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15 pages, 3307 KiB  
Article
Exposure to Cadmium and Other Trace Elements Among Individuals with Mild Cognitive Impairment
by Teresa Urbano, Marco Vinceti, Chiara Carbone, Lauren A. Wise, Marcella Malavolti, Manuela Tondelli, Roberta Bedin, Giulia Vinceti, Alessandro Marti, Annalisa Chiari, Giovanna Zamboni, Bernhard Michalke and Tommaso Filippini
Toxics 2024, 12(12), 933; https://doi.org/10.3390/toxics12120933 - 22 Dec 2024
Viewed by 321
Abstract
Background: A limited number of studies have investigated the role of environmental chemicals in the etiology of mild cognitive impairment (MCI). We performed a cross-sectional study of the association between exposure to selected trace elements and the biomarkers of cognitive decline. Methods: During [...] Read more.
Background: A limited number of studies have investigated the role of environmental chemicals in the etiology of mild cognitive impairment (MCI). We performed a cross-sectional study of the association between exposure to selected trace elements and the biomarkers of cognitive decline. Methods: During 2019–2021, we recruited 128 newly diagnosed patients with MCI from two Neurology Clinics in Northern Italy, i.e., Modena and Reggio Emilia. At baseline, we measured serum and cerebrospinal fluid (CSF) concentrations of cadmium, copper, iron, manganese, and zinc using inductively coupled plasma mass spectrometry. With immuno-enzymatic assays, we estimated concentrations of β-amyloid 1-40, β-amyloid 1-42, Total Tau and phosphorylated Tau181 proteins, neurofilament light chain (NfL), and the mini-mental state examination (MMSE) to assess cognitive status. We used spline regression to explore the shape of the association between exposure and each endpoint, adjusted for age at diagnosis, educational attainment, MMSE, and sex. Results: In analyses between the serum and CSF concentrations of trace metals, we found monotonic positive correlations between copper and zinc, while an inverse association was observed for cadmium. Serum cadmium concentrations were inversely associated with amyloid ratio and positively associated with Tau proteins. Serum iron concentrations showed the opposite trend, while copper, manganese, and zinc displayed heterogeneous non-linear associations with amyloid ratio and Tau biomarkers. Regarding CSF exposure biomarkers, only cadmium consistently showed an inverse association with amyloid ratio, while iron was positively associated with Tau. Cadmium concentrations in CSF were not appreciably associated with serum NfL levels, while we observed an inverted U-shaped association with CSF NfL, similar to that observed for copper. In CSF, zinc was the only trace element positively associated with NfL at high concentrations. Conclusions: In this cross-sectional study, high serum cadmium concentrations were associated with selected biomarkers of cognitive impairment. Findings for the other trace elements were difficult to interpret, showing complex and inconsistent associations with the neurodegenerative endpoints examined. Full article
(This article belongs to the Special Issue Cadmium and Trace Elements Toxicity)
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Graphical abstract
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<p>Study flowchart. Abbreviations: CSF, cerebrospinal fluid; SCD, subjective cognitive decline (SCD).</p>
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<p>Violin plots distribution of trace element concentrations in serum and cerebrospinal fluid (CSF) according to sex (M, males; F, females). MCI, serum n = 89; CSF n = 45. Abbreviations: Cd, cadmium; Cu, copper; Fe, iron; MCI, mild cognitive impairment; Mn, manganese; NfL, neurofilament light chain; SCD, subjective cognitive decline; Zn, zinc.</p>
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<p>Spline regression analysis of the association between trace element concentration in serum and cerebrospinal fluid among patients with mild cognitive impairment. The solid line indicates the multivariable analysis; the shaded area represents upper and lower confidence interval limits. The dashed line represents association assuming linearity. Diamonds represent individual observations (n = 45). Abbreviations: Cd, cadmium; CSF, cerebrospinal fluid; Cu, copper; Fe, iron; Mn, manganese; NfL, neurofilament light chain; Zn, zinc.</p>
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<p>Spline regression analysis of the association between trace element concentration in serum (dark blue) and cerebrospinal fluid (CSF-light blue) with serum <b>(A)</b> and CSF neurofilament light (NfL) concentrations <b>(B)</b> among patients with mild cognitive impairment. The solid line indicates the multivariable analysis; the shaded area represents the upper and lower confidence interval limits. The dashed line represents the association assuming linearity. Diamonds represent individual observations (serum n = 89; CSF n = 45). Abbreviations: Cd, cadmium; Cu, copper; Fe, iron; MMSE, mini-mental state examination; Mn, manganese; Zn, zinc.</p>
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<p>Spline regression analysis of the association between trace element concentration in serum (dark green) and cerebrospinal fluid (CSF—light green) with CSF concentration of amyloid ratio (<b>A</b>), Total Tau (<b>B</b>) and phosphorylated Tau (p-Tau181) protein (<b>C</b>) among patients with mild cognitive impairment. The solid line indicates the multivariable analysis; the shaded area represents the upper and lower confidence interval limits. The dashed line represents the association assuming linearity. Diamonds represent individual observations (serum n = 89; CSF n = 45). Red lines represent laboratory cut-offs (&gt;0.069 for amyloid ratio; &lt;400 pg/mL for Total Tau; &lt;56.5 pg/mL for p-Tau181). Abbreviations: Cd, cadmium; Cu, copper; Fe, iron; MMSE, mini-mental state examination; Mn, manganese; Zn, zinc.</p>
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17 pages, 8649 KiB  
Article
LPS Disrupts Endometrial Receptivity by Inhibiting STAT1 Phosphorylation in Sheep
by Xing Fan, Jinzi Wei, Yu Guo, Juan Ma, Meiyu Qi, He Huang, Peng Zheng, Wenjie Jiang and Yuchang Yao
Int. J. Mol. Sci. 2024, 25(24), 13673; https://doi.org/10.3390/ijms252413673 - 21 Dec 2024
Viewed by 294
Abstract
Uterine infections reduce ruminant reproductive efficiency. Reproductive dysfunction caused by infusion of Gram-negative bacteria is characterized by the failure of embryo implantation and reduced conception rates. Lipopolysaccharide (LPS), a major component of the outer membrane of Gram-negative bacteria, is highly abortogenic. In this [...] Read more.
Uterine infections reduce ruminant reproductive efficiency. Reproductive dysfunction caused by infusion of Gram-negative bacteria is characterized by the failure of embryo implantation and reduced conception rates. Lipopolysaccharide (LPS), a major component of the outer membrane of Gram-negative bacteria, is highly abortogenic. In this study, the effects of LPS infusion on the endometrial receptivity of sheep were studied during three critical periods of embryo implantation. The results showed that LPS infusion on d12, d16, and d20 of pregnancy in vivo interfered with the expression of prostaglandins (PGs) and affected the expression of adhesion-related factors (ITGB1/3/5, SPP1), key implantation genes (HOXA10, HOXA11 and LIF), and progestational elongation genes (ISG15, RSAD2 and CXCL10) during embryo implantation. In addition, after LPS infusion on d12, d16, and d20, the phosphorylation level of STAT1 significantly decreased and the protein expression level of IRF9 significantly increased on d12, suggesting that LPS infusion in sheep impairs endometrial receptivity through the JAK2/STAT1 pathway. Sheep endometrial epithelial cells were treated with 17 β-estrogen, progesterone, and/or interferon-tau in vitro to mimic the receptivity of the endometrium during early pregnancy for validation. LPS and the p-STAT1 inhibitor fludarabine were both added to the model, which resulted in reduced p-STAT1 protein expression, significant inhibition of PGE2/PGF2α, and significant suppression of the expression of key embryo implantation genes. Collectively, these results indicate that LPS infusion in sheep on d12, d16, and d20 impairs endometrial receptivity through the JAK2/STAT1 pathway, which is responsible for LPS-associated pregnancy failure. Full article
(This article belongs to the Section Molecular Biology)
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Figure 1
<p>Effect of LPS on prostaglandin expression in sheep endometrium. (<b>A</b>) The secretion of PGE2 and PGF2α in endometrial tissue was measured on d12, d16, and d20 of pregnancy using an ELISA kit. (<b>B</b>) The secretion of PGE2 and PGF2α in endometrial tissue was measured on d12, d16, and d20 of pregnancy using an ELISA kit. (<b>C</b>) The ratio of PGE2 and PGF2α in endometrial tissue on d12, d16, and d20 of pregnancy. (<b>D</b>) The rate-limiting enzymes <span class="html-italic">PTGS1</span>, <span class="html-italic">PTGS2</span> (<b>E</b>), <span class="html-italic">PTGES</span> (<b>F</b>), and <span class="html-italic">PGFS</span> (<b>G</b>) of synthesized PGs in endometrial tissue on d12, d16, and d20 of pregnancy were measured by real-time quantitative PCR. All data are presented as the mean ± SEM, <span class="html-italic">n</span> ≥ 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effect of LPS on endometrial receptivity genes in sheep. (<b>A</b>) The pro-conceptus elongation gene <span class="html-italic">ISG15</span>, <span class="html-italic">RSAD2</span> (<b>B</b>), and <span class="html-italic">CXCL10</span> (<b>C</b>) on d12, d16, and d20 of pregnancy in endometrial tissue were measured by real-time quantitative PCR. (<b>D</b>) The adhesion molecules <span class="html-italic">ITGB1</span>, <span class="html-italic">ITGB3</span> (<b>E</b>), <span class="html-italic">ITGB5</span> (<b>F</b>), <span class="html-italic">SPP1</span> (<b>G</b>), and <span class="html-italic">MUC1</span> (<b>H</b>) on d12 of pregnancy in endometrial tissue were measured by real-time quantitative PCR. (<b>I</b>) The endometrial receptivity markers <span class="html-italic">HOXA10</span>, <span class="html-italic">HOXA11</span> (<b>J</b>), and <span class="html-italic">LIF</span> (<b>K</b>) on d12 of pregnancy in endometrial tissue were measured by real-time quantitative PCR. All data are presented as the mean ± SEM, <span class="html-italic">n</span> ≥ 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>LPS affected JAK2/STAT1 pathways. (<b>A</b>) The protein level of p-JAK2, T-JAK2, p-STAT1, T-STAT1, and IRF9 on d12, d16, and d20 of pregnancy in sheep endometrial tissue. (<b>B</b>) p-JAK2/β-actin, T-JAK2/β-actin (<b>C</b>), and p-JAK2/T-JAK2 (<b>D</b>) ratio on d12, d16, and d20 of pregnancy in sheep endometrial tissue. (<b>E</b>) p-STAT1/β-actin, T-STAT1/β-actin (<b>F</b>), and p-STAT1/T-STAT1 (<b>G</b>) ratio on d12, d16, and d20 of pregnancy in sheep endometrial tissue. (<b>H</b>) The IRF9/β-actin ratio on d12, d16, and d20 of pregnancy in sheep endometrial tissue. All data are presented as the mean ± SEM, <span class="html-italic">n</span> ≥ 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Establishment of a receptive sheep endometrial epithelial cell model for sheep. (<b>A</b>) Confocal microscopy was used to observe the morphology of sEECs. Red: Cy3-labeled cytokeratin 18 protein; blue, DAPI-labeled nuclei; scale bar: 20 µm. (<b>B</b>) Expression of ISG15 was measured under different concentrations in sEECs. (<b>C</b>–<b>E</b>) The endometrial receptivity-related genes <span class="html-italic">ISG15</span>, <span class="html-italic">RSAD2</span>, <span class="html-italic">CXCL10</span>, <span class="html-italic">HOXA10</span>, <span class="html-italic">HOXA11</span>, <span class="html-italic">LIF</span>, <span class="html-italic">ESR1</span>, <span class="html-italic">ESR2</span>, and <span class="html-italic">PGR</span> in sEECs were measured by real-time quantitative PCR. GAPDH (sheep) was used as the reference gene in all samples. sEECs: sheep endometrial epithelial cells. All data are presented as the mean ± SEM, <span class="html-italic">n</span> ≥ 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effect of LPS or fludarabine treatment on the expression of endometrial receptivity-related genes under hormone treatment. (<b>A</b>) The protein level of p-STAT1 and T-STAT1 in sEECs. (<b>B</b>) The secretion of PGE2 and PGF2α in sEECs. (<b>C</b>–<b>E</b>) The pro-conceptus elongation genes <span class="html-italic">ISG15</span>, <span class="html-italic">RSAD2</span>, <span class="html-italic">CXCL10</span>, adhesion molecules <span class="html-italic">ITGB1/3/5</span>, <span class="html-italic">MUC1</span>, <span class="html-italic">SPP1</span>, and receptivity markers <span class="html-italic">HOXA10</span>, <span class="html-italic">HOXA11</span>, <span class="html-italic">LIF</span> mRNA expression levels in sEECs. GAPDH (sheep) was used as the reference gene in all samples. (<b>F</b>) Confocal microscope images of SPP1 expression in four treatment groups. Red: Cy3-labeled SPP1 protein; blue, DAPI-labeled nuclei; scale bar: 20 µm. All data are presented as the mean ± SEM, <span class="html-italic">n</span> ≥ 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Schematic characterization of the cellular mechanism of LPS infusion effects on endometrial receptivity in sheep during early pregnancy. LPS blocked the effect of IFN-τ in the three stages of sheep embryo implantation and impaired the endometrial receptivity, which is characterized by interfering with the secretion of prostaglandins, hindering the elongation of the conceptus, and reducing the adhesion of the embryo by inhibiting the phosphorylation of STAT1.</p>
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28 pages, 1384 KiB  
Review
Pathology and Treatments of Alzheimer’s Disease Based on Considering Changes in Brain Energy Metabolism Due to Type 2 Diabetes
by Hidekatsu Yanai, Hiroki Adachi, Mariko Hakoshima and Hisayuki Katsuyama
Molecules 2024, 29(24), 5936; https://doi.org/10.3390/molecules29245936 - 16 Dec 2024
Viewed by 453
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with cognitive dysfunction, memory decline, and behavioral disturbance, and it is pathologically characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. Although various hypotheses have been proposed to explain the pathogenesis [...] Read more.
