Abstract
Sporadic Parkinson’s Disease (sPD) is a progressive neurodegenerative
disorder caused by multiple genetic and environmental factors.
Mitochondrial dysfunction is one contributing factor, but its role at
different stages of disease progression is not fully understood. Here,
we showed that neural precursor cells and dopaminergic neurons derived
from induced pluripotent stem cells (hiPSCs) from sPD patients
exhibited a hypometabolism. Further analysis based on transcriptomics,
proteomics, and metabolomics identified the citric acid cycle,
specifically the α-ketoglutarate dehydrogenase complex (OGDHC), as
bottleneck in sPD metabolism. A follow-up study of the patients
approximately 10 years after initial biopsy demonstrated a correlation
between OGDHC activity in our cellular model and the disease
progression. In addition, the alterations in cellular metabolism
observed in our cellular model were restored by interfering with the
enhanced SHH signal transduction in sPD. Thus, inhibiting overactive
SHH signaling may have potential as neuroprotective therapy during
early stages of sPD.
Subject terms: Parkinson's disease, Induced pluripotent stem cells,
Mechanisms of disease
__________________________________________________________________
Mitochondrial dysfunction is a contributing factor in Parkinson’s
disease. Here the authors carry out a multilayered omics analysis of
Parkinson’s disease patient-derived neuronal cells, which reveals a
reversible hypometabolism mediated by α-ketoglutarate dehydrogenase
deficiency, which is correlated with disease progression in the
donating patients.
Introduction
Parkinson’s disease (PD) is a neurodegenerative disorder characterized
in advanced stages by motor disabilities and the loss of dopaminergic
neurons (DAns) specifically in the substantia nigra pars
compacta^[70]1. The vast majority of PD cases are sporadic, induced by
a combination of genetic and environmental risk factors. Only about 15%
are associated with heritable familial mutations^[71]2. The
degenerative processes in PD patients start and develop long before the
characteristic motor symptoms occur, which are needed to finalize the
diagnosis. This prodromal phase can last up to 20 years^[72]3,[73]4.
Understanding this early phase of PD at the molecular level is of the
highest importance for the development of disease-modifying or
neuroprotective therapies, which require intervention at the earliest
stages of disease in order to prevent progressive neurodegeneration.
To investigate disease-causing molecular and cellular mechanisms, model
systems carrying PD-associated mutations are widely used which allowed
identifying molecular and cellular dysfunctions associated with PD such
as mitochondrial impairment, autophagy, protein aggregation,
proteasomal degradation, and primary cilia dysfunction^[74]4,[75]5.
With the majority of PD-associated genes including PARK7 (DJ1), PINK1,
PRKN, SNCA, and LRRK2 directly affecting mitochondria, mitochondrial
dysfunction has emerged as a central factor contributing to PD
etiology. Overall, these genes are involved in mitochondrial
homeostatic control as well as basic functions such as energy
production and oxidative stress which may crosstalk with other
PD-associated pathways. Also in postmortem studies analyzing brain
tissue of sporadic Parkinson’s disease (sPD) patients, mitochondrial
alterations could be identified amongst others varying degrees of
complex I and complex II deficiency (from ~30 to 60%)^[76]6–[77]8.
Still, the relevance of these alterations for the etiology of sporadic
Parkinson’s disease (sPD) remains largely elusive due to the lack of
suitable human model systems^[78]9,[79]10. The ascent of human induced
pluripotent stem cells (hiPSCs) allowed the use of patient-specific
stem cells as model systems to expand our knowledge of human physiology
at the cellular level^[80]11. During the reprogramming of
patient-derived fibroblasts into hiPSCs these cells are rejuvenated
regarding their epigenetic state, transcriptome, telomeres, and
mitochondrial function^[81]12–[82]15. Thus, in contrast to analyses of
postmortem material from sPD patients, hiPSCs and their derivatives are
thought to recapitulate early disease events.
In order to establish a human cellular model of early sPD, we analyzed
sPD patient-derived hiPSCs as well as their differentiation
products—neural precursor cells (hNPCs) and DAns—for metabolic and
mitochondrial alterations. We could show that sPD neural cells develop
a state of hypometabolism also due to a bottleneck within the citric
acid cycle at the level of the α-ketoglutarate dehydrogenase complex
(OGDHC). Thereby, alterations in sPD metabolism were introduced by
enhanced primary cilia (PC)-mediated sonic hedgehog (SHH) signal
transduction, as alterations in cellular metabolism could be rescued in
our cellular model of sPD by interfering with SHH signaling. Thus,
dysfunctional PC signaling pathways, especially SHH signaling, induce
major metabolic rearrangements associated with sPD. This suggests that
metabolic dysfunction modifiable by SHH signaling via PC is a central
factor contributing to the pathoetiology of sPD implying a novel
therapeutic option.
Results
In this study, we used hiPSCs derived from fibroblasts from 7 sPD
patients and 5 age- and sex-matched Ctrl individuals, which were
cultivated in vitro for approximately 60 passages. sPD patients were
clinically examined and screened for the absence of known PD-causing
familial mutations (PARK1-18)^[83]16. hiPSCs were repeatedly
characterized for copy number variations and their differentiation
potential^[84]17. To establish a model system for sPD, hiPSCs were
differentiated into human neural precursor cells (hNPCs) and further
into dopaminergic neurons (DAns), which are vulnerable to degeneration
in PD^[85]1. Differentiation stages were confirmed using
immunohistochemical staining for characteristic markers such as the
precursor markers NESTIN, SOX2, and SOX1 (Supplementary Fig. [86]1a) or
the DAn marker TUBB3, RBFOX3 (synonym: NeuN), and TH^[87]17. Expression
of these differentiation markers was not affected in sPD hNPCs
(Supplementary Fig. [88]1b–d), nor was the abundance and morphology of
DAns derived thereof^[89]17. This indicates that the DAn
differentiation process assessed at various stages was comparable
between Ctrl and sPD cells.
Previous studies using these hiPSC-derived neuronal cells indicated
that mitochondrial functionality regarding the cellular respiration as
well as complex I deficiency are impaired in sPD^[90]17. The present
study aims to decipher the molecular mechanisms underlying these
PD-associated changes.
Mitochondrial dysfunction in neural cells derived from sPD patients
Mitochondrial function was assessed in hiPSCs, as well as their
derivatives—hNPCs and DAns—using Seahorse XF analysis. It allows the
assessment of multiple parameters, like basal respiration, proton leak,
ATP-linked respiration, and maximal respiratory capacity. Furthermore,
different energy substrates (glucose or pyruvate) allow to investigate
the contribution of specific metabolic pathways to cellular respiration
and can be used to determine possible bottlenecks.
Using glucose as an energy substrate, we observed no significant
differences in mitochondrial respiration in sPD hiPSC and DAns.
However, the basal, ATP-linked, and maximal respiratory capacity of sPD
hNPCs was decreased (trend) (Fig. [91]1). This might indicate an
sPD-specific defect in cellular respiration possibly in glucose uptake,
glycolysis, the citric acid cycle, or oxidative phosphorylation.
Fig. 1. Alterations in cellular respiration in sPD patient-derived cells.
[92]Fig. 1
[93]Open in a new tab
a Mitochondrial stress test performed in hiPSCs, b thereof
differentiated hNPCs, and c DAns using a Seahorse XFe96 Extracellular
Flux Analyzer. Cells were measured in Seahorse XF assay medium
supplemented with 25 mM glucose or 5 mM pyruvate (as shown in ref.
^[94]17). Injected were (A) Oligomycin (1 μg/ml), (B) carbonyl cyanide
p-trifluoro-methoxyphenyl hydrazone (FCCP; 0.5 μM), (C) Rotenone
(5 μM)/Antimycin A (2 μM), and (D) 2-deoxyglucose (2-DG; 100 mM).
Measurement progression is shown with means ± standard error of the
mean (SEM). Boxplots display the median and range from the 25th to 75th
percentile. Whiskers extend from the min to max value. Each dot
represents one patient. n = 5 Ctrl and 7 sPD patient-derived cell
clones, in triplicates. p-values were determined by one-way ANOVA with
Sidak’s post hoc test. ^#p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001.
Source data including p-values are provided as a Source Data file.
To discriminate limitations within glycolysis from mitochondrial ones,
pyruvate was supplied as a substrate that can directly enter the citric
acid cycle. Using pyruvate as an energy substrate, the basal,
ATP-linked, and maximal mitochondrial respiration of sPD hNPCs and DAns
was significantly reduced. In contrast, the original hiPSCs were still
not affected (Fig. [95]1).
Since cellular respiration was even more impaired when pyruvate was
used as an energy substrate, this indicated a defect downstream of
glycolysis in sPD hNPCs and DAns. Thus, we expected the defect either
in the substrate delivery for the respiratory chain by the citric acid
cycle or the respiratory chain itself.
To get a more comprehensive overview of cellular metabolism, the
glycolytic flux based on the extracellular acidification rate (ECAR)
was also assessed. The glycolytic flux was only analyzed in cells
supplied with glucose as an energy substrate. The oxidation of glucose
during glycolysis depends on ATP hydrolysis and results in the
production of protons, pyruvate, and often lactate. ECAR mainly
correlates with lactate/H^+ secretion and can be masked by various
other cellular processes leading to acidification. Thus, it only offers
a rough overview of glycolytic rates and has to be interpreted with
caution^[96]18.
The glycolytic flux analysis did not show significant differences in
the ECAR between sPD and Ctrl hiPSCs, hNPCs, and DAns (Supplementary
Fig. [97]2a–c), supporting our hypothesis of a defect downstream of
glycolysis as indicated by the OCR measurements using pyruvate as a
substrate. It might, however, also indicate that reduced mitochondrial
ATP production in sPD is not compensated by an increased glycolytic
flux and conversion of pyruvate to lactate.
To further elucidate the impact of glycolytic flux and mitochondrial
respiration to energy production, we calculated the ECAR to OCR ratio
and visualized the total levels by plotting the ECAR against the OCR
(Supplementary Fig. [98]2d–f). The higher the ratio, the lower the
proportion of mitochondrial respiration for energy production should
be. Indicative for the well-known glycolytic switch during neuronal
differentiation, the ECAR to OCR ratio is declining during this process
with the highest values in hiPSCs and the lowest ones in DAns.
Furthermore, the ratio is significantly increased in sPD hNPCs and
tends to be increased in hiPSC and DAns. This indicates a decreased
proportion of mitochondrial respiration to total energy production in
sPD.
Mitochondrial health is not affected in neural cells derived from sPD
patients
Aiming to identify the underlying causes of reduced mitochondrial
respiration, we first investigated mitochondrial mass and morphology.
Alterations in both have been previously described in various PD
models^[99]19–[100]22.
The amount of total mitochondrial numbers and morphology was determined
using two different independent stainings. On one hand, we stained the
ATP synthase with an antibody against ATP5F1A in hNPCs (Fig. [101]2a)
and DAns derived thereof (Fig. [102]2b). To specifically visualize
functional/active mitochondria, we used MitoTracker which accumulates
specifically in mitochondria with intact membrane potential
(Fig. [103]2a, b). In total, five characteristics of mitochondrial
morphology were assessed: The number of mitochondria per cell, the mean
area and fluorescence intensity of the mitochondria per cell, as well
as the mean length of their morphological skeleton, and its number of
branch points per cell. However, neither in hNPCs (Fig. [104]2c) nor in
DAns (Fig. [105]2d) derived thereof any significant differences between
Ctrl and sPD clones could be observed, indicating that mitochondrial
content or general health is not impaired in neural cells derived from
sPD hiPSCs.
Fig. 2. Mitochondrial abundance and morphologies are not altered in
patient-derived cells.
[106]Fig. 2
[107]Open in a new tab
a Quantification of total mitochondrial mass and functional
mitochondria in hNPCs or b DAns. Total mitochondrial mass was
visualized using immunostainings with an antibody against the ATP
synthase (ATP5F1A). Functional mitochondria with an active membrane
potential were visualized using a MitoTracker probe (200 nM for
20 min). Images are exemplarily shown for hNPCs of clone i1E4-R1-003
(Ctrl), and iR66-R1-007 (sPD); for DAns of clone i1E4-R1-003
(mitoTracker—Ctrl), iR66-R1-007 (mitoTracker—sPD), i1JF-R1-018
(ATP5F1A—Ctrl), and iJ2C-R1-015 (ATP5F1A—sPD). Scale bar = 20 μm. c
Violin plots highlight some morphological characteristics of
mitochondria in hNPCs or d DAns: number of mitochondria (count),
mitochondrial area, skeleton length, number of branch points, and mean
intensity. On average, 112 (hNPCs—ATP5F1A), 160 (hNPCs—mitoTracker),
370 (DAns—ATP5F1A), 360 (DAns—mitoTracker) cells per clone were
analyzed. e To assess alterations in the mitochondrial fusion
machinery, expression of the mitofusions MFN1, MFN2, and OPA1 was
quantified in hNPCs by RT-qPCR. Cells were cultivated on the energy
substrates used in the Seahorse XF analysis (25 mM glucose or 5 mM
pyruvate). f To assess alterations in the mitochondrial fission
machinery, expression of DRP1 and its phosphorylation on Ser^616 was
quantified in hNPCs by western blot. Protein levels were normalized to
ACTB. g Western blots are exemplary shown for some hNPC clones.
Boxplots display the median and range from the 25th to 75th percentile.
Whiskers extend from the min to max value or to the most extreme data
point which is no more than 1.5 times the interquartile range (c, d).
Each dot represents one patient. n = 5 Ctrl and 7 sPD patient-derived
cell clones, in triplicates. p-values were determined by linear mixed
effects model (c, d); one-way ANOVA with Sidak’s Post hoc test
(p-values are provided together with the source data) (e, f).
*p < 0.05; **p < 0.01; ***p < 0.001. Source data are provided as a
Source Data file.
In addition, we characterized the abundance and post-translational
modifications of the mitochondrial fusion and fission machinery which
is essential for mitochondrial quality control and functioning. These
processes allow cells to adapt the mitochondrial morphology to cellular
metabolic demands and substrate supply^[108]23 and is a measure for
mitochondrial stress. Fusion of the outer and inner mitochondrial
membrane is mainly facilitated by the mitofusions (MFN1 and MFN2), as
well as OPA1, respectively, and mitochondrial fission is mainly
mediated by DRP1^[109]23. As fusion is regulated by the expression of
the corresponding components, expression levels of MFN1, MFN2, and OPA1
were assessed on mRNA level using RT-qPCR. Contrary, fission is mainly
regulated by post-translational modifications which were quantified on
protein level using western blots. For example, phosphorylation of
Serine at position 616 is thought to activate fission
activity^[110]23,[111]24. As mitochondrial function highly relies on
substrate availability, cells were supplied with glucose or pyruvate as
during the mitochondrial respiration analysis to unmask possible
deficits in adjusting mitochondrial morphology to cellular demands in
sPD clones. However, no significant differences in the expression of
MFN1, MFN2, or OPA1 (Fig. [112]2e) as well as in the abundance of DRP1,
or DRP1-pSerine^616 (Fig. [113]2f, g) could be identified in sPD hNPCs
neither with glucose nor with pyruvate as energy substrate. Upon
changing the substrate supply from glucose to pyruvate, a significant
upregulation in the expression of MFN2 and thus the fusion machinery
could be observed for both Ctrl and sPD clones, possibly as a reaction
to the loss of glycolytic energy production. These results further
validate our previous results and exclude a decrease in mitochondrial
mass or alterations in mitochondrial dynamics as an explanation for the
respiratory deficiency that was observed in the Seahorse analysis.
