Abstract
Parkinson’s disease (PD) is a devastating neurodegenerative disorder
with growing prevalence worldwide and, as yet, no effective treatment.
Drug repurposing is invaluable for detecting novel PD therapeutics.
Here, we compiled gene expression data from 1231 healthy human brain
samples and 357 samples across tissues, ethnicities, brain regions,
Braak stages, and disease status. By integrating them with
multiple-source genomic data, we found a PD-associated gene
co-expression module, and its alignment with the CMAP database
successfully identified drug candidates. Among these, meclofenoxate
hydrochloride (MH) and sodium phenylbutyrate (SP) are indicated to be
able to prevent mitochondrial destruction, reduce lipid peroxidation,
and protect dopamine synthesis. MH was validated to prevent neuronal
death and synaptic damage, improve motor function, and reduce anhedonic
and depressive-like behaviors of PD mice. The interaction of MH with a
PD-related protein, sigma1, was confirmed experimentally. Thus, our
findings support that MH potentially ameliorates PD by interacting with
sigma1.
Subject terms: Target identification, Parkinson's disease
Introduction
Parkinson’s disease (PD) is a chronic neurodegenerative movement
disorder characterized by a large number of motor symptoms, including
resting tremor, rigidity and postural instability. The non-motor
symptoms of PD include autonomic, psychiatric, sensory and cognitive
impairments, as well as dementia^[44]1. As one of the most common
neurodegenerative disorders, PD affects 2–3% of 65-year-olds and is
responsible for more than 100,000 deaths worldwide each year^[45]2. PD
can be broadly categorized into two genetic forms monogenic PD, caused
by mutations in single genes (5–10%), and complex forms of PD resulting
from the interplay between multiple genetic risk factors and
environmental influences (90–95%). The heritability of PD is
approximately 27–34%^[46]3–[47]5. However, our understanding of the
etiology of PD remains incomplete. Thus, treatments for PD are still
limited in their efficacy, e.g., dopamine replacement therapy, the most
commonly used therapeutic strategy for PD, is capable of improving
clinical symptoms but is unable to halt disease progression^[48]6.
Thanks to decades of research, it is clear that PD exhibits
considerable locus heterogeneity^[49]7; an increasing number of disease
genes/pathogenic mutations are being identified in PD patients by means
of whole genome sequencing (WGS) or whole exome sequencing (WES)
studies^[50]8, and both autosomal recessive and dominant forms have
been described among the monogenic forms of PD. Such marked locus
heterogeneity underlying PD not only represents a major obstacle in
identifying common disease mechanisms, but also restricts our options
for appropriate therapeutic intervention. This notwithstanding, many
mutations have been repeatedly identified among PD
patients^[51]9–[52]11, thereby linking the familial and sporadic forms
of PD mechanistically^[53]12,[54]13. Thus, mutations in the α-synuclein
(SNCA) gene appear to be involved in both the familial and sporadic
forms of PD; SNCA function/homeostasis is modulated by various
contributory risk factors for PD, including oxidative stress,
mitochondrial dysfunction, post-translational modifications, and
concentrations of fatty acids^[55]14,[56]15. It has therefore been
reasoned that perturbation of the molecular networks involving multiple
genes might commonly underlie the pathogenesis or progression of PD,
and that the elucidation of these networks could facilitate the future
development of therapeutic interventions. So far, the global effort
toward this goal has led to the establishment of multiple valuable
sources of information that have facilitated the compilation of such
gene networks^[57]16,[58]17. Instead of focusing on single genes,
considerable emphasis has been placed on utilizing data-driven
frameworks at the system or network level to generate
biologically/clinically meaningful gene modules comprising sets of
functionally associated genes whose homeostasis may be altered by
specific pathophysiological events^[59]15–[60]19. For instance, the
weighted gene co-expression network analysis (WGCNA) and Differential
Co-expression (DiffCoEx) are designed to identify gene co-expression
modules by analyzing gene expression through coefficient
test^[61]18,[62]19. The application of such an approach has led to the
identification of network modules that are implicated in
neurodevelopmental processes, metabolism, and the immune system^[63]20.
An analysis of GEO data of PD (n = 128) identified modules associated
with RNA metabolism pathology as a potential cause of PD by sorting
differentially active pathways between brain transcriptomics samples
from PD patients and controls^[64]21,[65]22. However, these studies
were generally based on patient gene expression data, and may have been
biased due to insufficient numbers of samples and inter-patient
heterogeneity.
One important application of the molecular network is in drug
repurposing. Drug repurposing represents an attractive avenue in drug
discovery due to its relatively low cost and fewer safety concerns. By
definition, drug repurposing is designed to redirect new or additional
indications for three kinds of therapeutic molecules i.e., drugs
approved for a particular indication, drugs that have already been
well-characterized during their clinical development and accompanied by
thorough post-market surveillance data, and drugs which have undergone
some clinical development but were subsequently
abandoned^[66]23,[67]24. Often, biological networks combined with
Genome-Wide Association Studies (GWAS) are the most commonly employed
sources of information for drug repurposing, as GWAS studies are
intended to impartially link controlled factors to genetic or
transcriptomic alterations in human subjects with no specific emphasis
on a single gene or fixed set of genes^[68]25,[69]26. Thus, developing
a method for detecting the gene network perturbations caused by
PD-associated variants through combining large-scale human genomic
data, including functional interactions between genes from healthy
humans, is emerging as a useful approach to drug repurposing.
To test the effects of medicines for PD, an animal model is often used.
The most common PD model involves a neurotoxin approach, such as the
rotenone-induced PD model^[70]27–[71]29. From 2000 onwards, researchers
used rotenone to create a PD animal model, and it has been proven to be
very informative. However, there are limitations of the rotenone-based
model, which are low reproducibility and acute toxicity^[72]30,[73]31.
The chemical-induced models, like rotenone, may not fully recapitulate
the genetic diversity observed in PD patients. In contrast, genetic
models, such as those involving mutations in SNCA, LRRK2, or PINK1,
specifically replicate familial forms of PD but may not capture the
environmental factors implicated in sporadic cases. Thus, the chemical
models may lack genetic relevance, while genetic models may not reflect
the complexity of sporadic PD^[74]32. From 2000 onwards, researchers
used rotenone to create a PD animal model^[75]33, and it has proven to
be very informative. It is thought to cause dopaminergic degeneration
by inducing oxidative stress, as well as inducing in vivo aggregation
of α-synuclein, which is the major component of Lewy bodies^[76]34.
Recently, Ahn et al. constructed a rotenone-induced PD mouse model in
order to explore the role of δ-secretase in cleaving both α-Syn at N103
and Tau at N368^[77]35. Moreover, multiple studies have used the
rotenone-induced mouse model in the study of PD-targeted medicines. For
example, Liu et al. have investigated the protective effects of
piperlongumine in rotenone-induced PD cell and mouse models^[78]36.
Another study that used the rotenone-induced C57Bl/6 J mouse model
indicated the potential role of anle138b in the treatment of PD^[79]37.
To explore drug repurposing for Parkinson’s Disease (PD), we developed
a computational framework called iGOLD, which integrates multi-source
genomic data with gene co-expression modules (Fig. [80]1). This
framework (available at [81]https://github.com/fanc232CO/iGOLD_pipline)
was used to identify gene co-expression modules affected by PD-related
genes and SNPs. We evaluated the gene co-expression modules that were
significantly enriched in PD-associated genes and SNPs through
conservation analysis using seven gene expression datasets spanning
various ethnicities, brain regions, tissues, Braak stages, and PD
disease status. The highly conserved modules were then utilized for
drug discovery. Subsequent experiments involved rotenone-induced
primary neuronal cells and a mouse model to assess the efficacy of the
identified drugs in promoting neuronal survival, enhancing hippocampal
function, and modifying PD-related behaviors. Finally, we conducted
additional experiments focused on mitochondrial functions and metabolic
factors to elucidate the specific mechanisms through which these drugs
exert their effects.
Fig. 1. Schematics of iGOLD for drug repurposing.
[82]Fig. 1
[83]Open in a new tab
Step 1: constructed gene co-expression modules using 1231 healthy human
samples from ten brain regions. CCM—concurrently co-expressed modules
expressed in ten brain regions, SCM--brain region-specific co-expressed
modules. Step 2: from the constructed gene co-expression modules, we
selected the PD-associated module through enrichment of PD-associated
genes, enrichment of PD-associated SNPs (by Chi-square test and
stratified LDSC (sLDSC) analysis, respectively), and fraction of
PD-associated differential expression genes. DEGs differentially
expressed genes. Step 3: testing the conservation of the gene
co-expression relationships in the validated modules across
ethnicities, tissue, and disease development stages. Step 4: inside the
selected PD-associated module, the differentially expressed genes in
the PD patients were used for drug discovery with the Connectivity Map
(CMAP). The enrichment of up-regulated and down-regulated genes by the
drug-induced gene expression profiles was tested, and drugs that
reverse the differential gene expression in PD were considered as the
lead compound candidates.
Results
Overview of the study
Here, we first developed a computational architecture integrating
multiple-source genomic data with gene co-expression modules for drug
repurposing (iGOLD) for drug repurposing. The source code of iGOLD is
available at ([84]https://github.com/fanc232CO/iGOLD_pipline). As shown
in Fig. [85]1, it comprises by four main steps: (1) using gene
expression data of 1231 healthy human brain samples across ten brain
regions^[86]38–[87]40 to construct gene co-expression modules
associated with normal brain functions (Table [88]1); (2) identifying
the co-expression modules enriched with disease-associated genes, SNPs
and genes expressed significantly different in patients and controls;
(3) examining the conservation of the selected co-expression modules in
the gene expression data from brain tissues across different brain
regions, disease status, and ethnicities, and in the gene expression
data from blood and multiple other cell types; (4) aligning the highly
conserved modules to the gene expression profiles perturbed by small
molecular compounds in CMAP database^[89]41,[90]42, and identifying the
gene expression profiles enriched in genes from the conserved modules.
The small molecular compound was considered a drug candidate. The drug
candidate was further validated by primary neurons and mouse models.
The binding targets of the candidate drugs were predicted by
DStruBTarget^[91]43. The interactions between the candidate drug and
the drug target were validated experimentally.
Table 1.
Sample size of each brain region in the [92]GSE60862 dataset
Brain region name Sample size
Cerebellar cortex 130
Frontal cortex 127
Hippocampus 122
Medulla 119
Occipital cortex 129
Putamen 129
Substantia nigra 101
Temporal cortex 119
Thalamus 124
White matter 131
Total 1231
[93]Open in a new tab
The gene co-expression modules in hippocampi and substantia nigra as being
associated with PD
Using the gene expression data from the healthy human brain, iGOLD
constructed 19 concurrently co-expressed modules expressed in ten brain
regions (CCM) (Supplementary Table [94]1), and 68 modules (brain
region-specific co-expressed modules, SCM) specifically expressed in
one of the ten brain regions but not in the other nine brain regions
(Supplementary Fig. [95]1). The functional similarity of these modules
was then evaluated by determining the number of overlapping genes
between each pair of modules expressed in two brain regions. The width
of the line in Supplementary Fig. [96]1 represents the significance of
the number of overlapping genes (Chi-square test) between one pair of
modules from different brain regions compared to the modules from other
brain regions (Supplementary Table [97]2).
Among these modules, one CCM module, M3, and four SCM modules, BR7M4
(Substantia Nigra), BR9M3 (Thalamus), BR6M3 (Putamen) and BR3M2
(Hippocampus), were suggested as enriched (FDR < 0.05) with both
PD-associated genes and PD-associated SNPs (P[Fisher’s exact
test] < 0.05 and P[sLDSC] < 0.05) (Supplementary Fig. [98]2, Table
[99]1, and Supplementary Tables [100]1 and [101]3–[102]5). The
principle of selecting SCM modules includes: (1) PD-associated
genes/SNPs enriched in the module, (2) the number of PD-associated
genes in the module is larger than five, and (3) the module size
(number of co-expressed genes inside this module) is less than 3000. A
supplementary Excel file
([103]https://github.com/fanc232CO/iGOLD_pipline
/tree/main/supplementary_material/Module_enrichment_details.xlsx) is
provided to show the significance of the enrichment and the number of
PD-associated genes in the module. Accordingly, four SCM modules were
selected, which are BR7M4, BR9M3, BR6M3, and BR3M2. We further examined
the enrichment of DEGs in these five modules (Fig. [104]2A). The DEGs
were obtained from two GEO gene expression datasets^[105]44, [106]GPL96
and [107]GPL97 (Supplementary Table [108]6). The DEGs in [109]GPL96 and
[110]GPL97 were respectively termed [111]GPL96-DEGs and
[112]GPL97-DEGs. The proportions of [113]GPL96-DEGs and [114]GPL97-DEGs
in BR7M4 are significantly higher than in other modules (Fig. [115]2A
and Supplementary Fig. [116]4), indicating that the BR7M4 module might
best describe the gene expression profile characteristic of PD.
Fig. 2. Detecting and validating gene co-expression modules associated with
PD.
[117]Fig. 2
[118]Open in a new tab
A Enrichment of PD-associated genes and SNPs, and the proportion of
differentially expressed genes (DEGs) in co-expression modules that are
most likely associated with PD compared to the other modules,
specifically expressed in the same brain region. From the outer ring to
the inner, the circles sequentially represent the brain regions, the
module names, the fraction of DEGs (dark green for [119]GPL96 and light
green for [120]GPL97) in the co-expression modules, the enrichment of
PD-associated genes by the co-expression modules, the enrichment of
PD-associated SNPs by the co-expression modules tested by Chi-square
analysis, and the heritability enrichment of PD-associated SNPs tested
by sLDSC analysis. TC temporal cortex, TM thalamus, WM white matter, CC
cerebellar cortex, FC frontal cortex, HC hippocampus, MD medulla, OC
occipital cortex, PM putamen, SN substantia Nigra. B Conservation of
the BR7M4 genes in three brain regions of Japanese samples across
different Braak stages, including Braak 0, Braak I–II, Braak III–IV,
and Braak V–VI. Conservation of BR7M4 genes expressed in brain regions,
IPSC-induced dopaminergic neurons, and peripheral blood of PD patients
and healthy controls. Gene co-expression conservation of BR7M4 module
in the brain regions of hippocampus and substantia nigra, respectively.
