-C without reduction and alkylation steps.

Results: Simple sample preparation reduced turnaround time by 1.5 h and neat goat serum was chosen as an optimal calibration matrix for accurate protein quantification. Assay precision, linearity, correlation, accuracy, limit of detection (LOD), limit of quantitation (LOQ), and carryover were validated according to CLSI guidelines over 41 days using more than 100 human serum samples. Good correlation compared with turbidimetric immunoassay (TIA) was observed by Deming regression for all analytes.

Conclusions and clinical relevance: A high-throughput LC-MS/MS and BRAVO assay for simultaneous apolipoprotein analysis was validated using a simple preparation method with a human serum calibrator in goat serum matrix. The assay is readily expandable to include other target serum proteins and/or their isoforms.

Purpose: Robust, affordable plasma proteomic biomarker workflows are needed for large-scale clinical studies. We evaluated aspects of sample preparation to allow liquid chromatography-mass spectrometry (LC-MS) analysis of more than 1500 samples from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial of adults with type 2 diabetes.

Methods: Using LC-MS with data-independent acquisition we evaluated four variables: plasma protein depletion, EDTA or citrated anti-coagulant blood collection tubes, plasma lipid depletion strategies and plasma freeze-thaw cycles. Optimised methods were applied in a pilot study of FIELD participants.

Results: LC-MS of undepleted plasma conducted over a 45 min gradient yielded 172 proteins after excluding immunoglobulin isoforms. Cibachrome-blue-based depletion yielded additional proteins but with cost and time expenses, while immunodepleting albumin and IgG provided few additional identifications. Only minor variations were associated with blood collection tube type, delipidation methods and freeze-thaw cycles. From 65 batches involving over 1500 injections, the median intra-batch quantitative differences in the top 100 proteins of the plasma external standard were less than 2%. Fenofibrate altered seven plasma proteins.

Conclusions and clinical relevance: A robust plasma handling and LC-MS proteomics workflow for abundant plasma proteins has been developed for large-scale biomarker studies that balance proteomic depth with time and resource costs.

Purpose: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide, mainly due to late diagnosis and poor prognosis. Saliva is an important source for discovering biomarkers and contains an abundance of biological information. The purpose of this study was to determine whether galactosylation levels of salivary proteins are associated with LC.

Experimental design: First, we analyzed the alterations of the glycopatterns recognized by Bandeiraea Simplicifolia Lectin I (BS-I) in five groups (healthy volunteers [HV]: 28, benign pulmonary disease [BPD]: 27, lung adenocarcinoma [ADC]: 39, squamous cell carcinoma [SCC]: 28, small-cell lung cancer [SCLC]: 22) of 144 saliva samples using lectin microarrays. Pooled samples from each group were subsequently validated by the lectin blotting technique. Finally, the N-glycan profiles of their salivary glycoproteins isolated by the BS-I-magnetic particle conjugates from pooled samples for each group were analyzed by MALDI-TOF/TOF-MS.

Results: The results showed that the expression level of galactosylated glycans recognized by BS-I was significantly increased in patients with LC compared with BPD and HV. Receiver operating characteristic (ROC) analysis indicated that the levels of salivary glycopattern recognized by BS-I could discriminate lung disease (BPD, ADC, SCC, and SCLC) and HV with an AUC of 0.700 (95% CI: 0.589-0.812), and discriminate LC and BPD with an AUC of 0.860 (95% CI: 0.763-0.956). Also, the proportion of galactosylated N-glycans in ADC (38.4%), SCC (43.1%), and SCLC (39.5%) increased compared to HV (30.1%) and BPD (33.7%), and two galactosylated N-glycan peaks (m/z 1828.683, 2418.853) could be identified only in the LC groups (ADC, SCC, and SCLC).

Conclusions and clinical relevance: These findings could provide crucial information on galactosylated N-linked glycans associated with LC and facilitate the study of LC biomarkers based on precise alterations of galactosylated N-glycans in saliva.