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with cognitive dysfunction, memory decline, and behavioral disturbance, and it is pathologically characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. Although various hypotheses have been proposed to explain the pathogenesis of AD, including the amyloid beta hypothesis, oxidative stress hypothesis, and abnormal phosphorylation of tau proteins, the exact pathogenic mechanisms underlying AD remain largely undefined. Furthermore, effective curative treatments are very limited. Epidemiologic studies provide convincing evidence for a significant association between type 2 diabetes and AD. Here, we showed energy metabolism using glucose, lactate, ketone bodies, and lipids as energy substrates in a normal brain, and changes in such energy metabolism due to type 2 diabetes. We also showed the influences of such altered energy metabolism due to type 2 diabetes on the pathology of AD. Furthermore, we comprehensively searched for risk factors related with type 2 diabetes for AD and showed possible therapeutic interventions based on considering risk factors and altered brain energy metabolism due to type 2 diabetes for the development of AD. Full article
(This article belongs to the Special Issue Chemical Biology in Asia)
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<p>Glucose transporters, insulin receptor, insulin receptor substrate, and monocarboxylate transporters in normal brain. Red up and down arrows indicate increase and decrease in phenomenon, substances, and expression of molecules, respectively. Black arrows indicate the flow of substances. BBB, blood–brain barrier; G, glucose; GLUT, glucose transporter; Gly, glycogen; IR, insulin receptor; IRS, insulin receptor substrate; L, lactate; MCT, monocarboxylate transporter.</p>
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<p>Lipid metabolism in normal brain. C, cholesterol; Ce, ceramide; E, apo E; FA, fatty acid; FABP, FA binding protein; FATP, FA transport proteins; GLUT, glucose transporter; K, ketone body; L, lactate; LP, lipoprotein-like particle; LPL, lipoprotein lipase; MCT, monocarboxylate transporter; P, phospholipid; S, sphingolipid; SR, scavenger receptor.</p>
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<p>Risk factors related with type 2 diabetes for the development of AD. Aβ, amyloid beta; AGEs, advanced glycation end products; APP, amyloid precursor protein; GSK-3, glycogen synthase kinase-3; HDL-C, high-density lipoprotein cholesterol; IAPP, islet amyloid polypeptide; IGF, insulin-like growth factor; LDL-C, low-density lipoprotein cholesterol; PI3K, phosphatidyl-inositide 3-kinases; RAGE, receptor for AGEs; TG, triglyceride.</p>
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<p>Effects of changes in energy metabolism in brain due to type 2 diabetes on the development of AD. AO, antioxidant; E, apo E; FA, fatty acid; G, glucose; K, ketone body; L, lactate; LP, lipoprotein-like particle; LPL, lipoprotein lipase. Upward and downward arrows indicate an increase and decrease in expression, activity, and phenomenon, respectively.</p>
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20 pages, 2502 KiB  
Review
The Search for a Universal Treatment for Defined and Mixed Pathology Neurodegenerative Diseases
by Danton H. O’Day
Int. J. Mol. Sci. 2024, 25(24), 13424; https://doi.org/10.3390/ijms252413424 - 14 Dec 2024
Viewed by 430
Abstract
The predominant neurodegenerative diseases, Alzheimer’s disease, Parkinson’s disease, dementia with Lewy Bodies, Huntington’s disease, amyotrophic lateral sclerosis, and frontotemporal dementia, are rarely pure diseases but, instead, show a diversity of mixed pathologies. At some level, all of them share a combination of one [...] Read more.
The predominant neurodegenerative diseases, Alzheimer’s disease, Parkinson’s disease, dementia with Lewy Bodies, Huntington’s disease, amyotrophic lateral sclerosis, and frontotemporal dementia, are rarely pure diseases but, instead, show a diversity of mixed pathologies. At some level, all of them share a combination of one or more different toxic biomarker proteins: amyloid beta (Aβ), phosphorylated Tau (pTau), alpha-synuclein (αSyn), mutant huntingtin (mHtt), fused in sarcoma, superoxide dismutase 1, and TAR DNA-binding protein 43. These toxic proteins share some common attributes, making them potentially universal and simultaneous targets for therapeutic intervention. First, they all form toxic aggregates prior to taking on their final forms as contributors to plaques, neurofibrillary tangles, Lewy bodies, and other protein deposits. Second, the primary enzyme that directs their aggregation is transglutaminase 2 (TGM2), a brain-localized enzyme involved in neurodegeneration. Third, TGM2 binds to calmodulin, a regulatory event that can increase the activity of this enzyme threefold. Fourth, the most common mixed pathology toxic biomarkers (Aβ, pTau, αSyn, nHtt) also bind calmodulin, which can affect their ability to aggregate. This review examines the potential therapeutic routes opened up by this knowledge. The end goal reveals multiple opportunities that are immediately available for universal therapeutic treatment of the most devastating neurodegenerative diseases facing humankind. Full article
(This article belongs to the Section Molecular Neurobiology)
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<p>Toxic protein biomarkers shared by specific and mixed pathology NDDs. The references supporting these relationships are cited in the main body of the text. The color of the individual toxic biomarkers indicates the primary NDD with which they are associated. <span class="html-italic">Abbreviations</span>: amyloid beta (Aβ), phosphorylated Tau (pTau), alpha-synuclein (αSyn), mutant huntingtin (mHtt), fused in sarcoma (FUS), superoxide dismutase 1 (SOD1) and TAR DNA-binding protein 43 (TDP-43).</p>
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<p>Calcium–calmodulin mediated signal transduction in normal neurons (<b>top panel</b>) and neurodegeneration (<b>lower panel</b>). This essential signaling pathway mediates learning and memory as well as the essential functions and survival of brain cells. During neurodegeneration, the unregulated influx of calcium ions mediated by various receptors and ion channels increases cytosolic calcium to toxic levels, in turn increasing the level of Ca<sup>2+</sup>-CaM, causing changes implicated in memory loss, nerve degeneration, and death. Abbreviations: ER, endoplasmic reticulum; Mito, mitochondria; Lyso, lysosomes.</p>
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<p>The regulation of transglutaminase 2 (TGM2). The enzyme is inactive when bound to GTP, but upon calcium binding, the protein unfolds, becoming active. The binding of CaM in the catalytic core is suggested. Calmodulin binding enhances TGM2 enzyme activity 3-fold. The details of these events are summarized in the main text. The color of the catalytic core area is suggestive of activity level (yellow, inactive; light green, active; dark green, enhanced activity). Figure after [<a href="#B131-ijms-25-13424" class="html-bibr">131</a>].</p>
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<p>A diagrammatic representation of the potential interactions between calcium (Ca<sup>2+</sup>), calmodulin (CaM), and transglutaminase 2 (TGM2) and their potential implications for the aggregation of toxic biomarkers in neurodegenerative diseases. (<b>A</b>) Basic model for calcium-regulated TGM2 mediating toxic protein (Aβ, Tau, αSyn, mHtt, SOD1) aggregation. (<b>B</b>) Enhanced aggregation directed by Ca<sup>2+</sup>-CaM-bound TGM2. (<b>C</b>) The potential involvement of calmodulin binding to the aggregation of specific toxic proteins (Aβ, Tau, αSyn, mHtt). See text for details.</p>
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25 pages, 1179 KiB  
Review
Dietary Strategies to Mitigate Alzheimer’s Disease: Insights into Antioxidant Vitamin Intake and Supplementation with Microbiota–Gut–Brain Axis Cross-Talk
by Wan Zurinah Wan Ngah, Hajar Fauzan Ahmad, Sheril June Ankasha, Suzana Makpol and Ikuo Tooyama
Antioxidants 2024, 13(12), 1504; https://doi.org/10.3390/antiox13121504 - 10 Dec 2024
Viewed by 664
Abstract
Alzheimer’s disease (AD), which is characterized by deterioration in cognitive function and neuronal death, is the most prevalent age-related progressive neurodegenerative disease. Clinical and experimental research has revealed that gut microbiota dysbiosis may be present in AD patients. The changed gut microbiota affects [...] Read more.
