scholarly journals Transcriptomic Analysis of Mating Responses in Bemisia tabaci MED Females

Insects ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 308
Author(s):  
Zhijia Huo ◽  
Yating Liu ◽  
Jinjian Yang ◽  
Wen Xie ◽  
Shaoli Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Bemisia Tabaci ◽  
Differentially Expressed Gene ◽  
Transcriptomic Analysis ◽  
Gene Transcripts ◽  
Transcriptomic Changes ◽  
Related Proteins ◽  
Protein Ligases ◽  
Differentially Expressed Gene Transcripts

Mating triggers substantial changes in gene expression and leads to subsequent physiological and behavioral modifications. However, postmating transcriptomic changes responding to mating have not yet been fully understood. Here, we carried out RNA sequencing (RNAseq) analysis in the sweet potato whitefly, Bemisia tabaci MED, to identify genes in females in response to mating. We compared mRNA expression in virgin and mated females at 24 h. As a result, 434 differentially expressed gene transcripts (DEGs) were identified between the mated and unmated groups, including 331 up- and 103 down-regulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that many of these DEGs encode binding-related proteins and genes associated with longevity. An RT-qPCR validation study was consistent with our transcriptomic analysis (14/15). Specifically, expression of P450s (Cyp18a1 and Cyp4g68), ubiquitin-protein ligases (UBR5 and RNF123), Hsps (Hsp68 and Hsf), carboxylase (ACC-2), facilitated trehalose transporters (Tret1-2), transcription factor (phtf), and serine-protein kinase (TLK2) were significantly elevated in mated females throughout seven assay days. These combined results offer a glimpe of postmating molecular modifications to facilitate reproduction in B. tabaci females.

scholarly journals Analysis of Gene Expression in Intracranial Aneurysms

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Jia Wang
Keyword(s):  
Gene Expression ◽  
Intracranial Aneurysm ◽  
Intracranial Aneurysms ◽  
Expression Profiles ◽  
Superficial Temporal Artery ◽  
Differentially Expressed Gene ◽  
Gene Expression Profiles ◽  
Temporal Artery ◽  
Inflammatory Reactions ◽  
Gene Transcripts

Abstract INTRODUCTION Different gene expression profiles are observed in intracranial aneurysm tissue. Understanding these genes and what regulates their expression will help us to understand intracranial aneurysm pathogenesis. We investigated whether there are differences in gene expression in intracranial aneurysms. METHODS A total of 16 intracranial aneurysm tissues were compared with 16 matched samples from the superficial temporal artery as controls. We detected the gene expression profiles in these samples with the Human U133 Plus 2.0 GeneChip. RESULTS A total of 2357 differentially expressed gene transcripts were detected based on the gene expression profile. Verification analysis showed that the VCAM1, MAGI2, PPP2R2B, PPP2R3A genes were associated with the occurrence and development of intracranial aneurysm. These genes mainly encode cell adhesion molecules (CAMs) and ERK/JNK signaling pathways. CONCLUSION Changes of genes expression involved in immune and inflammatory reactions, CAMs may be associated with the development of aneurysms.

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

2020 ◽  
Vol 26 (29) ◽  
pp. 3619-3630
Author(s):  
Saumya Choudhary ◽  
Dibyabhaba Pradhan ◽  
Noor S. Khan ◽  
Harpreet Singh ◽  
George Thomas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Gene ◽  
Therapeutic Targets ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Keratinocyte Differentiation ◽  
Hub Genes ◽  
Lesional Skin ◽  
Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

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