scholarly journals Effects of a Sudden Drop in Salinity on Immune Response Mechanisms of Anadara kagoshimensis

2019 ◽  
Vol 20 (18) ◽  
pp. 4365 ◽  
Author(s):  
Mo Zhang ◽  
Li Li ◽  
Ying Liu ◽  
Xiaolong Gao
Keyword(s):  
Immune Response ◽  
Signaling Pathway ◽  
Transcriptome Sequencing ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Total Count ◽  
Sudden Drop ◽  
Blood Lymphocytes ◽  
Anadara Kagoshimensis ◽  
Comparison Groups

In this experiment, the effects of a sudden drop of salinity on the immune response mechanisms of the ark shell Anadara kagoshimensis were examined by simulating the sudden drop of salinity that occurs in seawater after a rainstorm. Additionally, the differentially expressed genes (DEGs) were identified using transcriptome sequencing. When the salinity dropped from 30‰ (S30) to 14‰ (S14), the phagocytic activity of blood lymphocytes, the O2− levels produced from respiratory burst, the content of reactive oxygen species, and the activities of lysozymes and acid phosphatases increased significantly, whereas the total count of blood lymphocytes did not increase. Total count of blood lymphocytes in 22‰ salinity (S22) was significantly higher than that in any other group. The raw data obtained from sequencing were processed with Trimmomatic (Version 0.36). The expression levels of unigenes were calculated using transcripts per million (TPM) based on the effects of sequencing depth, gene length, and sample on reads. Differential expression analysis was performed using DESeq (Version 1.12.4). Transcriptome sequencing revealed 269 (101 up-regulated, 168 down-regulated), 326 (246 up-regulated, 80 down-regulated), and 185 (132 up-regulated, 53 down-regulated) significant DEGs from comparison of the S14 vs. S22, S22 vs. S30, and S14 vs. S30 groups, respectively. Gene Ontology enrichment analysis of the DEGs in these salinity comparison groups revealed that the cellular amino acid metabolic process, the regulation of protein processing, the regulation of response to stress, and other terms were significantly enriched. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that nucleotide-binding, oligomerization domain (NOD)-like receptor signaling pathway (ko04621), apoptosis-multiple species (ko04215), Toll and Imd signaling pathway (ko04624), NF-κB signaling pathway (ko04064), apoptosis (ko04210), and focal adhesion (ko04510) were significantly enriched in all salinity comparison groups. qRT-PCR verification of 12 DEGs in the above six pathways was conducted, and the results were consistent with the transcriptome sequencing results in terms of up-regulation and down-regulation, which illustrates that the transcriptome sequencing data are credible. These results were used to preliminarily explore the effects of a sudden drop of salinity on blood physiological and biochemical indexes and immunoregulatory mechanisms of A. kagoshimensis. They also provide a theoretical basis for the selection of bottom areas optimal for release and proliferation of A. kagoshimensis required to restore the declining populations of this species.

Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

2020 ◽  
Vol 23 (8) ◽  
pp. 805-813
Author(s):  
Ai Jiang ◽  
Peng Xu ◽  
Zhenda Zhao ◽  
Qizhao Tan ◽  
Shang Sun ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Microarray Data ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Functional Enrichment ◽  
Joint Disease ◽  
Support Vector ◽  
Diagnostic Model ◽  
Data Sets

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

scholarly journals Integrated Transcriptional Profiling Analysis and Immune-Related Risk Model Construction for Intracranial Aneurysm Rupture

2021 ◽  
Vol 15 ◽  
Author(s):  
Dezhi Shan ◽  
Xing Guo ◽  
Guozheng Yang ◽  
Zheng He ◽  
Rongrong Zhao ◽  
...  
Keyword(s):  
Immune Response ◽  
Inflammatory Response ◽  
Signaling Pathway ◽  
Immune Cell ◽  
Risk Model ◽  
Enrichment Analysis ◽  
Cell Infiltration ◽  
Enzyme Linked Immunosorbent Assay ◽  
Immune Cell Infiltration ◽  
Related Risk

