Biology subjects

Ravichandran, J.

Publications and source records attributed to Ravichandran, J..

11 recordsLinked to original sources

bioRxiv2024

UVREK: Development and analysis of an expression profile knowledgebase of biomolecules induced by ultraviolet radiation exposure

Humans encounter diverse environmental factors which can have impact on their health. One such environmental factor is the ultraviolet (UV) radiation, which is part of the physical component of the exposome. UV radiation is the leading cause of skin cancer and is a significant global health concern. A large body of published research has been conducted to uncover the mechanisms underlying the adverse outcomes of UV radiation exposure on living beings. These studies involve identifying the biomolecules induced upon UV radiation exposure. A few previous efforts have attempted to compile this information in the form of a database, but such earlier efforts have certain limitations. To fill this gap, we present a structured database named UVREK, containing manually curated data on biomolecules induced by UV radiation exposure from published literature. UVREK has compiled information on 985 genes, 470 proteins, 54 metabolites and 77 miRNAs along with their metadata. Thereafter, an enrichment analysis performed on the human gene set of UVREK database showed the importance of transcription related processes in UV related response, and enrichment of pathways involved in cancer and aging. While significantly contributing towards characterizing the physical component of the exposome, we expect the compiled data in UVREK will serve as a valuable resource for development of better UV protection mechanisms such as UV sensors and sunscreens. Noteworthy, UVREK is the only resource to date, compiling varied types of biomolecular responses to UV radiation with corresponding metadata. UVREK is openly accessible at: https://cb.imsc.res.in/uvrek/.

bioinformatics↗

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

Machine learning models for prediction of xenobiotic chemicals with high propensity to transfer into human milk

Breast milk serves as a vital source of essential nutrients for infants. However, human milk contamination via transfer of environmental chemicals from maternal exposome is a significant concern for infant health. Machine learning based predictive toxicology models can be valuable in predicting chemicals with high propensity to transfer into human milk. To this end, we build such classification- and regression-based models by employing multiple machine learning algorithms and leveraging the largest curated dataset to date of 375 chemicals with known Milk to Plasma concentration (M/P) ratios. Our Support Vector Machine (SVM) based classifier outperforms other models in terms of different performance metrics, when evaluated on both (internal) test data and external test dataset. Specifically, the SVM based classifier on (internal) test data achieved a classification accuracy of 77.33%, specificity of 84%, sensitivity of 64%, and F-score of 65.31%. When evaluated on an external test dataset, our SVM based classifier is found to be generalizable with sensitivity of 77.78%. While we were able to build highly predictive classification models, our best regression models for predicting the M/P ratio of chemicals could achieve only moderate R2 values on the (internal) test data. As noted in earlier literature, our study also highlights the challenges in developing accurate regression models for predicting the M/P ratio of xenobiotic chemicals. We have made our complete workflow, train and test datasets, and computer codes for the classification and regression models publicly available via a dedicated GitHub repository. Overall, this study attests the immense potential of predictive computational toxicology models in characterizing the myriad chemicals in the human exposome.

pharmacology and toxicology↗

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↗

bioRxiv2022

Identification of activity cliffs in structure-activity landscape of androgen receptor binding chemicals

Androgen mimicking environmental chemicals can bind to Androgen receptor (AR) and can cause severe effects on the reproductive health of males. Predicting such endocrine disrupting chemicals (EDCs) in the human exposome is vital for improving current chemical regulations. To this end, QSAR models have been developed to predict androgen binders. However, a continuous structure-activity relationship (SAR) wherein chemicals with similar structure have similar activity does not always hold. Activity landscape analysis can help map the structure-activity landscape and identify unique features such as activity cliffs. Here we performed a systematic investigation of the chemical diversity along with the global and local structure-activity landscape of a curated list of 144 AR binding chemicals. Specifically, we clustered the AR binding chemicals and visualized the associated chemical space. Thereafter, consensus diversity plot was used to assess the global diversity of the chemical space. Subsequently, the structure-activity landscape was investigated using SAS maps which capture the activity difference and structural similarity among the AR binders. This analysis led to a subset of 41 AR binding chemicals forming 86 activity cliffs, of which 14 are activity cliff generators. Additionally, SALI scores were computed for all pairs of AR binding chemicals and the SALI heatmap was also used to evaluate the activity cliffs identified using SAS map. Finally, we provide a classification of the 86 activity cliffs into six categories using structural information of chemicals at different levels. Overall, this investigation reveals the heterogeneous nature of the structure-activity landscape of AR binding chemicals and provides insights which will be crucial in preventing false prediction of chemicals as androgen binders and developing predictive computational toxicity models in the future.

pharmacology and toxicology↗

bioRxiv2021

Investigation of a derived adverse outcome pathway (AOP) network for endocrine-mediated perturbations

