Clinical proteomics最新文献

Background: The pathogenesis of type 2 diabetes (T2DM) remained to be fully understood. Meanwhile, exosome have shown its potential to further advance diabetes research as a rich source of biomarkers. This study aims to explore the proteomic profiles of circulating plasma exosomes in individuals with varying glucose statuses and offer potentially new perspective on the pathogenesis of T2DM.

Methods: Participants with different glucose status were recruited according to the diagnostic criteria of the American diabetes association. Plasma exosomes were collected and went through data independent acquisition mass spectrometry quantitative proteomics analysis. Differential proteins identified through pairwise group comparisons underwent further analysis like protein-protein interaction (PPI) and gene ontology (GO) to reveal their functions and interactions.

Results: A total of 75 participants (25 euglycemia; 25 prediabetes; 25 diabetes) were included in this study. Principal coordinates analysis showed that the proteomic patterns of exosomes in groups with prediabetes and diabetes exhibited certain similarities, contrasting with those in euglycemic individuals. From pairwise differential protein comparison, 32 proteins were selected for PPI and functional analysis, of which 7 were deemed significant within the network. GO annotations highlighted a close link between immunity and T2DM. Local STRING clustering, Reactome and KEGG pathway analysis all indicated great significance of complement and coagulation cascades.

Conclusion: The proteomic patterns of exosomes in groups with different glucose levels exhibited that even before overt diabetes manifests, the circulating exosome cargo signals immune and coagulatory activation distinct from normal physiology.

Introduction: Amyloidosis typing is crucial to determine the best therapeutic strategy for patients. Since conventional histological techniques often fail, the identification of amyloid precursors by mass spectrometry has become the new standard. However, without quantification, selecting the amyloid precursor from proteins that may be ubiquitous under non-pathological conditions can be equivocal. Therefore, we quantified protein enrichment in amyloid deposits to improve amyloidosis typing.

Methods: Protein enrichment was measured by extracted ion chromatogram-based label-free quantification by comparing a microdissected amyloid area to a non-amyloid area. We assessed the discrimination ability of candidate precursors with this approach compared to the two practiced identification methods.

Results: As a proof of concept, we selected 9 cases including the most common amyloidosis subtypes, 6 typed by immunohistochemistry and 3 inconclusive by immunohistochemistry. Proteins associated with amyloid deposits were identified in all samples, confirming the pathology. Where the routine clinical mass spectrometric identification techniques allowed unambiguous conclusions for 3 of 9 cases, quantification of the enrichment ratio in amyloid deposits allowed unambiguous precursor selection in all cases.

Conclusion: Quantification of precursor enrichment in amyloid deposits is a promising optimization for amyloidosis typing, which should be studied in larger cohorts. Incorporated into routine clinical processes, it could improve patient care in difficult diagnostic situations.

Background: Alzheimer's disease (AD) biomarkers in plasma and cerebrospinal fluid (CSF) are useful for disease diagnosis, prognosis, risk assessment and monitoring therapy response, as well as for uncovering altered disease pathways. Previously, we and others cloned a novel gene, KLK6, which encodes a serine protease of the kallikrein family. The protein (hK6) is highly expressed in the brain, spinal cord and cerebellum.

Methods: To examine the correlation of hK6 concentration in CSF with various clinicopathological variables in AD, we used a quantitative ELISA system. The variables examined included patient age, sex, MMSE score, APOE status, amyloid β 1-42 (Αβ1-42), phosphorylated Tau 181 (p-Tau181), total Tau (t-Tau). Previously, using a cohort of Swedish and Norwegian patients, we established a positive correlation between CSF hK6 and age as well as the levels of core AD biomarkers in four groups of patients (cognitively normal, MCI without progression to AD, MCI with progression to AD within 2 years and AD dementia). In this investigation, our goal was to validate these previous data with a large and independent patient cohort from Spain.

Results: We found that CSF hK6 is minimally or not affected by patient age and sex, but it significantly correlates with MMSE score and CSF Aβ1-42, p-Tau1811 and t-Tau.

Conclusions: We conclude that these correlations further support our previous findings and suggest that hK6 may be an additional biomarker for AD and may play some role in the pathogenesis of AD.

