Cerebellum mitochondrial DNA copy number is increased in Parkinson’s disease

Talia Beglarian; David R Tyrpak; J Raphael Gibbs; John Andrew MacKay; Sonja W Scholz; Bryan J Traynor; Marilyn S Albert; Liana S Rosenthal; Ted M Dawson; Juan C Troncoso; Dena G Hernandez; Mark R Cookson; Charles H Adler; Geidy Serrano; Andrew B Singleton; Thomas G Beach; Brooke E Hjelm

doi:10.1093/braincomms/fcaf296

. 2025 Aug 20;7(4):fcaf296. doi: 10.1093/braincomms/fcaf296

Cerebellum mitochondrial DNA copy number is increased in Parkinson’s disease

Talia Beglarian ^1,^#, David R Tyrpak ^2,^3,^#, J Raphael Gibbs ⁴, John Andrew MacKay ⁵, Sonja W Scholz ^6,⁷, Bryan J Traynor ⁸, Marilyn S Albert ⁹, Liana S Rosenthal ¹⁰, Ted M Dawson ¹¹, Juan C Troncoso ¹², Dena G Hernandez ¹³, Mark R Cookson ¹⁴, Charles H Adler ¹⁵, Geidy Serrano ¹⁶, Andrew B Singleton ^17,¹⁸, Thomas G Beach ¹⁹, Brooke E Hjelm ^20,^✉

¹ Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy St. NRT 2502, Los Angeles, CA 90033, USA

² Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy St. NRT 2502, Los Angeles, CA 90033, USA

³ Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90089, USA

⁴ Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA

⁵ Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90089, USA

⁶ Neurodegenerative Diseases Research Section, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892, USA

⁷ Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD 21287, USA

⁸ Neuromuscular Diseases Research Section, National Institute on Aging, Bethesda, MD 20892, USA

⁹ Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD 21287, USA

¹⁰ Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD 21287, USA

¹¹ Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD 21287, USA

¹² Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD 21287, USA

¹³ Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA

¹⁴ Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA

¹⁵ Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ 85259, USA

¹⁶ Banner Sun Health Research Institute, Sun City, AZ 85351, USA

¹⁷ Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA

¹⁸ Center for Alzheimer’s and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA

¹⁹ Banner Sun Health Research Institute, Sun City, AZ 85351, USA

²⁰ Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy St. NRT 2502, Los Angeles, CA 90033, USA

^✉

Correspondence to: Brooke E. Hjelm, PhD Department of Translational Genomics, Keck School of Medicine, University of Southern California 1450 Biggy St. NRT 2502, Los Angeles, CA 90033, USA E-mail: bhjelm@usc.edu

Talia Beglarian and David R Tyrpak contributed equally to this work.

PMCID: PMC12378876 PMID: 40873892

Abstract

Bioinformatics methods can be used to quantify mitochondrial DNA copy number from whole genome sequencing (WGS) data. We evaluated mitochondrial DNA copy number from human brain-derived WGS data using the fastMitoCalc tool. 341 Parkinson’s Disease cerebellum samples were compared with 74 age-matched controls from the North American Brain Expression Consortium. Parkinson’s Disease cerebellum had significantly higher mitochondrial DNA copy number compared with controls (P = 4.15e–7), and this effect was reproducible in four of five brain banks when analysis was restricted to each resource that contributed Parkinson’s Disease samples to this genetic dataset. Follow-on analyses of 128 Parkinson’s Disease cerebellum samples and 33 controls that had paired neuropathology data and clinical scores demonstrated a significant increase in mitochondrial DNA copy number with Unified Staging System for Lewy Body disorders stages and Unified Parkinson’s Disease Rating Scale (off meds) motor scores. Analysis of Lewy Body scores from ten brain regions showed cerebellum mitochondrial DNA copy number increased upon pathological infestation of α-synuclein aggregates in the brainstem and limbic system but did not increase after late-stage neocortical involvement. This genetics dataset supports previous observations of cerebellum activation in Parkinson’s Disease and suggests mitochondrial DNA copy number may increase to support this regional activation as a compensatory mechanism to pathology or motor symptoms.

Keywords: cerebellum, mitochondrial DNA copy number, Parkinson’s disease, WGS, Lewy body

Beglarian et al. reported that cerebellum mitochondrial DNA copy number is elevated in Parkinson’s Disease and correlates with increased α-synuclein aggregates in the brainstem and limbic system but not with late-stage neocortical involvement. This suggests cerebellum mitochondrial DNA copy number may increase in response to disease pathology and/or motor symptoms.

Graphical Abstract

Introduction

Mitochondria are essential for cellular processes and functions that require energy and are unique organelles that harbor their own 16.5 kb double-stranded, circular genome. The mitochondrial genome can be sequenced directly and/or evaluated in genomics data using whole genome (WGS) or whole exome (WES) sequencing.¹ Mitochondrial DNA copy number (mtDNA CN), or the ratio of mitochondrial DNA to nuclei, can be quantified by several methods including quantitative PCR of mitochondrial and nuclear gene fragments and with WGS/WES data using bioinformatics tools such as fastMitoCalc.^1,2 While the majority of WGS/WES data available for complex adult diseases is from blood or saliva, due to the easy access of these biofluids and study goals of assessing germline variants, several large-scale genetic studies on neurodegenerative diseases have sequenced DNA from human brain tissue instead.^3-7 This brain-derived sequencing data, generated by collaborative efforts of brain banks and genomic research groups, has special utility because it can be used to study somatic DNA effects (such as mtDNA CN) in the human brain and in neurodegenerative disease contexts such as Parkinson’s Disease (PD).

mtDNA CN is highly variable across brain regions (even under healthy/normal conditions), with the highest levels observed in midbrain regions like the substantia nigra (SN) and ventral tegmental area and the lowest levels observed in the cerebellum (CB).^8-10 In PD, mtDNA CN has been observed to be decreased in the SN and prefrontal cortex compared with controls.^11-13 In PD blood, mtDNA CN has also been shown to be decreased.^11,13-15 However, mtDNA CN studies of PD blood may need to be interpreted with caution, given many significant genome-wide association study (GWAS) loci that are associated with mtDNA CN are affected by neutrophil cell counts, and neutrophil cell proportions and gene expression have been reported to be increased in PD blood.^16-20 Levels of mtDNA CN in other brain regions of PD patients have not been comprehensively evaluated. One previous study of mtDNA CN in the CB using WES data reported a decrease in Alzheimer’s Disease (Ad) and Creutzfeldt-Jakob Disease compared with controls, but there was no significant effect observed for PD.²¹ However, that study combined PD and Dementia with Lewy Body (DLB) subjects into one group (PD + DLB), and did not assess neuropathology levels directly.

