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22 pages, 2131 KiB  
Article
A Comprehensive Transcriptomic and Proteomics Analysis of Candidate Secretory Proteins in Rose Grain Aphid, Metopolophium dirhodum (Walker)
by Atsbha Gebreslasie Gebrekidan, Yong Zhang and Julian Chen
Curr. Issues Mol. Biol. 2024, 46(12), 13383-13404; https://doi.org/10.3390/cimb46120798 (registering DOI) - 23 Nov 2024
Viewed by 176
Abstract
The Rose grain aphid, a notable agricultural pest, releases saliva while feeding. Yet, there is a need for a comprehensive understanding of the specific identity and role of secretory proteins released during probing and feeding. Therefore, a combined transcriptomic and proteomic approach was [...] Read more.
The Rose grain aphid, a notable agricultural pest, releases saliva while feeding. Yet, there is a need for a comprehensive understanding of the specific identity and role of secretory proteins released during probing and feeding. Therefore, a combined transcriptomic and proteomic approach was employed in this study to identify putative secretory proteins. The transcriptomic sequencing result led to the assembly of 18,030 unigenes out of 31,344 transcripts. Among these, 705 potential secretory proteins were predicted and functionally annotated against publicly accessible protein databases. Notably, a substantial proportion of secretory genes (71.5%, 69.08%, and 60.85%) were predicted to encode known proteins in Nr, Pfam, and Swiss-Prot databases, respectively. Conversely, 27.37% and 0.99% of gene transcripts were predicted to encode known proteins with unspecified functions in the Nr and Swiss-Prot databases, respectively. Meanwhile, the proteomic analysis result identified, 15 salivary proteins. Interestingly, most salivary proteins (i.e., 60% of the proteins) showed close similarity to A. craccivora, while 46.67% showed close similarity to A. glycines, M. sacchari and S. flava. However, to verify the expression of these secretory genes and characterize the biological function of salivary proteins further investigation should be geared towards gene expression and functional analysis. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Aphid feeding on artificial diet and saliva collection.</p>
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<p>The schematic illustration of salivary protein bands of <span class="html-italic">M. dirhodum</span> (MDE_SA) in reference to marker (M).</p>
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<p>Sequence length distribution of genes and transcripts of the trinity generated with de novo assembly driven out of the raw reads of rose grain aphid transcriptome.</p>
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<p>Homology analysis of <span class="html-italic">M. dirhodum</span> genes against non-redundant protein sequence database. (<b>A</b>) Expected threshold value (E-value) in which the number BLAST hits of each gene appear by chance (e-value &lt; 1.0 × 10<sup>−5</sup>), (<b>B</b>) Similarity distribution, (<b>C</b>) Species classification based on sequence similarity of genes among different organisms.</p>
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<p>The biological, molecular and cellular classification of genes associated with each functional category.</p>
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<p>Genes annotated based on Eukaryotic Orthologous Groups classification (KOG).</p>
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<p>Functional classification of unigenes associated with cellular processes (A), environmental information processing (B), genetic information processing (C), metabolism (D) and organismal systems (E) pathways.</p>
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15 pages, 3332 KiB  
Article
Isolation and Characterization of a Lytic Phage PaTJ Against Pseudomonas aeruginosa
by Jiayu Gu, Xinqiao Zhang, Tianlang Liu and Yunxue Guo
Viruses 2024, 16(12), 1816; https://doi.org/10.3390/v16121816 - 21 Nov 2024
Viewed by 367
Abstract
Pseudomonas aeruginosa is a major global threat to human health, and phage therapy has emerged as a promising strategy for treating infections caused by multidrug-resistant pathogens. In this study, we isolated and characterized a Pseudomonas lytic phage, PaTJ, from wastewater. PaTJ belongs to [...] Read more.
Pseudomonas aeruginosa is a major global threat to human health, and phage therapy has emerged as a promising strategy for treating infections caused by multidrug-resistant pathogens. In this study, we isolated and characterized a Pseudomonas lytic phage, PaTJ, from wastewater. PaTJ belongs to the phage family Mesyanzhinovviridae, and is featured by short latency (30 min) and large burst size (103 PFU per infected cell). Our investigation revealed that PaTJ utilizes the type IV Pili (T4P) as a receptor. Transcriptome analysis of PaTJ infected host at latent stage showed distinct expression patterns of PaTJ encoding genes involved in replication and structure assembly, without expression of the majority of toxic accessory genes responsible for phage release. In addition, host bacteria exhibited specific induction of host metabolism-related genes in response to the PaTJ’s infection. Furthermore, our findings demonstrated the PaTJ’s potential in degrading biofilms. This work sheds light on the multifaceted impact of this lytic phage PaTJ on P. aeruginosa, presenting potential applications in both gene expression modulation and biofilm management. Full article
(This article belongs to the Section Bacterial Viruses)
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<p>Identification and morphology of <span class="html-italic">Pseudomonas</span> phage PaTJ. (<b>A</b>) Plaques for the isolation of <span class="html-italic">Pseudomonas</span> phage PaTJ on a wild-type MPAO1 lawn after double layer agar plating (top), and the PaTJ plaques selected for further phage propagation were indicated by red arrows. The plate used for the preparation of high titer phage lysate in this study (bottom). (<b>B</b>) TEM images of negatively stained phage PaTJ particles are presented at two magnifications. Representative images were shown.</p>
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<p>One-step growth curve and killing kinetics of PaTJ. (<b>A</b>) The one-step growth curve of PaTJ on the <span class="html-italic">P. aeruginosa</span> strain MPAO1 was conducted at MOI 0.1, and three independent cultures were used. The data are presented as mean ± SD. (<b>B</b>) Killing curves of <span class="html-italic">P. aeruginosa</span> strain MPAO1 by PaTJ were generated at various MOIs (1000, 100, 10, 1, 0.1, and 0.00001). The growth of MPAO1 cells without the addition of phages (MOI = 0) was used as the control. (<b>C</b>) Phage titers in cultures in B at MOIs 10 and 0.1, and the data are presented as mean ± SD.</p>
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<p>PaTJ uses T4P as its receptor. The PaTJ phages were serially diluted by a factor of 10 and were plated onto lawns of the wild-type MPAO1 strain as well as two T4P mutant strains, Δ<span class="html-italic">pilC</span> and Δ<span class="html-italic">pilA</span>, respectively. Three independent phage samples were utilized and were designated as #1, #2, and #3.</p>
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<p>Comparative genome analysis of the <span class="html-italic">P. aeruginosa</span> phages PaTJ, Rocky, vB-Pa-PAC4, and PAE1.</p>
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<p>The maximum likelihood tree based on the whole genome sequences of phage PaTJ (red) and the available phages listed in <a href="#app1-viruses-16-01816" class="html-app">Supplementary Table S2</a>.</p>
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<p>MPAO1 cells were infected with phage PaTJ at an OD<sub>600</sub> of 1.0 with MOI 0.01 for 30 min. Normalized gene expression (lgRPKM) of all PaTJ-encoding genes was shown on the left. The gene clusters with continuous similar expression patterns were also depicted on the right, and genes encoding hypothetical proteins were shown in gray. Phage structural proteins were depicted in blue, while those encoding homologs of known phage accessory proteins were depicted in brown. MCP, major capsid protein. Three independent cultures were used for both groups.</p>
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<p>The impact of PaTJ infection on expression of host genes. Normalized expression of MPAO1 genes in samples in <a href="#viruses-16-01816-f006" class="html-fig">Figure 6</a> was also analyzed. Only KEGG pathways related to metabolism were significantly enriched and the expression levels (lgRPKM) of genes in enriched pathways were depicted in heatmaps. Three independent cultures were used for both groups.</p>
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<p>Biofilms in a static growth phase were exposed to PaTJ phages, and the residual biofilm was assessed at 0, 4, and 8 h post-infection. The relative biofilm was calculated by comparing it to the initial time point (time 0) when the PaTJ phages were introduced at different MOIs. Three independent cultures were used for each MOI and the data were presented as the mean ± standard deviation.</p>
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22 pages, 9495 KiB  
Article
De Novo Transcriptome Assembly of Anoectochilus roxburghii for Morphological Diversity Assessment and Potential Marker Development
by Wenting Zhang, Ke Chen, Yu Mei and Jihua Wang
Plants 2024, 13(23), 3262; https://doi.org/10.3390/plants13233262 - 21 Nov 2024
Viewed by 240
Abstract
Anoectochilus roxburghii is a rare and precious medicinal and ornamental plant of Orchidaceae. Abundant morphological characteristics have been observed among cultivated accessions. Our understanding of the genetic basis of morphological diversity is limited due to a lack of sequence data and candidate genes. [...] Read more.
