ArticlePDF Available

Meta-Analysis of Immune Induced Gene Expression Changes in Diverse Drosophila melanogaster Innate Immune Responses

Authors:
Article

Meta-Analysis of Immune Induced Gene Expression Changes in Diverse Drosophila melanogaster Innate Immune Responses

Abstract and Figures

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 examine this, we performed meta-analysis of gene expression data collected from Drosophila melanogaster following infection with a wide array of pathogens. We identified 62 genes that are significantly induced by infection. While many of these infection-induced genes encode known immune response factors, we also identified 21 genes that have not been previously associated with host immunity. Examination of the upstream flanking sequences of the infection-induced genes lead to the identification of two conserved enhancer sites. These sites correspond to conserved binding sites for GATA and nuclear factor κB (NFκB) family transcription factors and are associated with higher levels of transcript induction. We further identified 31 genes with predicted functions in metabolism and organismal development that are significantly downregulated following infection by diverse pathogens. Our study identifies conserved gene expression changes in Drosophila melanogaster following infection with varied pathogens, and transcription factor families that may regulate this immune induction.
Content may be subject to copyright.
Citation: Waring, A.L.; Hill, J.; Allen,
B.M.; Bretz, N.M.; Le, N.; Kr, P.; Fuss,
D.; Mortimer, N.T. Meta-Analysis of
Immune Induced Gene Expression
Changes in Diverse Drosophila
melanogaster Innate Immune
Responses. Insects 2022,13, 490.
https://doi.org/10.3390/
insects13050490
Academic Editors: Sourav Roy and
Rebecca Spokony
Received: 17 April 2022
Accepted: 19 May 2022
Published: 23 May 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
insects
Article
Meta-Analysis of Immune Induced Gene Expression Changes
in Diverse Drosophila melanogaster Innate Immune Responses
Ashley L. Waring, Joshua Hill, Brooke M. Allen, Nicholas M. Bretz, Nguyen Le , Pooja Kr, Dakota Fuss
and Nathan T. Mortimer *
School of Biological Sciences, Illinois State University, Normal, IL 61790, USA; alwarin@ilstu.edu (A.L.W.);
jrhill2@ilstu.edu (J.H.); bmalle5@ilstu.edu (B.M.A.); nbretz@ilstu.edu (N.M.B.); nvle1@ilstu.edu (N.L.);
pkadaba@ilstu.edu (P.K.); dfuss@ilstu.edu (D.F.)
*Correspondence: ntmorti@ilstu.edu
Simple Summary:
Organisms can be infected by a wide range of pathogens, including bacteria,
viruses, and parasites. Following infection, the host mounts an immune response to attempt to
eliminate the pathogen. These responses are often specific to the type of pathogen and mediated
by the expression of specialized genes. We have characterized the expression changes induced in
host Drosophila fruit flies following infection by multiple types of pathogens, and identified a small
number of genes that show expression changes in each infection. This includes genes that are known
to be involved in pathogen resistance, and others that have not been previously studied as immune
response genes. These findings provide new insight into transcriptional changes that accompany
Drosophila immunity. They may suggest possible roles for the differentially expressed genes in innate
immune responses to diverse classes of pathogens, and serve to identify candidate genes for further
empirical study of these processes.
Abstract:
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 charac-
terized by the induction of gene expression, however, the extent to which expression changes are
shared among responses to distinct pathogens is largely unknown. To examine this, we performed
meta-analysis of gene expression data collected from Drosophila melanogaster following infection with
a wide array of pathogens. We identified 62 genes that are significantly induced by infection. While
many of these infection-induced genes encode known immune response factors, we also identified
21 genes that have not been previously associated with host immunity. Examination of the upstream
flanking sequences of the infection-induced genes lead to the identification of two conserved enhancer
sites. These sites correspond to conserved binding sites for GATA and nuclear factor
κ
B (NF
κ
B)
family transcription factors and are associated with higher levels of transcript induction. We further
identified 31 genes with predicted functions in metabolism and organismal development that are
significantly downregulated following infection by diverse pathogens. Our study identifies conserved
gene expression changes in Drosophila melanogaster following infection with varied pathogens, and
transcription factor families that may regulate this immune induction.
Keywords:
Drosophila melanogaster; innate immunity; gene expression; transcriptome analysis;
pathogen infection
1. Introduction
Organisms encounter a broad range of pathogens in their natural environments and
have evolved immune defenses that allow them to survive infection by diverse pathogen
classes. Most metazoan hosts make use of a highly conserved suite of innate immune
responses to defend against pathogen infection [
1
]. These responses are typified by a
multi-step process, beginning with pathogen recognition and resulting in activation of the
Insects 2022,13, 490. https://doi.org/10.3390/insects13050490 https://www.mdpi.com/journal/insects
Insects 2022,13, 490 2 of 19
appropriate immune mechanism [
2
]. This immune activation is accompanied by changes
in gene expression, including both the induction of immune response transcripts and
the downregulation of transcripts encoding proteins with non-immune functions [
3
,
4
].
The induced immune gene products then play a role in either eliminating the pathogen
(pathogen resistance) or allowing the organism to survive despite infection (pathogen
tolerance). Differential gene expression analysis has proven to be a valuable approach
to uncover the genetic basis for a variety of traits [
5
8
], including the immune response
to infection [
9
11
]. These studies have revealed the importance of conserved immune
signaling pathways including Toll, Immune deficiency (IMD) and JAK-STAT across diverse
organisms [1219].
While hosts may encounter numerous distinct pathogenic species, the pathogens are
often categorized into four general classes: bacterial pathogens, fungal pathogens, viruses,
and parasites [
20
,
21
]. Innate immune responses can be highly specific to the category of
pathogen encountered and may include both cellular and humoral mechanisms [
2
,
22
]. For
instance, bacterial infection is often countered by the production of secreted antimicrobial
peptides (AMPs) and immune cell mediated phagocytosis of the invading microbes [
23
,
24
].
Alternatively, antiviral immunity may include distinct features such as RNA interference or
the recognition and cytolysis of infected host cells [
25
,
26
]. Highly conserved transcriptional
signatures in response to distinct pathogen types have been uncovered, but less is known
about common patterns of gene induction against multiple pathogens [2730].
The genetic model organism Drosophila melanogaster is a commonly used and powerful
system to understand conserved innate immune processes [
31
33
]. Like other animals,
Drosophila mount specific responses to infection by distinct pathogen classes [
20
,
22
]. Nu-
merous 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
40
]. These studies provide a
unique opportunity for the comparative analysis of immune responses by a single host
organism against diverse pathogens. A useful method to perform comparative analyses
is the meta-analysis of gene expression studies. Such meta-analyses attempt to directly
compare results from multiple studies while controlling for inter-study differences [
41
].
This 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 expres-
sion 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.
Table 1. List of datasets used in the meta-analysis.
GEO Accession Pathogen Type Pathogen Host Stage Reference
-1Bacteria Escherichia coli +Micrococcus luteus Adult [34]
-1Bacteria E. coli,M. luteus +Enterococcus faecalis Adult [18]
GSE37708 Bacteria E. coli Adult [35]
GSE5489 Bacteria E. coli Larva [36]
-1Fungus Beauvaria bassiana Adult [34]
-1Fungus Aspergillus fumigatus Adult [18]
GSE2828 Virus Drosophila C Virus Adult [37]
GSE42726 Virus Sindbis virus (transgenic Drosophila model) Adult [38]
GSE31542 Virus Flock House Virus Adult [39]
GSE31542 Virus Sindbis virus Adult [39]
GSE25522 Parasite Ganaspis xanothopoda Larva [40]
GSE8938 Parasite Leptopilina boulardi Larva [19]
1Data available at http://www.fruitfly.org/expression/immunity/.
Insects 2022,13, 490 3 of 19
2. Materials and Methods
2.1. Drosophila Melanogaster Genome Data
Gene expression data analyzed in the current study are available through the Gene
Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/ study accessions: GSE37708,
GSE2828, GSE42726, GSE25522, GSE8938, GSE5489, GSE31542; accessed on 23 May 2014),
and through the fruitfly.org Expression Database (http://www.fruitfly.org/expression/
immunity/). 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/
convert/id) [
42
]. Second, the gene expression datasets were filtered to remove any genes
that are not represented in all of the datasets. This step resulted in the identification of
10,818 common genes that were retained for subsequent analysis. Finally, gene expression
fold change values were log
2
transformed wherever necessary to be used as input for meta-
analysis (next section). Data for the D. melanogaster genome (release 6.38) and individual
gene reports were accessed through FlyBase (version FB2021_01; https://flybase.org/) [
42
].
2.2. Meta-Analysis of Gene Expression Studies
Meta-analysis of immune gene expression studies was performed in the R statistical
computing environment [
43
], using the RankProd package [
44
,
45
]. The log
2
fold change for
each gene in each dataset was used as the input, and the rank and rank-product (RP) were
calculated for each gene. Significance 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.
2.3. Chromosomal Distribution
The chromosomal location of each gene identified as significantly differentially ex-
pressed in the meta-analysis was retrieved from FlyBase (version FB2021_01). The propor-
tion of identified genes found on each chromosomal arm was compared to the proportion
of all genes for that arm using a 2-sample test for equality of proportions with continuity
correction in R.
2.4. Gene Locus Uniformity and Clustering
To determine the uniformity of gene spacing across D. melanogaster chromosome
arms, the up- and down-regulated gene lists were used as input for the Cluster Locator
webserver (http://clusterlocator.bnd.edu.uy/ accessed on 24 April 2021) [
46
] using default
parameters. Uniformity is tested using a two-sided Kolmogorov–Smirnov test. Clusters
were identified using the Cluster Locator webserver with the default max-gap value of
5. Gene clustering is statistically tested by comparing clustering of the input lists with
randomly selected lists.
2.5. Motif Finding
Putative transcription factor binding motifs were predicted using iMotifs [
47
,
48
]. The
250 bp of sequence immediately upstream of each gene of interest was downloaded from
FlyBase, and these sequences were used as input to iMotifs. These predicted motifs were
then mapped onto the input and unchanged control sequences using the FIMO tool [
49
].
To identify the likely transcription factor interacting with the discovered motifs, the motifs
were compared with experimentally validated D. melanogaster transcription factor binding
sites using the TomTom Motif Comparison Tool [
50
,
51
] with a significance threshold of
p< 0.05
. Position-weight-matrices for the identified transcription factor binding sites were
accessed through the OnTheFly database [
52
]. OnTheFly accession numbers: GATAe:
OTF0433.1; dl: OTF0107.2; Hr46/Hr3: OTF0227.1.
Insects 2022,13, 490 4 of 19
2.6. Statistics
All statistical tests were performed in R using the base stats package and graphs were
produced using ggplot2 [53].
3. Results
3.1. Meta-Analysis of Genome-Wide Transcript Levels following Pathogen Infection
We 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. In this method, the observed fold-change of each gene is ranked within each study
and the rank-product of each gene is calculated as the geometric mean of the ranks of a
given gene across all of the studies. Genes with rank-products that significantly differ
from a uniform distribution are considered to be up- or down-regulated [
45
,
54
]. The 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). These downregulated genes have an average logFC of
0.75, with a logFC range of 0.26 to 1.40.
The identified genes were mapped onto their chromosomal locations (Figure 1) and
found to be distributed throughout the autosomal chromosome arms, with few genes
mapping to the X chromosome and none on chromosome 4. Given that only ~80 genes
of ~18,000 total genes in the D. melanogaster genome are found chromosome 4 [
55
,
56
], the
lack of immune regulated genes on chromosome 4 is unsurprising. On the other hand,
the apparent lack of immune regulated genes on the X chromosome was unanticipated.
