GAAD: A Gene and Autoimmiune Disease Association Database

ViCEKb: Vitiligo-linked Chemical Exposome Knowledgebase

Vitiligo is a complex disease wherein the environmental factors, in conjunction with the underlying genetic predispositions, trigger the autoimmune destruction of melanocytes, ultimately leading to depigmented patches on the skin. While genetic factors have been extensively studied, the knowledge on environmental triggers remains sparse and less understood. To address this knowledge gap, we present the first comprehensive knowledgebase of vitiligo-triggering chemicals namely, Vitiligo-linked Chemical Exposome Knowledgebase (ViCEKb). ViCEKb involves an extensive and systematic manual effort in curation of published literature and subsequent compilation of 113 unique chemical triggers of vitiligo. ViCEKb standardizes various chemical information, and categorizes the chemicals based on their evidences and sources of exposure. Importantly, ViCEKb contains a wide range of metrics necessary for different toxicological evaluations. Notably, we observed that ViCEKb chemicals are present in a variety of consumer products. For instance, Propyl gallate is present as a fragrance substance in various household products, and Flutamide is used in medication to treat prostate cancer. These two chemicals have the highest level of evidence in ViCEKb, but are not regulated for their skin sensitizing effects. Furthermore, an extensive cheminformatics-based investigation revealed that ViCEKb chemical space is structurally diverse and comprises unique chemical scaffolds in comparison with skin specific regulatory lists. For example, Neomycin and 2,3,5-Triglycidyl-4-aminophenol have unique chemical scaffolds and the highest level of evidence in ViCEKb, but are not regulated for their skin sensitizing effects. Finally, a transcriptomics-based analysis of ViCEKb chemical perturbations in skin cell samples highlighted the commonality in their linked biological processes. Overall, we present the first comprehensive effort in compilation and exploration of various chemical triggers of vitiligo. We believe such a resource will enable in deciphering the complex etiology of vitiligo and aid in the characterization of human chemical exposome. ViCEKb is freely available for academic research at: https://cb.imsc.res.in/vicekb.

RNA2Immune: A Database of Experimentally Supported Data Linking Non-coding RNA Regulation to The Immune System

Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as important regulators of the immune system and are involved in the control of immune cell biology, disease pathogenesis, as well as vaccine responses. A repository of ncRNA-immune associations will facilitate our understanding of ncRNA-dependent mechanisms in the immune system and advance the development of therapeutics and prevention for immune disorders. Here, we describe a comprehensive database, RNA2Immune, which aims to provide a high-quality resource of experimentally supported database linking ncRNA regulatory mechanisms to immune cell function, immune disease, cancer immunology, and vaccines. The current version of RNA2Immune documents 50,433 immune-ncRNA associations in 42 host species, including (1) 6690 ncRNA associations with immune functions involving 31 immune cell types; (2) 38,672 ncRNA associations with 348 immune diseases; (3) 4833 ncRNA associations with cancer immunology; and (4) 238 ncRNA associations with vaccine responses involving 26 vaccine types targeting 22 diseases. RNA2Immune provides a user-friendly interface for browsing, searching, and downloading ncRNA-immune system associations. Collectively, RNA2Immune provides important information about how ncRNAs influence immune cell function, how dysregulation of these ncRNAs leads to pathological consequences (immune diseases and cancers), and how ncRNAs affect immune responses to vaccines. RNA2Immune is available at http://bio-bigdata.hrbmu.edu.cn/rna2immune/home.jsp.

Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in moyamoya disease

Background: Growing evidence suggests the links between moyamoya disease (MMD) and autoimmune diseases. However, the molecular mechanism from genetic perspective remains unclear. This study aims to clarify the potential roles of autoimmune-related genes (ARGs) in the pathogenesis of MMD. Methods: Two transcription profiles (GSE157628 and GSE141025) of MMD were downloaded from GEO databases. ARGs were obtained from the Gene and Autoimmune Disease Association Database (GAAD) and DisGeNET databases. Differentially expressed ARGs (DEARGs) were identified using "limma" R packages. GO, KEGG, GSVA, and GSEA analyses were conducted to elucidate the underlying molecular function. There machine learning methods (LASSO logistic regression, random forest (RF), support vector machine-recursive feature elimination (SVM-RFE)) were used to screen out important genes. An artificial neural network was applied to construct an autoimmune-related signature predictive model of MMD. The immune characteristics, including immune cell infiltration, immune responses, and HLA gene expression in MMD, were explored using ssGSEA. The miRNA-gene regulatory network and the potential therapeutic drugs for hub genes were predicted. Results: A total of 260 DEARGs were identified in GSE157628 dataset. These genes were involved in immune-related pathways, infectious diseases, and autoimmune diseases. We identified six diagnostic genes by overlapping the three machine learning algorithms: CD38, PTPN11, NOTCH1, TLR7, KAT2B, and ISG15. A predictive neural network model was constructed based on the six genes and presented with great diagnostic ability with area under the curve (AUC) = 1 in the GSE157628 dataset and further validated by GSE141025 dataset. Immune infiltration analysis showed that the abundance of eosinophils, natural killer T (NKT) cells, Th2 cells were significant different between MMD and controls. The expression levels of HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DRB6, HLA-F, and HLA-G were significantly upregulated in MMD. Four miRNAs (mir-26a-5p, mir-1343-3p, mir-129-2-3p, and mir-124-3p) were identified because of their interaction at least with four hub DEARGs. Conclusion: Machine learning was used to develop a reliable predictive model for the diagnosis of MMD based on ARGs. The uncovered immune infiltration and gene-miRNA and gene-drugs regulatory network may provide new insight into the pathogenesis and treatment of MMD.

Comprehensive analysis of autoimmune-related genes in amyotrophic lateral sclerosis from the perspective of 3P medicine

Background

Although growing evidence suggests close correlations between autoimmunity and amyotrophic lateral sclerosis (ALS), no studies have reported on autoimmune-related genes (ARGs) from the perspective of the prognostic assessment of ALS. The purpose of this study was to investigate whether the circulating ARD signature could be identified as a reliable biomarker for ALS survival for predictive, preventive, and personalized medicine.

Methods

The whole blood transcriptional profiles and clinical characteristics of 454 ALS patients were downloaded from the Gene Expression Omnibus (GEO) database. A total of 4371 ARGs were obtained from GAAD and DisGeNET databases. Wilcoxon test and multivariate Cox regression were applied to identify the differentially expressed and prognostic ARGs. Then, unsupervised clustering was performed to classify patients into two distinct autoimmune-related clusters. PCA method was used to calculate the autoimmune index. LASSO and multivariate Cox regression was performed to establish risk model to predict overall survival for ALS patients. A ceRNA regulatory network was then constructed for regulating the model genes. Finally, we performed single-cell analysis to explore the expression of model genes in mutant SOD1 mice and methylation analysis in ALS patients.

Results

Based on the expressions of 85 prognostic ARGs, two autoimmune-related clusters with various biological features, immune characteristics, and survival outcome were determined. Cluster 1 with a worsen prognosis was more active in immune-related biological pathways and immune infiltration than Cluster 2. A higher autoimmune index was associated with a better prognosis than a lower autoimmune index, and there were significant adverse correlations between the autoimmune index and immune infiltrating cells and immune responses. Nine model genes (KIF17, CD248, ENG, BTNL2, CLEC5A, ADORA3, PRDX5, AIM2, and XKR8) were selected to construct prognostic risk signature, indicating potent potential for survival prediction in ALS. Nomogram integrating risk model and clinical characteristics could predict the prognosis more accurately than other clinicopathological features. We constructed a ceRNA regulatory network for the model genes, including five lncRNAs, four miRNAs, and five mRNAs.