Alzheimer’s disease (AD), which is characterized by deterioration in cognitive function and neuronal death, is the most prevalent age-related progressive neurodegenerative disease. Clinical and experimental research has revealed that gut microbiota dysbiosis may be present in AD patients. The changed gut microbiota affects brain function and behavior through several mechanisms, including tau phosphorylation and increased amyloid deposits, neuroinflammation, metabolic abnormalities, and persistent oxidative stress. The lack of effective treatments to halt or reverse the progression of this disease has prompted a search for non-pharmaceutical tools. Modulation of the gut microbiota may be a promising strategy in this regard. This review aims to determine whether specific dietary interventions, particularly antioxidant vitamins, either obtained from the diet or as supplements, may support the formation of beneficial microbiota in order to prevent AD development by contributing to the systemic reduction of chronic inflammation or by acting locally in the gut. Understanding their roles would be beneficial as it may have the potential to be used as a future therapy option for AD patients. Full article
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<p>The mechanisms of the controllable factors that support the gut microbiome related to AD prevention. The growth of beneficial bacteria and SCFA-producing bacteria have the ability to maintain intestinal permeability and the production of metabolites, all of which can influence the brain and behavior via direct or indirect signaling pathways.</p>
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<p>Host genetic factors present that may be modifiable by antioxidants and interactions across the microbiota–gut–brain axis (MGBA) in AD in a balanced environment between the host and microorganisms, which affects immunological and metabolic functions.</p>
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15 pages, 1189 KiB  
Article
Cognitive Impairment in Cerebral Amyloid Angiopathy: A Single-Center Prospective Cohort Study
by Aikaterini Theodorou, Athanasia Athanasaki, Konstantinos Melanis, Ioanna Pachi, Angeliki Sterpi, Eleftheria Koropouli, Eleni Bakola, Maria Chondrogianni, Maria-Ioanna Stefanou, Efthimios Vasilopoulos, Anastasios Kouzoupis, Georgios P. Paraskevas, Georgios Tsivgoulis and Elias Tzavellas
J. Clin. Med. 2024, 13(23), 7427; https://doi.org/10.3390/jcm13237427 - 6 Dec 2024
Viewed by 547
Abstract
Background/Objectives: Cognitive impairment represents a core and prodromal clinical feature of cerebral amyloid angiopathy (CAA). We sought to assess specific cognitive domains which are mainly affected among patients with CAA and to investigate probable associations with neuroimaging markers and Cerebrospinal Fluid (CSF) biomarkers. [...] Read more.
Background/Objectives: Cognitive impairment represents a core and prodromal clinical feature of cerebral amyloid angiopathy (CAA). We sought to assess specific cognitive domains which are mainly affected among patients with CAA and to investigate probable associations with neuroimaging markers and Cerebrospinal Fluid (CSF) biomarkers. Methods: Thirty-five patients fulfilling the Boston Criteria v1.5 or v2.0 for the diagnosis of probable/possible CAA were enrolled in this prospective cohort study. Brain Magnetic Resonance Imaging and CSF biomarker data were collected. Every eligible participant underwent a comprehensive neurocognitive assessment. Spearman’s rank correlation tests were used to identify possible relationships between the Addenbrooke’s Cognitive Examination—Revised (ACE-R) sub-scores and other neurocognitive test scores and the CSF biomarker and neuroimaging parameters among CAA patients. Moreover, linear regression analyses were used to investigate the effects of CSF biomarkers on the ACE-R total score and Mini-Mental State Examination (MMSE) score, based on the outcomes of univariate analyses. Results: Cognitive impairment was detected in 80% of patients, and 60% had a coexistent Alzheimer’s disease (AD) pathology based on CSF biomarker profiles. Notable correlations were identified between increased levels of total tau (t-tau) and phosphorylated tau (p-tau) and diminished performance in terms of overall cognitive function, especially memory. In contrast, neuroimaging indicators, including lobar cerebral microbleeds and superficial siderosis, had no significant associations with cognitive scores. Among the CAA patients, those without AD had superior neurocognitive test performance, with significant differences observed in their ACE-R total scores and memory sub-scores. Conclusions: The significance of tauopathy in cognitive impairment associated with CAA may be greater than previously imagined, underscoring the necessity for additional exploration of the non-hemorrhagic facets of the disease and new neuroimaging markers. Full article
(This article belongs to the Section Clinical Neurology)
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<p>Correlations between the levels of CSF t-tau and p-tau and the total ACE-R scores and memory sub-scores among the CAA participants. Legend: Negative correlations are depicted between the CSF t-tau levels and ACE-R (<b>A</b>), p-tau levels and ACE-R (<b>B</b>), t-tau levels and memory sub-score (<b>C</b>), and p-tau levels and memory sub-score (<b>D</b>). The Spearman’s rank correlation test was used to determine significant correlations.</p>
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<p>Radar plot depicting differences in multiple cognitive domains (total ACE-R and sub-scores) among CAA patients with and without AD coexistence. Legend: Statistically significant differences were detected in total ACE-R scores and memory sub-scores among CAA patients with and without coexistent AD.</p>
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10 pages, 620 KiB  
Article
Serum Tau Species in Progressive Supranuclear Palsy: A Pilot Study
by Costanza Maria Cristiani, Luana Scaramuzzino, Elvira Immacolata Parrotta, Giovanni Cuda, Aldo Quattrone and Andrea Quattrone
Diagnostics 2024, 14(23), 2746; https://doi.org/10.3390/diagnostics14232746 - 5 Dec 2024
Viewed by 457
Abstract
Background/Objectives: Progressive Supranuclear Palsy (PSP) is a tauopathy showing a marked symptoms overlap with Parkinson’s Disease (PD). PSP pathology suggests that tau protein might represent a valuable biomarker to distinguish between the two diseases. Here, we investigated the presence and diagnostic value of [...] Read more.
Background/Objectives: Progressive Supranuclear Palsy (PSP) is a tauopathy showing a marked symptoms overlap with Parkinson’s Disease (PD). PSP pathology suggests that tau protein might represent a valuable biomarker to distinguish between the two diseases. Here, we investigated the presence and diagnostic value of six different tau species (total tau, 4R-tau isoform, tau aggregates, p-tau202, p-tau231 and p-tau396) in serum from 13 PSP and 13 PD patients and 12 healthy controls (HCs). Methods: ELISA commercial kits were employed to assess all the tau species except for t-tau, which was assessed by a single molecule array (SIMOA)-based commercial kit. Possible correlations between tau species and biological and clinical features of our cohorts were also evaluated. Results: Among the six tau species tested, only p-tau396 was detectable in serum. Concentration of p-tau396 was significantly higher in both PSP and PD groups compared to HC, but PSP and PD patients showed largely overlapping values. Moreover, serum concentration of p-tau396 strongly correlated with disease severity in PSP and not in PD. Conclusions: Overall, we identified serum p-tau396 as the most expressed phosphorylated tau species in serum and as a potential tool for assessing PSP clinical staging. Moreover, we demonstrated that other p-tau species may be present at too low concentrations in serum to be detected by ELISA, suggesting that future work should focus on other biological matrices. Full article
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<p>Serum concentration of p-tau396 in PSP (<span class="html-italic">n</span> = 13), PD (<span class="html-italic">n</span> = 13) and HC (<span class="html-italic">n</span> = 12). Data are summarized as box plots. Ranges are depicted as vertical lines while median, 25th percentile and 75th percentile are depicted as middle, lower and upper lines, respectively. Data were analyzed by ANOVA followed by Turkey’s LSD post hoc test. PSP = progressive supranuclear palsy; PD = Parkinson’s disease; HC = healthy control.</p>
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<p>Correlation between serum p-tau396 levels and PSP Rating Scale in PSP patients. The analysis was performed by Spearman’s correlation test, and the obtained rho coefficient and <span class="html-italic">p</span>-value are reported in the plot.</p>
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15 pages, 12643 KiB  
Article
Tau Protein and β-Amyloid Associated with Neurodegeneration in Myelin Oligodendrocyte Glycoprotein-Induced Experimental Autoimmune Encephalomyelitis (EAE), a Mouse Model of Multiple Sclerosis
by Grażyna Pyka-Fościak, Ewa Jasek-Gajda, Bożena Wójcik, Grzegorz J. Lis and Jan A. Litwin
Biomedicines 2024, 12(12), 2770; https://doi.org/10.3390/biomedicines12122770 - 5 Dec 2024
Viewed by 448
Abstract
Background: The levels of β-amyloid precursor protein (β-APP), tau protein, and phosphorylation of tau (p-tau) protein were examined by quantitative immunohistochemistry in the spinal cord sections of mice suffering from experimental autoimmune encephalomyelitis (EAE) in the successive phases of the disease: onset, peak, [...] Read more.
