scholarly journals Investigating Both Mucosal Immunity and Microbiota in Response to Gut Enteritis in Yellowtail Kingfish

2020 ◽  
Vol 8 (9) ◽  
pp. 1267
Author(s):  
Thibault P. R. A. Legrand ◽  
James W. Wynne ◽  
Laura S. Weyrich ◽  
Andrew P. A. Oxley
Keyword(s):  
Gene Expression ◽  
Sequence Data ◽  
Early Stage ◽  
Significant Loss ◽  
Differentially Expressed ◽  
Gastrointestinal Microbiota ◽  
Health States ◽  
Gut Inflammation ◽  
Mucosal Surfaces ◽  
Yellowtail Kingfish

The mucosal surfaces of fish play numerous roles including, but not limited to, protection against pathogens, nutrient digestion and absorption, excretion of nitrogenous wastes and osmotic regulation. During infection or disease, these surfaces act as the first line of defense, where the mucosal immune system interacts closely with the associated microbiota to maintain homeostasis. This study evaluated microbial changes across the gut and skin mucosal surfaces in yellowtail kingfish displaying signs of gut inflammation, as well as explored the host gene expression in these tissues in order to improve our understanding of the underlying mechanisms that contribute to the emergence of these conditions. For this, we obtained and analyzed 16S rDNA and transcriptomic (RNA-Seq) sequence data from the gut and skin mucosa of fish exhibiting different health states (i.e., healthy fish and fish at the early and late stages of enteritis). Both the gut and skin microbiota were perturbed by the disease. More specifically, the gastrointestinal microbiota of diseased fish was dominated by an uncultured Mycoplasmataceae sp., and fish at the early stage of the disease showed a significant loss of diversity in the skin. Using transcriptomics, we found that only a few genes were significantly differentially expressed in the gut. In contrast, gene expression in the skin differed widely between health states, in particular in the fish at the late stage of the disease. These changes were associated with several metabolic pathways that were differentially expressed and reflected a weakened host. Altogether, this study highlights the sensitivity of the skin mucosal surface in response to gut inflammation.

scholarly journals Transcriptome analysis ofSchistosoma mansonilarval development using serial analysis of gene expression (SAGE)

Parasitology ◽  
2009 ◽  
Vol 136 (5) ◽  
pp. 469-485 ◽  
Author(s):  
A. S. TAFT ◽  
J. J. VERMEIRE ◽  
J. BERNIER ◽  
S. R. BIRKELAND ◽  
M. J. CIPRIANO ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sequence Data ◽  
Subsequent Development ◽  
Differentially Expressed ◽  
Cdna Libraries ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Expression ◽  
Cell Conditioned Medium

SUMMARYInfection of the snail,Biomphalaria glabrata, by the free-swimming miracidial stage of the human blood fluke,Schistosoma mansoni, and its subsequent development to the parasitic sporocyst stage is critical to establishment of viable infections and continued human transmission. We performed a genome-wide expression analysis of theS. mansonimiracidia and developing sporocyst using Long Serial Analysis of Gene Expression (LongSAGE). Five cDNA libraries were constructed from miracidia andin vitrocultured 6- and 20-day-old sporocysts maintained in sporocyst medium (SM) or in SM conditioned by previous cultivation with cells of theB. glabrataembryonic (Bge) cell line. We generated 21 440 SAGE tags and mapped 13 381 to theS. mansonigene predictions (v4.0e) either by estimating theoretical 3′ UTR lengths or using existing 3′ EST sequence data. Overall, 432 transcripts were found to be differentially expressed amongst all 5 libraries. In total, 172 tags were differentially expressed between miracidia and 6-day conditioned sporocysts and 152 were differentially expressed between miracidia and 6-day unconditioned sporocysts. In addition, 53 and 45 tags, respectively, were differentially expressed in 6-day and 20-day cultured sporocysts, due to the effects of exposure to Bge cell-conditioned medium.

scholarly journals Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7821 ◽  
Author(s):  
Xiaoming Zhang ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
Zhengguo He ◽  
Cun Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Value ◽  
Early Stage ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

scholarly journals Identification of Potentially Functional Circular RNAs hsa_circ_0070934 and hsa_circ_0004315 as Prognostic Factor of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Pejman Morovat ◽  
Saman Morovat ◽  
Arash M. Ashrafi ◽  
Shahram Teimourian
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Factor ◽  
Sequence Data ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Ppi Network ◽  
Hub Genes ◽  
Cerna Network

