Gene Co-expression Research Articles

WGCNA combined with machine learning to explore potential biomarkers and treatment strategies for acute liver failure, with experimental validation

Background and aimsTo identify biomarkers to predict acute liver failure and investigate the mechanisms and immune-related pathways linked to its onset and progression. MethodsWe analyzed gene expression differences between patients with acute liver failure (ALF) and controls in the GSE14668 dataset. Clinically relevant modules and key ALF-associated genes were identified using weighted gene co-expression network analysis (WGCNA) in conjunction with differential gene expression (DEG) analysis. Enrichment analysis was carried out and protein–protein interaction networks were constructed to understand the functions and pathways. Six potential diagnostic biomarkers were identified using machine learning algorithms. Diagnostic performance was assessed via column charts and area under the curve calculations. Single-sample gene set enrichment analysis evaluated the relationship between known marker gene sets and potential biomarker expression. We also examined diagnostic biomarker mRNA levels in ALF models in vivo and in vitro. We estimated the relative infiltration levels of 22 immune cell subpopulations in ALF samples, and explored the link between diagnostic biomarkers and infiltrating immune cells. ResultWe found 352 DEGs associated with ALF. WGCNA analysis and intersecting DEGs identified 191 significant ALF-related genes. Machine learning identified HORMAD2, WNT10A, ATP6V1E2, CMBL, ARRDC4, and LPIN2 as potential diagnostic biomarkers. Cell experiments and quantitative real-time polymerase chain reaction supported the therapeutic potential of eriodictyol for ALF. Immune infiltration analysis suggested that plasma cells, CD4 memory resting and activated T cells, macrophages, and neutrophils might play roles in the progression of ALF. ConclusionWe identified HORMAD2, WNT10A, ATP6V1E2, CMBL, ARRDC4, and LPIN2, as diagnostic biomarkers for ALF and demonstrated the effectiveness of eriodictyol for treating ALF. Immune cell infiltration may play a significant role in the pathogenesis and progression of ALF.

Multi-omics profiling reveals elevated CO2-enhanced tolerance of Trifolium repens L. to lead stress through environment-plant-microbiome interactions

The increasing atmospheric CO2 resulting from human activities over the past two centuries, which is projected to persist, has significant implications for plant physiology. However, our predictive understanding of how elevated CO2 (eCO2) modifies plant tolerance to metal stress remains limited. In this study, we collected roots and rhizosphere soils from Trifolium repens L. subjected to lead (Pb) stress under ambient and elevated CO2 conditions, generating transcriptomic data for roots, microbiota data for rhizospheres, and conducting comprehensive multi-omics analyses. Our findings show that eCO2 reduced the accumulation of Pb-induced reactive oxygen species (ROS) and promoted plant growth by 72% to 402%, as well as increases shoot Pb uptake by 79% compared to ambient CO2. Additionally, eCO2 triggers specific defense response in T. repens, elevating the threshold for stress response. We observed a adaptive reconfiguration of transcriptional network that enhances energy efficiency and optimizes photosynthetic product utilization. Notably, eCO2 induces salicylic acid biosynthesis and activates defense pathways related to redox balance and ROS scavenging processes, thereby enhancing abiotic stress resistance. Through weighted gene co-expression network analysis, our comprehensive investigation reveals a holistic regulatory network encompassing plant traits, gene expression patterns, and bacterial structure potentially linked to metal accumulation as well as tradeoffs between growth and defense in plants under elevated CO2. These insights shed light on the plant stress responses under elevated CO2 and while contributing to a broader comprehension of plant-environment interactions.

Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes.

Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool for uncovering individual-specific genomic interactions.

Integrative Analysis of Metabolome and Transcriptome Reveals Molecular Mechanisms Regulating Oil and Protein Content in Peanut (Arachis hypogaea L).

