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Volume 25
Issue 18
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Int. J. Mol. Sci., Volume 25, Issue 18 (September-2 2024) – 474 articles

Cover Story (view full-size image): The coordination of ICI therapy and the gut microbiota triggers a synergistic anti-tumour immune response. The inflammation induced by ICI promotes the uptake of gut microbiota by the intestinal dendritic cells (DCs), leading to the upregulation of MHC, CD86, and CCR7, which facilitates the transport of microbiota-loaded DCs into the MLNs. Additionally, ICI therapy induces lymphangiogenesis and the dilation of high endothelial venules in the MLNs, enhancing the translocation of bacteria to the TDLNs and the tumour itself. In the TME, the microbiota modifies the intrinsic tumour factors and influences the anti-tumour immune response, resulting in increased leukocyte infiltration and enhanced secretion of Granzyme B and perforin by the effector immune cells, such as CD8 killer T cells and NKT cells, ultimately driving tumour elimination. View this paper
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14 pages, 2865 KiB  
Review
Lectin-Based Approaches to Analyze the Role of Glycans and Their Clinical Application in Disease
by Hiroko Ideo, Akiko Tsuchida and Yoshio Takada
Int. J. Mol. Sci. 2024, 25(18), 10231; https://doi.org/10.3390/ijms251810231 - 23 Sep 2024
Viewed by 1233
Abstract
Lectin-based approaches remain a valuable tool for analyzing glycosylation, especially when detecting cancer-related changes. Certain glycans function as platforms for cell communication, signal transduction, and adhesion. Therefore, the functions of glycans are important considerations for clinical aspects, such as cancer, infection, and immunity. [...] Read more.
Lectin-based approaches remain a valuable tool for analyzing glycosylation, especially when detecting cancer-related changes. Certain glycans function as platforms for cell communication, signal transduction, and adhesion. Therefore, the functions of glycans are important considerations for clinical aspects, such as cancer, infection, and immunity. Considering that the three-dimensional structure and multivalency of glycans are important factors for their function, their binding characteristics toward lectins provide vital information. Glycans and lectins are inextricably linked, and studies on lectins have also led to research on the roles of glycans. The applications of lectins are not limited to analysis but can also be used as drug delivery tools. Moreover, mammalian lectins are potential therapeutic targets because certain lectins change their expression in cancer, and lectin regulation subsequently regulates several molecules with glycans. Herein, we review lectin-based approaches for analyzing the role of glycans and their clinical applications in diseases, as well as our recent results. Full article
(This article belongs to the Special Issue Glycobiology of Health and Diseases)
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<p>Functions of lectins and glycans.</p> Full article ">Figure 2
<p>Concanavalin A (Con A)-unbound glycoforms in prostate-specific antigens (PSAs) from cancer cells. (<b>A</b>) Elution profiles of PSAs from cancer cells using Con A column chromatography. (<b>a</b>) LNCaP. (<b>b</b>) CTOS. Black arrows indicate the positions where the buffers were switched to those containing 0.3 M α-methyl glycoside (α-MG). Western blot analysis of PSAs from cancer cells. (<b>c</b>) PSAs from LNCaP in Con A (−) fraction with and without Peptide/N-glycosidase F (PNGF) treatment. (<b>d</b>) PSAs in Con A (−) and bound (+) fraction from CTOS. (<b>e</b>) PSAs in Con A (−) fraction from CTOS with and without PNGF treatment. The position of each high molecular and low molecular PSA is indicated by closed and open triangles, respectively. Following PNGF treatment, both high molecular weight forms (32 and 31 kDa) changed to the low molecular weight form (29 kDa). The majority of the 29-kDa form (open triangle) in Con A (−) fraction did not change, suggesting that it was non-glycosylated. (<b>B</b>) Analysis of PSA glycopeptides from LNCaP using matrix-assisted laser desorption ionization–mass spectrometry (MALDI-MS). MALDI-TOF MS spectra of glycopeptides in Con A (−) (<b>a</b>) and Con A (+) (<b>b</b>) fractions. Mass spectra were acquired in negative ion mode. (<b>A</b>,<b>B</b>) are reproduced from our previous study [<a href="#B12-ijms-25-10231" class="html-bibr">12</a>].</p> Full article ">Figure 3
<p>(<b>A</b>) Schema of galectin-4 (Gal4)/mucin-type 1 (MUC1)-enzyme-linked immunosorbent assay (ELISA) and standard curve. (<b>B</b>) Gal4/MUC1-ELISA of intact (solid bars) and sialidase-treated sera (white bars) from patients with breast cancer. Solutions of 1% sera were applied to anti-MUC1-coated plates and detected using Gal4-horseradish peroxidase (HRP). Background values, which were obtained from plates with blocking reagents only, were subtracted to account for nonspecific binding. (<b>C</b>) Receiver operating characteristic (ROC) plots displaying the specificities and sensitivities of (<b>a</b>) Gal4/MUC1 and (<b>b</b>) cancer antigen 15-3 (CA15-3) from patients with breast cancer with recurrence/metastasis, and the specificities and sensitivities of (<b>c</b>) Gal4/MUC1 and (<b>d</b>) CA15-3 from patients with primary breast cancer. (<b>A</b>,<b>C</b>) are reproduced from our previous study [<a href="#B23-ijms-25-10231" class="html-bibr">23</a>].</p> Full article ">Figure 4
<p>Lectin microarray strategies. (<b>A</b>) Direct assay. (<b>B</b>) Lectin-overlay antibody sandwich array. (<b>C</b>) Antibody-overlay lectin sandwich array. (<b>A</b>–<b>C</b>) are produced referring to the figure in the reference [<a href="#B27-ijms-25-10231" class="html-bibr">27</a>].</p> Full article ">Figure 5
<p>(<b>A</b>) Analysis of galectin-4 knockout (KO) cells in vivo. Macroscopic view of mesentery from mice inoculated with wild-type and KO cells. (<b>B</b>) The structures of the neutral GSL glycans from wild-type and galectin-4KO NUGC4 cells. The compositions and identified structures of the GSL glycans were acquired in positive ion mode. The glycan structures were identified using glycosidase digestion and multi-stage mass spectrometry (MS<sup>n</sup>) analysis. (<b>C</b>) Total weight of tumors in the peritoneal cavity in two animal experiments. The first experiment (black circles) and the second experiment (blank circles). The horizontal line in the middle of columns shows the average tumor weight of mice inoculated with NUGC4 clone cells (Control, B3GALT5 low expression, high expression H1 and H2). The accompanying vertical line indicates standard deviation (SD). ** <span class="html-italic">p</span> &lt; 0.005 (<b>A</b>,<b>C</b>) are reproduced from our previous study [<a href="#B37-ijms-25-10231" class="html-bibr">37</a>,<a href="#B38-ijms-25-10231" class="html-bibr">38</a>,<a href="#B39-ijms-25-10231" class="html-bibr">39</a>].</p> Full article ">Figure 6
<p>(<b>A</b>) Possible GSL biosynthetic pathways in NUGC4 cells. The synthesis of the three series of mammalian GSLs with the possible gene names. (<b>B</b>) Relative expression levels of beta-1,3-galactosyltransferase (B3GALT)1, 2, and 5 genes in the synthesis of the GSLs using quantitative real-time polymerase chain reaction (PCR). The normalized expression ratio of each gene against the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene is represented on the vertical axis. (<b>A</b>,<b>B</b>) are reproduced from our previous study [<a href="#B39-ijms-25-10231" class="html-bibr">39</a>].</p> Full article ">Figure 7
<p>(<b>A</b>) Drug resistance is a major barrier to successful treatment. (<b>a</b>) Before treatment, tumors consist of various cancer cells with different molecular features. (<b>b</b>) Drug A and B kill some cancer cells that express the drug targets. (<b>c</b>) The cancer cells that are resistant to drugs grow, thereby contributing to re-growth of the tumor. (<b>B</b>) (<b>a</b>) Treatments that target specific molecules, such as molecular-target drugs and monoclonal antibodies. (<b>b</b>) Drugs that target lectins can regulate many molecules with specific glycans which lectin recognizes, thereby modulating cell properties.</p> Full article ">
18 pages, 3870 KiB  
Article
A Machine Learning Algorithm Suggests Repurposing Opportunities for Targeting Selected GPCRs
by Shayma El-Atawneh and Amiram Goldblum
Int. J. Mol. Sci. 2024, 25(18), 10230; https://doi.org/10.3390/ijms251810230 - 23 Sep 2024
Viewed by 918
Abstract
Repurposing utilizes existing drugs with known safety profiles and discovers new uses by combining experimental and computational approaches. The integration of computational methods has greatly advanced drug repurposing, offering a rational approach and reducing the risk of failure in these efforts. Recognizing the [...] Read more.
Repurposing utilizes existing drugs with known safety profiles and discovers new uses by combining experimental and computational approaches. The integration of computational methods has greatly advanced drug repurposing, offering a rational approach and reducing the risk of failure in these efforts. Recognizing the potential for drug repurposing, we employed our Iterative Stochastic Elimination (ISE) algorithm to screen known drugs from the DrugBank database. Repurposing in our hands is based on computer models of the actions of ligands: the ISE algorithm is a machine learning tool that creates ligand-based models by distinguishing between the physicochemical properties of known drugs and those of decoys. The models are large sets of “filters” made out, each, of molecular properties. We screen and score external sets of molecules (in our case- the DrugBank molecules) by our agonism and antagonism models based on published data (i.e., IC50, Ki, or EC50) and pick the top-scoring molecules as candidates for experiments. Such agonist and antagonist models for six G-protein coupled receptors (GPCRs) families facilitated the identification of repurposing opportunities. Our screening revealed 5982 new potential molecular actions (agonists, antagonists), which suggest repurposing candidates for the cannabinoid 2 (CB2), histamine (H1, H3, and H4), and dopamine 3 (D3) receptors, which may be useful to treat conditions such as neuroinflammation, obesity, allergic dermatitis, and drug abuse. These sets of best candidates should now be examined by experimentalists: based on previous such experiments, there is a very high chance of discovering novel highly bioactive molecules. Full article
(This article belongs to the Special Issue G Protein-Coupled Receptors: Signaling, Regulation and Therapeutic Opportunities)
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<p>(<b>A</b>) Reported interactions for six GPCR families: 5-Hydroxytryptamine (serotonin, 5-HT)—blue, Cannabinoids—green, Dopamine (D1–5)—yellow, Histamine (H1–4)—pink, Muscarinic (M1–5)—gray and Opioid (Delta, Kappa, and Mu)—orange. (<b>B</b>) The number of reported interactions per drug. (<b>C</b>) Predicted interactions above a score of 0.7. 5120 interactions involving 1283 drugs interacting with the 31 GPCRs: 5-Hydroxytryptamine (serotonin, 5-HT)—blue, Cannabinoids—green, Dopamine (D1–5)—yellow, Histamine (H1–4)—pink, Muscarinic (M1–5)—gray and Opioid (Delta, Kappa, and Mu)—orange.</p> Full article ">Figure 1 Cont.
<p>(<b>A</b>) Reported interactions for six GPCR families: 5-Hydroxytryptamine (serotonin, 5-HT)—blue, Cannabinoids—green, Dopamine (D1–5)—yellow, Histamine (H1–4)—pink, Muscarinic (M1–5)—gray and Opioid (Delta, Kappa, and Mu)—orange. (<b>B</b>) The number of reported interactions per drug. (<b>C</b>) Predicted interactions above a score of 0.7. 5120 interactions involving 1283 drugs interacting with the 31 GPCRs: 5-Hydroxytryptamine (serotonin, 5-HT)—blue, Cannabinoids—green, Dopamine (D1–5)—yellow, Histamine (H1–4)—pink, Muscarinic (M1–5)—gray and Opioid (Delta, Kappa, and Mu)—orange.</p> Full article ">Figure 2
<p>Docking positions at the CB2R agonist pocket. (<b>A</b>) Amiodarone (blue docking score = −12 Kcal/mol) aligned with native agonist LEI-102 (orange). (<b>B</b>) Ipratropium bromide (gray, docking score = −10.5 Kcal/mol) aligned with native LEI-102 (orange). H-bonds are shown as dashed red lines, pi-pi stacking as teal dashed lines.</p> Full article ">Figure 3
<p>Docking poses at the H3R (red). The native antagonist, PF-03654746, is presented as green sticks aligned to the different docked drugs: (<b>A</b>) Alizapride (DB01425, gray sticks); (<b>B</b>) Eprazinone (DB08990, violet sticks); (<b>C</b>) Sumatriptan (DB00669, azure sticks). Hydrogen bonds, salt bridges, and pi-cation interactions are shown as red, pink, and green dashed lines, respectively.</p> Full article ">Figure 4
<p>Docking poses at the H1R (gray). The native antagonist, Doxepin, is presented as green sticks aligned to the docked drugs: (<b>A</b>) Fluspirilene (DB04842, azure sticks); (<b>B</b>) DB07330 (orange sticks). Hydrogen bonds, salt bridges, and pi-pi stacking are shown as red, violet, and azure dashed lines, respectively.</p> Full article ">Figure 5
<p>Docking poses at the D3R (violet). The native antagonist, Eticlopride, is presented as lime sticks aligned to the docked drugs: (<b>A</b>) Vardenafil (DB00862, dark gray sticks); (<b>B</b>) Halofantrine (DB01218, red sticks). Hydrogen bonds, salt bridges, and pi-pi stacking are shown as red, violet, and azure dashed lines, respectively.</p> Full article ">
13 pages, 4324 KiB  
Article
Glial-Cell-Line-Derived Neurotrophic Factor Promotes Glioblastoma Cell Migration and Invasion via the SMAD2/3-SERPINE1-Signaling Axis
by Xiaoxiao Guo, Han Zhou, Yifang Liu, Wei Xu, Kouminin Kanwore and Lin Zhang
Int. J. Mol. Sci. 2024, 25(18), 10229; https://doi.org/10.3390/ijms251810229 - 23 Sep 2024
Viewed by 939
Abstract
Glial-cell-line-derived neurotrophic factor (GDNF) is highly expressed and is involved in the malignant phenotype in glioblastomas (GBMs). However, uncovering its underlying mechanism for promoting GBM progression is still a challenging work. In this study, we found that serine protease inhibitor family E member [...] Read more.
Glial-cell-line-derived neurotrophic factor (GDNF) is highly expressed and is involved in the malignant phenotype in glioblastomas (GBMs). However, uncovering its underlying mechanism for promoting GBM progression is still a challenging work. In this study, we found that serine protease inhibitor family E member 1 (SERPINE1) was a potential downstream gene of GDNF. Further experiments confirmed that SERPINE1 was highly expressed in GBM tissues and cells, and its levels of expression and secretion were enhanced by exogenous GDNF. SERPINE1 knockdown inhibited the migration and invasion of GBM cells promoted by GDNF. Mechanistically, GDNF increased SERPINE1 by promoting the phosphorylation of SMAD2/3. In vivo experiments demonstrated that GDNF facilitated GBM growth and the expressions of proteins related to migration and invasion via SERPINE1. Collectively, our findings revealed that GDNF upregulated SERPINE1 via the SMAD2/3-signaling pathway, thereby accelerating GBM cell migration and invasion. The present work presents a new mechanism of GDNF, supporting GBM development. Full article
(This article belongs to the Section Biochemistry)
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<p>SERPINE1 is increased in GBMs. (<b>A</b>) GO enrichment analysis of upregulated DEGs in C6 cells treated with GDNF. The top 30 significant GO terms were exhibited according to <span class="html-italic">p</span>-value. (<b>B</b>) SERPINE1 mRNA expressions were compared between the GBM and normal tissues from the UALCAN database. (<b>C</b>) The overall survival was analyzed to evaluate the association between the mRNA level of SERPINE1 and the outcome of GBM patients by the Oncomine website. (<b>D</b>,<b>E</b>) The protein expressions of SERPINE1 in GBM tissues and cells were determined by Western blotting. NB: normal brain; RAs: rat astrocytes; HAs: human astrocytes (vs. NB: **, <span class="html-italic">p</span> &lt; 0.01. vs. RAs: *, <span class="html-italic">p</span> &lt; 0.05. vs. HAs: #, <span class="html-italic">p</span> &lt; 0.05; ##, <span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 2
<p>GDNF promotes SERPINE1 expressions and secretion in GBM cells. (<b>A</b>) Western blotting (left) was used to detect the protein expressions of SERPINE1 in C6 and U251 cells treated with various doses of GDNF (0, 20, 40, 80, 100 ng/mL) for 48 h. The statistical analysis (right) of the left bands was performed. (<b>B</b>) The contents of SERPINE1 were tested in C6 and U251 cells after 24 and 48 h of treatment with different concentrations of GDNF using ELISA kits (vs. 0 ng/mL: *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 3
<p>SERPINE1 knockdown strikingly suppresses the migration and invasion of GBM cells enhanced by GDNF. (<b>A</b>,<b>B</b>) qPCR and Western blotting were applied to assess the knockdown efficiency of SERPINE1 in C6 and U251 cells. (<b>C</b>,<b>D</b>) Wound healing assay was performed to examine the effects of SERPINE1 deficiency on cell migration in C6 and U251 cells separately treated with 80 and 20 ng/mL GDNF for 48 h. Bar = 100 µm. (<b>E</b>,<b>F</b>) The migratory abilities were detected by transwell migration assay in SERPINE1 silenced C6 and U251 cells treated with 80 and 20 ng/mL GDNF for 48 h, respectively. Bar = 50 µm. (<b>G</b>) Transwell invasion assay was conducted to evaluate the influences of SERPINE1 knockdown on cell invasion in C6 and U251 cells after 48 h of separate treatment with 80 and 20 ng/mL GDNF. Bar = 50 µm (*, <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).</p> Full article ">Figure 4
<p>GDNF upregulates SERPINE1 via SMAD2/3 phosphorylation in GBM cells. (<b>A</b>,<b>B</b>) The levels of SMAD2/3 phosphorylation were analyzed in C6 and U251 cells separately treated with 80 and 20 ng/mL GDNF for 0, 1, 2, and 4 h using Western blotting. (<b>C</b>) The effects of SMAD2/3 deficiency on SERPINE1 protein expressions were assessed in C6 cells in the presence of 80 ng/mL GDNF for 48 h by Western blotting (*, <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).</p> Full article ">Figure 5
<p>GDNF accelerates GBM growth in vivo. (<b>A</b>,<b>B</b>) The tumor tissues were exfoliated and weighed at 14 days after subcutaneous tumor formation. (<b>C</b>) The mean tumor volumes were calculated every 2 days until 14 days. (<b>D</b>) Hematoxylin-Eosin (HE) staining of the tumor tissues was performed (×20). (<b>E</b>,<b>F</b>) IHC staining was used to detect the levels of Ki-67, GFAP, MMP2 and MMP9 (×20). <span class="html-italic">n</span> = 5 (*, <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).</p> Full article ">
20 pages, 2200 KiB  
Article
Synergistic Effect between the APOE ε4 Allele with Genetic Variants of GSK3B and MAPT: Differential Profile between Refractory Epilepsy and Alzheimer Disease
by Danira Toral-Rios, Pavel Pichardo-Rojas, Elizabeth Ruiz-Sánchez, Óscar Rosas-Carrasco, Rosa Carvajal-García, Dey Carol Gálvez-Coutiño, Nancy Lucero Martínez-Rodríguez, Ana Daniela Rubio-Chávez, Myr Alcántara-Flores, Arely López-Ramírez, Alma Rosa Martínez-Rosas, Ángel Alberto Ruiz-Chow, Mario Alonso-Vanegas and Victoria Campos-Peña
Int. J. Mol. Sci. 2024, 25(18), 10228; https://doi.org/10.3390/ijms251810228 - 23 Sep 2024
Viewed by 1450
Abstract
Temporal Lobe Epilepsy (TLE) is a chronic neurological disorder characterized by recurrent focal seizures originating in the temporal lobe. Despite the variety of antiseizure drugs currently available to treat TLE, about 30% of cases continue to have seizures. The etiology of TLE is [...] Read more.
Temporal Lobe Epilepsy (TLE) is a chronic neurological disorder characterized by recurrent focal seizures originating in the temporal lobe. Despite the variety of antiseizure drugs currently available to treat TLE, about 30% of cases continue to have seizures. The etiology of TLE is complex and multifactorial. Increasing evidence indicates that Alzheimer’s disease (AD) and drug-resistant TLE present common pathological features that may induce hyperexcitability, especially aberrant hyperphosphorylation of tau protein. Genetic polymorphic variants located in genes of the microtubule-associated protein tau (MAPT) and glycogen synthase kinase-3β (GSK3B) have been associated with the risk of developing AD. The APOE ε4 allele is a major genetic risk factor for AD. Likewise, a gene-dose-dependent effect of ε4 seems to influence TLE. The present study aimed to investigate whether the APOE ɛ4 allele and genetic variants located in the MAPT and GSK3B genes are associated with the risk of developing AD and drug-resistant TLE in a cohort of the Mexican population. A significant association with the APOE ε4 allele was observed in patients with AD and TLE. Additional genetic interactions were identified between this allele and variants of the MAPT and GSK3B genes. Full article
(This article belongs to the Special Issue Neurogenetics of Diseases)
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<p>Linkage disequilibrium (LD) of GSK3B SNPs in epilepsy and Alzheimer’s disease. (<b>A</b>) D’ values are shown within cells. The standard LD color scheme was used, with white to red colors representing increasing LD strength. Significant values, represented by haplotype blocks, are observed between variants rs334558 and rs6438552 located in the GSK3B gene. (<b>B</b>) Significant haplotypes for each study group. Values are in bold to emphasize their significance.</p> Full article ">Figure 2
<p>Interaction network diagram in the genetic variants and patients according to the MDR analysis. Patients with nHS-TLE (1), HS-TLE (2) and AD (3) compared to their controls. (<b>a</b>,<b>c</b>,<b>e</b>), Dendrogram interaction plots, generated by hierarchical cluster analysis, illustrate presence, strength and type of epistatic effects. The color of the line indicates the type of interaction. Red and orange indicate a synergistic relationship (i.e., epistasis). Green and blue suggest redundancy or linkage. (<b>b</b>,<b>d</b>,<b>f</b>) Circular graphs; the percentage at the bottom of each variable represents its entropy, and the percentage on each line represents the interaction of the percentage of entropy between two variables.</p> Full article ">Figure 3
<p>Schematic of tau-GSK3b interaction in Alzheimer’s disease and epilepsy. Overactivation of GSK3b promotes tau phosphorylation and aggregation into neurofibrillary tangles (NFTs), which induce neuronal death and imbalance hippocampal neuronal networks, promoting hyperexcitability and seizure development.</p> Full article ">
10 pages, 603 KiB  
Article
Influence of Intraocular Pressure on the Expression and Activity of Sodium–Potassium Pumps in the Corneal Endothelium
by Princia Anney, Pascale Charpentier and Stéphanie Proulx
Int. J. Mol. Sci. 2024, 25(18), 10227; https://doi.org/10.3390/ijms251810227 - 23 Sep 2024
Viewed by 788
Abstract
The corneal endothelium is responsible for pumping fluid out of the stroma in order to maintain corneal transparency, which depends in part on the expression and activity of sodium–potassium pumps. In this study, we evaluated how physiologic pressure and flow influence transcription, protein [...] Read more.
