Biology subjects

Chivukula, N.

Publications and source records attributed to Chivukula, N..

15 recordsLinked to original sources

bioRxiv2026

An integrated resource for systems-level analysis of aging hallmarks and associated genes

Aging is a complex biological process involving progressive cellular dysfunction, tissue decline, and increased susceptibility to multiple chronic diseases. A systemic view of aging through its established hallmarks provides a structured framework to understand this complexity and drive therapeutic discovery. To this end, we present AgingHallmarksDB, an interactive web platform that enables systems-level analysis of hallmark-associated gene sets. Aging-related genes were first curated from seven established resources, and those present in at least two of these resources were considered as consensus aging-related genes. Using functional annotations derived from GO, KEGG, and Reactome, a total of 3111 genes were mapped to the 11 aging hallmarks, of which 2593 were supported by additional experimental or manually curated evidence, with 1089 of these forming the consensus set. Further, AgingHallmarksDB supplements hallmarks and gene annotations with tissue or cell-type class specificity, exosomal profiles, and regulatory interactions, enabling users to perform hallmark enrichment, protein-protein and regulatory interaction-based analysis. To elucidate the interconnectedness of hallmarks, network separation and proximity analyses of hallmark-associated modules were performed, revealing functional overlap between hallmarks on the human interactome. Furthermore, the utility of AgingHallmarksDB was demonstrated through network topological and regulatory interaction analyses of common hallmark genes, hallmark enrichment and network proximity analyses of eight chronic age-related diseases and seven developmental and congenital diseases, and gene set enrichment analysis of a PM2.5-associated skin transcriptome. Together, these analyses highlight the utility of AgingHallmarksDB for aging hallmark-centred systems biology research. The resource is accessible at https://cb.imsc.res.in/aginghallmarksdb/.

bioinformatics↗

bioRxiv2026

Benchmarking Artificial Intelligence Models for Predicting Nuclear Receptor Activity from Tox21 Assays

Tox21 assays compile extensive chemical bioactivity data across diverse biological targets, making them widely utilized resources for in silico model development. Nuclear receptor-specific assays within this dataset are particularly valuable for screening potential endocrine disrupting chemicals. This study presents a comprehensive benchmarking of diverse machine learning (ML), deep learning (DL), and transformer-based architectures with varied chemical feature representations across nuclear receptor assays. First, 43 datasets associated with 18 nuclear receptors within Tox21 assays were systematically curated from ToxCast invitrodb v4.3. Upon testing across these datasets, model performance was found to be dependent on the degree of class imbalance. Tree-based ML models such as random forest (RF) and extreme gradient boosting (XGBoost) trained on descriptors, or combination of descriptors and fingerprints, consistently outperformed in datasets with higher proportions of active chemicals (>10%), while DL models showed greater robustness for those with moderate proportions (5-10%). Further analysis revealed that approximately 40% of misclassified active chemicals occupied structurally isolated regions of the chemical space, suggesting absence of close structural analogues in the training set potentially contributed to their misclassification. External validation using in vitro and in vivo androgen and estrogen receptor bioactivity data showed generally good concordance. Finally, a systematic literature review revealed that the models in this study span wider range of architectures, feature representations, and assay endpoints, and are broadly comparable to or better than existing work. Overall, insights from this study can inform the development of more reliable in silico tools supporting new approach methodologies for nuclear receptor bioactivity predictions.

pharmacology and toxicology↗

bioRxiv2026

A comprehensive resource on chemicals used in aquaculture and their ecotoxicity

Sustainable aquaculture requires comprehensive chemical oversight, as compounds used in aquaculture can persist in ecosystems, bioaccumulate through food chains, and affect aquatic life and human health. This study presents ReCAnt (Resource on Chemicals used in Aquaculture and their Ecotoxicity), which compiles information on 690 aquaculture chemicals, with data on toxic effects and therapeutic potential curated from published literature. It was observed that only a fraction of the 690 chemicals are currently regulated, revealing gaps in existing regulations. Integration of data from the Comparative Toxicogenomics Database revealed associations with genes, phenotypes, and diseases, while ECOTOX data provided toxicity and bioconcentration information. Predicted biotransformation pathways and partition coefficients indicated microbial degradation potential and fate across environmental media. Further, food web network analysis identified species vulnerable to trophic transfer and common entry points for chemicals into aquatic ecosystems. This resource can aid in developing evidence-based regulatory frameworks and promoting sustainable chemical management.

