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19 pages, 2896 KiB  
Article
Quantitative Proteomics Analysis Reveals XDH Related with Ovarian Oxidative Stress Involved in Broodiness of Geese
by Ning Zhou, Yaoyao Zhang, Youluan Jiang, Wang Gu, Shuai Zhao, Wanwipa Vongsangnak, Yang Zhang, Qi Xu and Yu Zhang
Animals 2025, 15(2), 182; https://doi.org/10.3390/ani15020182 (registering DOI) - 11 Jan 2025
Viewed by 146
Abstract
Studies have demonstrated significant alterations in ovarian oxidative stress levels, ovarian degeneration, and follicular atresia during the broody period in geese. The results of this study showed that during the broody period, geese exhibited degraded ovarian tissues, disrupted follicular development, a thinner granulosa [...] Read more.
Studies have demonstrated significant alterations in ovarian oxidative stress levels, ovarian degeneration, and follicular atresia during the broody period in geese. The results of this study showed that during the broody period, geese exhibited degraded ovarian tissues, disrupted follicular development, a thinner granulosa cell layer, and lower levels of ovarian hormones E2, P4, and AMH. Antioxidant activity (GSH, CAT, SOD, T-AOC, and the content of H2O2) and the mRNA expression levels of antioxidant genes (GPX, SOD-1, SOD-2, CAT, COX-2, and Hsp70) were significantly higher in pre-broody geese compared to laying geese, while the expression of apoptosis-related genes (p53, Caspase-3, and Caspase-9) increased and the anti-apoptotic gene Bcl-2 decreased. Additionally, proteomic analysis identified 703 differentially expressed proteins (DEPs), primarily concentrated in the GO categories of the biological process (biological regulation, response to stimulus, etc.) and enriched in the KEGG pathways (PI3K-Akt signaling pathway, etc.). Among them, XDH was central to the regulatory network. Furthermore, Western blotting revealed higher expression of XDH in the ovaries of pre-broody geese than those of laying geese. Pearson correlation analysis indicated a significant correlation between XDH expression and oxidative stress markers in the ovaries of geese (r >0.75). Overall, these results demonstrated that geese experience ovarian atrophy and remarkably increased oxidative stress during the broody period, suggesting that XDH may be a key driver of broodiness in geese. Full article
(This article belongs to the Section Poultry)
22 pages, 1396 KiB  
Article
Ethyl Acetate Extract of Cichorium glandulosum Activates the P21/Nrf2/HO-1 Pathway to Alleviate Oxidative Stress in a Mouse Model of Alcoholic Liver Disease
by Shuwen Qi, Chunzi Zhang, Junlin Yan, Xiaoyan Ma, Yewei Zhong, Wenhui Hou, Juan Zhang, Tuxia Pang and Xiaoli Ma
Metabolites 2025, 15(1), 41; https://doi.org/10.3390/metabo15010041 - 10 Jan 2025
Viewed by 298
Abstract
Background: Alcoholic liver disease (ALD) is a significant global health concern, primarily resulting from chronic alcohol consumption, with oxidative stress as a key driver. The ethyl acetate extract of Cichorium glandulosum (CGE) exhibits antioxidant and hepatoprotective properties, but its detailed mechanism of [...] Read more.
Background: Alcoholic liver disease (ALD) is a significant global health concern, primarily resulting from chronic alcohol consumption, with oxidative stress as a key driver. The ethyl acetate extract of Cichorium glandulosum (CGE) exhibits antioxidant and hepatoprotective properties, but its detailed mechanism of action against ALD remains unclear. This study investigates the effects and mechanisms of CGE in alleviating alcohol-induced oxidative stress and liver injury. Methods: Ultra-Performance Liquid Chromatography coupled with Quadrupole-Orbitrap Mass Spectrometry (UPLC-Q-Orbitrap-MS) was used to identify CGE components. A C57BL/6J mouse model of ALD was established via daily oral ethanol (56%) for six weeks, with CGE treatment at low (100 mg/kg) and high doses (200 mg/kg). Silibinin (100 mg/kg) served as a positive control. Liver function markers, oxidative stress indicators, and inflammatory markers were assessed. Transcriptomic and network pharmacology analyses identified key genes and pathways, validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blotting. Results: UPLC-Q-Orbitrap-MS identified 81 CGE compounds, mainly including terpenoids, flavonoids, and phenylpropanoids. CGE significantly ameliorated liver injury by reducing alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP) levels and enhancing antioxidative markers such as total antioxidant capacity (T-AOC) and total superoxide dismutase (T-SOD) while lowering hepatic malondialdehyde (MDA) levels. Inflammation was mitigated through reduced levels of Tumor Necrosis Factor Alpha (TNF-α), Interleukin-1 Beta (IL-1β), and C-X-C Motif Chemokine Ligand 10 (CXCL-10). Transcriptomic and network pharmacology analysis revealed seven key antioxidant-related genes, including HMOX1, RSAD2, BCL6, CDKN1A, THBD, SLC2A4, and TGFβ3, validated by RT-qPCR. CGE activated the P21/Nuclear Factor Erythroid 2-Related Factor 2 (Nrf2)/Heme Oxygenase-1 (HO-1) signaling axis, increasing P21, Nrf2, and HO-1 protein levels while suppressing Kelch-like ECH-associated Protein 1 (Keap1) expression. Conclusions: CGE mitigates oxidative stress and liver injury by activating the P21/Nrf2/HO-1 pathway and regulating antioxidant genes. Its hepatoprotective effects and multi-target mechanisms highlight CGE’s potential as a promising therapeutic candidate for ALD treatment. Full article
(This article belongs to the Special Issue Plants and Plant-Based Foods for Metabolic Disease Prevention)
15 pages, 3191 KiB  
Article
High Concentrations of Tilmicosin Promote the Spread of Multidrug Resistance Gene tolC in the Pig Gut Microbiome Through Mobile Genetic Elements
by Tao Chen, Minxing Zhao, Majian Chen, Xiaoyue Tang, Yuliang Qian, Xiaoting Li, Yan Wang, Xindi Liao and Yinbao Wu
Animals 2025, 15(1), 70; https://doi.org/10.3390/ani15010070 - 31 Dec 2024
Viewed by 387
Abstract
The impact of antibiotic therapy on the spread of antibiotic resistance genes (ARGs) and its relationship to gut microbiota remains unclear. This study investigated changes in ARGs, mobile genetic elements (MGEs), and gut microbial composition following tilmicosin administration in pigs. Thirty pigs were [...] Read more.
The impact of antibiotic therapy on the spread of antibiotic resistance genes (ARGs) and its relationship to gut microbiota remains unclear. This study investigated changes in ARGs, mobile genetic elements (MGEs), and gut microbial composition following tilmicosin administration in pigs. Thirty pigs were randomly divided into control (CK), low-concentration (0.2 g/kg; L), and high-concentration (0.4 g/kg; H) groups. Tilmicosin concentration in manure peaked on day 16 of dosing and dropped below detectable levels by day 13 of the withdrawal period. While tilmicosin did not significantly affect the total abundance of macrolide resistance genes (MRGs) (p > 0.05), it significantly increased the abundance of the multidrug resistance gene tolC in the H group compared with the L and CK groups during the withdrawal period (p < 0.05). This increase was associated with a coincidental rise in the abundance of MGEs (e.g., int1 and int2) and the growth of potential tolC-hosting bacteria such as Paenalcaligenes and Proteiniclasticum. Redundancy analysis showed gut microbial composition as the primary driver of MRG abundance, with MGEs, tilmicosin concentration, and manure physicochemical properties playing secondary roles. These findings suggest that high-dose tilmicosin may alter the gut microbiota and promote ARG spread via MGE-mediated transfer. Full article
(This article belongs to the Special Issue Antibiotic Use in Animals—Second Edition)
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<p>Changes in the concentration of tilmicosin in pig manure from each experimental group (n = 6). Data are expressed as mean ± SD based on dry matter (DM).</p>
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<p>Abundance of ARGs in pig manure after tilmicosin treatment. Absolute (<b>A</b>) and relative (<b>B</b>) abundance of <span class="html-italic">tol</span>C, total absolute abundance (<b>C</b>), and total relative abundance (<b>D</b>) of ARGs. Groups labeled with different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Abundance of MGEs in pig manure after tilmicosin treatment. Absolute (<b>A</b>) and relative (<b>B</b>) abundance of MGEs and (<b>C</b>) Pearson’s correlation coefficient of ARGs and MGEs. * 0.01 &lt; <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. Groups labeled with different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Changes in the diversity and structure of the pig manure flora following tilmicosin treatment. (<b>A</b>,<b>B</b>) α diversity assessed by Chao1 and Shannon indices, (<b>C</b>) relative abundance of bacteria at the phylum level, and (<b>D</b>) relative abundance of bacteria at the genus level. Groups labeled with different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Potential host bacteria for ARGs. (<b>A</b>) Network analysis of the CK group, (<b>B</b>) network analysis of the L group, (<b>C</b>) network analysis of the H group, and (<b>D</b>) mean abundance of <span class="html-italic">Escherichia/Shigella</span>, <span class="html-italic">Paenalcaligenes</span>, <span class="html-italic">Solibacillus, Proteiniclasticum</span>, and <span class="html-italic">Anaerostipes</span> in the CK, L, and H groups and their correlation with <span class="html-italic">tol</span>C. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Impacts of antibiotics, bacterial flora, physicochemical properties, and MGEs on ARGs. Black arrows, red arrows, and bluish-purple arrows represent ARGs, physicochemical properties, and bacteria, respectively.</p>
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17 pages, 3800 KiB  
Article
miR-217-5p NanomiRs Inhibit Glioblastoma Growth and Enhance Effects of Ionizing Radiation via EZH2 Inhibition and Epigenetic Reprogramming
by Jack Korleski, Sweta Sudhir, Yuan Rui, Christopher A. Caputo, Sophie Sall, Amanda L. Johnson, Harmon S. Khela, Tanmaya Madhvacharyula, Anisha Rasamsetty, Yunqing Li, Bachchu Lal, Weiqiang Zhou, Karen Smith-Connor, Stephany Y. Tzeng, Jordan J. Green, John Laterra and Hernando Lopez-Bertoni
Cancers 2025, 17(1), 80; https://doi.org/10.3390/cancers17010080 - 30 Dec 2024
Viewed by 494
Abstract
Background/Objectives: CSCs are critical drivers of the tumor and stem cell phenotypes of glioblastoma (GBM) cells. Chromatin modifications play a fundamental role in driving a GBM CSC phenotype. The goal of this study is to further our understanding of how stem cell-driving [...] Read more.
