Search Results (832)

Background: Psoralea corylifolia is a widely used traditional medicinal plant, with its dried mature fruits as the main medicinal part. Flavonoids and coumarins are the primary bioactive compounds of this species. However, the tissue-specific metabolic profiles, gene expression patterns, and potential regulatory factors underlying active compound biosynthesis remain largely uncharacterized across different tissues of P. corylifolia; Methods: In this study, five tissue types (roots, stems, leaves, flowers and fruits) of P. corylifolia were collected as experimental materials. We performed integrated widely targeted metabolomic and transcriptomic analysis, combined with weighted gene co-expression network analysis (WGCNA), to screen co-expression modules and candidate regulatory factors associated with flavonoid and coumarin accumulation; Results: Distinct tissue specificity was observed at both metabolomic and transcriptomic levels among different tissues, with the most remarkable difference between fruits and other tissues. Signature bioactive compounds including isobavachalcone, bavachin and corylin were specifically and highly accumulated in fruits. Differentially expressed genes were mainly enriched in phenylpropanoid biosynthesis, flavonoid biosynthesis and isoflavonoid biosynthesis pathways. WGCNA revealed that the magenta module was significantly positively correlated with fruit tissues and the contents of the above bioactive metabolites. Six candidate transcription factors were identified from this module and classified into three candidate-priority tiers based on a TF–pathway gene co-expression network (344 edges, |r| ≥ 0.8, p < 0.05) and connectivity metrics. The prioritized hubs were Cluster_22013.0 (C3H-type zinc finger transcription factor) and Cluster_21217.8 (NF-YA; Arabidopsis homolog NFYA9/AT3G20910), with Cluster_9299.0 (Rcd1-like) and Cluster_20910.0 (NAC; Arabidopsis homolog NAC002/AT5G04410) as highly connected positively correlated candidates, while Cluster_31326.1 (bZIP) and Cluster_11436.0 (C2H2) were identified as negatively correlated candidates, with all their significant edges representing negative correlations with pathway genes; Conclusions: This study characterizes tissue-specific metabolic and transcriptomic patterns in P. corylifolia, and identifies candidate co-expression modules and transcription factors associated with flavonoid and coumarin accumulation in fruits. The prioritized TF tiers, including candidate hub and negatively correlated TFs, provide a foundation for future functional studies on the regulation of active compound biosynthesis in P. corylifolia. Full article

Coronary artery disease (CAD) and non-obstructive azoospermia (NOA) have distinct aetiologies. We examined whether separately analysed public transcriptomic cohorts contained overlapping exploratory candidate signals without assuming a shared causal mechanism. We re-analysed five Gene Expression Omnibus datasets using differential-expression screening, weighted gene co-expression network analysis (WGCNA), an archived neural-network candidate ranking, xCell enrichment scoring, and single-cell transcriptomic mapping. Nominal differential-expression screening identified 978 CAD-associated and 2562 NOA-associated candidate transcripts. WGCNA showed a moderate correlation between the MElightyellow module and NOA status (r = 0.58, p = 0.007) in GSE45887, a small, imbalanced, non-independent subset of GSE45885. An archived neural-network (NNET) ranking prioritised HSPA1B, PLCL2, ISLR2, STRN, and AQP7 for descriptive analyses; the ranking was generated from the same 20 specimens and is treated as heuristic. xCell produced marker-gene enrichment scores rather than direct measurements of cell abundance or function. Single-cell mapping was descriptive because GSE149512 combined heterogeneous NOA aetiologies with paediatric and adult comparator tissues. The analyses generate hypotheses from separate CAD and NOA cohorts. They do not establish a shared causal pathway, a sex- or age-independent association, temporal sequence, clinical diagnostic utility, or direct correspondence between testicular and coronary cell states. Full article