Transcriptome analysis reveals differential expression of genes associated
with the electron transport chain in sPD
To get insights into the molecular underpinnings for the observed
respiratory deficiency in sPD hNPCs—which showed the most pronounced
phenotype—we first performed a pathway enrichment analysis using our
recently published single-cell transcriptome data (bulk-like) of these
cells (Supplementary Data [114]2)^[115]17. On the pathway level, mainly
processes associated with the respiratory chain were dysregulated in
sPD (Fig. [116]3a). Remarkably, the expression of almost every gene
encoding a subunit of the electron transport chain was affected in sPD,
and in almost every case the expression on mRNA level was downregulated
(Fig. [117]3b). This was also true for all 13 mitochondrial-encoded
subunits, which were downregulated to an even higher extent than the
remaining nuclear-encoded subunits. However, mitochondrial-encoded
tRNAs were not differentially expressed in sPD indicating that there
exists not a general problem with the mitochondrial genome and
transcriptional processes (Supplementary Data 5 of ref. ^[118]17).
Fig. 3. Abundance of the electron transport chain complexes is not affected
in sPD.
[119]Fig. 3
[120]Open in a new tab
a Metabolic KEGG pathways enriched in sPD hNPCs. Single-cell
transcriptome data (bulk-like) were previously published^[121]17.
FDR-corrected p-values are represented by q-values. b Visualization of
the ‘Electron Transport Chain (OXPHOS system in mitochondria)’ pathway
based on the single-cell transcriptome data with manual annotations.
Color intensities for up- (red) and downregulated (blue) genes are
proportional to the fold change. Not significantly altered genes are
colored in white. c The abundance of mitochondrial complexes I – V was
quantified by western blot with antibodies against the labile subunits
NDUFB8 (complex I), SDHB (complex II), UQCRC2 (Complex III), MT-CO2
(Complex IV), and ATP5F1A (Complex V). Expression levels were
normalized to ACTB or α-Tubulin levels. Western blots are exemplarily
shown for some hNPC clones. Quantifications of protein levels are shown
for d hNPCs and e DAns. n = 5 Ctrl and 7 sPD patient-derived cell
clones, in triplicates. Boxplots display the median and range from the
25th to 75th percentile. Whiskers extend from the min to max value.
Each dot represents one patient. p-values were determined by one-sided
hypergeometric tests (a), two-sided t-test (d) (complex I: p = 0.69;
complex II: p = 0.86; complex III: p = 0.89; complex IV: p = 0.66;
complex V: p = 0.83), e (complex I: p = 0.35; complex II: p = 0.99;
complex III: p = 0.62; complex IV: p = 0.50; complex V: p = 0.90).
*p < 0.05; **p < 0.01; ***p < 0.001. Source data are provided as a
Source Data file.
Consequently, the abundance of the mitochondrial complexes I - V was
determined on the protein level using western blots for labile subunits
of each complex. Contrary to our expectations, sPD and Ctrl hNPCs
(Fig. [122]3c, d) and DAns (Fig. [123]3e) expressed the labile subunits
in similar amounts. Thus, it can be concluded that the total abundance
of the respiratory chain complexes was not altered in sPD on the
protein level.
Although the total abundance of mitochondrial complexes on the protein
level was not affected in sPD, their activity may be altered due to
differences in complex assembly and structure^[124]25. Indeed, a
reduced complex I activity by ~30% has been described for these sPD
hNPCs^[125]17. This corresponds well with literature describing complex
I deficiency in different PD models as well as in postmortem brain
tissues of PD patients^[126]26,[127]27.
Since hNPCs behaved like DAns and recapitulated the main findings of
sPD-specific alterations in cellular respiration, these hNPCs are
perfect for further investigations to unravel the underlying
pathological mechanisms and represent—as published earlier^[128]17—a
suitable model for sPD.
Proteome analysis reveals dysregulation of pathways associated with
mitochondrial function in sPD
To further characterize dysfunctional cellular processes, we performed
a proteome analysis in these hNPCs (Supplementary Data [129]3).
Principal Component Analysis (PCA), as well as correlation analysis and
hierarchical clustering, were used to visualize the variability within
the samples to detect possible outliers (Fig. [130]4a and Supplementary
Fig. [131]3a). Two human-derived cell lines (sPD—iR66-R1-007 and
Ctrl—i1JF-R1-018) seemed to cluster separately and thus were removed as
outliers for downstream analysis. In total 7943 proteins were
quantified (Fig. [132]4b and Supplementary Data [133]4) and out of
these 1667 were significantly dysregulated (differentially expressed
proteins—DEPs) in sPD based on a negative binomial generalized linear
model and Wald tests (q-value < 0.05). A heatmap visualizing expression
levels of all DEPs shows the similarity between patients and highlights
differences in sPD (Fig. [134]4c). Similar to the
transcriptome^[135]17, levels of most DEPs were only slightly altered.
However, up- and downregulation of DEPs was more balanced
(Fig. [136]4b) compared to the transcriptome level where ~88% of
differentially expressed genes (DEGs) were downregulated.
Fig. 4. Proteome analysis points towards primary cilia and citric acid cycle
defects in sPD.
[137]Fig. 4
[138]Open in a new tab
a Principal component analysis visualizes the variability within
technical replicates and conditions. b Proteome analysis identified
1667 (out of 7943 proteins) which were dysregulated in sPD hNPCs.
Dotted line indicates the significance threshold (q < 0.05). c Heatmap
showing log2-transformed fold changes (FC) with columns scaled by
z-score for differentially expressed proteins (DEPs). d Enriched
Reactome terms based on all DEPs. e Overlap of genome-wide predicted
GLI3 target genes^[139]17 with DEPs. f Correlation between DEGs and
DEPs. FC for DEGs is plotted on the x-axis, FC of DEPs on the y-axis.
Linear regressions between upregulated DEGs and DEPs (red),
downregulated DEGs and DEPs (blue), upregulated DEGs and downregulated
DEPs (orange), downregulated DEGs and upregulated DEPs (light blue) are
displayed together with their respective Pearson correlation
coefficients and p-values. g Enriched Reactome terms for DEG-DEP pairs
with a negative correlation (downregulated DEG and upregulated DEP;
upregulated DEG and downregulated DEP) between transcriptome and
proteome level as well as a h positive correlation (downregulated DEG
and DEP; upregulated DEG and DEP). n = 5 Ctrl and 7 sPD patient-derived
cell clones, in triplicates. p-values were determined by one-sided
hypergeometric tests (d, g, h); one-sided Fisher’s Exact test (e)
(p = 0.014); two-sided t-test (f). p-values corrected for multiplicity
are represented by q-values. *p < 0.05; **p < 0.01; ***p < 0.001. See
also Supplementary Fig. [140]3 and Supplementary Data [141]4, [142]5,
and [143]6.
To determine disease-associated cellular processes, we performed a
pathway enrichment analysis based on all DEPs using multiple pathway
databases (KEGG, Reactome, and WikiPathways)^[144]28. Significantly
enriched pathways (q-value < 0.05) could be grouped into the categories
metabolism (citric acid cycle), primary cilia (PC), the CCT/TriC
complex, RHO/RAC GTPase cycle, growth factor signaling, and cell cycle
(Fig. [145]4d, Supplementary Fig. [146]3b, c, and Supplementary
Data [147]5).
Two of these categories thereby directly translate from the
transcriptome to the proteome namely cell cycle and cellular processes
related to PC function. Alterations within cell cycle-related processes
are not surprising due to the cycling nature of neural precursor cells.
Still, alterations within these pathways did not affect cellular
proliferation^[148]17. In addition, the intraflagellar transport (IFT)
pathway associated with PC has been reported on the transcriptome level
before^[149]17. PC are hair-like organelles that extrude from the cell
surface and function as cellular antennas that are thought to mediate
the transduction of a myriad of external signaling events including SHH
signaling^[150]29,[151]30. The consequences of a dysfunctional IFT
system or its interaction with the sorting system, especially the
BBSome, are alterations in signal transduction^[152]17,[153]31. This
has been validated for PC-mediated alterations in SHH signaling, which
was enhanced in sPD hNPCs^[154]17. For SHH signaling, three
transcription factors are known to mediate its signal transduction
namely GLI1, GLI2, and GLI3^[155]32. Especially the latter one is of
particular interest, as it is thought that SHH signaling during brain
development is mainly to restrict the repressing effect of
GLI3^[156]33,[157]34. Thus, a significant enrichment of GLI3 target
genes within DEGs (~22.5%)^[158]17 and DEPs (~31.7%; Fisher’s Exact
test p = 0.014) (Fig. [159]4e), as well as an enrichment of
misexpressed IFT components also on protein level (Fig. [160]4d)
further strengthens the relevance of altered PC function in sPD
etiology.
Interestingly, the massive downregulation of subunits of the electron
transport chain on the transcriptome level was not observed on the
proteome level. Here mainly components of complex IV seemed to be
affected, rather than of complex I. Out of 18 complex IV subunits,
levels of MT-CO3 (COX3), COX7A2, COX8A, and NDUFA4 were altered next to
the assembly factors COX20, COX17, and COX11^[161]35. Although complex
I is the largest complex of the electron transport chain consisting of
14 central subunits that are involved in energy conservation and
roughly 30 accessory subunits^[162]36, not a single complex I central
subunit was misexpressed on the protein level. Instead, the assembly
factors^[163]36 NDUFAF1, NDUFAF2, and TIMMDC1 as well as one accessory
subunit namely NDUFA11 were dysregulated. This is consistent with the
unchanged total complex I abundance in sPD hNPCs analyzed by
quantifying the labile subunit NDUFB8 by western blot (Fig. [164]3d,
e). Thus, alterations in the assembly of complex I rather than
dysfunctional central subunits might contribute to its observed reduced
activity in sPD. Also, the abundance of the other labile subunits SDHB
(complex II), UQCRC2 (Complex III), MT-CO2 (Complex IV), and ATP5F1A
(Complex V) was not altered in the proteome analysis confirming the
western blot results (Fig. [165]3d, e) as well as the quantification of
total mitochondrial abundance (Fig. [166]2).
A strong pattern of dysregulation on the proteome level was evident for
the citric acid cycle, a pathway significantly enriched when using
KEGG, Reactome, and WikiPathway terms (Fig. [167]4d and Supplementary
Fig. [168]3b, c). The citric acid cycle is the central common pathway
for the oxidation of fuel molecules (carbohydrates, amino acids e.g.
glutamine, and fatty acids) and includes a series of redox reactions
resulting in the oxidation of substrates into CO[2], yielding ATP,
NADH, or FADH[2]^[169]37,[170]38. If dysfunctional, it can create
bottlenecks in the NADH/FADH[2] production rates that fuel the electron
transport chain or in the production of intermediates for fatty acid
and amino acid anabolism.
Surprisingly, expression of the citric acid cycle enzymes seemed to be
mainly downregulated on the transcriptome level (bulk-like DEGs) but
upregulated on the proteome level. In total, 1250 or 75.0% of the DEPs
were also detected as DEGs in the transcriptome. Out of these, 586
(46.9%) DEP-DEG pairs showed a positive (downregulated DEG and
downregulated DEP or upregulated DEG and upregulated DEP) and 664
(53.1%) a negative correlation (downregulated DEG and upregulated DEP
or upregulated DEG and downregulated DEP) (Fig. [171]4f). Pathway
enrichment analysis based on DEP-DEG pairs with a negative
(Fig. [172]4g) or positive (Fig. [173]4h, Supplementary Fig. [174]3d,
and Supplementary Data [175]6) correlation, respectively, confirmed the
previous observation of the citric acid cycle enzymes following a
negative correlation between transcriptome and proteome.
Taken together, the proteome analysis further validated PC and
especially IFT dysfunction in sPD. It identified sPD-specific
alterations in basal metabolism regarding the citric acid cycle. In
order to determine the consequences of these changes in pathways
affecting basal metabolism a metabolome analysis is warranted.
Non-targeted metabolome analysis highlights the citric acid cycle as a
bottleneck in sPD metabolism
To gain a better understanding of metabolic alterations under basal
conditions also referring to possible alterations within the citric
acid cycle, we performed a non-targeted metabolomics analysis in hNPCs.
PCA was used to visualize the variability within the samples to detect
possible outliers (Fig. [176]5a). The observed variation between
technical replicates seemed to be lower than the variation between
samples from different patients. No cell line clustered separately and
thus no sample was removed as an outlier. In total 223 metabolites
passed quality control and 45 metabolites were significantly
dysregulated in sPD based on Student’s t-tests. Interestingly, most of
the metabolites detected and all significantly dysregulated metabolites
were downregulated in sPD indicating a state of hypometabolism in sPD
hNPCs (Fig. [177]5b and Supplementary Data [178]7).
Fig. 5. Metabolic alterations propose a citric acid cycle bottleneck in sPD.
[179]Fig. 5
[180]Open in a new tab
a Principal component analysis visualizes the variability within
technical replicates and conditions. b Non-targeted metabolomic
analysis identified 45 metabolites (out of 223) that were significantly
dysregulated in sPD hNPCs. Dotted line indicates the significance
threshold (q < 0.05). c Integrative analysis of transcriptome and
metabolome data d or proteome and metabolome data identified metabolic
pathways enriched in sPD hNPCs. Node color is based on p-values and
node radius on the pathway impact values. Dotted line indicates the
significance threshold (q < 0.05). e Visualization of the “citric acid
cycle” pathway with manual annotations. Color intensities for up- (red)
and downregulated (blue) differentially expressed genes (DEG)/proteins
(DEP) or metabolites are proportional to the fold change. Unchanged
genes/proteins/metabolites are colored in white, not quantified
metabolites or proteins are colored in gray. f Based on the
non-targeted metabolomic analysis, levels of the citric acid cycle
metabolites succinate, fumarate, malate, and NADH were reduced in sPD.
NAD^+ levels were not affected. g Quantification of NADH and NAD^+
levels in sPD hNPCs. h The ATP production rate was calculated from the
basal mitochondrial respiration linked to ATP production measured in
Seahorse XF analysis from hNPCs (Fig. [181]1b). i Cellular ATP levels
(in µM) were analyzed in hNPCs and normalized to the genomic DNA
content. j The abundance of OGDHL was quantified in hNPCs by western
blot. Expression levels were normalized to GAPDH. Boxplots display the
median and range from the 25th to 75th percentile. Whiskers extend from
the min to max value. Each dot represents one patient. n = 5 Ctrl and
seven sPD patient-derived cell clones, in triplicates or six replicates
for the non-targeted metabolome analysis. p-values were determined by
one-sided hypergeometric tests (c, d); two-sided t-test (f) (see
Supplementary Data [182]7), g (NADH: p = 0.0006; NAD^+ : p = 0.72), h
(p = 0.005), i (p = 0.88), j (p = 0.030). *p < 0.05; **p < 0.01;
***p < 0.001. See also Supplementary Fig. [183]7 and Supplementary
Data [184]7 and [185]8. Source data are provided as a Source Data file.
