Biology subjects

Singh, G.

Publications and source records attributed to Singh, G..

6 recordsLinked to original sources

bioRxiv2026

Spatial profiling and neurovascular communication in the developing and adolescent cortex following prenatal alcohol exposure

Fetal alcohol spectrum disorders (FASD) constitute a wide range of developmental, cognitive, and behavioral impairments caused by prenatal alcohol exposure (PAE). Although neuronal and vascular consequences of PAE have been studied, how alcohol affects the cerebrovasculature within the framework of the neurovascular unit (NVU) across development remains poorly understood. At minimum, the NVU comprises neurons, astrocyte endfeet, and endothelial cells (ECs), which coordinate to maintain brain homeostasis. Here, we used the NanoString Digital Spatial Profiling platform to characterize spatial transcriptomic data from neurons, astrocytes, and ECs from PAE and saccharin (SAC) control cortices at embryonic day 18 (E18) and postnatal day 28 (P28). Differentially expressed genes were then used for Ingenuity Pathway Analysis (IPA) to identify altered biological pathways and perform comparison analyses across developmental time points, while CellChat was used to infer cell cell communication networks. We uncovered thousands of differentially expressed genes and numerous altered pathways and biological processes in PAE cortices across development. Both IPA and CellChat analyses implicated dysregulation of vascular and extracellular matrix (ECM) remodeling, cell adhesion, and neuroinflammatory signaling. CellChat further predicted the loss of several key bidirectional relationships and altered ligand-receptor interactions among neurovascular cell types at E18 and P28. Overall, these findings identify PAE associated alterations in neurovascular gene expression and intercellular signaling across development, providing potential mechanisms by which PAE may disrupt neurodevelopment.

molecular biology↗

bioRxiv2018

Variational Infinite Heterogeneous Mixture Model for Semi-supervised Clustering of Heart Enhancers

MotivationPMammalian genomes can contain thousands of enhancers but only a subset are actively driving gene expression in a given cellular context. Integrated genomic datasets can be harnessed to predict active enhancers. One challenge in integration of large genomic datasets is the increasing heterogeneity: continuous, binary and discrete features may all be relevant. Coupled with the typically small numbers of training examples, semi-supervised approaches for heterogeneous data are needed; however, current enhancer prediction methods are not designed to handle heterogeneous data in the semi-supervised paradigm.\n\nResultsWe implemented a Dirichlet Process Heterogeneous Mixture model that infers Gaussian, Bernoulli and Poisson distributions over features. We derived a novel variational inference algorithm to handle semi-supervised learning tasks where certain observations are forced to cluster together. We applied this model to enhancer candidates in mouse heart tissues based on heterogeneous features. We constrained a small number of known active enhancers to appear in the same cluster, and 47 additional regions clustered with them. Many of these are located near heart-specific genes. The model also predicted 1176 active promoters, suggesting that it can discover new enhancers and promoters.\n\nAvailabilityWe created the dphmix Python package: https://pypi.org/project/dphmix/\n\nContactalan.moses@utoronto.ca

bioinformatics↗

bioRxiv2018

The exon junction complex undergoes a compositional switch that alters mRNP structure and nonsense-mediated mRNA decay activity

The exon junction complex (EJC) deposited upstream of mRNA exon junctions shapes structure, composition and fate of spliced mRNA ribonucleoprotein particles (mRNPs). To achieve this, the EJC core nucleates assembly of a dynamic shell of peripheral proteins that function in diverse post-transcriptional processes. To illuminate consequences of EJC composition change, we purified EJCs from human cells via peripheral proteins RNPS1 and CASC3. We show that EJC originates as an SR-rich mega-dalton sized RNP that contains RNPS1 but lacks CASC3. After mRNP export to the cytoplasm and before translation, the EJC undergoes a remarkable compositional and structural remodeling into an SR-devoid monomeric complex that contains CASC3. Surprisingly, RNPS1 is important for nonsense-mediated mRNA decay (NMD) in general whereas CASC3 is needed for NMD of only select mRNAs. The promotion of switch to CASC3-EJC slows down NMD. Overall, the EJC compositional switch dramatically alters mRNP structure and specifies two distinct phases of EJC-dependent NMD.

molecular biology↗

bioRxiv2018

Structural, functional and molecular dynamics analysis of cathepsin B gene SNPs associated with tropical calcific pancreatitis, a rare disease of tropics.