Background/Objectives: CSCs are critical drivers of the tumor and stem cell phenotypes of glioblastoma (GBM) cells. Chromatin modifications play a fundamental role in driving a GBM CSC phenotype. The goal of this study is to further our understanding of how stem cell-driving events control changes in chromatin architecture that contribute to the tumor-propagating phenotype of GBM. Methods: We utilized computational analyses to identify a subset of clinically relevant genes that were predicted to be repressed in a Polycomb repressive complex 2 (PRC2)-dependent manner in GBM upon induction of stem cell-driving events. These associations were validated in patient-derived GBM neurosphere models using state-of-the-art molecular techniques to express, silence, and measure microRNA (miRNA) and gene expression changes. Advanced Poly(β-amino ester) nanoparticle formulations (PBAEs) were used to deliver miRNAs in vivo to orthotopic human GBM tumor models. Results: We show that glioma stem cell (GSC) formation and tumor propagation involve the crosstalk between multiple epigenetic mechanisms, resulting in the repression of the miRNAs that regulate PRC2 function and histone H3 lysine 27 tri-methylation (H3K27me3). We also identified miR-217-5p as an EZH2 regulator repressed in GSCs and showed that miR-217-5p reconstitution using advanced nanoparticle formulations re-activates the PRC2-repressed genes, inhibits GSC formation, impairs tumor growth, and enhances the effects of ionizing radiation in an orthotopic model of GBM. Conclusions: These findings suggest that inhibiting PRC2 function by targeting EZH2 with miR-217-5p advanced nanoparticle formulations could have a therapeutic benefit in GBM. Full article
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<p>Transcriptome analysis identifies the PRC2 complex as a downstream target of Oct4 and Sox2 in GSCs. (<b>A</b>) Volcano plot comparing genes that are upregulated in Oct4/Sox2-overexpressing cells. Number of genes correspond to those that have a fold change larger than 1 or −1 with an adjusted <span class="html-italic">p</span> value less than 0.05. (<b>B</b>) Heatmap highlighting significant genes that are up- or downregulated after overexpression of Oct4 and Sox2 in two cells lines. (<b>C</b>) Gene set enrichment analysis showing genes downregulated in the setting of transgenic Oct4 and Sox2 expression. (<b>D</b>) Western blot analysis showing expression of Oct4, Sox2, and EZH2 protein in GSCs expressing transgenic Oct4 and Sox2. Actin used as a loading control. (<b>E</b>) Western blot showing histone modifications in GSCs expressing transgenic Oct4 and Sox2. H3 used as loading control. (<b>F</b>) qRT-PCR measuring EZH2 expression in GSCs after sorting for cells based on expression of the stem cell markers CD133 and SSEA1. (<b>G</b>) qRT-PCR analysis to measure expression of EZH2 in primary GSC isolates (<span class="html-italic">n</span> = 20) and glial progenitor cells (<span class="html-italic">n</span> = 4). (<b>H</b>) EZH2 expression in three primary GBM cells lines (GBM1A, GBM1B, and 612) after growth in neurosphere media (Sph.) or serum containing media (Diff). (<b>I</b>) EZH2 expression in normal CNS tissue vs. GBM tissue in the TCGA, Rembrandt (REMB) or Gravendeel (Grav.) datasets (<b>J</b>) Kaplan–Meier survival curves for patients with tumors expressing high or low levels of EZH2 in the TCGA, Rembrandt or Gravendeel databases. Two sample <span class="html-italic">t</span>-tests used to test for differences in (<b>F</b>–<b>H</b>). Log-rank test used to test for differences in (<b>J</b>). * denotes <span class="html-italic">p</span>-value &lt; 0.05. Original Western blot figures can be found in <a href="#app1-cancers-17-00080" class="html-app">Supplementary File S1</a>.</p>
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<p>Oct4/Sox2 represses a subset of genes associated with tumor suppression in a PRC2-dependent manner. (<b>A</b>) Venn diagram showing intersection of genes directly bound by EZH2, EED, SUZ12, and H3K27me3 in human embryonic stem cells. (<b>B</b>) Heatmap of RNA-seq expression from TCGA (HG-U133A) showing expression of the 44 genes identified in (<b>A</b>). Fifteen genes highlighted in red are downregulated in GBM compared to non-tumor tissue. (<b>C</b>) qRT-PCR analysis showing expression of the 15 predicted PRC2 targets in GSCs expressing transgenic Oct4 and Sox2. Genes highlighted in red show consistent changes in 2 distinct GSC isolates. (<b>D</b>) ChIP-PCR for H3K27me<sup>3</sup> at the promotor region for six punitive PRC2 targets in GSCs expressing transgenic Oct4 and Sox2. GSCs expressing GFP were used as controls. (<b>E</b>) qRT-PCR for predicted PRC2 targets with and without the EZH2 inhibitors CPI and EPZ in control vs. Oct4/Sox2 overexpression in the primary GBM cell line GBM1A. Two sample <span class="html-italic">t</span>-tests used to test for differences in (<b>C</b>,<b>D</b>). ANOVA with a post hoc Tukey’s test used to test for significance in (<b>E</b>). * denotes <span class="html-italic">p</span>-value &lt; 0.05.</p>
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<p>miR-217-5p expression is regulated by DNA methylation in GBM neurospheres. (<b>A</b>) Venn diagram showing miRNAs predicted to target EZH2 from PicTAR, miRNA.org, and TarBase that overlap with miRNAs repressed by Oct4 and Sox2. (<b>B</b>) Western blots measuring EZH2, H3, and H3K27me<sup>3</sup> after treatment with antagomiRs to miR-124, miR-217-5p, or in combination, respectively. GAPDH and H3 were used as loading controls (left panel). qRT-PCR to measure expression of miR-124 or miR-217-5p after treatment with antagomiRs (right panel). (<b>C</b>) Expression of miR-217-5p in cells that are positive for the stem cell markers CD133 or SSEA1. (<b>D</b>) Expression of miR-217-5p in GSCs after growth in neurosphere media (Sph.) or serum-containing media (Diff). (<b>E</b>) miR-217-5p expression in primary GSC isolates (<span class="html-italic">n</span> = 20) and glial progenitor cells (<span class="html-italic">n</span> = 4). (<b>F</b>) Expression of miR-217-5p after treatment with the DNMT inhibitor 5-azacytidine (5-aza) in neurospheres expressing transgenic Oct4 and Sox2. (<b>G</b>) Methylation pattern of the putative miR-217-5p promoter via bisulfate sequencing in cell grown in neurosphere media (left panel), serum-containing media (Diff.) (middle panel), or expressing transgenic Oct4 and Sox2 (right panel). ANOVA with a post hoc Tukey’s test used to test for significance in (<b>B</b>,<b>F</b>). Two sample <span class="html-italic">t</span>-tests used to test for differences in (<b>C</b>–<b>E</b>). * denotes <span class="html-italic">p</span>-value &lt; 0.05. Original Western blot figures can be found in <a href="#app1-cancers-17-00080" class="html-app">Supplementary File S1</a>.</p>
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<p>miR-217-5p regulates EZH2 expression and the stem cell phenotype in GSCs. (<b>A</b>) Correlation analysis of miR-217-5p and EZH2 expression in primary GSCs isolates. (<b>B</b>) Schematic showing conserved miR-217-5p binding sites in the 3′UTR of EZH2. (<b>C</b>) Western blots to measure EZH2 and H3K27me<sup>3</sup> protein levels 3 days after GSCs were transfected with an antagomiR against miR-217-5p or miR-217-5p mimic. Actin used as a loading control. (<b>D</b>) Luciferase assay 48 h after co-transfecting a reporter containing the 3′-UTR of EZH2 with an antagomiR against miR-217-5p (AM-217) or miR-217-5p mimics (217). (<b>E</b>) Expression of luciferase-containing reporter assay with the 3′-UTR of EZH2 in two primary GSC isolates 3 days after forced differentiation. (<b>F</b>) Limiting dilution assay to measure stem cell frequency in GSCs after treatment with an antagomiRs against miR-217-5p (AM-217) or miR-217-5p mimics (217). (<b>G</b>) qRT-PCR expression of EZH2, stem cell, and neuronal lineage markers 5 days after treatment with miR-217-5p mimic (<b>G</b>) or amiR-217-5p antagomir (<b>H</b>). Equal numbers of cells were dissociated into single-cell suspensions and cultured in neurosphere medium after transfection with either miR-217-5p mimic or a control miRNA. Cells were counted at the indicated intervals using Trypan blue exclusion to measure total number of cells (<b>I</b>) and viability (<b>J</b>). ANOVA with a post hoc Tukey’s test used to test for significance in (<b>D</b>). Two sample <span class="html-italic">t</span>-tests used to test for differences in (<b>E</b>,<b>G</b>,<b>H</b>). Two-way ANOVA with post hoc Bonferroni test used to test for significance in (<b>I</b>). * denotes <span class="html-italic">p</span>-value &lt; 0.05; *** denotes <span class="html-italic">p</span>-value &lt; 0.001. Original Western blot figures can be found in <a href="#app1-cancers-17-00080" class="html-app">Supplementary File S1</a>.</p>
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<p>miR-217-5p nanomiRs inhibit GBM growth and enhance effects of IR. (<b>A</b>) Western blot analysis showing EZH2 and H3K27me3 expression 3 days after miR-217-5p nanomiR transfection. (<b>B</b>) Sphere formation assay 14 days after GSCs received control or miR-217-5p nanomiRs. Untreated GSCs were included as a control. (<b>C</b>) qRT-PCR for predicted tumor suppressor PRC2 targets 4 days after GSCs received control or miR-217-5p nanomiRs. (<b>D</b>) Schematic of in vivo nanomiR treatment +/− ionizing radiation (IR). (<b>E</b>) Hematoxylin &amp; Eosin (H&amp;E) representative images of each treatment group (left panel). Measured tumor volumes for each animal (right panel) that received control nanomiR (<span class="html-italic">n</span> = 7); control nanomiR and IR (<span class="html-italic">n</span> = 7); miR-217-5p nanomiR (<span class="html-italic">n</span> = 9); miR-217-5p nanomiR and IR (<span class="html-italic">n</span> = 10). Red bar = mean. ANOVA with a post hoc Tukey’s test used to test for significance in (<b>B</b>,<b>C</b>,<b>E</b>). * denotes <span class="html-italic">p</span>-value &lt; 0.05. Original Western blot figures can be found in <a href="#app1-cancers-17-00080" class="html-app">Supplementary File S1</a>.</p>
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28 pages, 8013 KiB  
Article
A Comparison of Rice Root Microbial Dynamics in Organic and Conventional Paddy Fields
by Fangming Zhu, Takehiro Kamiya, Toru Fujiwara, Masayoshi Hashimoto, Siyu Gong, Jindong Wu, Hiromi Nakanishi and Masaru Fujimoto
Microorganisms 2025, 13(1), 41; https://doi.org/10.3390/microorganisms13010041 - 29 Dec 2024
Viewed by 616
Abstract
The assembly of plant root microbiomes is a dynamic process. Understanding the roles of root-associated microbiomes in rice development requires dissecting their assembly throughout the rice life cycle under diverse environments and exploring correlations with soil properties and rice physiology. In this study, [...] Read more.
