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15 pages, 13437 KiB

Open AccessArticle

Integrative Analysis of Transcriptomic Profiles and Physiological Responses Provide New Insights into Drought Stress Tolerance in Oil Palm (Elaeis guineensis Jacq.)

by Fernan Santiago Mejía-Alvarado, Arley Fernando Caicedo-Zambrano, David Botero-Rozo, Leonardo Araque, Cristihian Jarri Bayona-Rodríguez, Seyed Mehdi Jazayeri, Carmenza Montoya, Iván Ayala-Díaz, Rodrigo Ruiz-Romero and Hernán Mauricio Romero

Int. J. Mol. Sci. 2024, 25(16), 8761; https://doi.org/10.3390/ijms25168761 (registering DOI) - 12 Aug 2024

Abstract

Oil palm (Elaeis guineensis Jacq.) is a highly productive crop economically significant for food, cosmetics, and biofuels. Abiotic stresses such as low water availability, salt accumulation, and high temperatures severely impact oil palm growth, physiology, and yield by restricting water flux among [...] Read more.

Oil palm (Elaeis guineensis Jacq.) is a highly productive crop economically significant for food, cosmetics, and biofuels. Abiotic stresses such as low water availability, salt accumulation, and high temperatures severely impact oil palm growth, physiology, and yield by restricting water flux among soil, plants, and the environment. While drought stress’s physiological and biochemical effects on oil palm have been extensively studied, the molecular mechanisms underlying drought stress tolerance remain unclear. Under water deficit conditions, this study investigates two commercial E. guineensis cultivars, IRHO 7001 and IRHO 2501. Water deficit adversely affected the physiology of both cultivars, with IRHO 2501 being more severely impacted. After several days of water deficit, there was a 40% reduction in photosynthetic rate (A) for IRHO 7001 and a 58% decrease in IRHO 2501. Further into the drought conditions, there was a 75% reduction in A for IRHO 7001 and a 91% drop in IRHO 2501. Both cultivars reacted to the drought stress conditions by closing stomata and reducing the transpiration rate. Despite these differences, no significant variations were observed between the cultivars in stomatal conductance, transpiration, or instantaneous leaf-level water use efficiency. This indicates that IRHO 7001 is more tolerant to drought stress than IRHO 2501. A differential gene expression and network analysis was conducted to elucidate the differential responses of the cultivars. The DESeq2 algorithm identified 502 differentially expressed genes (DEGs). The gene coexpression network for IRHO 7001 comprised 274 DEGs and 46 predicted HUB genes, whereas IRHO 2501’s network included 249 DEGs and 3 HUB genes. RT-qPCR validation of 15 DEGs confirmed the RNA-Seq data. The transcriptomic profiles and gene coexpression network analysis revealed a set of DEGs and HUB genes associated with regulatory and transcriptional functions. Notably, the zinc finger protein ZAT11 and linoleate 13S-lipoxygenase 2-1 (LOX2.1) were overexpressed in IRHO 2501 but under-expressed in IRHO 7001. Additionally, phytohormone crosstalk was identified as a central component in the response and adaptation of oil palm to drought stress. Full article

(This article belongs to the Special Issue Recent Research in Plant Abiotic Stress)

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Figure 1
Physical appearance of two oil palm cultivars, Deli × La Mé, (IRHO 7001 and IRHO 2501) in response to water deficit. Ninety-day-old palms were maintained under field capacity (well-watered) or subjected to water deprivation for three weeks (drought stress). Full article ">Figure 2
Predawn leaf water potential (Ψleaf) of two oil palm cultivars, Deli × La Mé (IRHO 7001 and IRHO 2501), in response to water deficit. Ninety-day-old palms were maintained under field capacity (well-watered) or subjected to water deprivation until the photosynthetic rate of the IRHO 7001 cultivar dropped 40% (40%), which is considered moderate drought stress, or until it dropped 75% (75%), which is considered severe drought stress. Each box corresponds to the mean ± SD (n = 6). Full article ">Figure 3
Physiological response of two oil palm cultivars, Deli × La Mé, IRHO 7001 (7001) and IRHO 2501 (2501) in response to water deficit. Ninety-day-old palms were maintained under field capacity (well-watered) or subjected to water deprivation until the photosynthetic rate of the IRHO 7001 cultivar dropped 40% (40%), which is considered moderate drought stress, or until it dropped 75% (75%), which is considered severe drought stress. Each box corresponds to the mean ± SD. (n = 6). (A). photosynthetic rate (A), (B). stomatal conductance (gs), (C). transpiration rate (E), and (D). instantaneous leaf-level water use efficiency (WUE). Full article ">Figure 4
DEGs of two oil palm cultivars, Deli × La Mé, (IRHO 7001 and IRHO 2501) in response to water deficit. Ninety-day-old palms were maintained under field capacity (well-watered) or subjected to water deprivation until the photosynthetic rate of the IRHO 7001 cultivar dropped 40% (40%), which is considered moderate drought stress, or until it dropped 75% (75%), which is considered severe drought stress. (A) Heatmap of the RNA-Seq samples. A tendency toward red indicates under-expression, while a tendency toward blue indicates overexpression. (B) Unique and shared DEGs between two contrasting oil palm genotypes and drought stress conditions. The color key scale corresponds to the L2FC, tendency to blue correspond to underexpressed genes, while tendency to red indicates overexpressed. Full article ">Figure 5
Gene coexpression networks of two oil palm cultivars, Deli × La Mé, (IRHO 7001 and IRHO 2501) in response to water deficit. (A) General; (B) IRHO 7001; and (C) IRHO 2501. The igraph R package was used to construct the general and specific cultivar coexpression networks under drought stress. Each node (sphere or bead-like shape) represents a gene, and groups of nodes highlighted with the same color indicate a module of genes. The black edges represent direct correlations between genes, and the red lines represent inverse correlations. The size of each node is proportional to the mean expression level of the gene represented by the node. Full article ">Figure 6
Relative quantification of 15 genes by RT–qPCR compared against RNA-Seq in two oil palm cultivars, Deli × La Mé, (IRHO 7001 and IRHO 2501) in response to water deficit. Ninety-day-old palms were maintained under field capacity (well-watered) or subjected to water deprivation until the photosynthetic rate of the IRHO 7001 cultivar dropped 40% (40%), which is considered moderate drought stress, or until it dropped 75% (75%), which is considered severe drought stress. Yellow bars indicate the relative expression value obtained by RT-qPCR. Lite blue diamonds indicate the RNA-Seq value. (A) WRKY transcription factor 51; (B) NAC transcription factor NAM-B2-like_ NAM-B2; (C) beta-xylosidase alpha-L-arabinofuranosidase 2-like OsI_08964_ BXL1; (D) Leucine-rich repeat receptor-like serine_ At1g17230; (E) Calcium-binding protein CML42; (F) Ser/threo-protein phosphatase 6 regulatory ankyrin repeat subunit B; (G) Pectinesterase-like; (H) Pentatricopeptide repeat-containing protein_ At5g39980; (I) Multiple C2 and transmembrane domain-containing protein 2-like, (J) Non-specific lipid-transfer protein 2-like; (K) Transcription factor bHLH35-like isoform X1; (L) Mitogen-activated protein kinase kinase kinase 2-like; (M) Bidirectional sugar transporter SWEET14-like; (N) Galactinol synthase 1-like_ GOLS1; and (O) Xyloglucan endotransglucosylase/hydrolase protein 22-like XTH22. Full article ">Figure 7
Phytohormone crosstalk and signal cascades of two oil palm cultivars, Deli × La Mé, (IRHO 7001 and IRHO 2501) in response to water deficit. The numbers indicate the step-by-step signaling cascade response in oil palms under drought stress. Numbers 1 and 2 indicate the stimulus and signal perception. 3 indicates signal transduction. 4, 5, and 6 indicate phytohormone metabolism and TFs activation/ inactivation. 7 and 8 indicate drought stress-induced genes and ROS metabolism balance. Gene expression levels are indicated for each cultivar, where 7001 = IRHO 7001 and 2501 = IRHO 2501. The square color corresponds to the gene expression color scale in the L2FC bar. The question mark indicates no gene expression. Arrows colors indicate flux of water (blue), ABA (green), and ROS (red) from soil or roots to leaves. The figure was partly generated using plant icon adaptations licensed and created by Guillaume Lobet (<a href="https://figshare.com/authors/Plant_Illustrations/3773596" target="_blank">https://figshare.com/authors/Plant_Illustrations/3773596</a> is licensed under CC-BY 4.0 Unported <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a>, accessed on 16 April 2024). Full article ">

12 pages, 4362 KiB

Open AccessArticle

Quantification of Photoreceptors’ Changes in a Diabetic Retinopathy Model with Two-Photon Imaging Microscopy

by Nazario Bautista-Elivar, Marcelino Avilés-Trigueros and Juan M. Bueno

Int. J. Mol. Sci. 2024, 25(16), 8756; https://doi.org/10.3390/ijms25168756 (registering DOI) - 11 Aug 2024

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Abstract

Emerging evidence suggests that retinal neurodegeneration is an early event in the pathogenesis of diabetic retinopathy (DR), preceding the development of microvascular abnormalities. Here, we assessed the impact of neuroinflammation on the retina of diabetic-induced rats. For this aim we have used a [...] Read more.

