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Identification of pathways, gene networks and paralogous gene families in Daphnia pulex responding to exposure to the toxic cyanobacterium Microcystis aeruginosa. Jana Asselman, Dieter IM De Coninck, Stephen Glaholt, John Colbourne, Colin Janssen, Joseph Shaw, and Karel De Schamphelaere Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es301100j • Publication Date (Web): 16 Jul 2012 Downloaded from http://pubs.acs.org on July 22, 2012
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Identification of pathways, gene networks and paralogous gene families in
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Daphnia pulex responding to exposure to the toxic cyanobacterium
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Microcystis aeruginosa.
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Jana Asselman1,*, Dieter IM De Coninck1, Stephen Glaholt2, John K Colbourne3, Colin R Janssen1, Joseph R
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Shaw2,3, Karel AC De Schamphelaere1
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1
Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, Belgium
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2
The School of Public and Environmental Affairs, Indiana University, Bloomington, Indiana USA
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3
The Center for Genomics and Bioinformatics, Indiana University, Bloomington, Indiana USA
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*
Corresponding author
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Email:
[email protected] 12
Postal Address:
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Laboratory of Environmental Toxicology and Aquatic Ecology
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J. Plateaustraat 22
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B-9000 Gent
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Belgium
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Tel: +32 9 264 37 10
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Fax: +32 9 264 37 66
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Abstract
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Although cyanobacteria produce a wide range of natural toxins that impact aquatic organisms, food webs and
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water quality, the mechanisms of toxicity are still insufficiently understood. Here, we implemented a whole-
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genome expression microarray to identify pathways, gene networks and paralogous gene families responsive to
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Microcystis stress in Daphnia pulex. Therefore, neonates of a sensitive isolate were given a diet contaminated
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with Microcystis to contrast with those given a control diet for sixteen days. The microarray revealed 2247
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differentially expressed (DE) genes (7.6% of the array) in response to Microcystis, of which 17% are lineage
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specific( i.e., these genes have no detectable homology to any other gene in currently available databases) and
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49% are gene duplicates (paralogs). We identified four pathways/gene networks and eight paralogous gene
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families affected by Microcystis. Differential regulation of the ribosome, including 3 paralogous gene families
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encoding 40S, 60S and mitochondrial ribosomal proteins, suggests an impact of Microcystis on protein synthesis
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of D. pulex. In addition, differential regulation of the oxidative phosphorylation pathway (including the NADH
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ubquinone oxidoreductase gene family) and the trypsin paralogous gene family (a major component of the
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digestive system in D. pulex) could explain why fitness is reduced based on energy budget considerations.
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Introduction
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Cyanobacterial blooms in water reservoirs have important ecological, economical and human health
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consequences (1). These blue-green algal blooms have been studied since the end of the 19th century (1), yet
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gained prevalence during recent decades because of their links to climate change and anthropogenic
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eutrophication of water reservoirs (2). They produce a wide range of natural toxins (3-7) that have the potential
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to impact aquatic ecosystems, human health and ecosystem services, which now fuels an increased public and
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scientific awareness (8-11).
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The most common and best studied cyanobacterium is Microcystis aeruginosa (12). This cyanobacterium is
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primarily known for its production of microcystins (a group of toxic cyclic heptapeptides) and aeruginosins - a
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group of toxins with a peptide-like structure without any standard L-amino acids (13-14). Microcystins are
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inhibitors of eukaryotic protein serine/threonine phosphatases (13), while aeruginosins inhibit serine proteases at
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the protein level (14-15).
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Our goal was to investigate the effects of cyanobacterial stress from M. aeruginosa on the transcriptome of the
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freshwater microcrustacean Daphnia pulex. Daphnia is a keystone species in aquatic food webs and a prominent
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model organism for the study of ecotoxicology, evolutionary biology and ecology (16). The recently described
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draft genome sequence has also made available a large suite of tools to investigate gene functions (17).
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The effects of Microcystis stress on Daphnia at physiological and life-history level have been studied since the
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1980s (18-20) and have primarily been related to three different factors: lack of essential nutrients such as
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essential fatty acids or lipids (18, 21, 22), deter feeding (19, 21), or toxin production (19, 21, 23). Current
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literature (21, 23) remains undecided whether the effect of Microcystis on Daphnia can be contributed to only
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one of these factors or a combination of them.
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We described the transcriptional stress response of D. pulex feeding on M. aeruginosa by using a comprehensive
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transcriptome microarray. We aimed to identify pathways or gene networks that characterize the response of
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Microcystis stress. We chose to study the effects of exposure to live cells of M. aeruginosa and not the effects of
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individual purified cyanobacterial toxins because the effects of purified toxins are known to be different than
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those of live cyanobacteria cells (21, 24-28). Moreover, studying the responses to feeding on Microcystis
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provides a more realistic understanding of its impact in the environment.
