Weighted Correlation Network Analysis Research Articles

Transcriptomic and metabolomic dissection of skeletal muscle of crossbred Chongming white goats with different meat production performance

BackgroundThe transcriptome and metabolome dissection of the skeletal muscle of high- and low- growing individuals from a crossbred population of the indigenous Chongming white goat and the Boer goat were performed to discover the potential functional differentially expressed genes (DEGs) and differential expression metabolites (DEMs).ResultsA total of 2812 DEGs were detected in 6 groups at three time stages (3,6,12 Month) in skeletal muscle using the RNA-seq method. A DEGs set containing seven muscle function related genes (TNNT1, TNNC1, TNNI1, MYBPC2, MYL2, MHY7, and CSRP3) was discovered, and their expression tended to increase as goat muscle development progressed. Seven DEGs (TNNT1, FABP3, TPM3, DES, PPP1R27, RCAN1, LMOD2) in the skeletal muscle of goats in the fast-growing and slow-growing groups was verified their expression difference by reverse transcription-quantitative polymerase chain reaction. Further, through the Liquid chromatography-mass spectrometry (LC-MS) approach, a total of 183 DEMs in various groups of the muscle samples and these DEMs such as Queuine and Keto-PGF1α, which demonstrated different abundance between the goat fast-growing group and slow-growing group. Through weighted correlation network analysis (WGCNA), the study correlated the DEGs with the DEMs and identified 4 DEGs modules associated with 18 metabolites.ConclusionThis study benefits to dissection candidate genes and regulatory networks related to goat meat production performance, and the joint analysis of transcriptomic and metabolomic data provided insights into the study of goat muscle development.

Joint transcriptomic and metabolomic analysis provides new insights into drought resistance in watermelon (Citrullus lanatus)

IntroductionWatermelon is an annual vine of the family Cucurbitaceae. Watermelon plants produce a fruit that people love and have important nutritional and economic value. With global warming and deterioration of the ecological environment, abiotic stresses, including drought, have become important factors that impact the yield and quality of watermelon plants. Previous research on watermelon drought resistance has included analyzing homologous genes based on known drought-responsive genes and pathways in other species.MethodsHowever, identifying key pathways and genes involved in watermelon drought resistance through high-throughput omics methods is particularly important. In this study, RNA-seq and metabolomic analysis were performed on watermelon plants at five time points (0 h, 1 h, 6 h, 12 h and 24 h) before and after drought stress.ResultsTranscriptomic analysis revealed 7829 differentially expressed genes (DEGs) at the five time points. The DEGs were grouped into five clusters using the k-means clustering algorithm. The functional category for each cluster was annotated based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database; different clusters were associated with different time points after stress. A total of 949 metabolites were divided into 10 categories, with lipids and lipid-like molecules accounting for the most metabolites. Differential expression analysis revealed 22 differentially regulated metabolites (DRMs) among the five time points. Through joint analysis of RNA-seq and metabolome data, the 6-h period was identified as the critical period for watermelon drought resistance, and the starch and sucrose metabolism, plant hormone signal transduction and photosynthesis pathways were identified as important regulatory pathways involved in watermelon drought resistance. In addition, 15 candidate genes associated with watermelon drought resistance were identified through joint RNA-seq and metabolome analysis combined with weighted correlation network analysis (WGCNA). Four of these genes encode transcription factors, including bHLH (Cla97C03G068160), MYB (Cla97C01G002440), HSP (Cla97C02G033390) and GRF (Cla97C02G042620), one key gene in the ABA pathway, SnRK2-4 (Cla97C10G186750), and the GP-2 gene (Cla97C05G105810), which is involved in the starch and sucrose metabolism pathway.DiscussionIn summary, our study provides a theoretical basis for elucidating the molecular mechanisms underlying drought resistance in watermelon plants and provides new genetic resources for the study of drought resistance in this crop.

