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21 pages, 13028 KiB  
Article
Integrative Targeted Metabolomics and Transcriptomics Reveal the Mechanism of Leaf Coloration in Impatiens hawkeri ‘Sakimp005’
by Jia-Qi He, Dou-Cheng Yu, Si-Yu Ren, Xiao-Li Zhang, Xin-Yi Li, Mei-Juan Huang and Hai-Quan Huang
Int. J. Mol. Sci. 2025, 26(1), 174; https://doi.org/10.3390/ijms26010174 (registering DOI) - 28 Dec 2024
Abstract
One of the most important characteristics of ornamental plants is leaf color, which enhances the color of plant landscapes and attracts pollinators for reproduction. The leaves of Impatiens hawkeri ‘Sakimp005’ are initially green, then the middle part appears yellow, then gradually become white, [...] Read more.
One of the most important characteristics of ornamental plants is leaf color, which enhances the color of plant landscapes and attracts pollinators for reproduction. The leaves of Impatiens hawkeri ‘Sakimp005’ are initially green, then the middle part appears yellow, then gradually become white, while the edge remains green. In the study, leaves of I. hawkeri ‘Sakimp005’, in four developmental stages (S1-G, S2-C, S3-C, and S4-C), were selected for the determination of pigment content, chromaticity values, integrative metabolomics, and transcriptomics analyses. The carotenoid content of leaves varied significantly and regularly at four stages, and the colorimetric values corroborated the phenotypic observations. The results of integrative metabolomics and transcriptomics analysis show that the accumulation of two carotenoids (lutein and zeaxanthin), to different degrees in the leaves of I. hawkeri ‘Sakimp005’ at four stages, led to the vary yellowing phenomenon. We speculated that the carotenoid biosynthesis (containing two branches: α-branch and β-branch) in leaves by IhLUT1 and IhLUT5 in the α-branch and IhBCH2 genes in the β-branch differed. These findings provide a molecular basis for Impatiens plants’ leaf color breeding and improve the knowledge of the leaf color mechanism. Full article
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Figure 1

Figure 1
<p>Determination of total chlorophylls, total carotenoids, and total flavonoids in the leaves of <span class="html-italic">I. hawkeri</span> ‘Sakimp005’ at four developmental stages. Different lowercase letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>PLA-DA assay. (<b>A</b>) PLS-DA score diagram; (<b>B</b>) PLS-DA replacement inspection diagram.</p>
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<p>Clustering heatmap of various carotenoids in the leaves of <span class="html-italic">I. hawkeri</span> ‘Sakimp005’ at four developmental stages. Red indicates a high level and green indicates a low level.</p>
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<p>Proportion of different carotenoids in the leaves of <span class="html-italic">I. hawkeri</span> ‘Sakimp005’ at four developmental stages. (<b>A</b>) S1-G; (<b>B</b>) S2-C; (<b>C</b>) S3-C; and (<b>D</b>) S4-C.</p>
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<p>Lutein and zeaxanthin content in the leaves of <span class="html-italic">I. hawkeri</span> ‘Sakimp005’ at four developmental stages. Different lowercase letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Unigenes function annotation diagrams. (<b>A</b>) GO annotation diagram; (<b>B</b>) KOG annotation diagram.</p>
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<p>Violin diagram of gene expression.</p>
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<p>DEG volcano diagrams for each comparison group. (<b>A</b>) S2-C vs. S1-G; (<b>B</b>) S3-C vs. S1-G; (<b>C</b>) S4-C vs. S1-G; (<b>D</b>) S3-C vs. S2-C; (<b>E</b>) S4-C vs. S2-C; and (<b>F</b>) S4-C vs. S3-C.</p>
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<p>DEG function enrichment bubble maps. (<b>A</b>) GO enrichment bubble map; (<b>B</b>) KEGG enrichment bubble map.</p>
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<p>Integrative analysis of DEGs and DEMs. (<b>A</b>) Heatmap of correlation clustering between DEMs and DEGs; (<b>B</b>) co-expression network analysis diagram of DEMs and DEGs.</p>
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<p>Heatmap of carotenoid metabolic pathways. Red indicates the gene is up-regulated and green indicates the gene is down-regulated.</p>
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<p>The expression pattern of carotenoid biosynthesis-related genes in the leaves of <span class="html-italic">I. hawkeri</span> ‘Sakimp005’ at four developmental stages. (<b>A</b>) <span class="html-italic">IhLUT1</span>; (<b>B</b>) <span class="html-italic">IhLUT5</span>; (<b>C</b>) <span class="html-italic">IhBCH2-1</span>; and (<b>D</b>) <span class="html-italic">IhBCH2-1</span>. Different lowercase letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>A proposed model for the leaf coloration in <span class="html-italic">I. hawkeri</span> ‘Sakimp005’. Red arrows indicate up-regulation and green arrows indicate down-regulation.</p>
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<p>Leaf coloration process in <span class="html-italic">I. hawkeri</span> ‘Sakimp005’. S1-S4 were the four crucial developmental stages during leaf coloration; G: green; and C: color.</p>
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16 pages, 3843 KiB  
Article
Optimal Vase Solution for Gerbera hybrida Cut Flower Keeping Fresh by Activating SA and Cytokinin Signaling and Scavenging Reactive Oxygen Species
by Chaoshui Xia, Yiyang Cao, Weixin Gan, Huifeng Lin, Huayang Li, Fazhuang Lin, Zhenhong Lu and Weiting Chen
Biology 2025, 14(1), 18; https://doi.org/10.3390/biology14010018 (registering DOI) - 28 Dec 2024
Abstract
Gerbera (Gerbera hybrida) is a popular cut flower on the market, so extending its vase life (VL) is an important goal in the horticultural industry. The aim of this study was to improve the freshness of gerbera cut flowers through the [...] Read more.
Gerbera (Gerbera hybrida) is a popular cut flower on the market, so extending its vase life (VL) is an important goal in the horticultural industry. The aim of this study was to improve the freshness of gerbera cut flowers through the optimal solution (OS) and to analyze its preservation mechanism. We used chitosan (COS), calcium chloride (CaCl2), and citric acid (CA) as the main ingredients of the vase solution and determined the OS ratio of 104 mg/L of COS, 92 mg/L of CA, and 93 mg/L of CaCl2 using the Box–Behnken design-response surface method (BBD-RSM). Gerbera preservation results showed that the VL of the OS was 14.5 days, which was significantly longer than that of flowers maintained in the Basic Vase Solution (BVS) and the Commercial Formulation (CF) and was highly consistent with the theoretical VL of 14.57 d. Transcriptome analysis indicated that the OS might extend VL by regulating phytohormone signaling pathways, such as cytokinin and salicylic acid signaling. The qRT-PCR analysis of key candidate genes supported these findings, with significant upregulation observed in genes related to cytokinin synthesis (e.g., GhIPT1 and GhIPT9), salicylic acid signaling related to pathogen defense (e.g., GhTGA1, GhTGA4, GhNPR1, and GhRBOHA), and plant wax synthesis and stress response (e.g., GhKCS5, GhCUT1, and GhKCS6). Further, transcriptome GO-enrichment and physiological analysis showed that the OS might extend VL of Gerbera cut flowers by scavenging reactive oxygen species, including by activating the expression of genes related to oxidoreductase activity and the activities of antioxidant-system-related enzymes catalase (CAT), peroxidase (POD), ascorbate peroxidase (APX), and superoxide dismutase (SOD), while decreasing the malondialdehyde (MDA) content. These results provide valuable insights into the mechanisms underlying the extended VL of gerbera cut flowers and offer a foundation for developing more effective preservation techniques. Full article
(This article belongs to the Special Issue Molecular Biology of Plants)
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Figure 1
<p>Effect of a single-factor chemical agent for freshness preservation. (<b>A</b>) Effect of antibacterial agent on VL of gerbera cut flower. (<b>B</b>) Effects of organic acids on the VL of cut gerbera flowers. (<b>C</b>) Influence of inorganic salts on VL of gerbera cut flowers. (<b>D</b>) Effect of antibacterial agent on maximum flower diameter of gerbera cut flower. (<b>E</b>) Effects of organic acids on the maximum flower diameter of cut gerbera flowers. (<b>F</b>) Influence of inorganic salts on maximum flower diameter of gerbera cut flowers. In the context of statistical analysis, “ns” stands for “not significant,” which means that the differences between the two groups have not reached a level of statistical significance. “*” denotes “significant,” and is commonly used to indicate a <span class="html-italic">p</span>-value less than 0.05, suggesting that the differences between the groups are statistically significant. “**” signifies “highly significant”, which typically corresponds to a <span class="html-italic">p</span>-value less than 0.01, indicating that the differences between the groups are highly statistically significant.</p>
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<p>Response surface diagram (<b>top</b>) and contour plot (<b>bottom</b>) of interaction of different insurance agents on VL of gerbera cut flowers.</p>
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<p>(<b>A</b>) Different stages and treatments of gerbera cut flowesr bottling state. (<b>B</b>) Effect of OS preservative on VL and maximum flower diameter of gerbera cut flowers, BVS: Basic Vase Solution; OS: optimal solution; CF: Commercial formulation.The dots in the graph represent the number of repetitions. The dots with different colors in subfigure. (<b>B</b>) represent different groups or categories of data. Different uppercase letters indicate that the differences between them are significant at the <span class="html-italic">p</span> &lt; 0.01 level of significance.</p>
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<p>(<b>A</b>) Principal component analysis (PCA) of the samples of BVS vs. OS gerbera cut flowers. (<b>B</b>) Sample correlation heat map.</p>
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<p>(<b>A</b>) Numbers and (<b>B</b>) volcano plot of up- and down-regulated DEGs in control (BVS) vs. OS gerbera cut flower, and (<b>C</b>) clustering heat map of DEGs in BVS vs. OS gerbera cut flower.</p>
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<p>The top 20 enriched KEGG pathways for the upregulated DEGs in BVS vs. OS gerbera cut flowers. (<b>A</b>) The KEGG pathways were enriched in the upregulated DEGs. (<b>B</b>) Parts of the cytokinin and salicylic acid signaling pathways were significantly expressed under treatment (red). (<b>C</b>) Detection of key differentially expressed genes using qRT-PCR. Data in (<b>C</b>) represent the means ± SD from four independent experiments. Error bars indicate SD. “**” above the bars indicates significant differences (<span class="html-italic">p</span> &lt; 0.01) calculated by Fisher’s protected <span class="html-italic">t</span>-test. The dots in the graph represent the number of repetitions. The pathways marked in red are Closely related to cytokinin signaling and salicylic acid signaling.</p>
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<p>(<b>A</b>) Up-down normalization (<b>B</b>): The top 20 enriched GO terms for the DEGs in BVS vs. OS gerbera cut flowers. (<b>C</b>) Analysis of antioxidant enzyme activity and MDA content in gerbera cut flowers. The dots in the graph represent the number of repetitions. Different uppercase letters indicate that the differences between them are significant at the <span class="html-italic">p</span> &lt; 0.01 level of significance.</p>
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23 pages, 5382 KiB  
Article
Transcriptional Analysis of Tissues in Tartary Buckwheat Seedlings Under IAA Stimulation
by Yingying Gao, Jialing Lai, Chenglu Feng, Luyang Li, Qihang Zu, Juan Li and Dengxiang Du
Genes 2025, 16(1), 30; https://doi.org/10.3390/genes16010030 (registering DOI) - 27 Dec 2024
Abstract
Background: Fagopyrum tataricum, commonly referred to as tartary buckwheat, is a cultivated medicinal and edible crop renowned for its economic and nutritional significance. Following the publication of the buckwheat genome, research on its functional genomics across various growth environments has gradually [...] Read more.