CB alterations, activity, and pathology in PD have been extensively reviewed by Wu and Hallett in 2013, and by Li, Le, and Jankovic in 2023.^22,23 In PD, there is evidence of CB activation that may be a compensatory mechanism due to damage of dopaminergic pathways in the basal ganglia.^22-27 Reports of increased CB activity in PD have primarily come from brain imaging studies using functional magnetic resonance imaging (fMRI) and glucose metabolism (FDG-PET).^{22,24-26,28-30} These foundational studies have demonstrated that CB activation is high in early-to-mid stages of PD and may already be increased before the onset of symptoms.^{22,24-26,28,29} Supporting evidence in humans includes a positive correlation between the Unified Parkinson’s Disease Rating Scale (UPDRS) score and fMRI regional homogeneity within the CB. Studies of PD animal models have observed an increased firing rate in CB nuclei neurons compared with controls and activation of CB Purkinje cells by immunostaining with c-Fos that correlated with dopaminergic neuronal loss in the SN.²³ As multiple brain regions linked to the CB via neural circuitry are affected by α-synuclein aggregates [i.e. Lewy Bodies (LB) and neurites], including the SN, locus coeruleus, amygdala, and cortex, it is of interest to evaluate if any of these diseased brain regions correlate with CB activation.^31-34 Here, we evaluated mtDNA CN in PD subjects vs. age-matched controls using CB-derived WGS data previously used in PD GWAS.^3,4 We further analyzed whether (a) Unified Staging System for Lewy Body disorders (USSLB) stages,³⁵ (b) LB scores in the brainstem, limbic system, or neocortex regions, and/or (c) UPDRS (off meds) were associated with CB mtDNA CN.

Materials and methods

WGS data

The larger cohort of both PD and control WGS data from the CB (n = 415) was obtained from six brain banks: Banner Sun Health Research Institute (BSHRI), Johns Hopkins University (JHU), Sepulveda Research Corporation, University of Maryland, Harvard University, and University of Miami. All of these samples are included in the Billingsley et al.⁴ study, and are referred to as the ‘NIH Laboratory of Neurogenetics pathologically confirmed collection’.⁴ The age-matched control WGS data is also a part of the North American Brain Expression Consortium (NABEC).³

DNA from the NABEC controls CB samples were all extracted at NIH using the DNeasy Blood and Tissue Kit as per the manufacturer’s instructions (Qiagen Inc., Valencia, CA). DNA from PD samples was extracted at each brain bank resource. DNA from the BSHRI PD samples and the JHU PD samples was extracted using a phenol:chloroform extraction procedure as previously described.³⁶ All WGS libraries were prepared using the Illumina TruSeq PCR Free DNA sample prep protocol. Statistical analysis of diagnosis of PD and control CB all samples included site (i.e. brain bank) as a co-variate to factor in potential variation in dissection and extraction methods. Additional analyses of samples with neuropathology measures from BSHRI were performed, including PD subjects alone, to control for variation in both site and DNA extraction method.

LB scores, USSLB and UPDRS

A subset of the above WGS data set obtained from BSHRI had LB scores annotated for ten brain regions and had USSLB stage determined following neuropathology testing (n = 161). This BSHRI cohort was used for additional testing of PD stage. Some of these subjects also had UPDRS scores (off meds; part 3 motor scores) (n = 97). Neuropathology scores were quantified as previously described.^35,36

Mitochondrial DNA CN quantification

WGS data was aligned to hg38 as previously described.⁴ Aligned BAM files were used for mtDNA CN quantification using the fastMitoCalc tool (https://github.com/HSGU-NIA/mitoAnalyzer/).² As the fastMitoCalc.pl script uses samtools for depth calculations, users should be sure to use a version of samtools that is 1.13 or greater, as the previous default maximum depth of 8000× may cause errors for mtDNA CN estimations in tissues with high mitochondrial content. The fastMitoCalc output files included both mtDNA CN and autosomal coverage, which were used in downstream analysis.

Statistical analysis

The proportion of male and female subjects in each group was tested using Chi-squared proportion tests using MedCalc Software Ltd.³⁷ All other statistical tests were performed with R v4.2.1. Spearman’s correlation tests were performed as two-sided tests with the cor.test R function or the ggcorrplot R library. Statistical tests of two independent groups (test of age between groups, and tests of Lewy Body and UPDRS scores in tables) were performed as Wilcoxon Rank Sum two-sided tests with the wilcox.test R function. Regression models that included co-variates of sex and age (± site) were performed as Rank-based estimation regression tests using the Rfit R package and library and rfit function. Statistical tests of three or more groups were performed as Kruskal-Wallis tests using the kruskal.test R function; significant results were tested for pairwise differences with the Dunn’s Test using the FSA R package and library and dunnTest R function and included Bonferroni corrections for multiple comparisons.³⁸ Robust linear models predicting CB mtDNA CN (in PD subjects alone) from LB scores were performed using the MASS R package and library and rlm function, and included sex and age as co-variates. LB scores from each brain region were tested for significant effect in rlm models using the Wald test and f.robtest function from the sfsmisc R package and library. Standardized beta values from rlm models were obtained using the lm.beta package and library. rlm models were graphed using the sjPlot R package and library and plot_model function. All analyses of CB mtDNA CN were performed after log10 transformation. All remaining graphs were made in RStudio with ggplot2 v3.4.3. Figures and the graphical abstract were generated using Microsoft® PowerPoint version 16.66.1. Brain illustration in the graphical abstract was modified from NIAID NIH BIOART Source (bioart.niaid.nih.gov/bioart/60)³⁹ using GIF Maker by Braincraft Ltd, the Photos app by Apple Inc., and Microsoft® PowerPoint version 16.66.1.

Results

CB mitochondrial DNA CN in PD across brain banks

The goal of this study was to assess mtDNA CN within the CB at different stages of PD. A total of 415 CB samples were collected from six different brain banks and WGS was performed as previously described.⁴ The sites that provided CB tissue are shown in Supplementary Table 1. 341 PD subjects were compared with 74 age-matched controls. The PD and control groups were not significantly different by age (PD: 78.7 ± 7.9, Control: 75.6 ± 16.0; Wilcoxon test: W = 11 894; P = 0.440). Both groups had a higher proportion of male subjects, reflective of disease incidence; however, they were not significantly different by sex (chi-squared 1.353; P = 0.245).

