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22 pages, 94356 KiB  
Article
Propolis Reduces Inflammation and Dyslipidemia Caused by High-Cholesterol Diet in Mice by Lowering ADAM10/17 Activities
by Ertugrul Yigit, Orhan Deger, Katip Korkmaz, Merve Huner Yigit, Huseyin Avni Uydu, Tolga Mercantepe and Selim Demir
Nutrients 2024, 16(12), 1861; https://doi.org/10.3390/nu16121861 (registering DOI) - 13 Jun 2024
Abstract
Atherosclerosis is one of the most important causes of cardiovascular diseases. A disintegrin and metalloprotease (ADAM)10 and ADAM17 have been identified as important regulators of inflammation in recent years. Our study investigated the effect of inhibiting these enzymes with selective inhibitor and propolis [...] Read more.
Atherosclerosis is one of the most important causes of cardiovascular diseases. A disintegrin and metalloprotease (ADAM)10 and ADAM17 have been identified as important regulators of inflammation in recent years. Our study investigated the effect of inhibiting these enzymes with selective inhibitor and propolis on atherosclerosis. In our study, C57BL/6J mice (n = 16) were used in the control and sham groups. In contrast, ApoE-/- mice (n = 48) were used in the case, water extract of propolis (WEP), ethanolic extract of propolis (EEP), GW280264X (GW-synthetic inhibitor), and solvent (DMSO and ethanol) groups. The control group was fed a control diet, and all other groups were fed a high-cholesterol diet for 16 weeks. WEP (400 mg/kg/day), EEP (200 mg/kg/day), and GW (100 µg/kg/day) were administered intraperitoneally for the last four weeks. Animals were sacrificed, and blood, liver, aortic arch, and aortic root tissues were collected. In serum, total cholesterol (TC), triglycerides (TGs), and glucose (Glu) were measured by enzymatic colorimetric method, while interleukin-1β (IL-1β), paraoxonase-1 (PON-1), and lipoprotein-associated phospholipase-A2 (Lp-PLA2) were measured by ELISA. Tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), myeloperoxidase (MPO), interleukin-6 (IL-6), interleukin-10 (IL-10), and interleukin-12 (IL-12) levels were measured in aortic arch by ELISA and ADAM10/17 activities were measured fluorometrically. In addition, aortic root and liver tissues were examined histopathologically and immunohistochemically (ADAM10 and sortilin primary antibody). In the WEP, EEP, and GW groups compared to the case group, TC, TG, TNF-α, IL-1β, IL-6, IL-12, PLA2, MPO, ADAM10/17 activities, plaque burden, lipid accumulation, ADAM10, and sortilin levels decreased, while IL-10 and PON-1 levels increased (p < 0.003). Our study results show that propolis can effectively reduce atherosclerosis-related inflammation and dyslipidemia through ADAM10/17 inhibition. Full article
(This article belongs to the Section Nutrition and Metabolism)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Aortic arch a disintegrin and metalloprotease 10 activity. * statistically significant compared to control (<span class="html-italic">p</span> &lt; 0.05); <sup>#</sup> statistically significant compared to case (<span class="html-italic">p</span> &lt; 0.05) (<span class="html-italic">n</span> = 8, One-way ANOVA, the data are expressed as mean).</p>
Full article ">Figure 2
<p>Aortic arch a disintegrin and metalloprotease 17 activity. * statistically significant compared to control (<span class="html-italic">p</span> &lt; 0.05); <sup>#</sup> statistically significant compared to case (<span class="html-italic">p</span> &lt; 0.05) (<span class="html-italic">n</span> = 8, One-way ANOVA, the data are expressed as mean).</p>
Full article ">Figure 3
<p>Experimental groups’ body weight (BW) changes. Black dashed line: start of injection. * statistically significant compared to control (<span class="html-italic">p</span> &lt; 0.01); <sup>#</sup> statistically significant compared to case (<span class="html-italic">p</span> &lt; 0.01) (<span class="html-italic">n</span> = 8, One-way ANOVA, the data are expressed as mean).</p>
Full article ">Figure 4
<p>Aortic arch cytokine levels. (<b>A</b>) TNF-α levels, (<b>B</b>) IL-12 levels, (<b>C</b>) IL-6 levels, (<b>D</b>) MPO levels, (<b>E</b>) IFN-γ levels, (<b>F</b>) IL-10 levels. <sup>a</sup> statistically significant compared to control (<span class="html-italic">p</span> &lt; 0.003); <sup>b</sup> statistically significant compared to case (<span class="html-italic">p</span> &lt; 0.003); <sup>c</sup> statistically significant compared to WEP (<span class="html-italic">p</span> &lt; 0.003); (<span class="html-italic">n</span> = 8, Mann–Whitney U, the data are expressed as median (Q1–Q3). WT: wild-type mice, ApoE<sup>-/-</sup>: Apolipoprotein-E knockout mice, CD: control diet, HCD: high-cholesterol diet. Gray: Control and Sham (C57BL-6J), Red (ApoE<sup>-/-</sup>, 400 mg/kg WEP), Blue (ApoE<sup>-/-</sup>, 200 mg/kg EEP), Green (ApoE<sup>-/-</sup>, 100 µg/kg GW), Purple (ApoE<sup>-/-</sup>, Case), Brown (ApoE<sup>-/-</sup>, 10% DMSO), Turquoise (ApoE<sup>-/-</sup>, 30% Ethanol).</p>
Full article ">Figure 5
<p>Representative light microscopic image of sections of aortic root tissue stained with H&amp;E. (<b>A</b>) (×20) and (<b>B</b>) (×40): Control group: In the aortic root tissue sections of the control group, it is observed that the endothelium and sub-endothelial connective tissue in the tunica intima layer have a typical structure. In addition, it is observed that the tunica media layer and tunica adventitia layer have a regular structure. (<b>C</b>) (×20) and (<b>D</b>) (×40): Sham group: In the aortic root tissue sections of the sham group, subendothelial widespread adipocyte accumulations (curved arrow) are observed in the tunica intima. In addition, adipocyte accumulations (curved arrow) are observed occasionally in the tunica media. (<b>E</b>) (×20) and (<b>F</b>) (×40): Case group: Dense adipocyte accumulations (curved arrow) are observed in the subendothelial and tunica media in the aortic sections of the case group, (<b>G</b>) (×20) and (<b>H</b>) (×40): WEP group: In the aortic root sections of the WEP administration group, it is observed that adipocyte accumulations (curved arrow) in the subendothelial and tunica media have decreased. (<b>I</b>) (×20) and (<b>J</b>) (×40): EEP group: It is observed that adipocytes have decreased in number (curved arrow) in the aortic sections of the EEP administration group. (<b>K</b>) (×20) and (<b>L</b>) (×40): GW group: In the aortic root sections of the GW administration group, it is observed that adipocyte accumulations in the tunica intima and tunica media layers have decreased (curved arrow). (<b>M</b>) (×20) and (<b>N</b>) (×40): DMSO group: In the aortic root sections of the DMSO administration group, dense adipocyte accumulations (curved arrow) are observed, especially in the subendothelial layer of the tunica intima and the tunica media layers. (<b>O</b>) (×20) and (<b>P</b>) (×40): Ethanol group: In the aortic root sections of the ethanol administration group, dense adipocyte accumulations (curved arrow) are observed, especially in the subendothelial layer of the tunica intima and the tunica media layers.</p>
Full article ">Figure 6
<p>Representative light microscopic image of sections of aortic root tissue stained with ORO. (L: lumen). (<b>A</b>) (×10): Control group: In the aortic root sections of the control group, it is observed that the endothelium and subendothelial connective tissue in the tunica intima layer have a typical structure (arrow). In addition, it is observed that the tunica media layer and tunica adventitia layer have a regular structure. (<b>B</b>) (×10): Sham group: In the aortic root tissue sections of the sham group, subendothelial widespread adipocyte accumulations (tailed arrow) are observed in the tunica intima. In addition, adipocyte accumulations (tailed arrow) are observed occasionally in the tunica media. (<b>C</b>) (×10): Case group: Dense adipocyte accumulations (tailed arrow) are observed in the subendothelial and tunica media in the aortic sections of the case group. (<b>D</b>) (×10): WEP group: In the aortic root sections of the WEP administration group, it is observed that adipocyte accumulations (tailed arrow) in the subendothelial and tunica media have decreased. (<b>E</b>) (×10): EEP group: It is observed that adipocytes have decreased in number (arrow) in the aortic sections of the EEP administration group. (<b>F</b>) (×10): GW group: In the aortic root sections of the GW administration group, it is observed that the adipocyte accumulations in the tunica intima and tunica media layers have decreased (tailed arrow). (<b>G</b>) (×10): DMSO group: Dense adipocyte accumulations (tailed arrow) are observed in the aortic sections of the DMSO administration group, especially in the subendothelial layer of the tunica intima and the tunica media layer. (<b>H</b>) (×10): Ethanol group: Dense adipocyte accumulations (tailed arrow) are observed in the aortic sections of the ethanol administration group, especially in the subendothelial layer of the tunica intima and the tunica media layer.</p>
Full article ">Figure 6 Cont.
<p>Representative light microscopic image of sections of aortic root tissue stained with ORO. (L: lumen). (<b>A</b>) (×10): Control group: In the aortic root sections of the control group, it is observed that the endothelium and subendothelial connective tissue in the tunica intima layer have a typical structure (arrow). In addition, it is observed that the tunica media layer and tunica adventitia layer have a regular structure. (<b>B</b>) (×10): Sham group: In the aortic root tissue sections of the sham group, subendothelial widespread adipocyte accumulations (tailed arrow) are observed in the tunica intima. In addition, adipocyte accumulations (tailed arrow) are observed occasionally in the tunica media. (<b>C</b>) (×10): Case group: Dense adipocyte accumulations (tailed arrow) are observed in the subendothelial and tunica media in the aortic sections of the case group. (<b>D</b>) (×10): WEP group: In the aortic root sections of the WEP administration group, it is observed that adipocyte accumulations (tailed arrow) in the subendothelial and tunica media have decreased. (<b>E</b>) (×10): EEP group: It is observed that adipocytes have decreased in number (arrow) in the aortic sections of the EEP administration group. (<b>F</b>) (×10): GW group: In the aortic root sections of the GW administration group, it is observed that the adipocyte accumulations in the tunica intima and tunica media layers have decreased (tailed arrow). (<b>G</b>) (×10): DMSO group: Dense adipocyte accumulations (tailed arrow) are observed in the aortic sections of the DMSO administration group, especially in the subendothelial layer of the tunica intima and the tunica media layer. (<b>H</b>) (×10): Ethanol group: Dense adipocyte accumulations (tailed arrow) are observed in the aortic sections of the ethanol administration group, especially in the subendothelial layer of the tunica intima and the tunica media layer.</p>
Full article ">Figure 7
<p>Representative light microscopic image of liver tissue sections stained with ORO. (<b>A</b>) (×10): Control group: Observed with remark cords consisting of hepatocytes with typical structure (arrow). Sinusoids are observed between the remark cords (tailed arrow). (<b>B</b>) (×10): Sham group: Multifocal hepatic steatosis is observed, formed by degenerative hepatocytes containing numerous lipid droplets and centrally located nuclei. Degenerative hepatocytes containing numerous lipid droplets and centrally located nuclei are observed to have centrilobular and periportal zone involvement (tailed arrow). (<b>C</b>) (×10): Case group: Hepatic steatosis (tailed arrow) in the multifocal centrilobular and periportal zones formed by degenerative hepatocytes that commonly contain lipid vacuoles in their cytoplasm. (<b>D</b>) (×10): WEP group: It is observed that multifocal steatosis caused by degenerative hepatocytes containing lipid vacuoles in their cytoplasm has decreased (tailed arrow). Hepatocytes (arrow). (<b>E</b>) (×10): EEP group: Degenerative hepatocytes containing cytoplasmic lipid vacuoles causing hepatic steatosis are observed to be decreased in the centrilobular and periportal zones (tailed arrow). Hepatocytes (arrow). (<b>F</b>) (×10): GW group: Although it is observed that the degenerative hepatocytes that cause multifocal hepatic steatosis have decreased (tailed arrow), hepatocytes with a typical structure are observed (arrow). (<b>G</b>) (×10): DMSO group: Hepatic steatosis (tailed arrow) is observed in the multifocal centrilobular and periportal zones formed by degenerative hepatocytes that commonly contain cytoplasmic lipid vacuoles. (<b>H</b>) (×10): Ethanol group: Degenerative hepatocytes (tailed arrow) with diffuse cytoplasmic vacuole content causing hepatic steatosis in the centrilobular and periportal zones with a multifocal pattern are observed in the remark cords.</p>
Full article ">Figure 7 Cont.