Background: Fagopyrum tataricum, commonly referred to as tartary buckwheat, is a cultivated medicinal and edible crop renowned for its economic and nutritional significance. Following the publication of the buckwheat genome, research on its functional genomics across various growth environments has gradually begun. Auxin plays a crucial role in many life processes. Analyzing the expression changes in tartary buckwheat after IAA treatment is of great significance for understanding its growth and environmental adaptability. Methods: This study investigated the changes in auxin response during the buckwheat seedling stage through high-throughput transcriptome sequencing and the identification and annotation of differentially expressed genes (DEGs) across three treatment stages. Results: After IAA treatment, there are 3355 DEGs in leaves and 3974 DEGs in roots identified. These DEGs are significantly enriched in plant hormone signaling, MAPK signaling pathways, phenylpropanoid biosynthesis, and flavonoid biosynthesis pathways. This result suggests a notable correlation between these tissues in buckwheat and their response to IAA, albeit with significant differences in response patterns. Additionally, the identification of tissue-specific expression genes in leaves and other tissues revealed distinct tissue variations. Conclusions: Following IAA treatment, an increase in tissue-specific expression genes observed, indicating that IAA significantly regulates the growth of buckwheat tissues. This study also validated certain genes, particularly those in plant hormone signaling pathways, providing a foundational dataset for the further analysis of buckwheat growth and tissue development and laying the groundwork for understanding buckwheat growth and development. Full article
22 pages, 8635 KiB  
Article
Comprehensive Analysis of the GiTCP Gene Family and Its Expression Under UV-B Radiation in Glycyrrhiza inflata Bat
by Ziliang Liu, Jiaang Zhao, Ying Xiao, Caijuan Li, Rong Miao, Sijin Chen, Dan Zhang, Xiangyan Zhou and Mengfei Li
Int. J. Mol. Sci. 2025, 26(1), 159; https://doi.org/10.3390/ijms26010159 - 27 Dec 2024
Abstract
TCP is a plant-specific transcription factor that plays an important role in plant growth and development. In this study, we used bioinformatics to identify the entire genome of the TCP gene family in Glycyrrhiza inflata Bat, and we analyzed the expression characteristics [...] Read more.
TCP is a plant-specific transcription factor that plays an important role in plant growth and development. In this study, we used bioinformatics to identify the entire genome of the TCP gene family in Glycyrrhiza inflata Bat, and we analyzed the expression characteristics of GiTCP genes under UV-B radiation using qRT-PCR. The results were as follows: (1) 24 members of the TCP gene family were identified in G. inflata, evenly distributed on its 24 chromosomes. (2) The GiTCP genes contained 0–4 introns and 0–5 exons. (3) The GiTCP genes were phylogenetically divided into three subfamilies—PCF, CIN, and CYC/TB1, with 14, 9, and 1 GiTCP proteins, respectively. (4) A covariance analysis showed that two pairs of GiTCP genes underwent a fragmentary duplication event. (5) A cis-element analysis showed that the cis-responsive elements of the GiTCP genes’ promoter regions were mainly comprised of light-responsive, stress-responsive, hormone-regulated, growth and development, and metabolic-regulated elements. (6) A protein network interaction analysis revealed a total of 14 functional molecules of TCPs and 8 potential interacting proteins directly related to GiTCP proteins. (7) GO annotation showed that the GiTCP genes were mainly enriched in BP, CC, and MF groups and had corresponding functions. (8) RNA-seq and qRT-PCR further indicated that GiTCP3, 6, 7, 8, 12, 14, 17, 23, and 24 were up- or down-regulated in G. inflata after UV-B radiation, demonstrating that these genes responded to UV-B radiation in G. inflata. (9) Subcellular localization analysis showed that the GiTCP8 protein was localized in the nucleus. The results of this study provide a basis for further exploration of the function of the GiTCP gene family in the growth and development of G. inflata. Full article
(This article belongs to the Section Molecular Plant Sciences)
15 pages, 4712 KiB  
Article
A Protein with Unknown Function, Ps495620, Is Critical for the Sporulation and Oospore Production of Phytophthora sojae
by Xiaoran Du, Yan Zeng, Yiying Li, Qin Peng, Jianqiang Miao and Xili Liu
J. Fungi 2025, 11(1), 12; https://doi.org/10.3390/jof11010012 - 27 Dec 2024
Abstract
While the rapid rise in bioinformatics has facilitated the identification of the domains and functions of many proteins, some still have no domain annotation or largely uncharacterized functions. However, the biological roles of unknown proteins were not clear in oomycetes. An analysis of [...] Read more.
While the rapid rise in bioinformatics has facilitated the identification of the domains and functions of many proteins, some still have no domain annotation or largely uncharacterized functions. However, the biological roles of unknown proteins were not clear in oomycetes. An analysis of the Phytophthora sojae genome database identified the protein Ps495620, which has no domain annotations and functional predictions in Phytophthora. This study used a CRISPR/Cas9-mediated gene replacement system to knock out Ps495620 to elucidate its function. The Ps495620-knockout mutants exhibited significantly increased oospore production and decreased sporangium formation compared to the wild-type strain P6497. Transcriptomics showed that it is a key regulator of nitrogen, pyruvate, ascorbate, and adorate metabolism in P. sojae. Our findings indicate that Ps495620 is critical in regulating sporangium formation and oospore production in P. sojae. Full article
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Figure 1
<p>Sequence and expression profile analysis of Ps495620. (<b>a</b>) Full-length Ps495620 gene; (<b>b</b>) phylogenetic tree of Ps495620 homologous proteins from <span class="html-italic">P. capsici</span>, <span class="html-italic">P. infestans</span>, <span class="html-italic">P. ramorum</span>, <span class="html-italic">P. palmivora</span>, <span class="html-italic">P. cinnamomi</span>, <span class="html-italic">P. nicotianae</span>, and <span class="html-italic">P. cactorum</span>, based on Bayesian inference. The size of a star of bootstrap stands for confidence in the branching of phylogenetic trees; (<b>c</b>) expression profile of Ps495620 at various developmental and infection stages, measured by qRT-PCR. Samples include mycelia (MY), sporangium (SP), zoospore (ZO), cystospores (CY), cyst germination (CG), and different infection stages of strain P6497 (at 1.5 h, 3 h, 6 h, 12 h, 24 h, and 48 h). Statistical significance was determined using one-way ANOVA, with asterisks indicating significant differences (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Phenotypic analysis of <span class="html-italic">Ps495620</span> knockout transformants. (<b>a</b>) Colony diameter (first row) on V8 medium, with microscopic visualization of sporangia (second row) and oospores (third row) of wild-type strain P6497 (WT), <span class="html-italic">Ps495620</span> knockout transformants (∆Ps495620-1, 2, 3) and complemented transformant (Ps495620-C); (<b>b</b>) colony diameter on V8 medium; (<b>c</b>) pathogenicity on soybean leaves; (<b>d</b>) sporangia number; (<b>e</b>) zoospore number; (<b>f</b>) oospore number; (<b>g</b>) cyst germination. Experiments were repeated three times. ns: not significant. *: At <span class="html-italic">p</span> &lt; 0.05, significant difference. **: At <span class="html-italic">p</span> &lt; 0.01, significant difference. ***: At <span class="html-italic">p</span> &lt; 0.001, significant difference. The scale bar represents 50 µm.</p>
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<p>Phenotypic analysis of <span class="html-italic">Ps495620</span> knockout transformants under various stress conditions. (<b>a</b>) Mycelial growth under temperature stress; (<b>b</b>,<b>c</b>) mycelial growth under osmotic stress (sorbitol and KCl); (<b>d</b>) mycelial growth under oxygen stress. A one-way ANOVA was used for statistical analysis. *: At <span class="html-italic">p</span> &lt; 0.05, significant difference. **: At <span class="html-italic">p</span> &lt; 0.01, significant difference. ***: At <span class="html-italic">p</span> &lt; 0.001, significant difference. ****: At <span class="html-italic">p</span> &lt; 0.0001, significant difference. ns, not significant.</p>
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<p>Transcriptomic analysis of <span class="html-italic">Ps495620</span> knockout transformants and P6497 (WT) in sporangia stage. (<b>a</b>) Volcanic diagrams displayed the differentially expressed genes (DEGs) among Ps495620 knockout transformants (KT20) and P6497 (WT) in sporangia stage; (<b>b</b>) differential gene expression analysis of <span class="html-italic">Ps495620</span> knockout transformants and P6497 (WT) in sporangia stage, measured by qRT-PCR. *: At <span class="html-italic">p</span> &lt; 0.01, significant difference.</p>
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<p>Differential gene expression enrichment analysis of <span class="html-italic">Ps495620</span> knockout transformant and P6497 (WT) in sporangia stage. (<b>a</b>) KEGG functional enrichment analysis; The size of the circle stand for the count of genes, and the change in the color of the circle stand for <span class="html-italic">p</span>-adjusted (padj); (<b>b</b>) GO functional enrichment analysis; red bars represent biological process (BP), green bars represent cellular component (CC), and blue bars represent Molecular Function (MF). The numbers on top of each bar indicate the number of genes involved in each process.</p>
