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17 pages, 1864 KiB  
Article
An Analysis of Capsaicin, Dihydrocapsaicin, Vitamin C and Flavones in Different Tissues during the Development of Ornamental Pepper
by June Wang, Xudong Duan, Yu An, Jinyao He, Jiaxin Li, Jingqi Xian and Daofen Zhou
Plants 2024, 13(15), 2038; https://doi.org/10.3390/plants13152038 (registering DOI) - 24 Jul 2024
Abstract
As a fruit and vegetable crop, the ornamental pepper is not just highly ornamental but also rich in nutritional value. The quality of ornamental pepper fruits is given in their contents of capsaicin, vitamin C (VC), flavonoids and total phenols. The study concentrated [...] Read more.
As a fruit and vegetable crop, the ornamental pepper is not just highly ornamental but also rich in nutritional value. The quality of ornamental pepper fruits is given in their contents of capsaicin, vitamin C (VC), flavonoids and total phenols. The study concentrated on the accumulation of capsaicin and dihydrocapsaicin in different tissues of 18 peppers during fruit growth and development. The results showed that the pericarp and placenta contained significantly higher levels of capsaicin than dihydrocapsaicin. Additionally, the placenta contained significantly higher levels of both capsaicin and dihydrocapsaicin compared to the pericarp. The content of capsaicin was in the range of 0-6.7915 mg·g−1, the range of dihydrocapsaicin content was 0–5.329 mg·g−1. Interestingly, we found that the pericarp is rich in VC (5.4506 mg·g−1) and the placenta is high in flavonoids (4.8203 mg·g−1) and total phenols (119.63 mg·g−1). The capsaicin is the most important component using the correlation analysis and principal component analysis. The qPCR results substantiated that the expression of genes in the placenta was significantly higher than that in the pericarp and that the expression of genes in green ripening stage was higher than that in red ripening stage. This study could be utilized to select the best ripening stages and tissues to harvest peppers according to the use of the pepper and to the needs of producers. It not only provides a reference for quality improvement and processing for consumers and market but also provides a theoretical basis for high-quality pepper breeding. Full article
(This article belongs to the Section Horticultural Science and Ornamental Plants)
18 pages, 1221 KiB  
Article
Plasticity Comparison of Two Stem Cell Sources with Different Hox Gene Expression Profiles in Response to Cobalt Chloride Treatment during Chondrogenic Differentiation
by Sahar Khajeh, Vahid Razban, Yasaman Naeimzadeh, Elham Nadimi, Reza Asadi-Golshan, Zahra Heidari, Tahereh Talaei-Khozani, Farzaneh Dehghani, Zohreh Mostafavi-Pour and Masoud Shirali
Biology 2024, 13(8), 560; https://doi.org/10.3390/biology13080560 (registering DOI) - 24 Jul 2024
Abstract
The limited self-repair capacity of articular cartilage is a challenge for healing injuries. While mesenchymal stem/stromal cells (MSCs) are a promising approach for tissue regeneration, the criteria for selecting a suitable cell source remain undefined. To propose a molecular criterion, dental pulp stem [...] Read more.
The limited self-repair capacity of articular cartilage is a challenge for healing injuries. While mesenchymal stem/stromal cells (MSCs) are a promising approach for tissue regeneration, the criteria for selecting a suitable cell source remain undefined. To propose a molecular criterion, dental pulp stem cells (DPSCs) with a Hox-negative expression pattern and bone marrow mesenchymal stromal cells (BMSCs), which actively express Hox genes, were differentiated towards chondrocytes in 3D pellets, employing a two-step protocol. The MSCs’ response to preconditioning by cobalt chloride (CoCl2), a hypoxia-mimicking agent, was explored in an assessment of the chondrogenic differentiation’s efficiency using morphological, histochemical, immunohistochemical, and biochemical experiments. The preconditioned DPSC pellets exhibited significantly elevated levels of collagen II and glycosaminoglycans (GAGs) and reduced levels of the hypertrophic marker collagen X. No significant effect on GAGs production was observed in the preconditioned BMSC pellets, but collagen II and collagen X levels were elevated. While preconditioning did not modify the ALP specific activity in either cell type, it was notably lower in the DPSCs differentiated pellets compared to their BMSCs counterparts. These results could be interpreted as demonstrating the higher plasticity of DPSCs compared to BMSCs, suggesting the contribution of their unique molecular characteristics, including their negative Hox expression pattern, to promote a chondrogenic differentiation potential. Consequently, DPSCs could be considered compelling candidates for future cartilage cell therapy. Full article
(This article belongs to the Special Issue Mesenchymal Stem Cells: What We Have Learned and How to Manage Them)
13 pages, 3431 KiB  
Article
Fabrication of Apparatus Specialized for Measuring the Elasticity of Perioral Tissues
by Ryo Takemoto, Junya Kobayashi, Yuko Oomori, Kojiro Takahashi, Isao Saito, Mika Kawai and Tetsu Mitsumata
Materials 2024, 17(15), 3654; https://doi.org/10.3390/ma17153654 (registering DOI) - 24 Jul 2024
Viewed by 85
Abstract
On the human face, the lips are one of the most important anatomical elements, both morphologically and functionally. Morphologically, they have a significant impact on aesthetics, and abnormal lip morphology causes sociopsychological problems. Functionally, they play a crucial role in breathing, articulation, feeding, [...] Read more.
On the human face, the lips are one of the most important anatomical elements, both morphologically and functionally. Morphologically, they have a significant impact on aesthetics, and abnormal lip morphology causes sociopsychological problems. Functionally, they play a crucial role in breathing, articulation, feeding, and swallowing. An apparatus that can accurately and easily measure the elastic modulus of perioral tissues in clinical tests was developed, and its measurement sensitivity was evaluated. The apparatus is basically a uniaxial compression apparatus consisting of a force sensor and a displacement sensor. The displacement sensor works by enhancing the restoring force due to the deformation of soft materials. Using the apparatus, the force and the displacement were measured for polyurethane elastomers with various levels of softness, which are a model material of human tissues. The stress measured by the developed apparatus increased in proportion to Young’s modulus, and was measured by the compression apparatus at the whole region of Young’s modulus, indicating that the relation can be used for calibration. Clinical tests using the developed apparatus revealed that Young’s moduli for upper lip, left cheek, and right cheek were evaluated to be 45, 4.0, and 9.9 kPa, respectively. In this paper, the advantages of this apparatus and the interpretation of the data obtained are discussed from the perspective of orthodontics. Full article
(This article belongs to the Special Issue Advanced Polymeric Materials Studies for Oral Health)
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Figure 1
<p>Photographs of the appearance of the (<b>a</b>) apparatus developed and for (<b>b</b>) force and displacement sensors. Views of (<b>c</b>) force measurement for a PU elastomer and of the (<b>d</b>) deformation of elastomers with Young’s moduli of 44 kPa (left) and 10 kPa (right). (<b>e</b>) Time response of force and displacement for PU elastomers with a plasticizer concentration of 70 wt.% when three indentation tests were carried out in approximately 3 min.</p>
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<p>(<b>a</b>) Stress–strain curves and (<b>b</b>) the relationship between Young’s modulus and the plasticizer concentration for PU elastomers synthesized in this study.</p>
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<p>Time response of force (<b>tops</b>) and displacement (<b>bottoms</b>) for PU elastomers with a plasticizer concentration of 70 wt.% at various diameters of the force sensor, indicated as a function of the set values of displacement.</p>
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<p>Average force (<b>a</b>–<b>c</b>) and average displacement (<b>d</b>–<b>f</b>) as a function of the diameter of the force sensor for PU elastomers with various plasticizer concentrations.</p>
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<p>Relationship between the average force and the diameter of force sensor at various displacements for PU elastomers with various plasticizer concentrations. Schematic illustrations represent the deformation of elastomers around the force sensor when pressurized by the apparatus.</p>
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<p>Schematic illustrations representing the deformation and mechanical response when a stress was applied by the indenter without (<b>a</b>) and with (<b>b</b>) a displacement sensor.</p>
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<p>The relationship between the stress measured by the apparatus developed in this study and Young’s modulus determined by a uniaxial compression apparatus for PU elastomers with various plasticizer concentrations at various displacements.</p>
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<p>Time responses of (<b>a</b>) force and (<b>b</b>) displacement for perioral tissues measured at <span class="html-italic">ϕ</span> = 9 mm and <span class="html-italic">d</span> = 3 mm. (<b>c</b>) The relationship between the average force and the diameter of the force sensor for perioral tissues at a displacement of 3 mm. (<b>d</b>) Young’s modulus in vivo for perioral tissues. (<b>e</b>) The appearance of the skin surface before and after three indentation tests. The indenter was applied to the circle in (<b>e</b>).</p>
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18 pages, 10556 KiB  
Article
The Drought Tolerance Function and Tanscriptional Regulation of GhAPX7 in Gossypium hirsutum
by Tingwei Wang, Quanjia Chen, Yaping Guo, Wenju Gao, Hu Zhang, Duolu Li, Shiwei Geng, Yuxiang Wang, Jieyin Zhao, Jincheng Fu, Yilei Long, Pengfei Liu, Yanying Qu and Qin Chen
Plants 2024, 13(15), 2032; https://doi.org/10.3390/plants13152032 (registering DOI) - 24 Jul 2024
Viewed by 157
Abstract
Drought stress significantly affects the growth, development, and yield of cotton, triggering the response of multiple genes. Among them, ascorbate peroxidase (APX) is one of the important antioxidant enzymes in the metabolism of reactive oxygen species in plants, and APX enhances the ability [...] Read more.