Peanut (Arachis hypogaea L.) is an important source of edible vegetable oils and plant proteins globally. However, the complex mechanisms regulating the oil and protein contents of peanut seeds remain unclear. Here, comparative broad-target metabolomics and quantitative lipidomics, together with transcriptome analysis, of peanut seeds at four developmental stages from the high-oil content variety "YH15" and high-protein content variety "KB008" were performed to search for oil and protein content control genes. A total of 984 differential metabolites, including 128 amino acids and derivatives and 310 differentially accumulated lipids, were identified between "YH15" and "KB008" in four seed developmental stages. The weighted gene coexpression network analysis and module-trait relationship analysis revealed that MEbrown, MEyellow, and MEturquoise modules were key contributors to the quality discrepancies observed between "YH15" and "KB008." Crucial genes potentially regulating the differences in oil and protein contents between "YH15" and "KB008" were identified within the aforementioned three modules, including genes involved in amino acid synthesis and degradation, nitrogen allocation, triglyceride synthesis and degradation, and fatty acid synthesis and degradation, as well as transcription factors. Overall, this study provides valuable insights into the molecular regulation of oil and protein contents in peanut seeds and may help cultivate specialized peanut varieties with enhanced nutritional and economic values.

Network hub gene detection using the entire solution path information.

Gene co-expression networks typically comprise modules and their associated hub genes, which are regulating numerous downstream interactions within the network. Methods for hub screening, as well as data-driven estimation of hub co-expression networks using graphical models, can serve as useful tools for identifying these hubs. Graphical model-based penalization methods typically have one or multiple regularization terms, each of which encourages some favorable characteristics (e.g., sparsity, hubs, power-law) to the estimated complex gene network. It is common practice to find a single optimal graphical model corresponding to a specific value of the regularization parameter(s). However, instead of doing this, one could aggregate information across several graphical models, all of which depend on the same data set, along the solution path in the hub gene detection process. We propose a novel method for detecting hub genes that utilizes the information available in the solution path. Our procedure is related to stability selection, but we replace resampling with a simple statistic. This procedure amalgamates information from each node of the data-driven graphical models into a single influence statistic, similar to Cook's distance. We call this statistic the Mean Degree Squared Distance (MDSD). Our simulation and empirical studies demonstrate that the MDSD statistic maintains a good balance between false positive and true positive hubs. An R package MDSD is publicly available on GitHub under the General Public License https://github.com/markkukuismin/MDSD.

Liver transcriptomics-metabolomics integration reveals biological pathways associated with fetal programming in beef cattle

We investigated the long-term effects of prenatal nutrition on pre-slaughter Nelore bulls using integrative transcriptome and metabolome analyses of liver tissue. Three prenatal nutritional treatments were administered to 126 cows: NP (control, mineral supplementation only), PP (protein-energy supplementation in the third trimester), and FP (protein-energy supplementation throughout pregnancy). Liver samples from 22.5 ± 1-month-old bulls underwent RNA-Seq and targeted metabolomics. Weighted correlation network analysis (WGCNA) identified treatment-associated gene and metabolite co-expression modules, further analyzed using MetaboAnalyst 6.0 (metabolite over-representation analysis and transcriptome-metabolome integrative analysis) and Enrichr (gene over-representation analysis). We identified several significant gene and metabolite modules, as well as hub components associated with energy, protein and oxidative metabolism, regulatory mechanisms, epigenetics, and immune function. The NP transcriptome-metabolome analysis identified key pathways (aminoacyl t-RNA biosynthesis, gluconeogenesis, and PPAR signaling) and hub components (glutamic acid, SLC6A14). PP highlighted pathways (arginine and proline metabolism, TGF-beta signaling, glyoxylate and dicarboxylate metabolism) with arginine and ODC1 as hub components. This study highlights the significant impact of prenatal nutrition on the liver tissue of Nelore bulls, shedding light on critical metabolic pathways and hub components related to energy and protein metabolism, as well as immune system and epigenetics.

Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.

Abdominal aortic aneurysm (AAA) is a life-threatening vascular condition. This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development. Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. Additionally, a separate dataset (GSE7084) was designated as the validation set. Enrichment analyses were carried out to explore the underlying biological mechanisms using Disease Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology. We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. Finally, the single sample gene set enrichment analysis algorithm was applied to probe the immune landscape in AAA and its connection to the selected feature genes. We discovered 72 differentially expressed genes (DEGs) when comparing healthy and AAA samples, including 36 upregulated and 36 downregulated genes. Functional enrichment analysis revealed that the DEGs associated with AAA are primarily involved in inflammatory regulation and immune response. By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. The diagnostic performance of all these genes was strong, as revealed by the ROC analysis. A significant increase in 15 immune cell types in AAA samples was observed, based on the analysis of immune cell infiltration. In addition, the 3 feature genes show a strong linkage with different types of immune cells. Three feature genes (MRAP2, PPP1R14A, and PLN) related to the development of AAA were identified. These genes are linked to immune cell activity and the inflammatory microenvironment, providing potential biomarkers for early detection and a basis for further research into AAA progression.

DGKH-mediated phosphatidic acid oncometabolism as a driver of self-renewal and therapy resistance in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is characterized by metabolic pathway aberrations, which enable cancer cells to meet their energy demands and accelerate malignant progression. Identifying novel metabolic players governing therapy resistance and self-renewal in HCC is crucial, as these properties are likely responsible for tumor recurrence. Clinical traits and RNA-seq of HCC patients in TCGA were used for weighted gene co-expression network analysis, where one module was significantly correlated with advanced pathological stage and stem cell population maintenance. Further analysis of this module by integrating data obtained from HCC patient nonresponders to tyrosine kinase inhibitors identified 361 commonly deregulated genes significantly enriched in the intracellular signal transduction pathway, with diacylglycerol kinase eta (DGKH) ranked as the most enriched gene in poorly differentiated HCC tumors. Clinically, DGKH was elevated in tumor tissues compared to non-tumor tissues. Patients with higher DGKH expression exhibited a more undifferentiated state and were less responsive to TKIs. Functional assays using DGKH-manipulated HCC cell lines demonstrated that DGKH augmented aggressive features, including cancer stemness, therapy resistance, and metastasis. Upstream of DGKH, we discovered that the E1A-associated protein p300 (EP300) binds to DGKH’s promoter region, thereby increasing its transcriptomic expression. Mechanistically, DGKH promotes mTOR signaling by producing phosphatidic acid (PA). In an immunocompetent mouse model, co-treatment with sorafenib and liver-directed AAV8-mediated Dgkh depletion significantly reduced tumor burden, self-renewal, PA production and mTOR signaling. Our research demonstrated that DGKH is a crucial oncometabolic regulator of cancer stemness and therapy resistance, inhibition of which may lead to more effective hepatocellular carcinoma treatment.

Abstract 4138334: Identification and Immunological Characterization of Arrhythmia-Related Molecular Clusters in Ischemic Cardiomyopathy

Ischemic cardiomyopathy, a severe cardiac condition resulting from prolonged myocardial ischemia, is characterized by ventricular dilation, dysfunction, and an increased risk of life-threatening arrhythmias. Arrhythmia, a common complication in ischemic cardiomyopathy, is associated with poor clinical outcomes. Recent studies have indicated that the molecular heterogeneity of ischemic cardiomyopathy may contribute to the development of arrhythmias. Therefore, our study aimed to explore arrhythmia-related molecular clusters in ischemic cardiomyopathy and establish a prediction model for risk stratification. Based on gene expression profiles of ischemic cardiomyopathy patients, we analyzed the dysregulation of arrhythmia-associated genes and the associated immune characteristics (Figure 1A-C). Using unsupervised clustering analysis, we identified distinct arrhythmia-related molecular clusters in ischemic cardiomyopathy patients (Figure 1D-F). Cluster-specific differentially expressed genes were further identified using weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis revealed significant differences in biological processes and pathways between the clusters (Figure 1G-H). Subsequently, we developed a prediction model using a random forest algorithm, which showed superior performance in distinguishing patients with different arrhythmia risks (Figure 1I-J). This forest model demonstrated excellent performance in one external validation datasets with AUC values of 0.891. In conclusion, our study provides novel insights into the molecular basis of arrhythmias in ischemic cardiomyopathy and establishes a prediction model with potential clinical utility for risk stratification and targeted therapies. Further investigation into the specific mechanisms underlying the identified molecular clusters may reveal new therapeutic targets for the management of arrhythmias in ischemic cardiomyopathy patients.

