Biology subjects

Parthasarathi, K. T. S.

Publications and source records attributed to Parthasarathi, K. T. S..

2 recordsLinked to original sources

bioRxiv2026

An integrated database of ion channels

Ion channels play critical roles in a wide range of physiological processes, including the propagation of nerve impulses, insulin secretion and regulation of heart rate. Alterations in ion-channels function or expression can contribute to the development of various diseases, including cancers, genetic disorders, and infectious diseases. Ion channels represent the second-largest class of membrane proteins considered potential targets for therapeutic intervention. However, a comprehensive data resource integrating information on human ion channels and their associations with diseases has been limited. Therefore, the Integrated Database of Ion Channels (IDIC) was developed, comprising information on 493 human ion channels. The database contains a broad range of ion-channel annotations, including gene and protein information, structural details, protein signature sequences, protein domains, nuclear localization signals, and signal peptides. It also provides cross-references to several established biological resources, including the Human Genome Organization Gene Nomenclature Committee (HGNC), National Center for Biotechnology Information (NCBI) Gene, Vertebrate Genome Annotation (VEGA), Ensembl, and the Protein Data Bank (PDB). Furthermore, IDIC incorporates genomic variations identified in various tumors and disease associations involving ion channels, which have been systematically curated from published literature. Through concise search methods and user-friendly browsing functionalities, IDIC serves as an integrated resource for exploring human ion channels and investigating their structural, functional, genetic, and disease-related characteristics. Thus, the database can facilitate a better understanding of ion channels and their potential involvement in diverse human diseases.

bioinformatics↗

bioRxiv2025

ONCOchannelome: A computational framework to investigate altered ion channels across tumor types

Conventional approaches for analyzing ion channels in cancer primarily focus on the detection of membrane potential to record the polarization states of the cellular membrane. Although these approaches provide meaningful insights during validation, they face challenges in discovery, especially in identifying the ion channels that govern the membrane potential. Moreover, ion channels are known to exhibit context-dependent activity that often depends on the cancer type and metabolic state of the tumor. Here, we developed a computational framework, ONCOchannelome, that allows the identification of potential ion channels via transcriptomic datasets. We collected publicly available RNA-Seq datasets corresponding to 2421 normal, 5442 primary tumor and 588 metastatic samples of 15 tumor types. Using the data, we designed a computational strategy to merge the datasets, identifying differentially expressed ion channels in the tumor and metastatic states and aiding in the determination of ion channels in different tumor states. We subsequently developed strategies to determine the correlation of altered ion channels with epithelial-to-mesenchymal transition and to identify differentially coexpressed ion channel networks along with their possible transcription factors. The ONCOchannelome allows visualization of altered ion channels in the tumor and metastatic states in addition to revealing changes resulting in changes in biological processes and molecular functions as the tumor progresses. The ONCOchannelome enhances our understanding of the dependence of ion channels in different tumors in addition to revealing their observed alterations in progressing from the primary tumor state to the metastatic state.

bioinformatics↗