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31 pages, 1158 KiB  
Article
Protective Effects of Resveratrol Against Perfluorooctanoic Acid-Induced Testicular and Epididymal Toxicity in Adult Rats Exposed During Their Prepubertal Period
by R. Pavani, K. Venkaiah, P. Gnana Prakasam, Vijaya R. Dirisala, P. Gopi Krishna, B. Kishori and S. B. Sainath
Toxics 2025, 13(2), 111; https://doi.org/10.3390/toxics13020111 (registering DOI) - 29 Jan 2025
Abstract
The antioxidant properties of resveratrol (RES) against oxidative toxicity induced by testicular toxicants are well documented. The current study aimed to investigate the probable beneficial role of RES on male reproduction in adult rats following prepubertal exposure to perfluorooctanoic acid (PFOA). Healthy rats [...] Read more.
The antioxidant properties of resveratrol (RES) against oxidative toxicity induced by testicular toxicants are well documented. The current study aimed to investigate the probable beneficial role of RES on male reproduction in adult rats following prepubertal exposure to perfluorooctanoic acid (PFOA). Healthy rats of the Wistar strain (23 days old) were allocated into four groups. Rats in group I did not receive any treatment, while rats in groups II, III, and IV received RES, PFOA, and RES + PFOA, respectively, between days 23 and 56 and were monitored for up to 90 days. Exposure to PFOA resulted in a significant reduction in spermiogram parameters, testicular 3β- and 17β-HSD activity levels, and circulatory levels of testosterone. A significant elevation in LPx, PCs, H2O2, and O2, associated with a concomitant reduction in SOD, CAT, GPx, GR, and GSH, was noticed in the testes, as well as region-specific changes in pro- and antioxidants in the epididymides of exposed rats compared to controls. A significant increase in serum FSH and LH, testicular cholesterol levels, and caspase-3 activity was observed in PFOA-exposed rats compared to controls. Histological analysis revealed that the integrity of the testes was deteriorated in PFOA-exposed rats. Transcriptomic profiling of the testes and epididymides revealed 98 and 611 altered genes, respectively. In the testes, apoptosis and glutathione pathways were disrupted, while in the epididymides, glutathione and bile secretion pathways were altered in PFOA-exposed rats. PFOA exposure resulted in the down-regulation in the testes of 17β-HSD, StAR, nfe2l2, ar, Lhcgr, and mRNA levels, associated with the up-regulation of casp3 mRNA, and down-regulation of alpha 1 adrenoceptor, muscarinic choline receptor 3, and androgen receptor in the epididymides of exposed rats compared to the controls. These events might lead to male infertility in PFOA-exposed rats. In contrast, restoration of selected reproductive variables was observed in RES plus PFOA-exposed rats compared to rats exposed to PFOA alone. Taken together, we postulate that prepubertal exposure to PFOA triggered oxidative damage and altered genes in the testes and epididymides, leading to suppressed male reproductive health in adult rats, while RES, with its steroidogenic, antiapoptotic, and antioxidant effects, restored PFOA-induced fertility potential in rats. Full article
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29 pages, 1880 KiB  
Article
Inter- and Transgenerational Effects of In Ovo Stimulation with Bioactive Compounds on Cecal Tonsils and cecal Mucosa Transcriptomes in a Chicken Model
by Mariam Ibrahim, Marek Bednarczyk, Katarzyna Stadnicka and Ewa Grochowska
Int. J. Mol. Sci. 2025, 26(3), 1174; https://doi.org/10.3390/ijms26031174 - 29 Jan 2025
Abstract
Backgroub/Objectives: Exploring how early-life nutritional interventions may impact future generations, this study examines the inter- and transgenerational effects of in ovo injection of bioactive compounds on gene expression in the cecal tonsils and cecal mucosa using a chicken model. Methods: Synbiotic PoultryStar® [...] Read more.
Backgroub/Objectives: Exploring how early-life nutritional interventions may impact future generations, this study examines the inter- and transgenerational effects of in ovo injection of bioactive compounds on gene expression in the cecal tonsils and cecal mucosa using a chicken model. Methods: Synbiotic PoultryStar® (Biomin) and choline were injected in ovo on the 12th day of egg incubation. Three experimental groups were established in the generation F1: (1) a control group (C) receiving 0.9% physiological saline (NaCl), (2) a synbiotic group (SYN) receiving 2 mg/embryo, and (3) a combined synbiotic and choline group (SYNCH) receiving 2 mg synbiotic and 0.25 mg choline per embryo. For the generations F2 and F3, the SYN and SYNCH groups were each divided into two subgroups: (A) those injected solely in F1 (SYNs and SYNCHs) and (B) those injected in each generation (SYNr and SYNCHr). At 21 weeks posthatching, cecal tonsil and cecal mucosa samples were collected from F1, F2, and F3 birds for transcriptomic analysis. Results: Gene expression profiling revealed distinct intergenerational and transgenerational patterns in both tissues. In cecal tonsils, a significant transgenerational impact on gene expression was noted in the generation F3, following a drop in F2. In contrast, cecal mucosa showed more gene expression changes in F2, indicating intergenerational effects. While some effects carried into F3, they were less pronounced, except in the SYNs group, which experienced an increase compared to F2. Conclusions: The study highlights that transgenerational effects of epigenetic modifications are dynamic and unpredictable, with effects potentially re-emerging in later generations under certain conditions or fading or intensifying over time. This study provides valuable insights into how epigenetic nutritional stimulation during embryonic development may regulate processes in the cecal tonsils and cecal mucosa across multiple generations. Our findings provide evidence supporting the phenomenon of epigenetic dynamics in a chicken model. Full article
22 pages, 1663 KiB  
Article
Transcriptional Responses of In Vitro Blood–Brain Barrier Models to Shear Stress
by Koji L. Foreman, Benjamin D. Gastfriend, Moriah E. Katt, Sean P. Palecek and Eric V. Shusta
Biomolecules 2025, 15(2), 193; https://doi.org/10.3390/biom15020193 - 29 Jan 2025
Abstract
Endothelial cells throughout the body sense blood flow, eliciting transcriptional and phenotypic responses. The brain endothelium, known as the blood–brain barrier (BBB), possesses unique barrier and transport properties, which are in part regulated by blood flow. We utilized RNA sequencing to analyze the [...] Read more.
Endothelial cells throughout the body sense blood flow, eliciting transcriptional and phenotypic responses. The brain endothelium, known as the blood–brain barrier (BBB), possesses unique barrier and transport properties, which are in part regulated by blood flow. We utilized RNA sequencing to analyze the transcriptome of primary cultured rat brain microvascular endothelial cells (BMECs), as well as three human induced pluripotent stem cell-derived models. We compared the transcriptional responses of these cells to either low (0.5 dyne/cm2) or high (12 dyne/cm2) shear stresses, and subsequent analyses identified genes and pathways that were influenced by shear including key BBB-associated genes (SLC2A1, LSR, PLVAP) and canonical endothelial shear-stress-response transcription factors (KLF2, KLF4). In addition, our analysis suggests that shear alone is insufficient to rescue the de-differentiation caused by in vitro primary BMEC culture. Overall, these datasets and analyses provide new insights into the influence of shear on BBB models that will aid in model selection and guide further model development. Full article
(This article belongs to the Special Issue Barrier Formation and Homeostasis in the Vertebrate Brain)
19 pages, 5996 KiB  
Article
Effect of Photoperiod on Ascorbic Acid Metabolism Regulation and Accumulation in Rapeseed (Brassica napus L.) Seedlings
by Chao Wang, Lieqiong Kuang, Ze Tian, Xinfa Wang, Jinxing Tu, Hanzhong Wang and Xiaoling Dun
Antioxidants 2025, 14(2), 160; https://doi.org/10.3390/antiox14020160 - 29 Jan 2025
Abstract
Ascorbic acid (AsA) is an important antioxidant for human health. The concept of “oil-vegetable-duel-purpose” can significantly enhance the economic benefits of the rapeseed industry. Rapeseed, when utilized as a vegetable, serves as a valuable food source of AsA. In this study, we integrated [...] Read more.
