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Introduction: Newcastle disease is one of the significant issues in the poultry industry, having catastrophic effects worldwide. The lung is one of the essential organs which harbours Bronchus-associated lymphoid tissue and plays a vital role in the immune response. Leghorn and Fayoumi breeds are known to have differences in resistance to Newcastle disease. Along with genes and long non-coding RNAs (lncRNAs) are also known to regulate various biological pathways through gene regulation. Methods: This study analysed the lung transcriptome data and identified the role of genes and long non-coding RNAs in differential immune resistance. The computational pipeline, FHSpipe, as used in our previous studies on analysis of harderian gland and trachea transcriptome was used to identify genes and lncRNAs. This was followed by differential expression analysis, functional annotation of genes and lncRNAs, identification of transcription factors, microRNAs and finally validation using qRT-PCR. Results and discussion: A total of 8219 novel lncRNAs were identified. Of them, 1263 lncRNAs and 281 genes were differentially expressed. About 66 genes were annotated with either an immune-related GO term or pathway, and 12 were annotated with both. In challenge and breed-based analysis, most of these genes were upregulated in Fayoumi compared to Leghorn, and in timepoint-based analysis, Leghorn challenge chicken showed downregulation between time points. A similar trend was observed in the expression of lncRNAs. Co-expression analysis has revealed several lncRNAs co-expressing with immune genes with a positive correlation. Several genes annotated with non-immune pathways, including metabolism, signal transduction, transport of small molecules, extracellular matrix organization, developmental biology and cellular processes, were also impacted. With this, we can understand that Fayoumi chicken showed upregulated immune genes and positive cis-lncRNAs during both the non-challenged and NDV-challenge conditions, even without viral transcripts in the tissue. This finding shows that these immune-annotated genes and coexpressing cis-lncRNAs play a significant role in Fayoumi being comparatively resistant to NDV compared to Leghorn. Our study affirms and expands upon the outcomes of previous studies and highlights the crucial role of lncRNAs during the immune response to NDV. Conclusion: This analysis clearly shows the differences in the gene expression patterns and lncRNA co-expression with the genes between Leghorn and Fayoumi, indicating that the lncRNAs and co-expressing genes might potentially have a role in differentiating these breeds. We hypothesise that these genes and lncRNAs play a vital role in the higher resistance of Fayoumi to NDV than Leghorn. This study can pave the way for future studies to unravel the biological mechanism behind the regulation of immune-related genes.
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Pollos , Perfilación de la Expresión Génica , Pulmón , MicroARNs , Enfermedad de Newcastle , Virus de la Enfermedad de Newcastle , Enfermedades de las Aves de Corral , ARN Largo no Codificante , Transcriptoma , Animales , Pollos/genética , Pollos/inmunología , Enfermedad de Newcastle/inmunología , Enfermedad de Newcastle/virología , Enfermedad de Newcastle/genética , Pulmón/inmunología , Pulmón/virología , Virus de la Enfermedad de Newcastle/inmunología , Virus de la Enfermedad de Newcastle/genética , ARN Largo no Codificante/genética , ARN Largo no Codificante/metabolismo , Enfermedades de las Aves de Corral/virología , Enfermedades de las Aves de Corral/inmunología , Enfermedades de las Aves de Corral/genética , MicroARNs/genética , MicroARNs/metabolismo , Regulación de la Expresión Génica , Resistencia a la Enfermedad/genética , Biología Computacional/métodos , Interacciones Huésped-Patógeno/genética , Interacciones Huésped-Patógeno/inmunologíaRESUMEN
Background: Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF. Methods: We retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the shared genes within the modules related to CS and AF. The STRING database was used to build a protein-protein interaction (PPI) network, facilitating the discovery of hub genes within the network. Finally, several common used machine learning approaches were applied to construct the clinical predictive model of CS and AF. ROC curve analysis to evaluate the diagnostic value of the identified biomarkers for AF and CS. Results: Functional enrichment analysis indicated that pathways intrinsic to the immune response may be significantly involved in CS and AF. PPI network analysis identified a potential association of 4 key genes with both CS and AF, specifically PIK3R1, ITGAM, FOS, and TLR4. Conclusion: In our study, we utilized WGCNA, PPI network analysis, and machine learning to identify four hub genes significantly associated with CS and AF. Functional annotation outcomes revealed that inherent pathways related to the immune response connected to the recognized genes might could pave the way for further research on the etiological mechanisms and therapeutic targets for CS and AF.