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<p>Differential ABC transporter genes expression analysis of <span class="html-italic">Ps495620</span> knockout transformants and P6497 (WT) in sporangia stage. (<b>a</b>) Clustering heat map of differentially expressed ABC transporter genes among <span class="html-italic">Ps495620</span> knockout transformants (KT20) and P6497 (WT) in KEGG and GO enrichment analysis. Three to four biological replicates at different time intervals were used for RNA-seq analysis. The color gradient represents the relative sequence abundance; numbers in the color key indicate log2 fold change (FC); (<b>b</b>) expression profile of ABC transporter proteins of <span class="html-italic">Ps495620</span> knockout transformants and P6497 (WT) in sporangia stage, measured by qRT-PCR. *: At <span class="html-italic">p</span> &lt; 0.01, significant difference.</p>
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<p>Phylogenetic tree of differential ABC transporter proteins expression analysis of <span class="html-italic">Ps495620</span> knockout transformants and P6497 (WT) in sporangia stage in <span class="html-italic">Phytophthora sojae</span>. The colored ranges stand for the kinds of ABC transporter proteins. The size of a circle of bootstrap stands for confidence in the branching of phylogenetic trees. The red triangles stand for ABC transporter proteins from RNA-seq of <span class="html-italic">Ps495620</span> knockout transformants and P6497 (WT) in sporangia stage.</p>
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27 pages, 1697 KiB  
Article
Differential microRNA and Target Gene Expression in Scots Pine (Pinus sylvestris L.) Needles in Response to Methyl Jasmonate Treatment
by Baiba Krivmane and Dainis Edgars Ruņģis
Genes 2025, 16(1), 26; https://doi.org/10.3390/genes16010026 - 27 Dec 2024
Abstract
Background/objectives: Methyl jasmonate is a plant signaling molecule involved in a wide range of functions, including stress responses. This study investigates the relative differential expression of microRNAs and their target genes in response to methyl jasmonate treatment of Scots pine needles. Methods: A [...] Read more.
Background/objectives: Methyl jasmonate is a plant signaling molecule involved in a wide range of functions, including stress responses. This study investigates the relative differential expression of microRNAs and their target genes in response to methyl jasmonate treatment of Scots pine needles. Methods: A combined strategy of high-throughput sequencing and in silico prediction of potential target genes was implemented. Results: a total of 58 differentially expressed (DE) microRNAs (miRNAs) (43 up-regulated and 15 down-regulated), belonging to 29 miRNA families, were identified. The 41 DE miRNAs from 17 families were conifer-specific miRNA families—miR946, miR947, miR950, miR1312, miR1313, miR1314, miR3693, miR3107, miR11452, miR11466, miR11487, miR11490, miR11504, miR11511, miR11532, miR11544, and miR11551. The other DE miRNAs (miR159, miR164, miR169, miR396, miR397, miR398, miR408, miR535) were conserved miRNAs, which are also found in angiosperm species. Transcriptome analysis identified 389 gene transcripts with 562 miRNA-target sites targeted by 57 of the 58 DE miRNAs. Of these, 250 target genes with 138 different GO annotations were found for the 41 DE conifer-specific conserved miRNAs. Conclusions: The 26 DE miRNAs from 14 DE miRNA families, of which almost all (12 families, 24 miRNAs) are conifer specific, and were associated with 68 disease resistance and TMV resistance proteins, TIR-NBS-LRR, LRR receptor-like serine/threonine-protein kinase, putative CC-NBS-LRR protein, and putative NBS-LRR protein target transcripts with 29 target gene GO term descriptions. Some of the genes targeted by conifer-specific miRNAs have been previously reported to be targeted by other miRNAs in angiosperms, indicating that the miRNA-target gene regulation system can vary between species. Full article
(This article belongs to the Special Issue Plant Small RNAs: Biogenesis and Functions)
16 pages, 2568 KiB  
Article
Polystyrene Microplastics Induce Photosynthetic Impairment in Navicula sp. at Physiological and Transcriptomic Levels
by Xi Li, Zunyan Wang, Yiyong Chen and Qi Li
Int. J. Mol. Sci. 2025, 26(1), 148; https://doi.org/10.3390/ijms26010148 - 27 Dec 2024
Abstract
The rising concentration of microplastics (MPs) in aquatic environments poses increasing ecological risks, yet their impacts on biological communities remain largely unrevealed. This study investigated how aminopolystyrene microplastics (PS-NH2) affect physiology and gene expression using the freshwater alga Navicula sp. as [...] Read more.
The rising concentration of microplastics (MPs) in aquatic environments poses increasing ecological risks, yet their impacts on biological communities remain largely unrevealed. This study investigated how aminopolystyrene microplastics (PS-NH2) affect physiology and gene expression using the freshwater alga Navicula sp. as the test species. After exposing Navicula sp. to high PS-NH2 concentrations for 24 h, growth was inhibited, with the most significant effect seen after 48 h. Increasing PS-NH2 concentrations reduced chlorophyll content, maximum photochemical quantum yield (Fv/Fm), and the photochemical quenching coefficient (Qp), while the non-photochemical quenching coefficient (NPQ) increased, indicating a substantial impact on photosynthesis. PS-NH2 exposure, damaged cell membrane microstructures, activated antioxidant enzymes, and significantly increased malondialdehyde (MDA), glutathione peroxidase (GPX), and superoxide dismutase (SOD) activities. Transcriptomic analysis revealed that PS-NH2 also affected the gene expression of Navicula sp. The differentially expressed genes (DEGs) are mainly related to porphyrin and chlorophyll metabolism, carbon fixation in photosynthesis, endocytosis, and glycolysis/gluconeogenesis. Protein–protein interaction (PPI) analysis revealed significant interactions among DEGs, particularly within photosystem II. These findings shed insights into the toxic mechanisms and environmental implications of microplastic interactions with phytoplankton, deepening our understanding of the potential adverse effects of microplastics in aquatic ecosystems. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Effects of varying PS-NH<sub>2</sub> concentrations on <span class="html-italic">Navicula</span> sp. growth inhibition rate: (<b>A</b>) growth inhibition rate; (<b>B</b>–<b>D</b>) dose–response curves at 24 h, 48 h, and 96 h, respectively. “*” denotes statistical significance (<span class="html-italic">p</span> &lt; 0.05) between the experimental and control groups, and data are expressed as mean ± standard error (<span class="html-italic">n</span> = 3).</p>
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<p>Impact of different PS-NH<sub>2</sub> concentrations on <span class="html-italic">Navicula</span> sp. photosynthetic parameters: (<b>A</b>) chlorophyll content; (<b>B</b>) Fv/Fm; (<b>C</b>) NPQ; (<b>D</b>) Qp. “*” indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05) between the experimental and control groups, and data are expressed as mean ± standard error (<span class="html-italic">n</span> = 3).</p>
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<p>Influence of varying PS-NH<sub>2</sub> concentrations on antioxidative enzymes in <span class="html-italic">Navicula</span> sp. (<b>A</b>) TP; (<b>B</b>) MDA; (<b>C</b>) CAT; (<b>D</b>) SOD; (<b>E</b>) GPX. “*” denotes significant differences (<span class="html-italic">p</span> &lt; 0.05) between the experimental and control groups, and data are expressed as mean ± standard error (<span class="html-italic">n</span> = 3).</p>
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<p>(<b>A</b>,<b>B</b>) Volcano plot illustrating DEGs between control and exposure groups. The <span class="html-italic">x</span>-axis displays log<sub>2</sub>FC (fold-change), and the <span class="html-italic">y</span>-axis displays −log<sub>10</sub> (<span class="html-italic">q</span>-value). Red represents significantly upregulated genes, blue represents significantly downregulated genes, gray represents insignificantly expressed genes, with each circle representing one gene. (<b>C</b>) Number of DEGs; (<b>D</b>) Venn diagram depicting common and unique DEGs in response to the two stressors. C: Control group; L: low concentration; H: high concentration.</p>
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<p>Histogram displaying enriched subcategories from GO analysis of DEGs in <span class="html-italic">Navicula</span> sp. after exposure to (<b>A</b>) Low concentration and (<b>B</b>) High concentration of PS-NH<sub>2</sub>. The <span class="html-italic">x</span>-axis presents GO terms related to the main ontologies (biological process, cellular component, and molecular function), while the <span class="html-italic">y</span>-axis indicates the number of genes. C: Control group; L: low concentration; H: high concentration.</p>
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<p>Analysis of protein–protein interactions among differentially abundant genes in <span class="html-italic">Navicula</span> sp. under PS-NH<sub>2</sub> exposure stress. Each node corresponds to the protein encoded by the respective gene, and line thickness indicates data support strength.</p>
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18 pages, 967 KiB  
Review
Application of Spatial Transcriptomics in Digestive System Tumors
by Bowen Huang, Yingjia Chen and Shuqiang Yuan
Biomolecules 2025, 15(1), 21; https://doi.org/10.3390/biom15010021 - 27 Dec 2024
Abstract
In the field of digestive system tumor research, spatial transcriptomics technologies are used to delve into the spatial structure and the spatial heterogeneity of tumors and to analyze the tumor microenvironment (TME) and the inter-cellular interactions within it by revealing gene expression in [...] Read more.