The corneal endothelium is responsible for pumping fluid out of the stroma in order to maintain corneal transparency, which depends in part on the expression and activity of sodium–potassium pumps. In this study, we evaluated how physiologic pressure and flow influence transcription, protein expression, and activity of Na+/K+-ATPase. Native and engineered corneal endothelia were cultured in a bioreactor in the presence of pressure and flow (hydrodynamic culture condition) or in a Petri dish (static culture condition). Transcription of ATP1A1 was assessed using qPCR, the expression of the α1 subunit of Na+/K+-ATPase was measured using Western blots and ELISA assays, and Na+/K+-ATPase activity was evaluated using an ATPase assay in the presence of ouabain. Results show that physiologic pressure and flow increase the transcription and the protein expression of Na+/K+-ATPase α1 in engineered corneal endothelia, while they remain stable in native corneal endothelia. Interestingly, the activity of Na+/K+-ATPase was increased in the presence of physiologic pressure and flow in both native and engineered corneal endothelia. These findings highlight the role of the in vivo environment on the functionality of the corneal endothelium. Full article
(This article belongs to the Special Issue Functional Roles of Epithelial and Endothelial Cells)
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<p><span class="html-italic">ATP1A1</span> gene expression. (<b>a</b>) Threshold value (Ct) of <span class="html-italic">ATP1A1</span> mRNA of native and tissue-engineered (TE) endothelia cultured under static (S) or hydrodynamic (H) conditions. Results are normalized over B2M. Two-way ANOVA, ** <span class="html-italic">p</span> &lt; 0.01, ns: not significant. (<b>b</b>) Hydrodynamic over static <span class="html-italic">ATP1A1</span> 2<sup>−ΔΔCt</sup> fold change in native and tissue-engineered endothelia. Welch’s T test, ns: not significant. Results are presented as mean ± SD. <span class="html-italic">n</span> = 3, in triplicate.</p> Full article ">Figure 2
<p>Na+/K+-ATPase α1 protein expression. (<b>a</b>) Representative Western blots of Na+/K+-ATPase α1 of native and tissue-engineered (TE) endothelia cultured under static (S) or hydrodynamic (H) conditions; (<b>b</b>) Western blots Na+/K+-ATPase α1 band intensity relative to β-actin, Welch’s T test, ns: not significant; (<b>c</b>) ELISA quantification of Na+/K+-ATPase α1 protein of native and tissue-engineered endothelia cultured under static or hydrodynamic conditions, normalized to total proteins. Welch’s T test ** <span class="html-italic">p</span> &lt; 0.01, ns: not significant. Results are presented as mean ± SD. <span class="html-italic">n</span> = 3, in triplicate.</p> Full article ">Figure 3
<p>Ouabain dose–response assay. (<b>a</b>) ATPase activity in the absence and in the presence of different concentrations of ouabain; (<b>b</b>) percentage of inhibition of ATPase activity by different concentrations of ouabain. <span class="html-italic">n</span> = 1, in duplicate.</p> Full article ">Figure 4
<p>Na+/K+-ATPase activity (<b>a</b>) Na+/K+-ATPase activity of native and tissue-engineered (TE) endothelia cultured under static (S) or hydrodynamic (H) conditions. Results are standardized to total DNA. Two-way ANOVA, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001; (<b>b</b>) hydrodynamic over static Na+/K+-ATPase activity fold change in native and tissue-engineered endothelia. Welch’s T test, ns: not significant. Results are presented as mean ± SD. <span class="html-italic">n</span> = 3, in triplicate.</p> Full article ">
15 pages, 3493 KiB  
Article
Ascochlorin Attenuates the Early Stage of Adipogenesis via the Wnt/β-Catenin Pathway and Inhibits High-Fat-Diet-Induced Obesity in Mice
by Mi-Hee Yu, Yun-Jeong Jeong, Sung Wook Son, So Yoon Kwon, Kwon-Ho Song, Ho-Sang Son, Eon-Ju Jeon and Young-Chae Chang
Int. J. Mol. Sci. 2024, 25(18), 10226; https://doi.org/10.3390/ijms251810226 - 23 Sep 2024
Viewed by 675
Abstract
This study investigated the effects of ascochlorin (ASC), a natural compound derived from the fungus Ascochyta viciae, on adipogenesis and obesity. We determined the effects of ASC on 3T3-L1 preadipocytes and whether it ameliorated to mitigate high-fat diet (HFD)-induced obesity in C57BL/6J mice. [...] Read more.
This study investigated the effects of ascochlorin (ASC), a natural compound derived from the fungus Ascochyta viciae, on adipogenesis and obesity. We determined the effects of ASC on 3T3-L1 preadipocytes and whether it ameliorated to mitigate high-fat diet (HFD)-induced obesity in C57BL/6J mice. We found that ASC significantly inhibited the differentiation of preadipocytes by modulating the Wnt/β-catenin signaling pathway, a key regulator of adipogenic processes. Treatment with ASC not only reduced the mRNA and protein expression of key adipogenic transcription factors such as C/EBPα and PPARγ, but also reduced lipid accumulation both in vitro and in vivo. In addition, treatment HFD-fed mice with ASC significantly reduced their weight gain and adiposity vs. control mice. These results suggest that ASC has considerable potential as a therapeutic agent for obesity, owing to its dual action of inhibiting adipocyte differentiation and reducing lipid accumulation. Thus, ASC represents a promising candidate as a natural anti-obesity agent. Full article
(This article belongs to the Special Issue Molecular Research in Obesity and Obesity Related Disorders: 2nd Edition)
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<p>Effect of various compounds on lipid accumulation, TG content, and cell viability in 3T3-L1 adipocytes. (<b>A</b>) Two-day post-confluency preadipocytes were incubated with differentiation medium in the presence of various compounds (0, 1, 10, 20, and 30 µM) for 48 h. Cell viability was detected by MTT. (<b>B</b>,<b>D</b>) Accumulation of intracellular lipid droplets in Oil Red O staining of the undifferentiated control (UC) and differentiated control (DC) or compound-treated cells on day 8 (D8) after differentiation induction. Metformin (Met 10 mM) was used as a positive control. 3T3-L1 cells were imaged using a microscope (×200). (<b>C</b>,<b>E</b>) Quantification of intracellular lipid content (O.D.<sub>510</sub>). (<b>F</b>) Measurement of intracellular triglyceride (TG) content on D8. Rosiglitazone (Rosi 10 µM) was used as a negative control. Data represent the mean ± SD of three independent experiments. *** <span class="html-italic">p</span> &lt; 0.001 compared to the DC.</p> Full article ">Figure 2
<p>Effect of ASC on lipid accumulation and lipogenic gene expression in 3T3-L1 cells. (<b>A</b>) Representative image of BODIPY-stained lipid droplets in DC- or ASC (10 µM)-treated mature 3T3-L1 adipocytes. (<b>B</b>) Densitometric analysis of each BODIPY fluorescence ratio was performed using ImageJ (version 2018). (<b>C</b>) Two-day post-confluency preadipocytes were incubated with differentiation medium in the presence of ASC (0, 1, 5, and 10 µM). Western blotting of the cells treated with a series of doses of ASC as indicated for PPARγ, C/EBPα, FASN, SREBP1, FABP4, and adiponectin. Protein expression was quantified using ImageJ software and normalized against β-actin as a loading control. (<b>D</b>) The expression of PPARγ, C/EBPα, and FASN was evaluated by qRT-PCR with specific primer pairs on D8. (<b>E</b>) The expression of KLF2, Pref1, and GATA2 was evaluated by qRT-PCR on D2. (<b>F</b>) The expression of Perillipin, FAS, HSL, and SREBP1c was evaluated by qRT-PCR on D8. The relative qRT-PCR values were corrected to expression levels and normalized with respect to the control. Data represent the mean ± SD of three independent experiments. * <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 compared to the DC.</p> Full article ">Figure 3
<p>Effect of ASC on MCE and adipogenic differentiation in 3T3-L1 cells. Cells were differentiated according to the standard protocol. (<b>A</b>) Flow cytometry analysis of cells treated with MDI and 10 µM ASC for 18 h. (<b>B</b>) Cellular extracts were isolated and analyzed for the expression of the proteins indicated by Western blotting. Protein expression was quantified using ImageJ software and normalized against β-actin as a loading control. (<b>C</b>) Two-day post-confluency preadipocytes were incubated with differentiation medium in the presence of 10 µM ASC for 48 h. 3T3-L1 cells were subjected to adipocyte differentiation and harvested on day 0, 2, 4, 6, and 8 for qRT-PCR. mRNA levels of anti-adipogenesis-associated genes (Pref1, GATA2, and KLF2). (<b>D</b>) Protein levels of pre-adipogenesis-associated genes (PPARγ, c/EBPα, FASN, and FABP4). (<b>E</b>) mRNA levels of pre-adipogenesis-associated genes (PPARγ, c/EBPα, and FAS). Data represent the mean ± SD of three independent experiments. * <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 compared to the DC.</p> Full article ">Figure 4
<p>Activation of Wnt/β-catenin signaling by ASC blocked adipogenesis. (<b>A</b>) mRNA expression levels of β-catenin in 3T3-L1 adipocytes treated with ASC (10 µM) at days 2 and 8. (<b>B</b>) The expression of β-catenin, KLF2, Pref1, and GATA2 was evaluated by qRT-PCR with specific primer pairs on D8. (<b>C</b>) Representative Western blot images and quantification of β-catenin and p-GSK3β protein levels in fully matured 3T3-L1 adipocytes. Protein expression was quantified using ImageJ software and normalized against β-actin as a loading control. (<b>D</b>) The expression of Wnt10b, Wnt16, and id2 was evaluated by qRT-PCR with specific primer pairs on D8. (<b>E</b>) 3T3-L1 cells were transfected with control siRNA or β-catenin siRNA, and then treated with ASC (10 µM) and harvested on day 8. Cells were analyzed by Western blotting for the β-catenin, PPARγ, and c/EBPα. β-actin was used as a loading control. (<b>F</b>) Representative images of 3T3-L1 cells were transfected with control siRNA or β-catenin siRNA, and then treated with ASC (10 µM) and stained with Oil Red O on day 8. (<b>G</b>) Quantification of intracellular lipid content. Data represent the mean ± SD of three independent experiments. ns: not significant, * <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 compared to the DC.</p> Full article ">Figure 5
<p>Effect of ASC on HFD-induced obesity. (<b>A</b>) Weekly change in total body weight and (<b>B</b>) representative appearance of mice fed with normal diet (ND, <span class="html-italic">n</span> = 5), high-fat diet (HFD, <span class="html-italic">n</span> = 5), ASC (5 mg/kg, <span class="html-italic">n</span> = 5), and metformin (Met, 200 mg/kg, <span class="html-italic">n</span> = 3). (<b>C</b>) Representative appearance of liver, inguinal subcutaneous (iWAT), and epididymal fat tissue (eWAT). (<b>D</b>) Changes in liver and (<b>E</b>) eWAT and (<b>F</b>) iWAT weight. (<b>G</b>–<b>K</b>) Plasma level of TC, TG, HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), and alanine aminotransferase (ALT). (<b>L</b>) Representative Western blot images and quantification of β-catenin protein levels in eWAT are shown. Protein expression was quantified using ImageJ software and normalized against β-actin as a loading control. (<b>M</b>,<b>N</b>) The expression of Wnt10b and id2 was evaluated in eWAT by qRT-PCR using specific primer pairs. (<b>O</b>) H&amp;E-stained images of liver tissue. (<b>P</b>) β-catenin expression of the liver tissues by immunohistochemistry, 20×. Data represent the mean ± SD of three independent experiments. ns: not significant, * <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 compared to the HFD.</p> Full article ">
12 pages, 971 KiB  
Review
Current Understanding of Cardiovascular Calcification in Patients with Chronic Kidney Disease
by Sijie Chen, Rining Tang and Bicheng Liu
Int. J. Mol. Sci. 2024, 25(18), 10225; https://doi.org/10.3390/ijms251810225 - 23 Sep 2024
Viewed by 888
Abstract
The burden of chronic kidney disease (CKD) is increasing, posing a serious threat to human health. Cardiovascular calcification (CVC) is one of the most common manifestations of CKD, which significantly influences the morbidity and mortality of patients. The manifestation of CVC is an [...] Read more.
The burden of chronic kidney disease (CKD) is increasing, posing a serious threat to human health. Cardiovascular calcification (CVC) is one of the most common manifestations of CKD, which significantly influences the morbidity and mortality of patients. The manifestation of CVC is an unusual accumulation of mineral substances containing calcium and phosphate. The main component is hydroxyapatite. Many cells are involved in this process, such as smooth muscle cells (SMCs) and endothelial cells. CVC is an osteogenic process initiated by complex mechanisms such as metabolic disorders of calcium and phosphorus minerals, inflammation, extracellular vesicles, autophagy, and micro-RNAs with a variety of signaling pathways like Notch, STAT, and JAK. Although drug therapy and dialysis technology continue to advance, the survival time and quality of life of CVC patients still face challenges. Therefore, early diagnosis and prevention of CKD-related CVC, reducing its mortality rate, and improving patients’ quality of life have become urgent issues in the field of public health. In this review, we try to summarize the state-of-the-art understanding of the progression of CVC and hope that it will help in the prevention and treatment of CVC in CKD. Full article
(This article belongs to the Special Issue Signaling Pathways and Novel Therapies in Heart Disease)
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<p>The mechanism diagram of CVC. P: phosphorus, PTH: parathyroidhormone, IS: Indoxylsulfate, VSMCs: vascular smooth muscle cells, miRNAs: micro-RNAs, EVs: extracellular vesicles, Runx2: runt-related transcription factor 2, BMP2: bone morphogenetic protein, TNF: tumor necrosis factor, IL: interleukin, ROS: reactive oxygen species, EndMT: endothelial-to-mesenchymal transition, STAT: transcriptional activation factor, NICD: Notch intracellular domain.</p> Full article ">
12 pages, 3820 KiB  
Article
Rapid Diagnostic PCR Assay Method for Species Identification of Mantidis Ootheca (Sangpiaoxiao) Based on Cytochrom C Oxidase I (COI) Barcode Analysis
by Sumin Noh, Wook Jin Kim, Ji-Min Cha, Goya Choi, Sungyu Yang, Jun-Ho Song and Byeong Cheol Moon
Int. J. Mol. Sci. 2024, 25(18), 10224; https://doi.org/10.3390/ijms251810224 - 23 Sep 2024
Viewed by 694
Abstract
Mantidis Ootheca (sangpiaoxiao), the egg case of the mantis, is a type of insect-derived traditional medicine widely used in East Asia. However, species identification based on egg morphology is challenging, leading to the distribution of counterfeit and adulterated products. The use of inauthentic [...] Read more.
Mantidis Ootheca (sangpiaoxiao), the egg case of the mantis, is a type of insect-derived traditional medicine widely used in East Asia. However, species identification based on egg morphology is challenging, leading to the distribution of counterfeit and adulterated products. The use of inauthentic ingredients can pose serious health risks to consumers. This study aimed to develop PCR markers that can rapidly and accurately differentiate between authentic and counterfeit Mantidis Ootheca. The mitochondrial cytochrome c oxidase I (COI) region was sequenced in thirteen samples from four mantis species: Tenodera angustipennis, Statilia maculata, Hierodula patellifera, and T. sinensis. Four sets of SCAR primers were designed based on species-specific nucleotide polymorphisms, and a multiplex SCAR assay was developed by combining all sets of the primers. The sequence-characterized amplified region (SCAR) primers successfully produced amplicons for each target species, even with low-DNA templates or templates containing DNA from multiple samples. No amplification was observed for nontarget species. This study presents a novel approach for identifying authentic Mantidis Ootheca species using DNA-based diagnostic marker assays, which enable rapid and precise species identification. The SCAR assays developed in this study will aid in maintaining quality control and promoting the standardization of commercial Mantidis Ootheca products. Full article
(This article belongs to the Special Issue Advances in Diagnostics: Applications of Nucleic Acids and Their Analogs)
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<p>Comparative sequence analysis of the COI regions of the four mantis species. Box-shaped arrows indicate the positions and directions of the four species-specific SCAR primer pairs.</p> Full article ">Figure 2
<p>(<b>A</b>) PCR amplicons produced using species-specific SCAR markers developed in this study. (<b>B</b>) Sensitivity analysis of SCAR markers using serially diluted template DNA. NTC, non-template control; *, minimum detectable DNA concentration for each SCAR marker. The SCAR marker names (TA-F/R, SM-F/R, HP-F/R, and TS-F/R) presented on the left of the gel images correspond to the primer information listed in <a href="#ijms-25-10224-t001" class="html-table">Table 1</a>, and details of the mantis DNA sample (TA, SM, HP, and TS) are listed in Table 4. Numbers on the right indicate amplicon sizes. M, 100 bp DNA ladder with band sizes as indicated.</p> Full article ">Figure 3
<p>(<b>A</b>) PCR amplicons produced using the multiplex SCAR assay developed in this study. (<b>B</b>) Discrimination capacity of multiplex SCAR assay. DNA from 2 to 4 mantis species was combined and used as template. All PCRs used all four species-specific primer pairs (TA-F/R, SM-F/R, HP-F/R, and TS-F/R), with the template DNA samples indicated above the gel image. Details of the mantis DNA samples (TA, SM, HP, and TS) are listed in Table 4. M, 100 bp DNA ladder with band sizes as indicated. Numbers on the right indicate amplicon sizes.</p> Full article ">Figure 4
<p>Analysis of commercial Mantidis Ootheca products using both species-specific and multiplex SCAR assays. The SCAR marker names (TA-F/R, SM-F/R, HP-F/R, and TS-F/R) presented on the left of the gel images correspond to the primer information listed in <a href="#ijms-25-10224-t001" class="html-table">Table 1</a>. Details of control and commercial samples are listed in Table 4 and <a href="#app1-ijms-25-10224" class="html-app">Table S2</a>, respectively. M, 100 bp DNA ladder with band sizes as indicated; #, counterfeit commercial products according to the country of distribution.</p> Full article ">
14 pages, 3482 KiB  
Article
Exosomal miRNAs Differentiate Chronic Total Occlusion from Acute Myocardial Infarction
by Ji-Hye Son, Jeong Kyu Park, Ji-Hong Bang, Dongeon Kim, Inki Moon, Min Gyu Kong, Hyun-Woo Park, Hyung-Oh Choi, Hye-Sun Seo, Yoon Haeng Cho, Hun Soo Chang and Jon Suh
Int. J. Mol. Sci. 2024, 25(18), 10223; https://doi.org/10.3390/ijms251810223 - 23 Sep 2024
Viewed by 736
Abstract
Although coronary artery occlusion can have a negative effect on the myocardium, chronic total occlusion (CTO) exhibits different clinical features from those of acute myocardial infarction (AMI). In this study, we identify the differential associations of exosomal miRNAs with CTO and AMI. Exosomes [...] Read more.