An integrated analysis of metabolome data together with the
transcriptome (bulk-like DEGs) (Fig. [186]5c and Supplementary
Data [187]8) or proteome data (Fig. [188]5d and Supplementary
Data [189]8) allowed refining pathway enrichment analysis of metabolic
pathways affected in sPD. This analysis pointed toward significantly
altered processes associated with “pyruvate metabolism” and the “one
carbon cycle” (Fig. [190]5e), as well as towards the “citric acid
cycle”. Regarding the latter, the abundance of three consecutive
intermediate metabolites, namely succinate, fumarate, and malate
(Fig. [191]5f), as well as the expression levels of most citric acid
cycle associated genes were reduced in sPD (Fig. [192]5e). Also on the
protein level, the levels of many citric acid cycle associated enzymes
were altered in sPD (Fig. [193]5e).
As levels of the metabolites aconitate (cis and trans), and
α-ketoglutarate were not affected (Fig. [194]5e), we hypothesized that
the conversion of α-ketoglutarate to succinate may be a bottleneck in
sPD metabolism causing a reduced citric acid cycle flux and thus
results in a reduced abundance of citric acid cycle intermediates.
Changes in the citric acid cycle may directly affect the flux through
the electron transport chain and thus mitochondrial respiration. In
line with this, the abundance of the electron transport chain
substrates NADH and succinate, produced within the citric acid cycle,
were reduced in sPD (Fig. [195]5e, f). Interestingly, NAD^+ levels were
not affected in sPD thus being not the limiting factor within the
NAD^+ /NADH balance (Fig. [196]5f). Both NADH and NAD^+ levels could
also be validated independently (Fig. [197]5g). These reduced levels of
electron transport chain substrates are further supported by a
decreased mitochondrial ATP production rate (calculated from the basal
mitochondrial respiration) in sPD hNPCs (Figs. [198]5h and [199]1b
(pyr)), whereas the total ATP levels (Fig. [200]5i) were not affected.
Our assumption of an α-ketoglutarate dehydrogenase complex (OGDHC)
deficiency in sPD clones was supported by the misregulation of its
rate-limiting subunits OGDH and the brain-specific isoform OGDHL
(Fig. [201]5e, j) on both transcriptome and proteome level.
Taken together, the non-targeted metabolomics data highlight a state of
hypometabolism in neural cells derived from sPD patients. Furthermore,
they strengthen the assumption of metabolic defects in the citric acid
cycle in sPD, possibly at the level of the α-ketoglutarate
dehydrogenase complex.
Reduced glucose uptake in neural cells derived from sPD patients is not
caused by alterations in glucose transporters
Based on the reduced mitochondrial respiration and ATP production, as
well as on the massive reduction of metabolites we hypothesized that an
sPD-specific state of hypometabolism develops in neural cells derived
from sPD hiPSCs. To further investigate this state of hypometabolism,
uptake and secretion rates of the main carbon sources glucose, lactate,
glutamine, and glutamate within a 24 h period were analyzed in hNPCs
(Fig. [202]6a). The glucose and glutamine uptake rates were
significantly reduced in sPD hNPCs indicating a reduced total uptake of
carbon sources for biomass and energy production. These measurements
nicely correlated with clinical observations from sPD
patients^[203]39–[204]41. The reduced glucose uptake rate was further
validated by using different methods again in hNPCs (Fig. [205]6b) but
also in DAns derived thereof (Fig. [206]6c). This further strengthens
our hypothesis of hypometabolism observed in neuronal cells (hNPCs and
DAns) derived from sPD hiPSCs.
Fig. 6. Metabolite uptake and secretion rates indicate a state of
hypometabolism in sPD.
[207]Fig. 6
[208]Open in a new tab
a Uptake/secretion rates of main exchange metabolites of hNPCs measured
in the medium after 24 h using a YSI biochemistry analyzer. b Glucose
uptake rates were validated independently in hNPCs and c DAns. Values
were subtracted from blanks and normalized to mean levels of Ctrl
clones. d Normalized gene expression levels of glucose transporters
analyzed on mRNA level by RT-qPCR in hNPCs. e The abundance of glucose
transporters on the protein level was quantified by western blot.
Western blots are shown exemplarily for some hNPC clones.
Quantification of protein levels is shown for f hNPCs and g DAns. h The
cellular localization of glucose transporters was visualized using
immunostainings. Immunostainings are exemplarily shown for hNPCs of
clone i1JF-R1-018 (SLC2A1—Ctrl), iM89-R1-005 (SLC2A1—sPD), iO3H-R1-005
(SLC2A3—Ctrl), and iAY6-R1-003 (SLC2A3—sPD). Scale bar = 20 μm. i To
quantify the radial distribution of glucose transporters in hNPCs, the
cytoplasmic area was separated into ten rings around the center of the
corresponding nuclei, with ring ten being the outermost. On average,
3960 (SLC2A1), 4062 (SLC2A3) cells per clone were analyzed. j
Quantification of cellular 2-deoxyglucose (2-DG) levels of hNPCs after
a 15 min incubation period. Boxplots display the median and range from
the 25th to 75th percentile. Whiskers extend from the min to max value
or to the most extreme data point which is no more than 1.5 times the
interquartile range (i). Each dot represents one patient. n = 5 Ctrl
and seven sPD patient-derived cell clones, in triplicates or six
replicates (a). p-values were determined by linear mixed effects model
(a) (glucose: p = 0.038; lactate: p = 0.14; glutamine: p = 2.7 × 10^−4;
glutamate: p = 0.075) (i); two-sided Mann–Whitney U test (b)
(p = 0.003), c (p = 0.009), d (SLC2A2: p = 0.76); two-sided t-test (d)
(SLC2A1: p = 0.88; SLC2A3: p = 0.24; SLC2A4: p = 0.031), f (SLC2A1:
p = 0.16; SLC2A3: p = 0.14), g (SLC2A1: p = 0.46; SLC2A3: p = 0.15), j
(p = 0.038). *p < 0.05; **p < 0.01; ***p < 0.001. Source data are
provided as a Source Data file.
To identify the mechanism underlying the hypometabolism observed in
sPD, we first analyzed the expression and function of glucose
transporters^[209]42,[210]43. Expression levels of the most abundant
glucose transporters SLC2A3 and SLC2A1 on mRNA level were not altered
in sPD hNPCs (Fig. [211]6d). Expression of SLC2A4 was significantly
decreased in sPD, however, as SLC2A4 and SLC2A2 were only very weakly
expressed, their relevance in neural cells is questionable. Also on
protein level, the expression of the most abundant glucose transporters
SLC2A3 and SLC2A1 was not affected, neither in sPD hNPCs (Fig. [212]6e,
f) nor in DAns (Fig. [213]6g). In addition, we quantified the cellular
and membrane localization of these glucose transporters using
immunohistochemical staining in hNPCs (Fig. [214]6h). Again, the mean
fluorescence intensity as well as the radial distribution of SLC2A1 and
SLC2A3 was not altered in sPD hNPCs (Fig. [215]6i).
Still, to assess the overall function of glucose transporters and
hexokinase-1 (HK1) which mediates the first step of glycolysis to
capture glucose within cells, the uptake and phosphorylation of the
glucose analog 2-deoxyglucose was quantified. 2-deoxyglucose is
phosphorylated by HK1, but inhibits further glycolytic processing and
thus accumulates within cells^[216]44. Interestingly, within a short
period of 15 min, sPD hNPCs consumed significantly more 2-deoxyglucose
than Ctrl cells (Fig. [217]6j), suggesting that the glucose uptake and
fixation per se is not affected in sPD and thus, is not the limiting
factor responsible for the observed hypometabolism.
Metabolic flux analysis confirms a bottleneck within the citric acid cycle at
the level of the OGDHC in sPD
Based on our assumption of metabolic defects in the citric acid cycle
in sPD hNPCs, we performed stable-isotope tracing with either
[U-^13C]Glutamine or [U-^13C]Glucose followed by GC-MS measurements of
mainly citric acid cycle related metabolites (Supplementary Fig. [218]4
and Supplementary Data [219]9). The contribution of ^13C to metabolites
of the central carbon metabolism showed that roughly 20% of carbon
within the citric acid cycle was derived from glucose, whereas ~60% was
derived from glutamine, highlighting the relevance of also glutamine
metabolism for basal metabolic function and energy production
(Fig. [220]7a).
Fig. 7. Metabolic flux analysis validates a bottleneck within the citric acid
cycle in sPD.
[221]Fig. 7
[222]Open in a new tab
a Fractional contribution of [U-^13C]Glutamine (Gln) and
[U-^13C]Glucose (Glc) metabolism to metabolite abundance. Calculated
from MIDs determined by isotopic tracing after a 24 h incubation period
in hNPCs. b Overview of carbon transitions for [U-^13C]Glutamine and
[U-^13C]Glucose metabolism. c Cycling of carbons originating from
[U-^13C]Glucose through the citric acid cycle. Ratio of M4- over
M2-citrate. d Quantification of the oxidative glutamine flux
originating from [U-^13C]Glutamine. Ratio of M3- over M5-glutamate. e
Quantification of the reductive glutamine flux originating from
[U-^13C]Glutamine. Ratio of M5-citrate over M5-glutamate. f
Quantification of the α-ketoglutarate dehydrogenase complex activity
for carbons originating from [U-^13C]Glutamine. Ratio of M4-succinate
over M5-α-ketoglutarate. g Activity of the α-ketoglutarate
dehydrogenase complex of (left) hNPCs or (right) DAns. Values were
normalized to mean levels of Ctrl hNPCs or DAns, respectively. h
Metabolic flux map for sPD hNPCs. Arrow thickness and colors reflect
flux values in units of mM/ h/ mg DNA. Fold changes (sPD / Ctrl hNPCs)
are noted next to the respective fluxes. Fluxes colored in gray could
not be determined with sufficient reliability. i Immunostainings for
aging-associated histone markers H3K9me3 and H3K27me3. Immunostainings
are exemplarily shown for hNPCs of clone iO3H-R1-003. Scale bar =
100 μm. j Violin plots show the fluorescence intensity per condition.
In total, 600 cells per clone were analyzed. Boxplots display the
median and range from the 25th to 75th percentile. Whiskers extend from
the min to max value or to the most extreme data point which is no more
than 1.5 times the interquartile range (j). Each dot represents one
patient. n = 5 Ctrl and 7 sPD patient-derived cell clones, in
triplicates. p-values were determined by linear mixed effects model (a,
c) (p = 0.15), d (p = 0.014), e (p = 0.047), f (p = 0.046), j two-sided
t-test (g) (hNPCs: p = 0.024; DAns: p = 0.011). *p < 0.05; **p < 0.01;
***p < 0.001. See also Supplementary Figs. [223]4–[224]6 and
Supplementary Data [225]9 and [226]10. Source data are provided as a
Source Data file.
If using a [U-^13C]Glucose tracer, the entrance of carbon via
Acetyl-CoA into the citric acid cycle produced M2-citrate isotopologues
or M4-citrate if M2-citrate already passed the citric acid cycle once
(Fig. [227]7b). Thus, the unaltered ratio of M4/M2-citrate between sPD
hNPCs and Ctrl indicated a normal flux—also in patient-derived
hNPCs—through the citric acid cycle of carbons derived from
[U-^13C]Glucose (Fig. [228]7c).
Alternatively, [U-^13C]Glutamine can enter the citric acid cycle via
oxidative or reductive metabolism producing M4-citrate or M5-citrate
isotopologues, respectively (Fig. [229]7b). At this level, there seemed
to be a rerouting of carbons through the citric acid cycle in sPD, as
oxidative glutamine flux was significantly decreased (Fig. [230]7d)
whereas the reductive flux was significantly increased (Fig. [231]7e).
Most likely due to a reduced activity of the α-ketoglutarate
dehydrogenase complex (OGDHC) in sPD, based on the ratio of
M4-succinate to M5-α-ketoglutarate (Fig. [232]7f). This was indeed
validated by analyzing the OGDHC activity in cell lysates of these
hNPCs as well as DAns differentiated thereof (Fig. [233]7g) and
correlates with the OGDHC activity measured in postmortem PD
patients^[234]45. As only three members of the family of α-ketoacid
dehydrogenase multienzyme complexes have been described so far^[235]46,
we also assessed the activity of the pyruvate dehydrogenase complex
(PDC). The PDC activity, however, was similar in Ctrl and sPD hNPCs
(Supplementary Fig. [236]7b), indicating that it is not a general
dysfunction of this enzyme class per se but is rather specifically
affecting the OGDHC activity.
For a better comparison of metabolic differences between Ctrl and sPD,
we performed a ^13C-metabolic flux analysis (Supplementary
Data [237]10) by integrating the mass isotopomer distributions yielded
from the [U-^13C]Glucose and [U-^13C]Glutamine labeling experiments
(Supplementary Data [238]9) with the extracellular uptake/secretion
rates (Fig. [239]6a) into a metabolic network model^[240]47,[241]48. A
simplified metabolic flux map for sPD is shown in Fig. [242]7h (direct
comparison of Ctrl and sPD maps is shown in Supplementary Fig. [243]5).
The fold changes relative to Ctrl hNPCs are indicated next to the
respective fluxes. Generally, sPD hNPCs displayed a reduced metabolic
flux (0.75-fold) within glycolysis correlating with a reduced uptake
and metabolization of glucose (Fig. [244]6a). Consequently, also the
citric acid cycle flux was reduced in sPD, however, to various extents.
The initial steps from Acetyl-CoA fixation to α-ketoglutarate oxidation
were reduced by 19%, while the conversion of α-ketoglutarate to
succinate was reduced by 36%. Most likely due to a reduced activity of
the OGDHC (Fig. [245]7f, g) as well as an increased reductive glutamine
metabolism (Fig. [246]7e). Instead, the net flux from α-ketoglutarate
to glutamate was inverted (−2.22 fold) in sPD hNPCs compared to Ctrl.
Thus, indicating a net carbon efflux from the citric acid cycle also
supplying the GABA shunt, which is thought to partially compensate for
the measured reduction in OGDHC activity. A similar metabolic pattern
has been predicted using transcriptome data from substantia nigra of
sPD patients^[247]49. The reduced citric acid cycle flux resulted in a
reduced NADH production rate and consequently a reduced O[2]
consumption and mitochondrial ATP production rate (0.77-fold). Similar
values for NADH (Fig. [248]5e, f, g) and ATP (Fig. [249]5h) production
as well as O[2] consumption (Fig. [250]1b) have been measured in
experiments included in this manuscript and further validate the
proposed metabolic model for sPD.
Another pathway that seemed to be heavily affected in sPD was the
serine-glycine-one-carbon metabolism, which is essential for providing
methyl groups for DNA, lipid, and protein modifications. Both the
exchange flux from serine to glycine producing M3-serine, M2-glycine,
and a methyl group (Supplementary Fig. [251]4b), as well as the total
flux (0.38 fold, Fig. [252]7h) feeding glycine and serine into the
central carbon metabolism and producing M0- or M2-serine, was strongly
reduced in sPD. A reduced supply of methyl groups by the one-carbon
cycle may further affect methylation patterns in sPD. Indeed,
methylation levels of histones shown for H3K9me3 and H3K27me3 were
significantly reduced in sPD hNPCs and DAns (Fig. [253]7i, j and
Supplementary Fig. [254]6). An interference with chromatin organization
in sPD may also explain the pattern of global gene dysregulation
observed in sPD hNPCs, with most genes being slightly
downregulated^[255]17. Interestingly, alterations in histone
modifications were not present in the original patient-derived
fibroblasts (Fig. [256]7j). Both a reduced mitochondrial function
(Fig. [257]1), as well as reduced levels of the histone marks H3K9me3
and H3K27me3 are also well-characterized hallmarks of cellular
aging^[258]50, with aging being the greatest risk factor for developing
PD^[259]51.