In the field of digestive system tumor research, spatial transcriptomics technologies are used to delve into the spatial structure and the spatial heterogeneity of tumors and to analyze the tumor microenvironment (TME) and the inter-cellular interactions within it by revealing gene expression in tumors. These technologies are also instrumental in the diagnosis, prognosis, and treatment of digestive system tumors. This review provides a concise introduction to spatial transcriptomics and summarizes recent advances, application prospects, and technical challenges of these technologies in digestive system tumor research. This review also discusses the importance of combining spatial transcriptomics with single-cell RNA sequencing (scRNA-seq), artificial intelligence, and machine learning in digestive system cancer research. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Medicine)
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<p>Schematic diagram of three different spatial transcriptomics methods. (<b>A</b>) In situ hybridization methods sequence the transcripts within the tissue after rolling circle amplification (RCA), while in situ sequencing methods identify the transcripts by hybridizing with fluorescent probes. (<b>B</b>) In sequencing-based methods, transcripts within the tissue are captured by poly(dT) on spatially barcoded microarray slides, after which transcripts are reverse transcribed and sequenced by next-generation sequencing (NGS).</p>
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<p>Considerations for selecting a suitable spatial transcriptomics method. When selecting a suitable spatial transcriptomics method, the experiment objective, the capture efficiency and spatial resolution, the sample tissue area, the quality of mRNAs, and the sensitivity and detection efficiency should be taken into consideration.</p>
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19 pages, 16034 KiB  
Article
Comprehensive Analysis of β-1,3-Glucanase Genes in Wolfberry and Their Implications in Pollen Development
by Xin Zhang, Pinjie Zheng, Xurui Wen and Zhanlin Bei
Plants 2025, 14(1), 52; https://doi.org/10.3390/plants14010052 - 27 Dec 2024
Abstract
β-1,3-Glucanases (Glu) are key enzymes involved in plant defense and physiological processes through the hydrolysis of β-1,3-glucans. This study provides a comprehensive analysis of the β-1,3-glucanase gene family in wolfberry (Lycium barbarum), including their chromosomal distribution, evolutionary relationships, and expression profiles. [...] Read more.
β-1,3-Glucanases (Glu) are key enzymes involved in plant defense and physiological processes through the hydrolysis of β-1,3-glucans. This study provides a comprehensive analysis of the β-1,3-glucanase gene family in wolfberry (Lycium barbarum), including their chromosomal distribution, evolutionary relationships, and expression profiles. A total of 58 Glu genes were identified, distributed across all 12 chromosomes. Evolutionary analysis revealed six distinct branches within wolfberry and nine distinct branches when compared with Arabidopsis thaliana. Expression analysis showed that 45 Glu genes were expressed in berries, with specific genes also being expressed in flowers and leaves. Notably, LbaGlu28 exhibited significant expression during the tetrad stage of pollen development and was localized in the cell wall. These findings provide valuable insights into the functional significance of Glu genes in wolfberry, highlighting their roles in development and their potential involvement in reproductive processes, particularly in pollen development. Full article
(This article belongs to the Special Issue Bioinformatics and Functional Genomics in Modern Plant Science)
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<p>Chromosomal distribution and collinearity analysis of <span class="html-italic">Glu</span> family members in wolfberry. (<b>A</b>) Chromosomal localization of <span class="html-italic">Glu</span> genes on the 12 chromosomes of wolfberry. Each colored bar represents a chromosome, and <span class="html-italic">Glu</span> genes are denoted by vertical lines along the chromosomes. (<b>B</b>) Collinearity analysis showing replication relationships between <span class="html-italic">Glu</span> gene family members within the wolfberry genome. Genes connected by lines represent collinear relationships. (<b>C</b>) Inter-species collinearity analysis between wolfberry and <span class="html-italic">Arabidopsis thaliana</span>.</p>
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<p>Gene structure and conserved motif analysis of <span class="html-italic">Glu</span> family members in wolfberry. (<b>A</b>) Conserved motif analysis revealing the presence of up to 15 motifs among <span class="html-italic">Glu</span> genes, with most genes possessing motifs 1 and 7. (<b>B</b>) Gene structure analysis showing the exon–intron organization of <span class="html-italic">Glu</span> genes. The number of exons and introns varies among different <span class="html-italic">Glu</span> family members. (<b>C</b>) Gene structure and conserved motif analysis of <span class="html-italic">Glu</span> family members in wolfberry.</p>
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<p>Gene structure and conserved motif analysis of <span class="html-italic">Glu</span> family members in wolfberry. (<b>A</b>) Conserved motif analysis revealing the presence of up to 15 motifs among <span class="html-italic">Glu</span> genes, with most genes possessing motifs 1 and 7. (<b>B</b>) Gene structure analysis showing the exon–intron organization of <span class="html-italic">Glu</span> genes. The number of exons and introns varies among different <span class="html-italic">Glu</span> family members. (<b>C</b>) Gene structure and conserved motif analysis of <span class="html-italic">Glu</span> family members in wolfberry.</p>
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<p>Prediction of cis-acting elements of <span class="html-italic">Glu</span> family members in wolfberry. (<b>A</b>) Heatmap visualization of the distribution patterns of cis-regulatory elements among <span class="html-italic">Glu</span> genes. Elements are classified into 22 distinct categories based on their sequence features and putative functions. (<b>B</b>) Comparison of cis-regulatory element frequencies, highlighting higher occurrences of ARBE and G-box elements in specific <span class="html-italic">Glu</span> gene family members.</p>
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<p>Prediction of cis-acting elements of <span class="html-italic">Glu</span> family members in wolfberry. (<b>A</b>) Heatmap visualization of the distribution patterns of cis-regulatory elements among <span class="html-italic">Glu</span> genes. Elements are classified into 22 distinct categories based on their sequence features and putative functions. (<b>B</b>) Comparison of cis-regulatory element frequencies, highlighting higher occurrences of ARBE and G-box elements in specific <span class="html-italic">Glu</span> gene family members.</p>
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<p>Evolutionary relationship analysis of <span class="html-italic">Glu</span> genes in wolfberry and interspecific comparison with <span class="html-italic">Arabidopsis thaliana</span>. (<b>A</b>) Evolutionary tree of <span class="html-italic">Glu</span> genes within wolfberry, categorized into 6 branches based on DNA sequence similarity. (<b>B</b>) Interspecific evolutionary analysis comparing <span class="html-italic">Glu</span> genes from wolfberry (<span class="html-italic">Lycium barbarum</span>) and <span class="html-italic">Arabidopsis thaliana</span>. The tree reveals 9 distinct branches, indicating evolutionary divergence and potential common ancestry between the <span class="html-italic">Glu</span> gene families of the two species.</p>
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<p>Expression of <span class="html-italic">Glu</span> gene family members in different tissues of wolfberry. Heatmap representation of <span class="html-italic">Glu</span> gene expression across various tissues, including flowers, leaves, and berries. Tissue-specific expression patterns indicate potential roles of <span class="html-italic">Glu</span> genes in different developmental processes within the plant.</p>
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<p>The potential role of <span class="html-italic">Glu</span> genes in wolfberry reproductive development. Microscopic observation of anther development stages in fertile (NQ1, <b>E</b>–<b>H</b>) and male-sterile (NQ5, <b>A</b>–<b>D</b>) wolfberry cultivars, revealing differences in sporopollenin metabolism and pollen grain formation. (<b>I</b>) Transcriptomic analysis and qRT-PCR identifying differential expression of <span class="html-italic">LbaGlu28</span> between fertile and male-sterile anthers. (<b>J</b>) Enzymatic assays demonstrating glucanase activity patterns in NQ1 and NQ5 anthers, indicative of aberrant sporopollenin metabolism in the male-sterile cultivar. ** indicates a highly significant difference (<span class="html-italic">p</span> ≤ 0.01).</p>
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<p>Subcellular localization of LbaGlu28 protein in wolfberry cells. (<b>A</b>) Schematic representation of the recombinant vector p35S:LbaGlu28:YFP used for subcellular localization analysis. (<b>B</b>) Confocal microscopy images showing the subcellular localization of LbaGlu1:YFP fusion protein in wolfberry cells. Yellow fluorescence indicates the presence of LbaGlu28 protein at the cell wall and plasma membrane, confirmed by the absence of co-localization with the red cell membrane marker during plasmolysis.</p>
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<p>Prediction of three-dimensional structures and signaling network of Glu proteins. (<b>A</b>) Predicted three-dimensional structure of LbaGlu28 generated using homology modeling. The structure provides insights into the spatial arrangement of amino acids within the protein. (<b>B</b>) Protein–protein interaction (PPI) network analysis predicting potential interactions between proteins and LbaGlu28, highlighting their functional associations within cellular processes.</p>
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21 pages, 3895 KiB  
Article
Transcriptomic Analysis of Wheat Under Multi LED Light Conditions
by Lei Sun, Ding Li, Chunhong Ma, Bo Jiao, Jiao Wang, Pu Zhao, Fushuang Dong and Shuo Zhou
Plants 2025, 14(1), 46; https://doi.org/10.3390/plants14010046 - 27 Dec 2024
Viewed by 85
Abstract
Light is a vital environmental cue that profoundly influences the development of plants. LED lighting offers significant advantages in controlled growth environments over fluorescent lighting. Under monochromatic blue LED light, wheat plants exhibited reduced stature, accelerated spike development, and a shortened flowering period [...] Read more.