Abstract Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide, which has a high mortality rate and poor treatment outcomes with yet unknown molecular basis. It seems that gene expression plays a pivotal role in the pathogenesis of the disease. Circular RNAs (circRNAs) can interact with microRNAs (miRNAs) to regulate gene expression in various malignancies by acting as competitive endogenous RNAs (ceRNAs). However, the potential pathogenesis roles of the ceRNA network among circRNA/miRNA/mRNA in HCC are unclear. In this study, first, the HCC circRNA expression data were obtained from three Gene Expression Omnibus microarray datasets (GSE164803, GSE94508, GSE97332), and the differentially expressed circRNAs (DECs) were identified using R limma package. Also, the liver hepatocellular carcinoma (LIHC) miRNA and mRNA sequence data were retrieved from TCGA, and differentially expressed miRNAs (DEMIs) and mRNAs (DEGs) were determined using the R DESeq2 package. Second, CSCD website was used to uncover the binding sites of miRNAs on DECs. The DECs' potential target miRNAs were revealed by conducting an intersection between predicted miRNAs from CSCD and downregulated DEMIs. Third, some related genes were uncovered by intersecting targeted genes predicted by miRWalk and targetscan online tools with upregulated DEGs. The ceRNA network was then built using the Cytoscape software. The functional enrichment and the overall survival time of these potential targeted genes were analyzed, and a PPI network was constructed in the STRING database. Network visualization was performed by Cytoscape, and ten hub genes were detected using the CytoHubba plugin tool. Four DECs (hsa_circ_0000520, hsa_circ_0008616, hsa_circ_0070934, hsa_circ_0004315) were obtained and six miRNAs (hsa-miR-542-5p, hsa-miR-326, hsa-miR-511-5p, hsa-miR-195-5p, hsa-miR-214-3p, and hsa-miR-424-5p) which are regulated by the above DECs were identified. Then 543 overlapped genes regulated by six miRNAs mentioned above were predicted. Functional enrichment analysis showed that these genes are mostly associated with cancer regulation functions. Ten hub genes (TTK،AURKB, KIF20A، KIF23، CEP55، CDC6، DTL، NCAPG، CENPF، PLK4) have been screened from the PPI network of the 204 survival-related genes. KIF20A, NCAPG, TTK, PLK4, and CDC6 were selected for the highest significant p-values. In the end, a circRNA-miRNA-mRNA regulatory axis was established for five final selected hub genes. This study implies the potential pathogenesis of the obtained network and proposes that the two DECs (has_circ_0070934 and has_circ_0004315) may be important prognostic factor for HCC.

scholarly journals Liver development is restored by blastocyst complementation of HHEX knockout in mice and pigs

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
M. Ruiz-Estevez ◽  
A. T. Crane ◽  
P. Rodriguez-Villamil ◽  
F. L. Ongaratto ◽  
Yun You ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Tissue ◽  
Early Stage ◽  
Significant Loss ◽  
Embryonic Lethality ◽  
Innovative Technology ◽  
Specific Gene ◽  
Liver Development ◽  
Blastocyst Complementation ◽  
Gene And Protein Expression

Abstract Background There are over 17,000 patients in the US waiting to receive liver transplants, and these numbers are increasing dramatically. Significant effort is being made to obtain functional hepatocytes and liver tissue that can for therapeutic use in patients. Blastocyst complementation is a challenging, innovative technology that could fundamentally change the future of organ transplantation. It requires the knockout (KO) of genes essential for cell or organ development in early stage host embryos followed by injection of donor pluripotent stem cells (PSCs) into host blastocysts to generate chimeric offspring in which progeny of the donor cells populate the open niche to develop functional tissues and organs. Methods The HHEX gene is necessary for proper liver development. We engineered loss of HHEX gene expression in early mouse and pig embryos and performed intraspecies blastocyst complementation of HHEX KO embryos with eGFP-labeled PSCs in order to rescue the loss of liver development. Results Loss of HHEX gene expression resulted in embryonic lethality at day 10.5 in mice and produced characteristics of lethality at day 18 in pigs, with absence of liver tissue in both species. Analyses of mouse and pig HHEX KO fetuses confirmed significant loss of liver-specific gene and protein expression. Intraspecies blastocyst complementation restored liver formation and liver-specific proteins in both mouse and pig. Livers in complemented chimeric fetuses in both species were comprised of eGFP-labeled donor-derived cells and survived beyond the previously observed time of HHEX KO embryonic lethality. Conclusions This work demonstrates that loss of liver development in the HHEX KO can be rescued via blastocyst complementation in both mice and pigs. This complementation strategy is the first step towards generating interspecies chimeras for the goal of producing human liver cells, tissues, and potentially complete organs for clinical transplantation.

