scholarly journals Differential Expression of Immune Genes between Two Closely Related Beetle Species with Different Immunocompetence following Attack by Asecodes parviclava

2020 ◽  
Vol 12 (5) ◽  
pp. 522-534 ◽  
Author(s):  
Xuyue Yang ◽  
Lisa Fors ◽  
Tanja Slotte ◽  
Ulrich Theopold ◽  
Mahesh Binzer-Panchal ◽  
...  
Keyword(s):  
Differential Expression ◽  
Evolutionary Dynamics ◽  
Healing Process ◽  
Differential Expression Analysis ◽  
Immune Defense ◽  
Differentially Expressed ◽  
Host Immune System ◽  
Beetle Species ◽  
Immune Genes ◽  
Galerucella Pusilla

Abstract Endoparasitoid wasps are important natural enemies of many insect species and are major selective forces on the host immune system. Despite increased interest in insect antiparasitoid immunity, there is sparse information on the evolutionary dynamics of biological pathways and gene regulation involved in host immune defense outside Drosophila species. We de novo assembled transcriptomes from two beetle species and used time-course differential expression analysis to investigate gene expression differences in closely related species Galerucella pusilla and G. calmariensis that are, respectively, resistant and susceptible against parasitoid infection by Asecodes parviclava parasitoids. Approximately 271 million and 224 million paired-ended reads were assembled and filtered to form 52,563 and 59,781 transcripts for G. pusilla and G. calmariensis, respectively. In the whole-transcriptome level, an enrichment of functional categories related to energy production, biosynthetic process, and metabolic process was exhibited in both species. The main difference between species appears to be immune response and wound healing process mounted by G. pusilla larvae. Using reciprocal BLAST against the Drosophila melanogaster proteome, 120 and 121 immune-related genes were identified in G. pusilla and G. calmariensis, respectively. More immune genes were differentially expressed in G. pusilla than in G. calmariensis, in particular genes involved in signaling, hematopoiesis, and melanization. In contrast, only one gene was differentially expressed in G. calmariensis. Our study characterizes important genes and pathways involved in different immune functions after parasitoid infection and supports the role of signaling and hematopoiesis genes as key players in host immunity in Galerucella against parasitoid wasps.

scholarly journals Expression profiling of ileal mucosa in asthma reveals upregulation of innate immunity and genes characteristic of Paneth and goblet cells

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jan K. Nowak ◽  
Marzena Dworacka ◽  
Nazgul Gubaj ◽  
Arystan Dossimov ◽  
Zhumabek Dossimov ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Differential Expression ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Goblet Cells ◽  
Ileal Mucosa ◽  
Significant Differential Expression ◽  
Immune Genes ◽  
Asthma Patients

Abstract Background The expression profiles of the intestinal mucosa have not been comprehensively investigated in asthma. We aimed to explore this in the Correlated Expression and Disease Association Research (CEDAR) patient cohort. Methods Differential expression analysis of ileal, transverse colon, and rectal biopsies were supplemented by a comparison of transcriptomes from platelets and leukocytes subsets, including CD4+, CD8+, CD14+, CD15+, and CD19+ cells. Asthma patients (n = 15) and controls (n = 15) had similar age (p = 0.967), body mass index (p = 0.870), similar numbers of females (80%) and smoking rates (13.3%). Results Significant differential expression was found in the ileum alone, and not in any other cell/tissue types. More genes were found to be overexpressed (1,150) than under-expressed (380). The most overexpressed genes included Fc Fragment of IgG Binding Protein (FCGBP, logFC = 3.01, pFDR = 0.015), Mucin 2 (MUC2, logFC = 2.78, pFDR = 0.015), and Alpha 1B Defensin (DEFA1B, logFC = 2.73, pFDR = 0.024). Gene ontology implicated the immune system, including interleukins 4 and 13, as well as antimicrobial peptides in this overexpression. There was concordance of gene over- (STAT1, XBP1) and underexpression (NELF, RARA) in asthma and Crohn’s disease ileum when our results were compared to another dataset (p = 3.66 × 10–7). Conclusion Ileal mucosa in asthma exhibits a specific transcriptomic profile, which includes the overexpression of innate immune genes, mostly characteristic of Paneth and goblet cells, in addition to other changes that may resemble Crohn’s disease.

