Mitochondrial Respiratory Chain Protein Co-Regulation in the Human Brain

bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Caroline Trumpff1, Edward Owusu-Ansah2, Hans-Ulrich Klein3, Annie Lee3, Vladislav Petyuk4, Thomas S.

Wingo5, Aliza P. Wingo6, Madhav Thambisetty7, Luigi Ferrucci8, Nicholas T. Seyfried9, David A.

Bennett10, Philip L. De Jager3, Martin Picard1,11,12*

1 Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center,

New York, USA

2 Department of Physiology and Cellular Biophysics, Columbia University Irving Medical Center, New

York, USA

3 Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia

University Irving Medical Center, New York, USA

4 Pacific Northwest National Laboratory, Richland, Washington State, USA

5 Departments of Neurology and Human Genetics, Emory University, Atlanta, GA, USA

6 Atlanta VA Medical Center, Decatur, GA, USA and Department of Psychiatry, Emory University, Atlanta,

GA, USA

7 Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National

Institute on Aging Intramural Research Program, Baltimore, USA

8 Longitudinal Study Section, National Institute on Aging, Baltimore, USA

9 Department of Biochemistry, Emory University, Atlanta, USA

10 Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA

11 Department of Neurology, H. Houston Merritt Center, Columbia Translational Neuroscience Initiative,

Columbia University Irving Medical Center, New York, USA

12 New York State Psychiatric Institute, New York, USA

* Correspondence:

Department of Psychiatry, Division of Behavioral Medicine, Columbia University Medical Center, New

York, USA.

Email: [email protected]

Summary

Mitochondrial respiratory chain (RC) function requires the stoichiometric interaction among

dozens of proteins but their co-regulation has not been defined in the human brain. Here, using

quantitative proteomics across three independent cohorts we systematically characterized the co-

regulation patterns of mitochondrial RC proteins in the human dorsolateral prefrontal cortex

(DLPFC). Whereas the abundance of RC protein subunits that physically assemble into stable

complexes were correlated, indicating their co-regulation, RC assembly factors exhibited modest

co-regulation. Within complex I, nuclear DNA-encoded subunits exhibited >2.5-times higher co-

regulation than mitochondrial (mt)DNA-encoded subunits. Moreover, mtDNA copy number was

unrelated to mtDNA-encoded subunits abundance, suggesting that mtDNA content is not limiting.

Alzheimer’s disease (AD) brains exhibited reduced abundance of complex I RC subunits, an effect

largely driven by a 2-4% overall lower mitochondrial protein content. These findings provide

foundational knowledge to identify molecular mechanisms contributing to age- and disease-

related erosion of mitochondrial function in the human brain.

Keywords

Mitochondrial respiratory chain, post-mortem brain, Alzheimer disease, proteomics, ROSMAP,

BLSA, Banner. bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Introduction

The brain contains energivorous networks of interacting neural and glial cells whose

energy demands are largely sustained by mitochondria. Mitochondria contain approximately

1,300 proteins (Rath et al., 2020), many of which physically interact with others to produce

functional organelles. Key molecular operations in mitochondria are performed by multi-subunit

protein complexes, and there is mounting evidence that the stoichiometry of specific protein

subunits can influence metabolism and cellular functions. One key example is the mitochondrial

respiratory chain (RC), also known as the electron transport chain, which converts reducing

equivalents into an electrochemical gradient (Nicholls, 2013) through a process that depends on

the stoichiometry of multiple protein subunits.

Perturbing RC protein subunit stoichiometry has functional consequences on

mitochondrial and organ function. For instance, mice overexpressing NDUFAB1 (one of the

complex I subunits) in skeletal muscles are refractory to obesity and insulin resistance when fed

a high-fat diet (Zhang et al., 2019). In addition, overexpression of NDUFAB1 in the heart enhances

mitochondrial bioenergetics and limits reactive oxygen species (ROS) production to protect the

heart from ischemia-reperfusion injury (Hou et al., 2019). Other studies have shown that the level

of expression of NDUFA13 (a complex I subunit also referred to as GRIM-19) is reduced in

hepatocellular carcinoma (HCC) cells relative to wildtype controls; and overexpression of

NDUFA13 in HCC cells arrests cell proliferation and stimulates apoptosis (Kong et al., 2014).

Therefore, the relative abundance of individual subunits in relation to their molecular binding

partners is emerging as a contributor to overall organelle and cellular function.

The RC is located within the inner mitochondrial membrane (IMM) and includes five

protein complexes. The RC complexes assemble with the help of assembly factors that only

temporarily interact with core and accessory protein subunits that are later released from the

assembled complexes (Formosa et al., 2018; Mukherjee and Ghosh, 2020). In neurons and other

brain cells, RC function ultimately powers not only adenosine triphosphate (ATP) synthesis but bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

also calcium uptake and other biological functions essential to brain function and cognition (Picard

and McEwen, 2014; Rangaraju et al., 2019). The ability of the RC to produce adequate amounts

of ATP therefore depends on the coordinated expression and stability of its components, the RC

protein subunits that are encoded both by the nucleus and by the mitochondrial DNA (mtDNA).

The entry point for the electron transfer is complex I which is the largest RC complex.

Complex I contains 45 subunits in humans and assembles in 3 major modular intermediates

(Formosa et al., 2018; Garcia et al., 2017). Assembled complex I can further assemble as

multimeric supercomplexes with complexes III and IV to enhance electron transfer efficiency

(Acín-Pérez et al., 2008; Lapuente-Brun et al., 2013; Letts and Sazanov, 2017; Milenkovic et al.,

2017; Vartak et al., 2013). In complex I, 14 of the 45 subunits that form the catalytic centers are

referred to as the core or central subunits. Although mitochondrial protein synthesis is considered

a highly regulated process (Kummer and Ban, 2021), the extent to which mtDNA-encoded

proteins are coregulated to achieve equimolar abundance and the extent to which their

abundance is matched to nuclear DNA (nDNA) encoded subunits have not been defined in the

human brain.

Mitochondrial RC dysfunction has emerged as a putative cause of neurodegeneration in

Alzheimer’s disease (AD) (Holper et al., 2019; Swerdlow et al., 2014) and cognitive decline

(Mostafavi et al., 2018; Wingo et al., 2019), as well as Parkinson’s disease (Grünewald et al.,

2016), highlighting the importance of understanding brain mitochondrial biology. In particular, we

still lack a molecular-resolution understanding of the mitochondrial alterations or recalibrations

that may underlie brain dysfunction and AD. Here we investigate mitochondrial RC components

in molecularly-profiled post-mortem human brains in relation to AD status and sex. Using

proteomic datasets in the human dorsolateral prefrontal cortex (DLPFC), an associative brain

region involved in AD, we specifically investigate the co-regulation patterns among known RC

protein subunits.

Results

We characterized the co-regulation of the DLPFC mitochondrial RC proteins by

repurposing high-throughput quantitative proteomics measured by liquid chromatography coupled

with tandem mass spectrometry (LC-MS/MS). Data from three independent cohorts were

analyzed: ROSMAP (N=400) (Bennett et al., 2018; Robins et al., 2019), BLSA (N=47) (Ferrucci,

2008; Swarup et al., 2020; Wingo et al., 2019), and Banner (N=201) (Beach et al., 2015; Swarup

et al., 2020; Wingo et al., 2019). Each cohort included both women and men who had either

normal cognition, mild cognitive impairment, or AD at the time of death (see fig. 1A, fig. S1 and

table S1).

The coverage of RC proteins by all three datasets ranged from 72% to 91% and was

largest (91%) in ROSMAP (illustrated in fig. S1B). In the ROSMAP dataset, both nDNA-encoded

and mtDNA-encoded protein subunits were available, while only nDNA-encoded protein subunits

were detected in Banner and BLSA, which used a different proteomics approach to determine

protein abundance characterized by less depth than the one used in ROSMAP. Thus, we focused

most of our analyses on ROSMAP, the most comprehensively profiled cohort.

Our goal was to assess the degree to which physically-related or non-physically-

interacting RC proteins are co-regulated within the human DLPFC. To estimate co-regulation, we

performed protein covariation analysis (Kustatscher et al., 2019) by computing the Spearman

correlations for each protein pairs and averaging the correlation coefficients (for visualization see

fig. S2). Additionally, we performed statistical analysis to compare the average co-regulation by

subsets of subunits.

RC protein subunits show co-regulation across RC complexes

Across the three cohorts, the nDNA encoded RC protein subunits exhibited significant co-

regulation. This means that brains with higher abundance of a specific RC protein also have more

of other related RC proteins, as expected from their assembly in the same complexes (fig. 1B, bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

see fig. S3-S5 for the heatmaps of RC subunits and assembly factors). Computing average co-

regulation indices for each RC complex in ROSMAP showed that subunits of Complex I (average

across three cohorts r=0.53) were most strongly correlated with each other.

Patterns of co-regulation within each RC complex can also be visualized as frequency

histograms indicating the number of protein pairs exhibiting increasing strengths of correlations

with other subunits (fig. 1C). If co-regulation was absent, we would expect a median distribution

value of 0. Here the distribution ranged between r=0.27 - 0.64 which suggests moderate to large

co-regulation among RC protein subunits. Between RC complexes, the highest degree of co-

regulation was found for Complex I across the three cohorts (average r=0.48). To validate this

observation, we also used selected reaction monitoring (SRM), a targeted proteomics approach

on a subset of RC proteins in a larger sample of DLPFC samples from the ROSMAP cohort

(N=1,228). Again, Complex I subunits showed the highest degree of correlation, suggesting

particularly high stability of Complex I and of its subunits in the human brain (fig. S6). Of all

complexes, Complex IV (average r=0.30) showed the lowest degree of co-regulation. Complex II

was not assessed individually since it includes too few (four) subunits.

Across the RC proteins, assembly factors are not part of the final assembled RC

complexes. If the physical interactions of subunits among assembled complexes contribute to co-

regulation, assembly factors should exhibit lower co-regulation. Indeed, compared to RC subunits

across all complexes (average r=0.37), we found approximately two-third lower co-regulation for

assembly factors (average r=0.14) (fig. 1D). Among actual RC protein subunits, accessory

subunits, which represent the structural components of the RC, showed lower co-regulation

(average r=0.33) than core subunits responsible for performing the catalytic activities (average

r=0.65) (fig. 1E).

Co-expression of RC subunits in RNAseq

To examine if the observed co-regulation of RC subunits is a feature only present at the

protein level, or if transcriptional activity (i.e., gene expression) could contribute to these effects,

we also examined the co-expression of RC components based on mRNA transcripts. Bulk

RNAseq data was examined in the DLPFC (N=1,092), as well as two other brain regions: the

posterior cingulate cortex (PCC, N=661) and the anterior caudate (AC, N=731).

Similar to our observations at the protein level, in the DLPFC, PCC and AC, individuals

with higher mRNA levels for specific subunits tended to also have higher levels of other related

transcripts, consistent with the co-expression of RC genes as part of a common genetic program

(fig. S7-9). In the DLPFC, co-regulation estimates based on proteins were slightly weaker (-7.3%)

than RNA levels (fig. S7C). Thus, these data suggest that co-regulation of RC subunits occurs at

both the protein and transcript levels.

Partial evidence for the existence of RC supercomplexes in the human brain

Research in drosophila and mammalian model systems suggests that the RC complexes

I, III and IV assemble as supercomplexes (Acín-Pérez et al., 2008; Lapuente-Brun et al., 2013;

Letts and Sazanov, 2017; Milenkovic et al., 2017; Vartak et al., 2013). If supercomplexes (SC)

also existed in the human brain, we would expect RC subunits between these complexes to be

more highly co-regulated than with subunits of non-SC subunits (complex II and V). We therefore

asked if pairs of subunits derived from complex I-III-IV are more strongly co-regulated with each

other than other RC complexes (II, V). In the more comprehensive ROSMAP dataset, we find that

co-regulation between SC RC complexes was 46% higher compared to non-SC RC complexes

in ROSMAP (fig. 1E, fig. S11A-C). Similarly, in the Banner dataset, the co-regulation between

complex I, III and IV was 43% higher (p<0.01) than for complex I and II. This effect, however, was

not observed in the BLSA cohort, perhaps due to lower power in detecting a difference in co-

regulation or possibly due to other inherent differences in participants characteristics between bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

cohorts. Higher co-regulation of SC was also observed at the RNA levels across three brain

regions, DLPFC, PCC, AC (ROSMAP) (fig. S10, fig. S11D-F).

Variation in RC protein abundance, mitochondrial mass, and mtDNA density

To understand what drives the variation in RC subunits in the DLPFC, we assessed the

correlation between nDNA- and mtDNA-encoded RC subunits and i) markers of mitochondrial

content, ii) mtDNA copy number (mtDNAcn), and iii) neuronal proportion (fig. 2 A,B, see table S1

for the descriptive statistics). We built an index of mitochondrial content using the average of the

scaled values of four housekeeping proteins including the translocator of the outer membrane

(TOMM20.Q15388), voltage-dependent anion channel 1 (VDAC1.P21796, also known as porin),

and citrate synthase (CS.O75390, CS.H0YIC4). As expected, brains with higher general

mitochondrial content also had higher RC protein subunits abundance, as indicated by the

positive correlation of the mitochondrial content index and individual proteins (fig. 2 A,B). Based

on these estimates, overall mitochondrial content explains ~23% of the variance in RC protein

abundance. The unexplained variance suggests that mitochondria may vary in their RC protein

density (i.e., how much RC each mitochondrial unit contains) (McLaughlin et al., 2020).

Modest positive associations were found between mtDNAcn quantified from whole

genome sequencing (WGS) (N=454) and either nDNA- and mtDNA-encoded RC subunits, such

that mtDNAcn accounted for only 0.1 % of the variance in RC protein abundance. Thus, mtDNAcn

alone or in combination with mitochondrial content, accounts for at most 24% of inter-individual

differences in RC protein abundance in the DLPFC (fig. 2).

To examine whether the number of mtDNA copies per mitochondrion is a fixed quantity or

varies between individuals, we divided mtDNA copies by the mitochondrial content index. The

resulting metric of mtDNA density per mitochondrion differed by up to 6% between individuals

with the highest and lowest mtDNA density, establishing the range and natural variability of this

parameter in the human DLPFC. Interestingly, brains with greater mtDNA density tended to have bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

lower total RC protein abundance, including mtDNA-encoded proteins (fig. 2 A,B and C for an

example). This suggests that increased mtDNA copies for a given mitochondrial mass does not

lead to higher mitochondrial RC abundance. Rather, elevated mtDNA per mitochondrion might be

the result of compensatory mechanisms activated by low RC protein concentration, as seen in

genetic cases of mitochondrial dysfunction (Giordano et al., 2014; Yu-Wai-Man et al., 2010).

Neurons are rich in mitochondria, and differences in the number of neurons per unit of

brain tissue that may occur secondary to neuronal loss with neurodegeneration (Gómez-Isla et

al., 1997) could theoretically contribute to RC chain abundance. As an exploratory analysis, we

also examined to what extent RC protein abundance may be driven by neuronal abundance. In

other words: do brains with more neurons relative to other cell types have higher RC protein

abundance? On average less than 5% of the variance in RC protein abundance was explained

by neuron density derived from RNA-seq within the DLPFC (fig. 2 A,B), ruling out neuronal

density as a major confounding factor in these analyses, and confirming that non-neuronal cells

make a significant contribution to the brain proteomics signal.