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Patients with thymoma (THYM)-associated myasthenia gravis (MG) typically have a poor prognosis and recurring illness. This study aimed to discover important biomarkers associated with immune cell infiltration and THYM-associated MG (THYM-MG) development. Gene expression microarray data were downloaded from The Cancer Genome Atlas website (TCGA) and Gene Expression Omnibus (GEO). A total of 102 differentially expressed genes were investigated. According to the immune infiltration data, the distribution of Tfh cells, B cells, and CD4 T cells differed significantly between the THYM-MG and THYM-NMG groups. WGCNA derived 25 coexpression modules; one hub module (the blue module) strongly correlated with Tfh cells. Combining differential genes revealed 21 intersecting genes. LASSO analysis subsequently revealed 16 hub genes as potential THYM-MG biomarkers. ROC curve analysis of the predictive model revealed moderate diagnostic value. The association between the 16 hub genes and infiltrating immune cells was further evaluated in TIMER2.0 and the validation dataset. Draggability analysis identified the therapeutic target genes PTGS2 and ALB, along with significant drugs including Firocoxib, Alclofenac, Pyridostigmine, and Stavudine. This was validated through MD simulation, PCA, and MM-GBSA analyses. The interaction between numerous activated B cells and follicular helper T cells is closely associated with THYM-MG pathogenesis from a bioinformatics perspective. Hub genes (including SP6, SCUBE3, B3GNT7, and MAGEL2) may be downregulated in immune cells in THYM-MG and associated with progression.
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Purpose: Although the application of heterosis has significantly increased crop yield over the past century, the mechanisms underlying this phenomenon still remain obscure. Here, we applied transcriptome sequencing to unravel the impacts of parental expression differences and transcriptomic reprogramming in cotton heterosis. Methods: A high-quality transcriptomic atlas covering 15 developmental stages and tissues was constructed for XZM2, an elite hybrid of upland cotton (Gossypium hirsutum L.), and its parental lines, CRI12 and J8891. This atlas allowed us to identify gene expression differences between the parents and to characterize the transcriptomic reprogramming that occurs in the hybrid. Results: Our analysis revealed abundant gene expression differences between the parents, with pronounced tissue specificity; a total of 1,112 genes exhibited single-parent expression in at least one tissue. It also illuminated transcriptomic reprogramming in the hybrid XZM2, which included both additive and non-additive expression patterns. Coexpression networks between parents and hybrid constructed via weighted gene coexpression network analysis identified modules closely associated with fiber development. In particular, key regulatory hub genes involved in fiber development showed high-parent dominant or over dominant patterns in the hybrid, potentially driving the emergence of heterosis. Finally, high-depth resequencing data was generated and allele-specific expression patterns examined in the hybrid, enabling the dissection of cis- and trans-regulation contributions to the observed expression differences. Conclusion: Parental transcriptional differences and transcriptomic reprogramming in the hybrid, especially the non-additive upregulation of key genes, play an important role in shaping heterosis. Collectively, these findings provide new insights into the molecular basis of heterosis in cotton.
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Isoflavone is a secondary metabolite of the soybean phenylpropyl biosynthesis pathway with physiological activity and is beneficial to human health. In this study, the isoflavone content of 205 soybean germplasm resources from 3 locations in 2020 showed wide phenotypic variation. A joint genome-wide association study (GWAS) and weighted gene coexpression network analysis (WGCNA) identified 33 single-nucleotide polymorphisms and 11 key genes associated with soybean isoflavone content. Gene ontology enrichment analysis, gene coexpression, and haplotype analysis revealed natural variations in the Glyma.12G109800 (GmOMT7) gene and promoter region, with Hap1 being the elite haplotype. Transient overexpression and knockout of GmOMT7 increased and decreased the isoflavone content, respectively, in hairy roots. The combination of GWAS and WGCNA effectively revealed the genetic basis of soybean isoflavone and identified potential genes affecting isoflavone synthesis and accumulation in soybean, providing a valuable basis for the functional study of soybean isoflavone.
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Regulación de la Expresión Génica de las Plantas , Estudio de Asociación del Genoma Completo , Glycine max , Isoflavonas , Proteínas de Plantas , Polimorfismo de Nucleótido Simple , Semillas , Glycine max/genética , Glycine max/metabolismo , Glycine max/química , Isoflavonas/metabolismo , Isoflavonas/análisis , Semillas/genética , Semillas/química , Semillas/metabolismo , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Redes Reguladoras de GenesRESUMEN
Heat shock proteins (Hsp) function as crucial molecular chaperones, playing pivotal roles in insects' response to stress stimuli. Apolygus lucorum, known for its broad spectrum of host plants and significant crop damage potential, presents a compelling subject for understanding stress response mechanisms. Hsp is important for A. lucorum to tolerate temperature and insecticide stress and may be involved in the formation of resistance to the interactive effects of temperature and insecticide. Here, we employed comprehensive genomic approaches to identify Hsp superfamily members in its genome. In total, we identified 42 Hsp genes, including 3 Hsp90, 16 Hsp70, 13 Hsp60, and 10 Hsp20. Notably, we conducted motif analysis and gene structures for Hsp members, which suggested the same families are relatively conserved. Furthermore, leveraging the weighted gene coexpression network analysis, we observed diverse expression patterns of different Hsp types across various tissues, with certain Hsp70 showing tissue-specific bias. Noteworthy among the highly expressed Hsp genes was testis-specific, which may serve as a pivotal hub gene regulating the gene network. Our findings shed light on the molecular evolutionary dynamics and temperature stress response mechanisms of Hsp genes in A. lucorum, offering insights into its adaptive strategies and potential targets for pest management.
