Bioinformatic Approaches to Study Mitochondrial DNA Heteroplasmy

Mitochondria serve as signalling centres; they are primarily responsible for producing cellular energy and contain multiple copies of their own small, circular DNA, known as mitochondrial DNA(mtDNA). Mutations in mtDNA can be maternally inherited or somatic, when they occur after fertilisation. There are mechanisms at both the extra- and intracellular levels to prevent the transmission of these mutations across generations and cell cycles. The condition where both wild type and mutated mtDNA are present within the same cell is called hetero plasmy. Diverse and multi-system symptoms only appear when the level of mutated DNA becomes critical and exceeds above a threshold. While healthy individuals often carry low levels of mutations, primary mitochondrial diseases occur in patients with a high mutational load, affecting about 1 in 5,000 people. Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) is a primary mitochondrial disease that mainly affects the nervous system and muscles. It is most often caused by a heteroplasmic mutation at position m.3243A>G. To investigate the disease’s progression, we cultivated brain organoids from MELAS patients for either 100 or 200 days. Computational analysis of single cell RNA sequencing revealed the cellular mechanisms behind disease progression. We found evidence of calcium trafficking dysregu lation, which is associated with changes in mitochondrial morphology and function. This dysregulation can lead to axonal and dendritic abnormalities, predominantly affecting mature neurons and resulting in cellular developmental delays. Additionally, I address the main challenges of working with single-cell multiome data, with a focus on mitochondrial genomics. A key objective in the field is to estimate hetero plasmy at the single-cell level. The ATAC library of multiome experiments yields a large number of mitochondrial reads; however, determining the minimum mtDNA coverage needed to accurately estimate heteroplasmy remains to be defined. I approached this issue through comprehensive computational and statistical analysis, establishing standards that can benefit the entire research community. Finally, I developed a computational pipeline for analysing single-cell multiome datasets, with an emphasis on mitochondrial genomics. The advantages of a standardised pipeline include ensuring the reproducibility of results, as all software versions are documented. Additionally, the pipeline adheres to nf-core standards and is compartmentalised to prevent software incompatibilities. By parallelising processes and optimising computing resources, the overall run time of the pipeline is significantly reduced. I demonstrate the pipeline’s performance using both mouse and human test datasets.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• Lyons, Camilla

Advisor dc:contributor.advisor

• Chinnery, Patrick

• mitochondria
• bioinformatics

Licence

• https://www.repository.cam.ac.uk/bitstreams/16585325-7e0b-4610-b761-ed57c42fe347/download
• http://purl.org/NET/rdflicense/allrightsreserved

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.118291

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/384219