scholarly journals Exosomal microRNAs are novel circulating biomarkers in cigarette, waterpipe smokers, E-cigarette users and dual smokers

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Kameshwar P. Singh ◽  
Krishna P. Maremanda ◽  
Dongmei Li ◽  
Irfan Rahman
Keyword(s):  
Differential Expression ◽  
Tobacco Smoking ◽  
Dna Analysis ◽  
Electronic Cigarettes ◽  
Differential Expression Analysis ◽  
High Sensitivity ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Cigarette Smokers ◽  
Non Coding Rnas

Abstract Background Electronic cigarettes (e-cigs) vaping, cigarette smoke, and waterpipe tobacco smoking are associated with various cardiopulmonary diseases. microRNAs are present in higher concentration in exosomes that play an important role in various physiological and pathological functions. We hypothesized that the non-coding RNAs transcript may serve as susceptibility to disease biomarkers by smoking and vaping. Methods Plasma exosomes/EVs from cigarette smokers, waterpipe smokers and dual smokers (cigarette and waterpipe) were characterized for their size, morphology and TEM, Nanosight and immunoblot analysis. Exosomal RNA was used for small RNA library preparation and the library was quantified using the High Sensitivity DNA Analysis on the Agilent 2100 Bioanalyzer system and sequenced using the Illumina NextSeq 500 and were converted to fastq format for mapping genes. Results Enrichment of various non-coding RNAs that include microRNAs, tRNAs, piRNAs, snoRNAs, snRNAs, Mt-tRNAs, and other biotypes are shown in exosomes. A comprehensive differential expression analysis of miRNAs, tRNAs and piRNAs showed significant changes across different pairwise comparisons. The seven microRNAs that were common and differentially expressed of when all the smoking and vaping groups were compared with non-smokers (NS) are hsa-let-7a-5p, hsa-miR-21-5p, hsa-miR-29b-3p, hsa-let-7f-5p, hsa-miR-143-3p, hsa-miR-30a-5p and hsa-let-7i-5p. The e-cig vs. NS group has differentially expressed 5 microRNAs (hsa-miR-224-5p, hsa-miR-193b-3p, hsa-miR-30e-5p, hsa-miR-423-3p, hsa-miR-365a-3p, and hsa-miR-365b-3p), which are not expressed in other three groups. Gene set enrichment analysis of microRNAs showed significant changes in the top six enriched functions that consisted of biological pathway, biological process, molecular function, cellular component, site of expression and transcription factor in all the groups. Further, the pairwise comparison of tRNAs and piRNA in all these groups revealed significant changes in their expressions. Conclusions Plasma exosomes of cigarette smokers, waterpipe smokers, e-cig users and dual smokers have common differential expression of microRNAs which may serve to distinguish smoking and vaping subjects from NS. Among them has-let-7a-5p has high sensitivity and specificity to distinguish NS with the rest of the users, using ROC curve analysis. These findings will pave the way for the utilizing the potential of exosomes/miRNAs as a novel theranostic agents in lung injury and disease caused by tobacco smoking and vaping.

scholarly journals Differential variation and expression analysis

2018 ◽  
Author(s):  
Haim Bar ◽  
Elizabeth D. Schifano
Keyword(s):  
Differential Expression ◽  
Mixture Model ◽  
Expression Analysis ◽  
Empirical Bayes ◽  
Differential Expression Analysis ◽  
Normal Component ◽  
Differentially Expressed ◽  
Biological Knowledge ◽  
Report Generation ◽  
Mixture Density

