Biology subjects

Daulatabad, S. V.

Publications and source records attributed to Daulatabad, S. V..

7 recordsLinked to original sources

bioRxiv2026

Integrated multi-omic analysis of pediatric metastatic osteosarcoma reveals endothelial cell plasticity and lineage infidelity.

While recent research has increasingly focused on the role of fibroblasts and macrophages in osteosarcoma, the tumor vasculature remains poorly understood, particularly in metastatic disease. To address this gap, we performed single-nuclei multi-ome (RNA+ATAC) sequencing on 24 human metastatic osteosarcoma specimens. We found that endothelial cells adopt a hybrid endothelial-mesenchymal state resembling endothelial-to-mesenchymal transition (EndMT) and that a subset of diploid endothelial cells expresses osteoblastic transcriptional profiles and gene regulatory networks (GRN). Joint copy-number analysis further identified osteosarcoma cells with endothelial transcriptional programs and GRNs, consistent with vascular mimicry. In vitro assays and syngeneic lineage-tracing experiments validated that tumor educated endothelial cells acquire osteoblast-like features. Together, these findings reveal substantial plasticity among endothelial and osteosarcoma cells in human and murine metastatic osteosarcoma, provide new insight into the how the metastatic microenvironment shapes the tumor vasculature, and challenge current models of osteosarcoma biology.

cancer biology↗

bioRxiv2022

Monoallelically-expressed Noncoding RNAs form nucleolar territories on NOR-containing chromosomes and regulate rRNA expression

Out of the several hundred copies of rRNA genes that are arranged in the nucleolar organizing regions (NOR) of the five human acrocentric chromosomes, [~]50% remain transcriptionally inactive. NOR-associated sequences and epigenetic modifications contribute to differential expression of rRNAs. However, the mechanism(s), controlling the dosage of active versus inactive rRNA genes in mammals is yet to be determined. We have discovered a family of ncRNAs, SNULs (Single NUcleolus Localized RNA), which form constrained sub-nucleolar territories on individual NORs and influences rRNA expression. Individual members of the SNULs monoallelically associate with specific NOR-containing chromosome. SNULs share sequence similarity to pre-rRNA and localize in the sub-nucleolar compartment with pre-rRNA. Finally, SNULs control rRNA expression by influencing pre-rRNA sorting to the DFC compartment and pre-rRNA processing. Our study discovered a novel class of ncRNAs that by forming constrained nucleolar territories on individual NORs contribute to rRNA expression.

cell biology↗

bioRxiv2022

Nm-Nano: Predicting 2'-O-methylation (Nm) Sites in Nanopore RNA Sequencing Data

Nm (2-O-methylation) is one of the most abundant modifications of mRNAs and non-coding RNAs. It has a great contribution in many biological processes such as the normal functioning of tRNA, the protection of mRNA against degradation by DXO protein, and the biogenesis and specificity of rRNA. Recently, the single-molecule sequencing techniques for long reads of RNA sequences data offered by Oxford Nanopore technologies have enabled the direct detection of RNA modifications on the molecule that is being sequenced. In this paper, we propose a bio-computational framework, Nm-Nano for predicting the existence of Nm sites in Nanopore direct RNA sequencing reads of human cell lines. This addresses the limitations of Nm predictors presented in the literature that were only able to detect those sites on short reads of RNA sequences data of cell lines of different species or long read sequencing data of non-human cell lines (yeast). Nm-Nano framework integrates two supervised machine learning (ML) models for predicting Nm sites in Nanopore direct RNA sequencing data, namely the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) with K-mers embedding models. XGBoost is trained with the features extracted from the modified and unmodified Nanopore signals and their corresponding K-mers resulting from the reported underlying RNA sequence obtained by base-calling, while RF model is trained with the same set of features used to train XGBoost, in addition to a dense vector representation of RNA K-mers generated by word2vec technique. Results on benchmark data sets from Hela and Hek293 cell lines demonstrate high accuracy (99% with XGBoost and 92% with RF) in identifying Nm sites. Deploying Nm-Nano on Hela and Hek293 cell lines reveals the frequently Nm-modified genes. In Hela cell lines, 125 genes are identified as frequently Nm-modified, showing enrichment in ontologies related to immune response and cellular processes. In Hek293 cell lines, 61 genes are identified as frequently Nm-modified, with enrichment in processes like glycolysis and protein localization. These findings underscore the diverse regulatory roles of Nm modifications in metabolic pathways, protein degradation, and cellular processes. The source code of Nm-Nano can be freely accessed at https://github.com/Janga-Lab/Nm-Nano.