Anoectochilus roxburghii is a rare and precious medicinal and ornamental plant of Orchidaceae. Abundant morphological characteristics have been observed among cultivated accessions. Our understanding of the genetic basis of morphological diversity is limited due to a lack of sequence data and candidate genes. In this study, a high-quality de novo transcriptome assembly of A.roxburghii was generated. A total of 138,385 unigenes were obtained, and a BUSCO (Benchmarking Universal Single-Copy Orthologs) analysis showed an assembly completeness of 98.8%. Multiple databases were used to obtain a comprehensive annotation, and the unigenes were functionally categorized using the GO (Gene Ontology), KOG (Eukaryotic Orthologous Groups), KEGG (Kyoto Encyclopedia of Genes and Genomes), and Nr databases. After comparing the phenotypic characteristics of five representative cultivars, a set of cultivar-specific, highly expressed unigenes was identified based on a comparative transcriptome analysis. Then, a WGCNA (Weighted Gene Co-expression Network Analysis) was performed to generate gene regulatory modules related to chlorophyll content (red) and sucrose synthase activity (black). In addition, the expression of six and four GO enrichment genes in the red and black modules, respectively, was analyzed using qRT-PCR to determine their putative functional roles in the leaves of the five cultivars. Finally, in silico SSR (Simple Sequence Repeat) mining of the assembled transcriptome identified 44,045 SSRs. Mononucleotide was the most dominant class of SSRs, followed by complex SSRs. In summary, this study reports on the phenomic and genomic resources of A. roxburghii, combining SSR marker development and validation. This report aids in morphological diversity assessments of Anoectochilus roxburghii. Full article
(This article belongs to the Section Plant Genetics, Genomics and Biotechnology)
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<p>Transcriptome sequencing and sugar composition analysis of <span class="html-italic">A. roxburghii</span> (JXL28). (<b>A</b>) The leaf, stem, and root of JXL28, aged six (up) and twelve (down) months, respectively, are utilized for the assessment of total polysaccharide levels. (<b>B</b>) The total polysaccharide levels in the leaf, stem, and root of <span class="html-italic">A. roxburghii</span> after six and twelve months of growth. (<b>C</b>) A PCA analysis of various tissues of JXL28 transcriptome. (<b>D</b>) Length distribution of assembled transcripts and unigenes of the JXL28 transcriptome. (<b>E</b>) A Venn diagram showing differentially expressed unigenes unique to or shared among differential groups. (<b>F</b>) Heatmap of the levels of ten sugars in various tissues of JXL28. Ara: <span class="html-italic">D</span>-Arabinose; Fru: <span class="html-italic">D</span>-Fructose; Fuc: <span class="html-italic">L</span>-Fucose; Glu: Glucose; Mal: Maltose; Rha: <span class="html-italic">L</span>-Rhamnose; Suc: Sucrose; Tre: Trehalose.</p>
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<p>Functional annotation of unigenes. (<b>A</b>) GO ontology annotation of the <span class="html-italic">A. roxburghii</span> (JXL28) transcriptome showing the major GO terms in the molecular function, biological process, and cellular component categories. (<b>B</b>) Histogram representation of the cluster of orthologous group (COG) classification for assembled unigenes. (<b>C</b>) A KEGG analysis of the JXL28 transcriptome showing the top 20 highly represented KEGG pathways. The <span class="html-italic">X</span>-axis indicates the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, and the <span class="html-italic">Y</span>-axis indicates the number of transcripts in each pathway. (<b>D</b>) Species-based distribution of blastx matches for each clustered unitranscript of the JXL28 transcriptome. The species with a match &lt; 1% are grouped in the “Other” category.</p>
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<p>Morphological diversity of <span class="html-italic">A. roxburghii.</span> (<b>A</b>) Morphology of leaf adaxial, leaf abaxial, and seedling of five representative “Jinxianlian” cultivars. (<b>B</b>) An analysis of chlorophyll content (<b>up</b>) and sucrose synthase activity (<b>down</b>); analysis of five representative “Jinxianlian” cultivars. (<b>C</b>) Investigation of ten agronomic characteristics, including weight, height, leaf number, diameter, aerial root number, stem node number, leaf length, and leaf width. The hollow circles represent discrete values. (<b>D</b>) Comparison of leaf surface morphology among five representative “Jinxianlian” cultivars using scanning electron microscopy (SEM). ad: adaxial; ab: abaxial.</p>
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<p>Transcriptome analysis of five representative <span class="html-italic">A. roxburghii</span> cultivars. (<b>A</b>) Results of 3D-PCA of five “Jinxianlian” cultivars based on the expression level of all unigenes, with each dot representing an independent experimental repeat. (<b>B</b>) Numbers of up- and down-regulated DEGs in each comparison (others vs. JXL28). (<b>C</b>) Venn diagram showing the number of DEGs in each combination. (<b>D</b>) Results of five main clusters from <span class="html-italic">K</span>-means clustering analysis. (<b>E</b>) RNA-seq results for several transcription factor candidate from five main clusters.</p>
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<p>Identification of important modules and biomarkers based on a WGCNA. (<b>A</b>) A cluster dendrogram and the color display of co-expression network modules for all unigenes. (<b>B</b>) A correlation matrix of the module eigengene values obtained from the WGCNA. Nine modules were identified, and each module eigengene was tested for correlation with traits. In each cell, the upper values are the correlation coefficients between the module eigengenes and the traits; the lower values are the corresponding <span class="html-italic">p</span>-values; the co-expression modules significantly associated with the content of Chl a, Chl b, and total chlorophyll content and sucrose synthase activity are highlighted in red boxes. (<b>C</b>,<b>D</b>) A scatterplot describing the relationship between MM and GS in the red (<b>C</b>) and black (<b>D</b>) modules; key genes are screened out in the upper-right area, where GS &gt; 0.8 and MM &gt; 0.8. (<b>E</b>,<b>F</b>) A heatmap of the genes in the red (<b>E</b>) and black (<b>F</b>) modules; (<b>G</b>,<b>H</b>) A dotplot of the GO enrichment analysis of the genes in the red (<b>G</b>) and black (<b>H</b>) modules.</p>
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<p>Verification of RNA-seq results via qRT-PCR of candidate unigenes. (<b>A</b>) Six unigenes selected from the hub gene of the red module. (<b>B</b>) Two unigenes selected from the hub gene of the black module. (<b>C</b>) One unigene selected from the DEGs. Error bars indicate SD (n = 3).</p>
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<p>Characterization of potential simple sequence repeat (SSR) markers using MISA software. (<b>A</b>) The distribution of the different nucleotide repeat types (complex; Mono—mononucleotide; Di—dinucleotide; Tr—trinucleotide; Tetra—tetranucleotide; Penta—pentanucleotide; Hexa—hexanucleotide). (<b>B</b>) A stacked bar chart representing the abundance of trinucleotide repeats. (<b>C</b>) PCR amplification of genic-SSR markers in 20 <span class="html-italic">A. roxburghii</span> genotypes.</p>
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18 pages, 3968 KiB  
Article
Comparative and Spatial Transcriptome Analysis of Rhododendron decorum Franch. During the Flowering Period and Revelation of the Plant Defense Mechanism
by Weiwei Liu, Chenghua Yu, Kaiye Yang, Ling Wang, Zhongyu Fan and Xinchun Mo
Genes 2024, 15(11), 1482; https://doi.org/10.3390/genes15111482 - 18 Nov 2024
Viewed by 425
Abstract
Background: Rhododendron is a globally distributed and extensive genus, comprising over 1000 species. In the southwestern mountains of China, there exists a remarkable diversity of Rhododendron, with Yunnan Province alone harboring more than 600 species. R. decorum Franch. has long been utilized [...] Read more.
Background: Rhododendron is a globally distributed and extensive genus, comprising over 1000 species. In the southwestern mountains of China, there exists a remarkable diversity of Rhododendron, with Yunnan Province alone harboring more than 600 species. R. decorum Franch. has long been utilized by local communities for its medicinal and edible properties. However, the transcriptional regulation function, medicinal properties, and edibility characteristics of R. decorum Franch. currently lack a solid theoretical basis. Methods: Total RNA was extracted from leaves, corollas and androecium/gynoecium of R. decorum Franch. in Heqing county, followed by the construction of cDNA libraries and the de novo assembly of transcriptomes. Results: A total of 63,050 unigenes were extracted from the flowers and leaf organs of R. decorum Franch. Among these unigenes, 43,517 were predicted to be coding sequences, with 32,690 being effectively annotated. Differential gene expression enrichment was observed among different organs within their respective transcriptomes; notably floral organs exhibited significant defense against plant diseases along with signal transduction functions. Furthermore, during the flower harvesting period, all floral organs exhibited gene enrichment pathways associated with carbohydrate metabolism. Additionally, the stamen and pistil displayed flavonoid metabolism pathways, suggesting their potential applications as functional food or medicine. Conclusions: Our results shed light on plant–pathogen defense mechanisms and the molecular bias of flavonoids biosynthesis on flower organs during the flowering period, which might help to understand the consumption of R. decorum Franch. corollas by the Bai nationality of Heqing county. Full article
(This article belongs to the Special Issue Molecular Genetics and Multi-omics in Medicinal Plants)
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<p>GO functional classifications of <span class="html-italic">Rhododendron decorum</span> Franch. unigenes.</p>
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<p>KEGG annotation of <span class="html-italic">R. decorum</span> Franch. unigenes. (<b>A</b>) KEGG functional classification of assembled unigenes. (<b>B</b>) Classifications of subcategory “metabolism of terpenoids and polyketides”. (<b>C</b>) Classifications of the subcategory “biosynthesis of other secondary metabolites”.</p>
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<p>Statistics of differential expressed genes and comparisons between different groups. (<b>A</b>) Differentially expressed genes in different compared groups. The horizontal axis represents distinct sets of differentially expressed genes, where blue color represents the differential expressed genes in different groups, red color indicates upregulated, and green signifies downregulated. Meanwhile, the vertical axis corresponds to the number of differential expressed genes. (<b>B</b>) Volcano plot of differentially expressed genes in HQI/HQL. (<b>C</b>) Volcano plot of differentially expressed genes in HQI/HQO. (<b>D</b>) Volcano plot of differentially expressed genes in HQO/HQL. Each point on the graph represents a gene, with the horizontal axis indicating the logarithmic-fold change in gene expression between two samples. The vertical axis represents the negative logarithm of the false discovery rate. Blue points represent downregulated differentially expressed genes, red points represent upregulated differentially expressed genes, and gray points represent non-differentially expressed genes.</p>
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<p>GO enrichment analysis of differential expression genes within different organ comparison groups. (<b>A</b>) GO terms of DEGs in HQI/HQL group. (<b>B</b>) GO terms of DEGs in HQI/HQO group. (<b>C</b>) GO terms of DEGs in HQO/HQL group.</p>
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<p>The heatmaps of DEG expression patterns in different groups of <span class="html-italic">R. decorum</span> Franch. (<b>A</b>) DEG expression patterns in HQO/HQL group. (<b>B</b>) DEG expression patterns in HQI/HQL group. (<b>C</b>) DEG expression patterns in HQI/HQO group. Clustering plot depicting the differential expression analysis of genes belonging to the EF-hand protein family. Blue color represents the downregulated and red color the upregulated.</p>
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15 pages, 11288 KiB  
Article
De Novo Transcriptome Assembly of Rice Bean (Vigna umbellata) and Characterization of WRKY Transcription Factors Response to Aluminum Stress
by Gunasekaran Ariharasutharsan, Manoharan Akilan, Manickam Dhasarathan, Manivel Amaravel, Sankaran Divya, Mariyappan Deivamani, Manickam Sudha, Muthaiyan Pandiyan, Adhimoolam Karthikeyan and Natesan Senthil
Plants 2024, 13(22), 3170; https://doi.org/10.3390/plants13223170 - 12 Nov 2024
Viewed by 544
Abstract
Rice bean is an underutilized legume crop cultivated in Asia, and it is a good source of protein, minerals, and essential fatty acids for human consumption. Moreover, the leaves left over after harvesting rice bean seeds contain various biological constituents beneficial to humans [...] Read more.