Background: The levels of β-amyloid precursor protein (β-APP), tau protein, and phosphorylation of tau (p-tau) protein were examined by quantitative immunohistochemistry in the spinal cord sections of mice suffering from experimental autoimmune encephalomyelitis (EAE) in the successive phases of the disease: onset, peak, and chronic. Methods: EAE was induced in C57BL/6 mice by immunization with MOG35–55 peptide. The degree of pathological changes was assessed in cross-sections of the entire spinal cord. Results: β-APP expression was observed in the white matter and colocalized with some Iba-1-positive macrophages/microglia. It increased in the peak phase of EAE and remained at the same level in the chronic phase. During the onset and peak phases of EAE, expression of tau protein was observed in nerve fibers and nerve cell perikaryons, with a predominance of nerve fibers, whereas in the chronic phase, tau was labeled mainly in the perikaryons of nerve cells, with its content significantly decreased. P-tau immunostaining was seen only in nerve fibers. Conclusions: The expression of p-tau increased with the progression of EAE, reaching the maximum in the chronic phase. The correlation between these proteins and neurodegeneration/neuroinflammation highlights their potential roles in the progression of neurodegenerative mechanisms in MS. Full article
(This article belongs to the Special Issue Pharmacological Targets for Neuroinflammation)
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<p>The progression of clinical severity in the successive phases of EAE: on day 14—onset phase, day 19—peak phase, and day 30—chronic phase (the dashed lines). No clinical scores for control mice. Each data point represents the average score (+/−S.E.M.) for disease severity at the indicated post-immunization time points (<b>A</b>). EAE cumulative score as the sum of individual EAE courses (+/−S.E.M.) (<b>B</b>).</p>
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<p>Quantitative histology of spinal cord samples of EAE and control mice. Hematoxylin and eosin (H&amp;E) staining reveals inflammatory cell infiltration (<b>A</b>,<b>B</b>,<b>A’</b>,<b>B’</b>), Luxol Fast Blue (LFB) shows demyelination (<b>C</b>,<b>D</b>,<b>C’</b>,<b>D’</b>), and Bielschovsky silver impregnation (BSI) visualizes axonal loss (<b>E</b>,<b>F</b>,<b>E’</b>,<b>F’</b>) in the spinal cord on day 19 after immunization for EAE (<b>A</b>,<b>C</b>,<b>E</b>) and in control mice, which did not show any pathologies (<b>B</b>,<b>D</b>,<b>F</b>,<b>B’</b>,<b>D’</b>,<b>F’</b>). Diagrams show the number of inflammatory plaques (<b>G</b>), degree of inflammation (<b>H</b>), demyelination (<b>I</b>), and axonal loss (<b>J</b>) in three phases of EAE progression. Areas enclosed within yellow dashed squares (<b>A</b>–<b>F</b>) are magnified bellow, respectively (<b>A’</b>–<b>F’</b>). To assess the degree of pathological changes, the areas occupied by inflammatory infiltrate (<b>A</b>,<b>A’</b>), pale areas of demyelination (<b>C</b>,<b>C’</b>), and the brighter area for axonal loss (<b>E</b>,<b>E’</b>) (all marked by arrowheads) were quantified as the percentage of the cross-sectioned spinal cord section area (<b>G</b>–<b>J</b>). The data are expressed as means ± SEM; <span class="html-italic">n</span> = 5 per group. Statistical significance was verified using ANOVA with Bonferroni’s multiple comparisons test at a 0.05 confidence level (*** <span class="html-italic">p</span> &lt; 0.001). Scale bars: (<b>A</b>–<b>F</b>) = 200 µm, insets (<b>A’</b>–<b>F’</b>) = 50 µm.</p>
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<p>A schematic illustration depicting pathological changes in the white and gray matter of the spinal cord: inflammation, amyloid aggregation, and intracellular neurofibrillary tangles in nerve cells (<b>A</b>). Congo red staining visualizes intraneuronally accumulated tau ((<b>B</b>), inset (<b>C</b>)) and amyloid deposits (arrowheads) associated with inflammatory conditions (high density of leukocyte nuclei) in the chronic phase of EAE ((<b>D</b>), inset (<b>E</b>)) and the spinal cord for control ((<b>F</b>), inset (<b>G</b>)). Areas enclosed within yellow dashed squares (<b>B</b>,<b>D</b>,<b>F</b>) are magnified on the right, respectively (<b>C</b>,<b>E</b>,<b>G</b>). Scale bars: (<b>B</b>,<b>D</b>,<b>F</b>) = 100 µm, and insets (<b>C</b>,<b>E</b>,<b>G</b>) = 20 µm.</p>
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<p>Intercellular β-amyloid (β-APP) aggregation associated with inflammatory infiltration (DAPI staining of the nuclei) ((<b>A</b>), inset (<b>B</b>)). No β-APP deposition in control mice ((<b>C</b>), inset (<b>D</b>)) Quantification of β-APP. (<b>E</b>) Yellow dashed line indicates the border between the white and gray matter of the spinal cord. Areas enclosed within white (<b>A</b>,<b>C</b>) are magnified on the right, respectively (<b>B</b>,<b>D</b>). The data are expressed as means ± SEM; <span class="html-italic">n</span> = 5 per group. Statistical significance was verified using ANOVA, with Bonferroni’s multiple comparisons test at a 0.05 confidence level (*** <span class="html-italic">p</span> &lt; 0.001). Scale bars: (<b>A</b>,<b>C</b>) = 200 µm, and inset (<b>B</b>,<b>D</b>) = 50 µm.</p>
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<p>Area of inflammatory infiltration in the white matter of the spinal cord in the EAE peak phase. Immunostaining of β-APP (<b>A</b>,<b>D</b>), CD45 (<b>B</b>), and IBA-1 (<b>E</b>) and the overlap of β-APP/CD45 (<b>C</b>) and β-APP/IBA-1 (<b>F</b>), accompanied by DAPI nuclear staining (<b>C</b>,<b>E</b>). Colocalization for β-APP/IBA-1 ((<b>F</b>), white arrows). Scale bars: (<b>A</b>–<b>F</b>) = 50 µm.</p>
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<p>Tau (<b>A</b>–<b>D</b>) and p-tau (<b>H</b>–<b>J</b>) immunofluorescence in the chronic phase of EAE and tau expression in the control group (<b>E</b>–<b>G</b>). Tau is expressed in EAE mice, mainly in the perikaryons of nerve cells and in a few nerve fibers (<b>A</b>–<b>D</b>), and in control mice, it is expressed in both nerve fibers and nerve cell perikaryons (<b>E</b>–<b>G</b>). Immunolabeling of p-tau was observed predominantly in nerve fibers of EAE mice (<b>H</b>–<b>J</b>). A yellow dashed line indicates the border between the white and gray matter of the spinal cord. Areas enclosed within white (<b>B</b>,<b>F</b>,<b>I</b>) are magnified on the right, respectively (<b>C</b>,<b>D</b>,<b>G</b>,<b>J</b>). Nuclei were visualized by DAPI staining. Scale bars: (<b>A</b>,<b>B</b>,<b>E</b>,<b>F</b>,<b>H</b>,<b>I</b>) = 100 µm; insets (<b>C</b>,<b>D</b>,<b>G</b>,<b>J</b>) = 50 µm.</p>
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<p>Tau (<b>A</b>) and p-tau (<b>B</b>) contents in the spinal cord at different phases of EAE. Tau expression was quantified as the percentage of the immunostained area in EAE mice in comparison to control mice. The data are expressed as means ± SEM; <span class="html-italic">n</span> = 5 per group. Statistical significance was verified using ANOVA with Bonferroni’s multiple comparisons test at a 0.05 confidence level (*** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Correlations of β-amyloid (<b>A</b>–<b>C</b>), tau (<b>D</b>–<b>F</b>), and p-tau contents (<b>G</b>–<b>I</b>) with inflammation, demyelination, and axonal loss for EAE mice, as well as tau/p-tau content (<b>L</b>) with β-amyloid (<b>J</b>,<b>K</b>). The heatmap displays the Pearson correlation coefficients between APP, tau, p-tau, and histological parameters. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 (<b>M</b>).</p>
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26 pages, 2977 KiB  
Article
Therapeutic Efficacy of the Inositol D-Pinitol as a Multi-Faceted Disease Modifier in the 5×FAD Humanized Mouse Model of Alzheimer’s Amyloidosis
by Dina Medina-Vera, Antonio J. López-Gambero, Julia Verheul-Campos, Juan A. Navarro, Laura Morelli, Pablo Galeano, Juan Suárez, Carlos Sanjuan, Beatriz Pacheco-Sánchez, Patricia Rivera, Francisco J. Pavon-Morón, Cristina Rosell-Valle and Fernando Rodríguez de Fonseca
Nutrients 2024, 16(23), 4186; https://doi.org/10.3390/nu16234186 - 4 Dec 2024
Viewed by 763
Abstract
Background/Objectives: Alzheimer’s disease (AD), a leading cause of dementia, lacks effective long-term treatments. Current therapies offer temporary relief or fail to halt its progression and are often inaccessible due to cost. AD involves multiple pathological processes, including amyloid beta (Aβ) deposition, insulin resistance, [...] Read more.
Background/Objectives: Alzheimer’s disease (AD), a leading cause of dementia, lacks effective long-term treatments. Current therapies offer temporary relief or fail to halt its progression and are often inaccessible due to cost. AD involves multiple pathological processes, including amyloid beta (Aβ) deposition, insulin resistance, tau protein hyperphosphorylation, and systemic inflammation accelerated by gut microbiota dysbiosis originating from a leaky gut. Given this context, exploring alternative therapeutic interventions capable of addressing the multifaceted components of AD etiology is essential. Methods: This study suggests D-Pinitol (DPIN) as a potential treatment modifier for AD. DPIN, derived from carob pods, demonstrates insulin-sensitizing, tau hyperphosphorylation inhibition, and antioxidant properties. To test this hypothesis, we studied whether chronic oral administration of DPIN (200 mg/kg/day) could reverse the AD-like disease progression in the 5×FAD mice. Results: Results showed that treatment of 5×FAD mice with DPIN improved cognition, reduced hippocampal Aβ and hyperphosphorylated tau levels, increased insulin-degrading enzyme (IDE) expression, enhanced pro-cognitive hormone circulation (such as ghrelin and leptin), and normalized the PI3K/Akt insulin pathway. This enhancement may be mediated through the modulation of cyclin-dependent kinase 5 (CDK5). DPIN also protected the gut barrier and microbiota, reducing the pro-inflammatory impact of the leaky gut observed in 5×FAD mice. DPIN reduced bacterial lipopolysaccharide (LPS) and LPS-associated inflammation, as well as restored intestinal proteins such as Claudin-3. This effect was associated with a modulation of gut microbiota towards a more balanced bacterial composition. Conclusions: These findings underscore DPIN’s promise in mitigating cognitive decline in the early AD stages, positioning it as a potential disease modifier. Full article
(This article belongs to the Section Lipids)
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<p>Anhedonic and anxiety-like behavior after 18 weeks of D-Pinitol treatment in 5×FAD mice. (<b>A</b>) Experimental procedure where mice were supplemented with D-Pinitol (DPIN, 200 mg/kg/day) ad libitum in their drinking water for 18 weeks. All animals were about 14 weeks of age (3.5 months old) at the beginning of the experiment. Experimental groups: non-transgenic (Non-Tg-DPIN; n = 16; 7 males; 9 females) and transgenic 5×FAD (5×FAD-DPIN; n = 17; 10 males; 7 females) mice. Control groups of both genotypes: non-transgenic (Non-Tg-CTR; n = 16; 7 males; 9 females), and 5×FAD transgenic mice (5×FAD-CTR; n = 14; 7 males; 7 females) received water as a vehicle solution. Animal control weight (CW) was recorded at 3.5–5.5–6.5–7.5 months of age). Behavioral tests were performed at baseline point (3.5 months old) and after 16 weeks with DPIN treatment (8 month old): sucrose preference test (SPT) and elevated plus maze (EPM). The Morris water maze (MWM) behavioral test began at 7.5 months of age and was finalized at 8 months of age. The animals were sacrificed at 32 weeks of age (8 months) and tissue samples were rapidly removed. (<b>B</b>) Body weight in grams (g). Two-way ANOVA test: (*) <span class="html-italic">p</span>&lt; 0.05 genotype effect; (#) <span class="html-italic">p</span>&lt; 0.05 age effect. (<b>C</b>) Sucrose preference test (%) at the baseline point and after 18 weeks of DPIN treatment. Dashed lines represent the criterion for anhedonia ≤ 65%. Two-way ANOVA and Tukey’s test: (##) <span class="html-italic">p</span> &lt; 0.01 between 5×FAD (5×FAD-CTR and 5×FAD-DPIN) compared to Non-Tg mice (Non-Tg-CTR and Non-Tg-DPIN) at the baseline point. (<b>D</b>) Time spent in seconds (s) in the open arms at the baseline point and after 18 weeks of DPIN treatment in the EPM. Two-way ANOVA and Tukey’s test: (##) <span class="html-italic">p</span> &lt; 0.01 between 5×FAD (5×FAD-CTR and 5×FAD-DPIN) compared to Non-Tg mice (Non-Tg-CTR and Non-Tg-DPIN) at the baseline point and after 18 weeks of DPIN. (<b>E</b>) Total distance moved in centimeters (cm) at the baseline point and after 18 weeks of DPIN treatment in the EPM test. Results are shown as the mean ± SEM. Two-way ANOVA and Tukey’s test from (<b>C</b>–<b>E</b>): (*) <span class="html-italic">p</span> &lt; 0.05 and (**) <span class="html-italic">p</span> &lt; 0.01 in the 5×FAD mice after 18 weeks of DPIN treatment.</p>
