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In last week’s WCMR Science Seminar Series, we heard from Dr Katja Menger about the role of topoisomerases in mtDNA maintenance, replication and transcription, and from Dr Mahmoud Fassad who discussed phenotype-driven analysis of mitochondrial disorders in Genomics England Dataset. Read on to find out more.

How to untangle mitochondrial DNA – Dr Katja Menger.

Imagine a circular piece of yarn, and now try to separate the individual threads from each other. You will soon end up with a knotted structure that cannot easily be put back together again. In order to smooth it all out you would probably end up using scissors to cut some of the strands, and move other strands through the gaps to separate them.

Mitochondrial DNA (mtDNA) is faced with a similar scenario. mtDNA is like the circular piece of yarn and the scissors are enzymes (topoisomerases) that cut, untwist, untangle, and rejoin the ends. The genetic information that is stored in mtDNA needs to be copied (through DNA replication) as well as expressed as proteins (through transcription). This can only happen when the DNA is unwound, and this unwinding creates problems with the twisting and knotting of DNA.

It is important to understand how topoisomerases work on mtDNA during these processes, so that we can identify when and why things go wrong. Using different sequencing techniques, we have looked at two different types of mitochondrial topoisomerases and their roles during mitochondrial DNA replication and transcription. We find that both are important to maintain mtDNA integrity, with more or less dominant roles. There is also evidence for some redundancy in the system, possibly explaining why variants in only one of these topoisomerases has to date been identified as a cause of mitochondrial disease.


Solve The Unsolved – Dr Mahmoud Fassad

Manifestations of mitochondrial diseases are highly variable and can occur at any age. Due to this high variability and the overlap between the clinical symptoms of mitochondrial diseases and other rare diseases, the clinical diagnosis of mitochondrial disease is very challenging and can be easily missed.

We have developed a computationally efficient unbiased way to identify patients with mitochondrial diseases that were otherwise classified in Genomic England dataset. This is based on list of symptoms more frequently reported in participants classified with mitochondrial diseases compared to participants otherwise classified. We applied several genetic analysis approaches to define the underlying genetic diagnosis in these participants.

This will help us to expand our understanding of the clinical symptoms and identify the underlying genetic causes of mitochondrial diseases.

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