Search Results (3,600)

Plant immunity research is being reshaped by integrative multi-omics approaches that connect transcriptomic, proteomic, and interactomic data to build systems-level views of plant–pathogen interactions. This review outlines the scope and methodological landscape of these approaches, with particular emphasis on how transcriptomic and proteomic insights converge through network-based analyses to elucidate defense regulation. Transcriptomics captures infection-induced transcriptional reprogramming, while proteomics reveals protein abundance changes, post-translational modifications, and signaling dynamics essential for immune activation. Network-driven computational frameworks including iOmicsPASS, WGCNA, and DIABLO enable the identification of regulatory modules, hub genes, and concordant or discordant molecular patterns that structure plant defense responses. Interactomic techniques such as yeast two-hybrid screening and affinity purification–mass spectrometry further map host–pathogen protein–protein interactions, highlighting key immune nodes such as receptor-like kinases, R proteins, and effector-targeted complexes. Recent advances in machine learning and gene regulatory network modeling enhance the predictive interpretation of transcription–translation relationships, especially under combined or fluctuating stress conditions. By synthesizing these developments, this review clarifies how integrative multi-omics and network-based frameworks deepen understanding of the architecture and coordination of plant immune networks and support the identification of molecular targets for engineering durable pathogen resistance. Full article

Background/Objectives: In severe peripheral artery disease (PAD) with limb ischemia, hypercholesterolemia (HC) is associated with impaired neovascularization. Extracellular vesicles (EVs) are present within ischemic muscles, and they contain microRNAs (miRs) involved in several biological functions, including angiogenesis and neovascularization. Methods: We used a mouse model of PAD and compared the response to hindlimb ischemia in hypercholesterolemic ApoE^−/− vs. normocholesterolemic mice. Next-generation sequencing (NGS) was used to perform full miR expression profiling in ischemic skeletal muscles and in EVs of varying sizes—large EVs (lEVs) and small EVs (sEVs)—within these muscles. Results: We identified several miRs with potential pro-angiogenic effects (angiomiRs) that are reduced by HC in lEVs (Let-7b-5p, miR-151-3p, Let-7c-5p) or sEVs (miR-21a-5p, miR-196b-5p, miR-340-5p). As proof of principle, we showed that the overexpression of Let-7b-5p in lEVs, or miR-21a-5p in sEVs, can significantly increase the angiogenic capacity of these EVs in vitro. HC also impaired the enrichment of specific angiomiRs in lEVs (miR-100-5p), sEVs (miR-142a-3p), or in both lEVs and sEVs (miR-146b-5p). In silico approaches, including the prediction of miR targets, pathway unions, and gene unions, identified the resulting predictive effects of HC-modulated miRs in EVs on processes with key roles in the modulation of angiogenesis and neovascularization, such as the regulation of the actin cytoskeleton and focal adhesion and the HIF-1, MAPK, AMPK, and PI3K-Akt signaling pathways. Conclusions: Our results constitute an important first step towards the identification of specific miRs that could be targeted to improve EV angiogenic function in hypercholesterolemic conditions and reduce tissue ischemia in patients with severe PAD. Full article

Dentathalia scutellariae (Hymenoptera: Athaliidae) is a major pest of Scutellaria baicalensis, a plant of significant economic and medicinal value. To date, no genomic resources have been available for this species, limiting research into its biology and control. Here, we reported a genome assembly of D. scutellariae with high accuracy and contiguity, sequenced by PacBio HiFi long-read and MGI-Seq short-read methods. The genome assembly is 157.00 Mb in length with a contig N50 of 4.04 Mb. The complete BUSCO score was 98.8%. The genome contained 14.73 Mb of repetitive elements, representing 9.38% of the total genome size. We predicted 14,904 protein-coding genes, of which 12,327 genes were annotated functionally. Gene family analysis of D. scutellariae revealed 422 expanded and 113 contracted gene families. Notably, genes within expanded families were significantly enriched in retinol metabolism and drug metabolism–cytochrome P450 pathways. We present the first high-quality genome assembly of D. scutellariae, which serves as a foundational genomic resource. This dataset will facilitate future studies on the molecular basis of D. scutellariae’s pest status, host adaptation, and the development of targeted control strategies. Full article

