Next Article in Journal
Identification of Genomic Signatures for Colorectal Cancer Survival Using Exploratory Data Mining
Previous Article in Journal
Evaluating the Anti-Osteoarthritis Potential of Standardized Boswellia serrata Gum Resin Extract in Alleviating Knee Joint Pathology and Inflammation in Osteoarthritis-Induced Models
Previous Article in Special Issue
NLRC5 Deficiency Reduces LPS-Induced Microglial Activation via Inhibition of NF-κB Signaling and Ameliorates Mice’s Depressive-like Behavior
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 25
Issue 6
10.3390/ijms25063219
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Peripheral Upregulation of Parkinson’s Disease-Associated Genes Encoding α-Synuclein, β-Glucocerebrosidase, and Ceramide Glucosyltransferase in Major Depression

by
Razvan-Marius Brazdis
1,
Claudia von Zimmermann
1,
Bernd Lenz
1,2,
Johannes Kornhuber
1 and
Christiane Mühle
1,*
1
Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen and Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
2
Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(6), 3219; https://doi.org/10.3390/ijms25063219
Submission received: 1 February 2024 / Revised: 7 March 2024 / Accepted: 9 March 2024 / Published: 12 March 2024
(This article belongs to the Special Issue Molecular Research on Depression)
Browse Figures
Versions Notes

Abstract

:
Due to the high comorbidity of Parkinson’s disease (PD) with major depressive disorder (MDD) and the involvement of sphingolipids in both conditions, we investigated the peripheral expression levels of three primarily PD-associated genes: α-synuclein (SNCA), lysosomal enzyme β-glucocerebrosidase (GBA1), and UDP-glucose ceramide glucosyltransferase (UGCG) in a sex-balanced MDD cohort. Normalized gene expression was determined by quantitative PCR in patients suffering from MDD (unmedicated n = 63, medicated n = 66) and controls (remitted MDD n = 39, healthy subjects n = 61). We observed that expression levels of SNCA (p = 0.036), GBA1 (p = 0.014), and UGCG (p = 0.0002) were higher in currently depressed patients compared to controls and remitted patients, and expression of GBA1 and UGCG decreased in medicated patients during three weeks of therapy. Additionally, in subgroups, expression was positively correlated with the severity of depression and anxiety. Furthermore, we identified correlations between the gene expression levels and PD-related laboratory parameters. Our findings suggest that SNCA, GBA1, and UGCG analysis could be instrumental in the search for biomarkers of MDD and in understanding the overlapping pathological mechanisms underlying neuro-psychiatric diseases.

1. Introduction

Major depressive disorder (MDD) is one of the most severe and common psychiatric disorders. It strongly impairs psychosocial functioning and diminishes quality of life. Depression has an extreme global economic burden and has been listed as one of the leading causes of disability by the World Health Organization (WHO). According to the WHO, by 2030, depression will be the leading cause of disease burden in high-income countries [1]. A major depressive episode (MDE) is characterized by a combination of affective, social, and somatic symptoms, such as persistently low or depressed mood, anhedonia or decreased interest in pleasurable activities, fatigue, feelings of guilt or worthlessness, poor concentration, changes in appetite and weight, sleep disturbances, and/or suicidal thoughts [2]. A variety of pathomechanisms have been proposed to underlie the etiology of the disease and the effects of antidepressants. These mechanisms span from disrupted monoamines, oxidative pathways, and the hypothalamic–pituitary–adrenal axis to neurotrophic homeostasis and inflammatory processes [3,4]. Additionally, prenatal factors are believed to augment the risk for depression and suicide in adulthood [5]. In recent years, considerable evidence has emerged regarding the dysregulation of sphingolipid metabolism in depression and comorbid anxiety [6,7], supported by findings from animal models [8,9,10] and clinical studies revealing significantly elevated levels of the central sphingolipid ceramide in patients with MDD, alongside its correlation with the severity of depression [11,12].
Depression is also one of the most important and frequent non-motor symptoms in Parkinson’s disease (PD) with an overall prevalence of about 40% [13,14]. PD is the most common neurodegenerative movement disorder affecting 1–2% of the population aged over 65 years [15]. Age is considered to be the greatest risk factor for PD [16] and according to sociodemographic developments, the incidence rate of PD increases. Over the course of their PD, male patients experience neuropsychiatric disturbances, including depression, anxiety, sleep disturbances, psychosis, and behavioral and cognitive changes [17]. The main neuropathological hallmark of PD is the presence of eosinophilic neuronal inclusions, mainly consisting of abnormal aggregated α-synuclein (α-Syn) and known as Lewy bodies (LBs) [18]. α-Syn is a small (~14 kDa) presynaptic soluble neuronal protein that aggregates through monomers, oligomeric intermediates to insoluble amyloid fibrils and finally deposits along with other proteins into LBs [19]. α-Syn gene (SNCA) missense mutations, as well as its duplication and triplication, cause early-onset parkinsonism [20], pointing out the importance of α-Syn pathology for PD and other synucleinopathies.
PD is also intricately connected to sphingolipid metabolism. Abundantly present in the eukaryotic cell membrane, sphingolipids also play a crucial role in diverse cellular processes, including cell differentiation, signal transduction, and apoptosis [21]. Reduced activity of acid sphingomyelinase, which catalyzes the hydrolysis of sphingomyelin to ceramide, has been linked to an earlier onset of PD [22]. Different mutations of the corresponding gene SMPD1 have been associated with an increased risk for PD in the Ashkenazi Jewish as well as Chinese populations [23]. Mutations in the β-glucocerebrosidase gene (GBA1) that encodes for the lysosomal enzyme β-glucocerebrosidase (GCase) are considered to be the most important genetic risk factor for PD [24]. Functional loss of GCase, which cleaves glycosylceramide to yield ceramide, has been linked with increased α-Syn aggregation and neurotoxicity [25]. Accordingly, pharmacologically driven reduction in glycosphingolipid levels in a human-induced pluripotent stem cell-derived model can revert toxic α-Syn oligomers to their normal physiological forms [26]. Moreover, UDP-glucose ceramide glucosyltransferase (UGCG), the enzyme responsible for the de novo synthesis of glucosylceramide, is also associated with several diseases including PD [27]. Mechanistically, sphingolipids are thought to act as modulators of α-Syn aggregation as they could compete with α-Syn for binding sites on cellular membranes, potentially altering membrane fluidity and protein trafficking, or directly with α-Syn, modulating its folding and aggregation [28]. For example, the GCase substrate glycosylceramide was found to directly stabilize the soluble oligomeric intermediate form of α-Syn. Vice versa, α-Syn inhibited the activity of wild-type GCase such that the bidirectional effect may lead to a self-propagating disease [29,30].
Another hallmark of PD is the progressive degeneration of dopaminergic neurons in the substantia nigra pars compacta [31]. Dopamine is critical for movement, and its progressive loss causes the cardinal motor features of PD, including bradykinesia, rigidity, tremor, and postural instability [32]. The non-motor symptoms such as depression, anxiety, or psychosis are based on the dysregulation of dopaminergic processes of mainly mesocortical and mesolimbic pathways, non-dopaminergic processes, and their interactions [33,34]. The subsequent risk of PD is significantly higher in patients with depression as compared with healthy individuals [35,36]. These connections may run deeper, as PD may directly precipitate mood disorders. Alterations in the brain due to PD can trigger depression and anxiety. Also, depression can exacerbate other PD symptoms. Notably, approximately 12–37% of PD patients receive a depression diagnosis before the clinical identification of their motor symptoms [37].
The high incidence of comorbidity suggests that depression and PD may be related by a shared cellular pathway involving sphingolipids and α-Syn. Despite accumulating evidence supporting the involvement of sphingolipids in PD [38,39], research examining PD-related genes in MDD remains limited. This prompted us to conduct an analysis of gene expression, with a special focus on SNCA, GBA1, and UGCG. We utilized samples from our large, sex-balanced cohort of patients experiencing an MDE, along with matched controls, to investigate their potential as biomarkers for depression and anxiety. Building on previous research, our main hypothesis posited group differences in the expression of these three genes within peripheral blood cells between depressed patients and healthy individuals. Upon observing these differences, we delved deeper into the associations of gene expressions with the severity of depression and anxiety. Additionally, we studied their correlations with laboratory parameters related to PD.

2. Results

2.1. Cohort Characteristics

The study involved a cohort of 230 patients and controls with 227 individuals providing at least one gene expression data point for analysis (see Table 1). Sex distribution was evenly balanced among unmedicated and medicated patients as well as healthy controls, except for remitted patients with substantially more females than males. Overall, there were no significant differences in age, education years, or body mass index (BMI) between currently depressed patients and the combined group of remitted patients and healthy controls. However, medicated patients presented with higher BMI, likely due to antidepressant side effects. Elevated scores across all three depression scales (Hamilton Depression Rating Scale (HAM-D), Montgomery and Åsberg Depression Rating Scale (MADRS), and Beck Depression Inventory-II (BDI-II)) highlighted the medium to high severity of depression among patients with a current MDE. These scores showed a decline from baseline to follow-up approximately 3 weeks later (all p < 10−7), indicating therapeutic effectiveness. Remitted patients displayed lower depression scores compared to those with current MDE, although scores remained higher than those observed in healthy controls.
There were no significant differences in the gene expression of SNCA, GBA, and UGCG between males and females, neither in the entire cohort nor within subgroup analyses (all p > 0.088, Table 1), enabling a combined assessment. Moreover, none of the gene expressions at T1 was associated with age, duration of education, or body mass index in any of the four groups (all p > 0.074).