<p>Representative light microscopic image of liver tissue sections stained with ORO. (<b>A</b>) (×10): Control group: Observed with remark cords consisting of hepatocytes with typical structure (arrow). Sinusoids are observed between the remark cords (tailed arrow). (<b>B</b>) (×10): Sham group: Multifocal hepatic steatosis is observed, formed by degenerative hepatocytes containing numerous lipid droplets and centrally located nuclei. Degenerative hepatocytes containing numerous lipid droplets and centrally located nuclei are observed to have centrilobular and periportal zone involvement (tailed arrow). (<b>C</b>) (×10): Case group: Hepatic steatosis (tailed arrow) in the multifocal centrilobular and periportal zones formed by degenerative hepatocytes that commonly contain lipid vacuoles in their cytoplasm. (<b>D</b>) (×10): WEP group: It is observed that multifocal steatosis caused by degenerative hepatocytes containing lipid vacuoles in their cytoplasm has decreased (tailed arrow). Hepatocytes (arrow). (<b>E</b>) (×10): EEP group: Degenerative hepatocytes containing cytoplasmic lipid vacuoles causing hepatic steatosis are observed to be decreased in the centrilobular and periportal zones (tailed arrow). Hepatocytes (arrow). (<b>F</b>) (×10): GW group: Although it is observed that the degenerative hepatocytes that cause multifocal hepatic steatosis have decreased (tailed arrow), hepatocytes with a typical structure are observed (arrow). (<b>G</b>) (×10): DMSO group: Hepatic steatosis (tailed arrow) is observed in the multifocal centrilobular and periportal zones formed by degenerative hepatocytes that commonly contain cytoplasmic lipid vacuoles. (<b>H</b>) (×10): Ethanol group: Degenerative hepatocytes (tailed arrow) with diffuse cytoplasmic vacuole content causing hepatic steatosis in the centrilobular and periportal zones with a multifocal pattern are observed in the remark cords.</p>
Full article ">Figure 8
<p>Representative light microscopic picture of liver tissue sections incubated with ADAM10 primary antibody. (<b>A</b>) (×10): Control group: Liver tissue consisting of immune-negative normal-structured hepatocytes (arrow) is observed in the liver tissue sections of the control group. (<b>B</b>) (×10): Sham group: In the liver tissue sections of the sham group, it is observed that there are a large number of hepatocytes showing intense ADAM10 immune positivity (tailed arrow) in the remark cords. (<b>C</b>) (×10): Case group: In the liver tissue sections of the case group, intense ADAM10 immune positivity is observed in many hepatocytes (tailed arrow), especially in the centrilobular region. (<b>D</b>) (×10): WEP group: In the liver tissue sections of the WEP administration group, it is observed that the number of hepatocytes showing ADAM10 immune positivity has decreased (arrow). (<b>E</b>) (×10): EEP group: In the liver tissue sections of the EEP administration group, it is observed that the number of ADAM10 immune-positive hepatocytes in the remark cords has decreased (arrow). (<b>F</b>) (×10): GW group: In the liver tissue sections of the GW administration group, it is observed that the number of ADAM10 immune-positive hepatocytes in the remark cords has decreased (arrow). (<b>G</b>) (×10): DMSO group: It is observed that liver tissue sections belonging to the DMSO group contain hepatocytes showing intense ADAM10 immune positivity (tailed arrow) in the perizonal areas, especially in the centripetal region. (<b>H</b>) (×10): Ethanol group: It is observed that there are many hepatocytes showing intense ADAM10 immune positivity (tailed arrow), especially in the remark cords of the liver tissue sections of the ethanol group.</p>
Full article ">Figure 8 Cont.
<p>Representative light microscopic picture of liver tissue sections incubated with ADAM10 primary antibody. (<b>A</b>) (×10): Control group: Liver tissue consisting of immune-negative normal-structured hepatocytes (arrow) is observed in the liver tissue sections of the control group. (<b>B</b>) (×10): Sham group: In the liver tissue sections of the sham group, it is observed that there are a large number of hepatocytes showing intense ADAM10 immune positivity (tailed arrow) in the remark cords. (<b>C</b>) (×10): Case group: In the liver tissue sections of the case group, intense ADAM10 immune positivity is observed in many hepatocytes (tailed arrow), especially in the centrilobular region. (<b>D</b>) (×10): WEP group: In the liver tissue sections of the WEP administration group, it is observed that the number of hepatocytes showing ADAM10 immune positivity has decreased (arrow). (<b>E</b>) (×10): EEP group: In the liver tissue sections of the EEP administration group, it is observed that the number of ADAM10 immune-positive hepatocytes in the remark cords has decreased (arrow). (<b>F</b>) (×10): GW group: In the liver tissue sections of the GW administration group, it is observed that the number of ADAM10 immune-positive hepatocytes in the remark cords has decreased (arrow). (<b>G</b>) (×10): DMSO group: It is observed that liver tissue sections belonging to the DMSO group contain hepatocytes showing intense ADAM10 immune positivity (tailed arrow) in the perizonal areas, especially in the centripetal region. (<b>H</b>) (×10): Ethanol group: It is observed that there are many hepatocytes showing intense ADAM10 immune positivity (tailed arrow), especially in the remark cords of the liver tissue sections of the ethanol group.</p>
Full article ">Figure 9
<p>Representative light microscopic picture of liver tissue sections incubated with sortilin primary antibody. (<b>A</b>) (×10): Control group: Liver tissue consisting of immune-negative hepatocytes (arrow) is observed in the liver tissue sections of the control group. (<b>B</b>) (×10): Sham group: It is observed that there are a large number of hepatocytes (spiral arrow) showing intense sortilin immune positivity in the liver tissue sections of the sham group. (<b>C</b>) (×10): Case group: In the liver tissue sections of the case group, intense sortilin immune positivity (spiral arrow) is observed in many hepatocytes in the remark cords. (<b>D</b>) (×10): WEP group: In the liver tissue sections of the WEP administration group, it is observed that the number of sortilin immune-positive hepatocytes (spiral arrow) in the remark cords has decreased (arrow). (<b>E</b>) (×10): EEP group: In the liver tissue sections of the EEP administration group, it is observed that hepatocytes showing sortilin immune positivity (tailed arrow) have decreased (arrow). (<b>F</b>) (×10): GW group: In the liver tissue sections of the GW administration group, it is observed that the number of hepatocytes showing sortilin immune positivity (tailed arrow) in the remark cords has decreased (arrow). (<b>G</b>) (×10): DMSO group: It is observed that liver tissue sections belonging to the DMSO group contain hepatocytes showing intense sortilin immune positivity in the perizonal areas, especially in the centripetal region (arrow). (<b>H</b>) (×10): Ethanol group: It is observed that there are many hepatocytes showing intense sortilin immune positivity, especially in the remark cords of the liver tissue sections belonging to the ethanol group (arrow).</p>
Full article ">Figure 9 Cont.
<p>Representative light microscopic picture of liver tissue sections incubated with sortilin primary antibody. (<b>A</b>) (×10): Control group: Liver tissue consisting of immune-negative hepatocytes (arrow) is observed in the liver tissue sections of the control group. (<b>B</b>) (×10): Sham group: It is observed that there are a large number of hepatocytes (spiral arrow) showing intense sortilin immune positivity in the liver tissue sections of the sham group. (<b>C</b>) (×10): Case group: In the liver tissue sections of the case group, intense sortilin immune positivity (spiral arrow) is observed in many hepatocytes in the remark cords. (<b>D</b>) (×10): WEP group: In the liver tissue sections of the WEP administration group, it is observed that the number of sortilin immune-positive hepatocytes (spiral arrow) in the remark cords has decreased (arrow). (<b>E</b>) (×10): EEP group: In the liver tissue sections of the EEP administration group, it is observed that hepatocytes showing sortilin immune positivity (tailed arrow) have decreased (arrow). (<b>F</b>) (×10): GW group: In the liver tissue sections of the GW administration group, it is observed that the number of hepatocytes showing sortilin immune positivity (tailed arrow) in the remark cords has decreased (arrow). (<b>G</b>) (×10): DMSO group: It is observed that liver tissue sections belonging to the DMSO group contain hepatocytes showing intense sortilin immune positivity in the perizonal areas, especially in the centripetal region (arrow). (<b>H</b>) (×10): Ethanol group: It is observed that there are many hepatocytes showing intense sortilin immune positivity, especially in the remark cords of the liver tissue sections belonging to the ethanol group (arrow).</p>
Full article ">
12 pages, 3300 KiB  
Article
Lysophosphatidylcholines Promote Influenza Virus Reproduction through the MAPK/JNK Pathway in PMA-Differentiated THP-1 Macrophages
by Min-Ho Cha, Hee-Jeong Choi and Jin-Yeul Ma
Int. J. Mol. Sci. 2024, 25(12), 6538; https://doi.org/10.3390/ijms25126538 (registering DOI) - 13 Jun 2024
Abstract
Obesity and metabolic syndrome alter serum lipid profiles. They also increase vulnerability to viral infections and worsen the survival rate and symptoms after infection. How serum lipids affect influenza virus proliferation is unclear. Here, we investigated the effects of lysophosphatidylcholines on influenza A [...] Read more.
