A genome-wide association study reveals ARL15, a novel non-HLA susceptibility gene for rheumatoid arthritis in North Indians

Gene selection by incorporating genetic networks into case-control association studies

Large-scale genome-wide association studies (GWAS) have been successfully applied to a wide range of genetic variants underlying complex diseases. The network-based regression approach has been developed to incorporate a biological genetic network and to overcome the challenges caused by the computational efficiency for analyzing high-dimensional genomic data. In this paper, we propose a gene selection approach by incorporating genetic networks into case-control association studies for DNA sequence data or DNA methylation data. Instead of using traditional dimension reduction techniques such as principal component analyses and supervised principal component analyses, we use a linear combination of genotypes at SNPs or methylation values at CpG sites in a gene to capture gene-level signals. We employ three linear combination approaches: optimally weighted sum (OWS), beta-based weighted sum (BWS), and LD-adjusted polygenic risk score (LD-PRS). OWS and LD-PRS are supervised approaches that depend on the effect of each SNP or CpG site on the case-control status, while BWS can be extracted without using the case-control status. After using one of the linear combinations of genotypes or methylation values in each gene to capture gene-level signals, we regularize them to perform gene selection based on the biological network. Simulation studies show that the proposed approaches have higher true positive rates than using traditional dimension reduction techniques. We also apply our approaches to DNA methylation data and UK Biobank DNA sequence data for analyzing rheumatoid arthritis. The results show that the proposed methods can select potentially rheumatoid arthritis related genes that are missed by existing methods.

HLA sequencing identifies novel associations and suggests clinical relevance of DPB1*04:01 in ANCA-associated Granulomatosis with polyangiitis

Granulomatosis with polyangiitis (GPA) is a rare systemic autoimmune disease. Major contributions of HLA genes have been reported; however, HLA typing-based diagnosis or risk prediction in GPA has not been established. We have performed a sequencing-based HLA genotyping in a north Indian GPA cohort and controls to identify clinically relevant novel associations. PR3-ANCA-positive 40 GPA patients and 40 healthy controls from north India were recruited for the study. Targeted sequencing of HLA-A,-B,-C,-DRB1,-DQB1, and -DPB1 was performed. Allelic and haplotypic associations were tested. Molecular docking of susceptibility HLA alleles with reported super-antigen epitopes was performed. The association of substituted amino acids located at the antigen-binding domain of HLA was evaluated. Genetic association of five HLA-alleles was identified in GPA. The novel association was identified for C*15:02 (p = 0.04; OR = 0.27(0.09-0.88)). The strongest association was observed for DPB1*04:01 (p < 0.0001; OR = 6.2(3.08-11.71)), previously reported in European studies. 35 of 40 GPA subjects had at least one DPB1*04:01 allele, and its significant risk was previously not reported from the Indian population. Significantly associated haplotypes DRB1*03:01-DQB1*02:01-DPB1*04:01 (p = 0.02; OR = 3.46(1.11-12.75)) and DRB1*07:01-DQB1*02:02-DPB1*04:01 (p = 0.04; OR = 3.35(0.95-14.84)) were the most frequent in GPA patients. Ranging from 89 % to 100 % of GPA patients with organ involvement can be explained by at least one DPB1*04:01 allele. A strong interaction between the HLA and three epitopes of the reported super antigen TSST-1 of Staphylococcus aureus was confirmed. Our study highlighted the potential applicability of HLA typing for screening and diagnosis of GPA. A large multi-centric study and genotype-phenotype correlation analysis among GPA patients will enable the establishment of HLA-typing based GPA diagnosis.

ARL15, a GTPase implicated in rheumatoid arthritis, potentially repositions its truncated N-terminus as a function of guanine nucleotide binding

The ADP ribosylation factor like protein 15 (ARL15) gene encodes for an uncharacterized GTPase associated with rheumatoid arthritis (RA) and other metabolic disorders. Investigation of the structural and functional attributes of ARL15 is important to position the protein as a potential drug target. Using spectroscopy, we demonstrated that ARL15 exhibits properties inherent of GTPases. The Km and Vmax of the enzyme were calculated to be 100 μM and 1.47 μmole/min/μL, respectively. The equilibrium dissociation constant (Kd) of GTP binding with ARL15 was estimated to be about eight-fold higher than that of GDP. Small Angle X-ray Scattering (SAXS) data indicated that in solution, the apo state of monomeric ARL15 adopts a shape characterized by a globe of maximum linear dimension (Dmax) of 6.1 nm, and upon binding to GTP or GDP, the vector distribution profile changes to peak-n-tail shoulder with Dmax extended to 7.6 and 7.7 nm, respectively. Structure restoration using a sequence-based template and experimental SAXS data provided the first visual insight revealing that the folded N-terminal in the unbound state of the protein may toggle open upon binding to guanine nucleotides. The conformational dynamics observed in the N-terminal region offer a scope to develop drugs that target this unique GTPase, potentially providing treatments for a range of metabolic disorders.