Taken together, the ^13C labeling experiments allowed us to identify
the citric acid cycle, specifically at the step of the OGDHC, as a
bottleneck in sPD metabolism. Consequently, glucose uptake and flux
were reduced in sPD hNPCs, mitochondrial respiration was reduced, as
well as fluxes through the one-carbon cycle.
The state of hypometabolism in sPD is introduced by alterations in SHH signal
transduction
We previously reported a connection between the reduced basal
mitochondrial respiration (Fig. [260]1) and an enhanced SHH signal
transduction mediated by primary cilia (PC) dysfunction^[261]17. PC are
hair-like organelles that extrude from the cell surface and function as
cellular antennas that are thought to mediate the transduction of
external signaling events, among which SHH signaling is very
prominent^[262]29,[263]30. SHH binding to its receptor PTCH1 results in
its removal from PC, which allows the translocation of SMO into the PC.
Subsequently, SMO interferes with the post-translational modification
of the respective transcription factors, the GLIs, resulting in
distinct transcriptional changes. Using SMO interactors such as
cyclopamine (cyc), a cell-permeable steroidal alkaloid, it is possible
to repress SMO activity and thus SHH signal
transduction^[264]52,[265]53. By repressing the overactive SHH signal
transduction in sPD hNPCs to similar levels as in Ctrl, we previously
showed that mitochondrial respiration analyzed by Seahorse XF was
restored in sPD to levels similar as in Ctrl^[266]17.
This might be mediated by a direct regulation of OGDHC abundance and
thus activity through SHH signaling, as the limiting OGDHC subunit
OGDHL is a predicted target gene of the SHH transcription factors GLI2
and GLI3, as well as FOXA2 (Fig. [267]8a). In line with this,
expression levels of OGDHL were misregulated on the transcriptome and
proteome level (Figs. [268]5e, j and [269]8b), and the reduced OGDHL
protein levels could be restored to Ctrl levels upon cyc treatment
(Fig. [270]8b and Supplementary Fig. [271]7a). Thus, the bottleneck
within the citric acid cycle, the OGDHC activity, seemed to be
introduced by alterations in PC-dependent SHH signaling in sPD, as also
OGDHC activity was rescued after OGDHL expression was normalized by cyc
treatment (Fig. [272]8c). By treating hNPCs with cyc, also the reduced
glucose uptake and thus metabolization was rescued in sPD to similar
levels as in Ctrls (Fig. [273]8d). This indicates that the observed
state of hypometabolism, the reduced glucose uptake and flux through
the central carbon metabolism, and the reduced mitochondrial
respiration were introduced specifically in sPD by altered SHH signal
transduction. The increased metabolic rate in sPD hNPCs following cyc
treatment seemingly also resulted in a sufficient supply of methyl
groups for, e.g. post-translational modifications, as also homocysteine
levels (Fig. [274]8e), as well as levels of the histone marks H3K9me3
(Fig. [275]8f) and H3K27me3 (Fig. [276]8g) were restored in sPD to
similar levels as in Ctrl.
Fig. 8. Alterations in SHH signal transduction underly the hypometabolism
observed in sPD.
[277]Fig. 8
[278]Open in a new tab
a Visualization of the human OGDHL locus and its alignment to the mouse
genome. The transcription factor bindings sites of GLI1, GLI2, GLI3,
and FOXA2 are highlighted. b The abundance of OGDHL was quantified by
western blot. Expression levels were normalized to GAPDH. c Activity of
the α-ketoglutarate dehydrogenase complex. Values were normalized to
mean levels of Ctrl hNPCs. d Glucose uptake rates. Quantified were
glucose levels in the growth medium after a 24 h incubation period.
Values were subtracted from blank values and normalized to mean levels
of Ctrl hNPCs. e Cellular homocysteine (HCY) levels. Values were
normalized to mean levels of Ctrl hNPCs. f Quantification of histone
marks H3K9me3 and g H3K27me3. Analyzed were 600 cells per clone. h
Analysis of relative complex I activity. Values were normalized to mean
levels of Ctrl hNPCs. Boxplots display the median and range from the
25th to 75th percentile. Whiskers extend from the min to max value or
to the most extreme data point which is no more than 1.5 times the
interquartile range (f, g). Each dot represents one patient.
Experiments were performed in hNPCs (DMSO ctrl and cyc treated—10 µM
for 4 days). n = 5 Ctrl and 7 sPD patient-derived cell clones, in
triplicates. p-values were determined by linear mixed effects model (f)
(untreated: p = 0.56; cyc: p = 5.2 × 10^−5), (g) (untreated:
p = 0.004; cyc: p = 0.85); two-sided t-test (b) (untreated: p = 0.030;
cyc: p = 0.10), (c) (untreated: p = 0.024; cyc: p = 0.30), (e)
(untreated: p = 0.017; cyc: p = 0.88), (h) (untreated: p = 0.048; cyc:
p = 0.032); two-sided Mann–Whitney-U test (d) (untreated: p = 0.003;
cyc: p = 0.11). *p < 0.05; **p < 0.01; ***p < 0.001. See also
Supplementary Fig. [279]7. Source data are provided as a Source Data
file.
Interestingly, these SHH-dependent changes in basal and also maximal
mitochondrial respiration occurred independently of the reduced
activity of the complex I of the electron transport chain. Following
cyc treatment, complex I activity was still reduced by ~30% in sPD
hNPCs (Fig. [280]8h), indicating that the observed complex I deficiency
is not reflected in the mitochondrial respiration measured by Seahorse
XF, nor is it affecting basal cellular metabolism.
Thus, complex I deficiency is a pathological hallmark independent of
alterations in SHH signaling and OGDHC deficiency. However, it is not
yet a limiting factor contributing to the more severe metabolic
alterations associated with sPD that have been observed in hNPCs and
DAns.
Multiple-factor analysis based on the characterization of hNPCs and DAns
allows to stratify patients
In a next step, the experimental data were used to stratify the
patients that donated the skin fibroblasts used for hiPSC generation
according to the severity of their molecular alterations. To do so, a
multiple-factor analysis (MFA) was performed including detailed
information gained from the scRNA-seq, proteome analysis, and
non-targeted metabolomics analysis. In total, five groups of variables
were used: the top 100 DEGs, the top 100 DEPs, the significantly
altered metabolites (45 metabolites), as well as the key metabolic and
ciliary parameters (61 functional parameters), and as a supplementary
group, the information about the disease state (Ctrl or
sPD—Table [281]1).
Table 1.
Disease progression in sPD patients within 10 years after biopsy
Time of biopsy Clinical changes ~10 years after biopsy
Patient ID Gender Years of illness Δ H&Y Δ UPDRS III [points] Δ L-Dopa
equivalent [mg] Δ ADL
J2C m 3 3 24 400 −0.6
M89 m 3 3 53 1563 −0.6
C99 m 7 2.5 36 610 −0.5
R66 m 3 Follow-up not available
AY6 M 4 1 −1 800 −0.2
PX7 M 1 1 0 1900 −0.1
88H F 6 2 7 640 −0.3
[282]Open in a new tab
Left: Description of sPD patients at the timepoint of skin biopsy
(Gender, and time in years between sPD diagnosis and tissue biopsy).
Right: Long-term history of sPD patients. Changes in the Hoehn&Yahr
scale (ΔH&Y), in motor examinations (Part III) according to the Unified
Parkinson Disease Rating Scale (ΔUPDRS III), medication requirement
(ΔL-Dopa equivalent), and activities of daily living (ΔADL) monitored
within 9–12 years after skin biopsy.
The results show that dimensions 1 and 2 explain together about 42% of
the variability observed in patient-derived cells (Fig. [283]9a). The
first dimension represents mainly the functional parameters and altered
metabolites, whereas DEGs and DEPs mainly contribute to dimension 2
(Fig. [284]9b). Of particular interest is the separation of Ctrl and
sPD patients by dimension 1 (Fig. [285]9a). The larger the distance
between a sPD patient and the average of Ctrl patients, the larger is
the molecular deviation which might reflect the severity of the disease
state.
Fig. 9. OGDHC activity and metabolic alterations correlate with disease
progression in sPD patients.
[286]Fig. 9
[287]Open in a new tab
a Multiple-factor analysis using differentially expressed genes (DEGs;
bulk-like; top 100) (Fig. [288]3 and ref. ^[289]17), differentially
expressed proteins (DEPs; top 100) (Fig. [290]4), significantly altered
metabolites (45 metabolites) (Fig. [291]5), as well as the key
experimental variables displayed in Figs. [292]1–[293]7 and ref.
^[294]17 describing alterations in sPD (61). The group points and lines
represent the patient coordinates conditioned by the corresponding
group variables. b Contributions of quantitative variables to the
dimension 1 and 2 of the multiple-factor analysis. c Correlogram
visualizing the Pearson correlation coefficients of variables measured
in sPD hNPCs and DAns, as well as parameters associated with disease
progression in the sPD patients over a period of 9–12 years
(Table [295]1). Disease progression markers are highlighted in
green. Correlations with the ∆H&Y scale are highlighted in yellow.
Colors and dot sizes are proportional to the correlation coefficients.
Negative correlations are colored in blue, positive correlations in
red. d Visualization of interesting dependencies within measured
variables and parameters of disease progression in sPD patients. Each
point represents one patient. Linear regressions were calculated for
each group and are displayed with a 95% confidence interval. The
Pearson correlation coefficient (R) is displayed next to the
corresponding p-value (p) (two-sided). Correlations with p < 0.1 were
considered significant. e Graphical overview summarizing the main
findings.
To validate this hypothesis, the sPD patients were clinically examined
at the timepoint of skin biopsy and were assessed again after a mean of
10.7 years (range 9–12 years). Both at baseline and at follow-up, all
PD patients fulfilled the clinical diagnostic criteria for PD^[296]54
and were defined as having sPD by the absence of known PD-causing
familial mutations (PARK1–18) and a negative family history of
PD^[297]16. Disease progression was assessed by monitoring changes in
PD-associated scores determined according to the Hoehn & Yahr scale
(H&Y), part III (motor symptoms) of the Unified Parkinson’s Disease
Rating Scale (UPDRS), as well as the activities of daily living (ADL)
scale (Table [298]1).
Indeed, dimension 1 can also be used to separate subgroups of sPD
patients with slow versus fast disease progression (see also
Table [299]1). As patient R66, who was lost to follow-up, clustered
together with the fast progression group, it may be possible that R66
also exhibited a faster disease progression.
OGDHC deficiency and metabolic alterations correlate with disease progression
in sPD patients
In a further translational effort, we correlated our metabolic in vitro
findings with disease progression of the sPD patients that donated the
skin fibroblasts used for hiPSCs generation. A subset of variables is
displayed in Fig. [300]9c or as scatter plots in Fig. [301]9d.
In line with our observations regarding the impact of OGDHC deficiency
in sPD, a strong link existed between the rate of disease progression
and OGDHC activity. In both, hNPCs and DAns, OGDHC activity correlated
with disease progression (∆H&Y (hNPCs – R = -0.93; p = 0.006)
(DAns – R = -0.96; p = 0.009), ∆ADL (hNPCs – R = 0.95; p = 0.004)
(DAns – R = 0.89; p = 0.046), and in parts ∆UPDRS III
(hNPCs – R = -0.89; p = 0.017)). Furthermore, in patients but not in
Ctrls OGDHC activity correlated with cellular glucose consumption
indicating a link between reduced glucose uptake and the reduced
activity of the enzyme complex. Complementary, also the cellular
glucose uptake was linked to disease progression (e.g. hNPCs – R = 0.9;
p = 0.015).
In addition, OGDHC deficiency in sPD remained stable during the
differentiation from hNPCs to DAns (R = 0.82; p = 0.043). This again
validates the usability of hNPCs for disease modeling.
Interestingly, the ciliary capacity to transduce SHH signaling
correlated with metabolic parameters. The capacity was assessed by
monitoring the nuclear levels of GLI3-full length (GLI3-FL) which is
thought to function as a weak transcriptional enhancer and the
truncated GLI3-repressor (GLI3-R) form. An increase of the
GLI3-FL/GLI3-R (enhancer/repressor) ratio positively correlated with
the protein abundance of the predicted SHH target OGDHL in hNPCs
(R = 0.98; p- = 0.00047). OGDHL protein levels thereby positively
correlated with overall OGDHC activity (R = 0.73; p = 0.098). These
findings together with the correlations of disease progression with
OGDHC activity and OGDHL protein levels (∆H&Y (R = -0.82; p = 0.046)
further strengthen the observed impact of ciliary-mediated SHH
signaling on sPD onset and progression.
A similar separation of patients versus controls as by dimension 1
(Fig. [302]9a) could be achieved by only plotting the OGDHC activity
measured in hNPCs against the respective complex I activity
(Fig. [303]9d). Although activity levels of both enzymes did not
significantly correlate in sPD (R = -0.64; p = 0.12) or Ctrl
(R = -0.66; p = 0.22) samples, they clearly separated the individuals
by the disease state. Furthermore, the separation was clearer in this
case than if parameters would have been considered alone (Fig. [304]7g
and ref. ^[305]17).
In sum, we present here a human cellular model system that combines—and
allows to model— most of the known sporadic PD-associated metabolic
alterations. Based on our findings, we propose a mechanism in which PC
dysfunction underlies the onset of most of these metabolic alterations
(Fig. [306]9e). Dysfunctional PC thereby affect SHH signal transduction
which in turn results in altered gene expression patterns amongst
others of the OGDHC. This creates a bottleneck within the citric acid
cycle and thus reduces the flux through the main metabolic routes of
glycolysis, citric acid cycle, and OXPHOS resulting in a sPD-specific
state of hypometabolism. Contrary, complex I deficiency seems to evolve
independently of the PC-mediated metabolic alterations. Thus, we
present a model in which complex I deficiency and the SHH-mediated
hypometabolism develop as two independent hits that negatively impact
cellular energy supply and could be used to predict disease progression
in sPD patients.
Discussion
The patient-derived hiPSC-based system described in this study to model
sporadic Parkinson’s disease is the first that integrates most
patient-associated metabolic phenotypes into one comprehensive
molecular and mechanistic model. Focusing on mitochondrial dysfunction,
we showed that neuronal cells—hNPCs and DAns—derived from sPD patients
progress into a symptomatic state with severe mitochondrial alterations
in e.g. mitochondrial ATP production and complex I functionality.
Although these cells exhibit an sPD-specific mitochondrial phenotype,
they do not degenerate^[307]17. Thus, this model system can provide
unique insights into cellular processes and molecular mechanisms early
during sporadic PD etiology before the onset of neurodegeneration.
Processes that can be targeted to slow down, halt, or even prevent
disease progression before the brain is irreversibly damaged.
To unravel these mechanisms, we performed a multilayered omics analysis
based on transcriptomics, proteomics, and metabolomics using neuronal
cells of the dopaminergic lineage. The multilayered omics analysis
allowed us to identify a crucial bottleneck in sPD metabolism within
the citric acid cycle, which is the most important metabolic pathway
for the cellular energy supply, especially within the brain.
