Mukerji, Mitali

Coronary artery disease (CAD) has a high mortality rate. Despite various therapeutic targets, non-responsiveness to drugs remains a prevalent issue. Pharmacogenomics assesses the way an individual's genetic attributes affect their likely response to drug therapy. Single-nucleotide polymorphisms play a crucial role in determining these outcomes. This review offers an overview of single-nucleotide polymorphisms investigated in clinical studies and their associations with drug response/nonresponse in the treatment of CAD. A total of 104 studies of whole sets of chromosomes and several genes were explored. A total of 161 polymorphisms exhibited associations with drug response/nonresponse in CAD across diverse ethnic populations. This pool can serve as a pharmacogenomic biomarker for predicting response to drug therapy in patients with CAD.

Precision medicine aims to move from traditional reactive medicine to a system where risk groups can be identified before the disease occurs. However, phenotypic heterogeneity amongst the diseased and healthy poses a major challenge for identification markers for risk stratification and early actionable interventions. In Ayurveda, individuals are phenotypically stratified into seven constitution types based on multisystem phenotypes termed “Prakriti”. It enables the prediction of health and disease trajectories and the selection of health interventions. We hypothesize that exome sequencing in healthy individuals of phenotypically homogeneous Prakriti types might enable the identification of functional variations associated with the constitution types. Exomes of 144 healthy Prakriti stratified individuals and controls from two genetically homogeneous cohorts (north and western India) revealed differential risk for diseases/traits like metabolic disorders, liver diseases, and body and hematological measurements amongst healthy individuals. These SNPs differ significantly from the Indo-European background control as well. Amongst these we highlight novel SNPs rs304447 (IFIT5) and rs941590 (SERPINA10) that could explain differential trajectories for immune response, bleeding or thrombosis. Our method demonstrates the requirement of a relatively smaller sample size for a well powered study. This study highlights the potential of integrating a unique phenotyping approach for the identification of predictive markers and the at-risk population amongst the healthy.

Extreme complexity in the Human Leukocyte Antigens (HLA) system and its nomenclature makes it difficult to interpret and integrate relevant information for HLA associations with diseases, Adverse Drug Reactions (ADR) and Transplantation. PubMed search displays ~ 146,000 studies on HLA reported from diverse locations. Currently, IPD-IMGT/HLA (Robinson et al., Nucleic Acids Research 48:D948–D955, 2019) database houses data on 28,320 HLA alleles. We developed an automated pipeline with a unified graphical user interface HLA-SPREAD that provides a structured information on SNPs, Populations, REsources, ADRs and Diseases information. Information on HLA was extracted from ~ 28 million PubMed abstracts extracted using Natural Language Processing (NLP). Python scripts were used to mine and curate information on diseases, filter false positives and categorize to 24 tree hierarchical groups and named Entity Recognition (NER) algorithms followed by semantic analysis to infer HLA association(s). This resource from 109 countries and 40 ethnic groups provides interesting insights on: markers associated with allelic/haplotypic association in autoimmune, cancer, viral and skin diseases, transplantation outcome and ADRs for hypersensitivity. Summary information on clinically relevant biomarkers related to HLA disease associations with mapped susceptible/risk alleles are readily retrievable from HLASPREAD. The resource is available at URL http://hla-spread.igib.res.in/. This resource is first of its kind that can help uncover novel patterns in HLA gene-disease associations.

