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18 pages, 1406 KiB  
Review
Novel Insight of N6-Methyladenosine in Cardiovascular System
by Huan Zhang, Wei Lu, Haoyue Tang, Aiqun Chen, Xiaofei Gao, Congfei Zhu and Junjie Zhang
Medicina 2025, 61(2), 222; https://doi.org/10.3390/medicina61020222 - 26 Jan 2025
Viewed by 389
Abstract
N6-methyladenosine (m6A) is the most common and abundant internal co-transcriptional modification in eukaryotic RNAs. This modification is catalyzed by m6A methyltransferases, known as “writers”, including METTL3/14 and WTAP, and removed by demethylases, or “erasers”, such as FTO and ALKBH5. It is [...] Read more.
N6-methyladenosine (m6A) is the most common and abundant internal co-transcriptional modification in eukaryotic RNAs. This modification is catalyzed by m6A methyltransferases, known as “writers”, including METTL3/14 and WTAP, and removed by demethylases, or “erasers”, such as FTO and ALKBH5. It is recognized by m6A-binding proteins, or “readers”, such as YTHDF1/2/3, YTHDC1/2, IGF2BP1/2/3, and HNRNPA2B1. Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality worldwide. Recent studies indicate that m6A RNA modification plays a critical role in both the physiological and pathological processes involved in the initiation and progression of CVDs. In this review, we will explore how m6A RNA methylation impacts both the normal and disease states of the cardiovascular system. Our focus will be on recent advancements in understanding the biological functions, molecular mechanisms, and regulatory factors of m6A RNA methylation, along with its downstream target genes in various CVDs, such as atherosclerosis, ischemic diseases, metabolic disorders, and heart failure. We propose that the m6A RNA methylation pathway holds promise as a potential therapeutic target in cardiovascular disease. Full article
(This article belongs to the Section Cardiology)
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Figure 1

Figure 1
<p>The regulation of m6A modification. m6A modification is established by m6A methyltransferases (“writers”) and removed by m6A demethylases (“erasers”). m6A-binding proteins (“readers”) recognize and bind to m6A-modified RNA, playing essential roles in RNA metabolism. <span class="html-italic">METTL3</span>, in collaboration with <span class="html-italic">METTL14/16</span>, <span class="html-italic">KIAA1429</span>, <span class="html-italic">WTAP</span>, <span class="html-italic">RBM15/15B</span>, and <span class="html-italic">ZC3H13</span>, forms the core methylation complex. This modification is reversible, with demethylases such as <span class="html-italic">ALKBH5</span> and <span class="html-italic">FTO</span> serving as m6A erasers. The modified transcripts are recognized by readers, including <span class="html-italic">YTHDF1/2/3</span>, <span class="html-italic">YTHDC1/2</span>, <span class="html-italic">IGF2BP1/2/3</span>, and <span class="html-italic">hnRNPG/C/A2B</span>, which subsequently influence various aspects of RNA function, such as translation promotion, stability, localization, splicing, and nuclear export.</p>
Full article ">Figure 2
<p>The role of m6A modulators in cardiovascular diseases and biological processes. <span class="html-italic">METTL3</span>-mediated m6A modification contributes to atherosclerosis by affecting inflammatory pathways, including <span class="html-italic">NLRP1</span>, <span class="html-italic">KLF4</span>, and <span class="html-italic">JAK2</span>. <span class="html-italic">METTL14</span> up-regulation induces inflammation and plaque formation by enhancing <span class="html-italic">FOXO1</span> translation. Reduced <span class="html-italic">FTO</span> expression promotes smooth muscle cell proliferation and inflammation by stabilizing <span class="html-italic">NR4A3</span> mRNA, accelerating atherosclerosis. In stent restenosis, <span class="html-italic">WTAP</span> promotes smooth muscle cell proliferation and migration by increasing <span class="html-italic">P16</span> mRNA via m6A modification. In myocardial ischemic disease, <span class="html-italic">METTL3</span> up-regulation decreases <span class="html-italic">TFEB</span> expression, impairing autophagic flux and enhancing cell apoptosis. During myocardial infarction repair, <span class="html-italic">ALKBH5</span> up-regulation inhibits autophagy by destabilizing mRNAs like <span class="html-italic">SPHK1</span>, <span class="html-italic">ERB4</span>, and <span class="html-italic">YAP</span>, promoting infarct repair. <span class="html-italic">METTL3</span> also mediates miR-34a maturation, which down-regulates <span class="html-italic">SIRT1</span> and promotes inflammatory infiltration in abdominal aortic aneurysm. Decreased <span class="html-italic">FTO</span> expression is linked to reduced <span class="html-italic">Serca2a</span> levels, leading to impaired cardiac contractility and heart failure. In heart failure, m6A-modified <span class="html-italic">MYH7</span> mRNA and miR-133a play protective roles in ventricular remodeling. Additionally, <span class="html-italic">ALKBH5</span> increases <span class="html-italic">IL-11</span> mRNA, inhibiting macrophage-to-myofibroblast transition. In hypoxic pulmonary hypertension, up-regulated <span class="html-italic">MAGED1</span> and down-regulated <span class="html-italic">PTEN</span>, both m6A-modified, contribute to smooth muscle cell proliferation, inflammation, and pulmonary vascular remodeling.</p>
Full article ">Figure 3
<p>Relative amounts of methylated regulatory enzymes in the cardiovascular system and other systems. Heatmap shows differentially expressed methylated regulatory enzymes in different tissues. Data derived from PRJEB4337 of HPA RNA-seq normal tissues, in which RNA-seq was performed on tissue samples from 95 human individuals representing 27 different tissues in order to determine the tissue specificity of all protein-coding genes.</p>
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14 pages, 1038 KiB  
Article
Profiling of snoRNAs in Exosomes Secreted from Cells Infected with Influenza A Virus
by Wojciech Rozek, Malgorzata Kwasnik, Wojciech Socha, Bartosz Czech and Jerzy Rola
Int. J. Mol. Sci. 2025, 26(1), 12; https://doi.org/10.3390/ijms26010012 - 24 Dec 2024
Viewed by 712
Abstract
Small nucleolar RNAs (snoRNAs) are non-coding RNAs (ncRNAs) that regulate many cellular processes. Changes in the profiles of cellular ncRNAs and those secreted in exosomes are observed during viral infection. In our study, we analysed differences in expression profiles of snoRNAs isolated from [...] Read more.
Small nucleolar RNAs (snoRNAs) are non-coding RNAs (ncRNAs) that regulate many cellular processes. Changes in the profiles of cellular ncRNAs and those secreted in exosomes are observed during viral infection. In our study, we analysed differences in expression profiles of snoRNAs isolated from exosomes of influenza (IAV)-infected and non-infected MDCK cells using high-throughput sequencing. The analysis revealed 133 significantly differentially regulated snoRNAs (131 upregulated and 2 downregulated), including 93 SNORD, 38 SNORA, and 2 SCARNA. The most upregulated was SNORD58 (log2FoldChange = 9.61), while the only downregulated snoRNAs were SNORD3 (log2FC = −2.98) and SNORA74 (log2FC = −2.67). Several snoRNAs previously described as involved in viral infections were upregulated, including SNORD27, SNORD28, SNORD29, SNORD58, and SNORD44. In total, 533 interactors of dysregulated snoRNAs were identified using the RNAinter database with an assigned confidence score ≥ 0.25. The main groups of predicted interactors were transcription factors (TFs, 169 interactors) and RNA-binding proteins (RBPs, 130 interactors). Among the most important were pioneer TFs such as POU5F1, SOX2, CEBPB, and MYC, while in the RBP category, notable interactors included Polr2a, TNRC6A, IGF2BP3, and FMRP. Our results suggest that snoRNAs are involved in pro-viral activity, although follow-up studies including experimental validation would be beneficial. Full article
(This article belongs to the Special Issue Exosomes and Non-Coding RNA Research in Health and Disease)
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Figure 1

Figure 1
<p>List of the 50 most upregulated snoRNAs, ranked by their log2Fold Change values. Colours indicate the classification of upregulated snoRNAs into specific types: C/D box snoRNAs (SNORD)—blue, H/ACA box snoRNAs (SNORA)—red, and SCARNAs—yellow.</p>
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<p>Graphical representation of snoRNA interactions: (<b>A</b>). Main categories of predicted interactors for dysregulated snoRNAs. Groups of interactors within each category are marked with colours: transcription factors (TF)—blue, RNA-binding proteins (RBP)—orange, microRNAs (miRNA)—grey, messenger RNAs (mRNA)—yellow, long non-coding RNAs (lncRNA)—light blue, histone modifications—green, snoRNAs—dark blue, and others—dark orange. (<b>B</b>). A circus plot illustrating the proportion of predicted interactions between various types of snoRNAs and the main categories of interactors. The width of the lines connecting the two halves of the plot represents the number of interactions. In the lower half of the plot, specific types of snoRNAs are indicated by distinct colours: SNORA—red, SNORD—blue, and SCARNA—green. In the upper half of the plot, the main categories of interactors are represented by the following colours: transcription factors (TF)—grey, histone modifications—orange, long non-coding RNAs (lncRNA)—light blue, microRNAs (miRNA)—light green, messenger RNAs (mRNA)—black, RNA-binding proteins (RBP)—dark red, and others—pink.</p>
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18 pages, 5017 KiB  
Article
Identification of Three POMCa Genotypes in Largemouth Bass (Micropterus salmoides) and Their Differential Physiological Responses to Feed Domestication
by Jie Hu, Jie Yang, Huan Zhong, Qifang Yu, Jun Xiao and Chun Zhang
Animals 2024, 14(24), 3638; https://doi.org/10.3390/ani14243638 - 17 Dec 2024
Viewed by 573
Abstract
Diverse feeding habits in teleosts involve a wide range of appetite-regulating factors. As an appetite-suppressing gene, the polymorphisms of POMCa in largemouth bass (Micropterus salmoides) were validated via sequencing and high-resolution melting (HRM). The frequency distribution of different POMCa genotypes were [...] Read more.
