Epiregulin (EREG) and human V-ATPase (TCIRG1): genetic variation, ethnicity and pulmonary tuberculosis susceptibility in Guinea-Bissau and The Gambia

Discussion on the Antipruritic Mechanism of Qiwei Antipruritic Based on Network Pharmacology and Molecular Docking Technology

Objective

To explore the mechanism of Qiwei antipruritic by using network pharmacology and molecular docking technology.

Methods

The components and related targets of Qiwei antipruritic were screened by using the traditional Chinese medicine system pharmacology database (TCMSP and symmap databases). GeneCards and OMIM databases were used to screen itch-related targets. The protein-protein interaction (PPI) network between active ingredient targets and pruritus disease targets was constructed using STRING database. Cytoscape 3.8.0 software was used to draw the visualization network of "drug-component-target-signaling pathway" and screen the core targets. Gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using R software. AutoDock vina software was used to perform molecular docking of key targets and their corresponding key components.

Results

There were 44 main components of Qiwei antipruritic compound, 118 corresponding targets and 3869 itch-related genes. A total of 82 predicted targets of Qiwei antipruritic in the treatment of pruritus were obtained. Eleven key targets were screened. Among the 23 KEGG enriched pathways, 12 signaling pathways were related to skin pruritus. Molecular docking results showed that the core components of Qiwei antipruritic, including quercetin, kaempferol, β-sitosterol, stigmasterol, luteolin, and preskimmianine, had good binding ability with ESR1, PPARG, IL6, TP53, and EGFR, and the docking scores were all less than -4.

Conclusion

The mechanism of Qiwei antipruritic may be related to histamine activation mechanism, calcium channel mechanism, inhibition of inflammatory signaling pathway, inhibition of neurotransmitters, and regulation of immune pathways. The traditional Chinese medicine compound Qiwei antipruritic can treat clinical pruritus through multiple targets and pathways.

CLEC4E (Mincle) genetic variation associates with pulmonary tuberculosis in Guinea-Bissau (West Africa)

Tuberculosis (TB) is the leading cause of death from a single infectious agent. According to the WHO, 85% of cases in 2018 were pulmonary tuberculosis (PTB), making it the most prevalent form of the disease. Although the bacillus responsible for disease, Mycobacterium tuberculosis (MTB), is estimated to infect 1.7 billion people worldwide, only a small portion of those infected (5-10%) will transition into active TB. Because such a small fraction of infected people develop active disease, we hypothesized that underlying host genetic variation associates with developing active pulmonary disease. Variation in CLEC4E has been of interest in previous association studies showing either no effect or protection from PTB. For our study we assessed 60 SNPs in 11 immune genes, including CLEC4E, using a case-control study from Guinea-Bissau. The 289 cases and 322 controls differed in age, sex, and ethnicity all of which were included in adjusted models. Initial association analysis with unadjusted logistic regression revealed putative association with seven SNPs (p < 0.05). All SNPs were then assessed in an adjusted model. Of the six SNPs that remained significant, three of them were assigned to the CLEC4E gene (rs12302046, rs10841847, and rs11046143). Of these, only rs10841847 passed FDR adjustment for multiple testing. Adjusted regression analyses showed that the minor allele at rs10841847 associated with higher risk of developing PTB (OR = 1.55, CI = 1.22-1.96, p-value = 0.00036). Based on these initial association tests, CLEC4E seemed to be the predictor of interest for PTB risk in this population. Haplotype analysis (2-SNP and 3-SNP windows) showed that minor alleles in segments including rs10841847 were the only ones to pass the threshold of global significance, compared to other haplotypes (p-value < 0.05). Linkage disequilibrium patterns showed that rs12302046 is in high LD with rs10841847 (r2 = 0.67), and all other SNPs lost significance when adjusted for rs10841847 effects. These findings indicate that rs10841847 in CLEC4E is the single best predictor of pulmonary tuberculosis risk in our study population. These results provide evidence for the hypothesis that genetic variation of CLEC4E influences risk to TB in Guinea-Bissau.