Emerging evidence suggests that retinal neurodegeneration is an early event in the pathogenesis of diabetic retinopathy (DR), preceding the development of microvascular abnormalities. Here, we assessed the impact of neuroinflammation on the retina of diabetic-induced rats. For this aim we have used a two-photon microscope to image the photoreceptors (PRs) at different eccentricities in unstained retinas obtained from both control (N = 4) and pathological rats (N = 4). This technique provides high-resolution images where individual PRs can be identified. Within each image, every PR was located, and its transversal area was measured and used as an objective parameter of neuroinflammation. In control samples, the size of the PRs hardly changed with retinal eccentricity. On the opposite end, diabetic retinas presented larger PR transversal sections. The ratio of PRs suffering from neuroinflammation was not uniform across the retina. Moreover, the maximum anatomical resolving power (in cycles/deg) was also calculated. This presents a double-slope pattern (from the central retina towards the periphery) in both types of specimens, although the values for diabetic retinas were significantly lower across all retinal locations. The results show that chronic retinal inflammation due to diabetes leads to an increase in PR transversal size. These changes are not uniform and depend on the retinal location. Two-photon microscopy is a useful tool to accurately characterize and quantify PR inflammatory processes and retinal alterations. Full article

(This article belongs to the Special Issue Retinal Degenerative Diseases: 2nd Edition)

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18 pages, 23890 KiB

Open AccessArticle

Exploration of Response Mechanisms in the Gills of Pacific Oyster (Crassostrea gigas) to Cadmium Exposure through Integrative Metabolomic and Transcriptomic Analyses

by Luyao Dong, Yanan Sun, Muyang Chu, Yuxin Xie, Pinyi Wang, Bin Li, Zan Li, Xiaohui Xu, Yanwei Feng, Guohua Sun, Zhongping Wang, Cuiju Cui, Weijun Wang and Jianmin Yang

Animals 2024, 14(16), 2318; https://doi.org/10.3390/ani14162318 (registering DOI) - 9 Aug 2024

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Abstract

Marine mollusks, including oysters, are highly tolerant to high levels of cadmium (Cd), but the molecular mechanisms underlying their molecular response to acute Cd exposure remain unclear. In this study, the Pacific oyster Crassostrea gigas was used as a biological model, exposed to [...] Read more.

Marine mollusks, including oysters, are highly tolerant to high levels of cadmium (Cd), but the molecular mechanisms underlying their molecular response to acute Cd exposure remain unclear. In this study, the Pacific oyster Crassostrea gigas was used as a biological model, exposed to acute Cd stress for 96 h. Transcriptomic analyses of their gills were performed, and metabolomic analyses further validated these results. In our study, a total of 111 differentially expressed metabolites (DEMs) and 2108 differentially expressed genes (DEGs) were identified under acute Cd exposure. Further analyses revealed alterations in key genes and metabolic pathways associated with heavy metal stress response. Cd exposure triggered physiological and metabolic responses in oysters, including enhanced oxidative stress and disturbances in energy metabolism, and these changes revealed the biological response of oysters to acute Cd stress. Moreover, oysters could effectively enhance the tolerance and detoxification ability to acute Cd exposure through activating ABC transporters, enhancing glutathione metabolism and sulfur relay system in gill cells, and regulating energy metabolism. This study reveals the molecular mechanism of acute Cd stress in oysters and explores the molecular mechanism of high tolerance to Cd in oysters by using combined metabolomics and transcriptome analysis. Full article

(This article belongs to the Topic Anthropic Impacts in Marine Coastal Waters: Assessment, Case Studies and Solutions)

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Biochemical alterations of the acute Cd exposure C. gigas. (a) Increased intracellular Cd level in Cd-exposed oyster individuals. The bar chart depicts mean levels and standard deviation (SD) values, with n = 3. Significance differences were determined by t-test (p < 0.05), denoted by asterisks (*). (b) The survival rate of oysters after 96 h of acute Cd exposure. (c–f) After Cd exposure, oxidative stress markers such as SOD (c), CAT (d), MDA (e), and GPx (f) were measured. Data were presented as the mean ± SD (n = 3). Distinguishing letters were assigned to indicate significant differences (p < 0.05). Star plots depict the impact of Cd exposure on gill samples. Full article ">Figure 2
Analysis of oyster transcriptome following a 96-hour exposure to Cd. (a) A volcano plot of DEGs is graphically represented with upregulated genes in red and downregulated genes in blue. (b) Hierarchical clustering based on the DEGs, where red signifies upregulation and blue signifies downregulation. (c) Enrichment of DEGs in GO terms categorized into cellular components, biological processes, and molecular functions. (d) KEGG pathway enrichment analysis of DEGs, with colors indicating p-value significance and bubble size reflecting the count of enriched genes. Full article ">Figure 3
Analysis of oyster metabolomes following a 96-hour exposure to Cd. (a) OPLS-DA score plot of the metabolomic data. (b) PLS-DA sorting test plot of the metabolomic data. (c) Hierarchical clustering was performed using 111 DEMs, with red indicating upregulation and blue representing downregulation, respectively. (d) Sample comparisons for the matchstick diagram. The top 20 metabolites of up and down are displayed in the matchstick diagram. The x-axis of the matchstick diagram represents log2 (Fold Change) values, the y-axis represents metabolites, and the size of the points corresponds to VIP values. The metabolites that are upregulated and downregulated are represented by the red and blue points, respectively. Full article ">Figure 4
Pathway analysis of the DEMs. (a) Top 20 KEGG pathway results. p-values are represented by colors, while pathway impact is indicated by the size of the bubbles. (b) KEGG regulatory network diagram. Red circles represent individual metabolic pathways, yellow circles depict enzyme information related to specific substances, green circles indicate background substances for a metabolic pathway, purple circles represent information on molecular modules of a certain substance category, blue circles represent chemical interactions involving a specific substance, and green squares denote differentially expressed substances identified in this comparison. Full article ">Figure 5
Correlation heatmap analysis of DEGs and DEMs. The DEMs are depicted on the y-axis, while the x-axis displays the DEGs. A correlation coefficient less than 0 is indicative of a negative correlation, and a coefficient greater than 0 indicates a positive correlation. Negative correlations are symbolized by the color blue, while positive correlations are represented by the color red. Full article ">Figure 6
Pathway enrichment analysis of DEGs and DEMs. (a) Bubble plot of KEGG enrichment for DEGs and DEMs. The x-axis represents the ratio of the number of enriched differential metabolites or genes annotated to metabolites or genes in the pathway to the total number in that pathway (ratio). The y-axis represents KEGG pathways jointly enriched in the metabolome and transcriptome. The count indicates the number of enriched metabolites or genes in the pathway. The colors represent p-values, with brighter colors indicating smaller p-values and more significant pathway enrichment. (b) iPath pathway map of shared enriched pathways. Colored boxes represent enriched pathways, nodes depict various biochemical molecules, lines represent biochemical reactions, and blue lines within the pathways indicate pathways jointly enriched with DEGs and DEMs. Full article ">Figure 7
Main biological pathway responses to acute Cd exposure in oysters. The blue boxes represent four core Cd exposure response pathways. The orange boxes represent DEMs associated with the main pathways. The green boxes represent downregulated DEGs related to the main pathways. The red elliptical frames represent upregulated DEGs associated with the main pathways. Full article ">

27 pages, 6716 KiB

Open AccessArticle

Comparative Metabolome and Transcriptome Analysis of Rapeseed (Brassica napus L.) Cotyledons in Response to Cold Stress

by Xinhong Liu, Tonghua Wang, Ying Ruan, Xiang Xie, Chengfang Tan, Yiming Guo, Bao Li, Liang Qu, Lichao Deng, Mei Li and Chunlin Liu

Plants 2024, 13(16), 2212; https://doi.org/10.3390/plants13162212 (registering DOI) - 9 Aug 2024

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Abstract

Cold stress affects the seed germination and early growth of winter rapeseed, leading to yield losses. We employed transmission electron microscopy, physiological analyses, metabolome profiling, and transcriptome sequencing to understand the effect of cold stress (0 °C, LW) on the cotyledons of cold-tolerant [...] Read more.