The assembly of plant root microbiomes is a dynamic process. Understanding the roles of root-associated microbiomes in rice development requires dissecting their assembly throughout the rice life cycle under diverse environments and exploring correlations with soil properties and rice physiology. In this study, we performed amplicon sequencing targeting fungal ITS and the bacterial 16S rRNA gene to characterize and compare bacterial and fungal community dynamics of the rice root endosphere and soil in organic and conventional paddy fields. Our analysis revealed that root microbial diversity and composition was significantly influenced by agricultural practices and rice developmental stages (p < 0.05). The root microbiome in the organic paddy field showed greater temporal variability, with typical methane-oxidizing bacteria accumulating during the tillering stage and the amount of symbiotic nitrogen-fixing bacteria increasing dramatically at the early ripening stage. Redundancy analysis identified ammonium nitrogen, iron, and soil organic matter as key drivers of microbial composition. Furthermore, correlation analysis between developmental stage-enriched bacterial biomarkers in rice roots and leaf mineral nutrients showed that highly mobile macronutrient concentrations positively correlated with early-stage biomarkers and negatively correlated with later-stage biomarkers in both paddy fields. Notably, later-stage biomarkers in the conventional paddy field tended to show stronger correlations with low-mobility nutrients. These findings suggest potential strategies for optimizing microbiome management to enhance productivity and sustainability. Full article
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<p>Temporal shifts in alpha-diversity of rice root and soil microbial communities across paddy types. (<b>A</b>–<b>D</b>) Shannon index and observed feature richness of bacterial (<b>A</b>,<b>B</b>) and fungal (<b>C</b>,<b>D</b>) communities in the root endosphere and bulk soil across four rice developmental stages: tillering, elongating, early ripening, and maturing. The box plots are colored as follows: brownish red for “Conv. Root”, beige for “Org. Root”, light-green for “Conv. Soil”, and blue for “Org. Soil”, “Conv.” and “Org.” represent conventional and organic paddies, respectively. Statistical significance was tested by one-way ANOVA followed by Tukey’s post hoc test. Different letters indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05). A dagger (†) indicates a large effect size (Cohen’s <span class="html-italic">d</span> &gt; 0.8) between organic and conventional paddies where no statistically significant difference was detected.</p>
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<p>Temporal shifts in beta-diversity of rice root and soil microbial communities across paddy types. (<b>A</b>–<b>F</b>) Principal coordinate analysis (PCoA) based on Bray–Curtis distances illustrating the beta diversity of microbial communities across compartments (the root endosphere and bulk soil) and paddy types (organic and conventional). (<b>A</b>,<b>B</b>) PCoA plots of the overall bacterial (<b>A</b>) and fungal (<b>B</b>) communities, with root (red) and bulk soil (blue) samples under organic (circles) and conventional (triangles) paddies. (<b>C</b>,<b>D</b>) PCoA plots of bacterial (<b>C</b>) and fungal (<b>D</b>) communities in the root endosphere across four developmental stages and paddy types. (<b>E</b>,<b>F</b>) PCoA plots of bacterial (<b>E</b>) and fungal (<b>F</b>) communities in bulk soil across four developmental stages and paddy types. Significance values from PERMANOVA analysis, including R<sup>2</sup> values and <span class="html-italic">p</span>-values, were annotated on the PCoA plots to highlight the degree of variation explained by compartment, paddy type, and developmental stage.</p>
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<p>Temporal shifts in rice root and soil microbial community structure across paddy types. (<b>A</b>–<b>D</b>) Dominant microbial taxonomic groups in different compartments (the root endosphere and bulk soil) and paddy types (organic and conventional). (<b>A</b>,<b>B</b>) Bacterial community composition in the root endosphere (<b>A</b>) and bulk soil (<b>B</b>). (<b>C</b>,<b>D</b>) Fungal community composition in the root endosphere (<b>C</b>) and bulk soil (<b>D</b>). Stages 1, 2, 3, and 4 correspond to the tillering, elongating, early ripening, and maturing stages, respectively.</p>
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<p>Core genera of rice root and soil bacterial communities. (<b>A</b>–<b>F</b>) Venn diagrams and pie charts of bacterial genera in the rice root endosphere and bulk soil across four developmental stages under organic and conventional paddies. (<b>A</b>,<b>B</b>) Venn diagrams showing the unique and shared genera in the root endosphere under organic (<b>A</b>) and conventional (<b>B</b>) paddies. (<b>C</b>,<b>D</b>) Venn diagrams showing the unique and shared genera in bulk soil under organic (<b>C</b>) and conventional (<b>D</b>) paddies. Each of the colored ovals represents the sampled stage. Values within intersections represent shared genera, while values outside intersections are unique to each stage. (<b>E</b>,<b>F</b>) Pie charts of the shared core bacterial microbiome, with 69 genera in the root endosphere (<b>E</b>) and 280 genera in bulk soil (<b>F</b>), summarizing the dominant phyla in the shared microbiomes. The top three phyla, ranked by proportion, are highlighted in red.</p>
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<p>Correlation between dominant rice root and soil microbial communities and soil physicochemical properties. (<b>A</b>,<b>B</b>) Redundancy analysis (RDA) plots showing relationships between the dominant bacterial phyla in the root endosphere (<b>A</b>) and bulk soil (<b>B</b>) and soil physicochemical properties. (<b>C</b>,<b>D</b>) RDA plots showing relationships between the dominant fungal phyla in the root endosphere (<b>C</b>) and bulk soil (<b>D</b>) and soil physicochemical properties. Red and black arrows indicate soil physicochemical factors and dominant microbial phyla, respectively. Key soil physicochemical variables include ammonium nitrogen (NH₄⁺-N), total nitrogen (TN), total carbon (TC), nitrate nitrogen (NO<sub>3</sub>⁻-N), electrical conductivity (EC), soil organic matter (SOM), available silica (AS), pH, magnesium (Mg), zinc (Zn), manganese (Mn), copper (Cu), potassium (K), and iron (Fe). Arrow lengths denote the strength of influence, and the angle between soil physicochemical factors and microbial taxa indicates their relationship: acute angles represent positive correlations, while obtuse angles indicate negative correlations. The percentage of variance explained by each RDA axis is indicated along the axes. Asterisks indicate a significant correlation at <span class="html-italic">p</span> &lt; 0.05, while double asterisks indicate a highly significant correlation at <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Differential rice root and soil microbial biomarkers between organic and conventional paddies. (<b>A</b>–<b>D</b>) Linear discriminant analysis (LDA) scores of bacterial and fungal biomarkers with LDA &gt; 2 in the root endosphere (<b>A</b>,<b>C</b>) and bulk soil (<b>B</b>,<b>D</b>). Red bars indicate biomarkers enriched in the conventional paddy, while green bars represent biomarkers enriched in the organic paddy. (<b>E</b>–<b>H</b>) Heatmaps showing the relative abundance of biomarkers across four developmental stages in organic and conventional paddies, for the root endosphere (<b>E</b>,<b>G</b>) and bulk soil (<b>F</b>,<b>H</b>). The relative abundance of each genus was normalized to a maximum value of 1.00 for comparison. Stages 1, 2, 3, and 4 correspond to the tillering, elongating, early ripening, and maturing stages, respectively. Colors represent relative abundance, with red indicating higher abundance and blue indicating lower abundance. Detailed taxonomy information corresponding to microbial taxa in the microbial databases (the SILVA 138 database for bacteria and the UNITE database for fungi) is provided in <a href="#app1-microorganisms-13-00041" class="html-app">Table S6</a>. The ’unidentified’ taxa represent sequences not confidently assigned to known species in the microbial reference databases. These taxa in different plots are independently derived and may not correspond to the same microbial groups.</p>
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<p>Developmental stage-enriched rice root bacterial biomarkers and their correlation with leaf mineral nutrient concentrations. (<b>A</b>,<b>B</b>) Heatmaps showing the relative abundance of bacterial biomarkers across each developmental stage in the rice root endosphere in organic (<b>A</b>) and conventional (<b>B</b>) paddies. The relative abundance of each genus was normalized to a maximum value of 1.00 for comparison. (<b>C</b>,<b>D</b>) Heatmaps showing the correlations between bacterial biomarkers in the root endosphere and concentrations of leaf mineral nutrients, including six macronutrients and seven micronutrients, in rice under organic (<b>C</b>) and conventional (<b>D</b>) paddies. The concentrations of each mineral nutrient were normalized such that the maximum deviation from the mean was set to 1.0 for comparison. Red and blue indicate highly mobile and low-mobility mineral nutrients, respectively. Significant correlations are marked by * (<span class="html-italic">p</span> &lt; 0.05) and ** (<span class="html-italic">p</span> &lt; 0.01). Stages 1, 2, 3, and 4 correspond to the tillering, elongating, early ripening, and maturing stages, respectively. The colors represent biomarkers enriched at different developmental stages: light-pink for tillering, light-beige for elongating, pastel-green for early ripening, and pale-blue for maturing. Detailed taxonomy information corresponding to microbial taxa in the SILVA 138 database is provided in <a href="#app1-microorganisms-13-00041" class="html-app">Table S7</a>.</p>
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<p>Proposed model for root-associated microbial functions in conventional and organic paddy fields. The diagram illustrates key differences in microbial processes between conventional (<b>left</b>) and organic (<b>right</b>) paddy fields. The white area represents the atmosphere. The blue area represents the water layer. The brown background represents the paddy soil environment. Dots of different colors represent bacteria with different functions, specifically, light-yellow and light-green dots represent <span class="html-italic">Bradyrhizobium</span> and <span class="html-italic">Azospirillum</span>, respectively. In conventional paddies, plant growth is promoted by chemical fertilizers that supply NH₄⁺, while disease prevention relies on pathogen suppression through microbicides. In organic paddies, rice growth is promoted via symbiotic nitrogen fixation by beneficial bacteria, such as <span class="html-italic">Bradyrhizobium</span> and <span class="html-italic">Azospirillum</span>, which convert atmospheric N<sub>2</sub> into NH<sub>4</sub>⁺, enhancing nutrient absorption and root growth. Disease prevention is achieved through microbial antagonism, with diverse fungi and bacteria suppressing pathogens, resulting in improved stress tolerance and resistance.</p>
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16 pages, 1004 KiB  
Review
Role of NF2 Mutation in the Development of Eleven Different Cancers
by Shervin Hosseingholi Nouri, Vijay Nitturi, Elizabeth Ledbetter, Collin W. English, Sean Lau, Tiemo J. Klisch and Akash J. Patel
Cancers 2025, 17(1), 64; https://doi.org/10.3390/cancers17010064 - 29 Dec 2024
Viewed by 459
Abstract
Background/Objectives: With the rise in prevalence of diagnostic genetic techniques like RNA sequencing and whole exome sequencing (WES), as well as biological treatment regiments for cancer therapy, several genes have been implicated in carcinogenesis. This review aims to update our understanding of [...] Read more.
