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14 pages, 3439 KiB  
Article
Overexpression Analysis of PtrLBD41 Suggests Its Involvement in Salt Tolerance and Flavonoid Pathway in Populus trichocarpa
by Jiewan Wang, Yi Liu and Xingshun Song
Int. J. Mol. Sci. 2024, 25(22), 12349; https://doi.org/10.3390/ijms252212349 (registering DOI) - 17 Nov 2024
Abstract
Soil salinization is a significant environmental stress factor, threatening global agricultural yield and ecological security. Plants must effectively cope with the adverse effects of salt stress on survival and successful reproduction. Lateral Organ Boundaries (LOB) Domain (LBD) genes, a gene family encoding plant-specific [...] Read more.
Soil salinization is a significant environmental stress factor, threatening global agricultural yield and ecological security. Plants must effectively cope with the adverse effects of salt stress on survival and successful reproduction. Lateral Organ Boundaries (LOB) Domain (LBD) genes, a gene family encoding plant-specific transcription factors (TFs), play important roles in plant growth and development. Here, we identified and functionally characterized the LBD family TF PtrLBD41 from Populus trichocarpa, which can be induced by various abiotic stresses, including salt, dehydration, low temperature, and Abscisic Acid (ABA). Meanwhile, transgenic plants overexpressing PtrLBD41 showed a better phenotype and higher tolerance than the wild-type (WT) plants under salt stress treatment. Transcriptome analysis found that the differentially expressed genes (DEGs) between the WT and overexpression (OE) line were enriched in the flavonoid biosynthetic process, in which chalcone synthases (CHS), naringenin 3-dioxygenase (F3H), and chalcone isomerase (CHI) were significantly up-regulated under salt stress conditions through qRT-PCR analysis. Therefore, we demonstrate that PtrLBD41 plays an important role in the tolerance to salt stress in P. trichocarpa. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Contrast and evolutionary relationship between PtrLBD41 and LOB domain-containing proteins in different species, and prediction of PtrLBD41 protein domains and structure. (<b>A</b>) Comparison between homology of PtrLBD41 protein and LOB domain-containing proteins in other plants, with conserved amino acids shaded in different colors. The conserved regions of the amino acid sequence are marked by black and red boxes. The accession numbers are as follows: PnLBD41-like (XP_061947259.1, <span class="html-italic">Populus nigra</span>), PeLBD41-like (XP_011021242.1, <span class="html-italic">Populus euphratica</span>), PaLBD41-like (XP_034889549.1, <span class="html-italic">Populus alba</span>), SvLBD38 (KAJ6684372.1, <span class="html-italic">Salix viminalis</span>), SkLBD40 (KAJ6738787.1, <span class="html-italic">Salix koriyanagi</span>), MeLBD41 (XP_021602536.1, <span class="html-italic">Manihot esculenta</span>), HbLBD40 (XP_021643936.1, <span class="html-italic">Hevea brasiliensis</span>), RcLBD40 (XP_048231049.1, <span class="html-italic">Ricinus communis</span>), TcLBD41-like (XP_017975845.1, <span class="html-italic">Theobroma cacao</span>), ZjLBD41 (XP_015880531.3, <span class="html-italic">Ziziphus jujuba</span>), AgLBD41 (XP_062150573.1, <span class="html-italic">Alnus glutinosa</span>), PpLBD41 (XP_007215745.1, <span class="html-italic">Prunus persica</span>), PvLBD40-like (XP_031250561.1, <span class="html-italic">Pistacia vera</span>), and AtLBD41 (NP_566175.1, <span class="html-italic">Arabidopsis thaliana</span>). Red block: The CX2CX6CX3C zinc finger-like domain. (<b>B</b>) Phylogenetic tree analysis of NAC protein in <span class="html-italic">Fragaria vesca</span> and other plants. The red frame is the target protein. (<b>C</b>) Predicted protein secondary structure of PtrLBD41 protein using the SPOMA. (<b>D</b>) Tertiary structure of PtrLBD41 protein predicted by Expasy.</p>
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<p>Subcellular localization of PtrLBD41 protein in tobacco leaf lower epidermal cells, based on visualization of green fluorescent protein (GFP) in tobacco leaves transformed with a fusion construct (35S: PtrLBD41-GFP) or empty vector (35S: GFP). Bright-field images, GFP fluorescence, and merged images are displayed from left to right. Fluorescence was observed by confocal microscopy. Scale bar is 20 μm.</p>
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<p>Expression pattern analysis of <span class="html-italic">PtrLBD41</span> in <span class="html-italic">Populus trichocarpa</span> under abiotic stress by qRT-PCR. Time-course of <span class="html-italic">PtrLBD41</span> expression in young leaf in the control and under (<b>A</b>) salt (200 mM NaCl), (<b>B</b>) low-temperature (4 °C), (<b>C</b>) dehydration, and (<b>D</b>) abscisic acid (100 μM ABA) treatments. Significant differences are marked with asterisks above the error bar (Student’s <span class="html-italic">t</span>-test; ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant).</p>
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<p>Salt treatment of transgenic <span class="html-italic">P. trichocarpa</span> lines overexpressing <span class="html-italic">PtrLBD41</span>. (<b>A</b>) Relative expression level of <span class="html-italic">PtrLBD41</span> in WT, and overexpression lines (OE2, OE4, and OE7). (<b>B</b>) Phenotypes of the WT and transgenic lines (OE4 and OE7) grown in the control environment and salt treatment (irrigation with 200 mM NaCl for 7 days). Chlorophyll contents (<b>C</b>), Fv/Fm (<b>D</b>), and MDA contents (<b>E</b>) in the WT and transgenic lines (OE4 and OE7) under NaCl treatment for 7 days. Significant differences are marked with asterisks above the error bar (Student’s <span class="html-italic">t</span>-test; * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant).</p>
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<p>Transcriptome analysis of <span class="html-italic">PtrLBD41</span> transgenic plants and WT plants. (<b>A</b>) Histogram analysis of the number of DEGs of WT and <span class="html-italic">PtrLBD41</span>-OE plants. (<b>B</b>) Heat map of DEGs. (<b>C</b>) GO analysis of DEGs. The vertical coordinate indicates GO entries; the horizontal coordinate indicates the ratio of the number of genes enriched in the entry to the total number of genes; the color indicates <span class="html-italic">p</span> adjust, the redder the color, the higher the significance; the size of the bubble indicates the number of genes enriched in the entry, a bigger bubble indicates more genes. (<b>D</b>) KEGG analysis of DEGs. The vertical coordinate indicates the name of the pathway; the horizontal coordinate indicates the proportion of the number of genes enriched in the pathway to the total number of genes; the color indicates p-adjust, the redder the color, the higher the significance; the size of the bubbles indicates the number of genes enriched in the pathway, bigger bubbles indicate more genes. MF: molecular function, CC: cellular component, BP: biological process.</p>
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<p>Verification of salt tolerance genes. (<b>A</b>) The FPKM value of the selected genes. (<b>B</b>) Analysis of the relative expression levels of the selected genes by qRT-PCR. Significant differences are marked with asterisks above the error bar (Student’s <span class="html-italic">t</span>-test; ** <span class="html-italic">p</span> ≤ 0.01, **** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant).</p>
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15 pages, 406 KiB  
Communication
RNA-Seq Analysis of Pubertal Mammary Epithelial Cells Reveals Novel n-3 Polyunsaturated Fatty Acid Transcriptomic Changes in the fat-1 Mouse Model
by Connor D. C. Buchanan, Rahbika Ashraf, Lyn M. Hillyer, Wangshu Tu, Jing X. Kang, Sanjeena Subedi and David W. L. Ma
Nutrients 2024, 16(22), 3925; https://doi.org/10.3390/nu16223925 (registering DOI) - 17 Nov 2024
Abstract
Background: The early exposure of nutrients during pubertal mammary gland development may reduce the risk of developing breast cancer later in life. Anticancer n-3 polyunsaturated fatty acids (n-3 PUFA) are shown to modulate pubertal mammary gland development; however, the mechanisms [...] Read more.
Background: The early exposure of nutrients during pubertal mammary gland development may reduce the risk of developing breast cancer later in life. Anticancer n-3 polyunsaturated fatty acids (n-3 PUFA) are shown to modulate pubertal mammary gland development; however, the mechanisms of action remain unclear. Prior work focused on effects at the whole tissue level, and little is known at the cellular level, such as at the level of mammary epithelial cells (MECs), which are implicated in cancer development. Methods: This pilot study examined the effects of lifelong n-3 PUFA exposure on the transcriptome by RNA-Seq in the isolated MECs of pubertal (6–8-week-old) female fat-1 transgenic mice capable of de novo n-3 PUFA synthesis. edgeR and DESeq2 were used separately for the differential expression analysis of RNA sequencing data followed by the Benjamani–Hochberg procedure for multiple testing correction. Results: Nine genes were found concordant and significantly different (p ≤ 0.05) by both the DESeq2 and edgeR methods. These genes were associated with multiple pathways, suggesting that n-3 PUFA stimulates estrogen-related signaling (Mlltl0, Galr3, and Nrip1) and a glycolytic profile (Soga1, Pdpr, and Uso1) while offering protective effects for immune and DNA damage responses (Glpd1, Garre1, and Rpa1) in MECs during puberty. Conclusions: This pilot study highlights the utility of RNA-Seq to better understanding the mechanistic effects of specific nutrients such as n-3 PUFA in a cell-specific manner. Thus, further studies are warranted to investigate the cell-specific mechanisms by which n-3 PUFA influences pubertal mammary gland development and breast cancer risk later in life. Full article
(This article belongs to the Special Issue Nutrition and Gene Interaction)
14 pages, 5199 KiB  
Article
Identification of Key Genes Involved in Seed Germination of Astragalus mongholicus
by Junlin Li, Shuhong Guo, Xian Zhang, Yuhao He, Yaoqin Wang, Hongling Tian and Qiong Zhang
Int. J. Mol. Sci. 2024, 25(22), 12342; https://doi.org/10.3390/ijms252212342 (registering DOI) - 17 Nov 2024
Abstract
Seed germination is a fundamental process in plant reproduction, and it involves a series of complex physiological mechanisms. The germination rate of Astragalus mongholicus (AM) seeds is significantly lower under natural conditions. To investigate the key genes associated with AM seed germination, seeds [...] Read more.