Research hotspots and trends of artificial intelligence in rheumatoid arthritis: A bibliometric and visualized study

Artificial intelligence (AI) applications on rheumatoid arthritis (RA) are becoming increasingly popular. In this bibliometric study, we aimed to analyze the characteristics of publications relevant to the research of AI in RA, thereby developing a thorough overview of this research topic. Web of Science was used to retrieve publications on the application of AI in RA from 2003 to 2022. Bibliometric analysis and visualization were performed using Microsoft Excel (2019), R software (4.2.2) and VOSviewer (1.6.18). The overall distribution of yearly outputs, leading countries, top institutions and authors, active journals, co-cited references and keywords were analyzed. A total of 859 relevant articles were identified in the Web of Science with an increasing trend. USA and China were the leading countries in this field, accounting for 71.59% of publications in total. Harvard University was the most influential institution. Arthritis Research & Therapy was the most active journal. Primary topics in this field focused on estimating the risk of developing RA, diagnosing RA using sensor, clinical, imaging and omics data, identifying the phenotype of RA patients using electronic health records, predicting treatment response, tracking the progression of the disease and predicting prognosis and developing new drugs. Machine learning and deep learning algorithms were the recent research hotspots and trends in this field. AI has potential applications in various fields of RA, including the risk assessment, screening, early diagnosis, monitoring, prognosis determination, achieving optimal therapeutic outcomes and new drug development for RA patients. Incorporating machine learning and deep learning algorithms into real-world clinical practice will be a future research hotspot and trend for AI in RA. Extensive collaboration to improve model maturity and robustness will be a critical step in the advancement of AI in healthcare.

Association between PYTPN22 rs2476601, VEGF rs833070, TNFAIP3 rs6920220 Polymorphisms and Risk for Rheumatoid Arthritis in Early Undifferentiated Arthritis Patients: A Pilot Study

Background and Objectives: About 40% of early undifferentiated arthritis (UA) progresses to rheumatoid (RA) or other chronic arthritis. Novel diagnostic tools predicting the risk for this progression are needed to identify the patients who would benefit from early aggressive treatment. Evidence on the role of single-nucleotide polymorphisms (SNPs) in the development of RA has emerged. The aim of our study was to investigate the association between rs2476601, rs833070, and rs6920220 SNPs and UA progression to RA. Materials and Methods: Ninety-two UA patients were observed for 12 months. At study entry, demographic and clinical characteristics were recorded, musculoskeletal ultrasonography was performed, and blood samples were drawn to investigate levels of inflammatory markers, rheumatoid factor (RF), anti-citrullinated protein antibodies (anti-CCP)detect SNPs. After 12 months, UA outcomes were assessed, and patients were divided into two (RA and non-RA) groups. The association between the risk of progression to chronic inflammatory arthritis and analyzed SNPs was measured by computing odds ratios (OR). Results: After a 12-month follow-up, 27 (29.3%) patients developed RA, and 65 (70.7%) patients were assigned to the non-RA group. The arthritis of 21 patients (22.8%) from the non-RA group resolved completely, while the other 44 (47.2%) patients were diagnosed with another rheumatic inflammatory disease. The patients who developed RA had a significantly greater number of tender and swollen joints (p = 0.010 and p = 0.021 respectively) and were more frequently RF or anti-CCP (p < 0.001), and both RF and anti-CCP positive (p < 0.001) at the baseline as compared with the patients in the non-RA group. No significant association between rs2476601 (OR = 0.99, p = 0.98), rs833070 (OR = 1.0, p = 0.97), and rs6920220 (OR = 0.48, p = 0.13) polymorphisms and the risk of developing RA were found. Conclusions: No association between analyzed SNPs and a greater risk to progress from UA to RA was confirmed, although patients with rs6920220 AA + AG genotypes had fewer tender joints at the disease onset.

Structural insights into regulation of CNNM-TRPM7 divalent cation uptake by the small GTPase ARL15

Cystathionine-β-synthase (CBS)-pair domain divalent metal cation transport mediators (CNNMs) are an evolutionarily conserved family of magnesium transporters. They promote efflux of Mg2+ ions on their own and influx of divalent cations when expressed with the transient receptor potential ion channel subfamily M member 7 (TRPM7). Recently, ADP-ribosylation factor-like GTPase 15 (ARL15) has been identified as CNNM-binding partner and an inhibitor of divalent cation influx by TRPM7. Here, we characterize ARL15 as a GTP and CNNM-binding protein and demonstrate that ARL15 also inhibits CNNM2 Mg2+ efflux. The crystal structure of a complex between ARL15 and CNNM2 CBS-pair domain reveals the molecular basis for binding and allowed the identification of mutations that specifically block binding. A binding deficient ARL15 mutant, R95A, failed to inhibit CNNM and TRPM7 transport of Mg2+ and Zn2+ ions. Structural analysis and binding experiments with phosphatase of regenerating liver 2 (PRL2 or PTP4A2) showed that ARL15 and PRLs compete for binding CNNM to coordinate regulation of ion transport by CNNM and TRPM7.

Case-control association study of congenital heart disease from a tertiary paediatric cardiac centre from North India

BACKGROUND

Congenital Heart diseases (CHDs) account for 1/3rd of all congenital birth defects. Etiopathogenesis of CHDs remain elusive despite extensive investigations globally. Phenotypic heterogeneity witnessed in this developmental disorder reiterate gene-environment interactions with periconceptional factors as risk conferring; and genetic analysis of both sporadic and familial forms of CHD suggest its multigenic basis. Significant association of de novo and inherited variants have been observed. Approximately 1/5th of CHDs are documented in the ethnically distinct Indian population but genetic insights have been very limited. This pilot case-control based association study was undertaken to investigate the status of Caucasian SNPs in a north Indian cohort.

