The innate immune responses in Drosophila against parasites and pathogens are regulated through several signal transductions pathways, in particular the Toll, Imd, and JAK/STAT pathways. These pathways are activated in a parasite-/pathogen-specific manner, and it was recognized before the genomic era that the pathways interact for several types of infection (e.g. Lemaitre et al., 1996). Post-genomic technology has been successful in further elucidating components of these pathways, the interactions, and other genes and pathways involved in the regulation of the immune response.
Transcriptomic data describe the levels of gene expression by measuring the relative abundances of all mRNAs within a biological sample. Comparing infected and uninfected samples yields information on the genes for which expression is up- or downregulated in response to the immune challenge. Microarrays have been used to capture the genome-wide transcriptomic changes in Drosophila during the immune responses against most of the pathogens and parasites that can infect them (bacteria and fungi, De Gregorio et al, 2001; bacteria and fungi, Irving et al., 2001; bacteria, Boutros et al., 2002; bacteria, fungi, microsporidia, and viruses, Roxstrom-Lindquist et al, 2004; viruses, Dostert et al., 2005; parasitoids, Wertheim et al., 2005; parasitoids, Schlenke et al., 2007; Wolbachia, Xi et al, 2008).
One of the primary strengths of transcriptomic data is that they enable identification of a variety of genes that are involved with, affected by, or associated with an immune response, and their sequence of action. Although a change in expression is insufficient evidence for involvement in immunity without experimental validation, transcriptomic data are valuable as a first step in identifying novel genes with a putative role in the genetic control of immunity. The first transcriptomic studies focused on the antimicrobial responses, which involve primarily the humoral immune response. These studies yielded several hundred novel putative immunity genes that are still being characterized in follow-up studies (e.g. Maillet et al., 2008). Parasitism induces the cellular immune response, which is less well studied than the humoral immune response. Comparing the expression in parasitized and control larvae at nine time points after attack by the braconid parasitoid wasp A. tabida, revealed approximately 160 genes as being differentially expressed after parasitoid attack, most of which had not previously been associated with immunity functions (Wertheim et al, 2005).
To get an indication of the functional roles of (novel) genes in the immune responses, comparisons with the simultaneous expression patterns of all other genes may provide an additional benefit of microarrays. Several suites of genes responded similarly across time after parasitoid attack, and shared functional annotations to a larger degree than expected by chance. For example, a group of genes involved in proteolysis and peptidoly-sis was upregulated during the encapsulation/ melanization phase of the immune response (Wertheim et al., 2005). For unannotated genes within such suites of co-expressed genes, the similarity in their expression pattern to genes with annotations provides a starting point for putative functional annotations.
We can also use expression data to screen for candidate proteins with specific functional domains that are relevant during a certain stage of the immune response. This may provide information on the key genes for particular processes, but may also reveal new insights in the nature of the processes itself. For example, to recognize invading organisms the Drosophila genome codes for various types of pattern-recognition receptors (PRRs), such as lectins and receptors for microbial peptides. Non-self recognition by PRRs leads to triggering of immune-signalling pathways. This avoids the costs of constitutive immune defences and ensures the production of the appropriate defence molecules for a particular type of infection. During the first few hours after parasitoid attack, two out of the 20
peptidoglycan-recognition proteins (PGRP-LB and PGRP-SB1) were differentially expressed, while during the encapsulation phase one of the 30 C-type lectins in the Drosophila genome (lectin-24A) showed a striking increase in expression (Wertheim et al., 2005). The PGRP molecules recognize a component in the bacterial cell wall, and their increased expression may be a response to low-level micro-bial infections following the puncturing of the cuticle by the parasitoid. Lectins not only function in recognition, but are also thought to be important in changing cell-adhesion properties. It is therefore plausible that lectin-24A is a key player in recruiting the haemocytes to the parasitoid egg, and the subsequent formation of the multilayered cellular encasing of the egg. Another transcriptomic study compared the expression in Drosophila larvae after attack by two different parasitoid species (the figitids L. boulardi and L. heterotoma; Schlenke et al., 2007). Both species can successfully suppress encapsulation by Drosophila, but L. boulardi appears to invoke a complete immune response that is only sabotaged at the final stage, whereas L. heterotoma appears to achieve a near-complete lack of a tran-scriptional immune response. Interestingly, L. bou-lardi induced a similar massive upregulation of the same lectin-24A gene during the first 2-5 h after infection, while the lectin was not upregulated at all after attack by L. heterotoma.
