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19 pages, 708 KiB  
Review
Menin Inhibitors: New Targeted Therapies for Specific Genetic Subtypes of Difficult-to-Treat Acute Leukemias
by Pasquale Niscola, Valentina Gianfelici, Marco Giovannini, Daniela Piccioni, Carla Mazzone and Paolo de Fabritiis
Cancers 2025, 17(1), 142; https://doi.org/10.3390/cancers17010142 (registering DOI) - 4 Jan 2025
Abstract
Menin (MEN1) is a well-recognized powerful tumor promoter in acute leukemias (AL) with KMT2A rearrangements (KMT2Ar, also known as MLL) and mutant nucleophosmin 1 (NPM1m) acute myeloid leukemia (AML). MEN1 is essential for sustaining leukemic transformation due to its interaction with wild-type KMT2A [...] Read more.
Menin (MEN1) is a well-recognized powerful tumor promoter in acute leukemias (AL) with KMT2A rearrangements (KMT2Ar, also known as MLL) and mutant nucleophosmin 1 (NPM1m) acute myeloid leukemia (AML). MEN1 is essential for sustaining leukemic transformation due to its interaction with wild-type KMT2A and KMT2A fusion proteins, leading to the dysregulation of KMT2A target genes. MEN1 inhibitors (MIs), such as revumenib, ziftomenib, and other active small molecules, represent a promising new class of therapies currently under clinical development. By disrupting the MEN1-KMT2Ar complex, a group of proteins involved in chromatin remodeling, MIs induce apoptosis and differentiation AL expressing KMT2Ar or NPM1m AML. Phase I and II clinical trials have evaluated MIs as standalone treatments and combined them with other synergistic drugs, yielding promising results. These trials have demonstrated notable response rates with manageable toxicities. Among MIs, ziftomenib received orphan drug and breakthrough therapy designations from the European Medicines Agency in January 2024 and the Food and Drug Administration (FDA) in April 2024, respectively, for treating R/R patients with NPM1m AML. Additionally, in November 2024, the FDA approved revumenib for treating R/R patients with KMT2Ar-AL. This review focuses on the pathophysiology of MI-sensitive AL, primarily AML. It illustrates data from clinical trials and discusses the emergence of resistance mechanisms. In addition, we outline future directions for the use of MIs and emphasize the need for further research to fully realize the potential of these novel compounds, especially in the context of specific genetic subtypes of challenging AL. Full article
(This article belongs to the Section Cancer Therapy)
17 pages, 5065 KiB  
Article
Genome-Wide microRNA Expression Profiling in Human Spermatozoa and Its Relation to Sperm Quality
by Nino-Guy Cassuto, Florence Boitrelle, Hakima Mouik, Lionel Larue, Gwenola Keromnes, Nathalie Lédée, Laura Part-Ellenberg, Geraldine Dray, Léa Ruoso, Alexandre Rouen, John De Vos and Said Assou
Genes 2025, 16(1), 53; https://doi.org/10.3390/genes16010053 (registering DOI) - 4 Jan 2025
Abstract
Background: Sperm samples are separated into bad and good quality samples in function of their phenotype, but this does not indicate their genetic quality. Methods: Here, we used GeneChip miRNA arrays to analyze microRNA expression in ten semen samples selected based on high-magnification [...] Read more.
Background: Sperm samples are separated into bad and good quality samples in function of their phenotype, but this does not indicate their genetic quality. Methods: Here, we used GeneChip miRNA arrays to analyze microRNA expression in ten semen samples selected based on high-magnification morphology (score 6 vs. score 0) to identify miRNAs linked to sperm phenotype. Results: We found 86 upregulated and 21 downregulated miRNAs in good-quality sperm (score 6) compared with bad-quality sperm samples (score 0) (fold change > 2 and p-value < 0.05). MiR-34 (FC × 30, p = 8.43 × 10−8), miR-30 (FC × 12, p = 3.75 × 10−6), miR-122 (FC × 8, p = 0.0031), miR-20 (FC × 5.6, p = 0.0223), miR-182 (FC × 4.83, p = 0.0008) and miR-191 (FC × 4, p = 1.61 × 10−6) were among these upregulated miRNAs. In silico prediction algorithms predicted that miRNAs upregulated in good-quality sperm targeted 910 genes involved in key biological functions of spermatozoa, such as cell death and survival, cellular movement, molecular transport, response to stimuli, metabolism, and the regulation of oxidative stress. Genes deregulated in bad-quality sperm were involved in cell growth and proliferation. Conclusions: This study reveals that miRNA profiling may provide potential biomarkers of sperm quality. Full article
(This article belongs to the Section Human Genomics and Genetic Diseases)
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<p>Differences in the global miRNA expression profiles of S6 and S0 sperm samples. (<b>A</b>). Unsupervised 3D PCA representing the miRNA expression patterns of S6 spermatozoa (<span class="html-italic">n</span> = 5 samples) and S0 spermatozoa (<span class="html-italic">n</span> = 5 samples). Each sample was analyzed using the GeneChip<sup>®</sup> miRNA 4.0 Array. Red dots, S6 samples; blue dots, S0 samples. (<b>B</b>). Hierarchical clustering of the samples using the differentially expressed miRNAs with the highest variation. S6 and S0 samples (n = 5/each group) are clustered in two distinct groups. (<b>C</b>). Heat map of the S6 and S0 miRNA signatures based on the 107 miRNAs that are differentially expressed between S6 and S0 samples. Each column corresponds to a specific miRNA, and each row represents a sperm sample. The color scale reflects the relative miRNA expression levels, with red indicating higher expression and blue indicating lower expression. (<b>D</b>). Violin plots showing the expression of the top 10 upregulated miRNAs in S6 samples based on the TAC analysis of the microarray data. S6: good quality samples, S0: bad quality samples.</p>
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<p>Analysis of GO terms associated with S6-miRNA targets and their functions. (<b>A</b>). Analysis of significantly represented GO terms. Pathway enrichment analyses were carried out using the human gene names of S6-miRNA targets. The size of the blue dots reflects the degree of enrichment, with larger dots representing more significant <span class="html-italic">p</span>-values. (<b>B</b>). GSEA was conducted using the S6-miRNA targets. The heat map illustrates the clustering of genes within the leading-edge subsets, emphasizing the dynamic expression of genes associated with programmed cell death regulation, phosphorylation, positive regulation of cell proliferation, and metabolic processes. Genes are shown on the vertical bars colored from deep blue (top rank) to blank (lowest rank). (<b>C</b>). Bubble plot of the overlapping canonical pathways associated with S6-miRNA targets. The circle size reflects the number of genes involved in the pathway. The canonical pathways were categorized into various types based on the IPA database.</p>
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<p>Top-ranked functional networks of the S6-miRNA target genes. Top networks identified by IPA of S6-miRNA target genes related cell growth and proliferation, cell cycle regulation, DNA replication and repair, system development and function, tissue morphology, reproductive system disorders, cell morphology, cellular assembly and organization, cellular function and maintenance, cell death and survival, and developmental disorders. Green nodes represent genes regulated by S6-miRNAs. Dashed lines represent indirect relationships, while solid lines indicate direct molecular interactions. Within each network, the edge types are defined as follows: a line without an arrowhead signifies binding only, a line ending with a vertical bar represents inhibition, and a line with an arrowhead indicates an “acts on” relationship. *: indicate that several gene identifiers in the dataset file correspond to a single gene in the Global Molecular Network.</p>
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<p>Networks of the S6-miRNA target genes. The IPA tool was used to generate the networks based on the predicted miRNA–mRNA interactions. Pink nodes represent the miRNAs upregulated in S6 samples and green nodes represent the genes targeted by S6-miRNAs. Solid lines represent direct interactions and dashed lines indirect interactions. *: indicate that several gene identifiers in the dataset file correspond to a single gene in the Global Molecular Network.</p>
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<p>The promoters of the predicted S6-miRNA target genes are not differentially methylated. Integrative Genome Viewer snapshots illustrating the methylation levels at individual CpG sites (0–100%) across the examined genes. Each promoter region (red arrow) overlaps with a CpG island (green box).</p>
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<p>Enrichment of S6-miRNA targets in critical signaling pathways and their expression in testes. (<b>A</b>). Pathway analysis (KEGG pathway) using the Pathview server (<a href="https://pathview.uncc.edu/" target="_blank">https://pathview.uncc.edu/</a> (accessed on 17 June 2024)). Highlighted genes are pathway components identified as targets of S6 miRNAs. (<b>B</b>). Expression profile of candidate genes in various human tissues. Expression levels (in Log2 RPKM) of <span class="html-italic">PDGFA</span>, <span class="html-italic">PDGFRA</span>, <span class="html-italic">GRB2</span>, <span class="html-italic">MECP2</span>, <span class="html-italic">MAP2K1</span>, <span class="html-italic">ARHGDIA</span>, and <span class="html-italic">MET</span> in 30 tissues from GTEx. For each gene, the colored circle corresponding to each tissue represents the RPKM value averaged across all samples within that tissue. RPKM stands for reads per kilobase of transcript per million mapped reads.</p>
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14 pages, 1663 KiB  
Article
Investigating the Role of Gut Microbiota in Pediatric Patients with Severe COVID-19 or MIS-C
by Elena Franchitti, Paolo Bottino, Francesca Sidoti, Andrea Carpino, Giulia Pruccoli, Ugo Ramenghi, Cristina Costa, Ugo Ala, Emilia Parodi and Deborah Traversi
Microorganisms 2025, 13(1), 83; https://doi.org/10.3390/microorganisms13010083 (registering DOI) - 4 Jan 2025
Viewed by 96
Abstract
Severe COVID-19 and MIS-C are rare but serious outcomes associated with SARS-CoV-2 infection. The onset of MIS-C often involves the gastrointestinal system, suggesting a potential connection with gut microbiota. This study aims to compare the gut microbiota of children with severe COVID-19 and [...] Read more.
