SIX1 attenuates inflammation and rheumatoid arthritis by silencing MyD88-dependent TLR1/2 signaling

An effective and validated prognostic model for uterine corpus endometrial cancer based on gene main effects and gene-gene interactions

Uterine corpus endometrial carcinoma (UCEC) poses a significant to women's health. Accurate prediction of prognosis plays a crucial role in facilitating clinical decision-making processes. Therefore, this study aimed to develop a robust prognostic model based on gene expression profile. Gene expression profile of 546 UCEC samples of The Cancer Genome Atlas were retrieved. A multi-step strategy was employed to develop and validate a prognostic model predicting all-cause mortality rates. Receiver operating characteristic curve and decision curve analysis were performed to assess the predictive accuracy and net benefit of the model. Besides, model-associated immunological features were explored. The UCEC Prognostic Model (TUPM) performed well in identifying patients at high mortality risk. Patients with risk scores above the upper quartile had significantly decreased overall survival compared to patients with risk scores below the lower quartile (HR = 12.56, CI95: 4.629-34.09, P = 6.76E-7), indicating a prominent discriminability. The model accurately predicted patient survival from 1 to 5-year (area under the curve [AUC]1-year = 0.766, AUC2-year = 0.816, AUC3-year = 0.764, AUC4-year = 0.783, AUC5-year = 0.814) and provided excellent calibration. Meanwhile, The UCEC Prognostic Model encompassing transcriptome scores yielded a higher net clinical benefit than the baseline model that only included patient age and clinical stage. Furthermore, the prolonged survival in the low-risk group may be associated with increased infiltration of follicular T cells and regulatory T cells in the tumor microenvironment. We have developed a robust prognostic model for UCEC that may provide preliminary evidence for individualized management and treatment modality decision.

Genetic Architecture of Idiopathic Inflammatory Myopathies From Meta-Analyses

OBJECTIVE

Idiopathic inflammatory myopathies (IIMs, myositis) are rare systemic autoimmune disorders that lead to muscle inflammation, weakness, and extramuscular manifestations, with a strong genetic component influencing disease development and progression. Previous genome-wide association studies identified loci associated with IIMs. In this study, we imputed data from two prior genome-wide myositis studies and analyzed the largest myositis data set to date to identify novel risk loci and susceptibility genes associated with IIMs and its clinical subtypes.

METHODS

We performed association analyses on 14,903 individuals (3,206 patients and 11,697 controls) with genotypes and imputed data from the Trans-Omics for Precision Medicine reference panel. Fine-mapping and expression quantitative trait locus colocalization analyses in myositis-relevant tissues indicated potential causal variants. Functional annotation and network analyses using the random walk with restart (RWR) algorithm explored underlying genetic networks and drug repurposing opportunities.

RESULTS

Our analyses identified novel risk loci and susceptibility genes, such as FCRLA, NFKB1, IRF4, DCAKD, and ATXN2 in overall IIMs; NEMP2 in polymyositis; ACBC11 in dermatomyositis; and PSD3 in myositis with anti-histidyl-transfer RNA synthetase autoantibodies (anti-Jo-1). We also characterized effects of HLA region variants and the role of C4. Colocalization analyses suggested putative causal variants in DCAKD in skin and muscle, HCP5 in lung, and IRF4 in Epstein-Barr virus (EBV)-transformed lymphocytes, lung, and whole blood. RWR further prioritized additional candidate genes, including APP, CD74, CIITA, NR1H4, and TXNIP, for future investigation.

CONCLUSION

Our study uncovers novel genetic regions contributing to IIMs, advancing our understanding of myositis pathogenesis and offering new insights for future research.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

Background

Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis.

Methods

Next-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes.

Results

A total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network.

Conclusions

This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

Identification of potential drug targets for allergic diseases from a genetic perspective: A mendelian randomization study

BACKGROUND

Allergic diseases typically refer to a heterogeneous group of conditions primarily caused by the activation of mast cells or eosinophils, including atopic dermatitis (AD), allergic rhinitis (AR), and asthma. Asthma, AR, and AD collectively affect approximately one-fifth of the global population, imposing a significant economic burden on society. Despite the availability of drugs to treat allergic diseases, they have been shown to be insufficient in controlling relapses and halting disease progression. Therefore, new drug targets are needed to prevent the onset of allergic diseases.

METHOD

We employed a Mendelian randomization approach to identify potential drug targets for the treatment of allergic diseases. Leveraging 1798 genetic instruments for 1537 plasma proteins from the latest reported Genome-Wide Association Studies (GWAS), we analyzed the GWAS summary statistics of Ferreira MA et al. (nCase = 180,129, nControl = 180,709) using the Mendelian randomization method. Furthermore, we validated our findings in the GWAS data from the FinnGen and UK Biobank cohorts. Subsequently, we conducted sensitivity tests through reverse causal analysis, Bayesian colocalization analysis, and phenotype scanning. Additionally, we performed protein-protein interaction analysis to determine the interaction between causal proteins. Finally, based on the potential protein targets, we conducted molecular docking to identify potential drugs for the treatment of allergic diseases.

