Comparison of the genomewide studies

The various genome-wide studies all used different assays and control treatments, statistical criteria for inclusions in gene lists, and a range of pathogens or parasites. These differences between assays also provided important insights. For example, in the bacterial and viral infection assays, the patho-genicity as well as the number of differentially expressed genes were markedly less when infection was induced by feeding, compared to injury with a septic needle (Irving et al., 2001; Roxström-Lindquist et al, 2004; Dostert et al, 2005). This marked difference suggested that the epithelial barrier was largely influential as a first line of defence, which has indeed been confirmed in other studies (reviewed in Lemaitre and Hoffmann, 2007).

A comparison of the differentially expressed genes across the various studies also illustrates the degree of specificity for Drosophila immune responses. Although some immunity genes change expression after most types of immune challenge (dorsal, Spätzle, Relish, IM2, attacin-A and -B, Metchnikowin, Jonah 25Bii, CG6687), the majority of genes are only reported as induced after one or few immune challenges (Table 12.2). For the PRRs, we find the expected specificity, but some overlap too: many different immune challenges induce the expression of PGRP-SA, which can activate the Toll pathway in response to Gram-positive bacteria, but not to fungi (Michel et al, 2001). The Gram-negative-binding proteins (GNBPs) appear to be induced after various types of microbial infection, while lectins are upregulated after infection with various macro-parasites. A large family of serine-protease-like proteins in the Drosophila genome (with catalytic site (serine proteases) or without (serine protease homologues, SPHs)) and their inhibitors (serpins) form proteolytic cascades. An extensive comparison of the 201 trypsin-like serine proteases also included the published transcriptomic data after infection with either bacteria/fungi or parasites/parasitoids (Shah et al, 2008). The authors reported that half of the serine protease-like proteins were differentially expressed during immune responses, with a subset induced upon infection by either microbes or parasites and a subset affected by only one type of immune challenge (Shah et al., 2008). Some serine proteases and serpins were massively upregulated after various immune challenges (CG6639, CG6687, CG18563), while others responded to one challenge only (CG4793, CG7219, CG18477, Jonah 99Fi). Among the serine proteases with monophenol mono-oxygenase activity, the overlap seems to be larger across immune challenges (CG3066, Cyp4p3, and SPE are induced by three different immune challenges). The intracellular infections (viral and Wolbachia) appear to barely change the expression in any of the serine proteases or prophenoloxidase (proPO) genes, although they do show upregulation of the antimicrobial peptides. Additionally, they show changes in the expression for genes related to temperature stress (various heat-shock proteins, Dna-J, and Frost). The latter molecules bind to unfolded proteins or ATP, and are important for refolding or decondensing (loosening) of chromosomal sites. It is unclear whether these changes in expression reflect a stress response to the infection or an immunity reaction. Finally, the Drosophila genome contains six thioester-containing protein (Tep) genes, three that are upregulated after infection under the control of the JAK/STAT pathway and are thought to act as opsonins (Lagueux et al., 2000). The transcriptomic data seem to suggest that Tep1 is only upregulated in parasitized larvae. This is misleading, as Tep1 is also upregulated after microbial challenge in larvae (Lagueux et al, 2000), but the antimicrobial microarray experiments were all performed on adults.

One caveat of these studies is that they were performed on whole organisms or cell lines, while enormous differences in expression exist among tissues. Although tissue-specific analyses in expression data are not as crucial as in prote-omic data, expression differences within a tissue can be completely obscured in whole-fly analyses (Chintapalli et al, 2007). This problem has implications for both the sensitivity of the analysis, as well as for the detection of tissue-specific effector genes. For example, the Toll pathway is required for the production of antimicrobial proteins in the fat body, whereas in the lymph gland it induces the production of haemocytes (Qiu et al, 1998). One example where tissue-specificity was taken into

Table 12.2 Genes with changed expression after infection. The table was composed from the published gene lists, using the criteria of the respective authors. Listing implies that the gene was considered to be differentially expressed in at least one study, although other studies may not have found this. Genes with a highest reported fold change of more than 8-fold are shown in bold.