Seed germination is a fundamental process in plant reproduction, and it involves a series of complex physiological mechanisms. The germination rate of Astragalus mongholicus (AM) seeds is significantly lower under natural conditions. To investigate the key genes associated with AM seed germination, seeds from AM plants were collected at 0, 12, 24, and 48 h for a transcriptomic analysis, weighted gene co-expression network analysis (WGCNA), and machine learning (ML) analysis. The primary pathways involved in AM seed germination include plant-pathogen interactions and plant hormone signaling. Four key genes were identified through the WGCNA and ML: Cluster-28,554.0, FAS4, T10O24.10, and EPSIN2. These findings were validated using real-time quantitative reverse transcription PCR (qRT-PCR), and results from RNA sequencing demonstrated a high degree of concordance. This study reveals, for the first time, the key genes related to AM seed germination, providing potential gene targets for further research. The discovery of N4-acetylcysteine (ac4C) modification during seed germination not only enhances our understanding of plant ac4C but also offers valuable insights for future functional research and application exploration. Full article
(This article belongs to the Section Molecular Informatics)
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<p>DEGs during germination of AM seeds. (<b>A</b>) Upregulation and downregulation of DEGs at 12 h, 24 h, and 48 h, respectively, compared to 0 h. (<b>B</b>) Venn diagram illustrating DEGs. (<b>C</b>) GO enrichment analysis of DEGs. (<b>D</b>) KEGG enrichment analysis.</p>
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<p>Expression network analysis of genes related to AM seed germination. (<b>A</b>) Appropriate soft thresholds were established to construct the scale-free network. (<b>B</b>) The cluster dendrogram illustrates the results of hierarchical clustering among genes, with different modules indicated by distinct colors at the bottom of the figure. Each module represents a set of highly co-expressed genes. (<b>C</b>) The module–sample relationship illustrates the correlation between various gene modules and sample features. (<b>D</b>) The eigengene adjacency heatmap displays the similarity between genes characterized by their respective modules.</p>
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<p>KEGG and GO analysis of module genes related to AM seed germination. (<b>A</b>) Key module KEGG pathway analysis: The horizontal axis represents the number of genes enriched in the top 20 pathways, while the vertical axis indicates the names of the KEGG pathways. (<b>B</b>) Key module GO function analysis: The horizontal axis displays the names of the GO entries, and the vertical axis represents the number of genes enriched in the top 10 GO functions.</p>
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<p>Feature gene selection for AM seed germination. (<b>A</b>) DEGs and WGCNA of the overlapping screened genes. (<b>B</b>) GBM screening of the characterized genes, with the horizontal axis representing the character importance score and the vertical axis representing the gene name. (<b>C</b>) RF algorithm screening of the characterized genes, with the horizontal axis indicating the average Gini index decline value and the vertical axis indicating the gene name. (<b>D</b>) Feature genes identified from RF and GBM screening.</p>
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<p>Key genes involved in AM seed germination. (<b>A</b>) LASSO coefficient path diagram. The horizontal axis represents the log λ value, while the vertical axis displays the regression coefficient of the gene. At the optimal λ value (indicated by the vertical dashed line in the figure), the LASSO method identifies the key genes. (<b>B</b>) Cross-validation error plot of LASSO. The horizontal axis shows the log λ value, and the vertical axis represents the mean deviation. The red solid line indicates the mean deviation, while the gray shading represents the standard error. The vertical dashed line marks the optimal λ value, which corresponds to the smallest error. (<b>C</b>) Plot of expression changes of the four key genes at different time points. The horizontal axis denotes the time points, the vertical axis indicates gene expression on the left side, and gene counts on the right side. The bar graph represents gene counts, and the line graph illustrates gene expression. (<b>D</b>) KEGG pathway enrichment analysis graph for the purple module. (<b>E</b>) KEGG pathway enrichment analysis graph for the green module. (<b>F</b>) KEGG pathway enrichment analysis for the tan module.</p>
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<p>qRT-PCR validation of four key genes. The black lines represent the qRT-PCR results for the key genes, while the blue bars indicate the RNA-seq values. Each sample was analyzed with three biological replicates for qRT-PCR. Error bars represent the standard deviation of the relative expression levels from the three biological replicates. (<b>A</b>) Expression level of Cluster-28,554.0 in RNA-Seq and qRT-PCR validation results; (<b>B</b>) Expression level of FAS4 in RNA-Seq and qRT-PCR validation results; (<b>C</b>) Expression level of T10O24.10 in RNA-Seq and qRT-PCR validation results; (<b>D</b>) Expression level of EPSIN2/EPN2 in RNA-Seq and qRT-PCR validation results.</p>
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<p>Morphological characteristics of AM seed germination at four stages: (<b>A</b>) Seed dormancy (0 h). (<b>B</b>) Seed water absorption and swelling (12 h). (<b>C</b>) Seed coat dehiscence (24 h). (<b>D</b>) Radicle breakthrough (48 h).</p>
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27 pages, 4824 KiB  
Review
Cadmium (Cd) Tolerance and Phytoremediation Potential in Fiber Crops: Research Updates and Future Breeding Efforts
by Adnan Rasheed, Pengliang He, Zhao Long, Syed Faheem Anjum Gillani, Ziqian Wang, Kareem Morsy, Mohamed Hashem and Yucheng Jie
Agronomy 2024, 14(11), 2713; https://doi.org/10.3390/agronomy14112713 (registering DOI) - 17 Nov 2024
Abstract
Heavy metal pollution is one of the most devastating abiotic factors, significantly damaging crops and human health. One of the serious problems it causes is a rise in cadmium (Cd) toxicity. Cd is a highly toxic metal with a negative biological role, and [...] Read more.
Heavy metal pollution is one of the most devastating abiotic factors, significantly damaging crops and human health. One of the serious problems it causes is a rise in cadmium (Cd) toxicity. Cd is a highly toxic metal with a negative biological role, and it enters plants via the soil–plant system. Cd stress induces a series of disorders in plants’ morphological, physiological, and biochemical processes and initiates the inhibition of seed germination, ultimately resulting in reduced growth. Fiber crops such as kenaf, jute, hemp, cotton, and flax have high industrial importance and often face the issue of Cd toxicity. Various techniques have been introduced to counter the rising threats of Cd toxicity, including reducing Cd content in the soil, mitigating the effects of Cd stress, and genetic improvements in plant tolerance against this stress. For decades, plant breeders have been trying to develop Cd-tolerant fiber crops through the identification and transformation of novel genes. Still, the complex mechanism of Cd tolerance has hindered the progress of genetic breeding. These crops are ideal candidates for the phytoremediation of heavy metals in contaminated soils. Hence, increased Cd uptake, accumulation, and translocation in below-ground parts (roots) and above-ground parts (shoots, leaves, and stems) can help clean agricultural lands for safe use for food crops. Earlier studies indicated that reducing Cd uptake, detoxification, reducing the effects of Cd stress, and developing plant tolerance to these stresses through the identification of novel genes are fruitful approaches. This review aims to highlight the role of some conventional and molecular techniques in reducing the threats of Cd stress in some key fiber crops. Molecular techniques mainly involve QTL mapping and GWAS. However, more focus has been given to the use of transcriptome and TFs analysis to explore the potential genomic regions involved in Cd tolerance in these crops. This review will serve as a source of valuable genetic information on key fiber crops, allowing for further in-depth analyses of Cd tolerance to identify the critical genes for molecular breeding, like genetic engineering and CRISPR/Cas9. Full article
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
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<p>Cd is discharged from various sources, enters the soil, and is eventually taken up by plants through the roots and transported to the shoots. This Figure was made with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Cd toxicity decreases seed germination, seedling growth, and antioxidant activities and reduces protein content. Different factors, like organic acids and stress-related signaling, affect Cd uptake in plants. This Figure was made with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Conventional and molecular breeding tools are crucial in genetically improving Cd tolerance in fiber crops. The identification of Cd-tolerant genes led to an increase in the phytoremediation potential of fiber crops. This Figure was made with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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19 pages, 15576 KiB  
Article
IPEC-J2 Autophagy Induced by TLR4 and NSP6 Interactions Facilitate Porcine Epidemic Diarrhea Virus Replication
by Haiyuan Zhao, Dianzhong Zheng, Qinyuan Chang, Hailin Zhang, Yilan Shao, Jiaxuan Li, Wen Cui, Yanping Jiang, Lijie Tang, Yijing Li and Xiaona Wang
Viruses 2024, 16(11), 1787; https://doi.org/10.3390/v16111787 (registering DOI) - 17 Nov 2024
Abstract
Autophagy is an important cellular response against intracellular pathogens. However, some viruses have evolved mechanisms to hijack this defensive process to provide favorable conditions for virus replication in host cells. The porcine epidemic diarrhea virus (PEDV) has been shown to alter autophagy pathways; [...] Read more.
