RNA-seq analysis of workers' brain reveals that queen and brood affect bumble bee worker reproduction via similar genetic pathways

Worker reproduction in social insects is often regulated by the queen’s presence but can be regulated by other colony members, such as the brood and nestmates. Adults and brood may induce the same outcomes in subordinates but may use different mechanisms. Here, we compared gene expression patterns in bumble bee workers (Bombus impatiens) in response to the queen, the brood, both or none. RNA‐seq analysis of workers’ brain identified 27 differentially expressed genes regulated by the queen and the brood. Expression levels of 8 candidate genes were re-tested using qRT-PCR in worker brain and fat body. Our results show that the brood’s effect on gene expression is substantially weaker than the queen, and a greater impact on gene expression was caused by the combined presence of the queen and the brood. All the genes that were explained by the brood presence were also regulated by the queen presence. A significant amount of the variation in gene expression was explained by the queen, that regulated the expression of key regulators of reproduction and brood care across insects, such as neuroparsin and vitellogenin. A comparison of the data with similar datasets in the honeybee and the raider ant revealed that neuroparsin is the only differentially expressed gene shared by all species. These data highlight the need to consider components other than the queen when examining mechanisms regulating worker sterility and provide information on key genes regulating reproduction that are likely to play an important role in the evolution of sociality.

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The queen, but not the brood, drives brain gene expression in bumble bee workers

10.22541/au.162349344.47800387/v2 ◽

2021 ◽

Author(s):

Priscila Santos ◽

Jesse Starkey ◽

David Galbraith ◽

Etya Amsalem

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Egg Laying ◽

Differentially Expressed ◽

Bumble Bee ◽

Brain Gene Expression ◽

Evolution Of Sociality ◽

Worker Sterility ◽

And Behavior

Worker reproduction in social insects is often regulated by the queen, but can be regulated by the brood and nestmates, who may use different mechanisms to induce the same outcomes in subordinates. Analysis of brain gene expression patterns in bumble bee workers (Bombus impatiens) in response to the presence of the queen, the brood, both or neither, identified 18 differentially expressed genes, 17 of them are regulated by the queen and none are regulated by the brood. Overall, brain gene expression differences in workers were driven by the queen’s presence, despite recent studies showing that brood reduces worker egg laying and provides context to the queen pheromones. The queen affected important regulators of reproduction and brood care across insects, such as neuroparsin and vitellogenin, and a comparison with similar datasets in the honeybee and the raider ant revealed that neuroparsin is differentially expressed in all species. These data emphasize the prominent role of the queen in regulating worker physiology and behavior, and the need to consider components other than the queen when examining regulators of worker sterility. Genes that serve as key regulators of workers’ reproduction are likely to play an important role in the evolution of sociality.

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The queen, but not the brood, drives early changes in brain gene expression in bumble bee workers

10.22541/au.162349344.47800387/v3 ◽

2021 ◽

Author(s):

Priscila Santos ◽

Jesse Starkey ◽

David Galbraith ◽

Etya Amsalem

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Egg Laying ◽

Differentially Expressed ◽

Bumble Bee ◽

Brood Care ◽

Brain Gene Expression ◽

Evolution Of Sociality ◽

And Behavior

Worker reproduction in social insects is often regulated by the queen, but can be regulated by the brood and nestmates, who may use different mechanisms to induce the same outcomes in subordinates. Analysis of brain gene expression patterns in bumble bee workers (Bombus impatiens) in response to the presence of the queen, the brood, both or neither, identified 18 differentially expressed genes, 17 of them are regulated by the queen and none are regulated by the brood. Overall, brain gene expression differences in workers were driven by the queen’s presence, despite recent studies showing that brood reduces worker egg laying and provides context to the queen pheromones. The queen affected important regulators of reproduction and brood care across insects, such as neuroparsin and vitellogenin, and a comparison with similar datasets in the honey bee and the clonal raider ant revealed that neuroparsin is differentially expressed in all species. These data emphasize the prominent role of the queen in regulating worker physiology and behavior. Genes that serve as key regulators of workers’ reproduction are likely to play an important role in the evolution of sociality.

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Transcriptomic analysis reveals distinct resistant response by physcion and chrysophanol against cucumber powdery mildew

PeerJ ◽

10.7717/peerj.1991 ◽

2016 ◽

Vol 4 ◽

pp. e1991 ◽

Cited By ~ 9

Author(s):

Yanping Li ◽

Shilin Tian ◽

Xiaojun Yang ◽

Xin Wang ◽

Yuhai Guo ◽

...