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We therefore fed Daphnia pulex neonates with a diet ratio containing a mixture of M. aeruginosa and a normal
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green algae. The former diet ratio has been shown to invoke a 50% decline in reproductive output in the D. pulex
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isolate used here (11). Therefore, measured gene expression responses to the toxic diet mixture are tied to effects
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on the animal’s fitness. An additional aim of this study was to demonstrate how researchers can continue to
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ecologically annotate the Daphnia pulex genome, which is characterized by a very high number of lineage
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specific genes (i.e they have no detectable sequence homology to genes in any of the current genome databases
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(17)), based on the knowledge of the response of such genes to specific environmental conditions (here
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Microcystis exposure).
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Material and Methods
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Exposure Experiments
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Animals
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Dahpnia pulex were obtained from isoclonal laboratory cultures of an isolate, originating from the BassHaunt
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Lake, Dorset region, Ontario, Canada. D. pulex were housed in 3L borosilicate beakers (20 per beaker), held at a
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constant temperature (20 ± 1°C) and photoperiod (16:8 light-dark). Animals were cultured in no N, no P
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COMBO medium (29) which was renewed weekly. They were fed daily Ankistrodesmus falcatus at a rate of 1.5
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mg C L-1. For all experiments, neonates (< 24 h old) were isolated from unexposed maintenance cultures. These
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cultures were synchronized with respect to time to maturity of the adults producing neonates for experiments.
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Culturing of cyanobacteria
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The cyanobacterial strain used was a microcystin producing Microcystis aeruginosa strain (UTEX LB2385).
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Microcystis was cultured in modified COMBO medium (29) under standard and sterile conditions in 6L
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culturing flasks. Cultures were incubated at 20±1°C under constant light conditions with gentle aeration and
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allowed to grow until mid-late log phase. Cyanobacteria were concentrated by centrifugation and cleaned by
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resuspension and centrifugation using reconstituted water (modified COMBO medium) three times before use.
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Density of the cultures was determined with a counting chamber. Toxicity of the strain was verified by HPLC
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analysis, in which subsamples of the concentrated cyanobacteria suspension were analyzed for
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cylindrospermopsin, anatoxin-a and 4 different types of microcystins, i.e. microcystin-RR, YR, LR, LA, LF and
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LW, which differing in two variable amino acids residues (30) (Environmental Analysis Laboratory, Lake
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Superior State University, Sault Ste. Marie, MI, USA). An administered volume of this M. aeruginosa
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suspension per day, i.e. 50% of the diet based on dry weight, resulted in an exposure to 0.069 µg microcystin-YR
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and 0.050 µg microcystin-LR per day.
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Exposure experiment
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Animals were exposed in 1L polyethylene beakers (18 neonates per beaker) for a period of sixteen days under a
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constant photoperiod (16:8 light dark) and constant temperature of 20 ± 1°C. Both control and exposed
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treatments consisted of four biological replicates, i.e. 4 beakers. All animals were fed with a diet in which the
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final feeding concentration was 1.5 mg C L-1 on dry weight basis. The diet of the exposed animals contained
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50% of Microcystis aeruginosa and 50% of Ankistrodesmus falcatus, control diet consisted of 100% A. falcatus.
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This ratio was based on De Schamphelaere et al. (11), where it resulted in a decline of 50% in reproduction in
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exposed animals. This diet ratio resulted in an administered volume of 2.67 mL of this M. aeruginosa suspension
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per day. During the experiment, pH of the media was monitored on regular intervals. The experiment was
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repeated twice. First, animals were isolated for gene expression analysis. Second, animals were isolated for
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microcystin measurements.
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Analysis of microcystin levels in exposed animals
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Wet weight of the animals was determined on a microbalance at the end of the exposure. Afterwards animals
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were frozen and stored at -80°C until microcystin analysis. The sample preparation was conducted as follows: all
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animals were macerated with a pipet tip and then subjected to 5 freeze-thaw cycles (freezing at min 80°C, then
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thawing at room temperature). Next, 250 µL of 70% methanol (analytical grade, Sigma Aldrich, MO, USA) was
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added and samples were left to stand for 24 hours at room temperature. This was followed by a sonication of the
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samples. Finally, samples were filtered through a 0.2 µm syringe filter and filtrate was stored at -20°C until
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ELISA analysis. Microcystins levels were analyzed using the ELISA technique provided in the QuantiPlateTM
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Microcystin Kit (Envirologix, Maine, USA) following manufacturer’s protocol.
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mRNA Extraction, Labeling and Hybridization
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RNA was extracted with the RNeasy kit and Qiashredder (Qiagen, Venlo, Netherlands) following
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manufacturer’s protocol. All animals (18 in total) from one beaker were pooled into one sample and will further
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be referred to as one biological replicate. DNA contamination was removed by a DNAse treatment (Qiagen,
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Venlo, Netherlands). RNA quantity and quality were determined with the spectrophotometer (Nanodrop, Thermo
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Fisher Scientific, Wilmington, DE, USA) and with the Bioanalyzer 2100 (Agilent Technologies, Santa Clara,
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CA, USA) respectively. Samples were stored at -80°C until RNA amplification.