Autophagy is an important cellular response against intracellular pathogens. However, some viruses have evolved mechanisms to hijack this defensive process to provide favorable conditions for virus replication in host cells. The porcine epidemic diarrhea virus (PEDV) has been shown to alter autophagy pathways; however, it is still unknown through which receptors PEDV induces autophagy in IPEC-J2 cells, whether autophagy facilitates PEDV replication, and which functional domains of PEDV proteins are primarily responsible for inducing autophagy. Here, we found that PEDV infection induces autophagy in host cells via distinct and uncoupled molecular pathways. RNA-seq technology was used to analyze the expression patterns of intracellular genes in PEDV-infected IPEC-J2 cells using transcriptomics. The results demonstrate that PEDV triggers autophagy via the cellular pathogen receptor TLR4 and the AKT-mTOR pathway. As evidenced by autophagosome detection, PEDV infection increases autophagosomes and light chain 3 (LC3)-II as well as downregulated AKT-mTOR phosphorylation. Our study revealed that the binding of the viral protein NSP61-2C (56-151aa) to TLR4 triggers autophagy and inactivates the AKT-mTOR pathway, both of which are critical for facilitating PEDV infection. Through screening and analysis, TLR4 was found to be a key gene involved in PEDV-induced autophagy. The screening of the key functional domains of NSP6 (56-151aa) for their ability to induce autophagy in IPEC-J2 cells provided a basis for the in-depth analysis of the pathogenic mechanism of PEDV infection-induced autophagy and promotion of self-replication and also provided an important target for the study of PEDV antiviral drugs. In conclusion, we elucidated that the PEDV infection of IPEC-J2 cells could induce autophagy and found that PEDV could use autophagy to promote its own replication. Full article
(This article belongs to the Section Animal Viruses)
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<p>PEDV infection in IPEC-J2 cell-induced autophagy. (<b>a</b>,<b>b</b>) Western blot was used to detect changes in the expression of LC3-II, p62, and PEDV-N proteins in PEDV-infected IPEC-J2 cells at 6 h, 12 h, 18 h, 24 h, 30 h, 36 h, 42 h, and 48 h post-infection. Cell samples from non-infected cultures at the same time points were used as the control. (<b>c</b>) Quantitative analysis of LC3-II and β-actin. (<b>d</b>) Quantitative analysis of P62a and β-actin. (<b>e</b>) Quantitative analysis of PEDV-N and β-actin. (<b>f</b>) Determination of TCID<sub>50</sub> of the PEDV. * (<span class="html-italic">p</span> &lt; 0.05) and ** (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Autophagosome observed using TEM. (<b>a</b>) Cell control group (Scale, 1 μm) and magnified autophagosome structure in the control group (Scale, 500 nm); (<b>b</b>) PEDV-infected cells (Scale, 1 μm) and magnified autophagosome structure in the PEDV-infected group (Scale, 500 nm).</p>
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<p>The role of autophagy in PEDV replication. (<b>a</b>) Expression levels of LC3-II and p62 in IPEC-J2 cells pretreated with different concentrations of chloroquine. (<b>b</b>) Expression level of LC3-II in IPEC-J2 cells treated with different concentrations of insulin. (<b>c</b>) Expression level of LC3-II in IPEC-J2 cells treated with different concentrations of rapamycin. The control group included normal cells. (<b>d</b>) The autophagic flow was detected using GFP-mCherry-LC3 dual fluorescence labeling. * (<span class="html-italic">p</span> &lt; 0.05) and ** (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Statistics, volcano plot, and pattern clustering heat maps of differentially expressed mRNAs. (<b>a</b>) Statistical map of differential genes. (<b>b</b>) Volcano plot of differential gene comparison. (<b>c</b>) Cluster heat map of differential genes.</p>
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<p>PEDV N gene expression in TLR4<sup>−/−</sup>, TFRC<sup>−/−</sup>, and GABRG3<sup>−/−</sup> IPEC-J2 cells. * (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The effects of TLR4<sup>−/−</sup>, TFRC<sup>−/−</sup>, and GABRG3<sup>−/−</sup> on autophagy, as assessed using Western blotting. (<b>a</b>) The changes in the protein expression of LC3-II, PEDV-N, and p62 in PEDV-J2-infected IPEC-J2 cells treated with siRNA detected using Western blotting. (<b>b</b>) The quantitative analysis of LC3-II and β-actin. (<b>c</b>) The quantitative analysis of p62 and β-actin. (<b>d</b>) The quantitative analysis of PEDV-N and β-actin. * (<span class="html-italic">p</span> &lt; 0.05) and ** (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Analysis of AKT-mTOR phosphorylation using Western blotting. (<b>a</b>) The comparison of protein phosphorylation levels (AKT vs. p-AKT; mTOR vs. p-mTOR) and TLR4 expression using Western blotting in PEDV-infected cells in TLR4<sup>−/−</sup> and WT cells. (<b>b</b>) The quantitative analysis of TLR4, β-actin, p-AKT, AKT, p-mTOR, and mTOR in TLR4<sup>−/−</sup> IPEC-J2 cells. (<b>c</b>) The quantitative analysis of TLR4, β-actin, p-AKT, AKT, p-mTOR, and mTOR in WT IPEC-J2 cells. * (<span class="html-italic">p</span> &lt; 0.05) and ** (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Analysis of LC3-II and p62 protein levels by Western blotting. (<b>a</b>) Changes in the expression of LC3-II and p62 in IPEC-J2 cells induced with NSP6 and its truncated proteins detected using Western blotting. (<b>b</b>) The quantitative analysis of LC3-II and β-actin. (<b>c</b>) The quantitative analysis of p62 and β-actin. * (<span class="html-italic">p</span> &lt; 0.05) and ** (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Autophagic flow was detected using GFP-mCherry-LC3 dual fluorescence labeling.</p>
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<p>Analysis of LC3-II and p62 expression in IPEC-J2 cells using Western blotting. (<b>a</b>) Changes in the expression of LC3-II and p62 in IPEC-J2 cells induced with NSP61A, NSP61B, and NSP61-2C were detected using Western blotting. (<b>b</b>) The quantitative analysis of LC3-II and β-actin. (<b>c</b>) The quantitative analysis of p62 and β-actin. * (<span class="html-italic">p</span> &lt; 0.05) and ** (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>The autophagic flow was detected using GFP-mCherry-LC3 dual fluorescence labeling.</p>
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<p>Analysis of AKT-mTOR signaling pathway activation using Western blotting. (<b>a</b>) Changes in the expression of AKT, p-AKT, mTOR, p-mTOR, and TLR4 proteins induced by NSP61-2C in TLR4<sup>−/−</sup> and in WT IPEC-J2 cultures. (<b>b</b>) The quantitative analysis of p-AKT, AKT, p-mTOR, mTOR, TLR4, and β-actin in the NSP61-2C group. (<b>c</b>) The quantitative analysis of p-AKT, AKT, p-mTOR, mTOR, TLR4, and β-actin in the TLR4<sup>−/−</sup> group. * (<span class="html-italic">p</span> &lt; 0.05) and ** (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Detection of TLR4 and NSP61-2C co-localization using an indirect immunofluorescent assay.</p>
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<p>Molecular mechanism model diagram of autophagy induced by PEDV. <b>×</b>: represents the phosphorylation of AKT-mTOR is inhibited. The curved arrow represents the fusion of lysosomes and phagophore (autophagosomes).</p>
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16 pages, 4649 KiB  
Article
Altered Photoprotective Mechanisms and Pigment Synthesis in Torreya grandis with Leaf Color Mutations: An Integrated Transcriptome and Photosynthesis Analysis
by Yujia Chen, Lei Wang, Jing Zhang, Yilu Chen and Songheng Jin
Horticulturae 2024, 10(11), 1211; https://doi.org/10.3390/horticulturae10111211 (registering DOI) - 17 Nov 2024
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Abstract
Torreya grandis is a widely cultivated fruit species in China that is valued for its significant economic and agricultural importance. The molecular mechanisms underlying pigment formation and photosynthetic performance in Torreya leaf color mutants remain to be fully elucidated. In this study, we [...] Read more.
Torreya grandis is a widely cultivated fruit species in China that is valued for its significant economic and agricultural importance. The molecular mechanisms underlying pigment formation and photosynthetic performance in Torreya leaf color mutants remain to be fully elucidated. In this study, we performed transcriptome sequencing and measured photosynthetic performance indicators to compare mutant and normal green leaves. The research results indicate that the identified Torreya mutant differs from previously reported mutants, exhibiting a weakened photoprotection mechanism and a significant reduction in carotenoid content of approximately 33%. Photosynthetic indicators, including the potential maximum photosynthetic capacity (Fv/Fm) and electron transport efficiency (Ψo, φEo), decreased significantly by 32%, 52%, and 49%, respectively. While the quantum yield for energy dissipation (φDo) increased by 31%, this increase was not statistically significant, which may further reduce PSII activity. A transcriptome analysis revealed that the up-regulation of chlorophyll degradation-related genes—HCAR and NOL—accelerates chlorophyll breakdown in the Torreya mutant. The down-regulation of carotenoid biosynthesis genes, such as LCY1 and ZEP, is strongly associated with compromised photoprotective mechanisms and the reduced stability of Photosystem II. Additionally, the reduced expression of the photoprotective gene psbS weakened the mutant’s tolerance to photoinhibition, increasing its susceptibility to photodamage. These changes in gene expression accelerate chlorophyll degradation and reduce carotenoid synthesis, which may be the primary cause of the yellowing in Torreya. Meanwhile, the weakening of photoprotective mechanisms further impairs photosynthetic efficiency, limiting the growth and adaptability of the mutants. This study emphasizes the crucial roles of photosynthetic pigments and photosystem structures in regulating the yellowing phenotype and the environmental adaptability of Torreya. It also provides important insights into the genetic regulation of leaf color in relation to photosynthesis and breeding. Full article
(This article belongs to the Special Issue Advances in Developmental Biology in Tree Fruit and Nut Crops)
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<p>Leaf appearance of wild type and mutant <span class="html-italic">Torreya</span>.</p>
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<p>(<b>A</b>) Prompt chlorophyll a fluorescence (PF). (<b>B</b>) Normalized curve; V<sub>t</sub> = [(F<sub>t</sub> − F<sub>O</sub>)/(F<sub>M</sub> − F<sub>o</sub>)], J reflects early electron transport blockage; I reflects the size of the PQ pool and the efficiency of electron flow; P represents the maximum PSII photochemical efficiency. (<b>C</b>) Delayed chlorophyll a fluorescence (DF). I<sub>1</sub> represents the redox state of Q<sub>A</sub> and PSII functionality; I<sub>2</sub> represents the reduction of the PQ pool and the efficiency of electron transfer; D<sub>2</sub> represents the charge separation stability and recombination dynamics. (<b>D</b>) The decay kinetics of DF at the characteristic maxima I<sub>1</sub> (7 ms). Each curve represents the mean value derived from three replicate measurements.</p>
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<p>Radar plot of energy fluxes in mutant and wild type <span class="html-italic">Torreya</span>. The radar plot reflected specific activity values at individual PSII reaction centers (RCs) and cross-sections (CSs). Each data point represents the mean value derived from three replicate measurements.</p>
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<p>Differentially expressed genes (DEGs) in different leaves of <span class="html-italic">Torreya</span>. (<b>A</b>) DEGs were displayed in the form of a volcano plot. The red dots denote up-regulated genes, while the blue dots denote down-regulated genes. (<b>B</b>) Hierarchical cluster analysis of DEGs. Each row represents a gene, with red indicating a more pronounced up-regulation and green indicating a more pronounced down-regulation.</p>
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<p>Functional annotation of DEGs. (<b>A</b>) GO classification of differentially expressed genes—the top 30 enriched GO terms. (<b>B</b>) KEGG enrichment of differentially expressed genes; the larger the bubble, the more DEGs that are enriched.</p>
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<p>Regulation of gene expression and metabolic pathways associated with leaf color at the transcriptional level. (<b>A</b>) Analysis of differentially expressed genes related to chlorophyll biosynthesis and degradation pathways. (<b>B</b>) Differential expressions of genes related to carotenoid biosynthesis and degradation pathways. (<b>C</b>) Transcriptome data (FPKM) were used for heat mapping.</p>
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<p>The expression levels of DEGs in wild-type and mutant <span class="html-italic">Torreya</span>. (<b>A</b>) Gene ID: TG8G02048 (CHL2). (<b>B</b>) Gene ID: TG5G03735 (LCY1). (<b>C</b>) Gene ID: TG3G00961 (CCD4). (<b>D</b>) Gene ID: TG9G00978 (NOL). (<b>E</b>) Gene ID: TG7G03643 (chlN). (<b>F</b>) Gene ID: TG3G0175 (ZEP). (<b>G</b>) Gene ID: TG8G003353 (psaA). (<b>H</b>) Gene ID: TG4G00167 (psbS). (<b>I</b>) Gene ID: TG11G01233 (HCAR). (<b>J</b>) Gene ID: TG3G00946 (NECD1). Each error bar represents the SD calculated from three biological replicates, each of which includes three technical replicates.</p>
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8 pages, 1339 KiB  
Brief Report
Optimized Protocol for RNA Isolation from Penicillium spp. and Aspergillus fumigatus Strains
by Aleksandra Siniecka-Kotula, Martyna Mroczyńska-Szeląg, Anna Brillowska-Dąbrowska and Lucyna Holec-Gąsior
Curr. Issues Mol. Biol. 2024, 46(11), 13050-13057; https://doi.org/10.3390/cimb46110778 (registering DOI) - 17 Nov 2024
Viewed by 163
Abstract
Efficient RNA isolation from filamentous fungi is crucial for gene expression studies, but it poses significant technical challenges due to the robust cell walls and susceptibility of RNA to degradation by ribonucleases. This study presents the effectiveness of two RNA isolation protocols for [...] Read more.
