Yannick Pauchet
email: y.pauchet@exeter.ac.uk

Paul Wilkinson

Manuella Van Munster

Sylvie Augustin

David Pauron

Richard H Ffrench-Constant

Pyrosequencing of the midgut transcriptome of the poplar leaf beetle Chrysomela tremulae reveals new gene families in Coleoptera

Keywords: Insect midgut Chitin deacetylase Cellulase Endopolygalacturonase Chrysomela Bt resistance EST, expressed sequence tag, PM, peritrophic membrane, Bt, Bacillus thuringiensis toxin, SNP, single nucleotide polymorphism

The insect midgut is the primary target site for Bt-derived insecticides and Bt alternatives. However, despite extensive recent study, the precise role and nature of different Bt receptors remains a subject of considerable debate. This problem is fuelled by a lack of understanding of the genes expressed in the insect midgut and their physiological roles. The poplar leaf beetle, Chrysomela tremulae, is an important model for understanding the mode of action of, and resistance to, coleopteran-specific Bt toxins and currently shows the only known naturally occurring case of resistance to Cry3A toxins. Moreover it belongs to the same family as the corn rootworm, Diabrotica virgifera, an economically important beetle pest and target of hybrid corn expressing Cry3 toxins. Pyrosequencing is a fast and efficient way of defining the transcriptome of specific insect tissues such as the larval midgut. Here we use 454 based pyrosequencing to sample the larval midgut transcriptome of C. tremulae. We identify candidate genes of putative Bt receptors including transcripts encoding cadherin-like proteins, aminopeptidase N and alkaline phosphatase. We also describe a wealth of new transcripts predicting rapidly evolving gene families involved in plant tissue digestion, which have no homologs in the genome of the stored product pest the Red Flour beetle, Tribolium castaneum.

Introduction

Traditionally the major target site for small molecule insecticides has been the insect nervous system [START_REF] Buckingham | Insect GABA receptors: splicing, editing, and targeting by antiparasitics and insecticides[END_REF][START_REF] Dong | Insect sodium channels and insecticide resistance[END_REF][START_REF] Ffrench-Constant | The genetics and genomics of insecticide resistance[END_REF][START_REF] Millar | Nicotinic acetylcholine receptors: targets for commercially important insecticides[END_REF][START_REF] Raymond-Delpech | Ion channels: molecular targets of neuroactive insecticides[END_REF]. However with the advent of Bt toxins, and Bt transformed insect resistant crops, the midgut has become the primary focus for the development of novel insecticidal proteins [START_REF] Pigott | Role of receptors in Bacillus thuringiensis crystal toxin activity[END_REF]. Interest in insect midgut physiology has been strengthened by concerns over emerging Bt resistance and by ongoing research into a growing number of candidate Bt receptors and how these may become altered in Bt resistant insects [START_REF] Pigott | Role of receptors in Bacillus thuringiensis crystal toxin activity[END_REF]. Identifying and understanding midgut receptors will also be important in the study of new toxins proposed as alternatives to toxins from Bacillus, such as the Toxin complexes (Tc's) from Photorhabdus and Xenorhabdus bacteria [START_REF] Bowen | Insecticidal toxins from the bacterium Photorhabdus luminescens[END_REF][START_REF] Ffrench-Constant | Insecticidal toxins from Photorhabdus bacteria and their potential use in agriculture[END_REF]. However, despite extensive recent study, the precise role and nature of different Bt receptors remains a subject of considerable debate [START_REF] Pigott | Role of receptors in Bacillus thuringiensis crystal toxin activity[END_REF][START_REF] Soberon | Signaling versus punching hole: how do Bacillus thuringiensis toxins kill insect midgut cells?[END_REF]. This problem is fuelled by a lack of understanding of the genes expressed in the insect midgut and their respective roles in digestion, response to invading micro-organisms, homeostatic regulation, and the production and remodeling of the peritrophic membrane. We aim to address this problem by describing the midgut transcriptome of representatives of key insect pest groups such as leaf feeding beetles and Lepidoptera.

Beetles represent approximately half of all insect species recorded to date and within this group the chrysomelids are one of the largest families [START_REF] Hunt | A comprehensive phylogeny of beetles reveals the evolutionary origins of a superradiation[END_REF], many of which are important defoliating and/or root damaging pests such as the corn rootworm Diabrotica virgifera [START_REF] Gray | Adaptation and invasiveness of western corn rootworm: intensifying research on a worsening pest[END_REF]. With the recent sequencing of the complete genome of the Red Flour Beetle, Tribolium castaneum, we are now uniquely poised to understand more about the genomics and transcriptomics of beetles. Moreover, given the rather unusual lifestyle of T. castaneum in feeding exclusively on stored products, comparisons with the commoner lifestyle of leaf-, root-or bark-feeding are a priority. The poplar leaf beetle, Chrysomela

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tremulae, is an important model for understanding Bt receptors and Bt resistance, and this is currently the only known beetle for which Cry3A resistance alleles have been found in the field (Genissel et al., 2003a). A Cry3A resistant C. tremulae strain was selected on Bt transformed poplar trees expressing the Cry3A toxin (Genissel et al., 2003b). This strain was derived from an isofemale line established from field-caught insects that generated F2 offspring that survived on this Bt poplar clone [START_REF] Augustin | Genetics of resistance to transgenic Bacillus thuringiensis poplars in Chrysomela tremulae (Coleoptera: Chrysomelidae)[END_REF]. Resistance to Cry3A in C. tremulae is under control of a single, completely recessive, autosomal trait [START_REF] Augustin | Genetics of resistance to transgenic Bacillus thuringiensis poplars in Chrysomela tremulae (Coleoptera: Chrysomelidae)[END_REF], suggesting that changes in a single receptor, or other gene product, may be involved in resistance. Resistance to Cry3A in the related Chrysomela scripta can be overcome using Cyt1Aa [START_REF] Federici | Cyt1Aa protein of Bacillus thuringiensis is toxic to the cottonwood leaf beetle, Chrysomela scripta, and suppresses high levels of resistance to Cry3Aa[END_REF], a protein from a different class of Bt toxins, suggesting some potential specificity in the nature of the resistance mechanism. In the Lepidoptera, Bt resistance has been associated with changes in midgut associated aminopeptidase N, cadherin-like proteins, alkaline phosphatase and, via work in model nematodes, midgut glycolipids [START_REF] Pigott | Role of receptors in Bacillus thuringiensis crystal toxin activity[END_REF]. However the molecular basis of Cry3A resistance in beetles has not been reported to date.