Non-AIDS-defining cancers (NADCs) have emerged as an increasingly prominent cause of non-AIDS-related morbidity and mortality among people living with HIV (PLWH). However, the scarcity of NADC clinical samples, compounded by privacy and security constraints, continues to present formidable obstacles to advancing pathological and clinical investigations. In this study, we adopted a joint analysis strategy and deeply integrated and analyzed transcriptomic data from 12,486 PLWH and cancer patients to systematically identify potential key regulators for 23 NADCs. This effort culminated in NADCdb—a database specifically engineered for NADC pathological exploration, structured around three mechanistic frameworks rooted in the interplay of immunosuppression, chronic inflammation, carcinogenic viral infections, and HIV-derived oncogenic pathways. The “rNADC” module performed risk assessment by prioritizing genes with aberrant expression trajectories, deploying bidirectional stepwise regression coupled with logistic modeling to stratify the risks for 21 NADCs. The “dNADC” module, synergized patients’ dysregulated genes with their regulatory networks, using Random Forest (RF) and Conditional Inference Trees (CITs) to identify pathogenic drivers of NADCs, with an accuracy exceeding 75% (in the external validation cohort, the prediction accuracy of the HIV-associated clear cell renal cell carcinoma model exceeded 90%). Meanwhile, “iPredict” identified 1905 key immune biomarkers for 16 NADCs based on the distinct immune statuses of patients. Importantly, we conducted multi-dimensional profiling of these key determinants, including in-depth functional annotations, phenotype correlations, protein–protein interaction (PPI) networks, TF-miRNA-target regulatory networks, and drug prediction, to deeply dissect their mechanistic roles in NADC pathogenesis. In summary, NADCdb serves as a novel, centralized resource that integrates data and provides analytical frameworks, offering fresh perspectives and a valuable platform for the scientific exploration of NADCs. Full article

Liver fibrosis induced by Schistosoma japonicum Katsurada, 1904 (S. japonicum) infection lacks effective diagnostic markers and specific anti-fibrotic therapies. Although dysregulated iron homeostasis and ferroptosis pathways may contribute to its pathogenesis, the core regulatory mechanisms remain elusive. To unravel the ferroptosis-related molecular features, this study integrated transcriptomic datasets (GSE25713 and GSE59276) from S. japonicum-infected mouse livers. Following batch effect correction and normalization, ferroptosis-related differentially expressed genes (FRDEGs) were identified. Subsequently, core hub genes were screened through the construction of a protein–protein interaction (PPI) network, functional enrichment analysis, immune infiltration evaluation, and receiver operating characteristic (ROC) analysis. The expression patterns of these hub genes were further validated in an S. japonicum-infected mouse model using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The study identified 7 hub genes (Lcn2, Timp1, Cth, Cp, Hmox1, Cbs, and Gclc) as key regulatory molecules. Functional enrichment analysis revealed that these hub genes are closely associated with sulfur amino acid metabolism and oxidative stress responses. Specifically, key enzymes involved in cysteine and glutathione (GSH) synthesis (Cth, Cbs, Gclc) were consistently downregulated, suggesting a severe impairment of the host antioxidant defense capacity. Conversely, pro-fibrotic and pro-inflammatory markers (Timp1, Lcn2, Hmox1) were upregulated. This molecular pattern was significantly associated with a remodeled immune microenvironment, characterized by increased infiltration of neutrophils and eosinophils. In vivo validation confirmed the expression trends of 6 hub genes, corroborating the bioinformatics predictions, while the discrepancy in Cp expression highlighted the complexity of post-transcriptional regulation in vivo. The identified hub genes demonstrated excellent diagnostic potential, with Timp1 achieving an area under the curve (AUC) of 1.000. This study elucidates the molecular link between S. japonicum infection and the ferroptosis pathway, suggesting that these hub genes may drive liver fibrosis progression by regulating sulfur metabolism and the immune microenvironment. These findings offer potential diagnostic biomarkers and novel therapeutic targets for schistosomal liver fibrosis. Full article