Ascorbic acid (AsA) is an important antioxidant for human health. The concept of “oil-vegetable-duel-purpose” can significantly enhance the economic benefits of the rapeseed industry. Rapeseed, when utilized as a vegetable, serves as a valuable food source of AsA. In this study, we integrated transcriptome and metabolome analyses, along with substrate feeding, to identify the L-galactose pathway as the primary source for AsA production, which is primarily regulated by light. Through seven different photoperiod treatments from 12 h/12 h (light/dark) to 24 h/0 h, we found that AsA content increased with longer photoperiods, as well as chlorophyll, carotenoids, and soluble sugars. However, an excessively long photoperiod led to photooxidative stress, which negatively affected biomass accumulation in rapeseed seedlings and subsequently impacted the total accumulation of AsA. Furthermore, different enzymes respond differently to different photoperiods. Analysis of the correlation between the expression levels of AsA biosynthesis-related genes and AsA content highlighted a dynamic balancing mechanism of AsA metabolism in response to different photoperiods. The study revealed that the 16 h/8 h photoperiod is optimal for long-term AsA accumulation in rapeseed seedlings. However, extending the photoperiod before harvest can enhance AsA content without compromising yield. These findings offer novel insights into an effective strategy for the biofortification of AsA in rapeseed. Full article
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<p>Overview of transcriptome analysis. (<b>A</b>) Comparison of AsA content of high and low AsA accessions at 6DAS and 12DAS. (<b>B</b>) PCA of gene expression for high- and low-AsA mixed pools. (<b>C</b>) Venn graph for DEGs from L6DAS-vs-H6DAS and L12DAS-vs-H12DAS. (<b>D</b>) The GO enrichment analysis of overlapped DEGs. (<b>E</b>) The KEGG pathway enrichment analysis of overlapped DEGs. The different letters above the bars denote significance groupings (<span class="html-italic">p</span> &lt; 0.05) as determined by ANOVA. FW, fresh weight.</p>
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<p>Overview of metabolome analysis. (<b>A</b>) PCA of metabolites for AsA mixed pools. (<b>B</b>) Venn graph for DAMs from L6DAS-vs-H6DAS and L12DAS-vs-H12DAS. (<b>C</b>) The KEGG pathway enrichment analysis of overlapped DAMs.</p>
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<p>Comprehensive analysis of gene expression, metabolites, and substrate feeding related to AsA in rapeseed seedlings. (<b>A</b>) Expression levels of genes related to AsA synthesis in the L-galactose pathway. <span class="html-italic">PGI</span>: Glucose-6-phosphate isomerase; <span class="html-italic">PMI</span>: Mannose-6-phosphate isomerase; <span class="html-italic">PMM</span>: Phosphomannomutase; <span class="html-italic">GMP</span>: GDP-D-mannose pyrophosphorylase; <span class="html-italic">GME</span>: GDP-mannose-3,5-epimerase; <span class="html-italic">GGP</span>: GDP-L-galactose phosphorylase; <span class="html-italic">GPP</span>: L-galactose-1-phosphate phosphatase; <span class="html-italic">GDH</span>: L-galactose dehydrogenase; <span class="html-italic">GLDH</span>: L-galactose-1,4-lactone dehydrogenase; (<b>B</b>) Expression levels of genes related to AsA synthesis in the recycling pathway. <span class="html-italic">AAO</span>: Ascorbate oxidase; <span class="html-italic">APX</span>: Ascorbate peroxidase; <span class="html-italic">DHAR:</span> Dehydroascorbate reductase; <span class="html-italic">MDHAR</span>: Monodehydroascorbate reductase. (<b>C</b>) DAMs in the AsA synthesis pathway identified between high- and low-AsA pools. (<b>D</b>) Comparison of AsA content in rapeseed seedlings of three accessions by adding substrates related to four AsA synthesis pathways. The different letters above the bars denote significance groupings (<span class="html-italic">p</span> &lt; 0.05) as determined by ANOVA. FW, fresh weight.</p>
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<p>Changes in AsA concentration (<b>A</b>), shoot fresh weight (<b>B</b>), total AsA content of per strain (<b>C</b>), chlorophyll content (<b>D</b>), carotenoids content (<b>E</b>), and soluble sugars content (<b>F</b>) during rapeseed seedlings growth under seven photoperiod treatments. Values and bars represent the means of three replicates ± SD. Different letters indicate significant difference (<span class="html-italic">p</span> &lt; 0.05) as obtained by one-way ANOVA test. “1 day, 2 day, 3 day, and 4 day” represents the growth days under different photoperiod conditions. FW, fresh weight.</p>
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<p>Changes in ROS-related indicators and key enzyme activities during rapeseed seedlings growth under different photoperiod treatments. (<b>A</b>) O<sup>2−</sup> content. (<b>B</b>) H<sub>2</sub>O<sub>2</sub> content. (<b>C</b>) MDA content. (<b>D</b>) GMP activity. (<b>E</b>) GGP activity. (<b>F</b>) GLDH activity. (<b>G</b>) APX activity. (<b>H</b>) DHAR activity. (<b>I</b>) MDHAR activity. Values and bars represent the means of three replicates ± SD. Different letters indicate significant difference (<span class="html-italic">p</span> &lt; 0.05) as obtained by one-way ANOVA test. FW, fresh weight.</p>
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<p>PCA (<b>A</b>) and correlation (<b>B</b>) analysis of AsA-related physiological parameters under different photoperiods. SFW, shoot fresh weight. T-AsA, total accumulation of AsA content, Chl, chlorophyll, Car, carotenoids, SS, soluble sugars.</p>
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<p>The pathways of L-galactose and recycling of AsA and related gene expression levels during rapeseed seedlings growth under different photoperiods. Values are shown as means ± SD (n = 3). The green arrows indicated the L-galactose pathway in the AsA synthesis route. The blue arrow indicated the recycling pathway in the AsA synthesis route.</p>
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25 pages, 7090 KiB  
Article
Combined Bulked Segregant Analysis-Sequencing and Transcriptome Analysis to Identify Candidate Genes Associated with Cold Stress in Brassica napus L
by Jiayi Jiang, Rihui Li, Kaixuan Wang, Yifeng Xu, Hejun Lu and Dongqing Zhang
Int. J. Mol. Sci. 2025, 26(3), 1148; https://doi.org/10.3390/ijms26031148 - 28 Jan 2025
Abstract
Cold tolerance in rapeseed is closely related to its growth, yield, and geographical distribution. However, the mechanisms underlying cold resistance in rapeseed remain unclear. This study aimed to explore cold resistance genes and provide new insights into the molecular mechanisms of cold resistance [...] Read more.