Soil salinization severely limits forage crop productivity, yet the regulatory networks that govern salt stress adaptation in alfalfa, a moderately salt-tolerant leguminous forage, remain largely unexplored. Here, we examined the physiological and transcriptomic dynamics of alfalfa leaves under 200 mM NaCl stress across three time points. Salt stress induced a progressive elevation of the Na⁺/K⁺ ratio, biphasic activation of antioxidant enzymes and concurrent accumulation of malondialdehyde. Time-course RNA-seq analysis identified 3631 differentially expressed genes (DEGs) and 132 core salt-responsive transcription factors (TFs). Pathway and functional annotation analyses indicated that these DEGs were prominently involved in cell wall biogenesis, redox homeostasis, and the carotenoid biosynthesis pathway, with carotenoid accumulation strongly activated under salt stress. Using weighted gene co-expression network analysis (WGCNA), nine distinct co-expression clusters were constructed. Notably, the brown module, which showed a positive correlation with Na⁺ accumulation and the Na⁺/K⁺ ratio, was significantly enriched in the plant hormone signal transduction pathway, within which 72.7% of the enriched genes belonged to the TIFY family. Among them, a core hub gene, MsTIFY11B, was isolated for functional characterization. Subcellular localization demonstrated that MsTIFY11B is exclusively localized to the nucleus. Heterologous expression in yeast showed that MsTIFY11B overexpression enhanced tolerance to salinity and alkalinity, whereas it conferred negligible protection against mannitol-induced drought stress. Taken together, our findings provide a comprehensive temporal framework of the alfalfa transcriptomic response to salinity and suggest that MsTIFY11B may contribute to salt–alkali tolerance, making it a promising candidate for further functional characterization and potential application in the development of stress-adapted alfalfa varieties. Full article

Crop productivity around the world is largely constrained by salt-induced stress, a key abiotic factor. Although oat (Avena sativa L.) can withstand challenging environmental conditions, the physiological and molecular responses underlying salt tolerance during germination and early seedling development remain insufficiently understood. To investigate these responses, 28 oat varieties were evaluated at the germination stage, and two contrasting varieties, the salt-tolerant Mengshi No. 1 (MS) and salt-sensitive Morgan (MG), were selected for detailed analysis under a severe NaCl treatment (300 mM) during early seedling stages. Under severe salt stress, the two oat varieties exhibited distinct growth and physiological responses, including changes in growth traits, chlorophyll content, membrane stability, osmotic adjustment, and antioxidant responses. Transcriptomic analysis revealed 14,109 differentially expressed genes (DEGs) between salt-treated MG and its respective control (CK), 19,405 between salt-treated MS and its CK, and 6161 between salt-treated MG and salt-treated MS, suggesting different transcriptional response patterns between the salt-tolerant and salt-sensitive varieties under severe salt stress. Weighted gene co-expression network analysis (WGCNA) revealed a salt-responsive module associated with MS, from which five hub genes, AVESA.00010b.r2.1CG0087930 (MGL), AVESA.00010b.r2.19DG0180280 (MGL), AVESA.00010b.r2.4CG1272260 (BCH1), AVESA.00010b.r2.5DG0989800 (GPAT7), and AVESA.00010b.r2.6CG1124100 (TPR10), were identified as candidate genes potentially associated with salt tolerance and stress responses. Full article

Smut disease is a major constraint on proso millet (Panicum miliaceum L.) production. Our previous metabolomic work in the cultivar ‘Chishu 13’ outlined a two-stage defense strategy involving energy metabolism at jointing and phenylpropanoid activation at heading, yet the transcriptional regulation underlying this metabolic shift remained unknown. In this follow-up study, we performed RNA-seq on the same biological samples used in our prior metabolomics analysis to directly link transcriptomic changes to metabolic outcomes. We compared asymptomatic inoculated (IA) and symptomatic inoculated (IS) plants across four developmental stages (seedling, tillering, jointing, and heading) using time-series profiling, weighted gene co-expression network analysis (WGCNA), and multi-omics integration. Transcriptional reprogramming was minimal during early infection but escalated sharply from jointing (5498 differentially expressed genes) to heading (6818 DEGs), matching the previously observed metabolic divergence. K-means clustering revealed two distinct transcriptional programs: TCA cycle and oxidative phosphorylation pathways dominated at jointing, while phenylpropanoid biosynthesis and plant–pathogen interaction pathways peaked at heading. By combining WGCNA with temporal clustering, we identified 337 high-confidence Stage 1 and 408 Stage 2 candidate genes. Seven hub genes encoding TCA cycle enzymes correlated positively with citrate and succinate accumulation only in IA plants at jointing, whereas eight phenylpropanoid-related hub genes strongly associated with L-phenylalanine and cinnamic acid levels in IA plants at heading. These coordinated transcriptional–metabolic modules were disrupted in IS plants, and RT-qPCR confirmed phenotypic-group-specific expression of key hub genes. This study presents a temporally resolved transcriptional atlas of proso millet response to Anthracocystis destruens, extending beyond prior single-time-point multi-omics studies by integrating matched transcriptomic and metabolomic time-series data to establish a dynamic multi-omics framework for smut resistance. We demonstrate that effective defense requires precise sequential coordination of energy and phenylpropanoid metabolism, providing validated hub genes as targets for molecular breeding. Full article

