Genome-wide differential expression analysis explores antibacterial molecular mechanisms of zebrafish intestine upon pathogenic Streptococcus agalactiae challenge

Abstract Grapevine productivity is severely affected by fungal diseases worldwide and for the diseases control in eco-friendly way, it is essential to understand the molecular mechanisms of fungal resistance in grapes. Therefore, we performed genome-wide identification of various Resistance (R) genes expressed during PM and DM infection in grapevine. Consequently, we identified 6, 21, 2, 5, 3 and 48 EDS1, NDR1, PAD4, NPR, RAR and PR genes respectively in the grapevine genome. Further, differential expression analysis resulted in identification of 2, 4, 7, 2, 4, 1 and 7 differentially expressed PM-responsive Resistance (R) genes (NBS-LRR, EDS1, NDR1, PAD4, NPR, RAR1 and PR) and 28, 2, 5, 4, 1 and 19 differentially expressed DM-responsive Resistance (R) genes (NBS-LRR, EDS1, NDR1, NPR, RAR1 and PR) in V. vinifera. These genes are involved in salicylic acid mediated Effector-triggered immunity (ETI) pathway, therefore, we examined their co-expression to determine the sequence of events that occurs during a signaling cascade in order to respond against PM and DM-infection. Altogether, the PM and DM responsive R genes of ETI pathway found in this study can be used in future to produce new and improved grape varieties that are immune to biotic stresses.

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Identification of Critical Functional Modules and Signaling Pathways in Osteoporosis

Current Bioinformatics ◽

10.2174/1574893615999200706002411 ◽

2020 ◽

Vol 15 ◽

Author(s):

Xiaowei Jiang ◽

Pu Ying ◽

Yingchao Shen ◽

Yiming Miu ◽

Wenbin Kong ◽

...

Keyword(s):

Signaling Pathways ◽

Differential Expression ◽

Expression Analysis ◽

Network Topology ◽

Molecular Mechanisms ◽

Differential Expression Analysis ◽

Osteoclast Differentiation ◽

Enrichment Analysis ◽

Osteoclast Formation ◽

Functional Modules

Background: Osteoporosis is the most common bone metabolic disease. Abnormal osteoclast formation and resorption play a fundamental role in osteoporosis pathogenesis. Recent researches have greatly broadened our understanding of molecular mechanisms of osteoporosis. However, the molecular mechanisms leading to osteoporosis are still not entirely clear. Objective: The purpose of this work is to study the critical regulatory genes, functional modules, and signaling pathways. Methods: Differential expression analysis, network topology-based analysis, and overrepresentation enrichment analysis (ORA) were used to identify differentially expressed genes (DEGs), gene subnetworks, and signaling pathways related to osteoporosis, respectively. Results: Differential expression analysis identified DEGs, such as POGLUT1, DAPK3 and NFKBIA, associated with osteoclastogenesis, which highlighted Notch, apoptosis and NF-kB signaling pathways. Network topology-based analysis identified the upregulated subnetwork characterized by EXOSC8 and DIS3L from the RNA exosome complex, and the downregulated subnetwork composed of histone deacetylases and the cofactors, MORF4L1 and JDP2. Furthermore, the overrepresentation enrichment analysis highlighted that corticotrophin-releasing hormone signaling pathway may affect osteoclastogenesis through its component NR4A1, and suppressing osteoclast differentiation and osteoclast bone resorption with urocortin (UCN). Conclusion: Our systematic analysis not only discovered novel molecular mechanisms, but also proposed potential drug targets for osteoporosis.

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Genome-Wide Association Study and Transcriptome Differential Expression Analysis of the Feather Rate in Shouguang Chickens

Frontiers in Genetics ◽

10.3389/fgene.2020.613078 ◽

2020 ◽

Vol 11 ◽

Author(s):

Xiayi Liu ◽

Zhou Wu ◽

Junying Li ◽

Haigang Bao ◽

Changxin Wu

Keyword(s):

Differential Expression ◽

Candidate Genes ◽

Rna Sequencing ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Serine Phosphorylation ◽

Differentially Expressed ◽

Genome Wide Association ◽

Sequencing Analysis ◽

Genome Wide

The feather rate phenotype in chicks, including early-feathering and late-feathering phenotypes, are widely used as a sexing system in the poultry industry. The objective of this study was to obtain candidate genes associated with the feather rate in Shouguang chickens. In the present study, we collected 56 blood samples and 12 hair follicle samples of flight feathers from female Shouguang chickens. Then we identified the chromosome region associated with the feather rate by genome-wide association analysis (GWAS). We also performed RNA sequencing and analyzed differentially expressed genes between the early-feathering and late-feathering phenotypes using HISAT2, StringTie, and DESeq2. We identified a genomic region of 10.0–13.0 Mb of chromosome Z, which is statistically associated with the feather rate of Shouguang chickens at one-day old. After RNA sequencing analysis, 342 differentially expressed known genes between the early-feathering (EF) and late-feathering (LF) phenotypes were screened out, which were involved in epithelial cell differentiation, intermediate filament organization, protein serine kinase activity, peptidyl-serine phosphorylation, retinoic acid binding, and so on. The sperm flagellar 2 gene (SPEF2) and prolactin receptor (PRLR) gene were the only two overlapping genes between the results of GWAS and differential expression analysis, which implies that SPEF2 and PRLR are possible candidate genes for the formation of the chicken feathering phenotype in the present study. Our findings help to elucidate the molecular mechanism of the feather rate in chicks.