Efficient RNA isolation from filamentous fungi is crucial for gene expression studies, but it poses significant technical challenges due to the robust cell walls and susceptibility of RNA to degradation by ribonucleases. This study presents the effectiveness of two RNA isolation protocols for four species of filamentous fungi: Penicillium crustosum, Penicillium rubens, Penicillium griseofulvum, and Aspergillus fumigatus. Both protocols utilized Fenzol Plus for cell lysis but varied in the mechanical disruption methods: bead-beating versus manual vortexing. The results show that the bead-beater method (Protocol 1) yielded significantly higher RNA quantities, with better purity and integrity, as demonstrated by higher A260/A280 and A260/A230 ratios. RNA concentrations ranged from 30 to 96 µg/g of dry biomass in Penicillium species and up to 52 µg/g in A. fumigatus. The use of chloroform in Protocol 1 also enhanced RNA purity, effectively separating contaminants such as DNA, proteins, and polysaccharides. This optimized protocol is highly efficient and can be applied in routine laboratories handling large numbers of fungal samples, making it a robust method for downstream applications such as cDNA synthesis and transcriptome analysis. Full article
(This article belongs to the Section Molecular Microbiology)
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<p>RNA isolated from <span class="html-italic">Penicillium crustosum</span> (1–6); <span class="html-italic">Penicillium rubens</span> (7–10).</p>
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<p>RNA isolated from Penicillium griseofulvum (1–6); Aspergillus fumigatus (7–10).</p>
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15 pages, 8589 KiB  
Article
Genome-Wide Identification and Expression Analysis of the Alfalfa (Medicago sativa L.) U-Box Gene Family in Response to Abiotic Stresses
by Shuaixian Li, Xiuhua Chen, Meiyan Guo, Xiaoyue Zhu, Wangqi Huang, Changhong Guo and Yongjun Shu
Int. J. Mol. Sci. 2024, 25(22), 12324; https://doi.org/10.3390/ijms252212324 (registering DOI) - 17 Nov 2024
Viewed by 247
Abstract
E3 ubiquitin ligases known as plant U-box (PUB) proteins regulate a variety of aspects of plant growth, development, and stress response. However, the functions and characteristics of the PUB gene family in alfalfa remain unclear. This work involved a genome-wide examination of the [...] Read more.
E3 ubiquitin ligases known as plant U-box (PUB) proteins regulate a variety of aspects of plant growth, development, and stress response. However, the functions and characteristics of the PUB gene family in alfalfa remain unclear. This work involved a genome-wide examination of the alfalfa U-box E3 ubiquitin ligase gene. In total, 210 members were identified and divided into five categories according to their homology with the members of the U-box gene family in Arabidopsis thaliana. The phylogenetic analysis, conserved motifs, chromosomal localization, promoters, and regulatory networks of this gene were investigated. Chromosomal localization and covariance analyses indicated that the MsPUB genes expanded MsPUB gene family members through gene duplication events during evolution. MsPUB genes may be involved in the light response, phytohormone response, growth, and development of several biological activities, according to cis-acting element analysis of promoters. In addition, transcriptome analysis and expression analysis by qRT-PCR indicated that most MsPUB genes were significantly upregulated under cold stress, drought stress, and salt stress treatments. Among them, MsPUBS106 and MsPUBS185 were significantly and positively correlated with cold resistance in alfalfa. MsPUBS110, MsPUBS067, MsPUBS111 and MsPUB155 were comprehensively involved in drought stress, low temperature, and salt stress resistance. All things considered, these discoveries offer fresh perspectives on the composition, development, and roles of the PUB gene family in alfalfa. They also provide theoretical guidance for further investigations into the mechanisms regulating the development, evolution, and stress tolerance of MsPUB. Full article
(This article belongs to the Special Issue Advance in Plant Abiotic Stress: 2nd Edition)
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<p>Phylogenetic relationships between <span class="html-italic">MsPUB</span> genes. Sequence comparison of 210 MsPUB proteins. Using 1000 bootstrap iterations, the neighbor-joining (NJ) method was used to construct the phylogenetic tree. The five groups of members of the MsPUB gene family are II, III, IV, VI, and VII. Five distinct colors are used to point out the genes in each group.</p>
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<p>Distribution of conserved motifs in group III of the <span class="html-italic">MsPUB</span> genes in alfalfa.</p>
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<p>Distribution and replication of <span class="html-italic">MsPUB</span> genes. The 210 <span class="html-italic">MsPUB</span> genes are located in a ring formed by the 32 chromosomes (chr1.1–chr8.4) of alfalfa. Gene interactions are represented by the colors inside the circles.</p>
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<p>Analysis of cis-acting elements in the promoter regions of the top 70 <span class="html-italic">MsPUB</span> genes. The promoter region of <span class="html-italic">MsPUB</span> genes contains fifteen cis-acting elements; the more cis-acting elements, the darker the color. The ARE, LTR, MBS, TC-rich repeats, and the WUN motif are related to biotic and abiotic stresses; phytohormone responsiveness is associated with the AuxRR-core, P-box, ABRE, TGACG motif, and TGA element. Plant growth and development are associated with the A-box, CAT-box, circadian, GA motif, and GCN4 motif.</p>
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<p>Analysis of the interactions between <span class="html-italic">MsPUB</span> genes in alfalfa using gene regulatory networks. The gene regulatory network (GRN) of <span class="html-italic">MsPUB</span> genes and their interactions was built using Cytoscape and was based on relationships between genes in Arabidopsis thaliana. The cyan line shows the interaction of alfalfa, the pink nodes relate to <span class="html-italic">MsPUB</span> genes, and the purple nodes correspond to genes interacting with <span class="html-italic">MsPUB</span> genes.</p>
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<p>A review of the genes that interact with <span class="html-italic">MsPUB</span> genes and their gene ontology enrichment. The GO terms for molecular functions (MFs), cellular components (CCs), and biological processes (BPs) are shown as red, green, and blue dots, respectively. The GO term is displayed on the <span class="html-italic">Y</span>-axis, while the <span class="html-italic">X</span>-axis displays the <span class="html-italic">p</span>-value of the topGO enrichment analysis with −log 10 transformation or −log 10 (<span class="html-italic">p</span>). The number of genes involved in the GO keywords is reflected in the size of the circles.</p>
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<p>Expression profiles of <span class="html-italic">MsPUB</span> under stress conditions. (<b>a</b>) Eight varieties of alfalfa were analyzed under cold stress. (<b>b</b>) Under drought stress, the performance of Wilson and PI467895 alfalfa was studied in three tissues of the root, stem, and leaf. (<b>c</b>) Under salt stress, the performance of Wilson and PI467895 alfalfa in three tissues of rhizome leaves was investigated. The R platform was used to present the mean expression levels (FPKM values), which were measured using Salmon software (version 0.12.0).</p>
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<p>qRT-PCR analysis of <span class="html-italic">MsPUBs</span> under abiotic stress. qRT-PCR analysis of <span class="html-italic">MsPUBs</span> under salt, drought, and cold stresses. Eleven abiotic stress-responsive genes were selected for qRT-PCR experiments. The <span class="html-italic">X</span>-axis represents control, salt treatment, drought treatment, and cold treatment. The <span class="html-italic">Y</span>-axis represents the relative expression level of <span class="html-italic">MsPUB</span> genes. The relative expression was calculated using the 2<sup>−ΔΔCT</sup> method, with the expression level of the “control” set to 1. <span class="html-italic">GAPDH</span> was used as the internal control. The blue color represents <span class="html-italic">Zhaodong</span> and the green color represents WL525HQ. Statistical significance was determined using the <span class="html-italic">t</span>-test (** <span class="html-italic">p</span> &lt; 0.01). Asterisks indicate significant differences.</p>
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21 pages, 4494 KiB  
Article
A New Approach of Detecting ALK Fusion Oncogenes by RNA Sequencing Exon Coverage Analysis
by Galina Zakharova, Maria Suntsova, Elizaveta Rabushko, Tharaa Mohammad, Alexey Drobyshev, Alexander Seryakov, Elena Poddubskaya, Alexey Moisseev, Anastasia Smirnova, Maxim Sorokin, Victor Tkachev, Alexander Simonov, Egor Guguchkin, Evgeny Karpulevich and Anton Buzdin
Cancers 2024, 16(22), 3851; https://doi.org/10.3390/cancers16223851 (registering DOI) - 16 Nov 2024
Viewed by 402
Abstract
Background: In clinical practice, various methods are used to identify ALK gene rearrangements in tumor samples, ranging from “classic” techniques, such as IHC, FISH, and RT-qPCR, to more advanced highly multiplexed approaches, such as NanoString technology and NGS panels. Each of these methods [...] Read more.
