List of datasets used in the meta-analysis.

List of datasets used in the meta-analysis.

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Article
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Organisms are commonly infected by a diverse array of pathogens and mount functionally distinct responses to each of these varied immune challenges. Host immune responses are characterized by the induction of gene expression, however, the extent to which expression changes are shared among responses to distinct pathogens is largely unknown. To exam...

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... other animals, Drosophila mount specific responses to infection by distinct pathogen classes [20,22]. Numerous studies have investigated changes in gene expression in Drosophila hosts following infection by a wide range of pathogens including multiple species of bacterial and fungal pathogens, viruses, and parasitoid wasps (Table 1) [18,19,[34][35][36][37][38][39][40]. These studies provide a unique opportunity for the comparative analysis of immune responses by a single host organism against diverse pathogens. ...
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... approach is particularly useful as it allows for the reuse of existing datasets to address novel research questions, while providing a statistically rigorous framework [41]. Here, we use a common meta-analysis approach to perform a comparative analysis on multiple previously described Drosophila infection studies (Table 1) to identify genes whose expression are similarly altered across infection by distinct pathogen classes. Our meta-analysis approach allows us to take a broader view of infection induced transcriptional changes than would be otherwise possible, and extends the findings of the original studies. ...
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... Accession numbers and other metadata are listed in Table 1. Gene expression data were then pre-processed before meta-analysis. First, gene identifiers were converted to the most recent FlyBase gene identification number (FBgn) using the FlyBase Upload and Validate IDs tool (version FB2021_01; https://flybase.org/ ...
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... was determined using the estimated percentage of false prediction (pfp) with a threshold of 0.05. Genes with significantly altered expression are listed in Table S1 (62 upregulated genes) and Table S2 (31 downregulated genes). A control set of 62 genes with unchanged expression was selected from the RP result as listed in Table S4. ...
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... performed a meta-analysis on 10,818 genes across 12 gene expression studies following infection by a variety of pathogens (listed in Table 1). To identify genes showing significant expression changes across these studies, we used the non-parametric rank products approach with an estimated percentage of false prediction (pfp) threshold of 0.05. ...
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... use of ranks, rather than experimental values, makes this approach robust to differences between experimental platforms and allows for the comparison between multiple studies [44,45]. Using this approach, we identified 62 genes that were induced across these infection conditions (Table S1) with an average log 2 fold change (logFC) of 1.17, and a logFC range of 0.62 to 2.27. We further identified 31 genes that were significantly downregulated across these infection conditions (Table S2). ...
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... represents a significant degree of clustering compared to background controls (induced p = 1.76 × 10 −7 ; downregulated: p = 0.003). We identified 5 clusters of induced genes (annotated in Table S1) including the Bomanin family gene clusters (found on chromosomes 2R and 3R), clusters comprising the Diptericin (chromosome 2R) and Cecropin (chromosome 3R) antimicrobial peptide families, and a cluster of two unstudied genes on chromosome 2L (CG9928 and CG16978). We also identified 2 clusters of downregulated genes (annotated in Table S2) including a cluster of Trypsin genes (chromosome 2R) and a cluster of predicted S1A family serine protease genes (CG18179 and CG18180; chromosome 3L). ...
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... Materials: The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/insects13050490/s1, Table S1: All significantly induced genes from the RankProduct analysis listed by FBgn and gene name; Table S2: All significantly downregulated genes from the RankProduct analysis listed by FBgn and gene name; Table S3: All significantly enriched Gene Ontology terms among genes identified as induced by the RankProduct analysis; Table S4: Genes identified as unchanged from the RankProduct analysis and used as a representative background set for motif analysis; Table S5: All significantly enriched Gene Ontology terms among genes identified as downregulated by the RankProduct analysis. Table 1. ...
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... S1: All significantly induced genes from the RankProduct analysis listed by FBgn and gene name; Table S2: All significantly downregulated genes from the RankProduct analysis listed by FBgn and gene name; Table S3: All significantly enriched Gene Ontology terms among genes identified as induced by the RankProduct analysis; Table S4: Genes identified as unchanged from the RankProduct analysis and used as a representative background set for motif analysis; Table S5: All significantly enriched Gene Ontology terms among genes identified as downregulated by the RankProduct analysis. Table 1. ...

Citations

... Several reasons might account for this. On the one hand, it is possible that there are significant differences in responses to infections with different pathogens, an aspect that is certainly important (38,39). On the other hand, these differences may also represent secondary effects that could be explained by different degrees of infection events and responses caused by them (40, 41). ...
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Article
The fruit fly Drosophila is an excellent model to study the response of different immunocompetent organs during systemic infection. In the present study, we intended to test the hypothesis that the only professional immune organs of the fly, the fat body and hemocytes, show substantial similarities in their responses to systemic infection. However, comprehensive transcriptome analysis of isolated organs revealed highly divergent transcript signatures, with the few commonly regulated genes encoding mainly classical immune effectors from the antimicrobial peptide family. The fat body and the hemocytes each have specific reactions that are not present in the other organ. Fat body-specific responses comprised those enabling an improved peptide synthesis and export. This reaction is accompanied by transcriptomic shifts enabling the use of the energy resources of the fat body more efficiently. Hemocytes, on the other hand, showed enhanced signatures related to phagocytosis. Comparing immune-induced signatures of both cell types with those of whole-body responses showed only a minimal correspondence, mostly restricted again to antimicrobial peptide genes. In summary, the two major immunocompetent cell types of Drosophila show highly specific responses to infection, which are closely linked to the primary function of the respective organ in the landscape of the systemic immune response.