A comprehensive database for integrated analysis of omics data in autoimmune diseases

Epigenetic insights into Familial Mediterranean Fever: Increased RGS10 expression and histone modifications accompanies inflammation in familial Mediterranean fever disease

BACKGROUND

Familial Mediterranean fever (FMF) is an autosomal recessive autoinflammatory disease characterized by recurring fever, erythema, joint pain, and abdominal discomfort during acute episodes. While FMF patients typically share MEFV gene mutations, they display varying clinical manifestations, suggesting the involvement of modifying genes, epigenetic mechanisms, or environmental factors. G protein regulator signal 10 (RGS10), a member of the RGS protein family, exhibits anti-inflammatory effects in autoinflammatory diseases. There are no studies on the role of plays in FMF pathogenesis or histone modification in FMF.

AIMS

This study aimed to shed light on the epigenetic regulation of FMF from several perspectives. The relationship between RGS10 DNA hypermethylation in FMF clinical parameters and the regulation of 22 histone modifications were examined in FMF attack patients and the control group.

METHODS

Sixty FMF (remission/attack) and thirty healthy individuals were included in the study. First, RNA was isolated from the blood of patients/controls, and the expression of RGS10 was examined. Then, DNA was isolated from the patients, and gene-specific hypermethylation was investigated using the bisulfite conversion method. Finally, histone extraction was performed for FMF patients and controls and 22 histone H3 modifications were determined. In addition, using ADEX bioinformatics tools, RGS10 expression and methylation profiles were detected in different autoinflammatory diseases.

RESULTS

This study indicate that RGS10 expression decreased in attack-free/attack patients than control, attributed to DNA methylation. In addition, there were a positive correlation between FMF patients and attack, WBC, neutrophil, MCHC and MPV. Moreover, higher H3K4 me3, H3K9 me2, and H3K14ac levels were observed in patients with FMF attacks. This research also showed a consistent decrease in RGS10 expression in patients with SjS, SSc, and T1D compared with controls. I also obtained five prognosis-related CpGs (cg17527393, cg19653161, cg20445950, cg18938673 and cg13975098) of RGS10 in patients with SjS, RA, SSc, SLE and T1D.

CONCLUSION

The present study provides insights into the complex relationship between RGS10, epigenetic modifications, and immune responses in FMF. While RGS10 may initially enhance immune responses, genetic mutations and epigenetic changes associated with FMF acute episode may override this regulatory effect, resulting in increased inflammation and clinical symptoms. Moreover, our study revealed elevated levels of specific histone modifications in the context of FMF, suggesting significant epigenetic changes that could contribute to the disease pathogenesis. Understanding these associations opens new avenues for research and potential therapeutic interventions, potentially involving epigenetic therapies targeting histone modifications.

IBDTransDB: a manually curated transcriptomic database for inflammatory bowel disease

Inflammatory Bowel Disease (IBD) therapies are ineffective in at least 40% patients, and transcriptomic datasets have been widely used to reveal the pathogenesis and to identify the novel drug targets for these patients. Although public IBD transcriptomic datasets are available from many web-based tools/databases, due to the unstructured metadata and data description of these public datasets, most of these tools/databases do not allow querying datasets based on multiple keywords (e.g. colon and infliximab). Furthermore, few tools/databases can compare and integrate the datasets from the query results. To fill these gaps, we have developed IBDTransDB (https://abbviegrc.shinyapps.io/ibdtransdb/), a manually curated transcriptomic database for IBD. IBDTransDB includes a manually curated database with 34 transcriptomic datasets (2932 samples, 122 differential comparisons) and a query system supporting 35 keywords from 5 attributes (e.g. tissue and treatment). IBDTransDB also provides three modules for data analyses and integration. IBDExplore allows interactive visualization of differential gene list, pathway enrichment, gene signature and cell deconvolution analyses from a single dataset. IBDCompare supports comparisons of selected genes or pathways from multiple datasets across different conditions. IBDIntegrate performs meta-analysis to prioritize a list of genes/pathways based on user-selected datasets and conditions. Using two case studies related to infliximab treatment, we demonstrated that IBDTransDB provides a unique platform for biologists and clinicians to reveal IBD pathogenesis and identify the novel targets by integrating with other omics data. Database URL: https://abbviegrc.shinyapps.io/ibdtransdb/.

Artificial intelligence-driven drug development against autoimmune diseases

Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments.