Rice bean is an underutilized legume crop cultivated in Asia, and it is a good source of protein, minerals, and essential fatty acids for human consumption. Moreover, the leaves left over after harvesting rice bean seeds contain various biological constituents beneficial to humans and animals. In our study, we performed a de-novo transcriptome assembly of rice bean, characterized the WRKY transcription factors, and studied their response to aluminum stress. A total of 46.6 million clean reads, with a GC value of 43%, were generated via transcriptome sequencing. De novo assembly of the clean reads resulted in 90,933 transcripts and 74,926 unigenes, with minimum and maximum lengths of 301 bp and 24,052 bp, and N50 values of 1801 bp and 1710 bp, respectively. A total of 27,095 and 28,378 unigenes were annotated and subjected to GO and KEGG analyses. Among the unigenes, 15,593, 20,770, and 15,385 unigenes were identified in the domains of biological process, molecular function, and cellular component, respectively. A total of 16,132 unigenes were assigned to 188 pathways, including metabolic pathways (5500) and secondary metabolite biosynthesis (2858). Transcription factor analysis revealed 4860 unigenes from 98 different transcription factor families. For WRKY, a total of 95 unigenes were identified. Further analysis revealed the diverse response of WRKY transcription factors to aluminum stress. Collectively, the results of this study boost genomic resources and provide a baseline for further research on the role of WRKY transcription factors in aluminum tolerance in rice bean. Full article
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<p>(<b>A</b>) Distribution of unigenes and transcript lengths, and summary of annotation statistics from the NR database; (<b>B</b>) species distribution; (<b>C</b>) similarity distribution; and (<b>D</b>) E-value distribution.</p>
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<p>Summary of KOG classification. The X-axis represents the names of the KOG group and the Y-axis represents the number of transcripts under this group in the total annotated genes.</p>
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<p>(<b>A</b>) Gene ontology classification of unigenes. The histogram shows the results of unigene classification under three major categories of GO terms: biological processes (BP), molecular functions (MF), and cellular components (CC). (<b>B</b>) KEGG pathway unigene assignments. (<b>C</b>) Details of secondary metabolite biosynthesis.</p>
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<p>Transcription factors identified from the rice bean transcriptome.</p>
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<p>Phylogenetic analysis of WRKY transcription factors from rice bean and Arabidopsis.</p>
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<p>Expression of WRKY transcription factors analyzed in aluminum treated roots, stems, and leaves of rice bean by RT-qPCR analysis. The different letters indicate significant changes at <span class="html-italic">p</span> &lt; 0.05.</p>
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19 pages, 5529 KiB  
Article
Integration of mRNA and miRNA Analysis Sheds New Light on the Muscle Response to Heat Stress in Spotted Sea Bass (Lateolabrax maculatus)
by Cong Liu, Haishen Wen, Yuan Zheng, Chong Zhang, Yonghang Zhang, Lingyu Wang, Donglei Sun, Kaiqiang Zhang, Xin Qi and Yun Li
Int. J. Mol. Sci. 2024, 25(22), 12098; https://doi.org/10.3390/ijms252212098 - 11 Nov 2024
Viewed by 380
Abstract
Temperature is a crucial environmental factor for fish. Elevated temperatures trigger various physiological and molecular responses designed to maintain internal environmental homeostasis and ensure the proper functioning of the organism. In this study, we measured biochemical parameters and performed mRNA–miRNA integrated transcriptomic analysis [...] Read more.
Temperature is a crucial environmental factor for fish. Elevated temperatures trigger various physiological and molecular responses designed to maintain internal environmental homeostasis and ensure the proper functioning of the organism. In this study, we measured biochemical parameters and performed mRNA–miRNA integrated transcriptomic analysis to characterize changes in gene expression profiles in the muscle tissue of spotted sea bass (Lateolabrax maculatus) under heat stress. The measurement of biochemical parameters revealed that the activities of nine biochemical enzymes (ALP, γ-GT, AST, GLU, CK, ALT, TG, LDH and TC) were significantly affected to varying degrees by elevated temperatures. A total of 1940 overlapping differentially expressed genes (DEGs) were identified among the five comparisons in the muscle tissue after heat stress. Protein–protein interaction (PPI) analysis of DEGs indicated that heat shock protein genes (HSPs) were deeply involved in the response to heat stress. In addition, we detected 462 differential alternative splicing (DAS) events and 618 DAS genes, which are closely associated with sarcomere assembly in muscle, highlighting the role of alternative splicing in thermal response regulation. Moreover, 32 differentially expressed miRNAs (DEMs) were identified in response to heat stress, and 599 DEGs were predicted as potential target genes of those DEMs, generating 846 DEG–DEM negative regulatory pairs potentially associated with thermal response. Function enrichment analysis of the target genes suggested that lipid metabolism-related pathways and genes were regulated by miRNAs. By analyzing PPIs of target genes, we identified 28 key negative regulatory pairs, including 13 miRNAs (such as lma-miR-122, lma-miR-200b-5p and novel-miR-444) and 15 target genes (such as hspa13, dnaja1, and dnajb1a). This study elucidates the molecular mechanisms of response to high-temperature stress and offers valuable information for the selection and breeding of heat-tolerant strains of spotted sea bass. Full article
(This article belongs to the Section Molecular Biology)
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<p>Differentially expressed genes (DEGs) at different time points after heat stress. (<b>A</b>) Histogram of statistics of the number of up-regulated, down-regulated and non-significant-difference genes in each comparison group. (<b>B</b>) Volcano plot of top 10 genes with DEGs in each comparison group. (<b>C</b>) Venn diagram of up-regulated DEGs. (<b>D</b>) Venn diagram of down-regulated DEGs.</p>
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<p>(<b>A</b>) Cluster 1 and (<b>B</b>) Cluster 2 display the PPI network of common DEGs. The color of the nodes represent the connectivity degree. (<b>C</b>) Expression profiles of hub genes of spotted sea bass after heat stress. Genes with up-regulated expression are marked in red.</p>
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<p>Functional enrichment and validation results for differential alternative splicing (DAS) genes in muscles after heat stress. (<b>A</b>) KEGG enrichment analysis results for DAS genes. Significant pathways are linked with their involved genes via various color ribbons. (<b>B</b>) GO enrichment analysis results for DAS genes. (<b>C</b>) Verification of DAS genes by PCR. (<b>D</b>) AS patterns of two validated genes. Purple frames represent the exon in where the AS occurs.</p>
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<p>Differential expression analysis of miRNAs under heat stress. (<b>A</b>) The volcano plot of differential expression analysis between the CG and HG. (<b>B</b>) Heatmap of expression levels for DEMs of the CG and HG.</p>
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<p>(<b>A</b>) Venn diagram of target gene prediction of DEMs. Different colors indicate different prediction tools. (<b>B</b>) KEGG pathway enrichment analysis of target genes. (<b>C</b>) Validation of DEMs and negative regulation relationships with target genes by qPCR.</p>
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<p>Hub target genes and regulatory network in spotted sea bass coping with thermal stress. (<b>A</b>) Identification of hub target genes. Different colors indicate the degree of connectivity. (<b>B</b>) Hub regulatory network of DEMs–DEGs. Green ellipses represent down-regulated expressed DEGs, plink ellipses represent up-regulated expressed DEGs, yellow squares represent up-regulated expressed DEMs, and blue squares represent down-regulated expressed DEMs.</p>
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<p>Schematic diagram of predicted molecular mechanism in the thermal response in muscle tissue of spotted sea bass.</p>
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18 pages, 8769 KiB  
Article
Analysis of Rfo-Mediated Network in Regulating Fertility Restoration in Brassica oleracea
by Miaomiao Xing, Yuanyuan Xu, Yuyu Lu, Jiyong Yan and Aisong Zeng
Int. J. Mol. Sci. 2024, 25(22), 12026; https://doi.org/10.3390/ijms252212026 - 8 Nov 2024
Viewed by 364
Abstract
Ogura cytoplasmic male sterility (CMS) lines play a crucial role in the utilization of heterosis. However, valuable traits, such as disease resistance genes from Ogura CMS hybrids, are challenging to incorporate for germplasm innovation, particularly in cabbage and broccoli. To date, the Rfo [...] Read more.
Ogura cytoplasmic male sterility (CMS) lines play a crucial role in the utilization of heterosis. However, valuable traits, such as disease resistance genes from Ogura CMS hybrids, are challenging to incorporate for germplasm innovation, particularly in cabbage and broccoli. To date, the Rfo-mediated network regulating fertility restoration remains largely unexplored. In this study, we conducted a transcriptomic analysis of broccoli flower buds from Ogura CMS SFB45 and its Rfo-transgenic fertility restoration line, pRfo, at different stages of pollen development. Gene Ontology (GO) terms such as “pollen exine formation”, “flavonoid metabolic and biosynthetic processes”, and “pollen wall assembly”, along with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways including “flavonoid biosynthesis”, “MAPK signaling pathway-plant”, and “ABC transporters”, were significantly enriched. We identified five differentially expressed genes (DEGs) involved in tapetum-mediated callose metabolism, thirty-four DEGs related to tapetum-mediated pollen wall formation, three DEGs regulating tapetum programmed cell death (PCD), five MPKs encoding DEGs, and twelve DEGs associated with oxidative phosphorylation. Additionally, yeast two-hybrid and bimolecular fluorescence complementation (BiFC) assays demonstrated that RFO directly interacts with ORF138 at the protein level. These findings provide valuable insights into the fertility recovery mechanisms regulated by Rfo in broccoli and offer important clues for breeders aiming to enhance Ogura CMS hybrids in Brassica oleracea. Full article
(This article belongs to the Special Issue Advances in Brassica Crop Metabolism and Genetics)
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<p>Morphology of the Ogura CMS line SFB45 and the <span class="html-italic">Rfo</span>-transgenic restorer line pRfo. (<b>A</b>) Comparison of plants. Scale bar = 1 cm. (<b>B</b>) Comparison of flowers. Scale bar = 3 mm. (<b>C</b>) Pollen viability of pRfo. Scale bar = 50 μm. (<b>D</b>) Results of Southern blotting for <span class="html-italic">Rfo</span>-transgenic lines. M: marker; Line 1: positive control transgenic carrier vector (<span class="html-italic">pro</span>::<span class="html-italic">Rfo</span>); Line 2: negative control SFB45; Line 3: pRfo; lines 4–10 represent other <span class="html-italic">Rfo</span>-transgenic lines.</p>
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<p>Principal component analysis (PCA) and differentially expressed gene (DEG) statistics. (<b>A</b>) PCA clustering based on gene expression. (<b>B</b>) Number of DEGs detected in different comparison groups. (<b>C</b>) Expression of <span class="html-italic">Rfo</span> in different samples. Error bars represent standard deviations. (<b>D</b>) Analysis of the shared DEGs in L_DEGs, S_DEGs, and <span class="html-italic">Rfo</span> co-expressed genes.</p>
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<p>Top 20 of GO enrichment terms of DEGs in SFB45_S vs. pRfo_S. (<b>A</b>) and SFB45_L vs. pRfo_L (<b>B</b>). Circles from outer to inner sides indicate the following: 1. entry numbers of enriched terms; 2. number of all genes within particular annotated term; 3. number of DEGs within the annotated term (purple block: up-regulated genes; blue block: down-regulated genes); 4. rich factor of the term.</p>
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<p>Top 20 KEGG enrichment pathways in SFB45_S vs. pRfo_S (<b>A</b>) and SFB45_L vs. pRfo_L (<b>B</b>).</p>
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<p>Key genes associated with tapetum development and function that are involved in pollen development. DYT1, DYSFUNCTIONAL TAPETUM1; TDF1, TAPETAL DEVELOPMENT and FUNCTION1; AMS, ABORTED MICROSPORES; TEK, TRANSPOSABLE ELEMENT SILENCING VIA AT-HOOK; Cals5, Callose synthase5; A6, Anther-specific protein6; QRT3, QUARTET3; ABCG, ABC subfamily G; LTP, LIPID TRANSFER PROTEINS; MS2, MALE STERILITY 2; TKPR, TETRAKETIDE alpha-PYRONE REDUCTASE; PKS, POLYKETIDE SYNTHASE; CYP, Cytochrome P450; EXL, EXTRACELLULAR LIPASE; GRP, GLYCINE-RICH PROTEIN; AGP, ARABINOGALACTAN PROTEIN; βVPE, beta VACUOLAR PROCESSING ENZYME; CEP1, CYSTEINE PROTEASE1; PCD, PROGRAMMED CELL DEATH.</p>
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<p>The expression pattern of DEGs involved in tapetum-regulated pollen development. (<b>A</b>) Key DEGs involved in tapetum-mediated callose metabolism. (<b>B</b>) Key DEGs involved in tapetum-mediated pollen wall formation. (<b>C</b>) Key DEGs regulating the programmed cell death of the tapetum. (<b>D</b>) Other DEGs related to pollen development. <span class="html-italic">A6</span>, <span class="html-italic">Anther-specific protein6</span>; <span class="html-italic">QRT3</span>, <span class="html-italic">QUARTET3</span>; <span class="html-italic">Cals5</span>, <span class="html-italic">Callose synthase5</span>; <span class="html-italic">βVPE</span>, <span class="html-italic">beta VACUOLAR PROCESSING ENZYME</span>; <span class="html-italic">CEP1</span>, <span class="html-italic">CYSTEINE PROTEASE1</span>; <span class="html-italic">DYT1</span>, <span class="html-italic">DYSFUNCTIONAL TAPETUM1</span>; <span class="html-italic">AMS</span>, <span class="html-italic">ABORTED MICROSPORES</span>; <span class="html-italic">ABCG</span>, <span class="html-italic">ABC subfamily G</span>; <span class="html-italic">LTPs</span>, <span class="html-italic">LIPID TRANSFER PROTEINs</span>; <span class="html-italic">EXLs</span>, <span class="html-italic">EXTRACELLULAR LIPASEs</span>; <span class="html-italic">CYPs</span>, <span class="html-italic">cytochrome P450</span>; <span class="html-italic">PKS</span>, <span class="html-italic">POLYKETIDE SYNTHASE</span>; <span class="html-italic">TKPR</span>, <span class="html-italic">TETRAKETIDE alpha-PYRONE REDUCTASE</span>; <span class="html-italic">GRPs</span>, <span class="html-italic">GLYCINE-RICH PROTEINs</span>; <span class="html-italic">ACOS5</span>, <span class="html-italic">acyl-CoA synthetase5</span>. <span class="html-italic">MPK</span>, <span class="html-italic">Mitogen Activated Protein Kinase</span>.</p>
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<p>Validation of the expression profiles of the selected DEGs using RT-qPCR. Actin was used as an internal reference gene. Relative gene expression levels were calculated using the 2<sup>−ΔΔCt</sup> method. Values are means of three independent biological samples and error bars represent standard deviations. Significant differences (<span class="html-italic">p</span> ≤ 0.01) based on <span class="html-italic">t</span>-test are highlighted by two asterisks. Red represents RNA-seq results based on FPKM values, green represents RT-qPCR results based on relative expression levels.</p>
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<p>RFO interacts with ORF138 at the protein level in living cells. (<b>A</b>) Yeast growth assays on selective media containing the RFO bait and ORF138 prey vectors. (<b>B</b>) <span class="html-italic">N. benthamiana</span> leaves co-infiltrated with RFO-NYFP and ORF138-CYFP. SDEL1-NYFP and SPX4-CYFP were co-expressed as a positive control. Bar = 20 μm.</p>
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12 pages, 2245 KiB  
Article
OHDLF: A Method for Selecting Orthologous Genes for Phylogenetic Construction and Its Application in the Genus Camellia
by Junhao Cai, Cui Lu, Yuwei Cui, Zhentao Wang and Qunjie Zhang
Genes 2024, 15(11), 1404; https://doi.org/10.3390/genes15111404 - 30 Oct 2024
Viewed by 500
Abstract
Accurate phylogenetic tree construction for species without reference genomes often relies on de novo transcriptome assembly to identify single-copy orthologous genes. However, challenges such as whole-genome duplication (WGD), heterozygosity, gene duplication, and loss can hinder the selection of these genes, leading to limited [...] Read more.
Accurate phylogenetic tree construction for species without reference genomes often relies on de novo transcriptome assembly to identify single-copy orthologous genes. However, challenges such as whole-genome duplication (WGD), heterozygosity, gene duplication, and loss can hinder the selection of these genes, leading to limited data for constructing reliable species trees. To address these issues, we developed a new analytical pipeline, OHDLF (Orthologous Haploid Duplication and Loss Filter), which filters orthologous genes from transcript data and adapts parameter settings based on genomic characteristics for further phylogenetic tree construction. In this study, we applied OHDLF to the genus Camellia and evaluated its effectiveness in constructing phylogenetic trees. The results highlighted the pipeline’s ability to handle challenges like high heterozygosity and recent gene duplications by selectively retaining genes with a missing rate and merging duplicates with high similarity. This approach ensured the preservation of informative sites and produced a highly supported consensus tree for Camellia. Additionally, we evaluate the accuracy of the OHDLF phylogenetic trees for different species, demonstrating that the OHDLF pipeline provides a flexible and effective method for selecting orthologous genes and constructing accurate phylogenetic trees, adapting to the genomic characteristics of various plant groups. Full article
(This article belongs to the Special Issue Advances in Genetics and Genomics of Plants)
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<p>The distribution of synonymous mutation substitutions and the primary sources of highly similar homologous genes in the genus <span class="html-italic">Camellia</span>. Synonymous mutation substitution distribution of transcripts from Trinity assembly data (<b>a</b>), transcripts from a single haploid genome (<b>b</b>), transcripts from a diploid genome with phased haplotypes (<b>c</b>), and transcripts with alternative splicing data in the genome (<b>d</b>). The red dashed lines represent heterozygous peaks, while the blue and green dashed lines represent the two known rounds of WGDs.</p>
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<p>Characteristics of orthogroups and the phylogenetic tree construction process in <span class="html-italic">Camellia</span> species. (<b>a</b>) The OHDLF pipeline addresses three major issues: heterozygosity, recent segmental duplications, and ancient duplications and losses encountered in the analysis of orthologous genes. (<b>b</b>) Detailed steps of the OHDLF process. (<b>c</b>,<b>d</b>) Data distribution across orthogroups with different mission rates. (<b>e</b>) Distribution of the maximum copy number within the same orthogroup in a single species. Light green bars represent the number of orthogroups with different mission rates as identified by OrthoFinder. Dark green bars represent the number of orthogroups meeting the criteria of Max_copy &lt;= 10 and pairwise_identity &gt;= 95%.</p>
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<p>Phylogenetic trees constructed using the OHDLF pipeline. Concatenation (<b>a</b>) and coalescent (<b>b</b>) phylogenetic trees of <span class="html-italic">Camellia</span> species; (<b>c</b>) the number of selected orthogroups by OrthoFinder, DISCO [<a href="#B17-genes-15-01404" class="html-bibr">17</a>], and OHDLF can be used for phylogenetic tree construction. (<b>d</b>) Bootstrap value distribution of different tree-building methods. Red squares indicate a bootstrap value of 100; pink squares indicate a bootstrap value of 75 to 99; pale blue squares indicate a bootstrap value of 50 to 74; deep blue squares indicate a bootstrap value below 50. The Latin names and grouping information for species used in this figure can be found in <a href="#app1-genes-15-01404" class="html-app">Supplemental Table S1</a>.</p>
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<p>A comparison of OHDLF usage across different species. (<b>a</b>) Phylogenetic tree of family Theaceae. The number of selected orthogroups (<b>b</b>) and bootstrap value distribution (<b>c</b>) by OrthoFinder and DISCO for the phylogenetic tree of family Theaceae. (<b>d</b>) Concatenation phylogenetic trees of <span class="html-italic">Oryza</span> species. The number of selected orthogroups (<b>e</b>) and bootstrap value distribution (<b>f</b>) comparison for the phylogenetic tree of genus <span class="html-italic">Oryza</span>. Red squares indicate a bootstrap value of 100; pink squares indicate a bootstrap value of 75 to 99; pale blue squares indicate a bootstrap value of 50 to 74; deep blue squares indicate a bootstrap value below 50 for (<b>a</b>,<b>c</b>,<b>d</b>,<b>f</b>). The Latin names and data source for the species used in (<b>a</b>,<b>d</b>) can be found in <a href="#app1-genes-15-01404" class="html-app">Supplemental Tables S1 and S2</a>.</p>
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<p>The time tree of family Theaceae. Ages of stratigraphic boundaries were from the Int. Chronostrat. Chart [<a href="#B43-genes-15-01404" class="html-bibr">43</a>] (Pl: Pliocene; Qu: Quaternary), in millions of years ago (Mya). Blue bars at each node show the 95% highest posterior density (HPD) with posterior probability &gt; 0.5.</p>
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14 pages, 2891 KiB  
Article
Agave schidigera Transcriptome Reveals Stress-Responsive Phenylalanine ammonia-lyase Genes in Agave
by Xuxia Wang, Xiaoli Hu, Chen Lin, Qingqing Liu, Yubo Li, Dengxiang Du, Dietram Mkapa, Weiyi Zhang, Xing Huang and Kexian Yi
Agronomy 2024, 14(11), 2520; https://doi.org/10.3390/agronomy14112520 - 26 Oct 2024
Viewed by 896
Abstract
Agave is a significant fiber crop in tropical regions, known for its high fiber strength. Lignin is closely associated with fiber strength, and phenylalanine ammonia-lyase (PAL) serves as the initial enzyme in biosynthesis of lignin. Hence, it is of considerable significance to study [...] Read more.
