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. 2019 Jun 4;47(10):e59.
doi: 10.1093/nar/gkz164.

Splice-Break: exploiting an RNA-seq splice junction algorithm to discover mitochondrial DNA deletion breakpoints and analyses of psychiatric disorders

Brooke E Hjelm  1   2 , Brandi Rollins  1 , Ling Morgan  1 , Adolfo Sequeira  1 , Firoza Mamdani  1 , Filipe Pereira  3 , Joana Damas  4 , Michelle G Webb  2 , Matthieu D Weber  1 , Alan F Schatzberg  5 , Jack D Barchas  6 , Francis S Lee  6 , Huda Akil  7 , Stanley J Watson  7 , Richard M Myers  8 , Elizabeth C Chao  9 , Virginia Kimonis  9 , Peter M Thompson  10 , William E Bunney  1 , Marquis P Vawter  1
Affiliations

Splice-Break: exploiting an RNA-seq splice junction algorithm to discover mitochondrial DNA deletion breakpoints and analyses of psychiatric disorders

Brooke E Hjelm et al. Nucleic Acids Res. .

Abstract

Deletions in the 16.6 kb mitochondrial genome have been implicated in numerous disorders that often display muscular and/or neurological symptoms due to the high-energy demands of these tissues. We describe a catalogue of 4489 putative mitochondrial DNA (mtDNA) deletions, including their frequency and relative read rate, using a combinatorial approach of mitochondria-targeted PCR, next-generation sequencing, bioinformatics, post-hoc filtering, annotation, and validation steps. Our bioinformatics pipeline uses MapSplice, an RNA-seq splice junction detection algorithm, to detect and quantify mtDNA deletion breakpoints rather than mRNA splices. Analyses of 93 samples from postmortem brain and blood found (i) the 4977 bp 'common deletion' was neither the most frequent deletion nor the most abundant; (ii) brain contained significantly more deletions than blood; (iii) many high frequency deletions were previously reported in MitoBreak, suggesting they are present at low levels in metabolically active tissues and are not exclusive to individuals with diagnosed mitochondrial pathologies; (iv) many individual deletions (and cumulative metrics) had significant and positive correlations with age and (v) the highest deletion burdens were observed in major depressive disorder brain, at levels greater than Kearns-Sayre Syndrome muscle. Collectively, these data suggest the Splice-Break pipeline can detect and quantify mtDNA deletions at a high level of resolution.

