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Proteomic characterization of the normal human medial meniscus body using data-independent acquisition mass spectrometry.

ABSTRACT: Recent research suggests an important role of the meniscus in the development of knee osteoarthritis. We, therefore, aimed to analyze the proteome of the normal human meniscus body, and specifically to gain new knowledge on global protein expression in the different radial zones. Medial menisci were retrieved from the right knees of 10 human cadaveric donors, from which we cut a 2 mm radial slice from the mid-portion of the meniscal body. This slice was further divided into three zones: inner, middle, and peripheral. Proteins were extracted and prepared for mass spectrometric analysis using data-independent acquisition. We performed subsequent data searches using Spectronaut Pulsar and used fixed-effect linear regression models for statistical analysis. We identified 638 proteins and after statistical analysis, we observed the greatest number of differentially expressed proteins between the inner and peripheral zones (163 proteins) and the peripheral and middle zones (136 proteins), with myocilin being the protein with the largest fold-change in both comparisons. Chondroadherin was one of eight proteins that differed between the inner and middle zones. Functional enrichment analyses showed that the peripheral one-third of the medial meniscus body differed substantially from the two more centrally located zones, which were more similar to each other. This is probably related to the higher content of cells and vascularization in the peripheral zone, whereas the middle and inner zones of the meniscal body appear to be more similar to hyaline cartilage, with high levels of extracellular matrix proteins such as aggrecan and collagen type II.

SUBMITTER: Folkesson E

PROVIDER: S-EPMC7610686 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHalting progression of chronic kidney disease (CKD) to established end stage kidney disease is a major goal of global health research. The mechanism of CKD progression involves pro-inflammatory, pro-fibrotic, and vascular pathways, but pathophysiological differentiation is currently lacking.MethodsPlasma samples of 414 non-dialysis CKD patients, 170 fast progressors (with ∂ eGFR-3 ml/min/1.73 m2/year or worse) and 244 stable patients (∂ eGFR of - 0.5 to + 1 ml/min/1.73 m2/year) with a broad range of kidney disease aetiologies, were obtained and interrogated for proteomic signals with SWATH-MS. We applied a machine learning approach to feature selection of proteins quantifiable in at least 20% of the samples, using the Boruta algorithm. Biological pathways enriched by these proteins were identified using ClueGo pathway analyses.ResultsThe resulting digitised proteomic maps inclusive of 626 proteins were investigated in tandem with available clinical data to identify biomarkers of progression. The machine learning model using Boruta Feature Selection identified 25 biomarkers as being important to progression type classification (Area Under the Curve = 0.81, Accuracy = 0.72). Our functional enrichment analysis revealed associations with the complement cascade pathway, which is relevant to CKD as the kidney is particularly vulnerable to complement overactivation. This provides further evidence to target complement inhibition as a potential approach to modulating the progression of diabetic nephropathy. Proteins involved in the ubiquitin-proteasome pathway, a crucial protein degradation system, were also found to be significantly enriched.ConclusionsThe in-depth proteomic characterisation of this large-scale CKD cohort is a step toward generating mechanism-based hypotheses that might lend themselves to future drug targeting. Candidate biomarkers will be validated in samples from selected patients in other large non-dialysis CKD cohorts using a targeted mass spectrometric analysis.

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Proteomic characterization of the normal human medial meniscus body using data-independent acquisition mass spectrometry.

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