Furthermore, the citric acid cycle is thought to be the central wheel
connecting almost all individual metabolic pathways so that a blockage
within the cycle can affect the abundance of metabolites involved in
most metabolic processes^[308]37,[309]55,[310]56. Especially the
α-ketoglutarate dehydrogenase complex (OGDHC) activity was
significantly reduced in sPD hNPCs and DAns derived thereof, which is
the rate-limiting step in the citric acid cycle. This reduced activity
might be caused by the altered abundance of OGDHC subunits.
Interestingly, the abundance of the rate-limiting subunit OGDH and its
brain-specific isoform OGDHL was affected, both of which are essential
for normal OGDHC function in neuronal cells. On the protein level, the
ubiquitously expressed OGDH is upregulated in sPD, and contrary, the
brain-specific OGDHL^[311]57 isoform is downregulated.
Interestingly, alterations in OGDHC activity have been implicated in
multiple neurodegenerative diseases such as Huntington’s disease,
Alzheimer’s disease (reduction of ~30–90%^[312]58), and Parkinson’s
disease (reduction of ~50%^[313]45,[314]59) to various degrees. Also,
OGDHC abundance is reduced in postmortem brain regions of PD
patients^[315]60. A central role of OGDHC activity for the progression
of sPD is also implied by the present correlation analysis revealing
that OGDHC activity is tightly correlated with clinical markers of
disease progression over a long-term follow-up.
As a consequence of altered OGDHC function in sPD, also several
OGDHC-dependent cellular processes could be affected as well, such as
α-ketoglutarate metabolization or cellular reactive oxygen species
(ROS) signaling.
Concerning the α-ketoglutarate metabolization, the OGDHC activity is
thought to determine if the citric acid cycle acts as an oxidative
pathway to produce energy in form of reducing equivalents or as a
reductive pathway to produce intermediates for biosynthetic processes.
A reduced OGDHC activity due to, e.g. OGDHL mutations, has been shown
to result in reduced mitochondrial respiration and ATP
production^[316]61, an depletion of fumarate, and malate^[317]61, as
well as a reduced cortical glucose consumption^[318]62,[319]63. In
fact, this is in line with our data concerning the metabolic
alterations in sPD. In sPD hNPCs and DAns, a reduced OGDHC activity is
combined with a reduced mitochondrial ATP production rate, reduced
levels of both fumarate, and malate, as well as reduced glucose
consumption. Furthermore, α-ketoglutarate is rerouted at the step of
the OGDHC^[320]58 instead of being processed to succinate. The
oxidative glutamine flux was significantly decreased, whereas the
reductive flux was significantly increased. This resulted in sPD hNPCs
in an efflux of carbon out of the citric acid cycle at the level of
α-ketoglutarate, diminishing the production of reducing equivalents.
This is in line with metabolomic flux analysis that revealed enhanced
reductive carboxylation upon genetic deletion of OGDHC
subunits^[321]64. Thus overall, the alterations in OGDHC activity
seemed to introduce an sPD-specific state of hypometabolism marked by
reduced glucose and glutamine metabolization, a reduced mitochondrial
ATP production rate, as well as a reduced abundance of most quantified
metabolites. Alterations in glucose metabolization have also been
previously linked to sPD using positron emission tomography (PET)
imaging^[322]39–[323]41. Basal ganglia neurons, DAns, and also
cholinergic neurons receiving the dopaminergic inputs, might be
especially vulnerable as they heavily rely on the citric acid cycle and
electron transport chain due to their high energetic burden and thus
exhibit high levels of OGDHC^[324]58.
Second, the OGDHC is also thought to function as a redox sensor to
reduce the NADH output of the citric acid cycle to the electron
transport chain in situations of oxidative stress^[325]65. Depending on
ROS levels, the OGDHC activity is reversibly reduced. This might be
particularly crucial for ROS homeostasis and thus cellular health in
combination with an already reduced OGDHC activity, as well as a
misassembled and dysfunctional complex I as observed in our cellular
model^[326]17 and in postmortem material of sPD
patients^[327]26,[328]66. Furthermore, cellular antioxidant capacities
may also be limited due to a reduced NAD(P)H production rate as a
consequence of the observed hypometabolism/metabolic flux, since NADPH
powers the majority of ROS-detoxifying enzymes such as the glutathione
reductases or thioredoxin reductases^[329]67. Besides being sensitive
to ROS, the OGDHC is also a prominent source of superoxide or H[2]O[2]
production exceeding the respective levels of complex I by about eight
times^[330]68.
In sum, we present here a human cellular system that combines—and
allows to model—most of the known sporadic PD-associated metabolic
alterations such as reduced glucose metabolization, reduced
mitochondrial respiration, and thus ATP production, as well as an OGDHC
and complex I deficiency. Thus, it can provide unique insights into the
molecular mechanisms underlying the metabolic alterations early during
sporadic PD etiology that can be targeted for therapeutic purposes. It
further allows to investigate causalities between these PD-associated
alterations.
Possibly of therapeutic potential is the finding that most of the
described metabolic alterations, especially the observed hypometabolism
were evoked by altered SHH signal transduction in sPD hNPCs. We
previously reported that these sPD hNPCs and DAns developed alterations
in primary cilia function which manifest in an overactive SHH signal
transduction in sPD^[331]17. Upon inhibiting the enhanced SHH signal
transduction in sPD, glucose uptake and the activity of the OGDHC could
be restored. Interestingly, the misexpressed OGDHL subunit which is
thought to result in altered OGDHC activity is also a predicted
SHH/GLI3 target gene. Thus, primary cilia dysfunction and alterations
in SHH signal transduction are the underlying causes of OGDHC
dysfunction which in turn creates a bottleneck within the citric acid
cycle causing an sPD-specific state of hypometabolism. Therefore,
inhibiting the overactive SHH signaling may be one part of a potential
neuroprotective therapy during early stages of sPD to attenuate the
metabolic alterations. Independently of the SHH signaling mediated
alterations in metabolism seems to develop the complex I deficiency in
sPD hNPCs, displaying a second hit that further contributes to the
severe metabolic alterations.
Astonishing, however, is that external stimuli that are thought to have
a beneficial effect on DAn functionality and survival negatively impact
internal metabolic processes in an sPD-specific manner. Under normal
conditions, SHH signaling mediates cellular and neurochemical
homeostasis within the nigrostriatal circuits, whereas diminished SHH
signaling results in DAn degeneration^[332]69. Contrary, our sPD
patient-derived hNPCs already react to physiological SHH signaling
unusually sensitive, initiating a cascade that ultimately results in an
sPD-specific state of hypometabolism. The mechanisms underlying the
development of primary cilia dysfunction and thus most metabolic
alterations remain still unknown and have to be investigated in the
future.
Based on these data, we present a model in which complex I deficiency
and the SHH-mediated hypometabolism develop as two independent hits
that negatively impact cellular energy supply as well as ROS
homeostasis, but also each other, thereby creating a vicious cycle of
self-destruction^[333]66,[334]70. To interfere with both processes by
firstly inhibiting the overactive SHH signaling and secondly by
rescuing complex I deficiency might be a combinatorial neuroprotective
therapy beneficial during early stages of sPD.
Methods
Ethical compliance
The use of patient-related material, information, and cell culture work
was approved by local ethics committees (No. 4485, No. 4120, No.
17-259, FAU Erlangen-Nuernberg, Germany; and No 422-13 and 357/19 S,
Technical University Munich, Germany) and all participants or their
legal guardians gave written informed consent. Participants were not
compensated. All related examinations, experiments, and methods were
performed in accordance with relevant guidelines and regulations.
Individuals were recruited independently of their sex/gender, race,
ethnicity, or other socially relevant categories. The sex and gender
was determined based on self-report.
sPD and Ctrl hiPSC lines were established, characterized, and provided
by the ForIPS consortium^[335]16,[336]17. hiPSC lines from 7 sPD
patients and 5 Ctrls were used, in total 24 hiPSC lines with two clones
per patient (Supplementary Data [337]1). To exchange selected sPD lines
for research purposes the scientific board of the UKER biobank will
consider each request.
Cell culture hiPSCs
hiPSCs were maintained under feeder-free conditions on Geltrex (Thermo
Fisher Scientific) coating and in mTesR1 medium (StemCell Technologies)
at 37 °C, 5% CO[2], 21% O[2]. At 70% confluency, cells were detached
using StemMACS Passaging Solution XF (Miltenyi Biotec) for 6 min at
37 °C. StemMACS Passaging Solution XF was aspirated and hiPSC colonies
were harvested in 1 ml mTeSR1 and chopped using a 1000 µl pipette tip.
Harvested hiPSCs were diluted and seeded on Geltrex-coated plates.
hiPSCs were cultured for ~200 days with passaging every 5–7 days (~60
passages). hiPSCs were screened regularly for pluripotency and stable
karyotype^[338]17.
Neural precursor differentiation
hiPSCs were differentiated into human small molecule neural progenitor
cells (hNPCs) by embryoid body (EB) formation as described
previously^[339]17. At 70% confluency, hiPSC colonies were detached
using 2 mg/ml collagenase type IV (Thermo Fisher Scientific) for 40 min
at 37 °C, 5% CO[2], 21% O[2]. Detached hiPSC colonies were harvested in
knockout serum replacement medium (80% KnockOut DMEM (Life
Technologies) supplemented with 20% Knockout serum replacement (Life
Technologies), 10 µM SB431542 (Miltenyi Biotec), 0.5 µM purmorphamine
(Tocris Bioscience), 1 µM dorsomorphin (Tocris Bioscience), 3 µM
CHIR99021 (Tocris Bioscience), 4.44 nM FGF-8b (Miltenyi Biotec), 1%
non-essential amino acids (Life Technologies), 1% l-glutamine (Life
Technologies), 150 µM ascorbic acid 2-phosphate (Sigma), and 0.02%
beta-mercaptoethanol (Life Technologies)). hiPSCs were cultivated for
two days on an orbital shaker at 37 °C, 5% CO[2], 21% O[2] and 80 rpm
with daily media changes. At day 3, EBs were transferred into neuronal
precursor medium (50% DMEM/F12-GlutaMAX (Life Technologies), 50%
Neurobasal (Life Technologies) supplemented with 1% B27 (minus vitamin
A, 12587010, Life Technologies), 0.5% N2 (Life Technologies), 10 µM
SB431542, 0.5 µM purmorphamine, 1 µM dorsomorphin, 3 µM CHIR99021,
4.44 nM FGF-8b, 150 µM ascorbic acid 2-phosphate, and 0.02%
beta-mercaptoethanol). EBs were cultivated for 4 days at 37 °C, 7%
CO[2], 21% O[2], and 80 rpm with daily media changes. On day 6, EBs
were transferred into neural precursor maintenance medium (neural
precursor medium deprived of purmorphamine and CHIR99021). The
following day, EBs were seeded unharmed on Geltrex-coated plates and
expanded for 3–4 days in neural precursor maintenance medium at 37 °C,
7% CO[2], 21% O[2] with daily media changes. At day 10-11, hNPCs were
dissociated using Accutase (Sigma) for 10 min at 37 °C, 7% CO[2], 21%
O[2], chopped with a 1000 µl pipette tip and diluted in 5 ml neural
precursor maintenance medium. hNPCs were harvested at 200 × g for
5 min, resuspended in 1 ml neural precursor maintenance medium and
seeded on Geltrex-coated plates. Thereafter, hNPCs were cultivated at
37 °C, 7% CO[2], 21% O[2] with daily medium changes and passaged with
Accutase at 80% confluency. After two passages, 0.5 µM purmorphamine
was added to the maintenance medium.
Dopaminergic neuron differentiation
DAn differentiation was done as described previously^[340]17. hNPCs
were differentiated to DAns using 15 µg/mL poly-l-ornithine (Sigma) and
10 µg/mL laminin (Thermo Fisher Scientific) coating and differentiation
steps were done precisely as described previously^[341]17 using the
following growth factors and supplements: 10 ng/mL human BDNF (Miltenyi
Biotec); 10 ng/mL human GDNF (Miltenyi Biotec); 1 ng/mL human Tgf-beta3
(Miltenyi Biotec); 100 ng/mL human FGF-8b (Miltenyi Biotec); 200 µM
ascorbic acid 2-phosphate (Sigma); 1 µM purmorphamine (Tocris); 500 µM
dbcAMP (Sigma). DAns were differentiated at 37 °C, 7% CO[2], 21% O[2]
for at least 50 days.
Respiratory analysis
Respiratory analysis was done as described previously^[342]17.
hiPSCs were maintained on Geltrex coating and in mTesR1 medium at
37 °C, 5% CO[2], 21% O[2]. At 70% confluency, hiPSCs colonies were
passaged using Accutase for 6–8 min at 37 °C, sheared to single cells
by pipetting using a 1000 µl tip, and diluted in mTeSR medium
containing 10 µM ROCK inhibitor Y27632. hiPSCs were harvested at 100 ×
g for 5 min and resuspended in 1 ml mTeSR medium containing 10 µM ROCK
inhibitor Y27632. Cell number was determined and 5000 cells per well
with at least 8 replicates per cell line were seeded in mTeSR medium
containing 10 µM ROCK inhibitor Y27632 on Geltrex-coated XF96 cell
culture microplates and incubated for 6 days with daily medium changes.
hNPCs were maintained on Geltrex coating and in neural precursor
maintenance medium at 37 °C, 7% CO[2], 21% O[2]. At 80% confluency,
hNPCs were passaged, cell number was determined and 70,000 cells per
well with at least 8 replicates per cell line were seeded on
Geltrex-coated XF96 cell culture microplates and incubated for 48 h
with daily medium changes.
DAns were passaged on day 9 of differentiation according to the
differentiation protocol, cell number was determined and 5000 cells per
well with at least 8 replicates per cell line were seeded on 15 µg/mL
poly-l-ornithine and 10 µg/mL laminin coated XF96 cell culture
microplates. DAns were matured for at least 30 days at 37 °C, 7% CO[2],
21% O[2] with medium changes every 2–5 days.
The respiratory analysis was performed using an XF96 Analyzer (Seahorse
Bioscience) and XF Assay medium (Seahorse Bioscience) supplemented with
25 mM glucose (Sigma) or 5 mM pyruvate (Sigma). Prior to measurements,
cells were washed once with XF Assay medium and at least 4 replicates
per patient were incubated with XF Assay medium containing glucose or
pyruvate for 1 h at 37 °C, 0% CO[2], 21% O[2]. XFe96 Sensor Cartridges
(Seahorse Bioscience) were hydrated with 200 µl Calibrant (Seahorse
Bioscience) per well overnight and ports were loaded with (A) 10 µg/mL
oligomycin (Sigma), (B) 5 µM carbonyl cyanide
p-trifluoromethoxyphenylhydrazone (FCCP) (Sigma), (C) 50 µM rotenone
(Sigma) and 20 µM antimycin A (Sigma), and (D) 1 M 2-deoxyglucose
(Sigma). After equilibration, the analysis was performed with a total
of four basal measuring points (mix for 1 min, time delay for 2 min,
and measure for 3 min). Subsequent to port injections, respiratory and
glycolytic flux analysis was performed with three measuring points.