We therefore used 2-sample proportion tests to assay the distribution of genes on each
chromosome arm (Table 2). We find that induced genes are significantly enriched (
χ2= 8.74
,
p= 0.0031), and that downregulated genes are significantly under-represented (
χ2
= 4.56,
p= 0.033
), on chromosome 2R. Additionally both induced and downregulated genes are
under-represented on the X chromosome (induced:
χ2
= 3.88, p= 0.049; downregulated:
χ2
= 11.13, p= 8.5
×
10
4
). This relative lack of genes was unexpected given the pres-
ence of numerous immune response genes on the X chromosome, although interestingly,
the antimicrobial peptide class of immune effectors is also under-represented on the X
chromosome [57].
Table 2. Distribution of analyzed genes across chromosome arms.
Sample X 2L 2R 3L 3R 4 U Total
Dataset 1793 1951 2129 2103 2734 65 43 10,818
Induced genes 4b11 22 a8 17 0 0 62
Downregulated genes 0b65b9 11 0 0 31
The Dataset category contains the genes that were measured in all 12 datasets.
a
enriched relative to Dataset
control,
b
under-represented relative to Dataset control; determined by p< 0.05 from 2-sample test for equality of
proportions with continuity correction.
Next, we assayed the distribution of the identified genes within each chromosome arm
for overall uniformity and the presence of gene clusters. Using the Kolmogorov–Smirnov
test of uniformity, we find that the genes are evenly distributed along chromosomes
(Table 3), but this analysis did identify the presence of 7 gene clusters. This 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
Insects 2022,13, 490 5 of 19
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).
Insects 2022, 13, 490 5 of 21
in Table S2) including a cluster of Trypsin genes (chromosome 2R) and a cluster of pre-
dicted S1A family serine protease genes (CG18179 and CG18180; chromosome 3L).
Figure 1. Chromosomal location of altered genes. Each identified gene has been mapped to its chro-
mosomal location, indicated by its position on each chromosome arm of the Drosophila melanogaster
genome (AE). For each panel, the x axis represents the genomic position, inverted cyan triangles
indicate the positions of induced genes and the magenta triangles indicate the positions of down-
regulated genes.
Figure 1.
Chromosomal location of altered genes. Each identified gene has been mapped to its chro-
mosomal location, indicated by its position on each chromosome arm of the Drosophila melanogaster
genome (
A
E
). For each panel, the x axis represents the genomic position, inverted cyan trian-
gles indicate the positions of induced genes and the magenta triangles indicate the positions of
downregulated genes.
Insects 2022,13, 490 6 of 19
Table 3. Uniformity of altered genes within chromosome arms.
Chromosome Arm DUp pValue Up DDown pValue Down
X 0.62 0.05 - -
2L 0.17 0.87 0.41 0.20
2R 0.24 0.15 0.51 0.10
3L 0.41 0.10 0.25 0.52
3R 0.17 0.66 0.31 0.19
The uniformities of induced and downregulated genes within each chromosome arm were independently assessed
by Kolmogorov–Smirnov test. Dis the calculated Kolmogorov–Smirnov distance, Up = induced (upregulated)
genes, Down = downregulated genes.
3.2. Infection-Induced Genes in Host Immunity
Our meta-analysis identified 62 genes that are significantly upregulated following
infection. Of these, 42 have been previously linked with host immunity (Table 4). This list
includes genes that have been previously implicated in resistance to each of the pathogen
categories. The list also includes genes with membership in the Toll, IMD, JAK-STAT and
Jun N-terminal kinase (JNK) conserved immune signaling pathways. Accordingly, Gene
Ontology (GO) term analysis revealed that the immune induced genes are enriched in
multiple biological processes linked to responses to external stimuli including immune
response (GO:0009655), response to biotic stimulus (GO:0009607), response to wounding
(GO:0009611), and response to stress (GO:0006950) (Figure 2, Table S3).
Table 4.
Infection induced genes with previous links to immune function or immune signaling pathways.
Gene Name Function Immune Pathway References
AttA Antimicrobial peptide IMD [58,59]
AttD Antimicrobial peptide IMD [58]
Bbd
Production of AMP-like peptides
Toll [60]
BomBc1 AMP-like Toll [61,62]
BomBc2 AMP-like Toll [61,62]
BomBc3 AMP-like Toll [61,62]
BomS1 AMP-like Toll [6163]
BomS2 AMP-like Toll [6163]
BomS3 AMP-like Toll [6163]
BomS5 AMP-like Toll [6163]
BomS6 AMP-like Toll [6163]
BomT2 AMP-like Toll [61,62]
BomT3 AMP-like Toll [61,62]
CecB Antimicrobial peptide IMD [64,65]
CecC Antimicrobial peptide IMD [64,66]
Def Antimicrobial peptide IMD, Toll [18,67]
DptA Antimicrobial peptide IMD [64,68]
DptB Antimicrobial peptide IMD [64,69]
Drs Antimicrobial peptide Toll [70,71]
Ets21C Transcription factor IMD [72]
BaraA Antimicrobial peptide Toll [73]
Irc Oxidant detoxification - [74]
lectin-24A Carbohydrate binding - [75]
Listericin Antimicrobial peptide JAK-STAT [76]
mat - JAK-STAT [77,78]
Mtk Antimicrobial peptide IMD, Toll [79,80]
nec Serpin Toll [81]
NimB1
Pathogen recognition (predicted)
- [82,83]
PGRP-SA Pathogen recognition Toll [84,85]
PGRP-SB1 Antimicrobial effector IMD [86,87]
PGRP-SD Pathogen recognition Toll [88]
Rel Transcription factor IMD [89,90]
Insects 2022,13, 490 7 of 19
Table 4. Cont.
Gene Name Function Immune Pathway References
Sid DNA endonuclease Toll [18,91]
Sp7 S1A Serine Protease Toll [92,93]
SPE S1A Serine Protease Toll [94,95]
Tep2 Thioester-containing Protein Toll [96]
TotM - JAK-STAT [97,98]
CG13675 Chitin Binding IMD [99]
CG14957 Chitin Binding JNK [78]
CG3505 S1A Serine Protease Toll/IMD [100]
CG5909 S1A Serine Protease Toll/IMD [18]
Insects 2022, 13, 490 6 of 21
Table 2. Distribution of analyzed genes across chromosome arms.
Sample X 2L 2R 3L 3R 4 U Total
Dataset 1793 1951 2129 2103 2734 65 43 10,818
Induced genes 4 b 11 22
a 8 17 0 0 62
Downregulated genes 0 b 6 5
b 9 11 0 0 31
The Dataset category contains the genes that were measured in all 12 datasets. a enriched relative to
Dataset control, b under-represented relative to Dataset control; determined by p < 0.05 from 2-sam-
ple test for equality of proportions with continuity correction.
Table 3. Uniformity of altered genes within chromosome arms.
Chromosome Arm D Up p Value Up D Down p Value Down
X 0.62 0.05 - -
2L 0.17 0.87 0.41 0.20
2R 0.24 0.15 0.51 0.10
3L 0.41 0.10 0.25 0.52
3R 0.17 0.66 0.31 0.19
The uniformities of induced and downregulated genes within each chromosome arm were inde-
pendently assessed by Kolmogorov–Smirnov test. D is the calculated Kolmogorov–Smirnov dis-
tance, Up = induced (upregulated) genes, Down = downregulated genes.
3.2. Infection-Induced Genes in Host Immunity
Our meta-analysis identified 62 genes that are significantly upregulated following
infection. Of these, 42 have been previously linked with host immunity (Table 4). This list
includes genes that have been previously implicated in resistance to each of the pathogen
categories. The list also includes genes with membership in the Toll, IMD, JAK-STAT and
Jun N-terminal kinase (JNK) conserved immune signaling pathways. Accordingly, Gene
Ontology (GO) term analysis revealed that the immune induced genes are enriched in
multiple biological processes linked to responses to external stimuli including immune
response (GO:0009655), response to biotic stimulus (GO:0009607), response to wounding
(GO:0009611), and response to stress (GO:0006950) (Figure 2, Table S3).
Figure 2. Gene ontology analysis of infection induced genes. The log2 fold enrichment for selected
GO terms. The Biological Process (BP) category is shown in cyan, and the Cellular Component (CC)
category is shown in magenta. See Table S3. for complete GO term analysis of induced genes.
Table 4. Infection induced genes with previous links to immune function or immune signaling path-
ways.
Gene Name Function Immune Pathway References
A
ttA Antimicrobial peptide IMD [58,59]
AttD Antimicrobial peptide IMD [58]
Figure 2.
Gene ontology analysis of infection induced genes. The log
2
fold enrichment for selected
GO terms. The Biological Process (BP) category is shown in cyan, and the Cellular Component (CC)
category is shown in magenta. See Table S3. for complete GO term analysis of induced genes.
Our meta-analysis results suggest that infection by a broad range of pathogens can
lead to differential regulation of immune signaling pathways. We found that several genes
implicated in pathogen recognition (NimB1,lectin-24A and Tep2), along with regulators
of the Toll (nec,PGRP-SA,SPE and Sp7) and IMD (PGRP-SD and Rel) pathways, are
induced following infection. We also identified a broad range of immune effector molecules
including antimicrobial peptides (AMPs) and AMP-like immune induced genes. The
D. melanogaster
genome encodes numerous AMP families [
101
], and we identify members
of nearly all of these families including the Attacins (AttA and AttD), Cecropins (CecB
and CecC), Defensin, Diptericins (DptA and DptB), Drosomycin, IMPPP/Baramycin A,
Listericin, and Metchnikowin (Table 4). This broad induction is particularly interesting
given that the Toll and IMD signaling pathways and many of these AMP/AMP-like families
have distinct pathogen targets [24].
Members of the Bomanin (Bom) AMP-like gene family have been shown to act down-
stream of Toll pathway signaling in antimicrobial immunity [
61
,
62
]. We identified 10 of the
12 Bom family genes as induced in our meta-analysis (Table 4); of the other 2 Bom genes,
BomS4 is significantly induced following parasite infection, but not following infection by
the other pathogens, and BomT1 is not represented in our dataset. Bom genes are found
in the genome in two clusters, and we identified genes from both clusters in our analysis.
We additionally identified the bombardier (bbd) gene as induced in our analysis (Table 4).
Like the Bom genes, bbd also acts in antimicrobial immunity downstream of Toll, and bbd
mutants fail to produce the short-form class of Bom peptides (BomS) [
60
]. The finding that
bbd is induced alongside Bom genes lends further support for a role for Bom family activity
following infection.
Insects 2022,13, 490 8 of 19
3.3. Predicted Functions of Infection Induced Genes
The proteins encoded by the infection induced genes have a wide array of predicted
molecular functions. This includes both immune associated functions like antimicrobial
activity and peptidoglycan recognition, and a variety of other functions such as ion trans-
port (MFS12), deoxyribonuclease activity (Sid), and acyl transferase activity (CG14219).
Notably, we identified 5 members of the S1A protease family (SPE,Sp7,CG3505,CG18563
and CG5909). The S1A family is comprised of more than 200 genes and includes both active
proteases and catalytically inactive protease homologs [
102
]. S1A family members have
been previously linked to immune responses against a variety of pathogens [
84
,
94
,
103
105
].
Due to the wide array of encoded protein activities, our GO term analysis did not identify
any significant enrichment for molecular function. However, we did identify an enrich-
ment of genes encoding proteins that are secreted into the extracellular space (GO:0005615)
and an under-representation of genes encoding cytosolic (GO:0005737) and intracellular
membrane-bounded organelle localized proteins (GO:0043231) (Figure 2, Table S3).
