- Research article
- Open Access
Transcriptome sequencing and microarray development for the Manila clam, Ruditapes philippinarum: genomic tools for environmental monitoring
© Milan et al; licensee BioMed Central Ltd. 2011
- Received: 19 November 2010
- Accepted: 12 May 2011
- Published: 12 May 2011
The Manila clam, Ruditapes philippinarum, is one of the major aquaculture species in the world and a potential sentinel organism for monitoring the status of marine ecosystems. However, genomic resources for R. philippinarum are still extremely limited. Global analysis of gene expression profiles is increasingly used to evaluate the biological effects of various environmental stressors on aquatic animals under either artificial conditions or in the wild. Here, we report on the development of a transcriptomic platform for global gene expression profiling in the Manila clam.
A normalized cDNA library representing a mixture of adult tissues was sequenced using a ultra high-throughput sequencing technology (Roche 454). A database consisting of 32,606 unique transcripts was constructed, 9,747 (30%) of which could be annotated by similarity. An oligo-DNA microarray platform was designed and applied to profile gene expression of digestive gland and gills. Functional annotation of differentially expressed genes between different tissues was performed by enrichment analysis. Expression of Natural Antisense Transcripts (NAT) analysis was also performed and bi-directional transcription appears a common phenomenon in the R. philippinarum transcriptome. A preliminary study on clam samples collected in a highly polluted area of the Venice Lagoon demonstrated the applicability of genomic tools to environmental monitoring.
The transcriptomic platform developed for the Manila clam confirmed the high level of reproducibility of current microarray technology. Next-generation sequencing provided a good representation of the clam transcriptome. Despite the known limitations in transcript annotation and sequence coverage for non model species, sufficient information was obtained to identify a large set of genes potentially involved in cellular response to environmental stress.
- Gene Ontology
- Digestive Gland
- Venice Lagoon
- Manila Clam
- Basic Local Alignment Search Tool Search
The Manila clam Ruditapes philippinarum (Adams & Reeve, 1850) is a bivalve mollusc of the family Veneridae native to the Indo-Pacific region. Because of its commercial value as seafood, this species has been introduced to other regions, where it has become permanently established. In Europe it was first imported in 1972 in France. Additional introductions occurred from Oregon to the United Kingdom, followed by numerous transfers within European waters for aquaculture purposes (Portugal, Ireland, Spain, and Italy). Natural reproduction of introduced individuals favored geographical expansion into the wild, particularly in Italy, France, Spain and Ireland where the Manila clam proved to be more resistant and grew faster than the endemic carpet-shell clam, R. decussatus. Consequently, R. philippinarum displaced its autochthonous congeneric species in most areas, and now represents the most important species for commercial clam landings in Europe. Globally, harvest of R. philippinarum has experienced a dramatic increase in the last 20 years, currently representing one of the major aquacultured species in the world (3.36 million metric tons in 2008). China is by far the leading producer (97.4% of total annual production) while Italy has a smaller but yet conspicuous production of over 65,000 tonnes per year .
Despite the relevance of Manila clam landings in world aquaculture, genomic resources for R. philippinarum are still extremely limited . A small set of genetic markers is available  and only 5,707 transcripts has been sequenced and are already available on GENBANK. Although R. philippinarum is considered a robust species, capable of adapting to a wide range of environments, infectious diseases, chronic parasitic (e.g. Perkinsus -like microorganisms) and bacterial (e.g. brown ring bacterial disease) infections, it has been suffering mass mortality that have caused severe production losses in different areas (European Atlantic waters, Yellow Sea) . The impact of infections is often aggravated under particular environmental conditions, such as extreme temperatures or limited availability of oxygen or nutrients. However, massive mortalities are rarely explained by a single parameter. An understanding of the interactions among different biotic and abiotic factors influencing survival is therefore a high priority for clam aquaculture. Functional genomics, or more specifically physiological genomics, i.e. a global analysis of transcriptome responses to different conditions, offers unprecedented opportunities to achieve such a goal. For instance, a genomic analysis was recently used to investigate summer mortality in the Pacific oyster . To this end, the development of transcriptomic tools for the Manila clam is the first necessary step.
A second and possibly more important application of global gene expression profiling in R. philippinarum is environmental monitoring. Genomic technologies are increasingly used to evaluate the biological effects of various chemical pollutants on aquatic animals under either controlled conditions or in natural environments (e.g. [5, 6]). While several hurdles remain to be overcome, the outlook for eco-toxicogenomics is extremely promising . A sessile, filter-feeding organism living in the seafloor sediment, R. philippinarum represents an excellent "sentinel" species to assess the quality of marine environment. Two recent studies correlating different biochemical, cellular, and organismal markers with levels of pollutants in the sediment  or accumulated in the animals  support this view. However, a limited set of multiple biomarkers is usually employed in most of the studies. Therefore, a transcriptomic approach could provide a much broader analysis of different biological processes allowing for an integrated description of responses to xenobiotics [5, 6].
The aim of the present study was to fill the gap in transcriptome sequence data available for the Manila clam and to develop a reliable and informative platform for global gene expression profiling, to be then applied to environmental monitoring. To this end, next-generation sequencing was coupled with a technology, in situ synthesized oligo array, which has provided a robust and flexible microarray platform in other species using conventional Sanger sequencing [10–18].
To date, 454 mollusc data are available only for Mytilus galloprovincialis and Bathymodiolus azoricus [19, 20], and to our knowledge, this is the first report of an oligo DNA microarray developed using ultra-high throughput pyrosequencing in a mollusc species. A free web-accessible database including extensive transcript annotation and a blast search option was also developed in support of the gene expression platform.
In order to assess the feasibility of this newly developed R.philippinarum microarray to toxicogenomics, a preliminary investigation has been performed by profiling gene expression in gills and digestive glands of clams sampled in the industrial area of Marghera, a highly polluted site of the Venice Lagoon, compared to animals sampled in a clean area of the lagoon of Venice.
Next-generation sequencing and hybrid contig assembly
Summary of generated Ruditapes philippinarum ESTs and assembly results with statistics describing different properties of transcriptome contig sequences available in Ruditapes philippinarum Database (compgen.bio.unipd.it/RuphiBase)
First run of hybrid assembly
Meta-contigs of second assembly
Total (Ruditapes philippinarum transcriptome)
Mean length (bp)
Max length (bp)
Mean Average quality (Phred)
Putative identities of assembled contigs and meta-contigs were obtained by running Blastx and Blastn similarity searches on several protein and nucleotide databases (see Additional file 1). Of 32,606 unique sequences, 7,907 (24%) showed at least one significant match (e < 10-5) in the NCBI non-redundant protein database. The use of Blast2GO software allowed the association of one or more GO terms to 6,867 R. philippinarum data base entries. Of these, 2,788 were linked to "Biological Process" (BP) GO entries, 2,880 to "Cellular Component" (CC) entries, and 3,141 to "Molecular Function" (MF) entries. Unique GO terms represented in R. philippinarum entries were 1,515 for BP, 380 for CC, and 655 for MF. A simplified view of these GO terms using a "Generic GO Slim" showed 46 BP, 30 CC, and 34 MF classes (see Additional File 2).
