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Supplementary text:
Supplementary Materials and Methods
(a) Microarray analysis of differential gene expression
Total RNA was extracted from each sample by the standard procedure of FlyChip
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(http://www.flychip.org.uk/). Two sub-samples (40 g RNA) of each experimental and control
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sample were reverse-transcribed using 2 l Superscript III reverse transcriptase, following
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manufacturer’s instructions with oligo(dT)23 anchored primers (Sigma). Samples were directly
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labelled: the reference samples were pooled to give single Cy3- and Cy5- labelled samples, and
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then each Cy3- and Cy5- labelled experimental sample was combined with an equal volume of
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the pooled Cy5- and Cy3- labelled reference sample, respectively.
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nucleotides were removed on AutoSeq G-50 columns, following manufacturer’s instructions
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(Amersham), and the column eluate was reduced to 2-5 l in a Speed Vac. Two l sonicated
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salmon sperm DNA (10 mg ml-1) (Invitrogen) was added, followed by 140 l Ocimum
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Hybridization Buffer (BioSolutions). The sample was incubated at 100oC for 2 min, centrifuged
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at 13,000 rpm for 1 min and then applied to the spotted slide in GeneTac Hybridization Station.
Unincorporated dye and
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The analysis was conducted on a FL002 microarray of FlyChip with the metagrid of 18,240
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elements in the format of 48 grids (12 x 4) of 380 spots (20 x 19) (details at
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http://www.flychip.org.uk/services/core/FL002).
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buffer, pH 8.5, was arrayed onto PowerMax slides (Full Moon BioSystems) using Genetix
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Qarray2 contact-printing instrument and 48 Genetix aQu75 split-pins, to give spots of mean
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diameter 90-120 m. After hybridization, the slides were scanned using Genepix 4000B dual
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laser scanner (Axon Istruments, Molecular Devices) and fluorescent spot signal finding,
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quantification and annotation were performed using the software tool ‘Dapple’(BUHLER et al.
Probe DNA in 150 mM sodium phosphate
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2003). Low intensity spots were flagged using filters in Dapple and omitted from analysis. Data
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were normalised using the package VSN (the variance stabilising normalisation library (HUBER
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et al. 2002) within Bioconductor (GENTLEMAN et al. 2004).
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Bioconductor were applied to identify differentially-expressed genes: ‘siggenes’, which uses the
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Significance Analysis of Microarrays approach (TUSHER et al. 2001) (SAM: to perform a multi-
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class analysis using the default settings) and Microarray Significant Profiles (CONESA et al.
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2006) (MaSigPro: single timeseries analysis with parameters alfa=0.05/3; degree = 2; Q = 0.05;
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step.method = "backward"; rsq = 0.7; min.obs = 3). The two methods (SAMS and MaSigPro)
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applied to identify genes that were differentially-expressed genes in Buchnera-infected cells
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yielded closely similar results. Of all the genes identified as differentially-expressed, >80% were
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detected by both methods (Supplementary Table 1), and no functional difference between the
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genes represented in the two methods were obtained by functional analysis in FATIGO.
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MaSigPro which is designed specifically for analysing microarray timecourse data (CONESA et
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al. 2006) was selected for the detailed analysis presented here. K-means clustering within
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MaSigPro was conducted on the set of significantly differentially-expressed genes to identify 9
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clusters of gene expression profiles.
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Two alternative packages in
(b) Quantitative real-time PCR (qPCR) of gene expression
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The expression of four Drosophila genes (AttB CG18372, Cec A2 CG1367, dipt CG12763 and
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βtub56D CG9277) was quantified by QRT-PCR. S2 cells were exposed in triplicate to Buchnera
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and an equivalent extract from aposymbiotic aphids for 6 hours. They were then centrifuged at
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300 g for 3 min, washed once with centrifugation in PBS and lysed in TRIzol reagent
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(Invitrogen) with mild homogenization using a hand-held glass homogeniser. RNA was
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extracted following manufacturer’s instructions (Invitrogen). To remove contaminating DNA,
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the RNA was incubated with RNase-free DNase (Qiagen) for 15 min at 20oC before purification
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with RNeasy minikit (Qiagen) using the RNA cleanup protocol in the manufacturer’s handbook.