3.4. Motif Finding Analysis of Infection Induced Genes
The timing and levels of mRNA transcription are tightly regulated by the activity of
a wide array of transcription factors. Multiple transcription factor families, including the
nuclear factor
κ
B (NF
κ
B), nuclear factor of activated T cells (NFAT), signal transducer and
activator of transcription (STAT) and erythroblast transformation specific (ETS) factors have
been linked to transcription induction following infection [
106
109
]. We predicted that the
induced genes uncovered in our meta-analysis are co-transcriptionally regulated, and share
a common set of transcription factors. To test this prediction, we analyzed the 250 bp of
genomic sequence upstream of the annotated transcription start site of each of the induced
genes using the iMotifs de novo motif finder [
47
]. We reasoned that these sequences likely
included the promoter and proximal enhancers for each gene, and our approach would
allow us to test whether conserved binding motifs for any of these transcription factor
families are found in the immune induced genes, and uncover any motif that was found in
the upstream region of the majority of the induced genes.
Our analysis led to the identification of 3 consensus motifs (Figure 3A–C). These con-
sensus sequences were searched against the complete database of known D. melanogaster
transcription factor binding sites using the Tomtom web server [
51
]. We found that our
Motif 1 showed significant similarity to the binding site for the GATA factor GATAe
(
p= 6.0 ×106
; Figure 3A), and that our Motif 2 showed significant similarity to the
binding site for the NF
κ
B factor dl (p= 5.6
×
10
4
; Figure 3B). It can be challenging to
correctly identify the binding site for a specific member for either of these transcription
factor families [
110
,
111
], and so we will refer to the sites by their general classifications
as GATA and NF
κ
B sites for Motifs 1 and 2, respectively. We did not find motifs that
show similarity to the NFAT, STAT or ETS transcription factor families, and Motif 3 did
not show significant similarity to any known D. melanogaster transcription factor binding
site. However, its core sequence matches the TATA box characteristic of many eukaryotic
core promoters (Figure 3C) [
112
,
113
]. This finding supports our use of upstream genomic
sequences to capture gene promoter regions.
Since both GATA and NF
κ
B factors have been previously linked to infection induced
transcription in Drosophila [
89
,
114
116
], we next tested whether these sites are more com-
mon in the upstream regions of our induced genes than of a control list of unchanged
genes from the meta-analysis (Table S4). We find that our identified induced genes are
significantly more likely that the background control to contain either a Motif 1/GATA or
Motif 2/NF
κ
B site (Fisher’s Exact Test odds ratio 7.5, p= 1.6
×
10
6
). More specifically,
both Motif 1 and Motif 2 are enriched in the upstream regions of our induced genes (Motif
1: odds ratio 4.7, p= 1.0
×
10
4
; Motif 2: odds ratio 5.2, p= 2.5
×
10
5
; Table 5), and the
induced genes are also significantly more likely to contain both motifs (odds ratio 16.2,
p= 5.3 ×106; Table 5).
Insects 2022,13, 490 9 of 19
Insects 2022, 13, 490 9 of 21
3C) [112,113]. This finding supports our use of upstream genomic sequences to capture
gene promoter regions.
Figure 3. Motifs associated with infection induced genes. De novo motif finding identified 3 motifs
(Motifs 1–3) that are enriched in the upstream sequences of the induced genes. The consensus motifs
are represented as sequence logos (AC, top). Motif matching identifies Motif 1 as being signifi-
cantly similar to the GATAe binding site (A). Motif 2 shows significant similarity to the dl binding
site (B). (DF) Box-whisker plots showing the distribution of rank-products for induced genes with
and without the indicated motifs. A lower rank-product is indicative of higher expression levels.
Figure 3.
Motifs associated with infection induced genes. De novo motif finding identified 3 motifs
(Motifs 1–3) that are enriched in the upstream sequences of the induced genes. The consensus
motifs are represented as sequence logos (
A
C
, top). Motif matching identifies Motif 1 as being
significantly similar to the GATAe binding site (
A
). Motif 2 shows significant similarity to the dl
binding site (
B
). (
D
F
) Box-whisker plots showing the distribution of rank-products for induced
genes with and without the indicated motifs. A lower rank-product is indicative of higher expression
levels. (
D
) Induced genes with Motif 1 in the upstream region have significantly lower rank-products.
(
E
) The presence of Motif 2 does not impact the rank-product distribution. (
F
) Induced genes with
both motifs have significantly lower rank-products. Asterisk (*) indicates p< 0.05 relative to induced
genes without the indicated motif.
Insects 2022,13, 490 10 of 19
Table 5.
Enrichment of predicted transcription factor binding sites in the induced genes compared to
unchanged control genes.
Gene Set Motif 1 Motif 2 Both Neither Total
Induced 36 * 39 * 22 * 9 * 62
Unchanged 14 15 2 35 62
*p> 0.05 compared to unchanged control by Fisher’s exact test.
While this enrichment is suggestive that GATA and NF
κ
B factors have an impact on
immune induced expression, we wanted to test this hypothesis more explicitly. We used
the Wilcoxon rank sum exact test to test the effect of each site on the rank-product of the
induced genes. We would predict that if the presence of Motif 1/GATA or Motif 2/NF
κ
B
(or both) sites has a positive effect on expression, then transcripts with these sites would
have significantly lower rank-products (indicative of higher expression). We find that genes
with Motif 1 have significantly lower rank-products that those without (W = 595, p= 0.035;
Figure 3D), while the presence of Motif 2 alone has little to no impact on rank-product
(
W = 510
,p= 0.189; Figure 3E). In contrast, transcripts with both motifs have significantly
lower rank-products (W = 616, p= 4.53
×
10
3
; Figure 3F), with the strength of this effect
hinting at a possible synergistic effect of GATA and NFκB factors.
3.5. Analysis of Downregulated Transcripts
Our meta-analysis identified 31 genes that are significantly downregulated following
infection with the various pathogens (Table S2). Most of these genes may be predicted
to be influenced by life history tradeoffs that occur following infection. Mounting an
immune response is energetically costly, and following infection organismal metabolism is
altered [
117
119
]. We find a wide range of genes linked to metabolism are downregulated
following infection including genes linked to amino acid metabolism (Lsp1
β
,Lsp2,Srr),
lipid metabolism (CG17192,mag), and carbohydrate metabolism (LManVI,Sodh-1). The
shifting of resources towards immunity is often at the expense of organismal development
or fertility [
120
122
], and among our downregulated genes, we find genes associated with
these processes including fln,Act88F,CG33259,Cpr92F, and TpnC47D.
In many organisms, pathogen infection leads to coordinated changes in host physiol-
ogy and behaviour, known as sickness behaviour [
123
]. These changes include decreased
host appetite and feeding following infection in a wide range of host species, includ-
ing Drosophila [
123
127
]. Accordingly, we find that genes involved in feeding behaviour
(fit) and nutritional stress (CG18179,CG18180), along with putative digestive enzymes
in the Trypsin and Jonah protease families are all downregulated following pathogen
infection [
128
130
]. Interestingly, despite the widespread prevalence of infection induced
anorexia, this mechanism is not uniformly protective, and instead can lower host resistance
to certain pathogens [127,131,132].
Our GO term analysis (Figure 4, Table S5) of these downregulated genes reveals an
enrichment in genes involved in proteolysis (GO:0006508), and specifically in serine-type
peptidase activity (GO:0008236). The enrichment in genes found in the larval serum protein
complex (GO:0005616) likely reflects the observed metabolic change. Like with the induced
genes, we also find an under-representation of genes encoding proteins that localize to
intracellular membrane-bounded organelles (GO:0043231).
We used motif finding software to scan the 250 bp regions upstream of the transcrip-
tion start sites of the downregulated genes, and identified a putative transcription factor
binding site (Motif D1, Figure 5A). This motif is homologous to the identified Hr3 binding
site (
p= 0.005
; Figure 5A). However, we find that Motif D1 is not enriched among downreg-
ulated genes in comparison with our unchanged control gene set (odds ratio 1.14, p= 0.826),
and that the presence of Motif D1 does not have a significant effect on the rank-product
among downregulated genes (W = 97, p= 0.811; Figure 5B), suggesting that this site is
likely not mediating the downregulation observed following infection.
Insects 2022,13, 490 11 of 19
Insects 2022, 13, 490 11 of 21
gen infection [128–130]. Interestingly, despite the widespread prevalence of infection in-
duced anorexia, this mechanism is not uniformly protective, and instead can lower host
resistance to certain pathogens [127,131,132].
Our GO term analysis (Figure 4, Table S5) of these downregulated genes reveals an
enrichment in genes involved in proteolysis (GO:0006508), and specifically in serine-type
peptidase activity (GO:0008236). The enrichment in genes found in the larval serum pro-
tein complex (GO:0005616) likely reflects the observed metabolic change. Like with the
induced genes, we also find an under-representation of genes encoding proteins that lo-
calize to intracellular membrane-bounded organelles (GO:0043231).
Figure 4. Gene ontology analysis of downregulated genes. The log2 fold enrichment for selected GO
terms. The Biological Process (BP) category is shown in cyan, the Molecular Function (MF) category
is shown in yellow, and the Cellular Component (CC) category is shown in magenta. See Table S5
for complete GO term analysis of downregulated genes.
We used motif finding software to scan the 250 bp regions upstream of the transcrip-
tion start sites of the downregulated genes, and identified a putative transcription factor
binding site (Motif D1, Figure 5A). This motif is homologous to the identified Hr3 binding
site (p = 0.005; Figure 5A). However, we find that Motif D1 is not enriched among down-
regulated genes in comparison with our unchanged control gene set (odds ratio 1.14, p =
0.826), and that the presence of Motif D1 does not have a significant effect on the rank-
product among downregulated genes (W = 97, p = 0.811; Figure 5B), suggesting that this
site is likely not mediating the downregulation observed following infection.
Figure 4.
Gene ontology analysis of downregulated genes. The log
2
fold enrichment for selected GO
terms. The Biological Process (BP) category is shown in cyan, the Molecular Function (MF) category
is shown in yellow, and the Cellular Component (CC) category is shown in magenta. See Table S5 for
complete GO term analysis of downregulated genes.
Insects 2022, 13, 490 12 of 21
Figure 5. Motif associated with downregulated genes. De novo motif finding identified 1 motif (Mo-
tif D1) that is enriched in the upstream sequences of the induced genes. The consensus motif is
represented as a sequence logo (A, top). Motif matching identifies Motif D1 as being significantly
similar to the Hr3 binding site (A). (B) Box-whisker plot showing the distribution of rank-products
for the downregulated genes with and without Motif D1. The presence of Motif D1 does not impact
the rank-product distribution.
4. Discussion
Our findings have uncovered a subset of Drosophila melanogaster genes whose expres-
sion is altered following infection by a range of pathogens. These genes are likely mediat-
ing known responses to infection, for instance we find that known immune response
genes predominate among the induced genes on our list. Many of the downregulated
genes we identified are linked to metabolism, feeding, development, and reproduction;
processes which are all altered following infection. Additionally, our analysis has uncov-
ered a putative transcriptional mechanism that regulates gene expression following infec-
tion with diverse pathogens.
Figure 5.
Motif associated with downregulated genes. De novo motif finding identified 1 motif
(Motif D1) that is enriched in the upstream sequences of the induced genes. The consensus motif is
represented as a sequence logo (
A
, top). Motif matching identifies Motif D1 as being significantly
similar to the Hr3 binding site (
A
). (
B
) Box-whisker plot showing the distribution of rank-products
for the downregulated genes with and without Motif D1. The presence of Motif D1 does not impact
the rank-product distribution.
Insects 2022,13, 490 12 of 19
4. Discussion
Our findings have uncovered a subset of Drosophila melanogaster genes whose expres-
sion is altered following infection by a range of pathogens. These genes are likely mediating
known responses to infection, for instance we find that known immune response genes
predominate among the induced genes on our list. Many of the downregulated genes we
identified are linked to metabolism, feeding, development, and reproduction; processes
which are all altered following infection. Additionally, our analysis has uncovered a pu-
tative transcriptional mechanism that regulates gene expression following infection with
diverse pathogens.
