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.

Mitochondrial DNA Haplogroups and SNPs: Risk Factors in Multiple Cancers Based on a Cross-Tumor Analysis in Chinese Population.

BACKGROUND: Mitochondrial DNA's (mtDNA) haplogroups and SNPs were associated with the risk of different cancer. However, there is no evidence that the same haplogroup or mitochondrial SNP (mtSNP) exhibits the pleiotropic effect on multiple cancers. METHODS: We recruited 2,489 participants, including patients with colorectal, hepatocellular, lung, ovarian, bladder, breast, pancreatic, and renal cell carcinoma. In addition, 715 healthy individuals from Northern China served as controls. Next, cross-tumor analysis was performed to determine whether mtDNA variation is associated with multiple cancers. RESULTS: Our results revealed a significant decrease in the occurrence risk of multiple cancers among individuals belonging to haplogroup A [OR = 0.553, 95% confidence interval (CI) = 0.375-0.815, P = 0.003]. Furthermore, we identified 11 mtSNPs associated with multiple cancers and divided the population into high-risk and low-risk groups. Low-risk groups showed a significantly reduced risk of occurrence compared with high-risk groups (OR = 0.614, 95% CI = 0.507-0.744, P < 0.001). Furthermore, using interaction analysis, we identified a special group of individuals belonging to haplogroup A/M7 and the low-risk population, who exhibit a lower risk of multiple cancers compared with other populations (OR = 0.195, 95% CI = 0.106-0.359, P < 0.001). Finally, gene set enrichment analysis confirmed that haplogroup A/M7 patients had lower expression levels of cancer-related pathway genes compared with haplogroup D patients. CONCLUSIONS: We found that specific mtDNA haplogroups and mtSNPs may play a role in predicting multiple cancer predisposition in Chinese populations. IMPACT: This may provide a potential tool for early screening in clinical settings for individuals in the Chinese population.