2.2. Elevated Gene Expression of SNCA, GBA1, and UGCG in Depressed Patients

In the absence of sex-based differences, our initial analysis compared all patients experiencing a current MDE (unmedicated and medicated) with controls devoid of depression (patients with remitted MDD and healthy controls). We identified significantly elevated expression levels of all three genes in individuals with depression: SNCA (+27%, p = 0.036), GBA1 (+56%, p = 0.014), and UGCG (+75%, p = 0.0002) (Figure 1a–c). In exploratory analyses stratified by sex, we noted similarly heightened levels in both male and female subgroups, with UGCG exhibiting the most pronounced effects. Upon subgroup comparison, healthy controls and remitted patients did not display significant differences; however, medicated patients exhibited slightly elevated expression levels (20–29%) compared to unmedicated patients, with statistical significance observed solely for UGCG (p = 0.028, Supplementary Table S1).
Throughout the three-week period of standard treatment until the follow-up assessment, elevated levels did not revert to baseline in patients (Figure 1d–f). Notably, a significant reduction in levels was observed solely among medicated patients (both males and females), with GBA decreasing by 21% (p = 0.028) and UGCG by 23% (p = 0.032) for the total groups.

2.3. Associations of SNCA, GBA1, and UGCG Expressions with Depression Severity and Anxiety

Based on the elevated gene expression levels observed in depressed patients compared to unaffected individuals, we conducted an exploratory analysis to determine whether high gene expression levels correlated with depression severity as assessed by clinicians (HAM-D, MADRS) or self-evaluation (BDI-II) with subsequent sex-stratified analysis in the event of nominally significant effects. However, we found that only SNCA mRNA levels exhibited a significant positive correlation with BDI-II scores, but not with other scales, in the group of medicated and unmedicated patients at inclusion (ρ = 0.190, p = 0.031) and in the female subgroup (ρ = 0.256, p = 0.034, see Table 2). Furthermore, we did not observe any significant predictive association between gene expression levels and follow-up scores or their changes during therapy or associations with anxiety in patients with MDE.
Notably, elevated expression levels of SNCA, GBA1, and UGCG genes correlated with higher HAM-D and MADRS depression scores in patients with remitted MDD, observed across the entire cohort and specifically in the female subgroup (refer to Table 3). These associations demonstrated consistently moderate effects (Figure 2).
Consistent with the high prevalence of comorbidity between depression and anxiety, sum scores for trait anxiety (with a theoretical range of 20–80) were elevated, nearly doubling in both medicated and unmedicated patients compared to healthy controls (Table 1), surpassing the suggested cut-off point of 39–40 for clinical significance [40]. Therefore, akin to depression severity scores, we examined the correlation between SNCA, GBA1, and UGCG expressions and anxiety scores. Remarkably, only among remitted patients, elevated SNCA and UGCG expression levels were associated with higher trait anxiety scores at baseline (see Table 3). This association was observed specifically in female patients, with no significant correlation noted in males (Figure 2).

2.4. Associations of SNCA, GBA1, and UGCG Expressions with Routine Blood Parameters

In our exploratory analysis, we investigated potential correlations between PD-related gene expression levels and routine blood parameters, particularly those with a potential link to PD. We identified a weak trend associating SNCA and GBA1 expressions with human serum albumin levels in depressive patients at inclusion (ρ = 0.164, p = 0.064, and ρ = 0.157, p = 0.079, respectively), which appeared stronger in the male subgroup (ρ = 0.315, p = 0.014; ρ = 0.230, p = 0.077), and particularly evident in medicated patients (ρ = 0.287, p = 0.019; ρ = 0.255, p = 0.042).
In relation to blood calcium levels, we observed sex-specific effects, with contrasting correlations noted for male (ρ = −0.548, p = 0.006) and female (ρ = 0.336, p = 0.065) currently depressed patients who were unmedicated at inclusion, concerning the relative change in SNCA expression levels from inclusion to follow-up.
Additionally, we uncovered a robust correlation between gene expression and serum lactate dehydrogenase activity, particularly notable among males within the subgroup of patients with remitted MDD (refer to Table 4).
Lastly, the expression levels of the three genes exhibited associations with creatine kinase in individuals currently not affected by depression (see Table 4), displaying a notably stronger negative correlation among male healthy controls for GBA1 and UGCG (ρ = −0.410, p = 0.024 and ρ = −0.427, ρ = 0.023, respectively).