In the absence of
in vivo
experimental data, we are unable to draw conclusions about
the roles of the genes we’ve identified, however our bioinformatic and meta-analyses do
allow us to generate interesting hypotheses for future testing. We believe that our lists of
induced and downregulated genes can provide insight in host immunity, particularly due
to the presence of genes with experimentally defined functions that align with observed
immune response mechanisms as highlighted below; however, these roles need to be
empirically tested in future studies. Hopefully our analyses have provided an interesting
list of candidate genes whose study can begin to unravel important immune mechanisms.
Intriguingly, we find that many specific immune effector encoding genes are induced
following infection by diverse pathogens. For instance, antimicrobial and AMP-like pep-
tides are not known to play a role in the antiparasite immune response, and yet are induced
following parasitoid wasp infection (Table 4) [
19
,
133
,
134
]. A possible model to explain
this observation is that the Drosophila genome encodes a conserved set of immune effector
genes that act against all types of pathogen infection. This model is unlikely given the long
history of findings suggesting that specific immune effectors are used to target distinct
pathogens [
24
,
135
]. Additionally, multiple studies have demonstrated that selection for flies
with resistance to a particular pathogen does not translate to cross-resistance to additional
pathogens [
136
,
137
]. Specifically, flies selected for resistance to the parasitoid Asobara tabida
do not show increased resistance to bacterial or fungal pathogens [
136
], and fly lines se-
lected for resistance to the bacterial pathogen Bacillus cereus do not display cross-resistance
to Sigma virus [137].
Instead, our findings may suggest a second model in which sensing pathogen infection
leads to the induction of genes that play a role in surveillance and resistance to possible
coinfecting pathogens. Coinfections are commonly observed in natural populations of
various species [
138
140
]. It has been demonstrated that coinfection can lead to decreased
host resistance in nature and laboratory experiments [
140
142
], and negatively impact
host health [
139
,
143
]. These previous findings suggest that avoidance of coinfection would
increase host fitness, and support a model in which infection by any pathogen may provoke
a generalized prophylactic response against coinfection alongside the specific response
to the primary pathogen. Indeed, we have identified a large number of induced genes
that encode immune recognition proteins and regulators of the Toll, IMD, and JAK-STAT
immune signaling pathways, along with immune effectors that target distinct classes of
pathogens. These pathways are among the main immune response pathways in Drosophila,
and in combination with the breadth of induced immune effectors, our findings suggest
that infected flies are primed to respond to the possibility of coinfection.
This model is also supported by previous findings. For instance, AMP expression is
seen at early time points following parasitoid infection [
19
,
133
], but little to no expression
of antimicrobial immune effectors is observed at late time points following parasitoid
infection [
134
]. These findings may make sense in the light of the coinfection prevention
model. In nature, Drosophila larvae are found in the microbe-rich environment of decay-
ing fruit [
144
]. Parasitoid infection of Drosophila larvae results in the wasp ovipositor
puncturing a hole in the larval cuticle; this wound will be healed, however the healing
of epidermal wounds can take several hours [
145
,
146
]. Immediately following parasitoid
infection, and before healing is complete, the wound can therefore provide a readily avail-
able infection route for environmental microorganisms. The expression of antimicrobial
Insects 2022,13, 490 13 of 19
factors and surveillance for any surviving microbes may therefore play an important role
in pre-empting this possible route of coinfection.
Additional support for this model is provided by an in-depth time course study of the
transcriptional response to IMD pathway activation [
147
]. In this study, stimulation of the
IMD pathway resulted in the expression of Toll pathway regulated genes, including Bom
family genes, and stress response genes, including TotM, all of which we also identified in
our meta-analysis. Interestingly, the high resolution time course provided by this study
illustrates that the Toll and stress response genes were induced as part of an early and
transient response to IMD pathway stimulation, in contrast to the sustained transcription
of known IMD responsive genes [
147
]. This pattern would fit with our expectations under
a coinfection prevention model, in which immune stimulation simultaneously triggers a
specific response against the identified pathogen (IMD pathway genes) and leads to the
production of a temporary prophylactic state (Toll and stress response genes) to guard
against possible coinfection.
Using de novo motif finding analysis, we identified putative binding sites for NF
κ
B
and GATA family transcription factors. The degree of gene induction in our meta-analysis
correlates with the presence of these factors, and suggests a possible synergistic relationship
between them. NF
κ
B and GATA factor activity have been linked to immune responses,
and have been previously demonstrated to work in concert to promote immune gene
expression [
148
], supporting our idea that these factors may be underlying the response to
infection by diverse pathogens. The Drosophila genome encodes three NF
κ
B family genes
(Dif,dl,Rel), all of which have been previously linked to immunity, and 5 GATA factors
(pnr,srp,grn,dGATAd,dGATAe) of which srp and dGATAe have been previously linked
to immune responses [
89
,
114
116
,
149
]. The difficulty in distinguishing between paralog-
specific binding site motifs within these families leaves us unable to speculate whether the
response is driven by a particular family member, or whether multiple members may play a
role. Cross-regulation of gene expression has been observed between the NF
κ
B-dependent
Toll (Dif and dl dependent) and IMD (Rel dependent) pathways [
150
], perhaps suggesting
some redundancy between NF
κ
B factors. In order to build a model of transcriptional
regulation the role of individual factors must still be tested experimentally, and the results
may help in understanding the immune response to infection.
While downregulated genes are often overlooked in studies of gene expression, they
may still provide insight into the process being studied. Our meta-analysis identified
a small number of transcripts that are downregulated following infection by diverse
pathogens. The functions of these genes suggest that they may be playing a role in the
switch to an altered metabolic state following infection. Infected flies display altered feeding
behaviour, and prioritize using energy resources for immunity ahead of development or
reproduction [
120
,
121
,
127
,
151
]. Accordingly, we find that genes previously linked with
these processes are downregulated in infected flies. The further study of these genes
may shed light on the largely unknown mechanisms underlying the life history tradeoffs
induced by pathogen infection.
5. Conclusions
Our meta-analysis has identified 93 genes whose transcript levels are significantly al-
tered following infection by diverse pathogens. Analysis of the experimentally determined
and predicted functions of the proteins encoded by these genes suggests that they may
play a role in immune function, immune metabolism and infection induced life history
tradeoffs. Follow up studies on the roles of these genes following infection will be neces-
sary to verify their importance and will likely improve our understanding of conserved
immune functions.
Supplementary 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
Insects 2022,13, 490 14 of 19
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.
Author Contributions:
Conceptualization, N.T.M.; investigation, A.L.W., J.H., B.M.A., N.M.B.,
N.L., P.K., D.F. and N.T.M.; data curation, A.L.W.; writing—original draft preparation, N.T.M.;
writing—review
and editing, A.L.W., J.H., B.M.A., N.M.B., N.L., P.K. and D.F.; funding acquisition,
N.T.M. All authors have read and agreed to the published version of the manuscript.
Funding:
Research reported in this publication was supported by the National Institute Of General
Medical Sciences of the National Institutes of Health under Award Number R35GM133760 to N.T.M.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
All data generated by this project are included in the paper and
Supplementary Materials. The analyzed data are publicly available via accession numbers provided
in Table 1.
Acknowledgments:
We would like to acknowledge Alysia Vrailas-Mortimer and members of the
Mortimer Cellular Immunology Lab for discussion of results.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
References
1.
Danilova, N. The evolution of immune mechanisms. J. Exp. Zoöl. Part B Mol. Dev. Evol.
2006
,306, 496–520. [CrossRef] [PubMed]
2. Chaplin, D.D. Overview of the immune response. J. Allergy Clin. Immunol. 2010,125, S3–S23. [CrossRef] [PubMed]
3. Fairfax, B.P.; Knight, J.C. Genetics of gene expression in immunity to infection. Curr. Opin. Immunol. 2014,30, 63–71. [CrossRef]
[PubMed]
4.
Zhang, Q.; Cao, X. Epigenetic regulation of the innate immune response to infection. Nat. Rev. Immunol.
2019
,19, 417–432.
[CrossRef]
5.
Groitl, B.; Dahl, J.-U.; Schroeder, J.W.; Jakob, U. Pseudomonas aeruginosa defense systems against microbicidal oxidants. Mol.
Microbiol. 2017,106, 335–350. [CrossRef]
6.
Kar, S.; Mai, H.-J.; Khalouf, H.; Ben Abdallah, H.; Flachbart, S.; Fink-Straube, C.; Bräutigam, A.; Xiong, G.; Shang, L.;
Panda, S.K.; et al
. Comparative Transcriptomics of Lowland Rice Varieties Uncovers Novel Candidate Genes for Adaptive
Iron Excess Tolerance. Plant Cell Physiol. 2021,62, 624–640. [CrossRef]
7.
Nica, A.C.; Dermitzakis, E.T. Using gene expression to investigate the genetic basis of complex disorders. Hum. Mol. Genet.
2008
,
17, R129–R134. [CrossRef]
8.
Petruccelli, E.; Brown, T.; Waterman, A.; Ledru, N.; Kaun, K.R. Alcohol Causes Lasting Differential Transcription in Drosophila
Mushroom Body Neurons. Genetics 2020,215, 103–116. [CrossRef]
9.
Scheid, A.D.; Van Keulen, V.P.; Felts, S.J.; Neier, S.C.; Middha, S.; Nair, A.A.; Techentin, R.W.; Gilbert, B.K.; Jen, J.;
Neuhauser, C.; et al
. Gene Expression Signatures Characterized by Longitudinal Stability and Interindividual Variability
Delineate Baseline Phenotypic Groups with Distinct Responses to Immune Stimulation. J. Immunol.
2018
,200, 1917–1928.
[CrossRef]
10.
Mola, S.; Foisy, S.; Boucher, G.; Major, F.; Beauchamp, C.; Karaky, M.; Goyette, P.; Lesage, S.; Rioux, J.D. A transcriptome-
based approach to identify functional modules within and across primary human immune cells. PLoS ONE
2020
,15, e0233543.
[CrossRef]
11.
Ding, P.; Ngou, B.P.M.; Furzer, O.J.; Sakai, T.; Shrestha, R.K.; MacLean, D.; Jones, J.D.G. High-resolution expression profiling of
selected gene sets during plant immune activation. Plant Biotechnol. J. 2020,18, 1610–1619. [CrossRef] [PubMed]
12.
Edgerton, E.B.; McCrea, A.R.; Berry, C.T.; Kwok, J.Y.; Thompson, L.K.; Watson, B.; Fuller, E.M.; Nolan, T.J.; Lok, J.B.; Povelones, M.
Activation of mosquito immunity blocks the development of transmission-stage filarial nematodes. Proc. Natl. Acad. Sci. USA
2020,117, 3711–3717. [CrossRef] [PubMed]
13.
Brutscher, L.M.; Daughenbaugh, K.F.; Flenniken, M.L. Virus and dsRNA-triggered transcriptional responses reveal key compo-
nents of honey bee antiviral defense. Sci. Rep. 2017,7, 1–15. [CrossRef]
14.
Yang, X.; Fors, L.; Slotte, T.; Theopold, U.; Binzer-Panchal, M.; Wheat, C.W.; Hambäck, P.A. Differential Expression of Immune
Genes between Two Closely Related Beetle Species with Different Immunocompetence following Attack by Asecodes parviclava.
Genome Biol. Evol. 2020,12, 522–534. [CrossRef] [PubMed]
Insects 2022,13, 490 15 of 19
15.
Roesel, C.L.; Vollmer, S.V. Differential gene expression analysis of symbiotic and aposymbiotic Exaiptasia anemones under
immune challenge with Vibrio coralliilyticus. Ecol. Evol. 2019,9, 8279–8293. [CrossRef]
16.
Byadgi, O.; Chen, Y.-C.; Maekawa, S.; Wang, P.-C.; Chen, S.-C. Immune-Related Functional Differential Gene Expression in Koi
Carp (Cyprinus carpio) after Challenge with Aeromonas sobria.Int. J. Mol. Sci. 2018,19, 2107. [CrossRef] [PubMed]
17.