Conclusion

Expression of ARGs is correlated with immune characteristics of ALS, and seven ARG signatures may have practical application as an independent prognostic factor in patients with ALS, which may serve as target for the future prognostic assessment, targeted prevention, patient stratification, and personalization of medical services in ALS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00299-w.

3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk

Background

Genome-wide association studies (GWAS) have enabled the discovery of single nucleotide polymorphisms (SNPs) that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the identified variants lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. To address this problem, we developed a variant filtering workflow called 3DFAACTS-SNP to link genetic variants to target genes in a cell-specific manner. Here, we use 3DFAACTS-SNP to identify candidate SNPs and target genes associated with the loss of immune tolerance in regulatory T cells (Treg) in T1D.

Results

Using 3DFAACTS-SNP, we identified from a list of 1228 previously fine-mapped variants, 36 SNPs with plausible Treg-specific mechanisms of action. The integration of cell type-specific chromosome conformation capture data in 3DFAACTS-SNP identified 266 regulatory regions and 47 candidate target genes that interact with these variant-containing regions in Treg cells. We further demonstrated the utility of the workflow by applying it to three other SNP autoimmune datasets, identifying 16 Treg-centric candidate variants and 60 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of all known common (> 10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 9376 candidate variants and 4968 candidate target genes, generating a list of potential sites for future T1D or other autoimmune disease research.

Conclusions

We demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function, and illustrate the power of using cell type-specific multi-omics datasets to determine disease mechanisms. Our workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13072-022-00456-5.

A transcriptome atlas and interactive analysis platform for autoimmune disease

With the rapid development of next-generation sequencing technology, many laboratories have produced a large amount of single-cell transcriptome data of blood and tissue samples from patients with autoimmune diseases, which enables in-depth studies of the relationship between gene transcription and autoimmune diseases. However, there is still a lack of a database that integrates the large amount of autoimmune disease transcriptome sequencing data and conducts effective analysis. In this study, we developed a user-friendly web database tool, Interactive Analysis and Atlas for Autoimmune disease (IAAA), which integrates bulk RNA-seq data of 929 samples of 10 autoimmune diseases and single-cell RNA-seq data of 783 203 cells in 96 samples of 6 autoimmune diseases. IAAA also provides customizable analysis modules, including gene expression, difference, correlation, similar gene detection and cell–cell interaction, and can display results in three formats (plot, table and pdf) through custom parameters. IAAA provides valuable data resources for researchers studying autoimmune diseases and helps users deeply explore the potential value of the current transcriptome data. IAAA is available.

Database URL: http://galaxy.ustc.edu.cn/IAAA

Expression of Autoimmunity-Related Genes in Melanoma

Simple Summary

The present study selected four genes strongly related to autoimmunity. Their expression was found to be significantly altered in melanoma patients according to a multi-validation procedure carried out on 1948 patients. Such genes may represent suitable molecular targets to further investigate the role autoimmunity may play in melanoma setup and development. Our data suggest that autoimmunity may play a beneficial role in melanoma set up, at least to some extent.

Abstract

(1) Background. Immune response dysregulation plays a key role in melanoma, as suggested by the substantial prognosis improvement observed under immune-modulation therapy. Similarly, the role of autoimmunity is under large investigation in melanoma and other cancers. (2) Methods. Expression of 98 autoimmunity-related genes was investigated in 1948 individuals (1024 melanoma and 924 healthy controls). Data were derived from four independent databases, namely, GEO in the selection phase, and Ist Online, GEPIA2 and GENT2, in three sequential validation-steps. ROC analyses were performed to measure the ability to discriminate melanoma from controls. Principal Component Analysis (PCA) was used to combine expression data; survival analysis was carried out on the GEPIA2 platform. (3) Results. Expression levels of NOD2, BAX, IL-18 and ADRB2 were found to be significantly different in melanoma vs. controls and discriminate melanoma from controls in an extremely effective way, either as single molecules (AUC > 0.93 in all cases) or as a profile, according to the PCA analysis. Patients showing high-expression of NOD2 and of IL-18 also show a significant survival improvement as compared to low-expression patients. (4) Conclusions. Four genes strongly related to autoimmunity show a significant altered expression in melanoma samples, highlighting the role they may play in melanoma.