Prognostic significance and identification of m6A regulator genes and hub genes associated with m6A in breast cancer

This research endeavors to investigate the functions of N6-methyladenosine (m6A) regulatory genes and key genes linked to m6A modifications within the context of breast cancer (BC). The objective is to identify a promising predictive biomarker related to m6A modifications and validate its significance in BC through experimental methodologies. Utilizing data from The Cancer Genome Atlas (TCGA) database, a model for predicting prognosis was developed. Key genes connected to m6A modifications were discerned using weighted gene co-expression network analysis (WGCNA) coupled with LASSO and Cox regression analyses, which were then utilized to construct a predictive model. The influence of ZNF260 within BC was probed experimentally. The predictive model formulated using m6A regulatory genes and key m6A-associated genes demonstrated the capability to categorize BC patients into distinct risk groups effectively (all P < 0.001). Clinical sample analyses revealed notably elevated expression levels of ZNF260 in hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR + /HER2−) BC tissues compared to adjacent non-tumor tissues (all P < 0.001). Reduction in ZNF260 expression was shown to inhibit the proliferation, clonogenicity, migration, and invasiveness of MCF-7 cells while concomitantly enhancing apoptosis (all P < 0.001).This investigation uniquely uncovered ZNF260 as a novel key gene, suggesting its potential utility as a predictive biomarker associated with m6A modifications specifically in HR + /HER2− BC.

EPCO-33. OMICON: A CLOUD PLATFORM FOR FAIR RESEARCH ON GENE COEXPRESSION NETWORKS IN NORMAL HUMAN BRAIN AND BRAIN TUMORS

Abstract Genome-wide coexpression analysis of bulk human brain samples is a powerful approach for identifying highly reproducible transcriptional signatures of cell types and cell states, since it can survey huge numbers of individuals, cells, and transcripts. However, it is difficult to optimize gene coexpression network construction, compare gene coexpression modules from independent datasets, or even locate gene expression datasets with similar attributes. To address these challenges, we have developed the OMICON platform (theomicon.ucsf.edu) to promote FAIR (Findable, Accessible, Interoperable, Reusable) research practices involving human brain gene coexpression networks. OMICON contains structured gene expression data for &amp;gt;17K bulk human brain samples (~10K normal and ~7K malignant glioma), which were collected from diverse public repositories and consortia (e.g., GEO, TCGA, CGGA, CPTAC, REMBRANDT, IvyGAP, GTEx, NABEC, the Allen Institute). To promote interoperability, we have standardized metadata for hundreds of dataset and sample attributes, including single-nucleotide and copy-number mutations. For each dataset, we have used the FindModules R function to construct dozens of gene coexpression networks by iterating over module detection parameters. These efforts have identified ~100K gene coexpression modules, which have been extensively characterized via enrichment analysis with thousands of curated gene sets. All datasets, metadata, gene coexpression networks, enrichment results, and analysis steps can be browsed with an interactive workflow visualization tool, which promotes accessibility, reusability, and reproducibility by maintaining complete data provenance with unique identifiers. To promote findability, we have built an advanced search engine to identify datasets, samples, modules, and more, by filtering standardized metadata (e.g., find gene coexpression modules in human glioblastoma datasets that are significantly enriched with markers of T-cells). A detailed description of OMICON’s functionality is described on our Help page: theomicon.ucsf.edu/home/help. Through this functionality, OMICON seeks to build community and focus therapeutic efforts around reproducible analyses of transcriptional variation in normal human brain and brain tumors.