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First strand cDNA was synthesized using superscript II reverse transcriptase (Invitrogen)
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following the manufacturer’s instruction and using 1.6 to 2.4 ng of template RNA and 200 ng of
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random primers (Invitrogen). All assays included RT controls consisting of the reaction without
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the reverse transcriptase enzyme. Real time amplification of cDNAs was performed in 96-well
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plates using 1 µl of cDNA in 19 µl of reaction mix consisting of 1X Power Sybr Green PCR
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Mater mix (Applied Biosystems), 300 nM of forward and reverse primers (Supplementary Table
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2). The reaction were carried out in an CFX96 real time system on a C1000 thermal cycler
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(Biorad) with the following thermal profile 10 min at 95˚C followed by 40 cycles of 15 sec at
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95˚C and 1 min at 65˚C. An additional melt curve was run for each pair of primers from 65˚C to
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95˚C with an increment of 0.5˚C; all the primers pairs showed a single peak and were considered
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specific. All samples were run in duplicates with template free and –RT samples as control. The
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gene expression was assayed using the 2-ΔΔCt method as described in Livak and Schmittgen
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(2001) with β-tubulin as the reference gene.
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Supplementary Results
(a) Microarray analysis
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To investigate the patterns of gene expression over the timecourse of the experiment, the
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microarray data were analysed by step-wise regression and k-means clustering in MaSigPro.
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The gene expression profiles were grouped by k-means into a pre-defined set of 9 clusters.
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FATIGO (http://babelomics.bioinfo.cipf.es/EntryPoint?loadForm=fatigo) was applied to identify
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the major functions over-represented in each cluster. GO terms could be allocated to all the
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clusters except clusters 1 and 4 (Supplementary Figure 3). From these data and a further
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FATIGO comparison between all differentially regulated genes and genes with equivalent
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expression, five broad functional groupings of differentially expressed genes could be identified:
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genes implicated in defence, uptake and intracellular transport, cytoskeleton organisation,
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carbohydrate metabolism and rRNA processing (Supplementary Table 3).
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The genes with defence function against bacteria (Supplementary Table 3a) were allocated
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predominantly to clusters-2 and -5, comprising genes with sustained over-expression in
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Buchnera-infected cells over the full time course. These genes are considered in the main text.
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The differentially expressed genes with functions relating to rearrangements of the actin
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cytoskeleton (Supplementary Table 3b) and vesicle trafficking (Supplementary Table 3c) are
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likely related to phagocytosis, including the insertion of new membrane at sites of phagosome
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assembly, and maturation of the phagosome. Most of the differentially regulated genes with
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these functions were upregulated exclusively at the first time-point, 1 h, of the analysis (i.e.
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members of clusters 3, 4 and 8), accounting for 79% (19/24) of the genes associated with
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cytoskeletal rearrangement and 69% (20/36) of the genes associated with uptake and transport.
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Of particular note among these genes are three in cluster-3: CG3055 (homologue of the
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lysosomal associated membrane protein LAMP1); and CG12770 (vps28) and CG8055 (vps32),
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components of the endosomal sorting complex for transport (vps28 in ESCRT I and vps32 in
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ESCRTIII), required for the degradation of membrane proteins and production of multivesicular
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bodies (SLAGSVOLD et al. 2006).
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Other functions represented in the microarray data were genes involved in glycolysis and
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sugar metabolism (Supplementary Table 3d), which were predominantly in clusters 6 and 7
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(down-regulated in the B. aphidicola-infected cells at 1 h and 6 h after infection). In particular,
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cluster 6, with depressed expression at 1 h after infection, included 5 of the 9 genes in glycolysis,
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with glyceraldehyde 3P dehydrogenase and pyruvate kinase also with significantly depressed
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expression, in cluster 7 and cluster 4, respectively. Impacts of bacterial infection on the
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metabolism of infected cells have been reported previously, but we are unaware of any previous
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description of suppressed glycolysis.