Jasmonate enhances cold acclimation in jojoba by promoting flavonol synthesis

Abstract Jojoba is an industrial oil crop planted in tropical arid areas, and its low-temperature sensitivity prevents its introduction into temperate areas. Studying the molecular mechanisms associated with cold acclimation in jojoba is advantageous for developing breeds with enhanced cold tolerance. In this study, metabolomic analysis revealed that various flavonols accumulate in jojoba during cold acclimation. Time-course transcriptomic analysis and weighted correlation network analysis (WGCNA) demonstrated that flavonol biosynthesis and jasmonates (JAs) signaling pathways played crucial roles in cold acclimation. Combining the biochemical and genetic analyses showed that ScMYB12 directly activated flavonol synthase gene (ScFLS). The interaction between ScMYB12 and transparent testa 8 (ScTT8) promoted the expression of ScFLS, but the negative regulator ScJAZ13 in the JA signaling pathway interacted with ScTT8 to attenuate the transcriptional activity of the ScTT8 and ScMYB12 complex, leading to the downregulation of ScFLS. Cold acclimation stimulated the production of JA in jojoba leaves, promoted the degradation of ScJAZ13, and activated the transcriptional activity of ScTT8 and ScMYB12 complexes, leading to the accumulation of flavonols. Our findings reveal the molecular mechanism of JA-mediated flavonol biosynthesis during cold acclimation in jojoba and highlight the JA pathway as a promising means for enhancing cold tolerance in breeding efforts.

Bioinformatic analysis of the role of immune checkpoint genes and immune infiltration in the pathogenesis and development of premature ovarian insufficiency.

With advances in immunology, increasing evidence suggests that immunity is involved in premature ovarian insufficiency (POI) pathogenesis. This study investigated the roles of immune checkpoint genes and immune cell infiltration in POI pathogenesis and development. The GSE39501 dataset and immune checkpoint genes were obtained from the Gene Expression Omnibus database and related literature. The two datasets were intersected to obtain immune checkpoint-related differentially expressed genes (ICRDEGs), which were analyzed using Gene Ontology and Kyoto Encyclopedia of Gene and Genomes enrichment analysis, weighted correlation network analysis, protein-protein interaction and related microRNAs, transcription factors, and RNA binding proteins. The immune cell infiltration of ICRDEGs was explored, and receiver operating characteristic curves were used to validate the diagnostic value of ICRDEGs in POI. We performed ICRDEG functional enrichment analysis and found that these genes were closely related to immune processes, such as T cell activation. Specifically, they are enriched in various biological processes and pathways, such as cell adhesion molecule and T cell receptor signaling pathways. Weighted correlation network analysis identified seven hub genes: Cd200, Cd274, Cd28, neurociliary protein-1, Cd276, Cd40lg, and Cd47. Furthermore, we identified 112 microRNAs, 17 RNA-binding proteins, and 101 transcription factors. Finally, immune infiltration analysis showed a clear positive correlation between hub genes and multiple immune cell types. Bioinformatic analysis identified seven potential ICRDEGs associated with POI, among which the immune checkpoint molecules CD200 and neurociliary protein-1 may be involved in the pathogenesis of POI.

Abstract PO5-24-10: Clinical significance and immune landscape analyses of the immune related genes based prognostic signature for HER2 positive breast cancer

Abstract Objective: To date, significant breakthrough has yet to be achieved in immunotherapy for HER2-positive breast cancer (HPBC). In this study, we aimed to construct a risk model for predicting prognosis and therapy response and evaluating the tumor immune landscape in HPBC patients. Methods: Differentially expressed immune-related genes (IRGs) were used as input to weighted correlation network analysis (WGCNA) to identify modules of highly correlated gene. An optimal prognostic TPBC-IRGs signature were constructed by least absolute shrinkage and selection operator (LASSO) cox regression. Survival analysis and ROC curves were analyzed to identify the predictive value in a training cohort and external validation cohorts. With three GEO datasets and samples from our center, the anti-HER2 therapeutic efficacy in HPBC patients was predicted through this prognostic model. Additionally, the immunotherapy outcome was forecasted using IMvigor210 dataset. We also investigated the connection between high and low-risk populations and immune checkpoints and analyzed immune infiltration levels of these populations with several algorithms. Ultimately, we develop a combined model and nomogram by integrating the risk score with clinical factors. Results: 11 key genes associated with overall survival (OS) in the blue module were selected for further analysis. A 3-gene signature (CX3CL1, RARB, TANK) based prognostic risk model was developed. The Kaplan-Meier survival curve revealed that patients with high-risk score had shorter OS compared with the low-risk group. The AUC of the training cohort at 1-, 3- and 5-year were 0.718, 0.784 and 0.767. The performance of the risk model was validated with GSE25066 and GSE55348. It’s showed that the risk scores of HPBC who were resistant to trastuzumab were significantly higher than those of patients who were sensitive to trastuzumab. The results indicated that the levels of immune checkpoints expression (such as PD-1, CTLA4 and TIGIT) and immune infiltration were higher in low-risk group than in high-risk group. The combined model showed better prognostic prediction accuracy compared to the clinical model or gene signature alone (1-year AUC=0.910;3-year AUC=0.832;5-year AUC=0.882). Conclusion: The immune gene-based risk profile and combined prognostic model in this study may hold promise for clinical use in predicting the prognosis and treatment efficacy of HPBC patients. figure3. Construction of HPBC-IRGs signature based prognostic model. Citation Format: Xiaofen Li, Wenfen Fu, Yushuai Yu, Jie Zhang, Chuangui Song. Clinical significance and immune landscape analyses of the immune related genes based prognostic signature for HER2 positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-24-10.

