Search Results (719)

Members of the genus Nemipterus are economically important fish species distributed in the tropical and subtropical Indo-West Pacific region. The majority of species in this genus inhabit waters with sandy–muddy substrates on the continental shelf, although different species are found at slightly varying water depths. In this study, we sequenced seven species within the genus Nemipterus after identifying the specimens using complementary morphological analysis and DNA barcoding. Each species yielded over 40,000,000 clean reads, totaling over 300,000,000 clean reads across the seven species. A total of 276,389 unigenes were obtained after de novo assembly and a total of 168,010 (60.79%) unigenes were annotated in the protein database. The comprehensive functional annotation based on the KOG, GO, and KEGG databases revealed that these unigenes are mainly associated with numerous physiological, metabolic, and molecular processes, and that the seven species exhibit similarity in these aspects. By constructing a phylogenetic tree and conducting divergence time analysis, we found that N. bathybius and N. virgatus diverged most recently, approximately during the Neogene Period (14.9 Mya). Compared with other species, N. bathybius and N. virgatus are distributed in deeper water layers. Therefore, we conducted selection pressure analysis using these two species as the foreground branches and identified several environmental-responsive genes. The results indicate that genes such as aqp1, arrdc3, ISP2, Hip, ndufa1, ndufa3, pcyt1a, ctsk, col6a2, casp2 exhibit faster evolutionary rates during long-term adaptation to deep-water environments. Specifically, these genes are considered to be associated with adaptation to aquatic osmoregulation, temperature fluctuations, and skeletal development. This comprehensive analysis provides valuable insights into the evolutionary biology and environmental adaptability of threadfin breams, contributing to the conservation and sustainable management of these species. Full article

Background: Endophytic fungi are prolific sources of bioactive metabolites with potential in pharmaceutical and biotechnological applications. Methods: Here, the endophytic fungus, Alternaria alstroemeriae S6, was isolated from Veronica acinifolia (speedwell), and conducted its anti-microbial activities, whole-genome sequencing and metabolome analysis. Results: The ethyl acetate extract of this fungus exhibited strong anti-bacterial activity and the inhibition zones, induced by the fungal extract at 20 mg/mL, reached 16.25 ± 0.5 mm and 26.5 ± 0.5 mm against Gram-positive and Gram-negative bacteria. To unravel the biosynthetic potential for anti-bacterial compounds, whole-genome sequencing was conducted on A. alstroemeriae S6, resulting in a high-quality assembly of 42.93 Mb encoding 13,885 protein-coding genes. Comprehensive functional genome annotation analyses, including gene ontology (GO) terms, clusters of orthologous groups (COGs), Kyoto encyclopedia of genes and genomes (KEGG), carbohydrate-active enzymes (CAZymes), and antibiotics and secondary metabolites analysis shell (antiSMASH) analyses, were performed. According to the antiSMASH analysis, 58 biosynthetic gene clusters (BGCs), including 16 non-ribosomal peptide synthetases (NRPSs), 21 terpene synthases, 12 polyketide synthetases (PKSs), and 9 hybrids, were identified. In addition, succinic acid was identified as the major metabolite within the fungal extract, while 20 minor bioactive compounds were identified through LC-MS/MS-based molecular networking on a GNPS database. Conclusions: These findings support the biotechnological potential of A. alstroemeriae S6 as an alternative producer of succinic acid, as well as novel anti-bacterial agents. Full article

Severe COVID-19 disproportionately impacts patients with comorbidities such as type 1 diabetes (T1D), type 2 diabetes (T2D), obesity (OBCD), cardiovascular disease (CVD), hypertension (HTN), and cerebrovascular disease (CeVD), affecting 10–30% of cases. This study elucidates shared molecular mechanisms by identifying common hub genes and genetic variants across these conditions using an integrative bioinformatics approach. We curated 5463 COVID-19-related genes from DisGeNET, GeneCards, T-HOD, and other databases, comparing them with gene sets for T1D (324 genes), T2D (497), OBCD (835), CVD (1756), HTN (837), and CeVD (1421). Functional similarity analysis via ToppGene, hub gene prediction with cytoHubba, and Cytoscape-based protein–protein interaction networks identified four hub genes—CCL2, IL6, IL10, and TLR4—consistently shared across all conditions (p < 1.0 × 10⁻⁵). Enrichr-based gene ontology and KEGG analyses revealed cytokine signaling and inflammation as key drivers of COVID-19 cytokine storms. Polymorphisms like IL6 rs1800795 and TLR4 rs4986790 contribute to immune dysregulation, consistent with previous genomic studies. These genes suggest therapeutic targets, such as tocilizumab for IL6-driven inflammation. While computational, requiring biochemical validation, this study illuminates shared pathways, advancing prospects for precision medicine and multi-omics research in high-risk COVID-19 populations. Full article