Although coronary artery occlusion can have a negative effect on the myocardium, chronic total occlusion (CTO) exhibits different clinical features from those of acute myocardial infarction (AMI). In this study, we identify the differential associations of exosomal miRNAs with CTO and AMI. Exosomes were isolated from the plasma obtained from coronary arteries of patients undergoing percutaneous coronary intervention to treat CTO (n = 29) and AMI (n = 24), followed by small RNA sequencing, target gene predictions, and functional enrichment analyses. Promising miRNA markers were validated using real-time PCR in 35 CTO, 35 AMI, and 10 normal subjects. A total of 205 miRNAs were detected in all subjects, and 20 and 12 miRNAs were upregulated and downregulated in CTO compared to AMI patients, respectively (|fold change| > 4, FDR q < 0.05). The target genes of miRNAs that were higher in CTO patients were associated with “regulation of cell cycle phase transition”, “cell growth”, and “apoptosis”. The target genes of miRNAs that were lower in CTO patients were enriched in terms such as “muscle cell differentiation”, “response to oxygen levels”, and “artery morphogenesis”. On qRT-PCR analysis, the expression levels of miR-9-5p and miR-127-3p were significantly different between CTO and AMI patients. The miRNA expression levels accurately distinguished CTO from AMI patients with 79% specificity and 97% sensitivity. The miRNA contents of plasma exosomes were significantly different between CTO and AMI patients. The miRNAs may play important roles in CTO and AMI. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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<p>Characterization of EVs from plasma. (<b>A</b>) Diameter and concentration distribution of EVs from plasma obtained from coronary artery. (<b>B</b>) Western blot analysis of the expression of the EV-markers CD9, CD63, and CD81, and a cytoplasmic marker beta-actin in EVs from plasma.</p> Full article ">Figure 2
<p>Differentially expressed miRNAs between patients with CTO and AMI. (<b>A</b>) Volcano plot shows the number and distribution of miRNAs. (<b>B</b>) Heatmap of the differentially expressed miRNAs.</p> Full article ">Figure 3
<p>Biological process of gene ontology (<b>A</b>) and KEGG pathway (<b>B</b>) enrichment of 247 genes targeted by 10 upregulated miRNAs in patients with CTO compared to AMI patients. The color intensity indicates Maximal Clique Centrality (MCC) rank.</p> Full article ">Figure 4
<p>Clusters (<b>A</b>–<b>C</b>) and key genes (<b>D</b>) of protein–protein interaction network of the 247 genes targeted by 10 upregulated miRNAs in patients with CTO.</p> Full article ">Figure 5
<p>Biological process of gene ontology (<b>A</b>) and KEGG pathway (<b>B</b>) enrichment of 219 genes targeted by three downregulated miRNAs in patients with CTO compared to AMI patients.</p> Full article ">Figure 6
<p>Clusters (<b>A</b>–<b>C</b>) and key genes (<b>D</b>) of protein–protein interaction network of the 219 genes targeted by 3 downregulated miRNAs in patients with CTO. The color intensity indicates Maximal Clique Centrality (MCC) rank.</p> Full article ">Figure 7
<p>The expression levels of miR-127-3p (<b>A</b>), miR-9-5p (<b>B</b>), and miR-21-5p (<b>C</b>) in plasma EVs analyzed using qRT-PCR and ROC curve (<b>D</b>) for miR-127-3p and miR-9-5p in discrimination between the CTO and AMI (AUC = 0.948 with 79% specificity and 97% sensitivity). Box plots represent median and quartile ranges.</p> Full article ">
24 pages, 969 KiB  
Review
Novel Insights into Diabetic Kidney Disease
by Ewelina Młynarska, Dominika Buławska, Witold Czarnik, Joanna Hajdys, Gabriela Majchrowicz, Filip Prusinowski, Magdalena Stabrawa, Jacek Rysz and Beata Franczyk
Int. J. Mol. Sci. 2024, 25(18), 10222; https://doi.org/10.3390/ijms251810222 - 23 Sep 2024
Viewed by 2464
Abstract
Diabetic kidney disease (DKD) is a major complication of diabetes mellitus (DM), affecting over one-third of type 1 and nearly half of type 2 diabetes patients. As the leading cause of end-stage renal disease (ESRD) globally, DKD develops through a complex interplay of [...] Read more.
Diabetic kidney disease (DKD) is a major complication of diabetes mellitus (DM), affecting over one-third of type 1 and nearly half of type 2 diabetes patients. As the leading cause of end-stage renal disease (ESRD) globally, DKD develops through a complex interplay of chronic hyperglycemia, oxidative stress, and inflammation. Early detection is crucial, with diagnosis based on persistent albuminuria and reduced estimated glomerular filtration rate (eGFR). Treatment strategies emphasize comprehensive management, including glycemic control, blood pressure regulation, and the use of nephroprotective agents such as angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs), sodium-glucose cotransporter-2 (SGLT2) inhibitors, and glucagon-like peptide-1 (GLP-1) receptor agonists. Ongoing research explores novel therapies targeting molecular pathways and non-coding RNAs. Preventive measures focus on rigorous control of hyperglycemia and hypertension, aiming to mitigate disease progression. Despite therapeutic advances, DKD remains a leading cause of ESRD, highlighting the need for continued research to identify new biomarkers and innovative treatments. Full article
(This article belongs to the Special Issue Advances in the Pathogenesis of Diabetic Kidney Disease: Second Edition)
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<p>Structural changes in the diabetic kidney. GBM—glomerular basement membrane; TEC—renal tubular epithelial cells; TBM—tubular basement membrane.</p> Full article ">Figure 2
<p>Mechanisms of hyperglycemia in diabetic kidney disease. VEGF—vascular endothelial growth factor; PTEN K27-polyUb—phosphatase and tensin homolog (PTEN) K27-polyubiquitinated; TGF-β—transforming growth factor β; NLRP3—NLR family pyrin domain containing 3; MAPK—mitogen-activated protein kinases; JAK/STAT—Janus kinase/signal transducers and activators of transcription; NFκB—nuclear factor kappa-light-chain-enhancer of activated B cells; NETs—neutrophil extracellular traps; EMT—epithelial–mesenchymal transition; ROS—reactive oxygen species; NO—nitric oxide; ECM—extracellular matrix; STING/PINK1—stimulator of interferon genes/PTEN-induced kinase 1; ABCA1—ATP-binding cassette sub-family A member 1; SIRT1—sirtuin 1; PPARγ—peroxisome proliferators-activated receptor γ; Nrf2—nuclear factor erythroid 2-related factor 2.</p> Full article ">
22 pages, 6569 KiB  
Article
Bioinformatics Identification and Expression Analysis of Acetyl-CoA Carboxylase Reveal Its Role in Isoflavone Accumulation during Soybean Seed Development
by Xu Wu, Zhenhong Yang, Yina Zhu, Yuhang Zhan, Yongguang Li, Weili Teng, Yingpeng Han and Xue Zhao
Int. J. Mol. Sci. 2024, 25(18), 10221; https://doi.org/10.3390/ijms251810221 - 23 Sep 2024
Cited by 1 | Viewed by 783
Abstract
Isoflavones belong to the class of flavonoid compounds, which are important secondary metabolites that play a crucial role in plant development and defense. Acetyl-CoA carboxylase (ACCase) is a biotin-dependent enzyme that catalyzes the conversion of Acetyl-CoA into Malonyl-CoA in plants. It is a [...] Read more.
Isoflavones belong to the class of flavonoid compounds, which are important secondary metabolites that play a crucial role in plant development and defense. Acetyl-CoA carboxylase (ACCase) is a biotin-dependent enzyme that catalyzes the conversion of Acetyl-CoA into Malonyl-CoA in plants. It is a key enzyme in fatty acid synthesis and also catalyzes the production of various secondary metabolites. However, information on the ACC gene family in the soybean (Glycine max L. Merr.) genome and the specific members involved in isoflavone biosynthesis is still lacking. In this study, we identified 20 ACC family genes (GmACCs) from the soybean genome and further characterized their evolutionary relationships and expression patterns. Phylogenetic analysis showed that the GmACCs could be divided into five groups, and the gene structures within the same groups were highly conserved, indicating that they had similar functions. The GmACCs were randomly distributed across 12 chromosomes, and collinearity analysis suggested that many GmACCs originated from tandem and segmental duplications, with these genes being under purifying selection. In addition, gene expression pattern analysis indicated that there was functional divergence among GmACCs in different tissues. The GmACCs reached their peak expression levels during the early or middle stages of seed development. Based on the transcriptome and isoflavone content data, a weighted gene co-expression network was constructed, and three candidate genes (Glyma.06G105900, Glyma.13G363500, and Glyma.13G057400) that may positively regulate isoflavone content were identified. These results provide valuable information for the further functional characterization and application of GmACCs in isoflavone biosynthesis in soybean. Full article
(This article belongs to the Special Issue Genetics- and Genomics-Based Crop Improvement and Breeding: 3rd Edition)
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<p>Phylogenetic analysis of <span class="html-italic">ACC</span> genes from soybean, <span class="html-italic">Arabidopsis thaliana</span>, rice, maize, and peanut. A phylogenetic tree constructed using full-length protein sequences. Different shades of color were used to distinguish different branches of Group I–VII indicating the classification of the <span class="html-italic">ACCase</span> gene family.</p> Full article ">Figure 2
<p>Conserved motifs, gene structure maps of 20 <span class="html-italic">GmACCs</span>, and <span class="html-italic">cis</span>-elements in the promoter sequences of <span class="html-italic">GmACCs</span>. (<b>A</b>) Phylogenetic tree of 20 <span class="html-italic">GmACCs</span>. (<b>B</b>) Motif composition of 20 <span class="html-italic">GmACCs</span>; the black line indicates the protein length. (<b>C</b>) Gene structures of 20 <span class="html-italic">GmACCs</span>. Black lines indicate introns, green boxes indicate UTR, and blue boxes indicate CDS. (<b>D</b>) Schematic model of 19 <span class="html-italic">cis</span>-elements in the promoter sequences of <span class="html-italic">GmACCs</span>.</p> Full article ">Figure 3
<p>Chromosomal locations and collinearity analysis of the <span class="html-italic">ACC</span> genes. (<b>A</b>) Chromosomal locations the <span class="html-italic">ACC</span> genes. The scale is in megabases (Mbs). (<b>B</b>) The intrachromosomal segmental duplication map of the <span class="html-italic">GmACCs</span>. Chromosomes 01–20 are represented by yellow rectangles. Along these rectangles, blue lines and a heatmap indicate the gene density on the chromosomes. The red lines represent the segmental duplication pairs between the <span class="html-italic">GmACCs</span> and the gray lines represent the segmental duplication pairs in the whole soybean genome. Different colors on the chromosomes represent gene density, with red indicating high-density regions and blue indicating low-density regions. (<b>C</b>) Collinearity analysis of the <span class="html-italic">ACC</span> genes between soybean and four other plant species. Red lines represent the syntenic <span class="html-italic">ACC</span> gene pairs. Gray lines indicate collinear blocks.</p> Full article ">Figure 4
<p>The expression profiles of <span class="html-italic">GmACCs</span> in soybean seeds. (<b>A</b>) The expression profilez of <span class="html-italic">GmACCs</span> during seed development. The figure illustrates the hierarchical clustering and heatmap of the dynamic expression levels of 20 <span class="html-italic">GmACCs</span> in soybean tissues. The vertical bars on the right side of the figure represent the five groups. (<b>B</b>) Relative expression of 16 <span class="html-italic">GmACCs</span> from seed development stages (R5–R7) detected by RT-qPCR. The blue line represents the low isoflavone variety, and the red line represents the high isoflavone variety. Each bar indicates the mean of three repeats. Similar results were obtained from three independent biological experiments.</p> Full article ">Figure 5
<p>Correlation between <span class="html-italic">GmACCs</span> and isoflavone accumulation. The color of lines refers to Mantel’s r for statistics of corresponding distance correlations, and Edge width of lines represents the statistical significances. The size of the circles indicates the statistical significance, while the color of the circles represents the Spearman’s correlation coefficients for pairwise comparisons of <span class="html-italic">GmACC</span> expression levels.</p> Full article ">Figure 6
<p>WGCNA reveals modules associated with isoflavone content. (<b>A</b>) Dendrogram of average network adjacency for identifying gene co-expression modules. (<b>B</b>) Analysis of the correlation between gene modules and traits.</p> Full article ">
21 pages, 1564 KiB  
Review
NMDARs in Alzheimer’s Disease: Between Synaptic and Extrasynaptic Membranes
by Sergio Escamilla, Javier Sáez-Valero and Inmaculada Cuchillo-Ibáñez
Int. J. Mol. Sci. 2024, 25(18), 10220; https://doi.org/10.3390/ijms251810220 - 23 Sep 2024
Viewed by 1236
Abstract
N-methyl-D-aspartate receptors (NMDARs) are glutamate receptors with key roles in synaptic communication and plasticity. The activation of synaptic NMDARs initiates plasticity and stimulates cell survival. In contrast, the activation of extrasynaptic NMDARs can promote cell death underlying a potential mechanism of neurodegeneration occurring [...] Read more.
N-methyl-D-aspartate receptors (NMDARs) are glutamate receptors with key roles in synaptic communication and plasticity. The activation of synaptic NMDARs initiates plasticity and stimulates cell survival. In contrast, the activation of extrasynaptic NMDARs can promote cell death underlying a potential mechanism of neurodegeneration occurring in Alzheimer’s disease (AD). The distribution of synaptic versus extrasynaptic NMDARs has emerged as an important parameter contributing to neuronal dysfunction in neurodegenerative diseases including AD. Here, we review the concept of extrasynaptic NMDARs, as this population is present in numerous neuronal cell membranes but also in the membranes of various non-neuronal cells. Previous evidence regarding the membranal distribution of synaptic versus extrasynaptic NMDRs in relation to AD mice models and in the brains of AD patients will also be reviewed. Full article
(This article belongs to the Collection Feature Papers in Molecular Neurobiology)
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<p>Classification of neuronal NMDARs as synaptic or extrasynaptic according to the technique of choice. Schematic illustration of a glutamatergic synapse, including the pre (blue)- and postsynaptic (orange) terminals. Different populations of NMDARs are represented: (1) presynaptic, (2) those located in the PSD, and (3) extrasynaptic. Synaptic NMDARs include those in the PSD and the presynaptic ones when the technique of choice is confocal microscopy, especially when the synaptic marker is a presynaptic protein, such as synaptophysin and syntaxin 1. However, when the technique is biochemical fractionation, presynaptic NMDARs will reside in the extrasynaptic fraction, and the synaptic fraction will be composed mainly of the PSD. In addition, electron microscopy allows us to distinguish pre- from postsynaptic terminals and, thus, presynaptic NMDARs and those in the PSD. Created in <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p> Full article ">Figure 2
<p>Classification of NMDARs as synaptic or extrasynaptic according to the technique of choice and cell type. The schematic table contains columns for the technique of choice, the criterion to define an NMDAR as synaptic, and which NMDAR populations will be considered as SynNMDARs or ExsynNMDARs attending to subcellular localization or cell type origin. Created in <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p> Full article ">Figure 3
<p>Model of altered levels of NMDARs in the AD brain.</p> Full article ">
16 pages, 2333 KiB  
Article
Discovery and Prediction Study of the Dominant Pharmacological Action Organ of Aconitum carmichaeli Debeaux Using Multiple Bioinformatic Analyses
by Musun Park, Eun-Hye Seo, Jin-Mu Yi and Seongwon Cha
Int. J. Mol. Sci. 2024, 25(18), 10219; https://doi.org/10.3390/ijms251810219 - 23 Sep 2024
Viewed by 789
Abstract
Herbs, such as Aconitum carmichaeli Debeaux (ACD), have long been used as therapies, but it is difficult to identify which organs of the human body are affected by the various compounds. In this study, we predicted the organ where the drug predominantly acts [...] Read more.
Herbs, such as Aconitum carmichaeli Debeaux (ACD), have long been used as therapies, but it is difficult to identify which organs of the human body are affected by the various compounds. In this study, we predicted the organ where the drug predominantly acts using bioinformatics and verified it using transcriptomics. We constructed a computer-aided brain system network (BSN) and intestinal system network (ISN). We predicted the action points of ACD using network pharmacology (NP) analysis and predicted the dockable proteins acting in the BSN and ISN using statistical-based docking analysis. The predicted results were verified using ACD-induced transcriptome analysis. The predicted results showed that both the NP and docking analyses predominantly acted on the BSN and showed better hit rates in the hub nodes. In addition, we confirmed through verification experiments that the SW1783 cell line had more than 10 times more differentially expressed genes than the HT29 cell line and that the dominant acting organ is the brain, using network dimension spanning analysis. In conclusion, we found that ACD preferentially acts in the brain rather than in the intestine, and this multi-bioinformatics-based approach is expected to be used in future studies of drug efficacy and side effects. Full article
(This article belongs to the Special Issue Molecular Mechanisms of Action of Adaptogens—in Search of Natural Methods of Restoring Homeostasis)
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Figure 1

Figure 1
<p>Action points of the organ networks derived from NP analysis. Proteins interacting with ACD were predicted by NP analysis, and the predicted proteins were projected to the BSN and ISN. The nodes of the network are the proteins that constitute the BSN and ISN, and the edges of the network are the connections with interaction scores of 700 or more in the STRING database. Green nodes represent interacting proteins in the network pharmacology analysis results, and yellow circles represent the locations of proteins that act as hubs in the network. (<b>A</b>) BSN and interacting proteins projected to BSN. (<b>B</b>) ISN and interacting proteins projected to ISN. NP, network pharmacology; ACD, <span class="html-italic">Aconitum carmichaeli</span> Debeaux; BSN, brain system network; ISN, intestinal system network.</p> Full article ">Figure 2
<p>Action points of the organ networks derived from MD analysis. Using large-scale MD analysis, proteins interacting with ACD were predicted, and the predicted proteins were projected to the BSN and ISN. The nodes of the network are the proteins that constitute the BSN and ISN, and the edges of the network are the connections with interaction scores of 700 or more in the STRING database. Orange nodes are dockable proteins that have more than 10 hits in the docking analysis results, and yellow circles indicate the positions of proteins that act as hubs in the network. (<b>A</b>) BSN and dockable proteins projected to BSN. (<b>B</b>) ISN and dockable proteins projected to ISN. MD, molecular docking; ACD, <span class="html-italic">Aconitum carmichaeli</span> Debeaux; BSN, brain system network; ISN, intestinal system network.</p> Full article ">Figure 3
<p>Results of validation experiments using transcriptomes. The number of DEGs whose expression levels were changed after treatment with ACD in the SW1783 and HT29 cell lines was identified and visualized. The water extract of ACD was administered at high doses (500 µg/mL), medium doses (100 µg/mL), and low doses (20 µg/mL), and wortmannin was administered as a positive control. The results of the number of DEGs were compared. (<b>A</b>) The number of DEGs expressed after ACD was administered to the SW1783 cell line. (<b>B</b>) The number of DEGs expressed after ACD was administered to the HT29 cell line. In (<b>A</b>,<b>B</b>), the upregulated DEGs are indicated by red lines, and the downregulated DEGs are indicated by green lines. (<b>C</b>) DEGs of SW1783 projected to the BSN. (<b>D</b>) DEGs of HT29 projected to the ISN. In (<b>C</b>,<b>D</b>), upregulated DEGs are represented by red nodes, and downregulated DEGs are represented by green nodes. DEG, differentially expressed gene; ACD, <span class="html-italic">Aconitum carmichaeli</span> Debeaux; BSN, brain system network; ISN, intestinal system network.</p> Full article ">Figure 4
<p>Results of network spanning analysis using transcriptome data. The results of the analysis performed using transcriptome data derived after administration of ACD to the SW1783 and HT29 cell lines are visualized. (<b>A</b>) Visualization of brain DP and DEG projections to BSN in SW1783 cell line. (<b>B</b>) Visualization of intestine DP and DEG projections to ISN in HT29 cell line. In (<b>A</b>,<b>B</b>), red nodes are DPs, blue nodes are DEGs, and purple nodes correspond to both. (<b>C</b>) Analysis of spanning rate using DPs, DEGs, and neighbor nodes of the integrated protein list in BSN. (<b>D</b>) Analysis of spanning rate using DPs, DEGs, and neighbor nodes of the integrated protein list in ISN. Green nodes in (<b>C</b>,<b>D</b>) include all action points, DPs, DEGs, and neighbor nodes in the network. The spanning rate described below the figure represents the ratio of action points to the total number of nodes in the network. ACD, <span class="html-italic">Aconitum carmichaeli</span> Debeaux; DP, dockable protein; DEG, differentially expressed gene; BSN, brain system network; ISN, intestinal system network.</p> Full article ">Figure 5
<p>ORA results using action points of the BSN. ORA results performed on the BSN using DPs of ACD that act on the brain proteins and ACD-induced DEGs of the SW1783 cell line. The longer the bar graph and the lighter the color, the higher the statistical significance of the term. (<b>A</b>) ORA results using the KEGG pathway gene set. (<b>B</b>) ORA results using the GOBP gene set. ORA, over-representation analysis; BSN, brain system network; DP, druggable protein; ACD, <span class="html-italic">Aconitum carmichaeli</span> Debeaux; DEG, differentially expressed gene; KEGG, Kyoto Encyclopedia of Genes and Genomes; GOBP, Gene Ontology biological process.</p> Full article ">
88 pages, 16393 KiB  
Review
Structural Features, Chemical Diversity, and Physical Properties of Microporous Sodalite-Type Materials: A Review
by Nikita V. Chukanov and Sergey M. Aksenov
Int. J. Mol. Sci. 2024, 25(18), 10218; https://doi.org/10.3390/ijms251810218 - 23 Sep 2024
Viewed by 725
Abstract
This review contains data on a wide class of microporous materials with frameworks belonging to the sodalite topological type. Various methods for the synthesis of these materials, their structural and crystal chemical features, as well as physical and chemical properties are discussed. Specific [...] Read more.