Cold tolerance in rapeseed is closely related to its growth, yield, and geographical distribution. However, the mechanisms underlying cold resistance in rapeseed remain unclear. This study aimed to explore cold resistance genes and provide new insights into the molecular mechanisms of cold resistance in rapeseed. Rapeseed M98 (cold-sensitive line) and D1 (cold-tolerant line) were used as parental lines. In their F2 population, 30 seedlings with the lowest cold damage levels and 30 with the highest cold damage levels were selected to construct cold-tolerant and cold-sensitive pools, respectively. The two pools and parental lines were analyzed using bulk segregant sequencing (BSA-seq). The G’-value analysis indicated a single peak on Chromosome C09 as the candidate interval, which had a 2.59 Mb segment with 69 candidate genes. Combined time-course and weighted gene co-expression network analyses were performed at seven time points to reveal the genetic basis of the two-parent response to low temperatures. Twelve differentially expressed genes primarily involved in plant cold resistance were identified. Combined BSA-seq and transcriptome analysis revealed BnaC09G0354200ZS, BnaC09G0353200ZS, and BnaC09G0356600ZS as the candidate genes. Quantitative real-time PCR validation of the candidate genes was consistent with RNA-seq. This study facilitates the exploration of cold tolerance mechanisms in rapeseed. Full article
(This article belongs to the Special Issue Molecular Genetics and Plant Breeding, 5th Edition)
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<p>Phenotypic observation of rapeseed seedlings in response to cold stress. (<b>a</b>) Rapeseed <span class="html-italic">D1</span> and <span class="html-italic">M98</span> seedlings grown under normal conditions (22 °C) in the plant chamber. (<b>b</b>) Phenotypes of <span class="html-italic">D1</span> and <span class="html-italic">M98</span> seedlings after cold treatment (−4 °C, 24 h) in the plant chamber. (<b>c</b>) <span class="html-italic">D1</span> and <span class="html-italic">M98</span> seedlings at 24 days of recovery (22 °C) in the plant chamber. The survival rates were evaluated at this stage, as shown in the images. (<b>d</b>–<b>h</b>) The phenotype of seedlings with cold damage level 0–4 in the field, respectively. Bar, 5 cm (<b>a</b>–<b>h</b>).</p>
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<p>Manhattan plots showing the distribution of G’-Value on the chromosomes. (<b>a</b>) Manhattan plots showing the distribution of G’-Value on all 19 chromosomes. The blue box is a single pink on the chromosome C09. (<b>b</b>) Enlarged view of chromosome C09 in (<b>a</b>), highlighting the single peak on chromosome C09. The blue box is the candidate interval for this study.</p>
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<p>Variation types of candidate genes in the candidate interval (CI).</p>
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<p>Statistics of the expressed genes. (<b>a</b>) Statistics of the expressed genes in all samples. (<b>b</b>) Statistics of the DEGs in <span class="html-italic">D1</span>, <span class="html-italic">M98,</span> and D1 vs. M98. (<b>c</b>) Statistics of the DEGs between two adjacent low-temperature treatment time points in <span class="html-italic">D1</span> and <span class="html-italic">M98</span>. Line charts showing the number of the expressed genes during different sampling time points (1st–7th are described in the methods) in rapeseed <span class="html-italic">D1</span> and <span class="html-italic">M98</span>. CK: control; T, low-temperature treatment; D1 vs. M98, a gene set after removing D1_CK from the D1_T vs. a gene set after removing M98_CK from the M98_T.</p>
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<p>Enrichment analyses of the GO annotation and KEGG pathway. The GO annotation analysis of gene set in the 6th vs. 5th time points in <span class="html-italic">D1</span> (<b>a</b>) and <span class="html-italic">M98</span> (<b>c</b>). The GO annotation analysis of the intersection of the 5th and 6th time points in <span class="html-italic">D1</span> (<b>b</b>). The KEGG pathway analysis of gene set in the 6th vs. 5th time points in <span class="html-italic">D1</span> (<b>d</b>). The KEGG pathway analysis of the intersection of the 5th and 6th time points in <span class="html-italic">D1</span> (<b>e</b>) and <span class="html-italic">M98</span> (<b>f</b>).</p>
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<p>Different gene expression patterns based on the time-course analysis. Each cluster represents a trend of gene expression, and the numbers at the bottom indicate the number of genes in the cluster. Different colored curves represent cultivars under low-temperature treatment conditions, and each curve represents the median profile of genes at different low-temperature treatment time points. CK, control; T, treatment; Sampling time points 1st–7th are described in the methods.</p>
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<p>GO enrichment analysis of the predominant expression in <span class="html-italic">D1</span> and <span class="html-italic">M98</span>. GO enrichment analysis of the predominant expression in <span class="html-italic">D1</span> (<b>a</b>) and <span class="html-italic">M98</span> (<b>b</b>), including biological process, cellular component, and molecular function. The different colors represent different clusters. The X-axis represents −log<sub>10</sub> (<span class="html-italic">p</span>-value), and the enriched GO terms are indicated on the Y-axis.</p>
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<p>Co-expression network construction and overlapping analysis with MaSigPro. (<b>a</b>,<b>b</b>) Weighted gene co-expression network analysis of the genes with dominant expression in <span class="html-italic">D1</span> and <span class="html-italic">M98</span> at seven time points of low-temperature treatment. Each row represents a module, and the correlation coefficient and the <span class="html-italic">p</span>-value calculated using Fisher’s exact test are shown in each square. The table is color-coded by correlation according to the color legend. The intensity and direction of correlations are indicated on the right-hand side of the heat map (red, positive; blue, negative). (<b>c</b>,<b>d</b>) Overlapping analyses of genes in the five clusters and eight modules in <span class="html-italic">D1</span> and the four clusters and nine modules in <span class="html-italic">M98</span>. Sampling time points 1st–7th are described in the methods.</p>
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17 pages, 3404 KiB  
Article
Unraveling the Role of RSPRY1 in TGF-β Pathway Dysregulation: Insights into the Pathogenesis of Spondyloepimetaphyseal Dysplasia
by Gozde Imren, Beren Karaosmanoglu, Bihter Muratoglu, Cansu Ozdemir, Gulen Eda Utine, Pelin Ozlem Simsek-Kiper and Ekim Z. Taskiran
Int. J. Mol. Sci. 2025, 26(3), 1134; https://doi.org/10.3390/ijms26031134 - 28 Jan 2025
Abstract
Skeletal dysplasias, characterized by bone, cartilage, and connective tissue abnormalities, often arise due to disruptions in extracellular matrix (ECM) dynamics and growth factor-dependent signaling pathways. RSPRY1, a secreted protein with RING and SPRY domains, has been implicated in bone development, yet its exact [...] Read more.
Skeletal dysplasias, characterized by bone, cartilage, and connective tissue abnormalities, often arise due to disruptions in extracellular matrix (ECM) dynamics and growth factor-dependent signaling pathways. RSPRY1, a secreted protein with RING and SPRY domains, has been implicated in bone development, yet its exact role remains to be determined. RSPRY1 gene mutations are associated with spondyloepimetaphyseal dysplasia (SEMD), a rare skeletal disorder characterized by severe epiphyseal and metaphyseal deformities. This study aimed to determine the molecular and cellular mechanisms by which RSPRY1 deficiency affects skeletal homeostasis. Transcriptome analysis of fibroblasts from patients with homozygous RSPRY1 mutations showed there was significant enrichment of transforming growth factor beta (TGF-β) signaling and ECM-related pathways. Functional wound healing assays showed that RSPRY1 knockout fibroblasts exhibited enhanced motility, a phenotype that was abrogated in RSPRY1 + SMAD3 double knockout fibroblasts, highlighting the SMAD3-dependence of RSPRY1′s effects. The observed limited response to exogenous TGF-β in RSPRY1-deficient cells indicated that there was constitutive pathway activation. These findings show that RSPRY1 is a critical regulator of TGF-β signaling in ECM dynamics and cell motility, contributing to the pathophysiology of SEMD. An improvement in our understanding of the molecular roles of RSPRY1 might yield novel therapeutic strategies that target TGF-β signaling in patients with SEMD and other skeletal dysplasias. Full article
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<p>GO enrichment analysis and volcano plot of DEGs. This figure provides an overview of the GO enrichment and differential gene expression analysis of dermal fibroblasts with homozygous RSPRY1 mutations, as compared to CTRL fibroblasts. Each GO category, (<b>A</b>) Cellular Component (<b>B</b>) Molecular Function and (<b>C</b>) Biological Process, shows the enrichment score (−log10 of the <span class="html-italic">p</span> value) on the x-axis, with a dot size proportional to the number of genes contributing to the term and color intensity indicating statistical significance (<span class="html-italic">p</span> value). (<b>D</b>) The volcano plot shows the distribution of DEGs based on fold change (x-axis) and statistical significance (−log10 of the <span class="html-italic">p</span> value, y-axis). Red dots represent significantly upregulated genes. Blue dots represent significantly downregulated genes. Non-significant genes are shown in gray. Green dots represent the genes related to the TGF-β regulation of the ECM.</p>
