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Search Results (2,441)

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Keywords = HSP90

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14 pages, 1696 KiB  
Article
TRPA1 Influences Staphylococcus Aureus Skin Infection in Mice and Associates with HIF-1a and MAPK Pathway Modulation
by Manoj Yadav, Prem Prashant Chaudhary, Grace Ratley, Brandon D’Souza, Mahaldeep Kaur, Sundar Ganesan, Juraj Kabat and Ian A. Myles
Int. J. Mol. Sci. 2024, 25(18), 9933; https://doi.org/10.3390/ijms25189933 (registering DOI) - 14 Sep 2024
Viewed by 192
Abstract
Infections caused by methicillin-resistant Staphylococcus aureus (MRSA) are a major public health burden. Emerging antibiotic resistance has heightened the need for new treatment approaches for MRSA infection such as developing novel antimicrobial agents and enhancing the host’s defense response. The thermo-ion channels Transient [...] Read more.
Infections caused by methicillin-resistant Staphylococcus aureus (MRSA) are a major public health burden. Emerging antibiotic resistance has heightened the need for new treatment approaches for MRSA infection such as developing novel antimicrobial agents and enhancing the host’s defense response. The thermo-ion channels Transient Receptor Potential (TRP-) A1 and V1 have been identified as modulators of S. aureus quorum sensing in cell culture models. However, their effects on in vivo infection control are unknown. In this study, we investigated the therapeutic effect of natural TRP ion channel inhibitors on MRSA skin infection in mice. While deletion of TRPV1 did not affect lesion size or inflammatory markers, TRPA1−/− mice demonstrated significantly reduced infection severity and abscess size. Treatment with natural inhibitors of TRPA1 with or without blockade of TRPV1 also reduced abscess size. Tissue transcriptomic data coupled with immunohistochemistry revealed that TRPA1 inhibition impacted heat shock protein expression (HSP), modulated the HIF-1a and MAPK pathways, and reduced IL4 expression. Additionally, metabolomics data showed an impact on purine and glycosaminoglycan pathways. Multi-omic integration of transcriptomic and metabolic data revealed that diacylglycerol metabolism was the likely bridge between metabolic and immunological impacts. Our findings suggest that TRPA1 antagonism could provide a promising and cost-effective therapeutic approach for reducing the severity of MRSA infection, and presents a novel underlying molecular mechanism. Full article
12 pages, 2290 KiB  
Article
Mild Heat Stress Alters the Physical State and Structure of Membranes in Triacylglycerol-Deficient Fission Yeast, Schizosaccharomyces pombe
by Péter Gudmann, Imre Gombos, Mária Péter, Gábor Balogh, Zsolt Török, László Vígh and Attila Glatz
Cells 2024, 13(18), 1543; https://doi.org/10.3390/cells13181543 - 13 Sep 2024
Viewed by 239
Abstract
We investigated whether the elimination of two major enzymes responsible for triacylglycerol synthesis altered the structure and physical state of organelle membranes under mild heat shock conditions in the fission yeast, Schizosaccharomyces pombe. Our study revealed that key intracellular membrane structures, lipid [...] Read more.
We investigated whether the elimination of two major enzymes responsible for triacylglycerol synthesis altered the structure and physical state of organelle membranes under mild heat shock conditions in the fission yeast, Schizosaccharomyces pombe. Our study revealed that key intracellular membrane structures, lipid droplets, vacuoles, the mitochondrial network, and the cortical endoplasmic reticulum were all affected in mutant fission yeast cells under mild heat shock but not under normal growth conditions. We also obtained direct evidence that triacylglycerol-deficient cells were less capable than wild-type cells of adjusting their membrane physical properties during thermal stress. The production of thermoprotective molecules, such as HSP16 and trehalose, was reduced in the mutant strain. These findings suggest that an intact system of triacylglycerol metabolism significantly contributes to membrane protection during heat stress. Full article
(This article belongs to the Special Issue Advances in Biophysics of Cellular Membranes)
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Figure 1
<p>Analysis of lipid droplets in WT and DKO cells subjected to HS. (<b>A</b>) LD540 staining of LDs in WT and DKO cells before and after HS. (<b>B</b>) Quantitative analysis of the LDs in the WT and mutant cells. Data are mean ± SEM of <span class="html-italic">n</span> = 3 independent experiments with ≥500 cells/experiment; * <span class="html-italic">p</span> &lt; 0.05, 30 °C vs. 40 °C, 1 h; <span>$</span> <span class="html-italic">p</span> &lt; 0.05 WT vs. DKO.</p>
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<p>Analysis of vacuoles of WT and DKO exposed to HS. (<b>A</b>) Representative images of MDY-64-stained WT and DKO cells. Red arrows indicate enlarged vacuoles in heat-shocked DKO cells. Analysis of the vacuolar size (<b>B</b>), quantity (<b>C</b>), and surface/volume (<b>D</b>) after HS in both strains. Data are mean ± SEM of <span class="html-italic">n</span> = 3 independent experiments with &gt;200 vacuoles analyzed; * <span class="html-italic">p</span> &lt; 0.05, 30 °C vs. 40 °C 1 h.</p>
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<p>Changes in the mitochondrial network and cortical ER of heat-stressed WT and DKO <span class="html-italic">S. pombe</span> cells. (<b>A</b>) MitoTracker CMXRos staining; disordered parts of the mitochondrial network are indicated by arrows. (<b>B</b>) ER-thermo-yellow staining; dot-like ER structures are indicated by red arrows.</p>
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<p>Assessment of lipid packing of membranes in the wild-type and DKO strains. (<b>A</b>) False colored GP images of the WT (upper panel) and DKO (lower panel) cells during heat shock. (<b>B</b>) Median GP values of plasma membranes (PM) and endomembranes (EM) of control and heat-shocked WT and DKO cells. Data are mean ± SEM of <span class="html-italic">n</span> = 4 independent experiments, &gt;90 cells per data point analyzed; * <span class="html-italic">p</span> &lt; 0.05 30 °C vs. 40 °C, 1 h; <sup><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.05 WT vs. DKO; <sup>+</sup> <span class="html-italic">p</span> &lt; 0.05 PM vs. EM. GP, generalized polarization.</p>
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<p>Comparison of the expression of the thermoprotectants HSP16 and trehalose in heat-stressed WT and DKO cells. (<b>A</b>) Induction of HSP16-GFP in the WT (BRC40) and DKO (BRC62) backgrounds. (<b>B</b>) Accumulation of trehalose in WT and DKO cells during heat treatment. Data are mean ± SD for n = 3 independent experiments; * <span class="html-italic">p</span> &lt; 0.05 at 30 °C vs. 40 °C for 1 h; <sup><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.05 in WT vs. DKO.</p>
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17 pages, 4106 KiB  
Article
Replicative Endothelial Cell Senescence May Lead to Endothelial Dysfunction by Increasing the BH2/BH4 Ratio Induced by Oxidative Stress, Reducing BH4 Availability, and Decreasing the Expression of eNOS
by Ignacio Hernandez-Navarro, Laura Botana, Javier Diez-Mata, Laura Tesoro, Beatriz Jimenez-Guirado, Claudia Gonzalez-Cucharero, Nunzio Alcharani, Jose Luis Zamorano, Marta Saura and Carlos Zaragoza
Int. J. Mol. Sci. 2024, 25(18), 9890; https://doi.org/10.3390/ijms25189890 - 13 Sep 2024
Viewed by 174
Abstract
Vascular aging is associated with the development of cardiovascular complications, in which endothelial cell senescence (ES) may play a critical role. Nitric oxide (NO) prevents human ES through inhibition of oxidative stress, and inflammatory signaling by mechanisms yet to be elucidated. Endothelial cells [...] Read more.