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Proteínas de Choque Térmico , Proteínas de Insectos , Animales , Proteínas de Choque Térmico/genética , Proteínas de Choque Térmico/metabolismo , Proteínas de Insectos/genética , Proteínas de Insectos/metabolismo , Genoma de los Insectos , Heterópteros/genética , Heterópteros/metabolismo , Filogenia , Redes Reguladoras de Genes , Familia de MultigenesRESUMEN
INTRODUCTION: Interspecific introgression between Gossypium hirsutum and G. barbadense allows breeding cotton with outstanding fiber length (FL). However, the dynamic gene regulatory network of FL-related genes has not been characterized, and the functional mechanism through which the hub gene GhTUB5 mediates fiber elongation has yet to be determined. METHODS: Coexpression analyses of 277 developing fiber transcriptomes integrated with QTL mapping using 250 introgression lines of different FL phenotypes were conducted to identify genes related to fiber elongation. The function of GhTUB5 was determined by ectopic expression of two TUB5 alleles in Arabidopsis and knockout of GhTUB5 in upland cotton. Yeast two-hybrid, split-luciferase and pull-down assays were conducted to screen for interacting proteins, and upstream genes were identified by yeast one-hybrid, dual-LUC and electrophoretic mobility shift assays. RESULTS: The 32,612, 30,837 and 30,277 genes expressed at 5, 10 and 15 days postanthesis (dpa) were grouped into 19 distinct coexpression modules, and 988 genes in the MEblack module were enriched in the cell wall process and exhibited significant associations with FL. A total of 20 FL-QTLs were identified, each explaining 3.34-16.04 % of the phenotypic variance in the FL. Furthermore, several FL-QTLs contained 15 genes that were differentially expressed in the MEblack module including the tubulin beta gene (TUB5). Compared with the wild type, the overexpression of GhTUB5 and GbTUB5 in Arabidopsis suppressed root cell length but promoted cellulose synthesis. Knockout of GhTUB5 resulted in longer fiber lines. Protein-based experiments revealed that GhTUB5 interacts with GhZFP6. Additionally, GhTUB5 was directly activated by GhHD-ZIP7, a homeobox-leucine zipper transcription factor, and its paralogous gene was previously reported to mediate fiber elongation. CONCLUSION: This study opens a new avenue to dissect functional mechanism of cotton fiber elongation. Our findings provide some molecular details on how GhTUB5 mediates the FL phenotype in cotton.
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Engineering acid-tolerant microbial strains is a cost-effective approach to overcoming acid stress during industrial fermentation. We previously constructed an acid-tolerant strain (Escherichia coli SC3124) with enhanced growth robustness and productivity under mildly acidic conditions by fine-tuning the expression of synthetic acid-tolerance module genes consisting of a proton-consuming acid resistance system (gadE), a periplasmic chaperone (hdeB), and ROS scavengers (sodB, katE). However, the precise acid-tolerance mechanism of E. coli SC3124 remained unclear. In this study, the growth of E. coli SC3124 under mild acid stress (pH 6.0) was determined. The final OD600 of E. coli SC3124 at pH 6.0 was 131% and 124% of that of the parent E. coli MG1655 at pH 6.8 and pH 6.0, respectively. Transcriptome analysis revealed the significant upregulation of the genes involved in oxidative phosphorylation, the tricarboxylic acid (TCA) cycle, and lysine-dependent acid-resistance system in E. coli SC3124 at pH 6.0. Subsequently, a weighted gene coexpression network analysis was performed to systematically determine the metabolic perturbations of E. coli SC3124 with mild acid treatment, and we extracted the gene modules highly associated with different acid traits. The results showed two biologically significant coexpression modules, and 263 hub genes were identified. Specifically, the genes involved in ATP-binding cassette (ABC) transporters, oxidative phosphorylation, the TCA cycle, amino acid metabolism, and purine metabolism were highly positively associated with mild acid stress responses. We propose that the overexpression of synthetic acid-tolerance genes leads to metabolic changes that confer mild acid stress resistance in E. coli. Integrated omics platforms provide valuable information for understanding the regulatory mechanisms of mild acid tolerance in E. coli and highlight the important roles of oxidative phosphorylation and ABC transporters in mild acid stress regulation. These findings offer novel insights to better the design of acid-tolerant chasses to synthesize value-added chemicals in a green and sustainable manner.