Cold stress affects the seed germination and early growth of winter rapeseed, leading to yield losses. We employed transmission electron microscopy, physiological analyses, metabolome profiling, and transcriptome sequencing to understand the effect of cold stress (0 °C, LW) on the cotyledons of cold-tolerant (GX74) and -sensitive (XY15) rapeseeds. The mesophyll cells in cold-treated XY15 were severely damaged compared to slightly damaged cells in GX74. The fructose, glucose, malondialdehyde, and proline contents increased after cold stress in both genotypes; however, GX74 had significantly higher content than XY15. The pyruvic acid content increased after cold stress in GX74, but decreased in XY15. Metabolome analysis detected 590 compounds, of which 32 and 74 were differentially accumulated in GX74 (CK vs. cold stress) and XY15 (CK vs. cold stressed). Arachidonic acid and magnoflorine were the most up-accumulated metabolites in GX74 subjected to cold stress compared to CK. There were 461 and 1481 differentially expressed genes (DEGs) specific to XY15 and GX74 rapeseeds, respectively. Generally, the commonly expressed genes had higher expressions in GX74 compared to XY15 in CK and cold stress conditions. The expression changes in DEGs related to photosynthesis-antenna proteins, chlorophyll biosynthesis, and sugar biosynthesis-related pathways were consistent with the fructose and glucose levels in cotyledons. Compared to XY15, GX74 showed upregulation of a higher number of genes/transcripts related to arachidonic acid, pyruvic acid, arginine and proline biosynthesis, cell wall changes, reactive oxygen species scavenging, cold-responsive pathways, and phytohormone-related pathways. Taken together, our results provide a detailed overview of the cold stress responses in rapeseed cotyledons. Full article

(This article belongs to the Special Issue Genetics and Genomics of Crop Breeding and Improvement)

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(A) Visual comparison of injury to the cotyledons of cold-sensitive (XY15) and -tolerant (GX74) B. rapa in response to cold stress (LW) compared to control (CK). (B) Transmission electron microscopic observations of mesophyll cells of XY15 and GX74 before and after cold stress treatment. Mi = mitochondria, CP = chloroplast, red arrow = cell wall, orange arrow = starch grains, green arrow = osmiophilic granules, blue arrow = crenellate, purple arrow = vacuole, and yellow arrow = lamellar. The red circles show that the outer membrane of the mesophyll cells is dissolved/ruptured. Full article ">Figure 2
Physiological performance of cold-tolerant (GX74) and -sensitive (XY15) genotypes of B. rapa cotyledons before (LW) and after (18 h) of cold stress (LW). (A). Fructose content. (B). Glucose content. (C). Malondialdehyde content. (D). Hydrogen peroxide (H2O2) content. (E). Relative electrical conductivity. (F). Pyruvic acid content. (G). Proline content. Bars of a genotype with * and ns indicate significant and non-significant difference, respectively, between CK and LW. Error bars represent standard error of means of three replicates. Full article ">Figure 3
Metabolome analysis of B. rapa (XY15 and GX74) cotyledons challenged with cold stress. (A) Hierarchical heatmap clusters, (B) principal component analysis, and (C) Pearson’s correlation coefficient analyses of the detected metabolites in negative (upper panel) and positive modes (lower panel). (D–F) Differential metabolome analysis of B. rapa (XY15 and GX74) cotyledons in response to cold stress. (A) Venn diagram showing differential metabolites in GX74 (CKvsLW) and XY15 (CKvsLW). Log2 fold change of the highly up- and down-accumulated metabolites in (B) GX74 (CKvsLW) and (C) XY15 (CKvsLW). Full article ">Figure 4
Overview of gene expression in B. rapa cotyledons in response to cold stress. (A) Distribution of average TPM and (B) principal component analysis in GX74 and XY15 before (CK) and after cold stress (LW). The numbers (1–3) with treatments indicate replicates. (C) Summary of differentially expressed genes and (D) Venn diagram of differentially expressed genes in GX74 and XY15 before and after cold stress. The up and down arrows indicate the number of up- or downregulated genes in cold treated samples compared to CK. KEGG pathway (top 20) enrichment barplots of DEGs in (E) GX74 (CKvsLW) and (F) XY15 (CKvsLW). x- and y-axis in (E,F) represent no. of DEGs and KEGG pathways. The color bars represent p-value (adjusted). The lower the p-value, the more significant the enrichment results. Full article ">Figure 5
Effects of cold stress on gene expression related to physiological and biochemical changes, ion homeostasis, and ROS scavenging. The heatmaps are based on the log2 fold change values. The left and right columns of the heatmap represent XY15 (CKvsLW) and GX74 (CKvsLW), respectively. The colors of the boxes in the pathway correspond to the borders of the heatmaps. Full article ">Figure 6
Expression changes in sugar biosynthesis-related KEGG pathways. The genes given as red semi-circles were differentially expressed in the B. rapa (XY15 and GX74) cotyledons before and after cold stress treatment. The heatmap on the right panel represents log2 fold change values of the DEGs enriched in the KEGG pathways; gene id is followed by a number (given as |x|) corresponding to the number given in the red semi-circle. The number is followed by gene annotation according to KEGG database. Full article ">Figure 7
Expression changes in auxin, abscisic acid, and cold-responsive genes in B. rapa (XY15 and GX74) cotyledons in response to cold stress. The heatmaps represent log2 fold change values. Full article ">Figure 8
Quantitative real-time PCR analysis of B. napus genes in XY15 and GX74 before and after cold stress. The bars are means of three replicates. The error bars represent standard deviation. Full article ">Figure 9
Contrasting cold stress responses of GX74 and XY15 cotyledons as revealed by full-length transcriptome analyses. The red, green, and black dots show the up-, down-, and not regulated genes within each pathway. The reduction in red and green color intensity of the dots indicates a higher and lower number of DEGs involved in each pathway, respectively. The dark yellow circles indicate the metabolites were differentially accumulated related to those pathways. Full article ">

13 pages, 3950 KiB

Open AccessArticle

MaAzaR Influences Virulence of Metarhizium acridum against Locusta migratoria manilensis by Affecting Cuticle Penetration

by Geng Hong, Siqing Wang, Yuxian Xia and Guoxiong Peng

J. Fungi 2024, 10(8), 564; https://doi.org/10.3390/jof10080564 (registering DOI) - 9 Aug 2024

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Abstract

The entomopathogenic fungus (EPF) Metarhizium acridum is a typical filamentous fungus and has been used to control migratory locusts (Locusta migratoria manilensis). This study examines the impact of the Zn(II)2Cys6 transcription factor, MaAzaR, in the virulence of M. acridum. Disruption [...] Read more.

The entomopathogenic fungus (EPF) Metarhizium acridum is a typical filamentous fungus and has been used to control migratory locusts (Locusta migratoria manilensis). This study examines the impact of the Zn(II)2Cys6 transcription factor, MaAzaR, in the virulence of M. acridum. Disruption of MaAzaR (ΔMaAzaR) diminished the fungus’s ability to penetrate the insect cuticle, thereby decreasing its virulence. The median lethal time (LT₅₀) for the ΔMaAzaR strain increased by approximately 1.5 d compared to the wild-type (WT) strain when topically inoculated, simulating natural infection conditions. ΔMaAzaR compromises the formation, turgor pressure, and secretion of extracellular hydrolytic enzymes in appressoria. However, the growth ability of ΔMaAzaR within the hemolymph is not impaired; in fact, it grows better than the WT strain. Moreover, RNA-sequencing (RNA-Seq) analysis of ΔMaAzaR and WT strains grown for 20 h on locust hindwings revealed 87 upregulated and 37 downregulated differentially expressed genes (DEGs) in the mutant strain. Pathogen–host interaction database (PHI) analysis showed that about 40% of the total DEGs were associated with virulence, suggesting that MaAzaR is a crucial transcription factor that directly regulates the expression of downstream genes. This study identifies a new transcription factor involved in EPF cuticle penetration, providing theoretical support and genetic resources for the developing highly virulent strains. Full article

(This article belongs to the Special Issue Fungi and Insect Interactions: Pathogenicity, Immune Defenses and Biocontrol)