Drought stress significantly affects the growth, development, and yield of cotton, triggering the response of multiple genes. Among them, ascorbate peroxidase (APX) is one of the important antioxidant enzymes in the metabolism of reactive oxygen species in plants, and APX enhances the ability of plants to resist oxidation, thus increasing plant stress tolerance. Therefore, enhancing the activity of APX in cells is crucial to improving plant stress resistance. Previous studies have isolated differentially expressed proteins under drought stress (GhAPX7) in drought-resistant (KK1543) and drought-sensitive (XLZ26) plants. Thus, this study analyzed the expression patterns of GhAPX7 in different cotton tissues to verify the drought resistance function of GhAPX7 and explore its regulatory pathways. GhAPX7 had the highest expression in cotton leaves, which significantly increased under drought stress, suggesting that GhAPX7 is essential for improving antioxidant capacity and enzyme activities in cotton. GhAPX7 silencing indirectly affects pronounced leaf yellowing and wilting in drought-resistant and drought-sensitive plants under drought stress. Malondialdehyde (MDA) content was significantly increased and chlorophyll and proline content and APX enzyme activity were generally decreased in silenced plants compared to the control. This result indicates that GhAPX7 may improve drought resistance by influencing the contents of MDA, chlorophyll, proline, and APX enzyme activity through increased expression levels. Transcriptome analysis revealed that the drought-related differentially expressed genes between the control and treated groups enriched plant hormone signal transduction, MAPK signaling, and plant–pathogen interaction pathways. Therefore, the decreased expression of GhAPX7 significantly affects the expression levels of genes in these three pathways, reducing drought resistance in plants. This study provides insights into the molecular mechanisms of GhAPX7 and its role in drought resistance and lays a foundation for further research on the molecular mechanisms of response to drought stress in cotton. Full article
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<p>The <span class="html-italic">GhAPX7</span> gene expression patterns. (<b>a</b>) Tissue-specific expression of <span class="html-italic">GhAPX7</span> in XLZ26. (<b>b</b>) Tissue-specific expression of <span class="html-italic">GhAPX7</span> in KK1543. Statistical analysis was conducted using a one-way analysis of variance (ANOVA). * represents <span class="html-italic">p</span> &lt; 0.05, indicating significant differences; ** represents <span class="html-italic">p</span> &lt; 0.01, indicating highly significant differences; and *** represents <span class="html-italic">p</span> &lt; 0.001, indicating extremely significant differences.</p>
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<p>VIGS experiment with <span class="html-italic">GhAPX7</span>. (<b>a</b>) Whitening phenotype of pTRV2::CLA in XLZ26 and KK1543; (<b>b</b>) silencing efficiency detection using one-way analysis of variance (ANOVA); (<b>c</b>,<b>d</b>) <span class="html-italic">GhAPX7</span> expression in pTRV2::00 plants after drought stress; and (<b>e</b>) phenotypic comparison between pTRV2::00 and pTRV2::<span class="html-italic">GhAPX7</span> materials under normal watering and soil moisture content below 10% for 3 days. Statistical analysis was performed using a two-way analysis of variance (ANOVA); *** represents <span class="html-italic">p</span> &lt; 0.001, indicating significant differences.</p>
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<p>Silencing of relative leaf water content after normal watering and drying in <span class="html-italic">GhAPX7</span> cotton. pTRV2:00: control subjects; pTRV2::<span class="html-italic">GhAPX7</span>: test group. Silencing efficiency detection using one-way analysis of variance (ANOVA); and *** represents <span class="html-italic">p</span> &lt; 0.001, indicating significant differences.</p>
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<p>Physiological and biochemical assays of <span class="html-italic">GhAPX7</span> VIGS materials. (<b>a</b>,<b>b</b>), MDA, (<b>c</b>,<b>d</b>), Pro, (<b>e</b>,<b>f</b>), Chl, (<b>g</b>,<b>h</b>), APX, in pTRV2::00 and pTRV2::<span class="html-italic">GhAPX7</span> in drought-stressed XLZ26 and KK1543, respectively. Statistical analysis was performed using a two-way analysis of variance (ANOVA). * represents <span class="html-italic">p</span> &lt; 0.05, indicating a significant difference; ** represents <span class="html-italic">p</span> &lt; 0.01, indicating a highly significant difference; *** represents <span class="html-italic">p</span> &lt; 0.001, indicating an extremely significant difference.</p>
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<p>RT-qPCR analysis of drought-responsive marker genes (<b>a</b>–<b>j</b>) shows the relative expression of GhABF2, ABF2, ERF1, GhABI5, and SOS2 in drought-stressed pTRV2::00 and pTRV2::<span class="html-italic">GhAPX7</span> plants of XLZ26 and KK1543. Statistical analysis was performed using a two-way analysis of variance (ANOVA). * indicates <span class="html-italic">p</span> &lt; 0.05, indicating significant difference; ** indicates <span class="html-italic">p</span> &lt; 0.01, indicating highly significant difference; *** indicates <span class="html-italic">p</span> &lt; 0.001, indicating extremely significant difference.</p>
Full article ">Figure 5 Cont.
<p>RT-qPCR analysis of drought-responsive marker genes (<b>a</b>–<b>j</b>) shows the relative expression of GhABF2, ABF2, ERF1, GhABI5, and SOS2 in drought-stressed pTRV2::00 and pTRV2::<span class="html-italic">GhAPX7</span> plants of XLZ26 and KK1543. Statistical analysis was performed using a two-way analysis of variance (ANOVA). * indicates <span class="html-italic">p</span> &lt; 0.05, indicating significant difference; ** indicates <span class="html-italic">p</span> &lt; 0.01, indicating highly significant difference; *** indicates <span class="html-italic">p</span> &lt; 0.001, indicating extremely significant difference.</p>
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<p>Differentially expressed genes (DEGs) and GO annotation statistics. Note: (<b>a</b>) GO annotation classification statistics of DEGs in A−vs−E; (<b>b</b>) GO annotation classification statistics of DEGs in I−vs−K; (<b>c</b>) GO annotation classification statistics of DEGs in B−vs−F; (<b>d</b>) GO annotation classification statistics of DEGs in J−vs−L. Note: the horizontal axis represents the GO categories, the vertical axis represents the number of genes, and different colors represent different primary classifications.</p>
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<p>Bubble plots of differentially expressed genes (DEGs) that enrich the KEGG pathways. Note: (<b>a</b>) bubble plot of DEGs that enriched the KEGG pathways for A−vs−E; (<b>b</b>) bubble plot of DEGs that enriched the KEGG pathways for the I_ vs_ K group; (<b>c</b>) bubble plot of DEGs that enrich the KEGG pathways for B−vs−F; and (<b>d</b>) bubble plot of DEGs that enrich the KEGG pathways for J_vs _L.</p>
Full article ">Figure 8
<p>Venn diagrams and heatmaps of differentially expressed genes (DEGs). (<b>a</b>) Venn diagram of DEGs between A−vs−E and I−vs−K; (<b>b</b>) Venn diagram of DEGs between B−vs−F and J−vs−L; (<b>c</b>) Venn diagram of DEGs among A−vs−E, I−vs−K, B−vs−F, and J−vs−L; (<b>d</b>) heatmap of the four overlapping genes between A−vs−E and I−vs−K; and (<b>e</b>) heatmap of the nine overlapping genes between B−vs−F and J−vs−L.</p>
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<p>The correlation heatmap of pairwise samples.</p>
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<p>Principal component analysis plot.</p>
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14 pages, 1963 KiB  
Article
Treatment of Intrabony Defects with Non-Surgical Subgingival Debridement: A Radiographic Evaluation of Bone Gain Using an Experimental Digital Software “Bone Defect Analysis (BDA)”
by Alessia Pardo, Laura Bonfante, Annarita Signoriello, Andrea Benetti, Marco Barillari, Piero Zanutto and Giorgio Lombardo
J. Clin. Med. 2024, 13(15), 4315; https://doi.org/10.3390/jcm13154315 (registering DOI) - 24 Jul 2024
Viewed by 127
Abstract
Background: The aim of this study was to retrospectively evaluate the 3-year radiographic outcomes of periodontal intrabony defects treated with non-surgical subgingival therapy (NST), assessing radiographic bone gain (RBG) through experimental digital software, named “Bone Defect Analysis (BDA)”. Methods: The study included 17 [...] Read more.
