High-throughput sequencing and analysis of the gill tissue transcriptome from the deep-sea hydrothermal vent mussel Bathymodiolus azoricus
© Bettencourt et al; licensee BioMed Central Ltd. 2010
Received: 19 July 2010
Accepted: 11 October 2010
Published: 11 October 2010
Bathymodiolus azoricus is a deep-sea hydrothermal vent mussel found in association with large faunal communities living in chemosynthetic environments at the bottom of the sea floor near the Azores Islands. Investigation of the exceptional physiological reactions that vent mussels have adopted in their habitat, including responses to environmental microbes, remains a difficult challenge for deep-sea biologists. In an attempt to reveal genes potentially involved in the deep-sea mussel innate immunity we carried out a high-throughput sequence analysis of freshly collected B. azoricus transcriptome using gills tissues as the primary source of immune transcripts given its strategic role in filtering the surrounding waterborne potentially infectious microorganisms. Additionally, a substantial EST data set was produced and from which a comprehensive collection of genes coding for putative proteins was organized in a dedicated database, "DeepSeaVent" the first deep-sea vent animal transcriptome database based on the 454 pyrosequencing technology.
A normalized cDNA library from gills tissue was sequenced in a full 454 GS-FLX run, producing 778,996 sequencing reads. Assembly of the high quality reads resulted in 75,407 contigs of which 3,071 were singletons. A total of 39,425 transcripts were conceptually translated into amino-sequences of which 22,023 matched known proteins in the NCBI non-redundant protein database, 15,839 revealed conserved protein domains through InterPro functional classification and 9,584 were assigned with Gene Ontology terms. Queries conducted within the database enabled the identification of genes putatively involved in immune and inflammatory reactions which had not been previously evidenced in the vent mussel. Their physical counterpart was confirmed by semi-quantitative quantitative Reverse-Transcription-Polymerase Chain Reactions (RT-PCR) and their RNA transcription level by quantitative PCR (qPCR) experiments.
We have established the first tissue transcriptional analysis of a deep-sea hydrothermal vent animal and generated a searchable catalog of genes that provides a direct method of identifying and retrieving vast numbers of novel coding sequences which can be applied in gene expression profiling experiments from a non-conventional model organism. This provides the most comprehensive sequence resource for identifying novel genes currently available for a deep-sea vent organism, in particular, genes putatively involved in immune and inflammatory reactions in vent mussels.
The characterization of the B. azoricus transcriptome will facilitate research into biological processes underlying physiological adaptations to hydrothermal vent environments and will provide a basis for expanding our understanding of genes putatively involved in adaptations processes during post-capture long term acclimatization experiments, at "sea-level" conditions, using B. azoricus as a model organism.
Deep-sea hydrothermal vent ecosystems are driven by unique physical, geochemical and biological processes with specialized energy sources at the origin of the trophic web. Since the discovery of hydrothermal vents and their associated fauna in the Galapagos Rift, evidence of the establishment of dense faunal communities based on chemosynthesis have mounted over the past decades, and generally in relation to areas where tectonic movements and deep ocean volcanism are active . Hydrothermal vent ecosystems are characterized by the synthesis of organic matter by means of chemo-autotrophic bacteria using reduced elements extracted from the hydrothermal fluids as source of energy [2, 3].
Mussels in the genus Bathymodiolus are biomass dominant at many known deep-sea hydrothermal vent and cold seep habitats. Survival in such extreme conditions requires unique anatomical and physiological adaptations. For example the development of specialized gill epithelial cells harboring methanotrophic and thiotrophic endosymbiont bacteria constitutes one the best recognized adaptation strategies to chemosynthetic environments . Dual symbiosis thus provides a clear nutritional advantage to Bathymodiolid mussels, allowing them to obtain energy from both sulfide and methane at the vent sites [5–7]. Near the Mid-Atlantic Ridge, and in the vicinity of the Azores region, Bathymodiolus azoricus subsists at vent sites, amid unusual levels of heavy metals, pH, temperature, CO2, methane and sulfide, while coping successfully with environmental microbes .