Obesity and metabolic syndrome alter serum lipid profiles. They also increase vulnerability to viral infections and worsen the survival rate and symptoms after infection. How serum lipids affect influenza virus proliferation is unclear. Here, we investigated the effects of lysophosphatidylcholines on influenza A virus (IAV) proliferation. IAV particles in the culture medium were titrated using extraction-free quantitative PCR, and viral RNA and protein levels were assessed using real-time PCR and Western blot, respectively. RNA sequencing data were analyzed using PCA and heatmap analysis, and pathway analysis was performed using the KEGG mapper and PathIN tools. Statistical analysis was conducted using SPSS21.0. LPC treatment of THP-1 cells significantly increased IAV proliferation and IAV RNA and protein levels, and saturated LPC was more active in IAV RNA expression than unsaturated LPC was. The functional analysis of genes affected by LPCs showed that the expression of genes involved in IAV signaling, such as suppressor of cytokine signaling 3 (SOCS3), phosphoinositide-3-kinase regulatory subunit 3 (PI3K) and AKT serine/threonine kinase 3 (AKT3), Toll-like receptor 7 (TKR7), and interferon gamma receptor 1 (IFNGR1), was changed by LPC. Altered influenza A pathways were linked with MAPK and PI3K/AKT signaling. Treatment with inhibitors of MAPK or PI3K attenuated viral gene expression changes induced by LPCs. The present study shows that LPCs stimulated virus reproduction by modifying the cellular environment to one in which viruses proliferated better. This was mediated by the MAPK, JNK, and PI3K/AKT pathways. Further animal studies are needed to confirm the link between LPCs from serum or the respiratory system and IAV proliferation. Full article
(This article belongs to the Special Issue Lysophosphatidic Acid Signaling in Health and Disease)
Show Figures

Figure 1

Figure 1
<p>Increase in IAV titer after LPC treatment. (<b>A</b>) Viability of THP-1 macrophages pretreated with different doses of LPCs and exposed to IAV for 24 or 36 h. (<b>B</b>) Number of virus particles released into the culture medium of cells treated with LPCs before IAV infection. Color boxs indicated control (□), IAV (■) and IAV with LPC (▒). (<b>C</b>) Relative quantification of the number of virus particles released into the culture medium at different times after IAV infection of cells pretreated with LPCs for 8 h. (<b>D</b>) Relative amount of viral genome in cells immediately after viral infection. THP-1 macrophages were treated with LPCs at the indicated concentrations for 8 h and then infected with IAV for 2 h. After replacing the medium with fresh medium, cells were incubated for the indicated times. Cell viability was determined using the CCK-8 assay, and IAV titer was detected using RT-qPCR. The data are representative of three independent experiments and are presented as means ± SDs of three independent experiments. Statistical significance was assessed using an unpaired Student <span class="html-italic">t</span>-test. *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.005, * <span class="html-italic">p</span> &lt; 0.05 compared with the values obtained for cells infected with IAV.</p>
Full article ">Figure 2
<p>Increase in viral RNA and protein levels by LPCs. (<b>A</b>) Relative expression levels of IAV genes in THP-1 cells treated with different dosages of LPCs. Color boxes indicated control (□), IAV (■) and IAV with LPC (▒). (<b>B</b>) The protein level of IAV proteins in THP-1 cells treated with LPCs. RNA levels were determined using RT-qPCR, and protein expression was detected using Western blotting. Data represent the means ± SDs of three independent experiments. Statistical significance was assessed using an unpaired Student <span class="html-italic">t</span>-test. *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.005, * <span class="html-italic">p</span> &lt; 0.05 compared with the values obtained for cells infected with IAV. M2: matrix-2; M1: matrix-1; PA: polymerase acidic protein; PB1: polymerase basic 1; NP: nucleoprotein; NS1: non-structural protein; PB2: polymerase basic 2; HA: hemagglutinin; NA: neuraminidase.</p>
Full article ">Figure 3
<p>Differential expression of viral RNAs according to the type of LPCs. The relative expression levels of IAV genes were determined using RT-qPCR. Data represent the means ± SDs of three independent experiments. Statistical significance was assessed using an unpaired Student <span class="html-italic">t</span>-test. *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.005, * <span class="html-italic">p</span> &lt; 0.05 compared with the values obtained for cells infected with IAV. Color boxes indicated control (□), IAV (■) and IAV with LPCs (▒). M2: matrix-2; M1: matrix-1; PA: polymerase acidic protein; PB1: polymerase basic 1; NP: nucleoprotein; NS1: non-structural protein; PB2: polymerase basic 2; HA: hemagglutinin; NA: neuraminidase.</p>
Full article ">Figure 4
<p>RNA expression profiles in THP-1 cells treated with LPCs. (<b>A</b>) PCA of RNA profiles in control cells and cells treated with 40 µM LPCs. (<b>B</b>) Heatmap of genes with a more than twofold expression changes in response to LPCs (<span class="html-italic">p</span> &lt; 0.01). Heatmap was obtained using ClustVis (<a href="https://biit.cs.ut.ee/clustvis/" target="_blank">https://biit.cs.ut.ee/clustvis/</a>; accessed on 2 November 2023). (<b>C</b>) KEGG pathway analysis of 1082 genes with decreased or enhanced expression in response to LPCs.</p>
Full article ">Figure 5
<p>Effects of LPCs on influenza A signaling pathways. (<b>A</b>) Genes involved in influenza A signaling pathways and altered by LPC treatment of THP-1 cells. Pink indicates genes of which the expression increased 2-fold upon LPC treatment, respectively. Green and yellow indicate genes with a more than 2- or 1.5-fold decrease in expression upon LPC treatment, respectively. (<b>B</b>) Interaction of pathways linked with influenza A signaling and affected by LPCs. (<b>C</b>) The protein level of p38, JNK, and AKT and their phosphorylated forms in THP-1 cells treated with LPCs. The protein expression was detected using Western blotting. p38: mitogen-activated protein kinase P38 alpha; JNK: C-Jun N-terminal kinase 1; AKT: AKT serine/threonine kinase 1.</p>
Full article ">Figure 6
<p>Attenuation of viral gene expression by MAP kinase inhibitor (SB203580), JNK inhibitor, and PI3K inhibitor (LY294002) in LPC-treated THP-1 cells. Relative expression levels of IAV genes were determined using RT-qPCR. Data represent the means ± SDs of three independent experiments. Statistical significance was assessed using an unpaired Student <span class="html-italic">t</span>-test. *** <span class="html-italic">p</span> &lt; 0.001 compared with values obtained for cells infected with IAV, # <span class="html-italic">p</span> &lt; 0.05 compared with values obtained for cells infected with IAV and treated with LPC. Color boxes indicated control (□), IAV (■) and IAV with LPC and inhibitors (▒). M2: matrix-2; M1: matrix-1; PA: polymerase acidic protein; PB1: polymerase basic 1; NP: nucleoprotein; NS1: non-structural protein; PB2: polymerase basic 2; HA: hemagglutinin; NA: neuraminidase.</p>
Full article ">
20 pages, 4670 KiB  
Article
Nutraceutical Potential of Djulis (Chenopodium formosanum) Hull: Phytochemicals, Antioxidant Activity, and Liver Protection
by Yu-Chen Huang, Chun-Liang Tung, Shang-Tse Ho, Wei-Sung Li, Shiming Li, Yu-Tang Tung and Jyh-Horng Wu
Antioxidants 2024, 13(6), 721; https://doi.org/10.3390/antiox13060721 (registering DOI) - 13 Jun 2024
Abstract
Djulis (Chenopodium formosanum), a traditional Taiwanese crop enriched with phenolic compounds and betalain pigments, is associated with various health benefits, including antioxidant and hepatoprotective effects. This study analysed the phytochemical content and antioxidant capacity of extracts from both the hull and [...] Read more.
Djulis (Chenopodium formosanum), a traditional Taiwanese crop enriched with phenolic compounds and betalain pigments, is associated with various health benefits, including antioxidant and hepatoprotective effects. This study analysed the phytochemical content and antioxidant capacity of extracts from both the hull and kernel of Djulis. The hull extract, which contained higher levels of flavonoids and exhibited superior antioxidant activity compared to the kernel extract, was selected for further in vivo studies. These experiments showed that oral administration of the Djulis hull crude extract significantly mitigated lipopolysaccharide (LPS)-induced acute liver injury (ALI) in mice by increasing the activity of the antioxidant enzyme glutathione peroxidase (GPx), reducing plasma levels of pro-inflammatory cytokine interferon gamma (IFN-γ), and enhancing liver levels of the anti-inflammatory cytokine interleukin-4 (IL-4). Additionally, the extract demonstrated potential in inhibiting the TLR4/NF-κB pathway, a critical signalling pathway in inflammation and apoptosis, offering insights into its protective mechanisms. These findings underscore Djulis hull’s potential as a functional food ingredient for ALI prevention and propose a valuable application for agricultural by-products. Full article
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<p>The component analysis of three strains of Djulis hull and kernel crude extracts using UPLC-MS/MS. (<b>A</b>) Molecular networking of three strains of Djulis hull and kernel crude extracts. <b>12</b>, <b>18</b>, and <b>20</b> belong to the flavonoid group. (<b>B</b>) Differences in flavonoids in Djulis hull and kernel crude extracts. (<b>C</b>) Differences in flavonoids in three strains of Djulis hull crude extracts. The widths and shades of connecting lines represent the similarity between connected nodes (Edge Score), while the sizes of circular nodes represent the total spectra number for each compound (spectra number). The numbers in the figures corresponded to the compound name, parent mass, and spectra number, as detailed in <a href="#app1-antioxidants-13-00721" class="html-app">Supplementary Tables S1–S3</a>.</p>
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<p>(<b>A</b>) Total phenolic contents, (<b>B</b>) total flavonoid contents, (<b>C</b>) DPPH radical scavenging activity, and (<b>D</b>) half maximal inhibitory concentrations of three strains of Djulis hull and kernel crude extracts. DPPH, 1,1-diphenyl-2-picrylhydrazyl. The statistics were determined by one-way ANOVA with Tukey’s multiple comparisons test. Values are represented as the mean ± SD (<span class="html-italic">n</span> = 3), and different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of Djulis hull and kernel crude extracts on (<b>A</b>) body weight, (<b>B</b>) body weight change, (<b>C</b>) relative liver weight, and (<b>D</b>) white blood cell type of mice in LPS-induced acute liver injury. The arrow represents the time point induced by LPS. A one-tailed Mann-Whitney U test was used for statistical analysis. Values are represented as the mean ± SEM (<span class="html-italic">n</span> = 6), where <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 compared with the control group.</p>
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<p>Effects of Djulis hull and kernel crude extracts on the (<b>A</b>) liver histopathology and Suzuki scores of mice with an LPS-induced acute liver injury, including (<b>B</b>) vacuolation, (<b>C</b>) necrosis, (<b>D</b>) congestion, and (<b>E</b>) the total score. H&amp;E staining (200×). Solid arrows represent vacuolization, * represents necrosis, and hollow arrows represent congestion. A one-tailed Mann-Whitney U test was used for statistical analysis. Values are represented as the mean ± SEM (<span class="html-italic">n</span> = 6), where <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 compared with the control group, and * <span class="html-italic">p</span> &lt; 0.05 compared with the water group.</p>
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<p>Effects of Djulis hull and kernel crude extracts on the activities of liver antioxidant enzymes, including (<b>A</b>) SOD, (<b>B</b>) GPx, and (<b>C</b>) CAT, as well as the (<b>D</b>) TBARS content of mice in LPS-induced acute liver injury. SOD, superoxide dismutase; GPx, glutathione peroxidase; CAT, catalase; TBARS, thiobarbituric acid reactive substances. A one-tailed Mann-Whitney U test was used for statistical analysis. Values are represented as the mean ± SEM (<span class="html-italic">n</span> = 6), where <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 compared with the control group, and * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared with the water group.</p>
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<p>Effects of Djulis hull and kernel crude extracts on plasma pro-inflammatory cytokines (<b>A</b>) IFN-γ, (<b>B</b>) IL-6, and (<b>C</b>) TNF-α and liver pro-inflammatory cytokines (<b>D</b>) IFN-γ, (<b>E</b>) IL-1β, (<b>F</b>) IL-6, and (<b>G</b>) TNF-α and liver anti-inflammatory cytokines (<b>H</b>) IL-4 and (<b>I</b>) IL-10 in mice with LPS-induced acute liver injury. IFN-γ, interferon gamma; IL-6, interleukin-6; TNF-α, tumour necrosis factor alpha; IL-1β, interleukin-1 beta; IL-4, interleukin-4; IL-10, interleukin-10. A one-tailed Mann-Whitney U test was used for statistical analysis. Values are represented as the mean ± SEM (<span class="html-italic">n</span> = 6), where <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 compared with the control group, and * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared with the water group.</p>
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<p>(<b>A</b>) Effects of Djulis hull and kernel crude extracts on the protein expression levels of (<b>B</b>) MyD88 and (<b>C</b>) IκBα in the TLR4/NF-κB pathway, (<b>D</b>) HO-1 in the Nrf2/HO-1 pathway, and (<b>E</b>) BAX, (<b>F</b>) cleaved caspase-8, and (<b>G</b>) cleaved caspase-3 in the apoptotic pathway in the livers of mice with LPS-induced acute liver injury. MyD88, myeloid differentiation primary response protein 88; IκBα, nuclear factor-kappa B inhibitor alpha; HO-1, haem oxygenase-1; BAX, Bcl2-associated X protein. A one-tailed Mann-Whitney U test was used for statistical analysis. Values are represented as the mean ± SEM (<span class="html-italic">n</span> = 6), where <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 compared with the control group.</p>
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<p>Mechanisms underlying the effects of Djulis hull crude extract on the LPS-induced acute liver injury model in mice (created with BioRender.com). Red solid arrows indicate a significant increase; red dotted arrows indicate a potential increase; blue solid arrows indicate a significant decrease; blue dotted arrows indicate a potential decrease [<a href="#B11-antioxidants-13-00721" class="html-bibr">11</a>,<a href="#B12-antioxidants-13-00721" class="html-bibr">12</a>,<a href="#B13-antioxidants-13-00721" class="html-bibr">13</a>,<a href="#B14-antioxidants-13-00721" class="html-bibr">14</a>,<a href="#B49-antioxidants-13-00721" class="html-bibr">49</a>,<a href="#B52-antioxidants-13-00721" class="html-bibr">52</a>,<a href="#B53-antioxidants-13-00721" class="html-bibr">53</a>].</p>
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11 pages, 2819 KiB  
Communication
Rare Filaggrin Variants Are Associated with Pustular Skin Diseases in Asians
by Luca Lo Piccolo, Wasinee Wongkummool, Phatcharida Jantaree, Teerada Daroontum, Suteeraporn Chaowattanapanit, Charoen Choonhakarn, Warayuwadee Amornpinyo, Romanee Chaiwarith, Salin Kiratikanon, Rujira Rujiwetpongstorn, Napatra Tovanabutra, Siri Chiewchanvit, Chumpol Ngamphiw, Worrachet Intachai, Piranit Kantaputra and Mati Chuamanochan
Int. J. Mol. Sci. 2024, 25(12), 6466; https://doi.org/10.3390/ijms25126466 - 12 Jun 2024
Viewed by 176
Abstract
Reactive pustular eruptions (RPEs) can manifest in a variety of conditions, including pustular psoriasis (PP) and adult-onset immunodeficiency syndrome due to anti-interferon-γ autoantibody (AOID). These RPEs can be attributed to different causes, one of which is genetic factors. However, the genetic basis for [...] Read more.
