Downregulation of peripheral PTGS2/COX-2 in response to valproate treatment in patients with epilepsy

Antiepileptic drug therapy has significant inter-patient variability in response towards it. The current study aims to understand this variability at the molecular level using microarray-based analysis of peripheral blood gene expression profiles of patients receiving valproate (VA) monotherapy. Only 10 unique genes were found to be differentially expressed in VA responders (n = 15) and 6 genes in the non-responders (n = 8) (fold-change >2, p < 0.05). PTGS2 which encodes cyclooxygenase-2, COX-2, showed downregulation in the responders compared to the non-responders. PTGS2/COX-2 mRNA profiles in the two groups corresponded to their plasma profiles of the COX-2 product, prostaglandin E(2) (PGE(2)). Since COX-2 is believed to regulate P-glycoprotein (P-gp), a multidrug efflux transporter over-expressed at the blood-brain barrier (BBB) in drug-resistant epilepsy, the pathway connecting COX-2 and P-gp was further explored in vitro. Investigation of the effect of VA upon the brain endothelial cells (hCMEC/D3) in hyperexcitatory conditions confirmed suppression of COX-2-dependent P-gp upregulation by VA. Our findings suggest that COX-2 downregulation by VA may suppress seizure-mediated P-gp upregulation at the BBB leading to enhanced drug delivery to the brain in the responders. Our work provides insight into the association of peripheral PTGS2/COX-2 expression with VA efficacy and the role of COX-2 as a potential therapeutic target for developing efficacious antiepileptic treatment

Supplementary Table 3a

<p>Gene ontology enrichment results for differentially expressed genes among FDR, CeD and Control</p

Supplementary Table 4a

<p>List of probes/genes with their gene expression pattern among CeD, FDR and Control.</p

Novel approach to analysis of AMR: looking at the composite resistance phenotype

Submission of detail report with codes for the Vivli AMR Surveillance Open Data Re-use Data Challenge 2023 Team ID: 9049 [Lead: Shraddha Karve] Objectives: The escalating challenge of antimicrobial resistance (AMR) poses a significant global concern for public healthcare systems1,2. Current AMR surveillance and molecular mechanism studies traditionally focus on specific drug-bug combinations, like carbapenem-resistant Klebsiella pneumoniae, designated as a priority pathogen by the WHO3. While wastewater and environmental surveillance aim to detect Klebsiella species and genes conferring carbapenem resistance4, it is known that resistance genes for one antibiotic often coexist with genes for resistance to others5. To address these complexities, we propose a novel analysis approach using Klebsiella pneumoniae as a model. We consider the resistance profile of an isolate for a set of common antibiotics across two datasets, ATLAS and GEARS. We term this composite phenotype, encompassing resistance/sensitivity to a group of antibiotics, a 'subtype' of the pathogen. Our primary objective is to track and study the prevalence of different subtypes across time and space, enabling a more comprehensive understanding of AMR dynamics. We then explore the impact of climatic parameters on the prevalence of different Klebsiella pneumoniae subtypes, aiming to uncover additional insights into antibiotic resistance patterns. Rising temperatures and climate change have been associated with recent antibiotic resistance developments, as bacterial growth and genetic material dissemination are closely tied to temperature conditions6. Heavy rainfall has been linked to bacterial mutagenesis and antibiotic resistance gene expression7. Rising local temperatures in the United States and Europe have shown correlations with increased antibiotic resistance at the population level in various pathogens8,9. GitHub repository: https://github.com/KutumLab/amr-vivli-ashoka-submissio

Extensive copy number variations in admixed Indian population of African ancestry: potential involvement in adaptation

