Project description:Cultivated eggplant XN, a waterlogging-tolerant variety, were treated with waterlogging stress, and the root of XN eggplant were harvested at the time point of 0, 6, 12, and 24 h post treatment ,relatively. iTRAQ-based quantitative proteomics was performed to analyze protein dynamics in eggplant root.
Project description:Transcriptome analysis of Eggplant cv. PPL during fruit development at 0, 5, 10, 20 and 50 dpa. Eggplant is third most important solanaceae crop species after potato and tomato. It is a versatile crop adapted to different agro-climatic regions and can be grown throughout the year. Unripe eggplant fruit is consumed as cooked vegetable in various ways. It is low in calories and fats, contains mostly water, some protein, fibre and carbohydrates. To decipher molecular mechanisms involved in fruit development eggplant fruit were collected at 0, 5, 10, 20 and 50 dpa and gene expression profiles were analyzed using Affymetrix tomato GeneChip Genome array.
Project description:MiRNAs are a class of non-coding, small RNAs that play important roles in the regulation of gene expression. Although plant miRNAs have been extensively studied in model systems, less is known in other plants with limited genome sequence data, including eggplant (Solanum melongena L.). To identify miRNAs in eggplant and their response to Verticillium dahliae infection, a fungal pathogen for which effective cure methods and a clear understanding of its mechanisms are currently lacking, we deep-sequenced two small RNA (sRNA) libraries prepared from mocked and infected seedlings of eggplants. Specifically, 30,830,792 reads produced 7,716,328 unique small RNAs have been identified.
Project description:Transcriptome analysis of Eggplant cv. PPL during fruit development at 0, 5, 10, 20 and 50 dpa. Eggplant is third most important solanaceae crop species after potato and tomato. It is a versatile crop adapted to different agro-climatic regions and can be grown throughout the year. Unripe eggplant fruit is consumed as cooked vegetable in various ways. It is low in calories and fats, contains mostly water, some protein, fibre and carbohydrates. To decipher molecular mechanisms involved in fruit development eggplant fruit were collected at 0, 5, 10, 20 and 50 dpa and gene expression profiles were analyzed using Affymetrix tomato GeneChip Genome array. Eggplant plants were was grown under controlled conditions in glasshouse. Flowers were hand-pollinated at anthesis and samples were collected at 0, 5, 10, 20 and 50 days post anthesis (dpa). Total RNA was isolated using SpectrumTM Plant Total RNA kit (Sigma, USA) according to the manufacturerM-bM-^@M-^Ys protocol. Affymetrix tomato GeneChip Genome array (Affymetrix, USA) having 10,000 probe sets was used for transcriptome analysis. Three biological replicates were maintained to test the reproducibility and quality of the chip hybridization. cDNA labeling, array hybridization, staining and washing procedures were carried out as described in the Affymetrix protocols. CEL files having estimated probe intensity values were analyzed with GeneSpring GX-11.5 software (Agilent Technologies, USA) to get differentially expressed transcripts. The Robust Multiarray Average (RMA) algorithm was used for the back ground correction, quantile normalization and median polished probe set summarization to generate single expression value for each probe set. Normalized expression values were log2-transformed and differential expression analysis was performed using unpaired t-test. The p-values were corrected by applying the false discovery rate (FDR) correction (Benjamini and Hochberg, 2000).
Project description:BACKGROUND: Western flower thrips are considered the major insect pest of horticultural crops worldwide, causing economic and yield loss to Solanaceae crops. The eggplant (Solanum melongena L.) resistance against thrips remains largely unexplored. This work aims to identify thrips-resistant eggplants and dissect the molecular mechanisms underlying this resistance using the integrated metabolomic and transcriptomic analyses of thrips-resistant and -susceptible cultivars. RESULTS: We developed a micro-cage thrips bioassay to identify thrips-resistant eggplant cultivars, and highly resistant cultivars were identified from wild eggplant relatives. Metabolomic profiles of thrips-resistant and -susceptible eggplant were compared using the gas chromatography-mass spectrometry (GC-MS)-based approach, resulting in the identification of a higher amount of quinic acid in thrips-resistant eggplant compared to the thrips-susceptible plant. RNA-sequencing analysis identified differentially expressed genes (DEGs) by comparing genome-wide gene expression changes between thrips-resistant and -susceptible eggplants. Consistent with metabolomic analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs revealed that the starch and sucrose metabolic pathway in which quinic acid is a metabolic by-product was highly enriched. External application of quinic acid enhances the resistance of susceptible eggplant to thrips. CONCLUSION: Our results showed that quinic acid plays a key role in the resistance to thrips. These findings highlight a potential application of quinic acid as a biocontrol agent to manage thrips and expand our knowledge to breed thrips-resistant eggplant.
Project description:This dataset comprises spatial multi-omic profiling of malignant pleural mesothelioma (MPM) tissue, enabling simultaneous measurement of transcriptomic and protein expression at spatial resolution. To address the lack of spatially resolved proteomic measurements in standard spatial transcriptomics (ST) technologies, we generated this in-house MPM spatial transcriptomics and proteomics dataset and used it to train and evaluate DGAT (Dual-Graph Attention Network), a deep learning framework for imputing protein expression from ST data. DGAT integrates transcriptomic, proteomic, and spatial information using graph attention networks and jointly reconstructs mRNA and protein profiles through multi-task learning. This dataset serves both as a biological resource for understanding the tumor immune microenvironment in MPM and as a benchmark for spatial protein inference. DGAT’s predictions on this dataset enabled improved identification of immune phenotypes and tumor–stroma spatial organization, with implications for biomarker discovery and therapeutic targeting in mesothelioma. Spatial transcriptomics (ST) technologies provide genome-wide mRNA profiles in tissue context but lack direct protein-level measurements, which are critical for interpreting cellular function and microenvironmental organization. We present DGAT (Dual-Graph Attention Network), a deep learning framework that imputes spatial protein expression from transcriptomics-only ST data by learning RNA–protein relationships from spatial CITE-seq datasets. DGAT constructs heterogeneous graphs integrating transcriptomic, proteomic, and spatial information, encoded using graph attention networks. Task-specific decoders reconstruct mRNA and predict protein abundance from a shared latent representation.