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BACKGROUND: The cold tolerance of rice is closely related to its production and geographic distribution. The identification of cold tolerance-related genes is of important significance for developing cold-tolerant rice. Dongxiang wild rice (Oryza rufipogon Griff.) (DXWR) is well-adapted to the cold climate of northernmost-latitude habitats ever found in the world, and is one of the most valuable rice germplasms for cold tolerance improvement. RESULTS: Transcriptome analysis revealed genes differentially expressed between Xieqingzao B (XB; a cold sensitive variety) and 19H19 (derived from an interspecific cross between DXWR and XB) in the room temperature (RT), low temperature (LT), and recovery treatments. The results demonstrated that chloroplast genes might be involved in the regulation of cold tolerance in rice. A high-resolution SNP genetic map was constructed using 120 BC5F2 lines derived from a cross between 19H19 and XB based on the genotyping-by-sequencing (GBS) technique. Two quantitative trait loci (QTLs) for cold tolerance at the early seedling stage (CTS), qCTS12 and qCTS8, were detected. Moreover, a total of 112 candidate genes associated with cold tolerance were identified based on bulked segregant analysis sequencing (BSA-seq). These candidate genes were divided into eight functional categories, and the expression trend of candidate genes related to 'oxidation-reduction process' and 'response to stress' differed between XB and 19H19 in the RT, LT and recovery treatments. Among these candidate genes, the expression level of LOC_Os12g18729 in 19H19 (related to 'response to stress') decreased in the LT treatment but restored and enhanced during the recovery treatment whereas the expression level of LOC_Os12g18729 in XB declined during recovery treatment. Additionally, XB contained a 42-bp deletion in the third exon of LOC_Os12g18729, and the genotype of BC5F2 individuals with a survival percentage (SP) lower than 15% was consistent with that of XB. Weighted gene coexpression network analysis (WGCNA) and modular regulatory network learning with per gene information (MERLIN) algorithm revealed a gene interaction/coexpression network regulating cold tolerance in rice. In the network, differentially expressed genes (DEGs) related to 'oxidation-reduction process', 'response to stress' and 'protein phosphorylation' interacted with LOC_Os12g18729. Moreover, the knockout mutant of LOC_Os12g18729 decreased cold tolerance in early rice seedling stage signifcantly compared with that of wild type. CONCLUSIONS: In general, study of the genetic basis of cold tolerance of rice is important for the development of cold-tolerant rice varieties. In the present study, QTL mapping, BSA-seq and RNA-seq were integrated to identify two CTS QTLs qCTS8 and qCTS12. Furthermore, qRT-PCR, genotype sequencing and knockout analysis indicated that LOC_Os12g18729 could be the candidate gene of qCTS12. These results are expected to further exploration of the genetic mechanism of CTS in rice and improve cold tolerance of cultivated rice by introducing the cold tolerant genes from DXWR through marker-assisted selection.
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Frío , Oryza , Sitios de Carácter Cuantitativo , Plantones , Oryza/genética , Oryza/fisiología , Sitios de Carácter Cuantitativo/genética , Plantones/genética , Plantones/fisiología , Plantones/crecimiento & desarrollo , Genes de Plantas , RNA-Seq , Mapeo Cromosómico , Perfilación de la Expresión Génica , Regulación de la Expresión Génica de las Plantas , Respuesta al Choque por Frío/genéticaRESUMEN
BACKGROUND: Knee osteoarthritis (KOA) is a common degenerative joint disease characterized by cartilage degradation, inflammation, and pain. Traditional Chinese Medicine, including JDJM (a herbal formula derived from the renowned Du Huo Ji Sheng Tang), has been used to alleviate symptoms of KOA, but its underlying mechanisms remain unclear. OBJECTIVE: This study aims to elucidate the potential therapeutic mechanisms of JDJM in treating KOA through network pharmacology, weighted gene co-expression network analysis (WGCNA), molecular docking, and experimental validation in animal models. METHODS: The active compounds of JDJM were identified through TCMSP database searches, and their potential targets were predicted using network pharmacology. WGCNA was employed to identify key modules and hub genes associated with KOA. Molecular docking was performed to assess the binding affinities of key compounds to critical inflammatory targets. Molecular dynamics (MD) simulations were used to evaluate the stability of the protein-ligand complexes. An experimental KOA model in rabbits was used to validate the therapeutic effects of JDJM. Histopathological examinations and inflammatory marker analyses were conducted to confirm the findings. RESULTS: Network pharmacology and WGCNA analyses identified 21 key targets and pathways potentially involved in the therapeutic effects of JDJM. Molecular docking results showed that Glyasperin C had the highest docking scores with EGF and IL-1ß, followed by Stigmasterol with IL-6, Myricanone with INS, and Sesamin with VEGFA. MD simulations confirmed the stability of these protein-ligand complexes, indicating strong and stable interactions. In the rabbit KOA model, JDJM treatment significantly improved knee joint morphology and reduced the levels of inflammatory markers, such as IL-6 and TNF-α. Histopathological analysis revealed reduced cartilage degradation and inflammation in the JDJM-treated group compared to controls. CONCLUSION: JDJM exhibits promising anti-inflammatory and cartilage-protective effects, making it a potential therapeutic option for KOA patients. Further experimental and clinical studies are warranted to confirm these findings and translate them into clinical practice.
