Alladi Ramakrishnan Hall

Computational data-driven investigation of chemical exposome and its links to human and ecosystem health (Presynopsis Talk)

Ajaya Kumar Sahoo

IMSc, Chennai

This is a hybrid talk and zoom link to attend is:
zoom.us/j/99127472570
Meeting ID: 991 2747 2570
Passcode: 217863

ABSTRACT

Humans and ecosystems are frequently exposed to myriad of chemicals, including chemicals in consumer products, industrial pollutants, pesticides, among others, which collectively constitute the chemical exposome. These chemicals can persist in the environment and bioaccumulate, leading to detrimental effects on humans and other organisms, as well as long-term ecological impacts. Therefore, it is imperative to characterize the chemical exposome and its impact on human and ecosystem health. To this end, traditional toxicity testing often relies on animal models which can be low-throughput, expensive and time consuming, and therefore, computational approaches have emerged as effective alternatives to expedite the characterization of the ever-expanding chemical exposome. In this thesis, we employ different computational approaches to investigate the structure-activity landscape of environmental chemicals in the exposome, and further, integrate heterogeneous toxicological datasets to elucidate chemical-induced adverse effects on both human and ecosystem.

For the analysis of the structure-activity landscape of endocrine disruptors, we focus on two distinct environmental chemical spaces, namely, androgen receptor (AR) binding chemicals and thyroid stimulating hormone receptor (TSHR) binding chemicals. For both chemical spaces, we employ several computational approaches to analyze the presence of heterogeneity in the structure-activity landscape of these chemical spaces and identify the presence of activity cliffs, i.e., structurally similar chemicals exhibiting large differences in their activities against a target receptor. Further we classify the identified activity cliffs based on the information of their chemical structures. Additionally, we analyze the structure-mechanism relationships of the TSHR binding chemicals and identify structurally similar chemicals differing in their mechanism of actions. In sum, the inferences from these computational analyses will aid in development of improved toxicity predictors for characterization of the chemical exposome.

In addition, to investigate the adverse health effects induced by environmental chemicals, we focus on certain chemical classes namely, inorganic cadmium compounds (heavy metal), plastic additives and petroleum hydrocarbons (PHs). For each case, we curate a list of chemicals by relying on published reports and existing resources. We then integrate the biological endpoint data from various toxicological resources to identify associations between the chemicals and the high confidence adverse outcome pathways (AOPs) within AOP-Wiki. Further we construct chemical-specific AOP networks and analyze toxicity pathways to understand the mechanisms underlying chemical-induced adverse effects in both humans and aquatic organisms. Additionally, we assess the toxicities of the PHs across diverse ecological species using network-based approaches and perform an ecological risk assessment for a group of PHs.

In conclusion, this thesis presents a systematic computational investigation of environmental chemicals and their adverse health effects on humans and ecosystems, thereby providing a holistic overview of chemical exposome and its implications on health from a One Health perspective.