Vascular aging is associated with the development of cardiovascular complications, in which endothelial cell senescence (ES) may play a critical role. Nitric oxide (NO) prevents human ES through inhibition of oxidative stress, and inflammatory signaling by mechanisms yet to be elucidated. Endothelial cells undergo an irreversible growth arrest and alter their functional state after a finite number of divisions, a phenomenon called replicative senescence. We assessed the contribution of NO during replicative senescence of human aortic (HAEC) and coronary (CAEC) endothelial cells, in which accumulation of the senescence marker SA-β-Gal was quantified by β-galactosidase staining on cultured cells. We found a negative correlation in passaged cell cultures from P0 to P12, between a reduction in NO production with increased ES and the formation of reactive oxygen (ROS) and nitrogen (ONOO) species, indicative of oxidative and nitrosative stress. The effect of ES was evidenced by reduced expression of endothelial Nitric Oxide Synthase (eNOS), Interleukin Linked Kinase (ILK), and Heat shock protein 90 (Hsp90), alongside a significant increase in the BH2/BH4 ratio, inducing the uncoupling of eNOS, favoring the production of superoxide and peroxynitrite species, and fostering an inflammatory environment, as confirmed by the levels of Cyclophilin A (CypA) and its receptor Extracellular Matrix Metalloprotease Inducer (EMMPRIN). NO prevents ES by preventing the uncoupling of eNOS, in which oxidation of BH4, which plays a key role in eNOS producing NO, may play a critical role in launching the release of free radical species, triggering an aging-related inflammatory response. Full article
(This article belongs to the Special Issue Molecular Mechanisms of Endothelial Dysfunction: Fourth Edition)
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Graphical abstract

Graphical abstract
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<p>Passage-induced senescence in cultured HAEC and CAEC. Representative Beta-Galactosidase staining of cultured HAEC and CAEC from Passage P1 to P12. N = 3 by triplicate, Mean ± SD. * <span class="html-italic">p</span> &lt; 0.05 P5 vs. P1. ** <span class="html-italic">p</span> &lt; 0.01 P8, P10, P12 vs. P1 (HAEC&amp;CAEC). Scale bars, 50 µm.</p>
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<p>ES promotes the expression of inflammatory markers in ECs. Representative immunoblot detection of Cyclophilin A (<b>A</b>) and its receptor EMMPRIN (high glycosylated (HG) and low glycosylated (LG) forms) (<b>B</b>). N = 3 by triplicate. Mean ± SD * <span class="html-italic">p</span> &lt; 0.01, ** <span class="html-italic">p</span> &lt; 0.001, *** <span class="html-italic">p</span> &lt; 0.0001 P1 vs. selected passages. (<b>C</b>,<b>D</b>) Representative expression of matrix metalloproteinases MM9 (<b>C</b>) and MMP13 (<b>D</b>) in the same cells. N = 3 by triplicate. Mean ± SD. * <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 P1 vs. indicated passages.</p>
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<p>ES reduces the expression of eNOS and promotes oxidation of BH4 in HAEC and CAEC. (<b>A</b>) Representative expression of eNOS from P1 to P12 (N = 3 by triplicate. Mean ± SD. * <span class="html-italic">p</span> &lt; 0.01 selected passages vs. P1). (<b>B</b>) Representative ratio BH2/BH4 in cultured HAEC and CAEC from P1 to P12 (N = 3 by triplicate. Mean ± SD. * <span class="html-italic">p</span> &lt; 0.01, ** <span class="html-italic">p</span> &lt; 0.001). (<b>C</b>,<b>D</b>) Representative expression of GTP cyclohydrolase and dihydrofolate reductase (DHFR), respectively, in cultured HAEC and CAEC from P1 to P12 (N = 3 by triplicate. Mean ± SD. * <span class="html-italic">p</span> &lt; 0.01 vs. selected passages).</p>
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<p>Cell passaging induces eNOS uncoupling from Hsp90 in senescent cells. Representative confocal microscopy of eNOS (FITC, green) and Hsp90 (Alexa 647, red) in HAEC and CAEC from P1 to P12. Merged panels show co-localization of both signals (yellow). N = 3 by triplicate. Mean ± SD. * <span class="html-italic">p</span> &lt; 0.01 vs. selected passages (HAEC). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 vs. selected passages (CAEC). Scale bars, 25 µm.</p>
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<p>Cell passaging induces eNOS uncoupling from ILK in senescent cells. Representative confocal microscopy of eNOS (FITC, green) and ILK (Alexa 647, red) in HAEC and CAEC from P1 to P12. Merged panels show co-localization of both signals (yellow). N = 3 by triplicate. Mean ± SD. * <span class="html-italic">p</span> &lt; 0.05 P1 vs. P8. ** <span class="html-italic">p</span> &lt; 0.01 P1 vs. P10, P12 (HAEC). * <span class="html-italic">p</span> &lt; 0.01 P1 vs. P5, P8. ** <span class="html-italic">p</span> &lt; 0.001 P1 vs. P10, P12 (CAEC). Scale bars, 25 µm.</p>
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<p>ES induces oxidative and nitrosative stress. Representative confocal microscopy detection of superoxide (<b>A</b>) and peroxynitrite (<b>B</b>) radicals in HAEC and CAEC from P1 to P12. (<b>C</b>) Representative immunoblot detection NOX in HAEC and CAEC from P1 to P12 (N = 3 mean by triplicate ± SD. * <span class="html-italic">p</span> &lt; 0.05 P1 vs. indicated passages. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.01 P1 vs. indicated passages). Scale bars, 25 µm.</p>
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12 pages, 2544 KiB  
Article
A Novel MAG Variant Causes Hereditary Spastic Paraplegia in a Consanguineous Pakistani Family
by Rabia Akram, Haseeb Anwar, Humaira Muzaffar, Valentina Turchetti, Tracy Lau, Barbara Vona, Ehtisham Ul Haq Makhdoom, Javed Iqbal, Shahid Mahmood Baig, Ghulam Hussain, Stephanie Efthymiou and Henry Houlden
Genes 2024, 15(9), 1203; https://doi.org/10.3390/genes15091203 - 13 Sep 2024
Viewed by 305
Abstract
Background and objectives: Hereditary spastic paraplegia (HSP) is characterized by unsteady gait, motor incoordination, speech impairment, abnormal eye movement, progressive spasticity and lower limb weakness. Spastic paraplegia 75 (SPG75) results from a mutation in the gene that encodes myelin associated glycoprotein (MAG). Only [...] Read more.
Background and objectives: Hereditary spastic paraplegia (HSP) is characterized by unsteady gait, motor incoordination, speech impairment, abnormal eye movement, progressive spasticity and lower limb weakness. Spastic paraplegia 75 (SPG75) results from a mutation in the gene that encodes myelin associated glycoprotein (MAG). Only a limited number of MAG variants associated with SPG75 in families of European, Middle Eastern, North African, Turkish and Palestinian ancestry have been documented so far. This study aims to provide further insight into the clinical and molecular manifestations of HSP. Methods: Using whole-exome sequencing, we investigated a consanguineous Pakistani family where three individuals presented with clinical signs of HSP. Sanger sequencing was used to carry out segregation analysis on available family members, and a minigene splicing assay was utilized to evaluate the effect of the splicing variant. Results: We identified a novel homozygous pathogenic splice donor variant in MAG (c.46 + 1G > T) associated with SPG75. RNA analysis revealed exon skipping that resulted in the loss of a start codon for ENST00000361922.8 isoform. Affected individuals exhibited variable combinations of nystagmus, developmental delay, cognitive impairments, spasticity, dysarthria, delayed gait and ataxia. The proband displayed a quadrupedal stride, and his siblings experienced frequent falls and ataxic gait as one of the prominent features that have not been previously reported in SPG75. Conclusions: Thus, the present study presents an uncommon manifestation of SPG75, the first from the Pakistani population, and broadens the spectrum of MAG variants. Full article
(This article belongs to the Section Human Genomics and Genetic Diseases)
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Figure 1
<p>(<b>A</b>) Five-generation pedigree of the family showing three affected siblings. All affected individuals were homozygous (G &gt; T) while the mother was heterozygous (Proband: V.1; Affected sister: V.2; Affected sister: V.3). The sample for IV.1 was unavailable for further study. (<b>B</b>) Sequence chromatograms of <span class="html-italic">MAG</span> showing a likely pathogenic c.46 + 1G &gt; T variant.</p>
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<p>RNA functional studies of the <span class="html-italic">MAG</span> c.46 + 1G &gt; T variants. (<b>A</b>) RT-PCR amplicons for the <span class="html-italic">MAG</span> c.46 + 1G wild-type, c.46 + 1T mutant, and empty pSPL3 vector were separated by gel electrophoresis. The PCR and transfection negative controls performed as expected. (<b>B</b>) The in vitro splice assay’s vector construct displays the variant-containing (lower splice profile) and wild-type (upper splice profile) amplicons that are placed between pSPL3 vector’s exons A and B. Each variant’s splicing schematic is displayed below. The c.46 + 1G &gt; A variant causes skipping of exon 3, resulting in a deletion of 46 bp of coding exon 3 (r.1_46del), p.?, including the start codon of the ENST00000361922.8/NM_080600.3 isoforms. (<b>C</b>) Sequencing of the exon–exon junctions for the wild-type (left) and variant, appearing as the empty vector control (right).</p>
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<p>MAG interacts with RTN4R/NgR and prevents axonal sprouting. MAG contains five Ig domains (1–5) and a intramembrane segment. MAG: Myelin associated glycoprotein; RTN4R: reticulon-4 receptor; NgR: Nogo Receptor; Ig: Immunoglobulin domain; Transmembrane domain: TM. Created with BioRender.com, accessed on 6 September 2024.</p>
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13 pages, 2091 KiB  
Article
From Nature to Treatment: The Impact of Pterostilbene on Mitigating Retinal Ischemia–Reperfusion Damage by Reducing Oxidative Stress, Inflammation, and Apoptosis
by Beáta Pelles-Taskó, Réka Szekeres, Barbara Takács, Anna Szilágyi, Dóra Ujvárosy, Mariann Bombicz, Dániel Priksz, Balázs Varga, Rudolf Gesztelyi, Zoltán Szabó, Zoltán Szilvássy and Béla Juhász
Life 2024, 14(9), 1148; https://doi.org/10.3390/life14091148 - 11 Sep 2024
Viewed by 265
Abstract
Retinal ischemia–reperfusion (I/R) injury is a critical pathogenic mechanism in various eye diseases, and an effective therapeutic strategy remains unresolved. Natural derivatives have recently reemerged; therefore, in our present study, we examined the potential therapeutic effects of a stilbenoid that is chemically related [...] Read more.