Data were analyzed using Wave 2.6.1. Data were normalized to DNA
content analyzed on an equally seeded and grown plate (fibroblasts,
hiPSCs, hNPCs) or the same plate (DAns) using the Quant-iT PicoGreen
dsDNA Assay Kit (Thermo Fisher Scientific). The copy plate (medium
aspirated) or the same plate was stored at −20 °C immediately after the
Seahorse run was performed and thawed on ice for 30 min prior to
analysis. For the copy plate, cells were lysed in 60 µl RIPA buffer
(50 mM Tris-HCL (Sigma), 150 mM NaCl (Sigma), 1% Triton X-100, 0.5%
sodium deoxycholate (Sigma), 0.1% SDS (Sigma), 3 mM EDTA (Sigma)) per
well. For the same plate, 10 µl Proteinase K (20 mg/ml; Sigma-Aldrich)
was added per well and cells were lysed at 37 °C for 1 h. The Quant-iT
PicoGreen dsDNA assay was performed according to the manufacturer’s
instructions. DNA concentrations were calculated with a linear
regression curve using Lambda DNA standards. For statistical analysis
of basal and maximal mitochondrial respiration as well as proton leak,
the mean OCR values per patient of measuring points prior to port A
injection, after port B and prior to port C injection, or after port A
and prior to port B injection, respectively, were used. The last two
measuring points were used to subtract non-mitochondrial respiration or
non-glycolytic acidification. For DAns, only the last measuring point
was used to subtract the non-glycolytic acidification. Mitochondrial
ATP production rates were calculated using the following equation with
a P/O ratio of 2.75 pmol ATP/pmol O according to the manufacturer’s
instructions
([343]https://www.agilent.com/cs/library/whitepaper/public/whitepaper-q
uantify-atp-production-rate-cell-analysis-5991-9303en-agilent.pdf).
[MATH: mitoATPProductionRate(pmolATP/min)=(OCRbasal(pmolO2/min)−<
msub>OCRprotonleak(pmolO2/min)
)*2(pmolO/pmolO2)*P/O(pmolATP/pmolO) :MATH]
1
Analysis of total ATP levels
At 80% confluency, hNPCs were passaged, cell number was determined, and
10,000 cells per well with at least three replicates per cell line were
seeded on Geltrex-coated white 96-well plates and incubated for 48 h
with daily medium changes at 37 °C, 7% CO[2], 21% O[2]. Total ATP
levels were analyzed using a Luminescent ATP Detection Assay Kit
(Abcam) according to the manufacturer’s instructions. Values were
normalized to DNA content analyzed on an equally seeded and grown copy
plate using the Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher
Scientific) as described above for Respiratory analysis.
Analysis of total NADH/NAD^+ levels
At 80% confluency, hNPCs were passaged, cell number was determined, and
5,000,000 cells per sample were resuspended in 100 µl lysis buffer.
Total NADH and NAD^+ levels were analyzed using a NAD/NADH Assay Kit
(ab176723; Abcam) according to the manufacturer’s instructions.
Fluorescence intensity was quantified using a SpectraMax M5 (Molecular
Devices). Values were normalized to genomic DNA content analyzed using
additional 5,000,000 cells from the same cell solution. Cells were
lysed in 100 µl RIPA buffer and genomic DNA was quantified using the
Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific) as
described above for Respiratory analysis.
Glucose uptake
The cellular glucose uptake was quantified using the Glucose Assay Kit
(ab272532, Abcam) and the Glucose Uptake-Glo™ Assay (J1341, Promega)
according to the manufacturer’s instructions.
For the Glucose Assay Kit (ab272532, Abcam), hNPCs were passaged, cell
number was determined, and 40,000 cells per well with at least 2
replicates per cell line were seeded on Geltrex-coated 96-well plates
and incubated for 48 h with daily medium changes at 37 °C, 7% CO[2],
21% O[2]. The medium was aspirated, and cells were incubated in fresh
neural precursor maintenance medium for exactly 24 h. The medium was
collected and diluted 1:2 in ddH[2]O. In total, 2 µl of each dilution
were mixed with 200 µl reagent and incubated at 95 °C for 8 min and at
8 °C for 4 min. The light absorbance at a wavelength of 630 nm of each
sample was measured using a SpectraMax M (Molecular Devices). Glucose
concentrations were calculated with a linear regression curve using the
provided standards. To calculate the glucose uptake rates, average
glucose concentrations per cell line were subtracted from the average
blank value measured from two equally treated wells only containing
medium.
For normalization of the glucose uptake rates, remaining cells were
lysed in 60 µl RIPA buffer and genomic DNA was quantified using the
Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific) as
described above for the Respiratory analysis.
For the Glucose Uptake-Glo™ Assay (J1341, Promega), hNPCs were
passaged, cell number was determined, and 20,000 cells per well with at
least two replicates per cell line were seeded on Geltrex-coated
96-well plates and incubated for 48 h with daily medium changes at
37 °C, 7% CO[2], 21% O[2]. The medium was aspirated, cells were washed
with PBS, and incubated in PBS containing 1 mM 2-deoxyglucose for
15 min at 37 °C. The 2-deoxyglucose uptake was assessed according to
the manufacturer’s instructions. Luminescence intensity was quantified
using a SpectraMax M5 (Molecular Devices). Values were normalized to
the DNA content of an equally seeded and treated copy plate quantified
using the Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific)
as described above for the Respiratory analysis.
Pyruvate dehydrogenase activity assay
At 80% confluency, hNPCs were passaged, cell number was determined, and
1,000,000 cells per sample were resuspended in 100 µl ice-cold assay
buffer. The PDH activity was assessed using the pyruvate dehydrogenase
Activity Assay Kit (MAK183, Sigma) according to the manufacturer’s
instructions. The absorbance at 450 nm was quantified every 5 min for
30 min using a SpectraMax M5 (Molecular Devices). Values were
normalized to genomic DNA content analyzed using additional 1,000,000
cells from the same cell solution. Cells were lysed in 100 µl RIPA
buffer and genomic DNA was quantified using the Quant-iT PicoGreen
dsDNA Assay Kit (Thermo Fisher Scientific) as described above for
Respiratory analysis.
α-ketoglutarate dehydrogenase activity assay
At 80% confluency, hNPCs were passaged, cell number was determined, and
1,000,000 cells per sample were resuspended in 100 µl ice-cold assay
buffer. The OGDHC activity was assessed using the alpha-Ketoglutarate
Dehydrogenase Activity Assay Kit (Colorimetric) (ab185440, Abcam)
according to the manufacturer’s instructions. The absorbance at 450 nm
was quantified every 5 min for 30 min using a SpectraMax M5 (Molecular
Devices). Values were normalized to genomic DNA content analyzed using
additional 1,000,000 cells from the same cell solution. Cells were
lysed in 100 µl RIPA buffer and genomic DNA was quantified using the
Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific) as
described above for Respiratory analysis.
Quantification of human homocysteine levels
At 80% confluency, hNPCs were rinsed with PBS, collected using a cell
scraper in 1 ml PBS, homogenized, and stored overnight at −20 °C. After
two freeze-thaw cycles, the homogenates were centrifuged for 5 minutes
at 5000×g, 4 °C. The supernatant was collected and the protein content
was quantified using the Pierce BCA Protein Assay Kit (Thermo Fisher
Scientific) according to the manufacturer’s instructions. Samples were
diluted to obtain a 30 µg protein/100 µl PBS solution which was assayed
immediately using the human homocysteine (HCY) ELISA kit (CSB-E13814h,
Cusabio) according to the manufacturer’s instructions. The absorbance
at 450 nm and 540 nm was quantified within 5 min using a SpectraMax M5
(Molecular Devices). Absorbance at 540 nm was subtracted from the
absorbance at 450 nm, and a standard curve was used to calculate the
homocysteine concentration in nmol/ml.
Isolation of RNA, cDNA synthesis, and quantitative real-time PCR
Gene expression was analyzed by RT-qPCR as described
previously^[344]17. hNPCs were maintained on Geltrex-coated six-well
plates and in neural precursor maintenance medium at 37 °C, 7% CO[2],
21% O[2] for at least 48 h. Total RNA was extracted using RNeasy Plus
Mini Kit (Qiagen) according to the manufacturer’s instructions and
reverse transcribed to cDNA using the SuperScript VILO cDNA Synthesis
Kit (Thermo Fisher Scientific). For RT-qPCR, 25 ng (278 ng/µl) cDNA was
quantitatively amplified on a QuantStudio 7 Flex (Thermo Fisher
Scientific) using TaqMan universal PCR MM no Ung (Thermo Fisher
Scientific) and gene-specific TaqMan primers (Thermo Fisher
Scientific): ACTB (Hs99999903_m1); SLC2A1 (Hs00892681_m1); SLC2A2
(Hs01096908_m1); SLC2A3 (Hs00359840_m1); SLC2A4 (Hs00168966_m1). The
comparative Ct method was used to analyze differences in gene
expression, values were normalized to ACTB.
Immunostaining
Cells were cultured on Geltrex-coated 96-well plates or on glass
coverslips for at least 72 h. Cells were fixed with 10% Formalin for
20 min at 37 °C, washed twice with PBS, and permeabilized/blocked with
PBS containing 1% BSA (Sigma-Aldrich) and 0.3% Triton X-100
(Sigma-Aldrich) for 15 min at room temperature. Primary antibodies were
diluted in PBS containing 1% BSA and 0.3% Triton X-100 and antibody
incubation was performed at 4 °C overnight. Cells were washed twice
with PBS and incubated with secondary antibodies diluted in PBS
containing 1% BSA and 0.3% Triton X-100 for 2 h at room temperature.
Nuclei were stained using a 0.1 µg/ml DAPI (Sigma)—PBS solution for
10 min at room temperature. Cells were washed twice with PBS and
coverslips were mounted using Aqua-Poly/Mount (Polysciences Inc.) or
Aqua-Poly/Mount was added per well. Primary antibodies were diluted as
follows: ATP5F1A (ab14748, Abcam; 1:500), NES (Ma1110, Thermo Fisher
Scientific; 1:250), Histone H3 (tri methyl K27) (ab6002, Abcam; 1:100),
Histone H3 (tri methyl K9) (ab8898, Abcam; 1:500), SLC2A1 (MA5-31960,
Invitrogen; 1:500), SLC2A3 (PA5-72331, Thermo Fisher Scientific;
1:500), SOX1 (Ab87775, Abcam; 1:500), SOX2 (sc17320, Santa Cruz;
1:500), TUBB3 (T5076, Sigma-Aldrich; 1:1000). Secondary antibodies were
diluted as follows: donkey-anti-mouse IgG Alexa 488 (A21202, Thermo
Fisher Scientific; 1:500), donkey-anti-rabbit IgG Alexa 488 (A21206,
Thermo Fisher Scientific; 1:500). If necessary, images were processed
using ImageJ 1.53c.
Image quantification
Quantification of histone levels and hNPC markers
For cells grown on 96-well plates, images were acquired using a
Cellinsight NXT platform (Thermo Fisher Scientific) with a 20 × 0.4 NA
objective (field size of 454,41 by 454.41 µm) and analyzed using HCS
Studio 2.0 (Thermo Fisher Scientific). The acquisition was configured
individually for every analyzed antibody and chromophore. Nuclear DNA
fluorescence intensity (DAPI dye) was assessed in channel 1 and a
nuclear mask was created using the image analysis segmentation
algorithm to identify viable cells as valid objects according to their
object area, shape, and intensity. The nuclear mask was used to
quantify stained proteins of valid objects in channel 2 or 3 with a
fixed exposure time. A ring (thickness 13 pixels) was used to quantify
the cytoplasmic markers with a fixed exposure time. The amount of
analyzed valid objects per well and cell line used for statistical
analysis is stated in the corresponding figure legends. Violin plots
were generated using the functions ggplot + geom_violin of the R
package ggplot2 v3.4.2. For the statistical analysis, a linear mixed
effects model (lm) was fit using the lmer function (R package lme4
Version 1.1-34), where unique cells were included but nested within
donors (formula: Parameter measured per cell ~disease state + 1 |
disease state:Patients; REML = FALSE). p-values for lm were calculated
using the Anova function (R package car Version 3.1-2).
Quantification of mitochondrial abundance and morphology
hNPCs were maintained on Geltrex-coated glass coverslips and in neural
precursor maintenance medium at 37 °C, 7% CO[2], 21% O[2] for at least
48 h. The same amount of prewarmed neural precursor medium supplemented
with 200 nM MitoTracker Deep Red ([345]M22426, Thermo Fisher
Scientific) was added per well and cells were stained for 20 min at
37 °C, 7% CO[2], 21% O[2]. Cells were washed twice with PBS and fixed
with 10% formalin (Sigma) for 20 min at 37 °C. ATP5F1A, TUBB3, and DAPI
staining was performed as described above.
Images were taken using a Leica SP8 laser scanning confocal microscope
equipped with 488, 561 and 633 nm lasers and a 63× objective. Images
were processed using CellProfiler v4.2.5 (Broad Institute). Multiple
images per well/cell line were loaded and assigned to the groups
Patient and Disease state. Images were processed as described in ref.
^[346]70. In brief, fluorescence intensities of all channels were
rescaled, illumination was corrected, primary and secondary (based on
“Distance — B” or “Propagation”) objects were identified, objects were
filtered (based on shape and size), and tertiary objects were assigned.
Mitochondrial speckles (MitoTracker or ATP5F1A staining) were enhanced
and quantified as primary objects within the tertiary cell objects
(based on ‘Distance – B’ or “Propagation”). A typical diameter of 3–300
pixel units was assumed for mitochondrial content and a threshold
strategy with a threshold smoothing scale of 0, and a threshold
correction factor of 2 was applied, as well as an object smoothing
filter of 20. Next, the shape, area, and intensity of mitochondrial
primary objects were quantified, and mitochondrial objects were related
to tertiary cell objects. Additionally, the mitochondrial skeleton was
quantified. For this purpose, the mitochondrial primary objects were
converted to an image, and the morphological skeleton was identified.
The lengths and branch points of the mitochondrial skeletons were
quantified. Violin plots were generated using the functions ggplot +
geom_violin of the R package ggplot2 v3.4.2. For the statistical
analysis, a linear mixed effects model (lm) was fit using the lmer
function (R package lme4 Version 1.1-34), where unique cells were
included but nested within donors (formula: Parameter measured per cell
~disease state + 1 | disease state:Patients; REML = FALSE). p-values
for lm were calculated using the Anova function (R package car Version
3.1-2).
Quantification of glucose transporters
For cells grown on 96-well plates, images were acquired as described
for the quantification of histone marks. Acquired images were exported
from HCS Studio 2.0 and analyzed using CellProfiler v4.2.5 (Broad
Institute). Multiple images per well/cell line were loaded and assigned
to the groups Patient and Disease state. Images were processed as
described for the quantification of mitochondrial abundance and
morphology. The distribution of glucose transporters/fluorescence
intensity within the identified tertiary objects (based on
“Propagation”) was measured using MeasureObjectIntensityDistribution in
ten bins around the center of the corresponding primary object with bin
1 being close to the center and bin 10 close to the edges. Violin plots
were generated using the functions ggplot + geom_violin of the R
package ggplot2 v3.4.2. For the statistical analysis, a linear mixed
effects model (lm) was fit using the lmer function (R package lme4
Version 1.1-34), where unique cells were included but nested within
donors (formula: Parameter measured per cell ~disease state + 1 |
disease state:Patients; REML = FALSE). p-values for lm were calculated
using the Anova function (R package car Version 3.1-2).