Robledo, D.; Taggart, J.B.; Ireland, J.H.; McAndrew, B.J.; Starkey, W.G.; Haley, C.S.; Hamilton, A.; Guy, D.R.; Mota-Velasco, J.C.;
Gheyas, A.A.; et al. Gene expression comparison of resistant and susceptible Atlantic salmon fry challenged with Infectious
Pancreatic Necrosis virus reveals a marked contrast in immune response. BMC Genom. 2016,17, 279. [CrossRef]
18.
De Gregorio, E.; Spellman, P.T.; Tzou, P.; Rubin, G.; Lemaitre, B. The Toll and Imd pathways are the major regulators of the
immune response in Drosophila. EMBO J. 2002,21, 2568–2579. [CrossRef]
19.
Schlenke, T.A.; Morales, J.; Govind, S.; Clark, A. Contrasting Infection Strategies in Generalist and Specialist Wasp Parasitoids of
Drosophila melanogaster.PLoS Pathog. 2007,3, e158. [CrossRef]
20.
Brennan, C.A.; Anderson, K.V. Drosophila: The Genetics of Innate Immune Recognition and Response. Annu. Rev. Immunol.
2004
,
22, 457–483. [CrossRef]
21.
Crum-Cianflone, N.F. Bacterial, Fungal, Parasitic, and Viral Myositis. Clin. Microbiol. Rev.
2008
,21, 473–494. [CrossRef] [PubMed]
22.
Lemaitre, B.; Hoffmann, J. The Host Defense of Drosophila melanogaster.Annu. Rev. Immunol.
2007
,25, 697–743. [CrossRef]
[PubMed]
23.
Gordon, S.; Plüddemann, A.; Estrada, F.M. Macrophage heterogeneity in tissues: Phenotypic diversity and functions. Immunol.
Rev. 2014,262, 36–55. [CrossRef] [PubMed]
24.
Lemaitre, B.; Reichhart, J.-M.; Hoffmann, J.A. Drosophila host defense: Differential induction of antimicrobial peptide genes after
infection by various classes of microorganisms. Proc. Natl. Acad. Sci. USA 1997,94, 14614–14619. [CrossRef] [PubMed]
25.
Koonin, E.V. Evolution of RNA- and DNA-guided antivirus defense systems in prokaryotes and eukaryotes: Common ancestry
vs. convergence. Biol. Direct 2017,12, 5. [CrossRef]
26. Weber, F. Antiviral Innate Immunity: Introduction. Encycl. Virol. 2020, 577–583. [CrossRef]
27.
Beutler, B.; Jiang, Z.; Georgel, P.; Crozat, K.; Croker, B.; Rutschmann, S.; Du, X.; Hoebe, K. GENETIC ANALYSIS OF HOST
RESISTANCE: Toll-Like Receptor Signaling and Immunity at Large. Annu. Rev. Immunol. 2006,24, 353–389. [CrossRef]
28.
Lemaitre, B.; Nicolas, E.; Michaut, L.; Reichhart, J.-M.; Hoffmann, J.A. The Dorsoventral Regulatory Gene Cassette spät-
zle/Toll/cactus Controls the Potent Antifungal Response in Drosophila Adults. Cell 1996,86, 973–983. [CrossRef]
29. Medzhitov, R. Toll-like receptors and innate immunity. Nat. Rev. Immunol. 2001,1, 135–145. [CrossRef]
30.
O’Shea, J.J.; Plenge, R. JAK and STAT Signaling Molecules in Immunoregulation and Immune-Mediated Disease. Immunity
2012
,
36, 542–550. [CrossRef]
31.
Hoffmann, J.A.; Kafatos, F.C.; Janeway, C.A.; Ezekowitz, R.A.B. Phylogenetic Perspectives in Innate Immunity. Science
1999
,284,
1313–1318. [CrossRef] [PubMed]
32.
Howell, L.; Sampson, C.J.; Xavier, M.J.; Bolukbasi, E.; Heck, M.M.S.; Williams, M.J. A directed miniscreen for genes involved in
the Drosophila anti-parasitoid immune response. Immunogenetics 2011,64, 155–161. [CrossRef] [PubMed]
33.
Stroschein-Stevenson, S.L.; Foley, E.; O’Farrell, P.H.; Johnson, A.D. Identification of Drosophila Gene Products Required for
Phagocytosis of Candida albicans. PLoS Biol. 2005,4, e4. [CrossRef] [PubMed]
34.
De Gregorio, E.; Spellman, P.T.; Rubin, G.M.; Lemaitre, B. Genome-wide analysis of the Drosophila immune response by using
oligonucleotide microarrays. Proc. Natl. Acad. Sci. USA 2001,98, 12590–12595. [CrossRef] [PubMed]
35.
Felix, T.M.; Hughes, K.A.; Stone, E.A.; Drnevich, J.M.; Leips, J. Age-Specific Variation in Immune Response in Drosophila
melanogaster Has a Genetic Basis. Genetics 2012,191, 989–1002. [CrossRef]
36.
Pal, S.; Wu, J.; Wu, L.P. Microarray analyses reveal distinct roles for Rel proteins in the Drosophila immune response. Dev. Comp.
Immunol. 2008,32, 50–60. [CrossRef]
37.
Dostert, C.; Jouanguy, E.; Irving, P.; Troxler, L.; Galiana-Arnoux, D.; Hetru, C.; Hoffmann, J.A.; Imler, J.-L. The Jak-STAT signaling
pathway is required but not sufficient for the antiviral response of drosophila. Nat. Immunol. 2005,6, 946–953. [CrossRef]
38.
Huang, Z.; Kingsolver, M.B.; Avadhanula, V.; Hardy, R.W. An Antiviral Role for Antimicrobial Peptides during the Arthropod
Response to Alphavirus Replication. J. Virol. 2013,87, 4272–4280. [CrossRef]
39.
Kemp, C.; Mueller, S.; Goto, A.; Barbier, V.; Paro, S.; Bonnay, F.; Dostert, C.; Troxler, L.; Hetru, C.; Meignin, C.; et al. Broad RNA
Interference–Mediated Antiviral Immunity and Virus-Specific Inducible Responses in Drosophila.J. Immunol.
2012
,190, 650–658.
[CrossRef]
40.
Lee, M.J.; Mondal, A.; Small, C.; Paddibhatla, I.; Kawaguchi, A.; Govind, S. A database for the analysis of immunity genes in
Drosophila. Fly 2011,5, 155–161. [CrossRef]
41.
Toro-Domínguez, D.; García, J.A.V.; Martorell-Marugán, J.; Román-Montoya, Y.; Alarcón-Riquelme, M.E.; Carmona-Sáez, P. A
survey of gene expression meta-analysis: Methods and applications. Brief. Bioinform. 2020,22, 1694–1705. [CrossRef] [PubMed]
42.
Larkin, A.; Marygold, S.J.; Antonazzo, G.; Attrill, H.; dos Santos, G.; Garapati, P.V.; Goodman, J.L.; Gramates, L.S.; Millburn,
G.; Strelets, V.B.; et al. FlyBase: Updates to the Drosophila melanogaster knowledge base. Nucleic Acids Res.
2020
,49, D899–D907.
[CrossRef] [PubMed]
43.
R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna,
Austria, 2021.
Insects 2022,13, 490 16 of 19
44.
Del Carratore, F.; Jankevics, A.; Eisinga, R.; Heskes, T.; Hong, F.; Breitling, R. RankProd 2.0: A refactored bioconductor package
for detecting differentially expressed features in molecular profiling datasets. Bioinformatics
2017
,33, 2774–2775. [CrossRef]
[PubMed]
45.
Hong, F.; Breitling, R.; McEntee, C.W.; Wittner, B.S.; Nemhauser, J.L.; Chory, J. RankProd: A bioconductor package for detecting
differentially expressed genes in meta-analysis. Bioinformatics 2006,22, 2825–2827. [CrossRef]
46.
Obregón, F.P.; Soto, P.; Lavín, J.L.; Cortazar, A.R.; Barrio, R.; Aransay, A.M.; Cantera, R. Cluster Locator, online analysis and
visualization of gene clustering. Bioinformatics 2018,34, 3377–3379. [CrossRef]
47.
Piipari, M.; Down, T.A.; Saini, H.; Enright, A.; Hubbard, T.J. iMotifs: An integrated sequence motif visualization and analysis
environment. Bioinformatics 2010,26, 843–844. [CrossRef]
48.
Ryan, S.M.; Wildman, K.; Oceguera-Perez, B.; Barbee, S.; Mortimer, N.T.; Vrailas-Mortimer, A.D. Evolutionarily conserved
transcription factors drive the oxidative stress response in Drosophila. J. Exp. Biol. 2020,223, jeb221622. [CrossRef]
49.
Grant, C.E.; Bailey, T.L.; Noble, W.S. FIMO: Scanning for occurrences of a given motif. Bioinformatics
2011
,27, 1017–1018.
[CrossRef]
50. Bailey, T.L.; Johnson, J.; Grant, C.E.; Noble, W.S. The MEME Suite. Nucleic Acids Res. 2015,43, W39–W49. [CrossRef]
51.
Gupta, S.; Stamatoyannopoulos, J.A.; Bailey, T.L.; Noble, W.S. Quantifying similarity between motifs. Genome Biol.
2007
,8, R24.
[CrossRef]
52.
Shazman, S.; Lee, H.; Socol, Y.; Mann, R.S.; Honig, B. OnTheFly: A database of Drosophila melanogaster transcription factors and
their binding sites. Nucleic Acids Res. 2013,42, D167–D171. [CrossRef] [PubMed]
53. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2009; ISBN 978-0-387-98141-3.
54.
Breitling, R.; Armengaud, P.; Amtmann, A.; Herzyk, P. Rank products: A simple, yet powerful, new method to detect differentially
regulated genes in replicated microarray experiments. FEBS Lett. 2004,573, 83–92. [CrossRef] [PubMed]
55.
Riddle, N.C.; Elgin, S.C.R. The Drosophila Dot Chromosome: Where Genes Flourish Amidst Repeats. Genetics
2018
,210, 757–772.
[CrossRef]
56.
Kaufman, T.C. A Short History and Description of Drosophila melanogaster Classical Genetics: Chromosome Aberrations, Forward
Genetic Screens, and the Nature of Mutations. Genetics 2017,206, 665–689. [CrossRef] [PubMed]
57.
Hill-Burns, E.M.; Clark, A.G. X-Linked Variation in Immune Response in Drosophila melanogaster.Genetics
2009
,183, 1477–1491.
[CrossRef]
58.
Hedengren, M.; Borge, K.; Hultmark, D. Expression and Evolution of the Drosophila Attacin/Diptericin Gene Family. Biochem.
Biophys. Res. Commun. 2000,279, 574–581. [CrossRef] [PubMed]
59.
Wang, L.-N.; Yu, B.; Han, G.-Q.; Chen, D.-W. Molecular cloning, expression in Escherichia coli of Attacin A gene from Drosophila
and detection of biological activity. Mol. Biol. Rep. 2009,37, 2463–2469. [CrossRef]
60.
Lin, S.J.H.; Fulzele, A.; Cohen, L.B.; Bennett, E.J.; Wasserman, S.A. Bombardier Enables Delivery of Short-Form Bomanins in the
Drosophila Toll Response. Front. Immunol. 2020,10, 3040. [CrossRef]
61.
Clemmons, A.W.; Lindsay, S.A.; Wasserman, S.A. An Effector Peptide Family Required for Drosophila Toll-Mediated Immunity.
PLoS Pathog. 2015,11, e1004876. [CrossRef]
62.
Hanson, M.A.; Dostálová, A.; Ceroni, C.; Poidevin, M.; Kondo, S.; Lemaitre, B. Synergy and remarkable specificity of antimicrobial
peptides in vivo using a systematic knockout approach. eLife 2019,8, e44341. [CrossRef]
63.