Background: The aim of this study was to retrospectively evaluate the 3-year radiographic outcomes of periodontal intrabony defects treated with non-surgical subgingival therapy (NST), assessing radiographic bone gain (RBG) through experimental digital software, named “Bone Defect Analysis (BDA)”. Methods: The study included 17 intrabony defects in 14 patients. BDA software (version 1) was used on radiographs to calculate RBG (in %) and variations in defect angle (in °) between baseline (T0) and 3-year follow-up (T1). Soft tissue conditions were registered, reporting bleeding on probing (BOP), probing pocket depth (PPD), and clinical attachment level (CAL). Defects were analyzed according to angles less (group A) or greater (group B) than 30°. Results: Nine and eight defects were, respectively, analyzed in groups A and B. Three years after treatment, an average RBG of 12.28% was found overall, with 13.25% and 10.11% for groups A and B, respectively (p = 0.28). Clinically, a mean CAL of 6.05 mm at T1 (from 10.94 mm at T0) was found, with 6.88 mm and 5.12 mm in groups A and B, respectively (p = 0.07). Conclusions: BDA software demonstrated predictability in the evaluation of bone variations after NST, revealing better clinical findings for intrabony defects with an initial smaller angle. Full article
(This article belongs to the Section Dentistry, Oral Surgery and Oral Medicine)
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Figure 1

Figure 1
<p>RSA in percentage is the ratio between segments D’B and C’B. C’ and D’ are obtained by projecting C and D on the line AB, defined by points A (coronal margin) and B (root apex). C’B thus corresponds to the length of the root and D’B to the length of the root completely covered by bone tissue.</p>
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<p>The angle at the base of the intrabony defect (in black) is defined by two segments: respectively, CD, which represents the surface of the involved tooth, and ED, which represents the surface of the bone defect; AB (green line) represents the distance between coronal margin and root apex.</p>
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<p>From the software calculation, the mesial surface of the root completely covered by bone tissue is equal to 46.2%, which means that the bone loss is approximately equal to 54%. Points A and B are connected by the green line; points C, D, and E are, respectively, visible as blue, red, and yellow indicators. By selecting point E and connecting it with points C and D, it is possible to detect the angle at the base of the intrabony defect (in black).</p>
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<p>Radiographic case of a bone defect between the mesial surface of element 1.1 and the mesial surface of element 2.1: (<b>a</b>) angle at T0 is 45.75° and RSA at T0 is 54.43%; (<b>b</b>) angle at T1 is 14.72° and RSA at T1 is 62.65%. The green line represents the distance between coronal margin and root apex. The red dots represent the angle at the base of the intrabony defect, respectively at T0 and at T1.</p>
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<p>Radiographic case of a bone defect between the mesial surface of element 1.1 and the mesial surface of element 2.1: (<b>a</b>) angle at T0 is 30.4° and RSA at T0 is 55.49%; (<b>b</b>) angle at T1 is 37.63° and RSA at T1 is 56.82%. The green line represents the distance between coronal margin and root apex. The red dots represent the angle at the base of the intrabony defect, respectively at T0 and at T1.</p>
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18 pages, 10657 KiB  
Article
A CLRN3-Based CD8+ T-Related Gene Signature Predicts Prognosis and Immunotherapy Response in Colorectal Cancer
by Zhiwen Gong, Xiuting Huang, Qingdong Cao, Yuanquan Wu and Qunying Zhang
Biomolecules 2024, 14(8), 891; https://doi.org/10.3390/biom14080891 (registering DOI) - 24 Jul 2024
Viewed by 100
Abstract
Background: Colorectal cancer (CRC) ranks among the most prevalent malignancies affecting the gastrointestinal tract. The infiltration of CD8+ T cells significantly influences the prognosis and progression of tumor patients. Methods: This study establishes a CRC immune risk model based on CD8+ [...] Read more.
Background: Colorectal cancer (CRC) ranks among the most prevalent malignancies affecting the gastrointestinal tract. The infiltration of CD8+ T cells significantly influences the prognosis and progression of tumor patients. Methods: This study establishes a CRC immune risk model based on CD8+ T cell-related genes. CD8+ T cell-related genes were identified through Weighted Gene Co-expression Network Analysis (WGCNA), and the enriched gene sets were annotated via Gene Ontology (GO) and Reactome pathway analysis. Employing machine learning methods, including the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Random Forest (RF), we identified nine genes associated with CD8+ T-cell infiltration. The infiltration levels of immune cells in CRC tissues were assessed using the ssGSEA algorithm. Results: These genes provide a foundation for constructing a prognostic model. The TCGA-CRC sample model’s prediction scores were categorized, and the prediction models were validated through Cox regression analysis and Kaplan–Meier curve analysis. Notably, although CRC tissues with higher risk scores exhibited elevated levels of CD8+ T-cell infiltration, they also demonstrated heightened expression of immune checkpoint genes. Furthermore, comparison of microsatellite instability (MSI) and gene mutations across the immune subgroups revealed notable gene variations, particularly with APC, TP53, and TNNT1 showing higher mutation frequencies. Finally, the predictive model’s efficacy was corroborated through the use of Tumor Immune Dysfunction and Exclusion (TIDE), Immune Profiling Score (IPS), and immune escape-related molecular markers. The predictive model was validated through an external cohort of CRC and the Bladder Cancer Immunotherapy Cohort. CLRN3 expression levels in tumor and adjacent normal tissues were assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot. Subsequent in vitro and in vivo experiments demonstrated that CLRN3 knockdown significantly attenuated the malignant biological behavior of CRC cells, while overexpression had the opposite effect. Conclusions: This study presents a novel prognostic model for CRC, providing a framework for enhancing the survival rates of CRC patients by targeting CD8+ T-cell infiltration. Full article
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Figure 1
<p>Identification of CD8<sup>+</sup> T cell-related gene signature using WGCNA. (<b>a</b>) Scale-free fitting index for various soft threshold powers and average connectivity analysis. (<b>b</b>) WGCNA TOM cluster tree: Different colored branches correspond to different modules. A dynamic tree cut represents the original module, while a merge represents the final module. (<b>c</b>) Correlation analysis of 11 modules with immune cell phenotype. (<b>d</b>) Enrichment analysis of Reactome pathways for genes in the brown module. (<b>e</b>,<b>f</b>) Functional enrichment analysis of brown module genes: Annotated maps of biological process (BP) and molecular function (MF).</p>
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<p>Construction of immune prediction models using CD8<sup>+</sup> T cell-associated genes. (<b>a</b>,<b>b</b>) Genes selected through LASSO regression screening. (<b>c</b>,<b>d</b>) Genes identified through Random Forest screening. (<b>e</b>) Intersection of genes identified by LASSO regression and Random Forest screening. (<b>f</b>) Kaplan–Meier survival analysis following score prediction using the TCGA-CRC sample model. (<b>g</b>,<b>i</b>) ROC curves for the TCGA-CRC training set and validation set. (<b>h</b>) Model grading grouping and multivariate Cox regression analysis of CRS.</p>
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<p>Analysis of immunoinfiltration and immune checkpoint expression in high- and low-risk groups. (<b>a</b>,<b>c</b>) Predicted infiltration of 28 types of immune cells using ssGSEA. (<b>b</b>,<b>d</b>) Correlation analysis between model genes and mRNA expression of 28 types of immune-cell infiltration scores, as well as immune checkpoint genes. (<b>e</b>) Grouping of high and low ratings based on immune escape-related molecular marker scores. (<b>f</b>) Comparison of TIDE score and IPS score between high- and low-ranking groups. ns no significance, * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Somatic mutation analysis based on high- and low-risk grouping. (<b>a</b>) Overview of gene mutations in the high-low rating grouping. (<b>b</b>) Survival analysis based on high and low tumor mutation burden. (<b>c</b>) Comparison of tumor mutation load between high- and low-rating groups. (<b>d</b>) Mutation patterns in tumor-related pathways within the high-low rating group. (<b>e</b>) Variations in risk scores among different MSI groups. ns no significance, * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>The predictive model validated in an external Bladder Cancer Immunotherapy Cohort. (<b>a</b>) Survival analysis of the Bladder Cancer Immunotherapy Cohort based on model prediction. (<b>b</b>) Comparison of immunotherapy responses between high- and low-rating groups in the Bladder Cancer Immunotherapy Cohort. (<b>c</b>) Differences in IPS scores between high- and low-rating groups in the Bladder Cancer Immunotherapy Cohort. (<b>d</b>,<b>e</b>) Comparison of ssGSEA immune-cell infiltration between high- and low-rating groups in the validation cohort. (<b>f</b>) Comparison of molecular markers related to immune escape between high- and low-rating groups. ns no significance, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>The expression levels of CLRN3 mRNA and protein in both CRC tissues and cell lines. (<b>a</b>) qRT-PCR was used to analyze CLRN3 mRNA levels in CRC tissues compared to paired normal tissues. (<b>b</b>) The expression levels of CLRN3 mRNA in patients with different clinical stages of colorectal cancer (I/II vs. III/IV). (<b>c</b>) mRNA expression levels of CLRN3 in a normal colorectal cell line (HCoEpiC) and CRC cell lines (LoVo, SW480, HT-29, HCT116, and Caco2) were evaluated by qRT-PCR. (<b>d</b>,<b>e</b>) Western blotting was used to analyze quantified CLRN3 protein levels in paired CRC and normal tissues. ns no significance, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>CLRN3 enhances the malignancy of CRC cells in vitro and in vivo. (<b>a</b>,<b>b</b>) Western blot analysis was used to detect CLRN3 protein levels in both overexpressed and knockdown CRC cells. (<b>c</b>,<b>d</b>) The proliferation abilities of these cells were evaluated using the CCK8 assay. (<b>e</b>,<b>g</b>) The effects of CLRN3 overexpression on CRC cell migration and proliferation were confirmed through transwell and colony-formation assays in Caco2 cell lines. (<b>f</b>,<b>h</b>) Similar assays in SW480 cell lines with CLRN3 knockdown assessed the impact of knockdown CLRN3 expression. (<b>i</b>) A subcutaneous xenograft model in BALB/c-nude mice (n = 5) was established using HCT116 cells with stable overexpression or silencing of CLRN3, with vector and sh- Ctrl serving as controls. (<b>j</b>,<b>k</b>) Tumor volume and weight were measured and analyzed. * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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21 pages, 2025 KiB  
Review
Long Non-Coding RNA AGAP2-AS1: A Comprehensive Overview on Its Biological Functions and Clinical Significances in Human Cancers
by Feng Ma, Bingbing Zhang, Yiqi Wang and Chenghua Lou
Molecules 2024, 29(15), 3461; https://doi.org/10.3390/molecules29153461 - 24 Jul 2024
Viewed by 171
Abstract
Long non-coding RNAs (lncRNAs) are well known for their oncogenic or anti-oncogenic roles in cancer development. AGAP2-AS1, a new lncRNA, has been extensively demonstrated as an oncogenic lncRNA in various cancers. Abundant experimental results have proved the aberrantly high level of AGAP2-AS1 [...] Read more.