Objective: Neodymium nitrate (Nd(NO₃)₃) is widely used globally, raising concerns about its occupational and environmental safety. It enters the human body via the digestive system, accumulates in organs, and causes toxicity, including potential hepatotoxicity. However, the role of non-coding RNAs (ncRNAs) in Nd(NO₃)₃-induced liver injury remains unclear. This study aimed to identify key genes and regulatory pathways involved in Nd(NO₃)₃-induced hepatic injury using RNA sequencing (RNA-seq) and differential gene expression analysis. Methods: Mouse hepatocytes (AML12 cells) were exposed to Nd(NO₃)₃, and RNA-seq was performed to analyze the expression profiles of miRNA, lncRNA, circRNA, and mRNA. qPCR was used to validate the RNA-seq results. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore the functions and pathways associated with differentially expressed genes (DEGs). Results: Nd(NO₃)₃ exposure altered the expression of ferroptosis-related genes and induced significant changes in mRNA, miRNA, circRNA, and lncRNA expression levels. GO and KEGG analyses revealed that DEGs were closely related to cellular ferroptosis pathways. Specific miRNAs, lncRNAs, and circRNAs were significantly upregulated, suggesting their potential as biomarkers for Nd(NO₃)₃-induced ferroptosis and liver injury. Conclusion: This study provides the first comprehensive transcriptome database for Nd(NO₃)₃-induced liver injury, highlighting the involvement of ncRNAs in hepatotoxicity. These findings offer valuable insights for developing biomarkers and understanding the mechanisms underlying Nd(NO₃)₃-induced hepatic injury. Full article

Objectives: To explore the metabolic changes in zebrafish larvae after infection with Mycobacterium marinum, this study adopted a widely targeted metabolomic approach to analyze the changes in the overall metabolic profiles of zebrafish larvae infected for 5 days. Methods: Data were collected by liquid chromatography–tandem mass spectrometry (LC-MS/MS). Mass spectrometry data were processed using Analyst 1.6.3 and MultiQuant 3.0.3 software, and multivariate statistical analysis was carried out. The KEGG database, HMDB database, and CHEBI database were used to screen and identify differential metabolites, and metabolic pathway enrichment analysis was performed through KEGG pathways. Results: A total of 329 metabolites were detected, among which 61 differential metabolites were screened. Specifically, 41 metabolites, such as kynurenine, isoallolithocholic acid, 2′-deoxyguanosine, indole-3-carboxaldehyde, and L-lactic acid, were downregulated, while 20 metabolites, such as L-palmitoylcarnitine, myristoyl-L-carnitine, dodecanoylcarnitine, 2-isopropyl-malic acid, and 2-methylsuccinic acid, were upregulated. KEGG metabolic pathway enrichment analysis indicated that these differential metabolites were mainly involved in metabolic pathways such as pyrimidine metabolism, nucleotide metabolism, the pentose phosphate pathway, and purine metabolism. Conclusions: This study demonstrated that significant changes occurred in multiple metabolites and metabolic pathways in zebrafish larvae after infection with M. marinum. The research results have improved the understanding of zebrafish as a model organism in the field of Mycobacterium research and laid a solid foundation for subsequent metabolomic-related research using zebrafish. Full article