Identification of Potential Therapeutic Drugs for Diabetic Cardiomyopathy

This study focused on identifying potential therapeutic drugs and mechanisms of action for diabetic cardiomyopathy (DCM). Using gene expression profiles from the GSE197850 dataset, we applied Weighted Correlation Network Analysis, Limma, and Gene Set Variation Analysis (GSVA) to uncover DCM-related gene sets and pathways. Subsequently, we conducted protein interaction network analysis with String and identified 10 hub genes through Cytoscape: ACTN2, ITGA1, CASP3, PXN, PCNA, CAV1, GAPDH, FEN1, PTPN11, and ESR1. In vitro validation using Rat H9C2 cardiomyocytes showed upregulation of FEN1, PCNA, PTPN11, CAV1, GAPDH, CASP3, PXN, and ACTN2, and downregulation of ESR1 and ITGA11 in high-glucose conditions. We further performed immune infiltration analysis with CIBERSORT and explored potential therapeutic agents through molecular docking with Autodock Vina. Our findings identified estradiol, valproic acid, acetaminophen, and resveratrol as potential drugs for DCM. Among these, resveratrol showed promise by promoting autophagy. This study leveraged comprehensive bioinformatic and experimental methods to pinpoint DCM-related genes, elucidate key hub genes, and propose resveratrol as a latent drug for DCM.

Ecophysiological responses of Phragmites australis populations to a tidal flat gradient in the Yangtze River Estuary, China

Phragmites australis is a prevalent species in the Chongming Dongtan wetland and is capable of thriving in various tidal flat environments, including high salinity habitats. P. australis population displays inconsistent ecological performances, highlighting the need to uncover their survival strategies and mechanisms in tidal flats with diverse soil salinities. Upon comparing functional traits of P. australis at multiple tidal flats (low, middle, and high) and their responses to soil physicochemical properties, this study aimed to clarify the salt-tolerant strategy of P. australis and the corresponding mechanisms. These results showed that leaf characteristics, such as specific leaf area and leaf dry matter content, demonstrated more robust stability to soil salinity than shoot height and dry weight. Furthermore, as salt stress intensified, the activities of superoxide dismutase (SOD), catalase (CAT) and peroxisome (POD) in P. australis leaves at low tidal flat exhibited an increased upward trend compared to those at other tidal flats. The molecular mechanism of salt tolerance in Phragmites australis across various habitats was investigated using transcriptome sequencing. Weighted correlation network analysis (WGCNA) combined with differentially expressed genes (DEGs) screened out 3 modules closely related to high salt tolerance and identified 105 core genes crucial for high salt tolerance. Further research was carried out on the few degraded populations at low tidal flat, and 25 core genes were identified by combining WGCNA and DEGs. A decrease in the activity of ferroptosis marker gonyautoxin-4 and an increase in the content of Fe3+ in the degenerated group were observed, indicating that ferroptosis might participate in degradation. Furthermore, correlation analysis indicated a possible regulatory network between salt tolerance and ferroptosis. In short, this study provided new insights into the salt tolerance mechanism of P. australis population along tidal flats.

Analysis of drought and heat stress response genes in rice using co-expression network and differentially expressed gene analyses.

Studies on Oryza sativa (rice) are crucial for improving agricultural productivity and ensuring global sustenance security, especially considering the increasing drought and heat stress caused by extreme climate change. Currently, the genes and mechanisms underlying drought and heat resistance in rice are not fully understood, and the scope for enhancing the development of new strains remains considerable. To accurately identify the key genes related to drought and heat stress responses in rice, multiple datasets from the Gene Expression Omnibus (GEO) database were integrated in this study. A co-expression network was constructed using a Weighted Correlation Network Analysis (WGCNA) algorithm. We further distinguished the core network and intersected it with differentially expressed genes and multiple expression datasets for screening. Differences in gene expression levels were verified using quantitative real-time polymerase chain reaction (PCR). OsDjC53, MBF1C, BAG6, HSP23.2, and HSP21.9 were found to be associated with the heat stress response, and it is also possible that UGT83A1 and OsCPn60a1, although not directly related, are affected by drought stress. This study offers significant insights into the molecular mechanisms underlying stress responses in rice, which could promote the development of stress-tolerant rice breeds.