METHOD

A total of 306 CHD cases sub-classified into n = 198 acyanotic and n = 108 cyanotic types were recruited from a dedicated tertiary paediatric cardiac centre in Palwal, Haryana. 23 SNPs primarily prioritized from Genome-wide association studies (GWAS) on Caucasians were genotyped using Agena MassARRAY Technology and test of association was performed with adequately numbered controls.

RESULTS

Fifty percent of the studied SNPs were substantially associated in either allelic, genotypic or sub-phenotype categories validating their strong correlation with disease manifestation. Of note, strongest allelic association was observed for rs73118372 in CRELD1 (p < 0.0001) on Chr3, rs28711516 in MYH6 (p = 0.00083) and rs735712 in MYH7 (p = 0.0009) both on Chr 14 and were also significantly associated with acyanotic, and cyanotic categories separately. rs28711516 (p = 0.003) and rs735712 (p = 0.002) also showed genotypic association. Strongest association was observed with rs735712(p = 0.003) in VSD and maximum association was observed for ASD sub-phenotypes.

CONCLUSIONS

Caucasian findings were partly replicated in the north Indian population. The findings suggest the contribution of genetic, environmental and sociodemographic factors, warranting continued investigations in this study population.

Progression of pre-rheumatoid arthritis to clinical disease of joints: Potential role of mesenchymal stem cells

Rheumatoid arthritis (RA) related autoimmunity is developed at mucosal sites due to the interplay between genetic risk factors and environmental triggers. The pre-RA phase that leads to anti-citrullinated protein antibodies, rheumatoid factor, and other autoantibodies spread in the systemic circulation may not affect articular tissue for years until a mysterious second hit triggers the localization of RA-related autoimmunity in joints. Several players in the joint microenvironment mediate the synovial innate and adaptive immunological processes, eventually leading to clinical synovitis. There still exists a gap in the early phase of RA pathogenesis, i.e., the progression of diseases from the systemic circulation to joints. The lack of better understanding of these events results in the inability to answer questions about why only after a certain point of time the disease appears in joints and why in some cases, it simply remains latent and doesn't affect joints at all. In the current review, we focused on the immunomodulatory and regenerative role of mesenchymal stem cells and associated exosomes in RA pathology. We also highlighted the age-related dysregulations in activities of mesenchymal stem cells and how that might trigger homing of systemic autoimmunity to joints.

Structural insights into regulation of TRPM7 divalent cation uptake by the small GTPase ARL15

Cystathionine-β-synthase (CBS)-pair domain divalent metal cation transport mediators (CNNMs) are an evolutionarily conserved family of magnesium transporters. They promote efflux of Mg ²⁺ ions on their own or uptake of divalent cations when coupled to the transient receptor potential ion channel subfamily M member 7 (TRPM7). Recently, ADP-ribosylation factor-like GTPase 15 (ARL15) has been identified as CNNM binding partner and an inhibitor of divalent cation influx by TRPM7. Here, we characterize ARL15 as a GTP-binding protein and demonstrate that it binds the CNNM CBS-pair domain with low micromolar affinity. The crystal structure of the complex between ARL15 GTPase domain and CNNM2 CBS-pair domain reveals the molecular determinants of the interaction and allowed the identification of mutations in ARL15 and CNNM2 mutations that abrogate binding. Loss of CNNM binding prevented ARL15 suppression of TRPM7 channel activity in support of previous reports that the proteins function as a ternary complex. Binding experiments with phosphatase of regenerating liver 2 (PRL2 or PTP4A2) revealed that ARL15 and PRLs compete for binding CNNM, suggesting antagonistic regulation of divalent cation transport by the two proteins.

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Arl15 upregulates the TGFβ family signaling by promoting the assembly of the Smad-complex

The hallmark event of the canonical transforming growth factor β (TGFβ) family signaling is the assembly of the Smad-complex, consisting of the common Smad, Smad4, and phosphorylated receptor-regulated Smads. How the Smad-complex is assembled and regulated is still unclear. Here, we report that active Arl15, an Arf-like small G protein, specifically binds to the MH2 domain of Smad4 and colocalizes with Smad4 at the endolysosome. The binding relieves the autoinhibition of Smad4, which is imposed by the intramolecular interaction between its MH1 and MH2 domains. Activated Smad4 subsequently interacts with phosphorylated receptor-regulated Smads, forming the Smad-complex. Our observations suggest that Smad4 functions as an effector and a GTPase activating protein (GAP) of Arl15. Assembly of the Smad-complex enhances the GAP activity of Smad4 toward Arl15, therefore dissociating Arl15 before the nuclear translocation of the Smad-complex. Our data further demonstrate that Arl15 positively regulates the TGFβ family signaling.