EPCO-18. DECIPHERING SPATIAL DIVERSITY OF GLIOBLASTOMA GENETIC ARCHITECTURE

Abstract Glioblastoma (GBM) is hallmarked by profound inter- and intratumoral heterogeneity. Current research has mapped key genetic drivers to distinct transcriptional profiles within GBM. Nevertheless, the intricate interactions between genetic alterations and spatial-transcriptional heterogeneity remain elusive. We utilized nestin-Tva C57BL/6 mice to induce de novo GBM via oncogene overexpression alongside shRNA-mediated silencing or deletion of tumor suppressors. We modeled critical driver mutations: PDGFB, NF1, and EGFR. Employing 10X Genomics platform for single-cell RNA sequencing, combined with spatially resolved transcriptomics and proteomics (Nanostring GeoMx), we integrated data using both horizontal (mutual nearest neighbors) and vertical (weighted nearest neighbors) approaches, analyzed through the SPATA2 software. Advanced computational techniques, including graph neural networks and single-cell deconvolution, were leveraged to unravel the genotypic and spatial variability within tumors and their microenvironments. Our findings uncover persistent cellular heterogeneity across diverse genetic contexts. However, specific genetic mutations precipitated distinct dominant transcriptional states: NF1 and EGFR mutations drove mesenchymal and astrocytic-like states, respectively, whereas PDGFB fostered a neural progenitor/cell-like state. These transcriptional shifts were closely linked to unique microenvironmental modifications, such as elevated neuronal signaling in PDGFB-driven GBMs and significant immune infiltration in NF1 and EGFR models. Through weighted co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA) paired with spatial-niche deconvolution, we identified a mesenchymal transition leading to an immunosuppressive microenvironment in NF1-loss driven GBMs and increased lymphocyte presence around blood vessels in EGFR-amplified GBMs. These patterns were consistent with human spatially resolved transcriptomic data, emphasizing the genetic complexity and heterogeneity of human GBMs. Our genetically engineered mouse models serve as a robust platform for dissecting GBM with defined genetic mutations. Leveraging sophisticated analytical techniques and computational modeling, this study underscores the profound interplay between genetic drivers and the tumor microenvironment, paving the way for targeted therapeutic strategies and precision medicine in GBM.

EPCO-37. LONGITUDINAL TUMOR-WIDE SAMPLING OF GLIOBLASTOMA REVEALS EARLY EVOLUTIONARY DIVERGENCE OF RECURRENCE AND CLUES TOWARDS IDENTIFYING THE BIOLOGICAL TUMOR CENTER