Keyword(s):

Gene Expression ◽

Disease Resistance ◽

Powdery Mildew ◽

Differentially Expressed Genes ◽

Expression Patterns ◽

Defense Responses ◽

Differentially Expressed ◽

Rna Seq ◽

Sequencing Data ◽

Transcriptional Change

Physcion and chrysophanol induce defense responses against powdery mildew in cucumbers. The combination of these two compounds has synergistic interaction against the disease. We performed RNA-seq on cucumber leaf samples treated with physcion and chrysophanol alone and with their combination. We generated 17.6 Gb of high-quality sequencing data (∼2 Gb per sample) and catalogued the expressions profiles of 12,293 annotated cucumber genes in each sample. We identified numerous differentially expressed genes that exhibited distinct expression patterns among the three treatments. The gene expression patterns of the Chr and Phy treatments were more similar to each other than to the Phy × Chr treatment. The Phy × Chr treatment induced the highest number of differentially expressed genes. This dramatic transcriptional change after Phy × Chr treatment leaves reflects that physcion combined with chrysophanol treatment was most closely associated with induction of disease resistance. The analysis showed that the combination treatment caused expression changes of numerous defense-related genes. These genes have known or potential roles in structural, chemical and signaling defense responses and were enriched in functional gene categories potentially responsible for cucumber resistance. These results clearly demonstrated that disease resistance in cucumber leaves was significantly influenced by the combined physcion and chrysophanol treatment. Thus, physcion and chrysophanol are appealing candidates for further investigation of the gene expression and associated regulatory mechanisms related to the defense response.

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MSF: Modulated Sub-graph Finder

F1000Research ◽

10.12688/f1000research.16005.3 ◽

2019 ◽

Vol 7 ◽

pp. 1346

Author(s):

Mariam R. Farman ◽

Ivo L. Hofacker ◽

Fabian Amman

Keyword(s):

Expression Patterns ◽

Differentially Expressed Gene ◽

Data Interpretation ◽

Differentially Expressed ◽

Rna Seq ◽

Novel Approach ◽

Differential Gene ◽

External Stimuli ◽

Flow Of Information

High throughput techniques such as RNA-seq or microarray analysis have proven to be invaluable for the characterizing of global transcriptional gene activity changes due to external stimuli or diseases. Differential gene expression analysis (DGEA) is the first step in the course of data interpretation, typically producing lists of dozens to thousands of differentially expressed genes. To further guide the interpretation of these lists, different pathway analysis approaches have been developed. These tools typically rely on the classification of genes into sets of genes, such as pathways, based on the interactions between the genes and their function in a common biological process. Regardless of technical differences, these methods do not properly account for cross talk between different pathways and most of the methods rely on binary separation into differentially expressed gene and unaffected genes based on an arbitrarily set p-value cut-off. To overcome this limitation, we developed a novel approach to identify concertedly modulated sub-graphs in the global cell signaling network, based on the DGEA results of all genes tested. To this end, expression patterns of genes are integrated according to the topology of their interactions and allow potentially to read the flow of information and identify the effectors. The described software, named Modulated Sub-graph Finder (MSF) is freely available at https://github.com/Modulated-Subgraph-Finder/MSF.

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In Silico Analysis and Characterization of Differentially Expressed Genes to Distinguish Glioma Stem Cells from Normal Neural Stem Cells

10.1101/2021.04.05.438487 ◽

2021 ◽

Author(s):

Urja Parekh ◽

Mohit Mazumder ◽

Harpreet Kaur ◽

Elia Brodsky

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Neural Stem Cells ◽

Differentially Expressed Genes ◽

Expression Patterns ◽

Principal Component ◽

Differentially Expressed ◽

Glioma Stem Cells ◽

Rna Seq ◽

Bioinformatics Analyses

AbstractGlioblastoma multiforme (GBM) is a heterogeneous, invasive primary brain tumor that develops chemoresistance post therapy. Theories regarding the aetiology of GBM focus on transformation of normal neural stem cells (NSCs) to a cancerous phenotype or tumorigenesis driven via glioma stem cells (GSCs). Comparative RNA-Seq analysis of GSCs and NSCs can provide a better understanding of the origin of GBM. Thus, in the current study, we performed various bioinformatics analyses on transcriptional profiles of a total 40 RNA-seq samples including 20 NSC and 20 GSC, that were obtained from the NCBI-SRA (SRP200400). First, differential gene expression (DGE) analysis using DESeq2 revealed 358 significantly differentially expressed genes between GSCs and NSCs (padj. value <0.05, log2fold change ±3) with 192 upregulated and 156 downregulated genes in GSCs in comparison to NSCs. Subsequently, exploratory data analysis using the principal component analysis (PCA) based on key significant genes depicted the clear separation between both the groups. Further, the Hierarchical clustering confirmed the distinct clusters of GSC and NSC samples. Eventually, the biological enrichment analysis of the significant genes showed their enrichment in tumorigenesis pathways such as Wnt-signalling, VEGF- signalling and TGF-β-signalling pathways. Conclusively, our study depicted significant differences in the gene expression patterns between NSCs and GSCs. Besides, we also identified novel genes and genes previously unassociated with gliomagenesis that may prove to be valuable in establishing diagnostic, prognostic biomarkers and therapeutic targets for GBM.