Long non-coding RNAs (lncRNAs) are well known for their oncogenic or anti-oncogenic roles in cancer development. AGAP2-AS1, a new lncRNA, has been extensively demonstrated as an oncogenic lncRNA in various cancers. Abundant experimental results have proved the aberrantly high level of AGAP2-AS1 in a great number of malignancies, such as glioma, colorectal, lung, ovarian, prostate, breast, cholangiocarcinoma, bladder, colon and pancreatic cancers. Importantly, the biological functions of AGAP2-AS1 have been extensively demonstrated. It could promote the proliferation, migration and invasion of cancer cells. Simultaneously, the clinical significances of AGAP2-AS1 were also illustrated. AGAP2-AS1 was exceptionally overexpressed in various cancer tissues. Clinical studies disclosed that the abnormal overexpression of AGAP2-AS1 was tightly connected with overall survival (OS), lymph nodes metastasis (LNM), clinical stage, tumor infiltration, high histological grade (HG), serous subtype and PFI times. However, to date, the biological actions and clinical significances of AGAP2-AS1 have not been systematically reviewed in human cancers. In the present review, the authors overviewed the biological actions, potential mechanisms and clinical features of AGAP2-AS1 according to the previous studies. In summary, AGAP2-AS1, as a vital oncogenic gene, is a promising biomarker and potential target for carcinoma prognosis and therapy. Full article
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<p>Related information of <span class="html-italic">AGAP2-AS1</span>. (<b>A</b>) The genomic localization of <span class="html-italic">AGAP2-AS1</span> (<a href="https://www.ncbi.nlm.nih.gov" target="_blank">https://www.ncbi.nlm.nih.gov</a>, accessed on 6 July 2024). (<b>B</b>) Secondary structure of <span class="html-italic">AGAP2-AS1</span>. (<b>C</b>) Three-dimensional structure of <span class="html-italic">AGAP2-AS1</span>. (<b>D</b>) Motif analysis of <span class="html-italic">AGAP2-AS1</span>. (<b>E</b>) The expression level of <span class="html-italic">AGAP2-AS1</span> in clinical carcinomatous (red color) and non-carcinomatous (blue color) tissues was analyzed using the UALCAN database (<a href="https://ualcan.path.uab.edu/" target="_blank">https://ualcan.path.uab.edu/</a>, accessed on 18 May 2024). BLCA: Bladder urothelial carcinoma; BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma; CHOL: Cholangiocarcinoma; COAD: Colon adenocarcinoma; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and neck squamous cell carcinoma; KICH: Kidney chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and paraganglioma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SARC: Sarcoma; THYM: Thymoma; THCA: Thyroid carcinoma; UCEC: Uterine corpus endometrial carcinoma.</p>
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<p>The potential molecular mechanisms of <span class="html-italic">AGAP2-AS1</span> in human carcinomas. <span class="html-italic">AGAP2-AS1</span> promoted cancer proliferation, migration and invasion in various cancers, including glioma, PTC, LC, melanoma, ccRCC, GC, PC, EC, BC, CRC and CLC.</p>
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<p>The <span class="html-italic">AGAP2-AS1</span> expression profile, and its correlation with cancer stage, tumor histology and survival analyzed using the UALCAN database (<a href="https://ualcan.path.uab.edu/" target="_blank">https://ualcan.path.uab.edu/</a>, accessed on 20 May 2024).</p>
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<p>The <span class="html-italic">AGAP2-AS1</span> expression in normal, tumor, and metastatic tumor tissues using the TNM plot (<a href="https://tnmplot.com/analysis/" target="_blank">https://tnmplot.com/analysis/</a>, accessed on 22 May 2024).</p>
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19 pages, 4445 KiB  
Article
Simulated Microgravity Alters Gene Regulation Linked to Immunity and Cardiovascular Disease
by Candice G. T. Tahimic, Sonette Steczina, Aimy Sebastian, Nicholas R. Hum, Metadel Abegaz, Masahiro Terada, Maria Cimini, David A. Goukassian, Ann-Sofie Schreurs, Tana M. Hoban-Higgins, Charles A. Fuller, Gabriela G. Loots, Ruth K. Globus and Yasaman Shirazi-Fard
Genes 2024, 15(8), 975; https://doi.org/10.3390/genes15080975 (registering DOI) - 24 Jul 2024
Viewed by 151
Abstract
Microgravity exposure induces a cephalad fluid shift and an overall reduction in physical activity levels which can lead to cardiovascular deconditioning in the absence of countermeasures. Future spaceflight missions will expose crew to extended periods of microgravity among other stressors, the effects of [...] Read more.
Microgravity exposure induces a cephalad fluid shift and an overall reduction in physical activity levels which can lead to cardiovascular deconditioning in the absence of countermeasures. Future spaceflight missions will expose crew to extended periods of microgravity among other stressors, the effects of which on cardiovascular health are not fully known. In this study, we determined cardiac responses to extended microgravity exposure using the rat hindlimb unloading (HU) model. We hypothesized that exposure to prolonged simulated microgravity and subsequent recovery would lead to increased oxidative damage and altered expression of genes involved in the oxidative response. To test this hypothesis, we examined hearts of male (three and nine months of age) and female (3 months of age) Long–Evans rats that underwent HU for various durations up to 90 days and reambulated up to 90 days post-HU. Results indicate sex-dependent changes in oxidative damage marker 8-hydroxydeoxyguanosine (8-OHdG) and antioxidant gene expression in left ventricular tissue. Three-month-old females displayed elevated 8-OHdG levels after 14 days of HU while age-matched males did not. In nine-month-old males, there were no differences in 8-OHdG levels between HU and normally loaded control males at any of the timepoints tested following HU. RNAseq analysis of left ventricular tissue from nine-month-old males after 14 days of HU revealed upregulation of pathways involved in pro-inflammatory signaling, immune cell activation and differential expression of genes associated with cardiovascular disease progression. Taken together, these findings provide a rationale for targeting antioxidant and immune pathways and that sex differences should be taken into account in the development of countermeasures to maintain cardiovascular health in space. Full article
(This article belongs to the Topic Animal Models of Human Disease 2.0)
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<p>Body weights of animals included in this study. NL: normally loaded; HU: hindlimb unloading; Rel: reambulated (reloading). The HU group was compared to their age- and sex-matched NL controls by Student’s <span class="html-italic">t</span>-test. Sample sizes: N = 5–8/group for all groups except for 90D older males, where N = 3. Values depicted are means and standard deviation. * Significant at <span class="html-italic">p</span> &lt; 0.05 by Student’s <span class="html-italic">t</span>-test.</p>
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<p>8-hydroxydeoxyguanosine (8-OHdG) levels in left ventricular tissue as measured by ELISA. NL: normally loaded control, HU: hindlimb unloading, 7D Rel: 90D HU + 7D reloading and normally loaded control (NL). The HU groups were compared to their age- and sex-matched NL controls by Student’s <span class="html-italic">t</span>-test. Sample sizes: N = 5–8/group for all groups except for 90D older males, where N = 3. Values depicted are means and standard deviation. * Significant at <span class="html-italic">p</span> &lt; 0.05 by Student’s <span class="html-italic">t</span>-test.</p>
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<p>Transcript levels of (<b>A</b>) <span class="html-italic">Nfe2l2</span>, (<b>B</b>) <span class="html-italic">Sod1</span>, (<b>C</b>) <span class="html-italic">Sod2</span>, and (<b>D</b>) <span class="html-italic">Sirt1</span> in left ventricular wall as measured by qPCR. Values depicted are mean fold changes relative to young male control at 14 days of treatment as determined by the ΔΔCt method. Errors bars show upper and lower ranges. NL: normally loaded control, HU: hindlimb unloading, 90D Rel: 90D HU + 90D reloading and NL control. Sample sizes: N = 3–7/group. * Significant at <span class="html-italic">p</span> &lt; 0.05 by Student’s <span class="html-italic">t</span>-test by comparing HU with age- and sex-matched NL control.</p>
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<p>PCA plot of transcriptomic data from older males that underwent 14 days of HU (gray triangles) and corresponding NL controls (black circles). Animal ID is indicated by the letter “R” succeeded by numbers.</p>
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<p>Heatmap showing normalized counts of differentially expressed genes from older male 14D NL and HU groups. Each cell corresponds to a gene. Red: Upregulated in 14D HU relative to NL group. Blue: Downregulated in 14D HU relative to NL group. Magnitude of upregulation or downregulation is proportional to the intensity of red or blue. Deepest red: most upregulated; deepest blue: most downregulated.</p>
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<p>Top 10 upregulated and downregulated genes in older male 14D HU relative to NL groups. Log2 FC: Log2 fold change. Refer to <a href="#genes-15-00975-t001" class="html-table">Table 1</a> for full list of DEGs.</p>
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<p>Select enriched gene ontology (GO) terms for biological processes in older male 14D HU group relative to NL group. Gene count refers to the number of DEGs that matched the GO term. The vertical bar represents the color scale of the FDR with black representing the lowest FDR. Refer to <a href="#app1-genes-15-00975" class="html-app">Table S2</a> for the full list of GO terms.</p>
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26 pages, 4664 KiB  
Article
A Preliminary Study on Factors That Drive Patient Variability in Human Subcutaneous Adipose Tissues
by Megan K. DeBari, Elizabeth K. Johnston, Jacqueline V. Scott, Erica Ilzuka, Wenhuan Sun, Victoria A. Webster-Wood and Rosalyn D. Abbott
Cells 2024, 13(15), 1240; https://doi.org/10.3390/cells13151240 - 24 Jul 2024
Viewed by 129
Abstract
Adipose tissue is a dynamic regulatory organ that has profound effects on the overall health of patients. Unfortunately, inconsistencies in human adipose tissues are extensive and multifactorial, including large variability in cellular sizes, lipid content, inflammation, extracellular matrix components, mechanics, and cytokines secreted. [...] Read more.