An adverse outcome pathway (AOP) is a compact representation of the available mechanistic information on observed adverse effects upon environmental exposure. Sharing of events across individual AOPs has led to the emergence of AOP networks. Since AOP networks are expected to be functional units of toxicity prediction, there is current interest in their development tailored to specific research question or regulatory problem. To this end, we have developed a detailed workflow to construct a comprehensive endocrine-specific AOP (ED-AOP) network. Connectivity analysis of the ED-AOP network comprising 48 AOPs reveals 7 connected components and 12 isolated AOPs. Subsequently, we apply standard network measures to perform an in-depth analysis of the two largest connected components of the ED-AOP network. Notably, the graph-theoretic analyses led to the identification of important events including points of convergence or divergence in the ED-AOP network. Detailed analysis of the largest component in the ED-AOP network gives insights on the systems-level perturbations caused by endocrine disruption, emergent paths, and stressor-event associations. In sum, the derived ED-AOP network can be used to address the current knowledge gaps in the existing regulatory framework and aid in better risk assessment of environmental chemicals.

pharmacology and toxicology↗

bioRxiv2021

Constitutive signaling by the C-terminal fragment of polycystin-1 is mediated by a tethered peptide agonist

Mutation of the PKD1 gene, encoding polycystin-1 (PC1), is the primary cause of autosomal dominant polycystic kidney disease. PC1 is an 11-transmembrane domain protein that binds and modulates the activity of multiple heterotrimeric G protein families and is thought to function as a non-canonical G protein-coupled receptor (GPCR). PC1 shares a conserved GPCR autoproteolysis inducing [GAIN] domain with the adhesion family of GPCRs, that promotes an auto-catalytic, cis-cleavage at the GPCR proteolysis site (GPS) located proximal to the first transmembrane domain. GPS cleavage divides these receptors into two associated subunits, the extracellular N-terminal (NTF) and transmembrane C-terminal (CTF) fragments. For the adhesion GPCRs, removal of the NTF leads to activation of G protein signaling as a result of the exposure and subsequent intramolecular binding of the extracellular N-terminal stalk of the CTF, i.e., the tethered cryptic ligand or tethered agonist model. Here, we test the hypothesis that PC1-mediated signaling is regulated by an adhesion GPCR-like, tethered agonist mechanism. Using cell-based reporter assays and mutagenesis of PC1 expression constructs, we show that the CTF form of PC1 requires the stalk for signaling activation and synthetic peptides derived from the PC1 stalk sequence can re-activate signaling by a stalk-less CTF. In addition, we demonstrate that ADPKD-associated missense mutations within the PC1 stalk affect signaling and can inhibit GPS cleavage. These results provide a foundation for beginning to understand the molecular mechanism of G protein regulation by PC1 and suggest that a tethered agonist-mediated mechanism can contribute to PKD pathogenesis. SIGNIFICANCE STATEMENTMutations of the PKD1 gene, encoding polycystin-1, are the predominant cause of autosomal dominant polycystic kidney disease (ADPKD), a systemic disease that is the 4th leading cause of kidney failure. Polycystin-1 functions as an atypical GPCR capable of binding or activating heterotrimeric G proteins, which is essential for preventing renal cystogenesis. However, little is known regarding its regulation. Polycystin-1 shares structural features with the Adhesion family of GPCRs. In this work, we combined mutagenesis and cellular signaling assays which demonstrated that constitutive activation of signaling by polycystin-1 involves an Adhesion GPCR-like molecular mechanism. This study provides new knowledge regarding the structure-function relationships of polycystin-1 which will stimulate additional areas of investigation and reveal novel avenues of therapeutic intervention for ADPKD.

biochemistry↗

bioRxiv2021

NeurotoxKb: compilation, curation and exploration of a knowledgebase of environmental neurotoxicants specific to mammals

Exposure to environmental neurotoxicants is a significant concern due to their potential to cause permanent or irreversible damage to the human nervous system. Here, we present the first dedicated knowledgebase, NeurotoxKb, on environmental neurotoxicants specific to mammals. Using a detailed workflow, we have compiled 475 potential non-biogenic neurotoxicants from 835 published studies with evidence of neurotoxicity specific to mammals. A unique feature of NeurotoxKb is the manual curation effort to compile and standardize the observed neurotoxic effects for the potential neurotoxicants from 835 published studies. For the 475 potential neurotoxicants, we have compiled diverse information such as chemical structures, environmental sources, chemical classification, physicochemical properties, molecular descriptors, predicted ADMET properties, and target human genes. To better understand the prospect of human exposure, we have explored the presence of potential neurotoxicants in external exposomes via two different analyses. By analyzing 55 chemical lists representing global regulations and guidelines, we reveal potential neurotoxicants both in regular use and produced in high volume. By analyzing human biospecimens, we reveal potential neurotoxicants detected in them. Lastly, a construction of the chemical similarity network and ensuing analysis revealed the diversity of the toxicological space of 475 potential neurotoxicants. NeurotoxKb is accessible online at: https://cb.imsc.res.in/neurotoxkb/.