This review contains data on a wide class of microporous materials with frameworks belonging to the sodalite topological type. Various methods for the synthesis of these materials, their structural and crystal chemical features, as well as physical and chemical properties are discussed. Specific properties of sodalite-related materials make it possible to consider they as thermally stable ionic conductors, catalysts and catalyst carriers, sorbents, ion exchangers for water purification, matrices for the immobilization of radionuclides and heavy metals, hydrogen and methane storage, and stabilization of chromophores and phosphors. It has been shown that the diversity of properties of sodalite-type materials is associated with the chemical diversity of their frameworks and extra-framework components, as well as with the high elasticity of the framework. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Recent Advances in Molecular Crystal Materials)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The general view of the SOD-type topology of the framework (coordination tetrahedra are shown in yellow) with the channels (blue lines) running through the SOD-cages.</p> Full article ">Figure 2
<p>Representation of (<b>a</b>) the unit cell of ZIFs with an SOD topology and (<b>b</b>) the basic unit of ZIFs with the different functional groups [<a href="#B88-ijms-25-10218" class="html-bibr">88</a>].</p> Full article ">Figure 3
<p>(a) Coordination mode of zinc atoms of ZTIF-8; (<b>b</b>) SOD cage constructed by Zn–tetrazolate–imidazolate; (<b>c</b>) view of the 3D framework of ZTIF-8 along the (111) direction; (<b>d</b>) topology of ZTIF-8. Reprinted with permission from [<a href="#B92-ijms-25-10218" class="html-bibr">92</a>]. Copyright 2020 American Chemical Society.</p> Full article ">Figure 4
<p>Cage size for SOD structure, ZIF-8, and Zr-sod-ZMOF-1. Reprinted (adapted) with permission from [<a href="#B95-ijms-25-10218" class="html-bibr">95</a>]. Copyright 2020 American Chemical Society.</p> Full article ">Figure 5
<p>The breathing behavior and expansion magnitude of ZIF-65(Zn) is selective and responsive depending on the nature of the guest molecules [<a href="#B97-ijms-25-10218" class="html-bibr">97</a>].</p> Full article ">Figure 6
<p>The general view of the crystal structure of a rare-earth hexahydride. The <span class="html-italic">REE</span> atom is shown with gray-blue.</p> Full article ">Figure 7
<p>Correlation between Si–O–Al angles and framework densities (FD) for 30 individual aluminosilicate frameworks of SOD-type compounds crystallizing in space group <span class="html-italic">P</span>-43<span class="html-italic">n</span> (Pearson’s <span class="html-italic">R</span><sup>2</sup> is 0.95) [<a href="#B111-ijms-25-10218" class="html-bibr">111</a>].</p> Full article ">Figure 8
<p>Sodalite (<b>a</b>) unit-cell volume, (<b>b</b>) bond lengths, and (<b>c</b>) Si–O–Al bridging angle variations as a function of pressure [<a href="#B113-ijms-25-10218" class="html-bibr">113</a>]. In panel (<b>a</b>), the results of Hazen and Sharp [<a href="#B81-ijms-25-10218" class="html-bibr">81</a>] are added for a direct comparison.</p> Full article ">Figure 9
<p>Diagrams of the seismic wave velocities (Lambert equal-area upper hemisphere projections) of sodalite at 12.8 GPa, showing (<b>a</b>) the phase velocities <span class="html-italic">v</span><sub>P</sub> (km/s), (<b>b</b>) the group velocities <span class="html-italic">v</span><sub>P</sub> (km/s), (<b>c</b>) the enhancement factor <span class="html-italic">A</span>, and (<b>d</b>) the power flow angle (PF, °) [<a href="#B113-ijms-25-10218" class="html-bibr">113</a>].</p> Full article ">Figure 10
<p>The 3D AFM topography of sodalite (<b>a</b>) and PCL/sodalite nanocomposite (<b>b</b>), and the corresponding relationship between elastic modulus and displacement for sodalite (<b>c</b>) and PCL/sodalite nanocomposite (<b>d</b>) [<a href="#B115-ijms-25-10218" class="html-bibr">115</a>]. The images were acquired with sizes of 2.56 μm × 2.56 μm.</p> Full article ">Figure 11
<p>The sections of the diffraction pattern of modulated monoclinic LRM with <b>q</b>~0.43<b>c</b> in the reciprocal space by the planes <span class="html-italic">h</span> = 2, <span class="html-italic">k</span> = 0, <span class="html-italic">k</span> = 2 and <span class="html-italic">l</span> = 2 (the pictures (<b>a</b>), (<b>b</b>), (<b>c</b>) and (<b>d</b>), respectively).</p> Full article ">Figure 12
<p>The sections of the diffraction pattern of S<sub>4</sub>-bearing LRM in the reciprocal space by the planes <span class="html-italic">h</span> = 2, <span class="html-italic">h</span> = 4, <span class="html-italic">k</span> = 4, and <span class="html-italic">l</span> = 4 (the pictures (<b>a</b>), (<b>b</b>), (<b>c</b>), and (<b>d</b>), respectively). In (<b>b</b>–<b>d</b>), the reciprocal lattice is shown with black net.</p> Full article ">Figure 13
<p>A typical sodalite penetration twin.</p> Full article ">Figure 14
<p>Sodalite contact twin. Photographer: V. Heck.</p> Full article ">Figure 15
<p>Crystal structures of (<b>a</b>) reflection and (<b>b</b>) rotation twins of sodalite.</p> Full article ">Figure 16
<p>Raman spectra of (<b>a</b>) initial slyudyankaite, (<b>b</b>) slyudyankaite preheated for three days at 700 °C, over the Fe-FeS buffer, and (<b>c</b>) preheated slyudyankaite additionally annealed at 800 °C in air for one day. The inset shows the Raman spectrum of initial slyudyankaite in the range 1200–3750 cm<sup>−1</sup>.</p> Full article ">Figure 17
<p>The ESR spectra of unheated S<sub>4</sub>- and CO<sub>2</sub>-bearing haüyne and product of its thermal transformation at 800 °C (2) [<a href="#B24-ijms-25-10218" class="html-bibr">24</a>]. The dots indicate the bands of S<sub>4</sub><sup>•−</sup> and the vertical lines show the bands of S<sub>3</sub><sup>•−</sup>.</p> Full article ">Figure 18
<p>Relative amounts of various sulfur radical anions in S<sub>4</sub>- and CO<sub>2</sub>-bearing haüyne heated at different temperatures. The values of the relative amounts are normalized to the maximum number of each of the radical anions.</p> Full article ">Figure 19
<p>Spectra of diffuse absorption of: unheated S<sub>4</sub>- and CO<sub>2</sub>-bearing haüyne (1) and products of its thermal conversions at 200 °C (2), 400 °C (3), 600 °C (4), and 800 °C (5) [<a href="#B24-ijms-25-10218" class="html-bibr">24</a>].</p> Full article ">Figure 20
<p>Examples of sodalite-group minerals containing various polysulfide chromophores: (<b>a</b>) bolotinaite (S<sub>2</sub><sup>•−</sup>, yellow), (<b>b</b>) nosean without chromophores, (<b>c</b>) slyudyankaite (combination of S<sub>6</sub>, yellow, S<sub>3</sub><sup>•−</sup>, blue and <span class="html-italic">cis</span>-S<sub>4</sub>, red; the presence of <span class="html-italic">trans</span>-S<sub>4</sub>, green is not excluded), (<b>d</b>) haüyne (combination of S<sub>3</sub><sup>•−</sup>, blue and S<sub>2</sub><sup>•−</sup>, yellow), (<b>e</b>) intermediate member of the haüyne–lazurite solid–solution series (S<sub>3</sub><sup>•−</sup>, blue) and (<b>f</b>) S<sub>4</sub>-rich haüyne (combination of <span class="html-italic">cis</span>-S<sub>4</sub>, red and minor S<sub>3</sub><sup>•−</sup>, blue). The color centers were identified using a complex of spectroscopic methods [<a href="#B18-ijms-25-10218" class="html-bibr">18</a>,<a href="#B21-ijms-25-10218" class="html-bibr">21</a>,<a href="#B23-ijms-25-10218" class="html-bibr">23</a>,<a href="#B24-ijms-25-10218" class="html-bibr">24</a>,<a href="#B25-ijms-25-10218" class="html-bibr">25</a>,<a href="#B26-ijms-25-10218" class="html-bibr">26</a>].</p> Full article ">Figure 20 Cont.
<p>Examples of sodalite-group minerals containing various polysulfide chromophores: (<b>a</b>) bolotinaite (S<sub>2</sub><sup>•−</sup>, yellow), (<b>b</b>) nosean without chromophores, (<b>c</b>) slyudyankaite (combination of S<sub>6</sub>, yellow, S<sub>3</sub><sup>•−</sup>, blue and <span class="html-italic">cis</span>-S<sub>4</sub>, red; the presence of <span class="html-italic">trans</span>-S<sub>4</sub>, green is not excluded), (<b>d</b>) haüyne (combination of S<sub>3</sub><sup>•−</sup>, blue and S<sub>2</sub><sup>•−</sup>, yellow), (<b>e</b>) intermediate member of the haüyne–lazurite solid–solution series (S<sub>3</sub><sup>•−</sup>, blue) and (<b>f</b>) S<sub>4</sub>-rich haüyne (combination of <span class="html-italic">cis</span>-S<sub>4</sub>, red and minor S<sub>3</sub><sup>•−</sup>, blue). The color centers were identified using a complex of spectroscopic methods [<a href="#B18-ijms-25-10218" class="html-bibr">18</a>,<a href="#B21-ijms-25-10218" class="html-bibr">21</a>,<a href="#B23-ijms-25-10218" class="html-bibr">23</a>,<a href="#B24-ijms-25-10218" class="html-bibr">24</a>,<a href="#B25-ijms-25-10218" class="html-bibr">25</a>,<a href="#B26-ijms-25-10218" class="html-bibr">26</a>].</p> Full article ">Figure 21
<p>CIE (Commission Internationale de l’Éclairage) color space chromaticity diagram for S-bearing aluminosilicate sodalite-group minerals [<a href="#B24-ijms-25-10218" class="html-bibr">24</a>].</p> Full article ">Figure 22
<p>UV-Vis absorption spectra of differently colored sodalite-group minerals [<a href="#B27-ijms-25-10218" class="html-bibr">27</a>]: (1) lilac S<sub>4</sub>-bearing haüyne, (2) light blue (with greenish hue) SO<sub>3</sub><sup>2−</sup>-, S<sub>2</sub><sup>•−</sup>-, and S<sub>3</sub><sup>•−</sup>-bearing haüyne, with a minor amount of S<sub>4</sub> groups (3) blue S<sub>3</sub><sup>•−</sup>-bearing haüyne, (4) deep-blue S<sub>3</sub><sup>•−</sup>-rich haüyne, and (5) dark blue lazurite with 0.55 S<sub>3</sub><sup>•−</sup> groups per formula unit.</p> Full article ">Figure 23
<p>Photoluminescence spectra of the light greenish-blue haüyne sample under 405 nm excitation measured at room temperature (curve 1) and 77 K (curve 2) [<a href="#B27-ijms-25-10218" class="html-bibr">27</a>].</p> Full article ">Figure 24
<p>Absorption spectrum of tugtupite: almost colorless sample (black curve) and purple after irradiation with UV light (red curve).</p> Full article ">Figure 25
<p>Excitation spectrum of tugtupite monitored at 605 nm (curve 1) and luminescence spectrum of tugtupite monitored at 400 nm (curve 2).</p> Full article ">Figure 26
<p>Initially almost-colorless hackmanite from Sar-e Sang, Afghanistan after exposure by visible light.</p> Full article ">Figure 27
<p>Pseudomorphs of natrolite after sodalite crystals (rhombic dodecahedra) from a hydrothermally altered peralkaline pegmatite at Marchenko Peak, Khibiny alkaline complex, Kola Peninsula.</p> Full article ">Figure 28
<p>Representative IR spectra of (<b>a</b>) H<sub>2</sub>O-bearing tsaregorodtsevite, [N(CH<sub>3</sub>)<sub>4</sub>]<sub>2−x</sub>(Al<sub>2−<span class="html-italic">x</span></sub>Si<sub>10+<span class="html-italic">x</span></sub>O<sub>24</sub>)·<span class="html-italic">n</span>H<sub>2</sub>O, (<b>b</b>) H<sub>3</sub>O<sup>+</sup>-, H<sub>2</sub>O-, and CO<sub>2</sub>-bearing haüyne, (Na,K,H<sub>3</sub>O)<sub>6</sub>Ca<sub>2−<span class="html-italic">x</span></sub>(Al<sub>6</sub>Si<sub>6</sub>O<sub>24</sub>)(SO<sub>4</sub>)<sub>2−<span class="html-italic">x</span></sub>(CO<sub>2</sub>)<span class="html-italic"><sub>y</sub></span>(H<sub>2</sub>O)<sub>z</sub> (<span class="html-italic">x</span>, <span class="html-italic">y</span>, <span class="html-italic">z</span> &lt;&lt; 1), and (<b>c</b>) H<sub>3</sub>O<sup>+</sup>- and H<sub>2</sub>O-bearing tugtupite, (Na,H<sub>3</sub>O)<sub>8−<span class="html-italic">x</span></sub>(Al<sub>2</sub>Be<sub>2</sub>Si<sub>8</sub>O<sub>24</sub>)Cl<sub>2−x</sub>H<sub>2</sub>O)<sub>y</sub> (<span class="html-italic">x</span>, <span class="html-italic">y</span> &lt;&lt; 1). The spectra are offset for comparison.</p> Full article ">Figure 29
<p>Representative IR spectra of (<b>a</b>) Mn-bearing genthelvite, (Zn,Mn)<sub>8</sub>(Be<sub>6</sub>Si<sub>6</sub>O<sub>24</sub>)S<sub>2</sub>, (<b>b</b>) sodalite, Na<sub>8</sub>(Si<sub>6</sub>Al<sub>6</sub>O<sub>24</sub>)Cl<sub>2</sub>, and (<b>c</b>) bicchuite, Ca<sub>8</sub>(Al<sub>8</sub>Si<sub>4</sub>O<sub>24</sub>)(OH)<sub>8</sub>. The spectra are offset for comparison.</p> Full article ">Figure 30
<p>Representative IR spectra of (<b>a</b>) CO<sub>2</sub>-rich and H<sup>+</sup>- and HS<sup>−</sup>-bearing bolotinaite, H<sub>0.17</sub>Na<sub>5.92</sub>K<sub>0.82</sub>Ca<sub>0.10</sub>(Si<sub>6.33</sub>Al<sub>5.67</sub>O<sub>24</sub>)(SO<sub>4</sub>)<sub>0.17</sub>F<sub>0.84</sub>Cl<sub>0.16</sub>(H<sub>2</sub>O)<sub>3.36</sub>(CO<sub>2</sub>)<sub>0.38</sub> and (<b>b</b>) sodalite containing minor H<sup>+</sup> and H<sub>3</sub>O<sup>+</sup>.</p> Full article ">Figure 31
<p>IR spectrum of a CO<sub>2</sub>-rich lazurite-related mineral with the empirical formula (Na<sub>7.43</sub>K<sub>0.16</sub>Ca<sub>0.43</sub>)(Si<sub>6.17</sub>Al<sub>5.75</sub>Fe<sup>3+</sup><sub>0.08</sub>O<sub>24</sub>)(S<sup>2−</sup>,SO<sub>4</sub><sup>2−</sup>)<sub>1.21</sub>(S<sub>3</sub><sup>−</sup>)<sub>0.15</sub>Cl<sub>0.06</sub>(CO<sub>2</sub>)<sub>0.46</sub>(COS)<b><span class="html-italic"><sub>x</sub></span></b>·<span class="html-italic">n</span>H<sub>2</sub>O (<span class="html-italic">x</span> &lt;&lt; 1, <span class="html-italic">n</span>~1) [<a href="#B23-ijms-25-10218" class="html-bibr">23</a>]. The band at 2044 cm<sup>−1</sup> corresponds to a minor admixture of COS molecules.</p> Full article ">Figure 32
<p>Baseline-corrected Raman spectra of (<b>a</b>) Cl-bearing sapozhnikovite, Na<sub>8</sub>(Si<sub>6</sub>Al<sub>6</sub>O<sub>24</sub>)(HS,Cl)<sub>2</sub>, and (<b>b</b>) anhydrous nosean analogue, Na<sub>8</sub>(Al<sub>6</sub>Si<sub>6</sub>O<sub>24</sub>)(SO<sub>4</sub>).</p> Full article ">Figure 33
<p>Uncorrected Raman spectra of (<b>a</b>) S<sub>4</sub>- and S<sub>2</sub><sup>•−</sup>-rich and S<sub>3</sub><sup>•−</sup>-bearing haüyne, (Na,K)<sub>6</sub>Ca<sub>2−<span class="html-italic">x</span></sub>(Al<sub>6</sub>Si<sub>6</sub>O<sub>24</sub>)(SO<sub>4</sub><sup>2−</sup>,S<sub>4</sub>,S<sub>2</sub><sup>•−</sup>,S<sub>3</sub><sup>•−</sup>)<sub>2</sub>, and (<b>b</b>) SO<sub>3</sub><sup>2−</sup>- and S<sub>2</sub><sup>•−</sup>-bearing haüyne, (Na,K)<sub>6</sub>Ca<sub>2−<span class="html-italic">x</span></sub>(Al<sub>6</sub>Si<sub>6</sub>O<sub>24</sub>)(SO<sub>4</sub><sup>2−</sup>, SO<sub>4</sub><sup>2−</sup>,S<sub>3</sub><sup>•−</sup>,S<sub>2</sub><sup>•−</sup>)<sub>2</sub>. The luminescence is caused by the presence of the S<sub>2</sub><sup>•−</sup> radical anion.</p> Full article ">Figure 34
<p>Uncorrected Raman spectra of (<b>a</b>) CO<sub>2</sub>-rich and H<sup>+</sup>- and S<sub>2</sub><sup>•−</sup>-bearing bolotinaite and (<b>b</b>) HS<sup>−</sup>-bearing sodalite. The luminescence peaks with the maxima about 2650 and 3500 cm<sup>−1</sup> are caused by S<sub>2</sub><sup>•−</sup> and <sup>IV</sup>Fe<sup>3+</sup>, respectively.</p> Full article ">Figure 35
<p>Baseline-corrected Raman spectra of (<b>a</b>) S<sub>3</sub><sup>•−</sup>-bearing haüyne, (Na,K)<sub>6</sub>Ca<sub>2−<span class="html-italic">x</span></sub>(Al<sub>6</sub>Si<sub>6</sub>O<sub>24</sub>)(SO<sub>4</sub><sup>2−</sup>,S<sub>3</sub><sup>•−</sup>)<sub>2</sub>, and (<b>b</b>) lazurite neotype with the empirical formula (Na<sub>6.97</sub>Ca<sub>0.88</sub>K<sub>0.10</sub>)<sub>∑7.96</sub>[(Al<sub>5.96</sub>Si<sub>6.04</sub>)<sub>∑12</sub>O<sub>24</sub>](SO<sub>4</sub><sup>2−</sup>)<sub>1.09</sub>(S<sub>3</sub><sup>•−</sup>)<sub>0.55</sub>S<sup>2−</sup><sub>0.05</sub>Cl<sub>0.04</sub>·0.72H<sub>2</sub>O.</p> Full article ">Figure 36
<p>The arrangement of different sites of water molecules (red, blue and black balls) in H<sup>+</sup>-bearing bolotinaite. The site shown as the green ball may be occupied by F with minor admixtures of Cl and S (in the presence of H<sub>2</sub>O molecules at the sites shown as red balls) or by H<sup>+</sup> (in the presence of water molecules forming the tetrahedron). The unit cell is outlined.</p> Full article ">
12 pages, 861 KiB  
Article
Zinc and Ferritin Levels and Their Associations with Functional Disorders and/or Thyroid Autoimmunity: A Population-Based Case–Control Study
by Hernando Vargas-Uricoechea, Karen Urrego-Noguera, Hernando Vargas-Sierra and María Pinzón-Fernández
Int. J. Mol. Sci. 2024, 25(18), 10217; https://doi.org/10.3390/ijms251810217 - 23 Sep 2024
Viewed by 765
Abstract
Population zinc and iron status appear to be associated with an increased risk of thyroid function abnormalities and thyroid autoimmunity (AITD). In the present study, we aimed to determine whether zinc and/or iron levels (assessed by ferritin levels) were associated with the presence [...] Read more.
Population zinc and iron status appear to be associated with an increased risk of thyroid function abnormalities and thyroid autoimmunity (AITD). In the present study, we aimed to determine whether zinc and/or iron levels (assessed by ferritin levels) were associated with the presence of AITD and with alterations in thyroid function. A population-based case–control study (n = 1048) was conducted (cases: n = 524; controls: n = 524). Participants were measured for blood concentrations of zinc and ferritin, TSH, FT4, FT3, and thyroid autoantibodies. No significant differences were found in relation to ferritin levels between cases and controls. Among cases, the prevalence of low zinc levels in those with hypothyroidism (both subclinical and overt) was 49.1% [odds ratio (OR) of low zinc levels: 5.926; 95% CI: 3.756–9.351]. The prevalence of low zinc levels in participants with hyperthyroidism (both subclinical and overt) was 37.5% [OR of low zinc levels: 3.683; 95% CI: 1.628–8.33]. The zinc value that best discriminated the highest frequency of AITD was 70.4 µg/dL [sensitivity: 0.947, 1–specificity: 0.655, specificity: 0.345]. The highest frequency of AITD was calculated based on a zinc value <70 µg/dL (relative to a normal value), with this frequency being significantly higher in cases than in controls [OR: 9.3; 95% CI: 6.1–14.3 (p = 0.001)]. In conclusion, the results of our study suggest that zinc deficiency is associated with an increased frequency of functional thyroid disorders and thyroid autoimmunity. Full article
(This article belongs to the Special Issue The Role of Trace Elements in Health and Diseases)
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<p>Flow chart of the participants included in the study.</p> Full article ">Figure 2
<p>Area under the curve (AUC), which showed that the zinc value that best discriminated the risk of AITD was 70.4 µg/dL [AUC: 0.673 (95% CI: 0.640–0.706)].</p> Full article ">
16 pages, 6730 KiB  
Article
Hypoxia-Induced Adaptations of N-Glycomes and Proteomes in Breast Cancer Cells and Their Secreted Extracellular Vesicles
by Bojia Peng, Kai Bartkowiak, Feizhi Song, Paula Nissen, Hartmut Schlüter and Bente Siebels
Int. J. Mol. Sci. 2024, 25(18), 10216; https://doi.org/10.3390/ijms251810216 - 23 Sep 2024
Viewed by 941
Abstract
The hypoxic tumor microenvironment significantly impacts cellular behavior and intercellular communication, with extracellular vesicles (EVs) playing a crucial role in promoting angiogenesis, metastasis, and host immunosuppression, and presumed cancer progression and metastasis are closely associated with the aberrant surface N-glycan expression in EVs. [...] Read more.