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All of the 10 differentially expressed genes in the GO category rRNA processing
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(Supplementary Table 3e) were allocated to cluster 9, with upregulated expression at 6 h. From
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visual inspection of the genes in this cluster, the analysis was extended to the related function of
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ribosome biogenesis using the GO category ‘ribosome biogenesis’ and additional genes recently
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identified as Drosophila homologues to yeast genes involved in ribosome assembly (GUERTIN et
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al. 2006). This yielded a further 18 differentially-expressed genes, all in cluster 9, and the full
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list of 28 genes is shown in Supplementary Table 3e. Ribosome assembly genes are known to be
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regulated by the TOR pathway, which includes genes involved in various functions, including
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cell-cycle control, programmed cell death, amino acid signalling and protein targeting and
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folding. However, B. aphidicola-infection did not have a general effect on expression of TOR
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genes. Apart from those in ribosome biogenesis, just 5 of the 62 genes implicated in the TOR
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pathway in Drosophila cells, were differentially expressed; and these included Rheb/CG1081
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(upregulated in cluster 2) but not the other five key components of the TOR pathway
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(raptor/CG4320, tor/CG5092, s6k/CG10539, akt1/CG4006 and pten/CG5671).
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(b) QRT-PCR assays
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The expression of three immune genes, attacin A, cecropin A2 and diptericin, that were
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significantly upregulated in S2 cells exposed to Buchnera (Table 1 in main text), was
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investigated further in a supplementary experiment, using qPCR. The S2 cells were challenged
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with either Buchnera cells (as previously) or with an equivalent extract from aposymbiotic
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aphids (which lack Buchera cells). The data were normalized against the expression of the β-
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tubulin gene, β-tub56D, and compared to uninfected S2 cells. Attacin B, cecropin A2 and
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diptericin displayed significantly higher expression in S2 cells exposed to Buchnera than to the
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extract of aposymbiotic cells (Supplementary Figure 2, with statistical analysis in legend). These
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data verify that the impact of the Buchnera preparation on S2 gene expression cannot be
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attributed to non-Buchnera products that may contaminate the Buchnera preparation.
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BUHLER, J., T. IDEKER and D. HAYNOR, 2003 Dapple: improved techniques for finding spots on
DNA microarrays, pp. in Technical Report. University of Washington
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CONESA, A., M. J. NUEDA, A. FERRER and M. TALON, 2006 maSigPro: a method to identify
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significantly differential expression profiles in time-course microarray experiments.
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Bioinformatics 22: 1096-1102.
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GENTLEMAN, R. C., V. J. CAREY, D. M. BATES, B. BOLSTAD, M. DETTLING et al., 2004
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Bioconductor: open software development for computational biology and bioinformatics.
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Genome Biol 5: R80.
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GUERTIN, D. A., K. V. GUNTUR, G. W. BELL, C. C. THOREEN and D. M. SABATINI, 2006
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Functional genomics identifies TOR-regulated genes that control growth and division.
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Curr Biol 16: 958-970.
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HUBER, W., A. VON HEYDEBRECK, H. SULTMANN, A. POUSTKA and M. VINGRON, 2002 Variance
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stabilization applied to microarray data calibration and to the quantification of differential
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expression. Bioinformatics 18 Suppl 1: S96-104.
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SLAGSVOLD, T., K. PATTNI, L. MALEROD and H. STENMARK, 2006 Endosomal and non-
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endosomal functions of ESCRT proteins. Trends Cell Biol 16: 317-326.
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TUSHER, V. G., R. TIBSHIRANI and G. CHU, 2001 Significance analysis of microarrays applied to
the ionizing radiation response. Proc Natl Acad Sci U S A 98: 5116-5121.
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Legends for supplementary figures
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Supplementary Figure 1
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Viability of Buchnera isolated into insect culture medium (606-721 cells scored).
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Supplementary Figure 2
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Expression levels of three immune genes of Drosophila S2 cells infected with Buchnera cells or
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with Buchnera-free extracts from aposymbiotic aphids, normalised against β-tubulin (βtub56D)
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and relative to uninfected S2 cells (normalized at one unit). The genes which are significantly
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differentially expressed between the two groups are indicated by an asterisk (t-test with the
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critical probability of 0.0167, after Bonferroni correction for three tests).
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Supplementary Figure 3
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Expression patterns of genes in S2 cells over 24 h after challenge with B. aphidicola.
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The data are displayed as fold-difference relative to uninfected cells. Nine gene clusters were
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identified by MaSigPro.
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representation relative to genes with expression patterns that did not differ significantly between
Non-redundant GO categories with significantly different gene
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infected and uninfected cells are shown for biological processes (regular) and molecular function
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(italics) with the probability indicated.
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