Adipose tissue is a dynamic regulatory organ that has profound effects on the overall health of patients. Unfortunately, inconsistencies in human adipose tissues are extensive and multifactorial, including large variability in cellular sizes, lipid content, inflammation, extracellular matrix components, mechanics, and cytokines secreted. Given the high human variability, and since much of what is known about adipose tissue is from animal models, we sought to establish correlations and patterns between biological, mechanical, and epidemiological properties of human adipose tissues. To do this, twenty-six independent variables were cataloged for twenty patients, which included patient demographics and factors that drive health, obesity, and fibrosis. A factorial analysis for mixed data (FAMD) was used to analyze patterns in the dataset (with BMI > 25), and a correlation matrix was used to identify interactions between quantitative variables. Vascular endothelial growth factor A (VEGFA) and actin alpha 2, smooth muscle (ACTA2) gene expression were the highest loadings in the first two dimensions of the FAMD. The number of adipocytes was also a key driver of patient-related differences, where a decrease in the density of adipocytes was associated with aging. Aging was also correlated with a decrease in overall lipid percentage of subcutaneous tissue, with lipid deposition being favored extracellularly, an increase in transforming growth factor-β1 (TGFβ1), and an increase in M1 macrophage polarization. An important finding was that self-identified race contributed to variance between patients in this study, where Black patients had significantly lower gene expression levels of TGFβ1 and ACTA2. This finding supports the urgent need to account for patient ancestry in biomedical research to develop better therapeutic strategies for all patients. Another important finding was that TGFβ induced factor homeobox 1 (TGIF1), an understudied signaling molecule, which is highly correlated with leptin signaling, was correlated with metabolic inflammation. Furthermore, this study draws attention to what we define as “extracellular lipid droplets”, which were consistently found in collagen-rich regions of the obese adipose tissues evaluated here. Reduced levels of TGIF1 were correlated with higher numbers of extracellular lipid droplets and an inability to suppress fibrotic changes in adipose tissue. Finally, this study indicated that M1 and M2 macrophage markers were correlated with each other and leptin in patients with a BMI > 25. This finding supports growing evidence that macrophage polarization in obesity involves a complex, interconnecting network system rather than a full switch in activation patterns from M2 to M1 with increasing body mass. Overall, this study reinforces key findings in animal studies and identifies important areas for future research, where human and animal studies are divergent. Understanding key drivers of human patient variability is required to unravel the complex metabolic health of unique patients. Full article
(This article belongs to the Special Issue Fibrosis in Chronic Inflammatory Diseases)
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<p>Study design showing how patient demographics (green) and tissue characterization techniques (red) fed into two different statistical models, where (1) each quantitative variable (gray box) was plotted in a correlation matrix against the other quantitative variables to determine correlative relationships and (2) qualitative and quantitative variables (all of the variables in the black box) were fed into a factorial analysis of mixed data (FAMD) to determine significant relationships between the variables. Correlations were used to compare the human data to published data from animal models (i.e., in animals, TGFβ1 gene expression is related to enhanced collagen deposition, and is verified in this study in human samples) and to discover new correlations (i.e., there is a correlation of gene expression between TGIF and leptin). The FAMD indicated what variables account for the most variation in the dataset (with BMI &gt; 25).</p>
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<p>(<b>A</b>–<b>D</b>) Representative images used to analyze extracellular lipid diameter. Samples were stained with AdipoRed (red) and Phalloidin 488 (green). Extracellular lipids appear red, while adipocytes (stained by both AdipoRed and Phalloidin 488) appear green. Scale bars are 200 μm. Each image is from a different patient. (<b>E</b>) Each dot on the graph represents an extracellular lipid droplet diameter measurement, with the mean represented by the red line (mean—7.27 μm, standard deviation—8.16 μm). (<b>F</b>) Histogram showing frequency of extracellular lipids with specific diameters, where the majority of extracellular lipids measured were under 15 μm. <span class="html-italic">n</span> = 1904.</p>
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<p>Amongst quantitative variables, many correlations exist in BMI, age, mechanical properties, cell number (DNA content), metabolic activity, doubling time of the stromal vascular fraction, collagen content (hydroxyproline), advanced glycation end products, morphological measurements, histological quantification, and gene expression. The correlation matrix indicates the correlation between quantitative variables (samples from N = 20 patients with BMI &gt; 25). A value greater than 0.7 is considered a high correlation, 0.5–0.7 is considered a moderate correlation, and 0.3–0.5 is considered a low correlation.</p>
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<p>Gene expression of VEGFA was correlated to stromal vascular doubling time and adiponectin gene expression. VEGFA gene expression is plotted versus stromal vascular doubling time (<b>A</b>) and adiponectin (ADIPOQ) gene expression (<b>B</b>). Gene expression is represented as a delta CT from the housekeeping (HK) gene (ΔCT = CT<sub>SDHA</sub> − CT<sub>Target gene</sub>). Therefore, the HK gene is equal to 0 on the plots. With the formula used, gene expression is relative to the housekeeping gene and increases at higher values. The red lines indicate a linear regression best fit line for each dataset. Correlations from the correlation matrix are indicated on each plot.</p>
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<p>Contribution results generated from the FAMD analysis normalized to the highest contribution in each dimension. In each column, the contribution of variables to the nine dimensions is shown in a heat map, where a value of 1 indicates the largest loading of a variable and the highest contributor in that dimension. For example, in dimension 1, VEGFA is the highest contributor (1.00) followed closely by TGIF1 (0.982).</p>
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<p>Increased gene expression of transforming growth factor-β induced factor homeobox 1 (TGIF1) was correlated with an increase in gene expression of leptin, interleukin-6 (IL6), CD163, CD86, and no bariatric surgery and a decrease in the number of extracellular lipid droplets. TGIF1 gene expression was plotted versus leptin (LEP) gene expression (<b>A</b>), IL6 gene expression (<b>B</b>), CD163 gene expression (<b>C</b>), CD86 gene expression (<b>D</b>), and the number of extracellular lipid droplets counted in histological images (<b>E</b>) and separated by whether the patient had undergone bariatric surgery previously or not (<b>F</b>). Gene expression is represented as a delta CT from the housekeeping (HK) gene (ΔCT = CT<sub>SDHA</sub> − CT<sub>Target gene</sub>). Therefore, the HK gene is equal to 0 on the plots. With the formula used, gene expression is relative to the housekeeping gene and increases from a negative value to a higher positive value. The red lines indicate a linear regression best fit line for each dataset. Correlations from the correlation matrix are indicated on each plot. A summary of the results and current literature findings are illustrated (<b>G</b>), where the literature indicates that insulin upregulates TGIF1 (Horie, 2008) [<a href="#B60-cells-13-01240" class="html-bibr">60</a>], blocking TGFβ1 transcriptional changes (Wotton, 1999) [<a href="#B57-cells-13-01240" class="html-bibr">57</a>], inducing adipocyte differentiation (Horie, 2008) [<a href="#B60-cells-13-01240" class="html-bibr">60</a>], and increasing lipid accumulation (Bjork, 2021) [<a href="#B61-cells-13-01240" class="html-bibr">61</a>].</p>
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<p>Increased gene expression of actin alpha 2, smooth muscle (ACTA2) gene expression was correlated with an increase in transforming growth factor-β (TGFβ1) gene expression and collagen content (hydroxyproline), with a corresponding decrease in adipocyte diameter. ACTA2 gene expression was plotted versus TGFβ1 (<b>A</b>), hydroxyproline content (<b>B</b>), and measurements of adipocyte diameter in histological images (<b>C</b>). Gene expression is represented as a delta CT from the housekeeping (HK) gene (ΔCT = CT<sub>SDHA</sub> − CT<sub>Target gene</sub>). Therefore, the HK gene is equal to 0 on the plots. With the formula used, gene expression is relative to the housekeeping gene and increases from a negative value to a higher positive value. The red lines indicate a linear regression best fit line for each dataset. Correlations from the correlation matrix are indicated on each plot.</p>
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<p>With increasing age, there is a decline in the number of adipocytes and the percentage of lipids and an increase in TGFβ1 and the number of lipid droplets deposited extracellularly. The age of patients at the time of surgery is plotted versus the number of adipocytes measured in histological images (<b>A</b>), the lipid percent measured in the same images (<b>B</b>), the gene expression of TGFβ1 (<b>C</b>), and the number of extracellular lipid droplets counted in histological images (<b>D</b>). TGFβ1 gene expression is represented as a delta CT from the housekeeping gene (ΔCT = CT<sub>SDHA</sub> − CT<sub>Target gene</sub>). With the formula used, gene expression is relative to the housekeeping gene and increases from a negative value to a higher positive value. The red lines indicate a linear regression best fit line for each dataset. Correlations from the correlation matrix are indicated on each plot.</p>
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<p>Significantly lower fibrotic gene expression (TGFβ1 and ACTA2) and trends related to collagen content, TGIF1, leptin, extracellular lipid droplets, and adiponectin were observed in self-identified Black patients compared to White patients and were independent of BMI. Samples derived from Black patients had significantly lower gene expression levels of TGFβ1 (<b>A</b>) and ACTA2 (<b>B</b>) and a trend towards lower hydroxyproline collagen content (<b>C</b>) than those derived from White patients (all patients identified as non-Hispanic). On average, samples from Black patients also had higher gene expression levels of TGIF1 (<b>D</b>) and leptin (<b>E</b>) and fewer extracellular lipid droplets (<b>F</b>) compared to White patients. Adiponectin levels were also lower in Black patient samples compared to White patient samples (<b>G</b>). Differences between the population groups are independent of body mass index (BMI), as there were no significant differences in BMI between the groups (<b>H</b>). Samples derived from Black patients are represented as dots and were color-coded by patient source (i.e., all of the blue dots are from the same patient) to highlight that the outliers in TGIF1, leptin, hydroxyproline, and number of extracellular lipid droplets were all from the same patient (red dots). The patient sample represented by the red dots follows the same trends as the aggregated data (with combined ancestries) where reduced levels of TGIF1 are linked with higher numbers of extracellular lipid droplets and an inability to suppress fibrotic changes in adipose tissue. Samples derived from White patients are represented as black squares. Black lines indicate the mean of the data. Statistical significance was determined by an unpaired <span class="html-italic">t</span>-test, where ** indicates <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Gene expression of the classically activated CD86 macrophage surface marker and the alternatively activated CD163 surface marker were correlated with each other and with leptin gene expression. CD86 gene expression is plotted versus CD163 gene expression (<b>A</b>) and leptin (LEP) gene expression (<b>B</b>). Gene expression is represented as a delta CT from the housekeeping (HK) gene (ΔCT = CT<sub>SDHA</sub> − CT<sub>Target gene</sub>). Therefore, the HK gene is equal to 0 on the plots. With the formula used, gene expression is relative to the housekeeping gene and increases from a negative value to a higher positive value. The red lines indicate a linear regression best fit line for each dataset. Correlations from the correlation matrix are indicated on each plot.</p>
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21 pages, 3957 KiB  
Systematic Review
Stable Nitroxide as Diagnostic Tools for Monitoring of Oxidative Stress and Hypoalbuminemia in the Context of COVID-19
by Ekaterina Georgieva, Julian Ananiev, Yovcho Yovchev, Georgi Arabadzhiev, Hristo Abrashev, Vyara Zaharieva, Vasil Atanasov, Rositsa Kostandieva, Mitko Mitev, Kamelia Petkova-Parlapanska, Yanka Karamalakova, Vanya Tsoneva and Galina Nikolova
Int. J. Mol. Sci. 2024, 25(15), 8045; https://doi.org/10.3390/ijms25158045 (registering DOI) - 24 Jul 2024
Viewed by 174
Abstract
Oxidative stress is a major source of ROS-mediated damage to macromolecules, tissues, and the whole body. It is an important marker in the severe picture of pathological conditions. The discovery of free radicals in biological systems gives a “start” to studying various pathological [...] Read more.