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Insect bioassay and simulation of Metarhizium acridum cuticle penetration. (A) Survival of Locusta migratoria manilensis topically inoculated with each strain. (B) Calculated LT50 (median lethal time, i.e., time until death) values of L. migratoria in the topical inoculation bioassay. (C) Image of locust cadaver 7 days post-death in topical inoculation bioassay. (D) Cuticle penetration simulation assay; the white arrow points to the location of the conidia vaccinate. Data points represent the mean ± standard error of the mean (SEM). The same capital letters above two bars indicate no significant difference between the samples by one-way ANOVA and Tukey test (p > 0.01). Full article ">Figure 2
The deletion of MaAzaR affects cuticle penetration by influencing appressoria. (A) Relative expression of adhesin-like proteins and extracellular hydrolytic enzymes in appressoria after 24 h post-inoculation (hpi) with locust’s hind wings. (B) Appressoria formation rate on locust wings from 16 to 36 hpi of each strain. (C) The time required for 50% appressoria formation (AFT50). (D) The collapsed rate of appressoria treated with different concentrations of PEG-8000. (E) Concentration of PEG-8000 to make 50% of appressorium collapse (LD50). (F) Nile Red staining of lipid droplets in the appressorium (AP) and conidium (CO). (G) Fluorescence of lipid droplet staining with Nile Red was quantified. Data points represent the mean ± standard error of the mean (SEM). The same lowercase letters and capital letters above two bars indicate no significant difference between the samples by one-way ANOVA and Tukey test (p > 0.05 and p > 0.01, respectively). Full article ">Figure 3
Disruption of MaAzaR induces higher host immune responses during cuticle penetration. (A) Phenoloxidases (PO) enzyme activity of locust hemolymph after topical inoculation with each strain. (B) Relative expression of spaetzle at 8, 12, and 18 h post-inoculation (hpi) after topical inoculation. (C) Relative expression of myd88 at 8, 12, and 18 hpi after topical inoculation. (D) Relative expression of defensin at 12, 24, 36, and 48 hpi after topical inoculation. Data points represent the mean ± standard error of the mean (SEM). The same lowercase letters and capital letters above two bars indicate no significant difference between the samples by one-way ANOVA and Tukey test (p > 0.05 and p > 0.01, respectively). Full article ">Figure 4
In vivo growth stage does not cause the decreased virulence of ΔMaAzaR strain. (A) Microscopic images of hyphal bodies and insect hemocytes in hemolymph after topical inoculation. Black arrow: insect hemocytes. White arrow: hyphal bodies. (B) Survival of Locusta migratoria manilensis following intrahemocoel injection with different strains. (C) Calculated LT50 (median lethal time) values of L. migratoria in the intrahemocoel injection bioassay. (D) Quantification of DNA concentration of in vitro cultured hyphal bodies. Data points represent the mean ± standard error of the mean (SEM). The same lowercase letters above two bars indicate no significant difference between the samples by one-way ANOVA and Tukey test (p > 0.05). Full article ">Figure 5
Transcriptomic analysis of MaAzaR in Metarhizium acridum. (A) Volcano plot of differentially expressed genes (DEGs) between the knockout mutant ΔMaAzaR and the wild-type (WT) strain grown 20 h in locust hindwings. (B) Enrichment of total DEGs to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. (C) Enrichment of total DEGs to gene ontology (GO) item. BP, biological process; CC, cellular component; MF, molecular function. * indicates the significantly enriched terms (q < 0.05). (D) Quantification of total DEGs and homolog genes identified in the Pathogen–Host Interactions (PHI) database (phi-blasted DEGs). Full article ">

15 pages, 4940 KiB

Open AccessArticle

Integrated Transcriptomic–Metabolomic Analysis Reveals the Effect of Different Light Intensities on Ovarian Development in Chickens

by Xiaoli Zhou, Yuhang Xu, Cheng Fang, Chutian Ye, Weiming Liang, Zhexia Fan, Xuerong Ma, Aijun Liu, Xiquan Zhang and Qingbin Luo

Int. J. Mol. Sci. 2024, 25(16), 8704; https://doi.org/10.3390/ijms25168704 - 9 Aug 2024

Viewed by 176

Abstract

Light is a key environmental factor regulating reproduction in avians. However, the mechanism of light intensity regulating ovarian development is still unclear. In this study, 5-week-old (5 wk) partridge broiler breeders were randomly divided into a low-light-intensity group (LL group) and a natural-light-intensity [...] Read more.

Light is a key environmental factor regulating reproduction in avians. However, the mechanism of light intensity regulating ovarian development is still unclear. In this study, 5-week-old (5 wk) partridge broiler breeders were randomly divided into a low-light-intensity group (LL group) and a natural-light-intensity group (NL group) (n = 100). In the rearing period (5 wk to 22 wk), the light intensity of the LL group and NL group were 0.41 ± 0.05 lux and 45.39 ± 1.09 lux, and in the laying period (23 wk to 32 wk) they were 23.92 ± 0.06 lux and 66.93 ± 0.76 lux, respectively. Samples were collected on 22 wk and 32 wk. The results showed that the LL group had a later age at first egg and a longer laying period than the NL group. Serum P4 and LH levels in the LL group were higher than in the NL group on 22 wk (p < 0.05). On 32 wk, P4, E2, LH and FSH levels in the LL group were lower than in the NL group (p < 0.05). Ovarian transcriptomics and metabolomics identified 128 differentially expressed genes (DEGs) and 467 differential metabolites (DMs) on 22 wk; 155 DEGs and 531 DMs on 32 wk between two groups. An enrichment analysis of these DEGs and DMs identified key signaling pathways, including steroid hormone biosynthesis, neuroactive ligand-receptor interaction. In these pathways, genes such as CYP21A1, SSTR2, and NPY may regulate the synthesis of metabolites, including tryptamine, triglycerides, and phenylalanine. These genes and metabolites may play a dominant role in the light-intensity regulation of ovarian development and laying performance in broiler breeders. Full article

(This article belongs to the Section Molecular Genetics and Genomics)

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Figure 1
Effect of different light intensities on laying performance. (A) Experimental design of this study. Two hundred 5-week-old (5 wk) partridge broiler breeders with similar body weights were selected and randomly divided into low-light-intensity group (LL group) and natural-light-intensity group (NL group) (n = 100). The light intensity of the LL group and NL group from 5 wk to 22 wk were 0.41 ± 0.05 lux and 45.39 ± 1.09 lux, respectively; and from 23 wk to 32 wk, they were 23.92 ± 0.06 lux and 66.93 ± 0.76 lux, respectively. Tissue and blood samples were collected on 22 wk and 32 wk. (B) Laying rate. Full article ">Figure 2
Effects of different light intensities on GSI% and numbers of follicles. (A) GSI% (GSI% = gonadal weight/body weight × 100%); (B) Number of large yellow follicles (LYFs, follicle diameter > 8 mm); (C) Number of small yellow follicles (SYFs, follicle diameter = 6–8 mm); (D) Number of white follicles (WFs, follicle diameter < 6 mm). Data are presented as the mean ± SEM, n = 10. ** indicates p < 0.01. Full article ">Figure 3
Transcriptomic analysis in ovaries under different light intensities on 22 wk. (A) PCA plot; (B) DEGs bar plot; (C) Gene volcano plot; (D) DEGs clustering heatmap; (E) DEGs GO enrichment analysis plot; (F) DEGs KEGG enrichment analysis plot. Full article ">Figure 4
Transcriptomic analysis in ovaries under different light intensities on 32 wk. (A) PCA plot; (B) DEGs bar plot; (C) Gene volcano plot; (D) DEGs clustering heatmap; (E) DEGs GO enrichment analysis plot; (F) DEGs KEGG enrichment analysis plot. Full article ">Figure 5
Metabolomic analysis in ovaries under different light intensities on 22 wk. (A) PCA plot; (B) OPLS-DA scores plot; (C) OPLS-DA model validation plot; (D) DM clustering heatmap; (E) DM bar plot; (F) metabolites’ volcano plot; (G) DM KEGG enrichment analysis plot. Full article ">Figure 6
Metabolomic analysis in ovaries under different light intensities on 32 wk. (A) PCA plot; (B) OPLS-DA scores plot; (C) OPLS-DA model validation plot; (D) DM clustering heatmap; (E) DM bar plot; (F) metabolites’ volcano plot; (G) DM KEGG enrichment analysis plot. Full article ">Figure 7
Integrated analysis of transcriptomics and metabolomics in ovaries under different light intensities. (A) Venn diagram of intersection of DEG and DM enrichment signaling pathways on 22 wk; (B) top 10 pathways of the intersection of DEG and DM enrichment signaling pathways on 22 wk; (C) Venn diagram of intersection of DEG and DM enrichment signaling pathways on 32 wk; (D) top 10 pathways of the intersection of DEG and DM enrichment signaling pathways on 32 wk. Full article ">

21 pages, 4390 KiB

Open AccessArticle

A Combined Transcriptomic and Proteomic Analysis of Monkeypox Virus A23 Protein on HEK293T Cells

by Yihao Wang, Yihan Li, Mingzhi Li, Keyi Wang, Jiaqi Xiong, Ting Wang, Yu Wang, Yunli Guo, Lingbao Kong and Meifeng Li

Int. J. Mol. Sci. 2024, 25(16), 8678; https://doi.org/10.3390/ijms25168678 - 8 Aug 2024

Viewed by 402

Abstract

Monkeypox virus (MPXV) is a cross-kingdom pathogen infecting both humans and wildlife, which poses a significant health risk to the public. Although MPXV attracts broad attention, there is a lack of adequate studies to elucidate pathogenic mechanisms associated with viral infections. In this [...] Read more.