pharmacology and toxicology↗

bioRxiv2026

DEDuCT 3.0: An enhanced and expanded FAIR-compliant resource and toxicology knowledge graph for endocrine disrupting chemicals

Endocrine disrupting chemicals (EDCs) are of particular regulatory and research interest due to the increasing incidence of endocrine-related disorders, such as declining fertility rates and reproductive health problems. The Database of Endocrine Disrupting Chemicals and their Toxicity Profiles (DEDuCT) has gained importance in both academic and regulatory settings by systematically curating data from published literature to characterize these chemicals. Given the growing body of EDC literature, this study aimed to consolidate the latest research and update this critical database. First, more than 14000 research articles were screened through an extensive four-stage manual process, and integrated with the earlier version to create the updated DEDuCTv3.0, comprising 1043 unique EDCs and 796 unique endocrine-related endpoints curated from 3269 published articles. Thereafter, human- and rodent-specific biological endpoint data including interacting genes/proteins, phenotypes, diseases, and adverse outcome pathways (AOPs) were curated from toxicology-relevant databases and systematically integrated with DEDuCTv3.0 to construct a large-scale toxicology knowledge graph for EDCs, termed DEDuCT-KG. DEDuCT-KG was then hosted on a Neo4j database and made easily accessible through a novel interactive user interface. The utility of DEDuCT-KG was demonstrated by exploring potential mechanisms of action associated with obesogenic EDCs within DEDuCTv3.0. Furthermore, the constructed EDC-AOP network, linking 949 EDCs to 381 AOPs within AOP-Wiki, revealed diverse toxicity mechanisms associated with EDCs. Integration with consumer product database and regulatory chemical lists showed that some of these EDCs are present in food contact materials, personal care products, and daily use items, highlighting potential exposure pathways. Overall, all data compiled in this study have been integrated into the DEDuCT webserver, which has been further enhanced to align with FAIR principles. In sum, this study provides a much-needed update to DEDuCT and offers a single point of access to EDC-relevant data to accelerate research and regulation of EDCs.

pharmacology and toxicology↗

bioRxiv2025

Integrative network-based construction of ecologically relevant computational adverse outcome pathways for organic mercury-induced toxicity

Adverse outcome pathways (AOPs) describe mechanisms of toxicity by connecting molecular events with outcomes at higher levels of biological organization. Computational AOPs (cAOPs), constructed using existing toxicological data, can accelerate the early stages of AOP development, which is often a time-consuming and resource-intensive process. In this study, an integrative network-based framework was developed to construct cAOPs, with a particular focus on elucidating the toxicity of organic mercury in fish. First, 124 organic mercury compounds, corresponding fish-specific toxicity endpoints, and proteins were curated from CTD and ECOTOX. Next, molecular docking investigations were performed to determine novel molecular interactions with 16 zebrafish protein orthologs. Subsequently, the toxicity endpoints, including the identified molecular interactions, were standardized and harmonized using data from AOP-Wiki, Gene Ontology, and MeSH. These endpoints were integrated with event relationship information from AOP-Wiki and published literature to construct an organic mercury-associated toxicity network comprising 197 nodes and 243 edges. Thereafter, node- and edge-level filtration criteria were designed based on AOP definitions and utilized to identify biologically relevant pathways within the toxicity network. Further, these pathways were ranked based on their novelty with respect to existing AOPs within AOP-Wiki. Finally, based on extensive evidence from published literature, four top-ranked novel pathways that describe binding with glutathione peroxidase or alterations in metallothionein levels leading to neurologic manifestations or dysbiosis in fish were proposed as cAOPs. Overall, this study presents an integrative network-based framework for constructing cAOPs applicable to diverse contaminants and species, supporting New Approach Methodologies for toxicological risk assessment.

pharmacology and toxicology↗

bioRxiv2025

TICToK: A comprehensive knowledgebase of tattoo ink chemicals and investigation of their associated toxicities and regulations