Severe COVID-19 and MIS-C are rare but serious outcomes associated with SARS-CoV-2 infection. The onset of MIS-C often involves the gastrointestinal system, suggesting a potential connection with gut microbiota. This study aims to compare the gut microbiota of children with severe COVID-19 and those with MIS-C using various biomolecular approaches. Gut microbiota composition and specific microbial modulations were analyzed using fecal samples collected at hospital admission. The study included hospitalized patients (mean age 6 ± 5 years) diagnosed with severe COVID-19 (37 patients) or MIS-C (37 patients). Microbial differences were assessed using both NGS and qRT-PCR methodologies. In 75% of cases, pharmacological treatments included antibiotics and corticosteroids, which influenced the microbiota composition. Early age was found to have the most significant impact on microbiota diversity. Significant differences in alpha and beta diversity were observed between COVID-19 and MIS-C patients, particularly concerning low-abundance species. Levels of Bacteroides spp., Bifidobacterium spp., and Akkermansia muciniphila were comparable between groups, while an increased activity of Bifidobacterium spp. was noted in children with positive fecal samples (p = 0.019). An in-depth evaluation of lesser-known gut species may be key to reducing the risk of severe outcomes and developing microbiota-based biomarkers for the early diagnosis of MIS-C. Full article
(This article belongs to the Special Issue New Methods in Microbial Research, 4th Edition)
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<p>Principal coordinator analysis by Manhattan distance metric in relation to the patient condition (COVID-19 or MIS-C, red or blue color, respectively) and age class (cut off at 0.5 years, circle A—older; triangle B—younger).</p>
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<p>Relative abundance of main microbial species observed in COVID-19 (subdivided by age, cut off 0.5 years) and MIS-C.</p>
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<p>Box plot of the qRT-PCR results subdivided by disease, starting both from DNA and mRNA extracts: for Phyla (<b>A</b>) and for detected genera and species (<b>B</b>). The white dots represent the outliers.</p>
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24 pages, 6052 KiB  
Article
Urinary Proteome and Exosome Analysis Protocol for the Discovery of Respiratory Diseases Biomarkers
by Laura Martelo-Vidal, Sara Vázquez-Mera, Pablo Miguéns-Suárez, Susana Belén Bravo-López, Heidi Makrinioti, Vicente Domínguez-Arca, Javier de-Miguel-Díez, Alberto Gómez-Carballa, Antonio Salas, Francisco Javier González-Barcala, Francisco Javier Salgado and Juan José Nieto-Fontarigo
Biomolecules 2025, 15(1), 60; https://doi.org/10.3390/biom15010060 - 3 Jan 2025
Viewed by 261
Abstract
This study aims to develop a protocol for respiratory disease-associated biomarker discovery by combining urine proteome studies with urinary exosome components analysis (i.e., miRNAs). To achieve this, urine was DTT treated to decrease uromodulin, then concentrated and ultracentrifuged. Proteomic analyses of exosome-free urine [...] Read more.
This study aims to develop a protocol for respiratory disease-associated biomarker discovery by combining urine proteome studies with urinary exosome components analysis (i.e., miRNAs). To achieve this, urine was DTT treated to decrease uromodulin, then concentrated and ultracentrifuged. Proteomic analyses of exosome-free urine were performed using LC-MS/MS. Simultaneously, miRNA expression from urine exosomes was measured using either RTqPCR (pre-amplification) or nCounter Nanostring (non-amplication) analyses. We detected 548 different proteins in exosome-free urine samples (N = 5) with high confidence (FDR < 1%), many of them being expressed in different non-renal tissues. Specifically, lung-related proteins were overrepresented (Fold enrichment = 1.31; FDR = 0.0335) compared to whole human proteome, and 10–15% were already described as protein biomarkers for several pulmonary diseases. Urine proteins identified belong to several functional categories important in respiratory pathology. We could confirm the expression of miRNAs previously connected to respiratory diseases (i.e., miR-16-5p, miR-21-5p, miR-146a-5p, and miR-215-5p) in urine exosomes by RTqPCR. Finally, we detected 333 miRNAs using Nanostring, 15 of them up-regulated in T2high asthma (N = 4) compared to T2low asthma (N = 4) and healthy subjects (N = 4). Therefore, this protocol combining the urinary proteome (exosome free) with the study of urinary exosome components (i.e., miRNAs) holds great potential for molecular biomarker discovery of non-renal and particularly respiratory pathologies. Full article
20 pages, 1692 KiB  
Article
Serum hsa-miR-22-3p, hsa-miR-885-5p, Lipase-to-Amylase Ratio, C-Reactive Protein, CA19-9, and Neutrophil-to-Lymphocyte Ratio as Prognostic Factors in Advanced Pancreatic Ductal Adenocarcinoma
by Jakub Wnuk, Dorota Hudy, Joanna Katarzyna Strzelczyk, Łukasz Michalecki, Kamil Dybek and Iwona Gisterek-Grocholska
Curr. Issues Mol. Biol. 2025, 47(1), 27; https://doi.org/10.3390/cimb47010027 - 3 Jan 2025
Viewed by 214
Abstract
Pancreatic cancer (PC) is the seventh most common cause of cancer-related death worldwide. The low survival rate may be due to late diagnosis and asymptomatic early-stage disease. Most patients are diagnosed at an advanced stage of the disease. The search for novel prognostic [...] Read more.
Pancreatic cancer (PC) is the seventh most common cause of cancer-related death worldwide. The low survival rate may be due to late diagnosis and asymptomatic early-stage disease. Most patients are diagnosed at an advanced stage of the disease. The search for novel prognostic factors is still needed. Two miRNAs, miR-22-3p and miR-885-5p, which show increased expression in PC, were selected for this study. The aim of this study was to evaluate the utility of these miRNAs in the prognosis of PC. Other prognostic factors such as lipase-to-amylase ratio (LAR), neutrophil-to-lymphocyte ratio (NLR), and carbohydrate antigen 19-9 (CA19-9) were also evaluated in this study. This study was conducted in 50 patients previously diagnosed with pancreatic ductal adenocarcinoma in clinical stage (CS) III and IV. All patients underwent a complete medical history, physical examination, and routine laboratory tests including a complete blood count, C-reactive protein (CRP), CA19-9, lipase, and amylase. Two additional blood samples were taken from each patient to separate plasma and serum. Isolation of miRNA was performed using TRI reagent with cel-miR-39-3p as a spike-in control. Reverse transcription of miRNA was performed using a TaqMan Advanced miRNA cDNA Synthesis Kit. The relative expression levels of miR-22-3p and miR-885-5p were measured using RT-qPCR. Serum hsa-miR-22-3p was detected in 22 cases (44%), while hsa-miR-885-5p was detected in 33 cases (66%). There were no statistically significant differences in serum or plasma miRNA expression levels between patient groups based on clinical stage, gender, or BMI. There were no statistically significant differences in LAR between patients with different CS. For NLR, CRP and CA19-9 thresholds were determined using ROC analysis (6.63, 24.7 mg/L and 4691 U/mL, respectively). Cox’s F test for overall survival showed statistically significant differences between groups (p = 0.002 for NLR, p = 0.007 for CRP and p = 0.007 for CA19-9). Utility as prognostic biomarkers was confirmed in univariate and multivariate analysis for CA19-9, CRP, and NLR. The selected miRNAs and LAR were not confirmed as reliable prognostic markers in PC. Full article
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<p>Probability of survival according to clinical stage (Cox’s F test <span class="html-italic">p</span> = 0.043). CS—clinical stage.</p>
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<p>Probability of survival based on an age threshold of 72 years (Cox’s F test <span class="html-italic">p</span> &gt; 0.05; 0.07).</p>
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<p>Probability of survival based on CA19-9 levels (threshold: 4619 U/mL).</p>
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<p>Probability of survival based on C-reactive protein (CRP) level (threshold 24.7 mg/L).</p>
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<p>Probability of survival based on NLR values (threshold: 6.63).</p>
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15 pages, 2205 KiB  
Article
Agathisflavone Inhibits Viability and Modulates the Expression of miR-125b, miR-155, IL-6, and Arginase in Glioblastoma Cells and Microglia/Macrophage Activation
by Karina Costa da Silva, Irlã Santos Lima, Cleonice Creusa dos Santos, Carolina Kymie Vasques Nonaka, Bruno Solano de Freitas Souza, Jorge Mauricio David, Henning Ulrich, Ravena Pereira do Nascimento, Maria de Fátima Dias Costa, Balbino Lino dos Santos and Silvia Lima Costa
Molecules 2025, 30(1), 158; https://doi.org/10.3390/molecules30010158 - 3 Jan 2025
Viewed by 224
Abstract
Glioblastomas (GBM) are malignant tumours with poor prognosis. Treatment involves chemotherapy and/or radiotherapy; however, there is currently no standard treatment for recurrence, and prognosis remains unfavourable. Inflammatory mediators and microRNAs (miRNAs) influence the aggressiveness of GBM, being involved in the communication with the [...] Read more.
