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  Evaluation of RNA Epigenetic modifications as non-invasive biomarkers for human diseases


   School of Science, Engineering and Environment

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  Dr A Mukhopadhyay, Dr I Goodhead  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The proposed project is within the broad area of biomedical genomics for healthy ageing. Globally, the major focus is to identify biomarkers for human diseases, that can help early diagnosis and prognosis of the disease towards a better management and a better quality of life for an ageing population.

Cancer, and most other chronic conditions, can greatly benefit from early detection. Thus, most biomarker research focuses on early detection of molecular and physiological alterations that are detectable before the full-blown disease conditions manifest. Such biomarkers range from imaging (e.g. mammogram), microscopic analysis of cellular morphology (e.g. pap smear, biopsy) and genetic tests (e.g. DNA/cDNA sequencing). Most of these biomarkers are amenable only when an irreversible physiological change occur – unless surgically intervened. In other words, thus far, most biomarkers that has entered clinical management, are static – missing the even earlier physiological changes that are dynamic and reversible.

At a molecular level, epigenetic and epi-transcriptomic changes provide a window to this dynamic, often reversible changes at a molecular level. While epigenetic changes on DNA are limited, such changes on RNA are highly diverse and not yet well understood. Currently, around 20 different types of DNA epigenetic modifications are known, while the number is over 160 for RNA.

We have shown that epi-transcriptomics markers such as editing and fusion transcript can be important biomarkers in cancer. We have also built capacity to detect epi-transcriptomic features in extracellular cargo carried by the small vesicles (sEV). However, until now the technology to study epi-transcriptomes of small RNA present in sEVs has not been available. With the advent of third generation sequencing technology (e.g. Nanopore sequencing), it is now possible to natively identify epigenetic modification in RNA using direct RNA sequencing.

We propose to merge these two areas together and address the next step of the challenge.

The proposed supervisors for this proposal have the right background, expertise and complementary sets of skills to provide a multidisciplinary experience to the young researcher. Dr. Mukhopadhyay has extensively published on RNA as cancer biomarkers – both by genome-wide scans and by candidate validation. His group is one of the very few research groups in the north-west UK to have the technical knowledge and infrastructure to isolate and characterise sEV mediated cargo for biomarker discovery. To facilitate this, the university has recently invested (>£80,000) in setting up the infrastructure for nanoparticle tracking analysis – an essential tool for sEV research. The research group has also optimised a workflow to capture the epi-transcriptomic signatures using RNA from small extracellular vesicles (sEV) using massive parallel sequencing approaches.

Prof. Ian Goodhead, the co-supervisor, has core expertise in establishment and capacity building of genomics and transcriptomics technologies and applications. Of specific relevance to this project, Prof. Goodhead and his team has established a Genomics facility, including Nanopore Sequencing, within the University of Salford. His involvement in the team has already enabled the use of Illumina sequencing for sEV RNA cargo. His involvement will ensure that the adoption of Nanopore-based direct RNA sequencing can be delivered.

Biological Sciences (4)

Funding Notes

This project is currently unfunded, and we welcome applications from self-funded students. We are continuously trying to get the project funded from multiple sources and any such success will contribute towards running the project.

References

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• Zhang et al. 2021. Frontiers in Genetics. DOI: 10.3389/fgene.2021.672804.
• Erson-Bensan and Begik. 2017. MicroRNA. DOI: 10.2174/2211536606666170511102219
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