Background: Sepsis recognition in the ICU remains variable and relies on consensus clinical criteria rather than biomarker-defined rules. Routine laboratory and physiologic data often overlap with noninfectious critical illness, obscuring early identification. We evaluated whether discovery proteomics could prioritize a concise set of routinely obtainable clinical variables, yielding a practical, clinic-first model that distinguishes sepsis from other critical illness.

Methods: In a prospective, single-center pilot at an academic medical center, we enrolled adults within 48 h of critical illness onset (sepsis and non-sepsis comparators). Plasma proteomics by LC-MS/MS with diaPASEF identified proteins differentiating groups and guided selection of proteome-enriched routine variables for modeling. A Random Forest classifier was trained in a Discovery cohort (n = 55) and evaluated in an independent Validation cohort (n = 59), with prespecified attention to discrimination, parsimony, and feasibility for electronic health record (EHR) deployment.

Results: Twelve plasma proteins differed between groups at FDR < 0.10, supporting biological separation. A parsimonious model using routine predictors ± CCL3 achieved AUC 0.73 in Discovery and AUC 0.76 in the independent Validation cohort. Recursive feature elimination demonstrated a parsimony plateau at ~ 9 variables; beyond this threshold, further reduction degraded accuracy. Notably, blood urea nitrogen, CCL3 (measured by multiplex immunoassay), and creatinine were the final features retained before performance declined, aligning with renal stress and inflammatory signaling. Figures present ROC curves and the parsimony profile, highlighting a minimal variable set compatible with typical ICU workflows and decision-support systems.

Conclusions: A proteomics-informed, clinic-first strategy produced a parsimonious set of routine variables that discriminated sepsis from other ICU critical illness with clinically meaningful accuracy and an immediately actionable footprint. Because most predictors are routinely captured in the EHR, the model is EHR-compatible; CCL3 is readily measurable on standard immunoassay platforms if adopted locally. These findings justify multicenter studies to confirm generalizability and calibration, evaluate real-time integration into ICU workflows, and test whether an early recognition adjunct improves timeliness of sepsis care and patient outcomes.

Background: Previous investigations have shown that the absence of typical breast symptoms is associated with unfavorable outcomes after an acute myocardial infarction (AMI). Delayed diagnosis and therapy could not explain these results, so other causes seem to be involved. Therefore, in the present analysis the association between inflammatory plasma proteins and typical chest pain symptoms in hospitalized patients with acute ST-elevation myocardial infarction (STEMI) was investigated.

Methods: Data from 395 STEMI patients registered by the population-based Myocardial Infarction Registry Augsburg between 2009 and 2013 were used for analysis. The OLINK inflammatory panel including a total of 92 cytokines was measured in arterial blood samples, which were obtained immediately after hospital admission within the scope of cardiac catheterization. The associations between the inflammation markers and typical chest pain were examined by multiple logistic regression analyses.

Results: Altogether, 10.9% of the STEMI patients did not present with typical chest pain. The inflammatory markers IL8, IL6, FGF-21, CD40, CST5, ADA, OPG, PD-L1, TNFRSF9 and STAMBP were significantly inversely associated with typical chest pain after FDR-adjustment. The strongest associations were found for FGF-21, CST5 and CD40.

Conclusions: These results suggest that a dysregulated inflammatory status is associated with a lack of typical chest pain in AMI patients. Beyond acute-phase inflammatory interleukins elevated within the early phase of an AMI, such as IL-6, hepatokines and transmembrane proteins seem to be associated with AMI symptoms. Further research into the causal mechanisms of these associations is necessary.

Background: Pseudomonas aeruginosa is a multidrug-resistant pathogenic bacterium that poses a substantial threat to global public health because of its resistance to antibiotics, and especially to last-resort colistin. The aim of this study is to perform a comparative analysis between the antibiotic-sensitive isolate A and the resistant isolate C (8 µg/mL for isolate A and 128 µg/mL for isolate C), with the intent of elucidating the discrete molecular mechanisms underpinning resistance. This investigation seeks to distinguish between pathways inherently expressed in the absence of antibiotic exposure (acquired resistance) and those activated in response to antibiotic challenge (induced resistance), thereby providing deeper insights into the multifaceted nature of antimicrobial resistance in P. aeruginosa.