Abstract Therapy resistance in glioblastoma is in part attributed to intratumoral heterogeneity and treatment-resistant cell populations. Understanding heterogeneity requires more complete tumor sampling at diagnosis and at recurrence. We used a whole-tumor sampling approach to obtain 43 spatially mapped biopsies from 3 glioblastomas at diagnosis and recurrence, Pyclone and ClonEvol imputed clonal evolution from exome data, and weighted gene co-expression network analysis inferred gene expression programs from RNA sequencing. Across the cohort, tumor-wide clonal alterations representing evolutionarily early expansions included canonical changes (i.e. Chr7 gain, EGFR amplification) and a diverse set of copy number variations (Chr19/20 gain), driver mutations (i.e. PTEN, KDR), and fusions (LIMCH1::UCHL1, KANK::DOCK8). In 2/3 patients, the founding clone was the only subclone common to primary and recurrence samples and 37% of cancer drivers across the cohort appeared after evolutionary divergence of the primary and recurrent tumors. We identified thirty-two transcriptional programs, six of which were differentially expressed between timepoints. Programs enriched for mitochondrial activity, endothelium/pericytes and classical glioblastoma subtype signature were more highly expressed in primary samples. Programs enriched for mesenchymal-like state, oligodendrocytes and inhibitory interneurons were more highly expressed in recurrence samples. At diagnosis, 11/16 samples showed higher expression of the classical subtype program compared to mesenchymal-like, but at recurrence the mesenchymal program dominated in 18/18 samples. Expression of the programs correlated more strongly with position relative to the contrast-enhancing rather than T2 tumor centroid (p&amp;lt;0.001). These findings reveal novel diversity and complexity in the genetic roots of individual glioblastoma. Recurrences arose from subclones diverging early in evolution of the primary tumor and contained clonal drivers not detected in the primary. Expression data revealed transition from an intratumorally heterogeneous mixture of classical and mesenchymal signatures to tumor-wide mesenchymal at recurrence and suggests that the contrast enhancing centroid may serve as the biological center of the tumor.

Identification of a coagulation-related gene signature for predicting prognosis in recurrent glioma

BackgroundRecurrent gliomas rapidly progress and have high mortality and poor prognosis in the central nervous system. Therefore, further investigation of prognostic and therapeutic markers is needed.MethodsThe mRNA expression, clinical data, and coagulation-related genes (CRGs) associated with recurrent glioma were obtained and calculated from the Chinese Glioma Genome Atlas and Kyoto Encyclopedia of Genes and Genomes databases. The significant CRGs were calculated by Weighted gene co-expression network analysis and PPI network. A prediction model was constructed using the least absolute shrinkage and selection operator regression analysis. Recurrent gliomas were stratified into high and low-risk groups based on the median risk score (RS). The Kaplan–Meier curve was used to analyze the difference in overall survival (OS) between these groups, while the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the gene model at 1-, 3-, and 5-years. Clinical factors, including age, gender, MGMT methylation status, radiotherapy, chemotherapy, and RS, were included in the univariate and multivariate regression analysis. A prognostic nomogram and calibration curve were established based on these factors.ResultsOverall, seven CRGs associated with the prognosis were identified, including BTK, ITGB1, GNAI3, CFH, LYN, CFI, and F3. OS and survival rates were lower in the high-risk group compared with the low-risk group. The ROC curve demonstrated the area under the curve values >0.65 at 1-, 3-, and 5-years, confirming the reliability of the prognostic model. The univariate regression analysis indicated that tumor grade (grades 2, 3, and 4), histopathology (GBM or not), chemotherapy, IDH mutation, and 1p19q co-deletion status were independent prognostic indicators. Univariate and multivariate regression analyses indicated that RS was an independent prognostic factor for patients with recurrent glioma. Immune analysis revealed low infiltration of resting dendritic cells and high expression of programmed death receptor 1 in the high-risk group.ConclusionThis study comprehensively investigated the correlation between CRGs and recurrent glioma prognosis, offering crucial insights for further research into glioma recurrence mechanisms and treatment strategies.

Transcriptome and Metabolome Analysis Reveals the Effect of Temperature on the Vegetative Mycelium of Morchella sextelata