The S100A7 nuclear interactors in autoimmune diseases: a coevolutionary study in mammals

S100A7, a member of the S100A family of Ca²⁺-binding proteins, is considered a key effector in immune response. In particular, S100A7 dysregulation has been associated with several diseases, including autoimmune disorders. At the nuclear level, S100A7 interacts with several protein-binding partners which are involved in transcriptional regulation and DNA repair. By using the BioGRID and GAAD databases, S100A7 nuclear interactors with a putative involvement in autoimmune diseases were retrieved. We selected fatty acid-binding protein 5 (FABP5), autoimmune regulator (AIRE), cystic fibrosis transmembrane conductance regulator (CFTR), chromodomain helicase DNA-binding protein 4 (CHD4), epidermal growth factor receptor (EGFR), estrogen receptor 1 (ESR1), histone deacetylase 2 (HDAC2), v-myc avian myelocytomatosis viral oncogene homolog (MYC), protection of telomeres protein 1 (POT1), telomeric repeat-binding factor (NIMA-interacting) 1 (TERF1), telomeric repeat-binding factor 2 (TERF2), and Zic family member 1 (ZIC1). Linear correlation coefficients between interprotein distances were calculated with MirrorTree. Coevolution clusters were also identified with the use of a recent version of the Blocks in Sequences (BIS2) algorithm implemented in the BIS2Analyzer web server. Analysis of pair positions identified interprotein coevolving clusters between S100A7 and the binding partners CFTR and TERF1. Such findings could guide further analysis to better elucidate the function of S100A7 and its binding partners and to design drugs targeting for these molecules in autoimmune diseases.

Pan-Cancer Analysis Reveals Alternative Splicing Characteristics Associated With Immune-Related Adverse Events Elicited by Checkpoint Immunotherapy

Immune-related adverse events (irAEs) can impair the effectiveness and safety of immune checkpoint inhibitors (ICIs) and restrict the clinical applications of ICIs in oncology. The predictive biomarkers of irAE are urgently required for early diagnosis and subsequent management. The exact mechanism underlying irAEs remains to be fully elucidated, and the availability of predictive biomarkers is limited. Herein, we performed data mining by combining pharmacovigilance data and pan-cancer transcriptomic information to illustrate the relationships between alternative splicing characteristics and irAE risk of ICIs. Four distinct classes of splicing characteristics considered were associated with splicing factors, neoantigens, splicing isoforms, and splicing levels. Correlation analysis confirmed that expression levels of splicing factors were predictive of irAE risk. Adding DHX16 expression to the bivariate PD-L1 protein expression-fPD1 model markedly enhanced the prediction for irAE. Furthermore, we identified 668 and 1,131 potential predictors based on the correlation of the incidence of irAEs with splicing frequency and isoform expression, respectively. The functional analysis revealed that alternative splicing might contribute to irAE pathogenesis via coordinating innate and adaptive immunity. Remarkably, autoimmune-related genes and autoantigens were preferentially over-represented in these predictors for irAE, suggesting a close link between autoimmunity and irAE occurrence. In addition, we established a trivariate model composed of CDC42EP3-206, TMEM138-211, and IRX3-202, that could better predict the risk of irAE across various cancer types, indicating a potential application as promising biomarkers for irAE. Our study not only highlights the clinical relevance of alternative splicing for irAE development during checkpoint immunotherapy but also sheds new light on the mechanisms underlying irAEs.

