Biology subjects

Narasimhan, B.

Publications and source records attributed to Narasimhan, B..

3 recordsLinked to original sources

bioRxiv2026

Epithelial-Mesenchymal Wnt Crosstalk Directs Planar Cell Polarity in the Developing Cochlea

Cochlear hair cells are crucial for sound transduction and are precisely oriented by planar cell polarity (PCP). Wnt proteins direct PCP in vertebrates, but their roles in regulating cochlear PCP remain unclear. Here, we inhibited Wnt secretion by ablating Wntless in the cochlear epithelium and found shortened cochlea, mild hair cell misorientation and mislocalization of PCP proteins, Fzd6 and Dvl2, representing defects less severe than classic PCP mutants. Computational cell-communication analysis predicted that candidate Wnts from the cochlear epithelium (Wnt5a, Wnt7a, Wnt7b) and surrounding periotic mesenchyme (Wnt5a) act on developing hair cells. Deletion of Wnt5a, Wnt7a, and Wnt7b additively shortened the cochlea without causing hair cell misorientation or mislocalization of PCP proteins. Moreover, deletion of Wnt5a alone in the periotic mesenchyme failed to cause PCP defects. However, ablating both epithelial and mesenchymal Wnts caused a severely shortened cochlea with apically malrotated hair cells devoid of polarized core PCP proteins. Thus, Wnts serve as global instructive cues directing cochlear outgrowth and hair cell polarization, with remarkable redundancy of distinct Wnts across epithelial and mesenchymal compartments to ensure a fail-safe developmental program.

developmental biology↗

bioRxiv2021

Fast Numerical Optimization for Genome Sequencing Data in Population Biobanks

We develop two efficient solvers for optimization problems arising from large-scale regularized regressions on millions of genetic variants sequenced from hundreds of thousands of individuals. These genetic variants are encoded by the values in the set {0, 1, 2, NA}. We take advantage of this fact and use two bits to represent each entry in a genetic matrix, which reduces memory requirement by a factor of 32 compared to a double precision floating point representation. Using this representation, we implemented an iteratively reweighted least square algorithm to solve Lasso regressions on genetic matrices, which we name snpnet-2.0. When the dataset contains many rare variants, the predictors can be encoded in a sparse matrix. We utilize the sparsity in the predictor matrix to further reduce memory requirement and computational speed. Our sparse genetic matrix implementation uses both the compact 2-bit representation and a simplified version of compressed sparse block format so that matrix-vector multiplications can be effectively parallelized on multiple CPU cores. To demonstrate the effectiveness of this representation, we implement an accelerated proximal gradient method to solve group Lasso on these sparse genetic matrices. This solver is named sparse-snpnet, and will also be included as part of snpnet R package. Our implementation is able to solve group Lasso problems on sparse genetic matrices with more than 1, 000, 000 columns and almost 100, 000 rows within 10 minutes and using less than 32GB of memory.

bioinformatics↗

bioRxiv2018

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight novel adipocyte biology

To characterize latent components of genetic associations, we applied truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identified key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits, and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across human phenotypes will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.

genetics↗