Retinal ischemia–reperfusion (I/R) injury is a critical pathogenic mechanism in various eye diseases, and an effective therapeutic strategy remains unresolved. Natural derivatives have recently reemerged; therefore, in our present study, we examined the potential therapeutic effects of a stilbenoid that is chemically related to resveratrol. Pterostilbene, recognized for its anti-inflammatory, anti-carcinogenic, anti-diabetic, and neuroprotective properties, counteracts oxidative stress during I/R injury through various mechanisms. This study explored pterostilbene as a retinoprotective agent. Male Sprague Dawley rats underwent retinal I/R injury and one-week reperfusion and were treated with either vehicle or pterostilbene. After this functional electroretinographical (ERG) measurement, Western blot and histological analyses were performed. Pterostilbene treatment significantly improved retinal function, as evidenced by increased b-wave amplitude on ERG. Histological studies showed reduced retinal thinning and preserved the retinal structure in the pterostilbene-treated groups. Moreover, Western blot analysis revealed a decreased expression of glial fibrillary acidic protein (GFAP) and heat shock protein 70 (HSP70), indicating reduced glial activation and cellular stress. Additionally, the expression of pro-apoptotic and inflammatory markers, poly(ADP-ribose) polymerase 1 (PARP1) and nuclear factor kappa B (NFκB) was significantly reduced in the pterostilbene-treated group. These findings suggest that pterostilbene offers protective effects on the retina by diminishing oxidative stress, inflammation, and apoptosis, thus preserving retinal function and structure following I/R injury. This study underscores pterostilbene’s potential as a neuroprotective therapeutic agent for treating retinal ischemic injury and related disorders. Full article
(This article belongs to the Special Issue Advances in the Biomedical Applications of Plants and Plant Extracts)
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Figure 1
<p>Comparison of a-wave and b-wave responses in ERG: (<b>A</b>) amplitude analysis of a-wave responses across different experimental groups; (<b>B</b>) amplitude analysis of b-wave responses across different experimental groups. Data represent mean ± SEM; * <span class="html-italic">p</span> &lt; 0.05. ns = no significant difference.</p>
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<p>Statistically most significant comparisons in scotopic ERG measurements; flash intensity: mcd·s·m<sup>−2</sup>: (<b>A</b>) average b-wave amplitudes (µV) of the different groups at 1000 mcd·s·m<sup>−2</sup> light intensity; (<b>B</b>) average b-wave amplitudes (µV) at 3000 mcd·s·m<sup>−2</sup> light intensity; (<b>C</b>) average b-wave amplitudes (µV) at 25,000 mcd·s·m<sup>−2</sup> light intensity (µV). All results are plotted as group average ± SEM. Statistically significant comparisons are denoted by * <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; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Implicit time analysis of a-wave and b-wave responses in electroretinography (ERG): (<b>A</b>) comparison of a-wave implicit times among different treatment groups; (<b>B</b>) comparison of b-wave implicit times among different treatment groups. The statistical analysis showed no significant differences in implicit times among the different treatment groups. Data represent mean ± SEM; <span class="html-italic">p</span> &gt; 0.05. ns = no significant difference.</p>
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<p>Expression levels of glial fibrillary acidic protein (GFAP) and heat shock protein 70 (HSP70) in retinal tissue: (<b>A</b>) Western blot analysis revealing a notable increase in GFAP expression in different groups; (<b>B</b>) Western blot analysis demonstrating a similar pattern with elevated levels of HSP70 in different groups. Data represent mean ± SEM; *** <span class="html-italic">p</span> &lt; 0.001, <span class="html-italic">**** p</span> &lt; 0.0001, <span class="html-italic">n</span> = 4 per group. Expression levels of poly(ADP-ribose) polymerase 1 (PARP1) and nuclear factor kappa B (NFkB) in retinal tissue; (<b>C</b>) Western blot analysis showing significantly elevated expression of PARP1 in different groups; (<b>D</b>) Western blot analysis revealing a similar pattern with significantly increased levels of NFkB in different groups. Data represent mean ± SEM; ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001, <span class="html-italic">n</span> = 4 per group.</p>
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<p>Microscopic analysis of rat retinal sections: (<b>A</b>) representative images of retinal thickness in the different treatment groups (from left to right): MUCI NO IR, MUCI IR, PTER NO IR, and PTER IR; (<b>B</b>) graphs showing statistical analysis results of histology sections of the different groups. Data represent mean ± SEM; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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17 pages, 3251 KiB  
Article
17α-Ethynylestradiol and Levonorgestrel Exposure of Rainbow Trout RTL-W1 Cells at 18 °C and 21 °C Mainly Reveals Thermal Tolerance, Absence of Estrogenic Effects, and Progestin-Induced Upregulation of Detoxification Genes
by Margarida Vilaça, Célia Lopes, Rosária Seabra and Eduardo Rocha
Genes 2024, 15(9), 1189; https://doi.org/10.3390/genes15091189 - 10 Sep 2024
Viewed by 269
Abstract
Fish are exposed to increased water temperatures and aquatic pollutants, including endocrine-disrupting compounds (EDCs). Although each stressor can disturb fish liver metabolism independently, combined effects may exist. To unveil the molecular mechanisms behind the effects of EDCs and temperature, fish liver cell lines [...] Read more.
Fish are exposed to increased water temperatures and aquatic pollutants, including endocrine-disrupting compounds (EDCs). Although each stressor can disturb fish liver metabolism independently, combined effects may exist. To unveil the molecular mechanisms behind the effects of EDCs and temperature, fish liver cell lines are potential models needing better characterisation. Accordingly, we exposed the rainbow trout RTL-W1 cells (72 h), at 18 °C and 21 °C, to ethynylestradiol (EE2), levonorgestrel (LNG), and a mixture of both hormones (MIX) at 10 µM. The gene expression of a selection of targets related to detoxification (CYP1A, CYP3A27, GST, UGT, CAT, and MRP2), estrogen exposure (ERα, VtgA), lipid metabolism (FAS, FABP1, FATP1), and temperature stress (HSP70b) was analysed by RT-qPCR. GST expression was higher after LNG exposure at 21 °C than at 18 °C. LNG further enhanced the expression of CAT, while both LNG and MIX increased the expressions of CYP3A27 and MRP2. In contrast, FAS expression only increased in MIX, compared to the control. ERα, VtgA, UGT, CYP1A, HSP70b, FABP1, and FATP1 expressions were not influenced by the temperature or the tested EDCs. The RTL-W1 model was unresponsive to EE2 alone, sensitive to LNG (in detoxification pathway genes), and mainly insensitive to the temperature range but had the potential to unveil specific interactions. Full article
(This article belongs to the Section Genes & Environments)
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Figure 1
<p>Cell density and viability after 72 h of exposure to 17α-ethynylestradiol (EE2) and levonorgestrel (LNG) at 18 °C and 21 °C. C—control; EE2—10 µM of EE2, LNG—10 µM of LNG, and MIX—10 µM of EE2 + 10 µM of LNG. Data are presented as the mean ± standard deviation. <span class="html-italic">n</span> = 5 independent experiments per temperature.</p>
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<p>Relative gene expression of targets related to detoxification (<span class="html-italic">CYP1A</span>, <span class="html-italic">CYP3A27</span>, <span class="html-italic">CAT</span>, <span class="html-italic">GST</span>, <span class="html-italic">UGT</span>, <span class="html-italic">MRP2</span>) in RTL-W1 cells after 72 h of exposure to 17α-ethynylestradiol (EE2) and levonorgestrel (LNG) at 18 °C and 21 °C. C—control; EE2—10 µM of EE2, LNG—10 µM of LNG, and MIX—10 µM of EE2 + 10 µM of LNG. Data concerning <span class="html-italic">CYP1A</span> (<span class="html-italic">Cytochrome P450 1A</span>), <span class="html-italic">CYP3A27</span> (<span class="html-italic">Cytochrome P450 3A27</span>), <span class="html-italic">CAT</span> (<span class="html-italic">Catalase</span>), <span class="html-italic">GST</span> (<span class="html-italic">Glutathione S-transferase omega 1</span>), <span class="html-italic">UGT</span> (<span class="html-italic">Uridine diphosphate</span> (<span class="html-italic">UDP</span>)<span class="html-italic">-glucuronosyltransferase</span>), and <span class="html-italic">MRP2</span> (<span class="html-italic">Multidrug Resistance-Associated Protein 2</span>) relative expression levels are presented as the mean ± standard deviation. Different letters illustrate significant differences between conditions, according to two-way ANOVA followed by Tukey’s test, and the symbol ** denotes significant differences (<span class="html-italic">p</span> &lt; 0.01) between temperatures. <span class="html-italic">n</span> = 5 independent experiments per temperature.</p>
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<p>Relative gene expression of targets related to lipid metabolism (<span class="html-italic">FAS</span>, <span class="html-italic">FABP1</span>, <span class="html-italic">FATP1</span>), estrogenic effects (<span class="html-italic">ERα</span>, <span class="html-italic">VtgA</span>), and temperature-related stress (<span class="html-italic">HSP70b</span>) in RTL-W1 cells after 72 h of exposure to 17α-ethynylestradiol (EE2) and levonorgestrel (LNG) at 18 °C and 21 °C. C—control; EE2—10 µM of EE2, LNG—10 µM of LNG, and MIX—10 µM of EE2 + 10 µM of LNG. Data concerning <span class="html-italic">FAS</span> (<span class="html-italic">Fatty Acid Synthase</span>), <span class="html-italic">FABP1</span> (<span class="html-italic">Fatty Acid Binding Protein 1</span>), <span class="html-italic">FATP1</span> (<span class="html-italic">Fatty Acid Transport Protein 1</span>), <span class="html-italic">HSP70b</span> (<span class="html-italic">Heat Shock Protein 70 b</span>), <span class="html-italic">ERα</span> (<span class="html-italic">Estrogen Receptor α</span>), and <span class="html-italic">VtgA</span> (<span class="html-italic">Vitellogenin A</span>) relative expression levels are presented as the mean ± standard deviation. Different letters illustrate significant differences between conditions, according to two-way ANOVA followed by Tukey’s test. <span class="html-italic">n</span> = 5 independent experiments per temperature.</p>
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<p>Principal component analysis scatter plot for all the experimental groups (each depicted with a convex hull, distinctly filled with unique colours). C—Control; 17α-ethynylestradiol (EE2)—10 µM; levonorgestrel (LNG)—10 µM, and MIX—10 µM of EE2 + 10 µM of LNG at 18 °C and 21 °C.</p>
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18 pages, 3595 KiB  
Article
Pro-Inflammatory Characteristics of Extracellular Vesicles in the Vitreous of Type 2 Diabetic Patients
by Shengshuai Shan, Abdulaziz H. Alanazi, Yohan Han, Duo Zhang, Yutao Liu, S. Priya Narayanan and Payaningal R. Somanath
Biomedicines 2024, 12(9), 2053; https://doi.org/10.3390/biomedicines12092053 - 10 Sep 2024
Viewed by 368
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness, yet its molecular mechanisms are unclear. Extracellular vesicles (EVs) contribute to dysfunction in DR, but the characteristics and functions of vitreous EVs are unclear. This study investigated the inflammatory properties of type 2 diabetic [...] Read more.