pharmacology and toxicology↗

bioRxiv2020

ExHuMId: A curated resource and analysis of Exposome of Human Milk across India

Human milk is a vital source of nourishment for infants, containing nutrients, immunoprotective components, and bioactive substances. However, several environmental contaminants find their way into human milk. Although lactation physiology has been well documented, the effect of human milk contaminants on maternal and infant health remains unclear. Human milk is the major route of contaminant exposure to infants; these contaminants and their effects can themselves be considered an exposome. While there are chemical regulations in India and scientific literature on environmental contaminants is available, yet there is a lack of systematic compilation, monitoring, and risk management of human milk contaminants. We have harnessed the potential of this large body of literature to develop the Exposome of Human Milk across India (ExHuMId) containing detailed information on 101 environmental contaminants detected in human milk samples, studied across 13 Indian states, compiled from 36 research articles. ExHuMId also compiles the detected concentrations of the contaminants, structural and physicochemical properties, and factors associated with the donor of the sample. Here, we also present findings from a three-pronged analysis of ExHuMId and two other resources on human milk contaminants, with a focus on the Indian scenario. Through a comparative analysis with global chemical regulations and guidelines, we identify human milk contaminants of high concern, such as potential carcinogens, endocrine disruptors and neurotoxins. We then study the physicochemical properties of the contaminants to gain insights on their propensity to transfer into human milk. Further, we employ a systems biology approach to shed light on potential effects of human milk contaminants on maternal and infant health, by identifying contaminant-gene interactions associated with lactation, cytokine signalling and production, and protein-mediated transport. ExHuMId is accessible online at: https://cb.imsc.res.in/exhumid/.

pharmacology and toxicology↗

bioRxiv2020

An exploratory analysis of the current chemical regulations and guidelines from the perspective of endocrine disrupting chemicals using public resources

The regulatory assessment of endocrine disrupting chemicals (EDCs) is complex due to the lack of a standardized definition of EDCs and validated testing criteria. In spite of these challenges, there is growing scientific interest in EDCs which has resulted in the rapid expansion of published literature on endocrine disruption upon chemical exposure. Here, we explore how academic research leading to curated knowledgebases can inform current chemical regulations on EDCs. To this end, we present an updated knowledgebase, DEDuCT 2.0, containing 792 potential EDCs with supporting evidence from 2218 research articles. Thereafter, we study the distribution of potential EDCs across several chemical lists that reflect guidelines for use or regulations. Further, to understand the scale of possible exposure to the potential EDCs present in chemical lists, we compare them with high production volume chemicals. Notably, we find many potential EDCs are in use across various product categories such as Food additives and Food contact materials and Cosmetics and household products. Several of these EDCs are also produced or manufactured in high volume across the world. Lastly, we illustrate using an example how diverse information in curated knowledgebases such as DEDuCT 2.0 can be helpful in the risk assessment of EDCs. In sum, we highlight the need to bridge the gap between academic and regulatory aspects of chemical safety, as a step towards the better management of environment and health hazards such as EDCs.

pharmacology and toxicology↗

bioRxiv2019

A curated knowledgebase enabling a network perspective on endocrine disrupting chemicals and their biological systems-level perturbations

Human well-being can be affected by exposure to several chemicals in the environment. One such group is endocrine disrupting chemicals (EDCs) that can perturb the hormonal homeostasis leading to adverse health effects. In this work, we have developed a detailed workflow to identify EDCs with supporting evidence of endocrine disruption in published experiments in humans or rodents. Thereafter, this workflow was used to manually evaluate more than 16000 published research articles and identify 686 potential EDCs with published evidence in humans or rodents. Importantly, we have compiled the observed adverse effects or endocrine-specific perturbations along with the dosage information for the potential EDCs from their supporting published experiments. Subsequently, the potential EDCs were classified based on the type of supporting evidence, their environmental source and their chemical properties. Additional compiled information for potential EDCs include their chemical structure, physicochemical properties, predicted ADMET properties and target genes. In order to enable future research based on this compiled information on potential EDCs, we have built an online knowledgebase, Database of Endocrine Disrupting Chemicals and their Toxicity profiles (DEDuCT), accessible at: https://cb.imsc.res.in/deduct/. After building this comprehensive resource, we employed a network biology approach to study the chemical space of EDCs and its potential link to the biological space of target genes of EDCs. Specifically, we have constructed two networks of EDCs using our resource based on similarity of chemical structures or target genes. Ensuing analysis of these two networks revealed that EDCs can differ both in their chemical structure and set of target genes. Though our detailed results highlight potential challenges in developing predictive models for EDCs, the compiled information in our resource will undoubtedly enable future research in the field, especially, those focussed towards mechanistic understanding of the systems-level perturbations caused by EDCs.

bioinformatics↗