[go: up one dir, main page]

Next Issue
Volume 25, March-1
Previous Issue
Volume 25, February-1
 
 
ijms-logo

Journal Browser

Journal Browser

Int. J. Mol. Sci., Volume 25, Issue 4 (February-2 2024) – 505 articles

Cover Story (view full-size image): Traditional nanoparticle delivery faces challenges such as short circulation time and immune recognition. Cell-membrane-coated nanoparticle technology is a recent breakthrough that improves stability and bioavailability and enables controlled drug/gene delivery to specific cells or tissues. The procedure involves the disruption of the membranes of selected cell types by hypotonic lysis and homogenization, followed by several centrifugation steps before the nanoparticles are coated by sonication or extrusion. This review explores current developments in cell-membrane-coated nanoparticles, emphasizing their potential for targeted drug delivery and therapeutic applications. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
25 pages, 2713 KiB  
Review
The Evolving Role of Dendritic Cells in Atherosclerosis
by Simone Britsch, Harald Langer, Daniel Duerschmied and Tobias Becher
Int. J. Mol. Sci. 2024, 25(4), 2450; https://doi.org/10.3390/ijms25042450 - 19 Feb 2024
Viewed by 1479
Abstract
Atherosclerosis, a major contributor to cardiovascular morbidity and mortality, is characterized by chronic inflammation of the arterial wall. This inflammatory process is initiated and maintained by both innate and adaptive immunity. Dendritic cells (DCs), which are antigen-presenting cells, play a crucial role in [...] Read more.
Atherosclerosis, a major contributor to cardiovascular morbidity and mortality, is characterized by chronic inflammation of the arterial wall. This inflammatory process is initiated and maintained by both innate and adaptive immunity. Dendritic cells (DCs), which are antigen-presenting cells, play a crucial role in the development of atherosclerosis and consist of various subtypes with distinct functional abilities. Following the recognition and binding of antigens, DCs become potent activators of cellular responses, bridging the innate and adaptive immune systems. The modulation of specific DC subpopulations can have either pro-atherogenic or atheroprotective effects, highlighting the dual pro-inflammatory or tolerogenic roles of DCs. In this work, we provide a comprehensive overview of the evolving roles of DCs and their subtypes in the promotion or limitation of atherosclerosis development. Additionally, we explore antigen pulsing and pharmacological approaches to modulate the function of DCs in the context of atherosclerosis. Full article
(This article belongs to the Special Issue Molecular Mechanisms and Pathophysiology of Atherosclerosis 2.0)
Show Figures

Figure 1

Figure 1
<p>Development of conventional dendritic cells and plasmacytoid dendritic cells from pluripotent hematopoietic stell cells in mice and summary of transcription factors, expression markers and activation markers in conventional dendritic cells 1 and 2 and plasmacytoid dendritic cells according to [<a href="#B42-ijms-25-02450" class="html-bibr">42</a>]. BATF3, basic leucine zipper ATF-like transcription factor 3. BST2, bone marrow stromal cell antigen 2. CADM1, cell adhesion molecule 1. CD, cluster of differentiation. ID2, inhibitor of DNA binding 2. IRF8, interferon regulatory factor 8. Ly-6C, lymphocyte antigen 6 family member G6C. NFIL3, nuclear factor, interleukin 3 regulated. TCF4, transcription factor 4. XCR1, X-C motif chemokine receptor 1. CLEC9A, C-type lectin domain containing 9A. <sup>1</sup> Pre-dendritic cells can give rise to conventional dendritic cells 1 and 2 (pre-cDCs) and plasmacytoid dendritic cells (pre-pDCs). <sup>2</sup> Expression markers used to identify and differentiate dendritic cells in humans.</p>
Full article ">Figure 2
<p>Location, expression profile and properties of dendritic cells in the development of atherosclerosis. ATLO, adventitial artery tertiary lymphoid organs. BST2, bone marrow stromal cell antigen 2. cDC, conventional dendritic cells. Clec4a4, c-type lectin domain family 4, member a4. Ly6C, lymphocyte antigen 6 family member C1. XCR1, X-C motif chemokine receptor 1. pDC, plasmacytoid dendritic cells. Changes in DC subtypes in atherosclerosis according to [<a href="#B72-ijms-25-02450" class="html-bibr">72</a>].</p>
Full article ">
23 pages, 10678 KiB  
Article
Microbiota Alterations in Lung, Ileum, and Colon of Guinea Pigs with Cough Variant Asthma
by Chongyang Dou, Lin Hu, Xian Ding, Fangfang Chen, Xi Li, Guihua Wei and Zhiyong Yan
Int. J. Mol. Sci. 2024, 25(4), 2449; https://doi.org/10.3390/ijms25042449 - 19 Feb 2024
Viewed by 1297
Abstract
Alterations in the microbiota composition, or ecological dysbiosis, have been implicated in the development of various diseases, including allergic diseases and asthma. Examining the relationship between microbiota alterations in the host and cough variant asthma (CVA) may facilitate the discovery of novel therapeutic [...] Read more.
Alterations in the microbiota composition, or ecological dysbiosis, have been implicated in the development of various diseases, including allergic diseases and asthma. Examining the relationship between microbiota alterations in the host and cough variant asthma (CVA) may facilitate the discovery of novel therapeutic strategies. To elucidate the diversity and difference of microbiota across three ecological niches, we performed 16S rDNA amplicon sequencing on lung, ileum, and colon samples. We assessed the levels of interleukin-12 (IL-12) and interleukin-13 (IL-13) in guinea pig bronchoalveolar lavage fluid using the enzyme-linked immunosorbent assay (ELISA). We applied Spearman’s analytical method to evaluate the correlation between microbiota and cytokines. The results demonstrated that the relative abundance, α-diversity, and β-diversity of the microbial composition of the lung, ileum, and colon varied considerably. The ELISA results indicated a substantial increase in the level of IL-13 and a decreasing trend in the level of IL-12 in the CVA guinea pigs. The Spearman analysis identified a correlation between Mycoplasma, Faecalibaculum, and Ruminococcus and the inflammatory factors in the CVA guinea pigs. Our guinea pig model showed that core microorganisms, such as Mycoplasma in the lung, Faecalibaculum in the ileum, and Ruminococcus in the colon, may play a crucial role in the pathogenesis of CVA. The most conspicuous changes in the ecological niche were observed in the guinea pig ileum, followed by the lung, while relatively minor changes were observed in the colon. Notably, the microbial structure of the ileum niche approximated that of the colon niche. Therefore, the results of this study suggest that CVA development is closely related to the dysregulation of ileal, lung, and colon microbiota and the ensuing inflammatory changes in the lung. Full article
(This article belongs to the Section Molecular Microbiology)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Changes in BALF inflammatory factors in guinea pigs. (<b>A</b>) Changes in interleukin 12 in BALF. (<b>B</b>) Changes in interleukin 13 in BALF. ** <span class="html-italic">p</span> &lt; 0.01, compared to the normal group. The ns indicates that no statistical difference was observed between the two groups. Data are expressed as the mean ± SD (<span class="html-italic">n</span> = 6/group). The black column represents the normal group. The grey column represents the model group.</p>
Full article ">Figure 2
<p>Rarefaction Curve and Rank−Abundance curve of all samples. (<b>A</b>) The Rarefaction Curve shows the trend of changes in species richness of each sample with sequencing depth. (<b>B</b>) The Rank−Abundance curve displays the relationship between the abundance of individual species and the number of individual species in each sample. IN (the microbiota in the ileum of the normal group), CN (the microbiota in the colon of the normal group), BN (the microbiota in the BALF of the normal group), IM (the microbiota in the ileum of the CVA model group), CM (the microbiota in the colon of the CVA model group), BM (the microbiota in the BALF of the CVA model group). Guinea pigs were randomly divided into a normal group and a model group, with six guinea pigs in each group. Sequencing analysis was conducted on four samples from each group.</p>
Full article ">Figure 3
<p>Comparison of microorganisms in three different niches in normal guinea pigs. (<b>A</b>) Alpha diversity. (<b>B</b>) Clustering tree based on unweighted UniFrac distances. (<b>C</b>) PCoA based on unweighted UniFrac. The horizontal axis denotes the first principal coordinate, with percentages indicating the contribution of the first axis to the differences among samples. The vertical axis represents the second principal coordinate, with percentages indicating the contribution of the second axis to the differences among samples. Each point on the graph represents a sample, with samples from the same group represented by the same color. Confidence ellipses indicate the 95% confidence interval for each group. (<b>D</b>) Distribution of high-abundance microbial groups at the phylum level, with sample names on the horizontal axis and relative abundance on the vertical axis, and different colors representing different taxa. (<b>E</b>) Distribution of high-abundance microbial groups at the genus level, with sample names on the horizontal axis and relative abundance on the vertical axis, and different colors representing different taxa.</p>
Full article ">Figure 4
<p>Comparison of ileal microbiota between normal and CVA model groups. (<b>A</b>) Alpha diversity. ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) Clustering tree based on unweighted UniFrac distances. (<b>C</b>) PCoA based on unweighted UniFrac. (<b>D</b>) Distribution of high-abundance microbial groups at the phylum level, with sample names on the horizontal axis and relative abundance on the vertical axis, and different colors representing different taxa. (<b>E</b>) Changes in dominant microbiota at the genus level. *** <span class="html-italic">p</span> &lt; 0.001. The ns indicates that no statistical difference was observed between the two groups. (<b>F</b>) Heatmap of the top 30 abundant genera selected based on abundance ranking, with sample names sorted on the horizontal axis and the vertical axis arranged according to the total average abundance from top to bottom, making it easier to observe which genera have higher average abundance and how they vary between samples.</p>
Full article ">Figure 5
<p>Differential species analysis of the gut microbiota in the ileum. (<b>A</b>) The horizontal axis represents the magnitude of the LDA score, where the length of the bar indicates the importance of the corresponding group. The color represents different groups, indicating that these groups have significantly enriched features, with the highest average abundance and significant differences between groups. (<b>B</b>) The positive values of the horizontal axis Log2FC represent up-regulated species, while negative values represent down-regulated species, and the vertical axis represents the taxonomic classification of the species, with three columns indicating phylum, genus, and OTU. The color represents the degree of significance.</p>
Full article ">Figure 6
<p>Comparison of pulmonary microbiota between normal and CVA model groups. (<b>A</b>) Alpha diversity. (<b>B</b>) Clustering tree based on unweighted UniFrac distances. (<b>C</b>) PCoA based on unweighted UniFrac. (<b>D</b>) Distribution of high-abundance microbial groups at the phylum level. (<b>E</b>) Changes in dominant microbiota at the genus level. * <span class="html-italic">p</span> &lt; 0.05. The ns indicates that no statistical difference was observed between the two groups. (<b>F</b>) Heatmap of the top 30 abundant genera selected based on abundance ranking.</p>
Full article ">Figure 7
<p>Differential species analysis of the lung microbiota. (<b>A</b>) The horizontal axis represents the size of the LDA score, with longer bars indicating more important taxa. The colors represent different groups, indicating that these taxa are significantly enriched in this group, with the highest average abundance and significant inter-group differences. (<b>B</b>) The horizontal axis represents the Log2FC value, with positive values indicating up-regulated species and negative values indicating down-regulated species. The vertical axis represents the taxonomic attributes of species, with three columns representing phylum, genus, and OTU. The colors represent the level of significance.</p>
Full article ">Figure 8
<p>Comparison of colonic microbiota between normal and CVA model groups. (<b>A</b>) Alpha diversity. * <span class="html-italic">p</span> &lt; 0.05. (<b>B</b>) Clustering tree based on unweighted UniFrac distances. (<b>C</b>) PCoA based on unweighted UniFrac. (<b>D</b>) Distribution of high-abundance microbial groups at the phylum level. (<b>E</b>) Changes in dominant microbiota at the genus level. ** <span class="html-italic">p</span> &lt; 0.01. The ns indicates that no statistical difference was observed between the two groups. (<b>F</b>) Heatmap of the top 30 abundant genera selected based on abundance ranking.</p>
Full article ">Figure 9
<p>Differential species analysis of the gut microbiota in the colon. (<b>A</b>) The horizontal axis represents the size of the LDA score, with longer bars indicating more important taxa. The colors represent different groups, indicating that these taxa are significantly enriched in this group, with the highest average abundance and significant inter-group differences. (<b>B</b>) The horizontal axis represents the Log2FC value, with positive values indicating up-regulated species and negative values indicating down-regulated species. The vertical axis represents the taxonomic attributes of species, with three columns representing phylum, genus, and OTU. The colors represent the level of significance.</p>
Full article ">Figure 10
<p>Comparison of microorganisms in three different niches in CVA model guinea pigs. (<b>A</b>) Alpha diversity. (<b>B</b>) Clustering tree based on unweighted UniFrac distances. (<b>C</b>) PCoA based on unweighted UniFrac. (<b>D</b>) Distribution of high-abundance microbial groups at the phylum level. (<b>E</b>) Distribution of high-abundance microbial groups at the genus level.</p>
Full article ">Figure 11
<p>The correlation between microbiota and inflammatory factors. (<b>A</b>) Heat map showing the correlation between the top 20 abundant genera of pulmonary microbial communities and lung inflammatory factors. The redder the color, the stronger the positive correlation, while the bluer the color, the stronger the negative correlation. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) Heat map showing the correlation between the top 20 abundant genera of ileal microbial communities and lung inflammatory factors. (<b>C</b>) Heat map showing the correlation between the top 20 abundant genera of colonic microbial communities and lung inflammatory factors. (<b>D</b>) Network diagram depicting the correlation between the top 20 abundant genera of pulmonary microbial communities and lung inflammatory factors. Circular nodes represent microbial communities at the genus level, while square nodes represent inflammatory factors. The degree indicates the number of edges connected to the node, and a larger degree indicates that the species is more central in the network. The species point size is correlated with degree, the color of the smallest species point value is light blue−green, and the color of the largest selected species point value is light orange. The color with the smallest inflammatory factor score is pale yellow and the color with the largest inflammatory factor point value is orange. (<b>E</b>) Network diagram depicting the correlation between the top 20 abundant genera of ileal microbial communities and inflammatory factors. (<b>F</b>) Network diagram depicting the correlation between the top 20 abundant genera of colonic microbial communities and inflammatory factors.</p>
Full article ">Figure 12
<p>Establishment of a guinea pig model of cough variant asthma.</p>
Full article ">
22 pages, 3385 KiB  
Article
Alcohol Impairs Bioenergetics and Differentiation Capacity of Myoblasts from Simian Immunodeficiency Virus-Infected Female Macaques
by Danielle E. Levitt, Brianna L. Bourgeois, Keishla M. Rodríguez-Graciani, Patricia E. Molina and Liz Simon
Int. J. Mol. Sci. 2024, 25(4), 2448; https://doi.org/10.3390/ijms25042448 - 19 Feb 2024
Viewed by 1263
Abstract
Alcohol misuse and HIV independently induce myopathy. We previously showed that chronic binge alcohol (CBA) administration, with or without simian immunodeficiency virus (SIV), decreases differentiation capacity of male rhesus macaque myoblasts. We hypothesized that short-term alcohol and CBA/SIV would synergistically decrease differentiation capacity [...] Read more.
Alcohol misuse and HIV independently induce myopathy. We previously showed that chronic binge alcohol (CBA) administration, with or without simian immunodeficiency virus (SIV), decreases differentiation capacity of male rhesus macaque myoblasts. We hypothesized that short-term alcohol and CBA/SIV would synergistically decrease differentiation capacity and impair bioenergetic parameters in female macaque myoblasts. Myoblasts from naïve (CBA/SIV), vehicle [VEH]/SIV, and CBA/SIV (N = 4–6/group) groups were proliferated (3 days) and differentiated (5 days) with 0 or 50 mM ethanol (short-term). CBA/SIV decreased differentiation and increased non-mitochondrial oxygen consumption rate (OCR) versus naïve and/or VEH/SIV. Short-term alcohol decreased differentiation; increased maximal and non-mitochondrial OCR, mitochondrial reactive oxygen species (ROS) production, and aldolase activity; and decreased glycolytic measures, ATP production, mitochondrial membrane potential (ΔΨm), and pyruvate kinase activity. Mitochondrial ROS production was closely associated with mitochondrial network volume, and differentiation indices were closely associated with key bioenergetic health and function parameters. Results indicate that short-term alcohol and CBA non-synergistically decrease myoblast differentiation capacity. Short-term alcohol impaired myoblast glycolytic function, driving the bioenergetic deficit. Results suggest potentially differing mechanisms underlying decreased differentiation capacity with short-term alcohol and CBA, highlighting the need to elucidate the impact of different alcohol use patterns on myopathy. Full article
(This article belongs to the Special Issue Mitochondrial Metabolism Alterations in Health and Disease)
Show Figures