Agave is a significant fiber crop in tropical regions, known for its high fiber strength. Lignin is closely associated with fiber strength, and phenylalanine ammonia-lyase (PAL) serves as the initial enzyme in biosynthesis of lignin. Hence, it is of considerable significance to study the genes of PAL family to analyze the characteristics and mechanism of sisal fiber development. In this research, we conducted a transcriptomic analysis of Agave schidigera, a widely recognized ornamental plant in agave. Approximately 29.85 million clean reads were acquired through Illumina sequencing. In total, 116,602 transcripts including 72,160 unigenes were assembled, and 22.06~63.56% of those unigenes were annotated in public databases. Two, six, six and six PAL genes were successfully identified and cloned from A. schidigera, A. deserti, A. tequilana and A. H11648, respectively. After phylogenetic analysis, these genes were clustered into two branches. Genes AhPLA2a and AhPLA2c exhibited higher expression levels compared to other genes but had different expression patterns. Moreover, AhPLA2a and AhPLA2c were expressed at high levels under full-nutrient, nitrogen-free and phosphorus-free stresses. Most PAL genes were induced by Phytophthora nicotianae Breda, especially AhPAL1a, AhPAL1b, AhPAL2b and AhPAL2c. This research is the first work to present a de novo transcriptome dataset for A. schidigera, enriching its bioinformation of transcripts. The cloned PAL genes and the expression analyses will form the basis of future research on lignin biosynthesis, the relationship between lignin and fiber strength, and stress resistance in Agave species. Full article
(This article belongs to the Special Issue Molecular Advances in Crop Protection and Agrobiotechnology)
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<p>The length distribution of all transcripts. The chart presents data categorized by DNA sequence lengths of all transcripts (bp). The sequences are grouped into the following length ranges: 201–400, 401–600, 801–1000, 1001–1200, 1201–1400, 1401–1600, 1601–1800, 1801–2000, and greater than 2000. The y-axis, labeled as “count”, indicates the number of sequences within each length category.</p>
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<p>GO annotation of all transcripts. The GO annotation categorizes the function of genes into three main categories, denoted by green (biological processes), blue (cellular components), and purple (molecular functions).</p>
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<p>KEGG annotation of all transcripts.</p>
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<p>KOG functional classification of the transcripts.</p>
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<p>Phylogenetic tree of the <span class="html-italic">PAL</span> gene family. The <span class="html-italic">PAL</span> genes of <span class="html-italic">Arabidopsis thaliana</span> (At), <span class="html-italic">Oryza sativa</span> (Os), <span class="html-italic">Agave tequilana</span> (Atq); <span class="html-italic">Agave</span> H11648 (Ah), <span class="html-italic">Agave deserti</span> (Ad) and <span class="html-italic">Agave schidigera</span> (Asc) are marked in blue, green, red, pink, purple and yellow, respectively.</p>
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<p>The relative expression of <span class="html-italic">PAL</span> genes in <span class="html-italic">A.</span> H11648 at different leaf developmental stages and under different stresses. Y-axis: expression level. X-axis: L0 (shoot), L1 (unexpanded leaf), L2 (expanded leaf). F (full nutrient), N- (nitrogen free), P- (phosphorus free), K- (potassium free) and W (Sterile water); T0 (Negative control), T1 (<span class="html-italic">P. nicotianae</span> Breda inoculation stress 24 h) and T2 (<span class="html-italic">P. nicotianae</span> Breda inoculation stress 48 h). The relative expression quantities were obtained with <span class="html-italic">PP2A</span> as an endogenous reference gene under the qRT-PCR technique. All data were generated from three biological replicates. The error bar represents the standard error. In each column of the bar charts, L0, F and T0 of the X-axis indicate the control. The expression values of L0, F and T0 were normalized as 1. “*”and “**” demonstrate differences at the 0.05 and 0.01 probability levels, respectively.</p>
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25 pages, 12175 KiB  
Article
Analysis of Stress Response Genes in Microtuberization of Potato Solanum tuberosum L.: Contributions to Osmotic and Combined Abiotic Stress Tolerance
by Lisset Herrera-Isidron, Braulio Uribe-Lopez, Aaron Barraza, José Luis Cabrera-Ponce and Eliana Valencia-Lozano
Plants 2024, 13(21), 2996; https://doi.org/10.3390/plants13212996 - 26 Oct 2024
Viewed by 744
Abstract
Wild Solanum species have contributed many introgressed genes during domestication into current cultivated potatoes, enhancing their biotic and abiotic stress resistance and facilitating global expansion. Abiotic stress negatively impacts potato physiology and productivity. Understanding the molecular mechanisms regulating tuber development may help solve [...] Read more.