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BACKGROUND: Pruning is an important cultivation management option that has important effects on peach yield and quality. However, the effects of pruning on the overall genetic and metabolic changes in peach leaves and fruits are poorly understood. RESULTS: The transcriptomic and metabolomic profiles of leaves and fruits from trees subjected to pruning and unpruning treatments were measured. A total of 20,633 genes and 622 metabolites were detected. Compared with those in the control, 1,127 differentially expressed genes (DEGs) and 77 differentially expressed metabolites (DEMs) were identified in leaves from pruned and unpruned trees (pdLvsupdL), whereas 423 DEGs and 29 DEMs were identified in fruits from the pairwise comparison pdFvsupdF. The content of three auxin analogues was upregulated in the leaves of pruned trees, the content of all flavonoids detected in the leaves decreased, and the expression of almost all genes involved in the flavonoid biosynthesis pathway decreased. The phenolic acid and amino acid metabolites detected in fruits from pruned trees were downregulated, and all terpenoids were upregulated. The correlation analysis revealed that DEGs and DEMs in leaves were enriched in tryptophan metabolism, auxin signal transduction, and flavonoid biosynthesis. DEGs and DEMs in fruits were enriched in flavonoid and phenylpropanoid biosynthesis, as well as L-glutamic acid biosynthesis. CONCLUSIONS: Pruning has different effects on the leaves and fruits of peach trees, affecting mainly the secondary metabolism and hormone signalling pathways in leaves and amino acid biosynthesis in fruits.
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Frutas , Perfilación de la Expresión Génica , Metabolómica , Hojas de la Planta , Prunus persica , Hojas de la Planta/metabolismo , Hojas de la Planta/genética , Prunus persica/genética , Prunus persica/metabolismo , Prunus persica/crecimiento & desarrollo , Frutas/metabolismo , Frutas/genética , Frutas/crecimiento & desarrollo , Regulación de la Expresión Génica de las Plantas , Metaboloma , Transcriptoma , Flavonoides/metabolismo , Ácidos Indolacéticos/metabolismoRESUMEN
BACKGROUND: Delayed cerebral ischemia (DCI) is a common and serious complication of subarachnoid hemorrhage (SAH). Its pathogenesis is not fully understood. Here, we developed a predictive model based on peripheral blood biomarkers and validated the model using several bioinformatic multi-analysis methods. METHODS: Six datasets were obtained from the GEO database. Characteristic genes were screened using weighted correlation network analysis (WGCNA) and differentially expressed genes. Three machine learning algorithms, elastic networks-LASSO, support vector machines (SVM-RFE) and random forests (RF), were also used to construct diagnostic prediction models for key genes. To further evaluate the performance and predictive value of the diagnostic models, nomogram model were constructed, and the clinical value of the models was assessed using Decision Curve Analysis (DCA), Area Under the Check Curve (AUC), Clinical Impact Curve (CIC), and validated in the mouse single-cell RNA-seq dataset. Mendelian randomization(MR) analysis explored the causal relationship between SAH and stroke, and the intermediate influencing factors. We validated this by retrospectively analyzing the qPCR levels of the most relevant genes in SAH and SAH-DCI patients. This experiment demonstrated a statistically significant difference between SAH and SAH-DCI and normal group controls. Finally, potential small molecule compounds interacting with the selected features were screened from the Comparative Toxicogenomics Database (CTD). RESULTS: The fGSEA results showed that activation of Toll-like receptor signaling and leukocyte transendothelial cell migration pathways were positively correlated with the DCI phenotype, whereas cytokine signaling pathways and natural killer cell-mediated cytotoxicity were negatively correlated. Consensus feature selection of DEG genes using WGCNA and three machine learning algorithms resulted in the identification of six genes (SPOCK2, TRRAP, CIB1, BCL11B, PDZD8 and LAT), which were used to predict DCI diagnosis with high accuracy. Three external datasets and the mouse single-cell dataset showed high accuracy of the diagnostic model, in addition to high performance and predictive value of the diagnostic model in DCA and CIC. MR analysis looked at stroke after SAH independent of SAH, but associated with multiple intermediate factors including Hypertensive diseases, Total triglycerides levels in medium HDL and Platelet count. qPCR confirmed that significant differences in DCI signature genes were observed between the SAH and SAH-DCI groups. Finally, valproic acid became a potential therapeutic agent for DCI based on the results of target prediction and molecular docking of the characterized genes. CONCLUSION: This diagnostic model can identify SAH patients at high risk for DCI and may provide potential mechanisms and therapeutic targets for DCI. Valproic acid may be an important future drug for the treatment of DCI.
