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New Mechanisms and Therapeutics in Neurological Diseases 3.0
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New Mechanisms and Therapeutics in Neurological Diseases 3.0

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 (31 July 2024) | Viewed by 17814

Special Issue Editor

Prof. Dr. Barbara Mroczko

E-Mail Website
Guest Editor
Department of Biochemical Diagnostics, Medical University of Bialystok, 15-269 Bialystok, Poland
Interests: neurodegeneration; neuroinflammation; neurodegenerative diseases; neurodevelopmental disorders; tumor markers; specific proteins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The term “neurodegenerative diseases” (NDs) collectively defines a group of pathological conditions of the nervous system characterized by the degeneration of neurons. This group of disorders includes different diseases leading to dementia or motor neuron disfunction, resulting in disability. Among them, Alzheimer’s (AD) and Parkinson’s diseases (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS) have the greatest importance because of their devastating outcomes and enormous socio-economic impact. Although they have a variegated etiology, an increasing role in their pathology is ascribed to neuroinflammatory processes. Neuroinflammation may affect not only neurons but also non-neuronal astrocytes and microglia cells, as well as immune cells entering the nervous system. These cells cooperate in both the damage and the repair of diseased brain tissue. Interactions between cells and the extracellular environment have emerged as new targets for the treatment of neurodegenerative disorders. However, the heterogeneity of the molecular and cellular mechanisms underlying these diseases hinders efforts at slowing down the progression of these diseases and their effective treatment. Moreover, the availability of biomarkers with appropriate sensitivity and specificity that could predict treatment success is very limited.

Inflammatory and infectious diseases of the central nervous system (CNS) may also be involved in the pathogenesis of neurodegeneration. Microbial infection has emerged as a new risk factor for NDs, and new evidence supports the universal hypothesis that some bacteria, viruses, and even fungi could be involved not only in brain inflammation but also in neurodegeneration and dementia. The diagnosis of CNS infections and the identification of potential pathogenic pathways of these diseases are also topics of interest for this Special Issue, as well as the therapy and prevention of these diseases, including vaccination.

Studies on malignant primary brain tumors are also welcome. These tumors are a highly heterogeneous group of malignancies, with varied frequency within different age groups. Among them, glioblastoma is the most common and most malignant primary CNS tumor, affecting patients of all ages, from children to adults. Glioblastoma multiforme is an especially fatal tumor type, and only moderate progress has been achieved in its clinical management in recent years.

The goal of this Special Issue is to collect original research manuscripts, short communications, and reviews on the latest advances regarding new mechanisms of and therapeutics for neurological diseases, including neurodegeneration, neuroinflammation, and tumors of the central nervous system.

Topics of interest include (but are not limited to):

  • Biological mechanisms related to neurodegeneration, inflammation, and tumorigenesis within the central nervous system;
  • Neurodegenerative diseases as proteinopathies;
  • Relationship between neurodegeneration and inflammation;
  • New potential biomarkers of Alzheimer’s disease and other neurodegenerative diseases, including mild cognitive impairment, multiple sclerosis, Parkinson’s disease, Lewy body dementia, frontotemporal dementia, amyotrophic lateral sclerosis, Huntington’s disease, and prion diseases;
  • Prognostic value of biomarkers of neurodegeneration in the conversion from mild cognitive impairment to fully symptomatic dementia;
  • Cytokines, chemokines, and matrix metalloproteinases as prognostic factors in the carcinogenesis of CNS malignant tumors;
  • Mediators of inflammation, chemokines, and their receptors as novel tumor markers in malignant tumors of the central nervous system in relation to the histological type of tumors;
  • Relationships between COVID-19 and neurological diseases.

This Special Issue is supervised by Prof. Dr. Barbara Mroczko and assisted by our Topical Advisory Panel Member Dr. Kristina Mlinac-Jerković (University of Zagreb).

Prof. Dr. Barbara Mroczko
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • neurodegeneration
  • neuroinflammation
  • neurodegenerative diseases
  • neurodevelopmental disorders
  • tumor markers
  • specific proteins

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22 pages, 3870 KiB  
Article
Network Analysis of Enhancer–Promoter Interactions Highlights Cell-Type-Specific Mechanisms of Transcriptional Regulation Variation
by Justin Koesterich, Jiayi Liu, Sarah E. Williams, Nan Yang and Anat Kreimer
Int. J. Mol. Sci. 2024, 25(18), 9840; https://doi.org/10.3390/ijms25189840 - 11 Sep 2024
Viewed by 1486
Abstract
Gene expression is orchestrated by a complex array of gene regulatory elements that govern transcription in a cell-type-specific manner. Though previously studied, the ability to utilize regulatory elements to identify disrupting variants remains largely elusive. To identify important factors within these regions, we [...] Read more.
Gene expression is orchestrated by a complex array of gene regulatory elements that govern transcription in a cell-type-specific manner. Though previously studied, the ability to utilize regulatory elements to identify disrupting variants remains largely elusive. To identify important factors within these regions, we generated enhancer–promoter interaction (EPI) networks and investigated the presence of disease-associated variants that fall within these regions. Our study analyzed six neuronal cell types across neural differentiation, allowing us to examine closely related cell types and across differentiation stages. Our results expand upon previous findings of cell-type specificity of enhancer, promoter, and transcription factor binding sites. Notably, we find that regulatory regions within EPI networks can identify the enrichment of variants associated with neuropsychiatric disorders within specific cell types and network sub-structures. This enrichment within sub-structures can allow for a better understanding of potential mechanisms by which variants may disrupt transcription. Together, our findings suggest that EPIs can be leveraged to better understand cell-type-specific regulatory architecture and used as a selection method for disease-associated variants to be tested in future functional assays. Combined with these future functional characterization assays, EPIs can be used to better identify and characterize regulatory variants’ effects on such networks and model their mechanisms of gene regulation disruption across different disorders. Such findings can be applied in practical settings, such as diagnostic tools and drug development. Full article
(This article belongs to the Special Issue New Mechanisms and Therapeutics in Neurological Diseases 3.0)
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Figure 1