Background: In clinical practice, various methods are used to identify ALK gene rearrangements in tumor samples, ranging from “classic” techniques, such as IHC, FISH, and RT-qPCR, to more advanced highly multiplexed approaches, such as NanoString technology and NGS panels. Each of these methods has its own advantages and disadvantages, but they share the drawback of detecting only a restricted (although sometimes quite extensive) set of preselected biomarkers. At the same time, whole transcriptome sequencing (WTS, RNAseq) can, in principle, be used to detect gene fusions while simultaneously analyzing an incomparably wide range of tumor characteristics. However, WTS is not widely used in practice due to purely analytical limitations and the high complexity of bioinformatic analysis, which requires considerable expertise. In particular, methods to detect gene fusions in RNAseq data rely on the identification of chimeric reads. However, the typically low number of true fusion reads in RNAseq limits its sensitivity. In a previous study, we observed asymmetry in the RNAseq exon coverage of the 3′ partners of some fusion transcripts. In this study, we conducted a comprehensive evaluation of the accuracy of ALK fusion detection through an analysis of differences in the coverage of its tyrosine kinase exons. Methods: A total of 906 human cancer biosamples were subjected to analysis using experimental RNAseq data, with the objective of determining the extent of asymmetry in ALK coverage. A total of 50 samples were analyzed, comprising 13 samples with predicted ALK fusions and 37 samples without predicted ALK fusions. These samples were assessed by targeted sequencing with two NGS panels that were specifically designed to detect fusion transcripts (the TruSight RNA Fusion Panel and the OncoFu Elite panel). Results: ALK fusions were confirmed in 11 out of the 13 predicted cases, with an overall accuracy of 96% (sensitivity 100%, specificity 94.9%). Two discordant cases exhibited low ALK coverage depth, which could be addressed algorithmically to enhance the accuracy of the results. It was also important to consider read strand specificity due to the presence of antisense transcripts involving parts of ALK. In a limited patient sample undergoing ALK-targeted therapy, the algorithm successfully predicted treatment efficacy. Conclusions: RNAseq exon coverage analysis can effectively detect ALK rearrangements. Full article
(This article belongs to the Special Issue The Role of RNAs in Cancers)
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<p>Structure of the wild-type <span class="html-italic">ALK</span> gene and the corresponding protein. MAM—methylthioalkymalate synthase-like domain; LDLa—low-density lipoprotein receptor class A; EGF-like—epidermal growth factor-like domain; TM—transmembrane domain; and TK—tyrosine kinase domain.</p>
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<p>Schematic representation of the formation and functional role of an <span class="html-italic">ALK</span> fusion. FAM150—ALK ligand Augmentor α (FAM150A) or Augmentor β (FAM150B); DD—dimerization domain; and TK—tyrosine kinase domain.</p>
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<p>The <span class="html-italic">ALK</span> coverage plots are based on RNAseq data and normalized to the exon length and total number of reads in the sample. The coverage of the <span class="html-italic">ALK</span> sense reads is shown on a positive scale, while the <span class="html-italic">ALK</span> antisense reads are shown on a negative scale. (<b>a</b>) The ALK_9 sample showed pronounced coverage asymmetry and overexpression of exons 20–29. (<b>b</b>) The NS_20 sample showed uniformly high coverage of all exons. TK—tyrosine kinase domain-related exons; non-TK—exons not related to the tyrosine kinase domain; and non-TK/TK coverage—ratios of the mean coverage of five non-TK exons (exons 2–6) and five TK exons (exons 20–24).</p>
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<p>ALK immunostaining using clone D5F3 (Ventana) for sample LuC_103.</p>
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<p><span class="html-italic">ALK</span> coverage plots based on targeted RNAseq data, obtained with the TruSight panel, normalized to exon length and the total number of reads in the sample. Coverage of <span class="html-italic">ALK</span> sense reads is shown on a positive scale, while <span class="html-italic">ALK</span> antisense reads are shown on a negative scale. (<b>a</b>) The ALK_10 sample demonstrates clear <span class="html-italic">ALK</span> coverage asymmetry and a high number of fusion-supporting reads. (<b>b</b>) The NS_20 sample shows the detectable expression of all <span class="html-italic">ALK</span> exons. (<b>c</b>–<b>f</b>) ALK_4, ALK_5, ALK_12, and ALK_16 samples, respectively, with <span class="html-italic">ALK</span> coverage asymmetry but very few (or no) fusion-supporting reads. TK—tyrosine kinase domain-related exons; non-TK—exons not related to the kinase domain; and non-TK and TK coverage—mean coverage of five non-TK exons (exons 2–6) and of five TK exons (exons 20–24), respectively.</p>
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<p><span class="html-italic">ALK</span> coverage plots based on targeted RNAseq data, obtained with the TruSight panel, normalized to exon length and the total number of reads in the sample. Coverage of <span class="html-italic">ALK</span> sense reads is shown on a positive scale, while <span class="html-italic">ALK</span> antisense reads are shown on a negative scale. (<b>a</b>) The ALK_10 sample demonstrates clear <span class="html-italic">ALK</span> coverage asymmetry and a high number of fusion-supporting reads. (<b>b</b>) The NS_20 sample shows the detectable expression of all <span class="html-italic">ALK</span> exons. (<b>c</b>–<b>f</b>) ALK_4, ALK_5, ALK_12, and ALK_16 samples, respectively, with <span class="html-italic">ALK</span> coverage asymmetry but very few (or no) fusion-supporting reads. TK—tyrosine kinase domain-related exons; non-TK—exons not related to the kinase domain; and non-TK and TK coverage—mean coverage of five non-TK exons (exons 2–6) and of five TK exons (exons 20–24), respectively.</p>
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<p>Kaplan–Meier plots for PFS data in lung cancer patients receiving ALK-specific targeted therapies: (<b>a</b>) for first-line ALK-specific therapy and (<b>b</b>) for second-line ALK-specific therapy. PFS—progression-free survival; HR—hazard ratio; CI—confidence interval; and P_val—<span class="html-italic">p</span>-value.</p>
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18 pages, 4498 KiB  
Article
The Mechanism of Aniline Blue Degradation by Short-Chain Dehydrogenase (SDRz) in Comamonas testosteroni
by Chuanzhi Zhang, Yong Huang, Jiaxin He, Lei He, Jinyuan Zhang, Lijing Yu, Elshan Musazade, Edmund Maser, Guangming Xiong, Miao Xu and Liquan Guo
Molecules 2024, 29(22), 5405; https://doi.org/10.3390/molecules29225405 (registering DOI) - 15 Nov 2024
Viewed by 373
Abstract
Dye wastewater pollution, particularly from persistent and toxic polycyclic organic pollutants, such as aniline blue, poses a significant environmental challenge. Aniline blue, a triphenylmethane dye widely used in the textile, leather, paper, and pharmaceutical industries, is notoriously difficult to treat owing to its [...] Read more.
