Substance abuse disorder (SUD) is a global problem that affects the health, social and economic welfare of all societies (1). Opioid use disorder (OUD) is characterized by compulsive use of opioids despite adverse consequences from continued use and the development of a withdrawal syndrome when opioid use ends (2) involves a complex interplay of genetic, epigenetic and host–microbiome interactions (3,4). The goal of this PhD project will be to exploit the outbred (NIH/H) rat model of opioid abuse currently being developed as part of the US NIH consortium award (Hardiman, QUB PI) (5,6).
A rodent model NIH heterogeneous stock rats for operant self-administration escalation has been developed by the consortium where >1,000 animals are being stratified based on resistance or vulnerability to opioid abuse. This design is implemented at two different research sites in the USA and Europe and is addressing genetic make-up and environment contributions to opioid abuse. By triangulating different measures of the motivation and perseverance to seek heroin, we are dissecting the endophenotype of the powerful, dysregulated drive to use and seek heroin that develops with continued use and dependence. Epigenetics data has been generated from a sub-sample of these animals as they transition over time from initial heroin use to dependence. This PhD project will perform an integrative multi-Omics analysis on this unique resource (for example see recent collaborative paper from Hardiman Lab) (7) and integrate genetic, metagenomics, transcriptomics, epigenetics, behavioural and neurobiological data. Systems level modelling on these data will provide novel insights into heroin addiction.
Addiction is a relapsing brain disease where an individual pathologically pursues reward and/or relief by substance use and other behaviours. The goal of the PhD project is to exploit a rodent model to advance understanding of the contribution of genetics to opioid use disorder (OUD), which includes the use of illicit drugs such as heroin as well as legally prescribed pain relievers. This PhD project will exploit a unique resource, i.e. an advanced behavioural rat model that has been developed to screen outbred NIH/H rats for vulnerability or resistance to the development of compulsive opiate self-administration at two distinct geographical research sites in the USA and Italy. To date multidimensional data sets have been generated that include whole genome and transcriptomic sequencing, and behavioural measures. These are providing unique insights into the molecular genetic processes underlying the causes and consequences of drug addiction. The long-term objective of this PhD is to define in this rodent model the genetic, epigenetic, and transcriptomic architecture of OUD via a systems level integrative analysis and uncover new insights into this disease pathophysiology. This will be accomplished through three interlinked objectives:
O1: Analysis of the landscape of noncoding RNAs in the human blood transcriptome in the transition from initial heroin use to dependence. Extracellular vesicles (EV) are secretory membrane derived elements used by cells to transport proteins, lipids, mRNAs, and microRNAs (miRNAs). Due to their ability to transport cargo between cells, EVs have been identified as important regulators of various pathophysiological conditions and can therefore influence treatment outcomes. This objective will examine 1) if plasma EV-miRNAs become dysregulated in the transition from initial heroin use to dependence and 2) if differences exist between vulnerable and resilient animals.
O2: Analysis of the landscape of noncoding RNAs in brain region transcriptomes in the transition from initial heroin use to dependence: This objective will exploit whole genome sequence data for all the animals in this study and test the hypothesis that transcriptomic regulation of non-coding RNA variants in defined brain regions results in functional differences that are associated with substance abuse.
O3: Machine Learning: This project will delineate the genetic basis of drug addiction by integrating host genetics, behavioural measures, and non-coding regulatory RNAs and interrogate the data using machine learning (ML) approaches. Studying and mining these data types to identify biological factors involved in substance misuse is increasingly important because technologic advances have improved the ability of researchers to single out individual genes or brain processes that may inform new prevention and treatment interventions. This will provide deeper insights into the mechanism of drug addiction and provide novel therapeutic targets.
Specific skills/experience required: Full training will be provided, but previous experience in working with high dimensional genomic data, such as sequencing data, gene expression, genotype, CNV, sequence and/or data from other high throughput biological technologies desired. Prior research in neuroscience, brain research, addiction, and molecular techniques e.g. RNA sequencing, although not essential, is desired.
Start Date: 1 October 2023
Duration: 3 years
How to apply: Applications must be submitted online via: https://dap.qub.ac.uk/portal/user/u_login.php
Continue with Facebook