Sjögren’s Disease (SjD), Rheumatoid Arthritis (RA), and Systemic Lupus Erythematosus (SLE) are autoimmune diseases with overlapping genetic features, yet the etiologies of these diseases are poorly understood. Using these rheumatic diseases as an example of proof of concept, our aim was to develop a tool that simplifies analysis of gene–disease associations applicable to any disease and to perform comparisons. This tool is meant to provide insights into associated gene symbols and gene expression data to identify candidate biomarkers in common among these diseases. The Diseasesv2.0 and GTExv8 databases were utilized for data collection, providing searchable disease names, affiliated gene symbols, confidence scores (ranging from 0 to 5, with 5 being the most confident), and gene expression across the panel of 54 tissue types present in GTExv8. Data infrastructure was established on a Postgres database using Plotlyv5.17.0 and Streamlitv1.27.2 Python packages. The resulting database was used to investigate the genetic associations among SjD, RA, and SLE, including confidence scores from 2.50 to 5.00. STRINGv12 analysis determined significant pathways (FDR < 0.05). Analysis using our tool revealed the following refined gene associations for each disease: SjD based on ‘Sjogren’ search term (n = 12 genes), RA (n = 231 genes), and SLE (n = 137 genes). We found seven genes in common, namely, CD4, CD8A, IL6, IL17A, TNFS13B, TNF, and TRIM21. With the exception of IL17A, these genes were expressed in tissue types known or suggested to be affected by SjD. STRINGv12 determined significant KEGG pathways involving interleukin signaling, cytokine signaling, and the immune system. We developed a tool that simplifies the data mining process, allowing users to search for diseases of interest and view common gene associations and gene expression. Some of the genes identified through our tool may be further explored to better understand SjD pathogenesis and systemic impact. Full article

Diabetic nephropathy (DN) represents a severe microvascular complication of diabetes mellitus. As a Traditional Chinese Medicine (TCM) with extensive clinical applications, Ligustri Lucidi Fructus (LLF) exhibits significant anti-DN activity. However, the underlying pharmacological mechanisms, crucial components, and targets for LLF in DN treatment remain unclear. By integrating network pharmacology, molecular docking, and molecular dynamics simulations, the bioactive compounds, potential therapeutic targets, and underlying mechanisms of LLF in the treatment of DN were elucidated, followed by biological validation in a palmitic acid (PA)-induced MPC5 podocyte injury model. Among the 383 DN-related LLF targets identified, TNF emerged as a pivotal one, demonstrating potential binding interaction with the active components salidroside (Sal), apigenin (Api), and tormentic acid (TA). Moreover, Gene Expression Omnibus (GEO) database and KEGG enrichment analysis collectively highlighted the cytosolic DNA-sensing pathway. Notably, the cGAS-STING pathway is central to this pathway. Experimental studies further demonstrated that LLF-containing serum exerted a protective effect on MPC5 podocytes through cGAS-STING pathway suppression. Overall, these findings elucidate the pleiotropic mechanisms underlying LLF’s protective effects against DN, integrating compound–target–pathway interactions and thus offering a rationale for further investigation. Full article

Oral squamous cell carcinoma (OSCC) is a malignancy that affects the oral mucosa and is characterized by indurated oral lesions. The RNAseq of formalin-fixed, paraffin-embedded (FFPE) samples is readily available in clinical settings. Such samples have long-term preservation and can provide highly accurate transcriptomic information regarding gene fusions, isoforms, and allele-specific expression. We determined differentially expressed genes using the transcriptomic profiles of oral potentially malignant disorder (OPMD) FFPE oral lesion samples of patients who developed OSCC over years. A technical comparison was completed comparing breast cancer (BC) FFPE publicly available data in this proof-of-concept pilot study. OSCC FFPE samples were collected from patients (N = 3) who developed OSCC 3 to 5 years following OPMD diagnosis (n = 3) and were analyzed using RNAseq. RNAseq sequences from the FFPE OSCC samples and publicly available FFPE samples of BC patients (n = 6) (Gene Expression Omnibus Database, GSE58135) aligned to human reference (GRCh38.p13). Genes were counted using the Spliced Transcripts Alignment to a Reference (STARv2.7.9a) software. Differential expression was determined in R using DESeq2v1.40.2 comparing OSCC to BC samples. Principal component analysis (PCA) plots were completed. Differential Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined via the Pathviewv.1.40.0 program. STRING v12.0 was used to determine protein–protein interactions between genes represented in more than one KEGG pathway. STARv2.7.9a identified 27,237 and 30,343 genes among the OSCC and BC groups, respectively. DESeq2v1.40.2 determined 9194 differentially expressed genes (DEPs), 4466 being upregulated (OSCC > BC) and 4728 being downregulated (BC > OSCC) (padj < 0.05). Most significant genes included KRT6B, SERPINB5, and DSC3 (5- to 10-fold change range; padj < 10 × 10−100). PCA showed that BC and OSCC samples clustered as separate groups. Pathviewv.1.40.0 identified 17 downregulated KEGG pathways in OSCC compared to the BC group. No upregulated KEGG pathways were identified. STRINGv12.0 determined Gene Ontology Biological Process enrichments for leukocytes and apoptosis in upregulated KEGG genes including multiple PIK3 genes and NIK/NF-kappaB signaling and metabolic responses from lipopolysaccharides in downregulated KEGG genes including CHUK and NFKB1. Using FFPE samples, we determined DEPs characteristic of OSCC and distinct from BC. KRT-family genes and lipopolysaccharide producing periodontal pathogens may be further investigated for their involvement in the OPMD to OSCC transition. Full article