Lindsay, S.A.; Lin, S.J.; Wasserman, S.A. Short-Form Bomanins Mediate Humoral Immunity in Drosophila. J. Innate Immun.
2018
,
10, 306–314. [CrossRef] [PubMed]
64.
Lemaitre, B.; Kromer-Metzger, E.; Michaut, L.; Nicolas, E.; Meister, M.; Georgel, P.; Reichhart, J.M.; Hoffmann, J.A. A recessive
mutation, immune deficiency (imd), defines two distinct control pathways in the Drosophila host defense. Proc. Natl. Acad. Sci.
USA 1995,92, 9465–9469. [CrossRef] [PubMed]
65.
Samakovlis, C.; Kimbrell, D.A.; Kylsten, P.; Engström, A.; Hultmark, D. The immune response in Drosophila: Pattern of cecropin
expression and biological activity. EMBO J. 1990,9, 2969–2976. [CrossRef] [PubMed]
66.
Tryselius, Y.; Samakovlis, C.; Kimbrell, D.A.; Hultmark, D. CecC, a cecropin gene expressed during metamorphosis in Drosophila
pupae. JBIC J. Biol. Inorg. Chem. 1992,204, 395–399. [CrossRef]
67.
Tzou, P.; Reichhart, J.-M.; Lemaitre, B. Constitutive expression of a single antimicrobial peptide can restore wild-type resistance to
infection in immunodeficient Drosophila mutants. Proc. Natl. Acad. Sci. USA 2002,99, 2152–2157. [CrossRef]
68. Wicker, C.; Reichhart, J.M.; Hoffmann, D.; Hultmark, D.; Samakovlis, C.; Hoffmann, J.A. Insect immunity. Characterization of a
Drosophila cDNA encoding a novel member of the diptericin family of immune peptides. J. Biol. Chem.
1990
,265, 22493–22498.
[CrossRef]
69.
Lee, J.H.; Cho, K.S.; Lee, J.; Yoo, J.; Lee, J.; Chung, J. Diptericin-like protein: An immune response gene regulated by the
anti-bacterial gene induction pathway in Drosophila. Gene 2001,271, 233–238. [CrossRef]
70.
Chiu, H.; Ring, B.C.; Sorrentino, R.P.; Kalamarz, M.; Garza, D.; Govind, S. dUbc9 negatively regulates the Toll-NF-
κ
B pathways in
larval hematopoiesis and drosomycin activation in Drosophila. Dev. Biol. 2005,288, 60–72. [CrossRef]
71.
Zhang, Z.; Zhu, S. Functional role of charged residues in drosomycin, a Drosophila antifungal peptide. Dev. Comp. Immunol.
2010
,
34, 953–958. [CrossRef]
72.
Ayres, J.S.; Freitag, N.; Schneider, D.S. Identification of Drosophila Mutants Altering Defense of and Endurance to Listeria
monocytogenes Infection. Genetics 2008,178, 1807–1815. [CrossRef]
Insects 2022,13, 490 17 of 19
73.
Hanson, M.A.; Cohen, L.B.; Marra, A.; Iatsenko, I.; Wasserman, S.A.; Lemaitre, B. The Drosophila Baramicin polypeptide gene
protects against fungal infection. PLoS Pathog. 2021,17, e1009846. [CrossRef] [PubMed]
74.
Ha, E.-M.; Oh, C.-T.; Ryu, J.-H.; Bae, Y.-S.; Kang, S.-W.; Jang, I.-H.; Brey, P.T.; Lee, W.-J. An Antioxidant System Required for Host
Protection against Gut Infection in Drosophila. Dev. Cell 2005,8, 125–132. [CrossRef] [PubMed]
75.
Keebaugh, E.S.; Schlenke, T.A. Adaptive Evolution of a Novel Drosophila Lectin Induced by Parasitic Wasp Attack. Mol. Biol.
Evol. 2011,29, 565–577. [CrossRef] [PubMed]
76.
Goto, A.; Yano, T.; Terashima, J.; Iwashita, S.; Oshima, Y.; Kurata, S. Cooperative Regulation of the Induction of the Novel
Antibacterial Listericin by Peptidoglycan Recognition Protein LE and the JAK-STAT Pathway. J. Biol. Chem.
2010
,285, 15731–15738.
[CrossRef]
77.
Pal, S.; Leger, R.J.S.; Wu, L. Fungal Peptide Destruxin A Plays a Specific Role in Suppressing the Innate Immune Response in
Drosophila melanogaster. J. Biol. Chem. 2007,282, 8969–8977. [CrossRef]
78.
Brun, S.; Vidal, S.; Spellman, P.; Takahashi, K.; Tricoire, H.; Lemaitre, B. The MAPKKK Mekk1 regulates the expression of Turandot
stress genes in response to septic injury in Drosophila. Genes Cells 2006,11, 397–407. [CrossRef]
79.
Levashina, E.A.; Ohresser, S.; Bulet, P.; Reichhart, J.-M.; Hetru, C.; Hoffmann, J.A. Metchnikowin, a Novel Immune-Inducible
Proline-Rich Peptide from Drosophila with Antibacterial and Antifungal Properties. Eur. J. Biochem.
1995
,233, 694–700. [CrossRef]
80.
Levashina, E.; Ohresser, S.; Lemaitre, B.; Imler, J.-L. Two distinct pathways can control expression of the gene encoding the
Drosophila antimicrobial peptide metchnikowin. J. Mol. Biol. 1998,278, 515–527. [CrossRef]
81.
Levashina, E.A.; Langley, E.; Green, C.; Gubb, D.; Ashburner, M.; Hoffmann, J.A.; Reichhart, J.-M. Constitutive Activation of
Toll-Mediated Antifungal Defense in Serpin-Deficient Drosophila.Science 1999,285, 1917–1919. [CrossRef]
82.
Kurucz, É.; Markus, R.; Zsámboki, J.; Folkl-Medzihradszky, K.; Darula, Z.; Vilmos, P.; Udvardy, A.; Krausz, I.; Lukacsovich, T.;
Gateff, E.; et al. Nimrod, a Putative Phagocytosis Receptor with EGF Repeats in Drosophila Plasmatocytes. Curr. Biol.
2007
,17,
649–654. [CrossRef]
83.
Somogyi, K.; Sipos, B.; Pénzes, Z.; Andó, I. A conserved gene cluster as a putative functional unit in insect innate immunity. FEBS
Lett. 2010,584, 4375–4378. [CrossRef] [PubMed]
84.
Buchon, N.; Poidevin, M.; Kwon, H.-M.; Guillou, A.; Sottas, V.; Lee, B.-L.; Lemaitre, B. A single modular serine protease integrates
signals from pattern-recognition receptors upstream of the Drosophila Toll pathway. Proc. Natl. Acad. Sci. USA
2009
,106,
12442–12447. [CrossRef] [PubMed]
85.
Gottar, M.; Gobert, V.; Michel, T.; Belvin, M.; Duyk, G.; Hoffmann, J.A.; Ferrandon, D.; Royet, J. The Drosophila immune response
against Gram-negative bacteria is mediated by a peptidoglycan recognition protein. Nat. Cell Biol.
2002
,416, 640–644. [CrossRef]
[PubMed]
86.
Mellroth, P.; Steiner, H. PGRP-SB1: An N-acetylmuramoyl l-alanine amidase with antibacterial activity. Biochem. Biophys. Res.
Commun. 2006,350, 994–999. [CrossRef]
87.
Zaidman-Rémy, A.; Poidevin, M.; Hervé, M.; Welchman, D.P.; Paredes, J.C.; Fahlander, C.; Steiner, H.; Mengin-Lecreulx, D.;
Lemaitre, B. Drosophila Immunity: Analysis of PGRP-SB1 Expression, Enzymatic Activity and Function. PLoS ONE
2011
,
6, e17231. [CrossRef]
88.
Bischoff, V.; Vignal, C.; Boneca, I.G.; Michel, T.; Hoffmann, J.A.; Royet, J. Function of the drosophila pattern-recognition receptor
PGRP-SD in the detection of Gram-positive bacteria. Nat. Immunol. 2004,5, 1175–1180. [CrossRef]
89.
Dushay, M.S.; Asling, B.; Hultmark, D. Origins of immunity: Relish, a compound Rel-like gene in the antibacterial defense of
Drosophila. Proc. Natl. Acad. Sci. USA 1996,93, 10343–10347. [CrossRef]
90.
Hedengren, M.; Bengtåsling, B.; Dushay, M.S.; Ando, I.; Ekengren, S.; Wihlborg, M.; Hultmark, D. Relish, a Central Factor in the
Control of Humoral but Not Cellular Immunity in Drosophila. Mol. Cell 1999,4, 827–837. [CrossRef]
91.
Seong, C.-S.; Varela-Ramirez, A.; Tang, X.; Anchondo, B.; Magallanes, D.; Aguilera, R.J. Cloning and Characterization of a Novel
Drosophila Stress Induced DNase. PLoS ONE 2014,9, e103564. [CrossRef]
92.
Castillejo-López, C.; Häcker, U. The serine protease Sp7 is expressed in blood cells and regulates the melanization reaction in
Drosophila. Biochem. Biophys. Res. Commun. 2005,338, 1075–1082. [CrossRef]
93.
Dudzic, J.P.; Hanson, M.A.; Iatsenko, I.; Kondo, S.; Lemaitre, B. More Than Black or White: Melanization and Toll Share Regulatory
Serine Proteases in Drosophila. Cell Rep. 2019,27, 1050–1061.e3. [CrossRef] [PubMed]
94.
Kambris, Z.; Brun, S.; Jang, I.-H.; Nam, H.-J.; Romeo, Y.; Takahashi, K.; Lee, W.-J.; Ueda, R.; Lemaitre, B. Drosophila Immunity:
A Large-Scale In Vivo RNAi Screen Identifies Five Serine Proteases Required for Toll Activation. Curr. Biol.
2006
,16, 808–813.
[CrossRef] [PubMed]
95.
Jang, I.-H.; Chosa, N.; Kim, S.-H.; Nam, H.-J.; Lemaitre, B.; Ochiai, M.; Kambris, Z.; Brun, S.; Hashimoto, C.; Ashida, M.; et al.
A Spätzle-Processing Enzyme Required for Toll Signaling Activation in Drosophila Innate Immunity. Dev. Cell
2006
,10, 45–55.
[CrossRef] [PubMed]
96.
Dostálová, A.; Rommelaere, S.; Poidevin, M.; Lemaitre, B. Thioester-containing proteins regulate the Toll pathway and play a role
in Drosophila defence against microbial pathogens and parasitoid wasps. BMC Biol. 2017,15, 79. [CrossRef] [PubMed]
97.
Boutros, M.; Agaisse, H.; Perrimon, N. Sequential Activation of Signaling Pathways during Innate Immune Responses in
Drosophila. Dev. Cell 2002,3, 711–722. [CrossRef]
Insects 2022,13, 490 18 of 19
98.
Zhong, W.; McClure, C.D.; Evans, C.R.; Mlynski, D.T.; Immonen, E.; Ritchie, M.G.; Priest, N.K. Immune anticipation of mating
in Drosophila:Turandot M promotes immunity against sexually transmitted fungal infections. Proc. R. Soc. B Boil. Sci.
2013
,
280, 20132018. [CrossRef]
99.
Wang, Z.; Berkey, C.D.; Watnick, P.I. The Drosophila Protein Mustard Tailors the Innate Immune Response Activated by the
Immune Deficiency Pathway. J. Immunol. 2012,188, 3993–4000. [CrossRef]
100.
Irving, P.; Troxler, L.; Heuer, T.S.; Belvin, M.; Kopczynski, C.; Reichhart, J.-M.; Hoffmann, J.A.; Hetru, C. A genome-wide analysis
of immune responses in Drosophila.Proc. Natl. Acad. Sci. USA 2001,98, 15119–15124. [CrossRef]
101.