3. Discussion

In this study, we investigated the expression of the PD-related genes SNCA, GBA1, and UGCG in human blood cells of MDD patients and matched controls and their association with the severity of depression, anxiety, and PD-related laboratory parameters. Our principal finding reveals significantly elevated expression levels of all three genes among moderately to severely depressed patients (n = 129) compared to a combined group consisting of patients with remitted MDD and healthy controls (n = 97). The increased levels of SNCA mRNA are in line with a previous smaller study involving 70 patients and 18 controls [41]. This is also supported by the detection of higher α-Syn protein levels in serum samples of 132 inpatients with MDD compared to controls independent of age suggesting that depression could influence α-Syn metabolism and thus possibly not only function as a prodromal symptom of Lewy body dementia but also as a potential causal risk factor [42]. Furthermore, the involvement of SNCA in psychiatric disorders was also observed in a study of eating disorders, which demonstrated a positive correlation between the severity of depressive symptoms and SNCA mRNA levels [43].
A potential mechanistic explanation for the linkage of SNCA to MDD involves the negative modulation of serotonin transporter activity by α-Syn through the formation of heteromeric complexes via direct protein–protein interactions, as suggested by co-immunoprecipitation studies, and the subsequent reduction in transporter levels at the plasma membrane due to increased SNCA expression [44]. Additionally, α-Syn has been observed to influence the activity and trafficking of the norepinephrine transporter, a process contingent upon its interactions with microtubules [45]. The translation of elevated SNCA expression levels into modifications of α-syn conformational states and bioavailability, as well as the correlation between peripheral and central protein levels, necessitate further investigation. Given that studies in rats exhibiting depressive-like symptoms have revealed an upregulation of γ-Syn in the frontal cortex [46], exploration of additional members of the synuclein family, namely β- and γ-synuclein, in patients is warranted.
To our knowledge, this study represents the first demonstration of elevated GBA1 (p = 0.014) and UGCG (p = 0.0002) mRNA expression levels in patients experiencing a current MDE compared to patients with remitted MDD and healthy controls. Importantly, these elevations persist even after conservative correction for multiple tests using the Bonferroni method, thus reinforcing the robustness of our findings. While the observed decrease in both GBA1 and UCGC expression levels during the three-week course of antidepressive therapy, leading toward normalization, was only noted in the subgroup of patients already receiving medication at the time of inclusion, it further underscores the potential involvement of these PD-related sphingolipid enzymes in MDD. It is worth noting that the relatively short time frame of three weeks may have limited the magnitude of effects, particularly in the group of patients who were not receiving medication at the time of inclusion. While data on UGCG in PD is limited, GCase (GBA1) has been thoroughly investigated, with lower activities associated with increased aggregation of α-Syn [25] and thus pathological conditions. Accordingly, overexpression of GCase in the central nervous system in mice reduces α-Syn levels with the potential to modulate the progression of alpha-synucleinopathies [47]. In this context, discovering elevated levels of GBA1 expression in individuals with the disease may seem unexpected and potentially protective for MDD patients and/or could also be part of a negative feedback loop regulating actual protein levels of the enzyme. Most studies have primarily focused on reduced GCase activity as it is strongly linked to PD. However, a study utilizing the small molecule chaperone ambroxol has demonstrated an increase in GCase activity, which corresponded with approximately a 20% decrease in both α-Syn and phosphorylated α-Syn protein levels in mice [48]. It is intriguing to observe an elevation in the expression levels of enzymes catalyzing opposing reactions, such as the de novo synthesis of glucosylceramide (UGCG) versus its cleavage (GBA1). However, such a phenomenon could be interpreted as indicative of increased turnover and/or the possibility that the specific localization of these sphingolipids may elicit distinct effects.
The potential involvement of α-Syn in MDD is further substantiated by the positive correlation observed between depression severity (self-assessment by BDI-II) and SNCA expression levels in both medicated and unmedicated patients. Even in patients with remitted MDD, where depression severity, as assessed by clinicians using HAM-D and MADRS, is notably lower, expression levels of all three genes still exhibit positive associations with medium effect sizes ranging from 0.3 to 0.4 (Spearman’s ρ).
Prior investigations indicate a notable comorbidity between depression and anxiety, with rates reaching up to 50–80% [49,50]. Strong evidence supports the existence of similar pathological mechanisms underlying both depressive and anxious disorders. They share numerous genetic [51], familial, and environmental risk factors [52], and are frequent features observed in PD. While anxiety risk in PD has been less extensively studied compared to depression, numerous studies highlight its high prevalence [53]. In our assessment, we evaluated trait anxiety using the STAI and noted that elevated SNCA and UGCG expressions coincided with high anxiety scores specifically within the group of remitted patients—similar to the associations identified for depression severity scores, with comparable effect sizes. This link was observed only for trait anxiety, a more stable personality characteristic, as opposed to state anxiety, which represents a temporary feeling and exhibits weaker or absent correlations with these gene expressions.
In an exploratory approach, we examined correlations between gene expression levels and laboratory parameters related to PD. Human serum albumin, known to decelerate α-Syn aggregation [54], was also identified within a 2D gel protein biomarker panel designed to differentiate PD patients from age-matched controls based on blood serum proteins [55]. In our study, we noted a positive correlation between serum albumin levels and SNCA expression among patients, particularly within the subgroup of male patients receiving medication.
Neuronal calcium homeostasis plays a pivotal role in regulating aging, and neurodegeneration, and also in triggering α-Syn oligomerization [56]. While numerous studies link elevated neuronal calcium levels with α-Syn aggregation, their peripheral interaction remains relatively understudied and less understood. Some investigations have highlighted a correlation between hypocalcemia and the severity of PD [57]. In our study, we identified a robust inverse correlation between the relative expression change in SNCA during therapy and serum calcium values in both male and female patients. Although this represents the sole contrasting sex-specific effect observed in our investigation, it is important to note that different biological mechanisms in males and females regarding depression have been documented [58] and warrant further exploration. Moreover, the concept of masculine depression, which refers to alternative depression symptoms more commonly associated with the male gender, such as externalizing behaviors, emotional suppression, substance misuse, and risk-seeking [59,60,61], deserves further attention.
Elevated lactate dehydrogenase (LDH) activity has been associated with neurodegenerative disorders, including PD, potentially reflecting neuronal damage and inflammation [62]. On the other hand, reduced levels of serum LDH were found to be associated with depression and suicide attempts [63]. While a definitive connection between SNCA, GBA1, UGCG expression, and LDH has not yet been clearly established [64,65], we observed a robust correlation between the expression of these genes and serum LDH levels in our male patients with remitted MDD.
Creatine kinase (CK) is one of the primary kinases involved in promoting α-Syn phosphorylation [66]. Studies have indicated that PD patients exhibit elevated levels of serum CK [67]. Moreover, the serum ubiquitous but not the sarcomeric forms of the mitochondrial CK activity were significantly decreased in PD patients compared to controls and correlated significantly with the disease progression rate, duration, and age at onset [68]. Interestingly, in our male control group, we observed a strong negative correlation between the expression levels of SNCA, GBA1, UGCG, and CK.
Our study exhibits several strengths. Firstly, the size of the sex-balanced cohort, encompassing the four subgroups and the monitoring of the treatment course, has to be highlighted. Although the follow-up period of approximately 3 weeks was relatively short and a longer duration would aid in the potential identification of (stronger) effects, the second visit still enabled us to analyze the predictive potential of gene expression. Another strength lies in the inclusion of three scales for depression severity, either rated by a clinician or through self-evaluation. Moreover, we meticulously excluded psychiatric comorbidities such as post-traumatic stress disorder, along with patients taking anti-inflammatory medications. These measures contribute to the robustness and specificity of our findings.
The present study is subject to several limitations. While the primary and novel finding of increased gene expression levels for GBA and UGCG in depressed patients remained significant after correction for multiple testing, the majority of our observations remain at an exploratory level with small to medium effect sizes. Additionally, the associational study design restricts our ability to draw causative conclusions or assume direct physiological links with gene expressions. Further investigations could include the analysis of protein levels of α-Syn in these samples [69,70,71] or isolated exosomes [72], as well as activities of the respective enzymes and their associations with sphingolipid levels of their substrates and products. It could be informative to extend activity assays to further enzymes of the sphingolipid pathway such as acid and neutral ceramidases and sphingomyelinases. Recent evidence suggests the latter to be detectable in human serum/plasma samples [73]. Neutral sphingomyelinase was also found to be strongly reduced in the hippocampus of PD-induced mice in association with neuroinflammation [74]. Moreover, inhibition of acid ceramidase in GC-ase deficient patient-derived dopaminergic neurons resulted in increased ceramide, and decreased glycosylsphingosine levels eventually reduced oxidized α-Syn levels suggesting acid ceramidase inhibition as a potential therapeutic strategy for synucleinopathies linked to GBA1 mutations [75]. In addition, exploring the pattern of splice variants and their effects could be valuable areas for future research. Moreover, we cannot exclude the possibility that observed alterations represent adverse metabolic effects of psychotropic drugs. Therefore, investigating the effect of nonpharmacological antidepressant treatments such as music therapy (Behzad et al., manuscript submitted) or bouldering and mental models [76] on the expression of PD-related genes could provide valuable insights. Given that up to 30% of patients with major depressive disorder are therapy refractory, which could be also related to inflammatory processes [77], it is crucial to further explore the underlying pathological mechanisms and their interactions with immune responses and stress, which are important factors in major depressive disorder and PD pathologies [78]. Additionally, our cohort, recruited at a university hospital from an academic surrounding, might not be fully representative of patients and healthy controls in other regions or cultures. Replication and extension of these results are certainly warranted in patients of both sexes, with sufficient group size to differentiate between different therapeutic approaches. We also acknowledge that the analysis of expression levels in peripheral leukocytes may not fully reflect the pathophysiological processes occurring within the central nervous system. Mouse models of PD and MDD, with easier access to region-specific brain material, could further substantiate the investigation of the interaction between sphingolipid and α-Syn pathology [79].

4. Materials and Methods

4.1. Study Description

The study was conducted utilizing samples and data from the CeraBiDe (“Ceramide-associated Biomarkers in Depression”) study [80,81,82,83,84]. Recruitment took place between 01/2014 and 01/2017, adhering to the ethical principles outlined in the sixth revision of the Declaration of Helsinki (Seoul 2008) and the International Conference on Harmonization Guidelines for Good Clinical Practice (1996) and had received approval from the Ethics Committee of the Medical Faculty of the Friedrich-Alexander University Erlangen-Nürnberg (FAU, ID 148_13 B, 17 July 2013). Written informed consent was obtained from all participants. Depressed patients were recruited from both inpatient and outpatient facilities of the Department of Psychiatry and Psychotherapy at the Universitätsklinikum Erlangen, along with other individuals meeting the inclusion criteria. Healthy control subjects were recruited from the local community through various means of advertisement and underwent a rigorous screening process to exclude severe somatic and psychiatric morbidity, except nicotine dependence. The study included 130 patients with a current MDE, of which 64 were not taking any antidepressants for at least 2 weeks, while 66 were on stable antidepressant regimes for the same duration. Additionally, 61 healthy control subjects and 39 patients with remitted MDD were included in the study. Remitted MDD patients were individuals with a first MDE onset before the age of 60 and no depressive episode in the preceding 12 months. Inclusion criteria comprised age between 18 and 75 years and a BMI of 18.5–35.0 kg/m2, while exclusion criteria included severe physical illness, autoimmune disorders, pregnancy, breastfeeding, and recent use of anti-inflammatory drugs or corticosteroids within the last 7 days (details provided in [17]). All participants underwent screening using the structured clinical interview for DSM-IV (SKID-I), blood sampling, and psychometric tests at inclusion (T1). A subset of 59 unmedicated and 60 medicated patients participated in a direct follow-up with blood sampling and psychometric scales 21 and 19 days post-inclusion (median), with interquartile ranges of 17–28 and 15–24, respectively (T2). Throughout the observation period, all patients received treatment as usual, including adjustments to psychotropic drug administration for some individuals (details provided in [81]).

4.2. Psychometric Scales

The depression severity was measured by trained staff using the 17-item version of Hamilton Depression Rating Scale (HAM-D, [85]) and the 10-item Montgomery and Åsberg Depression Rating Scale (MADRS, [86]). In addition, it was self-reported with the Beck Depression Inventory-II (BDI-II, [87]). The levels of trait and state anxiety were self-evaluated by employing the 40-item State–Trait Anxiety Inventory (STAI) [88].

4.3. Collection and Analysis of Blood Samples

Blood samples were obtained from all individuals in the morning following an overnight fast to reduce circadian variations at inclusion (T1) and at follow-up (T2). PAXgene TM Blood RNA tubes (Qiagen, Hilden, Germany) were stored at −80 °C for later isolation of RNA. Separate vials were collected for routine laboratory tests, which were carried out at the accredited Central Laboratory of the University Hospital Erlangen, Germany, in accordance with DIN EN ISO 15189 standards [89].