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Figures

Figure 1.
Figure 1.
Summary of the Splice-Break pipeline. This pipeline integrates long-range (LR) PCR of the mitochondrial genome with bead purification, NGS library prep and multiplex sequencing to generate mitochondrial sequence data that contains mtDNA deletion breakpoints. Alignment and mtDNA deletion breakpoint ‘junction’ calling were accomplished using MapSplice, an RNA-seq splice junction detection algorithm (34).
Figure 2.
Figure 2.
Analysis of artificial data. (A) Overlaying coverage plots of the mtDNA ‘common deletion’ from mixed ratios (0.01–50%) of deleted and wild type (undeleted) sequences, and (B) sensitivity analysis from this data and comparisons between Splice-Break (MapSplice (MS) alignment) and MitoDel (MS, BWA or STAR alignment). (C) Coverage plots of five Sanger-validated mtDNA deletions from 1:1 ratios of deleted and wild type (undeleted) sequences (50% each), and (D) sensitivity analysis for these deletions from a series of mixed ratios (1–50%) and comparisons between Splice-Break (MS alignment) and MitoDel (MS, BWA or STAR alignment). (E) Scatter plots of all junction calls observed for these five deletions before and after filtering steps when spiked-in at a heteroplasmy rate of 1%; arrows point to the actual breakpoint positions for that deletion. (F) Specificity (% true negative) assessed for these five deletions before and after filtering steps using a series of mixed ratios (1–50%). (G) Analysis of complex artificial deletion dataset combining 60 mtDNA deletions each at a rate of 1% (total expected heteroplasmy rate of 60%) and comparisons between Splice-Break (MS alignment) and MitoDel (MS, BWA or STAR alignment). Pie charts display detected sensitivity of cumulative deletion read %, and bar graphs display number of unique deletion breakpoints observed with each approach.
Figure 3.
Figure 3.
Global analysis of 4489 mtDNA deletions. (A) Chord diagrams display gene involvement across 4489 mtDNA deletions. Gene regions shown are not to scale with mitochondrial coordinates; the size of the gene shown represents the total number of deletion events (5′ breakpoints + 3′ breakpoints) within that gene. Ribbons are colored based on which gene contained the 5′ breakpoint. Gene locations for all (B) 5′ breakpoints and (C) 3′ breakpoints used in the 4489 mtDNA deletions; regions between protein-coding genes (that may contain several tRNAs) are referred to as gap1, gap2, etc. for simplicity. (D) Histogram of deletion sizes for all 4489 mtDNA deletions. Redundant usage of (E) 5′ and (F) 3′ breakpoints in multiple deletion species; arrows point to the 5′ and 3′ breakpoints for the 4977 bp ‘common deletion’, which were used in 31 and 52 deletions, respectively.
Figure 4.
Figure 4.
Characterization of the 30 most frequent mtDNA deletions. (A) Chord diagrams display gene involvement for the 30 most frequent mtDNA deletions. Gene regions shown are not to scale with mitochondrial coordinates; the size of the gene shown represents the total number of deletion events (5′ breakpoints + 3′ breakpoints) within that gene. Ribbons are colored based on which gene contained the 5′ breakpoint. (B) Histogram of deletion sizes for the 30 most frequent mtDNA deletions. (C) The proportion of each based used in the imperfect repeat sequences associated with the 30 deletion breakpoints compared to the entire mitochondrial genome, and results of Z score tests on proportions. (D) Condensed view of the artificial junctions.bed file generated for the 30 most frequent deletions (Supplemental Data S13) using the Integrative Genomics Viewer (IGV). (E) Pearson's correlation and (F) network analysis of significant correlations between the 30 most frequent deletions, and pH, PMI and donor age. OL (lagging strand origin-of-replication).
Figure 5.
Figure 5.
Tissue, age and paired analyses. Tissue analyses of (A) the cumulative deletion read %, (B) number of deletion species detected (per 10k coverage) and (C) the % burden per deletion. Statistical results shown (letters above tissue groups) are from Tukey's post-hoc analyses after correcting for coverage and age. Paired analyses of subject-matched brain regions (ACC x DLPFC, n = 35) and tissues (DLPFC × BLOOD, n = 9) for (D) the cumulative deletion read %, (E) number of deletion species detected (per 10k coverage) and (F) the % burden per deletion. Statistical results shown are from Pearson's correlation tests. Age analysis of (G) the cumulative deletion read %, (H) number of deletion species detected (per 10k coverage) and (I) the % burden per deletion in ACC (anterior cingulate cortex, n = 35), DLPFC (dorsolateral prefrontal cortex, n = 41), and BLOOD (whole blood, n = 9). Statistical results shown are linear regression models after correcting for coverage, pH and RIN.
Figure 6.
Figure 6.
Psychiatric disorder effects and comparisons to mitochondrial pathologies. Cumulative deletion read % across diagnoses in the (A) ACC (anterior cingulate cortex) and (B) DLPFC (dorsolateral prefrontal cortex), (C) the ratio of these two brain regions, and (D) pooled analysis of (C) after combining BD and SZ groups. Splice-Break coverage plots of mtDNA from (E) a typical blood sample with very few deletions, (F) a blood sample from a subject with Pearson's Syndrome, (G) a muscle sample from a subject with Kearns–Sayre syndrome, (H) an aging/adult brain sample with many low level mtDNA deletions (but no single, clonally amplified deletion), (I) two brain regions from one MDD subject, where the caudate nucleus (CAUN) displayed a large, clonally amplified deletion but the dorsolateral prefrontal cortex (DLPFC) did not and (J) two brain regions from another MDD subject, where both the anterior cingulate cortex (ACC) and DLPFC were affected by the same clonally amplified deletion. (K) Analysis of outliers based on threshold criteria (>2-fold more than 95th percentile of all other brain regions). Arrowheads point to one MDD subject where only the caudate nucleus (CAUN) was identified as an outlier, while all other brain regions from this subject were considered normal; full arrows denote one MDD subject where both brain regions analyzed (ACC and DLPFC) were identified as outliers. Positive controls of PS blood and KSS muscle are shown alongside for comparison. Statistics shown for are from one-way ANOVA (A–G) or hypergeometric mean distribution tests.
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