An Integrative Transcriptomic Analysis of Systemic Juvenile Idiopathic Arthritis for Identifying Potential Genetic Markers and Drug Candidates

Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis

BACKGROUND

Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear.

METHODS

To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk.

RESULTS

We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks.

CONCLUSION

Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

Identification of hub genes and drug candidates for NF2-related vestibular schwannoma by bioinformatics tools

Neurofibromatosis type 2 (NF2)-related vestibular schwannoma (NF2-VS) is a rare genetic disorder that results in bilateral acoustic neuromas. However, the exact pathogenesis of the disease is still unclear. This study aims to use bioinformatics analyses to identify potential hub genes and therapeutic. We retrieved the mRNA expression profiles (GSE108524 and GSE141801) of NF2-VS from the database, and selected the leading 25% genes with the most variance across samples for weighted correlation network analysis. Subsequently, we conducted gene ontology term and Kyoto Encyclopedia of Genes and Genomes signaling network enrichment analyses. The STRING database was employed for protein-protein interaction (PPI) axis construction. The mRNA-miRNA modulatory network was generated via the miRTarBase database. Differentially expressed genes (DEGs) were identified via the R package "limma" in both datasets, and hub genes were screened via intersection of common DEGs, candidate hub genes from the PPI axis, and candidate hub genes from the key module. Finally, common DEGs were uploaded onto the connectivity map database to determine drug candidates. Based on our observations, the blue module exhibited the most significant relation to NF2-VS, and it included the NF2 gene. Using enrichment analysis, we demonstrated that the blue modules were intricately linked to modulations of cell proliferation, migration, adhesion, junction, and actin skeleton. Overall, 356 common DEGs were screened in both datasets, and 33 genes carrying a degree > 15 were chosen as candidate hub genes in the PPI axis. Subsequently, 4 genes, namely, GLUL, CAV1, MYH11, and CCND1 were recognized as real hub genes. In addition, 10 drugs with enrichment scores < -0.7 were identified as drug candidates. Our conclusions offered a novel insight into the potential underlying mechanisms behind NF2-VS. These findings may facilitate the identification of novel therapeutic targets in the future.

Identification of biomarkers of acne based on transcriptome analysis and combined with network pharmacology to explore the therapeutic mechanism of Jinhuang ointment

The incidence of acne is on the rise due to unhealthy diet and living habits. Jinhuang ointment (JHO) is a classic prescription composed of 10 kinds of commonly used Chinese herbal medicine, which has been widely used in clinical prevention and treatment of skin inflammatory diseases since ancient times. However, the pharmacological mechanism and target of JHO are not clear. The acne microarray dataset was downloaded from gene expression omnibus database to identify differentially expressed genes (DEG). Immune infiltration was analyzed by CiberSort algorithm. HUB gene was identified by protein-protein interaction network. The gene expression omnibus dataset validates the biomarkers of acne with high diagnostic value. The potential active components and targets of JHO were obtained through Traditional Chinese Medicine Systems Pharmacology database, and the therapeutic targets were obtained by crossing with disease targets. R-packet is used for enrichment analysis. Molecular docking using Auto Dock Tools. A total of 202 DEGs were identified from 12 skin samples in the GSE6475. Immune infiltration analysis showed that there were a large number of macrophages and mast cells in acne skin. Gene set enrichment analysis analysis showed that DEGS was mainly involved in bacterial reaction, inflammatory reaction and so on. Six central genes and gene cluster modules were identified by Cytoscape software. A total of 185 JHO active components and 220 targets were obtained, of which 10 targets were potential targets for JHO in the treatment of acne. Kyoto encyclopedia of genes and genomes enrichment analysis showed that JHO treatment of acne was mainly related to Toll-like receptors, IL-17 and other signal pathways. The results of molecular docking showed that 5 active compounds in JHO had strong binding activity to the core protein receptor. IL-1 β, CXCL8, toll-like receptor 2, CXCL2, LCN2, and secretory phosphoprotein 1 may be potential biomarkers for early diagnosis of acne. JHO active components may regulate skin cell metabolism and inflammatory response and improve cellular immune microenvironment by acting on core targets (CXCL8, ESR1, IL-1 β, MMP1, MMP3, secretory phosphoprotein 1), thus achieving the purpose of treating acne. This is the result of the joint action of multiple targets and multiple pathways. It provides an idea for the development of a new combination of drugs for the treatment of acne.

An in-silico approach to studying a very rare neurodegenerative disease using a disease with higher prevalence with shared pathways and genes: Cerebral adrenoleukodystrophy and Alzheimer’s disease

Cerebral adrenoleukodystrophy (cALD) is a rare neurodegenerative disease characterized by inflammatory demyelination in the central nervous system. Another neurodegenerative disease with a high prevalence, Alzheimer’s disease (AD), shares many common features with cALD such as cognitive impairment and the alleviation of symptoms by erucic acid. We investigated cALD and AD in parallel to study the shared pathological pathways between a rare disease and a more common disease. The approach may expand the biological understandings and reveal novel therapeutic targets. Gene set enrichment analysis (GSEA) and weighted gene correlation network analysis (WGCNA) were conducted to identify both the resemblance in gene expression patterns and genes that are pathologically relevant in the two diseases. Within differentially expressed genes (DEGs), GSEA identified 266 common genes with similar up- or down-regulation patterns in cALD and AD. Among the interconnected genes in AD data, two gene sets containing 1,486 genes preserved in cALD data were selected by WGCNA that may significantly affect the development and progression of cALD. WGCNA results filtered by functional correlation via protein–protein interaction analysis overlapping with GSEA revealed four genes (annexin A5, beta-2-microglobulin, CD44 molecule, and fibroblast growth factor 2) that showed robust associations with the pathogeneses of cALD and AD, where they were highly involved in inflammation, apoptosis, and the mitogen-activated protein kinase pathway. This study provided an integrated strategy to provide new insights into a rare disease with scant publicly available data (cALD) using a more prevalent disorder with some pathological association (AD), which suggests novel druggable targets and drug candidates.

Integrative transcriptome-wide analysis of atopic dermatitis for drug repositioning

Atopic dermatitis (AD) is one of the most common inflammatory skin diseases, which significantly impact the quality of life. Transcriptome-wide association study (TWAS) was conducted to estimate both transcriptomic and genomic features of AD and detected significant associations between 31 expression quantitative loci and 25 genes. Our results replicated well-known genetic markers for AD, as well as 4 novel associated genes. Next, transcriptome meta-analysis was conducted with 5 studies retrieved from public databases and identified 5 additional novel susceptibility genes for AD. Applying the connectivity map to the results from TWAS and meta-analysis, robustly enriched perturbations were identified and their chemical or functional properties were analyzed. Here, we report the first research on integrative approaches for an AD, combining TWAS and transcriptome meta-analysis. Together, our findings could provide a comprehensive understanding of the pathophysiologic mechanisms of AD and suggest potential drug candidates as alternative treatment options.

Integrative genomic and transcriptomic analyses on publicly available data-sets together with in silico drug repositioning identifies alternative therapeutic options to treat atopic dermatitis.