Background/Objectives: With the rise in prevalence of diagnostic genetic techniques like RNA sequencing and whole exome sequencing (WES), as well as biological treatment regiments for cancer therapy, several genes have been implicated in carcinogenesis. This review aims to update our understanding of the Neurofibromatosis 2 (NF2) gene and its role in the pathogenesis of various cancers. Methods: A comprehensive search of five online databases yielded 43 studies that highlighted the effect of sporadic NF2 mutations on several cancers, including sporadic meningioma, ependymoma, schwannoma, mesothelioma, breast cancer, hepatocellular carcinoma, prostate cancer, glioblastoma, thyroid cancer, and melanoma. Of note were key biological pathways implicated in cancer formation resulting from sporadic NF2 mutations. Results: NF2 gene mutations are implicated in over 11 different cancers, including several CNS tumors, soli-organ tumors, and skin cancer. NF2 acts as a driver mutation in some cancers, as a non-driver mutation in some cancers, and has simple associated mutations with other cancers. In terms of biological pathway involvement, 8 of the 11 cancers with NF2 mutations show evidence of Hippo signaling cascade involvement. Conclusions: Several cancers characterized by mutations in the NF2 gene have associations with the Hippo signaling pathway. However, future studies remain to be done to further elucidate the role of the Hippo signaling pathway in the carcinogenesis of human NF2-mutant tumors. The findings of this review provide insights into the role of NF2 mutations in cancers, Hippo signaling in NF2-mutant cancers, and current gaps in our knowledge regarding the two. Full article
(This article belongs to the Collection Oncology: State-of-the-Art Research in the USA)
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<p>NF2/Merlin protein structure and conformational change upon phosphorylation.</p>
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<p>Diagram of Hippo Signaling Pathway “on” &amp; “off” states. “?” indicates unknown specific pathway interaction. Created in BioRender.com.</p>
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17 pages, 13346 KiB  
Article
Genome-Wide Identification of the ABC Gene Family in Rosaceae and Its Evolution and Expression in Response to Valsa Canker
by Chenglong Du, Hongqiang Yu, Huanhuan Hu, Zhiqi Dou, Cunwu Zuo and Junqiang Niu
Horticulturae 2025, 11(1), 1; https://doi.org/10.3390/horticulturae11010001 - 24 Dec 2024
Viewed by 292
Abstract
The ATP-binding cassette (ABC) transporter family plays a critical role in plant growth, development, and disease resistance. However, the evolution and functional characteristics of the ABC gene family in Rosaceae species have not been fully studied. In this study, we performed the first [...] Read more.
The ATP-binding cassette (ABC) transporter family plays a critical role in plant growth, development, and disease resistance. However, the evolution and functional characteristics of the ABC gene family in Rosaceae species have not been fully studied. In this study, we performed the first whole-genome identification, as well as an evolutionary analysis and comparative analysis of ABC genes in Rosaceae plants. We identified 3037 ABC genes in 20 plant species, classifying them into eight subfamilies. Comparative analysis revealed significant variations in family size and expansion patterns among species, suggesting adaptive evolution. Tandem duplication (TD: where genes are duplicated in sequence) and whole-genome duplication (WGD: duplication of the entire genome) were identified as the primary drivers of ABC family expansion. In pears, gene pairs produced by WGD underwent purifying selection. Gene ontology (GO) enrichment analysis indicated the involvement of ABC proteins in transmembrane transport and signal transduction pathways. Under Valsa pyri infection, most ABC genes were upregulated in the early stages, highlighting the role of ABCG genes in pathogen response. A weighted gene co-expression network analysis (WGCNA) identified five key ABCG genes potentially involved in pathogen resistance regulation. Our findings provide insights into the evolutionary adaptability of the ABC gene family and their potential applications in plant disease defense. Full article
(This article belongs to the Section Genetics, Genomics, Breeding, and Biotechnology (G2B2))
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<p>ABC System Analysis and Expansion Rate Analysis of 20 Plant Species. (<b>A</b>) The heat map shows the number of characteristics of the ABC subfamily of 20 species. (<b>B</b>) Box plot of the number of ABCs in Rosaceae. Different letters at the top of the map indicate significant differences in the expansion rate between subfamilies. (<b>C</b>) The heat map shows the expansion rate of different subfamilies in different species. (<b>D</b>) The box plot shows the expansion rate of different subfamilies in Rosaceae. Different letters at the top of the figure indicate significant differences in the expansion rate among subfamilies. Analysis of variance was used, followed by Tukey’s HSD test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Gene duplication event analysis. (<b>A</b>) Number of gene pairs of tandem duplication (TD) and whole-genome duplication (WGD) events in different species. (<b>B</b>) TD and WGD events in different subfamilies of 20 species. (<b>C</b>) Heat map of the number of TD events in Rosaceae. (<b>D</b>) Heat map of the number of WGD events in Rosaceae.</p>
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<p>Tissue specificity analysis. (<b>A</b>) The figure shows the distribution characteristics of ABCs in different subfamilies in different tissues. Significant differences among organizations were indicated by different letters at the top of the figure. Analysis of variance was used, followed by Tukey’s HSD test (<span class="html-italic">p</span> &lt; 0.05). (<b>B</b>) The distribution characteristics of different tissues in different subgroups, and blue is the average value of each tissue.</p>
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<p>Analysis of chromosome location and evolutionary selection. (<b>A</b>) Figure A shows the distribution of different subgroups of PbeABC on chromosomes, and the number on top is the number of genes. (<b>B</b>) Density plot representing the distribution of <span class="html-italic">Ka</span> and <span class="html-italic">Ks</span> values. (<b>C</b>) Figure C shows the <span class="html-italic">Ka/Ks</span> analysis of WGD gene pairs. The line is the regression line, and the gene ID of <span class="html-italic">Ka/Ks</span> &gt; 1 is displayed.</p>
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<p>PbeABC GO and KEGG enrichment analysis. (<b>A</b>) Bubble diagram showing the top 20 GO enrichments. (<b>B</b>) Network diagram showing KEGG enrichment analysis.</p>
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<p>PbeABC protein interaction network. The larger the circle is, the more central the node it represents.</p>
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<p>Analysis of PbeABC expression pattern. The figure shows the expression pattern of PbeABC when induced by <span class="html-italic">Vp</span>. All the differential genes in the ABC family are shown in the figure, and the value is log2FC. T1, T2, and T3 were treated for 1h, 3h, and 6h, respectively. FC: fold change.</p>
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<p>Weighted co-expression network diagram. The key node gene is shown on the right, and different colors represent different subfamilies. The weight is set to 0.4.</p>
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19 pages, 4606 KiB  
Article
MET Exon 14 Skipping and Novel Actionable Variants: Diagnostic and Therapeutic Implications in Latin American Non-Small-Cell Lung Cancer Patients
by Solange Rivas, Romina V. Sepúlveda, Ignacio Tapia, Catalina Estay, Vicente Soto, Alejandro Blanco, Evelin González and Ricardo Armisen
Int. J. Mol. Sci. 2024, 25(24), 13715; https://doi.org/10.3390/ijms252413715 - 22 Dec 2024
Viewed by 746
Abstract
Targeted therapy indications for actionable variants in non-small-cell lung cancer (NSCLC) have primarily been studied in Caucasian populations, with limited data on Latin American patients. This study utilized a 52-genes next-generation sequencing (NGS) panel to analyze 1560 tumor biopsies from NSCLC patients in [...] Read more.