AbstractWe propose an empirical Bayes approach using a three-component mixture model, the L2N model, that may be applied to detect both differential expression (mean) and variation. It consists of two log-normal components (L2) for the differentially expressed (dispersed) features (one component for under-expressed [dispersed] and the other for over-expressed [dispersed] features), and a single normal component (N) for the null features (i.e., non-differentially expressed [dispersed] features). Simulation results show that L2N can capture asymmetries in the numbers of over-and under-expressed (dispersed) features (e.g., genes) when they exist, can provide a better fit to data in which the distributions of the null and non-null features are not well-separated, but can also perform well under symmetry and separation. Thus the L2N model is particularly appealing when no a priori biological knowledge about the mixture density is available. The L2N model is implemented in an R package called DVX, for Differential Variation and eXpression analysis. The package also includes an implementation of differential expression analysis via the limma package, and a differential variation and expression analysis using a three-way normal mixture model. DVX is a user-friendly, graphical interface implemented via the ‘Shiny’ package [6], so that a user is not required to have R programming knowledge. It offers a set of diagnostics plots, data transformation tools, and report generation in Microsoft Excel- and Word-compatible formats. The package is available on the web, at https://haim-bar.uconn.edu/software/DVX/.

scholarly journals Predictive Analyses of Prognostic-Related Immune Genes and Immune Infiltrates for Glioblastoma

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 177
Author(s):  
Ping Liang ◽  
Yi Chai ◽  
He Zhao ◽  
Guihuai Wang
Keyword(s):  
Differential Expression ◽  
Roc Curve ◽  
Differential Expression Analysis ◽  
Model Performance ◽  
Predictive Biomarkers ◽  
Positive Interactions ◽  
Immune Genes ◽  
Kaplan Meier ◽  
Significant Difference ◽  
Immune Related Genes

Glioblastoma (GBM), the most common and aggressive brain tumor, has a very poor outcome and high tumor recurrence rate. The immune system has positive interactions with the central nervous system. Despite many studies investigating immune prognostic factors, there is no effective model to identify predictive biomarkers for GBM. Genomic data and clinical characteristic information of patients with GBM were evaluated by Kaplan–Meier analysis and proportional hazard modeling. Deseq2 software was used for differential expression analysis. Immune-related genes from ImmPort Shared Data and the Cistrome Project were evaluated. The model performance was determined based on the area under the receiver operating characteristic (ROC) curve. CIBERSORT was used to assess the infiltration of immune cells. The results of differential expression analyses showed a significant difference in the expression levels of 2942 genes, comprising 1338 upregulated genes and 1604 downregulated genes (p < 0.05). A population of 24 immune-related genes that predicted GBM patient survival was identified. A risk score model established on the basis of the expressions of the 24 immune-related genes was used to evaluate a favorable outcome of GBM. Further validation using the ROC curve confirmed the model was an independent predictor of GBM (AUC = 0.869). In the GBM microenvironment, eosinophils, macrophages, activated NK cells, and follicular helper T cells were associated with prognostic risk. Our study confirmed the importance of immune-related genes and immune infiltrates in predicting GBM patient prognosis.

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

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.

scholarly journals Exploring microRNA target genes and identifying hub genes in bladder cancer based on bioinformatic analysis

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hongjian Wu ◽  
Wubing Jiang ◽  
Guanghua Ji ◽  
Rong Xu ◽  
Gaobo Zhou ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Differential Expression ◽  
Expression Analysis ◽  
Target Genes ◽  
Cancer Genomics ◽  
Differential Expression Analysis ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Tumour Suppressor Genes ◽  
Hub Genes

