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Molecular Biomarkers in Neurological Diseases

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Neurobiology".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 46215

Special Issue Editor


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Guest Editor
Regional Neurogenetic Center (CRN), Department of Primary Care, ASP Catanzaro, 88046 Lamezia Terme, Italy
Interests: neurological diseases; biomarkers; translational research; precision medicine; diagnosis; prognosis

Special Issue Information

Dear Colleagues,

Neurological diseases remain a major cause of disability and mortality and exert a serious social and financial burden. However, our understanding of the pathogenesis of neurological diseases at the molecular level is expanding day by day. Due to technological developments and new holistic approaches to translational research, an ever-increasing number of biomarkers at genomic, transcriptomic, epigenomic and proteomic levels are identified. These molecular biomarkers could allow to facilitate diagnosis, predict prognosis and guide treatment choices of patients with neurological diseases.

This special Issue in International Journal of Molecular Sciences on “Molecular Biomarkers in Neurological Diseases” will provide a monographic portrait of the current state of knowledge of molecular biomarkers for the diagnosis, prognosis, and neurological patients. Are especially welcome original research studies, review articles (either systematic or discursive), and short communications of preliminary, but significant, experimental results.

Dr. Francesco Bruno
Guest Editor

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Keywords

  • neurological diseases
  • biomarkers
  • translational research
  • precision medicine
  • diagnosis
  • prognosis

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Published Papers (11 papers)