The larvae and damage symptoms of Cnaphalocrocis medinalis and Cnaphalocrocis patnalis exhibit a high degree of similarity, which often leads to confusion between the two species. This has posed challenges for research on their population dynamics and the development of effective control measures. To better understand their morphological and damage characteristics, population dynamics, species identification based on COI gene fragments, and potential future distribution, a searchlight trap monitoring program was conducted for C. medinalis and its closely related species C. patnalis across four sites in Longhua, Haitang, and Yazhou districts in Hainan Province from 2021 to 2023. The MaxEnt model was utilized to predict the potential global distribution of both species, incorporating known occurrence points and climate variables. The trapping results revealed that both species reached peak abundance between April and June, with a maximum of 1500 individuals captured in May at Beishan Village, Haitang District. Interannual population fluctuations of both species generally followed a unimodal pattern. Genetic analyses revealed distinct differences in the mitochondrial COI gene fragment, confirming that C. medinalis and C. patnalis are closely related yet distinct species. The population peak of C. patnalis occurred slightly earlier than that of C. medinalis, and its field damage was more severe. Infestations during the booting to heading stages of rice significantly reduced seed-setting rates and overall yield. Model predictions indicated that large areas of southern Eurasia are suitable for the survival of both species, with precipitation during the wettest month identified as the primary environmental factor shaping their potential distributions. At present, moderately and highly suitable habitats for C. medinalis account for 2.50% and 2.27% of the global land area, respectively, whereas those for C. patnalis account for 2.85% and 1.19%. These results highlight that climate change is likely to exacerbate the damage caused by both rice leaf-roller pests, particularly the emerging threat posed by C. patnalis. Overall, this study provides a scientific basis for invasion risk assessment and the development of integrated management strategies targeting the combined impacts of C. medinalis and C. patnalis. Full article

The gut microbiome is defined as the collective assembly of microbial communities inhabiting the gut, along with their genes and metabolic products. The gut microbiome systematically regulates host metabolism, immunity, and neuroendocrine homeostasis via interspecies interaction networks and inter-organ axes. Given the importance of the gut microbiome to the host, this review integrates the composition, function, and genetic basis of the gut microbiome with host genomics to provide a systematic overview of recent advances in microbiome–host interactions. This encompasses a complete technological pipeline spanning from in vitro to in vivo models to translational medicine. This technological pipeline spans from single-bacterium CRISPR editing, organoid–microbiome co-culture, and sterile/humanized animal models to multi-omics integrated algorithms, machine learning causal inference, and individualized probiotic design. It aims to transform microbiome associations into precision intervention strategies that can be targeted and predicted for clinical application through interdisciplinary research, thereby providing the cornerstone of a new generation of precision treatment strategies for cancer, metabolic, and neurodegenerative diseases. Full article

Background: Rhodopseudomonas palustris is a metabolically versatile bacterium with significant biotechnological potential, including the ability to catabolize lignin and its heterogeneous breakdown products. Understanding the molecular determinants of growth on lignin-derived compounds is essential for advancing lignin valorization strategies under both aerobic and anaerobic conditions. Methods: R. palustris was cultivated on multiple lignin breakdown products (LBPs), including p-coumaryl alcohol, coniferyl alcohol, sinapyl alcohol, p-coumarate, sodium ferulate, and kraft lignin. Condition-specific transcriptomics and proteomics datasets were generated and used as input features to train machine-learning models, with experimentally measured growth rates as the prediction target. Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Machine (SVM) models were evaluated and compared. Permutation feature importance analysis was applied to identify genes and proteins most influential for growth. Results: Among the tested models, ANNs achieved the highest predictive performance, with accuracies of 94% for transcriptomics-based models and 96% for proteomics-based models. Feature importance analysis identified the top twenty growth-associated genes and proteins for each omics layer. Integrating transcriptomic and proteomic results revealed eight key transport proteins that consistently influenced growth across LBP conditions. Re-training ANN models using only these eight transport proteins maintained high predictive accuracy, achieving 86% for proteomics and 76% for transcriptomics. Conclusions: This study demonstrates the effectiveness of ANN-based models for predicting growth-associated genes and proteins in R. palustris. The identification of a small set of key transport proteins provides mechanistic insight into lignin catabolism and highlights promising targets for metabolic engineering aimed at improving lignin utilization. Full article