The hypoxic tumor microenvironment significantly impacts cellular behavior and intercellular communication, with extracellular vesicles (EVs) playing a crucial role in promoting angiogenesis, metastasis, and host immunosuppression, and presumed cancer progression and metastasis are closely associated with the aberrant surface N-glycan expression in EVs. We hypothesize that hypoxic tumors synthesize specific hypoxia-induced N-glycans in response to or as a consequence of hypoxia. This study utilized nano-LC–MS/MS to integrate quantitative proteomic and N-glycomic analyses of both cells and EVs derived from the MDA-MB-231 breast cancer cell line cultured under normoxic and hypoxic conditions. Whole N-glycome and proteome profiling revealed that hypoxia has an impact on the asparagine N-linked glycosylation patterns and on the glycolysis/gluconeogenesis proteins in cells in terms of altered N-glycosylation for their adaptation to low-oxygen conditions. Distinct N-glycan types, high-mannose glycans like Man3 and Man9, were highly abundant in the hypoxic cells. On the other hand, alterations in the sialylation and fucosylation patterns were observed in the hypoxic cells. Furthermore, hypoxia-induced EVs exhibit a signature consisting of mono-antennary structures and specific N-glycans (H4N3F1S2, H3N3F1S0, and H7N4F3S2; H8N4F1S0 and H8N6F1S2), which are significantly associated with poor prognoses for breast tumors, presumably altering the interactions within the tumor microenvironment to promote tumorigenesis and metastasis. Our findings provide an overview of the N-glycan profiles, particularly under hypoxic conditions, and offer insights into the potential biomarkers for tracking tumor microenvironment dynamics and for developing precision medicine approaches in oncology. Full article
(This article belongs to the Section Molecular Oncology)
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<p>EV isolation and characterization. (<b>A</b>) Workflow for preparing EVs and conducting mass spectrometry for proteomic and N-glycomic analyses of EVs and cells. Key steps include enrichment of EVs secreted under normoxic or hypoxic conditions, isolation of peptides and N-glycans, EV characterization, and mass spectrometry analysis. (<b>B</b>) Western blot of hypoxic marker HIF1α in normoxic and hypoxic cell lysate. (<b>C</b>) Western blot of EV markers CD81, Flotillin-1, and Lamin C in normoxic and hypoxic EV lysate and cell lysate. (<b>D</b>,<b>E</b>) Size and concentration of EVs secreted under normoxia or hypoxia for MDA-MB-231 cell line, as measured by NTA. Histogram showing the calculated mean ± SD of size distribution by NTA analysis of normoxic and hypoxic EV. Red error bars indicate ± 1 standard error of the mean. Screenshots from video recorded using NanoSight LM14, showing the distribution of EVs from the cellulose-supernatant culture under normoxia or hypoxia. Three biological replicates were used for each condition.</p> Full article ">Figure 2
<p>Overview of the N-glycome profiles of EVs and cells under normoxic and hypoxic conditions of the MDA-MB-231 cell line. (<b>A</b>) The stacked bar chart of identified N-glycan compositions in cells (n = 6) and EVs (n = 6). The counts indicate the number of each monosaccharide building block present in the N-glycan structures. (<b>B</b>) A Venn diagram illustrating the number of N-glycans shared between cells and EVs. (<b>C</b>) All quantified N-glycans are plotted, depicting their log-transformed relative abundances against their rank within the dynamic range. (<b>D</b>) Distribution of common N-glycosylation features in cells and EVs under normoxic and hypoxic conditions. Significant values are marked with ns (no significant), * (<span class="html-italic">p</span> ≤ 0.05), ** (<span class="html-italic">p</span> ≤ 0.01), *** (<span class="html-italic">p</span> ≤ 0.001), or **** (<span class="html-italic">p</span> ≤ 0.0001).</p> Full article ">Figure 3
<p>Comparison of N-glycans found in cells and EVs under normoxia and hypoxia. (<b>A</b>) Scatter plot of PLS-DA analysis of the N-glycome profile of normoxia_Cell (yellow circles), hypoxia_Cell (orange circles), and normoxia_EV and hypoxia_EV (purple and cyan squares). Symbols and ellipses indicate each sample and the 95% confidence interval of the four clusters. R<sup>2</sup>Y = 0.953; Q<sup>2</sup>Y = 0.836; <span class="html-italic">p</span> = 0.05. (<b>B</b>) Volcano plots showing differentially expressed N-glycans between normoxic and hypoxic conditions in both cells and EVs (<span class="html-italic">p</span> &lt; 0.05 and fold change &gt; 1.5; <span class="html-italic">q</span>-value &lt; 0.05). The left panel presents a volcano plot of differentially expressed N-glycans between normoxic and hypoxic cells, while the right panel shows the differentially expressed N-glycans between normoxic and hypoxic EVs. The central diagram highlights the overlapping differentially expressed N-glycans identified in both conditions. (<b>C</b>) The heatmap for the hierarchical clustering of all <span class="html-italic">t</span>-test-significant N-glycans with different glycosylation traits in hypoxic vs. normoxic cells (<span class="html-italic">p</span> &lt; 0.05 and fold change &gt; 1.5; <span class="html-italic">q</span>-value &lt; 0.05). Twenty-seven altered N-glycans were classified by biosynthetic class. Each N-glycan is assigned to a Ballon plot depicting glycosylation features in the respective N-glycan. (<b>D</b>) The nine-quadrant map analysis of N-glycan expression changes regarding hypoxic vs. normoxic cells and hypoxic vs. normoxic EVs. Each dot corresponds to an N-glycan. N-glycans demonstrating a significant increase or decrease in abundance (fold change &gt; 1.5) both upon hypoxic vs. normoxic cells and hypoxic vs. normoxic EVs are shaded red. N-glycans demonstrating a significant increase or decrease in abundance (fold change &gt; 1.5) only upon hypoxic vs. normoxic cells or hypoxic vs. normoxic EVs are shaded orange and purple. N-glycans demonstrating no significant changes regarding either hypoxic vs. normoxic cells or hypoxic vs. normoxic EVs are shaded yellow.</p> Full article ">Figure 4
<p>Proteomic analysis of EVs and cells under normoxic and hypoxic conditions. (<b>A</b>) Scatter plot of PLS-DA analysis of proteome profiles of normoxia_Cell (yellow circles), hypoxia_Cell (orange circles), and normoxia_EV and hypoxia_EV (purple and cyan squares). Symbols and ellipses indicate each sample and the 95% confidence interval of the four clusters. R<sup>2</sup>Y = 0.661; Q<sup>2</sup>Y = 0.574; <span class="html-italic">p</span> = 0.05. (<b>B</b>) Venn diagram showing comparison of protein groups identified in MDA-MB-231 cell EVs by LC–MS/MS with ExoCarta, a public EV proteomics database. Overlapping parts represent shared proteins, and non-overlapping parts represent unique proteins. (<b>C</b>) The network displays enriched biological pathways identified from EV proteome using Metascape. Nodes represent enriched terms, edges denote similarities, and colors indicate cluster IDs, highlighting different functional groups. (<b>D</b>) Venn diagram shows protein groups identified in cells and EVs using LC–MS/MS procedures (n = 6 per group). (<b>E</b>) Volcano plots showing differentially expressed proteins between normoxic and hypoxic conditions in both cells and EVs (<span class="html-italic">p</span> &lt; 0.05 and fold change &gt; 1.5; <span class="html-italic">q</span>-value &lt; 0.05). The left panel presents a volcano plot of differentially expressed proteins between normoxic and hypoxic cells, while the right panel shows the differentially expressed proteins between normoxic and hypoxic EVs. The central diagram highlights the overlapping differentially expressed proteins identified in both conditions. (<b>F</b>) The top 20 entries of the Reactome pathway enriched for levels 1 and 2 by the differentially expressed proteins between normoxic and hypoxic EVs. Levels 1 and 2 indicate distinct biological themes enriched in the dataset.</p> Full article ">Figure 5
<p>The expression profile and functional enrichment of glycosylation-related proteins in normoxic and hypoxic cells. (<b>A</b>) Heatmap of the differentially expressed “Asparagine N-linked glycosylation” proteins in hypoxic cell compared to normoxic cell. (<b>B</b>) STRING network analyses of significant differentially abundant proteins involved in asparagine N-linked glycosylation. (<b>C</b>) Heatmap of the differentially expressed glycolysis/gluconeogenesis proteins in hypoxic cell compared to normoxic cell. (<b>D</b>,<b>E</b>) Correlation Circos plot of differentially expressed N-glycans in EVs and cells under normoxic and hypoxic conditions and dysregulated N-glycosylation-related proteins in cells. Pearson coefficient cut-offs set at ≥0.8 or ≤−0.8. Blue lines represent negative correlations, while orange lines represent positive correlations.</p> Full article ">
16 pages, 2437 KiB  
Article
Polymer Physics Models Reveal Structural Folding Features of Single-Molecule Gene Chromatin Conformations
by Mattia Conte, Alex Abraham, Andrea Esposito, Liyan Yang, Johan H. Gibcus, Krishna M. Parsi, Francesca Vercellone, Andrea Fontana, Florinda Di Pierno, Job Dekker and Mario Nicodemi
Int. J. Mol. Sci. 2024, 25(18), 10215; https://doi.org/10.3390/ijms251810215 - 23 Sep 2024
Viewed by 779
Abstract
Here, we employ polymer physics models of chromatin to investigate the 3D folding of a 2 Mb wide genomic region encompassing the human LTN1 gene, a crucial DNA locus involved in key cellular functions. Through extensive Molecular Dynamics simulations, we reconstruct in silico [...] Read more.
Here, we employ polymer physics models of chromatin to investigate the 3D folding of a 2 Mb wide genomic region encompassing the human LTN1 gene, a crucial DNA locus involved in key cellular functions. Through extensive Molecular Dynamics simulations, we reconstruct in silico the ensemble of single-molecule LTN1 3D structures, which we benchmark against recent in situ Hi-C 2.0 data. The model-derived single molecules are then used to predict structural folding features at the single-cell level, providing testable predictions for super-resolution microscopy experiments. Full article
(This article belongs to the Special Issue Computational Modelling and Molecular Dynamics Simulations of Biological Systems)
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<p>(<b>a</b>) In the SBS model, a chromatin region is represented by a self-avoiding polymer chain along which specific binding sites are arranged for diffusing cognate molecular binders. By bridging cognate sites on the chain, the binders drive the folding of the polymer-forming microphase-separated globular structures. The SBS binding domains of the studies <span class="html-italic">LTN1</span> locus (Chr21:28–30 Mb) in human WTC-11 cells are shown along with a schematic cartoon of the polymer model. (<b>b</b>) The polymer gyration radius <span class="html-italic">R</span><sub>g</sub> is shown here as a function of the MD time iteration steps (y-axis normalized by the <span class="html-italic">R</span><sub>g</sub> value at <span class="html-italic">t</span> = 0). The function exhibits a sharp drop around 10<sup>5</sup> time steps, signaling the collapse of the chain from an initial coil (i.e., randomly folded) to an equilibrium globule conformation [<a href="#B140-ijms-25-10215" class="html-bibr">140</a>]. A representative coil and phase-separated globule 3D structures are shown, respectively, below and above the phase transition point (gray shaded line in the figure).</p> Full article ">Figure 2
<p>The gyration radius of the polymer chain is shown here as a function of (<b>a</b>) the binder concentration, <span class="html-italic">c</span> (expressed in volume fraction), and (<b>b</b>) the bead-binder affinity (<span class="html-italic">E</span><sub>bb</sub>, in K<sub>B</sub>T units). For <span class="html-italic">c</span> ≃ 0 (i.e., no binders), or analogously for <span class="html-italic">E</span><sub>bb</sub> ≃ 0 (i.e., no bead-binder attractions), the polymer is a randomly folded chain (in the SAW universality class), as only random and fleeting contacts are established in the absence of binders [<a href="#B140-ijms-25-10215" class="html-bibr">140</a>]; as soon as the number of binders (or their energy affinity) grows above a characteristic threshold, the polymer collapses into an equilibrium globular state where specific contacts are established based on the underlying distribution of the inferred binding sites (see above). The characteristic threshold concentrations and affinities depend on model details: for the considered model parameters, they fall, respectively, around 0.1 and 1.0 K<sub>B</sub>T.</p> Full article ">Figure 3
<p>(<b>a</b>) In situ Hi-C 2.0 contact data of the studied 2 Mb wide <span class="html-italic">LTN1</span> locus in WTC-11 cells (left) are consistently captured by the SBS polymer model (right). The high Pearson and genomic distance corrected correlation values (respectively, r = 0.90 and r’ = 0.59) indicate that the model accurately captures the overall structure of <span class="html-italic">LTN1</span> pairwise interactions. (<b>b</b>) Comparison between the model and Hi-C contact probabilities at the <span class="html-italic">LTN1</span> locus in WTC-11 cells. Overall, the model consistently recapitulates the experimental profile across genomic scales (r = 0.98).</p> Full article ">Figure 4
<p>(<b>a</b>) Representative examples of model-predicted phase-separated single-molecule distance matrices of the <span class="html-italic">LTN1</span> locus. The interaction patterns broadly differ across the ensemble of polymer configurations, as the system can fold in a variety of 3D architectures [<a href="#B127-ijms-25-10215" class="html-bibr">127</a>]. (<b>b</b>) The structural heterogeneity of individual polymer structures is measured in the model by computing the r’ correlation between pairs of single-molecule distance matrices. The resulting distribution is broad (variance = 0.15) and has a non-zero average value (mean = 0.13), indicating that chromatin structures are highly variable from cell to cell yet have a residual degree of structural correlation.</p> Full article ">Figure 5
<p>(<b>a</b>) Distribution of ellipsoid semi-axis ratios, <span class="html-italic">a</span>/<span class="html-italic">c</span> (histogram in blue) and <span class="html-italic">b</span>/<span class="html-italic">c</span> (in orange), computed from the inertia tensor of single-molecule polymer structures. The black dashed line in the figure represents the expected value in the case of perfectly spherical conformations (<span class="html-italic">a</span> = <span class="html-italic">b</span> = <span class="html-italic">c</span>). Interestingly, model-predicted 3D structures of the <span class="html-italic">LTN1</span> locus appear prolate, as we find <span class="html-italic">a</span> &gt; <span class="html-italic">b</span> ≈ <span class="html-italic">c</span>. The two distributions, <span class="html-italic">a</span>/<span class="html-italic">c</span> and <span class="html-italic">b</span>/<span class="html-italic">c,</span> are statistically different from each other (two-sided Mann–Whitney <span class="html-italic">p</span>-value &lt; 0.001). (<b>b</b>) The ellipticity of model single molecules, calculated from the eigenvalues of their gyration tensor [<a href="#B159-ijms-25-10215" class="html-bibr">159</a>], exhibits a broad distribution (variance = 0.16) with an average value of 0.51, indicating a significant structural variability and a tendency towards a prolate shape.</p> Full article ">
12 pages, 2159 KiB  
Article
Genomic Landscape of Myelodysplastic/Myeloproliferative Neoplasms: A Multi-Central Study
by Fei Fei, Amar Jariwala, Sheeja Pullarkat, Eric Loo, Yan Liu, Parastou Tizro, Haris Ali, Salman Otoukesh, Idoroenyi Amanam, Andrew Artz, Feras Ally, Milhan Telatar, Ryotaro Nakamura, Guido Marcucci and Michelle Afkhami
Int. J. Mol. Sci. 2024, 25(18), 10214; https://doi.org/10.3390/ijms251810214 - 23 Sep 2024
Viewed by 875
Abstract
The accurate diagnosis and classification of myelodysplastic/myeloproliferative neoplasm (MDS/MPN) are challenging due to the overlapping pathological and molecular features of myelodysplastic syndrome (MDS) and myeloproliferative neoplasm (MPN). We investigated the genomic landscape in different MDS/MPN subtypes, including chronic myelomonocytic leukemia (CMML; n = [...] Read more.
The accurate diagnosis and classification of myelodysplastic/myeloproliferative neoplasm (MDS/MPN) are challenging due to the overlapping pathological and molecular features of myelodysplastic syndrome (MDS) and myeloproliferative neoplasm (MPN). We investigated the genomic landscape in different MDS/MPN subtypes, including chronic myelomonocytic leukemia (CMML; n = 97), atypical chronic myeloid leukemia (aCML; n = 8), MDS/MPN-unclassified (MDS/MPN-U; n = 44), and MDS/MPN with ring sideroblasts and thrombocytosis (MDS/MPN-RS-T; n = 12). Our study indicated that MDS/MPN is characterized by mutations commonly identified in myeloid neoplasms, with TET2 (52%) being the most frequently mutated gene, followed by ASXL1 (38.7%), SRSF2 (34.7%), and JAK2 (19.7%), among others. However, the distribution of recurrent mutations differs across the MDS/MPN subtypes. We confirmed that specific gene combinations correlate with specific MDS/MPN subtypes (e.g., TET2/SRSF2 in CMML, ASXL1/SETBP1 in aCML, and SF3B1/JAK2 in MDS/MPN-RS-T), with MDS/MPN-U being the most heterogeneous. Furthermore, we found that older age (≥65 years) and mutations in RUNX1 and TP53 were associated with poorer clinical outcomes in CMML (p < 0.05) by multivariate analysis. In MDS/MPN-U, CBL mutations (p < 0.05) were the sole negative prognostic factors identified in our study by multivariate analysis (p < 0.05). Overall, our study provides genetic insights into various MDS/MPN subtypes, which may aid in diagnosis and clinical decision-making for patients with MDS/MPN. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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<p>The workflow and study design of our cohort. BM, bone marrow; PB, peripheral blood. (Abbreviations: aCML, atypical myeloid leukemia; AML, acute myeloid leukemia; CMML, chronic myelomonocytic leukemia; MDS/MPN-U, myelodysplastic/myeloproliferative neoplasm-unclassified; and MDS/MPN-RS-T, myelodysplastic/myeloproliferative neoplasm with ring sideroblasts and thrombocytosis.)</p> Full article ">Figure 2
<p>Frequency of recurrent gene mutations in all myelodysplastic/myeloproliferative neoplasm (MDS/MPN) patients (n = 173).</p> Full article ">Figure 3
<p>Molecular and cytogenetic characteristics among the different MDS/MPN subtypes (n = 173). An oncoplot showing the mutated genes among the different MDS/MPN subtypes. Each column represents a patient. Thirty-one genes are grouped into eight categories based on their functions: DNA methylation, chromatin modification, RNA splicing, transcription factors, receptor kinases, cohesion, RAS pathways, and others. Green depicts the different MDS/MPN subtypes: CMML, CMML-AML, aCML, MDS/MPN-U, and MDS/MPN-RS-T. Red depicts a single gene mutation; purple depicts more than one mutation in the same gene, mainly corresponding to biallelic <span class="html-italic">TET2</span> mutations. Cytogenetic findings are divided into three groups: normal karyotype, abnormal karyotype, and complex karyotype. Myelofibrosis (MF) status is divided into five groups: MF 0, MF 1, MF 2, MF 3, and N/A. The frequency of recurrent gene mutations among the different MDS/MPN subtypes. (Abbreviations: aCML, atypical myeloid leukemia; AML, acute myeloid leukemia; CMML, chronic myelomonocytic leukemia; MDS/MPN-U, myelodysplastic/myeloproliferative neoplasm-unclassified; MDS/MPN-RS-T, myelodysplastic/myeloproliferative neoplasm with ring sideroblasts and thrombocytosis; MF, myelofibrosis; and N/A, not applicable.)</p> Full article ">Figure 4
<p>Frequency of mutations based on functional classification among different MDS/MPN subtypes. (<b>A</b>) CMML (n = 97); (<b>B</b>) CMML-AML (n = 12); (<b>C</b>) aCML (n = 8); (<b>D</b>) MDS/MPN-U (n = 44); and (<b>E</b>) MDS/MPN-RS-T (n = 12). (Abbreviations: aCML, atypical myeloid leukemia; AML, acute myeloid leukemia; CMML, chronic myelomonocytic leukemia; MDS/MPN-U, myelodysplastic/myeloproliferative neoplasm-unclassified; and MDS/MPN-RS-T, myelodysplastic/myeloproliferative neoplasm with ring sideroblasts and thrombocytosis).</p> Full article ">
16 pages, 2874 KiB  
Article
Age-Dependent Differences in Radiation-Induced DNA Damage Responses in Intestinal Stem Cells
by Guanyu Zhou, Tsutomu Shimura, Taiki Yoneima, Akiko Nagamachi, Akinori Kanai, Kazutaka Doi and Megumi Sasatani
Int. J. Mol. Sci. 2024, 25(18), 10213; https://doi.org/10.3390/ijms251810213 - 23 Sep 2024
Viewed by 940
Abstract
Age at exposure is a critical modifier of the risk of radiation-induced cancer. However, the effects of age on radiation-induced carcinogenesis remain poorly understood. In this study, we focused on tissue stem cells using Lgr5-eGFP-ires-CreERT2 mice to compare radiation-induced DNA damage responses [...] Read more.