Despite its prominence as a model to study physiological adaptation to extreme physical and chemical conditions , there is currently no large scale genome project for Bathymodiolus species. Gene expression profiles are limited to a few EST projects mainly originated from the Evolution and Genetics of Marine Populations team at the Biological Station of Roscoff, France. In a recent analysis, the screening of cDNA libraries from whole bodies of B. azoricus resulted in 362 contigs and 1,918 singletons. Many genes known to be involved in both metallic and oxidative stress responses were then identified . However, these data remain private and there is currently no published reports based on those sequences. More recently, the effect of temperature on the vent mussel B. thermophilus was investigated by means of subtractive suppression hybridization experiments aimed at the identification of genes differentially expressed in response to different temperatures regimes .
Thus far, knowledge of deep-sea biology or of the molecules involved in the maintenance of homeostasis in hydrothermal vent animals has been limited in part by the lack of information about their genomes and systematic sequencing of expressed sequence tags to identify protein coding genes on a large scale. The deep-sea vent biological systems represent the opportunity to study and provide new insights into the basic physiological principles that govern the defense mechanisms in vent animals and to understand how they cope with microbial infections. The problem of microbial threat and the need for immunity exist in both deep sea and shallow water bivalves however differences in the genes of marine organisms living in so distinct habitats are likely to occur. In order to significantly increase the number of B. azoricus genes in the public database and to discover new deep-sea vent adaptation-related genes in B. azoricus, and particularly for immune-related genes we have conducted a high-throughput experimental approach using pyrosequencing, on the 454 GS FLX (Roche-454 Life Sciences) with Titanium chemistry, to sequence the transcriptome of B. azoricus gill tissues. In the absence of a reference genome, this sequence method, which has not yet been widely applied to hydrothermal vent animals, holds great potential for discovery of genes and genetic markers in unconventional model species through de novo transcriptome sequence assembly. The assembled and annotated sequences were produced and have been organized in a dedicated database, accessible through the website, http://transcriptomics.biocant.pt:8080/deepSeaVent providing an extensive catalog of genes expressed in gill tissues harboring immune cells, the hemocytes, of the deep-sea vent mussel Bathymodiolus azoricus.
Results and Discussion
Summary of assembly and EST data
Number of Reads
Average read length after MIRA
Number of contigs
Average contig length
Range contig length
Number of singletons
Number of Contigs with 2 reads
Number of Contigs with > 2 reads
Contigs with BLASTx matches (E-value ≤ 10-6)
*Remaining contigs with additional matches (E-value ≤ 10-2)
Contigs determined by ESTscan
**Total number of transcripts
**Total number of putatively translated amino-acids sequences
The largest proportion of GO assigned sequences fell into broad categories for all three major GO functional domains as presented in Fig 2. Within the Biological Process, 31% and 32% of assignments corresponded to "Cellular Process" (GO:0009987) and "Metabolic Process (GO:0008152) respectively, followed by the "Localization" (GO:0051179, 7%) and "Establishment of Localization" (GO:0051234, 7%) GO categories. Furthermore, the matches of molecular function terms were most prevalent within the "Binding" (GO:0005488, 46%) and "Catalytic Activity" (GO:0003824, 33%), followed by the categories "Structural Molecule Activity" (GO:0005198, 8%) and "Transporter Activity" (GO:0005215, 5%). Finally for the Cellular Component GO the most evident matches were within the "Cell Part" (GO:0044464, 31%) and "Cell" (GO:0005623, 31%) terms, followed by "Organelle" (GO:0043226, 16%) and "Macromolecular Complex" (GO:0032991, 12%). Together, these GO classes accounted for most of the assignable transcripts, and may represent a general gene expression profile signature for B. azoricus from the Lucky Strike hydrothermal vent field.
The contigs without any homology may correspond to one of the following categories: a) novel or diverged amino acid coding sequences that are specific to Bathymodiolus species, b) represent mostly 3' or 5' untranslated regions (UTRs) that would lack protein matches as they are non-coding or c) contain sequences to short to result in significant hits.