Long RNA Sequencing and Ribosome Profiling of Inflamed β-Cells Reveal an Extensive Translatome Landscape

Type 1 diabetes (T1D) is an autoimmune disease characterized by autoreactive T cell-mediated destruction of the insulin-producing pancreatic β-cells. Increasing evidence suggest that the β-cells themselves contribute to their own destruction by generating neoantigens through the production of aberrant or modified proteins that escape central tolerance. We recently demonstrated that ribosomal infidelity amplified by stress could lead to the generation of neoantigens in human β-cells, emphasizing the participation of nonconventional translation events in autoimmunity, as occurring in cancer or virus-infected tissues. Using a transcriptome-wide profiling approach to map translation initiation start sites in human β-cells under standard and inflammatory conditions, we identify a completely new set of polypeptides derived from noncanonical start sites and translation initiation within long noncoding RNA. Our data underline the extreme diversity of the β-cell translatome and may reveal new functional biomarkers for β-cell distress, disease prediction and progression, and therapeutic intervention in T1D.

Personizing the prediction of future susceptibility to a specific disease

A traceable biomarker is a member of a disease's molecular pathway. A disease may be associated with several molecular pathways. Each different combination of these molecular pathways, to which detected traceable biomarkers belong, may serve as an indicative of the elicitation of the disease at a different time frame in the future. Based on this notion, we introduce a novel methodology for personalizing an individual's degree of future susceptibility to a specific disease. We implemented the methodology in a working system called Susceptibility Degree to a Disease Predictor (SDDP). For a specific disease d, let S be the set of molecular pathways, to which traceable biomarkers detected from most patients of d belong. For the same disease d, let S' be the set of molecular pathways, to which traceable biomarkers detected from a certain individual belong. SDDP is able to infer the subset S'' ⊆{S-S'} of undetected molecular pathways for the individual. Thus, SDDP can infer undetected molecular pathways of a disease for an individual based on few molecular pathways detected from the individual. SDDP can also help in inferring the combination of molecular pathways in the set {S'+S''}, whose traceable biomarkers collectively is an indicative of the disease. SDDP is composed of the following four components: information extractor, interrelationship between molecular pathways modeler, logic inferencer, and risk indicator. The information extractor takes advantage of the exponential increase of biomedical literature to automatically extract the common traceable biomarkers for a specific disease. The interrelationship between molecular pathways modeler models the hierarchical interrelationships between the molecular pathways of the traceable biomarkers. The logic inferencer transforms the hierarchical interrelationships between the molecular pathways into rule-based specifications. It employs the specification rules and the inference rules for predicate logic to infer as many as possible undetected molecular pathways of a disease for an individual. The risk indicator outputs a risk indicator value that reflects the individual's degree of future susceptibility to the disease. We evaluated SDDP by comparing it experimentally with other methods. Results revealed marked improvement.

ncRI: a manually curated database for experimentally validated non-coding RNAs in inflammation

BACKGROUND

Inflammation has been considered to be central to the onset, progression, and outcome of infectious diseases, especially as one of the hallmarks of cancer. Non-coding RNAs (ncRNAs), such as miRNAs and lncRNAs, have emerged as vital regulators in control of immune and inflammatory processes, and also play important roles in the inflammatory disease and immunotherapy.

RESULTS

In this study, we presented a database ncRI, which documented experimentally verified ncRNAs in inflammatory diseases, from published articles. Each entry contained the detailed information about ncRNA name, inflammatory diseases, mechanism, experimental techniques (e.g., microarray, RNA-seq, qRT-PCR), experimental samples (cell line and/or tissue), expression patterns of ncRNA (up-regulated or down-regulated), reference information (PubMed ID, year of publication, title of paper) and so on. Collectively, ncRI recorded 11,166 entries that include 1976 miRNAs, 1377 lncRNAs and 107 other ncRNAs across 3 species (human, mouse, and rat) from more than 2000 articles. All these data are free for users to search, browse and download.

CONCLUSION

In summary, the presented database ncRI provides a relatively comprehensive credible repository about ncRNAs and their roles in inflammatory diseases, and will be helpful for research on immunotherapy. The ncRI is now freely available to all users at http://www.jianglab.cn/ncRI/.