Glioblastomas (GBM) are malignant tumours with poor prognosis. Treatment involves chemotherapy and/or radiotherapy; however, there is currently no standard treatment for recurrence, and prognosis remains unfavourable. Inflammatory mediators and microRNAs (miRNAs) influence the aggressiveness of GBM, being involved in the communication with the cells of the tumour parenchyma, including microglia/macrophages, and maintaining an immunosuppressive microenvironment. Hence, the modulation of miRNAs and inflammatory factors may improve GBM treatments. In this study, we investigated the effects of agathisflavone, a biflavonoid purified from Cenostigma pyramidale (Tul.), on the growth and migration of GBM cells, on the expression of inflammatory cytokines and microRNAs, as well on the response of microglia. Agathisflavone (5–30 μM) induced a dose- and time-dependent reduction in the viability of both human GL-15 and rat C6 cells, as determined by the MTT test, and reduced cell migration, as determined by cell scratch assay. RT-qPCR analysis revealed that agathisflavone (5 μM) down-regulated the expression of miR-125b and miR-155 in the secretome derived from GL-15 cells, which was associated with upregulation of the mRNA expression of IL-6 and arginase-1 immunoregulatory factors. Exposure of human microglia/macrophage to the secretome from GL-15 GMB cells modulated proliferation and morphology, effects that were modulated by agathisflavone treatment. These results demonstrate the effect of flavonoids on the growth of GBM cells, which impacts cells in the microenvironment and can be considered for preclinical studies for adjuvant treatments. Full article
(This article belongs to the Special Issue Bioactive Phenolic and Polyphenolic Compounds, Volume III)
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<p>Cytotoxicity of agathisflavone to human GL15 and rat C6 rat glioma cells. The cells were treated with the flavonoid dilution vehicle (0.03% DMSO) or with agathisflavone (FAB) at a concentration of 1 to 30 µM, and the cell morphology and cytotoxicity was assessed after 24 h of exposure. (<b>A</b>,<b>C</b>) Phase contrast photomicrographs of GL15 and C6 cell cultures in control conditions or exposed to agathisflavone (5 or 10 μM) for 24 h; obj. ×20 scale bar = 100 μm. (<b>B</b>,<b>D</b>) Analysis of cell viability by the MTT assay in GL15 and C6 cell cultures exposed to agathisflavone at different concentrations; the results are expressed as the mean percentages ± SD (<span class="html-italic">n</span> = 3) in relation to the control group, which was considered 100%. (*) Statistically different, significance <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of agathisflavone on migration of human GL15 (<b>A</b>) and rat C6 glioma (<b>B</b>) cells. The cell cultures were treated with the flavonoid (FAB) at a concentration of 5 and 10 µM or maintained under control conditions (0.01% DMSO). Migration was assessed after 24 h exposure by the Scratch assay; (obj. ×10).</p>
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<p>Effect of the flavonoid agathisflavone (FAB) on the regulation of miRNAs (miR) in intracellular and in the secretome of GL-15 human GBM cells by RT-qPCR. (<b>A</b>) intracellular expression of miR-125b, miR-146a and miR-21n; (<b>B</b>) extracellular expression of miR-125b and miR-155. Results were expressed as mean values ± SD (<span class="html-italic">n</span> = 3) and compared to control (0.005% DMSO) expression; FAB 5: agathisflavone at 5 µM. significance was determined by an unpaired <span class="html-italic">t</span>-test; (****) Statistically different, significance <span class="html-italic">p</span> &lt; 0.0001; (*) Statistically different, significance <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>RT-qPCR analysis for mRNA cytokines’ expression in a culture of GL15 human glioblastoma cells. Expression of IL1-β, TGFβ, IL-10, TNFα, arginase 1 and IL-6. Results were expressed as means ± SD (<span class="html-italic">n</span> = 3) and compared to control (0.005% DMSO) expression; FAB 5: agathisflavone at 5 µM; (**) Statistically different, significance <span class="html-italic">p</span> &lt; 0.005; (*) Statistically different, significance <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effect of treatment with human GL15 GBM agathisflavone on the morphology and proliferation of human C20 microglia. GBM cells were treated for 24 h with agathisflavone (5 µM) or maintained in control condition (0.005% DMSO), and conditioned medium (CM) was collected after 24 h treatment. C20 microglia were exposed to fresh control medium (CM), to control CM of GBM cells (CGCM), or to agathisflavone GL15-treated CM (FGCM) for 24 h. (<b>A</b>) Cell morphology was assessed by phase contrast microscopy and proliferation by immunofluorescence for Ki67+ expression (red); the nuclear chromatin was stained with DAPI (blue); Obj. ×20, scale bar = 50 µM; the images are representative of three independent experiments. (<b>B</b>) Quantification of C20 microglia after 24 h of treatment in the different conditions; cells were counted in 20 aleatory fields in three independent cultures and were tested for significance using one-way ANOVA followed by Tukey’s post-hoc test; data presented as mean changes ± SEM times of controls. (****) Statistically different, significance <span class="html-italic">p</span> &lt; 0.0001.</p>
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22 pages, 22538 KiB  
Article
Physiological and Multi-Omics Integrative Analysis Provides New Insights into Tolerance to Waterlogging Stress in Sesame (Sesamum indicum L.)
by Lu Zhang, Suhua Wang, Xuele Yang, Luqiu He, Liqin Hu, Rui Tang, Jiguang Li and Zhongsong Liu
Int. J. Mol. Sci. 2025, 26(1), 351; https://doi.org/10.3390/ijms26010351 - 3 Jan 2025
Viewed by 244
Abstract
Plant growth and development require water, but excessive water hinders growth. Sesame (Sesamum indicum L.) is an important oil crop; it is drought-tolerant but sensitive to waterlogging, and its drought tolerance has been extensively studied. However, the waterlogging tolerance of sesame still [...] Read more.
Plant growth and development require water, but excessive water hinders growth. Sesame (Sesamum indicum L.) is an important oil crop; it is drought-tolerant but sensitive to waterlogging, and its drought tolerance has been extensively studied. However, the waterlogging tolerance of sesame still has relatively few studies. In this study, two kinds of sesame, R (waterlogging-tolerant) and S (waterlogging-intolerant), were used as materials, and they were treated with waterlogging stress for 0, 24, 72, and 120 h. Physiological analysis showed that after waterlogging, sesame plants responded to stress by increasing the contents of ascorbate peroxidase (APX), glutathione (GSH), and some other antioxidants. The results of the multi-omics analysis of sesame under waterlogging stress revealed 15,652 (R) and 12,156 (S) differentially expressed genes (DEGs), 41 (R) and 47 (S) differentially expressed miRNAs (DEMis), and 896 (R) and 1036 (S) differentially accumulated metabolites (DAMs). The combined DEMi-DEG analysis that 24 DEMis regulated 114 DEGs in response to waterlogging stress. In addition, 13 hub genes and three key pathways of plant hormone signal transduction, glutathione metabolism, and glyoxylate and dicarboxylate metabolism were identified by multi-omics analysis under waterlogging stress. The results showed that sesame regulated the content of hormones and antioxidants and promoted energy conversion in the plant through the above pathways to adapt to waterlogging stress. In summary, this study further analyzed the response mechanism of sesame to waterlogging stress and provides helpful information for the breeding of plants for waterlogging tolerance and genetic improvement. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Physiological changes in different kinds of sesame under waterlogging stress. (<b>A</b>–<b>F</b>). APX, GSH, POD, Pro, SS, and SP contents in different kinds of sesame under different waterlogging stresses. APX: ascorbate peroxidase; GSH: glutathione; POD: peroxidase; Pro: proline; SS: soluble sugar; SP: soluble protein. R and S represent two types of sesame: R is waterlogging-tolerant, and S is waterlogging-intolerant. One-way ANOVA processing was used for significance analysis; different lowercase letters indicate that there are significant differences under the same sesame waterlogging stress at different times (<span class="html-italic">p</span> &lt; 0.05); **, ***, and **** indicate that there is a significant difference between the two types of sesame plants at the same time (**: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001; ****: <span class="html-italic">p</span> &lt; 0.0001); and ns indicates no significant change. Bar means the average ± SD, <span class="html-italic">n</span> = 3.</p>