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Bone Signaling in Middle Ear Development: A Genome-Wide Differential Expression Analysis

The Anatomical Record ◽

10.1002/ar.22992 ◽

2014 ◽

Vol 297 (12) ◽

pp. 2349-2355 ◽

Cited By ~ 3

Author(s):

Michelle Christine Nielsen ◽

Tomas Martin Bertelsen ◽

Morten Friis ◽

Ole Winther ◽

Lennart Friis-Hansen ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Middle Ear ◽

Differential Expression Analysis ◽

Ear Development ◽

Genome Wide ◽

A Genome ◽

Middle Ear Development

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Circular RNA expression profiling in dorsal root ganglion of rats with peripheral nerve injury-induced neuropathic pain

10.21203/rs.2.24025/v1 ◽

2020 ◽

Author(s):

Wanxia Xiong ◽

Fan Liu ◽

jie wang ◽

zhiyao wang

Keyword(s):

Neuropathic Pain ◽

Differential Expression ◽

Expression Analysis ◽

Functional Annotation ◽

Molecular Mechanisms ◽

High Throughput Sequencing ◽

Differential Expression Analysis ◽

Mapk Signaling ◽

Circular Rnas ◽

Next Generation Sequencing Technology

Abstract Background : Circular RNAs (circRNAs) comprise a class of endogenous species of RNA consisting of a covalently closed loop structure that is crucial for genetic and epigenetic regulation. The significance of circRNA in neuropathic pain remains to be investigated. Methods : The sciatic nerve chronic constriction injury (CCI) model was established to induce neuropathic pain. We performed genome-wide circRNA analysis of 4 paired DRG sample from CCI and NC rats via next generation sequencing technology. The differentially expressed circRNAs (DEcircRNAs) were identified by differential expression analysis and the expression profile of circRNAs was validated by quantitative real-time PCR (qPCR). Functional annotation analysis was performed to predict the function of DEcircRNAs. Results : A total of 374 DEcirRNAs were identified between CCI and NC rats using circRNA High-throughput sequencing (HTS). Expression levels of 9 DEcircRNAs were validated by qPCR. Functional annotation analysis showed that DEcircRNAs were mainly enriched in pathways and functions such as ‘dopaminergic synapse’, ‘renin secretion’, ‘MAPK signaling pathway’ and ‘neurogenesis’. Competing endogenous RNAs analysis showed that top 50 circRNAs exhibited interactions with four pain related miRNAs. Circ:chr2:33950934-33955969 is the largest node in the circRNA-miRNA interaction network. Conclusion : DEcircRNAs may advance our understanding of the molecular mechanisms underlying neuropathic pain. Key words : neuropathic pain, circRNA, CCI, differential expression analysis

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Identification of rhizome-specific genes by genome-wide differential expression Analysis in Oryza longistaminata

BMC Plant Biology ◽

10.1186/1471-2229-11-18 ◽

2011 ◽

Vol 11 (1) ◽

pp. 18 ◽

Cited By ~ 35

Author(s):

Fengyi Hu ◽

Di Wang ◽

Xiuqin Zhao ◽

Ting Zhang ◽

Haixi Sun ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Genome Wide ◽

Oryza Longistaminata

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Construction of a ceRNA network in glioma and analysis of its clinical significance

BMC Genomics ◽

10.1186/s12864-021-08035-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Guangdong Liu ◽

Haihong Li ◽

Wenyang Ji ◽

Haidong Gong ◽

Yan Jiang ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Molecular Mechanisms ◽

Cox Regression ◽

Differential Expression Analysis ◽

Gene Set Enrichment Analysis ◽

Normal Brain ◽

Central Nervous System Tumor ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background Glioma is the most common central nervous system tumor with a poor survival rate and prognosis. Previous studies have found that long non-coding RNA (lncRNA) and competitive endogenous RNA (ceRNA) play important roles in regulating various tumor mechanisms. We obtained RNA-Seq data of glioma and normal brain tissue samples from TCGA and GTEx databases and extracted the lncRNA and mRNA expression data. Further, we analyzed these data using weighted gene co-expression network analysis and differential expression analysis, respectively. Differential expression analysis was also carried out on the mRNA data from the GEO database. Further, we predicted the interactions between lncRNA, miRNA, and targeted mRNA. Using the CGGA data to perform univariate and multivariate Cox regression analysis on mRNA. Results We constructed a Cox proportional hazard regression model containing four mRNAs and performed immune infiltration analysis. Moreover, we also constructed a ceRNA network including 21 lncRNAs, two miRNAs, and four mRNAs, and identified seven lncRNAs related to survival that have not been previously studied in gliomas. Through the gene set enrichment analysis, we found four lncRNAs that may have a significant role in tumors and should be explored further in the context of gliomas. Conclusions In short, we identified four lncRNAs with research value for gliomas, constructed a ceRNA network in gliomas, and developed a prognostic prediction model. Our research enhances our understanding of the molecular mechanisms underlying gliomas, providing new insights for developing targeted therapies and efficiently evaluating the prognosis of gliomas.

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