Oxidative stress is a major source of ROS-mediated damage to macromolecules, tissues, and the whole body. It is an important marker in the severe picture of pathological conditions. The discovery of free radicals in biological systems gives a “start” to studying various pathological processes related to the development and progression of many diseases. From this moment on, the enrichment of knowledge about the participation of free radicals and free-radical processes in the pathogenesis of cardiovascular, neurodegenerative, and endocrine diseases, inflammatory conditions, and infections, including COVID-19, is increasing exponentially. Excessive inflammatory responses and abnormal reactive oxygen species (ROS) levels may disrupt mitochondrial dynamics, increasing the risk of cell damage. In addition, low serum albumin levels and changes in the normal physiological balance between reduced and oxidized albumin can be a serious prerequisite for impaired antioxidant capacity of the body, worsening the condition in patients. This review presents the interrelationship between oxidative stress, inflammation, and low albumin levels, which are hallmarks of COVID-19. Full article
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<p>Immune response and redox imbalance during SARS-CoV-2 infection.</p>
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<p>The flow diagram shows the methodology for selecting articles. This methodology follows the recommendations given in PRISMA-P guideline rules [<a href="#B25-ijms-25-08045" class="html-bibr">25</a>] and PRISMA-S named “PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews” for reporting literature searches.</p>
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<p>Inflammation response and hepcidin during SARS-CoV-2 infection. Transported iron into cells (Fe<sup>2+</sup>) binds to ferritin or is transported into the bloodstream by ferroportin, where it is oxidized to Fe<sup>3+</sup>. Oxidized iron forms a complex with transferrin, which allows efficient iron transport in the body. The inflammatory response triggers the production of hepcidin, which is part of the body’s defense mechanism against pathogens. During SARS-CoV-2 infection, interleukin-6 (IL-6) induced hepcidin induction via the IL-6R/STAT3 pathway. It leads to high hepcidin levels and, as a result, to inhibiting and decreasing ferroportin activity.</p>
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<p>Redox reactions of nitroxides: diamagnetic non-radical forms hydroxyl amine (&gt;N-OH), and paramagnetic radical form (&gt;NO•) involvement of the hydroxylamine form of TEMPOL-H in redox reactions in the presence of ROS, in which the emergence of an EPR signal and recovery of the nitroxide radical form is observed.</p>
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<p>Redox reactions of TEMPOL in the presence of free iron, •OH, and H<sub>2</sub>O<sub>2</sub>. Efficiency of the nitroxide radical in metabolizing O<sub>2</sub>•<sup>−</sup> and H<sub>2</sub>O<sub>2</sub> or in protecting cells from •OH; TEMPOL sensitivity was found to be the highest for •OH.</p>
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10 pages, 1081 KiB  
Article
Left Atrial Coupling Index Predicts Heart Failure in Patients with End Stage Renal Disease
by Fulya Avcı Demir, Gülsüm Bingöl, Mustafa Uçar, Özge Özden, Emre Özmen, Haşim Tüner, Muharrem Nasifov and Serkan Ünlü
Medicina 2024, 60(8), 1195; https://doi.org/10.3390/medicina60081195 - 24 Jul 2024
Viewed by 178
Abstract
Background and Objectives: We aimed to ascertain the predictive power of the left atrial coupling index (LACI) in patients with end stage renal disease (ESRD) for heart failure with preserved ejection fraction (HFpEF). Materials and Methods: This is a retrospective study including 100 [...] Read more.
Background and Objectives: We aimed to ascertain the predictive power of the left atrial coupling index (LACI) in patients with end stage renal disease (ESRD) for heart failure with preserved ejection fraction (HFpEF). Materials and Methods: This is a retrospective study including 100 subjects between 18 and 65 years of age with ESRD and not on dialysis treatment. Patients were divided into groups with and without HFpEF. The LACI was defined as the ratio of the left atrial volume index (LAVI) to the a′ wave in tissue Doppler imaging (TDI). Statistical analyses were performed, including univariate and multivariate regression analyses. Results: The mean age of the participants was 47 ± 13.3 years. Individuals with HFpEF exhibited a higher LACI. Univariate and multivariate regression analyses demonstrated that the predictive capacity of the LACI for HFpEF was considerably higher than that of the LAVI and other echocardiographic parameters. Conclusions: Higher LACI levels were consistently related to the presence of HFpEF in ESRD patients. The LACI can be easily obtained in daily practice using conventional Doppler echocardiographic measurements during left atrial functional assessments. Full article
(This article belongs to the Section Cardiology)
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<p>Receiver operating curve analysis for LACI for the prediction of HFpEF (▬: receiver operating curve, <b>…</b>: upper- and lower 95% confidence interval boundaries of the receiver operating curve).</p>
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24 pages, 7397 KiB  
Article
Toxicity to the Male Reproductive System after Exposure to Polystyrene Nanoplastics: A Macrogenomic and Metabolomic Analysis
by Xue Zhang, Yueping Wu, Xufeng Fu, Shulan He, Liping Shi, Haiming Xu, Xiaojuan Shi, Yue Yang, Yongbin Zhu, Yanrong Wang, Hongyan Qiu, Hongmei Li and Jiangping Li
Toxics 2024, 12(8), 531; https://doi.org/10.3390/toxics12080531 (registering DOI) - 23 Jul 2024
Viewed by 218
Abstract
Nanoplastics (NPs) cause serious contamination of drinking water and potential damage to human health. This study aimed to investigate the effects of NPs with different particle sizes and concentrations on the reproductive function of male mice. In this study, free drinking water exposure [...] Read more.