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Biomarcadores , Isquemia Encefálica , Ácido Valproico , Humanos , Animales , Isquemia Encefálica/tratamiento farmacológico , Isquemia Encefálica/genética , Isquemia Encefálica/sangre , Isquemia Encefálica/inmunología , Ácido Valproico/uso terapéutico , Ratones , Biomarcadores/sangre , Hemorragia Subaracnoidea/sangre , Hemorragia Subaracnoidea/inmunología , Hemorragia Subaracnoidea/genética , Hemorragia Subaracnoidea/tratamiento farmacológico , Biología Computacional , Bases de Datos Genéticas , Aprendizaje AutomáticoRESUMEN
BACKGROUND: As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources. RESULTS: We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs. CONCLUSIONS: RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.
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Minería de Datos , Estudio de Asociación del Genoma Completo , Oryza , Sitios de Carácter Cuantitativo , Oryza/genética , Programas Informáticos , Epigenómica/métodos , Biología Computacional/métodos , Polimorfismo de Nucleótido Simple , Genómica/métodos , Genoma de Planta , Mapeo Cromosómico , Bases de Datos GenéticasRESUMEN
BACKGROUND: Drug-induced liver injury (DILI) is one of the most common adverse events of medication use, and its incidence is increasing. However, early detection of DILI is a crucial challenge due to a lack of biomarkers and noninvasive tests. AIM: To identify salivary metabolic biomarkers of DILI for the future development of noninvasive diagnostic tools. METHODS: Saliva samples from 31 DILI patients and 35 healthy controls (HCs) were subjected to untargeted metabolomics using ultrahigh-pressure liquid chromatography coupled with tandem mass spectrometry. Subsequent analyses, including partial least squares-discriminant analysis modeling, t tests and weighted metabolite coexpression network analysis (WMCNA), were conducted to identify key differentially expressed metabolites (DEMs) and metabolite sets. Furthermore, we utilized least absolute shrinkage and selection operato and random fores analyses for biomarker prediction. The use of each metabolite and metabolite set to detect DILI was evaluated with area under the receiver operating characteristic curves. RESULTS: We found 247 differentially expressed salivary metabolites between the DILI group and the HC group. Using WMCNA, we identified a set of 8 DEMs closely related to liver injury for further prediction testing. Interestingly, the distinct separation of DILI patients and HCs was achieved with five metabolites, namely, 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, tetradecanedioic acid, hypoxanthine, and inosine (area under the curve: 0.733-1). CONCLUSION: Salivary metabolomics revealed previously unreported metabolic alterations and diagnostic biomarkers in the saliva of DILI patients. Our study may provide a potentially feasible and noninvasive diagnostic method for DILI, but further validation is needed.