Reactive pustular eruptions (RPEs) can manifest in a variety of conditions, including pustular psoriasis (PP) and adult-onset immunodeficiency syndrome due to anti-interferon-γ autoantibody (AOID). These RPEs can be attributed to different causes, one of which is genetic factors. However, the genetic basis for pustular skin diseases remains poorly understood. In our study, we conducted whole-exome sequencing on a cohort of 17 AOID patients with pustular reactions (AOID-PR) and 24 PP patients. We found that 76% and 58% of the AOID-PR and PP patients, respectively, carried rare genetic variations within the filaggrin (FLG) gene family. A total of 12 out of 21 SNPs on FLG had previously received clinical classifications, with only p.Ser2706Ter classified as pathogenic. In contrast, none of the FLG3 SNPs identified in this study had prior clinical classifications. Overall, these variations had not been previously documented in cases of pustular disorders, and two of them were entirely novel discoveries. Immunohistochemical analysis of skin biopsies revealed that FLG variants like p.Ser860Trp, p.Gly3903Ter, p.Gly2440Glu, and p.Glu2133Asp caused reductions in FLG levels similar to the pathogenic FLG p.Ser2706Ter. These results highlight rare FLG variants as potential novel genetic risk factors contributing to pustule formation in both AOID and PP. Full article
(This article belongs to the Special Issue Dermatology: Advances on Pathophysiology and Therapies 2.0)
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<p>Multiple non-follicular pustules with coalescence on all extremities of an AOID patient (<b>A</b>,<b>B</b>) and multiple non-follicular pustules with coalescence on all extremities and the abdomen of a patient with generalized pustular psoriasis (<b>C</b>).</p>
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<p>Annotation of the FLG and FLG3 variants identified in this study. The primary structures of FLG (<b>A</b>), FLG3 (<b>B</b>), and the genetic variants identified in this study were visualized using SnapGene v.7.02. The primary structures of filaggrins were retrieved from Uniprot (FLG: P20930; FLG3: Q86YZ3). Red marks indicate previously unreported variants.</p>
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<p>Immunohistochemical comparison of filaggrin expression in healthy control skin to that in skin from anti-interferon-γ autoantibody (AOID) patients with pustular reactions (AOID-PR) and pustular psoriasis (PP) patients who carry FLG variants. Formalin-fixed, paraffin-embedded healthy skin was stained with a filaggrin antibody. Filaggrin was only expressed in well-differentiated keratinized epithelial cells, including hair follicles. (<b>A</b>) Healthy control skin (4X). (<b>B</b>) Healthy control skin (8X). AOID-PR patients and PP patients displayed reduced filaggrin immunoreactivity compared to healthy controls. (<b>C</b>) AOID-PR-FLG-2797 (8X). (<b>D</b>) AOID-PR-FLG-2925 (8X). (<b>E</b>) AOID-PR-FLG-2935 (8X). (<b>F</b>) AOID-PR-FLG-2926 (8X). (<b>G</b>) PP-FLG-2923 (8X). (<b>H</b>) AOID-PR-FLG-WT (8X).</p>
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<p>Quantitation of immunoreactive FLG protein in skin biopsies. Bar graph displaying means along with standard deviations (SDs). Mixed-effects analysis was utilized, followed by Dunnett’s multiple comparison test. The immunoreactivity of FLG in AOID-PR and PP was compared to that of healthy controls. Bars without any symbol indicate no statistical significance. HC = healthy control; FLG-WT = wild-type filaggrin. Statistical significance was defined as a <span class="html-italic">p</span>-value &lt; 0.05.</p>
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19 pages, 10049 KiB  
Article
Transcriptome Analysis in Air–Liquid Interface Porcine Respiratory Epithelial Cell Cultures Reveals That the Betacoronavirus Porcine Encephalomyelitis Hemagglutinating Virus Induces a Robust Interferon Response to Infection
by Kaitlyn M. Sarlo Davila, Rahul K. Nelli, Juan C. Mora-Díaz, Yongming Sang, Laura C. Miller and Luis G. Giménez-Lirola
Viruses 2024, 16(6), 939; https://doi.org/10.3390/v16060939 - 11 Jun 2024
Viewed by 400
Abstract
Porcine hemagglutinating encephalomyelitis virus (PHEV) replicates in the upper respiratory tract and tonsils of pigs. Using an air–liquid interface porcine respiratory epithelial cells (ALI-PRECs) culture system, we demonstrated that PHEV disrupts respiratory epithelia homeostasis by impairing ciliary function and inducing antiviral, pro-inflammatory cytokine, [...] Read more.
Porcine hemagglutinating encephalomyelitis virus (PHEV) replicates in the upper respiratory tract and tonsils of pigs. Using an air–liquid interface porcine respiratory epithelial cells (ALI-PRECs) culture system, we demonstrated that PHEV disrupts respiratory epithelia homeostasis by impairing ciliary function and inducing antiviral, pro-inflammatory cytokine, and chemokine responses. This study explores the mechanisms driving early innate immune responses during PHEV infection through host transcriptome analysis. Total RNA was collected from ALI-PRECs at 24, 36, and 48 h post inoculation (hpi). RNA-seq analysis was performed using an Illumina Hiseq 600 to generate 100 bp paired-end reads. Differential gene expression was analyzed using DeSeq2. PHEV replicated actively in ALI-PRECs, causing cytopathic changes and progressive mucociliary disruption. Transcriptome analysis revealed downregulation of cilia-associated genes such as CILK1, DNAH11, LRRC-23, -49, and -51, and acidic sialomucin CD164L2. PHEV also activated antiviral signaling pathways, significantly increasing the expression of interferon-stimulated genes (RSAD2, MX1, IFIT, and ISG15) and chemokine genes (CCL5 and CXCL10), highlighting inflammatory regulation. This study contributes to elucidating the molecular mechanisms of the innate immune response to PHEV infection of the airway epithelium, emphasizing the critical roles of the mucociliary, interferon, and chemokine responses. Full article
(This article belongs to the Special Issue Endemic and Emerging Swine Viruses 2024)
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<p>Air–liquid interface porcine respiratory epithelial cells (ALI-PREC) susceptibility toward porcine hemagglutinating encephalomyelitis virus (PHEV) infection. (<b>A</b>–<b>D</b>) Completely differentiated ALI-PRECs (day 30) treated with infection medium only, i.e., without virus (mock-inoculated) (<b>A</b>), (<b>B</b>) with HA (titer of 128) of PHEV 67 N for 24 h post-inoculation (hpi), (<b>C</b>) 36 hpi, and (<b>D</b>) 48 hpi. Bar, 100 μm. Representative images from two biological and three technical replicates. (<b>E</b>) Detection of PHEV nucleocapsid gene using reverse transcription-qPCR. RNA from the subnatants collected from ALI-PRECs treated with PHEV was analyzed using RT-qPCR developed by ISU and Tetracore. Collection time is shown in hours. A sample volume of 5 μL of extracted sample RNA along with internal control was added to the qPCR master mix. All qPCRs were performed with a negative extraction control (NEC), a positive extraction control (PEC), and a no-template control (NTC) included in each run. Samples from two biological replicates and three technical replicates. Statistical analysis was performed using Fisher’s LSD multiple-comparison test (GraphPad Prism 9.0.1). **, <span class="html-italic">p</span> value  &lt;  0.01, and ****, <span class="html-italic">p</span> value  &lt;  0.0001.</p>
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<p>PHEV-induced DEG in ALI-PRECs at 24, 36, and 48 h post-infection (hpi). Volcano plots show differential gene expression at 24 hpi (<b>A</b>), 36 hpi (<b>B</b>), and 48 hpi (<b>C</b>). Significant genes (Benjamini–Hochberg FDR <span class="html-italic">p</span> &lt; 0.15) downregulated are shown in green, while upregulated genes are shown in red. Genes that are not significantly differentially expressed are shown in grey. The number of DEGs shared between time points is plotted in the Venn diagram (<b>D</b>).</p>
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<p>Network visualization of DEG in ALI-PRECs at 24, 36, and 48 h post-infection (hpi) with PHEV. Percentage GO terms per group (grouped by kappa score) in PHEV-infected ALI-PRECs at (<b>A</b>) 24 hpi, (<b>C</b>) 36 hpi, (<b>E</b>) 48 hpi. Network of functionally grouped significant gene ontology terms at (<b>B</b>) 24 hpi, (<b>D</b>) 36 hpi, and (<b>F</b>) 48 hpi. The network elements were nodes representing molecules and edges representing the interaction between molecules. The node size represents the significance of the term. Upregulated genes associated with each term are shown in red, while downregulated are in green. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Differential gene expression at 24 h post-inoculation (hpi) within the canonical IPA pathway “Role of Hypercytokinemia/hyperchemokinemia in the Pathogenesis of Influenza”. Downregulated genes are shown with green fill, while upregulated genes were shown with pink fill. The greater the upregulation or downregulation, the darker the fill. A molecule activity predictor tool predicts downstream activity based on significant differential gene expression. Predicted activation is shown in orange, and predicted inhibition is shown in blue. The more confident the prediction, the darker the fill. Solid lines represent direct relationships, while dashed lines represent indirect relationships. * Denotes statistical significance.</p>
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<p>Differential gene expression at 36 h post-inoculation (hpi) within the canonical IPA pathway “Role of Hypercytokinemia/hyperchemokinemia in the Pathogenesis of Influenza”. Downregulated genes are shown with green fill, while upregulated genes are shown with pink fill. The greater the upregulation or downregulation, the darker the fill. A molecule activity predictor tool predicts downstream activity based on significant differential gene expression. Predicted activation is shown in orange, and predicted inhibition is shown in blue. The more confident the prediction, the darker the fill. Solid lines represent direct relationships, while dashed lines represent indirect relationships. * Denotes statistical significance.</p>
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<p>Differential gene expression at 48 h post-inoculation (hpi) within the canonical IPA pathway “Role of Hypercytokinemia/hyperchemokinemia in the Pathogenesis of Influenza”. Downregulated genes are shown with green fill, while upregulated genes are shown with pink fill. The greater the upregulation or downregulation, the darker the fill. A molecule activity predictor tool predicts downstream activity based on significant differential gene expression. Predicted activation is shown in orange, and predicted inhibition is shown in blue. The more confident the prediction, the darker the fill. Solid lines represent direct relationships, while dashed lines represent indirect relationships.</p>
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<p>Quantitative PCR analysis of ALI-PRECs inoculated with PHEV or mock inoculated with infection medium. Bar graph showing relative quantification (RQ) levels of <span class="html-italic">MDA5</span> (<b>A</b>), <span class="html-italic">STAT1</span> (<b>B</b>), <span class="html-italic">Mx1</span> (<b>C</b>), <span class="html-italic">CXCL10</span> (<b>D</b>), and <span class="html-italic">CCL5</span> (<b>E</b>) measured in ALI-PRECs treated with HA (titer of 128) of PHEV and mock inoculum at respective h post-infection (x axes). RQ values were calculated using the 2−ΔΔCT method. The data are normalized against the geometric mean for three endogenous control genes (<span class="html-italic">EIF3K</span>, <span class="html-italic">PPIA</span>, and <span class="html-italic">RPL10</span>). This graph is generated from three technical replicates and two biological replicates (2 pigs). Statistical analysis was performed using Fisher’s LSD multiple-comparison test (GraphPad Prism 9.0.1). *, <span class="html-italic">p</span> value  &lt;  0.05; **, <span class="html-italic">p</span> value &lt; 0.001; ***, <span class="html-italic">p</span> value &lt; 0.005; ****, <span class="html-italic">p</span> value &lt; 0.0001.</p>
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38 pages, 3209 KiB  
Review
Interferon-Regulated Expression of Cellular Splicing Factors Modulates Multiple Levels of HIV-1 Gene Expression and Replication
by Fabian Roesmann, Lisa Müller, Katleen Klaassen, Stefanie Heß and Marek Widera
Viruses 2024, 16(6), 938; https://doi.org/10.3390/v16060938 - 11 Jun 2024
Viewed by 481
Abstract
Type I interferons (IFN-Is) are pivotal in innate immunity against human immunodeficiency virus I (HIV-1) by eliciting the expression of IFN-stimulated genes (ISGs), which encompass potent host restriction factors. While ISGs restrict the viral replication within the host cell by targeting various stages [...] Read more.
