Analyzing Low-Level mtDNA Heteroplasmy—Pitfalls and Challenges from Bench to Benchmarking

Abstract

Massive parallel sequencing technologies are promising a highly sensitive detection of low-level mutations, especially in mitochondrial DNA (mtDNA) studies. However, processes from DNA extraction and library construction to bioinformatic analysis include several varying tasks. Further, there is no validated recommendation for the comprehensive procedure. In this study, we examined potential pitfalls on the sequencing results based on two-person mtDNA mixtures. Therefore, we compared three DNA polymerases, six different variant callers in five mixtures between 50% and 0.5% variant allele frequencies generated with two different amplification protocols. In total, 48 samples were sequenced on Illumina MiSeq. Low-level variant calling at the 1% variant level and below was performed by comparing trimming and PCR duplicate removal as well as six different variant callers. The results indicate that sensitivity, specificity, and precision highly depend on the investigated polymerase but also vary based on the analysis tools. Our data highlight the advantage of prior standardization and validation of the individual laboratory setup with a DNA mixture model. Finally, we provide an artificial heteroplasmy benchmark dataset that can help improve somatic variant callers or pipelines, which may be of great interest for research related to cancer and aging.

Implications of Standardized Uptake Values of Oral Squamous Cell Carcinoma in PET-CT on Prognosis, Tumor Characteristics and Mitochondrial DNA Heteroplasmy

“…An in-depth analysis on mtDNA mutations in the entire prospective cohort has recently been published [ 27 ]. Applying a threshold of 2% for the heteroplasmy detection, which after performing in-depth analysis has been shown to produce results less prone to artefacts [ 29 ], 51 mutations were detected overall (46 private and 5 shared). Heteroplasmies (HP) were found in 11 of the benign samples and 10 of the malignant samples and, in concordance with the overall cohort, private mutations in OSCC samples were more frequent compared to the paired benign tissue ( n = 28 vs. 18).…”

“…The data were analyzed using the mtDNA-Server as previously described [ 28 ] by annotating potential artefacts from nuclear encoded mitochondrial pseudogenes (NUMTs: nuclear inserts of mitochondrial genes). Additionally, based on a mixture model comparing different polymerase enzymes [ 29 ], we could detect a phantom mutation on position 3210, which was excluded for this analysis.…”

From Forensics to Clinical Research: Expanding the Variant Calling Pipeline for the Precision ID mtDNA Whole Genome Panel

“…On the other hand, mtDNA differences between the same sample are not unheard of, as DNA polymerases, amplification protocols, sequencing runs, and variant callers are frequent sources of disparity in genomics [ 35 , 44 ]. Discerning sequencing errors/false positives from true heteroplasmic variants is a challenging task, usually achieved through post-sequencing curation, looking for signs of poor amplification, strand bias, mutations in LCR, and the presence of NUMTs [ 41 , 45 , 46 , 47 , 48 , 49 ].…”

mtDNA Heteroplasmy: Origin, Detection, Significance, and Evolutionary Consequences

“…Thirdly, some knowledge of the template sequence is needed for all PCR-based methods. More recently, various next-generation sequencing (NGS) methods have revolutionized the study of heteroplasmy because they could detect and quantify very rare heteroplasmic variants using a great variety of sophisticated experimental techniques and bioinformatic tools [ 54 , 55 , 56 , 57 , 58 ]. The NGS methods revealed the extent of heteroplasmy in tissues and organisms, gave insight on its causes, and identified its role in mitochondrial diseases and aging [ 6 , 30 , 32 ].…”

“…While traditional lab techniques have been employed in the past, and they still have merit for NUMT detection, the modern, massive sequencing data, need sophisticated bioinformatic approaches to identify NUMTs in the genomes. Such methods have been recently developed [ 54 , 55 , 77 , 78 , 79 , 80 ], but they are more efficient in human genome. Even in that case, the bioinformatic tools which have been developed for general use might not be adequate to detect NUMTs that are large and recently embedded in the nuclear genome [ 72 , 73 ].…”