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<p>Assessment of cognitive function by Morris water maze after 18 weeks of D-Pinitol treatment. Data is presented as the mean ± SEM. Two-way ANOVA + Tukey’s test for multiple comparisons were performed. (<b>A</b>) Path length in centimeters (cm) (* <span class="html-italic">p</span> &lt; 0.05) during the habituation training. (<b>B</b>) During the visual training (2 days, visible platform; 4 trials/day), all experimental groups diminished the escape latency (s) on the second day (# <span class="html-italic">p</span> &lt; 0.05 day 2 vs. day 1). (<b>C</b>) Non-Tg-DPIN and 5×FAD-DPIN showed a reduced cumulative distance (cm) to reach the platform on the second training day (* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01). (<b>D</b>) On acquisition training, each subject received six trials per acquisition day (4 days, hidden platform; 6 trials/day). The escape latency (s) was decreased on the last and the fourth training day (* <span class="html-italic">p</span> &lt; 0.05), (<b>E</b>) being more significant in the Non-Tg-DPIN experimental group (* <span class="html-italic">p</span> &lt; 0.05). (<b>F</b>) The cumulative distance (cm) to reach the hidden platform was also evaluated and showed a similar profile to the escape latency outcomes on acquisition training. (<b>G</b>) On memory retention test 1 (without platform; 1 trial/day), all animals demonstrated similar measures of time (s) spent searching the target quadrant (Q1) (# <span class="html-italic">p</span> &lt; 0.05 Q1 vs. the other quadrants). (<b>H</b>) After 48 h, each subject received six trials for one day on the reversal spatial learning day (1 day, hidden platform; 6 trials/day). 5×FAD-DPIN reached the new hidden platform position significantly faster (s) than 5×FAD-CTR (* <span class="html-italic">p</span> &lt; 0.05) and (<b>I</b>) with less distance traveled (cm) (* <span class="html-italic">p</span> &lt; 0.05). (<b>J</b>) On memory retention test 2, 5×FAD-CTR exhibited impaired long-term spatial memory as measured by less time spent (s) in the new position of the platform (Q3) and persisted for a longer period on the Q1 position that they learned on the acquisition training compared to Non-Tg-CTR (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01). (<b>K</b>,<b>L</b>) shows a graphical representation of the path traveled by each group during the first and second memory retention tests.</p>
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<p>Regulation of hormones related to insulin and metabolic health after 18 weeks of D-Pinitol treatment. Plasma levels (pg/mL) of (<b>A</b>) insulin, (<b>B</b>) glucagon, (<b>C</b>) insulin/glucagon ratio, (<b>D</b>) plasminogen activator inhibitor-1 (PAI-1), (<b>E</b>) leptin, and (<b>F</b>) ghrelin. Histograms represent mean ± SEM (n = 10). Two-way ANOVA and Tukey’s test for multiple comparisons were performed: (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, and (***) <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Activation of the hippocampal PI3K/Akt pathway after 18 weeks of D-Pinitol treatment. Western blot analysis of the phosphorylation status of the p85 regulatory domain of (<b>A</b>) the phosphatidylinositol 3 kinase (p85-PI3K) phosphorylation at tyrosine 607, (<b>B</b>) and the quantity of total p85-PI3K, (<b>C</b>) protein Kinase B (Akt) phosphorylation on serine 473, (<b>D</b>) and the amount of total Akt, (<b>E</b>) glycogen synthase kinase 3β (GSK-3β) phosphorylation at serine 9, (<b>F</b>) and the amount of total GSK-3β, (<b>G</b>) cyclin-dependent kinase 5 (CDK5) subunits p25 (<b>H</b>) and p35, (<b>I</b>) and the total quantity of CDK5 on Non-Tg and 5×FAD with (DPIN) and without (controls = CTR) D-Pinitol treatment. (<b>J</b>) The blots represent all bands. Molecular weights (MWs) are expressed in kilodaltons (kDa). The corresponding expression of γ-Adaptin is shown as a loading control per lane. All samples were obtained simultaneously and processed in parallel. Histograms represent mean ± SEM (n = 4). Two-way ANOVA and Tukey’s test for multiple comparisons were performed: (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, (***) <span class="html-italic">p</span> &lt; 0.001 and (****) <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Amyloid beta clearance and tau dephosphorylation in the hippocampus of 5×FAD mice after 18 weeks of D-Pinitol treatment. Western blot analysis of (<b>A</b>) tau [AT8] phosphorylation on serine 202 and threonine 205, (<b>B</b>) tau [AT100] phosphorylation on threonine 212 and serine 214, (<b>C</b>) the total amount of tau, and (<b>D</b>) insulin-degrading enzyme (IDE) on Non-Tg and 5×FAD with (DPIN) and without (controls = CTR) D-Pinitol treatment. (<b>E</b>) The blots represent all bands. Molecular weights (MW) are expressed in kilodaltons (kDa). The corresponding expression of γ-Adaptin is shown as a loading control per lane. All samples were obtained simultaneously and processed in parallel. Histograms (<b>A</b>–<b>D</b>) represent mean ± SEM (n = 4). (<b>F</b>,<b>H</b>) Images correspond to representative immunostaining of Aβ 1-40 (Aβ 1-40) and Aβ 1-42 (Aβ 1-42) densitometry in the hippocampus of Non-Tg and 5×FAD controls (CTR) and after 18 weeks of continuous drinking treatment with D-Pinitol (DPIN). Scale bar: 100 µm. Histograms in (<b>G</b>,<b>I</b>) represent the mean ± SEM of the number of Aβ from all samples per group (n = 8). Two-way ANOVA and Tukey’s test for multiple comparisons were performed: (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, (***) <span class="html-italic">p</span> &lt; 0.001 and (****) <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Effects of D-Pinitol treatment on proinflammatory cytokine levels in plasma and small intestine. mRNA expression (in relative units) in the small intestine of (<b>A</b>) Claudin 3, (<b>B</b>) occludin, and (<b>C</b>) Toll-like receptor 4 (TLR4). Graphs (<b>D</b>–<b>G</b>) correspond to plasma levels (pg/mL) of (<b>D</b>) LPS plasma level (pg/mL) and the pro-inflammatory cytokines (<b>E</b>) Interleukin 5 (IL-5), (<b>F</b>) Interleukin 6 (IL-6), (<b>G</b>) Keratinocyte chemoattractant (KC)/human growth-regulated oncogene (GRO), and (<b>H</b>) Tumor necrosis factor alpha (TNF-α). Histograms represent mean ± SEM (n = 7) in the groups Non-Tg and 5×FAD with (DPIN) and without (controls = CTR) D-Pinitol treatment. Two-way ANOVA and Tukey’s test for multiple comparisons were performed: (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, (***) <span class="html-italic">p</span> &lt; 0.001, and (****) <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Differences in fecal microbiota by genotype and D-Pinitol treatment in Alzheimer’s transgenic and Non-Tg mice. (<b>A</b>) Taxonomic compositions obtained from the analysis of DNA sequences from fecal microbiota samples using QIIME2 (<a href="https://qiime2.org/" target="_blank">https://qiime2.org/</a>, accessed on 1 January 2024) were compared at the family level in terms of relative frequency (%). The sequences were grouped into operational taxonomic units (OTUs) using a 97% similarity threshold. Significant differences for ‘Genotype × Treatment’ variables have been detected mostly in seven families: (<b>B</b>) Prevotellaceae, (<b>C</b>) Eggerthellaceae, (<b>D</b>) Streptococcaceae, (<b>E</b>) Marinifilaceae, (<b>F</b>) Lachnospiraceae, (<b>G</b>) Acholeplasmataceae, and (<b>H</b>) Enterococcaceae. Histograms represent relative abundance (%) in the groups Non-Tg and 5×FAD with (DPIN) and without (controls = CTR) D-Pinitol treatment. Statistical inference was performed using the Kruskal–Wallis test and Mann–Whitney U for each OTU, allowing for comparisons and identification of significant differences between groups: (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, (***) <span class="html-italic">p</span> &lt; 0.001, and (****) <span class="html-italic">p</span> &lt; 0.0001.</p>
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16 pages, 638 KiB  
Article
Machine Learning for Early Detection of Cognitive Decline in Parkinson’s Disease Using Multimodal Biomarker and Clinical Data
by Raziyeh Mohammadi, Samuel Y. E. Ng, Jayne Y. Tan, Adeline S. L. Ng, Xiao Deng, Xinyi Choi, Dede L. Heng, Shermyn Neo, Zheyu Xu, Kay-Yaw Tay, Wing-Lok Au, Eng-King Tan, Louis C. S. Tan, Ewout W. Steyerberg, William Greene and Seyed Ehsan Saffari
Biomedicines 2024, 12(12), 2758; https://doi.org/10.3390/biomedicines12122758 - 3 Dec 2024
Viewed by 1121
Abstract
Background: Parkinson’s disease (PD) is the second most common neurodegenerative disease, primarily affecting the middle-aged to elderly population. Among its nonmotor symptoms, cognitive decline (CD) is a precursor to dementia and represents a critical target for early risk assessment and diagnosis. Accurate [...] Read more.
Background: Parkinson’s disease (PD) is the second most common neurodegenerative disease, primarily affecting the middle-aged to elderly population. Among its nonmotor symptoms, cognitive decline (CD) is a precursor to dementia and represents a critical target for early risk assessment and diagnosis. Accurate CD prediction is crucial for timely intervention and tailored management of at-risk patients. This study used machine learning (ML) techniques to predict the CD risk over five-year in early-stage PD. Methods: Data from the Early Parkinson’s Disease Longitudinal Singapore (2014 to 2018) was used to predict CD defined as a one-unit annual decrease or a one-unit decline in Montreal Cognitive Assessment over two consecutive years. Four ML methods—AutoScore, Random Forest, K-Nearest Neighbors and Neural Network—were applied using baseline demographics, clinical assessments and blood biomarkers. Results: Variable selection identified key predictors of CD, including education year, diastolic lying blood pressure, diastolic standing blood pressure, systolic lying blood pressure, Hoehn and Yahr scale, body mass index, phosphorylated tau at threonine 181, total tau, Neurofilament light chain and suppression of tumorigenicity 2. Random Forest was the most effective, achieving an AUC of 0.93 (95% CI: 0.89, 0.97), using 10-fold cross-validation. Conclusions: Here, we demonstrate that ML-based models can identify early-stage PD patients at high risk for CD, supporting targeted interventions and improved PD management. Full article
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<p>Feature importance ranked by mean decrease in Gini score.</p>
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<p>Calibration plot with 99% CI for Model 2 across different methods. The black points indicate the observed event rates for each bin, while the black line connects these points to show the trend. The dashed dark blue diagonal line represents the ideal calibration line, indicating perfect agreement between predicted probabilities and observed event rates.</p>
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21 pages, 10076 KiB  
Article
Late-Life Alcohol Exposure Does Not Exacerbate Age-Dependent Reductions in Mouse Spatial Memory and Brain TFEB Activity
by Hao Chen, Kaitlyn Hinz, Chen Zhang, Yssa Rodriguez, Sha Neisha Williams, Mengwei Niu, Xiaowen Ma, Xiaojuan Chao, Alexandria L. Frazier, Kenneth E. McCarson, Xiaowan Wang, Zheyun Peng, Wanqing Liu, Hong-Min Ni, Jianhua Zhang, Russell H. Swerdlow and Wen-Xing Ding
Biomolecules 2024, 14(12), 1537; https://doi.org/10.3390/biom14121537 - 30 Nov 2024
Viewed by 499
Abstract
Alcohol consumption is believed to affect Alzheimer’s disease (AD) risk, but the contributing mechanisms are not well understood. A potential mediator of the proposed alcohol-AD connection is autophagy, a degradation pathway that maintains organelle and protein homeostasis. Autophagy is regulated through the activity [...] Read more.