Figure 1
<p>EPI calling: Schematic of cell-type relation, data processing, and analysis steps. The depiction of cell-type relation to the other cell types analyzed in the study, in order of increasing differentiation stage. From left to right: Pluripotent Stem Cells (ESC), Neural Stem Cells (NSC), Neural Progenitor Cells (NPC), Mature Motor neurons (Motor), GABAergic neurons (AD), and glutamatergic neurons (Ngn2). Note that the Motor, AD, and Ngn2 cell types are shown together as they are all different end stages of neuron cell differentiation. The data processing describes the ATAC-Seq, RNA-Seq, and ChIP-Seq or CUT&amp;RUN information collected for each cell type. Due to only having Hi-C data available for the NPC and Motor neurons, we elected to incorporate the ABC model’s validated set of multi-cell-type-averaged Hi-C data [<a href="#B7-ijms-25-09840" class="html-bibr">7</a>] for all cell types to keep uniformity of the input data. The analysis steps then describe utilizing the ABC model to generate the cell-type EPI networks, and the types of analysis of such networks.</p> Full article ">Figure 2
<p>Regulatory elements distribution: (<b>A</b>) Grouped bar chart showing the number of total EPIs, total unique enhancers, and total unique promoters per cell type of the collapsed EPI network. (<b>B</b>) Bar charts display the percentage of enhancer and promoter regions that are only found within that cell type. (<b>C</b>) A heatmap containing Jaccard index values of shared enhancers (bottom left) and promoters (upper right) between any two cell types. A color gradient is created for each regulatory element between 0 and the max Jaccard index value across both the enhancers and promoters. The darker cells represent higher levels of shared elements between the two cell types.</p> Full article ">Figure 3
<p>Dotplot figures showing the top 5 enriched biological processes for each cell type. Enrichment was performed by taking all of the target genes predicted as part of an EPI in that cell type and the top 5 with the lowest <span class="html-italic">p</span>-values are shown. Dotplots are shown for all target genes in the cell type’s EPI network (<b>A</b>) and for only the target genes predicted to interact with cell-type-specific enhancers (<b>B</b>).</p> Full article ">Figure 4
<p>TF weighted binary matrix: (<b>A</b>) Heatmap displaying the amount of presence each TF (row) has within each cell-type group (column) From left to right the columns are ESC, NSC, NPC, AD, Ngn2, Motor. Values are the percentage of enhancers that the TF binds to that belong to that cell type, each column adding to 100%. Cell color denotes the relative percentage of TF binding within that cell type. A higher percentage of the TF binding uniquely to that cell type results in more red and less blue coloring. TF columns are organized via hierarchical clustering to identify clusters of TFs binding preferentially to certain cell types. (<b>B</b>) Gene Ontology Enrichment Dot plot for the top 10 enriched processes of the 2 TF subsets. The higher progenitor column consists of the TFs that have the highest relative percentage of predicted binding to enhancers within progenitor cell types and lower relative binding percentages in mature neurons (center section of <a href="#ijms-25-09840-f004" class="html-fig">Figure 4</a>A). The higher mature column consists of the TFs that have the highest relative percentage of predicted binding to enhancers within mature neuron cell types and lower relative binding percentages in progenitor cell types (bottom section of <a href="#ijms-25-09840-f004" class="html-fig">Figure 4</a>A).</p> Full article ">Figure 4 Cont.
<p>TF weighted binary matrix: (<b>A</b>) Heatmap displaying the amount of presence each TF (row) has within each cell-type group (column) From left to right the columns are ESC, NSC, NPC, AD, Ngn2, Motor. Values are the percentage of enhancers that the TF binds to that belong to that cell type, each column adding to 100%. Cell color denotes the relative percentage of TF binding within that cell type. A higher percentage of the TF binding uniquely to that cell type results in more red and less blue coloring. TF columns are organized via hierarchical clustering to identify clusters of TFs binding preferentially to certain cell types. (<b>B</b>) Gene Ontology Enrichment Dot plot for the top 10 enriched processes of the 2 TF subsets. The higher progenitor column consists of the TFs that have the highest relative percentage of predicted binding to enhancers within progenitor cell types and lower relative binding percentages in mature neurons (center section of <a href="#ijms-25-09840-f004" class="html-fig">Figure 4</a>A). The higher mature column consists of the TFs that have the highest relative percentage of predicted binding to enhancers within mature neuron cell types and lower relative binding percentages in progenitor cell types (bottom section of <a href="#ijms-25-09840-f004" class="html-fig">Figure 4</a>A).</p> Full article ">Figure 5
<p>EPI sub-structure: Each EPI is assigned to a sub-structure group (<b>A</b>) based on the number of promoters with which the EPI enhancer interacts and the number of enhancers with which the EPI promoter interacts. (<b>B</b>) Stacked bar plot breaking down the number of EPIs per cell type in the collapsed EPI network that belong to each sub-structure group. (<b>C</b>) Stacked barplot breaking down the number of EPIs per cell type in the subset EPI network that belong to each sub-structure group. The groups Progenitor and Mature consist of EPIs predicted active in at least 2 of the 3 cell types belonging to the progenitor or mature differentiation stage group, respectively. The Ubiquitous group consists of EPIs predicted to be active in at least 5 of the 6 cell types. The Progenitor Combined group consists of the union of EPIs from the ESC, NSC, NPC, and Progenitor subsets. The Mature Combined consists of the union of EPIs from the AD, Ngn2, Motor, and Mature subsets. Progenitor Combined and Mature Combined are included to represent the overlapping nature of the collapsed network.</p> Full article ">Figure 6