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Biomarcadores , Enfermedad Hepática Inducida por Sustancias y Drogas , Metabolómica , Saliva , Humanos , Biomarcadores/análisis , Biomarcadores/metabolismo , Enfermedad Hepática Inducida por Sustancias y Drogas/diagnóstico , Enfermedad Hepática Inducida por Sustancias y Drogas/etiología , Enfermedad Hepática Inducida por Sustancias y Drogas/metabolismo , Saliva/química , Saliva/metabolismo , Masculino , Femenino , Metabolómica/métodos , Persona de Mediana Edad , Adulto , Estudios de Casos y Controles , Espectrometría de Masas en Tándem/métodos , Curva ROC , Anciano , Cromatografía Líquida de Alta Presión , Diagnóstico PrecozRESUMEN
Maize (Zea mays), a major food crop worldwide, is susceptible to infection by the saprophytic fungus Aspergillus flavus that can produce the carcinogenic metabolite aflatoxin (AF) especially under climate change induced abiotic stressors that favor mold growth. Several studies have used "-omics" approaches to identify genetic elements with potential roles in AF resistance, but there is a lack of research identifying the involvement of small RNAs such as microRNAs (miRNAs) in maize-A. flavus interaction. In this study, we compared the miRNA profiles of three maize lines (resistant TZAR102, moderately resistant MI82, and susceptible Va35) at 8 h, 3 d, and 7 d after A. flavus infection to investigate possible regulatory antifungal role of miRNAs. A total of 316 miRNAs (275 known and 41 putative novel) belonging to 115 miRNA families were identified in response to the fungal infection across all three maize lines. Eighty-two unique miRNAs were significantly differentially expressed with 39 miRNAs exhibiting temporal differential regulation irrespective of the maize genotype, which targeted 544 genes (mRNAs) involved in diverse molecular functions. The two most notable biological processes involved in plant immunity, namely cellular responses to oxidative stress (GO:00345990) and reactive oxygen species (GO:0034614) were significantly enriched in the resistant line TZAR102. Coexpression network analysis identified 34 hubs of miRNA-mRNA pairs where nine hubs had a node in the module connected to their target gene with potentially important roles in resistance/susceptible response of maize to A. flavus. The miRNA hubs in resistance modules (TZAR102 and MI82) were mostly connected to transcription factors and protein kinases. Specifically, the module of miRNA zma-miR156b-nb - squamosa promoter binding protein (SBP), zma-miR398a-3p - SKIP5, and zma-miR394a-5p - F-box protein 6 combinations in the resistance-associated modules were considered important candidates for future functional studies.
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Plants produce diverse specialized metabolites (SMs) that do not participate in plant growth and development but help them adapt to various environmental conditions. In addition to aiding in plant adaptation, different SMs serve as active ingredients for pharmaceutical and cosmetics products. However, despite their significant role in plant adaptation and industrial importance, the genes involved in the biosynthesis and regulation of many SMs remain largely unknown. This hinders deciphering the specific role of SMs in plant adaptation and limits their industrial utilization. Since many SMs pathway genes are expected to act in tight association with each other within a coexpression network, the network biology approach, such as weighted gene coexpression network analysis, could be used to identify the unknown genes. This chapter describes a workflow for constructing a gene coexpression network to identify genes that could be associated with the biosynthesis and regulation of SMs.
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Regulación de la Expresión Génica de las Plantas , Redes Reguladoras de Genes , Plantas , Metabolismo Secundario , Metabolismo Secundario/genética , Plantas/genética , Plantas/metabolismo , Perfilación de la Expresión Génica/métodos , Biología Computacional/métodos , Genes de PlantasRESUMEN
This paper aims to elucidate the differentially coexpressed genes, their potential mechanisms, and possible drug targets in low-grade invasive serous ovarian carcinoma (LGSC) in terms of the biologic continuity of normal, borderline, and malignant LGSC. We performed a bioinformatics analysis, integrating datasets generated using the GPL570 platform from different studies from the GEO database to identify changes in this transition, gene expression, drug targets, and their relationships with tumor microenvironmental characteristics. In the transition from ovarian epithelial cells to the serous borderline, the FGFR3 gene in the "Estrogen Response Late" pathway, the ITGB2 gene in the "Cell Adhesion Molecule", the CD74 gene in the "Regulation of Cell Migration", and the IGF1 gene in the "Xenobiotic Metabolism" pathway were upregulated in the transition from borderline to LGSC. The ERBB4 gene in "Proteoglycan in Cancer", the AR gene in "Pathways in Cancer" and "Estrogen Response Early" pathways, were upregulated in the transition from ovarian epithelial cells to LGSC. In addition, SPP1 and ITGB2 genes were correlated with macrophage infiltration in the LGSC group. This research provides a valuable framework for the development of personalized therapeutic approaches in the context of LGSC, with the aim of improving patient outcomes and quality of life. Furthermore, the main goal of the current study is a preliminary study designed to generate in silico inferences, and it is also important to note that subsequent in vitro and in vivo studies will be necessary to confirm the results before considering these results as fully reliable.
