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14 pages, 2422 KiB  
Article
Modulation of PRC1 Promotes Anticancer Effects in Pancreatic Cancer
by Hajin Lee, An-Na Bae, Huiseong Yang, Jae-Ho Lee and Jong Ho Park
Cancers 2024, 16(19), 3310; https://doi.org/10.3390/cancers16193310 (registering DOI) - 27 Sep 2024
Abstract
Background: Pancreatic cancer, while relatively uncommon, is extrapolated to become the second leading cause of cancer-related deaths worldwide. Despite identifying well-known markers like the KRAS gene, the exact regulation of pancreatic cancer progression remains elusive. Methods: Clinical value of PRC1 was [...] Read more.
Background: Pancreatic cancer, while relatively uncommon, is extrapolated to become the second leading cause of cancer-related deaths worldwide. Despite identifying well-known markers like the KRAS gene, the exact regulation of pancreatic cancer progression remains elusive. Methods: Clinical value of PRC1 was analyzed using bioinformatics database. The role of PRC1 was further evaluated through cell-based assays, including viability, wound healing, and sensitivity with the drug. Results: We demonstrate that PRC1 was significantly overexpressed in pancreatic cancer compared to pancreases without cancer, as revealed through human databases and cell lines analysis. Furthermore, high PRC1 expression had a negative correlation with CD4+ T cells, which are crucial for the immune response against cancers. Additionally, PRC1 showed a positive correlation with established pancreatic cancer markers. Silencing PRC1 expression using siRNA significantly inhibited cancer cell proliferation and viability and increased chemotherapeutic drug sensitivity. Conclusions: These findings suggest that targeting PRC1 in pancreatic cancer may enhance immune cell infiltration and inhibit cancer cell proliferation, offering a promising avenue for developing anticancer therapies. Full article
(This article belongs to the Special Issue Treatment of Abdominal Tumors)
26 pages, 9744 KiB  
Article
Exploring the Genetic Basis of Calonectria spp. Resistance in Eucalypts
by Zhiyi Su, Wanhong Lu, Yan Lin, Jianzhong Luo, Guo Liu and Anying Huang
Curr. Issues Mol. Biol. 2024, 46(10), 10854-10879; https://doi.org/10.3390/cimb46100645 (registering DOI) - 27 Sep 2024
Abstract
Selecting high-quality varieties with disease resistance by artificial crossbreeding is the most fundamental way to address the damage caused by Calonectria spp. in eucalypt plantations. However, understanding the mechanism of disease-resistant heterosis occurrence in eucalypts is crucial for successful crossbreeding. Two eucalypt hybrids, [...] Read more.
Selecting high-quality varieties with disease resistance by artificial crossbreeding is the most fundamental way to address the damage caused by Calonectria spp. in eucalypt plantations. However, understanding the mechanism of disease-resistant heterosis occurrence in eucalypts is crucial for successful crossbreeding. Two eucalypt hybrids, the susceptible EC333 (H1522 × unknown) and the resistant EC338 (W1767 × P9060), were screened through infection with Calonectria isolates, a pathogen that causes eucalypt leaf blight. RNA-Seq was performed on the susceptible hybrid, the disease-resistant hybrid, and their parents. The gene differential expression analysis showed that there were 3912 differentially expressed genes between EC333 and EC338, with 1631 up-regulated and 2281 down-regulated genes. The expression trends of the differential gene sets in P9060 and EC338 were similar. However, the expression trend of W1767 was opposite that of EC338. The similarity of the expression and the advantage of stress resistance in E. pellita suggested that genes with significant differences in expression likely relate to disease resistance. A GSEA based on GO annotations revealed that the carbohydrate binding pathway genes were differentially expressed between EC338 and EC333. The gene pathways that were differentially expressed between EC338 and EC333 revealed by the GSEA based on KEGG annotations were the sesquiterpenoid and triterpenoid biosynthesis pathways. The alternative splicing analysis demonstrated that an AS event between EC338 and EC333 occurred in LOC104426602. According to our SNP analysis, EC338 had 626 more high-impact mutation loci than the male parent P9060 and 396 more than the female parent W1767; W1767 had 259 more mutation loci in the downstream region than EC338, while P9060 had 3107 fewer mutation loci in the downstream region than EC338. Additionally, EC338 had 9631 more mutation loci in the exon region than EC333. Modules were found via WGCNA that were strongly and oppositely correlated with EC338 and EC333, such as module MEsaddlebrown, likely associated with leaf blight resistance. The present study provides a detailed explanation of the genetic basis of eucalypt leaf blight resistance, providing the foundation for exploring genes related to this phenomenon. Full article
(This article belongs to the Section Molecular Plant Sciences)
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Figure 1

Figure 1
<p>Testing the distribution of gene expression levels in eucalypt genotypes. (<b>A</b>) A boxplot of gene expression levels, where the horizontal coordinate is the name of the sample (group), and the vertical coordinate is log2(FPKM+1). (<b>B</b>) A density plot of gene expression levels, where the horizontal coordinate is log2(FPKM+1), and the vertical coordinate is the density of gene. (<b>C</b>) A violin plot of gene expression levels, where the horizontal coordinate is the name of the sample (group), and the vertical coordinate is log2(FPKM+1). The width of each violin represents the number of genes at that expression level.</p>
Full article ">Figure 2
<p>Principal component analysis plot of gene expression for each eucalypt genotype (3D).</p>
Full article ">Figure 3
<p>Gene co-expression Venn diagram in eucalypt genotypes tested. (Venn diagram shows the overlap of differentially expressed genes between different comparison combinations. (<b>A</b>) Venn diagram of co-expressed genes EC338 vs. EC333. (<b>B</b>) Venn diagram of co-expressed genes EC338 vs. W1767. (<b>C</b>) Venn diagram of co-expressed genes EC338 vs. P9060. (<b>D</b>) Venn diagram of co-expressed genes EC333 vs. H1522.).</p>
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<p>Differential gene Venn diagram in eucalypt genotypes tested.</p>
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<p>Heatmap of differentially expressed gene clustering. (Horizontal coordinates are sample names, vertical coordinates are values of differentially expressed genes after FPKM normalization. The redder the color, the higher the expression level; the greener, the lower the expression level).</p>
Full article ">Figure 6
<p>Bar (<b>left</b>) and bubble (<b>right</b>) plots of the GO analysis for four pairs of differential gene sets. (<b>A</b>) GO enrichment of EC338 vs. EC333. (<b>B</b>) GO enrichment of EC338 vs. P9060. (<b>C</b>) GO enrichment of EC338 vs. W1767. (<b>D</b>) GO enrichment of EC333 vs. H1522.).</p>
Full article ">Figure 7
<p>Bar (<b>left</b>) and bubble (<b>right</b>) plots of KEGG analysis for four differential gene pairs. (<b>A</b>) KEGG enrichment of EC338 vs. EC333. (<b>B</b>) KEGG enrichment of EC338 vs. P9060. (<b>C</b>) KEGG enrichment of EC338 vs. W1767. (<b>D</b>) KEGG enrichment of EC333 vs. H1522.).</p>
Full article ">Figure 8
<p>GSEA using GO annotations. (The top row is a snapshot of the enrichment results. The enrichment map displays the green ES line graph at the top, revealing the highest peak at the ES value denoting the gene set ES value. In the hits barcode map located in the middle, the leading-edge subset represents the genes that make the most significant contribution to the enrichment score, ranging from 0 to the ES value. The top grayscale enrichment of the bottom sorting level value indicates the level of genotype expression. The distribution plot in the bottom row illustrates that if the final ES value (marked by the black dotted line) is relatively small, the pathway gene set is randomly distributed, whereas a relatively large ES value suggests non-random distribution. Subsequently, the hypothesis regarding substitution is then examined. (<b>A</b>) Carbohydrate binding. (<b>B</b>) Terpene synthase activity. (<b>C</b>) Carbon–oxygen lyase acting on phosphates. (<b>D</b>) Carbon–oxygen lyase activity.).</p>
Full article ">Figure 9
<p>GSEA using KEGG annotations. (Here, the legend is the same as that for <a href="#cimb-46-00645-f008" class="html-fig">Figure 8</a>. (<b>A</b>) Sesquiterpenoid and triterpenoid biosynthesis. (<b>B</b>) Phenylpropanoid biosynthesis. (<b>C</b>) Plant–pathogen interaction. (<b>D</b>) Flavonoid biosynthesis.).</p>
Full article ">Figure 10
<p>Alternative splicing SEs. (<b>A</b>) alternative splicing events of EC338 vs. EC333. (<b>B</b>) alternative splicing events of EC338 vs. P9060. (<b>C</b>) alternative splicing event of EC338 vs. W1767. (<b>D</b>) alternative splicing event of EC333 vs. H1522.).</p>
Full article ">Figure 11
<p>SNP variant loci region statistics. (The horizontal coordinates represent the samples. (<b>A</b>) histogram of each sample variant loci categorized based on function (SNP_function). (<b>B</b>) histogram of this by impact (INDEL_impact). (<b>C</b>) histogram of this by region (INDEL_region). (<b>D</b>) histogram of this by region (SNP_region).).</p>
Full article ">Figure 12
<p>Module division in WGCNA analysis. (<b>A</b>) Soft threshold plot. In the left plot, the horizontal coordinate is the soft threshold, and the vertical coordinate is the correlation between connectivity k and p (k). The right plot is the soft threshold versus the average connectivity of the network. (<b>B</b>) Module hierarchical clustering tree. The branches are gene modules, and the leaves are genes. Merged colors are the modules with dissimilarity coefficients less than 0.25 merged with colors to represent the different modules. (<b>C</b>) Inter-module correlation heatmap. The top part of the plot is the clustering based on the module’s eigenvalues, and the values of the vertical coordinates correspond to the similarity between different modules. The upper part is the clustering based on the module eigenvalues, and the value of the vertical coordinate is the similarity between different modules. The lower part is the clustering heatmap between different modules; each row and column in the graph represents one module. The darker the color (redder) in the box, the stronger the correlation; the lighter the color of the box, the weaker the correlation.).</p>
Full article ">Figure 13
<p>Heatmap of genotype and inter-module correlations in eucalypt genotypes. (The horizontal axis, known as the abscissa, denotes the sample, while the vertical axis, referred to as the ordinate, represents the module. Each cell in the grid contains a numerical value indicating the correlation between the module and the sample. A value closer to 1 signifies a more robust positive correlation between the module and the sample, whereas a value nearing −1 indicates a stronger negative correlation. The degree of negative correlation strength is proportional to the value. Additionally, the significance level, denoted by the value in parentheses, reflects the strength of significance, with smaller values indicating higher significance).</p>
Full article ">
20 pages, 3301 KiB  
Article
Comparative Metabolome and Transcriptome Analysis Reveals the Defense Mechanism of Chinese Cabbage (Brassica rapa L. ssp. pekinensis) against Plasmodiophora brassicae Infection
by Xiaochun Wei, Yingyi Du, Wenjing Zhang, Yanyan Zhao, Shuangjuan Yang, Henan Su, Zhiyong Wang, Fang Wei, Baoming Tian, Haohui Yang, Xiaowei Zhang and Yuxiang Yuan
Int. J. Mol. Sci. 2024, 25(19), 10440; https://doi.org/10.3390/ijms251910440 (registering DOI) - 27 Sep 2024
Abstract
Chinese cabbage (Brassica rapa L. ssp. pekinensis) ranks among the most cultivated and consumed vegetables in China. A major threat to its production is Plasmodiophora brassicae, which causes large root tumors, obstructing nutrient and water absorption and resulting in plant withering. [...] Read more.