Naive and regulatory B-cell transcription patterns guide the increased risk of papillary thyroid carcinoma in obesity.

A growing number of studies suggest a positive association between obesity and the high incidence of papillary thyroid cancer (PTC), suggesting that the abnormal levels of adipokines associated with obesity may be a risk factor for these aggressive thyroid cancers, but the underlying regulatory mechanisms are not yet clear. We downloaded bulk RNA sequence data for subcutaneous adipose tissue (SAT) in obesity and healthy population and tumor tissues of PTC from GEO database. Through analysis of Differential Expression Genes (DEGs), Gene Set Variation Analysis (GSVA) and Weighted Correlation Network Analysis (WGCNA), we identified co-expressed genes between obesity and PTC, and their pathways were mainly enriched in the regulation of B-cells. Furthermore, through TCGA-THCA (thyroid carcinoma) cohorts analysis, we identified B-cell regulatory-related genes LEF1, TNFRSF13C, SHLD2 and SHLD3 as independent prognostic markers of PTC. Next, we explored the transcriptional regulation mechanism of the increased risk of PTC in obesity through analysis of DNA methylation CpGs data and single-cell RNA sequences (scRNA-seq) from GEO database. PTC-induced hypomethylation of the promoter region may be involved in the transcriptional regulation of these genes, while these genes were further identified in naive and regulatory B-cells of both diseases. Notably, both of the gene expressions in naive and regulatory B-cells showed high similarity in both diseases. Our data reveals the high frequency of PTC in obese populations may be explained by the comparable transcriptional patterns of naive and regulatory B-cells, and offers novel insights for the analysis of critical genes and underlying biological mechanisms for obesity and PTC.

Network pharmacology of Dracaena sp. in Guangxi and its related species leaf secondary metabolites possess antioxidant properties

In this study, metabolite maps were conducted on leaf samples accessions of Dracaena sp. in Guangxi (C6, C13, CZ4, JX2, RM1, and BM3) and six related species including D. angustifolia (CH), D. elliptica (XZ), D. cochinchinensis (CB), D. cambodiana (HN), D. marginata (HB), and Yucca schidigera (SL). We identified 2,971 compounds including carboxylic acids and their derivatives, organooxygen compounds, prenol lipids, benzenes and their substituted derivatives, and flavonoids. Of those, 73 characteristic secondary metabolites were screened out by weighted correlation network analysis (WGCNA). Pterostilbene, 9-cis-retinal, and dracorubin were unique to the Dracaena sp. in Guangxi, and 3-hydroxytridecanoic acid and chrysin were highly abundant in them. An in vitro antioxidant activity assay disclosed that all 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) scavenging rates and all fluorescence recovery after photobleaching (FRAP) rates were in the ranges of 75–87 % and 3.10–3.25 μmol/mL, respectively, for the Dracaena sp. in Guangxi. Hence, the latter have relatively high and stable foliar antioxidant activity. A network pharmacological analysis of the top 20 metabolites in Dracaena leaf and their 446 antioxidant-related disease targets revealed ten core action targets, including GAPDH, AKT1, MAPK3, VEGFA, CASP3, TNF, MAPK1, SRC, EGFR, and MAPK8. An antioxidant-related compound-target-pathway network was constructed and verified by molecular docking. The results of this study provide a theoretical basis for mining and exploiting the foliar secondary metabolites of Dracaena.

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning.

Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction. The prevalence of AF and HF were progressively increasing over time. The co-existence of AF and HF presents a significant therapeutic challenge. In order to provide new ideas for the diagnosis of AF and HF, it is necessary to carry out biomarker related studies. The training set and validation set data of AF and HF patient samples were downloaded from the GEO database, 'limma' was used to compare the differences in gene expression levels between the disease group and the normal group to screen for differentially expressed genes (DEGs). Weighted correlation network analysis (WGCNA) identified the modules with the highest positive correlation with AF and HF. Functional enrichment and PPI network construction of key genes were carried out. Biomarkers were screened by machine learning. The infiltration of immune cells in AF and HF groups was evaluated by R-packet 'CIBERSORT'. The miRNA network was constructed and potential therapeutic agents for biomarker genes were predicted through the drugbank database. Through WGCNA analysis, it was found that the modules most positively correlated with AF and HF were MEturquoise (r=0.21, P value=0.09) and MEbrown (r=0.62, P value=8e-12), respectively. We screened 25 genes that were highly correlated with both AF and HF. Lasso regression analysis results showed 7 and 20 core genes in AF and HF groups, respectively. The top 20 important genes in AF and HF groups were obtained as core genes by RF model analysis. Four biomarkers were obtained after the intersection of core genes in four groups, namely, GLUL, NCF2, S100A12, and SRGN. The diagnostic efficacy of four genes in AF validation sets was good (AUC: GLUL 0.76, NCF2 0.64, S100A12 0.68, and SRGN 0.76), as well as in the HF validation set (AUC: GLUL 0.76, NCF2 0.84, S100A12 0.92, and SRGN 0.68). The highest correlation with neutrophils was observed for GLUL, NCF2, and S100A12, while SRGN exhibited the strongest correlation with T cells CD4 memory resting in the AF group. GLUL, NCF2, S100A12, and SRGN were most associated with neutrophils in the HF group. A total of 101 miRNAs were predicted by four genes, and GLUL, NCF2, and S100A12 predicted a total of 10 potential therapeutic agents. We identified four biological markers that are highly correlated with AF and HF, namely, GLUL, NCF2, S100A12, and SRGN. Our findings provide theoretical basis for the clinical diagnosis and treatment of AF and HF.

A nested case-control study of untargeted plasma metabolomics and lung cancer among never-smoking women within the prospective Shanghai Women's Health Study.

The etiology of lung cancer in never-smokers remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Here, we aimed to enhance our understanding of lung cancer pathogenesis among never-smokers using untargeted metabolomics. This nested case-control study included 395 never-smoking women who developed lung cancer and 395 matched never-smoking cancer-free women from the prospective Shanghai Women's Health Study with 15,353 metabolic features quantified in pre-diagnostic plasma using liquid chromatography high-resolution mass spectrometry. Recognizing that metabolites often correlate and seldom act independently in biological processes, we utilized a weighted correlation network analysis to agnostically construct 28 network modules of correlated metabolites. Using conditional logistic regression models, we assessed the associations for both metabolic network modules and individual metabolic features with lung cancer, accounting for multiple testing using a false discovery rate (FDR) < 0.20. We identified a network module of 121 features inversely associated with all lung cancer (p = .001, FDR = 0.028) and lung adenocarcinoma (p = .002, FDR = 0.056), where lyso-glycerophospholipids played a key role driving these associations. Another module of 440 features was inversely associated with lung adenocarcinoma (p = .014, FDR = 0.196). Individual metabolites within these network modules were enriched in biological pathways linked to oxidative stress, and energy metabolism. These pathways have been implicated in previous metabolomics studies involving populations exposed to known lung cancer risk factors such as traffic-related air pollution and polycyclic aromatic hydrocarbons. Our results suggest that untargeted plasma metabolomics could provide novel insights into the etiology and risk factors of lung cancer among never-smokers.

Effects of Long-Term Cryopreservation on the Transcriptomes of Giant Grouper Sperm.

The giant grouper fish (Epinephelus lanceolatus), one of the largest and rarest groupers, is a fast-growing economic fish. Grouper sperm is often used for cross-breeding with other fish and therefore sperm cryopreservation is important. However, freezing damage cannot be avoided. Herein, we performed a transcriptome analysis to compare fresh and frozen sperm of the giant grouper with frozen storage times of 0, 23, 49, and 61 months. In total, 1911 differentially expressed genes (DEGs), including 91 in El-0-vs-El-23 (40 upregulated and 51 downregulated), 251 in El-0-vs-El-49 (152 upregulated and 69 downregulated), and 1569 in El-0-vs-El-61 (984 upregulated and 585 downregulated), were obtained in the giant grouper sperm. DEGs were significantly increased at 61 months of cryopreservation (p < 0.05). GO and KEGG enrichment analyses of the DEGs revealed significant enrichment in the pilus assembly, metabolic process, MAPK signaling pathway, apoptosis, and P53 signaling pathway. Time-series expression profiling of the DEGs showed that consistently upregulated modules were also significantly enriched in signaling pathways associated with apoptosis. Four genes, scarb1, odf3, exoc8, and atp5f1d, were associated with mitochondria and flagella in a weighted correlation network analysis. These genes may play an important role in the response to sperm freezing. The experimental results show that long-term cryopreservation results in freezing damage to the giant grouper sperm. This study provides rich data for studies of the mechanism underlying frozen fish sperm damage as well as a technical reference and evaluation index for the long-term cryopreservation of fish sperm.