Stratification of rheumatoid arthritis cohort using Ayurveda based deep phenotyping approach identifies novel genes in a GWAS

Background and aim

Genome wide association studies have scaled up both in terms of sample size and range of complex disorders investigated, but these have explained relatively little phenotypic variance. Of the several reasons, phenotypic heterogeneity seems to be a likely contributor for missing out genetic associations of large effects. Ayurveda, the traditional Indian system of medicine is one such tool which adopts a holistic deep phenotyping approach and classifies individuals based on their body constitution/prakriti. We hypothesized that Ayurveda based phenotypic stratification of healthy and diseased individuals will allow us to achieve much desired homogeneous cohorts which would facilitate detection of genetic association of large effects. In this proof of concept study, we performed a genome wide association testing of clinically diagnosed rheumatoid arthritis patients and healthy controls, who were re-phenotyped into Vata, Pitta and Kapha predominant prakriti sub-groups.

Experimental procedure

Genotypes of rheumatoid arthritis cases (Vata = 49; Pitta = 117; Kapha = 78) and controls (Vata = 33; Pitta = 175; Kapha = 85) were retrieved from the total genotype data, used in a recent genome-wide association study performed in our laboratory. A total of 528461 SNPs were included after quality control. Prakriti-wise genome-wide association analysis was employed.

Results and conclusion

This study identified (i) prakriti-specific novel disease risk genes of high effect sizes; (ii) putative candidates of novel therapeutic potential; and (iii) a good correlation between genetic findings and clinical knowledge in Ayurveda. Adopting Ayurveda based deep phenotyping may facilitate explaining hitherto undiscovered heritability in complex traits and may propel much needed progress in personalized medicine.

Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions

INTRODUCTION

Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or 'omics' data). Such approaches may improve the management of patients with RA.

AREAS COVERED

In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: 'arthritis, rheumatoid' and 'machine learning'. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed.

EXPERT OPINION

ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.

Computational insight into the three-dimensional structure of ADP ribosylation factor like protein 15, a novel susceptibility gene for rheumatoid arthritis

The ARL15 gene (ADP ribosylation factor like protein 15) encodes for an uncharacterized small GTP-binding protein. Its exact role in human physiology remains unknown, but a number of genetic association studies have recognised different variants in this gene to be statistically associated with numerous traits and complex diseases. We have previously reported a novel association of ARL15 with rheumatoid arthritis (RA) based on a genome-wide association study in a north Indian cohort. Subsequent investigations have provided leads for its involvement in RA pathophysiology, especially its potential as a novel therapeutic target. However, the absence of an experimentally determined tertiary structure for ARL15 significantly hinders the understanding of its biochemical and physiological functions, as well as development of potential lead molecules. We, therefore, aimed to derive a high quality, refined model of the three dimensional structure of human ARL15 protein using two different computational protein structure prediction methods - template-based threading and ab initio modelling. The best model each from among the five each derived from both the approaches was selected based on stringent quality assessment and refinement. Molecular dynamics simulations over long timescales revealed the ab initio model to be relatively more stable, and it marginally outperformed the template-based model in the quality assessment as well. A putative GTP-binding site was also predicted using homology for the ARL15 protein, where potential competitive inhibitors can be targeted. This high quality predicted model may provide insights to the biological role(s) of ARL15 and inform and guide further experimental, structural and biochemical characterization efforts.Communicated by Ramaswamy H. Sarma.

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

Indonesians Human Leukocyte Antigen (HLA) Distributions and Correlations with Global Diseases

In Human, Major Histocompatibility Complex known as Human Leukocyte Antigen (HLA). The HLA grouped into three subclasses regions: the class I region, the class II region, and the class III region. There are thousands of polymorphic HLAs, many of them are proven to have correlations with diseases. Indonesia consists of diverse ethnicity people and populations. It carries a unique genetic diversity between one and another geographical positions. This paper aims to extract Indonesians HLA allele data, mapping the data, and correlating them with global diseases. From the study, it is found that global diseases, like Crohn's disease, rheumatoid arthritis, Graves' disease, gelatin allergy, T1D, HIV, systemic lupus erythematosus, juvenile chronic arthritis, and Mycobacterial disease (tuberculosis and leprosy) suspected associated with the Indonesian HLA profiles.

'RA and the microbiome: do host genetic factors provide the link?

Rheumatoid arthritis (RA) is a chronic autoimmune disease, characterised by painful synovium inflammation, bony erosions, immune activation and the circulation of autoantibodies. Despite recent advances in therapeutics enabling disease suppression, there is a considerable demand for alternative therapeutic strategies as well as optimising those available at present. The relatively low concordance rate between monozygotic twins, 20–30% contrasts with heritability estimates of ∼65%, indicating a substantive role of other risk factors in RA pathogenesis. There is established evidence that RA has an infective component to its aetiology. More recently, differences in the commensal microbiota in RA compared to controls have been identified. Studies have shown that the gut, oral and lung microbiota is different in new onset treatment naïve, and established RA patients, compared to controls. Key taxonomic associations are an increase in abundance of Porphyromonas gingivalis and Prevotella copri in RA patients, compared to healthy controls. Host genetics may provide the link between disease and the microbiome. Genetic influence may be mediated by the host immune system; a differential response to RA associated taxa is suggested. The gut microbiome contains elements which are as much as 30% heritable. A better understanding of the influence of host genetics will shed light onto the role of the microbiome in RA. Here we review the role of the microbiome in RA through the lens of host genetics, and consider future research areas addressing microbiome study design and bioinformatics approaches.

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Rheumatoid arthritis (RA) affects 1% of the population and is highly debilitating.

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RA is ~65% heritable, yet the concordance rate between monozygotic twins is just 20–30%, indicating a substantive role of other risk factors.

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Studies have shown that the gut, oral and lung microbiome is different in treatment naïve and established RA patients, compared to controls.

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Current findings suggest an important influence of host genetics on the microbiome, which may contribute to RA via the host immune system.

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Associations of the microbiome with RA described thus far are confounded by host genetics, and future studies need to take account of this.

Prerequisite of a Holistic Blend of Traditional and Modern Approaches of Cancer Management

Background:

With the advent of changes in lifestyle of people all around the world, cancer cases have been showing an exponential rise. Researchers from varied fields have been trying to solve this tricky issue.

Methods:

We undertook a systematic search of bibliographic databases of peer-reviewed research literature to evaluate the holistic blend of modern and traditional approaches, especially the Ayurvedic perspective of treatment of cancer along with the effect of our diet and lifestyle on the management (both prevention and cure) of cancer.

Results:

On the basis of extensive literature survey, it was found that Ayurveda as one of the ancient medicinal systems had been very well documented for utilizing its best practices for the treatment of various diseases including cancer, by utilization of several herbal plants and dietary interventions as therapeutics. Active components present in various herbs, which interfere with certain molecular targets to inhibit carcinogenesis are also summarized. Further, beneficial effects of yoga and exercise on psychological distress, cancer-related fatigue and global side-effects as well as their mechanism of action are also discussed. In addition, we recapitulate an upcoming field of Ayurgenomics to understand the possible correlation of Prakriti with genetics as well as epigenetics.

Conclusion:

Both genetic as well as environmental factors have shown their linkage with cancer. Substantial advancements in the field of targeted therapies have opened new horizons for the cancer patients. To fight with this grave situation, a combination of ancient and modern medicinal systems seems to be the need of the hour.

Investigation of common, low-frequency and rare genome-wide variation in anorexia nervosa

Anorexia nervosa (AN) is a complex neuropsychiatric disorder presenting with dangerously low body weight, and a deep and persistent fear of gaining weight. To date, only one genome-wide significant locus associated with AN has been identified. We performed an exome-chip based genome-wide association studies (GWAS) in 2158 cases from nine populations of European origin and 15 485 ancestrally matched controls. Unlike previous studies, this GWAS also probed association in low-frequency and rare variants. Sixteen independent variants were taken forward for in silico and de novo replication (11 common and 5 rare). No findings reached genome-wide significance. Two notable common variants were identified: rs10791286, an intronic variant in OPCML (P=9.89 × 10-6), and rs7700147, an intergenic variant (P=2.93 × 10-5). No low-frequency variant associations were identified at genome-wide significance, although the study was well-powered to detect low-frequency variants with large effect sizes, suggesting that there may be no AN loci in this genomic search space with large effect sizes.

Genome-Wide Gene-Potassium Interaction Analyses on Blood Pressure: The GenSalt Study (Genetic Epidemiology Network of Salt Sensitivity)

BACKGROUND

Gene-environmental interaction analysis can identify novel genetic factors for blood pressure (BP). We performed genome-wide analyses to identify genomic loci that interact with potassium to influence BP using single-marker (1 and 2 df joint tests) and gene-based tests among Chinese participants of the GenSalt study (Genetic Epidemiology Network of Salt Sensitivity).

METHODS AND RESULTS

Among 1876 GenSalt participants, the average of 3 urine samples was used to estimate potassium excretion. Nine BP measurements were taken using a random-zero sphygmomanometer. A total of 2.2 million single nucleotide polymorphisms were imputed using Affymetrix 6.0 genotype data and the Chinese Han of Beijing and Japanese of Tokyo HapMap reference panel. Promising findings (P<1.00×10-4) from GenSalt were evaluated for replication among 775 Chinese participants of the MESA (Multi-ethnic Study of Atherosclerosis). Single nucleotide polymorphism and gene-based results were meta-analyzed across the GenSalt and MESA studies to determine genome-wide significance. The 1 df tests identified interactions for ARL15 rs16882447 on systolic BP (P=2.83×10-9) and RANBP3L rs958929 on pulse pressure (P=1.58×10-8). The 2 df tests confirmed the ARL15 rs16882447 signal for systolic BP (P=1.15×10-9). Genome-wide gene-based analysis identified CC2D2A (P=2.59×10-7) at 4p15.32 and BNC2 (P=4.49×10-10) at 9p22.2 for systolic BP, GGNBP1 (P=1.18×10-8), and LINC00336 (P=1.36×10-8) at 6p21 for diastolic BP, DAB1 (P=1.05×10-13) at 1p32.2, and MIR4466 (P=5.34×10-8) at 6q25.3 for pulse pressure. The BNC2 (P=3.57×10-8) gene was also significant for mean arterial pressure.

CONCLUSIONS

We identified 2 novel BP loci and 6 genes through the examination of single nucleotide polymorphism- and gene-based interactions with potassium.

Attempts to replicate genetic associations with schizophrenia in a cohort from north India

Schizophrenia is a chronic, severe, heritable disorder. Genome-wide association studies, conducted predominantly among Caucasians, have indicated > 100 risk alleles, with most significant SNPs on chromosome 6. There is growing interest as to whether these risk alleles are relevant in other ethnic groups as well. Neither an Indian genome-wide association studies nor a systematic replication of GWAS findings from other populations are reported. Thus, we analyzed 32 SNPs, including those associated in the Caucasian ancestry GWAS and other candidate gene studies, in a north Indian schizophrenia cohort (n = 1009 patients; n = 1029 controls) using a Sequenom mass array. Cognitive functioning was also assessed using the Hindi version of the Penn Computerized Neuropsychological Battery in a subset of the sample. MICB (rs6916394) a previously noted Caucasian candidate, was associated with schizophrenia at the p = 0.02 level. One SNP, rs2064430, AHI1 (6q23.3, SZ Gene database SNP) was associated at the p = 0.04 level. Other candidates had even less significance with rs6932590, intergenic (p = 0.07); rs3130615, MICB (p = 0.08); rs6916921, NFKBIL1 (p = 0.08) and rs9273012, HLA-DQA1 (p = 0.06) and haplotypic associations (p = 0.01-0.05) of 6p SNPs were detected. Of note, nominally significant associations with cognitive variables were identified, after covarying for age and diagnostic status. SNPs with p < 0.01 were: rs3130375, with working memory (p = 0.007); rs377763, with sensorimotor (p = 0.004); rs6916921, NFKBIL1 with emotion (p = 0.01). This relative lack of significant positive associations is likely influenced by the sample size and/or differences in the genetic architecture of schizophrenia across populations, encouraging population specific studies to identify shared and unique genetic risk factors for schizophrenia.

POPULATION GENETICS

CAUCASIANS AND INDIANS EXHIBIT GENETIC DISJUNCTION IN SCHIZOPHRENIA: A tenuous link between schizophrenia's genetic basis in Caucasians and Indians calls for more comprehensive research on the latter. Large-scale analyses of the human genome have identified over a hundred genetic variations associated with schizophrenia; however, these have focused largely on European and North American populations. Researchers led by the University of Delhi's BK Thelma, and Smita Deshpande of the Dr. Ram Manohar Lohia Hospital, India, selected 32 gene variations from past studies to look for similar associations in Indians. Many assays met limited success, though the team found significant correlations between certain variations and specific cognitive hallmarks of schizophrenia. Aside from differences in genetic architecture, the lack of adequate and comparable genetic data on schizophrenia in Indians may contribute to this apparent difference to schizophrenia in Caucasian patients. This shows a clear need for more schizophrenia genetic studies in India.

Inclusion of Population-specific Reference Panel from India to the 1000 Genomes Phase 3 Panel Improves Imputation Accuracy

Imputation is a computational method based on the principle of haplotype sharing allowing enrichment of genome-wide association study datasets. It depends on the haplotype structure of the population and density of the genotype data. The 1000 Genomes Project led to the generation of imputation reference panels which have been used globally. However, recent studies have shown that population-specific panels provide better enrichment of genome-wide variants. We compared the imputation accuracy using 1000 Genomes phase 3 reference panel and a panel generated from genome-wide data on 407 individuals from Western India (WIP). The concordance of imputed variants was cross-checked with next-generation re-sequencing data on a subset of genomic regions. Further, using the genome-wide data from 1880 individuals, we demonstrate that WIP works better than the 1000 Genomes phase 3 panel and when merged with it, significantly improves the imputation accuracy throughout the minor allele frequency range. We also show that imputation using only South Asian component of the 1000 Genomes phase 3 panel works as good as the merged panel, making it computationally less intensive job. Thus, our study stresses that imputation accuracy using 1000 Genomes phase 3 panel can be further improved by including population-specific reference panels from South Asia.

Integrative analysis of genome-wide association studies and gene expression analysis identifies pathways associated with rheumatoid arthritis

Rheumatoid arthritis (RA) is a complex and systematic autoimmune disease, which is usually influenced by both genetic and environmental factors. Pathway analyses based on a single data type such as microarray data or SNP data have successfully revealed some biology pathways associated with RA. However, we found that the pathway analysis based on a single data type only provide limited understanding about the pathogenesis of RA. Gene-disease association is usually caused by many ways, such as genotype, gene expression and so on. Therefore, the integrative analysis method combining multiple levels of evidence can more precisely and comprehensively identify the pathway associations. In this study, we performed a pathway analysis by integrating GWAS and gene expression analysis to detect the RA-related pathways. The integrative analysis identified 28 pathways associated with RA. Among these pathways, 18 pathways were also found by both GWAS and gene expression analysis, 7 pathways are novel RA-related pathways, such as B cell receptor signaling pathway, Toll-like receptor signaling pathway, Fc gamma R-mediated phagocytosis and so on. Compared with pathway analyses using only one type genomic data, we found integrative analysis can increase the power to identify the real associations and provided more stable and accurate results. We believe these results will contribute to perform future genetic studies in RA pathogenesis and may promote the development of new therapeutic strategies by targeting these pathways.

Genomics era and complex disorders: Implications of GWAS with special reference to coronary artery disease, type 2 diabetes mellitus, and cancers

The Human Genome Project (HGP) has identified millions of single nucleotide polymorphisms (SNPs) and their association with several diseases, apart from successfully characterizing the Mendelian/monogenic diseases. However, the dissection of precise etiology of complex genetic disorders still poses a challenge for human geneticists. This review outlines the landmark results of genome-wide association studies (GWAS) with respect to major complex diseases - Coronary artery disease (CAD), type 2 diabetes mellitus (T2DM), and predominant cancers. A brief account on the current Indian scenario is also given. All the relevant publications till mid-2015 were accessed through web databases such as PubMed and Google. Several databases providing genetic information related to these diseases were tabulated and in particular, the list of the most significant SNPs identified through GWAS was made, which may be useful for designing studies in functional validation. Post-GWAS implications and emerging concepts such as epigenomics and pharmacogenomics were also discussed.

Network-assisted analysis of primary Sjögren's syndrome GWAS data in Han Chinese

Primary Sjögren's syndrome (pSS) is a complex autoimmune disorder. So far, genetic research in pSS has lagged far behind and the underlying biological mechanism is unclear. Further exploring existing genome-wide association study (GWAS) data is urgently expected to uncover disease-related gene combination patterns. Herein, we conducted a network-based analysis by integrating pSS GWAS in Han Chinese with a protein-protein interactions network to identify pSS candidate genes. After module detection and evaluation, 8 dense modules covering 40 genes were obtained for further functional annotation. Additional 31 MHC genes with significant gene-level P-values (sigMHC-gene) were also remained. The combined module genes and sigMHC-genes, a total of 71 genes, were denoted as pSS candidate genes. Of these pSS candidates, 14 genes had been reported to be associated with any of pSS, RA, and SLE, including STAT4, GTF2I, HLA-DPB1, HLA-DRB1, PTTG1, HLA-DQB1, MBL2, TAP2, CFLAR, NFKBIE, HLA-DRA, APOM, HLA-DQA2 and NOTCH4. This is the first report of the network-assisted analysis for pSS GWAS data to explore combined gene patterns associated with pSS. Our study suggests that network-assisted analysis is a useful approach to gaining further insights into the biology of associated genes and providing important clues for future research into pSS etiology.

Genetic Variants Influencing Joint Damage in Mexican Americans and European Americans With Rheumatoid Arthritis

Joint destruction in rheumatoid arthritis (RA) is heritable, but knowledge on specific genetic determinants of joint damage in RA is limited. We have used the Immunochip array to examine whether genetic variants influence variation in joint damage in a cohort of Mexican Americans (MA) and European Americans (EA) with RA. We studied 720 MA and 424 EA patients with RA. Joint damage was quantified using a radiograph of both hands and wrists, scored using Sharp's technique. We conducted association analyses with the transformed Sharp score and the Immunochip single nucleotide polymorphism (SNP) data using PLINK. In MAs, 15 SNPs from chromosomes 1, 5, 9, 17 and 22 associated with joint damage yielded strong p-values (p < 1 × 10(-4) ). The strongest association with joint damage was observed with rs7216796, an intronic SNP located in the MAP3K14 gene, on chromosome 17 (β ± SE = -0.25 ± 0.05, p = 6.23 × 10(-6) ). In EAs, 28 SNPs from chromosomes 1, 4, 6, 9, and 21 showed associations with joint damage (p-value < 1 × 10(-4) ). The best association was observed on chromosome 9 with rs59902911 (β ± SE = 0.86 ± 0.17, p = 1.01 × 10(-6) ), a synonymous SNP within the CARD9 gene. We also observed suggestive evidence for some loci influencing joint damage in MAs and EAs. We identified two novel independent loci (MAP3K14 and CARD9) strongly associated with joint damage in MAs and EAs and a few shared loci showing suggestive evidence for association.

Genome-wide association scan in north Indians reveals three novel HLA-independent risk loci for ulcerative colitis

OBJECTIVE

Over 100 ulcerative colitis (UC) loci have been identified by genome-wide association studies (GWASs) primarily in Caucasians (CEUs). Many of them have weak effects on disease susceptibility, and the bulk of the heritability cannot be ascribed to these loci. Very little is known about the genetic background of UC in non-CEU groups. Here we report the first GWAS on UC in a genetically distinct north Indian (NI) population.

DESIGN

A genome-wide scan was performed on 700 cases and 761 controls. 18 single-nucleotide polymorphisms (SNPs) (p<5×10(-5)) were genotyped in an independent cohort of 733 cases and 1148 controls. A linear mixed model was used for case-control association tests.

RESULTS

Seven novel human leucocyte antigen (HLA)-independent SNPs from chromosome 6, located in 3.8-1, BAT2, MSH5, HSPA1L, SLC44A4, CFB and NOTCH4, exceeded p<5×10(-8) in the combined analysis. To assess the independent biological contribution of such genes from the extended HLA region, we determined the percentage alternative pathway activity of complement factor B (CFB), the top novel hit. The activity was significantly different (p=0.01) between the different genotypes at rs12614 in UC cases. Transethnic comparisons revealed a shared contribution of a fraction of UC risk genes between NI and CEU populations, in addition to genetic heterogeneity.

CONCLUSIONS

This study shows varying contribution of the HLA region to UC in different populations. Different environmental exposures and the characteristic genetic structure of the HLA locus across ethnic groups collectively make it amenable to the discovery of causative alleles by transethnic resequencing. This may lead to an improved understanding of the molecular mechanisms underlying UC.

Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations

Genome-wide association studies (GWASs) are the method most often used by geneticists to interrogate the human genome, and they provide a cost-effective way to identify the genetic variants underpinning complex traits and diseases. Most initial GWASs have focused on genetically homogeneous cohorts from European populations given the limited availability of ethnic minority samples and so as to limit population stratification effects. Transethnic studies have been invaluable in explaining the heritability of common quantitative traits, such as height, and in examining the genetic architecture of complex diseases, such as type 2 diabetes. They provide an opportunity for large-scale signal replication in independent populations and for cross-population meta-analyses to boost statistical power. In addition, transethnic GWASs enable prioritization of candidate genes, fine-mapping of functional variants, and potentially identification of SNPs associated with disease risk in admixed populations, by taking advantage of natural differences in genomic linkage disequilibrium across ethnically diverse populations. Recent efforts to assess the biological function of variants identified by GWAS have highlighted the need for large-scale replication, meta-analyses and fine-mapping across worldwide populations of ethnically diverse genetic ancestries. Here, we review recent advances and new approaches that are important to consider when performing, designing or interpreting transethnic GWASs, and we highlight existing challenges, such as the limited ability to handle heterogeneity in linkage disequilibrium across populations and limitations in dissecting complex architectures, such as those found in recently admixed populations.

Clozapine-induced agranulocytosis is associated with rare HLA-DQB1 and HLA-B alleles

Clozapine is a particularly effective antipsychotic medication but its use is curtailed by the risk of clozapine-induced agranulocytosis/granulocytopenia (CIAG), a severe adverse drug reaction occurring in up to 1% of treated individuals. Identifying genetic risk factors for CIAG could enable safer and more widespread use of clozapine. Here we perform the largest and most comprehensive genetic study of CIAG to date by interrogating 163 cases using genome-wide genotyping and whole-exome sequencing. We find that two loci in the major histocompatibility complex are independently associated with CIAG: a single amino acid in HLA-DQB1 (126Q) (P=4.7 × 10(-14), odds ratio (OR)=0.19, 95% confidence interval (CI)=0.12-0.29) and an amino acid change in the extracellular binding pocket of HLA-B (158T) (P=6.4 × 10(-10), OR=3.3, 95% CI=2.3-4.9). These associations dovetail with the roles of these genes in immunogenetic phenotypes and adverse drug responses for other medications, and provide insight into the pathophysiology of CIAG.

Evaluation of European coeliac disease risk variants in a north Indian population

Studies in European populations have contributed to a better understanding of the genetics of complex diseases, for example, in coeliac disease (CeD), studies of over 23 000 European samples have reported association to the HLA locus and another 39 loci. However, these associations have not been evaluated in detail in other ethnicities. We sought to better understand how disease-associated loci that have been mapped in Europeans translate to a disease risk for a population with a different ethnic background. We therefore performed a validation of European risk loci for CeD in 497 cases and 736 controls of north Indian origin. Using a dense-genotyping platform (Immunochip), we confirmed the strong association to the HLA region (rs2854275, P=8.2 × 10⁻⁴⁹). Three loci showed suggestive association (rs4948256, P=9.3 × 10⁻⁷, rs4758538, P=8.6 × 10⁻⁵ and rs17080877, P=2.7 × 10⁻⁵). We directly replicated five previously reported European variants (P<0.05; mapping to loci harbouring FASLG/TNFSF18, SCHIP1/IL12A, PFKFB3/PRKCQ, ZMIZ1 and ICOSLG). Using a transferability test, we further confirmed association at PFKFB3/PRKCQ (rs2387397, P=2.8 × 10⁻⁴) and PTPRK/THEMIS (rs55743914, P=3.4 × 10⁻⁴). The north Indian population has a higher degree of consanguinity than Europeans and we therefore explored the role of recessively acting variants, which replicated the HLA locus (rs9271850, P=3.7 × 10⁻²³) and suggested a role of additional four loci. To our knowledge, this is the first replication study of CeD variants in a non-European population.

Population and genomic lessons from genetic analysis of two Indian populations

Indian demographic history includes special features such as founder effects, interpopulation segregation, complex social structure with a caste system and elevated frequency of consanguineous marriages. It also presents a higher frequency for some rare mendelian disorders and in the last two decades increased prevalence of some complex disorders. Despite the fact that India represents about one-sixth of the human population, deep genetic studies from this terrain have been scarce. In this study, we analyzed high-density genotyping and whole-exome sequencing data of a North and a South Indian population. Indian populations show higher differentiation levels than those reported between populations of other continents. In this work, we have analyzed its consequences, by specifically assessing the transferability of genetic markers from or to Indian populations. We show that there is limited genetic marker portability from available genetic resources such as HapMap or the 1,000 Genomes Project to Indian populations, which also present an excess of private rare variants. Conversely, tagSNPs show a high level of portability between the two Indian populations, in contrast to the common belief that North and South Indian populations are genetically very different. By estimating kinship from mates and consanguinity in our data from trios, we also describe different patterns of assortative mating and inbreeding in the two populations, in agreement with distinct mating preferences and social structures. In addition, this analysis has allowed us to describe genomic regions under recent adaptive selection, indicating differential adaptive histories for North and South Indian populations. Our findings highlight the importance of considering demography for design and analysis of genetic studies, as well as the need for extending human genetic variation catalogs to new populations and particularly to those with particular demographic histories.