Diabetic retinopathy (DR) is a leading cause of blindness, yet its molecular mechanisms are unclear. Extracellular vesicles (EVs) contribute to dysfunction in DR, but the characteristics and functions of vitreous EVs are unclear. This study investigated the inflammatory properties of type 2 diabetic (db) vitreous EVs. EVs isolated from the vitreous of db and non-db donors were used for nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), immunogold staining, Western blotting, and proteomic analysis by mass spectrometry. Intracellular uptake of vitreous EVs by differentiated macrophages was evaluated using ExoGlow membrane labeling, and the impact of EVs on macrophage (THP-1) activation was assessed by cytokine levels using RT-qPCR. NTA and TEM analysis of db and non-db vitreous EVs showed non-aggregated EVs with a heterogeneous size range below 200 nm. Western blot detected EV markers (Alix, Annexin V, HSP70, and Flotillin 1) and an upregulation of Cldn5 in db EVs. While the db EVs were incorporated into macrophages, treatment of THP-1 cells with db EVs significantly increased mRNA levels of TNFα and IL-1β compared to non-db EVs. Proteomic and gene enrichment analysis indicated pro-inflammatory characteristics of db EVs. Our results suggest a potential involvement of EC-derived Cldn5+ EVs in triggering inflammation, offering a novel mechanism involved and presenting a possible therapeutic avenue for DR. Full article
(This article belongs to the Special Issue Angiogenesis and Related Disorders)
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Figure 1
<p>Characterization of human vitreous EVs. (<b>A</b>,<b>B</b>) Size distribution of EVs purified from non-db and db vitreous. (<b>C</b>) Average diameters of non-db and db vitreous EVs. (<b>D</b>) Average particle numbers in non-db and db vitreous EV preparations. Size distribution was averaged for each group. (<b>E</b>) TEM images of isolated EVs from non-db and db vitreous EVs revealing non-aggregated EVs. ns, Not Significant. Scale bar = 200 nm.</p>
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<p>Db vitreous EVs exhibit increased expression of EV markers and Cldn5. (<b>A</b>) Representative Western blot images of known EV markers (Alix, annexin V, HSP70, and flotillin 1) in non-db and db vitreous EVs (EVs, <span class="html-italic">n</span> = 5, <span class="html-italic">p</span> &lt; 0.01). (<b>B</b>) Bar graph showing increased expression of endothelial junctional protein, Cldn5, in db compared to non-db vitreous EVs (<span class="html-italic">n</span> = 5, ** <span class="html-italic">p</span> &lt; 0.01; # <span class="html-italic">p</span> &lt; 0.001). (<b>C</b>–<b>F</b>) Bar graphs showing the presence of EV markers in both groups.</p>
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<p>Macrophages uptake db EVs to induce cytokine synthesis. (<b>A</b>) Immunogold labeling confirms the increased presence of Cldn5 in db vitreous EVs (red arrowhead). This confirms the significant presence of Cldn5 in the vitreous EVs of individuals with diabetes. (<b>B</b>) Fluorescence microscopy images showing the uptake of ExoGlow membrane-labeled human db vitreous EVs (10 ug/mL) by THP-1 macrophages after 24 h (40×). (<b>C</b>,<b>D</b>) qRT-PCR analysis of THP-1 macrophage post db vitreous EV treatment for 6 h (EV 10 ug/mL) demonstrating changes in the mRNA levels of pro-inflammatory cytokines TNFα and IL-1β, respectively (<span class="html-italic">n</span> = 5). Data are presented as mean ± SD. ** <span class="html-italic">p</span> &lt; 0.01; # <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Proteins with differential expression in the db vitreous EVs. (<b>A</b>) Venn diagram showing the detailed counts of identified proteins in db and non-db EVs. (<b>B</b>) Principal component analysis (PCA) indicating the two groups were separated. (<b>C</b>) The volcano plot of db versus non-db vitreous EVs demonstrating the proteins that are significantly altered among the groups. (<b>D</b>) The heatmap showing differentially expressed proteins in db compared to non-db vitreous EVs (<span class="html-italic">n</span> = 6).</p>
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<p>Gene enrichment analysis: (<b>A</b>) KEGG, Reactome, and Wiki pathway analysis of db vitreous EVs showing involvement of several inflammatory pathways. (<b>B</b>) GO analysis showing top enriched ontologies in the biological processes, cellular components, and molecular functions of deferentially expressed proteins in db vitreous EVs compared to non-db EV control.</p>
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<p>(<b>A</b>) Kinase enrichment analysis showing four significant protein kinases of differentially expressed protein. (<b>B</b>) Protein–protein interaction (PPI) network demonstrating 27 differentially regulated proteins generated by the STRING database.</p>
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22 pages, 7304 KiB  
Article
Integration of Transcriptomics and WGCNA to Characterize Trichoderma harzianum-Induced Systemic Resistance in Astragalus mongholicus for Defense against Fusarium solani
by Jingping Niu, Xiang Yan, Yuguo Bai, Wandi Li, Genglong Lu, Yuanyuan Wang, Hongjun Liu, Zhiyong Shi and Jianping Liang
Genes 2024, 15(9), 1180; https://doi.org/10.3390/genes15091180 - 8 Sep 2024
Viewed by 388
Abstract
Beneficial fungi of the genus Trichoderma are among the most widespread biocontrol agents that induce a plant’s defense response against pathogens. Fusarium solani is one of the main pathogens that can negatively affect Astragalus mongholicus production and quality. To investigate the impact of [...] Read more.
Beneficial fungi of the genus Trichoderma are among the most widespread biocontrol agents that induce a plant’s defense response against pathogens. Fusarium solani is one of the main pathogens that can negatively affect Astragalus mongholicus production and quality. To investigate the impact of Trichoderma harzianum on Astragalus mongholicus defense responses to Fusarium solani, A. mongholicus roots under T. harzianum + F. solani (T + F) treatment and F. solani (F) treatment were sampled and subjected to transcriptomic analysis. A differential expression analysis revealed that 6361 differentially expressed genes (DEGs) responded to T. harzianum induction. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the 6361 DEGs revealed that the genes significantly clustered into resistance-related pathways, such as the plant–pathogen interaction pathway, phenylpropanoid biosynthesis pathway, flavonoid biosynthesis pathway, isoflavonoid biosynthesis pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and plant hormone signal transduction pathway. Pathway analysis revealed that the PR1, formononetin biosynthesis, biochanin A biosynthesis, and CHIB, ROS production, and HSP90 may be upregulated by T. harzianum and play important roles in disease resistance. Our study further revealed that the H2O2 content was significantly increased by T. harzianum induction. Formononetin and biochanin A had the potential to suppress F. solani. Weighted gene coexpression network analysis (WGCNA) revealed one module, including 58 DEGs associated with T. harzianum induction. One core hub gene, RPS25, was found to be upregulated by T. harzianum, SA (salicylic acid) and ETH (ethephon). Overall, our data indicate that T. harzianum can induce induced systemic resistance (ISR) and systemic acquired resistance (SAR) in A. mongholicus. The results of this study lay a foundation for a further understanding of the molecular mechanism by which T. harzianum induces resistance in A. mongholicus. Full article
(This article belongs to the Section Plant Genetics and Genomics)
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<p>Response of <span class="html-italic">A. mongholicus</span> to <span class="html-italic">F. solani</span> under <span class="html-italic">T. harzianum</span> treatment. (<b>A</b>) Plant phenotype. (<b>B</b>) Plant wilting rate. F, plant infected with <span class="html-italic">F. solani</span>; T + F, plant infected with <span class="html-italic">F. solani</span> after <span class="html-italic">T. harzianum</span> treatment for 48 h. The error bars represent ±SD values, and different letters indicate significant differences between the two columns (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Volcano diagram and Venn diagram of differentially expressed genes (DEGs) between pairs of samples (i.e., T + F_0 h vs. F_0 h, T + F_24 h vs. F_24 h, and T + F_48 h vs. F_48 h). The x-axis shows the fold change in gene expression between samples, and the y-axis represents the statistical test result for the difference in gene expression. The blue dots represent the significantly upregulated DEGs, whereas the green dots represent the significantly downregulated DEGs. The orange dots represent the DEGs that were not significantly differentially expressed.</p>
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<p>KEGG enrichment analysis of the 6361 DEGs. The x-axis indicates the ratio of the number of DEGs in the pathway to all DEGs. The y-axis indicates the KEGG pathway.</p>
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<p>Plant–pathogen interaction pathway information for the DEGs. (<b>A</b>) Ca<sup>2+</sup> signaling pathway-related DEGs; (<b>B</b>) other key DEGs. The log<sub>2</sub>FPKM values are indicated by colors. Yellow, upregulated; blue, downregulated; black, no significant difference in expression in the comparison group.</p>
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<p>Determination of H<sub>2</sub>O<sub>2</sub> content in the T + F and F treatments. Error bars represent ±SD values, and different letters indicate significant differences between the two columns (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Formononetin and biochanin A biosynthesis pathway information for the DEGs. The log<sub>2</sub>FPKM values are indicated by colors. Yellow, upregulated; blue, downregulated; black, no significant difference in expression in the comparison group at the same time point.</p>
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<p>Antifungal activity of formononetin and biochanin A against <span class="html-italic">F. solani</span> in vitro. (<b>A</b>) Colony diameter after formononetin treatment. (<b>B</b>) Mycelial growth inhibition rate after formononetin treatment. (<b>C</b>) Colony diameter after biochanin A treatment. (<b>D</b>) Mycelial growth inhibition rate after biochanin A treatment. The error bars represent ±SD values, and different letters indicate significant differences between the two columns (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>MAPK signaling pathway and plant hormone signal transduction pathway information of the DEGs. (<b>A</b>) DEGs in the MAPK signaling pathway; (<b>B</b>) DEGs in the plant hormone signal transduction pathway. The log<sub>2</sub>FPKM values are colored. Yellow, upregulated; blue, downregulated; black, no significant difference in expression in the comparison group at the same time point.</p>
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<p>Soft threshold power (β value) determination by WGCNA and module detection. (<b>A</b>) Soft threshold determination. The number corresponding to the green line is the most appropriate β value. (<b>B</b>) Gene-cluster dendrogram and module colors. (<b>C</b>) Correlations between modules and the resistance trait. The correlation coefficients are shown above, and the <span class="html-italic">p</span> values are shown below.</p>
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<p>Coexpression networks of the 58 connected hub genes in the pink module related to the T + F treatment. The red circle represents the core hub gene <span class="html-italic">TRINITY_DN48668_c0_g2</span>, and the yellow circles represent the coexpressed hub genes <span class="html-italic">TRINITY_DN43430_c0_g1</span>, <span class="html-italic">TRINITY_DN11766_c0_g1</span>, <span class="html-italic">TRINITY_DN8962_c0_g1</span>, <span class="html-italic">TRINITY_DN12696_c0_g1</span>, and <span class="html-italic">TRINITY_DN105680_c0_g1</span>.</p>
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<p>Relative expression analysis of hub genes and coexpressed genes in the T + F and F treatments. The error bars represent ±SD values, and different letters indicate significant differences between the two columns (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Relative expression analysis of the hub genes under SA, ETH, and MeJA induction. The error bars represent ±SD values, and different letters indicate significant differences between the two columns (<span class="html-italic">p</span> &lt; 0.05).</p>
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18 pages, 10705 KiB  
Article
Heat Shock Protein 70 Genes Are Involved in the Thermal Tolerance of Hippodamia variegata
by Qing Yang and Yanhui Lu
Insects 2024, 15(9), 678; https://doi.org/10.3390/insects15090678 - 8 Sep 2024
Viewed by 371
Abstract
Previous studies have shown that the survival and reproduction of Hippodamia variegata are increasingly harmed by progressive increases in temperature (from 32 °C to 35 °C and 38 °C). In this study, transcriptome sequencing analysis was performed on H. variegata, after being [...] Read more.