The fastMitoCalc tool was used to calculate mtDNA CN from the cohort of PD and control subjects (Supplementary Table 1 and Fig. 1).² There was no significant association between CB mtDNA CN and age (Spearman’s Correlation: rho = −0.008; P = 0.877) (Fig. 1A). There was also no significant difference in CB mtDNA CN between sexes after correcting for age (Rank-based regression: t = 1.52; P = 0.130) (Fig. 1B). This result was also replicated when samples were split by diagnosis [sex effect: PD (t = 1.63; P = 0.105), Control (t = −0.78; P = 0.782)]. Lastly, there was no significant association between autosomal coverage and mtDNA CN abundance (Spearman’s Correlation: rho = −0.034; P = 0.486), which supports that any difference in mtDNA CN is not due to sequencing depth (Fig. 1C).

**Cerebellum mitochondrial DNA copy number (mtDNA CN) from Control and Parkinson’s Disease WGS data.** CB WGS data obtained from six brain banks was used for analysis (n = 415 subjects; see Supplementary Table 1). mtDNA CN was determined using the fastMitoCalc tool.² Subjects’ CB had no significant differences in mtDNA CN in relation to (A) age (Spearman’s Correlation: *rho* = −0.008; P = 0.877), (B) sex (Rank-based regression after correcting for age: t = 1.52; P = 0.130), or (C) autosomal coverage measured from WGS data (Spearman’s Correlation: *rho* = −0.034; P = 0.486). PD subjects had significantly higher CB mtDNA CN than controls when evaluating all available control samples in comparison to (D) PD samples from all brain banks combined (Rank-based regression after correcting for age, sex, and site: t = 5.15; P = 4.15e−7). PD samples from each brain bank were also compared with the control samples using Rank-based regression tests after correcting for age and sex: (E) Banner (BSHRI) PD vs. controls (t = 5.95; P = 9.80e−9), (F) JHU PD vs. controls (t = −0.63; P = 0.525), (G) Sepulveda PD vs. controls (t = 4.84, P = 4.05e−6), (H) U Maryland PD vs. controls (t = 2.42, P = 0.017), (I) Harvard PD vs. controls (t = 7.95, P = 6.65e−12). The black triangle on each boxplot represents the mean; the ‘×’ symbol on jitterplots denotes outliers [i.e. 1.5-fold more than the interquartile range (IQR)]. All analyses of CB mtDNA CN were performed after log10 transformation. Brain bank abbreviations: BSHRI (Banner Sun Health Research Institute); JHU (Johns Hopkins University); Sepulveda (Sepulveda Research Corporation); U Maryland (University of Maryland); Harvard (Harvard University).

Analysis of CB mtDNA CN by diagnosis was performed on all brain banks combined; PD samples from each brain bank were also compared with the combined cohort of controls (Fig. 1D–I). PD CB samples had significantly higher mtDNA CN than controls after correcting for age, sex, and site (i.e. brain bank) (Rank-based regression: t = 5.15; P = 4.15e−7) (Fig. 1D). This result was also reproduced for four of five brain banks that had PD subjects (Fig. 1E–I). Overall, the control CB samples had a mean mtDNA CN of 1300 ± 668. The PD CB samples had a mean mtDNA CN of 2054 ± 1333. When split by brain bank, the PD CB samples had a mean mtDNA CN of 2200 ± 1332 (Banner/BSHRI), 1699 ± 1465 (JHU), 2148 ± 1214 (Sepulveda), 1666 ± 847 (U Maryland), and 3029 ± 757 (Harvard).

CB mitochondrial DNA CN in subjects with LB pathology and clinical motor scores

A subset of the above WGS dataset obtained from BSHRI had LB scores annotated for 10 brain regions and had USSLB stage determined following neuropathology testing (n = 161) (Table 1). This cohort was used for additional testing of CB mtDNA CN relative to PD stage. 128 PD subjects were compared with 33 controls. This cohort included subjects categorized with the following USSLB stages: 0: no pathology (n = 28); I: olfactory bulb only (n = 1); IIa: brainstem (n = 18); IIb: limbic system (n = 5); III: brainstem and limbic system (n = 49); IV: neocortex (n = 64). Some of these subjects also had UPDRS scores (off meds; part 3 motor scores) (n = 97) (Table 1). The mean UPDRS score for the controls was 7.2 ± 4.9. The mean UPDRS scores for PD subjects was 44.0 ± 20.9. It should be noted there was a high degree of variation of when these measures were collected prior to death (controls: 17.0 ± 13.6 months; PD: 12.6 ± 12.2 months).

Table 1.

Subject demographics of cerebellum cohort with LB neuropathology scores ± UPDRS (off) measurements

	Total samples n	Age mean ± SD^a	Sum LB score^b mean ± SD^a	CB Mito copy number^c mean ± SD^a
All Cerebellum Samples
Total	161	81.2 ± 6.8	20.3 ± 12.1	1947 ± 1285
Female	54	82.5 ± 5.9	20.0 ± 13.4	1750 ± 1226
Male	107	80.5 ± 7.1	20.4 ± 11.4	2047 ± 1308
Control Cerebellum Samples
Total	33	85.5 ± 5.9	1.2 ± 3.4	1102 ± 410
Female	14	86.1 ± 4.7	0.6 ± 1.6	1099± 333
Male	19	84.9 ± 6.7	1.7 ± 4.2	1104 ± 468
Parkinson’s Disease (PD) Cerebellum Samples
Total	128	80.1 ± 6.5	25.2 ± 7.9	2165 ± 1343
Female	40	81.2 ± 5.9	26.9 ± 7.7	1978 ± 1342
Male	88	79.6 ± 6.8	24.5 ± 7.9	2250 ± 1343
All Cerebellum Samples [Split by Unified Lewy Body (LB) Stage]^d
USSLB Stage 0^d	24	85.3 ± 6.4	0.0 ± 0.0	1092 ± 433
USSLB Stage I^d	1	84.0 ± NA	1.0 ± NA	485 ± NA
USSLB Stage IIa^d	18	83.7 ± 5.8	10.4 ± 7.0	1261 ± 419
USSLB Stage IIb^d	5	80.2 ± 9.8	11.2 ± 6.5	2564 ± 2622
USSLB Stage III^d	49	78.9 ± 6.8	21.5 ± 5.1	2548 ± 1452
USSLB Stage IV^d	64	80.7 ± 6.1	30.8 ± 4.8	1976 ± 1130
Cerebellum Samples with UPDRS (off) Scores^e
All UPDRS (off)^e	97	82.3 ± 6.8	20.0 ± 12.7	1984 ± 1397
Control UPDRS (off)^e	23	85.7 ± 5.1	1.5 ± 3.9	1055 ± 421
PD^b UPDRS (off)^e	74	81.2 ± 6.9	25.7 ± 8.1	2273 ± 1469

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^aSD, Standard Deviation.