Nanoplastics (NPs) cause serious contamination of drinking water and potential damage to human health. This study aimed to investigate the effects of NPs with different particle sizes and concentrations on the reproductive function of male mice. In this study, free drinking water exposure was used to expose male BALB/C mice to PS-NPs (20 nm, 200 nm, and 1000 nm) at 0.1 mg/L, 1 mg/L, and 5 mg/L for 4 months. The male reproductive function of the mice was assessed after NPs exposure, and fecal and blood samples were collected for macrogenomics and metabolomics. The results showed that PS-NPs resulted in mice with reduced testicular organ coefficients, decreased sperm quality, altered testicular tissue structure, disturbed sex hormone levels, and abnormal levels of inflammatory factors and oxidative stress. Furthermore, this study found that NP exposure affected the alteration of gut communities and metabolic pathways related to male reproduction, such as Clostridium and glutathione metabolism. Importantly, we found an effect of NP particle size on reproductive function. In the future, more attention should be paid to the smaller particle sizes of NPs. Full article
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<p>Study subgroup design. Note: The figure was drawn by Figdraw at <a href="https://www.figdraw.com/static/index.html" target="_blank">https://www.figdraw.com/static/index.html</a> (accessed on 31 December 2023).</p>
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<p>Body weight and changes in the organ coefficient after drinking exposure to PS-NPS. Note: “#” represents comparisons among exposure groups of different particle sizes, “##” is <span class="html-italic">p</span> &lt; 0.01 and “###” is <span class="html-italic">p</span> &lt; 0.001. “*” represents comparisons between exposure groups of different particle sizes and the control group. “*” is <span class="html-italic">p</span> &lt; 0.05, “**” is <span class="html-italic">p</span> &lt; 0.01, and “***” is <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Changes in sperm parameters and sex hormones after drinking water exposure to PS-NPs. Note: Changes in sperm counts (<b>A</b>), sperm abnormality proportion (<b>B</b>), and sperm motility (<b>C</b>) after exposure. Representative images of sperm with Eosin staining (<b>D</b>). The pentagram indicates sperm with normal morphology, while “&amp;” indicates tailless, “→” indicates cervical folding, and “#” indicates a curly tail. The level of testosterone in testicular tissue (<b>E</b>) and FSH in serum (<b>F</b>). “#” represents comparisons among exposure groups of different particle sizes, “*” represents comparisons between exposure groups of different particle sizes and the control group. “*/#” is <span class="html-italic">p</span> &lt; 0.05, “**/##” is <span class="html-italic">p</span> &lt; 0.01, “***/###” is <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Changes in inflammatory factors and oxidative stress in testicular tissue after exposure. Note: Changes in inflammatory factors ((<b>A</b>): IL-6) and oxidative stress ((<b>B</b>): CAT, (<b>C</b>): MDA, and (<b>D</b>): SOD)) in testicular tissue after drinking exposure to PS-NPs. “#” represents comparisons among exposure groups of different particle sizes, “*” represents comparisons between exposure groups of different particle sizes and the control group. “*/#” is <span class="html-italic">p</span> &lt; 0.05, “**/##” is <span class="html-italic">p</span> &lt; 0.01, and “***/###” is <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Immunofluorescence assay for TNF-α and IL-6 expression in mouse testes. Note: Immunofluorescence staining of testicular tissue, with TNF-α fluorescence expression in red, IL-6 fluorescence expression in green, and DAPI nuclear staining in blue (<b>A</b>). Quantitative detection and analysis of testicular tissue immunofluorescence staining (<b>B</b>). “#” represents comparisons among exposure groups of different particle sizes, “###” is <span class="html-italic">p</span> &lt; 0.001. “*” represents comparisons between exposure groups of different particle sizes and the control group. “*” is <span class="html-italic">p</span> &lt; 0.05 and “**” is <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>HE staining of mouse testicular tissue (50 μm). Note: black arrows indicate sparse structure, red arrows indicate vacuolization of testicular tissue, and red boxes indicate detached germ cells.</p>
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<p>Electron microscopic structure of mouse tissue (2 μm). Note: Red arrows indicate the blood–testis barrier, and red boxes indicate structurally disrupted mitochondria.</p>
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<p>Changes in gut NR-annotated species diversity and community species composition. The comparison of the Alpha diversity of NR genes in different exposure groups at the species level (<b>A</b>). “*” is <span class="html-italic">p</span> &lt; 0.05 and “**” is <span class="html-italic">p</span> &lt; 0.01. The PCA analysis of NR genes in different exposure groups at the phylum level (<b>B</b>). Venn analysis of NR genes in different exposure groups at the genus level (<b>C</b>). Circos plot at the phylum level for difference exposure groups (<b>D</b>). Community bar plot analysis at the phylum level (<b>E</b>).</p>
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<p>Changes in the abundance of gut microbes. The differences in gut microbial species at the family level (<b>left</b>) and genus level (<b>right</b>) among the groups (<b>A</b>); The gut microbes associated with male reproductive function at the genus level (<b>B</b>). “*” is <span class="html-italic">p</span> &lt; 0.05 and “**” is <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Correlation analysis heatmap showing gut microbial abundance and testicular tissue levels of testosterone, inflammation, and oxidative stress. Note: R-values are shown in different colors in the figure. “*” is <span class="html-italic">p</span> &lt; 0.05 and “**” is <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Metabolite annotation information and composition.</p>
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<p>KEGG differential metabolite enrichment analysis. Note: 20 nm vs. control (<b>A</b>), 200 nm vs. control (<b>B</b>), 1000 nm vs. control (<b>C</b>). The figure is the KEGG enrichment analysis plot, where the horizontal coordinate indicates the pathway name and the vertical coordinate indicates the enrichment rate. “*” is <span class="html-italic">p</span> &lt; 0.05, “**” is <span class="html-italic">p</span> &lt; 0.01, and “***” is <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Differential metabolite analysis. Note: The horizontal coordinate of the left bar graph indicates the average relative abundance of a metabolite in different subgroups, the vertical coordinate indicates the subgroup category in a two-by-two comparison, and different colors indicate different subgroups. The middle area is the confidence interval set, the value corresponding to the dot indicates the difference in the average relative abundance of the metabolite in the two subgroups, the color of the dot is shown as the color of the subgroup whose metabolite abundance accounts for a larger proportion of the metabolite abundance, and the I-type intervals on the dots are the differences between the upper and lower values. “*” is <span class="html-italic">p</span> &lt; 0.05 and “**” is <span class="html-italic">p</span> &lt; 0.01.</p>
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31 pages, 1965 KiB  
Review
Unraveling the Dynamics of Estrogen and Progesterone Signaling in the Endometrium: An Overview
by Isabelle Dias Da Silva, Vincent Wuidar, Manon Zielonka and Christel Pequeux
Cells 2024, 13(15), 1236; https://doi.org/10.3390/cells13151236 - 23 Jul 2024
Viewed by 174
Abstract
The endometrium is crucial for the perpetuation of human species. It is a complex and dynamic tissue lining the inner wall of the uterus, regulated throughout a woman’s life based on estrogen and progesterone fluctuations. During each menstrual cycle, this multicellular tissue undergoes [...] Read more.
The endometrium is crucial for the perpetuation of human species. It is a complex and dynamic tissue lining the inner wall of the uterus, regulated throughout a woman’s life based on estrogen and progesterone fluctuations. During each menstrual cycle, this multicellular tissue undergoes cyclical changes, including regeneration, differentiation in order to allow egg implantation and embryo development, or shedding of the functional layer in the absence of pregnancy. The biology of the endometrium relies on paracrine interactions between epithelial and stromal cells involving complex signaling pathways that are modulated by the variations of estrogen and progesterone levels across the menstrual cycle. Understanding the complexity of estrogen and progesterone receptor signaling will help elucidate the mechanisms underlying normal reproductive physiology and provide fundamental knowledge contributing to a better understanding of the consequences of hormonal imbalances on gynecological conditions and tumorigenesis. In this narrative review, we delve into the physiology of the endometrium, encompassing the complex signaling pathways of estrogen and progesterone. Full article
(This article belongs to the Special Issue Breakthroughs in Cell Signaling in Health and Disease)
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<p>Estrogen levels throughout woman’s life, adapted from [<a href="#B8-cells-13-01236" class="html-bibr">8</a>,<a href="#B9-cells-13-01236" class="html-bibr">9</a>]. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 16 June 2024).</p>
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<p>Woman’s menstrual cycle according to estrogen and progesterone fluctuation levels. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 16 June 2024).</p>
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<p>ER Signaling in response to estrogens during proliferative phase. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 16 June 2024).</p>
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<p>PR signaling in response to progesterone during secretory phase. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 19 June 2024).</p>
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12 pages, 6504 KiB  
Article
Testosterone Inhibits Lipid Accumulation in Porcine Preadipocytes by Regulating ELOVL3
by Fuyin Xie, Yubei Wang, Shuheng Chan, Meili Zheng, Mingming Xue, Xiaoyang Yang, Yabiao Luo and Meiying Fang
Animals 2024, 14(15), 2143; https://doi.org/10.3390/ani14152143 - 23 Jul 2024
Viewed by 206
Abstract
Castration is commonly used to reduce stink during boar production. In porcine adipose tissue, castration reduces androgen levels resulting in metabolic disorders and excessive fat deposition. However, the underlying detailed mechanism remains unclear. In this study, we constructed porcine preadipocyte models with and [...] Read more.