Diverse feeding habits in teleosts involve a wide range of appetite-regulating factors. As an appetite-suppressing gene, the polymorphisms of POMCa in largemouth bass (Micropterus salmoides) were validated via sequencing and high-resolution melting (HRM). The frequency distribution of different POMCa genotypes were analyzed in two populations, and physiological responses of different POMCa genotypes to feed domestication were investigated. The indel of an 18 bp AU-rich element (ARE) in the 3′ UTR and four interlocked SNP loci in the ORF of 1828 bp of POMCa cDNA sequence were identified in largemouth bass and constituted three genotypes of POMC-A I, II, and III, respectively. POMC-A I and Allele I had increased frequencies in the selection population than in the non-selection population (p < 0.01), 63.55% vs. 43.33% and 0.7850 vs. 0.6778, respectively. POMC-A I possessed the lowest value of POMCa mRNA during fasting (p < 0.05) and exhibited growth and physiological advantages under food deprivation and refeeding according to the levels of body mass and four physiological indicators, i.e., cortisol (Cor), growth hormone (GH), insulin-like growth factor-1 (IGF-1), and glucose (Glu). The identification of three POMCa genotypes, alongside their varying physiological responses during feed domestication, suggests a selective advantage that could be leveraged in molecular marker-assisted breeding of largemouth bass that are adapted to feeding on formula diet. Full article
(This article belongs to the Section Aquatic Animals)
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Figure 1
<p>Structure of <span class="html-italic">POMC</span> cDNA and phylogenetic analysis of amino acid sequences between largemouth bass and other Perciform fishes. (<b>A</b>) 1828 bp of <span class="html-italic">POMC</span> cDNA sequence in largemouth bass, including a 96 bp of 5′ UTR, a 1075 bp of 3′ UTR, and a 657 bp of ORF. (<b>B</b>) Phylogenetic analysis of <span class="html-italic">POMC</span> in Perciformes. (<b>C</b>) POMC structure was analyzed in largemouth bass and several other Perciform fishes. The arrows indicated a set of conserved four cysteine residues located at NPP of the molecule. The boxes indicated the core motifs of peptide precursors of the <span class="html-italic">POMC</span> gene family: the “YGGF” sequence of β-endorphin and a core sequence of “HFRW” in α-MSH and β-MSH peptides.</p>
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<p>The indel of an 18 bp ARE “ATATCAATATTGTCTCGG” was found in the 3′ UTR of LMB <span class="html-italic">POMCa</span>. (<b>A</b>) “RACE” represents the sequence of 3′ UTR amplified according to the protocol of the SMARTer RACE 5′/3′ Kit. #13 and #14 represent the sequences of 3′ UTR amplified by the primer pair of POMC-F1/R1. “*” represents identical. (<b>B</b>) Chromatogram of POMC-A I 3′ UTR with an 18 bp homozygous insertion. (<b>C</b>) Chromatogram of POMC-A II 3′ UTR with a coexistence of the 18 bp ARE insertion and deletion. (<b>D</b>) Chromatogram of POMC-A III 3′ UTR with an 18 bp homozygous deletion.</p>
Full article ">Figure 3
<p>Genotyping of three <span class="html-italic">POMCa</span> genotypes by sequencing and HRM detection in largemouth bass. (<b>A</b>) SNP Chromatogram of three <span class="html-italic">POMCa</span> genotypes. Four SNP loci are tightly linked and indicated by arrows. (1) The homozygote POMC−A I with 220TT/327GG/452CC/504TT; (2) The heterozygote POMC−A II with 220CT/327AG/452CT/504CT; (3) The homozygote POMC−A III with 220CC/327AA/452TT/504CC. (<b>B</b>) Genotyping of three <span class="html-italic">POMCa</span> genotypes by HRM detection in largemouth bass. (1) Normalized temperature-shifted melting curve. (2) Temperature-shifted difference curve. The blue curve corresponds to the genotype of POMC−A I, the red indicates POMC−A II, and the green profiles represent POMC−A III. The blank control is shown in black, with GG as the reference cluster.</p>
Full article ">Figure 4
<p>Frequency distribution of three <span class="html-italic">POMCa</span> genotypes in largemouth bass. (<b>A</b>,<b>B</b>) Percentages of three genotypes in non-selection population (<b>A</b>) and selection population (<b>B</b>), respectively. (<b>C</b>,<b>D</b>) The genetic structure of the top 10%, 20%, and 30% of the largest and smallest individuals in the non-selection population (<b>C</b>) and selection population (<b>D</b>), respectively.</p>
Full article ">Figure 5
<p>Transcriptional levels of LMB <span class="html-italic">POMCa</span> in response to fasting and refeeding. (<b>A</b>) <span class="html-italic">POMC</span> expression in mixed genotypes (<span class="html-italic">n</span> = 9) of largemouth bass fasted for three days (3 d), one week (7 d), and then refed for three days (10 d) and one week (14 d). <span class="html-italic">n</span> = 3 for each genotype. (<b>B</b>) Differential expression of three <span class="html-italic">POMCa</span> genotypes in the treatment group, which was fasted for one week and then refed for three days. Fold changes of three <span class="html-italic">POMCa</span> genotypes in different groups were calculated using the 2<sup>−ΔΔCT</sup> method. Data were expressed as mean ± SE (<span class="html-italic">n</span> = 6 for each genotype). <sup>a,b,c,d</sup>, and, <sup>e</sup> represent the significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 6
<p>Physiological responses to fasting and refeeding among three <span class="html-italic">POMCa</span> genotypes. (<b>A</b>) mean values of Cor, GH, IGF-1, and Glu. The blue line corresponds to the genotype of POMC-A I, the yellow indicates POMC-A II, and the green represents POMC-A III. Data were expressed as mean ± SE (n ≥ 9) for each genotype in each group. Thirteen POMC-A I, 9 POMC-A II, and 13 POMC-A III for the control group; 9 POMC-A I, 9 POMC-A II, and 9 POMC-A III for the fasting group; 18 POMC-A I, 9 POMC-A II, and 10 POMC-A III for the refeeding group. (<b>B</b>) Body weights (mean ± SE) of three <span class="html-italic">POMCa</span> genotypes before fasting and after refeeding. <sup>a, b,</sup> and <sup>c</sup> represent the significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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16 pages, 37876 KiB  
Article
Circ_0000284 Is Involved in Arsenite-Induced Hepatic Insulin Resistance Through Blocking the Plasma Membrane Translocation of GLUT4 in Hepatocytes via IGF2BP2/PPAR-γ
by Shiqing Xu, Zhida Hu, Yujie Wang, Qiyao Zhang, Zhi Wang, Teng Ma, Suhua Wang, Xiaohui Wang and Li Wang
Toxics 2024, 12(12), 883; https://doi.org/10.3390/toxics12120883 - 4 Dec 2024
Viewed by 1060
Abstract
Arsenic exposure can induce liver insulin resistance (IR) and diabetes (DM), but the underlying mechanisms are not yet clear. Circular RNAs (circRNAs) are involved in the regulation of the onset of diabetes, especially in the progression of IR. This study aimed to investigate [...] Read more.
Arsenic exposure can induce liver insulin resistance (IR) and diabetes (DM), but the underlying mechanisms are not yet clear. Circular RNAs (circRNAs) are involved in the regulation of the onset of diabetes, especially in the progression of IR. This study aimed to investigate the role of circRNAs in arsenic-induced hepatic IR and its underlying mechanism. Male C57BL/6J mice were given drinking water containing sodium arsenite (0, 0.5, 5, or 50 ppm) for 12 months. The results show that sodium arsenite increased circ_0000284 expression, decreased insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) and peroxisome proliferator-activated receptor-γ (PPAR-γ), and inhibited cell membrane protein levels of insulin-responsive glucose transporter protein 4 (GLUT4) in the mouse livers, indicating that arsenic exposure causes liver damage and disruptions to glucose metabolism. Furthermore, sodium arsenite reduced glucose consumption and glycogen levels, increased the expression of circ_0000284, reduced the protein levels of IGF2BP2 and PPAR-γ, and inhibited GLUT4 protein levels in the cell membranes of insulin-treated HepG2 cells. However, a circ_0000284 inhibitor reversed arsenic exposure-induced reductions in IGF2BP2, PPAR-γ, and GLUT4 levels in the plasma membrane. These results indicate that circ_0000284 is involved in arsenite-induced hepatic insulin resistance through blocking the plasma membrane translocation of GLUT4 in hepatocytes via IGF2BP2/PPAR-γ. This study provides a scientific basis for finding early biomarkers for the control of arsenic exposure and type 2 diabetes mellitus (T2DM), and discovering new prevention and control measures. Full article
(This article belongs to the Special Issue Health Effects of Exposure to Environmental Pollutants)
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Figure 1
<p>Chronic exposure to sodium arsenite causes liver injury and hepatic IR in mice. C57BL/6J mice were allowed to drink water containing 0, 0.5, 5, or 50 ppm sodium arsenite for 12 months. (<b>A</b>) IPGTTs showing the blood glucose concentrations at 0, 15, 30, 60, and 120 min in the mice. The area under the curve (AUC) was calculated based on the IPGTTs results. (<b>B</b>) ITT assays were performed to determine the blood glucose concentrations of the mice at 0, 15, 30, and 60 min. The AUC of the ITTs was calculated. (<b>C</b>) A glycogen assay kit was used to determine the glycogen concentration in each mouse’s liver. (<b>D</b>) The detection of hepatic glycogen changes by PAS staining. (<b>E</b>) The HE staining images of livers indicating the liver injury level. The data are presented as the mean ± SD, <span class="html-italic">n</span> = 3. <sup>a</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with the 0 ppm NaAsO<sub>2</sub> group; <sup>b</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with the 0.5 ppm NaAsO<sub>2</sub> group; <sup>c</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with the 5 ppm NaAsO<sub>2</sub> group; <sup>d</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with the 50 ppm NaAsO<sub>2</sub> group.</p>
Full article ">Figure 1 Cont.