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Research

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12 pages, 621 KiB  
Article
Upregulation of APAF1 and CSF1R in Peripheral Blood Mononuclear Cells of Parkinson’s Disease
by Kuo-Hsuan Chang, Chia-Hsin Liu, Yi-Ru Wang, Yen-Shi Lo, Chun-Wei Chang, Hsiu-Chuan Wu and Chiung-Mei Chen
Int. J. Mol. Sci. 2023, 24(8), 7095; https://doi.org/10.3390/ijms24087095 - 12 Apr 2023
Cited by 1 | Viewed by 2183
Abstract
Increased oxidative stress and neuroinflammation play a crucial role in the pathogenesis of Parkinson’s disease (PD). In this study, the expression levels of 52 genes related to oxidative stress and inflammation were measured in peripheral blood mononuclear cells of the discovery cohort including [...] Read more.
Increased oxidative stress and neuroinflammation play a crucial role in the pathogenesis of Parkinson’s disease (PD). In this study, the expression levels of 52 genes related to oxidative stress and inflammation were measured in peripheral blood mononuclear cells of the discovery cohort including 48 PD patients and 25 healthy controls. Four genes, including ALDH1A, APAF1, CR1, and CSF1R, were found to be upregulated in PD patients. The expression patterns of these genes were validated in a second cohort of 101 PD patients and 61 healthy controls. The results confirmed the upregulation of APAF1 (PD: 0.34 ± 0.18, control: 0.26 ± 0.11, p < 0.001) and CSF1R (PD: 0.38 ± 0.12, control: 0.33 ± 0.10, p = 0.005) in PD patients. The expression level of APAF1 was correlated with the scores of the Unified Parkinson’s Disease Rating Scale (UPDRS, r = 0.235, p = 0.018) and 39-item PD questionnaire (PDQ-39, r = 0.250, p = 0.012). The expression level of CSF1R was negatively correlated with the scores of the mini-mental status examination (MMSE, r = −0.200, p = 0.047) and Montréal Cognitive Assessment (MoCA, r = −0.226, p = 0.023). These results highly suggest that oxidative stress biomarkers in peripheral blood may be useful in monitoring the progression of motor disabilities and cognitive decline in PD patients. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Neurological Diseases)
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<p>(<b>A</b>) Difference in expression level of <span class="html-italic">APAF1</span> between Parkinson’s disease (PD) patients at early (N = 81) and advanced stages (N = 20) compared to controls (N = 61). (<b>B</b>,<b>C</b>) The correlation between expression level of <span class="html-italic">APAF1</span> and scores of Unified Parkinson’s Disease Rating Scale (UPDRS) or 39-item PD questionnaire (PDQ-39). (<b>D</b>,<b>E</b>) The correlation between expression level of <span class="html-italic">CSF1R</span> and the scores of mini-mental state examination (MMSE) or Montreal Cognitive Assessment (MoCA). *: Statistically significant between two groups, <span class="html-italic">p</span> &lt; 0.05, one-way analysis of variance (ANOVA) with Tukey’s post hoc test.</p>
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16 pages, 3842 KiB  
Article
Saliva and Saliva Extracellular Vesicles for Biomarker Candidate Identification—Assay Development and Pilot Study in Amyotrophic Lateral Sclerosis
by Sebastian Sjoqvist and Kentaro Otake
Int. J. Mol. Sci. 2023, 24(6), 5237; https://doi.org/10.3390/ijms24065237 - 9 Mar 2023
Cited by 14 | Viewed by 3485
Abstract
Saliva is gaining increasing attention as a source of biomarkers due to non-invasive and undemanding collection access. Extracellular vesicles (EVs) are nano-sized, cell-released particles that contain molecular information about their parent cells. In this study, we developed methods for saliva biomarker candidate identification [...] Read more.
Saliva is gaining increasing attention as a source of biomarkers due to non-invasive and undemanding collection access. Extracellular vesicles (EVs) are nano-sized, cell-released particles that contain molecular information about their parent cells. In this study, we developed methods for saliva biomarker candidate identification using EV-isolation and proteomic evaluation. We used pooled saliva samples for assay development. EVs were isolated using membrane affinity-based methods followed by their characterization using nanoparticle tracking analysis and transmission electron microscopy. Subsequently, both saliva and saliva-EVs were successfully analyzed using proximity extension assay and label-free quantitative proteomics. Saliva-EVs had a higher purity than plasma-EVs, based on the expression of EV-proteins and albumin. The developed methods could be used for the analysis of individual saliva samples from amyotrophic lateral sclerosis (ALS) patients and controls (n = 10 each). The starting volume ranged from 2.1 to 4.9 mL and the amount of total isolated EV-proteins ranged from 5.1 to 42.6 µg. Although no proteins were significantly differentially expressed between the two groups, there was a trend for a downregulation of ZNF428 in ALS-saliva-EVs and an upregulation of IGLL1 in ALS saliva. In conclusion, we have developed a robust workflow for saliva and saliva-EV analysis and demonstrated its technical feasibility for biomarker discovery. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Neurological Diseases)
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<p>Study overview. (<b>a</b>) Pooled, healthy control saliva was used for assay development (90 mL × 3 isolates). The saliva was cleared using differential centrifugation followed by isolation of extracellular vesicles using exoEasy. The isolates then underwent buffer exchange using ultrafiltration. The EVs were analyzed for total protein concentration, albumin concentration, transmission electron microscopy, proximity extension assay and mass spectrometry-based proteomics. (<b>b</b>) As a feasibility and pilot study, amyotrophic lateral sclerosis and matched control saliva samples were used (<span class="html-italic">n</span> = 10 each) with a volume ranging from 2.1 to 4.9 mL. The isolation protocol was the same except for minor changes in the clearing step. Isolated EVs and whole saliva samples were analyzed using total protein concentration measurements and mass spectrometry-based proteomics.</p>
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<p>Characterization of saliva-EVs. Both total protein concentration (<b>a</b>) and albumin (<b>b</b>) were considerably reduced in the saliva-EVs compared to whole saliva. Transmission electron microscopy identified spherical structures at low (<b>c</b>) and high magnification (<b>d</b>). (<b>e</b>) Nanoparticle tracking analysis was used to measure vesicle concentration and size distribution. (<b>f</b>) One milliliter of saliva yielded around 6 × 10<sup>8</sup> particles with a mean size of approximately 300 nm. Error bars represent standard deviation.</p>
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<p>Proximity extension assay of saliva and saliva-EVs. (<b>a</b>) Scatter plot indicating the number of detected proteins in different dilutions of saliva and saliva-EVs. (<b>b</b>) Both saliva and saliva-EV samples showed a high correlation between samples within each group, while the inter-group correlation was lower. (<b>c</b>) A volcano plot demonstrating differences between the groups. The top 10 differentially expressed proteins in each group are labelled.</p>