In addition to the annotation with Blast2GO, Blast searches against UniProtKB/Swiss-Prot database, UniProtKB/TrEMBL database and 26 different species-specific data bases (see Additiona file 1) were implemented in order to further increase the number of putatively annotated R. philippinarum contigs (see Methods for details). This approach provided a significant match for additional 1,840 transcripts, which showed no previous correspondence with either the NCBI non-redundant protein or nucleotide database, and brought the final number of clam entries associated with a known protein or transcript to 9,747 (30%).
RuphiBase, a Ruditapes philippinarum database
All 32,606 contig sequences as well as different layers of results for data analysis are available through RuphiBase , a free web-accessible database implemented using MySQL and Django web framework. RuphiBase is centered on contig sequence and annotation, and can be searched by contig ID and key word match on different textual fields. Moreover, it allows the user to conduct a local BLAST search on the fly against the transcripts database, in order to identify one or more transcripts significantly similar to a given query sequence. Indeed, massive and customizable data retrieval is provided by a browsing system. For each contig, a gene-like entry shows different data and bioinformatic analyses results according to the scheme detailed below:
• Contig information. For each contig, identified by an ID and a preliminary description, the FASTA sequence is given, along with an informative contig description, which is defined by the Blast2GO natural language text mining functionality, applied to BLAST hits description. The best hit is used when a BLAST2GO description is unavailable.
• Assembly. The list of reads belonging to the contig is given together with two FASTA files which include all read sequences, contig with reads and ESTs sequences and ACE format multiple alignment of the contig with reads and ESTs.
• Gene Ontology. GO terms associated to each transcript are given for BP, MF, and CC, with hyper-link to the GO database.
BLAST results. BLAST results, for both nucleotide and protein database searches, are shown in a dedicated section in the classic BLAST output format. These results are hyperlinked to external databases, and include the list of alignment descriptions and details about the pairwise alignments of each transcript with the corresponding BLAST hits.
Microarray quality assessment
A total of seven microarray experiments (three biological replicates for gills and four for the digestive gland) were carried out. After data extraction, hybridization success for each probe was inferred if flag "glsFound" values was equal to 1 (see Methods). Across all experiments, only 131 probes (0.3%) never showed a signal higher than the background, while 19,360 probes (46%) were always successful and 37,379 (88%) were successful in at least four experiments.
Correlation coefficients on the entire set of expression values across biological replicates (**p-value < 0.01)
Digestive gland Pool 1
Digestive gland Pool 2
Digestive gland Pool 3
Digestive gland_Pool 2
Digestive gland _Pool 3
Digestive gland _Pool 4
Comparison of gene expression in the digestive gland and gills
Fluorescence data microarray experiments of three biological replicates consisting of pooled digestive glands and three pools of gills sampled in Alberoni, a clean area in the Venice Lagoon, were normalized and used to identify genes that were differentially expressed in different tissues.
GO terms significantly represented among up-regulated genes in digestive gland compared to gills tissue
dre00980:Metabolism of xenobiotics by cytochrome P450
GO:0040008~regulation of growth
GO:0001558~regulation of cell growth
GO:0009308~amine metabolic process
GO:0048699~generation of neurons
GO:0016052~carbohydrate catabolic process
GO:0048869~cellular developmental process
GO:0005975~carbohydrate metabolic process
GO:0007399~nervous system development
GO:0006066~alcohol metabolic process
GO:0009653~anatomical structure morphogenesis
GO:0006629~lipid metabolic process
GO:0016021~integral to membrane
GO:0031224~intrinsic to membrane
GO:0005509~calcium ion binding
GO:0070011~peptidase activity, acting on L-amino acid peptides
GO:0004197~cysteine-type endopeptidase activity
GO:0008234~cysteine-type peptidase activity
Biological Process (BP), Cellular Component (CC), KEGG pathways (KP) and Molecular Function (MF) significantly represented by at least 10 genes up-regulated in gills compared to digestive gland
dre04310:Wnt signaling pathway
dre04621:NOD-like receptor signaling pathway
dre04630:Jak-STAT signaling pathway
dre04622:RIG-I-like receptor signaling pathway
dre04620:Toll-like receptor signaling pathway
GO:0050794~regulation of cellular process
GO:0034622~cellular macromolecular complex assembly
GO:0050789~regulation of biological process
GO:0007166~cell surface receptor linked signal transduction
GO:0034621~cellular macromolecular complex subunit organization
GO:0022607~cellular component assembly
GO:0050896~response to stimulus
GO:0042981~regulation of apoptosis
GO:0065003~macromolecular complex assembly
GO:0032501~multicellular organismal process
GO:0009653~anatomical structure morphogenesis
GO:0001568~blood vessel development
GO:0043067~regulation of programmed cell death
GO:0010941~regulation of cell death
GO:0044085~cellular component biogenesis
GO:0003700~transcription factor activity
GO:0019001~guanyl nucleotide binding
GO:0032561~guanyl ribonucleotide binding
Strand orientation and antisense transcripts
Comparison between sense and antisense probes for each probe pair
FC < 1.5
1.5 < FC < 3
3 < FC < 10
FC > 10
F LS < 10
10 < F LS < 100
100 < F LS < 1000
F LS > 1000
F LS < 10
10 < F LS < 100
100 < F LS < 1000
F LS > 1000
Gene/transcripts represented with both sense and antisense probe pairs differentially expressed between gills and digestive gland (FC threshold set to 1.5)
Discordant S-AS pairs
Concordant S-AS pairs
UP-regulated in digestive gland
UP-regulated in gills
Clam genomic markers for environmental monitoring
A wide array of biochemical, cellular, and whole-organism markers have been applied to evaluate the biological effects of different types of pollutants in aquatic animals and to assess the status of marine ecosystems [31, 32]. For instance, over-expression of metallothioneins (MTs) has been associated with exposure to heavy metals, inhibition of acetylcholinesterase (AChE) with organophosphorous, pesticide exposure, and induction of Vitellogenin (Vg) proteins (egg-yolk precursors) with the presence of xenoestrogens (endocrine-disruptors).
In the R. philippinarum platform developed in this study at least four transcripts (ruditapes_c21946, ruditapes_c30181, ruditapes_c7664, ruditapes_c12315) that appear to be AChE precursors and ten different expressed sequences (ruditapes_lrc32058, ruditapes_lrc32676, ruditapes2_c61, ruditapes2_c830, ruditapes2_lrc2117, ruditapes2_lrc4331, ruditapes2_lrc4377, ruditapes2_lrc4388, ruditapes2_lrc5136, ruditapes2_lrc5747) coding for a putative metallothionein were incorporated into the microarray. Finally, a transcript (ruditapes_c16240) showing a significant match with invertebrate Vg proteins was also included. It is worth mentioning that the lack of a specific anti-Vg antibody for many species impairs direct measure of such biomarker, and only indirect estimates of Vg concentration can be obtained using an alkali-labile phosphate (ALP) assay.
