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Singh P, Guin D, Pattnaik B, Kukreti R. Mapping the genetic architecture of idiopathic pulmonary fibrosis: Meta-analysis and epidemiological evidence of case-control studies. Gene 2024; 895:147993. [PMID: 37977320 DOI: 10.1016/j.gene.2023.147993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/23/2023] [Accepted: 11/13/2023] [Indexed: 11/19/2023]
Abstract
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-β1] 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-β1), 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.
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Affiliation(s)
- Pooja Singh
- Academy of Scientific and Innovative Research (AcSIR), CSIR-HRDC, Ghaziabad, Uttar Pradesh, India; Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
| | - Debleena Guin
- Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, New Delhi, India; Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
| | - Bijay Pattnaik
- Centre of Excellence for Translational Research in Asthma and Lung Diseases, CSIR-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India; Department of Pulmonary, Critical Care and Sleep Medicine, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Ritushree Kukreti
- Academy of Scientific and Innovative Research (AcSIR), CSIR-HRDC, Ghaziabad, Uttar Pradesh, India; Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.
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Guin D, Hasija Y, Kukreti R. Assessment of clinically actionable pharmacogenetic markers to stratify anti-seizure medications. Pharmacogenomics J 2023; 23:149-160. [PMID: 37626111 DOI: 10.1038/s41397-023-00313-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 07/22/2023] [Accepted: 07/31/2023] [Indexed: 08/27/2023]
Abstract
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.
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Affiliation(s)
- Debleena Guin
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, 110007, India
- Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Delhi, 110042, India
| | - Yasha Hasija
- Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Delhi, 110042, India
| | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, 110007, India.
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India.
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Singh P, Srivastava A, Guin D, Thakran S, Yadav J, Chandna P, Sood M, Chadda RK, Kukreti R. Genetic Landscape of Major Depressive Disorder: Assessment of Potential Diagnostic and Antidepressant Response Markers. Int J Neuropsychopharmacol 2023; 26:692-738. [PMID: 36655406 PMCID: PMC10586057 DOI: 10.1093/ijnp/pyad001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 01/18/2023] [Indexed: 01/20/2023] Open
Abstract
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.
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Affiliation(s)
- Priyanka Singh
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR) - Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Ankit Srivastava
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR) - Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
- Department of Pharmacology, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
| | - Debleena Guin
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR) - Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
- Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Delhi, India
| | - Sarita Thakran
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR) - Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Jyoti Yadav
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR) - Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
| | - Puneet Chandna
- Indian Society of Colposcopy and Cervical Pathology (ISCCP), Safdarjung Hospital, New Delhi, India
| | - Mamta Sood
- Department of Psychiatry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Rakesh Kumar Chadda
- Department of Psychiatry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR) - Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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Kukal S, Thakran S, Kanojia N, Yadav S, Mishra MK, Guin D, Singh P, Kukreti R. Genic-intergenic polymorphisms of CYP1A genes and their clinical impact. Gene 2023; 857:147171. [PMID: 36623673 DOI: 10.1016/j.gene.2023.147171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/16/2022] [Accepted: 01/03/2023] [Indexed: 01/08/2023]
Abstract
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.
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Affiliation(s)
- Samiksha Kukal
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Sarita Thakran
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Neha Kanojia
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Saroj Yadav
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Manish Kumar Mishra
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India; Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi 110042, India
| | - Debleena Guin
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India; Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi 110042, India
| | - Pooja Singh
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
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Guin D, Yadav S, Singh P, Singh P, Thakran S, Kukal S, Kanojia N, Paul PR, Pattnaik B, Sardana V, Grover S, Hasija Y, Saso L, Agrawal A, Kukreti R. Human genetic factors associated with pneumonia risk, a cue for COVID-19 susceptibility. Infection, Genetics and Evolution 2022; 102:105299. [PMID: 35545162 PMCID: PMC9080029 DOI: 10.1016/j.meegid.2022.105299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/30/2022] [Accepted: 05/02/2022] [Indexed: 01/08/2023]
Abstract
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.