^bSum LB Score = Summation of the Lewy Body (LB) density scores across ten brain regions: olfactory bulb, three brain stem regions (cranial nerves ix x, SN, locus coeruleus), three limbic regions (amygdala, transentorhinal cortex, cingulate gyrus), and three neocortex regions (temporal lobe, frontal lobe, parietal lobe).³⁴

^cCB Mito Copy Number = Cerebellum (CB) mitochondrial copy number obtained from WGS data using the fastMitoCalc tool.²

^dUnified Staging System for Lewy Body disorders (USSLB) Stage = Neuropathology-based staging system that categorizes subjects based on the brain regions infiltrated with Lewy-type <-synuclein: I. Olfactory Bulb Only; IIa Brainstem Predominant; IIb Limbic Predominant; III Brainstem and Limbic; IV Neocortical.³⁴

^eUPDRS (off) = Unified Parkinson’s Disease Rating Scale Part 3—Motor (off medication).

PD and control samples obtained from Banner (BSHRI) (n = 161; 128 PD and 33 controls) were also analyzed to determine if there was an observable association between PD neuropathology and the observed increase in CB mtDNA CN. The CB mtDNA CN of subjects with USSLB stages 0 to IV is shown in Table 1. For these subjects, sum LB pathology density scores were calculated across ten brain regions: olfactory bulb, cranial nerves IX X, SN, locus coeruleus, amygdala, transentorhinal cortex, cingulate gyrus, temporal lobe, frontal lobe, and parietal lobe. A subgroup of 97 subjects also had UPDRS (off meds) motor scores available. Collectively, we observed PD subjects had approximately two-fold more CB mtDNA CN than control subjects, and this increase was observed at LB Stage IIb/III, and in PD subjects with clinical UPDRS measures (Table 1).

In this cohort with clinical/neuropathology scores, there was a significant increase in CB mtDNA CN in PD samples compared with controls after correcting for age and sex (Rank-based regression: t = 5.29; P = 4.06e−7) (Fig. 2A). There was no difference between PD subjects binned by duration of clinical disease (Supplementary Fig. 1A) (Kruskal-Wallis chi-squared 0.140; P = 0.933); however, we only obtained ten subjects with early PD (i.e. <5 years of disease). These ten subjects did have the highest CB mtDNA CN mean (2657 ± 2090) compared with mid (2074 ± 1302) and late (2039 ± 1260) stage subjects.

**Cerebellum mitochondrial DNA copy number (mtDNA CN) stratified by clinical and neuropathological (Lewy body) scores.** CB WGS data obtained from the BSHRI brain bank was used for analysis (n = 161 subjects; see Table 1). mtDNA CN was determined using the fastMitoCalc tool.² Only subjects with Unified Staging System for Lewy Body disorders (USSLB) results were included in this analysis.³⁵ (A) PD subjects had significantly higher CB mtDNA CN compared with controls (Rank-based regression after correcting for age and sex: t = 5.29; P = 4.06e−7). (B) There was a significant difference in groups split by USSLB stage (Kruskal-Wallis test: chi-squared = 40.88; P = 9.94e−8). Stage III had significantly higher mtDNA CN compared with Stages 0 (Dunn’s test: z = 5.51; P = 5.50e−7) and IIa (z = 4.15; P = 5.05e−4); Stages IV was also significantly higher than Stage 0 (z = 3.86; P = 1.69e−3). (C) UPDRS scores showed a positive correlation between mtDNA CN and clinical motor symptoms (Spearman’s Correlation: *rho* = 0.391; P = 7.55e−5). There was also a significant difference in groups when samples were split by LB scores in each brain region: (D) cranial nerves ix x (Kruskal-Wallis test: chi-squared = 26.65; P = 2.34e−5), (E) SN (Kruskal-Wallis test: chi-squared = 31.06; P = 2.98e−6), (F) locus coeruleus (Kruskal-Wallis test: chi-squared = 26.39; P = 2.64e−5), (G) amygdala (Kruskal-Wallis test: chi-squared = 34.78; P = 5.17e−7), (H) transentorhinal cortex (Kruskal-Wallis test: chi-squared = 33.03; P = 1.18e−6), (I) cingulate gyrus (Kruskal-Wallis test: chi-squared = 27.66; P = 1.46e−5), (J) temporal lobe (Kruskal-Wallis test: chi-squared = 15.78; P = 0.003), (K) frontal lobe (Kruskal-Wallis test: chi-squared = 20.98; P = 0.0003), (L) parietal lobe (Kruskal-Wallis test: chi-squared = 23.39; P = 0.0001). Symbols for P-values are from Dunn’s post-hoc tests after Bonferroni corrections: ^P < 0.05, *P < 0.01, **P < 0.001, ***P < 0.0001. The black triangle on each boxplot represents the mean; ‘×’ symbol on jitterplots denotes outliers [i.e. 1.5-fold more than the interquartile range (IQR)]. All analyses of CB mtDNA CN were performed after log10 transformation.

Samples were also analyzed based on USSLB stage using the Kruskal-Wallis test and Dunn’s post-hoc tests with Bonferroni corrections (Fig. 2B). Samples classified as Stage III were found to have significantly higher mtDNA CN compared with Stages 0 (i.e. controls with no pathology; Dunn’s test: z = 5.51; P (Bonferroni adj.) = 5.50e−7) and IIa (z = 4.15; P = 5.05e−4) (Fig. 2B). This increase was also observed between Stages 0 and IV, with higher mtDNA CN in Stage IV (z = 3.86; P = 1.69e−3). The PD subjects classified as Stage III had a higher mean than subjects at Stage IV (Stage III: 2548 ± 1,452, Stage IV: 1976 ± 1130); however, this difference was not statistically significant (z = 2.36; P = 0.277) (Fig. 2B). Analysis of UPDRS scores showed a positive correlation between CB mtDNA CN and clinical motor symptoms (Spearman’s Correlation: rho = 0.391; P = 7.55e−5) (Fig. 2C). Analysis of UPDRS scores from PD subjects alone did not show a significant correlation with CB mtDNA CN (Spearman’s Cor.: rho = 0.057; P = 0.631) (Supplementary Fig. 1B), suggesting the correlation between CB mtDNA CN and UPDRS in Fig. 2C was driven by the difference in motor symptoms between PD and control subjects. Together, these findings illustrate an increase in CB mtDNA CN is associated with PD neuropathology, with observable LB disease in the brainstem and limbic system and the loss of motor functioning observed in PD.