<p>Empirical <span class="html-italic">p</span>-values of disease variant overlap. This heatmap denotes the empirical <span class="html-italic">p</span>-value found after 1000 repetitions of randomly selecting enhancer (<b>A</b>), promoter (<b>B</b>), or target gene (<b>C</b>) regulatory regions and identifying the number of regions that contain a variant (<b>A</b>,<b>B</b>) or have been previously associated with ASD or NDD (<b>C</b>). <span class="html-italic">p</span>-values calculated as 1 minus the number of iterations where the randomly selected regulatory regions contained equal or more variants than observed, divided by 1000. <span class="html-italic">p</span>-values less than or equal to the adjusted threshold of 0.008333 are shaded in dark red. <span class="html-italic">p</span>-values of nominal significance between 0.008333 and 0.05 are shaded in bright red. The ASD variant space consisted of ~250,000 variants, with an approximately even split between case and control variants. The Schizophrenia variant space consisted of ~30,000 variants from GWAS-identified variants along with variants within LD &gt; 0.8 of the identified variants. The ASD-associated gene list consists of 252 genes. The NDD-associated gene list consists of 817 genes including the 252 ASD-associated genes.</p> Full article ">Figure 7
<p>Representative empirical analysis histograms: We highlight the empirical analysis histograms of the AD cell type as a representative of the analysis performed on the 6 cell types. The AD cell type was selected due to it having a significant enrichment for ASD cases (<b>A</b>) and not ASD controls (<b>B</b>), nominal enrichment for SCZ variants in enhancers (<b>E</b>), and enrichment for ASD- and NDD-associated genes (<b>G</b>,<b>H</b>). The rows of the figure for panels A–F represent the variants being analyzed for enrichment with the columns being the regulatory regions that the variants might be enriched in. For panels G and H, they are both testing the target genes of the AD EPI network and the columns denote if it is being analyzed for enrichment of ASD-associated genes (<b>G</b>) or NDD-associated genes (<b>H</b>). The <span class="html-italic">X</span>-axis of the histograms represents the number of regions containing variants (<b>A</b>–<b>F</b>) or disease-associated genes (<b>G</b>,<b>H</b>). The <span class="html-italic">Y</span>-axis of the histograms represents the frequency of the 1000 randomly selected iteration observed values. The red line represents the observed number of enhancer (<b>A</b>,<b>C</b>,<b>E</b>), promoter (<b>B</b>,<b>D</b>,<b>F</b>), or target-gene (<b>G</b>,<b>H</b>) regions containing variants or disease-associated genes in the AD cell type’s EPI network.</p> Full article ">
16 pages, 9707 KiB  
Article
Increased Expression of the Neuropeptides PACAP/VIP in the Brain of Mice with CNS Targeted Production of IL-6 Is Mediated in Part by Trans-Signalling
by Alessandro Castorina, Jurgen Scheller, Kevin A. Keay, Rubina Marzagalli, Stefan Rose-John and Iain L. Campbell
Int. J. Mol. Sci. 2024, 25(17), 9453; https://doi.org/10.3390/ijms25179453 - 30 Aug 2024
Viewed by 949
Abstract
Inflammation with expression of interleukin 6 (IL-6) in the central nervous system (CNS) occurs in several neurodegenerative/neuroinflammatory conditions and may cause neurochemical changes to endogenous neuroprotective systems. Pituitary adenylate cyclase-activating polypeptide (PACAP) and vasoactive intestinal polypeptide (VIP) are two neuropeptides with well-established protective [...] Read more.
Inflammation with expression of interleukin 6 (IL-6) in the central nervous system (CNS) occurs in several neurodegenerative/neuroinflammatory conditions and may cause neurochemical changes to endogenous neuroprotective systems. Pituitary adenylate cyclase-activating polypeptide (PACAP) and vasoactive intestinal polypeptide (VIP) are two neuropeptides with well-established protective and anti-inflammatory properties. Yet, whether PACAP and VIP levels are altered in mice with CNS-restricted, astrocyte-targeted production of IL-6 (GFAP-IL6) remains unknown. In this study, PACAP/VIP levels were assessed in the brain of GFAP-IL6 mice. In addition, we utilised bi-genic GFAP-IL6 mice carrying the human sgp130-Fc transgene (termed GFAP-IL6/sgp130Fc mice) to determine whether trans-signalling inhibition rescued PACAP/VIP changes in the CNS. Transcripts and protein levels of PACAP and VIP, as well as their receptors PAC1, VPAC1 and VPAC2, were significantly increased in the cerebrum and cerebellum of GFAP-IL6 mice vs. wild type (WT) littermates. These results were paralleled by a robust activation of the JAK/STAT3, NF-κB and ERK1/2MAPK pathways in GFAP-IL6 mice. In contrast, co-expression of sgp130Fc in GFAP-IL6/sgp130Fc mice reduced VIP expression and activation of STAT3 and NF-κB pathways, but it failed to rescue PACAP, PACAP/VIP receptors and Erk1/2MAPK phosphorylation. We conclude that forced expression of IL-6 in astrocytes induces the activation of the PACAP/VIP neuropeptide system in the brain, which is only partly modulated upon IL-6 trans-signalling inhibition. Increased expression of PACAP/VIP neuropeptides and receptors may represent a homeostatic response of the CNS to an uncontrolled IL-6 synthesis and its neuroinflammatory consequences. Full article
(This article belongs to the Special Issue New Mechanisms and Therapeutics in Neurological Diseases 3.0)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Enhanced PACAP and VIP levels in the cerebrum and cerebellum of GFAP-IL6 mice are partly rescued in bi-genic GFAP-IL6/sgp130Fc mice. PACAP and VIP protein concentrations in lysates from cerebra and cerebella of age-matched six-months-old wild-type (WT), monogenic GFAP-IL6 and bi-genic GFAP-IL6/sgp130Fc mice were determined using commercially available PACAP and VIP mouse ELISA Kits (for details refer to <a href="#sec4-ijms-25-09453" class="html-sec">Section 4</a>). Comparisons among WTs, monogenic and bi-genic mice groups (n = 3 per group) were conducted to assess the levels of PACAP or VIP in the cerebrum (<b>A</b>,<b>B</b>) and cerebellum (<b>C</b>,<b>D</b>). * <span class="html-italic">p</span> &lt; 0.05 or ** <span class="html-italic">p</span> &lt; 0.01 vs. WT. # <span class="html-italic">p</span> &lt; 0.05 or ## <span class="html-italic">p</span> &lt; 0.01 vs. GFAP-IL6 mice. n.s. = not significant. Tukey post-hoc test after analysis of variance.</p> Full article ">Figure 2