<p>Chronic exposure to sodium arsenite causes liver injury and hepatic IR in mice. C57BL/6J mice were allowed to drink water containing 0, 0.5, 5, or 50 ppm sodium arsenite for 12 months. (<b>A</b>) IPGTTs showing the blood glucose concentrations at 0, 15, 30, 60, and 120 min in the mice. The area under the curve (AUC) was calculated based on the IPGTTs results. (<b>B</b>) ITT assays were performed to determine the blood glucose concentrations of the mice at 0, 15, 30, and 60 min. The AUC of the ITTs was calculated. (<b>C</b>) A glycogen assay kit was used to determine the glycogen concentration in each mouse’s liver. (<b>D</b>) The detection of hepatic glycogen changes by PAS staining. (<b>E</b>) The HE staining images of livers indicating the liver injury level. The data are presented as the mean ± SD, <span class="html-italic">n</span> = 3. <sup>a</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with the 0 ppm NaAsO<sub>2</sub> group; <sup>b</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with the 0.5 ppm NaAsO<sub>2</sub> group; <sup>c</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with the 5 ppm NaAsO<sub>2</sub> group; <sup>d</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with the 50 ppm NaAsO<sub>2</sub> group.</p>
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<p>Chronic exposure to sodium arsenite induces increased levels of circ_0000284 and decreased levels of IGF2BP2 levels in livers of mice. C57BL/6J mice were allowed to drink water containing 0, 0.5, 5, or 50 ppm sodium arsenite for 12 months. Levels of circ_0000284 (<b>A</b>) and IGF2BP2 (<b>B</b>) in livers of mice were determined using qRT-PCR assay. (<b>C</b>) Western blots of protein bands and (<b>D</b>) relative protein levels of IGF2BP2 in livers of mice. Data are presented as mean ± SD, <span class="html-italic">n</span> = 3. <sup>a</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 0 ppm NaAsO<sub>2</sub> group; <sup>b</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 0.5 ppm NaAsO<sub>2</sub> group; <sup>c</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 5 ppm NaAsO<sub>2</sub> group; <sup>d</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 50 ppm NaAsO<sub>2</sub> group.</p>
Full article ">Figure 3
<p>Chronic arsenic exposure decreases levels of PPAR-γ and levels of membrane GLUT4 in livers of mice. C57BL/6J mice were allowed to drink water containing 0, 0.5, 5, or 50 ppm sodium arsenite for 12 months. (<b>A</b>) Western blots of protein bands and (<b>B</b>) relative protein levels of PPAR-γ in livers of mice. (<b>C</b>) Western blots of protein bands and (<b>D</b>) relative protein levels of GLUT4 in cytoplasm and membrane of mice livers; β-actin served as internal reference for cytoplasm proteins, and Na and K-ATPase as internal references for membrane proteins. (<b>E</b>) Ratio of GLUT4 protein levels in membrane to cytoplasm. Data are presented as mean ± SD, <span class="html-italic">n</span> = 3. <sup>a</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 0 ppm NaAsO<sub>2</sub> group; <sup>b</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 0.5 ppm NaAsO<sub>2</sub> group; <sup>c</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 5 ppm NaAsO<sub>2</sub> group; <sup>d</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 50 ppm NaAsO<sub>2</sub> group.</p>
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<p>Sodium arsenite causes decreased levels of glucose consumption and glycogen in insulin-treated HepG2 cells. HepG2 cells were treated with 0, 1, 2, 4, 8, 10, 20, or 30 μM sodium arsenite for 24 h. (<b>A</b>) Cell viability was detected by CCK-8 assay. After HepG2 cells were treated with 0, 2, 4, or 8 μM sodium arsenite for 24 h, they were then treated for 30 min with 100 nM insulin. Glucose consumption (<b>B</b>) and glycogen levels (<b>C</b>) in HepG2 cells were measured by glucose assay kits and glycogen assay kits. Data are presented as mean ± SD, <span class="html-italic">n</span> = 3. <sup>a</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 0 μM NaAsO<sub>2</sub> group; <sup>b</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 2 μM NaAsO<sub>2</sub> group; <sup>c</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 4 μM NaAsO<sub>2</sub> group; <sup>d</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 8 μM NaAsO<sub>2</sub> group.</p>
Full article ">Figure 5
<p>Sodium arsenite increases levels of circ_0000284 levels and decreases levels of IGF2BP2, PPAR-γ, and membrane GLUT4 in insulin-treated HepG2 cells. HepG2 cells were treated with 0, 2, 4, or 8 μM sodium arsenite for 24 h, and then treated for 30 min with 100 nM insulin. (<b>A</b>) Levels of circ_0000284 were quantified by qRT-PCR. (<b>B</b>) Western blots of protein bands and (<b>C</b>,<b>D</b>) corresponding relative protein levels of IGF2BP2 and PPAR-γ. (<b>E</b>) Western blots of protein bands and (<b>F</b>) relative protein levels of GLUT4 in cytoplasm and membrane; β-actin served as internal reference for cytoplasm proteins, and Na and K-ATPase as internal references for membrane proteins. (<b>G</b>) Ratio of GLUT4 expression levels in membrane proteins to cytoplasm proteins. Data are presented as mean ± SD, <span class="html-italic">n</span> = 3. <sup>a</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 0 μM NaAsO<sub>2</sub> group; <sup>b</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 2 μM NaAsO<sub>2</sub> group; <sup>c</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 4 μM NaAsO<sub>2</sub> group; <sup>d</sup>: <span class="html-italic">p</span> &lt; 0.05, compared with 8 μM NaAsO<sub>2</sub> group.</p>
Full article ">Figure 6
<p>Inhibition of circ_0000284 blocks sodium arsenite-induced increases in circ_0000284 levels and decreases in glucose consumption and glycogen levels in insulin-treated HepG2 cells. HepG2 cells were transfected with 0 or 50 nM si-circ_0000284 or si-circ_NC for 6 h, followed by treatment with 0 or 8 μM sodium arsenite for 24 h, respectively, and then treated for 30 min with 100 nM insulin. (<b>A</b>) Levels of circ_0000284 in HepG2 cells were detected by qRT-PCR assay. (<b>B</b>) Glucose consumption and glycogen levels in HepG2 cells were measured by glucose assay kits and glycogen assay kits. Data are presented as mean ± SD, <span class="html-italic">n</span> = 3. <sup>a</sup>: <span class="html-italic">p</span> &lt;0.05, compared with HepG2 cells without arsenite treatment; <sup>b</sup>: <span class="html-italic">p</span> &lt;0.05, compared with si-circ_0000284-treated HepG2 cells.</p>
Full article ">Figure 7
<p>Inhibition of circ_0000284 blocks sodium arsenite-induced decreases in IGF2BP2, PPAR-γ, and membrane GLUT4 levels in insulin-treated HepG2 cells. After HepG2 cells were transfected with 0 or 50 nM si-circ_0000284 or si-circ_NC for 6 h, and then with 0 or 8 μM sodium arsenite for 24 h, respectively, they were then treated for 30 min with 100 nM insulin. (<b>A</b>) Western blots of protein bands and (<b>B</b>,<b>C</b>) corresponding relative protein levels of IGF2BP2 and PPAR-γ. (<b>D</b>) Western blots of protein bands and (<b>E</b>) corresponding relative protein levels of GLUT4 in cytoplasm and membrane; β-actin served as internal reference for cytoplasm proteins, and Na and K-ATPase served as internal references for membrane proteins. (<b>F</b>) Ratio of GLUT4 expression levels in membrane proteins to cytoplasm proteins. Data are presented as mean ± SD, <span class="html-italic">n</span> = 3. <sup>a</sup>: <span class="html-italic">p</span> &lt;0.05, compared with HepG2 cells without arsenite treatment; <sup>b</sup>: <span class="html-italic">p</span> &lt;0.05, compared with si-circ_0000284-treated HepG2 cells.</p>
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25 pages, 2080 KiB  
Review
The Emerging Role of IGF2BP2 in Cancer Therapy Resistance: From Molecular Mechanism to Future Potential
by Die Li, Shiqi Hu, Jiarong Ye, Chaojie Zhai, Jipeng Liu, Zuao Wang, Xinchi Zhou, Leifeng Chen and Fan Zhou
Int. J. Mol. Sci. 2024, 25(22), 12150; https://doi.org/10.3390/ijms252212150 - 12 Nov 2024
Viewed by 1546
Abstract
Tumor resistance is one of the primary reasons for cancer treatment failure, significantly limiting the options and efficacy of cancer therapies. Therefore, overcoming resistance has become a critical factor in improving cancer treatment outcomes. IGF2BP2, as a reader of m6A methylation, plays a [...] Read more.
Tumor resistance is one of the primary reasons for cancer treatment failure, significantly limiting the options and efficacy of cancer therapies. Therefore, overcoming resistance has become a critical factor in improving cancer treatment outcomes. IGF2BP2, as a reader of m6A methylation, plays a pivotal role in the post-transcriptional regulation of RNA through the methylation of m6A sites. It not only contributes to cancer initiation and progression but also plays a key role in tumor drug resistance. This review provides a comprehensive summary of the mechanisms by which IGF2BP2 contributes to therapy resistance, with the aim of improving the efficacy of chemotherapy in cancer treatment. Advancing research in this area is crucial for developing more effective therapies that could significantly improve the quality of life for cancer patients. Full article
(This article belongs to the Section Molecular Oncology)
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Figure 1

Figure 1
<p>The process of m6A modification. After transcription, the N6 position of adenosine in mRNA can be modified by the addition of a CH3 group as the function of m6A writer, resulting in m6A modification. However, this modification is not stable, and the mRNA may be subject to degradation. Alternatively, specific m6A erasers can remove the methyl group, also leading to mRNA degradation. Conversely, if an m6A reader protein binds to the modified site, the m6A modification is stabilized, allowing the mRNA to be successfully translated into protein as well as degradation.</p>
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<p>Protein structure of IGF2BP2. (<b>a</b>,<b>b</b>) are 3D protein structure of IGF2BP2 while (<b>c</b>) is unfolding chain. Different colors represent different domains. RRM1 is represented by red, RRM2 is represented by blue, KH1 is represented by yellow, KH21 is represented by magenta, KH3 is represented by cyan, KH4 is represented by orange, and the remaining low-complexity region is represented by green.</p>
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<p>The mechanisms of IGF2BP2 regulating chemotherapy resistance. (<b>a</b>) circPBX3 can interact with the RNA-binding protein IGF2BP2 in the cytoplasm to form a complex. The complex increases the stability and translation of ATP7A mRNA and upregulates ATP7A protein levels. ATP7A is stored in the trans-Golgi (TGN). When platinum-based drugs enter cancer cells, ATP7A can bind and segregate them into the vesicles. Vesicles carrying platinum drugs will be translocated outside the cell, leading to drug resistance. (<b>b</b>) cPKM is a newly identified circRNA. It binds to IGF2BP2 and STMN1/TGFB1 mRNA 3′ UTR to form an RNA–protein complex, which promotes the interaction of IGF2BP2 with STMN1/TGFB1 mRNA and thus enhances the stability of STMN1/TGFB1 mRNA. TGFB1 is key to the transformation of activated HSCs to a myofibroblast-like phenotype. TGFB1 secreted by tumor cells in the hepatic microenvironment induces the activation of HSCs to produce many extracellular matrix components, which shapes the microenvironment for tumor progression, leading to the decrease in paclitaxel sensitivity in cancer cells. STMN1 reduces the sensitivity to paclitaxel of cancer cells by activating the PI3K/Akt pathway. (<b>c</b>) IGF2BP2 binds to the CAUC motif of circITGB6 in the cytoplasm via the KH1-2 bi-structural domain. Two CAAAC sites within circITGB6 can directly bind to AU-element-rich FGF9, leading to the formation of the circITGB6/IGF2BP2/FGF9 ternary complex. The formation of the complex enhances the stability of FGF9 mRNA, which leads to increased extracellular FGF9 secretion. This ultimately promotes an increase in FGF9 in the TME of ovarian cancer cells, induces macrophage polarization toward the M2 phenotype, and ultimately leads to platinum resistance in OC patients. (<b>d</b>) In the cytoplasm, A1BG–AS1 recruits IGF2BP2, and the ABCB1 3′ UTR has a very-high-confidence m6A modification site that is bound by IGF2BP2, preventing degradation and stabilizing the expression of ABCB1. ABCB-1 encodes P-gp, a protein located in the cell membrane, which is capable of binding to doxorubicin and utilizing the hydrolysis of ATP to provide energy for drug transport. HOXD-AS2 is an IncRNA that interacts with IGF2BP2 to form an RNA–protein complex to fulfill its molecular function. Upregulated HOXD–AS2/IGF2BP2 promotes STAT3 signaling, phosphorylates tyrosine 705 residue of STAT3, and upregulates the expression of BCL2L1 and MCL1, which promotes anti-apoptotic activity and reduces the responsiveness of cancer cells to chemotherapeutic drug TMZ.</p>
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<p>The mechanisms of IGF2BP2 regulating immunotherapy resistance. ① METTL3 modifies circQSOX1 with m6A, while ②a IGF2BP2 binds to the m6A modification on cicrQSOX1, stabilizing and upregulating cicrQSOX1, or the ②b unstable cicrQSOX1 would be degraded. miR-326 and miR330-5p can bind to cicrQSOX1 to ④ release PGAM1 RNA for translation. Meanwhile, whereas cicrQSOX1 adsorbs miR-326 and miR-330-5p, PGAM1 is upregulated, which ⑤ promotes lactate translocation to the extracellular compartment, ⑥ increases cytosolic glycolytic activity, and facilitates Treg cell-associated CRC immune escape, therefore ⑦ resisting the anti-cancer effects of the CTLA-14 antibody.</p>
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18 pages, 4360 KiB  
Article
The Pattern of Gene Expression (Igf Family, Muscle Growth Regulatory Factors, and Osteogenesis-Related Genes) Involved in the Growth of Skeletal Muscle in Pikeperch (Sander lucioperca) During Ontogenesis
by Fatemeh Lavajoo Bolgouri, Bahram Falahatkar, Miquel Perelló-Amorós, Fatemeh Moshayedi, Iraj Efatpanah and Joaquim Gutiérrez
Animals 2024, 14(21), 3089; https://doi.org/10.3390/ani14213089 - 26 Oct 2024
Viewed by 946
Abstract
The pikeperch (Sander lucioperca) is an economically important freshwater fish and a valuable food with high market acceptance. It is undergoing important changes in growth and regulatory metabolism during the ontogeny. Hence, the current study aims to investigate the mRNA expression [...] Read more.
