Cathepsin S, a new serum biomarker of sarcoidosis discovered by transcriptome analysis of alveolar macrophages

Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies

Sarcoidosis is a chronic granulomatous disorder characterized by unknown etiology, undetermined mechanisms, and non-specific therapies except TNF blockade. To improve our understanding of the pathogenicity and to predict the outcomes of the disease, the identification of new biomarkers and molecular endotypes is sorely needed. In this study, we systematically evaluate the biomarkers identified through Omics and non-Omics approaches in sarcoidosis. Most of the currently documented biomarkers for sarcoidosis are mainly identified through conventional "one-for-all" non-Omics targeted studies. Although the application of machine learning algorithms to identify biomarkers and endotypes from unbiased comprehensive Omics studies is still in its infancy, a series of biomarkers, overwhelmingly for diagnosis to differentiate sarcoidosis from healthy controls have been reported. In view of the fact that current biomarker profiles in sarcoidosis are scarce, fragmented and mostly not validated, there is an urgent need to identify novel sarcoidosis biomarkers and molecular endotypes using more advanced Omics approaches to facilitate disease diagnosis and prognosis, resolve disease heterogeneity, and facilitate personalized medicine.

Mucopolysaccharidosis Type IVA: Extracellular Matrix Biomarkers in Cardiovascular Disease

Cardiovascular disease (CVD) in Mucopolysaccharidosis Type IVA (Morquio A), signified by valvular disease and cardiac hypertrophy, is the second leading cause of death and remains untouched by current therapies. Enzyme replacement therapy (ERT) is the gold-standard treatment for MPS disorders including Morquio A. Early administration of ERT improves outcomes of patients from childhood to adulthood while posing new challenges including prognosis of CVD and ERT's negligible effect on cardiovascular health. Thus, having accurate biomarkers for CVD could be critical. Here we show that cathepsin S (CTSS) and elastin (ELN) can be used as biomarkers of extracellular matrix remodeling in Morquio A disease. We found in a cohort of 54 treatment naïve Morquio A patients and 74 normal controls that CTSS shows promising attributes as a biomarker in young Morquio A children. On the other hand, ELN shows promising attributes as a biomarker in adolescent and adult Morquio A. Plasma/urine keratan sulfate (KS), and urinary glycosaminoglycan (GAG) levels were significantly higher in Morquio A patients (p < 0.001) which decreased with age of patients. CTSS levels did not correlate with patients' phenotypic severity but differed significantly between patients (median range 5.45-8.52 ng/mL) and normal controls (median range 9.61-15.9 ng/mL; p < 0.001). We also studied α -2-macroglobulin (A2M), C-reactive protein (CRP), and circulating vascular cell adhesion molecule-1 (sVCAM-1) in a subset of samples to understand the relation between ECM biomarkers and the severity of CVD in Morquio A patients. Our experiments revealed that CRP and sVCAM-1 levels were lower in Morquio A patients compared to normal controls. We also observed a strong inverse correlation between urine/plasma KS and CRP (p = 0.013 and p = 0.022, respectively) in Morquio A patients as well as a moderate correlation between sVCAM-1 and CTSS in Morquio A patients at all ages (p = 0.03). As the first study to date investigating CTSS and ELN levels in Morquio A patients and in the normal population, our results establish a starting point for more elaborate studies in larger populations to understand how CTSS and ELN levels correlate with Morquio A severity.

Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis

Background: Atrial fibrillation (AF) is the most common tachyarrhythmia in the clinic, leading to high morbidity and mortality. Although many studies on AF have been conducted, the molecular mechanism of AF has not been fully elucidated. This study was designed to explore the molecular mechanism of AF using integrative bioinformatics analysis and provide new insights into the pathophysiology of AF. Methods: The GSE115574 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells. Differentially expressed genes (DEGs) were identified through the limma package in R language. Weighted gene correlation network analysis (WGCNA) was performed to cluster DEGs into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on DEGs in the significant module, and hub genes were identified based on the protein-protein interaction (PPI) network. Hub genes were then verified using quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of 2,350 DEGs were identified and clustered into eleven modules using WGCNA. The magenta module with 246 genes was identified as the key module associated with M1 macrophages with the highest correlation coefficient. Three hub genes (CTSS, CSF2RB, and NCF2) were identified. The results verified using three other datasets and qRT-PCR demonstrated that the expression levels of these three genes in patients with AF were significantly higher than those in patients with SR, which were consistent with the bioinformatic analysis. Conclusion: Three novel genes identified using comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism in AF, which provide potential therapeutic targets and new insights into the treatment and early detection of AF.