Light is a vital environmental cue that profoundly influences the development of plants. LED lighting offers significant advantages in controlled growth environments over fluorescent lighting. Under monochromatic blue LED light, wheat plants exhibited reduced stature, accelerated spike development, and a shortened flowering period with increased blue light intensity promoting an earlier heading date. In this study, we conducted a comprehensive transcriptome analysis to investigate the molecular mechanisms underlying wheat plants’ response to varying light conditions. We identified 34 types of transcription factors (TFs) and highlighted the dynamic changes of key families such as WRKY, AP2/ERF, MYB, bHLH, and NAC, which play crucial roles in light-induced gene regulation. Additionally, this study revealed differential effects of blue and red light on the expression levels of genes related to hormones such as cytokinin (CK) and salicylic acid (SA) synthesis as well as significant changes in pathways such as flavonoid biosynthesis, circadian rhythms, chlorophyll synthesis, and flowering. Particularly, blue light upregulated genes involved in chlorophyll synthesis, contrasting with the downregulation observed under red light. Furthermore, blue light enhanced the expression of anthocyanin synthesis-related genes, such as CHS, underscoring its role in promoting anthocyanin accumulation. These findings provide valuable insights into how light quality impacts crop growth and development. Full article
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<p>Effects of different light treatments on plant growth. (<b>A</b>) Development of spikes under blue light and white light treatments, (a) white light; (b) blue light; (<b>B</b>) height of the plants; and (<b>C</b>) development of spikes under varying light intensities, (a) 56.7 μmol/m<sup>2</sup> s; (b) 49.3 μmol/m<sup>2</sup> s; (c) 35.6 μmol/m<sup>2</sup> s; and (d) 23.7 μmol/m<sup>2</sup> s. (<b>D</b>,<b>E</b>) Statistics of spike length under blue and white light conditions and varying light intensities, respectively.</p>
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<p>Global view of transcriptome expression and differential gene expression. (<b>A</b>) The experimental design schematic. The experiment included four light treatments: blue light, red light, white light, and a 1:1 mixture of red and blue light. Exposure was initiated from dark conditions, and samples were taken at three time points: 1 h (1 h), 6 h (6 h), and 14 days (14 d). Differentially expressed genes (DEGs) were identified by comparing the gene expression profiles at each time point under the respective light treatments with those under white light conditions. (<b>B</b>) Spearman correlation coefficient (SCC) of gene expression profiles between samples; (<b>C</b>) principal component analysis (PCA) of samples distinguished by different colors with three biological repeats; (<b>D</b>) petal plot, where each petal represents the number of uniquely expressed genes during that time period; and (<b>E</b>) number of differentially expressed genes over time under white light conditions.</p>
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<p>Transcription factor ridge plot showing the changes in number and types of transcription factors under different light conditions compared to white light at 1 h, 6 h, and 14 d.</p>
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<p>Expression changes in hormone synthesis-related genes under different light treatments. Heatmaps represent the log2 fold change (log2FC) values of gene expression levels involved in the GA, SA, ABA, CK, JA, and ethylene synthesis pathways compared to white light conditions. Each time point is represented by three treatments in three colors: blue for blue light, red for red light, and gray for a 1:1 mixture of blue and red light.</p>
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<p>Analysis of pathway enrichment in response to different light conditions. (<b>A</b>) KEGG enrichment analysis. KEGG pathway enrichment analysis was performed on differentially expressed genes (DEGs) identified by comparing the gene expression profiles under each light treatment with those under white light conditions at the same time points. The pathways were ranked according to the total number of enriched genes across all conditions, and the results are visualized using a heatmap, with the specific differentially expressed genes and their enrichment analysis under various light conditions relative to white light. (<b>B</b>) GO enrichment analysis of unique DEGs. By comparing samples from each light condition with those from white light, genes that were significantly differentially expressed under each light condition were filtered, then unique condition-specific differential DEGs were screened for GO enrichment analysis. Left and right panels are GO enrichment analysis of unique DEGs in white light compared to blue and red light at 14 d, separately. (<b>C</b>) Changes in chlorophyll content after treatment under blue and red light.</p>
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<p>Genes associated with the circadian clock. (<b>A</b>) Partial and core genes in the circadian rhythm plant pathway. (<b>B</b>) Heatmap showing the expression levels of clock-related genes. (<b>C</b>) Heatmap illustrating the expression levels of VRN family genes, supplemented with qRT-PCR validation results. (<b>D</b>) qRT-PCR validation and RNA-seq expression profiles for selected genes.</p>
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<p>Gene co-expression network analysis. (<b>A</b>) Gene module classification heatmap showing the standardized log(fpkm) values of genes, which can be categorized into three classes based on their expression patterns. (<b>B</b>) Schematic diagram of selected gene network interactions. (<b>C</b>) Expression patterns of modules 3 and 7. (<b>D</b>) Expression patterns of four bHLH genes among 404 neighbor genes.</p>
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24 pages, 3209 KiB  
Article
Multi-Omics Analysis in Mouse Primary Cortical Neurons Reveals Complex Positive and Negative Biological Interactions Between Constituent Compounds of Centella asiatica
by Steven R. Chamberlin, Jonathan A. Zweig, Cody J. Neff, Luke Marney, Jaewoo Choi, Liping Yang, Claudia S. Maier, Amala Soumyanath, Shannon McWeeney and Nora E. Gray
Pharmaceuticals 2025, 18(1), 19; https://doi.org/10.3390/ph18010019 - 27 Dec 2024
Viewed by 125
Abstract
Background: A water extract of the Ayurvedic plant Centella asiatica (L.) Urban, family Apiaceae (CAW), improves cognitive function in mouse models of aging and Alzheimer’s disease and affects dendritic arborization, mitochondrial activity, and oxidative stress in mouse primary neurons. Triterpenes (TT) and caffeoylquinic [...] Read more.
Background: A water extract of the Ayurvedic plant Centella asiatica (L.) Urban, family Apiaceae (CAW), improves cognitive function in mouse models of aging and Alzheimer’s disease and affects dendritic arborization, mitochondrial activity, and oxidative stress in mouse primary neurons. Triterpenes (TT) and caffeoylquinic acids (CQA) are constituents associated with these bioactivities of CAW, although little is known about how interactions between these compounds contribute to the plant’s therapeutic benefit. Methods: Mouse primary cortical neurons were treated with CAW or equivalent concentrations of four TT combined, eight CQA combined, or these twelve compounds combined (TTCQA). Treatment effects on the cell transcriptome (18,491 genes) and metabolome (192 metabolites) relative to vehicle control were evaluated using RNAseq and metabolomic analyses, respectively. Results: Extensive differentially expressed genes (DEGs) were seen with all treatments, as well as evidence of interactions between compounds. Notably, many DEGs seen with TT treatment were not observed in the TTCQA condition, possibly suggesting CQA reduced the effects of TT. Moreover, additional gene activity seen with CAW as compared to TTCQA indicates the presence of additional compounds in CAW that further modulate TTCQA interactions. Weighted Gene Correlation Network Analysis (WGCNA) identified 4 gene co-expression modules altered by treatments that were associated with extracellular matrix organization, fatty acid metabolism, cellular response to stress and stimuli, and immune function. Compound interaction patterns were seen at the eigengene level in these modules. Interestingly, in metabolomics analysis, the TTCQA treatment saw the highest number of changes in individual metabolites (20), followed by CQA (15), then TT (8), and finally CAW (3). WGCNA analysis found two metabolomics modules with significant eigenmetabolite differences for TT and CQA and possible compound interactions at this level. Conclusions: Four gene expression modules and two metabolite modules were altered by the four treatment types applied. This methodology demonstrated the existence of both negative and positive interactions between TT, CQA, and additional compounds found in CAW on the transcriptome and metabolome of mouse primary cortical neurons. Full article
(This article belongs to the Special Issue Network Pharmacology of Natural Products)
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Graphical abstract

Graphical abstract
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<p>PCA plots for the two untargeted molecular domains of metabolomics and transcriptomics. (<b>A</b>) All treatments plotted together, including the vehicle control, for either metabolic or RNA-seq data. (<b>B</b>) RNA-seq data, each treatment compared individually to control. (<b>C</b>) Metabolic data, each treatment compared individually to control. For (<b>B</b>,<b>C</b>), the number of differentially expressed metabolites or genes (#DE) is shown in the lower right corner of each plot. The plots are also ordered left to right by increasing number of CA compounds in each treatment.</p>
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<p>Effect on gene expression of the TT and CQA compounds administered separately and interactions between these groups observed in the TTCQA-treated samples. The figure on the left shows a mapping of the interactions listed in the table on the right. Any gene with significant expression changes, relative to control, seen with any of the TT, CQA, or TTCQA treatments are represented in the figure and table (N = 2268 unique genes). The relative number of genes and the expression status are represented within each of the three columns labeled TT, CQA, or TTCQA (red = significant downregulation, green = significant upregulation, beige = no significant expression changes seen with this treatment). The TT and CQA compound ‘Interaction effect’ for the combined group is shown next to the TTCQA column (‘P’ = positive, or synergistic; ‘N’ = negative, or antagonistic; ‘none’ = additive, or no interaction). The gray ribbons show the number of genes interacting between TT and CQA, the interaction status, and the final expression status in the TTCQA treatment. The table on the right shows the number of genes in each category. As an example, a positive interaction has a higher fold change value in the TTCQA treatment than the sum of the two-fold change values from TT and CQA, and a negative interaction has a lower TTCQA fold change than the sum of TT and CQA fold changes. So a positive interaction could still result in a downregulated or unregulated TTCQA gene, and a negative interaction could result in an upregulated or unregulated TTCQA gene.</p>