Red dashed line—high conservation Z summary cutoff of 10. Cyan dashed
line—medium conservation Z summary cutoff of 2. EC entorhinal cortex,
FC temporal cortex, TC frontal cortex, PM putamen, LC locus coeruleus,
iPSC IDN IPSC-induced dopaminergic neurons, PB peripheral blood, SN
substantia nigra, HC hippocampus. C Interactions between BR7M4-novel
genes and known PD-associated genes. BR7M4-novel genes overlapping with
Mouse-DEGs are filled in red. Node size represents the significance of
genes in RNA-seq analysis from the ROT group and the NC group. The edge
between the two genes represents their expression correlation less than
0.85 (scored by WGCNA), and genes linked by them are highlighted as
triangles edged in black. D The gene expression profile of the NC
group, ROT group, ROT + MH group, and ROT + SP group. E The gene
expression profile was obtained by analyzing [121]GPL96 data.
The associations between BR7M4 and PD were tested using seven unrelated
publicly available brain expression datasets^[122]38,[123]45–[124]49
that together cover gene expression information across different ethnic
backgrounds, brain regions, tissues, and disease status (Supplementary
Table [125]7). As shown in Fig. [126]2B, module BR7M4 displays medium
conservation in the entorhinal cortex (EC), frontal cortex (FC), and
temporal cortex (TC) at four Braak NFT stages (0, I–II, III–IV, and
V–VI) in Japanese samples. We examined the conservation of BR7M4 in
putamen, locus coeruleus and IPSC-induced dopaminergic neurons using
Spanish samples, and found that the BR7M4 module exhibits medium
conservation in the putamen, high conservation in the locus coeruleus,
and medium conservation in IPSC-induced dopaminergic neurons (Fig.
[127]2B). When the BR7M4 module was tested in both hippocampus^[128]38
and substantia nigra samples^[129]38 from Europeans, it exhibited high
conservation in both brain regions (Fig. [130]2B). By contrast, BR7M4
showed low conservation in the peripheral blood of American samples
(Fig. [131]2B). Thus, unsurprisingly, the PD-associated functions of
the BR7M4 module appear to be expressed through brain regions (e.g.,
substantia nigra and hippocampus) and dopaminergic neurons rather than
through peripheral blood.
BR7M4 enriched with PD-associated SNPs from GCST007780
(P-value = 0.024) by LDSC analysis, and from GCST007780
(P-values = 0.041) and GCST010765 (P-values = 0.020) by Chi-square
analysis (Supplementary Fig. [132]2). Compared to other modules, BR7M4
is significantly (P[FDR-adjust] < 0.05) enriched with genes and SNPs
(P-value < 0.05) from the largest number of resources. Thus, we choose
BR7M4 for further validation.
Figure [133]2B is to shows the conversation scores of the module BR7M4
across tissues from multiple brain regions. The conservation score is
to represent the enrichment of genes expressed in specific tissues and
is estimated by modulePreservation, a function in the WGCNA R
package^[134]50. The higher score (Z[summary])means higher
conservation, and a Z[summary] higher than 10 is suggested as highly
conserved. When we performed the conservation analysis for module BR7M4
across the tissues from multiple brain regions of health controls, the
module BR7M4 has shown the highest conservation in Hippocampus (HC)
(Z[summary] = 10.53) and Substantia Nigra (SN) (Z[summary] = 33.93). In
comparison, it has shown conservation in putamen (Z[summary] = 7.98),
locus coeruleus (Z[summary] = 10.35) and IPSC-induced dopaminergic
neurons (Z[summary] = 1.85) using Spanish samples(Fig. [135]2B). The
lack of significance in the brain regions could be attributed to their
relatively lower expression of genes involved in the core pathological
processes of PD.
The module BR7M4 contained 399 genes whose interactions are shown in
Supplementary Figure [136]5. The association of BR7M4 with PD was
examined using hippocampal samples from mice since the expression of
BR7M4 is highly conserved in the hippocampus (Fig. [137]2B,
Zs[ummary] = 10.53). We performed RNA sequencing (RNA-seq) on the
hippocampi of ten mice into two groups of mice: DMSO (NC; n = 5) and
ROT (rotenone-induced group; n = 5) (Supplementary Table [138]8). The
ROT-induced C57 L/J mouse model can recapitulate many features of human
PD, including anatomical, neurochemical, behavioral, and
neuropathological features^[139]35,[140]51,[141]52. RNA-seq data
analysis identified 2195 genes (Mouse-DEGs) that were expressed
significantly [adjusted P < 0.05, absolute value of fold-change (FC)
greater than 2] differently between the NC and ROT groups. Among the
Mouse-DEGs, 50 genes were present in the BR7M4 module (termed
Mouse-DEGs-Mod), which is significantly (single-tailed binomial test
P = 0.016) more than the genes that were not expressed significantly
differently between the NC and ROT groups (termed Mouse-non-DEGs)
(Supplementary Fig. [142]5). Moreover, 39 of the Mouse-DEGs-Mod genes
were not PD-associated genes. Nevertheless, the expression levels of
these 39 genes were found to be closely correlated (WGCNA TOM
similarity > 0.15) with those of 41 PD-associated genes in BR7M4 (Fig.
[143]2C). Thus, the co-expression module BR7M4 is strongly associated
with PD.
In this study, we have used the gene expression data from different
ethnicities and tissues to evaluate the module conservation. To
evaluate the gender effects on the module conservation, we first
divided the samples into male and female groups to perform the module
conservation analysis. The result indicated that the module
conservation (Z[summary]) in the male group (905 samples) is 18.44, and
in the female group (326 samples) is 18.91. According to the widely
agreed standard that Z[summary] > 10 indicates high module
conservation, this result suggests that gender does not play a vital
role in module conservation in this study. As to the age factor,
because most of the samples are from elder PD patients, the module
conservation analysis has not been performed to check the influence of
age.
Meclofenoxate hydrochloride (MH) and sodium phenylbutyrate (SP) restore the
normal expression levels of PD-associated genes via different mechanisms
From BR7M4, we extracted DEGs for the discovery of PD candidate
therapeutics (Supplementary Table [144]9), from which two drugs, SP
(connectivity score −0.963 and ranked in top 0.05% of 6100 drugs) and
MH (connectivity score of −0.814 and ranked in top 0.2% of 6100 drugs),
were considered for further validation since they were not only
top-ranking candidates but were also able to pass through the blood
brain barrier. We have included the druglike and ADME data of the
potential candidates of Supplementary Table [145]9 in a separate
supplementary Excel file
(“[146]https://github.com/fanc232CO/iGOLD_pipline/tree/main/supplementa
ry_material/candidates_druglike_ADME.xlsx”). Both the druglike and the
ADME were predicted using the model of CMPNN (Communicative Message
Passing Neural Network, [147]https://github.com/SY575/CMPNN)^[148]53.
The CMPNN predicted 52 parameters to evaluate the drugs, as shown in
the file candidates_druglike_ADME.xlsx. The drug candidates were ranked
by their connectivity scores. From them, we selected those (absolute
values of connectivity scores higher than 0.8) ranked in the top 14 are
shown in Supplementary Table [149]9. Among them, Cyanocobalamin,
SC-58125, Dexamethasone, and Rofecoxib are well-studied PD drugs,
suggesting the reliability of our method in identifying drugs for PD.
Out of the remained drug candidates, SP and MH have been reported as
having the ability to pass through the blood-brain barrier, while
Carteolol has been explicitly with low penetrability to pass the
brain-blood barrier^[150]54. Thus, SP and MH were selected for further
validation. To assess the impact of MH or SP on PD-associated gene
expression, we performed RNA-sequencing on the hippocampi of mice from
the NC, ROT, ROT + SP, ROT + MH, SP, and MH groups (Supplementary Table
[151]8). We found 91 genes to be expressed significantly
(Bonferroni-corrected P < 0.05 and |log(FC)| > 2) differently between
the ROT group and the NC group, as well as between the ROT + SP group
and the ROT group. These genes were termed the SP-ROT set. Meanwhile,
we found 666 genes that were expressed significantly
(Bonferroni-corrected P < 0.05 and |log(FC)| > 2) differently between
the ROT group and the NC group, as well as between the ROT + MH group
and the ROT group. These genes were termed the MH-ROT set. Among them,
28 were in the BR7M4 module and displayed the same direction of
regulation as the [152]GPL96 dataset. The expression of these genes in
the ROT group, the NC group, the ROT + MH group and the ROT + SP group
are shown in Fig. [153]2D. The expression of these genes in the
[154]GPL96 dataset is shown in Fig. [155]2E. The expression profile of
these gene in controls in [156]GPL96 is similar to that of the NC
group, whereas the gene expression profile of the PD individuals in
[157]GPL96 is similar to that of the ROT group (Fig. [158]2D, [159]E).
Thus, after MH or SP treatment, gene expression in the ROT group was
restored such that it approximated the characteristics of the NC group,
suggesting a specific effect of MH or SP in remodeling the expression
pattern of PD-associated genes.
Of the genes in the MH-ROT set, 129 genes were not in the SP-ROT set,
whilst 74 genes from the SP-ROT set were not in the MH-ROT set, which
were then termed the Uni-MH-ROT set and Uni-SP-ROT set, respectively. A
STRING analysis was performed to detect the networks of protein-protein
interactions (PPIs) in the Uni-MH-ROT set and the Uni-SP-ROT set,
respectively. The PPIs of the Uni-MH-ROT genes were mainly enriched in
synapse-related functions (Supplementary Fig. [160]6A), whereas the
PPIs of the Uni-SP-ROT group were enriched in mitochondrial electron
transport and mitochondrial respiratory chain complex I assembly
functions (Supplementary Fig. [161]6B). Thus, the effect of MH on gene
transcription is potentially distinguishable from that of SP in terms
of its modulatory effect on genes with synapse-related functions in
murine hippocampus.
The potential targets of MH and SP were further examined by
DStruBTarget^[162]55 to predict those proteins that could directly bind
to MH or SP (Supplementary Material, Supplementary Table [163]10, and
Supplementary Fig. [164]7). All these proteins in Supplementary Table
[165]10 are predicted as binding with MH by the DStruBTarget model that
has been developed based on the fusion of protein-drug interaction and
ligand similarity methods. DStruBTarget indicated that the top ten
predicted proteins binding to MH were enriched in neuroactive
ligand-receptor interactions and neurotransmitter receptor activity
functions (P = 1.3 × 10^−7), whereas the top 10 DStruBTarget predicted
proteins binding to SP were enriched in inflammation-related functions
(Supplementary Table [166]11). Thus, MH and SP may bind to different
targets for restoring the normal expression levels of PD-associated
genes in the hippocampi of mice. Among the predicted binding targets,
DRD4, 5-HT1A, 5-HT2A, Sigma1 (σ1), PPARG, CNR1 and CNR2 have been
reported to be PD associated by previous studies^[167]56–[168]62. The
target proteins of MH have not been reported anywhere, and require
further experimental validation. The predicted MH-protein interactions,
if validated, may at least partially underlie the protective effect of
MH in treating PD.
Both SP and MH protect neurons against ROT-induced neurodegeneration
The neuronal nuclear protein (NeuN) is often used as a positive marker
for the functional state of postmitotic neurons. Thus, the
NeuN-positive rate of neurons is usually used to assess
neurodegeneration^[169]63–[170]65. Here, immunohistochemical (IHC)
staining with anti-NeuN was performed on the dentate gyrus (DG),
dentate gyrus2 (DG2), and cornu ammonis (CA1) of the hippocampus from
six groups of mice (NC, ROT, SP, ROT + SP, MH, and ROT + MH), with four
mice in each group. As shown in Fig. [171]3A, B, the average relative
numbers of NeuN-negative cells [quantified by ImageJ^[172]66] (29.5%)
increased by 27.9% in the ROT group as compared to those of the NC
group (1.6%) (P = 2.7 × 10^−3). In the SP + ROT and MH + ROT groups,
the average relative numbers of NeuN-negative cells (3.1% for SP
treatment and 2.4% for MH treatment) were respectively reduced by 26.4%
and 27.1% (P = 1.9 × 10^−3 and P = 1.3 × 10^−^2, respectively) compared
to the ROT group (Fig. [173]3A, [174]B). The average relative numbers
of NeuN-negative cells in the DG2 structure of the hippocampus in the
ROT group (30.6%) increased by 26.7% compared to the NC group (3.9%)
(P = 1.5 × 10^−^2). In the SP + ROT and MH + ROT groups, the average
relative number (5.2% and 4.6%, respectively) of NeuN-negative cells in
DG2 was reduced by 25.4% (P = 2.3 × 10^−2) and 26.0% (P = 3.6 × 10^−2),
respectively, compared to the ROT group (Fig. [175]3A, [176]C). In the
CA1 substructure of the hippocampus, the average relative number
(54.7%) of NeuN-negative cells in the ROT group increased by 50.9%
compared to the NC group (3.9%) with P = 1.7 × 10^−2. The average
relative numbers of NeuN-negative cells of ROT + SP (3.0%) and ROT + MH
(3.1%) in the CA1 substructure of the hippocampus were reduced by 51.7%
and 51.7%, respectively (P = 1.8 × 10^−2 and P = 3.5 × 10^−2) compared
with the ROT group (Fig. [177]3A, [178]D). Thus, MH and SP treatments
reduce the number of NeuN-negative cells in different parts of the
hippocampus.
Fig. 3. MH and SP can protect neurons against PD-related neurodegeneration.
[179]Fig. 3
[180]Open in a new tab
A IHC representation of NeuN in the DG, DG2, and CA1. Magnification
20×. Scale bar = 10 μm. B The relative number of NeuN-negative cells in
the DG structure of the hippocampus in different groups. C The relative
number of NeuN-negative cells in the DG2 structure of the hippocampus
in different groups. D The relative number of NeuN-negative cells in
the CA1 structure of the hippocampus in different groups. E IHC
representation of TH positive cells in the substantia nigra striatum
region of the experimental mice (N ≥ 3 mice/group). TH-positive cells
were reduced in the ROT-induced mice, and increased in the MH-treated
mice as compared with the NC. Magnification 10×. Scale bar = 50 μm. F
The TH-positive cells in the ROT group were significantly lower than
those in the NC group. SP treatment and MH treatment can increase the
TH-positive cells in the ROT-induced group. G GFAP in the hippocampus
of each group. The number of GFAP-labeled astrocytes was significantly
increased in the ROT group as compared to the NC group. The number of
GFAP-labeled astrocytes was significantly reduced in the
ROT + SP-treated mice and ROT + MH-treated mice as compared to the ROT
group. Magnification 10×. Scale bar = 50 μm. H The relative number of
GFAP-labeled astrocytes in the hippocampus structure in different
groups. I Fluorescence image of glucose metabolism capacity shown by
PET of the mouse brain. J The average change of SUVs in each group. K
The maximum change of SUVs in each group. SP and MH prevented
ROT-induced neurodegeneration. [^18F]-FDG ^18F-fluorodeoxyglucose,
α-SYN α-synuclein group, eGFP enhanced green fluorescent protein, PET
positron emission tomography, A anterior, P posterior, L left, R right.
