Biology subjects

Bhatnagar, S.

Publications and source records attributed to Bhatnagar, S..

6 recordsLinked to original sources

bioRxiv2018

Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank

Osteoarthritis is the most common musculoskeletal disease and the leading cause of disability globally. Here, we perform the largest genome-wide association study for osteoarthritis to date (77,052 cases and 378,169 controls), analysing 4 phenotypes: knee osteoarthritis, hip osteoarthritis, knee and/or hip osteoarthritis, and any osteoarthritis. We discover 64 signals, 52 of them novel, more than doubling the number of established disease loci. Six signals fine map to a single variant. We identify putative effector genes by integrating eQTL colocalization, fine-mapping, human rare disease, animal model, and osteoarthritis tissue expression data. We find enrichment for genes underlying monogenic forms of bone development diseases, and for the collagen formation and extracellular matrix organisation biological pathways. Ten of the likely effector genes, including TGFB1, FGF18, CTSK and IL11 have therapeutics approved or in clinical trials, with mechanisms of action supportive of evaluation for efficacy in osteoarthritis.

genetics↗

bioRxiv2018

Diverse chemical compounds target Plasmodium falciparum plasma membrane lipid homeostasis

Lipid homeostasis is essential for the maintenance of life. We previously reported that disruptions of the parasite Na+ homeostasis via inhibition of PfATP4 resulted in elevated cholesterol within the parasite plasma membrane as assessed by saponin sensitivity. A large number of compounds have been shown to target the parasite Na+ homeostasis. We therefore screened the same collection of 800 compounds to identify chemotypes that disrupted the parasite plasma membrane lipid homeostasis. Here, we show that the compounds disrupting parasite Na+ homeostasis also induced saponin sensitivity, an indication of parasite lipid homeostasis disruption. Remarkably, 13 compounds were identified that altered plasma membrane lipid composition independent of Na+ homeostasis disruption. Further studies suggest that these compounds target the Plasmodium falciparum Niemann-Pick Type C1-Related (PfNCR1) protein, which is hypothesized to be involved in maintaining plasma membrane lipid composition. PfNCR1, like PfATP4, appears to be targeted by multiple chemotypes with potential for drug discovery.

microbiology↗

bioRxiv2018

Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes

BackgroundGenomics-based prediction could be useful since genome-wide genotyping costs less than many clinical tests. We tested whether machine learning methods could provide a clinically-relevant genomic prediction of quantitative ultrasound speed of sound (SOS)--a risk factor for osteoporotic fracture.\n\nMethodsWe used 341,449 individuals from UK Biobank with SOS measures to develop genomically-predicted SOS (gSOS) using machine learning algorithms. We selected the optimal algorithm in 5,335 independent individuals and then validated it and its ability to predict incident fracture in an independent test dataset (N = 80,027). Finally, we explored whether genomic pre-screening could complement a UK-based osteoporosis screening strategy, based on the validated tool FRAX.\n\nResultsgSOS explained 4.8-fold more variance in SOS than FRAX clinical risk factors (CRF) alone (r2 = 23% vs. 4.8%). A standard deviation decrease in gSOS, adjusting for the CRF-FRAX score was associated with a higher increased odds of incident major osteoporotic fracture (1,491 cases / 78,536 controls, OR = 1.91 [1.70-2.14], P = 10-28) than that for measured SOS (OR = 1.60 [1.50-1.69], P = 10-52) and femoral neck bone mineral density (147 cases / 4,594 controls, OR = 1.53 [1.27-1.83], P = 10-6). Individuals in the bottom decile of the gSOS distribution had a 3.25-fold increased risk of major osteoporotic fracture (P = 10-18) compared to the top decile. A gSOS-based FRAX score, identified individuals at high risk for incident major osteoporotic fractures better than the CRF-FRAX score (P = 10-14). Introducing a genomic pre-screening step into osteoporosis screening in 4,741 individuals reduced the number of required clinical visits from 2,455 to 1,273 and the number of BMD tests from 1,013 to 473, while only reducing the sensitivity to identify individuals eligible for therapy from 99% to 95%.\n\nInterpretationThe use of genotypes in a machine learning algorithm resulted in a clinically-relevant prediction of SOS and fracture, with potential to impact healthcare resource utilization.\n\nResearch in ContextO_ST_ABSEvidence Before this StudyC_ST_ABSGenome-wide association studies have identified many loci associated with risk of clinically-relevant fracture risk factors, such as SOS. Yet, it is unclear if such information can be leveraged to identify those at risk for disease outcomes, such as osteoporotic fractures. Most previous attempts to predict disease risk from genotypes have used polygenic risk scores, which may not be optimal for genomic-prediction. Despite these obstacles, genomic-prediction could enable screening programs to be more efficient since most people screened in a population are not determined to have a level of risk that would prompt a change in clinical care. Genomic pre-screening could help identify individuals whose risk of disease is low enough that they are unlikely to benefit from screening.\n\nAdded Value of this StudyUsing a large dataset of 426,811 individuals we trained and tested a machine learning algorithm to genomically-predict SOS. This metric, gSOS, had performance characteristics for predicting fracture risk that were similar to measured SOS and femoral neck BMD. Implementing a gSOS-based pre-screening step into the UK-based osteoporosis treatment guidelines reduced the number of individuals who would require screening clinical visits and skeletal testing by approximately 50%, while having little impact on the sensitivity to identify individuals at high risk for osteoporotic fracture.\n\nImplications of all of the Available EvidenceClinically-relevant genomic prediction of heritable traits is feasible using the machine learning algorithm presented here in large sample sizes. Genome-wide genotyping is now less expensive than many clinical tests, needs to be performed once over a lifetime and could risk stratify for multiple heritable traits and diseases years prior to disease onset, providing an opportunity for prevention. The implementation of such algorithms could improve screening efficiency, yet their cost-effectiveness will need to be ascertained in subsequent analyses.