Results: Proteomic analysis, performed under basal conditions and after exposure to increasing doses of colistin, revealed that, although both isolates have intrinsic resistance to several antimicrobials, the mechanisms underlying colistin resistance diverge significantly. While isolate A showed a stable proteomic response, characterized by the overexpression of proteins related to membrane remodeling and efflux systems, isolate C demonstrated a more dynamic response, evidenced by metabolic adaptations and oxidative stress mitigation mechanisms. These differences suggest that each isolate employs specific strategies to cope with antimicrobial pressure, which has direct implications for the choice of alternative therapies and the development of optimized dosing regimens.

Conclusions: The findings have direct implications for the choice of alternative therapies and the development of optimized dosing regimen. In summary, the results reinforce the complexity of resistance mechanisms in P. aeruginosa and highlight the importance of personalized therapeutic approaches for the management of infections caused by multidrug-resistant isolates.

Background: Hypermobile Ehlers-Danlos syndrome (hEDS) and hypermobility spectrum disorders (HSD) are prevalent conditions characterized by symptomatic joint hypermobility and a substantial public health burden, for which no causal treatment is currently available. In the absence of a defined molecular basis or validated diagnostic biomarkers, diagnosis of hEDS relies solely on the 2017 clinical criteria, with individuals who do not meet these criteria classified as HSD. Although currently categorized as distinct entities, the biological relationship between hEDS and HSD remains a subject of active debate within the scientific community.

Methods: We performed targeted serum proteomic profiling using the proximity extension assay technology, quantifying 458 proteins in large cohorts of hEDS (n = 88) and HSD (n = 88) patients, alongside healthy controls (n = 176).

Results: Compared to controls, 54 proteins were differentially expressed in hEDS patients and 49 in HSD patients. No statistically significant differences were observed between hEDS and HSD groups. When the combined patient cohort was analyzed, 69 proteins showed differential expression relative to controls. The proteins were distributed across the predefined PEA panels, which include proteins involved in inflammatory, cardiometabolic, neurological, organ damage, and developmental processes.

Conclusions: This targeted serum proteomic analysis identified overlapping protein expression changes in hEDS and HSD relative to controls, while revealing no detectable differences between the two conditions. These findings suggest the presence of shared molecular features across the hEDS/HSD spectrum and identify a set of candidate circulating proteins that warrant further investigation and independent validation in larger, well-characterized cohorts.

Background: Estrogen receptor-positive (ER⁺) breast cancer, a leading cause of female cancer mortality, faces therapeutic challenges due to endocrine resistance. Plasma proteins, bridging genetic variation and disease phenotypes, offer potential biomarkers and therapeutic targets, yet their causal roles in the pathogenesis of ER⁺ breast cancer remain underexplored.

Methods: Using two-sample Mendelian randomization (TSMR) and Bayesian colocalization, we analyzed associations between plasma protein quantitative trait loci from deCODE/Fenland cohorts and ER⁺ breast cancer. DSigDB predicted drugs targeting identified protein, while TCGA assessed the prognostic value.

Results: TSMR identified 38 causal plasma proteins (deCODE), with Bayesian analysis prioritizing 12 candidates. IL3RA emerged as stable and novel protective factor, validated in Fenland data. TCGA revealed reduced IL3RA expression in ER⁺ tumors, with higher levels correlating with improved survival and favorable clinicopathological features, particularly in ER⁺/PR⁺ cases. Tumor microenvironment analysis revealed that IL3RA expression levels significantly correlated with immune landscape alterations in ER⁺ breast cancer. Immune infiltration analysis demonstrated significant associations between IL3RA expression levels and multiple immune cell populations in ER⁺ breast cancer, particularly CD8⁺ T cells, neutrophils, M0 macrophages, and M2 macrophages. DSigDB identified panobinostat, arbutin, clindamycin, cimetidine, and chlorzoxazone as IL3RA-targeting drugs.

Conclusions: Our study identified IL3RA as novel biomarker and therapeutic target for ER⁺ breast cancer. Further validation and mechanistic studies are warranted to advance precision oncology strategies for ER⁺ breast cancer management.