The pikeperch (Sander lucioperca) is an economically important freshwater fish and a valuable food with high market acceptance. It is undergoing important changes in growth and regulatory metabolism during the ontogeny. Hence, the current study aims to investigate the mRNA expression of the growth hormone (gh)/insulin-like growth factor (igf) axis (ghr, igfI, igfbp, igfr), muscle regulatory factors (pax7, myf5, myod, myogenin, mrf, mymk, mstn), and osteogenesis-related genes (colla1a, fib1a, on, op, ostn) from hatching through day 40th post-hatching (DPH). The average total length (TL) of larvae measured at hatching was 3.6 ± 0.4 mm (67 degree days), and at the end of the experiment (40 DPH, 777 degree days), it was 27.1 ± 1.1 mm. The results showed three phases of gene expression in day 0 (egg), larval, and juvenile stages of pikeperch, which can be a progression or transition from the initial state toward the juvenile state. The expression pattern of myf5, mymk, and fib1a genes showed the highest value at day 0. The growth hormone receptor gene (ghr) and igfbp5 were raised to 1 DPH, whereas increased expression of igfI, igfII, igf1bp4, igf1rb, myod2, and mrf4 was detected at 14 DPH. The myod1, pax7, op, ostc, on, igf1ra, and col1a1a genes were highly expressed at 21 DPH and juvenile stages. According to the PLS-DA model, the most relevant VIPs are myf5 and mymk as best markers of earlier stages and igf1ra, ostc, pax7, and ghr as markers of later stages of ontogeny. Results from this study suggest that basal metabolism, growth of body cells and muscles, and bone proliferation and development can be regulated by the dynamic changes in gene expression patterns in this species. The identified genes will help to understand the basic biological process of pikeperch larvae and development, which is very important in pikeperch farming Full article
(This article belongs to the Section Animal Genetics and Genomics)
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Graphical abstract

Graphical abstract
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<p>Feeding scheme for rearing of pikeperch (<span class="html-italic">Sander lucioperca</span>) and water temperature fluctuations during the first 40 days post-hatch.</p>
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<p>Relative gene expression of skeletal muscle <span class="html-italic">ghr</span> (<b>a</b>), <span class="html-italic">igfI</span> (<b>b</b>), <span class="html-italic">igfII</span> (<b>c</b>), <span class="html-italic">igf1bp4</span> (<b>d</b>), <span class="html-italic">igf1bp5</span> (<b>e</b>), <span class="html-italic">igf1ra</span> (<b>f</b>), and <span class="html-italic">igf1rb</span> (<b>g</b>) in pikeperch during the ontogenesis. Data are shown as means ± SEM (n = 10). Letters indicate significant differences (<span class="html-italic">p &lt;</span> 0.05) by one-way ANOVA and Tukey HSD test. Sample 0 pointed to the sample egg before the hatch.</p>
Full article ">Figure 2 Cont.
<p>Relative gene expression of skeletal muscle <span class="html-italic">ghr</span> (<b>a</b>), <span class="html-italic">igfI</span> (<b>b</b>), <span class="html-italic">igfII</span> (<b>c</b>), <span class="html-italic">igf1bp4</span> (<b>d</b>), <span class="html-italic">igf1bp5</span> (<b>e</b>), <span class="html-italic">igf1ra</span> (<b>f</b>), and <span class="html-italic">igf1rb</span> (<b>g</b>) in pikeperch during the ontogenesis. Data are shown as means ± SEM (n = 10). Letters indicate significant differences (<span class="html-italic">p &lt;</span> 0.05) by one-way ANOVA and Tukey HSD test. Sample 0 pointed to the sample egg before the hatch.</p>
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<p>Relative gene expression of skeletal muscle <span class="html-italic">pax7</span> (<b>a</b>), <span class="html-italic">myf5</span> (<b>b</b>), <span class="html-italic">myod1</span> (<b>c</b>), <span class="html-italic">myod2</span> (<b>d</b>), <span class="html-italic">myogenin</span> (<b>e</b>), <span class="html-italic">mrf4</span> (<b>f</b>), <span class="html-italic">mymk</span> (<b>g</b>), and <span class="html-italic">mstnb</span> (<b>h</b>) in pikeperch during the ontogenesis. Data are shown as means ± SEM (n = 10). Letters indicate significant differences (<span class="html-italic">p &lt;</span> 0.05) by one-way ANOVA and Tukey HSD test. Sample 0 indicated to the sample egg before hatch.</p>
Full article ">Figure 3 Cont.
<p>Relative gene expression of skeletal muscle <span class="html-italic">pax7</span> (<b>a</b>), <span class="html-italic">myf5</span> (<b>b</b>), <span class="html-italic">myod1</span> (<b>c</b>), <span class="html-italic">myod2</span> (<b>d</b>), <span class="html-italic">myogenin</span> (<b>e</b>), <span class="html-italic">mrf4</span> (<b>f</b>), <span class="html-italic">mymk</span> (<b>g</b>), and <span class="html-italic">mstnb</span> (<b>h</b>) in pikeperch during the ontogenesis. Data are shown as means ± SEM (n = 10). Letters indicate significant differences (<span class="html-italic">p &lt;</span> 0.05) by one-way ANOVA and Tukey HSD test. Sample 0 indicated to the sample egg before hatch.</p>
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<p>Relative gene expression of bone <span class="html-italic">col1a1a</span> (<b>a</b>), <span class="html-italic">fib1a</span> (<b>b</b>), <span class="html-italic">on</span> (<b>c</b>), <span class="html-italic">op</span> (<b>d</b>), and <span class="html-italic">ostn</span> (<b>e</b>) in pikeperch during the ontogenesis. Data are shown as means ± SEM (n = 10). Letters indicate significant differences (<span class="html-italic">p &lt;</span> 0.05) by one-way ANOVA and Tukey HSD test. Sample 0 pointed to the sample egg before the hatch.</p>
Full article ">Figure 4 Cont.
<p>Relative gene expression of bone <span class="html-italic">col1a1a</span> (<b>a</b>), <span class="html-italic">fib1a</span> (<b>b</b>), <span class="html-italic">on</span> (<b>c</b>), <span class="html-italic">op</span> (<b>d</b>), and <span class="html-italic">ostn</span> (<b>e</b>) in pikeperch during the ontogenesis. Data are shown as means ± SEM (n = 10). Letters indicate significant differences (<span class="html-italic">p &lt;</span> 0.05) by one-way ANOVA and Tukey HSD test. Sample 0 pointed to the sample egg before the hatch.</p>
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<p>Overview of the PLS-DA model with the % of variability explained by each component (<b>A</b>) and the quality assessment of the model with the R2 and Q2 values (<b>B</b>). The asterisk indicates which number of components combined gives a higher Q2 value.</p>
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<p>Scatter 3D score plot of the PLS-DA shows the discrimination between the clustered samples corresponding to the different ontogenic stages of <span class="html-italic">Sander lucioperca</span>. The position of each dot along the axis indicates the strength and direction of the relationship between the variable and the principal component.</p>
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<p>Overview of the main variable importance in projection (VIP) corresponding to the three main components (Comp), and the colored boxes on the right of each graph indicate the relative expression of the corresponding gene in each group. Only VIPs ≥ 1 are considered relevant for accurate prediction and robustness of the model. The colored boxes on the right of each graph indicate the relative expression of the corresponding gene in each group. 0 day (egg).</p>
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26 pages, 8774 KiB  
Review
RNA Binding Proteins as Potential Therapeutic Targets in Colorectal Cancer
by Vikash Singh, Amandeep Singh, Alvin John Liu, Serge Y. Fuchs, Arun K. Sharma and Vladimir S. Spiegelman
Cancers 2024, 16(20), 3502; https://doi.org/10.3390/cancers16203502 - 16 Oct 2024
Viewed by 2106
Abstract
RNA-binding proteins (RBPs) play critical roles in regulating post-transcriptional gene expression, managing processes such as mRNA splicing, stability, and translation. In normal intestine, RBPs maintain the tissue homeostasis, but when dysregulated, they can drive colorectal cancer (CRC) development and progression. Understanding the molecular [...] Read more.
RNA-binding proteins (RBPs) play critical roles in regulating post-transcriptional gene expression, managing processes such as mRNA splicing, stability, and translation. In normal intestine, RBPs maintain the tissue homeostasis, but when dysregulated, they can drive colorectal cancer (CRC) development and progression. Understanding the molecular mechanisms behind CRC is vital for developing novel therapeutic strategies, and RBPs are emerging as key players in this area. This review highlights the roles of several RBPs, including LIN28, IGF2BP1–3, Musashi, HuR, and CELF1, in CRC. These RBPs regulate key oncogenes and tumor suppressor genes by influencing mRNA stability and translation. While targeting RBPs poses challenges due to their complex interactions with mRNAs, recent advances in drug discovery have identified small molecule inhibitors that disrupt these interactions. These inhibitors, which target LIN28, IGF2BPs, Musashi, CELF1, and HuR, have shown promising results in preclinical studies. Their ability to modulate RBP activity presents a new therapeutic avenue for treating CRC. In conclusion, RBPs offer significant potential as therapeutic targets in CRC. Although technical challenges remain, ongoing research into the molecular mechanisms of RBPs and the development of selective, potent, and bioavailable inhibitors should lead to more effective treatments and improved outcomes in CRC. Full article
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Figure 1

Figure 1
<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor Ln7 with RNA binding protein Ln28 (PDBID: 5UDZ). (<b>C</b>) shows 2D representations of binding interactions of Ln7. (<b>D</b>) The binding energy of inhibitors with RNA binding protein Ln28 along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
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<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor R12–8–44–3 with RNA binding protein Musash1 (PDBID: 2RS2). (<b>C</b>) shows 2D representations of binding interactions of Musashi1. (<b>D</b>) The binding energy of inhibitors with RNA binding protein Musashi 1 along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
Full article ">Figure 2 Cont.
<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor R12–8–44–3 with RNA binding protein Musash1 (PDBID: 2RS2). (<b>C</b>) shows 2D representations of binding interactions of Musashi1. (<b>D</b>) The binding energy of inhibitors with RNA binding protein Musashi 1 along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
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<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor palmatine with RNA binding protein Musash2 (PDBID: 6DBP). (<b>C</b>) shows 2D representations of binding interactions of Musashi2. (<b>D</b>) The binding energy of inhibitors with RNA binding protein Musashi 2 along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
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<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor C11 with RNA binding protein HUR (PDBID: 4ED5). (<b>C</b>) shows 2D representations of binding interactions of HUR. (<b>D</b>) The binding energy of inhibitors with RNA binding protein HUR along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
Full article ">Figure 4 Cont.