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<p>Differentially expressed genes (DEGs) in sesame under waterlogging stress at different times. (<b>A</b>). Venn diagram showing the intersection of up- and downregulated DEGs in R and S sesame. (<b>B</b>). UpSet plot showing the intersection of DEGs between different comparisons. Yellow means DEGs under waterlogging stress in all groups, pink means DEGs only in R or S, purple means DEGs between R and S under the same waterlogging treatment time, orange means DEGs only in R under waterlogging stress, and blue mesns DEGs only in S under waterlogging stress. (<b>C</b>,<b>D</b>) Heatmaps showing genes differentially expressed at each time point in R (<b>C</b>) or S (<b>D</b>) before (0 h) and after (24, 72, and 120 h) waterlogging treatment. A total of 15,652 DEGs in R and 12,156 DEGs in S were analyzed via using k-means clustering. The color key represents the standardized gene expression levels from high (red) to low (blue).</p>
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<p>DEG functional analysis in sesame at different durations of waterlogging stress. (<b>A</b>). Gene Ontology (GO) analysis. Top 25 GO terms significantly enriched in DEGs upregulated in R and downregulated in S. (<b>B</b>). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. KEGG pathways significantly enriched in DEGs upregulated in R and downregulated in S. * indicates significantly enriched pathways in R or S. The number indicates the number of DEGs.</p>
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<p>Weighted gene co-expression network analysis (WGCNA) of sesame under waterlogging stress. (<b>A</b>). Cluster dendrogram. Cluster dendrogram of the top 8000 genes with the greatest variation among the genes upregulated in R and those downregulated in S. Each vertical line in the dendrogram represents a gene. All genes are clustered into 16 modules, which are represented by turquoise, magenta, black, green-yellow, green, midnight blue, salmon, pink, yellow, cyan, tan, brown, red, blue, purple, and grey, respectively. (<b>B</b>). Correlation heatmap between 16 modules and the genes upregulated in R and those downregulated in S. The upper numbers in the table are correlations, and the lower numbers in parentheses are significant. Bold is the strongest correlation. Positive and negative correlations are indicated in red and blue, respectively. (<b>C</b>,<b>D</b>). The correlation network of the magenta (<b>C</b>) and green-yellow (<b>D</b>) modules. Magenta represents magenta module, and green-yellow represents green-yellow module. The gene network was constructed via WGCNA, and each node represents a gene; the connecting line (edge) between genes represents the co-expression correlation. The size of the gene font and the depth of the color are determined by the co-expression correlation. The higher the co-expression correlation, the larger the gene font and the deeper the color. On the contrary, the smaller the gene font, the lighter the color. The top 20 genes combined with the centrality and number of edges were visualized via Cytoscape. (<b>E</b>). Venn diagram of the hub genes. (<b>F</b>). Heatmaps showing hub gene expression before and after waterlogging stress.</p>
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<p>Differentially expressed miRNAs (DEMis) in sesame at different waterlogging treatment times. (<b>A</b>). UpSet plot showing the intersection of DEMis between different comparisons. Yellow means DEMis under waterlogging stress in all groups, pink means DEMis only in R or S, purple means DEMis between R and S under the same waterlogging treatment time, orange means DEMis only in R under waterlogging stress, and blue mesns DEMis only in S under waterlogging stress. (<b>B</b>,<b>C</b>). Heatmaps showing the DEMis that were differentially expressed in R (<b>B</b>) and S (<b>C</b>). (<b>D</b>,<b>E</b>). KEGG analysis. Top 25 KEGG pathways enriched by DEMi target genes in R (<b>D</b>) and S (<b>E</b>).</p>
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<p>DEMi and DEG correlation network. The yellow circles in the network indicate the DEGs in R, the green circles indicate the DEGs in S, and the pink circles indicate the common DEGs both in R and S. The red dotted boxes in the network indicate the DEMis both in R and S. The arrow points to DEGs that miRNA can regulate.</p>
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<p>Analysis of differentially accumulated metabolites (DAMs) in sesame under waterlogging stress. (<b>A</b>). UpSet plot showing the DAMs under waterlogging stress. Yellow means DAMs under waterlogging stress in all groups, pink means DAMs only in R or S, purple means DAMs between R and S under the same waterlogging treatment time, orange means DAMs only in R under waterlogging stress, and blue mesns DAMs only in S under waterlogging stress. (<b>B</b>). Classification of DAMs. (<b>C</b>,<b>D</b>). KEGG analysis. Top 25 KEGG pathways enriched by DAMs in R (<b>C</b>) and S (<b>D</b>).</p>
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<p>Plant hormone signal transduction in sesame under waterlogging stress. (<b>A</b>). DAMs related to the plant hormone signal transduction pathway. Red represents the DAM. (<b>B</b>). DEGs related to the plant hormone signal transduction pathway. Heatmaps showing the expression of DEGs or DAMs at different waterlogging treatment times.</p>
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<p>Glutathione metabolism pathway in sesame under waterlogging stress. Red represents the DAM. Heatmaps showing the expression of DEGs or DAMs at different waterlogging durations.</p>
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<p>The glyoxylate and dicarboxylate metabolism pathway in sesame under waterlogging stress. Red represents the DAM. Heatmaps showing the expression of DEGs or DAMs at different waterlogging treatment times.</p>
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<p>qRT-PCR analysis of analysis of DEGs. The mRNAs were isolated from the roots of R and S with waterlogging treatment, respectively. The <span class="html-italic">α-tubulin</span> gene was chosen as the internal control. Student’s <span class="html-italic">t</span> test was used for significance analysis, and different lowercase letters indicate <span class="html-italic">p</span> &lt; 0.05. Bar means the average ± SD, n = 3.</p>
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15 pages, 4423 KiB  
Article
Analysis of the miRNA Transcriptome in Aconitum vilmorinianum and Its Regulation of Diterpenoid Alkaloid Biosynthesis
by Xing Zhao, Yiguo Li, Jun Shen, Caixia Guo, Jie Li, Mingzhu Chen, Huini Xu and Kunzhi Li
Int. J. Mol. Sci. 2025, 26(1), 348; https://doi.org/10.3390/ijms26010348 - 3 Jan 2025
Viewed by 334
Abstract
Aconitum vilmorinianum (A. vilmorinianum) is an important medicinal plant in the Aconitum genus that is known for its diterpenoid alkaloids, which exhibit significant pharmacological activity and toxicity, thus making it valuable for both medicinal use and as a biopesticide. Although the [...] Read more.