bioinformatics↗

bioRxiv2021

Penguin: A Tool for Predicting Pseudouridine Sites in Direct RNA Nanopore Sequencing Data

Pseudouridine is one of the most abundant RNA modifications, occurring when uridines are catalyzed by Pseudouridine synthase proteins. It plays an important role in many biological processes and also has an importance in drug development. Recently, the single-molecule sequencing techniques such as the direct RNA sequencing platform offered by Oxford Nanopore technologies enable direct detection of RNA modifications on the molecule that is being sequenced, but to our knowledge this technology has not been used to identify RNA Pseudouridine sites. To this end, in this paper, we address this limitation by introducing a tool called Penguin that integrates several developed machine learning (ML) models (i.e., predictors) to identify RNA Pseudouridine sites in Nanopore direct RNA sequencing reads. Penguin extracts a set of features from the raw signal measured by the Oxford Nanopore and the corresponding basecalled k-mer. Those features are used to train the predictors included in Penguin, which in turn, is able to predict whether the signal is modified by the presence of Pseudouridine sites. We have included various predictors in Penguin including Support vector machine (SVM), Random Forest (RF), and Neural network (NN). The results on the two benchmark data sets show that Penguin is able to identify Pseudouridine sites with a high accuracy of 93.38% and 92.61% using SVM in random split testing and independent validation testing respectively. Thus, Penguin outperforms the existing Pseudouridine predictors in the literature that achieved an accuracy of 76.0 at most with an independent validation testing. A GitHub of the tool is accessible at https://github.com/Janga-Lab/Penguin. HIGHLIGHTSO_LIPenguin integrates several developed ML learning models (i.e., predictors) to identify RNA {Psi} sites in Nanopore direct RNA sequencing reads. C_LIO_LIThe pipeline of penguin automates the data preprocessing including Nanopore direct RNA reads alignment using Minimap2, and Nanopore signal extraction using Nanopolish, feature extraction from raw Nanopore signal for training ML predictors integrated in its platform, and the prediction of RNA {Psi} sites with those predictors. C_LIO_LIPenguin can predict {Psi} sites with a performance that outperforms the performance of the state-of-the-art research methods existing in the literature. C_LIO_LIPenguin platform can be adopted to be used for predicting other/various types of RNA modification. C_LIO_LIThere are 6137606 U-mers samples predicted by penguin best ML model (SVM) as {Psi} ones from a total of 67491289 U-mers samples in the complete Hek293 cell line with 556813 unique genomic location of {Psi}. C_LIO_LIThere are 1193192 U-mers samples predicted by penguin best ML model (SVM) as {Psi} ones from a total of 229637931 U-mers samples in the complete Hela cell line with 39384 unique genomic locations of {Psi}. C_LIO_LIThere is a small fraction of 0.01% (6482 unique genomic locations) of {Psi} that are common (overlapped) between both Hek293 and Hela cell lines. C_LIO_LIThe extend of {Psi} modification (the number of U-mers samples predicted as {Psi} samples to the total number of U-mer samples in the complete RNA sequence of the cell line) in RNA sequence of Hek293 cell line is much greater than its counterpart for Hela cell line (9% for Hek293 versus 0. 5 % for Hela cell line). C_LI

genomics↗

bioRxiv2020

SARS-CoV-2 contributes to altering the post-transcriptional regulatory networks across human tissues by sponging RNA binding proteins and micro-RNAs