Tattoos and permanent make-up, involving dermal injection of chemicals, are increasingly popular, with 30-40% of young adults in North America and Europe having at least one tattoo. Recent studies have suggested a possible association between tattooing and health risks, including skin cancers and lymphomas, although no definitive link has yet been established. This study comprehensively catalogs tattoo ink chemicals and investigates their potential adverse effects, addressing the urgent need for greater understanding of tattoo-related health concerns. First, 364 unique tattoo chemicals were identified from various scientific and regulatory sources, with nearly half functioning as pigments. Hazardous chemicals were identified, revealing carcinogens, endocrine disruptors, neurotoxicants, and dermal toxicants. A regulatory analysis based on key EU regulations, including harmonised classifications under Classification, Labelling and Packaging (CLP) regulation, Restriction Entry 75 of the REACH regulation, Cosmetic Products Regulation (CPR), and SVHC candidate list, revealed existing regulatory coverage of tattoo ink chemicals. Curated chemical-disease associations highlighted that some tattoo chemicals are known to cause dermatitis. Further, diverse toxicological information, including experimental results from REACH dossiers, were integrated to construct stressor-AOP network linking 151 chemicals to 362 AOPs, revealing potential carcinogenic mechanisms associated with tattoo ink chemicals. A systems biology approach revealed potential immunomodulatory effects associated with these chemicals. Finally, all findings have been made available through the online database Tattoo Ink Chemicals and associated Toxicities Knowledgebase (TICToK; https://cb.imsc.res.in/tictok), which can support risk assessment and sustainable tattoo practices. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/668261v2_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@6ce8e3org.highwire.dtl.DTLVardef@2dc5aborg.highwire.dtl.DTLVardef@10c67e6org.highwire.dtl.DTLVardef@1795281_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2024

Network toxicology focused investigation on the impacts of inorganic arsenic and cadmium on human and ecosystem health

Heavy metals like arsenic and cadmium are persistent environmental pollutants that pose serious health risks to humans and ecosystems due to their toxicity, bioaccumulation potential, and frequent presence in consumer products. Network toxicology offers a holistic in silico framework to elucidate the complex biological mechanisms of toxicity, thereby supporting New Approach Methodologies (NAMs) for toxicity assessment. In this study, network toxicological tools were utilized to investigate arsenic- and cadmium-induced toxicities. Toxicity endpoints associated with inorganic arsenic and cadmium compounds were curated from six exposome-relevant databases and mapped to key events (KEs) across adverse outcome pathways (AOPs) cataloged in AOP-Wiki. This led to construction of stressor-AOP networks, revealing 51 AOPs associated with arsenic and 78 with cadmium, and facilitated mechanistic case studies of pathways relevant to human and ecological health. Toxicity concentrations and bioconcentration factors from the ECOTOX database were then used to construct stressor-species networks, that helped identify species that are vulnerable and potentially bioaccumalate these chemicals. Further, the construction of species sensitivity distributions (SSDs) and toxicity-normalized SSDs (SSDn), provided a comparative framework for prioritizing these compounds in risk assessments. Further, integrating SSD data with stressor-species networks identified species groups particularly sensitive to arsenic and cadmium exposure, enhancing these networks utility for ecological risk assessment. The networks and related data generated in this study are freely available for further research at https://cb.imsc.res.in/heavymetaltox/. Overall, this study offers a comprehensive perspective on the toxicological impact of inorganic arsenic and cadmium compounds, supporting a One Health approach to their regulatory and mitigation strategies.

pharmacology and toxicology↗

bioRxiv2024

Network-based investigation of petroleum hydrocarbons-induced ecotoxicological effects and their risk assessment