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<p>Enrichment and interaction analysis of TFs. TF enrichment and the interactions derived from the transcriptomic analysis of fibroblasts with RSPRY1 deficiency. (<b>A</b>) The bar chart shows the top enriched TFs based on their involvement in the DEGs. SMAD2 and SMAD3, both core components of the TGF-β signaling pathway, exhibited the highest enrichment, highlighting their dominant regulatory roles. The PPI network visualizes the interactions between the top enriched TFs. The asterisk indicates the most statistically significant TF. (<b>B</b>) The network shows the TFs’ collaborative regulatory activity in driving changes in gene expression, particularly within the TGF-β signaling pathway and ECM-related processes.</p>
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<p>The frequency of clinical findings associated with RSPRY1 mutations. “Frequency” in this figure refers to the number of overlapping clinical findings associated with RSPRY1 mutations observed in the analyzed dataset. The DEGs list obtained from 2 patients carrying RSPRY1 mutations matched the genes defined in the OMIM database and related clinical phenotypes. Firstly, clinical findings phenotypically overlapping with RSPRY1 mutations were identified, and then a frequency analysis of these findings was performed. Clinical findings were grouped based on DisGenet terms, and the most common phenotypes were ranked. Each bar represents the number of times a specific phenotype was identified as overlapping with the genes linked to RSPRY1-associated pathways.</p>
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<p>The top 10 genes and associated critical phenotypes network. This network visualization highlights the interactions between the top 10 genes with the highest association density and their linked phenotypic organ systems derived from analyzing RSPRY1-associated DEGs. Blue nodes represent the top 10 genes, including such key genes as SMAD3, COL1A1, and TRPV4, which are strongly associated with the TGF-β signaling pathway and ECM regulation. Orange nodes represent the phenotypic organ systems, including such critical features as eye, neck, short stature, and neurological phenotypes. Edges (lines) indicate the interaction or association between genes and phenotypic organ systems, with a denser connectivity representing a higher degree of association.</p>
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<p>Wound-healing assay. Representative wound-healing assay images at 0, 24, 48, and 96 h. (<b>A</b>). Wound-healing dynamics in fibroblast groups, including CTRL (Cas9), RSPRY1 KO, SMAD3 KO, and RSPRY1 + SMAD3 + double KO cells without TGF-β supplementation, highlighting differences in wound closure rates across conditions. (<b>B</b>). Wound-healing dynamics in the same experimental fibroblast groups under TGF-β supplementation. Yellow outlines indicate wound borders at each time point. Scale bar, 100 μm.</p>
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<p>Wound closure percentage. (<b>A</b>). Line graph showing the percentage of wound closure over time (0–96 h), with error bars representing standard deviations. (<b>B</b>). Bar graph comparing wound closure percentages across fibroblast groups at 96 h under TGF-β-supplemented and standard conditions.</p>
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16 pages, 2388 KiB  
Article
Polo-like Kinase 1 Inhibitors Demonstrate In Vitro and In Vivo Efficacy in Preclinical Models of Small Cell Lung Cancer
by Guojing Zhang, Abbe Pannucci, Andrey A. Ivanov, Jeffrey Switchenko, Shi-Yong Sun, Gabriel L. Sica, Zhentao Liu, Yufei Huang, John C. Schmitz and Taofeek K. Owonikoko
Cancers 2025, 17(3), 446; https://doi.org/10.3390/cancers17030446 - 28 Jan 2025
Abstract
Objective: To investigate the preclinical efficacy and identify predictive biomarkers of polo-like kinase 1 (PLK1) inhibitors in small cell lung cancer (SCLC) models. Methods: We tested the cytotoxicity of selective PLK1 inhibitors (rigosertib, volasertib, and onvansertib) in a panel of SCLC cell lines. [...] Read more.
Objective: To investigate the preclinical efficacy and identify predictive biomarkers of polo-like kinase 1 (PLK1) inhibitors in small cell lung cancer (SCLC) models. Methods: We tested the cytotoxicity of selective PLK1 inhibitors (rigosertib, volasertib, and onvansertib) in a panel of SCLC cell lines. We confirmed the therapeutic efficacy of subcutaneous xenografts of representative cell lines and in four patient-derived xenograft models generated from patients with platinum-sensitive and platinum-resistant SCLC. We employed an integrated analysis of genomic and transcriptomic sequencing data to identify potential biomarkers of the activity and mechanisms of resistance in laboratory-derived resistance models. Results: Volasertib, rigosertib, and onvansertib showed strong in vitro cytotoxicity at nanomolar concentrations in human SCLC cell lines. Rigosertib, volasertib, and onvansertib showed equivalent efficacy to that of standard care agents (irinotecan and cisplatin) in vivo with significant growth inhibition superior to cisplatin in PDX models of platinum-sensitive and platinum-resistant SCLC. There was an association between YAP1 expression and disruptive or inactivation TP53 gene mutations, with greater efficacy of PLK1 inhibitors. Comparison of lab-derived onvansertib-resistant H526 cells to parental cells revealed differential gene expression with upregulation of NAP1L3, CYP7B1, AKAP7, and FOXG1 and downregulation of RPS4Y1, KDM5D, USP9Y, and EIF1AY highlighting the potential mechanisms of resistance in the clinical setting. Conclusions: We established the efficacy of PLK1 inhibitors in vitro and in vivo using PDX models of platinum-sensitive and resistant relapsed SCLC. An ongoing phase II trial is currently testing the efficacy of onvansertib in patients with SCLC (NCT05450965). Full article
(This article belongs to the Section Molecular Cancer Biology)
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<p>Assessment of in vitro antiproliferative activity of targeted agents in a panel of SCLC cell lines (<b>a</b>); effect of volasertib (<b>b</b>) and onvansertib (<b>c</b>) on proliferation of SCLC cell lines. Cells were treated for 72 h with indicated agents. Cell proliferation was determined using colorimetric or luminescent assays depending on the degree of clustering of SCLC cell lines in culture. Values represent the mean ± S.D. from a minimum of 3 independent experiments. Blue and orange curves define cell lines with non-disruptive and disruptive p53 mutations, respectively. Basal protein expression in SCLC cell lines (<b>d</b>). SCLC subtype based on expression are indicated after each cell line: ASCL1 (A), POU2F3 (P), YAP1 (Y).</p>
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<p>In vivo efficacy of PLK1 inhibitors in SCLC. Mice bearing H526 xenografts were i.p. administered volasertib (20 mg/kg), irinotecan (25 mg/kg), or cisplatin (3 mg/kg) weekly. Tumor volumes represent the mean ± SEM from groups of 6 mice. *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Antitumor efficacy of PLK1 inhibitors in SCLC PDXs. Mice bearing platinum-resistant PDXs TKO-002 and TKO-008 (<b>a</b>,<b>b</b>) and platinum-sensitive PDXs TKO-005 and TKO-010 (<b>c</b>,<b>d</b>) were administered cisplatin (3 mg/kg; i.p. weekly), rigosertib (250 mg/kg; i.p. daily), and onvansertib (60 mg/kg; oral × 10 days, 4 days off). Tumor volumes represent the mean ± S.D. from groups of 6 mice per group. *: significant and ns: not significant versus control group. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Correlative analysis between <span class="html-italic">TP53</span>, <span class="html-italic">PLK1</span>, and <span class="html-italic">MYC</span> expression (NCBI public database Gene Expression Omnibus GSE55830 [<a href="#B30-cancers-17-00446" class="html-bibr">30</a>]) and cell line sensitivity to PLK1 inhibition (<b>a</b>); YAP1 expression in SCLC-Y cell lines versus other subtypes (<b>b</b>); volcano plot of differentially expressed genes between SCLC-Y and not SCLC-Y cell lines (<b>c</b>); analysis of therapeutic vulnerability based on differential sensitivity of YAP1-positive cell lines showing PLK1 inhibitor as a potential candidate (<b>d</b>); Reactome analysis of active cellular function based on DEG between SCLC-Y and not SCLC-Y lines identified major differences in immune regulation and muscle contraction (<b>e</b>); KEGG analysis of differentially activated signaling pathways between the 2 groups (<b>f</b>).</p>
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<p>Effect of <span class="html-italic">TP53</span> mutational status on PLK1 inhibitor sensitivity. Comparison of mean IC<sub>50</sub> to <span class="html-italic">TP53</span> gene mutation status (<b>a</b>). <span class="html-italic">TP53</span> gene status in 166 tumor samples in cbioportal.org (<b>b</b>) and 50 SCLC cell lines from publicly available CCLE data (<b>c</b>). Activity of PLK1 inhibitor onvansertib in parental and resistant H526 cells (IC<sub>50</sub> concentration in the resistant vs. parent: 447 nM vs. 51 nM) (<b>d</b>). Gene expression profiles of matched parental and PLK1 inhibitor resistant H526 cells from 3 separate samples (<b>e</b>). Heatmap shows the top differential gene expression (<span class="html-italic">p</span>-adj &lt; 0.5; logFC &gt; 4 cut-off) with red indicating high and blue indicating low natural log-transformed expression.</p>
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14 pages, 4345 KiB  
Article
Morphological and Transcriptome Analysis of the Near-Threatened Orchid Habenaria radiata with Petals Shaped Like a Flying White Bird
by Seiji Takeda, Yuki Nishikawa, Tsutomu Tachibana, Takumi Higaki, Tomoaki Sakamoto and Seisuke Kimura
Plants 2025, 14(3), 393; https://doi.org/10.3390/plants14030393 - 28 Jan 2025
Abstract
Orchids have evolved flowers with unique morphologies through coevolution with pollinators, such as insects. Among the floral organs, the lip (labellum), one of the three petals, exhibits a distinctive shape and plays a crucial role in attracting pollinators and facilitating pollination in many [...] Read more.