Aconitum vilmorinianum (A. vilmorinianum) is an important medicinal plant in the Aconitum genus that is known for its diterpenoid alkaloids, which exhibit significant pharmacological activity and toxicity, thus making it valuable for both medicinal use and as a biopesticide. Although the biosynthesis of terpenoids is well characterized, the potential gene regulatory role of microRNAs (miRNAs) in terpenoid biosynthesis in A. vilmorinianum remains unclear, and further research is needed to explore this aspect in this species. In this study, miRNA sequencing was conducted to analyze the miRNA population and its targets in A. vilmorinianum. A total of 22,435 small RNAs were identified across the nine samples. Through miRNA target gene association analysis, 356 target genes from 54 known miRNAs and 977 target genes from 151 novel miRNAs were identified. Target identification revealed that miR6300 targets the hydroxymethylglutaryl-CoA reductase (HMGR) gene, which is involved in the formation of the terpenoid backbone and regulates the synthesis of diterpenoid alkaloids. Additionally, preliminary findings suggest that miR4995 and miR5021 may be involved in the regulation of terpenoid biosynthesis, although further biochemical analysis is needed to confirm these potential roles. This study provides a foundational understanding of the molecular mechanisms by which miRNAs regulate terpenoid biosynthesis in A. vilmorinianum and offers scientific evidence for further research on the biosynthesis of diterpenoid alkaloids in this medicinal plant. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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<p>Length distribution of sRNA sequences in each sequencing sample. The samples were collected at three different stages: the initial stage of root formation in June (<b>R1</b>), the middle stage in August (<b>R2</b>), and the final stage in October (<b>R3</b>). Each stage had three biological replicates (labeled as (<b>R1-1</b>–<b>R3-3</b>)).</p>
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<p>Stem-loop hairpin structures of Novel-mir17, Novel-mir79, Novel-mir131, and Novel-mir150.</p>
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<p>Expression patterns of mature miRNAs and novel miRNAs at the initial, middle, and final stages of root formation. (<b>A</b>) The expression levels of known miRNAs. (<b>B</b>) The expression levels of novel miRNAs.</p>
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<p>Differentially expressed miRNAs at three developmental stages. Blue and green indicate the numbers of downregulated and upregulated miRNAs, respectively.</p>
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<p>Gene ontology (GO) analysis of the target genes of the differentially expressed miRNAs.</p>
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<p>Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of target genes of differentially expressed miRNAs.</p>
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<p>Validation of the expression of 15 differentially expressed miRNAs at three root developmental stages by qRT-PCR. 18S rRNA was used as an internal reference for normalization by qRT-PCR. All of the qRT-PCR reactions were performed with three biological and three technical replications.</p>
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1 pages, 136 KiB  
Retraction
RETRACTED: Gu et al. Long Coding RNA XIST Contributes to Neuronal Apoptosis through the Downregulation of AKT Phosphorylation and Is Negatively Regulated by miR-494 in Rat Spinal Cord Injury. Int. J. Mol. Sci. 2017, 18, 732
by Shixin Gu, Rong Xie, Xiaodong Liu, Jiajun Shou, Wentao Gu and Xiaoming Che
Int. J. Mol. Sci. 2025, 26(1), 347; https://doi.org/10.3390/ijms26010347 - 3 Jan 2025
Viewed by 147
Abstract
The journal retracts the article titled “Long Coding RNA XIST Contributes to Neuronal Apoptosis through the Downregulation of AKT Phosphorylation and Is Negatively Regulated by miR-494 in Rat Spinal Cord Injury” [...] Full article
(This article belongs to the Section Molecular Biology)
22 pages, 2905 KiB  
Review
Physical Exercise: A Promising Treatment Against Organ Fibrosis
by Xiaojie Ma, Bing Liu, Ziming Jiang, Zhijian Rao and Lifang Zheng
Int. J. Mol. Sci. 2025, 26(1), 343; https://doi.org/10.3390/ijms26010343 - 2 Jan 2025
Viewed by 260
Abstract
Fibrosis represents a terminal pathological manifestation encountered in numerous chronic diseases. The process involves the persistent infiltration of inflammatory cells, the transdifferentiation of fibroblasts into myofibroblasts, and the excessive deposition of extracellular matrix (ECM) within damaged tissues, all of which are characteristic features [...] Read more.
Fibrosis represents a terminal pathological manifestation encountered in numerous chronic diseases. The process involves the persistent infiltration of inflammatory cells, the transdifferentiation of fibroblasts into myofibroblasts, and the excessive deposition of extracellular matrix (ECM) within damaged tissues, all of which are characteristic features of organ fibrosis. Extensive documentation exists on fibrosis occurrence in vital organs such as the liver, heart, lungs, kidneys, and skeletal muscles, elucidating its underlying pathological mechanisms. Regular exercise is known to confer health benefits through its anti-inflammatory, antioxidant, and anti-aging effects. Notably, exercise exerts anti-fibrotic effects by modulating multiple pathways, including transforming growth factor-β1/small mother decapentaplegic protein (TGF-β1/Samd), Wnt/β-catenin, nuclear factor kappa-B (NF-kB), reactive oxygen species (ROS), microRNAs (miR-126, miR-29a, miR-101a), and exerkine (FGF21, irisin, FSTL1, and CHI3L1). Therefore, this paper aims to review the specific role and molecular mechanisms of exercise as a potential intervention to ameliorate organ fibrosis. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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<p>The pathogenesis of fibrosis: After tissue injury, immune cells (mainly macrophages) are activated and release cytokines (e.g., IL-4, IL-13, PDGF, TGF-β, etc.). Through signaling pathways such as TGF-β/Smad and Wnt/β-catenin, fibroblasts are transformed into myofibroblasts, and myofibroblasts produce a large amount of ECM, leading to the generation of fibrosis. IL-4: interleukin-4; IL-13: interleukin-13; IL-1β: interleukin-1β; TGF-β: transforming growth factor-β; smad: small mother decapentaplegic protein; PDGF: platelet-derived growth factor-D; ERK: extracellular signal-regulated kinase; p38: p38 mitogen-activated protein kinases; JNK: c-Jun N-terminal kinases; TAK1: transforming growth factor-β (TGF-β)-activated kinase 1; α-SMA: α-smooth muscle actin; COL1: Collagen 1; COL3: Collagen 3; MMP: matrix metallopeptidase; TIMP-1: tissue inhibitor of metal protease1. Created with Figdraw (<a href="http://www.figdraw.com" target="_blank">www.figdraw.com</a>), license ID: TTWOW4366f.</p>
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<p>The mechanisms of exercise modulation of pulmonary fibrosis. Exercise ameliorates paraquat-induced pulmonary fibrosis by impeding the Wnt/β-catenin pathway, dampening inflammation, oxidative stress, and EMT. In bleomycin-induced pulmonary fibrosis, exercise alleviates fibrosis by enhancing endogenous hydrogen sulfide (H<sub>2</sub>S) synthesis, thereby inhibiting the LRP-6/β-catenin and TGF-β1 signaling pathways or reducing lung inflammation and EMT via the suppression of serotonin (5-HT) and Akt phosphorylation. Additionally, in silica-induced silicosis, exercise attenuates lung fibrosis by suppressing the TLR4-TNF-α and SRB-NLRP3 pathways and further inhibiting the IL-17A-CXCL5-CXCR2 inflammatory axis. SRB: scavenger receptor B; NLPR3: NOD-like receptor thermal protein domain associated protein 3; TLR4: Toll-like receptor 4; TNF-α: tumor necrosis factor; IL-17A: interleukin-17A; CXCL5: CXC motif chemokine ligand 5; CXCR2: Chemokine (C-X-C motif) Receptor 2; 5-HT: serotonin; AKT: protein kinase B; LRP-6: low-density lipoprotein receptor-related proteins; H<sub>2</sub>S: hydrogen sulfide; TGF-β1: transforming growth factor-β1; smad: small mother decapentaplegic protein. Created with Figdraw (<a href="http://www.figdraw.com" target="_blank">www.figdraw.com</a>), license ID: PYPIP737ba.</p>
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<p>Mechanisms of exercise modulation of renal fibrosis. Exercise exerts its beneficial effects on renal fibrosis through various mechanisms: Exercise suppresses NOX4-dependent ROS production in the kidney, thereby inhibiting the NF-κB/NLPR3 inflammasome pathway, or by targeting Sirt1, which ultimately ameliorates renal fibrosis associated with diabetic nephropathy. Exercise inhibits the TGF-β/Smad pathway or reduces Ang II content, diminishes AT1R and Ang II binding, and inhibits the Ang II-AT1R-TGF-β pathway. These actions contribute to the mitigation of renal fibrosis development in hypertensive conditions. In aging kidneys, exercise improves renal fibrosis by inhibiting the TGF-β1/TAK1/MKK3/p38 MAPK signaling pathway, enhancing autophagy activity, and delaying the epithelial–mesenchymal transition, or activating PPAR α to reduce oxidative stress, inflammation, and lipid accumulation by inhibiting the expression of miR-21 and miR-34a. TGF-β1: transforming growth factor-β1; TAK1: transforming growth factor-β (TGF-β)-activated kinase 1; MKK3: mitogen-activated protein kinase (MAPK) kinase; p38MAPK: p38 mitogen-activated protein kinase; NOX4: NADPH oxidase 4; ROS: reactive oxygen species; NF-κB: nuclear factor kappa-B; NLPR3: NOD-like receptor thermal protein domain associated protein 3; AngⅡ: angiotensin II; AT1R: Ang II-angiotensin II type I receptor; α-SMA: α-smooth muscle actin; CTGF: connective tissue growth factor; Sirt1: silent information regulator 1; H<sub>2</sub>S: hydrogen sulfide; PGC-1α: peroxisome proliferator-activated receptor gamma coactivator 1-α; miR-21: microRNA-21; miR-34a: microRNA-34a. Created with Figdraw (<a href="http://www.figdraw.com" target="_blank">www.figdraw.com</a>), license ID: TRPRT70cb4.</p>
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<p>Mechanisms of exercise modulation of myocardial fibrosis. Aging, myocardial infarction, metabolic diseases, and hypertension can all lead to myocardial fibrosis. Aging: Exercise reduces collagen deposition by enhancing MMP-2 activity and decreasing the expression of fibrosis-associated factors (TGF-β1, TIMP-1, COL-I). It also restores endogenous H<sub>2</sub>S levels or inhibits the FGF-2/uPA/MMP-2 signaling pathway. Metabolic diseases: Exercise inhibits the TGF-β1/Smad signaling pathway or reduces ROS production, promoting HO-1 expression and inhibiting fibrosis-related factors. Myocardial infarction: Exercise inhibits TGF-β1 signaling through the NRG-1/ErbB signaling pathway or by upregulating miR-29a, miR-101a, and FGF-21 expression. In addition, exercise can also secrete several myokines such as irisin, CHI3L1, and FSTL1 to ameliorate myocardial fibrosis after myocardial infarction. Hypertension: Exercise attenuates myocardial fibrosis by inhibiting the LOXL-2/TGF-β signaling pathway and the expression of AT1R and FGF23, or by promoting the expression of ccdc80tide and AMPKα1. MMP: matrix metallopeptidase; TGF-β1: transforming growth factor-β1; TIMP-1: tissue inhibitor of metal protease1; H2S: hydrogen sulfide; COL-Ⅰ: Collagen 1; FGF-2: fibroblast growth factor 2; uPA: urokinase-type plasminogen activator; NRG1: Neuregulin 1; FGF-21: fibroblast Growth Factor 21; miR-29a: microRNA-29a; miR-101a: microRNA-101a; smad: small mother decapentaplegic protein; ROS: reactive oxygen species; CTGF: connective tissue growth factor; HO-1: heme oxygenase 1; JAK: janus kinase 2; STAT3: signal transducer and activator of transcription 3; AMPKα1: AMP-activated protein kinase α1; Sirt1: silent information regulator 1; PGC-1α: peroxisome proliferator-activated receptor gamma coactivator 1-α; AT1R: Ang II-angiotensin II type I receptor; FGF-23: fibroblast Growth Factor 23; LOXL2: Lysyl oxidase-like 2; miR-34a: microRNA-34a; miR-486a-5p: microRNA-486a-5p; miR-29: microRNA-29; miR-133: microRNA-133; TNF-α: tumor necrosis factor; NF-kB: nuclear factor kappa-B; AKT: protein kinase B; PI3K: phosphoinositide 3-kinase; ALCAT1: lysocardiolipin acyltransferase-1. Created with Figdraw (<a href="http://www.figdraw.com" target="_blank">www.figdraw.com</a>), license ID: PRPRW1c841.</p>
