Genetic evidence for the involvement of NOTCH4 in rheumatoid arthritis and alopecia areata

Causal relationship between primary sclerosing cholangitis and systemic lupus erythematosus: a bidirectional Mendelian randomization study

BACKGROUND

Observational studies have found a link between two autoimmune diseases, namely, primary sclerosing cholangitis (PSC) and systemic lupus erythematosus (SLE). However, the relationship remains unclear.

METHODS

Bidirectional Mendelian randomization (MR) analysis and statistical methods, including inverse variance weighting, weighted median, and MR-Egger tests, were performed using data from genome-wide association studies to detect a causal relationship between PSC and SLE. Sensitivity analyses were subsequently performed to assess the robustness of the results. Univariate MR methods were also investigated.

RESULTS

Results of MR analysis suggested that PSC was associated with an increased risk for SLE (odds ratio: 1.33, 95% confidence interval: 1.10-1.61, P=0.0039) However, SLE had no significant causal relationship with PSC.

CONCLUSION

Results of MR analysis revealed that patients with PSC were at an increased risk for SLE, which provides new insights into the relationship between these two autoimmune diseases.

Association between genetically predicted rheumatoid arthritis and alopecia areata: a two-sample Mendelian randomization study

Background

Although numerous observational studies have indicated a potential association between autoimmune diseases, such as rheumatoid arthritis (RA) and alopecia areata (AA), the research reports lack a clear causal relationship. In this study, our objective is to utilize the Mendelian randomization (MR) design to examine the potential causal association between RA and AA.

Methods

To investigate the causal relationship between RA and AA, we utilized large-scale gene aggregation data from genome-wide association studies (GWAS), including RA (n=58,284) and AA (n=361,822) based on previous observational studies. In our analysis, we mainly employed the inverse variance-weighted (IVW) method of the random effects model, supplemented by the weighted median (WM) method and the MR Egger method.

Results

The findings from the IVW methods revealed a significant association between genetically predicted RA and an increased likelihood of AA, as evidenced by an odds ratio of 1.21 (95%CI = 1.11-1.32; P < 0.001. Both the WM method and MR-Egger regression consistently showed significant directional outcomes (Both P < 0.05), indicating a robust association between RA and AA. Additionally, both the funnel plot and the MR-Egger intercepts provided evidence of the absence of directional pleiotropy, suggesting that the observed association is not influenced by other common genetic factors.

Conclusions

The results of the study suggest a possible link between genetically predicted RA and AA. This finding highlights the importance for individuals diagnosed with RA to remain vigilant and aware of the potential development of AA. Regular monitoring and early detection can be crucial in managing and addressing this potential complication.

Genome-Wide Association Study of Alopecia Areata in Taiwan: The Conflict Between Individuals and Hair Follicles

Purpose

Alopecia areata (AA) is one of the most prevalent autoimmune diseases affecting humans. Given that hair follicles are immune-privileged, autoimmunity can result in disfiguring hair loss. However, the genetic basis for AA in the Taiwanese population remains unknown.

Materials and Methods

A genome-wide association study was conducted using a cohort of 408 AA cases and 8167 controls. To link variants to gene relationships, we used 882 SNPs (P<1E-05) within 74 genes that were associated with AA group to build the biological networks by IPA software. HLA diplotypes and haplotypes were analyzed using Attribute Bagging (HIBAG)-R package and chi-square analysis.

Results

Seven single nucleotide polymorphisms (SNPs) including LINC02006 (rs531166736, rs187306735), APC (rs112800832_C_CAT), SRP19 (rs139948960, rs144784670), EGFLAM (rs16903975) and LDLRAD3 (rs79874564) were closely associated with the AA phenotype (P<5E-08). Examination of biological networks revealed that these genomic areas are associated with antigen presentation signaling, B cell and T cell development, Th1 and Th2 activation pathways, Notch signaling, crosstalk signaling between dendritic cells and natural killer cells, and phagosome maturation. Based on human leukocyte antigen (HLA) genotype analysis, four HLA genotypes (HLA-B*15:01-*40:01, HLA-DQA1*01:02-*03:03, HLA-DQA1*01:02, and HLA-DQB1*02:01) were found to be associated with AA (adjusted p-value<0.05). HLA-DQA1*01:02 is the most significantly related gene in the Taiwanese population (adjusted p-value = 2.09E-05).