Evidence of mito-nuclear crosstalk in human DLPFC

Both nDNA- and mtDNA-encoded protein subunits physically interact in the RC, and

coordination of mitochondrial and nuclear genomes (i.e., mitonuclear crosstalk) is considered an

important determinant of mitochondrial biology (Kim et al., 2018; Quirós et al., 2016). To indirectly

quantify evidence of co-regulation within and across genomes, we assessed the co-regulation

among nDNA-encoded subunits, and separately among mtDNA encoded subunits in ROSMAP.

Moreover, to estimate to what extent mito-nuclear crosstalk produces coordinated levels of

proteins encoded in both genomes, we specifically quantified the co-regulation between nDNA-

and mtDNA-encoded subunits (fig. 3A).

The extent of co-regulation among nDNA-encoded subunits was more than double

(r=0.38) that of mtDNA subunits (r=0.18) (fig. 3B). The degree of crosstalk between nDNA- and bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

mtDNA-encoded subunits was also low (r=0.23) (fig. 3B). Among individual RC complexes, the

lowest degree of nDNA-mtDNA crosstalk was found for complex III (r=0.08, <1% of shared

variance) and the highest was found for complex I and IV (r=0.31-0.33, ~10% shared variance)

(fig. 3C). For mtDNA-encoded genes, the observed strength of co-regulation was not associated

with the order of the genes or their distance from the initiation of transcription sites. Co-regulation

with nDNA-encoded subunits was largest for ND1, COX2, and ND5, and lowest for CYTB (fig. 3

D,E, fig. S12). Co-regulation differences between proteins were not confounded by their

abundance (fig. 3F), although we note the existence of a possible trend (excluding ND1 and ND2)

whereby lower abundance proteins may exhibit higher mito-nuclear crosstalk.

We then explored how much the observed inter-individual differences in the abundance

of nDNA- and mtDNA-encoded subunits are explained by canonical transcription factors (TFs)

believed to mediate mito-nuclear crosstalk. We examined TFs acting either on the nuclear (NRF1,

NRF2, PGC1a) or mitochondrial (TFAM) genomes, and quantified their expression measured by

RNA-seq with RC protein and transcript abundance. TFs expression did not correlate with RC

subunits abundance (all rs<0.1) (fig. 3G,H fig. S13) but expression of NRF2 and TFAM correlated

moderately with RC transcript abundance (fig. S14). Importantly, we also found either no or weak

associations between RC transcript (RNA-seq) (Raj et al., 2018) and protein abundance levels

(fig. S15).

Complex I proteins co-regulation by topology

Since co-regulation was highest for complex I in ROSMAP as well as in BLSA, and since

it is the largest RC complex with high-fidelity three-dimensional maps of its subunits, we refined

our covariance analysis of complex I subunits according to their topological location (fig. 4A). This

analysis focused on the ROSMAP cohort because it is the largest, has the best global coverage,

and included both nDNA and mtDNA-encoded proteins. Moreover, ROSMAP contains a bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

substantial number of individuals with either normal cognition or with an AD diagnosis, allowing

to examine associations with AD.

Fig. 4B displays the detailed correlation matrix of RC complex I subunits arranged by their

topological location. Fig. 4C shows the corresponding protein abundance for each subunit,

illustrating that co-regulation is not driven by protein content (fig. 4B,C). As observed across all

RC complexes, structural subunits that physically assemble into the protein complex showed

substantially higher co-regulation than assembly factors (fig. 4D). Among RC subunits, accessory

subunits had 30% larger co-regulation than core subunits, but this difference is mainly driven by

mtDNA-encoded subunits that show substantially lower co-regulation than nDNA encoded

subunits (r=0.26 vs r=0.76).

Next, we assessed whether RC protein co-regulation varies according complex I subunit

topological location. Mitochondrial complex I is an ‘L’ shaped enzyme composed of a hydrophilic

arm that protrudes into the matrix, and a hydrophobic arm that is embedded within the inner

membrane. Matrix-located subunits showed 24% higher co-regulation than membrane subunits

(fig. 4G). Proteins located in the N module (r=0.68) showed greater co-regulation than proteins

located in the Q (r=0.50) or P module (r=0.49) (fig. 4H). These topologically-sensitive covariance

patterns in the human DLPFC provide information about the relative co-regulation and/or stability,

or regulated turnover of all proteins within each module in vivo in the human brain.

We also note that some nDNA-encoded complex I subunits exhibited particularly low co-

regulation with any of the other subunits, such as NDUFA9.F5GY40, and NDUFV3.P56181.2. In

both cases, these proteins have a related isoform that has high co-regulation with neighboring

and other subunits, suggesting that these isoforms are generally not incorporated in the

assembled complex I in the human DLPFC.

Complex I proteins co-regulation by sex

Sex differences in energy metabolism have been observed in humans (Mittelstrass et al.,

2011), and studies of various aspects of mitochondrial biology in model organisms suggest that

mitochondria in males exhibit lower mitochondrial content and ATP production capacity compared

to females (Ventura-Clapier et al., 2017). Therefore, we examined whether males and females

exhibit differences in complex I co-regulation. To explore this question, we computed the same

covariance matrix for all complex I subunits as in figure 4, but separately for men (fig. S16A) and

women (fig. S16B), then generated a difference matrix (see fig. S16C for the matrix of women-

men difference where correlations that differ between the sexes appear in darker colors).

Differences in co-regulation ranged from -0.43 to 0.36 (average=0.02). We then ran an interaction

analysis (correlations of protein-protein x sex) on the top 100 most different correlation pairs, of

which the top-15 most significant are shown in fig. S16D. Although we did not find any systematic

difference between women and men, these data suggest potential differences among specific

protein pairs that could be of interest to future studies of sex differences. For example, ND2 and

NDUFA13 (both P module subunits) were strongly correlated in women, but not in men.

Complex I proteins co-regulation by clinical diagnosis

In relation to AD, multiple studies have found that Complex I enzymatic activity is reduced

in AD (see (Holper et al., 2019) for a meta-analysis). Therefore, we hypothesized that this

enzymatic deficiency could be associated with altered complex I protein co-regulation. To explore

differences in complex I protein co-regulation according to clinical diagnosis of AD in ROSMAP,

we assessed separately the correlation between subunits in individuals with normal cognitive

function at death (NCI, N=170, fig. S16F) and in individuals with AD (N=123, fig. S16E); then, we

assessed the difference in effect size between NCI and AD (fig. S16G). AD was defined using

the NIA-Reagan criteria for the pathologic diagnosis of AD (high or intermediate likelihood of AD)

(Bennett et al., 2006) and final clinical diagnosis of cognitive status (AD and no other cause of bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

cognitive impairment) as described previously (Klein et al., 2020). Differences in protein-protein

correlations ranged from -0.32 to 0.30 (average=-0.02). Then, we ran an interaction analysis on

the top 100 most different correlation pairs, of which the top-15 most significant are shown in fig.

S16H. As for sex differences, there was no systematic difference in complex I co-regulation

between AD and NCI, indicating that co-regulation of complex I subunits is unlikely to underlie

enzymatic activity defects in AD.

Complex I proteins abundance by sex

In the absence of systematic co-regulation differences between women and men, or

between AD and NCI, we reasoned that there may be a coordinated up or down regulation in

protein abundance between women/men and AD/NCI. Overall, men and women did not show a

difference in the overall complex I protein abundance. The average standardized effect size

Hedge's g was 0.004, indicating the absence of a difference in complex I subunit abundance in

relation to sex (fig. S17A).

Complex I proteins abundance by clinical diagnosis

In contrast, the analysis by AD status revealed a general downregulation of several

complex I subunits. Of the 44 complex I subunits available, 95% were significantly lower in AD

than in NCI (p<0.0001, Chi-square compared to chance (50%)), and none were upregulated. The

protein with the largest effect size was NDUFB7, decreased by an average of 7%, representing a

medium to large effect size (g=0.70) (fig. S17B).

RC proteins abundance and mitochondrial mass by sex and clinical diagnosis

We next determined whether the observed AD/NCI difference were i) specific to complex

I or whether they generalized to other RC complexes, ii) observed in other cohorts, and ii) driven

by reduced mitochondrial mass in AD brains. Therefore, we studied men/women and AD/NCI bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

differences in the five RC complexes (fig. 5) using the average fold change in individual subunits

(fig. 5A, D). We also used summary scores representing mitochondrial content and average RC

abundance by complex, adjusted or not for mitochondrial content (fig. 5B, E). While we did not

observe any systematic difference in RC abundance between women and men, mitochondrial

content tended to be 0.3-1.6% higher in men than in women (fig. 5C). Relative to NCI, AD brains

had on average 2.6-3.7% lower abundance of RC complexes, but this effect was largely driven

by a 1.9-3.9% lower mitochondrial content across all three cohorts. Therefore, when accounting

for mitochondrial content, mitochondria from AD and NCI do not appear to differ in their RC protein

abundance (fig. 5F). However, at the brain tissue level, our proteomics results are consistent with

and could explain the reported loss of cortical mitochondrial oxidative capacity in AD (Holper et

al., 2019).

Discussion

We deployed untargeted proteomics and covariance analysis to identify patterns of co-

regulation among mitochondrial RC proteins in the human DLPFC. A key aspect of this study was

the integration of known biological and topological information, particularly for complex I, to inform

the interpretation of protein co-regulation. Consistent with the physical assembly of bona fide

subunits into physical complexes that have coordinated turnover (Szczepanowska et al., 2020),

relative to assembly factors that assist in the assembly but are not part of the final assembled

complexes, our approach reliably detected higher co-regulation of assembled subunits than

assembly factors across all examined cohorts. Focusing on the largest cohort, ROSMAP, which

also has the highest proteomic coverage across both mtDNA- and nDNA-encoded subunits, we

find that the largest RC complex, complex I, exhibits a high level of co-regulation relative to other

complexes. Our data also identifies interesting patterns of regulation, notably the lack of

association between RC subunits and mtDNA abundance in brain tissue. bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Our data aligns well with a recent model of complex I maintenance in cultured cells

(Szczepanowska and Trifunovic, 2021) and extends its potential applicability to the human brain.

In particular, our analysis of protein-protein co-regulation and abundance for different complex I

subunits and isoforms in 400 individuals provides a platform to further examine mitochondrial RC

regulation. For example, specific isoforms exhibiting relatively normal abundance but no co-

regulation with other subunits may not assemble in vivo. The high co-regulation of groups of

subunits, such as those contained within the N-module of complex I, could be attributable to its

high turnover as a single module (Szczepanowska et al., 2020). The conservation of this pattern

from cultured cells to the human brain suggests that this process is robust and possibly a general

principle of mitochondrial RC maintenance.

Another noteworthy observation is the lack of apparent co-regulation between RC subunits

encoded in the nuclear and mitochondrial genomes. Although in theory transcription and

translation of gene products in both genomes are coupled via several mito-nuclear communication

mechanisms (Quirós et al., 2016), our results indicate a disconnect (i.e., lack of effective co-

regulation) between RC proteins, which may point to protein regulation mechanisms operating

relatively independently between mitochondrial and nuclear compartments in the human DLPFC.

In other words, brain mtDNA-related protein synthesis could occur relatively independently from

that of cytoplasmic nDNA-encoded proteins. Whether this potential mito-nuclear protein

imbalance in the human brain could contribute to activating compensatory mechanisms, such as

the mitochondrial unfolded protein response (UPRmt) (Houtkooper et al., 2013), could also be

examined in relation to proteotoxic stress and brain pathology. This point also may be consistent

with the fact that mtDNAcn was not found to be correlated with mtDNA- or nDNA-encoded RC

proteins, possibly implicating translational regulation (Kummer and Ban, 2021) and/or equally

influential degradation and turnover processes, as the main drivers of RC protein abundance,

rather than the number of mtDNA molecules per cell. Moreover, potential differences in

compartment-specific translation (Kummer and Ban, 2021) and degradation (Chung-ha et al., bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

2014) (e.g., in axons, synaptic terminals, cell bodies) that could not be resolved in our work should

also be considered in future studies.

In relation to mtDNA abundance, combining precise estimates of mtDNA copies per cell

from WGS and proteomic estimates of mitochondrial abundance showed two main points. First,

that mtDNAcn is not a good surrogate for mitochondrial abundance. It is recognized that mtDNAcn

is under the influence of a multitude of factors (Filograna et al., 2020) and is generally not a good

marker of mitochondrial content, at least in human skeletal muscle (Larsen et al., 2012). Our data

corroborate this notion in the human DLPFC. Furthermore, by quantifying the number of mtDNA

copies per mitochondria, or mtDNA density, we find that mitochondria with greater mtDNA density

have lower RC protein abundance. This inverse association could be similar to what occurs in

mitochondrial disease, where poorly functioning mitochondria (few RC proteins) activate signaling

cascades to produce a compensatory upregulation of mtDNA copies as an attempt to restore

mitochondrial oxidative capacity – an effect observed at the tissue and single-cell level (Giordano

et al., 2014; Vincent et al., 2018; Yu-Wai-Man et al., 2010). A meta-analysis showed that aging

(the majority of our population) and AD are associated with lower complex I and IV enzymatic

activities (Holper et al., 2019), suggesting that functional impairments could indeed exist in a

portion of the brains examined here. Combined with the marginally lower mitochondrial content

detected in our data, this could possibly drive elevation in mtDNA density in the DLPFC.

Therefore, we speculate that combined with other measures of mitochondrial function (such as a

ratio reflecting mtDNA abundance per mitochondrial mass), mtDNA density could represent a

potential marker of mitochondrial dysfunction to be examined in future studies.

Finally, similar to previous work (de Sousa Abreu et al., 2009; Maier et al., 2009) and to

recent observations across human tissues showing that RNA transcript levels are generally poorly

correlated with protein abundance (Jiang et al., 2020), we found no evidence of correlation

between RNA and protein levels for most RC proteins. Although RNA integrity is generally high

in all postmortem samples processed (Mostafavi et al., 2018), we cannot entirely rule out the bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

influence of uncontrolled confounds, such as degradation of RNA transcripts during the post-

mortem interval relative to greater protein stability, which would contribute to diminish RNA-

protein correlations. Regardless, our analyses of the human brain proteome show conserved and

expected co-regulation patterns consistent with RC composition and topology. Overall, this

comprehensive analysis of RC subunit co-regulation patterns in a large cohort of women and

men, with or without AD, adds molecular-resolution data to further examine the basis for normal

and abnormal mitochondrial function in the human brain.

Acknowledgments

We acknowledge participants of the ROS and MAP, Banner and BLSA studies. Support for this

work was provided by NIH grants P30AG16101 (DAB), R01AG15819 (DAB), R01AG17917

(DAB), U01AG46152 (PLD, DAB), U01AG61356 (PLD, DAB), R01AG036836 (PLD),

U01AG061357 (SNT), R01AG061800 (SNT), RF1AG062181 (SNT), GM119793 (MP) and

MH122706 (MP), the Intramural Program of the National Institute on Aging (MT), a NARSAD

young investigator award (CT) and the Nathaniel Wharton Fund (MP).