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<p>Mass spectrometry-based proteomics of saliva- and plasma-EVs. (<b>a</b>) We compared saliva-EVs to plasma-EVs and found that albumin, a contaminating protein, was more abundant in plasma-EVs, while there was no significant difference in APOA1. Several common transmembrane (<b>b</b>) and cytosolic (<b>c</b>) EV-associated proteins showed a higher expression in saliva-EVs compared to plasma-EVs. (<b>d</b>) A multi-scatter plot of saliva and plasma-EVs indicates strong intra-group correlations which are higher in the saliva-EVs compared to plasma-EVs. Unsupervised hierarchical clustering (<b>e</b>) and principal component analysis (<b>f</b>) could correctly cluster all samples. (<b>g</b>) A volcano plot was generated, depicting proteins which were upregulated in saliva-EVs (green squares), upregulated in plasma-EVs (gray squares) or with similar expression between the two groups (red squares). (<b>h</b>) To determine the minimum required amount of total protein for the detection of sufficient proteins, 0.5 µg, 2 µg and 20 µg of protein were analyzed. Two micrograms protein were deemed sufficient, with a minimal difference to 20 µg. * <span class="html-italic">p</span> = 0.05 ** <span class="html-italic">p</span> = 0.01, *** <span class="html-italic">p</span> = 0.001, **** <span class="html-italic">p</span> = 0.0001, ns—non-significant by two-sample <span class="html-italic">t</span>-test with permutation-based FDR. Error bars represent standard deviation.</p>
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<p>EV-isolation from patient and control saliva samples. (<b>a</b>) The starting volume and volume of cleared saliva for controls and ALS samples are plotted. Yellow dots represent whole saliva, green dots cleared saliva and red dots saliva with visible contamination. (<b>b</b>) Approximately 65% and 70% of the starting volume remained after clearing of control and ALS samples, respectively. A greater reduction was observed for contaminated samples (red dots) compared to non-contaminated samples (black dots). (<b>c</b>,<b>d</b>) The total protein concentrations in saliva (<b>c</b>) and saliva-EVs isolated from one milliliter saliva (<b>d</b>) were similar between the two groups, and within the range of pooled samples. (<b>e</b>) The EV-isolates represented approximately 0.75% of the total saliva protein concentration, with no significant difference between the sample groups and within the range of pooled samples. (<b>f</b>) The total EV-protein yield ranged from 42.6 µg to 5.1 µg, with no difference between the groups. For (<b>c</b>–<b>f)</b>: blue dots represent control samples, yellow squares ALS samples and gray dots pooled samples. Red dots or squares indicate samples with visible contamination. Error bars represent standard deviation.</p>
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<p>Proteomic analysis of saliva-EVs and cleared saliva. (<b>a</b>) There was no clear clustering of the samples based on principal component analysis and the contamination did not appear to affect the results. (<b>b</b>) There was no statistically significantly expressed protein between the groups, as depicted in a volcano plot, but there was a trend towards downregulation of ZNF428 in ALS saliva-EVs (<b>c</b>) black dots represent ALS samples, black squares control samples and red dots or squares represent samples with visible contamination. (<b>d</b>) Similarly, for saliva samples, there was no obvious clustering of the samples based on principal component analysis. (<b>e</b>) No proteins were statistically significantly expressed, although IGLL1 showed a complete separation between patients and controls (<b>f</b>) black dots represent ALS samples, black squares control samples and red dots or squares represent samples with visible contamination.</p>
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10 pages, 1233 KiB  
Article
Myelin Basic Protein in Oligodendrocyte-Derived Extracellular Vesicles as a Diagnostic and Prognostic Biomarker in Multiple Sclerosis: A Pilot Study
by Cristina Agliardi, Franca Rosa Guerini, Milena Zanzottera, Elisabetta Bolognesi, Silvia Picciolini, Domenico Caputo, Marco Rovaris, Maria Barbara Pasanisi and Mario Clerici
Int. J. Mol. Sci. 2023, 24(1), 894; https://doi.org/10.3390/ijms24010894 - 3 Jan 2023
Cited by 20 | Viewed by 5281
Abstract
Approximately 15% of multiple sclerosis (MS) patients develop a progressive form of disease from onset; this condition (primary progressive-PP) MS is difficult to diagnose and treat, and is associated with a poor prognosis. Extracellular vesicles (EVs) of brain origin isolated from blood and [...] Read more.
Approximately 15% of multiple sclerosis (MS) patients develop a progressive form of disease from onset; this condition (primary progressive-PP) MS is difficult to diagnose and treat, and is associated with a poor prognosis. Extracellular vesicles (EVs) of brain origin isolated from blood and their protein cargoes could function as a biomarker of pathological conditions. We verified whether MBP and MOG content in oligodendrocytes-derived EVs (ODEVs) could be biomarkers of MS and could help in the differential diagnosis of clinical MS phenotypes. A total of 136 individuals (7 clinically isolated syndrome (CIS), 18 PPMS, 49 relapsing remitting (RRMS)) and 70 matched healthy controls (HC) were enrolled. ODEVs were enriched from serum by immune-capture with anti-MOG antibody; MBP and MOG protein cargoes were measured by ELISA. MBP concentration in ODEVs was significantly increased in CIS (p < 0.001), RRMS (p < 0.001) and PPMS (p < 0.001) compared to HC and was correlated with disease severity measured by EDSS and MSSS. Notably, MBP concentration in ODEVs was also significantly augmented in PPMS compared to RRMS (p = 0.004) and CIS (p = 0.03). Logistic regression and ROC analyses confirmed these results. A minimally invasive blood test measuring the concentration of MBP in ODEVs is a promising tool that could facilitate MS diagnosis. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Neurological Diseases)
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<p><b>ODEVs characterization.</b> (<b>A</b>): Exo-Check™ Exosome Antibody Array on an exemplificative ODEVs lysate. In the image are visible exosomal associated markers: FLOT1 (flotillin-1), ICAM1 (intercellular adhesion molecule 1), ALIX (programmed cell death 6 interacting protein), CD81 and CD63 (tetraspanins), EpCAM (epithelial cell adhesion molecule), ANXA5 (annexin A5), TSG101 (tumor susceptibility gene 101) and controls (2 positive assay control, negative control: blank and GM130: cis-golgi matrix protein: control for cellular contamination). (<b>B</b>): Immuno-gold (OMGp antigen detected) TEM micrograph of an exemplificative ODEVs preparation. Scale bar: 100 nm. (<b>C</b>): Representative size distribution graph of nanoparticle tracking analysis (NTA) that shows size and concentration of enriched ODEVs in a sample from an RRMS patient; a frame of the video is also shown. Mean ODEVs concentration (particles/mL) ± SD and mean ODEVs diameter (nm) ± SD obtained by NTA analysis from five ODEVs samples from the three conditions (HC, PPMS, and RRMS). ANOVA tests <span class="html-italic">p</span> values are reported.</p>