Admixture mapping has been enormously resourceful in identifying genetic variations linked to phenotypes, adaptation, and diseases. In this study through analysis of copy number variable regions (CNVRs), we report extensive restructuring in the genomes of the recently admixed African-Indian population (OG-W-IP) that inhabits a highly saline environment in Western India. The study included subjects from OG-W-IP (OG), five different Indian and three HapMap populations that were genotyped using Affymetrix version 6.0 arrays. Copy number variations (CNVs) detected using Birdsuite were used to define CNVRs. Population structure with respect to CNVRs was delineated using random forest approach. OG genomes have a surprising excess of CNVs in comparison to other studied populations. Individual ancestry proportions computed using STRUCTURE also reveals a unique genetic component in OGs. Population structure analysis with CNV genotypes indicates OG to be distant from both the African and Indian ancestral populations. Interestingly, it shows genetic proximity with respect to CNVs to only one Indian population IE-W-LP4, which also happens to reside in the same geographical region. We also observe a significant enrichment of molecular processes related to ion binding and receptor activity in genes encompassing OG-specific CNVRs. Our results suggest that retention of CNVRs from ancestral natives and de novo acquisition of CNVRs could accelerate the process of adaptation especially in an extreme environment. Additionally, this population would be enormously useful for dissecting genes and delineating the involvement of CNVs in salt adaptation

Insights from a pan India sero-epidemiological survey (Phenome-india cohort) for SARS-CoV2.

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92–2.59, <0.0001), use of public transport (1.79, 1.43–2.24, <0.0001), not smoking (1.52, 1.16–1.99, 0.0257), non-vegetarian diet (1.67, 1.41–1.99, <0.0001), and B blood group (1.36, 1.15–1.61, 0.001)

Exhaled breath condensate metabolome clusters for endotype discovery in asthma

Abstract Background Asthma is a complex, heterogeneous disorder with similar presenting symptoms but with varying underlying pathologies. Exhaled breath condensate (EBC) is a relatively unexplored matrix which reflects the signatures of respiratory epithelium, but is difficult to normalize for dilution. Methods Here we explored whether internally normalized global NMR spectrum patterns, combined with machine learning, could be useful for diagnostics or endotype discovery. Nuclear magnetic resonance (NMR) spectroscopy of EBC was performed in 89 asthmatic subjects from a prospective cohort and 20 healthy controls. A random forest classifier was built to differentiate between asthmatics and healthy controls. Clustering of the spectra was done using k-means to identify potential endotypes. Results NMR spectra of the EBC could differentiate between asthmatics and healthy controls with 80% sensitivity and 75% specificity. Unsupervised clustering within the asthma group resulted in three clusters (n = 41,11, and 9). Cluster 1 patients had lower long-term exacerbation scores, when compared with other two clusters. Cluster 3 patients had lower blood eosinophils and higher neutrophils, when compared with other two clusters with a strong family history of asthma. Conclusion Asthma clusters derived from NMR spectra of EBC show important clinical and chemical differences, suggesting this as a useful tool in asthma endotype-discovery

MOESM1 of Exhaled breath condensate metabolome clusters for endotype discovery in asthma

Additional file 1: Figure S1. Dynamic adaptive binning achieved optimal binning on the spectra. Figure S2. Changes in error rates of the random forest model at different steps of optimization. Figure S3. Boxplots of annotated bins which are top predictors in random forest model showing difference between asthmatics and healthy controls (A) and a table of p values for compounds showing statistical significance (B). Table S1. Most important NMR bins according to the random forest model along with the compounds annotated at that particular position

Whole Exome Sequencing in Healthy Individuals of Extreme Constitution Types Reveals Differential Disease Risk: A Novel Approach towards Predictive Medicine

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

Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits

<div><p>In Ayurveda system of medicine individuals are classified into seven constitution types, “<i>Prakriti</i>”, for assessing disease susceptibility and drug responsiveness. <i>Prakriti</i> evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting <i>Prakriti</i> classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme <i>Prakriti types</i>, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for <i>Prakriti</i> respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme <i>Prakriti</i> classes. The supervised modelling approaches could classify individuals, with distinct <i>Prakriti</i> types, in the training and validation sets. This study is the first to demonstrate that <i>Prakriti</i> types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting <i>Prakriti</i> classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations.</p></div