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<p>Antifibrotic mechanisms of exercise: Exercise exerts its anti-fibrotic effects by directly or indirectly (secreting exerkines or targeting microRNAs) affecting multiple signaling pathways associated with fibrogenesis. Created with Figdraw (<a href="http://www.figdraw.com" target="_blank">www.figdraw.com</a>), license ID: IYAUA656e8.</p>
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23 pages, 8662 KiB  
Article
Identification of the EBF1/ETS2/KLF2-miR-126-Gene Feed-Forward Loop in Breast Carcinogenesis and Stemness
by Alessandra Gambacurta, Valentina Tullio, Isabella Savini, Alessandro Mauriello, Maria Valeria Catani and Valeria Gasperi
Int. J. Mol. Sci. 2025, 26(1), 328; https://doi.org/10.3390/ijms26010328 - 2 Jan 2025
Viewed by 260
Abstract
MicroRNA (miR)-126 is frequently downregulated in malignancies, including breast cancer (BC). Despite its tumor-suppressive role, the mechanisms underlying miR-126 deregulation in BC remain elusive. Through silencing experiments, we identified Early B Cell Factor 1 (EBF1), ETS Proto-Oncogene 2 (ETS2), and Krüppel-Like Factor 2 [...] Read more.
MicroRNA (miR)-126 is frequently downregulated in malignancies, including breast cancer (BC). Despite its tumor-suppressive role, the mechanisms underlying miR-126 deregulation in BC remain elusive. Through silencing experiments, we identified Early B Cell Factor 1 (EBF1), ETS Proto-Oncogene 2 (ETS2), and Krüppel-Like Factor 2 (KLF2) as pivotal regulators of miR-126 expression. These transcription factors were found to be downregulated in BC due to epigenetic silencing or a “poised but not transcribed” promoter state, impairing miR-126 expression. Gene Ontology analysis of differentially expressed miR-126 target genes in the Cancer Genome Atlas: Breast Invasive Carcinoma (TCGA-BRCA) cohort revealed their involvement in cancer-related pathways, primarily signal transduction, chromatin remodeling/transcription, and differentiation/development. Furthermore, we defined interconnections among transcription factors, miR-126, and target genes, identifying a potential feed-forward loop (FFL) crucial in maintaining cellular identity and preventing the acquisition of stemness properties associated with cancer progression. Our findings propose that the dysregulation of the EBF1/ETS2/KLF2/miR-126 axis disrupts this FFL, promoting oncogenic transformation and progression in BC. This study provides new insights into the molecular mechanisms of miR-126 downregulation in BC and highlights potential targets for therapeutic intervention. Further research is warranted to clarify the role of this FFL in BC, and to identify novel therapeutic strategies aimed at modulating this network as a whole, rather than targeting individual signals, for cancer management. Full article
(This article belongs to the Special Issue Cancer Genomics)
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<p>MiR-126 and EGFL7 expression in breast cancer. Expression of (<b>a</b>) miR-126 and (<b>b</b>) EGFL7 in normal and breast tumor samples from TCGA-BRCA RNA-seq and miRNA-seq data. For miR-a126, normal = 104 and tumor = 1078. For EGFL7, normal = 112 and tumor = 1093. (<b>c</b>) pre-miR-126, (<b>d</b>) miR-126, and (<b>e</b>) EGFL7 expression analysis, by qRT-PCR, in luminal breast tumor and matched-normal biopsies (n = 6). Solid black lines illustrate expression of pre-miR-126, miR-126 and EGFL7 for each patient. Each box plot with the whiskers indicates the median, maximum, and minimum expression value, and data are reported either as Log<sub>2</sub> expression (for TCGA-BRCA) or Log<sub>2</sub> expression + 1 (for human biopsies). **** <span class="html-italic">p</span> &lt; 0.0001 versus normal samples, calculated by Mann–Whitney test. * <span class="html-italic">p</span> = 0.0313 versus matched-normal tissues, calculated by Wilcoxon matched-pairs signed rank test.</p>
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<p>Involvement of KLF2, EBF1, and ETS2 TFs in <span class="html-italic">EGFL7/miR-126</span> transcription. (<b>a</b>) Genome binding/occupancy profiling of KLF2, EBF1, and ETS2 on the <span class="html-italic">EGFL7/miR-126</span> gene. Upper panel: Jaspar Core 2024 tracks showing predicted binding sites for the three TFs along the <span class="html-italic">EGFL7/miR-126</span> gene, visualized by UCSC Genome browser. Lower panel: IGV visualization of ChIP-seq datasets relative to KLF2 (red), EBF1 (blue), and ETS2 (green) binding sites in the promoter regions (highlighted in grey) of the <span class="html-italic">EGFL7/miR-126</span> gene. (<b>b</b>) IGV visualization of active (H3K4me1, H3K4me3, H3K9ac, H3K27ac, and H3K36me3) and repressive (H3K27me3 and H3K9me3) histone modifications, RNA Pol II occupancy, RNA-seq and GRO-seq of the <span class="html-italic">EGFL7/miR-126</span> gene, in HUVEC cells. (<b>c</b>) pre-miR-126, (<b>d</b>) miR-126, and (<b>e</b>) EGFL7 expression, in HUVECs, analyzed by qRT-PCR, after 48 h transient transfection with either KLF2 (red) or EBF1 (blue) or ETS2 (green) siRNAs. (<b>f</b>) EBF1, ETS2, KLF2 and (<b>g</b>) miR-126 expression in HMECs, analyzed by qRT-PCR, after 48 h transient transfection with either EBF1 (blue) or ETS2 (green) or KLF2 (red) siRNAs. Values are reported as percentage of scramble-transfected cells (scr), arbitrarily set to 100%. Data are shown as mean ± S.D. of three independent experiments, each performed in triplicate. * <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; 0.005, and **** <span class="html-italic">p</span> &lt; 0.001 versus scramble, calculated by Dunnett’s multiple comparisons test.</p>
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<p>Modulation of miR-126 levels in luminal BC and healthy mammary epithelial cells. (<b>a</b>) EBF1 expression in MCF-7 cells, analyzed by qRT-PCR, after 48 h transient transfection with either pCMV6-AC empty vector (ev) or pCMV6-AC-EBF1 (EBF1). (<b>b</b>) miR-126 expression in EBF1-overexpressing MCF-7 cells. Cells were treated as in (<b>a</b>) and analyzed by qRT-PCR. (<b>c</b>) CFU assay performed with HMECs transiently transfected with either scramble oligo (scramble), or miR-126-inhibitor (anti-miR-126) for 48 h. Photographs refer to the wells of a 6-well plate and are representative of three independent experiments. Histograms show colony number reported as percentage of scramble, arbitrarily set to 100% (absolute colony number = 45.17 ± 12.84). Data are shown as mean ± SD. ** <span class="html-italic">p</span> = 0.0079 and *** <span class="html-italic">p</span> = 0.0022 versus relative control [ev in (<b>a</b>,<b>b</b>); scramble-transfected cells in (<b>c</b>)], calculated by Mann–Whitney test.</p>
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<p><span class="html-italic">EBF1</span> gene epigenetic signatures in normal and tumor breast samples. (<b>a</b>) DNA methylation (blue) and DNAase hypersensitive sites (red) in the genome region containing the <span class="html-italic">EBF1</span> gene (box), in normal and tumor breast samples. (<b>b</b>) IGV visualization of histone modifications, RNA Pol II and RNA seq data of the <span class="html-italic">EBF1</span> gene in normal and tumor luminal breast samples. (<b>c</b>) IGV tracks showing ATAC-seq and GRO-seq in tumor luminal breast samples.</p>
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<p><span class="html-italic">ETS2</span> gene epigenetic signatures in normal and tumor breast samples. (<b>a</b>) DNA methylation (blue) and DNAase hypersensitive sites (red) in the genome region containing the <span class="html-italic">ETS2</span> gene (black box), in normal and tumor breast samples. (<b>b</b>) IGV visualization of histone modifications, RNA Pol II and RNA seq data of the <span class="html-italic">ETS2</span> gene in normal and tumor luminal breast samples. (<b>c</b>) IGV tracks showing ATAC-seq and GRO-seq in tumor luminal breast samples.</p>
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<p><span class="html-italic">KLF2</span> gene epigenetic signatures and KLF2-dependent regulation of EBF1 and ETS2 expression. (<b>a</b>) DNA methylation (blue) and DNAase hypersensitive sites (red) in the genome region containing the <span class="html-italic">KLF2</span> gene (black box), in normal and tumor breast samples. (<b>b</b>) IGV visualization of histone modifications, RNA Pol II and RNA seq data of the <span class="html-italic">KLF2</span> gene in normal and tumor luminal breast samples. (<b>c</b>) IGV tracks showing ATAC-seq and GRO-seq in tumor luminal breast samples. (<b>d</b>) EBF1 and ETS2 expression, analyzed by qRT-PCR, in HUVEC cells, after 48 h transient transfection with KLF2 siRNA. Values are reported as percentage of scramble-transfected cells (scr), arbitrarily set to 100%. Data are shown as mean ± S.D. of three independent experiments, each performed in triplicate. ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001 versus scramble, calculated by Dunnett’s multiple comparisons test.</p>
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<p>Transcription factor/miR-126 networks. (<b>a</b>) STRING analysis of EBF1/ETS2/KLF2/EGFL7 interacting networks. Colored nodes: query proteins and first shell of interactors; white nodes: second shell of interactors. Light blue line: database evidence; purple line: experimental evidence; green line: neighborhood evidence; blue line: co-occurrence evidence; red line: gene fusion evidence; yellow line: text mining evidence; black line: co-expression evidence; violet line: protein homology. (<b>b</b>) Hierarchical view of biological processes (BPs) related to genes shown in (<b>a</b>), by GOnet (<span class="html-italic">p</span> value threshold &lt; 0.00 × 10<sup>0</sup>). Squares: categories. Circles: genes. (<b>c</b>) Selected BP interaction network of miR-126 by miRTargetLink 2.0. Blue circle: miR-126; red circles: BP connected to cell differentiation; green circles: BP connected to either negative or positive regulation of gene expression.</p>
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<p>Gene Ontology (GO) analysis and network analysis of miR-126 target genes. GO and hierarchical clustering tree in “Biological Process (BP)” category for (<b>a</b>) upregulated and (<b>b</b>) downregulated miR-126 targets in TCGA-BRCA cohort, by ShinyGO. (<b>c</b>) Network analysis of miR-126 target genes in BP category, by GOnet (<span class="html-italic">p</span> value threshold &lt; 5.56 × 10<sup>−5</sup>). Squares: categories. Circles: genes (colored by expression).</p>
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<p>Gene Ontology (GO) analysis and network analysis of miR-126 target genes. GO and hierarchical clustering tree in “Biological Process (BP)” category for (<b>a</b>) upregulated and (<b>b</b>) downregulated miR-126 targets in TCGA-BRCA cohort, by ShinyGO. (<b>c</b>) Network analysis of miR-126 target genes in BP category, by GOnet (<span class="html-italic">p</span> value threshold &lt; 5.56 × 10<sup>−5</sup>). Squares: categories. Circles: genes (colored by expression).</p>
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31 pages, 8689 KiB  
Article
Impact of Ultraviolet C Radiation on Male Fertility in Rats: Suppression of Autophagy, Stimulation of Gonadotropin-Inhibiting Hormone, and Alteration of miRNAs
by Ahmed Mohamed Alahwany, Ahmed Hamed Arisha, Adel Abdelkhalek, Tarek Khamis, Taku Miyasho and Doaa Kirat
Int. J. Mol. Sci. 2025, 26(1), 316; https://doi.org/10.3390/ijms26010316 - 1 Jan 2025
Viewed by 511
Abstract
While ultraviolet C (UVC) radiation has beneficial applications, it can also pose risks to living organisms. Nevertheless, a detailed assessment of UVC radiation’s effects on mammalian male reproductive physiology, including the underlying mechanisms and potential protective strategies, has not yet been accomplished. This [...] Read more.
