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. 2024 Jul 8;227(3):iyae070.
doi: 10.1093/genetics/iyae070.

UV damage induces production of mitochondrial DNA fragments with specific length profiles

Gus Waneka  1 , Joseph Stewart  2   3 , John R Anderson  4 , Wentao Li  5 , Jeffrey Wilusz  4 , Juan Lucas Argueso  2   3 , Daniel B Sloan  1   3
Affiliations

UV damage induces production of mitochondrial DNA fragments with specific length profiles

Gus Waneka et al. Genetics. .

Abstract

UV light is a potent mutagen that induces bulky DNA damage in the form of cyclobutane pyrimidine dimers (CPDs). Photodamage and other bulky lesions occurring in nuclear genomes can be repaired through nucleotide excision repair (NER), where incisions on both sides of a damaged site precede the removal of a single-stranded oligonucleotide containing the damage. Mitochondrial genomes (mtDNAs) are also susceptible to damage from UV light, but current evidence suggests that the only way to eliminate bulky mtDNA damage is through mtDNA degradation. Damage-containing oligonucleotides excised during NER can be captured with antidamage antibodies and sequenced (XR-seq) to produce high-resolution maps of active repair locations following UV exposure. We analyzed previously published datasets from Arabidopsis thaliana, Saccharomyces cerevisiae, and Drosophila melanogaster to identify reads originating from the mtDNA (and plastid genome in A. thaliana). In A. thaliana and S. cerevisiae, the mtDNA-mapping reads have unique length distributions compared to the nuclear-mapping reads. The dominant fragment size was 26 nt in S. cerevisiae and 28 nt in A. thaliana with distinct secondary peaks occurring in regular intervals. These reads also show a nonrandom distribution of di-pyrimidines (the substrate for CPD formation) with TT enrichment at positions 7-8 of the reads. Therefore, UV damage to mtDNA appears to result in production of DNA fragments of characteristic lengths and positions relative to the damaged location. The mechanisms producing these fragments are unclear, but we hypothesize that they result from a previously uncharacterized DNA degradation pathway or repair mechanism in mitochondria.

Keywords: Arabidopsis thaliana; Saccharomyces cerevisiae; XR sequencing; cyclobutane pyrimidine dimer (CPD); mitochondrial genome (mtDNA); mtDNA degradation; nucleotide excision repair (NER); photodamage.

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Conflict of interest statement

Conflicts of interest The author(s) declare no conflicts of interest.

Figures

Fig. 1.
Fig. 1.
Overview of XR-seq protocol. Panel a shows direct capture of damage-containing excised oligomers as performed in experiments with S. cerevisiae, A. thaliana, and D. melanogaster. After immunoprecipitation with damage-specific antibodies, adaptors are attached to excised and the damaged sites are repaired by a photolyase before the molecules are amplified and sequenced. Panel b shows the alternative XR-seq approach, which includes an initial immunoprecipitation against transcription factor IIH (TFIIH; the enzymatic complex that associates with excised oligomers in mammalian cells). Then, adaptors are ligated to the ssDNA fragments before a second immunoprecipitation with antidamage antibodies, photolyase damage reversal, and library amplification/sequencing.
Fig. 2.
Fig. 2.
Read length distributions of nuclear and mitochondrial reads from anti-CPD and anti-(6-4)PP libraries from S. cerevisiae. These distributions exhibited a high degree of repeatability across samples and conditions. Pearson's correlation analyses reveal significant correlations between the anti-CPD and anti-(6-4)PP read length distributions (R = 0.9555, P = 1.6E-11) as well as between anti-CPD (5 vs 20 minutes; R = 0.9951, P = 2.2E-16) and anti-(6-4)PP (5 vs 20 minutes; R = 0.9765, P = 3.92E-14) timepoints.
Fig. 3.
Fig. 3.
Read length distributions of nuclear, mitochondrial, and plastid mapping reads from the A. thaliana anti-CPD libraries. Pearson's correlation analyses reveal significant correlations between in the mtDNA read length distributions between time points (2 vs 5 hours; R = 0.9899, P = 2.2E-16).
Fig. 4.
Fig. 4.
Read length distributions of nuclear and mitochondrial mapping reads from the d. melanogaster anti-CPD libraries.
Fig. 5.
Fig. 5.
Di-pyrimidine frequencies in the most abundant read length classes (26-, 24-, 22-, and 20-nt) from the S. cerevisiae anti-CPD libraries. Read lengths are denoted in the gray boxes at the top left of each panel. The pink horizontal lines show the frequency of TT dinucleotides in the S. cerevisiae mtDNA, providing a null expectation for TT dinucleotide frequencies in the XR-seq reads. Positions with TT peaks in the 26-nt reads are in red, and the equivalent positions in the 3′ aligned (right aligned) 24-, 22-, and 20-nt reads are also in red. We approximated the 95% confidence interval as two times the standard error of the expected TT frequency given the number of reads included for each di-pyrimidine calculation. Given the large number of reads analyzed, 95% confidence intervals are very small, ranging from 0.1472 ± 0.0022 for the CPD 26-nt reads to 0.1472 ± 0.0081 for the CPD 20-nt reads. As a result, all blue bars that appear above the pink line in the figure represent a significant statistical enrichment relative to the expectation and its 95% confidence interval.
Fig. 6.
Fig. 6.
Di-pyrimidine frequencies in the most abundant read length classes (32, 28, 24, 20, and 16 nt) from the A. thaliana mtDNA-mapping reads. Read lengths are denoted in the gray boxes at the top right of each panel. The pink horizontal lines show the frequency of TT dinucleotides in the A. thaliana mtDNA, providing a null expectation for TT dinucleotide frequencies in the XR-seq reads. See Fig. 5 for a description of calculating 95% confidence intervals around this expectation. These confidence intervals were very small due to the large number of reads, ranging from 0.0743 ± 0.0033 for the 16-nt reads to 0.0743 ± 0.0018 for the 32-nt reads.
Fig. 7.
Fig. 7.
Proposed alternative explanations for unique read length distributions and dinucleotide composition patterns in the S. cerevisiae anti-CPD and anti (6-4)PP datasets.
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