Western blot
hNPCs were maintained on Geltrex coating and in neural precursor
maintenance medium at 37 °C, 7% CO[2], 21% O[2] for at least 72 h.
Approximately 2 × 10^7 cells were lysed in RIPA buffer and subsequently
stored at −80 °C. Protein concentration was quantified using the Pierce
BCA Protein Assay Kit (Thermo Fisher Scientific) according to the
manufacturer’s instructions. 10 µg protein extract was diluted in RIPA
and NuPAGE (Novex) and incubated for 5 min at 95 °C. Protein extracts
from 5 Ctrl and 7 sPD hNPC lines and Protein Marker VI (AppliChem) were
separated on a Criterion XT Bis-Tris Gel, 4–12% (Bio-Rad) using
Tris/Glycine Buffer (Bio-Rad) and a Criterion Vertical Electrophoresis
Cell (Bio-Rad) at 120 V for 70 min. Proteins were blotted on
methanol-activated Immobilon—P Membranes (Millipore) using XT MOPS
buffer (Bio-Rad) and a Criterion Blotter (Bio-Rad) at 20 V, 4 °C
overnight. Membranes were blocked for 1 h with TBS containing 0.01%
Tween (TBST) and 5% milk. Primary antibodies NDUFB8 (459210, Novex;
1:500), Complex II- Subunit 30 (SDHB) (459230, Thermo Fisher
Scientific; 1:500), UQCRC2 (ab14745, Abcam; 1:2500), MT-CO2 (ab110258,
Abcam; 1:1000), ATP5F1A (ab14748, Abcam; 1:4000), DRP1 (5391, Cell
Signaling; 1:1000), DRP1 phospho-Ser616 (4494, Cell Signaling; 1:1000),
TUBA (GTX628802, Genetex; 1:20,000), ACTB (ABO145-200, OriGene;
1:2000), OGDHL (17110-1-AP, Proteintech, 1:5000), GAPDH (GTX627408
peroxidase coupled, Genetex, 1:20,000), SLC2A1 (MA5-31960, Invitrogen,
1:5000), SLC2A3 (PA5-72331, Thermo Fisher Scientific, 1:5000) were
incubated in blocking buffer overnight at 4 °C. Membrane was washed
with TBST and secondary antibodies rabbit-anti-mouse IgG peroxidase
(GTX213112-01, GeneTex; 1:10,000), goat-anti-rabbit IgG peroxidase
(111-035-003, Dianova; 1:10,000), and rabbit-anti-goat IgG peroxidase
(305-035-003, Dianova; 1:10,000) were incubated in blocking buffer at
room temperature for 2 h, respectively. Subsequently, the membrane was
washed with TBST and incubated with ECL substrate (GE Healthcare) for
1 min. Protein bands were visualized using a ChemiDoc Imager (Bio-Rad)
and quantified using Image Lab (Bio-Rad). Quantified expression levels
were normalized to ACTB or TUBA levels.
Analysis of complex I activity
Complex I activity was quantified using the complex I enzymatic
activity microplate assay kit (Abcam, ab109721) according to the
manufacturer’s instructions and as described previously^[347]17.
Kinetics were measured on a SpectraMax M5 (Molecular Devices) for
45 min. Values were normalized to total protein concentrations and
average Ctrl levels.
Proteome analysis
Sample preparation for total hNPC proteome analysis
hNPCs were maintained on Geltrex coating and in neural precursor
maintenance medium at 37 °C, 7% CO[2], 21% O[2] for at least 72 h. At
80% confluency, hNPCs were passaged using Accutase, cell number was
determined, and 3,000,000 cells per replicate with three replicates per
cell clone were collected. Cells were harvested at 200× g for 5 min,
medium was aspirated and cell pellets were snap frozen in liquid
nitrogen and stored at −80 °C. Frozen cell pellets were lysed in SDC
Buffer (1% sodium deoxycholate (wt/vol) in 100 mM Tris pH 8.5) and
boiled for 5 min at 95 °C. Lysates were then cooled on ice for 5 min
and sonicated using the Bioruptor sonication device for 30 min.
Reduction and alkylation was performed by adding
Tris(2-carboxyethyl)phosphine (TCEP) and 2-Chloracetamide (CAA) at the
final concentrations of 10 mM and 40 mM, respectively, and incubating
them for 5 min at 45 °C. Samples were digested overnight by the
addition of 1:50 LysC (1:50 wt/wt: Wako) and Trypsin (1:50 wt/wt:
Sigma-Aldrich) overnight at 37 °C with agitation (1500 rpm) on an
Eppendorf Thermomixer C. The next day, peptides were desalted using
SDB-RPS (Empore) StageTips. Briefly, samples were tenfold diluted using
1% TFA in isopropanol and then loaded onto the StageTips, which were
subsequently washed with 200 µL of 1% TFA in isopropanol and then with
0.2% TFA/2% acetonitrile (ACN) twice. Peptides were eluted using 75 µL
of 80% ACN/1.25% NH[4]OH and dried using a SpeedVac centrifuge
(Concentrator Plus; Eppendorf) for 1 h at 30 °C. Peptides were
resuspended in 0.2% TFA/2% ACN and peptide concentration was determined
using the Nanodrop 2000 (Thermo Scientific). In total, 200 ng of
peptides were subjected to LC-MS/MS analysis.
Data-independent acquisition LC-MS analysis for total hNPC proteome
Peptides were loaded on a 50 cm reversed-phase column (75 μm inner
diameter, packed in-house with ReproSil-Pur C18-AQ 1.9-μm resin). No
trap column was used. To maintain a column temperature of 60 °C, we
used a homemade column oven. An EASY-nLC 1200 system (Thermo Fisher
Scientific) was connected online with a mass spectrometer (Orbitrap
Exploris 480, Thermo Fisher Scientific) via nano-electrospray source.
Peptides were separated using a binary buffer system consisting of
buffer A (0.1% formic acid (FA)) and buffer B (80% ACN, 0.1% FA). We
used a constant flow rate of 300 nl/min. We loaded 200 ng of peptides
and eluted them with a 100 min gradient. The gradient starts with 5%
buffer B and increases consistently to 30% in 80 min, until it reaches
95% in 88 min and remains constant for another 4 min. At the end,
Buffer B decreases to 5% in 96 min and remains constant for another
4 min. The MS data was acquired using a data-independent acquisition
(DIA) mode with a full scan range of 300–1650 m/z at 120,000
resolution, automatic gain control (AGC) of 3e6 and a maximum injection
time of 60 ms. The stepped higher-energy collision dissociation (HCD)
was set to 25.5, 27.30. Each full scan was followed by 33 DIA scans
which were performed at a 30,000 resolution, an AGC of 1e6 and the
maximum injection time set to auto. Information regarding m/z
separation and number of windows are provided as Supplementary
Data [348]3.
Data processing and bioinformatic analysis of total hNPC proteome
DIA raw files were analyzed using directDIA in Spectronaut version 15
(Biognosys). The search was done against UniProt human proteome of
canonical and isoform sequences (downloaded July 2019) with 20,383
entries for final protein identification and quantification. Enzyme
specificity was set to trypsin with up to two missed cleavages. Maximum
and minimum peptide length was set to 52 and 7, respectively. The
search included carbamidomethylation as a fixed modification and
oxidation of methionine and N-terminal acetylation of proteins as
variable modifications. A protein and precursor FDR of 1% were used for
filtering and subsequent reporting in samples (q-value mode with no
imputation).
For bioinformatic analyses, intensities were log2-transformed. Next,
the dataset was filtered by a minimum of three valid values in at least
one experimental group and subsequently imputed using a Gaussian normal
distribution (width = 0.3 and downshift = 1.8) using Perseus version
1.6.1.3^[349]71. Further analysis was performed using R v4.1.0. PCA was
performed using the prcomp function and visualized using the package
ggbiplot v0.55 with probability ellipses (0.68 of normal probability).
The Pearson correlation between samples was calculated using the cor
function within R, and correlations were plotted in a heatmap using the
pheatmap function of the R package pheatmap v1.0.12. Agglomerative
hierarchical clustering by the hclust function (method = complete) was
applied to group samples.
Hypothesis testing was performed using the package DESeq2 v1.36.0 and
the design formula design = ~samples.n + samples.n:replicate +
condition with condition being either Ctrl or sPD, samples.n being a
unique number per patient-derived cell line (1 to 12), and replicate
indicating the different replicates per cell line (1, 2, or 3). P
values were adjusted for multiple testing within DESeq2 by Benjamini
and Hochberg^[350]72. Proteins with a p.adjust-value (q-value) <0.05
were considered significantly altered in sPD.
Based on the complete set of DEPs a volcano plot and heatmap was
produced. For the volcano plot, log2(fold change) was plotted versus
the -log10(p.adjust-value) on the x- and y-axis, respectively. The
Volcano plot was generated using the EnhancedVolcano function of the R
package EnhancedVolcano v1.12.0 with pCutoff = 0.05 and FCcutoff=0.26.
The heatmap was generated by using the heatmap.2 function within the
gplots v3.1.1 package. Agglomerative hierarchical clustering by the
hclust function (method = complete) was applied to group samples or
proteins. Log2(fold changes) of DEPs were scaled and represented as
z-score.
Transcriptome–Proteome correlation
Bulk-like DEG^[351]17 and DEP lists were filtered for common
genes/proteins yielding a list of 1250 DEG-DEP pairs. This list was
further subset according to pairs exhibiting a positive correlation
(downregulate DEG and downregulated DEP - 553 pairs; upregulated DEG
and upregulated DEP - 33 pairs) or a negative correlation (downregulate
DEG and upregulated DEP - 582 pairs; upregulated DEG and downregulated
DEP - 82 pairs). The correlation between DEGs and DEPs was assessed
using Pearson’s correlation within the base R function cor.test. Plots
were generated using ggplot2 v3.4.0 and trend lines were added using
geom_smooth(method = lm).
Non-targeted metabolomics analysis
Cell preparation
hNPCs were seeded at a density of 700,000 cells/well in six replicates
in Geltrex-coated six-well plates and cultured in neural precursor
maintenance medium with daily medium changes at 37 °C, 7% CO[2,] 21%
O[2] for at least 72 h to achieve a desired cell number of
1,000,000–1,500,000 cells per well. One replicate was used to determine
the cell number. hNPCs were dissociated using Accutase for 10 min at
37 °C, 7% CO[2], 21% O[2], chopped with a 1000 µl pipette tip, and
diluted in 5 ml neural precursor maintenance medium. Cell number was
determined using a Neubauer-improved cell counting chamber. The
remaining replicates were used for metabolite extraction. Cells were
washed twice with warm PBS and metabolism was quenched by adding
precooled extraction solvent (80% methanol (AppliChem)) which contained
4 standard compounds to monitor the extraction efficiency. The amount
of extraction solvent used was adjusted to the cell count, 1 ml solvent
was used for 1,000,000 cells. Cells were harvested by scraping them
with the extraction solvent, were collected in precooled microtubes
(Sarstedt), and stored at −80 °C.
For Ctrl samples, Geltrex-coated six-well plates containing neural
precursor maintenance medium were incubated at 37 °C, 7% CO[2], 21%
O[2]for at least 72 h. Medium was changed daily. After 72 h, medium was
collected in precooled microtubes and stored at −80 °C. Geltrex-coated
wells were washed twice with warm PBS and 1 ml extraction solvent was
added. After scraping, the Geltrex–extraction solvent mixture was
collected in precooled microtubes and stored at −80 °C.
For analysis, 80 mg glass beads (0.5 mm; VK-05, PeqLab) were added to
the samples and cells were homogenized using a Preccellys24 (PeqLab)
for two times 25 s at 5500 rpm, 4 °C. The homogenates were used for
fluorometric DNA quantification and for metabolomic analysis.
Fluorometric DNA quantification
Fluorometric DNA quantification in homogenates was performed as
described by^[352]73. In brief, 80 µl Hoechst 33342 (20 µg/ml in PBS;
Thermo Fisher Scientific) were mixed with 20 µl of the homogenate or
88% Methanol (blank) in a black 96-well plate (Thermo Fisher
Scientific) and incubated in the dark for 30 min at room temperature.
Fluorescence intensity was quantified using a GloMax Multi Detection
System (Promega) with an UV filter (λ[Ex] 365 nm, λ[Em] 410–460 nm;
Promega). Values were normalized to blank levels and the average of 4
replicates per sample was used for normalization of metabolite levels.
LC-MS/MS-based metabolomics analysis
The cell homogenates were centrifuged for 5 min at 3250× g, 4 °C. Eight
aliquots of 50 µl of the supernatant were loaded onto four 96-well
microplates. Two (i.e. early and late eluting compounds) aliquots for
analysis by ultra-high performance liquid chromatography-tandem mass
spectrometry (UPLC-MS/MS) in positive ion mode electrospray ionization
(ESI), one for analysis by UPLC-MS/MS in negative ion mode ESI, and one
for analysis by (HILIC)/UPLC-MS/MS in negative ion mode ESI. Three
types of quality control samples were included into each plate: samples
generated from a pool of human plasma, samples generated from a small
portion of each experimental samples served as technical replicate
throughout the dataset, and extracted water samples served as process
blanks. Experimental samples and controls were randomized across the
metabolomics analysis. The samples were dried on a TurboVap 96
(Zymark).
Prior to UPLC-MS/MS analysis, the dried samples were reconstituted in
acidic or basic LC-compatible solvents, each of which contained a
series of standard compounds at fixed concentrations to ensure
injection and chromatographic consistency. The UPLC-MS/MS platform
utilized a Waters Acquity UPLC with Waters UPLC BEH C18-2.1 × 100 mm,
1.7-μm columns and a Thermo Scientific Q-Exactive high
resolution/accurate mass spectrometer interfaced with a heated
electrospray ionization (HESI-II) source and Orbitrap mass analyzer
operated at 35,000 mass resolution. One aliquot reconstituted in acidic
positive ion conditions, chromatographically optimized for more
hydrophilic compounds. In this method, the extract was gradient eluted
from a C18 column (Waters UPLC BEH C18-2.1 × 100 mm, 1.7 µm) using
water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and
0.1% formic acid (FA). Another aliquot was also analyzed using acidic
positive ion conditions; however it was chromatographically optimized
for more hydrophobic compounds. In this method, the extract was
gradient eluted from the same aforementioned C18 column using methanol,
acetonitrile, water, 0.05% PFPA and 0.01% FA and was operated at an
overall higher organic content. Another aliquot was analyzed using
basic negative ion optimized conditions using a separate dedicated C18
column. The basic extracts were gradient eluted from the column using
methanol and water, however with 6.5 mM Ammonium Bicarbonate at pH 8.
The fourth aliquot was analyzed via negative ionization following
elution from a HILIC column (Waters UPLC BEH Amide 2.1 × 150 mm,
1.7 µm) using a gradient consisting of water and acetonitrile with
10 mM Ammonium Formate, pH 10.8. The MS analysis alternated between MS
and data-dependent MSn scans using dynamic exclusion. The scan range
varied slighted between methods but covered 70–1000 m/z.
