Quantitative Proteomic Analysis of Synovial Tissue Reveals That Upregulated OLFM4 Aggravates Inflammation in Rheumatoid Arthritis

An AI-assisted morphoproteomic approach is a supportive tool in esophagitis-related precision medicine

Esophagitis is a frequent, but at the molecular level poorly characterized condition with diverse underlying etiologies and treatments. Correct diagnosis can be challenging due to partially overlapping histological features. By proteomic profiling of routine diagnostic FFPE biopsy specimens (n = 55) representing controls, Reflux- (GERD), Eosinophilic-(EoE), Crohn's-(CD), Herpes simplex (HSV) and Candida (CA)-esophagitis by LC-MS/MS (DIA), we identified distinct signatures and functional networks (e.g. mitochondrial translation (EoE), immunoproteasome, complement and coagulations system (CD), ribosomal biogenesis (GERD)), and pathogen-specific proteins for HSV and CA. Moreover, combining these signatures with histological parameters in a machine learning model achieved high diagnostic accuracy (100% training set, 93.8% test set), and supported diagnostic decisions in borderline/challenging cases. Applied to a young patient representing a use case, the external GERD diagnosis could be revised to CD and ICAM1 was identified as highly abundant therapeutic target. This resulted in CyclosporinA as a personalized treatment recommendation by the local multidisciplinary molecular inflammation board. Our integrated AI-assisted morphoproteomic approach allows deeper insights in disease-specific molecular alterations and represents a promising tool in esophagitis-related precision medicine.

Defining the extracellular matrix in non-cartilage soft-tissues in osteoarthritis: a systematic review

Aims

Extracellular matrix (ECM) is a critical determinant of tissue mechanobiology, yet remains poorly characterized in joint tissues beyond cartilage in osteoarthritis (OA). This review aimed to define the composition and architecture of non-cartilage soft joint tissue structural ECM in human OA, and to compare the changes observed in humans with those seen in animal models of the disease.

Methods

A systematic search strategy, devised using relevant matrix, tissue, and disease nomenclature, was run through the MEDLINE, Embase, and Scopus databases. Demographic, clinical, and biological data were extracted from eligible studies. Bias analysis was performed.

Results

A total of 161 studies were included, which covered capsule, ligaments, meniscus, skeletal muscle, synovium, and tendon in both humans and animals, and fat pad and intervertebral disc in humans only. These studies covered a wide variety of ECM features, including individual ECM components (i.e. collagens, proteoglycans, and glycoproteins), ECM architecture (i.e. collagen fibre organization and diameter), and viscoelastic properties (i.e. elastic and compressive modulus). Some ECM changes, notably calcification and the loss of collagen fibre organization, have been extensively studied across osteoarthritic tissues. However, most ECM features were only studied by one or a few papers in each tissue. When comparisons were possible, the results from animal experiments largely concurred with those from human studies, although some findings were contradictory.

Conclusion

Changes in ECM composition and architecture occur throughout non-cartilage soft tissues in the osteoarthritic joint, but most of these remain poorly defined due to the low number of studies and lack of healthy comparator groups.

Knockdown of OLFM4 protects cardiomyocytes from sepsis by inhibiting apoptosis and inflammatory responses

Sepsis is a systemic inflammatory response that can result in cardiac insufficiency or heart failure known as septic myocardial injury. A previous study identified OLFM4 as an important gene in sepsis through bioinformatics analysis. However, there is limited research on the regulatory functions of OLFM4 in sepsis-triggered myocardial injury, and the related molecular mechanisms remain unclear. In this study, the protein expression of OLFM4 was found to be significantly elevated in LPS-stimulated H9C2 cells, and its suppression enhanced cell proliferation and reduced cell apoptosis in LPS-triggered H9C2 cells. The inflammatory factors TNF-α, IL-6, and IL-1β were increased after LPS treatment, and these effects were mitigated after silencing OLFM4. Moreover, it was confirmed that inhibition of OLFM4 attenuated the NF-κB signaling pathway. In conclusion, the knockdown of OLFM4 protected cardiomyocytes from sepsis by inhibiting apoptosis and inflammatory responses via the NF-κB pathway. These findings provide important insights into the regulatory functions of OLFM4 in the progression of septic myocardial injury.

The Genetics and Functional Genomics of Osteoarthritis

Osteoarthritis is the most prevalent whole-joint degenerative disorder, and is characterized by the degradation of articular cartilage and the underlying bone structures. Almost 600 million people are affected by osteoarthritis worldwide. No curative treatments are available, and management strategies focus mostly on pain relief. Here, we provide a comprehensive overview of the available human genetic and functional genomics studies for osteoarthritis to date and delineate how these studies have helped shed light on disease etiopathology. We highlight genetic discoveries from genome-wide association studies and provide a detailed overview of molecular-level investigations in osteoarthritis tissues, including methylation-, transcriptomics-, and proteomics-level analyses. We review how functional genomics data from different molecular levels have helped to prioritize effector genes that can be used as drug targets or drug-repurposing opportunities. Finally, we discuss future directions with the potential to drive a step change in osteoarthritis research.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

Integrative Proteomics and N-Glycoproteomics Analyses of Rheumatoid Arthritis Synovium Reveal Immune-Associated Glycopeptides