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<p>Sample eigengene distributions for the five gene co-expression modules with significant eigengene differences between at least one treatment and control. Boxplots for module eigengene distributions by each treatment group (* <span class="html-italic">p</span>-value ≤ 0.05). Module 1 = Extracellular Matrix Organization and Collagen Biosynthesis, Module 2 = Fatty Acid Metabolism, Module 3 = Cellular Response to Stress and Stimuli, Module 4 = Immune System, Module 5 = Electron Transport and Mitochondrial Biogenesis. Module 1 also shows a heat map of individual gene expression levels (blue = low, red = high). The highlighted section represents samples in the significant treatment for this module. See <a href="#app1-pharmaceuticals-18-00019" class="html-app">Supplementary Figure S6</a> for heatmaps for Modules 2–5.</p>
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<p>Metabolite co-abundance modules with significant eigenmetabolite differences between at least one treatment and control (<b>1.</b> Module 1 n = 25 metabolites, <b>2.</b> Module 2 n = 100 metabolites). (<b>A</b>) Boxplots for module eigenmetabolite distributions by each treatment group (* adj <span class="html-italic">p</span>-value ≤ 0.05). (<b>B</b>) Heatmap of individually scaled metabolite abundance for the module (red = high abundance, blue = low abundance). Samples are ordered by treatment group and labeled on the horizontal axis. Histogram shows eigenmetabolite values for each sample. (<b>C</b>) Metabolite category counts for all metabolites in the module.</p>
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<p>TT Integration Community Network Example. Network is derived from seed genes and metabolites in one community of the TT integrated composite network. Seed genes and metabolites are outlined in red. Genes are indicated in gray, and metabolites in yellow. Connector genes (not seed genes) are the red circles that are not outlined. The size of the circles is related to the number of network connections, or degree. This network enriched mostly fatty acid metabolism pathways.</p>
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<p>Omics integration methodology. Individual seed networks are shown in the center of the figure. For metabolite seeds two networks are shown, a primary metabolite-gene network and a secondary PPI created from the genes in the primary network. Seed genes are shown in this secondary PPI if present, but are not used to construct this network. For the seed genes, only one primary PPI network is created. The red connector (non-seed) genes (protein) are added to both PPIs, if necessary, to increase connectivity in the overall network for community detection that will better contain seeds of interest.</p>
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25 pages, 21059 KiB  
Article
Cytological, Physiological, and Transcriptome Analysis of Leaf-Yellowing Mutant in Camellia chekiangoleosa
by Bin Huang, Wenyin Huang, Zhenyu Liu, Yixuan Peng, Yanshu Qu, Wencai Zhou, Jianjian Huang, Huili Shu and Qiang Wen
Int. J. Mol. Sci. 2025, 26(1), 132; https://doi.org/10.3390/ijms26010132 - 27 Dec 2024
Viewed by 126
Abstract
Color variation in plant leaves has a significant impact on their photosynthesis and plant growth. Camellia chekiangoleosa yellow-leaf mutants are ideal materials for studying the mechanisms of pigment synthesis and photosynthesis, but their mechanism of leaf variation is not clear. We systematically elucidated [...] Read more.
Color variation in plant leaves has a significant impact on their photosynthesis and plant growth. Camellia chekiangoleosa yellow-leaf mutants are ideal materials for studying the mechanisms of pigment synthesis and photosynthesis, but their mechanism of leaf variation is not clear. We systematically elucidated the intrinsic causes of leaf yellowing in the new Camellia chekiangoleosa variety ‘Diecui Liuji’ in terms of changes in its cell structure, pigment content, and transcript levels. This study indicates that the incomplete structure of chloroplast-like vesicles, the decrease in blue-green chlorophyll a, and the increase in yellow-green chlorophyll b in yellowing leaves are the direct causes of yellowing-leaf formation. The high expression of genes that catalyze the degradation of chlorophyll a (PAO and RCCR) and its conversion to chlorophyll b (CAO) in yellowing leaves leads to a decrease in the chlorophyll a content, while the low expression of CLH genes is the main reason for the increase in the chlorophyll b content. We also found transcription factors such as ERF, E2F, WRKY, MYB, TPC, TGA, and NFYC may regulate their expression. RT-qPCR assays of 12 DEGs confirm the RNA-seq results. This study will provide a foundation for investigating the transcriptional and regulatory mechanisms of leaf color changes. Full article
(This article belongs to the Special Issue Molecular Research in Bamboo, Tree, Grass, and Other Forest Products)
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<p>‘Diecui Liujin’ whole plant (<b>a</b>). Three periods were selected from young leaf formation to leaf maturity—10 DALFs (days after initial leaf formation, S1), 50 DALFs (S2), and 90 DALFs (S3)—for collection. S1–S3 period of yellowing leaves (<b>b</b>–<b>d</b>); S1–S3 period of normal leaves (<b>e</b>–<b>g</b>).</p>
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<p>Chloroplast ultrastructure on normal green leaves and mutant leaves of <span class="html-italic">C. chekiangoleosa</span>. Bars = 10 μm (<b>a</b>,<b>d</b>) and 2 μm (<b>b</b>,<b>c</b>,<b>e</b>,<b>f</b>). S, starch granules; M, mitochondrion; N, nucleus; CH, chloroplast; CW, cell wall; T, thylakoid grana; O, osmiophilic granules; V, vesica; NL, normal leaf; ML, mutant leaf. The average number of chloroplasts per cell (<b>g</b>) and mean chloroplast size were lower in the mutant leaves (<b>h</b>).</p>
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<p>Changes in pigment content of normal leaves and mutant leaves (<b>a</b>–<b>d</b>). Changes in photosynthetically active radiation of normal leaves and mutant leaves (<b>e</b>). Changes in photosynthetic parameters of normal leaves and mutant leaves (<b>f</b>–<b>n</b>). S1, Stage 1; S2, Stage 2; S3, Stage 3. **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Changes in pigment content of normal leaves and mutant leaves (<b>a</b>–<b>d</b>). Changes in photosynthetically active radiation of normal leaves and mutant leaves (<b>e</b>). Changes in photosynthetic parameters of normal leaves and mutant leaves (<b>f</b>–<b>n</b>). S1, Stage 1; S2, Stage 2; S3, Stage 3. **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>PCA plots of non-targeted metabolomes of six sets of samples (<b>a</b>), Venn plots of differential metabolites of two leaf types at three developmental periods (<b>b</b>), and heat maps of relative metabolite contents of the pathways of interest (<b>c</b>). NL1, Stage 1 of normal leaf; NL2, Stage 2 of normal leaf; NL3, Stage 3 of normal leaf; ML1, Stage 1 of mutant leaf; ML2, Stage 2 of mutant leaf; ML3, Stage 3 of mutant leaf.</p>
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<p>PCA plots of expression profiles of 18 samples (<b>a</b>), counts of differential genes among different samples (<b>b</b>), Venn plots and counts of differential genes of two leaf types at three developmental periods (<b>c</b>), and KEGG categorization plots of common differential genes of two leaf types at three developmental periods (<b>d</b>).</p>
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<p>Differential expression of genes related to the chlorophyll metabolism pathway. The expression level was based on FPKM value. <span class="html-italic">HEMA</span>, glutamyl-tRNA reductase; <span class="html-italic">HEML</span>, glutamate-1-semialdehyde 2,1-aminomutase; <span class="html-italic">HEMB</span>, porphobilinogen synthase; <span class="html-italic">HEMC</span>, hydroxymethylbilane synthase; <span class="html-italic">HEMD</span>, Uroporphyrinogen-III synthase; <span class="html-italic">HEME</span>, uroporphyrinogen decarboxylase; <span class="html-italic">HEMF</span>, coproporphyrinogen-III oxidase; <span class="html-italic">HEMY</span>, oxygen-dependent protoporphyrinogen oxidase; <span class="html-italic">CHLH</span>, magnesium chelatase subunit H; <span class="html-italic">CHLE</span>, magnesium protoporphyrin IX monomethyl ester(oxidative) cyclase; <span class="html-italic">CHLM</span>, magnesium protoporphyrin IX methyltransferase; <span class="html-italic">DVR</span>, divinyl chlorophyllide a 8-vinyl-reductase; <span class="html-italic">POR</span>, protochlorophyllide reductase; <span class="html-italic">CAO</span>, chlorophyllide a oxygenase; <span class="html-italic">CLH</span>, chlorophyllase; <span class="html-italic">CHLG</span>, Chlorophyll synthase; <span class="html-italic">NYC1</span>, chlorophyll(ide) b reductase; <span class="html-italic">PAO</span>, pheophorbide a oxygenase; <span class="html-italic">RCCR</span>, red chlorophyll catabolite reductase.</p>
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<p>Differential expression of genes related to photosynthesis (light reactions). The expression level was based on FPKM value.</p>
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<p>Differential expression of genes related to the carotenoid metabolism pathway. The expression level was based on FPKM value. <span class="html-italic">PSY</span>, phytoene synthase; <span class="html-italic">PDS</span>, phytoene desaturase; <span class="html-italic">ZISO</span>, ζ-carotene isomerase; <span class="html-italic">ZDS</span>, ζ-carotene desaturase; <span class="html-italic">crtISO</span>, carotenoid isomerase; <span class="html-italic">Lcy E</span>, ε-cyclase; <span class="html-italic">Lcy B</span>, β-cyclase; <span class="html-italic">LUT5</span>, β-hydroxylase; <span class="html-italic">CCS1</span>, capsanthin/capsorubin synthase; <span class="html-italic">LUT1</span>, ε-cyclase; <span class="html-italic">VDE</span>, violaxanthin de-epoxidase; <span class="html-italic">ZEP</span>, zeaxanthin epoxidase; <span class="html-italic">NCED</span>, 9-cis-epoxycarotenoid dioxygenase.</p>
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<p>Differential expression of genes related to the anthocyanin metabolism pathway. The expression level was based on FPKM value. <span class="html-italic">PAL</span>, phenylalanine ammonia lyase; <span class="html-italic">C4H</span>, cinnamate 4-hydroxylase; <span class="html-italic">4CL</span>, 4-coumarate: CoA ligase; <span class="html-italic">CHS</span>, chalcone synthase; <span class="html-italic">CHI</span>, chalcone isomerase; <span class="html-italic">F3H</span>, flavanone 3-hydroxylase; <span class="html-italic">F3′H</span>, flavonoid-3′-hydroxylase; <span class="html-italic">F3′5′H</span>, flavonoid-3′,5′-hydroxylase; <span class="html-italic">DFR</span>, dihydroflavonol 4-reductase; <span class="html-italic">ANS</span>, anthocyanidin synthase; <span class="html-italic">UFGT,</span> UDP-glucose: anthocyanidin 3-O-glucosyltransferase; <span class="html-italic">UGT,</span> cyanidin 3-O-rutinoside 5-O-glucosyltransferase.</p>
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<p>(<b>A</b>) Clustering dendrograms of genes. (<b>B</b>) Heat map showing the expression profile of each cluster eigengene. (<b>C</b>) Heat map of the expression of functional genes and transcription factors in 3 periods of 2 leaf types. (<b>D</b>) Co-expression network between the functional genes and transcription factors.</p>
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<p>Relative expression levels and FPKM of 12 genes. qRT–PCR results are shown in the column configuration, and FPKM results are displayed as line charts.</p>
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16 pages, 21287 KiB  
Article
Comparative Transcriptome Analysis of Gene Expression Between Female and Monoecious Spinacia oleracea L.
