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 RNA sequencing reveals interacting key determinants of osteoarthritis acting in subchondral bone and articular cartilage

2020 ◽  
Author(s):  
Margo Tuerlings ◽  
Marcella van Hoolwerff ◽  
Evelyn Houtman ◽  
Eka (H.E.D.) Suchiman ◽  
Nico Lakenberg ◽  
...  
Keyword(s):  
Differential Expression ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Subchondral Bone ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Risk Genes ◽  
Joint Replacement Surgery ◽  
And Cartilage

ABSTRACTObjectiveThe aim of this study was to identify key determinants of the interactive osteoarthritis (OA) pathophysiological processes of subchondral bone and cartilage.MethodsWe performed RNA sequencing on macroscopically preserved and lesioned OA subchondral bone of patients that underwent joint replacement surgery due to OA (N=24 pairs; 6 hips, 18 knees, RAAK-study). Unsupervised hierarchical clustering and differential expression analyses were performed. Results were combined with previously identified, differentially expressed genes in cartilage (partly overlapping samples) as well as with recently identified OA risk genes.ResultsWe identified 1569 genes significantly differentially expressed between lesioned and preserved subchondral bone, including CNTNAP2 (FC=2.4, FDR=3.36×10−5) and STMN2 (FC=9.6, FDR=3.36×10−3). Among the identified genes, 305 were also differentially expressed and with same direction of effects in cartilage, including IL11 and CHADL, recently acknowledged OA susceptibility genes. Upon differential expression analysis stratifying for joint site, we identified 509 genes exclusively differentially expressed in subchondral bone of the knee, such as KLF11 and WNT4. These exclusive knee genes were enriched for involvement in epigenetic processes, characterized by for instance HIST1H3J and HIST1H3H.ConclusionTo our knowledge, we are the first to report on differential gene expression patterns of paired OA subchondral bone tissue using RNA sequencing. Among the most consistently differentially expressed genes with OA pathophysiology in both bone and cartilage were IL11 and CHADL. As these genes were recently also identified as robust OA risk genes they classify as attractive druggable targets acting on two OA disease relevant tissues.

scholarly journals Identification of Key Differentially Expressed MicroRNAs in Cancer Patients Through Pan-cancer Analysis

2018 ◽  
Author(s):  
Yu Hu ◽  
Hayley Dingerdissen ◽  
Samir Gupta ◽  
Robel Kahsay ◽  
Vijay Shanker ◽  
...  
Keyword(s):  
Differential Expression ◽  
Cancer Progression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Literature Mining ◽  
Wide Range ◽  
Cancer Types

AbstractA number of microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common expression patterns of abnormally expressed miRNAs and their potential roles in multiple cancer types have not yet been evaluated. To minimize the difference of patients, we collected miRNA sequencing data of 575 patients with tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA), and performed differential expression analysis using DESeq2 and edgeR. The results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) unique to one of the 14 cancers may affect patient survival rate, and 21 key SDEmiRNAs (nine overexpressed and 12 under-expressed) associated with at least eight cancers and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effect of these 21 SDEmiRNAs on cellular functions was evaluated through enrichment and pathway analysis of 7,186 protein-coding gene targets from literature mining with known differential expression profiles in cancers. It enables identification of their functional similarity in cell proliferation control across a wide range of cancers and to build common regulatory networks over cancer-related pathways. This is validated by construction of a regulatory network in PI3K pathway. This study provides evidence of the value of further analysis on SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

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 Expression Analysis Using A Model-Based Gene Clustering Algorithm for RNA-Seq Data

2020 ◽  
Author(s):  
Takayuki Osabe ◽  
Kentaro Shimizu ◽  
Koji Kadota
Keyword(s):  
Differential Expression ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Real Data ◽  
Gene Clustering ◽  
Rna Seq ◽  
Model Based ◽  
Group Data

Abstract Background RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report a model-based clustering algorithm, MBCluster.Seq, that can be implemented using an R package for DE analysis.Results The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.Conclusions MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

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 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 Integrated Analysis of a Competing Endogenous RNA Network Reveals a Prognostic lncRNA Signature in Bladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Mou Peng ◽  
Xu Cheng ◽  
Wei Xiong ◽  
Lu Yi ◽  
Yinhuai Wang
Keyword(s):  
Bladder Cancer ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Competing Endogenous Rna ◽  
Predictive Tool ◽  
Aberrant Expression ◽  
Cerna Network ◽  
Rna Interaction

Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs) to regulate mRNA expression through sponging microRNA in tumorigenesis and progression. However, following the discovery of new RNA interaction, the differentially expressed RNAs and ceRNA regulatory network are required to update. Our study comprehensively analyzed the differentially expressed RNA and corresponding ceRNA network and thus constructed a potentially predictive tool for prognosis. “DESeq2” was used to perform differential expression analysis. Two hundred and six differentially expressed (DE) lncRNAs, 222 DE miRNAs, and 2,463 DE mRNAs were found in this study. The lncRNA-mRNA interactions in the miRcode database and the miRNA-mRNA interactions in the starBase, miRcode, and mirTarBase databases were searched, and a competing endogenous RNA (ceRNA) network with 186 nodes and 836 interactions was subsequently constructed. Aberrant expression patterns of lncRNA NR2F1-AS1 and lncRNA AC010168.2 were evaluated in two datasets (GSE89006, GSE31684), and real-time polymerase chain reaction was also performed to validate the expression pattern. Furthermore, we found that these two lncRNAs were independent prognostic biomarkers to generate a prognostic lncRNA signature by univariate and multivariate Cox analyses. According to the lncRNA signature, patients in the high-risk group were associated with a poor prognosis and validated by an external dataset. A novel genomic-clinicopathologic nomogram to improve prognosis prediction of bladder cancer was further plotted and calibrated. Our study deepens the understanding of the regulatory ceRNA network and provides an easy-to-do genomic-clinicopathological nomogram to predict the prognosis in patients with bladder cancer.

Sign in / Sign up

Export Citation Format

Share Document