Biology subjects

Jeyasekharan, A.

Publications and source records attributed to Jeyasekharan, A..

8 recordsLinked to original sources

bioRxiv2025

PatternExtract: A facile, scalable pipeline for point pattern generation from spatial imaging data

We present PatternExtract, an open-source pipeline that generates accurate spatial point patterns from RGB pathology images and cell coordinate data without relying on composite channels or proprietary software. Using a novel two-kernel tissue segmentation method combined with automated pixel classification in QuPath, PatternExtract precisely excludes tissue artifacts such as necrosis and blood vessels to define spatial windows. Optimized on 568 diffuse large B-cell lymphoma images and validated on an independent cohort, the pipeline enables robust spatial analyses.

bioinformatics↗

bioRxiv2025

A single-cell atlas of ribosomal protein heterogeneity across human tissues reveals phenotypes of biological and clinical significance

Ribosomes, once considered homogenous, exhibit dynamic compositional heterogeneity driven by differential ribosomal protein (RP) gene expression, modulating translational control. The exact cellular contributions to ribosomal heterogeneity in human tissues and their biological or clinical significance remain largely unknown. This study addresses these by mapping the expression of 76 cytoplasmic RP genes across 161 cell types from 15 human tissues using single-cell RNA sequencing from the Human Cell Atlas. We reveal extensive tissue- and cell-type-specific RP expression patterns, with RPL23, RPS20, RPS17 and RPL27A showing variability across most tissues. The testis cells exhibited the greatest variability and the largest number of variable RP genes, with distinct RP signatures distinguishing germ and somatic lineages; these signatures form temporally coordinated expression modules throughout spermatogenesis. In the context of disease, a comparative analysis of male infertility patients revealed widespread RP gene dysregulation in testicular cell types, highlighting the importance of proper RP composition for reproductive health. Furthermore, given the link between RP gene mutations and inherited anaemia syndromes, we investigated RP gene expression during erythropoiesis. We observed disrupted RP gene expression in Diamond-Blackfan Anaemia patients, contrasting with stable patterns in normal erythropoiesis. Our findings underscore the underappreciated cellular specificity and dynamic regulation of RP gene expression, strongly implicating ribosome compositional heterogeneity as fundamental to both cellular identity and disease pathogenesis.

systems biology↗

bioRxiv2021

DruID: Personalized Drug Recommendations by Integrating Multiple Biomedical Databases for Cancer

Advances in next-generation sequencing technologies have led to the development of personalized genomic profiles in diagnostic panels that inform oncologists of alterations in clinically relevant genes. While targeted therapies for some alterations may be found, an effective therapeutic strategy should consider multiple and dependent genetic interactions that affect cancer progression, a task which remains challenging. There are ongoing efforts to profile cancer cells in-vitro, both to catalog their genomic information and study their sensitivity to various drugs. There is a need for tools that can interpret the personalized genomic profile of a patient in light of information from these biological and pre-clinical studies and recommend potentially useful drugs. To address this need, we develop a new algorithmic framework called DruID, to effectively combine drug efficacy predictions from a deep neural network model with information, such as drug sensitivity, drug-drug interactions and genetic dependencies, from multiple publicly available databases. We empirically evaluate DruID on cancer cell line data on which efficacy of many drugs have been experimentally determined. We find that DruID outperforms competing approaches and promises to be a useful tool in clinical decision-making.

bioinformatics↗

bioRxiv2020

ASTER: A Method to Predict Clinically Actionable Synthetic Lethal Interactions

A Synthetic Lethal (SL) interaction is a functional relationship between two genes or functional entities where the loss of either entity is viable but the loss of both is lethal. Such pairs can be used to develop targeted anticancer therapies with fewer side effects and reduced overtreatment. However, finding clinically actionable SL interactions remains challenging. Leveraging unified gene expression data of both disease-free and cancerous samples, we design a new technique based on statistical hypothesis testing, called ASTER, to identify SL pairs. We empirically find that the patterns of mutually exclusivity ASTER finds using genomic and transcriptomic data provides a strong signal of SL. For large-scale multiple hypothesis testing, we develop an extension called ASTER++ that can utilize additional input gene features within the hypothesis testing framework. Our extensive experiments demonstrate the efficacy of ASTER in identifying SL pairs with potential therapeutic benefits. CCS CONCEPTS* Applied computing [->] Computational genomics; Health informatics; * Mathematics of computing [->] Hypothesis testing and confidence interval computation. ACM Reference FormatHerty Liany, Anand Jeyasekharan, and Vaibhav Rajan. 2021. ASTER: A Method to Predict Clinically Actionable Synthetic Lethal Genetic Interactions. In Proceedings of ACM Conference. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

bioinformatics↗

bioRxiv2020

TIP60 acetylates H2AZ and regulates doxorubicin-induced DNA damage sensitivity through RAD51 transcription

