Next-generation sequencing has become the method of choice diagnosing diseases and risks, but the size difference between the nuclear and mitochondrial genomes complicates its value for mitochondrial diseases. A new study led by Rita Horváth at the University of Cambridge offers new hope for this technology.

Mitochondrial diseases affect about 1 in 4,300 people. Unfortunately, they have different manifestations at different times in development. They also overlap with other common diseases. Finally, there are only a limited number of biomarkers for blood samples.

The Horváth laboratory developed the MitoPhen database that includes genotype-phenotype information and mitochondria (mt)DNA variant levels in blood and tissues from published reports. They test MitoPhen for its ability to determine phenotype similarity scores for patients with mitochondrial diseases and then in a large European Solve-RD rare disease cohort. They then used those data to develop a workflow to identify mtDNA variants using MToolBox and annotated them with the MITOMAP database.

Using these data-management tools, the team was able to identify additional patients with mtDNA mutations. Other methods leave significant uncertainty. The workflow developed here allows analysis of DNA samples for possible mtDNA-based diseases to reveal rare mitochondrial diseases in patients not suspected of those diseases. Thus, the workflow and analysis provide another tool for diagnosing these rare disorders.

 

Reference

Ratnaike T, Paramonov I, Olimpio C, Hoischen A, Beltran S, Matalonga L, Solve-RD Consortium, Horváth R (2025) Mitochondrial DNA disease discovery through evaluation of genotype and phenotype data: The Solve-RD experience. Am J Hum Genet https://www.cell.com/ajhg/fulltext/S0002-9297(25)00144-2?rss=yes.

 

A discussion with Dr. Horváth:

What are the likely next steps in your research?

Thiloka: It will be really interesting to expand this phenotype-based prioritisation techniques to other primary mitochondrial diseases, and we have indeed compiled a large manually curated updated version of MitoPhen for this purpose. We are now looking at nuclear genes that cause mitochondrial diseases to see whether we can highlight patients with phenotypes that are suggestive of mitochondrial diseases, to prioritise known or novel variants for further evaluation.

 

You found a deceptively small additional number (0.4%) of patients where no disease was expected. Of course, that number spread across a large population would become a big number. Do you think it will expand interest in mitochondrial diseases?

Thiloka: Absolutely! Mitochondrial diseases are known to affect in 1 in 4300 individuals, so this group of conditions is one of the largest in the field of inherited metabolic conditions. Being able to confidently diagnose individuals affected by these conditions mean that our pool of families to invite for potential therapeutic strategies or clinical trials will grow, enabling advancements in this challenging field.

 

As you noted, there is overlap with some diseases (e.g., diabetes). Will your technique help to parse the different diseases?

Thiloka: Great point. We believe this technique can help understand contribution of the variant to the clinical features. For example, in our study, we diagnosed individuals with sensorineural hearing loss with mitochondrial DNA variants that cause this presentation, however, there was one individual where the mitochondrial DNA variant didn’t fully explain the phenotype which consisted of several more features than just sensorineural hearing impairment. That is important to know as well because we are increasingly finding dual genetic diagnoses in this era of genetic testing. We can only try to achieve this level of phenotypic certainty by adding to existing genotype-phenotype databases with curated data at the individual level.

 

Can your workflow be “translated” so that it can be transitioned into the clinic? What would that take?

Thiloka: I am aiming to a tool which could be used in the clinical setting to understand the probability of a person having a primary mitochondrial disease based on their clinical features. A tool such as this could be helpful in utilizing resources effectively to prioritise advanced genetic testing for individuals with a high likelihood of this diagnosis. However, to get to this stage we would need to have a comprehensive resource that has compiled individual level data on primary mitochondrial diseases and have been tested in the setting of individuals with other genetic conditions (non-mitochondrial). We are trying to achieve this currently with our updated MitoPhen database that now contains data on 117 genotypes of primary mitochondrial diseases, and we are testing the utility of this dataset in large datasets including the 100,000 Genomes Project and RD-Connect. The unexplored situation is that of ‘real world’ clinical data extracted from electronic health records, which is likely to contain more ‘noise’ in the sense of phenotypic features which may not be relevant to mitochondrial disease, but is very much the next major step to take in this research.

 

How did you first become interested in mitochondria?

Thiloka: I became interested in mitochondria because I wanted to understand the processes behind what caused my cousin’s fatal degenerative condition, known as Kearns-Sayre Syndrome (a primary mitochondrial disease). I undertook my PhD at Newcastle University where I worked on understanding what we could learn from muscle biopsy findings from patients, to explain disease progression in different primary mitochondrial diseases, but also how muscle mitochondrial function changes with exercise. Since the PhD, I have remained committed to trying to streamline the diagnostic process for families because I realized we could better use the clinical record to inform their disease profile. It has been challenging trying to juggle this with clinical and family commitments, while I train to become a Paediatric Neurologist, but keeping in close contact with the amazing Lily Foundation maintains my motivation and desire to help add to this scientific domain!