Triple-negative breast cancer (TNBC), which is characterized by a lack of the estrogen receptor, the progesterone receptor, and HER2 expression, is the most aggressive breast cancer subtype and has a poor prognosis and high recurrence rates because of frequent chemotherapy resistance. As a crucial epigenetic regulator, DNA methylation modulates gene expression through aberrant methylation patterns, contributing to tumor progression and therapeutic resistance. Early diagnosis and treatment of TNBC are vital for its prognosis. The development of DNA methylation testing technology and the application of liquid biopsy provide technological support for early diagnosis and treatment. Additionally, preclinical and early-phase clinical studies suggest that epigenetic therapies targeting DNA methylation may hold promise for TNBC treatment, pending larger clinical trials. Furthermore, research on DNA methylation-based prognostic models enables personalized precision treatment for patients, helping to reduce unnecessary therapies and improve overall survival. The emerging role of DNA methylation patterns in predicting the therapeutic response and overcoming drug resistance is highlighted. In this narrative review, we integrate current research findings and clinical perspectives. We propose that DNA methylation presents promising research prospects for the diagnosis, treatment and prognosis prediction of TNBC. Future efforts should focus on translating methylation-driven insights into clinically actionable strategies, ultimately advancing precision oncology for this challenging disease. Full article

Fungal diseases continue to limit global crop production and drive major economic losses. Conventional diagnostic and control approaches depend on time-consuming culture-based methods and broad-spectrum chemicals, which offer limited precision. Advances in molecular identification have changed this landscape. PCR, qPCR, LAMP, sequencing and portable platforms enable rapid and species-level detection directly from plant tissue. These tools feed into RNA-based control strategies, where knowledge of pathogen genomes and sRNA exchange enables targeted suppression of essential fungal genes. Host-induced and spray-induced gene silencing provide selective control without the long-term environmental costs associated with chemical use. CRISPR/Cas9 based tools now refine both diagnostics and resistance development, and bioinformatics improves target gene selection. Rising integration of artificial intelligence indicates a future in which disease detection, prediction and management connect in near real time. The major challenge lies in limited field validation and the narrow range of fungal species with complete molecular datasets, yet coordinated multi-site trials and expansion of annotated genomic resources can enable wider implementation. The combined use of molecular diagnostics and RNA-based strategies marks a shift from disease reaction to disease prevention and moves crop protection towards a precise, sustainable and responsive management system. This review synthesizes the information related to current molecular identification tools and RNA-based management strategies, and evaluates how their integration supports precise and sustainable approaches for fungal disease control under diverse environmental settings. Full article