At the cellular level, loss of lysosomal membrane integrity has been observed as a consequence of oxidative stress induced by several class of chemicals. Reduced lysosomal membrane stability is also linked to increased autophagy . To which extent these biochemical and cellular markers might be mirrored by gene expression markers present in RuphiBase based on GO-CC annotation, 73 lysosomal proteins including several cathepsins and other hydrolases could be found in the current clam transcriptome. Of note is a putative homolog (ruditapes_c23093) for Autophagic Transcript 12 (ATG12), an ubiquitin-like modifier necessary for macroautophagy, while several RuphiBase entries match with p14/ROBLD3, which is part of a protein complex that recruits mTOR (Mammalian Target Of Rapamycin), a key negative regulator of autophagy, to the lysosome membrane . Further studies may be conducted to test whether chemical pollutants affecting lysosomal stability can induce alterations in expression levels of lysosomal and/or autophagy-related proteins. Indeed, tributyltin chloride has recently been shown to inhibit mTOR in neuronal cells .
A correct classification of GST proteins is often difficult , but it is mostly important when correlating the expression of different GST-encoding genes with exposure to specific groups of environmental pollutants, as the various GST classes show diverse substrate specificities, catalytic properties, and tissue distribution.
Gene expression profiling of Manila clam sampled in a polluted area of the Venice lagoon
The Venice lagoon, the largest in the Mediterranean sea, is characterized by the presence of complex mixtures of xenobiotics, derived from both industrial and domestic effluents, which reach higher concentrations in specific areas, mainly close to the industrial zone of Marghera. Gene expression profiles of digestive glands and gills from Manila clams harvested in a cleaner area (Alberoni) of the Venice lagoon were compared to the corresponding tissues of clams sampled within the industrial area. This area shows high levels of contamination with different xenobiotics, as confirmed in various studies  and it is currently restricted for clam harvesting.
For each tissue and comparison, raw and normalized fluorescence have been deposited in the GEO data base  under accession number GEO:GSE27194. A two-unpaired class SAM test was carried out separately for digestive glands and gills on normalized data, enforcing a False Discovery Rate (FDR) of 10% and Fold Change (FC) of 1.5.
Comparison of expression profiles between the two areas revealed a remarkably large number of differentially expressed transcripts in both tissues, respectively 1,127 in the digestive gland and 2,432 in the gills. A limited set of transcripts (99) showed differential expression in both tissues. Fold-change differences varied from -174- to 1,446-fold in the gills, with a prevalence for up-regulated transcripts (1,412) compared to down-regulated ones (1,020) in samples collected in the industrial area. This trend is reversed for transcripts displaying the strongest signal, as 93 probes showed FC > 5 (13 with FC > 10), whereas 120 ones presented FC < -5 (22 with FC > 10). In the digestive gland, FC ranged between -30- and 62-fold. A significant bias toward up-regulated transcripts (852, 75% of all differentially expressed sequences, binomial test p < 0.00001) was observed in animals sampled in the industrial area, a bias that was even stronger for transcripts showing FC larger than ± 5-fold (94 with FC > 5, 26 with FC < -5; binomial test p < 0.000001).
Putative annotations were obtained respectively for 321 digestive gland- and 830 gills-specific transcripts by comparison against the NCBI protein non redundant database. When using the zebrafish transcriptome as a reference, respectively 247 (digestive gland) and 730 (gills) differentially expressed sequences could be associated with one D. rerio Ensembl Gene IDs (see Addition file 4).
In a comparison between natural population samples different environmental and/or physiological factors can influence gene expression profiles. The objective of the present study was to assess the role of chronic exposure to high levels of chemical pollution. To control for the effects of other factors, histological examination of collected animals was carried out showing similar sex ratio (1:1), comparable levels of parasitic contamination, average size (12.3 gr vs 14 gr), and reproductive stage (data not shown). Water temperature and salinity showed no significant differences between the two analyzed areas. Indeed, the temperature and salinity recorded at the time of sampling were 18°C and 32 ‰ and 20°C and 34‰ in Marghera and Alberoni respectively. Likewise, it seems quite difficult that strong genetic differentiation occurs at a such a small geographic scale (few kilometres), in the presence of a planktonic larval phase and a sustained water circulation within the Venice lagoon. Although evidence on population genetics for the Manila clam is limited, it has been shown that no genetic structure was present across four population samples in the Adriatic Sea, including the Venice lagoon .
GO terms significantly over-represented, among genes differentially expressed, between Alberoni and Marghera samples, in both gills and digestive gland
DAVID analysis of digestive gland differential expressed genes
GO:0010033~response to organic substance
GO:0046686~response to cadmium ion
GO:0051597~response to methylmercury
GO:0045259~proton-transporting ATP synthase complex
dre00980:Metabolism of xenobiotics by cytochrome P450
DAVID analysis of gills differential expressed genes
GO:0044267~cellular protein metabolic process
GO:0015980~energy derivation by oxidation of organic comp.
GO:0034645~cellular macromolecule biosynthetic process
GO:0019538~protein metabolic process
GO:0006091~generation of precursor metabolites and energy
GO:0044237~cellular metabolic process
GO:0003735~structural constituent of ribosome
GO:0005198~structural molecule activity
GO:0015078~hydrogen ion transmembrane transporter activity
GO:0016859~cis-trans isomerase activity
GO:0003755~peptidyl-prolyl cis-trans isomerase activity
GO:0008092~cytoskeletal protein binding
GO:0015075~ion transmembrane transporter activity
dre04260:Cardiac muscle contraction
dre00630:Glyoxylate and dicarboxylate metabolism
Four transcripts encoding MTs (ruditapes2_lrc4377, ruditapes_lrc32058, ruditapes2_c830, ruditapes2_lrc4331) and two encoding sulfotransferase (SULT) (ruditapes_c20565, ruditapes_c28883) (see Additional file 4) are over-expressed in samples from the industrial zone. MTs provide protection against metal toxicity, are involved in the regulation of physiological metals (Zn and Cu) and provide protection against oxidative stress. MTs can be induced either by essential metals (Cu and Zn) or non-essential ones (Cd, Ag and Hg) in both vertebrates and invertebrates. Increased levels of MTs after experimental exposure to high Cu concentrations had been already reported in the digestive gland of R. philippinarum , while higher MT protein expression had been found in clams collected at sites nearby the industrial zone of Marghera [43–45].
SULTs, a family of phase II detoxification enzymes, are involved in the homeostasis of endogenous compounds as well as in the protection against xenobiotics. It is well known that sulfated products of environmental xenobiotics are more water-soluble and easily excreted from the body. Channel catfish (Ictalurus punctatus) exposed to Polycyclic aromatic hydrocarbons (PAHs) showed a marked induction of phenol-type sulfotransferase enzyme activity . In addition, SULT1 was up-regulated in Gadus morhua male sampled in two contaminated sites of western Norway . Although these genes play a documented role in the defense from chemicals , to our knowledge they have never been proposed as biomarkers in bivalve species.
AChE enzymatic activity is inhibited in response to organophosphate insecticides and exposure to other pollutants. Eight different clam transcripts encoding a peptide with putative cholinesterase activity are represented in the R. philippinarum microarray.
