Professor Krasimira Tsaneva-Atanasova,College of Engineering, Mathematics and Physical Sciences, University of Exeter.
Assistant Professor Sanjay Chotirmall, Nanyang Technological University, Singapore
About the Programme:
The University of Exeter (UoE) and Nanyang Technological University (NTU), Singapore are offering six fully funded postgraduate studentships to undertake collaborative research projects at the two institutions, leading to PhD degrees (split-site) to be conferred either by the UoE or NTU.
Students pursuing these postgraduate research projects will benefit from the unique opportunity to conduct their research at both institutions. Students will be registered at one or other institution, where they will be based for the majority of their time, but will spend at least 12 and not more than 18 months at the partner institution over the duration of the programme. The frequency and length of stays at each institution will be agreed with successful candidates prior to offers being made.
Understanding how individual people respond to medical therapy is a key facet of improving the odds ratio that interventions will have a positive impact. Reducing the non-responder rate for an intervention or reducing complications associated with a particular treatment is the next stage for any medical advance. The Precision Medicine Initiative, launched in January 2015, set the stage for enhanced collaboration between researchers and medical professionals to develop next-generation techniques to aid patient treatment and recovery, and increased the opportunity for impactful pre-emptive care. The microbiome plays a crucial role in health and disease, as it influences endocrinology, physiology, and even neurology, altering the outcome of many disease states, including its ability to augment drug response and tolerance.
Therefore, in precision medicine, the focus is on the identification of effective approaches for particular patients based on their genetic, lifestyle and environmental factors. Asian and European phenotypes of respiratory disease and infection are unique and therefore require such precision. While such approaches have been successfully employed to investigate contrasting clinical phenotypes; and by disease trajectories, little is known about ‘precision through microbes’. Precision medicine can be applied to the lung microbiome that includes both bacteria and fungi and their associated metabolic states. These ‘microbial fingerprints’ permit patient stratification and we can identify particular disease phenotypes associated to clinical outcomes potentially amenable to precision and individualised intervention. It is clear that our microbes tell us something about disease, something representing a potential target for clinical intervention.
Using a well phenotyped and prospectively curated Asian and European dataset across a variety of chronic inflammatory respiratory disease states including severe asthma, chronic obstructive pulmonary disease (COPD) and bronchiectasis, this PhD project aims to perform the following:
1. Prospectively curate novel datasets focused on the Asian microbiome/mycobiome (and their associated metabolomic profiles) in patients with severe asthma, COPD and their associated overlap syndrome states (e.g. asthma-COPD overlap syndrome and bronchiectasis-COPD overlap syndrome). The candidate will gain experience in sequencing, bioinformatics and mass spectrometry. This work will be performed in Singapore.
2. Model mathematically microbiome and mycobiome populations and their interactions across a range of pulmonary disease states: this will utilize computational approaches to identify mathematically significant co-operative and competitive relationships within and between species. There is a scope for spatio-temporal modelling approaches, which would allow us to account for potential differences in anatomical distribution of the microbiome and mycobiome populations within the lung. This work will be performed in Exeter.
3. Apply the developed model systems to clinical settings in diagnosis, prognosis and predicting disease progression across a range of respiratory disease states This work will be performed in Singapore.
4. Finally, the use of microbial metabolomic datasets will further extend the developed models in order to take into account the affected pathways and signalling networks. This will further power our microbial airway interaction models and provide therapeutic and pharmaceutical relevance to their in vivo relationships. This work will be performed in Exeter.
To apply for this project, please visit: http://www.exeter.ac.uk/studying/funding/award/?id=3053