Orchids have evolved flowers with unique morphologies through coevolution with pollinators, such as insects. Among the floral organs, the lip (labellum), one of the three petals, exhibits a distinctive shape and plays a crucial role in attracting pollinators and facilitating pollination in many orchids. The lip of the terrestrial orchid Habenaria radiata is shaped like a flying white bird and is believed to attract and provide a platform for nectar-feeding pollinators, such as hawk moths. To elucidate the mechanism of lip morphogenesis, we conducted time-lapse imaging of blooming flowers to observe the extension process of the lip and analyzed the cellular morphology during the generation of serrations. We found that the wing part of the lip folds inward in the bud and fully expands in two hours after blooming. The serrations of the lip were initially formed through cell division and later deepened through polar cell elongation. Transcriptome analysis of floral buds revealed the expression of genes involved in floral organ development, cell division, and meiosis. Additionally, genes involved in serration formation are also expressed in floral buds. This study provides insights into the mechanism underlying the formation of the unique lip morphology in Habenaria radiata. Full article
(This article belongs to the Section Plant Development and Morphogenesis)
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Figure 1
<p>Flowering and withering of <span class="html-italic">Habenaria radiata</span> flowers captured by interval shooting (time-lapse). (<b>A</b>–<b>G</b>) Flowering and withering of flowers. The date (dd/mm) and time of capture are shown. Note that the upper flower, which bloomed later, withered earlier than the lower flower (<b>G</b>), probably due to the loss of pollinium (arrowheads in (<b>D</b>,<b>E</b>)) and subsequent pollination. (<b>H<sub>1</sub></b>–<b>H<sub>12</sub></b>,<b>I<sub>1</sub></b>–<b>I<sub>6</sub></b>) Side (<b>H<sub>1</sub></b>–<b>H<sub>12</sub></b>) and front (<b>I<sub>1</sub></b>–<b>I<sub>6</sub></b>) views of lip unfolding. The captured time is shown in each panel.</p>
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<p>Lip development. (<b>A</b>–<b>E</b>) Floral buds at different stages. Arrowheads in B, C, and D indicate the growing spur. (<b>F</b>–<b>J</b>) Lip inside the floral bud shown in (<b>A</b>–<b>E</b>). The lengths of the floral buds are 1 mm (<b>A</b>,<b>F</b>), 2 mm (<b>B</b>,<b>G</b>), 4 mm (<b>C</b>,<b>H</b>), 6 mm (<b>D</b>,<b>I</b>), and 7 mm (<b>E</b>,<b>J</b>). Scale bars: A, F = 0.5 mm; B, G = 1 mm; C, D, E, H, I, J = 2 mm.</p>
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<p>Cell shape changes during the development of lip serration. (<b>A</b>) Confocal laser microscopy images of petal margin cells. Petals from early to late (<b>A1</b>–<b>A5</b>) stages were excised, stained, and observed. (<b>B1</b>–<b>B5</b>) Distribution of cell area. (<b>C1</b>–<b>C5</b>) Elongation direction of each cell. The direction of serration elongation was set as 90 degrees, with the angles relative to this direction shown in different colors. (<b>D1</b>–<b>D5</b>) Scatter plots of cell area and elongation direction for each stage. Up to time point 3, cell proliferation occurs, and from time point 4 onward, the serrations deepen due to polarized cell elongation. Scale bars: 50 µm.</p>
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<p>Transcriptome analysis of floral buds. (<b>A</b>) Venn diagram showing genes expressed more than twice as much in floral buds of 3 mm, 4 mm, and 5 mm sizes compared to leaves. (<b>B</b>) Self-organizing map (SOM) analysis of the genes expressed in floral buds. The letters represent clusters with similar expression patterns, and the numbers indicate the gene number in each cluster. Clusters G, H, and I show an increase in expression during bud development, while clusters A, B, and C show a decrease in expression.</p>
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<p>RT-PCR of floral homeotic and MADS genes in <span class="html-italic">Habenaria radiata</span>. Underlined genes are reported for the first time in this work. HrACTIN was used as the control.</p>
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<p>Overexpression of <span class="html-italic">HrAP2</span> and <span class="html-italic">HrAG2</span> in <span class="html-italic">Arabidopsis thaliana</span>. (<b>A</b>) <span class="html-italic">ap2-3</span> mutant. (<b>B</b>) <span class="html-italic">35S:HrAP2</span> plant in the <span class="html-italic">ap2-3</span> background. Arrowhead indicates the petaloid stamen. Note that sepal-like organs are generated in the first whorl, and more stamens were produced compared to the <span class="html-italic">ap2-3</span> mutant. (<b>C</b>) <span class="html-italic">ag-1</span> flower. (<b>D</b>) <span class="html-italic">35S:HrAG2</span> plant in the <span class="html-italic">ag-1</span> background. Note that stamen-like organs are generated. (<b>E</b>,<b>F</b>) <span class="html-italic">35S:HrAG2</span> plants in <span class="html-italic">AG-1</span> (wild-type sibling) background. (<b>E</b>) Stamens are generated instead of petals in the second whorl. (<b>F</b>) Vegetative phenotype showing hyponastic growth in leaves, resulting in the curled leaves. Scale bars: A, B, C, D, E = 1 mm; F = 5 mm.</p>
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<p>Digital gene expression (DGE) of genes involved in serration formation in floral buds.</p>
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17 pages, 2706 KiB  
Article
Integrated Metabolomic and Transcriptomic Analysis Revealed the Mechanism of BHPF Exposure in Endometrium
by Xin Tan, Nengyong Ouyang, Wenjun Wang and Junting Qiu
Toxics 2025, 13(2), 100; https://doi.org/10.3390/toxics13020100 - 27 Jan 2025
Abstract
Fluorene-9-bisphenol (BHPF) has been increasingly used as a bisphenol A substitute in the synthesis of various products. Previous studies have suggested that BHPF can be released from plastic bottles into drinking water, and BHPF accumulation has been reported to cause various adverse effects [...] Read more.
Fluorene-9-bisphenol (BHPF) has been increasingly used as a bisphenol A substitute in the synthesis of various products. Previous studies have suggested that BHPF can be released from plastic bottles into drinking water, and BHPF accumulation has been reported to cause various adverse effects in humans. Nevertheless, the impact of BHPF exposure on endometrial epithelial cells remains largely unexplored. Here, we investigated the effects of exposure to different concentrations of BHPF on endometrial cells and used integrated metabolomic and transcriptomic methods to elucidate the underlying molecular mechanisms. Our results revealed significant associations between specific metabolites and genes, indicating that low-concentration exposure to BHPF affects endometrial epithelial cells by targeting pathways related to primary immunodeficiency, in which the key genes are IL7R and PTPRC. High-concentration exposure to BHPF decreased cell viability by regulating the purine metabolism pathway, as well as dysregulating the expression of PGM1, PDE3B, AK9, and ENTPD8. Our study highlights that the health risk of BHPF exposure to endometrial epithelial cells is concentration-dependent and that integrated analysis of metabolomic and transcriptomic data not only revealed the biological effects of BHPF and its underlying mechanisms, but also provided key candidate target genes for further exploration. Full article
(This article belongs to the Section Exposome Analysis and Risk Assessment)
18 pages, 6491 KiB  
Article
An Integrated Approach Utilizing Single-Cell and Bulk RNA-Sequencing for the Identification of a Mitophagy-Associated Genes Signature: Implications for Prognostication and Therapeutic Stratification in Prostate Cancer
by Yuke Zhang, Li Ding, Zhijin Zhang, Liliang Shen, Yadong Guo, Wentao Zhang, Yang Yu, Zhuoran Gu, Ji Liu, Aimaitiaji Kadier, Jiang Geng, Shiyu Mao and Xudong Yao
Biomedicines 2025, 13(2), 311; https://doi.org/10.3390/biomedicines13020311 - 27 Jan 2025
Abstract
Introduction: Prostate cancer, notably prostate adenocarcinoma (PARD), has high incidence and mortality rates. Although typically resistant to immunotherapy, recent studies have found immune targets for prostate cancer. Stratifying patients by molecular subtypes may identify those who could benefit from immunotherapy. Methods: [...] Read more.