^*P < 0.05, ^**P < 0.01, ^***P < 0.001.
IHC analyses were performed to examine the presence of tyrosine
hydroxylase (TH) positive cells in the substantia nigra tissues of the
mice, as TH is generally considered as an indicator of dopamine
production in neurons. As shown in Fig. [181]3E, [182]F, the number of
TH-positive cells in the ROT group (19.50) was significantly lower than
in the NC group (52.80) (P = 3.1
[MATH: ×10 :MATH]
^−3). Similarly, in the ROT + MH group, the number of TH positive cells
was 56.83, nearly 3 times higher than in the ROT group (P = 4.0
[MATH: ×10 :MATH]
^−4) (Fig. [183]3F). In the MH group, the number of TH positive cells
was 64.00, which was nearly 2-fold higher than in the ROT + SP group
(36.83) (P = 3.6
[MATH: ×10 :MATH]
^−2). The number of TH-positive cells in the MH group was comparable to
the NC group, significantly higher than in the ROT group (P = 1.0
[MATH: ×10 :MATH]
^−4). Although SP treatment increased the number of TH positive cells,
it did not significantly improve the damage to the substantia nigra. In
contrast, MH treatment improved the dopamine-producing capacity of
neurons.
Subsequently, IHC staining with anti-GFAP (glial fibrillary acidic
protein) was also performed to visualize the intermediate filament (IF)
protein expressed in numerous cell types of the central nervous system
(CNS) including astrocytes and ependymal cells, with the number of
GFAP-positive (GFAP^+) cells serving as an indicator for the activation
of the neuroinflammatory pathway in the murine hippocampus (Fig.
[184]3G). As shown in Fig. [185]3G, the proportion of GFAP^+ cells was
markedly increased in the hippocampus of the ROT group (50.17 ± 9.88)
compared to that in the NC group (25.50 ± 6.80) (P = 3.83
[MATH: ×10 :MATH]
^−7). By contrast, the number of GFAP^+ cells in the ROT + SP and
ROT + MH groups was significantly lower than that of the ROT group [SP:
29.08 ± 4.81 (P = 1.16
[MATH: ×10 :MATH]
^−6) and MH: 26.89 ± 8.87 (P = 2.23
[MATH: ×10 :MATH]
^−5)]. However, little or no difference was observed in terms of the
number of GFAP^+ cells between the ROT + SP and ROT + MH groups (Fig.
[186]3H). Similar results were obtained for the interleukin 1 complex
(IL-1), another proinflammatory cytokine, and GFAP, in the murine
striatum (Supplementary Fig. [187]8). Taken together, it is clear that
both MH and SP repress ROT-induced neuroinflammation in the
hippocampus, suggesting an anti-inflammatory effect of these drugs.
MH and SP both upregulate glucose metabolism in the brains of ROT-induced PD
mice
To measure glucose metabolism in mouse brains, mice from both the NC
and ROT groups (Supplementary Table [188]8) were subjected to
neuro-imaging through [^18F]-fluorodeoxyglucose positron emission
tomography (^18F-FDG PET) (Supplementary Materials). The
cross-sectional small animal PET images of mice from the six groups are
presented in Fig. [189]3I and are quantified in Fig. [190]3J. In Fig.
[191]3J, the average and maximum standardized uptake values (SUV) of
the ROT group are 1.19 and 1.30 which were decreased by 0.18 (P = 2.3
[MATH: ×10 :MATH]
^−2) and 0.18 (P = 3.2
[MATH: ×10 :MATH]
^−2), respectively compared to the NC group (average SUV = 1.37 and
maximum SUV = 1.48) (Fig. [192]3K), indicating higher intensity of
[^18F]-FDG uptake in the ROT-induced group than in the control group
(NC). These data clearly indicate that ROT treatment markedly
down-regulated glucose metabolism of the murine neurons. For the mice
in the SP + ROT group, the average SUV was 1.48 (Fig. [193]3J), whilst
the maximum SUV value was 1.62 (Fig. [194]3K), which were decreased by
0.28 (24.4%, P = 2.67
[MATH: ×10 :MATH]
^−6) and 0.32 (24.6%, P = 1.67
[MATH: ×10 :MATH]
^−6), compared with the ROT group. For the mice in the ROT + MH group,
the average SUV value was 1.48, with a maximum SUV value of 1.54, 0.32
(24.6%) and 0.24 (18.5%) higher than for the ROT group (P = 5.04
[MATH: ×10 :MATH]
^−5 and (P = 5.29
[MATH: ×10 :MATH]
^−4), respectively (Fig. [195]3J, [196]K). Thus, treatment with either
SP or MH appears to significantly promote glucose metabolism in
neuronal cells in ROT-induced PD mice.
Validating the action of SP and MH in preventing ROT-induced cell damage and
preserving neuronal cell morphology
We then tested the potential effects of SP and MH on the survival and
morphology of cells in primary neuron culture. The proportions of
viable cells, as well as the relative volume of cell bodies, were
calculated by ImageJ^[197]66. The volumes of the neuronal cell bodies
appeared to shrink to 25.9% of the NC in the presence of ROT (Fig.
[198]4Aa, b). After SP and MH treatment, the volumes of the neuronal
cells increased by 4.78 (P = 7.9
[MATH: ×10 :MATH]
^−3) and 6.59 (P = 5.1
[MATH: ×10 :MATH]
^−3), respectively, compared to the ROT group (Fig. [199]4Ab). The
average number of surviving cells in the ROT-induced group (9.14) was
significantly lower than in the NC group (154.43) (P = 6.2
[MATH: ×10 :MATH]
^−4) (Fig. [200]4B). Remarkably, the treatment of the ROT-induced cells
with either SP or MH increased cell survival from 9.14 to 63.71 or
47.43, respectively (Fig. [201]4B), strongly indicating that SP and MH
has the potential to protect neurons from ROT-induced damage.
Fig. 4. SP and MH prevent ROT-induced neuronal cell damage and protect
neuronal cell morphology.
[202]Fig. 4
[203]Open in a new tab
A a: Representative scans from immunocytochemical preparations acquired
with 4× and 100× objective lenses. SP protects NeuN+ and mouse neurons
against the deleterious effects of ROT with an ensuing increase in NeuN
and tubulin expression in PD-containing neurons. Insets correspond to
high magnification images. Data are representative of 20–40 neurons per
group obtained from seven independent cultures. b: The relative volume
of cells in the ROT group was significantly smaller than that in the
other three groups. B The number of tubulin+ cells with cell bodies and
synapses in more than three visual fields of each group. SP and MH
effectively prevent cell death caused by ROT-induced cytotoxicity and
increase the number of viable cells in each field. C The relative
expression of fluorescence of the ROT group was significantly lower
than that of the NC group. The relative expression of fluorescence
increased after SP and MH treatments (ROT + SP and ROT + MH groups). D
The length of the nerve synapse of the ROT group was significantly
lower than that of the NC group, and the length of the nerve synapses
increased after SP and MH treatments (ROT + SP and ROT + MH groups).
Scale bar: 20 μm in 4× and 500 μm in 100×. On average, 25–30 neurons
per condition were tested from three independent cultures.
^***P < 0.001, ^**P < 0.01 and ^*P < 0.05.
Furthermore, the relative expression of NeuN fluorescence
(fluorescence/area) in the ROT-induced group was 0.007, significantly
lower than that in the NC group (P = 2.0
[MATH: ×10 :MATH]
^−2), which increased to 0.013 and 0.018 after SP and MH treatment,
respectively, significantly higher than in the ROT-induced group
(P = 1.8
[MATH: ×10 :MATH]
^−2 and 1.2
[MATH: ×10 :MATH]
^−2, respectively) (Fig. [204]4C).
Moreover, we found that the average lengths of the nerve synapses in
the ROT-induced group were approximately 12.75 μm, significantly
shorter than that in the NC group (~44.98 μm) (P = 1.3
[MATH: ×10 :MATH]
^−2). After SP and MH treatment, the lengths of the nerve synapses
increased to 20.19μm (P = 2.6
[MATH: ×10 :MATH]
^−2) and 37.06 μm (P = 2.3
[MATH: ×10 :MATH]
^−2), respectively, significantly higher than for the ROT-induced
groups. Interestingly, the average length of the synapses in neurons
treated with MH was longer than that treated with SP (P = 2.1
[MATH: ×10 :MATH]
^−2) (Fig. [205]4D).
MH and SP improved motor function, anhedonia, and the depression-like
behaviors of PD mice
Both MH and SP significantly improved motor function, reduced
anhedonia, and alleviated depression-like behaviors in PD mice. To
further evaluate the potential of MH and SP as drug candidates for PD
treatment, we also examined their effects on PD-like behavior traits in
a mouse model induced by ROT, a known cause of motor function
abnormalities. The mice from each of the six groups mentioned above
underwent a motor function behavior test (for a detailed description of
the test, see Supplementary Material). As shown in Fig. [206]5A, the
footprints of the NC group (red) were straight, whereas the footprints
of the ROT group (orange) were irregular, indicating an unstable motor
function. The motor function behavior of the SP- (yellow) or MH-treated
(cyan) groups was also compared to that of the NC or ROT group. In the
ROT + SP group (green), the footprints were nearly straight, although
the walking directions of the front and rear feet on the same side were
not exactly parallel to each other. By comparison, for the mice in the
ROT + MH group (blue), the front and rear feet on the same side were
precisely in parallel, with the lines of the footprints being
straighter than those of the mice in the ROT + SP group (Fig. [207]5A).
Fig. 5. MH can ameliorate PD-related behaviors.
[208]Fig. 5
[209]Open in a new tab
A The influence of SP and MH on the behavior of the PD animal model
constructed by ROT (footprinting test). PD behavioral changes were
mainly disclosed as motor function changes, whilst the repetition rate
of footprints decreased. B Stride length of mice. The stride length of
PD mice treated with ROT + SP and ROT + MH was significantly less than
that of PD mice. C Stride width of mice. The stride width of mice
treated with ROT + SP and ROT + MH was significantly less than that of
PD mice, respectively. D Angular change per step in each group. E
Effects of SP and MH pretreatment on ROT-induced anhedonic behavior
evaluated by means of the sucrose preference test (SPT). F Effects of
SP and MH pretreatment on ROT-induced depressive-like behavior of mice
evaluated by the forced swim test (FST). G Effects of SP and MH
pretreatment on ROT-induced depressive-like behavior of mice evaluated
by the tail suspension experiment (TSE). H Results of the open field
test for each group. I Statistics on the number of center crossings. J
Activity levels of mice in each group. K Total distance traveled in the
open field test for each group. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001.
To quantitatively assess the motor function behavior of the mice, we
measured the stride lengths. As shown in Fig. [210]5B, the stride
lengths of the NC, SP, and MH groups were 5.00 mm, 5.18 mm, and
6.46 mm, respectively, with no statistically significant difference
evident between them (P å 0.05). However, the average stride length of
the ROT group was 12.67 mm, 2.5 times longer than that of the mice in
the NC group (P = 2.0
[MATH: ×10 :MATH]
^−3). SP treatment served to completely reverse the ROT-induced
increase in stride length, as the average stride length of the ROT + SP
group was 5.22 mm, almost 2.5 times shorter than that of the ROT group
(P = 1.0
[MATH: ×10 :MATH]
^−4). Similarly, in the ROT + MH group, the average stride length was
7.73 mm, significantly smaller than that of the ROT group (P = 1.0
[MATH: ×10 :MATH]
^−4) (Fig. [211]5B). Turning to the stride width, those of the NC, SP
and MH groups were 3.49 mm, 2.55 mm and 4.06 mm, again not
statistically different from each other (P å 0.05) (Fig. [212]5C).
Whilst the average stride width of the ROT group was 6.35 mm,
significantly greater than that of the NC group (P = 1.0
[MATH: ×10 :MATH]
^−3), the average stride widths measured for the SP + ROT and MH + ROT
groups were 4.59 mm (P = 1.4 × 10^−3) and 3.97 mm (P = 1.7
[MATH: ×10 :MATH]
^−2), respectively, suggesting a narrowing effect of SP and MH on the
stride width of the animals (Fig. [213]5C). The ROT group exhibited an
angular change of approximately 3.24° per step, while the NC group
showed an angular change of 0.88° per step (P < 0.1
[MATH: ×10 :MATH]
^−4). The SP group demonstrated an angular change of 1.03° per step
(P < 0.1
[MATH: ×10 :MATH]
^−4), and the ROT + SP group exhibited an angular change of 1.56° per
step (P < 0.1
[MATH: ×10 :MATH]
^-4). The MH group displayed an angular change of 0.97° per step
(P < 0.1
[MATH: ×10 :MATH]
^−4), while the ROT + MH group exhibited an angular change of 1.16° per
step (P < 0.1
[MATH: ×10 :MATH]
^−4) (Fig. [214]5D). Taken together, these data strongly indicate that
the administration of SP or MH can efficiently restore the ROT-induced
changes in mouse motor function behavior, suggesting that these drugs
hold promise for alleviating the motor function abnormality typically
observed in PD patients.
Clinically, anhedonia (lack of interest) and depression are commonly
noted in PD patients. Whilst a reduction in the sucrose preference
ratio in an experimental group compared to controls is held to be
indicative of anhedonia in animals^[215]67, the forced swimming test
(FST) and the tail suspension test (TST) have been devised to assay
depression-like behavior in the preclinical mouse model for
PD^[216]68,[217]69. The sucrose preference test was performed to
measure the percentage of sugar water intake in 24 h for the mouse
groups (Supplementary Material). The sucrose preference of the ROT
group (65.4%) was approximately 25% lower than that of the NC group
(81.4%) (P = 2.8
[MATH: ×10 :MATH]
^−2). Interestingly, the sucrose preference of the ROT + MH group
(77.5%) increased by approximately 20% compared to the ROT group
(P = 2.8
[MATH: ×10 :MATH]
^−2), whilst the sucrose preference of the ROT + SP group increased to
72.4%, but this increase was subtler and did not attain statistical
significance (P = 1.2
[MATH: ×10 :MATH]
^−1) (Fig. [218]5D).