Author contributions

CT, MP and EOA designed the analytic plan. CT performed the data analysis. CT, MP and EOA

drafted the original draft the manuscript. All authors participated to the writing, review and editing

of the manuscript.

Competing Interests

The authors declare no competing interests.

Figure Legends

Fig. 1. Mitochondrial respiratory chain (RC) complexes proteins co-regulation. (A) Study

design: we assessed co-regulation of mitochondrial RC proteins (or subunits) in the human bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

dorsolateral prefrontal cortex (DLPFC) across three independent cohorts: ROSMAP (N=400),

Banner (N=201) and BLSA (N=47) (for more details see Fig. S1). (B) Heatmap of the association

between RC nuclear DNA (nDNA) encoded subunits assessed with Spearman rank correlation in

ROSMAP, Banner and BLSA. Heatmaps with individual pairs of correlations are presented in Fig

S3-S5 and correlation matrices are presented in table S2. Average Spearman’s rho (95% CI) for

nDNA encoded subunits for RC complexes I, III, IV, V. (C) Distribution of correlation results

(Spearman’s rho) for RC complexes I, III, IV, V. Co-regulation of complex II was not studied

individually due to its small number of subunits. (D) Average Spearman’s rho (95% CI) for nDNA

encoded subunits and assembly factors and for (E) core and accessory subunits for all RC

complexes. (F) Average Spearman’s rho (95% CI) for RC complexes part of the hypothesized

supercomplexes (SC, I, III, IV) and other possible pairs of RC complexes. P-values from Dunn’s

multiple comparisons test are shown in Fig S11A-C. P-values from Mann-Whitney test (D-E) or

Kruskal-Wallis test with Dunn’s multiple comparisons test (B), p<0.05*, p<0.01**, p<0.001***,

p<0.0001****.

Fig. 2. Relationship between mitochondrial respiratory chain (RC) protein abundance,

mitochondrial mass, mtDNA density, mitochondrial DNA copy number (mtDNAcn) and

neuron proportion. Association between RC nuclear DNA (nDNA) and mtDNA encoded subunits

abundance and mitochondrial content (average of the scaled values of CS.O75390, CS.H0YIC4,

TOMM20.Q15388, VDAC1.P21796), mtDNAcn, mtDNA density (mtDNAcn/mito content) and

neuron proportion (neuron %) shown as (A) heatmaps of Spearman’s rho (B) and as average

Spearman’s rho (95% CI). P-value from Kruskal-Wallis test with Dunn’s multiple comparisons test

(B, left) or one-way ANOVA and Tukey’s multiple comparison test (B, right), p<0.0001****. (C)

Example of scatterplot of the association between a complex 1 subunit (NDUFA10.O95299) and

mtDNA density. Results from simple linear regression. ROSMAP, N=124-400.

Fig. 3. mtDNA and nuclear DNA (nDNA) encoded mitochondrial respiratory chain (RC)

proteins co-regulation, nuclear-mtDNA crosstalk and transcription factors. (A) Diagram of

nuclear-mtDNA crosstalk. (B) Average Spearman’s rho (95% CI) between nDNA encoded

subunits, between mtDNA encoded subunits and for the crosstalk between nDNA and mtDNA

subunits. (C) Average Spearman’s rho of nuclear-mtDNA crosstalk for RC complexes I, III, IV, V.

(D) Heatmap and (E) average Spearman’s rho (95% CI) of nuclear-mtDNA crosstalk presented

by mtDNA encoded subunits’ order of transcription. Note that ND4L is not shown (data not

available). P-values from Dunn’s multiple comparisons test are shown in Fig. S12, (F)

Relationship between mtDNA encoded RC protein abundance and nuclear-mtDNA crosstalk.

Results from simple linear regression. (G) Heatmap and (H) average Spearman’s rho (95% CI)

of RC subunits protein abundance and transcription factor gene expression (NRF2 corresponds

to GABPB1 gene). P-values from Dunn’s multiple comparisons test are shown in Fig S13.

ROSMAP, N =124-400. P-value from Kruskal-Wallis test with Dunn’s multiple comparisons test,

p<0.05*, p<0.01**, p<0.001***, p<0.0001****.

Fig. 4. Mitochondrial respiratory chain (RC) complex I proteins co-regulation. (A) Diagram

of complex I protein subunits topological location. (B) Heatmap of complex I protein co-regulation

arranged by modules and example of individual association (results from simple linear

regression). (C) Complex I subunit abundance (raw values). Average Spearman’s rho (95% CI)

coefficient for complex I (D) subunits and assembly factors, (E) core and accessory subunits (F)

nuclear DNA (nDNA) or mtDNA encoded subunits (F) subunits by localization and (G) by module.

ROSMAP, N=124-400.P-value from unpaired t-test, Mann-Whitney test, or Kruskal-Wallis test

with Dunn’s multiple comparisons test, p<0.05*, p<0.01**, p<0.001***, p<0.0001****.

Fig. 5. Difference in mitochondrial respiratory chain (RC) proteins abundance according to

sex and clinical diagnosis. Difference between men and women in proteins abundance shown bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

as fold change for the (A) RC individual subunits and (B) mitochondrial content and RC summary

scores. (C) violin plot of mito content by sex. P-value from one-way ANCOVA adjusted for

Alzheimer’s disease (AD) status and age at death. Difference between individuals with no

cognitive impairment (NCI) and AD at death in RC proteins abundance shown as (D) RC individual

subunits, (E) mitochondrial content and RC summary scores. (F) violin plot of mito content by AD

status. P-value from one-way ANCOVA adjusted for sex and age at death (as well as years of

education for ROSMAP and BLSA). Scores were adjusted for post-mortem interval, age at death,

study, and batch. *p<0.05, **p<0.01. Detailed results are shown in supplementary table S4.

STAR Methods

RESOURCE AVAILABILITY

RESOURCE SOURCE IDENTIFIER

Database

ROSMAP TMT proteomics (Robins et al., 2019) syn17015098

ROSMAP SRM proteomics (Yu et al., 2018) syn10468856

ROSMAP RNA-seq (Raj et al., 2018) syn3388564

ROSMAP mtDNAcn from WGS (Klein et al., 2021) syn25618990

Banner Raw Proteomic Data (Swarup et al., 2020) syn7170616

BLSA Raw Proteomic Data (Seyfried et al., 2017) syn3606086

Software and algorithms

RStudio https://www.rstudio.com/ v1.4

R https://cran.r-project.org/ v4.0.4

Prism https://www.graphpad.com/ V9.1.1

Lead contact

Further information and requests for resources should be directed to and will be fulfilled by the

lead contact, Martin Picard ([email protected]).

Materials availability

This study did not generate new unique materials or reagents.

Data and code availability

Access to full datasets from the ROS and MAP studies can be requested on the website:

https://www.radc.rush.edu/. The molecular data used in this study is available through the

synapse.org AMP-AD Knowledge Portal: www.synapse.org

EXPERIMENTAL MODEL AND SUBJECT DETAILS

We used data from ROSMAP (N=400) (Bennett et al., 2018; Robins et al., 2019; Yu et al.,

2020), BLSA (N=47) (Ferrucci, 2008; Swarup et al., 2020; Wingo et al., 2019), and Banner

(N=201) (Beach et al., 2015; Swarup et al., 2020; Wingo et al., 2019). These studies have

measured post-mortem DLPFC protein abundance using untargeted proteomics. Clinical

characteristics of the study participants included in the analysis are shown in supplementary table

S1.

ROSMAP

Participants

The Rush Memory and Ageing Project (MAP) and the Religious Orders Study (ROS) (A

Bennett et al., 2012a; A Bennett et al., 2012b; Bennett et al., 2018) are two ongoing cohort studies

of aging and dementia in older persons. The ROS study enrolls Catholic nuns, priests, and

brothers, from about 40 groups across the United States. The MAP study enrolls participants

primarily from about 40 retirement communities throughout northeastern Illinois, with additional

diverse participants via individual home visits. Participants in both cohorts were free of known

dementia at study enrollment and agreed to annual evaluations and organ donation on death.

Both studies were approved by an Institutional Review Board of Rush University Medical Center

and all participants signed an informed consent, Anatomical Gift Act, and a repository consent to

share data and biospecimens.

The clinical diagnosis of Alzheimer’s Disease (AD) was based on the analysis of the annual

clinical diagnosis of dementia by the study neurologist blinded to post-mortem data. Post-mortem

AD pathology was assessed as described previously (Bennett et al., 2006; Bennett et al., 2003),

and AD classification was defined based on the National Institutes of Ageing-Reagan criteria

(Hyman and Trojanowski, 1997). Dementia status was coded as no cognitive impairment (NCI),

mild cognitive impairment (MCI), or Alzheimer’s dementia (AD) from the final clinical diagnosis of

dementia. Pathologic AD was defined using the NIA-Reagan criteria for the pathologic diagnosis

of AD (high or intermediate likelihood of AD) (Bennett et al., 2006) and final clinical diagnosis of

cognitive status (AD and no other cause of cognitive impairment) as described previously (Klein

et al., 2020). MCI refers to those persons with cognitive impairment but who did not meet criteria

for dementia.

Additional cohorts: Banner and BLSA

Banner Participants

As a first replication sample, we used proteomic data of post-mortem DLPFC brain tissue

from the Banner Sun Health Research Institute's Brain and Body Donation Program. Data was

available from 101 cognitively normal (controls) and 100 Alzheimer’s disease (AD) cases (43%

women). Subjects were enrolled as cognitively unimpaired volunteers from the retirement

communities (Beach et al., 2015) or as patients by neurologists. The Banner study was approved

by the Banner Sun Health Research Institute Institutional Review Board-approved. Participants

in the Banner dataset provided informed consent for clinical assessments during life, brain

donation after death, and usage of donated biospecimens for approved future research (Beach

et al., 2015).

Post-mortem neuropathological evaluation was performed at Banner Sun Health

Research Institute with the assessment of amyloid plaque distribution according to CERAD

criteria and the neurofibrillary tangle pathology assessed with Braak staging. Control cases were bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

defined as cognitively healthy within on average 9 months of death when they presented low

CERAD (0.13 ±0.35) and Braak (2.26 ±0.94) measures of amyloid and tau neuropathology.

Participants were classified as AD cases when evaluated as demented at the last clinical research

assessment, and when the brains showed high CERAD (2.9 ±0.31) and Braak (5.4 ±0.82) scores

(consistent with moderate to severe neuropathological burden).

BLSA Participants

As a second replication sample, we used proteomic data performed on DLPFC brain

tissue samples from the National Institute on Aging’s Baltimore Longitudinal Study of Aging. Data

was available for 13 cognitively healthy individuals, 14 asymptomatic AD and 20 AD cases (36%

women). The BLSA study was approved by the Institutional Review Board and the National

Institute on Aging. Human research at the National Institutes of Health (NIH) and the BLSA

participants provided written informed consent (Ferrucci, 2008).

Post-mortem neuropathological evaluation was performed at the Johns Hopkins

Alzheimer’s Disease Research Center with the Uniform Data Set. Assessment included amyloid

plaque distribution according to the CERAD criteria and neurofibrillary tangle pathology assessed

with the Braak staging. Control cases were defined as cognitively healthy within on average 9

months of death when they presented low CERAD (0.13 ±0.35) and Braak (2.26 ±0.94) measures

of amyloid and tau neuropathology. Participants were classified as AD cases when evaluated as

demented at the last clinical research assessment, and when the brains showed high CERAD

(2.9 ±0.31) and Braak (5.4 ±0.82) scores (consistent with moderate to severe neuropathological

burden). Asymptomatic AD cases were cognitively normal proximate to death, and had high

CERAD (2.1 ±0.52) and moderate Braak (3.6 ±0.99).

METHOD DETAILS

ROSMAP

Tandem mass tag (TMT) isobaric labeling mass spectrometry

Untargeted proteomics was measured from post-mortem DLPFC (Broadman area 9)

tissue of 400 (70% women) individuals including 168 cognitively normal individuals, 101 MCI, 123

AD and 8 had other cause of dementia, using tandem mass tag (TMT) isobaric labeling mass

spectrometry methods for protein identification and quantification as previously described (Robins

et al., 2019). A total of 12,691 unique proteins (7,901 after quality control) were detected.

Briefly, tissue homogenization was performed (following the method described in (Ping et

al., 2018)) before protein digestion. An equal amount of protein from each sample was aliquoted

and digested in parallel to serve as the global pooled internal standard (GIS) in each TMT batch.

Samples were randomized by co-variates (age, sex, PMI, diagnosis, etc.) into 50 batches (8 cases

per batch) before TMT labeling. The samples (N=400) and the pooled global internal standards

(GIS) (N=100) were labelled using the TMT 10-plex kit (ThermoFisher 90406) as previously

described in (Johnson et al., 2018; Ping et al., 2018). In each batch, TMT channels 126 and 131

were used to label GIS standards and the 8 middle TMT channels were used for individual

samples following randomization. The labelling was followed by high-pH fractionation on an

Agilent 1100 HPLC system (as described in (Mertins et al., 2018; Robins et al., 2019)). Peptide

eluents were separated on a self-packed C18 (1.9 μm, Dr. Maisch, Germany) fused silica column

(25 cm × 75 μM internal diameter (ID); New Objective, Woburn, MA) by a Dionex UltiMate 3000

RSLCnano liquid chromatography system (ThermoFisher Scientific) and monitored on an

Orbitrap Fusion mass spectrometer (ThermoFisher Scientific). The RAW files were analyzed

using the Proteome Discoverer suite (version 2.3, ThermoFisher Scientific). The protein

abundances were log2 transformed and regressed for age at death, postmortem interval, study

and batch for downstream analyses.

Selection reaction monitoring (SRM) quantitative proteomics

In addition, a small set of targeted proteins (N=119) was measured from post-mortem

DLPFC brain tissue of 381 cognitively normal individuals, 286 MCI, 519 AD and 22 with other

cause of dementia (68% women) using selection reaction monitoring (SRM) quantitative

proteomics as described previously (Yu et al., 2018).

RNA-seq

RNA sequencing was performed from DLPFC (N=1102) using a method that has been

previously described (Raj et al., 2018). Briefly, samples were extracted using Qiagen's miRNeasy

mini kit (cat. no. 217004) and the RNase free DNase Set (cat. no. 79254), and quantified by

Nanodrop and quality was evaluated by Agilent Bioanalyzer. Sequencing was performed on the

Illumina HiSeq with 101-bp paired-end reads, the mean coverage for the samples to pass quality

control was 95 million reads (median 90 million reads). Data included in the analysis met quality

control criteria. For each gene, a normalized expression level was computed by subtracting the

mean expression for that gene across all samples and dividing by the SD. The expression levels

were adjusted for batch, library size, percentage of coding bases, percentage of aligned reads,

percentage of ribosomal bases, percentage of UTR base, median 5 prime to 3 prime base, median

CV coverage, post-mortem interval (PMI), and study (ROS or MAP). The data is available through

the synapse.org AMP-AD Knowledge Portal (www.synapse.org; SynapseID: syn3388564).