Despite that the Gene Ontology project is aimed at describing gene product characteristics and gene product annotation data, by ways of a direct acyclic graph (DAG) structure of controlled vocabularies, the process of assigning GO terms to gene products might prove difficult given the current GO structured vocabulary and the systematic relationship between the GO terms. This follows from the fact that GO describes how gene products behave in a cellular context and thus a gene product might be associated with or located in one or more cellular components; it is active in one or more biological processes, during which it performs one or more molecular functions.
Immunity, stress and bacterial genes in Bathymodiolus azoricus gills
B. azoricus genes putatively involved in immune response and inflammatory reactions.
Gene Ontology n°
Gene Ontology description
Peptidoglycan Recognition protein (PGRP)
N-acetylmuramoyl-L-alanine amidase activity
Chitin binding protein
Chitin binding; chitin metabolic process
Galectin 4-like protein
Cell adhesion; cell adhesion mediated by integrin
Glycoside hydrolase, Chitinase-like
Carbohydrate metabolic process
Contactin associated protein 2
Cell adhesion; protein binding
Tissue inhibitor of metalloproteinase
Metalloendopeptidase inhibitor activity; proteinaceous extracellular matrix
Serpin (serine protease inhibitor)
Serine-type endopeptidase inhibitor activity
α2-Macroblobulin (thioester-containing protein)
Endopeptidase inhibitor activity
Syndecan binding protein
Ras protein signal transduction; interleukin-5 receptor binding
Fibrinogen (pattern recognition receptor)
signal transduction; receptor binding
Ficolin (opsonin, contain fibrinogen and collagen-like domains)
signal transduction; receptor binding; opsonization
Scavenger receptor cysteine-rich protein (SRCR)
Scavenger receptor activity
LBP/BPI (LPS binding, Crassostrea homologue)
Innate immune response; signal transduction
Transmembrane receptor activity
TRAF (TNF receptor-associated factor)
Signal transduction; regulation of apoptosis
Cytokine-mediated signaling pathway; regulation of NF-κB
Protein kinase activity; protein amino acid phosphorylation
Protein kinase activity; stress-activated MAPK cascade
GO:0007411; GO: 0007219
Axon guidance; Notch signaling pathway
Epidermal growth factor receptor signaling Pathway; signal transduction
Signal transduction; regulation of apoptosis
Calcium ion binding; protein binding
Cadherin (EGF domain containing)
Membrane;homophilic cell adhesion
Integrin (fibronectin receptor)
Cell adhesion; integrin mediated signaling pathway
Nuclear Factor κB inhibitor
Regulation of NF-κB activity
Signal transducer activity; regulation of transcription
SH2 motif(Src homology 2
Signal transduction; peptidyl-tyrosine phosphorylation
Apoptosis; cellular response to DNA damage stimulus
AP-1 (Proto-oncogene c-jun)
Transcription factor activity; regulation of transcription
Tal (Crassostrea homologue)
Regulation of transcription; transcription regulator activity
Effector and modulator molecules
Defensin (big defensin)
Toxin metabolic process
Blood coagulation; serine-type endopeptidase activity
TNF (LPS-induced, α factor)
Immune response; defense response
Defense response to bacterium; regulation of innate immune response
Inflammatory response; induction of apoptosis
Glutathione peroxidase activity; response to oxidative stress
Response to oxidative stress; peroxidase activity
Metalloendopeptidase activity; proteinaceous extracellular matrix
Metal ion binding
Cellular iron ion homeostasis
Cell adhesion; response to wounding
Glucose-regulated protein 94
Response to stress
The identification of putative genes was based on GO annotation and querying the DeepSeaVent database
The selected putative genes are being currently investigated under controlled conditions in our laboratory, to assess the effect of long-term acclimatization in aquaria at atmospheric pressure, the effect of de novo hyperbaric stimulations in the IPOCAMP chamber and the effect of exposure to marine Vibrio bacteria, on B. azoricus transcript profiling experiments [20, 21].