<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor C11 with RNA binding protein HUR (PDBID: 4ED5). (<b>C</b>) shows 2D representations of binding interactions of HUR. (<b>D</b>) The binding energy of inhibitors with RNA binding protein HUR along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
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<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor compound27 with RNA binding protein CELFI (PDBID: 3NMR). (<b>C</b>) shows 2D representations of binding interactions of CELFI. (<b>D</b>) The binding energy of inhibitors with RNA binding protein CELFI along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
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20 pages, 8303 KiB  
Article
Interactive Structural Analysis of KH3-4 Didomains of IGF2BPs with Preferred RNA Motif Having m6A Through Dynamics Simulation Studies
by Muhammad Fakhar, Mehreen Gul and Wenjin Li
Int. J. Mol. Sci. 2024, 25(20), 11118; https://doi.org/10.3390/ijms252011118 - 16 Oct 2024
Viewed by 1144
Abstract
m6A modification is the most common internal modification of messenger RNA in eukaryotes, and the disorder of m6A can trigger cancer progression. The GGACU is considered the most frequent consensus sequence of target transcripts which have a GGAC m [...] Read more.
m6A modification is the most common internal modification of messenger RNA in eukaryotes, and the disorder of m6A can trigger cancer progression. The GGACU is considered the most frequent consensus sequence of target transcripts which have a GGAC m6A core motif. Newly identified m6A ‘readers’ insulin-like growth factor 2 mRNA-binding proteins modulate gene expression by binding to the m6A binding sites of target mRNAs, thereby affecting various cancer-related processes. The dynamic impact of the methylation at m6A within the GGAC motif on human IGF2BPs has not been investigated at the structural level. In this study, through in silico analysis, we mapped IGF2BPs binding sites for the GGm6AC RNA core motif of target mRNAs. Subsequent molecular dynamics simulation analysis at 400 ns revealed that only the KH4 domain of IGF2BP1, containing the 503GKGG506 motif and its periphery residues, was involved in the interaction with the GGm6AC backbone. Meanwhile, the methyl group of m6A is accommodated by a shallow hydrophobic cradle formed by hydrophobic residues. Interestingly, in IGF2BP2 and IGF2BP3 complexes, the RNA was observed to shift from the KH4 domain to the KH3 domain in the simulation at 400 ns, indicating a distinct dynamic behavior. This suggests a conformational stabilization upon binding, likely essential for the functional interactions involving the KH3-4 domains. These findings highlight the potential of targeting IGF2BPs’ interactions with m6A modifications for the development of novel oncological therapies. Full article
(This article belongs to the Section Molecular Informatics)
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Figure 1

Figure 1
<p>Comparative analysis of KH3-4 domains of human IGF2BPs and gallus gallus IGF2BP1. (<b>A</b>) Multiple sequence alignment of gallus gallus (GG1) IGF2BP1 and human IGF2BP1 (Hu1), IGF2BP2 (Hu2), and IGF2BP3 (Hu3)’s KH3-4 domains. The conserved motif involved in the binding is highlighted in light olive (GXXG) and green–red color (GKGG). The secondary structure is shown above the sequences. Alpha helices are indicated in black color, β-sheets in plum color, and loops in blue color. (<b>B</b>) Structural analysis of KH3-4 domains of all human IGF2BPs and gallus gallus IGF2BP1 with their respective colors. KH3 domains, linkers, and KH4 domains are represented in white, rosy brown, and dark grey colors, respectively.</p>
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<p>Binding pattern of GGm<sup>6</sup>AC RNA motif with KH3-4 domains of IGF2BPs. (<b>A</b>) Surface representation of gallus gallus KH3-4 domains (pink) with GGm<sup>6</sup>AC RNA (red). (<b>B</b>) The same complex is indicated using a ribbon for the protein and zoomed out for highlighting the binding residues with RNA motif. The human IGF2BP1,2 and 3 KH3-4 domains (<b>C</b>–<b>H</b>) are indicated in surface and ribbon representations with green, orange, and blue colors, respectively. In all complexes, the 503GKKG506 loop of KH4 (brown), GGm<sup>6</sup>AC RNA motif (red), and the binding region (yellow) are highlighted. The binding residues of KH4 domains are labeled in black color.</p>
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<p>Time-dependent analysis of MD trajectories for a 400 ns time scale to investigate the stability and deviation of apo IGF2BPs (KH3-4) and their bound states. (<b>A</b>) gallus gallus Apo_GG1 and GG1_bound are illustrated in black and pink colors, respectively. RMSD plots (<b>B</b>–<b>D</b>) for human Apo_Hu1, Apo_Hu2, Apo_Hu3 and their bound states. In all complexes, apo and bound systems are represented by black and pink colors, respectively.</p>
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<p>Comparative analysis of RMSD, Rg, and SASA of IGF2BPs (KH3-4) with GGm<sup>6</sup>AC complexes at a 400 ns MD simulation. (<b>A</b>) Root mean square deviation (RMSD) (<b>B</b>) Radius of gyration (Rg) throughout the simulation. (<b>C</b>) Solvent-accessible surface area (SASA).</p>
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<p>RMSF analysis and RMSD calculation by superimposition of Apo IGF2BPs (KH3-4) domains and their bound states with GGm<sup>6</sup>AC at 400 ns. (<b>A</b>) Comparative RMSF plots of gallus gallus Apo_GG1 and GG1_bound are illustrated in black and pink colors respectively. Similarly, RMSF plots for human Apo_Hu1, Apo_Hu2, Apo_Hu3 (<b>B</b>–<b>D</b>), and their bound states follow the same color scheme: black for apo and pink for bound. (<b>E</b>–<b>H</b>) Superimposition of 3D structures of Apo_GG1, Apo_Hu1, Apo_Hu2, and Apo_Hu3 with their respective bound complexes. Superimposed Apo and bound 3D structures are shown in purple and brown colors, respectively.</p>
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<p>Comparative analysis of RMSF and average number of hydrogen bonds of IGF2BPs with GGm<sup>6</sup>AC complexes at 400 ns MD simulation. (<b>A</b>) RMSF values of alpha carbon over the entire simulation. (<b>B</b>) Average number of hydrogen bonds over the entire simulation.</p>
Full article ">Figure 7
<p>Time-dependent binding dynamics of GGm<sup>6</sup>AC RNA motif at IGF2BPs KH3-4 domains. (<b>A</b>) gallus gallus GG1_bound (pink) and (<b>B</b>) human Hu1_bound (green) binding with GGm<sup>6</sup>AC (red) at 400 ns. (<b>C</b>) Hu2_bound (orange) and (<b>D</b>) Hu3_bound (blue) at 150 and 120 ns MD simulation time scales, respectively, showed an interaction with the GGm<sup>6</sup>AC (red) RNA motif. The binding region is highlighted in yellow color, and some core binding residues at the groove region are labeled in black color. The GGm<sup>6</sup>AC RNA motif is labeled in red color in all complexes.</p>
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<p>Principal component analysis 2D projection scatters plot of 400 ns MD trajectories for apo and bound IGF2BP1 (KH3-4) with GGm<sup>6</sup>AC. Panels (<b>A</b>) apo_GG1, (<b>B</b>) GG1_bound, (<b>C</b>) apo_Hu1, and (<b>D</b>) Hu1_bound represent 2 D plots.</p>
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<p>Per-residue decomposition of binding enthalpy from MD trajectories estimated by the MM/PBSA method. Binding energy decomposition at residue basis for (<b>A</b>) GG1_bound and (<b>B</b>) Hu1_bound complexes are indicated in pink and green colors, respectively.</p>
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<p>The methylation process of m<sup>6</sup>A in consensus motif of target transcripts. The modification of m<sup>6</sup>A is regulated by ‘writers’, ‘readers’, and ‘erasers’. ‘Writers’ such as METTL3, METTL14, an d WTAP regulate m<sup>6</sup>A methylation. RNA m<sup>6</sup>A demethylation is prompted by eraser proteins such as FTO and ALKBH5. IGF2BPs have a role, like other reader proteins, in reading the m<sup>6</sup>A binding sites of target mRNAs to protect mRNA from degradation and promote cancer proliferation.</p>
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13 pages, 3164 KiB  
Article
DNA 5mC and RNA m6A Collaborate to Upregulate Phosphoenolpyruvate Carboxykinase 2 for Kupffer Cell Activation
by Yulan Zhao, Wenbo Yuan, Yue Feng and Ruqian Zhao
Int. J. Mol. Sci. 2024, 25(18), 9894; https://doi.org/10.3390/ijms25189894 - 13 Sep 2024
Viewed by 1202
Abstract
Both DNA 5-methylcytosine (5mC) and RNA N6-methyladenosine (m6A) modifications are reported to participate in cellular stress responses including inflammation. Phosphoenolpyruvate carboxykinase 2 (PCK2) is upregulated in Kupffer cells (KCs) to facilitate the proinflammatory phosphorylation signaling cascades upon LPS stimulation, yet the [...] Read more.
Both DNA 5-methylcytosine (5mC) and RNA N6-methyladenosine (m6A) modifications are reported to participate in cellular stress responses including inflammation. Phosphoenolpyruvate carboxykinase 2 (PCK2) is upregulated in Kupffer cells (KCs) to facilitate the proinflammatory phosphorylation signaling cascades upon LPS stimulation, yet the role of 5mC and m6A in PCK2 upregulation remain elusive. Here, we report that the significantly augmented PCK2 mRNA and protein levels are associated with global 5mC demethylation coupled with m6A hypermethylation in LPS-activated KCs. The suppression of 5mC demethylation or m6A hypermethylation significantly alleviates the upregulation of PCK2 and proinflammatory cytokines in LPS-challenged KCs. Further reciprocal tests indicate 5mC demethylation is upstream of m6A hypermethylation. Specifically, CpG islands in the promoters of PCK2 and RNA methyltransferase (METTL3 and METTL14) genes are demethylated, while the 3′UTR of PCK2 mRNA is m6A hypermethylated, in LPS-stimulated KCs. These modifications contribute to the transactivation of the PCK2 gene as well as increased PCK2 mRNA stability and protein production via a m6A-mediated mechanism with IGF2BP1 as the reader protein. These results indicate that DNA 5mC and RNA m6A collaborate to upregulate PCK2 expression, respectively, at the transcriptional and post-transcriptional levels during KC activation. Full article
(This article belongs to the Section Molecular Biology)
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<p>Gluconeogenesis/glycolytic pathway variation in LPS-activated KCs is associated with PCK2 upregulation. (<b>A</b>). IL-6 and IL-1β mRNA expression. IL-6 and IL-1β levels in culture media. (n = 3). (<b>B</b>) RNA-sequencing Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of transcripts expression. (<b>C</b>) HK2, PFKP, and PKM2 mRNA expression (n = 3). (<b>D</b>) G6PC, PCK1, and PCK2 mRNA expression. (<b>E</b>) PCK2 protein expression (n = 3). (<b>F</b>) RNA-sequencing show that compared with PCK2 were significantly upregulated in LPS group; (<b>G</b>) Expression of IL-1β and IL-6 after inhibition of PCK2 and overexpression of PCK2. Values are means ± SE, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>(<b>A</b>) Total DNA 5mC modification (n = 3). (<b>B</b>) DNMT1, DNMT3A, DNMT3b, TET1, TET2, and TET3 mRNA expression (n = 3). (<b>C</b>) DNMT1, DNMT3A, and TET2 protein expression (n = 3). (<b>D</b>) Total RNA m<sup>6</sup>A modification (n = 3). (<b>E</b>) METTL3, METTL14, FTO, YTHDF1, YTHDF2, YTHDF3, and IGF2BP1 mRNA expression (n = 3). (<b>F</b>) METTL3, METTL14, FTO, YTHDF1, YTHDF2, YTHDF3, and IGF2BP1 protein expression (n = 3). Values are means ± SE, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Suppression of 5mC demethylation and m6A hypermethylation rectifies LPS-induced PCK2 upregulation. (<b>A</b>) Total DNA 5mC modification after TET2 knockdown (n = 3). (<b>B</b>) IL-6 and IL-1β levels in culture media after TET2 knockdown (n = 3). (<b>C</b>) mRNA and protein levels of PCK2 (n = 3). (<b>D</b>) Total RNA m<sup>6</sup>A modification after treatment with CYC (n = 3). (<b>E</b>) IL-6 and IL-1β levels in culture media after treatment with CYC (n = 3). (<b>F</b>) mRNA and protein levels of PCK2 after treatment with CYC. Values are means ± SE, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>CpG island of PCK2 promoter is 5mC-demethylated and 3′UTR of PCK2 mRNA is m6A-hypermethylated in LPS-activated KCs. (<b>A</b>) Schematic diagram of CpG islands on the promoter of PCK2 gene. (<b>B</b>) 5mC is reduced in fragment 1 (−304~−49) of PCK2 gene promoter region (n = 3). (<b>C</b>) 5mC has not change in fragment 1 (−1717~−1590) of PCK2 gene promoter region (n = 3). (<b>D</b>) The m<sup>6</sup>A site was predicted for the PCK2 3′UTR. The RRACU-compliant motif was named motif 1–3 (X1–X3 site); a non-m6A modification site of this mRNA was selected as the N Site. (<b>E</b>–<b>H</b>) No significant changes in cycle of threshold (Ct) were determined at N, X1, and X2 sites, while a significant increase in PCK2 3′UTR at the X3 site was observed. Values are means ± SE, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Locus-/site-specific 5mC demethylation and m6A hypermethylation contribute collectively to PCK2 upregulation in LPS-activated KCs. (<b>A</b>) 5mC increased in fragment 1 (−304~−49) of PCK2 gene promoter region after TET2 knockdown (n = 3). (<b>B</b>) TET2 knockdown reduced the increase in total mRNA in PCK2 induced by LPS (n = 3). (<b>C</b>) TET2 knockdown reduced the increase in un-spliced mRNA in PCK2 induced by LPS (n = 3). (<b>D</b>) P TET2 knockdown reduced the increase in spliced mRNA in PCK2 induced by LPS (n = 3). (<b>E</b>) CYC treatment rectified LPS-induced m6A hypermethylation on X3 site of PCK2 3′UTR (n = 3). (<b>F</b>) Knockdown IGF2BP1 mitigated PCK2 upregulation in LPS-activated KCs (n = 3). (<b>G</b>) CYC treatment reduced LPS resulting in increased PCK2 mRNA stability (n = 3). (<b>H</b>) Schematic representation of synthetic mRNAs containing PCK2 3′UTR and full-length firefly luciferase. (<b>I</b>) Synthetic mRNAs were transfected, and luciferase activity was detected in KCs. Values are means ± SE, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>METTL3/METTL14 genes are transactivated in LPS-activated KCs via promoter 5mC hypomethylation. (<b>A</b>) Total RNA m<sup>6</sup>A modification after TET2 knockdown (n = 3). (<b>B</b>) METTL3, METTL14, FTO, YTHDF1, YTHDF2, YTHDF3, and IGF2BP1 protein expression after TET2 knockdown (n = 3). (<b>C</b>) Total DNA 5mC modification after treatment with CYC (n = 3). (<b>D</b>) DNMT1, DNMT3A, and TET2 protein expression (n = 3) after treatment with CYC. (<b>E</b>) METTL3 and METTL14 mRNA expression after treatment with the 5mc inhibitor 5aza (n = 3). Schematic diagram of CpG islands on the promoter of METTL3 and METTL14 gene, and 5mC is reduced in METTL3 and METTL14 gene promoter region (n = 3). Values are means ± SE, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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16 pages, 4797 KiB  
Article
Fat Mass- and Obesity-Associated Protein (FTO) Promotes the Proliferation of Goat Skeletal Muscle Satellite Cells by Stabilizing DAG1 mRNA in an IGF2BP1-Related m6A Manner
by Jiangzhen Yao, Liang Xu, Zihao Zhao, Dinghui Dai, Siyuan Zhan, Jiaxue Cao, Jiazhong Guo, Tao Zhong, Linjie Wang, Li Li and Hongping Zhang
Int. J. Mol. Sci. 2024, 25(18), 9804; https://doi.org/10.3390/ijms25189804 - 11 Sep 2024
Cited by 1 | Viewed by 1070
Abstract
Skeletal muscle development is spotlighted in mammals since it closely relates to animal health and economic benefits to the breeding industry. Researchers have successfully unveiled many regulatory factors and mechanisms involving myogenesis. However, the effect of N6-methyladenosine (m6A) modification, [...] Read more.