In the forced swimming test (FST), the “immobility” time indicates the
state the experimental animals eventually adopt to avoid the stressor
(water, in this case), which could be quantified to indicate
depression-like behavior of mice^[219]68. As shown in Fig. [220]5E, the
immobility time for the ROT group (13.86 s) was longer than for the NC
group (7.38 s) (P = 8.0
[MATH: ×10 :MATH]
^−3). The immobility time of the ROT + MH group was significantly lower
(9.07 s) than for the ROT group (P = 1.5
[MATH: ×10 :MATH]
^−2). However, the average immobility time (11.42 s) of the ROT + SP
group was comparable to that of the mice that received only ROT (Fig.
[221]5E).
The tail suspension experiment (TSE) was quantified using a ratio of
static time to moving time. As shown in Fig. [222]5F, the ratio of
static time to moving time in the ROT group (84.5%) was approximately
20% greater than in the NC group (63.0%) (P = 3.3
[MATH: ×10 :MATH]
^−2). Remarkably, the ratio of static time to moving time in the
ROT + MH group (70.0%) was significantly lower than in the ROT group
(P = 3.2
[MATH: ×10 :MATH]
^−^2), whereas the average ratio of static time to moving time (80.5%)
in the ROT + SP group scarcely changed from that of the ROT group (Fig.
[223]5F).
We further employed the open field test to validate the preventive
effects of MH and SP on PD (Fig. [224]5H). The ROT group exhibited a
significantly reduced number of center crossings compared to the NC
group (51.00 vs 84.80, P = 2.7
[MATH: ×10 :MATH]
^−2). The number of center crossings in the SP group was similar to
that of the NC group (100.00 vs 84.80, P = 7.0
[MATH: ×10 :MATH]
^−1), while the ROT + SP group showed a marked increase in center
crossings compared to the ROT group (91.25 vs 51.00, P = 1.1
[MATH: ×10 :MATH]
^−2). Similarly, the number of center crossings in the MH group was
comparable to that of the NC group (107.60 vs 84.80, P = 2.4
[MATH: ×10 :MATH]
^−1), and the ROT + MH group showed a significant increase compared to
the ROT group (90.00 vs 51.00, P = 8.4
[MATH: ×10 :MATH]
^−3) (Fig. [225]5I).
The activity level in the ROT group was significantly lower than that
of the NC group (9.77 vs 12.75, P = 3.3
[MATH: ×10 :MATH]
^−2), whereas the activity level in the SP group was similar to the NC
group (15.36 vs 12.75, P = 1.1
[MATH: ×10 :MATH]
^−1). The ROT + SP group demonstrated a significant increase in
activity compared to the ROT group (14.10 vs 9.77, P = 2.0
[MATH: ×10 :MATH]
^−3). The MH group displayed higher activity than the NC group (16.94
vs 12.75, P = 1.5
[MATH: ×10 :MATH]
^−3), and the ROT + MH group showed a significant increase compared to
the ROT group (12.93 vs 9.77, P = 2.1 × 10^−2) (Fig. [226]5J).
In terms of movement distance of mice, those in the ROT group showed a
significant decrease compared to the NC group (2333 mm vs 3472 mm,
P = 4.7 × 10^−2). The total movement distance of mice in the SP group
was similar to that of the NC group (4291 mm vs 3472 mm, P = 3.1
[MATH: ×10 :MATH]
^−1), while the ROT + SP group exhibited a significant increase
compared to the ROT group (3671 mm vs 2333 mm, P = 2.3 × 10^−2). The
movement distance of mice in MH group was similar to that of the NC
group (4294 mm vs 3472 mm, P = 2.5
[MATH: ×10 :MATH]
^−1), and the ROT + MH group showed a significant improvement compared
to the ROT group (3675 mm vs 2333 mm, P = 1.4
[MATH: ×10 :MATH]
^−2) (Fig. [227]5K), suggesting effectiveness of the MH in alleviating
the movement symptoms of PD mice.
Taken together, we conclude that MH can significantly alleviate both
anhedonia and depression-like behavior in the ROT-induced mouse model
for PD, with considerably greater efficiency than SP.
Exploring the mechanisms by which MH protects mitochondrial function,
postsynaptic density, and synaptic functions in neuronal cultures and mouse
brain
Having found that the impact of MH on the behavior of PD mice was
significantly greater than that of SP (Fig. [228]5A–F), we next
directed our efforts toward elucidating the underlying mechanisms by
which MH treatment might be efficacious. We therefore sought to
ascertain the impact of MH on mitochondrial homeostasis and function in
the hippocampus of the ROT-induced PD mouse model. The mitochondria
presented themselves as continuous filaments in the hippocampal neurons
of the NC group, whilst they showed more obvious fragmentation after
treatment with ROT (Fig. [229]6Ac). Additionally, ROT treatment
appeared to lead to a concentration of clumped mitochondria in the cell
bodies of the neurons that were broken and lumpy in shape (Fig.
[230]6Aa–c). Remarkably, compared to the disrupted mitochondria in the
neurons from the ROT group, the ROT + MH group appeared to have
significantly restored the mitochondrial morphology in the neurons,
with most of them being maintained in the soma, and continuous in shape
(Fig. [231]6Ae, f). Interestingly, when the neuronal cells were treated
with MH alone, mitochondria were observed with many more synapses in
the neurites than in the NC group (Fig. [232]6Ad).
Fig. 6. MH can reduce mitochondrial damage in neurons.
[233]Fig. 6
[234]Open in a new tab
A Protective effects of MH on mitochondrial morphology in a ROT-induced
PD cell model. a: Mitochondrial morphology of control neurons. b:
Mitochondrial morphology in a ROT-induced PD cell model with 200 μM
ROT. c: Mitochondrial morphology in a ROT-induced PD cell model with
400 μM ROT. d: Mitochondrial morphology of neuronal cells treated with
MH alone. e: Mitochondrial morphology of the PD neuron model induced by
200 μM ROT and treated with MH. f: Mitochondrial morphology of the PD
neuron model induced by 400 μM ROT and treated with MH. B–E MH protects
mitochondrial function in neurons. Effects of MH pretreatment on
ROT-induced oxido-nitrosative stress assessed by B malondialdehyde
(MDA) level, C reduced glutathione (GSH) level, and D ATP levels of the
hippocampus of the mice in each group. E–H According to the results of
mitochondrial dysfunction, we performed western blotting for
mitochondrial-related proteins. The western blot (E) was quantified for
α-syn (F) and NeuN (G) in the murine hippocampi. The bands were
quantified using Sigma Gel software, and the differences are
represented by a histogram. GAPDH was used as a loading control. The
results showed that both MH and SP could restore the abnormal
expression of α-syn (F), NeuN (G), and VDAC (H). The western blot (I)
was quantified for TH (J) and HSP60 (K) in the substantia nigra. The
bands were quantified using Sigma Gel software, and the differences are
represented by a histogram. GAPDH was used as a loading control. The
results showed that both MH and SP could restore the abnormal
expression of TH (J) and HSP60 (K). All values are expressed as
mean ± SEM. All experiments were repeated more than three times
individually. Scale bar = 500 μm in 100×. ^*P < 0.05, ^**P < 0.01, and
^***P < 0.001.
Consistent with a protective effect of MH on the morphology of neuronal
mitochondria, MH treatment also appeared to improve mitochondrial
function of the hippocampi in the mice of the ROT + MH group compared
to those in the ROT group (Fig. [235]6B–D). The most commonly used
markers for mitochondrial functions include malondialdehyde (MDA) and
glutathione (GSH), and ATP. As shown in Fig. [236]6B, ROT increased the
MDA level from 1.00 to 1.22 in the hippocampi (P = 3.0
[MATH: ×10 :MATH]
^−3) of mice, but such a trend was efficiently reversed by MH
treatment, as the MDA level of the ROT + MH group decreased to 0.91
(25.4%) (P = 6.1
[MATH: ×10 :MATH]
^−3) (Fig. [237]6B). Meanwhile, ROT administration significantly
decreased the GSH level in the hippocampus (P = 2.3
[MATH: ×10 :MATH]
^−3) compared to NC, whereas in the MH + ROT group, the GSH level
(P = 3.8
[MATH: ×10 :MATH]
^−2) was elevated to 152.27, rather higher than the 100.77 in the ROT
groups (Fig. [238]6C). Moreover, in the ROT-induced mice, the ATP level
was found to have decreased from 124.02 to 75.61 in the hippocampi
(P = 5.8
[MATH: ×10 :MATH]
^−3) compared to the NC group. In the ROT + MH group, we observed a
significant elevation of the ATP level from 75.61 to 103.26 (P = 2.2
[MATH: ×10 :MATH]
^−2) compared to the ROT group (Fig. [239]6D).
To better characterize the protective effect of MH in the brain, we
examined the impact of MH on the homeostasis of mitochondrial marker
proteins (Fig. [240]6E). As shown in Fig. [241]6F, the level of
α-synuclein exhibited by mice in the ROT group increased from 1.06 to
1.35 (P = 3.5
[MATH: ×10 :MATH]
^−3) as compared to the NC group, whilst expression of α-synuclein
decreased from 1.35 to 1.00 in the ROT + MH group (P = 4.0
[MATH: ×10 :MATH]
^4) compared to the ROT group. The NeuN level of mice in the ROT group
decreased from 0.99 to 0.50 (P = 2.5
[MATH: ×10 :MATH]
^−2) as compared to the NC group, whereas the ROT + MH groups displayed
significant increases (from 0.50 to 0.82) in NeuN expression (P = 2.5
[MATH: ×10 :MATH]
^−2) as compared to the ROT group (Fig. [242]6G). The VDAC1 level in
the ROT group decreased from 1.49 to 1.06 (P = 1.4
[MATH: ×10 :MATH]
^−2) as compared to the NC group, whereas the ROT + MH groups displayed
significant increases (from 1.06 to 1.26) in VDAC expression (P = 4.1
[MATH: ×10 :MATH]
^−2) as compared to the ROT group (Fig. [243]6H).
We further utilized primary cells from the substantia nigra (SN) to
verify the effects of SP and MH on PD. We found that ROT significantly
decreased the expression of TH (P = 1.0 × 10^−4) and HSP60 (P = 1.8
[MATH: ×10 :MATH]
^−3) in primary neurons from the SN (Fig. [244]6I), indicating that ROT
impairs the ability of SN neurons to synthesize TH and reduces
mitochondrial content within the cells. SP effectively prevented the
ROT-induced reduction in SN neuronal TH synthesis (P = 2.9
[MATH: ×10 :MATH]
^−2), and MH similarly prevented this reduction (P = 2.0
[MATH: ×10 :MATH]
^−4) (Fig. [245]6J). Furthermore, SP effectively prevented the
ROT-induced decrease in HSP60 in SN neurons (P = 4.1
[MATH: ×10 :MATH]
^−3), and MH also successfully prevented this reduction (P = 9.2
[MATH: ×10 :MATH]
^−3) (Fig. [246]6K).
To improve our understanding of the mechanism(s) whereby MH improves
mitochondrial function in murine hippocampi and neurons, we used
qRT-PCR to test a set of genes (Ndufa12, Cox6b1, Atp5k, Src, Ndufb10,
and Dlgap3) in the Uni-MH-ROT set that are known to be involved in the
pathways pertaining to mitochondrial function and postsynaptic density
(Supplementary Fig. [247]7A). As shown in Fig. [248]7A–D, the levels of
Ndufa12, Cox6b1, Atp5k, and Src expression were restored to normal
after treatment with MH compared to the ROT group.
Fig. 7. MH regulates genes involved in mitochondrial function, postsynaptic
density, and mitochondrial metabolites in a PD mouse model.
[249]Fig. 7
[250]Open in a new tab
MH can prevent the dysregulation of lipid oxidation-related genes in
the brains of mice treated by ROT. Effects of MH on regulating
mitochondria-related genes were detected by qRT-PCR in the hippocampus
of mice, including A Ndufa12, B Cox6b1, C Atp5k, and D Src. The
expression values are given as mean ± SEM. The experimental data were
taken from more than three independent experiments. MH can prevent the
dysregulation of synapse-related genes in the brains of mice treated by
ROT. Effects of MH on regulating synapse-related genes were detected by
qRT-PCR in mouse hippocampus, including E Chrna4, F Syt11, G Cdh8, and
H Sncg. Expression values are expressed as mean ± SEM. The experimental
data were taken from more than three independent experiments.
^*P < 0.05, ^**P < 0.01, and ^***P < 0.001. The effect of MH on
mitochondrial metabolites of ROT-induced PD primary neuron model. I
OPLS VIP (variable influence on projection for the orthogonal
projections to latent structures model) and Student’s t-test P-values
of 22 metabolites whose VIP was higher than 1 and t-test P-values lower
than 0.1. ^*P < 0.05, ^**P < 0.01; ^***P < 0.001. J KEGG pathway
enrichment analysis on 22 metabolites (OPLS VIP > 1 and t-test P < 0.1)
combined with 10 genes (Chrna4, Syt11, Cdh8, Sncg, Ndufa12, Cox6b1,
Atp5k, Src, Ndufb10, and Dlgap3) that are indicated in this study as
expressed significantly differently in hippocampal regions of the ROT
and MH + ROT groups. Compound 1:
2-{(3S)-1-[4-(trifluoromethyl)benzyl]-3-pyrrolidinyl}-1,3-benzoxazole.
Compound 2: 2-heptyl-4-hydroxyquinoline-N-oxide. Compound 3:
5,5-dimethyl-2-{[(2-phenylacetyl)amino]methyl}-1,3-thiazolane-4-carboxy
lic acid. Compound 4: trans-cinnamoyl beta-D-glucoside. Compound 5:
6,9-dioxo-11R,15S-dihydroxy-13E-prostenoic acid. Compound 6:
17beta-hydroxy-5beta-androstan-3-one. NAFLD non-alcoholic fatty liver
disease, RE signaling retrograde endocannabinoid signaling, IMR of TRP
channels inflammatory mediator regulation of TRP channels, SH steroid
hormone, P.R.T biosynthesis phenylalanine, tyrosine and tryptophan
biosynthesis.
When analyzed the RNA-seq data from the brain tissue of mice treated
with ROT and the mice treated with both MH and ROT by GSEA analysis, we
found that genes down-regulated in brain tissue of ROT + MH mice are
significantly enriched with genes annotated as “regulation of
postsynaptic neurotransmitter receptor activity” in Gene Ontology
database (GO0098962), with P-value as 0.0016^** and adjusted P-value as
0.0145^* comparing to the brain tissue of mice treated with ROT
(Supplementary Fig. [251]9). Among the 21 genes annotated with
GO0098962, the differential expression of Src had been validated by
RT-PCR assay in mouse brain sample, as shown in Fig. [252]7D. The genes
related to neurotransmitter regulation are collected from the Gene
Ontology database (Supplementary Table [253]12). The detailed methods
for the GSEA analysis are shown in the Supplementary Material.