Molecular function

Bacteria1234

Fungi1,

Viruses45

Wolbachia46

Microsporia4

Parasitoids7,8

Pattern-recognition receptors (PPRs)

Immunity signal transduction pathways

Antimicrobial peptides

GNBP-like (CG13422)

GNBP-like (CG12780)

GNBP-like

PGRP-LB

PGRP-LC

PGRP-LF

PGRP-SA

PGRP-SB1

PGRP-SC2

PGRP-SD

Cactus

Dorsal

Necrotic Pelle

Relish

Spätzle

Thor

Toll

Andropin

Attacin-A

Attacin-B

Attacin-C

Attacin-D

Cecropin-A1

Cecropin-A2

Cecropin-B

Cecropin-C

CG15066 (IM23)

CG18279 (IM10)

Defensin

Diptericin

GNBP-like (CG12780) GNBP-like (CG13422) PGRP-SA PGRP-SC2 PGRP-SD

GNBP-like (CG12780) PGRP-SA

Cactus

Dorsal

Kayak

Necrotic

Pelle

Relish

Spätzle

Stat92E

Thor

Toll

Andropin

Attacin-A

Attacin-B

Cecropin-A1

Cecropin-A2

Defensin

Drosomycin

Drosomycin-5

IM2-like

Metchnikowin

Dome

Dorsal

Relish

Thor

Spätzle

Attacin-A

Attacin-B

Attacin-C

Cecropin-A1

Cecropin-A2

Diptericin-B

Drosomycin

Metchnikowin

Dorsal dJun

Ird5

Puckered

Relish

Spätzle

Attacin-A Attacin-B Attacin-C Attacin-D Diptericin-B

CG12780

Lectin-33A

Lectin-37Da/Db

CG2736

GNBP-like (CG13422) Lectin-24A

PGRP-LB PGRP-SA PGRP-SB1 PGRP-SD aPS4

Santa-maria (CG12789)

Cactus

CG14225

Dome

Embargoed

Necrotic

Nup214

Pelle

Relish

Stat92E

Toll

Attacin-A

Attacin-B

Attacin-D

Cecropin-C

CG15065

CG15066 (IM23) CG18279 (IM10) IM1

IM2 IM3 IM4

Metchnikowin

Table 12.2 Cont.

Molecular function Bacteria1'2'3 4 Fungi1'2 4

Diptericin-B

Drosocin

Drosomycin

Drosomycin-5 IM1

IM2 IM3

IM2-1ike (CG15065)

Metchnikowin

Defence/stress response Hsp26 CG4164

Hsp68 Frost

Hsp70Bc Peroxidasin

Peroxidasin Tepll

Tepll TepIV

TepIV Tollo

Tollo TotM

TotM Transferrin 1

Transferrin 3 Transferrin 3

Trypsin and serine protease-like Acp67A CG2045 (Ser7)

(SP and SPH)/serpins CG2045 (Ser7) CG2105 (Corin)

CG2056 (spirit) CG2145

CG2105 (Corin) CG3505

CG2229 CG3604

CG3505 CG5246

CG3604 CG5909

CG5909 CG6639

CG6361 CG6687

CG6467 CG7219

CG6639 CG8738

Viruses45

Wolbachia46 Microsporidia4 Parasitoids78

Frost

Hsp70Aa/Ab

Hemolectin

Hsp70Bc

Hsp70Ba/b/Bc/Bbb

Hsp60

Hsp68

Hsp83

Hsp67Bc

mthl2

Hsp27

Peroxidasin

Hsp22

Tepl

DnaJ-1

Tepll

TepIV

TotA

TotB

TotC

CG6687

Acp67A

CG2056 (spirit)

Jonah 25Bi

CG2229

CG2105 (Corin)

Jonah 25Bii

CG6289

CG3117

Jonah 99Ci

CG6663

CG3344

CG7542

CG3505

CG8952

CG3916

CG9564

CG4053

CG10477

CG4259

CG16749

CG4653

CG17571

CG4793

CG18180

CG5246

CG6687

CG8952

CG7219

CG9372

CG7695

CG9645

CG8215

CG9649

CG8571 (smid)