Pyrosequencing is a fast and efficient way of defining the transcriptome of specific insect tissues or cells and has recently been used with great effect to define the transcriptome of immune related tissues in Manduca sexta larvae [START_REF] Zou | Pyrosequence analysis of expressed sequence tags for Manduca sexta hemolymph proteins involved in immune responses[END_REF]. 454 based sequencing of Expressed Sequence Tags (ESTs) greatly increases the number and depth of sequence contigs that can be achieved using capillary based sequencing. Further, normalization of cDNA pools prior to sequencing reduces the frequency of highly abundant transcripts and ensures that a more even representation of transcripts is sampled during sequencing. Thus 454 sequencing looks poised to geometrically increase the number of known transcripts in many insect tissues. Here we use 454 based pyrosequencing to sample the larval midgut transcriptome of C. tremulae. We identify several classes of putative Bt receptors such as transcripts encoding putative cadherin-like proteins, aminopeptidase N and alkaline phosphatase. We also describe a wealth of putative new gene products which are part of rapidly evolving gene families involved in plant cell wall digestion. These new gene families are not present in the genome of T. castaneum but are common in EST collection from other plant feeding beetles, suggesting that additional leaf feeding genomic models, such as C. tremulae, will be needed in order to fully understand the physiology of the Coleoptera.

Methods and materials

2.1. Insect rearing and midgut RNA preparation C. tremulae larvae for midgut dissection were obtained from a strain established from a single mated pair collected in Vatan, France, in 1999 [START_REF] Augustin | Genetics of resistance to transgenic Bacillus thuringiensis poplars in Chrysomela tremulae (Coleoptera: Chrysomelidae)[END_REF]. The strain (Vatan125), originated from the offspring of an isofemale line lacking alleles conferring resistance to the Cry3Aa toxin, was maintained in standard rearing conditions, in a growth chamber at 20 C with a photoperiod of 16:8 (L:D) at URZF-INRA Orle ´ans. Larvae and adults were reared on fresh mature leaves detached from greenhouse-grown poplar hybrid clone (Populus tremula  Populus tremuloides, . Three day old third-instar larvae were used for dissection.

For RNA isolation, midguts were dissected from 10 larvae into PBS buffer. Midguts were cleared of food, peritrophic membrane and Malpighian tubules, before being flash frozen in liquid nitrogen and stored at À80 C prior to use. RNA was subsequently isolated using TRIzol reagent (Invitrogen) according to the manufacturer's protocol. Genomic DNA contamination was removed by DNAse treatment (TURBO DNAse, Ambion) for 30 min at 37 C. Midgut RNA was further purified by using the RNeasy MinElute Clean up Kit (Qiagen) following the manufacturer's protocol and eluted in 20 ml of RNA storage solution (Ambion).

Midgut cDNA library preparation

Full-length, enriched, cDNAs were generated from 2 mg of total RNA using the SMART PCR cDNA synthesis kit (BD Clontech) following the manufacturer's protocol. Reverse transcription was performed with the PrimeScript reverse transcription enzyme (Takara) for 60 min at 42 C and 90 min at 50 C. In order to prevent over-representation of the most common transcripts, the resulting double-stranded cDNAs were normalized using the Kamchatka crab duplex-specific nuclease method (Trimmer cDNA normalization kit, Evrogen) [START_REF] Zhulidov | Simple cDNA normalization using kamchatka crab duplexspecific nuclease[END_REF]. The resulting normalized cDNA midgut library was used for 454 pyrosequencing [START_REF] Margulies | Genome sequencing in microfabricated high-density picolitre reactors[END_REF].

Pyrosequencing, sequence preprocessing and assembly

For 454 pyrosequencing, a cDNA aliquot was sent to the Advanced Genomics facility at the University of Liverpool (http://www.liv.ac. uk/agf). A single full plate run was performed on the 454 GS-FLX pyrosequencer (Roche Applied Science) using 3 mg of normalized cDNAs processed by the ''shotgun'' method. The reads obtained were preprocessed by removing PolyA tails and SMART adapters using custom written Perl scripts kindly provided by Mark Blaxter of the University of Edinburgh. The processed reads were then clustered using the MIRA v2.9.26x3 assembler with the ''de novo, normal, EST, 454 00 parameters. Specifying a minimum read length of 40 nt, a minimum sequence overlap of 40 nt and a minimum percentage overlap identity of 80%.

Blast homology searches and sequence annotation

Homology searches (BLASTx and BLASTn) of unique sequences and functional annotation by gene ontology terms (GO; www. geneontology.org), InterPro terms (InterProScan, EBI), enzyme classification codes (EC), and metabolic pathways (KEGG, Kyoto Encyclopedia of Genes and Genomes) were determined using the BLAST2GO software suite v2.3.1 (www.blast2go.de) [START_REF] Conesa | Blast2GO: a comprehensive suite for functional analysis in plant genomics[END_REF]. Homology searches were performed remotely on the NCBI server through QBLAST, and followed a sequential strategy. First, sequences were searched against an NCBI non-redundant (nr) protein database via BLASTx using an E-value cut-off of 10 À3 and selecting predicted polypeptides of a minimum length of 10 amino acids. Second, sequences for which no BLASTx hit were searched again by BLASTn, against an NCBI nr nucleotide database using an E-value cut-off of 10 À10 .