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<p><b>MBP in enriched ODEVs in HC, CIS, RR-MS, and PP-MS.</b> (<b>A</b>): Multiple comparison graphs of MBP concentration in enriched ODEVs, respectively in HC, CIS, RRMS, and PPMS subjects; all data are plotted, and median and interquartile range (IQR) are reported. The reported global <span class="html-italic">p</span> values of the differences between the groups of subjects was calculated by Kruskal–Wallis test for non-parametric distributions. <span class="html-italic">p</span> values of post hoc Dwass–Steel–Critchlow–Fligner for pairwise comparisons are also reported. (<b>B</b>): Multiple comparison graphs of MBP concentration in enriched ODEVs, respectively in HC, (CIS + RRMS) and PP-MS subjects; all data are plotted; median and interquartile range (IQR) are reported. The reported global <span class="html-italic">p</span> values of the differences between the groups of subjects was calculated by Kruskal–Wallis test for non-parametric distributions. <span class="html-italic">p</span> values of post hoc Dwass–Steel–Critchlow–Fligner for pairwise comparisons are also reported.</p>
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<p><b>ROC curve analysis.</b> (<b>A</b>): ROC curves of MBP in enriched ODEVs: HC vs. MS (CIS + RRMS + PPMS). AUC and <span class="html-italic">p</span> value are reported. (<b>B</b>): ROC curves of MBP in enriched ODEVs: PPMS vs. (CIS + RRMS). AUC and <span class="html-italic">p</span> value are reported.</p>
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<p>(<b>A</b>): <b>Correlation between MBP in ODEVs and clinical scales</b>. Bivariate Pearson’s correlation between MBP concentration in enriched ODEVs and EDSS. (<b>B</b>): Bivariate Pearson’s correlation between MBP concentration in enriched ODEVs and MSSS.</p>
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14 pages, 983 KiB  
Communication
Rare Amyloid Precursor Protein Point Mutations Recapitulate Worldwide Migration and Admixture in Healthy Individuals: Implications for the Study of Neurodegeneration
by Paolo Abondio, Francesco Bruno, Amalia Cecilia Bruni and Donata Luiselli
Int. J. Mol. Sci. 2022, 23(24), 15871; https://doi.org/10.3390/ijms232415871 - 14 Dec 2022
Cited by 3 | Viewed by 1848
Abstract
Genetic discoveries related to Alzheimer’s disease and other dementias have been performed using either large cohorts of affected subjects or multiple individuals from the same pedigree, therefore disregarding mutations in the context of healthy groups. Moreover, a large portion of studies so far [...] Read more.
Genetic discoveries related to Alzheimer’s disease and other dementias have been performed using either large cohorts of affected subjects or multiple individuals from the same pedigree, therefore disregarding mutations in the context of healthy groups. Moreover, a large portion of studies so far have been performed on individuals of European ancestry, with a remarkable lack of epidemiological and genomic data from underrepresented populations. In the present study, 70 single-point mutations on the APP gene in a publicly available genetic dataset that included 2504 healthy individuals from 26 populations were scanned, and their distribution was analyzed. Furthermore, after gametic phase reconstruction, a pairwise comparison of the segments surrounding the mutations was performed to reveal patterns of haplotype sharing that could point to specific cross-population and cross-ancestry admixture events. Eight mutations were detected in the worldwide dataset, with several of them being specific for a single individual, population, or macroarea. Patterns of segment sharing reflected recent historical events of migration and admixture possibly linked to colonization campaigns. These observations reveal the population dynamics of the considered APP mutations in worldwide human groups and support the development of ancestry-informed screening practices for the improvement of precision and personalized approaches to neurodegeneration and dementia. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Neurological Diseases)
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<p>Amyloidogenic and nonamyloidogenic processing of the APP protein under physiological conditions. The region giving origin to the Aβ fragment is highlighted in red.</p>
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<p>Possible origin and cross-population sharing of mutation-carrying segments. The Caribbean population of admixed origin is indicated by red text and red dashed borders. The putative European contributions to individuals carrying mutations A713T in America and G708G in Vietnam are indicated by a blue question mark and blue dashed borders.</p>
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12 pages, 644 KiB  
Article
Association of LTA and SOD Gene Polymorphisms with Cerebral White Matter Hyperintensities in Migraine Patients
by Patrizia Ferroni, Raffaele Palmirotta, Gabriella Egeo, Cinzia Aurilia, Maria Giovanna Valente, Antonella Spila, Alberto Pierallini, Piero Barbanti and Fiorella Guadagni
Int. J. Mol. Sci. 2022, 23(22), 13781; https://doi.org/10.3390/ijms232213781 - 9 Nov 2022
Cited by 4 | Viewed by 2537
Abstract
White matter hyperintensities (WMHs) in migraine could be related to inflammatory and antioxidant events. The aim of this study is to verify whether migraine patients with WMHs carry a genetic pro-inflammatory/pro-oxidative status. To test this hypothesis, we analyzed lymphotoxin alpha (LTA; [...] Read more.
White matter hyperintensities (WMHs) in migraine could be related to inflammatory and antioxidant events. The aim of this study is to verify whether migraine patients with WMHs carry a genetic pro-inflammatory/pro-oxidative status. To test this hypothesis, we analyzed lymphotoxin alpha (LTA; rs2071590T and rs2844482G) and superoxide dismutase 1 (SOD1; rs2234694C) and 2 (SOD2; rs4880T) gene polymorphisms (SNPs) in 370 consecutive patients affected by episodic (EM; n = 251) and chronic (CM; n = 119) migraine and in unrelated healthy controls (n = 100). Brain magnetic resonance was available in 183/370 patients. The results obtained show that genotypes and allele frequencies for all tested SNPs did not differ between patients and controls. No association was found between single SNPs or haplotypes and sex, migraine type, cardiovascular risk factors or disorders. Conversely, the LTA rs2071590T (OR = 2.2) and the SOD1 rs2234694C (OR = 4.9) alleles were both associated with WMHs. A four-loci haplotype (TGCT haplotype: rs2071590T/rs2844482G/rs2234694C/rs4880T) was significantly more frequent in migraineurs with WMHs (7 of 38) compared to those without WMHs (4 of 134; OR = 8.7). We may, therefore, conclude by suggesting that that an imbalance between pro-inflammatory/pro-oxidative and antioxidant events in genetically predisposed individuals may influence the development of WMHs. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Neurological Diseases)