Rheumatoid arthritis (RA) is a typical autoimmune disease characterized by synovial inflammation, synovial tissue hyperplasia, and destruction of bone and cartilage. Protein glycosylation plays key roles in the pathogenesis of RA but in-depth glycoproteomics analysis of synovial tissues is still lacking. Here, by using a strategy to quantify intact N-glycopeptides, we identified 1260 intact N-glycopeptides from 481 N-glycosites on 334 glycoproteins in RA synovium. Bioinformatics analysis revealed that the hyper-glycosylated proteins in RA were closely linked to immune responses. By using DNASTAR software, we identified 20 N-glycopeptides whose prototype peptides were highly immunogenic. We next calculated the enrichment scores of nine types of immune cells using specific gene sets from public single-cell transcriptomics data of RA and revealed that the N-glycosylation levels at some sites, such as IGSF10_N2147, MOXD2P_N404, and PTCH2_N812, were significantly correlated with the enrichment scores of certain immune cell types. Furthermore, we showed that aberrant N-glycosylation in the RA synovium was related to increased expression of glycosylation enzymes. Collectively, this work presents, for the first time, the N-glycoproteome of RA synovium and describes immune-associated glycosylation, providing novel insights into RA pathogenesis.

Significance of differential expression of OLFM4 in the development of endometrial adenocarcinoma

The incidence of endometrial adenocarcinoma (EA) has increased worldwide in recent years due to the widespread use of estrogen therapy and the overall increase in life expectancy. However, we know of no sensitive molecular index that can be used to predict the onset of EA, evaluate the therapeutic effects of treatment agents, or provide prognostic benefit in post-treatment follow-up. To explore the correlation between human olfactomedin 4 (OLFM4) and the clinicopathologic parameters of EA, and to determine the precise involvement of OLFM4 as a related factor in the occurrence and development of EA. We enrolled 61 gynecologic patients for a retrospective study at the Tai'an Central Hospital of Shandong Province from January 1, 2016, to June 30, 2022. We determined the expression levels of estrogen receptor α (ERα), progesterone receptor (PR), and OLFM4 proteins in endometrial tissue with the immunohistochemical S-P staining method, and analyzed the correlations among ERα, PR, and OLFM4 protein expression levels and with the pathologic stage, histologic grade, myometrial invasiveness, and lymphatic metastasis of EA. The expression levels of OLFM4 in EA were higher than in normal endometrium (P = .036). The expression level of OLFM4 protein in stage II-III patients was higher than that in stage I patients (P = .034), and the expression levels of ERα and PR proteins in EA were lower than those in normal endometrial tissue (P = .014 and P = .0005). While we observed no correlation in endometrial tissues of disparate pathologic types between OLFM4 and the expression levels of ERα and PR proteins, we noted a positive correlation between the expression levels of ERα and PR protein. The expression level of OLFM4 protein increased with the malignant degree of endometrial lesions and OLFM4 protein expression was related to the FIGO stage of EA. And OLFM4 protein can be used as 1 of the potential diagnostic factors for endometrial lesions, which is worthy of further study.

Profiling of proteome changes in plasma of HIV-infected patients receiving antiretroviral therapy

PURPOSE

Antiretroviral therapy (ART) prevents human immunodeficiency virus (HIV)-1 onward transmission and disease progression, leading to excellent prognosis in people living with HIV-1 (PWH). However, side effects, complications, and impaired immune reconstitution persist in some patients treated with ART. We aimed to profile proteome changes in plasma before and after ART to identify the molecular pathways altered by ART.

EXPERIMENTAL DESIGN

Quantitative proteomics analysis based on tandem mass tag (TMT) labeling was used to profile proteome changes of paired plasma samples from HIV-1 patients before receiving ART and after ART treatment.

RESULTS

A total of 1398 protein groups (PGs) were identified, in which 18 proteins were downregulated and 50 were upregulated in plasma from ART treated patients. Based on Ingenuity Pathway analysis (IPA), gap junction signaling and actin cytoskeleton signaling were enriched among upregulated proteins, while downregulated proteins were mainly participated in IL-15 signaling pathway. Patients with the low level of CSF1R and the high levels of MINPP1 and TGM3 showed better CD4⁺ T-cell recovery.

CONCLUSIONS

The present study provided plasma proteome changes after ART to elucidate the underlying mechanistic pathways in response to ART, and also identified potential targets to prompt immune reconstitution.

Peroxiredoxin 4 regulates tumor-cell-like characteristics of fibroblast-like synoviocytes in rheumatoid arthritis through PI3k/Akt signaling pathway

Peroxiredoxin-4 (PRDX4), a member of PRDX family, which played an important role in scavenging reactive oxygen species (ROS). The up-regulation of PRDX4 in synovial tissue and synovial fluid from rheumatoid arthritis (RA) patients has been reported. However, the biological functions of PRDX4 in fibroblast-like synoviocytes (RA-FLS) remains unclear. In this research, we reveal that expression of PRDX4 was notably increased in RA synovial tissue, especially in hyperplastic synovial tissue. PRDX4 silencing significantly inhibited the tumor cell-like behaviors and mRNA expression of matrix metalloproteinases (MMPs) in RA-FLS. Furthermore, overexpression of PRDX4 markedly activated PI3K/Akt signaling pathway, which can be reverted by Akt inhibitor MK-2206. These observations identified elevated PRDX4 may regulates the tumor cell-like biological characteristic of RA-FLS via Pi3k/Akt pathway. Targeting PRDX4 and its downstream signaling pathway might provide a potential diagnostic markers and therapeutic target for RA.