Intracranial aneurysms (IAs) may cause lethal subarachnoid hemorrhage upon rupture, but the molecular mechanisms are poorly understood. The aims of this study were to analyze the transcriptional profiles to explore the functions and regulatory networks of differentially expressed genes (DEGs) in IA rupture by bioinformatics methods and to identify the underlying mechanisms. In this study, 1,471 DEGs were obtained, of which 619 were upregulated and 852 were downregulated. Gene enrichment analysis showed that the DEGs were mainly enriched in the inflammatory response, immune response, neutrophil chemotaxis, and macrophage differentiation. Related pathways include the regulation of actin cytoskeleton, leukocyte transendothelial migration, nuclear factor κB signaling pathway, Toll-like receptor signaling pathway, tumor necrosis factor signaling pathway, and chemokine signaling pathway. The enrichment analysis of 20 hub genes, subnetworks, and significant enrichment modules of weighted gene coexpression network analysis showed that the inflammatory response and immune response had a causal relationship with the rupture of unruptured IAs (UIAs). Next, the CIBERSORT method was used to analyze immune cell infiltration into ruptured IAs (RIAs) and UIAs. Macrophage infiltration into RIAs increased significantly compared with that into UIAs. The result of principal component analysis revealed that there was a difference between RIAs and UIAs in immune cell infiltration. A 4-gene immune-related risk model for IA rupture (IRMIR), containing CXCR4, CXCL3, CX3CL1, and CXCL16, was established using the glmnet package in R software. The receiver operating characteristic value revealed that the model represented an excellent clinical situation for potential application. Enzyme-linked immunosorbent assay was performed and showed that the concentrations of CXCR4 and CXCL3 in serum from RIA patients were significantly higher than those in serum from UIA patients. Finally, a competing endogenous RNA network was constructed to provide a potential explanation for the mechanism of immune cell infiltration into IAs. Our findings highlighted the importance of immune cell infiltration into RIAs, providing a direction for further research.

scholarly journals Identification of hydatidosis-related modules and key regulatory genes

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9280
Author(s):  
Jijun Song ◽  
Mingxin Song
Keyword(s):  
Transcription Factors ◽  
Signaling Pathway ◽  
Target Genes ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Bmp Signaling ◽  
Differentially Expressed ◽  
Regulatory Effects

Background Echinococcosis caused by larval of Echinococcus is prevalent all over the world. Although clinical experience showed that the presence of tapeworms could not be found in liver lesions, the repeated infection and aggravation of lesions still occur in the host. Here, this study constructed a multifactor-driven disease-related dysfunction network to explore the potential molecular pathogenesis mechanism in different hosts after E.multilocularis infection. Method First, iTRAQ sequencing was performed on human liver infected with E.multilocularis. Second, obtained microRNAs(miRNAs) expression profiles of humans and canine infected with Echinococcus from the GEO database. In addition, we also performed differential expression analysis, protein interaction network analysis, enrichment analysis, and crosstalk analysis to obtain genes and modules related to E.multilocularis infection. Pivot analysis is used to calculate the potential regulatory effects of multiple factors on the module and identify related non-coding RNAs(ncRNAs) and transcription factors(TFs). Finally, we screened the target genes of miRNAs of Echinococcus to further explore its infection mechanism. Results A total of 267 differentially expressed proteins from humans and 3,635 differentially expressed genes from canine were obtained. They participated in 16 human-related dysfunction modules and five canine-related dysfunction modules, respectively. Both human and canine dysfunction modules are significantly involved in BMP signaling pathway and TGF-beta signaling pathway. In addition, pivot analysis found that 1,129 ncRNAs and 110 TFs significantly regulated human dysfunction modules, 158 ncRNAs and nine TFs significantly regulated canine dysfunction modules. Surprisingly, the Echinococcus miR-184 plays a role in the pathogenicity regulation by targeting nine TFs and one ncRNA in humans. Similarly, miR-184 can also cause physiological dysfunction by regulating two transcription factors in canine. Conclusion The results show that the miRNA-184 of Echinococcus can regulate the pathogenic process through various biological functions and pathways. The results laid a solid theoretical foundation for biologists to further explore the pathogenic mechanism of Echinococcosis.