Hanson, M.A.; Lemaitre, B.; Unckless, R.L. Dynamic Evolution of Antimicrobial Peptides Underscores Trade-Offs Between
Immunity and Ecological Fitness. Front. Immunol. 2019,10, 2620. [CrossRef]
102.
Cao, X.; Jiang, H. Building a platform for predicting functions of serine protease-related proteins in Drosophila melanogaster and
other insects. Insect Biochem. Mol. Biol. 2018,103, 53–69. [CrossRef]
103.
Ligoxygakis, P.; Pelte, N.; Hoffmann, J.A.; Reichhart, J.-M. Activation of Drosophila Toll During Fungal Infection by a Blood Serine
Protease. Science 2002,297, 114–116. [CrossRef]
104.
Patrnogic, J.; Leclerc, V. The serine protease homolog spheroide is involved in sensing of pathogenic Gram-positive bacteria. PLoS
ONE 2017,12, e0188339. [CrossRef]
105.
Kr, P.; Lee, J.; Mortimer, N.T. The S1A Protease Family Members CG10764 and CG4793 Regulate Cellular Immunity in Dro-sophila.
Micropublication Biol. 2021,2021. [CrossRef]
106. Hayden, M.S.; Ghosh, S. NF-κB in immunobiology. Cell Res. 2011,21, 223–244. [CrossRef]
107.
Hogan, P.G. Calcium–NFAT transcriptional signalling in T cell activation and T cell exhaustion. Cell Calcium
2017
,63, 66–69.
[CrossRef]
108.
Villarino, A.; Kanno, Y.; Ferdinand, J.R.; O’Shea, J.J. Mechanisms of Jak/STAT Signaling in Immunity and Disease. J. Immunol.
2014,194, 21–27. [CrossRef]
109.
Gallant, S.; Gilkeson, G. ETS transcription factors and regulation of immunity. Arch. Immunol. Ther. Exp.
2006
,54, 149–163.
[CrossRef]
110.
Copley, R.R.; Totrov, M.; Linnell, J.; Field, S.; Ragoussis, J.; Udalova, I.A. Functional conservation of Rel binding sites in drosophilid
genomes. Genome Res. 2007,17, 1327–1335. [CrossRef]
111.
Immarigeon, C.; Bernat-Fabre, S.; Guillou, E.; Verger, A.; Prince, E.; Benmedjahed, M.A.; Payet, A.; Couralet, M.; Monte, D.;
Villeret, V.; et al. Mediator complex subunit Med19 binds directly GATA transcription factors and is required with Med1 for
GATA-driven gene regulation in vivo. J. Biol. Chem. 2020,295, 13617–13629. [CrossRef]
112.
Juven-Gershon, T.; Hsu, J.-Y.; Theisen, J.; Kadonaga, J.T. The RNA polymerase II core promoter—The gateway to transcription.
Curr. Opin. Cell Biol. 2008,20, 253–259. [CrossRef]
113.
Ngoc, L.V.; Kassavetis, G.A.; Kadonaga, J.T. The RNA Polymerase II Core Promoter in Drosophila.Genetics
2019
,212, 13–24.
[CrossRef] [PubMed]
114.
Reichhart, J.M.; Georgel, P.; Meister, M.; Lemaitre, B.; Kappler, C.; Hoffmann, J.A. Expression and nuclear translocation of the
rel/NF-kappa B-related morphogen dorsal during the immune response of Drosophila. C R Acad. Sci. III
1993
,316, 1218–1224.
[PubMed]
115.
Petersen, U.; Björklund, G.; Ip, Y.; Engström, Y. The dorsal-related immunity factor, Dif, is a sequence-specific trans-activator of
Drosophila Cecropin gene expression. EMBO J. 1995,14, 3146–3158. [CrossRef] [PubMed]
116.
Petersen, U.-M.; Kadalayil, L.; Rehorn, K.-P.; Hoshizaki, D.K.; Reuter, R.; Engström, Y. Serpent regulates Drosophila immunity
genes in the larval fat body through an essential GATA motif. EMBO J. 1999,18, 4013–4022. [CrossRef]
117.
Clark, R.I.; Tan, S.W.; Péan, C.B.; Roostalu, U.; Vivancos, V.; Bronda, K.; Pilátová, M.; Fu, J.; Walker, D.W.; Berdeaux, R.; et al.
MEF2 Is an In Vivo Immune-Metabolic Switch. Cell 2013,155, 435–447. [CrossRef]
118.
Bajgar, A.; Kucerova, K.; Jonatova, L.; Tomcala, A.; Schneedorferova, I.; Okrouhlik, J.; Dolezal, T. Extracellular Adenosine
Mediates a Systemic Metabolic Switch during Immune Response. PLoS Biol. 2015,13, e1002135. [CrossRef]
119.
Mihajlovic, Z.; Tanasic, D.; Bajgar, A.; Perez-Gomez, R.; Steffal, P.; Krejci, A. Lime is a new protein linking immunity and
metabolism in Drosophila. Dev. Biol. 2019,452, 83–94. [CrossRef]
120.
DiAngelo, J.R.; Bland, M.L.; Bambina, S.; Cherry, S.; Birnbaum, M.J. The immune response attenuates growth and nutrient storage
in Drosophila by reducing insulin signaling. Proc. Natl. Acad. Sci. USA 2009,106, 20853–20858. [CrossRef]
121.
Schwenke, R.A.; Lazzaro, B.P.; Wolfner, M.F. Reproduction–Immunity Trade-Offs in Insects. Annu. Rev. Èntomol.
2016
,61, 239–256.
[CrossRef]
122.
Martínez, B.A.; Hoyle, R.G.; Yeudall, S.; Granade, M.E.; Harris, T.E.; Castle, J.D.; Leitinger, N.; Bland, M.L. Innate immune
signaling in Drosophila shifts anabolic lipid metabolism from triglyceride storage to phospholipid synthesis to support immune
function. PLoS Genet. 2020,16, e1009192. [CrossRef]
123. Hart, B. Biological basis of the behavior of sick animals. Neurosci. Biobehav. Rev. 1988,12, 123–137. [CrossRef]
124.
Murray, M.J.; Murray, A.B. Anorexia of infection as a mechanism of host defense. Am. J. Clin. Nutr.
1979
,32, 593–596. [CrossRef]
[PubMed]
125.
Adamo, S.A. Parasitic suppression of feeding in the tobacco hornworm, Manduca sexta: Parallels with feeding depression after
an immune challenge. Arch. Insect Biochem. Physiol. 2005,60, 185–197. [CrossRef] [PubMed]
Insects 2022,13, 490 19 of 19
126.
Surendran, S.; Hückesfeld, S.; Wäschle, B.; Pankratz, M.J. Pathogen induced food evasion behavior in Drosophila larvae. J. Exp.
Biol. 2017,220, 1774–1780. [CrossRef] [PubMed]
127.
Ayres, J.S.; Schneider, D.S. The Role of Anorexia in Resistance and Tolerance to Infections in Drosophila. PLoS Biol.
2009
,
7, e1000150. [CrossRef]
128.
Sun, J.; Liu, C.; Bai, X.; Li, X.; Li, J.; Zhang, Z.; Zhang, Y.; Guo, J.; Li, Y. Drosophila FIT is a protein-specific satiety hormone
essential for feeding control. Nat. Commun. 2017,8, 14161. [CrossRef]
129.
Erkosar, B.; Kolly, S.; van der Meer, J.R.; Kawecki, T.J. Adaptation to Chronic Nutritional Stress Leads to Reduced Dependence on
Microbiota in Drosophila melanogaster.mBio 2017,8, e01496-17. [CrossRef]
130. Lemaitre, B.; Miguel-Aliaga, I. The Digestive Tract of Drosophila melanogaster.Annu. Rev. Genet. 2013,47, 377–404. [CrossRef]
131.
Wang, A.; Huen, S.C.; Luan, H.H.; Yu, S.; Zhang, C.; Gallezot, J.-D.; Booth, C.J.; Medzhitov, R. Opposing Effects of Fasting
Metabolism on Tissue Tolerance in Bacterial and Viral Inflammation. Cell 2016,166, 1512–1525.e12. [CrossRef]
132.
Howick, V.M.; Lazzaro, B.P. Genotype and diet shape resistance and tolerance across distinct phases of bacterial infection. BMC
Evol. Biol. 2014,14, 56. [CrossRef]
133.
Coustau, C.; Carton, Y.; Nappl, A.; Shotkoski, F.; Ffrench-Constant, R. Differential induction of antibacterial transcripts in
Drosophila susceptible and resistant to parasitism by Leptopilina boulardi.Insect Mol. Biol.
1996
,5, 167–172. [CrossRef] [PubMed]
134.
Nicolas, E.; Nappi, A.J.; Lemaitre, B. Expression of antimicrobial peptide genes after infection by parasitoid wasps in Drosophila.
Dev. Comp. Immunol. 1996,20, 175–181. [CrossRef]
135.
Leulier, F.; Parquet, C.; Pili-Floury, S.; Ryu, J.-H.; Caroff, M.; Lee, W.-J.; Mengin-Lecreulx, D.; Lemaitre, B. The Drosophila immune
system detects bacteria through specific peptidoglycan recognition. Nat. Immunol. 2003,4, 478–484. [CrossRef] [PubMed]
136.
Kraaijeveld, A.R.; Layen, S.J.; Futerman, P.H.; Godfray, H.C.J. Lack of Phenotypic and Evolutionary Cross-Resistance against
Parasitoids and Pathogens in Drosophila melanogaster. PLoS ONE 2012,7, e53002. [CrossRef]
137.
Bentz, M.L.; Humphrey, E.A.; Harshman, L.G.; Wayne, M.L. Sigma Virus (DMelSV) Incidence in Lines of Drosophila melanogaster
Selected for Survival following Infection with Bacillus cereus. Psyche A J. Èntomol. 2017,2017, e3593509. [CrossRef]
138.
Cattadori, I.; Boag, B.; Hudson, P. Parasite co-infection and interaction as drivers of host heterogeneity. Int. J. Parasitol.
2008
,38,
371–380. [CrossRef]
139.
Salam, N.; Mustafa, S.; Hafiz, A.; Chaudhary, A.A.; Deeba, F.; Parveen, S. Global prevalence and distribution of coinfection of
malaria, dengue and chikungunya: A systematic review. BMC Public 2018,18, 710. [CrossRef]
140.
Dieme, C.; Zmarlak, N.M.; Brito-Fravallo, E.; Travaillé, C.; Pain, A.; Cherrier, F.; Genève, C.; Alvarez, E.C.; Riehle, M.M.;
Vernick, K.D.; et al
. Exposure of Anopheles mosquitoes to trypanosomes reduces reproductive fitness and enhances susceptibility
to Plasmodium. PLoS Negl. Trop. Dis. 2020,14, e0008059. [CrossRef]
141.
Pokutnaya, D.; Molaei, G.; Weinberger, D.M.; Vossbrinck, C.R.; Diaz, A.J. Prevalence of Infection and Co-Infection and Presence
of Rickettsial Endosymbionts in Ixodes scapularis (Acari: Ixodidae) in Connecticut, USA. J. Parasitol.
2020
,106, 30–37. [CrossRef]
142.
Sheehan, G.; Tully, L.; Kavanagh, K.A. Candida albicans increases the pathogenicity of Staphylococcus aureus during polymicro-
bial infection of Galleria mellonella larvae. Microbiology 2020,166, 375–385. [CrossRef]
143. Diaz, J.H. Tickborne Coinfections in the United States. J. La. State Med. Soc. 2016,168, 44–53. [PubMed]
144. Markow, T.A. The secret lives of Drosophila flies. eLife 2015,4, e06793. [CrossRef] [PubMed]
145.