<p>Comparative analyses of STAT3, NFκB and ERK1/2<sup>MAPK</sup> phosphorylation in the cerebrum of GFAP-IL6 and GFAP-IL6/sgp130Fc mice. Co-expression of sgp130Fc reduces steady-state p-STAT3<sup>(Y705)</sup> and p-NFκB<sup>(S536)</sup> but not p-ERK1/2<sup>MAPK</sup> in the cerebrum of GFAP-IL6 mice. (<b>A</b>,<b>C</b>) Tissue lysates (15 μg protein per lane) from cerebra of 6-month-old mice were subjected to SDS-PAGE followed by immunoblotting. (<b>B</b>,<b>D</b>), X-ray films were quantified by densitometry (OD) using NIH ImageJ software (version 1.52) in (<b>B</b>) for p-STAT3, STAT3, p-NFκB, NFκB or reported as a ratio between phospho-specific and pan proteins and in (<b>D</b>) for p-ERK1/2, ERK1/2 or as a ratio. GAPDH was used as loading control. Values represent the mean ± SEM with n = 3 brains per genotype. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 or *** <span class="html-italic">p</span> &lt; 0.001 vs. WT. # <span class="html-italic">p</span> &lt; 0.05 or ### <span class="html-italic">p</span> &lt; 0.001 vs. GFAP-IL6 mice. One-Way ANOVA followed Tukey post-hoc test.</p> Full article ">Figure 3
<p>Comparative analyses of STAT3, NFκB and ERK1/2<sup>MAPK</sup> phosphorylation in the cerebellum of GFAP-IL6 and GFAP-IL6/sgp130Fc mice. Co-expression of sgp130Fc reduces steady-state p-STAT3<sup>(Y705)</sup> and p-NFκB<sup>(S536)</sup> but not p-ERK1/2<sup>MAPK</sup> in the cerebellum of GFAP-IL6 mice. (<b>A</b>,<b>C</b>) Tissue lysates (15 μg protein per lane) from cerebra of 6-month-old mice were subjected to SDS-PAGE followed by immunoblotting (<b>B</b>,<b>D</b>). X-ray films were quantified by densitometry (OD) using NIH ImageJ software in (<b>B</b>) for p-STAT3, STAT3, p-NFκB and NFκB or reported as a ratio between phospho-specific and pan proteins and in (<b>D</b>) for p-ERK1/2 and ERK1/2 or as a ratio. GAPDH was used as a loading control. Values represent the mean ± SEM, with n = 3 brains per genotype. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 or *** <span class="html-italic">p</span> &lt; 0.001 vs. WT. # <span class="html-italic">p</span> &lt; 0.05 or ## <span class="html-italic">p</span> &lt; 0.01 vs. GFAP-IL6 mice. One-Way ANOVA followed Tukey post-hoc test.</p> Full article ">Figure 4
<p>Expression of PAC1, VPAC1 and VPAC2 receptors in the cerebrum of GFAP-IL6 and GFAP-IL6/sgp130Fc mice. The presence of sgp130Fc does not prevent the increase in PACAP/VIP receptors in the cerebrum of GFAP-IL6 mice. (<b>A</b>,<b>C</b>,<b>E</b>) Western blots and (<b>B</b>,<b>D</b>,<b>F</b>) densitometry of bands obtained from lysates of cerebra from 6-month-old mice (n = 3 × genotype) that were separated by SDS-PAGE and quantified by NIH ImageJ software (version 1.52). GAPDH was used as the loading control. (<b>G</b>,<b>I</b>,<b>K</b>) PAC1, VPAC1 and VPAC2 immunohistochemistry was performed on paraformaldehyde fixed, paraffin-embedded sections (5 µm) of brains prepared from 6-month-old mice. Scale bar, 50 µm. (<b>H</b>,<b>J</b>,<b>L</b>) Semi-quantitative analyses of immunoreactivities were performed on at least three blinded sections per brain and on a minimum of four brains × genotype. Values represent the mean ± SEM with n = 4 brains per genotype. ** <span class="html-italic">p</span> &lt; 0.01 or *** <span class="html-italic">p</span> &lt; 0.001 vs. WT mice. One-Way ANOVA followed Tukey post-hoc test.</p> Full article ">Figure 5
<p>Expression of PAC1, VPAC1 and VPAC2 receptors in the cerebellum of GFAP-IL6 and GFAP-IL6/sgp130Fc mice. Inhibition of IL6 trans-signalling failed to reduce GFAP-IL6-driven induction of PACAP/VIP receptors in the mouse cerebellum. (<b>A</b>,<b>C</b>,<b>E</b>) Tissue lysates (15 µg protein per lane) from cerebellum of 6-month-old mice were subjected to SDS-PAGE followed by immunoblotting. (<b>B</b>,<b>D</b>,<b>F</b>) Quantification of band densities (n = 3 × genotype) by NIH ImageJ software. GAPDH was used as loading control. (<b>G</b>,<b>I</b>,<b>K</b>) PAC1, VPAC1 and VPAC2 immunohistochemistry was performed on paraformaldehyde fixed, paraffin-embedded sections (5 µm) of brains prepared from 6-month-old mice. Scale bar, 50 µm. (<b>H</b>,<b>J</b>,<b>L</b>) Stereological assessments of (<b>H</b>) PAC1<sup>+</sup>, (<b>J</b>) VPAC1<sup>+</sup> and (<b>L</b>) VPAC2<sup>+</sup> cells in each of the three cerebellar cortical layers was performed on at least three blinded sections per brain and using four brains × genotype. Values represent the mean ± SEM with n = 4 brains per genotype. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 or *** <span class="html-italic">p</span> &lt; 0.001 vs. WT mice. Western blots (<b>A</b>–<b>F</b>): One-Way ANOVA followed by Tukey post-hoc tests. Stereology (<b>G</b>–<b>L</b>): 2-Way ANOVA (factoring in both cell layers and genotypes) followed Tukey post-hoc tests. ML = Molecular layer; PC = Purkinje cells layer; GCL = Granular cell layer.</p> Full article ">
14 pages, 4478 KiB  
Article
Toxic Advanced Glycation End-Products Inhibit Axonal Elongation Mediated by β-Tubulin Aggregation in Mice Optic Nerves
by Hayahide Ooi, Ayako Furukawa, Masayoshi Takeuchi and Yoshiki Koriyama
Int. J. Mol. Sci. 2024, 25(13), 7409; https://doi.org/10.3390/ijms25137409 - 5 Jul 2024
Cited by 2 | Viewed by 1122
Abstract
Advanced glycation end-products (AGEs) form through non-enzymatic glycation of various proteins. Optic nerve degeneration is a frequent complication of diabetes, and retinal AGE accumulation is strongly linked to the development of diabetic retinopathy. Type 2 diabetes mellitus is a major risk factor for [...] Read more.
Advanced glycation end-products (AGEs) form through non-enzymatic glycation of various proteins. Optic nerve degeneration is a frequent complication of diabetes, and retinal AGE accumulation is strongly linked to the development of diabetic retinopathy. Type 2 diabetes mellitus is a major risk factor for Alzheimer’s disease (AD), with patients often exhibiting optic axon degeneration in the nerve fiber layer. Notably, a gap exists in our understanding of how AGEs contribute to neuronal degeneration in the optic nerve within the context of both diabetes and AD. Our previous work demonstrated that glyceraldehyde (GA)-derived toxic advanced glycation end-products (TAGE) disrupt neurite outgrowth through TAGE–β-tubulin aggregation and tau phosphorylation in neural cultures. In this study, we further illustrated GA-induced suppression of optic nerve axonal elongation via abnormal β-tubulin aggregation in mouse retinas. Elucidating this optic nerve degeneration mechanism holds promise for bridging the knowledge gap regarding vision loss associated with diabetes mellitus and AD. Full article
(This article belongs to the Special Issue New Mechanisms and Therapeutics in Neurological Diseases 3.0)
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Figure 1