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<p>Flow chart of patients’ recruitment.</p>
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11 pages, 1127 KiB  
Article
Cerebrospinal Fluid Alpha-Synuclein Improves the Differentiation between Dementia with Lewy Bodies and Alzheimer’s Disease in Clinical Practice
by Matthieu Lilamand, Josué Clery, Agathe Vrillon, François Mouton-Liger, Emmanuel Cognat, Sinead Gaubert, Claire Hourregue, Matthieu Martinet, Julien Dumurgier, Jacques Hugon, Elodie Bouaziz-Amar and Claire Paquet
Int. J. Mol. Sci. 2022, 23(21), 13488; https://doi.org/10.3390/ijms232113488 - 4 Nov 2022
Cited by 3 | Viewed by 2319
Abstract
Background: Alpha-synuclein, abnormally aggregated in Dementia with Lewy Bodies (DLB), could represent a potential biomarker to improve the differentiation between DLB and Alzheimer’s disease (AD). Our main objective was to compare Cerebrospinal Fluid (CSF) alpha-synuclein levels between patients with DLB, AD and Neurological [...] Read more.
Background: Alpha-synuclein, abnormally aggregated in Dementia with Lewy Bodies (DLB), could represent a potential biomarker to improve the differentiation between DLB and Alzheimer’s disease (AD). Our main objective was to compare Cerebrospinal Fluid (CSF) alpha-synuclein levels between patients with DLB, AD and Neurological Control (NC) individuals. Methods: In a monocentric retrospective study, we assessed CSF alpha-synuclein concentration with a validated ELISA kit (ADx EUROIMMUN) in patients with DLB, AD and NC from a tertiary memory clinic. Between-group comparisons were performed, and Receiver Operating Characteristic analysis was used to identify the best CSF alpha-synuclein threshold. We examined the associations between CSF alpha-synuclein, other core AD CSF biomarkers and brain MRI characteristics. Results: We included 127 participants (mean age: 69.3 ± 8.1, Men: 41.7%). CSF alpha-synuclein levels were significantly lower in DLB than in AD (1.28 ± 0.52 ng/mL vs. 2.26 ± 0.91 ng/mL, respectively, p < 0.001) without differences due to the stage of cognitive impairment. The best alpha-synuclein threshold was characterized by an Area Under the Curve = 0.85, Sensitivity = 82.0% and Specificity = 76.0%. CSF alpha-synuclein was associated with CSF AT(N) biomarkers positivity (p < 0.01) but not with hippocampal atrophy or white matter lesions. Conclusion: CSF Alpha-synuclein evaluation could help to early differentiate patients with DLB and AD in association with existing biomarkers. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Neurological Diseases)
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<p>Comparison of the concentration of α-syn between patients with AD, DLB and NC. ***: <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Comparison of the concentration of CSF total α-syn between patients with AD and DLB (MCI or Dementia) and NC. **: <span class="html-italic">p</span> &lt; 0.01, ***: <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Receiver Operating Characteristic curve: CSF α-syn for discrimination between AD and DLB. AUC: Area Under the Curve, Se: Sensitivity, Spe: Specificity.</p>
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13 pages, 1049 KiB  
Article
Elevated Cerebrospinal Fluid Proteins and Albumin Determine a Poor Prognosis for Spinal Amyotrophic Lateral Sclerosis
by Abdelilah Assialioui, Raúl Domínguez, Isidro Ferrer, Pol Andrés-Benito and Mónica Povedano
Int. J. Mol. Sci. 2022, 23(19), 11063; https://doi.org/10.3390/ijms231911063 - 21 Sep 2022
Cited by 14 | Viewed by 3335
Abstract
Amyotrophic lateral sclerosis (ALS) is a heterogeneous disease, both in its onset phenotype and in its rate of progression. The aim of this study was to establish whether the dysfunction of the blood–brain barrier (BBB) and blood–spinal cord barrier (BSCB) measured through cerebrospinal [...] Read more.
Amyotrophic lateral sclerosis (ALS) is a heterogeneous disease, both in its onset phenotype and in its rate of progression. The aim of this study was to establish whether the dysfunction of the blood–brain barrier (BBB) and blood–spinal cord barrier (BSCB) measured through cerebrospinal fluid (CSF) proteins and the albumin-quotient (QAlb) are related to the speed of disease progression. An amount of 246 patients diagnosed with ALS were included. CSF and serum samples were determined biochemically for different parameters. Survival analysis based on phenotype shows higher probability of death for bulbar phenotype compared to spinal phenotype (p-value: 0.0006). For the effect of CSF proteins, data shows an increased risk of death for spinal ALS patients as the value of CSF proteins increases. The same model replicated for CSF albumin yielded similar results. Statistical models determined that the lowest cut-off value for CSF proteins able to differentiate patients with a good prognosis and worse prognosis corresponds to CSF proteins ≥ 0.5 g/L (p-value: 0.0189). For the CSF albumin, the QAlb ≥0.65 is associated with elevated probability of death (p-value: 0.0073). High levels of QAlb are a bad prognostic indicator for the spinal phenotype, in addition to high CSF proteins levels that also act as a marker of poor prognosis. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Neurological Diseases)
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<p>Mortality probability in relation to phenotype as a function of follow-up time from onset. The differences observed in the two survival curves allow us to reject the null hypothesis of equality (Log-rank test, <span class="html-italic">p</span>-value: 0.0006).</p>
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<p>(<b>Left</b>) Mortality probability as a function of the follow-up time from onset, comparing levels of CK. The differences observed in the two survival curves according to the CK level do not allow us to reject the null hypothesis of equality (Log-rank test, <span class="html-italic">p</span>-value: 0.1731). (<b>Right</b>) Mortality probability as a function of the follow-up time from onset, comparing cholesterol levels. The differences observed in the two survival curves according to cholesterol level do not allow us to reject the null hypothesis of equality (Log-rank test, <span class="html-italic">p</span>-value: 0.7478).</p>
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<p>(<b>Left</b>) Mortality probability as a function of the follow-up time from onset when comparing levels of CSF proteins using 0.5 g/L as a cut-off point for those with a spinal phenotype. We observed an increased probability of death in patients with CSF protein ≥ 0.5 g/L when compared to those with CSF protein &lt; 0.5 g/L (<b>Right</b>). Mortality probability as a function of the follow-up time from onset comparing the two QAlb levels for the spinal phenotype. We observed an increased probability of death in patients with QAlb ≥ 0.65 compared to those with QAlb &lt;0.65.</p>
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Review

