About the Project
Additional Supervisor: Dr Filippo Menolascina
Background
Chronic Liver Disease (CLD) is the third most common cause of premature death in the UK, with enormous medical and societal costs. Non-alcoholic fatty liver disease (NAFLD) is now the leading driver for CLD and indication for liver transplantation. Although preclinical proof-of-concept has been demonstrated for many interventions, results of clinical trials in CLD have been disappointing and there are still no FDA- or EMA-approved therapies for liver fibrosis or NAFLD.
The failure in developing a “silver bullet” against liver fibrosis is likely due to biological complexity: a high level of redundancy and robust compensation mechanisms in dysregulated pathways make single targets unlikely to succeed. Combination therapy, whereby multiple compounds are administered in an orchestrated way, is increasingly considered a solution. Liver fibrosis offers a rich variety of drug targets that might be additive or synergistic1. However, to date, there have been very few drug combination trials for liver fibrosis with or without NAFLD.
As we cannot yet define a hierarchy of importance of metabolic/inflammatory pathways in NAFLD-fibrosis, attempts to stratify the value of potential therapies are empiric and designing a combination therapy “by intuition” is destined to be unsuccessful and costly. A systematic approach is needed, whereby combinations of targets/drugs are rationally identified and tested in silico, before biological assays are performed. Such an approach requires a model in which to “test” combinations. Despite a wealth of knowledge/data, computational models have seldom been employed for CLD2,3 and, more importantly, they have never been used in conjunction with experiments to design combination therapies tailored to patients.
To systematically identify drug targets/candidates we will build on our preliminary work (https://goo.gl/5GJaq9) and use publicly-accessible gene expression datasets/bioinformatics to identify genes differentially regulated in NAFLD-fibrosis vs healthy subjects (Fig. 1, top left). We will then build a “gene signature” of NAFLD-fibrosis and use computational methods (e.g. DrugvsDisease - https://saezlab.github.io/DrugVsDisease/ with the support of Dr. Francesco Iorio (http://www.ebi.ac.uk/~iorio/Francesco_Iorio_WebSite/Home.html - see letter of support)) to identify drugs which repress genes overexpressed and/or augment expression of genes silenced in disease. After shortlisting drug candidates in silico, we will use machine-learning to cluster them and identify primary and secondary modes of action to assemble them into groups of drugs for investigation in vitro. Next, we will adapt the microscopy/microfluidics platform available in Dr. Menolascina’s laboratory4 (see Fig. 1, top right) to test candidate combination therapies in vitro, using a microfluidic device to trap human liver cells in which maximally dysregulated genes have been fluorescently tagged. We will then program a computer algorithm to decide the dose/timing of each drug: the “control algorithm” will quantify the expression of genes in the disease signature of NAFLD-fibrosis and automatically determine the best drug to administer to reset gene expression to physiological levels. Finally, we will use statistical methods to determine, from experimental replicates, emergent patterns in the timing/dosing of drugs that can progress to preclinical evaluation in in vivo disease models established in Dr. Fallowfield’s lab
Aims
- To identify, systematically, new drug targets/candidates for co-development in NAFLD-fibrosis;
- To screen drug combinations in silico;
- To test the most promising combination therapies in human liver cell lines (e.g. TWNT-4, C3A) and then in in vivo disease models.
Training Outcomes
At the end of this project the candidate will have a comprehensive understanding of:
- Computational biology methods to interrogate large gene expression datasets;
- Mathematical modelling to develop a computational system to “test” in silico candidate combination therapies;
- Control engineering methods to automatically determine the best stimulus (drug) to correct a pathological phenoytpe given the gene - expression status of cells;
- Microscopy and microfluidics to implement, experimentally, the control algorithm;
- Testing antifibrotic drugs/combinations in vivo.
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:
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 are encouraged to contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.
For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine
Funding Notes
Start: September 2018
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 qualifications, in an appropriate science/technology area.
Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £14,553 (RCUK rate 2017/18) for UK and EU nationals that meet all required eligibility criteria.
Full eligibility details are available: http://www.mrc.ac.uk/skills-careers/studentships/studentship-guidance/student-eligibility-requirements/
Enquiries regarding programme: [Email Address Removed]
References
1. Ryall KA, Tan AC. Systems biology approaches for advancing the discovery of effective drug combinations. J Cheminform. 2015;7:7.
2. Lee S, Zhang C, Liu Z, Klevstig M, Mukhopadhyay B, Bergentall M, Cinar R, Ståhlman M, Sikanic N, Park JK, Deshmukh S, Harzandi AM, Kuijpers T, Grøtli M, Elsässer SJ, Piening BD, Snyder M, Smith U, Nielsen J, Bäckhed F, Kunos G, Uhlen M, Boren J, Mardinoglu A. Network analyses identify liver-specific targets for treating liver diseases. Molecular Systems Biology 2017;13:938.
3. Hicks DF, Goossens N, Blas-García A, Tsuchida T, Wooden B, Wallace MC, Nieto N, Lade A, Redhead B, Cederbaum AI, Dudley JT, Fuchs BC, Lee YA, Hoshida Y, Friedman SL. Transcriptome-based repurposing of apigenin as a potential anti-fibrotic agent targeting hepatic stellate cells. Sci Rep 2017;7:42563.
4. Menolascina F, Fiore G, Orabona E, De Stefano L, Ferry M, Hasty J, di Bernardo M, di Bernardo D. In-vivo real-time control of protein expression from endogenous and synthetic gene networks. PLoS Comput Biol 2014;10(5):e1003625.