Figure 1
<p>GA increased TAGE–β-tubulin and β-tubulin aggregation in the retina in a time-dependent manner. (<b>A</b>) TAGE levels were measured using slot blot analysis with an anti-TAGE antibody. The graph shows the intensity of the TAGE band in the slot blot. ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05 vs. day 0 (n = 3). (<b>B</b>–<b>E</b>) Level of β-tubulin aggregation detected using an anti-β-tubulin antibody. (<b>B</b>) Western blot obtained using the anti-β-tubulin antibody: U: Upper band, L: Lower band, M: Monomer band. (<b>C</b>) The intensity of the upper β-tubulin bands upon GA treatment. (<b>D</b>) The intensity of the lower β-tubulin bands upon GA treatment. (<b>E</b>) The intensity of the monomer β-tubulin bands upon GA treatment. ** <span class="html-italic">p</span> &lt; 0.05 vs. day 0 (n = 3).</p> Full article ">Figure 2
<p>TAGE colocalized with β-tubulin in the retina upon intraocular injection of GA. (<b>A</b>,<b>D</b>,<b>G</b>) Immunoreactivity of β-tubulin was increased in GCL and NFL at 1–3 days after intraocular injection of GA (<b>A</b>) day 0, (<b>D</b>) day 1, (<b>G</b>) day 3. (<b>B</b>,<b>E</b>,<b>H</b>) Immunoreactivity of TAGE, (<b>B</b>) day 0, (<b>E</b>) day 1, (<b>H</b>) day 3. (<b>C</b>,<b>F</b>,<b>I</b>) Merged images. Arrowhead: Colocalization of TAGE and β-tubulin. GCL: ganglion cell layer, NFL: nerve fiber layer. Scale = 100 μm.</p> Full article ">Figure 3
<p>PM inhibited GA-induced TAGE formation and β-tubulin aggregation in the retina. (<b>A</b>) TAGE levels were measured using slot blot analysis with an anti-TAGE antibody at 3 days of treatment. The histogram shows the intensity of the TAGE bands in the slot blot. ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05 vs. vehicle control. + <span class="html-italic">p</span> &lt; 0.01 vs. GA alone (n = 3). (<b>B</b>) β-Tubulin levels detected using an anti-β-tubulin antibody (3-day treated retinal samples). U: Upper band, L: Lower band, M: Monomer band. (<b>C</b>–<b>E</b>) The intensity of the upper (<b>C</b>), lower (<b>D</b>), and monomer (<b>E</b>) β-tubulin bands upon GA treatment. ** <span class="html-italic">p</span> &lt; 0.05 vs. vehicle control, + <span class="html-italic">p</span> &lt; 0.01 vs. GA alone (n = 3).</p> Full article ">Figure 4
<p>PM dose-dependently suppressed TAGE formation in GCL and NFL. (<b>A</b>–<b>D</b>) PM dose-dependently suppressed TAGE formation in GCL and NFL in retina. (<b>A</b>) 0 day, (<b>B</b>) GA, (<b>C</b>) GA plus 250 μM PM, (<b>D</b>) GA plus 500 μM PM. Scale = 100 μm.</p> Full article ">Figure 5
<p>Zymosan did not affect TAGE formation and TAGE–β-tubulin aggregation by GA in the retina after optic nerve injury. (<b>A</b>) TAGE levels were measured using slot blot analysis with an anti-TAGE antibody. The histogram shows the intensity of the TAGE bands in the slot blot. * <span class="html-italic">p</span> &lt; 0.01 vs. vehicle control (n = 3). (<b>B</b>–<b>E</b>) Levels of β-tubulin aggregation detected using an anti-β-tubulin antibody. (<b>B</b>) Western blot obtained using the anti-β-tubulin antibody. U: Upper band, L: Lower band, M: Monomer band. (<b>C</b>) The intensity of the upper β-tubulin band upon GA treatment. (<b>D</b>) The intensity of the lower β-tubulin band upon GA treatment. (<b>E</b>) The intensity of the monomer β-tubulin band upon GA treatment. * <span class="html-italic">p</span> &lt; 0.01 vs. vehicle control (n = 3). C: vehicle control, Z: zymosan (12.5 μg/mL), G: GA (1 mM), Z+G: zymosan plus GA.</p> Full article ">Figure 6
<p>Axonal elongation induced by GA was dependent on TAGE. (<b>A</b>–<b>D</b>) Longitudinal sections of the adult mouse optic nerve showing GAP-43-positive axons extending over the injury site (asterisks) after 10 days of optic nerve injury. (<b>A</b>) Vehicle control, (<b>B</b>) Zymosan, (<b>C</b>) Zymosan plus GA, (<b>D</b>) Zymosan plus GA plus PM, (<b>E</b>) Quantification of axonal elongation at a point 250 μm distant from the injury site. ** <span class="html-italic">p</span> &lt; 0.05 vs. vehicle control. + <span class="html-italic">p</span> &lt; 0.05 vs. zymosan alone. # <span class="html-italic">p</span> &lt; 0.01 vs. zymosan plus GA (n = 8, 6 mice per each group). Scale = 100 μm.</p> Full article ">Figure 7
<p>PM decreased the levels of phosphorylated tau induced by GA. (<b>A</b>) Western blot images showing the levels of total and phosphorylated tau (P-Tau). (<b>B</b>) Graphical representation of the intensity of Total-tau and (<b>C</b>) P-tau bands in the Western blot images shown in (<b>A</b>). ** <span class="html-italic">p</span> &lt; 0.01 vs. vehicle control, + <span class="html-italic">p</span> &lt; 0.01 vs. GA alone (n = 3).</p> Full article ">
14 pages, 1192 KiB  
Article
The Relationships between Cerebrospinal Fluid Glial (CXCL12, CX3CL, YKL-40) and Synaptic Biomarkers (Ng, NPTXR) in Early Alzheimer’s Disease
by Agnieszka Kulczyńska-Przybik, Maciej Dulewicz, Julia Doroszkiewicz, Renata Borawska, Agnieszka Słowik, Henrik Zetterberg, Jörg Hanrieder, Kaj Blennow and Barbara Mroczko
Int. J. Mol. Sci. 2023, 24(17), 13166; https://doi.org/10.3390/ijms241713166 - 24 Aug 2023
Cited by 7 | Viewed by 1801
Abstract
In addition to amyloid and tau pathology in the central nervous system (CNS), inflammatory processes and synaptic dysfunction are highly important mechanisms involved in the development and progression of dementia diseases. In the present study, we conducted a comparative analysis of selected pro-inflammatory [...] Read more.
In addition to amyloid and tau pathology in the central nervous system (CNS), inflammatory processes and synaptic dysfunction are highly important mechanisms involved in the development and progression of dementia diseases. In the present study, we conducted a comparative analysis of selected pro-inflammatory proteins in the CNS with proteins reflecting synaptic damage and core biomarkers in mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). To our knowledge, no studies have yet compared CXCL12 and CX3CL1 with markers of synaptic disturbance in cerebrospinal fluid (CSF) in the early stages of dementia. The quantitative assessment of selected proteins in the CSF of patients with MCI, AD, and non-demented controls (CTRL) was performed using immunoassays (single- and multiplex techniques). In this study, increased CSF concentration of CX3CL1 in MCI and AD patients correlated positively with neurogranin (r = 0.74; p < 0.001, and r = 0.40; p = 0.020, respectively), ptau181 (r = 0.49; p = 0.040), and YKL-40 (r = 0.47; p = 0.050) in MCI subjects. In addition, elevated CSF levels of CXCL12 in the AD group were significantly associated with mini-mental state examination score (r = −0.32; p = 0.040). We found significant evidence to support an association between CX3CL1 and neurogranin, already in the early stages of cognitive decline. Furthermore, our findings indicate that CXCL12 might be a useful marker for tract severity of cognitive impairment. Full article
(This article belongs to the Special Issue New Mechanisms and Therapeutics in Neurological Diseases 3.0)
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<p>CSF concentrations of CXCL12 and CX3CL1 across diagnostic groups (AD, MCI, CTRL). Statistically significant levels ** <span class="html-italic">p</span> &lt; 0.01 *** <span class="html-italic">p</span> = 0.001 **** <span class="html-italic">p</span> &lt; 0.001, ns—not significant.</p> Full article ">Figure 2
<p>Spearman correlations between CSF proteins, MMSE, as well as amyloid and tau biomarkers in patients with mild cognitive impairment (<b>A</b>) and Alzheimer’s disease (<b>B</b>). The X and Y axes display biomarkers that correlate with each other. The scale of the colors visualizes the strength of relationships between selected biomarkers: the more intense the color, the stronger the correlation. Additionally, the green color presents negative correlations, whereas purple presents positive correlations.</p> Full article ">