Resolving candidate genes of duck ovarian tissue transplantation via RNA-Seq and expression network analyses

This study aims to identify candidate genes related to ovarian development after ovarian tissue transplantation through transcriptome sequencing (RNA-seq) and expression network analyses, as well as to provide a reference for determining the molecular mechanism of improving ovarian development following ovarian tissue transplantation. We collected ovarian tissues from 15 thirty-day-old ducks and split each ovary into 4 equal portions of comparable sizes before orthotopically transplanting them into 2-day-old ducks. Samples were collected on days 0 (untransplanted), 3, 6, and 9. The samples were paraffin sectioned and then subjected to Hematoxylin-Eosin (HE) staining and follicular counting. We extracted RNA from ovarian samples via the Trizol method to construct a transcriptome library, which was then sequenced by the Illumina Novaseq 6000 sequencing platform. The sequencing results were examined for differentially expressed genes (DEG) through gene ontology (GO) function and the Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses, gene set enrichment analysis (GSEA), weighted correlation network analysis (WGCNA), and protein-protein interaction (PPI) networks. Some of the candidate genes were selected for verification using real-time fluorescence quantitative PCR (qRT-PCR). Histological analysis revealed a significant reduction in the number of morphologically normal follicles at 3, 6, and 9 d after ovarian transplantation, along with significantly higher abnormality rates (P < 0.05). The transcriptome analysis results revealed 2,114, 2,224, and 2,257 upregulated DEGs and 2,647, 2,883, and 2,665 downregulated DEGs at 3, 6, and 9 d after ovarian transplantation, respectively. Enrichment analysis revealed the involvement multiple pathways in inflammatory signaling, signal transduction, and cellular processes. Furthermore, WGCNA yielded 13 modules, with 10, 4, and 6 candidate genes mined at 3, 6 and 9 d after ovarian transplantation, respectively. Transcription factor (TF) prediction showed that STAT1 was the most important TF. Finally, the qRT-PCR verification results revealed that 12 candidate genes exhibited an expression trend consistent with sequencing data. In summary, significant differences were observed in the number of follicles in duck ovaries following ovarian transplantation. Candidate genes involved in ovarian vascular remodeling and proliferation were screened using RNA-Seq and WGCNA.

Methylation-driven mechanisms of allergic rhinitis during pollen and non-pollen seasons using integrated bioinformatics analysis.

Allergic rhinitis (AR) is a widespread allergic airway disease that results from a complex interplay between genetic and environmental factors and affects approximately 10%-40% of the global population. Pollen is a common allergen, and exposure to pollen can cause epigenetic changes. However, the mechanism underlying pollen-induced DNA methylation changes and their potential effects on the allergic march are still unclear. The purpose of this study was to explore the methylation-driven mechanisms of AR during the pollen and non-pollen seasons using bioinformatics analysis and to investigate their relationship with asthma. We downloaded DNA methylation and gene expression data from the GEO database (GSE50387: GSE50222, GSE50101) and identified differentially methylated positions (DMPs) and differentially expressed genes (DEGs) during the pollen and non-pollen seasons using the CHAMP and limma packages. Through correlation analysis, we identified methylation-driven genes and performed pathway enrichment analysis to annotate their functions. We incorporated external data on AR combined with asthma (GSE101720) for analysis to identify key CpGs that promote the transformation of AR to asthma. We also utilized external data on olive pollen allergy (GSE54522) for analysis to validate the methylation-driven genes. Weighted correlation network analysis (WGCNA) was employed to identify gene modules significantly correlated with pollen allergy. We extracted genes related to the key methylation-driven gene ZNF667-AS1 from the significant module and performed pathway intelligent clustering using KOBAS-i. We also utilized gene set enrichment analysis to explore the potential function of ZNF667-AS1. We identified 20 and 24 CpG-Gene pairings during the pollen and non-pollen seasons. After incorporating external data from GSE101720, we found that ZNF667-AS1 is a key gene that may facilitate the transformation of AR into asthma during the pollen season. This finding was further validated in another external dataset, GSE54522, which is associated with pollen allergy. WGCNA identified 17 modules, among which the blue module showed significant correlation with allergies. ZNF667-AS1 was located in the blue module. We performed pathway analysis on the genes correlated with ZNF667-AS1 extracted from the blue module and identified a prominent cluster of pathways in the KOBAS-i results, including Toll-like receptor (TLR) family, MyD88, MAPK, and oxidative stress. Gene set enrichment analysis around cg05508084 (paired with ZNF667-AS1) also indicated its potential involvement in initiating and modulating allergic inflammation from the perspective of TLR and MAPK signaling. We identified methylation-driven genes and their related pathways during the pollen and non-pollen seasons in patients with AR and identified key CpGs that promote the transformation of AR into asthma due to pollen exposure. This study provides new insights into the underlying molecular mechanisms of the transformation of AR to asthma.