TIP60, a lysine acetyltransferase and H2AZ, a histone H2A variant are involved in transcription and DNA repair. Recent studies suggest that H2AZ acetylation is dependent on TIP60. Here, we show that TIP60 acetylates both isoforms of H2AZ in vitro and in cells. Utilizing ChIP-seq and RNA-seq to identify the genes regulated by TIP60-dependent acetylation of H2AZ, we find that TIP60-dependent acetylation of H2AZ correlates with the expression of genes involved in DNA damage repair, amongst several other pathways. In line with this, TIP60-depleted cells exhibit increased sensitivity to the DNA damage-inducing drug doxorubicin. Restoring the expression level of RAD51, one of the genes involved in the DNA damage repair pathway, partially rescues the doxorubicin sensitivity due to TIP60 depletion. Overall, our study uncovers a role for TIP60 in regulating doxorubicin-induced DNA damage sensitivity in a manner dependent on RAD51 transcription.

molecular biology↗

bioRxiv2020

Primary platinum resistance and immune exclusion in ovarian carcinomas with high expression of the homologous recombination mediator RAD51

BackgroundHomologous recombination deficiency (HRD) in ovarian cancer confers increased sensitivity to Poly-ADP-Ribose-Polymerase (PARP) inhibitors and platinum. The homologous recombination (HR) mediator RAD51, however, is commonly overexpressed, potentially driving unregulated HR. Due to non-quantitative measurements and heterogeneous cohorts, the clinical relevance of RAD51 expression in ovarian cancer is unclear. MethodsFluorescent immunohistochemistry and multispectral imaging were used to quantitate RAD51 expression, in relation to other markers, in independent cohorts of ovarian carcinomas from British Columbia Cancer (BCC n=284) and the phase III SCOTROC4 trial (n=268). Independent cohorts (TCGA n=566, GSE9891 n=267, GSE26712 n=185 and GSE3149 n=146) were used for mRNA expression and immune infiltration analyses. ResultsRAD51-High tumours had shorter progression-free and overall survival compared to RAD51-Low cases in both BCC and SCOTROC4. The negative prognostic significance of high RAD51 was primarily evident in cases classified "HRD negative" by the Myriad genomic scar assay. Unexpectedly, overexpression of RAD51 in ovarian cancer cell lines did not affect sensitivity to platinum or PARP inhibitors, but modified the expression of immune-regulatory genes. Accordingly, tumours with high RAD51 mRNA showed consistent changes in immunomodulatory transcripts across four independent ovarian cancer cohorts. In-situ multiplexed imaging confirmed that high RAD51 tumours correlated with tumour exclusion of cytotoxic-T-cells, possibly explaining their poor outcomes after chemotherapy. ConclusionsHigh RAD51 expression, in conjunction with a HRD score <42, identifies a subgroup of EOC cases with platinum resistance and an immune excluded tumour microenvironment. Tumours with high RAD51 may require alternate or additional adjuvant therapeutic strategies to overcome immune exclusion. Statement of Translational RelevanceHomologous recombination deficiency (HRD) in ovarian cancer confers increased sensitivity to Poly-ADP-Ribose-Polymerase (PARP) inhibitors and platinum. Currently, methods to identify patients who do poorly on first-line platinum based chemotherapy in ovarian cancer represent an unmet clinical need. In this report we show that RAD51 expression can identify patients with primary platinum resistance in two large cohorts, including a unique phase-III trial of carboplatin as adjuvant monotherapy. The clinical relevance of a positive genomic scar HRD score, a biomarker for assessing HRD status, in predicting benefit to PARP inhibitors has been established following recent phase 3 trials in first line and 2nd line maintenance treatment of ovarian cancer. Our findings suggest that HRD negative ovarian tumours may be further stratified by RAD51 expression status in the context of platinum sensitivity, and may serve to guide future trials of HRD targeted agents.

cancer biology↗

bioRxiv2020

Mining Pathway Associations from Networks of Mutual Exclusivity Interactions

Study of pairwise genetic interactions such as mutual exclusivity or synthetic lethality has led to the development of targeted anticancer therapies, and mining the network of such interactions is a common approach used to obtain deeper insights into the mechanism of cancer. A number of useful graph clustering-based tools exist to mine interaction networks. These tools find subgraphs or groups of genes wherein each gene belongs to a single subgraph. However, a gene may be present in multiple groups - for instance, a gene can be involved in multiple signalling pathways. We develop a new network mining algorithm, that does not impose this constraint and can provide a novel pathway-centric view. Our approach is based on finding edge-disjoint bipartite subgraphs of highest weights in an input network of genes, where edge weights indicate the significance of the interaction and each set of nodes in every bipartite subgraph is constrained to belong to a single pathway. This problem is NP-hard and we develop an Integer Linear Program to solve this problem. We evaluate our algorithm on breast and stomach cancer data. Our algorithm mines dense between-pathway interactions that are known to play important roles in cancer and are therapeutically actionable. Our algorithm complements existing network mining tools and can be useful to study the mutational landscape of cancer and inform therapy development.

bioinformatics↗

bioRxiv2019

Predicting Synthetic Lethal Interactions usingHeterogeneous Data Sources

MotivationA synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources.\n\nResultsIn this paper we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering.\n\nAvailabilitySoftware available at https://github.com/lianyh\n\nContactvaibhav.rajan@nus.edu.sg

bioinformatics↗