Petroleum hydrocarbons (PHs) are compounds composed mostly of carbon and hydrogen, originating from crude oil and its derivatives. PHs are primarily released into the environment through the diffusion of oils, resulting from anthropogenic activities like transportation and offshore drilling, and accidental incidents such as oil spills. Once released, these PHs can persist in different ecosystems and cause long-term detrimental ecological impacts. While the hazards associated with such PH contaminations are often assessed by the concentrations of total petroleum hydrocarbons in the environment, studies focusing on the risks associated with individual PHs are limited. Here, we leveraged different network-based frameworks to explore and understand the adverse ecological effects associated with PH exposure. First, we systematically curated a list of 320 PHs from published reports. Next, we integrated biological endpoint data from toxicological databases, and constructed a stressor-centric adverse outcome pathway (AOP) network linking 75 PHs with 177 ecotoxicologically-relevant high confidence AOPs within AOP-Wiki. Further, we relied on stressor-species network constructions, based on reported toxicity concentrations and bioconcentration factors data for 80 PHs and 28 PHs, respectively, and found that crustaceans are documented to be affected by many of these PHs. Finally, we utilized the aquatic toxicity data within ECOTOX to construct species sensitivity distributions for polycyclic aromatic hydrocarbons (PAHs) prioritized by the US EPA, and derived their corresponding hazard concentrations (HC05) that protect 95% of species in the aquatic ecosystem. Overall, this study highlights the importance of using network-based approaches and risk assessment methods to understand the PH-induced toxicities effectively.

pharmacology and toxicology↗

bioRxiv2024

Leveraging integrative toxicogenomic approach towards development of stressor-centric adverse outcome pathway networks for plastic additives

Plastics are widespread pollutants found in atmospheric, terrestrial and aquatic ecosystems due to their extensive usage and environmental persistence. Plastic additives, that are utilized to achieve specific functionality in plastics, leach into the environment upon plastic degradation and pose considerable risk to ecological and human health. Limited knowledge concerning the presence of plastic additives throughout the plastic life cycle has hindered their effective regulation, thereby posing risks to product safety. In this study, we leveraged the adverse outcome pathway (AOP) framework to understand the mechanisms underlying plastic additives-induced toxicities. We first identified an exhaustive list of 6470 plastic additives from chemicals documented to be found in plastics. Next, we leveraged heterogenous toxicogenomics and biological endpoints data from five exposome-relevant resources, and identified associations between 1287 plastic additives and 322 complete and high quality AOPs within AOP-Wiki. Based on these plastic additive-AOP associations, we constructed a stressor-centric AOP network, wherein the stressors are categorized into 10 priority use sectors and AOPs are linked to 27 disease categories. We visualized the plastic additives-AOP network for each of the 1287 plastic additives and made them available in a dedicated website: https://cb.imsc.res.in/saopadditives/. Finally, we showed the utility of the constructed plastic additives-AOP network by identifying 28 highly relevant AOPs associated with benzo[a]pyrene, and thereafter, explored the associated toxicity pathways leading to respiratory and gastrointestinal system diseases in humans and developmental disorders in aquatic species. Overall, the constructed plastic additives-AOP network will enable regulatory risk assessment of plastic additives, thereby contributing towards a toxic-free circular economy for plastics.

pharmacology and toxicology↗

bioRxiv2023

An integrative data-centric approach to derivation and characterization of an adverse outcome pathway network for cadmium-induced toxicity

Cadmium is a prominent toxic heavy metal that contaminates both terrestrial and aquatic environments. Owing to its high biological half-life and low excretion rates, cadmium causes a variety of adverse biological outcomes. Adverse outcome pathway (AOP) networks were envisioned to systematically capture toxicological information to enable risk assessment and chemical regulation. Here, we leveraged AOP-Wiki and integrated heterogeneous data from four other exposome-relevant resources to build the first AOP network relevant for inorganic cadmium-induced toxicity. From AOP-Wiki, we filtered 309 high confidence AOPs, identified 312 key events (KEs) associated with inorganic cadmium, and thereafter, curated 30 cadmium relevant AOPs (cadmium-AOPs), using a data-centric approach. By constructing the undirected AOP network, we identified a large connected component of 18 cadmium-AOPs. Further, we analyzed the directed network of 59 KEs and 82 key event relationships (KERs) in the largest component using graph-theoretic approaches. Subsequently, we mined published literature using artificial intelligence-based tools to provide auxiliary evidence of cadmium association for all KEs in the largest component. Finally, we performed case studies to verify the rationality of cadmium-induced toxicity in humans and aquatic species. Overall, cadmium-AOP network constructed in this study will aid ongoing research in systems toxicology and chemical exposome.