Introduction: Prostate cancer, notably prostate adenocarcinoma (PARD), has high incidence and mortality rates. Although typically resistant to immunotherapy, recent studies have found immune targets for prostate cancer. Stratifying patients by molecular subtypes may identify those who could benefit from immunotherapy. Methods: We used single-cell and bulk RNA sequencing data from GEO and TCGA databases. We characterized the tumor microenvironment at the single-cell level, analyzing cell interactions and identifying fibroblasts linked to mitophagy. Target genes were narrowed down at the bulk transcriptome level to construct a PARD prognosis prediction nomogram. Unsupervised consensus clustering classified PARD into subtypes, analyzing differences in clinical features, immune infiltration, and immunotherapy. Furthermore, the cellular functions of the genes of interest were verified in vitro. Results: We identified ten cell types and 160 mitophagy-related single-cell differentially expressed genes (MR-scDEGs). Strong interactions were observed between fibroblasts, endothelial cells, CD8+ T cells, and NK cells. Fibroblasts linked to mitophagy were divided into six subtypes. Intersection of DEGs from three bulk datasets with MR-scDEGs identified 26 key genes clustered into two subgroups. COX regression analysis identified seven prognostic key genes, enabling a prognostic nomogram model. High and low-risk groups showed significant differences in clinical features, immune infiltration, immunotherapy, and drug sensitivity. In prostate cancer cell lines, CAV1, PALLD, and ITGB8 are upregulated, while CLDN7 is downregulated. Knockdown of PALLD significantly inhibits the proliferation and colony-forming ability of PC3 and DU145 cells, suggesting the important roles of this gene in prostate cancer progression. Conclusions: This study analyzed mitophagy-related genes in PARD, predicting prognosis and aiding in subtype identification and immunotherapy response analysis. This approach offers new strategies for treating prostate cancer with specific molecular subtypes and helps develop potential biomarkers for personalized medicine strategies. Full article
(This article belongs to the Section Cancer Biology and Oncology)
13 pages, 886 KiB  
Article
The Molecular Mechanism of Clock in Thermal Adaptation of Two Congeneric Oyster Species
by Zhuxiang Jiang, Chaogang Wang, Mingyang Du, Rihao Cong, Ao Li, Wei Wang, Guofan Zhang and Li Li
Int. J. Mol. Sci. 2025, 26(3), 1109; https://doi.org/10.3390/ijms26031109 - 27 Jan 2025
Abstract
Clock genes regulate physiological and metabolic processes by responding to changes in environmental light and temperature, and genetic variations in these genes may facilitate environmental adaptation, offering opportunities for resilience to climate change. However, the genetic and molecular mechanisms remain unclear in marine [...] Read more.
Clock genes regulate physiological and metabolic processes by responding to changes in environmental light and temperature, and genetic variations in these genes may facilitate environmental adaptation, offering opportunities for resilience to climate change. However, the genetic and molecular mechanisms remain unclear in marine organisms. In this study, we investigated the role of a key clock gene, the circadian locomotor output cycles kaput (Clock), in thermal adaptation using DNA affinity purification sequencing (DAP-Seq) and RNA interference (RNAi)-based transcriptome analysis. In cold-adapted Crassostrea gigas and warm-adapted Crassostrea angulata, Clock was subject to environmental selection and exhibited contrasting expression patterns. The transcriptome analysis revealed 2054 differentially expressed genes (DEGs) following the knockdown of the Clock expression, while DAP-Seq identified 150,807 genes regulated by Clock, including 5273 genes located in promoter regions. The combined analyses identified 201 overlapping genes between the two datasets, of which 98 were annotated in public databases. These 98 genes displayed distinct expression patterns in C. gigas and C. angulata under heat stress, which were potentially regulated by Clock, indicating its role in a molecular regulatory network that responds to heat stress. Notably, a heat-shock protein 70 family gene (Hsp12b) and a tripartite motif-containing protein (Trim3) were significantly upregulated in C. angulata but showed no significant changes in C. gigas, further highlighting their critical roles in thermal adaptation. This study preliminarily constructs a thermal regulatory network involving Clock, providing insights into the molecular mechanisms of clock genes in thermal adaptation. Full article
(This article belongs to the Section Molecular Biology)
13 pages, 1519 KiB  
Article
Transcriptome-Wide Analysis of N6-Methyladenosine-Modified Long Noncoding RNAs in Particulate Matter-Induced Lung Injury
by Yingying Zeng, Guiping Zhu, Wenjun Peng, Hui Cai, Chong Lu, Ling Ye, Meiling Jin and Jian Wang
Toxics 2025, 13(2), 98; https://doi.org/10.3390/toxics13020098 - 27 Jan 2025
Abstract
Background: N6-methyladenosine (m6A) modification plays a crucial role in the regulation of diverse cellular processes influenced by environmental factors. Nevertheless, the involvement of m6A-modified long noncoding RNAs (lncRNAs) in the pathogenesis of lung injury induced by particulate matter (PM) [...] Read more.
Background: N6-methyladenosine (m6A) modification plays a crucial role in the regulation of diverse cellular processes influenced by environmental factors. Nevertheless, the involvement of m6A-modified long noncoding RNAs (lncRNAs) in the pathogenesis of lung injury induced by particulate matter (PM) remains largely unexplored. Methods: Here, we establish a mouse model of PM-induced lung injury. We utilized m6A-modified RNA immunoprecipitation sequencing (MeRIP-seq) to identify differentially expressed m6A peaks on long non-coding RNAs (lncRNAs). Concurrently, we performed lncRNA sequencing (lncRNA-seq) to determine the differentially expressed lncRNAs. The candidate m6A-modified lncRNAs in the lung tissues of mice were identified through the intersection of the data obtained from these two sequencing approaches. Results: A total of 664 hypermethylated m6A peaks on 644 lncRNAs and 367 hypomethylated m6A peaks on 358 lncRNAs are confirmed. We use bioinformatic tools to analyze the potential functions and pathways of these m6A-modified lncRNAs, revealing their involvement in regulating inflammation, immune response, and metabolism-related pathways. Three key m6A-modified lncRNAs (lncRNA NR_003508, lncRNA uc008scb.1, and lncRNA ENSMUST00000159072) are identified through a joint analysis of the MeRIP-seq and lncRNA-seq data, and their validation is carried out using MeRIP-PCR and qRT-PCR. Analysis of the coding-non-coding gene co-expression network reveals that m6A-modified lncRNAs NR_003508 and uc008scb.1 participate in regulating pathways associated with inflammation and immune response. Conclusions: This study first provides a comprehensive transcriptome-wide analysis of m6A methylation profiling in lncRNAs associated with PM-induced lung injury and identifies three pivotal candidate m6A-modified lncRNAs. These findings shed light on a novel regulatory mechanism underlying PM-induced lung injury. Full article
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Figure 1
<p>Overview of m<sup>6</sup>A modification on lncRNAs in lung tissues of mice with or without PM exposure. (<b>A</b>) Venn diagram showing the numbers of unique and common m<sup>6</sup>A peaks on lncRNAs. (<b>B</b>) Venn diagram showing the numbers of unique and common m<sup>6</sup>A-modified lncRNAs. (<b>C</b>) Proportions of lncRNAs containing varying numbers of m<sup>6</sup>A peaks in both groups. (<b>D</b>) The motif enriched from m<sup>6</sup>A peaks in both groups. (<b>E</b>) Circos plot showing the distribution of differentially hypermethylated and hypomethylated m<sup>6</sup>A peaks in different chromosome. (<b>F</b>) Polar bar diagram displaying the number of hypotheylated and hypermethylated m<sup>6</sup>A peaks per chromosome in two groups. PM, particulate matter.</p>
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<p>GO enrichment and KEGG analysis of lncRNAs with differentially methylated m<sup>6</sup>A peaks. (<b>A</b>) GO enrichment of lncRNAs with differentially hypermethylated m<sup>6</sup>A peaks in the biological process category. (<b>B</b>) GO enrichment of lncRNAs with differentially hypomethylated m<sup>6</sup>A peaks in the biological process category. (<b>C</b>) GO enrichment of lncRNAs with differentially hypermethylated m<sup>6</sup>A peaks in the cellular component category. (<b>D</b>) GO enrichment of lncRNAs with differentially hypomethylated m<sup>6</sup>A peaks in the cellular component category. (<b>E</b>) GO enrichment of lncRNAs with differentially hypermethylated m<sup>6</sup>A peaks in the molecular function category. (<b>F</b>) GO enrichment of lncRNAs with differentially hypomethylated m<sup>6</sup>A peaks in the molecular function category. (<b>G</b>) KEGG analysis of lncRNAs with differentially hypermethylated m<sup>6</sup>A peaks. (<b>H</b>) KEGG analysis of lncRNAs with differentially hypomethylated m<sup>6</sup>A peaks. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes.</p>