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Kukal S, Guin D, Rawat C, Bora S, Mishra MK, Sharma P, Paul PR, Kanojia N, Grewal GK, Kukreti S, Saso L, Kukreti R. Multidrug efflux transporter ABCG2: expression and regulation. Cell Mol Life Sci 2021; 78:6887-6939. [PMID: 34586444 PMCID: PMC11072723 DOI: 10.1007/s00018-021-03901-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 06/24/2021] [Accepted: 07/15/2021] [Indexed: 12/15/2022]
Abstract
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/β-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.
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Affiliation(s)
- Samiksha Kukal
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, Delhi, 110007, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Debleena Guin
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, Delhi, 110007, India
- Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India
| | - Chitra Rawat
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, Delhi, 110007, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Shivangi Bora
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, Delhi, 110007, India
- Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India
| | - Manish Kumar Mishra
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, Delhi, 110007, India
- Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India
| | - Priya Sharma
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, Delhi, 110007, India
| | - Priyanka Rani Paul
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, Delhi, 110007, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Neha Kanojia
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, Delhi, 110007, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Gurpreet Kaur Grewal
- Department of Biotechnology, Kanya Maha Vidyalaya, Jalandhar, Punjab, 144004, India
| | - Shrikant Kukreti
- Nucleic Acids Research Lab, Department of Chemistry, University of Delhi (North Campus), Delhi, 110007, India
| | - Luciano Saso
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, P. le Aldo Moro 5, 00185, Rome, Italy
| | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, Delhi, 110007, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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Guin D, Rani J, Singh P, Grover S, Bora S, Talwar P, Karthikeyan M, Satyamoorthy K, Adithan C, Ramachandran S, Saso L, Hasija Y, Kukreti R. Corrigendum: Global Text Mining and Development of Pharmacogenomic Knowledge Resource for Precision Medicine. Front Pharmacol 2020; 11:614445. [PMID: 33343377 PMCID: PMC7746840 DOI: 10.3389/fphar.2020.614445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 10/16/2020] [Indexed: 11/20/2022] Open
Affiliation(s)
- Debleena Guin
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Jyoti Rani
- Department of Biomedical Sciences, Acharya Narayan Dev College, University of Delhi, New Delhi, India.,G N Ramachandran Knowledge Centre, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
| | - Priyanka Singh
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Academy of Scientific & Innovative Research (AcSIR), New Delhi, India
| | - Sandeep Grover
- Institute of Medical Biometry and Statistics, University of Lübeck University Medical Center Schleswig-Holstein - Campus Lübeck, Lübeck, Germany
| | - Shivangi Bora
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Puneet Talwar
- Institute of Human Behaviour and Allied Sciences, Delhi, India
| | | | - K Satyamoorthy
- School of Life Sciences, Manipal University, Manipal, India
| | - C Adithan
- Central Inter-Disciplinary Research Facility (CIDRF), Pondicherry, India
| | - S Ramachandran
- G N Ramachandran Knowledge Centre, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Academy of Scientific & Innovative Research (AcSIR), New Delhi, India
| | - Luciano Saso
- Department of Physiology and Pharmacology "Vittorio Erspamer," Sapienza University of Rome, Rome, Italy
| | - Yasha Hasija
- Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Academy of Scientific & Innovative Research (AcSIR), New Delhi, India
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Rawat C, Guin D, Talwar P, Grover S, Baghel R, Kushwaha S, Sharma S, Agarwal R, Bala K, Srivastava AK, Kukreti R. Clinical predictors of treatment outcome in North Indian patients on antiepileptic drug therapy: A prospective observational study. Neurol India 2019; 66:1052-1059. [PMID: 30038093 DOI: 10.4103/0028-3886.237000] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
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.