CB mitochondrial DNA CN vs. brainstem, limbic system, and neocortex LB pathology

LB pathology scores indicating the degree of α-synuclein aggregation were determined for three brainstem regions: cranial nerves ix x, SN, and locus coeruleus (Fig. 2D–F). mtDNA CN was significantly increased in the CB of subjects with scores of 3 and 4 compared with a score of 0 in cranial nerves ix x (Dunn’s test: z > 4.13; P (Bonferroni adj.) < 3.63e−4) (Fig. 2D). Subjects with LB scores 1–4 in the SN had significantly increased CB mtDNA CN when compared with those with a LB score of 0 (z > 3.06; P < 0.023); the PD subjects with the highest average CB mtDNA CN had LB scores of 2 in the SN (Fig. 2E). Lastly, there were statistically significant increases in CB mtDNA CN between scores of 0 and 3 or 4 in the locus coeruleus (z > 3.62; P < 2.93e−3) (Fig. 2F).

LB pathology scores were obtained for three limbic system regions: amygdala, transentorhinal cortex, and cingulate gyrus (Fig. 2G–I). mtDNA CN was significantly increased in the CB of subjects with LB scores of 3 and 4 in comparison to a score of 0 in the amygdala (z > 4.81; P < 1.49e−5) (Fig. 2G). Subjects with LB scores 2–4 in the transentorhinal cortex had significantly increased CB mtDNA CN when compared with those with a LB score of 0 (z > 4.14; P < 3.42e−4) (Fig. 2H). Lastly, there was a significant increase in CB mtDNA CN between scores 1–4 compared with 0 in the cingulate gyrus (z > 2.82; P < 0.049) (Fig. 2I).

Finally, LB pathology scores were obtained for three neocortex regions: temporal lobe, frontal lobe, and parietal lobe (Fig. 2J–L). There was a statistically significant difference in CB mtDNA CN for subjects with LB scores of 0 and 1 in the temporal lobe (z = 3.77; P = 0.0016) (Fig. 2J), frontal lobe (z = 4.12; P = 0.0004) (Fig. 2K), and parietal lobe (z = 4.68; P = 2.84e−5) (Fig. 2L). Subjects with higher LB pathology in the neocortex (2 or greater) did not display a significant increase in CB mtDNA CN compared with subjects with no neocortical pathology (i.e. score 0) (Fig. 2J–L).

LB scores of PD subjects binned by high vs. low CB mitochondrial DNA CN

To further evaluate the relationship between LB pathology in the brain and CB mtDNA CN, we analyzed the PD subjects alone (excluding controls) that had neuropathology measures (n = 128) ± UPDRS scores (n = 74). PD samples were binned based on whether they had a CB mtDNA CN< or >2,000, which is near the average of all PD subjects (Table 2). In a second analysis, PD samples were binned based on whether they had a CB mtDNA CN< or >4000, which represents the top ten PD subjects in the neuropathology cohort (Supplementary Table 2). We compared (i) LB sum scores for the entire brain, brainstem, limbic system, and neocortex, (ii) LB scores for each of the ten brain regions, and (iii) UPDRS scores between these bins of PD subjects using Wilcoxon Rank Sum two-sided tests (Tables 2 and 3).

Table 2.

Lewy body and UPDRS (off) motor scores in PD subjects binned by cerebellum mtDNA CN values

Total PD samples n (n with UPDRS)	CB mtDNA^a CN < 2000 mean ± SD^b	CB mtDNA^a CN > 2000 mean ± SD^b	P-value Wilcoxon Test^c
Parkinson’s Disease (PD) Cerebellum Samples
CB mtDNA CN < 2000^a	77 (40)
CB mtDNA CN > 2000^a	51 (34)
LB Sum Scores
10 Brain Regions^d	24.4 ± 8.8	26.4 ± 6.2	0.3214
Brain Stem^e	8.8 ± 2.6	9.7 ± 2.2	0.0497
Limbic System^f	8.4 ± 3.3	9.2 ± 2.0	0.3848
Neocortex^g	4.4 ± 3.1	4.5 ± 2.5	0.8223
LB Scores
Olfactory Bulb	2.8 ± 1.2	3.1 ± 1.1	0.2510
Brain Stem—Cranial Nerves ix x	3.1 ± 1.0	3.4 ± 0.9	0.0273
Brain Stem—Substantia Nigra	2.7 ± 1.0	2.9 ± 0.9	0.5895
Brain Stem—Locus Coeruleus	3.0 ± 1.0	3.4 ± 0.9	0.0094
Limbic System—Amygdala	3.3 ± 1.0	3.7 ± 0.6	0.1585
Limbic System—Transentorhinal Cortex	2.6 ± 1.3	2.9 ± 0.9	0.3149
Limbic System—Cingulate Gyrus	2.4 ± 1.3	2.6 ± 1.1	0.4219
Neocortex—Temporal Lobe	1.7 ± 1.1	1.7 ± 1.1	0.9697
Neocortex—Frontal Lobe	1.4 ± 1.1	1.4 ± 0.8	0.6845
Neocortex—Parietal Lobe	1.3 ± 1.1	1.4 ± 0.8	0.4755
UPDRS (off)^h
UPDRS (off) Score	43.9 ± 21.9	44.2 ± 20.0	0.9870
UPDRS (off)—Months prior to death	13.5 ± 14.3	11.0 ± 8.0	0.7899

Open in a new tab

^aCB Mito Copy Number = Cerebellum (CB) mitochondrial DNA (mtDNA) copy number obtained from WGS data using the fastMitoCalc tool.²

^bSD = Standard Deviation.

^cTwo-sided Wilcoxon Rank Sum Test.

^dSum LB Score = Summation of the Lewy Body (LB) density scores across 10 brain regions: olfactory bulb, three brain stem regions.

^eCranial nerves ix x, SN, locus coeruleus, three limbic regions.

^fAmygdala, transentorhinal cortex, cingulate gyrus, and three neocortex regions.

^gTemporal lobe, frontal lobe, parietal lobe.³⁴

^hUPDRS (off) = Unified Parkinson’s Disease Rating Scale Part 3—Motor (off medication).

P-values < 0.05 are in Bold.

Table 3.