An epistatic interaction between pre-natal smoke exposure and socioeconomic status has a significant impact on bronchodilator drug response in African American youth with asthma

BACKGROUND

Asthma is one of the leading chronic illnesses among children in the United States. Asthma prevalence is higher among African Americans (11.2%) compared to European Americans (7.7%). Bronchodilator medications are part of the first-line therapy, and the rescue medication, for acute asthma symptoms. Bronchodilator drug response (BDR) varies substantially among different racial/ethnic groups. Asthma prevalence in African Americans is only 3.5% higher than that of European Americans, however, asthma mortality among African Americans is four times that of European Americans; variation in BDR may play an important role in explaining this health disparity. To improve our understanding of disparate health outcomes in complex phenotypes such as BDR, it is important to consider interactions between environmental and biological variables.

RESULTS

We evaluated the impact of pairwise and three-variable interactions between environmental, social, and biological variables on BDR in 233 African American youth with asthma using Visualization of Statistical Epistasis Networks (ViSEN). ViSEN is a non-parametric entropy-based approach able to quantify interaction effects using an information-theory metric known as Information Gain (IG). We performed analyses in the full dataset and in sex-stratified subsets. Our analyses identified several interaction models significantly, and suggestively, associated with BDR. The strongest interaction significantly associated with BDR was a pairwise interaction between pre-natal smoke exposure and socioeconomic status (full dataset IG: 2.78%, p = 0.001; female IG: 7.27%, p = 0.004)). Sex-stratified analyses yielded divergent results for females and males, indicating the presence of sex-specific effects.

CONCLUSIONS

Our study identified novel interaction effects significantly, and suggestively, associated with BDR in African American children with asthma. Notably, we found that all of the interactions identified by ViSEN were "pure" interaction effects, in that they were not the result of strong main effects on BDR, highlighting the complexity of the network of biological and environmental factors impacting this phenotype. Several associations uncovered by ViSEN would not have been detected using regression-based methods, thus emphasizing the importance of employing statistical methods optimized to detect both additive and non-additive interaction effects when studying complex phenotypes such as BDR. The information gained in this study increases our understanding and appreciation of the complex nature of the interactions between environmental and health-related factors that influence BDR and will be invaluable to biomedical researchers designing future studies.

Polymorphism in the EREG gene confers susceptibility to tuberculosis

BACKGROUND

Host genetic factors affect the immune response to Mycobacterium tuberculosis (Mtb) infection as well as the progression of the disease. Epiregulin (EREG) belongs to the epidermal growth factor (EGF) family, which binds to the epidermal growth factor receptor (EGFR) to regulate the immune response of the host during infections. Our study aimed to compare EREG levels in tuberculosis (TB) patients and healthy controls and assess whether polymorphisms in EREG increase the risk of TB.

METHODS

We used ELISA to determine the plasma EREG level from 30 healthy controls and 50 tuberculosis patients. By evaluating the EREG gene from 624 TB patients and 600 healthy controls, we determined the allelic and genotypic frequencies for association with susceptibility to TB infections in this group.

RESULTS

This paper shows that the pulmonary tuberculosis (PTB) and extrapulmonary tuberculosis (EPTB) groups showed a significantly higher plasma EREG level (1014 ± 733.9 pg/ml, 700.2 ± 676.6 pg/ml, respectively) than the healthy controls (277 ± 105.4 pg/ml). The rs2367707 polymorphism was associated with a higher risk of PTB and EPTB (P = 0.00051, P = 0.0012). Analyses of haplotype frequencies found that people with the haplotype CACAT had a higher risk of PTB and EPTB (P = 0.00031, OR = 1.43; P = 0.000053, OR = 1.65). Moreover, the rs6446993 polymorphism of the EREG gene was found to be associated with EPTB (P = 0.00087, OR = 1.54; 95% CI = 1.23-1.94).

CONCLUSIONS

Compared to that of healthy controls, the level of EREG in the plasma of TB patients increased significantly. Based on these data, we demonstrated that EREG polymorphisms are genetic factors for susceptibility to TB and various forms of TB.

Genomics of human pulmonary tuberculosis: from genes to pathways

Purpose of review

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major public health threat globally. Several lines of evidence support a role for host genetic factors in resistance/susceptibility to TB disease and MTB infection. However, results across candidate gene and genome-wide association studies (GWAS) are largely inconsistent, so a cohesive genetic model underlying TB risk has not emerged.