Review

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37 pages, 3007 KiB  
Review
High Blood Pressure and Impaired Brain Health: Investigating the Neuroprotective Potential of Magnesium
by Khawlah Alateeq, Erin I. Walsh and Nicolas Cherbuin
Int. J. Mol. Sci. 2024, 25(22), 11859; https://doi.org/10.3390/ijms252211859 - 5 Nov 2024
Viewed by 1414
Abstract
High blood pressure (BP) is a significant contributor to the disease burden globally and is emerging as an important cause of morbidity and mortality in the young as well as the old. The well-established impact of high BP on neurodegeneration, cognitive impairment, and [...] Read more.
High blood pressure (BP) is a significant contributor to the disease burden globally and is emerging as an important cause of morbidity and mortality in the young as well as the old. The well-established impact of high BP on neurodegeneration, cognitive impairment, and dementia is widely acknowledged. However, the influence of BP across its full range remains unclear. This review aims to explore in more detail the effects of BP levels on neurodegeneration, cognitive function, and dementia. Moreover, given the pressing need to identify strategies to reduce BP levels, particular attention is placed on reviewing the role of magnesium (Mg) in ageing and its capacity to lower BP levels, and therefore potentially promote brain health. Overall, the review aims to provide a comprehensive synthesis of the evidence linking BP, Mg and brain health. It is hoped that these insights will inform the development of cost-effective and scalable interventions to protect brain health in the ageing population. Full article
(This article belongs to the Special Issue New Mechanisms and Therapeutics in Neurological Diseases 3.0)
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<p>The figure shows the global rise in brain ageing cases and the increasing incidence of dementia, highlighting the urgent need for prevention. A major risk factor is high blood pressure (BP), which shares several common risk factors with brain ageing. High BP affects a significant portion of the population worldwide, with recent findings indicating an earlier onset of hypertension in younger individuals. Higher magnesium intake is associated with reduced BP and improved cardiovascular health. Additionally, the protective effects of magnesium extend to lowering the risk of cognitive decline and brain ageing.</p> Full article ">Figure 2
<p>Schematic overview of the relationship between blood pressure (BP), ageing mechanisms, brain changes, and cognitive function. It depicts how increased BP adversely affects endothelial function, leading to oxidative stress, inflammation, vascular atherosclerosis, and calcification, which may further elevate BP. Chronically high BP is associated with microscopic changes in the brain, including blood–brain barrier (BBB) disruption, microglia activation, pro-inflammatory responses, demyelination, and the accumulation of amyloid plaques and tau protein. These microscopic alterations result in macroscopic changes such as larger white matter lesions and reduced brain size, ultimately contributing to accelerated brain ageing and an increased risk of dementia.</p> Full article ">Figure 3
<p>Schematic overview illustrating the relationship between magnesium (Mg), ageing mechanisms, and micro- and macrostructural brain changes. It shows how higher dietary Mg intake supports vascular health by improving endothelial function and regulating blood pressure (BP). Mg also contributes to reducing inflammatory processes, thereby mitigating the harmful effects of chronic inflammation on brain tissue. Additionally, Mg exhibits neuroprotective properties, such as the ability to block NMDAR (N-methyl-D-aspartate receptors), which is involved in excitotoxicity—a process that can damage neurons during ageing. These beneficial effects of Mg lead to a reduction in various brain-ageing pathologies, including glial cell loss, microglia activation, oxidative stress, neuroinflammation, demyelination, amyloid accumulation, and tau phosphorylation. By counteracting these pathological mechanisms, Mg intake may help preserve brain health and slow down age-related brain degeneration, ultimately reducing the risk of cognitive decline.</p> Full article ">Figure 4
<p>Schematic overview of mechanisms mediating the neuroprotective effects of magnesium on brain ageing. The antihypertensive effect of magnesium may be linked to slower ageing, although recent research by Alateeq et al. [<a href="#B307-ijms-25-11859" class="html-bibr">307</a>] found no significant effects in a large population sample. Emerging evidence suggests that inflammation may partially mediate the association between magnesium intake and brain volume, indicating that the anti-inflammatory properties of magnesium could help reduce brain ageing. Furthermore, multiple pathways, such as NMDA receptor modulation, may play a role in the neuroprotective effects of magnesium. While evidence from human studies on NMDA modulation remains limited, further research is needed to fully elucidate these interconnected mechanisms.</p> Full article ">
15 pages, 522 KiB  
Review
The Role of ACE2 in Neurological Disorders: From Underlying Mechanisms to the Neurological Impact of COVID-19
by Jingwen Li, Xiangrui Kong, Tingting Liu, Meiyan Xian and Jianshe Wei
Int. J. Mol. Sci. 2024, 25(18), 9960; https://doi.org/10.3390/ijms25189960 - 15 Sep 2024
Cited by 1 | Viewed by 1644
Abstract
Angiotensin-converting enzyme 2 (ACE2) has become a hot topic in neuroscience research in recent years, especially in the context of the global COVID-19 pandemic, where its role in neurological diseases has received widespread attention. ACE2, as a multifunctional metalloprotease, not only plays a [...] Read more.
Angiotensin-converting enzyme 2 (ACE2) has become a hot topic in neuroscience research in recent years, especially in the context of the global COVID-19 pandemic, where its role in neurological diseases has received widespread attention. ACE2, as a multifunctional metalloprotease, not only plays a critical role in the cardiovascular system but also plays an important role in the protection, development, and inflammation regulation of the nervous system. The COVID-19 pandemic further highlights the importance of ACE2 in the nervous system. SARS-CoV-2 enters host cells by binding to ACE2, which may directly or indirectly affect the nervous system, leading to a range of neurological symptoms. This review aims to explore the function of ACE2 in the nervous system as well as its potential impact and therapeutic potential in various neurological diseases, providing a new perspective for the treatment of neurological disorders. Full article