Morchella, a highly valued medicinal and edible mushrooms, is experiencing an increasing demand; however, its cultivation is significantly influenced by climatic conditions and soil characteristics. Consequently, elucidating the mechanisms underlying Morchella mycelium’s response to temperature stress holds substantial importance for enhancing Morchella cultivation practices. In this study, we used Morchella sextelata as the research object and employed integrated transcriptomic and metabolomic analyses to evaluate the effects of cultivation temperatures set at 33 °C, 20 °C, and 4 °C on the vegetative mycelium of Morchella. Through these comprehensive analyses, we identified 2998 differentially expressed genes alongside 678 differentially accumulated metabolites. Utilizing Weighted Gene Co-expression Network Analysis (WGCNA), we constructed a co-expression network that revealed hub genes and metabolites within each module. Furthermore, through KEGG pathway analysis, we pinpointed significant metabolic pathways responsive to temperature stress—particularly those involved in purine metabolism, RNA degradation, two-component systems, ABC transporters, and pyruvate metabolism. Overall findings indicated that elevated temperatures exerted a more pronounced effect on M. sextelata mycelium compared to lower temperatures. These insights enhance our comprehension of the adaptive mechanisms of M. sextelata to thermal variations while providing valuable references for optimizing environmental temperature regulation in their cultivation as well as offering clues for selecting varieties capable of thriving under diverse thermal conditions.

Study of red vine phenotypic plasticity across central-southern Italy sites: an integrated analysis of the transcriptome and weather indices through WGCNA.

The grapevine (Vitis spp., family Vitaceae) is characterized by marked phenotypic plasticity. Its ability to withstand specific environmental conditions depends on the activation of highly coordinated responses resulting from interactions among genotypes (G) and environmental factors (E). In this study, the transcriptomes of commercially ripe berries of the Cabernet Sauvignon and Aglianico genotypes grown in open fields at three different sites in central-southern Italy (Campania, Molise and Sicily) were analyzed with RNA sequencing. These transcriptomic data were integrated with a comprehensive set of weather course indices through weighted gene co-expression network analysis (WGCNA). A total of 11,887 differentially expressed genes (DEGs) were retrieved, most of which were associated with the Aglianico genotype. The plants from the Sicilian site presented the greatest number of DEGs for both genotypes. Most of the weather course data (daily maximum air temperature, relative humidity, air pressure, dew point, and hours of sun radiation) were significantly correlated with the "lightcyan1" module, confirming WGCNA as a powerful method for identifying genes of high biological interest. Within this module, the gene encoding the ACA10 cation transporter was highly expressed in plants of both genotypes from Campania, where the lowest anthocyanin content was recorded. The transcriptome was also correlated with quality traits, such as total soluble solids and polyphenol content. This approach could lead to the identification of a transcriptomic profile that may specifically identify a genotype and its growing site and to the discovery of hub genes that might function as markers of wine quality.

Changes in the chemical composition and medicinal effects of black ginseng during processing.

To study the changes in the chemical composition and medicinal effects of black ginseng during processing. The contents of ginsenosides Rg1, Re, Rh1, Rb1, 20-(S)-Rg3, 20-(R)-Rg3, and Rg5 were determined using high-performance liquid chromatography (HPLC), and the percentage of rare saponins was calculated. Furthermore, changes in the contents of reducing sugars and amino acids (i.e., Maillard reaction (MR) substrates) were measured to assess the relationship between processing and the MR. Compounds were identified using HPLC-MS and their cleavage patterns were analyzed. Gene co-expression network bioinformatics techniques were applied to identify the pharmacological mechanism of black ginseng. The changes in the physicochemical characteristics of black ginseng during processing were determined based on the MR. Rare saponins accumulated during black ginseng processing. In addition, reducing sugars were produced through polysaccharide pyrolysis and the MR; thus, their content initially increased and then decreased. The amino acid content gradually decreased as the number of evaporation steps increased, indicating that both amino acids and reducing sugars acted as substrates for the MR during black ginseng processing. Thirty-one saponins, 18 sugars, and 58 amino acids were identified based on the MS analysis. Transcriptomics results demonstrated that black ginseng can regulate signaling pathways such as the TNF, IL-17, MAPK, and PI3K-Akt pathways. This finding helps us understand the observed proliferation and differentiation of immune-related cells and positively regulated cell adhesion.

Multiomics dissection of Brassica napus L. lateral roots and endophytes interactions under phosphorus starvation

Many plants associate with endophytic microbes that improve root phosphorus (P) uptake. Understanding the interactions between roots and endophytes can enable efforts to improve P utilization. Here, we characterize the interactions between lateral roots of endophytes in a core collection of 50 rapeseed (Brassica napus L.) genotypes with differing sensitivities to low P conditions. With the correlation analysis result between bacterial abundance and plant physiological indices of rapeseeds, and inoculation experiments on plates and soil, we identify one Flavobacterium strain (C2) that significantly alleviates the P deficiency phenotype of rapeseeds. The underlying mechanisms are explored by performing the weighted gene coexpression network analysis (WGCNA), and conducting genome-wide association studies (GWAS) using Flavobacterium abundance as a quantitative trait. Under P-limited conditions, C2 regulates fatty acid and lipid metabolic pathways. For example, C2 improves metabolism of linoleic acid, which mediates root suberin biosynthesis, and enhances P uptake efficiency. In addition, C2 suppresses root jasmonic acid biosynthesis, which depends on α-linolenic acid metabolism, improving C2 colonization and activating P uptake. This study demonstrates that adjusting the endophyte composition can modulate P uptake in B. napus plants, providing a basis for developing agricultural microbial agents.

Machine learning and network analysis with focus on the biofilm in Staphylococcus aureus

Research on biofilm formation in Staphylococcus aureus has greatly benefited from the generation of high-throughput sequencing data to drive molecular analysis. The accumulation of high-throughput sequencing data, particularly transcriptomic data, offers a unique opportunity to unearth the network and constituent genes involved in biofilm formation using machine learning strategies and co-expression analysis. Herein, the available RNA sequencing data related to Staphylococcus aureus biofilm studies and identified influenced functional pathways and corresponding genes in the process of the transition of bacteria from planktonic to biofilm state by employing machine learning and differential expression analysis. Using weighted gene co-expression analysis and previously developed online prediction platform, important functional modules, potential biofilm-associated proteins, and subnetworks of the biofilm-formation pathway were uncovered. Additionally, several novel protein interactions within these functional modules were identified by constructing a protein-protein interaction (PPI) network. To make this data more straightforward for experimental biologists, an online database named SAdb was developed (http://sadb.biownmcli.info/), which integrates gene annotations, transcriptomics, and proteomics data. Thus, the current study will be of interest to researchers in the field of bacteriology, particularly those studying biofilms, which play a crucial role in bacterial growth, pathogenicity, and drug resistance.

Immune Response and Transcriptome Analysis of the Head Kidney to Different Concentrations of Aeromonas veronii in Common Carp (Cyprinus carpio).

The common carp (Cyprinus carpio), a major economic freshwater fish, is suffering from a variety of bacterial infectious diseases because of its high-density, factory and intensive farming patterns. Aeromonas veronii is the causative agent of high mortality in common carp, causing severe economic losses in aquaculture. However, the regulatory mechanisms involved in the response of common carp to this bacterial infection remain poorly understood. In this study, we compared mortality, blood serum LZM (Lysozyme) and IgM (Immunoglobulin M) levels and transcriptome patterns of head kidney tissues after infection with different concentrations of Aeromonas veronii. We observed that mortality increased progressively with an increasing pathogen concentration. The concentrations of blood serum LZM and IgM significantly increased after infection. A total of 13 and 925 differentially expressed genes (DEGs) were identified after infection with low (T4) and high (T9) concentrations of bacterial suspension, respectively. KEGG and GO analyses of the DEGs highlighted multiple immune-related signaling pathways. Weighted gene co-expression network analysis (WGCNA) revealed that 136 and 83 hub genes were related to blood serum LZM and IgM, respectively. Finally, the gene expression in the head kidney was validated via RT-qPCR to be consistent with the transcriptome. These results provide insights into the mechanisms of the immune response to infection with different concentrations of Aeromonas veronii and offer useful information for further studies on immune defense mechanisms in common carp.