Biology subjects

Nagarajan, R.

Publications and source records attributed to Nagarajan, R..

3 recordsLinked to original sources

bioRxiv2022

Control of contextual memory through interneuronal α5-GABAA receptors

{gamma}-aminobutyric acid type A receptors that incorporate 5 subunits (5-GABAARs) are highly enriched in the hippocampus and are strongly implicated in control of learning and memory. Receptors located on pyramidal neuron dendrites have long been considered responsible, but here we report that their selective knockout from either interneurons (5-i-KO) or pyramidal neurons (5-pyr-KO) interferes with the ability of the general anesthetic etomidate to suppress contextual conditioning. Using Ca2+ imaging of CA1 pyramidal neuron activity in freely exploring mice to assess hippocampal function directly, we found that etomidate blocked the development of place cells and spatial engrams in wild type (WT) and 5-pyr-KO mice, but not in 5-i-KO mice. In addition, 5-i-KO mice developed weaker spatial engrams than WT mice under control conditions. These findings show that interneuronal 5-GABAARs serve a physiological role in promoting spatial learning, and that they mediate the suppression of hippocampus-dependent memory by etomidate.

neuroscience↗

bioRxiv2021

Mapping QTLs for phenotypic and morpho-physiological traits related to grain yield under late sown conditions in wheat (Triticum aestivum L.)

The elevating temperature makes heat stress one of the major issues for wheat production globally. To elucidate genetic basis and map heat tolerance traits, a set of 166 doubled haploid lines (DHLs) derived from the cross between PBW3438/IC252874 was used. The population was evaluated under Normal sown (NS) and late sown (LS) conditions, by exposing to heat stress during rabi season. The canopy temperature (CT) showed positive correlations with grain yield, whereas Soil plant analysis development (SPAD) was not significantly correlated and associated with GY in both the normal and late sown conditions. Composite interval mapping (CIM) identified total 12 Quantitative trait loci (QTLs) viz., 2 (Normal sown), 10 (late sown) mapped on linkage groups 1A, 1D, 2B, 2D, 3B, 4D, 5B and 6D, during both the crop seasons 2017-18 and 2018-19. Combining the results of these QTLs revealed a major stable QTL for grain yield (GY) on chromosome 3B with 11.84% to 21.24% explaining phenotypic variance under both sowing conditions. QTL for CT and SPAD was detected on chromosome 1A while QTL for GY on chromosome 3B and 5B. The identified QTLs in the genomic regions could be targeted for genetic improvement and marker assisted selection for heat tolerance in wheat. The tools like SPAD and CT could be exploited to screen the large number of breeding lines.

plant biology↗

bioRxiv2021

Development of the Wheat Practical Haplotype Graph Database as a Resource for Genotyping Data Storage and Genotype Imputation

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 93% imputation accuracy with 0.01x sequence coverage, which was only slightly lower than the accuracy obtained using the 0.5x sequence coverage (96.9%). Compared to Beagle, on average, PHG imputation was ~4% (p-value = 0.00027) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. The reduced accuracy of imputation with GBS data (90.4%) is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequlibrium and proportion of identity-by-descent regions among accessions in our reference panel. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

genomics↗