Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Group of Professor Kristijan Ramadan has published an original scientific article in Nature Communications today. The article has the first report of a new autophagy receptor TEX264, which may be involved in tumours developing drug resistance.

 

Autophagy (self-eating) is a critical cell process that allows cells to degrade and cycle damaged cell components.  In this case, TEX264 is key to identifying and recycling DNA with a permanent chemical link to a protein.

Covalent attachment of proteins to DNA is a constant problem.  Unless cells can clear their DNA of attached proteins they would die, so we knew they could do this, we just didn’t know how.

 Kristijan Ramadan’s group has uncovered how cells handle one example of this, the formation of a permanent link between DNA and a protein called Topoisomerase 1 to give a Topoisomerase 1 -cleavage complex (Top1-ccs).  Topoisomerase 1 (Top-1) is usually involved in unwinding DNA strands so that they may be easily copied, but if Top-1 gets permanently linked to DNA copying would get stuck and the cell would suffer further DNA damage. 

Specialised DNA repair machinery composed of the p97 ATPase, SPRTN protease and an autophagy receptor TEX264, tackles the linked protein-DNA.   Inactivation of the p97-SPRTN-TEX264 complex leads to accumulation of Top1-ccs and genomic instability. Pathological accumulation of Top1-ccs is linked to neurodegeneration and cancer.

The discovery of TEX264 and role it plays in resolving problems associated with covalent linking of proteins to DNA is important for cancer therapy, as Topoisomerase 1 inhibitors are one of the commonly used chemotherapeutics and they kill cancer cells by accumulation of Top1-ccs. Kristijan Ramadan’s report could be vital in treating tumours which have become resistant to Topoisomerase 1 inhibitors.

 

The full publication can be read here

Similar stories

Machine Learning Predicts SETD2 Mutation Status with Unprecedented Accuracy using DNA methylation

In a pan-cancer analysis spanning 24 different cancer type, researchers shed light on the critical role of SETD2 in tumourigenesis.

Oxford to launch UK’s first trials unit dedicated to conducting precision prevention and early detection studies

Oxford researchers have been given a £1 million boost to support their strategy of developing cancer prevention treatments and early diagnostic tools for people at high risk of cancer.

Multi-cancer blood test shows real promise in NHS trial

An NHS trial of a new blood test for more than 50 types of cancer correctly revealed two out of every three cancers in more than 5,000 people who had visited their GP with suspected symptoms, in England or Wales. The test also correctly identified the original site of cancer in 85% of those cases.

The Howat Foundation to fund Chair in Clinical Oncology

Oxford Cancer announce the endowment of a Chair in Clinical Oncology, thanks to generous philanthropic support from The Howat Foundation

New Oxford and Nottingham developed tool uses existing health records to predict people’s risk of developing lung cancer within the next 10 years

A team of researchers from the University of Oxford and the University of Nottingham have developed a new tool, called ‘CanPredict’, aimed at identifying the people most at risk of developing lung cancer over the next 10 years, and put them forward for screening tests earlier, saving time, money and, most importantly, lives.

Scientists find genetic ‘marker’ linked to serious side-effects from skin cancer treatment

New research from the Fairfax Group has identified a genetic marker that could be used to predict a patient’s risk of developing serious side-effects when undergoing immunotherapy treatment for metastatic melanoma.