Previous studies have shown that the survival and reproduction of Hippodamia variegata are increasingly harmed by progressive increases in temperature (from 32 °C to 35 °C and 38 °C). In this study, transcriptome sequencing analysis was performed on H. variegata, after being exposed to different temperatures (from 32 to 38 °C) for 24 h, using high-throughput sequencing technology. We found the largest number of differentially expressed genes (DEGs) in the 35 °C vs. 32 °C group (1151) followed by the 38 °C vs. 32 °C group (1054) and then the 38 °C vs. 35 °C group (901), indicating that H. variegata expressed the largest number of newly mobilized genes under medium-high temperature (35 °C). Gene functional analysis showed that a large number of DEGs were involved in “Catalytic activity”, “Oxidoreductase activity”, “Metabolic pathways”, and “Longevity regulating pathway-multiple species” gene groups. We randomly selected nine DEGs for validation using qRT-PCR. The results of qRT-PCR were consistent with the transcriptome data, confirming their reliability. Finally, the RNAi results showed that adult survival, longevity, and fecundity were lower in the group in which gene expression of the heat shock proteins (Hsp70-01 and Hsp68) was suppressed than in the control group (injection ds-GFP) at all the experimental temperatures (32, 35, and 38 °C). Our results indicate the important role of the heat shock proteins (Hsp70-01 and Hsp68) in resistance to high-temperature stress in H. variegata and provide a molecular basis for analyzing its thermotolerance mechanism. Full article
(This article belongs to the Section Insect Molecular Biology and Genomics)
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<p>All unigene sequences for <span class="html-italic">Hippodamia variegata</span> that had blast annotations in the NR database were analyzed for species distribution.</p>
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<p>Differentially expressed genes (DEGs) in <span class="html-italic">Hippodamia variegata</span> under different degrees of temperature stress. (<b>A</b>) Total number of individual transcripts that were significantly up- or downregulated in different temperature groups. (<b>B</b>) Venn diagram illustrating the number of upregulated genes in the different temperature groups. (<b>C</b>) Venn diagram illustrating the number of downregulated genes in the different temperature groups.</p>
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<p>GO enrichment analysis of adult <span class="html-italic">Hippodamia variegata</span> under different levels of temperature stress.</p>
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<p>KEGG enrichment analysis of adult <span class="html-italic">Hippodamia variegata</span> under different levels of temperature stress.</p>
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<p>Real-time PCR validation of adult <span class="html-italic">Hippodamia variegata</span> DEGs under different levels of temperature stress. The results are shown as the means ± SE. Different letters above the bars indicate statistically significant differences between different temperatures in terms of gene expression (Tukey’s post hoc test; <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Adult survival rate of <span class="html-italic">Hippodamia variegata</span> under different temperatures when the <span class="html-italic">Hsp70-01</span> and <span class="html-italic">Hsp68</span> genes were either fully functional (the control) or partly suppressed by the injection of dsRNA-RNAi gene sequences. Different letters with the same color indicate that the corresponding color curve is different from the control (log-rank, <span class="html-italic">p</span> &lt; 0.05, individual = 20).</p>
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<p>Average adult longevity (<b>A</b>) and fecundity (<b>B</b>) of <span class="html-italic">Hippodamia variegata</span> under different temperatures after the <span class="html-italic">Hsp70-01</span> and <span class="html-italic">Hsp68</span> genes were suppressed with dsRNA-RNAi gene sequences. The results are shown as the means ± SE. The control was injected with the ds-<span class="html-italic">GFP</span>. The asterisk (*) above each bar indicates significant differences (Student’s <span class="html-italic">t</span>-test, <span class="html-italic">p</span> &lt; 0.05) between the dsRNA treatments and the control. **, <span class="html-italic">p</span> &lt; 0.01.</p>
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19 pages, 12053 KiB  
Article
A Comprehensive Study on the Mid-Infrared Variability of Blazars
by Xuemei Zhang, Zhipeng Hu, Weitian Huang and Lisheng Mao
Universe 2024, 10(9), 360; https://doi.org/10.3390/universe10090360 - 7 Sep 2024
Viewed by 342
Abstract
We present a comprehensive investigation of mid-infrared (MIR) flux variability at 3.4 μm (W1 band) for a large sample of 3816 blazars, using Wide-field Infrared Survey Explorer (WISE) data through December 2022. The sample consists of 1740 flat-spectrum radio quasars (FSRQs), 1281 BL [...] Read more.
We present a comprehensive investigation of mid-infrared (MIR) flux variability at 3.4 μm (W1 band) for a large sample of 3816 blazars, using Wide-field Infrared Survey Explorer (WISE) data through December 2022. The sample consists of 1740 flat-spectrum radio quasars (FSRQs), 1281 BL Lac objects (BL Lacs), and 795 blazars of uncertain type (BCUs). Considering Fermi Large Area Telescope detection, we classify 2331 as Fermi blazars and 1485 as non-Fermi blazars. Additionally, based on synchrotron peak frequency, the sample includes 2264 low-synchrotron peaked (LSP), 512 intermediate-synchrotron peaked (ISP), and 655 high-synchrotron peaked (HSP) sources. We conduct a comparative analysis of short- and long-term intrinsic variability amplitude (σm), duty cycle (DC), and ensemble structure function (ESF) across blazar subclasses. The median short-term σm values were 0.1810.106+0.153, 0.1040.054+0.101, 0.1350.076+0.154, 0.1730.097+0.158, 0.1770.100+0.156, 0.0960.050+0.109, and 0.1060.058+0.100 mag for FSRQs, BL Lacs, Fermi blazars, non-Fermi blazars, LSPs, ISPs, and HSPs, respectively. The median DC values were 71.0322.48+14.17, 64.0222.86+16.97, 68.9625.52+15.66, 69.4022.17+14.42, 71.2421.36+14.25, 63.0333.19+16.93, and 64.6324.26+15.88 percent for the same subclasses. The median long-term σm values were 0.1370.105+0.408, 0.1710.132+0.206, 0.2820.184+0.332, 0.0710.062+0.143, 0.2180.174+0.386, 0.1730.132+0.208, and 0.1010.077+0.161 mag for the same subclasses, respectively. Our results reveal significant differences in 3.4 μm flux variability among these subclasses. FSRQs (LSPs) exhibit larger σm and DC values compared to BL Lacs (ISPs and HSPs). Fermi blazars display higher long-term σm but lower short-term σm relative to non-Fermi blazars, while DC distributions between the two groups are similar. ESF analysis further confirms the greater variability of FSRQs, LSPs, and Fermi blazars across a wide range of time scales compared to BL Lacs, ISPs/HSPs, and non-Fermi blazars. These findings highlight a close correlation between MIR variability and blazar properties, providing valuable insights into the underlying physical mechanisms responsible for their emission. Full article
(This article belongs to the Section Galaxies and Clusters)
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Figure 1
<p>Sky distribution of selected blazars in Hammer-Aitoff projection with Galactic coordinates.</p>
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<p>Redshift distributions of FSRQs, BL Lacs, and BCUs in the sample. The black dashed lines indicate the median redshifts of the three subsamples.</p>
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<p>Light curves of the BL Lac 5BZB J1135+3200 (z = 0.511) in the W1 band: (<b>Left</b>) long-term light curve; (<b>Right</b>) representative short-term light curve.</p>
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<p>Distributions of (<b>Left</b>) short-term variability amplitude quantified by <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>m</mi> </msub> </semantics></math>, (<b>Middle</b>) duty cycle, and (<b>Right</b>) long-term variability amplitude quantified by <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>m</mi> </msub> </semantics></math> for the entire sample. The pink dashed lines mark the median values of the data set.</p>
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<p>Distributions of short-term <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>m</mi> </msub> </semantics></math> for different subclasses of blazars. (<b>Left</b>) panels show normalized histograms. (<b>Middle</b>) panels display CDFs with 95% confidence intervals. (<b>Right</b>) panels present Q–Q plots, with red lines representing 45-degree lines.</p>
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<p>Distributions of duty cycle (DC) for different subclasses of blazars. Left panels show normalized histograms. Middle panels display CDFs with 95% confidence intervals. Right panels present Q–Q plots, with red lines representing 45-degree lines.</p>
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<p>Distributions of duty cycle (DC) for different subclasses of blazars. Left panels show normalized histograms. Middle panels display CDFs with 95% confidence intervals. Right panels present Q–Q plots, with red lines representing 45-degree lines.</p>
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<p>Distributions of long-term <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>m</mi> </msub> </semantics></math> for different subclasses of blazars. (<b>Left</b>) panels show normalized histograms. (<b>Middle</b>) panels display CDFs with 95% confidence intervals. (<b>Right</b>) panels present Q–Q plots, with red lines representing 45-degree lines.</p>
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<p>Ensemble structure functions (ESFs) for blazar subclasses. ESFs are shown in the rest frame (log–log scale) for various blazar subclasses. Upper panels: Comparison of ESF between (<b>left</b>) FSRQs and BL Lacs, and (<b>right</b>) Fermi and non-Fermi blazars. (<b>lower</b>): Comparison of ESF among LSP, ISP, and HSP blazars. Dashed lines represent power-law fits to the ESFs (SF ∝ <math display="inline"><semantics> <msup> <mrow> <mo>Δ</mo> <mi>τ</mi> </mrow> <mi>β</mi> </msup> </semantics></math>) within the approximate <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>τ</mi> </mrow> </semantics></math> range of 120 to 2000 days. Best-fit <math display="inline"><semantics> <mi>β</mi> </semantics></math> values are shown near the lines.</p>
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17 pages, 10495 KiB  
Article
Genome-Wide Identification and Analysis of Maize DnaJ Family Genes in Response to Salt, Heat, and Cold at the Seedling Stage
by Gang Li, Ziqiang Chen, Xinrui Guo, Dagang Tian, Chenchen Li, Min Lin, Changquan Hu and Jingwan Yan
Plants 2024, 13(17), 2488; https://doi.org/10.3390/plants13172488 - 5 Sep 2024
Viewed by 244
Abstract
DnaJ proteins, also known as HSP40s, play a key role in plant growth and development, and response to environmental stress. However, little comprehensive research has been conducted on the DnaJ gene family in maize. Here, we identify 91 ZmDnaJ genes from maize, which [...] Read more.