Dye wastewater pollution, particularly from persistent and toxic polycyclic organic pollutants, such as aniline blue, poses a significant environmental challenge. Aniline blue, a triphenylmethane dye widely used in the textile, leather, paper, and pharmaceutical industries, is notoriously difficult to treat owing to its complex structure and potential for bioaccumulation. In this study, we explored the capacity of Comamonas testosteroni (CT1) to efficiently degrade aniline blue, focusing on the underlying enzymatic mechanisms and degradation pathways. Through prokaryotic transcriptome analysis, we identified a significantly upregulated short-chain dehydrogenase (SDRz) gene (log2FC = 2.11, p < 0.05) that plays a crucial role in the degradation process. The SDRz enzyme possessed highly conserved motifs and a typical short-chain dehydrogenase structure. Functional validation using an SDRz-knockout strain (CT-ΔSDRz) and an SDRz-expressioning strains (E-SDRz) confirmed that SDRz is essential for aniline blue degradation. The knockout strain CT-ΔSDRz exhibited a 1.27-fold reduction in the degradation efficiency, compared to CT1 strain after 12 h; while the expression strain E-SDRz showed a 1.24-fold increase compared to Escherichia coli DH5α after 12 h. Recombinant SDRz (rSDRz) was successfully produced, showing significant enzymatic activity (1.267 ± 0.04 mmol·L−1·min−1 protein), with kinetic parameters Vmax = 2.870 ± 0.0156 mmol·L⁻1·min⁻1 protein and Km = 1.805 ± 0.0128 mM·mL−1. Under optimal conditions, the rSDRz achieved a degradation efficiency of 62.17% for aniline blue. Gas chromatography–mass spectrometry (GC-MS) analysis identified several intermediate metabolites in the degradation pathway, including benzeneacetaldehyde, a, a-diphenyl, 2-amino-4-methylbenzophenone, benzene, 1-dimethylamino-4-phenylmethyl, benzenesulfonic acid, methyl ester, further elucidating the biodegradation mechanism. These findings highlight SDRz as a critical enzyme in the biodegradation of aniline blue, offering valuable insights and a robust theoretical foundation for developing advanced bioremediation strategies to address dye wastewater pollution. Full article
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<p>Seven macrocyclic-degrading bacterial strains were capable of degrading aniline blue. Each data point represents N = 3, with values expressed as mean ± standard deviation (<math display="inline"><semantics> <mrow> <mover> <mi mathvariant="normal">x</mi> <mo>¯</mo> </mover> </mrow> </semantics></math> ± SD). Statistical significance was determined at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Analysis of gene expression of strain CT1. (<b>A</b>) Venn diagram of expressed genes shared among three groups which labeled on graph are defined as: CK_blue—control cultures, CT1_blue2—aniline blue concentrations of 200 m·L<sup>−1</sup> treated cultures, CT1_blue5—aniline blue concentrations of 500 mg·L<sup>−1</sup> treated cultures. All groups were cultured at 27 °C at 180 rpm for 12 h. (<b>B</b>) Gene expression profile.</p>
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<p>Volcano plots of differentially expressed genes. (<b>A</b>) Volcano plot between CK group and CT1_blue2 group; (<b>B</b>) Volcano plot between CK group and CT1_blue5 group.</p>
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<p>KEGG analysis of differentially expressed genes.</p>
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<p>GO annotation analysis of up-regulated genes. (<b>A</b>) GO annotation analysis between CK group and CT1_blue2 group; (<b>B</b>) GO annotation analysis between CK group and CT1_blue5 group.</p>
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<p>Aniline blue degradation by the wild-type CT1 and CT-ΔSDRz mutant.</p>
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<p>Aniline blue degradation by wild-type <span class="html-italic">E. coli</span> DH5α and the E-SDRz.</p>
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<p>GC-MS analysis of aniline blue metabolites after degradation by rSDRz for 5 min. (<b>A</b>) benzeneacetaldehyde, a, a-diphenyl; (<b>B</b>) 2-amino-4-methylbenzophenone; (<b>C</b>) benzene, 1-dimethylamino-4-phenylmethyl; (<b>D</b>) benzenesulfonic acid, methyl ester.</p>
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<p>Proposed aniline blue degradation pathway in rSDRz.</p>
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13 pages, 3241 KiB  
Article
Up-Regulated Expression of Thioredoxin-Interacting Protein (TXNIP) in Mesenchymal Stem Cells Associated with Severe Aplastic Anemia in Children
by Ying-Hsuan Peng, Chang-Wei Li, Kang-Hsi Wu, Ju-Pi Li, Shun-Fa Yang and Yu-Hua Chao
Int. J. Mol. Sci. 2024, 25(22), 12298; https://doi.org/10.3390/ijms252212298 (registering DOI) - 15 Nov 2024
Viewed by 315
Abstract
The pathogenic mechanisms of severe aplastic anemia (SAA) in children are not completely elucidated. The insufficiency of the bone marrow microenvironment, in which mesenchymal stem cells (MSCs) are an important element, can be a potential factor associated with hematopoietic impairment in SAA. In [...] Read more.
The pathogenic mechanisms of severe aplastic anemia (SAA) in children are not completely elucidated. The insufficiency of the bone marrow microenvironment, in which mesenchymal stem cells (MSCs) are an important element, can be a potential factor associated with hematopoietic impairment in SAA. In the present study, we compared bone marrow MSCs from five children with SAA and five controls. We found a higher intensity of senescence-associated β-galactosidase activity in SAA MSCs, indicating the increased senescence in these cells. Further RNA sequencing analysis identified a distinctive profile of transcriptomes in SAA MSCs. After conducting a survey of the differentially expressed genes, we found that the up-regulated expression of TXNIP may compromise the proliferative potential of MSCs and probably relate to the pathogenesis of SAA. These results were validated by qPCR. To explore the molecular mechanism involving aberrant TXNIP regulation in SAA MSCs, the expression levels of IGF-1 and IGFBP-1 were measured. A significant increase in IGFBP-1 expression was noted in SAA MSCs despite the wide range of IGF-1 expressions. Accordingly, we postulated a novel pathogenic mechanism of SAA: a compensated increase in the expression of IGF-1 in MSCs to down-regulate TXNIP expression in the face of SAA, which is offset by the up-regulated expression of IGFBP-1. Full article
(This article belongs to the Special Issue Advances in Cell Signaling Pathways and Signal Transduction)
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<p>MSC identification. MSCs in both SAA and control groups were in accordance with the criteria of the International Society for Cellular Therapy. (<b>A</b>) Morphology (×200). (<b>B</b>) Immunophenotypic expression detected by flow cytometry. (<b>C</b>) Adipogenesis after 2-week adipogenic induction (oil red O staining, ×200). (<b>D</b>) Osteogenesis after 2-week osteogenic induction (von Kossa staining, ×200).</p>
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<p>Comparison of senescence-associated β-galactosidase activity in MSCs between patients with SAA and controls. (<b>A</b>) Cytochemical staining (×200). (<b>B</b>) Fluorescence detection (×200) and quantification by flow cytometry. Each dot in the boxplot represents the intensity of β-galactosidase fluorescence for each sample. * Mann-Whitney U test.</p>
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<p>Comparison of gene expression profiling in MSCs between the patients with SAA and the control. (<b>A</b>) Differential expression volcano plot. Red dots represent genes that are significantly up-regulated, and blue dots represent genes that are significantly down-regulated. In total, 49 down-regulated genes and 73 up-regulated genes were significantly differentially expressed in the SAA sample. (<b>B</b>) Hierarchical cluster analysis of the differentially expressed genes. Up-regulated genes are in red, and down-regulated genes are in blue. (<b>C</b>) GO enrichment histogram. The number of differentially expressed genes in each GO term is illustrated with the specification of the relevant biological process (red), cellular component (green), and molecular function (blue). The top 30 most prominent GO categories are shown. (<b>D</b>) KEGG enrichment histogram. The top 30 most significantly enriched pathways are shown. (<b>E</b>) Scatter plot of KEGG] enrichment. The size of the dot is positively correlated with the number of differentially expressed genes in the pathway. Color code indicates different ranges of adjusted <span class="html-italic">p</span>-values.</p>
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<p>Comparison of expression of TXNIP, IGF-1, and IGFBP-1 in MSCs between patients with SAA and controls by qPCR. * Mann-Whitney U test.</p>
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<p>Regulation of TXNIP expression by IGF-1 and IGFBP-1. <b>Left</b> panel: the normal physiological condition. <b>Right</b> panel: the condition of SAA. Up-regulated IGFBP-1 expression leads to an increase in the binding of IGF-1 to IGFBP-1 and thus attenuates the inhibition of TXNIP expression by IGF-1. Augmented TXNIP levels may initiate deregulated cell growth and apoptosis.</p>
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15 pages, 3230 KiB  
Article
Transcriptome Analysis of Porcine Immune Cells Stimulated by Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) and Caesalpinia sappan Extract
by Chaiwat Arjin, Patipan Hnokaew, Patchara Tasuksai, Marninphan Thongkham, Kidsadagon Pringproa, Jirapat Arunorat, Terdsak Yano, Mintra Seel-audom, Pornchai Rachtanapun, Korawan Sringarm and Phongsakorn Chuammitri
Int. J. Mol. Sci. 2024, 25(22), 12285; https://doi.org/10.3390/ijms252212285 (registering DOI) - 15 Nov 2024
Viewed by 287
Abstract
The current level of knowledge on transcriptome responses triggered by Caesalpinia sappan (CS) extract in porcine peripheral blood mononuclear cells (PBMCs) after porcine reproductive and respiratory syndrome virus (PRRSV) infection is limited. Therefore, in the present study, we aimed to detect significant genes [...] Read more.