Monkeypox virus (MPXV) is a cross-kingdom pathogen infecting both humans and wildlife, which poses a significant health risk to the public. Although MPXV attracts broad attention, there is a lack of adequate studies to elucidate pathogenic mechanisms associated with viral infections. In this study, a high-throughput RNA sequencing (RNA-seq) and liquid chromatography–tandem mass spectrometry (LC-MS/MS) approach was used to explore the transcriptional and metabolic responses of MPXV A23 protein to HEK293T cells. The protein–protein interactions and signaling pathways were conducted by GO and KEGG analyses. The localization of A23 protein in HEK293T cells was detected by immunofluorescence. A total of 648 differentially expressed genes (DEGs) were identified in cells by RNA-Seq, including 314 upregulated genes and 334 downregulated genes. Additionally, liquid chromatography–tandem mass spectrometry (LC-MS/MS) detected 115 cellular proteins that interact with the A23 proteins. Transcriptomic sequencing analysis revealed that transfection of MPXV A23 protein modulated genes primarily associated with cellular apoptosis and DNA damage repair. Proteomic analysis indicated that this protein primarily interacted with host ribosomal proteins and histones. Following the identification of the nuclear localization sequence RKKR within the A23 protein, a truncated mutant A23_ΔRKKR was constructed to investigate the subcellular localization of A23 protein. The wild-type A23 protein exhibits a significantly higher nuclear-to-cytoplasmic ratio, exceeding 1.5, in contrast to the mutant A23_ΔRKKR, which has a ratio of approximately 1. Immunofluorescence assays showed that the A23 protein was mainly localized in the nucleus. The integration of transcriptomics and proteomics analysis provides a comprehensive understanding of the interaction between MPXV A23 protein and the host. Our findings highlight the potential role of this enzyme in suppressing host antiviral immune responses and modulating host gene expression. Full article

(This article belongs to the Special Issue Recent Advances in Herpesviruses)

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15 pages, 2520 KiB

Open AccessArticle

Cataloging the Genetic Response: Unveiling Drought-Responsive Gene Expression in Oil Tea Camellia (Camellia oleifera Abel.) through Transcriptomics

by Zhen Zhang, Yanming Xu, Caixia Liu, Longsheng Chen, Ying Zhang, Zhilong He, Rui Wang, Chengfeng Xun, Yushen Ma, Xiaokang Yuan, Xiangnan Wang, Yongzhong Chen and Xiaohu Yang

Life 2024, 14(8), 989; https://doi.org/10.3390/life14080989 - 8 Aug 2024

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Abstract

Drought stress is a critical environmental factor that significantly impacts plant growth and productivity. However, the transcriptome analysis of differentially expressed genes in response to drought stress in Camellia oleifera Abel. is still unclear. This study analyzed the transcriptome sequencing data of C. [...] Read more.

Drought stress is a critical environmental factor that significantly impacts plant growth and productivity. However, the transcriptome analysis of differentially expressed genes in response to drought stress in Camellia oleifera Abel. is still unclear. This study analyzed the transcriptome sequencing data of C. oleifera under drought treatments. A total of 20,674 differentially expressed genes (DEGs) were identified under drought stress, with the number of DEGs increasing with the duration of drought. Specifically, 11,793 and 18,046 DEGs were detected after 8 and 15 days of drought treatment, respectively, including numerous upregulated and downregulated genes. Gene Ontology (GO) enrichment analysis showed that the DEGs were primarily involved in various biological processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that carbon metabolism, glyoxylate and dicarboxylate metabolism, proteasome, glycine, serine, and threonine metabolism were the main affected pathways. Among the DEGs, 376 protein kinases, 42 proteases, 168 transcription factor (TF) genes, and 152 other potential functional genes were identified, which may play significant roles in the drought response of C. oleifera. The expression of relevant functional genes was further validated using quantitative real-time PCR (qRT-PCR). These findings contribute to the comprehension of drought tolerance mechanisms in C. oleifera and bolster the identification of drought-resistant genes for molecular breeding purposes. Full article

(This article belongs to the Special Issue Plant Functional Genomics and Breeding)

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19 pages, 1594 KiB

Open AccessArticle

Transcriptomic Analysis of Vitrified–Warmed vs. Fresh Mouse Blastocysts: Cryo-Induced Physiological Mechanisms and Implantation Impact

by Chi-Ying Lee, Han-Ni Tsai, En-Hui Cheng, Tsung-Hsien Lee, Pin-Yao Lin, Maw-Sheng Lee and Chun-I Lee

Int. J. Mol. Sci. 2024, 25(16), 8658; https://doi.org/10.3390/ijms25168658 - 8 Aug 2024

Viewed by 285

Abstract

Blastocyst vitrification has significantly improved embryo transfer methods, leading to higher implantation success rates and better pregnancy outcomes in subsequent frozen embryo transfer cycles. This study aimed to simulate the transcriptional changes caused by vitrifying human blastocysts using mouse blastocysts as a model [...] Read more.

Blastocyst vitrification has significantly improved embryo transfer methods, leading to higher implantation success rates and better pregnancy outcomes in subsequent frozen embryo transfer cycles. This study aimed to simulate the transcriptional changes caused by vitrifying human blastocysts using mouse blastocysts as a model and to further investigate these changes’ effects. Utilizing a human vitrification protocol, we implanted both vitrified and fresh embryos into mice. We observed the implantation success rates and performed transcriptomic analysis on the blastocysts. To validate the results from messenger RNA sequencing, we conducted reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) to measure the expression levels of specific genes. Based on mRNA profiling, we predicted the microRNAs responsible for the regulation and used qPCR basic microRNA assays for validation. Our observations revealed a higher implantation success rate for vitrified embryos than fresh embryos. Transcriptomic analysis showed that vitrified–warmed blastocysts exhibited differentially expressed genes (DEGs) primarily associated with thermogenesis, chemical carcinogenesis-reactive oxygen species, oxidative phosphorylation, immune response, and MAPK-related signaling pathways. RT-qPCR confirmed increased expression of genes such as Cdk6 and Nfat2, and decreased expression of genes such as Dkk3 and Mapk10. Additionally, gene-microRNA interaction predictions and microRNA expression analysis identified twelve microRNAs with expression patterns consistent with the predicted results, suggesting potential roles in uterine epithelial cell adhesion, trophectoderm development, invasive capacity, and immune responses. Our findings suggest that vitrification induces transcriptomic changes in mouse blastocysts, and even small changes in gene expression can enhance implantation success. These results highlight the importance of understanding the molecular mechanisms underlying vitrification to optimize embryo transfer techniques and improve pregnancy outcomes. Full article

(This article belongs to the Special Issue Genetic and Epigenetic Regulation of Reproduction)

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The most abundant GO terms correspond to the (A) upregulated and (B) downregulated gene expression of vitrified–warmed blastocysts. A plot of gene characteristics depicts the functional categories derived from molecular functions, biological processes, and cellular components. The GO enrichment analysis reveals critical insights into the physiological impacts of vitrification and warming on mouse blastocysts. The upregulated DEGs, highly associated with mitochondria and chromatin, suggest enhanced metabolic activity and potential chromatin remodeling. In contrast, the downregulated DEGs, linked to the endoplasmic reticulum-Golgi intermediate compartment and glycerolipid biosynthesis, indicate a reduction in lipid metabolism and intracellular transport processes. These findings highlight the distinct molecular adaptations occurring in response to cryopreservation, providing a deeper understanding of its effects on embryo viability and development potential. Larger data points indicate lower FDR. Red, green, and blue dots represent biological processes, cellular components, and molecular functions, respectively. Full article ">Figure 2
Enrichment analysis of KEGG pathways associated with DEGs in vitrified–warmed blastocysts. The scatterplot depicts the enriched KEGG pathways corresponding to upregulated (red) and downregulated (blue) DEGs. With a false discovery rate (FDR) < 0.2 and fold-change ≥ 1.5, the upregulated DEGs were enriched for 6 pathways, while the downregulated DEGs were enriched for 7 pathways. Enriched pathways of upregulated DEGs such as “Thermogenesis”, “Chemical carcinogenesis-reactive oxygen species”, and “Oxidative phosphorylation”. These pathways promote ATP generation, affecting cell survival, proliferation, migration, and differentiation via the “MAPK signaling pathway”. Enriched pathways of downregulated DEGs mostly involved in immune responses, such as “Herpes simplex virus 1 infection”, “NF-kappa B signaling pathway”, “Autophagy-animal”, “Fc epsilon RI signaling pathway”, and “Glycosylphosphatidylinositol (GPI)-anchor biosynthesis”. The x-axis represents the fold enrichment, calculated as the ratio of the observed gene frequency in the pathway to the expected frequency, based on a random distribution. The y-axis represents the −log10 transformed false discovery rate (FDR), where larger dot points indicate lower FDR values and higher statistical significance. Pathways positioned towards the left exhibit more significant fold enrichments, indicating a higher representation of DEGs in those pathways compared to random expectation. Full article ">Figure 3
Physiological Mechanisms Induced by Cryopreservation. Cold overfeeding leads to cellular damage, prompting the thermogenesis process to generate heat. This process activates downstream pathways, such as “Oxidative phosphorylation” and “MAPK signaling pathway,” which are essential for ATP production and cellular functions. Consequently, the cryopreservation and warming process necessitates increased ATP production to sustain cell survival and proliferation. The KEGG database indicates associations between pathway enrichment results, represented as rectangles, and downstream mechanism predictions, depicted as ovals. Hexagons denote vital compounds. Full article ">Figure 4
Validation of gene expression results by RT-qPCR. The Y-axis represents the log-2-fold change between vitrified–warmed and fresh blastocysts. The dark gray bar represents the ratio generated by RT-qPCR, and the light gray bar represents the ratio calculated by the NGS result. The dashed line represents the two-fold change difference between the vitrified and fresh cells. Full article ">