Chinese cabbage (Brassica rapa L. ssp. pekinensis) ranks among the most cultivated and consumed vegetables in China. A major threat to its production is Plasmodiophora brassicae, which causes large root tumors, obstructing nutrient and water absorption and resulting in plant withering. This study used a widely targeted metabolome technique to identify resistance-related metabolites in resistant (DH40R) and susceptible (DH199S) Chinese cabbage varieties after inoculation with P. brassicae. This study analyzed disease-related metabolites during different periods, identifying 257 metabolites linked to resistance, enriched in the phenylpropanoid biosynthesis pathway, and 248 metabolites linked to susceptibility, enriched in the arachidonic acid metabolism pathway. Key metabolites and genes in the phenylpropanoid pathway were upregulated at 5 days post-inoculation (DPI), suggesting their role in disease resistance. In the arachidonic acid pathway, linoleic acid and gamma-linolenic acid were upregulated at 5 and 22 DPI in resistant plants, while arachidonic acid was upregulated at 22 DPI in susceptible plants, leading to the conclusion that arachidonic acid may be a response substance in susceptible plants after inoculation. Many genes enriched in these pathways were differentially expressed in DH40R and DH199S. The research provided insights into the defense mechanisms of Chinese cabbage against P. brassicae through combined metabolome and transcriptome analysis. Full article
(This article belongs to the Special Issue Advances in Brassica Crop Metabolism and Genetics)
18 pages, 5216 KiB  
Article
Characterization, Evolution, Expression and Functional Divergence of the DMP Gene Family in Plants
by Zeeshan Ahmad, Dingyan Tian, Yan Li, Isah Mansur Aminu, Javaria Tabusam, Yongshan Zhang and Shouhong Zhu
Int. J. Mol. Sci. 2024, 25(19), 10435; https://doi.org/10.3390/ijms251910435 (registering DOI) - 27 Sep 2024
Abstract
The DMP (DOMAIN OF UNKNOWN FUNCTION 679 membrane protein) domain, containing a family of membrane proteins specific to green plants, is involved in numerous biological functions including physiological processes, reproductive development and senescence in Arabidopsis, but their evolutionary relationship and biological function [...] Read more.
The DMP (DOMAIN OF UNKNOWN FUNCTION 679 membrane protein) domain, containing a family of membrane proteins specific to green plants, is involved in numerous biological functions including physiological processes, reproductive development and senescence in Arabidopsis, but their evolutionary relationship and biological function in most crops remains unknown. In this study, we scrutinized phylogenetic relationships, gene structure, conserved domains and motifs, promoter regions, gene loss/duplication events and expression patterns. Overall, 240 DMPs were identified and analyzed in 24 plant species selected from lower plants to angiosperms. Comprehensive evolutionary analysis revealed that these DMPs underwent purifying selection and could be divided into five groups (I–V). DMP gene structure showed that it may have undergone an intron loss event during evolution. The five DMP groups had the same domains, which were distinct from each other in terms of the number of DMPs; group III was the largest, closely followed by group V. The DMP promotor region with various cis-regulatory elements was predicted to have a potential role in development, hormone induction and abiotic stresses. Based on transcriptomic data, expression profiling revealed that DMPs were primarily expressed in reproductive organs and were moderately expressed in other tissues. Evolutionary analysis suggested that gene loss events occurred more frequently than gene duplication events among all groups. Overall, this genome-wide study elucidates the potential function of the DMP gene family in selected plant species, but further research is needed in many crops to validate their biological roles. Full article
(This article belongs to the Section Molecular Plant Sciences)
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Figure 1

Figure 1
<p>Phylogenetic relationship of 240 <span class="html-italic">DMP</span> genes from 24 species, including <span class="html-italic">Arabidopsis</span> and cotton, showing a classification of <span class="html-italic">DMP</span>s in various groups. (<b>A</b>) MEGA 11 software was used to construct phylogenetic trees using the neighbor-joining (NJ) method with 1000 bootstrap replications. Each color shows a different group (I–V). Species are denoted with different colors and symbols in the phylogenetic tree. (<b>B</b>) Classification and arrangement of <span class="html-italic">DMP</span> genes in various groups based on a phylogenetic tree. Plant species classes are highlighted in various colors on the left. Five groups on the right side are made based on the phylogenetic tree containing <span class="html-italic">DMP</span> genes.</p>
Full article ">Figure 1 Cont.
<p>Phylogenetic relationship of 240 <span class="html-italic">DMP</span> genes from 24 species, including <span class="html-italic">Arabidopsis</span> and cotton, showing a classification of <span class="html-italic">DMP</span>s in various groups. (<b>A</b>) MEGA 11 software was used to construct phylogenetic trees using the neighbor-joining (NJ) method with 1000 bootstrap replications. Each color shows a different group (I–V). Species are denoted with different colors and symbols in the phylogenetic tree. (<b>B</b>) Classification and arrangement of <span class="html-italic">DMP</span> genes in various groups based on a phylogenetic tree. Plant species classes are highlighted in various colors on the left. Five groups on the right side are made based on the phylogenetic tree containing <span class="html-italic">DMP</span> genes.</p>
Full article ">Figure 2
<p>Comparison of the gene structure, conserved motifs and domains in <span class="html-italic">DMP</span> genes between <span class="html-italic">Arabidopsis</span>, cotton and 22 other species. (<b>A</b>) The NJ phylogenetic tree was constructed in MEGA 11 software using <span class="html-italic">DMP</span> sequences from 24 species. (<b>B</b>) The conserved protein motif composition of all <span class="html-italic">DMP</span>s and motif 1–10 is shown in various colored boxes. (<b>C</b>) Representation of the domain of <span class="html-italic">Arabidopsis</span> and other species. Yellow boxes represent the <span class="html-italic">DMP</span> domain in the sequences. (<b>D</b>) Exon and Intron structure of <span class="html-italic">DMP</span> genes of all Selected species. The red box represents exon or coding region, and the grey line represents intron with the scale at the bottom for measurement length of sequences.</p>
Full article ">Figure 3
<p>Promoter region analysis of <span class="html-italic">DMP</span> sequences from two monocot and two dicot species along with phylogenetic tree of the <span class="html-italic">DMP</span>s for each species separately. (<b>A</b>,<b>B</b>) = Dicots (<span class="html-italic">Arabidopsis thaliana</span> and <span class="html-italic">Gossypium raimondi</span>). (<b>C</b>,<b>D</b>) = Monocots (<span class="html-italic">Sorghum bicolor</span> and <span class="html-italic">Zea mays</span>).</p>
Full article ">Figure 4
<p>Evolutionary events in the <span class="html-italic">DMP</span> gene family in plants. Numbers of gene losses are shown in red color after “+” and duplications in blue color after “−”in the phylogenetic tree on the left side.</p>
Full article ">Figure 5
<p>Expression profiles of <span class="html-italic">DMP</span> genes in various organ tissue from two monocot and two dicot species. Raw RNA-seq data were procured from relevant databases. (<b>A</b>) <span class="html-italic">Arabidopsis thaliana</span> from TAIR database (TAIR, v.11, <a href="http://www.arabidopsis.org" target="_blank">http://www.arabidopsis.org</a>). (<b>B</b>) <span class="html-italic">Avena sativa</span> and (<b>C</b>) <span class="html-italic">Glycine max</span> from Phytozome (<a href="https://phytozome.jgi.doe.gov/pz/portal.html/" target="_blank">https://phytozome.jgi.doe.gov/pz/portal.html/</a>, accessed on 5 March 2024). (<b>D</b>) <span class="html-italic">Gossypium hirsutum</span> from COTTONGEN (<a href="http://www.cottongen.org/" target="_blank">http://www.cottongen.org/</a>, accessed on 7 March 2024) and the Cotton Functional Genomics Database (CottonFGD, <a href="https://cottonfgd.org/" target="_blank">https://cottonfgd.org/</a>)<span class="html-italic">. DMP</span> gene expression levels are represented with various colors, with blue color indicating the least expression and red indicates the highest expression. Eudicot species (<b>A</b>,<b>C</b>,<b>D</b>); monocot species (<b>B</b>).</p>
Full article ">Figure 6
<p>Expression levels of <span class="html-italic">DMP</span> sequences in various tissues of cotton. <span class="html-italic">GhActin</span> was used as a reference. Error bars denote standard deviation measured from three experiments.</p>
Full article ">
30 pages, 16404 KiB  
Article
Interplay of RNA m6A Modification-Related Geneset in Pan-Cancer
by Boyu Zhang, Yajuan Hao, Haiyan Liu, Jiarun Wu, Lu Lu, Xinfeng Wang, Akhilesh K. Bajpai and Xi Yang
Biomedicines 2024, 12(10), 2211; https://doi.org/10.3390/biomedicines12102211 (registering DOI) - 27 Sep 2024
Abstract
Background: N6-methyladenosine (m6A), is the most common modification found in mRNA and lncRNA in higher organisms and plays an important role in physiology and pathology. However, its role in pan-cancer has not been explored. Results: A total of 31 [...] Read more.
Background: N6-methyladenosine (m6A), is the most common modification found in mRNA and lncRNA in higher organisms and plays an important role in physiology and pathology. However, its role in pan-cancer has not been explored. Results: A total of 31 m6A modification regulators, including 12 writers, 2 erasers, and 17 readers are identified in the current study. The functional analysis of the regulators results in the enrichment of processes, primarily related to RNA modification and metabolism, and the PPI network reveals multiple interactions among the regulators. The mRNA expression analysis reveals a high expression for most of the regulators in pan-cancer. Most of the m6A regulators are found to be mutated across the cancers, with ZC3H13, VIRMA, and PRRC2A having a higher frequency rate. Significant correlations of the regulators with clinicopathological parameters, such as age, gender, tumor stage, and grade are identified in pan-cancer. The m6A regulators’ expression is found to have significant positive correlations with the miRNAs in pan-cancer. The expression pattern of the m6A regulators is able to classify the tumors into different subclusters as well as into high- and low-risk groups. These tumor groups show differential patterns in terms of their immune cell infiltration, tumor stemness score, genomic heterogeneity score, expression of immune regulatory/checkpoint genes, and correlations between the regulators and the drugs. Conclusions: Our study provide a comprehensive overview of the functional roles, genetic and epigenetic alterations, and prognostic value of the RNA m6A regulators in pan-cancer. Full article
(This article belongs to the Special Issue Molecular Biomarkers of Tumors: Advancing Genetic Studies)
44 pages, 20348 KiB  
Article
Testing Green Tea Extract and Ammonium Salts as Stimulants of Physical Performance in a Forced Swimming Rat Experimental Model
by Ekaterina A. Korf, Artem V. Novozhilov, Igor V. Mindukshev, Andrey S. Glotov, Igor V. Kudryavtsev, Ekaterina V. Baidyuk, Irina A. Dobrylko, Natalia G. Voitenko, Polina A. Voronina, Samarmar Habeeb, Afrah Ghanem, Natalia S. Osinovskaya, Maria K. Serebryakova, Denis V. Krivorotov, Richard O. Jenkins and Nikolay V. Goncharov
Int. J. Mol. Sci. 2024, 25(19), 10438; https://doi.org/10.3390/ijms251910438 (registering DOI) - 27 Sep 2024
Abstract
The study of drugs of natural origin that increase endurance and/or accelerate recovery is an integral part of sports medicine and physiology. In this paper, decaffeinated green tea extract (GTE) and two ammonium salts—chloride (ACL) and carbonate (ACR)—were tested individually and in combination [...] Read more.
