Biology subjects

Parekh, A.

Publications and source records attributed to Parekh, A..

5 recordsLinked to original sources

bioRxiv2025

Deep Learning-coupled Proximity Proteomics to Deconvolve Kinase Signaling In Vivo

Deconvolving the substrates of hundreds of kinases linked to phosphorylation networks driving cellular behavior is a fundamental, unresolved biological challenge, largely due to the poorly understood interplay of kinase selectivity and substrate proximity. We introduce KolossuS, a deep learning framework leveraging protein language models to decode kinase-substrate specificity. KolossuS achieves superior prediction accuracy and sensitivity across mammalian kinomes, enabling proteome-wide predictions and evolutionary insights. By integrating KolossuS with CRISPR-based proximity proteomics in vivo, we capture kinase-substrate recognition and spatial context, obviating prior limitations. We show this combined framework identifies kinase substrates associated with physiological states such as sleep, revealing both known and novel Sik3 substrates during sleep deprivation. This novel integrated computational-experimental approach promises to transform systematic investigations of kinase signaling in health and disease.

cell biology↗

bioRxiv2024

A role for YAP-mediated regulation of invadopodia in HNSCC cells

The objective of this study was to determine whether nuclear translocation of the transcriptional coactivator Yes-associated protein (YAP) was sensitive to extracellular matrix (ECM) rigidity and promoted Rho-associated kinase 2 (ROCK2) expression to affect invadopodia maturation and ECM degradation. ECM rigidity mimicking head and neck squamous cell carcinoma (HNSCC) tumor mechanical properties was simulated in vitro using a well-established model based on fibronectin-conjugated polyacrylamide gels (PAAs). The ratio of nuclear to cytoplasmic YAP and overall cellular ROCK2 levels were evaluated in HNSCC cells using quantitative immunofluorescence. YAP-mediated ROCK2 expression in HNSCC cells was determined using nested PCR and Western blot in response to the YAP inhibitor verteporfin. Invadopodia and ECM degradation were evaluated in HNSCC cells with siRNA-mediated inhibition of YAP using quantitative immunofluorescence in invadopodia assays. Both YAP nuclear translocation and ROCK2 cellular levels increased with ECM rigidity. Inhibition of YAP activity with verteporfin decreased ROCK2 gene and protein expression. Knockdown of YAP with siRNA inhibited the formation of mature invadopodia and ECM degradation but not total invadopodia (i.e., mature and immature or not degrading). Our study suggests that tumor-associated ECM rigidity can promote mechanically-induced transcriptional regulation to control proteolytic activity by affecting invadopodia maturation.

cancer biology↗

bioRxiv2023

Navigating the manifold of single-cell gene coexpression to discover interpretable gene programs

The utility of single-cell RNA sequencing (scRNA-seq) is premised on the notion that transcriptional state can faithfully reflect cell phenotype. However, scRNA-seq measurements are noisy and sparse, with individual transcript counts showing limited correlation with cell phenotype markers such as protein expression. To better characterize cell states from scRNA-seq data, researchers analyze gene programs---sets of covarying genes---rather than individual transcripts. We hypothesized that more accurate estimation of gene covariation, especially at a local (i.e., cell-state) rather than global (i.e., experimental) scale, could better capture cell phenotypes. However, the field lacks appropriate mathematical frameworks for analyzing gene covariation: coexpression is quantified as a symmetric positive-definite matrix, where even basic operations like arithmetic differences lack biological interpretability. Here we introduce Sceodesic, which exploits the Riemannian manifold structure of gene coexpression matrices to quantify cell state-specific coexpression patterns using the log-Euclidean metric from differential geometry. Unlike principal components analysis and non-negative matrix factorization, which infer only global covariation, Sceodesic efficiently discovers local covariation patterns and organizes them into interpretable, linear gene programs. Sceodesic outperforms existing approaches in predicting protein expression levels, distinguishing transcriptional responses to gene perturbations, and identifying biologically meaningful programs in fetal development. By respecting the mathematical structure of gene coexpression, Sceodesic bridges the gap between biological variability and statistical analysis of scRNA-seq data, enabling more accurate characterization of cell phenotypes. Software availabilityhttps://singhlab.net/Sceodesic

bioinformatics↗

bioRxiv2020

Heterogeneous distribution of mRNAs within flight muscle fibers, and implications for function

Muscle heterogeneity has been explored in terms of fiber-type distribution, structural organisation, and differences at their junctions with neurons and tendons. We amplify on such observation to additionally suggest that muscle syncytia have nonuniform protein requirements along their length, deployed for developmental and functional uses. An exploration of regionalized proteins or their mRNA across muscle syncytia has not been done. We investigated mRNA localization in regions of Drosophila melanogaster dorsal longitudinal muscle (DLM) syncytia over their entire transcriptome. Dissection of muscle regions, their RNA-seq and stringent Differential Gene Expression analysis indeed reveals statistically significant regionalization of nearly a hundred mRNA over the length of DLMs. Functions of over half of these genes require experimental verification. A preponderance of mRNA coding for catabolic and proteolytic enzymes is conspicuous among transcripts enriched in the posterior of DLMs. Our findings provide a foundation for exploring molecular processes that contribute to syncytial maturation and muscle homeostasis in a spatially non-homogenous manner.

developmental biology↗

bioRxiv2017

Multichannel Sleep Spindle Detection Using Sparse Low-Rank Optimization

BackgroundWe propose a multichannel spindle detection method that detects global and local spindle activity across all channels of scalp EEG in a single run\n\nNew MethodUsing a non-linear signal model, which assumes the multichannel EEG to be a sum of a transient component and an oscillatory component, we propose a multichannel transient separation algorithm. Consecutive overlapping blocks of the multichannel oscillatory component are assumed to be low-rank whereas the transient component is assumed to be piecewise constant with a zero baseline. The estimated multichannel oscillatory component is used in conjunction with a bandpass filter and the Teager operator for detecting sleep spindles\n\nResults and comparison with other methodsSeveral examples are shown to illustrate the utility of the proposed method in detecting global and local spindle activity. The proposed method is applied to two publicly available databases and compared with 7 existing single-channel automated detectors. F1 scores for the proposed spindle detection method averaged 0.66 (0.02) and 0.62 (0.06) for the two databases, respectively. For an overnight 6 channel EEG signal, the proposed algorithm takes about 4 minutes to detect sleep spindles simultaneously across all channels with a single setting of corresponding algorithmic parameters\n\nConclusionsThe proposed method aims to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindle in EEG channels other than the central channel visible on a digital screen

neuroscience↗