For gene ontology mapping, the program extracts the GO terms associated with homologies identified with NCBI's QBLAST and returns a list of GO annotations represented as hierarchical categories of increasing specificity. BLAST2GO allows the selection of a significance level for the False Discovery Rate (FDR) which was used as a cut-off at the 0.05% probability level. Then, GO terms were modulated using the annotation augmentation tool ANNEX [START_REF] Myhre | Additional gene ontology structure for improved biological reasoning[END_REF] followed by GOSlim. GOSlim consists of a subset of the gene ontology vocabulary encompassing key ontological terms and a mapping function between the full GO and the GOSlim. Here, we used the ''generic'' GOSlim mapping (goslim_generic.obo) available in BLAST2GO. The data presented represent the level 2 analysis, illustrating general functional categories. Enzyme classification codes and KEGG metabolic pathway annotations are generated from the direct mapping of GO terms to their enzyme code equivalents. Finally, InterPro searches were performed remotely from

ARTICLE IN PRESS

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BLAST2GO to the InterProEBI web server. The default settings of BLAST2GO were used in every annotation step. For more details on BLAST2GO functionalities see [START_REF] Conesa | Blast2GO: a comprehensive suite for functional analysis in plant genomics[END_REF].

Capillary based sequencing of highly abundant messages

The over-abundant w1.3 kb band observed in the nonnormalized cDNA lane (Fig. 1) was gel-extracted and purified using the Illustra GFX PCR DNA and Gel Band Purification kit (GE healthcare). The resulting DNA was re-amplified by 25 PCR cycles using the 5 0 PCR Primer II A provided in the SMART PCR cDNA synthesis kit (BD Clontech). After checking on a 1.5% agarose gel that only one band of w1300 bp was re-amplified, the PCR product was cloned into the pCR4 TOPO vector (Invitrogen) and ligations transformed into chemically competent Escherichia coli TOP10 (Invitrogen). For capillary sequencing, DNA template was prepared on a Nucleoplex automated system using the Nucleoplex Plasmid Mini Prep kit (Tepnel Life Sciences). Capillary sequencing was carried out on an ABI 3130xl automatic DNA sequencer (Applied Biosystems). Unique cDNAs found in at least two clones were fully sequenced by primer walking. The unique cDNA sequences obtained were searched by BLAST and fully annotated using BLAST2GO. Sequences were submitted to Genbank with accession numbers FJ654706-FJ654727.

Phylogenetic analysis

MAFFT software (Multiple Alignment using Fast Fourier Transform, www.ebi.ac.uk/mafft/) was used to perform multiple sequence alignment of the chitin deacetylase catalytic domains prior to subsequent phylogenetic analysis. Predicted protein sequences corresponding to cellulases, deduced from C. tremulae 454 contigs and coleopteran-specific ESTs collected from Genbank db_est database, were screened for the presence of a putative signal peptide using the SignalP software. After removal of the predicted signal peptide, sequences were aligned using MAFFT software prior to Phylogenetic analysis. The MEGA4.0 software [START_REF] Tamura | MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0[END_REF] was used to construct the consensus phylogenetic tree using the UPGMA method (Unweighted Pair Method with Arithmetic Mean). To evaluate the branch strength of the phylogenetic tree, bootstrap analysis of 1000 replications was performed.

Results

Pyrosequencing, assembly and annotation

Normalization of the midgut cDNA resulted in reduction of the over-abundant 0.8-3.0 kb transcripts and production of an even distribution of transcripts ranging from 0.5 to 3.0 kb in size (Fig. 1). 454-mediated pyrosequencing of the normalized library, using a full sequencing plate of a Roche GS-FLX sequencer, generated a total of 264,698 reads of raw nucleotide sequence data with an average read length of 226 bp (Table 1). After quality scoring using MIRA v2.9.26x3, 210,885 of these reads were taken forward for assembly. Assembly resulted in 23,238 contigs and a residual 87 singletons, defined as sequences that did not assemble into a contig using the defined assembly parameters. The mean contig size was 418 bp, with contigs ranging from as small as 40 bp to as large as 2959 bp (Table 1).

For BLAST annotation, contigs were first searched using BLASTx against the non-redundant NCBI protein database using a cut-off E-value of 10 À3 . Contigs returning below cut-off BLASTx results were then re-searched using BLASTn against the non-redundant NCBI nucleotide database using a cut-off E-value of 10 À10 , using this sequential approach, just over half (56.1%) of all contigs returned an above cut-off BLAST result (Table 1).

Functional classifications of predicted proteins

The E-value and similarity distributions of the C. tremulae BLAST hits against the non-redundant database are shown in Fig. 2. Most (67%) of the BLAST hits are to the T. castaneum genome (Fig. 2C), as to date this is the only fully sequenced beetle genome and therefore T. castaneum sequences currently represent the vast majority of beetle sequences in current databases. Gene ontology (GO) assignments and enzyme classifications (EC) were used to classify the functions of the predicted midgut proteins (Fig. 3). A dominance of metabolic, catalytic and binding activities is predicted (Fig. 3B andC), as expected for midgut proteins. Within the range of enzyme activities hydrolases dominate accounting for over half (51%) of midgut enzymes (Fig. 3D). The InterPro database was also used to classify the likely functions of predicted midgut proteins and a summary of the 20 most frequent classifications is shown in Table 2. Of these, enzymes involved in protein digestion (including a range of cysteine and serine proteinases) or xenobiotic metabolism (cytochrome P450s, glutathione-S-transferases and carboxylesterases) are dominant, as again predicted for the role of the insect gut in digestion and xenobiotic metabolism. In Table 3, we list candidate genes of interest for different functional roles discussed below, including cell wall degradation, putative Bt receptors and peritrophic membrane associated proteins. Six contigs predict a C. tremulae cadherin-like protein showing 40-64% amino acid identity to other coleopteran and lepidopteran cadherin-like proteins. Contig 2921 shows 64% identity and 80% similarity to the cadherin-like protein from D. virgifera [START_REF] Sayed | A novel cadherin-like gene from western corn rootworm, Diabrotica virgifera virgifera (Coleoptera: Chrysomelidae), larval midgut tissue[END_REF] and is therefore a candidate Bt receptor. An alignment of contig 2921 with coleopteran cadherin-like proteins is shown in Fig. 4. The predicted protein sequence of contig 2921 corresponds to part of the transmembrane spanning repeats and to the whole cytosolic region of cadherin-like proteins.