Parkinson’s disease (PD) involves not only the loss of dopaminergic neurons in the substantia nigra but also spinal cord degeneration. However, the molecular mechanisms of spinal cord degeneration remain unclear. This study investigated the spinal cord transcriptomic characteristics of MPTP-induced PD mice via transcriptome sequencing and weighted gene co-expression network analysis (WGCNA) to identify key gene modules and potential therapeutic targets. An MPTP-induced PD mouse model was established, and spinal cord transcriptome sequencing was conducted to screen differentially expressed genes (DEGs). Functional enrichment analysis, WGCNA for phenotype-correlated modules, and protein–protein interaction analysis were subsequently conducted to identify key genes. In total, 3473 DEGs were identified (1775 upregulated, 1698 downregulated). Downregulated genes were predominantly enriched in pathways related to oxidative phosphorylation and post-transcriptional regulation, whereas upregulated genes were associated with glutamatergic synapses, axonogenesis, and negative regulation of neurogenesis. Among the five co-expression modules, the brown and yellow modules were most strongly correlated with the PD phenotype, enriched in calcium signaling, inflammation, and spliceosome pathways. Key genes like Akt1, Nlrp3, Tgfb1, Lingo1, and Olig2 were upregulated, whereas Vps35 and Omg were downregulated. This study characterizes the spinal transcriptome of PD mice, suggesting that dysregulated post-transcriptional processes, abnormal oxidative phosphorylation, glutamatergic excitotoxicity, and neuroinflammation may be potential candidate mechanisms and vital involved factors in spinal cord degeneration. These findings provide novel insights into the pathological mechanisms of spinal cord degeneration in PD and lay a foundation for targeted therapy, deserving further investigation. Full article

Evodia rutaecarpa is a valuable medicinal plant, yet its non-medicinal tissues remain largely underexplored. Here, we integrated ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)-based widely targeted metabolomics and RNA sequencing (RNA-seq) transcriptomics to systematically profile the metabolic and transcriptional landscapes of roots, stems, leaves, and flowers of Evodia rutaecarpa (Juss.) Benth. Our aim was to characterize tissue-specific metabolic differentiation and its underlying transcriptional regulatory mechanisms. Metabolomic analysis, employing principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) with robust model parameters (R²Y > 0.9, Q² > 0.5), identified 3090 differential metabolite features (variable importance in projection, VIP > 1.0; p < 0.05) across the four tissues, which exhibited distinct tissue-specific clustering patterns. Integrated Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and weighted gene co-expression network analysis (WGCNA) revealed that roots specifically accumulated quinolone alkaloids and flavonoid glycosides, accompanied by the coordinated upregulation of genes involved in flavonoid and phenylpropanoid biosynthetic pathways. In contrast, stems, leaves, and flowers were enriched in indole alkaloids (evodiamine and rutaecarpine) and volatile oil precursors, with concurrent upregulation of genes involved in tryptophan metabolism and indole alkaloid biosynthesis (e.g., tryptophan decarboxylase, TDC; s N-methyltransferase, NMT). Notably, leaves and flowers displayed particularly high accumulation levels of these bioactive alkaloids, suggesting their potential as alternative sources for industrial and pharmaceutical applications. WGCNA further identified multiple transcription factors and structural gene modules tightly correlated with evodiamine accumulation, offering promising candidate regulators for future biosynthetic pathway engineering. Collectively, this multi-omics integration study systematically elucidates the tissue-partitioned secondary metabolism of Evodia rutaecarpa (Juss.) Benth. and provides a solid scientific foundation for full-plant resource utilization, targeted development of non-medicinal tissues, and future metabolic engineering of indole alkaloid production. Full article