Wild Solanum species have contributed many introgressed genes during domestication into current cultivated potatoes, enhancing their biotic and abiotic stress resistance and facilitating global expansion. Abiotic stress negatively impacts potato physiology and productivity. Understanding the molecular mechanisms regulating tuber development may help solve this global problem. We made a transcriptomic analysis of potato microtuberization under darkness, cytokinins, and osmotic stress conditions. A protein–protein interaction (PPI) network analysis identified 404 genes with high confidence. These genes were involved in important processes like oxidative stress, carbon metabolism, sterol biosynthesis, starch and sucrose metabolism, fatty acid biosynthesis, and nucleosome assembly. From this network, we selected nine ancestral genes along with eight additional stress-related genes. We used qPCR to analyze the expression of the selected genes under osmotic, heat–osmotic, cold–osmotic, salt–osmotic, and combined-stress conditions. The principal component analysis (PCA) revealed that 60.61% of the genes analyzed were associated with osmotic, cold–osmotic, and heat–osmotic stress. Seven out of ten introgression/domestication genes showed the highest variance in the analysis. The genes H3.2 and GAPCP1 were involved in osmotic, cold–osmotic, and heat–osmotic stress. Under combined-all stress, TPI and RPL4 were significant, while in salt–osmotic stress conditions, ENO1, HSP70-8, and PER were significant. This indicates the importance of ancestral genes for potato survival during evolution. The targeted manipulation of these genes could improve combined-stress tolerance in potatoes, providing a genetic basis for enhancing crop resilience. Full article
(This article belongs to the Special Issue Potato Physiology, Genetics and Breeding)
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<p>(<b>A</b>): Stolon explants of potato in control medium MR1-G3-2iP; no MTs were observed. (<b>B</b>–<b>F</b>): MT development in potato <span class="html-italic">S. tuberosum</span> cv. Alpha, after fifteen days in culture in MR8-G6-2iP medium (osmotic stress) plus NaCl 50 mM (salt–osmotic stress), MR8-G6-2iP exposed at 38 °C for 24 h (heat–osmotic stress), MR8-G6-2iP exposed at 4 °C for 24 h (cold–osmotic stress), and the combination of osmotic stress in MR8-G6-2iP exposed to NaCl 50 mM, followed by heat–osmotic and cold–osmotic stress. Scale bar represents 1 cm.</p>
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<p>PPI network of upregulated genes derived from the STRING database v12.0 of potato <span class="html-italic">S. tuberosum</span> from the transcriptomic-wide analysis with high confidence (0.800). Circles are related to the most important genes in different stresses: red for osmotic/heat/cold, blue for combined-all stresses, and black for salinity. Numbered circles correspond to the level of importance according to the PCA in different stresses. Green stars represent INT genes from wild-type ancestors during potato domestication. Blue stars represent genes involved in DOMc from landraces to cultivated potatoes.</p>
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<p>Reconciliation trees of introgressed genes. As can be seen in all trees, the INT genes were conserved in the evolution of species. PGSC0003DMT400006945 (M0ZS78—<span class="html-italic">PK1</span>—Pyruvate kinase 1), PGSC0003DMT400029242 (M1ASG7—<span class="html-italic">GAPCP1</span>—Glyceraldehyde-3-phosphate dehydrogenase), PGSC0003DMT400032266 (M1AX44—<span class="html-italic">MMDH</span>—Malate dehydrogenase), PGSC0003DMT400035521 (M1B2E4—<span class="html-italic">PER</span>—Peroxidase 7), PGSC0003DMT400077358 (M1CYA5—<span class="html-italic">HSP70-8</span>—Heat shock 70 kDa protein 8). Blue diamonds (duplications or introgressions). Red circles (speciations). Black circles without lines (losses).</p>
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<p>Reconciliation trees of domesticated genes: PGSC0003DMT400002870 (M0ZKT2—<span class="html-italic">H3.2</span>—Histone H3.2-like), PGSC0003DMT400062986 (M1C9X0—<span class="html-italic">ENO1</span>—Enolase 1), PGSC0003DMT400071725 (M1CP75—<span class="html-italic">RPL4</span>—60S ribosomal protein L4-1-like), PGSC0003DMT400071330 (M1CNK1—<span class="html-italic">TPI</span>—Triosephosphate isomerase). Blue diamonds (duplications or introgressions). Red circles (speciations). Black circles without lines (losses).</p>
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<p>Quantitative PCR analysis of the stolon explants producing MTs of seventeen selected genes under osmotic (<b>A</b>), heat–osmotic (<b>B</b>), cold–osmotic (<b>C</b>), salt–osmotic (<b>D</b>), and combined-all stresses (<b>E</b>). Relative expression estimation levels are represented in Log2-Fold Change.</p>
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<p>(<b>A</b>) Principal component analysis (PCA) from the relative gene expression under different type of stresses during the microtuberization of potato under darkness. The group of genes with the highest variance were <span class="html-italic">H3.2</span> and <span class="html-italic">GAPCP1</span>, involved in osmotic, cold–osmotic, and heat–osmotic stress. Combined-all stresses was associated with <span class="html-italic">FPS1</span>, <span class="html-italic">TPI</span>, <span class="html-italic">RPL4</span>, and <span class="html-italic">SOD/Fe</span>, and salt–osmotic stress was associated with <span class="html-italic">KAS2</span>, <span class="html-italic">ENO1</span>, <span class="html-italic">HSP70-8</span>, and <span class="html-italic">PER</span>. (<b>B</b>) In Corr PCA, dimension 1 (PC1) shows a uniform distribution in the number of genes, indicating a general variability in gene expression without bias toward a specific treatment. In contrast, dimension 2 (PC2) reveals a high load of genes associated with osmotic stress response, highlighting their predominant relevance in this dimension.</p>
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<p>Correlation network between different stresses during the microtuberization of potato under darkness. Variance was used in each component, as the greater the variance in the treatment, the greater its value. Heat–osmotic, cold–osmotic, osmotic, and combined-all stresses have a major impact in gene regulation. Salt–osmotic stress is not interacting with the others.</p>
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<p>Heat map of the variable contribution of genes during MT development in potato exposed to different stresses. The gene set enrichment analysis elucidates the overall activity under different stresses, like osmotic, heat–osmotic, cold–osmotic, salt–osmotic, and combined-all stresses. Notably, genes associated with osmotic, cold–osmotic, and heat–osmotic stress (<span class="html-italic">H3.2</span> and <span class="html-italic">GAPCP1</span>) have a key role in abiotic stress adaptation, exhibiting distinctive regulatory dynamics and potential functional implications across different stresses.</p>
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<p>Cis-acting elements present in the genes with the highest variance in different stresses during the MT development of potato.</p>
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<p>A model proposal for the analysis of stress response genes in the microtuberization of potato <span class="html-italic">S. tuberosum</span>. Contributions to osmotic, heat–osmotic, cold–osmotic, salt–osmotic, and combined-all stresses tolerance. Osmotic/heat–osmotic/cold–osmotic stress (blue), salt–osmotic stress (green), and combined-all stresses (violet).</p>
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<p>Methodology of microtuberization of potato <span class="html-italic">S. tuberosum</span> cv. Alpha under different stresses and genetic analysis of stress resistance genes.</p>
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22 pages, 14858 KiB  
Article
Clam Genome and Transcriptomes Provide Insights into Molecular Basis of Morphological Novelties and Adaptations in Mollusks
by Xiujun Sun, Xi Chen, Biao Wu, Liqing Zhou, Yancui Chen, Sichen Zheng, Songlin Wang and Zhihong Liu
Biology 2024, 13(11), 870; https://doi.org/10.3390/biology13110870 - 25 Oct 2024
Viewed by 852
Abstract
Bivalve mollusks, comprising animals enclosed in two shell valves, are well-adapted to benthic life in many intertidal zones. Clams have evolved the buried lifestyle, which depends on their unique soft tissue structure and their wedge-shaped muscular foot and long extendible siphons. However, molecular [...] Read more.
Bivalve mollusks, comprising animals enclosed in two shell valves, are well-adapted to benthic life in many intertidal zones. Clams have evolved the buried lifestyle, which depends on their unique soft tissue structure and their wedge-shaped muscular foot and long extendible siphons. However, molecular mechanisms of adaptative phenotype evolution remain largely unknown. In the present study, we obtain the high-quality chromosome-level genome of Manila clam R. philippinarum, an economically important marine bivalve in many coastal areas. The genome is constructed by the Hi-C assisted assembly, which yields 19 chromosomes with a total of 1.17 Gb and BUSCO integrity of 92.23%. The de novo assembled genome has a contig N50 length of 307.7 kb and scaffold N50 of 59.5 Mb. Gene family expansion analysis reveals that a total of 24 single-copy gene families have undergone the significant expansion or contraction, including E3 ubiquitin ligase and dynein heavy chain. The significant expansion of transposable elements has been also identified, including long terminal repeats (LTR) and non-LTR retrotransposons. The comparative transcriptomics among different clam tissues reveals that extracellular matrix (ECM) receptors and neuroactive ligand receptors may play the important roles in tissue structural support and neurotransmission during their infaunal life. These findings of gene family expansion and tissue-specific expression may reflect the unique soft tissue structure of clams, suggesting the evolution of lineage-specific morphological novelties. The high-quality genome and transcriptome data of R. philippinarum will not only facilitate the genetic studies on clams but will also provide valuable information on morphological novelties in mollusks. Full article
(This article belongs to the Section Marine Biology)
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<p>The genome-wide Hi-C map for Manila clam <span class="html-italic">Ruditapes philippinarum</span>. The names from Chr1 to Chr19 represent the 19 pseudochromosomes. The color blocks represent the correlation between one location and the other locations in the assembled genome.</p>
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<p>The circular genomic map for Manila clam <span class="html-italic">R. philippinarum</span>. From outer to inner circles: 19 chromosomes, gene density (blue), repeat density (green) and GC density (red) across the genome (a sliding window of 500 k). For the outer circle, the rectangular color bars represent different chromosomes in <span class="html-italic">R. philippinarum</span> genome.</p>
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<p>Phylogenetic analysis of <span class="html-italic">R. philippinarum</span> with other 12 invertebrate species. The polychaeta <span class="html-italic">Capitella teleta</span> was used as the outgroup. Estimated divergence times (million years ago) between lineages and 95% confidential intervals are labeled at each branch site. (<b>A</b>) The phylogenetic tree constructed by single-copy genes, showing divergence times (million years ago, Ma) and 95% confidence limits of divergence times in parentheses; (<b>B</b>) Comparison of the number of homologous gene families among species. The vertical axis represents different species, while the horizontal axis represents the number of gene families in each species.</p>