Furthermore, this search-based analysis was also particularly important to determine a "bacterial fingerprint" in B. azoricus gill tissues, since we expected the vent mussel to have a rich microorganismal community and more specifically a substantial accumulation of endosymbiont bacteria within its gill tissues. The selection of poly-A RNA as the starting material for our transcriptome library likely eliminated many potential microbial sequences. However, 3,522 contigs in DeepSeaVent presented protein match hits to bacterial phylotypes, supporting the evidence for the presence of bacteria in gill tissues of B. azoricus, and representing thus a potential bacterial fingerprint, most likely of chemoautotrophic nature, in deep-sea hydrothermal vent mussels. Additionally, a number of bacterial sequences were ascribed to several non-cultured marine bacteria, to chemolithoautotrophic, sulfur- or methane-oxidizing bacteria as evidenced, for instance, by the presence of the SoxB, SoxY, SoxH, methane monooxygenase, Biopolymer transport protein ExbD/TolR genes when querying our database using InterPro or GO terms as "methane" or "sulfur". Similarly, searches using the genera Calyptogena and Riftia names returned several hundreds of putative protein sequences, the majority of which associated to endosymbionts from the giant hydrothermal vesicomyid clam and vestimentiferan tubeworm, respectively from the East Pacific Rise hydrothermal region.
Such an approach has the potential to reveal sequences that have apparent bacterial origin including many genera of species that have been associated with mussel pathogens or as normal flora in the gut system . In this case, mutualistic interaction between symbiont bacteria and their animal hosts may be taken to another level of analysis based on transcriptome sequencing. New genes involved in host recognition of endosymbionts and immune-effector mechanisms underlying host defense responses may shed light into understanding mutualism better and provide explanations as to how endosymbiont bacteria, living inside the bacteriocytes of vent mussels, are able to evade immune recognition, during early stages of acquisition and how mutualism is maintained. One possible explanation might lie within the immune response itself, where constitutive immune responses of bacteriocytes towards the dense population of endosymbionts, might be expressed at different levels of the rest of the body and therefore, keeping endosymbionts under control . This hypothesis is currently under experimental consideration in our laboratory.
Deep-sea vent mussel comparison to shallow water mussel
Comparison between Mytibase and DeepSeaVent database
DeepSeaVent and Mytibase comparison
Matched proteins with InterPro annotation
Immune system process
Multicellular organismal process
Cellular component biogenesis
Cell wall organization or biogenesis
Structural molecule activity
Electron carrier activity
Enzyme regulator activity
Molecular transducer activity
The biological significance of these findings was not immediately evaluated due the comparatively low amount of amino-acid sequences available in the Mytibase as compared to DeepSeaVent. However, representatives of broad GO categories are present in both Mytibase and DeepSeaVent databases, suggesting that mussels originated from distinct marine habitat may share common biological processes, cellular components and molecular functions.
Such inter-database computational analyses offers now the potential to unravel genes specifically involved in hydrostatic pressure and chemosynthetic environmental adaptations by comparing transcriptome profiles from two closely related Mytilid family members living in very distinct marine habitats.
Comparison of our results with recently published transcriptomic studies on B. azoricus confirms the efficacy of 454 sequencing to reveal a large number of putative transcripts and significantly improve the genomic knowledge on this deep sea animal. The use of 454 pyrosequencing to develop a new EST collection containing potentially 39,425 new transcripts provides a new resource for genome-wide association studies of vent mussel physiological variations, which is the focus of ongoing projects in our laboratory, addressing in particular the molecular adaptation mechanisms of B. azoricus to deep-sea hydrothermal vent environments. This new resource now gathered in the DeepSeaVent database will set the stage for innovative work and the establishment of large scale expression studies to validate the deep-sea vent mussel as a bone fide experimental model to study the biology of adaptation to deep-sea hydrothermal vent environments.