The study of drugs of natural origin that increase endurance and/or accelerate recovery is an integral part of sports medicine and physiology. In this paper, decaffeinated green tea extract (GTE) and two ammonium salts—chloride (ACL) and carbonate (ACR)—were tested individually and in combination with GTE as stimulants of physical performance in a forced swimming rat experimental model. The determined parameters can be divided into seven blocks: functional (swimming duration); biochemistry of blood plasma; biochemistry of erythrocytes; hematology; immunology; gene expression of slow- and fast-twitch muscles (m. soleus, SOL, and m. extensor digitorum longus, EDL, respectively); and morphometric indicators of slow- and fast-twitch muscles. Regarding the negative control (intact animals), the maximum number of changes in all blocks of indicators was recorded in the GTE + ACR group, whose animals showed the maximum functional result and minimum lactate values on the last day of the experiment. Next, in terms of the number of changes, were the groups ACR, ACL, GTE + ACL, GTE and NaCl (positive control). In general, the number of identified adaptive changes was proportional to the functional state of the animals of the corresponding groups, in terms of the duration of the swimming load in the last four days of the experiment. However, not only the total number but also the qualitative composition of the identified changes is of interest. The results of a comparative analysis suggest that, in the model of forced swimming we developed, GTE promotes restoration of the body and moderate mobilization of the immune system, while small doses of ammonium salts, especially ammonium carbonate, contribute to an increase in physical performance, which is associated with satisfactory restoration of skeletal muscles and the entire body. The combined use of GTE with ammonium salts does not give a clearly positive effect. Full article
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Figure 1

Figure 1
<p>Positive control group (NaCl). Expression of the <span class="html-italic">Myhc</span> genes (<b>a</b>,<b>c</b>), which determine the phenotype of slow-twitch SOL muscles (<b>a</b>,<b>b</b>) and fast-twitch EDL muscles (<b>c</b>,<b>d</b>), and genes encoding proteins that regulate the balance of Ca<sup>2+</sup> ions (<b>b</b>,<b>d</b>). *, **—differences from the negative control group are statistically significant (<span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively).</p>
Full article ">Figure 2
<p>GTE group. Expression of the <span class="html-italic">Myhc</span> genes (<b>a</b>,<b>c</b>), which determine the phenotype of slow-twitch SOL muscles (<b>a</b>,<b>b</b>) and fast-twitch EDL muscles (<b>c</b>,<b>d</b>), and genes encoding proteins that regulate the balance of Ca<sup>2+</sup> ions (<b>b</b>,<b>d</b>). *—differences from the negative control group are statistically significant (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>ACL group. Expression of the <span class="html-italic">Myhc</span> genes (<b>a</b>,<b>c</b>), which determine the phenotype of slow-twitch SOL muscles (<b>a</b>,<b>b</b>) and fast-twitch EDL muscles (<b>c</b>,<b>d</b>), and genes encoding proteins that regulate the balance of Ca<sup>2+</sup> ions (<b>b</b>,<b>d</b>). *—differences from the negative control group are statistically significant (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 4
<p>ACR group. Expression of <span class="html-italic">Myhc</span> genes (<b>a</b>,<b>c</b>), which determine the phenotype of slow-twitch SOL muscles (<b>a</b>,<b>b</b>) and fast-twitch EDL muscles (<b>c</b>,<b>d</b>), and genes encoding proteins that regulate the balance of Ca<sup>2+</sup> ions (<b>b</b>,<b>d</b>). *, **—differences from the negative control group are statistically significant (<span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively).</p>
Full article ">Figure 5
<p>GTE + ACL group. Expression of <span class="html-italic">Myhc</span> genes (<b>a</b>,<b>c</b>), which determine the phenotype of slow-twitch SOL muscles (<b>a</b>,<b>b</b>) and fast-twitch EDL muscles (<b>c</b>,<b>d</b>), and genes encoding proteins that regulate the balance of Ca<sup>2+</sup> ions (<b>b</b>,<b>d</b>). *, ***—differences from the negative control group are statistically significant (<span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.001, respectively).</p>
Full article ">Figure 6
<p>GTE + ACR group. Expression of <span class="html-italic">Myhc</span> genes (<b>a</b>,<b>c</b>), which determine the phenotype of slow-twitch SOL muscles (<b>a</b>,<b>b</b>) and fast-twitch EDL muscles (<b>c</b>,<b>d</b>), and genes encoding proteins that regulate the balance of Ca<sup>2+</sup> ions (<b>b</b>,<b>d</b>). *, **—differences from the negative control group are statistically significant (<span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively).</p>
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<p>M. soleus of rats: (<b>a</b>) negative (intact) control group; (<b>b</b>) positive (NaCl) control group; (<b>c</b>) GTE group; (<b>d</b>) ACL group; (<b>e</b>) ACR group; (<b>f</b>) GTE + ACL group; (<b>g</b>) GTE + ACR group. Abbreviations: M—mitochondria; TC—T-tubules, cross section; TL—T-tubules, longitudinal section; Z—Z-line; H—H-zone.</p>
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<p>M. soleus of rats: (<b>a</b>) negative (intact) control group; (<b>b</b>) positive (NaCl) control group; (<b>c</b>) GTE group; (<b>d</b>) ACL group; (<b>e</b>) ACR group; (<b>f</b>) GTE + ACL group; (<b>g</b>) GTE + ACR group. Abbreviations: M—mitochondria; TC—T-tubules, cross section; TL—T-tubules, longitudinal section; Z—Z-line; H—H-zone.</p>
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<p>M. soleus of rats: (<b>a</b>) negative (intact) control group; (<b>b</b>) positive (NaCl) control group; (<b>c</b>) GTE group; (<b>d</b>) ACL group; (<b>e</b>) ACR group; (<b>f</b>) GTE + ACL group; (<b>g</b>) GTE + ACR group. Abbreviations: M—mitochondria; TC—T-tubules, cross section; TL—T-tubules, longitudinal section; Z—Z-line; H—H-zone.</p>
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<p>M. extensor digitorum longus of rats: (<b>a</b>) negative (intact) control group; (<b>b</b>) positive (NaCl) control group; (<b>c</b>) GTE group; (<b>d</b>) ACL group; (<b>e</b>) ACR group; (<b>f</b>) GTE + ACL group; (<b>g</b>) GTE + ACR group. Abbreviations: M—mitochondria; TC—T-tubules, cross section; TL—T-tubules, longitudinal section; Z—Z-line; H—H-zone.</p>
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<p>M. extensor digitorum longus of rats: (<b>a</b>) negative (intact) control group; (<b>b</b>) positive (NaCl) control group; (<b>c</b>) GTE group; (<b>d</b>) ACL group; (<b>e</b>) ACR group; (<b>f</b>) GTE + ACL group; (<b>g</b>) GTE + ACR group. Abbreviations: M—mitochondria; TC—T-tubules, cross section; TL—T-tubules, longitudinal section; Z—Z-line; H—H-zone.</p>
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<p>M. extensor digitorum longus of rats: (<b>a</b>) negative (intact) control group; (<b>b</b>) positive (NaCl) control group; (<b>c</b>) GTE group; (<b>d</b>) ACL group; (<b>e</b>) ACR group; (<b>f</b>) GTE + ACL group; (<b>g</b>) GTE + ACR group. Abbreviations: M—mitochondria; TC—T-tubules, cross section; TL—T-tubules, longitudinal section; Z—Z-line; H—H-zone.</p>
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<p>A schematic representation of the experimental design.</p>
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18 pages, 2700 KiB  
Article
Ascorbic Acid and Graphene Oxide Exposure in the Model Organism Acheta domesticus Can Change the Reproduction Potential
by Barbara Flasz, Monika Tarnawska, Andrzej Kędziorski, Łukasz Napora-Rutkowski, Joanna Szczygieł, Łukasz Gajda, Natalia Nowak and Maria Augustyniak
Molecules 2024, 29(19), 4594; https://doi.org/10.3390/molecules29194594 (registering DOI) - 27 Sep 2024
Abstract
The use of nanoparticles in the industry carries the risk of their release into the environment. Based on the presumption that the primary graphene oxide (GO) toxicity mechanism is reactive oxygen species production in the cell, the question arises as to whether well-known [...] Read more.
The use of nanoparticles in the industry carries the risk of their release into the environment. Based on the presumption that the primary graphene oxide (GO) toxicity mechanism is reactive oxygen species production in the cell, the question arises as to whether well-known antioxidants can protect the cell or significantly reduce the effects of GO. This study focused on the possible remedial effect of vitamin C in Acheta domesticus intoxicated with GO for whole lives. The reproduction potential was measured at the level of Vitellogenin (Vg) gene expression, Vg protein expression, hatching success, and share of nutrition in the developing egg. There was no simple relationship between the Vg gene’s expression and the Vg protein content. Despite fewer eggs laid in the vitamin C groups, hatching success was high, and egg composition did not differ significantly. The exceptions were GO20 and GO20 + Vit. C groups, with a shift in the lipid content in the egg. Most likely, ascorbic acid impacts the level of Vg gene expression but does not affect the production of Vg protein or the quality of eggs laid. Low GO concentration in food did not cause adverse effects, but the relationship between GO toxicity and its concentration should be investigated more thoroughly. Full article
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<p><span class="html-italic">Vg</span> gene expression levels in <span class="html-italic">A. domesticus</span> fat body on the 5th and 15th days of adult life. Data were shown as a relative expression compared to β-actin and expressed as means ± SE. Abbreviations: Control—animals fed uncontaminated food; Vit. C.—animals fed with Vitamin C in the food; GO20 + Vit. C.—animals fed with graphene oxide and Vitamin C; GO20—animals fed with graphene oxide; significant differences were measured using ANOVA (Fisher test; <span class="html-italic">p</span> &lt; 0.05); different letters denote differences among the experimental groups within time points.</p>
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<p>Total Vg protein content in the fat body of <span class="html-italic">A. domesticus</span> on the 5th and 15th days of adult life. Abbreviations: Control—animals fed uncontaminated food; Vit. C.—animals fed with Vitamin C in the food; GO20 + Vit. C.—animals fed with graphene oxide and Vitamin C in the food; GO20—animals fed with graphene oxide in the food; significant differences were measured using ANOVA (Fisher test; <span class="html-italic">p</span> &lt; 0.05); different letters denote differences among the experimental groups and time points.</p>
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<p>Semi-quantitative analysis of Vg protein in the fat body of <span class="html-italic">A. domesticus</span> on the 5th and 15th days of adult life. The graph presents the precursor’s Vg expression (~200 kDa) (<b>A</b>,<b>B</b>) and the subunits 130 kDa (<b>C</b>,<b>D</b>), 97 kDa (<b>E</b>,<b>F</b>). Abbreviations: Control—animals fed uncontaminated food; Vit. C.—animals fed with Vitamin C in the food; GO20 + Vit. C.—animals fed with graphene oxide and Vitamin C; GO20—animals fed with graphene oxide; Expression measured as band density compared to the reference (control, day 5th). All the groups were compared to controls presented as 100% (red line).</p>
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<p>Egg laying (48 h) and hatching success of <span class="html-italic">A. domesticus</span>. (<b>A</b>) the average number of eggs laid per female (dark grey), the average number of larvae enclosed per female (light grey), and (<b>B</b>) the average total hatching success measured as a percent of enclosed eggs to the total number of laid eggs in the experimental group. Abbreviations: Control—animals fed uncontaminated food; Vit. C.—animals fed with Vitamin C in the food; GO20 + Vit. C.—animals fed with graphene oxide and Vitamin C; GO20—animals fed with graphene oxide; significant differences were measured using ANOVA (Fisher test; <span class="html-italic">p</span> &lt; 0.05); different letters denote differences among the experimental groups (lower case: eggs/female, capital letters: larvae/female).</p>
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<p>Share of major energetic components (lipids, glucose, glycogen, and total protein content) in the eggs of <span class="html-italic">A. domesticus</span> females collected on the 5th and 15th days of adult life. Abbreviations: Control—animals fed uncontaminated food; Vit. C.—animals fed with Vitamin C in the food; GO20 + Vit. C.—animals fed with graphene oxide and Vitamin C; GO20—animals fed with graphene oxide.</p>
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<p>Major energetic components: lipids (<b>A</b>), glucose (<b>B</b>), glycogen (<b>C</b>), and total protein content (<b>D</b>) in the eggs of <span class="html-italic">A. domesticus</span> females collected on the 5th and 15th day of adult life. Abbreviations: Control—animals fed uncontaminated food; Vit. C.—animals fed with Vitamin C in the food; GO20 + Vit. C.—animals fed with graphene oxide and Vitamin C; GO20—animals fed with graphene oxide; significant differences were measured using ANOVA (Fisher test; <span class="html-italic">p</span> &lt; 0.05); different letters denote differences among the experimental groups (small letters for the 5th day, and capital letters for the 15th day); an asterisk (*) and hashtag (#) show differences between corresponding groups on days 5th and 15th.</p>
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<p>Image of graphene oxide (<b>A</b>) SEM Magnification: 10,000×; scale bar 10 µm; (<b>B</b>) AFM.</p>
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19 pages, 4219 KiB  
Article
Exploring Molecular Drivers of PARPi Resistance in BRCA1-Deficient Ovarian Cancer: The Role of LY6E and Immunomodulation
by Tirzah Braz Petta and Joseph Carlson
Int. J. Mol. Sci. 2024, 25(19), 10427; https://doi.org/10.3390/ijms251910427 (registering DOI) - 27 Sep 2024
Abstract
Approximately 50% of patients diagnosed with ovarian cancer harbor tumors with mutations in BRCA1, BRCA2, or other genes involved in homologous recombination repair (HR). The presence of homologous recombination deficiency (HRD) is an approved biomarker for poly-ADP-ribose polymerase inhibitors (PARPis) as a maintenance [...] Read more.