The outbreak of a novel coronavirus SARS-CoV-2 responsible for COVID-19 pandemic has caused worldwide public health emergency. Due to the constantly evolving nature of the coronaviruses, SARS-CoV-2 mediated alteration on post-transcriptional gene regulation across human tissues remains elusive. In this study, we analyze publicly available genomic datasets to systematically dissect the crosstalk and dysregulation of human post-transcriptional regulatory networks governed by RNA binding proteins (RBPs) and micro-RNAs (miRs), due to SARS-CoV-2 infection. We uncovered that 13 out of 29 SARS-CoV-2 encoded proteins directly interact with 51 human RBPs of which majority of them were abundantly expressed in gonadal tissues and immune cells. We further performed a functional analysis of differentially expressed genes in mock-treated versus SARS-CoV-2 infected lung cells that revealed enrichment for immune response, cytokine-mediated signaling, and metabolism associated genes. This study also characterized the alternative splicing events in SARS-CoV-2 infected cells compared to control demonstrating that skipped exons and mutually exclusive exons were the most abundant events that potentially contributed to differential outcomes in response to viral infection. Motif enrichment analysis on the RNA genomic sequence of SARS-CoV-2 clearly revealed the enrichment for RBPs such as SRSFs, PCBPs, ELAVs, and HNRNPs suggesting the sponging of RBPs by SARS-CoV-2 genome. A similar analysis to study the interactions of miRs with SARS-CoV-2 revealed functionally important miRs that were highly expressed in immune cells, suggesting that these interactions may contribute to the progression of the viral infection and modulate host immune response across other human tissues. Given the need to understand the interactions of SARS-CoV-2 with key post-transcriptional regulators in the human genome, this study provides a systematic computational analysis to dissect the role of dysregulated post-transcriptional regulatory networks controlled by RBPs and miRs, across tissues types during SARS-CoV-2 infection.

bioinformatics↗

bioRxiv2019

Sequoia: An interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets

Sequoia is a visualization tool that allows biologists to explore characteristics of signals generated by the Oxford Nanopore Technologies (ONT) in detail. From Fast5 files generated by ONT, the tool displays relative similarities between signals using the dynamic time warping and the t-SNE algorithms. Raw signals can be visualized through mouse actions while particular signals of interest can also be exported as a CSV file for further analysis. Sequoia consists of two major components: the command-line back-end that performs necessary computations using Python and the front-end that displays the visualization through a web interface. Two datasets are used to conduct a case study in order to illustrate the usability of the tool.

bioinformatics↗

bioRxiv2019

Long non-coding RNA expression levels modulate cell-type specific splicing patterns by altering their interaction landscape with RNA-binding proteins

BackgroundRecent developments in our understanding of the interactions between long non-coding RNAs (lncRNAs) and cellular components have improved treatment approaches for various human diseases including cancer, vascular diseases, and neurological diseases. Although investigation of specific lncRNAs revealed their role in the metabolism of cellular RNA, our understanding of their contribution to post-transcriptional regulation is relatively limited. In this study, we explore the role of lncRNAs in modulating alternative splicing and their impact on downstream protein-RNA interaction networks.\n\nResultsAnalysis of alternative splicing events across 39 lncRNA knockdown and wildtype RNA-sequencing datasets from three human cell lines: HeLa (Cervical Cancer), K562 (Myeloid Leukemia), and U87 (Glioblastoma), resulted in high confidence (fdr < 0.01) identification of 11630 skipped exon events and 5895 retained intron events, implicating 759 genes to be impacted at post-transcriptional level due to the loss of lncRNAs. We observed that a majority of the alternatively spliced genes in a lncRNA knockdown were specific to the cell type, in tandem, the functions annotated to the genes affected by alternative splicing across each lncRNA knockdown also displayed cell type specificity. To understand the mechanism behind this cell-type specific alternative splicing patterns, we analyzed RNA binding protein (RBP)-RNA interaction profiles across the spliced regions, to observe cell type specific alternative splice event RBP binding preference.\n\nConclusionsDespite limited RBP binding data across cell lines, alternatively spliced events detected in lncRNA perturbation experiments were associated with RBPs binding in proximal intron-exon junctions, in a cell type specific manner. The cellular functions affected by alternative splicing were also affected in a cell type specific manner. Based on the RBP binding profiles in HeLa and K562 cells, we hypothesize that several lncRNAs are likely to exhibit a sponge effect in disease contexts, resulting in the functional disruption of RBPs, and their downstream functions. We propose that such lncRNA sponges can extensively rewire the post-transcriptional gene regulatory networks by altering the protein-RNA interaction landscape in a cell-type specific manner.

bioinformatics↗