{"doi":"10.1002/ctm2.1055","title":"Novel attributes of cell‐free plasma mitochondrial DNA in traumatic injury","abstract":"Plasma mitochondrial DNA (mtDNA) fragment abundance has emerged as a biomarker in multiple human disorders, thus pointing to the prospect that mtDNA, like nuclear DNA (nDNA), could be a useful substrate for liquid biopsy.1-4 Structural attributes of plasma mtDNA fragments, may contain prognostic information beyond quantitative-polymerase chain reaction (PCR) measured abundance.5, 6 While deep sequencing could be informative, the method is limited by the low concentration of mtDNA relative to nDNA in plasma.7 In this communication, we describe a combined target-bait enrichment, sequencing and analytical protocol to improve quantitation and structural insights into plasma mtDNA fragments. Plasma obtained on hospital admission from 30 consecutive patients admitted to a single academic surgical-trauma intensive care unit was utilized to determine if mtDNA damage-associated molecular patterns (DAMP) abundance or other parameters were associated with acute complications. Attributes of mtDNA DAMPs enriched from plasma utilizing a commercially available target-bait capture kit were explored using Next Generation sequencing on an Illumina platform. We then developed an alignment and filtering strategy to fully quantify mtDNA DAMP abundance over the entire mitochondrial genome, characterize fragment lengths, and identify mtDNA heteroplasmy. (Figure 1A). Due to high sequence homology, we assumed the target-bait capture method would also enrich nuclear mitochondrial (NUMT) pseudogenes.8 There are two varieties of NUMTs; those enumerated in the reference genome, and a second that is polymorphic, meaning they are found sporadically in the population.8 About 1500 reference NUMTs have been identified, spanning ∼100 Kbp of the nuclear genome. A simulation analysis of mitochondrial sequences and NUMT sequences determined that while most reads were uniquely aligned, there were multiple reads that align with both the mitochondrial and nuclear genomes (Figure S1A–C). While quality scores >20 improve mapping efficiency, some reads cannot be uniquely aligned. Therefore, to improve accuracy, our algorithm judiciously excludes all reads that ambiguously align to both nuclear and mitochondrial genomes. While this potentially undercounts mtDNA DAMP abundance, it improves the rigour of variant classification. Detection of polymorphic NUMTs is far more challenging than the enumerated NUMT population because the latter often share greater than 99% homology, are not contained in the reference assembly, and can only be discovered by matching paired reads that align to both the nuclear and mitochondrial genomes.8 As depicted, polymorphic NUMTs captured by target-bait enrichment can be identified by sequenced fragments of nDNA outside of the NUMT insertion point (Figure 1B). Some read pairs aligned to both the nuclear and mitochondrial genomes, while the regions flanking the polymorphic NUMT insertion sites were not homologous to the mtDNA genome (Figure 1C). There is currently no available strategy that can completely eliminate polymorphic NUMTs. However, because of the apparent low frequency of this type of NUMT, their inadvertent inclusion is unlikely to lead to a significant overestimation of the authentic mtDNA DAMP abundance. In four patients from a protocol development study, we found that the enrichment and alignment strategy produced 1412 ± 1333 (mean ± SD)-fold enrichment of mtDNA as compared to WGS (Figure 2A). We explored the utility of our protocol by characterizing plasma mtDNA DAMPs in 30 consecutively-enrolled trauma patients (Figure 2B). First, to determine if there were differences in mtDNA DAMP abundance or fragment lengths as a function of the mtDNA sequence from which they aligned, we normalized these parameters into 100 bp bins and depicted means ± S.D. as a function of the bin from which they originated, finding no sequence-dependent differences (Figure 2C,D). Thus, in this cohort and patient population, quantitation of mtDNA DAMP abunda","journal":"Clinical and Translational Medicine","year":2022,"id":293322,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9566,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":828823,"name":"Viktor M. Pastukh","orcid":null,"position":1,"is_corresponding":false},{"id":828824,"name":"Yong B. Tan","orcid":null,"position":2,"is_corresponding":false},{"id":519036,"name":"C. Michael Francis","orcid":"0000-0001-6001-2618","position":3,"is_corresponding":false},{"id":828825,"name":"C. Zack Aggen","orcid":null,"position":4,"is_corresponding":false},{"id":828826,"name":"S. Chris Groark","orcid":null,"position":5,"is_corresponding":false},{"id":828827,"name":"Carson Edwards","orcid":null,"position":6,"is_corresponding":false},{"id":978041,"name":"Madhuri S. Mulekar","orcid":"0000-0001-8659-728X","position":7,"is_corresponding":false},{"id":828828,"name":"Mohammad Hamo","orcid":null,"position":8,"is_corresponding":false},{"id":828829,"name":"Jon D. Simmons","orcid":null,"position":9,"is_corresponding":false},{"id":417808,"name":"Matthew Kutcher","orcid":"0000-0003-4566-5359","position":10,"is_corresponding":false},{"id":828415,"name":"Emily M. Hartsell","orcid":"0000-0003-0268-9916","position":11,"is_corresponding":false},{"id":438260,"name":"Darrell L. Dinwiddie","orcid":"0000-0002-3283-9367","position":12,"is_corresponding":false},{"id":71872,"name":"Zachary M. Turpin","orcid":"0000-0002-6488-2503","position":13,"is_corresponding":false},{"id":71877,"name":"Hank W. Bass","orcid":"0000-0003-0522-0881","position":14,"is_corresponding":false},{"id":71871,"name":"Justin T. Roberts","orcid":"0000-0003-4433-1234","position":15,"is_corresponding":false},{"id":71876,"name":"Mark N. Gillespie","orcid":"0009-0000-1278-1666","position":16,"is_corresponding":false},{"id":332316,"name":"Raymond J. Langley","orcid":"0000-0001-8849-9325","position":17,"is_corresponding":false},{"id":71874,"name":"Grant T. Daly","orcid":"0000-0002-4109-0546","position":0,"is_corresponding":true}],"reference_count":9,"raw_metadata":null,"created_at":"2026-07-19T00:30:53.818562Z","pmid":"36245326","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}