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The microarray protocol follows detailed instructions by Lopez and Colbourne, 2011 (31). Samples were
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amplified using a T7-based RNA amplification technique. One microgram of total RNA was amplified with the
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MessageAmp II aRNA Amplification kit (Ambion, Applied Biosystems, Carlsbad, CA, USA) following
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manufacturer’s protocol. Quantity and quality of the amplified RNA were determined with the
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spectrophotometer and Bioanalyzer 2100.
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Double stranded cDNA was synthetized with SuperScript Double-Stranded cDNA Synthesis Kit (Invitrogen,
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Carlsbad, CA, USA) following clean up (alkaline hydrolysis and Qiaquick columns, Qiagen). Concentration and
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integrity of the cDNA were determined with Nanodrop spectrophotometer and Bioanalyzer 2100.
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Samples were labeled with Dual-Color DNA Labeling Kit (Roche Nimblegen, Madison, WI, USA) following
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manufacturers protocol. Quantity and quality of the samples were again determined with the spectrophotometer
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and Bioanalyzer 2100.
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The microarray design (Table S1) consisted of four arrays, each containing two samples, i.e. a control and a
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Micrcosytis exposed sample. Different biological replicates were used for each array and dye swaps were
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conducted. All 8 labeled samples were pooled according to the design (i.e. one control biological replicate was
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pooled with one Microcystis exposed replicate), resulting in 4 pools to be hybridized to 4 arrays. Each pool was
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dried and resuspended in hybridization buffer according to Roche NimbleGen’s User Guide for Expression
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Analysis for Cy-labeled cDNA derived from Eukaryote systems. Subsequent hybridization of each of these
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pools on the respective arrays followed the same protocol (31) and was executed with the NimbleGen
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Hybridization Kit (Roche Nimblegen, Madison, WI, USA). After hybridization the slides were washed with
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NimbleGen Hybridization Wash Buffers (Roche Nimblegen, Madison, WI, USA). The microarray itself is a
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transcriptome array developed by the Centre for Genomics and Bioinformatics (Indiana University,
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Bloomington, IN, USA) and is in the National Center for Biotechnology Information (NCBI) Gene Expression
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Omnibus under the accession number (GEO: GPL11278). Finally, arrays were scanned with the NimbleGen MS
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200 Microarray Scanner to measure fluorescence and images were processed with NimbleScan 2.6 Software
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(GEO: GSE36635).
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Image Analysis and Data Processing
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Microarray images were analyzed with the statistical software package R (32) and bioconductor (33). We used
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the LIMMA (34) package with additions and modifications according to Colbourne et al. (17). All signal
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distributions were quantile normalized across arrays, samples and replicates. Differential expression of a gene
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was determined based on the mean M-value of probes that represent the gene in question. The M-value for a
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gene was defined as the log2 ratio of the expression in the exposed animals and the expression of the animals in
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the control treatment. Linear models were constructed with lmFit and empirical Bayes Statistics were
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implemented with eBayes function. Benjamin-Hochberg method (35) was implemented to adjust p-values for
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multiple testing.
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Analysis of Gene lists
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The analysis of the gene lists were combined with annotation information on each gene available through
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wFleabase.org (36), KEGG database (37) and KOG (clusters of eukaryotic orthologous groups) database (38) in
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R. Annotation information from wFleabase.org including KOG annotation, and enzyme classification numbers,
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was downloaded in batch and combined with gene expression lists in R. Annotation information from the KEGG
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database was obtained with KAAS (39), for which all protein sequences of the draft genome sequence were
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uploaded to the KAAS server. All results were stored in a txt file for further use in R (Files S1-S3). Hence, the
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gene lists were analyzed in three different steps: KOG grouping analysis, pathway analysis and analysis of
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paralogous gene families. To assess the impact of duplicated genes, both KOG and KEGG analysis were
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executed once with and once without duplicated genes (i.e. only single copy genes were considered in the latter
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analysis). Duplicated genes were excluded based on their grouping into a paralog family as defined on
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wFleabase.org,
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predictions/dpulex1_gnomon_paralog_mcl2ids.tab). KOG analysis was executed based on KOG classification as
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defined by the Joint Genome Institute (http://genome.jgi-psf.org/cgi-bin/kogBrowser?db=Dappu1) where p-
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value was calculated with a Fisher exact Test (40) and correct for multiple testing with the Benjamin-Hochberg
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method (35). Pathway analysis with KEGG reference pathway maps revealed differential expression of
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pathways, where p-value was calculated with a Fisher Exact Test (40) and corrected with Benjamin-Hochberg
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method (35) for multiple testing. KEGGSOAP package (37) was used in R to query KEGG databases for full
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pathway annotation. Pathway analysis was executed with both annotated enzyme number classification and
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Kegg Orthology classification as input identifiers. A global metabolic pathway map was created within KEGG
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through KEGGSOAP from R. In addition, gene lists were analyzed with Ingenuity Pathway Analysis
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(Ingenuity® Systems, www.ingenuity.com). The input identifier for Ingenuity Pathway Analysis was the
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UniProt accession number for each gene. Genes from the dataset that had an absolute M-value larger than 1 and
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a q-value