scholarly journals Weighted Gene Co-Expression Network Analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

2020 ◽  
Author(s):  
Xin Li ◽  
Chenxin Wang ◽  
Xiaoqing Zhang ◽  
Jiali Liu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
R Package ◽  
Diagnostic Value ◽  
Wilcoxon Test ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Ppar Signaling

Abstract Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

scholarly journals miR-590-3 and SP1 Promote Neuronal Apoptosis in Patients with Alzheimer’s Disease via AMPK Signaling Pathway

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanqun Cao ◽  
Xiangxiang Tan ◽  
Quzhe Lu ◽  
Kai Huang ◽  
Xiaoer Tang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Signaling Pathway ◽  
Neuronal Apoptosis ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Hub Gene ◽  
Pathogenic Mechanisms ◽  
Ampk Signaling ◽  
Regulatory Effects

Alzheimer’s disease (AD) is a progressive neurological degenerative illness with a hidden onset. Its pathogenesis is complicated, although with molecular biology research on cancer and targeted research on pathogenic mechanisms, good progress has not yet been made. Therefore, this work built a multifactor-driven neuronal apoptosis dysfunction module for the purpose of probing its underlying pathogenic mechanisms. We performed differential expression analysis, coexpression analysis, enrichment analysis, and hypergeometric tests to calculate the underlying regulatory effects of multifactors on the modules by the way of the whole gene expression profile of AD and identify a series of ncRNA (miR-320a) and TF (NFKB1). Additionally, we screened 10 modules corresponding to the Hub gene, which tend to regulate the physiological progress of inflammation, regulation of autophagy, cerebral cortex neuron differentiation, glial cell apoptotic, and so on. Meanwhile, Alzheimer’s disease is triggered by signaling pathways such as the MPK signaling pathway. In this study, a dysfunction module is utilized to verify that miR-590-3 and SP1 motility factors can regulate neurons in Alzheimer’s disease through the MPK signaling pathway, not only providing new insights into the pathogenesis of Alzheimer’s disease but also laying a solid theoretical foundation for the biologists to further cure Alzheimer’s disease.

scholarly journals Identification of Neuropeptides as Potential Crosstalks Linking Down Syndrome and Periodontitis Revealed by Transcriptomic Analyses

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yue Chen ◽  
Xiaofei Yu ◽  
Jia Kong
Keyword(s):  
Down Syndrome ◽  
Signaling Pathway ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Mapk Signaling ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Future Research ◽  
Biological Processes

Background. This bioinformatics study was aimed to investigate the relationship between periodontitis (PD) and Down Syndrome (DS) regarding potential crosstalk genes, related neuropeptides, and biological processes. Methods. Data for PD (GSE23586, GSE10334 and GSE16134) and DS (GSE35665) were downloaded from NCBI Gene Expression Omnibus (GEO). Following normalization and merging of PD data, differential expression analysis was performed ( p value < 0.05 and ∣ log   FC ∣ ≥ 0.5 ). The common deregulated genes between PD and DS were considered as crosstalk genes. The significantly differentially expressed genes were used to construct the coexpression network and to further identify coexpression gene modules. To acquire the significant modules, the significant expression level of genes in the module was used to analyze the enrichment of genes in each module. Neuropeptides were assessed from NeuroPedia database. Neuropeptide genes and crosstalk genes were merged and mapped into PPI network, and the correlation coefficient (Spearman) was determined for the crosstalk genes. Results. 138 crosstalk genes were predicted. According to the functional enrichment analysis, these genes significantly regulated different biological processes and pathways. In enrichment analysis, the significant module of DS was pink module, and turquoise module was significant in PD. Four common crosstalk genes were acquired, i.e., CD19, FCRL5, FCRLA, and HLA-DOB. In the complex network, INS and IGF2 interacted with CASP3 and TP53, which commonly regulated the MAPK signaling pathway. Moreover, the results showed that TP53 interacted with IGF2 and INS inducing the dysregulation of PI3K-Akt signaling pathway. UBL was positively correlated with crosstalk genes in both diseases. LEP was revealed to be both a neuropeptide and crosstalk gene and was positively correlated with other crosstalk genes. Conclusion. Different crosstalk genes, related neuropeptides, and biological pathways and processes were revealed between PD and DS, which can serve as a theoretical basis for future research.