Approximately 50% of patients diagnosed with ovarian cancer harbor tumors with mutations in BRCA1, BRCA2, or other genes involved in homologous recombination repair (HR). The presence of homologous recombination deficiency (HRD) is an approved biomarker for poly-ADP-ribose polymerase inhibitors (PARPis) as a maintenance treatment following a positive response to initial platinum-based chemotherapy. Despite this treatment option, the development of resistance to PARPis is common among recurrent disease patients, leading to a poor prognosis. In this study, we conducted a comprehensive analysis using publicly available datasets to elucidate the molecular mechanisms driving PARPi resistance in BRCA1-deficient ovarian cancer. Our findings reveal a central role for the interferon (IFN) pathway in mediating resistance in the context of BRCA1 deficiency. Through integrative bioinformatics approaches, we identified LY6E, an interferon-stimulated gene, as a key mediator of PARPi resistance, with its expression linked to an immunosuppressive tumor microenvironment (TME) encouraging tumor progression and invasion. LY6E amplification correlates with poor prognosis and increased expression of immune-related gene signatures, which is predictive of immunotherapy response. Interestingly, LY6E expression upon PARPi treatment resistance was found to be dependent on BRCA1 status. Gene expression analysis in the Orien/cBioPortal database revealed an association between LY6E and genes involved in DNA repair, such as Rad21 and PUF60, emphasizing the interplay between DNA repair pathways and immune modulation. Moreover, PUF60, Rad21, and LY6E are located on chromosome 8q24, a locus often amplified and associated with the progression of ovarian cancer. Overall, our study provides novel insights into the molecular determinants of PARPi resistance and highlights LY6E as a promising prognostic biomarker in the management of HRD ovarian cancer. Future studies are needed to fully elucidate the molecular mechanisms underlying the role of LY6E in PARPi resistance. Full article
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<p>BRCA1-deficient cells resistant to Olaparib activate inflammatory response through interferon signaling pathway. (<b>A</b>) Graphic summary of the three datasets used to analyze the response to PARPi resistance in this study. 96 h is 96 hours. (<b>B</b>) Images generated using the web tool GEO2R to explore the dataset GSE235980 with a Venn diagram comparing the samples of the BRCA1-deficient (BRCA def) and BRCA1 complemented with BRCA1 gene (BRCA compl) treated with Olaparib using DESeq2 with padj &lt; 0.05. (<b>C</b>) Uniform manifold approximation and projection for dimension reduction plot showing the distribution of the 8 samples, with the colors, as explained in this Figure legend. (<b>D</b>) Volcano plot with DEG BRCA-deficient Olaparib versus BRCA-deficient control. Red represents genes with high expression, blue represents genes with low expression, and gray represents genes with equal expression. (<b>E</b>) Canonical pathways in the comparison analysis within the 2 groups, BRCA-deficient Olaparib versus control and BRCA-complemented Olaparib versus control. Z-score cutoff: 2.</p>
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<p>IPA in PARPi-resistant cells. (<b>A</b>) Upstream regulators in the comparison analysis within the 2 groups, BRCA-deficient Olaparib versus control and BRCA-complemented Olaparib versus control. Z-score cutoff: 2. (<b>B</b>) Network generated from <a href="#ijms-25-10427-f001" class="html-fig">Figure 1</a>A where we can see all the genes present in the dataset that are related to IRF1 and their impact (activation or inhibition) as displayed in the legend. (<b>C</b>) GO analysis with the pathways with positive or negative regulation in the two groups from <a href="#ijms-25-10427-f001" class="html-fig">Figure 1</a>A. The orange rectangle with genes activated in the group BRCA-complemented and the red rectangle in the group BRCA-deficient. (<b>D</b>) PPI network using the list of genes upstream regulators generated using STRING database. The core with the highest FDR (FDR = 4.87 × 10<sup>−13</sup>) according to KEGG is related to Cytosolic DNA-sensing pathway (hsa04623). MCL clustering analysis highlights the genes STING1, TREX1, and RNASEH2B, in yellow, as part of this network.</p>
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<p>The role of DNA exonucleases and cGAS–STING pathway in PARPi resistance. (<b>A</b>) Network with STING1 expression in BRCA1-deficient and BRCA1-complemented cells, with their relationship with other molecules as explained in the predict legend on the upper right corner. Dashed lines predict indirect relationships. (<b>B</b>) Gene expression level as TPM normalized in the dataset GSE235980 of the genes cGAs and STING. See the legend in this Figure for the colors of the bars. Two-way ANOVA multiple comparison test with adjusted <span class="html-italic">p</span>-value applied to the asterisks. (<b>C</b>) Gene expression level as TPM normalized in the dataset GSE235980 and GSE237361 of the genes MRE11 and Exo1. See the legend in this Figure for the colors of the bars. Two-way ANOVA multiple comparison test with adjusted <span class="html-italic">p</span>-value applied to the asterisks. Ns = <span class="html-italic">p</span> &gt; 0.05; * <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.</p>
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<p>HRD Niraparib-resistant vs. -sensitive PDX tumors RAD51-deficient (from the dataset GSE165054). (<b>A</b>) Expression level of the gene LY6E in resistant and sensitive phenotype. *** <span class="html-italic">p</span> ≤ 0.001. (<b>B</b>) Heatmap showing two clusters (red and blue) according to the z-score of the phenotypes Sensitive or Resistant. The green rectangle highlights the genes IFI27, OAS2, and IFI16. (<b>C</b>) Volcano plot with the 1415 genes with FC &lt; −2 and &gt;2, <span class="html-italic">p</span>-value &lt; 0.05, highlighting the LY6E and RAD51C genes. (<b>D</b>) GSEA analysis of top 100 DEGs comparing Sensitive x Resistant. Reactome, EnrichR.</p>
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<p>The LY6E as a prognostic biomarker for ovarian cancer. (<b>A</b>) PPI network. (<b>B</b>) Volcano plot from BRCA1-deficient cells resistant to Olaparib from the dataset GSE235980 using the 243 DEG used in the first analysis. The gene LY6E is highlighted. (<b>C</b>) Tissue-wise expression profile of LY6E in different cancer types using a dot plot according to data in GEPIA2 for normal tissues (green) and tumor (red). The highest expression is observed in ovarian cancer. Abbreviated cancer names are as per TCGA. (<b>D</b>) Oncoplot of ovarian tumors for LY6E and RAD21. The legend is displayed in this Figure. (<b>E</b>) LY6E mRNA expression in ovarian cancer according to the type of genetic mutation (deep deletion, shallow deletion, diploid, gain, and amplification. Each point represents one tumor. The legend is displayed in this Figure. (<b>F</b>) Kaplan–Meier curve with overall survival of patients with ovarian cancer distributed according to CNV in the gene LY6E as low ((<b>A</b>) 0 to 3 events) or high ((<b>B</b>) 3 to 19 events). <span class="html-italic">p</span>-value is displayed in this Figure’s panel. (<b>G</b>) AYERS2017_IFNG signature of ovarian tumors classified according to the number of CNV events in LY6E as (<b>A</b>) low or (<b>B</b>) high. <span class="html-italic">p</span>-value = 4.048 × 10<sup>−4</sup>, Wilcoxon test. AYERS2017_EXPANDEDIMMUNE, <span class="html-italic">p</span>-value 7.695 × 10<sup>−3</sup>. (<b>H</b>) CIBERSTORTx analysis of TCGA Ovarian Tumors according to the expression of the gene LY6E divided into Low and High according to the median of the z-score.</p>
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<p>The co-expression of LY6E, PUF60, and RAD21 are important for PARPi resistance. (<b>A</b>) RAD21 mRNA expression according to LY6E CNV groups low or high. <span class="html-italic">p</span>-value are indicated in this Figure. (<b>B</b>) Pearson correlation coefficient of the genes LY6E and RAD21 in normal tissue (R = 0.12; <span class="html-italic">p</span>-value = 0.29) and ovarian tumors (R = 0.29; <span class="html-italic">p</span>-value = 9.2 × 10<sup>−10</sup>). (<b>C</b>) Kaplan–Meier curve with overall survival of ovarian cancer patients according to RAD21 CNV low (0 to 3 events) or high (3 to 20 events). Log rank <span class="html-italic">p</span>-value is displayed in this Figure. (<b>D</b>) AYERS2017_IFNG signature in ovarian tumors divided in low and high RAD21 CNV. <span class="html-italic">p</span>-value = 1.145 × 10<sup>−3</sup>. (<b>E</b>) mRNA of the gene PUF60 in ovarian tumors according to low or high CNV in the genes LY6E and RAD21. <span class="html-italic">p</span>-values are indicated in this Figure. (<b>F</b>) Kaplan–Meier curve with overall survival of patients according to PUF60 CNV low (0 to 3 events) or high (3 to 20 events). (<b>G</b>) PPI network analysis in STRING formed by the proteins LY6E, RAD21, PUF60. Nodes and edges colors are as per STRING legend pattern. (<b>H</b>) Gene expression level as TPM normalized in the dataset GSE237361 and GSE235980 of the genes PUF60 and LY6E. See the legend in this Figure for the colors of the bars. ns = <span class="html-italic">p</span> &gt; 0.05; * <span class="html-italic">p</span> ≤ 0.05; *** <span class="html-italic">p</span> ≤ 0.001 and **** <span class="html-italic">p</span> ≤ 0.0001.</p>
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16 pages, 5781 KiB  
Article
Genome-Wide Identification of the Remorin Gene Family in Poplar and Their Responses to Abiotic Stresses
by Zihui Li, Hang Wang, Chuanqi Li, Huimin Liu and Jie Luo
Life 2024, 14(10), 1239; https://doi.org/10.3390/life14101239 (registering DOI) - 27 Sep 2024
Abstract
The Remorin (REM) gene family is a plant-specific, oligomeric, filamentous family protein located on the cell membrane, which is important for plant growth and stress responses. In this study, a total of 22 PtREMs were identified in the genome of Populus [...] Read more.