genomics↗

bioRxiv2018

Plasmodium falciparum Niemann-Pick Type C1-Related Protein is a Druggable Target Required for Parasite Membrane Homeostasis

Plasmodium parasites possess a protein with homology to Niemann-Pick Type C1 proteins (Plasmodium falciparum Niemann-Pick Type C1-Related protein, PfNCR1). We isolated parasites with resistance-conferring mutations in PfNCR1 during selections with three diverse small-molecule antimalarial compounds and show that the mutations are causative for compound resistance. PfNCR1 protein knockdown results in severely attenuated growth and confers hypersensitivity to the compounds. Compound treatment or protein knockdown leads to increased sensitivity of the parasite plasma membrane (PPM) to the amphipathic glycoside saponin and engenders digestive vacuoles (DVs) that are small and malformed. Immuno-electron microscopy and split-GFP experiments localize PfNCR1 to the PPM. Our experiments show that PfNCR1 activity is critically important for the composition of the PPM and is required for DV biogenesis, suggesting PfNCR1 as a novel antimalarial drug target.

microbiology↗

bioRxiv2018

Cell-intrinsic genetic regulation of peripheral memory-phenotype T cell frequencies

Memory T and B lymphocyte numbers are thought to be regulated by recent and cumulative microbial exposures. We report here that memory-phenotype lymphocyte frequencies in B, CD4 and CD8 T-cells in 3-monthly serial bleeds from healthy young adult humans were relatively stable over a 1-year period, while recently activated -B and -CD4 T cell frequencies were not, suggesting that recent environmental exposures affected steady state levels of recently activated but not of memory lymphocyte subsets. Frequencies of memory B and CD4 T cells were not correlated, suggesting that variation in them was unlikely to be determined by cumulative antigenic exposures. Immunophenotyping of adult siblings showed high concordance in memory, but not of recently activated lymphocyte subsets, suggesting genetic regulation of memory lymphocyte frequencies. To explore this possibility further, we screened effector memory (EM)-phenotype T cell frequencies in common independent inbred mice strains. Using two pairs from these strains that differed predominantly in either CD4EM and/or CD8EM frequencies, we constructed bi-parental bone marrow chimeras in F1 recipient mice, and found that memory T cell frequencies in recipient mice were determined by donor genotypes. Together, these data suggest cell-autonomous determination of memory T niche size, and suggest mechanisms maintaining immune variability.

immunology↗

bioRxiv2017

Transient exposure to oxygen or nitrate reveals ecophysiology of fermentative and sulfate-reducing benthic microbial populations

Conflict of interestThe authors declare no conflict of interest.\n\nShort summaryFermentation coupled to sulfate reduction is a globally important process for the remineralization of organic carbon in marine sediments. The present study uses long-term, replicated continuous culture bioreactors and meta-omics to investigate the ecophysiology of the involved microbial populations at an unprecedented resolution. We reveal complex trophic networks, in which fermenters and sulfate reducers coexist with nitrate- and oxygen respirers, we indicate strategies and niches of the microbial populations, and describe a novel and widespread, yet uncultured fermentative organism. These insights are crucial to understand fermentation coupled to sulfate reduction and relevant to assess microbial dynamics and community-level responses in coastal ecosystems.\n\nFor the anaerobic remineralization of organic matter in marine sediments, sulfate reduction coupled to fermentation plays a key role. Here, we enriched sulfate-reducing/fermentative communities from intertidal sediments under defined conditions in continuous culture. We transiently exposed the cultures to oxygen or nitrate twice daily and investigated the community response. Chemical measurements, provisional genomes and transcriptomic profiles revealed trophic networks of microbial populations. Sulfate reducers coexisted with facultative nitrate reducers or aerobes enabling the community to adjust to nitrate or oxygen pulses. Exposure to oxygen and nitrate impacted the community structure, but did not suppress fermentation or sulfate reduction as community functions, highlighting their stability under dynamic conditions. The most abundant sulfate reducer in all cultures, related to Desulfotignum balticum, appeared to have coupled acetate oxidation to sulfate reduction. We described a novel representative of the widespread uncultured phylum Candidatus Fermentibacteria (formerly candidate division Hyd24-12). For this strictly anaerobic, obligate fermentative bacterium, we propose the name Ca. \"Sabulitectum silens\" and identify it as a partner of sulfate reducers in marine sediments. Overall, we provide insights into the metabolic network of fermentative and sulfate-reducing microbial populations, their niches, and adaptations to a dynamic environment.

microbiology↗