Targeted therapy indications for actionable variants in non-small-cell lung cancer (NSCLC) have primarily been studied in Caucasian populations, with limited data on Latin American patients. This study utilized a 52-genes next-generation sequencing (NGS) panel to analyze 1560 tumor biopsies from NSCLC patients in Chile, Brazil, and Peru. The RNA sequencing reads and DNA coverage were correlated to improve the detection of the actionable MET exon 14 skipping variant (METex14). The pathogenicity of MET variants of uncertain significance (VUSs) was assessed using bioinformatic methods, based on their predicted driver potential. The effects of the predicted drivers VUS T992I and H1094Y on c-MET signaling activation, proliferation, and migration were evaluated in HEK293T, BEAS-2B, and H1993 cell lines. Subsequently, c-Met inhibitors were tested in 2D and 3D cell cultures, and drug affinity was determined using 3D structure simulations. The prevalence of MET variants in the South American cohort was 8%, and RNA-based diagnosis detected 27% more cases of METex14 than DNA-based methods. Notably, 20% of METex14 cases with RNA reads below the detection threshold were confirmed using DNA analysis. The novel actionable T992I and H1094Y variants induced proliferation and migration through c-Met/Akt signaling. Both variants showed sensitivity to crizotinib and savolitinib, but the H1094Y variant exhibited reduced sensitivity to capmatinib. These findings highlight the importance of RNA-based METex14 diagnosis and reveal the drug sensitivity profiles of novel actionable MET variants from an understudied patient population. Full article
(This article belongs to the Section Molecular Oncology)
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<p>The mutational profiles of NSCLC actionable genes in South America evidenced a high prevalence of MET variants. (<b>A</b>) Each column of the oncoplots represents a patient, and the rows show the prevalence of the variants in the eight NSCLC actionable genes. (<b>B</b>) Comparison of variant prevalence in eight actionable NSCLC genes. (<b>C</b>) The gender, subject country, tumor stage, and tobacco use information of the patients with variants of the <span class="html-italic">MET</span> gene. (<b>D</b>) MET variant categorization according to the clinical significance, the number, and the percentage of the patients. * <span class="html-italic">p</span>-value ≤ 0.05; ** <span class="html-italic">p</span>-Value ≤ 0.01.</p>
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<p>The RNA and DNA MET sequencing analysis evidenced differences in diagnosing the MET exon 14 skipping variant. (<b>A</b>) DNA regions of exons 13, 14, 15, and introns 14 and 15 of the MET gene (GRCh37.p13). Below are the DNA variants’ locations, which affect the coding sequence of exon 14 and the splicing donor region of the MET gene (variants located in red in rows) of each patient with a DNA variant in the SD region. (<b>B</b>) The broad spectrum of the RNA reads for the METex14 variant is shown in the x-axis. Each column represents a patient; the dashedline shows the threshold (120 reads) for the positive <span class="html-italic">METex14</span> diagnosis [<a href="#B26-ijms-25-13715" class="html-bibr">26</a>]. (<b>C</b>) The number of patients categorized as negative and positive for <span class="html-italic">METex14,</span> according to the numbers of RNA reads. (<b>D</b>) The conceptual map represents all TBx from the NSCLC patients with a pair of RNA- and DNA-sequenced QC pass data. (<b>E</b>) The Pearson correlation between the RNA and DNA reads is represented by a continous line and the standard error as a shadow. (<b>F</b>) The Pearson correlation analysis between the allele frequency of the positive <span class="html-italic">METex14</span> DNA variants (X-axis) and the number of RNA reads (Y-axis). (<b>G</b>) Altered genes in the tumor profile of the patients with low RNA reads for <span class="html-italic">METex14</span>.</p>
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<p>T992I and H1094Y were the most prevalent and bioinformatically predicted drivers and actionable. (<b>A</b>) All the VUSs were localized in the Met protein domains. The green, red, blue, and yellow rectangles represent the location of the Sema, PSI, TIG, and kinase protein domains, respectively. Above the lolliplot, (I) blue dots represent regions sensitive to targeted therapies, according to Oncokb. (II) The exons are represented by blue and light-blue boxes. (III-IV-V) The subcellular location of the mature protein. (<b>B</b>) The driver prediction of the VUSs located at the JM and TK domains (x-axis) using the bioinformatic algorithms CGI, Cadd13, polyphen2, mutation taster, and sift. Light pink and white represent predicted passengers and tolerated variants; green represents those variants’ predicted drivers.</p>
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<p>The VUSs predicted to be drivers, T992I and H1094Y, promote the survival of proliferative non-tumor cells and migration in tumor cells. (<b>A</b>) Representative Western blots of total Met and β-actin expression, evaluated for the H1993 GFP (basal), METex14, T992I, and H1094Y cells. (<b>B</b>) Densitometry levels of total normalized Met/β-actin (+SEM). The graph represents the normalized average from 3 independent experiments, ±SEM. (<b>C</b>) The representative Western blots of total Met and β-actin expression were evaluated for the HEK293T GFP (basal), METex14, T992I, and H1094Y cells. (<b>D</b>) Densitometry levels of total normalized Met/β-actin (+SEM). The graph represents the normalized average from 3 independent experiments, ±SEM. (<b>E</b>) The absorbance averages of HEK293T and H1993 cells expressing GFP, METex14, T992I, and H1094Y; the cells incubated with and without HGF. (<b>F</b>) Representative microphotographies of the wound healing at 0 and 24 h of H1993 cells expressing METex14, T992I, and H1094Y, treated with and without HGF were taken at 4×. (<b>G</b>) The wound closure percentage was calculated for each experimental condition. Finally, three independent experiments averaging the ±SEM are shown. A two-way ANOVA with Tukey correction was applied, and the <span class="html-italic">p</span>-values were adjusted for multiple comparisons. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001; n.s. non-significant.</p>
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<p>The VUSs predicted to be drivers, T992I and the H1094Y, increased Met-activating phosphorylation and the downstream Akt signaling pathway. (<b>A</b>) Representative Western blot images of total Met, total Akt, β-actin, Met p(Y1230-1234-1235), and Akt p(S473) protein expression of HEK293T cells. (<b>B</b>,<b>C</b>) The densitometry levels of Met phosphorylation, Akt phosphorylation, and β-actin were normalized relative to the total Met and Akt. (<b>D</b>) Representative Western blots of total Met, total Akt, β-actin, Met p(Y1230-1234-1235), and Akt p(S473) protein expression of H1993 cells. (<b>E</b>,<b>F</b>) Densitometry levels of Met phosphorylation, Akt phosphorylation, and β-actin normalized relative to the total Met and Akt for H1993 cells. Graphs represent the normalized average from 3 independent experiments, ±SEM. (<b>G</b>) Densitometry levels of Metp, relative to β-actin levels. (<b>H</b>) Pearson correlation between the Metp and Met total, relative to β-actin levels. A one-way ANOVA with Tukey correction was applied, and the <span class="html-italic">p</span>-values were adjusted for multiple comparisons. * <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; n.s. non-significant.</p>
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<p>The 2D and 3D cell cultures expressing the Met-predicted driver variants were sensitive to c-Met inhibitors. (<b>A</b>) A total of 2000 HEK293T-expressing variants were seeded in 2D and incubated for 24 h with crizotinib, capmatinib, and savolitinib. The absorbance was calculated from three independent experiments and normalized, relative to the non-treatment culture cells. (<b>B</b>) Representative microphotographs (taken at 10×) were captured with a Cytation3 imaging reader of the 3D H1993 cells (spheroid) on each day of their life. On day<span class="html-small-caps"> 2</span> of spheroid formation (~200 µm sphere diameter), the drugs were incubated, and then the cells were released from the treatment until day 5. (<b>C</b>) The spheroids were treated with savolitinib for 24 h. Each experimental condition consisted of triplicates, averaged for each experimental condition. The three independent experiments were averaged, ±SEM. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001 and n.s non-significant.</p>
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<p>Molecular dynamics simulations illustrate the savolitinib–MET protein system binding at an approximate mean distance of 3.0. The stability of the pyridazinone ring of savolitinib within the binding site of the MET WT:SLB (<b>A</b>), MET H1094Y:SLB (<b>B</b>), and MET T992I:SLB (<b>C</b>) complexes is largely determined by hydrophobic interactions. Importantly, within the METex14:SLB complex (<b>D</b>), savolitinib is incapable of achieving a stable conformation due to substantial modifications in the initial loop that precedes the tyrosine kinase domain. Therefore, an unstable pocket site was produced. To clarify its proximity to savolitinib, the H1094 residue in the METex14:SLB complex (<b>D</b>) is highlighted in yellow in this context.</p>
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21 pages, 3763 KiB  
Communication
Fixation of Expression Divergences by Natural Selection in Arabidopsis Coding Genes
by Cheng Qi, Qiang Wei, Yuting Ye, Jing Liu, Guishuang Li, Jane W. Liang, Haiyan Huang and Guang Wu
Int. J. Mol. Sci. 2024, 25(24), 13710; https://doi.org/10.3390/ijms252413710 - 22 Dec 2024
Viewed by 406
Abstract
Functional divergences of coding genes can be caused by divergences in their coding sequences and expression. However, whether and how expression divergences and coding sequence divergences coevolve is not clear. Gene expression divergences in differentiated cells and tissues recapitulate developmental models within a [...] Read more.
Functional divergences of coding genes can be caused by divergences in their coding sequences and expression. However, whether and how expression divergences and coding sequence divergences coevolve is not clear. Gene expression divergences in differentiated cells and tissues recapitulate developmental models within a species, while gene expression divergences between analogous cells and tissues resemble traditional phylogenies in different species, suggesting that gene expression divergences are molecular traits that can be used for evolutionary studies. Using transcriptomes and evolutionary proxies to study gene expression divergences among differentiated cells and tissues in Arabidopsis, expression divergences of coding genes are shown to be strongly anti-correlated with phylostrata (gene ages), indicators of selective constraint Ka/Ks (nonsynonymous replacement rate/synonymous substitution rate) and indicators of positive selection (frequency of loci with Ka/Ks > 1), but only weakly or not correlated with indicators of neutral selection (Ks). Our results thus suggest that expression divergences largely coevolve with coding sequence divergences, suggesting that expression divergences of coding genes are selectively fixed by natural selection but not neutral selection, which provides a molecular framework for trait diversification, functional adaptation and speciation. Our findings therefore support that positive selection rather than negative or neutral selection is a major driver for the origin and evolution of Arabidopsis genes, supporting the Darwinian theory at molecular levels. Full article
(This article belongs to the Special Issue Power Up Plant Genetic Research with Genomic Data 2.0)
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<p>Gene expression abundance (GEA) was anti-correlated with phylostratum (PS). (<b>A</b>) ESTs/locus was strongly anti-correlated with PS. (<b>B</b>) cDNAs/locus was strongly anti-correlated with PS. (<b>C</b>) Log<sub>10</sub>(RPMs) from RNA-Seq of seedlings was strongly anti-correlated with PS. Each data point is an average of 100 loci grouped by expression amount. RPMs: reads per million. (<b>D</b>) Log<sub>10</sub>(microarray signals) from microarray data [<a href="#B36-ijms-25-13710" class="html-bibr">36</a>] was strongly anti-correlated with PS. Each data point was an average of 100 loci grouped by expression amount. (<b>E</b>) Genes expressed in one sample (black bars) had a higher PS than genes expressed in more than one sample (unfilled bars). *, **, and *** indicate significant differences at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, and <span class="html-italic">p</span> &lt; 0.001 (<span class="html-italic">t</span>-test), respectively. (<b>F</b>) Genes with smaller GEB (narrow expression) had a higher PS than genes with larger GEB (broad expression). Letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.001 (One-way ANOVA). Grey lines indicate 95% confidence intervals and triangles represent data points (<b>A</b>–<b>D</b>). Error bars are standard deviation (<b>E</b>,<b>F</b>).</p>