Deconvolution of cell type proportion

Proportion of neurons were estimated from DLPFC RNA-seq data by applying the Digital

Sorting Algorithm (DSA) (Zhong et al., 2013) to genes previously used to deconvolute cortical

RNA-seq data (Wang et al., 2020). RNA-seq data was TMM normalized and technical variables

were regressed out. A mean transcription level ³2 cpm was used to include marker genes of bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

neurons (n=90). The median transcription level of all marker genes per cell type was calculated

as described previously by (Klein et al., 2021); Wang et al. (2020).

Assessment of DLPFC WGS mtDNAcn

WGS libraries were prepared using the KAPA Hyper Library Preparation Kit in accordance

with the manufacturer’s instructions. Briefly, Covaris LE220 sonicator (adaptive focused

acoustics) was used to shear 650ng of DNA. Bead-based size selection was performed and

selected DNA fragments were then end-repaired, adenylated, and ligated to Illumina sequencing

adapters. Fluorescent-based assays including qPCR with the Universal KAPA Library

Quantification Kit and Fragment Analyzer (Advanced Analytics) or BioAnalyzer (Agilent 2100)

were used to evaluate final libraries. Libraries were sequenced on an Illumina HiSeq X sequencer

(v2.5 chemistry) using 2 x 150bp cycles.

R/Bioconductor (packages GenomicAlignments and GenomicRanges) was used to

calculate the median sequence coverages of the autosomal chromosomes and of the

mitochondrial genome. The intra-contig ambiguity mask from the BSgenome package was used

to exclude ambiguous regions. The mtDNAcn was defined as (covmt/covnuc) × 2. the mtDNAcn

was z-standardized and then logarithmized as described previously (Klein et al., 2021).

Banner AND BLSA LC-MS/MS proteomics

In Banner and BLSA, protein identification and quantification were performed using Liquid

Chromatography Coupled to Tandem Mass Spectrometry (LC-MS/MS) as described in (Seyfried

et al., 2017; Swarup et al., 2020). Briefly, tissue homogenization and protein digestion were

performed. Resulting peptides were desalted with a Sep-Pak C18 column (Waters) and dried

under vacuum. Peptide mixtures were separated on a self-packed C18 (1.9 um Dr. Maisch,

Germany) fused silica column (25 cm x 75 μM internal diameter; New Objective, Woburn, MA) by

a NanoAcquity UHPLC (Waters, Milford, FA) and monitored on a Q-Exactive Plus mass bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

spectrometer (ThermoFisher Scientific, San Jose, CA). MaxQuant (v1.5.2.) with Thermo

Foundation 2.0 for RAW file reading capability was used to generate label-free quantification. The

quantitation method only considered razor plus unique peptides for protein level quantitation. The

protein abundances were log2 transformed and regressed for age at death and postmortem

interval.

QUANTIFICATION AND STATISTICAL ANALYSIS

To assess the extent to which mitochondrial proteins are correlated with each other within RC

complexes or between RC we used Spearman correlation matrices. Spearman’s r matrixes were

first computed and averaged to compare between groups of protein subunits. The same method

was used to assess the cross-talk between nDNA and mtDNA proteins. As well as the overall

correlation between RC proteins abundance and mito content, mtDNAcn, mtDNA density and

neuron proportion. Before comparing levels of co-regulation between groups of subunits, we

tested whether the variables were normally distributed using the Kolmogorov-Smirnov test. For

the comparison of two groups, we used either the unpaired t-test or the non-parametric Mann-

Whitney test when the variables were not normally distributed. For the comparison of more than

two groups, we tested equality of variance using the Brown–Forsyathe test, we used one-way

ANOVA with Tukey’s multiple comparison test or the non-parametric equivalent Kruskal-Wallis

test with Dunn’s multiple comparisons test when the variables were not normally distributed.

Multivariate linear models were used to compare group difference in RC abundance by sex

(adjusted for AD status and age at death) and AD status (adjusted by sex and age at death (and

years of education in ROSMAP and BLSA)). Effect sizes between groups were computed as

Hedges’ g (g) to quantify the magnitude of group differences. Statistical analyses were performed

with Prism 8 (GraphPad, CA) and RStudio version 1.3.1056. Statistical significance was set at

p<0.05.

Definition of mitochondrial mass and RC content

Each datapoint was transformed into a z-score (x-mean/sd) before being transformed

into a t-score with an average of 100 and a standard deviation of 10 ((x*10)+100).

Index of mitochondrial content (mass)

To build a multivariate index of mitochondrial content, we averaged the t-score protein

levels of the citrate synthase (CS), the translocator of the outer mitochondrial membrane (TOM20)

and voltage dependent anion channel (VDAC1). CS is a matrix-located Krebs cycle enzyme

whose enzymatic activity has been validated has a robust marker of mitochondrial mass in human

skeletal muscle (Larsen et al., 2012) and has been used as marker of mitochondrial content in

the brain (e.g. (Scaini et al., 2010)). TOM20 and VDAC1 are integral proteins of the outer and

inner mitochondrial membranes, respectively, and are widely used as a marker of mitochondrial

mass (Gouspillou et al., 2014; Narendra et al., 2008; Rehman et al., 2012).

Indices of mitochondrial respiratory chain abundance

For each RC complex, t-score protein levels were averaged to build a score representing

the average abundance based on all subunits of RC complex I, II, III, IV and V. The proteins

included for each complex, and their annotation, are provided in Table S5.

Index of mitochondrial DNA density

The ratio of mtDNA copies per cell (mtDNAcn derived from WGS) relative to mitochondrial

content per cell (mito content index) was computed to build a score representing mtDNA density

per mitochondrion.

SUPPLEMENTAL ITEM TITLES

Supplementary figures:

Fig. S1. Study design

Fig. S2. Analytical procedure to compute co-regulation.

Fig. S3. Mitochondrial respiratory chain (RC) complexes proteins co-regulation in ROSMAP (TMT).

Fig. S4. Mitochondrial respiratory chain (RC) proteins co-regulation in Banner.

Fig. S5. Mitochondrial respiratory chain (RC) proteins co-regulation in BLSA.

Fig. S6. Mitochondrial respiratory chain (RC) proteins co-regulation in ROSMAP. Data from Selected reaction monitoring proteomics (SRM).

Fig. S7. Mitochondrial respiratory chain (RC) transcript co-regulation of the dorsolateral prefrontal cortex (DLPFC).

Fig. S8. Mitochondrial respiratory chain (RC) transcript co-regulation of the posterior cingulate cortex (PCC).

Fig. S7. Mitochondrial respiratory chain (RC) transcript co-regulation of the dorsolateral prefrontal cortex (DLPFC).

Fig. S8. Mitochondrial respiratory chain (RC) transcript co-regulation of the posterior cingulate cortex (PCC).

Fig. S9. Mitochondrial respiratory chain (RC) transcript co-regulation of the anterior caudate (AC).

Fig. S10. Mitochondrial respiratory chain (RC) supercomplexes transcript co-regulation (RNA- seq).

Fig. S11. Mitochondrial respiratory chain (RC) supercomplexes proteins and transcripts co- regulation.

Fig. S12. Nuclear-mtDNA crosstalk by mtDNA subunits.

Fig. S13. Mitochondrial transcription factors (TFs) gene expression and respiratory chain (RC) protein abundance.

Fig. S14. Association between nDNA-encoded mitochondrial respiratory chain (RC) transcript and transcription factors (TFs) levels (RNA-seq).

Fig. S15. Correlations between mitochondrial respiratory chain (RC) proteins (TMT) and transcript (RNAseq) levels in human dorsolateral prefrontal cortex (DLPFC).

Fig. S16. Mitochondrial respiratory chain (RC) proteins co-regulation according to sex and cognitive status. bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Fig. S17. Difference in mitochondrial respiratory chain (RC) complex I proteins abundance according to sex and cognitive status.

Supplementary tables:

Supplementary table 1: Participant descriptive statistics

Supplementary table 2: Correlation matrices

Supplementary table 3: Correlation matrices stratified by sex and AD status

Supplementary table 4: Difference in RC protein abundance by sex and AD status

Supplementary table 5: Annotated protein list

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bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923ROSMAP ; this version posted JulyBANNER 19, 2021. The copyright holder for this preprintBLSA (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A B Complex I II III IV V Complex I II III IV V Complex I II III IV V NDUFA12.Q9UI09 NDUFA2.O43678 NDUFA6.A0A0C4DGS0 NDUFA7.O95182 NDUFS1.P28331 NDUFS4.O43181 NDUFS6.D6RBT3 NDUFS6.O75380 NDUFV1.P49821.2 NDUFV2.P19404 NDUFV3.P56181 NDUFV3.P56181.2 NDUFA1.O15239 NDUFA13.Q9P0J0 NDUFA3.O95167 NDUFA8.P51970 NDUFA10.A0A087WXC5 NDUFA11.Q86Y39 NDUFC1.O43677 NDUFC2.O95298 NDUFS5.O43920 NDUFB1.O75438 NDUFB10.O96000 NDUFB11.Q9NX14 NDUFB4.O95168 NDUFB5.O43674 NDUFB2.C9IZW8 NDUFB3.O43676 NDUFB6.A0A087WZX2 NDUFB7.P17568 NDUFB8.O95169.3 NDUFB9.Q9Y6M9 NDUFAB1.O14561 NDUFA5.Q16718 NDUFA9.Q16795 NDUFS2.O75306 NDUFS3.O75489 NDUFS7.O75251 NDUFS8.O00217 SDHA.P31040 SDHB.P21912 SDHC.Q99643 SDHD.O14521.2 CYC1.P08574 CYCS.C9JFR7 UQCR10.Q9UDW1 UQCR11.O14957 UQCRB.P14927 UQCRC1.P31930 UQCRC2.P22695 UQCRH.P07919 UQCRQ.O14949 COX4I1.P13073 COX5A.P20674 COX5B.P10606 COX6A1.P12074 COX6B1.P14854 COX6C.P09669 COX7A1.P24310 COX7A2.P14406 COX7A2L.O14548 COX7C.P15954 COX8A.P10176 ATP5A1.P25705 ATP5A1.P25705.2 ATP5B.F8W0P7 ATP5B.P06576 ATP5C1.P36542 ATP5D.P30049 ATP5E.P56381 ATP5F1.P24539 ATP5H.O75947 ATP5I.P56385 ATP5J.P18859 ATP5J2.P56134.4 ATP5L.O75964 ATP5O.P48047 ATP5S.Q99766 ATP6AP1.Q15904 ATP6AP2.O75787.2 C C C NDUFA5.Q16718 NDUFA9.Q16795 NDUFS2.O75306 NDUFS3.O75489 NDUFS7.A0A087WXF6 NDUFS7.O75251 NDUFS8.O00217 NDUFAB1.O14561 NDUFB3.O43676 NDUFB6.A0A087WZX2 NDUFB7.P17568 NDUFB8.O95169.3 NDUFB9.Q9Y6M9 NDUFB1.O75438 NDUFB10.H3BPJ9 NDUFB11.Q9NX14 NDUFB4.O95168.2 NDUFB5.O43674 NDUFA10.A0A087WXC5 NDUFA11.Q86Y39 NDUFC2.O95298 NDUFS5.O43920 NDUFA1.O15239 NDUFA13.Q9P0J0 NDUFA3.S4R3I5 NDUFA8.P51970 NDUFA12.Q9UI09 NDUFA2.O43678 NDUFA6.A0A0C4DGS0 NDUFA7.O95182 NDUFS1.P28331 NDUFS4.O43181 NDUFS6.O75380 NDUFS6.D6RBT3 NDUFV1.P49821.2 NDUFV2.P19404 NDUFV3.P56181 NDUFV3.P56181.2 SDHA.P31040 SDHB.P21912 SDHC.Q99643.5 CYC1.P08574 CYCS.C9JFR7 UQCR10.Q9UDW1 UQCR11.O14957 UQCRB.P14927 UQCRC1.P31930 UQCRC2.P22695 UQCRH.P07919 UQCRQ.O14949 COX4I1.P13073 COX5A.P20674 COX5B.P10606 COX6A1.P12074 COX6B1.P14854 COX6C.P09669 COX7A1.P24310 COX7A2.P14406 COX7A2L.O14548 COX7C.P15954 COX8A.P10176 ATP5A1.P25705 ATP5A1.P25705.2 ATP5B.P06576 ATP5C1.P36542.2 ATP5D.P30049 ATP5F1.P24539 ATP5H.O75947 ATP5I.P56385 ATP5J.P18859 ATP5J2.P56134.4 ATP5L.O75964 ATP5O.P48047 ATP5S.Q99766 ATP6AP1.Q15904 ATP6AP2.O75787.2 NDUFA12.Q9UI09 NDUFA5.Q16718 1 1 NDUFA2.O43678 NDUFA9.Q16795 C C C C C NDUFA6.A0A0C4DGS0 NDUFS2.O75306 NDUFA7.O95182 NDUFS3.O75489 NDUFS1.P28331 NDUFS4.O43181 NDUFS7.A0A087WXF6 NDUFS6.D6RBT3 NDUFS7.O75251 NDUFS6.O75380 NDUFS8.O00217 NDUFV1.P49821.2 NDUFAB1.O145610.8 0.8 NDUFV2.P19404 NDUFB3.O43676 NDUFV3.P56181 NDUFB6.A0A087WZX2 NDUFV3.P56181.2 NDUFB7.P17568 NDUFA1.O15239 NDUFB8.O95169.3 NDUFA13.Q9P0J0 NDUFB9.Q9Y6M9 NDUFA3.O95167 NDUFB1.O75438 NDUFA8.P51970 NDUFB10.H3BPJ9 NDUFA10.A0A087WXC5 0.6 NDUFB11.Q9NX14 NDUFA11.Q86Y39 0.6 NDUFB4.O95168.2 NDUFC1.O43677 NDUFB5.O43674 DLPFC NDUFC2.O95298 NDUFA10.A0A087WXC5 NDUFS5.O43920 NDUFB1.O75438 NDUFA11.Q86Y39 NDUFB10.O96000 NDUFC2.O95298 NDUFB11.Q9NX14 NDUFS5.O43920 NDUFB4.O95168 0.4 NDUFA1.O15239 NDUFB5.O43674 NDUFA13.Q9P0J0 0.4 NDUFB2.C9IZW8 NDUFA3.S4R3I5 NDUFB3.O43676 NDUFA8.P51970 NDUFB6.A0A087WZX2 NDUFA12.Q9UI09 NDUFB7.P17568 NDUFA2.O43678 NDUFB8.O95169.3 NDUFA6.A0A0C4DGS0 NDUFB9.Q9Y6M9 NDUFA7.O95182 NDUFAB1.O14561 0.2 NDUFS1.P28331 NDUFA5.Q16718 0.2 NDUFS4.O43181 NDUFA9.Q16795 NDUFS6.O75380 NDUFS2.O75306 NDUFS3.O75489 NDUFS6.D6RBT3 NDUFS7.O75251 0.8 NDUFV1.P49821.2 NDUFS8.O00217 NDUFV2.P19404 0.8 SDHA.P31040 NDUFV3.P56181 0.8 SDHB.P21912 0 NDUFV3.P56181.2 SDHC.Q99643 SDHA.P31040 0 **** SDHD.O14521.2 SDHB.P21912 * ** CYC1.P08574 SDHC.Q99643.5 CYCS.C9JFR7 CYC1.P08574 UQCR10.Q9UDW1 CYCS.C9JFR7 UQCR11.O14957 UQCR10.Q9UDW1 UQCRB.P14927 0.6 UQCR11.O14957 UQCRC1.P31930 −0.2 UQCRB.P14927 0.6 0.6 UQCRC2.P22695 −0.2 UQCRC1.P31930 UQCRH.P07919 UQCRC2.P22695 UQCRQ.O14949