Jump to: Research

13 pages, 1033 KiB  
Review
Gender Differences in Cortisol and Cortisol Receptors in Depression: A Narrative Review
by Chuin Hau Teo, Ally Chai Hui Wong, Rooba Nair Sivakumaran, Ishwar Parhar and Tomoko Soga
Int. J. Mol. Sci. 2023, 24(8), 7129; https://doi.org/10.3390/ijms24087129 - 12 Apr 2023
Cited by 17 | Viewed by 8357
Abstract
Stress is known to have a significant impact on mental health. While gender differences can be found in stress response and mental disorders, there are limited studies on the neuronal mechanisms of gender differences in mental health. Here, we discuss gender and cortisol [...] Read more.
Stress is known to have a significant impact on mental health. While gender differences can be found in stress response and mental disorders, there are limited studies on the neuronal mechanisms of gender differences in mental health. Here, we discuss gender and cortisol in depression as presented by recent clinical studies, as well as gender differences in the role of glucocorticoid receptors (GRs) and mineralocorticoid receptors (MRs) in stress-associated mental disorders. When examining clinical studies drawn from PubMed/MEDLINE (National Library of Medicine) and EMBASE, salivary cortisol generally showed no gender correlation. However, young males were reported to show heightened cortisol reactivity compared to females of similar age in depression. Pubertal hormones, age, early life stressors, and types of bio-samples for cortisol measurement affected the recorded cortisol levels. The role of GRs and MRs in the HPA axis could be different between males and females during depression, with increased HPA activity and upregulated MR expression in male mice, while the inverse happened in female mice. The functional heterogeneity and imbalance of GRs and MRs in the brain may explain gender differences in mental disorders. This knowledge and understanding will support the development of gender-specific diagnostic markers involving GRs and MRs in depression. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Neurological Diseases)
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<p>The role of the GR/MR in the HPA axis under depression. The hypothalamus releases corticotropin-releasing hormone (CRH) and arginine-vasopressin (AVP), acting on the pituitary gland which in turn releases adrenocorticotropic hormone (ACTH) to stimulate the adrenal gland. The adrenal gland plays a role in the negative feedback loop for the hypothalamus and the pituitary gland, while releasing glucocorticoids such as cortisol. Glucocorticoids travel into the cell and bind to GRs or MRs, with the help of Hsp90 and FKBP51, before being transported into the nucleus with the aid of FKBP52 where they activate GREs for subsequent gene transcription. During depression, CRH and AVP are elevated, resulting in increased ACTH and increased glucocorticoid release such as cortisol. Chaperone proteins such as FKBP51 may be elevated while others such as Hsp90 may be impaired in function.</p>
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<p>Genetic gender differences in glucocorticoid receptor and mineralocorticoid receptor expressions.</p>
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31 pages, 8332 KiB  
Review
Ubiquitin Proteasome Gene Signatures in Ependymoma Molecular Subtypes
by Jerry Vriend, Thatchawan Thanasupawat, Namita Sinha and Thomas Klonisch
Int. J. Mol. Sci. 2022, 23(20), 12330; https://doi.org/10.3390/ijms232012330 - 15 Oct 2022
Cited by 4 | Viewed by 3184
Abstract
The ubiquitin proteasome system (UPS) is critically important for cellular homeostasis and affects virtually all key functions in normal and neoplastic cells. Currently, a comprehensive review of the role of the UPS in ependymoma (EPN) brain tumors is lacking but may provide valuable [...] Read more.
The ubiquitin proteasome system (UPS) is critically important for cellular homeostasis and affects virtually all key functions in normal and neoplastic cells. Currently, a comprehensive review of the role of the UPS in ependymoma (EPN) brain tumors is lacking but may provide valuable new information on cellular networks specific to different EPN subtypes and reveal future therapeutic targets. We have reviewed publicly available EPN gene transcription datasets encoding components of the UPS pathway. Reactome analysis of these data revealed genes and pathways that were able to distinguish different EPN subtypes with high significance. We identified differential transcription of several genes encoding ubiquitin E2 conjugases associated with EPN subtypes. The expression of the E2 conjugase genes UBE2C, UBE2S, and UBE2I was elevated in the ST_EPN_RELA subtype. The UBE2C and UBE2S enzymes are associated with the ubiquitin ligase anaphase promoting complex (APC/c), which regulates the degradation of substrates associated with cell cycle progression, whereas UBE2I is a Sumo-conjugating enzyme. Additionally, elevated in ST_EPN_RELA were genes for the E3 ligase and histone deacetylase HDAC4 and the F-box cullin ring ligase adaptor FBX031. Cluster analysis demonstrated several genes encoding E3 ligases and their substrate adaptors as EPN subtype specific genetic markers. The most significant Reactome Pathways associated with differentially expressed genes for E3 ligases and their adaptors included antigen presentation, neddylation, sumoylation, and the APC/c complex. Our analysis provides several UPS associated factors that may be attractive markers and future therapeutic targets for the subtype-specific treatment of EPN patients. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Neurological Diseases)
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Graphical abstract