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<p>The habitat adaptation in mollusks. (<b>A</b>) the heatmap for the conserved domains of expanded gene families in six molluscan species with different lifestyles. Manila clam (<span class="html-italic">R. philippinarum</span>), Oyster (<span class="html-italic">C. gigas</span>), Yesso scallop (<span class="html-italic">P. yessoensis</span>), Zhikong scallop (<span class="html-italic">Chlamys farreri</span>), Blood clam (<span class="html-italic">Scapharca broughtonii</span>), and Octopus (<span class="html-italic">Octopus bimaculoides</span>). The selected gene families (z-score standardization) are displayed as y-axis, and x-axis represents the number of genes in each species in the corresponding gene family. The darker color represents the greater number of gene families, highlighting the significant expansion of <span class="html-italic">R. philippinarum</span>. (<b>B</b>) Rph (<span class="html-italic">R. philippinarum</span>), Cgi (<span class="html-italic">C. gigas</span>), Pye (<span class="html-italic">P. yessoensis</span>), Cfa (<span class="html-italic">C. farreri</span>), Sbr (<span class="html-italic">S. broughtonii</span>), and <span class="html-italic">Obi</span> (<span class="html-italic">O. bimaculoides</span>).</p>
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<p>GO and KEGG enrichment results for expanded gene families. The horizontal axis of RichFactor represents the ratio of differential genes located in GO and KEGG.</p>
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<p>The chromosomal location and significant expansion of non-LTR retrotransposon elements in clam <span class="html-italic">R. philippinarum</span>. (<b>A</b>) The location of non-LTR retrotransposons in chromosomes 2 and 7; (<b>B</b>) Phylogenetic tree of non-LTR retrotransposons (<span class="html-italic">pol-like</span>, <span class="html-italic">jockey</span>_I, <span class="html-italic">jockey</span>_II, and <span class="html-italic">jockey</span>_IV) in Rph (<span class="html-italic">R. philippinarum</span>), Cgi (<span class="html-italic">C. gigas</span>), Pye (<span class="html-italic">P. yessoensis</span>), Cfa (<span class="html-italic">Chlamys farreri</span>), Sbr (<span class="html-italic">Scapharca broughtonii</span>) and <span class="html-italic">Obi</span> (<span class="html-italic">Octopus bimaculoides</span>).</p>
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<p>The general anatomy and tissue-specific expression of non-LTR transposable elements in the clam <span class="html-italic">R. philippinarum</span>. (<b>A</b>) The soft body structure of the clam, having foot, siphons, mantle, gill, adductor muscles, gonad and hepatopancreas; (<b>B</b>) The heatmap for tissue-specific expression of non-LTR retrotransposons. According to the normalized z-score FPKM values, the mean level of relative expression was calculated for each tissue using three biological replicates. The scale at the top right denoted normalized expression levels (red, high expression; blue, low expression). The heatmap for non-LTR retrotransposon expression was constructed by the normalized z-score FPKM values from tissue transcriptomic data.</p>
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<p>The significant expansion and tissue-specific expression of dynein heavy chain (DHC) in clam <span class="html-italic">R. philippinarum</span>. (<b>A</b>) phylogeny of the 13 copies of DHC in <span class="html-italic">R. philippinarum</span>; (<b>B</b>) the heatmap showing high expression of DHC in gill tissues. The mean relative expression level for each tissue was calculated using normalized z-score FPKM values in three replicates. The scale at the top right denoted normalized expression levels (red, high expression; blue, low expression).</p>
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<p>The mRNA heatmap and immunohistochemical (IHC) staining for laminin in different tissues of clams. The positive staining signals for laminin protein expression were indicated by the red asterisks. (<b>A</b>) mRNA expression heatmap for laminin among tissues; (<b>B</b>) laminin staining in foot showing the ciliated columnar epithelium (FEP), with the positive staining signals indicated by the red asterisks; (<b>C</b>) IHC for laminin indicating the skeletal bars of filaments (FB) in gills; (<b>D</b>) IHC for laminin in siphon epithelium (SEP); (<b>E</b>) IHC for laminin mantle tissue. MCP, mantle-cavity epithelium; SSP, shell-side epithelium; HS, hemolymph sinus; MF, muscle fibers. The mean relative expression level for each tissue was calculated using normalized z-score FPKM values in three replicates. The scale at the top right denoted normalized expression levels (red, high expression; blue, low expression).</p>
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<p>The mRNA heatmap and immunohistochemical (IHC) staining for collagen in different tissues of clams. The positive staining signals for collagen protein expression were indicated by the red asterisks. (<b>A</b>) mRNA expression heatmap for collagen among tissues; (<b>B</b>) collagen expressed under the foot epithelium (FEP), with the positive staining signals indicated by the red asterisks; (<b>C</b>) collagen detected under the frontal cilia (FR) of gills; (<b>D</b>) IHC for collagen in siphon epithelium (SEP); (<b>E</b>) IHC for collagen in mantle tissue. MCP, mantle-cavity epithelium; SSP, shell-side epithelium; HS, hemolymph sinus; MF, muscle fibers; FR, frontal cilia. The mean relative expression level for each tissue was calculated using normalized z-score FPKM values in three replicates. The scale at the top right denoted normalized expression levels (red, high expression; blue, low expression).</p>
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<p>The mRNA heatmap and Nissl staining in different tissues of clams. (<b>A</b>) mRNA expression heatmap for neuroactive ligand among tissues; (<b>B</b>) abundant neuron cells identified in the foot sub-epithelium (FSE); (<b>C</b>) amplification of neuron cells in the foot sub-epithelium (FSE); (<b>D</b>) neuron cells detected in the shell-side epithelium (SSP) of mantle tissue; (<b>E</b>) neuron cells identified in the edge of major plica (MAP) of gill; (<b>F</b>) slight positive staining detected in siphon sub-epithelium (SSE); (<b>G</b>) No positive staining in the adductor muscle. MCP, mantle-cavity epithelium; SSP, shell-side epithelium; HS, hemolymph sinus; MF, muscle fibers. The mean relative expression level for each tissue was calculated using normalized z-score FPKM values in three replicates. The scale at the top right denoted normalized expression levels (red, high expression; blue, low expression).</p>
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18 pages, 5747 KiB  
Article
Comparative Transcriptome Analysis of Non-Organogenic and Organogenic Tissues of Gaillardia pulchella Revealing Genes Regulating De Novo Shoot Organogenesis
by Yashika Bansal, A. Mujib, Mahima Bansal, Mohammad Mohsin, Afeefa Nafees and Yaser Hassan Dewir
Horticulturae 2024, 10(11), 1138; https://doi.org/10.3390/horticulturae10111138 - 25 Oct 2024
Viewed by 620
Abstract
Gaillardia pulchella is an important plant species with pharmacological and ornamental applications. It contains a wide array of phytocompounds which play roles against diseases. In vitro propagation requires callogenesis and differentiation of plant organs, which offers a sustainable, alternative synthesis of compounds. The [...] Read more.
Gaillardia pulchella is an important plant species with pharmacological and ornamental applications. It contains a wide array of phytocompounds which play roles against diseases. In vitro propagation requires callogenesis and differentiation of plant organs, which offers a sustainable, alternative synthesis of compounds. The morphogenetic processes and the underlying mechanisms are, however, known to be under genetic regulation and are little understood. The present study investigated these events by generating transcriptome data, with de novo assembly of sequences to describe shoot morphogenesis molecularly in G. pulchella. The RNA was extracted from the callus of pre- and post-shoot organogenesis time. The callus induction was optimal using leaf segments cultured onto MS medium containing α-naphthalene acetic acid (NAA; 2.0 mg/L) and 6-benzylaminopurine (BAP; 0.5 mg/L) and further exhibited a high shoot regeneration/caulogenesis ability. A total of 68,366 coding sequences were obtained using Illumina150bpPE sequencing and transcriptome assembly. Differences in gene expression patterns were noted in the studied samples, showing opposite morphogenetic responses. Out of 10,108 genes, 5374 (53%) were downregulated, and there were 4734 upregulated genes, representing 47% of the total genes. Through the heatmap, the top 100 up- and downregulating genes’ names were identified and presented. The up- and downregulated genes were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Important pathways, operative during G. pulchella shoot organogenesis, were signal transduction (13.55%), carbohydrate metabolism (8.68%), amino acid metabolism (5.11%), lipid metabolism (3.75%), and energy metabolism (3.39%). The synthesized proteins displayed phosphorylation, defense response, translation, regulation of DNA-templated transcription, carbohydrate metabolic processes, and methylation activities. The genes’ product also exhibited ATP binding, DNA binding, metal ion binding, protein serine/threonine kinase -, ATP hydrolysis activity, RNA binding, protein kinase, heme and GTP binding, and DNA binding transcription factor activity. The most abundant proteins were located in the membrane, nucleus, cytoplasm, ribosome, ribonucleoprotein complex, chloroplast, endoplasmic reticulum membrane, mitochondrion, nucleosome, Golgi membrane, and other organellar membranes. These findings provide information for the concept of molecular triggers, regulating programming, differentiation and reprogramming of cells, and their uses. Full article
(This article belongs to the Special Issue Plant Tissue and Organ Cultures for Crop Improvement in Omics Era)
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<p>(<b>A</b>) Non-organogenic callus, and (<b>B</b>) organogenic callus of <span class="html-italic">G. pulchella</span> with arrow indicating the origin of shoot from the callus mass.</p>
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<p>Workflow and tools used for mRNA sequence analysis of non-organogenic and organogenic callus of <span class="html-italic">G. pulchella</span>.</p>
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<p>Length distribution of primary assembly and unigenes of <span class="html-italic">G. pulchella</span>.</p>
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<p>KEGG pathway classification for <span class="html-italic">G. pulchella</span>.</p>
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<p>Top hit species distribution pattern showing the number of genes identified in <span class="html-italic">G. pulchella</span> matching with the other plant species.</p>
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<p>Gene ontology annotation for all a ssembled unigenes in the <span class="html-italic">G. pulchella</span> transcriptome.</p>
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<p>Volcano plot showing the comparison of differential expressed genes.</p>
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<p>Heatmap representing the gene expression of the top 100 differentially expressed genes in the non-organogenic and organogenic calluses of <span class="html-italic">G. pulchella</span>.</p>
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<p>Principal component analysis (PCA) plot showing the relationship between the non-organogenic and organogenic calluses of <span class="html-italic">G. pulchella</span>.</p>
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13 pages, 6856 KiB  
Article
Dual RNA-Seq Unveils Candidate Key Virulence Genes of Vibrio harveyi at the Early Stage of Infection in Hybrid Grouper (♀ Epinephelus polyphekadion × ♂ E. fuscoguttatus)
by Yan-Hua Zeng, Wen Li, He Xu, Xiao-Xiao Gong, Yu-Mei Zhang, Hao Long and Zhen-Yu Xie
Microorganisms 2024, 12(11), 2113; https://doi.org/10.3390/microorganisms12112113 - 22 Oct 2024
Viewed by 648
Abstract
Vibrio harveyi is a major bacterial pathogen that causes disease in aquaculture animals worldwide. Although V. harveyi consistently harbors a range of traditional virulence genes, it remains unclear which specific genes are crucial for virulence at different infection stages. Dual RNA-seq is a [...] Read more.