Lewy body and UPDRS (off) motor scores in PD subjects binned by cerebellum mtDNA CN values

Total PD Samples n (n with UPDRS)	CB mtDNA^a CN < 4000 mean ± SD^b	CB mtDNA^a CN > 4000 mean ± SD^b	P-value Wilcoxon Test^c
Parkinson’s Disease (PD) Cerebellum Samples
CB mtDNA CN < 4000^a	118 (66)
CB mtDNA CN > 4000^a	10 (8)
LB Sum Scores^a
10 Brain Regions^d	25.5 ± 8.0	22.5 ± 5.3	0.1562
Brain Stem^e	9.2 ± 2.5	8.4 ± 3.1	0.4640
Limbic System^f	8.7 ± 3.0	8.8 ± 1.7	0.6350
Neocortex^g	4.6 ± 2.9	2.5 ± 1.7	0.0347
LB Scores
Olfactory Bulb	2.9 ± 1.2	3.0 ± 1.1	0.9887
Brain Stem—Cranial Nerves ix x	3.2 ± 1.0	3.1 ± 1.0	0.6647
Brain Stem—Substantia Nigra	2.8 ± 1.0	2.4 ± 1.0	0.1443
Brain Stem—Locus Coeruleus	3.2 ± 0.9	2.9 ± 1.6	0.9885
Limbic System—Amygdala	3.5 ± 0.9	3.7 ± 0.5	0.5864
Limbic System—Transentorhinal Cortex	2.7 ± 1.2	3.0 ± 0.7	0.6811
Limbic System—Cingulate Gyrus	2.5 ± 1.2	2.1 ± 1.3	0.3116
Neocortex—Temporal Lobe	1.7 ± 1.1	1.1 ± 0.7	0.0848
Neocortex—Frontal Lobe	1.4 ± 1.0	0.6 ± 0.5	0.0047
Neocortex—Parietal Lobe	1.4 ± 1.0	0.8 ± 0.6	0.0610
UPDRS (off)^h
UPDRS (off) Motor Score	45.4 ± 20.9	32.8 ± 18.3	0.1233
UPDRS (off)—Months prior to death	12.3 ± 12.1	13.3 ± 11.5	0.7525

Open in a new tab

^aCB Mito Copy Number = Cerebellum (CB) mitochondrial DNA (mtDNA) copy number obtained from WGS data using the fastMitoCalc tool.²

^bSD = Standard Deviation.

^cTwo-sided Wilcoxon Rank Sum Test.

^dSum LB Score = Summation of the Lewy Body (LB) density scores across 10 brain regions: olfactory bulb, three brain stem regions.

^eCranial nerves ix x, SN, locus coeruleus, three limbic regions.

^fAmygdala, transentorhinal cortex, cingulate gyrus, and three neocortex regions.

^gTemporal lobe, frontal lobe, parietal lobe.³⁴

^hUPDRS (off) = Unified Parkinson’s Disease Rating Scale Part 3—Motor (off medication).

P-values < 0.05 are in Bold.

For PD samples binned by CB mtDNA CN values < or >2000 (Table 2), subjects with higher mtDNA CN had significantly higher LB sum scores in the brainstem (Wilcoxon test: W = 1564.5; P = 0.0497). The other LB sum scores were not significantly different. Within the brainstem, subjects with higher mtDNA CN had significantly higher LB scores in cranial nerves ix x (Wilcoxon test: W = 1545; P = 0.0273) and the locus coeruleus (Wilcoxon test: W = 1470; P = 0.0094). It is worth noting that LB scores in these two brainstem regions have more positive correlations with one another than they do with the SN (Supplementary Fig. 2). The LB scores from the other brain regions were not significantly different. There was also no difference in UPDRS scores or the months prior to death these measurements were collected.

For PD samples binned by CB mtDNA CN values < or >4000 (Table 3), subjects with higher mtDNA CN had significantly lower LB sum scores in the neocortex (Wilcoxon test: W = 825; P = 0.0347). The other LB sum scores were not significantly different. Within the neocortex, subjects with higher mtDNA CN had significantly lower LB scores in the frontal lobe (Wilcoxon test: W = 882; P = 0.0047). The other two neocortex regions followed a similar trend but were not statistically significant: temporal lobe (Wilcoxon test: W = 777; P = 0.0848) and parietal lobe (Wilcoxon test: W = 787; P = 0.0610). The LB scores from the other brain regions were not significantly different. There was also no difference in UPDRS scores or the months prior to death these measurements were collected; however, the lower UPDRS scores for PD subjects with CB mtDNA CN > 4000 (32.8 ± 18.3) compared with those <4000 (45.4 ± 20.9) warrants further investigation in larger cohorts.

Prediction of CB mitochondrial DNA CN from LB scores in PD subjects

LB neuropathology scores from each brain region were also evaluated to determine their effect on CB mtDNA CN in PD subjects alone (Fig. 3 and Supplementary Fig. 3). All PD subjects with neuropathology measures (n = 128) were included in analyses using robust linear models and included co-variates of sex and age. Models show predicted values of CB mtDNA CN across LB scores (0–4) in each brain region, along with t-values for the LB score variable and P-values from Wald tests (Fig. 3). The two brainstem regions that had a significant difference of LB scores in the binned analysis in Table 2 (i.e. the cranial nerves ix x and locus coeruleus) both had positive t-values (t = +1.18 and +1.70) for LB score in robust linear models (Fig. 3A and C; Supplementary Fig. 3B and D). In the limbic system, two regions (i.e. the amygdala and transentorhinal cortex) both had positive t-values (t = +2.08 and +1.22) for LB score in robust linear models, with the amygdala showing a significant improvement of CB mtDNA CN prediction (Wald test: F = 4.417; P = 0.038) (Fig. 3D and E; Supplementary Fig. 3A and C). LB scores in the SN, cingulate gyrus, and olfactory bulb showed little to no effect (Fig. 3B and F; Supplementary Fig. 3E–G). Conversely, the three neocortex regions (i.e. the temporal, frontal and parietal lobes) all had negative t-values (−1.12, −1.00, and −0.83) for LB score in robust linear models (Fig. 3G–I; Supplementary Fig. 3H–J). The effect of sex and age are also shown, with positive t-values (i.e. higher CB mtDNA CN) observed in male PD subjects compared with females in all models (Supplementary Fig. 3).

**Predicted cerebellum mtDNA copy number in PD subjects.** CB WGS data from *PD subjects only* obtained from the BSHRI brain bank was used for analysis (n = 128 subjects; see Table 1). mtDNA CN was determined using the fastMitoCalc tool.² Robust linear model (rlm) results predicting cerebellum mtDNA copy number from LB pathology scores from 9 brain regions encompassing the (**A–C**) brainstem, (**D–F**) limbic system, and (**G–I**) neocortex, with co-variates of sex and age. Predicted values for CB mtDNA CN are shown (y-axis) across LB Scores (0–4) for each brain region (x-axis). Regressor values and statistics (t-values) for the LB score are shown, along with F values and statistics (P-values) from Wald tests. Unstandardized coefficients and t-values from rlm tests are as follows: (A) cranial nerves ix x (value = 0.061; t = +1.18), (B) SN (value = 0.004; t = +0.07), (C) locus coeruleus (value = 0.086; t = +1.70), (D) amygdala (value = 0.120; t = +2.08), (E) transentorhinal cortex (value = 0.056; t = +1.22), (F) cingulate gyrus (value = −0.007; t = −0.17), (G) temporal lobe (value = −0.052; t = −1.12), (H) frontal lobe (value = −0.054; t = −1.00), (I) and parietal lobe (value = −0.045; t = −0.83). Standardized beta values and P-values from Wald tests are as follows: (A) cranial nerves ix x (β = 0.108; P = 0.240), (B) SN (β = 0.007; P = 0.943), (C) locus coeruleus (β = 0.153; P = 0.101), (D) amygdala (β = 0.193; P = 0.038), (E) transentorhinal cortex (β = 0.116; P = 0.220), (F) cingulate gyrus (β = −0.016; P = 0.863), (G) temporal lobe (β = −0.105; P = 0.264), (H) frontal lobe (β = −0.095; P = 0.318), (I) and parietal lobe (β = −0.080; P = 0.406). Cerebellum mtDNA CN predictions are back-transformed to original response scale, but standard errors are still on log transformed scale. Model results split by sex and age, and t-values for these variables, are shown in Supplementary Fig. 3.