Objective: Alu repeats have gained huge importance in the creation and modification of regulatory networks. We previously reported a unique isoform of human CYP20A1 i.e. CYP20A1_Alu-LT with 23 Alu repeats exonized in its 9 kb long 3’UTR with 4742 potential binding sites for 994 miRNAs. The role of this transcript was hypothesized as a potential miRNA sponge in primary neurons as its expression correlated with that of 380 genes having shared miRNA sites and enriched in neuro-coagulopathy. This study provides experimental evidence for the miRNA sponge activity of CYP20A1_Alu-LT in neuronal cell lines. Results: We studied the Alu-rich fragment of the CYP20A1_Alu-LT extended 3’UTR with > 10 binding sites for miR-619-5p and miR-3677-3p. Enrichment of the Alu-rich fragment with Ago2 confirmed miRNA association of this transcript. Cloning the fragment downstream of a reporter gene led to a 90% decrease in luciferase activity. Overexpression and knockdown studies revealed a positive correlation between the expression of CYP20A1_Alu-LT and miR-619-5p / miR-3677-3p target genes. GAP43, one of the key modulators of nerve regeneration, was significantly altered by the expression of CYP20A1_Alu-LT. This study, for the first time, provides evidence for a unique regulatory function of exonized Alu repeats as miRNA sponges.

Background: Viral infections have a history of abrupt and severe eruptions through the years in the form of pandemics. And yet, definitive therapies or preventive measures are not present. Herbal medicines have been a source of various antiviral compounds such as Oseltamivir, extracted using shikimic acid from star anise (Illicium verum) and Acyclovir from Carissa edulis are FDA (Food and Drug Administration) approved antiviral drugs. In this study, we dissect the anti-coronavirus infection activity of Cissampelos pareira L (Cipa) extract using an integrative approach. Methods: We analysed the signature similarities between predicted antiviral agents and Cipa using the connectivity map (https://clue.io/). Next, we tested the anti-SARS-COV-2 activity of Cipa in vitro. Molecular docking analyses of constituents of with key targets of SARS-CoV2 protein viz. spike protein, RNA‑dependent RNA‑polymerase (RdRp) and 3C‑like proteinase. was also performed. A three-way comparative analysis of Cipa transcriptome, COVID-19 BALF transcriptome and CMAP signatures of small compounds was also performed. Results: Several predicted antivirals showed a high positive connectivity score with Cipa such as apcidin, emetine, homoharringtonine etc. We also observed 98% inhibition of SARS-COV-2 replication in infected Vero cell cultures with the whole extract. Some of its prominent pure constituents e.g. pareirarine, cissamine, magnoflorine exhibited 40–80% inhibition. Comparison of genes between BALF and Cipa showed an enrichment of biological processes like transcription regulation and response to lipids, to be downregulated in Cipa while being upregulated in COVID-19. CMAP also showed that Triciribine, torin-1 and VU-0365114–2 had positive connectivity with BALF 1 and 2, and negative connectivity with Cipa. Amongst all the tested compounds, Magnoflorine and Salutaridine exhibited the most potent and consistent strong in silico binding profiles with SARS-CoV2 therapeutic targets.

Predictive medicine for a holistic and proactive approach to health management is steadily replacing the reactive healthcare model as the dominant paradigm in the twenty-first century. The Ayurvedic medical system, which incorporates all parts of predictive medicine, divides people into seven constitution types, or Prakriti, to help practitioners determine their initial homeostatic conditions. This article uses data on the phenotypic characteristics of 217 healthy people who fall into three extreme Prakriti types to conduct a study for predicting Prakriti classes. Those who fit the Prakriti type are drawn from two genetically different northern and western India cohorts. In order to dichotomize inter-individual variability in various individuals, eight machine learning (ML) classifiers are used. The prediction skills of the ML algorithms are evaluated here using ten pairs of predefined training and testing datasets for each cohort. Lastly, a performance comparison of various ML algorithms is carried out using six crucial performance criteria. The study aims to investigate and appraise using artificial intelligence (AI) to evaluate Prakriti in Ayurveda. The use of AI in Prakriti assessment may have several advantages, including enhancing the consistency and accuracy of assessments and minimizing reliance on subjective judgements. This study aims to further our knowledge of how technology can be applied to enhance the practice of Ayurveda and possibly improve patient outcomes.