Skeletal muscle development is spotlighted in mammals since it closely relates to animal health and economic benefits to the breeding industry. Researchers have successfully unveiled many regulatory factors and mechanisms involving myogenesis. However, the effect of N6-methyladenosine (m6A) modification, especially demethylase and its regulated genes, on muscle development remains to be further explored. Here, we found that the typical demethylase FTO (fat mass- and obesity-associated protein) was highly enriched in goats’ longissimus dorsi (LD) muscles. In addition, the level of m6A modification on transcripts was negatively regulated by FTO during the proliferation of goat skeletal muscle satellite cells (MuSCs). Moreover, a deficiency of FTO in MuSCs significantly retarded their proliferation and promoted the expression of dystrophin-associated protein 1 (DAG1). m6A modifications of DAG1 mRNA were efficiently altered by FTO. Intriguingly, the results of DAG1 levels and its m6A enrichment from FB23-2 (FTO demethylase inhibitor)-treated cells were consistent with those of the FTO knockdown, indicating that the regulation of FTO on DAG1 depended on m6A modification. Further experiments showed that interfering FTO improved m6A modification at site DAG1-122, recognized by Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) and consequently stabilized DAG1 transcripts. Our study suggests that FTO promotes the proliferation of MuSCs by regulating the expression of DAG1 through m6A modification. This will extend our knowledge of the m6A-related mechanism of skeletal muscle development in animals. Full article
(This article belongs to the Section Molecular Biology)
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<p>Deficiency of FTO suppresses the proliferation of goat MuSCs. (<b>A</b>) Expression of <span class="html-italic">FTO</span> in different tissues of goats. (<b>B</b>) Cells immunofluorescent stained with anti-PAX7 (MuSCs cultured in growth medium (GM) for 2 days) and anti-MYHC (MuSCs cultured in differentiation medium (DM) for 6 days). Scale bar: 200 μm. (<b>C</b>) <span class="html-italic">FTO</span> mRNA and m<sup>6</sup>A changes during MuSCs (cultured in the growth medium for 1 and 2 days and differentiation medium for 1, 3, 5, and 7 days). (<b>D</b>) mRNA level of <span class="html-italic">FTO</span> in cells treated with siFTO. (<b>E</b>) The m<sup>6</sup>A of total RNA affected by FTO knockdown. (<b>F</b>) Effect of FTO knockdown on gene expression of m<sup>6</sup>A modified enzymes. (<b>G</b>) mRNA changes in myoblast proliferation marker genes after FTO knockdown. (<b>H</b>) Protein of myoblast proliferation genes affected by deficiency of FTO. (<b>I</b>) CCK8 assay of the viability of MuSCs. (<b>J</b>) The number of new cells stained with EdU. Scale bar: 200 μm. Results are represented as the mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and ns indicate insignificance.</p>
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<p>The FTO-targeted gene DAG1 inhibits cell proliferation. (<b>A</b>) <span class="html-italic">DAG1</span> mRNA increased by FTO knockdown. (<b>B</b>) DAG1 protein elevated by inhibiting FTO. (<b>C</b>) <span class="html-italic">DAG1</span> mRNA stability affected by FTO knockdown. (<b>D</b>) The profile of DAG1 in cell proliferation and differentiation. (<b>E</b>) siRNA targeting <span class="html-italic">DAG1</span> knockdown on mRNA expression. (<b>F</b>) mRNA changes in cell proliferation marker genes in cells deficiency of DAG1. (<b>G</b>) Effect of DAG1 knockdown on protein of cell proliferation marker genes. (<b>H</b>) Viability of cells tested by CCK8. (<b>I</b>) EdU staining cells altered by siDAG1. Scale bar: 200 μm. Results are represented as the mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and ns indicates no significance. In the picture marked with lower case letters, means shared at least one letter indicate no significance (<span class="html-italic">p</span> &gt; 0.05), and on the contrary, no common letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>FTO regulates DAG1 and other proliferation genes in an m<sup>6</sup>A-dependent manner. (<b>A</b>) The m<sup>6</sup>A modification sites on <span class="html-italic">DAG1</span> mRNA predicted using the SRAMP. (<b>B</b>) The m<sup>6</sup>A modification of <span class="html-italic">DAG1</span> mRNA verified by MeRIP-qPCR. (<b>C</b>) The FTO binding sites on <span class="html-italic">DAG1</span> mRNA predicted by RBPsuite. (<b>D</b>) FTO binding on <span class="html-italic">DAG1</span> mRNA verified by RIP-qPCR. (<b>E</b>) The wild-type (WT) and mutant (MUT) m<sup>6</sup>A motif dual luciferase reporter vectors. (<b>F</b>) Effect of interfering FTO on luciferase activity in m<sup>6</sup>A-modified fragments of <span class="html-italic">DAG1</span> mRNA. (<b>G</b>) DAG1 m<sup>6</sup>A modification levels affected by interfering FTO. (<b>H</b>) Changes in total RNA m<sup>6</sup>A modification of cells treated by FB23-2. (<b>I</b>) MeRIP-qPCR of DAG1-122 after FB23-2 treatment. (<b>J</b>) FTO and <span class="html-italic">DAG1</span> mRNA altered by FB23-2. (<b>K</b>) Effect of FB23-2 on <span class="html-italic">DAG1</span> stability. (<b>L</b>) mRNA levels of cell proliferation marker genes changed by FB23-2. (<b>M</b>) Protein of proliferation marker genes influenced by FB23-2. Results are represented as the mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and ns indicates no significance.</p>
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<p>IGF2BP1 stabilizes <span class="html-italic">DAG1</span> mRNA through recognizing its m<sup>6</sup>A modification. (<b>A</b>) The DAG1-IGF2BP1 interaction sites predicted by RBPsuite. (<b>B</b>) <span class="html-italic">DAG1</span> mRNA enriched by IGF2BP1 protein. (<b>C</b>) Luciferase activity of DAG1-122 altered by interfering IGF2BP1. (<b>D</b>) IGF2BP1 and <span class="html-italic">DAG1</span> mRNA altered by interfering IGF2BP1. (<b>E</b>) <span class="html-italic">DAG1</span> mRNA stability caused by knockdown of IGF2BP1. (<b>F</b>) mRNA profiles of cell proliferation marker genes altered by deficiency of IGF2BP1. (<b>G</b>) Protein of cell proliferation marker genes affected by interfering IGF2BP1. (<b>H</b>) Expression of IGF2BP1 transcripts in cells treated with FB23-2 combined with siIGF2BP1. (<b>I</b>) <span class="html-italic">DAG1</span> mRNA, (<b>J</b>) <span class="html-italic">mki67</span> mRNA, and <span class="html-italic">PCNA</span> mRNA in cells cotransfected with FB23-2 and siIGF2BP1. Results are represented as the mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and ns indicates no significance. Means with totally different lowercase letters indicate <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Proposed mechanism of FTO/IGF2BP1/DAG1 on myocyte proliferation.</p>
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28 pages, 3251 KiB  
Article
Insulin-like Growth Factor-Binding Protein 2 in Severe Aortic Valve Stenosis and Pulmonary Hypertension: A Gender-Based Perspective
by Elke Boxhammer, Vera Paar, Kristen Kopp, Sarah X. Gharibeh, Evelyn Bovenkamp-Aberger, Richard Rezar, Michael Lichtenauer, Uta C. Hoppe and Moritz Mirna
Int. J. Mol. Sci. 2024, 25(15), 8220; https://doi.org/10.3390/ijms25158220 - 27 Jul 2024
Viewed by 1161
Abstract
Severe aortic valve stenosis (AS) and pulmonary hypertension (PH) are life-threatening cardiovascular conditions, necessitating early detection and intervention. Recent studies have explored the role of Insulin-like Growth Factor-Binding Protein 2 (IGF-BP2) in cardiovascular pathophysiology. Understanding its involvement may offer novel insights into disease [...] Read more.