Another merit of transcriptomic data is that it can help unravel genetic networks and interactions. These network interactions can be crucially important in understanding the regulation of immune responses and the associated costs. By applying bioinformatic approaches to the transcrip-tomic data, suites of genes with simultaneously changing expression patterns can be investigated for shared regulatory elements (e.g. promoter or enhancer sites), which could indicate that the genes are under control of the same (co-)transcription factors. Several suites of genes that were differentially expressed genes at various stages after parasitoid attack, harboured a significant over-representation of three transcription factor DNA-binding motifs (TFBMs) in their upstream regions (for Stat92E, NFxB-like, and serpent (srp)) (Wertheim et al., 2005). In many cases, the upstream regions contained several replicates of two or three of these TFBMs, suggesting that the pathways could jointly regulate the expression of target genes. Transcriptomic studies after microbial infection in single and double mutants for the Toll, Imd, and JAK/STAT pathways also showed that regulation of target genes during an immune response can be compensated for by the other pathway (redundancy), or can partially or wholly depend on both pathways (co-regulation, cross-regulation, or synergism; De Gregorio et al., 2002; Brun et al., 2006). Transcriptomic studies also identified the c-Jun N-terminal kinase (JNK) pathway as a separate branch of the Imd pathway, and its involvement in immune responses (Boutros et al, 2002; Silverman et al, 2003; Park et al., 2004). These findings all support the existence of extensive genetic interactions among the various immunity pathways.
This technique compares and describes all proteins (and their modified varieties) among biological samples (e.g. infected and uninfected). Proteomic studies have focused mainly on the peptides in the haemolymph after microbial or fungal challenge (Levy et al., 2004; Loseva and Engstrom, 2004; Vierstraete et al., 2004a, 2004b; de Morais Guedes et al, 2005). Proteins are the final gene products, and proteomics thus provides a more direct measurement of the 'actors' in the immune response. Proteomics is complicated by the need to analytically determine the identity of each expressed protein in the sample while relying on incompletely developed databases for doing so, it requires minimized variation in cell types, necessitating tissue-specific analyses, and it is less sensitive to small changes in abundance. However, in contrast to transcriptomics, these measurements also incorporate the effects of post-transcriptional regulation and post-translational modifications. Therefore, this technique provides important additional information on the regulation of the innate immune response, including proteolytic cascades.
A proteomic study identified 37 instantly released peptides after immune challenge that were not induced after sterile injury (Vierstraete et al, 2004b). Insects can stockpile proteins in a pre-active stage that can be immediately deployed in the event of an invasion, through, for example, phosphorylation and cleavage. Proteolytic cascades often form the start of signalling pathways, and the activity of such peptides cannot be detected by transcriptomic studies (although the subsequent replenishment of the proteins may be measured).
126.96.36.199 RNA interference (RNAi) screens Finally, RNAi screens have been used for the elimination of specific genes, to investigate their roles in the immune response. With this technique, genes are silenced post-transcriptionally by introducing double-stranded RNA for a target gene, resulting in the degradation of the targeted mRNAs. The effect of the loss of function for each gene is then measured in a screen for phenotype or signalling pathway activity (e.g. using reporter constructs to visualize whether the target genes of the pathway are switched on). One large advantage of this technology is that it already includes a degree of experimental validation. In Drosophila, it has been used to investigate immunity signalling pathways or functional groups of molecules (Imd pathway, Foley and Farrell, 2004; JAK/STAT pathway, Muller et al., 2005; serine proteases, Kambris et al, 2006). The RNAi constructs can be introduced most easily to cell cultures, but can also be used in vivo in whole organisms (reviewed in Boutros and Ahringer, 2008).
A genome-wide RNAi screen was used on a Drosophila haemocyte-like cell line and identified approximately 90 novel genes that were under influence of the JAK/STAT pathways (Muller et al., 2005). To identify the components and negative regulators of the Imd pathway and their relative position in the pathway, an RNAi screen was performed on a reporter cell line that was exposed to lipopolysaccharide, a bacterial cell-wall component known to trigger the Imd pathway. This study not only revealed several novel components, but also many inhibitors that either keep the pathway inactive in the absence of infection, or downregu-late the response to infection (Foley and Farrell, 2004). RNAi was also applied in vivo against 75 serine proteases in the Drosophila genome to study the serine protease cascade upstream of the Toll receptor. The study identified five serine proteases that are required for activation of the pathway (Kambris et al., 2006).
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