Castration is commonly used to reduce stink during boar production. In porcine adipose tissue, castration reduces androgen levels resulting in metabolic disorders and excessive fat deposition. However, the underlying detailed mechanism remains unclear. In this study, we constructed porcine preadipocyte models with and without androgen by adding testosterone exogenously. The fluorescence intensity of lipid droplet (LD) staining and the fatty acid synthetase (FASN) mRNA levels were lower in the testosterone-treated cells than in the untreated control cells. In contrast, the mRNA levels of adipose triglycerides lipase (ATGL) and androgen receptor (AR) were higher than in the testosterone-treated cells than in the control cells. Subsequently, transcriptomic sequencing of porcine preadipocytes incubated with and without testosterone showed that the mRNA expression levels of very long-chain fatty acid elongase 3 (ELOVL3), a key enzyme involved in fatty acids synthesis and metabolism, were high in control cells. The siRNA-mediated knockdown of ELOVL3 reduced LD accumulation and the mRNA levels of FASN and increased the mRNA levels of ATGL. Next, we conducted dual-luciferase reporter assays using wild-type and mutant ELOVL3 promoter reporters, which showed that the ELOVL3 promoter contained an androgen response element (ARE); furthermore, its transcription was negatively regulated by AR overexpression. In conclusion, our study reveals that testosterone inhibits fat deposition in porcine preadipocytes by suppressing ELOVL3 expression. Moreover, our study provides a theoretical basis for further studies on the mechanisms of fat deposition caused by castration. Full article
(This article belongs to the Section Pigs)
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<p>Testosterone treatment inhibited fat accumulation and altered the expression of lipid metabolism-related genes: (<b>A</b>) Oil Red O was used to stain porcine preadipocytes on the 6 d in the testosterone (T) and control groups (bar = 100 μm). (<b>B</b>) Quantification of LDs using Oil Red O analysis on the 6 d in the testosterone (T) and control groups. (<b>C</b>) The levels of <span class="html-italic">FASN</span>, <span class="html-italic">ATGL,</span> and <span class="html-italic">AR</span> mRNA in T group and control group on the 6 d of adipogenic differentiation of porcine preadipocytes were analyzed using quantitative RT-PCR. (<b>D</b>) LDs stained with BODIPY 493/503 in the T, testosterone + flutamide (T + F), and control groups (bar = 130 μm); (<b>E</b>) Quantitative RT-PCR analysis of the mRNA levels of <span class="html-italic">FASN</span>, <span class="html-italic">ATGL,</span> and <span class="html-italic">AR</span> on the 6 d in the T, T + F, and control groups. <span class="html-italic">n</span> = 3 per group. Statistical comparisons were performed with a <span class="html-italic">t</span>-test with one-way ANOVA. All data are expressed as means ± SEM. Results of the <span class="html-italic">t</span>-test are denoted by asterisks: * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001 compared to the control (on the bars) or between the indicated groups. Results of one-way ANOVA are indicated by letters. The absence of significant differences is indicated by identical letters (<span class="html-italic">p</span> &gt; 0.05), while different lowercase letters denote significant differences (<span class="html-italic">p</span> &lt; 0.05), and different capital letters signify highly significant differences (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Transcriptomic profiles of porcine preadipocytes incubated with testosterone: (<b>A</b>) Volcano plot of the statistically significant DEGs (adjusted <span class="html-italic">p</span> &lt; 0.05 and |log2FoldChange| &gt; 1) between the testosterone-treated and control preadipocytes. (<b>B</b>) GO analysis of the DEGs. (<b>C</b>) KEGG analysis of the down-regulated DEGs; (<b>D</b>) KEGG analysis of the up-regulated DEGs; (<b>E</b>) KEGG analysis of metabolism-related DEGs. <span class="html-italic">n</span> = 3 per group.</p>
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<p>Knockdown of <span class="html-italic">ELOVL3</span> expression inhibited porcine fat accumulation: (<b>A</b>) <span class="html-italic">ELOVL3</span> mRNA levels in the different groups were tested with qRT-PCR. (<b>B</b>) Porcine preadipocytes were transfected with <span class="html-italic">ELOVL3</span> siRNAs (si-713-<span class="html-italic">ELOVL3</span>, si-394-<span class="html-italic">ELOVL3</span>, and si-410-<span class="html-italic">ELOVL3</span>) or a negative control siRNA, and <span class="html-italic">ELOVL3</span> expression interference efficiency was analyzed at 48 h after transfection. The relative expression of <span class="html-italic">ELOVL3</span> was normalized, and the relative values were expressed as the fold of induction relative to the negative control. (<b>C</b>) LD accumulation in preadipocytes transfected with either si-713-<span class="html-italic">ELOVL3</span> or a negative control siRNA was analyzed using Oil Red O staining at 6 d of induction (bar = 100 μm). (<b>D</b>) Quantification of LDs using Oil Red O analysis on the 6 d in the si-713-<span class="html-italic">ELOVL3</span> and negative control groups. (<b>E</b>) The mRNA levels of <span class="html-italic">ELOVL3</span>, <span class="html-italic">FASN</span>, and <span class="html-italic">ATGL</span> in the si-713-<span class="html-italic">ELOVL3</span> and negative control group were confirmed as measured using qRT-PCR. <span class="html-italic">n</span> = 3 per group. All data are expressed as means ± SEM. Results of the <span class="html-italic">t</span>-test are denoted by asterisks: * <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 control (on the bars) or between the indicated groups. Results of one-way ANOVA are indicated by letters. The absence of significant differences is indicated by identical letters (<span class="html-italic">p</span> &gt; 0.05), while different lowercase letters denote significant differences (<span class="html-italic">p</span> &lt; 0.05), and different capital letters signify highly significant differences (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p><span class="html-italic">AR</span> targets the <span class="html-italic">ELOVL3</span> promoter and inhibits its transcriptional activity: (<b>A</b>) Relative luciferase activity (Firefly: Renilla) at 48 h after transfection with different 5′ deletion <span class="html-italic">ELOVL3</span> promoter constructs (−2000 bp/+100 bp, −1460 bp/+100 bp, −920 bp/+100 bp, −380 bp/+100 bp, −158 bp/+100 bp). (<b>B</b>) Effects of <span class="html-italic">AR</span> overexpression on wild-type and mutant <span class="html-italic">ELOVL3</span> promoter activity. <span class="html-italic">n</span> = 3 per group. The luciferase activity was normalized, and the relative values were expressed as the fold of induction relative to the pcDNA3.1-EGFP vector activity. A one-way ANOVA test was used to assess the differences in luciferase activity. The absence of significant differences is denoted by the same letters (<span class="html-italic">p</span> &gt; 0.05), while distinct lowercase letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05), and distinct capital letters signify a significant difference (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Testosterone inhibits lipid accumulation in porcine preadipocytes by regulating <span class="html-italic">ELOVL3</span>.</p>
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16 pages, 8128 KiB  
Article
Analysis of the Aging-Related AP2/ERF Transcription Factor Gene Family in Osmanthus fragrans
by Gongwei Chen, Tianqi Shao, Yixiao Zhou, Fengyuan Chen, Dandan Zhang, Heng Gu, Yuanzheng Yue, Lianggui Wang and Xiulian Yang
Int. J. Mol. Sci. 2024, 25(15), 8025; https://doi.org/10.3390/ijms25158025 - 23 Jul 2024
Viewed by 133
Abstract
Ethylene-Responsive Factor (ERF) is a key element found in the middle and lower reaches of the ethylene signal transduction pathway. It is widely distributed in plants and plays important roles in plant growth and development, hormone signal transduction, and various stress processes. Although [...] Read more.
Ethylene-Responsive Factor (ERF) is a key element found in the middle and lower reaches of the ethylene signal transduction pathway. It is widely distributed in plants and plays important roles in plant growth and development, hormone signal transduction, and various stress processes. Although there is research on AP/ERF family members, research on AP2/ERF in Osmanthus fragrans is lacking. Thus, in this work, AP2/ERF in O. fragrans was extensively and comprehensively analyzed. A total of 298 genes encoding OfAP2/ERF proteins with complete AP2/ERF domains were identified. Based on the number of AP2/ERF domains and the similarity among amino acid sequences between AP2/ERF proteins from A. thaliana and O. fragrans, the 298 putative OfAP2/ERF proteins were divided into four different families, including AP2 (45), ERF (247), RAV (5), and SOLOIST (1). In addition, the exon–intron structure characteristics of these putative OfAP2/ERF genes and the conserved protein motifs of their encoded OfAP2/ERF proteins were analyzed, and the results were found to be consistent with those of the population classification. A tissue-specific analysis showed the spatiotemporal expression of OfAP2/ERF in the stems and leaves of O. fragrans at different developmental stages. Specifically, 21 genes were not expressed in any tissue, while high levels of expression were found for 25 OfAP2/ERF genes in several tissues, 60 genes in the roots, 34 genes in the stems, 37 genes in young leaves, 34 genes in old leaves, 32 genes in the early flowering stage, 18 genes in the full flowering stage, and 37 genes in the late flowering stage. Quantitative RT-PCR experiments showed that OfERF110a and OfERF110b had the highest expression levels at the full-bloom stage (S4), and this gradually decreased with the senescence of petals. The expression of OfERF119c decreased first and then increased, while the expression levels of OfERF4c and OfERF5a increased constantly. This indicated that these genes may play roles in flower senescence and the ethylene response. In the subsequent subcellular localization experiments, we found that ERF1-4 was localized in the nucleus, indicating that it was expressed in the nucleus. In yeast self-activation experiments, we found that OfERF112, OfERF228, and OfERF23 had self-activation activity. Overall, these results suggest that OfERFs may have the function of regulating petal senescence in O. fragrans. Full article
(This article belongs to the Special Issue Regulation of Transcription Factor–Hormone Networks in Plants)
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<p>Phylogenetic analysis of AP2/ERF transcription factors of <span class="html-italic">A. thaliana</span> and <span class="html-italic">Osmanthus fragrans</span>. Red represents AP2/ERF factors in <span class="html-italic">A. thaliana</span>; black represents AP2/ERF factors in <span class="html-italic">O. fragrans</span>.</p>
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<p>Conserved motifs in the protein sequences.</p>
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<p>Conserved motif analysis for the protein family members.</p>
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<p>Chromosomal distribution of AP2/ERF genes in <span class="html-italic">O. fragrans</span>. Blue lines represent tandem repeats.</p>
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<p>Duplicated AP2/ERF gene pairs in the <span class="html-italic">O. fragrans</span> genome. Green lines represent duplicated genes, blue lines represent tandem repeats.</p>
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<p>Expression patterns of AP2/ERF genes in different tissues of <span class="html-italic">O. fragrans</span>.</p>
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<p>Expression patterns of AP2/ERF genes at different flowering stages of <span class="html-italic">O. fragrans</span>. S1: Linggeng stage, S2: Xiangyan stage, S3: initial flowering stage, S4: full flowering stage, S5: late flowering stage. ‘a, b’ are part of the gene name.</p>
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<p>Subcellular localization of the OfERFs-GFP gene.</p>
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<p>Yeast self−activation verification.</p>
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