Severe aortic valve stenosis (AS) and pulmonary hypertension (PH) are life-threatening cardiovascular conditions, necessitating early detection and intervention. Recent studies have explored the role of Insulin-like Growth Factor-Binding Protein 2 (IGF-BP2) in cardiovascular pathophysiology. Understanding its involvement may offer novel insights into disease mechanisms and therapeutic targets for these conditions. A total of 102 patients (46 female, 56 male) with severe AS undergoing a transcatheter aortic valve replacement (TAVR) in a single-center study were classified using echocardiography tests to determine systolic pulmonary artery pressure (sPAP) and the presence (sPAP ≥ 40 mmHg) or absence (sPAP < 40 mmHg) of PH. Additionally, serial laboratory determinations of IGF-BP2 before, and at 24 h, 96 h, and 3 months after intervention were conducted in all study participants. Considering the entire cohort, patients with PH had significant and continuously higher serum IGF-BP2 concentrations over time than patients without PH. After subdivision by sex, it could be demonstrated that the above-mentioned results were only verifiable in males, but not in females. In the male patients, baseline IGF-BP2 levels before the TAVR was an isolated risk factor for premature death after intervention and at 1, 3, and 5 years post-intervention. The same was valid for the combination of male and echocardiographically established PH patients. The predictive role of IGF-BP2 in severe AS and concurrent PH remains unknown. A more profound comprehension of IGF-BP2 mechanisms, particularly in males, could facilitate the earlier consideration of the TAVR as a more effective and successful treatment strategy. Full article
(This article belongs to the Special Issue Novel Biomarkers for Cardiovascular Diseases)
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<p>Kaplan–Meier curves with corresponding numbers at risk and annual log-rank tests for detection of 1- to 5-year survival in overall cohort (<b>A</b>) and with dependence on gender ((<b>B</b>) male; (<b>C</b>) female).</p>
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<p>Kaplan–Meier curves with corresponding numbers at risk and annual log-rank tests for detection of 1- to 5-year survival in overall cohort (<b>A</b>) and with dependence on gender ((<b>B</b>) male; (<b>C</b>) female).</p>
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<p>Serum concentration of IGF-BP2 in overall patient cohort (<b>A</b>) and with dependence on gender ((<b>B</b>) male; (<b>C</b>) female). * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001. o = outlier.</p>
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<p>Serum concentration of IGF-BP2 in overall patient cohort (<b>A</b>) and with dependence on gender ((<b>B</b>) male; (<b>C</b>) female) with an sPAP &lt; 40 mmHg and ≥ 40 mmHg. * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001. o = outlier.</p>
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<p>Serum concentration of IGF-BP2 in overall patient cohort (<b>A</b>) and with dependence on gender ((<b>B</b>) male; (<b>C</b>) female) with an sPAP &lt; 40 mmHg and ≥ 40 mmHg. * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001. o = outlier.</p>
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<p>IGF-BP2 levels of overall cohort and male and female patients over time regarding different stages of aortic stenosis classified according to Généreux et al. [<a href="#B16-ijms-25-08220" class="html-bibr">16</a>].</p>
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<p>Serum concentration of IGF-BP2 in male patient cohort with an sPAP ≥ 40 mmHg regarding survival or death after 1 (<b>A</b>), 3 (<b>B</b>), and 5 years (<b>C</b>) after TAVR. o = outlier.</p>
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<p>Graphic summary of the main findings of the study (Created with BioRender.com). Image material of CoreValve™ Evolut™ was kindly provided by <b>©</b> Medtronic Inc. * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001; o = outlier.</p>
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16 pages, 4134 KiB  
Article
High Muscle Expression of IGF2BP1 Gene Promotes Proliferation and Differentiation of Chicken Primary Myoblasts: Results of Transcriptome Analysis
by Jintang Luo, Zhuliang Yang, Xianchao Li, Cong Xiao, Hong Yuan, Xueqin Yang, Biyan Zhou, Yan Zheng, Jiayi Zhang and Xiurong Yang
Animals 2024, 14(14), 2024; https://doi.org/10.3390/ani14142024 - 9 Jul 2024
Cited by 1 | Viewed by 900
Abstract
Muscle development is a multifaceted process influenced by numerous genes and regulatory networks. Currently, the regulatory network of chicken muscle development remains incompletely elucidated, and its molecular genetic mechanisms require further investigation. The Longsheng-Feng chicken, one of the elite local breeds in Guangxi, [...] Read more.
Muscle development is a multifaceted process influenced by numerous genes and regulatory networks. Currently, the regulatory network of chicken muscle development remains incompletely elucidated, and its molecular genetic mechanisms require further investigation. The Longsheng-Feng chicken, one of the elite local breeds in Guangxi, serves as an excellent resource for the selection and breeding of high-quality broiler chickens. In this study, we conducted transcriptome sequencing of the pectoral muscles of Longsheng-Feng chickens and AA broiler chickens with different growth rates. Through comprehensive bioinformatics analysis, we identified differentially expressed genes that affect muscle growth and showed that IGF2BP1 is a key participant in chicken muscle development. Subsequently, we employed QRT-PCR, EdU staining, and flow cytometry to further investigate the role of IGF2BP1 in the proliferation and differentiation of chicken myogenic cells. We identified 1143 differentially expressed genes, among which IGF2BP1 is intimately related to the muscle development process and is highly expressed in muscle tissues. Overexpression of IGF2BP1 significantly promotes the proliferation and differentiation of chicken primary myoblasts, while knockdown of IGF2BP1 significantly inhibits these processes. In summary, these results provide valuable preliminary insights into the regulatory roles of IGF2BP1 in chicken growth and development. Full article
(This article belongs to the Section Poultry)
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<p>Identification of RNA-Seq Data and Differentially Expressed Genes (DEGs). (<b>A</b>) Correlation analysis between samples. (<b>B</b>) Principal component analysis. (<b>C</b>) Volcano plot depicting the distribution of differentially expressed genes. (<b>D</b>) Hierarchical clustering of differentially expressed gene expression.</p>
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<p>Bioinformatics Analysis of Differentially Expressed Genes (DEGs). (<b>A</b>) Enriched terms in biological processes for DEGs. (<b>B</b>) Enriched terms in cellular components for DEGs. (<b>C</b>) Enriched terms in molecular functions for DEGs. (<b>D</b>) KEGG enrichment analysis of DEGs.</p>
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<p>Analysis of protein-protein interactions of differentially expressed genes and screening of candidate genes. (<b>A</b>) Top 10 hub genes identified by Cytoscape CytoHubba. (<b>B</b>,<b>C</b>) Top 3 protein-protein interaction clusters. (<b>D</b>) Protein-protein interaction cluster of IGF2BP1. (<b>E</b>) Validation of RNA-seq data by QRT-PCR. (<b>F</b>) Expression of IGF2BP1 in 10 different chicken tissues measured by QRT-PCR (<span class="html-italic">n</span> = 6). Significance between groups is expressed using alphabetical notation, where the same letter indicates that the differences are not significant.</p>
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<p>Effects of IGF2BP1 Interference on the Proliferation and Differentiation of Chicken Skeletal Muscle Cells. (<b>A</b>) Light microscopy images of chicken skeletal muscle cells before and after transfection, 100×. (<b>B</b>) Relative mRNA expression level of IGF2BP1 after interference. (<b>C</b>) EdU fluorescence staining, 100×. (<b>D</b>) EdU-positive cell rate. (<b>E</b>) Flow cytometry analysis of the cell cycle after IGF2BP1 interference. (<b>F</b>) QRT-PCR detection of the mRNA expression levels of proliferation marker genes in chicken skeletal muscle cells transfected with si-IGF2BP1 or si-NC. (<b>G</b>) QRT-PCR detection of the mRNA expression levels of differentiation marker genes in chicken skeletal muscle cells transfected with si-IGF2BP1 or si-NC. si-NC represents negative control. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effects of IGF2BP1 Overexpression on the Proliferation and Differentiation of Chicken Skeletal Muscle Cells. (<b>A</b>) 100×, images of chicken skeletal muscle cells 48 h after transfection with pEGFP-IGF2B1, (<b>a</b>,<b>d</b>) pEGFP-N1, (<b>b</b>,<b>e</b>) pEGFP-IGF2BP1, and (<b>c</b>,<b>f</b>) negative control. (<b>B</b>) Relative mRNA expression level of IGF2BP1 after overexpression. (<b>C</b>) EdU fluorescence staining, 100×. (<b>D</b>) EdU-positive cell rate. (<b>E</b>) Flow cytometry analysis of the cell cycle after IGF2BP1 overexpression. (<b>F</b>) QRT-PCR detection of the mRNA expression levels of proliferation marker genes in chicken skeletal muscle cells transfected with pEGFP-IGF2B1 or pEGFP-N1. (<b>G</b>) QRT-PCR detection of the mRNA expression levels of differentiation marker genes in chicken skeletal muscle cells transfected with pEGFP-IGF2B1 or pEGFP-N1. pEGFP-N1 represents negative control. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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14 pages, 2423 KiB  
Article
Phenotypic Identification, Genetic Characterization, and Selective Signal Detection of Huitang Duck
by Haojie Ma, Bingjin Lin, Zhiyao Yan, Yueyue Tong, Huichao Liu, Xi He and Haihan Zhang
Animals 2024, 14(12), 1747; https://doi.org/10.3390/ani14121747 - 10 Jun 2024
Cited by 3 | Viewed by 1494
Abstract
The Huitang duck (HT), a long-domesticated elite local breed from Hunan Province, China, with excellent meat quality, has not had its population genetic structure and genomic selective sweeps extensively studied to date. This study measured the phenotypic characteristics of HT and conducted comparative [...] Read more.