Gut microbiota is a potential therapeutic target for type 2 diabetes (T2D), but its role remains unclear. Investigating causal associations between them could further our understanding of their biological and clinical significance. A two-sample Mendelian randomization (MR) analysis was conducted to assess the causal relationship between gut microbiota and T2D. Key genes and mechanisms were identified through the integration of Genome-Wide Association Studies (GWAS) and cis-expression quantitative trait loci (cis-eQTL) data. Network pharmacology was applied to identify potential drugs and targets. Additionally, gut microbiota community analysis and machine learning models were used to construct a diagnostic model for T2D. MR analysis identified 17 gut microbiota taxa associated with T2D, with three showing significant associations: Actinomyces (odds ratio [OR] = 1.106; 95% confidence interval [CI]: 1.06–1.15; p < 0.01; adjusted p-value [p_adj] = 0.0003), Ruminococcaceae (UCG010 group) (OR = 0.897; 95% CI: 0.85–0.95; p < 0.01; p_adj = 0.018), and Deltaproteobacteria (OR = 1.072; 95% CI: 1.03–1.12; p < 0.01; p_adj = 0.029). Ten key genes, such as EXOC4 and IGF1R, were linked to T2D risk. Network pharmacology identified INSR and ESR1 as target driver genes, with drugs like Dienestrol showing promise. Gut microbiota analysis revealed reduced α-diversity in T2D patients (p < 0.05), and β-diversity showed microbial community differences (R² = 0.012, p = 0.001). Furthermore, molecular docking confirmed the binding affinity of potential therapeutic agents to their targets. Finally, we developed a class-weight optimized Extreme Gradient Boosting (XGBoost) diagnostic model, which achieved an area under the curve (AUC) of 0.84 with balanced sensitivity (95.1%) and specificity (83.8%). Integrating machine learning predictions with MR causal inference highlighted Bacteroides as a key biomarker. Our findings elucidate the gut microbiota-T2D causal axis, identify therapeutic targets, and provide a robust tool for precision diagnosis. Full article

Background: Pulmonary fibrosis (PF) currently lacks effective therapeutic interventions. Roxadustat, an oral small-molecule inhibitor of hypoxia-inducible factor prolyl hydroxylase, has been shown in several studies to attenuate the progression of fibrotic diseases. However, its therapeutic efficacy in PF remains to be fully elucidated. The aim of this study was to evaluate roxadustat’s therapeutic benefits on PF as well as the underlying mechanisms of action. Methods: Bleomycin was administered intraperitoneally to establish a PF mouse model. H&E staining, Masson staining, and immunohistochemistry (IHC) were used to assess histopathological and fibrotic changes. Changes in the expression levels of inflammatory mediators, including IL-1β, TGF-β1, and TNF-α, were examined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Network pharmacology combined with transcriptomic analysis was employed to identify potential target genes and associated signaling pathways. Subsequently, RT-qPCR and Western blot analyses were carried out to experimentally validate the predicted targets and pathways and to verify the protective effects of roxadustat in PF mice. Results: Roxadustat markedly ameliorated bleomycin-induced pulmonary fibrosis in mice. The therapeutic effect was evidenced by a reduction in alveolar damage, thinner alveolar septa, diminished infiltration of inflammatory cells, and decreased collagen deposition. Concomitantly, the expression levels of inflammatory mediators, including IL-1β, TGF-β1, and TNF-α, were significantly lowered. Integrated network pharmacology and transcriptomic analyses revealed the involvement of critical signaling pathways, specifically nuclear factor-kappa B (NF-κB) and peroxisome proliferator-activated receptor (PPAR). Experimental validation further demonstrated that roxadustat downregulated the expression of key genes (S100A8, S100A9, and Fos) in murine lung tissues. It also suppressed the protein ratios of phosphorylated p65 to total p65 and phosphorylated IκBα to total IκBα. Moreover, roxadustat treatment upregulated PPARγ protein expression. Conclusions: These data indicate that roxadustat ameliorates bleomycin-induced PF in mice, an effect associated with modulation of the NF-κB and PPAR signaling pathways. The findings provide a preclinical rationale for further investigation of roxadustat as a potential treatment for PF. Full article

