PhD Study : Multiomics approach to biomarkers discovery in COVID-19 severity

Summary

​​Background: The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a single-stranded RNA virus, first reported in Wuhan, China, that causes the COVID-19 disease. COVID-19 was declared a pandemic by the World Health Organization (WHO) on March 11 of 2020 and imposed the biggest public health challenge with unforeseen impacts in the coming years. Over last two years utilizing several past and ongoing grants we have generated multi-omic data including RNA seq, WGS, proteomics, metatrancriptomics and metagenomics for 500 COVID-19 patients. This PhD project aims to integrate all the omic data to generate combinatorial profiles for senescence biomarkers to predict severity of COVID-19. Problem: Even though COVID is a debilitating condition, not much is known about (a) why patients develop it, (b) whether the patients will recover in the long term, (c) why two different individuals have a completely different set of Long COVID symptoms. Individuals whose symptoms are initially mild may develop joint pain, loss of smell, taste, anxiety and depression.

Hypothesis: We hypothesize senescence omic signatures act in combinatorial fashion and could be the reason for this variability in COVID-19 severity.

Aim: This proposal aims to develop a multiplexed omics approach for the identification of key senescence biomarkers (sendotypes) which can be used to predict COVID-19 severity.

Aim 1. Multiplex proteomic, transcriptomic, genomic, and metaomic analyses to evaluate role of senescence in the pathogenesis of COVID-19.

Aim 2. Machine learning techniques will be applied to the secretory senescence proteins to yield omic combinations (sendotypes) that will accurately differentiate severe COVID-19 patients vs patients with mild to no symptoms. This translational research has the potential to change the way we diagnose and manage risk for COVID-19 patients.

Aim 3: Directly compare suitability of identified sendotypes as compared to signatures from unbiased data analytics. In one scenario senescence outperforms traditional analysis and this could lead to establishment of new paradigm to study age associated diseases. In other scenario sendotypes will perform no better than traditional biomarkers; this is acceptable too, as to our knowledge no study has compared multi-omic data for severity of COVID-19. This will lead to publishable impactful insight into molecular changes in COVID-19.

The project is entirely desk based and will suit computing and biomedical students with keen interest in developing a career in data analytics. The supervisory team amongst themselves have necessary expertise in both biology and data analytics to provide required training for the candidate to finish the project on time. This research is supported by funding from HSC R and D grant (COM/5618/20) to Dr Rai and several other ongoing grants at NICSM on which Dr Rai is Co-I and members of the team are PI.

The project also has full ethical approvals in place for data analytics. Further ethical approval will be applied for if the need arises.

For any informal enquiry and  to discuss more about the project, please contact the supervisors: Dr Taranjit Singh Rai (t.rai@ulster.ac.uk), Dr Shu-Dong Zhang (sd.zhang@ulster.ac.uk) and Dr David Gibson (d.gibson@ulster.ac.uk)

Please note: Applications for more than one PhD studentships are welcome, however if you apply for more than one PhD project within Biomedical Sciences, your first application on the system will be deemed your first-choice preference and further applications will be ordered based on the sequential time of submission. If you are successfully shortlisted, you will be interviewed only on your first-choice application and ranked accordingly. Those ranked highest will be offered a PhD studentship. In the situation where you are ranked highly and your first-choice project is already allocated to someone who was ranked higher than you, you may be offered your 2nd or 3rd choice project depending on the availability of this project

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• Clearly defined research proposal detailing background, research questions, aims and methodology

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• Completion of Masters at a level equivalent to commendation or distinction at Ulster
• Experience using research methods or other approaches relevant to the subject domain
• Sound understanding of subject area as evidenced by a comprehensive research proposal
• Work experience relevant to the proposed project
• Publications record appropriate to career stage
• Experience of presentation of research findings
• A comprehensive and articulate personal statement
• Relevant professional qualification and/or a Degree in a Health or Health related area

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living