Identification of microsatellite repeats

Microsatellite loci with !5 di-, tri-, tetra-and pentanucleotide repeat units were identified from 67 C. tremulae 454 contigs. Thanks to the high coverage enabled by 454 pyrosequencing, variation in the number of tandem repeats could be assessed directly from the assembly of 3 contigs (Fig. 5). These 3 loci were found in the 5 0 -UTR of a transcript similar to a T. castaneum sodium-dependent phosphate transporter XP_974340.1 (Fig. 5A), the 3 0 -UTR of a homolog of a Nasonia vitripennis predicted protein XP_001607853.1 (Fig. 5B), and the CDS of a homolog of a Bombyx mori fumarylacetoacetate hydrolase NP_001103763.1, matching codons for Tyr (TAT) and Ile (ATA) amino acids (Fig. 5C).

Capillary sequencing of abundant midgut transcripts

Following the excision of abundant 1.2-1.5 kb transcripts from the agarose gel of non-normalized midgut cDNA, the resulting product was cloned and individual cDNAs sequenced via conventional capillary sequencing. A representative sample of the most abundant cDNAs recovered is listed in Table 4, whilst a complete list of transcripts and their associated InterPro predictions are given in the Supplementary materials (Table S1 andS2). The most abundant transcripts, with 40 clones recovered, were two isoforms of a chitin

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deacetylase differing by eight SNPs resulting in only two amino acid changes. These two isoforms are putatively two alleles of the same chitin deacetylase gene. Phylogenetic comparison with chitin deacetylases from a range of different insects (Fig. 6), shows that these belong to the midgut-specific Group V, according to the classification described by [START_REF] Dixit | Domain organization and phylogenetic analysis of proteins from the chitin deacetylase gene family of Tribolium castaneum and three other species of insects[END_REF], with the closest relative being chitin deacetylase 6 from T. castaneum. A full amino acid alignment of the putative catalytic domains used to derive the phylogeny is shown in the Supplementary materials (Fig. S1). A total of 13 contigs, corresponding to the two alleles described above, and also to homologs of T. castaneum chitin deacetylase 2 and 5 were found in the 454 data (Table 3).

Identification of new beetle gene families

After searching C. tremulae contigs using BLASTx against the T. castaneum gene objects (16,404 entries) (Richards et al., 2008), 12,897 positive hits were returned corresponding to 5489 (33.5%) unique T. castaneum gene objects. Contigs returning below cut-off BLASTx results were re-searched against a non-redundant arthropod database returning a further 428 positive hits. A large portion of these contigs that did not hit any Tribolium predicted gene objects were identified as putative plant tissue degrading enzymes such as cellulases (EC:3.2.1.4) and endopolygalacturonases (EC:3.2.1.15).

To determine if such genes are common in other Coleoptera, although absent from T. castaneum, we used the predicted proteins of the corresponding C. tremulae contigs to search public coleopteran EST collections present in the Genbank db_est database. We retrieved 130 ESTs corresponding to cellulases that clustered into 20 unique contigs, and 29 ESTs corresponding to pectinases that clustered into 18 unique contigs. A further three coleopteran cellulase sequences were retrieved from Genbank together with two endopolygalacturonase sequences. All these genes were found in coleopteran species feeding on fresh plant tissues such as the pine engraver Ips pini, the western corn rootworm D. virgifera, the Colorado potato beetle Leptinotarsa decemlineata, the coffee berry borer Hypothenemus hampei, the mustard beetle Phaedon cochleariae and the citrus root weevil Diaprepes abbreviatus. These sequence data are summarized in Table S3.

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The SignalP software predicted the presence of a putative signal peptide in all the cellulase protein sequences that appeared to be full-length CDS. An alignment of all these sequences is provided as Supplementary material (Fig. S2). In addition, an InterProScan search predicted a glycoside hydrolase family 45 (GH45) conserved domain (IPR000334) in every sequence. The active site consensus

pattern for GH45 ([STA]-T-R-Y-[FYW]-D-x(5)-[CA]
) is also conserved in all sequences (Fig. 7A). As many as six different cellulase sequences could be found in some species such as I. pini, but only two contigs were present in the C. tremulae 454 data (Fig. 7A). A phylogenetic analysis suggested the presence of at least five sub-classes in this gene family (Fig. 7B).

Thirty-seven contigs corresponding to putative endopolygalacturonase genes were found in the C. tremulae dataset suggesting a highly diverse gene family. An alignment of the predicted proteins deduced from the 11 longest contigs together with the sequence found in the abundant 1.3 kb band (Pect-1, Table 4) is shown in Fig. 8A. All the C. tremulae sequences as well as the other coleopteran ones share a glycoside hydrolase family 28 (GH28) conserved domain (IPR000743). The InterProScan search did not hit the PRO-SITE consensus pattern of GH28 proteins (PS00502) which is S3).

[GSDENKRH]-x(2)-[VMFC]-x(2)-[GS]-H-G-[LIVMAG]-x(1,2)-[LIVM]- G-S.

Discussion

Pyrosequencing looks set to dramatically increase the frequency and depth of EST collections for specific insect tissues and non-Drosophilid insects as a whole. Such datasets not only provide extensive nucleotide sequence data for insects lacking genomic data but also provide a plethora of genetic markers, such as microsatellites (Fig. 5), that can be used to derive high density linkage maps for mapping traits of biological relevance. In this fashion 454 sequencing can rapidly promote both sequence data and genetic markers in species for which no previous sequence data is available. For example, 454-mediated sequencing of cDNA from immune S3).