pharmacology and toxicology↗

bioRxiv2023

ViCEKb: Vitiligo-linked Chemical Exposome Knowledgebase

Vitiligo is a complex disease wherein the environmental factors, in conjuction with the underlying genetic predispositions, trigger the autoimmune destruction of melanocytes, ultimately leading to depigmented patches on the skin. Apart from being susceptible to other autoimmune disorders, the affected patients may face social stigmatization leading to a decreased quality in life. While genetic factors have been extensively studied, the knowledge on environmental triggers remains sparse and less understood. Therefore, a comprehensive understanding of the environemental triggers will not only explain the complex etiology of the disease, it will also enable the prioritization of potential toxic chemicals in human chemcical exposome. Towards this, we present the first comprehensive knowledgbase of vitiligo triggering chemicals compiled from published literature namely, Vitiligo-linked Chemical Exposome Knowledgbase (ViCEKb). ViCEKb involved an extensive and systematic manual effort in curation of published literature and subsequent compilation of 113 unique chemical triggers of vitiligo. ViCEKb standardises various chemical information, and categorizes the chemicals based on their evidences and sources of exposure. Importantly, ViCEKb contains a wide range of metrics necessary for different toxicological evaluations. Moreover, our extensive cheminformatics-based analysis of the ViCEKb chemical space highlighted its diversity and uniqueness in comparison to other skin specific chemical regulatory lists. We also observed that ViCEKb chemicals are present in various consumer products and are not regulated. Additionally, a transcriptomics based analysis of ViCEKb chemical perturbations in skin cell samples highlighted the commonality in their linked biological processes. Overall, we present the first comprehensive effort in compilation and exploration of various chemical triggers of vitiligo. We believe such a resource will enable in deciphering the complex etiology of vitiligo and aid in the characterization of human chemical exposome. ViCEKb is freely available for academic research at: https://cb.imsc.res.in/vicekb.

bioinformatics↗

bioRxiv2023

Cheminformatics analysis of multi-target structure-activity landscape of environmental chemicals binding to human endocrine receptors

In human exposome, environmental chemicals can target, and disrupt different endocrine axes, ultimately leading to several endocrine disorders. Such chemicals, termed endocrine disrupting chemicals (EDCs), can promiscuously bind to different endocrine receptors and lead to varying biological endpoints. Thus, understanding the complexity in molecule-receptor binding of environmental chemicals can aid in the development of robust toxicity predictors. Towards this, the ToxCast project has generated the largest resource on the chemical-receptor activity data for environmental chemicals that were screened across various endocrine receptors. However, the heterogeneity in the multi-target structure-activity landscape of such chemicals is not yet explored. In this study, we systematically curated the chemicals targeting 8 human endocrine receptors, their activity values and biological endpoints from the ToxCast chemical library. We employed dual-activity difference and triple-activity difference maps to identify single-, dual-, and triple-target cliffs across different target combinations. We annotated the identified activity cliffs through matched molecular pair (MMP) based approach, and observed that a small fraction of activity cliffs form MMPs. Further, we structurally classified the activity cliffs and observed that R-group cliffs form the highest fraction among the cliffs identified in various target combinations. Finally, we leveraged the mechanism of action (MOA) annotations to analyze structure-mechanism relationships, identified strong MOA-cliffs and weak MOA-cliffs, for each of the 8 endocrine receptors. Overall, insights from this first study analyzing the structure-activity landscape of environmental chemicals targeting multiple human endocrine receptors, will likely contribute towards the development of better toxicity prediction models for characterizing the human chemical exposome.

pharmacology and toxicology↗

bioRxiv2023

Analysis of structure-activity and structure-mechanism relationships among thyroid stimulating hormone receptor binding chemicals by leveraging ToxCast library