0.45 COX4I1.P13073 UQCRH.P07919 COX5A.P20674 UQCRQ.O14949 0.45 0.61 0.61 COX5B.P10606 **** COX4I1.P13073 0.36 COX6A1.P12074 COX5A.P20674 1.0 COX6B1.P14854 −0.4 MT.ND1.P03886 0.18 COX5B.P10606-0.01 -0.09 1.0 COX6C.P09669 MT.ND1.P03886 0.18 *** -0.01COX6A1.P12074 -0.09 −0.4 0.4 0.4 COX7A1.P24310 0.4 COX6B1.P14854 COX7A2.P14406 COX6C.P09669 COX7A2L.O14548 MT.ND2.P03891 0.26 0.04COX7A1.P24310 -0.11

0.45 MT.ND2.P03891 0.26 0.04 -0.11 0.45 COX7C.P15954 COX7A2.P14406 COX8A.P10176 COX7A2L.O14548 0.5 0.5 ATP5A1.P25705 COX7C.P15954

ATP5A1.P25705.2 −0.6 0.39 MT.ND3.P03897MT.ND3.P03897 0.21 0.14 COX8A.P10176 0.05 0.37 0.21 0.14 0.05 0.36 ATP5B.F8W0P7

0.35 ATP5A1.P25705 −0.6 ATP5B.P06576 0.34 ATP5A1.P25705.2 ATP5C1.P36542 0.33 0.2 0.2 ATP5D.P30049 MT.ND4.P03905MT.ND4.P03905 0.32 0.20 ATP5B.P06576 -0.01

0.2 0.320.29 0.20 -0.01 ATP5E.P56381 ATP5C1.P36542.2 0 0 ATP5F1.P24539 ATP5D.P30049 ATP5H.O75947 ATP5F1.P24539 ATP5I.P56385 −0.8MT.ND5.P03915MT.ND5.P03915 0.350.35 0.190.19 -0.05ATP5H.O75947-0.05 ATP5J.P18859 ATP5I.P56385 −0.8 Average Spearman's rho Average Spearman's rho Average ATP5J2.P56134.4 Spearman's rho Average ATP5J.P18859 ATP5L.O75964 ATP5J2.P56134.4 -0.5 MT.ND6.P03923 0.420.16 0.13 -0.14 ATP5O.P48047 -0.5 MT.ND6.P03923 0.42 0.13 -0.14 ATP5L.O75964 ATP5S.Q99766 0.0 0.0 ATP5O.P48047 ATP6AP1.Q15904 ATP5S.Q99766 ATP6AP2.O75787.2 0.0

N=201 rho Spearman’s I N=400 I −1MT.CYB.P00156MT.CYB.P00156 0.230.23 -0.09-0.09 N=47-0.21-0.21ATP6AP1.Q15904 V V ATP6AP2.O75787.2 III IV III IV I -1.0−1 -1.0 MT.CO1.P00395MT.CO1.P00395 0.31III 0.31IV V 0.020.02 -0.13-0.13 MT.CO2.P00403MT.CO2.P00403 0.550.55 -0.04-0.04 -0.35-0.35 SubunitsMT.CO3.P00414MT.CO3.P00414 & assembly 0.330.33 Core0.080.08 & accessory-0.12-0.12 mt massmt mass mtcnmtcn mt densitymt density Frequency distributions of co-regulation by RC complex factors (AF) subunits **** C D 0.8 0.8 0.8 E 0.8 0.8 0.8 ROSMAP Banner BLSA **** ****

Predicted Median Median 0.6 0.6 0.6 0.6 0.6 0.72 0.6 0.42 **** 0.64 0.56 0.63

**** 0.60 5 2.5 2.0 I 0.4 0.4 0.4 **** 0.4 0.4 0.4

4 0.43 0.38 2.0 0.39 Null hypothesis 1.5 0.35 0.2 0.2 0.2 0.31 0.2 0.2 0.2 3 (no co-regulation 0.27 between proteins) 1.5 Average Spearman's rho Average 0.19 1.0 0.17 0.0 0.0 0.0 0.0 0.0 0.0 2 1.0 S S S AF AF AF

Proportion (%) Core Core Core Proportion (%) 1 Proportion (%) 0.5 0.5 ROSMAP Banner BLSA Accessory Accessory Accessory ROSMAP Banner BLSA 0 0.0 0.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 Effect sizes (r) Effect sizes (r) Effect sizes (r) Higher co-regulation among 0.30 0.70 0.34 F “supercomplexes” (SC)? 5 10 6 cc III SC Non-SC 8 3_ 4 su bn 4 I 3 6 dn IV II a_ V III 2 4 all 2

_p Proportion (%) Proportion (%) Proportion (%) 1 2 air s Co-regulation between RC complexes 0 0 0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 0.8 Effect sizes (r) Super- Non-SC pairs of Effect sizes (r) Effect sizes (r) complexes complexes 0.33 0.19 0.31 8 0.6 5 4 ROSMAP IV cc cc 4_ 4_ 4 6 0.4 3 su su 0.40 0.40 0.38 0.35 0.35 bn b_ 0.35

3 dn all 0.2 0.29 4 0.27 2 a_ _p 0.21 0.19 0.16 2 all air Spearman's rho Average

_p s Proportion (%) 2 0.0 Proportion (%) Proportion (%) 1 1 air 0.8 Banner s 0 0 0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 0.6 Effect sizes (r)

Effect sizes (r) Effect sizes (r) 0.57 c1c3_subndna_all_pairsc1c4_subndna_all_pairsc3c4_subndna_all_pairsc1c2_subndna_all_pairsc1c5_subndna_all_pairsc2c3_subndna_all_pairsc2c4_subndna_all_pairsc2c5_subndna_all_pairsc3c5_subndna_all_pairsc4c5_subndna_all_pairs

c1c3c4_subndna_all_pairs0.52

0.27 0.55 0.32 0.4 0.49 5 5 4 0.43

cc 0.40 0.37 0.37 5_ V 4 4 0.2 0.30 3 su 0.27 0.23 0.22 bn 3 3 dn 0.0 2 a_ 0.8 2 all 2 BLSA _p Proportion (% Proportion (% Proportion (%) 1 1 air 1 0.6 s 0.57 0 0 0 c1c3_subndna_all_pairsc1c4_subndna_all_pairsc3c4_subndna_all_pairsc1c2_subndna_all_pairsc1c5_subndna_all_pairsc2c3_subndna_all_pairsc2c4_subndna_all_pairsc2c5_subndna_all_pairsc3c5_subndna_all_pairsc4c5_subndna_all_pairs -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 0.4c1c3c4_subndna_all_pairs 0.40

Effect sizes (r) Effect sizes (r) Effect sizes (r) 0.37 0.35

0.2 0.30 0.27 0.26 0.25 0.24 0.24 0.17 0.0

I & III I & IV I & II I & V II & V III & IV II & IIIII & IV III & VIV & V I, III & IV Fig. 1. Mitochondrial respiratory chain (RC) complexes proteins co-regulation. (A) Study design: we assessed co-regulation of mitochondrial RC proteins (or subunits) in the human dorsolateral prefrontal cortex (DLPFC) across three independent cohorts: ROSMAP (N=400), Banner (N=201) and BLSA (N=47) (for more details see Fig. S1). (B) Heatmap of the association between RC nuclear DNA (nDNA) encoded subunits assessed with Spearman rank correlation in ROSMAP, Banner and BLSA. Heatmaps with individual pairs of correlations are presented in Fig S3-S5 and correlation matrices are presented in table S2. Average Spearman’s rho (95% CI) for nDNA encoded subunits for RC complexes I, III, IV, V. (C) Distribution of correlation results (Spearman’s rho) for RC complexes I, III, IV, V. Co- regulation of complex II was not studied individually due to its small number of subunits. (D) Average Spearman’s rho (95% CI) for nDNA encoded subunits and assembly factors and for (E) core and accessory subunits for all RC complexes. (F) Average Spearman’s rho (95% CI) for RC complexes part of the hypothesized supercomplexes (SC, I, III, IV) and other possible pairs of RC complexes. P-values from Dunn’s multiple comparisons test are shown in Fig S11A-C. P-values from Mann-Whitney test (D-E) or Kruskal-Wallis test with Dunn’s multiple comparisons test (B), p<0.05*, p<0.01**, p<0.001***, p<0.0001****. et o oewy NV ad ue’ mlil cmaio ts (, ih) p000**. ( p<0.0001****. right), (B, test comparison (NDUFA10.O95299) 1 subunit a complex between ( mtDNA and density. multiple ROSMAP, regression. linear from simple Results Tukey’s as N=124-400 and ANOVA shown one-way %) or (neuron left) proportion (B, test comparisons multiple neuron Dunn’s with test Kruskal-Wallis from P-value CI). and (95% rho Spearman’s average as content) and (B) rho TOMM20.Q15388, Spearman’s CS.H0YIC4, (mtDNAcn/mito CS.O75390, density of mtDNA values scaled mtDNAcn, the of VDAC1.P21796), density, (average content mtDNA mitochondrial mass, and abundance mitochondrial proportion. subunits neuron abundance, encoded and protein (mtDNAcn) (RC) number chain copy DNA respiratory mitochondrial mitochondrial between Relationship 2. Fig. UQCR10.Q9UDW1 NDUFB11.Q9NX14 bioRxiv preprint NDUFV3.P56181.2 NDUFAB1.O14561 NDUFB10.O96000 NDUFA13.Q9P0J0 NDUFA11.Q86Y39 COX7A2L.O14548 NDUFA10.O95299 ATP5C1.P36542.2 NDUFA12.Q9UI09 NDUFB9.Q9Y6M9 ATP6AP2.O75787 ATP6AP1.Q15904 NDUFB2.H7C5B8 NDUFV2.E7EPT4 UQCRC2.P22695 UQCRC1.P31930 NDUFA9.F5GY40 NDUFC2.O95298 NDUFC1.O43677 NDUFS8.O00217 NDUFS7.O75251 NDUFS6.O75380 NDUFS5.O43920 NDUFS4.O43181 NDUFS3.O75489 NDUFS2.O75306 NDUFB8.O95169 NDUFB6.O95139 NDUFB5.O43674 NDUFB4.O95168 NDUFB3.O43676 NDUFB1.O75438 UQCR11.O14957 NDUFA9.Q16795 NDUFA7.O95182 NDUFA5.Q16718 NDUFA3.O95167 NDUFA2.O43678 NDUFV3.P56181 NDUFS1.P28331 NDUFB7.P17568 COX7A2.P14406 COX7A1.P24310 COX6B1.P14854 COX6A1.P12074 NDUFA8.P51970 NDUFA6.P56556 NDUFV1.G3V0I5 UQCRQ.O14949 ATP5C1.P36542 UQCRH.P07919 ATP5A1.P25705 UQCRB.P14927 ATP5F1.P24539 COX4I1.P13073 ATP5B.F8W0P7 MT.CO3.P00414 MT.CO2.P00403 MT.CO1.P00395 MT.CYB.P00156 COX7C.P15954 COX6C.P09669 MT.ND6.P03923 MT.ND5.P03915 MT.ND4.P03905 MT.ND3.P03897 MT.ND2.P03891 MT.ND1.P03886 COX5B.P10606 COX5A.P20674 COX7B.P24311 ATP5H.O75947 ATP5S.Q99766 ATP5O.P48047 ATP5D.P30049 ATP5L.O75964 ATP5E.P56381 ATP5B.P06576 CYCS.C9JFR7 SDHD.O14521 SDHC.Q99643 ATP5J.P18859 SDHB.P21912 SDHA.P31040 ATP5I.P56385 CYC1.P08574 A MT.CO3.P00414 MT.CO2.P00403 MT.CO1.P00395 MT.CYB.P00156 MT.ND6.P03923 MT.ND5.P03915 MT.ND4.P03905 MT.ND3.P03897 MT.ND2.P03891 MT.ND1.P03886 MT.CO3.P00414 MT.CO2.P00403 MT.CO1.P00395 MT.CYB.P00156 mtDNAMT.ND6.P03923 MT.ND5.P03915 MT.ND4.P03905 MT.ND3.P03897 MT.ND2.P03891 nDNA-encodedMT.ND1.P03886 subunits content mt mass mt mass mito Correlations with covariates with Correlations was notcertifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. doi:

mt mass 0.33 0.55 0.31 0.23 0.42 0.35 0.32 0.21 0.26 0.18 https://doi.org/10.1101/2021.07.19.452923 mtDNAcn mt mass mtcn 0.33 0.55 0.31 0.23 0.42 0.35 0.32 0.21 0.26 0.18 mtcn UQCR10.Q9UDW1 NDUFB11.Q9NX14 NDUFV3.P56181.2 NDUFAB1.O14561 NDUFB10.O96000 NDUFA13.Q9P0J0 NDUFA11.Q86Y39 COX7A2L.O14548 NDUFA10.O95299 ATP5C1.P36542.2 NDUFA12.Q9UI09 NDUFB9.Q9Y6M9 ATP6AP2.O75787 ATP6AP1.Q15904 NDUFB2.H7C5B8 NDUFV2.E7EPT4 UQCRC2.P22695 UQCRC1.P31930 NDUFA9.F5GY40 NDUFC2.O95298 NDUFC1.O43677 NDUFS8.O00217 NDUFS7.O75251 NDUFS6.O75380 NDUFS5.O43920 NDUFS4.O43181 NDUFS3.O75489 NDUFS2.O75306 NDUFB8.O95169 NDUFB6.O95139 NDUFB5.O43674 NDUFB4.O95168 NDUFB3.O43676 NDUFB1.O75438 UQCR11.O14957 NDUFA9.Q16795 NDUFA7.O95182 NDUFA5.Q16718 NDUFA3.O95167 NDUFA2.O43678 NDUFV3.P56181 NDUFS1.P28331 NDUFB7.P17568 COX7A2.P14406 COX7A1.P24310 COX6B1.P14854 COX6A1.P12074 NDUFA8.P51970 NDUFA6.P56556 NDUFV1.G3V0I5 UQCRQ.O14949 ATP5C1.P36542 UQCRH.P07919 ATP5A1.P25705 UQCRB.P14927 ATP5F1.P24539 COX4I1.P13073 ATP5B.F8W0P7 COX7C.P15954 COX6C.P09669 COX5B.P10606 COX5A.P20674 COX7B.P24311 ATP5H.O75947 ATP5S.Q99766 ATP5O.P48047 ATP5D.P30049 ATP5L.O75964 ATP5E.P56381 ATP5B.P06576 CYCS.C9JFR7 SDHD.O14521 SDHC.Q99643 ATP5J.P18859 MT.ATP8.P03928 MT.ATP6.P00846 SDHB.P21912 SDHA.P31040 ATP5I.P56385 CYC1.P08574 -0.04 -0.09 -0.01 MT.CO3.P00414 MT.CO2.P00403 MT.CO1.P00395 MT.CYB.P00156 MT.ND6.P03923 MT.ND5.P03915 MT.ND4.P03905 MT.ND3.P03897 MT.ND2.P03891 MT.ND1.P03886 0.08 0.02 0.13 0.19 0.20 0.14 0.04 mtcn mt density mt density mtDNA density -0.04 -0.09 -0.01 0.08 0.02 0.13 0.19 0.20 0.14 0.04 mtcn mt density -0.12 -0.35 -0.13 -0.21 -0.14 -0.05 -0.01 -0.09 -0.11 0.05 mt density neurons % % % neurons -0.12 -0.35 -0.13 -0.21 -0.14 -0.05 -0.01 -0.09 -0.11 % % neurons 0.05 0.5 1.0 0 -1.0 -0.5 0.5 1.0 0 -1.0 -0.5 0.5 1.0 0 -1.0 -0.5 0.5 1.0 0 0.5 1.0 -1.0 -0.5 0 -1.0 -0.5 0.5 1.0 0 Spearman’s rho -1.0 -0.5 B