To better understand how MH might impact synaptic function in the PD
mouse model, qRT-PCR was performed to assess the drug’s effect on the
expression of several genes (Chrna4, Syt11, Cdh8, and Sncg) that are
known to play a role in synaptic pathways^[254]70–[255]74 in the NC,
ROT, MH or MH + ROT groups. As shown in Fig. [256]7E–H, the expression
levels of Chrna4, Syt11, and Cdh8 were significantly reversed in the
ROT + MH group as compared to the ROT group (more detailed results are
given in Supplementary Material and Supplementary Fig. [257]10).
Identification of a metabolic signature in mitochondria linked to
aminoacyl-tRNA and phenylalanine, tyrosine, and tryptophan biosynthesis in an
MH-treated PD neuronal cell model
To study the metabolic alterations in mitochondria after MH treatment
of ROT-induced PD neuronal cells, a global metabolomic analysis was
performed in the mitochondria of ROT-induced PD neurons, as well as in
ROT + MH-treated neuronal cells. Interestingly, eight metabolites
measured in whole neuronal cells were significantly (P < 0.05) altered
in both ROT-induced PD neuronal cells and ROT + MH-treated neuronal
cells, and with variable influence on projection (VIP) more than 1.0
calculated by the Orthogonal Projections to Latent Structures (OPLS)
method (Fig. [258]7I and Supplementary Table [259]13). Among them, the
compound of
2-{(3S)-1-[4-(Trifluoromethyl)benzyl]-3-pyrrolidinyl}-1,3-benzoxazole
was newly collected in the PubChem database in May 2021, whilst the
other seven metabolites have been reported as being related to PD
therapeutic or neuroprotective effects^[260]75–[261]87.
We performed KEGG analysis using MetaboAnalytic^[262]88,[263]89 by
combining the 22 metabolites (OPLS VIP > 1 and P < 0.1) and 10 genes
(Chrna4, Syt11, Cdh8, Sncg, Ndufa12, Cox6b1, Atp5k, Src, Ndufb10, and
Dlgap3) involved in mitochondrial function or synaptic function, which
were expressed significantly differently between the ROT and ROT + MH
groups in murine hippocampus. As shown in Fig. [264]7J, these
metabolites and genes are significantly (P < 0.05) enriched in seven
typical KEGG pathways including mitochondrial function pathways
(thermogenesis and oxidative phosphorylation), brain disease pathways
(PD, Alzheimer’s disease and Huntington disease), pathways closely
related to PD mechanisms such as NAFLD^[265]90, and the prolactin
signaling pathway^[266]91. Therefore, mechanistically, MH may protect
neurons from ROT-induced damage by modulating the function of neuronal
mitochondria with an impact on mitochondria-linked metabolic pathways.
KEGG pathways with 8 out of 22 metabolites significantly different
between ROT+/− MH groups, removed genes of Nfugb10 and Dlgap3 that were
not significantly different between groups, were shown in Supplementary
Fig. [267]11.
The pharmacological mechanism of MH in the treatment of PD
As shown in Supplementary Table [268]10, ten proteins are predicted as
the most possible drug targets binding with MH by DStruBTarget^[269]55,
among which DRD4, 5-HT1A, 5-HT2A, σ1, PPARG, CNR1, and CNR2 are known
PD-related proteins, suggesting the potential molecular mechanisms of
MH in influencing PD. We additionally identified the proteins that are
known to bind with drugs in a similar structure (Tanimoto score > 0.4)
as MH. These proteins are THRα, Beta3, and Alpha1D. The interactions of
these proteins and MH were examined by four assays. Among the assays
used in this study, TR-FRET Thyroid Receptor alpha Coactivator Assay
(Invitrogen, Cat PV4587) is used for detecting the interactions between
MH and THRα^[270]92, TR-FRET PPARγ Competitive Binding Kit (Invitrogen,
Cat PV4894) is used to evaluate the interactions between MH and
PPARγ^[271]93, and cAMP Detection Kit (Cisbio Cat
62AM4PEJ)^[272]94,[273]95 is used for identifying the interactions
between MH and Beta3 (Supplementary Table [274]15). The interactions of
MH with σ1, DRD4, CNR2, 5-HT1A, and Alpha1D are evaluated by Filtration
Binding Assay (Supplementary Table [275]14). In Supplementary Fig.
[276]12, we provided the details on the inhibition rates of the
reference drugs on these predicted target proteins. All the results and
experimental assays are shown in Supplementary Table [277]14. As shown
in Supplementary Table [278]14, the experiments indicated that the MH
(1 μM) significantly reduced the activity of σ1 with an inhibition rate
of 75.4%, suggesting potential interaction between σ1 and MH. Here, the
inhibition rate = (1 − (Sample Well − LC)/(HC − LC)) × 100% (sample
well: the interaction between MH and the target protein experimentally
measured by the sample in one well; LC: positive control; HC: DMSO). In
comparison, the inhibition rates of MH on other protein targets are
close to 0 or negative. Thus, MH has not shown obvious effects on
inhibiting the activity of other drug targets.
SN neurons were stained using Sigma1-Receptor (S1R) immunofluorescence.
The S1R levels were significantly increased in the MH group, while ROT
reduced S1R levels compared to the NC, SP, and MH groups. Thus, MH is
able to protect the S1R in the SN from ROT-induced damage (Fig.
[279]8C).
Fig. 8. The IC50 determination of the inhibition of MH and the positive
control, haloperidol, in σ1.
[280]Fig. 8
[281]Open in a new tab
A The IC[50] of the positive control (Haloperidol) in inhibiting the
σ1. Eight different concentrations of haloperidol were tested. B The
IC[50] of MH in inhibiting the σ1. Eight different concentrations of MH
were tested. C IHC representation of σ1 receptor expression across
different groups.
To further validate interaction between MH and σ1, we measured half
maximal inhibitory concentration (IC[50]) of MH in inhibiting σ1 by
filtration binding assay. Figure [282]8A is the IC[50] of positive
control, Haloperidol, in inhibiting σ1. Figure [283]8B describes the
potential of MH in binding with σ1, indicating MH (with an IC[50] value
of 1.6 μM) binds to σ1, reducing the interaction between σ1 and
radiolabeled ligand. No existing study has been found to report the
interactions between MH and σ1. DMSO functioned as the negative
control, while haloperidol served as the positive control. Detailed
results can be found in Supplementary Table [284]14 and Supplementary
Fig. [285]12.
Discussion
We established a method, integrating gene co-expression modules in
normal human brain with disease-associated genes or SNPs, to identify
disease-associated gene co-expression modules that were further used
for drug repurposing. iGOLD is able to interpret the impact of
individual genes on disease, especially those genes expressed
differentially in patients and controls (DEGs) obtained using
insufficient samples, which are unable to provide information on the
signaling circuitry of disease-associated pathways^[286]96–[287]98. It
can also explain the role of SNPs in regulating gene co-expression
modules in the normal human brain. These SNPs were discovered as part
of large population-based GWAS studies, and their functional roles in
regulating gene co-expression modules from normal human brain have
remained unclear^[288]99,[289]100. When these genes are used for drug
discovery, iGOLD determines the drug efficiency not in terms of its
target proteins but rather in terms of its ability to restore the
normal gene expression profile. This makes iGOLD a powerful tool in
drug discovery by dint of its considering multiple genes in one
network. This approach can be generally applied to repurposing drugs
for other brain disorders simply by connecting any disease-associated
genes or SNPs with the gene co-expression modules associated with
normal human brain samples.
When we used the conservation score to represent the enrichment of
genes expressed in specific tissues, we found that the conservation
score of the BR7M4 module in SN is 33.93 and in HC is 10.53. The
conservation score larger than 10 is defined as highly conserved by a
previous study^[290]50. The conservation analysis of the gene
co-expression modules across tissues is to find the modules showing
high expression in specific tissues, especially brain tissues. These
modules have high potential to be related to PD. Because we assume PD
is a disease directly related to the brain, the gene co-expression
modules that have shown high expression in other tissues are not
further investigated, although they may represent another mechanism of
PD. In addition to BR3M2, it is highly conserved in the HC brain area.
However, the BR3M2 is not highly enriched with PD-associated genes
compared to the BR7M4. The result of the enrichment analysis of
PD-associated genes in these two modules is shown in Supplementary
Table [291]5. Thus, we selected BR7M4 for further study. This result
had been exhibited in Fig. [292]2A and Supplementary Table [293]5.
To test sensitivity of the PD associated gene co-expression modules
identified by this study, we have performed RNA-seq analysis for the
brain tissues from hippocampi and substantia nigra brain regions of PD
mouse model. The results were compared to the mice in the controls. The
result indicated that the module, BR7M4, highly conserved in
hippocampi, is enriched with genes expressed differently (DEGs) in the
PD mice and the controls, indicating the reliability of our method in
identifying PD-associated brain regions. Moreover, gene expressions of
39 DEGs in BR7M4 are significantly correlated with the PD-associated
genes. The roles of these genes in PD require further clarification in
the future.
Although both hippocampus and the substantia nigra have shown high
potential to be associated with PD in the module conservation analysis,
the RNA-seq analysis is performed on the hippocampus of mice.
Indeed, BR7M4 shows the highest conservation in two brain regions,
Substantia Nigra and hippocampus that are also among the most affected
brain regions in PD. The commonly accepted mechanism of PD is the
degeneration of dopaminergic neurons in the Substantia Nigra^[294]101.
McGregor and Nelson^[295]101 highlight the crucial role of SN in motor
control and its profound involvement in PD pathogenesis. The loss of
these neurons in substantia Nigra in PD is well-documented in both
clinical and preclinical studies by Dauer and Przedborski^[296]29. In
2017, Surmeier et aldiscuss the selective vulnerability of substantia
Nigra dopaminergic neurons in PD, emphasizing the molecular and
cellular mechanisms that make this region particularly susceptible to
neurodegeneration^[297]102. Furthermore, Poewe et al. provide a
comprehensive review of PD pathophysiology, including the progressive
loss of dopaminergic neurons in substantia nigra and its impact on
motor dysfunction^[298]103. These studies collectively reinforce the
pivotal role of the Substantia Nigra in PD progression and pathology.
Hippocampus is also critically implicated in PD, especially concerning
non-motor symptoms such as cognitive decline, depression, and anxiety.
Hijaz and Volpicelli-Daley discuss how α-synuclein aggregation,
particularly in regions like the hippocampus, contributes to cognitive
deficits in PD^[299]104. A wide range of cognitive impairments in PD
patients is considered associated with the hippocampal
dysfunctions^[300]105. Regarding the molecular mechanisms of PD, the
change of NOX4 in the hippocampus is known to be involved in PD by
investing human PD patients^[301]106. Our findings of significant
enrichment in hippocampal modules are in line with these studies,
suggesting a deeper connection between PD-related cognitive dysfunction
and hippocampal gene expression.
Our study also found that BR7M4 has shown marginal conservation in the
other two regions, the Thalamus and the Putamen. The Thalamus and
Putamen, both regions, are integral parts of the motor control circuit,
often implicated in PD due to their involvement in motor and sensory
processing. Dorsey et al. provide evidence for the important role of
these regions in PD progression, particularly the putamen, which is
affected by dopaminergic dysfunction^[302]107. The Thalamus, as part of
the basal ganglia-thalamocortical circuit, also shows evidence of
alterations in PD patients, further supporting our findings.
The commonly used drug for PD is levodopa. However, the shortcoming of
levodopa is the drug resistance. In this study, we found that MH
treatment was associated with improving PD-related behaviors. It has
the potential to be an alternative choice for PD patients showing
resistance to levodopa. MH is known as a psychostimulant in the
nootropic agent group, and is an accepted treatment for traumatic
cataphora, alcohol poisoning, anoxia neonatorum, and children’s
enuresis^[303]108. Oral administration of MH to rats in chronic
hypoperfusion improved behavioral dysfunction, suggesting an ability of
MH to attenuate neuronal damage after ischemia^[304]109. Previous
studies have examined the potential effect of MH on the states of α-syn
in yeast, and similar effects were also observed in dopaminergic
neurons of worms expressing^[305]110. Additionally, MH was also found
to improve muscle tone and brain lipid peroxidation in a rat
model^[306]111. However, it remained entirely unclear whether MH would
have any effect on PD-related behaviors or symptoms, as neither yeast
nor worm was an ideal model for PD, and the etiology and progression of
PD were far more complicated than deregulated muscle tone or brain
lipid peroxidation. Here, we constructed a rotenone-induced mouse model
to validate the biological effects of MH. Many studies have employed
rotenone to generate an experimental animal model of PD to mimic the
PD-like symptoms, such as motor deficit, cognitive decline, and
depression^[307]34,[308]35,[309]112–[310]114. Most of the previous
studies focus on the functions of MH in improving
memory^[311]115–[312]118. Our findings demonstrate that MH effectively
improves PD-related behaviors of mice, as measured by changes in motor
function, sucrose preference, forced swim test, and the tail suspension
experiment. Recent research has provided evidence supporting a
correlation between motor dysfunction observed in PD and the
hippocampal region, as it has been confirmed that the hippocampal
region can project to the midcingulate motor area and the supplementary
motor area^[313]119. Furthermore, there is a strong association between
the hippocampus and non-motor symptoms of PD. A study^[314]120
demonstrated that increased iron levels in the early stages of the
hippocampus can trigger the occurrence of non-motor symptoms. Another
review^[315]121 discussed the link between the hippocampus and
non-motor symptoms, including depression and fatigue. Therefore, our
research on the hippocampus aims not only to verify the motor symptoms
of PD but also to demonstrate the role of MH in protecting the
hippocampus and improving PD-related non-motor symptoms. Moreover, we
have provided extensive evidence to support the mechanisms underlying
the beneficial effects of MH. Our findings demonstrate that MH can
prevent neuronal death, synaptic damage, and mitochondrial destruction,
reduce lipid peroxidation, protect dopamine synthesis, and reverse
abnormal mitochondrial metabolism. These results highlight the ability
of MH to improve both mitochondrial metabolism and brain function, thus
ameliorating the most overt symptoms of PD. Moreover, no study has
reported the binding target of MH. This is the first study providing
evidence that MH plays a role in PD through binding with σ1.
Although many psychostimulant and cholinergic drugs were reported to
promote the REDOX metabolism of brain cells, and MH is prescribed in
China and elsewhere to treat a variety of CNS conditions, the actual
effect of MH on mitochondrial function has never been tested before. We
then set out to test whether and how MH may act on
mitochondria-associated metabolic pathways in primary neurons. First,
as shown in Fig. [316]6A, MH was found to restore mitochondrial
morphology that was altered upon ROT treatment, indicating its positive
impact on overall mitochondrial homeostasis in primary neurons.
