scholarly journals Integrated Gene Co-expression Analysis and Metabolites Profiling Highlight the Important Role of ZmHIR3 in Maize Resistance to Gibberella Stalk Rot

2021 ◽  
Vol 12 ◽  
Author(s):  
Yali Sun ◽  
Xinsen Ruan ◽  
Qing Wang ◽  
Yu Zhou ◽  
Fang Wang ◽  
...  
Keyword(s):  
Cell Death ◽  
Resistance Genes ◽  
Quantitative Trait ◽  
Molecular Mechanisms ◽  
Differentially Expressed ◽  
Joint Analysis ◽  
Hub Genes ◽  
Positive Role ◽  
Stalk Rot ◽  
Key Genes

Gibberella stalk rot (GSR) caused by Fusarium graminearum is one of the most devastating diseases causing significant yield loss of maize, and GSR resistance is a quantitative trait controlled by multiple genes. Although a few quantitative trait loci/resistance genes have been identified, the molecular mechanisms underlying GSR resistance remain largely unexplored. To identify potential resistance genes and to better understand the molecular mechanism of GSR resistance, a joint analysis using a comparative transcriptomic and metabolomic approaches was conducted using two inbred lines with contrasting GSR resistance, K09 (resistant) and A08 (susceptible), upon infection with F. graminearum. While a substantial number of differentially expressed genes associated with various defense-related signaling pathways were identified between two lines, multiple hub genes likely associated with GSR resistance were pinpointed using Weighted Gene Correlation Network Analysis and K-means clustering. Moreover, a core set of metabolites, including anthocyanins, associated with the hub genes was determined. Among the complex co-expression networks, ZmHIR3 showed strong correlation with multiple key genes, and genetic and histological studies showed that zmhir3 mutant is more susceptible to GSR, accompanied by enhanced cell death in the stem in response to infection with F. graminearum. Taken together, our study identified differentially expressed key genes and metabolites, as well as co-expression networks associated with distinct infection stages of F. graminearum. Moreover, ZmHIR3 likely plays a positive role in disease resistance to GSR, probably through the transcriptional regulation of key genes, functional metabolites, and the control of cell death.

scholarly journals Hub microRNAs and genes in the development of atrial fibrillation identified by weighted gene co-expression network analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qiang Qu ◽  
Jin-Yu Sun ◽  
Zhen-Ye Zhang ◽  
Yue Su ◽  
Shan-Shan Li ◽  
...  
Keyword(s):  
Network Analysis ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Expression Patterns ◽  
Mirna Gene ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Interaction Patterns ◽  
Hub Genes ◽  
Key Genes

AbstractCo-expression network may contribute to better understanding molecular interaction patterns underlying cellular processes. To explore microRNAs (miRNAs) expression patterns correlated with AF, we performed weighted gene co-expression network analysis (WGCNA) based on the dataset GSE28954. Thereafter, we predicted target genes using experimentally verified databases (ENOCRI, miRTarBase, and Tarbase), and overlapped genes with differentially expressed genes (DEGs) from GSE79768 were identified as key genes. Integrated analysis of association between hub miRNAs and key genes was conducted to screen hub genes. In general, we identified 3 differentially expressed miRNAs (DEMs) and 320 DEGs, predominantly enriched in inflammation-related functional items. Two significant modules (red and blue) and hub miRNAs (hsa-miR-146b-5p and hsa-miR-378a-5p), which highly correlated with AF-related phenotype, were detected by WGCNA. By overlapping the DEGs and predicted target genes, 38 genes were screened out. Finally, 9 genes (i.e. ATP13A3, BMP2, CXCL1, GABPA, LIF, MAP3K8, NPY1R, S100A12, SLC16A2) located at the core region in the miRNA-gene interaction network were identified as hub genes. In conclusion, our study identified 2 hub miRNAs and 9 hub genes, which may improve the understanding of molecular mechanisms and help to reveal potential therapeutic targets against AF.

