About the Project
We have developed cohort-wide repositories of ‘–omics’ data from various participants’ tissue samples such as blood, fat and vessels using state-of-the-art techniques to allow wide-ranging hypothesis testing, exploratory analysis and data integration. Then to validate the disease biomarker candidates or drug targets generated from the discovery phase we have established biobanks of primary cells isolated from the aforementioned participants’ tissues available for students to perform all the necessary in vitro validation assays including pre-adipocytes from AT and vascular cells from human vessels. Moreover, for in vivo validation assays we have multiple mouse models. Our approach led us to demonstrate that AT can secrete a large number of active molecules able to affect cardiovascular redox state in a paracrine and endocrine manner (Akoumianakis Science Transl Med 2019 and Akoumianakis Science Transl Med 2020). We have also discovered novel “inside to outside” signals from the cardiovascular system to the AT that drive the biology of epicardial/perivascular adipocytes, which “sense” cardiovascular inflammation and oxidation modifying their adipogenic capacity and secretory profile; these bi-directional communication channels play a role in the cardiovascular complications of obesity/insulin resistance and diabetes.
The basic science project we are offering aims to investigate the molecular effects of new therapeutics such as SGLT2 inhibitors on cardiovascular biology, by performing ex vivo experiments in human arteries and myocardial tissue, as well as primary cardiovascular cells and adipocytes. Clinical trials have suggested that SGLT2 inhibitors can reduce cardiovascular risk in diabetic patients; however, their role in the cross-talk between AT and the cardiovascular system has not been established yet. Hence, this project aims to explore this important gap in our knowledge, providing mechanistic insights on SGLT2 signalling as a therapeutic target to prevent cardiovascular disease progression in diabetes.
Students joining the clinical imaging team will join in continuing the exciting body of work related to the radiomic assessment of cardiac CT images, and possibly the analysis of other images such as CT carotid artery angiogram and CT aortograms. Projects would focus on the early detection of risk for disease relevant to the vessels and structures being imaged. Applicants with an aptitude to image analysis including utilising artificial intelligence techniques would be welcome.
The successful candidate will receive training in several techniques relevant to the execution of the project that are already available in the lab, such as isolation and culturing of primary cells from human vessels, myocardial and fat biopsies; co-culture bioassays; vasomotor studies on human vessels; superoxide measurements on human tissues/cells; pertinent techniques of biochemistry and cellular and molecular biology. The student will be also encouraged to undertake any training outside the lab that is necessary for a successful execution of the project.
Clinical students will receive training on the analysis of CT images and the development of reproducible image analysis techniques, data handling, and the statistical techniques required for the analysis of large and complex datasets
Students are encouraged to attend the MRC Weatherall Institute of Molecular Medicine DPhil Course, which takes place in the autumn of their first year. Running over several days, this course helps students to develop basic research and presentation skills, as well as introducing them to a wide-range of scientific techniques and principles, ensuring that students have the opportunity to build a broad-based understanding of differing research methodologies.
For October 2021 entry, the application deadline is 8th January 2021 at 12 noon midday, UK time.
Please visit our website for more information on how to apply.
Oikonomou EK, Williams MC, Kotanidis CP, Desai MY, Marwan M, Antonopoulos AS, Thomas KE, Thomas S, Akoumianakis I, Fan LM, Kesavan S, Herdman L, Alashi A, Centeno EH, Lyasheva M, Griffin BP, Flamm SD, Shirodaria C, Sabharwal N, Kelion A, Dweck MR, Van Beek EJR, Deanfield J, Hopewell JC, Neubauer S, Channon KM, Achenbach S, Newby DE, Antoniades C. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur Heart J. 2019 Sep 3.
Antoniades C, Antonopoulos AS, Deanfield J. Imaging residual inflammatory cardiovascular risk. Eur Heart J. 2019 Jul 16. pii: ehz474
Oikonomou EK, Marwan M, Desai MY, Mancio J, Alashi A, Hutt Centeno E, Thomas S, Herdman L, Kotanidis CP, Thomas KE, Griffin BP, Flamm SD, Antonopoulos AS, Shirodaria C, Sabharwal N, Deanfield J, Neubauer S, Hopewell JC, Channon KM, Achenbach S, Antoniades C. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet. 2018 Sep 15;392(10151):929-939
Antonopoulos AS, Sanna F, Sabharwal N, Thomas S, Oikonomou EK, Herdman L, Margaritis M, Shirodaria C, Kampoli AM, Akoumianakis I, Petrou M, Sayeed R, Krasopoulos G, Psarros C, Ciccone P, Brophy CM, Digby J, Kelion A, Uberoi R, Anthony S, Alexopoulos N, Tousoulis D, Achenbach S, Neubauer S, Channon KM, Antoniades C. Detecting human coronary inflammation by imaging perivascular fat. Science Transl Med. 2017 Jul 12;9(398). pii: eaal2658
Oikonomou EK, Antoniades C. The role of adipose tissue in cardiovascular health and disease. Nat Rev Cardiol. 2019 Feb;16(2):83-99
Antonopoulos AS, Margaritis M, Verheule S, Recalde A, Sanna F, Herdman L, Psarros C, Nasrallah H, Coutinho P, Akoumianakis I, Brewer AC, Sayeed R, Krasopoulos G, Petrou M, Tarun A, Tousoulis D, Shah AM, Casadei B, Channon KM, Antoniades C. Mutual Regulation of Epicardial AT and Myocardial Redox State by PPAR-γ/Adiponectin Signalling. Circ Res. 2016 Mar 4;118(5):842-55
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