Age at exposure is a critical modifier of the risk of radiation-induced cancer. However, the effects of age on radiation-induced carcinogenesis remain poorly understood. In this study, we focused on tissue stem cells using Lgr5-eGFP-ires-CreERT2 mice to compare radiation-induced DNA damage responses between Lgr5+ and Lgr5- intestinal stem cells. Three-dimensional immunostaining analyses demonstrated that radiation induced apoptosis and the mitotic index more efficiently in adult Lgr5- stem cells than in adult Lgr5+ stem cells but not in infants, regardless of Lgr5 expression. Supporting this evidence, rapid and transient p53 activation occurred after irradiation in adult intestinal crypts but not in infants. RNA sequencing revealed greater variability in gene expression in adult Lgr5+ stem cells than in infant Lgr5+ stem cells after irradiation. Notably, the cell cycle and DNA repair pathways were more enriched in adult stem cells than in infant stem cells after irradiation. Our findings suggest that radiation-induced DNA damage responses in mouse intestinal crypts differ between infants and adults, potentially contributing to the age-dependent susceptibility to radiation carcinogenesis. Full article
(This article belongs to the Special Issue DNA Damage and DNA Repair Pathways in Cancer Development)
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<p>Age-dependent apoptosis after radiation exposure in intestinal crypts from wild-type mice. (<b>A</b>) Representative immunofluorescence images stained with cleaved caspase-3 of intestinal crypts 2 h after irradiation. Scale bars: 100 μm. (<b>B</b>) Number of apoptotic cells per crypt (<b>left</b>) and percentage of apoptotic crypts (<b>right</b>). Data are presented as means ± <span class="html-italic">SD</span> from three mice. Experiments were performed in duplicates. A one-way ANOVA was used to evaluate differences in means among groups. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) The number of apoptotic cells per crypt was plotted as a function of cell position from the crypt base. Fisher’s exact test was used to evaluate differences in the proportion of cells undergoing apoptosis between infants and adults at different positions from each crypt. * <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 2
<p>Age-dependent apoptosis after radiation exposure in intestinal crypts from <span class="html-italic">Lgr5-eGFP-ires</span>-<span class="html-italic">Cre<sup>ERT2</sup></span> mice. (<b>A</b>) Number of cleaved caspase-3 positive cells per crypt as a function of time after irradiation. Data are from Lgr5- populations (<b>left</b>) and Lgr5+ populations (<b>right</b>) in the intestinal crypt. Data are presented as means ± <span class="html-italic">SD</span> from three mice. Experiments were performed in duplicates. A one-way ANOVA was used to evaluate differences in means among groups. * <span class="html-italic">p</span> &lt; 0.05 vs. the non-irradiated group. <sup>†</sup> <span class="html-italic">p</span> &lt; 0.05 vs. the group at the same time point after irradiation. (B) Percentage of crypts containing cleaved caspase-3 positive cells as a function of time after irradiation. Data are from Lgr5- populations (left) and Lgr5+ populations (right) in the intestinal crypt. Data are presented as means ± <span class="html-italic">SD</span> from three mice. Experiments were performed in duplicates. A one-way ANOVA was used to evaluate differences in means among groups. * <span class="html-italic">p</span> &lt; 0.05 vs. the non-irradiated group. <sup>†</sup> <span class="html-italic">p</span> &lt; 0.05 vs. the infant group at the corresponding time point after irradiation.</p> Full article ">Figure 3
<p>Age-dependent mitotic index after radiation exposure in intestinal crypts from <span class="html-italic">Lgr5-eGFP-ires</span>-<span class="html-italic">Cre<sup>ERT2</sup></span> mice. (<b>A</b>) Number of phospho-histone H3-positive cells per crypt as a function of time after irradiation. Data are from Lgr5- populations (<b>left</b>) and Lgr5+ populations (<b>right</b>) in the intestinal crypt. Data are presented as means ± <span class="html-italic">SD</span> from three mice. Experiments were performed in duplicates. A one-way ANOVA was used to evaluate differences in means among groups. * <span class="html-italic">p</span> &lt; 0.05 vs. the non-irradiated group. <sup>†</sup> <span class="html-italic">p</span> &lt; 0.05 vs. the infant group at the same time point after irradiation. (<b>B</b>) Percentage of crypts containing phospho-histone H3-positive cells as a function of time after irradiation. Data are from Lgr5- populations (<b>left</b>) and Lgr5+ populations (<b>right</b>) in the intestinal crypt. Data are presented as means ± <span class="html-italic">SD</span> from three mice. Experiments were performed in duplicates. A one-way ANOVA was used to evaluate differences in means among groups. * <span class="html-italic">p</span> &lt; 0.05 vs. the non-irradiated group. <sup>†</sup> <span class="html-italic">p</span> &lt; 0.05 vs. the infant group at the corresponding time point after irradiation.</p> Full article ">Figure 4
<p>Average p53 immunofluorescence intensity in mouse intestinal crypts as a function of time after irradiation. (<b>A</b>) Quantification of p53 intensity across the intestinal crypts of mice treated with irradiation and analyzed at the indicated time points. (<b>B</b>) Quantification of phospho-p53 intensity across the intestinal crypts of mice treated with irradiation and analyzed at the indicated time points. (<b>A</b>,<b>B</b>) Data are from infant (<b>left</b>) and adult (<b>right</b>) mice. Bold bar shows mean and 95% confidential intervals. Dots represent individual cells. Experiments were performed in duplicates.</p> Full article ">Figure 5
<p>Volcano plot of DEGs between non-irradiated and irradiated groups from infant (<b>left</b>) and adult (<b>right</b>) stem cell populations. Red dots, upregulated DEGs; blue dots, downregulated DEGs; grey dots, nonsignificant DEGs.</p> Full article ">Figure 6
<p>Heatmap of DEGs from the infant non-irradiated (Infant-C), infant irradiated (Infant-IR), adult non-irradiated (Adult-C), and adult irradiated (Adult-IR) groups. The color keys of blue (<b>low</b>), white (<b>medium</b>), and red (<b>high</b>) represent the express levels of different genes. Their expression patterns can be classified mainly into six clusters.</p> Full article ">Figure 7
<p>Bubble charts of the KEGG classifications of assembled DEGs. Top 15 enriched KEGG pathway analysis on DEGs after irradiation in infant (<b>left</b>) and adult (<b>right</b>) stem cell populations. The number of DEGs enriched in the pathway is indicated by circle size. The Rich factor is the ratio of the number of DEGs annotated in a pathway to the number of all genes annotated in this pathway. The color saturation from green to red indicates the Q value.</p> Full article ">
14 pages, 4597 KiB  
Article
Needle and Branch Trait Variation Analysis and Associated SNP Loci Mining in Larix olgensis
by Ying Cui, Jiawei Yan, Luping Jiang, Junhui Wang, Manman Huang, Xiyang Zhao and Shengqing Shi
Int. J. Mol. Sci. 2024, 25(18), 10212; https://doi.org/10.3390/ijms251810212 - 23 Sep 2024
Viewed by 627
Abstract
Needles play key roles in photosynthesis and branch growth in Larix olgensis. However, genetic variation and SNP marker mining associated with needle and branch-related traits have not been reported yet. In this study, we examined 131 samples of unrelated genotypes from L. [...] Read more.
Needles play key roles in photosynthesis and branch growth in Larix olgensis. However, genetic variation and SNP marker mining associated with needle and branch-related traits have not been reported yet. In this study, we examined 131 samples of unrelated genotypes from L. olgensis provenance trails. We investigated phenotypic data for seven needle and one branch-related traits before whole genome resequencing (WGRS) was employed to perform a genome-wide association study (GWAS). Subsequently, the results were used to screen single nucleotide polymorphism (SNP) loci that were significantly correlated with the studied traits. We identified a total of 243,090,868 SNP loci, and among them, we discovered a total of 161 SNP loci that were significantly associated with these traits using a general linear model (GLM). Based on the GWAS results, Kompetitive Allele-Specific PCR (KASP), designed based on the DNA of population samples, were used to validate the loci associated with L. olgensis phenotypes. In total, 20 KASP markers were selected from the 161 SNPs loci, and BSBM01000635.1_4693780, BSBM01000114.1_5114757, and BSBM01000114.1_5128586 were successfully amplified, were polymorphic, and were associated with the phenotypic variation. These developed KASP markers could be used for the genetic improvement of needle and branch-related traits in L. olgensis. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Phenotypic correlations among needle and branch traits. The blank squares indicate that there is no significant correlation (** <span class="html-italic">p</span> ≤ 0.01). NL: Needle length; NWC: Needle water content; NF: Needle fascicles; BBL: Biennial branch length; Chl a: Chlorophyll a; Chl b:Chlorophyll b; Chl (a+b): Chlorophyll (a+b); Car: Carotenoid.</p> Full article ">Figure 2
<p>Frequency distribution of needle and branch traits in <span class="html-italic">Larix olgensis</span>.</p> Full article ">Figure 3
<p>QQ plot results from the GWAS using the GLM model for needle and branch traits.</p> Full article ">Figure 4
<p>Manhattan map of KASP markers SNP loci. (<b>A</b>) Biennial branch length: SNP4693780. (<b>B</b>,<b>C</b>) Chlorophyll total: SNP1282296 and SNP1341609. (<b>D</b>–<b>G</b>) Carotenoid: SNP5128581, SNP 5128586, SNP5128609, SNP11554808, SNP5114757, SNP5294345, SNP5261660, SNP5117368, SNP5135633, SNP5182255, SNP5294993, SNP5117379, SNP5170545, and SNP5184295. The red frames are the KASP markers success loci.</p> Full article ">Figure 5
<p>Genotyping of 20 KASP markers (<b>A</b>–<b>T</b>) The red, blue, purple, and black dots represent 5-Carboxyfluorescein (FAM), Hexachloro fluorescein (HEX), heterozygous, and unknown alleles, respectively.</p> Full article ">Figure 6
<p>Box plot of biennial branch length labeled by KASP in <span class="html-italic">Larix. olgensis</span> population.</p> Full article ">Figure 7
<p>Box plot of carotenoid labeled by KASP in <span class="html-italic">Larix. olgensis</span> population. (<b>A</b>) Box plot of BSBM01000114.1_5114757 loci. (<b>B</b>) Box plot of BSBM01000114.1_5128586 loci.</p> Full article ">
26 pages, 2109 KiB  
Review
Bioavailability as Proof to Authorize the Clinical Testing of Neurodegenerative Drugs—Protocols and Advice for the FDA to Meet the ALS Act Vision
by Sarfaraz K. Niazi
Int. J. Mol. Sci. 2024, 25(18), 10211; https://doi.org/10.3390/ijms251810211 - 23 Sep 2024
Viewed by 819
Abstract
Although decades of intensive drug discovery efforts to treat neurodegenerative disorders (NDs) have failed, around half a million patients in more than 2000 studies continue being tested, costing over USD 100 billion, despite the conclusion that even those drugs which have been approved [...] Read more.
Although decades of intensive drug discovery efforts to treat neurodegenerative disorders (NDs) have failed, around half a million patients in more than 2000 studies continue being tested, costing over USD 100 billion, despite the conclusion that even those drugs which have been approved have no better effect than a placebo. The US Food and Drug Administration (FDA) has established multiple programs to innovate the treatment of rare diseases, particularly NDs, providing millions of USD in funding primarily by encouraging novel clinical trials to account for issues related to study sizes and adopting multi-arm studies to account for patient dropouts. Instead, the FDA should focus on the primary reason for failure: the poor bioavailability of drugs reaching the brain (generally 0.1% at most) due to the blood–brain barrier (BBB). There are several solutions to enhance entry into the brain, and the FDA must require proof of significant entry into the brain as the prerequisite to approving Investigational New Drug (IND) applications. The FDA should also rely on factors other than biomarkers to confirm efficacy, as these are rarely relevant to clinical use. This study summarizes how the drugs used to treat NDs can be made effective and how the FDA should change its guidelines for IND approval of these drugs. Full article
(This article belongs to the Section Molecular Pharmacology)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Human innate immune system [Shutterstock-licensed image, Shutterstock_2146447853].</p> Full article ">Figure 2
<p>The blood–brain barrier is the strongest barrier, protecting the brain through multiple mechanisms [Shutterstock-licensed image, shutterstock_1933594793].</p> Full article ">Figure 3
<p>Adjusted sample size per arm by effect size and number of arms. Blue: 1 arm (no control; single-arm study); orange: 2 arms; green: 3 arms; red: 4 arms; and purple: 5 arms [author-created image].</p> Full article ">Figure 4
<p>Technologies for imaging.</p> Full article ">
16 pages, 9353 KiB  
Article
Discovery of Novel Biomarkers with Extended Non-Coding RNA Interactor Networks from Genetic and Protein Biomarkers
by Gregor Jezernik, Damjan Glavač, Pavel Skok, Martina Krušič, Uroš Potočnik and Mario Gorenjak
Int. J. Mol. Sci. 2024, 25(18), 10210; https://doi.org/10.3390/ijms251810210 - 23 Sep 2024
Viewed by 670
Abstract
Curated online interaction databases and gene ontology tools have streamlined the analysis of highly complex gene/protein networks. However, understanding of disease pathogenesis has gradually shifted from a protein-based core to complex interactive networks where non-coding RNA (ncRNA) is thought to play an essential [...] Read more.
Curated online interaction databases and gene ontology tools have streamlined the analysis of highly complex gene/protein networks. However, understanding of disease pathogenesis has gradually shifted from a protein-based core to complex interactive networks where non-coding RNA (ncRNA) is thought to play an essential role. As current gene ontology is based predominantly on protein-level information, there is a growing need to analyze networks with ncRNA. In this study, we propose a gene ontology workflow integrating ncRNA using the NPInter V5.0 database. To validate the proposed workflow, we analyzed our previously published curated biomarker datasets for hidden disease susceptibility processes and pharmacogenomics. Our results show a novel involvement of melanogenesis in psoriasis response to biological drugs in general. Hyperpigmentation has been previously observed in psoriasis following treatment with currently indicated biological drugs, thus calling attention to melanogenesis research as a response biomarker in psoriasis. Moreover, our proposed workflow highlights the need to critically evaluate computed ncRNA interactions within databases and a demand for gene ontology analysis of large miRNA blocks. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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<p>The mRNA-miRNA-lncRNA network of pediatric IBD and IBD-like gene dataset. Magenta nodes represent mRNA, green nodes represent miRNA and orange nodes represent lncRNA. Wavy lines connect mRNA and miRNA, dashed lines connect miRNA and lncRNA while backwards slash lines connect mRNA and lncRNA.</p> Full article ">Figure 2
<p>The mRNA-miRNA-lncRNA network of adult complex IBD gene dataset. Magenta nodes represent mRNA, green nodes represent miRNA and orange nodes represent lncRNA. Dashed lines connect miRNA and lncRNA while backwards slash lines connect mRNA and lncRNA.</p> Full article ">Figure 3
<p>The mRNA-miRNA-lncRNA network of rheumatoid arthritis pharmagenomics dataset. Magenta nodes represent mRNA, green nodes represent miRNA and orange nodes represent lncRNA. Dashed lines connect miRNA and lncRNA while backwards slash lines connect mRNA and lncRNA.</p> Full article ">Figure 4
<p>The mRNA-miRNA-lncRNA network of psoriasis pharmagenomics dataset. Magenta nodes represent mRNA, green nodes represent miRNA and orange nodes represent lncRNA. Wavy lines connect mRNA and miRNA, dashed lines connect miRNA and lncRNA while backwards slash lines connect mRNA and lncRNA.</p> Full article ">Figure 5
<p>(<b>a</b>–<b>c</b>) A psoriasis mRNA-miRNA-lncRNA network following gene expression hypothesis. Magenta nodes represent mRNA, green nodes represent miRNA, orange nodes represent lncRNA and gray nodes represent inactive or lowly expressed RNA. Wavy lines connect mRNA and miRNA, dashed lines connect miRNA and lncRNA, backwards slash lines connect mRNA and lncRNA while thin dotted lines represent inactive connections [<a href="#B9-ijms-25-10210" class="html-bibr">9</a>].</p> Full article ">Figure 5 Cont.
<p>(<b>a</b>–<b>c</b>) A psoriasis mRNA-miRNA-lncRNA network following gene expression hypothesis. Magenta nodes represent mRNA, green nodes represent miRNA, orange nodes represent lncRNA and gray nodes represent inactive or lowly expressed RNA. Wavy lines connect mRNA and miRNA, dashed lines connect miRNA and lncRNA, backwards slash lines connect mRNA and lncRNA while thin dotted lines represent inactive connections [<a href="#B9-ijms-25-10210" class="html-bibr">9</a>].</p> Full article ">Figure 6
<p>The mRNA-miRNA-lncRNA network of the ALS gene dataset. Magenta nodes represent mRNA, green nodes represent miRNA and orange nodes represent lncRNA. Wavy lines connect mRNA and miRNA, dashed lines connect miRNA and lncRNA while backwards slash lines connect mRNA and lncRNA.</p> Full article ">Figure 7
<p>The genes governed by lncRNA interactors of the NEAT1-MALAT1 miRNA block. Magenta nodes represent mRNA, green nodes represent miRNA and orange nodes represent lncRNA. Dashed lines connect miRNA and lncRNA while backwards slash lines connect mRNA and lncRNA.</p> Full article ">
24 pages, 7372 KiB  
Article
Insights into the Detoxification of Spruce Monoterpenes by the Eurasian Spruce Bark Beetle
by Aisha Naseer, Vivek Vikram Singh, Gothandapani Sellamuthu, Jiří Synek, Kanakachari Mogilicherla, Ladislav Kokoska and Amit Roy
Int. J. Mol. Sci. 2024, 25(18), 10209; https://doi.org/10.3390/ijms251810209 - 23 Sep 2024
Viewed by 721
Abstract
Plant defence mechanisms, including physical barriers like toughened bark and chemical defences like allelochemicals, are essential for protecting them against pests. Trees allocate non-structural carbohydrates (NSCs) to produce secondary metabolites like monoterpenes, which increase during biotic stress to fend off pests like the [...] Read more.
