Bioinformatics approach to identifying effective sensitising therapeutics for rheumatoid arthritis

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Summary

Rheumatoid arthritis (RA) is a chronic destructive disease of joints, which affects ~ 25 million adults in the world. RA is a painful and disabling disease and presents significant healthcare burden to society. Connectivity mapping (Lamb et al 2006) is an advanced bioinformatics technique that establishes connections among different biological states via their gene expression profiles/signatures. An important application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state, or more generally, altering a disease state towards a more favourable condition, for example, enabling patients to be more responsive to an otherwise ineffective treatment. Our team has undertaken pioneering research in this area, by developing a robust framework for connectivity mapping, releasing tested tools for such high throughput tasks, and successfully applying the developed technologies to different disease areas including cancers and inflammatory diseases [Zhang & Gant 2008, 2009; McArt & Zhang2011; McArt et al 2013; Wen et al 2015, 2016; Ramsey et al 2013;Malcomson et al 2016, PNAS;Wen et al 2017].

In this project, we will apply the connectivity mapping principle to rheumatoid arthritis (RA). This project aims to develop an integrated Bioinformatics and Big Data approach to the identification of sensitising agents that can improve RA patients’ response to biological treatments, such as infliximab (IFX), tocilizumab (TCZ), or abatacept (ABT).  Successful identification of such effective sensitising agents can help to address important unmet clinical needs, and bring great benefits to patients and NHS. Although this project focuses on non-responsive rheumatoid arthritis as a target disease, the approach developed and tested is also readily applicable to other disease areas of stratified medicine.

The Aims of the research project are:  1) To identify suitable public data sets of RA patients with gene expression profiling data (eg, those deposited in public data repository GEO and/or ArrayExpress) and clinical data on their response to standard DMARDs and biological treatments (eg. IFX, TCZ, ABT). 2) Use the multi-omics data and clinical data available in the retrieved public data sets, to identify gene signatures that are characteristic of response to certain biological treatments for RA.  3) To process and analyse the drug-induced gene expression data to construct reference gene expression profiles for FDA-approved drugs and experimental compounds available through the LINCS database.  4) To establish the connections between the IFX-response gene signatures generated in Aim 2 and the reference drug profiles in Aim 3, to identify candidate chemical agents, particularly FDA approved drugs, which have the desirable effects on the specific IFX-response gene signatures and therefore be able to sensitise IFX-treatments and induce remission in otherwise non-responsive RA patients  5) To validate the predicted effects of candidate drugs on model systems of the disease.

This is an interdisciplinary bioinformatics and BigData  research project; it requires an individual with good computational skills and statistical knowledge.  A good understanding of basic biological processes and some experience with basic biological lab experiments would be desirable, but training on these aspects will be provided during the course of PhD study.

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Essential criteria

• Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)

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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.

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Funding

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Other information

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The Doctoral College at Ulster University

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Reviews

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