Background: Coagulation markers are used to guide clinical anticoagulation decisions. We aimed to identify circulating metabolites that are associated with coagulation markers in children with congenital heart disease (CHD). Methods: Plasma samples were separated from whole blood under consistent conditions. Untargeted metabolomic data were measured in plasma from up to 203 young patients (age range: 0 days–24 years) with CHD before cardiac surgery. Coagulation markers included activated partial thromboplastin time (aPTT), prothrombin time (PT), and activated clotting time (ACT). Weighted Gene Co-expression Network Analysis (WGCNA) was performed to explore metabolite modules (clusters). Associations of metabolites with the coagulation markers were assessed cross-sectionally with regression models, with false discovery rate (FDR) correction for multiple comparison. Associations between coagulation markers and “eigenmetabolites” from WGCNA modules were assessed by correlation analysis. Results: A total of 776 metabolites were included in the final analysis. Among these, 20 metabolites were associated with PT and one (valine) with ACT (FDR q value < 0.05). Among the metabolites associated with PT, the top three were retinol, 1-palmitoyl-GPI (16:0), and X-25371 (identity unknown). One module from WGCNA with metabolites from the lipid super pathway was correlated with PT (p = 0.004). Conclusions: In this first attempt to identify novel metabolites for coagulation markers, we report 21 metabolites associated with PT or ACT in children with CHD. Future studies are needed to replicate these findings in independent cohorts and to elucidate the biological mechanisms linking these metabolites to hemostatic regulation. Full article

The cytochrome P450 superfamily is vital for plant growth and stress responses, yet the CYP90 gene family in Brassica napus remains poorly characterized. In this study, we systematically identified 25 BnCYP90 genes across 16 chromosomes, classifying them into CYP90A, CYP90B, CYP90C, and CYP90D phylogenetic groups. Synteny and evolutionary analyses were consistent with purifying selection acting on this gene family. Promoter analysis identified diverse stress- and hormone-responsive elements, while network analyses predicted BnCYP90_4, BnCYP90_6, and BnCYP90_11 as highly connected nodes in candidate protein–protein interaction and miRNA-mediated regulatory networks. Furthermore, single nucleotide polymorphism (SNP) analysis of a natural population demonstrated that a missense variant (C09:66,297,283) in BnCYP90_25 was significantly associated with seed germination rate and aboveground biomass under salt stress. Transcriptome and RT-qPCR analyses highlighted distinct spatiotemporal expression profiles under abiotic stresses: BnCYP90_25 showed sustained activation under salt stress, BnCYP90_9 and BnCYP90_20 exhibited mid-stage responses to osmotic stress, and BnCYP90_21 and BnCYP90_25 rapidly responded to alkaline stress. Finally, weighted gene co-expression network analysis (WGCNA) identified BnCYP90_13 as a hub gene in the T1 developmental stage. Overall, this study comprehensively characterizes the BnCYP90 family, providing valuable candidate genes for future functional validation in breeding stress-tolerant B. napus. Full article

Korean ginseng (Panax ginseng C. A. Meyer) is a perennial medicinal crop highly sensitive to waterlogging stress. Although excess soil moisture is known to induce oxygen limitation in the rhizosphere, the resulting metabolic constraints may precede visible shoot symptoms such as wilting, chlorosis, or necrosis. To examine the molecular responses under sustained high soil moisture before overt shoot necrosis, one-year-old ‘Yunpoong’ plants were exposed to ambient waterlogging at 45–55% volumetric soil water content (VSWC), while control plants were maintained at 25% VSWC. Phenotypic observations and hyperspectral imaging were conducted immediately after treatment initiation (week 0) and at weeks 1, 2, and 3, whereas whole-plant samples for RNA-seq were collected at weeks 1–3. Hyperspectral imaging and normalized difference vegetation index (NDVI) analysis indicated limited temporal changes in canopy-level spectral traits during the treatment period. In contrast, transcriptomic analyses revealed time-dependent changes in whole-plant gene expression. A total of 6448 unique differentially expressed genes (DEGs) were identified, of which 4369 were specific to week 3. Gene Ontology (GO) enrichment analysis suggested a stepwise adaptive pattern consisting of early priming, transient stabilization, and long-term remodeling. Hypoxia-, jasmonic acid-, and lignin-related processes were enriched at week 1, water deprivation and protein quality control responses were adjusted at week 2, and glycolysis, phosphate starvation, and repression of growth-related processes became prominent at week 3. Weighted gene co-expression network analysis (WGCNA) and STRING-based protein–protein interaction (PPI) analysis further identified functional modules associated with RNA processing, photosystem regulation, proteostasis, and central carbon, energy, and phosphate metabolism. Integrated transcriptome and network analyses prioritized six candidate genes, GAPC2, MDH, PGI1, PPC1, Lhb1B1, and RD21A, for further functional validation in relation to prolonged ambient waterlogging responses. Full article

