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.

Mutational signature of mtDNA confers mechanistic insight into oxidative metabolism remodeling in colorectal cancer.

Rationale: Mitochondrial dysfunction caused by mitochondrial DNA (mtDNA) mutations and subsequent metabolic defects are closely involved in tumorigenesis and progression in a cancer-type specific manner. To date, the mutational pattern of mtDNA somatic mutations in colorectal cancer (CRC) tissues and its clinical implication are still not completely clear. Methods: In the present study, we generated a large mtDNA somatic mutation dataset from three CRC cohorts (432, 1,015, and 845 patients, respectively) and then most comprehensively characterized the CRC-specific evolutionary pattern and its clinical implication. Results: Our results showed that the mtDNA control region (mtCTR) with a high mutation density exhibited a distinct mutation spectrum characterizing a high enrichment of L-strand C > T mutations, which was contrary to the H-strand C > T mutational bias observed in the mtDNA coding region (mtCDR) (P < 0.001). Further analysis clearly confirmed the relaxed evolutionary selection of mtCTR mutations, which was mainly characterized by the similar distribution of hypervariable region (HVS) and non-HVS mutation density. Moreover, significant negative selection was identified in mutations of mtDNA complex V (ATP6/ATP8) and tRNA loop regions. Although our data showed that oxidative metabolism was commonly increased in CRC cells, mtDNA somatic mutations in CRC tissues were not closely associated with mitochondrial biogenesis, oxidative metabolism, and clinical progression, suggesting a cancer-type specific relationship between mtDNA mutations and mitochondrial metabolic functions in CRC cells. Conclusion: Our study identified the CRC-specific evolutionary mode of mtDNA mutations, which is possibly matched to specific mitochondrial metabolic remodeling and confers new mechanic insight into CRC tumorigenesis.

Mitochondrial DNA haplogroup M7: A predictor of poor prognosis for colorectal cancer patients in Chinese population.

Haplogroups and single-nucleotide polymorphisms (SNP) of mitochondrial DNA (mtDNA) were associated with the prognosis of many types of cancer patients. However, whether mtDNA haplogroups contribute to clinical outcomes of colorectal cancer (CRC) in Chinese population remains to be determined. In this study, mtDNA of tissue samples from 445 CRC patients from Northwestern China was sequenced to evaluate the association between haplogroup and prognosis. The mtDNA sequencing data of 1015 CRC patients from Southern China were collected for validation. We found patients with mtDNA haplogroup M7 had a significantly higher death risk when compared with patients with other haplogroups in both Northwestern (Hazard ratio [HR] = 3.093, 95% CI = 1.768-5.411, p < 0.001) and Southern (HR = 1.607, 95% CI = 1.050-2.459, p = 0.029) China. Then, a haplogroup M7-based mtSNP classifier was selected by using LASSO Cox regression analysis. A nomogram comprising the mtSNP classifier and clinicopathological variables was developed to predict the prognosis of CRC patients (area under the curve [AUC] 0.735, 95% CI = 0.679-0.791). Furthermore, patients with high- and low-risk scores calculated by the haplogroup M7-based mtSNP classifier exhibited significantly different overall survival (OS) and recurrence-free survival (RFS) (all p < 0.001). Finally, RNA-seq and immunohistochemical analyses indicated the poor prognosis of patients with haplogroup M7 may be related to mitochondrial dysfunction and immune abnormalities in CRC tissues. In conclusion, the haplogroup M7 and haplogroup M7-based mtSNP classifier seems to be a practical and reliable prognostic predictor for CRC patients, which provides a potential tool of clinical decision-making for patients with haplogroup M7 in Chinese population.

Programmable optical real-time processing system for ultra-wide bandwidth electronic reconnaissance.