Mussels were collected from the hydrothermal vent field Lucky Strike (37° 13.52' N, 32° 26.18' W; 1700 m depth), on the Mid-Atlantic Ridge (MAR), with the American R/V Revelle using the ROV Jason II (Woods Hole Oceanographic Institution), during the MAR08 cruise (July 9th - August 16th 2008) led by Chief Scientist Dr Anna-Louise Reysenbach. Once the mussels were brought to the surface, they were immediately processed onboard for subsequent manipulation of RNA or immediately stored at -80°C for long-term preservation of tissue samples.
cDNA library construction and pyrosequencing
Gill tissues from 6 different animals were dissected from -80°C preserved animals and processed for total RNA extraction using the RiboPure(tm) kit (Ambion(r), Austin, TX). The quality of total RNA was verified on a 1.4% (w/v) agarose-MOPS-formaldehyde denaturating gels and by assessing the A260/280 and A260/230 ratios using the NanoVue spectrophotometer (GE Healthcare, Piscataway, NJ). Poly-A RNA was extracted from each total RNA sample using the Poly(A)Purist(tm) mRNA Purification Kit according to manufacturer's instructions (Ambion Inc, Applied Biosystems). The mRNA from each gill tissue sample from all 6 animals (approximately 20-50 μg/ml) was pooled and used as the source of starting material for cDNA synthesis and the production of normalized cDNA intended for 454 sequencing. The normalization process was performed by Evrogen (Moscow, Russia) and based on the SMART double-stranded cDNA synthesis methodology using a modified template-switching approach that allows the introduction of known adapter sequences to both ends of the first-strand cDNA. cDNA was further amplified by PCR and normalized using Duplex-Specific Nuclease-technology [24, 25].
Four micrograms of normalized cDNA were sequenced in a full plate of 454 GS FLX Titanium according to the standard manufacturer's instructions (Roche-454 Life Sciences, Brandford, CT, USA) at Biocant (Cantanhede, Portugal).
Sequence processing, data analysis and functional annotation
Following 454 sequencing, the quality trimming and size selection of reads were determined by the 454 software after which the SMART adaptor sequences were removed from reads using a custom script and the poly-A masked using MIRA, to assure correct assembly of raw sequencing reads . A total of 778,996 quality reads were subjected to the MIRA assembler  (version 3.0.5), with default parameters, yet only 582,650 reads were assembled. For some reads, after masking the poly-A, the sequence length was shorter than 40 bp, otherwise the minimum length assumed by the MIRA default parameter settings. The software also disregards all reads that do not match any other read or that belong to the megahub group, i.e. a read that is massively repetitive with respect to other reads. Such reads are considered singlets and were not included in the final assembly result. On the other hand, singletons were defined as reads that match other reads but neither do have sufficient coverage nor present conditions to assemble with them. The entire set of reads used for final assembly was submitted to the NCBI Sequence Read Archive under the accession n° SRA024338 (Submission: SRA024338.1/Bathymodiolus azoricus).
The entire collection of sequences of at least 30 amino-acid long, resulting from the BLASTx and the ESTScan procedures, was processed by InterProScan for the prediction of protein domain signatures and Gene Ontology terms. All the results were compiled into a SQL database developed as an information management system.
The distribution of sequences into GO categories was calculated at each level and were passed to the parent GO at the top of the broad ontology domains, considering that each single assignment into a GO child was only counted once in the total sum. This information was also useful to establish the number of amino-acid sequences shared between the two Mytibase and DeepSeaVent databases.
Identification of candidate genes putatively associated with immunity and inflammatory reactions
Forward and reverse primer sequences used in semi quantitative RT-PCR analyses.
GenBank acc. no
5'-3' forward primer
5'-3' reverse primer
STAT long form
STAT (SH2 motif)
Primers targeting the immune candidate genes were designed using the Primer-Blast  from NCBI http://www.ncbi.nlm.nih.gov/, specifying an expected PCR product of 200-300 bp and primer annealing temperatures between 56°C and 58°C. 25 μl PCR volume reactions were set with 1 μl of each forward and reverse primers (0.5 μM final concentration) and using a 2× PCR mix from PROMEGA (Madison, USA). PCR cycling conditions were according to Bettencourt et al. 2009 . PCR products were examined by agarose gel electrophoresis according to standard methods.