Raw data was extracted, peak-identified and QC processed using
Metabolon’s hardware and software (Metabolon, Inc., North Carolina,
USA). Compounds were identified by comparison to library entries of
purified standards or recurrent unknown entities, based on three
criteria: retention index within a narrow RI window of the proposed
identification, accurate mass match to the library +/− 10 ppm, and the
MS/MS forward and reverse scores between the experimental data and
authentic standards. The MS/MS scores are based on a comparison of the
ions present in the experimental spectrum to the ions present in the
library spectrum. While there may be similarities between these
molecules based on one of these factors, the use of all three data
points can be utilized to distinguish and differentiate biochemicals.
Data analysis
OrigScale values were median normalized, adjusted to DNA levels and
imported into R v4.1.0. Only metabolites detected in more than 30% of
the samples were used for further analysis. To approximate normal
distribution, values were log2-transformed. Unsupervised PCA was used
to discover differential variation features and confirmed close
relationship between replicates of samples. PCA was performed using the
prcomp function in R and visualized using the R package ggbiplot v0.55
with probability ellipses (0.68 of normal probability). Thus, average
values of all replicates per sample and metabolite were used for
statistical analysis. Hypothesis testing was performed using Student’s
t-test (t.test function in R) and p-values were adjusted for multiple
testing using the R package fdrtool v1.2.15 (statistic = pvalue,
cutoff.method = ptc0). Metabolites with a q-value < 0.05 were
considered significantly altered in sPD. A volcano plot was produced
using the R package EnhancedVolcano v1.12.0 by plotting the log2(fold
change) versus the –log10(p-value).
Isotopic labeling
Cell preparation
hNPCs were seeded on Geltrex-coated 6-well plates containing neural
precursor maintenance medium at a density of 1,000,000 cells/well with
six replicates per cell line. hNPCs were cultured at 37 °C, 7% CO[2],
21% O[2] for at least 72 h. Medium was replaced by a labeling medium
(50% DMEM/F12-GlutaMAX (L0091500, Biowest), 50% Neurobasal (A2477501,
Life Technologies) supplemented with 1% B27 (minus vitamin A, 12587010,
Life Technologies), 0.5% N2 (Life Technologies), 10 µM SB431542, 0.5 µM
purmorphamine, 1 µM dorsomorphin, 4.44 nM FGF-8b, 150 µM ascorbic acid
2-phosphate, 0.02% beta-mercaptoethanol, 0.125 mM sodium pyruvate
(Sigma), 21.25 mM glucose (Sigma), and 1.25 mM l-glutamine (Sigma))
containing the respective stable-isotope tracer instead of its
unlabeled variant. Cells were cultured with either 1.25 mM
[U-13C]-glutamine or 21.25 mM [U-13C]-glucose (all tracers: Cambride
Isotope Laboratories, USA) for 24 h. Subsequently, cell culture
supernatant was stored for profiling the extracellular metabolome.
Three replicates per cell line and blanks were washed with 0.9% NaCl
and quenched with ice-cold methanol and ice-cold ddH[2]O (containing
1 µg/ml D6-glutaric acid as internal standard). Cells were scraped and
extracts were added into tubes containing ice-cold chloroform.
Following vortexing at 1400 rpm for 20 min at 4 °C and centrifugation
at 17,000× g for 5 min at 4 °C, 300 µl of the polar phase were
transferred into GC glass vials with microinsert and dried under vacuum
at 4 °C.
Fluorometric DNA quantification
The other three replicates per cell line were used for the
quantification of genomic DNA using the Quant-iT PicoGreen dsDNA Assay
Kit (Thermo Fisher Scientific). Cells were lysed in 200 µl RIPA buffer
(50 mM Tris-HCL (Sigma), 150 mM NaCl (Sigma), 1% Triton X-100, 0.5%
sodium deoxycholate (Sigma), 0.1% SDS (Sigma), 3 mM EDTA (Sigma)) per
well. The Quant-iT PicoGreen dsDNA assay was performed according to the
manufacturer’s instructions. DNA concentrations were calculated with a
linear regression curve using Lambda DNA standards. Average values per
replicates were calculated and used for normalization.
GC-MS-based metabolomics analysis
GC-MS measurement of isotopic enrichment and relative metabolite
abundance was performed as previously published^[353]74. Briefly, dried
extracts were derivatized using equal amounts of methoxylamine
(20 mg/ml in pyridine) and MTBSTFA and injected into an Agilent 7890B
gas chromatograph equipped with a 30 m DB-35 ms and 5 m Duruguard
capillary column. Metabolites were detected in selected ion mode by an
Agilent 5977 MSD system. The MetaboliteDetector software was used to
analyze chromatograms, calculate mass isotopomer distributions (MIDs)
and perform relative comparison of metabolite levels^[354]75.
Quantification of medium concentrations
Concentrations of glucose, lactate, glutamine, and glutamate in cell
culture supernatants and control/blank media were determined using a
YSI 2950D Biochemistry Analyzer. Quantification was performed by
measuring respective reference compounds. Uptake rates were calculated
by subtracting sample concentrations from the measured blank
concentrations. Only for blank glutamine, the medium glutamine
concentration of 1.25 mM was assumed, as the measurements were
erroneous. Uptake rates were normalized to the genomic DNA content
measured per sample.
Data analysis
Selected MIDs, as well as uptake rates, and total cellular metabolite
concentrations normalized to the genomic DNA content per sample were
used for statistical analysis in R v4.1.0. Hypothesis testing was
performed for selected MIDs using the package lme4 v1.1-31 and the
formula values ~condition + (1 | condition:replicate) with condition
being either Ctrl or sPD and replicate indicating the different
replicates per patient-derived cell line (1, 2, or 3). p-values were
calculated using the Anova function (R package car Version 3.0-10).
Selected MIDs, uptake rates, or cellular metabolites with a
p-value < 0.05 were considered significantly altered in sPD. The
fractional contribution of glucose- or glutamine-derived carbons to the
total abundance of metabolite carbon was calculated by dividing the sum
of MID fractions (except M0) by the number of metabolite carbons.
Growth curve
hNPCs were seeded on Geltrex-coated 24-well plates containing neural
precursor maintenance medium at a density of 100,000 cells/well with 15
replicates per cell line. hNPCs were cultured at 37 °C, 7% CO[2], 21%
O[2] for at least 5 days with daily medium changes. Each day, three
replicates per cell line were used for genomic DNA preparation. Medium
was aspirated, cells were washed with PBS once and lysed in 100 µl RIPA
buffer. Genomic DNA was quantified using the Quant-iT PicoGreen dsDNA
Assay Kit (Thermo Fisher Scientific) according to the manufacturer’s
instructions. DNA concentrations were calculated with a linear
regression curve using Lambda DNA standards. If assuming an exponential
cell growth, a growth rate of 0.001312/h was determined. Both Ctrl and
sPD hNPCs exhibited a comparable proliferation rate^[355]17.
Metabolic flux analysis
Estimation of metabolic fluxes was performed using isotopic
non-stationary ^13C-metabolic flux analysis (MFA) based on a metabolic
network including the main pathways of the central carbon
metabolism—glycolysis, citric acid cycle, and amino acid metabolism.
The network reactions and carbon transitions are listed in
Supplementary Data [356]10. To estimate the metabolic fluxes, corrected
MIDs calculated from [U-13C]-glutamine and [U-13C]-glucose labeling
experiments, as well as the extracellular exchange rates (for glucose,
lactate, glutamine, and glutamate), and the effluxes for biomass
formation (estimated from the cellular growth rates as described in
ref. ^[357]76 were integrated into the metabolic network. Most
extracellular exchange rates were considered reversible to allow
balancing extracellular pools, only uptake rates for essential amino
acids and glucose were considered irreversible.
Non-stationary ^13C-MFA was performed using INCA v2.1^[358]47,[359]77
within MATLAB R2018a (installed packages: Statistics and Optimization
Toolbox) as described in ref. ^[360]78. In brief, INCA estimated
pathway fluxes using the elementary metabolite unit method. Fluxes were
estimated by minimizing the variance-weighted sum of squared residuals
between experimental and simulated data by least-squares regression. To
find the global optimum, model prediction was repeated for at least 5
times. The predictions were subjected to qui-square statistical testing
to evaluate the goodness-of-fit. Parameter continuation was used to
calculate the 95% confidence intervals. A redundancy analysis was
automatically performed by INCA. Data overfitting was avoided by
limiting the metabolic network to experimentally measured
reactions/metabolites, thus to the minimum of reactions necessary to
accurately simulate measured cellular carbon flows.
Pathway enrichment analysis
DEGs/DEPs were investigated for enrichment in KEGG, KEGG-Module and
WikiPathway (WP) terms using the enrichKEGG, enrichMKEGG, or enrichWP
function of the R package clusterProfiler v4.2.2 (one-sided
hypergeometric test). Enrichment of Reactome terms was assessed using
the enrichPathway function of the R package ReactomePA v1.38.0
(one-sided hypergeometric test). p-values were adjusted for multiple
testing by Benjamini and Hochberg. Enrichment maps were generated using
the treeplot function of clusterProfiler v4.2.2.
Integrated pathway enrichment analysis of previous transcriptome
(bulk-like DEGs)^[361]16 or present proteome data (DEPs) and present
metabolome (metabolites with q-value < 0.05) data was performed in
MetaboAnalyst v5.0
[[362]https://www.metaboanalyst.ca/MetaboAnalyst/home.xhtml]
(Enrichment analysis = Hypergeometric Test, Topology measure = Degree
Centrality, Integration method = Combine queries) using KEGG (Oct2019)
metabolic pathways.
The ‘citric acid cycle’ pathway and the ‘Electron Transport Chain
(OXPHOS system in mitochondria)’ pathway (source: WikiPathways) were
visualized and annotated using cytoscape v3.9.1 (installed apps:
WikiPathways v3.3.10, CyTargetLinker v4.1.0, stringApp v1.7.0, and
enhancedGraphics v1.5.4) and the R package RCy3 v2.14.2.
Prediction of transcription factor binding sites
The human OGDHL promoter region (GXP_638603) was obtained from
Genomatix by using the ElDorado database version ElDorado 04-2021
(genome build GRCh38). Transcription factor binding sites for GLI2
(MA0734.1; MA0734.2), GLI3 (MA1491.1; MA1491.2), and FOXA2 (MA0047.3)
within this promoter region and with a minimal relative profile score
of 80% were predicted using JASPAR, release 9 (2022). The human OGDHL
locus (genome build GRCh38) was visualized using the UCSC genome
browser. The alignment between the human and mouse (genome build
GRCm38/mm10) genome was obtained from the UCSC genome browser track
Vertebrate Multiz Alignment & Conservation (downloaded 11.2022).
Correlation of candidate experimental variables with patient-derived clinical
parameters
Key experimental variables per patient- or Ctrl-derived cell lines as
well as clinical information related to disease progression were
collected. This yielded a dataset of 77 parameters describing the
patients or Ctrl individuals. As no information about disease
progression can be available for Ctrl individuals, the dataset was also
separated based on the disease state. The Pearson correlation between
parameters for the whole dataset or the sPD dataset was performed using
the function rcorr of the R package Hmisc v5.1-0. A correlogram showing
the Pearson correlation coefficients was generated using the function
corrplot of the R package corrplot v0.92. Parameters were ordered based
on hierarchical clustering. Scatter plots with linear regression lines
and 95% confidence intervals were generated using the function
ggscatter of the R package ggpubr v0.6.0. Pearson correlation
coefficients with p-values were added using the function stat_cor.
Parameter pairs with p-values < 0.1 were considered as significant
correlations.
Multiple-factor analysis
In total 5 groups of variables were used for the multiple-factor
analysis: the top 100 DEGs, the top 100 DEPs, the significantly altered
metabolites (45 metabolites), as well as the experimental variables
used for the correlation analysis (61 functional parameters), and as a
supplementary group, the information about the disease state (Ctrl or
sPD). The multiple-factor analysis in the sense of Escofler-Pages with
a supplementary group of variables was performed using the function MFA
of the R package FactoMineR v2.8. The multiple-factor analysis was
visualized with confidence ellipses using the function fviz_mfa_ind of
the R package factoextra v1.0.7. The contribution of groups to
dimensions was visualized using the function fviz_contrib of the R
package factoextra v1.0.7.
Statistics and reproducibility
No statistical methods were used to predetermine the sample size. If
not stated otherwise, every analysis (except the omics analysis) was
performed thrice using independently collected material from 5 Ctrl and
7 sPD patient-derived cell clones e.g. from three different passages
(fibroblasts and hiPSCs) or three independent differentiation
approaches (hNPCs and DAns). Each data point represents the average of
these three replicates for each individual. If two clones per patient
were used for experiments, first the average value of these clones per
individual and replicate was calculated. Average values per patients
were used for any further statistical analysis performed using GraphPad
Prism 6. For two-group comparisons and in case of normal distribution,
unpaired, two-tailed t-test was applied. In the case of non-Gaussian
distribution, two-tailed Mann–Whitney U test was applied. For the
comparison of multiple groups, one-way ANOVA with Sidak’s multiple
comparison test was performed. Data displaying a measurement
progression are shown with mean ± standard error of the mean (SEM).
Boxplots are displayed from min to max values with all data points
shown.
If not stated otherwise, R version 4.2.2 and RStudio 2022.12.0 Build
353 was used for further analysis. Violin plots were used to visualize
the distributions of repeated measurements per individual cell line
(e.g. if multiple cells per cell line were analyzed). Violin plots were
generated using the functions ggplot + geom_violin of the R package
ggplot2 v3.4.2. For the statistical analysis, a linear mixed effects
model (lm) was fit using the lmer function (R package lme4 Version
1.1-31), where unique cells were included but nested within donors
(formula: Parameter measured per cell ~disease state + 1 | disease
state:Patients; REML = FALSE). p-values for lm were calculated using
the Anova function (R package car Version 3.0-10). Boxplots summarizing
measurements per individual cell line were generated using the
functions ggplot + geom_boxplot of the R package ggplot2 v3.4.2. These
boxplots display the median and range from the 25th to 75th percentile.
Whiskers extend to the most extreme data point which is no more than
1.5 times the interquartile range.
If not stated otherwise, p-values or q-values below 0.05 were
considered significant. Differences with a p-value or q-value between
0.05 and 0.1 were considered as trends (#). Not-significant differences
are not indicated.
Reporting summary
Further information on research design is available in the [363]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[364]Supplementary Information^ (12.1MB, pdf)
[365]Peer Review File^ (9.1MB, pdf)
[366]41467_2023_42862_MOESM3_ESM.pdf^ (115.2KB, pdf)
Description of Additional Supplementary Files
[367]Supplementary Data 1^ (10.7KB, xlsx)
[368]Supplementary Data 2^ (13.6KB, xlsx)
[369]Supplementary Data 3^ (10.8KB, xlsx)
[370]Supplementary Data 4^ (189KB, xlsx)
[371]Supplementary Data 5^ (35.7KB, xlsx)
[372]Supplementary Data 6^ (48.1KB, xlsx)
[373]Supplementary Data 7^ (16.5KB, xlsx)
[374]Supplementary Data 8^ (22.6KB, xlsx)
[375]Supplementary Data 9^ (79.9KB, xlsx)
[376]Supplementary Data 10^ (27KB, xlsx)
[377]Reporting Summary^ (91.8KB, pdf)
Source data
[378]Source Data^ (130.3MB, xlsx)
Acknowledgements