Alcohol consumption is believed to affect Alzheimer’s disease (AD) risk, but the contributing mechanisms are not well understood. A potential mediator of the proposed alcohol-AD connection is autophagy, a degradation pathway that maintains organelle and protein homeostasis. Autophagy is regulated through the activity of Transcription factor EB (TFEB), which promotes lysosome and autophagy-related gene expression. The purpose of this study is to explore whether chronic alcohol consumption worsens the age-related decline in TFEB-mediated lysosomal biogenesis in the brain and exacerbates cognitive decline associated with aging. To explore the effect of alcohol on brain TFEB and autophagy, we exposed young (3-month-old) and aged (23-month-old) mice to two alcohol-feeding paradigms and assessed biochemical, transcriptome, histology, and behavioral endpoints. In young mice, alcohol decreased hippocampal nuclear TFEB staining but increased SQSTM1/p62, LC3-II, ubiquitinated proteins, and phosphorylated Tau. Hippocampal TFEB activity was lower in aged mice than it was in young mice, and Gao-binge alcohol feeding did not worsen the age-related reduction in TFEB activity. Morris Water and Barnes Maze spatial memory tasks were used to characterize the effects of aging and chronic alcohol exposure (mice fed alcohol for 4 weeks). The aged mice showed worse spatial memory acquisition in both tests. Alcohol feeding slightly impaired spatial memory in the young mice, but had little effect or even slightly improved spatial memory acquisition in the aged mice. In conclusion, aging produces greater reductions in brain autophagy flux and impairment of spatial memory than alcohol consumption. Full article
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<p>Aging and chronic plus binge EtOH (Gao-binge) feeding impair TFEB-mediated autophagy in mouse brains. Male 3-month-old young mice (3M) and 23-month-old aged mice (23M) were subjected to Gao-binge alcohol feeding. Representative images of immunohistochemistry staining for TFEB (<b>A</b>) and ubiquitin (UB) (<b>B</b>) are shown. Black and white arrows denote positive TFEB and UB stained cells whereas red arrows denote decreased TFEB and UB staining in hippocampi CA1 (Cornu Ammonis) region. (<b>C</b>) Total lysates from the hippocampus were subjected to western blot analysis. (<b>D</b>) Densitometry analysis from (<b>C</b>). Data are presented as mean ± SEM (N = 3–4). CD: Control diet, ED: ethanol diet with a binge.</p>
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<p>Electron microscopy analysis of the ultrastructure of Gao-binge alcohol-fed young and aged mouse hippocampi. Male 3-month-old young mice (3M) and 23-month-old aged mice (23M) were subjected to Gao-binge alcohol feeding. Representative EM images of mouse hippocampi from 3M control (<b>A</b>), ED (<b>B</b>), 23M control (<b>C</b>), and 23M ED (<b>D</b>) mice. The right panels are enlarged images from the boxed area in (<b>B</b>,<b>D</b>). The left panels are enlarged images from (<b>C</b>). Red arrows denote lysosomes (LY); white arrows denote lipid droplets (LD) in lysosomes; black arrows denote damaged mitochondria; blue arrows denote myelinated axon with lamellar sheath; and green arrows denote endoplasmic reticulum. LY: lysosome, LD: lipid droplet, M: mitochondria, N: nucleus. Bar: 500 nm.</p>
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<p>Transcriptomic analysis of Gao-binge alcohol-fed young and aged mouse hippocampi. Male 3-month-old young mice (3M) and 23-month-old aged mice (23M) were subjected to Gao-binge alcohol feeding. mRNAs were extracted from the mouse hippocampi after the feeding and subjected to RNAseq analysis. (<b>A</b>–<b>D</b>) Circos plot analysis from the RNAseq data set. Red: 23M CD (control diet) vs. 3M CD; blue: 23M ED vs. 23M CD; green: 3M ED vs. 3M CD. The inner circle represents gene lists, where hits are arranged along the arc. Purple curves link identical genes. Genes that hit multiple lists are colored in dark orange, and genes unique to a list are shown in light orange. Each arc represents a distinct group of genes, color-coded to align with the corresponding experimental conditions, while the interconnecting chords depict shared ontology terms, including the shared term level, where blue curves link genes that belong to the same enriched ontology term. The thickness of each chord is proportional to the number of genes that contribute to the common functional annotation analysis (<b>C</b>) for up-regulated genes, and (<b>D</b>) for down-regulated genes. (<b>E</b>) Volcano plots illustrating differential gene expression across experimental conditions. The x-axis represents the log2 fold change, and the y-axis represents the log10 <span class="html-italic">p</span>-value, indicating the magnitude and statistical significance of gene expression changes, respectively. Upregulated genes are marked in red, downregulated genes in blue, and non-significant changes in gray. Key genes with a fold change greater than 2 and a <span class="html-italic">p</span>-value less than 0.05 are labeled.</p>
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<p>Heatmap analysis from the RNAseq dataset of Gao-binge alcohol-fed young and aged mouse hippocampi. Male 3-month-old young mice (3M) and 23-month-old aged mice (23M) were subjected to Gao-binge alcohol feeding. mRNAs were extracted from the mouse hippocampi after the feeding and subjected to RNAseq analysis. The color scale represents the magnitude of gene expression, with warmer colors (red) indicating higher expression and cooler colors (blue) indicating lower expression. The x-axis represents different experimental conditions, while the y-axis corresponds to the genes of interest in different pathways. The heatmap was generated using R with the ggplot2 package (version 3.5.1). (<b>A</b>) Heatmap analysis of alcohol metabolism gene expression from the RNAseq dataset. (<b>B</b>) Heatmap analysis of immune response gene expression from the RNAseq dataset. Heatmap analysis of TFEB target genes (<b>C</b>) and autophagy genes (<b>D</b>) from the RNAseq dataset.</p>
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<p>Immunohistochemistry staining of cortices and hippocampi TFEB in chronic EtOH-fed young mice and aged mice. Male 3-month-old young mice (3M) and 23-month-old aged mice (23M) were subjected to chronic EtOH feeding for 4 weeks. (<b>A</b>) Representative images of TFEB IHC staining of mouse cortices and (<b>B</b>) hippocampi are shown. Arrows denote cytosolic TFEB staining. CA: Cornu Ammonis, DG: dentate gyrus.</p>
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<p>Chronic EtOH feeding does not affect Tau levels but increases levels of protein ubiquitination in young but not in aged mouse brains. Male 3-month-old young mice and 23-month-old aged mice were subjected to chronic EtOH feeding for 4 weeks. (<b>A</b>) Cortex brain lysates were subjected to western blot analysis followed by (<b>B</b>–<b>F</b>) Densitometry analysis of (<b>A</b>), which are normalized to loading control β-actin. Data are presented as mean ± SEM (n = 3–4).</p>
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<p>Chronic EtOH feeding increases apoptotic-like cell populations in young mice but not in aged mice brains, and without affecting body weight and food intake. Male 3-month-old young mice and 23-month-old aged mice were subjected to Gao-binge alcohol feeding. (<b>A</b>) Food intake, and (<b>B</b>) body weight (BW) were measured (n = 4–6). (<b>C</b>) Representative brain hematoxylin and eosin images are shown. Lower panels are enlarged images from the boxed areas. Original magnifications (4× and 10×).</p>
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<p>Chronic ethanol feeding has no effects on synapses but synapses are reduced in aged mice compared to young mice. Male 3-month-old young mice (3M) and 23-month-old aged mice (23M) were subjected to chronic EtOH feeding for 4 weeks. (<b>A</b>) Cortex brain lysates were subjected to western blot analysis followed by (<b>B</b>) Densitometry analysis of (<b>A</b>), which are normalized to loading control GAPDH. Data are presented as mean ± SEM (n = 3–4) in (<b>B</b>). <span class="html-italic">p</span>-value; one-way analysis of variance with Bonferroni’s post hoc test.</p>
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<p>Chronic EtOH feeding impairs spatial learning and memory of young mice but not aged mice in the Morris Water Maze test. Male 3-month-old young mice (3M) and 23-month-old aged mice (23M) were subjected to chronic EtOH feeding for 4 weeks, followed by a Morris Water Maze test. (<b>A</b>) Swimming speed of indicated four mouse groups. (<b>B</b>) Representative images of swimming paths of indicated four mice groups during the probe test. (<b>C</b>) Escape latency decreased across the training days, representing the spatial learning ability of four groups of young and aged mice fed with or without EtOH. 3M control vs. 23M control. <span class="html-italic">* p &lt;</span> 0.05<span class="html-italic">;</span> one-way analysis of variance with Bonferroni’s post hoc test. (<b>D</b>) Spatial reference memory of indicated four mice groups evaluated by time spent in the target quadrant and (<b>E</b>) number of times crossing the target platform. Data are presented as mean ± SEM. 3-month-old mice: Control, <span class="html-italic">n =</span> 4; EtOH, <span class="html-italic">n =</span> 4; 23-month-old mice: Control, <span class="html-italic">n =</span> 4; EtOH, <span class="html-italic">n =</span> 6; one-way analysis of variance with Bonferroni’s post hoc test.</p>
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<p>Aging but not chronic EtOH feeding impairs mouse spatial learning and memory in the Barnes Maze test. Male 3-month-old young mice (3M) and 23-month-old aged mice (23M) were subjected to chronic EtOH feeding for 4 weeks, followed by Barnes Maze testing. (<b>A</b>) Representative heat maps of Days 1 and 5 from each treatment group. (<b>B</b>) The average latency to escape for each test day. (<b>C</b>) Average distance traveled to escape for young and aged mice fed with and without EtOH. (<b>D</b>) Day 1 average latency to escape for 3-month-old and 23-month-old mice fed with or without EtOH. 3M control vs. 23M control (<b>B</b>,<b>C</b>). * <span class="html-italic">p</span> &lt; 0.05; one-way analysis of variance with Dunn’s post hoc test. (<b>E</b>) Day 1 average distance traveled to escape for 3-month-old and 23-month-old mice fed with or without EtOH. (<b>F</b>) Day 5 average latency to escape for 3-month-old and 23-month-old mice fed with or without EtOH. (<b>G</b>) Day 5 average distance traveled to escape for 3-month-old and 23-month-old mice fed with or without EtOH. Data are presented as mean ± SEM. 3-month-old: CD, n = 5; ED, n = 5; 23-month-old: CD, n = 5; ED, n = 4. * <span class="html-italic">p</span> &lt; 0.05; one-way analysis of variance with Dunn’s post hoc test.</p>
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14 pages, 3255 KiB  
Article
Anti-Inflammatory and Neurotrophic Factor Production Effects of 3,5,6,7,8,3′,4′-Heptamethoxyflavone in the Hippocampus of Lipopolysaccharide-Induced Inflammation Model Mice
by Toshiki Omasa, Atsushi Sawamoto, Mitsunari Nakajima and Satoshi Okuyama
Molecules 2024, 29(23), 5559; https://doi.org/10.3390/molecules29235559 - 25 Nov 2024
Viewed by 443
Abstract
Citrus fruits contain several bioactive components. Among them, one of the major components is 3,5,6,7,8,3′,4′-heptamethoxyflavone (HMF), which has previously shown protective effects in the brain in some disease models; moreover, HMF has been shown to penetrate the brain. In recent years, inflammation has [...] Read more.