The current level of knowledge on transcriptome responses triggered by Caesalpinia sappan (CS) extract in porcine peripheral blood mononuclear cells (PBMCs) after porcine reproductive and respiratory syndrome virus (PRRSV) infection is limited. Therefore, in the present study, we aimed to detect significant genes and pathways involved in CS extract supplementation responsiveness of PBMCs after PRRSV infection. RNA sequencing was conducted on PBMCs, which were isolated from six weaned piglets. The resultant transcriptional responses were examined by mRNA sequencing. Differential expression analysis identified 263 and 274 differentially expressed genes (DEGs) between the PRRSV and CTRL groups, and the PRRSV+CS and CTRL groups, respectively. Among these, ZNF646 and KAT5 emerged as the most promising candidate genes, potentially influencing the interaction between PRRSV-infected PBMCs and CS extract supplementation through the regulation of gene networks and cellular homeostasis during stress. Two pathways were detected to be associated with CS extract supplementation responsiveness: the cellular response to stress pathway and the NF-kB signaling pathway. Consequently, our study reveals a novel mechanism underlying cellular stress response and the NF-κB signaling pathway in PRRSV-infected PBMCs, and identifies a potential application of CS extract for activating the NF-κB signaling pathway. In conclusion, by supplementing CS extract in PBMC cells infected with PRRSV, we found that CS extract modulates PRRSV infection by inducing cellular stress, which is regulated by the NF-κB signaling pathway. This induced stress creates an adverse environment for PRRSV survival. This study contributes to a deeper understanding of the therapeutic targets and pathogenesis of PRRSV infection. Importantly, our results demonstrate that CS extract has the potential to be a candidate for modulating PRRSV infection. Full article
(This article belongs to the Section Bioactives and Nutraceuticals)
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<p>Characterization of differential expression analysis. (<b>A</b>) Raw read counts (Log2) of three treatment groups generated by treatment group enrichment analysis. (<b>B</b>) Bar diagram illustrates representative up- and downregulation for pairwise comparison between three treatment groups, absolute LFC of genes &gt; 2 from DESeq2. (<b>C</b>) Principal component analysis (PCA) of gene expression profile of all samples. Control PBMS cells (CTRL) are shown in green circles; PRRSV-infected cells (PRTE) are presented in blue circles; PRRSV-infected cells with CS extract supplementation (PRTE+CS) are illustrated in purple circles. (<b>D</b>) Heatmap constructed using LFC of a total of 50 genes in matched comparisons; colors indicate the up- (red) and downregulation (green) of gene expression.</p>
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<p>Summary plots for differential expression analysis using DESeq2. (<b>A</b>,<b>B</b>) UpSet plot illustrates unique and shared DEGs of pairwise comparisons PRRSV vs. CTRL (<b>A</b>), PRRSV+CS vs. CTRL (<b>B</b>). (<b>C</b>,<b>D</b>) Volcano plot shows statistically significant DEGs of pairwise comparisons PRRSV vs. CTRL (<b>C</b>), PRRSV+CS vs. CTRL (<b>D</b>). Red dots indicate significant up- and downregulation, absolute LFC &gt; 2, FDR &lt; 0.05. (<b>E</b>,<b>F</b>) MA plot from DESeq2 of pairwise comparisons PRRSV vs. CTRL (<b>E</b>), PRRSV+CS vs. CTRL (<b>F</b>), shows LFC (<span class="html-italic">y</span>-axis) and the Log<sub>2</sub> mean expression (<span class="html-italic">x</span>-axis) across all samples. Absolute LFC of genes &gt; 2 from DESeq2 is color highlighted. Red represents upregulation, blue represents downregulated genes, and grey dots represent no change.</p>
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<p>RNA-seq data reveal GO biological processes among differentially expressed genes between the PRRSV group and the CTRL group (<b>A</b>), and between the PRRSV+CS group and the CTRL group (<b>B</b>). The bubble plot represents the number of genes (<span class="html-italic">x</span>-axis), fold enrichment (bubble size), and FDR value (gradient colors) in each GO biological process term (<span class="html-italic">y</span>-axis). Representative bubbles were enriched at FDR &lt; 0.05.</p>
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<p>Protein–protein interaction network (PPI) of DEGs produced by STRING analysis. STRING analysis was used to analyze DEGs in PBMC cells of PRRSV versus CTRL (<b>A</b>) and PRRSV+CS versus CTRL (<b>B</b>). Differently colored lines represent eight types of evidence used in predicting associations. Blue line: curated databases evidence; dark purple: experimentally determined evidence; green line: gene neighborhood evidence; red line: gene fusion evidence; dark-blue line: gene co-occurrence evidence; light-green line: textmining evidence; black line: co-expression evidence; and purple line: protein homology evidence.</p>
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<p>Gene landscapes of selected genomic regions and DEGs in PBMC cells infected with PRRSV (PRRSV) and PBMC cells infected with PRRSV with CS extract supplementation (PRRSV+CS) versus control samples (CTRL). Gene track views of raw mapped reads of <span class="html-italic">AKAP3</span> (<b>A</b>), <span class="html-italic">ARHGAP9</span> (<b>B</b>), <span class="html-italic">HAUS3</span> (<b>C</b>), <span class="html-italic">NT5C3B</span> (<b>D</b>), <span class="html-italic">PHOX2A</span> (<b>E</b>), and <span class="html-italic">PRSS58</span> genes (<b>F</b>). RNA-seq peaks of mRNA expression (read density) are shown and compared across six samples by genome browser (IGV) screenshots. Genomic coordinates of each gene are given underneath each gene track. (<b>G</b>) Bar graphs show <span class="html-italic">AKAP3</span>, <span class="html-italic">ARHGAP9</span>, <span class="html-italic">HAUS3</span>, <span class="html-italic">NT5C3B</span>, <span class="html-italic">PHOX2A</span>, and <span class="html-italic">PRSS58</span> transcript mRNA cycle quantification (Cq) by RT-PCR. Data are mean ± standard error mean (SEM). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt;0.001, ns = no significance.</p>
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18 pages, 3031 KiB  
Article
Combined Transcriptomic and Metabolomic Analyses Reveal the Mechanisms by Which the Interaction Between Sulfur and Nitrogen Affects Garlic Yield and Quality
by Licai Ren, Xudong Pan, Yang Deng, Zhengkang Ge, Shiyuan Li, Dong Su, Guoqian Zhao, Hui Tang and Xiangfei Wang
Horticulturae 2024, 10(11), 1203; https://doi.org/10.3390/horticulturae10111203 - 15 Nov 2024
Viewed by 240
Abstract
Nitrogen and sulfur are essential macronutrients in plant growth and development, and their interaction profoundly influences gene expression, metabolic activities, and adaptability in plants, directly affecting plant growth and yield. Garlic (Allium sativum L.) is a crop of significant economic and medicinal [...] Read more.
Nitrogen and sulfur are essential macronutrients in plant growth and development, and their interaction profoundly influences gene expression, metabolic activities, and adaptability in plants, directly affecting plant growth and yield. Garlic (Allium sativum L.) is a crop of significant economic and medicinal value. However, despite the critical role of the nitrogen–sulfur interaction in garlic’s adaptability, yield, and quality, the specific mechanisms underlying these effects remain unclear. In this study, transcriptomic and metabolomic analyses were employed to investigate the effects of combined sulfur and nitrogen application on garlic bulb tissues. The results show that the combined application of sulfur and nitrogen significantly increased the diameter and weight of garlic bulbs by 14.96% and 35.47%, respectively. The content of alliin increased by 28.48%, while the levels of abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), and gibberellin (GA) increased by 15.82%, 12.94%, 32.34%, and 48.13%, respectively. Additionally, the activities of alliinase, superoxide dismutase (SOD), and catalase (CAT) were enhanced by 7.93%, 4.48%, and 19.74%, respectively. Moreover, the application of sulfur and nitrogen significantly reduced the malondialdehyde (MDA) content and peroxidase (POD) activity in garlic bulbs by 29.66% and 9.42%, respectively, thereby improving garlic’s adaptability and growth potential. Transcriptomic analysis revealed differentially expressed genes in several key pathways, including plant hormone signal transduction, RNA degradation, glutathione metabolism, amino acid biosynthesis, and glycerophospholipid metabolism. Metabolomic analysis identified 80 differentially abundant metabolites primarily consisting of amino acids, indole carboxylic acids, and fatty acids. The integrated transcriptomic and metabolomic analyses highlighted the pivotal roles of glutathione metabolism, glycerophospholipid metabolism, and amino acid biosynthesis pathways in the synergistic effects of sulfur and nitrogen. This study not only provides critical scientific evidence for understanding the mechanisms underlying the nitrogen–sulfur interaction’s impact on the yield and quality of garlic but also offers a scientific basis for optimizing nutrient management strategies to enhance garlic yield and quality. Full article
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<p>Effects of sulfur and nitrogen fertilizers on garlic yield, medicinal quality, hormone and malondialdehyde content, and antioxidant enzyme activity. (<b>A</b>) Diameter, weight, alliin content, and alliinase activity. (<b>B</b>) ABA, SA, JA, and GA content. (<b>C</b>) MDA content and SOD, POD, and CAT activity. Note: Different lowercase letters on data of the same indicator treated with different concentrations indicate significant differences at the 5% level, the same below.</p>
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<p>Overview of differential gene expression changes. (<b>A</b>) The number of upregulated and downregulated DEGs between each treatment group and the CG group. (<b>B</b>) Venn diagram illustrating the overlap of DEGs among the groups.</p>
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<p>Results of KEGG enrichment analysis for differential genes. (<b>A</b>) KEGG enrichment analysis results for CG_SSF. (<b>B</b>) KEGG enrichment analysis results for CG_LSF. (<b>C</b>) KEGG enrichment analysis results for CG_MR. (<b>D</b>) KEGG enrichment analysis results for DEGs.</p>
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<p>Results of the transcription factors, transcriptional regulation, and Weighted Gene Co-expression Network Analysis (WGCNA). (<b>A</b>) The top 10 TF and TR families by quantity. (<b>B</b>) Hierarchical clustering tree of co-expression modules identified by WGCNA. (<b>C</b>) Module–sample relationship. (<b>D</b>) Correlation network of hub genes in the turquoise module. (<b>E</b>) Correlation network of hub genes in the blue module.</p>
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<p>Hierarchical clustering heatmap, principal component analysis, and orthogonal partial least squares discriminant analysis. (<b>A</b>) Hierarchical clustering heatmap of the top 30 metabolites by content. (<b>B</b>) Principal component analysis plot. (<b>C</b>) Orthogonal partial least squares discriminant analysis plot.</p>
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<p>Metabolite changes under different treatments. (<b>A</b>) Stacked bar chart showing the percentage of metabolites playing biological roles. (<b>B</b>) Stacked bar chart of the top 20 metabolites by content. (<b>C</b>) The number of differentially expressed metabolites (DEMs) that are upregulated and downregulated in each group compared to the control group (CG). (<b>D</b>) Venn diagram of DEMs.</p>
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<p>KEGG enrichment map of differential metabolites. (<b>A</b>) KEGG enrichment analysis results for CG_SSF. (<b>B</b>) KEGG enrichment analysis results for CG_LSF. (<b>C</b>) KEGG enrichment analysis results for CG_MR. (<b>D</b>) KEGG enrichment analysis results for DEMs.</p>
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<p>Illustrates the changes in the primary metabolic pathways of garlic resulting from the interaction between sulfur and nitrogen.</p>
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19 pages, 19487 KiB  
Article
RNA-Seq Profiling in Chicken Spleen and Thymus Infected with Newcastle Disease Virus of Varying Virulence
by Xiaoquan Wang, Xiaolong Lu, Mingzhu Wang, Qiwen Zhou, Xiyue Wang, Wenhao Yang, Kaituo Liu, Ruyi Gao, Tianxing Liao, Yu Chen, Jiao Hu, Min Gu, Shunlin Hu, Xiufan Liu and Xiaowen Liu
Vet. Sci. 2024, 11(11), 569; https://doi.org/10.3390/vetsci11110569 - 15 Nov 2024
Viewed by 342
Abstract
Newcastle disease virus (NDV), known as avian paramyxovirus-1, poses a significant threat to poultry production worldwide. Vaccination currently stands as the most effective strategy for Newcastle disease control. However, the mesogenic vaccine strain Mukteswar has been observed to evolve into a velogenic variant [...] Read more.