While ultraviolet C (UVC) radiation has beneficial applications, it can also pose risks to living organisms. Nevertheless, a detailed assessment of UVC radiation’s effects on mammalian male reproductive physiology, including the underlying mechanisms and potential protective strategies, has not yet been accomplished. This study aimed to examine the critical roles of oxidative stress, autophagy, reproductive hormonal axis, and microRNAs in UVC-induced reproductive challenges in male rats. Semen, biochemical, molecular, and in silico analyses revealed significant dysregulation of testicular steroidogenesis, impaired spermatogenesis, deteriorated sperm quality, and altered reproductive hormonal profiles, which ultimately lead to a decline in fertility in male rats exposed to UVC radiation. Our data indicated that the suppression of autophagy, stimulation of gonadotropin-inhibiting hormone (GnIH), and alteration of microRNAs serve as key mediators of UVC-induced stress effects in mammalian reproduction, potentially contributing to male infertility. Targeting these pathways, particularly through pretreatment with hesperidin (HES), offers a promising strategy to counteract UVC-induced male infertility. In conclusion, the present findings emphasize the importance of understanding the molecular mechanisms behind UVC-induced male infertility and offer valuable insights into the protective mechanisms and prospective role of HES in safeguarding male reproductive health. Full article
(This article belongs to the Special Issue Advances in Spermatogenesis and Male Infertility)
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Figure 1
<p>Effects of UVC irradiation, alone or in combination with hesperidin, on testicular weight (<b>A</b>) and gonadosomatic index (<b>B</b>) in male rats. Rats exposed to total body irradiation with low or high doses of artificial UVC for 8 h/day for 8 consecutive weeks showed a dose-dependent reduction in testicular weight and Gonadosomatic Index. HES alleviates the decrease of testicular weight and gonadosomatic index caused by UVC radiation. A gross morphology photograph of rat testicles is shown above the bar charts. Data are expressed as mean ± SE from six rats per group. Asterisks indicate a statistically significant difference: **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of UVC irradiation, alone or in combination with HES, on the rat spermiogram. Sperm quality parameters: (<b>A</b>) sperm count, (<b>B</b>) live sperm%, (<b>C</b>) abnormal sperm%, and (<b>D</b>–<b>F</b>) sperm motility%. Data are expressed as mean ± SE from six rats per group. Statistically significant at **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Sperm-related morphological abnormalities of rats exposed to UVC radiation. Photographs of different types of sperm abnormalities were assessed in eosin–nigrosin stained smears and viewed under the oil-immersion objective of a light microscope. Spermatozoa in all photographs are shown at ×100 magnification.</p>
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<p>Effects of UVC irradiation, alone or in combination with HES, on oxidative stress biomarkers in male rat testes. Oxidative stress biomarkers, namely, (<b>A</b>) malondialdehyde (MDA), (<b>B</b>) superoxide dismutase (SOD), and (<b>C</b>) total antioxidant capacity (TAC), were evaluated and are represented as mean ± SE. For each group, (<span class="html-italic">n</span> = 6). Asterisks indicate a statistically significant difference: **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of UVC irradiation, alone or in combination with HES, on serum levels of reproductive hormones in male rats. The levels of (<b>A</b>) luteinizing hormone (LH), (<b>B</b>) follicle stimulating hormone (FSH), and (<b>C</b>) testosterone were determined in the sera of control and UVC-irradiated rats with or without HES treatment. Values represent the mean ± SE from six rats per group. Asterisks indicate a statistically significant difference: **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of UVC irradiation, alone or in combination with HES, on the expression levels of reproductive-related genes in the hypothalamus of male rats. Rats exposed to total body irradiation with low or high doses of artificial UVC for 8 h/day for 8 consecutive weeks showed dose-dependent substantial changes in the hypothalamic gene expression of (<b>A</b>) gonadotropin-inhibiting hormone (GnIH), (<b>B</b>) kisspeptin-1receptor (Kiss1r), and (<b>C</b>) gonadotropin-releasing hormone (GnRH). HES significantly reversed the effects caused by exposure to UVC radiation. Data are expressed as mean ± SE from six rats per group. Asterisks indicate a statistically significant difference: **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of UVC irradiation, alone or in combination with HES, on the expression levels of reproductive-related genes in the pituitary glands of male rats. Rats exposed to total body irradiation with low or high doses of artificial UVC for 8 h/day for 8 consecutive weeks showed dose-dependent substantial changes in the pituitary gene expression of (<b>A</b>) gonadotropin-releasing hormone receptor (GnRHr), (<b>B</b>) follicle-stimulate hormone β1 (FSHβ1), and (<b>C</b>) luteinizing hormone β1 (LHβ1). HES significantly mitigates the effects caused by exposure to UVC radiation. Data are expressed as mean ± SE from six rats per group. Asterisks indicate a statistically significant difference: **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of UVC irradiation, alone or in combination with HES, on the expression of steroidogenic enzymes in testicles of male rats. Expression levels of testicular steroidogenic genes (<b>A</b>) steroidogenic acute regulatory (StAR), (<b>B</b>) hydroxysteroid dehydrogenase-3-beta 17 (HSD17B3), (<b>C</b>) cytochrome P450 family 11 subfamily A member 1 (CYP11A1), (<b>D</b>) cytochrome P450 family 17 subfamily A member 1 (CYP17A1), and (<b>E</b>) cytochrome P450 family 19 subfamily A member 1 (CYP19A1) were evaluated using real-time PCR analysis in control and UVC-irradiated rats with or without HES pretreatment. Data are expressed as mean ± SE from six rats per group. Asterisks indicate a statistically significant difference: **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of UVC irradiation, alone or in combination with HES, on testicular autophagy-related gene expression in male rats. Expression of autophagy-related genes [(<b>A</b>) mammalian target of rapamycin (mTOR), (<b>B</b>) Beclin1, and (<b>C</b>) microtubule-associated protein-light chain 3 II (LC3II)] were examined using real-time PCR analysis in testes of control and UVC-irradiated rats, with or without HES pretreatment. Data are expressed as mean ± SE from six rats per group. Asterisks indicate a statistically significant difference: **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effects of UVC irradiation alone or in combination with HES on the gene expression of miR- 20a and miR-137-3p and their potential targets in male rats. Bioinformatic predictions of SQSTM1/p62 and Kiss1 regulation by miRNAs. (<b>A</b>,<b>F</b>) Schematic representation of predicted binding sites for miR-137-3p and miR-20-5p in rat 3’UTR of Kiss1 gene and SQSTM1/p62, respectively. Seed regions of miR-20-5p and miR-137-3p are highlighted in red (A and F). Expression of hypothalamic and testic-ular miR-137-3p (<b>B</b>,<b>D</b>) and its target Kiss1 (<b>C</b>,<b>E</b>), as well as testicular miR-20-5p (<b>G</b>) and its target SQSTM1/p62 (<b>H</b>) were examined using real-time PCR analyses in control and UVC-irradiated rats with or without HES pretreatment. Data is expressed as mean ± SE from six rats per group. Asterisks indicate a statistically significant difference: **** <span class="html-italic">p</span> &lt; 0.0001, *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Molecular docking studies of HES against reproductive and autophagic receptors in male rats. Docking studies were conducted on the active sites of six target receptor proteins [(<b>I</b>) GnRHr, (<b>II</b>) LHr, (<b>III</b>) FSHr, (<b>IV</b>) Kiss1r, (<b>V</b>) mTORC1r, and (<b>VI</b>) SQSTM/p62] with HES: (A) 3D images showing the binding affinity of HES with each receptor; (B) 3D images showing the binding pockets between HES and each receptor; (C) 3D images showing the types of interactions between HES and the receptor protein residues; and (D) 2D images showing different types of bonds and interactions between HES and the assessed receptor protein.</p>
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22 pages, 3001 KiB  
Article
Potential Regulation of ARID1A by miR-129-5p and miR-3613-3p and Their Prognostic Value in Gastric Cancer
by Irina V. Bure, Ekaterina A. Vetchinkina, Alexey I. Kalinkin, Ekaterina B. Kuznetsova, Artem D. Molchanov, Alevtina E. Kiseleva, Ekaterina A. Alekseeva, Neonila V. Gorokhovets, Ivan V. Rodionov and Marina V. Nemtsova
Int. J. Mol. Sci. 2025, 26(1), 305; https://doi.org/10.3390/ijms26010305 - 1 Jan 2025
Viewed by 313
Abstract
Gastric cancer (GC) remains the most common malignant tumor of the gastrointestinal tract and one of the leading causes of cancer-related deaths worldwide. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), are involved in the pathogenesis and progression of GC and, therefore, may be potential [...] Read more.
Gastric cancer (GC) remains the most common malignant tumor of the gastrointestinal tract and one of the leading causes of cancer-related deaths worldwide. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), are involved in the pathogenesis and progression of GC and, therefore, may be potential diagnostic and prognostic biomarkers. Our work was aimed at investigating the predicted regulation of ARID1A by miR-129-5p and miR-3613-3p and the clinical value of their aberrant expression in GC. The study included tumor and adjacent non-tumor tissues from 110 GC patients, 38 sectional normal gastric tissue samples, as well as 65 plasma samples of GC patients and 49 plasma samples of healthy donors. Expression levels of ARID1A and both miRNAs were quantified by reverse transcription-polymerase chain reaction (RT-PCR). We have identified significant associations of their expression with the clinical and pathological characteristics of GC patients both in tissues and plasma. To validate predicted target pairs miR-129-5p/ARID1A and miR-3613-3p/ARID1A, in vitro experiments on cancer cell lines were conducted. The obtained results suggest a complex role of ARID1A, miR-129-5p and miR-3613-3p in GC and potential regulation of ARID1A expression by both miRNAs. Full article
(This article belongs to the Special Issue Role of MicroRNAs in Human Diseases)
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<p>The expression levels of <span class="html-italic">ARID1A</span> (<b>a</b>), miR-129-5p (<b>b</b>) and miR-3613-3p (<b>c</b>) in tumor and adjacent non-tumor tissues of GC patients and in normal gastric tissue samples (colored in different shades of grey). Statistically significant <span class="html-italic">p</span>-values are indicated with an asterisk.</p>
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<p>Relative expression of <span class="html-italic">ARID1A</span> and miR-129-5p (colored in different shades of grey) in tumor tissues (<b>a</b>) and in the adjacent non-tumor tissues (<b>b</b>) of GC patients; relative expression of <span class="html-italic">ARID1A</span> and miR-3613-3p in tumor tissues (<b>c</b>) and the adjacent non-tumor tissues (<b>b</b>) of GC patients, and in normal gastric tissue samples (<b>d</b>). Statistically significant <span class="html-italic">p</span>-values are indicated with an asterisk.</p>
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<p>Linear regression showing the negative correlation of expression levels in pars <span class="html-italic">ARID1A</span>/miR-129-5p and <span class="html-italic">ARID1A</span>/miR-3613-3p in tumor (<b>a</b>,<b>c</b>) and non-tumor tissue (<b>b</b>,<b>d</b>) of GC patients.</p>
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<p><span class="html-italic">ARID1A</span> mRNA abundance in cancer cells transfected with miR-129-5p (<b>a</b>) and miR-3613-3p (<b>b</b>) expression plasmid (light grey bar) and empty vector (dark grey bar).</p>
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<p>Kaplan–Meier survival curves for the overall survival of GC patients, depending on the expression of ARID1A (<b>a</b>), miR-129-5p (<b>b</b>) and miR-3613-3p (<b>c</b>). Red survival curves are for the group of patents with upregulated in the tumor tissue transcripts (T &gt; N) and green survival curves are for the group of patents with upregulated in the adjacent non-tumor tissue transcripts (T &lt; N).</p>
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<p>The expression level of miR-129-5p (<b>a</b>) and miR-3613-3p (<b>b</b>) in the plasma of GC patients and healthy donors. Statistically significant <span class="html-italic">p</span>-values are indicated with an asterisk.</p>
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<p>Associations of miR-129-5p and miR-3613-3p abundance in plasma of GC patients with the primary tumor size (<b>a</b>), metastases to regional lymph nodes (<b>b</b>), distant metastases (<b>c</b>), the clinical stage of GC (<b>d</b>). ROC-curves: blue—miR-129-5p, green—miR-3613-3p, black—a combination of both microRNAs.</p>
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22 pages, 8118 KiB  
Article
The Association of Cell-Free LncH19 and miR-29b Expression with the PI3K/AKT/HIF-1/VEGF Pathway in Patients with Diabetic Nephropathy: In Silico Prediction and Clinical Validation
by Noha M. Abd El-Fadeal, Basma Osman Sultan, Asmaa K. K. AbdelMaogood, Essam Al Ageeli, Fatma Tohamy Mekhamer, Sherihan Rohayem, Ahmed Shahidy, Nora Hosny, Manal S. Fawzy, Mohammed M. Ismail and Hidi A. A. Abdellatif
Curr. Issues Mol. Biol. 2025, 47(1), 20; https://doi.org/10.3390/cimb47010020 - 31 Dec 2024
Viewed by 269
Abstract
Diabetic nephropathy (DN) affects about one-third of patients with diabetes and can lead to end-stage renal disease despite numerous trials aimed at improving diabetic management. Non-coding RNAs (ncRNAs) represent a new frontier in DN research, as increasing evidence suggests their involvement in the [...] Read more.