Background: Chronic obstructive pulmonary disease (COPD) is associated with systemic inflammation and increased cardiovascular comorbidity, yet peripheral blood molecular markers for cardiovascular comorbidity-related stratification in COPD remain poorly defined. Methods: In this study, PBMC transcriptomic datasets from a COPD cohort (GSE42057) and a CAD cohort (GSE113079) were analyzed using weighted gene co-expression network analysis (WGCNA) to identify disease-associated modules, followed by overlapping gene screening. Machine-learning models were then applied to prioritize the shared genes. External validation was performed in an independent COPD cohort (GSE54837) and an independent CAD cohort (GSE250283). Immune-cell deconvolution and single-cell transcriptomic analysis of a CAD dataset (GSE269269) were further used to characterize the immune and cellular context of the leading candidate. Finally, RT–qPCR was performed in an institutional PBMC cohort for experimental validation. Results: A total of 169 shared genes were identified, with enrichment in immune, mitochondrial, oxidative phosphorylation, and metabolic pathways. RTN4 was the most consistently validated candidate across COPD and CAD cohorts and was associated with poorer lung function, advanced GOLD stages, and monocyte-related immune patterns. In CAD single-cell data, RTN4-associated signals were mainly localized to monocytes, particularly intermediate monocytes under plaque rupture conditions, with enrichment of immune, antigen-presentation, oxidative-stress, and metabolic pathways. RT–qPCR confirmed increased RTN4 mRNA expression in COPD and a further increase in patients with COPD and comorbid CAD despite comparable FEV1% predicted between the two COPD groups. Conclusions: These findings suggest that elevated RTN4 expression may serve as a PBMC-derived, monocyte-associated candidate molecular feature related to COPD–CAD comorbidity. Full article

Drought stress poses a major threat to global wheat (Triticum aestivum L.) productivity by impairing physiological processes and inducing oxidative damage. Mutation breeding provides a valuable approach to generate novel genetic variation and identify stress-tolerant germplasms. In this study, phenotypic, physiological, and transcriptomic analyses were integrated to elucidate the drought adaptation mechanisms of a gamma-ray-induced mutant wheat line, PL6, alongside its wild-type parent, PL1. Under osmotic stress and soil drought conditions, PL6 exhibited an enhanced germination rate and higher photosynthetic efficiency (Fv/Fm). Furthermore, PL6 maintained lower malondialdehyde (MDA) accumulation, which was supported by elevated activities of antioxidant enzymes including SOD, APX, and CAT. Time-series transcriptomic analysis via WGCNA and GSEA revealed that PL6 actively maintains environmental sensing, transmembrane transport, and photosynthetic processes under PEG-induced osmotic stress. Conversely, pathways associated with the cell cycle and DNA metabolism were transiently suppressed. To isolate key regulatory genes without computational bias, a multi-algorithm machine learning framework—combining Random Forest, LightGBM, and LASSO—was applied to variance-stabilizing transformed (VST) expression profiles. This approach successfully identified 45 consensus core drought-responsive genes enriched in targeted protein turnover, redox balance, cell wall restructuring, and lipid metabolism, from which ten representative candidate genes were experimentally validated via qRT-PCR. Collectively, this study demonstrates an effective analytical framework for selection of high-confidence transcripts, providing candidate targets for future targeted gene editing and molecular breeding in wheat. Full article

