Unbiased PCR-free spatio-temporal mapping of the mtDNA mutation spectrum reveals brain region-specific responses to replication instability

Abstract

Background: The accumulation of mtDNA mutations in different tissues from various mouse models has been widely studied especially in the context of mtDNA mutation-driven ageing but has been confounded by the inherent limitations of the most widely used approaches. By implementing a method to sequence mtDNA without PCR amplification prior to library preparation, we map the full unbiased mtDNA mutation spectrum across six distinct brain regions from mice.

Results: We demonstrate that ageing-induced levels of mtDNA mutations (single nucleotide variants and deletions) reach stable levels at 50 weeks of age but can be further elevated specifically in the cortex, nucleus accumbens (NAc), and paraventricular thalamic nucleus (PVT) by expression of a proof-reading-deficient mitochondrial DNA polymerase, Polg^D181A. The increase in single nucleotide variants increases the fraction of shared SNVs as well as their frequency, while characteristics of deletions remain largely unaffected. In addition, Polg^D181A also induces an ageing-dependent accumulation of non-coding control-region multimers in NAc and PVT, a feature that appears almost non-existent in wild-type mice.

Conclusions: Our data provide a novel view of the spatio-temporal accumulation of mtDNA mutations using very limited tissue input. The differential response of brain regions to a state of replication instability provides insight into a possible heterogenic mitochondrial landscape across the brain that may be involved in the ageing phenotype and mitochondria-associated disorders.

Keywords: Ageing; Dorsal raphe; Mitochondrial DNA; Mitochondrion; Nucleus accumbens; Paraventricular thalamic nucleus; Polg; Polymerase gamma; Substantia nigra; mtDNA mutation.

Fig. 1

Ageing increases the load of both SNVs and deletions in mtDNA across all brain regions. a Schematic illustration of the workflow from mouse to prepared library. Briefly, brain regions of interest were rapidly sampled and total DNA was extracted. Linear DNA was enzymatically degraded by exonuclease (ExoV), and non-linear DNA is purified and used for library preparation. FL: full-length mtDNA molecule, ∆: mtDNA molecule with deletion. b Overview of the analysis workflow to optimise mtDNA variant detection. Shortly, after quality filtering, reads were mapped to mm10 without the mitochondrial chromosome (MT). Unmapped reads were then re-mapped to a modified MT reference (dMT: two MT references in tandem) and variants called. c Overview of mouse mtDNA. Green: rRNA encoding genes; blue: protein-coding genes; red: tRNA-encoding genes; orange: non-coding region (NCR). d Schematic showing the areas isolated as the cortex (COR), caudate putamen (CP), dorsal raphe (DR), nucleus accumbens (NAc), paraventricular nucleus of the thalamus (PVT), and substantia nigra (SN). e DNA stored before and after ExoV digestion was subjected to qPCR to determine the relative levels of three mtDNA and three nuclear targets before and after digestion (shown for two different mice, A and B). Mouse C was treated as A and B but without the addition of ExoV. Bars show the mean of target signals and the standard deviation is indicated. †: nDNA after ExoV treatment was not detected or only detected at a very low level by qPCR and may not be visible in the bar plot. f Dot plot illustrating the age-dependent increase in the load of SNVs (left) and deletions (right) across the investigated brain regions (as indicated by the colour legend). All samples have been normalised to the mean of the variants at 10 weeks. Grey diamonds indicate the mean of all regions at the indicated age, and the 95% confidence interval is shown. Three-way ANOVA showed age, not region or animal, significantly (p < 0.01) contributed to SNV and deletion levels. Tukey’s test was used post hoc to determine p values between each age group

Fig. 2

SNVs heterogeneously accumulate across brain regions in Polg^D181A mice and cause mtDNA position-specific mutational patterns. a Dot plot illustrating the age-dependent increase in the load of SNVs in Polg^D181A mice across the investigated brain regions (as indicated by the colour legend) normalised to the mean of WT samples at 10 weeks. Grey diamonds indicate the mean of WT-derived brain region samples for reference (same as in Fig. 1f). Red diamonds indicate the mean of Polg^D181A-derived brain region samples and the 95% confidence interval is shown. Three-way ANOVA (age, region, and animal) of Polg^D181A-derived samples showed that age significantly contributed to SNV levels (p values of post hoc Tukey’s test are shown). Three-way ANOVA showed a significant contribution of all variables (age, genotype, region). p values of post hoc Tukey’s test comparing WT and Polg^D181A at each age are shown. For region contribution, we found a significant contribution of COR, NAc, and PVT to SNV levels in Polg^D181A mice using a linear model for main effects. b SNVs were counted in 10-bp non-overlapping bins for WT (grey) and Polg^D181A (red) mice at 10, 50, and 80 weeks, and the number of regions with SNV in each bin calculated. Note that in the case that one region has more than one SNV in a bin, it is only counted as one instance of an SNV. The overlap was visualised for non-overlapping bins (“1”), bins shared across two or three regions (“2–3”), and bins shared across four to six regions (“4–6”). c SNVs were counted in 10-bp non-overlapping bins for WT (grey) and Polg^D181A (red) mice at 50 weeks, and the number of individual animals with SNVs in each bin calculated. Note that in the case that one animal has more than one SNV in a bin, it is only counted as one instance of an SNV. The overlap was visualised for non-overlapping bins (“1”), bins shared across two or three animals (“2–3”), and bins shared by four or more animals (“≥ 4”). d Cumulative percentage of SNVs detected in each examined brain region (thin lines) for both WT (grey) and Polg^D181A (red) at 10, 50, and 80 weeks old. Bold lines indicate the smooth conditional mean for each genotype. e The relative average SNV allele frequency for each region for WT (grey) and Polg^D181A (red) mice at 10, 50, and 80 weeks as indicated shown as boxplots. p values of two-sided t tests are shown. f SNVs across brain regions were pooled for each genotype at each age and divided into 100-bp bins across the mtDNA reference and the allele fraction for SNVs in each bin summed and normalised (i.e. highest peak set to 1). Grey areas indicate mtDNA regions where peaks are found across all variables (α), peaks that are ageing-dependent (β), and ageing-induced Polg^D181A-dependent peaks (γ)