Vibrio harveyi is a major bacterial pathogen that causes disease in aquaculture animals worldwide. Although V. harveyi consistently harbors a range of traditional virulence genes, it remains unclear which specific genes are crucial for virulence at different infection stages. Dual RNA-seq is a cutting-edge RNA sequencing technology that is ideal for investigating the gene expression patterns of pathogens within the host, which is highly effective in identifying key virulence genes. In previous artificial infection experiments, we have identified the liver of hybrid grouper (♀ Epinephelus polyphekadion × ♂ E. fuscoguttatus) as the main target organ for pathogenic V. harveyi GDH11385 during the initial infection phase. To further explore the key virulence factors of V. harveyi at the early stage of infection, the liver of the hybrid grouper infected with strain GDH11385 was analyzed here by dual RNA-seq. The transcriptome data were compared with that of in vitro cultured bacteria. The results showed that 326 and 1140 DEGs (differentially expressed genes) were significantly up- and down-regulated, respectively, at 4 h post-infection (hpi). Further pathway enrichment analyses revealed that these up-regulated DEGs in vivo were mainly enriched in siderophore biosynthesis and transport, type VI secretion system (T6SS), flagellar assembly, glycolysis/gluconeogenesis, and ribosome. Notably, all genes involved in the metabolism and utilization of vibrioferrin (a carboxylate class of siderophore produced by Vibrio), and most of the genes within one of three T6SSs, were significantly up-regulated in vivo. This indicates that siderophore-dependent iron competition and T6SS-mediated delivery of virulence factors are vital for the successful colonization of V. harveyi at the early stage of infection. This study provides more precise clues to reveal the virulence mechanism of V. harveyi during the initial phase of infection. Full article
(This article belongs to the Special Issue Pathogens and Aquaculture)
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<p>Correlation analysis of gene expression between two experimental groups. (<b>A</b>) Venn diagram illustrating the commonly and uniquely expressed genes in <span class="html-italic">V. harveyi</span> GDH11385 in the 4 hpi liver (T4h) and in the in vitro bacterial pure culture control (CK). (<b>B</b>) PCA analysis based on the expression level of all genes showing the correlation of gene expression between groups. Values in parentheses on the axes represent the percentage of the total variance explained by each principal component. Samples from the same group are represented by the same color and shape, where the red circle represents the 4 hpi liver sample (T4h) and the blue triangle corresponds to the in vitro bacterial pure culture control (CK).</p>
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<p>The DEGs visualized by volcano plot and hierarchical clustering heatmap. (<b>A</b>) Volcano plot showing the differences in expression profiles of strain GDH11385 between two experimental groups. Each dot in the graph represents a gene: red dots signify up-regulated genes, blue dots signify down-regulated genes, and black dots indicate genes that are not significantly different. (<b>B</b>) Hierarchical clustering heatmap tree of DEGs.</p>
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<p>Functional enrichment analysis of DEGs. (<b>A</b>,<b>B</b>) show the KEGG pathway enrichment results for up-regulated and down-regulated DEGs, respectively. (<b>C</b>,<b>D</b>) display the GO enrichment circles of up-regulated and down-regulated DEGs, respectively.</p>
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<p>Analysis of siderophore biosynthesis and transporter genes of <span class="html-italic">V. harveyi</span> GDH11385. (<b>A</b>) The gene organization of two siderophore (vibrioferrin and amphi-enterobactin) gene clusters encoded by strain GDH11385 determined by genome mining. (<b>B</b>) Heatmap showing the variations in gene expression of siderophore (vibrioferrin and amphi-enterobactin) biosynthesis and transporter genes of strain GDH11385 in the host compared to expression in pure culture in vitro. The abundance of gene transcripts was represented as log10-transformed TPM values. Red color denoted higher expression levels, while blue indicated lower expression levels. Significantly altered genes were marked with an asterisk (*). Gene names or protein annotations were located on the right side of the heatmap.</p>
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<p>Analysis of type VI secretion system (T6SS)-associated genes of <span class="html-italic">V. harveyi</span> GDH11385. (<b>A</b>) The gene organization of three T6SS gene clusters encoded by strain GDH11385 determined by genome mining. (<b>B</b>) Heatmap presenting the variations in gene expression of one T6SS gene cluster of strain GDH11385 in the host compared to expression in pure culture in vitro. The abundance of gene transcripts was represented as log10-transformed TPM values. Red color denoted higher expression levels, while blue indicated lower expression levels. Gene names or KO IDs were located on the right side of the heatmap.</p>
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<p>Analysis of genes involved in flagellar assembly in <span class="html-italic">V. harveyi</span> GDH11385. (<b>A</b>) Schematic representation of the flagellar complex of strain GDH11385 as determined by KEGG pathway mapping. Red borders indicate up-regulated DEGs, and blue borders represent down-regulated DEGs. (<b>B</b>) Heatmap illustrating the variations in gene expression of flagellar assembly genes of strain GDH11385 in the host compared to expression in pure culture in vitro. The abundance of gene transcripts was represented as log10-transformed TPM values. Red color denoted higher expression levels, while blue indicated lower expression levels. The red-colored genes on the right side of the heatmap indicate up-regulated DEGs, while the blue-colored genes represent down-regulated DEGs.</p>
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13 pages, 26304 KiB  
Article
Assessing Genetic Diversity in Endangered Plant Orchidantha chinensis: Chloroplast Genome Assembly and Simple Sequence Repeat Marker-Based Evaluation
by Yiwei Zhou, Jianjun Tan, Lishan Huang, Yuanjun Ye and Yechun Xu
Int. J. Mol. Sci. 2024, 25(20), 11137; https://doi.org/10.3390/ijms252011137 - 17 Oct 2024
Viewed by 503
Abstract
Orchidantha chinensis T. L. Wu, an endemic species in China, is listed as a key protected wild plant in Guangdong Province. However, the lack of reports on the chloroplast genome and simple sequence repeat (SSR) markers has hindered the assessment of its genetic [...] Read more.
Orchidantha chinensis T. L. Wu, an endemic species in China, is listed as a key protected wild plant in Guangdong Province. However, the lack of reports on the chloroplast genome and simple sequence repeat (SSR) markers has hindered the assessment of its genetic diversity and conservation strategies. The limited number of molecular markers to assess the genetic diversity of this species, and thus develop proper conservation strategies, highlighted the urgent need to develop new ones. This study developed new SSR markers and investigated genetic variation using 96 samples of O. chinensis from seven populations. Through high-throughput sequencing, a complete chloroplast genome of 134,407 bp was assembled. A maximum-likelihood phylogenetic tree, based on the chloroplast genome, showed that O. chinensis is closely related to Ravenala madagascariensis. The study identified 52 chloroplast SSRs (cpSSRs) and 5094 expressed sequence tag SSRs (EST-SSRs) loci from the chloroplast genome and leaf transcriptome, respectively. Twenty-one polymorphic SSRs (seven cpSSRs and fourteen EST-SSRs) were selected to evaluate the genetic variation in 96 accessions across seven populations. Among these markers, one cpSSR and 11 EST-SSRs had high polymorphism information content (>0.5). Cluster, principal coordinate, and genetic structure analyses indicated that groups G1 and G6 were distinct from the other five groups. However, an analysis of molecular variance showed greater variation within groups than among groups. The genetic distance among the populations was significantly positively correlated with geographical distance. These findings provide new markers for studying the genetic variability of O. chinensis and offer a theoretical foundation for its conservation strategies. Full article
(This article belongs to the Special Issue Plant Phylogenomics and Genetic Diversity (2nd Edition))
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<p>The chloroplast genome of <span class="html-italic">O. chinensis</span> and the phylogenetic tree of representative species from eight families in Zingiberales. (<b>A</b>) Plants of <span class="html-italic">O. chinensis</span>. (<b>B</b>) The inflorescence of <span class="html-italic">O. chinensis</span>. (<b>C</b>) The chloroplast genome map of <span class="html-italic">O. chinensis</span>. (<b>D</b>) The maximum-likelihood phylogeny tree obtained from eight complete chloroplast sequences in Zingiberales. The position of <span class="html-italic">O. chinensis</span> has been highlighted in red.</p>
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<p>Genetic distance between different individuals (<b>A</b>) and populations (<b>B</b>) of <span class="html-italic">O. chinensis</span>.</p>
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<p>Population structure analysis of 96 <span class="html-italic">O. chinensis</span> accessions across seven groups.</p>
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<p>AMOVA analysis and general linear regression analysis between genetic distance and geographical distance. (<b>A</b>) Percentages of molecular variance. (<b>B</b>) General linear regression analysis. “**” indicates <span class="html-italic">p</span> &lt; 0.01.</p>
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14 pages, 734 KiB  
Review
Integrative Omics Strategies for Understanding and Combating Brown Planthopper Virulence in Rice Production: A Review
by Xinfeng Wang, Yaxuan Wang, Houhong Yang, Fang Liu, Yubiao Cai, Jing Xiao, Qiang Fu and Pinjun Wan
Int. J. Mol. Sci. 2024, 25(20), 10981; https://doi.org/10.3390/ijms252010981 - 12 Oct 2024
Viewed by 964
Abstract
The brown planthopper (Nilaparvata lugens, BPH) is a serious insect pest responsible for causing immense economic losses to rice growers around the globe. The development of high-throughput sequencing technologies has significantly improved the research on this pest, and its genome structure, [...] Read more.
The brown planthopper (Nilaparvata lugens, BPH) is a serious insect pest responsible for causing immense economic losses to rice growers around the globe. The development of high-throughput sequencing technologies has significantly improved the research on this pest, and its genome structure, gene expression profiles, and host–plant interactions are being unveiled. The integration of genomic sequencing, transcriptomics, proteomics, and metabolomics has greatly increased our understanding of the biological characteristics of planthoppers, which will benefit the identification of resistant rice varieties and strategies for their control. Strategies like more optimal genome assembly and single-cell RNA-seq help to update our knowledge of gene control structure and cell type-specific usage, shedding light on how planthoppers adjust as well. However, to date, a comprehensive genome-wide investigation of the genetic interactions and population dynamics of BPHs has yet to be exhaustively performed using these next-generation omics technologies. This review summarizes the recent advances and new perspectives regarding the use of omics data for the BPH, with specific emphasis on the integration of both fields to help develop more sustainable pest management strategies. These findings, in combination with those of post-transcriptional and translational modifications involving non-coding RNAs as well as epigenetic variations, further detail intricate host–brown planthopper interaction dynamics, especially regarding resistant rice varieties. Finally, the symbiogenesis of the symbiotic microbial community in a planthopper can be characterized through metagenomic approaches, and its importance in enhancing virulence traits would offer novel opportunities for plant protection by manipulating host–microbe interactions. The concerted diverse omics approaches collectively identified the holistic and complex mechanisms of virulence variation in BPHs, which enables efficient deployment into rice resistance breeding as well as sustainable pest management. Full article
(This article belongs to the Section Molecular Plant Sciences)
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<p>Key tools at the different levels of omics for the harmfulness of brown planthopper discovery.</p>
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