Consistently, MH seemed to also protect mitochondrial function from
ROT-induced oxido-nitrosative stress in neurons. Remarkably, when we
examined the metabolites of mitochondria in primary neurons, the MH
treatment significantly (Student t-test P < 0.05) decreased the
homeostatic levels of 17a-Ethynylestradiol, L-Indospicine,
2-{(3S)-1-[4-(Trifluoromethyl)benzyl]-3-pyrrolidinyl}-1,3-benzoxazole,
cotinine, cypridinaluciferin, 5-HETE, D-(+)-maltose and oxprenolol,
comparing to the primary neurons only treated with ROT (Supplementary
Table [317]13). As these metabolites have been shown as involved in the
pathways associated with PD in previous
studies^[318]75,[319]77,[320]79,[321]81,[322]82,[323]122, our data thus
collectively supported the notion that MH did significantly protect
mitochondrial function, which may at least partially underlie its
effects on PD.
Additionally, we found that MH may prevent the further deterioration of
Parkinsonian symptoms by improving mitochondrial function, such as
impacting the expression of markers for lipid peroxidation and
mitochondrial proteins. Moreover, it is widely believed that
mitochondrial-associated neurodegenerative diseases involve the
perturbation of calcium flux or energy generation^[324]123,[325]124.
Thus, we measured the ATP levels in the hippocampi of the mice in each
group, and noted that MH significantly restores the ATP level in the
ROT + MH group to a level comparable to that of the NC group (Fig.
[326]6D). The improved mitochondrial function consequent to MH
treatment might also be due, at least in part, to the restoration of
normal expression of Ndufa12, Cox6b1, Atp5k, and Src genes in the
ROT-induced PD mouse model. The NDUFA12 gene has been shown to encode a
key member of the mitochondrial respiratory chain^[327]125,[328]126.
Low expression of the Cox6b1 gene has been associated with Alzheimer’s
disease^[329]127. In a similar vein, ATP5K is known to be involved in
mitochondrial ATP synthesis-coupled proton transport^[330]128.
We also investigated the impact of MH on mitochondria by means of
mitochondrial metabolomics, and disclosed several specific metabolites
that were regulated by MH, suggesting that MH may influence
mitochondrial function by reprogramming metabolic pathways.
Understanding drug-metabolite associations is crucial for research into
pharmacoepidemiology and for improving drug efficiency^[331]129. One
recent study has demonstrated that metabolic abnormalities can alter
neuronal excitability in the brain^[332]130. We found that MH treatment
can restore normal levels of several metabolites associated with PD
pathogenesis, including 5-HETE and L-indospicine. For example, 5-HETE
(OPLS VIP = 1.250 and t-test P = 3.9 × 10^−^2) has been reported as a
biomarker of oxidative damage in PD; 5-HETE interacts with SRC,
regulates the TRPV1 gene, which has been reported to be associated with
PD development^[333]131–[334]136. Another compound, L-indospicine (OPLS
VIP = 1.107, P = 5.0 × 10^−4), has been reported to be a potent
inhibitor of arginase that can cause a shift in L-arginine metabolism
to the NOS pathway^[335]64 closely related to PD
development^[336]137–[337]139.
Damage to synaptic plasticity is also known to be related to the onset
and progression of both the motor and cognitive symptoms of
PD^[338]140. Previous studies have employed immunohistochemistry to
investigate the protective potential of MH in relation to
synapses^[339]141,[340]142. However, these studies could not determine
the true length and number of synapses. In order to confirm the
protective action of MH on synapses, we performed in vitro and in vivo
experiments, as well as cluster analysis to demonstrate that MH can
protect synapses in terms of synaptic length. Additional q-PCR
experiments indicated that MH treatment of ROT-induced PD primary
neurons restores normal expression of the Chrna4, Syt11, and Cdh8
genes. These genes have been previously shown to encode proteins with
functions pertaining to synaptic
function^[341]71–[342]73,[343]143–[344]146. Thus, our study supports
the view that MH may protect synapses by impacting the pathways in
which both mitochondria-related genes, and metabolic factors such as
maltose and cotinine, are involved.
To further reveal the molecular mechanism of MH in affecting PD, we
used our previously developed method, DStruBTarget, to predict MH and
protein interactions. DStruBTarget has provided the top 10 most
MH-protein interactions. Among them, the MH-σ1 interaction is validated
by the Filtration Binding Assay. Usually, σ1 is considered a crucial
target for preventing and treating PD^[345]56,[346]147,[347]148. σ
receptors have been recognized as unique receptors, initially thought
to be a subtype of opioid receptors^[348]149,[349]150. σ1 receptor is
implicated in aging and various diseases, including schizophrenia,
depression, Alzheimer’s disease, and ischemia^[350]148,[351]151. Other
studies suggest that the σ1 receptor is involved in regulating dopamine
synthesis and release^[352]152–[353]154. Our research predicts, on one
hand, that MH can interact with the σ1. The interaction was further
validated through Filtration Binding Assay, demonstrating robust
interaction of MH to σ 1 with IC[50] = 1636 nM (Fig. [354]8B). This is
the first study confirming MH plays roles in PD through σ 1.
The Filtration Binding Assay used in this study is the radiolabeled
binding assay that is often applied to evaluate protein-drug
interactions. This method has been used in many previous
studies^[355]155–[356]157. All these studies suggested that the
filtration binding assay is reliable in evaluating the interactions
between drugs and proteins by filtering receptor samples using a vacuum
processing system, assessing their ability to interfere with the
specific binding of a radiolabeled ligand to the receptor. This method
can be employed for accurate and universal high-throughput screening.
Our studies do, however, have several limitations. Firstly, the tissue
chip can only interrogate part of the synapses, and is unable to fully
observe the protective effect of MH on the murine synapse. New
technology for observing whole synapses will be required to confirm the
protective effect of MH. Secondly, the CMAP database only includes a
limited number of drugs, which may hinder the identification of more
effective drugs for repurposing. Thirdly, the effectiveness of the
drugs themselves still requires further supporting evidence from
clinical studies. One of the limitations of the study is that the iGOLD
is dependent on the gene co-expression module analysis. The inaccurate
modules influence the determination of the effective drugs.
Another limitation of this study is that we have not performed a
dopamine transporter molecular imaging to clarify the most responsive
region of MH, although the molecular experiments have indicated the
roles of MH in the hippocampus. Finally, no clinical validation has yet
been performed in this study.
In conclusion, this study revealed MH as a potential drug candidate for
PD. Subsequent experiments indicated that MH is able to improve
PD-related behavior and protect neurons by regulating
mitochondrial-related genes, synaptic pathways, and metabolite
pathways. Thus, it would appear that MH may help to arrest the
progressive deterioration of Parkinsonian symptoms.
Methods
Study design
Here, we designed a computational architecture, iGOLD, for drug
repurposing. This approach involved the construction of gene
co-expression modules of normal human brain by applying weighted gene
co-expression network analysis (WGCNA)^[357]18 and DiffCoEx^[358]19,
and analyzing gene expression data of 1231 brain samples from ten brain
regions of healthy humans. The sample size for each brain region is
shown in Table [359]1. Then, iGOLD was used to identify the modules
enriched in PD-associated genes and PD-associated SNPs by employing 11
datasets encompassing PD-associated genes, SNPs, and DEGs between PD
and controls. The identified modules were evaluated in relation to
their expression conservation in brain samples across ethnicities,
brain regions, and disease stages of PD by ModulePreservation, a
function in the WGCNA R package. This analysis was based upon seven
datasets with sample sizes ranging from 4 to 57. The highly conserved
modules were used for drug repurposing by CMAP^[360]41,[361]42. The
drug candidates were ranked by their connectivity scores. From them, we
selected those ranked in the top 15 (Supplementary Table [362]9) and
having the ability to pass through blood blood-brain barrier for
further validation. The source code of iGOLD and related data used in
this study are available at
[363]https://github.com/fanc232CO/iGOLD_pipline.
The experimental validations of the drug effects were conducted in
primary neurons and a mouse model. The primary neurons were obtained
from the hippocampi of mice on postnatal days 0–3. We used Rotenone
(ROT) to treat the primary neurons as described previously in ref.
^[364]158 since ROT has been shown to induce PD-like symptoms in
human^27and animal models^[365]28,[366]29. The effectiveness of drugs
in protecting neuronal damage was evaluated by immunofluorescence
marking the tubulin of synapses and primary neurons, and a
mitochondrial fluorescent probe for mitochondrial morphology. The
protective effects of the drug on mitochondrial functions were tested
by determining the malondialdehyde (MDA) level, the reduction in the
glutathione (GSH) level, ATP levels, and mitochondrial proteins. The
regulatory effects of drugs on mitochondrial metabolites were assessed
in murine primary neurons.
The mouse model was constructed using ROT-induced C57BL/6J male mice
(n = 86, 8-weeks-old) (Supplementary Material). We used PET/CT imaging
to examine glucose metabolism in the brains of mice (Supplementary
Material). The protective ability of the drugs on cranial nerve damage
was evaluated by immunohistochemistry of NeuN (in the DG, DG2, and
CA1), NeuN-negative cells in the DG structure of the hippocampus,
NeuN-negative cells in the DG2 structure of the hippocampus,
NeuN-negative cells in the CA1 structure of the hippocampus, and TH in
the hippocampus. Expression of mitochondrial-related proteins was
measured by western blotting for α-syn(F) and NeuN(G) in the murine
hippocampi. RNA-seq and qRT-PCR were used to measure the expression of
mitochondrial-related genes in the hippocampi of mice. The
effectiveness of the drugs was further examined in terms of their
influence on the PD behaviors of mice, including the footprint test,
sucrose preference test, and forced swim test.
Gene expression data used for identifying modules associated with PD
Gene expression data were obtained from the publicly available GEO
dataset^[367]159. From GEO, we downloaded eight gene expression
datasets [one for co-expression module building, five for module
conservation analysis, and two for enrichment analysis of Parkinson’s
disease (PD)-associated DEGs].
The co-expression modules were constructed by WGCNA analysis on the
[368]GSE60862^[369]38–[370]40, a gene expression dataset obtained from
the platform of Affymetrix Human Exon 1.0 ST Array. The samples covered
ten brain regions, including cerebellar cortex, frontal cortex,
occipital cortex, temporal cortex, hippocampus, putamen, thalamus,
medulla, white matter and substantia nigra, from 1231 individuals of
European descent collected by the UK Brain Expression Consortium
(UKBEC).
The module conservation analysis was performed on six GEO datasets,
[371]GSE131617^[372]45,[373]160, [374]GSE23290^[375]46,
[376]GSE34516^[377]47, [378]GSE51922^[379]48, [380]GSE18838^[381]49,
and [382]GSE34865
([383]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), which were
obtained from the platform of the Affymetrix Human Exon 1.0 ST Array.
These datasets covered multiple ethnicities, brain regions, tissues,
Braak stages, and PD disease status. In detail, [384]GSE131617 includes
transcriptome data from 213 post-mortem brain tissue specimens (=71
subjects × 3 BRs), which covered three brain regions (entorhinal,
temporal and frontal cortices) of 71 Japanese brain-donor subjects in
four Braak stages^[385]161–[386]163 (0, I–II, III–IV, and V–VI).
[387]GSE23290 included putamen tissues from the 8 idiopathic PD (IPD)
patients, 3 LRRK2-associated PD (G2019S mutation) patients, 5
neurologically healthy controls, and one asymptomatic LRRK2 mutation
carrier^[388]46, the asymptomatic carrier ([389]GSM745539) was removed
from analysis. [390]GSE34516 included the locus coeruleus post-mortem
tissues from idiopathic PD (IPD) and LRRK2-associated 6 European PD
patients^[391]47. [392]GSE51922 is built on the RNA profile of
IPSC-derived dopaminergic neurons from idiopathic and genetic forms
(LRRK2) of PD^[393]43. [394]GSE18838 included peripheral blood
collected from 18 PD patients and 12 healthy controls^[395]49.
[396]GSE34865 included gene expression data of substantia nigra samples
from 57 healthy adults. Details of these datasets can be found in
Supplementary Table [397]7.
[398]GSE8397^[399]44,[400]164 were downloaded for the generation of
PD-associated differentially expressed genes (DEGs). [401]GSE8397 is
built on the gene expression of substantia nigra split into medial and
lateral portions, and frontal cortex from 24 PD patients and 15
controls. Gene expression was accessed through two platforms,
[402]GPL96 and [403]GPL97^[404]44,[405]164. Details of these datasets
can be found in Supplementary Table [406]6.
Conservation analysis of co-expression gene module
Conservation of modules was estimated by module preservation through
the calculation of Z[summary]. The Z[summary] is determined by
estimating the density and connectivity of the test modules and the
reference module. Briefly, the calculation of Z[summary] is based on
permutation tests to assess the mean and variance of Z statistics under
the null hypothesis of no relationship between the module assignment in
reference and test modules. The reference gene expression data in this
study were derived from seven GEO databases (Supplementary Table
[407]7), while the test modules are the PD-associated modules suggested
by iGOLD. Modules with Z[summary] scores above 10 were interpreted as
being highly conserved, Z[summary] scores between 2 and 10 were deemed
to be moderately conserved, whilst Z[summary] scores below 2 were
regarded as incompletely conserved.
Stratified LD score regression (sLDSC) analyzing PD-associated SNPs
sLDSC analysis^[408]165 was conducted using the parameters and
pipelines provided by tutorials in LDSC
([409]https://github.com/bulik/ldsc/wiki). First, we mapped all SNPs to
the co-expressed modules if they were within 10 kb of the locations of
exon probes. The LD score and heritability were calculated for each
co-expressed module. The enrichment of the SNPs in the co-expression
modules was defined as the summation of SNP heritability divided by the
number of SNPs in that module. Standard errors of the SNP enrichment in
the co-expression modules were estimated by a block jackknife^[410]166
and were further used to evaluate Z-scores, P-values, and false
discovery rates (FDRs) of the SNP enrichment in the co-expression
modules^[411]87.
Construction of co-expression networks for the normal human brain
The [412]GSE60862^[413]38–[414]40 dataset was used to construct the
gene co-expression networks. The genes expressed in ten brain regions
were grouped into co-expression modules by two different approaches,
consensus weighted gene co-expression network analysis (WGCNA)^[415]18
and DiffCoEx^[416]19. The WGCNA was applied to detect co-expression
modules common to all ten brain regions (consensus co-expressed
modules, CCM). DiffCoEx was used to identify gene modules specifically
expressed in each of the ten brain regions compared to the other nine
brain regions (specific co-expressed modules, SCM).
