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Precision Medicine DTP - Analysis of intestinal metagenomes using ASAR


Project Description

Additional Supervisors: Dr Anatoly Sorokin & Dr Vidya Rajasekaran

Background
Understanding risk factors to Colorectal Cancer (CRC) can aid in its prevention. According to Cancer Research UK, around 54% of CRC is already preventable and this could be increased by understanding the genetic and environmental influences and tailoring screening approaches.
An ever-increasing understanding and use of metagenomics and the influence of the gut microbiome on health and disease, will also likely reduce CRC incidence. The diversity of intestinal bacteria can mediate many beneficial effects, some of which is via producing short-chain fatty acids via fermentation of dietary fibre. These fatty acids can then influence both lipid homeostasis and indeed reduce inflammation, both important factors in colorectal disease progression. The microbiome can be affected by numerous different factors including diet, lifestyle, antibiotic use and there is great interest in microbiome based biomarkers, with several studies demonstrating its potential for early colorectal cancer detection (Amitay, Krilaviciute et al. 2018). An added public health advantage is that bacterial DNA can be successfully isolated from qFIT (quantitative faecal immunochemical test) cartridges (Baxter, Koumpouras et al. 2016) and hence used for risk prediction/stratification which would complement the current population based CRC screening programmes which use qFIT, potentially lowering costs and improving efficiency of these programmes.
In this project we aim to utilize a new metagenomic tool ASAR (Orakov, et al. 2017), which had already been successfully applied to analyse metagenomes, including human microbiomes (Vasieva, Sorokin, et al. 2019). This will be applied to data from over 230 intestinal mucosa samples for bacterial presence and diversity identification. These colorectal normal mucosa samples are from different regions of the intestine and have had high quality RNA extracted and sequenced using NGS technologies.

This data can be analysed for bacterial 16sRNA sequences using standard analysis and the new ASAR tool to determine the bacterial abundance and functional abilities. The available metadata for these samples will allow characterization of the gut microbiome by sex, location in the intestine and indeed whether from an affected case or an unaffected control. Verification of the results will be explored using histological based approaches of available samples and if appropriate, how the gut microbiome changes during the carcinogenic process with analysis of matched colorectal tumour samples.

Overall, this interdisciplinary research project will further the knowledge of the intestinal microbiome and could help in the development of CRC risk prediction models, providing additional power to the current CRC screening programme and substantially improve risk stratification and ultimately personalized and targeted medicine.

Amitay EL, Krilaviciute A, Brenner H. Systematic review: Gut microbiota in fecal samples and detection of colorectal neoplasms. Gut Microbes. 2018; 9(4):293-307.
Baxter NT, Koumpouras CC, Rogers MA, Ruffin MT 4th, Schloss PD. DNA from fecal immunochemical test can replace stool for detection of colonic lesions using a microbiota-based model. Microbiome. 2016; 4(1):59.
A.N. Orakov, NK Sakenova, A Sorokin, II Goryanin. ASAR: visual analysis of metagenomes in R Bioinformatics 34 (8), 1404-1405, 2, 2017
Vasieva O, Sorokin A, Murzabaev M, Babiak P, Goryanin I A Study on the Analysis of Personal Gut Microbiomes. J Comput Sci Syst Biol, 12:7, 2019

Aims
To help develop and validate a new metagenomic tool and methods.
To determine the composition of abundance of bacterial species in human colorectal mucosa.
To assess this by sex, site and affected cancer status.
To validate the findings in patient samples.

Training outcomes
Developing expertise in the background of colorectal genetics, the carcinogenesis process and the influence of the microbiome.
Developing analytical skills and knowledge in the understanding of NGS platforms and the process of utilising large datasets and analysing them for bacterial species.
Practical skills of validation, both in dry-lab analyses and patient samples.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2020

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area.
Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,009 (RCUK rate 2019/20) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:

References

[1] Amitay EL, Krilaviciute A, Brenner H. Systematic review: Gut microbiota in fecal samples and detection of colorectal neoplasms. Gut Microbes. 2018; 9(4):293-307.

[2] Baxter NT, Koumpouras CC, Rogers MA, Ruffin MT 4th, Schloss PD. DNA from fecal immunochemical test can replace stool for detection of colonic lesions using a microbiota-based model. Microbiome. 2016; 4(1):59.

[3] A.N. Orakov, NK Sakenova, A Sorokin, II Goryanin. ASAR: visual analysis of metagenomes in R Bioinformatics 34 (8), 1404-1405, 2, 2017

[4] Vasieva O, Sorokin A, Murzabaev M, Babiak P, Goryanin I A Study on the Analysis of Personal Gut Microbiomes. J Comput Sci Syst Biol, 12:7, 2019

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