Graphical abstract
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<p>Differential expression of genes encoding E1 ubiquitin activating enzymes by Anova, <span class="html-italic">UBA3</span> (F = 20.60, <span class="html-italic">p</span> = 9.29 × 10<sup>−21</sup>) and <span class="html-italic">UBA6</span> (F = 14.19, <span class="html-italic">p</span> = 6.22 × 10<sup>−15</sup>) in EPN subtypes. * Expression of <span class="html-italic">UBA3</span> and <span class="html-italic">UBA6</span> were significantly elevated in ST_EPN_RELA compared to all other EPN subtypes by at least <span class="html-italic">p</span> &lt; 0.01 (with the exception of SP_MPE for <span class="html-italic">UBA3</span>) as determined by <span class="html-italic">t</span>-test. Dataset GSE64415.</p>
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<p>Increased <span class="html-italic">IFIH1</span> expression in the PF_EPN_B subtype (by Anova, F = 19.36, <span class="html-italic">p</span> = 1.08 × 10<sup>−19</sup>). * significantly different from all other groups by <span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.01. Dataset GSE64415.</p>
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<p>Expression of <span class="html-italic">UBE2C</span> and <span class="html-italic">UBE2S</span> is significantly increased in the ST_EPN_RELA subtype (F = 9.92, <span class="html-italic">p</span> = 1.28 × 10<sup>−10</sup>; F = 16.00, <span class="html-italic">p</span> = 1.19 × 10<sup>−16</sup>). * <span class="html-italic">UBE2C</span>-all significantly higher compared to other groups by <span class="html-italic">t</span>-test except <span class="html-italic">ST</span>-<span class="html-italic">EPN_YAP1 group</span> * <span class="html-italic">UBE2S</span>-all significantly higher compared to each of the other groups by <span class="html-italic">t</span>-test. Dataset GSE64415.</p>
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<p>* By <span class="html-italic">t</span>-test <span class="html-italic">UBE2I</span> mean of the St_Epn_Rela group is significantly greater (by Anova, F = 22.36, <span class="html-italic">p</span> = 3.06 × 10<sup>−22</sup>) than that of all other groups by at least <span class="html-italic">p</span> &lt; 0.05. The most significant difference was between the PF-EPN-A and the RELA groups (t = 9.88, <span class="html-italic">p</span> &lt; 0.0001). By <span class="html-italic">t</span>-test mean of <span class="html-italic">UBE2Z</span> SP_MPE is significantly greater than that of all other groups, <span class="html-italic">p</span> &lt; 0.0001. By Anova of <span class="html-italic">UBE2Z</span>, F = 28.73, <span class="html-italic">p</span> = 3.14 × 10<sup>−27</sup>. Dataset GSE64415.</p>
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<p>Cluster analysis of differentially expressed ubiquitin E3 ligase genes in different EPN subtypes. Genes are listed again in the order they appear in the heatmap (top to bottom) for better readability. Genes on the right of the heatmap are listed again for better visibility: <span class="html-italic">RNF19A</span>, <span class="html-italic">PPARG</span>, <span class="html-italic">TRIM47</span>, <span class="html-italic">TRIM71</span>, <span class="html-italic">ZNRF3</span>, <span class="html-italic">DPF3</span>, <span class="html-italic">RNF43</span>, <span class="html-italic">DTX1</span>, <span class="html-italic">PHF21B</span>, <span class="html-italic">UHRF1</span>, <span class="html-italic">MID1</span>, <span class="html-italic">DTX4</span>, <span class="html-italic">MDM2</span>, <span class="html-italic">UBR5</span>, <span class="html-italic">BAZ1B</span>, <span class="html-italic">KDM2A</span>, <span class="html-italic">KMT2A</span>, <span class="html-italic">MAP3K1</span>, <span class="html-italic">JADE2</span>, <span class="html-italic">BRAP</span>, <span class="html-italic">RNF34</span>, <span class="html-italic">RNF114</span>, <span class="html-italic">PIAS1</span>, <span class="html-italic">TRIM2</span>, <span class="html-italic">TRIM22</span>, <span class="html-italic">HERC6</span>, <span class="html-italic">TRIM9</span>, <span class="html-italic">LDB2</span>, <span class="html-italic">RNF112</span>, <span class="html-italic">GMNN</span>, <span class="html-italic">HACE1</span>, <span class="html-italic">RNF146</span>, <span class="html-italic">KAT6B</span>, <span class="html-italic">HDAC4</span>, <span class="html-italic">RNF103</span>, <span class="html-italic">INTS1</span>, <span class="html-italic">RMND5A</span>, <span class="html-italic">PJA1</span>, <span class="html-italic">SMURF2</span>, <span class="html-italic">BARD1</span>, <span class="html-italic">PFH13</span>, <span class="html-italic">AREL1</span>, <span class="html-italic">TRIM27</span>, <span class="html-italic">UBE4B</span>, <span class="html-italic">WHSC1</span>, <span class="html-italic">ZMIZ1</span>, <span class="html-italic">PDZRN4</span>, <span class="html-italic">BAZ1A</span>, <span class="html-italic">FANCL</span>, <span class="html-italic">RNFT2</span>, <span class="html-italic">TRIM59</span>, <span class="html-italic">MALT1</span>, <span class="html-italic">TRIM45</span>, <span class="html-italic">MARCH3</span>, <span class="html-italic">ANKRD28</span>, <span class="html-italic">RNF150</span>, <span class="html-italic">PDLIM2</span>, <span class="html-italic">LONRF2</span>, <span class="html-italic">HECW2</span>, <span class="html-italic">CADPS2</span>, <span class="html-italic">RNF32</span>, <span class="html-italic">ARMC4</span>, <span class="html-italic">UBE3D</span>, <span class="html-italic">CAPS2</span>, <span class="html-italic">DZIP3</span>, <span class="html-italic">MNAT1</span>, <span class="html-italic">RNF20</span>, <span class="html-italic">TRIM56</span>, <span class="html-italic">RNF175</span>, <span class="html-italic">KDM2B</span>, <span class="html-italic">SIAH3</span>, <span class="html-italic">TRIM16</span>, <span class="html-italic">RSPRY1</span>, <span class="html-italic">RNF13</span>, <span class="html-italic">ARMCX3</span>.</p>
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<p>Reactome network of ubiquitin E3 ligase genes in ependymoma.</p>
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<p>Significantly increased expression of the <span class="html-italic">HDAC4</span> gene in the ST_EPN_RELA subtype (by Anova, F = 44.88, <span class="html-italic">p</span> = 7.86 × 10<sup>−38</sup>). * by <span class="html-italic">t</span>-test significantly different from all other groups <span class="html-italic">p</span> &lt; 0.001. Dataset GSE64415.</p>
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<p>Over-expression of <span class="html-italic">JAG1</span> in SP_MPE and ST_EPN_RELA subtypes (by Anova, F = 108.15, <span class="html-italic">p</span> = 1.23 × 10<sup>−64</sup>). * significantly different from all other groups not indicated by *, at <span class="html-italic">p</span> &lt; 0.001. Dataset GSE64415.</p>
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<p>Gene encoding stem cell marker and cell adhesion factor, <span class="html-italic">L1CAM</span>, overexpressed in ST_EPN_RELA (by Anova, F = 173.55, <span class="html-italic">p</span> = 1.35 × 10<sup>−81</sup>). * significantly different from all other groups at <span class="html-italic">p</span> &lt; 0.001. Dataset GSE64415.</p>
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<p><span class="html-italic">HNF1B</span> expression in the SP-MPE subtype (by Anova, F = 151.68, <span class="html-italic">p</span> = 1.27 × 10<sup>−76</sup>) * by <span class="html-italic">t</span>-test significantly different from all other groups at <span class="html-italic">p</span> &lt; 0.001. Dataset GSE64415.</p>
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<p>Gene expression in the homebox cluster of chromosome 17 (17q21.32) in the SPE_MPE subtype (by Anova, <span class="html-italic">HOXB9</span>, F = 25.31, <span class="html-italic">p</span> = 5.26 × 10<sup>−25</sup>; <span class="html-italic">HOXB13</span>, F = 160.28, <span class="html-italic">p</span> = 1.21 × 10<sup>−78</sup>; <span class="html-italic">PRAC1</span>, F = 953.53, <span class="html-italic">p</span> = 1.96 × 10<sup>−150</sup>). * for <span class="html-italic">HOXB9 p</span> &lt; 0.01; for HOXB13, <span class="html-italic">p</span> &lt; 0.0001; for <span class="html-italic">PRAC1</span>, <span class="html-italic">p</span> &lt; 0.0001. Dataset GSE64415.</p>
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<p>The <span class="html-italic">NEFL</span> gene is selectively overexpressed in SP_MPE (F = 1505.18, <span class="html-italic">p</span> = 7.05 × 10<sup>−170</sup>). * <span class="html-italic">p</span> &lt; 0.001 by <span class="html-italic">t</span>-test, compared to all other groups. Dataset GSE64415.</p>
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<p>The <span class="html-italic">IGF2BP1</span> gene expression is selectively elevated in the PF_EPN_A subtype (F = 30.48, <span class="html-italic">p</span> = 1.66 × 10<sup>−28</sup>) and GATA4 expression is elevated in the ST_SE subtype (F = 57.00, <span class="html-italic">p</span> = 2.16 × 10<sup>−44</sup>). * significantly different from other groups by <span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.001 for <span class="html-italic">IGF2BP1</span> and <span class="html-italic">p</span> &lt; 0.01 for <span class="html-italic">GATA4.</span> Dataset GSE64415.</p>
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<p>Elevated expression of E3 ligase RNF8 in the SP_EPN subtype (by Anova, F = 11.86, <span class="html-italic">p</span> = 1.28 × 10<sup>−12</sup>). * significantly different from all other groups by <span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.01. Dataset GSE64415.</p>
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<p>Cluster analysis of ubiquitin E3 ligase adaptor gene expression in EPN subtypes.</p>
