BBSRC NLD Doctoral Training Partnership: Defining the links between ROS and inflammageing in a human model of neutrophil-driven inflammation

Viruses transmitted by mosquitoes (also known as arboviruses) greatly threaten animal and human health, and food security, worldwide. Those viruses are disproportionately prominent in global emerging infectious diseases and will likely continue to have substantial impact on health and economic stability.

Most current research focuses on the climatic suitability for established mosquito vectors, such as Aedes aegypti and albopictus. However, many local mosquito species have proven to be competent for arbovirus transmission. Notably, Zika, West Nile, chikungunya, and Usutu viruses have all recently expanded their global reach, largely due to previously unexposed mosquitoes becoming new vectors.

The ability to computationally predict whether a local mosquito species can transmit a virus, before an incursion, will greatly improve risk assessment, preparedness, and outbreak mitigation.

This project therefore aims to develop an Artificial Intelligence (AI) framework to predict mosquito vector-competence for arboviruses. This framework can then be used to estimate risk of transmission by local mosquitoes (in new regions); and to prioritise virus/mosquito combinations for laboratory testing.

Working with a dataset encompassing all arbovirus mosquito-competence studies conducted to date, you will investigate the following:

1. Which traits of the virus and mosquito correlate with arbovirus competence?

As drivers of competence remain unknown, and are likely to be complex, you will develop an array of variables, including virus genomic traits, mosquito phylogeny, and ecological characteristics. You will further expand the computational pipelines developed by supervisor 1 to uncover which of those variables correlate with competence.

2. What are the predictive blind-spots, and how can they be overcome?

You will collaborate closely with your primary supervisor, Dr Maya Wardeh, to develop an Active Learning (AL) system, and integrate it into the predictive framework. AL is a machine-learning technique which can identify the testable novel arbovirus-mosquito combinations, out of the numerous unknown combinations, which once known would maximise predictive performance. In other words, AL can help us achieve greater prediction accuracy, with substantially fewer number of experiments than other methods.

You will have the opportunity to test some of your predictions in the lab of your second supervisor Dr Marcus Blagrove towards the end of the PhD. You will also have the opportunity to collect wild mosquitoes for novel virus/mosquito combination testing with the group of Dr Olena Riabinina (Durham University).

HOW TO APPLY:

Applications should be made by emailing bbsrcdtp@liverpool.ac.uk with:

• a CV (including contact details of at least two academic (or other relevant) referees);
• a covering letter – clearly stating your first-choice project, and optionally 2nd ranked project, as well as including whatever additional information you feel is pertinent to your application; you may wish to indicate, for example, why you are particularly interested in the selected project(s) and at the selected University;
• copies of your relevant undergraduate degree transcripts and certificates;
• a copy of your passport (photo page).

A GUIDE TO THE FORMAT REQUIRED FOR THE APPLICATION DOCUMENTS IS AVAILABLE AT https://www.nld-dtp.org.uk/how-apply. Applications not meeting these criteria may be rejected.

In addition to the above items, please email a completed copy of the Additional Details Form (as a Word document) to bbsrcdtp@liverpool.ac.uk. A blank copy of this form can be found at: https://www.nld-dtp.org.uk/how-apply.

Informal enquiries may be made to maya.wardeh@liverpool.ac.uk

The deadline for all applications is 12noon on Monday 22nd July 2024.

Part-Time Study Options

All NLD DTP PhDs are available as part time or full time, with part time being a minimum of 50% of full time. Please discuss potential part time arrangements with the primary supervisor before applying to the programme.

Project CASE Status

This project is not a CASE project. While individual applicant quality is our overriding criterion for selection, the NLD DTP has a commitment to fund 8 CASE projects per year - as such, CASE projects may be favoured in shortlisting applicants when candidates are otherwise deemed to be equal or a consensus on student quality cannot be reached.