by Yingjie Zhao, Zhiyuan Liu, Hongbing She, Zhaosheng Xu, Helong Zhang, Shaowen Zheng and Wei Qian
Genes 2025, 16(1), 24; https://doi.org/10.3390/genes16010024 - 27 Dec 2024
Viewed by 279
Abstract
Background: Spinach (Spinacia oleracea L.) is an important leafy vegetable with dioecious and occasional monoecious plants. Monoecious lines are more suitable for hybrid production than dioecious lines due to their extended flowering period. However, genetic research on the sex determination of monoecism [...] Read more.
Background: Spinach (Spinacia oleracea L.) is an important leafy vegetable with dioecious and occasional monoecious plants. Monoecious lines are more suitable for hybrid production than dioecious lines due to their extended flowering period. However, genetic research on the sex determination of monoecism remains limited. Methods: In this study, RNA-seq analysis of monoecious and female spinach plants was performed at two distinct flowering stages. In total, we identified 4586 differentially expressed genes (DEGs), which were primarily involved in biological processes such as hormone signaling, cell wall biosynthesis, photosynthesis, and flower development, based on Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Results: Among these DEGs, 354 transcription factors, including 27 genes associated with the ABCDE gene, were discovered. Furthermore, a co-expression gene regulatory network was built, identifying nine key genes that play important roles in regulating sex differentiation between female and monoecious plants. Conclusions: Our findings provide crucial molecular insights into the mechanisms of monoecism in spinach and offer a scientific basis for future spinach breeding. Full article
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<p>Sampling time periods of Sp139 and Sp140. (<b>a</b>) First day of flowering in Sp139; (<b>b</b>) first day of flowering in Sp140; (<b>c</b>) eighth day of flowering in Sp139; and (<b>d</b>) eighth day of flowering in Sp140.</p>
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<p>Repeated correlation assessment and PCA. (<b>a</b>) Pearson correlation coefficients for comparisons among all samples and (<b>b</b>) PCA based on all expressed genes, showing three distinct sample groups.</p>
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<p>Identification of DEGs in different comparison groups. (<b>a</b>) Number of up- and downregulated DEGs in four comparisons and (<b>b</b>) Venn diagram of DEGs in four comparisons between monoecious and female plants.</p>
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<p>qRT-PCR was performed using four major genes. (<b>a</b>) Relative expression of <span class="html-italic">SOV2g030600</span>; (<b>b</b>) <span class="html-italic">SOV6g032730</span>; (<b>c</b>) <span class="html-italic">SOV3g046810</span>; and (<b>d</b>) <span class="html-italic">SOV2g009980</span>.</p>
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<p>Top-20 pathways in the GO enrichment analysis of DEGs in five comparisons. (<b>a</b>) GO enrichment dot plot of Biological_Processes; (<b>b</b>) Cell_Components; (<b>c</b>) Molecular_Function; (<b>d</b>) GO terms and hierarchical relationship of Biological_Processes; (<b>e</b>) Cell_Components; and (<b>f</b>) Molecular_Function. Note: Each node represents a GO term, and the box represents the GO with an enrichment level of Top 5. The depth of the box (or ellipse) color represents the enrichment level, and the darker the color, the higher the significance. The name of the term and the q-value of the enrichment analysis are displayed on each node.</p>
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<p>Top-20 pathways in KEGG enrichment analysis of DEGs in 5 comparisons.</p>
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<p>DEGs on the first and eighth day of flowering. (<b>a</b>) Top-20 pathways analyzed for KEGG enrichment of 102 DEGs in AvsB and (<b>b</b>) 113 DEGs in CvsD.</p>
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<p>K-means clustering was used to group the expression profiles of the transcriptome into 12 clusters. Gene numbers are shown in each box. The light-gray background represents the individual expression profiles of genes within each cluster, while the blue line in the foreground depicts the overall dynamic expression trends fitted to the sample data.</p>
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16 pages, 1287 KiB  
Article
The Transcription Factor VvbHLH053 Regulates the Expression of Copper Homeostasis-Associated Genes VvCTr5/6 and VvFRO4 and Confers Root Development in Grapevine
by Songqi Li, Xufei Li, Pengwei Jing, Min Li, Yadan Sun, Leilei Wang, Qiaofang Shi and Yihe Yu
Int. J. Mol. Sci. 2025, 26(1), 128; https://doi.org/10.3390/ijms26010128 - 26 Dec 2024
Viewed by 303
Abstract
Chlormequat chloride (CCC) has been demonstrated to inhibit plant growth and strengthen seedlings. The present study demonstrated that the root growth of Thompson seedless grapevine seedlings was significantly enhanced by the application of CCC treatment. Nevertheless, the precise mechanism by which CCC regulates [...] Read more.