DnaJ proteins, also known as HSP40s, play a key role in plant growth and development, and response to environmental stress. However, little comprehensive research has been conducted on the DnaJ gene family in maize. Here, we identify 91 ZmDnaJ genes from maize, which are likely distributed in the chloroplast, nucleus, and cytoplasm. Our analysis revealed that ZmDnaJs were classified into three types, with conserved protein motifs and gene structures within the same type, particularly among members of the same subfamily. Gene duplication events have likely contributed to the expansion of the ZmDnaJ family in maize. Analysis of cis-regulatory elements in ZmDnaJ promoters suggested involvement in stress responses, growth and development, and phytohormone sensitivity in maize. Specifically, four cis-acting regulatory elements associated with stress responses and phytohormone regulation indicated a role in adaptation. RNA-seq analysis showed constitutive expression of most ZmDnaJ genes, some specifically in pollen and endosperm. More importantly, certain genes also responded to salt, heat, and cold stresses, indicating potential interaction between stress regulatory networks. Furthermore, early responses to heat stress varied among five inbred lines, with upregulation of almost tested ZmDnaJ genes in B73 and B104 after 6 h, and fewer genes upregulated in QB1314, MD108, and Zheng58. After 72 h, most ZmDnaJ genes in the heat-sensitive inbred lines (B73 and B104) returned to normal levels, while many genes, including ZmDnaJ55, 79, 88, 90, and 91, remained upregulated in the heat-tolerant inbred lines (QB1314, MD108, and Zheng58) suggesting a synergistic function for prolonged protection against heat stress. In conclusion, our study provides a comprehensive analysis of the ZmDnaJ family in maize and demonstrates a correlation between heat stress tolerance and the regulation of gene expression within this family. These offer a theoretical basis for future functional validation of these genes. Full article
(This article belongs to the Special Issue Responses of Crops to Abiotic Stress)
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<p>Phylogenetic tree of ZmDnaJ and AtDnaJ proteins. The phylogenetic tree was constructed using MEGA X with 1000 bootstrap replications and an optimal Jones–Taylor–Thornton (JTT) model, incorporating the DnaJ proteins from maize and <span class="html-italic">Arabidopsis</span>. The new names for maize proteins are listed in <a href="#app1-plants-13-02488" class="html-app">Table S1</a>, and the accession numbers for <span class="html-italic">Arabidopsis</span> proteins are based on Rajan et al. [<a href="#B8-plants-13-02488" class="html-bibr">8</a>]. Different background colors represent the three types. A and B represent the DnaJ_C subfamily; C represents the DnaJ-X subfamily; D represents the DUF3444 subfamily; E represents the Jiv90 subfamily; F represents the Fer4_8 subfamily.</p>
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<p>Phylogenetic relationship, conserved motif, and gene structure of <span class="html-italic">ZmDnaJ</span> members. Different colored boxes represent various motifs or exons, while black lines indicate non-conserved sequences and introns. A and B represent the DnaJ_C subfamily; C represents the DnaJ-X subfamily; D represents the DUF3444 subfamily; E represents the Jiv90 subfamily; F represents the Fer4_8 subfamily.</p>
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<p>Gene duplication of <span class="html-italic">ZmDnaJ</span> genes on the 10 chromosomes of the maize genome. Red lines connect the duplicated gene pairs, while gray lines represent all synteny blocks within the maize genome. Detailed relationships of the duplicated genes are provided in <a href="#app1-plants-13-02488" class="html-app">Table S2</a>.</p>
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<p>Cis-element number analysis in the <span class="html-italic">ZmDnaJ</span> gene family. The grid’s varying color intensities and numbers represent the number of different promoter elements within the <span class="html-italic">ZmDnaJ</span> genes. The histogram, with its different colors, shows the total number of cis-acting elements for each category.</p>
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<p>Expression pattern analysis of <span class="html-italic">ZmDnaJ</span> family genes in different tissues and developmental stages based on transcript data. The expression patterns of 91 genes were examined across 13 different tissues and developmental stages using public transcriptome data. The FPKM values (fragments per kilobase of transcript per million fragments) of all genes were transformed to the log2 scale. Red and blue colors indicate higher and lower relative transcript enrichment, respectively.</p>
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<p>Gene expression profiles of <span class="html-italic">ZmDnaJ</span> genes in response to salt, heat, and cold stress treatments based on transcript data. The changes in expression of <span class="html-italic">ZmDnaJ</span> genes under salt, heat, and cold stress compared to normal conditions were calculated. Blue and red scales indicate low and high expression levels, respectively.</p>
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<p>Relative expression levels of 14 selected <span class="html-italic">ZmDnaJ</span> genes and the phenotypes of five inbred maize lines under heat treatment. (<b>A</b>) The relative expression levels of 14 <span class="html-italic">ZmDnaJ</span> genes in the seedling leaves of five inbred lines were measured using qRT-PCR, with ZmUbi as the reference gene. Results are shown as mean ± standard deviation, with significance indicated by * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 (Student’s <span class="html-italic">t</span>-test). Leaf samples were collected 6 h after heat treatment. (<b>B</b>) Left panel: Relative expression levels of the 14 <span class="html-italic">ZmDnaJ</span> genes in seedling leaves at 72 h after heat treatment. Right panel: Phenotypes of the five inbred lines (B73, B104, Zheng 58, QB1314, and MD108) at 72 h after heat treatment.</p>
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11 pages, 2200 KiB  
Article
Effect of Serotonin (5-Hydroxytryptamine) on Follicular Development in Porcine
by Yan Zhang, Yu Han, Rui Yang, Bo-Yang Zhang, Yan-Sen Zhao, Yue-Qi Wang, Dao-Zhen Jiang, An-Tong Wang, Xue-Ming Zhang and Bo Tang
Int. J. Mol. Sci. 2024, 25(17), 9596; https://doi.org/10.3390/ijms25179596 - 4 Sep 2024
Viewed by 338
Abstract
5-Hydroxytryptamine (5-HT) is an inhibitory neurotransmitter widely distributed in mammalian tissues, exerting its effects through binding to various receptors. It plays a crucial role in the proliferation of granulosa cells (GCs) and the development of follicles in female animals, however, its effect on [...] Read more.