In the present study, an AChE-encoding gene (ruditapes_c12315) was over-expressed in both gills and digestive glands of clams sampled in Marghera. A similar finding has been already reported by Somnuek et al. (2009) , who demonstrated up-regulation of AChE gene expression in hybrid catfish exposed to chlorpyrifos and proposed this gene as biomarker for detecting the effects of organophosphate insecticides. The apparently opposite transcriptional response on AChE gene expression likely represents a compensatory modification to counteract inhibition of enzyme activity after xenobiotic exposure.
Several GST-coding transcripts were also found up-regulated in samples collected in the polluted area. Glutathione S-transferase (GST) catalyses the conjugation of reduced glutathione to electrophilic centers on a wide variety of substrates. This activity detoxifies endogenous compounds (e.g. peroxidised lipids) as well as xenobiotics and an increased of GSTs activity has been observed after exposure to a broad set of xenobiotics.
Up-regulated GST coding transcripts found up-regulated in samples collected in the polluted area of Marghera
Whole-transcriptome analysis holds the promise to shed light on the genetic mechanisms underlying cellular and organismal response to physiological and pathological conditions (environmental stress, infections, chemical pollution). This is of particular importance for improved shellfish aquaculture and for cost-effective environmental monitoring. The aim of the present paper was to lay the foundations for transcriptomics in the Manila clam. To which extent this goal has been achieved? As demonstrated in previous studies , the use of next-generation sequencing technology yielded a number of expressed sequences unattainable until only recently. In our study, sequence assembly, annotation and development of a dedicated database resulted in a searchable, functionally annotated transcriptome for R. philippinarum (RuphiBase), which was then used to design a species-specific in-situ synthesized oligo microarray. This genomic platform has proven to provide reliable and highly reproducible results for global gene expression profiling [10–18]. Moreover, validation of the clam oligo microarray showed tissue-specific expression profiles and highly significant correlations across biological replicates. The current version of RuphiBase appears to offer already a good representation of the clam transcriptome, as shown by the broad array of potential markers of response to xenobiotics. Of particular relevance is the large number (>100) of GST-encoding transcripts observed in the Manila clam, which suggested a potential relationship between filter-feeding behaviour, ability to cope with high levels of pollution and availability of a wide array of detoxifying enzymes. The possible use of this microarray platform for toxicogenomic studies has been also demonstrated by comparative analysis of digestive glands and gills pool of Manila clam sampled in areas with different levels of chemical pollution of the Venice Lagoon.
On the other hand, despite the use of ultra-high throughput sequencing on normalized cDNA libraries constructed from all adult tissues, representation of the clam transcriptome is still incomplete. For instance, the signaling pathway for autophagy consists of at least 18 different components , yet only one of these, ATG12, a protein involved in autophagic vescicle assembly, was identified. The problem of incomplete representation of protein-coding transcripts will likely be solved in the near future, when reduction of sequencing costs and an increase in sequencing throughput will allow a much deeper sequence coverage even for non-model species transcriptomes. A more difficult issue to solve is the limited percentage of clam transcripts that can be matched against a known protein-coding gene. The large phylogenetic distance of the phylum Mollusca from other metazoan model species (e.g. Drosophila melanogaster, Caenorhabditis elegans, Danio rerio, Mus musculus, Homo sapiens) greatly reduces the power of a comparative approach for functional annotation. The only molluscan genome sequenced so far is that of L. gigantea, a gasteropod snail, which is functionally and evolutionarily distant from the class Bivalvia.
To conclude on a positive note, the next "call on (genomic) stage" is for the Pacific oyster, Crassostrea gigas. For this bivalve mollusk species, a high quality draft genome sequence is expected in 2011 thanks to the efforts of the Oyster Genome Consortium. Furthermore, worldwide aquaculture production of oysters amounts to over 4 million metric tons. The economic importance of the Pacific oyster has fuelled a large number of studies on the ecology, physiology, immunology, and genetics of C. gigas populations, and the possibility of targeted gene knock down has been recently demonstrated . The opportunity of having a bivalve model species available would allow a more accurate genome annotation for other important molluscs such as the Manila clam.
Sampling, cDNA library costruction and sequencing
Samples of R. philippinarum were bought in a local market in Faro. In order to improve RNA representatively, clams were stressed by submitting them to quick changes of temperature and salinity prior to be sacrificed. Total RNA was extracted from all tissues of 20 individuals using the acid guanidinium thiocyanate-phenol-chloroform method .
Two libraries were constructed, one using a mixture of adult tissues and a second one using gonadal tissues and 2 to 4 mm long larvae.
A cDNA library was constructed using equal amounts of RNA and normalized for sequencing. The SMART (Switching Mechanism At 5' end of RNA Template) kit from BD Biosciences Clontech was used to construct the cDNA libraries which were later normalised using the duplex-specific nuclease (DSN) method .
Approximately 15 μg of normalized cDNA were used for sequencing library construction at the Max Planck Institute, following procedures described in . Sequencing was performed using GS FLX Titanium series reagents and using one single region on a Genome Sequencer FLX instrument. Bases were called with 454 software by processing the pyroluminescence intensity for each bead-containing well in each nucleotide incorporation. Reads were trimmed to remove adapter sequences.
A total of 457,717 sequence reads were produced using Roche 454 FLX technology from the normalized cDNA library constructed using a mixture of adult tissues (see above). The same library was previously used to obtain 2,866 ESTs with Sanger sequencing. An additional set of 2,790 ESTs was available from a second normalized cDNA library (whole larvae and adult gonads). In addition, 51 mRNA sequences available in NCBI (as to 11th November 2009) for R. philippinarum were available.
454 Sequence reads and all previously ESTs accessible in the NCBI database were then assembled into contigs, representing putative transcripts, by using a custom procedure based on two runs of MIRA3 assembly  and quality-based filtering. All contigs obtained with the first run of hybrid assembly were used for a second run to eliminate contig redundancy. Threshold values on contigs length and sequence quality were then applied to obtain a final set of contigs representing R. philippinarum transcripts.
The Basic Local Alignment Search Tool (BLAST) was used to perform annotation of R. philippinarum contigs. Batch Blast similarity searches for the entire set of contigs were locally conducted against NCBI (National Centre for Biotechnology Information) amino acidic non redundant (nr) database (release of October 4 2009) using Blastx option. Alignments with an E-value of at most 1 E-3 were considered significant and up to 20 hits per contig were taken into account.
To improve the number of annotated contigs five different approaches were attempted (see Additional file 1): i) blastx searches (cut off e-value of < 1.0 E-3) against protein database UniProtKB/SwissProt and UniProtKB/TrEMBL , ii) blastx (cut off e-value of < 1.0 E-3) and blastn (cut off e-value of < 1.0 E-5) searches against proteins and high quality draft trascriptomes of Danio rerio, Gasterosteus aculeatus, Oryzias latipes, Takifugu rubripes, Tetraodon nigroviridis, Homo sapiens, Drosophila melanogaster available on Ensembl Genome Browser (release 56) , iii) blastx (cut off e-value of < 1.0 E-3) and blastn (cut off e-value of < 1.0 E-5) searches against proteins, transcripts and assembly scaffolds of Lottia gigantea v1.0 database , iv) blastn search (cut off e-value of < 1.0 E-5) against D. rerio, L. gigantea, O. latipes, T. rubripes, Salmo salar, H. sapiens, Oncorhynchus mykiss databases stored in NCBI UniGene database , v) blastn search (cut off e-value of < 1.0 E-5) against Crassostrea gigas transcripts database  and Argopecten irradians EST database .