14 pages, 4035 KiB

Open AccessArticle

Transcriptomic Analysis of Alfalfa Flowering and the Dual Roles of MsAP1 in Floral Organ Identity and Flowering Time

by Xu Jiang, Huiting Cui, Zhen Wang, Ruicai Long, Qingchuan Yang and Junmei Kang

Agronomy 2024, 14(8), 1741; https://doi.org/10.3390/agronomy14081741 - 8 Aug 2024

Viewed by 313

Abstract

Flowering, the transition from the vegetative to the reproductive stage, is vital for reproductive success, affecting forage quality, the yield of aboveground biomass, and seed production in alfalfa. To explore the transcriptomic profile of alfalfa flowering transition, we compared gene expression between shoot [...] Read more.

Flowering, the transition from the vegetative to the reproductive stage, is vital for reproductive success, affecting forage quality, the yield of aboveground biomass, and seed production in alfalfa. To explore the transcriptomic profile of alfalfa flowering transition, we compared gene expression between shoot apices (SAs) at the vegetative stage and flower buds (FBs) at the reproductive stage by mRNA sequencing. A total of 3,409 DEGs were identified, and based on gene ontology (GO), 42.53% of the most enriched 15 processes were associated with plant reproduction, including growth phase transition and floral organ development. For the former category, 79.1% of DEGs showed higher expression levels in SA than FB, suggesting they were sequentially turned on and off at the two test stages. For the DEGs encoding the components of circadian rhythm, sugar metabolism, phytohormone signaling, and floral organ identity genes, 60.71% showed higher abundance in FB than SA. Among them, MsAP1, an APETALA1 (AP1) homolog of Arabidopsis thaliana, showed high expression in flower buds and co-expressed with genes related to flower organ development. Moreover, ectopic expression of MsAP1 in Arabidopsis resulted in dwarfism and early flowering under long-day conditions. The MsAP1-overexpression plant displayed morphological abnormalities including fused whorls, enlarged pistils, determinate inflorescence, and small pods. In addition, MsAP1 is localized in the nucleus and exhibits significant transcriptional activity. These findings revealed a transcriptional regulation network of alfalfa transition from juvenile phase to flowering and provided genetic evidence of the dual role of MsAP1 in flowering and floral organ development. Full article

(This article belongs to the Topic Advances in Industrial Crops Physioecology and Sustainable Cultivation)

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Transcriptome analysis of alfalfa shoot apical tissues on day 25 and the floral buds on day 35. (a) The kinetic growth analysis of alfalfa in terms of plant height under the normal conditions. (b,c) Image of alfalfa shoot apex (SA) and floral bud (FB) on day 25 and day 35, respectively. The tissues were used for mRNA sequencing. (d) Heatmap of differential gene expression profiles of SA (day 25) and FB (day 35). The heatmap was constructed using FPKM values and normalized to a range of zero to one. Red represents high FPKM values, and blue for low values. (e) DEGs identified in this study with cut off |log2Foldchange| > 1. Red stands for the upregulated genes in FB relative to SA, and blue for the downregulated genes. (f) Linear regression analysis between mRNA sequencing data and the expression level test by RT-qPCR of the 15 randomly selected DEGs. Full article ">Figure 2
Classification of the DEGs enriched in terms of biological process and pathway. (a) Analysis of biological processes of the DEGs. GO terms for growth stage transition are marked with dots, while GO terms for flower organ development are indicated with asterisks. (b) Top 15 enriched pathways via Mapman. (c) The most enriched GO function of the putative transcription factors. (d) Transcript profile of the DEGs involved in phytohormone IAA, GA, and CTK signaling. The scale represents normalized FPKM for the annotated genes via sequence homology. The gradient colors from red to blue denote high and low expression, respectively. Full article ">Figure 3
Analysis of floral formation-related TFs. (a) Gene expression profiles of DEGs related to the timing of meristematic phase transition. The color scale indicates normalized FPKM changes in gene expression levels in alfalfa shoot apical and flower bud tissue. The gradient colors from red to blue indicate the abundance of gene expression from high to low. The arrow symbol represents the activation relationship. LFY, LEAFY; FUL, FRUITFULL; WUS, WUSCHEL; SOC1, SUPPRESSOR OF CONSTANS OVEREXPRESSION 1; SPL, squamosa promoter-binding-like protein. (b) Network analysis of AP1 and AP2 and their network genes. Pale purple lines indicate co-expression network and pale red lines indicate physical interaction in Arabidopsis. The red arrow symbol represents upregulated expression of genes in flower buds, while the blue symbol represents downregulated expression. (c) Analysis of MsAP1 expression pattern in different organs in vegetative and reproductive growth phase. Tissue sampling during the vegetative growth stage was performed on the 25th day after harvesting, floral meristem tissues were collected on the 30th day, and stems, leaves, flower buds, and flowers during the flowering stage were collected on day 40. Data represent mean values (with error bars indicating standard deviations from 3 biological replicates), and different letters denote significance levels < 0.01, determined by statistical analysis using one-way ANOVA. Full article ">Figure 4
Transcriptional activity assay and subcellular localization investigation of MsAP1. (a) The subcellular localization of the MsAP1-GFP fusion protein transiently expressed in tobacco leaves. Images were captured using a confocal microscope. Label the two constructs (control upper and recombinant vector lower panel, respectively); scale bars: 100 µm. Green represents GFP fluorescence signal, and blue dots represent cell nuclei labeled with DAPI (4’,6-diamidino-2-phenylindole). (b) Schematic diagram of His reporter gene expression activated by MsAP1 in a yeast cell. GAL4-BD represents the binding domain of GAL4. (c) Assay of the transcriptional activation of MsAP1 in yeast (Y2H) cells. Yeast were transfected with pGBKT7-MsAP1 (BD-MsAP1), pGBKT7-GAL4AD (positive control), and pGBKT7 (BD, negative control), respectively. The transformed cells were streaked on SC/-T and selective medium (SC/-T-H + 15 mM 3-AT) to assess growth. SC/-T: synthetic dropout (SC) yeast growth medium lacking tryptophan, SC/-L-H: SC medium lacking tryptophan and histidine, and supply with 15 mM 3-AT. Full article ">Figure 5
Overexpression of MsAP1 promoted flowering and altered floral organ morphology in Arabidopsis. (a) The relative transcription level of MsAP1. (b) Image of the homozygous T2 seedlings on day 20 after germination (DAG) under the long-day conditions. WT: Col-0, OE1, and OE3 represented the two independent transgenic Arabidopsis lines (35S::MsAP1-GFP). Bar = 2 cm. (c) Flowering time analysis in terms of days to bolting under the long-day conditions. (d) Analysis of rosette leaf number at the emergence of the first flower under the long-day conditions. (e) Phenotypes of the MsAP1 overexpressing Arabidopsis terminal flowers, bar = 3 mm. (f) Phenotype of the fruit of Arabidopsis thaliana. Bar = 5 mm. (g) Relative transcription levels of the key genes related to Arabidopsis floral transition. Asterisks indicate significant difference at p < 0.01 compared with wild type by Student’s t-test. Full article ">

26 pages, 2855 KiB

Open AccessArticle

Transcriptome Profiling and Weighted Gene Correlation Network Analysis Reveal Hub Genes and Pathways Involved in the Response to Polyethylene-Glycol-Induced Drought Stress of Two Citrus Rootstocks

by Emanuele Scialò, Angelo Sicilia, Alberto Continella, Alessandra Gentile and Angela Roberta Lo Piero

Biology 2024, 13(8), 595; https://doi.org/10.3390/biology13080595 - 7 Aug 2024

Viewed by 378

Abstract

Agriculture faces the dual challenge of increasing food production and safeguarding the environment. Climate change exacerbates this challenge, reducing crop yield and biomass due to drought stress, especially in semi-arid regions where Citrus plants are cultivated. Understanding the molecular mechanisms underlying drought tolerance [...] Read more.