Citrus fruits contain several bioactive components. Among them, one of the major components is 3,5,6,7,8,3′,4′-heptamethoxyflavone (HMF), which has previously shown protective effects in the brain in some disease models; moreover, HMF has been shown to penetrate the brain. In recent years, inflammation has been identified as a defense response in the body; however, a chronic inflammatory response may trigger several diseases. Inflammation in the peripheral tissues spreads to the brain and is suggested to be closely associated with diseases of the central nervous system. HMF has shown anti-inflammatory effects in the hippocampus following global cerebral ischemia; however, its effects on acute and chronic inflammation in the brain remain unclear. Therefore, in the present study, we examined the effects of HMF in a mouse model of systemic inflammation induced by lipopolysaccharide (LPS) administration. In this study, HMF suppressed LPS-induced microglial activation in the brains of acute inflammation model mice two days after LPS administration. In addition, 24 days after the administration of LPS in a chronic inflammation model, HMF promoted BDNF production and neurogenesis in the brain, which also tended to suppress tau protein phosphorylation at Ser396. These results suggest that HMF has anti-inflammatory and neurotrophic effects in the brains of model mice with lipopolysaccharide-induced systemic inflammation. Full article
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<p>Chemical structure of 3,5,6,7,8,3′,4′-heptamethoxyflavone (HMF).</p>
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<p>Experimental scheme. (<b>a</b>) Acute inflammation model mice were dissected two days after LPS administration. (<b>b</b>) Chronic inflammation model mice were dissected 24 days after LPS administration.</p>
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<p>Changes in body weight and measurement of spontaneous locomotive activity after HMF administration and LPS treatment in acute inflammation model mice. Body weight was measured on days 1, 5, and 7. (<b>a</b>) Changes in body weight after HMF administration from days 1 to 5. (<b>b</b>) Changes in body weight two days after LPS administration. (<b>c</b>) Total distance traveled by mice in the open-field test in 10 min. Data were analyzed by performing a one-way ANOVA followed by Dunnett’s multiple comparison test. ** <span class="html-italic">p</span> &lt; 0.01 or *** <span class="html-italic">p</span> &lt; 0.001 indicates a significant difference between CON and LPS. Values are presented as mean ± SEM (n = 6–8/group).</p>
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<p>The quantification of activated microglia between the stratum lacunosum moleculare (SLM) and stratum radiatum (SR) and in the CA3 region in the hippocampus of acute inflammation model mice. (<b>a</b>) The locations of the captured images and quantification in the hippocampus are shown with squares. (<b>b</b>) Representative images of microglia stained with an anti-Iba1 antibody in the SLM and SR. Scale bar = 100 μm. (<b>c</b>) The quantification of Iba1-positive signals in the SLM and SR. (<b>d</b>) The quantification of Iba1-positive signals in the CA3 region. Data were analyzed by performing a one-way ANOVA followed by Dunnett’s multiple comparison test. ** <span class="html-italic">p</span> &lt; 0.01 or *** <span class="html-italic">p</span> &lt; 0.001 indicates a significant difference between CON and LPS; * <span class="html-italic">p</span> &lt;0.05 indicates a significant difference between LPS and HMF-M; ** <span class="html-italic">p</span> &lt; 0.01 indicates a significant difference between LPS and HMF-H. Values are presented as the mean ± SEM (n = 3–4 brain sections/mouse in each group).</p>
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<p>The quantification of activated microglia between the stratum lacunosum moleculare (SLM) and stratum radiatum (SR) and in the CA3 region in the hippocampus of chronic inflammation model mice. (<b>a</b>) The locations of the captured images and quantification in the hippocampus are shown with squares. (<b>b</b>) Representative microglia pictures stained with an anti-Iba1 antibody in the SLM and SR. The scale bar represents 100 μm. (<b>c</b>) The quantification of Iba1-positive signals in the SLM and SR. (<b>d</b>) The quantification of Iba1-positive signals in the CA3 region. Data were analyzed by performing a one-way ANOVA followed by Dunnett’s multiple comparison test. *** <span class="html-italic">p</span> &lt; 0.001 indicates a significant difference between CON and LPS. Values are presented as the means ± SEMs (n = 3–4 brain sections/mouse in each group).</p>
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<p>The quantification of astrocytes and BDNF between the stratum lacunosum moleculare (SLM) and stratum radiatum (SR) and in the CA3 region in the hippocampus of chronic inflammation model mice. (<b>a</b>) The locations of the captured images and quantification in the hippocampus are shown with squares. (<b>b</b>) Representative images of astrocytes, BDNF, and merged, stained with either an anti-GFAP or anti-BDNF antibody in the SLM and SR. Scale bar = 100 μm. (<b>c</b>) Quantification of GFAP-positive signals in the SLM and SR. (<b>d</b>) The quantification of BDNF-positive signals in the SLM and SR. (<b>e</b>) The quantification of GFAP-positive signals in the CA3 region. (<b>f</b>) The quantification of BDNF-positive signals in the CA3 region. Data were analyzed by performing a one-way ANOVA followed by Dunnett’s multiple comparison test. *** <span class="html-italic">p</span> &lt; 0.001 indicates a significant difference between CON and LPS; * <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 indicates a significant difference between LPS and HMF-H. Values are presented as the mean ± SEM (n = 3–4 brain sections/mouse in each group).</p>
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<p>The quantification of DCX in the subgranular zone (SGZ) of the hippocampus of chronic inflammation model mice. (<b>a</b>) The location of the captured images and quantification in the hippocampus is shown with a square. (<b>b</b>) Representative images of DCX stained with anti-DCX antibody. Scale bar = 100 μm. (<b>c</b>) The quantification of DCX-positive cells with nuclei in the SGZ of the hippocampus. Data were analyzed by performing a one-way ANOVA followed by Dunnett’s multiple comparison test. * <span class="html-italic">p</span> &lt; 0.05 indicates a significant difference between CON and LPS; ** <span class="html-italic">p</span> &lt; 0.01 indicates a significant difference between LPS and HMF-H. Values are represented as the mean ± SEM (n = 2 brain sections/mouse in each group).</p>
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<p>The quantification of phosphorylated tau protein at Ser396 in the CA3 region of the hippocampus of chronic inflammation model mice. (<b>a</b>) The location of the captured images and quantification in the hippocampus is shown with a square. (<b>b</b>) Representative images of pSer396 stained with anti-pSer396 antibody. Scale bar = 100 μm. (<b>c</b>) Immunopositive signals of pSer396 in the medial hippocampal CA3 region were quantified. Data were analyzed by performing a one-way ANOVA followed by Dunnett’s multiple comparison test. Values are presented as the mean ± SEM (n = 3–4 brain sections/mouse in each group).</p>
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16 pages, 1527 KiB  
Review
Food Functional Factors in Alzheimer’s Disease Intervention: Current Research Progress
by Rong-Zu Nie, Huo-Min Luo, Ya-Ping Liu, Shuang-Shuang Wang, Yan-Jie Hou, Chen Chen, Hang Wang, Hui-Lin Lv, Xing-Yue Tao, Zhao-Hui Jing, Hao-Kun Zhang and Pei-Feng Li
Nutrients 2024, 16(23), 3998; https://doi.org/10.3390/nu16233998 - 22 Nov 2024
Viewed by 1086
Abstract
Alzheimer’s disease (AD) is a complex multifactorial neurodegenerative disease. With the escalating aging of the global population, the societal burden of this disease is increasing. Although drugs are available for the treatment of AD, their efficacy is limited and there remains no effective [...] Read more.
Alzheimer’s disease (AD) is a complex multifactorial neurodegenerative disease. With the escalating aging of the global population, the societal burden of this disease is increasing. Although drugs are available for the treatment of AD, their efficacy is limited and there remains no effective cure. Therefore, the identification of safe and effective prevention and treatment strategies is urgently needed. Functional factors in foods encompass a variety of natural and safe bioactive substances that show potential in the prevention and treatment of AD. However, current research focused on the use of these functional factors for the prevention and treatment of AD is in its initial stages, and a complete theoretical and application system remains to be determined. An increasing number of recent studies have found that functional factors such as polyphenols, polysaccharides, unsaturated fatty acids, melatonin, and caffeine have positive effects in delaying the progression of AD and improving cognitive function. For example, polyphenols exhibit antioxidant, anti-inflammatory, and neuroprotective effects, and polysaccharides promote neuronal growth and inhibit inflammation and oxidative stress. Additionally, unsaturated fatty acids inhibit Aβ production and Tau protein phosphorylation and reduce neuroinflammation, and melatonin has been shown to protect nerve cells and improve cognitive function by regulating mitochondrial homeostasis and autophagy. Caffeine has also been shown to inhibit inflammation and reduce neuronal damage. Future research should further explore the mechanisms of action of these functional factors and develop relevant functional foods or nutritional supplements to provide new strategies and support for the prevention and treatment of AD. Full article
(This article belongs to the Section Nutrition and Public Health)
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<p>The proportion of Alzheimer’s disease patients over 65 years by age group.</p>
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<p>Molecular mechanisms of food functional factors in Alzheimer’s disease.</p>
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25 pages, 4018 KiB  
Article
A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer’s Disease
by Min-Koo Park, Jinhyun Ahn, Jin-Muk Lim, Minsoo Han, Ji-Won Lee, Jeong-Chan Lee, Sung-Joo Hwang and Keun-Cheol Kim
Cells 2024, 13(22), 1920; https://doi.org/10.3390/cells13221920 - 19 Nov 2024
Viewed by 755
Abstract
The clinical spectrum of Alzheimer’s disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been developed, current therapies can only delay disease progression rather [...] Read more.