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<p>Linkage disequilibrium near positive selection (ω &gt; 1) loci in <span class="html-italic">Arabidopsis</span>. Linkage disequilibrium near ω &gt; 1 loci derived from orthologous gene pairs between <span class="html-italic">A. thaliana</span> and <span class="html-italic">A. lyrata</span> (interspecies; (<b>A</b>)) and between <span class="html-italic">A. thaliana</span> and <span class="html-italic">C. rubella</span> (intergenus; (<b>B</b>)). The 0 represents ω &gt; 1 loci; 1–5 represents loci closest to locus 0 (5 on each side), while 6–10 and 11–15 represent positions of loci farther away from locus 0, with the distribution of ω for loci in each group (all loci, 1–5, 6–10 and 11–15, respectively). (<b>C</b>) Linkage disequilibrium near ω &gt; 1 loci from orthologous gene pairs within <span class="html-italic">Arabidopsis</span> species. The 0 represents ω &gt; 1 loci; 1–2 represents loci closest to locus 0 (2 on each side), while 3–4 and 5–6 represent positions of loci farther away from locus 0, distribution of ω for loci in each group (all loci, 1–2, 3–4 and 5–6, respectively). Letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (<span class="html-italic">t</span>-test). Error bars are standard deviation.</p>
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<p>Gene expression abundance (GEA) was strongly anti-correlated with selective constraint (ω) and the incidence of ω &gt; 1 loci (positive selection markers) derived from orthologous gene pairs between <span class="html-italic">A. thaliana</span> and <span class="html-italic">A. lyrata</span> (interspecies). GEA for ESTs, cDNAs, microarray and RNA-Seq data was treated as described in the main text and methods, as well as in <a href="#app1-ijms-25-13710" class="html-app">Tables S10–S13</a> (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>), while GEA in (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>) was divided into low, medium and high levels and then correlated with ω &gt; 1 loci. ESTs/locus was anti-correlated strongly with ω (<b>A</b>) and the incidence of positive selection (<b>B</b>) but weakly correlated with Ks (<b>A</b>). cDNAs/locus was anti-correlated only with ω (<b>C</b>) and the incidence of ω &gt; 1 loci (<b>D</b>) but not with Ks (<b>C</b>). Log<sub>10</sub>(RPMs) from RNA-Seq of seedlings was strongly anti-correlated with ω (<b>E</b>) and the incidence of ω &gt; 1 loci (<b>F</b>) but correlated weakly with Ks (<b>E</b>). RPMs: reads per million. Log<sub>10</sub>(microarray signals) from microarray data [<a href="#B36-ijms-25-13710" class="html-bibr">36</a>] was strongly anti-correlated with ω (<b>G</b>) and the incidence of ω &gt; 1 loci (<b>H</b>) but weakly correlated with Ks (<b>G</b>). Each data point was an average of 100 loci grouped by expression amount (<b>E</b>,<b>G</b>). For (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>), the letters indicated the significant difference detected by χ<sup>2</sup> test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Gene expression breadth (GEB) was strongly anti-correlated with selective constraint (ω) and the incidence of ω &gt; 1 loci (positive selection markers) derived from orthologous gene pairs between <span class="html-italic">A. thaliana</span> and <span class="html-italic">A. lyrata</span> (interspecies). (<b>A</b>) GEB was strongly anti-correlated with ω. Letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.001 (One-way ANOVA) for ω between any adjacent groups with differential GEB. Error bars are standard deviation. (<b>B</b>) GEB was only minimally anti-correlated with Ks (neutral selection markers). Letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.01 (One-way ANOVA). There was no significant difference for Ks between any adjacent groups. Error bars are standard deviation. (<b>C</b>) GEB was strongly anti-correlated with the incidence of ω &gt; 1 loci (positive selection markers). Narrowly expressed genes had a significantly higher incidence of ω &gt; 1 loci than did broadly expressed genes. The parameter is the result of a chi-squared test between the levels of GEB and whether the gene is under positive selection (χ<sup>2</sup> = 843.1, <span class="html-italic">p</span> &lt; 0.0001). Error bars are standard deviation (<b>A</b>,<b>B</b>).</p>
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<p>Functional enrichment analysis of putative ω &gt;1 loci in <span class="html-italic">Arabidopsis</span>. (<b>A</b>) GO enrichment analysis of putative positively selected genes. (<b>B</b>) KEGG enrichment analysis of putative positively selected genes. Yellow strips represent the enrichment score [−log10(<span class="html-italic">p</span>-value)] of the pathway. Significantly enriched KEGG pathways (<span class="html-italic">p</span> &lt; 0.05) are presented.</p>
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21 pages, 2210 KiB  
Review
EWSR1::ATF1 Translocation: A Common Tumor Driver of Distinct Human Neoplasms
by Julia Raffaella Bianco, YiJing Li, Agota Petranyi and Zsolt Fabian
Int. J. Mol. Sci. 2024, 25(24), 13693; https://doi.org/10.3390/ijms252413693 - 21 Dec 2024
Viewed by 783
Abstract
Cancer is among the leading causes of mortality in developed countries due to limited available therapeutic modalities and high rate of morbidity. Although malignancies might show individual genetic landscapes, recurring aberrations in the neoplastic genome have been identified in the wide range of [...] Read more.
Cancer is among the leading causes of mortality in developed countries due to limited available therapeutic modalities and high rate of morbidity. Although malignancies might show individual genetic landscapes, recurring aberrations in the neoplastic genome have been identified in the wide range of transformed cells. These include translocations of frequently affected loci of the human genetic material like the Ewing sarcoma breakpoint region 1 (EWSR1) of chromosome 22 that results in malignancies with mesodermal origin. These cytogenetic defects frequently result in the genesis of fusion genes involving EWSR1 and a number of genes from partner loci. One of these chromosomal rearrangements is the reciprocal translocation between the q13 and q12 loci of chromosome 12 and 22, respectively, that is believed to initiate cancer formation by the genesis of a novel, chimeric transcription factor provoking dysregulated gene expression. Since soft-tissue neoplasms carrying t(12;22)(q13;q12) have very poor prognosis and clinical modalities specifically targeting t(12;22)(q13;q12)-harboring cells are not available to date, understanding this DNA aberration is not only timely but urgent. Here, we review our current knowledge of human malignancies carrying the specific subset of EWSR1 rearrangements that leads to the expression of the EWSR1::ATF1 tumor-driver chimeric protein. Full article
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<p>Structure of <span class="html-italic">EWSR1</span>. <span class="html-italic">EWSR1</span> spans about 40 kb within the 12.2 locus of chromosome 22. It is most closely surrounded by genes in both forward and reverse orientations encoding nuclear proteins involved in interactions between chromosomes and the cytoskeleton (<span class="html-italic">GAS2L1</span>), and inhibition of cellular proliferation (<span class="html-italic">RASL10A</span>), as well as <span class="html-italic">RHBDD3</span> that encodes an integral membrane protein predicted to be involved in protein metabolism. Its 17 exons generate a primary transcript that can give rise to various mature mRNAs by alternative splicing. Many of them, apparently, dictate translation of the corresponding polypeptides. The most well-documented alternative transcripts are depicted in the figure. Different colors of transcript variants represent spliced neighboring exons. The figure was created with <a href="http://Biorender.com" target="_blank">Biorender.com</a>.</p>
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<p>Structure and functions of EWSR1. Wild-type EWSR1 has an N-terminal low complexity domain (LCD) that is mainly composed of serine–tyrosine–glycine–glutamine (SYGQ) repeats. The LCD is the subject of extensive post-translational glycosylations and phosphorylations. The C-terminal half consists of multiple domains that affect EWSR1 affinity to distinct nucleic acid species. These include three arginine–glycine–glycine-rich domains (RGG) flanking a conserved RNA recognition motif (RRM) and a zinc finger domain (ZF). The RRM consists of four anti-parallel β-strands and two α-helices arranged in a β-α-β-β-α-β fold with side chains that stack with RNA bases. Specificity of RNA binding is determined by multiple contacts with surrounding amino acids in the RGG and ZF domains [<a href="#B47-ijms-25-13693" class="html-bibr">47</a>]. These interactions are affected by multiple post-translation modifications of the RGG and ZF motifs including arginine methylations and lysine acetylations, respectively. The figure was created with <a href="http://Biorender.com" target="_blank">Biorender.com</a>.</p>
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<p>Structure of <span class="html-italic">ATF1</span>. <span class="html-italic">ATF1</span> spans about 57 kb along the plus strand of the q13.12 locus of chromosome 12. It is most closely surrounded by genes in similar forward orientations encoding a transmembrane serine protease (<span class="html-italic">TMPRSS12</span>) involved in the regulation of chromosomal synapsis formation and double-strand break repair, and <span class="html-italic">DIP2B</span> encoding a polypeptide that is predicted to participate in DNA methylation, up- and downstream, respectively. The seven exons of <span class="html-italic">ATF1</span> generate a primary transcript that, via alternative splicing, can give rise to three protein-coding mature mRNAs (<span class="html-italic">ATF1-201</span>, <span class="html-italic">-204</span> and <span class="html-italic">205</span>) and a minimum of two additional transcripts (<span class="html-italic">ATF1-202</span> and -<span class="html-italic">203</span>) that undergo nonsense mRNA-mediated decay. Different colors of transcript variants represent spliced neighboring exons. The figure was created with <a href="http://Biorender.com" target="_blank">Biorender.com</a>.</p>
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<p>Variants of known <span class="html-italic">EWSR1::ATF1</span> fusion transcripts found in clear cell carcinomas. Numbers indicate exons of <span class="html-italic">EWSR1</span> and <span class="html-italic">ATF1</span>. Different colors of transcript variants represent spliced neighboring exons. The figure was created with <a href="http://Biorender.com" target="_blank">Biorender.com</a>.</p>
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<p>Structure of the most common EWSR1::ATF1 in CCS. EWSR1::ATF1 contains the N- and C-terminal regions of EWSR1 and ATF1, respectively. Black numbers represent amino acids of the full-length chimera, color-coded numbers refer to the portions of EWSR1 (red) and ATF1 (blue) fused in the chimeric proteins. The figure was created with <a href="http://Biorender.com" target="_blank">Biorender.com</a>.</p>
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24 pages, 2006 KiB  
Review
Current Non-Viral-Based Strategies to Manufacture CAR-T Cells
by Leon Gehrke, Vasco Dos Reis Gonçalves, Dominik Andrae, Tamas Rasko, Patrick Ho, Hermann Einsele, Michael Hudecek and Sabrina R. Friedel
Int. J. Mol. Sci. 2024, 25(24), 13685; https://doi.org/10.3390/ijms252413685 - 21 Dec 2024
Viewed by 573
Abstract
The successful application of CAR-T cells in the treatment of hematologic malignancies has fundamentally changed cancer therapy. With increasing numbers of registered CAR-T cell clinical trials, efforts are being made to streamline and reduce the costs of CAR-T cell manufacturing while improving their [...] Read more.