5-Hydroxytryptamine (5-HT) is an inhibitory neurotransmitter widely distributed in mammalian tissues, exerting its effects through binding to various receptors. It plays a crucial role in the proliferation of granulosa cells (GCs) and the development of follicles in female animals, however, its effect on porcine follicle development is not clear. The aim of this study is to investigate the expression of 5-HT and its receptors in various parts of the pig ovary, as well as the effect of 5-HT on porcine follicular development by using ELISA, quantitative real-time PCR (qPCR) and EdU assays. Firstly, we examined the levels of 5-HT and its receptors in porcine ovaries, follicles, and GCs. The findings revealed that the expression of different 5-HT receptors varied among follicles of different sizes. To investigate the relationship between 5-HT and its receptors, we exposed the GCs to 5-HT and found a decrease in 5-HT receptor expression compared to the control group. Subsequently, the treatment of GCs with 0.5 μM, 5 μM, and 50 μM 5-HT showed an increase in the expression of cell cycle-related genes, and EdU results indicated cell proliferation after the 0.5 μM 5-HT treatment. Additionally, the expression of genes involved in E2 synthesis was examined after the treatment of granulosa cells with 0.5 μM 5-HT. The results showed that CYP19A1 and HSP17β1 expression was decreased. These results suggest that 5-HT might affect the development of porcine follicle by promoting the proliferation of GCs and inhibiting the synthesis of estrogen. This provides a new finding for exploring the effect of 5-HT on follicular development, and lays a foundation for further research on the mechanism of 5-HT in follicles. Full article
(This article belongs to the Section Molecular Endocrinology and Metabolism)
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<p>Effect of follicle size on 5-HT and 5-HT receptors. RNA was derived from the theca. SF: Small follicle; MF: Middle follicle; LF: Large follicle. 5-HT1A, 5-HT2A, 5-HT3A, 5-HT4, 5-HT5A, 5-HT6 and 5-HT7 are the seven receptors of 5-HT. (<b>A</b>) The content of 5-HT in follicles of different sizes was detected by ELISA. qPCR was used to detect the levels of 5-HT1A receptor (<b>B</b>), 5-HT2A receptor (<b>C</b>), 5-HT3A receptor (<b>D</b>), 5-HT4 receptor (<b>E</b>), 5-HT5A receptor (<b>F</b>), 5-HT6 receptor (<b>G</b>), and 5-HT7 receptor (<b>H</b>) in follicles of different sizes. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Expression of 5-HT receptors in pGCs and ovaries. RNA was derived from GCs and ovarian tissue. SF: Small follicle; MF: Middle follicle; LF: Large follicle. 5-HT1A, 5-HT2A, 5-HT3A, 5-HT4, 5-HT5A, 5-HT6 and 5-HT7 are the seven receptors of 5-HT. qPCR was used to detect the levels of 5-HT1A receptor (<b>A</b>), 5-HT2A receptor (<b>B</b>), 5-HT3A receptor (<b>C</b>), 5-HT4 receptor (<b>D</b>), 5-HT5A receptor (<b>E</b>), 5-HT6 receptor (<b>F</b>), and 5-HT7 receptor (<b>G</b>) in GCs of different sizes of follicles. (<b>H</b>) The expression of 5-HT receptors in ovary was detected by qPCR. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The effect of 5-HT on the first polar body of oocytes rate. The first polar body was treated with 0 μM, 500 μM, and 1000 μM 5-HT, respectively. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of 5-HT exposure on pGCs. (<b>A</b>). q-PCR was used to detect the expression of cell cycle genes CyclinB1, CyclinD1 and CyclinE1. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>). 5-HT exposure promotes proliferation of pGCs. The proliferation of pGCs determined by EdU assay. The chi-square test was conducted to evaluate the statistical difference in the proportion of positive cells between the control group (<span class="html-italic">n</span> = 215, positive = 13, negative = 202) and the experimental group (<span class="html-italic">n</span> = 317, positive = 45, negative = 272). The test revealed a significant difference (χ<sup>2</sup> = 85.43, df = 1, <span class="html-italic">p</span> &lt; 0.05), indicating that the proportion of positive cells varied significantly between the two groups. NC: Control group; 5-HT: 5-HT group.</p>
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<p>Effects of 5-HT exposure on 5-HT receptors in pGCs. 5-HT1A, 5-HT2A, 5-HT3A, 5-HT4, 5-HT5A, 5-HT6 and 5-HT7 are the seven receptors of 5-HT. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effects of 5-HT exposure on E2 synthesis in pGCs. Changes in genes involved in E2 synthesis, including (<b>A</b>) Cyp19a1 and (<b>B</b>) HSP17β1, after 5-HT exposure. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 vs. CON (<span class="html-italic">t</span>-test).</p>
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12 pages, 5807 KiB  
Article
Maize Class C Heat Shock Factor ZmHSF21 Improves the High Temperature Tolerance of Transgenic Arabidopsis
by Yurong Xie and Yuhan Ye
Agriculture 2024, 14(9), 1524; https://doi.org/10.3390/agriculture14091524 - 4 Sep 2024
Viewed by 329
Abstract
High temperatures seriously threaten the global yield of maize. The objectives of the present study were to explore the key candidate gene involved in heat shock responses in maize and its potential biological function to heat stress. Here, we identified a Class C [...] Read more.
High temperatures seriously threaten the global yield of maize. The objectives of the present study were to explore the key candidate gene involved in heat shock responses in maize and its potential biological function to heat stress. Here, we identified a Class C heat shock factor, ZmHSF21, from maize leaves and used molecular biological and plant physiological assays to investigate its roles in transgenic Arabidopsis. ZmHSF21 encodes a putative protein of 388 amino acids. We showed that ZmHSF21 was expressed in most tissues of maize with relatively high expression in leaves and silks but rather low in roots and stalks, and its expression level in leaves was significantly up-regulated by heat treatment. We also showed that overexpression of ZmHSF21 in Arabidopsis significantly improved the seed germination frequency and plant survival rate when exposed to heat stress. We demonstrated that, compared with wild-type plants, the activities of peroxidase, superoxide dismutase, and catalase increased while the reactive oxygen species accumulation decreased in ZmHSF21 overexpressors under heat stress conditions. We further demonstrated that ZmHSF21 promoted the transcriptional level of AtAPX2, AtGolS1, and several AtHSPs. Collectively, the first-class C HSF in maize (ZmHSF21) is cloned in this study, and the combined results suggest that ZmHSF21 is a positive regulator of heat shock response and can be applied to develop maize high-temperature-tolerant varieties for more yield. Full article
(This article belongs to the Special Issue Breeding and Genetics of Maize)
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<p>Phylogenetic relationship between maize ZmHSF21 and Class C HSFs from other species. The software Clustal X (version 3.0 46) was used to align the protein sequence of all HSFs. The software Treeview (version 1.16r4 47) was adopted to produce the phylogenetic tree by the neighbor jointing method. The <span class="html-italic">ZmHSF21</span> cloned in this study is in red. The red open box indicated the Class C HSFs from <span class="html-italic">Arabidopsis</span>, maize, rice, wheat, and <span class="html-italic">Festuca arundinacea</span>. The amino acid sequences of all proteins aligned are given in the <a href="#app1-agriculture-14-01524" class="html-app">supplementary datasheet</a>.</p>
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<p>Expression patterns of <span class="html-italic">ZmHSF21</span> in different tissues and under heat stress. (<b>a</b>) Comparison of expression levels of <span class="html-italic">ZmHSF21</span> between different tissues. Bar represents standard deviation (SD). Values are means ± SD (<span class="html-italic">n</span> = 3 biological replicates). The different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. (<b>b</b>) The up-regulation of <span class="html-italic">ZmHSF21</span> expression after high temperature treatment. V3-stage maize seedlings were treated with high temperature (45 °C) for a time course (0, 15, 30, 60, 120, and 240 min), and the second leaves were harvested for RNA extraction. All data represent the mean ± SD of three biological replicates. The different lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Identification of <span class="html-italic">ZmHSF21</span> transgenic plants. (<b>a</b>) Screening of T<sub>1</sub> transgenic seedlings. Five-day-old T<sub>1</sub> seedlings were sprayed with 10% Basta (1:2000 dilution) for three times. The white triangles indicated the Basta-resistant (positive) seedlings. (<b>b</b>) Genotyping of <span class="html-italic">ZmHSF21</span> transgenic plants. The genomic DNA from WT plants was used as a negative control. (<b>c</b>) Detection of the expression level of <span class="html-italic">ZmHSF21</span> in different homozygous transgenic lines. All data represent the mean ± SD of three independent biological replicates. The different lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. The T<sub>3</sub> homozygous seeds of <span class="html-italic">OE#1</span>, <span class="html-italic">OE#4</span>, and <span class="html-italic">OE#11</span> were used for further study.</p>
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<p>Comparison of survival rates of WT seedlings after different durations of high temperature treatment. Five-day-old seedlings were placed at 45 °C for given time and then resumed growth at 22 °C. (<b>a</b>) 0 h; (<b>b</b>) 0.5 h; (<b>c</b>) 1.0 h; (<b>d</b>) 1.5 h; and (<b>e</b>) 2.0 h. (<b>f</b>) Statistical analysis of survival rates of seedlings with or without heat treatment. The data represent the mean ± SD (n = 3 biological replicates) of three independent. The different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The reactive oxygen species is significantly accumulated with heat stress. (<b>a</b>) NBT staining showing the SOD accumulation. (<b>b</b>) Quantification of the content of ROS and the enzyme activities. Values are means ± SDs of three replicates. The different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Comparison of seed germination frequency of WT and <span class="html-italic">ZmHSF21</span> transgenic seeds under heat stress. (<b>a</b>) Control; (<b>b</b>) After heat shock; (<b>c</b>) Diagram of the seeds in (<b>a</b>); (<b>d</b>) Statistical analysis of germination frequency of seeds with or without heat treatment. Seeds were placed at 50 °C for 60 min and then transferred to 22 °C for germination and growth. The data represent the means ± SDs (n = 3). The different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (Student’s <span class="html-italic">t</span>-test).</p>
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<p>Survival rates of WT and <span class="html-italic">ZmHSF21</span> transgenic seedlings under heat stress. (<b>a</b>) Control; (<b>b</b>) After heat shock; (<b>c</b>) Diagram of the seedlings in (<b>a</b>); (<b>d</b>) Statistical analysis of survival rate of seedlings with or without heat treatment. Five-day-old seedlings were placed at 45 °C for 90 min and then resumed growth at 22 °C for 7 days. The data represent the means ± SDs (n = 3). The different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (Student’s <span class="html-italic">t</span>-test).</p>
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<p><span class="html-italic">ZmHSF21</span> significantly up-regulated the expression of <span class="html-italic">HSP</span>s. Ten-day-old seedlings grown under normal conditions were harvested for RNA extraction. All data represent the mean ± SD of three independent biological replicates. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01. <span class="html-italic">AtACTIN2</span> was used as an internal control.</p>
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<p>Comparison of the ROS content in the leaves between WT plants and <span class="html-italic">ZmHSF21</span> overexpressors with or without high temperature treatment. (<b>a</b>) NBT staining showing the SOD accumulation between WT plants and <span class="html-italic">ZmHSF21</span> overexpressors before heat stress (HS) or after HS. (<b>b</b>) Quantification of the content of ROS and the activities of CAT, SOD, and POD between WT leaves and <span class="html-italic">ZmHSF21</span> overexpressors. Values are means ± SDs of three replicates. The different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p>
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14 pages, 2225 KiB  
Article
Supplementation with Fish Oil and Selenium Protects Lipolytic and Thermogenic Depletion of Adipose in Cachectic Mice Treated with an EGFR Inhibitor
by Hang Wang, Yi-Lin Chan, Yi-Han Chiu, Tsung-Han Wu, Simon Hsia and Chang-Jer Wu
Cells 2024, 13(17), 1485; https://doi.org/10.3390/cells13171485 - 4 Sep 2024
Viewed by 439
Abstract
Lung cancer and cachexia are the leading causes of cancer-related deaths worldwide. Cachexia is manifested by weight loss and white adipose tissue (WAT) atrophy. Limited nutritional supplements are conducive to lung cancer patients, whereas the underlying mechanisms are poorly understood. In this study, [...] Read more.