The Gene Ontology (GO) terms associations for "Biological process", "Molecular function" and "Cellular component" were performed using Blastx algorithm against the NCBI amino acid nr database implemented in Blast2GO software . The "Generic GO slim"  set of the CateGOrizer program  was used to have an overview of the gene ontology content by simplifying the results of the GO annotation.
DNA microarray design
Probe design started with selection of target sequences to be represented onto the R. philippinarum microarray. All annotated entries (9,747) were included. Non annotated transcripts were considered only if sequence length was ≥400 bp and average Phred sequence quality was ≥30, yielding 24,291 target sequences. As most sequence reads were obtained from a non directional cDNA library, sense strand orientation was inferred putatively from that of homologuous protein sequences of other species (see Methods).
One probe for annotated transcripts with known orientation was designed to construct a high-density oligo-DNA microarray, while two probes with both orientations were designed for contigs with ambiguous orientation. The same strategy was applied to unknown unique transcripts. For 8,239 contigs, the putative orientation was unambiguous across different databases and a single sense probe was designed. Two probes with opposite orientation (sense and antisense) were designed for a fraction of clam annotated transcripts (1,508 contigs) with ambiguous putative orientation and for non annotated sequences (14,544). Probe design was carried out using the Agilent eArray interface , which applies proprietary prediction algorithms to design 60 mer oligo-probes. Microarrays were synthesized in situ using the Agilent ink-jet technology with a 4 × 44 K format. Each array included default positive and negative controls.
A total of 40,332 out of 40,343 (99.9%) probes, representing 24,281 R. philippinarum transcripts were successfully obtained. Of these, 2,000 probes designed on known-orientation transcripts, were synthesized in duplicate on the array in order to test for "reproducibility-within-array". The percentage of annotated transcripts represented on the microarray was 40.1%. Probe sequences and other details on the microarray platform can be found in the GEO database  under accession number GEO:GPL10900.
Sample collection, RNA extraction, labeling and hybridization
The common bivalves R. philippinarum were collected during autumn 2009 in two different areas of Venice Lagoon characterized by different levels of environmental pollutants: Marghera and Alberoni (see Additional file 5).
Digestive gland and gills were dissected from 20 Manila clamsfor each sampling area. Four and three independent pools, for digestive gland and gills respectively, each consisting of 5 digestive gland or gills, were created.
Total RNA was extracted from pooled tissue samples using the RNAeasy Mini Kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. RNA concentration was determined using a UV-Vis spectrophotometer, NanoDrop® ND-1000 (NanoDrop Technologies, Wilmington, USA). RNA integrity and quality was finally estimated on an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA).
Sample labeling and hybridization were performed according to the Agilent One-Color Microarray-Based Gene Expression Analysis protocol. Briefly, for each pool 200 ng of total RNA were linearly amplified and labeled with Cy3-dCTP. A mixture of 10 different viral poly-adenilated RNAs (Agilent Spike-In Mix) was added to each RNA sample before amplification and labeling, to monitor microarray analysis work-flow. Labeled cRNA was purified with Qiagen RNAeasy Mini Kit, and sample concentration and specific activity (pmol Cy3/μg cRNA) were measured in a NanoDrop® ND-1000 spectrophotometer. A total of 1,650 ng of labeled cRNA was prepared for fragmentation adding 11 μl 10X Blocking Agent and 2.2 μl of 25X Fragmentation Buffer, heated at 60°C for 30 min, and finally diluted by addition with 55 μl 2X GE Hybridization buffer. A volume of 100 μl of hybridization solution was then dispensed in the gasket slide and assembled to the microarray slide (each slide containing four arrays). Slides were incubated for 17 h at 65°C in an Agilent Hybridization Oven, subsequently removed from the hybridization chamber, quickly submerged in GE Wash Buffer 1 to disassembly the slides and then washed in GE Wash Buffer 1 for approximately 1 minute followed by one additional wash in pre-warmed (37°C) GE Wash Buffer 2.
Data acquisition and analysis
Hybridized slides were scanned at 5 μm resolution using an Agilent G2565BA DNA microarray scanner. Default settings were modified to scan the same slide twice at two different sensitivity levels (XDR Hi 100% and XDR Lo 10%). The two linked images generated were analyzed together and data were extracted and background subtracted using the standard procedures contained in the Agilent Feature Extraction (FE) Software version 9.5.1. The software returns a series of spot quality measures in order to evaluate the goodness and the reliability of spot intensity estimates. All control features (positive, negative, etc.), except for Spike-in (Spike-in Viral RNAs), were excluded from subsequent analyses. Spike-in control intensities were used to identify the best normalization procedure for each dataset. After normalization, spike intensities are expected to be uniform across the experiments of a given dataset. Normalization procedures were performed using R statistical software . Quantile normalization always outperformed cyclic lowess and quantile-normalized data were used in all subsequent analyses.
Statistical tests implemented in the program Significance Analysis of Microarray (SAM)  were used to identify differentially expressed genes between digestive gland and gill tissues. The same approach was used to identify differentially expressed genes in both digestive glands and gills between MA and AL sampled individuals.
Pearson correlation coefficients were estimated within and among arrays with Statgraphics Centurion XVI to evaluate repeatability and precision of the obtained microarray data.
Functional enrichment of differentially expressed genes
Functional annotation analysis of differentially expressed genes was performed using the DAVID (Database for Annotation, Visualization and Integrated Discovery) web-server .
Functional annotation of differentially expressed genes between gills and digestive glands was achieved using DAVID software. "Biological process", "Molecular function" and "Cellular component" annotation was carried out by setting gene count = 10 and ease = 0.05. KEGG pathway analysis was then performed with gene count = 4 and ease = 0.05. David analyses of differentially expressed genes between Manila clam tissues sampled in Alberoni and Marghera were performed by setting gene count = 2 and ease = 0.1 Since DAVID databases contain functional annotation data for a limited number of species, it was necessary to link R. philippinarum transcripts with sequence identifiers that could be recognized in DAVID (Ensembl Human Gene IDs and Ensembl Zebrafish Gene IDs). This was carried out through dedicated Blast searches implemented as follows: i) blastx and blastn options were both used to search significant matches of the Manila clam sequences directly against human Ensembl proteins and transcripts respectively, ii) a first search was performed using either blastn or blastx against all zebrafish Ensembl proteins. Finally, Homo sapiens Ensembl Gene IDs were obtained from the corresponding Ensembl protein entries using the BIOMART data mining tool .
Evolutionary analyses were performed to determine patterns of divergence of the GST genes in R. philippinarum and to define putative orthology between GST genes in different species. Protein sequences of GST domains were aligned using TCoffee  applying default settings, while GBlock  was used to eliminate poorly aligned positions and divergent regions prior to phylogenetic analysis.