Agriculture faces the dual challenge of increasing food production and safeguarding the environment. Climate change exacerbates this challenge, reducing crop yield and biomass due to drought stress, especially in semi-arid regions where Citrus plants are cultivated. Understanding the molecular mechanisms underlying drought tolerance in Citrus is crucial for developing adaptive strategies. Plants of two citrus rootstocks, Carrizo Citrange and Bitters (C22), were grown in aerated half-strength Hoagland’s nutrient solution. Post-acclimation, the plants were exposed to a solution containing 0% (control) or 15% PEG-8000 for 10 days. Leaf malonyl dialdehyde (MDA) and hydrogen peroxide (H₂O₂) content were measured to assess the reached oxidative stress level. Total RNA was extracted, sequenced, and de novo-assembled. Weighted Gene Correlation Network Analysis (WGCNA) was conducted to examine the relationship between gene expression patterns and the levels of MDA and H₂O₂ used as oxidative stress indicators. Plant visual inspection and MDA and H₂O₂ contents clearly indicate that Bitters is more tolerant than Carrizo towards PEG-induced drought stress. RNA-Seq analysis revealed a significantly higher number of differentially expressed genes (DEGs) in Carrizo (6092) than in Bitters (320), with most being associated with drought sensing, ROS scavenging, osmolyte biosynthesis, and cell wall metabolism. Moreover, the WGCNA identified transcription factors significantly correlated with MDA and H₂O₂ levels, thus providing insights into drought-coping strategies and offering candidate genes for enhancing citrus drought tolerance. Full article

(This article belongs to the Section Genetics and Genomics)

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Malondialdehyde (MDA) (A) and hydrogen peroxide (H2O2) (B) content in treated (PEG) and control (CK) plants of two citrus rootstock genotypes after 10 days of PEG treatment. Each point represents the mean value of three replicates. Different letters indicate significantly different values (ANOVA, p < 0.05); CAR, Carrizo Citrange; C22, Bitters. Full article ">Figure 2
Volcano plot showing the differentially expressed genes (DEGs) in the CAR_PEG vs. CAR_CK (A) and the C22_PEG vs. C22_CK (B) comparisons. Red dots represent the upregulated genes with statistical significance, the blue dots represent the downregulated genes with statistical significance, and the grey dots (ns) are DEGs with −log10padj < 1.3, adopting a log2 Fold Change threshold of 1 (2.0 fold change). The X-axis is the gene expression change, and the Y-axis is the p-value adjusted after normalisation. Full article ">Figure 3
Gene Ontology (GO) enrichment analysis for the DEGs in the CAR_PEG vs. CAR_CK (A) and the C22_PEG vs. C22_CK (B) comparisons. The X-axis indicates the -log10(FDR), and the Y-axis indicates the GO terms within each category. Black dots indicate significantly enriched terms (FDR < 0.05), while grey dots indicate non-significantly enriched terms (FDR ≥ 0.05). Symbols indicate the GO category (circles indicate the Molecular function category, triangles indicate the Biological process category, and squares indicate the Cellular component category). The dot size indicates the Gene Ratio. Full article ">Figure 4
Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the DEGs in the CAR_PEG vs. CAR_CK (A) and the C22_PEG vs. C22_CK (B) comparisons. The X-axis indicates the −log10(padj), and the Y-axis indicates the KEGG pathways. Black dots indicate significantly enriched terms (padj < 0.05), while grey dots indicate non-significantly enriched terms (padj ≥ 0.05). The dot size indicates the Gene Ratio. Full article ">Figure 5
Distribution of the ten most abundant families of transcription factors responsive to drought stress in the CAR_PEG vs. CAR_CK (A) and the C22_PEG vs. C22_CK (B) comparisons. Full article ">Figure 6
Heatmap of the correlation between modules and MDA and H2O2 levels. Full article ">Figure 7
Diagrams illustrating the abundance and distribution of eigengenes associated with the grey60 module across various traits and comparisons. Full article ">Figure 8
Diagrams illustrating the abundance and distribution of eigengenes associated with the turquoise and darkturquoise modules across various traits and comparisons. Full article ">

5 pages, 1047 KiB

Open AccessProceeding Paper

Lung Cancer Biomarker Identification from Differential Expression Analysis Using RNA-Seq Data for Designing Multitargeted Drugs

by Syed Naseer Ahmad Shah and Rafat Parveen

Biol. Life Sci. Forum 2024, 35(1), 2; https://doi.org/10.3390/blsf2024035002 - 7 Aug 2024

Viewed by 143

Abstract

Lung cancer presents a global health challenge, demanding exploration of its molecular intricacies for treatment targets. The goal is to delay progression and intervene early, reducing patient burden. Novel biomarkers are urgently needed for early diagnosis. We analysed RNA sequencing on lung cancer [...] Read more.

Lung cancer presents a global health challenge, demanding exploration of its molecular intricacies for treatment targets. The goal is to delay progression and intervene early, reducing patient burden. Novel biomarkers are urgently needed for early diagnosis. We analysed RNA sequencing on lung cancer samples from NCBI’s SRA database. Using Bioconductor in R, we identified key genes, including hub genes TOP2A and TMEM100, crucial for cellular processes. Additionally, FDA-approved drugs are repurposed as multitargeted inhibitors against upregulated genes, validated through simulations. This approach aims to inhibit the function of crucial genes, potentially offering effective treatment for lung cancer within a comprehensive strategy. Full article

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16 pages, 6594 KiB

Open AccessArticle

GhSWEET42 Regulates Flowering Time under Long-Day Conditions in Arabidopsis thaliana

by Mengxue Du, Deying Wang, Jingyu Li, Taotao Zhu, Peng Lyu, Gang Li, Yi Ding, Xinxin Liu, Qingmei Men, Xiaofei Li, Yongwang Sun, Lingzhi Meng and Shangjing Guo

Plants 2024, 13(16), 2181; https://doi.org/10.3390/plants13162181 - 6 Aug 2024

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Abstract

Flowering in plants is pivotal for initiating and advancing reproductive processes, impacting regional adaptation and crop yield. Despite numerous cloned and identified flowering time genes, research in cotton remains sparse. This study identified GhSWEET42 as a key determinant of the flowering time in [...] Read more.

Flowering in plants is pivotal for initiating and advancing reproductive processes, impacting regional adaptation and crop yield. Despite numerous cloned and identified flowering time genes, research in cotton remains sparse. This study identified GhSWEET42 as a key determinant of the flowering time in cotton, demonstrating that its heterologous expression in Arabidopsis accelerated flowering under LD conditions compared to WT. Transgenic plants exhibited upregulated expression of the flowering inducers AtFT, AtSOC1, AtGI, and AtFKF1, alongside downregulated expression of the repressors AtTSF, AtFLC, and AtRGL2, correlating with the earlier flowering phenotype. GhSWEET42 showed a constitutive expression pattern, with elevated levels in the leaves, petals, and flower buds, and was notably higher in early-maturing cotton varieties. Subcellular localization assays confirmed GhSWEET42’s presence on the cell membrane. Transcriptome analysis between WT and GhSWEET42-overexpressing Arabidopsis plants revealed 2393 differentially expressed genes (DEGs), spanning 221 biological processes, 93 molecular functions, and 37 cellular components according to Gene Ontology (GO) enrichment analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis categorized the DEGs into metabolism and environmental information processing. These findings enhance the understanding of GhSWEET42’s function and provide a foundation for elucidating the molecular mechanisms governing flowering time regulation in cotton. Full article

(This article belongs to the Special Issue Molecular Mechanisms of Flower Development and Plant Reproduction)

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14 pages, 11637 KiB

Open AccessArticle

Comparison of Root Transcriptomes against Clubroot Disease Pathogens in a Resistant Chinese Cabbage Cultivar (Brassica rapa cv. ‘Akimeki’)

by Eun-Seok Oh, Hyeonseon Park, Kwanuk Lee, Donghwan Shim and Man-Ho Oh

Plants 2024, 13(15), 2167; https://doi.org/10.3390/plants13152167 - 5 Aug 2024

Viewed by 367

Abstract

Clubroot, caused by Plasmodiophora brassicae, is one of the diseases that causes major economic losses in cruciferous crops worldwide. Although prevention strategies, including soil pH adjustment and crop rotation, have been used, the disease’s long persistence and devastating impact continuously remain in [...] Read more.