-0.5 Average spearman's rho 0.0 0.5 1.0

mt mass ; mtDNAcn nDNA subunits mtDNA density 0.48 this versionpostedJuly19,2021. ****

neurons % -0.5 0.0 0.5 1.0

mt mass mtDNA subunits

soito bten C ula DA nN) n mtDNA and (nDNA) DNA nuclear RC between Association mtDNAcn mtDNA density 0.30 ****

neurons % The copyrightholderforthispreprint(which ) Example of scatterplot of the association association the of scatterplot of Example C) C NDUFA10.O95299 -0.6 -0.4 -0.2 0.0 0.2 0.4 Increasing mtDNAIncreasing mitochondrion per N =156 P =0.0078 R 2 = 0.04 0.8 mtDNA and density protein abundance protein mtDNA density 1.0 1.2 1.4 hamp of heatmaps A) bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. mtDNA encoded subunits 1. A nDNA B mtDNA-nDNA C Crosstalk by D 0 0.8 crosstalk 0.8 ND1 ND2 COX1 COX2 ATP8 ATP6 COX3 ND3 ND4 ND5 ND6 CYB expression RC complexNDUFA10.O95299 0. NDUFA13.Q9P0J0NDUFA11.Q86Y39NDUFA12.Q9UI09 NDUFA5.Q16718NDUFA3.O95167NDUFA2.O43678 5 NDUFA7.O95182NDUFA8.P51970NDUFA6.P56556 **** NDUFAB1.O14561NDUFA9.F5GY40NDUFA9.Q16795 NDUFB11.Q9NX14NDUFB10.O96000NDUFB1.O75438 0.6 0.6 NDUFB2.H7C5B8NDUFB4.O95168NDUFB3.O43676 0 NDUFB6.O95139NDUFB5.O43674NDUFB7.P17568 NDUFB9.Q9Y6M9NDUFC1.O43677NDUFB8.O95169 NDUFC2.O95298NDUFS2.O75306NDUFS1.P28331 *** NDUFS5.O43920NDUFS4.O43181NDUFS3.O75489 1.0 -0. NDUFA10_2 0.43 **NDUFS8.O00217NDUFS7.O75251NDUFS6.O753800.36 0.35 0.43 0.42 0.44 0.25 0.21 0.07 0.09 0.25 0.02 0.24 0.11 0.15 Co-expression? NDUFV2.E7EPT4NDUFV3.P56181NDUFV1.G3V0I5** 5 NDUFV3.P56181.2SDHB.P21912SDHA.P31040 0.4 NDUFA10_10.4 0.32 0.29SDHD.O14521SDHC.Q99643CYC1.P085740.25 0.31 0.32 0.31 0.15 0.17 0.06 0.08 0.15 0.01 0.15 0.04 0.06 0.5 UQCR10.Q9UDW1UQCR11.O14957CYCS.C9JFR7 -1. NDUFA5_2 0.39 UQCRC2.P22695UQCRC1.P31930UQCRB.P149270.35 0.31 0.36 0.36 0.37 0.22 0.23 0.07 0.09 0.24 0.07 0.22 0.05 0.13 UQCRQ.O14949UQCRH.P07919COX4I1.P13073 0 0.38 COX6A1.P12074COX5B.P10606COX5A.P20674 0 NDUFA5_1 0.44 COX7A1.P24310COX6B1.P14854COX6C.P096690.40 0.34 0.43 0.43 0.42 0.23 0.24 0.05 0.08 0.25 0.03 0.21 0.09 0.14 COX7A2L.O14548COX7A2.P144060.33 0.31 COX7B.P24311 0.2 NDUFA6_20.2 0.42 ATP5A1.P25705COX7C.P159540.35 0.39 0.43 0.42 0.44 0.27 0.22 0.11 0.09 0.26 0.03 0.25 0.10 0.15 ATP5B.F8W0P7ATP5B.P065760.28 ATP5C1.P36542.2ATP5C1.P36542ATP5D.P30049 -0.5 0.23 ATP5F1.P24539ATP5H.O75947ATP5E.P56381 NDUFA7_2 0.38 0.31ATP5I.P563850.35 nDNA encoded subunits 0.40 0.38 0.42 0.19 0.17 0.04 0.05 0.24 0.03 0.23 0.10 0.15 Average Spearman's rho Average ATP5J.P18859

0.18 ATP5L.O75964 ATP5S.Q99766ATP5O.P48047 Spearman's rho mtDNA NDUFA7_1 0.39 ATP6AP2.O75787ATP6AP1.Q159040.35 0.36 0.42 0.40 0.41 0.22 0.18 0.06 0.07 0.22 0.04 0.23 0.08 0.12 -1.0 0.0 0.0 expression NDUFS6_1 0.35 0.29 0.31 0.38 0.35 0.39 0.25 0.23 0.04 0.04 0.24 0.11 0.23 0.08 0.12 I NDUFS6_2 0.44 III 0.39IV V 0.37 0.45 0.44 0.45 0.24 0.22Direction0.04 of transcription0.07 0.25 0.01 0.23 0.08 0.13 nDNA mtDNA NDUFV1_2 0.37 0.31 0.27 0.35 0.35 0.35 0.21 0.19 0.06 0.08 0.21 0.04 0.22 0.07 0.15 crosstalk MT.ND1.P03886MT.ND2.P03891MT.CO1.P00395MT.CO2.P00403MT.ATP8.P03928MT.ATP6.P00846MT.CO3.P00414MT.ND3.P03897MT.ND4.P03905MT.ND5.P03915MT.ND6.P03923MT.CYB.P00156 NDUFV1_1 0.42 0.36 0.34 0.40 0.39 0.40 0.22 0.21 0.10 0.10 0.24 0.06 0.22 0.10 0.13 SDHA_2 0.17 0.13 0.11 0.18 Transcription0.14 0.18 factors0.31 RNAseq0.28 0.20 0.15 0.18 0.08 nDNA-encoded0.16 0.04 0.16 mtDNA E F SDHB_1 0.15 0.17 0.10 G0.13 0.12 0.13 0.22 0.25 0.14 0.19 0.15 H -3 0.13 0.02 0.08 Average strength of co-regulation with -3.620.15×10 subunits0.15 SDHB_2 0.23 0.24 0.17 0.21 0.20 0.21 0.26 0.27 0.09 0.16 0.20 0.07 0.16 0.07 0.13 0.8 nDNA-encoded subunits 1 NRF1 NRF2 TFAM PGC1⍺ 1.0

ATP8 NDUFA10.O95299 SDHC_1 0.09 0.08 0.04 0.08 0.09 0.08 0.11 0.16 0.15 0.11 0.08 0.50.04 0.06 -0.02 0.03 NDUFA11.Q86Y39 NDUFA12.Q9UI09 0 NDUFA13.Q9P0J0 NDUFA2.O43678 -0.5 NDUFA3.O95167 NDUFA5.Q16718 0.10 0.10 -1.0 COX1 NDUFA6.P56556 NDUFA7.O95182 NDUFA8.P51970 SDHC_2 0.05 0.04NDUFA9.F5GY400.02 0.07 0.07 0.07 0.08 0.11 0.10 0.10 0.06 0.02 0.04 -0.02 0.01 ND3 ND6NDUFA9.Q16795 NDUFAB1.O14561 NDUFB1.O75438 NDUFB10.O96000 NDUFB11.Q9NX14 NDUFB2.H7C5B8 NDUFB3.O43676 NDUFB4.O95168 SDHD_1 0.18 0.12NDUFB5.O436740.09 0.17 0.16 0.17 0.17 0.21 0.08 0.07 0.14 0.04 0.11 -0.03 0.11 0 NDUFB6.O95139 NDUFB7.P17568 0.6 ND4 NDUFB8.O95169 ATP6NDUFB9.Q9Y6M9 NDUFC1.O43677 NDUFC2.O95298 0.05 0.05 NDUFS1.P28331 NDUFS2.O75306 -3 NDUFS3.O75489 SDHD_2 0.08 0.08NDUFS4.O431810.03 0.09 0.09 0.09 0.12 0.19 0.12 0.08 0.09 0.05 0.09 1.84×10 0.07 NDUFS5.O43920 COX3NDUFS6.O75380

NDUFS7.O75251 0.07 NDUFS8.O00217COX2 0.06 NDUFV1.G3V0I5

NDUFV2.E7EPT4 0.06 CYB NDUFV3.P56181 NDUFV3.P56181.2 CYC1_1 0.22 0.14 SDHA.P310400.14 0.19 0.17 0.18 0.16 0.18 0.06 0.09 0.22 0.09 0.24 0.04 0.16 SDHB.P21912 SDHC.Q99643 SDHD.O14521ND5 CYC1.P08574 CYCS.C9JFR7 UQCR10.Q9UDW1 UQCR11.O14957 0.00 0.00 UQCRB.P14927 UQCRC1.P31930 -1 CYC1_2 0.26 0.23UQCRC2.P226950.19 0.26 0.23 0.24 0.22 0.21 0.07 0.08 0.24 0.06 0.27 0.07 0.16 0.4 UQCRH.P07919 UQCRQ.O14949 COX4I1.P13073 COX5A.P20674 1.0 COX5B.P10606 COX6A1.P12074 NDUFA10_2 0.43 0.36 0.35 0.43 0.42 0.44 0.25 0.21 COX6B1.P148540.07 0.09 0.25 0.02 0.24 0.11 0.15 COX6C.P09669 UQCR10_1 0.26 0.19COX7A1.P243100.22 0.25 0.26 0.24 0.25 0.18 -0.01 0.02 0.22 0.32 0.25 0.10 0.12 COX7A2.P14406 COX7A2L.O14548 COX7B.P24311 COX7C.P15954

0.40 ATP5A1.P25705 ATP5B.F8W0P7

0.38 ATP5B.P06576

NDUFA10_1 0.32 0.29 0.25 0.31 0.32 0.31 0.15 0.17 ATP5C1.P365420.06 0.08 0.15 0.01 0.15 0.04 0.06 -0.05 -0.06 -0.05 ATP5C1.P36542.2 UQCR10_2 0.18 0.15 ATP5D.P300490.11 0.17 0.16 0.15 0.16 0.11 0.02 0.060.5 0.12 0.03 0.19 0.09 0.12 ATP5E.P56381 ATP5F1.P24539 ATP5H.O75947 ATP5I.P56385 ATP5J.P18859

0.32 ATP5L.O75964 NDUFA5_2 0.39 0.35 0.31 0.36 0.36 0.37 0.22 0.23 ATP5O.P480470.07 0.09 0.24 0.07 0.22 0.05 0.13 -2 2 ATP5S.Q99766 0.2 0.30 UQCRC2_1 0.27 0.24ATP6AP1.Q159040.21 0.29 0.24 0.28 0.23 0.22 0.10 0.14 0.26 0.06 0.26 0.04 0.15 ATP6AP2.O75787R = 0.02

nDNA encoded subunits nDNA NRF1 GABPB1 TFAM PPARGC1A Protein abundance

0.26 ND2 NDUFA5_1 0.44 0.40 0.340.26 0.43 0.43 0.42 0.23 0.24P = 0.670.05 0.08 0.25 0.03 0.21 0.09 0.14 0 -0.10 -0.10

Average Spearman's rho Average UQCRC2_2 0.25 0.23 0.16 0.26 0.22 0.24 0.22 0.20 0.07 0.07 0.22 Spearman's rho Average 0.05 0.26 0.09 0.17 1.0 MT.ND1.P03886 0.20

0.19 MT.ND2.P03891 NDUFA6_2 0.42 0.35 0.39 0.43 0.42 0.44 0.27 0.22N =MT.ND3.P03897 120.11 0.09 0.26 0.03 0.25 0.10 0.15 0.5 MT.ND4.P03905 0.16

0.15 COX7B_2 0.11 0.05 0.06 0.14 0.11 0.17 0.14 0.15 0.13 0.05 0.14 0.10 0.17 0.06 0.11 ND1 MT.ND5.P03915 -0.5 0 MT.ND6.P03923 MT.CYB.P00156 -0.5 0.0 NDUFA7_2 0.38 0.31 0.35 0.40 0.38-3 0.42 0.19 0.17 MT.CO1.P003950.04 0.05 0.24 0.03 0.23 0.10 0.15 ATP5F1_2 0.14 0.13 MT.CO2.P004030.08 0.11 0.11 0.13 0.21 0.20 0.12 0.10 0.17 -0.150.05 0.18 0.04-0.15 0.23 -1.0 MT.CO3.P00414

MT.ATP6.P00846 Spearman's rho NDUFA7_1 0.39 0.35 0.36 0.42 0.40 0.0 0.410.1 0.20.22 0.30.18 0.4MT.ATP8.P039280.060.5 0.07mtDNA 0.22 0.04 0.23 0.08 0.12 -1.0 ATP5F1_1nuclear-mtDNA0.12 crosstalk0.16 0.07 0.10 NRF10.08 GABPB10.11 0.16TFAM0.17PPARGC1A0.10 0.12 0.16 0.01 0.16 0.01⍺ 0.23 ⍺ ND1 ND2 NDUFS6_1ATP8 ATP60.35 ND30.29ND4 ND50.31 ND6 0.38CYB 0.35 0.39 0.25 0.23 0.04 0.04 0.24 0.11 0.23 0.08 0.12 COX1 COX2 COX3 NRF1 NRF2 TFAM NRF1 NRF2 TFAM PGC1 PGC1 NDUFS6_2 0.44 0.39 0.37 0.45 0.44 0.45 0.24 0.22 0.04 0.07 0.25 0.01 0.23 0.08 0.13 NDUFV1_2 0.37 0.31 0.27 0.35 0.35 0.35 0.21 0.19 0.06 0.08 0.21 0.04 0.22 0.07 0.15 Fig. 3. mtDNANDUFV1_1 and0.42 nuclear0.36 DNA0.34 0.40 (nDNA)0.39 encoded0.40 0.22 mitochondrial0.21 0.10 0.10 respiratory0.24 0.06 chain0.22 0.10 (RC)0.13 proteins co-regulation, nuclear-mtDNA CYC1.P08574 SDHA.P31040 SDHB.P21912