Abstract Background Bladder cancer (BC) is the second most frequent malignancy of the urinary system. The aim of this study was to identify key microRNAs (miRNAs) and hub genes associated with BC as well as analyse their targeted relationships. Methods According to the microRNA dataset GSE112264 and gene microarray dataset GSE52519, differentially expressed microRNAs (DEMs) and differentially expressed genes (DEGs) were obtained using the R limma software package. The FunRich software database was used to predict the miRNA-targeted genes. The overlapping common genes (OCGs) between miRNA-targeted genes and DEGs were screened to construct the PPI network. Then, gene ontology (GO) analysis was performed through the “cluster Profiler” and “org.Hs.eg.db” R packages. The differential expression analysis and hierarchical clustering of these hub genes were analysed through the GEPIA and UCSC Cancer Genomics Browser databases, respectively. KEGG pathway enrichment analyses of hub genes were performed through gene set enrichment analysis (GSEA). Results A total of 12 DEMs and 10 hub genes were identified. Differential expression analysis of the hub genes using the GEPIA database was consistent with the results for the UCSC Cancer Genomics Browser database. The results indicated that these hub genes were oncogenes, but VCL, TPM2, and TPM1 were tumour suppressor genes. The GSEA also showed that hub genes were most enriched in those pathways that were closely associated with tumour proliferation and apoptosis. Conclusions In this study, we built a miRNA-mRNA regulatory targeted network, which explores an understanding of the pathogenesis of cancer development and provides key evidence for novel targeted treatments for BC.

scholarly journals SnoRNAs and miRNAs Networks Underlying COVID-19 Disease Severity

Vaccines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1056
Author(s):  
Aijaz Parray ◽  
Fayaz Ahmad Mir ◽  
Asmma Doudin ◽  
Ahmad Iskandarani ◽  
Ibn Mohammed Masud Danjuma ◽  
...  
Keyword(s):  
Blood Cell ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Rapid Identification ◽  
Differentially Expressed ◽  
Differentially Expressed Mirnas ◽  
Wbc Count ◽  
Nucleolar Rna ◽  
Nucleolar Rnas

There is a lack of predictive markers for early and rapid identification of disease progression in COVID-19 patients. Our study aims at identifying microRNAs (miRNAs)/small nucleolar RNAs (snoRNAs) as potential biomarkers of COVID-19 severity. Using differential expression analysis of microarray data (n = 29), we identified hsa-miR-1246, ACA40, hsa-miR-4532, hsa-miR-145-5p, and ACA18 as the top five differentially expressed transcripts in severe versus asymptomatic, and ACA40, hsa-miR-3609, ENSG00000212378 (SNORD78), hsa-miR-1231, hsa-miR-885-3p as the most significant five in severe versus mild cases. Moreover, we found that white blood cell (WBC) count, absolute neutrophil count (ANC), neutrophil (%), lymphocyte (%), red blood cell (RBC) count, hemoglobin, hematocrit, D-Dimer, and albumin are significantly correlated with the identified differentially expressed miRNAs and snoRNAs. We report a unique miRNA and snoRNA profile that is associated with a higher risk of severity in a cohort of SARS-CoV-2 infected patients. Altogether, we present a differential expression analysis of COVID-19-associated microRNA (miRNA)/small nucleolar RNA (snoRNA) signature, highlighting their importance in SARS-CoV-2 infection.

scholarly journals Confronting false discoveries in single-cell differential expression

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jordan W. Squair ◽  
Matthieu Gautier ◽  
Claudia Kathe ◽  
Mark A. Anderson ◽  
Nicholas D. James ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Cell Type ◽  
Mouse Spinal Cord ◽  
Cell Type Specific ◽  
False Discoveries ◽  
Biological Differences

AbstractDifferential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulations. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. To exemplify these principles, we exposed true and false discoveries of differentially expressed genes in the injured mouse spinal cord.

scholarly journals Reproductive workers show queen-like gene expression in an intermediately eusocial insect, the buff-tailed bumble beeBombus terrestris.