The yellow peach moth (YPM), Conogethes punctiferalis, is an important agricultural insect pest causing severe damage to corn in eastern China. Beauveria bassiana is an effective, eco-friendly, and promising alternative agent for controlling this insect pest. However, insect immunity can limit the ability of fungal infections. In order to understand the immune response mechanism of YPM, a comparative proteomic analysis of non-infected and B. bassiana-infected larvae was conducted using the isobaric tags for relative and absolute quantification (iTRAQ) technique. On the basis of proteomic analysis, 4195 quantifiable proteins were identified from a total of 29,155 peptides. The functions of the identified proteins were annotated in four databases (GO, COG, KEGG, and IPR). A total of 132 immune-related proteins were screened, including 46 pathogen recognition proteins, 27 extracellular signal modulation proteins, and 59 immune effectors. Furthermore, 70 differentially expressed proteins (DEPs) were identified, including 57 up-regulated proteins and 13 down-regulated proteins. Among these, four DEPs were related to immunity, namely one defense protein and three peptidoglycan recognition proteins. Six randomly selected immune-related proteins associated with target genes were validated for qRT-PCR, and the results indicated that the accuracy and reliability of the proteome sequencing data were high. Taken together, the results enrich the fundamental knowledge of YPM immune responses to B. bassiana infection and provide a new insight into insect−pathogen interactions. Full article

Alcoholic liver disease (ALD) is a type of liver disease with complex pathogenic factors. In 2019, alcohol caused 11 million life-years to be lost globally, and the mortality rate has continued to rise. This study aims to explore the exclusive gene profile of AH and construct an mRNA-lncRNA regulatory network through an integrative analysis and database validation to reveal potential key biomarkers. We obtained expression data for alcoholic hepatitis from the GEO database; screened differentially expressed genes (DEGs) through a weighted gene co-expression network analysis (WGCNA); conducted a GO&KEGG analysis; and focused on the enrichment pathways for the top 20 genes. Hub genes were selected using cytoHubba and MCODE to construct the mRNA-lncRNA regulatory network, and key genes were confirmed using GSE167308 and GSE28619. We obtained 2552 differentially expressed mRNAs and 555 differentially expressed lncRNAs from three databases. Differentially expressed genes are mainly involved in pathways such as lipid metabolism disorders, complement activation, the activation of cancer-related pathways, the excessive activation of inflammatory immunity, and the initiation of cell adhesion and fibrosis. Based on the hub gene analysis, we screened out 43 key genes. By constructing the key mRNA-lncRNA–pathway network, we identified 12 mRNAs (AQP1, ELOVL7, ITPR3, KRT19, KRT23, LAMC2, MMP7, PROM1, SPINT1, STK39, TNFRSF21, and VTCN1) and 14 lncRNAs that play an important role in the occurrence and development of alcoholic hepatitis. To sum up, this article mainly expounds upon the key genes in the occurrence and development of alcoholic hepatitis. The key genes are mainly concentrated within signaling pathways such as metabolic pathways, fatty acid metabolism, and cancer pathways. Twelve differentially expressed mRNAs in the co-expression network can be used as biomarkers and intervention targets for the diagnosis and treatment of alcoholic hepatitis. Full article