Collectively, our analysis of Lewy Body scores from ten brain regions (Figs 2 and 3 and Tables 2 and 3) showed CB mtDNA CN increased upon pathological infestation of α-synuclein aggregates in the brainstem and limbic system but did not increase after late-stage neocortical involvement.

Discussion

There is evidence of CB activation in PD patients, which may be a natural compensatory response to increase or sustain motor control that is being negatively affected by α-synuclein aggregates in the basal ganglia or other brain regions.^22-27 Most of the evidence of CB activation in PD has come from fMRI or FDG-PET imaging data; however, most subjects that donate brain tissue do not have this type of imaging data available, so it is typically not possible to correlate CB activation with LB pathology scores. By using WGS data from the CB to analyze mtDNA CN changes, we provide a novel strategy to evaluate CB mitochondrial alterations and neuropathology levels in the same subjects. We observed a significant increase in mtDNA CN in PD CB compared with healthy controls using this approach. We also observed increased CB mtDNA CN in PD subjects alone with higher LB pathology scores in the brainstem and limbic system, with the most positive effects in the locus coeruleus and amygdala. This is especially interesting given the established projections and mono/di-synaptic circuits these regions have with the CB, and suggests compensatory responses in PD CB may not be exclusive to the cortico-basal ganglia-cerebellar network.^{22-27,33,40,41} The lack of difference of LB scores in the SN in analyses of PD subjects alone does not preclude its involvement in CB activation, and it still may be a primary driver of the differences observed between cases and controls, but these data suggest other brain circuits may also be involved.

The difference in CB mtDNA CN may reflect activation in cerebellar circuits but may also be due to increased reactive oxygen species from α-synuclein pathology in the CB of PD subjects.^22,23,42-45 Lewy Bodies are not abundant in the CB and this region is not evaluated in the USSLB or Braak staging schemes^23,46,47; however, future studies may need to include assessment of α-synuclein aggregation in order to determine if CB mtDNA changes are due to circuit activation, neuropathology, or both.

There is one published study on the CB across several neurodegenerative diseases that used WES to evaluate the abundance of mtDNA CN and determine if there was a significant difference based on fold changes.²¹ This study did not observe a significant increase in PD; however, that analysis included both PD and DLB in the same group and did not report the number of DLB cases nor the LB pathology stage of any of the subjects. Those PD + DLB samples were part of the Medical Research Council Brain Tissue Resource where 60% of cases had DLB and 40% had PD.⁴⁸ As such, this report does not necessarily contradict our findings of increased CB mtDNA CN in early-mid PD, but suggests an increase in PD may have been missed due to several factors including mixed diagnosis (PD + DLB), less robust sequencing methods, reduced sample size, and/or batch effects. It is worth noting that the PD + DLB group in that study did have the highest median and upper quartile for mtDNA CN, and that DLB subjects often have robust Ad and/or neocortex pathology (and the Ad group in that study had reduced mtDNA CN). We observed a similar trend where the PD subjects with the ten highest CB mtDNA CN values had less LB pathology in their neocortex. Additional studies of DLB cases alone are warranted and should include regional measurements of both Ad and PD pathology if available. Without postmortem neuropathology data, the classification of subjects with synucleinopathies becomes complicated, especially at late ages when the co-occurrence of multiple neurodegenerative diseases is common.

Overall, analyses of large cohorts of brain samples that were dissected across multiple sites and may also include variability in DNA extraction procedures and neuropathology can make reproducibility of mtDNA CN results challenging. Differences in cell type composition (i.e. neutrophil levels) due to differential inflammatory responses levels may be responsible for the reduced mtDNA CN reported in PD blood,^16-20 but differences in pieces of frozen brain tissue are likely more susceptible to differences in brain regions/substructures and dissection techniques (e.g. that may inadvertently influence proportion of grey vs. white matter where mtDNA CN and mtDNA deletion content is dramatically different).^49,50 We do not have cell counts or surrogate measurements to evaluate the exact pieces of CB tissue used in this study, but we do not believe cell composition is responsible for the observed increase of CB mtDNA CN in PD given the reproducible signature across (four of five) brain banks and the associations to LB neuropathology scores we observed.

Future mtDNA CN studies of PD brains should explore additional brain regions using tissue with paired LB pathology scores, as well as other relevant measurements such as Ad pathology (i.e. extracellular beta-amyloid plaques and intracellular tau tangles), the presence/absence of risk variants in genes associated with PD (e.g. LRRK2, GBA1, or SNCA), and/or polygenic risk scores.⁵¹ Pairing these mitochondrial measures with genomic technologies such as single-nuclei RNA-Seq or spatial transcriptomics, in addition to measures of α-synuclein aggregation, autophagy, or mitochondrial respiration would also provide additional insights into why this brain region is changing in mtDNA content and if leveraging this effect could have therapeutic benefits or diagnostic utility.

Supplementary Material

fcaf296_Supplementary_Data

fcaf296_supplementary_data.zip^{(434.7KB, zip)}

Acknowledgements

Brain tissue for the NABEC cohort was obtained from the Baltimore Longitudinal Study on Aging at the Johns Hopkins School of Medicine, the NICHD Brain and Tissue Bank for Developmental Disorders at the University of Maryland, the Banner Sun Health Research Institute (BSHRI) Brain and Body Donation Program, and from the University of Kentucky Alzheimer's Disease Center Brain Bank. PD brain tissue was obtained from BSHRI, Johns Hopkins University, Sepulveda Research Corporation, University of Maryland, and Harvard University. We are greatly appreciative to all of the autopsy donors and their families, without whom this work would not be possible.