Quantitative PCR (qPCR) was further used to assess and quantify the relative expression of the candidate genes previously tested on semi-quantitative RT-PCR. The non-normalized cDNA was obtained as previously described and consisting of the same cDNA utilized for subsequent normalization and 454 sequencing procedure. Quantitative PCR reactions were performed on the CFX96™ Real Time PCR System mounted onto the C1000 Thermal Cycling platform (Bio-Rad, CA, USA). Amplifications were carried out using 0.5 μl (10 μM) of the specific primers as for semi-quantitative PCR and mixed to 10 μl of SsoFast™ Eva Green SuperMix (SYBR based system, Bio-Rad) and 50 ng of cDNA in a final volume of 20 μl. PCR cycling conditions were 95°C for 3 min, followed by 35 cycles of 95 °C 10 s, 58 °C 15 s and 72 °C 30 s. 6 replicates were performed for each gene tested in real time PCR reactions. Melt curves profiles were analyzed for each gene tested. The 28S rRNA gene was used as the housekeeping gene and for normalization of expression of gene of interest or immune-related target genes. The comparative CT method (ΔΔCT) for the relative quantification of gene expression was used for assessing the normalized expression value of immune-related genes using the 28S rRNA as the control transcript (CFX Manager™ Software, Bio-Rad). Data were transferred to Excel files and plotted as histograms of normalized fold expression of target genes.
We thank the shipboard nautical, technical and scientific parties of the American R/V Revelle/ROV Jason II during the MAR08 cruise http://www.deepseavoyage.research.pdx.edu as well as the captain and crew members of the Azorean R/V Arquipélago for assistance in transferring scientific parties to and from the Revelle during a mid-term cruise exchange of participants. We are most grateful to Breea Govenar and Brandi Cron for onboard assistance with mussel sampling and tissue preparation after the Lucky Strike dives.
We acknowledge the Portuguese Foundation for Science and Technology, FCT-Lisbon and the Regional Azorean Directorate for Science and Technology, DRCT-Azores, for pluri-annual and programmatic PIDDAC and FEDER funding to IMAR/DOP Research Unit #531 and the Associated Laboratory #9 (ISR-Lisboa); the Luso-American Foundation FLAD (Project L-V-173/2006); the Biotechnology and Biomedicine Institute of the Azores (IBBA), project M.2.1.2/I/029/2008-BIODEEPSEA and the project n° FCOMP-01-0124-FEDER-007376 (ref: FCT PTDC/MAR/65991/2006-IMUNOVENT; coordinated by RB) under the auspices of the COMPETE program.
- Childress JJ, Fisher CR: The biology of hydrothermal vent animals: physiology, biochemistry, and autotrophic symbioses. Oceanogr Mar Biol Annu Rev. 1992, 30: 337-441.Google Scholar
- Desbruyères D, Biscoito M, Caprais JC, Colaço A, Comtet T, Crassous Ph, Fouquet Y, Khripounoff A, Le Bris N, Olu K, Riso R, Sarradin PM, Segonzac M, Vangriesheim A: Variations in deep-sea hydrothermal vent communities on the Mid-Atlantic Ridge near the Azores plateau. Deep Sea Res Pt I. 2001, 48: 1325-1346. 10.1016/S0967-0637(00)00083-2.View ArticleGoogle Scholar