Electronic reconnaissance is to detect signals, extract their parameters, modulation types or direction of arrival and so on from a wide bandwidth range. It is difficult for digital signal processing device to process in real time under an ultra-wide bandwidth environment. This paper proposed a programmable optical system which can process signals from an instantaneous bandwidth up to 40GHz in real time. In the optical system, the signals are reconstructed at wavefront of a laser beam. The laser beam carrying signals passes through an optical system composed by lens, beam splitter, light modulator, etc. Signal processing operation is accomplished when laser beam arrives at a focal plane, and processing results are acquired by a high-speed camera. Typical pulse description words can be yielded from the results. The proposed optical system has a nano-second processing delay due to its meter-length light path.

Next-Generation Sequencing-Based Analysis of Urine Cell-Free mtDNA Reveals Aberrant Fragmentation and Mutation Profile in Cancer Patients.

BACKGROUND: Many studies have demonstrated the high efficacy of cell-free nuclear DNA in cancer diagnostics. Compared to nuclear DNA, mitochondrial DNA (mtDNA) exhibits distinct characteristics, including multiple copies per cell and higher mutation frequency. However, the potential applicability of cell-free mtDNA (cf-mtDNA) in plasma and urine remains poorly investigated. METHODS: Here, we comprehensively analyzed the fragmentomic and mutational characteristics of cf-mtDNA in urine and plasma samples from controls and cancer patients using next-generation sequencing. RESULTS: Compared to plasma cf-mtDNA, urine cf-mtDNA exhibited increased copy numbers and wider spread in fragment size distributions. Based on 2 independent animal models, urine cf-mtDNA originated predominantly from local shedding and transrenal excretion. Further analysis indicated an enhanced fragmentation of urine cf-mtDNA in renal cell carcinoma (RCC) and colorectal cancer (CRC) patients. Using the mtDNA sequence of peripheral blood mononuclear cells for reference, the mutant fragments were shorter than wild-type fragments in urine cf-mtDNA. Size selection of short urine cf-mtDNA fragments (<150 bp) significantly enhanced the somatic mutation detection. Our data revealed remarkably different base proportions of fragment ends between urine and plasma cf-mtDNA that also were associated with fragment size. Moreover, both RCC and CRC patients exhibited significantly higher T-end and lower A-end proportions in urine cf-mtDNA than controls. By integrating the fragmentomic and mutational features of urine cf-mtDNA, our nomogram model exhibited a robust efficacy for cancer diagnosis. CONCLUSIONS: Our proof-of-concept findings revealed aberrant fragmentation and mutation profiles of urine cf-mtDNA in cancer patients that have diagnostic potential.

mitoDataclean: A machine learning approach for the accurate identification of cross-contamination-derived tumor mitochondrial DNA mutations.

Next-generation sequencing (NGS) of mitochondrial DNA (mtDNA) has widespread applications in aging and cancer studies. However, cross-contamination of mtDNA constitutes a major concern. Previous methods for the detection of mtDNA contamination mainly focus on haplogroup-level phylogeny, but neglect haplotype-level differences, leading to limited sensitivity and accuracy. In our study, we present mitoDataclean, a random-forest-based machine learning package for accurate identification of cross-contamination, evaluation of contamination levels and detection of contamination-derived variants in mtDNA NGS data. Comprehensive optimization of mitoDataclean revealed that training simulation with mixtures of small haplogroup distance and low polymorphic difference was critical for optimal modeling. Compared to existing methods, mitoDataclean exhibited significantly improved sensitivity and accuracy for the detection of sample contamination in simulated data. In addition, mitoDataclean achieved area under the curve values of 0.91 and 0.97 for discerning genuine and contamination-derived mtDNA variants in a simulated Western dataset and private sequencing contamination data, respectively, suggesting that this tool may be applicable for different populations and samples with different sources of contamination. Finally, mitoDataclean was further evaluated in several private and public datasets and showed a robust ability for contamination detection. Altogether, our study demonstrates that mitoDataclean may be used for accurate detection of contaminated samples and contamination-derived variants in mtDNA NGS data.