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Shift in worker physiology and gene expression pattern from reproductive to diapause-like with colony age in the bumble bee Bombus impatiens

10.1101/758367 ◽

2019 ◽

Author(s):

Erin D. Treanore ◽

Jacklyn M. Kiner ◽

Mackenzie E. Kerner ◽

Etya Amsalem

Keyword(s):

Gene Expression ◽

Body Size ◽

Cold Tolerance ◽

Fat Body ◽

Expression Patterns ◽

Egg Laying ◽

Bumble Bee ◽

Seasonal Patterns ◽

In Captivity ◽

Ovarian Activation

AbstractInsects maximize their fitness by exhibiting predictable and adaptive seasonal patterns in response to changing environmental conditions. These seasonal patterns are often expressed even when insects are kept in captivity, suggesting they are functionally and evolutionary important.In this study we examined whether workers of the eusocial bumble bee Bombus impatiens maintained a seasonal signature when kept in captivity. We used an integrative approach and compared worker egg-laying, ovarian activation, body size and mass, lipid content in the fat body, cold tolerance and expression of genes related to cold tolerance, metabolism, and stress throughout colony development.We found that bumble bee worker physiology and gene expression patterns shift from reproductive-like to diapause-like as the colony ages. Workers eclosing early in the colony cycle had increased egg-laying and ovarian activation, and reduced cold tolerance, body size, mass, and lipid content in the fat body, in line with a reproductive-like profile, while late-eclosing workers exhibited the opposite characteristics. Furthermore, expression patterns of genes associated with reproduction and diapause differed between early- and late-eclosing workers, partially following the physiological patterns.We suggest that a seasonal signature, innate to individual workers, the queen or the colony is used by workers as a social cue determining the phenology of the colony and discuss possible implications for understanding reproductive division of labor in bumble bee colonies and the evolutionary divergence of female castes in the genus Bombus.

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Gene expression profiles and pathway enrichment analysis to identification of differentially expressed gene and signaling pathways in epithelial ovarian cancer based on high-throughput RNA-seq data

10.1101/566331 ◽

2019 ◽

Author(s):

A Siavoshi ◽

M Taghizadeh ◽

E Dookhe ◽

M Piran

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Leucine Zipper ◽

Expression Profiles ◽

Differentially Expressed Gene ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Statistical Interpretation ◽

Rna Seq

ABSTRACTEpithelial ovarian cancer (EOC) can be considered as a stressful and challenging disease among all women in the world, which has been associated with a poor prognosis and its molecular pathogenesis has remained unclear. In recent years, RNA Sequencing (RNA-seq) has become a functional and amazing technology for profiling gene expression. In the present study, RNA-seq raw data from Sequence Read Archive (SRA) of six tumor and normal ovarian sample was extracted, and then analysis and statistical interpretation was done with Linux and R Packages from the open-source Bioconductor. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of key genes and pathways involved in EOC. We identified 1091 Differential Expression Genes (DEGs) which have been reported in various studies of ovarian cancer as well as other types of cancer. Among them, 333 genes were up-regulated and 273 genes were down-regulated. In addition, Differentially Expressed Genes (DEGs) including RPL41, ALDH3A2, ERBB2, MIEN1, RBM25, ATF4, UPF2, DDIT3, HOXB8 and IL17D as well as Ribosome and Glycolysis/Gluconeogenesis pathway have had the potentiality to be used as targets for EOC diagnosis and treatment. In this study, unlike that of any other studies on various cancers, ALDH3A2 was most down-regulated gene in most KEGG pathways, and ATF4 was most up-regulated gene in leucine zipper domain binding term. In the other hand, RPL41 as a regulatory of cellular ATF4 level was up-regulated in many term and pathways and augmentation of ATF4 could justify the increase of RPL41 in the EOC. Pivotal pathways and significant genes, which were identified in the present study, can be used for adaptation of different EOC study. However, further molecular biological experiments and computational processes are required to confirm the function of the identified genes associated with EOC.

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

Current Pharmaceutical Design ◽

10.2174/1381612826666200311130133 ◽

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.