2014 ◽  
Author(s):  
Mark C Harrison ◽  
Robert L Hammond ◽  
Eamonn B Mallon
Keyword(s):  
Differential Expression ◽  
Bombus Terrestris ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Division Of Labour ◽  
Differentially Expressed ◽  
Bumble Bee ◽  
Strong Shift ◽  
Single Genome ◽  
Eusocial Species

Bumble bees represent a taxon with an intermediate level of eusociality within Hymenoptera. The clear division of reproduction between a single founding queen and the largely sterile workers is characteristic for highly eusocial species, whereas the morphological similarity between the bumble bee queen and the workers is typical for more primitively eusocial hymenopterans. Also, unlike other highly eusocial hymenopterans, division of labour among worker sub-castes is plastic and not predetermined by morphology or age. We conducted a differential expression analysis based on RNA-seq data from 11 combinations of developmental stage and caste to investigate how a single genome can produce the distinct castes of queens, workers and males in the buff-tailed bumble beeBombus terrestris. Based on expression patterns, we found males to be the most distinct of all adult castes (2,411 transcripts differentially expressed compared to non-reproductive workers). However, only relatively few transcripts were differentially expressed between males and workers during development (larvae: 71, pupae: 162). This indicates the need for more distinct expression patterns to control behaviour and physiology in adults compared to those required to create different morphologies. Among female castes, reproductive workers and their non-reproductive sisters displayed differential expression in over ten times more transcripts compared to the differential expression found between reproductive workers and their mother queen. This suggests a strong shift towards a more queen-like behaviour and physiology when a worker becomes fertile. This contrasts with eusocial species where reproductive workers are more similar to non-reproductive workers than the queen.

scholarly journals Individualized lncRNA differential expression profile reveals heterogeneity of breast cancer

Oncogene ◽  
2021 ◽  
Author(s):  
Zhangxiang Zhao ◽  
YingYing Guo ◽  
Yaoyao Liu ◽  
Lichun Sun ◽  
Bo Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differential Expression ◽  
Epithelial Mesenchymal Transition ◽  
Expression Profiles ◽  
Breast Cancer Subtype ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Migration And Invasion ◽  
Response Analysis ◽  
Mesenchymal Transition

AbstractLong non-coding RNAs (lncRNAs) play key regulatory roles in breast cancer. However, population-level differential expression analysis methods disregard the heterogeneous expression of lncRNAs in individual patients. Therefore, we individualized lncRNA expression profiles for breast invasive carcinoma (BRCA) using the method of LncRNA Individualization (LncRIndiv). After evaluating the robustness of LncRIndiv, we constructed an individualized differentially expressed lncRNA (IDElncRNA) profile for BRCA and investigated the subtype-specific IDElncRNAs. The breast cancer subtype-specific IDElncRNA showed frequent co-occurrence with alterations of protein-coding genes, including mutations, copy number variation and differential methylation. We performed hierarchical clustering to subdivide TNBC and revealed mesenchymal subtype and immune subtype for TNBC. The TNBC immune subtype showed a better prognosis than the TNBC mesenchymal subtype. LncRNA PTOV1-AS1 was the top differentially expressed lncRNA in the mesenchymal subtype. And biological experiments validated that the upregulation of PTOV1-AS1 could downregulate TJP1 (ZO-1) and E-Cadherin, and upregulate Vimentin, which suggests PTOV1-AS1 may promote epithelial-mesenchymal transition and lead to migration and invasion of TNBC cells. The mesenchymal subtype showed a higher fraction of M2 macrophages, whereas the immune subtype was more associated with CD4 + T cells. The immune subtype is characterized by genomic instability and upregulation of immune checkpoint genes, thereby suggesting a potential response to immunosuppressive drugs. Last, drug response analysis revealed lncRNA ENSG00000230082 (PRRT3-AS1) is a potential resistance biomarker for paclitaxel in BRCA treatment. Our analysis highlights that IDElncRNAs can characterize inter-tumor heterogeneity in BRCA and the new TNBC subtypes indicate novel insights into TNBC immunotherapy.

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