4.4. Gene Expression Analysis by Quantitative PCR

The PAXgene TM Blood RNA Kit (Qiagen, Hilden, Germany) was used to isolate total RNA from PAX blood tubes according to the manufacturer’s instructions. The concentration and purification of RNA were determined photometrically using a NanoDrop ND-1000 UV–Vis spectrophotometer (Peqlab, Erlangen, Germany), and the integrity of representative samples was verified on agarose gels in adherence with accepted guidelines for quality control [90]. Five hundred nanograms of RNA were used in a 20 µL reverse transcription reaction using the Quanta cDNA Kit (Gaithersburg, MD, USA) to synthesize cDNA.
The expression levels of SNCA, GBA1, and UGCG were assessed via quantitative PCR (qPCR) using the LightCycler System (LightCycler® SW 1.5, Roche Diagnostics GmbH, Mannheim, Germany). Duplicate 5 μL reactions were set up in 384-well plates using GoTaq qPCR Master Mix containing a dsDNA binding dye (Promega, Madison, WI, USA), 2 μL of 1:20 diluted cDNA, and 200 nM of the following gene-specific primers: 5′-ATGTTGGAGGAGCAGTGGTG-3′ and 5′-CTGTGGGGCTCCTTCTTCA-3′ for SNCA, 5′-ATGGAGCGGTGAATGGGAAG-3′ and 5′-GTGCTCAGCATAGGCATCCAG-3′ for GBA1, and 5′-GAATGGCCGTCTTCGGGTT-3′ and 5′-AGGTGTATCGGGTGTAGATGAT-3′ for UGCG. The cycling conditions for all three genes included an initial denaturation step at 95 °C for 2 min, followed by 50 cycles of amplification (3 s denaturation at 95 °C, 20 s annealing and amplification at 60 °C), and a cooling step at 40 °C for 30 s. A melting profile was incorporated to verify product specificity. The expression levels of reference genes beta-actin (ACTB), ornithine decarboxylase 1 (ODC1), and beta-2-microglobulin (B2M) were determined in a multiplex qPCR using double fluorescently labeled probes, as previously described [84]. Quantification cycles were determined using the “Abs Quant/2nd Derivative Max” analysis method provided by the LightCycler Software. The geometric mean of the duplicates was adjusted for separately determined gene-specific efficiencies. Each target gene expression was normalized to the geometric mean of the three reference genes. Finally, outliers lying beyond 2.5 standard deviations from the mean based on logarithmized expression values were removed (1 and 2 for SNCA for T1 and T2, respectively, 3 and 3 for GBA, 3 and 3 for UGCG).

4.5. Statistics

The data underwent analysis using IBM SPSS Statistics Version 29 for Windows (SPSS Inc., Chicago, IL, USA) and were visualized using GraphPad Prism 9.5.1 (733) (GraphPad Software Inc., San Diego, CA, USA). We utilized nonparametric methods as the gene expression values did not follow a normal distribution, as confirmed by the Kolmogorov–Smirnov test. Bivariate correlations were evaluated using Spearman’s method. Group differences for continuous variables were tested using the Mann–Whitney U test and for nominal variables using the χ2 test, while differences between inclusion and follow-up were assessed using the Wilcoxon signed-rank test for related samples. Continuous data were expressed as the median and interquartile ranges within tables, calculated using SPSS’s custom tables function. Subjects with missing data points were excluded from specific analyses. Three individuals were completely excluded for the lack of any gene expression data at T1. A significance level of p < 0.05 for two-sided tests was considered nominally statistically significant. To maintain transparency, p values were not corrected for multiple testing, and nominal p values are reported in tables and graphs. Initially, female and male subjects were analyzed together for primary hypotheses, followed by explorative sex-specific analysis due to well-established sex differences in depression [58].

5. Conclusions

In summary, our findings reveal a significantly elevated peripheral gene expression of SNCA, GBA1, and UGCG among patients currently experiencing depression which partially normalizes for GBA1 and UGCG in the group of medicated patients and also reflects increased depression severity in subgroups. These findings further support the convergent mechanisms between depression and PD [91] involving sphingolipid pathways [92]. They hold promise for informing the development of diagnostic biomarkers and elucidating pathological mechanisms of depression, with the ultimate goal of advancing novel therapeutic approaches. Given their association with PD, these genes may also suggest specific overlapping pathomechanisms, prompting further investigation into potential biological interactions between these two conditions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms25063219/s1.

Author Contributions

Conceptualization, R.-M.B., J.K. and C.M.; methodology, R.-M.B. and C.M.; validation, R.-M.B. and C.M.; formal analysis, R.-M.B. and C.M.; investigation, R.-M.B. and C.M.; resources, B.L., C.v.Z., J.K. and C.M.; data curation, R.-M.B. and C.M.; writing—original draft preparation, R.-M.B. and C.M.; writing—review and editing, all authors; visualization, R.-M.B. and C.M.; supervision, C.M.; project administration, C.M.; funding acquisition, R.-M.B., J.K. and C.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by grants of the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) KO 947/13 to J.K., and the German Federal Ministry of Education and Research (BMBF) 01EE1401C to J.K. The work was also supported by intramural grants from the Universitätsklinikum of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). R.-M.B. is a fellow of the Interdisciplinary Center for Clinical Research (IZKF) of the FAU, which has also funded his laboratory rotation within the Clinician Scientist Programme. C.M. is a member of the research training group 2162 “Neurodevelopment and Vulnerability of the Central Nervous System” of the DFG (GRK2162/270949263).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Medical Faculty of the Friedrich-Alexander Universität Erlangen-Nürnberg (ID 148 13 B, 17 July 2013).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available upon request.