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Affiliation(s)
- Chitra Rawat
- Institute of Genomics and Integrative Biology (IGIB); Academy of Scientific and Innovative Research (AcSIR), Council of Scientific and Industrial Research (CSIR), New Delhi, India
| | - Debleena Guin
- Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
| | - Puneet Talwar
- Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
| | - Sandeep Grover
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany
| | - Ruchi Baghel
- Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
| | - Suman Kushwaha
- Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India
| | - Sangeeta Sharma
- Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India
| | - Rachna Agarwal
- Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India
| | - Kiran Bala
- Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India
| | - Achal K Srivastava
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Ritushree Kukreti
- Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
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Guin D, Rani J, Singh P, Grover S, Bora S, Talwar P, Karthikeyan M, Satyamoorthy K, Adithan C, Ramachandran S, Saso L, Hasija Y, Kukreti R. Global Text Mining and Development of Pharmacogenomic Knowledge Resource for Precision Medicine. Front Pharmacol 2019; 10:839. [PMID: 31447668 PMCID: PMC6692532 DOI: 10.3389/fphar.2019.00839] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 07/01/2019] [Indexed: 11/20/2022] Open
Abstract
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.
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Affiliation(s)
- Debleena Guin
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Jyoti Rani
- Department of Biomedical Sciences, Acharya Narayan Dev College, University of Delhi, New Delhi, India.,G N Ramachandran Knowledge Centre, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
| | - Priyanka Singh
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Academy of Scientific & Innovative Research (AcSIR), New Delhi, India
| | - Sandeep Grover
- Institute of Medical Biometry and Statistics, University of Lübeck University Medical Center Schleswig-Holstein - Campus Lübeck, Lübeck, Germany
| | - Shivangi Bora
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Puneet Talwar
- Institute of Human Behaviour and Allied Sciences, Delhi, India
| | | | - K Satyamoorthy
- School of Life Sciences, Manipal University, Manipal, India
| | - C Adithan
- Central Inter-Disciplinary Research Facility (CIDRF), Pondicherry, India
| | - S Ramachandran
- G N Ramachandran Knowledge Centre, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Academy of Scientific & Innovative Research (AcSIR), New Delhi, India
| | - Luciano Saso
- Department of Physiology and Pharmacology "Vittorio Erspamer," Sapienza University of Rome, Rome, Italy
| | - Yasha Hasija
- Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Academy of Scientific & Innovative Research (AcSIR), New Delhi, India
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Jajodia A, Kaur H, Srivastava A, Kumari K, Baghel R, Guin D, Sood M, Satyamoorthy K, Jain S, Chadda RK, Kukreti R. Schizophrenia susceptibility and neuregulin signaling pathway genes: A rare haplotype combination based association study in Indian population. Psychiatry Res 2018; 262:628-630. [PMID: 29074073 DOI: 10.1016/j.psychres.2017.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 09/28/2017] [Accepted: 10/01/2017] [Indexed: 11/19/2022]
Affiliation(s)
- Ajay Jajodia
- Genomics and Molecular Medicine, CSIR-Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India
| | - Harpreet Kaur
- Genomics and Molecular Medicine, CSIR-Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India
| | - Ankit Srivastava
- Genomics and Molecular Medicine, CSIR-Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India
| | - Kalpana Kumari
- Department of Psychiatry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
| | - Ruchi Baghel
- Genomics and Molecular Medicine, CSIR-Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India
| | - Debleena Guin
- Genomics and Molecular Medicine, CSIR-Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India
| | - Mamta Sood
- Department of Psychiatry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
| | - Kapaettu Satyamoorthy
- Department of Biotechnology, School of Life Sciences, Manipal University, Manipal 576104, India
| | - Sanjeev Jain
- Molecular Genetic Laboratory, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Hosur Road, Bengaluru 560029, India
| | - Rakesh Kumar Chadda
- Department of Psychiatry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
| | - Ritushree Kukreti
- Genomics and Molecular Medicine, CSIR-Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India.