Thyroid stimulating hormone receptor (TSHR) is an integral part of the hypothalamic-pituitary-thyroid axis. Notably, dysregulation in TSHR activation in humans can lead to adverse effects such as Graves disease, hypothyroidism and Hashimotos disease. Moreover, animal studies have shown that binding of endocrine disrupting chemicals (EDCs) with TSHR can lead to developmental toxicity. Several such chemicals have also been screened for their adverse physiological effects in human cell lines through various high-throughput assays under the ToxCast project. The vast resource of data generated through ToxCast has enabled the development of different toxicity predictors, but they can be limited in their predictive ability due to the heterogeneity in structure-activity relationships among chemicals. In an attempt to explore this heterogeneity, we systematically investigated structure-activity and structure-mechanism relationships among the TSHR binding chemicals from ToxCast. By employing structure-activity similarity (SAS) map, we identified 79 activity cliffs among 509 chemicals in the TSHR agonist dataset and 69 activity cliffs among 650 chemicals in the TSHR antagonist dataset. Further, by using the matched molecular pair (MMP) approach, we find that the resultant activity cliffs (MMP-cliffs) are a subset of activity cliffs identified via the SAS map approach. Moreover, by leveraging ToxCast mechanism of action (MOA) annotations for chemicals common to both TSHR agonist and antagonist datasets, we identified 3 chemical pairs as Strong MOA-cliffs and 19 chemical pairs as Weak MOA-cliffs. In conclusion, the insights from this systematic analysis of the structure-activity as well as the structure-mechanism relationships of TSHR binding chemicals are likely to inform ongoing efforts towards development of better predictive toxicity models for characterizing the chemical exposome.

pharmacology and toxicology↗

bioRxiv2023

T9GPred: A Comprehensive Computational Tool for the Prediction of Type 9 Secretion System, Gliding Motility and the Associated Secreted Proteins

Type 9 secretion system (T9SS) is one of the least characterized secretion systems exclusively found in the Bacteroidetes phylum which comprise various environmental and economically relevant bacteria. While T9SS plays a central role in bacterial movement termed gliding motility, survival and pathogenicity, there is an unmet need for a comprehensive tool that predicts T9SS, gliding motility and proteins secreted via T9SS. In this study, we develop such a computational tool, Type 9 secretion system and Gliding motility Prediction (T9GPred). To build this tool, we manually curated published experimental evidence and identified mandatory components for T9SS and gliding motility prediction. We also compiled experimentally characterized proteins secreted via T9SS and determined the presence of three unique types of C-terminal domain signals, and these insights were leveraged to predict proteins secreted via T9SS. Notably, using recently published experimental evidence, we show that T9GPred has high predictive power. Thus, we used T9GPred to predict the presence of T9SS, gliding motility and associated secreted proteins across 693 completely sequenced Bacteroidetes strains. T9GPred predicted 402 strains to have T9SS, of which 327 strains are also predicted to exhibit gliding motility. Further, T9GPred also predicted putative secreted proteins for the 402 strains. In a nutshell, T9GPred is a novel computational tool for systems-level prediction of T9SS and streamlining future experimentation. The source code of the computational tool is available in our GitHub repository: https://github.com/asamallab/T9GPred. The tool and its predicted results are compiled in a web server available at: https://cb.imsc.res.in/t9gpred/.

bioinformatics↗

bioRxiv2022

EPEK: creation and analysis of an Ectopic Pregnancy Expression Knowledgebase

Ectopic pregnancy (EP) is one of the leading causes of maternal mortality, where the fertilized embryo grows outside of the uterus. Recent experiments on mice have uncovered the importance of genetic factors in the transport of embryos inside the uterus. In the past, efforts have been made to identify possible gene or protein markers in EP in humans through multiple expression studies. Although there exist comprehensive gene resources for other maternal health disorders, there is no specific resource that compiles the genes associated with EP from such expression studies. Here, we address that knowledge gap by creating a computational resource, Ectopic Pregnancy Expression Knowledgebase (EPEK), that involves manual compilation and curation of expression profiles of EP in humans from published articles. In EPEK, we compiled information on 314 differentially expressed genes, 17 metabolites, and 3 SNPs associated with EP. Computational analyses on the gene set from EPEK showed the implication of cellular signaling processes in EP. We also identified possible exosome markers that could be clinically relevant in the diagnosis of EP. In a nutshell, EPEK is the first and only dedicated resource on the expression profile of EP in humans. EPEK is accessible at https://cb.imsc.res.in/epek.

bioinformatics↗