Acknowledgments

We gratefully appreciate the support of Lea Böhm, Anna-Isabell Fischer, Cornelia Musenbichler, Felicitas von Nippold, and Merle Winkelmann in recruiting patients and healthy control subjects. We are thankful to Alexander Gagel, Bruno Gegenhuber, and Hedya Riesop for their excellent technical support with the blood samples.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Malhi, G.S.; Mann, J.J. Depression. Lancet 2018, 392, 2299–2312. [Google Scholar] [CrossRef]
  2. Kennedy, S.H. Core symptoms of major depressive disorder: Relevance to diagnosis and treatment. Dialogues Clin. Neurosci. 2008, 10, 271–277. [Google Scholar] [CrossRef]
  3. Dean, J.; Keshavan, M. The neurobiology of depression: An integrated view. Asian J. Psychiatry 2017, 27, 101–111. [Google Scholar] [CrossRef]
  4. Schaller, G.; Sperling, W.; Richter-Schmidinger, T.; Muhle, C.; Heberlein, A.; Maihofner, C.; Kornhuber, J.; Lenz, B. Serial repetitive transcranial magnetic stimulation (rTMS) decreases BDNF serum levels in healthy male volunteers. J. Neural Transm. 2014, 121, 307–313. [Google Scholar] [CrossRef]
  5. Lenz, B.; Thiem, D.; Bouna-Pyrrou, P.; Muhle, C.; Stoessel, C.; Betz, P.; Kornhuber, J. Low digit ratio (2D:4D) in male suicide victims. J. Neural Transm. 2016, 123, 1499–1503. [Google Scholar] [CrossRef] [PubMed]
  6. van der Heijden, A.R.; Houben, T. Lipids in major depressive disorder: New kids on the block or old friends revisited? Front. Psychiatry 2023, 14, 1213011. [Google Scholar] [CrossRef] [PubMed]
  7. Muhle, C.; Bilbao Canalejas, R.D.; Kornhuber, J. Sphingomyelin Synthases in Neuropsychiatric Health and Disease. Neurosignals 2019, 27, 54–76. [Google Scholar] [CrossRef]
  8. Zoicas, I.; Muhle, C.; Schumacher, F.; Kleuser, B.; Kornhuber, J. Development of Comorbid Depression after Social Fear Conditioning in Mice and Its Effects on Brain Sphingolipid Metabolism. Cells 2023, 12, 1355. [Google Scholar] [CrossRef] [PubMed]
  9. Wang, M.; Yang, L.; Chen, Z.; Dai, L.; Xi, C.; Wu, X.; Wu, G.; Wang, Y.; Hu, J. Geniposide ameliorates chronic unpredictable mild stress induced depression-like behavior through inhibition of ceramide-PP2A signaling via the PI3K/Akt/GSK3beta axis. Psychopharmacology 2021, 238, 2789–2800. [Google Scholar] [CrossRef]
  10. Zoicas, I.; Muhle, C.; Schmidtner, A.K.; Gulbins, E.; Neumann, I.D.; Kornhuber, J. Anxiety and Depression Are Related to Higher Activity of Sphingolipid Metabolizing Enzymes in the Rat Brain. Cells 2020, 9, 1239. [Google Scholar] [CrossRef] [PubMed]
  11. Schumacher, F.; Edwards, M.J.; Muhle, C.; Carpinteiro, A.; Wilson, G.C.; Wilker, B.; Soddemann, M.; Keitsch, S.; Scherbaum, N.; Muller, B.W.; et al. Ceramide levels in blood plasma correlate with major depressive disorder severity and its neutralization abrogates depressive behavior in mice. J. Biol. Chem. 2022, 298, 102185. [Google Scholar] [CrossRef] [PubMed]
  12. Lv, H.; Wang, H.; Xie, L.; Zou, D.; Liu, P.; Hu, Z.; Ma, R.; Shi, Y.; Zheng, G.; Zhang, G. Serum ceramide concentrations are associated with depression in patients after ischemic stroke-A two-center case-controlled study. Clin. Chim. Acta 2021, 518, 110–115. [Google Scholar] [CrossRef]
  13. Stefanova, N.; Seppi, K.; Scherfler, C.; Puschban, Z.; Wenning, G.K. Depression in alpha-synucleinopathies: Prevalence, pathophysiology and treatment. J. Neural Transm. Suppl. 2000, 335–343. [Google Scholar] [CrossRef]
  14. Cong, S.; Xiang, C.; Zhang, S.; Zhang, T.; Wang, H.; Cong, S. Prevalence and clinical aspects of depression in Parkinson’s disease: A systematic review and meta-analysis of 129 studies. Neurosci. Biobehav. Rev. 2022, 141, 104749. [Google Scholar] [CrossRef] [PubMed]
  15. Ntetsika, T.; Papathoma, P.E.; Markaki, I. Novel targeted therapies for Parkinson’s disease. Mol. Med. 2021, 27, 17. [Google Scholar] [CrossRef] [PubMed]
  16. Raket, L.L.; Oudin Astrom, D.; Norlin, J.M.; Kellerborg, K.; Martinez-Martin, P.; Odin, P. Impact of age at onset on symptom profiles, treatment characteristics and health-related quality of life in Parkinson’s disease. Sci. Rep. 2022, 12, 526. [Google Scholar] [CrossRef]
  17. Dujardin, K.; Sgambato, V. Neuropsychiatric Disorders in Parkinson’s Disease: What Do We Know About the Role of Dopaminergic and Non-dopaminergic Systems? Front. Neurosci. 2020, 14, 25. [Google Scholar] [CrossRef]
  18. Spillantini, M.G.; Schmidt, M.L.; Lee, V.M.; Trojanowski, J.Q.; Jakes, R.; Goedert, M. Alpha-synuclein in Lewy bodies. Nature 1997, 388, 839–840. [Google Scholar] [CrossRef]
  19. Srinivasan, E.; Chandrasekhar, G.; Chandrasekar, P.; Anbarasu, K.; Vickram, A.S.; Karunakaran, R.; Rajasekaran, R.; Srikumar, P.S. Alpha-Synuclein Aggregation in Parkinson’s Disease. Front. Med. 2021, 8, 736978. [Google Scholar] [CrossRef]
  20. Ross, O.A.; Braithwaite, A.T.; Skipper, L.M.; Kachergus, J.; Hulihan, M.M.; Middleton, F.A.; Nishioka, K.; Fuchs, J.; Gasser, T.; Maraganore, D.M.; et al. Genomic investigation of alpha-synuclein multiplication and parkinsonism. Ann. Neurol. 2008, 63, 743–750. [Google Scholar] [CrossRef]
  21. Jamjoum, R.; Majumder, S.; Issleny, B.; Stiban, J. Mysterious sphingolipids: Metabolic interrelationships at the center of pathophysiology. Front. Physiol. 2023, 14, 1229108. [Google Scholar] [CrossRef]
  22. Alcalay, R.N.; Mallett, V.; Vanderperre, B.; Tavassoly, O.; Dauvilliers, Y.; Wu, R.Y.J.; Ruskey, J.A.; Leblond, C.S.; Ambalavanan, A.; Laurent, S.B.; et al. SMPD1 mutations, activity, and alpha-synuclein accumulation in Parkinson’s disease. Mov. Disord. 2019, 34, 526–535. [Google Scholar] [CrossRef]
  23. Gan-Or, Z.; Orr-Urtreger, A.; Alcalay, R.N.; Bressman, S.; Giladi, N.; Rouleau, G.A. The emerging role of SMPD1 mutations in Parkinson’s disease: Implications for future studies. Park. Relat. Disord. 2015, 21, 1294–1295. [Google Scholar] [CrossRef]
  24. Smith, L.; Schapira, A.H.V. GBA Variants and Parkinson Disease: Mechanisms and Treatments. Cells 2022, 11, 1261. [Google Scholar] [CrossRef]
  25. Klein, A.D.; Outeiro, T.F. Glucocerebrosidase mutations disrupt the lysosome and now the mitochondria. Nat. Commun. 2023, 14, 6383. [Google Scholar] [CrossRef] [PubMed]
  26. Zunke, F.; Moise, A.C.; Belur, N.R.; Gelyana, E.; Stojkovska, I.; Dzaferbegovic, H.; Toker, N.J.; Jeon, S.; Fredriksen, K.; Mazzulli, J.R. Reversible Conformational Conversion of alpha-Synuclein into Toxic Assemblies by Glucosylceramide. Neuron 2018, 97, 92–107.e110. [Google Scholar] [CrossRef] [PubMed]
  27. Ishibashi, Y.; Ito, M.; Hirabayashi, Y. The sirtuin inhibitor cambinol reduces intracellular glucosylceramide with ceramide accumulation by inhibiting glucosylceramide synthase. Biosci. Biotechnol. Biochem. 2020, 84, 2264–2272. [Google Scholar] [CrossRef]
  28. Kiechle, M.; Grozdanov, V.; Danzer, K.M. The Role of Lipids in the Initiation of alpha-Synuclein Misfolding. Front. Cell Dev. Biol. 2020, 8, 562241. [Google Scholar] [CrossRef]
  29. Munoz, S.S.; Petersen, D.; Marlet, F.R.; Kucukkose, E.; Galvagnion, C. The interplay between Glucocerebrosidase, alpha-synuclein and lipids in human models of Parkinson’s disease. Biophys. Chem. 2021, 273, 106534. [Google Scholar] [CrossRef]
  30. Mazzulli, J.R.; Xu, Y.H.; Sun, Y.; Knight, A.L.; McLean, P.J.; Caldwell, G.A.; Sidransky, E.; Grabowski, G.A.; Krainc, D. Gaucher disease glucocerebrosidase and alpha-synuclein form a bidirectional pathogenic loop in synucleinopathies. Cell 2011, 146, 37–52. [Google Scholar] [CrossRef] [PubMed]
  31. Kalia, L.V.; Lang, A.E. Parkinson’s disease. Lancet 2015, 386, 896–912. [Google Scholar] [CrossRef] [PubMed]
  32. Fahn, S. Description of Parkinson’s disease as a clinical syndrome. Ann. N. Y. Acad. Sci. 2003, 991, 1–14. [Google Scholar] [CrossRef] [PubMed]
  33. Chaudhuri, K.R.; Schapira, A.H. Non-motor symptoms of Parkinson’s disease: Dopaminergic pathophysiology and treatment. Lancet Neurol. 2009, 8, 464–474. [Google Scholar] [CrossRef] [PubMed]
  34. Todorova, A.; Jenner, P.; Ray Chaudhuri, K. Non-motor Parkinson’s: Integral to motor Parkinson’s, yet often neglected. Pract. Neurol. 2014, 14, 310–322. [Google Scholar] [CrossRef] [PubMed]
