PhD Study : Identification of Peripheral Alzheimer’s Disease Senescence Signature (AzSenSig)

Summary

Alzheimer’s disease is an incurable, complex, age associated disorder that leads to progressive and debilitating cognitive decline. Currently, there is no treatment that can cure or even halt the progression of the disease. Effective treatments are urgently needed and, in part, will be aided by better classification and early detection of the disease. Alzheimer’s disease is the most common cause of dementia. It is a multifactorial disease; one cause being build up of protein structures such as plaques and tangles. Pathologically, -amyloid peptide and hyperphosphorylated tau are major drivers of neurotoxicity in the brain, however mechanisms are poorly understood.

One mechanism discovered recently (Bussian TJ et al Nature Sep 2018), implicated cellular senescence as a  key driving force for cognitive decline. Cellular senescence is a process by which cells damaged by various stressors are either removed from the body or maintained in a state of non-division. Senescent cells secrete a variety of inflammatory cytokines, growth factors and other  soluble and insoluble factors known as the senescence-associated secretory phenotype (SASP). Various features of senescent cells, such as the SASP, can cause damage to surrounding tissue. SASP secreted by senescent cells can alter the tissue microenvironment.  Interestingly, there is a significant overlap between cytokines and chemokines secreted by tau impacted cells and senescent cells. What role if any Senescence/SASP plays in Alzheimer’s is unknown.

We hypothesise that the presence of senescent cell markers precedes development of severe cognitive decline. An Alzheimer’s disease specific senescence signal (AzSenSig) will be developed using published datasets (n>500). These comprise of senescence specific transcriptomic, proteomics and metabolomics datasets. While the growing availability of such diverse senescence data offers huge opportunities to generate a more thorough and comprehensive view of biological problems, mining such abundant information poses great challenges to research communities. Using artificial intelligence and machine learning, we will develop advanced integrative data analysis algorithms and tools to capture the usability of senescence markers in prediction of outcomes for Alzheimer’s. Its clinical potential will be evaluated by analysing proteomic analyses from samples collected within the Northern Ireland Centre for Stratified Medicine. The bioinformatic discovery will be complemented by in vitro studies as follows.

We will establish a cellular model of neuronal senescence. Briefly neurons will be made senescent by treating them with 50 uM of etoposide for 24 hours to induce senescence. RNA will be extracted to generate a unique senescence specific transcriptomic dataset. Differentially expressed trancripts will be compared to AzSenSig for concordance. Validation of findings from data mining of publicly available datasets will be conducted in samples collected locally. Proteomic signatures in individuals with Alzheimer’s disease, mild cognitive impairment and apparently healthy controls will be evaluated. Comprehensive clinical history and neurocognitive assessments are available for all patients.

The main aim of this project is to determine specific senescence signatures associated with Alzheimer’s disease, and to identify if such signatures can be assessed in peripheral blood samples.

The proposed 3-year project will be based at the Centre for Personalized Medicine (CPM) under the supervision of Dr Rai, Dr McClean and Dr Shukla, research lecturers and alongside Professor Bjourson, centre director.

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

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.

• First Class Honours (1st) Degree
• Masters at 65%
• Research project completion within taught Masters degree or MRES
• Practice-based research experience and/or dissemination
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• 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