Type I interferons (IFN-Is) are pivotal in innate immunity against human immunodeficiency virus I (HIV-1) by eliciting the expression of IFN-stimulated genes (ISGs), which encompass potent host restriction factors. While ISGs restrict the viral replication within the host cell by targeting various stages of the viral life cycle, the lesser-known IFN-repressed genes (IRepGs), including RNA-binding proteins (RBPs), affect the viral replication by altering the expression of the host dependency factors that are essential for efficient HIV-1 gene expression. Both the host restriction and dependency factors determine the viral replication efficiency; however, the understanding of the IRepGs implicated in HIV-1 infection remains greatly limited at present. This review provides a comprehensive overview of the current understanding regarding the impact of the RNA-binding protein families, specifically the two families of splicing-associated proteins SRSF and hnRNP, on HIV-1 gene expression and viral replication. Since the recent findings show specifically that SRSF1 and hnRNP A0 are regulated by IFN-I in various cell lines and primary cells, including intestinal lamina propria mononuclear cells (LPMCs) and peripheral blood mononuclear cells (PBMCs), we particularly discuss their role in the context of the innate immunity affecting HIV-1 replication. Full article
(This article belongs to the Special Issue Innate Sensing and Restriction of Retroviruses)
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Graphical abstract

Graphical abstract
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<p>The complexity of the HIV-1 genome. Schematic overview of the nine major open reading frames (ORFs) encoded by HIV-1 using all three open reading frames (frames 1–3). The long terminal repeats (LTRs) are located at the terminal ends of the integrated genome serving as provirus. The trans-activation response (TAR) element is indicated at the 5′ LTR (gray box). The stem-loop and respective slippery sequence (U UUU UUA), required for programmed ribosomal frameshifting, is indicated at the frameshift site of the <span class="html-italic">pol</span> mRNA. The Rev-response element (RRE) within the <span class="html-italic">env</span>-coding region is indicated as a small box. <span class="html-italic">Cis</span>-acting repressive sequences (CRSs) according to [<a href="#B3-viruses-16-00938" class="html-bibr">3</a>] are shown in red lines above the respective sequence. Protease cleavage sites are indicated with gray arrows. The gene products encoded by the respective HIV-1 mRNA classes are represented in the following colors: 9 kb in green, 4 kb in turquoise, 2 kb in purple. Nucleotide positions are referenced relative to HXB2 (GenBank accession number K03455). Illustration and protease cleavage sites are adapted from ViralZone/SwissBioPics [<a href="#B4-viruses-16-00938" class="html-bibr">4</a>].</p>
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<p>A schematic drawing depicting the simplified HIV-1 replication cycle highlighting post-integration steps. Following attachment and glycoprotein-mediated membrane fusion, the viral particle is transported to the nuclear pore complex, while genomic RNA is reverse-transcribed into dsDNA. The viral dsDNA is integrated into the host genome serving as provirus. LTR-driven transcription by the RNAPII enables the synthesis of the HIV-1 full-length pre-mRNA, which subsequently undergoes extensive alternative splicing. This process maintains an equilibrium of protein coding mRNA isoforms of 9, 4, and 2 kb size, which are then exported into the cytoplasm and serve as a template for the translation of viral proteins. The newly assembled virions exit the host cell by budding. The extracellular space is shown in white, the cytoplasm in gray, and the nucleus in aquamarine.</p>
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<p>Schematic drawing of the structural overview of SRSF proteins. A schematic representation illustrating the modular organization and domain architecture of SRSF proteins. SRSF proteins typically consist of one or more RRMs responsible for RNA binding, followed by a serine/arginine-rich (RS) domain crucial for protein–protein interactions and splicing regulation. Auxiliary domains, RS-like domain, ZnF domain, or RRM-like motifs are depicted in the indicated colors. Illustration adapted from [<a href="#B69-viruses-16-00938" class="html-bibr">69</a>].</p>
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<p>Schematic drawing of the structural landscape and domain architecture of hnRNPs. hnRNP families A, A/B, C, D, E, F, G, H, I, J, K, L, M, P, Q, R, and U are depicted. RNA recognition motif (RRM), quasi-RNA recognition motif (qRRM), K-homology (KH), arginine–glycine–glycine (RGG), as well as glycine-rich, proline-rich, and acidic-rich domains are depicted in the indicated colors. Illustration was adapted from [<a href="#B106-viruses-16-00938" class="html-bibr">106</a>].</p>
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<p>Schematic representation of an HIV-1 long terminal repeat (LTR) architecture. Interactions with cellular transcription factors are illustrated. Schematic drawing of the LTR of the HIV-1 genome with nucleotide positions relative to the transcriptional start site (+1). The promotor comprises three main regions: U3, R, and U5 encompass specific functional domains; the U3 region contains the modulatory segment; the R region includes the TAR loop structure that is essential for HIV-1 transcriptional regulation. Colored boxes highlight potential binding sites for cellular transcription factors. Figure adapted from [<a href="#B206-viruses-16-00938" class="html-bibr">206</a>].</p>
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<p>Splicing-associated <span class="html-italic">cis</span>-regulatory elements in the HIV-1 genome. The arrangement of SREs (splicing regulatory elements) within the HIV-1 genome is illustrated. Exons are represented by gray boxes, whereas introns are depicted as black lines. Splicing enhancers are highlighted in green, while splicing silencers are indicated in red. Illustration was modified according to [<a href="#B43-viruses-16-00938" class="html-bibr">43</a>].</p>
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<p>Schematic illustration of the HIV-1 programmed ribosomal frameshifting. Schematic representation depicting −1 programmed ribosomal frameshifting (-1PRF) in HIV-1. The ribosome is depicted with the slippery site highlighted, comprising the sequence “UUUUUUA”, where the frameshift occurs. Two reading frames are illustrated: <span class="html-italic">gag</span>–<span class="html-italic">pol</span> being dependent on PRF and <span class="html-italic">gag</span> being independent of PRF. The hairpin structure of the mRNA downstream the slippery site is discernible, aiding in understanding the mechanism of frameshifting. Red arrows indicate the frame shifting direction. Illustration was modified according to [<a href="#B296-viruses-16-00938" class="html-bibr">296</a>].</p>
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<p>The net antiviral state of an interferon-stimulated cell is established by the interplay of host restriction and host dependency factors. Interferon (IFN) stimulation induces a cellular antiviral state, altering the expression of interferon-stimulated genes (ISGs) and interferon-repressed genes (IRepGs).</p>
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18 pages, 11668 KiB  
Article
Drug-Resistance Biomarkers in Patient-Derived Colorectal Cancer Organoid and Fibroblast Co-Culture System
by Kyoung-Bin Ryu, Jeong-ah Seo, Kyerim Lee, Juhyun Choi, Geon Yoo, Ji-hye Ha and Mee Ryung Ahn
Curr. Issues Mol. Biol. 2024, 46(6), 5794-5811; https://doi.org/10.3390/cimb46060346 - 11 Jun 2024
Viewed by 296
Abstract
Colorectal cancer, the third most commonly occurring tumor worldwide, poses challenges owing to its high mortality rate and persistent drug resistance in metastatic cases. We investigated the tumor microenvironment, emphasizing the role of cancer-associated fibroblasts in the progression and chemoresistance of colorectal cancer. [...] Read more.
Colorectal cancer, the third most commonly occurring tumor worldwide, poses challenges owing to its high mortality rate and persistent drug resistance in metastatic cases. We investigated the tumor microenvironment, emphasizing the role of cancer-associated fibroblasts in the progression and chemoresistance of colorectal cancer. We used an indirect co-culture system comprising colorectal cancer organoids and cancer-associated fibroblasts to simulate the tumor microenvironment. Immunofluorescence staining validated the characteristics of both organoids and fibroblasts, showing high expression of epithelial cell markers (EPCAM), colon cancer markers (CK20), proliferation markers (KI67), and fibroblast markers (VIM, SMA). Transcriptome profiling was conducted after treatment with anticancer drugs, such as 5-fluorouracil and oxaliplatin, to identify chemoresistance-related genes. Changes in gene expression in the co-cultured colorectal cancer organoids following anticancer drug treatment, compared to monocultured organoids, particularly in pathways related to interferon-alpha/beta signaling and major histocompatibility complex class II protein complex assembly, were identified. These two gene groups potentially mediate drug resistance associated with JAK/STAT signaling. The interaction between colorectal cancer organoids and fibroblasts crucially modulates the expression of genes related to drug resistance. These findings suggest that the interaction between colorectal cancer organoids and fibroblasts significantly influences gene expression related to drug resistance, highlighting potential biomarkers and therapeutic targets for overcoming chemoresistance. Enhanced understanding of the interactions between cancer cells and their microenvironment can lead to advancements in personalized medical research.. Full article
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<p>Morphological analysis by bright-field images and immunofluorescence (IF) staining with colorectal cancer markers (CK20, KI67, EPCAM) and fibroblast markers (VIM, SMA). (<b>A</b>) Bright-field images of PDO and CAF. (<b>B</b>) IF staining of CRC PDO with CK20, EPCAM, KI67, and DAPI (DNA) as indicated. (<b>C</b>) IF staining of CRC patient-derived CAF with VIM, SMA, and DAPI (DNA) as indicated. Images were taken with a Zeiss LSM 700 confocal microscope(Zeiss, Jena, Germany). Scale bars indicate 500 µm in bright-field images and 200 µm in IF images. The images confirm the preservation of original characteristics in both organoids and CAFs, validating the suitability of these samples for further experimental use.</p>
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<p>Next generation sequencing (NGS) and gene expression pattern of mRNA seq results. (<b>A</b>) Heatmap showing the expression levels of genes in six samples of organoids. Hierarchical cluster analysis revealed distinct cluster formations, highlighting a clear differentiation between monoculture organoids and co-culture organoids. (<b>B</b>) Principal component analysis of each sample. The same color indicates the number of repetitions of the experiment, and the experiment was conducted in triplicate per sample. C_Ctrl, co-culture organoid control; C_5FU, co-culture organoid treated with 5FU; C_Oxa, co-culture organoid treated with oxaliplatin; Co, co-culture organoid control; M_Ctrl, monoculture organoid; M_5FU, monoculture organoid treated with 5FU; M_Oxa, monoculture organoid treated with oxaliplatin; Mono, monoculture organoid.</p>
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<p>Differentially expressed genes (DEGs) analysis of each organoid sample. Hierarchical clustering heatmaps of significant genes in (<b>A</b>) M_Ctrl vs. C_Ctrl, (<b>B</b>) M_5FU vs. C_5FU, and (<b>C</b>) M_Oxa vs. C_Oxa. Volcano plots of significant genes in (<b>D</b>) M_Ctrl vs. C_Ctrl, (<b>E</b>) M_5FU vs. C_5FU, and (<b>F</b>) M_Oxa vs. C_Oxa. Red indicates upregulation, and blue indicates downregulation. Cut off (dotted line) drawn at equivalent of adjusted <span class="html-italic">p</span> = 0.05 and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>l</mi> <mi>o</mi> <mi>g</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>(fold change) of 1. The hierarchical clustering heatmaps and volcano plots provide a detailed visualization of the gene expression differences between co-culture and monoculture conditions under different treatments, highlighting significant DEGs that may be involved in chemoresistance mechanisms. C_Ctrl, co-culture organoid control; C_5FU, co-culture organoid treated with 5FU; C_Oxa, co-culture organoid treated with oxaliplatin; M_Ctrl, monoculture organoid; M_5FU, monoculture organoid treated with 5FU; M_Oxa, monoculture organoid treated with oxaliplatin.</p>