  35. Bareeqa, S.B.; Samar, S.S.; Kamal, S.; Masood, Y.; Allahyar; Ahmed, S.I.; Hayat, G. Prodromal depression and subsequent risk of developing Parkinson’s disease: A systematic review with meta-analysis. Neurodegener. Dis. Manag. 2022, 12, 155–164. [Google Scholar] [CrossRef] [PubMed]
  36. Wang, S.; Mao, S.; Xiang, D.; Fang, C. Association between depression and the subsequent risk of Parkinson’s disease: A meta-analysis. Prog. Neuropsychopharmacol. Biol. Psychiatry 2018, 86, 186–192. [Google Scholar] [CrossRef]
  37. Allain, H.; Schuck, S.; Mauduit, N. Depression in Parkinson’s disease. BMJ 2000, 320, 1287–1288. [Google Scholar] [CrossRef]
  38. Paciotti, S.; Albi, E.; Parnetti, L.; Beccari, T. Lysosomal Ceramide Metabolism Disorders: Implications in Parkinson’s Disease. J. Clin. Med. 2020, 9, 594. [Google Scholar] [CrossRef]
  39. Motyl, J.A.; Strosznajder, J.B.; Wencel, A.; Strosznajder, R.P. Recent Insights into the Interplay of Alpha-Synuclein and Sphingolipid Signaling in Parkinson’s Disease. Int. J. Mol. Sci. 2021, 22, 6277. [Google Scholar] [CrossRef]
  40. Julian, L.J. Measures of anxiety: State-Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Arthritis Care Res. 2011, 63 (Suppl. S11), S467–S472. [Google Scholar] [CrossRef]
  41. Rotter, A.; Lenz, B.; Pitsch, R.; Richter-Schmidinger, T.; Kornhuber, J.; Rhein, C. Alpha-Synuclein RNA Expression is Increased in Major Depression. Int. J. Mol. Sci. 2019, 20, 2029. [Google Scholar] [CrossRef] [PubMed]
  42. Ishiguro, M.; Baba, H.; Maeshima, H.; Shimano, T.; Inoue, M.; Ichikawa, T.; Yasuda, S.; Shukuzawa, H.; Suzuki, T.; Arai, H. Increased Serum Levels of alpha-Synuclein in Patients With Major Depressive Disorder. Am. J. Geriatr. Psychiatry 2019, 27, 280–286. [Google Scholar] [CrossRef] [PubMed]
  43. Frieling, H.; Gozner, A.; Romer, K.D.; Wilhelm, J.; Hillemacher, T.; Kornhuber, J.; de Zwaan, M.; Jacoby, G.E.; Bleich, S. Alpha-synuclein mRNA levels correspond to beck depression inventory scores in females with eating disorders. Neuropsychobiology 2008, 58, 48–52. [Google Scholar] [CrossRef] [PubMed]
  44. Wersinger, C.; Rusnak, M.; Sidhu, A. Modulation of the trafficking of the human serotonin transporter by human alpha-synuclein. Eur. J. Neurosci. 2006, 24, 55–64. [Google Scholar] [CrossRef] [PubMed]
  45. Jeannotte, A.M.; Sidhu, A. Regulation of the norepinephrine transporter by alpha-synuclein-mediated interactions with microtubules. Eur. J. Neurosci. 2007, 26, 1509–1520. [Google Scholar] [CrossRef]
  46. Jeannotte, A.M.; McCarthy, J.G.; Redei, E.E.; Sidhu, A. Desipramine modulation of alpha-, gamma-synuclein, and the norepinephrine transporter in an animal model of depression. Neuropsychopharmacology 2009, 34, 987–998. [Google Scholar] [CrossRef] [PubMed]
  47. Sardi, S.P.; Clarke, J.; Viel, C.; Chan, M.; Tamsett, T.J.; Treleaven, C.M.; Bu, J.; Sweet, L.; Passini, M.A.; Dodge, J.C.; et al. Augmenting CNS glucocerebrosidase activity as a therapeutic strategy for parkinsonism and other Gaucher-related synucleinopathies. Proc. Natl. Acad. Sci. USA 2013, 110, 3537–3542. [Google Scholar] [CrossRef]
  48. Migdalska-Richards, A.; Daly, L.; Bezard, E.; Schapira, A.H. Ambroxol effects in glucocerebrosidase and alpha-synuclein transgenic mice. Ann. Neurol. 2016, 80, 766–775. [Google Scholar] [CrossRef]
  49. Kaiser, T.; Herzog, P.; Voderholzer, U.; Brakemeier, E.L. Unraveling the comorbidity of depression and anxiety in a large inpatient sample: Network analysis to examine bridge symptoms. Depress. Anxiety 2021, 38, 307–317. [Google Scholar] [CrossRef]
  50. Lamers, F.; van Oppen, P.; Comijs, H.C.; Smit, J.H.; Spinhoven, P.; van Balkom, A.J.; Nolen, W.A.; Zitman, F.G.; Beekman, A.T.; Penninx, B.W. Comorbidity patterns of anxiety and depressive disorders in a large cohort study: The Netherlands Study of Depression and Anxiety (NESDA). J. Clin. Psychiatry 2011, 72, 341–348. [Google Scholar] [CrossRef]
  51. Brainstorm, C.; Anttila, V.; Bulik-Sullivan, B.; Finucane, H.K.; Walters, R.K.; Bras, J.; Duncan, L.; Escott-Price, V.; Falcone, G.J.; Gormley, P.; et al. Analysis of shared heritability in common disorders of the brain. Science 2018, 360, eaap8757. [Google Scholar] [CrossRef]
  52. Kessler, R.C.; Berglund, P.; Demler, O.; Jin, R.; Merikangas, K.R.; Walters, E.E. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 2005, 62, 593–602. [Google Scholar] [CrossRef] [PubMed]
  53. Kulisevsky, J.; Pagonabarraga, J.; Pascual-Sedano, B.; Garcia-Sanchez, C.; Gironell, A.; Trapecio Group, S. Prevalence and correlates of neuropsychiatric symptoms in Parkinson’s disease without dementia. Mov. Disord. 2008, 23, 1889–1896. [Google Scholar] [CrossRef]
  54. Bellomo, G.; Bologna, S.; Cerofolini, L.; Paciotti, S.; Gatticchi, L.; Ravera, E.; Parnetti, L.; Fragai, M.; Luchinat, C. Dissecting the Interactions between Human Serum Albumin and alpha-Synuclein: New Insights on the Factors Influencing alpha-Synuclein Aggregation in Biological Fluids. J. Phys. Chem. B 2019, 123, 4380–4386. [Google Scholar] [CrossRef] [PubMed]
  55. Goldknopf, I.L.; Bryson, J.K.; Strelets, I.; Quintero, S.; Sheta, E.A.; Mosqueda, M.; Park, H.R.; Appel, S.H.; Shill, H.; Sabbagh, M.; et al. Abnormal serum concentrations of proteins in Parkinson’s disease. Biochem. Biophys. Res. Commun. 2009, 389, 321–327. [Google Scholar] [CrossRef] [PubMed]
  56. Rcom-H’cheo-Gauthier, A.N.; Osborne, S.L.; Meedeniya, A.C.; Pountney, D.L. Calcium: Alpha-Synuclein Interactions in Alpha-Synucleinopathies. Front. Neurosci. 2016, 10, 570. [Google Scholar] [CrossRef]
  57. Emad, E.M.; Elmotaym, A.S.E.; Ghonemy, M.m.A.; Badawy, A.E. The effect of hypocalcemia on motor symptoms of Parkinson’s disease. Egypt. J. Neurol. Psychiatry Neurosurg. 2022, 58, 76. [Google Scholar] [CrossRef]
  58. Ma, L.; Xu, Y.; Wang, G.; Li, R. What do we know about sex differences in depression: A review of animal models and potential mechanisms. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2019, 89, 48–56. [Google Scholar] [CrossRef]
  59. von Zimmermann, C.; Hubner, M.; Muhle, C.; Muller, C.P.; Weinland, C.; Kornhuber, J.; Lenz, B. Masculine depression and its problem behaviors: Use alcohol and drugs, work hard, and avoid psychiatry! Eur. Arch. Psychiatry Clin. Neurosci. 2023, 274, 321–333. [Google Scholar] [CrossRef]
  60. von Zimmermann, C.; Bruckner, L.; Muhle, C.; Weinland, C.; Kornhuber, J.; Lenz, B. Bioimpedance Body Measures and Serum Lipid Levels in Masculine Depression. Front. Psychiatry 2022, 13, 794351. [Google Scholar] [CrossRef]
  61. Sedlinska, T.; Muhle, C.; Richter-Schmidinger, T.; Weinland, C.; Kornhuber, J.; Lenz, B. Male depression syndrome is characterized by pronounced Cluster B personality traits. J. Affect. Disord. 2021, 292, 725–732. [Google Scholar] [CrossRef]
  62. Yang, C.; Pan, R.Y.; Guan, F.; Yuan, Z. Lactate metabolism in neurodegenerative diseases. Neural Regen. Res. 2024, 19, 69–74. [Google Scholar] [CrossRef]
  63. Yao, Q.; Liu, H.; Li, Y. Low levels of serum LDH are associated with depression and suicide attempts. Gen. Hosp. Psychiatry 2022, 79, 42–49. [Google Scholar] [CrossRef]
  64. Abounit, S.; Bousset, L.; Loria, F.; Zhu, S.; de Chaumont, F.; Pieri, L.; Olivo-Marin, J.C.; Melki, R.; Zurzolo, C. Tunneling nanotubes spread fibrillar alpha-synuclein by intercellular trafficking of lysosomes. EMBO J. 2016, 35, 2120–2138. [Google Scholar] [CrossRef] [PubMed]
  65. White, A.J.; Wijeyekoon, R.S.; Scott, K.M.; Gunawardana, N.P.; Hayat, S.; Solim, I.H.; McMahon, H.T.; Barker, R.A.; Williams-Gray, C.H. The Peripheral Inflammatory Response to Alpha-Synuclein and Endotoxin in Parkinson’s Disease. Front. Neurol. 2018, 9, 946. [Google Scholar] [CrossRef] [PubMed]
  66. Li, G.; Liu, J.; Guo, M.; Gu, Y.; Guan, Y.; Shao, Q.; Ma, W.; Ji, X. Chronic hypoxia leads to cognitive impairment by promoting HIF-2alpha-mediated ceramide catabolism and alpha-synuclein hyperphosphorylation. Cell Death Discov. 2022, 8, 473. [Google Scholar] [CrossRef] [PubMed]
