Biology subjects

Haque, M. N.

Publications and source records attributed to Haque, M. N..

4 recordsLinked to original sources

bioRxiv2025

Reduced neuronal self-avoidance in mouse starburst amacrine cells with only one Pcdhg isoform

The clustered protocadherins (cPcdhs) are a family of [~]60 homophilic cell adhesion molecules expressed across three gene clusters (Pcdha, Pcdhb, and Pcdhg) with a variety of essential roles in the developing nervous system. Some of these roles rely on specific isoforms, while others are more consistent with a model of isoform redundancy or a requirement for diversity. The {gamma}-Pcdhs (expressed from the Pcdhg gene cluster) are particularly important for neuronal self-avoidance in starburst amacrine cells in the mouse retina. Here, we used mouse mutants to test two of the C-type isoforms - {gamma}C4 and {gamma}C5 - and found that neither was required for normal self-avoidance. Conversely, when we analyzed a mutant with only {gamma}C4 intact, we found significant failures in self-avoidance that could not be completely rescued by overexpression of this isoform from a transgene. We have recently found that this isoform is essential for normal neuronal survival during development, and our new findings here support the hypothesis that {gamma}C4 is specialized for the survival function at the expense of a significant role in self-avoidance.

neuroscience↗

bioRxiv2024

The (α, β)-k Boolean Signatures of Molecular Toxicity: Microcystin as a Case Study

BackgroundThe (, {beta})-k-Feature Set Problem is a combinatorial problem, that has been proven as alternative to typical methods for reducing the dimensionality of large datasets without compromising the performance of machine learning classifiers. ResultWe present a case study that shows that solutions of the (, {beta})-k-Feature Set Problem help to identify molecular substructures related to toxicity. The dataset investigated in this study is based on the inhibition of ser/thr-proteinphosphatases by Microcystin (MC) congeners. MC congeners are a class of structurally similar cyanobacterial toxins, which are critical to human consumption. ConclusionWe show that it is possible to identify biologically meaningful toxicity signatures by applying the (, {beta})-k feature sets on extended connectivity fingerprint representations of MC congeners. Boolean rules were derived from the feature sets to classify toxicity and can be mapped on the chemical structure, leading to insights on the absence/presence of substructures that can explain toxicity. The presented method can be applied on any other molecular data set and is therefore transferrable to other use cases.

bioinformatics↗

bioRxiv2023

Hummingbirds use wing inertial effects to improve maneuverability

Hummingbirds outperform other birds in terms of aerial agility at low flight speeds. To reveal the key mechanisms that enable such unparalleled agility, we reconstructed body and wing motion of hummingbird escape maneuvers from high-speed videos; then, we performed computational fluid dynamics modeling and flight mechanics analysis, in which each wingbeat was resolved. We found that the birds utilized the inertia of their wings to achieve peak body rotational acceleration within half a wingbeat before the aerodynamic forces became dominant. The aerodynamic forces instead counteracted the reversed inertial forces in the other half wingbeat, thereby to sustain body rotation, albeit at a lower acceleration. Thus, individual wingbeat cycles that generated body rotations can be split into an agility phase with rapid inertial acceleration, and a stability phase with counteracting aerodynamic and inertial acceleration. This mechanism involving inertial steering enables hummingbirds to generate instantaneous body acceleration at any phase of a wingbeat, and it is likely the key to understanding the unique dexterity distinguishing them from aircraft that solely rely on aerodynamics for maneuvering.

animal behavior and cognition↗

bioRxiv2020

Use of relevancy and complementary information for discriminatory gene selection from high-dimensional cancer data

With the advent of high-throughput technologies, life sciences are generating a huge amount of biomolecular data. Global gene expression profiles provide a snapshot of all the genes that are transcribed or not in a cell or in a tissue at a particular moment under a particular condition. The high-dimensionality of such gene expression data (i.e., very large number of features/genes analyzed in relatively much less number of samples) makes it difficult to identify the key genes (biomarkers) that are truly and more significantly attributing to a particular phenotype or condition, such as cancer or disease, de novo. With the increase in the number of genes, simple feature selection methods show poor performance for both selecting the effective and informative features and capturing biological information. Addressing these issues, here we propose Mutual information based Gene Selection method (MGS) for selecting informative genes and two ranking methods based on frequency (MGSf) and Random Forest (MGSrf) for ranking the selected genes. We tested our methods on four real gene expression datasets derived from different studies on cancerous and normal samples. Our methods obtained better classification rate with the datasets compared to recently reported methods. Our methods could also detect the key relevant pathways with a causal relationship to the phenotype.

bioinformatics↗