Glioblastoma multiforme (GBM) is the most aggressive primary brain cancer (with a median survival time of 14.5 months), characterized by heterogeneity. Identifying prognostic molecular subtypes could provide a deeper exposition of GBM biology with potential therapeutic implications. In this study, we classified GBM into two prognostic subtypes, C1-GBM (n = 57; OS: 313 days) and C2-GBM (n = 109; OS: 452 days), using pathway-based signatures derived from RNA-seq data. Unsupervised consensus clustering revealed that only binary classification (cluster number, CN = 2; mean cluster consensus score = 0.84) demonstrated statistically prognostic differences. We characterized C1 and C2 based on oncogenic pathway and immune signatures. Specifically, C1-GBM was categorized as an immune-infiltrated “hot” tumor, with high infiltration of immune cells, particularly macrophages and CD4⁺ T cells, while C2-GBM as an “inherent driving” subtype, showing elevated activity in G2/M checkpoint genes. To predict the C1 or C2 classification and explore therapeutic interventions, we developed a neural network model. By using Weighted Correlation Network Analysis (WGCNA), we obtained the gene co-expression module based on both gene expression pattern and distribution among patients in TCGA dataset (n = 166) and identified nine hub genes as potentially prognostic biomarkers for the neural network. The model showed strong accuracy in predicting C1/C2 classification and prognosis, validated by the external CGGA-GBM dataset (n = 85). Based on the classification of the BP neural network model, we constructed a Cox nomogram prognostic prediction model for the TCGA-GBM dataset. We predicted potential therapeutic small molecular drugs by targeting subtype-specific oncogenic pathways and validated drug sensitivity (C1-GBM: Methotrexate and Cisplatin; C2-GBM: Cytarabine) by assessing IC₅₀ values against GBM cell lines (divided into C1/C2 subtypes based on the nine hub genes) from the Genomics of Drug Sensitivity in Cancer database. This study introduces a pathway-based prognostic molecular classification of GBM with “hot” (C1-GBM) and “inherent driving” (C2-GBM) tumor subtypes, providing a prediction model based on hub biomarkers and potential therapeutic targets for treatments. Full article

Codon optimization is widely used to improve heterologous gene expression in Escherichia coli. However, many existing methods focus primarily on maximizing the codon adaptation index (CAI) and neglect broader aspects of biological context. In this study, we present ColiFormer, a transformer-based codon optimization framework fine-tuned on 3676 high-expression E. coli genes curated from the NCBI database. Built on the CodonTransformer BigBird architecture, ColiFormer employs self-attention mechanisms and a mathematical optimization method (the augmented Lagrangian approach) to balance multiple biological objectives simultaneously, including CAI, GC content, tRNA adaptation index (tAI), RNA stability, and minimization of negative cis-regulatory elements. Based on in silico evaluations on 37,053 native E. coli genes and 80 recombinant protein targets commonly used in industrial studies, ColiFormer demonstrated significant improvements in CAI and tAI values, maintained GC content within biologically optimal ranges, and reduced inhibitory cis-regulatory motifs compared with established codon optimization approaches, while maintaining competitive runtime performance. These results represent computational predictions derived from standard in silico metrics; future experimental work is anticipated to validate these computational predictions in vivo. ColiFormer has been released as an open-source tool alongside the benchmark datasets used in this study. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by dopaminergic neuronal loss and abnormal α-synuclein aggregation. Circulating microRNAs (miRNAs) have emerged as promising biomarkers and potential modulators of PD-related molecular pathways. In this study, we investigated the expression levels of four candidate miRNAs—miR-15a-5p, miR-16-5p, miR-139-5p, and miR-34a-3p—in patients with PD compared with healthy controls. A total of 47 PD patients and 45 age- and sex-matched controls were enrolled. Plasma miRNA levels were quantified using standardized RNA extraction, cDNA synthesis, and qPCR protocols. We observed marked upregulation of miR-15a-5p and robust downregulation of both miR-139-5p and miR-34a-3p in PD patients, whereas miR-16-5p showed no significant difference between groups. Target gene prediction and functional enrichment analysis identified 432 unique genes, with enrichment in biological processes related to protein ubiquitination and catabolic pathways, and signaling cascades such as mTOR, PI3K-Akt, MAPK, and Hippo pathways, all of which are implicated in neurodegeneration. Elevated miR-15a-5p may contribute to pro-apoptotic mechanisms, while reduced miR-139-5p and miR-34a-3p expression may reflect impaired mitochondrial function, diminished neuroprotection, or compensatory regulatory responses. Together, these dysregulated circulating miRNAs provide novel insight into PD pathophysiology and highlight their potential as accessible, non-invasive biomarkers. Further longitudinal studies in larger and more diverse cohorts are warranted to validate their diagnostic and prognostic value and to explore their utility as therapeutic targets. Full article