Fig. 3

Accumulation of deletions induced by Polg^D181A expression are brain region-specific and ageing-dependent. a Dot plot illustrating the age-dependent increase in the load of SNVs in Polg^D181A mice across the investigated brain regions (as indicated by the colour legend) normalised to the mean of WT samples at 10 weeks. Grey diamonds indicate the mean of WT-derived brain region samples for reference (same as in Fig. 1f). Red diamonds indicate the mean of Polg^D181A-derived brain region samples and the 95% confidence interval is shown. Three-way ANOVA (age, region, and animal) of Polg^D181A-derived samples showed that age significantly contributed to deletion levels (p values of post hoc Tukey’s test are shown). p values of three-way ANOVA (age, genotype, region) with post hoc Tukey’s test are shown for each age group. For region contribution, we found a significant contribution of COR, NAc, and PVT to deletion levels in Polg^D181A mice using a linear model for main effects. b Chord diagrams indicating the deletions accumulated at 10, 50, and 80 weeks in DR and PVT from WT and Polg^D181A mice. Data is normalised pr. brain region, and the width of each gene indicates the summed allele fraction of deletions spanning the indicated gene(s). The colour of the chord indicates the gene in which the breakpoint 5′ position is located. Plots were made using circlize

Fig. 4

Characteristics of deletions change with age, but not the expression of Polg^D181A. a Density plot of deletion sizes for WT (grey) and Polg^D181A (red) for 10- (dotted line), 50- (dashed line), and 80-week-old (full line) mice. b The shortest average distance from 5′ and 3′ deletion breakpoint pairs to a direct repeat pair in the mitochondrial genome for the observed deletions (darker colour) and a random in silico generated deletion length-matched library (lighter colour) for both WT (left, in grey) and Polg^D181A (right, in red) using pooled data from all ages and brain regions examined for each genotype. p values of two-sided t tests are shown. c Needle identity score calculated in a ± 10 bp window at the 5′ and 3′ deletion breakpoints as a function of deletion size after pooling of WT and Polg^D181A samples. Correlation for each age is indicated by the full lines and correlation data indicated in the same colour code

Fig. 5

Putative NCR multimers are Polg^D181A-specific and accumulate with age in a highly brain region-specific manner. a Dot plot illustrating the age-dependent increase in the load of VLRDs in Polg^D181A mice across the investigated brain regions (as indicated by the colour legend). All samples have been normalised to the sample with the lowest number of detected variants. b Cumulative percentage of 5′ position of VLRDs (i.e. start position of the putative multimeric sequence) summed across brain regions for WT (grey) and Polg^D181A (red) for 10- (dotted line), 50- (dashed line), and 80-week-old (full line) mice. c Mean number of discordant reads as extracted by samtools at 10, 50, and 80 weeks for WT (grey) and Polg^D181A (red) and standard deviation is indicated. p values of two-sided t tests are shown. d Summed analysis of mtDNA breakpoints of VLRDs from Polg^D181A mice in the NCR and surrounding region. 5′ (purple) and 3′ (dark turquoise) breakpoints are summed at each position across all brain regions at either 50 (left) or 80 (right) weeks old, and smooth conditional means are plotted. The lower panel shows the phastCons conservation score via the UCSC genome browser in the same region. p values of two-sided t tests are shown. e Boxplot showing the shortest average distance to a direct repeat of all Polg^D181A VLRDs separated by VLRD 5′ position into < 15 kb (light blue) or > 15 kb (dark blue). p values of two-sided t tests are shown. f Boxplot showing the Needle identity score of WT and Polg^D181A-derived VLRDs pooled across ages and brain regions examined split into VLRDs with 3′ position < 15 kb (light blue) and > 15 kb (dark blue). p values of two-sided Wilcoxon tests are shown

Fig. 6

Levels of different variants correlate across mtDNA genes. a Identified variants plotted across the mtDNA reference for WT (top) and Polg^D181A (bottom) samples across all regions at 10, 50, and 80 weeks as indicated. Tracks from outside to inside: (1) mtDNA gene names (note that tRNA gene names are not shown), (2) mtDNA genes by length, (3) the relative level of VLRDs (e.g. multimers and inversions), (4) SNVs detected across all regions with height indicating log2-transformed allele frequency, and (5) deletions plotted as lines connected to start and end positions. Note that the start and end points of deletions are not indicated. b Load of SNV and deletion for each mtDNA gene divided by the gene length are plotted for WT (grey) and Polg^D181A (red) for 10-, 50-, and 80-week-old mice. Data was scaled (from 0 to 1) before plotting for clarity. Pearson correlation and significance of the correlation is shown below each plot for both WT (grey) and Polg^D181A (red)