Y. Pauchet et al. / Insect Biochemistry and Molecular Biology xxx (2009)

U N C O R R E C T E D P R O O F related M
. sexta tissues has already dramatically increased the number of ESTs available [START_REF] Zou | Pyrosequence analysis of expressed sequence tags for Manduca sexta hemolymph proteins involved in immune responses[END_REF] and sequencing of other Manduca tissues, such as the midgut, will give us the first overview of a complete insect transcriptome from this physiological model which currently lacks any genomic framework. To our knowledge, the data described here is the first publication of a 454 derived insect midgut EST collection, and this dataset will also dramatically increase the number of midgut ESTs from collections previously derived via conventional capillary sequencing, such as those from the corn rootworm D. virgifera [START_REF] Siegfried | Expressed sequence tags from Diabrotica virgifera virgifera midgut identify a coleopteran cadherin and a diversity of cathepsins[END_REF], the grass grub beetle Costelytra zealandica [START_REF] Marshall | Serine proteases identified from a Costelytra zealandica (White) (Coleoptera: Scarabaeidae) midgut EST library and their expression through insect development[END_REF] or the pine engraver I. pini [START_REF] Eigenheer | Comparison of gene representation in midguts from two phytophagous insects, Bombyx mori and Ips pini, using expressed sequence tags[END_REF]. Thus pyrosequencing of non-model insects is a dramatic and rapid way of providing genomic and transcriptomic resources in an organism where none are previously available. For example, with this publication, for C. tremulae the number of nucleotide sequences (reported contigs) has risen instantaneously from 8 (Genbank, 1st of February 2009) to over 23,000 (Table 1). When systematically applied to different tissues of insect models pyrosequencing will similarly dramatically increase resources available for all non-Drosophilids.

With specific reference to the midgut ESTs for C. tremulae described here, many of these predict proteins with functions expected for the midgut with likely roles in digestion, xenobiotic metabolism or immunity, whereas many either show unexpected functions or no similarity to predicted proteins in current databases. Expected roles in the midgut physiology of a leaf eating beetle involve destruction of the plant cell wall, thus the dominance of cellulases, pectinases and glucanases. However, what is perhaps more interesting is the absence of these predicted proteins from the T. castaneum genome. This predicted difference in the midgut enzyme composition of beetles feeding on stored products (T. castaneum) and fresh leaves (C. tremulae), begins to illustrate the potential power of comparing such datasets between insects with different lifestyles. We also note that enzymes degrading plant cell walls are of considerable current interest in biotechnology for the degradation of plant material for use in biofuels. 454-mediated sequencing of insect midguts, or salivary glands, may therefore be a powerful new way of finding enzymes important for plant biotechnology.

One of the central aims of this EST project was to identify contigs encoding candidate Bt receptors to use as probes in a dissection of the molecular basis of Cry3A resistance in C. tremulae. Contigs encoding three clear candidate Bt receptors were identified. First, a cadherin-like protein showing strong similarity to cadherin-like proteins from three other beetles, T. castaneum, Tenebrio molitor and D. virgifera. Second, eleven contigs encoding proteins similar to aminopeptidase N. Third, a contig encoding an alkaline phosphatase. However no contigs encoding ADAM10-like metalloproteases, recently reported as involved in the processing of Cry3A in Colorado potato beetle [START_REF] Ochoa-Campuzano | An ADAM metalloprotease is a Cry3Aa Bacillus thuringiensis toxin receptor[END_REF], were found.

Another expected function for proteins in the insect midgut is remodeling of peritrophic membrane (Table 3). The peritrophic membrane (PM) separates the food bolus in the insect midgut, in this case poplar leaves, from the midgut epithelium and associated brush border membrane. This membrane is composed of chitin and proteins and forms a permeable barrier that protects the midgut epithelium from erosion by the food bolus whilst allowing for the passage of enzymes and solutes involved in extracellular digestion in the gut [START_REF] Caldeira | Digestive enzyme compartmentalization and recycling and sites of absorption and secretion along the midgut of Dermestes maculatus (Coleoptera) larvae[END_REF]. The PM requires continuous repair and replacement and it is therefore interesting to note dominance of enzymes involved in the binding (peritrophins, seven contigs), breakdown (chitinases, 36 contigs) and remodeling (chitin synthase, seven contigs, and chitin deacetylase, 13 contigs) of chitin (Table 3), suggesting that maintenance and remodeling of chitin in the PM occupies a major part of the C. tremulae transcriptional midgut machinery. It is interesting to note that a member of the group V chitin deacetylase was the most abundant transcript recovered from the C. tremulae non-normalized midgut cDNAs. This group of putative enzymes has attracted significant recent attention in several lepidopteran species [START_REF] O R R E C T E D P R O O F Campbell | Proteomic analysis of the peritrophic matrix from the gut of the caterpillar, Helicoverpa armigera[END_REF][START_REF] Guo | A novel chitin-binding protein identified from the peritrophic membrane of the cabbage looper, Trichoplusia ni[END_REF][START_REF] Pauchet | Mapping the larval midgut lumen proteome of Helicoverpa armigera, a generalist herbivorous insect[END_REF][START_REF] Toprak | A chitin deacetylase and putative insect intestinal lipases are components of the Mamestra configurata (Lepidoptera: Noctuidae) peritrophic matrix[END_REF], however their role in midgut physiology remains unclear.

As well as forming the major organ for processing of insect food, the midgut also forms the first line of defense against ingested xenobiotics and micro-organisms, the midgut therefore also plays a critical role in xenobiotic metabolism and insect immunity. With respect to xenobiotic metabolism, cytochrome P450s (25 hits) and glutathione-S-transferases (34 hits) are highly represented in the InterPro families detected (Table 2) and as full sequences for these gene families appear it will be interesting to compare the full complement of P450 and GST genes present in this leaf feeding beetle with that found in the grain feeding T. castaneum. For example, given that C. tremulae specializes in a single species of plant we might expect the range of enzymes required to metabolize secondary plant compounds to be reduced and somewhat specialized in their role.