Bioinformatics analyses of combined databases identify shared differentially expressed genes in cancer and autoimmune disease

BACKGROUND

Inadequate immunity caused by poor immune surveillance leads to tumorigenesis, while excessive immunity due to breakdown of immune tolerance causes autoimmune genesis. Although the function of immunity during the onset of these two processes appears to be distinct, the underlying mechanism is shared. To date, gene expression data for large bodies of clinical samples are available, but the resemblances of tumorigenesis and autoimmune genesis in terms of immune responses remains to be summed up.

METHODS

Considering the high disease prevalence, we chose invasive ductal carcinoma (IDC) and systemic lupus erythematosus (SLE) to study the potential commonalities of immune responses. We obtained gene expression data of IDC/SLE patients and normal controls from five IDC databases (GSE29044, GSE21422, GSE22840, GSE15852, and GSE9309) and five SLE databases (GSE154851, GSE99967, GSE61635, GSE50635, and GSE17755). We intended to identify genes differentially expressed in both IDC and SLE by using three bioinformatics tools including GEO2R, the limma R package, and Weighted Gene Co-expression Network Analysis (WGCNA) to perform function enrichment, protein-protein network, and signaling pathway analyses.

RESULTS

The mRNA levels of signal transducer and activator of transcription 1 (STAT1), 2'-5'-oligoadenylate synthetase 1 (OAS1), 2'-5'-oligoadenylate synthetase like (OASL), and PML nuclear body scaffold (PML) were found to be differentially expressed in both IDC and SLE by using three different bioinformatics tools of GEO2R, the limma R package and WGCNA. From the combined databases in this study, the mRNA levels of STAT1 and OAS1 were increased in IDC while reduced in SLE. And the mRNA levels of OASL and PML were elevated in both IDC and SLE. Based on Kyoto Encyclopedia of Genes and Genomes pathway analysis and QIAGEN Ingenuity Pathway Analysis, both IDC and SLE were correlated with the changes of multiple components involved in the Interferon (IFN)-Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway.

CONCLUSION

The expression levels of STAT1 and OAS1 manifest the opposite expression tendency across cancer and autoimmune disease. They are components in the IFN-JAK-STAT signaling pathway related to both tumorigenesis and autoimmune genesis. STAT1 and OAS1-associated IFN-JAK-STAT signaling could explain the commonalities during tumorigenesis and autoimmune genesis and render significant information for more precise treatment from the point of immune homeostasis.

Differential diagnosis of systemic lupus erythematosus and Sjögren's syndrome using machine learning and multi-omics data

Systemic lupus erythematosus and primary Sjogren's syndrome are complex systemic autoimmune diseases that are often misdiagnosed. In this article, we demonstrate the potential of machine learning to perform differential diagnosis of these similar pathologies using gene expression and methylation data from 651 individuals. Furthermore, we analyzed the impact of the heterogeneity of these diseases on the performance of the predictive models, discovering that patients assigned to a specific molecular cluster are misclassified more often and affect to the overall performance of the predictive models. In addition, we found that the samples characterized by a high interferon activity are the ones predicted with more accuracy, followed by the samples with high inflammatory activity. Finally, we identified a group of biomarkers that improve the predictions compared to using the whole data and we validated them with external studies from other tissues and technological platforms.

The Binding Specificity of PAB1 with Poly(A) mRNA, Regulated by Its Structural Folding

The poly(A)-binding protein cytoplasmic 1 (PAB1 or PABPC1) protein is associated with the long poly(A) mRNA tails, inducing stability. Herein, we investigated the dynamics of the PABPC1 protein, along with tracing its mRNA binding specificity. During molecular dynamics simulations (MDS), the R176-Y408 amino acids (RRM3-4 domains; RNA recognition motifs) initiated a folded structure that resulted in the formation of different conformations. The RRM4 domain formed high-frequency intramolecular interactions, despite such induced flexibility. Residues D45, Y54, Y56, N58, Q88, and N100 formed long-lasting interactions, and specifically, aromatic residues (Y14, Y54, Y56, W86, and Y140) gained a unique binding pattern with the poly(A) mRNA. In addition, the poly(A) mRNA motif assembled a PABPC1-specific conformation, by inducing movement of the center three nucleotides to face towards RRM1-2 domains. The majority of the high-frequency cancer mutations in PAB1 reside within the RRM4 domain and amino acids engaging in high-frequency interactions with poly(A) mRNA were found to be preserved in different cancer types. Except for the G123C variant, other studied cancer-derived mutants hindered the stability of the protein. Molecular details from this study will provide a detailed understanding of the PABPC1 structure, which can be used to modulate the activity of this gene, resulting in production of mutant peptide or neoantigens in cancer.

Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression

OBJECTIVES

Systemic Lupus Erythematosus is a complex autoimmune disease that leads to significant worsening of quality of life and mortality. Flares appear unpredictably during the disease course and therapies used are often only partially effective. These challenges are mainly due to the molecular heterogeneity of the disease, and in this context, personalized medicine-based approaches offer major promise. With this work we intended to advance in that direction by developing MyPROSLE, an omic-based analytical workflow for measuring the molecular portrait of individual patients to support clinicians in their therapeutic decisions.

METHODS

Immunological gene-modules were used to represent the transcriptome of the patients. A dysregulation score for each gene-module was calculated at the patient level based on averaged z-scores. Almost 6100 Lupus and 750 healthy samples were used to analyze the association among dysregulation scores, clinical manifestations, prognosis, flare and remission events and response to Tabalumab. Machine learning-based classification models were built to predict around 100 different clinical parameters based on personalized dysregulation scores.

RESULTS

MyPROSLE allows to molecularly summarize patients in 206 gene-modules, clustered into nine main lupus signatures. The combination of these modules revealed highly differentiated pathological mechanisms. We found that the dysregulation of certain gene-modules is strongly associated with specific clinical manifestations, the occurrence of relapses or the presence of long-term remission and drug response. Therefore, MyPROSLE may be used to accurately predict these clinical outcomes.

CONCLUSIONS

MyPROSLE (https://myprosle.genyo.es) allows molecular characterization of individual Lupus patients and it extracts key molecular information to support more precise therapeutic decisions.

Gene Expression Meta-Analysis of Potential Shared and Unique Pathways between Autoimmune Diseases under Anti-TNFα Therapy

While anti-TNFα has been established as an effective therapeutic approach for several autoimmune diseases, results from clinical trials have uncovered heterogeneous patients' response to therapy. Here, we conducted a meta-analysis on the publicly available gene expression cDNA microarray datasets that examine the differential expression observed in response to anti-TNFα therapy with psoriasis (PsO), inflammatory bowel disease (IBD) and rheumatoid arthritis (RA). Five disease-specific meta-analyses and a single combined random-effects meta-analysis were performed through the restricted maximum likelihood method. Gene Ontology and Reactome Pathways enrichment analyses were conducted, while interactions between differentially expressed genes (DEGs) were determined with the STRING database. Four IBD, three PsO and two RA datasets were identified and included in our analyses through our search criteria. Disease-specific meta-analyses detected distinct pro-inflammatory down-regulated DEGs for each disease, while pathway analyses identified common inflammatory patterns involved in the pathogenesis of each disease. Combined meta-analyses further revealed DEGs that participate in anti-inflammatory pathways, namely IL-10 signaling. Our analyses provide the framework for a transcriptomic approach in response to anti-TNFα therapy in the above diseases. Elucidation of the complex interactions involved in such multifactorial phenotypes could identify key molecular targets implicated in the pathogenesis of IBD, PsO and RA.

Integrated single-cell transcriptomics and epigenomics reveals strong germinal center-associated etiology of autoimmune risk loci

[Figure: see text].

Pathogenetic analysis of polycystic ovary syndrome from the perspective of omics

Polycystic ovary syndrome (PCOS) is the most common gynecological endocrine disease, involving multiple genes, multiple pathways, and complex hormone secretion processes. Hence, the pathogenesis of PCOS cannot be explained by a single factor. Omics analysis includes genomics, transcriptomics, and proteomics, which are fast and effective methods for studying the pathogenesis of diseases. PCOS is primarily characterized by androgen excess, and reproductive and metabolic dysfunctions. The application of omics analysis in the body fluids, blood, cells or tissues of women with PCOS offers the potential for unexpected molecular advantages in explaining new mechanisms of PCOS etiology and pathophysiology, and provides new perspectives for identifying potential biomarkers and developing new therapeutic targets. At present, several omics analyses have been applied to produce complex datasets. In this manuscript, the recent advances in omics research on PCOS are summarized, aiming at an important and parallel review of the newly published research.