The function blockwiseModules in WGCNA, was utilized to construct the
co-expression modules as previously described in refs.
^[417]167,[418]168. The parameters were set as follows, β = 7 (chosen
based on the scale-free topology criterion r^2 > 0.8),
minModuleSize = 30, mergeCutHeight = 0.25, maxBlockSize = 6000, and
corType = ‘pearson’. For each pair of genes, the topological overlap
matrix (TOM) was calculated and scaled based on the adjacency matrix.
The component-wise minimum of the TOMs in each brain region was then
extracted to generate a consensus TOM. This TOM was clustered by using
the average hierarchical clustering method to obtain a consensus TOM,
defining it as a 1-consensus TOM according to the difference of genetic
connectivity. A consensus co-expression module was defined as a branch
of a cluster tree generated by a dynamic tree cut.
The differential co-expression network analysis was carried out by the
DiffCoEx method in R software as previously described^[419]19. To
identify gene co-expression differences between transcripts from the
substantia nigra brain region and transcripts from the other nine brain
regions, we used the function of DiffCoex based on the WGCNA framework
by calculating a TOM generated from a matrix of adjacency differences
between these brain regions.
Module conservation analysis
The conservation of the association between the gene co-expression
modules and PD was evaluated according to the enrichment of the modules
in DEGs of PD patients and controls from five different
sources^[420]45–[421]49,[422]160. These datasets were all obtained from
the [423]GPL5175 platforms, and the samples in the datasets were
divided into healthy control and PD groups. The samples in this dataset
are accompanied by information on Braak stages, indicating the disease
severity^[424]161–[425]163. These samples were partitioned in terms of
two ethnicities, three brain tissues, five brain regions, and six PD
disease states. Further information is provided in Supplementary Table
[426]7. The degree of module conservation was estimated by
modulePreservation, a function in the WGCNA R package.
Enrichment analysis of PD-associated genes
We validated the association between PD and CCM and SCM modules by the
enrichment analysis of PD-associated genes and PD-associated SNPs. The
PD-associated genes were obtained from DisGeNet^[427]169–[428]171
([429]https://www.disgenet.org/home/). DisGeNET covers the full
spectrum of human genetic diseases, as well as normal and abnormal
traits. The currently released version of DisGeNET includes more than
24,000 different genetic diseases and traits, 17,000 genes, and 117,000
genomic variants^[430]171. As shown in Supplementary Table [431]3,
searching under the term “Parkinson disease” (UMLS CUI: C0030567)
allowed the collation of six types of PD-associated genes by the
DisGeNet database, of which “CausalMutation” was filtered out before
enrichment analysis because it contained only one gene. Gene enrichment
analysis in the remaining five gene sets was evaluated by means of the
single-tailed Fisher’s exact test, and further adjusted by the FDR. The
background genes for the enrichment analysis are those that are not
PD-associated genes by considering a total number of human genes as
20,814.
Enrichment analysis of genes expressed significantly differently in PD
patients and controls
Differentially expressed genes (DEGs) between PD patients and controls
were obtained by analyzing gene expression data from GEO with access ID
[432]GSE8397^[433]44,[434]164 (Supplementary Table [435]6). In
[436]GSE8397, samples from the whole substantia nigra (combination of
samples from lateral and medial substantia nigra regions) were used for
DEG analysis. The DEG analysis was performed by means of the GEO2R
tool^[437]172,[438]173 in the GEO website
([439]https://www.ncbi.nlm.nih.gov/geo/). DEGs were selected with a
fold change (FC) threshold of 1.2 and an adjusted P-value threshold of
0.05. Enrichment was evaluated by a single-tailed Fisher’s exact test
adjusted by the FDR.
RNA-seq data processing
In the RNA-seq analysis of the hippocampi of mice, we first used FASTP
([440]https://github.com/OpenGene/fastp) to carry out data
preprocessing. All parameters of FASTP were kept as default to
preprocess the raw data, including stripping adapters, filtering out
low-quality reads, correcting mismatched base pairs, and trimming poly
G ends. After the preprocessing, the RNAseq data were delivered to the
Salmon tool to generate a gene expression matrix by aligning the reads
to the GRCm9 ([441]http://asia.ensembl.org/Mus_musculus/Info/Index)
gene annotation file downloaded from Ensembl. The genes that exhibited
raw counts of 0 in each sample were excluded. Finally, we obtained the
counts of 29,324 gene symbols of 28 mouse samples.
Drug discovery by CMAP
First, the DEGs were generated by analyzing gene expression data
([442]GSE8397) of substantia nigra samples obtained from the platform
of [443]GPL96 (Affymetrix) (15 healthy controls and 24 PD). The
overlapping genes between these DEGs and the genes in PD-related
modules were extracted and mapped to the [444]GPL96 probe, and then
delivered to the Connectivity Map (CMAP)^[445]41,[446]42
([447]https://portals.broadinstitute.org/cmap/) to predict potential PD
drug candidates. We ranked the output drug candidates by their
connectivity scores. When the connectivity scores were close to −1, the
drugs were deemed to have strong potential to restore the normal gene
expression profile of the PD-associated genes. In this study, drug
candidates were selected for further analysis if their connectivity
scores were lower than −0.8.
Neuronal cell culture
The hippocampal primary neurons were obtained from mice on postnatal
days 0–3. Hippocampal neurons were plated on poly-D-lysine
(Sigma)-coated chamber slides and Six-hole plates for 2 h to allow
neurons to adhere. The cultured neurons were maintained with complete
culture medium composed of B27 supplement (Gibco, USA), L-glutamine
(Life Technologies), and Neurobasal-A medium (DMEM/F12) (Gibco, USA) at
37 °C in a 7% CO[2] incubator for 7 days to ensure the growth of nerve
synapses.
Construction of neuron models
The primary neurons were randomly divided into six groups: the normal
control (NC) group, the ROT-induced group, the SP-treated group, the
MH-treated group, the ROT + SP-treated group, and the ROT + MH-treated
group.
To construct the ROT-induced group, ROT with a concentration of 400 nM
was applied directly to the culture medium for 24 h. To create
ROT + SP(2 μM) and ROT + MH(10 μM) groups, we pretreated murine primary
neurons with SP or MH for 2 h, and then used 400 nM rotenone (ROT) to
treat the neuronal cells for 24 h. The SP and MH groups of the murine
primary neurons were treated with SP or MH for 2 h. We then used
immunofluorescence, marking the tubulin of synapses and primary neurons
to evaluate the cell damage from ROT.
Construction of a mouse model
C57BL/6 J male mice (n = 86, 8-weeks-old, 30 g) were purchased from the
Guangdong Medical Laboratory Animal Center (Foshan, China). The mice
were randomly assigned to six groups. Unless otherwise specified, they
were provided with ad libitum access to food and water and were housed
four to five per cage in a temperature-controlled (23 ± 1 °C) and
humidity-controlled room (40–60%) with a 14-h light and 10-h dark
cycle. All animal care and experimental procedures were conducted in
accordance with protocols approved by the Institutional Animal Care and
Use Committee of Sun Yat-sen University. The mice were anesthetized
with an intraperitoneal injection of 1% sodium pentobarbital (60 mg/kg)
and were euthanized by cervical dislocation.
C57BL/6 J male mice were randomly assigned into six groups, namely the
control group (NC), the Sodium phenylbutyrate (SP) group, the Rotenone
(ROT) group, the ROT + SP group, the Meclofenoxate-hydrochloride (MH)
group, and the ROT + MH group. The mice in the NC group received
dimethylsulfoxide (DMSO) (olive oil only); the mice in the SP group
were treated with SP (300 mg/kg bw/d; intraperitoneal [i.p.]) for 4
consecutive weeks; the mice in the ROT group were given rotenone
(1 μg/g bw/d; i.p.) for 3 consecutive weeks; the mice in the ROT + SP
group received SP prophylaxis (300 μg/g bw/d; i.p.) for 1 week followed
by ROT (1 μg/g bw/d, i.p.) from 1 week onwards for the next three
consecutive weeks; the mice in the MH group were administered with MH
(50 μg/g bw/d; i.p.) for four consecutive weeks; the mice in the
ROT + MH group received MH prophylaxis (50 μg/g bw/d; i.p.) for 1 week
followed by ROT challenge (1 μg/g bw/d, i.p.) from 1 week onwards for
the next 3 weeks.
Body weight and food intake of the ROT-induced PD mouse model
In this study, 36 C57BL/6 J mice were randomly divided into six groups
that were treated with DMSO (NC), ROT, SP, MH, ROT + SP, and ROT + MH,
each group containing six mice. Administration of SP and MH did not
elicit any behavioral alterations during the experimental period, nor
were any significant changes in food intake evident. Mice treated with
ROT showed no decrease in body weight during the treatment period. Mice
in other groups did not exhibit any significant decrease in body weight
during the treatment period nor any significant changes in food intake,
except for the ROT group (Supplementary Table [448]8).
Hippocampus sample collection
From each group of mice, more than three hippocampi were collected. The
mice were sacrificed by anesthetization, and their brains were
extracted within 24 h after the last injection of ROT. The hippocampi
were then isolated under a microscope.
Biospecimen collection
The striatum brain regions of mice were separated and processed to
obtain both cytosolic and mitochondrial fractions, after they were
sacrificed by anesthetization, within 2 h. The biochemical
investigations were conducted, and 4–5 murine striata (from each group)
were processed for histopathological examination.
RT-PCR
Total cell RNA extraction was performed using TRIzol (Invitrogen) and
reverse transcribed according to the manufacturer’s protocol (Takara).
qRT-PCR was carried out using SYBR Premix qRT-PCR ExTaq™ II (Takara)
and analyzed on a Bio-Rad CFX96 real-time PCR cycler (Bio-Rad,
Netherlands). The primer sequences are given in Supplementary Table
[449]15. Differences in mRNA expression were calculated by means of the
formula N = (2)^−ΔΔCT ^[450]174.
Immunofluorescence
Samples were washed three times with 0.01 M PBS and fixed with 3.7%
paraformaldehyde for 15 min at room temperature. The samples were then
permeabilized in 0.5% Triton X-100 for 3 min and blocked with 3% goat
serum albumin for 30 min prior to incubation with a primary antibody,
namely, anti-α-synuclein, mouse anti-TH, SIGMAR1 Ab (Affinity
Biosciences, Cat.#: DF7363) and anti-NeuN, at dilutions of 1:100
overnight at 4 °C. Secondary antibodies, anti-rabbit or anti-mouse
(Tianjin Sungene Biotech Co., China) at 1:200 dilution, for 1 h at room
temperature. Nuclei were stained with DAPI.
Immunohistochemistry
The slides were washed twice for 15 min in 0.01 M PBS, and proteinase K
was added to the tissue and incubated at 37 °C for 5 min. This step was
followed by quenching for 10 min in a solution of methanol containing
30% hydrogen peroxidase and further incubating for 1 h in blocking
solution containing 5% normal goat serum and 1% Triton X-100 in 0.01 M
PBS. After blocking, the slides were incubated overnight in rabbit
anti-caspase-3 antibody (Catalog No.: 10842-1-AP, Proteintech),
anti-α-synuclein antibody (Catalog No.: 10842-1-AP, Proteintech,
China), anti-TH antibody (Catalog No.: 25859-1-AP, Proteintech, China),
and anti-NeuN antibody (Catalog No.: A19086, ABclonal, China) diluted
1:100 in blocking solution. Following incubation with primary antibody,
the sections were incubated for 2 h in biotinylated goat antirabbit
secondary antibody diluted 1:500 in 0.01 M PBS and subsequently
incubated with ABC reagents (Standard Vectastain ABC Elite Kit; Vector
Laboratories, Burlingame, CA, USA) for 20 min in the dark at room
temperature. The sections were washed twice with 0.01 M PBS and
incubated in 3,3‘-diaminobenzidine tetrahydrochloride (DAB); sections
were washed with distilled water, dehydrated in graded ethanol (70%,
85%, 95%, and 100%), placed in xylen, and cover-slipped using mounting
medium. We then analyzed and counted the active caspase-3 positive
cells in the DG, DG2, and CA1 regions of the hippocampus using the
ImageJ program analysis.
Mitochondrial fluorescent probe staining analysis
Mitochondrial staining was performed with the mitochondrial probe
MitoTracker Red CM-H2XRos (Invitrogen, USA) according to protocols
provided by the manufacturer. After being washed with 0.01 M PBS, the
cells were counterstained with DAPI for 10 min and imaged with an
Olympus BX63 microscope (Olympus, Japan).
Neurons from differentiated groups were stained with MitoTracker Deep
Red (200 ng/ml) (Yeasen, Shanghai, China) for mitochondria for 60 min,
then fixed with 4% paraformaldehyde for 15 min and permeabilised with
Triton X-100 at 0.04% as previously described in ref. ^[451]175. All
cells (nuclei) were stained with DAPI (4‘,6-diamidino-2-phenylindole,
1 µg/ml). Images were obtained using an Olympus BX63 microscope
(Olympus, Japan). Quantification and analysis of the neuronal network
were performed using Image J software.
Footprint test
The motor function patterns of mice were assessed by the footprint test
as described previously in ref. ^[452]176. The apparatus comprises an
open field (60 × 60 × 40 cm), in which a runway (4.5 × 40 × 12 cm) was
arranged to lead out into a dark wooden box. The motor function
parameters were measured by wetting forepaws and hindpaws with
commercially available non-toxic colored inks and allowing the mice to
trot onto a strip of paper on the runway. Pawprints made at the
beginning and the end of the run were excluded. Various motor function
parameters, such as stride length (Differences in the forward distance
between each fore paw and hind paw footprint with each step), stride
width (lateral distance between opposite left and right fore paw and
opposite left and right hind paw), and foot direction, were measured.
Sucrose preference test
The mice were housed individually and were first trained to adapt to
sugary drinking water in a quiet room by putting two water bottles in
each cage. Both bottles were filled with 1% sucrose water. The mice
were tested with respect to sucrose preference using the following
process: (1) the mice were prevented from drinking for 24 h before
administration of the Sucrose Preference Test; (2) each mouse was given
a pre-quantified bottle of 1% sucrose water and a bottle of distilled
water; (3) the position of the two bottles of water was changed every
12 h; (4) the two bottles of water were taken and weighed after 24 h to
calculate the consumption of sucrose water, distilled water and total
liquid consumption for each mouse. Sucrose water preference
(%) = (sucrose water consumption/total liquid consumption) × 100%.