GST sequences described from Homo sapiens, Haliotis discus, Unio timidus, Cristaria plicata, Mytilus edulis, Mytilus galloprovincialis, Danio rerio, Dreissena polymorpha, Chlamys farreri, Corbicula fluminea, Mercenaria, mercenaria, Laternula elliptica, Mactra veneriformis, Cipangopaludina cathayensis were included in the alignment.
Phylogenetic trees were inferred by the maximum likelihood (ML) method  using the Phyml 2.4.4 program . Non-parametric bootstrap resamplings were performed to evaluate the robustness of tree topology.
We would like to thank Lino Pavan for providing access to the samples and TINAMENOR S.A. for providing clam larvae. This work was partially supported by a grant from European Union-funded Network of Excellence "Marine Genomics Europe". CS wishes to acknowledge additional funding from the Ministry of Education and Science (Spain) through grant AGL2007-60049. MM had a PhD scholarship from the University of Florence, Italy. RL was recipient of PhD fellowship SFRH/BD/30112/2006, from the Portuguese Science and Technology Foundation (FCT) and LC and RL acknowledge a grant from FCT project ISOPERK (PTDC/CVT/72083/2006). We thank also two anonymous reviewers for their useful comments on an earlier version of the manuscript.
- FAO: [http://www.fao.org/fishery/culturedspecies/Ruditapes_philippinarum/en]
- Saavedra C, Bachère E: Bivalve genomics. Aquaculture. 2006, 256: 1-14. 10.1016/j.aquaculture.2006.02.023.View ArticleGoogle Scholar
- Yasuda N, Nagai S, Yamaguchi S, Lian CL, Hamaguchi M: Development of microsatellite markers for the Manila clam Ruditapes philippinarum. Mol Ecol Notes. 2007, 7: 43-45.View ArticleGoogle Scholar
- Fleury E, Moal J, Boulo V, Daniel JY, Mazurais D, Hénaut A, Corporeau C, Boudry P, Favrel P, Huvet A: Microarray-Based Identification of Gonad Transcripts Differentially Expressed Between Lines of Pacific Oyster Selected to Be Resistant or Susceptible to Summer Mortality. Mar Biotechnol (NY). 2010, 12: 326-39. 10.1007/s10126-009-9227-9.View ArticleGoogle Scholar
- Leaver MJ, Diab A, Boukouvala E, Williams TD, Chipman JK, Moffat CF, Robinson CD, George SG: Hepatic gene expression in flounder chronically exposed to multiply polluted estuarine sediment: Absence of classical exposure 'biomarker' signals and induction of inflammatory, innate immune and apoptotic pathways. Aquat Toxicol. 2009, 96: 234-245.View ArticlePubMedGoogle Scholar
- Falciani F, Diab AM, Sabine V, Williams TD, Ortega F, George SG, Chipman JK: Hepatic transcriptomic profiles of European flounder (Platichthys flesus) from field sites and computational approaches to predict site from stress gene responses following exposure to model toxicants. Aquat Toxicol. 2008, 90: 92-101. 10.1016/j.aquatox.2008.07.020.View ArticlePubMedGoogle Scholar
- Van Aggelen G, Ankley GT, Baldwin WS, Bearden DW, Benson WH, Chipman JK, Collette TW, Craft JA, Denslow ND, Embry MR, Falciani F, George SG, Helbing CC, Hoekstra PF, Iguchi T, Kagami Y, Katsiadaki I, Kille P, Liu L, Lord PG, McIntyre T, O'Neill A, Osachoff H, Perkins EJ, Santos EM, Skirrow RC, Snape JR, Tyler CR, Versteeg D, Viant MR, Volz DC, Williams TD, Yu L: Integrating omic technologies into aquatic ecological risk assessment and environmental monitoring: hurdles, achievements, and future outlook. Environ Health Perspect. 2010, 118: 1-5.PubMedGoogle Scholar
- Moschino V, Delaney E, Meneghetti F, Ros LD: Biomonitoring approach with mussel Mytilus galloprovincialis (Lmk) and clam Ruditapes philippinarum (Adams and Reeve, 1850) in the Lagoon of Venice. Environ Monit Assess. 2010Google Scholar
- Matozzo V, Binelli A, Parolini M, Locatello L, Marin MG: Biomarker responses and contamination levels in the clam Ruditapes philippinarum for biomonitoring the Lagoon of Venice (Italy). J Environ Monit. 2010, 12: 776-86. 10.1039/b920536e.View ArticlePubMedGoogle Scholar
- Millán A, Gómez-Tato A, Fernández C, Pardo GB, Álvarez-Dios JA, Calaza M, Bouza C, Vázquez M, Cabaleiro S, Martínez P: Design and Performance of a Turbot (Scophthalmus maximus) Oligo-microarray Based on ESTs from Immune Tissues. Mar Biotechnol. 2009, 12: 452-465.View ArticlePubMedGoogle Scholar
- Ferraresso S, Milan M, Pellizzari C, Vitulo N, Reinhardt R, Canario AV, Patarnello T, Bargelloni L: Development of an oligo DNA microarray for the European sea bass and its application to expression profiling of jaw deformity. BMC Genomics. 2010, 3 (11): 354-View ArticleGoogle Scholar
- Salem M, Kenney PB, Rexroad CE III, Yao J: Development of a 37 k high-density oligonucleotide microarray: a new tool for functional genome research in rainbow trout. J Fish Biol. 2008, 72: 2187-2206. 10.1111/j.1095-8649.2008.01860.x.View ArticleGoogle Scholar
- Kane MD, Sringer JA, Iannotti NV, Gough E, Johns SM, Schlueter SD, Sepulveda MS: Identification of development and tissue-specific gene expression in the fathead minnow Pimephales promelas, Rafinesque using computational and DNA microarray methods. J Fish Biol. 2008, 72: 2341-2353. 10.1111/j.1095-8649.2008.01889.x.View ArticleGoogle Scholar
- Villeneuve DL, Knoebl I, Larkin P, Miracle AL, Carter BJ, Denslow ND, Ankley GT: Altered gene expression in the brain and liver of female fathead minnows Pimephales promelas Rafinesque exposed to fadrozole. J Fish Biol. 2008, 72: 2281-2340. 10.1111/j.1095-8649.2008.01897.x.View ArticleGoogle Scholar
- Klaper R, Carter BJ, Richter CA, Drevnick PE, Sandheinrich MB, Tillitt DE: Use of a 15 k gene microarray to determine gene expression changes in response to acute and chronic methylmercury exposure in the fathead minnow Pimephales promelas Rafinesque. J Fish Biol. 2008, 72: 2207-2280. 10.1111/j.1095-8649.2008.01899.x.View ArticleGoogle Scholar
- Li T, Brouwer M: Gene expression profile of grass shrimp Palaemonetes pugio exposed to chronic hypoxia. Comp Biochem Physiol Part D Genomics Proteomics. 2009, 4: 196-208. 10.1016/j.cbd.2009.03.004.View ArticlePubMedGoogle Scholar