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<p>Differentially expressed genes (DEGs) analysis of each organoid sample. Hierarchical clustering heatmaps of significant genes in (<b>A</b>) M_Ctrl vs. C_Ctrl, (<b>B</b>) M_5FU vs. C_5FU, and (<b>C</b>) M_Oxa vs. C_Oxa. Volcano plots of significant genes in (<b>D</b>) M_Ctrl vs. C_Ctrl, (<b>E</b>) M_5FU vs. C_5FU, and (<b>F</b>) M_Oxa vs. C_Oxa. Red indicates upregulation, and blue indicates downregulation. Cut off (dotted line) drawn at equivalent of adjusted <span class="html-italic">p</span> = 0.05 and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>l</mi> <mi>o</mi> <mi>g</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>(fold change) of 1. The hierarchical clustering heatmaps and volcano plots provide a detailed visualization of the gene expression differences between co-culture and monoculture conditions under different treatments, highlighting significant DEGs that may be involved in chemoresistance mechanisms. C_Ctrl, co-culture organoid control; C_5FU, co-culture organoid treated with 5FU; C_Oxa, co-culture organoid treated with oxaliplatin; M_Ctrl, monoculture organoid; M_5FU, monoculture organoid treated with 5FU; M_Oxa, monoculture organoid treated with oxaliplatin.</p>
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<p>Significant genes in co-culture organoids compared to monoculture organoids by differential gene expression analysis. (<b>A</b>) Venn diagram depicting significant genes in co-culture organoids compared to monoculture organoids, identified based on the criteria of adjusted <span class="html-italic">p</span>-value &lt; 0.05 and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>l</mi> <mi>o</mi> <mi>g</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> fold change &gt; |1| in each sample. A total of 154 significant genes (red circle) exhibiting altered expression during co-culture organoids in comparison to monoculture organoids were identified. (<b>B</b>) Enrichment analysis of GO biological process. (<b>C</b>) Enrichment analysis of GO cellular component. (<b>D</b>) Enrichment analysis of GO molecular function. The statistical significance was evaluated with an adjusted <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Significant genes in co-culture organoids compared to monoculture organoids by differential gene expression analysis. (<b>A</b>) Venn diagram depicting significant genes in co-culture organoids compared to monoculture organoids, identified based on the criteria of adjusted <span class="html-italic">p</span>-value &lt; 0.05 and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>l</mi> <mi>o</mi> <mi>g</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> fold change &gt; |1| in each sample. A total of 154 significant genes (red circle) exhibiting altered expression during co-culture organoids in comparison to monoculture organoids were identified. (<b>B</b>) Enrichment analysis of GO biological process. (<b>C</b>) Enrichment analysis of GO cellular component. (<b>D</b>) Enrichment analysis of GO molecular function. The statistical significance was evaluated with an adjusted <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Potential drug resistance genes among 154 co-culture significant genes. (<b>A</b>) Hierarchical clustering heatmap of 154 co-culture significant genes. Yellow box indicates commonly upregulated genes, and blue box indicates commonly downregulated genes. Red indicates upregulation, and blue indicates downregulation. (<b>B</b>) GO enrichment analysis of up- and downregulated genes. Red pie indicates downregulated genes, and blue pie indicates upregulated genes. (<b>C</b>) Selected groups of protein–protein interaction (PPI) analysis by physical affinity (STRING physical score &gt; 0.132). (<b>D</b>) Functional enrichment analysis of selected genes by STRING. Pathways represent GO: 0006952, defense response (red spheres); GO: 0050896, response to stimulus (yellow spheres); GO: 0002831, regulation of response to biotic stimulus (green spheres); and GO: 0019221, cytokine-mediated signaling pathway (blue spheres).</p>
Full article ">Figure 5 Cont.
<p>Potential drug resistance genes among 154 co-culture significant genes. (<b>A</b>) Hierarchical clustering heatmap of 154 co-culture significant genes. Yellow box indicates commonly upregulated genes, and blue box indicates commonly downregulated genes. Red indicates upregulation, and blue indicates downregulation. (<b>B</b>) GO enrichment analysis of up- and downregulated genes. Red pie indicates downregulated genes, and blue pie indicates upregulated genes. (<b>C</b>) Selected groups of protein–protein interaction (PPI) analysis by physical affinity (STRING physical score &gt; 0.132). (<b>D</b>) Functional enrichment analysis of selected genes by STRING. Pathways represent GO: 0006952, defense response (red spheres); GO: 0050896, response to stimulus (yellow spheres); GO: 0002831, regulation of response to biotic stimulus (green spheres); and GO: 0019221, cytokine-mediated signaling pathway (blue spheres).</p>
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14 pages, 4681 KiB  
Article
Effect of Prior ChAdOx1 COVID-19 Immunisation on T-Cell Responses to ChAdOx1-HBV
by Charlotte Davis, Dave Singh, Katie Anderson, Antonella Vardeu, Jakub Kopycinski, Alice Bridges-Webb, Alice Trickett, Susanne O’Brien, Matthew Downs, Randip Kaur, Radka Kolenovska, Louise Bussey, Kathryn Rutkowski, Sarah Sebastian, Tamsin Cargill, Eleanor Barnes, Thomas G. Evans and Paola Cicconi
Vaccines 2024, 12(6), 644; https://doi.org/10.3390/vaccines12060644 - 9 Jun 2024
Viewed by 463
Abstract
There are varying data concerning the effect of prior anti-vector immunity on the T-cell response induced by immunisation with an identical vectored vaccine containing a heterologous antigen insert. To determine whether prior exposure to ChAdOx1-SARS-CoV2 immunisation (Vaxzevria®) impacts magnitudes of antigen-specific [...] Read more.
There are varying data concerning the effect of prior anti-vector immunity on the T-cell response induced by immunisation with an identical vectored vaccine containing a heterologous antigen insert. To determine whether prior exposure to ChAdOx1-SARS-CoV2 immunisation (Vaxzevria®) impacts magnitudes of antigen-specific T-cell responses elicited by subsequent administration of the same viral vector (encoding HBV antigens, ChAdOx1-HBV), healthy volunteers that had received Vaxzevria® (n = 15) or the Pfizer or Moderna mRNA COVID-19 vaccine (n = 11) between 10 and 18 weeks prior were recruited to receive a single intramuscular injection of ChAdOx1-HBV. Anti-ChAdOx1-neutralising antibody titers were determined, and vector or insert-specific T-cell responses were measured by a gamma-interferon ELISpot and intracellular cytokine staining (ICS) assay using multiparameter flow cytometry. Participants were followed for three months after the ChAdOx1-HBV injection, which was well-tolerated, and no dropouts occurred. The baseline ChAdOx1 neutralisation titers were higher in the Vaxzevria® cohort (median of 848) than in the mRNA cohort (median of 25). T-cell responses to HBV antigens, measured by ELISpot, were higher on day 28 in the mRNA group (p = 0.013) but were similar between groups on day 84 (p = 0.441). By ICS, these differences persisted at the last time point. There was no clear correlation between the baseline responses to the adenoviral hexon and the subsequent ELISpot responses. As vaccination within 3 months using the same viral vector backbone affected the insert-specific T-cell responses, a greater interval after prior adenoviral immunisation using heterologous antigens may be warranted in settings in which these cells play critical roles. Full article
(This article belongs to the Section Vaccine Efficacy and Safety)
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<p>The sum of HBV-specific IFNγ ELISpot responses. (<b>A</b>,<b>B</b>): Line graphs represent the sum of HBV-specific peptide responses (Core, Pol1+2, Pol3+4, PreS1/S2+S, and SFU/10<sup>6</sup> PBMC) for each participant across the study for PC and PM, respectively. Red arrow indicates ChAdOx1-HBV administration. (<b>C</b>,<b>D</b>): Stacked bar graphs represent the sum of mean responses to HBV-specific peptide pools for PC and PM, respectively.</p>
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<p>Gamma-interferon ELISpot responses to the AdV5 Hexon peptide pool. (<b>A</b>,<b>B</b>): Line graphs represent AdV5 Hexon responses (SFU/10<sup>6</sup> PBMC) for each participant across the study for PC and PM cohorts, respectively. Red arrow indicates ChAdOx1-HBV administration. (<b>C</b>,<b>D</b>): Correlation of baseline AdV5 Hexon responses to the sum of HBV-specific peptide response (Core, Pol1+2, Pol3+4, and PreS1/S2+S) on day 14 and day 28, respectively (SFU/10<sup>6</sup> PBMC). Correlations were assessed using Spearman tests.</p>
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<p>The sum of HBV-specific CD8+ or CD4+ IFNγ+ ICS responses. Line graphs represent the sum of CD8+ or CD4+ IFNγ+ HBV-specific peptide responses (Core, Pol1+2, Pol3+4, PreS1/S2+S, % of total population) for each participant across the study for PC and PM, respectively. Red arrow indicates ChAdOx1-HBV administration. (<b>A</b>), CD8+, PC, (<b>B</b>), CD8+, PM, (<b>E</b>), CD4+, PC, and (<b>F</b>), CD4+, PM. Stacked bar graphs represent the sum of mean responses to HBV-specific peptide pools. (<b>C</b>), CD8+, PC, (<b>D</b>), CD8+, PM, (<b>G</b>), CD4+, PC, and (<b>H</b>), CD4+, PM. Note: difference in scale of the Y-axes between CD8+ and CD4+ plots.</p>
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<p>The sum of HBV-specific CD8+ or CD4+ TNFα+ ICS responses. Line graphs represent the sum of CD8+ or CD4+ TNFα+ HBV-specific peptide responses (Core, Pol1+2, Pol3+4, and PreS1/S2+S, % of total population) for each participant across the study for PC and PM, respectively. Red arrow indicates ChAdOx1-HBV administration. (<b>A</b>), CD8+, PC, (<b>B</b>), CD8+, PM, (<b>E</b>), CD4+, PC, and (<b>F</b>), CD4+, PM. Stacked bar graphs represent the sum of mean responses to HBV-specific peptide pools. (<b>C</b>), CD8+, PC and (<b>D</b>), CD8+, PM, (<b>G</b>), CD4+, PC, and (<b>H</b>), CD4+, PM. Note: difference in scale of the Y-axes between CD8+ and CD4+ stacked bar plots.</p>
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<p>The sum of HBV-specific CD8+ CD107a+ ICS responses. (<b>A</b>,<b>B</b>): Line graphs represent the sum of CD8+ CD107a+ HBV-specific peptide responses (Core, Pol1+2, Pol3+4, and PreS1/S2+S, % of total CD8+ population) for each participant across the study for PC and PM, respectively. Red arrow indicates ChAdOx1-HBV administration. (<b>C</b>,<b>D</b>): Stacked bar graphs represent the sum of mean responses to HBV-specific peptide pools for PC and PM, respectively.</p>
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<p>The sum of HBV-specific CD4+ CD154+ ICS responses. (<b>A</b>,<b>B</b>): Line graphs represent the sum of CD4+ CD154+ HBV-specific peptide responses (Core, Pol1+2, Pol3+4, and PreS1/S2+S, % of total CD4+ population) for each participant across the study for PC and PM, respectively. Red arrow indicates ChAdOx1-HBV administration. (<b>C</b>,<b>D</b>): Stacked bar graphs represent the sum of mean responses to HBV-specific peptide pools for PC and PM, respectively.</p>
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<p>Adenoviral neutralisation titers of sera in PC and PM on day 0 and day 84. (<b>A</b>): Neutralising antibody (nAb) titers grouped by time point, compared using a Mann–Whitney test (day 0: <span class="html-italic">p</span> &lt; 0.0001 (****); day 84: <span class="html-italic">p</span> = 0.0007 (***)). (<b>B</b>): nAb titers grouped by cohort, compared using a Wilcoxon signed-rank test for paired data (PC: <span class="html-italic">p</span> = 0.034 (*), PM: <span class="html-italic">p</span> = 0.002 (**)).</p>
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<p>Correlation of baseline (D0) neutralisation titers to peak gamma-interferon ELISpot responses at day 14 (<b>A</b>,<b>C</b>) and day 28 (<b>B</b>,<b>D</b>) post immunisation. (<b>A</b>,<b>B</b>): PC and PM data. (<b>C</b>,<b>D</b>): PC-only data. SUM IFNγ ELISpot response refers to the sum of HBV-specific peptide responses (Core, Pol1+2, Pol3+4, and PreS1/S2+S). Correlations were assessed using Spearman tests (*, <span class="html-italic">p</span> &lt; 0.05). Black dots, PC. Red dots, PM.</p>
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24 pages, 6241 KiB  
Article
Discovery and Characterization of the ddx41 Gene in Atlantic Salmon: Evolutionary Implications, Structural Functions, and Innate Immune Responses to Piscirickettsia salmonis and Renibacterium salmoninarum Infections
by Alejandro J. Yañez, Claudia A. Barrientos, Adolfo Isla, Marcelo Aguilar, Sandra N. Flores-Martin, Yassef Yuivar, Adriana Ojeda, Pablo Ibieta, Mauricio Hernández, Jaime Figueroa, Rubén Avendaño-Herrera and Marcos Mancilla
Int. J. Mol. Sci. 2024, 25(12), 6346; https://doi.org/10.3390/ijms25126346 - 8 Jun 2024
Viewed by 268
Abstract
The innate immune response in Salmo salar, mediated by pattern recognition receptors (PRRs), is crucial for defending against pathogens. This study examined DDX41 protein functions as a cytosolic/nuclear sensor for cyclic dinucleotides, RNA, and DNA from invasive intracellular bacteria. The investigation determined [...] Read more.