The Remorin (REM) gene family is a plant-specific, oligomeric, filamentous family protein located on the cell membrane, which is important for plant growth and stress responses. In this study, a total of 22 PtREMs were identified in the genome of Populus trichocarpa. Subcellular localization analysis showed that they were predictively distributed in the cell membrane and nucleus. Only five PtREMs members contain both Remorin_C- and Remorin_N-conserved domains, and most of them only contain the Remorin_C domain. A total of 20 gene duplication pairs were found, all of which belonged to fragment duplication. Molecular evolutionary analysis showed the PtREMs have undergone purified selection. Lots of cis-acting elements assigned into categories of plant growth and development, stress response, hormone response and light response were detected in the promoters of PtREMs. PtREMs showed distinct gene expression patterns in response to diverse stress conditions where the mRNA levels of PtREM4.1, PtREM4.2 and PtREM6.11 were induced in most cases. A co-expression network centered by PtREMs was constructed to uncover the possible functions of PtREMs in protein modification, microtube-based movement and hormone signaling. The obtained results shed new light on understanding the roles of PtREMs in coping with environmental stresses in poplar species. Full article
(This article belongs to the Special Issue Plant Biotic and Abiotic Stresses 2024)
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<p>The gene (<b>A</b>) and protein (<b>B</b>) structures of PtREMs in <span class="html-italic">P. trichocarpa</span>. (<b>A</b>) Exon–intron structure of <span class="html-italic">PtREMs</span> in <span class="html-italic">P. trichocarpa</span>. Purple represents the UTR; green denotes CDS; and the black line represents introns; (<b>B</b>) analysis of the conserved domains of PtREM proteins. Yellow indicates the Remorin_N domain and blue indicates the Remorin_C domain.</p>
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<p>Phylogenetic analysis of REMs in <span class="html-italic">Arabidopsis</span> and <span class="html-italic">P</span>. <span class="html-italic">trichocarpa</span>. Different colored lines represent different groups.</p>
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<p>Analysis of collinearity relationships of <span class="html-italic">REM</span> in <span class="html-italic">P. trichocarpa</span> (<b>A</b>) and among different plant species (<b>B</b>). (<b>A</b>) Collinearity analysis of <span class="html-italic">PtREMs</span> in <span class="html-italic">P. trichocarpa</span>. Repeated <span class="html-italic">PtREM</span> gene pairs were ligated with bluish-green lines. (<b>B</b>) Synteny analysis of <span class="html-italic">REM</span> genes in <span class="html-italic">P. trichocarpa</span>, <span class="html-italic">A. thaliana</span>, and <span class="html-italic">O. sativa</span>. The gray lines in the background represent the collinearity in the genomes of <span class="html-italic">P. trichocarpa</span> and other plant species, and the blue lines highlight the collinearity of the <span class="html-italic">REM</span> genes.</p>
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<p>The expression of <span class="html-italic">PtREMs</span> in different tissues and different treatments. Orange bars indicate upregulation and blue bars indicate downregulation.</p>
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<p>The <span class="html-italic">cis</span>-acting element analysis of <span class="html-italic">PtREMs</span> in <span class="html-italic">P. trichocarpa</span> (<b>A</b>) and the expression of <span class="html-italic">PtREMs</span> under different stresses (<b>B</b>). The number of different promoter elements in the <span class="html-italic">PtREMs</span> is represented by different intensity colors and numbers. The different colors in the histogram represent the percentage of <span class="html-italic">cis</span>-acting elements in the four functional categories. In the heatmap, orange and blue colors indicate upregulation and downregulation, respectively. The stars in cells indicate significance.</p>
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<p>Co-expression network of <span class="html-italic">PtREMs</span> in <span class="html-italic">P. trichocarpa</span> (<b>A</b>), as well as the GO and KEGG enrichment analyses of genes in the co-expression network (<b>B</b>). In the co-expression network, the red and green nodes represent <span class="html-italic">PtREMs</span> and their co-expressed genes, respectively. The edges of the network indicate the co-expression relationships between <span class="html-italic">PtREMs</span> and their co-expressed genes.</p>
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31 pages, 15181 KiB  
Article
Transcriptomic and Metabolomic Profiling of Root Tissue in Drought-Tolerant and Drought-Susceptible Wheat Genotypes in Response to Water Stress
by Ling Hu, Xuemei Lv, Yunxiu Zhang, Wanying Du, Shoujin Fan and Lingan Kong
Int. J. Mol. Sci. 2024, 25(19), 10430; https://doi.org/10.3390/ijms251910430 (registering DOI) - 27 Sep 2024
Abstract
Wheat is the most widely grown crop in the world; its production is severely disrupted by increasing water deficit. Plant roots play a crucial role in the uptake of water and perception and transduction of water deficit signals. In the past decade, the [...] Read more.
Wheat is the most widely grown crop in the world; its production is severely disrupted by increasing water deficit. Plant roots play a crucial role in the uptake of water and perception and transduction of water deficit signals. In the past decade, the mechanisms of drought tolerance have been frequently reported; however, the transcriptome and metabolome regulatory network of root responses to water stress has not been fully understood in wheat. In this study, the global transcriptomic and metabolomics profiles were employed to investigate the mechanisms of roots responding to water stresses using the drought-tolerant (DT) and drought-susceptible (DS) wheat genotypes. The results showed that compared with the control group, wheat roots exposed to polyethylene glycol (PEG) had 25941 differentially expressed genes (DEGs) and more upregulated genes were found in DT (8610) than DS (7141). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs of the drought-tolerant genotype were preferably enriched in the flavonoid biosynthetic process, anthocyanin biosynthesis and suberin biosynthesis. The integrated analysis of the transcriptome and metabolome showed that in DT, the KEGG pathways, including flavonoid biosynthesis and arginine and proline metabolism, were shared by differentially accumulated metabolites (DAMs) and DEGs at 6 h after treatment (HAT) and pathways including alanine, aspartate, glutamate metabolism and carbon metabolism were shared at 48 HAT, while in DS, the KEGG pathways shared by DAMs and DEGs only included arginine and proline metabolism at 6 HAT and the biosynthesis of amino acids at 48 HAT. Our results suggest that the drought-tolerant genotype may relieve the drought stress by producing more ROS scavengers, osmoprotectants, energy and larger roots. Interestingly, hormone signaling plays an important role in promoting the development of larger roots and a higher capability to absorb and transport water in drought-tolerant genotypes. Full article
(This article belongs to the Special Issue Plant Physiology and Molecular Nutrition)
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<p>Effects of drought stress on the shoots and roots phenotype characterization (<b>a</b>) at 4 DAT, dry weight (scale bar of root is 5 cm), (<b>b</b>,<b>c</b>) and water contents (<b>d</b>,<b>e</b>) at 0, 2 and 4 DAT in DT and DS. Values are means ± SD from three biological replicates each with 10 plants (<b>a</b>) or with six plants (<b>b</b>–<b>e</b>). According to Tukey’s significant deference test using SPSS software, bars with the different letters are significantly different at <span class="html-italic">p</span> &lt; 0.05 and bars marked with an asterisk (*) are significantly different at <span class="html-italic">p</span> &lt; 0.01. DT-CK: JM262 grown in control conditions; DT-PEG: JM262 treated with PEG; DS-CK: YN24 grown in control conditions; DS-PEG: YN24 treated with PEG.</p>
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<p>Effects of drought stress on root structure in DT (JM262) and DS (YN24), such as the total root length (<b>a</b>), total root area (<b>b</b>), total root volume (<b>c</b>), total root tips (<b>d</b>) and root average diameter (<b>e</b>) at 0, 2 and 4 DAT. Values are means ± SD from six biological replicates each with one plants. Bars with different letters are significantly different (<span class="html-italic">p</span> &lt; 0.05) according to Tukey’s significant deference test using SPSS software. DT-CK: JM262 grown in control conditions; DT-PEG: JM262 treated with PEG; DS-CK: YN24 grown in control conditions; DS-PEG: YN24 treated with PEG.</p>
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<p>Effects of PEG treatment on the contents of root O<sub>2</sub><sup>•−</sup> (<b>a</b>), H<sub>2</sub>O<sub>2</sub> (<b>b</b>), MDA (<b>c</b>), sucrose (<b>d</b>), trehalose (<b>e</b>) and proline (<b>f</b>) in DT (JM262) and DS (YN24) at 6, 24, 48 and 72 HAT. Values are means ± SD from three biological replicates, each with 0.5 g samples. Bars with different letters are significantly different at <span class="html-italic">p</span> &lt; 0.05 according to Tukey’s significant deference test using SPSS software. DT-CK: JM262 grown in control conditions; DT-PEG: JM262 treated with PEG; DS-CK: YN24 grown in control conditions; DS-PEG: YN24 treated with PEG.</p>
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<p>Venn diagram of DEGs between DT (JM262) and DS (YN24). (<b>a</b>,<b>b</b>): Upregulated genes at 6 and 48 HAT. (<b>c</b>,<b>d</b>): Downregulated genes at 6 and 48 HAT. Three biological replicates (each with 10 plants) were performed for the two genotypes.</p>
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<p>The top 10 GO terms for categories of biological process, cellular component and molecular function in DT (<b>a</b>–<b>c</b>) and DS (<b>d</b>–<b>f</b>). The left y-axis represents the number of enriched DEGs and the right y-axis is the percentage (%) of DEGs. The red and blue bar plots represent the number of upregulated and downregulated DEGs, respectively.</p>
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<p>KEGG pathway affected by drought stress in DT (JM262) and DS (YN24). KEGG pathway enrichment analysis was performed for lists of significantly up- and down-regulated genes for each genotype. The heatmap presents statistical significance by log<sub>10</sub>(corrected <span class="html-italic">p</span>-value).</p>
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<p>Effects of drought stress on the root antioxidant activities. Heatmap of DEGs related to <span class="html-italic">SOD</span> (<b>a</b>), <span class="html-italic">CAT</span> (<b>b</b>), <span class="html-italic">APX</span> (<b>c</b>) and <span class="html-italic">GPX</span> (<b>d</b>). Volcanic and heatmap of DEGs related <span class="html-italic">POD</span> (<b>e</b>) and <span class="html-italic">GST</span> (<b>f</b>). The activities of SOD (<b>g</b>), CAT (<b>h</b>), APX (<b>i</b>), GPX (<b>j</b>), POD (<b>k</b>) and GST (<b>l</b>). Relative levels of expression are shown by a color gradient from low (blue) to high (red) in genes. The squares are ordered from left to right: DT_6 h, DT_48 h, DS_6 h and DS_48 h. The numbers in the scale bar stand for the log<sub>2</sub>(FC) in expression. Values are means ± SD from three biological replicates, each with 0.5 g samples. Bars with different letters are significantly different at <span class="html-italic">p</span> &lt; 0.05 according to Tukey’s significant deference test using SPSS software. DT-CK: JM262 grown in control conditions; DT-PEG: JM262 treated with PEG; DS-CK: YN24 grown in control conditions; DS-PEG: YN24 treated with PEG.</p>
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<p>DEGs involved in the biosynthesis of IAA (<b>a</b>), ABA (<b>b</b>) and CKs (<b>c</b>) during PEG treatment and contents of root IAA (<b>d</b>), ABA (<b>e</b>) and Z + ZR (<b>f</b>). Relative levels of expression are shown by a color gradient from low (blue) to high (red) in genes. The squares are ordered from left to right: DT_6 h, DT_48 h, DS_6 h and DS_48 h. The numbers in the scale bar stand for the log<sub>2</sub>(FC) in expression. Values are means ± SD from three biological replicates, each with 0.5 g samples. Bars with different letters are significantly different at <span class="html-italic">p</span> &lt; 0.05 according to Tukey’s significant deference test using SPSS software. DT-CK: JM262 grown in control conditions; DT-PEG: JM262 treated with PEG; DS-CK: YN24 grown in control conditions; DS-PEG: YN24 treated with PEG.</p>
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<p>The pathway of suberin biosynthesis. Genes were displayed in different colors. Relative levels of expression are shown by a color gradient from low (blue) to high (red). The squares are ordered from left to right: DT_6 h, DT_48 h, DS_6 h and DS_48 h. The numbers in the scale bar stand for the log<sub>2</sub>FC(fold changes) in expression.</p>
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<p>Principal component analysis of metabolic profiles of DT (JM262) and DS (YN24) under control and PEG treatment. Three biological replicates (each with 10 plants) were performed for the two genotypes.</p>
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<p>The pathways of phenylpropanoid biosynthesis, lignin biosynthesis, flavonoid biosynthesis and anthocyanin biosynthesis. Deferential expression of genes and accumulation of metabolites are presented by different colors. Relative levels of expression are shown by a color gradient from low (blue) to high (red) in genes and from low (green) to high (brown) in metabolites. The squares are ordered from left to right: DT_6 h, DT_48 h, DS_6 h and DS_48 h. The numbers in the scale bar stand for the log<sub>2</sub>FC(fold changes) in expression.</p>
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<p>The pathways of carbon and amino acids metabolism. Genes (red and blue) and metabolites (brown and green) were displayed in different colors. The expression levels of genes are shown by a color gradient from low (blue) to high (red) in genes and from low (green) to high (brown) in metabolites. The squares are ordered from left to right: DT_6 h, DT_48 h, DS_6 h and DS_48 h. Numbers in the scale bar stand for the log<sub>2</sub>FC in expression.</p>
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15 pages, 2068 KiB  
Article
The G-Protein-Coupled Estrogen Receptor Agonist G-1 Mediates Antitumor Effects by Activating Apoptosis Pathways and Regulating Migration and Invasion in Cervical Cancer Cells
by Abigail Gaxiola-Rubio, Luis Felipe Jave-Suárez, Christian David Hernández-Silva, Adrián Ramírez-de-Arellano, Julio César Villegas-Pineda, Marisa de Jesús Lizárraga-Ledesma, Moisés Ramos-Solano, Carlos Daniel Diaz-Palomera and Ana Laura Pereira-Suárez
Cancers 2024, 16(19), 3292; https://doi.org/10.3390/cancers16193292 (registering DOI) - 27 Sep 2024
Abstract
Background/Objectives: Estrogens and HPV are necessary for cervical cancer (CC) development. The levels of the G protein-coupled estrogen receptor (GPER) increase as CC progresses, and HPV oncoproteins promote GPER expression. The role of this receptor is controversial due to its anti- and pro-tumor [...] Read more.
