A co-evolutionary approach to autophagy and infection
Lead Research Organisation:
University of Warwick
Department Name: Warwick Medical School
Abstract
Programme overview:
This MRC-funded doctoral training partnership (DTP) brings together cutting-edge molecular and analytical sciences with innovative computational approaches in data analysis to enable students to address hypothesis-led biomedical research questions. This is a 4-year programme whose first year involves a series of taught modules and two laboratory-based research projects that lead to an MSc in Interdisciplinary Biomedical Research. The first two terms consist of a selection of taught modules that allow students to gain a solid grounding in multidisciplinary science. Students also attend a series of masterclasses led by academic and industry experts in areas of molecular, cellular and tissue dynamics, microbiology and infection, applied biomedical technologies and artificial intelligence and data science. During the third and summer terms students conduct two eleven-week research projects in labs of their choice.
Project overview:
Living cells accumulate damaged, dangerous or excess material, which needs to be recycled. Autophagy (self-cannibalisation) enables cells to digest such material. Autophagy is especially important for defence against infection, since it allows cells to digest pathogens, which have invaded the cytoplasm. We know that agents of infectious diseases can manipulate autophagy in their hosts in order to enhance infection. If we can understand which effector proteins pathogens use to achieve this, we will be able to develop better treatments for infections. We might also be able to copy the methods that pathogens are using to manipulate autophagy, and use those methods to treat other diseases.
There are so many potential host/pathogen interactions that it is not feasible to use laboratory experimentation to identify all proteins used by pathogens to manipulate autophagy. In this project, we will get around this problem using a computational approach. Any protein that a host uses to defend against a pathogen, or protein that a pathogen uses to manipulate a host, is subject to the ongoing evolutionary struggle between hosts and pathogens known as "host pathogen co-evolution".
Under the supervision of Dr Penman, the student will develop computer simulations of this process for pathogens interacting with the autophagy machinery of their hosts, and use these simulations to understand the genetic signatures of such co-evolution. They will then analyse databases of pathogen genetic variation in order to identify candidate manipulators of autophagy. The student will then test whether or not such candidate effectors do manipulate autophagy by conducting laboratory experiments under the supervision of Prof Nezis. The skills developed in this project will be: computer programming, evolutionary-epidemiological modelling, bioinformatic analysis and molecular/cell biology (protein expression, purification, in vitro binding assays, GST-pull down, western blotting).
This project meets the MRC strategic skill priorities of quantitative skills and interdisciplinary skills.
This MRC-funded doctoral training partnership (DTP) brings together cutting-edge molecular and analytical sciences with innovative computational approaches in data analysis to enable students to address hypothesis-led biomedical research questions. This is a 4-year programme whose first year involves a series of taught modules and two laboratory-based research projects that lead to an MSc in Interdisciplinary Biomedical Research. The first two terms consist of a selection of taught modules that allow students to gain a solid grounding in multidisciplinary science. Students also attend a series of masterclasses led by academic and industry experts in areas of molecular, cellular and tissue dynamics, microbiology and infection, applied biomedical technologies and artificial intelligence and data science. During the third and summer terms students conduct two eleven-week research projects in labs of their choice.
Project overview:
Living cells accumulate damaged, dangerous or excess material, which needs to be recycled. Autophagy (self-cannibalisation) enables cells to digest such material. Autophagy is especially important for defence against infection, since it allows cells to digest pathogens, which have invaded the cytoplasm. We know that agents of infectious diseases can manipulate autophagy in their hosts in order to enhance infection. If we can understand which effector proteins pathogens use to achieve this, we will be able to develop better treatments for infections. We might also be able to copy the methods that pathogens are using to manipulate autophagy, and use those methods to treat other diseases.
There are so many potential host/pathogen interactions that it is not feasible to use laboratory experimentation to identify all proteins used by pathogens to manipulate autophagy. In this project, we will get around this problem using a computational approach. Any protein that a host uses to defend against a pathogen, or protein that a pathogen uses to manipulate a host, is subject to the ongoing evolutionary struggle between hosts and pathogens known as "host pathogen co-evolution".
Under the supervision of Dr Penman, the student will develop computer simulations of this process for pathogens interacting with the autophagy machinery of their hosts, and use these simulations to understand the genetic signatures of such co-evolution. They will then analyse databases of pathogen genetic variation in order to identify candidate manipulators of autophagy. The student will then test whether or not such candidate effectors do manipulate autophagy by conducting laboratory experiments under the supervision of Prof Nezis. The skills developed in this project will be: computer programming, evolutionary-epidemiological modelling, bioinformatic analysis and molecular/cell biology (protein expression, purification, in vitro binding assays, GST-pull down, western blotting).
This project meets the MRC strategic skill priorities of quantitative skills and interdisciplinary skills.
Organisations
People |
ORCID iD |
| Ioannis Nezis (Primary Supervisor) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/N014294/1 | 01/10/2016 | 30/09/2025 | |||
| 2268792 | Studentship | MR/N014294/1 | 01/10/2019 | 30/09/2023 |