  67. Takubo, H.; Shimoda-Matsubayashi, S.; Mizuno, Y. Serum creatine kinase is elevated in patients with Parkinson’s disease: A case controlled study. Park. Relat. Disord. 2003, 9 (Suppl. S1), S43–S46. [Google Scholar] [CrossRef]
  68. Xu, J.; Fu, X.; Pan, M.; Zhou, X.; Chen, Z.; Wang, D.; Zhang, X.; Chen, Q.; Li, Y.; Huang, X.; et al. Mitochondrial Creatine Kinase is Decreased in the Serum of Idiopathic Parkinson’s Disease Patients. Aging Dis. 2019, 10, 601–610. [Google Scholar] [CrossRef]
  69. Aminabad, E.D.; Hasanzadeh, M.; Ahmadalipour, A.; Mahmoudi, T.; Feizi, M.A.H.; Safaralizadeh, R.; Mobed, A. Sensitive electrochemical recognition of alpha-synuclein protein in human plasma samples using bioconjugated gold nanoparticles: An innovative immuno-platform to assist in the early stage identification of Parkinson’s disease by biosensor technology. J. Mol. Recognit. 2023, 36, e2952. [Google Scholar] [CrossRef]
  70. Aminabad, E.D.; Mobed, A.; Hasanzadeh, M.; Hosseinpour Feizi, M.A.; Safaralizadeh, R.; Seidi, F. Sensitive immunosensing of alpha-synuclein protein in human plasma samples using gold nanoparticles conjugated with graphene: An innovative immuno-platform towards early stage identification of Parkinson’s disease using point of care (POC) analysis. RSC Adv. 2022, 12, 4346–4357. [Google Scholar] [CrossRef]
  71. Youssef, P.; Kim, W.S.; Halliday, G.M.; Lewis, S.J.G.; Dzamko, N. Comparison of Different Platform Immunoassays for the Measurement of Plasma Alpha-Synuclein in Parkinson’s Disease Patients. J. Park. Dis. 2021, 11, 1761–1772. [Google Scholar] [CrossRef]
  72. Zheng, H.; Xie, Z.; Zhang, X.; Mao, J.; Wang, M.; Wei, S.; Fu, Y.; Zheng, H.; He, Y.; Chen, H.; et al. Investigation of alpha-Synuclein Species in Plasma Exosomes and the Oligomeric and Phosphorylated alpha-Synuclein as Potential Peripheral Biomarker of Parkinson’s Disease. Neuroscience 2021, 469, 79–90. [Google Scholar] [CrossRef]
  73. Muhle, C.; Kornhuber, J. Characterization of a Neutral Sphingomyelinase Activity in Human Serum and Plasma. Int. J. Mol. Sci. 2023, 24, 2467. [Google Scholar] [CrossRef]
  74. Cataldi, S.; Arcuri, C.; Hunot, S.; Legeron, F.P.; Mecca, C.; Garcia-Gil, M.; Lazzarini, A.; Codini, M.; Beccari, T.; Tasegian, A.; et al. Neutral Sphingomyelinase Behaviour in Hippocampus Neuroinflammation of MPTP-Induced Mouse Model of Parkinson’s Disease and in Embryonic Hippocampal Cells. Mediat. Inflamm. 2017, 2017, 2470950. [Google Scholar] [CrossRef]
  75. Kim, M.J.; Jeon, S.; Burbulla, L.F.; Krainc, D. Acid ceramidase inhibition ameliorates alpha-synuclein accumulation upon loss of GBA1 function. Hum. Mol. Genet. 2018, 27, 1972–1988. [Google Scholar] [CrossRef]
  76. Kind, L.; Luttenberger, K.; Lessmann, V.; Dorscht, L.; Muhle, C.; Muller, C.P.; Siegmann, E.M.; Schneider, S.; Kornhuber, J. New ways to cope with depression-study protocol for a randomized controlled mixed methods trial of bouldering psychotherapy (BPT) and mental model therapy (MMT). Trials 2023, 24, 602. [Google Scholar] [CrossRef]
  77. Haroon, E.; Daguanno, A.W.; Woolwine, B.J.; Goldsmith, D.R.; Baer, W.M.; Wommack, E.C.; Felger, J.C.; Miller, A.H. Antidepressant treatment resistance is associated with increased inflammatory markers in patients with major depressive disorder. Psychoneuroendocrinology 2018, 95, 43–49. [Google Scholar] [CrossRef]
  78. Correia, A.S.; Cardoso, A.; Vale, N. Highlighting Immune System and Stress in Major Depressive Disorder, Parkinson’s, and Alzheimer’s Diseases, with a Connection with Serotonin. Int. J. Mol. Sci. 2021, 22, 8525. [Google Scholar] [CrossRef] [PubMed]
  79. Kalinichenko, L.S.; Kohl, Z.; Muhle, C.; Hassan, Z.; Hahn, A.; Schmitt, E.M.; Macht, K.; Stoyanov, L.; Moghaddami, S.; Bilbao, R.; et al. Sex-specific pleiotropic changes in emotional behavior and alcohol consumption in human alpha-synuclein A53T transgenic mice during early adulthood. J. Neurochem. 2024, 168, 269–287. [Google Scholar] [CrossRef] [PubMed]
  80. Muhle, C.; Wagner, C.J.; Farber, K.; Richter-Schmidinger, T.; Gulbins, E.; Lenz, B.; Kornhuber, J. Secretory Acid Sphingomyelinase in the Serum of Medicated Patients Predicts the Prospective Course of Depression. J. Clin. Med. 2019, 8, 846. [Google Scholar] [CrossRef] [PubMed]
  81. Wagner, C.J.; Musenbichler, C.; Bohm, L.; Farber, K.; Fischer, A.I.; von Nippold, F.; Winkelmann, M.; Richter-Schmidinger, T.; Muhle, C.; Kornhuber, J.; et al. LDL cholesterol relates to depression, its severity, and the prospective course. Prog. Neuropsychopharmacol. Biol. Psychiatry 2019, 92, 405–411. [Google Scholar] [CrossRef]
  82. von Zimmermann, C.; Winkelmann, M.; Richter-Schmidinger, T.; Muhle, C.; Kornhuber, J.; Lenz, B. Physical Activity and Body Composition Are Associated With Severity and Risk of Depression, and Serum Lipids. Front. Psychiatry 2020, 11, 494. [Google Scholar] [CrossRef]
  83. von Zimmermann, C.; Bohm, L.; Richter-Schmidinger, T.; Kornhuber, J.; Lenz, B.; Muhle, C. Ex vivo glucocorticoid receptor-mediated IL-10 response predicts the course of depression severity. J. Neural Transm. 2021, 128, 95–104. [Google Scholar] [CrossRef]
  84. Swoboda, C.; Deloch, L.; von Zimmermann, C.; Richter-Schmidinger, T.; Lenz, B.; Kornhuber, J.; Muhle, C. Macrophage Migration Inhibitory Factor in Major Depressive Disorder: A Multilevel Pilot Study. Int. J. Mol. Sci. 2022, 23, 5460. [Google Scholar] [CrossRef]
  85. Hamilton, M. A rating scale for depression. J. Neurol Neurosurg. Psychiatry 1960, 23, 56–62. [Google Scholar] [CrossRef]
  86. Montgomery, S.A.; Asberg, M. A new depression scale designed to be sensitive to change. Br. J. Psychiatry 1979, 134, 382–389. [Google Scholar] [CrossRef] [PubMed]
  87. Beck, A.T.; Steer, R.A.; Brown, G.K. Manual for the Beck Depression Inventory-II; Psychological Corporation: San Antonio, TX, USA, 1996; Volume 1. [Google Scholar]
  88. Spielberger, C.D.; Gorsuch, L.; Laux, L.; Glanzmann, P.; Schaffner, P. Das State-Trait-Angstinventar: STAI; Beltz Test: Göttingen, Germany, 2001. [Google Scholar]
  89. ISO 15189:2022; Medical Laboratories—Requirements for Quality and Competence. International Organization for Standardization: Geneva, Switzerland, 2022.
  90. Taylor, S.; Wakem, M.; Dijkman, G.; Alsarraj, M.; Nguyen, M. A practical approach to RT-qPCR-Publishing data that conform to the MIQE guidelines. Methods 2010, 50, S1–S5. [Google Scholar] [CrossRef] [PubMed]
  91. Tran, A.A.; De Smet, M.; Grant, G.D.; Khoo, T.K.; Pountney, D.L. Investigating the Convergent Mechanisms between Major Depressive Disorder and Parkinson’s Disease. Complex Psychiatry 2021, 6, 47–61. [Google Scholar] [CrossRef] [PubMed]
  92. Custodia, A.; Aramburu-Nunez, M.; Correa-Paz, C.; Posado-Fernandez, A.; Gomez-Larrauri, A.; Castillo, J.; Gomez-Munoz, A.; Sobrino, T.; Ouro, A. Ceramide Metabolism and Parkinson’s Disease-Therapeutic Targets. Biomolecules 2021, 11, 945. [Google Scholar] [CrossRef] [PubMed]
Ijms 25 03219 g001
Figure 1. Peripheral gene expressions of SNCA (a), GBA (b), and UGCG (c) were significantly higher in patients with current MDE (combined unmedicated patients (PU) and medicated patients (PM)) at inclusion compared to unaffected individuals (combined remitted patients (PR) and healthy subjects (HC)). These levels remained for SNCA (d) but decreased between inclusion (T1) and follow-up (T2) after on average three weeks of treatment as usual for GBA1 (e) and UGCG (f) in the group of initially medicated patients. Normalized gene expression relative to reference genes is shown on a logarithmic y-axis. The numbers of individuals are provided below the x-axis. p-values from Mann–Whitney U test (a–c) and Wilcoxon test for paired values (d–f). Box plots with median and interquartile range.
Figure 1. Peripheral gene expressions of SNCA (a), GBA (b), and UGCG (c) were significantly higher in patients with current MDE (combined unmedicated patients (PU) and medicated patients (PM)) at inclusion compared to unaffected individuals (combined remitted patients (PR) and healthy subjects (HC)). These levels remained for SNCA (d) but decreased between inclusion (T1) and follow-up (T2) after on average three weeks of treatment as usual for GBA1 (e) and UGCG (f) in the group of initially medicated patients. Normalized gene expression relative to reference genes is shown on a logarithmic y-axis. The numbers of individuals are provided below the x-axis. p-values from Mann–Whitney U test (a–c) and Wilcoxon test for paired values (d–f). Box plots with median and interquartile range.