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BACKGROUND: The development and progression of hepatocellular carcinoma (HCC) have been reported to be associated with immune-related genes and the tumor microenvironment. Nevertheless, there are not enough prognostic biomarkers and models available for clinical use. Based on seven prognostic genes, this study calculated overall survival in patients with HCC using a prognostic survival model and revealed the immune status of the tumor microenvironment (TME). AIM: To develop a novel immune cell-related prognostic model of HCC and depict the basic profile of the immune response in HCC. METHODS: We obtained clinical information and gene expression data of HCC from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. TCGA and ICGC datasets were used for screening prognostic genes along with developing and validating a seven-gene prognostic survival model by weighted gene coexpression network analysis and least absolute shrinkage and selection operator regression with Cox regression. The relative analysis of tumor mutation burden (TMB), TME cell infiltration, immune checkpoints, immune therapy, and functional pathways was also performed based on prognostic genes. RESULTS: Seven prognostic genes were identified for signature construction. Survival receiver operating characteristic curve analysis showed the good performance of survival prediction. TMB could be regarded as an independent factor in HCC survival prediction. There was a significant difference in stromal score, immune score, and estimate score between the high-risk and low-risk groups stratified based on the risk score derived from the seven-gene prognostic model. Several immune checkpoints, including VTCN1 and TNFSF9, were found to be associated with the seven prognostic genes and risk score. Different combinations of checkpoint blockade targeting inhibitory CTLA4 and PD1 receptors and potential chemotherapy drugs hold great promise for specific HCC therapies. Potential pathways, such as cell cycle regulation and metabolism of some amino acids, were also identified and analyzed. CONCLUSION: The novel seven-gene (CYTH3, ENG, HTRA3, PDZD4, SAMD14, PGF, and PLN) prognostic model showed high predictive efficiency. The TMB analysis based on the seven genes could depict the basic profile of the immune response in HCC, which might be worthy of clinical application.
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Our study focused on human brain transcriptomes and the genetic risks of cigarettes per day (CPD) to investigate the neurogenetic mechanisms of individual variation in nicotine use severity. We constructed whole-brain and intramodular region-specific coexpression networks using BrainSpan's transcriptomes, and the genomewide association studies identified risk variants of CPD, confirmed the associations between CPD and each gene set in the region-specific subnetworks using an independent dataset, and conducted bioinformatic analyses. Eight brain-region-specific coexpression subnetworks were identified in association with CPD: amygdala, hippocampus, medial prefrontal cortex (MPFC), orbitofrontal cortex (OPFC), dorsolateral prefrontal cortex, striatum, mediodorsal nucleus of the thalamus (MDTHAL), and primary motor cortex (M1C). Each gene set in the eight subnetworks was associated with CPD. We also identified three hub proteins encoded by GRIN2A in the amygdala, PMCA2 in the hippocampus, MPFC, OPFC, striatum, and MDTHAL, and SV2B in M1C. Intriguingly, the pancreatic secretion pathway appeared in all the significant protein interaction subnetworks, suggesting pleiotropic effects between cigarette smoking and pancreatic diseases. The three hub proteins and genes are implicated in stress response, drug memory, calcium homeostasis, and inhibitory control. These findings provide novel evidence of the neurogenetic underpinnings of smoking severity.
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Estudio de Asociación del Genoma Completo , Nicotina , Humanos , Transcriptoma , Encéfalo , Cuerpo EstriadoRESUMEN
BACKGROUND: Biomarkers and pathways associated with renal ischemia reperfusion injury (IRI) had not been well unveiled. This study was intended to investigate and summarize the regulatory networks for related hub genes. Besides, the immunological micro-environment features were evaluated and the correlations between immune cells and hub genes were also explored. METHODS: GSE98622 containing mouse samples with multiple IRI stages and controls was collected from the GEO database. Differentially expressed genes (DEGs) were recognized by the R package limma, and the GO and KEGG analyses were conducted by DAVID. Gene set variation analysis (GSVA) and weighted gene coexpression network analysis (WGCNA) had been implemented to uncover changed pathways and gene modules related to IRI. Besides the known pathways such as apoptosis pathway, metabolic pathway, and cell cycle pathways, some novel pathways were also discovered to be critical in IRI. A series of novel genes associated with IRI was also dug out. An IRI mouse model was constructed to validate the results. RESULTS: The well-known IRI marker genes (Kim1 and Lcn2) and novel hub genes (Hbegf, Serpine2, Apbb1ip, Trip13, Atf3, and Ncaph) had been proved by the quantitative real-time polymerase chain reaction (qRT-PCR). Thereafter, miRNAs targeted to the dysregulated genes were predicted and the miRNA-target network was constructed. Furthermore, the immune infiltration for these samples was predicted and the results showed that macrophages infiltrated to the injured kidney to affect the tissue repair or fibrosis. Hub genes were significantly positively or negatively correlated with the macrophage abundance indicating they played a crucial role in macrophage infiltration. CONCLUSIONS: Consequently, the pathways, hub genes, miRNAs, and the immune microenvironment may explain the mechanism of IRI and might be the potential targets for IRI treatments.