Background/Objectives: Estrogens and HPV are necessary for cervical cancer (CC) development. The levels of the G protein-coupled estrogen receptor (GPER) increase as CC progresses, and HPV oncoproteins promote GPER expression. The role of this receptor is controversial due to its anti- and pro-tumor effects. This study aimed to determine the effect of GPER activation, using its agonist G-1, on the transcriptome, cell migration, and invasion in SiHa cells and non-tumorigenic keratinocytes transduced with the HPV16 E6 or E7 oncogenes. Methods: Transcriptome analysis was performed to identify G-1-enriched pathways in SiHa cells. We evaluated cell migration, invasion, and the expression of associated proteins in SiHa, HaCaT-16E6, and HaCaT-16E7 cells using various assays. Results: Transcriptome analysis revealed pathways associated with proliferation/apoptosis (TNF-α signaling, UV radiation response, mitotic spindle formation, G2/M cell cycle, UPR, and IL-6/JAK/STAT), cellular metabolism (oxidative phosphorylation), and cell migration (angiogenesis, EMT, and TGF-α signaling) in SiHa cells. Key differentially expressed genes included PTGS2 (pro/antitumor), FOSL1, TNFRSF9, IL1B, DIO2, and PHLDA1 (antitumor), along with under-expressed genes with pro-tumor effects that may inhibit proliferation. Additionally, DKK1 overexpression suggested inhibition of cell migration. G-1 increased vimentin expression in SiHa cells and reduced it in HaCaT-16E6 and HaCaT-16E7 cells. However, G-1 did not affect α-SMA expression or cell migration in any of the cell lines but increased invasion in HaCaT-16E7 cells. Conclusions: GPER is a promising prognostic marker due to its ability to activate apoptosis and inhibit proliferation without promoting migration/invasion in CC cells. G-1 could potentially be a tool in the treatment of this neoplasia. Full article
(This article belongs to the Special Issue The Estrogen Receptor and Its Role in Cancer)
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<p>The identification of differentially expressed genes and enriched pathways modulated by G-1 in the SiHa cell line. (<b>a</b>) A volcano plot illustrating DEGs with fold changes of −2 or lower and 2 or higher, and a <span class="html-italic">p</span>-value of less than 0.05. Red circles on the right indicate upregulated genes, while red circles on the left indicate downregulated genes. (<b>b</b>) An enrichment analysis of the Broad Institute’s Molecular Signature Database Hallmark gene collection was evaluated using the version 4.2.3 of the GSEA software. The left panel shows statistically significant pathway names and the right panel shows the false discovery rate (FDR) and normalized enrichment score (NES) values. The numbers in the bar graph indicate the number of enriched (blue) and non-enriched (pink) genes within each pathway. An FDR &lt; 0.25 was set as the selection criteria. (<b>c</b>) A heatmap of DEGs selected by −2 ≤ Log2 ≥ 2 and a <span class="html-italic">p</span>-value &lt; 0.05. The numbers on the left, shown in red and blue, represent the fold change values. Red numbers indicate gene upregulation, while blue numbers signify gene downregulation. Color coding indicates the detailed analysis of previous publications related to each gene.</p>
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<p>The effect of GPER activation on the expression of (<b>a</b>) vimentin and (<b>b</b>) αSMA and the induction of (<b>c</b>) migration and (<b>d</b>) invasion in SiHa cell line. This was cultured and stimulated with 1 μM of G-1 for 24 h. Immunofluorescence was performed using a secondary antibody conjugated to FITC (green) and DAPI staining (blue). Merged images are presented at 40×. The migration/invasion assays were performed in transwell chambers. The results are shown as the mean ± SD (**** <span class="html-italic">p</span> ≤ 0.0001; ns: not significant).</p>
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<p>The effect of GPER activation on the expression of (<b>a</b>) vimentin and (<b>b</b>) αSMA and the induction of (<b>c</b>) migration and (<b>d</b>) invasion in HaCaT-pLVX, HaCaT-16E6, and HaCaT-16E7 cell lines. These were cultured and stimulated with 1μM of G-1 for 24 h. Immunofluorescence was performed using a secondary antibody conjugated to FITC (green) and DAPI staining (blue). Merged images are presented at 40×. The migration/invasion assays were performed in transwell chambers. The results are shown as the mean ± SD (* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001; ns: not significant).</p>
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<p>Identification of differentially expressed genes by G-1 in HaCaT-16E7. (<b>a</b>) A volcano plot illustrating DEGs with fold changes of −2 or lower and 2 or higher, and a <span class="html-italic">p</span>-value of less than 0.05. Red circles on the right indicate upregulated genes, while red circles on the left indicate downregulated genes. (<b>b</b>) An enrichment analysis of the Broad Institute’s Molecular Signature Database Hallmark gene collection was evaluated using the version 4.2.3 of the GSEA software. The left panel shows statistically significant pathway names and the right panel shows the false discovery rate (FDR) and normalized enrichment score (NES) values. The numbers in the bar graph indicate the number of enriched (blue) and non-enriched (pink) genes within each pathway. An FDR &lt; 0.25 was set as the selection criteria. (<b>c</b>) A heatmap of DEGs selected by −2 ≤ Log2 ≥ 2 and a <span class="html-italic">p</span>-value &lt; 0.05. The numbers on the left, shown in red and blue, represent the fold change values. Red numbers indicate gene upregulation, while blue numbers signify gene downregulation. Color coding indicates the detailed analysis of previous publications related to each gene.</p>
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19 pages, 7940 KiB  
Article
Molecular and Physiological Responses of Toona ciliata to Simulated Drought Stress
by Linxiang Yang, Peixian Zhao, Xiaobo Song, Yongpeng Ma, Linyuan Fan, Meng Xie, Zhilin Song, Xuexing Zhang and Hong Ma
Horticulturae 2024, 10(10), 1029; https://doi.org/10.3390/horticulturae10101029 - 27 Sep 2024
Abstract
Drought stress, as one of the most common environmental factors, seriously affects seed- ling establishment as well as plant growth and productivity. The growth of Toona ciliata is constrained by soil moisture deficit, and drought stress can reduce its productivity and limit its [...] Read more.
Drought stress, as one of the most common environmental factors, seriously affects seed- ling establishment as well as plant growth and productivity. The growth of Toona ciliata is constrained by soil moisture deficit, and drought stress can reduce its productivity and limit its suitable growing environment. To explore the molecular mechanism of Toona ciliata responding to drought stress, leaves of two-year-old Toona ciliata seedlings were used as experimental materials for transcriptome sequencing and physiological index measurements. Under drought stress, the contents of Chl, MDA, POD, SP, SS, and RWC all change differently. We performed transcriptome sequencing, obtaining 4830 differential genes. The enrichment analysis indicates that the primary effects on the leaves of Toona ciliata under drought stress are related to photosynthesis and responses to plant hormone signal transduction. Transcription factor families associated with drought resistance include the NAC, WRKY, bZIP, bHLH, AP2-EREBP, C3H, GRAS, and FRAI transcription factor families. A weighted gene co-expression network analysis (WGCNA) analysis successfully identified 10 hub genes in response to drought stress in Toona ciliata leaves. Real-time quantitative PCR (RT-qPCR) validated the reliability of the transcriptomic data, and the analysis of its results showed a close correlation with the data obtained from RNA-seq. This study clarifies the transcriptional response of Toona ciliata to drought stress, contributing to the revelation of the molecular mechanisms of drought adaptation. Full article
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<p>Physiological indices of <span class="html-italic">Toona ciliata</span> leaves. (<b>A</b>) Chlorophyll (Chl) content, (<b>B</b>) malondialdehyde (MDA) content, (<b>C</b>) peroxidase (POD) activity, (<b>D</b>) soluble protein (SP) content, (<b>E</b>) soluble sugar (SS) content, (<b>F</b>) leaf relative water (RWC) content. The different lowercase letters indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Transcriptional changes in <span class="html-italic">Toona ciliata</span> leaves: (<b>A</b>) number of differential genes, (<b>B</b>) Venn diagram with overlapping differential genes in five groups, (<b>C</b>) Venn diagram with overlapping up-regulated differential genes in five groups, (<b>D</b>) Venn diagram with overlapping down-regulated differential genes in five groups.</p>
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<p>Trend analysis of expressed genes from 0 d to 9 d. Some genes will have similar expression patterns at different time stages, and based on the expression amount information of the genes, they can be clustered into time-related gene clusters, and the genes with consistent expression patterns will be clustered in the same cluster, and the center line represents the trend of the expression amount of the genes with consistent expression patterns over time. The color represents the distance from the centerline; purple is close to the centerline, and floral indicates distance from the centerline.</p>
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<p>Transcription factor families and GO enrichment analysis. BP is a biological process, CC is a cellular component, and MF is a molecular function. The horizontal axis is Rich Ratio, with larger values indicating a higher height of enriched cars at that entry. (<b>A</b>) Transcription factor families, (<b>B</b>) 1 d vs. 0 d GO enrichment analysis, (<b>C</b>) 3 d vs. 0 d GO enrichment analysis, (<b>D</b>) 5 d vs. 0 d GO enrichment analysis, (<b>E</b>) 7 d vs. 0 d GO enrichment analysis, (<b>F</b>) 9 d vs. 0 d GO enrichment analysis.</p>
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<p>Transcription factor families and GO enrichment analysis. BP is a biological process, CC is a cellular component, and MF is a molecular function. The horizontal axis is Rich Ratio, with larger values indicating a higher height of enriched cars at that entry. (<b>A</b>) Transcription factor families, (<b>B</b>) 1 d vs. 0 d GO enrichment analysis, (<b>C</b>) 3 d vs. 0 d GO enrichment analysis, (<b>D</b>) 5 d vs. 0 d GO enrichment analysis, (<b>E</b>) 7 d vs. 0 d GO enrichment analysis, (<b>F</b>) 9 d vs. 0 d GO enrichment analysis.</p>
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<p>Differential gene KEGG enrichment analysis: (<b>A</b>) 1 d vs. 0 d KEGG enrichment analysis; (<b>B</b>) 3 d vs. 0 d KEGG enrichment analysis; (<b>C</b>) 5 d vs. 0 d KEGG enrichment analysis; (<b>D</b>) 7 d vs. 0 d KEGG enrichment analysis: (<b>E</b>) 9 d vs. 0 d KEGG enrichment analysis; (<b>F</b>) module correlation graphs with physiological indicators.</p>
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<p>Differential gene KEGG enrichment analysis: (<b>A</b>) 1 d vs. 0 d KEGG enrichment analysis; (<b>B</b>) 3 d vs. 0 d KEGG enrichment analysis; (<b>C</b>) 5 d vs. 0 d KEGG enrichment analysis; (<b>D</b>) 7 d vs. 0 d KEGG enrichment analysis: (<b>E</b>) 9 d vs. 0 d KEGG enrichment analysis; (<b>F</b>) module correlation graphs with physiological indicators.</p>
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<p>Cluster analysis of shared pathways: (<b>A</b>) circadian rhythm–plant pathway cluster analysis, (<b>B</b>) carbon fixation in photosynthetic organism pathway cluster analysis, (<b>C</b>) porphyrin metabolism pathway cluster analysis, (<b>D</b>) carbon metabolism pathway cluster analysis, (<b>E</b>) glycolysis/gluconeogenesis pathway cluster analysis.</p>
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<p>Blue modules and red co-expression networks. Nodes represent genes in the module, while lines represent correlations between two genes. Connectivity is defined as the number of edges of all nodes. Node size and color shades reflect connectivity between genes. Darker red color indicates higher connectivity. The yellow part in the center indicates the screened hub genes. (<b>A</b>) Analysis of gene interaction network of the blue module; (<b>B</b>) analysis of gene interaction network of the red module.</p>
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<p>A total of 12 DEGs’ relative expression levels as determined by RNA-Seq and qRT-PCR.</p>
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13 pages, 6038 KiB  
Article
The VEGFA-Induced MAPK-AKT/PTEN/TGFβ Signal Pathway Enhances Progression and MDR in Gastric Cancer
by Hongming Fang, Yujuan Zhou, Xue Bai, Wanlin Che, Wenxuan Zhang, Danying Zhang, Qingmei Chen, Wei Duan, Guochao Nie and Yingchun Hou
Genes 2024, 15(10), 1266; https://doi.org/10.3390/genes15101266 - 27 Sep 2024
Abstract
Background/Objectives: Gastric cancer (GC) is a globally frequent cancer, in particular leading in mortality caused by digestive tract cancers in China. Vascular endothelial growth factor A (VEGFA) is excessively expressed in cancers including GC; its involvement in GC development, particularly in multidrug resistance [...] Read more.