Plant defence mechanisms, including physical barriers like toughened bark and chemical defences like allelochemicals, are essential for protecting them against pests. Trees allocate non-structural carbohydrates (NSCs) to produce secondary metabolites like monoterpenes, which increase during biotic stress to fend off pests like the Eurasian spruce bark beetle, ESBB (Ips typographus). Despite these defences, the ESBB infests Norway spruce, causing significant ecological damage by exploiting weakened trees and using pheromones for aggregation. However, the mechanism of sensing and resistance towards host allelochemicals in ESBB is poorly understood. We hypothesised that the exposure of ESBB to spruce allelochemicals, especially monoterpenes, leads to an upsurge in the important detoxification genes like P450s, GSTs, UGTs, and transporters, and at the same time, genes responsible for development must be compromised. The current study demonstrates that exposure to monoterpenes like R-limonene and sabiene effectively elevated detoxification enzyme activities. The differential gene expression (DGE) analysis revealed 294 differentially expressed (DE) detoxification genes in response to R-limonene and 426 DE detoxification genes in response to sabiene treatments, with 209 common genes between the treatments. Amongst these, genes from the cytochrome P450 family 4 and 6 genes (CP4 and CP6), esterases, glutathione S-transferases family 1 (GSTT1), UDP-glucuronosyltransferase 2B genes (UDB), and glucose synthesis-related dehydrogenases were highly upregulated. We further validated 19 genes using RT-qPCR. Additionally, we observed similar high expression levels of detoxification genes across different monoterpene treatments, including myrcene and α-pinene, suggesting a conserved detoxification mechanism in ESBB, which demands further investigation. These findings highlight the potential for molecular target-based beetle management strategies targeting these key detoxification genes. Full article
(This article belongs to the Special Issue Molecular Signalling in Multitrophic Systems Involving Arthropods)
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<p>Monoterpene bioassay (via fumigation method). The bark beetle survival distribution curve against the five tested monoterpenes was plotted against a 12 h interval for 72 h against the tested dose (<span class="html-italic">n</span> = 60 per dose per chemical). Different colours represent the dose applied in µL per 20 mL of air in the vials.</p> Full article ">Figure 2
<p>The geNorm comprehensive ranking of the least stable and most stable reference gene combination after monoterpene treatment based on their stability value plotted on the <span class="html-italic">y</span>-axis for each of the 12 genes on the <span class="html-italic">x</span>-axis. Three individual monoterpene treatments, R-limonene, sabiene, and 3-carene, were tested to select the suitable reference gene. Five different algorithms were used based on the Ct-values generated for each of the 12 housekeeping genes after RT-qPCR (<span class="html-italic">n</span> = 4), <span class="html-italic">viz.</span>, the ΔCt method, BestKeeper, RefFinder, and NormFinder. The comprehensive ranking was generated using geNorm.</p> Full article ">Figure 3
<p>Differential gene expression analysis in fumigated <span class="html-italic">I. typographus</span> for the two selected monoterpenes. (<b>A</b>) Bar graph representing the number of total genes and DEGs in R-limonene vs. control in green colour and those in sabiene vs. control in blue colour after a cut-off of probability value &gt; 0.9 and log fold change, M ± 1. (<b>B</b>) Venn diagram representing the number of common DEGs between the two comparisons. (<b>C</b>) Stacked bar graphs comparatively represent the number of expressed detoxification genes in the two treatments. Different colours represent the number of upregulated and downregulated genes in the two chemical treatments, with the individual number of transcripts of each gene family plotted on the <span class="html-italic">y</span>-axis. Number inside each bar represents number of transcripts for the corresponding comparison.</p> Full article ">Figure 4
<p>Comprehensive figure representing the distribution of 209 common detoxification genes in <span class="html-italic">I. typographus</span> between the two tested monoterpene fumigations. (<b>B</b>) Venn diagram showing the common 209 detoxification genes. (<b>A</b>) Percentage representation of the detoxification family genes among the 209 common transcripts and the GEO function distribution. (<b>C</b>) Comparison of the 209 common genes with four different available transcriptome datasets of <span class="html-italic">I. typographus</span> with different treatments: 3-carene vs. control (<span class="html-italic">unpublished data</span>); larval stage 2 vs. pupal stage [<a href="#B31-ijms-25-10209" class="html-bibr">31</a>]; callow male (CM) vs. sclerotised male (SM) [<a href="#B40-ijms-25-10209" class="html-bibr">40</a>]; and <span class="html-italic">I. typographus</span> fed on MeJA-treated bark vs. stored bark (Sellamuthu et al., <span class="html-italic">unpublished data</span>). * Non-intersecting circles (<b>C</b>) do not mean that the comparisons do not have common transcript sequences between them.</p> Full article ">Figure 5
<p>Heat map showing the categorical fold change expression of the 209 common detoxification gene families across the various comparisons used in the study. (<b>A</b>,<b>B</b>) phase I detoxification enzymes, (<b>C</b>) phase II detoxification enzymes, and (<b>D</b>) phase III detoxification enzymes. Different comparisons were formulated: (I) R-limonene vs. control, (II) sabiene vs. control, (III) 3-carene vs. control, (IV) MeJA-induced (high) vs. stored (low), (V) L2 vs. pupa, (VI) adult vs. pupa, (VII) callow male vs. sclerotised male. Blue colour represents upregulation, red colour represents downregulation, and white represents that the transcript was either absent or not differentially expressed in the respective comparison (more details in <a href="#app1-ijms-25-10209" class="html-app">Supplementary File S4</a>). Pink dots mark genes selected for RT-qPCR (refer to Table 5). Expression levels (numbers) for comparisons (I) and (II) were represented based on M value ≥ 1 with probability ≥0.9; the rest of the comparisons (III–VII) were based on logFC ± 1.</p> Full article ">Figure 6
<p>Relative fold change expression (2<sup>(−∆∆Ct)</sup> ± S.E.) of the 19 important detoxification genes in F1 beetles fumigated with R-limonene and sabiene (<span class="html-italic">n</span> = 3). F1 control (pink), F1 R-limonene (green), and F1 sabiene (blue) on the x-axis plotted against their fold change on the y-axis. An independent <span class="html-italic">t</span>-test was performed to check the statistical difference between the control and the treatment, and accordingly, <span class="html-italic">p</span>-values were generated. * represent <span class="html-italic">p</span> &lt; 0.05, ** represent <span class="html-italic">p</span> &lt; 0.01, and *** represent <span class="html-italic">p</span> &lt; 0.001. <span class="html-italic">ABCGK</span>–ABC transporter G family member 20, <span class="html-italic">IDLDH-a</span>–Ipsdienol dehydrogenase, <span class="html-italic">ST1B1</span>–Sulfotransferase 1B1, <span class="html-italic">CP6A2</span>–Cytochrome P450 6a2, <span class="html-italic">UDB17</span>–UDP-glucuronosyltransferase 2B17, <span class="html-italic">GST</span>–Glutathione S-transferase, <span class="html-italic">AK1A1</span>–Alcohol dehydrogenase, <span class="html-italic">SDR1-a</span>–Farnesol dehydrogenase, <span class="html-italic">IDLDH-b</span>–Ipsdienol dehydrogenase, <span class="html-italic">SDHA</span>–Succinate dehydrogenase, <span class="html-italic">NCPR</span>–NADPH-cytochrome P450 reductase, <span class="html-italic">DHB12</span>–17-beta-hydroxysteroid dehydrogenase 12, <span class="html-italic">PA1B2</span>–Platelet-activating factor acetylhydrolase IB subunit beta homolog, <span class="html-italic">DHGL</span>–Glucose dehydrogenase, <span class="html-italic">HYEP1-a</span>–Juvenile hormone epoxide hydrolase 1, <span class="html-italic">HYEP1-b</span>–Juvenile hormone epoxide hydrolase 1, <span class="html-italic">SDR1-b</span>–Farnesol dehydrogenase, <span class="html-italic">EST6</span>–Venom carboxylesterase-6, and <span class="html-italic">GSTT1</span>–Glutathione S-transferase 1.</p> Full article ">Figure 7
<p>Relative fold change expression (2<sup>(−∆∆Ct)</sup> ± S.E.) of the selected 9 important detoxification genes in F0 beetles fumigated with myrcene (dark green), α-pinene (green), R-limonene (blue), and sabiene (orange) at LC<sub>70</sub>-48 h (<span class="html-italic">n</span> = 4); and F0 beetles treated with a double dose of LC<sub>70</sub> of R-limonene (yellow) and sabiene (brown) each (<span class="html-italic">n</span> = 5) for 48 h on the <span class="html-italic">x</span>-axis plotted against their fold change on the <span class="html-italic">y</span>-axis. An independent <span class="html-italic">t</span>-test was performed to check the statistical difference between the control and the treatment, and accordingly, <span class="html-italic">p</span>-values were generated. * represent <span class="html-italic">p</span> &lt; 0.05, ** represent <span class="html-italic">p</span> &lt; 0.01, and *** represent <span class="html-italic">p</span> &lt; 0.001. <span class="html-italic">ABCGK</span>–ABC transporter G family member 20, <span class="html-italic">IDLDH-a</span>–Ipsdienol dehydrogenase, <span class="html-italic">ST1B1</span>–Sulfotransferase 1B1, <span class="html-italic">CP6A2</span>–Cytochrome P450 6a2, <span class="html-italic">UDB17</span>–UDP-glucuronosyltransferase 2B17, <span class="html-italic">GST</span>–Glutathione S-transferase, <span class="html-italic">AK1A1</span>–Alcohol dehydrogenase, <span class="html-italic">SDR1-a</span>–Farnesol dehydrogenase, <span class="html-italic">IDLDH-b</span>–Ipsdienol dehydrogenase, <span class="html-italic">SDHA</span>–Succinate dehydrogenase, <span class="html-italic">NCPR</span>–NADPH-cytochrome P450 reductase, <span class="html-italic">DHB12</span>–17-beta-hydroxysteroid dehydrogenase 12, <span class="html-italic">PA1B2</span>–Platelet-activating factor acetylhydrolase IB subunit beta homolog, <span class="html-italic">DHGL</span>–Glucose dehydrogenase, <span class="html-italic">HYEP1-a</span>–Juvenile hormone epoxide hydrolase 1, <span class="html-italic">HYEP1-b</span>–Juvenile hormone epoxide hydrolase 1, <span class="html-italic">SDR1-b</span>–Farnesol dehydrogenase, <span class="html-italic">EST6</span>–Venom carboxylesterase-6, and <span class="html-italic">GSTT1</span>–Glutathione S-transferase 1.</p> Full article ">Figure 8
<p>The activities of esterase (EST), cytochrome P450 reductase (CPR), and glutathione S-transferase (GST) measured on (<b>A</b>) F1 population of <span class="html-italic">I. typographus</span> fumigated with R-limonene and sabiene and (<b>B</b>) wild beetle (F0) population fumigated with R-limonene, sabiene, and additionally with myrcene and α-pinene (<span class="html-italic">n</span> = 3). An independent <span class="html-italic">t</span>-test was performed to check the statistical difference between the control and the treatment, and accordingly, <span class="html-italic">p</span>-values were generated. Esterase enzyme activity is expressed as nmol/mg, and that of CPR and GST is expressed as nmol/min/mg. * represent <span class="html-italic">p</span> &lt; 0.05, and ** represent <span class="html-italic">p</span> &lt; 0.01. F0–wild beetles, F1–first lab-reared generation, Con–control, Lim-(R)-limonene, Sab–sabiene, Myr–myrcene, and α-Pin–α-pinene.</p> Full article ">Figure 9
<p>Summary of the key findings in the RNA-seq analysis and RT-qPCR validation of important genes. Monoterpenes (M) are applied to filter paper, and beetles are fumigated for 48 h. M passes through phase I and produces a reactive intermediate (M-OH) after oxidation, reduction, or hydroxylation of the lipophilic compound (M), or it passes directly to phase II. M-OH get conjugated with reduced glutathione or UDP-glucose in phase II to produce a hydrophilic compound (M-R). This hydrophilic compound is transported through the cell membrane into body fluid and then excreted out of the insect body in phase III. Key detoxification genes reported to be upregulated in this study in each phase are denoted by green up-head arrows. Simultaneously, some development-related genes were downregulated, denoted by red down-head arrows. <span class="html-italic">EST</span>–esterases, <span class="html-italic">HYEP1</span>–juvenile hormone epoxide 1, <span class="html-italic">CP6A2</span>–Cytochrome P450 6a2, <span class="html-italic">CP4C1</span>–Cytochrome P450 4c1, <span class="html-italic">NCPR</span>–NADPH-cytochrome P450 reductase, <span class="html-italic">GSTT1</span>–Glutathione S-transferase 1, <span class="html-italic">ST1B1</span>–Sulfotransferase 1B1, <span class="html-italic">UDB17</span>–UDP-glucuronosyltransferase 2B17, <span class="html-italic">ABC</span>–ABC transporter family members, <span class="html-italic">MDP</span>–Multidrug resistance-associated protein.</p> Full article ">
27 pages, 2288 KiB  
Review
Role of Gut Microbial Metabolites in Cardiovascular Diseases—Current Insights and the Road Ahead
by Sayantap Datta, Sindhura Pasham, Sriram Inavolu, Krishna M. Boini and Saisudha Koka
Int. J. Mol. Sci. 2024, 25(18), 10208; https://doi.org/10.3390/ijms251810208 - 23 Sep 2024
Cited by 1 | Viewed by 1612
Abstract
Cardiovascular diseases (CVDs) are the leading cause of premature morbidity and mortality globally. The identification of novel risk factors contributing to CVD onset and progression has enabled an improved understanding of CVD pathophysiology. In addition to the conventional risk factors like high blood [...] Read more.
Cardiovascular diseases (CVDs) are the leading cause of premature morbidity and mortality globally. The identification of novel risk factors contributing to CVD onset and progression has enabled an improved understanding of CVD pathophysiology. In addition to the conventional risk factors like high blood pressure, diabetes, obesity and smoking, the role of gut microbiome and intestinal microbe-derived metabolites in maintaining cardiovascular health has gained recent attention in the field of CVD pathophysiology. The human gastrointestinal tract caters to a highly diverse spectrum of microbes recognized as the gut microbiota, which are central to several physiologically significant cascades such as metabolism, nutrient absorption, and energy balance. The manipulation of the gut microbial subtleties potentially contributes to CVD, inflammation, neurodegeneration, obesity, and diabetic onset. The existing paradigm of studies suggests that the disruption of the gut microbial dynamics contributes towards CVD incidence. However, the exact mechanistic understanding of such a correlation from a signaling perspective remains elusive. This review has focused upon an in-depth characterization of gut microbial metabolites and their role in varied pathophysiological conditions, and highlights the potential molecular and signaling mechanisms governing the gut microbial metabolites in CVDs. In addition, it summarizes the existing courses of therapy in modulating the gut microbiome and its metabolites, limitations and scientific gaps in our current understanding, as well as future directions of studies involving the modulation of the gut microbiome and its metabolites, which can be undertaken to develop CVD-associated treatment options. Clarity in the understanding of the molecular interaction(s) and associations governing the gut microbiome and CVD shall potentially enable the development of novel druggable targets to ameliorate CVD in the years to come. Full article
(This article belongs to the Special Issue Endothelial Dysfunction and Cardiovascular Diseases)
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<p>A schematic representation of the protective role of BAs in hypertriglyceridemia. The BA-associated activation of the Farnesoid X receptor and Pregnane X receptor attenuates pro-inflammatory NF-κB signaling and downregulates triglyceride levels.</p> Full article ">Figure 2
<p>Schematic representation of TMAO generation and associated pathophysiological correlations. High fat dietary intake leads to trimethylamine (TMA) generation upon gut microbial breakdown. TMA undergoes oxidization via hepatic flavin monooxygenase-3 (FMO3) and produces TMAO. This TMAO correlates with varied pathophysiological onset viz atherosclerosis, heart failure and platelet hyper-reactivity.</p> Full article ">Figure 3
<p>Schematic representation of SCFA-mediated cardioprotective signaling influence. SCFA-mediated GPR41 activation deactivates adenylyl cyclase and attenuates cAMP release, ameliorating blood pressure anomalies. SCFAs also augment regulatory T cell (Treg)-associated anti-inflammatory IL-10 signaling and attenuate cardiovascular injury.</p> Full article ">Figure 4
<p>Schematic representation of TMAO-driven CVD risk. TMAO increases NLRP3 inflammasome activity, mitochondrial ROS levels and NADPH oxidase activity and culminates in CVD onset viz atherosclerosis and deep vein thrombosis.</p> Full article ">
15 pages, 2800 KiB  
Article
bZIP Transcription Factor PavbZIP6 Regulates Anthocyanin Accumulation by Increasing Abscisic Acid in Sweet Cherry
by Shilin Gai, Bingyang Du, Yuqin Xiao, Xiang Zhang, Maihemuti Turupu, Qisheng Yao, Xinyu Wang, Yongzhen Yan and Tianhong Li
Int. J. Mol. Sci. 2024, 25(18), 10207; https://doi.org/10.3390/ijms251810207 - 23 Sep 2024
Viewed by 676
Abstract
Basic leucine zipper (bZIP) transcription factors (TFs) play a crucial role in anthocyanin accumulation in plants. In addition to bZIP TFs, abscisic acid (ABA) increases anthocyanin biosynthesis. Therefore, this study aimed to investigate whether bZIP TFs are involved in ABA-induced anthocyanin accumulation in [...] Read more.
Basic leucine zipper (bZIP) transcription factors (TFs) play a crucial role in anthocyanin accumulation in plants. In addition to bZIP TFs, abscisic acid (ABA) increases anthocyanin biosynthesis. Therefore, this study aimed to investigate whether bZIP TFs are involved in ABA-induced anthocyanin accumulation in sweet cherry and elucidate the underlying molecular mechanisms. Specifically, the BLAST method was used to identify bZIP genes in sweet cherry. Additionally, we examined the expression of ABA- and anthocyanin-related genes in sweet cherry following the overexpression or knockdown of a bZIP candidate gene. In total, we identified 54 bZIP-encoding genes in the sweet cherry genome. Basic leucine zipper 6 (bZIP6) showed significantly increased expression, along with increased anthocyanin accumulation in sweet cherry. Additionally, yeast one-hybrid and dual-luciferase assays indicated that PavbZIP6 enhanced the expression of anthocyanin biosynthetic genes (PavDFR, PavANS, and PavUFGT), thereby increasing anthocyanin accumulation. Moreover, PavbZIP6 interacted directly with the PavBBX6 promoter, thereby regulating PavNCED1 to promote abscisic acid (ABA) synthesis and enhance anthocyanin accumulation in sweet cherry fruit. Conclusively, this study reveals a novel mechanism by which PavbZIP6 mediates anthocyanin biosynthesis in response to ABA and contributes to our understanding of the mechanism of bZIP genes in the regulation of anthocyanin biosynthesis in sweet cherry. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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<p>Phylogenetic analysis of PavbZIP6 protein in sweet cherry (<span class="html-italic">Prunus avium</span>). The protein indicated by the marked red dot is PavbZIP6.</p> Full article ">Figure 2
<p>Analysis of anthocyanin content and bZIP gene family expression in sweet cherry fruits at different developmental stages. (<b>A</b>) Phenotypes of sweet cherry ‘Tieton’ fruit at different developmental stages. Scale bar: 1 cm. (<b>B</b>) Anthocyanin content of sweet cherry during three stages of fruit development: big green (BG); yellow white (YW); and full red (FR). Data are expressed as means ± standard deviation (SD) of three measurements from at least 10 sweet cherry fruits, and significant differences were assessed (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01). (<b>C</b>) Expression analysis of the <span class="html-italic">PavbZIP</span> gene family in sweet cherry ‘Tieton’ fruits at different developmental periods.</p> Full article ">Figure 3
<p>Subcellular localisation and self-activation activity analysis of PavbZIP6. (<b>A</b>) Subcellular localisation analysis of PavbZIP6 protein; Scale bar = 50 μm. Green represents GFP and the fusion protein of PavbZIP6 with GFP. Yellow indicates the color resulting from the combination of nuclear-localized <span class="html-italic">mCherry</span> and GFP fluorescence. (<b>B</b>) Analysis of the self-activating activity of the PavbZIP6 protein.</p> Full article ">Figure 4
<p>Generation of <span class="html-italic">PavbZIP6</span> overexpression and knockdown fruits. (<b>A</b>) Phenotypic analysis of sweet cherry following transient overexpression and silencing of <span class="html-italic">PavbZIP6</span>; scale bar: 1 cm. (<b>B</b>) Expression analysis of <span class="html-italic">PavbZIP6</span> in transgenic fruits. (<b>C</b>) Determination of anthocyanin content in sweet cherry fruit following <span class="html-italic">PavbZIP6</span> overexpression and silencing. (<b>D</b>) Expression analysis of genes related to anthocyanin synthesis in fruits overexpressing <span class="html-italic">PavbZIP6</span>. (<b>E</b>) Expression analysis of genes related to anthocyanin synthesis in fruits following <span class="html-italic">PavbZIP6</span> silencing. Data are expressed as means ± standard deviation (SD) of three measurements from at least 10 sweet cherry fruits, and significant differences were assessed (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 5
<p>PavbZIP6 regulates the expression of <span class="html-italic">PavDFR</span>, <span class="html-italic">PavANS</span> and <span class="html-italic">PavUFGT.</span> (<b>A</b>) Yeast one-hybrid assay to verify that PavbZIP6 binds the promoter of the anthocyanin synthesis gene. (<b>B</b>) Transient LUC imaging assays showing that PavbZIP6 activate the transcription of the report gene. Representative images of LUC activity in N. benthamiana leaves 48 h after infiltration. (<b>C</b>) Dual-luciferase assay showing promoter activity expressed as the ratio of luciferase (LUC) to <span class="html-italic">35S::</span>Renilla (REN). Data are expressed as means ± standard deviation (SD; <span class="html-italic">n</span> = 3), and significant differences were assessed (** <span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 6
<p>PavbZIP6 regulates the expression of PavBBX6 and the accumulation of abscisic acid (ABA). (<b>A</b>) mRNA expression of <span class="html-italic">PavBBX6/9</span> and <span class="html-italic">PavHY5</span> in transgenic fruits. (<b>B</b>) Yeast one-hybrid assay to verify the binding of <span class="html-italic">PavbZIP6</span> to the promoter of <span class="html-italic">PavBBX6/9</span> and <span class="html-italic">PavHY5.</span> (<b>C</b>) Transient LUC imaging assays showing that PavbZIP6 activate the transcription of the report gene. Representative images of LUC activity in <span class="html-italic">N. benthamiana</span> leaves 48 h after infiltration. (<b>D</b>) Dual-luciferase assay showing promoter activity expressed as the ratio of luciferase (LUC) to <span class="html-italic">35S::</span>Renilla (REN). (<b>E</b>) ABA contents in transgenic sweet cherry fruit and control. (<b>F</b>) mRNA expression of <span class="html-italic">PavNCED1</span>, <span class="html-italic">PavNCED2</span>, and <span class="html-italic">PavNCED3</span> in transgenic fruits. Data are expressed as means ± standard deviation (SD; <span class="html-italic">n</span> = 3), and significant differences were assessed (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, n.s.: no signifcant difference).</p> Full article ">
15 pages, 2300 KiB  
Article
Vitexin Mitigates Haloperidol-Induced Orofacial Dyskinesia in Rats through Activation of the Nrf2 Pathway
by Shu-Mei Chen, Mao-Hsien Wang, Kuo-Chi Chang, Chih-Hsiang Fang, Yi-Wen Lin and Hsiang-Chien Tseng
Int. J. Mol. Sci. 2024, 25(18), 10206; https://doi.org/10.3390/ijms251810206 - 23 Sep 2024
Viewed by 609
Abstract
Vitexin (VTX), a C-glycosylated flavone found in various medicinal herbs, is known for its antioxidant, anti-inflammatory, and neuroprotective properties. This study investigated the protective effects of VTX against orofacial dyskinesia (OD) in rats, induced by haloperidol (HPD), along with the neuroprotective mechanisms underlying [...] Read more.
Vitexin (VTX), a C-glycosylated flavone found in various medicinal herbs, is known for its antioxidant, anti-inflammatory, and neuroprotective properties. This study investigated the protective effects of VTX against orofacial dyskinesia (OD) in rats, induced by haloperidol (HPD), along with the neuroprotective mechanisms underlying these effects. OD was induced by administering HPD (1 mg/kg i.p.) to rats for 21 days, which led to an increase in the frequency of vacuous chewing movements (VCMs) and tongue protrusion (TP). VTX (10 and 30 mg/kg) was given intraperitoneally 60 min after each HPD injection during the same period. On the 21st day, following assessments of OD, the rats were sacrificed, and nitrosative and oxidative stress, antioxidant capacity, mitochondrial function, neuroinflammation, and apoptosis markers in the striatum were measured. HPD effectively induced OD, while VTX significantly reduced HPD-induced OD, decreased oxidative stress, enhanced antioxidant capacity, prevented mitochondrial dysfunction, and reduced neuroinflammatory and apoptotic markers in the striatum, and the protective effects of VTX on both behavioral and biochemical aspects of HPD-induced OD were significantly reduced when trigonelline (TGN), an inhibitor of the nuclear factor erythroid-2-related factor 2 (Nrf2)-mediated pathway, was administered. These findings suggest that VTX provides neuroprotection against HPD-induced OD, potentially through the Nrf2 pathway, indicating its potential as a therapeutic candidate for the prevention or treatment of tardive dyskinesia (TD) in clinical settings. However, further detailed research is required to confirm these preclinical findings and fully elucidate VTX’s therapeutic potential in human studies. Full article
(This article belongs to the Special Issue The Impact of Natural Bioactive Compounds on Human Health and Disease)
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<p>Impact of VTX on behaviors associated with HPD-induced orofacial dyskinesia (OD). (<b>a</b>) Vacuous chewing movements (VCMs) and (<b>b</b>) tongue protrusions (TP) observed in rats over days 1 to 21. Data are shown as mean ± SEM (<span class="html-italic">n</span> = 8). Data were analyzed by using two-way ANOVA with Tukey’s post hoc test: a corresponds to <span class="html-italic">p</span> &lt; 0.001 vs. C; b corresponds to <span class="html-italic">p</span> &lt; 0.001 vs. H.</p> Full article ">Figure 2
<p>Impact of VTX on nitrosative and oxidative stress in the striatum induced by HPD was assessed by measuring (<b>a</b>) nitrite levels and (<b>b</b>) thiobarbituric acid reactive substances (TBARSs) in rats. Data are presented as mean ± SEM (<span class="html-italic">n</span> = 8). Data were analyzed by using one-way ANOVA with Tukey’s post hoc test to determine differences between groups. *** <span class="html-italic">p</span> &lt; 0.001 vs. C; ### <span class="html-italic">p</span> &lt; 0.001 vs. H.</p> Full article ">Figure 3
<p>Impact of VTX on the reduction in striatal antioxidative capacity induced by HPD. (<b>a</b>) Glutathione (GSH), (<b>b</b>) superoxide dismutase (SOD), and (<b>c</b>) catalase (CAT) in rats. Data are shown as mean ± SEM (<span class="html-italic">n</span> = 8). Statistical analysis was performed using one-way ANOVA with Tukey’s post hoc test: *** <span class="html-italic">p</span> &lt; 0.001 vs. C; ### <span class="html-italic">p</span> &lt; 0.001 vs. H.</p> Full article ">Figure 4
<p>The impact of VTX on HPD-induced mitochondrial dysfunction in the striatum, covering (<b>a</b>) succinate dehydrogenase (SDH), (<b>b</b>) total ATPase, (<b>c</b>) NADH–cytochrome c reductase (complexes I–III), and (<b>d</b>) succinate–cytochrome c reductase (complexes II–III). The data are expressed as mean ± SEM (<span class="html-italic">n</span> = 8). Data were analyzed by using one-way ANOVA followed by Tukey’s post hoc test: *** <span class="html-italic">p</span> &lt; 0.001 vs. C; ### <span class="html-italic">p</span> &lt; 0.001 vs. H.</p> Full article ">Figure 5
<p>The VTX effects on HPD-induced increases in striatal neuroinflammation and apoptotic markers. (<b>a</b>) TNF-α, (<b>b</b>) IL-1β, (<b>c</b>) IL-6, and (<b>d</b>) caspase-3. The data are expressed as mean ± SEM (<span class="html-italic">n</span> = 8). Post hoc Tukey’s test after one-way ANOVA. *** <span class="html-italic">p</span> &lt; 0.001 vs. C; ### <span class="html-italic">p</span> &lt; 0.001 vs. H.</p> Full article ">Figure 6
<p>Experimental design and drug treatment paradigm.</p> Full article ">
19 pages, 2334 KiB  
Article
Accumulation of Cerebrospinal Fluid, Ventricular Enlargement, and Cerebral Folate Metabolic Errors Unify a Diverse Group of Neuropsychiatric Conditions Affecting Adult Neocortical Functions
by Lena Ikeda, Adrià Vilaseca Capel, Dhruti Doddaballapur and Jaleel Miyan
Int. J. Mol. Sci. 2024, 25(18), 10205; https://doi.org/10.3390/ijms251810205 - 23 Sep 2024
Viewed by 1000
Abstract
Cerebrospinal fluid (CSF) is a fluid critical to brain development, function, and health. It is actively secreted by the choroid plexus, and it emanates from brain tissue due to osmolar exchange and the constant contribution of brain metabolism and astroglial fluid output to [...] Read more.