Conclusion

This study successfully identified susceptibility loci associated with AA in the Taiwanese population. These findings not only shed light on the origins of AA within the Taiwanese context but also contribute to a comprehensive understanding of the genetic factors influencing AA susceptibility.

Polymorphisms and gene expression of Notch4 in pulmonary tuberculosis

Background

Tuberculosis (TB) is a serious public health problem to human health, but the pathogenesis of TB remains elusive.

Methods

To identify novel candidate genes associated with TB susceptibility, we performed a population-based case control study to genotype 41SNPs spanning 21 genes in 435 pulmonary TB patients and 375 health donors from China.

Results

We found Notch4 gene rs206018 and rs422951 polymorphisms were associated with susceptibility to pulmonary tuberculosis. The association was validated in another independent cohort including 790 TB patients and 1,190 healthy controls. Moreover, we identified that the rs206018 C allele was associated with higher level of Notch4 in PBMCs from pulmonary TB patients. Furthermore, Notch4 expression increased in TB patients and higher Notch4 expression correlated with the severer pulmonary TB. Finally, we explored the origin and signaling pathways involved in the regulation of Notch4 expression in response to Mycobacterium tuberculosis (Mtb) infection. We determine that Mtb induced Notch4 and its ligand Jagged1expression in macrophages, and Notch4 through TLR2/P38 signaling pathway and Jagged1 through TLR2/ERK signaling pathway.

Conclusion

Our work further strengthens that Notch4 underlay an increased risk of TB in humans and is involved in the occurrence and development of TB, which could serve as a novel target for the host-targeted therapy of TB.

How Our Microbiome Influences the Pathogenesis of Alopecia Areata

Alopecia areata is a multifactorial autoimmune-based disease with a complex pathogenesis. As in all autoimmune diseases, genetic predisposition is key. The collapse of the immune privilege of the hair follicle leading to scalp loss is a major pathogenic event in alopecia areata. The microbiota considered a bacterial ecosystem located in a specific area of the human body could somehow influence the pathogenesis of alopecia areata, as it occurs in other autoimmune diseases. Moreover, the Next Generation Sequencing of the 16S rRNA bacterial gene and the metagenomic methodology have provided an excellent characterization of the microbiota. The aim of this narrative review is to examine the published literature on the cutaneous and intestinal microbiota in alopecia areata to be able to establish a pathogenic link. In this review, we summarize the influence of the microbiota on the development of alopecia areata. We first introduce the general pathogenic mechanisms that cause alopecia areata to understand the influence that the microbiota may exert and then we summarize the studies that have been carried out on what type of gut and skin microbiota is found in patients with this disease.

NOTCH4 Exhibits Anti-Inflammatory Activity in Activated Macrophages by Interfering With Interferon-γ and TLR4 Signaling

NOTCH4 is a member of the NOTCH family of receptors whose expression is intensively induced in macrophages after their activation by Toll-like receptors (TLR) and/or interferon-γ (IFN-γ). In this work, we show that this receptor acts as a negative regulator of macrophage activation by diminishing the expression of proinflammatory cytokines, such as IL-6 and IL-12, and costimulatory proteins, such as CD80 and CD86. We have observed that NOTCH4 inhibits IFN-γ signaling by interfering with STAT1-dependent transcription. Our results show that NOTCH4 reprograms the macrophage response to IFN-γ by favoring STAT3 versus STAT1 phosphorylation without affecting their expression levels. This lower activation of STAT1 results in diminished transcriptional activity and expression of STAT1-dependent genes, including IRF1, SOCS1 and CXCL10. In macrophages, NOTCH4 inhibits the canonical NOTCH signaling pathway induced by LPS; however, it can reverse the inhibition exerted by IFN-γ on NOTCH signaling, favoring the expression of NOTCH-target genes, such as Hes1. Indeed, HES1 seems to mediate, at least in part, the enhancement of STAT3 activation by NOTCH4. NOTCH4 also affects TLR signaling by interfering with NF-κB transcriptional activity. This effect could be mediated by the diminished activation of STAT1. These results provide new insights into the mechanisms by which NOTCH, TLR and IFN-γ signal pathways are integrated to modulate macrophage-specific effector functions and reveal NOTCH4 acting as a new regulatory element in the control of macrophage activation that could be used as a target for the treatment of pathologies caused by an excess of inflammation.

kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes

The power of genotype-phenotype association mapping studies increases greatly when contributions from multiple variants in a focal region are meaningfully aggregated. Currently, there are two popular categories of variant aggregation methods. Transcriptome-wide association studies (TWAS) represent a set of emerging methods that select variants based on their effect on gene expressions, providing pretrained linear combinations of variants for downstream association mapping. In contrast to this, kernel methods such as sequence kernel association test (SKAT) model genotypic and phenotypic variance use various kernel functions that capture genetic similarity between subjects, allowing nonlinear effects to be included. From the perspective of machine learning, these two methods cover two complementary aspects of feature engineering: feature selection/pruning and feature aggregation. Thus far, no thorough comparison has been made between these categories, and no methods exist which incorporate the advantages of TWAS- and kernel-based methods. In this work, we developed a novel method called kernel-based TWAS (kTWAS) that applies TWAS-like feature selection to a SKAT-like kernel association test, combining the strengths of both approaches. Through extensive simulations, we demonstrate that kTWAS has higher power than TWAS and multiple SKAT-based protocols, and we identify novel disease-associated genes in Wellcome Trust Case Control Consortium genotyping array data and MSSNG (Autism) sequence data. The source code for kTWAS and our simulations are available in our GitHub repository (https://github.com/theLongLab/kTWAS).

Aggregated network centrality shows non-random structure of genomic and proteomic networks

Network analysis is a powerful tool for modelling biological systems. We propose a new approach that integrates the genomic interaction data at population level with the proteomic interaction data. In our approach we use chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) data from human genome to construct a set of genomic interaction networks, considering the natural partitioning of chromatin into chromatin contact domains (CCD). The genomic networks are then mapped onto proteomic interactions, to create protein-protein interaction (PPI) subnetworks. Furthermore, the network-based topological properties of these proteomic subnetworks are investigated, namely closeness centrality, betweenness centrality and clustering coefficient. We statistically confirm, that networks identified by our method significantly differ from random networks in these network properties. Additionally, we identify one of the regions, namely chr6:32014923-33217929, as having an above-random concentration of the single nucleotide polymorphisms (SNPs) related to autoimmune diseases. Then we present it in the form of a meta-network, which includes multi-omic data: genomic contact sites (anchors), genes, proteins and SNPs. Using this example we demonstrate, that the created networks provide a valid mapping of genes to SNPs, expanding on the raw SNP dataset used.

Notch receptors and ligands in inflammatory arthritis - a systematic review

BACKGROUND

Notch pathway is highly conserved across species and is involved in the regulation of cell differentiation and activity both in embryonic development and adult life. Notch signaling has an important role in the development of hematopoietic stem cells and their differentiation to committed lineages, as well as in the regulation of several non-hematopoietic cell lines.

OBJECTIVE

As Notch signaling has been implicated in various inflammatory and autoimmune diseases, it is of interest to elucidate what role do Notch receptors and ligands have in inflammatory arthritides.

METHODS

We performed a search on the role of Notch receptors (1-4) and Notch ligands Delta-like (DLL) 1, 3, 4 and Jagged (Jag) 1 and 2 in animal models of inflammatory arthritis and most common types of human inflammatory arthritis (rheumatoid arthritis, psoriatic arthritis or ankylosing spondylitis). The initial search identified 135 unique articles, of which 24 were ultimately deemed relevant and included in this systematic review.

RESULTS

Overall, identified articles describe roles for Notch ligands and receptors in inflammatory arthritis, with Notch activation resulting in enhanced Th1/17 polarization, osteoclast differentiation, macrophage activation and fibroblast-like synoviocyte proliferation. However, the inhibitory role of Notch signaling, especially by Jag1 is also described.

CONCLUSION

There is evidence that Notch pathway activation affects multiple cell lineages present within the arthritic environment, therefore potentially acting as one of the drivers of disease pathogenesis. Since cell lineage-selective transgenic mouse models and specific Notch receptor inhibitors are becoming increasingly available, it can be expected that future research will evaluate whether Notch signaling components initiate crucial pathogenic impulses and, therefore, present viable therapeutic targets in inflammatory arthritis.