(This article belongs to the Special Issue New Mechanisms and Therapeutics in Neurological Diseases 3.0)
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<p>The role of ACE2 in neurological diseases and its impact on the nervous system under SARS-CoV-2 infection. (Created with Biorender.com). (<b>A</b>). Angiotensinogen is cleaved by renin to form Ang I. ACE converts Ang I to Ang II, which is the main effector peptide of RAS. Ang II triggers its cellular effects by activating its main receptors, AT1R and angiotensin II receptor 2 (AT2R), thereby counteracting the effects of AT1R activation. ACE2 cleaves Ang II to form Ang 1-7, activating MasR and counteracting Ang II-mediated effects. ACE2 also cleaves Ang I to form Ang 1-9, which is then cleaved by ACE to produce Ang 1-7. Ang 1-9 can also be formed and activated by neuropeptidases, such as NEP, to form AT2R. Some of the effects mediated by Ang 1-7 may also involve AT2R. (<b>B</b>). ACE2 not only directly participates in the protection of neurons, but also maintains the homeostasis of the nervous system by regulating inflammatory responses. It can promote anti-inflammatory reactions, inhibit the production of neurotoxic substances, participate in physiological processes such as neuronal vasodilation and antioxidant stress, and thus play an important role in neurological diseases such as AD, PD, IS, depression, and anxiety. (<b>C</b>). The SARS-CoV-2 virus enters host cells by binding to ACE2 receptors, exacerbating neuronal damage.</p> Full article ">
29 pages, 942 KiB  
Review
Genetic and Epigenetic Biomarkers Linking Alzheimer’s Disease and Age-Related Macular Degeneration
by Snježana Kaštelan, Tamara Nikuševa-Martić, Daria Pašalić, Antonela Gverović Antunica and Danijela Mrazovac Zimak
Int. J. Mol. Sci. 2024, 25(13), 7271; https://doi.org/10.3390/ijms25137271 - 2 Jul 2024
Cited by 2 | Viewed by 1946
Abstract
Alzheimer’s disease (AD) represents a prominent neurodegenerative disorder (NDD), accounting for the majority of dementia cases worldwide. In addition to memory deficits, individuals with AD also experience alterations in the visual system. As the retina is an extension of the central nervous system [...] Read more.
Alzheimer’s disease (AD) represents a prominent neurodegenerative disorder (NDD), accounting for the majority of dementia cases worldwide. In addition to memory deficits, individuals with AD also experience alterations in the visual system. As the retina is an extension of the central nervous system (CNS), the loss in retinal ganglion cells manifests clinically as decreased visual acuity, narrowed visual field, and reduced contrast sensitivity. Among the extensively studied retinal disorders, age-related macular degeneration (AMD) shares numerous aging processes and risk factors with NDDs such as cognitive impairment that occurs in AD. Histopathological investigations have revealed similarities in pathological deposits found in the retina and brain of patients with AD and AMD. Cellular aging processes demonstrate similar associations with organelles and signaling pathways in retinal and brain tissues. Despite these similarities, there are distinct genetic backgrounds underlying these diseases. This review comprehensively explores the genetic similarities and differences between AMD and AD. The purpose of this review is to discuss the parallels and differences between AMD and AD in terms of pathophysiology, genetics, and epigenetics. Full article
(This article belongs to the Special Issue New Mechanisms and Therapeutics in Neurological Diseases 3.0)
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<p>Genetic and epigenetic factors contributing to Alzheimer’s disease.</p> Full article ">Figure 2
<p>The role of interplays of genetic and epigenetic factors in AMD pathogenesis.</p> Full article ">
25 pages, 1811 KiB  
Review
Extracellular Vesicles as Mediators of Neuroinflammation in Intercellular and Inter-Organ Crosstalk
by Andrea Cabrera-Pastor
Int. J. Mol. Sci. 2024, 25(13), 7041; https://doi.org/10.3390/ijms25137041 - 27 Jun 2024
Cited by 8 | Viewed by 2007
Abstract
Neuroinflammation, crucial in neurological disorders like Alzheimer’s disease, multiple sclerosis, and hepatic encephalopathy, involves complex immune responses. Extracellular vesicles (EVs) play a pivotal role in intercellular and inter-organ communication, influencing disease progression. EVs serve as key mediators in the immune system, containing molecules [...] Read more.
Neuroinflammation, crucial in neurological disorders like Alzheimer’s disease, multiple sclerosis, and hepatic encephalopathy, involves complex immune responses. Extracellular vesicles (EVs) play a pivotal role in intercellular and inter-organ communication, influencing disease progression. EVs serve as key mediators in the immune system, containing molecules capable of activating molecular pathways that exacerbate neuroinflammatory processes in neurological disorders. However, EVs from mesenchymal stem cells show promise in reducing neuroinflammation and cognitive deficits. EVs can cross CNS barriers, and peripheral immune signals can influence brain function via EV-mediated communication, impacting barrier function and neuroinflammatory responses. Understanding EV interactions within the brain and other organs could unveil novel therapeutic targets for neurological disorders. Full article
(This article belongs to the Special Issue New Mechanisms and Therapeutics in Neurological Diseases 3.0)
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<p><b>Biogenesis of Extracellular Vesicles (EVs).</b> The biogenesis of exosomes involves a tightly regulated process that begins with the formation of early endosomes. These early endosomes mature into multivesicular bodies (MVBs), which contain intraluminal vesicles harboring specific cargo molecules. Some of these MVBs directly fuse with lysosomes and degrade, some are transported to the Golgi for recovery, and some fuse with the cell membrane to release small vesicles outside of the cell and form exosomes. Many molecules play an important role in exosome biogenesis and abscission. First, the endosomal sorting complex required for transport (ESCRT) and other proteins, such as tumor susceptibility gene 101 protein (TSG101) and ALG-2 interacting protein X (ALIX), are involved in cargo sorting into exosomes. In addition, other ESCRT-independent mechanisms, including lipid rafts and tetraspanins CD63 and CD81, are conducive to exosome biogenesis. Finally, the Rab-GTPase family contributes to the intracellular trafficking and fusion of MVBs with the cell membrane to release exosomes. Exosomes are small EVs, typically ranging