- Ramsey JS, Wilson AC, de Vos M, Sun Q, Tamborindeguy C, Winfield A, Malloch G, Smith DM, Fenton B, Gray SM, Jander G: Genomic resources for Myzus persicae: EST sequencing, SNP identification, and microarray design. BMC Genomics. 2007, 8: 423-10.1186/1471-2164-8-423.View ArticlePubMedPubMed CentralGoogle Scholar
- Ferraresso S, Vitulo N, Mininni AN, Romualdi C, Cardazzo B, Negrisolo E, Reinhardt R, Canario AV, Patarnello T, Bargelloni L: Development and validation of a gene expression oligo microarray for the gilthead sea bream (Sparus aurata). BMC Genomics. 2008, 9: 580-10.1186/1471-2164-9-580.View ArticlePubMedPubMed CentralGoogle Scholar
- Craft JA, Gilbert JA, Temperton B, Dempsey KE, Ashelford K, Tiwari B, Hutchinson TH, Chipman JK: Pyrosequencing of Mytilus galloprovincialis cDNAs: tissue-specific expression patterns. PLoS One. 2010, 25 (5): 8875-View ArticleGoogle Scholar
- Bettencourt R, Pinheiro M, Egas C, Gomes P, Afonso M, Shank T, Santos RS: High-throughput sequencing and analysis of the gill tissue transcriptome from the deep-sea hydrothermal vent mussel Bathymodiolus azoricus. BMC Genomics. 2010, 11: 559-10.1186/1471-2164-11-559.View ArticlePubMedPubMed CentralGoogle Scholar
- RuphiBase. [http://compgen.bio.unipd.it/ruphibase/]
- GEO data base. [http://www.ncbi.nlm.nih.gov/geo/]
- DAVID. [http://david.abcc.ncifcrf.gov/]
- Forrest AR, Carninci P: Whole genome transcriptome analysis. RNA Biol. 2009, 6: 107-12. 10.4161/rna.6.2.7931.View ArticlePubMedGoogle Scholar
- Kapranov P, Willingham AT, Gingeras TR: Genome-wide transcription and the implications for genomic organization. Nat Rev Genet. 2007, 8: 413-23.View ArticlePubMedGoogle Scholar
- Carninci P: RNA dust: where are the genes?. DNA Res. 2010, 17: 51-9. 10.1093/dnares/dsq006.View ArticlePubMedPubMed CentralGoogle Scholar
- Ponting CP, Grant Belgard T: Transcribed dark matter: meaning or myth?. Hum Mol Genet. 2010, 19: 162-168. 10.1093/hmg/ddq362.View ArticleGoogle Scholar
- Zhang Y, Liu XS, Liu QR, Wei L: Genome-wide in silico identification and analysis of cis natural antisense transcripts (cis-NATs) in ten species. Nucleic Acids Res. 2006, 34: 3465-3475. 10.1093/nar/gkl473.View ArticlePubMedPubMed CentralGoogle Scholar
- Lapidot M, Pilpel Y: Genome-wide natural antisense transcription: coupling its regulation to its different regulatory mechanisms. EMBO Rep. 2006, 7: 1216-1222. 10.1038/sj.embor.7400857.View ArticlePubMedPubMed CentralGoogle Scholar
- Werner A, Sayer JA: Naturally occurring antisense RNA: function and mechanisms of action. Curr Opin Nephrol Hypertens. 2009, 18: 343-349. 10.1097/MNH.0b013e32832cb982.View ArticlePubMedGoogle Scholar
- Galloway TS: Biomarkers in environmental and human health risk assessment. Mar Pollut Bull. 2006, 53: 606-13. 10.1016/j.marpolbul.2006.08.013.View ArticlePubMedGoogle Scholar
- Losso C, Ghirardini AV: Overview of ecotoxicological studies performed in the Venice Lagoon (Italy). Environ Int. 2010, 36: 92-121. 10.1016/j.envint.2009.07.017.View ArticlePubMedGoogle Scholar
- Moore MN, Allen JI, McVeigh A, Shaw J: Lysosomal and autophagic reactions as predictive indicators of environmental impact in aquatic animals. Autophagy. 2006, 2: 217-20.View ArticlePubMedGoogle Scholar
- Sancak Y, Bar-Peled L, Zoncu R, Markhard AL, Nada S, Sabatini DM: Ragulator-Rag complex targets mTORC1 to the lysosomal surface and is necessary for its activation by amino acids. Cell. 2010, 141: 290-303. 10.1016/j.cell.2010.02.024.View ArticlePubMedPubMed CentralGoogle Scholar
- Nakatsu Y, Kotake Y, Takai N, Ohta S: Involvement of autophagy via mammalian target of rapamycin (mTOR) inhibition in tributyltin-induced neuronal cell death. J Toxicol Sci. 2010, 35: 245-51. 10.2131/jts.35.245.View ArticlePubMedGoogle Scholar
- Blanchette B, Feng X, Singh BR: Marine glutathione S-transferases. Mar Biotechnol (NY). 2007, 9: 513-42. 10.1007/s10126-007-9034-0.View ArticleGoogle Scholar
- Huse SM, Huber JA, Morrison HG, Sogin ML, Welch DM: Accuracy and quality of massively parallel DNA pyrosequencing. Genome Biol. 2007, 8: 143-10.1186/gb-2007-8-7-r143.View ArticleGoogle Scholar
- Konishi T, Kato K, Araki T, Shiraki K, Takagi M, Tamaru Y: A new class of glutathione S-transferase from the hepatopancreas of the red sea bream Pagrus major. Biochem J. 2005, 388: 299-307. 10.1042/BJ20041578.View ArticlePubMedPubMed CentralGoogle Scholar
- Whalen KE, Morin D, Lin CY, Tjeerdema RS, Goldstone JV, Hahn ME: Proteomic identification, cDNA cloning and enzymatic activity of glutathione S-transferases from the generalist marine gastropod Cyphoma gibbosum. Arch Biochem Biophys. 2008, 478: 7-17. 10.1016/j.abb.2008.07.007.View ArticlePubMedGoogle Scholar
- Micheletti C, Critto A, Marcomini A: Assessment of ecological risk from bioaccumulation of PCDD/Fs and dioxin-like PCBs in a coastal lagoon. Environment International. 2007, 33: 45-55. 10.1016/j.envint.2006.06.023.View ArticlePubMedGoogle Scholar
- Passamonti M, Mantovani B, Scali V: Allozymic characterization and genetic relationships among four species of Tapetinae (Bivalvia Veneridae). Italian Journal of Zoology. 1997, 64: 117-124. 10.1080/11250009709356183.View ArticleGoogle Scholar
- Irato P, Santovito G, Cassini A, Piccinni E, Albergoni V: Metal Accumulation and Binding Protein Induction in Mytilus galloprovincialis, Scapharca inaequivalvis, and Tapes philippinarum from the Lagoon of Venice. Arch Environ Contam Toxicol. 2003, 44: 476-484. 10.1007/s00244-002-1262-8.View ArticlePubMedGoogle Scholar
- Moschino V, Delaney E, Meneghetti F, Ros LD: Biomonitoring approach with mussel Mytilus galloprovincialis (Lmk) and clam Ruditapes philippinarum (Adams and Reeve, 1850) in the Lagoon of Venice. Environ Monit Assess. 2010Google Scholar