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<p>Conjoint analysis of MeRIP-seq and lncRNA-seq data. (<b>A</b>) Four-quadrant graph showing the lncRNAs with differentially methylated m<sup>6</sup>A peaks. The red dots represent the downregulated lncRNAs with m<sup>6</sup>A hypermethylation, the purple dots represent the upregulated lncRNAs with m<sup>6</sup>A hypermethylation, the blue dots represent downregulated lncRNAs with m<sup>6</sup>A hypomethylation, and the green dots represent upregulated lncRNAs with m<sup>6</sup>A hypomethylation. (<b>B</b>) GO analysis of the lncRNAs harboring differentially methylated m<sup>6</sup>A peaks. (<b>C</b>) KEGG analysis of the lncRNAs harboring differentially methylated m<sup>6</sup>A peaks. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes.</p>
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<p>The validation of three m<sup>6</sup>A-modified lncRNAs in the lung tissues from PM-exposed mice. (<b>A</b>) Visualization of m<sup>6</sup>A peaks on lncRNA NR_003508, lncRNA uc008scb.1, and lncRNA ENSMUST00000159072 in lung tissues of mice with or without PM exposure. (<b>B</b>) MeRIP-qPCR indicating the level of m<sup>6</sup>A modification on lncRNA NR_003508, lncRNA uc008scb.1, and lncRNA ENSMUST00000159072 in lung tissues of PM-exposed mice. (<b>C</b>) RT-qPCR showing the relative expression of lncRNA NR_003508, lncRNA uc008scb.1, and lncRNA ENSMUST00000159072 in lung tissues of mice exposed to PM. Values represent mean ± SEM; *, <span class="html-italic">p</span> &lt; 0.05, **, <span class="html-italic">p</span> &lt; 0.01, compared with the control group, n = 3. PM, particulate matter.</p>
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<p>Construction of lncRNA-mRNA co-expression network for three m<sup>6</sup>A-modified lncRNAs. (<b>A</b>) The lncRNA-mRNA co-expression network of three lncRNAs (lncRNA NR_003508, lncRNA uc008scb.1, and lncRNA ENSMUST00000159072) and their co-expressed mRNAs. (<b>B</b>) The Sankey diagram showing the potential pathways regulated by lncRNA NR_003508 and lncRNA uc008scb.1, along with their co-expressed mRNAs.</p>
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20 pages, 5808 KiB  
Article
Genomically Selected Genes Associated with a High Rate of Egg Production in Puan Panjiang Black-Bone Chickens
by Xiaomeng Miao, Zhiying Huang, Jia Liu, Li Zhang, Yulong Feng, Yalan Zhang, Diyan Li and Zhonghua Ning
Animals 2025, 15(3), 363; https://doi.org/10.3390/ani15030363 - 27 Jan 2025
Abstract
Puan Panjiang black-bone chickens are renowned for their distinctive traits, deep black coloration, and high-quality protein content, making them a focus of genetic research due to their unique egg-laying abilities. In this study, 110 Puan Panjiang black-bone chickens were used to investigate the [...] Read more.
Puan Panjiang black-bone chickens are renowned for their distinctive traits, deep black coloration, and high-quality protein content, making them a focus of genetic research due to their unique egg-laying abilities. In this study, 110 Puan Panjiang black-bone chickens were used to investigate the effects of natural and artificial selection influencing egg production. Whole-genome resequencing data from red junglefowl (RJF) and high-egg-production (HEP) and low-egg-production (LEP) groups of Puan Panjiang black-bone chickens revealed significant genetic variants associated with egg production traits. Additionally, transcriptome analysis of 47 samples from ovary stroma, small white follicles (SWFs), small yellow follicles (SYFs), and liver tissues from 6 HEP and 6 LEP groups identified differentially expressed genes. Notably, differences in egg production were linked to small yellow follicles rather than ovary stroma or SWFs. Key candidate genes, including TRIM7, CASR, SPTBN5, GAL1, ZP1, IL4I1, and CCL19, were identified as potential contributors to egg-laying performance. This study underscores the genetic diversity within this breed and provides valuable insights for future breeding programs to enhance egg production, supporting the sustainable development of this local resource. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>The geographical location and sampling locations of Pu’an County, Guizhou Province, China.</p>
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<p>The comparative egg production performance of HEP and LEP. (<b>A</b>) Violin plot comparing egg production in the HEP and LEP groups. Significant differences in egg production were observed, with HEP laying more eggs than LEP (<span class="html-italic">p</span> = 2.22 × 10<sup>−16</sup>). (<b>B</b>) Bar graph showing egg production for each sample in the HEP and LEP groups.</p>
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<p>Genomic regions displaying robust selective sweep signals were examined in Guizhou local chicken breeds and RJFs. Venn diagrams showing the number of selection genes identified by comparing RJF and the two chicken populations separately (<b>A</b>) and when the two populations are compared with each other (<b>B</b>). Genome-wide selective scanning analysis between HEP (<b>C</b>) and LEP (<b>D</b>) and RJF. Horizontal dashed lines show the significance level of α = 0.05. The distribution of θπ ratios (<span class="html-italic">θπ</span>, domestic/<span class="html-italic">θπ</span>, RJF) and <span class="html-italic">FST</span> values for HEP and LEP (<b>E</b>–<b>H</b>) is presented, calculated within 40 kb windows sliding in 10 kb increments. Data points situated to the left and right of the left and right vertical dashed lines, respectively (corresponding to the 5% left and right tails of the empirical <span class="html-italic">θπ</span> ratio distribution) and above the horizontal dashed line (representing the 5% right tail of the empirical <span class="html-italic">FST</span> distribution), were identified as selected regions for HEP and LEP (depicted as red points), respectively.</p>
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<p>Enrichment analysis of selected genes in two chicken populations. KEGG, Reactome, and GO analyses of selected genes in HEP (<b>A</b>–<b>C</b>) and LEP (<b>D</b>–<b>F</b>) in comparison with RJF are shown.</p>
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<p>Enrichment analysis of selected genes in two chicken populations. KEGG, Reactome, and GO analyses of selected genes in HEP (<b>A</b>–<b>C</b>) and LEP (<b>D</b>–<b>F</b>) in two chicken populations compared with each other are shown.</p>
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<p>Analysis of differentially expressed genes. (<b>A</b>–<b>D</b>) Principal component analysis (PCA) plots of liver (<b>A</b>), stroma (<b>B</b>), SWF (<b>C</b>), and SYF (<b>D</b>) between HEP and LEP. (<b>E</b>–<b>H</b>) Volcano plots of differentially expressed genes in the liver (<b>E</b>), stroma (<b>F</b>), SWF (<b>G</b>), and SYF (<b>H</b>) between HEP and LEP. The horizontal axis reflects the fold change distribution of the differentially expressed genes, typically represented as Log2(fold change). The further a point deviates from the center, the greater the fold change. The vertical axis is −Log10(adjusted <span class="html-italic">p</span>-value), with points closer to the top and bottom indicating higher significance. In the plot, red points represent upregulated genes, blue points represent downregulated genes, and gray points indicate genes with no significant difference.</p>
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<p>Functional enrichment analysis of differentially expressed genes in liver samples. (<b>A</b>) Gene ontology (GO) of differentially expressed genes in liver samples from the HEP group compared to the LEP group. (<b>B</b>) KEGG enrichment analysis of differentially expressed genes in liver samples from the HEP group compared to the LEP group.</p>
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<p>Functional enrichment analysis of differentially expressed genes in SYF samples. (<b>A</b>) Gene ontology (GO) of differentially expressed genes in SYF samples from the HEP group compared to the LEP group. (<b>B</b>) KEGG enrichment analysis of differentially expressed genes in SYF samples from the HEP group compared to the LEP group.</p>
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14 pages, 5874 KiB  
Article
Multi-Omics Sequencing Dissects the Atlas of Seminal Plasma Exosomes from Semen Containing Low or High Rates of Sperm with Cytoplasmic Droplets
by Zilu Zhang, Xiaoxian Xu, Fumei Chen, Qingyou Liu, Zhili Li, Xibang Zheng and Yunxiang Zhao
Int. J. Mol. Sci. 2025, 26(3), 1096; https://doi.org/10.3390/ijms26031096 - 27 Jan 2025
Abstract
Sperm cytoplasmic droplets (CDs) are remnants of cytoplasm that can cause a number of problems if it not shed from the sperm after ejaculation. Exosomes can rapidly bind to sperm, but it is not clear whether exosomes can affect the migration and shedding [...] Read more.