SDHA_2 0.17 0.13 0.11 0.18 0.14 0.18 0.31 0.28 0.20 0.15 0.18 0.08 0.16 0.04 0.16SDHC.Q99643 SDHD.O14521 COX7B.P24311 ATP5F1.P24539 NDUFV1.G3V0I5 NDUFA6.P56556 NDUFA5.Q16718 NDUFA7.O95182 NDUFS6.O75380 -3 UQCRC2.P22695

crosstalk andSDHB_1 transcription0.15 0.17 factors.0.10 0.13 (A0.12) Diagram0.13 0.22 ofNDUFA10.O95299 nuclear-mtDNA0.25 0.14 0.19 crosstalk.0.15 -3.62×10 (B0.13) Average0.02 0.08 Spearman’s rho (95% CI) between nDNA UQCR10.Q9UDW1 encoded subunits,SDHB_2 between0.23 0.24 mtDNA0.17 encoded0.21 0.20 subunits0.21 0.26 and0.27 for the0.09 crosstalk0.16 0.20 between0.07 0.16 nDNA0.07 and0.13 mtDNA subunits. (C) Average Spearman’s SDHC_1 0.09 0.08 0.04 0.08 0.09 0.08 0.11 0.16 0.15 0.11 0.08 0.04 0.06 -0.02 0.03 rho of nuclear-mtDNASDHC_2 0.05 crosstalk0.04 0.02 for RC0.07 complexes0.07 0.07 I,0.08 III, IV,0.11 V. 0.10(D) Heatmap0.10 0.06 and0.02 (E)0.04 average-0.02 Spearman’s0.01 rho (95% CI) of nuclear-mtDNA crosstalk presentedSDHD_1 0.18by mtDNA0.12 0.09encoded0.17 subunits’0.16 0.17 order0.17 of0.21 transcription.0.08 0.07 Note0.14 0.04that ND4L0.11 -0.03is not0.11 shown (data not available). P-values from SDHD_2 0.08 0.08 0.03 0.09 0.09 0.09 0.12 0.19 0.12 0.08 0.09 0.05 0.09 1.84×10-3 0.07 Dunn’s multipleCYC1_1 comparisons0.22 0.14 test0.14 are0.19 shown0.17 in0.18 Fig.0.16 S12,0.18 (F) Relationship0.06 0.09 0.22 between0.09 0.24mtDNA0.04 encoded0.16 RC protein abundance and nuclear- mtDNA crosstalk.CYC1_2 Results0.26 0.23 from0.19 simple0.26 linear0.23 regression.0.24 0.22 0.21(G) Heatmap0.07 0.08 and0.24 (H)0.06 average0.27 Spearman’s0.07 0.16 rho (95% CI) of RC subunits protein abundance andUQCR10_1 transcription0.26 0.19 factor0.22 gene0.25 0.26expression0.24 0.25 (NRF20.18 corresponds-0.01 0.02 0.22 to GABPB10.32 0.25 gene).0.10 P-values0.12 from Dunn’s multiple comparisons UQCR10_2 0.18 0.15 0.11 0.17 0.16 0.15 0.16 0.11 0.02 0.06 0.12 0.03 0.19 0.09 0.12 test are shownUQCRC2_1 in Fig0.27 S13.0.24 ROSMAP,0.21 0.29 N0.24 =124-400.0.28 0.23 P-value0.22 0.10 from0.14 Kruskal-Wallis0.26 0.06 0.26 test 0.04 with Dunn’s0.15 multiple comparisons test, p<0.05*, p<0.01**, p<0.001***,UQCRC2_2 0.25 p<0.0001****.0.23 0.16 0.26 0.22 0.24 0.22 0.20 0.07 0.07 0.22 0.05 0.26 0.09 0.17 COX7B_2 0.11 0.05 0.06 0.14 0.11 0.17 0.14 0.15 0.13 0.05 0.14 0.10 0.17 0.06 0.11 ATP5F1_2 0.14 0.13 0.08 0.11 0.11 0.13 0.21 0.20 0.12 0.10 0.17 0.05 0.18 0.04 0.23 ATP5F1_1 0.12 0.16 0.07 0.10 0.08 0.11 0.16 0.17 0.10 0.12 0.16 0.01 0.16 0.01 0.23 CYC1.P08574 SDHA.P31040 SDHB.P21912 SDHC.Q99643 SDHD.O14521 COX7B.P24311 ATP5F1.P24539 NDUFV1.G3V0I5 NDUFA6.P56556 NDUFA5.Q16718 NDUFA7.O95182 NDUFS6.O75380 UQCRC2.P22695 NDUFA10.O95299 UQCR10.Q9UDW1 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.19.452923; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Co-regulation matrix mtDNA-encoded proteins

N Q ND1 ND2 ND4 ND5 A Complex I structure B b NDUFA12.Q9UI09

NDUFA2.O43678 Protein abundance

NDUFA12.Q9UI09 NDUFA2.O43678 NDUFA6.P56556 NDUFA7.O95182 NDUFS1.P28331 NDUFS4.O43181 NDUFS6.O75380 NDUFV1.G3V0I5 NDUFV2.E7EPT4 NDUFV3.P56181 NDUFV3.P56181.2 NDUFA5.Q16718 NDUFA9.F5GY40 NDUFA9.Q16795 NDUFS2.O75306 NDUFS3.O75489 NDUFS7.O75251 NDUFS8.O00217 MT.ND1.P03886 NDUFA13.Q9P0J0 NDUFA3.O95167 NDUFA8.P51970 MT.ND2.P03891 MT.ND3.P03897 MT.ND6.P03923 NDUFA10.O95299 NDUFA11.Q86Y39 NDUFC1.O43677 NDUFC2.O95298 NDUFS5.O43920 MT.ND4.P03905 NDUFB1.O75438 NDUFB10.O96000 NDUFB11.Q9NX14 NDUFB4.O95168 NDUFB5.O43674 MT.ND5.P03915 NDUFB2.H7C5B8 NDUFB3.O43676 NDUFB6.O95139 NDUFB7.P17568 NDUFB8.O95169 NDUFB9.Q9Y6M9 NDUFAB1.O14561 C 1 RC1 Physical neighbor NDUFA12.Q9UI09 NDUFA6.P56556NDUFA12.Q9UI09 NDUFA12.Q9UI09

NDUFA2.O43678 NDUFA7.O95182NDUFA2.O43678 NDUFA2.O43678 0.6 NDUFA6.P56556 NDUFS1.P28331NDUFA6.P56556 NDUFA6.P56556 0.4 NDUFA7.O95182 NDUFS4.O43181NDUFA7.O95182 NDUFA7.O95182

0.2 NDUFS1.P28331 NDUFS6.O75380NDUFS1.P28331 0.8 NDUFS1.P28331

NDUFS4.O43181 NDUFV1.G3V0I5NDUFS4.O43181 NDUFS4.O43181 0.0 NDUFS6.O75380 NDUFV2.E7EPT4NDUFS6.O75380 NDUFS6.O75380 2 -0.2 R =0.53 NDUFV1.G3V0I5 NDUFV1.G3V0I5 NDUFV1.G3V0I5 NDUFV1 NDUFV3.P56181 P<0.0001 NDUFV2.E7EPT4 NDUFV2.E7EPT4 NDUFV2.E7EPT4 -0.4 N=400 NDUFV3.P56181.2 0.6 NDUFV3.P56181 NDUFV3.P56181 NDUFA5.Q16718NDUFV3.P56181 -0.6 NDUFV3.P56181.2 NDUFV3.P56181.2 -0.6 -0.4 -0.2 0.0 0.2 0.4 NDUFA9.F5GY40NDUFV3.P56181.2

NDUFA5.Q16718 NDUFA5.Q16718 NDUFV2 0.6 Within-module NDUFA9.Q16795NDUFA5.Q16718 NDUFA9.F5GY40 NDUFA9.F5GY40 NDUFS2.O75306NDUFA9.F5GY40 0.4 NDUFA9.Q16795 NDUFA9.Q16795 0.4 NDUFA9.Q16795 NDUFS3.O75489 NDUFS2.O75306 NDUFS2.O75306 NDUFS2.O75306 0.2 NDUFS7.O75251 NDUFS3.O75489 NDUFS3.O75489 NDUFS3.O75489 0.0 NDUFS8.O00217 NDUFS7.O75251 NDUFS7.O75251 NDUFS7.O75251 MT.ND1.P03886 -0.2 NDUFS8.O00217 NDUFS8.O00217 NDUFS8.O00217 NDUFA6 R2=0.51 NDUFA13.Q9P0J0 0.2 MT.ND1.P03886 MT.ND1.P03886 MT.ND1.P03886 -0.4 P<0.0001 NDUFA3.O95167 N=400 NDUFA13.Q9P0J0 NDUFA13.Q9P0J0 NDUFA13.Q9P0J0 NDUFA8.P51970 -0.6 NDUFA3.O95167 NDUFA3.O95167 NDUFA3.O95167 -0.4 -0.2 0.0 0.2 0.4 0.6 MT.ND2.P03891 0.8 Between-module NDUFA8.P51970 NDUFA8.P51970 NDUFA8.P51970 NDUFS6 MT.ND3.P03897 0 MT.ND2.P03891 MT.ND2.P03891 MT.ND2.P03891 0.6 MT.ND6.P03923 MT.ND3.P03897 MT.ND3.P03897 MT.ND3.P03897 0.4 NDUFA10.O95299 MT.ND6.P03923 MT.ND6.P03923 MT.ND6.P03923 NDUFA11.Q86Y39 0.2 NDUFA10.O95299 NDUFA10.O95299 NDUFA10.O95299 NDUFC1.O43677 NDUFA11.Q86Y39 NDUFA11.Q86Y39 −0.2 NDUFA11.Q86Y39 0.0 2 NDUFC2.O95298 NDUFS6 R =0.54 NDUFC1.O43677 NDUFC1.O43677 NDUFC1.O43677 P<0.0001 NDUFS5.O43920 -0.2 NDUFC2.O95298 NDUFC2.O95298 NDUFC2.O95298 N=400 MT.ND4.P03905 NDUFS5.O43920 NDUFS5.O43920 NDUFS5.O43920 -0.4 NDUFB1.O75438 Co-regulation by topology -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 MT.ND4.P03905 MT.ND4.P03905 MT.ND4.P03905 NDUFB10.O96000 −0.4 NDUFS7 0.4 mtDNA and nDNA NDUFB1.O75438 NDUFB1.O75438 NDUFB1.O75438 Module NDUFB11.Q9NX14 Core NDUFB10.O96000 D1.0 1.0E Subunits 1.0F 1.0 G H1.0 NDUFB10.O96000 NDUFB10.O96000 NDUFB4.O95168 * 0.2 NDUFB11.Q9NX14 NDUFB11.Q9NX14 NDUFB11.Q9NX14 **** NDUFB5.O43674 **** NDUFB4.O95168 NDUFB4.O95168 NDUFB4.O95168 MT.ND5.P03915 NDUFB5.O43674 NDUFB5.O43674 −0.6 NDUFB5.O43674 **** **** 0.0 0.8 0.8 0.8 0.8 0.8 NDUFB2.H7C5B8 MT.ND5.P03915 MT.ND5.P03915 MT.ND5.P03915

MTND1 NDUFB3.O43676 **** R2=0.01 NDUFB2.H7C5B8 NDUFB2.H7C5B8 NDUFB2.H7C5B8 -0.2 P=0.10 NDUFB3.O43676NDUFB6.O95139NDUFB3.O43676 NDUFB3.O43676 0.76 N=328 0.6 0.6 0.6 0.6 0.6 NDUFB6.O95139NDUFB7.P17568NDUFB6.O95139 −0.8 NDUFB6.O95139 0.68 -0.4 -5 -4 -3 -2 -1 0 1 NDUFB8.O95169NDUFB7.P17568NDUFB7.P17568 NDUFB7.P17568 Different isoforms 0.61 NDUFA12 0.0 NDUFB9.Q9Y6M9NDUFB8.O95169NDUFB8.O95169 NDUFB8.O95169 0.58 NDUFAB1.O14561NDUFB9.Q9Y6M9NDUFB9.Q9Y6M9 NDUFB9.Q9Y6M9

0.4 0.53 0.4 0.4 0.4 0.4 0.50 0.49 NDUFAB1.O14561 0.49 -0.2 NDUFAB1.O14561 NDUFAB1.O14561 NDUFA12.Q9UI09 NDUFA2.O43678 NDUFA6.P56556 NDUFA7.O95182 NDUFS1.P28331 NDUFS4.O43181 NDUFS6.O75380 NDUFV1.G3V0I5 NDUFV2.E7EPT4 NDUFV3.P56181 NDUFV3.P56181.2 NDUFA5.Q16718 NDUFA9.F5GY40 NDUFA9.Q16795 NDUFS2.O75306 NDUFS3.O75489 NDUFS7.O75251 NDUFS8.O00217 MT.ND1.P03886 NDUFA13.Q9P0J0 NDUFA3.O95167 NDUFA8.P51970 MT.ND2.P03891 MT.ND3.P03897 MT.ND6.P03923 NDUFA10.O95299 NDUFA11.Q86Y39 NDUFC1.O43677 NDUFC2.O95298 NDUFS5.O43920 MT.ND4.P03905 NDUFB1.O75438 NDUFB10.O96000 NDUFB11.Q9NX14 NDUFB4.O95168 NDUFB5.O43674 MT.ND5.P03915 NDUFB2.H7C5B8 NDUFB3.O43676 NDUFB6.O95139 NDUFB7.P17568 NDUFB8.O95169 NDUFB9.Q9Y6M9 NDUFAB1.O14561 NDUFA12.Q9UI09 1 NDUFA2.O43678 −1 NDUFA6.P56556

NDUFA7.O95182 0.42 NDUFS1.P28331 0.8 NDUFS4.O43181 1.0 NDUFS6.O75380 NDUFA10_2 0.43 0.36 0.35 0.43 0.42 NDUFV1.G3V0I50.44 0.25 0.21 0.07 0.09 0.25 0.02 0.24 0.11 0.15 NDUFV2.E7EPT4 0.6 -6 -4 -2 0 2 NDUFV3.P56181

NDUFV3.P56181.2

NDUFA5.Q16718 NDUFA9.F5GY40 -0.4 NDUFA10_1 0.32 0.29 0.25 0.31 0.32 0.31 0.15NDUFA9.Q16795 0.17 0.06 0.08 0.15 0.01 0.15 0.04 0.06 0.4 0.2 0.2 NDUFS2.O75306 0.5 log2-transformed ratio 0.2 0.2 0.2 NDUFS3.O75489 -6 -4 -2 0 2 -6 -4 -2 0 2 NDUFS7.O75251 NDUFS8.O00217 0.2 MT.ND1.P03886 NDUFA5_2 0.39 0.35 0.31 0.36 0.36 0.37 0.22 0.23 NDUFA13.Q9P0J00.07 0.09 0.24 0.07 0.22 0.05 0.13 NDUFA3.O95167