The successful application of CAR-T cells in the treatment of hematologic malignancies has fundamentally changed cancer therapy. With increasing numbers of registered CAR-T cell clinical trials, efforts are being made to streamline and reduce the costs of CAR-T cell manufacturing while improving their safety. To date, all approved CAR-T cell products have relied on viral-based gene delivery and genomic integration methods. While viral vectors offer high transfection efficiencies, concerns regarding potential malignant transformation coupled with costly and time-consuming vector manufacturing are constant drivers in the search for cheaper, easier-to-use, safer, and more efficient alternatives. In this review, we examine different non-viral gene transfer methods as alternatives for CAR-T cell production, their advantages and disadvantages, and examples of their applications. Transposon-based gene transfer methods lead to stable but non-targeted gene integration, are easy to handle, and achieve high gene transfer rates. Programmable endonucleases allow targeted integration, reducing the potential risk of integration-mediated malignant transformation of CAR-T cells. Non-integrating CAR-encoding vectors avoid this risk completely and achieve only transient CAR expression. With these promising alternative techniques for gene transfer, all avenues are open to fully exploiting the potential of next-generation CAR-T cell therapy and applying it in a wide range of applications. Full article
(This article belongs to the Special Issue Chimeric Antigen Receptors against Cancers and Autoimmune Diseases)
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<p>Overview of different strategies for gene transfer. Viruses, transposases, and programmable endonucleases mediate stable integration of the GOI into the genome, and therefore, stable CAR expression. Non-integrating vectors do not induce gene integration and thus induce transient CAR expression as long as the vector is present in the cell. The respective mechanisms and methods of delivery are depicted in a generalized but not necessarily inclusive manner. Transposase protein (blue ellipses); transposon ITRs (red DNA); CAR/GOI (green RNA/DNA/protein); genomic DNA (violet).</p>
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<p>Transposon-based cut-and-paste gene transfer. Transposon and Transposase are encoded separately. The transposase can be delivered as DNA, mRNA, or protein. A transposon carrying the GOI requires delivery as circular DNA. The SB protein binds to the ITR region of the transposon vector and forms a synaptic complex, in which both ends of the transposon are held together and excised from the DNA vector. For SB, the transposon is integrated at a random TA target site in the host cell genome, resulting in stable expression of the GOI. SB protein (blue); transposon ITRs (red); GOI (green).</p>
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<p>Targeted transgene integration using double-strand break induction via programmable nucleases: Genomic DNA containing the targeted sequence is cleaved by protein-DNA interactions or RNA-guided endonucleases. The resulting double-strand break (DSB) is repaired either by the error-prone non-homologous end-joining (NHEJ) pathway or by homology-directed repair (HDR). This results in correct repair or insertions and deletions (INDELs). Supplying a single- or double-stranded DNA donor template carrying homologous sequences can facilitate precise integration of the GOI at the target locus. Concurrent delivery and cleavage of a non-homologue DNA donor template can facilitate non-directional targeted integration. Targeted genomic DNA sequence (violet); GOI (green); INDEL (red with halo).</p>
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20 pages, 2736 KiB  
Article
Dysregulated miRNA Expression and Androgen Receptor Loss in Racially Distinct Triple-Negative Breast Cancer
by Shristi Bhattarai, Bruna M. Sugita, Emanuelle Nunes-Souza, Aline S. Fonseca, Darshan Shimoga Chandrashekar, Mahak Bhargava, Luciane R. Cavalli and Ritu Aneja
Int. J. Mol. Sci. 2024, 25(24), 13679; https://doi.org/10.3390/ijms252413679 - 21 Dec 2024
Viewed by 425
Abstract
Androgen receptor (AR)-negative triple-negative breast cancer (TNBC), often termed quadruple-negative breast cancer (QNBC), disproportionately impacts women of African descent, leading to poorer overall survival (OS). MiRNAs regulate the expression of gene drivers involved in critical signaling pathways in TNBC, such as the AR [...] Read more.
Androgen receptor (AR)-negative triple-negative breast cancer (TNBC), often termed quadruple-negative breast cancer (QNBC), disproportionately impacts women of African descent, leading to poorer overall survival (OS). MiRNAs regulate the expression of gene drivers involved in critical signaling pathways in TNBC, such as the AR gene, and their expression varies across races and breast cancer subtypes. This study investigates whether differentially expressed miRNAs influence AR transcription, potentially contributing to the observed disparities between African American (AA) and European American (EA) QNBC patients. Race-annotated TNBC samples (n = 129) were analyzed for AR expression status and revealed the prevalence of QNBC in AA patients compared to EA (76.6% vs. 57.7%) and a significant association of AR loss with poor survival among AAs. The Cancer Genome Atlas (TCGA) RNA-seq data showed that AAs with TNBC (n = 32) had lower AR mRNA levels than EAs (n = 67). Among TCGA patients in the AR-low group, AAs had significantly poorer OS than EAs. In our cohort, 46 miRNAs exhibited differential expression between AAs and EAs with QNBC. Ten of these miRNAs (miR-1185-5p, miR-1305, miR-3161, miR-3690, miR-494-3p, miR-509-3-5p, miR-619-3p, miR-628-3p, miR-873-5p, and miR-877-5p) were predicted to target the AR gene/signaling. The loss of AR expression is linked to poorer prognoses in AA women. The understanding of the specific miRNAs involved and their regulatory mechanisms on AR expression could provide valuable insights into why AA women are more prone to QNBC. Full article
(This article belongs to the Special Issue Breast Cancer: From Pathophysiology to Novel Therapies)
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<p>AR immunostaining and overall survival analysis in TNBC samples of EA and AA patients. (<b>A</b>) Micrographs representing AR staining in tumor tissues from EAs (80%) and AAs (6%), AR (brown) and nuclei (blue), Insets: 20× objective; (<b>B</b>) Kaplan–Meier plots of overall survival in TNBC (n = 40) and QNBC (n = 89) patients; (<b>C</b>) cases that were EA and AA TNBC and QNBC by IHC.</p>
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<p>DNA methylation of <span class="html-italic">AR</span> gene of AA and EA patients of TCGA database. (<b>A</b>) Methylation status of <span class="html-italic">AR</span> promoter region in normal and QNBC (AA vs. EA). Beta value ranges from 0 to 1 (no to complete methylation); significance based on unpaired <span class="html-italic">t</span>-test; (<b>B</b>) oncoprint showing <span class="html-italic">AR</span> mutations and CNAs in AR-low samples (n = 278).</p>
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<p>Supervised hierarchical cluster (SHC) of global miRNA expression profiling of QNBC cases of EA and AA patients. Heatmap shows 46 DE miRNAs between AA QNBC (n = 33, blue bars) and EA QNBC (n = 9, green bars) from discovery cohort. Shown below clinical data of patients [age (&gt;50 years, 50 years), tumor size (&gt;3 cm, ≤3 cm, and grade (2, 3), and survival status (alive, deceased)].</p>
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<p>MiRNA expression profiling analysis of QNBC cases of EA and AA patients. (<b>A</b>) Partial supervised hierarchical cluster (SHC) of global miRNA profiling showing DE miRNAs between AA QNBC (n = 10, blue bars) and EA QNBC (n = 9, green bars) (validation cohort). (<b>B</b>) Six miRNAs DE between AA QNBC and EA QNBC with expression directions common in discovery and validation cohorts.</p>
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<p>ROC analysis and corresponding <span class="html-italic">p</span>-values of six miRNAs (common to both discovery and validation cohorts of patients). Black line: test real classifier value; Red line: random classifier value (AUC = 0.5).</p>
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<p>Network of six miRNAs and experimentally validated targets of <span class="html-italic">AR</span> gene (color circles). Solid lines: protein–protein interaction; dashed lines: miRNA–mRNA interaction (Cytoscape 3.9.1).</p>
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18 pages, 20472 KiB  
Article
Genome-Wide Identification and Evolutionary Analysis of Functional BBM-like Genes in Plant Species
by Zhengyuan Hong, Linghong Zhu, Chaolei Liu, Kejian Wang, Yuchun Rao and Hongwei Lu
Genes 2024, 15(12), 1614; https://doi.org/10.3390/genes15121614 - 17 Dec 2024
Viewed by 579
Abstract
Background/Objectives: BABY BOOM (BBM), a transcription factor from the APETALA2 (AP2) protein family, plays a critical role in somatic embryo induction and apomixis. BBM has now been widely applied to induce apomixis or enhance plant transformation and regeneration efficiency through overexpression or [...] Read more.
