Mutational profiling of mitochondrial DNA reveals an epithelial ovarian cancer-specific evolutionary pattern contributing to high oxidative metabolism.

BACKGROUND: Epithelial ovarian cancer (EOC) heavily relies on oxidative phosphorylation (OXPHOS) and exhibits distinct mitochondrial metabolic reprogramming. Up to now, the evolutionary pattern of somatic mitochondrial DNA (mtDNA) mutations in EOC tissues and their potential roles in metabolic remodelling have not been systematically elucidated. METHODS: Based on a large somatic mtDNA mutation dataset from private and public EOC cohorts (239 and 118 patients, respectively), we most comprehensively characterised the EOC-specific evolutionary pattern of mtDNA mutations and investigated its biological implication. RESULTS: Mutational profiling revealed that the mitochondrial genome of EOC tissues was highly unstable compared with non-cancerous ovary tissues. Furthermore, our data indicated the delayed heteroplasmy accumulation of mtDNA control region (mtCTR) mutations and near-complete absence of mtCTR non-hypervariable segment (non-HVS) mutations in EOC tissues, which is consistent with stringent negative selection against mtCTR mutation. Additionally, we observed a bidirectional and region-specific evolutionary pattern of mtDNA coding region mutations, manifested as significant negative selection against mutations in complex V (ATP6/ATP8) and tRNA loop regions, and potential positive selection on mutations in complex III (MT-CYB). Meanwhile, EOC tissues showed higher mitochondrial biogenesis compared with non-cancerous ovary tissues. Further analysis revealed the significant association between mtDNA mutations and both mitochondrial biogenesis and overall survival of EOC patients. CONCLUSIONS: Our study presents a comprehensive delineation of EOC-specific evolutionary patterns of mtDNA mutations that aligned well with the specific mitochondrial metabolic remodelling, conferring novel insights into the functional roles of mtDNA mutations in EOC tumourigenesis and progression.

Comparison of capture-based mtDNA sequencing performance between MGI and illumina sequencing platforms in various sample types.

BACKGROUND: Mitochondrial genome abnormalities can lead to mitochondrial dysfunction, which in turn affects cellular biology and is closely associated with the development of various diseases. The demand for mitochondrial DNA (mtDNA) sequencing has been increasing, and Illumina and MGI are two commonly used sequencing platforms for capture-based mtDNA sequencing. However, there is currently no systematic comparison of mtDNA sequencing performance between these two platforms. To address this gap, we compared the performance of capture-based mtDNA sequencing between Illumina's NovaSeq 6000 and MGI's DNBSEQ-T7 using tissue, peripheral blood mononuclear cell (PBMC), formalin-fixed paraffin-embedded (FFPE) tissue, plasma, and urine samples. RESULTS: Our analysis indicated a high degree of consistency between the two platforms in terms of sequencing quality, GC content, and coverage. In terms of data output, DNBSEQ-T7 showed higher rates of clean data and duplication compared to NovaSeq 6000. Conversely, the amount of mtDNA data obtained by per gigabyte sequencing data was significantly lower in DNBSEQ-T7 compared to NovaSeq 6000. In terms of detection mtDNA copy number, both platforms exhibited good consistency in all sample types. When it comes to detection of mtDNA mutations in tissue, FFPE, and PBMC samples, the two platforms also showed good consistency. However, when detecting mtDNA mutations in plasma and urine samples, significant differenceof themutation number detected was observed between the two platforms. For mtDNA sequencing of plasma and urine samples, a wider range of DNA fragment size distribution was found in NovaSeq 6000 when compared to DNBSEQ-T7. Additionally, two platforms exhibited different characteristics of mtDNA fragment end preference. CONCLUSIONS: In summary, the two platforms generally showed good consistency in capture-based mtDNA sequencing. However, it is necessary to consider the data preferences generated by two sequencing platforms when plasma and urine samples were analyzed.