In conclusion, here we have shown that 454-mediated pyrosequencing is a rapid and dramatic way of increasing the size and depth of insect midgut EST collections. Many of the predicted enzymes are currently only reported in the leaf beetle C. tremulae, sequenced here. Indeed some of the enzymes recovered are the first insect representatives. For example, the cellulose cellobiosidases (Table 3) appear to only have representatives from yeast, fungi and bacteria in current sequence databases. The approach taken here, of normalizing cDNAs from a specific insect tissue, attempts to define all of the unique transcripts associated with a tissue, often termed an 'unigene set'. We note, however, that cDNA shearing associated with preparation of the sample for 454 sequencing may introduce an element of bias resulting in uneven sequence coverage of representative cDNAs. However, the depth of EST coverage generated by 454 sequencing far outweighs that generated in conventional capillary led projects. 454 sequencing of other insect tissues will therefore not only increase the depth of existing capillary generated EST collections but will also identify new insect representatives for different enzymes and receptors. Further comparison of such datasets in insects with different lifestyles and food sources will begin to tell us how different insects exploit their different niches.

Fig. 1 .

 1 Fig. 1. Agarose gel of C. tremulae midgut cDNA before (non-norm) and after (norm) normalization. Note the high relative abundance of several discrete bands in the nonnormalized sample which disappear following normalization. The over-abundant transcripts (w1.3 kb) were excised from the gel, cloned and sequenced via capillary sequencing to characterize highly abundant larval midgut transcripts (see text). Size markers are shown at left.

Fig. 2 .

 2 Fig. 2. Summary of homology searches (BLASTx and BLASTn) of C. tremulae larval midgut 454 data against the non-redundant database at NCBI. (A) E-value distribution of the top BLAST hit for each unique sequence. Cut-offs used in each case were 10 À3 for BLASTx and 10 À10 for BLASTn. (B) Similarity distribution of the top BLAST hit for each unique sequence. (C) Species distribution of the top BLAST hit for each unique sequence. Note that nearly 67% of top hits are to the beetle Tribolium whose complete genome has recently been sequenced.

Fig. 3 .

 3 Fig. 3. Gene Ontology (GO) assignments and Enzyme Classifications (EC) for the C. tremulae larval midgut transcriptome. (A) Cellular component GO terms at level 2. (B) Biological process GO terms at level 2. (C) Molecular function GO terms at level 2. (C) General EC terms: Oxidoreductases (EC:1.x.x), Transferases (EC:2.x.x), Hydrolases (EC: 3.x.x), Lyases (EC:4.x.x), Isomerases (EC:5.x.x), and Ligases (EC:6.x.x). Note that one sequence can be associated with more than one GO term (see text for discussion).

Fig. 4 .Fig. 5 .

 45 Fig. 4. Amino acid alignment of known coleopteran cadherin-like genes with C. tremulae contig 2921, showing part of the predicted transmembrane spanning repeats. Sequences were aligned using the MAFFT multiple alignment program and identical residues are boxed with dark shading, and conserved residues boxed with light shading. Sequences are from Diabrotica virgifera (Dvir, AAV88529.2), Tribolium castaneum (Tcas, XP_971388.2), Tenebrio molitor (Tmol, ABL86001.1) and C. tremulae (Ctre, contig_2921).

Fig. 6 .

 6 Fig. 6. Phylogenetic analysis of putative chitin deacetylases from different insects. Phylogenetic analysis was conducted in MEGA4.0 using the UPGMA method and the percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches. Positions containing alignment gaps and missing data were eliminated only in pairwise sequence comparisons (Pairwise deletion option). ChBD: Chitin-binding domain; LDL: Low density lipoprotein domain; CDA: Chitin deacetylase domain. Tribolium castaneum (Tcas), Apis mellifera (Amel), Anopheles gambiae (Agam), Helicoverpa armigera (Harm), Bombyx mori (Bmor), Drosophila melanogaster (Dmel), Trichoplusia ni (Tni), Mamestra configurata (Mcon), C. tremulae (Ctre).Note that the alignment of the CDA domains used in this phylogeny is provided as a Supplementary material (Fig.S1).

  This is because the active site consensus pattern of the coleopteran endopolygalacturonases is slightly different, we deduced it to be [SDENKRQT]-x(3)-C-x-G-[GS]-H-G-[LF]-x(3)-[IVTS]-G-x-S.

Fig. 7 .

 7 Fig. 7. Amino acid alignment and Phylogenetic analysis of the coleopteran cellulose gene family. (A) Predicted protein sequences deduced from coleopteran cellulase transcripts without their predicted signal peptide were aligned using MAFFT multiple alignment program and identical residues are boxed with dark shading, and conserved residues boxed with light shading. The predicted active site consensus pattern is underlined. (B) Phylogenetic analysis was conducted in MEGA4.0 using the UPGMA method and the percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches. Positions containing alignment gaps and missing data were eliminated only in pairwise sequence comparisons (Pairwise deletion option). Details on how these sequences were obtained are given as Supplementary materials (TableS3).

Y

  . Pauchet et al. / Insect Biochemistry and Molecular Biology xxx (2009) 1-11 8 ARTICLE IN PRESS IB2039_proof 16 April 2009 8/11 Please cite this article in press as: Pauchet, Y., et al., Pyrosequencing of the midgut transcriptome of the poplar leaf beetle Chrysomela..., Insect Biochemistry and Molecular Biology (2009), doi:10.1016/j.ibmb.2009.04

Fig. 8 .

 8 Fig.8. Amino acid alignment of putative C. tremulae and other coleopteran endopolygalacturonases. Sequences were aligned using the MAFFT multiple alignment program and identical residues are boxed with dark shading, and conserved residues boxed with light shading. The predicted active site consensus pattern is underlined. Details on how these sequences were obtained are given as Supplementary materials (TableS3).