scholarly journals Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

2021 ◽  
Vol 22 (12) ◽  
pp. 6505
Author(s):  
Jishizhan Chen ◽  
Jia Hua ◽  
Wenhui Song
Keyword(s):  
Stem Cells ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Negative Control ◽  
Gene Set Enrichment Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Two Samples ◽  
Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

scholarly journals Bioinformatics analysis of differentially expressed genes and identification of an miRNA–mRNA network associated with entorhinal cortex and hippocampus in Alzheimer’s disease

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Haoming Li ◽  
Linqing Zou ◽  
Jinhong Shi ◽  
Xiao Han
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differentially Expressed Genes ◽  
Entorhinal Cortex ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Differentially Expressed ◽  
Hub Genes

Abstract Background Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis. Methods We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network. Results Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway. Conclusions Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.

scholarly journals Identification of Hypoxia-Related Differentially Expressed Genes and Construction of the Clinical Prognostic Predictor in Hepatocellular Carcinoma by Bioinformatic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hao Guo ◽  
Jing Zhou ◽  
Yanjun Zhang ◽  
Zhi Wang ◽  
Likun Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Model ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Clinical Prognosis ◽  
Prognostic Predictor ◽  
Key Genes

Background. Hypoxia closely relates to malignant progression and appears to be prognostic for outcome in hepatocellular carcinoma (HCC). Our research is aimed at mining the hypoxic-related genes (HRGs) and constructing a prognostic predictor (PP) model on clinical prognosis in HCC patients. Methods. RNA-sequencing data about HRGs and clinical data of patients with HCC were obtained from The Cancer Genome Atlas (TCGA) database portal. Differentially expressed HRGs between HCC and para-carcinoma tissue samples were obtained by applying the Wilcox analysis in R statistical software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene functional enrichment analyses. Then, the patients who were asked to follow up for at least one month were enrolled in the following study. Cox proportional risk regression model was applied to obtain key HRGs which related to overall survival (OS) in HCC. PP was constructed and defined, and the accuracy of PP was validated by constructing the signature in a training set and validation set. Connectivity map (CMap) was used to find potential drugs, and gene set cancer analysis (GSCA) was also performed to explore the underlying molecular mechanisms. Results. Thirty-seven differentially expressed HRGs were obtained. It contained 28 upregulated and 9 downregulated genes. After the univariate Cox regression model analysis, we obtained 27 prognosis-related HRGs. Of these, 25 genes were risk factors for cancer, and 2 genes were protective factors. The PP was composed by 12 key genes (HDLBP, SAP30, PFKP, DPYSL4, SLC2A1, HMOX1, PGK1, ERO1A, LDHA, ENO2, SLC6A6, and TPI1). GSCA results showed the overall activity of these 12 key genes in 10 cancer-related pathways. Besides, CMap identified deferoxamine, crotamiton, talampicillin, and lycorine might have effects with HCC. Conclusions. This study firstly reported 12 prognostic HRGs and constructed the model of the PP. This comprehensive research of multiple databases helps us gain insight into the biological properties of HCC and provides deferoxamine, crotamiton, talampicillin, and lycorine as potential drugs to fight against HCC.

A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

2020 ◽  
Author(s):  
Yanjie Han ◽  
Xinxin Li ◽  
Jiliang Yan ◽  
Chunyan Ma ◽  
Xin Wang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Growth Factor Receptor ◽  
Principal Component ◽  
Distant Metastases ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

scholarly journals PSXIII-30 Analysis of gene expression profiles in liver and muscle tissue of pigs divergent in feed efficiency

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 475-475
Author(s):  
Stafford Vigors ◽  
Torres Sweeney
Keyword(s):  
Energy Metabolism ◽  
Immune Function ◽  
Feed Efficiency ◽  
Molecular Mechanisms ◽  
Protein Targeting ◽  
Expression Profiles ◽  
Cell Signalling ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Joint Analysis