Background/Objectives: BABY BOOM (BBM), a transcription factor from the APETALA2 (AP2) protein family, plays a critical role in somatic embryo induction and apomixis. BBM has now been widely applied to induce apomixis or enhance plant transformation and regeneration efficiency through overexpression or ectopic expression. However, the structural and functional evolutionary history of BBM genes in plants is still not well understood. Methods: The protein sequences of 10 selected plant species were used to locate the branch of BBM-Like by key domain identification and phylogenetic tree construction. The identified BBML genes were used for further conserved motif identification, gene structural analysis, miRNA binding site prediction, cis-acting element prediction, collinear analysis, protein–protein interaction network construction, three-dimensional structure modeling, molecular docking, and expression pattern analysis. Results: A total of 24 BBML proteins were identified from 10 representative plant species. Phylogenetic relationship analysis displayed that BBML proteins from eudicots and monocots were divided into two clusters, with monocots exhibiting a higher number of BBMLs. Gene duplication events indicated that whole genome/segmental duplication were the primary drivers of BBML genes’ evolution in the tested species, with purifying selection playing a key role during evolution processes. Comparative analysis of motif, domains, and gene structures revealed that most BBMLs were highly evolutionarily conserved. The expression patterns of BBML genes revealed significant tissue specificity, particularly in the root and embryo. We also constructed protein–protein interaction networks and molecular docking models to identify functional pathways and key amino acid residues of BBML proteins. The functions of BBMLs may differ between monocots and eudicots, as suggested by the functional enrichment of interacting proteins. Conclusions: Our research delved into the molecular mechanism, evolutionary relationships, functional differentiation, and expression patterns of BBML genes across plants, laying the groundwork for further investigations into the molecular properties and biological roles of BBMLs. Full article
(This article belongs to the Special Issue Genetics and Genomics of Rice)
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<p>Phylogenetic analysis of BBML proteins from ten tested species. The phylogenetic tree was constructed based on the maximum-likelihood method, divided into three groups that were identified as euAP2, basalANT, and euANT. The branch in red represents putative BBMLs. The circle size indicates the bootstrap value.</p>
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<p>Phylogenetic evolutionary tree, conserved motifs, and gene structures of 24 BBML proteins. (<b>A</b>) Phylogenetic tree of BBML proteins. (<b>B</b>) Conserved motifs of the BBML proteins. Diverse colors indicate fourteen motifs. (<b>C</b>) Structural composition of <span class="html-italic">BBML</span> genes. Black lines, yellow boxes, and green boxes represent introns, CDSs, and UTRs, respectively. The scale at the bottom contrasts gene and protein lengths.</p>
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<p>Predicted cis-acting elements of <span class="html-italic">BBML</span> genes. The number in each box represents the number of corresponding elements involved in the extracted promoter regions.</p>
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<p>Extensive microcollinearity of BBM gene pairs across tested species. The chromosomes of different plant are represented by distinct colors. Amtr, Ata, Os, Ta, Zm, At, Nn, Vv, Sl, and Gm represent <span class="html-italic">A. trichopoda</span>, <span class="html-italic">A. tatarinowii</span>, <span class="html-italic">O. sativa</span>, <span class="html-italic">T. aestivum</span>, <span class="html-italic">Z. mays</span>, <span class="html-italic">A. thaliana</span>, <span class="html-italic">N. nucifera</span>, <span class="html-italic">V. vinifera</span>, <span class="html-italic">S. lycopersicum</span>, and <span class="html-italic">G. max</span>, respectively. The red curved lines denote inter-collinear relationships, and the green line represent intra-collinear relationships, as well as segmental duplication events. The gray lines symbolize the duplication events in other regions. Only the <span class="html-italic">BBML</span>-containing chromosomes were included.</p>
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<p>The heatmap of the sequence similarity of interacting proteins from <span class="html-italic">A. thaliana</span>, <span class="html-italic">G. max</span>, <span class="html-italic">O. sativa</span>, <span class="html-italic">S. lycopersicum</span>, <span class="html-italic">T. aestivum</span>, and <span class="html-italic">Z. mays</span>.</p>
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<p>Gene ontology (GO) enrichment of interacting proteins in <span class="html-italic">A. thaliana</span>, <span class="html-italic">G. max</span>, <span class="html-italic">O.sativa</span>, and <span class="html-italic">T. aestivum</span>.</p>
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<p>Three-dimensional structure modeling and molecular docking of BBML proteins. (<b>A</b>–<b>D</b>) Three-dimensional protein structure of AtBBM (<b>A</b>), BnBBM1 (<b>B</b>), OsBBM1 (<b>C</b>), and PsASGR-BBML (<b>D</b>). Red symbolizes <math display="inline"><semantics> <mi>α</mi> </semantics></math>-helix, yellow symbolizes <math display="inline"><semantics> <mi>β</mi> </semantics></math>-fold, and green symbolizes irregular curl. (<b>E</b>,<b>F</b>) The receptor–ligand interaction of interacting proteins with BBML active sites. The blue and red colors symbolize the receptor and ligand, respectively. (<b>E</b>) Molecular docking of AtBBM with AtRKD5, (<b>F</b>) molecular docking of AtBBM with AtTKL, (<b>G</b>) molecular docking of OsBBM1 with OsRAC5, and (<b>H</b>) molecular docking of TaBBM with TraesCS1B02G107000.</p>
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<p>The expression heatmap of <span class="html-italic">BBMLs</span> based on the RNA-seq database in various tissues from representative species, including <span class="html-italic">A. thaliana</span> (<b>A</b>), <span class="html-italic">G. max</span> (<b>B</b>), <span class="html-italic">T. aestivum</span> (<b>C</b>), <span class="html-italic">Z. mays</span> (<b>D</b>), <span class="html-italic">O. sative</span> (<b>E</b>), respectively. The values in each box represent the relative expression levels.</p>
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19 pages, 2110 KiB  
Review
Exosome-Derived microRNAs: Bridging the Gap Between Obesity and Type 2 Diabetes in Diagnosis and Treatment
by Iva Vukelić, Branislav Šuša, Sanja Klobučar, Sunčica Buljević, Ana-Marija Liberati Pršo, Andrej Belančić, Dario Rahelić and Dijana Detel
Diabetology 2024, 5(7), 706-724; https://doi.org/10.3390/diabetology5070052 - 17 Dec 2024
Viewed by 509
Abstract
Obesity and type 2 diabetes represent global public health challenges that are continuously growing at an alarming rate. The etiology of obesity is complex and multifactorial, with a substantial interplay between behavioral, biological, and environmental factors. Dysregulation of immunometabolism through chronic low-intensity inflammation [...] Read more.
Obesity and type 2 diabetes represent global public health challenges that are continuously growing at an alarming rate. The etiology of obesity is complex and multifactorial, with a substantial interplay between behavioral, biological, and environmental factors. Dysregulation of immunometabolism through chronic low-intensity inflammation in obesity has long been recognized as the main driver of insulin resistance and the development of type 2 diabetes. However, the intricate mechanisms underlying these alterations have yet to be fully elucidated. Exosomes are extracellular vesicles that carry biomolecules including various types of RNA molecules. Of particular importance are microRNAs (miRNAs), known as modulators of gene expression whose altered expression is observed in various pathophysiological conditions. Recent research suggests that exosome-derived miRNAs, such as miR-155, miR-27a, and miR-29, play an essential role in the regulation of inflammatory processes, while miR-122 and miR-192 are associated with metabolic dysfunction. These and many other miRNAs influence signaling pathways that are critical for maintaining insulin sensitivity, thereby contributing to the development of insulin resistance in individuals with obesity. Hence, there is a growing interest in the potential of exosomes and miRNAs as biomarkers for the early detection of insulin resistance and other obesity-related complications, as well as promising therapeutic targets or next-generation drug delivery carriers. This review provides a comprehensive overview of the interplay between exosome-derived miRNA, obesity, and type 2 diabetes and summarizes the latest findings in exosome biology. Full article
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<p>The interplay between fat accumulation and adipose tissue-derived exosomal miRNAs in macro-phage activation and polarization. The polarization of macrophages from the M2 to the M1 phenotype leads to a shift from the production of anti-inflammatory cytokines to pro-inflammatory cytokines, including TNF-α, IL6, and IL1β. This pro-inflammatory cytokine production is driven by the activation of TLR 2 and 4, which are stimulated by elevated levels of saturated fatty acids. TLR, toll-like receptor; GLUT1, glucose transporter type 1; TNF, tumor necrosis factor; TGF, transforming growth factor; IL, interleukin; miR, microRNA; ↑, up-regulation.</p>
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<p>The structure and content of an exosome. Alix, ALG-2-interacting protein X; Tsg101, tumor susceptibility gene 101; HSP, heat shock protein; HSC, heat shock cognate; FasL, fas ligand; TNF, tumor necrosis factor; mRNA, messenger RNA; miR, microRNA.</p>
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<p>The involvement of adipose tissue-derived exosomal miRNAs in the development of obesity-induced insulin resistance and the subsequent progression of T2D. The figure illustrates miRNAs originating from exosomes of altered adipose tissue that contribute to enhanced inflammation in obesity and the induction of insulin resistance through studied mechanisms. Additionally, another list of miRNAs highlights those with altered expression in the plasma of obese and/or T2D patients, with or without an explained mechanism and origin. miR, microRNA; ↑↑, up-regulation; ↓↓, down-regulation.</p>
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15 pages, 1292 KiB  
Article
Whole-Exome Sequencing, Mutational Signature Analysis, and Outcome in Multiple Myeloma—A Pilot Study
by Lorenz Oelschläger, Axel Künstner, Friederike Frey, Theo Leitner, Lisa Leypoldt, Niklas Reimer, Niklas Gebauer, Lorenz Bastian, Katja Weisel, Verena-Wilbeth Sailer, Christoph Röcken, Wolfram Klapper, Björn Konukiewitz, Eva Maria Murga Penas, Michael Forster, Natalie Schub, Helal M. M. Ahmed, Jutta Kirfel, Nikolas Christian Cornelius von Bubnoff, Hauke Busch and Cyrus Khandanpouradd Show full author list remove Hide full author list
Int. J. Mol. Sci. 2024, 25(24), 13418; https://doi.org/10.3390/ijms252413418 - 14 Dec 2024
Viewed by 686
Abstract
The complex and heterogeneous genomic landscape of multiple myeloma (MM) and many of its clinical and prognostic implications remains to be understood. In other cancers, such as breast cancer, using whole-exome sequencing (WES) and molecular signatures in clinical practice has revolutionized classification, prognostic [...] Read more.
The complex and heterogeneous genomic landscape of multiple myeloma (MM) and many of its clinical and prognostic implications remains to be understood. In other cancers, such as breast cancer, using whole-exome sequencing (WES) and molecular signatures in clinical practice has revolutionized classification, prognostic prediction, and patient management. However, such integration is still in its early stages in MM. In this study, we analyzed WES data from 35 MM patients to identify potential mutational signatures and driver mutations correlated with clinical and cytogenetic characteristics. Our findings confirm the complex mutational spectrum and its impact on previously described ontogenetic and epigenetic pathways. They show TYW1 as a possible new potential driver gene and find no significant associations of mutational signatures with clinical findings. Further studies are needed to strengthen the role of mutational signatures in the clinical context of patients with MM to improve patient management. Full article
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Figure 1
<p>Oncoplot displaying potential driver genes inferred by MutSigCV (<span class="html-italic">p</span> &lt; 0.001, <span class="html-italic">n</span> = 35). Bar plots refer to individual tumor burden (upper bar plot in mutations per megabase), −<span class="html-italic">log</span><sub>10</sub> <span class="html-italic">p</span> values retrieved from MutSigCV (<b>left</b>), and the number of samples harboring mutations in a given gene (<b>right</b>). Different classes of mutations are color-coded, and additional covariates are shown below (Revised International Scoring System (R-ISS)).</p>
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<p>Oncogenic pathways are affected by mutations found in the cohort. (<b>A</b>) Heatmap showing the individual sample contributions to affected pathways and the frequency of affected pathways in percentage; (<b>B</b>) bar graphs showing the fraction of genes mutated in a particular pathway.</p>
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<p>Somatic interactions between mutated genes selected by MutSigCV (<span class="html-italic">p</span> &lt; 0.001). Higher co-occurrence of gene mutations is shown in red, while blue refers to mutually exclusive mutations. Gene names on the left and upper side with the number of affected patients in the cohort; <span class="html-italic">p</span>-values for statistical significance marked with (<span class="html-italic">p</span> &lt; 0.05) or * (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>COSMIC single-base substitution (SBS) signatures found in the analyzed cohort. Bar graphs show the color-coded proportion of somatic signatures per individual sample.</p>
Full article ">Figure 5
<p>Kaplan–Meier curve differences in progression-free survival with mutational status of <span class="html-italic">KRAS</span>.</p>
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