from 30 to 150 nanometers in diameter. In contrast, microvesicles are formed by the outward budding and shedding of the plasma membrane, resulting in the direct release of vesicles into the extracellular environment. Microvesicles are larger EVs, generally ranging from 100 to 1000 nanometers in size. The cargo of EVs includes a diverse array of bioactive molecules, such as nucleic acids (mRNA, miRNA, DNA, lncRNA), proteins, and lipids. The different surface proteins are transmembrane proteins such as tetraspanins (such as CD9, CD63, CD81), antigen-presenting molecules (MHC I and II), adhesion molecules (such as integrins, P-selectin), and other signaling receptors (such as TNFR, FasL, TfR); proteins in the EV lumen, such as heat shock proteins (HSPs), cytoskeletal proteins (such as actin, tubulin, vimentin), ESCRT components (such as Alix, TSG-101), membrane transport and fusion proteins (such as GTPases, Annexin, Flotillin, Clathrin), growth factors and cytokines (such as TNF-α, TGF-β, TRAIL), and metabolic enzymes (such as GAPDH, PKM2, PGK1, PDIA3). EVs also comprise multiple lipids, such as cholesterol, ceramides, sphingomyelin, phosphatidylinostol (PI), phosphatidylserine (PS), phosphatidylcholine (PC), phosphatidylethanolamine (PE), and gangliosides (GM). Importantly, the composition of EV cargo is influenced by the originating cell type and its physiological state. TNF-α = tumor necrosis alpha, TGF-β = transforming growth factor beta, TRAIL = TNF-related apoptosis-inducing ligand, messenger RNA (mRNA), microRNA (miRNA), lncRNA = long non-coding RNAs, GAPDH = Glyceraldehyde 3-phosphate dehydrogenase, PKM2 = Pyruvate kinase isozyme M2, PGK1 = Phosphoglycerate Kinase 1, PDIA3 = Protein disulfide-isomerase A3, TNFR = tumor necrosis factor receptor, FasL = Fas ligand, and TfR = Transferrin receptor.</p> Full article ">Figure 2
<p><b>EV-mediated intercellular crosstalk among glial-neuron cells.</b> EVs released by glial cells (astrocytes, oligodendrocytes, and microglia) or neurons have several target cells within the brain and not only orchestrate inflammatory reactions but also provide neurotrophic support and contribute to the maintenance of homeostasis. EVs from glial cells modulate synaptic activity, neuronal survival, neurogenesis, and myelination process, and, in an inflammatory environment, propagate the activation of inflammatory signaling pathways. Neuron-derived EVs also contribute to the homeostasis of astrocytes and microglia but, in neuroinflammatory conditions, they contribute to the activation of both. <span class="html-italic">CAM/ECM: cell adhesion/extracellular matrix</span>.</p> Full article ">Figure 3
<p><b>Scheme of EVs in inter-organ crosstalk.</b> EVs disseminate inflammatory signals between organs. In brain–heart crosstalk, increased plasma astrocyte-derived EVs have been shown in post-ischemic stroke. The content of the EVs participates in the disruption of endothelial function and activation of coagulation factors. Moreover, several miRNAs have been found to be linked to both heart and brain pathophysiology. EVs from adipose tissue are involved in processes such as axonal growth, tract connectivity, oligodendrogenesis, and remyelination following subcortical ischemic stroke. Other emerging evidence suggests that EVs derived from mesenchymal stem cells, such as ADSCs (adipose-derived stem cells), possess anti-inflammatory properties and mitigate neuroinflammation in various pathological conditions. Plasma EVs from animal models with minimal hepatic encephalopathy (MHE) are able to induce altered neurotransmission. Age-related thyroid deficiency can enhance the transport of Apolipoprotein E4-containing EVs from the liver to the brain, contributing to Alzheimer’s disease-related dementia and neuronal dysfunction. In the gut–brain axis, EVs found in the intestinal microenvironment originate from both microorganisms, such as bacteria, and intestinal cells, and are involved in transmitting signals (LPS, DNA, RNA, miRNAs, etc.) to the brain through the vagus nerve or the bloodstream. These gut-derived EVs can induce neuroinflammation, modulate neuronal function, and increase the permeability of the blood–brain barrier (BBB).</p> Full article ">
26 pages, 1413 KiB  
Review
Common and Trace Metals in Alzheimer’s and Parkinson’s Diseases
by Julia Doroszkiewicz, Jakub Ali Farhan, Jan Mroczko, Izabela Winkel, Maciej Perkowski and Barbara Mroczko
Int. J. Mol. Sci. 2023, 24(21), 15721; https://doi.org/10.3390/ijms242115721 - 29 Oct 2023
Cited by 29 | Viewed by 4712
Abstract
Trace elements and metals play critical roles in the normal functioning of the central nervous system (CNS), and their dysregulation has been implicated in neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). In a healthy CNS, zinc, copper, iron, and [...] Read more.
Trace elements and metals play critical roles in the normal functioning of the central nervous system (CNS), and their dysregulation has been implicated in neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). In a healthy CNS, zinc, copper, iron, and manganese play vital roles as enzyme cofactors, supporting neurotransmission, cellular metabolism, and antioxidant defense. Imbalances in these trace elements can lead to oxidative stress, protein aggregation, and mitochondrial dysfunction, thereby contributing to neurodegeneration. In AD, copper and zinc imbalances are associated with amyloid-beta and tau pathology, impacting cognitive function. PD involves the disruption of iron and manganese levels, leading to oxidative damage and neuronal loss. Toxic metals, like lead and cadmium, impair synaptic transmission and exacerbate neuroinflammation, impacting CNS health. The role of aluminum in AD neurofibrillary tangle formation has also been noted. Understanding the roles of these elements in CNS health and disease might offer potential therapeutic targets for neurodegenerative disorders. The Codex Alimentarius standards concerning the mentioned metals in foods may be one of the key legal contributions to safeguarding public health. Further research is needed to fully comprehend these complex mechanisms and develop effective interventions. Full article
(This article belongs to the Special Issue New Mechanisms and Therapeutics in Neurological Diseases 3.0)
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<p>Prisma flow diagram depicting the methods for including studies in the review.</p> Full article ">Figure 2
<p>Beneficial effects of Zn, Cu, Fe, and Mn on a healthy brain.</p> Full article ">Figure 3
<p>Schematic depiction of dopaminergic transmission changed in Parkinson’s disease.</p> Full article ">
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