Comparative Transcriptomic Analysis of Gossypium hirsutum Fiber Development in Mutant Materials (xin w 139) Provides New Insights into Cotton Fiber Development.

Cotton is the most widely planted fiber crop in the world, and improving cotton fiber quality has long been a research hotspot. The development of cotton fibers is a complex process that includes four consecutive and overlapping stages, and although many studies on cotton fiber development have been reported, most of the studies have been based on cultivars that are promoted in production or based on lines that are used in breeding. Here, we report a phenotypic evaluation of Gossypium hirsutum based on immature fiber mutant (xin w 139) and wild-type (Xin W 139) lines and a comparative transcriptomic study at seven time points during fiber development. The results of the two-year study showed that the fiber length, fiber strength, single-boll weight and lint percentage of xin w 139 were significantly lower than those of Xin W 139, and there were no significant differences in the other traits. Principal component analysis (PCA) and cluster analysis of the RNA-sequencing (RNA-seq) data revealed that these seven time points could be clearly divided into three different groups corresponding to the initiation, elongation and secondary cell wall (SCW) synthesis stages of fiber development, and the differences in fiber development between the two lines were mainly due to developmental differences after twenty days post anthesis (DPA). Differential expression analysis revealed a total of 5131 unique differentially expressed genes (DEGs), including 290 transcription factors (TFs), between the 2 lines. These DEGs were divided into five clusters. Each cluster functional category was annotated based on the KEGG database, and different clusters could describe different stages of fiber development. In addition, we constructed a gene regulatory network by weighted correlation network analysis (WGCNA) and identified 15 key genes that determined the differences in fiber development between the 2 lines. We also screened seven candidate genes related to cotton fiber development through comparative sequence analysis and qRT-PCR; these genes included three TFs (GH_A08G1821 (bHLH), GH_D05G3074 (Dof), and GH_D13G0161 (C3H)). These results provide a theoretical basis for obtaining an in-depth understanding of the molecular mechanism of cotton fiber development and provide new genetic resources for cotton fiber research.

Identifying SLC2A6 as the novel protective factor in breast cancer by TP53-related genes affecting M1 macrophage infiltration.

The high heterogeneity of breast cancer (BC) caused by pathogenic gene mutations poses a challenge to immunotherapy, but the underlying mechanism remains unknown. The difference in the infiltration of M1 macrophages induced by TP53 mutations has a significant impact on BC immunotherapy. The aim of this study was to develop a TP53-related M1 macrophage infiltration molecular typing risk signature in BC and evaluate the biological functions of the key gene to find new immunotherapy biomarkers. Weighted correlation network analysis (WGCNA) and negative matrix factorization (NMF) were used for distinguishing BC subtypes. The signature and the nomogram were both constructed and evaluated. Biological functions of the novel signature gene SLC2A6 were confirmed through in vitro and in vivo experiments. RNA-Sequencing and protein profiling were used for detecting the possible mechanism of SLC2A6. The results suggested that four BC subtypes were distinguished by TP53-related genes that affect M1 macrophage infiltration. The signature constructed by molecular typing characteristics could evaluate BC's clinical features and tumor microenvironment. The nomogram could accurately predict the prognosis. The signature gene SLC2A6 was found to have an abnormally low expression in tumor tissues. Overexpression of SLC2A6 could inhibit proliferation, promote mitochondrial damage, and result in apoptosis of tumor cells. The HSP70 family member protein HSPA6 could bind with SLC2A6 and increase with the increased expression of SLC2A6. In summary, the risk signature provides a reference for BC risk assessment, and the signature gene SLC2A6 could act as a tumor suppressor in BC.

Analysis of huanglongbing-associated RNA-seq data reveals disturbances in biological processes within Citrus spp. triggered by Candidatus Liberibacter asiaticus infection.