NDUFA8.P51970

0.26 0 MT.ND2.P03891 2 MT.ND3.P03897 R =0.04 MT.ND6.P03923 0 NDUFA5_1 0.44 0.40 0.34 0.43 0.43 0.42 0.23 0.24 0.05 NDUFA10.O952990.08 0.25 0.03 0.21 0.09 0.14 NDUFA11.Q86Y39 −0.2 NDUFA9.1 NDUFC1.O43677-0.6

NDUFC2.O95298 0.20 NDUFS5.O43920 P=0.0015 Average Spearman's rho Average MT.ND4.P03905 −0.4 NDUFA6_2 0.42 0.35 0.39 0.43 0.42 0.44 0.27 0.22 0.11 0.09 0.26 NDUFB1.O754380.03 0.25 0.10 0.15 NDUFB10.O96000 NDUFB11.Q9NX14 -0.5 NDUFB4.O95168 N=224 NDUFB5.O43674 −0.6

MT.ND5.P03915 0.0 0.0 0.0 0.0 0.0 NDUFA7_2 0.38 0.31 0.35 0.40 0.38 0.42 0.19 0.17 0.04 0.05 0.24 0.03 NDUFB2.H7C5B80.23 0.10 0.15 NDUFB3.O43676 NDUFB6.O95139 −0.8 NDUFB7.P17568

-0.8 NDUFB8.O95169 Spearman's rho NDUFA7_1 0.39 0.35 0.36 0.42 0.40 0.41 0.22 0.18 0.06 0.07 0.22 0.04 0.23 0.08NDUFB9.Q9Y6M90.12 NDUFAB1.O14561 -1.0 -1 0 1 2 −1 S P Q N AF NDUFS6_1 0.35 0.29 0.31 0.38 0.35 0.39 0.25 0.23 0.04 0.04 0.24 NDUFA9.20.11 0.23 0.08 0.12 core NDUFS6_2 0.44 0.39 0.37 0.45 0.44 0.45 0.24 0.22 0.04 0.07 0.25 0.01 0.23 0.08 0.13 nDNA matrix mtDNA NDUFV1_2 0.37 0.31 0.27 0.35 0.35 0.35 0.21 0.19 0.06 0.08 0.21 0.04 0.22 0.07 0.15 accessory membrane NDUFV1_1 0.42 0.36 0.34 0.40 0.39 0.40 0.22 0.21 0.10 0.10 0.24 0.06 0.22 0.10 0.13 SDHA_2 0.17 0.13 0.11 0.18 0.14 0.18 0.31 0.28 0.20 0.15 0.18 0.08 0.16 0.04 0.16 SDHB_1 0.15 0.17 0.10 0.13 0.12 0.13 0.22 0.25 0.14 0.19 0.15 -3.62×10-3 0.13 0.02 0.08 SDHB_2 0.23 0.24 0.17 0.21 0.20 0.21 0.26 0.27 0.09 0.16 0.20 0.07 0.16 0.07 0.13 Fig. 4. Mitochondrial respiratory chain (RC)SDHC_1 complex0.09 0.08 0.04 0.08 I proteins0.09 0.08 0.11 co-regulation.0.16 0.15 0.11 0.08 0.04 0.06 (A-0.02) 0.03 Diagram of complex I protein subunits SDHC_2 0.05 0.04 0.02 0.07 0.07 0.07 0.08 0.11 0.10 0.10 0.06 0.02 0.04 -0.02 0.01 topological location. (B) Heatmap of complex I SDHD_1protein0.18 0.12 co-regulation0.09 0.17 0.16 0.17 arranged0.17 0.21 0.08 by0.07 modules0.14 0.04 0.11 and-0.03 example0.11 of individual association (results SDHD_2 0.08 0.08 0.03 0.09 0.09 0.09 0.12 0.19 0.12 0.08 0.09 0.05 0.09 1.84×10-3 0.07 from simple linear regression). (C) Complex CYC1_1 I subunit0.22 0.14 0.14 abundance0.19 0.17 0.18 0.16 (raw0.18 values).0.06 0.09 0.22 Average0.09 0.24 0.04 Spearman’s0.16 rho (95% CI) coefficient for CYC1_2 0.26 0.23 0.19 0.26 0.23 0.24 0.22 0.21 0.07 0.08 0.24 0.06 0.27 0.07 0.16 UQCR10_1 0.26 0.19 0.22 0.25 0.26 0.24 0.25 0.18 -0.01 0.02 0.22 0.32 0.25 0.10 0.12 complex I (D) subunits and assembly factors, UQCR10_2(E) core0.18 0.15 and0.11 accessory0.17 0.16 0.15 0.16subunits0.11 0.02 (0.06F) 0.12nuclear0.03 0.19 DNA0.09 0.12 (nDNA) or mtDNA encoded subunits (F) UQCRC2_1 0.27 0.24 0.21 0.29 0.24 0.28 0.23 0.22 0.10 0.14 0.26 0.06 0.26 0.04 0.15 subunits by localization and (G) by module. ROSMAP,UQCRC2_2 0.25 0.23 N=124-400.P-value0.16 0.26 0.22 0.24 0.22 0.20 0.07from0.07 unpaired0.22 0.05 0.26 t-test,0.09 0.17 Mann-Whitney test, or Kruskal-Wallis COX7B_2 0.11 0.05 0.06 0.14 0.11 0.17 0.14 0.15 0.13 0.05 0.14 0.10 0.17 0.06 0.11 ATP5F1_2 0.14 0.13 0.08 0.11 0.11 0.13 0.21 0.20 0.12 0.10 0.17 0.05 0.18 0.04 0.23 test with Dunn’s multiple comparisons test, p<0.05*,ATP5F1_1 0.12 p<0.01**,0.16 0.07 0.10 p<0.001***,0.08 0.11 0.16 0.17 p<0.0001****.0.10 0.12 0.16 0.01 0.16 0.01 0.23 CYC1.P08574 SDHA.P31040 SDHB.P21912 SDHC.Q99643 SDHD.O14521 COX7B.P24311 ATP5F1.P24539 NDUFV1.G3V0I5 NDUFA6.P56556 NDUFA5.Q16718 NDUFA7.O95182 NDUFS6.O75380 UQCRC2.P22695 NDUFA10.O95299 UQCR10.Q9UDW1 bioRxiv preprint doi:Sex https://doi.org/10.1101/2021.07.19.452923 differences for RC subunits levels ; this version postedAD-NCI July differences 19, 2021. for RCThe subunits copyright levels holder for this preprint (which was not certified by peer review) is the author/funder.Higher in men All rights reserved. No reuse allowed without permission.Higher in AD

NDUFA10.O95299 1.05 NDUFA10.O95299 NDUFA11.Q86Y39 1.05 1.00 NDUFA11.Q86Y39 1.00NDUFA12.Q9UI09 0.95 NDUFA12.Q9UI09 0.95NDUFA13.Q9P0J0 0.90 NDUFA13.Q9P0J0 1.10.90NDUFA2.O43678 1.1 NDUFA2.O43678 ATP5A1.P25705 0.85NDUFA3.O95167 ATP5A1.P25705

NDUFA3.O95167 NDUFA5.Q16718

NDUFA5.Q16718 NDUFA6.P56556 NDUFA6.P56556A D NDUFA7.O95182

NDUFA7.O95182 NDUFA8.P51970

NDUFA8.P51970 NDUFA9.F5GY40

NDUFA9.F5GY40 NDUFA9.Q16795

NDUFA9.Q16795 NDUFAB1.O14561

NDUFAB1.O14561 NDUFB1.O75438

NDUFB1.O75438 NDUFB10.O96000

NDUFB10.O96000 NDUFB11.Q9NX14

NDUFB11.Q9NX14 NDUFB3.O43676

NDUFB3.O43676 NDUFB4.O95168

NDUFB4.O95168 NDUFB5.O43674

NDUFB5.O43674 NDUFB6.O95139 NDUFB6.O95139 ATP5A1.P25705 ATP5A1.P25705 NDUFB7.P17568

NDUFB7.P17568 NDUFB8.O95169

NDUFB8.O95169 NDUFB9.Q9Y6M9

NDUFB9.Q9Y6M9 NDUFC2.O95298

NDUFC2.O95298 NDUFS1.P28331 NDUFS1.P28331Complex I NDUFS2.O75306Complex I NDUFS2.O75306 NDUFS3.O75489

NDUFS3.O75489 NDUFS4.O43181

NDUFS4.O43181 NDUFS5.O43920

NDUFS5.O43920 NDUFS6.O75380 NDUFS6.O75380 1.0 NDUFS6.O75380 1.0 NDUFS6.O75380 NDUFS6.O75380

NDUFS6.O75380 NDUFS6.O75380

NDUFS6.O75380 NDUFS7.O75251

NDUFS7.O75251 NDUFS7.O75251 NDUFS7.O75251 ATP5B.F8W0P7 ATP5B.F8W0P7 NDUFS8.O00217

NDUFS8.O00217 NDUFV1.G3V0I5

NDUFV1.G3V0I5 NDUFV2.E7EPT4

NDUFV2.E7EPT4 NDUFV3.P56181.2

NDUFV3.P56181.2 NDUFV3.P56181.2

NDUFV3.P56181.2 NDUFV3.P56181

NDUFV3.P56181.2 NDUFV3.P56181.2

NDUFV3.P56181 NDUFV3.P56181.2

NDUFV3.P56181 NDUFV3.P56181

NDUFV3.P56181 NDUFV3.P56181 NDUFV3.P56181 1 2 3 1ATP5B.P06576 2 3 ATP5B.P06576

0.91.1 Fold change 0.91.1 Fold change SDHA.P31040 SDHA.P31040 ATP5C1.P36542.2 ATP5C1.P36542.2 SDHB.P21912 Lower in men SDHB.P21912 Lower in AD II ATP5D.P30049 1.0 II ATP5D.P30049 SDHC.Q99643 1.0 SDHC.Q99643 ATP5H.O75947 ATP5H.O75947 CYC1.P08574 1 2 3 1.1 CYC1.P08574 1 2 3 1.10.9 UQCR10.Q9UDW1 0.9 ATP5J.P18859 ATP5J.P18859 1.0 UQCR10.Q9UDW1 1.0 UQCRB.P14927 UQCRB.P14927III ATP5O.P48047 III ATP5O.P48047 0.9 UQCRC2.P22695 UQCRC2.P22695 0.9 ATP6AP1.Q15904 UQCRQ.O14949 ATP6AP1.Q15904 UQCRQ.O14949 1 2 3 COX4I1.P13073 1.1 1 2 3 COX4I1.P13073 1.05COX5A.P20674 COX5B.P10606 1.0 1.00 COX5B.P10606 1 2 3 COX6A1.P12074 1 2 3 0.95COX6B1.P14854 IV IV 0.9 COX6B1.P14854 0.90COX6C.P09669 COX7A1.P24310 0.85 COX7A1.P24310 COX7A2.P14406 COX7A2L.O14548 COX7A2L.O14548 COX7C.P15954

1 2 3 1 2 3 ATP5A1.P25705 1.1 ATP5A1.P25705 1.1 ATP5A1.P25705 1.0 ATP5A1.P25705 ATP5B.F8W0P7 1.0 ATP5B.F8W0P7 ATP5B.P06576 0.9 ATP5B.P06576 ATP5C1.P36542.2 0.9 ATP5C1.P36542.2 ATP5D.P30049V V ATP5D.P30049 ATP5H.O75947 ATP5H.O75947 ATP5J.P18859 ATP5J.P18859 ATP5O.P48047 ATP5O.P48047 ATP6AP1.Q15904 ATP6AP1.Q15904

1 2 3 1 2 3 ROSMAP Banner BLSA ROSMAP Banner BLSA B Summary score E Summary score

mito content 1.01 1.00 1.02 1.1 mito content 0.98 0.98 0.96 1.1 nc1 1.00 1.00 1.01 1.1 nc1 0.96 0.97 0.96 mito content 1.01 Unadjusted1.00 1.02 Adjusted for mito content mito content 0.98 Unadjusted0.98 0.96 Adjusted for1.1 mito content 1.1 nc2 0.98 1.00 1.01 nc2 0.96 0.97 0.92 1.1 nc1 1.00 1.00 1.01nc1_score 1.00.99 1.00 0.99 nc1 0.96 0.97 0.96nc1_score 0.99 0.991.0 0.99 nc3 0.97 0.97 1.00 nc2nc3 0.98 1.00 0.981.01 nc2_score 1.00.97 0.99 1.01 nc2 0.96 0.97 0.92nc2_score 0.99 0.991.0 0.96 nc4 1.00 1.01 1.02 1.0 nc4 0.98 0.97 0.97 1.0 nc3 0.98 1.00 0.98nc3_score 0.90.98 1.00 0.95 nc3 0.97 0.97 1.00nc3_score 1.00 0.990.9 1.04 nc5 0.99 1.00 0.98 nc5 0.99 0.99 0.97 nc4 1.00 1.01 1.02nc4_score 0.99 1.00 0.99 nc4 0.98 0.97 0.97nc4_score 1.01 0.99 1.01 0.9 0.9 nc5 0.991 1.002 0.98nc5_score3 0.98 0.99 0.95 0.9 nc5 0.991 0.992 0.97nc5_score3 1.01 1.01 1.01 0.9

ROSMAP1 Banner2 BLSA3 ROSMAP1 Banner2 BLSA3 ROSMAP1 Banner2 BLSA3 ROSMAP1 Banner2 BLSA3 ATP5C1.P36542/MT.CYB.P00156ATP5C1.P36542/MT.CYB.P00156ATP5C1.P36542/MT.CYB.P00156 ATP5C1.P36542/MT.CYB.P00156ATP5C1.P36542/MT.CYB.P00156ATP5C1.P36542/MT.CYB.P00156

C F ROSMAP Banner BLSA 130 ROSMAP130 Banner130 BLSA 130 130 130 * ** p=0.08 120 120 120 120 120 120

110 110 110 110 110 110

100 100 100 100 100 100

90 90 90 90 90 90 Mitocontent Mitocontent -1.98% 80 80 80 80 80 80 -0.74% 0.32% -1.63% -2.47% -3.91% 70 70 70 70 70 70

Men Men Men NCI AD NCI AD NCI AD Women Women Women

Fig. 5. Difference in mitochondrial respiratory chain (RC) proteins abundance according to sex and clinical diagnosis. Difference between men and women in proteins abundance shown as fold change for the (A) RC individual subunits and (B) mitochondrial content and RC summary scores. (C) violin plot of mito content by sex. P-value from one-way ANCOVA adjusted for Alzheimer’s disease (AD) status and age at death. Difference between individuals with no cognitive impairment (NCI) and AD at death in RC proteins abundance shown as (D) RC individual subunits, (E) mitochondrial content and RC summary scores. (F) violin plot of mito content by AD status. P- value from one-way ANCOVA adjusted for sex and age at death (as well as years of education for ROSMAP and BLSA). Scores were adjusted for post-mortem interval, age at death, study, and batch. *p<0.05, **p<0.01. Detailed results are shown in supplementary table S4.