Forced swim test
The mice were placed individually into an open cylindrical container
(10 cm diameter, 30 cm height), containing water (25 ± 2 °C) to a depth
of 20 cm. Each mouse was forced to swim for 6 min, and the total
duration of immobility in seconds was measured during the last 4 min.
The water was changed after each animal experiment was finished. After
the experiment, the mice were wiped with a towel until their fur was
dry. The immobility time was defined in terms of the absence of
escape-oriented behavior.
Open field test
Mice were first acclimated in a quiet laboratory with constant room
temperature for 1 h. After the acclimation period, each mouse was
carefully placed in the center of the open field box, and its
spontaneous activity was observed for 5 min. After each test, the box
was thoroughly cleaned to remove any debris, and 75% ethanol was used
to eliminate residual scents from the previous mouse to avoid
influencing the behavior of subsequent mice. The number of center
crossings, activity level, and total movement distance were recorded.
PET/CT imaging and data analysis
The PET/CT imaging was performed on 36 C57BL/6 J mice that were divided
into NC, SP, ROT, ROT + SP, MH, and ROT + MH groups. Each group
contains six mice. All PET imaging studies were performed on a Biograph
TrueV (Siemens Healthcare) scanner. This PET/CT device is equipped with
a 64-slice spiral CT component. After a 3-h fasting period, mice were
injected in the tail vein with a solution of ^18F-FDG (16–32 MBq) or
^18F-FLT (32–37 MBq). The volume of the syringes was always kept below
0.2 ml in order to meet the requirements of our ethics committee. To
minimize muscle and brown fat uptake in the case of ^18F-FDG imaging,
animals were kept anesthetized under warming lights for a 20-min period
after injection. Animals were imaged simultaneously in groups of three
with the PET/CT scanner; one was placed at the center of the field of
view (FOV) whilst the two others were placed on each side of the
central animal, at a 5-cm and at a −5-cm radial offset.
The PET/CT was initiated with a CT scan acquired with the following
parameters: 80 mA, 130 kV, pitch 0.8, and 64 × 0.6 mm collimation.
Then, an emission scan was obtained in 3D mode. PET images were
reconstructed in a reduced FOV (35 cm), applying a scaling factor of 2.
Images were reconstructed with an algorithm that models the point
spread function of the scanner and leads to a 2.2 mm spatial resolution
at the center of the FOV. The following parameters were used: six
iterations, 16 subsets, no filtering, and a matrix size of 3362,
resulting in a 1.02 × 1.02 × 1 mm voxel size. Scatter and attenuation
corrections were applied.
Brain activity was obtained from a volume of interest (VOI)
encompassing the entire brain. The VOI was determined by means of an
isocontour, which was set so that the VOI matched the apparent brain
volume on PET and CT images. When discordance was encountered between
the PET metabolic volume and the CT volume, the VOI was drawn according
to CT images, so that PET/CT images could be compared to ex vivo
counting of the entire hippocampus, for which the entire brain was
harvested, irrespective of the presence of non-viable areas. Data were
analyzed using Statistical Parametric Mapping software (SPM 12, The
Wellcome Trust Center for Neuroimaging, London, UK).
Relative [^18F] FDG uptake images were analyzed by using microQ
(Siemens/Concorde Microsystems, Knoxville, TN, USA). Subsequently, we
utilized a voxel-by-voxel approach to obtain maximal use of information
without a priori knowledge, using IRW (Siemens/Concorde Microsystems,
Knoxville, TN, USA). In short, we used a flexible factorial design
depending on time point (after all treatment) and group (NC, SP, ROT,
SP + ROT, MH, and MH + ROT), as previously described (1, 2). T-maps
were interrogated at a P[height] ≤ 0.005 (uncorrected) peak level and
extended threshold of kE > 200 voxels (1.6 mm^3)^[453]177. Only
significant clusters with P[height] < 0.05 (corrected for multiple
comparisons) were retained.
ELISA analysis
α-synuclein was quantified in the striatum region of the mouse brain
utilizing the Enzyme-linked Immunosorbent Assay (ELISA) Kit from
Invitrogen (Cat No.: KHB0061) following the manufacturer’s
instructions.
Western blot (WB) analysis
For WBs, cells were extracted in RIPA buffer (Sigma-Aldrich, USA),
separated by 10% SDS-PAGE, and transferred onto a polyvinylidene
difluoride membrane (Millipore, USA). After blocking with 5% skimmed
milk, the membrane was incubated with specific primary antibodies
directed against α-synuclein, TH, and GAPDH (Abcam plc, USA) at a
dilution of 1:1000 overnight at 4 °C. The protein expression levels
were normalized with GAPDH. The membrane was incubated with horseradish
peroxidase-conjugated anti-rabbit secondary antibody for 1 h at 37 °C.
Measurement of oxidative stress markers
The markers of oxidative impairment were studied in the cytosol. MDA
(malondialdehyde) contents were quantitatively detected using the Lipid
Peroxidation MDA-Assay Kit (Beyotime). The unit weight of MDA was
calculated by a MDA standard curve measured at 532 nm. MDA was
considered a biomarker of lipid peroxidation in tissues and
organs^[454]178.
Adenosine triphosphate (ATP) measurement
ATP was measured using ATP assay kits (Beyotime, Shanghai, China).
After being diluted with dilution buffer, the ATP detection reagent was
added to a 96-well plate. After homogenization followed by
centrifugation at 12,000 g at 4 °C for 5 min, the samples were added
into the wells and mixed with the detection solution. Then, the levels
of ATP were measured with a SpectraMax M5 microplate reader (Molecular
Devices, San Jose, CA, USA). The ATP content was normalized to the ATP
protein content on the basis of the standard curve.
Quantitative analysis of GSH levels
Estimation of GSH: glutathione (GSH) was measured in the supernatant of
the hippocampus tissue in the mouse brain. GSH level was measured with
the enzyme-linked immunoassay (ELISA) kit from Elabscience (Bethesda,
MD) according to the manufacturer’s instructions.
Sample preparation and library preparation for transcriptome sequencing
Hippocampal RNA was extracted by TRIzol. RNA quantification and
qualification were evaluated as follows: (1) RNA degradation and
contamination were monitored on 1% agarose gels; (2) RNA purity was
checked using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA);
and (3) RNA integrity was assessed using the RNA Nano 6000 Assay Kit of
the Bioanalyzer 2100 system (Agilent Technologies, CA, USA).
A total of 1 µg RNA per sample was used as input material for the RNA
sample preparations. Sequencing libraries were generated using NEBNext®
UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following the
manufacturer’s recommendations; index codes were added to attribute
sequences to each sample. Briefly, mRNA was purified from total RNA
using poly-T oligo-attached magnetic beads. Fragmentation was carried
out using divalent cations under elevated temperature in NEBNext First
Strand Synthesis Reaction Buffer (5×). First-strand cDNA was
synthesized using a random hexamer primer and M-MuLV Reverse
Transcriptase (RNase H-). Second-strand cDNA synthesis was subsequently
performed using DNA Polymerase I and RNase H. Remaining overhangs were
converted into blunt ends via exonuclease/polymerase activities. After
adenylation of the 3’ ends of the DNA fragments, NEBNext Adapters with
hairpin loop structure were ligated in preparation for hybridization.
In order to select cDNA fragments of preferentially ~250–300 bp in
length, the library fragments were purified with the AMPure XP system
(Beckman Coulter, Beverly, USA). Then, 3 µl USER Enzyme (NEB, USA) was
used with size-selected, adapter-ligated cDNA at 37 °C for 15 min
followed by 5 min at 95 °C before PCR. Then, PCR was performed with
Phusion High-Fidelity DNA polymerase, Universal PCR primers, and Index
(X) Primer. At last, PCR products were purified (AMPure XP system) and
library quality was assessed on the Agilent Bioanalyzer 2100 system.
Clustering and transcriptome sequencing
The clustering of the index-coded samples was performed on a cBot
Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS
(Illumina) according to the manufacturer’s instructions. After cluster
generation, the library preparations were sequenced on an Illumina
Novaseq platform, and 150 bp paired-end reads were generated.
Metabolite extraction and UPLC-MS/MS analysis
The mitochondrial metabolite tests were performed on murine primary
neurons. Global metabolic profiles were obtained from the cells using
the Metabolon Platform. The principle of the Metabolon Platform has
been previously described^[455]138–[456]140. Approximately 5 × 10^6
primary neuronal cells were involved in the test. These cells were
extracted from mice at postnatal days 0-3 as described above. The
primary neuronal cells were extracted and cultured for 7 days, treated
with MH or DMSO for 2 h, and then with ROT or DMSO for 24 h, to form a
PD cell model, which was used for the experiments described below. To
lyse the cells, 1120 μl lysis system (800 μl pre-cooled
methanol + 320 μl ice water) was added to the wells of the six-well
plate; the cellular metabolites were placed in a 2 ml EP tube, and
800 μl pre-cooled chloroform was injected into the tube prior to
vortexing for 15 min. The mixture was then centrifuged at 12,000 rpm
for 15 min before being separated into supernatant and precipitate,
which were transferred to other microtubes. The supernatant was then
dried by a continuous flow of nitrogen gas to render it solid; the
solid was re-dissolved with 100 µl acetonitrile-water (1:1), which was
centrifuged at 14,000 rpm for 5 min; a mixture of supernatants of the
sample (10 µl) was transferred to a quality control (QC) vial. The
samples were kept on ice throughout the procedure unless centrifuged.
Ratios above were all according to volume.
For UPLC-MS/MS analysis, each sample was reconstituted with a methanol
solution with a density of 80% (80% methanol: 20% water) (by an 80-μl
volume of methanol with 20-μl H[2]O). The methanol solution was
centrifuged at 12,000 g for 10 min. The samples were then prepared for
liquid chromatography-mass spectrometry. Briefly, the samples were
injected into a Waters ACQUITY UPLC BEH Amide Column (2.1 × 100 mm,
1.7 µm) at column temperature, 40 °C, with a flow rate of 0.35 ml/min.
The mobile phase includes phase A and B (Phase A: 95:5 (acetonitrile:
water) containing 10 mM ammonium formate, 0.1% formic acid; Phase B:
50:50 (acetonitrile: water) containing 10 mM ammonium formate, 0.1%
formic acid). The gradient elution ratio is shown in Supplementary
Table [457]16. Subsequent analyses were performed using
ThermoScientific Ultimate 3000 UPLC coupled with Orbitrap Exploris 480
MS from Sun Yat-Sen Memorial Hospital. Identification of known chemical
entities was based on comparison with metabolomic library entries of
purified standards. Each biochemical was rescaled to set the median
equal to 1. Values for each sample were normalized by Bradford protein
concentration.
Metabolomic instrumentation and analytical conditions
LC-MS/MS was used for detection, with at least three replicates for
each experimental condition.
Binding target prediction for MH and SP
Binding targets of MH and SP were predicted using the tool
DStruBTarget^[458]55. The inputs of DStruBTarget are structures of MH
and SP, which were obtained from the PubChem database
([459]https://pubchem.ncbi.nlm.nih.gov/) in the format of 3D sdf
(compound CID: 4039 for MH and 5258 for SP). The parameters of
DStruBTarget were employed as the defaults in the prediction. The top
ten binding targets with the highest scores were analyzed in this
study.
Experimental validation of interactions between MH and PD-related proteins
The interactions of MH with σ1, DRD4, CNR2, 5-HT1A, and Alpha1D targets
were determined by the Filtration Binding Assay. The radiolabeled
ligands used by this study include ^3H-DTG (Perkin Elmer, Cat. no.
PE-NET986250UC) for σ1validation, ^3H-methylspiperone (Perkin Elmer,
Cat. no. PE-NET856250UC) for DRD4 validation, ^3H CP 55940 (Perkin
Elmer, Cat. no. PE-NET1051250UC) for CNR2 validation, ^3H 8-OH-DPAT
(Perkin Elmer, Cat. no. PE-NET929250UC) for 5-HT1A validation, and
^3H-prazosin (Perkin Elmer, Cat. no. PE-NET823250UC) for Alpha1D
validation. The experimental process includes the following steps: (1)
transfer 1 μl of compounds to the assay plate according to the plate
layout, which leads to the final concentration of the testing compound
in the assay plate being 10 μM in duplicate. (2) Transfer 1 μl of
non-specific binding compounds to the plate according to the plate map.
Summarize the total binding by transferring 1 μl of DMSO onto the assay
plate. (3) Allocating specified volumes of membrane material to the
plate. Soaking the Unifilter-96 GF/C filter plates in 0.3% PEI
(Polyethyleneimine) at room temperature with 50 μl per well for 30 min.
(4) Using the Perkin Elmer Filtermate Harvester to filter the reaction
mixture through the GF/C plate, then wash each plate four times with
cold wash buffer. (5) Dry the filtration plate at 50° for 1 h. After
drying, seal the bottom of the filter plate holes with Perkin Elmer
Unifilter-96 backing seal tape. Adding 50 μl of Perkin Elmer Microscint
20 cocktail. Seal the top of filter plates with Perkin Elmer TopSeal-A
sealing film. Count 3H trapped on the filter using the Perkin Elmer
MicroBeta2 Reader. The key reagents utilized in the Filtration Binding
Assay include: Ascorbic acid (TCI-A0537), DTT (Sigma, Cat: 43815, Lot:
BCBD7009V), Ultima Gold cocktail (PerkinElmer, Cat: 6013329, Lot:
77-16371), and Microscint 20 cocktail (PerkinElmer- 6013329).
The interaction between MH and THRα was validated by using the
LanthaScreen™ TR-FRET co-activator assay (Invitrogen, Cat# PV4587, Lot#
2322026). The LanthaScreen™ TR-FRET has been previously used for
detecting the interactions of hERα, and the Tb3 + -labeled anti-GST
antibody y (fluorescence donor) with the fluorescein-labeled ligand
Fluormone ES2™ (fluorescence acceptor 1)^[460]92.
The interaction between MH and PPARγ (Peroxisome Proliferator-Activated
Receptor γ) was validated by the LanthaScreen™ TR-FRET competitive
binding assay kit (Invitrogen-PV4894). The experimental methods used by
this study are the same as those in a previous study for detecting the
interactions between PPAR-γ and pioglitazone^[461]93.
We used the Cisbio cAMP detection kit to validate the interactions of
MH with Beta3. The Cisbio cAMP detection kit (Cat # 62 AM4PEJ) was
previously used to identify the interactions between PAGly and
β2AR^[462]95. Here, we used a similar experimental procedure.
Supplementary information
[463]Supplemental Material^ (1.8MB, docx)
Acknowledgements