Objective: This study aims to elucidate the potential mechanisms by which Fespixon cream promotes diabetic foot ulcer (DFU) healing using network pharmacology, molecular docking, and RT-qPCR validation in clinical tissue samples. Methods: Active components of Fespixon cream were screened from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and relevant literature, and their corresponding targets were standardized using the Universal Protein Resource (UniProt) database. Diabetic foot ulcer (DFU)-related targets were retrieved and filtered from the GeneCards database and the Online Mendelian Inheritance in Man (OMIM) database. The intersection of drug and disease targets was identified, and a protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The interaction network was visualized using Cytoscape version 3.7.2 software. The potential mechanisms of the shared targets were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using R software packages, and results were visualized through Bioinformatics online tools. Molecular docking was performed to validate the binding between key active compounds of Fespixon cream and core DFU targets using AutoDock Vina version 1.1.2 and PyMOL software. Furthermore, RT-qPCR analysis was performed on wound edge tissue samples from DFU patients treated with Fespixon cream to experimentally verify the mRNA expression levels of predicted hub genes. Results: Network pharmacology analysis identified eight active compounds in Fespixon cream, along with 153 potential therapeutic targets related to diabetic foot ulcer (DFU). Among these, 21 were determined as core targets, with the top five ranked by degree value being RAC-αserine/threonine-protein kinase (AKT1), Cellular tumor antigen p53 (TP53), Tumor necrosis factor (TNF), Interleukin-6 (IL6), and Mitogen-activated protein kinase 1 (MAPK1). GO enrichment analysis indicated that the targets of Fespixon cream were primarily involved in various biological processes related to cellular stress responses. KEGG pathway enrichment revealed that these targets were significantly enriched in pathways associated with diabetic complications, atherosclerosis, inflammation, and cancer. Molecular docking confirmed stable binding interactions between the five major active compounds—quercetin, apigenin, rosmarinic acid, salvigenin, and cirsimaritin—and the five core targets (AKT1, TP53, TNF, IL6, MAPK1). Among them, quercetin exhibited the strongest binding affinity with AKT1. RT-qPCR validation in clinical DFU tissue samples demonstrated consistent expression trends with computational predictions: AKT1 was significantly upregulated, while TP53, TNF, IL6, and MAPK1 were markedly downregulated in the Fespixon-treated group compared to controls (p < 0.001), supporting the proposed multi-target therapeutic mechanism. Conclusions: Our study reveals the potential mechanisms by which Fespixon cream exerts therapeutic effects on DFUs. The efficacy of Fespixon cream in treating DFUs is attributed to the synergistic actions of its bioactive components through multiple targets and multiple signaling pathways. Full article

Background: Gastric cancer is a leading cause of cancer-related mortality worldwide, with approximately one million new cases diagnosed annually. While Helicobacter pylori infection remains a primary etiological factor, mounting evidence implicates obesity and lipid metabolic dysregulation, particularly in hypercholesterolemia, as emerging drivers of gastric tumorigenesis. This study investigates the molecular intersections between gastric cancer, obesity, and hypercholesterolemia through a comprehensive multi-omics and systems biology approach. Methods: We conducted integrative transcriptomic analysis of gastric adenocarcinoma using The Cancer Genome Atlas (TCGA) RNA-sequencing dataset (n = 623, 8863 genes), matched with standardized clinical metadata (n = 413). Differential gene expression between survival groups was assessed using Welch’s t-test with Benjamini–Hochberg correction (FDR < 0.05, |log₂FC| ≥ 1). High-confidence gene sets for obesity (n = 128) and hypercholesterolemia (n = 97) were curated from the OMIM, STRING (confidence ≥ 0.7), and KEGG databases using hierarchical evidence-based prioritization. Overlapping gene signatures were identified, followed by pathway enrichment via Enrichr (KEGG 2021 Human) and protein–protein interaction (PPI) analysis using STRING v11.5 and Cytoscape v3.9.0. CD36’s prognostic value was evaluated via Kaplan–Meier and log-rank testing alongside clinicopathological correlations. Results: We identified 36 genes shared between obesity and gastric cancer, and 31 genes shared between hypercholesterolemia and gastric cancer. CD36 emerged as the only gene intersecting all three conditions, marking it as a unique molecular integrator. Enrichment analyses implicated dysregulated fatty acid uptake, adipocytokine signaling, cholesterol metabolism, and NF-κB-mediated inflammation as key pathways. Elevated CD36 expression was significantly correlated with higher tumor stage (p = 0.016), reduced overall survival (p = 0.001), and race-specific expression differences (p = 0.007). No sex-based differences in CD36 expression or survival were observed. Conclusions: CD36 is a central metabolic–oncogenic node linking obesity, hypercholesterolemia, and gastric cancer. It functions as both a mechanistic driver of tumor progression and a clinically actionable biomarker, particularly in metabolically comorbid patients. These findings provide a rationale for targeting CD36-driven pathways as part of a precision oncology strategy and highlight the need to incorporate metabolic profiling into gastric cancer risk assessment and treatment paradigms. Full article