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Gene Set Correlation Analysis and Visualization Using Gene Expression Data

Current Bioinformatics ◽

10.2174/1574893615999200629124444 ◽

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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OP0021 IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES IN EARLY RHEUMATOID ARTHRITIS PATIENTS RESPONDING TO TOCILIZUMAB

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3668 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 12.2-12

Author(s):

I. Muller ◽

M. Verhoeven ◽

H. Gosselt ◽

M. Lin ◽

T. De Jong ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

T Cells ◽

Gene Ontology ◽

Regulatory T Cells ◽

Early Rheumatoid Arthritis ◽

Whole Blood ◽

Differentially Expressed ◽

Rna Seq ◽

Double Blind

Background:Tocilizumab (TCZ) is a monoclonal antibody that binds to the interleukin 6 receptor (IL-6R), inhibiting IL-6R signal transduction to downstream inflammatory mediators. TCZ has shown to be effective as monotherapy in early rheumatoid arthritis (RA) patients (1). However, approximately one third of patients inadequately respond to therapy and the biological mechanisms underlying lack of efficacy for TCZ remain elusive (1). Here we report gene expression differences, in both whole blood and peripheral blood mononuclear cells (PBMC) RNA samples between early RA patients, categorized by clinical TCZ response (reaching DAS28 < 3.2 at 6 months). These findings could lead to identification of predictive biomarkers for TCZ response and improve RA treatment strategies.Objectives:To identify potential baseline gene expression markers for TCZ response in early RA patients using an RNA-sequencing approach.Methods:Two cohorts of RA patients were included and blood was collected at baseline, before initiating TCZ treatment (8 mg/kg every 4 weeks, intravenously). DAS28-ESR scores were calculated at baseline and clinical response to TCZ was defined as DAS28 < 3.2 at 6 months of treatment. In the first cohort (n=21 patients, previously treated with DMARDs), RNA-sequencing (RNA-seq) was performed on baseline whole blood PAXgene RNA (Illumina TruSeq mRNA Stranded) and differential gene expression (DGE) profiles were measured between responders (n=14) and non-responders (n=7). For external replication, in a second cohort (n=95 therapy-naïve patients receiving TCZ monotherapy), RNA-seq was conducted on baseline PBMC RNA (SMARTer Stranded Total RNA-Seq Kit, Takara Bio) from the 2-year, multicenter, double-blind, placebo-controlled, randomized U-Act-Early trial (ClinicalTrials.gov identifier: NCT01034137) and DGE was analyzed between 84 responders and 11 non-responders.Results:Whole blood DGE analysis showed two significantly higher expressed genes in TCZ non-responders (False Discovery Rate, FDR < 0.05): urotensin 2 (UTS2) and caveolin-1 (CAV1). Subsequent analysis of U-Act-Early PBMC DGE showed nine differentially expressed genes (FDR < 0.05) of which expression in clinical TCZ non-responders was significantly higher for eight genes (MTCOP12, ZNF774, UTS2, SLC4A1, FECH, IFIT1B, AHSP, and SPTB) and significantly lower for one gene (TND2P28M). Both analyses were corrected for baseline DAS28-ESR, age and gender. Expression of UTS2, with a proposed function in regulatory T-cells (2), was significantly higher in TCZ non-responders in both cohorts. Furthermore, gene ontology enrichment analysis revealed no distinct gene ontology or IL-6 related pathway(s) that were significantly different between TCZ-responders and non-responders.Conclusion:Several genes are differentially expressed at baseline between responders and non-responders to TCZ therapy at 6 months. Most notably, UTS2 expression is significantly higher in TCZ non-responders in both whole blood as well as PBMC cohorts. UTS2 could be a promising target for further analyses as a potential predictive biomarker for TCZ response in RA patients in combination with clinical parameters (3).References:[1]Bijlsma JWJ, Welsing PMJ, Woodworth TG, et al. Early rheumatoid arthritis treated with tocilizumab, methotrexate, or their combination (U-Act-Early): a multicentre, randomised, double-blind, double-dummy, strategy trial. Lancet. 2016;388(10042):343-55.[2]Bhairavabhotla R, Kim YC, Glass DD, et al. Transcriptome profiling of human FoxP3+ regulatory T cells. Human Immunology. 2016;77(2):201-13.[3]Gosselt HR, Verhoeven MMA, Bulatovic-Calasan M, et al. Complex machine-learning algorithms and multivariable logistic regression on par in the prediction of insufficient clinical response to methotrexate in rheumatoid arthritis. Journal of Personalized Medicine. 2021;11(1).Disclosure of Interests:None declared

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