Biology subjects

Arunachalam, S.

Publications and source records attributed to Arunachalam, S..

4 recordsLinked to original sources

bioRxiv2026

LinkDTI: Drug-Target Interactionsprediction through a Link Predictionframework on Biomedical KnowledgeGraph

Computational drug-target interactions (DTI) prediction serves as a valuable tool for drug discovery and repurposing by cost-effectively narrowing down the potential drug-target space. This paper presents LinkDTI, a computational framework that predicts DTIs by identifying connections within a heterogeneous knowledge graph of drugs, proteins, diseases, and side effects. Unlike methods that rely on mathematical techniques like matrix completion or similarity-based scoring, LinkDTI uses an advanced graph-based approach to capture relationships between biomedical entities. Specifically, LinkDTI applies a modified version of the multilayer GraphSAGE model that learns from the heterogeneous knowledge graph and predicts potential drug-target interactions. Our model incorporates negative sampling that balances the data to address the issue of having more negative than positive interactions. Our results show that LinkDTI consistently performs better in AUROC and AUPRC than baseline methods by at least 2.5% across different sampling ratios and conditions. Subsequently, it identifies approximately 945 new potential DTIs, marking a 49.14% increase over known DTIs. Overall, LinkDTI offers a simple yet effective method for integrating diverse biomedical data to identify potential drug-target interactions. The code and data can be found at https://github.com/hub2nature/LinkDTI_heterogenous_KG.git.

bioinformatics↗

bioRxiv2024

What's in Cow Dung Manure? Insights into Its Potential for Supporting Plant Defense System

Solanum lycopersicum, a prominent vegetable crop, suffers major yield losses from early blight disease driven by Alternaria solani. Sustainable alternatives to conventional chemical fungicides are urgently needed to mitigate these losses while minimizing environmental impact. This study investigates the application of the microbial consortium derived from Gir cow dung for enhancing tomato resistance against early blight. Metagenomic analysis of Gir cow dung revealed a rich diversity of beneficial microbes, with Bacillus species being one of the prominent. Three key strains Bacillus subtilis, Bacillus tequilensis, and Bacillus licheniformis were isolated, characterized, and tested for their antagonistic activity against A. solani. The microbial consortium exhibited significant antifungal properties, and the application of the consortium on tomato plants led to a marked reduction in disease progression. Histochemical analyses of treated plants revealed a pronounced increase of hydrogen peroxide, phenolic compounds, and superoxide dismutase, suggesting enhanced activation of plant defense pathways. Comparative gene expression profiling showed the upregulation of key defense-related genes, including PR2b, Chi3, and Pto kinase, in Bo-treated plants, indicating a systemic activation of host defense mechanisms. Untargeted metabolomic analysis using LC-MS/MS further demonstrated significant metabolic reprogramming in microbial consortium-treated plants. Pathway enrichment analysis revealed enhanced accumulation of secondary metabolites, including phenolics and terpenoids, which are known to play vital roles in pathogen defense. Additionally, hierarchical clustering of metabolite profiles highlighted distinct metabolic signatures between Bo-treated and control plants, underscoring the broad biochemical impact of the microbial treatment. Collectively, our findings demonstrate that the microbial consortium derived from Gir cow dung significantly enhances tomato plant resistance to A. solani through multifaceted biochemical, molecular, and metabolic mechanisms. This study provides a sustainable biocontrol strategy that could effectively replace chemical fungicides, contributing to resilient tomato production and advancing the field of agroecological plant disease management.

plant biology↗

bioRxiv2019

Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis

Patient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.

cancer biology↗

bioRxiv2017

Identification of Novel Breast Tumor Expression Dimensions within the PAM50 and Discovery of a Breast Cancer Susceptibility Locus at 12q15

It is well-known that breast tumors exhibit different expression patterns that can be used to assign intrinsic subtypes - the PAM50 assay, for example, categorizes tumors into: Luminal A, Luminal B, HER2-enriched and Basal-like - yet tumors are often more complex than categorization can describe. We used 911 sporadic breast tumors to reparameterize expression from the PAM50 genes to five orthogonal tumor dimensions using principal components (PC). Three dimensions captured intrinsic subtype, two dimensions were novel, and all replicated in 945 TCGA tumors. By definition dimensions are independent, an important attribute for inclusion in downstream studies exploring effects of tumor diversity. One application where tumor subtyping has failed to provide impact is susceptibility genetics. Germline genetic heterogeneity reduces power for gene-finding. The identification of heritable tumor characteristics has potential to increase homogeneity. We compared 238 breast tumors from high-risk pedigrees not attributable to BRCA1 or BRCA2 to 911 sporadic breast tumors. Two PC dimensions were significantly enriched in the pedigrees (intrinsic subtypes were not). We performed proof-of-concept gene-mapping in one enriched pedigree and identified a 0.5 Mb genomewide significant region at 12q15 that segregated to the 8 breast cancer cases with the most extreme PC tumors through 32 meioses (p=2.6x10-8). In conclusion, our study: suggests a new approach to describe tumor diversity; supports the hypothesis that tumor characteristics are heritable providing new avenues for germline studies; and proposes a new breast cancer locus. Reparameterization of expression patterns may similarly inform other studies attempting to model the effects of tumor heterogeneity.

genetics↗