Biology subjects

Patel, U.

Publications and source records attributed to Patel, U..

4 recordsLinked to original sources

bioRxiv2025

TRIP13 protects pancreatic cancer cells against intrinsic and therapy-induced DNA replication stress

Oncogene activation in normal untransformed cells induces DNA replication stress and creates a dependency on DNA Damage Response (DDR) mechanisms for cell survival. Different oncogenic stimuli signal via distinct mechanisms in every cancer setting. The DDR is also pathologically re-programmed and deployed in diverse ways in different cancers. Because mutant KRAS is the driver oncogene in 90% of Pancreatic Ductal Adenocarcinomas (PDAC), here we have investigated DDR mechanisms by which KRAS-induced DNA replication stress is tolerated in normal human pancreatic epithelial cells (HPNE). Using a candidate screening approach, we identify TRIP13 as a KRASG12V-induced mRNA that is also expressed at high levels in PDAC relative to normal tissues. Using genetic and pharmacological tools, we show that TRIP13 is necessary to sustain ongoing DNA synthesis and viability specifically in KRASG12V-expressing cells. TRIP13 promotes survival of KRASG12V-expressing HPNE cells in a Homologous Recombination (HR)-dependent manner. KRASG12V-expressing HPNE cells lacking TRIP13 acquire hallmark HR-deficiency (HRD) phenotypes including sensitivity to inhibitors of Trans-Lesion Synthesis (TLS) and Poly-ADP Ribose Polymerase (PARP). Established PDAC cell lines are also sensitized to intrinsic DNA damage and therapy-induced genotoxicity following TRIP13-depletion. Taken together our results expose TRIP13 as an attractive new and therapeutically-tractable vulnerability of KRAS-mutant PDAC.

cancer biology↗

bioRxiv2024

scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery

PurposeSingle-cell RNA sequencing (scRNA-seq) is producing vast amounts of individual cell profiling data. Analysis of such datasets presents a significant challenge in accurately annotating cell types and their associated biomarkers. scRNA-seq datasets analysis will help us understand diseases such as Alzheimers, Cancer, Diabetes, Coronavirus disease 2019 (COVID-19), Systemic Lupus Ery-thematosus (SLE), etc. Recently different pipelines based on machine learning (ML) and Deep Neural Network (DNN) methods have been employed to tackle these issues utilizing scRNA-seq datasets. These pipelines have arisen as a promising resource and are capable of extracting meaningful and concise features from noisy, diverse, and high-dimensional data to enhance annotations and subsequent analysis. Existing tools require high computational resources to execute large sample datasets. MethodsWe have developed a cutting-edge platform known as scaLR (Single Cell Analysis using Low Resource) that efficiently processes data in batches, and reduces the required resources for processing large datasets and running NN models. scaLR is equipped with data processing, feature extraction, training, evaluation, and downstream analysis. The data processing module consists of sample-wise & standard scaler normalization and splitting of data. Its novel feature extraction algorithm, first trains the model on a feature subset and stores feature importance for all the features in that subset. At the end of this process, top K features are selected based on their importance. The model is trained on top K features, its performance evaluation and associated downstream analysis provide significant biomarkers for different cell types and diseases/traits. ResultsTo showcase the capabilities of scaLR, we utilized several scRNA-seq datasets of Peripheral Blood Mononuclear Cells (PBMCs), Alzheimers patients, and large datasets from human and mouse embryonic development. Our findings indicate that scaLR offers comparable prediction accuracy and requires less model training time and compute resources than existing Python-based pipelines and frameworks. Moreover, scaLR efficiently handles large sample datasets (>11.4 million cells) with minimal resource usage (29GB RAM, 12GB GPU, and 8 CPUs) while maintaining high prediction accuracy and being capable of ranking the biomarker association with specific cell types and diseases. ConclusionWe present scaLR a Python-based platform, engineered to utilize minimal computational resources while maintaining comparable execution times to existing frameworks. It is highly scalable and capable of efficiently handling datasets containing millions of cell samples and providing their classification and important biomarkers.

bioinformatics↗

bioRxiv2023

Conformational landscape of the transcription factor ATF4 is dominated by disordered-mediated inter-domain coupling

Transient intramolecular interactions between transactivation domain and DNA binding domain of transcription factors are known to play important functional roles, including modulation of DNA binding affinity and specificity. Similar type of inter-domain interactions has recently been reported for the transcription factor ATF4/CREB-2, a key regulator of the Integral Stress Response. In the case of ATF4, transient coupling between the transactivation and basic-leucine zipper (bZip) domains regulates the degree of phosphorylation of the disordered transactivation domain achievable by the casein kinase CK2. Despite the crucial importance of these inter-domain interactions, their structural and molecular basis remain ill-determined. In the present study, we use a combination of experimental and computational techniques to determine the precise nature of the long-range contacts established between the transactivation and bZip domains of ATF4 prior to its association with protein partners and DNA. Solution NMR spectroscopy experiments reveal that the isolated bZip domain of ATF4 is predominantly disordered and display evidence of conformational dynamics over a wide range of timescales. These experimental findings are supported by multi-microsecond timescale all-atom molecular simulations that unveil the molecular basis of the long-range interactions between the transactivation and bZip domains of ATF4. We found that inter-domain coupling is primarily driven by disorder-mediated interactions between a leucine-rich region of the transactivation domain and the leucine-zipper region of the bZip domain. This study uncovers the role played by structural disorder in facilitating the formation of long-range intramolecular interactions that shape the conformational ensemble of ATF4 in a critical manner.

biophysics↗

bioRxiv2022

Podocyte lineage marker expression is preserved across Wilms tumor subtypes and enhanced in tumors harboring the SIX1/2-Q177R mutation

Wilms tumors present as an amalgam of varying proportions of three tissues normally located within the developing kidney, one being the multipotent nephron progenitor population. While incomplete differentiation of the nephron progenitors is widely-considered the underlying cause of tumor formation, where this barrier occurs along the differentiation trajectory and how this might promote therapeutic resistance in high-risk blastemal-predominant tumors is unclear. Comprehensive integrated analysis of genomic datasets from normal human fetal kidney and high-risk Wilms tumors has revealed conserved expression of genes indicative of podocyte lineage differentiation in tumors of all subtypes. Comparatively upregulated expression of several of these markers, including the non-canonical WNT ligand WNT5A, was identified in tumors with the relapse-associated mutation SIX1/2 p.Q177R. These findings highlight the shared progression of cellular differentiation towards the podocyte lineage within Wilms tumors and enhancement of this differentiation program through promotion of non-canonical WNT/planar cell polarity signaling in association with SIX1/2 p.Q177R.

cancer biology↗