The clinical spectrum of Alzheimer’s disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been developed, current therapies can only delay disease progression rather than halt it entirely. Therefore, the early detection of MCI and the identification of MCI patients at high risk of progression to AD remain urgent unmet needs in the super-aged era. This study utilized transcriptomics data from cognitively unimpaired (CU) individuals, MCI, and AD patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and leveraged machine learning models to identify biomarkers that differentiate MCI from CU and also distinguish AD from MCI individuals. Furthermore, Cox proportional hazards analysis was conducted to identify biomarkers predictive of the progression from MCI to AD. Our machine learning models identified a unique set of gene expression profiles capable of achieving an area under the curve (AUC) of 0.98 in distinguishing those with MCI from CU individuals. A subset of these biomarkers was also found to be significantly associated with the risk of progression from MCI to AD. A linear mixed model demonstrated that plasma tau phosphorylated at threonine 181 (pTau181) and neurofilament light chain (NFL) exhibit the prognostic value in predicting cognitive decline longitudinally. These findings underscore the potential of integrating machine learning (ML) with transcriptomic profiling in the early detection and prognostication of AD. This integrated approach could facilitate the development of novel diagnostic tools and therapeutic strategies aimed at delaying or preventing the onset of AD in at-risk individuals. Future studies should focus on validating these biomarkers in larger, independent cohorts and further investigating their roles in AD pathogenesis. Full article
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<p>Study design and workflow for identifying RNA biomarkers predictive of MCI or AD. Methods for biomarker identification and functional annotation are depicted in grey round squares. The numbers in parentheses at the bottom indicate the RNA probes that met the selection criteria and were subsequently identified using ML algorithms. Abbreviations: CU (cognitively unimpaired), MCI (mild cognitive impairment), AD (Alzheimer’s disease), DEG (differentially expressed gene), and GSEA (gene set enrichment analysis).</p>
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<p>Comparison of the discriminating performances of multivariate models. (<b>a</b>) Receiver operating characteristic (ROC) curves for RNA biomarkers combined with demographic variables in the CU vs. MCI comparison. (<b>b</b>) ROC curves for RNA biomarkers in combination with demographics and neuropsychological measures in the CU vs. MCI comparison. (<b>c</b>) ROC curves for RNA biomarkers combined with demographic variables in the MCI vs. AD comparison. (<b>d</b>) ROC curves for RNA biomarkers in combination with demographics and neuropsychological measures in the MCI vs. AD comparison.</p>
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<p>Comparison of the discriminating performances of multivariate models. (<b>a</b>) Receiver operating characteristic (ROC) curves for RNA biomarkers combined with demographic variables in the CU vs. MCI comparison. (<b>b</b>) ROC curves for RNA biomarkers in combination with demographics and neuropsychological measures in the CU vs. MCI comparison. (<b>c</b>) ROC curves for RNA biomarkers combined with demographic variables in the MCI vs. AD comparison. (<b>d</b>) ROC curves for RNA biomarkers in combination with demographics and neuropsychological measures in the MCI vs. AD comparison.</p>
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<p>GSEA and gene network analysis for DEGs discriminating MCI from CU individuals. Ten hub genes from upregulated DEGs are displayed in the center small circle, representing those that overlap more than three clusters. Nodes with a deeper red color represent higher rank scores.</p>
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<p>GSEA and gene network analysis for DEGs discriminating AD from MCI. Ten hub genes from upregulated DEGs are displayed in the center small circle, representing those that overlap more than three clusters.</p>
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<p>Sensitivity analysis result. (<b>a</b>) Confusion matrix for the generalized regression; the sensitivity analysis misclassified 26 cases as CU and 22 cases as MCI out of the 712 MCI and 296 CU cases, respectively. (<b>b</b>) ROC curve of the sensitivity analysis, with an AUC of 0.9801.</p>
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<p>Representative Cox proportional hazard curves for MCI-to-AD converters. Expression values normalized using the robust multi-chip average method per each tertile are indicated in figure insets: (<b>a</b>) GPD1; (<b>b</b>) NPPA; (<b>c</b>) CAV1; (<b>d</b>) LILRB3. Year “0” marks the baseline diagnosis. Tick marks represent participants who were AD conversion-free at the last follow-up or who were censored at that time point.</p>
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<p>Prediction of longitudinal neuropsychological score alterations based on baseline plasma protein levels. Trajectories were derived from the LMM, with the baseline plasma pTau181 and NFL levels as predictors, being adjusted for age, sex, ApoE ε4, and years of education. MMSE trajectories were stratified by (<b>a</b>) pTau181 or (<b>b</b>) NFL tertiles, while ADNI-MEM trajectories were stratified by (<b>c</b>) pTau181 or (<b>d</b>) NFL tertiles. The trajectories depict changes in the MMSE or ADNI-MEM scores over time influenced by different tertiles of baseline pTau181 or NFL levels. The slope, indicative of the rate of cognitive decline, appears steeper for individuals with higher protein levels. The red line represents the highest tertile for each protein, while the blue and green lines represent the intermediate and lowest tertiles, respectively. Shaded areas indicate the 95% confidence intervals of the regression lines. This figure displays the mean levels within each covariate (age and years of education), with females as the reference group. The time span is capped at four years, corresponding to four follow-up assessments.</p>
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<p>Prediction of longitudinal neuropsychological score alterations based on baseline plasma protein levels. Trajectories were derived from the LMM, with the baseline plasma pTau181 and NFL levels as predictors, being adjusted for age, sex, ApoE ε4, and years of education. MMSE trajectories were stratified by (<b>a</b>) pTau181 or (<b>b</b>) NFL tertiles, while ADNI-MEM trajectories were stratified by (<b>c</b>) pTau181 or (<b>d</b>) NFL tertiles. The trajectories depict changes in the MMSE or ADNI-MEM scores over time influenced by different tertiles of baseline pTau181 or NFL levels. The slope, indicative of the rate of cognitive decline, appears steeper for individuals with higher protein levels. The red line represents the highest tertile for each protein, while the blue and green lines represent the intermediate and lowest tertiles, respectively. Shaded areas indicate the 95% confidence intervals of the regression lines. This figure displays the mean levels within each covariate (age and years of education), with females as the reference group. The time span is capped at four years, corresponding to four follow-up assessments.</p>
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12 pages, 2064 KiB  
Article
Oxidative Stress Triggers a Pivotal Peptide Linked to Alzheimer’s Disease
by Nikki Evans, Kashif Mahfooz, Sara Garcia-Rates and Susan Greenfield
Int. J. Mol. Sci. 2024, 25(22), 12413; https://doi.org/10.3390/ijms252212413 - 19 Nov 2024
Viewed by 807
Abstract
An aberrant recapitulation of a developmental mechanism driven by a 14 mer peptide (‘T14’) derived from acetylcholinesterase (AChE) has been implicated in Alzheimer’s disease. T14 was suggested as an upstream driver of neurodegeneration due to its ability to stimulate the production of phosphorylated [...] Read more.
An aberrant recapitulation of a developmental mechanism driven by a 14 mer peptide (‘T14’) derived from acetylcholinesterase (AChE) has been implicated in Alzheimer’s disease. T14 was suggested as an upstream driver of neurodegeneration due to its ability to stimulate the production of phosphorylated tau and amyloid beta. The activation of this mechanism in adulthood is thought to be brought upon by insult to the primarily vulnerable subcortical nuclei. Here, we show that oxidative stress, induced by high glucose and confirmed by an analysis of antioxidant enzyme mRNA expression, increased the levels of T14 peptide in PC12 cells. This increase in T14 corresponded with an increase in the mRNA expression of AChE and a decrease in the cell viability. The increase in T14 could be blocked by the cyclic form of T14, NBP14, which prevented any cytotoxic effects. These observations suggest that oxidative stress can directly trigger the inappropriate activation of T14 in the adult brain through the upregulation of Ache mRNA. Full article
(This article belongs to the Section Molecular Neurobiology)
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<p>The cell viability was reduced with 150 mM glucose. The PC12 cells were treated with 50 mM glucose, 75 mM glucose, 100 mM glucose, and 150 mM glucose for 24 h before the cell viability was determined. The bars represent the mean number of live cells expressed as a percentage relative to the control. All bars are presented as the mean ± SEM, where n = 3. Student’s <span class="html-italic">t</span>-test. ** <span class="html-italic">p</span> &lt; 0.01. ns: not significant.</p>
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<p>High glucose increased the expressions of <span class="html-italic">Cat</span>, <span class="html-italic">Sod2</span>, and <span class="html-italic">Gs</span>. Relative mRNA expression of the antioxidant enzymes: <span class="html-italic">Cat</span>, <span class="html-italic">Sod2</span>, and <span class="html-italic">Gs</span> in PC12 with 150 mM glucose treatment, normalised to the <span class="html-italic">Gapdh</span> mRNA expression. All bars are presented as the mean ± SEM, where n = 3–4. Student’s <span class="html-italic">t</span>-test. * <span class="html-italic">p</span> &lt; 0.05. ** <span class="html-italic">p</span> &lt; 0.01. <span class="html-italic">Cat</span>, catalase; <span class="html-italic">Sod</span>, superoxide dismutase; <span class="html-italic">Gs</span>, glutathione synthase.</p>
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<p>There was a significant increase in the <span class="html-italic">Ache</span> mRNA expression and T14 levels with high glucose. (<b>A</b>) Relative AChE mRNA expression, normalised to the <span class="html-italic">Gapdh</span> mRNA expression. (<b>Bi</b>) Representative Western blot of the high glucose (150 mM)-treated PC12 for vinculin (117 kDa), T14 (55 kDa), and GAPDH (37 kDa). C represents the control samples and G represents the high-glucose samples. (<b>Bii</b>) Relative T14 expression. The T14 expression was normalised to vinculin and GAPDH expression and expressed relative to the control. All bars represent the mean ± SEM, where n = 3–6. Student’s <span class="html-italic">t</span>-test. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Co-treatment with NBP14 prevented the increase in T14 levels and reduction in cell viability seen with high glucose. (<b>Ai</b>) Representative Western blot of the high glucose (150 mM)-treated and high glucose (150 mM) + NBP14 (15 µM)-treated PC12 for vinculin (117 kDa), T14 (55 kDa), and GAPDH (37 kDa). C represents control samples, G represents high-glucose samples, and N represents high glucose + NBP14 samples. (<b>Aii</b>) Relative T14 expression normalised to vinculin and GAPDH expression and expressed relative to the control. (<b>B</b>) The cell viability of the PC12 cells treated with high glucose and high glucose + NBP14 for 24 h. The bars represent the mean number of live cells expressed as a percentage relative to the control. All bars represent the mean ± SEM, where n = 3. One-way ANOVA followed by Dunnett’s post hoc test. * <span class="html-italic">p</span> &lt; 0.05. ** <span class="html-italic">p</span> &lt; 0.01. ns: not significant.</p>
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<p>Co-treatment with NBP14 (15µM) attenuated the increase in <span class="html-italic">Sod2</span> and <span class="html-italic">Cat</span> mRNA expression with high glucose (150 mM) but had no effect on <span class="html-italic">Gs</span> mRNA expression. (<b>A</b>) Relative <span class="html-italic">Cat</span> mRNA expression. (<b>B</b>) Relative <span class="html-italic">Sod2</span> mRNA expression. (<b>C</b>) Relative <span class="html-italic">Gs</span> mRNA expression. All mRNA expressions were normalised to the <span class="html-italic">Gapdh</span> expression. All bars represent the mean ± SEM, where n = 3–4. One-way ANOVA followed by Dunnett’s post hoc test. * <span class="html-italic">p</span> &lt; 0.05. ** <span class="html-italic">p</span> &lt; 0.01. ns: not significant.</p>
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<p>Proposed diagram of high-glucose-induced oxidative stress leading to elevated T14 levels and its downstream effects. (1) Glucose uptake occurs through GLUT transporters, which, for neuronal cells, are the GLUT1 and GLUT3 transporters [<a href="#B31-ijms-25-12413" class="html-bibr">31</a>]. (2) Once inside the cell, glucose activates increased mitochondrial electron transport chain activity [<a href="#B7-ijms-25-12413" class="html-bibr">7</a>], (3) generating ROS and contributing to the oxidative stress. (4) Elevated ROS activates the increased transcription of antioxidant enzymes and AChE. (5) The upregulated mRNA is transported outside the nucleus, where it is translated into protein at the endoplasmic reticulum. (6) Increased levels of produced AChE protein are released into the extracellular space, (7) where it is cleaved by proteases into T14 [<a href="#B32-ijms-25-12413" class="html-bibr">32</a>]. (8) T14 binds to the α7 nicotinic acetylcholine receptor [<a href="#B11-ijms-25-12413" class="html-bibr">11</a>,<a href="#B12-ijms-25-12413" class="html-bibr">12</a>], which (9) activates the mitochondria [<a href="#B14-ijms-25-12413" class="html-bibr">14</a>] downstream, further generating more ROS and contributing to cell death. (10) At the same time, AChE release from the reticulum is also stimulated [<a href="#B13-ijms-25-12413" class="html-bibr">13</a>], which is cleaved into T14, exacerbating the process. (11) NBP14 prevents T14 from binding to the α7 receptor by displacing it [<a href="#B30-ijms-25-12413" class="html-bibr">30</a>], thus blocking cascades activated by T14 and preventing the aberrant downstream effects by alleviating the generation of ROS from the mitochondria and AChE release. Figure created using BioRender.</p>
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