- Bellucci LG, Frignani M, Paolucci D, Ravanelli M: Distribution of heavy metals in sediments of the Venice Lagoon: The role of the industrial area. Science of the Total Environment. 2002, 295: 35-49. 10.1016/S0048-9697(02)00040-2.View ArticlePubMedGoogle Scholar
- Bernardello M, Secco T, Pellizzato F, Chinellato M, Sfriso A, Pavoni B: The changing state of contamination in the Lagoon of Venice. Part 2: Heavy metals. Chemosphere. 2006, 64: 1334-1345. 10.1016/j.chemosphere.2005.12.033.View ArticlePubMedGoogle Scholar
- Gaworecki KM, Rice CD, van den Hurk P: Induction of phenol-type sulfotransferase and glucuronosyltransferase in channel catfish and mummichog. Mar Environ Res. 2004, 58: 525-528. 10.1016/j.marenvres.2004.03.041.View ArticlePubMedGoogle Scholar
- Lie KK, Lanzen A, Breilid H, Olsvik PA: Gene expression profiling in Atlantic cod (Gadus morhua L.) from two contaminated sites using a custom-made cDNA microarray. Environ Toxicol Chem. 2009, 28: 1711-21. 10.1897/08-517.1.View ArticlePubMedGoogle Scholar
- Gamage N, Barnett A, Hempel N, Duggleby RG, Windmill KF, Martin JL, McManus ME: Human sulfotransferases and their role in chemical metabolism. Toxicol Sci. 2006, 90: 5-22.View ArticlePubMedGoogle Scholar
- Somnuek C, Boonphakdee C, Cheevaporn V, Tanaka K: Gene expression of acetylcholinesterase in hybrid catfish (Clarias gariepinus X Clarias macrocephalus) exposed to chlorpyrifos and carbaryl. J Environ Biol. 2009, 30: 83-8.PubMedGoogle Scholar
- Boutet I, Tanguy A, Moraga D: Characterisation and expression of four mRNA sequences encoding glutathione S-transferases pi, mu, omega and sigma classes in the Pacific oyster Crassostrea gigas exposed to hydrocarbons and pesticides. Marine Biology. 2004, 146: 53-64. 10.1007/s00227-004-1423-6.View ArticleGoogle Scholar
- Xu C, Pan L, Liu N, Wang L, Miao J: Cloning, characterization and tissue distribution of a pi-class glutathione S-transferase from clam (Venerupis philippinarum): Response to benzo[alpha]pyrene exposure. Comp Biochem Physiol C Toxicol Pharmacol. 2010, 152: 160-166. 10.1016/j.cbpc.2010.03.011.View ArticlePubMedGoogle Scholar
- Falfushynska HI, Gnatyshyna LL, Golubev AP, Stoliar OB: Main partitioning criteria for the characterization of the health status in the freshwater mussel Anodonta cygnea from spontaneously polluted area in western ukraine. Environ Toxicol. 2010,Google Scholar
- Bourgeault A, Gourlay-Francé C, Vincent-Hubert F, Palais F, Geffard A, Biagianti-Risbourg S, Pain-Devin S, Tusseau-Vuillemin MH: Lessons from a transplantation of zebra mussels into a small urban river: An integrated ecotoxicological assessment. Environ Toxicol. 2010, 25: 468-78. 10.1002/tox.20591.View ArticlePubMedGoogle Scholar
- Vera JC, Wheat CW, Fescemyer HW, Frilander MJ, Crawford DL, Hanski I, Marden JH: Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing. Mol Ecol. 2008, 17: 1636-47. 10.1111/j.1365-294X.2008.03666.x.View ArticlePubMedGoogle Scholar
- Maiuri MC, Zalckvar E, Kimchi A, Kroemer G: Self-eating and self-killing:crosstalk between autophagy and apoptosis. Nat Rev Mol Cell Biol. 2007, 8: 741-52. 10.1038/nrm2239.View ArticlePubMedGoogle Scholar
- Fabioux C, Corporeau C, Quillien V, Favrel P, Huvet A: In vivo RNA interference in oyster--vasa silencing inhibits germ cell development. FEBS J. 2009, 276: 2566-73. 10.1111/j.1742-4658.2009.06982.x.View ArticlePubMedGoogle Scholar
- Chomczynski P, Sacchi : Single step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem. 1987, 162: 156-159.View ArticlePubMedGoogle Scholar
- Kozhemyako VB, Matz MV, Meleshkevitch E, Moroz LL, Lukyanov SA, Shagin DA: Simple cDNA normalization using kamchatka crab 694 duplex-specific nuclease. Nucleic Acids Res. 2004, 32: e37-10.1093/nar/gnh031.View ArticlePubMedPubMed CentralGoogle Scholar
- Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, et al: Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005, 437: 376-380.PubMedPubMed CentralGoogle Scholar
- Chevreux B, Pfisterer T, Drescher B, et al: Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res. 2004, 14: 1147-59. 10.1101/gr.1917404.View ArticlePubMedPubMed CentralGoogle Scholar
- UniProt. [http://www.ebi.ac.uk/uniprot/]
- Ensembl Genome Browser. [http://www.ensembl.org/index.html]
- Lottia gigantea v1.0 database. [http://genome.jgi-psf.org/Lotgi1/Lotgi1.download.ftp.html]
- NCBI UniGene database. [http://www.ncbi.nlm.nih.gov/unigene]
- Sigenae. [http://www.sigenae.org/]
- Bay scallop EST project. [http://www.mbl.edu/aquaculture/EST/]
- Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talón M, Dopazo J, Conesa A: High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008, 36: 3420-35. 10.1093/nar/gkn176.View ArticlePubMedPubMed CentralGoogle Scholar
- Geneontology. [http://www.geneontology.org/GO.slims.shtml]
- Hu ZL, Bao J, Reecy JM: CateGOrizer: A web-based program to batch analyze gene ontology classification categories. Online J Bioinform. 2008, 9: 108-112.Google Scholar
- Agilent eArray. [https://earray.chem.agilent.com/earray/]
- R statistical software. [http://www.r-project.org]
- Thusher V, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001, 98: 5116-5121. 10.1073/pnas.091062498.View ArticleGoogle Scholar
- Ensembl BioMart. [http://www.ensembl.org/biomart/martview/]
- Notredame C, Higgins D, Heringa J: T-Coffee: A novel method for multiple sequence alignments. Journal of Molecular Biology. 2000, 302: 205-217. 10.1006/jmbi.2000.4042.View ArticlePubMedGoogle Scholar
- Castresana J: Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Molecular Biology and Evolution. 2000, 17: 540-552.View ArticlePubMedGoogle Scholar
- Felsenstein J: Inferring Phylogenies. Sinauer, Sunderland, MA. Syst Biol. 2004, 53: 669-670. 10.1080/10635150490468530.View ArticleGoogle Scholar
- Guindon S, Gascuel O: A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 2003, 52: 696-704. 10.1080/10635150390235520.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.