Tropical Calcific Pancreatitis (TCP) is a neglected juvenile form of chronic non-alcoholic pancreatitis. Cathepsin B (CTSB), a lysososmal protease involved in cellular degradation process, is recently been studied as a potential candidate gene in the pathogenesis of TCP. According to cathepsin B hypothesis, mutated CTSB can lead to premature intracellular activation of trypsinogen, which is a key regulatory mechanism in pancreatitis. So far, CTSB mutations have been studied in pancreatitis and neurodegenerative disorders but little is known about the structural and functional effect of variants in CTSB. In this study, we investigated the effect of single nucleotide variants (SNVs) associated with TCP, using molecular dynamics and simulation algorithms. There were two non-synonymous variants in the coding region (L26V and S53G) of CTSB, located in the propeptide region. We tried to predict the effect of these variants on structure and function using multiple algorithms: SIFT, Polyphen2, Panther, SDM sever, i-Mutant2.0 suite, mCSM algorithm and Vadar. Further, using databases like miRdbSNP, PolymiRTS and miRNASNP, two SNPs in 3UTR region were predicted to affect the miRNA binding sites. Structural mutated models of nsSNP mutants (L26V and S53G) were prepared by MODELLER v9.15 and evaluated using TM-Align, Verify 3D, ProSA and Ramachandran plot. The results showed that the models (L26V and S53G) were of high accuracy. The 3D mutated structures were simulated using GROMACS 5.0 to predict the impact of these SNPs on protein stability. The results from in silico analysis and molecular dynamics simulations suggested that these variants in the propeptide region of cathepsin B could lead to structural and functional changes in the protein. Hence, the structural and functional analysis results have given interim conclusions that these variants can have deleterious effect in TCP and thus should be screen in samples from all TCP patients to decipher its distribution in patient population.

bioinformatics↗

bioRxiv2017

Lipid-protein interactions are unique fingerprints for membrane proteins

Cell membranes contain hundreds of different proteins and lipids in an asymmetric arrangement. Understanding the lateral organization principles of these complex mixtures is essential for life and health. However, our current understanding of the detailed organization of cell membranes remains rather elusive, owing to the lack of experimental methods suitable for studying these fluctuating nanoscale assemblies of lipids and proteins with the required spatiotemporal resolution. Here, we use molecular dynamics simulations to characterize the lipid environment of ten membrane proteins. To provide a realistic lipid environment, the proteins are embedded in a model plasma membrane, where more than 60 lipid species are represented, asymmetrically distributed between leaflets. The simulations detail how each protein modulates its local lipid environment through local lipid composition, thickness, curvature and lipid dynamics. Our results provide a molecular glimpse of the complexity of lipid-protein interactions, with potentially far reaching implications for the overall organization of the cell membrane.

biophysics↗

bioRxiv2017

Effects of mechanical loading on cortical defect repair using a novel mechanobiological model of bone healing

Mechanical loading is an important aspect of post-surgical care. The timing of load application relative to the injury event is thought to differentially regulate repair depending on the stage of healing. Here, we show using a novel mechanobiological model of cortical defect repair that daily loading (5 N peak load, 2 Hz, 60 cycles, 4 consecutive days) during hematoma consolidation and inflammation disrupts the injury site and activates cartilage formation on the periosteal surface adjacent to the defect. We also show that daily loading during the matrix deposition phase enhances both bone and cartilage formation at the defect site, while loading during the remodeling phase results in an enlarged woven bone regenerate. All loading regimens resulted in abundant cellular proliferation within the regenerate and at the periosteal surface and fibrous tissue formation directly above the defect. Stress was concentrated at the edges of the defect during exogenous loading, and finite element (FE)-modeled longitudinal strain ({varepsilon}zz) values along the anterior and posterior borders of the defect (~2200 {varepsilon}) were an order of magnitude larger than strain values on the proximal and distal borders (~50-100 {varepsilon}). These findings demonstrate that all phases of cortical defect healing are sensitive to physical stimulation. In addition, the proposed novel mechanobiological model offers several advantages including its technical simplicity and its well-characterized and spatially confined repair program, making effects of physical and biological interventions more easily assessed.

bioengineering↗