- Salerno JL, Macko SA, Hallam SJ, Bright M, Won YJ, McKiness Z, Van Dover CL: Characterization of Symbiont Populations in Life-History Stages of Mussels From Chemosynthetic Environments. Biol Bull. 2005, 208: 145-155. 10.2307/3593123.PubMedView ArticleGoogle Scholar
- Won YJ, Hallam SJ, O'Mullan GD, Pan IL, Buck KR, Vrijenhoek RC: Environmental Acquisition of Thiotrophic Endosymbionts by Deep-Sea Mussels of the Genus Bathymodiolus. Appl Environ Microbiol. 2003, 69: 6785-6792. 10.1128/AEM.69.11.6785-6792.2003.PubMed CentralPubMedView ArticleGoogle Scholar
- Duperron S, Bergin C, Zielinski F, Blazejak A, Pernthaler A, McKiness ZP, Dechaine E, Cavanaugh CM, Dubilier N: A dual symbiosis shared by two mussel species, Bathymodiolus azoricus and Bathymodiolus puteoserpentis (Bivalvia: Mytilidae), from hydrothermal vents along the northern Mid-Atlantic Ridge. Environ Microbiol. 2006, 8: 1441-47. 10.1111/j.1462-2920.2006.01038.x.PubMedView ArticleGoogle Scholar
- Fiala-Medioni A, McKiness ZP, Dando P, Boulegue J, Mariotti A, Alayse-Danet AM, Robinson JJ, Cavanaugh CM: Ultrastructural, biochemical, and immunological characterization of two populations of the mytilid mussel Bathymodiolus azoricus from the Mid-Atlantic Ridge: evidence for a dual symbiosis. Mar Biol. 2002, 141: 1035-1043. 10.1007/s00227-002-0903-9.View ArticleGoogle Scholar
- Distel DL, Lee HKW, Cavanaugh CM: Intracellular coexistence of methanotrophic and thioautotrophic bacteria in a hydrothermal vent mussel. Proc Natl Acad Sci USA. 1995, 92: 9598-9602. 10.1073/pnas.92.21.9598.PubMed CentralPubMedView ArticleGoogle Scholar
- Bettencourt R, Roch P, Stefanni S, Rosa D, Colaço A, Serrão Santos R: Deep sea immunity: unveiling immune constituents from the hydrothermal vent mussel Bathymodiolus azoricus. Mar Environ Res. 2007, 64: 108-27. 10.1016/j.marenvres.2006.12.010.PubMedView ArticleGoogle Scholar
- Kadar E, Powell J: Post-capture investigation of the hydrothermal vent macro-invertebrates to study adaptation to extreme environments. Rev Env Sci Biotechnol. 2006, 5: 193-201. 10.1007/s11157-006-0006-z.View ArticleGoogle Scholar
- Tanguy A, Bierne N, Saavedra C, Pina B, Bachère E, Kube M, Bazin E, Bonhomme F, Boudry P, Boulo V, Boutet I, Cancela L, Dossat C, Favrel P, Huvet A, Jarque S, Jollivet D, Klages S, Lapègue S, Leite R, Moal J, Moraga D, Reinhardt R, Samain J-F, Zouros E, Canario A: Increasing genomic information in bivalves through new EST collections in four species: Development of new genetic markers for environmental studies and genome evolution. Gene. 2008, 408: 27-36. 10.1016/j.gene.2007.10.021.PubMedView ArticleGoogle Scholar
- Boutet I, Jollivet D, Shillito B, Moraga D, Tanguy A: Molecular identification of differentially regulated genes in the hydrothermal-vent species Bathymodiolus thermophilus and Paralvinella pandorae in response to temperature. BMC Genomics. 2009, 10: 222-10.1186/1471-2164-10-222.PubMed CentralPubMedView ArticleGoogle Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215: 403-10.PubMedView ArticleGoogle Scholar
- Iseli C, Jongeneel CV, Bucher P: ESTScan: a program for detecting, evaluating, and reconstructing potential coding regions in EST sequences. Proc Int Conf Intell Syst Mol Biol. 1999: 138-48.