Newcastle disease virus (NDV), known as avian paramyxovirus-1, poses a significant threat to poultry production worldwide. Vaccination currently stands as the most effective strategy for Newcastle disease control. However, the mesogenic vaccine strain Mukteswar has been observed to evolve into a velogenic variant JS/7/05/Ch during poultry immunization. Here, we aimed to explore the mechanisms underlying virulence enhancement of the two viruses. Pathogenically, JS/7/05/Ch mediated stronger virulence and pathogenicity in vivo compared to Mukteswar. Comparative transcriptome analysis revealed 834 differentially expressed genes (DEGs), comprising 339 up-regulated and 495 down-regulated genes, in the spleen, and 716 DEGs, with 313 up-regulated and 403 down-regulated genes, in the thymus. Gene Ontology (GO) analysis indicated that these candidate targets primarily participated in cell and biological development, extracellular part and membrane composition, as well as receptor and binding activity. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis unveiled a substantial portion of candidate genes predominantly involved in cellular processes, environmental information processing, metabolism, and organismal systems. Additionally, five DEGs (TRAT1, JUP, LPAR4, CYB561A3, and CXCR5) were randomly identified through RNA-seq analysis and subsequently confirmed via quantitative real-time polymerase chain reaction (qRT-PCR). The findings revealed a marked up-regulation in the expression levels of these DEGs induced by JS/7/05/Ch compared to Mukteswar, with CYB561A3 and CXCR5 exhibiting significant increases. The findings corroborated the sequencing accuracy, offering promising research directions. Taken together, we comprehensively evaluated transcriptomic alterations in chicken immune organs infected by NDV strains of diverse virulence. This study establishes a basis and direction for NDV virulence research. Full article
(This article belongs to the Section Veterinary Microbiology, Parasitology and Immunology)
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<p>Experimental design strategy in vivo. SPF chickens were injected with NDV and PBS through wing veins. On the 4th day post-infection, the two infected groups were dissected, and the spleen and thymus were collected for subsequent sample quality control, RNA-Seq, and analysis. The schematic representation is created with Biorender.com.</p>
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<p>Viral pathogenic characteristics of NDV in vivo. (<b>A</b>) Determination of virus load in the spleen and thymus following NDV infection. (<b>B</b>) Histopathology of the spleen and thymus following NDV infection. Magnification 200×, scale bar 100 μm. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. Bar-plot error bars indicate SDs.</p>
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<p>Pearson correlation map between samples.</p>
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<p>Analysis of read distribution and gene expression. (<b>A</b>) Analysis of read distribution. Distribution of reads in gene composition of NDV-infected spleen and thymus. The figures illustrate the proportion of exonic, intronic, and intergenic reads. (<b>B</b>) The number of genes in NDV-infected spleen and thymus. (<b>C</b>) Evaluation of saturation of sequencing results. The <span class="html-italic">X</span>-axis represents the proportion of sequencing reads (expressed as a percentage), while the <span class="html-italic">Y</span>-axis indicates the count of identified genes. (<b>D</b>) Gene expression density map. Utilizing RPKM density distribution mapping to assess the collective gene expression pattern of the sample, regional statistical analysis of genome-wide gene expression levels was conducted to depict the overall expression profile of the sample.</p>
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<p>Volcano plot of DEG analysis. (<b>A</b>) NDV-infected spleen. (<b>B</b>) NDV-infected thymus. The <span class="html-italic">X</span>-axis illustrates variations in gene expression multiples across various samples, while the <span class="html-italic">Y</span>-axis denotes the statistical significance of disparities in gene expression levels. Red dots indicate genes significantly up-regulated, whereas green dots denote genes significantly down-regulated. Gray dots indicate genes with no significant regulation. The significant differential expression was based on both fold change (|log2FoldChange| &gt; 1) and statistical significance (q-value &lt; 0.001).</p>
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<p>Gene Ontology (GO) functional enrichment of DEGs. The classification map illustrated the GO function enrichment of DEGs in the spleen (<b>A</b>) and thymus (<b>B</b>) following NDV infection. The <span class="html-italic">Y</span>-axis delineates the three fundamental categories of GO (Biological Process, Cellular Component, and Molecular Function) along with their respective specific terms. The <span class="html-italic">X</span>-axis indicates the <span class="html-italic">p</span>-value associated with each term, alongside the count of genes annotated to that particular term.</p>
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<p>Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation of DEGs. (<b>A</b>) KEGG pathway bubble map of DEGs. This visualization presents the statistical enrichment of pathways in NDV-infected spleen. (<b>B</b>) KEGG pathway annotation for DEGs in the NDV-infected spleen. (<b>C</b>) KEGG pathway bubble map of DEGs. This visualization presents the statistical enrichment of pathways in NDV-infected thymus. (<b>D</b>) KEGG pathway annotation for DEGs in the NDV-infected thymus. KEGG pathway bubble map: the <span class="html-italic">X</span>-axis denotes the proportion of enriched differential genes within the pathway’s background gene set, while the <span class="html-italic">Y</span>-axis lists the pathway names. The size of each bubble represents the number of enriched differential genes, while the color indicates the associated <span class="html-italic">p</span>-value. KEGG pathway annotation: the <span class="html-italic">Y</span>-axis represents the categories of KEGG pathways, while the <span class="html-italic">X</span>-axis indicates the <span class="html-italic">p</span>-value of each term along with the number of genes annotated to a particular term.</p>
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<p>Screening and identification of DEGs. (<b>A</b>) A heatmap of the top 100 DEGs was generated in the R package heatmap. (<b>B</b>) Expression levels of five candidate genes were assessed. The relative expression of genes randomly selected from the list of candidates was detected using RT-qPCR. * <span class="html-italic">p</span> &lt; 0.05. Bar-plot error bars indicate SDs.</p>
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19 pages, 3594 KiB  
Article
A Multi-Omics View of Maize’s (Zea mays L.) Response to Low Temperatures During the Seedling Stage
by Tao Yu, Jianguo Zhang, Xuena Ma, Shiliang Cao, Wenyue Li and Gengbin Yang
Int. J. Mol. Sci. 2024, 25(22), 12273; https://doi.org/10.3390/ijms252212273 - 15 Nov 2024
Viewed by 210
Abstract
Maize (Zea mays L.) is highly sensitive to temperature during its growth and development stage. A 1 °C drop in temperature can delay maturity by 10 days, resulting in a yield reduction of over 10%. Low-temperature tolerance in maize is a complex [...] Read more.
Maize (Zea mays L.) is highly sensitive to temperature during its growth and development stage. A 1 °C drop in temperature can delay maturity by 10 days, resulting in a yield reduction of over 10%. Low-temperature tolerance in maize is a complex quantitative trait, and different germplasms exhibit significant differences in their responses to low-temperature stress. To explore the differences in gene expression and metabolites between B144 (tolerant) and Q319 (susceptible) during germination under low-temperature stress and to identify key genes and metabolites that respond to this stress, high-throughput transcriptome sequencing was performed on the leaves of B144 and Q319 subjected to low-temperature stress for 24 h and their respective controls using Illumina HiSeqTM 4000 high-throughput sequencing technology. Additionally, high-throughput metabolite sequencing was conducted on the samples using widely targeted metabolome sequencing technology. The results indicated that low-temperature stress triggered the accumulation of stress-related metabolites such as amino acids and their derivatives, lipids, phenolic acids, organic acids, flavonoids, lignin, coumarins, and alkaloids, suggesting their significant roles in the response to low temperature. This stress also promoted gene expression and metabolite accumulation involved in the flavonoid biosynthesis pathway. Notably, there were marked differences in gene expression and metabolites related to the glyoxylate and dicarboxylate metabolism pathways between B144 and Q319. This study, through multi-omics integrated analysis, provides valuable insights into the identification of metabolites, elucidation of metabolic pathways, and the biochemical and genetic basis of plant responses to stress, particularly under low-temperature conditions. Full article
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<p>PCA score of differential genes and differential metabolites in response to low temperature.</p>
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<p>Nine-quadrant diagram of the correlation between differential metabolites and differential genes. Note: (<b>A</b>) Nine-quadrant diagram of MBCK vs. MB24 and TBCK vs. TB24. (<b>B</b>) Nine-quadrant diagram of MQCK vs. MQ24 and TQCK vs. TQ24. T stands for transcriptome, and M stands for metabolome.</p>
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<p><span class="html-italic">p</span>-value histogram of enrichment analysis of differential gene and differential metabolite. Note: The horizontal axis in the KEGG enrichment diagram represents metabolic pathways, and the red color in the vertical axis represents the enrichment <span class="html-italic">p</span>-value of differential genes, while the green color represents the enrichment <span class="html-italic">p</span>-value of differential metabolites, represented by −log(<span class="html-italic">p</span>-value). The higher the vertical axis, the stronger the enrichment degree. (<b>A</b>) shows the B144 KEGG enrichment diagram, and (<b>B</b>) shows the Q319 KEGG enrichment diagram.</p>
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<p>Cluster analysis of differential genes and differential metabolites. Note: (<b>A</b>) shows the differential expression gene and differential metabolite cluster heatmap after B144 low-temperature stress, and (<b>B</b>) shows the differential expression gene and differential metabolite cluster heatmap after Q319 low-temperature stress.</p>
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<p>Correlation network analysis of differential genes and differential metabolites. Note: (<b>A</b>) shows the network diagram of differentially expressed genes and differentially metabolized glyoxylate and dicarboxylate metabolism (ko00630) after B144 low-temperature stress. (<b>B</b>) shows the network diagram of differentially expressed genes and metabolized glyoxylate and dicarboxylate metabolism (ko00630) after Q319 low-temperature stress. Metabolites are marked in green, and genes are marked in red. Solid lines represent positive correlation, and dashed lines represent negative correlation.</p>
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<p>O2PLS model loading graph. Note: (<b>A</b>) represents the transcriptome loading plot, and (<b>B</b>) represents the metabolome loading plot.</p>
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<p>Detection of mRNA expression level of candidate genes in B144 by qPCR. Note: (<b>A</b>) represents the expression level of <span class="html-italic">LOC</span>103633247; (<b>B</b>) represents the expression level of <span class="html-italic">LOC</span>100273222; (<b>C</b>) represents the expression level of <span class="html-italic">gst2</span>; (<b>D</b>) represents the expression level of <span class="html-italic">LOC</span>103629384; (<b>E</b>) represents the expression level of <span class="html-italic">LOC</span>103629437. * and ** denote levels of significance at <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>Detection of mRNA expression level of candidate genes in Q319 by qPCR. Note: (<b>A</b>) represents the expression level of <span class="html-italic">LOC</span>103633247; (<b>B</b>) represents the expression level of <span class="html-italic">LOC</span>100273222; (<b>C</b>) represents the expression level of <span class="html-italic">gst2</span>; (<b>D</b>) represents the expression level of <span class="html-italic">LOC</span>103629384; (<b>E</b>) represents the expression level of <span class="html-italic">LOC</span>103629437. * and ** denote levels of significance at <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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