Biology subjects

Bhatia, G.

Publications and source records attributed to Bhatia, G..

6 recordsLinked to original sources

bioRxiv2026

Specialisation of photosynthetic biochemistry in C4 chloroplasts potentiates distinct cell-specific functionality of different chloroplast-to-nucleus retrograde signalling pathways.

Chloroplasts are key environmental sensors for plant cells. Upon exposure to abiotic stresses such as excess sunlight and heat, reactive oxygen species (ROS) in chloroplasts are sensed to trigger chloroplast-to-nucleus signalling pathways for acclimatory nuclear gene expression. There is extensive biochemical- and cell type-specialisation of chloroplasts, such as those in heat-resilient plants performing C4 photosynthesis, but how these specialisations intersect with chloroplast-to-nucleus signalling remains unclear. Here we show that specific chloroplast-to-nucleus signalling networks are functional in specialised leaf cell types of the C4 model species, Setaria viridis. RNA-sequencing, electron paramagnetic resonance (EPR) spectroscopy and metabolomics on cell type-enriched fractions, complemented with mutant analyses provided evidence for cell-specific enrichment of chloroplast-to-nucleus retrograde signalling pathways correlating with the photosynthetic specialisation in these cell types. Singlet oxygen (1O2)-responsive beta-cyclocitral ({beta}-CC) /dihydroactinidiolide (DHA) signalling and hydrogen peroxide (H2O2)-responsive 3'-phosphoadenosine 5'-phosphate (PAP) signalling pathways were specific to mesophyll cells, in line with enrichment of photosystem II and linear electron flow to photosystem I, respectively in this cell type. In contrast, bundle sheath cells were enriched for the rapid, ROS-independent retrograde signalling pathway mediated by the Calvin cycle product, dihydroxyacetone phosphate (DHAP), consistent with the exclusive localisation of the Calvin cycle to C4 bundle sheath cells. Taken together, our results provide insights into how chloroplast- and cell type-specialisation intersect with stress signalling in plant cells and identify photosynthetic biochemistry as a key driver of cell-specific cellular signalling.

plant biology↗

bioRxiv2020

New Pathways of Mutational Change in SARS-CoV-2 Proteomes Involve Regions of Intrinsic Disorder Important for Virus Replication and Release

The massive worldwide spread of the SARS-CoV-2 virus is fueling the COVID-19 pandemic. Since the first whole-genome sequence was published in January 2020, a growing database of tens of thousands of viral genomes has been constructed. This offers opportunities to study pathways of molecular change in the expanding viral population that can help identify molecular culprits of virulence and virus spread. Here we investigate the genomic accumulation of mutations at various time points of the early pandemic to identify changes in mutationally highly active genomic regions that are occurring worldwide. We used the Wuhan NC_045512.2 sequence as a reference and sampled 15,342 indexed sequences from GISAID, translating them into proteins and grouping them by month of deposition. The per-position amino acid frequencies and Shannon entropies of the coding sequences were calculated for each month, and a map of intrinsic disorder regions and binding sites was generated. The analysis revealed dominant variants, most of which were located in loop regions and on the surface of the proteins. Mutation entropy decreased between March and April of 2020 after steady increases at several sites, including the D614G mutation site of the spike (S) protein that was previously found associated with higher case fatality rates and at sites of the NSP 12 polymerase and the NSP13 helicase proteins. Notable expanding mutations include R203K and G204R of the nucleocapsid (N) protein inter-domain linker region and G251V of the viroporin encoded by ORF3a between March and April. The regions spanning these mutations exhibited significant intrinsic disorder, which was enhanced and decreased by the N-protein and viroporin 3a protein mutations, respectively. These results predict an ongoing mutational shift from the spike and replication complex to other regions, especially to encoded molecules known to represent major {beta}-interferon antagonists. The study provides valuable information for therapeutics and vaccine design, as well as insight into mutation tendencies that could facilitate preventive control.

bioinformatics↗

bioRxiv2018

Lipid Lowering Oxopropanylindole Hydrazone Derivatives with Antioxidant and Antihyperglycemic Activity

A series of substituted oxopropanylindole hydrazone derivatives was synthesized and evaluated for anti-oxidant and anti-dyslipidemic activity. Among these 12 compounds the three compounds 6c, 7b and 7d showed good anti-oxidant activity and the compound 6c attenuataed LDL oxidation by 32%. The compounds 6c and 7d also showed good anti-dyslipidemic activity by reducing serum levels of total cholesterol (TC), phospholipids (PL) and triglycerides (TG). These two compounds were further evaluated for anti-adipogenic and anti-hyperglycemic activity, where 6c was found most active compound with 44% reduction in lipid accumulation and 20.5% and 24.3% reduction in blood glucose at 5h and 24h respectively, as compared to standard drug metformin.

biochemistry↗

bioRxiv2017

Leveraging polygenic functional enrichment to improve GWAS power

Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, Functionally Informed Novel Discovery Of Risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N=130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to un-weighted raw p-values that do not use functional data. We replicated the novel loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66-0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N=416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.

genetics↗

bioRxiv2017

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Biological interpretation of GWAS data frequently involves analyzing unsigned genomic annotations comprising SNPs involved in a biological process and assessing enrichment for disease signal. However, it is often possible to generate signed annotations quantifying whether each SNP allele promotes or hinders a biological process, e.g., binding of a transcription factor (TF). Directional effects of such annotations on disease risk enable stronger statements about causal mechanisms of disease than enrichments of corresponding unsigned annotations. Here we introduce a new method, signed LD profile regression, for detecting such directional effects using GWAS summary statistics, and we apply the method using 382 signed annotations reflecting predicted TF binding. We show via theory and simulations that our method is well-powered and is well-calibrated even when TF binding sites co-localize with other enriched regulatory elements, which can confound unsigned enrichment methods. We further validate our method by showing that it recovers known transcriptional regulators when applied to molecular QTL in blood. We then apply our method to eQTL in 48 GTEx tissues, identifying 651 distinct TF-tissue expression associations at per-tissue FDR < 5%, including 30 associations with robust evidence of tissue specificity. Finally, we apply our method to 46 diseases and complex traits (average N = 289,617) and identify 77 annotation-trait associations at per-trait FDR < 5% representing 12 independent TF-trait associations, and we conduct gene-set enrichment analyses to characterize the underlying transcriptional programs. Our results implicate new causal disease genes (including causal genes at known GWAS loci), and in some cases suggest a detailed mechanism for a causal genes effect on disease. Our method provides a new way to leverage functional data to draw inferences about disease etiology.

genetics↗

bioRxiv2017

Quantitative analysis of population-scale family trees using millions of relatives

Family trees have vast applications in multiple fields from genetics to anthropology and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. Here, we collected 86 million profiles from publicly-available online data from genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of longevity by inspecting millions of relative pairs and to provide insights to population genetics theories on the dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data.\n\nOne Sentence SummaryUsing massive crowd-sourced genealogy data, we created a population-scale family tree resource for scientific studies.

genomics↗