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<p>Steps in the Neddylation cycle: 1. Activation of Nedd8 by NAE, 2. Loading of Nedd8 to E2 UBE2M or UBE2F, 3. Displacement of CAND1 protein from Cullin Ring by Nedd8, 4. Neddylation of Cullin ring, 5. Assembly of Cullin Ring Ligase (CRL) with adaptor and substrate receptor, 6. Ubiquitination of target substrate, 7. CSN (Cop9 signalosome) binding to Nedd8, 8. Deneddylation, 9. Disassembly of CRL, 10. CAND1 binding to CRL.</p>
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<p>Elevated expression of the genes encoding ubiquitin E3 ligase adaptor <span class="html-italic">FBXO31</span> and its substrate cyclin D1 (<span class="html-italic">CCND1</span>) (by F = 153.74, <span class="html-italic">p</span> = 4.09 × 10<sup>−77</sup>; F = 51.39, <span class="html-italic">p</span> = 1.77 × 10<sup>−41</sup>) in the ST_EPN_RELA subtype. * significantly different from all other groups by <span class="html-italic">t</span>-test. Dataset GSE64415.</p>
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<p>Elevated gene expression of <span class="html-italic">KLHL42</span> and <span class="html-italic">CISH</span> in ST_EPN_RELA (by Anova, F = 84.68, <span class="html-italic">p</span> = 1.75 × 10<sup>−56</sup>; F = 39.09, <span class="html-italic">p</span> = 2.55 × 10<sup>−34</sup>, respectively). * significantly different from all other groups by <span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.001. Dataset GSE64415.</p>
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<p>Elevated gene expression of <span class="html-italic">KCTD6</span> (pf_epn-a) and <span class="html-italic">GAN</span> (pf-se) (by Anova F = 21.73, <span class="html-italic">p</span> = 1.023–21 and F = 33.29, <span class="html-italic">p</span> = 1.71 × 10<sup>−30</sup>). * by <span class="html-italic">t</span>-test significantly different from other groups <span class="html-italic">p</span> &lt; 0.001, for <span class="html-italic">KCTD6</span> and <span class="html-italic">p</span> &lt; 0.01 for <span class="html-italic">GAN.</span> Dataset GSE64415.</p>
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<p>Differential expression of genes encoding E3 ligase adaptors related to the nuclear pore gene <span class="html-italic">NUP37</span> (by Anova, F = 43.18, <span class="html-italic">p</span> = 7.89 × 10<sup>−37</sup>). * PF_EPN_B and SP_EPN means are significantly higher than the other groups by <span class="html-italic">t</span>-test <span class="html-italic">p</span> &lt; 0.01. Dataset GSE64415.</p>
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<p>Differential expression of genes encoding E3 ligase adaptors associated with the dynein motor complex (by Anova, F = 13.835, <span class="html-italic">p</span> = 1.38 × 10<sup>−14</sup>; F = 27.89, <span class="html-italic">p</span> = 1.38 × 10<sup>−14</sup>). * by <span class="html-italic">t</span>-test DYNC1I1 expression is elevated in the SP_EPN group <span class="html-italic">p</span> &lt; 0.05, while <span class="html-italic">DYNC1I2</span> expression is significantly lower in the PF-EPN-B group <span class="html-italic">p</span> &lt; 0.001, compared to each of the other groups. Dataset GSE64415.</p>
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37 pages, 4734 KiB  
Review
Genetics, Functions, and Clinical Impact of Presenilin-1 (PSEN1) Gene
by Jaya Bagaria, Eva Bagyinszky and Seong Soo A. An
Int. J. Mol. Sci. 2022, 23(18), 10970; https://doi.org/10.3390/ijms231810970 - 19 Sep 2022
Cited by 44 | Viewed by 8168
Abstract
Presenilin-1 (PSEN1) has been verified as an important causative factor for early onset Alzheimer’s disease (EOAD). PSEN1 is a part of γ-secretase, and in addition to amyloid precursor protein (APP) cleavage, it can also affect other processes, such as Notch signaling, β-cadherin processing, [...] Read more.
Presenilin-1 (PSEN1) has been verified as an important causative factor for early onset Alzheimer’s disease (EOAD). PSEN1 is a part of γ-secretase, and in addition to amyloid precursor protein (APP) cleavage, it can also affect other processes, such as Notch signaling, β-cadherin processing, and calcium metabolism. Several motifs and residues have been identified in PSEN1, which may play a significant role in γ-secretase mechanisms, such as the WNF, GxGD, and PALP motifs. More than 300 mutations have been described in PSEN1; however, the clinical phenotypes related to these mutations may be diverse. In addition to classical EOAD, patients with PSEN1 mutations regularly present with atypical phenotypic symptoms, such as spasticity, seizures, and visual impairment. In vivo and in vitro studies were performed to verify the effect of PSEN1 mutations on EOAD. The pathogenic nature of PSEN1 mutations can be categorized according to the ACMG-AMP guidelines; however, some mutations could not be categorized because they were detected only in a single case, and their presence could not be confirmed in family members. Genetic modifiers, therefore, may play a critical role in the age of disease onset and clinical phenotypes of PSEN1 mutations. This review introduces the role of PSEN1 in γ-secretase, the clinical phenotypes related to its mutations, and possible significant residues of the protein. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Neurological Diseases)
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<p>Pathways, in which PSEN1 may be involved.</p>
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<p>Mutation sites in PSEN1 protein.</p>
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<p>Significant residues in PSN1 TM1.</p>
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<p>Residues in HL1 which may be critical in γ-secretase production.</p>
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<p>Potential significant residues in TM2 and HL2.</p>
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<p>Significant residues in TM3.</p>
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<p>Possible significant residues in TM4.</p>
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<p>Significant residues in TM5 and HL5.</p>
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<p>Significant residues in TM6 and HL6.</p>
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<p>Significant residues located in the hydrophobic stretch of large loop.</p>
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<p>Significant residues in TM7 and HL7.</p>
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<p>Significant residues located in TM8 and HL8.</p>
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<p>Significant residues located in TM9.</p>
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18 pages, 1148 KiB  
Review
LRRK2 and Proteostasis in Parkinson’s Disease
by María Dolores Pérez-Carrión, Inmaculada Posadas, Javier Solera and Valentín Ceña
Int. J. Mol. Sci. 2022, 23(12), 6808; https://doi.org/10.3390/ijms23126808 - 18 Jun 2022
Cited by 9 | Viewed by 3767
Abstract
Parkinson’s disease is a neurodegenerative condition initially characterized by the presence of tremor, muscle stiffness and impaired balance, with the deposition of insoluble protein aggregates in Lewy’s Bodies the histopathological hallmark of the disease. Although different gene variants are linked to Parkinson disease, [...] Read more.
Parkinson’s disease is a neurodegenerative condition initially characterized by the presence of tremor, muscle stiffness and impaired balance, with the deposition of insoluble protein aggregates in Lewy’s Bodies the histopathological hallmark of the disease. Although different gene variants are linked to Parkinson disease, mutations in the Leucine-Rich Repeat Kinase 2 (LRRK2) gene are one of the most frequent causes of Parkinson’s disease related to genetic mutations. LRRK2 toxicity has been mainly explained by an increase in kinase activity, but alternative mechanisms have emerged as underlying causes for Parkinson’s disease, such as the imbalance in LRRK2 homeostasis and the involvement of LRRK2 in aggregation and spreading of α-synuclein toxicity. In this review, we recapitulate the main LRRK2 pathological mutations that contribute to Parkinson’s disease and the different cellular and therapeutic strategies devised to correct LRRK2 homeostasis. In this review, we describe the main cellular control mechanisms that regulate LRRK2 folding and aggregation, such as the chaperone network and the protein-clearing pathways such as the ubiquitin–proteasome system and the autophagic-lysosomal pathway. We will also address the more relevant strategies to modulate neurodegeneration in Parkinson’s disease through the regulation of LRRK2, using small molecules or LRRK2 silencing. Full article
(This article belongs to the Special Issue Molecular Biomarkers in Neurological Diseases)
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Figure 1
<p>LRRK2 structure. LRRK2 contains seven structural domains, known as armadillo domain (Arm), ankyrin domain (ANK), leucine-rich repeat domain (LRR), ROC domain (Ras of Complex), COR domain (C-terminal of ROC), kinase domain (kinase) and WD40 repeat domain (WD40). PD associated-LRRK2 mutations and risk factors are indicated with a red line above the specific structural domain.</p>
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<p>LRRK2 homeostasis and quality-control mechanisms. There are several approaches to control LRRK2 homeostasis: chaperone system (1), chaperone-mediated autophagy (CMA) and macroautophagy (2) and the ubiquitin–proteasome system (UBP) (3). LRRK2 dysregulation contributes to α-synuclein (α-syn) aggregation (4), which is also recognized as CMA substrate.</p>
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<p>LRRK2 as therapeutic target for PD. Among the therapeutic options to manage LRRK2 activity in PD are included pharmacological strategies that involved the use of LRRK2 kinase inhibitors and gene therapy approaches to knock-down LRRK2 expression by zinc-finger nucleases (ZFN), short-hairpin RNA (shRNA) molecules or antisense oligonucleotides (ASO).</p>
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