Diabetic nephropathy (DN) affects about one-third of patients with diabetes and can lead to end-stage renal disease despite numerous trials aimed at improving diabetic management. Non-coding RNAs (ncRNAs) represent a new frontier in DN research, as increasing evidence suggests their involvement in the occurrence and progression of DN. A growing body of evidence suggests that long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in DN signaling pathways might serve as novel biomarkers or therapeutic targets, although this remains to be fully explored. Our study included four groups, each comprising 40 adults: patients with diabetes (a) without albuminuria, (b) with microalbuminuria, (c) with macroalbuminuria, and a control group. All participants underwent history-taking and clinicolaboratory assessments, including CBC, fasting blood sugar, HbA1c, lipid profile, liver function, and renal function tests. Additionally, expressions of lncRNA H19, miRNA-29b, PI3K, AKT, mTOR, and HIF-1 alpha were assessed using qPCR. lncRNA H19 expression was upregulated in patients with albuminuria compared to the DM group. Furthermore, based on qPCR, the level of lncRNA H19 was negatively correlated with eGFR and miRNA-29b expression. On the other hand, the lncRNA H19 level was positively correlated with PI3K, AKT, mTOR, and HIF-1 alpha levels. We also found that the lncH19/miRNA-29b ratio was significantly increased in patients with DN and macroalbuminuria. In conclusion, lncRNA H19 was upregulated in patients with DN, and this increase was associated with miRNA29b downregulation. Therefore, our study suggests a novel link between the lncH19/miRNA-29b ratio and DN, indicating that it might serve as a potential biomarker for the dynamic monitoring of DN. Full article
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Graphical abstract

Graphical abstract
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<p>A flow chart of in silico data analysis for the miRNA target prediction enrichment pathway analysis and miRNA–disease interaction.</p>
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<p>miR-29–lncH19 pairing showing the target binding sites. Source: starBase v2. database (<a href="http://starbase.sysu.edu.cn/" target="_blank">http://starbase.sysu.edu.cn/</a>, accessed 2 January 2024).</p>
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<p>MicroRNA-29B and lncH19 gene structural analysis. (<b>A</b>) miRNA-29b chromosomal location and (<b>B</b>) miR-29b secondary structure, (<b>C</b>) lncH19 chromosomal location, and (<b>D</b>) lncH19 secondary structure. Source: <a href="https://useast.ensembl.org/Homo_sapiens/Gene/" target="_blank">https://useast.ensembl.org/Homo_sapiens/Gene/</a>; last accessed 25 December 2024).</p>
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<p>MiR-29b-3p, lncH19, and VEGF evidence of cross interactions. (<b>A</b>) miR-2b with VEGF; (<b>B</b>) lncH19 loop interaction with miR-29b and VEGF.</p>
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<p>MiR-29b-3p, lncH19, and VEGF evidence of cross interactions. (<b>A</b>) miR-2b with VEGF; (<b>B</b>) lncH19 loop interaction with miR-29b and VEGF.</p>
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<p>Interaction analysis for “PI3K/AKT/HIF-1” pathway. (<b>A</b>) Target pathways and genes (red boxes) in enriched KEGG pathway. The original pathway is adopted with permission (<a href="https://www.kegg.jp/pathway/map04066" target="_blank">https://www.kegg.jp/pathway/map04066</a>) (last accessed 20 November 2024) [<a href="#B23-cimb-47-00020" class="html-bibr">23</a>], (<b>B</b>) protein–protein interaction and clusters, and (<b>C</b>) coexpression analysis (Data sources: STRING database).</p>
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<p>Gene expression among the four investigated groups, including the DM (green), DM with microalbuminuria (blue), and DM with macroalbuminuria (red) for lncH19 (panel <b>A</b>), miR-29b (panel <b>B</b>), their ratio (panel <b>C</b>) and target genes (i.e., panel <b>D</b>: PI3K, panel <b>E</b>: AKT, panel <b>F</b>: mTOR, and panel <b>G</b>: HIF-1). Data are presented as median and interquartile range; Kruskal–Wallis was employed to calculate the <span class="html-italic">p</span>-values, where * denotes a significant difference vs. the control group, # indicates a significant difference vs. the DM group, and <span>$</span> indicates significant differences vs. the microalbuminuria group at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>VEGFA protein analysis. (<b>A</b>) Circulating levels of serum VEGFA in the four studied groups. (<b>B</b>) The correlation between eGFR and the serum level of VEGFA. (Data were presented as median and interquartile range. Kruskal–Wallis tests were used to calculate the <span class="html-italic">p</span>-value where * denotes a statistically significant difference vs. the control group, # indicates a significant difference vs. the diabetic group, and <span>$</span> indicates a significant difference vs. the microalbuminuria group at <span class="html-italic">p</span> &lt; 0.05. R<sup>2</sup> indicates the results of the Spearman correlation test, and the significance was determined at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>An illustration of the correlation matrix showing the connection between lncH19 level and various study variables. The blue color represents positive correlations, while the red color represents negative correlations. The value of R represents the strength of the association. Statistical significance was determined if <span class="html-italic">p</span>-value &lt; 0.05.</p>
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<p>VEGF and lncH19 as an indicator for prognosis. ROC curve analysis for VEGF and lncH19 distinguishes patients with DN from those without DN. AUC: area under the curve, CI: Confidence Interval. * Statistical significance at <span class="html-italic">p</span>-value less than 0.05.</p>
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19 pages, 1761 KiB  
Article
microRNA Profile of High-Grade B-Cell Lymphoma with 11q Aberration
by Michalina Zajdel, Łukasz Michał Szafron, Agnieszka Paziewska, Grzegorz Rymkiewicz, Michalina Dąbrowska, Zbigniew Bystydzieński, Mariusz Kulińczak, Beata Grygalewicz, Maria Sromek, Katarzyna Błachnio, Maria Kulecka, Filip Hajdyła, Krzysztof Goryca, Magdalena Chechlińska and Jan Konrad Siwicki
Int. J. Mol. Sci. 2025, 26(1), 285; https://doi.org/10.3390/ijms26010285 - 31 Dec 2024
Viewed by 215
Abstract
High-grade B-cell lymphoma with 11q aberration (HGBCL-11q) is a rare germi-nal centre lymphoma characterised by a typical gain/loss pattern on chromo-some 11q but without MYC translocation. It shares some features with Burkitt lymphoma (BL), HGBCLs and germinal centre-derived diffuse large B-cell lym-phoma, not [...] Read more.
High-grade B-cell lymphoma with 11q aberration (HGBCL-11q) is a rare germi-nal centre lymphoma characterised by a typical gain/loss pattern on chromo-some 11q but without MYC translocation. It shares some features with Burkitt lymphoma (BL), HGBCLs and germinal centre-derived diffuse large B-cell lym-phoma, not otherwise specified (GCB-DLBCL-NOS). Since microRNA expression in HGBCL-11q remains unknown, we aimed to identify and compare the mi-croRNA expression profiles in HGBCL-11q, BL and in GCB-DLBCL-NOS. Next-generation sequencing (NGS)-based microRNA profiling of HGBCL-11q (n = 6), BL (n = 8), and GCB-DLBCL-NOS without (n = 3) and with MYC rearrange-ment (MYC-R) (n = 7) was performed. We identified sets of 39, 64, and 49 mi-croRNAs differentiating HGBCL-11q from BL, and from GCB-DLBCL-NOS without MYC-R, respectively. The expression levels of miR-223-3p, miR-193b-3p, miR-29b-3p, and miR-146a-5p consistently differentiated HGBCL-11q from both BL, GCB-DLBCL-NOS without MYC-R. In addition, HGBCL-11q presented greater heterogeneity in microRNA expression than BL. The expression profile of MYC-regulated microRNAs differed in HGBCL-11q and in BL, while also clearly distinguishing HGBCL-11q and BL from GCB-DLBCL-NOS. The microRNA pro-file of HGBCL-11q differs from those of BL and GCB-DLBCL-NOS, exhibiting greater heterogeneity compared to BL. The microRNA profile further supports that HGBCL-11q is a distinct subtype of B-cell lymphoma. Full article
(This article belongs to the Special Issue Molecular Pathology and Novel Therapies for Lymphoma)
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Figure 1
<p>Dendrogram and heatmap illustrating the hierarchical clustering of the HGBCL-11q and BL samples based on the expression levels of all the microRNA NGS reads for each individual sample. This analysis highlights the similarities and differences in the microRNA expression levels across individual samples. The colours, as indicated by the scale, represent differences in expression profiles, ranging from none (dark blue) to high (dark red).</p>
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<p>MDS analysis performed for the HGBCL-11q and BL cases based on the expression profiles of NGS reads for all microRNAs. dim1 and dim2 represent the first and second dimensions, equivalents of the first and second principal components in the PCA. The X and Y axes—measure of the diversity of each principal component.</p>
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<p>The pair of chromosomes 11 in HGBCL-11q with a duplication at locus 11q23.1 on the long arm of one of the chromosomes.</p>
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<p>Dendrogram and heatmap illustrating the hierarchical clustering of the HGBCL-11q and BL samples based on the expression levels of MYC-regulated microRNAs. The colours, as indicated by the scale, represent differences in expression levels, ranging from none (dark blue) to high (dark red).</p>
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<p>PCA performed for the HGBCL-11q and BL cases based on the expression profiles of NGS reads for the MYC-regulated microRNAs. PC1 and PC2 represent the first and second principal components. The X and Y axes are measures of the diversity of each principal component.</p>
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<p>Dendrogram and heatmap illustrating hierarchical clustering of the HGBCL-11q, BL GCB-DLBCL-NOS without <span class="html-italic">MYC</span>-R, and GCB-DLBCL-NOS with <span class="html-italic">MYC</span>-R samples based on the categorising of the expression levels of the MYC-regulated microRNAs. Dark blue colour indicates that the mean microRNA expression in a given group is lower than the median expression of that microRNA across all samples; red indicates that the mean microRNA expression in a given group is higher than or equal to the median expression of that microRNA across all the samples.</p>
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