  Please cite this article in press as: Pauchet, Y., et al., Pyrosequencing of the midgut transcriptome of the poplar leaf beetle Chrysomela..., Insect Biochemistry and Molecular Biology (2009), doi:10.1016/j.ibmb.2009.04.001

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Table 1

 1 Summary statistics for C. tremulae midgut EST assembly and annotation

	.	Q2

IB2039_proof 16 April 2009 3/11 Please cite this article in press as: Pauchet, Y., et al., Pyrosequencing of the midgut transcriptome of the poplar leaf beetle Chrysomela..., Insect Biochemistry and Molecular Biology (2009), doi:10.1016/j.ibmb.2009.04.001

Table 2

 2 Summary of TOP20 of InterPro families and domains represented in the C. tremulae larval midgut transcriptome.

	Family

Y. Pauchet et al. / Insect Biochemistry and Molecular Biology xxx (2009) 1-11 5 ARTICLE IN PRESS IB2039_proof 16 April 2009 5/11 Please cite this article in press as: Pauchet, Y., et al., Pyrosequencing of the midgut transcriptome of the poplar leaf beetle Chrysomela..., Insect Biochemistry and Molecular Biology (2009), doi:10.1016/j.ibmb.2009.04.001

Table 3

 3 Selection of genes of interest related to the C. tremulae larval midgut physiological functions.

		EC number	Total number
			of contigs
	Plant/fungi cell wall degradation
	Cellulase	3.2.1.4	2
	Cellulose 1,4-b-cellobiosidase	3.2.1.91	6
	Endopolygalacturonase (pectinase)	3.2.1.15	37
	Endo-b-1,3-glucanase	3.2.1.39	2
	Bt Cry3 toxin putative binding partners
	Cadherin-like protein	-	6
	Aminopeptidase N	3.4.11.2	11
	Alkaline phosphatase	3.1.3.1	1
	Peritrophic membrane biosynthesis, degradation and remodeling
	Chitinase	3.2.1.14	36
	Chitin synthase	2.4.1.16	7
	Chitin deacetylase	3.1.5.41	13
	Peritrophin (mucin)	-	7
	General digestion
	Cysteine proteinase all types	-	129
	Serine proteinase all types	-	69
	Aminopeptidase all types	-	33
	Carboxypeptidase all types	-	25
	Dipeptidylpeptidase all types	-	3
	Lipase	3.1.1.3	17
	a-amylase	3.2.1.1	3
	a-glucosidase (maltase)	3.2.1.20	10
	b-glucosidase	3.2.1.21	22

Table 4

 4 Capillary sequencing of over-abundant transcripts (w1.3 kb) in the non-normalized midgut cDNA.

	# clones	% total	Name	cDNA (bp)	Protein (aa)	Blastp vs nr	Species	E-Value
	40	41.7	Chitin deacetylase	1260	376	Chitin deacetylase 6	T. castaneum	3eÀ102
	10	10.42	Aspartic proteinase-1	1245	386	Cathepsin D	T. castaneum	3eÀ134
	10	10.42	16S rRNA	1306	-	-	-	-
	3	3.13	Chitinase-1	1278	389	Chitinase	P. cochleariae	1eÀ118
	3	3.13	Cysteine proteinase-1	1163	323	Cathepsin L-like	P. cochleariae	4eÀ138
	3	3.13	Endopolygalacturonase-1	1205	370	Polygalacturonase	S. oryzae	5eÀ97
	2	2.1	Tetraspanin-1	1136	305	Tetraspanin 29fb	T. castaneum	3eÀ67
	2	2.1	Unknown protein-1	1275	397	Hypothetical protein	T. castaneum	8eÀ69

  Please cite this article in press as: Pauchet, Y., et al., Pyrosequencing of the midgut transcriptome of the poplar leaf beetle Chrysomela..., Insect Biochemistry and Molecular Biology (2009), doi:10.1016/j.ibmb.2009.04.001

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Acknowledgments

The authors thank Marcel Amichot for fruitful discussion, Claudine Courtin for insect rearing and Philippe Lorme for greenhouse plant production. Supported by a BBSRC grant to R. ff-C.

There is no conflict of interest.

Appendix. Supplementary material

Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.ibmb.2009.04.001.


domain: sdv info

text:
Annie Robic
email: annie.robic@toulouse.inra.fr

Catherine Larzul

Michel M Bonneau

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

It has been drawn to the attention of the authors that in Figure 1 the common intermediate products of the transformation of progesterone and 5,16 androstadien-3bol into androstenone is 4,16-androstadien-3-ene (and not 4,16 androstadien-3-one). In addition, the list of the final products of androstenone sulfonation has been completed. This corrigendum published in issue 5, 2008 is freely available in electronic form on the web site of GSE (http://www.gse-journal.org/).

Figure 1 .

 1 Figure 1. Main enzymes involved in the porcine metabolism of androstenone.

Erratum

Genet. Sel. Evol. 40 (2008) 129130131132133134135136137138139140141142143Genetic and metabolic aspects of androstenone and skatole deposition in pig adipose tissue: A review


P Sellier

L Maignel

J P Bidanel

Genetic parameters for tissue and fatty acid composition of backfat, perirenal fat and longissimus muscle in Large White and Landrace pigs

Keywords: pig, genetic parameters, compositional traits, adipose tissue, muscle quality

Genetic parameters pertaining to the same chemical characteristics of three porcine tissues, that is backfat (BF), perirenal fat (PF) and longissimus muscle (LM), were estimated in centrally tested Large White and Landrace pigs. Animals were fed ad libitum. They were slaughtered at an average BW of 99.6 kg, and samples of BF (both inner and outer layers) and LM were removed at the 13th to 14th rib level of the carcass on the day after slaughter. The data set included 2483 animals recorded for average daily gain (ADG; 35 to 100 kg), estimated carcass lean percentage (LEAN) and lean tissue growth rate (LTGR). Among these animals, around 950 pigs were recorded for lipid content (L%) and water content (W%) of BF and LM and for fatty acid composition (FAC) of BF, whereas FAC of LM was measured on 297 pigs and L%, W%, and FAC of PF on around 210 pigs. Heritabilities (h 2 ) and genetic correlations (r a ) were estimated using REML-animal model methodology. Estimates of h 2 for L%, W% and FAC of BF, PF and LM were of moderate-to-high magnitude: for example 0.47 6 0.09 for L% of LM, 0.59 6 0.11 for W% of BF, 0.45 6 0.08 for the ratio of polyunsaturated to saturated fatty acids (P/S) of BF, 0.61 6 0.15 and 0.29 6 0.10 for the coefficient of unsaturation of lipids (UNSAT, average number of double bonds of unsaturated fatty acids) of PF and LM, respectively. Genetic correlations of L% with P/S or UNSAT were strongly negative (from 20.4 to 20.9) in BF and LM, but not in PF. The 'between-tissue' genetic correlations for homologous compositional traits were far from being unity (e.g. r a 5 0.57 6 0.05 'between' BF and PF for UNSAT). Genetic relationships between ADG and tissue compositional traits were globally weak. By contrast, genetic correlations were moderate-to-high between carcass leanness and tissue compositional traits, especially those of fat depots: for example 20.66 6 0.14 between LEAN and L% of BF, 0.50 6 0.07 between LEAN and UNSAT of PF, 20.44 6 0.08 between LEAN and L% of LM, and 0.27 6 0.03 between LEAN and UNSAT of LM. On the basis of the parameter estimates found here, breeding for higher LTGR is expected to increase the ratio of water to lipids and the unsaturation degree of lipids in subcutaneous BF and, to a lesser extent, in PF. Tissue composition and FAC of LM would be less affected.