CG10031

CG8952

CG10586

CG9631

CG10882

CG9645

CG11459

CG11459

CG11841

CG11836

CG11842

CG11841

CG11843

CG11842

CG11911

CG12558

CG16704

CG15046

CG16712

CG16030

CG16713

CG16713

CG16997

CG18180

CG18563

CG18563

CG31199/

CG31326/

CG31200

CG33109

CG31326/

CG33836

CG33109

Jonah 25Bii

Spn43Ad

Jonah 25Biii

Spn4

Jonah 99Cii

Spn 5

Spn27A (CG11331)

Spn42C

Spn88E

Spn43Ad

Spn3

Spn4

Spn5

\Jonah 25Bi Jonah 25Bii Jonah 25Biii Jonah 65Aiii Jonah 65Aiv Jonah 74E Jonah 99Cii Jonah 99Ciii Jonah 99Fi Trypsin 29F

CG6041 CG6639

CG6687 CG9240 CG9673

CG9675 (spheroide)

CG11912 CG12951 CG16704 CG16712

CG16713

CG17278

CG17475

CG17477

CG17572

CG18477

CG18478

CG18563

CG30414

CG30086

CG30090

CG30371

CG31266

CG31269

CG31780

CG31827

CG32374

CG32376

CG32483

CG33127

Jonah 25Bii

Jonah 65Aii

Jonah 65Aiii

Tequila

Trypsin (CG18681) ^Trypsin (CG12350)

Table 12.2 Cont.

Molecular function Bacteria1234 Fungi124 Viruses45 Wolbachia46 Microsporidia4 Parasitoids78

Prophenoloxidase cascade CG1102 CG3066 (Sp7) Cyp4p3 Cyp4d21 CG3066 (Sp7)

(proPO): mono/diphenol CG3066 (Sp7) CG16705 (SPE) CG9733

oxidase activity and melanin CG9733 Cyp4p3 CG11313

intermediates (including some CG16705 (SPE) Ddc CG16705 (SPE)

serine proteases) Cp19 pale Cyp4e3

Cyp26d1 Cyp12e1

Cyp4p3 Cyp309a1

Cyp-like Cyp6a17

Ddc Cyp12a5

Dihydropteridine reductase (Dhpr) Cyp12a4

Laccasse-like (CG3759) Cyp9f2

Pale Cyp9c1

Punch Dox-A3

yellow-f Dihydropteridine reductase (Dhpr) Fmo-2

yellow-c yellow-f yellow-f2 yellow-g

1 DeGregorio et al. (2001), Table 1; 2 Irving et al. (2001), Tables 1 and S2; 3 Boutros et al. (2002), Figure S4; 4 Roxstrom-Lindquist et al. (2004), Tables 1 and S2; 5 Dostert et al. (2005), Table S1; 6X¡ et al. (2008), Figures 4 and 5; 7 Wertheim et al. (2005), Tables 2 and S1, excluding cluster 9; 8 Schlenke et al. (2007), Tables S2a and S3, exclusing Lh vs Lb.

account was a genome-wide transcription study on the different types of haemocytes in larvae (Irving et al., 2005). This study showed that after infection some immunity genes are differentially expressed in the haemocytes but not in the fat body (e.g. Spätzle, attacin-D, cecropins B and C), some are not differentially expressed in haemocytes (e.g. necrotic), and some are expressed exclusively in a particular type of haemocyte (e.g. DoxA3 and aPS4 in lamellocytes).

Another important aspect of genomic studies is the use of appropriate experimental controls. As the technique (both transcriptomics and proteom-ics) relies on thousands of comparisons of relative abundances, rather than on actual quantification, it is vitally important to ensure that the only difference between a treated and a control sample is the treatment. For example, gene expression follows diurnal rhythms; it changes strongly over the course of a life (even at a day-to-day scale); and it may respond to handling and/or measurements (McDonald and Rosbash, 2001; Pletcher et al., 2002). In the case of a zero-hour control sample compared to a 12 h post-infection sample, for example, it becomes impossible to distinguish between genes that changed expression due to the infection and due to diurnal rhythms. To avoid such confounding effects, ideally control and infected samples should be collected in parallel and treated exactly the same throughout handling, except for the actual infection.

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