scholarly journals A Bioinformatics Approach to Identifying Potential Biomarkers for Cryptosporidium parvum: A Coccidian Parasite Associated with Fetal Diarrhea

Vaccines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1427
Author(s):  
Mumdooh J. Sabir ◽  
Ross Low ◽  
Neil Hall ◽  
Majid Rasool Kamli ◽  
Md. Zubbair Malik
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Cryptosporidium Parvum ◽  
Early Stage ◽  
Expression Profiles ◽  
Interaction Network ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Data Sets ◽  
Gene Interaction Network

Cryptosporidium parvum (C. parvum) is a protozoan parasite known for cryptosporidiosis in pre-weaned calves. Animals and patients with immunosuppression are at risk of developing the disease, which can cause potentially fatal diarrhoea. The present study aimed to construct a network biology framework based on the differentially expressed genes (DEGs) of C. parvum infected subjects. In this way, the gene expression profiling analysis of C. parvum infected individuals can give us a snapshot of actively expressed genes and transcripts under infection conditions. In the present study, we have analyzed microarray data sets and compared the gene expression profiles of the patients with the different data sets of the healthy control. Using a network medicine approach to identify the most influential genes in the gene interaction network, we uncovered essential genes and pathways related to C. parvum infection. We identified 164 differentially expressed genes (109 up- and 54 down-regulated DEGs) and allocated them to pathway and gene set enrichment analysis. The results underpin the identification of seven significant hub genes with high centrality values: ISG15, MX1, IFI44L, STAT1, IFIT1, OAS1, IFIT3, RSAD2, IFITM1, and IFI44. These genes are associated with diverse biological processes not limited to host interaction, type 1 interferon production, or response to IL-gamma. Furthermore, four genes (IFI44, IFIT3, IFITM1, and MX1) were also discovered to be involved in innate immunity, inflammation, apoptosis, phosphorylation, cell proliferation, and cell signaling. In conclusion, these results reinforce the development and implementation of tools based on gene profiles to identify and treat Cryptosporidium parvum-related diseases at an early stage.

scholarly journals Host gene expression at an early stage of virus resistance induction

2017 ◽  
Vol 38 (SI 2 - 6th Conf EFPP 2002) ◽  
pp. 502-503
Author(s):  
E. Gammelgård ◽  
M.L. Mohan ◽  
R.A. Andersson ◽  
J.P.T. Valkonen
Keyword(s):  
Gene Expression ◽  
Virus Resistance ◽  
Early Stage ◽  
Blot Hybridization ◽  
Systemic Infection ◽  
Subtractive Hybridization ◽  
Differentially Expressed ◽  
Host Gene Expression ◽  
Resistance Induction ◽  
Potato Virus A

Suppression subtractive hybridization (SSH) was carried out to detect genes differentially expressed in plants expressing resistance to systemic infection with Potato virus A (PVA), genus Potyvirus. Differential screening has up to now revealed 19 putative differentially expressed genes. Nothern blot hybridization has confirmed the differential expression of seven genes. Three of them were only induced by the virus, but four genes were also wound-induced.

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.

An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data

2019 ◽  
Vol 21 (9) ◽  
pp. 631-645 ◽  
Author(s):  
Saeed Ahmed ◽  
Muhammad Kabir ◽  
Zakir Ali ◽  
Muhammad Arif ◽  
Farman Ali ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Classification Accuracy ◽  
Early Stage ◽  
Small Sample Size ◽  
Feature Selection Method ◽  
Small Sample ◽  
Expression Data ◽  
Base Function

Aim and Objective: Cancer is a dangerous disease worldwide, caused by somatic mutations in the genome. Diagnosis of this deadly disease at an early stage is exceptionally new clinical application of microarray data. In DNA microarray technology, gene expression data have a high dimension with small sample size. Therefore, the development of efficient and robust feature selection methods is indispensable that identify a small set of genes to achieve better classification performance. Materials and Methods: In this study, we developed a hybrid feature selection method that integrates correlation-based feature selection (CFS) and Multi-Objective Evolutionary Algorithm (MOEA) approaches which select the highly informative genes. The hybrid model with Redial base function neural network (RBFNN) classifier has been evaluated on 11 benchmark gene expression datasets by employing a 10-fold cross-validation test. Results: The experimental results are compared with seven conventional-based feature selection and other methods in the literature, which shows that our approach owned the obvious merits in the aspect of classification accuracy ratio and some genes selected by extensive comparing with other methods. Conclusion: Our proposed CFS-MOEA algorithm attained up to 100% classification accuracy for six out of eleven datasets with a minimal sized predictive gene subset.

Sign in / Sign up

Export Citation Format

Share Document