Background/Objectives: Gastric cancer (GC) is a globally frequent cancer, in particular leading in mortality caused by digestive tract cancers in China. Vascular endothelial growth factor A (VEGFA) is excessively expressed in cancers including GC; its involvement in GC development, particularly in multidrug resistance (MDR), and the signal route it affects in GC remain unknown. To explore the roles VEGFA plays during progression and MDR formation in GC, we studied its function in a VEGFA-deleted GC cell platform. Methods: We initially assessed the importance of VEGFA in GC and MDR using database analysis. Then, using CCK8, wound healing, transwell, scanning electron microscopy, immunofluorescence, flow cytometry, and other techniques, the alterations in tumor malignancy-connected cell behaviors and microstructures were photographed and evaluated in a VEGFA-gene-deleted GC cell line (VEGFA−/−SGC7901). Finally, the mechanism of VEGFA in GC progression and MDR was examined by Western blot. Results: Database analysis revealed a strong correlation between high VEGFA expression and a poor prognosis for GC. The results showed that VEGFA deletion reduced GC cell proliferation and motility and altered microstructures important for motility, such as the depolymerized cytoskeleton. VEGFA deletion inhibited the growth of pseudopodia/filopodia and suppressed the epithelial–mesenchymal transition (EMT). The occurrence of MDR is induced by overactivation of the MAPK-AKT and TGFβ signaling pathways, while PTEN inhibits these pathways. Conclusions: All findings suggested that VEGFA acts as a cancer enhancer and MDR inducer in GC via the MAPK-AKT/PTEN/TGFβ signal pathway. Full article
(This article belongs to the Section Human Genomics and Genetic Diseases)
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<p>Different databases were evaluated to determine the effect of VEGFA on GC progression. (<b>A</b>,<b>B</b>) VEGFA expression in various cancers (<b>A</b>) and in GC (<b>B</b>). (<b>C</b>) The HPA database immunohistochemical samples (pictured above is normal tissue; below is GC tissue). (<b>D</b>) The relationship between VEGFA mutation and prognosis in GC patients. (<b>E</b>,<b>F</b>) VEGFA in different differentiation degree and pathological staging in GC. (<b>G</b>) Survival curve analysis of VEGFA in GC patients (TCGA). (<b>H</b>) VEGF family expression in various cancers. (<b>I</b>) The expression of VEGFA in SGC7901 cells was detected by RT-PCR. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt;0.0001.</p>
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<p>VEGFA-deleted SGC7901 cell line was generated with CRISPR/Cas9n. (<b>A</b>) The designation for VEGFA deletion. (<b>B</b>) The efficiency (&gt;80%) of the transfection of CRISPR/Cas9n vector at 60 h post-transfection (4×). (<b>C</b>,<b>D</b>) Sequencing assay for positive clone selection. (<b>E</b>–<b>G</b>) The validation of the VEGFA-deleted clone by Western blot ((<b>E</b>,<b>F</b>), **** <span class="html-italic">p</span> &lt; 0.0001) and immunocytochemistry assay ((<b>G</b>), 20×).</p>
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<p>VEGFA deletion reduced the multiplication and viability of SGC7901 cells. (<b>A</b>) Cell morphology of each group under light microscope (10×). (<b>B</b>,<b>C</b>) Results of CCK8 (<b>B</b>) and colony formation (<b>C</b>). (<b>G</b>,<b>H</b>) Digital assays for colony formatting efficiency (<b>G</b>) and clone counting (<b>H</b>). (<b>D</b>,<b>I</b>) Cell cycle analysis by FCM (<b>D</b>) and data assay (<b>I</b>). (<b>E</b>,<b>J</b>) Results of transwell. (<b>F</b>,<b>K</b>) Results of wound healing. (** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>VEGFA deletion caused cyto/nucleoskeletal remodeling. (<b>A</b>,<b>B</b>) Results of Coomassie Brilliant Blue staining (40×) under optical microscope (<b>A</b>) and SEM (<b>B</b>) (scale bar: 20 μm). (<b>C</b>–<b>E</b>) The cyto/nucleoskeletal microstructures displayed by immunocytochemistry (<b>C</b>) and immunofluorescence assays ((<b>D</b>), 40×, scale bar: 20 μm), and data assay for F-actin ((<b>E</b>), ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt;0.0001).</p>
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<p>The tendency of apoptosis in SGC7901 cells induced by VEGFA deletion. (<b>A</b>) MitoScene 633 staining and (<b>B</b>) DAPI staining (scale bar: 10 μm). (<b>C</b>–<b>E</b>) Apoptosis detection by annexin V FITC antibody and PI under a fluorescence microscope ((<b>C</b>), scale bar: 10 μm). (*** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>The mechanism VEGFA utilizes to enhance oncogenesis, progression, and MDR in GC. (<b>A</b>) Simulated spatial configuration of VEGFA (SWISS-MODEL database). (<b>B</b>) Prediction of interactions between VEGFA and other genes (STRING database). (<b>C</b>–<b>H</b>) Detection of the expression of the key signal genes (<b>C</b>,D), EMT-relevant genes (<b>E</b>,<b>F</b>), and MDR-relevant genes (<b>G</b>,<b>H</b>) in SGC7901 cells. (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>An illustration of the VEGFA regulatory mechanism in GC.</p>
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13 pages, 7301 KiB  
Article
Effect of Polystyrene Microplastic Exposure on Individual, Tissue, and Gene Expression in Juvenile Crucian Carp (Carassius auratus)
by Yuequn Huang, Wenjing Li, Kun Dong, Xiangtong Li, Wenrong Li and Dunqiu Wang
Fishes 2024, 9(10), 385; https://doi.org/10.3390/fishes9100385 - 27 Sep 2024
Abstract
Exposure to an environment containing microplastics can cause adverse effects on creatures through respiratory and digestive systems. In this paper, 50–500 μm polystyrene microplastics (exposure concentrations were 200 μg/L, 800 μg/L, and 3200 μg/L concentrations) were selected to study the distribution of polystyrene [...] Read more.
Exposure to an environment containing microplastics can cause adverse effects on creatures through respiratory and digestive systems. In this paper, 50–500 μm polystyrene microplastics (exposure concentrations were 200 μg/L, 800 μg/L, and 3200 μg/L concentrations) were selected to study the distribution of polystyrene microplastics (PS-MPs) and the effects on the growth, development, tissue damage and gene expression of crucian carp juveniles. The results showed that PS-MPs were enriched in the intestinal tract (GIT) and gill tissue of crucian carp, and the average number of PS-MPs was between 0 to 2.33 items per individual. It was found that the average number of MPs in the intestine was more than in the gills, and it was independent of the PS-MP concentration. However, the specific gravity of PS-MPs in excreta was concentration-dependent. In addition, it was found that the exposure of the medium concentration group promoted the weight of the crucian carp larvae, inhibited the growth rate, and reduced the weight in the low and high concentration groups. The histopathological results indicated that the intestinal, gill, brain, and liver tissues all showed different degrees of damage, and the higher the concentration of PS-MPs, the more severe damage to the tissue cells. This experiment evaluated 15 genes in three treatments, which found that PS-MPs had different effects on gene expression in the liver, intestine, and gill tissues, and the tested genes were involved in different response pathways associated with virulence. Full article
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<p>Distribution of the particle size range (50−500 μm) and morphology of the PS-MPs. (<b>a</b>) showed size map of PS-MPs detected by Malvin laser particle meter. (<b>b</b>) showed morphological electron microscope of 50–500 μm PS-MPs under a scanning electron microscope.</p>
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<p>Plot of PS-MPs in crucian carp sample gastrointestinal tract and gills.</p>
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<p>In−situ infrared spectrum of PS−MPs in fish organization and excreta. (<b>a</b>) shows the in situ infrared spectrum of PS-MPs in the gastrointestinal tract and gills, (<b>b</b>) shows the infrared spectrum of MPs in the excreta of low, medium and high concentration groups.</p>
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<p>Plot of PS-MPs in the crucian carp excreta in different concentrations, 0 μg/L (<b>a</b>), 200 μg/L (<b>b</b>), 800 μg/L (<b>c</b>), 3200 μg/L (<b>d</b>).</p>
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<p>Growth of crucian carp after exposure to different concentrations of microplastics, length (<b>a</b>), and weight (<b>b</b>).</p>
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<p>Pictures of growth factor of crucian carp (<b>a</b>) and Growth contras (<b>b</b>).</p>
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<p>Intestinal tract histological damage of crucian carp after 32 d of PS-MP exposure in different groups: (<b>a</b>) control; (<b>b</b>) low concentration; (<b>c</b>) medium concentration; (<b>d</b>) high concentration (red arrow indicated epithelial detachment and loss, green arrow indicated villus shortening, blue arrow indicated cell congestion, black arrow indicated leukocyte infiltration and orange arrow indicated villus division).</p>
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<p>Gill histological damage of crucian carp after 32 d of PS-MP exposure in different groups: (<b>a</b>) control; (<b>b</b>) low concentration; (<b>c</b>) medium concentration; (<b>d</b>) high concentration (red arrow indicated nuclear lysis, green arrow indicated cell lysis in the gill filament periphery, black arrow indicated off-center nuclei, orange indicated cell enlargement and deformation).</p>
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<p>Brain histology damage of crucian carp after 32 d of PS-MP exposure in different groups: (<b>a</b>) control; (<b>b</b>) low concentration; (<b>c</b>) medium concentration; (<b>d</b>) high concentration (red arrow indicated cell nucleus off-center).</p>
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<p>Liver histological changes of crucian carp after 32 d of PS-MP exposure in different groups: (<b>a</b>) control; (<b>b</b>) low concentration; (<b>c</b>) medium concentration; (<b>d</b>) high concentration (black arrows indicate cell congestion, red cells indicate cell vacuolization, green arrows indicate cell nucleus out of center, blue arrows indicate cell enlargement and necrosis).</p>
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<p>Gene sequence expression of the gill (<b>a</b>), GIT (<b>b</b>), and liver (<b>c</b>) tissue (* stand for <span class="html-italic">p</span> &lt; 0.05).</p>
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20 pages, 5997 KiB  
Article
RNA-Binding Proteins as Novel Effectors in Osteoblasts and Osteoclasts: A Systems Biology Approach to Dissect the Transcriptional Landscape
by Anastasia Meshcheryakova, Serhii Bohdan, Philip Zimmermann, Markus Jaritz, Peter Pietschmann and Diana Mechtcheriakova
Int. J. Mol. Sci. 2024, 25(19), 10417; https://doi.org/10.3390/ijms251910417 - 27 Sep 2024
Abstract
Bone health is ensured by the coordinated action of two types of cells—the osteoblasts that build up bone structure and the osteoclasts that resorb the bone. The loss of balance in their action results in pathological conditions such as osteoporosis. Central to this [...] Read more.