Figure 1

Figure 1
<p>Quantification of myotube differentiation indices. Fused nuclei (<b>A</b>), fusion index (<b>B</b>), myotubes per field (<b>C</b>), and total nuclei (<b>D</b>) after 3 days of proliferation plus 5 days of differentiation with representative images (20×) of myoblasts from macaques in the naïve (N = 5), vehicle (VEH)/simian immunodeficiency virus (SIV)(N = 4), and chronic binge alcohol (CBA)/SIV (N = 5) groups cultured with 0 mM (<b>E</b>–<b>G</b>) or 50 mM (<b>H</b>–<b>J</b>) EtOH. Scale bars (lower right in each image) indicate 50 µm. Main effects of short-term EtOH, * <span class="html-italic">p</span> &lt; 0.05; main effect of group (naïve, VEH/SIV, CBA/SIV) with post-hoc pairwise comparisons, # <span class="html-italic">p</span> &lt; 0.05, ## <span class="html-italic">p</span> &lt; 0.01; short-term EtOH × group (naïve, VEH/SIV, CBA/SIV) interaction effect with post-hoc pairwise comparisons, + <span class="html-italic">p</span> &lt; 0.05. Lengths of bracket legs indicate direction of differences.</p>
Full article ">Figure 2
<p>Mito Stress Test parameters. Basal (<b>A</b>) and ATP-linked (<b>B</b>) oxygen consumption rate (OCR) and coupling efficiency (<b>C</b>); ATP-linked OCR/baseline OCR × 100), maximal OCR (<b>D</b>) and spare capacity (<b>E</b>), non-mitochondrial OCR (<b>F</b>), and proton leak-linked OCR (<b>G</b>). Myoblasts from macaques in the naïve (N = 6), vehicle (VEH)/simian immunodeficiency virus (SIV) (N = 6), and chronic binge alcohol (CBA)/SIV (N = 6) groups were proliferated for 3 days with 0 mM or 50 mM EtOH. Main effects of short-term EtOH, * <span class="html-italic">p</span> &lt; 0.05; main effect of group (naïve, VEH/SIV, CBA/SIV) with post-hoc pairwise comparisons, # <span class="html-italic">p</span> &lt; 0.05. Lengths of bracket legs indicate direction of differences.</p>
Full article ">Figure 3
<p>Bioenergetic phenotype indices. Glycolytic function measured as extracellular acidification rate (ECAR) at baseline (<b>A</b>) and after oligomycin administration (<b>B</b>), the relative ratio or mitochondrial to glycolytic function quantified as the ratio of oxygen consumption (OCR) to extracellular acidification (ECAR) rates at baseline (<b>C</b>) and after oligomycin administration (<b>D</b>), and OCR (<b>E</b>) and ECAR (<b>F</b>) throughout the assay with indications for injections of oligomycin (Oligo), carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP), and rotenone/antimycin A (Rot/AA). Myoblasts from macaques in the naïve (N = 6), vehicle (VEH)/simian immunodeficiency virus (SIV) (N = 6), and chronic binge alcohol (CBA)/SIV (N = 6) groups were proliferated for 3 days with 0 mM or 50 mM EtOH. Main effects of short-term EtOH, *** <span class="html-italic">p</span> &lt; 0.001. Lengths of bracket legs indicate direction of differences.</p>
Full article ">Figure 4
<p>Glycolysis stress test parameters. Non-glycolytic acidification (<b>A</b>), basal glycolysis (<b>B</b>), glycolytic capacity (<b>C</b>), and glycolytic reserve (<b>D</b>) measured as extracellular acidification rates (ECAR) and ECAR traces throughout the assay (<b>E</b>) with indications for injections of glucose, oligomycin (Oligo), and 2-deoxyglucose (2-DG). Myoblasts from macaques in the naïve (N = 6), vehicle (VEH)/simian immunodeficiency virus (SIV) (N = 5), and chronic binge alcohol (CBA)/SIV (N = 6) groups were proliferated for 3 days with 0 mM or 50 mM EtOH. Main effects of short-term EtOH, * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001. Lengths of bracket legs indicate direction of differences.</p>
Full article ">Figure 5
<p>ATP rate assay parameters. Total ATP (<b>A</b>), ATP derived from mitochondrial metabolism (<b>B</b>) and anaerobic glycolysis (<b>C</b>), and oxygen consumption ((<b>D</b>); OCR) and extracellular acidification ((<b>E</b>); ECAR) rates throughout the assay, with indications for injections of oligomycin (Oligo) and rotenone/antimycin A (Rot/AA). Myoblasts from macaques in the naïve (N = 6), vehicle (VEH)/simian immunodeficiency virus (SIV) (N = 5), and chronic binge alcohol (CBA)/SIV (N = 6) groups were proliferated for 3 days with 0 mM or 50 mM EtOH. Main effects of short-term EtOH; * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001. Lengths of bracket legs indicate direction of differences.</p>
Full article ">Figure 6
<p>Mitochondrial health parameters. Mean fluorescence intensity (MFI) of tetramethylrhodamine methyl ester (TMRM) for mitochondrial membrane potential (ΔΨm; (<b>A</b>)), MitoSOX for mitochondrial reactive oxygen species (ROS; (<b>B</b>)), and MitoTracker Green for mitochondrial network volume (<b>C</b>) measured using flow cytometry. Mitochondrial network volume was significantly associated with ROS (<b>D</b>). Myoblasts from macaques in the naïve (N = 5 [<b>A</b>] or 4 [<b>B</b>–<b>D</b>]), vehicle (VEH)/simian immunodeficiency virus (SIV) (N = 4), and chronic binge alcohol (CBA)/SIV (N = 5) groups were proliferated for 3 days with 0 mM or 50 mM EtOH. Main effects of EtOH, * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, main effect of group with post-hoc pairwise comparisons, # <span class="html-italic">p</span> &lt; 0.05. Lengths of bracket legs indicate direction of differences.</p>
Full article ">Figure 7
<p>Activity of select glycolytic enzymes. Aldolase (<b>A</b>,<b>B</b>) and pyruvate kinase (<b>C</b>,<b>D</b>) activity in myoblasts from macaques in the naïve (N = 5), vehicle (VEH)/simian immunodeficiency virus (SIV) (N = 4), and chronic binge alcohol (CBA)/SIV (N = 5) groups after 3 days of proliferation (day 0 differentiation, (<b>A</b>,<b>C</b>)) and 5 days of differentiation (<b>B</b>,<b>D</b>) with 0 mM or 50 mM EtOH. Simplified schematic of the first 10 steps of anaerobic glycolysis (<b>E</b>) showing steps catalyzed by aldolase and pyruvate kinase (PK). Fructose-1,6-bisphosphate (F-1,6-BP); dihydroxyacetone phosphate (DHAP); glyceraldehyde-3-phosphate (G3P); phosphoenolpyruvate (PEP); dashed arrows indicate multiple steps between glycolysis intermediates. Main effects of short-term EtOH, * <span class="html-italic">p</span> &lt; 0.05. Lengths of bracket legs indicate direction of differences.</p>
Full article ">Figure 8
<p>Associations between differentiation indices and bioenergetics parameters. Spearman correlations were run between raw values for key differentiation indices and bioenergetics parameters for samples collected from all groups and conditions. Oxygen consumption rate (OCR); pyruvate kinase (PK). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 9
<p>Non-human primate study design. Arrows indicate timing of muscle sampling. The first arrow indicates vastus lateralis muscle biopsy for the naïve group before initiation of CBA or VEH. The second arrow indicates muscle collection at the study endpoint for the CBA/SIV and VEH/SIV groups. Chronic binge alcohol (CBA); vehicle (isovolumetric water; VEH); simian immunodeficiency virus (SIV); antiretroviral therapy (daily subcutaneous injections; ART).</p>
Full article ">
13 pages, 2432 KiB  
Article
Interleukin-1β/Interleukin (IL)-1-Receptor-Antagonist (IL1-RA) Axis in Invasive Bladder Cancer—An Exploratory Analysis of Clinical and Tumor Biological Significance
by Marko Vukovic, Jorge M. Chamlati, Jörg Hennenlotter, Tilman Todenhöfer, Thomas Lütfrenk, Sebastian Jersinovic, Igor Tsaur, Arnulf Stenzl and Steffen Rausch
Int. J. Mol. Sci. 2024, 25(4), 2447; https://doi.org/10.3390/ijms25042447 - 19 Feb 2024
Viewed by 1565
Abstract
Previous data indicate a role of IL-1 and IL-1RA imbalance in bladder carcinoma (BC); the inhibition of IL-1 signaling might be considered a treatment option. Objective: To assess expression patterns and the prognostic role of IL-1β and IL-1RA in invasive BC and to [...] Read more.
Previous data indicate a role of IL-1 and IL-1RA imbalance in bladder carcinoma (BC); the inhibition of IL-1 signaling might be considered a treatment option. Objective: To assess expression patterns and the prognostic role of IL-1β and IL-1RA in invasive BC and to evaluate their interaction with AKT signaling and proliferation. The study included two independent cohorts of n = 92 and n = 102 patients who underwent a radical cystectomy for BC. Specimen from BC and benign urothelium (n = 22 and n = 39) were processed to a tissue microarray and immunohistochemically stained for IL-1β, IL-1RA, AKT, and Ki-67. Expression scores were correlated to clinical variables and Ki-67 and AKT expression. An association with outcome was assessed using Wilcoxon Kruskal–Wallis tests, Chi-square tests or linear regression, dependent on the variable’s category. Kaplan–Meier and Cox proportional hazard analyses were used to estimate recurrence-free (RFS), cancer-specific (CSS) and overall survival (OS). Both IL-1β and IL-1RA were significantly overexpressed in invasive BC compared to benign urothelium in both cohorts (p < 0.005). IL-1β was associated with vascular invasion (210 vs. 183, p < 0.02), lymphatic invasion (210 vs. 180, <0.05) and G3 cancer (192 vs. 188, <0.04). The survival analysis revealed favorable RFS, CSS, and OS in the case of high IL-1β expression (p < 0.02, <0.03, and <0.006, respectively). Multivariate analyses revealed an independent impact of (low) IL1β expression on RFS, CSS, and OS. The IL-1β and IL-1β/IL-1RA ratios were positively correlated to the AKT expression (p < 0.05 and <0.01, respectively). Additionally, the high expression of Ki-67 (>15%) correlated with higher levels of IL-1β (p = 0.01). The overexpression of IL-1β and IL-1RA is frequently found in BC, with a prognostic significance observed for the IL-1β protein expression. The observed link between the IL-1β/IL-1RA axis and AKT signaling may indicate possible autophagy activation processes besides the known tumor-promoting effects of AKT. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>A</b>–<b>I</b>) Representative staining for immunohistochemistry of IL-1β/IL-1RA, AKT, and Ki-67 signaling pathway markers in bladder cancer and normal tissues. (<b>A</b>–<b>C</b>) Cytoplasmic IL-1β immunostaining in bladder tissue: (<b>A</b>)—staining in benign tissue; (<b>B</b>,<b>C</b>)—low and high staining in cancer tissue. (<b>D</b>–<b>F</b>) Cytoplasmic IL-1RA immunostaining in bladder tissue: (<b>D</b>)—staining in benign tissue; (<b>E</b>,<b>F</b>)—low and high staining in cancer tissue. (<b>G</b>) Cytoplasmic and partial nuclear staining of AKT in cancer tissue. (<b>H</b>,<b>I</b>) Low and strong (≥15%) nuclear staining of Ki-67 in cancer tissue. Magnification: 200-fold.</p>
Full article ">Figure 2
<p><b>A</b>–<b>B</b>. Expression patterns of IL-1β and IL-1RA. (<b>A</b>), Intensity of IL-1β cells expression and in (<b>B</b>), Intensity of IL-1RA cells expression in benign and malignant tissue; <span class="html-italic">p</span> = 0.0001 and <span class="html-italic">p</span> = 0.004, Wilcoxon Kruskal-Wallis tests).</p>
Full article ">Figure 3
<p>(<b>A</b>–<b>D</b>). (<b>A</b>), Correlation between expression patterns of IL-1β and AKT in tumor tissue (<span class="html-italic">p</span> = 0.04, r<sup>2</sup> corrected 0.14); (<b>B</b>), Positive correlation between AKT expression pattern and IL-1 ratio, for the determined cut-off value (0—under 0.7 cut-off; 1—over or = 0.7 cut-off); (<b>C</b>,<b>D</b>), Positive correlation between IL-1β expression and Ki-67 expression in tumor tissue (<span class="html-italic">p</span> = 0.011, r<sup>2</sup> = 0.095), as well as positive correlation trend of Ki-67 high expression towards IL-1 beta/IL-1RA ratio (<span class="html-italic">p</span> = 0.08).</p>
Full article ">Figure 4
<p>Postulated relationship between IL-1β/IL-1RA axis and autophagy signaling pathway in invasive bladder cancer. IL-interleukin, PIK3/AKT/mTOR—Phosphoinositidylinositol-3-Kinase/Proteinkinase B/mammalian Target of Rapamycin signaling pathway, PDK phosphoinositide-dependent protein kinase-1, TSC1/TSC2—Tuberous Sclerosis Complex 1 and 2, and p85—protein, the regulatory subunit of PI3K [<a href="#B12-ijms-25-02447" class="html-bibr">12</a>,<a href="#B13-ijms-25-02447" class="html-bibr">13</a>].</p>
Full article ">Figure 5
<p><b>A</b>–<b>C</b>: Kaplan-Meier diagrams for IL-1β expression: (<b>A</b>). recurrence-free survival, (<b>B</b>). cancer specific survival and (<b>C</b>). overall survival; red = expression &lt;160, log rank test, below numbers at risk.</p>
Full article ">
20 pages, 6586 KiB  
Article
Genetic Ablation of STE20-Type Kinase MST4 Does Not Alleviate Diet-Induced MASLD Susceptibility in Mice
by Mara Caputo, Emma Andersson, Ying Xia, Wei Hou, Emmelie Cansby, Max Erikson, Dan Emil Lind, Bengt Hallberg, Manoj Amrutkar and Margit Mahlapuu
Int. J. Mol. Sci. 2024, 25(4), 2446; https://doi.org/10.3390/ijms25042446 - 19 Feb 2024
Viewed by 2002
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) and its advanced subtype, metabolic dysfunction-associated steatohepatitis (MASH), have emerged as the most common chronic liver disease worldwide, yet there is no targeted pharmacotherapy presently available. This study aimed to investigate the possible in vivo function of [...] Read more.
Metabolic dysfunction-associated steatotic liver disease (MASLD) and its advanced subtype, metabolic dysfunction-associated steatohepatitis (MASH), have emerged as the most common chronic liver disease worldwide, yet there is no targeted pharmacotherapy presently available. This study aimed to investigate the possible in vivo function of STE20-type protein kinase MST4, which was earlier implicated in the regulation of hepatocellular lipotoxic milieu in vitro, in the control of the diet-induced impairment of systemic glucose and insulin homeostasis as well as MASLD susceptibility. Whole-body and liver-specific Mst4 knockout mice were generated by crossbreeding conditional Mst4fl/fl mice with mice expressing Cre recombinase under the Sox2 or Alb promoters, respectively. To replicate the environment in high-risk subjects, Mst4–/– mice and their wild-type littermates were fed a high-fat or a methionine–choline-deficient (MCD) diet. Different in vivo tests were conducted in obese mice to describe the whole-body metabolism. MASLD progression in the liver and lipotoxic damage to adipose tissue, kidney, and skeletal muscle were analyzed by histological and immunofluorescence analysis, biochemical assays, and protein and gene expression profiling. In parallel, intracellular fat storage and oxidative stress were assessed in primary mouse hepatocytes, where MST4 was silenced by small interfering RNA. We found that global MST4 depletion had no effect on body weight or composition, locomotor activity, whole-body glucose tolerance or insulin sensitivity in obese mice. Furthermore, we observed no alterations in lipotoxic injuries to the liver, adipose, kidney, or skeletal muscle tissue in high-fat diet-fed whole-body Mst4–/– vs. wild-type mice. Liver-specific Mst4–/– mice and wild-type littermates displayed a similar severity of MASLD when subjected to an MCD diet, as evidenced by equal levels of steatosis, inflammation, hepatic stellate cell activation, fibrosis, oxidative/ER stress, and apoptosis in the liver. In contrast, the in vitro silencing of MST4 effectively protected primary mouse hepatocytes against ectopic lipid accumulation and oxidative cell injury triggered by exposure to fatty acids. In summary, these results suggest that the genetic ablation of MST4 in mice does not mitigate the initiation or progression of MASLD and has no effect on systemic glucose or insulin homeostasis in the context of nutritional stress. The functional compensation for the genetic loss of MST4 by yet undefined mechanisms may contribute to the apparent discrepancy between in vivo and in vitro phenotypic consequences of MST4 silencing. Full article
(This article belongs to the Section Molecular Endocrinology and Metabolism)
Show Figures

Figure 1

Figure 1
<p>Global knockout of MST4 does not affect body weight or composition, locomotor activity, or glucose or insulin homeostasis in high-fat diet-fed mice. (<b>A</b>) Schematic illustration of the experimental design. (<b>B</b>) Body weight curves. (<b>C</b>) Total, fat, and lean body mass were measured using BCA. (<b>D</b>) Locomotor activity was assessed via the open-field test. (<b>E</b>,<b>F</b>) Fasting circulating levels of glucose (<b>E</b>) and insulin (<b>F</b>). (<b>G</b>) HOMA-IR was calculated using the equation (fasting glucose (mg/dL) × fasting insulin (ng/mL))/405. (<b>H</b>,<b>I</b>) Intraperitoneal GTT (<b>H</b>) and ITT (<b>I</b>); the area under the glucose curve in both tests is shown. Data are the mean ± SEM from 7 to 9 mice per group. HFD, high-fat diet; KO, knockout; WT, wild-type.</p>
Full article ">Figure 2
<p>Whole-body depletion of MST4 does not protect mice against high-fat diet-induced steatotoxicity in the liver. (<b>A</b>) Representative liver sections were stained with H&amp;E. The scale bars represent 25 µm. (<b>B</b>,<b>C</b>) The quantification of hepatic TAG (<b>B</b>) and glycogen (<b>C</b>) content. (<b>D</b>) Liver lysates were analyzed via Western blot using an anti-total OXPHOS antibody cocktail. Protein levels were measured using densitometry; representative Western blots are shown with vinculin used as a loading control. (<b>E</b>) Representative liver sections were stained with DHE (red) or processed for immunofluorescence with anti-cytochrome c or anti-CHOP (green) antibodies; nuclei were stained with DAPI (blue). The scale bars represent 25 µm. Quantification of the staining. (<b>F</b>,<b>G</b>) The relative mRNA expression of selected genes controlling oxidative/ER stress and apoptosis (<b>F</b>), as well as lipid metabolism (<b>G</b>), was assessed via qRT-PCR in the liver. Data are the mean ± SEM from 8 to 9 mice per group. CD, chow diet; HFD, high-fat diet; KO, knockout; ox., oxidative; WT, wild-type. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 3
<p>Global deficiency of MST4 has no effect on adipose tissue function in obese mice. (<b>A</b>) Representative eWAT sections were stained with H&amp;E. The scale bars represent 100 µm. (<b>B</b>,<b>C</b>) The average adipocyte size (<b>B</b>) and adipocyte size distribution with values representing the relative proportion of adipocytes in the given diameter class (<b>C</b>) in the eWAT. (<b>D</b>) Relative mRNA expression of adipokines and selected genes controlling lipid metabolism, oxidative/ER stress, and inflammation was assessed via qRT-PCR in the sWAT. (<b>E</b>) BAT lysates were analyzed via Western blot using antibodies specific for TH or UCP1, or applying the anti-total OXPHOS antibody cocktail. Protein levels were measured using densitometry; representative Western blots are shown with vinculin used as a loading control. Data are the mean ± SEM from 7 to 9 mice per group. CD, chow diet; HFD, high-fat diet; KO, knockout; WT, wild-type. *** <span class="html-italic">p</span> &lt; 0.001 for mice fed a high-fat vs. chow diet; <sup>†</sup> <span class="html-italic">p</span> &lt; 0.001 for wild-type mice fed a high-fat vs. chow diet; and <sup>#</sup> <span class="html-italic">p</span> &lt; 0.001 for <span class="html-italic">Mst4</span><sup>–/–</sup> mice fed a high-fat vs. chow diet.</p>
Full article ">Figure 4
<p>The whole-body depletion of MST4 has no effect on renal or skeletal muscle lipotoxicity in obese mice. (<b>A</b>,<b>B</b>) Assessment of TAG content (<b>A</b>) and the ratio of GSH/GSSH (<b>B</b>) in kidney lysates. (<b>C</b>) Measurement of urinary albumin, creatinine, and albumin-to-creatinine ratio. (<b>D</b>,<b>E</b>) Quantification of glycogen (<b>D</b>) and TAG (<b>E</b>) in gastrocnemius skeletal muscle lysates. (<b>F</b>) Representative gastrocnemius skeletal muscle sections were stained with Nile Red (red), nuclei were stained with DAPI (blue), or processed in enzymatic activity assays for NDH, SDH, or COX. The scale bars represent 25 µm. (<b>G</b>) The relative mRNA expression of selected genes controlling lipid and glucose metabolism was assessed by qRT-PCR in gastrocnemius skeletal muscle. Data are the mean ± SEM from 6 to 9 mice per group. CD, chow diet; KO, knockout; WT, wild-type. * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 5
<p>Liver-specific ablation of MST4 has no impact on systemic glucose or insulin homeostasis in MCD diet-fed mice. (<b>A</b>) Schematic illustration of the experimental design. (<b>B</b>–<b>D</b>) Body weight curves (<b>B</b>), liver weight estimated in absolute values (<b>C</b>), and when related to total body weight (<b>D</b>). (<b>E</b>,<b>F</b>) Fasting circulating levels of glucose (<b>E</b>) and insulin (<b>F</b>). (<b>G</b>) HOMA-IR was calculated using the equation (fasting glucose (mg/dL) × fasting insulin (ng/mL))/405. Data are the mean ± SEM from 8 to 12 mice per group. KO, knockout; WT, wild-type. * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 6
<p>Liver-specific inhibition of MST4 does not protect mice against MCD diet-induced hepatic steatosis or oxidative/ER stress. (<b>A</b>) Representative liver sections were stained with H&amp;E. The scale bars represent 25 µm. (<b>B</b>) Representative liver sections were stained with Oil Red O or DHE (red) or processed for immunofluorescence with anti-4-HNE (green), anti-8-oxoG (red), anti-KDEL (red), or anti-CHOP (green) antibodies; nuclei were stained with DAPI (blue). The scale bars represent 25 µm. Quantification of the staining. (<b>C</b>,<b>D</b>) Measurement of TBARS (<b>C</b>) and catalase activity (<b>D</b>) in the liver lysates. Data are the mean ± SEM from 7 to 9 mice per group. Data of Oil Red O-stained area in chow diet-fed mice were extracted from the previous study [<a href="#B36-ijms-25-02446" class="html-bibr">36</a>]. CD, chow diet; KO, knockout; ORO, Oil Red O; WT, wild-type. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 7
<p>Liver-specific <span class="html-italic">Mst4</span><sup>–/–</sup> mice are not protected against MCD diet-induced hepatic inflammation or fibrosis. (<b>A</b>) Representative liver sections were processed for immunofluorescence with anti-F4/80, anti-Gr1 (Ly6C), anti-collagen IV, anti-fibronectin (red), or anti-cleaved CASP3 (green) antibodies; nuclei stained with DAPI (blue). The scale bars represent 25 µm. Quantification of the staining. (<b>B</b>) Representative liver sections were stained with Picrosirius Red or TUNEL, or processed for immunohistochemistry with anti-αSMA antibodies; they were counterstained with Fast Green (Picrosirius Red), methyl green (TUNEL), or hematoxylin (αSMA). The scale bars represent 25 µm. (<b>C</b>,<b>D</b>) Relative mRNA expression of selected genes controlling inflammation (<b>C</b>) and fibrosis (<b>D</b>) was assessed via qRT-PCR in the liver. (<b>E</b>) Quantification of hepatic hydroxyproline. (<b>F</b>,<b>G</b>) Measurement of plasma ALT (<b>F</b>) and AST (<b>G</b>) activity. (<b>H</b>,<b>I</b>) Assessment of total NAS (<b>H</b>) and individual histological features of NAS (steatosis 0–3, inflammation 0–3, hepatocellular ballooning 0–2) (<b>I</b>) in H&amp;E-stained liver sections. (<b>J</b>) The fibrosis score was assessed based on the Kleiner/Brunt criteria adapted to rodents (0, no fibrosis; 1, focal pericellular fibrosis in zone 3; 2, perivenular and pericellular fibrosis confined to zones 2 and 3; 3, bridging fibrosis; and 4, cirrhosis) in the liver sections stained with Picrosirius Red and counterstained with Fast Green. Data are the mean ± SEM from 8 to 12 mice per group. CD, chow diet; inflam., inflammation; KO, knockout; WT, wild-type. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 8
<p>The transfection of primary mouse hepatocytes with <span class="html-italic">Mst4</span> siRNA suppresses lipotoxicity when compared with cells transfected with NTC siRNA, which is not replicated in hepatocytes derived from <span class="html-italic">Mst4</span><sup>–/–</sup> vs. wild-type mice. (<b>A</b>,<b>C</b>) Primary hepatocytes were isolated from wild-type mice, transfected with mouse <span class="html-italic">Mst4</span> siRNA or NTC siRNA, and cultured with oleate supplementation. (<b>A</b>) The relative mRNA expression of <span class="html-italic">Mst4</span> was assessed via qRT-PCR. (<b>C</b>) Representative images of hepatocytes stained with Bodipy 493/503 (green) or DHE (red); nuclei were stained with DAPI (blue). The scale bars represent 25 µm. Quantification of the staining. (<b>B</b>,<b>D</b>) Primary hepatocytes were isolated from whole-body <span class="html-italic">Mst4</span><sup>–/–</sup> and wild-type mice and cultured with oleate supplementation. (<b>B</b>) The relative mRNA expression of <span class="html-italic">Mst4</span> was assessed via qRT-PCR. In (<b>B</b>), statistical significance between the groups was evaluated using the nonparametric Kruskal–Wallis test, followed by Dunn’s multiple comparison test. (<b>D</b>) Representative images of hepatocytes stained with Bodipy 493/503 (green); nuclei were stained with DAPI (blue). The scale bars represent 25 µm. Quantification of the staining. Data are the mean ± SEM from 5 to 6 wells per group. KO, knockout; ND, not detected; WT, wild-type. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">
14 pages, 3937 KiB  
Article
Synthesis and Insecticidal Activity of Novel Anthranilic Diamide Insecticides Containing Indane and Its Analogs
by Zhihui Yang, Ruihan Hu, Jingjing Chen and Xiaohua Du
Int. J. Mol. Sci. 2024, 25(4), 2445; https://doi.org/10.3390/ijms25042445 - 19 Feb 2024
Viewed by 1283
Abstract
Diamide insecticides have always been a hot research topic in the field of pesticides. To further discover new compounds with high activity and safety, indane and its analogs were introduced into chlorantraniliprole, and a battery of chlorfenil derivatives, including indane and its analogs, [...] Read more.
Diamide insecticides have always been a hot research topic in the field of pesticides. To further discover new compounds with high activity and safety, indane and its analogs were introduced into chlorantraniliprole, and a battery of chlorfenil derivatives, including indane and its analogs, were designed and prepared for biological testing. Their characterization and verification were carried out through nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HRMS). Biological detection showed that all the compounds exhibited good insecticidal activity against Mythimna separata. At 0.8 mg/L, the insecticidal activity of compound 8q against Mythimna separata was 80%, which was slightly better than that of chlorantraniliprole. The results of the structure–activity relationship (SAR) analysis indicated that the indane moiety had a significant effect on insecticidal activity, especially in the R-configuration. The results indicated that chlorantraniliprole derivatives containing indane groups could serve as pilot compounds for the further development of new insecticides. Full article
(This article belongs to the Section Molecular Toxicology)
Show Figures

Figure 1

Figure 1
<p>Chemical structures of diamide insecticides.</p>
Full article ">Figure 2
<p>Chemical structures of indoxacarb, indaziflam, fluindapyr, and indaofan, and design strategies for their target compounds.</p>
Full article ">Figure 3
<p>(<b>A</b>) Theoretical binding pattern of chlorantraniliprole to the RyR N-terminal domain; (<b>B</b>) theoretical binding model of compound <b>8q</b> to the RyR N-terminal domain.</p>
Full article ">Scheme 1
<p>Synthesis route of chlorantraniliprole derivatives containing indane and its analogs.</p>
Full article ">
25 pages, 1595 KiB  
Review
Potential Therapeutic Application and Mechanism of Action of Stem Cell-Derived Extracellular Vesicles (EVs) in Systemic Lupus Erythematosus (SLE)
by Sushmitha Rajeev Kumar, Rajalingham Sakthiswary and Yogeswaran Lokanathan
Int. J. Mol. Sci. 2024, 25(4), 2444; https://doi.org/10.3390/ijms25042444 - 19 Feb 2024
Cited by 1 | Viewed by 2349
Abstract
Systemic lupus erythematosus (SLE) is a multisystemic autoimmune disease that affects nearly 3.41 million people globally, with 90% of the cases affecting women of childbearing age. SLE is a complex disease due to the interplay of various immunological pathways and mechanisms. This scoping [...] Read more.
Systemic lupus erythematosus (SLE) is a multisystemic autoimmune disease that affects nearly 3.41 million people globally, with 90% of the cases affecting women of childbearing age. SLE is a complex disease due to the interplay of various immunological pathways and mechanisms. This scoping review aims to highlight the latest research findings on the therapeutic mechanisms of action of EVs in SLE. Relevant research articles were identified using the PRISMA framework from databases such as PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), and Web of Science: Core Collection (Clarivate Analytics) from July 2023 to October 2023. Eleven studies met the inclusion criteria and thus were included in this scoping review. The findings showed that EVs have therapeutic effects on ameliorating the disease progression of SLE. EVs can reduce the pro-inflammatory cytokines and increase the anti-inflammatory cytokines. Moreover, EVs can increase the levels of regulatory T cells, thus reducing inflammation. EVs also have the potential to regulate B cells to alleviate SLE and reduce its adverse effects. The scoping review has successfully analysed the therapeutic potential in ameliorating the disease progression of SLE. The review also includes prospects to improve the effects of EVs further to increase the therapeutic effects on SLE. Full article
Show Figures

Figure 1

Figure 1
<p>Pathogenesis of the innate and adaptive immune response in SLE.</p>
Full article ">Figure 2
<p>Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Protocol.</p>
Full article ">Figure 3
<p>EVs and miRNAs that are related in ameliorating the disease progression of SLE. BMMSCS, bone marrow mesenchymal stromal cells; SHED, stem cells from human exfoliated deciduous teeth; hucMSCs, human umbilical cord mesenchymal stromal cells; AD-MSCS, adipose-derived mesenchymal stromal cells; UC-MSCs, umbilical cord mesenchymal stromal cells [<a href="#B1-ijms-25-02444" class="html-bibr">1</a>,<a href="#B43-ijms-25-02444" class="html-bibr">43</a>,<a href="#B44-ijms-25-02444" class="html-bibr">44</a>,<a href="#B45-ijms-25-02444" class="html-bibr">45</a>,<a href="#B46-ijms-25-02444" class="html-bibr">46</a>,<a href="#B47-ijms-25-02444" class="html-bibr">47</a>,<a href="#B48-ijms-25-02444" class="html-bibr">48</a>,<a href="#B49-ijms-25-02444" class="html-bibr">49</a>,<a href="#B50-ijms-25-02444" class="html-bibr">50</a>,<a href="#B51-ijms-25-02444" class="html-bibr">51</a>,<a href="#B52-ijms-25-02444" class="html-bibr">52</a>].</p>
Full article ">
15 pages, 5579 KiB  
Article
Non-Targeted Metabolomics Investigation of a Sub-Chronic Variable Stress Model Unveils Sex-Dependent Metabolic Differences Induced by Stress
by Seulgi Kang, Woonhee Kim, Jimin Nam, Ke Li, Yua Kang, Boyeon Bae, Kwang-Hoon Chun, ChiHye Chung and Jeongmi Lee
Int. J. Mol. Sci. 2024, 25(4), 2443; https://doi.org/10.3390/ijms25042443 - 19 Feb 2024
Viewed by 1349
Abstract
Depression is twice as prevalent in women as in men, however, most preclinical studies of depression have used male rodent models. This study aimed to examine how stress affects metabolic profiles depending on sex using a rodent depression model: sub-chronic variable stress (SCVS). [...] Read more.
Depression is twice as prevalent in women as in men, however, most preclinical studies of depression have used male rodent models. This study aimed to examine how stress affects metabolic profiles depending on sex using a rodent depression model: sub-chronic variable stress (SCVS). The SCVS model of male and female mice was established in discovery and validation sets. The stress-induced behavioral phenotypic changes were similar in both sexes, however, the metabolic profiles of female plasma and brain became substantially different after stress, whereas those of males did not. Four stress-differential plasma metabolites—β-hydroxybutyric acid (BHB), L-serine, glycerol, and myo-inositol—could yield biomarker panels with excellent performance to discern the stressed individuals only for females. Disturbances in BHB, glucose, 1,5-anhydrosorbitol, lactic acid, and several fatty acids in the plasma of stressed females implied a systemic metabolic shift to β-oxidation in females. The plasma levels of BHB and corticosterone only in stressed females were observed not only in SCVS but also in an acute stress model. These results collectively suggest a sex difference in the metabolic responses by stress, possibly involving the energy metabolism shift to β-oxidation and the HPA axis dysregulation in females. Full article
(This article belongs to the Section Molecular Endocrinology and Metabolism)
Show Figures

Figure 1

Figure 1
<p>Scheme for the establishment of SCVS model in the discovery and validation sets (<b>A</b>) and tail suspension test results (<b>B</b>). * and ** indicate significant differences with <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively. Results in (<b>B</b>) are displayed as mean ± S.E.M. Number of mice per group: n = 8 in the discovery set and n = 12 in the validation set.</p>
Full article ">Figure 2
<p>Hierarchical clustering analysis and heat map of the selected differential metabolites in brain (<b>A</b>) and plasma (<b>B</b>). Group identification: FC, female without SCVS; FS, female with SCVS; MC, male without SCVS; MS, male with SCVS.</p>
Full article ">Figure 3
<p>Overview of the integrated metabolic disturbances in the SCVS-treated female mice. Abbreviations: BCAA, branched chain amino acid; G6P, glucose-6-phosphate; G3P, glyceraldehyde-3-phosphate; 1,5-AG, 1,5-anhydroglucitol; BHB, 3-hydroxybutyric acid; C16:0, palmitic acid; C18:0, stearic acid; C18:1, oleic acid; C18:2, linoleic acid; C20:4, arachidonic acid.</p>
Full article ">Figure 4
<p>(<b>A</b>) Plasma concentrations of key biomolecules in the discovery set of SCVS model. Data are shown as mean ± S.E.M (n = 8 per group). (<b>B</b>) Correlation analysis among female mice. (<b>C</b>) Correlation analysis among male mice. Numbers and red lines in box (B and C) indicate the correlation coefficients and regression lines, respectively. *, **, ***, and **** indicate <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">p</span> &lt; 0.001, and <span class="html-italic">p</span> &lt; 0.0001, respectively. Abbreviations: CORT, corticosterone; BHB, 3-hydroxybutyric acid.</p>
Full article ">
29 pages, 5191 KiB  
Article
K2CO3-Modified Smectites as Basic Catalysts for Glycerol Transcarbonation to Glycerol Carbonate
by Yosra Snoussi, David Gonzalez-Miranda, Tomás Pedregal, Néji Besbes, Abderrahim Bouaid and Miguel Ladero
Int. J. Mol. Sci. 2024, 25(4), 2442; https://doi.org/10.3390/ijms25042442 - 19 Feb 2024
Viewed by 1272
Abstract
A novel and cost-effective heterogeneous catalyst for glycerol carbonate production through transesterification was developed by impregnating smectite clay with K2CO3. Comprehensive structural and chemical analyses, including X-ray diffraction Analysis (XRD), Scanning Electron Microscopy (SEM)-Electron Dispersion Spectroscopy (EDS), Fourier Transform [...] Read more.
A novel and cost-effective heterogeneous catalyst for glycerol carbonate production through transesterification was developed by impregnating smectite clay with K2CO3. Comprehensive structural and chemical analyses, including X-ray diffraction Analysis (XRD), Scanning Electron Microscopy (SEM)-Electron Dispersion Spectroscopy (EDS), Fourier Transform Infrared Spectroscopy (FTIR), and Brunauer-Emmett-Teller (BET) surface area analysis measurements, were employed to characterize the catalysts. Among the various catalysts prepared, the one impregnated with 40 wt% K2CO3 on smectite and calcined at 550 °C exhibited the highest catalytic activity, primarily due to its superior basicity. To enhance the efficiency of the transesterification process, several reaction parameters were optimized, including the molar ratio between propylene carbonate and glycerol reactor loading of the catalyst and reaction temperature. The highest glycerol carbonate conversion rate, approximately 77.13% ± 1.2%, was achieved using the best catalyst under the following optimal conditions: 2 wt% reactor loading, 110 °C reaction temperature, 2:1 propylene carbonate to glycerol molar ratio, and 6h reaction duration. Furthermore, both the raw clay and the best calcined K2CO3-impregnated catalysts demonstrated remarkable stability, maintaining their high activity for up to four consecutive reaction cycles. Finally, a kinetic analysis was performed using kinetic data from several runs employing raw clay and the most active K2CO3-modified clay at different temperatures, observing that a simple reversible second-order potential kinetic model of the quasi-homogeneous type fits perfectly to such data in diverse temperature ranges. Full article
(This article belongs to the Special Issue Advances in Biofuels and Green Catalysts)
Show Figures

Figure 1

Figure 1
<p>XRD patterns of smectite. Smectite catalysts were impregnated with different levels of potassium carbonate loading (10 to 40% by weight) and calcination temperature (450–800 °C) during calcination and prior to the transesterification reaction.</p>
Full article ">Figure 2
<p>FTIR spectra of smectite, K<sub>2</sub>CO<sub>3</sub>, and smectite-supported catalysts impregnated with different potassium carbonate loading levels (10–40 wt%) and calcination temperature (450–800 °C) during calcination and before the transesterification reaction.</p>
Full article ">Figure 3
<p>SEM micrographs of (<b>A</b>) K<sub>2</sub>CO<sub>3</sub> (10%)/smectite; (<b>B</b>) K<sub>2</sub>CO<sub>3</sub> (20%)/smectite; (<b>C</b>) K<sub>2</sub>CO<sub>3</sub> (30%)/smectite; (<b>D</b>) K<sub>2</sub>CO<sub>3</sub> (40%)/smectite catalysts with a calcination temperature of 550 °C at 20,000× magnification.</p>
Full article ">Figure 4
<p>SEM micrographs of (<b>A</b>) CC at 450 °C; (<b>B</b>) CC at 550 °C; (<b>C</b>) CC at 650 °C; (<b>D</b>) CC at 650 °C catalysts with 40% by weight of K<sub>2</sub>CO<sub>3</sub> at 20,000× magnification.</p>
Full article ">Figure 5
<p>Nitrogen adsorption-desorption isotherms of smectite catalysts impregnated with different potassium carbonate loading levels: (<b>b</b>) 10%<span class="html-italic">w</span>/<span class="html-italic">w</span> dry solid, (<b>c</b>) 20%<span class="html-italic">w</span>/<span class="html-italic">w</span> dry solid, (<b>d</b>) 10%<span class="html-italic">w</span>/<span class="html-italic">w</span> dry solid, and (<b>e</b>) 10%<span class="html-italic">w</span>/<span class="html-italic">w</span> dry solid to 40%<span class="html-italic">w</span>/<span class="html-italic">w</span> calcined at 550 °C in comparison to the raw or native clay (<b>a</b>).</p>
Full article ">Figure 6
<p>Nitrogen adsorption-desorption isotherms of impregnated smectite catalysts at different calcination temperatures: (<b>b</b>) 450 °C, (<b>c</b>) 550 °C, (<b>d</b>) 650 °C, and (<b>e</b>) 800 °C, in comparison to the raw clay (<b>a</b>).</p>
Full article ">Figure 7
<p>Expanded region (3.2 to 5.3 ppm) of <sup>1</sup>H NMR spectra in DMSO of a reaction sample at 120 min containing the mixture of Gly, EC, GC, and EG. The catalyst here used was raw clay (RC).</p>
Full article ">Figure 8
<p>Expanded region (3.2 to 5.3 ppm) of <sup>1</sup>H NMR spectra in DMSO of a reaction sample catalyzed by the most active K<sub>2</sub>CO<sub>3</sub>-modified smectite (CC) containing the mixture of Gly, EC, GC, and EG.</p>
Full article ">Figure 9
<p>(<b>A</b>) Effect of K<sub>2</sub>CO<sub>3</sub> loading impregnated on smectite at 100 °C with a molar ratio of PC/Gly 2:1. The conversion of glycerol is presented as a function of reaction time with 2% in quantity catalyst (RC or CC).Where <span style="color:#CCCC00">■</span> represents the RC (0% in charge of K<sub>2</sub>CO<sub>3</sub>), <span style="color:#FB2595">●</span> loading of 5% potassium carbonate, <span style="color:#ff66cc">▲</span> a K<sub>2</sub>CO<sub>3</sub> loading of 10%, <span style="color:#9966ff">▼</span> 20% loading, <span style="color:#a6a6a6">◆</span> 30% loading, and <span style="color:#ff9933">◀</span> 40% K<sub>2</sub>CO<sub>3</sub> loading.(<b>B</b>) Initial reaction rates observed for these runs.</p>
Full article ">Figure 10
<p>(<b>A</b>) Effect of calcination temperature with a K<sub>2</sub>CO<sub>3</sub> load equal to 40% at 100 °C, with a molar ratio of PC/Gly 2:1. The conversion of glycerol is presented as a function of the reaction time with 2% in the quantity of catalyst (RC or CC), where <span style="color:lime">■</span> represents the raw clay; <span style="color:#FF6600">●</span> a calcination temperature equal to 450 °C; <span style="color:#ff0066">▲</span> a calcination temperature equal to 550 °C; and <span style="color:#00ffff">▼</span> represents a temperature of 650 °C. (<b>B</b>) Initial reaction rates observed for these experiments.</p>
Full article ">Figure 11
<p>Effect of the quantity of catalyst, taking into account the effect of the nature of the catalyst (RC or CC) at 120 °C with a molar ratio of 2:1. The conversion of glycerol is presented as a function of the time of reaction with different catalyst loadings, where (<b>A</b>) represents a catalyst loading of 2%, (<b>B</b>) represents a catalyst loading of 4%, and (<b>C</b>) represents a catalyst loading of 6%.</p>
Full article ">Figure 12
<p>(<b>A</b>) Effect of CC loading at 120 °C and a CE/Gly ratio of 2 on Gly conversion as a function of reaction time with different catalyst loadings, where <span style="color:#538135">■</span> represents a loading of 2% catalyst, <span style="color:aqua">●</span> a catalyst loading of 4%, and <span style="color:#ff6600">▲</span> a catalyst loading of 6%. (<b>B</b>) Initial reaction rates recorded for these experiments.</p>
Full article ">Figure 13
<p>Effect of the initial molar ratio of the reactants, taking into account the effect of the nature of the catalyst (RC or CC) at 120 °C with a load of 2%. The conversion of glycerol is presented as a function of the reaction time with the PC/Gly ratio (M), where (<b>A</b>) M = 1.5, (<b>B</b>) M = 2, (<b>C</b>) M = 2.5, and (<b>D</b>) M = 3.</p>
Full article ">Figure 14
<p>(<b>A</b>) Effect of initial molar ratio of reactants using CC at 120 °C, with 2% loading on glycerol conversion as a function of reaction time with CE/Gly ratio (M), where <span style="color:#00B0F0">■</span> M = 1.5, <span style="color:#FF6600">●</span> M = 2, <span style="color:#993366">▲</span> M = 2.5, and <span style="color:#538135">▼</span> M = 3. (<b>B</b>) Effect of initial reaction speed with initial PC/Gly molar.</p>
Full article ">Figure 15
<p>Effect of the initial molar ratio of the reagents, taking into account the effect of the nature of the catalyst (RC or CC), with 2% load and with 2M. The conversion of glycerol is presented as a function of the reaction time with the ratio PC/Gly (M), where (<b>A</b>) T = 100, (<b>B</b>) T = 110, and (<b>C</b>) T = 120 °C.</p>
Full article ">Figure 16
<p>(<b>A</b>) Effect of initial molar ratio of reactants using CC at 120 °C, with 2% loading on glycerol conversion as a function of reaction time with CE/Gly ratio (M), where <span style="color:gray">■</span> T = 70 °C, <span style="color:red">●</span> T = 80 °C, <span style="color:#00b0f0">▲</span> T = 90 °C, <span style="color:#92D050">▼</span> T = 100 °C, <span style="color:#cc99ff">◆</span> T = 110 °C, and <span style="color:#ffcc00">◀</span> T = 120 °C. (<b>B</b>) Effect of the initial reaction speed with the initial PC/Gly molar.</p>
Full article ">Figure 17
<p>Reuse cycles of raw (<b>A</b>) and calcined (<b>B</b>) smectite catalyst (40% in K<sub>2</sub>CO<sub>3</sub> at 550 °C) at 120 °C (standard error percentage range fluctuates between ±0.5 and 5%).</p>
Full article ">Figure 18
<p>Fitting of the quasi-homogeneous kinetic model to glycerol conversion data (X<sub>Gly</sub>) in the transesterification reaction of glycerol with propylene carbonate driven by K<sub>2</sub>CO<sub>3</sub>-impregnated Tunisian smectite (CC) and raw clay (RC). Dots represent experimental data, while lines reflectthe fit of the model for each experimental data point. (<b>A</b>) Effect of the temperature on CC catalyst activity in the low-temperature interval (■ 70 °C; <span style="color:red">●</span> 80 °C; <span style="color:#3366FF">▲</span> 90 °C); (<b>B</b>) Temperature influence on CC catalyst activity in the high-temperature range (☐ 90 °C; <span style="color:red">❍</span> 100 °C; <span style="color:#59CB8C">△</span> 110 °C); and (<b>C</b>) Temperature effects on raw smectite (RC) activity in the high-temperature range (▣ 100 °C; <span style="color:red">◈</span> 110 °C; <span style="color:#00B050">▶</span> 120 °C). Common conditions: 2% <span class="html-italic">w</span>/<span class="html-italic">w</span> catalyst load and M = 2.</p>
Full article ">Figure 19
<p>Transcarbonation reaction catalyzed by smectite modified with K<sub>2</sub>CO<sub>3</sub> of glycerol and propylene carbonate to obtain monopropylene glycol and glycerol carbonate.</p>
Full article ">
15 pages, 7097 KiB  
Article
Myconoside and Calceolarioside E Restrain UV-Induced Skin Photoaging by Activating NRF2-Mediated Defense Mechanisms
by Iva D. Stoykova, Ivanka K. Koycheva, Biser K. Binev, Liliya V. Mihaylova, Maria Y. Benina, Kalina I. Alipieva and Milen I. Georgiev
Int. J. Mol. Sci. 2024, 25(4), 2441; https://doi.org/10.3390/ijms25042441 - 19 Feb 2024
Viewed by 2840
Abstract
Chronic and excessive ultraviolet (UVA/UVB) irradiation exposure is known as a major contributor to premature skin aging, which leads to excessive reactive oxygen species generation, disturbed extracellular matrix homeostasis, DNA damage, and chronic inflammation. Sunscreen products are the major preventive option against UVR-induced [...] Read more.
Chronic and excessive ultraviolet (UVA/UVB) irradiation exposure is known as a major contributor to premature skin aging, which leads to excessive reactive oxygen species generation, disturbed extracellular matrix homeostasis, DNA damage, and chronic inflammation. Sunscreen products are the major preventive option against UVR-induced photodamage, mostly counteracting the acute skin effects and only mildly counteracting accelerated aging. Therefore, novel anti-photoaging and photopreventive compounds are a subject of increased scientific interest. Our previous investigations revealed that the endemic plant Haberlea rhodopensis Friv. (HRE) activates the antioxidant defense through an NRF2-mediated mechanism in neutrophiles. In the present study, we aimed to investigate the photoprotective potential of HRE and two of its specialized compounds—the phenylethanoid glycosides myconoside (MYC) and calceolarioside E (CAL)—in UVA/UVB-stimulated human keratinocytes in an in vitro model of photoaging. The obtained data demonstrated that the application of HRE, MYC, and CAL significantly reduced intracellular ROS formation in UVR-exposed HaCaT cells. The NRF2/PGC-1α and TGF-1β/Smad/Wnt signaling pathways were pointed out as having a critical role in the observed CAL- and MYC-induced photoprotective effect. Collectively, CAL is worth further evaluation as a potent natural NRF2 activator and a promising photoprotective agent that leads to the prevention of UVA/UVB-induced premature skin aging. Full article
Show Figures

Figure 1

Figure 1
<p>Phototoxicity irradiation doses of UVA/UVB (<b>A</b>), UVA (<b>B</b>), and UVB (<b>C</b>) on cell viability in human keratinocytes. For each irradiation mode, the half-maximal inhibitory concentration (IC<sub>50</sub>) was calculated. Error bars indicate the mean ± SEM for cell viability expressed as a percentage from the native dark control.</p>
Full article ">Figure 2
<p>Photoprotective effects of <span class="html-italic">Haberlea rhodopensis</span> extract (HRE; (<b>A</b>)), myconoside (MYC; (<b>B</b>)), and calceolarioside E (CAL; (<b>C</b>)) in UVA/UVB-exposed human keratinocytes. Error bars indicate the mean ± SEM for cell viability expressed as a percentage from the dark control. Statistical significance between the groups was determined via one-way ANOVA, followed by Tukey’s post hoc test; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared to the UVA/UVB group.</p>
Full article ">Figure 3
<p>Myconoside (MYC) and calceolarioside E (CAL) reduce UVA/UVB-induced ROS production in human keratinocytes. The fluorescence images of the experimental groups stained with DCF-DA reagent after UVA/UVB irradiation were observed at 20x magnification (scale bar = 50 μm) with an FITC filter (<b>A</b>). Quantification of the normalized fluorescence intensity of intracellular ROS generation in HaCaT cells (<b>B</b>). The quantification of the stained oxygen radicals was measured as an average pixel intensity using ImageJ software version 1.53t and was represented as the normalized pixel intensity against the UVA/UVB group. Statistical significance between the groups was determined via one-way ANOVA, followed by Tukey’s post hoc test; ** <span class="html-italic">p</span> &lt; 0.01 compared to the UVA/UVB group.</p>
Full article ">Figure 4
<p>Gene expression profile modulation associated with UVA/UVB-induced photoaging by <span class="html-italic">H. rhodopensis</span> extract (HRE), calceolarioside E (CAL), and myconoside (MYC). Clustergram and heatmap of the relative gene expression analysis from the RT-qPCR. The results are expressed as the mean ± SEM compared to the UVA/UVB-exposed model group from three independent experiments. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared to the photoaging model group.</p>
Full article ">Figure 5
<p>Calceolarioside E (CAL) activates NRF2 signaling in UVA/UVB-photoaged HaCaT cells. Western blot of STAT1 (<b>A</b>) and NRF2 (<b>B</b>) 24 h after UVA/UVB irradiation and 1 hr pre-treatment with HRE (1, 5, and 10 μg/mL), MYC, or CAL (1, 5, and 10 μM) and representative bands from the Western blot analysis (<b>C</b>). The results are presented as the mean ± SEM from three independent experiments. ** <span class="html-italic">p</span> &lt; 0.01 compared to the UVA/UVB group.</p>
Full article ">Figure 6
<p>Molecular-based modeling of the anti-photoaging activity of myconoside (MYC) and calceolarioside E (CAL) mediated through NRF2/PGC-1α and TGF-β/Smad/Wnt signaling in UVA/UVB-exposed HaCaT cells.</p>
Full article ">
25 pages, 6954 KiB  
Review
The Pathophysiology and Treatment of Pyoderma Gangrenosum—Current Options and New Perspectives
by Magdalena Łyko, Anna Ryguła, Michał Kowalski, Julia Karska and Alina Jankowska-Konsur
Int. J. Mol. Sci. 2024, 25(4), 2440; https://doi.org/10.3390/ijms25042440 - 19 Feb 2024
Viewed by 3289
Abstract
Pyoderma gangrenosum (PG) is an uncommon inflammatory dermatological disorder characterized by painful ulcers that quickly spread peripherally. The pathophysiology of PG is not fully understood; however, it is most commonly considered a disease in the spectrum of neutrophilic dermatoses. The treatment of PG [...] Read more.
Pyoderma gangrenosum (PG) is an uncommon inflammatory dermatological disorder characterized by painful ulcers that quickly spread peripherally. The pathophysiology of PG is not fully understood; however, it is most commonly considered a disease in the spectrum of neutrophilic dermatoses. The treatment of PG remains challenging due to the lack of generally accepted therapeutic guidelines. Existing therapeutic methods focus on limiting inflammation through the use of immunosuppressive and immunomodulatory therapies. Recently, several reports have indicated the successful use of biologic drugs and small molecules administered for coexisting diseases, resulting in ulcer healing. In this review, we summarize the discoveries regarding the pathophysiology of PG and present treatment options to raise awareness and improve the management of this rare entity. Full article
(This article belongs to the Special Issue Skin, Autoimmunity and Inflammation 2.0)
Show Figures

Figure 1

Figure 1
<p>Clinical presentation of pyoderma gangrenosum. (<b>A</b>) A purulent ulcer with a raised, violaceous border localized on the lower extremity in the course of PG in a patient with ulcerative colitis. (<b>B</b>) Extensive purulent ulceration with a ragged, violaceous border on the abdomen in a patient with acute myeloid leukemia. (<b>C</b>) The same lesion after intensive 2-week treatment with cyclosporine A and high doses of prednisone.</p>
Full article ">Figure 2
<p>Pathophysiology of pyoderma gangrenosum. The pathophysiological mechanisms underlying the development of PG are complex and involve neutrophils, keratinocytes, T-cells, and other immune cells that produce pro-inflammatory cytokines. The clinically evident undermined border of the ulceration is attributed to the infiltration of neutrophils in the dermis. This figure was created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
Full article ">
15 pages, 704 KiB  
Article
Genetic Mapping and Characterization of Verticillium Wilt Resistance in a Recombinant Inbred Population of Upland Cotton
by Iain W. Wilson, Philippe Moncuquet, Yuman Yuan, Melanie Soliveres, Zitong Li, Warwick Stiller and Qian-Hao Zhu
Int. J. Mol. Sci. 2024, 25(4), 2439; https://doi.org/10.3390/ijms25042439 - 19 Feb 2024
Viewed by 1484
Abstract
Verticillium wilt (VW) is an important and widespread disease of cotton and once established is long-lived and difficult to manage. In Australia, the non-defoliating pathotype of Verticillium dahliae is the most common, and extremely virulent. Breeding cotton varieties with increased VW resistance is [...] Read more.
Verticillium wilt (VW) is an important and widespread disease of cotton and once established is long-lived and difficult to manage. In Australia, the non-defoliating pathotype of Verticillium dahliae is the most common, and extremely virulent. Breeding cotton varieties with increased VW resistance is the most economical and effective method of controlling this disease and is greatly aided by understanding the genetics of resistance. This study aimed to investigate VW resistance in 240 F7 recombinant inbred lines (RIL) derived from a cross between MCU-5, which has good resistance, and Siokra 1–4, which is susceptible. Using a controlled environment bioassay, we found that resistance based on plant survival or shoot biomass was complex but with major contributions from chromosomes D03 and D09, with genomic prediction analysis estimating a prediction accuracy of 0.73 based on survival scores compared to 0.36 for shoot biomass. Transcriptome analysis of MCU-5 and Siokra 1–4 roots uninfected or infected with V. dahliae revealed that the two cultivars displayed very different root transcriptomes and responded differently to V. dahliae infection. Ninety-nine differentially expressed genes were located in the two mapped resistance regions and so are potential candidates for further identifying the genes responsible for VW resistance. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
Show Figures

Figure 1

Figure 1
<p>Verticillium wilt resistance of Siokra 1–4 uninfected (<b>a</b>) and infected (<b>b</b>) and MCU-5 uninfected (<b>c</b>) and infected (<b>d</b>).</p>
Full article ">
8 pages, 769 KiB  
Communication
Screening for Rare Mitochondrial Genome Variants Reveals a Potentially Novel Association between MT-CO1 and MT-TL2 Genes and Diabetes Phenotype
by Tomasz Płoszaj, Sebastian Skoczylas, Karolina Gadzalska, Paulina Jakiel, Ewa Juścińska, Monika Gorządek, Agnieszka Robaszkiewicz, Maciej Borowiec and Agnieszka Zmysłowska
Int. J. Mol. Sci. 2024, 25(4), 2438; https://doi.org/10.3390/ijms25042438 - 19 Feb 2024
Viewed by 1374
Abstract
Variations in several nuclear genes predisposing humans to the development of MODY diabetes have been very well characterized by modern genetic diagnostics. However, recent reports indicate that variants in the mtDNA genome may also be associated with the diabetic phenotype. As relatively little [...] Read more.
Variations in several nuclear genes predisposing humans to the development of MODY diabetes have been very well characterized by modern genetic diagnostics. However, recent reports indicate that variants in the mtDNA genome may also be associated with the diabetic phenotype. As relatively little research has addressed the entire mitochondrial genome in this regard, the aim of the present study is to evaluate the genetic variations present in mtDNA among individuals susceptible to MODY diabetes. In total, 193 patients with a MODY phenotype were tested with a custom panel with mtDNA enrichment. Heteroplasmic variants were selected for further analysis via further sequencing based on long-range PCR to evaluate the potential contribution of frequent NUMTs (acronym for nuclear mitochondrial DNA) insertions. Twelve extremely rare variants with a potential damaging character were selected, three of which were likely to be the result of NUMTs from the nuclear genome. The variant m.3243A>G in MT-TL1 was responsible for 3.5% of MODY cases in our study group. In addition, a novel, rare, and possibly pathogenic leucine variant m.12278T>C was found in MT-TL2. Our findings also found the MT-CO1 gene to be over-represented in the study group, with a clear phenotype–genotype correlation observed in one family. Our data suggest that heteroplasmic variants in MT-COI and MT-TL2 genes may play a role in the pathophysiology of glucose metabolism in humans. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
Show Figures

Figure 1

Figure 1
<p>tRNA structures for leucine amino acid with marked variants. (Left: <span class="html-italic">MT-TL1</span>; right: <span class="html-italic">MT-TL2</span>). Source of images: [<a href="#B8-ijms-25-02438" class="html-bibr">8</a>].</p>
Full article ">Figure 2
<p>Genetic history of the family with the heteroplasmic variant in the <span class="html-italic">MT-COI</span> gene. (<b>a</b>) 5970G&gt;A variant, (<b>b</b>) 6036G&gt;A variant, (<b>c</b>) 6054G&gt;A variant. Genotype is shown underneath each symbol; − and + indicate wild-type and mutant allele, respectively, together with the proportion of heteroplasmic variants in %. The squares represent male family members, and the circles female members. Black-filled symbols denote patients with diabetes; an arrow denotes the proband in the family.</p>
Full article ">
15 pages, 5441 KiB  
Article
Identification of Novel Non-Nucleoside Inhibitors of Zika Virus NS5 Protein Targeting MTase Activity
by Diego Fiorucci, Micaela Meaccini, Giulio Poli, Maria Alfreda Stincarelli, Chiara Vagaggini, Simone Giannecchini, Priscila Sutto-Ortiz, Bruno Canard, Etienne Decroly, Elena Dreassi, Annalaura Brai and Maurizio Botta
Int. J. Mol. Sci. 2024, 25(4), 2437; https://doi.org/10.3390/ijms25042437 - 19 Feb 2024
Viewed by 1398
Abstract
Zika virus (ZIKV) is a positive-sense single-stranded virus member of the Flaviviridae family. Among other arboviruses, ZIKV can cause neurological disorders such as Guillain Barré syndrome, and it can have congenital neurological manifestations and affect fertility. ZIKV nonstructural protein 5 (NS5) is essential [...] Read more.
Zika virus (ZIKV) is a positive-sense single-stranded virus member of the Flaviviridae family. Among other arboviruses, ZIKV can cause neurological disorders such as Guillain Barré syndrome, and it can have congenital neurological manifestations and affect fertility. ZIKV nonstructural protein 5 (NS5) is essential for viral replication and limiting host immune detection. Herein, we performed virtual screening to identify novel small-molecule inhibitors of the ZIKV NS5 methyltransferase (MTase) domain. Compounds were tested against the MTases of both ZIKV and DENV, demonstrating good inhibitory activities against ZIKV MTase. Extensive molecular dynamic studies conducted on the series led us to identify other derivatives with improved activity against the MTase and limiting ZIKV infection with an increased selectivity index. Preliminary pharmacokinetic parameters have been determined, revealing excellent stability over time. Preliminary in vivo toxicity studies demonstrated that the hit compound 17 is well tolerated after acute administration. Our results provide the basis for further optimization studies on novel non-nucleoside MTase inhibitors. Full article
(This article belongs to the Special Issue Cutting-Edge Research on Antiviral Therapy)
Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) Analysis of protein dynamics and ligand–protein interactions. (<b>B</b>) Key interactions between SAM (green sticks) and NS5 MTase. (<b>C</b>) Pharmacophore model used to perform virtual screening of compounds into the SAM-binding pocket.</p>
Full article ">Figure 2
<p>Binding mode of ligand <b>1</b>. The hydrogen bonds are represented by yellow dotted lines.</p>
Full article ">Figure 3
<p>Design of experiment scheme.</p>
Full article ">Figure 4
<p>Kaplan–Meier survival rate curve for compound <b>17</b> after a single dose administration at the dose of 150 mg/kg.</p>
Full article ">
16 pages, 2728 KiB  
Article
Transcriptome Analysis Reveals Coexpression Networks and Hub Genes Involved in Papillae Development in Lilium auratum
by Yuntao Zhu, Jie Yang, Xiaolin Liu, Tingting Sun, Yiran Zhao, Fayun Xiang, Feng Chen and Hengbin He
Int. J. Mol. Sci. 2024, 25(4), 2436; https://doi.org/10.3390/ijms25042436 - 19 Feb 2024
Cited by 1 | Viewed by 1056
Abstract
Lilium is a genus of important ornamental plants with many colouring pattern variations. Lilium auratum is the parent of Oriental hybrid lilies. A typical feature of L. auratum is the presence of red-orange special raised spots named papillae on the interior tepals. Unlike [...] Read more.
Lilium is a genus of important ornamental plants with many colouring pattern variations. Lilium auratum is the parent of Oriental hybrid lilies. A typical feature of L. auratum is the presence of red-orange special raised spots named papillae on the interior tepals. Unlike the usual raised spots, the papillae are slightly rounded or connected into sheets and usually have hairy tips. To elucidate the potential genes regulating papillae development in L. auratum, we performed high-throughput sequencing of its tepals at different stages. Genes involved in the flavonoid biosynthesis pathway were significantly enriched during the colouration of the papillae, and CHS, F3H, F3′H, FLS, DFR, ANS, and UFGT were significantly upregulated. To identify the key genes involved in the papillae development of L. auratum, we performed weighted gene coexpression network analysis (WGCNA) and further analysed four modules. In total, 51, 24, 1, and 6 hub genes were identified in four WGCNA modules, MEbrown, MEyellow, MEpurple, and MEred, respectively. Then, the coexpression networks were constructed, and important genes involved in trichome development and coexpressed with anthocyanin biosynthesis genes, such as TT8, TTG1, and GEM, were identified. These results indicated that the papillae are essentially trichomes that accumulate anthocyanins. Finally, we randomly selected 12 hub genes for qRT-PCR analysis to verify the accuracy of our RNA-Seq analysis. Our results provide new insights into the papillae development in L. auratum flowers. Full article
(This article belongs to the Section Molecular Biology)
Show Figures

Figure 1

Figure 1
<p>Developmental stages of <span class="html-italic">L. auratum</span>. and identification and KEGG/GSEA enrichment analysis of DEGs. (<b>a</b>) In S1, the interior tepals were 5.5 cm long, and uncoloured papillae could be observed. In S2, the interior tepals were 7.5 cm long, and the papillae turned purple. In S3, the interior tepals were 13 cm long, and the papillae turned red-orange. The outer white area of the tepal was sampled and named S3 W (marked with a green line). The red-orange papillae were detached and sampled and named S3S (marked with a red line). For details on the sampling, see Materials and Methods. The scale bar (white line) represents 1 cm. (<b>b</b>) DEGs were identified with a threshold <span class="html-italic">p</span> value &lt; 0.05 and |log<sub>2</sub>FoldChange| (FC) &gt; 1. (<b>c</b>) KEGG enrichment analysis (left) and GSEA enrichment analysis (right) of DEGs between S2 and S1. The top 20 enriched KEGG pathways were used for the KEGG enrichment circle diagram (from the outside to the inside, the first circle represents the ko-id of the pathway; the second circle represents the number and <span class="html-italic">p</span> value of background genes in this pathway, and the more genes there are, the longer the bar is; the third circle represents the number of DEGs in this pathway, with upregulated genes annotated purple and downregulated genes annotated blue; and the fourth circle represents the value of the Rich factor in this pathway). (<b>d</b>) KEGG enrichment analysis (left) and GSEA enrichment analysis (right) of DEGs between S3S and S3 W.</p>
Full article ">Figure 2
<p>Schematic representation of the anthocyanin biosynthesis pathway in <span class="html-italic">L. auratum</span>. The expression abundance of the transcripts was normalised to the Z score and represented on a heatmap. The colour labels to the left of the heatmap indicate that the corresponding transcripts encode enzymes.</p>
Full article ">Figure 3
<p>Weighted gene coexpression network analysis (WGCNA). (<b>a</b>) Cluster dendrogram. Fourteen distinct modules and a grey module were obtained. (<b>b</b>) Eigengene adjacency heatmap of the modules. The cluster tree indicates the correlations between the modules. A heatmap representing the Pearson correlation coefficient is shown. (<b>c</b>) Graphical representation of the topological overlap matrix (TOM). This matrix was used to construct the coexpression network. (<b>d</b>) Gene expression profiles of four modules that were further analysed are represented using heatmaps. FPKMs were scaled and clustered by row.</p>
Full article ">Figure 4
<p>Coexpression network visualisation of hub genes from MEbrown, MEyellow, and MEred. (<b>a</b>) The coexpression network of the hub genes identified from the MEbrown cohort. The coexpression network consisted of 51 hub genes identified in MEbrown, ranked by degree, from <span class="html-italic">TT8</span> (inner circle) to <span class="html-italic">KDR</span> (outer circle) clockwise. (<b>b</b>) The coexpression network of the hub genes from MEyellow. The coexpression network consisted of 24 hub genes identified in MEyellow, ranked by their degree value, starting from <span class="html-italic">BBX19</span> clockwise. (<b>c</b>) The coexpression network of the hub genes from the MEred cohort. The coexpression network consisted of six hub genes identified in MEred, ranked by their degree value, starting from <span class="html-italic">NCED4</span> clockwise. The thicker the edge and the darker the colour, the greater the weight, indicating a stronger coexpression relationship.</p>
Full article ">Figure 5
<p>Gene expression profiles of hub genes in MEbrown, MEpurple, MEred, and MEyellow. The colour labels show the module to which the gene belongs. Gene expression was normalised to the Z score and represented using a heatmap.</p>
Full article ">Figure 6
<p>qRT-PCR validation of 12 hub genes. The average FPKMs obtained from RNA-seq analysis are shown as purple lines, corresponding to the left <span class="html-italic">Y</span>-axis. The relative gene expression levels obtained from qRT-PCR analysis are shown in histograms corresponding to the right <span class="html-italic">Y</span>-axis. β-actin was used as the internal control. The error bars represent the SDs of three biological replicates.</p>
Full article ">
22 pages, 5015 KiB  
Article
Antitumor Effects of Intravenous Natural Killer Cell Infusion in an Orthotopic Glioblastoma Xenograft Murine Model and Gene Expression Profile Analysis
by Takayuki Morimoto, Tsutomu Nakazawa, Ryosuke Matsuda, Ryosuke Maeoka, Fumihiko Nishimura, Mitsutoshi Nakamura, Shuichi Yamada, Young-Soo Park, Takahiro Tsujimura and Ichiro Nakagawa
Int. J. Mol. Sci. 2024, 25(4), 2435; https://doi.org/10.3390/ijms25042435 - 19 Feb 2024
Viewed by 1693
Abstract
Despite standard multimodality treatment, containing maximum safety resection, temozolomide, radiotherapy, and a tumor-treating field, patients with glioblastoma (GBM) present with a dismal prognosis. Natural killer cell (NKC)-based immunotherapy would play a critical role in GBM treatment. We have previously reported highly activated and [...] Read more.
Despite standard multimodality treatment, containing maximum safety resection, temozolomide, radiotherapy, and a tumor-treating field, patients with glioblastoma (GBM) present with a dismal prognosis. Natural killer cell (NKC)-based immunotherapy would play a critical role in GBM treatment. We have previously reported highly activated and ex vivo expanded NK cells derived from human peripheral blood, which exhibited anti-tumor effect against GBM cells. Here, we performed preclinical evaluation of the NK cells using an in vivo orthotopic xenograft model, the U87MG cell-derived brain tumor in NOD/Shi-scid, IL-2RɤKO (NOG) mouse. In the orthotopic xenograft model, the retro-orbital venous injection of NK cells prolonged overall survival of the NOG mouse, indirectly indicating the growth-inhibition effect of NK cells. In addition, we comprehensively summarized the differentially expressed genes, especially focusing on the expression of the NKC-activating receptors’ ligands, inhibitory receptors’ ligands, chemokines, and chemokine receptors, between murine brain tumor treated with NKCs and with no agents, by using microarray. Furthermore, we also performed differentially expressed gene analysis between an internal and external brain tumor in the orthotopic xenograft model. Our findings could provide pivotal information for the NK-cell-based immunotherapy for patients with GBM. Full article
Show Figures

Figure 1

Figure 1
<p>Enhanced growth inhibition of glioblastoma (GBM) cells by natural killer cells (NKCs). The graph on the left shows the growth curves of U87MG (<b>a</b>) and T98G cells (<b>b</b>) co-cultured with NKCs at effector-to-target cell ratios of 1:1 (red) and 1:2 (green). The blue curve represents cell lines only. The graphs on the right depict real-time cell analysis-based growth inhibition assays. NKC#1 and NKC#2 were derived from another donors. Blue bars represent cell lines only, red bars represent an effector-to-target cell ratio of 1:1, and green bars represent an effector-to-target cell ratio of 1:2. The X and Y axes indicate the co-culture time (min) and relative normalized cell index, respectively. Values represent mean ± standard deviation of 5–6 experiments. Statistical differences were determined by two-way analysis of variance, followed by Tukey’s test. **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05, ns: not significant.</p>
Full article ">Figure 2
<p>Antitumor effects of natural killer cells (NKCs) in an orthotopic xenograft murine model derived from a glioblastoma (GBM) cell line. (<b>a</b>) Schematic of the GBM xenograft model where mice were injected with NKCs via the retro-orbital sinus (<span class="html-italic">n</span> = 6/group). (<b>b</b>) Kaplan–Meier survival curves for mice treated (once or twice) or non-treated with NKCs. NKC#1 and NKC#2 were derived from another donors. Statistical differences were determined by two-way analysis of variance, followed by Tukey’s test. * <span class="html-italic">p</span> &lt; 0.05, ns: not significant. (<b>c</b>) Pathological validation of intracranial tumors from each group (negative background, NKC#1, and NKC#2) using hematoxylin and eosin staining. Scale bar, 50 µm.</p>
Full article ">Figure 2 Cont.
<p>Antitumor effects of natural killer cells (NKCs) in an orthotopic xenograft murine model derived from a glioblastoma (GBM) cell line. (<b>a</b>) Schematic of the GBM xenograft model where mice were injected with NKCs via the retro-orbital sinus (<span class="html-italic">n</span> = 6/group). (<b>b</b>) Kaplan–Meier survival curves for mice treated (once or twice) or non-treated with NKCs. NKC#1 and NKC#2 were derived from another donors. Statistical differences were determined by two-way analysis of variance, followed by Tukey’s test. * <span class="html-italic">p</span> &lt; 0.05, ns: not significant. (<b>c</b>) Pathological validation of intracranial tumors from each group (negative background, NKC#1, and NKC#2) using hematoxylin and eosin staining. Scale bar, 50 µm.</p>
Full article ">Figure 3
<p>Differential gene expression analysis between natural killer cell (NKC)-treated and non-treated intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), NKC-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>). Bar graphs illustrating the normalized enrichment score (NES) (<b>h</b>). Enrichment plot depicting downregulated gene sets belonging to different gene ontology (GO) categories (<b>i</b>).</p>
Full article ">Figure 3 Cont.
<p>Differential gene expression analysis between natural killer cell (NKC)-treated and non-treated intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), NKC-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>). Bar graphs illustrating the normalized enrichment score (NES) (<b>h</b>). Enrichment plot depicting downregulated gene sets belonging to different gene ontology (GO) categories (<b>i</b>).</p>
Full article ">Figure 3 Cont.
<p>Differential gene expression analysis between natural killer cell (NKC)-treated and non-treated intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), NKC-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>). Bar graphs illustrating the normalized enrichment score (NES) (<b>h</b>). Enrichment plot depicting downregulated gene sets belonging to different gene ontology (GO) categories (<b>i</b>).</p>
Full article ">Figure 3 Cont.
<p>Differential gene expression analysis between natural killer cell (NKC)-treated and non-treated intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), NKC-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>). Bar graphs illustrating the normalized enrichment score (NES) (<b>h</b>). Enrichment plot depicting downregulated gene sets belonging to different gene ontology (GO) categories (<b>i</b>).</p>
Full article ">Figure 3 Cont.
<p>Differential gene expression analysis between natural killer cell (NKC)-treated and non-treated intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), NKC-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>). Bar graphs illustrating the normalized enrichment score (NES) (<b>h</b>). Enrichment plot depicting downregulated gene sets belonging to different gene ontology (GO) categories (<b>i</b>).</p>
Full article ">Figure 4
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">Figure 4 Cont.
<p>Differential gene expression analysis between the external and internal layers of intracranial tumors from the orthotopic glioblastoma (GBM) xenograft model. (<b>a</b>) Volcano plot illustrating log<sub>2</sub>-scaled fold change (x-axis) and −log<sub>10</sub> <span class="html-italic">p</span>-value (y-axis) for each gene. (<b>b</b>–<b>g</b>) Heatmaps of the transcriptome-wide Clariom™ S array of gene expression related to glioma stem cell (GSC) markers (<b>b</b>), extracellular matrix (ECM) markers (<b>c</b>), chemokines (<b>d</b>), chemokine receptors (<b>e</b>), natural killer cell (NKC)-activating receptor ligands (<b>f</b>), and NKC inhibitory receptor ligands (<b>g</b>).</p>
Full article ">
19 pages, 12890 KiB  
Article
Selection and Validation of Reference Genes for Gene Expression in Bactericera gobica Loginova under Different Insecticide Stresses
by Hongshuang Wei, Jingyi Zhang, Mengke Yang, Yao Li, Kun Guo, Haili Qiao, Rong Xu, Sai Liu and Changqing Xu
Int. J. Mol. Sci. 2024, 25(4), 2434; https://doi.org/10.3390/ijms25042434 - 19 Feb 2024
Viewed by 918
Abstract
Insecticide resistance has long been a problem in crop pest control. Bactericera gobica is a major pest on the well-known medicinal plants Lycium barbarum L. Investigating insecticide resistance mechanisms of B. gobica will help to identify pesticide reduction strategies to control the pest. [...] Read more.
Insecticide resistance has long been a problem in crop pest control. Bactericera gobica is a major pest on the well-known medicinal plants Lycium barbarum L. Investigating insecticide resistance mechanisms of B. gobica will help to identify pesticide reduction strategies to control the pest. Gene expression normalization by RT-qPCR requires the selection and validation of appropriate reference genes (RGs). Here, 15 candidate RGs were selected from transcriptome data of B. gobica. Their expression stability was evaluated with five algorithms (Delta Ct, GeNorm, Normfinder, BestKeeper and RefFinder) for sample types differing in response to five insecticide stresses and in four other experimental conditions. Our results indicated that the RGs RPL10 + RPS15 for Imidacloprid and Abamectin; RPL10 + AK for Thiamethoxam; RPL32 + RPL10 for λ-cyhalothrin; RPL10 + RPL8 for Matrine; and EF2 + RPL32 under different insecticide stresses were the most suitable RGs for RT-qPCR normalization. EF1α + RPL8, EF1α + β-actin, β-actin + EF2 and β-actin + RPS15 were the optimal combination of RGs under odor stimulation, temperature, developmental stages and both sexes, respectively. Overall, EF2 and RPL8 were the two most stable RGs in all conditions, while α-TUB and RPL32 were the least stable RGs. The corresponding suitable RGs and one unstable RG were used to normalize a target cytochrome P450 CYP6a1 gene between adult and nymph stages and under imidacloprid stress. The results of CYP6a1 expression were consistent with transcriptome data. This study is the first research on the most stable RG selection in B. gobica nymphs exposed to different insecticides, which will contribute to further research on insecticide resistance mechanisms in B. gobica. Full article
(This article belongs to the Collection Feature Papers in Molecular Microbiology)
Show Figures

Figure 1

Figure 1
<p><span class="html-italic">Lycium barbarum</span> L. and its major pest <span class="html-italic">Bactericera gobica</span>. (<b>A</b>) Fresh ripe fruits of <span class="html-italic">L. barbarum</span> L. (<b>B</b>) Dried ripe fruits of <span class="html-italic">L. barbarum</span> L. (<b>C</b>) <span class="html-italic">B. gobica</span> adults laying eggs on young leaves of <span class="html-italic">L. barbarum</span> L. (<b>D</b>) <span class="html-italic">B. gobica</span> nymphs feeding on leaf of <span class="html-italic">L. barbarum</span> L.</p>
Full article ">Figure 2
<p>Specificity of primer pairs for RT-qPCR amplification in <span class="html-italic">B. gobica</span>. The melt peaks of primers for RT-qPCR amplification of 15 candidate RGs, including <span class="html-italic">β-actin</span>, <span class="html-italic">EF1α</span>, <span class="html-italic">EF2</span>, <span class="html-italic">GAPDH</span>, <span class="html-italic">α-TUB</span>, <span class="html-italic">β-TUB</span>, <span class="html-italic">Ferritin</span>, <span class="html-italic">GST</span>, <span class="html-italic">AK</span>, <span class="html-italic">RPL8</span>, <span class="html-italic">RPL10</span>, <span class="html-italic">RPL32</span>, <span class="html-italic">RPS11</span>, <span class="html-italic">RPS15</span> and <span class="html-italic">RPS20</span>. The straight lines with different colors indicated the template-free negative controls for different genes.</p>
Full article ">Figure 3
<p>Candidate RG expression profiles in <span class="html-italic">B. gobica</span>. The expression data are presented as mean Ct values of candidate RGs across all samples under different experimental conditions. Whiskers represent the maximum and minimum values. The lower and upper borders of boxes represent the 25th and 75th percentiles, respectively. The line across the box indicates the median Ct value.</p>
Full article ">Figure 4
<p>Expression stability ranking of 15 candidate RGs in five different insecticides was calculated using Delta Ct, GeNorm, NormFinder and BestKeeper. The five different insecticides included imidacloprid, thiamethoxam, λ-cyhalothrin, abamectin, matrine. The expression stability (standard value, SV) is listed (the lower, the most stable).</p>
Full article ">Figure 5
<p>GeNorm analysis of paired variation (V) values of 15 candidate RGs. Vn/Vn + 1 values are used to determine the optimal number of reference genes. The cut-off value to determine the optimal number of reference genes for RT-qPCR normalization is 0.15. (<b>A</b>) Genorm analysis of paired variation (V) values of 15 candidate RGs under five different insecticide treatments. (<b>B</b>) Genorm analysis of paired variation (V) values of 15 candidate RGs under different experimental conditions.</p>
Full article ">Figure 6
<p>Stability of 15 candidate RGs in <span class="html-italic">B. gobica</span> under five experimental conditions by RefFinder analysis. In a RefFinder analysis, increasing Geomean values corresponds to decreasing gene expression stability. The Geomean values for the following <span class="html-italic">B. gobica</span> samples are presented: Odor stimulation: samples treated with different odor compounds ((<span class="html-italic">E</span>)-2-Hexenal, Linalool and D-Limonene); insecticide treatments: samples treated with five different insecticides (Imidacloprid, Thiamethoxam, λ-cyhalothrin, Abamectin and Matrine); Temperature: samples treated with different temperatures (10 °C, 20 °C and 30 °C); Developmental stages: samples for three developmental stages (eggs, nymphs and adults); Sex: samples for male adults and female adults; All samples: all samples for all treatments under different experimental conditions. The details of 15 candidate RGs are listed in <a href="#ijms-25-02434-t001" class="html-table">Table 1</a>. The most stable genes are listed on the left, while the least stable genes are listed on the right.</p>
Full article ">Figure 7
<p>The expression profiles of <span class="html-italic">CYP6a1</span> in adult and nymph stages and under imidacloprid stress in <span class="html-italic">B. gobica</span>. (<b>A</b>) The expression levels of <span class="html-italic">CYP6a1</span> in the adult and nymph stages presented by FPKM value of transcriptome data. FPKM: fragments per kilobase of exon model per million mapped reads. Asterisks (**) indicate a significant difference was found between nymph and adult (mean ± SE, <span class="html-italic">n</span> = 3, Student’s <span class="html-italic">t</span> test, ** <span class="html-italic">p</span> &lt; 0.01). (<b>B</b>) The expression levels of <span class="html-italic">CYP6a1</span> in the adult and nymph stages by RT-qPCR. The expression levels of <span class="html-italic">CYP6a1</span> in nymphs were used for normalization. Namely, the relative expression levels were presented as fold changes relative to the transcript levels of <span class="html-italic">CYP6a1</span> in nymphs. <span class="html-italic">β-actin</span>, <span class="html-italic">EF2</span> and <span class="html-italic">β-actin</span> + <span class="html-italic">EF2</span> were used as the one or two most stable reference genes. <span class="html-italic">α-TUB</span> was used as the least stable reference gene. Data are represented as the mean ± SE. Bars labelled with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05, ANOVA followed by Tukey’s HSD multiple comparison test, <span class="html-italic">n</span> = 3). (<b>C</b>) The expression levels of <span class="html-italic">CYP6a1</span> in nymphs before/after imidacloprid treatment presented by FPKM value of transcriptome data. Control shows the nymphs without imidacloprid treatment. “ns” indicates no difference was between control and imidacloprid treatment (mean ± SE, <span class="html-italic">n</span> = 3, Student’s <span class="html-italic">t</span> test, ns <span class="html-italic">p</span> &lt; 0.01). (<b>D</b>) The expression levels of <span class="html-italic">CYP61a</span> in nymphs normalized to the top two stable genes (<span class="html-italic">RPL10</span> and <span class="html-italic">RPS15</span>) and an unstable gene (<span class="html-italic">β-actin</span>) by RT-qPCR. The expression level of <span class="html-italic">CYP6a1</span> in nymphs before imidacloprid treatment was used for normalization. Bars labelled with different letters are significantly different (mean ± SE, <span class="html-italic">n</span> = 3, Tukey’s HSD, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">
13 pages, 4113 KiB  
Article
TIGAR Deficiency Blunts Angiotensin-II-Induced Cardiac Hypertrophy in Mice
by Xiaochen He, Quinesha A. Williams, Aubrey C. Cantrell, Jessie Besanson, Heng Zeng and Jian-Xiong Chen
Int. J. Mol. Sci. 2024, 25(4), 2433; https://doi.org/10.3390/ijms25042433 - 19 Feb 2024
Viewed by 1273
Abstract
Hypertension is the key contributor to pathological cardiac hypertrophy. Growing evidence indicates that glucose metabolism plays an essential role in cardiac hypertrophy. TP53-induced glycolysis and apoptosis regulator (TIGAR) has been shown to regulate glucose metabolism in pressure overload-induced cardiac remodeling. In the present [...] Read more.
Hypertension is the key contributor to pathological cardiac hypertrophy. Growing evidence indicates that glucose metabolism plays an essential role in cardiac hypertrophy. TP53-induced glycolysis and apoptosis regulator (TIGAR) has been shown to regulate glucose metabolism in pressure overload-induced cardiac remodeling. In the present study, we investigated the role of TIGAR in cardiac remodeling during Angiotensin II (Ang-II)-induced hypertension. Wild-type (WT) and TIGAR knockout (KO) mice were infused with Angiotensin-II (Ang-II, 1 µg/kg/min) via mini-pump for four weeks. The blood pressure was similar between the WT and TIGAR KO mice. The Ang-II infusion resulted in a similar reduction of systolic function in both groups, as evidenced by the comparable decrease in LV ejection fraction and fractional shortening. The Ang-II infusion also increased the isovolumic relaxation time and myocardial performance index to the same extent in WT and TIGAR KO mice, suggesting the development of similar diastolic dysfunction. However, the knockout of TIGAR significantly attenuated hypertension-induced cardiac hypertrophy. This was associated with higher levels of fructose 2,6-bisphosphate, PFK-1, and Glut-4 in the TIGAR KO mice. Our present study suggests that TIGAR is involved in the control of glucose metabolism and glucose transporters by Ang-II and that knockout of TIGAR attenuates the development of maladaptive cardiac hypertrophy. Full article
(This article belongs to the Special Issue Metabolic Mechanisms of Cardiac Injury)
Show Figures

Figure 1

Figure 1
<p>Effect of TIGAR deficiency on systolic function in Ang-II-induced hypertension. (<b>A</b>) Schematic of the experimental design. (<b>B</b>) Systolic blood pressure after four weeks of Ang-II infusion measured by the tail-cuff method. (<b>C</b>) The representative echocardiographic images of WT and TIGAR KO mice subjected to either vehicle or Ang-II infusion for four weeks. (<b>D</b>–<b>F</b>) Left ventricular (LV) ejection fraction (EF), fractional shortening (FS), and LV mass measured by echocardiography in the indicated groups (n = 8). * <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; 0.0001.</p>
Full article ">Figure 2
<p>Effect of TIGAR deficiency on diastolic function in Ang-II-induced hypertension. (<b>A</b>) The representative pulsed-wave Doppler and tissue Doppler images from an apical four-chamber view of WT and TIGAR KO mice infused with either vehicle or Ang-II for four weeks. (<b>B</b>) The isovolumic relaxation time (IVRT) was increased in the WT and TIGAR KO mice after Ang-II infusion for four weeks. (<b>C</b>) The myocardial performance index (MPI) was also increased in the WT and TIGAR KO mice after Ang-II infusion for four weeks. (<b>D</b>) The representative pulsed-wave Doppler images of the proximal left coronary arteries of WT and TIGAR KO mice infused with either vehicle or Ang-II for four weeks. (<b>E</b>) The coronary flow reserve (CFR) was not affected by Ang-II infusion. n = 8, * <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>
Full article ">Figure 3
<p>Knockout of TIGAR attenuates Ang-II-induced cardiac hypertrophy. (<b>A</b>) The ratio of heart weight to tibia length in the indicated groups (n = 8). (<b>B</b>,<b>C</b>) The representative images of wheat germ agglutinin (WGA)–stained frozen heart sections and cardiomyocyte hypertrophy were assessed by cross-sectional areas in the indicated groups (n = 4–5). A minimum of 100 cardiomyocytes from each LV section of each mouse were measured. Bar = 50 μm. (<b>D</b>,<b>E</b>) The representative images of Picrosirius red-stained paraffin-embedded heart sections and quantification of the percentage of interstitial fibrosis area in the indicated groups (n = 3–4). Bar = 50 μm. (<b>F</b>–<b>H</b>) The representative images of Picrosirius red-stained paraffin-embedded heart sections show coronary arteries and perivascular fibrosis and quantification of perivascular fibrosis index and vascular remodeling in the indicated groups. Bar = 25 μm. n = 3–4, * <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>
Full article ">Figure 4
<p>Knockout of TIGAR increases glycolytic enzyme PFK-1 and glucose transporter Glut-4. (<b>A</b>–<b>F</b>) Representative immunoblots and quantitative analysis of TIGAR, PFKFB3, PFK-1, Glut-1, Glut-4, and corresponding GAPDH or β-tubulin in the indicated groups. The expression of PFK-1 and Glut-4 decreased in the WT mice after Ang-II infusion but not in the TIGAR KO mice. n = 3–4, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 5
<p>Effect of TIGAR deficiency on glycolytic enzymes in Ang-II-induced hypertension. (<b>A</b>) Cardiac F2,6-BP level was determined the coupled-enzymatic assay and expressed as the fold change to the WT sham group. n = 4. (<b>B</b>) Cardiac PFK-1 activity was determined by the coupled-enzymatic assay and expressed as the OD<sub>340nm</sub>/min/mg protein. n = 7–8. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">
34 pages, 6287 KiB  
Article
Immunoinformatic Identification of Multiple Epitopes of gp120 Protein of HIV-1 to Enhance the Immune Response against HIV-1 Infection
by Arslan Habib, Yulai Liang, Xinyi Xu, Naishuo Zhu and Jun Xie
Int. J. Mol. Sci. 2024, 25(4), 2432; https://doi.org/10.3390/ijms25042432 - 19 Feb 2024
Cited by 3 | Viewed by 2225
Abstract
Acquired Immunodeficiency Syndrome is caused by the Human Immunodeficiency Virus (HIV), and a significant number of fatalities occur annually. There is a dire need to develop an effective vaccine against HIV-1. Understanding the structural proteins of viruses helps in designing a vaccine based [...] Read more.
Acquired Immunodeficiency Syndrome is caused by the Human Immunodeficiency Virus (HIV), and a significant number of fatalities occur annually. There is a dire need to develop an effective vaccine against HIV-1. Understanding the structural proteins of viruses helps in designing a vaccine based on immunogenic peptides. In the current experiment, we identified gp120 epitopes using bioinformatic epitope prediction tools, molecular docking, and MD simulations. The Gb-1 peptide was considered an adjuvant. Consecutive sequences of GTG, GSG, GGTGG, and GGGGS linkers were used to bind the B cell, Cytotoxic T Lymphocytes (CTL), and Helper T Lymphocytes (HTL) epitopes. The final vaccine construct consisted of 315 amino acids and is expected to be a recombinant protein of approximately 35.49 kDa. Based on docking experiments, molecular dynamics simulations, and tertiary structure validation, the analysis of the modeled protein indicates that it possesses a stable structure and can interact with Toll-like receptors. The analysis demonstrates that the proposed vaccine can provoke an immunological response by activating T and B cells, as well as stimulating the release of IgA and IgG antibodies. This vaccine shows potential for HIV-1 prophylaxis. The in-silico design suggests that multiple-epitope constructs can be used as potentially effective immunogens for HIV-1 vaccine development. Full article
(This article belongs to the Section Molecular Immunology)
Show Figures

Figure 1

Figure 1
<p>B-cell epitopes were identified using various IEDB epitope servers. Epitopes exhibiting positive interactions are highlighted in yellow. (<b>A</b>) The surface accessibility of B-cell epitopes was assessed using the Emini tool. (<b>B</b>) The flexibility of selected epitopes was evaluated using the Karplus &amp; Schulz tool. (<b>C</b>) The antigenic potential of the final epitopes was observed using the Kolaskar &amp; Tongaonkar method. (<b>D</b>) The hydrophilicity of epitopes was examined using the Parker method. The residues with scores higher than the threshold were predicted to be part of an epitope, as indicated in yellow.</p>
Full article ">Figure 2
<p>The final vaccine construct displays a three-dimensional representation of conformational or discontinuous epitopes (<b>a</b>–<b>j</b>) found within the highest antigenic polyprotein of gp120. The epitopes are depicted on the surface using various colors, while the bulk of the polyprotein is represented as sky blue sticks.</p>
Full article ">Figure 3
<p>The population coverage (%) related to the selected epitopes’ HLA binding alleles was considered, taking into account both worldwide and average percentages. (<b>a</b>) For MHC class I restricted alleles, the selected epitopes represent a population coverage of 90.23% worldwide. (<b>b</b>) For MHC class II restricted alleles, the selected epitopes represent a population coverage of 72.95% worldwide. In the graph, the line (-o-) illustrates the cumulative percentage of population coverage for epitopes, while the bars depict the coverage for each individual epitope.</p>
Full article ">Figure 4
<p>Cluster analysis was performed on the HLA alleles for both MHC molecules, represented through a heat map. (<b>a</b>) The cluster of the MHC-I (VTVYYGVPVW, RAKWNNTLK, SVNFTDNAK, APTKAKRRVV, FNSTWFNSTW, GVAPTKAKR, KVQKEYAFFY, QKEYAFFYKL, TIGKIGNMR, and WQKVGKAMY) epitopes was represented. (<b>b</b>) The cluster of the MHC-II molecules (EIKNCSFNISTSIRG, EPLGVAPTKAKRRVV, FYKLDIIPIDNDTTS, GKVQKEYAFFYKLDI, INCTRPNNNTRKRIR, and GFAILKCNNKTFNGT) epitopes was represented.</p>
Full article ">Figure 5
<p>The sequences, locations, and representations of the immunodominant B-cell, CTL, and HTL epitopes in the final vaccine construct, as well as the 3D view and locations of the immunodominant epitopes of the gp120 monomer, are shown. (<b>a</b>) The sequences and positions of the immunodominant epitopes are depicted in a cartoon shape. (<b>b</b>) The immunodominant epitopes are represented in a 3D view using the PyMOL 2.3.4 program. The surface representation shows the locations of the immunodominant epitopes on the closed conformation of the 3D gp120 protein of HIV-1.</p>
Full article ">Figure 6
<p>Representation of the HIV-1 gp120 construct schematic diagram, including B-cell, CTL, and HTL epitopes, linkers, and adjuvants. The Histidine-Tag is connected to the B-cell epitopes through a GTG linker. The B-cell epitopes are connected to the CTL epitopes using GGTGG linkers. The CTL epitopes, in turn, are connected to the HTL epitopes using GGGGS linkers. Finally, the HTL epitopes are connected to the adjuvant through a GTG linker.</p>
Full article ">Figure 7
<p>Graphical representation of the secondary structure features of the final vaccine construct. The alpha helix residues are in pink, the beta strand residues are in yellow, and the coil residues are in grey. Upon analyzing the predicted secondary structure, it is revealed that the final vaccine comprises 10.2% alpha helix, 22.6% beta strand, and 60.2% coil.</p>
Full article ">Figure 8
<p>Disorder regions in the secondary structure of the designed vaccine predicted via the PSIPRED Tool. This tool has provided valuable insights into the secondary structure of the designed vaccine. In particular, it has identified disorder regions within the protein sequence. According to the analysis, amino acids in the input sequence are considered disordered when the dotted line exceeds the cutoff value of 0.5, representing the confidence threshold.</p>
Full article ">Figure 9
<p>Representation and Validation of the HIV-1 gp120 vaccine using in silico tools. (<b>a</b>) The HIV-1 gp120 vaccine construct was modeled in 3D, employing refined parameters through Galaxy Refine. (<b>b</b>) Prosa-web generated the Z-score and local model quality energy score, providing validation for our 3D vaccine construct. (<b>c</b>) The Ramachandran plot, associated with the vaccine’s 3D structure post-refinement, further validated the accuracy of our vaccine design. Additionally, the 3D prediction was verified through Verify 3D, indicating that the vaccine structure achieved a remarkable 92.70% residue score.</p>
Full article ">Figure 10
<p>Stability of the vaccine construct through disulfide bond engineering. (<b>a</b>) The original form of the vaccine construct is represented without any mutations. (<b>b</b>) In the mutant form of the vaccine construct, six pairs of amino acids, including Gly11-Gln73, Ala122-Asn125, Trp153-Asp308, and Lys202-Tyr205, are depicted in yellow sticks. These specific amino acid pairs have undergone modifications to incorporate disulfide bonds.</p>
Full article ">Figure 11
<p>Molecular docking of the TLR-2 complex and vaccine construct. (<b>a</b>) A cartoon representation of the Vaccine TLR-complex is depicted, with the TLR component shown in a vibrant magenta color, while the vaccine complex is represented in a striking cyan color. (<b>b</b>) Multiple interaction views of the TLR-2 and vaccine complex are generated using PyMOL. The interaction residues of the TLR-2 and vaccine complex are displayed in distinct colors, highlighting their respective roles in the binding interface.</p>
Full article ">Figure 12
<p>This figure illustrates the binding interaction between the active residues of a docked complex involving human TLR-2 and a vaccine construct. (<b>a</b>) In this complex, chain A represents the active residues of TLR-2, while chain B represents the active residues of the vaccine construct. (<b>b</b>) The docked conformation and interaction of TLR-2 and the vaccine construct are depicted via Ligplot analysis, highlighting the presence of hydrogen bonding and hydrophobic interactions between the construct.</p>
Full article ">Figure 13
<p>Molecular docking analysis was conducted to investigate the interaction between the HIV-1 gp120 vaccine construct and human toll-like receptors (TLRs). Cluspro was utilized to identify the docking regions between the vaccine construct and the TLRs. The figure displays the docking interaction between vaccine construct and TLRs, with a closer view of the interaction. (<b>b</b>) represents a closer view of TLR-3, while (<b>d</b>) depicts a closer view of TLR-4. Similarly, (<b>f</b>) illustrates a closer view of TLR-5, (<b>h</b>) portrays TLR-8 in closer detail, and (<b>j</b>) presents a closer view of TLR-9.</p>
Full article ">Figure 14
<p>The vaccine−TLR-2 docked complex underwent molecular dynamics simulation. (<b>a</b>) A main-chain deformability simulation was performed, identifying regions with high deformability known as hinges. (<b>b</b>) B-factor values were calculated using normal mode analysis, providing a measure of uncertainty for each atom. (<b>c</b>) The eigenvalue of the docked complex was determined, indicating the energy required to deform the structure. (<b>d</b>) The covariance matrix between pairs of residues was analyzed, with red indicating correlation, white indicating no correlation, and blue indicating anti-correlation. (<b>e</b>,<b>f</b>) An elastic network model was generated to visualize the connections between atoms and springs. The springs are more rigid if their shades of grey are darker.</p>
Full article ">Figure 15
<p>mRNA structure of the HIV-1 gp120 vaccine construct. The mRNA structure of the HIV-1 gp120 vaccine construct was determined using the RNAfold servers. The prediction yielded a minimal free energy score of −378.55 kcal/mol, indicating the stability of the vaccine design’s mRNA structure.</p>
Full article ">Figure 16
<p>C−ImmSimm represents the immune stimulation of the best-predicted HIV-1 gp120 vaccine. (<b>a</b>) The immunoglobulin and immunocomplex responses to the HIV-1 gp120 vaccine inoculations are indicated by colored lines. (<b>b</b>–<b>h</b>) The number of plasma B cells, HTLs, and CTLs exhibited progressive increases, indicating the robust development of an immune response characterized by high potency, immunological memory, and efficient removal of antigens from the host. (<b>i</b>–<b>l</b>) The increase in DCs and macrophages exhibited greater antigen presentation by APCs, and the activation of helper T cells demonstrated the superior adaptive immunity of the vaccine. (<b>m</b>,<b>n</b>) The vaccine was shown to be capable of inducing the production of IFN-γ, IL-23, IL-10, IL-8 and IL-6, which are vital for triggering immune feedback and protecting the body against viruses.</p>
Full article ">Figure 16 Cont.
<p>C−ImmSimm represents the immune stimulation of the best-predicted HIV-1 gp120 vaccine. (<b>a</b>) The immunoglobulin and immunocomplex responses to the HIV-1 gp120 vaccine inoculations are indicated by colored lines. (<b>b</b>–<b>h</b>) The number of plasma B cells, HTLs, and CTLs exhibited progressive increases, indicating the robust development of an immune response characterized by high potency, immunological memory, and efficient removal of antigens from the host. (<b>i</b>–<b>l</b>) The increase in DCs and macrophages exhibited greater antigen presentation by APCs, and the activation of helper T cells demonstrated the superior adaptive immunity of the vaccine. (<b>m</b>,<b>n</b>) The vaccine was shown to be capable of inducing the production of IFN-γ, IL-23, IL-10, IL-8 and IL-6, which are vital for triggering immune feedback and protecting the body against viruses.</p>
Full article ">
26 pages, 5084 KiB  
Article
An Integrated Comprehensive Peptidomics and In Silico Analysis of Bioactive Peptide-Rich Milk Fermented by Three Autochthonous Cocci Strains
by Martina Banić, Katarina Butorac, Nina Čuljak, Ana Butorac, Jasna Novak, Andreja Leboš Pavunc, Anamarija Rušanac, Željka Stanečić, Marija Lovrić, Jagoda Šušković and Blaženka Kos
Int. J. Mol. Sci. 2024, 25(4), 2431; https://doi.org/10.3390/ijms25042431 - 19 Feb 2024
Viewed by 1831
Abstract
Bioactive peptides (BPs) are molecules of paramount importance with great potential for the development of functional foods, nutraceuticals or therapeutics for the prevention or treatment of various diseases. A functional BP-rich dairy product was produced by lyophilisation of bovine milk fermented by the [...] Read more.
Bioactive peptides (BPs) are molecules of paramount importance with great potential for the development of functional foods, nutraceuticals or therapeutics for the prevention or treatment of various diseases. A functional BP-rich dairy product was produced by lyophilisation of bovine milk fermented by the autochthonous strains Lactococcus lactis subsp. lactis ZGBP5-51, Enterococcus faecium ZGBP5-52 and Enterococcus faecalis ZGBP5-53 isolated from the same artisanal fresh cheese. The efficiency of the proteolytic system of the implemented strains in the production of BPs was confirmed by a combined high-throughput mass spectrometry (MS)-based peptidome profiling and an in silico approach. First, peptides released by microbial fermentation were identified via a non-targeted peptide analysis (NTA) comprising reversed-phase nano-liquid chromatography (RP nano-LC) coupled with matrix-assisted laser desorption/ionisation-time-of-flight/time-of-flight (MALDI-TOF/TOF) MS, and then quantified by targeted peptide analysis (TA) involving RP ultrahigh-performance LC (RP-UHPLC) coupled with triple-quadrupole MS (QQQ-MS). A combined database and literature search revealed that 10 of the 25 peptides identified in this work have bioactive properties described in the literature. Finally, by combining the output of MS-based peptidome profiling with in silico bioactivity prediction tools, three peptides (75QFLPYPYYAKPA86, 40VAPFPEVFGK49, 117ARHPHPHLSF126), whose bioactive properties have not been previously reported in the literature, were identified as potential BP candidates. Full article
Show Figures

Figure 1

Figure 1
<p>Circular genome maps of (<b>A</b>) <span class="html-italic">Lc. lactis</span> subsp. <span class="html-italic">lactis</span> ZGBP5-51, (<b>B</b>) <span class="html-italic">E. faecium</span> ZGBP5-52 and (<b>C</b>) <span class="html-italic">E. faecalis</span> ZGBP5-53. From the inner to the outer rings: guanine and cytosine (GC) skew, GC content, genes for drug targets, genes for transporters, non-coding features, coding sequence on the reverse strand (CDS-REV), CDS on the forward strand (CDS-FWD), position and order of assembled contigs and reference position in the genome (×1 Mbp).</p>
Full article ">Figure 2
<p>Fermentation parameters, i.e., the degree of acidity (°SH), pH and cell viability of cocci grown on M17 agar, determined before and after 24 and 48 h of fermentation. The shaded coloured areas around the lines mark error bands, i.e., the dynamics of the change in the determined values of the fermentation parameters. The asterisks (*) indicate values with a significant difference (<span class="html-italic">p</span> &lt; 0.05) compared to the value of each fermentation parameter determined before fermentation (at 0 h).</p>
Full article ">Figure 3
<p>Representative MS/MS spectrum of peptide <sup>213</sup>TKVIPYVRYL<sup>222</sup> identified in the sample fermented with <span class="html-italic">Lactococcus</span> (<span class="html-italic">Lc.</span>) <span class="html-italic">lactis</span> subsp. <span class="html-italic">lactis</span> ZGBP5-51, <span class="html-italic">Enterococcus</span> (<span class="html-italic">E.</span>) <span class="html-italic">faecium</span> ZGBP5-52 and <span class="html-italic">E. faecalis</span> ZGBP5-53 after 24 h of fermentation. y ions are shown in blue and b ions in red. Unassigned black peaks could not be assigned to any sequence.</p>
Full article ">Figure 4
<p>Representative MRM chromatograms of the peptide <sup>213</sup>TKVIPYVRYL<sup>222</sup> (<span class="html-italic">m</span>/<span class="html-italic">z</span> 417.9 Da), recorded in a sample of fermented milk. (<b>A</b>) MRM chromatogram of the fragment ion used for quantification (transition 417.9→550.3). (<b>B</b>) Superimposed MRM chromatograms of all fragment ions. (<b>C</b>) MS spectrum of all fragment ions. The blue diamond indicates the <span class="html-italic">m</span>/<span class="html-italic">z</span> value of the precursor (417.9 Da).</p>
Full article ">Figure 5
<p>Graphical representation of the results of BP quantification by targeted MS analysis. The values of the area under the curve (AUC) correlate with the amount of each peptide ((<b>A</b>) <sup>213</sup>TKVIPYVRYL<sup>222</sup>, (<b>B</b>) <sup>74</sup>VYPFPGPIPN<sup>83</sup>, (<b>C</b>) <sup>208</sup>YQEPVLGPVRGPFPIIV<sup>224</sup>, (<b>D</b>) <sup>209</sup>QEPVLGPVRGPFPIIV<sup>224</sup>, (<b>E</b>) <sup>117</sup>ARHPHPHLSFM<sup>127</sup>, (<b>F</b>) <sup>207</sup>LYQEPVLGPVRGPFPIIV<sup>224</sup>, (<b>G</b>) <sup>181</sup>SQSKVLPVPQKAVPYPQ<sup>197</sup> and (<b>H</b>) <sup>198</sup>RDMPIQAF<sup>205</sup>) quantified using the MRM method. Asterisks indicate AUC values for each peptide with significant difference (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 and **** <span class="html-italic">p</span> &lt; 0.0001). The dashed lines (---) represent medians, while the dotted lines (⋯) represent the data quartiles.</p>
Full article ">Figure 6
<p>Peptide bitterness determined by (<b>A</b>) BERT4Bitter and (<b>B</b>) iBitter-Fuse computational models. Both tools were trained with a threshold of 0.5: any peptide with a predicted bitterness above a threshold of 0.5 was categorised as bitter.</p>
Full article ">Figure 7
<p>Prediction of (<b>A</b>) allergenicity and (<b>B</b>) half-life (s) of peptides identified in this work.</p>
Full article ">Figure 8
<p>Heatmap of each bioactivity probability score (<span class="html-italic">ps</span>) obtained with the CSM-peptides platform. A <span class="html-italic">ps</span> of less than 0.7 indicates a negative therapeutic relevance, while a <span class="html-italic">ps</span> of more than 0.7 indicates that the peptide could belong to one of the eight different classes of therapeutic peptides: <b>AAP</b>, antiangiogenic peptide; <b>ABP</b>, antibacterial peptide; <b>ACP</b>, anticancer peptide; <b>AIP</b>, anti-inflammatory peptide; <b>AVP</b>, antiviral peptide; <b>CPP</b>, cell-penetrating peptide; <b>QSP</b>, quorum-sensing peptide; and <b>SBP</b>, surface-binding peptide.</p>
Full article ">Figure 9
<p>Heatmap of bioactivity probability scores (<span class="html-italic">ps</span>) determined by the PeptideRanker server based on a novel N-to-1 neural network. A <span class="html-italic">ps</span> of less than 0.7 indicates a negative therapeutic significance, while a <span class="html-italic">ps</span> of more than 0.7 indicates the therapeutic significance of a particular peptide.</p>
Full article ">
20 pages, 4422 KiB  
Article
Rapid Antibacterial Activity Assessment of Chimeric Lysins
by Jin-Mi Park, Jun-Hyun Kim, Gun Kim, Hun-Ju Sim, Sun-Min Ahn, Kang-Seuk Choi and Hyuk-Joon Kwon
Int. J. Mol. Sci. 2024, 25(4), 2430; https://doi.org/10.3390/ijms25042430 - 19 Feb 2024
Viewed by 1095
Abstract
Various chimeric lysins have been developed as efficacious antibiotics against multidrug-resistant bacteria, but direct comparisons of their antibacterial activities have been difficult due to the preparation of multiple recombinant chimeric lysins. Previously, we reported an Escherichia coli cell-free expression method to better screen [...] Read more.
Various chimeric lysins have been developed as efficacious antibiotics against multidrug-resistant bacteria, but direct comparisons of their antibacterial activities have been difficult due to the preparation of multiple recombinant chimeric lysins. Previously, we reported an Escherichia coli cell-free expression method to better screen chimeric lysins against Staphylococcus aureus, but we still needed to increase the amounts of expressed proteins enough to be able to detect them non-isotopically for quantity comparisons. In this study, we improved the previous cell-free expression system by adding a previously reported artificial T7 terminator and reversing the different nucleotides between the T7 promoter and start codon to those of the T7 phage. The new method increased the expressed amount of chimeric lysins enough for us to detect them using Western blotting. Therefore, the qualitative comparison of activity between different chimeric lysins has become possible via the adjustment of the number of variables between samples without protein purification. We applied this method to select more active chimeric lysins derived from our previously reported chimeric lysin (ALS2). Finally, we compared the antibacterial activities of our selected chimeric lysins with reported chimeric lysins (ClyC and ClyO) and lysostaphin and determined the rank orders of antibacterial activities on different Staphylococcus aureus strains in our experimental conditions. Full article
(This article belongs to the Special Issue Recent Research on Antimicrobial Agents)
Show Figures

Figure 1

Figure 1
<p>Comparison of the previous and the present cell-free expression systems and the generation of ALS2-derived amidase domain-deleted/linker-length variants. (<b>A</b>) The schematic structure of the gene template for improved cell-free expression and (<b>B</b>) the nucleotide difference between the previous (V_T7-RBS) and the present (P_T7-RBS) upstream regions from the coding gene. The different nucleotides identical to the natural nucleotide sequences of T7 promoter and RBS regions are shown. (<b>C</b>) Comparison of the antibacterial activities of ALS2-dA-L31 prepared using the previous and present cell-free expression systems against human MRSA strains (CCARM3806 and CCARM3840 (turbidity reduction test)). Data were analyzed using one-way ANOVA followed by Dunnett’s test to determine the significance relative to the negative sample without DNA template (*** <span class="html-italic">p</span> &lt; 0.001). (<b>D</b>) The schematic structures of the reported ALS2 and the present amidase-deleted ALS2, ALS2-dA. (<b>E</b>) The predicted structure of the parent linker (L38) using Alphafold2 (blue: N-terminal, red: C-terminal). (<b>F</b>) The amino acid sequences of the tested linkers.</p>
Full article ">Figure 2
<p>The DNA templates of the linker variants of ALS2-dA for cell-free expression and quantification and the antibacterial activity test of the expressed chimeric lysins. (<b>A</b>) High-concentration purified DNA for cell-free protein synthetic systems in ten linker variants. Samples at a concentration of 400–600 ng/μL were loaded with 2 μL each on 1.3% agarose gel. Lane M, 1000 bp size marker; Lane 1: ALS2-dA-L38 (527 ng), 967 bp; Lane 2: ALS2-dA-35 (524 ng), 958 bp; Lane 3: ALS2-dA-L34 (551 ng), 955 bp; Lane 4: ALS2-dA-L31 (490 ng), 946 bp; Lane 5: ALS2-dA-L28 (555 ng), 937 bp; Lane 6: ALS2-dA-L27 (539 ng), 934 bp; Lane 7: ALS2-dA-L26 (493 ng), 931 bp; Lane 8: ALS2-dA-L25 (547 ng), 928 bp; Lane 9: ALS2-dA-L24 (544 ng), 925 bp; Lane 10: ALS2-dA-L21 (550 ng), 916 bp. (<b>B</b>) Western blotting using ECL and (<b>C</b>) the relative protein intensity of the ten linker variants. (<b>D</b>) Comparison of the antibacterial activities of the ten linker variants. The antibacterial activities of chimeric lysins produced via a cell-free expression system were tested with a turbidity reduction test against human MRSA strains (CCARM3806, RST10-2; CCARM3825, RST2-1; CCARM3832, RST2-1; CCARM3837, RST4-1). Due to the different susceptibilities of the strains, different volumes of chimeric lysins were used: CCARM3806 and CCARM3837 (0.5 μL) and CCARM3825 and CCARM3832 (1 μL). The experiment was performed in triplicate and mean ± standard deviation (SD) values are shown. Data were analyzed using one-way ANOVA followed by Dunnett’s test to determine the significance relative to the negative sample without DNA template (*** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>The antibacterial activity of <span class="html-italic">E. coli</span>-expressed ALS2-dA-L25. (<b>A</b>) Plate lysis test of the purified protein of ALS2-dA-L25. The protein formed a clear zone against various strains (isolated from bovine mastitis, chickens, and humans) at a concentration of 10/1/0.5 μg/10 μL. For negative samples, only protein purification buffer was used. (<b>B</b>) The turbidity reduction test for the antibacterial activity of the purified protein ALS2-dA-L25 against <span class="html-italic">S. aureus</span>. The turbidity ratio (TR) for each strain was determined by calculating the OD<sub>600</sub> value ratio of the ALS2-dA-L25-treated to untreated samples.</p>
Full article ">Figure 4
<p>Comparison of the amino acid sequences of the SH3 domains and the antibacterial activities of SH3 domain chimeras in ALS2-dA-L25. (<b>A</b>) The amino acid lengths of the eight SH3 domains are presented as numbers. Amino acids common to all eight SH3s are marked with (*) and those common to seven are marked with (:). Amino acids common to seven sequences are marked with ALS2-dA-L25, and the respective SH3 domain-linked proteins were reacted with <span class="html-italic">S. aureus.</span> (<b>B</b>) Turbidity reduction tests for SH3 domain chimeras in ALS2-dA-L25 using CCARM3806, CCARM3825, CCARM3832, and CCARM3837 for 6 h. The experiments were performed in triplicate, and the mean and SD values are shown. Data were tested for significance using one-way ANOVA and Dunnett’s test compared to negative samples without DNA template (*** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05). (<b>C</b>) Quantification of the SH3 domain chimeras of ALS2-dA-L25 using Western blotting. Lane M: protein molecular weight marker; Lane 1: ALS2-dA-L25; Lane 2: ALS2-dA-ClyC<sub>SH3</sub>; Lane 3: ALS2-dA-vB<sub>SH3</sub>; Lane 4: ALS2-dA-tras14<sub>SH3</sub>; Lane 5: ALS2-dA-ClyO<sub>SH3</sub>; Lane 6: ALS2-dA-Lsp; Lane 7: ALS2-dA-2638A<sub>SH3</sub>; Lane 8: ALS2-dA-2958<sub>SH3</sub>; Lane 9: DNA-negative sample. The molecular weight of the protein was approximately 26 kDa. (<b>D</b>) The relative intensity of each chimeric lysin was measured based on the relative intensity of each Western blotting band to ALS2-dA-2638A<sub>SH3</sub> using Image J 1.53.</p>
Full article ">Figure 5
<p>The determination of the minimal inhibitory volume of chimeric lysins (post-adjusting method). (<b>A</b>) Just after the cell-free expression of chimeric lysins, the antibacterial activity was measured using a turbidity reduction test against human MRSA strains (CCARM3806, CCARM3825, CCARM3832, and CCARM3837). The amount of protein used in the experiments ranged from 1 μL to 0.06 μL. The complete inhibition of bacterial growth was signified by the lack of changes in OD<sub>600</sub> values during the 0–6 h incubation period, and the presence of dotted reference lines. The experiments were performed in triplicate, and the mean ± standard deviation (SD) values are shown. Below the dotted line, there was no growth of <span class="html-italic">S. aureus</span> during the 6 h of the reaction. (<b>B</b>) Western blotting of chimeric lysins using an anti-histidine antibody and ECL. Lane M: protein molecular weight marker; Lane 1: ALS2-dA-L25 (25.6 kDa); Lane 2: ALS2-dA-ClyC<sub>SH3</sub> (25.6 kDa); Lane 3: Lysostaphin (28.1 kDa); Lane 4: ClyC (32.1 kDa); Lane 5: ClyO (29.6 kDa); Lane 6: Lsp-ClyC<sub>SH3</sub> (28.1 kDa); Lane 7: DNA-negative sample. (<b>C</b>) The relative intensity of the protein bands. The ratio of protein expression compared to lysostaphin was measured using Image J 1.53.</p>
Full article ">Figure 6
<p>Direct comparison of the antibacterial activities of different chimeric lysins after adjusting the protein concentration to be equal (pre-adjusting method). (<b>A</b>) Western blotting of chimeric lysins using an anti-histidine antibody and ECL. Lane M: protein molecular weight marker; Lane 1: ALS2-dA-L25 (25.7 kDa); Lane 2: ALS2-dA-ClyCSH3 (25.7 kDa); Lane 3: Lysostaphin (28.1 kDa); Lane 4: ClyC (32.1 kDa); Lane 5: ClyO (29.6 kDa); Lane 6: Lsp-ClyCSH3 (28.1 kDa); Lane 7: DNA-negative sample. (<b>B</b>) The relative intensity of the protein bands. The ratio of the protein expression compared to lysostaphin was measured using Image J 1.53. (<b>C</b>) The amount of each chimeric lysin was adjusted based on the RI (0.12) of Lsp-ClyC<sub>SH3</sub> to equal the final amount of protein: ALS2-dA-L25 (0.4 μL), ALS2-dA-ClyC<sub>SH3</sub> (0.27 μL), lysostaphin (0.12 μL), ClyC (0.65 μL), ClyO (0.06 μL), and Lsp-Cly<sub>SH3</sub> (1 μL). The proteins were reacted with the MRSA strains CCARM3806, CCARM3825, CCARM3832, and CCARM3837, and the turbidity ratio was calculated after 6 h. The amount of protein used in the experiments ranged from 2 μL to 0.12 μL. The complete inhibition of bacterial growth was determined by no changes in OD<sub>600</sub> values during the 0–6 h incubation period, and the reference lines are dotted. The experiments were performed in duplicate, and the mean ± standard deviation (SD) values are shown. Below the dotted line, there was no growth of <span class="html-italic">S. aureus</span> during the 6 h of the reaction.</p>
Full article ">Figure 7
<p>Comparison of the antibacterial activities of chimeric lysins in milk via a colony reduction test. According to the RI, the volume of each chimeric lysin was adjusted so that the final protein amount was the same: ALS2-dA-L25 (3.4 μL), ALS2-dA-ClyC<sub>SH3</sub> (1.85 μL), lysostaphin (1 μL), ClyC (5.9 μL), ClyO (0.5 μL), Lsp-ClyC<sub>SH3</sub> (10 μL), and the control (1 μL). The proteins were reacted with 10<sup>3</sup> CFU/mL of <span class="html-italic">S. aureus—</span>(<b>A</b>) CCARM3806, (<b>B</b>) CCARM3825, (<b>C</b>) CCARM3832, and (<b>D</b>) CCARM3837—in ultra-high-temperature processed commercial milk for 20 min. Experiments were performed in triplicate and mean ± standard deviation (SD) values are shown. Data were subjected to a <span class="html-italic">t</span>-test to determine the significance compared to the control (*** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">
11 pages, 3473 KiB  
Communication
Correlation of Experimental and Calculated Inhibition Constants of Protease Inhibitor Complexes
by Peter Goettig, Xingchen Chen and Jonathan M. Harris
Int. J. Mol. Sci. 2024, 25(4), 2429; https://doi.org/10.3390/ijms25042429 - 19 Feb 2024
Cited by 1 | Viewed by 1298
Abstract
Predicting the potency of inhibitors is key to in silico screening of promising synthetic or natural compounds. Here we describe a predictive workflow that provides calculated inhibitory values, which concord well with empirical data. Calculations of the free interaction energy ΔG with the [...] Read more.
Predicting the potency of inhibitors is key to in silico screening of promising synthetic or natural compounds. Here we describe a predictive workflow that provides calculated inhibitory values, which concord well with empirical data. Calculations of the free interaction energy ΔG with the YASARA plugin FoldX were used to derive inhibition constants Ki from PDB coordinates of protease–inhibitor complexes. At the same time, corresponding KD values were obtained from the PRODIGY server. These results correlated well with the experimental values, particularly for serine proteases. In addition, analyses were performed for inhibitory complexes of cysteine and aspartic proteases, as well as of metalloproteases, whereby the PRODIGY data appeared to be more consistent. Based on our analyses, we calculated theoretical Ki values for trypsin with sunflower trypsin inhibitor (SFTI-1) variants, which yielded the more rigid Pro14 variant, with probably higher potency than the wild-type inhibitor. Moreover, a hirudin variant with an Arg1 and Trp3 is a promising basis for novel thrombin inhibitors with high potency. Further examples from antibody interaction and a cancer-related effector-receptor system demonstrate that our approach is applicable to protein interaction studies beyond the protease field. Full article
(This article belongs to the Special Issue Biocatalysis: Mechanisms of Proteolytic Enzymes 2.0)
Show Figures

Figure 1

Figure 1
<p>Exemplary complex structures of trypsin-like serine proteases. (<b>A</b>) KLK4 complex with a highly potent SFTI-1 variant (cyan), containing Arg5 instead of the natural Lys5, as well as the mutations Phe2, Gln4, and Asn14 (<b>upper panel</b>). The lower panel shows a close-up of the active site, in which the P4 to P2′ residues of the SFTI variant bind to the corresponding S4 to S2′ specificity pockets as other canonical inhibitors similar to substrates via the standard mechanism. (<b>B</b>) Human α-thrombin in complex with the extremely strong inhibitor hirudin (green), an anticoagulant from the leech <span class="html-italic">Hirudo medicinalis</span> (<b>upper panel</b>). In contrast to canonical inhibitors hirudin binds in a reverse manner, with the N-terminal Ile1 occupying the S2 subsite, Thr2 the S1 subsite, and Tyr3 the S4 subsite (<b>lower panel</b>). However, Asp49 to Asn52 of hirudin correspond to P1′ to P4′ residues and bind the S1′ to S4′ subsites like canonical inhibitors, whereby further protease–inhibitor interactions occur in the prime side.</p>
Full article ">Figure 2
<p>Plot of K<sub>i</sub> values (nM) in logarithmic scale versus ΔG (kJ) for serine protease inhibitor complexes. The round symbols represent experimental K<sub>i</sub> and ΔG values from protease–inhibitor pairs, while the diamonds belong to calculated K<sub>i</sub> (K<sub>D</sub>) and ΔG derived from protease inhibitor complex coordinates: β-Try/PABA, Try-3/bikunin-D2, matriptase-SFTI, plasmin-SFTI, β-Try/SFTI-TCTR-N12-N14, KLK4/SFTI-FCQR-N14, β-Try/BPTI, and α-thrombin/hirudin (more β-Try structures with SFTI-1 variants are available). Essentially, free interaction energies were calculated with the YASARA plugin FoldX or with the web server PRODIGY. Overall, the FoldX results for serine protease inhibitor complexes correlated better with the experimental data. More details can be found in <a href="#ijms-25-02429-t001" class="html-table">Table 1</a>.</p>
Full article ">Figure 3
<p>Examples of cysteine, aspartic and metalloproteases. (<b>A</b>) SARS-CoV-2 MPro is a chymotrypsin-like protease with a catalytic dyad (His41, Cys145) in the half domains I and II, while domain III mediates dimerization (PDB 7RNW). The synthetic, cyclo-14-mer inhibits with a K<sub>i</sub> of roughly 4 nM. (<b>B</b>) Aspartic HIV protease forms a symmetrical active dimer, which binds a synthetic cyclo9-mer exhibiting an estimated K<sub>i</sub> of 3 nM (PDB 7YF6). (<b>C</b>) The catalytic domain of MMP-14 (MT1-MMP) binds the natural proteinaceous inhibitor TIMP-2 via a tight interaction to Zn<sup>2+</sup> from the N-terminal Cys1 and Thr2 in the S1′ pocket (PDB 1BUV), exhibiting a K<sub>i</sub> of 104 pM.</p>
Full article ">Figure 4
<p>Plot of K<sub>i</sub> values (nM) in logarithmic scale versus ΔG (kJ) for cysteine, aspartic and metallo-protease inhibitor complexes. The round symbols represent experimental K<sub>i</sub> and ΔG values from protease–inhibitor pairs, while the diamonds and triangles belong to calculated K<sub>i</sub> (K<sub>D</sub>) and ΔG derived from calculations with the YASARA plugin FoldX and the PRODIGY web server, respectively. The protease inhibitor complexes were legumain/cystatin E, SARS-CoV-2 Mpro/cyclo-14-mer, BACE-1/22-mer, HIV protease/cyclo-9-mer, MMP-14/TIMP-2, and MMP-3/TIMP-1. In five cases the correlation of experimental data was better with PRODIGY results. The cystatin E-K75A constant (19.8 nM) for human legumain corresponds better to the one derived from the coordinates of the structural data (46.4 kJ/mol) compared with the reported 0.011 nM. A better correlation was seen for energy minimized coordinates of the BACE-1 complex (−47.28 kJ/mol). More details can be found in <a href="#ijms-25-02429-t001" class="html-table">Table 1</a>.</p>
Full article ">
21 pages, 2536 KiB  
Article
Multiple Lines of Evidence Support 199 SARS-CoV-2 Positively Selected Amino Acid Sites
by Pedro Ferreira, Ricardo Soares, Hugo López-Fernández, Noé Vazquez, Miguel Reboiro-Jato, Cristina P. Vieira and Jorge Vieira
Int. J. Mol. Sci. 2024, 25(4), 2428; https://doi.org/10.3390/ijms25042428 - 19 Feb 2024
Viewed by 1420
Abstract
SARS-CoV-2 amino acid variants that contribute to an increased transmissibility or to host immune system escape are likely to increase in frequency due to positive selection and may be identified using different methods, such as codeML, FEL, FUBAR, and MEME. Nevertheless, when using [...] Read more.
SARS-CoV-2 amino acid variants that contribute to an increased transmissibility or to host immune system escape are likely to increase in frequency due to positive selection and may be identified using different methods, such as codeML, FEL, FUBAR, and MEME. Nevertheless, when using different methods, the results do not always agree. The sampling scheme used in different studies may partially explain the differences that are found, but there is also the possibility that some of the identified positively selected amino acid sites are false positives. This is especially important in the context of very large-scale projects where hundreds of analyses have been performed for the same protein-coding gene. To account for these issues, in this work, we have identified positively selected amino acid sites in SARS-CoV-2 and 15 other coronavirus species, using both codeML and FUBAR, and compared the location of such sites in the different species. Moreover, we also compared our results to those that are available in the COV2Var database and the frequency of the 10 most frequent variants and predicted protein location to identify those sites that are supported by multiple lines of evidence. Amino acid changes observed at these sites should always be of concern. The information reported for SARS-CoV-2 can also be used to identify variants of concern in other coronaviruses. Full article
(This article belongs to the Special Issue Genetic Variability and Molecular Evolution of SARS-CoV-2)
Show Figures

Figure 1

Figure 1
<p>Venn diagrams showing the overlap of the PSSs in the COV2Var database (COV2Var-int-5%) identified by each one of the methods used (FEL (blue), FUBAR (red), and MEME (green)), and those here identified (this work (yellow)), for the structural (S-E), non-structural (nsp1-16), and accessory (ORF3a-ORF10) proteins of SARS-CoV-2.</p>
Full article ">Figure 2
<p>Pie chart showing the contribution (in percentage) of each species to PSSs identified in non-SARS-CoV-2 species.</p>
Full article ">Figure 3
<p>PSS location on SARS-CoV-2 protein monomers. For each protein, from top to bottom: PSSs present in the COV2Var-int-5% list (in blue are PSSs, in gray are variable amino acid sites; no information is available for the E and NSP7 proteins), PSSs identified in this work (green—identified in this work and in the COV2Var-int-5% list; orange—identified in this work and in at least one other method in the COV2Var-int-5%; and red—only identified in this work; there are no PSSs for NSP2, NSP3, and NSP7), the top 10 variants (pink if it has a frequency over 5%, cyan otherwise; there is no information for ORF10), and regions identified as hot PSS regions in non-SARS-CoV-2 species (dark red). For NSP9, as well as for accessory proteins, there is no information for the latter.</p>
Full article ">Figure 4
<p>(<b>A</b>) Location of PSSs (labeled in red) supported by more than one type of evidence in the SARS-CoV-2 S homotrimer protein structure (PDB accession number 7DF4). Each monomer is shown in a different color. The PDB accession numbers of the docking partners of the S protein are shown above the respective structure (PDB accession numbers 7S0D and 7DF4). (<b>B</b>) Consurf projection of the S trimer as in the Consurf Database and its respective color code [<a href="#B70-ijms-25-02428" class="html-bibr">70</a>]. The PDB accession number 7DF4 was used as the query.</p>
Full article ">Figure 5
<p>(<b>A</b>) Location of PSSs (labeled in red) supported by more than one type of evidence in the SARS-CoV-2 RTC (PDB accession number 7EGQ). NSP7, NSP8, NSP9, NSP10, NSP12, NSP13, and NSP14 are labeled in yellow, violet, beige, brown, green, pink, and white, respectively. (<b>B</b>) Consurf projection of the replicase complex dimer as in the Consurf Database [<a href="#B70-ijms-25-02428" class="html-bibr">70</a>]. The PDB accession number 7EGQ was used as the query.</p>
Full article ">
16 pages, 11538 KiB  
Article
Molecular Mechanism of Different Rooting Capacity between Two Clones of Taxodium hybrid ‘Zhongshanshan’
by Jiaqi Liu, Lei Xuan, Chaoguang Yu, Jianfeng Hua, Ziyang Wang, Yunlong Yin and Zhiquan Wang
Int. J. Mol. Sci. 2024, 25(4), 2427; https://doi.org/10.3390/ijms25042427 - 19 Feb 2024
Viewed by 850
Abstract
The conifer Taxodium hybrid ‘Zhongshanshan’ (T. hybrid ‘Zhongshanshan’) is characterized by rapid growth, strong stress resistance, and high ornamental value and has significant potential for use in afforestation, landscaping, and wood production. The main method of propagating T. hybrid ‘Zhongshanshan’ is tender branch cutting, but [...] Read more.
The conifer Taxodium hybrid ‘Zhongshanshan’ (T. hybrid ‘Zhongshanshan’) is characterized by rapid growth, strong stress resistance, and high ornamental value and has significant potential for use in afforestation, landscaping, and wood production. The main method of propagating T. hybrid ‘Zhongshanshan’ is tender branch cutting, but the cutting rooting abilities of different T. hybrid ‘Zhongshanshan’ clones differ significantly. To explore the causes of rooting ability differences at a molecular level, we analyzed the transcriptome data of cutting base and root tissues of T. hybrid ‘Zhongshanshan 149’ with a rooting rate of less than 5% and T. hybrid ‘Zhongshanshan 118’ with rooting rate greater than 60%, at the developmental time points in this study. The results indicated that differentially expressed genes between the two clones were mainly associated with copper ion binding, peroxidase, and oxidoreductase activity, response to oxidative stress, phenylpropanoid and flavonoid biosynthesis, and plant hormone signal transduction, among others. The expression pattern of ThAP2 was different throughout the development of the adventitive roots of the two clone cuttings. Therefore, this gene was selected for further study. It was shown that ThAP2 was a nuclear-localized transcription factor and demonstrated a positive feedback effect on rooting in transgenic Nicotiana benthamiana cuttings. Thus, the results of this study explain the molecular mechanism of cutting rooting and provide candidate gene resources for developing genetic breeding strategies for optimizing superior clones of T. hybrid ‘Zhongshanshan’. Full article
(This article belongs to the Special Issue Plant Physiology and Molecular Nutrition)
Show Figures

Figure 1

Figure 1
<p>AR formation of <span class="html-italic">T. hybrid</span> ‘Zhongshanshan 149’ and <span class="html-italic">T. hybrid</span> ‘Zhongshanshan 118’ cuttings at different developmental time points. Three time points were used in the study. The first time point for sample was 0 day (The samples were marked as 149–0 d and 118–0 d), the second time point was 28 days after cutting (149–28 d and 118–28 d), and the third time point was 56 days after cutting (149–56 d and 118–56 d).</p>
Full article ">Figure 2
<p>GO annotation of the combined transcriptome data from <span class="html-italic">T. hybrid</span> ‘Zhongshanshan 149’ and <span class="html-italic">T. hybrid</span> ‘Zhongshanshan 118’.</p>
Full article ">Figure 3
<p>KEGG annotation of the combined transcriptome data from <span class="html-italic">T. hybrid</span> ‘Zhongshanshan 149’ and <span class="html-italic">T. hybrid</span> ‘Zhongshanshan 118’.</p>
Full article ">Figure 4
<p>PCA of mRNA populations of samples of <span class="html-italic">T. hybrid</span> ‘Zhongshanshan 149’ and <span class="html-italic">T. hybrid</span> ‘Zhongshanshan 118’.</p>
Full article ">Figure 5
<p>KEGG enrichment analysis of DEGs in the (<b>A</b>) 149–0 d/118–0 d, (<b>B</b>) 149–28 d/118–28 d, and (<b>C</b>) 149–56 d/118–56 d comparisons of <span class="html-italic">T. hybrid</span> ‘Zhongshanshan 149’ and <span class="html-italic">T. hybrid</span> ‘Zhongshanshan 118’.</p>
Full article ">Figure 6
<p>Transcript abundance of DEGs. (<b>A</b>) Clustering analysis of the DEGs into 20 clusters according to their expression profile. Comparison of GO enrichment of (<b>B</b>) cluster 0 and cluster 19 (<b>C</b>). Colored clusters were statistically significant (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 7
<p>Verification of the gene expression quantity of randomly selected genes obtained from RNA-Seq using qRT-PCR.</p>
Full article ">Figure 8
<p>Alignment results of <span class="html-italic">ThAP2</span> with <span class="html-italic">AP2</span> protein of other species. The highly-conserved regions are represented by different colors and <span class="html-italic">AP2</span> domain is marked with red line.</p>
Full article ">Figure 9
<p>The result of subcellular localization of the <span class="html-italic">ThAP2</span> gene in the lower epidermis of <span class="html-italic">N. benthamiana</span> leaves.</p>
Full article ">Figure 10
<p>Phenotype of <span class="html-italic">ThAP2</span> in <span class="html-italic">N. benthamiana</span> after 20 days of culture. (<b>A</b>) wild-type <span class="html-italic">N. benthamiana</span> and (<b>B</b>) <span class="html-italic">ThAP2</span>-transgenic <span class="html-italic">N. benthamiana</span>.</p>
Full article ">Figure 11
<p>IAA Levels of <span class="html-italic">ThAP2</span> in base and root tissue of <span class="html-italic">N. benthamiana</span> cuttings. (<b>A</b>) wild-type <span class="html-italic">N. benthamiana</span> and (<b>B</b>) <span class="html-italic">ThAP2</span>-transgenic <span class="html-italic">N. benthamiana</span>, <span class="html-italic">p</span> &lt; 0.05. “*” indicated a significant difference between the two sets of data.</p>
Full article ">
0 pages, 14539 KiB  
Article
Inhibition of Biofilm Formation in Cutibacterium acnes, Staphylococcus aureus, and Candida albicans by the Phytopigment Shikonin
by Yong-Guy Kim, Jin-Hyung Lee, Sanghun Kim, Sunyoung Park, Yu-Jeong Kim, Choong-Min Ryu, Hwi Won Seo and Jintae Lee
Int. J. Mol. Sci. 2024, 25(4), 2426; https://doi.org/10.3390/ijms25042426 - 19 Feb 2024
Cited by 1 | Viewed by 2200
Abstract
Skin microbiota, such as acne-related Cutibacterium acnes, Staphylococcus aureus, and fungal Candida albicans, can form polymicrobial biofilms with greater antimicrobial tolerance to traditional antimicrobial agents and host immune systems. In this study, the phytopigment shikonin was investigated against single-species and [...] Read more.
Skin microbiota, such as acne-related Cutibacterium acnes, Staphylococcus aureus, and fungal Candida albicans, can form polymicrobial biofilms with greater antimicrobial tolerance to traditional antimicrobial agents and host immune systems. In this study, the phytopigment shikonin was investigated against single-species and multispecies biofilms under aerobic and anaerobic conditions. Minimum inhibitory concentrations of shikonin were 10 µg/mL against C. acnes, S. aureus, and C. albicans, and at 1–5 µg/mL, shikonin efficiently inhibited single biofilm formation and multispecies biofilm development by these three microbes. Shikonin increased porphyrin production in C. acnes, inhibited cell aggregation and hyphal formation by C. albicans, decreased lipase production, and increased hydrophilicity in S. aureus. In addition, shikonin at 5 or 10 µg/mL repressed the transcription of various biofilm-related genes and virulence-related genes in C. acnes and downregulated the gene expression levels of the quorum-sensing agrA and RNAIII, α-hemolysin hla, and nuclease nuc1 in S. aureus, supporting biofilm inhibition. In addition, shikonin prevented multispecies biofilm development on porcine skin, and the antimicrobial efficacy of shikonin was recapitulated in a mouse infection model, in which it promoted skin regeneration. The study shows that shikonin inhibits multispecies biofilm development by acne-related skin microbes and might be useful for controlling bacterial infections. Full article
(This article belongs to the Special Issue New Insights in Bioactive Compounds as Antibiofilm Agents)
Show Figures

Figure 1

Figure 1
<p>Structures of phytopigments with antimicrobial and antibiofilm activities.</p>
Full article ">Figure 2
<p>Antibiofilm activity of shikonin. (<b>A</b>–<b>D</b>) Biofilm formation by <span class="html-italic">C. acnes</span>, <span class="html-italic">S. aureus</span>, and <span class="html-italic">C. albicans</span> in the presence of shikonin under anaerobic conditions; (<b>E</b>) color-coded images of <span class="html-italic">C. acnes</span> biofilms; (<b>F</b>) CLSM images of <span class="html-italic">C. acnes</span> biofilms; (<b>G</b>) COMSTAT results; and (<b>H</b>) SEM images. Black and white bars represent 100 µm, and yellow and cyan bars represent 1 and 5 µm, respectively. None: non-treated control. *, <span class="html-italic">p</span> &lt; 0.05 vs. non-treated controls (None).</p>
Full article ">Figure 3
<p>Inhibition of virulence factor production by shikonin. (<b>A</b>) Cell surface hydrophilicity, (<b>B</b>) cell agglutination, (<b>C</b>) porphyrin production, (<b>D</b>) extracellular lipase production, (<b>E</b>) cell aggregation, and (<b>F</b>) hyphal formation by <span class="html-italic">C. albicans</span>. Error bars indicate standard deviations. N/A means not applicable because of no cell growth under anaerobic conditions. *, <span class="html-italic">p</span> &lt; 0.05 vs. non-treated controls (None). Red and yellow bars represent 100 and 20 µm, respectively.</p>
Full article ">Figure 4
<p>Inhibitory effects of shikonin on multispecies biofilms. (<b>A</b>) The antibiofilm effects of shikonin on <span class="html-italic">C. acnes</span>/<span class="html-italic">S. aureus</span>/<span class="html-italic">C. albicans</span> biofilms after 7-day culture under anaerobic conditions, (<b>B</b>) planktonic cell growth of multiple species, (<b>C</b>) color-coded images of three-species biofilms, (<b>D</b>) CLSM images of the three-component biofilms, and (<b>E</b>) COMSTAT results. Black and white bars indicate 100 µm. None: non-treated control. *, <span class="html-italic">p</span> &lt; 0.05 vs. non-treated controls (None).</p>
Full article ">Figure 5
<p>Microscopic observations of multispecies biofilms. (<b>A</b>) SEM images of single-species biofilms and (<b>B</b>) a three-component biofilm formed in the presence of shikonin after culture under anaerobic conditions for 7 days. Red and yellow scale bars represent 3 and 10 µm, respectively. Red, cyan, and yellow triangles indicate cells of <span class="html-italic">C. acnes</span>, <span class="html-italic">S. aureus</span>, and <span class="html-italic">C. albicans</span>, respectively. None: non-treated control.</p>
Full article ">Figure 6
<p>Effects of shikonin on gene expression and putative mechanisms. Relative transcriptional profiles of biofilm-related genes in (<b>A</b>) <span class="html-italic">C. acnes</span> and (<b>B</b>) <span class="html-italic">S. aureus</span>. <span class="html-italic">C. acnes</span> cells were treated with shikonin at 5 μg/mL for 24 h without shaking under anaerobic conditions, and <span class="html-italic">S. aureus</span> cells were treated with shikonin at 5 and 10 μg/mL for 3 h with 250 rpm shaking under aerobic conditions. Fold changes indicate transcriptional differences observed in treated vs. untreated (None) cells as determined by qRT-PCR. <span class="html-italic">16s rRNA</span> was used as the housekeeping gene for <span class="html-italic">C. acnes</span> and <span class="html-italic">S. aureus</span>. *, <span class="html-italic">p</span> &lt; 0.05 vs. non-treated controls. Diagram of the putative mechanisms of shikonin in (<b>C</b>) <span class="html-italic">C. acnes</span> and (<b>D</b>) <span class="html-italic">S. aureus</span>. Blue arrows (<span style="color:blue">→</span>) indicate upregulation of gene expression or positively affecting a phenotype, red arrows (<span style="color:red">˫</span>) indicate downregulation of gene expression or negatively affecting a phenotype, and black dotted arrows indicate no change/no effect.</p>
Full article ">Figure 7
<p>Efficacy of shikonin in mice. Antimicrobial and skin wound-healing effects of shikonin on combined <span class="html-italic">S. aureus</span> and <span class="html-italic">C. albicans</span> infection. (<b>A</b>) Schematic of the experimental protocol used for the skin wound/infection mouse model. NIR: near-infrared. (<b>B</b>) Skin lesion areas were monitored from DPI 0 to 12 and are presented as (<b>C</b>) the percentages of total lesion areas to initial skin incision areas. (<b>D</b>,<b>F</b>) Bacterial counts of skin swabs at DPI 3 and (<b>E</b>,<b>G</b>) tissues at DPI 12 for <span class="html-italic">S. aureus</span> and <span class="html-italic">C. albicans</span>, respectively. (<b>H</b>) Hematoxylin and eosin-stained tissues were prepared for histopathological evaluation, and (<b>I</b>) re-epithelialization rates were calculated by measuring mean epithelial regeneration areas. Photomicrographs of epithelial layers (upper images; (<b>H</b>)) and corresponding higher magnification images (lower images; (<b>H</b>)) (original magnifications 100 and 400×, respectively) showing patterns of skin remodeling. Drug-alone means non-infected mice treated with 100 μg/mL of shikonin. Asterisks indicate regenerated epithelium, and arrows indicate necrotic tissue and inflammatory cells. (<b>H</b>) Black scale bars represent 200 µm and white scale bars 50 µm. Means ± standard deviations were calculated for 3 to 5 mice. *, <span class="html-italic">p</span> &lt; 0.05 vs. infected non-treated controls.</p>
Full article ">Figure 8
<p>Toxicities of shikonin in the plant germination and nematode models. (<b>A</b>) <span class="html-italic">B. rapa</span> seed germination was performed using Murashige and Skoog agar medium supplemented with or without shikonin at 25 °C. (<b>B</b>,<b>C</b>) Plant total lengths were analyzed over 10 days. Yellow scale bars indicate 1 cm. (<b>D</b>) <span class="html-italic">C. elegans</span> survival was assessed in the presence and absence of shikonin for 10 days.</p>
Full article ">
15 pages, 4119 KiB  
Article
Comprehensive Analysis of the GRAS Gene Family in Paulownia fortunei and the Response of DELLA Proteins to Paulownia Witches’ Broom
by Yixiao Li, Yabing Cao, Yujie Fan and Guoqiang Fan
Int. J. Mol. Sci. 2024, 25(4), 2425; https://doi.org/10.3390/ijms25042425 - 19 Feb 2024
Cited by 2 | Viewed by 1100
Abstract
The GRAS (GAI\RGA\SCL) gene family encodes plant-specific transcription factors that play crucial roles in plant growth and development, stress tolerance, and hormone network regulation. Plant dwarfing symptom is mainly regulated by DELLA proteins of the GRAS gene subfamily. In this study, the association [...] Read more.
The GRAS (GAI\RGA\SCL) gene family encodes plant-specific transcription factors that play crucial roles in plant growth and development, stress tolerance, and hormone network regulation. Plant dwarfing symptom is mainly regulated by DELLA proteins of the GRAS gene subfamily. In this study, the association between the GRAS gene family and Paulownia witches’ broom (PaWB) was investigated. A total of 79 PfGRAS genes were identified using bioinformatics methods and categorized into 11 groups based on amino acid sequences. Tandem duplication and fragment duplication were found to be the main modes of amplification of the PfGRAS gene family. Gene structure analysis showed that more than 72.1% of the PfGRASs had no introns. The genes PfGRAS12/18/58 also contained unique DELLA structural domains; only PfGRAS12, which showed significant response to PaWB phytoplasma infection in stems, showed significant tissue specificity and responded to gibberellin (GA3) in PaWB-infected plants. We found that the internodes were significantly elongated under 100 µmol·L−1 GA3 treatment for 30 days. The subcellular localization analysis indicated that PfGRAS12 is located in the nucleus and cell membrane. Yeast two-hybrid (Y2H) and bimolecular fluorescence complementation (BiFC) assays confirmed that PfGRAS12 interacted with PfJAZ3 in the nucleus. Our results will lay a foundation for further research on the functions of the PfGRAS gene family and for genetic improvement and breeding of PaWB-resistant trees. Full article
(This article belongs to the Section Molecular Plant Sciences)
Show Figures

Figure 1

Figure 1
<p>Chromosome distribution of <span class="html-italic">PfGRAS</span> gene family members. Vertical bars indicate the chromosomes. Tandem duplication genes are highlighted in blue and connected by blue lines. The left scale represents chromosome length.</p>
Full article ">Figure 2
<p>Phylogenetic and collinearity analysis of <span class="html-italic">PfGRAS</span> gene family members. (<b>A</b>) The phylogenetic tree of <span class="html-italic">pfGRAS</span>, <span class="html-italic">AtGRAS</span>, and OsG<span class="html-italic">RAS</span> divide the members into 11 groups. (<b>B</b>) The black lines represent the collinearity gene pair in the <span class="html-italic">Paulownia fortunei</span> (<span class="html-italic">P. fortunei</span>). The colored boxes represent gene density. (<b>C</b>) Collinearity analysis between <span class="html-italic">Arabidopsis thaliana</span> (<span class="html-italic">A. thaliana</span>) and <span class="html-italic">P. fortunei.</span> The black lines refer to the collinear gene pair in the <span class="html-italic">A. thaliana</span> and <span class="html-italic">P. fortunei</span>.</p>
Full article ">Figure 2 Cont.
<p>Phylogenetic and collinearity analysis of <span class="html-italic">PfGRAS</span> gene family members. (<b>A</b>) The phylogenetic tree of <span class="html-italic">pfGRAS</span>, <span class="html-italic">AtGRAS</span>, and OsG<span class="html-italic">RAS</span> divide the members into 11 groups. (<b>B</b>) The black lines represent the collinearity gene pair in the <span class="html-italic">Paulownia fortunei</span> (<span class="html-italic">P. fortunei</span>). The colored boxes represent gene density. (<b>C</b>) Collinearity analysis between <span class="html-italic">Arabidopsis thaliana</span> (<span class="html-italic">A. thaliana</span>) and <span class="html-italic">P. fortunei.</span> The black lines refer to the collinear gene pair in the <span class="html-italic">A. thaliana</span> and <span class="html-italic">P. fortunei</span>.</p>
Full article ">Figure 3
<p>Phylogenetic groups, motif compositions, and gene structures of <span class="html-italic">PfGRAS</span> gene family. MEME analysis revealed a schematic representation of the conserved motifs among the <span class="html-italic">PfGRAS</span> gene family. Each color represents a distinct motif; gene structures of the <span class="html-italic">PfGRAS</span> gene family are depicted with the coding sequences (CDS) and untranslated regions (UTR) appearing in green and yellow boxes, respectively.</p>
Full article ">Figure 4
<p>Structural domain and cis-acting element analysis of the <span class="html-italic">PfGRAS</span> gene family. Green and yellow colors represent the GRAS and DELLA structural domains, respectively; different colors represent different cis-acting elements.</p>
Full article ">Figure 5
<p>Expression analysis of <span class="html-italic">PfGRAS</span> gene family. (<b>A</b>) Heatmap of <span class="html-italic">PfGARS</span> gene expression in response to PaWB phytoplasma. The gene for |log2FC| &gt; 1.5 is highlighted. (<b>B</b>) qRT-qPCR of <span class="html-italic">PfGRAS</span> gene expression in response to PaWB phytoplasma. Significant and highly significant differences compared with the gene expression in PF are shown as * (<span class="html-italic">p</span> &lt; 0.05) and ** (<span class="html-italic">p</span> &lt; 0.01), respectively. (<b>C</b>) Heatmap of <span class="html-italic">PfGRAS</span> genes expression in response to methyl methanesulfonate (MMS) in PFI; 10(20) d, PFI seedlings treated with MMS for 10(20) days. (<b>D</b>) Heatmap of <span class="html-italic">PfGRAS</span> gene expression in response to rifampicin (Rif) in PFI; 10(20) d, PFI seedlings treated with Rif for 10(20) days. In the heatmaps (<b>A</b>,<b>C</b>,<b>D</b>), scaled log2FC expression values based on transcriptomics data are shown from blue to red, indicating low to high expression.</p>
Full article ">Figure 6
<p>Tissue specificity and expression patterns of DELLA proteins under gibberellin (GA3) treatment. (<b>A</b>) RNA from roots, stems, leaves, buds, and nodes of PF and PFI grown for 30 days were extracted for tissue-specific validation, and <span class="html-italic">PfGRAS18/58</span> was not expressed in the buds. The white and black bars indicate the amount of expression in PF and PFI at different tissues; (<b>B</b>) PFI grown for 30 days as a control; (<b>C</b>) PFI treated with 100 µmol·L<sup>−1</sup> GA3 for 30 days; (<b>D</b>) the gray bars indicate the amount of expression in the PFI at different treatment times. Significant and highly significant differences compared with the gene expression in PF are shown as * (<span class="html-italic">p</span> &lt; 0.05), ** (<span class="html-italic">p</span> &lt; 0.01), *** (<span class="html-italic">p</span> &lt; 0.001), and **** (<span class="html-italic">p</span> &lt; 0.0001), respectively.</p>
Full article ">Figure 7
<p>Subcellular localization of <span class="html-italic">PfGRAS12</span>; 35S: <span class="html-italic">PfGRAS12</span>-GFP construct was individually injected into the epidermal cells of <span class="html-italic">Nicotiana benthamiana</span> (<span class="html-italic">N. benthamiana</span>). The transient expression of <span class="html-italic">PfGRAS12</span>-GFP was observed and captured by a confocal laser scanning microscope. mCherry-PIP2;1 as a cell membrane marker. mCherry-H2B as a nucleus marker. Scale bars were 20 µm.</p>
Full article ">Figure 8
<p>Prediction and validation of proteins interactions. (<b>A</b>) Prediction of <span class="html-italic">PfGRAS</span> gene family protein interactions; (<b>B</b>) prediction of interacting proteins with RGA1 in <span class="html-italic">A. thaliana</span>; (<b>C</b>) BiFC experiments verify the interaction between PfGRAS12 and PfJAZ3; (<b>D</b>) yeast two-hybrid verifies the interaction between PfGRAS12 and PfJAZ3.</p>
Full article ">
15 pages, 3857 KiB  
Article
TGF-β1 Signaling Impairs Metformin Action on Glycemic Control
by Quan Pan, Weiqi Ai and Shaodong Guo
Int. J. Mol. Sci. 2024, 25(4), 2424; https://doi.org/10.3390/ijms25042424 - 19 Feb 2024
Viewed by 1171
Abstract
Hyperglycemia is a hallmark of type 2 diabetes (T2D). Metformin, the first-line drug used to treat T2D, maintains blood glucose within a normal range by suppressing hepatic glucose production (HGP). However, resistance to metformin treatment is developed in most T2D patients over time. [...] Read more.
Hyperglycemia is a hallmark of type 2 diabetes (T2D). Metformin, the first-line drug used to treat T2D, maintains blood glucose within a normal range by suppressing hepatic glucose production (HGP). However, resistance to metformin treatment is developed in most T2D patients over time. Transforming growth factor beta 1 (TGF-β1) levels are elevated both in the liver and serum of T2D humans and mice. Here, we found that TGF-β1 treatment impairs metformin action on suppressing HGP via inhibiting AMPK phosphorylation at Threonine 172 (T172). Hepatic TGF-β1 deficiency improves metformin action on glycemic control in high fat diet (HFD)-induced obese mice. In our hepatic insulin resistant mouse model (hepatic insulin receptor substrate 1 (IRS1) and IRS2 double knockout (DKO)), metformin action on glycemic control was impaired, which is largely improved by further deletion of hepatic TGF-β1 (TKObeta1) or hepatic Foxo1 (TKOfoxo1). Moreover, blockade of TGF-β1 signaling by chemical inhibitor of TGF-β1 type I receptor LY2157299 improves to metformin sensitivity in mice. Taken together, our current study suggests that hepatic TGF-β1 signaling impairs metformin action on glycemic control, and suppression of TGF-β1 signaling could serve as part of combination therapy with metformin for T2D treatment. Full article
(This article belongs to the Special Issue Drug Therapies for Diabetes)
Show Figures

Figure 1

Figure 1
<p>TGF-β1 signaling promotes AMPK phosphorylation at serine 485. (<b>A</b>) Western blot of pSmad3, Smad3, pAMPK-S485, AMPK, and β-actin in primary hepatocytes treated with different doses of TGF-β1 for 2 h. (<b>B</b>) Western blot of pSmad3, Smad3, pAMPK-S485, AMPK, and β-actin in primary hepatocytes treated with 5ng/mL TGF-β1 for the indicated periods of time.</p>
Full article ">Figure 2
<p>TGF-β1 signaling impairs metformin-induced AMPK activation. (<b>A</b>) Western blot of pSmad3, Smad3, pAMPK-S485, AMPK, and GAPDH in primary hepatocytes pretreated with 5 ng/mL TGF-β1 for 1 h and then treated with 100 μM metformin for 30 min. (<b>B</b>) Quantification of pAMPK-T172, pAMPK-S485, and AMPK analyzed by ImageJ. Data are presented as mean ± SEM. n.s. (no significance) <span class="html-italic">p</span> &gt; 0.05, * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001 between assigned groups using one-way ANOVA.</p>
Full article ">Figure 3
<p>TGF-β1 signaling impairs metformin action on suppressing HGP. Primary hepatocytes were isolated from WT mice and cultured in HGP buffer. Cells were pretreated with 5 ng/mL TGF-β1 for 1 h and then treated with 100 μM metformin for 3 h. HGP buffer was collected to determine glucose production rate. Data are presented as mean ± SEM. n.s. (no significance) <span class="html-italic">p</span> &gt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 between assigned groups using one-way ANOVA.</p>
Full article ">Figure 4
<p>TGF-β1 impairs metformin suppression of gluconeogenic gene expression in primary hepatocytes. Primary hepatocytes were isolated from WT mice and cultured in serum-free DMEM medium. Cells were pretreated with 5 ng/mL TGF-β1 for 1 h and then treated with 100 μM metformin for 3 h. (<b>A</b>–<b>C</b>) The mRNA was extracted from these cells and then subjected to cDNA synthesis. The mRNA expressions of <span class="html-italic">G6pc</span> (<b>A</b>)<span class="html-italic">, Pepck</span> (<b>B</b>), <span class="html-italic">Smad7</span> (<b>C</b>), and <span class="html-italic">cyclophilin</span> were determined by real-time qPCR. Data are presented as mean ± SEM. n.s. (no significance) <span class="html-italic">p</span> &gt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001 between assigned groups using one-way ANOVA.</p>
Full article ">Figure 5
<p>Hepatic TGF-β1 deficiency promotes metformin sensitivity in HFD-induced obese mice. (<b>A</b>) Primary hepatocytes were isolated from TGF-β1 L/L and L-TGF-β1KO mice and cultured in HGP buffer. Cells were pretreated with 5 ng/mL TGF-β1 for 1 h and then treated with 100 μM metformin for 3 h. HGP buffer was collected to determine glucose production rate. (<b>B</b>,<b>C</b>) Metformin tolerance test (actual blood glucose levels (<b>B</b>), ratio of initial level (<b>C</b>)) in TGF-β1 L/L (<span class="html-italic">n</span> = 9) and L-TGF-β1KO (<span class="html-italic">n</span> = 8) mice fed with HFD for 3 months. (<b>D</b>) Area under the curve of metformin tolerance test shown in (<b>C</b>). Data are presented as mean ± SEM. * <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 between assigned groups or vs. TGF-β1 L/L mice using one-way ANOVA or <span class="html-italic">t</span>-test.</p>
Full article ">Figure 6
<p>Hepatic TGF-β1 or Foxo1 deficiency restores metformin sensitivity in genetically diabetic mice. (<b>A</b>,<b>B</b>) Metformin tolerance test (actual blood glucose levels (<b>A</b>), ratio of initial level (<b>B</b>)) in WT (<span class="html-italic">n</span> = 10), DKO (<span class="html-italic">n</span> = 6), and TKObeta1 (<span class="html-italic">n</span> = 7) mice. (<b>C</b>) Area under the curve of metformin tolerance test shown in (<b>B</b>). (<b>D</b>,<b>E</b>) Metformin tolerance test (actual blood glucose levels (<b>D</b>), ratio of initial level (<b>E</b>)) in WT (<span class="html-italic">n</span> = 7), DKO (<span class="html-italic">n</span> = 6), and TKOfoxo1 (<span class="html-italic">n</span> = 5) mice. (<b>F</b>) Area under the curve of metformin tolerance test shown in (<b>E</b>). Data are presented as mean ± SEM. * <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 between WT and DKO, # <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 between DKO and TKObeta1 or TKOfoxo1 using one-way ANOVA.</p>
Full article ">Figure 7
<p>Pharmacological inhibition of TGFβ1 signaling improves metformin sensitivity. (<b>A</b>) Primary hepatocytes isolated from WT mice were pretreated with 10 μM LY2157299 (LY) for 30 min and then treated with 5 ng/mL TGF-β1 for 3 h. The pSmad3, Smad3, and β-actin protein levels were determined by Western blot. (<b>B</b>) Primary hepatocytes were isolated from WT mice and cultured in HGP buffer. Cells were pretreated with 10 μM LY for 30 min and then treated with 100 μM metformin for 3 h. HGP buffer was collected to determine glucose production rate. Data are presented as mean ± SEM. * <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, between assigned groups or vs. vehicle group using one-way ANOVA or <span class="html-italic">t</span>-test.</p>
Full article ">Figure 8
<p>Pharmacological inhibition of TGFβ1 signaling improves metformin sensitivity in HFD-fed mice (<span class="html-italic">n</span> = 8/group). (<b>A</b>,<b>B</b>) Metformin tolerance test (actual blood glucose levels (<b>A</b>), ratio of initial level (<b>B</b>)) in mice fed with HFD for 3 months with LY2157299 administration for 6 weeks. (<b>C</b>) Area under the curve of metformin tolerance test in (<b>B</b>). Data are presented as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 between assigned groups or vs. vehicle group using <span class="html-italic">t</span>-test.</p>
Full article ">Figure 9
<p>Schematic diagram of the interaction between TGFβ1 signaling and metformin in glycemic control. (1) Metformin suppresses HGP to treat hyperglycemia via promoting AMPK activation at T172 phosphorylation site; (2) TGFβ1 signaling impairs metformin action on AMPK T172 activation via promoting AMPK S485 phosphorylation; (3) Inhibition of TGFβ1 signaling by LY2157299 promotes metformin sensitivity for glycemic control. TβRs: TGF-β1 receptors; →: stimulation; ⊣: inhibition (Blue: The effects of metformin. Green: the effect of LY2157199. Red: the effects of TGF-b1).</p>
Full article ">
13 pages, 10364 KiB  
Review
Autism Spectrum Disorder: Brain Areas Involved, Neurobiological Mechanisms, Diagnoses and Therapies
by Jacopo Lamanna and Jacopo Meldolesi
Int. J. Mol. Sci. 2024, 25(4), 2423; https://doi.org/10.3390/ijms25042423 - 19 Feb 2024
Cited by 4 | Viewed by 4278
Abstract
Autism spectrum disorder (ASD), affecting over 2% of the pre-school children population, includes an important fraction of the conditions accounting for the heterogeneity of autism. The disease was discovered 75 years ago, and the present review, based on critical evaluations of the recognized [...] Read more.
Autism spectrum disorder (ASD), affecting over 2% of the pre-school children population, includes an important fraction of the conditions accounting for the heterogeneity of autism. The disease was discovered 75 years ago, and the present review, based on critical evaluations of the recognized ASD studies from the beginning of 1990, has been further developed by the comparative analyses of the research and clinical reports, which have grown progressively in recent years up to late 2023. The tools necessary for the identification of the ASD disease and its related clinical pathologies are genetic and epigenetic mutations affected by the specific interaction with transcription factors and chromatin remodeling processes occurring within specific complexes of brain neurons. Most often, the ensuing effects induce the inhibition/excitation of synaptic structures sustained primarily, at dendritic fibers, by alterations of flat and spine response sites. These effects are relevant because synapses, established by specific interactions of neurons with glial cells, operate as early and key targets of ASD. The pathology of children is often suspected by parents and communities and then confirmed by ensuing experiences. The final diagnoses of children and mature patients are then completed by the combination of neuropsychological (cognitive) tests and electro-/magneto-encephalography studies developed in specialized centers. ASD comorbidities, induced by processes such as anxieties, depressions, hyperactivities, and sleep defects, interact with and reinforce other brain diseases, especially schizophrenia. Advanced therapies, prescribed to children and adult patients for the control of ASD symptoms and disease, are based on the combination of well-known brain drugs with classical tools of neurologic and psychiatric practice. Overall, this review reports and discusses the advanced knowledge about the biological and medical properties of ASD. Full article
(This article belongs to the Special Issue Physiology and Pathology of Neurons 2.0)
Show Figures

Figure 1

Figure 1
<p>Examples of two types of dendrite arborization emerging from similar neuronal cell bodies and examples of dendritic spines. In (<b>A</b>), all dendritic fibers appear smooth because their post-synaptic structures, largely predominant in inhibitory neurons, are flat, i.e., they do not emerge or emerge only marginally from the fiber surface. In (<b>B</b>), the dendritic fibers predominant in stimulatory neurons are largely covered by spines, i.e., small stemming protrusions connected to fibers by their necks. (<b>A</b>,<b>B</b>) contain modified versions of <a href="#ijms-25-02423-f001" class="html-fig">Figure 1</a> from our previous publication [<a href="#B7-ijms-25-02423" class="html-bibr">7</a>]. (<b>C</b>) shows a fraction of an original figure by Santiago Ramon y Cajal (1896) (CAT 024, book Ciencia y Arte by the Instituto Cajal, Madrid, 2004) showing the heterogeneity of the spines emerging from dendritic fibers of pyramidal cells, illustrating in particular their variability in size, shape, density and distribution.</p>
Full article ">Figure 2
<p>ARID1B acts as a scaffolding protein that holds together the components of its complex ability to operate with specific chromatin components of reactive genes. (<b>A</b>,<b>C</b>) images illustrate two cells (orange) characterized by flat and spiny dendritic fibers, respectively. In the nucleus (violet) of these images, the ARID1B complex regulates the transcription of specific genes. The generated mRNA transcripts (small black dots) are transferred to the cytoplasm of the corresponding proteins addressed to the dendritic fibers (red arrows). In (<b>A</b>), the latter proteins contribute to the appropriate assembly of flat post-synaptic structures. The bottom (<b>B</b>) is analogous to (<b>A</b>) except that ARID1B has been knocked-down, the red pointed arrows do not move specific mRNAs, small white dots contain proteins different from those generated by ARID1B, the post-synapses are absent, and the pre-synapses are scattered in the space. (<b>C</b>) is like (<b>A</b>) except for one spine with black dots assembled close to two pre-synapses assembling whole synapses; (<b>D</b>) corresponds to (<b>C</b>) without ARID1B; thus, it is analogous to (<b>B</b>) with respect to (<b>A</b>). The change in (<b>D</b>) versus (<b>C</b>) is the tiny spine to which pre-synapses assemble to establish the whole synapse.</p>
Full article ">
16 pages, 330 KiB  
Review
From the Friend to the Foe—Enterococcus faecalis Diverse Impact on the Human Immune System
by Agnieszka Daca and Tomasz Jarzembowski
Int. J. Mol. Sci. 2024, 25(4), 2422; https://doi.org/10.3390/ijms25042422 - 19 Feb 2024
Cited by 1 | Viewed by 2716
Abstract
Enterococcus faecalis is a bacterium which accompanies us from the first days of our life. As a commensal it produces vitamins, metabolizes nutrients, and maintains intestinal pH. All of that happens in exchange for a niche to inhabit. It is not surprising then, [...] Read more.
Enterococcus faecalis is a bacterium which accompanies us from the first days of our life. As a commensal it produces vitamins, metabolizes nutrients, and maintains intestinal pH. All of that happens in exchange for a niche to inhabit. It is not surprising then, that the bacterium was and is used as an element of many probiotics and its positive impact on the human immune system and the body in general is hard to ignore. This bacterium has also a dark side though. The plasticity and relative ease with which one acquires virulence traits, and the ability to hide from or even deceive and use the immune system to spread throughout the body make E. faecalis a more and more dangerous opponent. The statistics clearly show its increasing role, especially in the case of nosocomial infections. Here we present the summarization of current knowledge about E. faecalis, especially in the context of its relations with the human immune system. Full article
(This article belongs to the Special Issue Diverse Responses of Immune Cells to Bacterial Infections)
14 pages, 3517 KiB  
Article
Novel Immortalized Human Multipotent Mesenchymal Stromal Cell Line for Studying Hormonal Signaling
by Alexandra Primak, Natalia Kalinina, Mariya Skryabina, Vladimir Usachev, Vadim Chechekhin, Maksim Vigovskiy, Elizaveta Chechekhina, Nikita Voloshin, Konstantin Kulebyakin, Maria Kulebyakina, Olga Grigorieva, Pyotr Tyurin-Kuzmin, Nataliya Basalova, Anastasia Efimenko, Stalik Dzhauari, Yulia Antropova, Ivan Plyushchii, Zhanna Akopyan, Veronika Sysoeva, Vsevolod Tkachuk and Maxim Karagyauradd Show full author list remove Hide full author list
Int. J. Mol. Sci. 2024, 25(4), 2421; https://doi.org/10.3390/ijms25042421 - 19 Feb 2024
Viewed by 1665
Abstract
Multipotent mesenchymal stromal cells (MSCs) integrate hormone and neuromediator signaling to coordinate tissue homeostasis, tissue renewal and regeneration. To facilitate the investigation of MSC biology, stable immortalized cell lines are created (e.g., commercially available ASC52telo). However, the ASC52telo cell line has an impaired [...] Read more.
Multipotent mesenchymal stromal cells (MSCs) integrate hormone and neuromediator signaling to coordinate tissue homeostasis, tissue renewal and regeneration. To facilitate the investigation of MSC biology, stable immortalized cell lines are created (e.g., commercially available ASC52telo). However, the ASC52telo cell line has an impaired adipogenic ability and a depressed response to hormones, including 5-HT, GABA, glutamate, noradrenaline, PTH and insulin compared to primary cells. This markedly reduces the potential of the ASC52telo cell line in studying the mechanisms of hormonal control of MSC’s physiology. Here, we have established a novel immortalized culture of adipose tissue-derived MSCs via forced telomerase expression after lentiviral transduction. These immortalized cell cultures demonstrate high proliferative potential (up to 40 passages), delayed senescence, as well as preserved primary culture-like functional activity (sensitivity to hormones, ability to hormonal sensitization and differentiation) and immunophenotype up to 17–26 passages. Meanwhile, primary adipose tissue-derived MSCs usually irreversibly lose their properties by 8–10 passages. Observed characteristics of reported immortalized human MSC cultures make them a feasible model for studying molecular mechanisms, which regulate the functional activities of these cells, especially when primary cultures or commercially available cell lines are not appropriate. Full article
Show Figures

Figure 1

Figure 1
<p>Telomerase expression in immortalized MSCs. (<b>a</b>) TERT mRNA expression level before (pMSCs) and after the immortalization procedure (iMSC-1 and iMSC-2) established using qPCR; (<b>b</b>) Western blot analysis of TERT protein content; (<b>c</b>) telomerase activity assessment by qPCR assay; (<b>d</b>) relative telomere length. *—<span class="html-italic">p</span> &lt; 0.05 (ANOVA multiple pairwise comparison, Tukey test), n = 3. Data are presented as mean ± standard deviation.</p>
Full article ">Figure 2
<p>iMSC proliferation. Proliferation of pMSC and iMSC cultures was assessed via measuring the dynamics of cell density (upper panels) and calculating the population doubling time (PDT, lower panels). #N—passage number, *—<span class="html-italic">p</span> &lt; 0.05—pMSCs PDT vs. iMSCs PDT at the corresponding passage, n ≥ 4. Data are presented as mean ± standard deviation.</p>
Full article ">Figure 3
<p>iMSCs senescence. Beta-galactosidase activity (blue color) was analyzed in pMSCs and ASC52telo cell line (upper panel’s row) as well as iMSCs-1 and iMSCs-2 cell lines. Passage number is indicated for each cell line.</p>
Full article ">Figure 4
<p>Flow cytometry analysis of MSC-specific markers in pMSC, iMSC and ASC52telo cell lines. #N—passage number.</p>
Full article ">Figure 5
<p>iMSC-2 differentiation potential. (<b>a</b>) MSCs cultured in appropriate differentiation medium for 2 weeks. Adipogenic differentiation was assessed by Nile red staining of accumulated lipids (green fluorescence), osteogenic differentiation—by Alizarin red staining of calcium deposits (red color) and chondrogenic differentiation—by Alcian blue staining of cartilage acidic glycosaminoglycans (light blue color); (<b>b</b>) relative expression level of marker genes of osteogenic (osteocalcin, Runx) and adipogenic (PPARgamma, adiponectin) differentiation in pMSCs and iMSCs cultured for 15 days in ordinary complete growth medium or appropriate differentiation medium.</p>
Full article ">Figure 6
<p>iMSC response to hormones. (<b>a</b>) The portion of cells, responded to glutamate (10<sup>−5</sup> M), GABA (2 × 10<sup>−5</sup> M), dopamine (10<sup>−5</sup> M), noradrenaline (10<sup>−6</sup> M), angiotensin II (10<sup>−8</sup> M), histamine (10<sup>−6</sup> M), 5-HT (10<sup>−5</sup> M) and PTH (10<sup>−8</sup> M) by increase in cytoplasmic Ca<sup>2+</sup> influx. (<b>b</b>,<b>c</b>) AKT and ERK phosphorylation in response to insulin. Western blot of MSC samples with P-AKT and P-ERK specific antibodies. (<b>c</b>) Vinculin content was used as a reference for densitometry of P-AKT and P-ERK bands.</p>
Full article ">
Previous Issue
Next Issue
Back to TopTop