Abstract The improvement of feed efficiency is a key economic goal within the pig production industry. The objective of this study was to examine transcriptomic differences in both the liver and muscle in pigs divergent for feed efficiency, thus improving our understanding of the molecular mechanisms influencing feed efficiency and enabling the identification of candidate biomarkers. Residual feed intake (RFI) was calculated in two populations of pigs from two different farms of origin. The 6 most efficient (LRFI) and 6 least efficient (HRFI) animals in each population were selected for further analysis of Longissimus Dorsi muscle and liver. Three different analysis were performed: 1) Identification of differentially expressed genes (DE) in liver, 2) Identification of DE genes in muscle and 3) Identification of genes commonly DE in both tissues. Hierarchical clustering revealed that transcriptomic data segregated based on the RFI value of the pig rather than farm of origin. A total of 6464 genes were identified as being differentially expressed (DE) in muscle, while 964 genes were identified as being DE in liver. In the muscle-only analysis, genes associated with RNA, protein synthesis and energy metabolism were downregulated in the LRFI animals while in the liver-only analysis, genes associated with cell signalling and lipid homeostasis were upregulated in the LRFI animals. Genes that were commonly DE between muscle and liver (n = 526) were used for the joint analysis. These 526 genes were associated with protein targeting to membrane, extracellular matrix organization and immune function. There are pathways common to both muscle and liver in particular genes associated with immune function. In contrast, tissue-specific pathways contributing to differences in feed efficiency were also identified with genes associated with energy metabolism identified in muscle and lipid metabolism in liver. This study identifies key mechanisms driving changes in feed efficiency in pigs.

scholarly journals A study on regional differences in decidualization of the mouse uterus

Reproduction ◽  
2017 ◽  
Vol 153 (5) ◽  
pp. 645-653 ◽  
Author(s):  
Miao Zhao ◽  
Wen-Qian Zhang ◽  
Ji-Long Liu
Keyword(s):  
Differentially Expressed Genes ◽  
Regional Differences ◽  
Molecular Mechanisms ◽  
Cell Metabolism ◽  
Gene Expression Pattern ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Promoter Regions ◽  
Mouse Uterus ◽  
Region Gene

Although regional differences in mouse decidualization have been recognized for decades, the molecular mechanisms remain understudied. In the present study, by using RNA-seq, we compared transcriptomic differences between the anti-mesometrial (AM) region and the mesometrial (M) region of mouse uterus on day 8 of pregnancy. A total of 1423 differentially expressed genes were identified, of which 811 genes were upregulated and 612 genes were downregulated in the AM region compared to those in the M region. Gene ontology analysis showed that upregulated genes were generally involved in cell metabolism and differentiation, whereas downregulated genes were associated with lymphocyte themes and immune response. Through network analysis, we identified a total of 6 hub genes. These hub genes are likely more important than other genes due to their key positions in the network. We also examined the promoter regions of differentially expressed genes for the enrichment of transcription factor-binding sites. In the end, we demonstrated that a similar regional gene expression pattern can be observed in the artificial decidualization model. Our study contributes to an increase in the knowledge on the molecular mechanisms underlying regional decidualization in mice.

scholarly journals Transcriptome analysis reveals ethylene-mediated defense responses to Fusarium oxysporum f. sp. cucumerinum infection in Cucumis sativus

2020 ◽  
Author(s):  
Jingping Dong ◽  
Yuean Wang ◽  
Qianqian Xian ◽  
Xuehao Chen ◽  
Jun Xu
Keyword(s):  
Fusarium Oxysporum ◽  
Cucumis Sativus ◽  
Fusarium Wilt ◽  
Defense Mechanisms ◽  
Molecular Mechanisms ◽  
Severe Disease ◽  
Defense Responses ◽  
Differentially Expressed ◽  
Pcr Analysis ◽  
Positive Role

Abstract Background: Fusarium wilt, caused by Fusarium oxysporum f. sp. cucumerinum (Foc), is a severe disease affecting cucumber (Cucumis sativus L.) production worldwide, but the molecular mechanisms underlying Fusarium wilt resistance in cucumber remain unknown. To gain an improved understanding of the defense mechanisms elicited in response to Foc inoculation, RNA sequencing-based transcriptomic profiling of responses of the Fusarium wilt-resistant cucumber line ‘Rijiecheng’ at 0, 24, 48, 96, and 192 h after Foc inoculation was performed.Results: We identified 4116 genes that were differentially expressed between 0 h and other time points after inoculation. All ethylene-related and pathogenesis-related genes from among the differentially expressed genes were filtered out. Real-time PCR analysis showed that ethylene-related genes were induced in response to Foc infection. Importantly, after Foc infection and exogenous application of ethephon, a donor of ethylene, these genes were highly expressed. In response to exogenous ethephon treatment in conjunction with Foc inoculation, the infection resistance of cucumber seedlings was enhanced and endogenous ethylene biosynthesis increased dramatically. Conclusion: Collectively, ethylene signaling pathways play a positive role in regulating the defense response of cucumber to Foc infection. The results provide insight into the cucumber Fusarium wilt defense mechanisms and provide valuable information for breeding new cucumber cultivars with enhanced Fusarium wilt tolerance.