Identification of novel genetic loci for osteoporosis and/or rheumatoid arthritis using cFDR approach

There are co-morbidity between osteoporosis (OP) and rheumatoid arthritis (RA). Some genetic risk factors have been identified for these two phenotypes respectively in previous research; however, they accounted for only a small portion of the underlying total genetic variances. Here, we sought to identify additional common genetic loci associated with OP and/or RA. The conditional false discovery rate (cFDR) approach allows detection of additional genetic factors (those respective ones as well as common pleiotropic ones) for the two associated phenotypes. We collected and analyzed summary statistics provided by large, multi-center GWAS studies of FNK (femoral neck) BMD (a major risk factor for osteoporosis) (n = 53,236) and RA (n = 80,799). The conditional quantile-quantile (Q-Q) plots can assess the enrichment of SNPs related to FNK BMD and RA, respectively. Furthermore, we identified shared loci between FNK BMD and RA using conjunction cFDR (ccFDR). We found strong enrichment of p-values in FNK BMD when conditional Q-Q was done on RA and vice versa. We identified 30 novel OP-RA associated pleiotropic loci that have not been reported in previous OP or RA GWAS, 18 of which located in the MHC (major histocompatibility complex) region previously reported to play an important role in immune system and bone health. We identified some specific novel polygenic factors for OP and RA respectively, and identified 30 novel OP-RA associated pleiotropic loci. These discovery findings may offer novel pathobiological insights, and suggest new targets and pathways for drug development in OP and RA patients.

Knowledge-based analysis of genetic associations of rheumatoid arthritis to inform studies searching for pleiotropic genes: a literature review and network analysis

Introduction

Pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. Gene variants directly affect the normal processes of a series of physiological and biochemical reactions, and therefore cause a variety of diseases traits to be changed accordingly. Moreover, a shared genetic susceptibility mechanism may exist between different diseases. Therefore, shared genes, with pleiotropic effects, are important to understand the sharing pathogenesis and hence the mechanisms underlying comorbidity.

Methods

In this study, we proposed combining genome-wide association studies (GWAS) and public knowledge databases to search for potential pleiotropic genes associated with rheumatoid arthritis (RA) and eight other related diseases. Here, a GWAS-based network analysis is used to recognize risk genes significantly associated with RA. These RA risk genes are re-extracted as potential pleiotropic genes if they have been proved to be susceptible genes for at least one of eight other diseases in the OMIM or PubMed databases.

Results

In total, we extracted 116 potential functional pleiotropic genes for RA and eight other diseases, including five hub pleiotropic genes, BTNL2, HLA-DRA, NOTCH4, TNXB, and C6orf10, where BTNL2, NOTCH4, and C6orf10 are novel pleiotropic genes identified by our analysis.

Conclusions

This study demonstrates that pleiotropy is a common property of genes associated with disease traits. Our results ascertained the shared genetic risk profiles that predisposed individuals to RA and other diseases, which could have implications for identification of molecular targets for drug development, and classification of diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-015-0715-1) contains supplementary material, which is available to authorized users.

What causes alopecia areata?

The pathobiology of alopecia areata (AA), one of the most frequent autoimmune diseases and a major unsolved clinical problem, has intrigued dermatologists, hair biologists and immunologists for decades. Simultaneously, both affected patients and the physicians who take care of them are increasingly frustrated that there is still no fully satisfactory treatment. Much of this frustration results from the fact that the pathobiology of AA remains unclear, and no single AA pathogenesis concept can claim to be universally accepted. In fact, some investigators still harbour doubts whether this even is an autoimmune disease, and the relative importance of CD8(+) T cells, CD4(+) T cells and NKGD2(+) NK or NKT cells and the exact role of genetic factors in AA pathogenesis remain bones of contention. Also, is AA one disease, a spectrum of distinct disease entities or only a response pattern of normal hair follicles to immunologically mediated damage? During the past decade, substantial progress has been made in basic AA-related research, in the development of new models for translationally relevant AA research and in the identification of new therapeutic agents and targets for future AA management. This calls for a re-evaluation and public debate of currently prevalent AA pathobiology concepts. The present Controversies feature takes on this challenge, hoping to attract more skin biologists, immunologists and professional autoimmunity experts to this biologically fascinating and clinically important model disease.