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Guin D, Mishra MK, Talwar P, Rawat C, Kushwaha SS, Kukreti S, Kukreti R. A systematic review and integrative approach to decode the common molecular link between levodopa response and Parkinson's disease. BMC Med Genomics 2017; 10:56. [PMID: 28927418 PMCID: PMC5606117 DOI: 10.1186/s12920-017-0291-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Accepted: 08/24/2017] [Indexed: 11/26/2022] Open
Abstract
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. Electronic supplementary material The online version of this article (10.1186/s12920-017-0291-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Debleena Guin
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, New Delhi, -110007, India
| | - Manish Kumar Mishra
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, New Delhi, -110007, India.,Department of Chemistry, Nucleic Acids Research Lab, University of Delhi (North Campus), Delhi, India
| | - Puneet Talwar
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, New Delhi, -110007, India
| | - Chitra Rawat
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, New Delhi, -110007, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR- Institute of Genomics and Integrative Biology (CSIR-IGIB) Campus, New Delhi, India
| | - Suman S Kushwaha
- Institute of Human Behaviour and Allied Sciences, Dilshad Garden, Delhi, India
| | - Shrikant Kukreti
- Department of Chemistry, Nucleic Acids Research Lab, University of Delhi (North Campus), Delhi, India
| | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, New Delhi, -110007, India. .,Academy of Scientific & Innovative Research (AcSIR), CSIR- Institute of Genomics and Integrative Biology (CSIR-IGIB) Campus, New Delhi, India.
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Prasher B, Varma B, Kumar A, Khuntia BK, Pandey R, Narang A, Tiwari P, Kutum R, Guin D, Kukreti R, Dash D, Mukerji M. Ayurgenomics for stratified medicine: TRISUTRA consortium initiative across ethnically and geographically diverse Indian populations. J Ethnopharmacol 2017; 197:274-293. [PMID: 27457695 DOI: 10.1016/j.jep.2016.07.063] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 07/02/2016] [Accepted: 07/21/2016] [Indexed: 06/06/2023]
Abstract
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.
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Affiliation(s)
- Bhavana Prasher
- CSIR Ayurgenomics Unit- TRISUTRA, CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India; Genomics and Molecular Medicine & CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India; Academy of Scientific & Innovative research, CSIR-IGIB, Delhi, India.
| | - Binuja Varma
- CSIR Ayurgenomics Unit- TRISUTRA, CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India
| | - Arvind Kumar
- CSIR Ayurgenomics Unit- TRISUTRA, CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India
| | - Bharat Krushna Khuntia
- CSIR Ayurgenomics Unit- TRISUTRA, CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India
| | - Rajesh Pandey
- CSIR Ayurgenomics Unit- TRISUTRA, CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India
| | - Ankita Narang
- CSIR Ayurgenomics Unit- TRISUTRA, CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India
| | - Pradeep Tiwari
- CSIR Ayurgenomics Unit- TRISUTRA, CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India; Academy of Scientific & Innovative research, CSIR-IGIB, Delhi, India
| | - Rintu Kutum
- G.N.Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India; Academy of Scientific & Innovative research, CSIR-IGIB, Delhi, India
| | - Debleena Guin
- Genomics and Molecular Medicine & CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India
| | - Ritushree Kukreti
- Genomics and Molecular Medicine & CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India
| | - Debasis Dash
- CSIR Ayurgenomics Unit- TRISUTRA, CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India; G.N.Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India; Academy of Scientific & Innovative research, CSIR-IGIB, Delhi, India
| | - Mitali Mukerji
- CSIR Ayurgenomics Unit- TRISUTRA, CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India; Genomics and Molecular Medicine & CSIR-Institute of Genomics and Integrative Biology, Mathura Road, New Delhi 110020, India; Academy of Scientific & Innovative research, CSIR-IGIB, Delhi, India.
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