Chlormequat chloride (CCC) has been demonstrated to inhibit plant growth and strengthen seedlings. The present study demonstrated that the root growth of Thompson seedless grapevine seedlings was significantly enhanced by the application of CCC treatment. Nevertheless, the precise mechanism by which CCC regulates plant root growth remains to be elucidated. Consequently, an RNA-sequencing (RNA-Seq) analysis was conducted on grapevine roots subjected to CCC treatment and those undergoing natural growth. A total of 819 differentially expressed genes were identified. Subsequently, Gene Ontology (GO) functional enrichment and weighted gene co-expression network analysis (WGCNA) identified the Copper (Cu) homeostasis-associated genes, VvCTr4/5/6/8 and VvFRO4, which play a pivotal role in mediating the effect of CCC. To further elucidate the transcription factor regulating these Cu homeostasis-associated genes, the key transcription factor VvbHLH053 was identified based on the PlantTFDB database, WGCNA results, and expression patterns under CCC treatment. Furthermore, multiple bHLH binding sites were identified on the promoters of VvCTr4/5/6 and VvFRO4. The GUS activity analysis and dual-luciferase assay demonstrated that VvbHLH053 can directly regulate the expression of VvCTr5/6 and VvFRO4. These findings reveal the feedback mechanism of grapevine root growth mediated by CCC and establish a direct functional relationship between CCC, VvbHLH053, and Cu homeostasis-associated genes that regulate root growth. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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<p>The effect of CCC on the root system of grapevine seedlings. (<b>A</b>) Alterations in the root phenotype of grapevine seedlings treated with CCC. The experiment was conducted with three biological replicates in both the treatment and control groups. (<b>B</b>) The statistical analysis of the root index encompasses the following parameters: root number, root length, and root width. The data presented are the mean ± standard deviation of three independent experiments, analyzed by Student’s <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>The number of expressed genes in the various root samples and the results of PCA. (<b>A</b>) The number of expressed genes in three biological replicates of root samples from a normal growing grapevine. (<b>B</b>) The number of expressed genes in three biological replicates of root samples treated with CCC. (<b>C</b>) PCA biplot of control and CCC-treated samples.</p>
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<p>The volcano plot and GO function of DEGs. (<b>A</b>) Volcano plot of DEGs in the table; each dot represents a gene. The plot depicts the expression levels of genes, with red dots representing those that are up-regulated, green dots representing those that are down-regulated, and black dots representing those that are not differentially expressed. (<b>B</b>) The top 20 most significantly enriched GO functional terms for the up-regulated DEGs. The figure depicts a visual representation of the GO function, wherein each circle represents a specific function. The color of the circle is indicative of the FDR value, and the size of the circle is proportional to the number of those enriched within that function. The three categories are as follows: biological processes (BP), cellular components (CC), and molecular functions (MF).</p>
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<p>The WGCNA was employed to identify genes associated with plant root development. (<b>A</b>) Hierarchical cluster tree displaying the co-expression of gene modules. Each branch of the dendrogram represented a cluster of interconnected genes, which were grouped together into a module. The modules colored accordingly are displayed in the lower panel. (<b>B</b>) The correlation between gene expression modules and plant roots and their respective indexes (root number, root length, and root width) across the entire co-expression gene network. The left panel depicts five modules, each represented by a distinct color. The right-hand panel presents a color scale for the features between modules, with values ranging from −1 to 1. The correlation value and <span class="html-italic">p</span>-value are displayed for each module.</p>
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<p>Identification and expression analysis of candidate genes involved in the root growth of grapevine induced by CCC. (<b>A</b>) The number of genes within the GO pathway that are associated with copper homeostasis in the turquoise module. (<b>B</b>) Heatmap analysis of <span class="html-italic">VvCTr4/5/6/8</span> and <span class="html-italic">VvFRO4</span> expression in grapevine roots after CCC treatment. The data used the log2 values of the DESeq2 normalized gene counts. (<b>C</b>) The expression patterns of <span class="html-italic">VvCTr4/5/6/8</span> and <span class="html-italic">VvFRO4</span> in grapevine roots at four stages under normal growth and CCC treatment. The error bars represent ± standard deviation (SD) (n = 3). Significant differences in values were determined by Student’s <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Identification of potential TFs of Cu homeostasis-associated genes. (<b>A</b>) The number of TFs exhibiting analogous expression patterns with regard to Cu homeostasis-associated genes within the turquoise module. (<b>B</b>) The expression of 14 TFs in the transcriptome was quantified using the log2 values of the ratio between the CCC treatment and the control group. TFs with the log2 values of the fold change exceeding 1.5 were deemed to be of particular significance and are indicated with an asterisk. (<b>C</b>) The expression patterns of <span class="html-italic">VvbHLH053</span> in different periods under CCC treatment. The error bars represent ± standard deviation (SD) (n = 3). Significant differences in values were determined by Student’s <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>VvbHLH053 promotes the transcription of Cu homeostasis-associated genes by binding to the MYC present elements in their respective promoters. (<b>A</b>) Predicted MYC elements in the promoters of <span class="html-italic">VvCTr4/5/6</span> and <span class="html-italic">VvFRO4</span>. The blue lines represent the promoter sequences of <span class="html-italic">VvCTr4/5/6</span> and <span class="html-italic">VvFRO4</span>. The red inverted triangles indicate the bHLH binding sites, designated as MYC. The golden rectangles represent the promoter fragments that were used for cloning purposes. (<b>B</b>) Analysis of GUS activities of the <span class="html-italic">VvCTr4/5/6</span> and <span class="html-italic">VvFRO4</span> promoters. The <span class="html-italic">GUS</span> gene lacking a promoter was employed as the negative control, while the <span class="html-italic">GUS</span> gene under the control of the CaMV 35S promoter served as the positive control. (<b>C</b>) Dual-luciferase assay. The upper part of the figure illustrates the schematic of the effector and reporter constructs, while the lower part depicts the impact of <span class="html-italic">VvbHLH053</span> overexpression on the transcriptional output of <span class="html-italic">VvCTr4/5/6</span> and <span class="html-italic">VvFRO4</span> promoters. The empty pSAK277 vector was employed as the control. The data are presented as mean ± SD (n = 3 replicates). Following the application of the Student’s <span class="html-italic">t</span>-test, the presence of statistically significant differences is indicated by the use of asterisks. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Model of <span class="html-italic">VvbHLH053</span> and Cu homeostasis-associated genes function in grapevine under CCC treatment. The solid lines represent direct regulatory relationships that have been validated by experimental data.</p>
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14 pages, 5431 KiB  
Article
Transcriptional Changes Associated with Amyoplasia
by Artem E. Komissarov, Olga E. Agranovich, Ianina A. Kuchinskaia, Irina V. Tkacheva, Olga I. Bolshakova, Evgenia M. Latypova, Sergey F. Batkin and Svetlana V. Sarantseva
Int. J. Mol. Sci. 2025, 26(1), 124; https://doi.org/10.3390/ijms26010124 - 26 Dec 2024
Viewed by 180
Abstract
Arthrogryposis, which represents a group of congenital disorders, includes various forms. One such form is amyoplasia, which most commonly presents in a sporadic form in addition to distal forms, among which hereditary cases may occur. This condition is characterized by limited joint mobility [...] Read more.
Arthrogryposis, which represents a group of congenital disorders, includes various forms. One such form is amyoplasia, which most commonly presents in a sporadic form in addition to distal forms, among which hereditary cases may occur. This condition is characterized by limited joint mobility and muscle weakness, leading to limb deformities and various clinical manifestations. At present, the pathogenesis of this disease is not clearly understood, and its diagnosis is often complicated due to significant phenotypic diversity, which can result in delayed detection and, consequently, limited options for symptomatic treatment. In this study, a transcriptomic analysis of the affected muscles from patients diagnosed with amyoplasia was performed, and more than 2000 differentially expressed genes (DEGs) were identified. A functional analysis revealed disrupted biological processes, such as vacuole organization, cellular and aerobic respiration, regulation of mitochondrion organization, cellular adhesion, ATP synthesis, and others. The search for key nodes (hubs) in protein–protein interaction networks allowed for the identification of genes involved in mitochondrial processes. Full article
(This article belongs to the Special Issue Genes and Human Diseases 2.0)
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<p>(<b>A</b>) List of muscle biopsy samples subjected to RNA-seq; and (<b>B</b>) heatmap displaying the Spearman correlations between samples.</p>
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<p>RNA-seq analysis showing differential gene expression in amyoplasia muscle and control muscle. Volcano plots showing −log (adjusted <span class="html-italic">p</span>-value) vs. log2 (fold change) for “lower” (<b>A</b>), “upper+lower” (<b>B</b>), and “upper” (<b>C</b>) groups. Dashed vertical lines mark log2 (fold change) &gt; |4|. Dashed horizontal line marks adjusted <span class="html-italic">p</span>-value &lt; 0.001. Blue dots represent downregulated genes and red dots represent upregulated genes. Venn plots illustrating the intersection of (<b>D</b>) downregulated DEGs and (<b>E</b>) upregulated DEGs between “lower”, “lower+upper”, and “upper” groups.</p>
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<p>Gene Ontology function and pathway enrichment analysis of downregulated (<b>A</b>) and upregulated (<b>B</b>) DEGs in the “lower” group. Dotplots for each of the GO analysis categories (biological processes, molecular function and cellular component) are presented.</p>
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<p>Gene Ontology function and pathway enrichment analysis of downregulated (<b>A</b>) and upregulated (<b>B</b>) DEGs in the “lower+upper” group. Dotplots for each of the GO analysis categories (biological processes, molecular function and cellular component) are presented.</p>
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<p>Gene Ontology function and pathway enrichment analysis of downregulated (<b>A</b>) and upregulated (<b>B</b>) DEGs in the “upper” group. Dotplots for each of the GO analysis categories (biological processes, molecular function and cellular component) are presented.</p>
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<p>Analysis of PPI networks for “lower” group sample: (<b>A</b>) MCODE-clustered subnetwork for downregulated DEGs; (<b>B</b>) MCODE-clustered subnetwork for upregulated DEGs. Hub genes identified by cytoHubba. (<b>C</b>) Hub genes of the PPI network for downregulated DEGs; (<b>D</b>) Hub genes of the PPI network for upregulated DEGs. Enrichment analysis of MCODE-clustered subnetwork by Metascape. (<b>E</b>) Enrichment analysis of downregulated DEGs; (<b>F</b>) Enrichment analysis of upregulated DEGs.</p>
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<p>Analysis of PPI networks for “upper+lower” group sample. (<b>A</b>) MCODE-clustered subnetwork for downregulated DEGs; (<b>B</b>) MCODE-clustered subnetwork for upregulated DEGs. Hub genes identified by cytoHubba. (<b>C</b>) Hub genes of the PPI network for downregulated DEGs; (<b>D</b>) Hub genes of the PPI network for upregulated DEGs. Enrichment analysis of MCODE-clustered subnetwork by Metascape. (<b>E</b>) Enrichment analysis of downregulated DEGs; (<b>F</b>) Enrichment analysis of upregulated DEGs.</p>
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<p>Analysis of PPI networks for “upper” group sample. (<b>A</b>) MCODE-clustered subnetwork for downregulated DEGs; (<b>B</b>) MCODE-clustered subnetwork for upregulated DEGs. Hub genes identified by cytoHubba. (<b>C</b>) Hub genes of the PPI network for downregulated DEGs; (<b>D</b>) Hub genes of the PPI network for upregulated DEGs. Enrichment analysis of MCODE-clustered subnetwork by Metascape. (<b>E</b>) Enrichment analysis of downregulated DEGs; (<b>F</b>) Enrichment analysis of upregulated DEGs.</p>
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