To elucidate the chemical basis underlying bitterness and astringency modulation in jasmine tea, four green tea bases and their corresponding jasmine teas were analyzed using weighted gene co-expression network analysis (WGCNA) combined with spiking validation experiments. WGCNA revealed that catechin, gallocatechin gallate, aspartic acid (Asp), glutamic acid (Glu), arginine (Arg), and alanine (Ala) were positively associated with sweetness and umami but negatively associated with bitterness and astringency. Spiking experiments showed that taste modulation was both concentration- and matrix-dependent. Glu and Asp effectively reduced bitterness at suitable concentrations, whereas the effects of theanine and Arg varied among tea matrices. Bitterness and astringency of jasmine tea infusion are modulated by the combined effects of the chemical matrix of tea base, providing a theoretical basis for tea-base selection and jasmine tea flavor optimization. Full article

Background: Prostate cancer (PCa) is a leading cause of cancer-related mortality worldwide, highlighting the need for improved prognostic tools. The integration of artificial intelligence (AI) and machine learning (ML) with multi-omics data offers new opportunities for biomarker discovery and risk stratification. Methods: We integrated bulk transcriptomic data from GSE116918 (training, n = 248) and three cross-cohort consistency evaluation cohorts (TCGA-PRAD, GSE70769, GSE46602), focusing on 1087 epithelial–mesenchymal transition (EMT)-associated genes. Using consensus clustering, weighted gene co-expression network analysis (WGCNA), and 91 machine learning algorithm combinations (including Random Forest, Lasso, and CoxBoost), we constructed a prognostic signature. SHAP analysis was used for model interpretability. Single-cell RNA sequencing (scRNA-seq, GSE268307, 10,672 cells) and spatial transcriptomics (10× Genomics Visium FFPE) provided hypothesis-generating evidence; spatial analysis was based on one tissue section. Results: A three-gene signature (INHBA, FAP, ITGBL1) effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly worse metastasis-free survival (HR = 1.61, 95% CI: 1.39–1.87; 4-year AUC = 0.93 in the training cohort; external AUCs ranged from 0.62 to 0.77). CytoTRACE inferred high differentiation potential of COMP+ fibroblasts, and Monocle3 inferred a transcriptional transition from COMP+ toward NELL2+ fibroblasts. BayesPrism deconvolution suggested that high inferred COMP+ fibroblast abundance was associated with poor prognosis and advanced T stage. NicheNet analysis prioritized BMP7 as a key upstream ligand, with downstream targets enriched in TGF-β signaling and stem cell pluripotency pathways. Conclusions: This study presents a machine learning-based multi-omics framework for prostate cancer risk stratification. The three-gene signature provides a new exploratory prognostic model while inferring a COMP+ to NELL2+ transcriptional transition. These findings may inform future hypothesis-driven studies of treatment sensitivity, pending experimental validation, and demonstrate the value of AI-driven multi-omics integration for precision oncology. Full article

The spread of Bursaphelenchus xylophilus has caused a critical shortage of traditional Poria cocos cultivation materials, making bag-based substrates an urgent alternative. Yet, how substrate stoichiometry shapes nutritional quality remains unclear. Using non-targeted metabolomics combined with redundancy analysis (RDA) and weighted gene co-expression network analysis (WGCNA), we profiled P. cocos cultivated on four substrates: healthy pine logs, pine wilt wood bags, oak bags, and pine needle/branch bags. Bag-cultivated P. cocos showed significantly elevated total amino acids, poria cocos acid, and total triterpenoids, with pine wilt wood bags (P1) performing best overall. Nitrogen, phosphorus, and the N/P ratio independently drove metabolomic variation (pairwise overlap < 5%). Nitrogen-line hub metabolites were negatively correlated with amino acid content, suggesting that suppressed lipid metabolism may free carbon skeletons for the accumulation of nitrogenous nutrients. Phosphorus-line hub metabolites were positively associated with polysaccharide indices, whereas the N/P ratio in line lipid amides showed strong negative correlations with polysaccharides under phosphorus limitation. These correlational patterns are consistent with a stoichiometric resource allocation model, although direct validation through controlled-element experiments is required. These findings provide quantitative guidance for optimizing bag-substrate formulations in P. cocos cultivation. Full article