The innate immune response in Salmo salar, mediated by pattern recognition receptors (PRRs), is crucial for defending against pathogens. This study examined DDX41 protein functions as a cytosolic/nuclear sensor for cyclic dinucleotides, RNA, and DNA from invasive intracellular bacteria. The investigation determined the existence, conservation, and functional expression of the ddx41 gene in S. salar. In silico predictions and experimental validations identified a single ddx41 gene on chromosome 5 in S. salar, showing 83.92% homology with its human counterpart. Transcriptomic analysis in salmon head kidney confirmed gene transcriptional integrity. Proteomic identification through mass spectrometry characterized three unique peptides with 99.99% statistical confidence. Phylogenetic analysis demonstrated significant evolutionary conservation across species. Functional gene expression analysis in SHK-1 cells infected by Piscirickettsia salmonis and Renibacterium salmoninarum indicated significant upregulation of DDX41, correlated with increased proinflammatory cytokine levels and activation of irf3 and interferon signaling pathways. In vivo studies corroborated DDX41 activation in immune responses, particularly when S. salar was challenged with P. salmonis, underscoring its potential in enhancing disease resistance. This is the first study to identify the DDX41 pathway as a key component in S. salar innate immune response to invading pathogens, establishing a basis for future research in salmonid disease resistance. Full article
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<p>Description of <span class="html-italic">ddx41</span> sequence of <span class="html-italic">S. salar</span>. (<b>A</b>) Nucleotide and amino acid sequence; (<b>B</b>) chromosome localization indicates intron and exons.</p>
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<p>Proteomic identification of DDX41 in head kidney of <span class="html-italic">S. salar</span>. (<b>A</b>) Peptide identification indicates mass and probability of identification; (<b>B</b>) sequence and peptide identify are highlighted in green; (<b>C</b>) fragmentation spectra of each peptide identify; pink background highlight cysteine-alkylation.</p>
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<p>Phylogenetic analysis, conducted with 52 coding sequences from unrelated vertebrates, elucidates the evolutionary relationships of <span class="html-italic">ddx41</span> genes. Using MrBayes and the maximum parsimony method, the bootstrap consensus tree was inferred from 1000 replicates. Bayesian posterior probability and bootstrap support value are indicated at each node, representing common ancestors, as well as branching points where species or sequences diverged during evolution. <span class="html-italic">D. melanogaster</span> was included as an outgroup.</p>
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<p>Multiple alignment of DDX41 deduced amino acid sequence from <span class="html-italic">S. salar</span>, <span class="html-italic">D. rerio</span>, <span class="html-italic">B. bufo</span>, <span class="html-italic">G. gallus</span>, <span class="html-italic">R. norvegicus,</span> and <span class="html-italic">H. sapiens</span>. Identical residues between organisms are highlighted in black color and amino acid with chemical similar characteristic are highlighted in grey color. Numbers on the right indicate the position of the last residue in the alignment relative to the complete amino acid sequence of each species. The conserver domain coiled coil, Q-motif, helicase ATP binding, helicase C-terminal, and ZnF_C2HC are indicated. The basic and acid residues, nuclear signal (NES), and DEAD box are boxed. Asterisk and triangles indicate the important residues in function of DDX41.</p>
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<p>Comparative analysis of predicted DDX41 protein structures between vertebrates, including <span class="html-italic">S. salar</span>, alongside the crystal structure of human DDX41. (<b>A</b>) Principal domain in <span class="html-italic">S. salar</span>, <span class="html-italic">D. rerio</span>, <span class="html-italic">G. gallus</span>, <span class="html-italic">R. norvegicus</span>, and <span class="html-italic">H. sapiens</span>. Domains are color-coded and depicted in box forms. (<b>B</b>) Aligned 3D protein model of DDX41 with <span class="html-italic">H. sapiens</span> (red) and <span class="html-italic">S. salar</span> (blue), with residues labeled at the start and end of the protein chain. (<b>C</b>) Helicase C-terminal domain is highlighted in a green box. (<b>D</b>) Helicase ATP binding domain with DEADc domain is highlighted in a yellow box.</p>
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<p>Immune response kinetics of <span class="html-italic">ddx41</span> gene expression were assessed from 0 (control) to 24 hpi in SHK1 cells following infections with <span class="html-italic">P. salmonis</span> and <span class="html-italic">R. salmoninarum</span>. qPCR quantified <span class="html-italic">ddx41</span> gene expression, normalized to elongation factor-1α (<span class="html-italic">elf1a</span>). Control: non-infected SHK-1 cells. Results, presented as mean ± SE, indicate significance with 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; (****) <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Innate immune response against intracellular pathogens in the SHK-1 cell line was assessed by measuring the gene expression levels of inflammatory genes <span class="html-italic">il1b</span> (<b>A</b>), <span class="html-italic">tnfa</span> (<b>B</b>), <span class="html-italic">ifng</span> (<b>C</b>), and <span class="html-italic">irf3</span> (<b>D</b>) during infection kinetics with <span class="html-italic">P. salmonis</span> and <span class="html-italic">R. salmoninarum</span> (0–24 hpi). Gene expression was analyzed using qPCR and normalized to the elongation factor-1a (<span class="html-italic">elf1a</span>) gene level. Control: non-infected SHK-1 cells. Data are presented as mean ± SE, with significance indicated 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; (****) <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Evaluation of innate response in <span class="html-italic">S. salar</span> to natural infection with <span class="html-italic">P. salmonis</span>; the cohabitant (naive) group exhibited mortalities starting from 28 dpc, reaching 70% at 49 dpc. Head kidney samples were collected from <span class="html-italic">S. salar</span> between 0 and 49 days post-<span class="html-italic">P. salmonis</span> challenge. qPCR analysis measured gene expression of <span class="html-italic">ddx41</span> (<b>A</b>), <span class="html-italic">il1b</span> (<b>B</b>), <span class="html-italic">tnfa</span> (<b>C</b>), <span class="html-italic">ifng</span> (<b>D</b>), and <span class="html-italic">irf3</span> (<b>E</b>), normalized to elongation factor-1a (<span class="html-italic">elf1a</span>) levels. Control: non-challenged <span class="html-italic">S. salar.</span> Results are presented as mean ± SE, with significance indicated 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.</p>
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16 pages, 664 KiB  
Review
Interferon-Stimulated Genes that Target Retrovirus Translation
by Niklas Jäger, Stefan Pöhlmann, Marina V. Rodnina and Shreya Ahana Ayyub
Viruses 2024, 16(6), 933; https://doi.org/10.3390/v16060933 - 8 Jun 2024
Viewed by 468
Abstract
The innate immune system, particularly the interferon (IFN) system, constitutes the initial line of defense against viral infections. IFN signaling induces the expression of interferon-stimulated genes (ISGs), and their products frequently restrict viral infection. Retroviruses like the human immunodeficiency viruses and the human [...] Read more.
The innate immune system, particularly the interferon (IFN) system, constitutes the initial line of defense against viral infections. IFN signaling induces the expression of interferon-stimulated genes (ISGs), and their products frequently restrict viral infection. Retroviruses like the human immunodeficiency viruses and the human T-lymphotropic viruses cause severe human diseases and are targeted by ISG-encoded proteins. Here, we discuss ISGs that inhibit the translation of retroviral mRNAs and thereby retrovirus propagation. The Schlafen proteins degrade cellular tRNAs and rRNAs needed for translation. Zinc Finger Antiviral Protein and RNA-activated protein kinase inhibit translation initiation factors, and Shiftless suppresses translation recoding essential for the expression of retroviral enzymes. We outline common mechanisms that underlie the antiviral activity of multifunctional ISGs and discuss potential antiretroviral therapeutic approaches based on the mode of action of these ISGs. Full article
(This article belongs to the Special Issue Innate Sensing and Restriction of Retroviruses)
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<p>Interferon-stimulated genes (ISGs) that inhibit viral translation. (1) Schlafen (SLFN) proteins degrade certain cellular tRNAs and rRNAs, which, in turn, inhibit viral translation. (2) Zinc finger antiviral protein (ZAP) binds eIF4A and prevents eIF4G binding, which is required for the formation of the eIF4G complex and translation initiation. The RNA-activated protein kinase (PKR) phosphorylates eIF2α and inhibits translation initiation by preventing eIF2B from generating functional eIF2–GTP from eIF2–GDP. (3) Shiftless (SFL) inhibits the −1PRF required for HIV-1 translation, potentially by recruiting eRF3-eRF1 to the ribosome at the slippery site and causing premature translation termination. (4) SFL inhibits the stop codon readthrough required for MLV translation by an unknown mechanism. Created with BioRender.com.</p>
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18 pages, 1811 KiB  
Review
The Two Levels of Podocyte Dysfunctions Induced by Apolipoprotein L1 Risk Variants
by Etienne Pays
Kidney Dial. 2024, 4(2), 126-143; https://doi.org/10.3390/kidneydial4020010 - 7 Jun 2024
Viewed by 354
Abstract
Apolipoprotein L1 (APOL1) nephropathy results from several podocyte dysfunctions involving morphological and motility changes, mitochondrial perturbations, inflammatory stress, and alterations in cation channel activity. I propose that this phenotype results from increased hydrophobicity of the APOL1 risk variants, which induces two distinct types [...] Read more.
Apolipoprotein L1 (APOL1) nephropathy results from several podocyte dysfunctions involving morphological and motility changes, mitochondrial perturbations, inflammatory stress, and alterations in cation channel activity. I propose that this phenotype results from increased hydrophobicity of the APOL1 risk variants, which induces two distinct types of podocyte dysfunctions. On one hand, increased hydrophobic interactions with APOL3 cause intracellular variant isoforms to impair both APOL3 control of Golgi PI(4)P kinase-B (PI4KB) activity and APOL3 control of mitochondrial membrane fusion, triggering actomyosin reorganisation together with mitophagy and apoptosis inhibition (hit 1). On the other hand, increased hydrophobic interactions with the podocyte plasma membrane may cause the extracellular variant isoforms to activate toxic Ca2+ influx and K+ efflux by the TRPC6 and BK channels, respectively (hit 2), presumably due to APOL1-mediated cholesterol clustering in microdomains. I propose that hit 2 depends on low HDL-C/high extracellular APOL1 ratio, such as occurs in cell culture in vitro, or during type I-interferon (IFN-I)-mediated inflammation. Full article
13 pages, 1251 KiB  
Review
Suppressing Anaphase-Promoting Complex/Cyclosome–Cell Division Cycle 20 Activity to Enhance the Effectiveness of Anti-Cancer Drugs That Induce Multipolar Mitotic Spindles
by Scott C. Schuyler, Hsin-Yu Chen and Kai-Ping Chang
Int. J. Mol. Sci. 2024, 25(12), 6329; https://doi.org/10.3390/ijms25126329 - 7 Jun 2024
Viewed by 192
Abstract
Paclitaxel induces multipolar spindles at clinically relevant doses but does not substantially increase mitotic indices. Paclitaxel’s anti-cancer effects are hypothesized to occur by promoting chromosome mis-segregation on multipolar spindles leading to apoptosis, necrosis and cyclic-GMP-AMP Synthase–Stimulator of Interferon Genes (cGAS-STING) pathway activation in [...] Read more.