Recent Findings

Despite the difficulties in identifying consistent genetic associations, genetic studies of TB and MTB infection have revealed a few well-documented loci. These well validated genes are presented in this review, but there remains a large gap in how these genes translate into better understanding of TB. To address this, we present a pathway based extension of standard association analyses, seeding the results with the best validated genes from candidate gene and GWAS studies.

Summary

Several pathways were significantly enriched using pathway analyses that may help to explain population patterns of TB risk. In conclusion, we advocate for novel approaches to the study of host genetic analysis of TB that extend traditional association approaches.

Immunological consequences of strain variation within the Mycobacterium tuberculosis complex

In 2015, there were an estimated 10.4 million new cases of tuberculosis (TB) globally, making it one of the leading causes of death due to an infectious disease. TB is caused by members of the Mycobacterium tuberculosis complex (MTBC), with human disease resulting from infection by M. tuberculosis sensu stricto and M. africanum. Recent progress in genotyping techniques, in particular the increasing availability of whole genome sequence data, has revealed previously under appreciated levels of genetic diversity within the MTBC. Several studies have shown that this genetic diversity may translate into differences in TB transmission, clinical manifestations of disease, and host immune responses. This suggests the existence of MTBC genotype‐dependent host–pathogen interactions which may influence the outcome of infection and progression of disease. In this review, we highlight the studies demonstrating differences in innate and adaptive immunological outcomes consequent on MTBC genetic diversity, and discuss how these differences in immune response might influence the development of TB vaccines, diagnostics and new therapies.

The Role of Host Genetics (and Genomics) in Tuberculosis

Familial risk of tuberculosis (TB) has been recognized for centuries. Largely through studies of mono- and dizygotic twin concordance rates, studies of families with Mendelian susceptibility to mycobacterial disease, and candidate gene studies performed in the 20th century, it was recognized that susceptibility to TB disease has a substantial host genetic component. Limitations in candidate gene studies and early linkage studies made the robust identification of specific loci associated with disease challenging, and few loci have been convincingly associated across multiple populations. Genome-wide and transcriptome-wide association studies, based on microarray (commonly known as genechip) technologies, conducted in the past decade have helped shed some light on pathogenesis but only a handful of new pathways have been identified. This apparent paradox, of high heritability but few replicable associations, has spurred a new wave of collaborative global studies. This review aims to comprehensively review the heritability of TB, critically review the host genetic and transcriptomic correlates of disease, and highlight current studies and future prospects in the study of host genomics in TB. An implicit goal of elucidating host genetic correlates of susceptibility to Mycobacterium tuberculosis infection or TB disease is to identify pathophysiological features amenable to translation to new preventive, diagnostic, or therapeutic interventions. The translation of genomic insights into new clinical tools is therefore also discussed.

Investigating the Role of Gene-Gene Interactions in TB Susceptibility

Tuberculosis (TB) is the second leading cause of mortality from infectious disease worldwide. One of the factors involved in developing disease is the genetics of the host, yet the field of TB susceptibility genetics has not yielded the answers that were expected. A commonly posited explanation for the missing heritability of complex disease is gene-gene interactions, also referred to as epistasis. In this study we investigate the role of gene-gene interactions in genetic susceptibility to TB using a cohort recruited from a high TB incidence community from Cape Town, South Africa. Our discovery data set incorporates genotypes from a large a number of candidate gene studies as well as genome-wide data. After limiting our search space to pairs of putative TB susceptibility genes, as well as pairs of genes that have been curated in online databases as potential interactors, we use statistical modelling to identify pairs of interacting SNPs. We attempt to validate the top models identified in our discovery data set using an independent genome-wide TB case-control data set from The Gambia. A number of models were successfully validated, indicating that interplay between the NRG1 - NRG3, GRIK1 - GRIK3 and IL23R - ATG4C gene pairs may modify susceptibility to TB. Gene pairs involved in the NF-κB pathway were also identified in the discovery data set (SFTPD - NOD2, ISG15 - TLR8 and NLRC5 - IL12RB1), but could not be tested in the Gambian study group due to lack of overlapping data.

Heuristic identification of biological architectures for simulating complex hierarchical genetic interactions

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects.