Mapping the genetic architecture of idiopathic pulmonary fibrosis: Meta-analysis and epidemiological evidence of case-control studies.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a rare and devastating fibrotic lung disorder with unknown etiology. Although it is believed that genetic component is an important risk factor for IPF, a comprehensive understanding of its genetic landscape is lacking. Hence, we aimed to highlight the susceptibility genes and pathways implicated in IPF pathogenesis through a two-staged systematic literature search of genetic association studies on IPF, followed by meta-analysis and pathway enrichment analysis. METHODS: This study was performed based on PRISMA guidelines (PROSPERO, registration number: CRD42022297970). The first search was performed (using PubMed and Web of Science) retrieving a total of 5642 articles, of which 52 were eligible for inclusion in the first stage. The second search was performed (using PubMed, Web of Science and Scopus) for ten polymorphisms, identified from the first search, with 2 or more studies. Finally, seven polymorphisms, [rs35705950/MUC5B, rs2736100/TERT, rs2609255/FAM13A, rs2076295/DSP, rs12610495/DPP9, rs111521887/TOLLIP and rs1800470/TGF-ß1] qualified for meta-analyses. The epidemiological credibility was evaluated using Venice criteria. RESULTS: From the systematic review, 222 polymorphisms in 118 genes showed a significant association with IPF susceptibility. Meta-analyses findings revealed significant association of rs35705950/T [OR = 3.92(3.26-4.57)], rs2609255/G [OR = 1.50(1.18-1.82)], rs2076295/G [OR = 1.19(0.82-1.756)], rs12610495/G [OR = 1.28(1.12-1.44)], rs2736100/C [OR = 0.68(0.54-0.82), rs111521887/G [OR = 1.34(1.06-1.61)] and suggestive evidence for rs1800470/T [OR = 1.08(0.82-1.34)] with IPF susceptibility. Four polymorphisms- rs35705950/MUC5B, rs2736100/TERT, rs2076295/DSP and rs111521887/TOLLIP, exhibited substantial epidemiological evidence supporting their association with IPF risk. Gene ontology and pathway enrichment analysis performed on IPF risk-associated genes identified a critical role of genes in mucin production, immune response and inflammation, host defence, cell-cell adhesion and telomere maintenance. CONCLUSIONS: Our findings present the most prominent IPF-associated genetic risk variants involved in alveolar epithelial injuries (MUC5B, TERT, FAM13A, DSP, DPP9) and epithelial-mesenchymal transition (TOLLIP, TGF-ß1), providing genetic and biological insights into IPF pathogenesis. However, further experimental research and human studies with larger sample sizes, diverse ethnic representation, and rigorous design are warranted.

Assessment of clinically actionable pharmacogenetic markers to stratify anti-seizure medications.

Epilepsy treatment is challenging due to heterogeneous syndromes, different seizure types and higher inter-individual variability. Identification of genetic variants predicting drug efficacy, tolerability and risk of adverse-effects for anti-seizure medications (ASMs) is essential. Here, we assessed the clinical actionability of known genetic variants, based on their functional and clinical significance and estimated their diagnostic predictability. We performed a systematic PubMed search to identify articles with pharmacogenomic (PGx) information for forty known ASMs. Functional annotation of the identified genetic variants was performed using different in silico tools, and their clinical significance was assessed using the American College of Medical Genetics (ACMG) guidelines for variant pathogenicity, level of evidence (LOE) from PharmGKB and the United States-Food and drug administration (US- FDA) drug labelling with PGx information. Diagnostic predictability of the replicated genetic variants was evaluated by calculating their accuracy. A total of 270 articles were retrieved with PGx evidence associated with 19 ASMs including 178 variants across 93 genes, classifying 26 genetic variants as benign/ likely benign, fourteen as drug response markers and three as risk factors for drug response. Only seventeen of these were replicated, with accuracy (up to 95%) in predicting PGx outcomes specific to six ASMs. Eight out of seventeen variants have FDA-approved PGx drug labelling for clinical implementation. Therefore, the remaining nine variants promise for potential clinical actionability and can be improvised with additional experimental evidence for clinical utility.

Genetic Landscape of Major Depressive Disorder: Assessment of Potential Diagnostic and Antidepressant Response Markers.

BACKGROUND: The clinical heterogeneity in major depressive disorder (MDD), variable treatment response, and conflicting findings limit the ability of genomics toward the discovery of evidence-based diagnosis and treatment regimen. This study attempts to curate all genetic association findings to evaluate potential variants for clinical translation. METHODS: We systematically reviewed all candidates and genome-wide association studies for both MDD susceptibility and antidepressant response, independently, using MEDLINE, particularly to identify replicated findings. These variants were evaluated for functional consequences using different in silico tools and further estimated their diagnostic predictability by calculating positive predictive values. RESULTS: A total of 217 significantly associated studies comprising 1200 variants across 545 genes and 128 studies including 921 variants across 412 genes were included with MDD susceptibility and antidepressant response, respectively. Although the majority of associations were confirmed by a single study, we identified 31 and 18 replicated variants (in at least 2 studies) for MDD and antidepressant response. Functional annotation of these 31 variants predicted 20% coding variants as deleterious/damaging and 80.6% variants with regulatory effect. Similarly, the response-related 18 variants revealed 25% coding variant as damaging and 88.2% with substantial regulatory potential. Finally, we could calculate the diagnostic predictability of 19 and 5 variants whose positive predictive values ranges from 0.49 to 0.66 for MDD and 0.36 to 0.66 for response. CONCLUSIONS: The replicated variants presented in our data are promising for disease diagnosis and improved response outcomes. Although these quantitative assessment measures are solely directive of available observational evidence, robust homogenous validation studies are required to strengthen these variants for molecular diagnostic application.

Genic-intergenic polymorphisms of CYP1A genes and their clinical impact.

The humancytochrome P450 1A (CYP1A) subfamily genes, CYP1A1 and CYP1A2, encoding monooxygenases are critically involved in biotransformation of key endogenous substrates (estradiol, arachidonic acid, cholesterol) and exogenous compounds (smoke constituents, carcinogens, caffeine, therapeutic drugs). This suggests their significant involvement in multiple biological pathways with a primary role of maintaining endogenous homeostasis and xenobiotic detoxification. Large interindividual variability exist in CYP1A gene expression and/or catalytic activity of the enzyme, which is primarily due to the existence of polymorphic alleles which encode them. These polymorphisms (mainly single nucleotide polymorphisms, SNPs) have been extensively studied as susceptibility factors in a spectrum of clinical phenotypes. An in-depth understanding of the effects of polymorphic CYP1A genes on the differential metabolic activity and the resulting biological pathways is needed to explain the clinical implications of CYP1A polymorphisms. The present review is intended to provide an integrated understanding of CYP1A metabolic activity with unique substrate specificity and their involvement in physiological and pathophysiological roles. The article further emphasizes on the impact of widely studied CYP1A1 and CYP1A2 SNPs and their complex interaction with non-genetic factors like smoking and caffeine intake on multiple clinical phenotypes. Finally, we attempted to discuss the alterations in metabolism/physiology concerning the polymorphic CYP1A genes, which may underlie the reported clinical associations. This knowledge may provide insights into the disease pathogenesis, risk stratification, response to therapy and potential drug targets for individuals with certain CYP1A genotypes.

Human genetic factors associated with pneumonia risk, a cue for COVID-19 susceptibility.

Pneumonia, an acute respiratory tract infection, is one of the major causes of mortality worldwide. Depending on the site of acquisition, pneumonia can be community acquired pneumonia (CAP) or nosocomial pneumonia (NP). The risk of pneumonia, is partially driven by host genetics. CYP1A1 is a widely studied pulmonary CYP family gene primarily expressed in peripheral airway epithelium. The CYP1A1 genetic variants, included in this study, alter the gene activity and are known to contribute in lung inflammation, which may cause pneumonia pathogenesis. In this study, we performed a meta-analysis to establish the possible contribution of CYP1A1 gene, and its three variants (rs2606345, rs1048943 and rs4646903) towards the genetic etiology of pneumonia risk. Using PRISMA guidelines, we systematically reviewed and meta-analysed case-control studies, evaluating risk of pneumonia in patients carrying the risk alleles of CYP1A1 variants. Heterogeneity across the studies was evaluated using I2 statistics. Based on heterogeneity, a random-effect (using maximum likelihood) or fixed-effect (using inverse variance) model was applied to estimate the effect size. Pooled odds ratio (OR) was calculated to estimate the overall effect of the risk allele association with pneumonia susceptibility. Egger's regression test and funnel plot were used to assess publication bias. Subgroup analysis was performed based on pneumonia type (CAP and NP), population, as well as age group. A total of ten articles were identified as eligible studies, which included 3049 cases and 2249 healthy controls. The meta-analysis findings revealed CYP1A1 variants, rs2606345 [T vs G; OR = 1.12 (0.75-1.50); p = 0.02; I2 = 84.89%], and rs1048943 [G vs T; OR = 1.19 (0.76-1.61); p = 0.02; I2 = 0.00%] as risk markers whereas rs4646903 showed no statistical significance for susceptibility to pneumonia. On subgroup analysis, both the genetic variants showed significant association with CAP but not with NP. We additionally performed a spatial analysis to identify the key factors possibly explaining the variability across countries in the prevalence of the coronavirus disease 2019 (COVID-19), a viral pneumonia. We observed a significant association between the risk allele of rs2606345 and rs1048943, with a higher COVID-19 prevalence worldwide, providing us important links in understanding the variability in COVID-19 prevalence.

Multidrug efflux transporter ABCG2: expression and regulation.

The adenosine triphosphate (ATP)-binding cassette efflux transporter G2 (ABCG2) was originally discovered in a multidrug-resistant breast cancer cell line. Studies in the past have expanded the understanding of its role in physiology, disease pathology and drug resistance. With a widely distributed expression across different cell types, ABCG2 plays a central role in ATP-dependent efflux of a vast range of endogenous and exogenous molecules, thereby maintaining cellular homeostasis and providing tissue protection against xenobiotic insults. However, ABCG2 expression is subjected to alterations under various pathophysiological conditions such as inflammation, infection, tissue injury, disease pathology and in response to xenobiotics and endobiotics. These changes may interfere with the bioavailability of therapeutic substrate drugs conferring drug resistance and in certain cases worsen the pathophysiological state aggravating its severity. Considering the crucial role of ABCG2 in normal physiology, therapeutic interventions directly targeting the transporter function may produce serious side effects. Therefore, modulation of transporter regulation instead of inhibiting the transporter itself will allow subtle changes in ABCG2 activity. This requires a thorough comprehension of diverse factors and complex signaling pathways (Kinases, Wnt/ß-catenin, Sonic hedgehog) operating at multiple regulatory levels dictating ABCG2 expression and activity. This review features a background on the physiological role of transporter, factors that modulate ABCG2 levels and highlights various signaling pathways, molecular mechanisms and genetic polymorphisms in ABCG2 regulation. This understanding will aid in identifying potential molecular targets for therapeutic interventions to overcome ABCG2-mediated multidrug resistance (MDR) and to manage ABCG2-related pathophysiology.

Corrigendum: Global Text Mining and Development of Pharmacogenomic Knowledge Resource for Precision Medicine.

[This corrects the article DOI: 10.3389/fphar.2019.00839.].

Genetic Landscape of Common Epilepsies: Advancing towards Precision in Treatment.

Epilepsy, a neurological disease characterized by recurrent seizures, is highly heterogeneous in nature. Based on the prevalence, epilepsy is classified into two types: common and rare epilepsies. Common epilepsies affecting nearly 95% people with epilepsy, comprise generalized epilepsy which encompass idiopathic generalized epilepsy like childhood absence epilepsy, juvenile myoclonic epilepsy, juvenile absence epilepsy and epilepsy with generalized tonic-clonic seizure on awakening and focal epilepsy like temporal lobe epilepsy and cryptogenic focal epilepsy. In 70% of the epilepsy cases, genetic factors are responsible either as single genetic variant in rare epilepsies or multiple genetic variants acting along with different environmental factors as in common epilepsies. Genetic testing and precision treatment have been developed for a few rare epilepsies and is lacking for common epilepsies due to their complex nature of inheritance. Precision medicine for common epilepsies require a panoramic approach that incorporates polygenic background and other non-genetic factors like microbiome, diet, age at disease onset, optimal time for treatment and other lifestyle factors which influence seizure threshold. This review aims to comprehensively present a state-of-art review of all the genes and their genetic variants that are associated with all common epilepsy subtypes. It also encompasses the basis of these genes in the epileptogenesis. Here, we discussed the current status of the common epilepsy genetics and address the clinical application so far on evidence-based markers in prognosis, diagnosis, and treatment management. In addition, we assessed the diagnostic predictability of a few genetic markers used for disease risk prediction in individuals. A combination of deeper endo-phenotyping including pharmaco-response data, electro-clinical imaging, and other clinical measurements along with genetics may be used to diagnose common epilepsies and this marks a step ahead in precision medicine in common epilepsies management.

Global Text Mining and Development of Pharmacogenomic Knowledge Resource for Precision Medicine.

Understanding patients' genomic variations and their effect in protecting or predisposing them to drug response phenotypes is important for providing personalized healthcare. Several studies have manually curated such genotype-phenotype relationships into organized databases from clinical trial data or published literature. However, there are no text mining tools available to extract high-accuracy information from such existing knowledge. In this work, we used a semiautomated text mining approach to retrieve a complete pharmacogenomic (PGx) resource integrating disease-drug-gene-polymorphism relationships to derive a global perspective for ease in therapeutic approaches. We used an R package, pubmed.mineR, to automatically retrieve PGx-related literature. We identified 1,753 disease types, and 666 drugs, associated with 4,132 genes and 33,942 polymorphisms collated from 180,088 publications. With further manual curation, we obtained a total of 2,304 PGx relationships. We evaluated our approach by performance (precision = 0.806) with benchmark datasets like Pharmacogenomic Knowledgebase (PharmGKB) (0.904), Online Mendelian Inheritance in Man (OMIM) (0.600), and The Comparative Toxicogenomics Database (CTD) (0.729). We validated our study by comparing our results with 362 commercially used the US- Food and drug administration (FDA)-approved drug labeling biomarkers. Of the 2,304 PGx relationships identified, 127 belonged to the FDA list of 362 approved pharmacogenomic markers, indicating that our semiautomated text mining approach may reveal significant PGx information with markers for drug response prediction. In addition, it is a scalable and state-of-art approach in curation for PGx clinical utility.

Systems Approach to Identify Common Genes and Pathways Associated with Response to Selective Serotonin Reuptake Inhibitors and Major Depression Risk.

Despite numerous studies on major depressive disorder (MDD) susceptibility, the precise underlying molecular mechanism has not been elucidated which restricts the development of etiology-based disease-modifying drug. Major depressive disorder treatment is still symptomatic and is the leading cause of (~30%) failure of the current antidepressant therapy. Here we comprehended the probable genes and pathways commonly associated with antidepressant response and MDD. A systematic review was conducted, and candidate genes/pathways associated with antidepressant response and MDD were identified using an integrative genetics approach. Initially, single nucleotide polymorphisms (SNPs)/genes found to be significantly associated with antidepressant response were systematically reviewed and retrieved from the candidate studies and genome-wide association studies (GWAS). Also, significant variations concerning MDD susceptibility were extracted from GWAS only. We found 245 (Set A) and 800 (Set B) significantly associated genes with antidepressant response and MDD, respectively. Further, gene set enrichment analysis revealed the top five co-occurring molecular pathways (p ≤ 0.05) among the two sets of genes: Cushing syndrome, Axon guidance, cAMP signaling pathway, Insulin secretion, and Glutamatergic synapse, wherein all show a very close relation to synaptic plasticity. Integrative analyses of candidate gene and genome-wide association studies would enable us to investigate the putative targets for the development of disease etiology-based antidepressant that might be more promising than current ones.

Clinical predictors of treatment outcome in North Indian patients on antiepileptic drug therapy: A prospective observational study.

BACKGROUND: Nearly 40%-50% of the individuals fail to respond to first line antiepileptic drug (AED) monotherapy and 30% are refractory, which calls for the need to recognize predictive markers for treatment failure. This study aims to identify clinical factors predictive of a poor prognosis in patients on AED therapy. MATERIALS AND METHODS: A prospective follow-up study involving 1056 patients with epilepsy (PWE) aged 5-67 years from North India on phenytoin (PHT, n = 247), carbamazepine (CBZ, n = 369), valproate (VA, n = 271), phenobarbital (PB, n = 50), and multitherapy (MultiT, n = 119) was conducted between 2005 and 2015. Seizure and epilepsy types were diagnosed based on the classifications by the International League Against Epilepsy (ILAE). Patients remaining seizure-free during the past 1 year were assigned to the "no seizure" group and patients experiencing seizure recurrence were assigned to the "recurrent seizures" group. RESULTS: Of the total, 786 (74.4%) patients were successfully followed up with 60% achieving 1-year seizure remission. Seizure recurrence was observed in the remaining 40% of the patients with a high likelihood in patients with the disease onset at ≤5 years of age [55% vs. 38%, P = 0.0016, odds ratio (OR) = 2.02 (95% confidence interval (CI) = 1.31-3.13)], in patients with cryptogenic epilepsy than with idiopathic/symptomatic epilepsy (48% vs. 32%, P = 0.0049, OR = 1.61 [95% CI = 1.16-2.24]), and in patients with pretreatment seizure frequency ≥12/year (46% vs. 27%, P < 0.0001, OR = 2.21 [95% CI = 1.61-3.05]). Logistic regression analysis also revealed a significant association of seizure recurrence (P < 0.05) with the three variables. CONCLUSION: Our findings suggest that an early disease onset, cryptogenic epilepsy, and a higher pretreatment seizure frequency are related to a poor prognosis or poor remission in people with epilepsy (PWE) on AED therapy.

A systematic review and integrative approach to decode the common molecular link between levodopa response and Parkinson's disease.

BACKGROUND: PD is a progressive neurodegenerative disorder commonly treated by levodopa. The findings from genetic studies on adverse effects (ADRs) and levodopa efficacy are mostly inconclusive. Here, we aim to identify predictive genetic biomarkers for levodopa response (LR) and determine common molecular link with disease susceptibility. A systematic review for LR was conducted for ADR, and drug efficacy, independently. All included articles were assessed for methodological quality on 14 parameters. GWAS of PD were also reviewed. Protein-protein interaction (PPI) analysis using STRING and functional enrichment using WebGestalt was performed to explore the common link between LR and PD. RESULTS: From 37 candidate studies on levodopa toxicity, 18 genes were found associated, of which, CAn STR 13, 14 (DRD2) was most significantly associated with dyskinesia, followed by rs1801133 (MTHFR) with hyper-homocysteinemia, and rs474559 (HOMER1) with hallucination. Similarly, 8 studies on efficacy resulted in 4 genes in which rs28363170, rs3836790 (SLC6A3) and rs4680 (COMT), were significant. To establish the molecular connection between LR with PD, we identified 35 genes significantly associated with PD. With 19 proteins associated with LR and 35 with PD, two independent PPI networks were constructed. Among the 67 nodes (263 edges) in LR, and 62 nodes (190 edges) in PD pathophysiology, UBC, SNCA, FYN, SRC, CAMK2A, and SLC6A3 were identified as common potential candidates. CONCLUSION: Our study revealed the genetically significant polymorphism concerning the ADRs and levodopa efficacy. The six common genes may be used as predictive markers for therapy optimization and as putative drug target candidates.

Ayurgenomics for stratified medicine: TRISUTRA consortium initiative across ethnically and geographically diverse Indian populations.

BACKGROUND: Genetic differences in the target proteins, metabolizing enzymes and transporters that contribute to inter-individual differences in drug response are not integrated in contemporary drug development programs. Ayurveda, that has propelled many drug discovery programs albeit for the search of new chemical entities incorporates inter-individual variability "Prakriti" in development and administration of drug in an individualized manner. Prakriti of an individual largely determines responsiveness to external environment including drugs as well as susceptibility to diseases. Prakriti has also been shown to have molecular and genomic correlates. We highlight how integration of Prakriti concepts can augment the efficiency of drug discovery and development programs through a unique initiative of Ayurgenomics TRISUTRA consortium. METHODS: Five aspects that have been carried out are (1) analysis of variability in FDA approved pharmacogenomics genes/SNPs in exomes of 72 healthy individuals including predominant Prakriti types and matched controls from a North Indian Indo-European cohort (2) establishment of a consortium network and development of five genetically homogeneous cohorts from diverse ethnic and geo-climatic background (3) identification of parameters and development of uniform standard protocols for objective assessment of Prakriti types (4) development of protocols for Prakriti evaluation and its application in more than 7500 individuals in the five cohorts (5) Development of data and sample repository and integrative omics pipelines for identification of genomic correlates. RESULTS: Highlight of the study are (1) Exome sequencing revealed significant differences between Prakriti types in 28 SNPs of 11 FDA approved genes of pharmacogenomics relevance viz. CYP2C19, CYP2B6, ESR1, F2, PGR, HLA-B, HLA-DQA1, HLA-DRB1, LDLR, CFTR, CPS1. These variations are polymorphic in diverse Indian and world populations included in 1000 genomes project. (2) Based on the phenotypic attributes of Prakriti we identified anthropometry for anatomical features, biophysical parameters for skin types, HRV for autonomic function tests, spirometry for vital capacity and gustometry for taste thresholds as objective parameters. (3) Comparison of Prakriti phenotypes across different ethnic, age and gender groups led to identification of invariant features as well as some that require weighted considerations across the cohorts. CONCLUSION: Considering the molecular and genomics differences underlying Prakriti and relevance in disease pharmacogenomics studies, this novel integrative platform would help in identification of differently susceptible and drug responsive population. Additionally, integrated analysis of phenomic and genomic variations would not only allow identification of clinical and genomic markers of Prakriti for application in personalized medicine but also its integration in drug discovery and development programs.