Paclitaxel induces multipolar spindles at clinically relevant doses but does not substantially increase mitotic indices. Paclitaxel’s anti-cancer effects are hypothesized to occur by promoting chromosome mis-segregation on multipolar spindles leading to apoptosis, necrosis and cyclic-GMP-AMP Synthase–Stimulator of Interferon Genes (cGAS-STING) pathway activation in daughter cells, leading to secretion of type I interferon (IFN) and immunogenic cell death. Eribulin and vinorelbine have also been reported to cause increases in multipolar spindles in cancer cells. Recently, suppression of Anaphase-Promoting Complex/Cyclosome–Cell Division Cycle 20 (APC/C-CDC20) activity using CRISPR/Cas9 mutagenesis has been reported to increase sensitivity to Kinesin Family 18a (KIF18a) inhibition, which functions to suppress multipolar mitotic spindles in cancer cells. We propose that a way to enhance the effectiveness of anti-cancer agents that increase multipolar spindles is by suppressing the APC/C-CDC20 to delay, but not block, anaphase entry. Delaying anaphase entry in genomically unstable cells may enhance multipolar spindle-induced cell death. In genomically stable healthy human cells, delayed anaphase entry may suppress the level of multipolar spindles induced by anti-cancer drugs and lower mitotic cytotoxicity. We outline specific combinations of molecules to investigate that may achieve the goal of enhancing the effectiveness of anti-cancer agents. Full article
(This article belongs to the Special Issue Current Research on Cancer Biology and Therapeutics 2.0)
12 pages, 1254 KiB  
Article
Disease Severity and Cytokine Expression in the Rhinovirus-Induced First Wheezing Episode
by Pekka Hurme, Miisa Kähkönen, Beate Rückert, Tero Vahlberg, Riitta Turunen, Tytti Vuorinen, Mübeccel Akdis, Cezmi A. Akdis and Tuomas Jartti
Viruses 2024, 16(6), 924; https://doi.org/10.3390/v16060924 - 7 Jun 2024
Viewed by 277
Abstract
Wheezing children infected with rhinovirus (RV) have a markedly increased risk of subsequently developing recurrencies and asthma. No previous studies have assessed the association between cytokine response and the severity of acute illness in the first wheezing episode in children infected with RV. [...] Read more.
Wheezing children infected with rhinovirus (RV) have a markedly increased risk of subsequently developing recurrencies and asthma. No previous studies have assessed the association between cytokine response and the severity of acute illness in the first wheezing episode in children infected with RV. Forty-seven children treated both as inpatients and as outpatients infected with RV only, aged 3–23 months, with severe first wheezing episodes were recruited. During acute illness, peripheral blood mononuclear cells (PBMCs) were isolated and stimulated with anti-CD3/anti-CD28 in vitro. A multiplex ELISA was used to quantitatively identify 56 different cytokines. The mean age of the children was 17 months, 74% were males, 79% were hospitalized, and 33% were sensitized. In adjusted analyses, the inpatient group was characterized by decreased expressions of interferon gamma (IFN-γ), interleukin 10 (IL-10), macrophage inflammatory protein 1 alpha (MIP-1α), RANTES (CCL5), and tumor necrosis factor-alpha (TNF-α) and an increased expression of ENA-78 (CXCL5) compared to the outpatient group. The cytokine response profiles from the PBMCs were different between the inpatient and outpatient groups. Our results support that firmly controlled interplay between pro-inflammatory and anti-inflammatory responses are required during acute viral infection to absolve the initial infection leading, to less severe illness. Full article
(This article belongs to the Special Issue Rhinoviruses and Asthma)
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Figure 1
<p>Study flow chart. Patients with cytology data were included. ICU, intensive care unit; PBMC, peripheral blood mononuclear cell; RV, rhinovirus.</p>
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<p>Differences in cytokine expression levels at time of study entry. Data are presented as median and lower (Q1) and upper (Q3) quartiles, and data falling outside Q1–Q3 range are plotted as outliers. In IFN-γ, for better visualization, one sample from both study groups were excluded from figure but included in analyses [inpatient (5200 pg/mL) and outpatient (2900 pg/mL)]. Cytokine concentrations are presented as pg/mL. Multiple significant differences in cytokine expression were observed between study groups (inpatient vs. outpatient, all <span class="html-italic">p</span> &lt; 0.05) (<b>a</b>–<b>f</b>).</p>
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<p>Differences in cytokine expression levels at time of study entry. Data are presented as median and lower (Q1) and upper (Q3) quartiles, and data falling outside Q1–Q3 range are plotted as outliers. In IFN-γ, for better visualization, one sample from both study groups were excluded from figure but included in analyses [inpatient (5200 pg/mL) and outpatient (2900 pg/mL)]. Cytokine concentrations are presented as pg/mL. Multiple significant differences in cytokine expression were observed between study groups (inpatient vs. outpatient, all <span class="html-italic">p</span> &lt; 0.05) (<b>a</b>–<b>f</b>).</p>
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20 pages, 1561 KiB  
Review
Host Cell Death and Modulation of Immune Response against Mycobacterium tuberculosis Infection
by Annie Vu, Ira Glassman, Giliene Campbell, Stephanie Yeganyan, Jessica Nguyen, Andrew Shin and Vishwanath Venketaraman
Int. J. Mol. Sci. 2024, 25(11), 6255; https://doi.org/10.3390/ijms25116255 - 6 Jun 2024
Viewed by 325
Abstract
Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis (TB), a prevalent infectious disease affecting populations worldwide. A classic trait of TB pathology is the formation of granulomas, which wall off the pathogen, via the innate and adaptive immune systems. Some [...] Read more.
Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis (TB), a prevalent infectious disease affecting populations worldwide. A classic trait of TB pathology is the formation of granulomas, which wall off the pathogen, via the innate and adaptive immune systems. Some key players involved include tumor necrosis factor-alpha (TNF-α), foamy macrophages, type I interferons (IFNs), and reactive oxygen species, which may also show overlap with cell death pathways. Additionally, host cell death is a primary method for combating and controlling Mtb within the body, a process which is influenced by both host and bacterial factors. These cell death modalities have distinct molecular mechanisms and pathways. Programmed cell death (PCD), encompassing apoptosis and autophagy, typically confers a protective response against Mtb by containing the bacteria within dead macrophages, facilitating their phagocytosis by uninfected or neighboring cells, whereas necrotic cell death benefits the pathogen, leading to the release of bacteria extracellularly. Apoptosis is triggered via intrinsic and extrinsic caspase-dependent pathways as well as caspase-independent pathways. Necrosis is induced via various pathways, including necroptosis, pyroptosis, and ferroptosis. Given the pivotal role of host cell death pathways in host defense against Mtb, therapeutic agents targeting cell death signaling have been investigated for TB treatment. This review provides an overview of the diverse mechanisms underlying Mtb-induced host cell death, examining their implications for host immunity. Furthermore, it discusses the potential of targeting host cell death pathways as therapeutic and preventive strategies against Mtb infection. Full article
(This article belongs to the Special Issue Programmed Cell Death and Oxidative Stress 2.0)
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<p>CpsA protein in LC3-associated phagocytosis. In LC3-associated phagocytosis, NADPH oxidase is recruited to the phagosome. There is a subsequent recruitment of LC3 and maturation of the phagophore, followed by a fusion of the subsequent autophagosome with a lysosome containing degradative enzymes, resulting in an autolysosome. The CpsA protein of <span class="html-italic">Mtb</span> may activate PRR but will inhibit the recruitment of NADPH oxidase.</p>
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<p><span class="html-italic">Mtb</span> inhibition of IFN-associated receptor (IFNAR) signaling pathway, preventing nitric oxide production.</p>
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<p>Caspase-dependent apoptosis may occur via the intrinsic or extrinsic pathway. The intrinsic pathway is triggered by internal cellular stress or by proapoptotic Bcl-2 proteins, which cause the release of factors such as cytochrome c and apoptosis-inducing factor (AIF) from the mitochondrial intermembrane space. Such factors activate caspase 9, which activates the apoptosome, which further activates caspase 3 or 7. Of note, nitric oxide (NO) is found to upregulate Bcl-2 proteins. IFN-γ-activated macrophages further upregulate nitric oxide synthase, thereby increasing NO and thus proapoptotic Bcl-2 proteins. In contrast, the extrinsic pathway is activated via death receptors, such as Fas or TNF receptor. This activates caspase 8, which further activates caspase 3 or 7. Of note, <span class="html-italic">Mtb</span> may secrete TNF-R2, which can inhibit the TNF receptor.</p>
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<p>Antioxidant defense in <span class="html-italic">Mtb</span> via superoxide dismutase.</p>
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17 pages, 2849 KiB  
Article
Tepilamide Fumarate as a Novel Potentiator of Virus-Based Therapy
by Akram Alwithenani, Rozanne Arulanandam, Boaz Wong, Marcus M. Spinelli, Andrew Chen, Glib Maznyi, Victoria H. Gilchrist, Tommy Alain and Jean-Simon Diallo
Viruses 2024, 16(6), 920; https://doi.org/10.3390/v16060920 - 5 Jun 2024
Viewed by 267
Abstract
Oncolytic virotherapy, using viruses such as vesicular stomatitis virus (VSVΔ51) and Herpes Simplex Virus-1 (HSV-1) to selectively attack cancer cells, faces challenges such as cellular resistance mediated by the interferon (IFN) response. Dimethyl fumarate (DMF) is used in the treatment of multiple sclerosis [...] Read more.
Oncolytic virotherapy, using viruses such as vesicular stomatitis virus (VSVΔ51) and Herpes Simplex Virus-1 (HSV-1) to selectively attack cancer cells, faces challenges such as cellular resistance mediated by the interferon (IFN) response. Dimethyl fumarate (DMF) is used in the treatment of multiple sclerosis and psoriasis and is recognized for its anti-cancer properties and has been shown to enhance both VSVΔ51 and HSV-1 oncolytic activity. Tepilamide fumarate (TPF) is a DMF analog currently undergoing clinical trials for the treatment of moderate-to-severe plaque psoriasis. The aim of this study was to evaluate the potential of TPF in enhancing the effectiveness of oncolytic viruses. In vitro, TPF treatment rendered 786-0 carcinoma cells more susceptible to VSVΔ51 infection, leading to increased viral replication. It outperformed DMF in both increasing viral infection and increasing the killing of these resistant cancer cells and other cancer cell lines tested. Ex vivo studies demonstrated TPF’s selective boosting of oncolytic virus infection in cancer cells without affecting healthy tissues. Effectiveness was notably high in pancreatic and ovarian tumor samples. Our study further indicates that TPF can downregulate the IFN pathway through a similar mechanism to DMF, making resistant cancer cells more vulnerable to viral infection. Furthermore, TPF’s impact on gene therapy was assessed, revealing its ability to enhance the transduction efficiency of vectors such as lentivirus, adenovirus type 5, and adeno-associated virus type 2 across various cell lines. This data underscore TPF’s potential role in not only oncolytic virotherapy but also in the broader application of gene therapy. Collectively, these findings position TPF as a promising agent in oncolytic virotherapy, warranting further exploration of its therapeutic potential. Full article
(This article belongs to the Special Issue Progress and Prospects in Oncolytic Virotherapy)
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