Autonomic modulation is critical during various physiological activities, including orthostatic stimuli and primarily evaluated by heart rate variability (HRV). Orthostatic stress affects people differently suggesting the possibility of identification of predisposed groups to autonomic dysfunction-related disorders in a healthy state. One way to understand this kind of variability is by using Ayurvedic approach that classifies healthy individuals into Prakriti types based on clinical phenotypes. To this end, we explored the differential response to orthostatic stress in different Prakriti types using HRV. HRV was measured in 379 subjects(Vata = 97, Pitta = 68, Kapha = 68, and Mixed Prakriti = 146) from two geographical regions(Vadu and Delhi NCR) for 5 min supine (baseline), 3 min head-up-tilt (HUT) at 60°, and 5 min resupine. We observed that Kapha group had lower baseline HRV than other two groups, although not statistically significant. The relative change (%Δ1&2) in various HRV parameters in response to HUT was although minimal in Kapha group. Kapha also had significantly lower change in HR, LF (nu), HF (nu), and LF/HF than Pitta in response to HUT. The relative change (%Δ1) in HR and parasympathetic parameters (RMSSD, HF, SD1) was significantly greater in the Vata than in the Kapha. Thus, the low baseline and lower response to HUT in Kapha and the maximum drop in parasympathetic activity of Vata may indicate a predisposition to early autonomic dysfunction and associated conditions. It emphasizes the critical role of Prakriti-based phenotyping in stratifying the differential responses of cardiac autonomic modulation in various postures among healthy individuals across different populations.

Bioactive fractions obtained from medicinal plants which have been used for the treatment of multiple diseases could exert their effects by targeting common pathways. Prior knowledge of their usage could allow us to identify novel molecular links. In this study, we explored the molecular basis of action of one such herbal formulation Cissampelos pareira L. (Cipa), used for the treatment of female hormone disorders and fever. Transcriptomic studies on MCF7 cell lines treated with Cipa extract carried out using Affymetrix arrays revealed a downregulation of signatures of estrogen response potentially modulated through estrogen receptor α (ERα). Molecular docking analysis identified 38 Cipa constituents that potentially bind (ΔG < − 7.5) with ERα at the same site as estrogen. The expression signatures in the connectivity map (https://clue.io/;) revealed high positive scores with translation inhibitors such as emetine (score: 99.61) and knockdown signatures of genes linked to the antiviral response such as ribosomal protein RPL7 (score: 99.92), which is a reported ERα coactivator. Further, gene knockdown experiments revealed that Cipa exhibits antiviral activity in dengue infected MCF7 cells potentially modulated through estrogen receptor 1. This approach reveals a novel pathway involving the ESR1-RPL7 axis which could be a potential target in dengue viral infection.

Background: Vesicoureteral reflux (VUR) is a genetically heterogenous disorder. We conducted this pilot study to explore the possibility of genetic discovery in patients with VUR using exome sequencing. Methods: Whole exome sequencing was performed on the DNA of ten Indian children (nine boys) with bilateral grade IV/V reflux and bilateral renal hypodysplasia (extreme phenotype) as well as that of a family with 2 affected male siblings with bilateral VUR and chronic kidney disease. For prioritization of single nucleotide variations (SNVs) in the ten children with extreme phenotype, extremely rare and deleterious variations were selected from a list of 778 genes of biological relevance. Hypothesis-free prioritization was performed in the VUR family. Copy-number variations (CNV) were also called and prioritized from whole exome data. Results: Among ten patients with extreme phenotype, rare and novel and deleterious variants related to VUR and renal hypodysplasia were identified in 7/10 patients each. At least one prioritized variant in either genes related to VUR per se or renal hypodysplasia was seen in 8/10 patients. All patients carried different variations. Among the remaining two patients, one carried a pathogenic CNV. In the VUR family, a rare and deleterious variant in SLIT1 gene was identified. Conclusion: Sharp phenotyping in combination with exome sequencing using a combination of approaches including hypothesis-driven and hypothesis-free for prioritization of SNVs and study of CNVs seems to be an optimal method of genetic discovery of VUR. VUR is genetically heterogenous.