Cerebrospinal fluid (CSF) is a fluid critical to brain development, function, and health. It is actively secreted by the choroid plexus, and it emanates from brain tissue due to osmolar exchange and the constant contribution of brain metabolism and astroglial fluid output to interstitial fluid into the ventricles of the brain. CSF acts as a growth medium for the developing cerebral cortex and a source of nutrients and signalling throughout life. Together with perivascular glymphatic and interstitial fluid movement through the brain and into CSF, it also acts to remove toxins and maintain metabolic balance. In this study, we focused on cerebral folate status, measuring CSF concentrations of folate receptor alpha (FOLR1); aldehyde dehydrogenase 1L1, also known as 10-formyl tetrahydrofolate dehydrogenase (ALDH1L1 and FDH); and total folate. These demonstrate the transport of folate from blood across the blood–CSF barrier and into CSF (FOLR1 + folate), and the transport of folate through the primary FDH pathway from CSF into brain FDH + ve astrocytes. Based on our hypothesis that CSF flow, drainage issues, or osmotic forces, resulting in fluid accumulation, would have an associated cerebral folate imbalance, we investigated folate status in CSF from neurological conditions that have a severity association with enlarged ventricles. We found that all the conditions we examined had a folate imbalance, but these folate imbalances were not all the same. Given that folate is essential for key cellular processes, including DNA/RNA synthesis, methylation, nitric oxide, and neurotransmitter synthesis, we conclude that ageing or some form of trauma in life can lead to CSF accumulation and ventricular enlargement and result in a specific folate imbalance/deficiency associated with the specific neurological condition. We believe that addressing cerebral folate imbalance may therefore alleviate many of the underlying deficits and symptoms in these conditions. Full article
(This article belongs to the Special Issue Multiplicity of Cerebrospinal Fluid Functions in Health and Disease)
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<p>Total protein content of CSF from different neurological conditions. Bars show mean with standard error bar. The proteins values are absolute values, mg/mL, of CSF. Most values are not significantly different from control values. MS, live dementia, IIH, and NPH are all significantly reduced. BP and SCZ are also reduced but not significantly with the number of samples in the study. Data were normalised using the general control sample (non-demented control, 1994-076) added to every Western blot and dot blot. This control sample was set as 1, and measurements from other CSF samples were adjusted according to their intensity relative to the control, including all controls used. * indicates significance at <span class="html-italic">p</span> ≤ 0.05, **** indicates significance at <span class="html-italic">p</span> ≤ 0.0001.</p> Full article ">Figure 2
<p>Relative (to the control) concentration of folate in CSF from the different neurological conditions. Epilepsy is not different from control, but PDC, PD, BP, and SCZ all show a non-significant decrease, while MS, AD, live dementia, IIH, and NPH all show significantly reduced folate levels. TBI is unique in showing a significantly increased folate concentration in CSF. Data were normalised using the general control sample (non-demented control, 1994-076) added to every Western blot and dot blot. This control sample was set as 1, and measurements from other CSF samples were adjusted according to their intensity relative to the control, including all controls. * indicates significance at <span class="html-italic">p</span> ≤ 0.05, ** indicates significance at <span class="html-italic">p</span> ≤ 0.01, *** indicates significance at <span class="html-italic">p</span> ≤ 0.001.</p> Full article ">Figure 3
<p>Relative (to the control) concentration of folate receptor alpha (FOLR1) in CSF from the different neurological conditions. All conditions show significantly reduced FOLR1 levels with barely detectable levels in the LP CSF of live dementia, IIH, and NPH. Bars are means plus SEM. Data were normalised using the general control sample (non-demented control, 1994-076) added to every Western blot and dot blot. This control sample was set as 1, and measurements from other CSF samples were adjusted according to their intensity relative to the control, including all controls used. * indicates significance at <span class="html-italic">p</span> ≤ 0.05, *** indicates significance at <span class="html-italic">p</span> ≤ 0.001 **** indicates significance at <span class="html-italic">p</span> ≤ 0.0001.</p> Full article ">Figure 4
<p>Relative (to the control) concentration of FDH in CSF from the different neurological conditions. Epilepsy, PDC, PD TBI, MS, and moderate AD have no significant difference from control levels of FDH in CSF. BP, SCZ, and severe AD have significantly reduced levels of FDH in CSF, while live dementia, IIH, and NPH have barely detectable levels in LP CSF. Bars are means plus SEM. Data were normalised using the general control sample (non-demented control, 1994-076) added to every Western blot and dot blot. This control sample was set as 1, and measurements from other CSF samples were adjusted according to their intensity relative to the control, including all controls used. * indicates significance at <span class="html-italic">p</span> ≤ 0.05, *** indicates significance at <span class="html-italic">p</span> ≤ 0.001 **** indicates significance at <span class="html-italic">p</span> ≤ 0.0001.</p> Full article ">Figure 5
<p>Schematic diagram (created using <a href="http://BioRender.com/o82e774" target="_blank">BioRender.com/o82e774</a>) illustrating proposed route of folate supply to the cerebral cortex. Folate is transported in blood, within erythrocytes and plasma bound to FOLR1 and folate-binding proteins. In the choroid plexus, fenestrated capillaries allow FOLR1 and free folate (and other blood components) to enter the interstitial space on the basal side of the choroid epithelium. Here, membrane-bound FOLR1 accepts free folate, while folate bound to FOLR1 inserts into the membrane. FOLR1 then invaginates to release folate into the cell and then merges with the apical membrane to produce vesicles containing FOLR1 and folate that are sent into the CSF. FDH is synthesised in radial glial and astroglial cells that send vesicles of FDH into the CSF. These fuse with the FOLR1–folate vesicles to transfer folate from FOLR1 to FDH. FDH–folate can then enter the cortex through the GFAP-negative–FDH-positive astroglial network. Folate may also enter directly from CSF into CSF-contacting neurons. Folate is transported around the CSF pathway as free folate and bound to FDH, and this is important to supply the grey matter with folate through the subarachnoid CSF and pial interface as well as the meninges.</p> Full article ">Figure 6
<p>Venn diagram illustrating the changes in FOLR1, folate, and FDH concentrations in the CSF of the conditions investigated in this study. No condition was normal. PDC, PD, epilepsy, and TBI had reduced FOLR1 but normal FDH and folate, indicating normal folate transport into the CSF but that folate may be entering the brain with FOLR1 rather than FDH, as we have described in severe AD [<a href="#B28-ijms-25-10205" class="html-bibr">28</a>]. Bipolar and SCZ have reduced FDH and FOLR1 but normal folate, suggesting a problem regarding transport into CSF, as well as into the brain. Moderate AD and MS have reduced folate and FOLR1, indicating a significant obstruction to folate transport into the CSF. IIH, NPH, live dementia, and severe AD have a reduction in all three molecules, indicating a profound cerebral folate deficiency. The table indicates the number of cases analysed for each condition.</p> Full article ">
22 pages, 1101 KiB  
Review
Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways
by Jinping Feng, Xinan Zhang and Tianhai Tian
Int. J. Mol. Sci. 2024, 25(18), 10204; https://doi.org/10.3390/ijms251810204 - 23 Sep 2024
Viewed by 964
Abstract
The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in [...] Read more.
The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in single-cell proteomic technologies have provided unprecedented opportunities to investigate the heterogeneity and noise within complex, multi-signaling networks across diverse cells and cell types. Mathematical modeling has become a powerful interdisciplinary tool that bridges mathematics and experimental biology, providing valuable insights into these intricate cellular processes. In addition, statistical methods have been developed to infer pathway topologies and estimate unknown parameters within dynamic models. This review presents a comprehensive analysis of how mathematical modeling of the MAPK pathway deepens our understanding of its regulatory mechanisms, enhances the prediction of system behavior, and informs experimental research, with a particular focus on recent advances in modeling and inference using single-cell proteomic data. Full article
(This article belongs to the Special Issue Computational Modelling and Molecular Dynamics Simulations of Biological Systems)
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<p>Schematic overview of the Ras–Raf–MEK–ERK module: In the cytosolic subsystem, the Ras–Raf–MEK–ERK pathway begins when the input signal Ras–GTP activates Raf, which subsequently activates MEK through a single-step processive module. MEK then activates ERK kinase in a two-step distributive manner. Both active and inactive forms of MEK and ERK are capable of freely diffusing between the cytosol and the nucleus. In the nuclear subsystem, activated MEK can further activate ERK. Specific phosphatases, such as Raf phosphatase, MKP, and STP, deactivate the active forms of Raf*, MEKpp, and ERKpp at various subcellular locations [<a href="#B71-ijms-25-10204" class="html-bibr">71</a>].</p> Full article ">Figure 2
<p>Schematic overview of the MAP kinase pathway and the PI3K/AKT pathway activated by EGF receptors. The box with the green dashed line includes the Ras–Raf–MEK–ERK module, as shown in <a href="#ijms-25-10204-f001" class="html-fig">Figure 1</a>, while the box with the blue solid line encompasses the EGF-induced MAPK pathway.</p> Full article ">Figure 3
<p>Mechanistic and data dual-driven approaches for modeling cell signaling pathways. (<b>A</b>) Mechanistic modeling approaches rely on experimentally discovered regulatory mechanisms, kinase activity data, and dynamic models to simulate cell signaling pathways. (<b>B</b>) Data-driven modeling approaches utilize static correlation network models, omics datasets, and statistical methods or machine learning algorithms to analyze signaling pathways. Two main combination techniques are employed for dual-driven approaches. The parallel structure approach uses weighting techniques to merge results from different models into a single output, whereas the serial structure approach uses the prediction of one model as the input for another model. (<b>C</b>) Inferred network model: The final network model is constructed by integrating predictions from the dual-driven approaches.</p> Full article ">Figure 4
<p>Modeling and simulation of cell signaling pathways using single-cell data. (<b>A</b>) Data types: Single-cell data include time-lapse and snapshot proteomic data, with pseudo-time trajectories generated from snapshot data using bioinformatics methods. (<b>B</b>) Model types: Stochastic models may involve chemical reaction systems (CRS) or stochastic differential equations (SDEs). Multi-scale models combine multiple model types, such as ODEs, CRS, and SDEs. (<b>C</b>) Simulation types: Stochastic simulations can be generated from either stochastic models or multi-scale models. (Red-line in deterministic: the average simulation of all simulations; lines in stochastic and NLMEM with different color: different simulations of the stochastic model and NLMEM model, respectively).</p> Full article ">
10 pages, 716 KiB  
Review
Follicular Skin Disorders, Inflammatory Bowel Disease, and the Microbiome: A Systematic Review
by Lauren Fleshner, Katie Roster, Banu Farabi, Rahim Hirani, Katharine Tepper, Capecomorin S Pitchumoni, Bijan Safai and Shoshana Marmon
Int. J. Mol. Sci. 2024, 25(18), 10203; https://doi.org/10.3390/ijms251810203 - 23 Sep 2024
Viewed by 971
Abstract
Follicular skin disorders, including hidradenitis suppurativa (HS), frequently coexist with systemic autoinflammatory diseases, such as inflammatory bowel disease (IBD) and its subtypes, Crohn’s disease and ulcerative colitis. Previous studies suggest that dysbiosis of the human gut microbiome may serve as a pathogenic link [...] Read more.
Follicular skin disorders, including hidradenitis suppurativa (HS), frequently coexist with systemic autoinflammatory diseases, such as inflammatory bowel disease (IBD) and its subtypes, Crohn’s disease and ulcerative colitis. Previous studies suggest that dysbiosis of the human gut microbiome may serve as a pathogenic link between HS and IBD. However, the role of the microbiome (gut, skin, and blood) in the context of IBD and various follicular disorders remains underexplored. Here, we performed a systematic review to investigate the relationship between follicular skin disorders, IBD, and the microbiome. Of the sixteen included studies, four evaluated the impact of diet on the microbiome in HS patients, highlighting a possible link between gut dysbiosis and yeast-exclusion diets. Ten studies explored bacterial colonization and HS severity with specific gut and skin microbiota, including Enterococcus and Veillonella. Two studies reported on immunological or serological biomarkers in HS patients with autoinflammatory disease, including IBD, and identified common markers including elevated cytokines and T-lymphocytes. Six studies investigated HS and IBD patients concurrently. Our systematic literature review highlights the complex interplay between the human microbiome, IBD, and follicular disorders with a particular focus on HS. The results indicate that dietary modifications hold promise as a therapeutic intervention to mitigate the burden of HS and IBD. Microbiota analyses and the identification of key serological biomarkers are crucial for a deeper understanding of the impact of dysbiosis in these conditions. Future research is needed to more thoroughly delineate the causal versus associative roles of dysbiosis in patients with both follicular disorders and IBD. Full article
(This article belongs to the Special Issue Interactions of Microbiome in Skin, Gastrointestinal, and Mucocutaneous Diseases)
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<p>PRISMA diagram depicting selection criteria for inclusion. Generated by Covidence.</p> Full article ">
17 pages, 10727 KiB  
Article
Supraphysiological Dose of Testosterone Impairs the Expression and Distribution of Sex Steroid Receptors during Endometrial Receptivity Development in Female Sprague–Dawley Rats
by Allia Najmie Muhammad Yusuf, Mohd Fariz Amri, Azizah Ugusman, Adila A Hamid and Mohd Helmy Mokhtar
Int. J. Mol. Sci. 2024, 25(18), 10202; https://doi.org/10.3390/ijms251810202 - 23 Sep 2024
Viewed by 569
Abstract
This study aims to investigate the effect of a supraphysiological dose of testosterone on the levels of sex steroid hormones and the expression and distribution of sex steroid receptors in the uterus during the endometrial receptivity development period. In this study, adult female [...] Read more.
This study aims to investigate the effect of a supraphysiological dose of testosterone on the levels of sex steroid hormones and the expression and distribution of sex steroid receptors in the uterus during the endometrial receptivity development period. In this study, adult female Sprague–Dawley rats (n = 24) were subcutaneously administered 1 mg/kg/day of testosterone alone or in combination with the inhibitors (finasteride or anastrozole or both) from day 1 to day 3 post-coitus, while a group of six untreated rats served as a control group. The rats were sacrificed on the evening of post-coital day 4 of to measure sex steroid hormone levels by ELISA. Meanwhile, gene expression and protein distribution of sex steroid receptors were analysed by quantitative polymerase chain reaction (qPCR) and immunohistochemistry (IHC), respectively. In this study, treatment with a supraphysiological dose of testosterone led to a significant reduction in oestrogen and progesterone levels compared to the control. The mRNA expression of the androgen receptor increased significantly in all treatment groups, while the mRNA expression of both the progesterone receptor and the oestrogen receptor-α decreased significantly in all treatment groups. The IHC findings of all sex steroid receptors were coherent with all mRNAs involved. This study shows that a supraphysiological dose of testosterone was able to interrupt the short period of the implantation window. This finding could serve as a basis for understanding the role of testosterone in endometrial receptivity in order to develop further therapeutic approaches targeting androgen-mediated disorders of endometrial receptivity. Full article
(This article belongs to the Special Issue Molecular Research on Embryo Developmental Potential)
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<p>Effects of testosterone on serum levels of (<b>A</b>) testosterone, (<b>B</b>) oestradiol, (<b>C</b>) progesterone. C: normal control; T: testosterone propionate; T + FIN: testosterone propionate + finasteride; T + ANA: testosterone propionate + anastrozole; T + FIN + ANA: testosterone propionate + finasteride + anastrozole. Error bars represent standard error of the mean (SEM). <span class="html-italic">n</span> = 6 in each group, * <span class="html-italic">p</span> &lt; 0.05 significant compared to normal control group.</p> Full article ">Figure 2
<p>Distribution of the androgen receptor (AR) in the uterus of rats. (<b>A</b>) The dark brown colouring, indicated by arrows, indicates the antibody binding site of the AR, which appears to be located in the stroma. Strong dark brown staining was observed in all groups only in the stroma, but not in the epithelial gland and the glandular epithelium. (<b>B</b>) Semi-quantitative evaluation of AR immunostaining for the stroma. C: normal control; T: testosterone propionate; T + FIN: testosterone propionate + finasteride; T + ANA: testosterone propionate + anastrozole; T + FIN + ANA: testosterone propionate + finasteride + anastrozole. LE: luminal epithelium; GE: glandular epithelium; L: endometrial lumen; S: stroma. Scale bar = 200 μm, scale bar inset = 1 mm, magnification 40× and 200×. Error bars represent standard error of the mean (SEM). <span class="html-italic">n</span> = 6 in each group, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 significance compared to normal control group.</p> Full article ">Figure 3
<p>Effects of testosterone on androgen receptor (AR) mRNA expression. C: normal control; T: testosterone propionate; T + FIN: testosterone propionate + finasteride; T + ANA: testosterone propionate + anastrozole; T + FIN + ANA: testosterone propionate + finasteride + anastrozole. Error bars represent standard error of the mean (SEM). <span class="html-italic">n</span> = 6 in each group, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 significant compared to the normal control group.</p> Full article ">Figure 4
<p>Distribution of oestrogen receptor-α (ERα) in the uterus of rats. (<b>A</b>) The dark brown colour, indicated by arrows, indicates the antibody-binding site of ERα, which appears to be present in the uterine lumen, uterine glandular epithelium and stroma. Strong dark brown staining was observed in the luminal epithelium in all four treatment groups compared to the control group, but to a lesser extent in the glandular epithelium and stroma. (<b>B</b>) Semi-quantitative scoring of ERα immunostaining for the endometrium compartments. C: normal control; T: testosterone propionate; T + FIN: testosterone propionate + finasteride; T + ANA: testosterone propionate + anastrozole; T + FIN + ANA: testosterone propionate + finasteride + anastrozole. LE: luminal epithelium; GE: glandular epithelium; L: endometrium lumen; S: stroma. Scale bar = 200 μm, scale bar inset = 1 mm, Magnification 40× and 200×. Error bars represent standard error of the mean (SEM). <span class="html-italic">n</span> = 6 in each group, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 significance compared to normal control group.</p> Full article ">Figure 5
<p>Effects of testosterone on oestrogen receptor alpha (ERα) mRNA expression. C: normal control; T: testosterone propionate; T + FIN: testosterone propionate + finasteride; T + ANA: testosterone propionate + anastrozole; T + FIN + ANA: testosterone propionate + finasteride + anastrozole. <span class="html-italic">n</span> = 6 in each group, * <span class="html-italic">p</span> &lt; 0.05 significant compared to normal control group.</p> Full article ">Figure 6
<p>Distribution of progesterone receptor (PR) in the uterus of rats. (<b>A</b>) The dark brown colour indicated by arrows indicates the antibody binding site of PR, which appears to be present in the luminal and glandular epithelium of the uterus as well as in the stroma. In all four treatment groups, a strong dark brown colouration was observed in the stroma compared to the control group, but less so in the luminal and glandular epithelium. (<b>B</b>) Semi-quantitative evaluation of PR immunostaining for the endometrial compartments. C: normal control; T: testosterone propionate; T + FIN: testosterone propionate + finasteride; T + ANA: testosterone propionate + anastrozole; T + FIN + ANA: testosterone propionate + finasteride + anastrozole. LE: Luminal epithelium; GE: Glandular epithelium; L: Lumen of the endometrium; S: Stroma. Scale bar = 200 μm, scale bar insert = 1 mm, magnification 40× and 200×. Error bars represent standard error of the mean (SEM). <span class="html-italic">n</span> = 6 in each group, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">Figure 7
<p>Effects of testosterone on mRNA expression of progesterone receptor (PR). C: normal control; T: testosterone propionate; T + FIN: testosterone propionate + finasteride; T + ANA: testosterone propionate + anastrozole; T + FIN + ANA: testosterone propionate + finasteride + anastrozole. Error bars represent standard error of the mean (SEM). <span class="html-italic">n</span> = 6 in each group, * <span class="html-italic">p</span> &lt; 0.05 significance compared to normal control group.</p> Full article ">
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