Breast, colon, and ovarian cancers are among the most prevalent malignancies worldwide, with distinct clinical features. This study aims to identify key proteins as common biomarkers for breast, colon, and ovarian cancer through protein analysis, molecular mechanisms, and patient sample validation. Data mining from curated databases identified 483 proteins for breast cancer, 521 for colon cancer, and 223 for ovarian cancer. Interaction network analysis revealed shared clusters involved in cancer progression, DNA repair, and cell proliferation. A core set of 27 proteins was found to be common across all three cancer types, participating in key biological processes such as DNA damage response, cell proliferation, and apoptosis. Notably, these proteins are implicated in KEGG pathways linked to multiple cancers. Differential gene expression analysis revealed significant alterations in the expressions of MSH2 and KIT across the three cancers, suggesting their potential as common biomarkers. The high expression of these proteins was associated with better survival outcomes, highlighting their potential as common biomarkers for breast, colon, and ovarian cancers. The in-silico methodology integrated various bioinformatic tools—including cluster identification, gene expression profiling, protein network visualization, and biomarker prediction—enhancing the understanding of shared molecular mechanisms and potential therapeutic targets. Full article

The eri silkworm Samia cynthia ricini (S. ricini) is an economically and scientifically significant lepidopteran species, though its genomic resources have remained limited. Here, we present a chromosome-level genome assembly for S. ricini generated through integrated long-read, short-read, and Hi-C sequencing data. The final 456.16 Mb assembly spans 14 chromosomes, exhibiting 98.5% BUSCO completeness and a 48.51% repetitive content. Functional annotation of the 15,729 protein-coding genes against five major databases (NR, SwissProt, Pfam, GO, and KEGG) revealed a maximum annotation rate of 92.71%, demonstrating high gene set quality. Comparative genomics with B. mori uncovered conserved syntenic blocks interspersed with chromosomal fusion/fission events and inversions. We further identified 4.27 million SNPs, 1.02 million InDels, and 53,367 SVs, establishing the first comprehensive variation map for this species. These genomic variations provide a foundation for marker-assisted breeding programs and trait association studies. All the genomic resources and interactive visualization tools were integrated into the SilkMeta database. This study establishes S. ricini as a pivotal resource for comparative lepidopteran genomics and accelerates molecular breeding programs for this agriculturally valuable insect. Full article

Soil salinization hinders plant growth and agricultural production, so breeding salt-tolerant crops is an economical way to exploit saline–alkali soils. However, the specific metabolites and associated pathways involved in salt tolerance of the dandelion have not been clearly elucidated so far. Here, we compared the transcriptome and metabolome responses of 0.7% NaCl-stressed dandelion ‘BINPU2’ (variety A) and ‘TANGHAI’ (variety B). Our results showed that 222 significantly altered metabolites mainly enriched in arginine biosynthesis and pyruvate metabolism according to a KEGG database analysis in variety A, while 147 differential metabolites were predominantly enriched in galactose metabolism and the pentose phosphate pathway in variety B. The transcriptome data indicated that the differentially expressed genes (DEGs) in variety A were linked to secondary metabolite biosynthesis, phenylpropanoid biosynthesis, and photosynthesis–antenna proteins. Additionally, KEGG annotations revealed the DEGs had functions assigned to general function prediction only, post-translation modification, protein turnover, chaperones, and signal transduction mechanisms in variety A. By contrast, the DEGs had functions assigned to variety B as plant–pathogen interactions, phenylpropanoid biosynthesis, and photosynthesis–antenna proteins, including general function prediction, signal transduction mechanisms, and secondary metabolite biosynthesis from the KOG database functional annotation. Furthermore, 181 and 162 transcription factors (TFs) expressed under saline stress conditions specifically were detected between varieties A and B, respectively, representing 36 and 37 TF families. Metabolomics combined with transcriptomics revealed that salt stress induced substantial changes in terpenoid metabolites, ubiquinone biosynthesis metabolites, and pyruvate metabolites, mediated by key enzymes from the glycoside hydrolase family, adenylate esterases family, and P450 cytochrome family at the mRNA and/or metabolite levels. These results may uncover the potential salt-response mechanisms in different dandelion varieties, providing insights for breeding salt-tolerant crop plants suitable for saline–alkali land cultivation. Full article