scholarly journals Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

Hereditas ◽  
2020 ◽  
Vol 157 (1) ◽  
Author(s):  
Xin Li ◽  
Chenxin Wang ◽  
Xiaoqing Zhang ◽  
Jiali Liu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
R Package ◽  
Diagnostic Value ◽  
Wilcoxon Test ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Ppar Signaling

Abstract Objective To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach. Methods The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the “pROC” R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the “proteasome” and a “PPAR signaling pathway,” respectively. Conclusions The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the “proteasome” and the “PPAR signaling pathway,” may play an important role in the development of HCM.

scholarly journals Co-expression Analysis Revealed Key Biomarkers Associated With the Diagnosis and Progression of Hypertrophic Cardiomyopathy

2020 ◽  
Author(s):  
Xin Li ◽  
Xiaoqing Zhang ◽  
Jiali Liu ◽  
Yu Wang ◽  
Chenxin Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
R Package ◽  
Diagnostic Value ◽  
Wilcoxon Test ◽  
Hub Genes ◽  
Ppar Signaling

Abstract Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYL7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYL7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYL7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYL7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

scholarly journals Weighted Gene Co-Expression Network Analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

2020 ◽  
Author(s):  
Xin Li ◽  
Chenxin Wang ◽  
Xiaoqing Zhang ◽  
Jiali Liu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
R Package ◽  
Diagnostic Value ◽  
Wilcoxon Test ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Ppar Signaling

Abstract Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

scholarly journals Immune Response of Eastern Honeybee Worker to Nosema ceranae Infection Revealed by Transcriptomic Investigation

Insects ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 728
Author(s):  
Wenhao Xing ◽  
Dingding Zhou ◽  
Qi Long ◽  
Minghui Sun ◽  
Rui Guo ◽  
...  
Keyword(s):  
Immune Response ◽  
Signaling Pathway ◽  
Apis Cerana ◽  
Enrichment Analysis ◽  
Nosema Ceranae ◽  
Pathway Enrichment Analysis ◽  
Post Inoculation ◽  
Sequencing Data ◽  
Humoral Immune ◽  
Immune Pathways

Here, a comparative transcriptome investigation was conducted based on high-quality deep sequencing data from the midguts of Apis cerana cerana workers at 7 d post-inoculation (dpi) and 10 dpi with Nosema ceranae and corresponding un-inoculated midguts. PCR identification and microscopic observation of paraffin sections confirmed the effective infection of A. c. cerana worker by N. ceranae. In total, 1127 and 957 N. ceranae-responsive genes were identified in the infected midguts at 7 dpi and 10 dpi, respectively. RT-qPCR results validated the reliability of our transcriptome data. GO categorization indicated the differentially expressed genes (DEGs) were respectively engaged in 34 and 33 functional terms associated with biological processes, cellular components, and molecular functions. Additionally, KEGG pathway enrichment analysis showed that DEGs at 7 dpi and 10 dpi could be enriched in 231 and 226 pathways, respectively. Moreover, DEGs in workers’ midguts at both 7 dpi and 10 dpi were involved in six cellular immune pathways such as autophagy and phagosome and three humoral immune pathways such as the Toll/Imd signaling pathway and Jak-STAT signaling pathway. In addition, one up-regulated gene (XM_017055397.1) was enriched in the NF-κB signaling pathway in the workers’ midgut at 10 dpi. Further investigation suggested the majority of these DEGs were engaged in only one immune pathway, while a small number of DEGs were simultaneously involved in two immune pathways. These results together demonstrated that the overall gene expression profile in host midgut was altered by N. ceranae infection and some of the host immune pathways were induced to activation during fungal infection, whereas some others were suppressed via host–pathogen interaction. Our findings offer a basis for clarification of the mechanism underlying the immune response of A. c. cerana workers to N. ceranae infection, but also provide novel insights into eastern honeybee-microsporodian interaction.

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