Ijms 25 03219 g001
Ijms 25 03219 g002
Figure 2. Positive correlations between depression severity assessed by HAM-D (a–c), MADRS (d–f), and STAI trait (g–i) with peripheral gene expressions of SNCA, GBA, and UGCG in patients with remitted major depressive disorder (PR) separated in female (red dots) and male (blue dots) subgroups at inclusion. Linear regression line for the combined group with 95% confidence interval and statistics (Spearman correlation, in bold for p < 0.05). Sex-stratified statistical data are in Table 3.
Figure 2. Positive correlations between depression severity assessed by HAM-D (a–c), MADRS (d–f), and STAI trait (g–i) with peripheral gene expressions of SNCA, GBA, and UGCG in patients with remitted major depressive disorder (PR) separated in female (red dots) and male (blue dots) subgroups at inclusion. Linear regression line for the combined group with 95% confidence interval and statistics (Spearman correlation, in bold for p < 0.05). Sex-stratified statistical data are in Table 3.
Ijms 25 03219 g002
Table 1. Study cohort description and group differences for unmedicated, medicated, and remitted patients and control subjects at study inclusion (T1) and follow-up (T2, 3 weeks later).
Table 1. Study cohort description and group differences for unmedicated, medicated, and remitted patients and control subjects at study inclusion (T1) and follow-up (T2, 3 weeks later).
Unmed. PatientsMedicated PatientsRemitted PatientsHealthy Controls
nMedian (IQR)nMedian (IQR)nMedian (IQR)nMedian (IQR)p (Sex)p (Groups)
Age (years)6346 (33–53)6646 (33–54)3850 (46–58)6041 (32–54)0.0810.502
Education (years)5615 (13–18)5814 (13–16)3414 (13–16)5016 (13–18)0.0080.594
BMI
(kg/m2)		6325.1
(22.5–27.4)		6628.5
(24.4–30.4)		3825.7
(23.0–29.1)		6024.5
(23.0–27.8)		0.0050.190
HAM-D T16321 (19–24)6623 (20–26)382 (0–3)600 (0–2)0.743<0.001
HAM-D T25918 (14–20)6015 (10–22) 0.048
MADRS T16326 (23–28)6628 (24–34)381 (0–3)600 (0–2)0.950<0.001
MADRS T25921 (18–25)6018 (13–26) 0.052
BDI-II T16328 (22–34)6629 (24–35)383 (0–3)602 (0–3)0.528<0.001
BDI-II T25919 (15–25)6020 (13–31) 0.009
STAI state T16350 (40–56)6654 (43–63)3832 (26–36)6028 (26–31)0.910<0.001
STAI state T25946 (37–52)6047 (42–57) 0.819
STAI trait mean6360 (55–66)6658 (52–66)3834 (27–40)6028 (25–33)0.624<0.001
SNCA expression (AU) T16317.7
(9.8–26.3)		6621.2
(10.1–36.2)		3813.8
(7.6–34.2)		5915.6
(6.7–29.3)		0.6660.036
SNCA expression (AU) T25514.2
(8.2–25.5)		6020.3
(9.9–31.3)		 0.420
SNCA expression rel. change55−0.016
(−0.450–0.656)		60−0.044
(−0.370–0.556)		 0.322
GBA1 expression (AU) T1630.237
(0.136–0.474)		640.302
(0.177–0.458)		380.194
(0.072–0.395)		590.178
(0.081–0.429)		0.5700.014
GBA1 expression (AU) T2560.208
(0.099–0.394)		570.245
(0.149–0.493)		 0.255
GBA1 expression rel. change56−0.131
(−0.587–0.873)		56−0.210
(−0.444–0.201)		 0.435
UGCG exp. (AU) T1630.136
(0.070–0.200)		640.174
(0.105–0.291)		380.093
(0.048–0.173)		570.089
(0.047–0.215)		0.123<0.001
UGCG exp. (AU) T2570.122
(0.072–0.229)		570.147
(0.092–0.271)		 0.147
UGCG exp rel. change570.173
(−0.577–1.018)		55−0.227
(−0.499–0.178)		 0.431
The table shows frequencies (n) and median with interquartile range (IQR) at inclusion (T1) and follow-up (T2, at median 3 weeks later) and p values (nominal p < 0.05 in bold) from Mann–Whitney U tests comparing females with males (p (sex)) or patient groups (combined PU + PM) with controls (combined PR + HC). See Supplementary Table S1 for all group-wise comparisons. Sex-separated group sizes (females/males at T1 and T2, respectively)—PU unmedicated depressive patients (37/26; 34/25), PM medicated depressive patients (32/34; 28/32), PR patients with remitted major depressive disorder (28/10) and HC healthy controls (30/30). χ2 test for T1 sex distribution (PU + PM versus PR + HC) p = 0.392. Parameters—BMI body mass index, BDI-II Beck Depression Inventory-II, HAM-D Hamilton Depression Rating Scale, MADRS Montgomery–Åsberg Depression Rating Scale, STAI State–Trait Anxiety Inventory, peripheral gene expression for SNCA α-synuclein, GBA1 β-glucocerebrosidase, UGCG UDP-glucose ceramide glucosyltransferase in arbitrary units (AU) normalized to reference genes.
Table 2. Correlation of SNCA expression with depression severity scores as assessed by self-rating (BDI-II) in unmedicated (PU) and medicated (PM) patients with major depressive disorder at inclusion, ρ, and p bold for p < 0.05.
Table 2. Correlation of SNCA expression with depression severity scores as assessed by self-rating (BDI-II) in unmedicated (PU) and medicated (PM) patients with major depressive disorder at inclusion, ρ, and p bold for p < 0.05.
SNCA Expression
in PU + PM		HAM-DMADRSBDI-II
nρpρpρp
All1290.0160.8550.1070.2270.1900.031
Female690.1500.2190.1970.1050.2560.034
Male60−0.1300.323−0.0300.8180.0460.726
Table 3. Correlations of SNCA, GBA1, and UGCG expression with depression severity (assessed by HAM-D, MADRS, BDI-II) and trait anxiety (STAI) at inclusion in patients with remitted MDD, ρ, and p bold for p < 0.05.
Table 3. Correlations of SNCA, GBA1, and UGCG expression with depression severity (assessed by HAM-D, MADRS, BDI-II) and trait anxiety (STAI) at inclusion in patients with remitted MDD, ρ, and p bold for p < 0.05.
Remitted HAM-DMADRSBDI-IISTAI Trait
Patientsnρpρpρpρp
SNCAAll380.3630.0250.3500.0310.1860.2630.3250.047
Female280.3660.0560.3870.0420.2370.2240.3380.079
Male100.3310.3510.2590.471−0.0320.9310.2730.446
GBA1All380.3360.0390.4040.0120.2780.0910.3130.056
Female280.2880.1370.3900.0400.2950.1280.2310.237
Male100.2480.4900.2590.4710.1270.7260.3580.310
UGCGAll380.3270.0450.3800.0190.2620.1110.4040.012
Female280.3440.0730.4750.0110.3080.1110.3850.043
Male100.1330.7130.0320.929−0.0250.9440.2480.489
Table 4. Correlations of SNCA, GBA1, and UGCG expression with lactate dehydrogenase (LDH) and creatine kinase (CK) at inclusion in patients with remitted MDD and the combined group of healthy controls and remitted patients; rho and p bold for p < 0.05.
Table 4. Correlations of SNCA, GBA1, and UGCG expression with lactate dehydrogenase (LDH) and creatine kinase (CK) at inclusion in patients with remitted MDD and the combined group of healthy controls and remitted patients; rho and p bold for p < 0.05.
SNCAGBA1UGCG
nρpρpρp
LDHAll38−0.3460.033−0.4210.008−0.3770.020
RemittedFemale28−0.1800.359−0.2830.144−0.2500.199
PatientsMale10−0.8690.001−0.8880.001−0.6990.024
CKAll97−0.1150.261−0.1830.073−0.1540.136
Healthy controlsFemale58−0.0000.999−0.0330.809−0.0330.806
and remitted patientsMale39−0.3530.027−0.3840.015−0.3790.019
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Brazdis, R.-M.; von Zimmermann, C.; Lenz, B.; Kornhuber, J.; Mühle, C. Peripheral Upregulation of Parkinson’s Disease-Associated Genes Encoding α-Synuclein, β-Glucocerebrosidase, and Ceramide Glucosyltransferase in Major Depression. Int. J. Mol. Sci. 2024, 25, 3219. https://doi.org/10.3390/ijms25063219

AMA Style

Brazdis R-M, von Zimmermann C, Lenz B, Kornhuber J, Mühle C. Peripheral Upregulation of Parkinson’s Disease-Associated Genes Encoding α-Synuclein, β-Glucocerebrosidase, and Ceramide Glucosyltransferase in Major Depression. International Journal of Molecular Sciences. 2024; 25(6):3219. https://doi.org/10.3390/ijms25063219

Chicago/Turabian Style

Brazdis, Razvan-Marius, Claudia von Zimmermann, Bernd Lenz, Johannes Kornhuber, and Christiane Mühle. 2024. "Peripheral Upregulation of Parkinson’s Disease-Associated Genes Encoding α-Synuclein, β-Glucocerebrosidase, and Ceramide Glucosyltransferase in Major Depression" International Journal of Molecular Sciences 25, no. 6: 3219. https://doi.org/10.3390/ijms25063219

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop