Nothing Special   »   [go: up one dir, main page]

Next Issue
Volume 45, August
Previous Issue
Volume 45, June
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
cimb-logo

Journal Browser

Journal Browser

Curr. Issues Mol. Biol., Volume 45, Issue 7 (July 2023) – 58 articles

Cover Story (view full-size image): Molecular allergy diagnosis is currently considered the best diagnostic tool in the allergy diagnosis process. The available laboratory tests for the molecular diagnosis of allergies differ in both the allergen panels and the analysis technique or technology used. Molecular allergy diagnostics also has features of personalized medicine; therefore, sometimes minor technological differences may translate into the clinical diagnostic value of the test. Specialist physicians, and sometimes patients, choose from among the available testing options on their own. In order for the choice to be accurate and appropriate for an individual patient, it is necessary to have knowledge of the available options. 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:
20 pages, 7378 KiB  
Article
Insight into the lncRNA–mRNA Co-Expression Profile and ceRNA Network in Lipopolysaccharide-Induced Acute Lung Injury
by Yue Shen, Linjing Gong, Fan Xu, Sijiao Wang, Hanhan Liu, Yali Wang, Lijuan Hu and Lei Zhu
Curr. Issues Mol. Biol. 2023, 45(7), 6170-6189; https://doi.org/10.3390/cimb45070389 - 24 Jul 2023
Cited by 3 | Viewed by 1839
Abstract
Long non-coding RNAs (lncRNAs) participate in acute lung injury (ALI). However, their latent biological function and molecular mechanism have not been fully understood. In the present study, the global expression profiles of lncRNAs and mRNAs between the control and lipopolysaccharide (LPS)-stimulated groups of [...] Read more.
Long non-coding RNAs (lncRNAs) participate in acute lung injury (ALI). However, their latent biological function and molecular mechanism have not been fully understood. In the present study, the global expression profiles of lncRNAs and mRNAs between the control and lipopolysaccharide (LPS)-stimulated groups of human normal lung epithelial cells (BEAS-2B) were determined using high-throughput sequencing. Overall, a total of 433 lncRNAs and 183 mRNAs were differentially expressed. A lncRNA–mRNA co-expression network was established, and then the top 10 lncRNAs were screened using topological methods. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis results showed that the key lncRNAs targeting mRNAs were mostly enriched in the inflammatory-related biological processes. Gene set variation analysis and Pearson’s correlation coefficients confirmed the close correlation for the top 10 lncRNAs with inflammatory responses. A protein–protein interaction network analysis was conducted based on the key lncRNAs targeting mRNAs, where IL-1β, IL-6, and CXCL8 were regarded as the hub genes. A competing endogenous RNA (ceRNA) modulatory network was created with five lncRNAs, thirteen microRNAs, and twelve mRNAs. Finally, real-time quantitative reverse transcription-polymerase chain reaction was employed to verify the expression levels of several key lncRNAs in BEAS-2B cells and human serum samples. Full article
(This article belongs to the Special Issue Studying the Function of RNAs Using Omics Approaches)
Show Figures

Figure 1

Figure 1
<p>Workflow and ALI model establishment of this study. (<b>a</b>) Flowchart of this work. (<b>b</b>,<b>c</b>) EDU assay for cell proliferation detection, (<b>d</b>) cell viability calculation, and (<b>e</b>,<b>f</b>) ROS production in LPS-stimulated BEAS-2B cells (green portion) and control group (blue portion). Values are the mean ± SD; * <span class="html-italic">p</span> &lt; 0.05 vs. the control group, ** <span class="html-italic">p</span> &lt; 0.01 vs. the control group.</p>
Full article ">Figure 2
<p>The mRNA and lncRNA expression profiles in LPS-induced BEAS-2B cells. (<b>a</b>,<b>b</b>) The heatmap of differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs). Three LPS-induced samples and three control samples are incorporated; each column represents a sample and each row represents a transcript. (<b>c</b>,<b>d</b>) Numbers of DEmRNAs and DElncRNAs. (<b>e</b>,<b>f</b>) The volcano plots of DEmRNAs and DElncRNAs. The filtered transcripts with no significance are in grey, upregulated transcripts are in red, and downregulated transcripts are in green. The top 10 significantly changed mRNAs and lncRNAs are labeled in volcano plots.</p>
Full article ">Figure 3
<p>Correlation of DElncRNAs and DEmRNAs. (<b>a</b>) Co-expression Circos map of the original CNC network. The outer layer is the distribution diagram of chromosomes. The second and third layers represent DEmRNAs displayed on chromosomes. The red line indicates up-regulation while the green indicates down-regulation, and a higher column implies more genes in this interval. The innermost two layers were the distribution of DElncRNAs on chromosomes. (<b>b</b>) Correlation of the top 10 lncRNAs with highly correlated mRNAs (<span class="html-italic">p</span> &lt; 0.05, |r| &gt; 0.99). (<b>c</b>) Co-expression network of the top 10 lncRNAs and their correlated mRNAs. The red color represents up-regulation and green represents down-regulation, while the circle shape represents mRNAs and the triangle represents lncRNAs.</p>
Full article ">Figure 4
<p>Functional enrichment of cis- and trans-regulated genes of DElncRNAs in the original CNC network. (<b>a</b>) The result of cis-regulation. The left and right of the <span class="html-italic">Y</span> axis are mRNA and lncRNA, respectively, and the <span class="html-italic">X</span>-axis is the distance between mRNA and lncRNA. A negative value indicates upstream and a positive value indicates downstream. (<b>b</b>) The result of trans-regulation. The green triangle represents mRNAs, and the red circle represents lncRNAs. (<b>c</b>) GO analysis of cis- and trans-regulated genes. (<b>d</b>) KEGG pathway analysis of cis- and trans-regulated genes. * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 5
<p>Functional enrichment of DEmRNAs, correlated with the top 10 lncRNAs. The top 10 lncRNAs were selected by several topological parameters, using the CytoHubba algorithm in Cytoscape. (<b>a</b>) GO analysis and (<b>b</b>) visualization of distribution on DEmRNAs in seven GO-enriched terms. (<b>c</b>) KEGG pathway analysis and (<b>d</b>) the interaction network for KEGG-enriched pathways. The size of the circle represents gene number in the pathway, and the color of the circle represents adjustive <span class="html-italic">p</span>-value.</p>
Full article ">Figure 6
<p>Correlation analysis for the expression of the top 10 lncRNAs with immune and inflammatory function enrichment scores. (<b>a</b>) The first heatmap shows the expression of the top 10 lncRNAs and the enrichment scores of immune and inflammatory functions in each sample calculated by GSVA. (<b>b</b>) The second heatmap presents a correlation of the top 10 lncRNAs with different immune and inflammatory responses. * <span class="html-italic">p</span> &lt; 0.05 vs. the control group, ** <span class="html-italic">p</span> &lt; 0.01 vs. the control group, *** <span class="html-italic">p</span> &lt; 0.001 vs. the control group.</p>
Full article ">Figure 7
<p>PPI network and ceRNA network. (<b>a</b>) PPI network of DEmRNAs correlated with the top 10 lncRNAs. (<b>b</b>) Hub gene module 1 in PPI network selected by MCODE. (<b>c</b>) Hub gene module 2 in PPI network selected by MCODE. (<b>d</b>,<b>e</b>) KEGG and GO analysis of all the hub genes from module 1 and module 2. (<b>f</b>) LncRNA–miRNA–mRNA ceRNA network; the orange color represents lncRNAs, green represents microRNAs and yellow represents mRNAs.</p>
Full article ">Figure 8
<p>Validation of the expression profiles of key lncRNAs and mRNAs by RT-qPCR. (<b>a</b>–<b>e</b>) Validation of five randomly selected top lncRNAs and (<b>f</b>–<b>h</b>) three hub genes in the control group, as well as LPS-induced BEAS-2B cells. (<b>i</b>) The expression of ENST00000627824 in human serum samples of healthy participants and ALI/ARDS patients. The round dots represented healthy participants and the square dots represented ALI/ARDS patients. Data are presented as the mean ± SD. * <span class="html-italic">p</span> &lt; 0.05 vs. the control group, ** <span class="html-italic">p</span> &lt; 0.01 vs. the control group.</p>
Full article ">
16 pages, 2669 KiB  
Article
Identification of a Novel ERK5 (MAPK7) Inhibitor, MHJ-627, and Verification of Its Potent Anticancer Efficacy in Cervical Cancer HeLa Cells
by Jeonghye Hwang, Hyejin Moon, Hakwon Kim and Ki-Young Kim
Curr. Issues Mol. Biol. 2023, 45(7), 6154-6169; https://doi.org/10.3390/cimb45070388 - 24 Jul 2023
Cited by 3 | Viewed by 2695
Abstract
Extracellular signal-regulated kinase 5 (ERK5), a member of the mitogen-activated protein kinase (MAPK) family, is involved in key cellular processes. However, overexpression and upregulation of ERK5 have been reported in various cancers, and ERK5 is associated with almost every biological characteristic of cancer [...] Read more.
Extracellular signal-regulated kinase 5 (ERK5), a member of the mitogen-activated protein kinase (MAPK) family, is involved in key cellular processes. However, overexpression and upregulation of ERK5 have been reported in various cancers, and ERK5 is associated with almost every biological characteristic of cancer cells. Accordingly, ERK5 has become a novel target for the development of anticancer drugs as inhibition of ERK5 shows suppressive effects of the deleterious properties of cancer cells. Herein, we report the synthesis and identification of a novel ERK5 inhibitor, MHJ-627, and verify its potent anticancer efficacy in a yeast model and the cervical cancer HeLa cell line. MHJ-627 successfully inhibited the kinase activity of ERK5 (IC50: 0.91 μM) and promoted the mRNA expression of tumor suppressors and anti-metastatic genes. Moreover, we observed significant cancer cell death, accompanied by a reduction in mRNA levels of the cell proliferation marker, proliferating cell nuclear antigen (PCNA), following ERK5 inhibition due to MHJ-627 treatment. We expect this finding to serve as a lead compound for further identification of inhibitors for ERK5-directed novel approaches for oncotherapy with increased specificity. Full article
(This article belongs to the Special Issue Advanced Molecular Solutions for Cancer Therapy)
Show Figures

Figure 1

Figure 1
<p>Design and synthesis of the new 1,4-dialkoxynaphthalen-2-acyl imidazolium salt, MHJ-627.</p>
Full article ">Figure 2
<p>MHJ-627 suppressed the kinase activity of Mpk1 and attenuated <span class="html-italic">MLP1</span> expression in an <span class="html-italic">S. cerevisiae</span> model. (<b>a</b>) Schematic representation of Mpk1 regulation in the <span class="html-italic">S. cerevisiae</span> model system, which is functionally homologous to the human ERK5. Inactivation of Mpk1 activity results in downregulated transcriptional activity of Rlm1 transcription factor and subsequent decrease in <span class="html-italic">MLP1</span> expression; (<b>b</b>) effect of MHJ-627 on expression of <span class="html-italic">MLP1</span> measured by β-galactosidase activity. Yeasts were transformed with <span class="html-italic">MLP1</span>-<span class="html-italic">lacZ</span> reporter plasmid and treated with 15 μL of DMSO (control) and MHJ-627 in 3 mL of media. The data were calibrated to the control value (DMSO control = 1). Data are presented as mean ± SD. Each experiment was performed in duplicate and repeated at least three times. Two-tailed unpaired Student’s <span class="html-italic">t</span> test (*** <span class="html-italic">p</span> &lt; 0.001) was used for significance.</p>
Full article ">Figure 3
<p>MHJ-627 reduced the kinase activity of human ERK5 in vitro. Relative ERK5 kinase activity following MHJ-627 treatment was measured via in vitro kinase assay. Kinase activity of ERK5 was reduced dose-dependently, supporting ERK5-inhibitory activity of MHJ-627 in vitro. Relative ERK5 kinase activity of the 0 μM control was set as 1. Data are presented as mean ± SD. Each experiment was performed in duplicate and repeated at least three times. One-way ANOVA (** <span class="html-italic">p</span> &lt; 0.01) was used for significance. All values were compared to the 0 μM control value to determine the significance.</p>
Full article ">Figure 4
<p>MHJ-627 suppressed ERK5 kinase activity to activate AP-1 transcription factor. To determine the ability of ERK5 to activate the transcription factor AP-1, luciferase reporter plasmid was transformed into HeLa cells and qRT-PCR was conducted to measure the mRNA level of luciferase after 24 h compound treatment. There was a decrease in luciferase mRNA levels, indicating reduced activity of AP-1 possibly caused by suppressed activity of ERK5 to activate AP-1. Relative AP-1 activity of the 0 μM control was set as 1. Data are presented as mean ± SD. Each experiment was performed in duplicate and repeated at least three times. One-way ANOVA (** <span class="html-italic">p</span> &lt; 0.01) was used for significance. All values were compared to the 0 μM control value to determine the significance.</p>
Full article ">Figure 5
<p>Alteration in mRNA expression pattern of the genes influenced by ERK5 after MHJ-627 treatment. (<b>a</b>) Decrease in mRNA expression of <span class="html-italic">PCNA</span>, which is a cell proliferation marker; (<b>b</b>) increase in mRNA expression of <span class="html-italic">DDIT4</span>, which is reported to increase when ERK5 is inhibited; (<b>c</b>) increase in mRNA expression of genes that encode transcription factors; (<b>d</b>) increase in mRNA expression of genes that encode immune-related proteins. Relative mRNA expression of genes influenced by ERK5 was measured via qRT-PCR analysis after 24 h compound treatment in HeLa cells. Relative mRNA expression of the 0 μM control was set as 1. Data are presented as mean ± SD. Each experiment was performed in duplicate and repeated at least three times. One-way ANOVA (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01) was used for significance. All values were compared to the 0 μM control value to determine the significance.</p>
Full article ">Figure 6
<p>MHJ-627 paradoxically upregulated the expression and phosphorylation of ERK5, possibly due to the stimulatory crosstalk of the ERK1/2 pathway. (<b>a</b>) Western blot image depicting the elevations in ERK5 protein expression and phosphorylation. Effect of MHJ-627 on the protein expression and phosphorylation of ERK5 was measured via Western blot analysis after HeLa cells were treated with compounds for 24 h; (<b>b</b>) quantitation of Western blot showing a paradoxical increase in ERK5 expression; (<b>c</b>) quantitation of Western blot showing a trend of increase in ERK5 phosphorylation; (<b>d</b>) the increase in ERK5 expression and phosphorylation was due to the compensatory action of ERK1/2. GAPDH was used as a loading control. Relative protein expression of the 0 μM control was set as 1. Western blot data were quantified using ImageJ software. Data are presented as mean ± SD. Each experiment was performed in duplicate and repeated at least three times. One-way ANOVA (* <span class="html-italic">p</span> &lt; 0.05) was used for significance. All values were compared to the 0 μM control value to determine the significance.</p>
Full article ">Figure 7
<p>MHJ-627 had an anti-proliferative effect through inhibition of ERK5. (<b>a</b>) Effect of XMD8-92 on viability of HeLa cells showed that inhibition of ERK5 exhibited an anti-proliferative effect for HeLa cells; (<b>b</b>) effect of MHJ-627 on viability of HeLa cells showed its potent anti-proliferative efficacy. Cell viability was determined via MTT assay after 24 h and 48 h compound treatments with indicated concentration. XMD8-92 was used as a positive control. MHJ-627 showed higher cytotoxicity compared to the positive control, suggesting its potent anticancer efficacy and ERK5-inhibitory activity since ERK5 activity is necessary for the survival of HeLa cells. (0 μM control = 100%). Data are presented as mean ± SD. Each experiment was performed in duplicate and repeated at least three times. One-way ANOVA (* <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) was used for significance. All values were compared to the 0 μM control value to determine the significance.</p>
Full article ">
14 pages, 11567 KiB  
Article
Interactions of piRNAs with the mRNA of Candidate Genes in Esophageal Squamous Cell Carcinoma
by Aizhan Rakhmetullina, Aigul Akimniyazova, Togzhan Niyazova, Anna Pyrkova, Makpal Tauassarova, Anatoliy Ivashchenko and Piotr Zielenkiewicz
Curr. Issues Mol. Biol. 2023, 45(7), 6140-6153; https://doi.org/10.3390/cimb45070387 - 23 Jul 2023
Cited by 3 | Viewed by 1869
Abstract
Recently, a database of human piRNAs (piwi-interacting RNAs) was created, which allows the study of the binding of many piRNAs to the mRNAs of genes involved in many diseases, including cancer. In the present work, we identified the piRNAs that can interact with [...] Read more.
Recently, a database of human piRNAs (piwi-interacting RNAs) was created, which allows the study of the binding of many piRNAs to the mRNAs of genes involved in many diseases, including cancer. In the present work, we identified the piRNAs that can interact with candidate esophageal squamous cell carcinoma (ESCC) genes. The binding of 480 thousand piRNAs with the mRNAs of 66 candidate ESCC genes was studied. Bioinformatic studies found that piRNAs bind only to the mRNAs of nine candidate genes: AURKA, BMP7, GCOM1, ERCC1, MTHFR, SASH1, SIX4, SULT1A1, and TP53. It has been shown that piRNAs can bind to mRNA by overlapping nucleotide sequences in limited 3′UTR and 5′UTR regions called clusters of binding sites (BSs). The existence of clusters of piRNA BSs significantly reduces the proportion of the nucleotide sequences of these sites in the mRNA of target genes. Competition between piRNAs occurs for binding to the mRNA of target genes. Individual piRNAs and groups of piRNAs that have separate BSs and clusters of BSs in the mRNAs of two or more candidate genes have been identified in the mRNAs of these genes. This organization of piRNAs BSs indicates the interdependence of the expression of candidate genes through piRNAs. Significant differences in the ability of genes to interact with piRNAs prevent the side effects of piRNAs on genes with a lack of the ability to bind such piRNAs. Individual piRNAs and sets of piRNAs are proposed and recommended for the diagnosis and therapy of ESCC. Full article
(This article belongs to the Special Issue Studying the Function of RNAs Using Omics Approaches)
Show Figures

Figure 1

Figure 1
<p>Nucleotide sequences of BSs of piRNAs and mRNA of the <span class="html-italic">AURKA</span> gene in the region from 522 nt to 560 nt. Note: The mRNA nucleotides are highlighted in red. Canonical pairs of piRNA and mRNA nucleotides are shown in violet, whereas noncanonical pairs are marked in green. The piRNAs’ names are followed by the start of their BSs.</p>
Full article ">Figure 2
<p>Nucleotide sequences of piRNAs and the characteristics of their interaction with the mRNA of the <span class="html-italic">BMP7</span> gene. The mRNA nucleotides are highlighted in red. Canonical pairs of piRNA and mRNA nucleotides are shown in violet, whereas noncanonical pairs are marked in green.</p>
Full article ">Figure 3
<p>Nucleotide sequences of the BSs of piRNAs and the mRNA of the <span class="html-italic">ERCC1</span> gene in the region from 2674 nt to 2710 nt. Note: The mRNA nucleotides are highlighted in red. Canonical pairs of piRNA and mRNA nucleotides are shown in violet, whereas noncanonical pairs are marked in green. The piRNAs’ names are followed by the start of their BSs.</p>
Full article ">Figure 4
<p>Nucleotide sequences of piRNAs and characteristics of their interaction with the mRNA of the <span class="html-italic">GCOM1</span> gene. The mRNA nucleotides are highlighted in red. Canonical pairs of piRNA and mRNA nucleotides are shown in violet, whereas noncanonical pairs are marked in green.</p>
Full article ">Figure 5
<p>Nucleotide sequences of the BSs of piRNAs and the mRNA of the <span class="html-italic">MTHFR</span> gene in the region from 6861 nt to 6893 nt. Note: The mRNA nucleotides are highlighted in red. Canonical pairs of piRNA and mRNA nucleotides are shown in violet, whereas noncanonical pairs are marked in green. The piRNAs’ names are followed by the start of their BSs.</p>
Full article ">Figure 6
<p>Nucleotide sequences of piRNAs and the characteristics of their interaction with the mRNA of the <span class="html-italic">SASH1</span> gene. The mRNA nucleotides are highlighted in red. Canonical pairs of piRNA and mRNA nucleotides are shown in violet, whereas noncanonical pairs are marked in green.</p>
Full article ">Figure 7
<p>Nucleotide sequences of the BS cluster in the 3′UTR of the mRNA of the <span class="html-italic">SIX4</span> gene from 4114 nt and the nucleotide sequences of 15 piRNAs. Note: The mRNA nucleotides are highlighted in red. Canonical pairs of piRNA and mRNA nucleotides are shown in violet, whereas noncanonical pairs are marked in green. The piRNAs’ names are followed by the start of their BSs.</p>
Full article ">Figure 8
<p>Nucleotide sequences of the BSs of piRNAs and the mRNA of the <span class="html-italic">SULT1A1</span> gene in the region from 1507 nt to 1543 nt. Note: The mRNA nucleotides are highlighted in red. Canonical pairs of piRNA and mRNA nucleotides are shown in violet, whereas noncanonical pairs are marked in green. The piRNAs’ names are followed by the start of their BSs.</p>
Full article ">Figure 9
<p>Nucleotide sequences of piRNAs and the characteristics of their interaction with the mRNA of the <span class="html-italic">TP53</span> gene. Note: The mRNA nucleotides are highlighted in red. Canonical pairs of piRNA and mRNA nucleotides are shown in violet, whereas noncanonical pairs are marked in green.</p>
Full article ">Figure 10
<p>Schemes of the interaction of identical piRNAs with the mRNAs of the <span class="html-italic">AURKA</span> and <span class="html-italic">SIX4</span> genes. Note: The mRNA nucleotides are highlighted in red. Canonical pairs of piRNA and mRNA nucleotides are shown in violet, whereas noncanonical pairs are marked in green.</p>
Full article ">Figure 11
<p>Schemes of the interaction between piRNAs and the mRNAs of candidate genes with ΔG/ΔGm equal to 92% to 100% and a ΔG value equal to -180 kJ/mol and higher. Canonical pairs of piRNA and mRNA nucleotides are shown in violet, whereas noncanonical pairs are marked in green.</p>
Full article ">
24 pages, 34881 KiB  
Article
Small Leucine-Rich Proteoglycan PODNL1 Identified as a Potential Tumor Matrix-Mediated Biomarker for Prognosis and Immunotherapy in a Pan-Cancer Setting
by Geyang Dai, Yue Sun, Rui Wei and Ling Xi
Curr. Issues Mol. Biol. 2023, 45(7), 6116-6139; https://doi.org/10.3390/cimb45070386 - 22 Jul 2023
Cited by 3 | Viewed by 2446
Abstract
The podocan-like protein 1 (PODNL1), an important member of the small leucine-rich proteoglycans (SLRP) family, is a crucial component of the tumor microenvironment (TME). But its prognostic values and the role in the TME have not been systematically estimated in a pan-cancer setting. [...] Read more.
The podocan-like protein 1 (PODNL1), an important member of the small leucine-rich proteoglycans (SLRP) family, is a crucial component of the tumor microenvironment (TME). But its prognostic values and the role in the TME have not been systematically estimated in a pan-cancer setting. Targeting PODNL1, a systematic exploration into the TCGA datasets, reconciling with the analyses of single-cell transcriptomes and immunotherapeutic cohorts in cancers, and validation by tissue microarray-based multiplex immunofluorescence staining was performed. PODNL1 was significantly correlated with the poor prognosis and immunotherapeutic responses in various cancers. In-depth demonstration of molecular mechanisms indicated that PODNL1 expressions were notably positively correlated with cancer-associated fibroblast (CAF) infiltration levels in 33 types of cancers. It also positively correlated with the pan-fibroblast TGF-β response signature score, and the hallmarks including TGF-β, TNF-α, inflammatory response, apical junction, epithelial–mesenchymal transition and hedgehog in pan-cancer. Furthermore, high PODNL1 expressions were positively related with the regulation of tumor-promoting TGF-β signaling through downregulating SMAD2/3:4 heterotrimer regulations transcription and up-regulating the pathway restricted SMAD protein phosphorylation. Single-cell transcriptome analyses and immunofluorescence validations indicated that PODNL1 was predominantly expressed in the cancer cells and CAFs in various cancers. Additionally, the heterogeneity of cancer genotype–phenotype cross-talking was also observed associated with PODNL1. Our systematic study indicates that PODNL1 plays an important role in the complex regulation network of tumor progression, and lays a foundation for further exploration to develop PODNL1 as a valuable matrix-mediated biomarker for cancer immunotherapy and prognosis in a pan-cancer setting. Full article
(This article belongs to the Special Issue Targeting Tumor Microenvironment for Cancer Therapy, 2nd Edition)
Show Figures

Figure 1

Figure 1
<p>The workflow and experimental framework of this study. Targeting PODNL1, a systematic exploration into the datasets including TCGA, reconciling with the analyses of single-cell transcriptomes and immunotherapeutic cohorts in cancers was performed, aiming to evaluate the correlations between PODNL1 differential expression levels and prognostic value in a pan-cancer setting, and further systematically explore the related immune mechanism contextualizing the tumor environment and immunotherapy responses. Further validation was conducted by tissue microarray-based multiplex immunofluorescence staining. All the abbreviations are listed in <a href="#app1-cimb-45-00386" class="html-app">Table S1</a>.</p>
Full article ">Figure 2
<p>Differential expression analyses of <span class="html-italic">PODNL1</span> among cancers. (<b>A</b>) Expression profiles of <span class="html-italic">PODNL1</span> analyzed with TCGA and GTEX databases. (<b>B</b>) The expression levels of PODNL1 in tumor cell lines in CCLE database. (<b>C</b>) The expression levels of PODNL1 in 69 cell lines from the HPA database. (<b>D</b>) The correlations between the <span class="html-italic">PODNL1</span> expressions and the clinical stages in 33 types of cancers. (<b>E</b>) The correlations between the <span class="html-italic">PODNL1</span> expressions and the clinical stages in ACC, KIRC, KIRP, THCA, KICH, OV, BLCA, SKCM, COAD, STAD, ESCA, and TGCT. (<b>F</b>) The respective analyses of correlations between the <span class="html-italic">PODNL1</span> expressions and the molecular subtypes in SKCM, THCA, SARC, BLCA, TGCT, GBM, CESC, HNSC, PAAD, LUAD, KIRP, KIRC, LUSC, THYM, and BRCA. * <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. All the abbreviations are listed in <a href="#app1-cimb-45-00386" class="html-app">Table S1</a>.</p>
Full article ">Figure 3
<p>Pan-cancer survival analysis of <span class="html-italic">PODNL1</span> using TCGA database. Through analysis using univariate cox regression, the forest plots representing the <span class="html-italic">PODNL1</span> expressions significantly associated with the following: (<b>A</b>) Overall survival time in days (OS) in KIRC, LGG, BLCA, ACC, OV, LIHC, MESO and KIRP, which indicated that <span class="html-italic">PODNL1</span> was a high-risk gene in these cancers, particularly in ACC and KIRP. (<b>B</b>) Progression-free survival (PFS) in LGG, GBM, KIRC, ACC, BLCA, KIRP, OV, and MESO, which indicated that <span class="html-italic">PODNL1</span> was a high-risk gene, particularly in ACC and KIRC. (<b>C</b>) Disease-free survival (DFS) in HNSC and KIRP. (<b>D</b>) Disease-special survival (DSS) in ACC, LGG, KIRC, BLCA, GBM, KIRP, MESO, and OV. (<b>E</b>–<b>N</b>) The correlations of the high expression levels of PODNL1 with the poor OS and PFS of patients with cancers including LGG, KIRC, KIRP, BLCA, OV, ACC, MESO, GBM, PAAD metastasis and recurrence, and STAD Stage M0, analyzed by the Kaplan–Meier (<b>K</b>–<b>M</b>) method, and the ROC curves measuring their predictive diagnostic values of the <span class="html-italic">PODNL1</span> expressions.</p>
Full article ">Figure 4
<p>Correlation analyses between the <span class="html-italic">PODNL1</span> expressions and the infiltration levels of immune cells among pan-cancer. (<b>A</b>) Heatmap of the correlations between the expression levels of PODNL1 and Immune Score, ESTIMATEScore, StromalScore, and TumorPurity, respectively. (<b>B</b>) Heatmap of the correlations between the expression levels of PODNL1 and immune cells evaluated by the TIMER, CIBERSORT, CIBERSORT-ABS, XCELL, EPIC, MCPCOUNTER, QUANTISEQ, and TIDE algorithms. Red showed the positive correlations and purple the negative. * <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>Single-cell sequencing analyzing <span class="html-italic">PODNL1</span> co-expression in pan-cancer. (<b>A</b>) The expression levels of PODNL1 in cells analyzed via the single-cell transcriptomes of normal tissues in HPA datasets. (<b>B</b>–<b>F</b>) The expression levels of PODNL1 analyzed via the single-cell sequencing datasets of BLCA, HNSC, GBM, KIRC, and OV, respectively.</p>
Full article ">Figure 6
<p>Correlations of <span class="html-italic">PODNL1</span> expressions with the core molecules and biology pathways related to tumor immunotherapeutic responses. Correlation heatmaps: (<b>A</b>) Pathways (including Pan_F_TBRs, EMT markers, angiogenesis, ferroptosis, tumor inflammation signature, tumor proliferation signature, homologous recombination, G2M checkpoint, DNA replication, DNA repair and antigen-processing machinery). (<b>B</b>) Immune checkpoint. (<b>C</b>) Immunostimulator. (<b>D</b>) Chemokine. (<b>E</b>) Chemokine receptor. (<b>F</b>) MHC. (<b>G</b>) Tumor stem cell marker. (<b>H</b>) TMB. (<b>I</b>) MSI. * <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 7
<p>Correlations of <span class="html-italic">PODNL1</span> expressions with <span class="html-italic">TGF</span>-β signaling. (<b>A</b>) The correlations between <span class="html-italic">PODNL1</span> expressions and <span class="html-italic">TGF</span>-β signaling pathway genes. (<b>B</b>,<b>C</b>) The Metascape MCODE networks and the corresponding pathway enrichment analysis identified for positively related gene lists (in red column) and negatively ones (in purple column), respectively. (<b>D</b>) GeneMANIA protein–protein interaction (PPI) networks of PODNL1 were analyzed using its positively correlated receptors including TGFBR1, TGFBR2, ACVR1, ACVR1C, ACVR2A, ACVR2B, ACVRL1, BMP8A, BMPR2 and ENG. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 8
<p>Immunotherapy responses and biomarkers related with the expression levels of PODNL1 in cancer immunotherapeutic cohorts. (<b>A</b>–<b>D</b>) The IMvigor210 cohort: (<b>A</b>) The K-M curve of OS. (<b>B</b>,<b>C</b>) Bar and box plots of immunotherapy responses (CR: complete response; PR: partial response; SD: stable disease; PD: progressive disease) and (<b>D</b>) the areas under the ROC curves (AUCs) of the PODNL1 expression, TMB, NEO, and their combination. (<b>E</b>–<b>H</b>) The GSE78220 cohort: (<b>E</b>) The K-M plot of OS. (<b>F</b>,<b>G</b>) Bar and box plots of immunotherapy responses (CR/PR/SD/ PD) and (<b>H</b>) the AUCs of the PODNL1 expressions. (<b>I</b>,<b>J</b>) The K-M curves of <span class="html-italic">PODNL1</span> expression in immunotherapy cohorts analyzed using the K-M plotter platform: (<b>I</b>) Anti-PD-1 cohorts. (<b>J</b>) Anti-CTLA4 cohorts. (<b>K</b>) The correlations of high <span class="html-italic">PODNL1</span> expressions with Risk, Risk.adj and ROC in immunotherapy cohorts enumerated in TIDE platform.</p>
Full article ">Figure 9
<p>Functional analyses on <span class="html-italic">PODNL1</span> expressions among 33 types of cancers. (<b>A</b>) The correlated HALLMARK_CANCER in pan-cancer analyzed via GSVA. (<b>B</b>–<b>G</b>) Correlation analyses in BLCA, GBM, LGG, KIRC, KIRP, and OV via GSEA of KEGG. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 10
<p>Mutational landscape and methylation of <span class="html-italic">PODNL1</span> in pan-cancer. (<b>A</b>) Mutation frequency of <span class="html-italic">PODNL1</span> in pan-cancer. (<b>B</b>) Mutation lollipop chart of <span class="html-italic">PODNL1</span>. (<b>C</b>) Correlation table of <span class="html-italic">PODNL1</span> methylation and CTL Cor, T Dysfunction, Risk and Risk.adj in 21 types of cancers from TCGA database, analyzed using the TIDE platform. (<b>D</b>) Correlation heatmap of <span class="html-italic">PODNL1</span> methylation sites and prognosis of 25 different human cancers from TCGA database, analyzed using the Methsurv platform. The red color represents a positive correlation while the purple negative. (* <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 11
<p>Oncoplots showed the somatic landscapes of (<b>A</b>) BLCA, (<b>B</b>) HNSC, (<b>C</b>) KICH, (<b>D</b>) BRCA, (<b>E</b>) GBM, (<b>F</b>) LGG, (<b>G</b>) THCA and (<b>H</b>) THYM tumor cohorts. Genes are ordered by their mutation frequencies, samples are ordered by disease histology, as indicated by the annotation bar. Waterfall plot shows mutation information for each gene for each sample. Color annotation of various cancer types are shown at the bottom. The bar plot above the legend shows the number of mutation burden. (<b>I</b>) The mutation profiles of different genes in high- and low-PODNL1-expression groups: <span class="html-italic">TP53</span> in BLCA, HNSC, KICH, GBM and LGG, <span class="html-italic">PIK3CA</span> in BRCA, <span class="html-italic">BRAF</span> in THCA and <span class="html-italic">GTF2I</span> in THYM.</p>
Full article ">Figure 12
<p>Expression correlation analysis of PODNL1 at the whole genome level in pan-cancer. (<b>A</b>) Upset plot showed the genes correlated with <span class="html-italic">PODNL1</span> expressions in pan-cancer. (<b>B</b>) Venn plots showed the genes correlated with <span class="html-italic">PODNL1</span> expressions in five types of cancers including BLCA, KIRC, ACC, OV and GBM. (<b>C1</b>–<b>E5</b>) The expression correlations of <span class="html-italic">PODNL1</span> with (<b>C1</b>–<b>C5</b>) <span class="html-italic">COL1A1</span>, (<b>D1</b>–<b>D5</b>) <span class="html-italic">COL5A1</span>, and (<b>E1</b>–<b>E5</b>) LINC01614 in BLCA, KIRC, ACC, OV and GBM, respectively.</p>
Full article ">Figure 13
<p>The digital image analyses of the relationship between the protein levels of PODNL1 and FAP using the TMA-based multiplex immunofluorescence staining detected from 52 paired samples of 20 types of cancer. (<b>A</b>–<b>J</b>) The merged images of the multiplex immunofluorescence staining (50× and 500×), the DAPI nuclear is stained in blue, while the protein levels of PODNL1 and FAP, respectively, are stained in yellow and green; (<b>A</b>) LUAD; (<b>B</b>) BLCA; (<b>C</b>) KIRC; (<b>D</b>) BRCA; (<b>E</b>) COAD; (<b>F</b>) OV; (<b>G</b>) UCEC; (<b>H</b>) CESC; (<b>I</b>) LUSC; (<b>J</b>) PAAD; (<b>K</b>) Comparison of the PONDL1 H-scores between the pairs of tumor and matched adjacent normal tissues; (<b>L</b>) The relationship between the PONDL1 and FAP H-scores. ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">
19 pages, 3934 KiB  
Article
Structural Analysis of Mitochondria in Cardiomyocytes: Insights into Bioenergetics and Membrane Remodeling
by Raquel A. Adams, Zheng Liu, Chongere Hsieh, Michael Marko, W. Jonathan Lederer, M. Saleet Jafri and Carmen Mannella
Curr. Issues Mol. Biol. 2023, 45(7), 6097-6115; https://doi.org/10.3390/cimb45070385 - 21 Jul 2023
Cited by 7 | Viewed by 2784
Abstract
Mitochondria in mammalian cardiomyocytes display considerable structural heterogeneity, the significance of which is not currently understood. We use electron microscopic tomography to analyze a dataset of 68 mitochondrial subvolumes to look for correlations among mitochondrial size and shape, crista morphology and membrane density, [...] Read more.
Mitochondria in mammalian cardiomyocytes display considerable structural heterogeneity, the significance of which is not currently understood. We use electron microscopic tomography to analyze a dataset of 68 mitochondrial subvolumes to look for correlations among mitochondrial size and shape, crista morphology and membrane density, and organelle location within rat cardiac myocytes. A tomographic analysis guided the definition of four classes of crista morphology: lamellar, tubular, mixed and transitional, the last associated with remodeling between lamellar and tubular cristae. Correlations include an apparent bias for mitochondria with lamellar cristae to be located in the regions between myofibrils and a two-fold larger crista membrane density in mitochondria with lamellar cristae relative to mitochondria with tubular cristae. The examination of individual cristae inside mitochondria reveals local variations in crista topology, such as extent of branching, alignment of fenestrations and progressive changes in membrane morphology and packing density. The findings suggest both a rationale for the interfibrillar location of lamellar mitochondria and a pathway for crista remodeling from lamellar to tubular morphology. Full article
(This article belongs to the Special Issue Mitochondrial Function and Dysfunction)
Show Figures

Figure 1

Figure 1
<p>Central slices from tomograms representing the four classes of crista morphology in the dataset, as described in text. (<b>A</b>,<b>B</b>) Lamellar cristae in orthodox (<b>A</b>) and condensed (<b>B</b>) conformations. (<b>C</b>,<b>D</b>) Tubular cristae in predominantly transverse (<b>C</b>) and longitudinal (<b>D</b>) views. (<b>E</b>,<b>F</b>) Transitional crista morphologies. (<b>G</b>,<b>H</b>) Mixed cristae with about equal fractions of lamellar—tubular (<b>G</b>) and tubular—transitional (<b>H</b>) morphologies. Scale bars in (<b>A</b>,<b>F</b>) represent 250 nm. The scale bar in (<b>A</b>) also applies to tomographic slices (<b>B</b>−<b>E</b>,<b>G</b>,<b>H</b>).</p>
Full article ">Figure 2
<p>Correlations between aspect ratios and areas (A<sub>MIT</sub>) of mitochondrial cross sections in central tomographic slices. (<b>A</b>) All mitochondria. (<b>B</b>) Only mitochondria with lamellar and tubular crista morphologies. (<b>C</b>) Mitochondria located in interfibrillar regions (IFM). (<b>D</b>) Mitochondria not located in interfibrillar regions (NonIFM). (<b>E</b>) Rectangular boundaries enclosing all but one IFM and all but two NonIFM, with corresponding % of mitochondria with lamellar and tubular crista morphologies. Diagonally striped symbols correspond to mitochondria with a few locally swollen cristae (described in text).</p>
Full article ">Figure 3
<p>Chart of mean areas and aspect ratios (standard deviations indicated) for the cross-section profiles of all mitochondria in the dataset, classified according to crista morphology as described in the text. An unpaired T-test (GraphPad Prism 9) was used to compare values for <span class="html-italic">Lam</span> mitochondria vs. each of the other three crista morphology classes. Statistically significant two-tailed <span class="html-italic">p</span>-values were obtained for areas and aspect ratios with <span class="html-italic">Lam</span> vs. <span class="html-italic">Tub</span> (0.017, 0.009) and <span class="html-italic">Lam</span> vs. <span class="html-italic">Mix</span> (0.027, 0.017), but not <span class="html-italic">Lam</span> vs. <span class="html-italic">Trans</span> (0.108, 0.07) mitochondria.</p>
Full article ">Figure 4
<p>Examples of manually traced membranes in tomographic slices used to calculate crista membrane density. (<b>A</b>) An interfibrillar mitochondrion (IFM) with lamellar cristae. (<b>B</b>,<b>B’</b>) A non-interfibrillar mitochondrion (nonIFM) with tubular cristae. (<b>C</b>,<b>C’</b>) A nonIFM with transitional cristae and local swelling of three cristae. (<b>D</b>) The large nonIFM with lamellar cristae. Color coding of traces in (<b>A</b>,<b>C</b>,<b>C’</b>,<b>D</b>) indicates cristae that are interconnected immediately above or below the central slice shown. Color coding in (<b>B</b>,<b>B’</b>) is random since interconnectivity of tubular cristae is difficult to assess, as explained in text. Scale bars correspond to 250 nm. Scale bar in (<b>A</b>) also applies to (<b>B</b>,<b>B’</b>,<b>D</b>). Scale bar in (<b>C</b>) also applies to (<b>C’</b>).</p>
Full article ">Figure 5
<p>Dependence of crista density on the (<b>A</b>) mitochondrial cross-section area and (<b>B</b>) extent of inner membrane folding for the subset of cardiomyocyte mitochondria described in the text. Crista densities were measured for central tomographic slices as L<sub>CRIS</sub>/A<sub>MIT</sub> where L<sub>CRIS</sub> is the length of crista membranes contained within area A<sub>MIT</sub>, and extent of IM folding is the ratio of L<sub>CRIS</sub> to L<sub>IM</sub>, the length of crista membranes plus IBM. Symbols are the same as in <a href="#cimb-45-00385-f002" class="html-fig">Figure 2</a>.</p>
Full article ">Figure 6
<p>Surface models of three cristae from the <span class="html-italic">Trans</span> mitochondrion of <a href="#cimb-45-00385-f004" class="html-fig">Figure 4</a>C,C’, displayed individually (<b>A</b>–<b>C</b>) and co-aligned as in the mitochondrion (<b>D</b>). Each view in the rows represents successive rotation by 45<b>°</b>. Arrows in (<b>A</b>) point to rows of fenestrations. Crista regions with flat, closely apposed membranes without fenestrations are circled in (<b>B</b>,<b>C</b>), as explained in text. Scale bar = 250 nm.</p>
Full article ">Figure 7
<p>Surface models of the <span class="html-italic">Lam</span> mitochondrion of <a href="#cimb-45-00385-f004" class="html-fig">Figure 4</a>D. (<b>A</b>) Top view of the tomogram with individual cristae sealed at top and bottom (for visual simplicity). Cristae are color coded; left to right: blue #1–4, purple #5–10, green #11, purple #12, brown #13, black #14, green #15, pink #16. (<b>B</b>) Top view of the upper-right quadrant, with open (unsealed) cristae. 2X zoom and colors changed to better discern crista branches and junctions with the IBM. (<b>C</b>) Side view of the mitochondrion (after 90° clockwise rotation) revealing the openings in the IBM corresponding to crista junctions (CJs), several of which are slots in this region. (<b>D</b>) Side view of a typical lamellar crista (#3) showing fenestrations. (<b>E</b>) Oblique view of the “transitional” crista (#14), which has a length of 1.1 μm. Scale bars = 200 nm. The scale bar in (<b>C</b>) also applies to (<b>B</b>).</p>
Full article ">Figure 8
<p>Structural parameters for individual cristae in the <span class="html-italic">Lam</span> mitochondrion of <a href="#cimb-45-00385-f007" class="html-fig">Figure 7</a>. Cristae are numbered from left to right as in <a href="#cimb-45-00385-f007" class="html-fig">Figure 7</a>A. (<b>A</b>) Crista volumes, V<sub>CRIS</sub>. The large increases in V<sub>CRIS</sub> correspond to extensive branching near the middle of the mitochondrion. (<b>B</b>) Surface-to-volume ratios, S<sub>CRIS</sub>/V<sub>CRIS</sub>. (<b>C</b>) Volume per crista junction opening, V<sub>CRIS</sub>/N<sub>CJ</sub>.</p>
Full article ">Figure 9
<p>Dependence of flux of ATP Synthase, J(AS), on shape, length and volume (V<sub>CRIS</sub>) of cristae. Data is re-plotted from published computer simulations that used idealized 3-D spatial models of mitochondria and a reduced mathematical model for ATP production [<a href="#B13-cimb-45-00385" class="html-bibr">13</a>]. Physiological conditions were used for which J(AS) varies directly with the matrix [ADP] (cytosolic [ADP] set to 37 μM). Spatial models: circles, 4 × 4 parallel array of 20-nm-wide tubes spaced 20 nm apart; squares, four parallel lamellar compartments, walls spaced 20 nm apart with widths of 150 nm (open symbols) or 450 nm (closed symbols). Crista lengths are indicated in black and S/V ratios in red. The reference J(AS)<sub>MAX</sub> (flux without crista compartments) is the same for all three curves. Vertical blue arrows connect data points for different crista types of equivalent volumes.</p>
Full article ">
12 pages, 573 KiB  
Review
Exosome Analysis in Prostate Cancer: How They Can Improve Biomarkers’ Performance
by Stefano Salciccia, Marco Frisenda, Giulio Bevilacqua, Luca Gobbi, Bruno Bucca, Martina Moriconi, Pietro Viscuso, Alessandro Gentilucci, Gianna Mariotti, Susanna Cattarino, Flavio Forte, Stefano Fais, Mariantonia Logozzi, Beatrice Sciarra and Alessandro Sciarra
Curr. Issues Mol. Biol. 2023, 45(7), 6085-6096; https://doi.org/10.3390/cimb45070384 - 21 Jul 2023
Cited by 7 | Viewed by 2645
Abstract
Exosomes are extracellular nanovesicles (EV), that is, carriers of different biomolecules such as lipids, proteins, nucleic acids. Their composition and the fact that their release dramatically increases in cases of tumorigenesis open up different scenarios on their possible application to research into new [...] Read more.
Exosomes are extracellular nanovesicles (EV), that is, carriers of different biomolecules such as lipids, proteins, nucleic acids. Their composition and the fact that their release dramatically increases in cases of tumorigenesis open up different scenarios on their possible application to research into new biomarkers. The first purpose of the present review was to specifically analyze and compare different methodologies available for the use of exosomes in prostate cancer (PC). The most widely applied methodologies include ultracentrifugation techniques, size-based techniques, immunoaffinity capture-based techniques (mainly ELISA), and precipitation. To optimize the acquisition of exosomes from the reference sample, more techniques can be applied in sequence for a single extraction, thereby determining an increase in labor time and costs. The second purpose was to describe clinical results obtained with the analysis of PSA-expressing exosomes in PC; this provides an incredibly accurate method of discriminating between healthy patients and those with prostate disease. Specifically, the IC-ELISA alone method achieved 98.57% sensitivity and 80.28% specificity in discriminating prostate cancer (PC) from benign prostatic hyperplasia (BPH). An immunocapture-based ELISA assay was performed to quantify and characterize carbonic anhydrase (CA) IX expression in exosomes. The results revealed that CA IX positive exosomes were 25-fold higher in plasma samples from PC patients than in those from healthy controls. The analysis of PC-linked exosomes represents a promising diagnostic model that can effectively distinguish patients with PC from those with non-malignant prostatic disease. However, the use of exosome analysis in clinical practice is currently limited by several issues, including a lack of standardization in the analytical process and high costs, which are still too high for large-scale use. Full article
(This article belongs to the Special Issue Exosomes in Cancers)
Show Figures

Figure 1

Figure 1
<p>Exosome isolation and characterization in prostate cancer cases. (<b>1</b>) PSA exosome extraction. From blood samples, after centrifugation, plasma is obtained. The protocol includes nanoparticle tracking analysis (NTA) for the quality control of plasmatic samples after ultracentrifugation; afterwards, both nanoscale flow cytometry and an immunocapture-based ELISA are used for the extracellular vesicles’ characterization and quantification. In both the analyses, an antibody specific for a typical exosome antigen (CD81) is exploited to identify exosomes within the pool of extracellular vesicles, and an antibody for PSA is used for the detection of plasmatic exosomes expressing PSA. (<b>2</b>) Carbonic anhydrase IX exosome extraction. Human plasma samples are collected from EDTA-treated whole blood. Nanoparticle tracking analysis (NTA) is used for size distribution and concentration measurements of exosome samples in liquid suspension. A Western blot analysis is performed. An ELISA for CA is performed, and then the exosomal pH is evaluated using nanoscale flow cytometry. Intracellular acidity is analyzed via confocal microscopy using fluorescent tracers. Figure created with BioRender.com, accessed on 10 May 2023.</p>
Full article ">
18 pages, 12742 KiB  
Article
Whole Transcriptome Analysis of Intervention Effect of Sophora subprostrate Polysaccharide on Inflammation in PCV2 Infected Murine Splenic Lymphocytes
by Yi Zhao, Nina Jia, Xiaodong Xie, Qi Chen and Tingjun Hu
Curr. Issues Mol. Biol. 2023, 45(7), 6067-6084; https://doi.org/10.3390/cimb45070383 - 20 Jul 2023
Cited by 2 | Viewed by 1747
Abstract
(1) Background: Sophora subprostrate, is the dried root and rhizome of Sophora tonkinensis Gagnep. Sophora subprostrate polysaccharide (SSP1) was extracted from Sophora subprostrate, which has shown good anti-inflammatory and antioxidant effects. Previous studies showed SSP1 could modulate inflammatory damage induced by [...] Read more.
(1) Background: Sophora subprostrate, is the dried root and rhizome of Sophora tonkinensis Gagnep. Sophora subprostrate polysaccharide (SSP1) was extracted from Sophora subprostrate, which has shown good anti-inflammatory and antioxidant effects. Previous studies showed SSP1 could modulate inflammatory damage induced by porcine circovirus type 2 (PCV2) in murine splenic lymphocytes, but the specific regulatory mechanism is unclear. (2) Methods: Whole transcriptome analysis was used to characterize the differentially expressed mRNA, lncRNA, and miRNA in PCV2-infected cells and SSP1-treated infected cells. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and other analyses were used to screen for key inflammation-related differentially expressed genes. The sequencing results were verified by RT-qPCR, and western blot was used to verify the key protein in main enriched signal pathways. (3) Results: SSP1 can regulate inflammation-related gene changes induced by PCV2, and its interventional mechanism is mainly involved in the key differential miRNA including miR-7032-y, miR-328-y, and miR-484-z. These inflammation-related genes were mainly enriched in the TNF signal pathway and NF-κB signal pathway, and SSP1 could significantly inhibit the protein expression levels of p-IκB, p-p65, TNF-α, IRF1, GBP2 and p-SAMHD1 to alleviate inflammatory damage. (4) Conclusions: The mechanism of SSP1 regulating PCV2-induced murine splenic lymphocyte inflammation was explored from a whole transcriptome perspective, which provides a theoretical basis for the practical application of SSP1. Full article
(This article belongs to the Special Issue Bioactives and Inflammation)
Show Figures

Figure 1

Figure 1
<p>The structure of SSP1.</p>
Full article ">Figure 2
<p>Histogram, cluster heat map and Venn chart of mRNA and LncRNA. 1607 differentially expressed mRNAs (<b>A</b>) and 549 lncRNAs (<b>F</b>) between C group and V group (FC ≥ 1.2, <span class="html-italic">p</span> &lt; 0.05). 630 differentially expressed mRNAs (<b>C</b>) and 357 lncRNAs (<b>H</b>) between V group and SV group (FC ≥ 1.2, <span class="html-italic">p</span> &lt; 0.05). Heat map generated by hierarchical clustering analysis of differentially expressed top 20 mRNAs (<b>B</b>,<b>D</b>) lncRNAs (<b>G</b>,<b>I</b>). 158 differentially co-expressed mRNAs (<b>E</b>) and 62 lncRNAs (<b>J</b>) were shown in Venn chart.</p>
Full article ">Figure 3
<p>Histogram, cluster heat map and Venn chart of mRNA and LncRNA. 144 differentially expressed miRNAs (<b>A</b>) and 21 circRNAs (<b>F</b>) between C group and V group (FC ≥ 1.2, <span class="html-italic">p</span> &lt; 0.05). 117 differentially expressed mRNAs (<b>C</b>) and 23 circRNAs (<b>H</b>) between V group and SV group (FC ≥ 1.2, <span class="html-italic">p</span> &lt; 0.05). Heat map generated by hierarchical clustering analysis of differentially expressed top 20 miRNAs (<b>B</b>,<b>D</b>) circRNAs (<b>G</b>,<b>I</b>). 45 differentially co-expressed miRNAs (<b>E</b>) and 3 circRNAs (<b>J</b>) were shown in Venn chart.</p>
Full article ">Figure 4
<p>Heat map of 19 differentially co-expression mRNAs related to inflammatory responses. Red represents higher expression and blue represents lower expression.</p>
Full article ">Figure 5
<p>GO term analysis of DEGs. C group and V group: Gene Ontology annotation of mRNA in BP (<b>A</b>), MF (<b>B</b>), CC (<b>C</b>). V group and SV group: Top 20 enrichment genes in BP (<b>D</b>), MF (<b>E</b>), CC (<b>F</b>). GO, Gene Ontology; CC, cellular component; MF, molecular function; BP, biological process.</p>
Full article ">Figure 6
<p>KEGG analysis of DE mRNAs. Enrichment and screening of KEGG pathway in C group and V group resulted in 325 signaling pathways, involving IL-17 signaling pathway, interaction of viral protein with cytokine and cytokine receptor, p53 signaling pathway, TNF signaling pathway, cytokine-cytokine receptor interaction, chemokine signaling pathway, C-type lectin receptor signaling pathway (<b>A</b>,<b>B</b>,<b>E</b>). 286 signaling pathways were obtained by enrichment and screening of the KEGG pathway in the V group and the SV group, which mainly involved primary immunodeficiency, natural killer cell-mediated cytotoxicity, NF-κB signaling pathway, B-cell receptor signaling pathway, FcγR-mediated phagocytosis, PI3K-Akt signaling pathway, cytokine-cytokine receptor interaction and TNF signaling pathway (<b>C</b>,<b>D</b>,<b>F</b>).</p>
Full article ">Figure 7
<p>Trend cluster maps from DEGs by STEM analysis. (<b>A</b>) 100 DE mRNAs were down-regulated. (<b>B</b>) 397 DE mRNAs were down-regulated and then stabilized. (<b>C</b>) 257 DE mRNAs were down-regulated and then up-regulated. (<b>D</b>) 141 DE mRNAs were stabilized and then down-regulated. (<b>E</b>) 158 DE mRNAs were stabilized and then up-regulated. (<b>F</b>) 257 DE mRNAs were up-regulated and then down-regulated. (<b>G</b>) 686 DE mRNAs were up-regulated and then stabilized. (<b>H</b>) 129 DE mRNAs were up-regulated.</p>
Full article ">Figure 8
<p>RT-qPCR validation of the expression of miRNA, lncRNA and mRNA (<span class="html-italic">n</span> = 3). Differentially expressed mRNA, LncRNA and miRNA were randomly chosen to validate the reliability of the RNA-seq analysis (<span class="html-italic">n</span> = 3). (<b>A</b>–<b>R</b>) The relative expression of partial mRNAs; (<b>S</b>–<b>X</b>) The relative expression of partial LncRNAs; (<b>ZA</b>–<b>ZD</b>) The relative expression of partial miRNAs. * represented significant or extremely significant difference compared with C group; # meant significant or extremely significant difference compared with V group.</p>
Full article ">Figure 9
<p>Effect of SSP1 on NF-κB/TNF/ cytosolic DNA sensing pathway in PCV2-infected murine splenic lymphocytes. (<b>A</b>) The protein bands of p-p65, p65, p-IκB and IκB. (<b>B</b>) Analysis of protein gray value of p-p65 / p65 and p-IκB / IκB. (<b>C</b>) Analysis of protein gray value of GBP2. (<b>D</b>) The protein bands of p-SAMHD1, SAMHD1, TNF-α, IRF1, and GBP2. (<b>E</b>) Analysis of protein gray value of TNF-α and IRF1. (<b>F</b>) Analysis of protein gray value of p-SAMHD1/ SAMHD1. * or ** represented significant or extremely significant difference compared with C group; ## meant significant or extremely significant difference compared with V group.</p>
Full article ">Figure 10
<p>Competing endogenous RNAs (ceRNAs) network construction. Differentially expressed mRNAs, miRNAs and lncRNAs, and in the C group and V group (<b>A</b>), and in the V group and SV group (<b>B</b>) were integrated to construct the ceRNA network. The up-regulated mRNA was represented as a red circle, while the down-regulated mRNA was represented as a blue circle. The up-regulated LncRNA was represented by a red diamond, while the down-regulated lncRNA was represented by a blue diamond. The up-regulated miRNA is represented by a red triangle, while the down-regulated miRNA is represented by a blue triangle.</p>
Full article ">Figure 11
<p>LncRNA, mRNA and miRNA interaction network diagram in V group and SV group.</p>
Full article ">
12 pages, 1350 KiB  
Article
Molecular Characterisation of Mycobacterium bovis Isolates from Cattle Slaughtered in Adamawa and Gombe States, North-Eastern Nigeria
by Sadiq Mohammed Damina, David Atomanyi Barnes, Bitrus Inuwa, Gulak Hussaini Ularamu, Mohammed Bello, Olu Solomon Okaiyeto, Ayuba Caleb Kudi, Jeewan Thapa, Chie Nakajima and Yasuhiko Suzuki
Curr. Issues Mol. Biol. 2023, 45(7), 6055-6066; https://doi.org/10.3390/cimb45070382 - 19 Jul 2023
Cited by 1 | Viewed by 1693
Abstract
Bovine tuberculosis is endemic in Nigeria with control measures as provided by the laws of the country being minimally enforced mostly at the abattoirs only. This study focused on bovine tuberculosis in Adamawa and Gombe States. Tuberculosis lesions were observed in 183 of [...] Read more.
Bovine tuberculosis is endemic in Nigeria with control measures as provided by the laws of the country being minimally enforced mostly at the abattoirs only. This study focused on bovine tuberculosis in Adamawa and Gombe States. Tuberculosis lesions were observed in 183 of 13,688 slaughtered cattle in the regions between June and December 2020. Analysis of tissue samples resulted in 17 Mycobacterium bovis isolates, predominantly from Gombe State. Spoligotyping identified four spoligotypes, including SB0944, SB1025, SB1104, and one novel pattern. MIRU-VNTR analysis further differentiated these spoligotypes into eight profiles. All isolates belonged to the Af1 clonal complex. The study emphasises the need for broader coverage and more isolates to comprehensively understand the molecular epidemiology of bovine tuberculosis in Nigeria. To enhance research and surveillance, a cost-effective approach is proposed, utilising a discriminatory VNTR panel comprising five or nine loci. The five-locus panel consists of ETR-C, QUB26, QUB11b, MIRU04, and QUB323. Alternatively, the nine-locus panel includes ETR-A, ETR-B, QUB11a, and MIRU26. Implementing this approach would provide valuable insights into the genetic diversity of M. bovis strains in Nigeria. These findings are crucial for developing effective control measures and minimising the impact of bovine tuberculosis on both animal and human health. Full article
Show Figures

Figure 1

Figure 1
<p>Map of Nigeria showing the neighbouring countries as well as the sample study sites. A: Gombe State; B: Adamawa State. ArcGIS 10.2.2 Software esri, available online: <a href="https://www.esri.com" target="_blank">https://www.esri.com</a> (accessed on 12 July 2023).</p>
Full article ">Figure 2
<p>Spoligotype and MIRU-VNTR profiles of isolates.</p>
Full article ">Figure 3
<p>UPGMA tree phylogenetic analysis of <span class="html-italic">Mycobacterium bovis</span> isolates from cattle slaughtered in Adamawa and Gombe states revealing relationships based on 24 MIRU-VNTR loci and spoligotype patterns (Blue: SB0944; Green: SB1104; Purple: SB1025; Red: New spoligotype pattern).</p>
Full article ">
15 pages, 1644 KiB  
Article
EMT Features in Claudin-Low versus Claudin-Non-Suppressed Breast Cancers and the Role of Epigenetic Modifications
by Ioannis A. Voutsadakis
Curr. Issues Mol. Biol. 2023, 45(7), 6040-6054; https://doi.org/10.3390/cimb45070381 - 19 Jul 2023
Cited by 3 | Viewed by 1755
Abstract
Background: Breast cancers are heterogeneous and are classified according to the expression of ER, PR and HER2 receptors to distinct groups with prognostic and therapeutic implications. Within the triple-negative group, with no expression of these three receptors, molecular heterogeneity exists but is currently [...] Read more.
Background: Breast cancers are heterogeneous and are classified according to the expression of ER, PR and HER2 receptors to distinct groups with prognostic and therapeutic implications. Within the triple-negative group, with no expression of these three receptors, molecular heterogeneity exists but is currently not exploited in the clinic. The claudin-low phenotype is present in a subset of triple-negative breast cancers and constitutes together with basal-like cancers the most extensive groups within triple-negative breast cancers. Suppression of epithelial cell adhesion molecules in claudin-low cancers is also a hallmark of Epithelial Mesenchymal Transition (EMT). Methods: The groups of claudin-low and claudin-non-suppressed breast cancers from the extensive publicly available genomic cohorts of the METABRIC study were examined to delineate and compare their molecular landscape. Genetic and epigenetic alterations of key factors involved in EMT and potentially associated with the pathogenesis of the claudin-low phenotype were analyzed in the two groups. Results: Claudin-low cancers displayed up-regulation of several core transcription factors of EMT at the mRNA level, compared with claudin-non-suppressed breast cancers. Global promoter DNA methylation was increased in both groups of triple-negative cancers and in claudin-low ER-positive cancers compared with the rest of ER-positive cancers. Histone modifier enzymes, including methyltransferases, demethylases, acetyltransferases and deacetylases displayed amplifications more frequently in claudin-non-suppressed triple-negative cancers than in claudin-low counterparts and the expression of some of these enzymes differed significantly between the two groups. Conclusion: Claudin-low and claudin-non-suppressed triple-negative breast cancers differ in their landscape of EMT core regulators and epigenetic regulators. These differences may be explored as targets for therapeutic interventions specific to the two groups of triple-negative breast cancers. Full article
(This article belongs to the Special Issue Advances in Molecular Pathogenesis Regulation in Cancer)
Show Figures

Figure 1

Figure 1
<p>mRNA expression calculated as z-scores relative to all samples (log RNA Seq RPKM) of claudins 3, 4 and 7, E cadherin and occludin in representative breast cancer cases. (<b>A</b>). ER-negative/PR-negative, basal-like cancers, (<b>B</b>). ER-negative/PR-negative, claudin-low cancers, (<b>C</b>). ER-positive/HER2-negative/proliferation-low, luminal A cancers, (<b>D</b>). ER-positive/HER2-negative/proliferation-low, claudin-low cancers. Data are from the METABRIC cohort. Red color denotes up-regulation and blue denotes suppression. Gene symbols <span class="html-italic">CLDN3</span>: Claudin 3, <span class="html-italic">CLDN4</span>: Claudin 4, <span class="html-italic">CLDN7</span>: Claudin 7, <span class="html-italic">CDH1</span>: E cadherin, <span class="html-italic">OCLN</span>: Occludin.</p>
Full article ">Figure 1 Cont.
<p>mRNA expression calculated as z-scores relative to all samples (log RNA Seq RPKM) of claudins 3, 4 and 7, E cadherin and occludin in representative breast cancer cases. (<b>A</b>). ER-negative/PR-negative, basal-like cancers, (<b>B</b>). ER-negative/PR-negative, claudin-low cancers, (<b>C</b>). ER-positive/HER2-negative/proliferation-low, luminal A cancers, (<b>D</b>). ER-positive/HER2-negative/proliferation-low, claudin-low cancers. Data are from the METABRIC cohort. Red color denotes up-regulation and blue denotes suppression. Gene symbols <span class="html-italic">CLDN3</span>: Claudin 3, <span class="html-italic">CLDN4</span>: Claudin 4, <span class="html-italic">CLDN7</span>: Claudin 7, <span class="html-italic">CDH1</span>: E cadherin, <span class="html-italic">OCLN</span>: Occludin.</p>
Full article ">Figure 2
<p>mRNA expression calculated as z-scores relative to all samples (log RNA Seq RPKM) of transcription regulators ZEB1, ZEB2, SNAIL (Gene symbol: SNAI1), Slug (Gene symbol: SNAI2), FOXC2 and TWIST1 in representative breast cancer cases. (<b>A</b>). ER-negative/PR-negative, basal-like cancers, (<b>B</b>). ER-negative/PR-negative, claudin-low cancers, (<b>C</b>). ER-positive/HER2-negative/proliferation-low, luminal A cancers, (<b>D</b>). ER-positive/HER2-negative/proliferation-low, claudin-low cancers. Data are from the METABRIC cohort. Red color denotes up-regulation and blue denotes suppression.</p>
Full article ">Figure 2 Cont.
<p>mRNA expression calculated as z-scores relative to all samples (log RNA Seq RPKM) of transcription regulators ZEB1, ZEB2, SNAIL (Gene symbol: SNAI1), Slug (Gene symbol: SNAI2), FOXC2 and TWIST1 in representative breast cancer cases. (<b>A</b>). ER-negative/PR-negative, basal-like cancers, (<b>B</b>). ER-negative/PR-negative, claudin-low cancers, (<b>C</b>). ER-positive/HER2-negative/proliferation-low, luminal A cancers, (<b>D</b>). ER-positive/HER2-negative/proliferation-low, claudin-low cancers. Data are from the METABRIC cohort. Red color denotes up-regulation and blue denotes suppression.</p>
Full article ">Figure 3
<p>Percentage of amplifications of transcription regulators ZEB1, ZEB2, SNAIL (Gene symbol: <span class="html-italic">SNAI1</span>), Slug (Gene symbol: <span class="html-italic">SNAI2</span>), FOXC2 and TWIST1 in ER-negative/PR-negative, basal-like breast cancers (grey bars) and ER-negative/PR-negative, claudin-low breast cancers (black bars). Data are from the METABRIC cohort. Fisher’s exact test <span class="html-italic">p</span> = 0.02 for the comparison of the presence of amplifications in any core EMT transcription factor in claudin-low versus basal-like breast cancers.</p>
Full article ">Figure 4
<p>mRNA expression calculated as z-scores relative to all samples (log RNA Seq RPKM) of methylation-sensitive genes in representative breast cancer cases. (<b>A</b>). ER-negative/PR-negative, basal-like cancers, (<b>B</b>). ER-negative/PR-negative, claudin-low cancers, (<b>C</b>). ER-positive/HER2-negative/proliferation-low, luminal A cancers, (<b>D</b>). ER-positive/HER2-negative/proliferation-low, claudin-low cancers. Data are from the METABRIC cohort. Red color denotes up-regulation and blue denotes suppression.</p>
Full article ">Figure 5
<p>mRNA expression calculated as z-scores relative to all samples (log RNA Seq RPKM) of DNA methyltransferases DNMT1, DNMT3A and DNMT3B in representative breast cancer cases. (<b>A</b>). ER-negative/PR-negative, basal-like cancers, (<b>B</b>). ER-negative/PR-negative, claudin-low cancers, (<b>C</b>). ER-positive/HER2-negative/proliferation-low, luminal A cancers, (<b>D</b>). ER-positive/HER2-negative/proliferation-low, claudin-low cancers. Data are from the METABRIC cohort. Red color denotes up-regulation and blue denotes suppression.</p>
Full article ">Figure 6
<p>Percentage of amplifications of genes encoding for histone methyltransferases in ER-negative/PR-negative, basal-like breast cancers (grey bars) and ER-negative/PR-negative, claudin-low breast cancers (black bars). Data are from the METABRIC cohort. Fisher’s exact test <span class="html-italic">p</span> &lt; 0.0001 for the comparison of the presence of amplifications in any histone methyltransferase in claudin-low versus basal-like breast cancers.</p>
Full article ">Figure 7
<p>Percentage of amplifications of genes encoding for histone demethylases in ER-negative/PR-negative, basal-like breast cancers (grey bars) and ER-negative/PR-negative, claudin-low breast cancers (black bars). Data are from the METABRIC cohort. Fisher’s exact test <span class="html-italic">p</span> &lt; 0.0001 for the comparison of the presence of amplifications in any histone demethylase in claudin-low versus basal-like breast cancers.</p>
Full article ">Figure 8
<p>mRNA expression calculated as z-scores relative to all samples (log RNA Seq RPKM) of genes encoding for histone methyltransferases in representative breast cancer cases. (<b>A</b>). ER-negative/PR-negative, basal-like cancers, (<b>B</b>). ER-negative/PR-negative, claudin-low cancers, Methyltransferases with significantly different expression between the 2 groups are shown. Red color denotes up-regulation and blue denotes suppression.</p>
Full article ">Figure 9
<p>Percentage of amplifications of genes encoding for histone acetyltransferases in ER-negative/PR-negative, basal-like breast cancers (grey bars) and ER-negative/PR-negative, claudin-low breast cancers (black bars). Data are from the METABRIC cohort. Fisher’s exact test <span class="html-italic">p</span> = 0.02 for the comparison of the presence of amplifications in any histone acetyltransferase in claudin-low versus basal-like breast cancers.</p>
Full article ">Figure 10
<p>Percentage of amplifications of genes encoding for histone deacetylases in ER-negative/PR-negative, basal-like breast cancers (grey bars) and ER-negative/PR-negative, claudin-low breast cancers (black bars). Data are from the METABRIC cohort. Fisher’s exact test <span class="html-italic">p</span> = 0.06 for the comparison of the presence of amplifications in any histone deacetylase in claudin-low versus basal-like breast cancers.</p>
Full article ">
16 pages, 2355 KiB  
Article
X-rays Stimulate Granular Secretions and Activate Protein Kinase C Signaling in Human Platelets
by Muhammad Shoaib Khan, Chunliang Liu, Fanbi Meng, Mengnan Yang, Kangxi Zhou, Renping Hu, Xuexiang Wang and Kesheng Dai
Curr. Issues Mol. Biol. 2023, 45(7), 6024-6039; https://doi.org/10.3390/cimb45070380 - 19 Jul 2023
Cited by 3 | Viewed by 1993
Abstract
X-rays can induce morphological as well as functional changes in cells. Platelets are anuclear cellular fragments originating from megakaryocytes and are the major regulators in hemostasis and thrombosis. Platelet products are irradiated to avoid medical complications associated with platelet transfusion. So far, gamma, [...] Read more.
X-rays can induce morphological as well as functional changes in cells. Platelets are anuclear cellular fragments originating from megakaryocytes and are the major regulators in hemostasis and thrombosis. Platelet products are irradiated to avoid medical complications associated with platelet transfusion. So far, gamma, UV, and laser radiation have been used for this purpose. However, scientists are divided about the effects of radiation on platelet quality. The present study was designed to explore the possible effects of X-rays in washed human platelets and understand the molecular mechanism behind them. In the present study, we exposed washed human platelets to 10 or 30 Gy X-rays at 0.25 Gy/min. Flow cytometry, aggregometry, and western blot were performed to investigate the effect of X-rays on platelet degranulation, integrin activation, platelet aggregation, and apoptosis. It was found that X-rays immediately induced granular secretions with no effect on GP IIb/IIIa activation. Not surprisingly, due to granule secretions in irradiated platelets, platelet aggregation was significantly reduced. In contrast to granular secretions and platelet aggregation, X-rays induced mitochondrial transmembrane potential depolarization in a time-dependent manner to induce apoptosis and activated protein kinase C (PKC) signaling. This study revealed and explained the molecular mechanism activated by X-rays in washed human platelets. Here we also introduced Gö 6983, a PKC inhibitor, as an agent that counteracts X-ray-induced changes and maintains the integrity of platelets. Full article
(This article belongs to the Section Biochemistry, Molecular and Cellular Biology)
Show Figures

Figure 1

Figure 1
<p>X-rays immediately induce CD62P exposure in washed human platelets. Washed human platelets were exposed to 10 or 30 Gy X-rays at 0.25 Gy/min. (<b>a</b>) Representative flow cytometric figures of CD62P exposure. (<b>b</b>) Quantification of CD62P exposure induced by X-rays; n = 5. Data are expressed as mean ± SD, <span class="html-italic">* p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 compared with control by Two-Way ANOVA followed by Tukey’s multiple comparison tests.</p>
Full article ">Figure 2
<p>X-rays induce ATP release in washed human platelets. Washed human platelets were exposed to 10 or 30 Gy X-rays at 0.25 Gy/min. ATP release assay was performed at using luciferin/luciferase reagents. (<b>a</b>) Original traces of ATP release at different time points without agonist incubation. (<b>b</b>) Quantification of ATP release at different time points without agonist incubation. (<b>c</b>–<b>e</b>) Quantification of ATP release in response to collagen, thrombin, or U46619 incubation after X-rays treatment; n = 4. Data are expressed as mean ± SD, <span class="html-italic">* p &lt;</span> 0.05, <span class="html-italic">** p &lt;</span> 0.01, <span class="html-italic">*** p</span> &lt; 0.001, compared with control by Two-Way (<b>a</b>,<b>b</b>) or One-Way ANOVA (<b>c</b>–<b>e</b>) followed by Tukey’s and Dunnett’s multiple comparison tests, respectively.</p>
Full article ">Figure 3
<p>X-rays significantly inhibit platelet aggregation. Washed human platelets were exposed to 10 or 30 Gy X-rays at 0.25 Gy/min. An hour later, irradiated or non-irradiated platelets were incubated with (<b>a</b>) thrombin, (<b>b</b>) collagen, (<b>c</b>) U46619, or (<b>d</b>) ADP at constant stirring. Platelet aggregation was monitored over 8–10 min. Data are expressed as mean ± SD obtained from three independent experiments. <span class="html-italic">* p &lt;</span> 0.05, <span class="html-italic">** p &lt;</span> 0.01, <span class="html-italic">*** p &lt;</span> 0.001, compared with control by One-Way ANOVA followed by Dunnett’s multiple comparison test.</p>
Full article ">Figure 4
<p>X-rays induce platelet apoptosis in washed human platelets. (<b>a</b>) Representative flow cytometric figures of ΔΨm depolarization. (<b>b</b>) Quantification of platelets ΔΨm depolarization; n = 4. (<b>c</b>) Representative western blot figure of caspase-3. (<b>d</b>) Quantification of cleaved caspase-3; n = 3. (<b>e</b>) Representative flow cytometric figures of PS externalization. (<b>f</b>) Quantification of PS externalization; n = 4. Data are expressed as mean ± SD; <span class="html-italic">* p &lt;</span> 0.05, <span class="html-italic">** p &lt;</span> 0.01, <span class="html-italic">*** p &lt;</span> 0.001, compared with control by One-Way (<b>c</b>,<b>d</b>) or Two-way ANOVA (<b>a</b>,<b>b</b>,<b>e</b>,<b>f</b>) followed by Bonferroni’s multiple comparison test.</p>
Full article ">Figure 5
<p>X-rays induce rearrangement of Bcl-2 family proteins and activate PKC signaling. (<b>a</b>,<b>b</b>) Representative western blot figures of P53, Bcl-xL, or Bak. (<b>c</b>) Quantification of Bcl-xL degradation; n = 3. (<b>d</b>) Quantification of Bak upregulation; n = 3. (<b>e</b>) Representative western blot figure of PKC α. (<b>f</b>) Quantification of PKC α; n = 3. (<b>g</b>) Representative flow cytometric figures. (<b>h</b>) Quantification of rise in Ca<sup>2+</sup> concentration; n = 3. (<b>i</b>) Representative western blot figure of PKC or cleaved PKC δ. (<b>j</b>) Quantification of cleaved PKC δ; n = 3. Data are expressed as mean and ± SD, <span class="html-italic">*** p &lt;</span> 0.001, compared with control by One-Way (<b>a</b>–<b>f</b>,<b>i</b>,<b>j</b>) or Two-Way ANOVA (<b>g</b>,<b>h</b>) followed by Dunnett’s or Tukey’s multiple comparison tests, respectively.</p>
Full article ">Figure 6
<p>Gö 6983 inhibits X-ray-induced CD62P and ΔΨm depolarization and rescues platelet aggregation. Washed human platelets were pretreated with 10 nM Gö 6983 for 2 h with gentle shake and then exposed to 10 or 30 Gy X-rays at 0.25 Gy/min. (<b>a</b>,<b>b</b>) Quantification of 10 Gy or 30 Gy induced CD62P exposure without or with Gö 6983 incubation; n = 3. (<b>c</b>) Quantification of platelet aggregation without or with Gö 6983 incubated irradiated platelets when stimulated with thrombin; n = 3. (<b>d</b>,<b>e</b>) Quantification of 10 Gy or 30 Gy induced ΔΨm depolarization without or with Gö 6983 incubation; n = 3. <span class="html-italic">* p &lt;</span> 0.05, <span class="html-italic">** p</span> &lt; 0.01, <span class="html-italic">*** p</span> &lt; 0.001, compared irradiated platelets to Gö 6983 incubated irradiated platelets by One-Way (<b>c</b>) or Two-Way (<b>a</b>,<b>b</b>,<b>d</b>,<b>e</b>) ANOVA.</p>
Full article ">
21 pages, 1517 KiB  
Article
A Clinical Qualification Protocol Highlights Overlapping Genomic Influences and Neuro-Autonomic Mechanisms in Ehlers–Danlos and Long COVID-19 Syndromes
by Golder N. Wilson
Curr. Issues Mol. Biol. 2023, 45(7), 6003-6023; https://doi.org/10.3390/cimb45070379 - 17 Jul 2023
Cited by 4 | Viewed by 3746
Abstract
A substantial fraction of the 15% with double-jointedness or hypermobility have the traditionally ascertained joint-skeletal, cutaneous, and cardiovascular symptoms of connective tissue dysplasia and its particular manifestation as Ehlers–Danlos syndrome (EDS). The holistic ascertainment of 120 findings in 1261 EDS patients added neuro-autonomic [...] Read more.
A substantial fraction of the 15% with double-jointedness or hypermobility have the traditionally ascertained joint-skeletal, cutaneous, and cardiovascular symptoms of connective tissue dysplasia and its particular manifestation as Ehlers–Danlos syndrome (EDS). The holistic ascertainment of 120 findings in 1261 EDS patients added neuro-autonomic symptoms like headaches, muscle weakness, brain fog, chronic fatigue, dyspnea, and bowel irregularity to those of arthralgia and skin laxity, 15 of these symptoms shared with those of post-infectious SARS-CoV-2 (long COVID-19). Underlying articulo-autonomic mechanisms guided a clinical qualification protocol that qualified DNA variants in 317 genes as having diagnostic utility for EDS, six of them identical (F2-LIFR-NLRP3-STAT1-T1CAM1-TNFRSF13B) and eighteen similar to those modifying COVID-19 severity/EDS, including ADAMTS13/ADAMTS2-C3/C1R-IKBKG/IKBKAP-PIK3C3/PIK3R1-POLD4/POLG-TMPRSS2/TMPRSS6-WNT3/WNT10A. Also, contributing to EDS and COVID-19 severity were forty and three genes, respectively, impacting mitochondrial functions as well as parts of an overlapping gene network, or entome, that are hypothesized to mediate the cognitive–behavioral, neuro-autonomic, and immune-inflammatory alterations of connective tissue in these conditions. The further characterization of long COVID-19 natural history and genetic predisposition will be necessary before these parallels to EDS can be carefully delineated and translated into therapies. Full article
Show Figures

Figure 1

Figure 1
<p>Clinical protocol for DNA variant qualification. Clinical DNA variant (column 4) and 1–4 + medical diagnostic utilities (last column) are added to consensus qualifications (column 2) as discussed in the text; DNA/protein change and gene abbreviations except for <span class="html-italic">MTHFR</span> (methylene tetrahydrofolate reductase) and <span class="html-italic">HBB</span> (beta-globin) are explained in <a href="#app1-cimb-45-00379" class="html-app">Tables S2 and S3</a>; single amino acid codes (A—alanine, D—aspartate, E—glutamate, I—isoleucine, L—leucine, M—methionine, P—proline, Q—glutamine, R—arginine, S—serine, T—threonine, X—stop, V—valine) used here; fs, frame-shift.</p>
Full article ">Figure 2
<p>Genes relevant to EDS or COVID-19 infection by tissue element or product type. (<b>A</b>) Connective tissue element/process relations (box, <a href="#cimb-45-00379-f002" class="html-fig">Figure 2</a>A bottom) are from associated diseases (<a href="#app1-cimb-45-00379" class="html-app">Tables S2 and S3</a>). COVID-19 percentages are those of 83 genes after 21 impacting viral-related processes were subtracted. (<b>B</b>) Gene product functions are explained in the legend to <a href="#app1-cimb-45-00379" class="html-app">Table S2</a>. COVID-19 percentages are of all 104 genes listed in <a href="#app1-cimb-45-00379" class="html-app">Table S3</a> (the <span class="html-italic">PNPLA3</span> gene associated with gastrointestinal disease is not listed). Colors indicate relative proportions for EDS (blue) and COVID19 (red). Significantly (<span class="html-italic">p</span> &lt; 0.05) lower X/ higher ↑ proportions (see Methods).</p>
Full article ">Figure 3
<p>Genes and symptoms related to EDS and COVID-19. Genes related to EDS (<a href="#app1-cimb-45-00379" class="html-app">Table S2</a>) and COVID-19 infection (<a href="#app1-cimb-45-00379" class="html-app">Table S3</a>) are envisioned as overlapping parts of a network (rhizome below) connected through pathogenic mechanisms (trunk sap, phloem) to common symptoms of EDS (<a href="#app1-cimb-45-00379" class="html-app">Table S1</a>) and long COVID-19 (canopy above). EDS symptom ranges are for females over age 10.5 years from the EDS1261database; long COVID percentages and ranges are taken from <a href="#cimb-45-00379-f002" class="html-fig">Figure 2</a> of the work by Deer et al. [<a href="#B37-cimb-45-00379" class="html-bibr">37</a>].</p>
Full article ">
22 pages, 1206 KiB  
Review
The Role of Genetic Risk Factors in Pathogenesis of Childhood-Onset Systemic Lupus Erythematosus
by Mario Sestan, Nastasia Kifer, Todor Arsov, Matthew Cook, Julia Ellyard, Carola G. Vinuesa and Marija Jelusic
Curr. Issues Mol. Biol. 2023, 45(7), 5981-6002; https://doi.org/10.3390/cimb45070378 - 17 Jul 2023
Cited by 8 | Viewed by 3924
Abstract
The pathogenesis of childhood-onset systemic lupus erythematosus (cSLE) is complex and not fully understood. It involves three key factors: genetic risk factors, epigenetic mechanisms, and environmental triggers. Genetic factors play a significant role in the development of the disease, particularly in younger individuals. [...] Read more.
The pathogenesis of childhood-onset systemic lupus erythematosus (cSLE) is complex and not fully understood. It involves three key factors: genetic risk factors, epigenetic mechanisms, and environmental triggers. Genetic factors play a significant role in the development of the disease, particularly in younger individuals. While cSLE has traditionally been considered a polygenic disease, it is now recognized that in rare cases, a single gene mutation can lead to the disease. Although these cases are uncommon, they provide valuable insights into the disease mechanism, enhance our understanding of pathogenesis and immune tolerance, and facilitate the development of targeted treatment strategies. This review aims to provide a comprehensive overview of both monogenic and polygenic SLE, emphasizing the implications of specific genes in disease pathogenesis. By conducting a thorough analysis of the genetic factors involved in SLE, we can improve our understanding of the underlying mechanisms of the disease. Furthermore, this knowledge may contribute to the identification of effective biomarkers and the selection of appropriate therapies for individuals with SLE. Full article
Show Figures

Figure 1

Figure 1
<p>Overview of the important genes involved in SLE pathogenesis. The most important genes are marked in red. Modified according to reference [<a href="#B26-cimb-45-00378" class="html-bibr">26</a>]. DNA: deoxyribonucleic acid; IFN-I: type I interferon; NFκB: nuclear factor κB; SLE: systemic lupus erythematosus; TLR: Toll-like receptor.</p>
Full article ">Figure 2
<p>Hypothetical model of polygenic SLE development: adaptive immune disorders lead to the generation of autoantibodies, specifically antibodies targeting self-antigens (right side of the figure, marked in pink). These autoantibodies progressively accumulate as a consequence of the innate immune dysfunction (left side of the figure, marked in blue). Modified according to reference [<a href="#B121-cimb-45-00378" class="html-bibr">121</a>]. APRIL: a proliferation-inducing ligand; B: B-cell; BAFF: B-cell-activating factor; BAFF-R: B-cell-activating factor receptor; BCMA: B-cell maturation antigen; BCR: B-cell antigen receptor; FcRγ: Fc receptor-γ; HLA class II: human leucocyte antigen class II; mDC: myeloid dendritic cell; Mϕ: macrophage; NET: neutrophil extracellular trap; ox-mDNA: oxidized mitochondrial DNA; pDC: plasmacytoid dendritic cell; Stat1: signal transducer and activator of transcription (a transcription factor); T: T-cell; TACI: transmembrane activator, calcium modulator and cyclophilin ligand interactor; T-bet: a T-box transcription factor; Tfh: T follicular helper; TLR7/9: Toll-like receptors 7 and 9.</p>
Full article ">Figure 3
<p>Pathways included in monogenic SLE development. The accumulation of nuclear material in the extracellular space resulting from apoptosis and NETosis triggers Toll-like receptors (TLRs). These TLRs, along with other pathways impacting the crucial transcription factor IRF3, are involved in nucleic acid recognition and degradation. Impairment in nucleic acid sensing or compromised handling of nucleic acid-containing waste products leads to a type I interferon response. This interferon response activates a set of interferon-stimulated genes. Genes are indicated within boxes. Modified according to references [<a href="#B122-cimb-45-00378" class="html-bibr">122</a>,<a href="#B123-cimb-45-00378" class="html-bibr">123</a>]. cGAS: cyclic GMP–AMP synthase; DAMP: damage-associated molecular patterns; IFNAR1: interferon alpha and beta receptor subunit 1; NET: neutrophil extracellular trap; PAMP: pathogen-associated molecular patterns; TLR: Toll-like receptors.</p>
Full article ">
14 pages, 7124 KiB  
Article
Alveolar Bone Preservation Using a Combination of Nanocrystalline Hydroxyapatite and Injectable Platelet-Rich Fibrin: A Study in Rats
by Andries Pascawinata, Gusti Revilla, Roni Eka Sahputra and Syukri Arief
Curr. Issues Mol. Biol. 2023, 45(7), 5967-5980; https://doi.org/10.3390/cimb45070377 - 17 Jul 2023
Cited by 2 | Viewed by 1694
Abstract
Alveolar bone resorption is a post-extraction complication wherein there is a reduction in the dimensions and quality of the alveolar bone. This study aimed to examine the effects of implantation of a combination of nanocrystalline hydroxyapatite (nHA) and injectable platelet-rich fibrin (IPRF) on [...] Read more.
Alveolar bone resorption is a post-extraction complication wherein there is a reduction in the dimensions and quality of the alveolar bone. This study aimed to examine the effects of implantation of a combination of nanocrystalline hydroxyapatite (nHA) and injectable platelet-rich fibrin (IPRF) on the expression of tartrate-resistant acid phosphatase (TRAP), alkaline phosphatase (ALP), osteocalcin (OCN), and new bone formation. A total of 32 male rats had their upper right incisors extracted under general anesthesia and were then divided into a control group, nHA group, IPRF group, and nHA-IPRF group. Decapitation was carried out on day 14 and day 28 in each group and the jaws of each rat were subjected to immunohistochemical and histological analysis. The results showed a decrease in TRAP expression in the nHA-IPRF group compared with the control group on day 14 (p = 0.074) and day 28 (p = 0.017). The study also showed an increase in ALP and OCN in the HA-IPRF group on day 14 and day 28 compared with the control group. New bone formation suggested a significant increase in the nHA-IPRF group compared with the control group on day 14 (p = 0.001) and day 28 (p = 0.001). nHA-IPRF implantation can suppress alveolar bone resorption, which is indicated by decreased TRAP expression, and it can increase bone growth, as indicated by increased expression of ALP, OCN, and new bone formation. Full article
(This article belongs to the Section Molecular Medicine)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>). Pensi shells, (<b>b</b>) hydroxyapatite synthesis, and (<b>c</b>) as-synthesized hydroxyapatite powder after calcination.</p>
Full article ">Figure 2
<p>XRD pattern of hydroxyapatite.</p>
Full article ">Figure 3
<p>SEM images of hydroxyapatite sample at (<b>a</b>) 10,000× and (<b>b</b>) 20,000× magnification.</p>
Full article ">Figure 4
<p>(<b>a</b>). Nanocrystalline hydroxyopathy: (<b>b</b>) (1) IPRF and (2) blood; (<b>c</b>) combination of HA and IPRF.</p>
Full article ">Figure 5
<p>Lower incisor in rat (<b>a</b>) before cutting and (<b>b</b>) after cutting to the gingival margin, and (<b>c</b>) rat tooth extraction.</p>
Full article ">Figure 6
<p>ROI of the dental socket (a) proximal area, (b) medial area (c) distal area. The plane (x) is a parallel plane to the mandibular plane and the plane (y) is a perpendicular plane to the mandibular plane.</p>
Full article ">Figure 7
<p>Immunohistochemical description of TRAP expression was detected as a brown color (red arrow) at 400× magnification. (<b>a</b>) Control group on day 14, (<b>b</b>) control group on day 28, (<b>c</b>) group IPRF on day 14, (<b>d</b>) group I-PRF on day 28, (<b>e</b>) nHA group day on 14, (<b>f</b>) nHA group on day 28, (<b>g</b>) nHA-IPRF group on day 14, (<b>h</b>) nHA-IPRF group on day 28, and (<b>i</b>) TRAP expression graph in each group. The control group on day 28 showed a significant difference from the nHA-IPRF group on the same day (<span class="html-italic">p</span> = 0.017).</p>
Full article ">Figure 8
<p>Immunohistochemical description of ALP expression was detected as a brown color (red arrow) at 400× magnification. (<b>a</b>) Control on day 14, (<b>b</b>) control on day 28, (<b>c</b>) IPRF on day 14, (<b>d</b>) IPRF on day 28, (<b>e</b>) nHA on day 14, (<b>f</b>) nHA on day 28, (<b>g</b>) nHA-IPRF on day 14, (<b>h</b>) nHA- IPRF on day 28, (<b>i</b>) and ALP expression graph in each group. There was an increase in ALP in the nHA-IPRF group on day 14 and 28 compared with the control group on the same days, although it was not statistically significant.</p>
Full article ">Figure 9
<p>Immunohistochemical description of OCN expression were detected as a brown color (red arrow) at 400× magnification. (<b>a</b>) Control group on day 14, (<b>b</b>) control group on day 28, (<b>c</b>) IPRF group on day 14, (<b>d</b>) IPRF group on day 28, (<b>e</b>) group nHA on day 14, (<b>f</b>) group nHA on day 28, (<b>g</b>) group nHa-IPRF on day 14, (<b>h</b>) group nHa-IPRF on day 28, and (<b>i</b>) OCN expression graph in each group. There was an increase in OCN in the nHA-IPRF group on day 14 compared with the control group on the same day, although it was not statistically significant.</p>
Full article ">Figure 10
<p>New bone formation description with hematoxilin/eosin (HE) staining. Base of extraction socket (black arrow), woven bone (green arrow), mature bone (blue arrow), nHA (white arrow), lymphocites (yellow arrow), (<b>a</b>) Control group on day 14, (<b>b</b>) control group on day 28, (<b>c</b>) IPRF group on day 14, (<b>d</b>) IPRF group on day 28, (<b>e</b>) nHa group on day 14, (<b>f</b>) nHA group on day 28, (<b>g</b>) nHA-IPRF group on day 14, and (<b>h</b>) nHA-IPRF group on day 28. (<b>i</b>) Calculation of new bone area in the control group on day 14, (<b>j</b>) calculation of new bone area in the nHA-IPRF group on day 14, and (<b>k</b>) new bone formation graph in each group. The control group on day 14 showed a significant difference from the nHA-IPRF group on the same day; likewise, the control group on day 28 showed a significant difference from the nHA-IPRF group on the same day.</p>
Full article ">
17 pages, 3333 KiB  
Article
Protocatechuic Acid and Syringin from Saussurea neoserrata Nakai Attenuate Prostaglandin Production in Human Keratinocytes Exposed to Airborne Particulate Matter
by Myeongguk Jeong, Yeongdon Ju, Hyeokjin Kwon, Yeeun Kim, Kyung-Yae Hyun and Go-Eun Choi
Curr. Issues Mol. Biol. 2023, 45(7), 5950-5966; https://doi.org/10.3390/cimb45070376 - 16 Jul 2023
Cited by 3 | Viewed by 1774
Abstract
Saussurea neoserrata Nakai offers a reliable and efficient source of antioxidants that can help alleviate adverse skin reactions triggered by air pollutants. Air pollutants, such as particulate matter (PM), have the ability to infiltrate the skin and contribute to the higher occurrence of [...] Read more.
Saussurea neoserrata Nakai offers a reliable and efficient source of antioxidants that can help alleviate adverse skin reactions triggered by air pollutants. Air pollutants, such as particulate matter (PM), have the ability to infiltrate the skin and contribute to the higher occurrence of cardiovascular, cerebrovascular, and respiratory ailments. Individuals with compromised skin barriers are particularly susceptible to the impact of PM since it can be absorbed more readily through the skin. This study investigated the impact of protocatechuic acid and syringin, obtained from the n-BuOH extract of S. neoserrata Nakai, on the release of PGE2 and PGD2 induced by PM10. Additionally, it examined the gene expression of the synthesis of PGE2 and PGD2 in human keratinocytes. The findings of this research highlight the potential of utilizing safe and efficient plant-derived antioxidants in dermatological and cosmetic applications to mitigate the negative skin reactions caused by exposure to air pollution. Full article
(This article belongs to the Section Bioorganic Chemistry and Medicinal Chemistry)
Show Figures

Figure 1

Figure 1
<p>Schematic representation of the extraction and fractionation process of <span class="html-italic">S. neoserrata</span> Nakai. Bold squares represent the fraction for which each single compound was identified using NMR spectra.</p>
Full article ">Figure 2
<p>The <sup>1</sup>H-NMR and <sup>13</sup>C-NMR spectrum of protocatechuic acid (77 mg).</p>
Full article ">Figure 3
<p>The <sup>1</sup>H-NMR and <sup>13</sup>C-NMR spectrum of syringin (10.4 mg).</p>
Full article ">Figure 4
<p>Effects of PM<sub>10</sub> on the viability and release of PGE<sub>2</sub> and PGD<sub>2</sub> in HaCaT keratinocytes were examined. The cells were exposed to different concentrations of PM<sub>10</sub> for a duration of 48 h in order to conduct the viability assay (<b>a</b>) and the PGE<sub>2</sub> (<b>b</b>) and PGD<sub>2</sub> (<b>c</b>) release assays. Control cells were treated with saline. Each bar represents the mean ± standard deviation (SD) (<span class="html-italic">n</span> = 4). All treatments were compared with the controls using one-way analysis of variance (ANOVA) followed by Dunnett′s test * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 5
<p>Effects of <span class="html-italic">S. neoserrata</span> Nakai extracts on the viability and the PGE<sub>2</sub> and PGD<sub>2</sub> release of HaCaT keratinocytes in response to PM<sub>10</sub>. The cells were treated with 12.5 µg/mL PM<sub>10</sub> in the presence of various concentrations of <span class="html-italic">S. neoserrata</span> Nakai extract for 48 h for the purposes of a viability assay (<b>a</b>) and PGE<sub>2</sub> (<b>b</b>) and PGD<sub>2</sub> (<b>c</b>) release assays. Each bar represents the mean ± SD (<span class="html-italic">n</span> = 4). All treatments were compared with the PM<sub>10</sub>–only control using one-way ANOVA followed by Dunnett′s test * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 6
<p>Effects of protocatechuic acid and syringin on PM<sub>10</sub>–induced keratinocyte cytotoxicity and PGE<sub>2</sub> and PGD<sub>2</sub> release. HaCaT keratinocytes were treated with various concentrations of protocatechuic acid and syringin for 48 h, and the resulting cell viability was measured (<b>a</b>,<b>b</b>). (<b>c</b>) Cells were treated with 12.5 μg/mL PM<sub>10</sub> in the presence or absence of protocatechuic acid at the indicated concentrations for 48 h for the PGE<sub>2</sub> release assay. (<b>d</b>) Cells were treated with 12.5 μg/mL PM<sub>10</sub> in the presence or absence of syringin at the indicated concentrations for 48 h for the PGE<sub>2</sub> release assay. (<b>e</b>) Cells were treated with 12.5 μg/mL PM<sub>10</sub> in the presence or absence of protocatechuic acid at the indicated concentrations for 48 h for the PGD<sub>2</sub> release assay. (<b>f</b>) Cells were treated with 12.5 μg/mL PM<sub>10</sub> in the presence or absence of syringin at the indicated concentrations for 48 h for the PGD<sub>2</sub> release assay. NAC (10 μg/mL) was used as a positive control antioxidant in each assay. Each bar represents the mean ± SD (<span class="html-italic">n</span> = 4). All treatments were compared with the PM<sub>10</sub>–only control using one-way ANOVA followed by Dunnett′s test * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 7
<p>Effect of <span class="html-italic">S. neoserrata</span> Nakai extract, protocatechuic acid, and syringin on the PM<sub>10</sub>-induced ROS production. Keratinocytes were exposed to a concentration of 12.5 μg/mL PM<sub>10</sub> for 24 h. The ROS production of keratinocytes was measured by flow cytometry using CM2-DCFDA. Compared to the control group, treatment with protocatechuic acid and syringin showed a concentration-dependent decrease in intracellular ROS production.</p>
Full article ">Figure 8
<p>Effects of protocatechuic acid on the PM<sub>10</sub>–induced gene expressions of the enzymes involved in PGD<sub>2</sub> synthesis. Cells were treated with 12.5 μg/mL PM<sub>10</sub> for 24 h in the presence or absence of protocatechuic acid at the indicated concentrations in order to determine the mRNA expression of enzymes involved in PGD<sub>2</sub> synthesis (L–PGDS and H–PGDS). Treatment with 20 μg/mL protocatechuic acid significantly reduced the expression of L–PGDS (<b>a</b>). However, H–PGDS expression was not reduced (<b>b</b>). N–acetyl cysteine (NAC) was employed as a positive control antioxidant. Each bar represents the mean ± SD (<span class="html-italic">n</span> = 4). All treatments were compared with the PM<sub>10</sub>–only control using one-way ANOVA followed by Dunnett′s test * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 9
<p>Effects of syringin on the PM<sub>10</sub>–induced gene expressions of the enzymes involved in the PGE<sub>2</sub> synthesis. To examine the mRNA expression of enzymes associated with PGD<sub>2</sub> synthesis (L–PGDS and H–PGDS), cells were exposed to 12.5 μg/mL PM<sub>10</sub> for 24 h with or without syringin at the specified concentrations. Treatment with 12.5 μg/mL PM<sub>10</sub> increased the expression of mPGES-1 at the mRNA level. However, treatment with syringin decreased the expression of mPGES–1 in a concentration–dependent manner (<b>a</b>). PM10 treatment at 12.5 μg/mL did not significantly increase the expression of mPGES–2 and cPGES at the mRNA level (<b>b</b>,<b>c</b>). N–acetyl cysteine (NAC) was employed as a positive control antioxidant. Each bar represents the mean ± SD (<span class="html-italic">n</span> = 4). All treatments were compared with the PM<sub>10</sub>–only control using one-way ANOVA followed by Dunnett′s test * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">
15 pages, 2257 KiB  
Article
The Synergistic Antitumor Effect of Decitabine and Vorinostat Combination on HepG2 Human Hepatocellular Carcinoma Cell Line via Epigenetic Modulation of Autophagy–Apoptosis Molecular Crosstalk
by Basant M. Salama, Maged W. Helmy, Hosny Fouad, Marium M. Shamaa and Maha E. Houssen
Curr. Issues Mol. Biol. 2023, 45(7), 5935-5949; https://doi.org/10.3390/cimb45070375 - 16 Jul 2023
Cited by 5 | Viewed by 2042
Abstract
Hepatocellular carcinoma (HCC) is a worldwide health issue. Epigenetic alterations play a crucial role in HCC tumorigenesis. Using epigenetic modulators for HCC treatment confers a promising therapeutic effect. The aim of this study was to explore the effect of a decitabine (DAC) and [...] Read more.
Hepatocellular carcinoma (HCC) is a worldwide health issue. Epigenetic alterations play a crucial role in HCC tumorigenesis. Using epigenetic modulators for HCC treatment confers a promising therapeutic effect. The aim of this study was to explore the effect of a decitabine (DAC) and vorinostat (VOR) combination on the crosstalk between apoptosis and autophagy in the HCC HepG2 cell line at 24 h and 72 h. Median inhibitory concentrations (IC50s) of VOR and DAC were assessed in the HepG2 cell line. The activity of caspase-3 was evaluated colorimetrically, and Cyclin D1(CCND1), Bcl-2, ATG5, ATG7, and P62 levels were assessed using ELISA at different time intervals (24 h and 72 h), while LC3IIB and Beclin-1gene expression were measured by using qRT-PCR. The synergistic effect of VOR and DAC was confirmed due to the observed combination indices (CIs) and dose reduction indices (DRIs). The combined treatment with both drugs inhibited the proliferation marker (CCND1), and enhanced apoptosis compared with each drug alone at 24 h and 72 h (via active caspase-3 upregulation and Bcl-2 downregulation). Moreover, the combination induced autophagy as an early event via upregulation of Beclin-1, LC3IIB, ATG5, and ATG7 gene expression. The initial induction of autophagy started to decrease after 72 h due to Beclin-1 downregulation, and there was decreased expression of LC3IIB compared with the value at 24 h. Herein, epigenetic modulation via the VOR/DAC combination showed an antitumor effect through the coordination of an autophagy–apoptosis crosstalk and promotion of autophagy-induced apoptosis, which ultimately led to the cellular death of HCC cancer cells. Full article
(This article belongs to the Section Molecular Medicine)
Show Figures

Figure 1

Figure 1
<p>Effects of VOR, DAC, and their combination on HepG2 cell viability after 24 h (<b>a</b>–<b>c</b>) and 72 h (<b>d</b>–<b>f</b>) treatment. The viability of HepG2 cells treated with vorinostat (VOR, 0.25–8 μM) (<b>a</b>,<b>d</b>), decitabine (DAC, 3.12–100 μM) (<b>b</b>,<b>e</b>), and combination of the two drugs (<b>c</b>,<b>f</b>). Data points indicate the mean ± SEM, each conducted in triplicate. *: A significant difference for VOR and/or DAC vs. the corresponding control group with <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 2
<p>HepG2 cell line morphology after 24 h and 72 h treatment with Decitabine (DAC, 50 µM), Vorinostat (VOR, 2.5 µM), and combination (50 µM for DAC + 2.5 µM for VOR).</p>
Full article ">Figure 3
<p>Effects of treatment with Decitabine (DAC, 50 µM), Vorinostat (VOR, 2.5 µM), and their combination (50 µM for DAC + 2.5 µM for VOR) for 24 h and 72 h on proliferation and apoptosis markers in HepG2 cells. The levels of tumor markers of proliferation, (<b>a</b>) (Cyclin D1; CCND1), and apoptosis, (<b>b</b>) (active caspase-3), (<b>c</b>) (Bcl-2), were measured using ELISA or colorimetrically. Data represented as the mean ± SEM of three samples, each conducted in triplicate. #: <span class="html-italic">p</span> &lt; 0.05 vs. control, π: <span class="html-italic">p</span> &lt; 0.05 vs. the DAC group, and Δ: <span class="html-italic">p</span> &lt; 0 05 vs. VOR group; these designations indicate statistically significant differences between groups at the same time interval, while significant differences between two time intervals (24 h and 72 h) in each group are designated as *: <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 4
<p>Effects of treatment with Decitabine (DAC, 50 µM), Vorinostat (VOR, 2.5 µM), and their combination (50 µM for DAC + 2.5 µM for VOR) for 24 h and 72 h on autophagy in HepG2 cells. qRT-PCR was used to determine the fold change (RQ) in <span class="html-italic">LC3II</span> (<b>a</b>) and <span class="html-italic">Beclin-1</span> (<b>b</b>) gene expression in each treatment group compared to the control group. Data are represented as the mean ± SEM of three samples, each conducted in triplicate. #: <span class="html-italic">p</span> &lt; 0.05 vs. control, π: <span class="html-italic">p</span> &lt; 0.05 vs. the DAC group, and Δ: <span class="html-italic">p</span> &lt; 0.05 vs. VOR group. These designations indicate statistically significant differences between groups at the same time interval, while significant differences between two- time intervals (24 h and 72 h) in each group are designated as *: <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 5
<p>Effects of treatment with Decitabine (DAC, 50 µM), Vorinostat (VOR, 2.5 µM), and combination (50 µM for DAC + 2.5 µM for VOR) for 24 h and 72 h on p62 expression in HepG2 cells measured using ELISA technique. Data are represented as the mean ± SEM of three samples, each conducted in triplicate. #: <span class="html-italic">p</span> &lt; 0.05 vs. control, and Δ: <span class="html-italic">p</span> &lt; 0.05 vs. VOR group.</p>
Full article ">Figure 6
<p>Effects of treatment with Decitabine (DAC, 50 µM), Vorinostat (VOR, 2.5 µM), and combination (50 µM for DAC + 2.5 µM for VOR) for 24 h and 72 h on ATG5 (<b>a</b>) and ATG7 (<b>b</b>) expression in HepG2 cells measured using ELISA technique. Data are represented as the mean ± SEM of three samples, each conducted in triplicate. #: <span class="html-italic">p</span> &lt; 0.05 vs. control, π: <span class="html-italic">p</span> &lt; 0.05 vs. DAC group, and Δ: <span class="html-italic">p</span> &lt; 0.05 vs. VOR group. These designations indicate statistically significant differences between groups at the same time interval, while significant differences between two time intervals (24 h and 72 h) in each group are designated as *: <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">
21 pages, 1989 KiB  
Review
How Plants Tolerate Salt Stress
by Haiqi Fu and Yongqing Yang
Curr. Issues Mol. Biol. 2023, 45(7), 5914-5934; https://doi.org/10.3390/cimb45070374 - 15 Jul 2023
Cited by 53 | Viewed by 5667
Abstract
Soil salinization inhibits plant growth and seriously restricts food security and agricultural development. Excessive salt can cause ionic stress, osmotic stress, and ultimately oxidative stress in plants. Plants exclude excess salt from their cells to help maintain ionic homeostasis and stimulate phytohormone signaling [...] Read more.
Soil salinization inhibits plant growth and seriously restricts food security and agricultural development. Excessive salt can cause ionic stress, osmotic stress, and ultimately oxidative stress in plants. Plants exclude excess salt from their cells to help maintain ionic homeostasis and stimulate phytohormone signaling pathways, thereby balancing growth and stress tolerance to enhance their survival. Continuous innovations in scientific research techniques have allowed great strides in understanding how plants actively resist salt stress. Here, we briefly summarize recent achievements in elucidating ionic homeostasis, osmotic stress regulation, oxidative stress regulation, and plant hormonal responses under salt stress. Such achievements lay the foundation for a comprehensive understanding of plant salt-tolerance mechanisms. Full article
(This article belongs to the Special Issue Stress and Signal Transduction in Plants)
Show Figures

Figure 1

Figure 1
<p>Ionic-stress signaling pathways that maintain ionic homeostasis and thereby help plants to adapt to salt stress. Under non-stress conditions (before salt stress (<b>A</b>)), plasma membrane (PM) H<sup>+</sup>-ATPase activity is repressed by PKS5; SOS2 kinase activity is repressed by PKS5, 14-3-3, ABI2, and GI; and SOS1 activity is inhibited by clade D PP2C (PP2C.D). Under salt stress (<b>B</b>), GIPC binds Na<sup>+</sup>, inducing an increase in calcium signaling. FER perceives changes in the cell wall under long-term salt stress and mediates calcium signaling. The calcium receptors SOS3 and SCaBP8 bind Ca<sup>2+</sup>, interacting with and activating SOS2, which then phosphorylates SOS1 to activate its Na<sup>+</sup>/H<sup>+</sup> antiporter activity. Salt stress induces PA accumulation, which promotes the kinase activity of MPK6. MPK6 then phosphorylates SOS1 to enhance the activity of SOS1. At the same time, SCaBP8 inhibits PP2C.D to relieve the inhibition of SOS1 by PP2C.D. ANN modulation of calcium signaling under salt stress positively regulates SCaBP8-activated SOS2; under long-term salt stress, SOS2 phosphorylates ANN4 and represses its Ca<sup>2+</sup> channel activity, creating a specific calcium signal for long-term stress. After salt stress (<b>C</b>), BIN2 phosphorylates SOS2 and inhibits its kinase activity, helping plants to recover from stress.</p>
Full article ">Figure 2
<p>ROS signal transduction response to salt stress. Salt stress induces a rapid increase in ROS accumulation. Sensors perceive the elevated ROS and transduce the ROS signal to stimulate plant responses. MAPK signaling cascades receive ROS signals and regulate the activity of the SOS pathway and ROS scavengers to modulate ionic homeostasis and ROS homeostasis, respectively. MAPKs also regulate gene expression to modulate plant growth under salt stress.</p>
Full article ">Figure 3
<p>Outline of antioxidant defense mechanisms in plants. SOD, superoxide dismutase; CAT, catalase; POX, peroxidase; AsA, ascorbate; DHA, dehydroascorbate; GSSG, oxidized glutathione; GSH, reduced glutathione; APX, ascorbate peroxidase; MDHA, monodehydroascorbate; MDHAR, monodehydroascorbate reductase; DHAR, dehydroascorbate reductase; GR, glutathione reductase; GST, glutathione S-transferase; GPX, glutathione peroxidase; PPO, polyphenol oxidase; PRX, peroxiredoxin; TRX, thioredoxin; NADPH, nicotinamide adenine dinucleotide phosphate; O, oxygen; H<sub>2</sub>O<sub>2</sub>, hydrogen peroxide; O2<sup>•−</sup>, superoxide radical; R, aliphatic, aromatic, or heterocyclic group; X, sulfate, nitrite, or halide group; ROOH, hydroperoxides; -SH, thiolate; -SOH, sulfenic acid.</p>
Full article ">
12 pages, 2932 KiB  
Communication
Escin Activates Canonical Wnt/β-Catenin Signaling Pathway by Facilitating the Proteasomal Degradation of Glycogen Synthase Kinase-3β in Cultured Human Dermal Papilla Cells
by Jae Young Shin, Jaeyoon Kim, Yun-Ho Choi, Sanghwa Lee and Nae-Gyu Kang
Curr. Issues Mol. Biol. 2023, 45(7), 5902-5913; https://doi.org/10.3390/cimb45070373 - 14 Jul 2023
Viewed by 1880
Abstract
Abnormal inactivation of the Wnt/β-catenin signaling pathway is involved in skin diseases like androgenetic alopecia, vitiligo and canities, but small-molecule activators are rarely described. In this study, we investigated the stimulatory effects of escin on the canonical Wnt/β-catenin signaling pathway in cultured human [...] Read more.
Abnormal inactivation of the Wnt/β-catenin signaling pathway is involved in skin diseases like androgenetic alopecia, vitiligo and canities, but small-molecule activators are rarely described. In this study, we investigated the stimulatory effects of escin on the canonical Wnt/β-catenin signaling pathway in cultured human dermal papilla cells (hDPCs). Escin stimulated Wnt/β-catenin signaling, resulting in increased β-catenin and lymphoid enhancer-binding factor 1 (LEF1), the accumulation of nuclear β-catenin and the enhanced expression of Wnt target genes in cultured hDPCs. Escin drastically reduced the protein level of glycogen synthase kinase (GSK)-3β, a key regulator of the Wnt/β-catenin signaling pathway, while the presence of the proteasome inhibitor MG-132 fully restored the GSK-3β protein level. The treatment of secreted frizzled-related proteins (sFRPs) 1 and 2 attenuated the activity of escin in Wnt reporter assays. Our data demonstrate that escin is a natural agonist of the canonical Wnt/β-catenin signaling pathway and downregulates GSK-3β protein expression by facilitating the proteasomal degradation of GSK-3β in cultured hDPCs. Our data suggest that escin likely stimulates Wnt signaling through direct interactions with frizzled receptors. This study underscores the therapeutic potential of escin for Wnt-related diseases such as androgenetic alopecia, vitiligo and canities. Full article
(This article belongs to the Special Issue Natural Products in Biomedicine and Pharmacotherapy)
Show Figures

Figure 1

Figure 1
<p>Chemical structure of escin.</p>
Full article ">Figure 2
<p>Escin activated Wnt/β-catenin signaling in HEK293 Wnt (TCF/LEF) reporter cells. Cells were treated with escin or recombinant Wnt3a for 24 h, lysed with 1× passive lysis buffer. GFP (488 nm/507 nm) was measured before luminescence measurement. Luciferase activity was measured by adding luciferase substrate. Luciferase activity, which refers to TCF/LEF transcriptional activity, was normalized to GFP signal, which is constitutively expressed in live cells. ** <span class="html-italic">p</span> &lt; 0.01 compared to non-treated control (n = 5). Data are expressed as mean ± SEM.</p>
Full article ">Figure 3
<p>Escin activated Wnt/β-catenin signaling in cultured hDPCs. Cells were treated with various concentrations of escin for 24 h. (<b>a</b>) The protein expression levels of β-catenin and LEF1 were evaluated via Western blot analysis (n = 5). N.S: not significant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 compared to the non-treated control. (<b>b</b>) The mRNA expression levels of β-catenin and LEF1 (n = 5) were monitored via real-time PCR. N.S: not significant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 compared to the non-treated control. (<b>c</b>) A representative picture of the translocation of β-catenin from the cytosol to the nucleus, visualized via immunocytochemistry (n = 5). Data are expressed as mean ± SD.</p>
Full article ">Figure 4
<p>Escin stimulated the mRNA expression of Wnt target genes and Wnt receptors. (<b>a</b>) The mRNA expression of CCND1, Myc and Dkk1 was evaluated in hDPCs treated with escin for 24 h and (<b>b</b>) the mRNA expression of Wnt receptors, including Fzd3, Fzd4, Fzd5 and Fzd7, was evaluated in hDPCs treated with escin for 24 h (n = 5). N.S: not significant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 compared to the non-treated control. Data are expressed as mean ± SD.</p>
Full article ">Figure 5
<p>Escin facilitated the proteasomal degradation of GSK3-β in cultured hDPCs. The cells were treated with various concentrations of escin for 24 h. The expression levels of (<b>a</b>) GSK3 and (<b>b</b>) Axin2 proteins were evaluated via Western blot analysis (n = 5). N.S: not significant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 compared to the non-treated control. (<b>c</b>) The mRNA expression levels of GSK3 and AXIN2 were monitored via real-time PCR analysis (n = 5). N.S: not significant. (<b>d</b>) The effect of the proteasome inhibitor MG132 on the protein levels of GSK3 and β-catenin in hDPCs treated with escin for 24 h (n = 5). N.S: not significant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 compared to non-treated control. Data are expressed as mean ± SD.</p>
Full article ">Figure 5 Cont.
<p>Escin facilitated the proteasomal degradation of GSK3-β in cultured hDPCs. The cells were treated with various concentrations of escin for 24 h. The expression levels of (<b>a</b>) GSK3 and (<b>b</b>) Axin2 proteins were evaluated via Western blot analysis (n = 5). N.S: not significant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 compared to the non-treated control. (<b>c</b>) The mRNA expression levels of GSK3 and AXIN2 were monitored via real-time PCR analysis (n = 5). N.S: not significant. (<b>d</b>) The effect of the proteasome inhibitor MG132 on the protein levels of GSK3 and β-catenin in hDPCs treated with escin for 24 h (n = 5). N.S: not significant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 compared to non-treated control. Data are expressed as mean ± SD.</p>
Full article ">Figure 6
<p>Escin-induced TOPFlash activity was abrogated by sFRP1 and sFRP2 treatment. WRHEK293A Wnt reporter cells were treated with escin in the presence of sFRP1or sFRP2 for 24 h. The cells were lysed, and GFP expression and TOPFlash activity were measured. Escin-induced TOPFlash activity was markedly abrogated by sFRP1 or sFRP2. Data are expressed as mean ± SD (n = 5). # <span class="html-italic">p</span> &lt; 0.05 compared to the non-treated control. ** <span class="html-italic">p</span> &lt; 0.01 compared to escin-treated control.</p>
Full article ">Figure 7
<p>Summarized in vitro effects of escin on Wnt/β-catenin signaling pathway of human dermal papilla cells. All these effects in combination could lead to hair growth promotion.</p>
Full article ">
23 pages, 3576 KiB  
Article
Genome-Wide Mining of Selaginella moellendorffii for Hevein-like Lectins and Their Potential Molecular Mimicry with SARS-CoV-2 Spike Glycoprotein
by Ahmed Alsolami, Amina I. Dirar, Emadeldin Hassan E. Konozy, Makarim El-Fadil M. Osman, Mohanad A. Ibrahim, Khalid Farhan Alshammari, Fawwaz Alshammari, Meshari Alazmi and Kamaleldin B. Said
Curr. Issues Mol. Biol. 2023, 45(7), 5879-5901; https://doi.org/10.3390/cimb45070372 - 14 Jul 2023
Cited by 1 | Viewed by 2222
Abstract
Multidisciplinary research efforts on potential COVID-19 vaccine and therapeutic candidates have increased since the pandemic outbreak of SARS-CoV-2 in 2019. This search has become imperative due to the increasing emergences and limited widely available medicines. The presence of bioactive anti-SARS-CoV-2 molecules was examined [...] Read more.
Multidisciplinary research efforts on potential COVID-19 vaccine and therapeutic candidates have increased since the pandemic outbreak of SARS-CoV-2 in 2019. This search has become imperative due to the increasing emergences and limited widely available medicines. The presence of bioactive anti-SARS-CoV-2 molecules was examined from various plant sources. Among them is a group of proteins called lectins that can bind carbohydrate moieties. In this article, we present ten novel, chitin-specific Hevein-like lectins that were derived from Selaginella moellendorffii v1.0’s genome. The capacity of these lectin homologs to bind with the spike protein of SARS-CoV-2 was examined. Using the HDOCK server, 3D-modeled Hevein-domains were docked to the spike protein’s receptor binding domain (RBD). The Smo446851, Smo125663, and Smo99732 interacted with Asn343-located complex N-glycan and RBD residues, respectively, with binding free energies of −17.5, −13.0, and −26.5 Kcal/mol. The molecular dynamics simulation using Desmond and the normal-state analyses via torsional coordinate association for the Smo99732-RBD complex using iMODS is characterized by overall higher stability and minimum deformity than the other lectin complexes. The three lectins interacting with carbohydrates were docked against five individual mutations that frequently occur in major SARS-CoV-2 variants. These were in the spike protein’s receptor-binding motif (RBM), while Smo125663 and Smo99732 only interacted with the spike glycoprotein in a protein–protein manner. The precursors for the Hevein-like homologs underwent additional characterization, and their expressional profile in different tissues was studied. These in silico findings offered potential lectin candidates targeting key N-glycan sites crucial to the virus’s virulence and infection. Full article
(This article belongs to the Special Issue Design, Synthesis and Discovery of Drug Candidates)
Show Figures

Figure 1

Figure 1
<p>Characterization of Hevein-like homologs from <span class="html-italic">Selaginella moellendorffii</span>. (<b>A</b>) Gene structure and (<b>B</b>) domain architecture (Hevein domains = purple boxes (PF00187), red domain = chitinase class I (PF00182), green domain = lytic transglycosylase domain (PF03330), and yellow domain = signal peptide). (<b>C</b>) Multiple sequence alignment of trimmed Hevein domains (red box = conserved cysteine residue) and (<b>D</b>) the duplication of the Hevein genes and the mechanism type. (<b>E</b>) Predicted subcellular localization of Hevein-like homologs and (<b>F</b>) phylogenetic tree of trimmed Hevein domains and their evolutionary relationship (bootstrap 1000, the lectin ID and accession numbers are followed by the number of the domains which reside in the same protein and indicated after the dash). Urtica dioica agglutinin (1ENM) was used as a reference for alignment and phylogenetic analysis.</p>
Full article ">Figure 2
<p>The 3D structural models of the Hevein-like lectins from <span class="html-italic">Selaginella moellendorffii</span>.</p>
Full article ">Figure 3
<p>Lectins–S protein complexes and the interaction analysis using PBDSum. Lectin chains in red are designated as (Chain A), and the S protein in blue is designated (as Chain B); the RBD range 319–541 is highlighted in yellow. To the right is the network of hydrogen bonds (dashed lines) anchoring NAG to the amino acid residues in both chains A and B.</p>
Full article ">Figure 4
<p>Docking of ACE to free and lectins–RBD complexes. (<b>A</b>) ACE2–RBD complex. (<b>B</b>) ACE2–Lectins–RBD complexes. The SARS-CoV-2 (Chain B, in blue), lectins Smo446851, Smo125663, and Smo99732 (Chain C, in red), and ACE2 (Chain C, in green). The residues in the RBD (yellow sphere) that are involved in the interaction include Lys417, Gln493, and Asn501, while the residues in the NTD of ACE2 include ASP30, His34, Lys353, and Tyr354.</p>
Full article ">Figure 5
<p>Normal-state analyses via iMODS server for the docked lectins (Smo446851, Smo125663, and Smo99732)–spike protein complexes. (<b>A</b>) B-factor indices, (<b>B</b>) deformability plot (the peaks indicated the non-rigid regions of the complexes), (<b>C</b>) eigenvalues, (<b>D</b>) variance plot (individual variances are purple, while cumulative variances are green), (<b>E</b>) covariance map (correlated (red), uncorrelated (white), or anti-correlated (blue) motions), and (<b>F</b>) elastic network analyses (darker grey regions indicate stiffer regions of the complex).</p>
Full article ">Figure 6
<p>Molecular dynamic simulation (MDS) for the spike protein’s RBD complexed with Smo99732. (<b>A</b>) RMSD, (<b>B</b>) RMSF, (<b>C</b>) number of hydrogen bonds, (<b>D</b>) radius gyration, and (<b>E</b>) SASA.</p>
Full article ">Figure 7
<p>Common mutations occurring in major SARS-CoV-2 variants within the region of the RBM.</p>
Full article ">
14 pages, 1633 KiB  
Article
Targeting RAF Isoforms and Tumor Microenvironments in RAS or BRAF Mutant Colorectal Cancers with SJ-C1044 for Anti-Tumor Activity
by Sungpyo Hong, Myeongjin Jeon, Jeonghee Kwon, Hanbyeol Park, Goeun Lee, Kilwon Kim and Soonkil Ahn
Curr. Issues Mol. Biol. 2023, 45(7), 5865-5878; https://doi.org/10.3390/cimb45070371 - 13 Jul 2023
Cited by 3 | Viewed by 2066
Abstract
Colorectal cancer (CRC) is a significant global health issue characterized by a high prevalence of KRAS gene mutations. The RAS/MAPK pathway, involving KRAS, plays a crucial role in CRC progression. Although some RAS inhibitors have been approved, their efficacy in CRC is limited. [...] Read more.
Colorectal cancer (CRC) is a significant global health issue characterized by a high prevalence of KRAS gene mutations. The RAS/MAPK pathway, involving KRAS, plays a crucial role in CRC progression. Although some RAS inhibitors have been approved, their efficacy in CRC is limited. To overcome these limitations, pan-RAF inhibitors targeting A-Raf, B-Raf, and C-Raf have emerged as promising therapeutic strategies. However, resistance to RAF inhibition and the presence of an immunosuppressive tumor microenvironment (TME) pose additional obstacles to effective therapy. Here, we evaluated the potential of a novel pan-RAF inhibitor, SJ-C1044, for targeting mutant KRAS-mediated signaling and inhibiting CRC cell proliferation. Notably, SJ-C1044 also exhibited inhibitory effects on immunokinases, specifically, CSF1R, VEGFR2, and TIE2, which play crucial roles in immune suppression. SJ-C1044 demonstrated potent antitumor activity in xenograft models of CRC harboring KRAS or BRAF mutations. Importantly, treatment with SJ-C1044 resulted in increased infiltration of T cells and reduced presence of tumor-associated macrophages and regulatory T cells within the TME. Thus, SJ-C1044 shows immunomodulatory potential and the ability to enhance antitumor responses. The study underscores the therapeutic potential of SJ-C1044 as a novel pan-RAF inhibitor capable of targeting oncogenic signaling pathways and overcoming immune suppression in CRC. Full article
Show Figures

Figure 1

Figure 1
<p>Chemical structure and binding mode of SJ-C1044 in BRAF V600E. (<b>A</b>) Chemical structure of SJ-C1044. (<b>B</b>) View of the binding mode of SJ-C1044 in BRAF V600E. SJ-C1044 and protein residues are represented as sticks with the following atom colors: carbon (SJ-C1044) yellow; carbon (BRAF V600E) brown; oxygen, red; nitrogen, blue; fluorine, cyan. The green lines with dashes indicate hydrogen bonding interactions. Molecular modeling was conducted in Discovery Studio using CHARMm.</p>
Full article ">Figure 2
<p>Effect of SJ-C1044 in mutant KRAS colorectal cancer cells, bone-marrow-derived macrophages (BMDMs), and human umbilical vein endothelial cells (HUVECs). (<b>A</b>) LS513 cells were treated with SJ-C1044 at the indicated concentrations. Western blot analysis was performed to evaluate the protein expression and phosphorylation of the indicated proteins. (<b>B</b>) BMDMs were treated with SJ-C1044 for 2 h, followed by stimulation with angiopoietin 2 (100 ng/mL) and CSF1 (100 ng/mL) for 15 min. Protein samples were obtained for Western blot analysis to evaluate the expression and phosphorylation status of the indicated proteins. (<b>C</b>) Tube formation induced by vascular endothelial growth factor (VEGF) in HUVECs was more effectively blocked by SJ-C1044 compared to LY3009120. (<b>D</b>) HUVECs were incubated with a concentration of 10 μM of LY3009120 or SJ-C1044, along with 10 nmol/L VEGF. Data are expressed as mean tube length ± SEM (<span class="html-italic">n</span> = 5). * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001 (Student’s <span class="html-italic">t</span>-test, two-tailed).</p>
Full article ">Figure 3
<p>Antitumor activity of SJ-C1044 in xenograft models of mutant KRAS or mutant BRAF. (<b>A</b>) Plasma concentration-time profile of SJ-C1044 in mice. SJ-C1044 was administered orally (po) at a dose of 40 mg/kg and intravenously (iv) at a dose of 5 mg/kg to male C57BL/6 mice. The blood concentrations of SJ-C1044 were measured at 30 min, 1 h, 2 h, 3 h, 4 h, 5 h, 6 h, and 24 h post-administration. (<b>B</b>–<b>D</b>) Anti-tumor growth activities of SJ-C1044 in LS513 (<b>B</b>), HCT116 (<b>C</b>), and HT29 (<b>D</b>) mouse xenograft models. SJ-C1044 was orally administered once daily (QD) at doses of 20, 40, or 80 mg/kg. Vehicle, blue line; SJ-C1044 20 mg/kg QD, red line; SJ-C1044 40 mg/kg QD, gray line; SJ-C1044 80 mg/kg QD, orange line. Error bars represent ± SEM (<span class="html-italic">n</span> = 10). *** <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 (Student’s <span class="html-italic">t</span>-test, two-tailed).</p>
Full article ">Figure 4
<p>Impact of SJ-C1044 in syngeneic mouse models. (<b>A</b>) Anti-tumor growth activities of SJ-C1044 in MC38 syngeneic mouse models. SJ-C1044 was orally administered once daily (QD) at doses of 80 mg/kg. Vehicle, blue line; SJ-C1044 80 mg/kg QD, red line. Error bars represent ± SEM (<span class="html-italic">n</span> = 10). *** <span class="html-italic">p</span> &lt; 0.001 (Student’s <span class="html-italic">t</span>-test, two-tailed). (<b>B</b>) Each dot represents the mean ± SEM of the tumor volume of the two groups. (<b>C</b>) Representative images showing the tumor burden acquired from each group. (<b>D</b>) TGI after 14 days of treatment with 80 mg/kg SJ-C1044.</p>
Full article ">Figure 5
<p>Effects of SJ-C1044 on angiogenesis, macrophages, and immune cell infiltration. (<b>A</b>) SJ-C1044 treatment reduced CD31 staining of tumor tissue, indicating an inhibitory effect on angiogenesis. SJ-C1044 decreased F4/80+ staining of tumor tissue, suggesting suppressed macrophage infiltration or activation (upper panel). SJ-C1044 administration caused a decrease in CD163+ or CD206+ staining, which are markers for M2 macrophages (middle panel). SJ-C1044 increased the infiltration of CD8+ T cells (bottom panel) and a simultaneous reduction in FOXP3+ cell population, including immunosuppressive regulatory T (Treg) cells, within the tumors. All staining intensities were normalized relative to the DAPI signals of the corresponding samples. Error bars represent ± SEM (<span class="html-italic">n</span> = 5). ** <span class="html-italic">p</span> &lt; 0.01; * <span class="html-italic">p</span> &lt; 0.05 (Student’s <span class="html-italic">t</span>-test, two-tailed). (<b>B</b>) SJ-C1044 treatment notably decreased IL-10 levels. Western blot analysis was performed to evaluate the protein expression of IL-10. Four independent Western blots were quantified using ImageJ. The protein expression of IL-10 was normalized relative to the β-actin signals of the corresponding samples. Mean ± SEM, *** <span class="html-italic">p</span> &lt; 0.001 (Student’s <span class="html-italic">t</span>-test, two-tailed).</p>
Full article ">
16 pages, 4026 KiB  
Article
A Metagenome from a Steam Vent in Los Azufres Geothermal Field Shows an Abundance of Thermoplasmatales archaea and Bacteria from the Phyla Actinomycetota and Pseudomonadota
by Roberto Marín-Paredes, Hermes H. Bolívar-Torres, Alberto Coronel-Gaytán, Esperanza Martínez-Romero and Luis E. Servín-Garcidueñas
Curr. Issues Mol. Biol. 2023, 45(7), 5849-5864; https://doi.org/10.3390/cimb45070370 - 13 Jul 2023
Viewed by 2125
Abstract
Los Azufres National Park is a geothermal field that has a wide number of thermal manifestations; nevertheless, the microbial communities in many of these environments remain unknown. In this study, a metagenome from a sediment sample from Los Azufres National Park was sequenced. [...] Read more.
Los Azufres National Park is a geothermal field that has a wide number of thermal manifestations; nevertheless, the microbial communities in many of these environments remain unknown. In this study, a metagenome from a sediment sample from Los Azufres National Park was sequenced. In this metagenome, we found that the microbial diversity corresponds to bacteria (Actinomycetota, Pseudomonadota), archaea (Thermoplasmatales and Candidatus Micrarchaeota and Candidatus Parvarchaeota), eukarya (Cyanidiaceae), and viruses (Fussellovirus and Caudoviricetes). The functional annotation showed genes related to the carbon fixation pathway, sulfur metabolism, genes involved in heat and cold shock, and heavy-metal resistance. From the sediment, it was possible to recover two metagenome-assembled genomes from Ferrimicrobium and Cuniculiplasma. Our results showed that there are a large number of microorganisms in Los Azufres that deserve to be studied. Full article
(This article belongs to the Collection Feature Papers in Current Issues in Molecular Biology)
Show Figures

Figure 1

Figure 1
<p>Geographic location of collection site. (<b>A</b>) Geographic location of collection site (19.78170609819753 N, −100.65805210414699 W). The Los Azufres geothermal field’s zone is shown in the image. Google-Earth-generated image. (<b>B</b>) Sample of sediment taken from the Los Azufres geothermal field. Inside the rectangle, a sample of the green sediment was obtained.</p>
Full article ">Figure 2
<p>Analysis of microbial diversity of sediment sample from a steam vent of the geothermal field of Los Azufres. In the graph, each phylum is denoted by a different alphabet letter. Kaiju was used to complete the analysis of the microbial diversity.</p>
Full article ">Figure 3
<p>Heatmap of functional diversity representation of each MAGs. The COG category is displayed on the y axis, and the x axis displays the MAGs.</p>
Full article ">Figure 4
<p>Phylogenomic tree of Thermoplasmatales archaea. The phylogenomic tree shows the predicted evolutionary relationships of genomes from the order Thermoplasmatales against genomes of Thermoplasmatales in NCBI database. MAGs recovered in this study are shown in bold letters. Aciduliprofundum boonei T469 was selected as an outgroup. Phylogenomic tree was generated using maximum likelihood model, and numbers at the branch points represent SH-like local support values. The scale bar represents the estimated number of amino acid changes per site.</p>
Full article ">Figure 5
<p>Phylogenomic tree of Actinobacteria. The phylogenomic tree shows the predicted evolutionary relationships of genomes from the phylum Actinomycetota against genomes of Actinomycetota in NCBI database. MAGs recovered in this study are shown in bold letters. <span class="html-italic">Sulfobacillus thermosulfidooxidans</span> strain ZJ was selected as an outgroup. Phylogenomic tree was generated using maximum likelihood model, and numbers at the branch points represent SH-like local support values. The scale bar represents the estimated number of amino acid changes per site.</p>
Full article ">
19 pages, 2095 KiB  
Review
The Role of the Piezo1 Mechanosensitive Channel in Heart Failure
by Weihua Yuan, Xicheng Zhang and Xiangming Fan
Curr. Issues Mol. Biol. 2023, 45(7), 5830-5848; https://doi.org/10.3390/cimb45070369 - 13 Jul 2023
Cited by 9 | Viewed by 4422
Abstract
Mechanotransduction (MT) is inseparable from the pathobiology of heart failure (HF). However, the effects of mechanical forces on HF remain unclear. This review briefly describes how Piezo1 functions in HF-affected cells, including endothelial cells (ECs), cardiac fibroblasts (CFs), cardiomyocytes (CMs), and immune cells. [...] Read more.
Mechanotransduction (MT) is inseparable from the pathobiology of heart failure (HF). However, the effects of mechanical forces on HF remain unclear. This review briefly describes how Piezo1 functions in HF-affected cells, including endothelial cells (ECs), cardiac fibroblasts (CFs), cardiomyocytes (CMs), and immune cells. Piezo1 is a mechanosensitive ion channel that has been extensively studied in recent years. Piezo1 responds to different mechanical forces and converts them into intracellular signals. The pathways that modulate the Piezo1 switch have also been briefly described. Experimental drugs that specifically activate Piezo1-like proteins, such as Yoda1, Jedi1, and Jedi2, are available for clinical studies to treat Piezo1-related diseases. The only mechanosensitive ion-channel-specific inhibitor available is GsMTx4, which can turn off Piezo1 by modulating the local membrane tension. Ultrasound waves can modulate Piezo1 switching in vitro with the assistance of microbubbles. This review provides new possible targets for heart failure therapy by exploring the cellular functions of Piezo1 that are involved in the progression of the disease. Modulation of Piezo1 activity may, therefore, effectively delay the progression of heart failure. Full article
(This article belongs to the Special Issue A Focus on Molecular Basis in Cardiac Diseases)
Show Figures

Figure 1

Figure 1
<p>Schematic diagram of the simple structure of piezo1 and piezo1 activated by mechanical force. Mechanical forces applied to the cell membrane cause the opening of Piezo1 channels, resulting in the influx of extracellular Ca<sup>2+</sup> and the conversion of mechanical signals into electrochemical and chemical signals.</p>
Full article ">Figure 2
<p>Schematic diagram of the mechanism of action of Piezo1 in cardiac fibroblasts. Mechanical stress induces Piezo1 opening in fibroblasts and promotes IL-6 expression via the p38α MAPK signaling pathway. Mechanical stretch induces fibroblasts to secrete natriuretic peptides and ECM, and the increase in matrix stiffness may further upregulate Piezo1 expression and promote fibrosis progression (created with <a href="https://www.figdraw.com/static/index.html" target="_blank">https://www.figdraw.com/static/index.html</a> accessed on 5 June 2023).</p>
Full article ">Figure 3
<p>Schematic diagram of the mechanism of action of Piezo1 in cardiomyocytes. Piezo1 helps cardiomyocytes transduce mechanical stretch into intracellular Ca<sup>2+</sup> signaling leading to ROS production. Mechanical overload results in increased Piezo1 expression in cardiomyocytes via the CaMKII-HDAC4–MEF2 pathway, ultimately leading to dilated cardiomyopathy (created with <a href="https://www.figdraw.com/static/index.html" target="_blank">https://www.figdraw.com/static/index.html</a> accessed on 5 June 2023).</p>
Full article ">Figure 4
<p>Schematic diagram of the role of Piezo1 in vascular endothelial cells. Piezo1 senses fluid shear, leading to Ca<sup>2+</sup> influx that activates the eNOS signaling pathway and contributing to the differentiation of monocytes into macrophages. In addition, it induces leukocyte entry and exit by activating the NF-κB pathway to open the endothelial barrier (created with <a href="https://www.figdraw.com/static/index.html" target="_blank">https://www.figdraw.com/static/index.html</a> accessed on 5 June 2023).</p>
Full article ">Figure 5
<p>Schematic diagram of the role of Piezo1 in immune cells. (1) Activation of piezo1 upon stimulation by hydrostatic pressure leads to Ca<sup>2+</sup> influx. In turn, the increase in Ca<sup>2+</sup> activates the AP1/HIF1ɑ signaling pathway, inducing macrophage differentiation into M1-like macrophages that secrete pro-inflammatory cytokines. (2) Mechanical stiffness or inflammatory signals activate piezo1 on DCs, leading to Ca<sup>2+</sup> influx and secretion of polarizing factors IL-12 and TGF-β and ultimately affecting the mutual differentiation of Th1 and Treg cells. (3) Activation of piezo1 on T cells affects their differentiation into CD<sup>4+</sup>/CD<sup>8+</sup> T cells and Th17/Treg cell reciprocally (created with <a href="https://www.figdraw.com/static/index.html" target="_blank">https://www.figdraw.com/static/index.html</a> accessed on 5 June 2023).</p>
Full article ">
6 pages, 205 KiB  
Editorial
Bioactives and Inflammation
by Guan-Ting Liu and Chan-Yen Kuo
Curr. Issues Mol. Biol. 2023, 45(7), 5824-5829; https://doi.org/10.3390/cimb45070368 - 13 Jul 2023
Cited by 1 | Viewed by 1392
Abstract
Inflammation is one of the body’s most complex physiological defense mechanisms against harmful substances [...] Full article
(This article belongs to the Special Issue Bioactives and Inflammation)
13 pages, 3072 KiB  
Article
A Comparative Analysis of NOX4 Protein Expression in Malignant and Non-Malignant Thyroid Tumors
by Salma Fenniche, Mohamed Oukabli, Yassire Oubaddou, Hafsa Chahdi, Amal Damiri, Abir Alghuzlan, Abdelilah Laraqui, Nadia Dakka, Youssef Bakri, Corinne Dupuy and Rabii Ameziane El Hassani
Curr. Issues Mol. Biol. 2023, 45(7), 5811-5823; https://doi.org/10.3390/cimb45070367 - 13 Jul 2023
Cited by 4 | Viewed by 1998
Abstract
The comparative analysis of the expression of the reactive oxygen species-generating NADPH oxidase NOX4 from TCGA data shows that the NOX4 transcript is upregulated in papillary thyroid carcinomas (PTC)-BRAFV600E tumors compared to PTC-BRAFwt tumors. However, a comparative analysis of NOX4 at [...] Read more.
The comparative analysis of the expression of the reactive oxygen species-generating NADPH oxidase NOX4 from TCGA data shows that the NOX4 transcript is upregulated in papillary thyroid carcinomas (PTC)-BRAFV600E tumors compared to PTC-BRAFwt tumors. However, a comparative analysis of NOX4 at the protein level in malignant and non-malignant tumors is missing. We explored NOX4 protein expression by immunohistochemistry staining in malignant tumors (28 classical forms of PTC (C-PTC), 17 follicular variants of PTC (F-PTC), and three anaplastic thyroid carcinomas (ATCs)) and in non-malignant tumors (six lymphocytic thyroiditis, four Graves’ disease, ten goiters, and 20 hyperplasias). We detected the BRAFV600E mutation by Sanger sequencing and digital droplet PCR. The results show that NOX4 was found to be higher (score ≥ 2) in C-PTC (92.9%) compared to F-PTC (52.9%) and ATC (33.3%) concerning malignant tumors. Interestingly, all C-PTC-BRAFV600E expressed a high score for NOX4 at the protein level, strengthening the positive correlation between the BRAFV600E mutation and NOX4 expression. In addition, independent of the mutational status of BRAF, we observed that 90% of C-PTC infiltrating tumors showed high NOX4 expression, suggesting that NOX4 may be considered a complementary biomarker in PTC aggressiveness. Interestingly, NOX4 was highly expressed in non-malignant thyroid diseases with different subcellular localizations. Full article
(This article belongs to the Special Issue Advanced Molecular Solutions for Cancer Therapy)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>BRAF<sup>V600E</sup> mutation in thyroid carcinomas. Association between BRAF<sup>V600E</sup> mutation and histological type of thyroid carcinomas (<span class="html-italic">n</span> = 48). In total, 100% of thyroid carcinoma harboring BRAF<sup>V600E</sup> are exclusively C-PTC. <span class="html-italic">ATC</span>: anaplastic thyroid carcinoma (<span class="html-italic">n</span> = 3). <span class="html-italic">C-PTC</span>: classical forms of papillary thyroid carcinoma (<span class="html-italic">n</span> = 28). <span class="html-italic">F-PTC</span>: follicular variants of papillary thyroid carcinoma (<span class="html-italic">n</span> = 17). BRAF<sup>V600E</sup> (<span class="html-italic">n</span> = 10), BRAF<sup>wt</sup> (<span class="html-italic">n</span> = 38). The statistical tests performed by GraphPad 8. * The statistical significance is affirmed by a <span class="html-italic">p</span>-value less than 0.05. *: <span class="html-italic">p</span>-value ≤ 0.05.</p>
Full article ">Figure 2
<p>NOX4 protein expression in thyroid carcinomas. (<b>a</b>) Representative example of NOX4 protein expression in C-PTC. Red lines represent tumor tissue sections, and green lines represent normal adjacent tissue (NAT) sections on the same slide (×10). There is a high expression level of NOX4 protein in C-PTC and a low expression level of NOX4 in its adjacent normal tissue (NAT). (<b>b</b>) Comparative analysis of NOX4 protein expression in human thyroid tumors (<span class="html-italic">n</span> = 48:28 C-PTC, 17 F-PTC, 3 ATC) compared to their normal adjacent tissues (46 NAT). Percentage calculated according to the number of each score &lt;2 and ≥2. The score &lt;2 represents an absence or very low and low expression of NOX4 protein (<span class="html-italic">n</span> = 58). The score ≥2 represents a middle or/and high expression level of NOX4 protein (<span class="html-italic">n</span> = 36). (<b>c</b>) Association between BRAF<sup>V600E</sup> mutation and NOX4 expression. In total, 100% of thyroid carcinoma harboring BRAF<sup>V600E</sup> mutation showed high expression level of NOX4 protein. BRAF<sup>V600E</sup> (<span class="html-italic">n</span> = 10), BRAF<sup>wt</sup> (<span class="html-italic">n</span> = 38). The statistical tests are performed by GraphPad 8. * The statistical significance is affirmed by a <span class="html-italic">p</span>-value under 0.05. *: <span class="html-italic">p</span>-value ≤ 0.05, ****: <span class="html-italic">p</span>-value ≤ 0.0001.</p>
Full article ">Figure 3
<p>Correlation of infiltrating character followed by the limitation of the border of the tumor with NOX4 protein expression and BRAF<sup>V600E</sup> mutation in PTC (<span class="html-italic">n</span> = 21: 17 c-PTC, 4 F-PTC). (<b>a</b>) In total, 83% (10/12) of papillary thyroid carcinoma with an infiltrating character show overexpression of NOX4 protein (<span class="html-italic">n</span> = 12). (<b>b</b>) C-PTC: high expression of NOX4 protein in infiltrating tumors (<span class="html-italic">n</span> = 10) (90% (9/10)) with a score ≥2. (<b>c</b>) C-PTC: 40% (4/10) of papillary thyroid carcinoma with an infiltrating character harbor BRAF<sup>V600E</sup> mutation. (<b>d</b>) In total, 100% of infiltrating C-PTC harboring BRAF<sup>V600E</sup> mutation overexpress NOX4 protein. Percentage calculated according to the total number of infiltrating tumors of each histological type.</p>
Full article ">Figure 4
<p>Immunohistochemical analysis of NOX4 protein expression and subcellular localization in thyroid carcinomas and non-malignant thyroid diseases. (<b>a</b>) NOX4 protein expression in 40 non-malignant thyroid diseases (six lymphocytic thyroiditis, four Graves’ disease, ten goiters, and 20 hyperplasias. (<b>b</b>) Subcellular localization of NOX4 protein in 40 non-malignant thyroid diseases (six lymphocytic thyroiditis, four Graves’ disease, ten goiters, and 20 hyperplasias). (<b>c</b>) Subcellular localization of NOX4 protein in 94 malignant thyroid tissues (28 c-PTC, 17 F-PTC, 3 ATC, and 46 NAT). The statistical tests performed by GraphPad 8. * The statistical significance is affirmed by a <span class="html-italic">p</span>-value less than 0.05. *: <span class="html-italic">p</span>-value ≤ 0.05, ****: <span class="html-italic">p</span>-value ≤ 0.0001.</p>
Full article ">
13 pages, 600 KiB  
Article
HLA-G Alleles Impact the Perinatal Father–Child HPV Transmission
by Nelli T. Suominen, Michel Roger, Marie-Claude Faucher, Kari J. Syrjänen, Seija E. Grénman, Stina M. Syrjänen and Karolina Louvanto
Curr. Issues Mol. Biol. 2023, 45(7), 5798-5810; https://doi.org/10.3390/cimb45070366 - 12 Jul 2023
Viewed by 1489
Abstract
The host factors that influence father-to-child human papillomavirus (HPV) transmission remain unknown. This study evaluated whether human leukocyte antigen (HLA)-G alleles are important in father-to-child HPV transmission during the perinatal period. Altogether, 134 father–newborn pairs from the Finnish Family HPV Study were included. [...] Read more.
The host factors that influence father-to-child human papillomavirus (HPV) transmission remain unknown. This study evaluated whether human leukocyte antigen (HLA)-G alleles are important in father-to-child HPV transmission during the perinatal period. Altogether, 134 father–newborn pairs from the Finnish Family HPV Study were included. Oral, semen and urethral samples from the fathers were collected before the delivery, and oral samples were collected from their offspring at delivery and postpartum on day 3 and during 1-, 2- and 6-month follow-up visits. HLA-G alleles were tested by direct sequencing. Unconditional logistic regression was used to determine the association of the father–child HLA-G allele and genotype concordance with the father–child HPV prevalence and concordance at birth and during follow-up. HLA-G allele G*01:01:03 concordance was associated with the father’s urethral and child’s oral high-risk (HR)-HPV concordance at birth (OR 17.00, 95% CI: 1.24–232.22). HLA-G allele G*01:04:01 concordance increased the father’s oral and child’s postpartum oral any- and HR-HPV concordance with an OR value of 7.50 (95% CI: 1.47–38.16) and OR value of 7.78 (95% CI: 1.38–43.85), respectively. There was no association between different HLA-G genotypes and HPV concordance among the father–child pairs at birth or postpartum. To conclude, the HLA-G allele concordance appears to impact the HPV transmission between the father and his offspring. Full article
(This article belongs to the Special Issue Complex Molecular Mechanism of Monogenic Diseases 2.0)
Show Figures

Figure 1

Figure 1
<p>Human leukocyte antigen G (HLA-G) (<b>a</b>) allele and (<b>b</b>) genotype concordance among the 134 father–child pairs from the Finnish Family HPV Study. Stacked bar columns showing the HLA-G (<b>a</b>) allele sharing (absent = both missing the allele; discordant = one homo- or heterozygous for the allele and the other absent; 2 common alleles = both heterozygous for the allele; 3 common alleles = one heterozygous for the allele and other homozygous for the allele; 4 common alleles = both homozygous for the allele) and (<b>b</b>) genotype concordance (discordance = one having the genotype and the other missing the genotype; concordance = both having the same genotype) between the 134 father–child pairs from the Finnish Family HPV Study. Those HLA-G alleles and genotypes that were ≥3% prevalent among the father–child pairs were included.</p>
Full article ">
22 pages, 2600 KiB  
Article
Genome-Wide SNP and Indel Discovery in Abaca (Musa textilis Née) and among Other Musa spp. for Abaca Genetic Resources Management
by Cris Francis C. Barbosa, Jayson C. Asunto, Rhosener Bhea L. Koh, Daisy May C. Santos, Dapeng Zhang, Ernelea P. Cao and Leny C. Galvez
Curr. Issues Mol. Biol. 2023, 45(7), 5776-5797; https://doi.org/10.3390/cimb45070365 - 12 Jul 2023
Cited by 4 | Viewed by 3200
Abstract
Abaca (Musa textilis Née) is an economically important fiber crop in the Philippines. Its economic potential, however, is hampered by biotic and abiotic stresses, which are exacerbated by insufficient genomic resources for varietal identification vital for crop improvement. To address these gaps, [...] Read more.
Abaca (Musa textilis Née) is an economically important fiber crop in the Philippines. Its economic potential, however, is hampered by biotic and abiotic stresses, which are exacerbated by insufficient genomic resources for varietal identification vital for crop improvement. To address these gaps, this study aimed to discover genome-wide polymorphisms among abaca cultivars and other Musa species and analyze their potential as genetic marker resources. This was achieved through whole-genome Illumina resequencing of abaca cultivars and variant calling using BCFtools, followed by genetic diversity and phylogenetic analyses. A total of 20,590,381 high-quality single-nucleotide polymorphisms (SNP) and DNA insertions/deletions (InDels) were mined across 16 abaca cultivars. Filtering based on linkage disequilibrium (LD) yielded 130,768 SNPs and 13,620 InDels, accounting for 0.396 ± 0.106 and 0.431 ± 0.111 of gene diversity across these cultivars. LD-pruned polymorphisms across abaca, M. troglodytarum, M. acuminata and M. balbisiana enabled genetic differentiation within abaca and across the four Musa spp. Phylogenetic analysis revealed the registered varieties Abuab and Inosa to accumulate a significant number of mutations, eliciting further studies linking mutations to their advantageous phenotypes. Overall, this study pioneered in producing marker resources in abaca based on genome-wide polymorphisms vital for varietal authentication and comparative genotyping with the more studied Musa spp. Full article
(This article belongs to the Special Issue Molecular Breeding and Genetics Research in Plants)
Show Figures

Figure 1

Figure 1
<p>Sequencing coverage (<b>a</b>) and mapping quality evaluation (<b>b</b>) results generated through Qualimap across abaca cultivars and <span class="html-italic">Musa</span> accessions. The graph was generated using Microsoft Excel 2010s.</p>
Full article ">Figure 2
<p>Frequency distribution of multiallelic SNPs and InDels within <span class="html-italic">M. textilis</span> and among <span class="html-italic">Musa</span> spp. The graph was generated using Microsoft Excel 2010.</p>
Full article ">Figure 3
<p>Percentage distribution of transitions and transversions among the discovered SNPs within <span class="html-italic">M. textilis</span> (<b>a</b>) and among <span class="html-italic">Musa</span> spp. (<b>b</b>). The pie chart was generated using Microsoft Excel 2010.</p>
Full article ">Figure 4
<p>PCA plot displaying genetic variation among 16 abaca varieties and accessions in terms of their genome-wide SNPs (<b>a</b>) and InDels (<b>b</b>). These are represented by 130,768 and 13,620 LD-pruned loci, respectively, with at most 10% missing genotypes. The first and second components in the SNP PCA plot were able to explain 39.4% and 14.4% of the variance, respectively. The first and second components in the InDel PCA plot were able to explain 17.8% and 11.4% of the variance, respectively. Plots were generated using RStudio 2022.02.3.</p>
Full article ">Figure 5
<p>Unrooted neighbor-joining tree displaying phylogenetic relationships among 16 abaca varieties and accessions in terms of their genome-wide SNPs (<b>a</b>) and InDels (<b>b</b>). These are represented by 130,768 and 13,620 LD-pruned loci, respectively, with at most 10% missing genotypes. Values on nodes represent bootstrap support out of 1000 NJ bootstrap sampling using Hamming distance for genetic distance calculation. The accessions SRR9696635, SRR8989639 and SRR9850642 were not identified in terms of their varietal identity [<a href="#B33-cimb-45-00365" class="html-bibr">33</a>], and hence were labeled as accession numbers. Trees were generated using RStudio 2022.02.3.</p>
Full article ">Figure 5 Cont.
<p>Unrooted neighbor-joining tree displaying phylogenetic relationships among 16 abaca varieties and accessions in terms of their genome-wide SNPs (<b>a</b>) and InDels (<b>b</b>). These are represented by 130,768 and 13,620 LD-pruned loci, respectively, with at most 10% missing genotypes. Values on nodes represent bootstrap support out of 1000 NJ bootstrap sampling using Hamming distance for genetic distance calculation. The accessions SRR9696635, SRR8989639 and SRR9850642 were not identified in terms of their varietal identity [<a href="#B33-cimb-45-00365" class="html-bibr">33</a>], and hence were labeled as accession numbers. Trees were generated using RStudio 2022.02.3.</p>
Full article ">Figure 6
<p>PCA plot displaying genetic variation among <span class="html-italic">Musa</span> species in terms of their genome-wide SNPs (<b>a</b>) and InDels (<b>b</b>). These are represented by 31,244 and 577 LD-pruned loci, respectively, with at most 10% missing genotypes. The first and second PCA components in the SNP PCA plot were able to explain 69.3% and 9.4% of the variance. The first and second PCA components in the InDel PCA plot were able to explain 27.0% and 7.9% of the variance. Species labels ‘Musa balbisiana’, ‘Musa troglodytarum’, ‘Musa acuminata’ and ‘Musa <span class="html-italic">textilis</span>’ represent the species <span class="html-italic">Musa balbisiana</span>, <span class="html-italic">M. troglodytarum</span>, <span class="html-italic">M. acuminata</span> and <span class="html-italic">M. textilis</span>, respectively. Plots were generated using RStudio 2022.02.3.</p>
Full article ">Figure 6 Cont.
<p>PCA plot displaying genetic variation among <span class="html-italic">Musa</span> species in terms of their genome-wide SNPs (<b>a</b>) and InDels (<b>b</b>). These are represented by 31,244 and 577 LD-pruned loci, respectively, with at most 10% missing genotypes. The first and second PCA components in the SNP PCA plot were able to explain 69.3% and 9.4% of the variance. The first and second PCA components in the InDel PCA plot were able to explain 27.0% and 7.9% of the variance. Species labels ‘Musa balbisiana’, ‘Musa troglodytarum’, ‘Musa acuminata’ and ‘Musa <span class="html-italic">textilis</span>’ represent the species <span class="html-italic">Musa balbisiana</span>, <span class="html-italic">M. troglodytarum</span>, <span class="html-italic">M. acuminata</span> and <span class="html-italic">M. textilis</span>, respectively. Plots were generated using RStudio 2022.02.3.</p>
Full article ">Figure 7
<p>Rooted unweighted pair group method with arithmetic mean (UPGMA) tree displaying phylogenetic relationships among <span class="html-italic">Musa</span> accessions in terms of their genome-wide SNPs (<b>a</b>) and InDels (<b>b</b>). These are represented by 31,244 and 577 LD-pruned loci, respectively, with at most 10% missing genotypes. Values on nodes represent bootstrap support out of 1000 UPGMA bootstrap sampling using Hamming distance for genetic distance calculation. <span class="html-italic">M. textilis</span> accessions are labeled in purple, <span class="html-italic">M. troglodytarum</span> accessions are labeled in pink, <span class="html-italic">M. acuminata</span> accessions are labeled in green and <span class="html-italic">M. balbisiana</span> accessions are labeled in orange. For both trees, the SRR9734079 <span class="html-italic">M. acuminata</span> accession was used as outgroup from where the trees were rooted. Trees were generated using RStudio 2022.02.3.</p>
Full article ">Figure 8
<p>Graphical comparison of homozygosity statistics per individual (variety and accession) based on genome-wide SNPs. Labels starting with ‘Mbalbisiana’, ‘Mtroglodytarum’, ‘Macuminata’ and ‘Mtextilis’ represent varieties/accessions under the <span class="html-italic">Musa balbisiana</span>, <span class="html-italic">M. troglodytarum</span>, <span class="html-italic">M. acuminata</span> and <span class="html-italic">M. textilis</span>, respectively. The specific varieties/accessions are indicated following the aforementioned labels. The pie chart was generated using Microsoft Excel 2010.</p>
Full article ">
11 pages, 2286 KiB  
Brief Report
Yeast One-Hybrid Screening to Identify Transcription Factors for IbMYB1-4 in the Purple-Fleshed Sweet Potato (Ipomoea batatas [L.] Lam.)
by Danwen Fu, Shaohua Yang, Rui Liu and Feng Gao
Curr. Issues Mol. Biol. 2023, 45(7), 5765-5775; https://doi.org/10.3390/cimb45070364 - 12 Jul 2023
Cited by 2 | Viewed by 2135
Abstract
IbMYB1 is a transcription factor involved in the biosynthesis of anthocyanin in the purple-fleshed sweet potato. So far, few studies have investigated transcription factors that are upstream of the promoter IbMYB1-4. In this study, a yeast one-hybrid screening aimed at identifying transcription [...] Read more.
IbMYB1 is a transcription factor involved in the biosynthesis of anthocyanin in the purple-fleshed sweet potato. So far, few studies have investigated transcription factors that are upstream of the promoter IbMYB1-4. In this study, a yeast one-hybrid screening aimed at identifying transcription factors upstream of the promoter IbMYB1-4 was performed in the storage roots of the purple-fleshed sweet potato, and IbPDC, IbERF1, and IbPGP19 were identified as upstream binding proteins for the promoter IbMYB1-4. A dual luciferase reporter assay, and yeast one-hybrid assays, were employed to confirm the interaction of these binding proteins with promoters. IbERF1 was found to be an upstream transcription factor for the promoter IbMYB1, and is implicated in the biosynthesis of anthocyanin in the purple-fleshed sweet potato. IbERF1 plays a major role in the biosynthesis of anthocyanin in the purple-fleshed sweet potato. Full article
(This article belongs to the Special Issue Molecular Breeding and Genetics Research in Plants)
Show Figures

Figure 1

Figure 1
<p>Transcriptional activation of IbERF1, IbPGP19, and IbPDC in yeast cells. The Y2H Gold strains successfully transformed with the corresponding vectors, and grown on SD/-His/AbA/X-α-Gal plates for 3–5 days at 30 °C. The ten points of each treatment were grown on a Petri dish. The growth status of yeast cells evaluated by an X-α-Gal assay was used to monitor transcription activation.</p>
Full article ">Figure 2
<p>The interaction of IbERF1, IbPGP19, and IbPDC with <span class="html-italic">PIbMYB1-4</span>, studied using a yeast one-hybrid assay. The Y1H Gold strains successfully transformed with corresponding vectors, and grown on SD/-Leu/AbA plates for 3–5 days at 30 °C. The four points of each treatment were grown on a Petri dish, and the interactions were confirmed using the yeast cell growth status.</p>
Full article ">Figure 3
<p>The IbERF1, IbPGP19, and IbPDC proteins activate the <span class="html-italic">IbMYB1-4</span> promoters in dual-luciferase assays. The error bars represent the standard deviation (SD). The significance tests are shown as a, b, c, and d. The different lowercase letters in the chart indicate that there is a significant difference (<span class="html-italic">p</span> &lt; 0.01).</p>
Full article ">Figure 4
<p>The subcellular localization of the IbERF1, IbPGP19, and IbPDC proteins in <span class="html-italic">Arabidopsis</span> protoplasts. The fusion proteins and the GFP control were expressed transiently in <span class="html-italic">Arabidopsis</span> protoplasts. The bars represent 20 µm. (<b>A</b>) GFP, (<b>B</b>) chloroplast, (<b>C</b>) light field, (<b>D</b>) merged graph.</p>
Full article ">Figure 5
<p>The relative expressions levels of <span class="html-italic">IbERF1</span>, <span class="html-italic">IbMYB1</span>, and the structural genes involved in the biosynthesis of anthocyanin at the different root stages in the purple- and white-fleshed sweet potato: the fibrous root (diameter &lt; 2 mm), thick root (2 mm &lt; diameter &gt; 5 mm), and storage root (diameter &gt; 5 mm).</p>
Full article ">
13 pages, 3322 KiB  
Article
Inhibition of Autophagy and the Cytoprotective Role of Smac Mimetic against ROS-Induced Cancer: A Potential Therapeutic Strategy in Relapse and Chemoresistance Cases in Breast Cancer
by Sahar Rafat, Mohammed Ageeli Hakami, Ali Hazazi, Ahad Amer Alsaiari, Summya Rashid, Mohammad Raghibul Hasan, Abdulaziz A. Aloliqi, Alaa Abdulaziz Eisa, Mohammad Irfan Dar, Mohd Faisal Khan and Kapil Dev
Curr. Issues Mol. Biol. 2023, 45(7), 5752-5764; https://doi.org/10.3390/cimb45070363 - 10 Jul 2023
Cited by 2 | Viewed by 1813
Abstract
With more than a million deaths each year, breast cancer is the top cause of death in women. Around 70% of breast cancers are hormonally responsive. Although several therapeutic options exist, cancer resistance and recurrence render them inefficient and insufficient. The major key [...] Read more.
With more than a million deaths each year, breast cancer is the top cause of death in women. Around 70% of breast cancers are hormonally responsive. Although several therapeutic options exist, cancer resistance and recurrence render them inefficient and insufficient. The major key reason behind this is the failure in the regulation of the cell death mechanism. In addition, ROS was also found to play a major role in this problem. The therapeutic benefits of Smac mimetic compound (SMC) BV6 on MCF7 were examined in the current study. Treatment with BV6 reduces viability and induces apoptosis in MCF7 breast cancer cells. BV6 suppresses autophagy and has demonstrated a defensive role in cancer cells against oxidative stress caused by H2O2. Overall, the present investigation shows that SMC has therapeutic and cytoprotective potential against oxidative stress in cancer cells. These Smac mimetic compounds may be used as anti-cancer drugs as well as antioxidants alone or in conjunction with other commonly used antioxidants. Full article
(This article belongs to the Section Molecular Medicine)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) The chemical structure of BV6 [<a href="#B23-cimb-45-00363" class="html-bibr">23</a>]. (<b>b</b>) Effect of an increasing concentration of H<sub>2</sub>O<sub>2</sub> (0–1200 µM) and (<b>c</b>) BV6 with H<sub>2</sub>O<sub>2</sub> (100 µM) on the viability of cancer cells measured by MTT assay. Cells were treated for 24 h. MTT incubation for 3 h, followed by DMSO, and the results were measured at a wavelength of 570 nm. The experiment was performed in triplicate, and data from three different trials are presented as mean ± SD. (*), (**), (***), and (****) show <span class="html-italic">p</span> ˂ 0.05, <span class="html-italic">p</span> ˂ 0.01, <span class="html-italic">p</span> ˂ 0.001, and <span class="html-italic">p</span> ˂ 0.0001, respectively, for each case.</p>
Full article ">Figure 2
<p>Evaluation of BV6 cytoprotective activity in cells exposed to H<sub>2</sub>O<sub>2</sub>: Cells were rinsed and exposed to 100 mM H<sub>2</sub>O<sub>2</sub> for 45 min after receiving BV6 treatment for 24 h and dyed for 30 min with DCFDA. Cells were taken and examined under a fluorescent microscope (<b>a</b>). Cells in a representative image with fluorescence distribution; scale bar = 100 µm. (<b>b</b>) The mean fluorescence intensity of H<sub>2</sub>O<sub>2</sub> and BV6-treated cells. (<b>c</b>) Flow cytometry analysis of ROS induced by H<sub>2</sub>O<sub>2</sub> in MCF7 cells treated with BV6. (****) show <span class="html-italic">p</span> &lt; 0.0001, respectively, and are carried out in triplicate and displayed as mean ± SD.</p>
Full article ">Figure 3
<p>Apoptosis analysis: Treatment of BV6 with H<sub>2</sub>O<sub>2</sub> (100 µM) for 24 h shows enhanced apoptosis induction compared to treatment with H<sub>2</sub>O<sub>2</sub> (100 µM). (<b>a</b>) The confocal microscopy images show cells stained with DAPI. Yellow arrows demonstrate nuclear morphology changes in response to H<sub>2</sub>O<sub>2</sub> (100 µM) with and without the Smac mimetic compound BV6 (1 µM). Scale bar = 50 µm. (<b>b</b>) The dot plot shows the cells mean gray value to quantify the DAPI fluorescence signals. Scale bar = 100 µm. (<b>c</b>) Images showing cells stained with AO/EtBr after treatment. (<b>d</b>) The quadrants after flow cytometry using Annexin V/PI stain display the live cells, early apoptotic cells, late apoptotic cells, and necrotic cells proportion. (<b>e</b>) Graphical representation of Annexin V-positive cells (%) in response to treatment. The data from three different trials is presented as mean ± SD and is statistically significant. (*), (**) show <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, respectively, for each case.</p>
Full article ">Figure 4
<p>Detection of autophagy in H<sub>2</sub>O<sub>2</sub>-induced breast cancer cells: (<b>a</b>) Transmission electron microscopy images displaying autophagic vacuoles in red arrows (autophagosomes) and yellow arrows (autolysosomes). M and N are mitochondria and nuclei, respectively. Scale bar = 1 µm. Images with yellow highlighted borders are the zoom images of the above TEM pictures. (<b>b</b>) MDC dye fluorescence microscopic images demonstrating the accumulated autophagy vacuoles in response to H<sub>2</sub>O<sub>2</sub> and BV6. Scale bar = 34 µm. (<b>c</b>) The relative change in the mean MDC dye fluorescence intensity is demonstrated by the histogram profile. (<b>d</b>) Fold change in Beclin1 and LC3 mRNA expression levels normalized by Actin after 24 h of treatment. (<b>e</b>) a Western blot demonstrating the protein expression of autophagic biomarkers, such as Beclin1 and LC3-II, in response to the treatment of BV6 and H<sub>2</sub>O<sub>2.</sub> Graphs represent the quantification of protein expression levels normalized by Actin. The data from three different trials is presented as mean ± SD and is statistically significant. (*) and (**) show <span class="html-italic">p</span> ˂ 0.05, <span class="html-italic">p</span> ˂ 0.01, respectively, for each case.</p>
Full article ">Figure 5
<p>Schematic representation of the therapeutic potential and the changes in the cellular activities instigated by the Smac mimetic compound BV6. The compound identified to decrease autophagy, ROS, and inhibitors of apoptosis (IAPs). The Smac mimetic compound BV6 leads to cell death via the resumption of apoptosis in cancer cells.</p>
Full article ">
11 pages, 667 KiB  
Article
Short Tandem Repeat (STR) Profiling of Earwax DNA Obtained from Healthy Volunteers
by Sayed Amin Amer, Maha Nawar Alotaibi, Sajjad Shahid, Mahmoud Alsafrani and Abdul Rauf Chaudhary
Curr. Issues Mol. Biol. 2023, 45(7), 5741-5751; https://doi.org/10.3390/cimb45070362 - 10 Jul 2023
Viewed by 3133
Abstract
The present study aimed to establish human earwax as a potential source of DNA evidence that could be effectively used in human identification. Sixty earwax samples were obtained from 15 healthy male and female Saudi volunteers living in Riyadh, Saudi Arabia. Four consecutive [...] Read more.
The present study aimed to establish human earwax as a potential source of DNA evidence that could be effectively used in human identification. Sixty earwax samples were obtained from 15 healthy male and female Saudi volunteers living in Riyadh, Saudi Arabia. Four consecutive earwax swab samples were obtained from each volunteer and stored for 1, 15, 30 and 60 days. Earwax samples were stored at room temperature (20–22 °C). Reference oral swab was also taken from each volunteer. DNA was extracted by QIAamp DNA Mini kit and quantified by real-time polymerase chain reaction (RT-PCR) on 7500 Thermal Cycler. Autosomal STR loci were amplified using AmpFLSTR™ Identifiler™ Plus PCR Amplification Kit (Thermo Fisher Scientific, Carlsbad, CA, USA). Amplified fragments were size separated and analyzed on a 3500 Genetic Analyzer. Complete autosomal STR profiles were obtained from the earwax swabs of all the volunteers stored up to 30 days after the collection. Some STR profiles were partially obtained 60 days after the earwax collection. Allelic drop-out, allelic drop-in, and stutters were seen in earwax samples analyzed 60 days after the collection. The results have shown that human earwax can be a potential source of DNA evidence for human identification up to 30 days after the earwax collection. It is recommended to quickly analyze earwax samples or store them at room temperature or at −10 °C after their recovery from the crime scene. Full article
(This article belongs to the Collection Feature Papers in Current Issues in Molecular Biology)
16 pages, 680 KiB  
Review
Correlation between Hepatocyte Growth Factor (HGF) with D-Dimer and Interleukin-6 as Prognostic Markers of Coagulation and Inflammation in Long COVID-19 Survivors
by Bena Zaira, Trilis Yulianti and Jutti Levita
Curr. Issues Mol. Biol. 2023, 45(7), 5725-5740; https://doi.org/10.3390/cimb45070361 - 8 Jul 2023
Cited by 5 | Viewed by 2308
Abstract
In general, an individual who experiences the symptoms of Severe Acute Respiratory Syndrome Coronavirus 2 or SARS-CoV-2 infection is declared as recovered after 2 weeks. However, approximately 10–20% of these survivors have been reported to encounter long-term health problems, defined as ‘long COVID-19’, [...] Read more.
In general, an individual who experiences the symptoms of Severe Acute Respiratory Syndrome Coronavirus 2 or SARS-CoV-2 infection is declared as recovered after 2 weeks. However, approximately 10–20% of these survivors have been reported to encounter long-term health problems, defined as ‘long COVID-19’, e.g., blood coagulation which leads to stroke with an estimated incidence of 3%, and pulmonary embolism with 5% incidence. At the time of infection, the immune response produces pro-inflammatory cytokines that stimulate stromal cells to produce pro-hepatocyte growth factor (pro-HGF) and eventually is activated into hepatocyte growth factor (HGF), which helps the coagulation process in endothelial and epithelial cells. HGF is a marker that appears as an inflammatory response that leads to coagulation. Currently, there is no information on the effect of SARS-CoV-2 infection on serum HGF concentrations as a marker of the prognosis of coagulation in long COVID-19 survivors. This review discusses the pathophysiology between COVID-19 and HGF, IL-6, and D-dimer. Full article
(This article belongs to the Special Issue Advances in Understanding Molecular Basis of Inflammatory Diseases)
Show Figures

Figure 1

Figure 1
<p>Proposed schematic for correlation between hepatocyte growth factor (HGF) with D-dimer and Interleukin 6 (IL-6) as a prognostic marker of coagulation and inflammation on long-term effects of COVID-19 survivor [<a href="#B37-cimb-45-00361" class="html-bibr">37</a>,<a href="#B46-cimb-45-00361" class="html-bibr">46</a>,<a href="#B47-cimb-45-00361" class="html-bibr">47</a>,<a href="#B56-cimb-45-00361" class="html-bibr">56</a>,<a href="#B70-cimb-45-00361" class="html-bibr">70</a>,<a href="#B71-cimb-45-00361" class="html-bibr">71</a>,<a href="#B72-cimb-45-00361" class="html-bibr">72</a>].</p>
Full article ">
17 pages, 9508 KiB  
Article
Molecular Markers of Ovarian Germ Cells of Banana Prawn (Fenneropenaeus merguiensis)
by Tatiyavadee Sengseng, Tomoyuki Okutsu, Anida Songnui, Jaruwan Boonchuay, Chanida Sakunrang and Monwadee Wonglapsuwan
Curr. Issues Mol. Biol. 2023, 45(7), 5708-5724; https://doi.org/10.3390/cimb45070360 - 7 Jul 2023
Viewed by 1452
Abstract
The banana prawn (Fenneropenaeus merguiensis) is a valuable prawn in the worldwide market. However, cultivation of this species is limited owing to the difficulty in culture management and limited knowledge of reproduction. Therefore, we studied the gene expression and molecular mechanisms [...] Read more.
The banana prawn (Fenneropenaeus merguiensis) is a valuable prawn in the worldwide market. However, cultivation of this species is limited owing to the difficulty in culture management and limited knowledge of reproduction. Therefore, we studied the gene expression and molecular mechanisms involved in oogenesis for elucidating ovarian germ cell development in banana prawns. The tissue-specific distribution of certain genes identified from previous transcriptome data showed that FmCyclinB, FmNanos, and nuclear autoantigenic sperm protein (FmNASP) were only expressed in gonads. The in situ hybridization (ISH) of these three genes showed different expression patterns throughout oogenesis. FmCyclinB was highly expressed in pre-vitellogenic oocytes. FmNanos was expressed at almost the same level during oogenesis but showed the most expression in late pre-vitellogenic stages. Based on the highest expression of FmCyclinB and FmNanos in mid pre-vitellogenic and late pre-vitellogenic oocytes, respectively, we suggested that FmNanos may suppress FmCyclinB expression before initiation of vitellogenesis. Meanwhile, FmNASP expression was detected only in pre-vitellogenesis. Moreover, quantitative real-time polymerase chain reaction (qRT-PCR) analysis of FmNASP expression was supported by FmNASP ISH analysis based on high expression of FmNASP in sub-adult ovaries, which contain most of pre-vitellogenic oocytes. In this study, we found three reliable ovarian markers for banana prawns and also found a dynamic change of molecular mechanism during the sub-stage of pre-vitellogenesis. We determined the expression levels of these genes involved in oogenesis. Our findings provide information for further studies on banana prawn reproduction which may assist in their cultivation. Full article
(This article belongs to the Section Biochemistry, Molecular and Cellular Biology)
Show Figures

Figure 1

Figure 1
<p>Tissue-specific distribution of candidate genes. Total RNA was isolated from T, V, O, I, B, H, Hp, M, Ep, G, and E. cDNA was amplified, and the full size of PCR product is in parenthesis: <span class="html-italic">Cyclin A</span> (899 bp), <span class="html-italic">NASP</span> (897 bp), <span class="html-italic">MARF1</span> (715 bp), <span class="html-italic">Cyclin E</span> (574 bp), <span class="html-italic">Nanos</span> (539 bp), and <span class="html-italic">Cyclin B</span> (528 bp). β-Actin gene (300 bp) was amplified as an internal control. A negative control without cDNA template is shown in lane NCT. T, testis; V, vas deferens; O, ovary; I, intestine; B, brain; H, heart; Hp, hepatopancreas; M, muscle; TG, thoracic ganglion; Ep, epidermis tissues; G, gills; E, eyestalks; <span class="html-italic">FmNASP</span>, banana-prawn-derived nuclear autoantigenic sperm protein; <span class="html-italic">MARF1</span>, meiosis regulator and mRNA stability factor 1.</p>
Full article ">Figure 2
<p>Phylogenetic tree analysis of banana prawn genes with that of other species. (<b>a</b>) <span class="html-italic">FmCyclinB</span>, (<b>b</b>) <span class="html-italic">FmNanos</span>, and (<b>c</b>) <span class="html-italic">FmNASP</span>. Phylogenetic was tree constructed using MEGA with Neighbor-Joining method based on the Poisson model. Values at the node represent the percentage of times that the particular node occurred in 1000 trees generated by bootstrapping the original aligned protein sequences. The asterisk mark (*) is for <span class="html-italic">F. merguiensis</span> in this study. Abbreviations: FM (<span class="html-italic">Fenneropenaeus merguiensis</span>), PM (<span class="html-italic">Penaeus monodon</span>), MJ (<span class="html-italic">Marsupenaeus japonicus</span>), ME (<span class="html-italic">Metapenaeus ensis</span>), MA (<span class="html-italic">M. affinis</span>), PV (<span class="html-italic">P. vannamei</span>), PP (<span class="html-italic">P. penicillatus</span>), PC (<span class="html-italic">Procambarus clarkia</span>), PaM (<span class="html-italic">Palaemon modestus</span>), MN (<span class="html-italic">Macrobrachium nipponense</span>), MR (<span class="html-italic">Macrobacium rosenbergii</span>), PT (<span class="html-italic">Portunus trituberculatus</span>), DR (<span class="html-italic">Danio rerio</span>), OS (<span class="html-italic">Octopus sinesis</span>), AC (<span class="html-italic">Aplysia californica</span>), PCh (<span class="html-italic">P. chiensis</span>), HA (<span class="html-italic">Homarus americanus</span>), AN (<span class="html-italic">Armadillidium nasatum</span>), SC (<span class="html-italic">Sycon ciliatum</span>), PL (<span class="html-italic">Pristina leidyi</span>), HP (<span class="html-italic">Hemicentrotus pulcherrimus</span>), HA (<span class="html-italic">Haliotis asinina</span>), NV (<span class="html-italic">Nematostella vectensis</span>), BF (<span class="html-italic">Branchiostoma floridae</span>), CC (<span class="html-italic">Caligus clemensi</span>), BM (<span class="html-italic">Bombyx mori</span>), PH (<span class="html-italic">Parhyale hawaiensis</span>), GG (<span class="html-italic">Gallus gallus</span>), OJ (<span class="html-italic">Oxyura jamaicensis</span>), PS (<span class="html-italic">Pelodiscus sinensis</span>), AM (<span class="html-italic">Alligator mississippiensis</span>), SA (<span class="html-italic">Sapajus apella</span>), HS (<span class="html-italic">Homo sapiens</span>), LA (<span class="html-italic">Loxodont Africana</span>), ERE (<span class="html-italic">Erinaceus europaeus</span>), ON (<span class="html-italic">Oreochromis niloticus</span>), PMa (<span class="html-italic">Petromyzon marinus</span>), AA (<span class="html-italic">Aedes aegypti</span>), DA (<span class="html-italic">Drosophila ananassae</span>).</p>
Full article ">Figure 3
<p>Multiple sequence alignment of deduced protein sequences of <span class="html-italic">FmCyclinB</span>, <span class="html-italic">FmNanos</span>, and <span class="html-italic">FmNASP</span> genes. (<b>a</b>) Multiple sequence alignment of banana prawn cyclin B amino acid with that of known crustacean cyclin B amino acid sequences. Red line and dot boxes show two signature cyclin superfamily motifs. The GenBank accession numbers of the cyclin B amino acids are as follows: merguiensi (<span class="html-italic">Fenneropenaeus merguiensis</span>), monodon (<span class="html-italic">Penaeus monodon</span>, XP_037794731.1), japonicus (<span class="html-italic">Marsupenaeus japonicus</span>, XP_042892405.1), ensis (<span class="html-italic">Metapenaeus ensis</span>, ADI86225.1), affinis (<span class="html-italic">Metapenaeus affinis</span>, ADI86226), clarkii (<span class="html-italic">Procambarus clarkii</span>, XP_045624571.1), nipponense (<span class="html-italic">Macrobachium nipponense</span>, ADB44902.1), rosenbergi (<span class="html-italic">Macrobacium rosenbergii</span>, ADP95148.1), modestus (<span class="html-italic">Palaemon modestus</span>, QDE09442.1), and vannamei (<span class="html-italic">Penaeus vannamei</span>, ACI46952.1). (<b>b</b>) Multiple sequence alignment of Nanos amino acid sequence with known crustacean Nanos amino acid sequences. Red and yellow boxes show two specific conserved Cys-Cys-His-Cys zinc finger motifs of Nanos. The GenBank accession numbers of the Nanos amino acids are as follows: merguiensi (<span class="html-italic">Fenneropenaeus merguiensis</span>), chinensis (<span class="html-italic">Penaeus chinensis</span>, XP_047487687.1), tritubercu (<span class="html-italic">Portunus trituberculatus</span>, XP_045137084.1), nasatum (<span class="html-italic">Armadillidium nasatum</span>, KAB7497688.1), and americanus (<span class="html-italic">Homarus americanus</span>, XP_042219141.1). (<b>c</b>) Multiple sequence alignment analysis of banana prawn <span class="html-italic">NASP</span> amino acid sequence with known crustacean <span class="html-italic">NASP</span> amino acid sequences. The GenBank accession numbers of the <span class="html-italic">NASP</span> amino acid sequences are as follows: merguiensi (<span class="html-italic">Fenneropenaeus merguiensis</span>), vannamei (<span class="html-italic">Penaeus vannamei</span>, ALR99738.1), and monodon (<span class="html-italic">Penaeus monodon</span>, ACM66845.1). NASP, nuclear autoantigenic sperm protein. The amino acid sequences were aligned using BioEdit and represented using GenDoc. Gaps that were introduced to maximize sequence homology are indicated by dashes. Shaded boxes indicate the conserved sequence. The asterisk is counting every 10 amino acid.</p>
Full article ">Figure 3 Cont.
<p>Multiple sequence alignment of deduced protein sequences of <span class="html-italic">FmCyclinB</span>, <span class="html-italic">FmNanos</span>, and <span class="html-italic">FmNASP</span> genes. (<b>a</b>) Multiple sequence alignment of banana prawn cyclin B amino acid with that of known crustacean cyclin B amino acid sequences. Red line and dot boxes show two signature cyclin superfamily motifs. The GenBank accession numbers of the cyclin B amino acids are as follows: merguiensi (<span class="html-italic">Fenneropenaeus merguiensis</span>), monodon (<span class="html-italic">Penaeus monodon</span>, XP_037794731.1), japonicus (<span class="html-italic">Marsupenaeus japonicus</span>, XP_042892405.1), ensis (<span class="html-italic">Metapenaeus ensis</span>, ADI86225.1), affinis (<span class="html-italic">Metapenaeus affinis</span>, ADI86226), clarkii (<span class="html-italic">Procambarus clarkii</span>, XP_045624571.1), nipponense (<span class="html-italic">Macrobachium nipponense</span>, ADB44902.1), rosenbergi (<span class="html-italic">Macrobacium rosenbergii</span>, ADP95148.1), modestus (<span class="html-italic">Palaemon modestus</span>, QDE09442.1), and vannamei (<span class="html-italic">Penaeus vannamei</span>, ACI46952.1). (<b>b</b>) Multiple sequence alignment of Nanos amino acid sequence with known crustacean Nanos amino acid sequences. Red and yellow boxes show two specific conserved Cys-Cys-His-Cys zinc finger motifs of Nanos. The GenBank accession numbers of the Nanos amino acids are as follows: merguiensi (<span class="html-italic">Fenneropenaeus merguiensis</span>), chinensis (<span class="html-italic">Penaeus chinensis</span>, XP_047487687.1), tritubercu (<span class="html-italic">Portunus trituberculatus</span>, XP_045137084.1), nasatum (<span class="html-italic">Armadillidium nasatum</span>, KAB7497688.1), and americanus (<span class="html-italic">Homarus americanus</span>, XP_042219141.1). (<b>c</b>) Multiple sequence alignment analysis of banana prawn <span class="html-italic">NASP</span> amino acid sequence with known crustacean <span class="html-italic">NASP</span> amino acid sequences. The GenBank accession numbers of the <span class="html-italic">NASP</span> amino acid sequences are as follows: merguiensi (<span class="html-italic">Fenneropenaeus merguiensis</span>), vannamei (<span class="html-italic">Penaeus vannamei</span>, ALR99738.1), and monodon (<span class="html-italic">Penaeus monodon</span>, ACM66845.1). NASP, nuclear autoantigenic sperm protein. The amino acid sequences were aligned using BioEdit and represented using GenDoc. Gaps that were introduced to maximize sequence homology are indicated by dashes. Shaded boxes indicate the conserved sequence. The asterisk is counting every 10 amino acid.</p>
Full article ">Figure 4
<p>Histological analysis of banana prawn ovaries visualized by hematoxylin and eosin staining. (<b>a</b>) OG, (<b>b</b>) 1PRO, (<b>c</b>) 2PRO, (<b>d</b>) 3PRO, (<b>e</b>) VO, (<b>f</b>) MO, Fc, and Cr (whitehead arrow line). Scale bars represent 20 µM. 1PRO, stage 1 previtellogenic oocyte; 2PRO, stage 2 previtellogenic oocyte; 3PRO, stage 3 previtellogenic oocyte; VO, vitellogenic oocyte; MO, mature oocyte; Fc, follicle cell; Cr, cortical rod.</p>
Full article ">Figure 5
<p>Histological characterization and cyclin B mRNA expression in ovaries of banana prawn, using ISH. Sequential sections of the ovary were divided and stained with (<b>a1</b>–<b>e1</b>) H&amp;E for representative cell characteristics and hybridized with (<b>a2</b>–<b>e2</b>) cyclin B antisense RNA probe and (<b>a3</b>–<b>e3</b>) sense RNA probes. Scale bars represent 20 µM. OG, oogonia; 1PRO, stage 1 previtellogenic oocyte; 2PRO, stage 2 previtellogenic oocyte; 3PRO, stage 3 previtellogenic oocyte; VO, vitellogenic oocyte; MO, mature oocyte; Fc, follicle cell; Cr, cortical rod; H&amp;E, hematoxylin and eosin.</p>
Full article ">Figure 6
<p>Histological characterization and <span class="html-italic">Nanos</span> mRNA expression in ovaries of banana prawn, using ISH. Continuous ovary sections were separated, stained with (<b>a1</b>–<b>e1</b>) H&amp;E, and then used as a reference cell for comparison with sections hybridized with (<b>a2</b>–<b>e2</b>) <span class="html-italic">Nanos</span> antisense and (<b>a3</b>–<b>e3</b>) sense RNA probe. Scale bars represent 10 µM in (<b>a</b>–<b>d</b>) and 20 µM in (<b>e</b>). OG, oogonia; 1PRO, stage 1 previtellogenic oocyte; 2PRO, stage 2 previtellogenic oocyte; 3PRO, stage 3 previtellogenic oocyte; VO, vitellogenic oocyte; MO, mature oocyte; Fc, follicle cell; Cr, cortical rod; H&amp;E, hematoxylin and eosin.</p>
Full article ">Figure 7
<p>Histological characterization and <span class="html-italic">NASP</span> mRNA expression in banana prawn ovaries, using ISH. Serial ovary sections were sorted for staining with (<b>a1</b>–<b>e1</b>) H&amp;E as reference ovarian cells and hybridized with (<b>a2</b>–<b>e2</b>) <span class="html-italic">NASP</span> antisense and (<b>a3</b>–<b>e3</b>) sense RNA probes. Scale bars represent 20 µM. <span class="html-italic">NASP</span>, nuclear autoantigenic sperm protein; OG, oogonia; 1PRO, stage 1 previtellogenic oocyte; 2PRO, stage 2 previtellogenic oocyte; 3PRO, stage 3 previtellogenic oocyte; VO, vitellogenic oocyte; MO, mature oocyte; Fc, follicle cell; Cr, cortical rod; H&amp;E, hematoxylin and eosin.</p>
Full article ">Figure 8
<p>Relative expression levels of ovarian germ cell-specific genes in sub-adult and adult ovaries, using qRT-PCR. Sections stained with hematoxylin and eosin were used for characterizing (<b>a</b>) sub-adult and (<b>b</b>) adult ovaries. Scale bar: 20 µM. Relative mRNA expression levels of (<b>c</b>) <span class="html-italic">Cyclin B</span>, (<b>d</b>) <span class="html-italic">Nanos</span>, and (<b>e</b>) <span class="html-italic">NASP</span>, respectively, between sub-adult and adult ovaries. The mRNA expression level of each gene was normalized to the expression level of the internal standard beta-actin. Asterisk indicates significant differences in relative mRNA expression level (<span class="html-italic">p</span> ≤ 0.05). <span class="html-italic">NASP</span>, nuclear autoantigenic sperm protein.</p>
Full article ">
Previous Issue
Next Issue
Back to TopTop