Clubroot, caused by Plasmodiophora brassicae, is one of the diseases that causes major economic losses in cruciferous crops worldwide. Although prevention strategies, including soil pH adjustment and crop rotation, have been used, the disease’s long persistence and devastating impact continuously remain in the soil. CR varieties were developed for clubroot-resistant (CR) Chinese cabbage, and ‘Akimeki’ is one of the clubroot disease-resistant cultivars. However, recent studies have reported susceptibility to several Korean pathotypes in Akimeki and the destruction of the resistance to P. brassicae in many Brassica species against CR varieties, requiring the understanding of more fine-tuned plant signaling by fungal pathogens. In this study, we focused on the early molecular responses of Akimeki during infection with two P. brassicae strains, Seosan (SS) and Hoengseong2 (HS2), using RNA sequencing (RNA-seq). Among a total of 2358 DEGs, 2037 DEGs were differentially expressed following SS and HS2 infection. Gene ontology (GO) showed that 1524 and 513 genes were up-regulated following SS and HS2 inoculations, respectively. Notably, the genes of defense response and jasmonic acid regulations were enriched in the SS inoculation condition, and the genes of water transport and light intensity response were enriched in the HS2 inoculation condition. Moreover, KEGG pathways revealed that the gene expression set were related to pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) mechanisms. The results will provide valuable information for developing CR cultivars in Brassica plants. Full article

(This article belongs to the Special Issue Molecular and Physiological Mechanisms Regulating Vegetable Crops Growth under Stressful Conditions)

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Phenotypes of the inoculation of CR B. rapa (Akimeki) with 2 types of P. brassicae isolates (Hoengseong2, pathotype 1; Seosan, pathotype 4). The photo was taken five weeks after inoculation. Full article ">Figure 2
(a) Heatmap of relative expression of 2358 DEGs with the indicated treatments. Expression values are log2-transformed median-centered TMM-normalized TPM. Color key indicates Z-scores of expression values. The x-axis dendrogram indicates sample similarity and y-axis dendrogram indicates the hierarchical clustering of genes with similar expression profiles. Distance and clustering algorithms used for the dendrogram are the complete linkage with Euclidean distances. (b) DEG analysis of differentially expressed genes (DEGs) between HS2 inoculation vs. SS inoculation, SS inoculation vs. Mock, and HS2 inoculation vs. Mock. Full article ">Figure 3
Gene ontology (GO) analysis and gene set enrichment analysis (GSEA) of genes in Akimeki by SS and HS2 inoculation. (a,b) Enriched GO biological process category of 1524 up-regulated DEGs following SS inoculation and 513 up-regulated DEGs following HS2 inoculation. The size and color depth of the circles represent the number of DEGs. (c) Enrichment plot for genes related to the defense responses (GO:0006952~defense response, GO:2000022~regulation of jasmonic acid-mediated signaling pathway). (d) Enrichment plot for genes related the water transport (GO:0006833~water transport). GSEA rank was calculated by HS2 vs. REST (Mock and SS). The green line indicates the enrichment profile. Full article ">Figure 4
Gene expression heatmaps of genes involved in the plant–pathogen interaction pathway of B. rapa in KEGG (Kyoto Encyclopedia of Genes and Genomes). The pathways are organized into three categories: (a) the perception of pathogens by pattern-recognition receptors (PRRs), (b) pattern-triggered immunity (PTI), and (c) effector-triggered immunity (ETI). Heatmap colors represent row-scaled Z-score of TMM-normalized TPM values. The functional descriptions corresponding to the gene are derived from the NCBI Eukaryotic Genome Annotation Pipeline (Annotation release ID: 103.20201202). The gene labels in red indicate DEGs with a fold-change of 4 or more induced by SS and HS2 strains. Full article ">Figure 5
Gene expression patterns related to jasmonic acid and salicylic acid defense pathways in B. rapa (Akimeki) against two P.brassicae isolates (SS and HS2). Heatmaps of the relative expression of key genes involved in (a) JA and (b) SA hormones. The colors represent the relatively highly and lowly regulated expression of genes, as indicated by the row-scaled Z-scores of TMM-normalized TPM values. Gene symbols were assigned based on their similarity to Arabidopsis genes, using a BLAST search. Differential gene expression induced by SS and HS2 strains is shown, with genes exhibiting a fold-change of 4 or more labeled in red and those with a fold-change of 2 or more labeled in blue. Full article ">Figure 6
Quantitative real-time PCR validation of the selected genes. Akimeki was inoculated 10 days after sowing, and samples were collected 72 h after inoculation. Isolated RNA was subjected to cDNA synthesis and qRT-PCR. HS2 (Hoengseong2); SS (Seosan). Gene expression values are normalized to BrACT1. Values are the mean and ± standard error of the mean. Full article ">

14 pages, 4342 KiB

Open AccessArticle

Identification of Autophagy-Related Biomarkers and Diagnostic Model in Alzheimer’s Disease

by Wei Xu, Xi Su, Jing Qin, Ye Jin, Ning Zhang and Shasha Huang

Genes 2024, 15(8), 1027; https://doi.org/10.3390/genes15081027 - 5 Aug 2024

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Abstract

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease. Its accurate pathogenic mechanisms are incompletely clarified, and effective therapeutic treatments are still inadequate. Autophagy is closely associated with AD and plays multiple roles in eliminating harmful aggregated proteins and maintaining cell homeostasis. This [...] Read more.

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease. Its accurate pathogenic mechanisms are incompletely clarified, and effective therapeutic treatments are still inadequate. Autophagy is closely associated with AD and plays multiple roles in eliminating harmful aggregated proteins and maintaining cell homeostasis. This study identified 1191 differentially expressed genes (DEGs) based on the GSE5281 dataset from the GEO database, intersected them with 325 autophagy-related genes from GeneCards, and screened 26 differentially expressed autophagy-related genes (DEAGs). Subsequently, GO and KEGG enrichment analysis was performed and indicated that these DEAGs were primarily involved in autophagy–lysosomal biological process. Further, eight hub genes were determined by PPI construction, and experimental validation was performed by qRT-PCR on a SH-SY5Y cell model. Finally, three hub genes (TFEB, TOMM20, GABARAPL1) were confirmed to have potential application for biomarkers. A multigenic prediction model with good predictability (AUC = 0.871) was constructed in GSE5281 and validated in the GSE132903 dataset. Hub gene-targeted miRNAs closely associated with AD were also retrieved through the miRDB and HDMM database, predicting potential therapeutic agents for AD. This study provides new insights into autophagy-related genes in brain tissues of AD patients and offers more candidate biomarkers for AD mechanistic research as well as clinical diagnosis. Full article

(This article belongs to the Special Issue Bioinformatics of Human Diseases)

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Figure 1

Figure 1
The flow chart of the analyses. Full article ">Figure 2
Volcano plot showing differential gene analysis in the GSE5281 dataset (a), Venn diagram indicating 26 DEAGs (b), and heatmap exhibiting the expression levels of DEAGs in AD and normal samples (c). Full article ">Figure 3
Bubble plot of GO analyses showing biological process (a), the cellular component (b), and the molecular function (c) of DEAGs, and circle plot of KEGG analysis indicating involved pathways of DEAGs (d). hsa04140: Autophagy—animal; hsa05131: Shigellosis; hsa04068; FoxO signaling pathway; hsa04621: NOD-like receptor signaling pathway; hsa04211: longevity-regulating pathway. Full article ">Figure 4
PPI network analysis of 26 DEAGs constructed using STRING (a) and 8 hub genes in two cluster networks determined using the MCODE analysis module of Cytoscape (b,c). Hub genes are highlighted in yellow. Full article ">Figure 5
The relationships among the 8 hub genes evaluated by p-value (a) and coefficient of correlation (b). Full article ">Figure 6
ROC curves for each hub gene. (a) ATG16L1, (b) BAG3, (c) GABARAPL1, (d) PKM, (e) LAMP2, (f) TFEB, (g) TOMM20, (h) VDAC1. Full article ">Figure 7
qRT-PCR validation (a–c) and external dataset validation in GSE132903 (d–f) of the three hub genes. Significance levels were given as follows: *** p < 0.001; ** p <0.01. Full article ">Figure 8
Multigenic prediction model constructed by three qRT-PCR-validated genes in the GSE5281 dataset (a) and validated in the GSE132903 dataset (b). Full article ">

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