Huanglongbing (HLB), a disease that's ubiquitous worldwide, wreaks havoc on the citrus industry. The primary culprit of HLB is the gram-negative bacterium Candidatus Liberibacter asiaticus (CLas) that infects the phloem, but its damaging mechanism is yet to be fully understood. In this study, a multitude of tools including weighted correlation network analysis (WGCNA), protein-protein interaction (PPI) network analysis and gene expression profiling are employed to unravel the intricacies of its pathogenesis. The investigation pinpoints various central genes, such as the ethylene-responsive transcription factor 9 (ERF9) and thioredoxin reductase 1 (TrxR1), that are associated with CLas invasion and resultant disturbances in numerous biological operations. Additionally, the study uncovers a range of responses through the detection of differential expressed genes (DEGs) across different experiments. The discovery of core DEGs leads to the identification of pivotal genes such as the sieve element occlusion (SEO) and the wall-associated receptor kinase-like 15 (WAKL15). PPI network analysis highlights potential vital proteins, while GO and KEGG pathway enrichment analysis illustrate a significant impact on multiple defensive and metabolic pathways. Gene set enrichment analysis (GSEA) indicates significant alterations in biological processes such as leaf senescence and response to biotic stimuli. This all-encompassing approach extends valuable understanding into the pathogenesis of CLas, potentially aiding future research and therapeutic strategies for HLB.

Vineyard microclimate alterations induced by black inter-row mulch through transcriptome reshaped the flavoromics of cabernet sauvignon grapes

BackgroundWeed control is essential for agricultural floor management in vineyards and the inter-row mulching is an eco-friendly practice to inhibit weed growth via filtering out photosynthetically active radiation. Besides weed suppression, inter-row mulching can influence grapevine growth and the accumulation of metabolites in grape berries. However, the complex interaction of multiple factors in the field challenges the understanding of molecular mechanisms on the regulated metabolites. In the current study, black geotextile inter-row mulch (M) was applied for two vintages (2016–2017) from anthesis to harvest. Metabolomics and transcriptomics analysis were conducted in two vintages, aiming to provide insights into metabolic and molecular responses of Cabernet Sauvignon grapes to M in a semi-arid climate.ResultsUpregulation of genes related to photosynthesis and heat shock proteins confirmed that M weakened the total light exposure and grapes suffered heat stress, resulting in lower sugar-acid ratio at harvest. Key genes responsible for enhancements in phenylalanine, glutamine, ornithine, arginine, and C6 alcohol concentrations, and the downward trend in ε-viniferin, anthocyanins, flavonols, terpenes, and norisoprenoids in M grapes were identified. In addition, several modules significantly correlated with the metabolic biomarkers through weighted correlation network analysis, and the potential key transcription factors regulating the above metabolites including VviGATA11, VviHSFA6B, and VviWRKY03 were also identified.ConclusionThis study provides a valuable overview of metabolic and transcriptomic responses of M grapes in semi-arid climates, which could facilitate understanding the complex regulatory network of metabolites in response to microclimate changes.

CDC167 exhibits potential as a biomarker for airway inflammation in asthma.

Current asthma treatments have been discovered to decrease the risk of disease progression. Herein, we aimed to characterize novel potential therapeutic targets for asthma. Differentially expressed genes (DEGs) for GSE64913 and GSE137268 datasets were characterized. Weighted correlation network analysis (WGCNA) was used to identify trait-related module genes within the GSE67472 dataset. The intersection of the module genes of interest, as well as the DEGs, comprised the key module genes that underwent additional candidate gene screening using machine learning. In addition, a bioinformatics-based approach was used to analyze the relative expression levels, diagnostic values, and reverently enriched pathways of the screened candidate genes. Furthermore, the candidate genes were silenced in asthmatic mice, and the inflammation and lung injury in the mice were validated. A total of 1710 DEGs were characterized in GSE64913 and GSE137268 for asthma patients. WGCNA identified 2367 asthma module genes, of which 285 overlapped with 1710 DEGs. Four candidate genes, CDC167, POSTN, SEC14L1, and SERPINB2, were validated using the intersection genes of three machine learning algorithms, including Least Absolute Shrinkage and Selection Operator, Random Forest, and Support Vector Machine. All the candidate genes were significantly upregulated in asthma patients and demonstrated diagnostic utility for asthma. Furthermore, silencing CDC167 reduced the levels of inflammatory cytokines significantly and alleviated lung injury in ovalbumin (OVA)-induced asthmatic mice. Our study demonstrated that CDC167 exhibits potential as diagnostic markers and therapeutic targets for asthma patients.