Bone health is ensured by the coordinated action of two types of cells—the osteoblasts that build up bone structure and the osteoclasts that resorb the bone. The loss of balance in their action results in pathological conditions such as osteoporosis. Central to this study is a class of RNA-binding proteins (RBPs) that regulates the biogenesis of miRNAs. In turn, miRNAs represent a critical level of regulation of gene expression and thus control multiple cellular and biological processes. The impact of miRNAs on the pathobiology of various multifactorial diseases, including osteoporosis, has been demonstrated. However, the role of RBPs in bone remodeling is yet to be elucidated. The aim of this study is to dissect the transcriptional landscape of genes encoding the compendium of 180 RBPs in bone cells. We developed and applied a multi-modular integrative analysis algorithm. The core methodology is gene expression analysis using the GENEVESTIGATOR platform, which is a database and analysis tool for manually curated and publicly available transcriptomic data sets, and gene network reconstruction using the Ingenuity Pathway Analysis platform. In this work, comparative insights into gene expression patterns of RBPs in osteoblasts and osteoclasts were obtained, resulting in the identification of 24 differentially expressed genes. Furthermore, the regulation patterns upon different treatment conditions revealed 20 genes as being significantly up- or down-regulated. Next, novel gene–gene associations were dissected and gene networks were reconstructed. Additively, a set of osteoblast- and osteoclast-specific gene signatures were identified. The consolidation of data and information gained from each individual analytical module allowed nominating novel promising candidate genes encoding RBPs in osteoblasts and osteoclasts and will significantly enhance the understanding of potential regulatory mechanisms directing intracellular processes in the course of (patho)physiological bone turnover. Full article
(This article belongs to the Special Issue Advances in Osteogenesis)
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Figure 1

Figure 1
<p>The expression patterns of genes encoding 180 RBPs in osteoblasts and osteoclasts. GENEVESTIGATOR-based analysis was performed to extract the expression levels of the genes encoding the 180 RBPs in osteoblasts and osteoclast. Analysis was performed on the basis of the Affymetrix Human Genome U133 Plus 2.0 Array platform; specific filters were set to define osteoblasts (n = 4, derived from the GSE12264 data set [<a href="#B26-ijms-25-10417" class="html-bibr">26</a>]) and osteoclasts (n = 3, derived from the GSE63009 data set [<a href="#B27-ijms-25-10417" class="html-bibr">27</a>]). For both cell types, only untreated/mock treated samples were included. Box-plots represent the expression levels of the individual genes encoding RBPs. The levels of expression are given as log2 transformed values and are sub-divided into low, medium, and high expression according to GENEVESTIGATOR. Color code: red, osteoblasts; blue, osteoclasts. Group comparison was performed using <span class="html-italic">t</span>-test; the correction for multiple testing was performed using the Bonferroni–Holm method. The <span class="html-italic">p</span>-value upon Bonferroni–Holm correction are indicated: * <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; only significant <span class="html-italic">p</span>-values are indicated. <span class="html-italic">LIN28A</span> showed low mRNA expression levels in both cell types; the biological relevance of this level of expression needs further validation. The data were assessed and extracted from GENEVESTIGATOR on 31 August 2021.</p>
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<p>The expression patterns of genes encoding 180 RBPs in osteoblasts and osteoclasts. GENEVESTIGATOR-based analysis was performed to extract the expression levels of the genes encoding the 180 RBPs in osteoblasts and osteoclast. Analysis was performed on the basis of the Affymetrix Human Genome U133 Plus 2.0 Array platform; specific filters were set to define osteoblasts (n = 4, derived from the GSE12264 data set [<a href="#B26-ijms-25-10417" class="html-bibr">26</a>]) and osteoclasts (n = 3, derived from the GSE63009 data set [<a href="#B27-ijms-25-10417" class="html-bibr">27</a>]). For both cell types, only untreated/mock treated samples were included. Box-plots represent the expression levels of the individual genes encoding RBPs. The levels of expression are given as log2 transformed values and are sub-divided into low, medium, and high expression according to GENEVESTIGATOR. Color code: red, osteoblasts; blue, osteoclasts. Group comparison was performed using <span class="html-italic">t</span>-test; the correction for multiple testing was performed using the Bonferroni–Holm method. The <span class="html-italic">p</span>-value upon Bonferroni–Holm correction are indicated: * <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; only significant <span class="html-italic">p</span>-values are indicated. <span class="html-italic">LIN28A</span> showed low mRNA expression levels in both cell types; the biological relevance of this level of expression needs further validation. The data were assessed and extracted from GENEVESTIGATOR on 31 August 2021.</p>
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<p>Existing knowledge on the 24 differentially expressed genes. PubMed-based search was conducted first for the gene name alone and then using the combination of keywords “Gene Name” (such as <span class="html-italic">RBFOX2</span>) AND the indicated term including “Bone”, “Osteo *”, Osteoblast”, and “Osteoclast” (assessed on 5 November 2023). The outcome is shown by a bar chart; color code: dark gray, “Gene name”; green, “Bone”; yellow, “Osteo *”; red, “Osteoblast”; blue, “Osteoclast”. The number of scientific articles found on PubMed for each search condition is indicated using a log scale. Genes were classified as “limited knowledge in bone homeostasis” if for the search terms “Bone”, “Osteo *”, Osteoblast”, and “Osteoclast” ≤ 2 publications were found.</p>
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<p>Up-regulation and down-regulation of genes encoding RBPS upon various treatment conditions. Bubble plot shows the 20 genes that were found to be significantly up- or down-regulated (<span class="html-italic">p</span>-value ≤ 0.05 and fold change ≥ |1.5|). The cell types, types of treatment and treatment durations are indicated. The success of the treatments was shown within the corresponding original publications; this includes a validation of the microarray results by quantitative real-time PCR [<a href="#B26-ijms-25-10417" class="html-bibr">26</a>,<a href="#B27-ijms-25-10417" class="html-bibr">27</a>,<a href="#B28-ijms-25-10417" class="html-bibr">28</a>]. The color indicates the strength and direction of the regulation (magenta, up-regulation; green, down-regulation). Dot size is proportional to the −log <span class="html-italic">p</span>-value. The data were assessed and extracted from GENEVESTIGATOR on 1 September 2021. The data were visualized using Spotfire software on 5 May 2022.</p>
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<p>Existing knowledge on the up-regulated and down-regulated genes. PubMed-based search was conducted first for the gene name alone and then using the combination of keywords “Gene Name” (such as <span class="html-italic">NUDT16L1</span>) AND the indicated term including “Bone”, “Osteo *”, Osteoblast”, and “Osteoclast” (assessed on 14 November 2023). The outcome is shown by a bar chart; color code: dark gray, “Gene name”; green, “Bone”; yellow, “Osteo *”; red, “Osteoblast”; blue, “Osteoclast”. The number of scientific articles found on PubMed for each search condition is indicated using a log scale. Genes were classified as “limited knowledge in bone homeostasis” if for the search terms “Bone”, “Osteo *”, Osteoblast”, and “Osteoclast” ≤ 2 publications were found.</p>
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<p>Hierarchical clustering of 180 genes encoding RBPs in osteoblasts and osteoclasts. Two-way hierarchical clustering based on Euclidean distance was performed on the expression of 180 genes across individual samples attributed to osteoblasts (n = 4, derived from the GSE12264 data set [<a href="#B26-ijms-25-10417" class="html-bibr">26</a>]) and osteoclasts (n = 3, derived from the GSE63009 data set [<a href="#B27-ijms-25-10417" class="html-bibr">27</a>]). Six sub-clusters (I–VI) are indicated. OC, osteoclasts; OB, osteoblasts. The data were assessed and extracted from GENEVESTIGATOR on 11 October 2021.</p>
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<p>The 25-gene osteoblast-specific gene signature and the 25-gene osteoclast-specific gene signature. Shown are the top 25 genes, which compose the specific gene signature for osteoblasts (<b>A</b>) and osteoclasts (<b>B</b>). The cell type of interest, for which the specific signature was dissected, is indicated by the checkmark. The expression pattern in other cell types/systems is indicated according to the color code in blue. The analysis was performed across the compendium of 777 anatomical parts on the basis of the Affymetrix Human Genome U133 Plus 2.0 Array platform. In (<b>A</b>), the name “FGF7P7, …” is indicative for the <span class="html-italic">FGF7P-1 to 8</span> pseudogenes. The data were assessed and extracted from GENEVESTIGATOR on 18 February 2022.</p>
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<p>Existing knowledge on the genes comprising the 25-gene osteoblast-specific gene signature and the 25-gene osteoclast-specific gene signature. PubMed-based search was conducted first for the gene name alone and then using the combination of keywords “Gene Name” (such as <span class="html-italic">IBSP</span>) AND the indicated term including “Bone”, “Osteo *”, Osteoblast”, and “Osteoclast” (assessed on 9 November 2023) for the 25-gene osteoblast-specific gene signature (<b>A</b>) and the 25-gene osteoclast-specific gene signature (<b>B</b>). The outcome is shown by a bar chart; color code: dark gray, “Gene name”; green, “Bone”; yellow, “Osteo *”; red, “Osteoblast”; blue, “Osteoclast”. The number of scientific articles found in PubMed for each search condition is indicated using a log scale. Genes were classified as “limited knowledge” in osteoblasts (<b>A</b>) or osteoclasts (<b>B</b>) if for the search terms “Osteoblast” or “Osteoclast”, respectively, ≤2 publications were found. The transcripts <span class="html-italic">AC004988.1</span>, <span class="html-italic">AL121933</span> and the pseudogenes <span class="html-italic">FGF7P-1 to 8</span> and <span class="html-italic">SDCBPP2</span>, where no information is available in NCBI Gene, were excluded from the analysis.</p>
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<p>The reconstructed gene networks and the promising candidate RBPs for osteoblasts and osteoclast. IPA software was used to reconstruct the gene networks on the basis of the 24 differentially expressed RBP genes (<b>A</b>) and the 20 RBP genes that were found to be significantly up- or down-regulated (<b>B</b>). A circular plot view was used for visualization. The types of molecules encoded by the corresponding genes are indicated in the figure legend. Lines display the IPA-identified associations between molecules; color code: red fill, genes encoding RBPs attributed to osteoblasts; blue fill, genes encoding RBPs attributed to osteoclasts; green outline, genes encoding RBPs identified as novel promising candidates. Insert: the IPA-based description of symbols and relationships. The data were assessed and extracted from IPA software on 22 and 23 April 2024.</p>
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