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Exploring The Utility of Transcriptomics Data in Toxicology


   School of Biological Sciences

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  Prof G Hardiman, Dr G Lopez-Campos  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

One of the challenges for the future of agriculture is to sustainable increase food production, using only the same or ideally less land area, to provide the quality and quantity required to meet the needs of an increasingly affluent & growing human population and simultaneously mitigating climate change and protecting or regenerating biodiversity. To achieve this average crops yields will need to improve globally. Therefore, crops will have to be protected against insect pests, diseases and competition from weeds.

This will require the increased use of novel active ingredients, including new chemical pesticides, that can protect crops to ensure this optimized agricultural production. Food safety remains a critically important objective. In the context of pesticides and public health, this is achieved through a risk assessment, which establishes the dose of a chemical that is without an impact on human health. Regulations then ensure that the uses of a pesticide do not exceed this highest safe dose. 

Since the 1940s, toxicology has relied on acute and chronic animal testing, particularly using rodent models, to determine the hazards a chemical may pose to humans based on a combination of cytological, physiologic, metabolic, and morphologic observations. Ethical considerations aside, animal studies are expensive, time-consuming and resource intensive. Evaluating the large number of emerging chemicals for crop protection poses a challenge to traditional toxicity testing. Indeed, more generally, the inability to perform safety assessments on the vast number of novel entities our society produces has been proposed as the Planetary Boundary for Novel Entities. This highlights the need for novel and scalable approaches to assess the potential toxicity of beneficial novel entities.

A major technological shift in the past decade has been the adoption of high throughput (HT) “omics” technologies to interrogate the genome, epigenome, transcriptome, metabolome and proteome in a massively parallel fashion. This has provided both unique discovery opportunities and challenges for computational and quantitative scientists in predicting phenotypic outcomes. ‘Big Data’ encompasses the collection of data sets derived from technologies and so large and complex that their processing is impractical using traditional data processing applications. Considerable effort has been devoted to investigating in vitro approaches and modern measurements to supplement or replace animal models for safety assessment studies. One strategy is Toxicogenomics which collects, interprets, and stores information on molecular alterations within a given cell or tissue in response to toxins, and derives predictive biomarkers.

Transitioning from current risk assessment practices to approaches more adequate for big data collection and integration requires a paradigm shift in implementation. The Adverse Outcome Pathway (AOP) is a relatively new analytical framework that organizes mechanistic and/or predictive relationships between initial chemical–biological interactions, biological pathways, gene, and protein networks, and adverse phenotypic or health outcomes.

This project will exploit transcriptomics data to develop software that provide improved data processing capability and effectively analyze toxicology data sets within the AOP framework so that the risks posed by pesticides in food can be better understood. The student, supported by the supervisors' complementary expertise, will develop a multidisciplinary workflow toward establishing the transcriptome-to-phenome link. The long-term goal of this project is to develop a robust toolkit for integration into the adverse outcome pathway (AOP) conceptual framework, whereby existing knowledge linking molecular-level perturbation of a biological system and an adverse biological outcome with predictive or regulatory relevance is generated. This project will pursue three objectives to integrate toxicogenomics data with interactome and pathological endpoints.

Objective 1. Develop a novel model for identification of key events by integrating toxicogenomics and protein-protein interaction data. This model will: 1) identify key exposure durations and dose levels associated with adverse endpoints; 2) improve power by sharing information among interacting genes and across compounds, time points, and dose levels; and 3) simultaneously identify gene subnetworks and their hub genes.

Objective 2. Develop a literature mining framework to identify gene-gene networks and hub genes associated with each compound exposure and adverse endpoint.

Objective 3. Develop a web interface by integrating transcriptome, interactome, and pathological endpoints.

Expected Outcomes. The project will promote understanding of complex cellular and organismal responses to toxins by identifying causal networks and associating them with pathological endpoints.

Student profile

Full training will be provided, but previous bioinformatics experience would be beneficial. Experience working with high dimensional genomic data, such as sequencing data, gene expression and/or data from other HT biological technologies desired.

Project Supervisors: Professor Gary Hardiman (QUB), Dr Guillermo Lopez Campos (QUB), Dr Dongjun Chun (Ohio State University), Dr Yeyejide Adeleye (Syngenta).

Start Date: 1 October 2022

Duration: 3 years

How to apply: Applications must be submitted via: https://dap.qub.ac.uk/portal/user/u_login.php


Funding Notes

This studentship is funded by the Northern Ireland Department for the Economy (DfE) in association with Syngenta.
Candidates must be normally resident in the UK for the three year period prior to 1 October 2022. For non-EU nationals, the main purpose of residence must not have been to receive full-time education. Non-UK or Irish nationals must also have pre-settled or settled status (EU nationals) or settled status (non-EU nationals).
Full eligibility criteria: https://www.economy-ni.gov.uk/sites/default/files/publications/economy/Postgraduate-studentships-terms-and-conditions.pdf

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