Contributor Information

Talia Beglarian, Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy St. NRT 2502, Los Angeles, CA 90033, USA.

David R Tyrpak, Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy St. NRT 2502, Los Angeles, CA 90033, USA; Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90089, USA.

J Raphael Gibbs, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.

John Andrew MacKay, Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA 90089, USA.

Sonja W Scholz, Neurodegenerative Diseases Research Section, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892, USA; Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD 21287, USA.

Bryan J Traynor, Neuromuscular Diseases Research Section, National Institute on Aging, Bethesda, MD 20892, USA.

Marilyn S Albert, Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD 21287, USA.

Liana S Rosenthal, Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD 21287, USA.

Ted M Dawson, Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD 21287, USA.

Juan C Troncoso, Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD 21287, USA.

Dena G Hernandez, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.

Mark R Cookson, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.

Charles H Adler, Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ 85259, USA.

Geidy Serrano, Banner Sun Health Research Institute, Sun City, AZ 85351, USA.

Andrew B Singleton, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA; Center for Alzheimer’s and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.

Thomas G Beach, Banner Sun Health Research Institute, Sun City, AZ 85351, USA.

Brooke E Hjelm, Department of Translational Genomics, Keck School of Medicine, University of Southern California, 1450 Biggy St. NRT 2502, Los Angeles, CA 90033, USA.

Supplementary material

Supplementary material is available at Brain Communications online.

Funding

This research was supported, in part, by the Intramural Research Program of the National Institutes of Health (NIH) [National Institute on Aging (NIA), National Institute of Neurological Disorders and Stroke (NINDS)]; project numbers 1ZIA-NS003154, Z01-AG000949-02, Z01-ES101986, and UK ADC NIA P30 AG 072946. T.B. and B.E.H. (and mitochondrial DNA data analyses) were supported by the University of Southern California Department of Translational Genomics. D.R.T. was supported by an F31 fellowship from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; F31 DK118881). J.A.M. was supported by the Gavin Herbert Professorship in Pharmaceutical Sciences. Funding for brain tissue procurement and WGS was provided by the intramural research program of the National Institute on Aging (NIA) and NINDS, NIH, a part of the U.S. Department of Health and Human Services, in addition to grants from the Michael J. Fox Foundation (MJFF) for Parkinson’s Research, and the Department of Defense. The study used tissue samples and data from the Johns Hopkins Morris K. Udall Center of Excellence for Parkinson’s Disease Research (NIH P50 NS38377). We are grateful to the NIH NeuroBioBank for the provision of tissue samples. Neuropathology and clinical information from the Banner Brain Bank Cohort were obtained from the Banner Sun Health Research Institute (BSHRI) Brain and Body Donation Program, which has been supported by NINDS (U24 NS072026 National Brain and Tissue Resource for Parkinson’s Disease and Related Disorders), NIA (P30 AG 019610 and P30 AG 072980, Arizona Alzheimer’s Disease Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson's Disease Consortium) and MJFF.

Competing interests

The authors report no competing interests.

Data availability

Subject demographics and raw data from the fastMitoCalc tool are provided in Supplementary Extended Data Table 1 for all CB samples. Regional LB scores, USSLB stage, and UPDRS scores (off meds; part 3 motor scores) for BSHRI samples are provided in Supplementary Extended Data Table 2. WGS CRAM files for the PD samples were obtained from the NIA/NIH Laboratory of Neurogenetics; LB scores, USSLB stage, UPDRS scores were obtained from BSHRI. WGS files for control samples were obtained from the NABEC and are available on the database of Genotypes and Phenotypes (dbGaP; accession phs001300.v4.p1).³ The R script used to generate graphs and perform statistical analysis (BrainComm2025_BH_graph-stats_git_v3.R) can be found on GitHub: https://github.com/brookehjelm/PD-cerebellum_mtDNA-CN.

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38. Ogle DH, Doll JC, Wheeler AP, Dinno A. FSA: Simple fisheries stock assessment methods. R package version 0.9.6; 2025. https://fishr-core-team.github.io/FSA/.
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45. Piao YS, Mori F, Hayashi S, et al. Alpha-synuclein pathology affecting bergmann glia of the cerebellum in patients with alpha-synucleinopathies. Acta Neuropathol. 2003;105(4):403–409. [DOI] [PubMed] [Google Scholar]
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47. Braak H, Del Tredici K, Rüb U, de Vos RA, Jansen Steur EN, Braak E. Staging of brain pathology related to sporadic Parkinson's disease. Neurobiol Aging. 2003;24(2):197–211. [DOI] [PubMed] [Google Scholar]
48. Keogh MJ, Wei W, Wilson I, et al. Genetic compendium of 1511 human brains available through the UK medical research council brain banks network resource. Genome Res. 2017;27(1):165–173. [DOI] [PMC free article] [PubMed] [Google Scholar]
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51. Program TGPsG, Leonard HL. Novel Parkinson’s disease genetic risk factors within and across European populations. medRxiv. [Preprint] doi: 10.1101/2025.03.14.24319455 [DOI]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

fcaf296_Supplementary_Data

fcaf296_supplementary_data.zip^{(434.7KB, zip)}

Data Availability Statement

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[fcaf296-B51] 51. Program TGPsG, Leonard HL. Novel Parkinson’s disease genetic risk factors within and across European populations. medRxiv. [Preprint] doi: 10.1101/2025.03.14.24319455 [DOI]

PERMALINK

Cerebellum mitochondrial DNA copy number is increased in Parkinson’s disease

Talia Beglarian

David R Tyrpak

J Raphael Gibbs

John Andrew MacKay

Sonja W Scholz

Bryan J Traynor

Marilyn S Albert

Liana S Rosenthal

Ted M Dawson

Juan C Troncoso

Dena G Hernandez

Mark R Cookson

Charles H Adler

Geidy Serrano

Andrew B Singleton

Thomas G Beach

Brooke E Hjelm

Abstract

Graphical Abstract

Graphical Abstract.

Introduction

Materials and methods

WGS data

LB scores, USSLB and UPDRS

Mitochondrial DNA CN quantification

Statistical analysis

Results

CB mitochondrial DNA CN in PD across brain banks

Figure 1.

CB mitochondrial DNA CN in subjects with LB pathology and clinical motor scores

Table 1.

Figure 2.

CB mitochondrial DNA CN vs. brainstem, limbic system, and neocortex LB pathology

LB scores of PD subjects binned by high vs. low CB mitochondrial DNA CN

Table 2.

Table 3.

Prediction of CB mitochondrial DNA CN from LB scores in PD subjects

Figure 3.

Discussion

Supplementary Material

Acknowledgements

Contributor Information

Supplementary material

Funding

Competing interests

Data availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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