RESULTS

At Bonferroni significance (p < 3.25 × 10-5), the Mendelian randomization analysis revealed 11 significantly associated protein-allergic disease pairs. Among these, the increased levels of TNFAIP3, ERBB3, TLR1, and IL1RL2 proteins were associated with a reduced risk of allergic diseases, with corresponding odds ratios of 0.82 (0.76-0.88), 0.74 (0.66-0.82), 0.49 (0.45-0.55), and 0.81 (0.75-0.87), respectively. Conversely, increased levels of IL6R, IL1R1, ITPKA, IL1RL1, KYNU, LAYN, and LRP11 proteins were linked to an elevated risk of allergic diseases, with corresponding odds ratios of 1.04 (1.03-1.05), 1.25 (1.18-1.34), 1.48 (1.25-1.75), 1.14 (1.11-1.18), 1.09 (1.05-1.12), 1.96 (1.56-2.47), and 1.05 (1.03-1.07), respectively. Bayesian colocalization analysis suggested that LAYN (coloc.abf-PPH4 = 0.819) and TNFAIP3 (coloc.abf-PPH4 = 0.930) share the same variant associated with allergic diseases.

CONCLUSIONS

Our study demonstrates a causal association between the expression levels of TNFAIP3 and LAYN and the risk of allergic diseases, suggesting them as potential drug targets for these conditions, warranting further clinical investigation.

System-based integrated metabolomics and microRNA analysis identifies potential molecular alterations in human X-linked cerebral adrenoleukodystrophy brain

X-linked adrenoleukodystrophy is a severe demyelinating neurodegenerative disease mainly affecting males. The severe cerebral adrenoleukodystrophy (cALD) phenotype has a poor prognosis and underlying mechanism of onset and progression of neuropathology remains poorly understood. In this study we aim to integrate metabolomic and microRNA (miRNA) datasets to identify variances associated with cALD. Postmortem brain tissue samples from five healthy controls (CTL) and five cALD patients were utilized in this study. White matter from ALD patients was obtained from normal-appearing areas, away from lesions (NLA) and from the periphery of lesions- plaque shadow (PLS). Metabolomics was performed by gas chromatography coupled with time-of-flight mass spectrometry and miRNA expression analysis was performed by next generation sequencing (RNAseq). Principal component analysis revealed that among the three sample groups (CTL, NLA and PLS) there were 19 miRNA, including several novel miRNA, of which 17 were increased with disease severity and 2 were decreased. Untargeted metabolomics revealed 13 metabolites with disease severity-related patterns with 7 increased and 6 decreased with disease severity. Ingenuity pathway analysis of differentially altered metabolites and miRNA comparing CTL with NLA and NLA with PLS, identified several hubs of metabolite and signaling molecules and their upstream regulation by miRNA. The transomic approach to map the crosstalk between miRNA and metabolomics suggests involvement of specific molecular and metabolic pathways in cALD and offers opportunity to understand the complex underlying mechanism of disease severity in cALD.

MyD88 dimerization inhibitor ST2825 targets the aggressiveness of synovial fibroblasts in rheumatoid arthritis patients

BACKGROUND

Dimerization of the myeloid differentiation primary response 88 protein (MyD88) plays a pivotal role in the exacerbated response to innate immunity-dependent signaling in rheumatoid arthritis (RA). ST2825 is a highly specific inhibitor of MyD88 dimerization, previously shown to inhibit the pro-inflammatory gene expression in peripheral blood mononuclear cells from RA patients (RA PBMC). In this study, we elucidated the effect of disrupting MyD88 dimerization by ST2825 on the pathological properties of synovial fibroblasts from RA patients (RA SFs).

METHODS

RA SFs were treated with varying concentrations of ST2825 in the presence or absence of bacterial lipopolysaccharides (LPS) to activate innate immunity-dependent TLR signaling. The DNA content of the RA SFs was quantified by imaging cytometry to investigate the effect of ST2825 on different phases of the cell cycle and apoptosis. RNA-seq was used to assess the global response of the RA SF toward ST2825. The invasiveness of RA SFs in Matrigel matrices was measured in organoid cultures. SFs from osteoarthritis (OA SFs) patients and healthy dermal fibroblasts were used as controls.

RESULTS

ST2825 reduced the proliferation of SFs by arresting the cells in the G0/G1 phase of the cell cycle. In support of this finding, transcriptomic analysis by RNA-seq showed that ST2825 may have induced cell cycle arrest by primarily inhibiting the expression of critical cell cycle regulators Cyclin E2 and members of the E2F family transcription factors. Concurrently, ST2825 also downregulated the genes encoding for pain, inflammation, and joint catabolism mediators while upregulating the genes required for the translocation of nuclear proteins into the mitochondria and members of the mitochondrial respiratory complex 1. Finally, we demonstrated that ST2825 inhibited the invasiveness of RA SFs, by showing decreased migration of LPS-treated RA SFs in spheroid cultures.

CONCLUSIONS

The pathological properties of the RA SFs, in terms of their aberrant proliferation, increased invasiveness, upregulation of pain and inflammation mediators, and disruption of mitochondrial homeostasis, were attenuated by ST2825 treatment. Taken together with the previously reported anti-inflammatory effects of ST2825 in RA PBMC, this study strongly suggests that targeting MyD88 dimerization could mitigate both systemic and synovial pathologies in a variety of inflammatory arthritic diseases.