Sperm cytoplasmic droplets (CDs) are remnants of cytoplasm that can cause a number of problems if it not shed from the sperm after ejaculation. Exosomes can rapidly bind to sperm, but it is not clear whether exosomes can affect the migration and shedding of CDs. We first extracted and characterized seminal plasma exosomes from boar semen containing sperm with low or high rates of CDs. Then, the transcriptomic and proteomic detection of these exosomes were performed to analyze the differences between the two groups of seminal plasma exosomes. The results revealed that 486 differentially expressed genes (DEGs), 40 differentially expressed proteins (DEPs), and 503 differentially expressed lncRNAs (DElncRNAs) were identified between the low CD rate group and high CD rate group. Integrative multi-omics analysis showed that exosome components may affect migration and shedding of cytoplasmic droplets by influencing cytoskeletal regulation and insulin signaling, including regulation of the actin cytoskeleton, ECM–receptor interaction, axon guidance, insulin secretion, and the insulin signaling pathway. Overall, our study systematically revealed the DEGs, DEPs, and DElncRNAs in seminal plasma exosomes between low CD rate semen and high CD rate semen, which will help broaden our understanding of the complex molecular mechanisms involved in the shedding of CDs. Full article
(This article belongs to the Section Molecular Informatics)
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Figure 1

Figure 1
<p><b>Characterization of exosomes.</b> (<b>A</b>) Overview of the experimental design for multi-omics analysis. (<b>B</b>) Statistical analysis of CD rate for the samples. *** <span class="html-italic">p</span> &lt; 0.001. (<b>C</b>) The typical morphological characteristics of isolated exosomes were detected using TEM. (<b>D</b>) The size distribution of serum exosomes was determined using NTA. (<b>E</b>) Membrane proteins of exosome were detected using Western blot.</p>
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<p><b>Seminal plasma exosome DEGs in semen containing sperm with low or high rates of CDs.</b> (<b>A</b>) Volcano map of 486 DEGs between the high CD rate group and low CD rate group. (<b>B</b>) Heatmap of DEGs. (<b>C</b>) KEGG pathway enrichment analysis of DEGs between the high CD rate group and low CD rate group. The top 10 significant pathways are displayed. (<b>D</b>) DEGs enriched in the ECM and cytoskeletal pathways, and trends in the expression of these genes. (<b>E</b>) ROC analyses of verified 4 exosomal mRNAs. ROC, receiver operator characteristic; AUC, area under the ROC curve.</p>
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<p><b>lncRNAs profile of seminal plasma exosomes in semen containing low or high rates of sperm with CDs.</b> (<b>A</b>) Volcano map of 503 DElncRNAs between the high CD rate group and low CD rate group. (<b>B</b>) Heatmap of DElncRNAs. (<b>C</b>) Prediction of target genes for DElncRNAs. (<b>D</b>) KEGG pathway enrichment analysis of DElncRNAs target genes. The top 10 significant pathways are displayed. (<b>E</b>) Core interaction network modules for the target genes of DElncRNAs. (<b>F</b>) Demonstration of target genes enriched in key pathways. (<b>G</b>) Schematic diagram of the insulin signaling pathway. In the figure, the relevant genes in the pathway represented in blue are the target genes of the DElncRNAs.</p>
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<p><b>Seminal plasma exosome DEPs in semen containing low or high rates of sperm with CDs.</b> (<b>A</b>) Volcano map of 40 DEPs between the high CD rate group and low CD rate group. (<b>B</b>) Heatmap of DEPs. (<b>C</b>) DEPs enriched in the pathways. (<b>D</b>) KEGG pathway enrichment analysis of DEPs between the high CD rate group and low CD rate group. The top 10 significant pathways are displayed. (<b>E</b>) The expression levels of PYGM and PYGB. * <span class="html-italic">p</span> &lt; 0.05 (<b>F</b>) ROC analyses of verified PYGM and PYGB proteins.</p>
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<p><b>Integrative multi-omics data analysis, including DEGs, DEPs, and DElncRNAs.</b> (<b>A</b>) Venn analysis of DEGs, DEPs, and DElncRNA. (<b>B</b>) Venn analysis of KEGG pathways from DEGs, DEPs, and DElncRNA. (<b>C</b>) We hypothesize that exosomes are involved in the migration and shedding of sperm cytoplasmic droplets by acting on cytoskeleton and insulin signaling. Blue color represents down-regulation of expression in the high residue group, red color represents up-regulation of expression in the high residue group (by Figdraw).</p>
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26 pages, 6007 KiB  
Article
Genome-Wide Identification and Expression Analysis of TCP Transcription Factors Responding to Multiple Stresses in Arachis Hypogaea L.
by Yanting Zhu, Sijie Niu, Jingyi Lin, Hua Yang, Xun Zhou, Siwei Wang, Xiaoyan Liu, Qiang Yang, Chong Zhang, Yuhui Zhuang, Tiecheng Cai, Weijian Zhuang and Hua Chen
Int. J. Mol. Sci. 2025, 26(3), 1069; https://doi.org/10.3390/ijms26031069 - 26 Jan 2025
Abstract
The TEOSINTE-BRANCHED1/CYCLOIDEA/PROLIFERATING-CELL-FACTOR (TCP) gene family, a plant-specific transcription factor family, plays pivotal roles in various processes such as plant growth and development regulation, hormone crosstalk, and stress responses. However, a comprehensive genome-wide identification and characterization of the TCP gene family in [...] Read more.
The TEOSINTE-BRANCHED1/CYCLOIDEA/PROLIFERATING-CELL-FACTOR (TCP) gene family, a plant-specific transcription factor family, plays pivotal roles in various processes such as plant growth and development regulation, hormone crosstalk, and stress responses. However, a comprehensive genome-wide identification and characterization of the TCP gene family in peanut has yet to be fully elucidated. In this study, we conducted a genome-wide search and identified 51 TCP genes (designated as AhTCPs) in peanut, unevenly distributed across 17 chromosomes. These AhTCPs were phylogenetically classified into three subclasses: PCF, CIN, and CYC/TB1. Gene structure analysis of the AhTCPs revealed that most AhTCPs within the same subclade exhibited conserved motifs and domains, as well as similar gene structures. Cis-acting element analysis demonstrated that the AhTCP genes harbored numerous cis-acting elements associated with stress response, plant growth and development, plant hormone response, and light response. Intraspecific collinearity analysis unveiled significant collinear relationships among 32 pairs of these genes. Further collinear evolutionary analysis found that peanuts share 30 pairs, 24 pairs, 33 pairs, and 100 pairs of homologous genes with A. duranensis, A. ipaensis, Arabidopsis thaliana, and Glycine max, respectively. Moreover, we conducted a thorough analysis of the transcriptome expression profiles in peanuts across various tissues, under different hormone treatment conditions, in response to low- and high-calcium treatments, and under low-temperature and drought stress scenarios. The qRT-PCR results were in accordance with the transcriptome expression data. Collectively, these studies have established a solid theoretical foundation for further exploring the biological functions of the TCP gene family in peanuts, providing valuable insights into the regulatory mechanisms of plant growth, development, and stress responses. Full article
(This article belongs to the Special Issue Signaling and Stress Adaptation in Plants)
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