- Apweiler R, Biswas M, Fleischmann W, Kanapin A, Karavidopoulou Y, Kersey P, Kriventseva EV, Mittard V, Mulder N, Phan I, Zdobnov E: Proteome Analysis Database: online application of InterPro and CluSTr for the functional classification of proteins in whole genomes. Nucleic Acids Res. 2001, 29: 44-48. 10.1093/nar/29.1.44.PubMed CentralPubMedView ArticleGoogle Scholar
- Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Das U, Daugherty L, Duquenne L, Finn RD, Gough J, Haft D, Hulo N, Kahn D, Kelly E, Laugraud A, Letunic I, Lonsdale D, Lopez R, Madera M, Maslen J, McAnulla C, McDowall J, Mistry J, Mitchell A, Mulder N, Natale D, Orengo C, Quinn AF, Selengut JD, Sigrist CJA, Thimma M, Thomas PD, Valentin F, Wilson D, Wu CH, Yeats C: InterPro: the integrative protein signature database. Nucleic Acids Res. 2008, 37: 211-215. 10.1093/nar/gkn785.View ArticleGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene Ontology: tool for the unification of biology. Nature Genet. 2000, 25: 25-29. 10.1038/75556.PubMed CentralPubMedView ArticleGoogle Scholar
- Rast JP, Smith LC, Loza-Coll M, Hibino T, Litman GW: Genomic insights into the immune system of the sea urchin. Science. 2006, 10: 952-956. 10.1126/science.1134301.View ArticleGoogle Scholar
- Iwanaga S, Lee BL: Recent Advances in the Innate Immunity of Invertebrate Animals. J Biochem Mol Bio. 2005, 38: 128-150.View ArticleGoogle Scholar
- Meyer E, Aglyamova GV, Wang S, Buchanan-Carter J, Abrego D, Colbourne JK, Willis BL, Matz MV: Sequencing and de novo analysis of a coral larval transcriptome using 454 GSFlx. BMC Genomics. 2009, 10: 219-10.1186/1471-2164-10-219.PubMed CentralPubMedView ArticleGoogle Scholar
- Kadar E, Tschuschke IG, Checa A: Post-capture hyperbaric stimulations to study the mechanism of shell regeneration of the deep-sea hydrothermal vent mussel Bathymodiolus azoricus (Bivalvia: Mytilidae). J Exp Mar Biol Ecol. 2008, 364: 80-90. 10.1016/j.jembe.2008.07.028.View ArticleGoogle Scholar
- Bettencourt R, Costa V, Laranjo M, Rosa D, Pires L, Colaço A, Lopes H, Serrão Santos R: Out of the deep sea into a land-based aquarium environment: investigating physiological adaptations in the hydrothermal vent mussel Bathymodiolus azoricus. ICES J Mar Sci. 2010,(first published online August 16, 2010),Google Scholar
- Stuart R, Rolff J: Immune function keeps endosymbionts under control. J Biol. 2008, 7: 28-10.1186/jbiol88.View ArticleGoogle Scholar
- Venier P, De Pitta C, Bernante F, Varotto L, De Nardi B, Bovo Roch, Novoa B, Figueras A, Pallavicini A, Lanfranchi G: MytiBase: a knowledgebase of mussel (M. galloprovincialis) transcribed sequences. BMC genomics. 2009, 10: 72-10.1186/1471-2164-10-72.PubMed CentralPubMedView ArticleGoogle Scholar
- Shcheglov A, Zhulidov P, Bogdanova E, Shagin D: Normalization of cDNA Libraries. Nucleic Acids Hybridization: Modern Applications. Edited by: Buzdin A, Lukyanov S. 2007, Dordrecht, Springer, 97-124. full_text.View ArticleGoogle Scholar
- Zhulidov PA, Bogdanova EA, Shcheglov AS, Shagina IA, Wagner LL, Khazpekov GL, Kozhemyako VV, Lukyanov SA, Shagin DA: A method for the preparation of normalized cDNA libraries enriched with full-length sequences. Bioorg Khim. 2005, 31: 186-94.PubMedGoogle Scholar
- Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Müller WE, Wetter T, Suhai S: 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.PubMed CentralPubMedView ArticleGoogle Scholar
- Stajich JE, Block D, Boulez K, Brenner SE, Chervitz SA, Dagdigian C, Fuellen G, Gilbert JG, Korf I, Lapp H, Lehväslaiho H, Matsalla C, Mungall CJ, Osborne BI, Pocock MR, Schattner P, Senger M, Stein LD, Stupka E, Wilkinson MD, Birney E: The Bioperl toolkit: Perl modules for the life sciences. Genome Res. 2002, 12: 1611-1618. 10.1101/gr.361602.PubMed CentralPubMedView ArticleGoogle Scholar
- Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol. 2000, 132: 365-86.PubMedGoogle Scholar
- Bettencourt R, Dando P, Collins P, Costa V, Allam B, Serrão Santos R: Innate Immunity in the deep sea hydrothermal vent mussel Bathymodiolus azoricus. Comp Biochem Physiol A. 2009, 152: 278-89. 10.1016/j.cbpa.2008.10.022.View ArticleGoogle 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.