Small, C.; Paddibhatla, I.; Rajwani, R.; Govind, S. An Introduction to Parasitic Wasps of Drosophila and the Antiparasite Immune
Response. J. Vis. Exp. 2012,63, e3347. [CrossRef] [PubMed]
146.
Galko, M.J.; Krasnow, M.A. Cellular and Genetic Analysis of Wound Healing in Drosophila Larvae. PLoS Biol.
2004
,2, e239.
[CrossRef]
147.
Schlamp, F.; Delbare, S.Y.N.; Early, A.; Wells, M.T.; Basu, S.; Clark, A.G. Dense time-course gene expression profiling of the
Drosophila melanogaster innate immune response. BMC Genom. 2021,22, 304. [CrossRef]
148.
Senger, K.; Armstrong, G.W.; Rowell, W.; Kwan, J.M.; Markstein, M.; Levine, M. Immunity Regulatory DNAs Share Common
Organizational Features in Drosophila. Mol. Cell 2004,13, 19–32. [CrossRef]
149.
Senger, K.; Harris, K.; Levine, M. GATA factors participate in tissue-specific immune responses in Drosophila larvae. Proc. Natl.
Acad. Sci. USA 2006,103, 15957–15962. [CrossRef]
150.
Tanji, T.; Hu, X.; Weber, A.N.R.; Ip, Y.T. Toll and IMD Pathways Synergistically Activate an Innate Immune Response in Drosophila
melanogaster.Mol. Cell. Biol. 2007,27, 4578–4588. [CrossRef]
151.
Unckless, R.L.; Rottschaefer, S.M.; Lazzaro, B.P. The Complex Contributions of Genetics and Nutrition to Immunity in Drosophila
melanogaster. PLoS Genet. 2015,11, e1005030. [CrossRef]
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
The fruit fly Drosophila melanogaster combats microbial infection by producing a battery of effector peptides that are secreted into the haemolymph. Technical difficulties prevented the investigation of these short effector genes until the recent advent of the CRISPR/CAS era. As a consequence, many putative immune effectors remain to be formally described, and exactly how each of these effectors contribute to survival is not well characterized. Here we describe a novel Drosophila antifungal peptide gene that we name Baramicin A . We show that BaraA encodes a precursor protein cleaved into multiple peptides via furin cleavage sites. BaraA is strongly immune-induced in the fat body downstream of the Toll pathway, but also exhibits expression in other tissues. Importantly, we show that flies lacking BaraA are viable but susceptible to the entomopathogenic fungus Beauveria bassiana . Consistent with BaraA being directly antimicrobial, overexpression of BaraA promotes resistance to fungi and the IM10-like peptides produced by BaraA synergistically inhibit growth of fungi in vitro when combined with a membrane-disrupting antifungal. Surprisingly, BaraA mutant males but not females display an erect wing phenotype upon infection. Here, we characterize a new antifungal immune effector downstream of Toll signalling, and show it is a key contributor to the Drosophila antimicrobial response.
Article
Full-text available
Background Immune responses need to be initiated rapidly, and maintained as needed, to prevent establishment and growth of infections. At the same time, resources need to be balanced with other physiological processes. On the level of transcription, studies have shown that this balancing act is reflected in tight control of the initiation kinetics and shutdown dynamics of specific immune genes. Results To investigate genome-wide expression dynamics and trade-offs after infection at a high temporal resolution, we performed an RNA-seq time course on D. melanogaster with 20 time points post Imd stimulation. A combination of methods, including spline fitting, cluster analysis, and Granger causality inference, allowed detailed dissection of expression profiles, lead-lag interactions, and functional annotation of genes through guilt-by-association. We identified Imd-responsive genes and co-expressed, less well characterized genes, with an immediate-early response and sustained up-regulation up to 5 days after stimulation. In contrast, stress response and Toll-responsive genes, among which were Bomanins, demonstrated early and transient responses. We further observed a strong trade-off with metabolic genes, which strikingly recovered to pre-infection levels before the immune response was fully resolved. Conclusions This high-dimensional dataset enabled the comprehensive study of immune response dynamics through the parallel application of multiple temporal data analysis methods. The well annotated data set should also serve as a useful resource for further investigation of the D. melanogaster innate immune response, and for the development of methods for analysis of a post-stress transcriptional response time-series at whole-genome scale.
Article
Full-text available
In nature, Drosophila melanogaster larvae are infected by parasitoid wasps and mount a cellular immune response to this infection. Several conserved signaling pathways have been implicated in coordinating this response, however our understanding of the integration and regulation of these pathways is incomplete. Members of the S1A serine protease family have been previously linked to immune functions, and our findings suggest roles for two S1A family members, CG10764 and CG4793 in the cellular immune response to parasitoid infection.
Article
Full-text available
Iron (Fe) toxicity is a major challenge for plant cultivation in acidic water-logged soil environments, where lowland rice is a major staple food crop. Only few studies addressed the molecular characterization of excess Fe tolerance in rice, and these highlight different mechanisms for Fe tolerance. Out of 16 lowland rice varieties we identified a pair of contrasting lines, Fe-tolerant Lachit and -susceptible Hacha. The two lines differed in their physiological and morphological responses to excess Fe, including leaf growth, leaf rolling, reactive oxygen species generation, Fe and metal contents. These responses were likely of genetic origin as they were mirrored by differential gene expression patterns, obtained through RNA-sequencing, and corresponding GO term enrichment in tolerant versus susceptible lines. 35 genes of the metal homeostasis category, mainly root-expressed, showed differential transcriptomic profiles suggestive of an induced tolerance mechanism. 22 out of these 35 metal homeostasis genes were present in selection sweep genomic regions, in breeding signatures and/or differentiated during rice domestication. These findings suggest that Fe excess tolerance is an important trait in the domestication of lowland rice, and the identified genes may further serve to design targeted Fe tolerance breeding of rice crops.
Article
Full-text available
FlyBase (flybase.org) is an essential online database for researchers using Drosophila melanogaster as a model organism, facilitating access to a diverse array of information that includes genetic, molecular, genomic and reagent resources. Here, we describe the introduction of several new features at FlyBase, including Pathway Reports, paralog information, disease models based on orthology, customizable tables within reports and overview displays (‘ribbons’) of expression and disease data. We also describe a variety of recent important updates, including incorporation of a developmental proteome, upgrades to the GAL4 search tab, additional Experimental Tool Reports, migration to JBrowse for genome browsing and improvements to batch queries/downloads and the Fast-Track Your Paper tool.
Article
Full-text available
During infection, cellular resources are allocated toward the metabolically-demanding processes of synthesizing and secreting effector proteins that neutralize and kill invading pathogens. In Drosophila , these effectors are antimicrobial peptides (AMPs) that are produced in the fat body, an organ that also serves as a major lipid storage depot. Here we asked how activation of Toll signaling in the larval fat body perturbs lipid homeostasis to understand how cells meet the metabolic demands of the immune response. We find that genetic or physiological activation of fat body Toll signaling leads to a tissue-autonomous reduction in triglyceride storage that is paralleled by decreased transcript levels of the DGAT homolog midway , which carries out the final step of triglyceride synthesis. In contrast, Kennedy pathway enzymes that synthesize membrane phospholipids are induced. Mass spectrometry analysis revealed elevated levels of major phosphatidylcholine and phosphatidylethanolamine species in fat bodies with active Toll signaling. The ER stress mediator Xbp1 contributed to the Toll-dependent induction of Kennedy pathway enzymes, which was blunted by deleting AMP genes, thereby reducing secretory demand elicited by Toll activation. Consistent with ER stress induction, ER volume is expanded in fat body cells with active Toll signaling, as determined by transmission electron microscopy. A major functional consequence of reduced Kennedy pathway induction is an impaired immune response to bacterial infection. Our results establish that Toll signaling induces a shift in anabolic lipid metabolism to favor phospholipid synthesis and ER expansion that may serve the immediate demand for AMP synthesis and secretion but with the long-term consequence of insufficient nutrient storage.
Article
Full-text available
The evolutionarily conserved multiprotein Mediator complex (MED) serves as an interface between DNA-bound transcription factors (TFs) and the RNA Pol II machinery. It has been proposed that each TF interacts with a dedicated MED subunit to induce specific transcriptional responses. But are these binary partnerships sufficient to mediate TF functions? We have previously established that the Med1 Mediator subunit serves as a cofactor of GATA TFs in Drosophila, as shown in mammals. Here, we observe mutant phenotype similarities between another subunit, Med19, and the Drosophila GATA TF Pannier (Pnr), suggesting functional interaction. We further show that Med19 physically interacts with the Drosophila GATA TFs, Pnr and Serpent (Srp), in vivo and in vitro through their conserved C-zinc finger domains. Moreover, Med19 loss of function experiments in vivo or in cellulo indicate that it is required for Pnr- and Srp- dependent gene expression, suggesting general GATA cofactor functions. Interestingly, Med19 but not Med1 is critical for the regulation of all tested GATA target genes, implying shared or differential use of MED subunits by GATAs depending on the target gene. Lastly, we show a direct interaction between Med19 and Med1 by GST-pull-down experiments indicating privileged contacts between these two subunits of the MED middle module. Together, these findings identify Med19/Med1 as a composite GATA TF interface and suggest that binary MED subunit - TF partnerships are probably oversimplified models. We propose several mechanisms to account for the transcriptional regulation of GATAs-targeted genes.
Article
Full-text available
As organisms are constantly exposed to the damaging effects of oxidative stress through both environmental exposure as well as internal metabolic processes, they have evolved a variety of mechanisms to cope with this stress. One such mechanism is the highly conserved p38 MAPK (p38K) pathway, which is known to be to post-translationally activated in response to oxidative stress resulting in the activation of downstream antioxidant targets. However, little is known about the role of p38K transcriptional regulation in response to oxidative stress. Therefore, we analyzed the p38K gene family across the genus Drosophila to identify conserved regulatory elements. We find that oxidative stress exposure results in increased p38K protein levels in multiple Drosophila species and is associated with increased oxidative stress resistance. We also find that the p38Kb genomic locus includes conserved AP-1 and lola-PT transcription factor consensus sites. Accordingly, over-expression of these transcription factors in D. melanogaster is sufficient to induce transcription of p38Kb and enhances resistance to oxidative stress. We further find that the presence of a putative lola-PT binding site in the p38Kb locus of a given species is predictive of the species' survival in response to oxidative stress. Through our comparative genomics approach, we have identified biologically relevant putative transcription factor binding sites that regulate the expression of p38Kb and are associated with resistance to oxidative stress. These findings reveal a novel mode of regulation for p38K genes and suggests that transcription may play as important a role in p38K mediated stress responses as post-translational modifications.
Article
Full-text available
Genome-wide transcriptomic analyses have provided valuable insight into fundamental biology and disease pathophysiology. Many studies have taken advantage of the correlation in the expression patterns of the transcriptome to infer a potential biologic function of uncharacterized genes, and multiple groups have examined the relationship between co-expression, co-regulation, and gene function on a broader scale. Given the unique characteristics of immune cells circulating in the blood, we were interested in determining whether it was possible to identify functional co-expression modules in human immune cells. Specifically, we sequenced the transcriptome of nine immune cell types from peripheral blood cells of healthy donors and, using a combination of global and targeted analyses of genes within co-expression modules, we were able to determine functions for these modules that were cell lineage-specific or shared among multiple cell lineages. In addition, our analyses identified transcription factors likely important for immune cell lineage commitment and/or maintenance.
Article
Full-text available
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 anti-parasitoid immunity, there is sparse information on the evolutionary dynamics of biological pathways and gene regulation involved in host immune defence 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 were exhibited in both species. The main difference between species appear to be immune response and wound healing process mounted by G. pusilla larvae. Using reciprocal BLAST against the D. 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 signalling, 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 signalling and hematopoiesis genes as key players in host immunity in Galerucella against parasitoid wasps.