The Huitang duck (HT), a long-domesticated elite local breed from Hunan Province, China, with excellent meat quality, has not had its population genetic structure and genomic selective sweeps extensively studied to date. This study measured the phenotypic characteristics of HT and conducted comparative analysis between HT and 16 different duck breeds, including wild, indigenous, and meat breeds, to characterize its population structure and genetic potential. The results revealed that HT is a dual-purpose indigenous breed with a genetic background closely related to the Youxian sheldrake and Linwu ducks. In the selective sweep analysis between HT and Linwu ducks, genes such as PLCG2, FN1, and IGF2BP2, which are associated with muscle growth and development, were identified near the 27 selection signals. The comparison between HT and Jinding ducks revealed 68 selective signals that contained important genes associated with ovarian development (GRIK4, MAP3K8, and TGIF1) and egg-laying behaviors (ERBB4). Selective sweep analysis between HT and Youxian sheldrake ducks found 93 selective regions covering genes related to both meat (IGF1R and IGFBP5) and egg-production (FOXO3 and ITPR1) traits. Our study may provide novel knowledge for exploring the population structure and genetic potential of HT, offering a theoretical basis for its breeding strategies in the future. Full article
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<p>The appearance of HT and comparative analysis of phenotypes between males and females. (<b>A</b>) Appearance of female and male HT. (<b>B</b>) Traits of HT ducks (BW90, 90-day body weight; AFWP, abdominal fat weight percentage; HEWP, percentage of half-eviscerated yield; EWP, eviscerated weight percentage). (<b>C</b>) Amino acid content in green- and white-shelled eggs. * <span class="html-italic">p ≤</span> 0.05; ** <span class="html-italic">p ≤</span> 0.01.</p>
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<p>Population structure analysis of 17 duck breeds (CV, Cherry Valley; ML, maple leaf; PK, Pekin; GY, Gaoyou duck; JD, Jinding duck; SX, Shaoxing duck; SM, Shanma duck; SS, Sansui duck; TW, Taiwan sheldrake; SC, Sichuan sheldrake; YX, Youxian sheldrake; HT, Huitang duck; LW, Linwu duck; M, Mei duck; MDN, Ningxia mallard; MDZ, Zhejiang mallard; SB, Chinese spot-billed duck). (<b>A</b>) Principal component analysis of the duck samples. Principal components 1 (40.34%) and 2 (29.33%) explained the variability among the 104 ducks. (<b>B</b>) Neighbor-joining phylogenetic tree analysis of 17 duck populations.</p>
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<p>Population ADMIXTURE and LD decay analysis. (<b>A</b>) Population genetic structure of 104 ducks, where the length of each colored segment represents the proportion of the individual’s genome inferred from ancestral populations (K = 2–4). The population names and production types are listed at the bottom (DP, dual-purpose). (<b>B</b>) Genome-wide linkage disequilibrium analysis of ducks (PK, JD LW, YX, HT, and MDN).</p>
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<p>Genomic regions with strong selective signals in ducks. (<b>A</b>) Distribution of <span class="html-italic">F</span><sub>ST</sub>, Tajima’s D, iHS, and XPEHH; the <span class="html-italic">x</span>-axis represents the chromosomes. The <span class="html-italic">F</span><sub>ST</sub> and XP-EHH were calculated for a single SNP between HT and MDN. Tajima’s D (5-kb window) and iHS (single SNP) were calculated for HT. The 1% of these statistics is considered indicative of selection in HT, with the thresholds set at <span class="html-italic">F</span><sub>ST</sub> &gt; 0.77, Tajima’s D &lt; −1.49, iHS &lt; −2.41, and XPEHH &gt; 3.37. The red dashed line represents the threshold for the statistical measure, while the red box delineates the 1% distribution range of the statistical measure. (<b>B</b>) Venn diagram depicting the number of unique and overlapping CDRs from the top 1% of <span class="html-italic">F</span><sub>ST</sub>, Tajima’s D, iHS, and XPEHH. Numbers represent the counts of CDRs in each group, along with annotations of genes related to meat and egg production in overlapping CDRs identified by the four statistics.</p>
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<p>Enrichment analysis of KEGG pathways. (<b>A</b>) Analysis of shared CDRs between HT and LW. (<b>B</b>) Analysis of shared CDRs between HT and JD. (<b>C</b>) Analysis of shared CDRs between HT and YX.</p>
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<p>The selective sweep analysis between HT and YX. (<b>A</b>) From outer to inner, the outermost circle represents SNP density on chromosomes, the second circle represents <span class="html-italic">F</span><sub>ST</sub>, the third circle represents Tajima’s D, the fourth circle represents iHS, and the innermost circle represents XP-EHH. Genes associated with important economic traits in HT are marked. (<b>B</b>) Violin plot of <span class="html-italic">F</span><sub>ST</sub>, Tajima’s D, iHS, and XP-EHH for duck genomic regions that have undergone strong selection, compared to the whole genome. The statistical significance was calculated using the Mann–Whitney U test. ** <span class="html-italic">p</span> &lt; 2.2 × 10<sup>−16</sup>.</p>
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15 pages, 17385 KiB  
Article
A Combination of Microarray-Based Profiling and Biocomputational Analysis Identified miR331-3p and hsa-let-7d-5p as Potential Biomarkers of Ulcerative Colitis Progression to Colorectal Cancer
by Pilar Chacon-Millan, Stefania Lama, Nunzio Del Gaudio, Antonietta Gerarda Gravina, Alessandro Federico, Raffaele Pellegrino, Amalia Luce, Lucia Altucci, Angelo Facchiano, Michele Caraglia and Paola Stiuso
Int. J. Mol. Sci. 2024, 25(11), 5699; https://doi.org/10.3390/ijms25115699 - 23 May 2024
Cited by 4 | Viewed by 1675
Abstract
Ulcerative colitis (UC), an inflammatory bowel disease (IBD), may increase the risk of colorectal cancer (CRC) by activating chronic proinflammatory pathways. The goal of this study was to find serum prediction biomarkers in UC to CRC development by combining low-density miRNA microarray and [...] Read more.
Ulcerative colitis (UC), an inflammatory bowel disease (IBD), may increase the risk of colorectal cancer (CRC) by activating chronic proinflammatory pathways. The goal of this study was to find serum prediction biomarkers in UC to CRC development by combining low-density miRNA microarray and biocomputational approaches. The UC and CRC miRNA expression profiles were compared by low-density miRNA microarray, finding five upregulated miRNAs specific to UC progression to CRC (hsa-let-7d-5p, hsa-miR-16-5p, hsa-miR-145-5p, hsa-miR-223-5p, and hsa-miR-331-3p). The circRNA/miRNA/mRNA competitive endogenous RNA (ceRNA) network analysis showed that the candidate miRNAs were connected to well-known colitis-associated CRC ACVR2A, SOCS1, IGF2BP1, FAM126A, and CCDC85C mRNAs, and circ-SHPRH circRNA. SST and SCARA5 genes regulated by hsa-let-7d-5p, hsa-miR-145-5p, and hsa-miR-331-3p were linked to a poor survival prognosis in a CRC patient dataset from The Cancer Genome Atlas (TCGA). Lastly, our mRNA and miRNA candidates were validated by comparing their expression to differentially expressed mRNAs and miRNAs from colitis-associated CRC tissue databases. A high level of hsa-miR-331-3p and a parallel reduction in SOCS1 mRNA were found in tissue and serum. We propose hsa-miR-331-3p and possibly hsa-let-7d-5p as novel serum biomarkers for predicting UC progression to CRC. More clinical sample analysis is required for further validation. Full article
(This article belongs to the Section Biochemistry)
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<p>Microarray low-intensity profile analysis between UC and nonrelated IBD progression to CRC. (<b>A</b>) Global and differential expression levels of miRNA across the screening set as shown by hierarchical clustering heatmap. The analysis summarizes the differential patterns of miRNA expression across the groups, ulcerative colitis (UC) and nonrelated IBD inflammation control miRNA expression profiles compared to colorectal cancer (CRC) miRNA expression pattern. Reddish values indicate upregulation, whereas greenish values show downregulation; (<b>B</b>) logarithm in base 10 FC of our miRNA candidates comparing UC and control to CRC progression.</p>
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<p>Upregulated miRNA candidates’ interaction with their inhibitors (circRNAs) and targets (mRNAs). (<b>A</b>) CeRNA circRNA-miRNA-mRNA links. A detailed description of the ceRNA members is provided in the <a href="#app1-ijms-25-05699" class="html-app">Supplementary Materials</a>; (<b>B</b>) ceRNA subnetwork for upregulated miRNAs. Only the most significant interactions (3 or more links to other molecules) are represented in this subnetwork, in addition to the circRNAs. miRNAs are shown in red; mRNA targeted by different miRNAs (blue) showing 1 (small node size), 2 (medium node size), and more than 3 (bigger node sizes); circRNAs are shown in orange.</p>
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<p>PPI network and its top ten hub genes constructed by miRNA targets. (<b>A</b>) PPI network built by using upregulated miRNA-mRNA targets; in total, 453 nodes and 1216 interactions between these nodes were established. In color, the different identified protein clusters are shown in the <a href="#app1-ijms-25-05699" class="html-app">Supplementary Materials</a>; (<b>B</b>) top ten hub genes analyzed from the upregulated miRNA targets; (<b>C</b>,<b>D</b>) the most significant genes regarding the number of their interactions with other genes; (<b>E</b>) the main clusters in which our top hub genes were identified next to the ACVR2A protein of interest.</p>
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<p>GO and KEGG pathway analysis of the upregulated miRNA targets. The GO top 10 terms of the biological processes (<b>A</b>), cellular components (<b>B</b>), and molecular functions (<b>C</b>). The length of each box correlates with the number of genes involved in each process, whereas the color is related to the significance of the value; (<b>D</b>) top 10 most significant KEGG pathways identified. The size of the circles represents the number of genes involved in each one of the pathways, whereas the color represents the significance of the value.</p>
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<p>Survival analysis of SST and SCARA5 mRNA expression. Downregulated mRNA expression is represented in red, while upregulated expression is represented in blue. The boxes describe the number of patients analyzed in each period. Each cross shows when a patient was excluded from the analysis. (<b>A</b>) SCARA5 mRNA upregulation was also linked to a worse tumoral prognosis (<span class="html-italic">p</span>-value = 0.034); (<b>B</b>) SST mRNA upregulation was significantly related to a poorer prognosis (<span class="html-italic">p</span>-value = 0.044).</p>
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16 pages, 2539 KiB  
Article
Strand-Specific RNA Sequencing Reveals Gene Expression Patterns in F1 Chick Breast Muscle and Liver after Hatching
by Jianfei Zhao, Meiying Chen, Zhengwei Luo, Pengxin Cui, Peng Ren and Ye Wang
Animals 2024, 14(9), 1335; https://doi.org/10.3390/ani14091335 - 29 Apr 2024
Viewed by 1828
Abstract
Heterosis refers to the phenomenon where hybrids exhibit superior performance compared to the parental phenotypes and has been widely utilized in crossbreeding programs for animals and crops, yet the molecular mechanisms underlying this phenomenon remain enigmatic. A better understanding of the gene expression [...] Read more.
Heterosis refers to the phenomenon where hybrids exhibit superior performance compared to the parental phenotypes and has been widely utilized in crossbreeding programs for animals and crops, yet the molecular mechanisms underlying this phenomenon remain enigmatic. A better understanding of the gene expression patterns in post-hatch chickens is very important for exploring the genetic basis underlying economically important traits in the crossbreeding of chickens. In this study, breast muscle and liver tissues (n = 36) from full-sib F1 birds and their parental pure lines were selected to identify gene expression patterns and differentially expressed genes (DEGs) at 28 days of age by strand-specific RNA sequencing (ssRNA-seq). This study indicates that additivity is the predominant gene expression pattern in the F1 chicken post-hatch breast muscle (80.6% genes with additivity) and liver (94.2% genes with additivity). In breast muscle, Gene Ontology (GO) enrichment analysis revealed that a total of 11 biological process (BP) terms closely associated with growth and development were annotated in the identified DEG sets and non-additive gene sets, including STAT5A, TGFB2, FGF1, IGF2, DMA, FGF16, FGF12, STAC3, GSK3A, and GRB2. Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation presented that a total of six growth- and development-related pathways were identified, involving key genes such as SLC27A4, GLUL, TGFB2, COX17, and GSK3A, including the PPAR signaling pathway, TGF-beta signaling pathway, and mTOR signaling pathway. Our results may provide a theoretical basis for crossbreeding in domestic animals. Full article
(This article belongs to the Section Poultry)
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<p>Experimental design and gene expression patterns. Meat-type: Recessive White chicken; Egg-type: Lohmann pink layer; F1 cross: Recessive White chicken × Lohmann pink layer cross; P1: Parent 1; P2: Parent 2; D28: 28-day-old.</p>
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<p>Top ten genes with the highest mRNA levels in breast muscle (<b>A</b>,<b>B</b>) and liver (<b>C</b>,<b>D</b>). All results are displayed as mean ± SEM. <span class="html-italic">n</span> = 3. <sup>a–c</sup> <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The number of DEGs in all groups (<b>A</b>), breast muscle (<b>B</b>), and liver (<b>C</b>). B_M_vs_B_F1: breast muscle, maternal line vs. F1 cross; B_P_vs_B_F1: breast muscle, paternal line vs. F1 cross; B_P_vs_B_M: breast muscle, paternal line vs. maternal line; L_M_vs_L_F1: liver, maternal line vs. F1 cross; L_P_vs_L_F1: liver, paternal line vs. F1 cross; L_P_vs_L_M: liver, paternal line vs. maternal line.</p>
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<p>DEGs identified between different groups. (<b>A</b>) Liver, paternal line vs. maternal line. (<b>B</b>) Liver, paternal line vs. F1 cross. (<b>C</b>) Liver, maternal line vs. F1 cross. (<b>D</b>) Breast muscle, paternal line vs. maternal line. (<b>E</b>) Breast muscle, paternal line vs. F1 cross. (<b>F</b>) Breast muscle, maternal line vs. F1 cross.</p>
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<p>Gene expression patterns in Liver (<b>A</b>) and breast muscle (<b>B</b>).</p>
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