scholarly journals Identification of Hub Genes in Anaplastic Thyroid Carcinoma: Evidence From Bioinformatics Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382096213
Author(s):  
Liqi Li ◽  
Mingjie Zhu ◽  
Hu Huang ◽  
Junqiang Wu ◽  
Dong Meng
Keyword(s):  
Thyroid Carcinoma ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Quantitative Polymerase Chain Reaction ◽  
Interaction Network ◽  
Anaplastic Thyroid Carcinoma ◽  
Rank Aggregation ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Rare Type

Anaplastic thyroid carcinoma (ATC) is a rare type of thyroid cancer that results in fatal clinical outcomes; the pathogenesis of this life-threatening disease has yet to be fully elucidated. This study aims to identify the hub genes of ATC that may play key roles in ATC development and could serve as prognostic biomarkers or therapeutic targets. Two microarray datasets (GSE33630 and GSE53072) were obtained from the Gene Expression Omnibus database; these sets included 16 ATC and 49 normal thyroid samples. Differential expression analyses were performed for each dataset, and 420 genes were screened as common differentially expressed genes using the robust rank aggregation method. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to explore the potential bio-functions of these differentially expressed genes (DEGs). The terms and enriched pathways were primarily associated with cell cycle, cell adhesion, and cancer-related signaling pathways. Furthermore, a protein-protein interaction network of DEG expression products was constructed using Cytoscape. Based on the whole network, we identified 7 hub genes that included CDK1, TOP2A, CDC20, KIF11, CCNA2, NUSAP1, and KIF2C. The expression levels of these hub genes were validated using quantitative polymerase chain reaction analyses of clinical specimens. In conclusion, the present study identified several key genes that are involved in ATC development and provides novel insights into the understanding of the molecular mechanisms of ATC development.

scholarly journals Transcriptional Insights into Key Genes and Pathways Underlying Muscovy Duck Subcutaneous Fat Deposition at Different Developmental Stages

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2099
Author(s):  
Liping Guo ◽  
Congcong Wei ◽  
Li Yi ◽  
Wanli Yang ◽  
Zhaoyu Geng ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Fatty Acid Biosynthesis ◽  
Developmental Stages ◽  
Subcutaneous Fat ◽  
Fat Deposition ◽  
Differentially Expressed ◽  
Muscovy Duck ◽  
Key Genes

Subcutaneous fat is a crucial trait for waterfowl, largely associated with meat quality and feed conversion rate. In this study, RNA-seq was used to identify differentially expressed genes of subcutaneous adipose tissue among three developmental stages (12, 35, and 66 weeks) in Muscovy duck. A total of 138 and 129 differentially expressed genes (DEGs) were identified between 35 and 12 weeks (wk), and 66 and 35 wk, respectively. Compared with 12 wk, subcutaneous fat tissue at 35 wk upregulated several genes related to cholesterol biosynthesis and fatty acid biosynthesis, including HSD17B7 and MSMO1, while it downregulated fatty acid beta-oxidation related genes, including ACOX1 and ACSL1. Notably, most of the DEGs (92.2%) were downregulated in 66 wk compared with 35 wk, consistent with the slower metabolism of aging duck. Protein network interaction and function analyses revealed GC, AHSG, FGG, and FGA were the key genes for duck subcutaneous fat from adult to old age. Additionally, the PPAR signaling pathway, commonly enriched between the two comparisons, might be the key pathway contributing to subcutaneous fat metabolism among differential developmental stages in Muscovy duck. These results provide several candidate genes and pathways potentially involved in duck subcutaneous fat deposition, expanding our understanding of the molecular mechanisms underlying subcutaneous fat deposition during development.

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