Lung cancer and cachexia are the leading causes of cancer-related deaths worldwide. Cachexia is manifested by weight loss and white adipose tissue (WAT) atrophy. Limited nutritional supplements are conducive to lung cancer patients, whereas the underlying mechanisms are poorly understood. In this study, we used a murine cancer cachexia model to investigate the effects of a nutritional formula (NuF) rich in fish oil and selenium yeast as an adjuvant to enhance the drug efficacy of an EGFR inhibitor (Tarceva). In contrast to the healthy control, tumor-bearing mice exhibited severe cachexia symptoms, including tissue wasting, hypoalbuminemia, and a lower food efficiency ratio. Experimentally, Tarceva reduced pEGFR and HIF-1α expression. NuF decreased the expression of pEGFR and HIF-2α, suggesting that Tarceva and NuF act differently in prohibiting tumor growth and subsequent metastasis. NuF blocked LLC tumor-induced PTHrP and expression of thermogenic factor UCP1 and lipolytic enzymes (ATGL and HSL) in WAT. NuF attenuated tumor progression, inhibited PTHrP-induced adipose tissue browning, and maintained adipose tissue integrity by modulating heat shock protein (HSP) 72. Added together, Tarceva in synergy with NuF favorably improves cancer cachexia as well as drug efficacy. Full article
(This article belongs to the Special Issue Second Edition of Advances in Adipose Tissue Biology)
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<p>Anticancer and antimetastatic effect of the combination of Tarveva and NuF in tumor-bearing mice. (<b>A</b>). The study involved a treatment regimen combining Tarceva with NuF in mice with tumors. On day 7, Lewis lung carcinoma (LLC) cells (3 × 10<sup>5</sup>) were injected subcutaneously into the right dorsal side of C57BL/6 mice. Tumor volume was measured using the following formula: 1/2 (x^2y), where x represents tumor width and y represents tumor length. Tumor-bearing mice were randomly assigned to four groups as follows: the control group with no treatment (T), the TT group (Tarceva at 2 mg/kg/day), the TN group (NuF at 1 g/mouse/day), and the TTN group (Tarceva at 2 mg/kg/day combined with NuF at 1 g/mouse/day). After 28 days, mice were sacrificed, and tumors, gastrocnemius muscles, white adipose tissue, brown adipose tissue (BAT), and lungs were collected for further analysis. (<b>B</b>). MTS assay for determining the inhibition of LLC cell growth by Tarveva. (<b>C</b>). Tumor weight. Results are based on three independent replicates. (<b>D</b>). Tumor weight distribution. (<b>E</b>). The average number of lung metastatic nodules. Representative photos of the lungs; arrows point to the metastatic nodules. (<b>F</b>). The image on the left displays the expression level of EGFR and its phosphorylated form in tumors from each group. Meanwhile, the image on the right illustrates the quantified ratio of phosphorylated EGFR to total EGFR (pEGFR/total EGFR) following treatment with Tarceva and NuF. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001, and <sup>####</sup> <span class="html-italic">p</span> &lt; 0.0001 compared to the T group. Data are expressed as means ± SD. N = 5–6 samples per group. Each group consisted of 5 to 6 mice.</p>
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<p>Anticachexic effect of the combination of Tarveva and NuF in tumor-bearing mice. (<b>A</b>) Albumin level. (<b>B</b>) Gastrocnemius muscle (Gastroc), epididymal fat (WAT) and interscapular brown adipose tissue (BAT) weight. (<b>C</b>) Image of BAT. (<b>D</b>) H&amp;E staining image of WAT. Scale bar = 200 µm. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 as compared to the T group. Different letters in the groups represent significant differences. Data are expressed as means ± SD. N = 5–6 samples per group.</p>
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<p>Co-administration of Tarveva and NuF inhibited adipocyte dysfunction factor in tumors. (<b>A</b>) Representative Western blots of IL-6, PTHrP, and β-actin in tumors from LLC tumor-bearing mice. (<b>B</b>) Relative mRNA expression levels of <span class="html-italic">Il6</span> and <span class="html-italic">PTHrP</span> were measured via RT-qPCR. Values are means of fluorescence signals expressed as a percentage of no-treatment tumor mice (T), and normalization to the <span class="html-italic">Gapdh</span> mRNA. (<b>C</b>) Western blot analysis for the expression of HIF-1α and β-actin in tumors. The graph represents the relative densitometric intensity of each band normalized to β-actin. (<b>D</b>) Western blot analysis for the expression of HIF-2α and β-actin in tumors. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 as compared to the T group. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 as compared between two groups. Data are expressed as means ± SD. N = 5–6 samples per group.</p>
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<p>NuF suppresses the expression of thermogenic and lipolytic factors in white adipose tissue (WAT). (<b>A</b>). Representative Western blots of pHSL, total HSL, UCP-1, and β-actin in WAT from LLC tumor-bearing mice. (<b>B</b>). Relative mRNA expression levels of <span class="html-italic">Il6</span>, <span class="html-italic">Ucp1</span>, <span class="html-italic">Argl</span>, and <span class="html-italic">HSL</span> were measured via RT-qPCR. Values are means of fluorescence signals expressed as a percentage of health control mice (NT group), and normalization to the <span class="html-italic">Gapdh</span> mRNA. (<b>C</b>). A representative Western blot of HSP25, HSP72, and β-actin expression in WAT. The graph represents the relative densitometric analysis of each band normalized to β-actin. (<b>D</b>). Diagram showing the hypothesized underlying mechanism for NuF inhibition of tumor progression and adipose tissue atrophy. UCP1, uncoupling protein 1. ATGL, adipocyte triglyceride lipase. HSL, hormone-sensitive lipase. HSP, Heat shock protein. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, and <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001 as compared to the T group. *** <span class="html-italic">p</span> &lt; 0.001 as compared between the two groups. Data are expressed as means ± SD. N = 5–6 samples per group.</p>
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14 pages, 2202 KiB  
Article
HSP-YOLOv8: UAV Aerial Photography Small Target Detection Algorithm
by Heng Zhang, Wei Sun, Changhao Sun, Ruofei He and Yumeng Zhang
Drones 2024, 8(9), 453; https://doi.org/10.3390/drones8090453 - 2 Sep 2024
Viewed by 621
Abstract
To address the larger numbers of small objects and the issues of occlusion and clustering in UAV aerial photography, which can lead to false positives and missed detections, we propose an improved small object detection algorithm for UAV aerial scenarios called YOLOv8 with [...] Read more.
To address the larger numbers of small objects and the issues of occlusion and clustering in UAV aerial photography, which can lead to false positives and missed detections, we propose an improved small object detection algorithm for UAV aerial scenarios called YOLOv8 with tiny prediction head and Space-to-Depth Convolution (HSP-YOLOv8). Firstly, a tiny prediction head specifically for small targets is added to provide higher-resolution feature mapping, enabling better predictions. Secondly, we designed the Space-to-Depth Convolution (SPD-Conv) module to mitigate the loss of small target feature information and enhance the robustness of feature information. Lastly, soft non-maximum suppression (Soft-NMS) is used in the post-processing stage to improve accuracy by significantly reducing false positives in the detection results. In experiments on the Visdrone2019 dataset, the improved algorithm increased the detection precision mAP0.5 and mAP0.5:0.95 values by 11% and 9.8%, respectively, compared to the baseline model YOLOv8s. Full article
(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones 2nd Edition)
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<p>Network structure diagram of YOLOv8.</p>
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<p>(<b>a</b>) Structure of Conv block; (<b>b</b>) structure of C2f block; (<b>c</b>) structure of SPPF block.</p>
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<p>Network structure diagram of HSP-YOLOv8 (see <a href="#drones-08-00453-f002" class="html-fig">Figure 2</a> for Conv, C2f, and SPPF blocks).</p>
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<p>(<b>a</b>) Prediction head of YOLOv8; (<b>b</b>) prediction head of HSP-YOLOv8.</p>
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<p>Structure diagram of SPD-Conv.</p>
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<p>Comparison of detection effects in different scenarios, (<b>a</b>) YOLOv5s, (<b>b</b>) YOLOv8s, (<b>c</b>) HSP-YOLO.</p>
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