Implications

Reducing fat quantity in the carcass has been a primary objective of pig farmers for a long time and considerable improvements have been accomplished in this domain. Increasing attention, including from a genetic point of view, has been devoted in the last few decades to the qualitative characteristics of the fat compartment of pig carcasses. Potential interest for pig meat industry and consumer acceptability refers to three main items, namely eating

Introduction

Along with the advances performed in pig nutrition and management, breeding effort has largely proven to be effective since the years 1950 to 1960 for reducing the whole fatness of pig carcasses. A lot of early results from breed comparisons or selection experiments (e.g. [START_REF] Wood | Fatty acid composition of backfat in Large White pigs selected for low backfat thickness[END_REF] a Present address: Canadian Centre for Swine Improvement, Central Experimental Building 54, Ottawa (Ontario) K1A0C6, Canada.

-E-mail: Pierre.Sellier@jouy.inra.fr [START_REF] Scott | Fatty acid composition of adipose tissue from lean and obese swine[END_REF] have rapidly shown that this quantitative decrease of fat deposition is accompanied by a number of more or less undesirable changes in composition and quality of fat depots throughout the body. Research workers have also addressed the question of to what extent carcass leanness is genetically correlated with quantitative development and chemical composition of intramuscular fat, as reviewed by [START_REF] Sellier | Genetics of meat and carcass traits[END_REF]. However, the amount of information remains fairly limited on the genetic parameters of most traits of interest in this field. In particular, studies dealing with the 'between-tissue' genetic relationships for homologous compositional traits are very scarce.

The purpose of our investigation mainly consisted of examining the genetic parameters (heritabilities (h 2 ) and genetic correlations (r a )) for compositional traits of three porcine tissues (subcutaneous back fat (BF), body cavity perirenal fat (PF) and longissimus muscle (LM)) in the widely used Large White (LW) and Landrace (LR) breeds.

Material and methods

Animals

Data for this study were collected on LW and LR female pig and castrated male pigs tested in four French central sibtesting stations. An 'all in-all out' (batch) system was practised in these stations. All batches comprised both LW and LR pigs. Some of the batches comprised both females and castrated males, but most batches consisted of either females or castrated males. A contemporary group was, therefore, defined here as a group of animals of the same gender and reared in the same barn and period.

A total of 2483 pigs (1882 LW and 601 LR) were reared in pens of two animals (two full-sibs, as a general rule) from 35 to 100 kg body weight (BW). Pigs were fed ad libitum a pelleted diet based on barley, wheat and soybean meal and containing 9.0 MJ/kg net energy and 170 g/kg crude protein. Data for lipid content (L%) and water content (W%) of BF and LM and fatty acid composition (FAC) of BF were collected on a subsample of 948 to 959 animals. In addition, 297 animals were recorded for FAC of muscle lipids and 209 to 215 animals for lipid content, water content and FAC of PF. Animals included in the various subsamples were chosen in order to have datasets with the most appropriate familial structure for an optimal estimation of genetic parameters.

Traits

Pigs were slaughtered at an average BW of 99.6 kg (s.d. 5 4.3 kg). After 24 h at 48C, cold carcass was weighed, and weights of seven cuts resulting from a standardised cutting of the left-hand side of the carcass (ham, loin, shoulder, belly, BF, PF, and feet) were recorded. Carcass lean percentage (LEAN) was estimated by the following formula: LEAN 5 20.75 1 80 H 1 106 L 1 48 B 2 50 BF 2 66 PF, where H, L, B, BF and PF are the ratios of ham, loin, belly, backfat and perirenal fat weights to half-carcass weight, respectively. Lean tissue growth rate (LTGR) was calculated from average daily gain (ADG) from 35 to 100 kg BW, killing out percentage and LEAN, under the assumption that the ratio of lean tissue weight to empty BW remains unchanged during the test period [START_REF] Fowler | Objectives in pig breeding[END_REF].

At the time of carcass cutting, samples of around 100 g of subcutaneous BF (both inner and outer layers) and around 100 g of LM were removed at the 13th to 14th rib level. Around 100 g of PF were also taken on a proportion of pigs. These tissue samples were stored at 2208C for subsequent laboratory analyses.

Total lipid content of adipose tissues was determined by using the refractometric method described by [START_REF] Arneth | Uber die Refraktometrische Schnellfettbestimmung nach Rudisher in Fleisch und Fleischwaren[END_REF]. Water content was calculated from the weight loss of a 10 g sample of fat after drying at 1058C for 2 h. After extraction of lipids by chloroform, methylation of fatty acids was performed on an aliquot of 50 mg of lipids [START_REF] Christopherson | Preparation of milk fat methyl esters by alcoholysis in an essentially nonalcoholic solution[END_REF]. FAC was determined using a Delsi Di-700 gas chromatography apparatus (Delsi-Nermag Instruments, Argentenil, France), equipped with a flame ionisation detector.

Regarding muscle tissue, total lipid content was determined on a 10 g sample following [START_REF] Arneth | Uber die Refraktometrische Schnellfettbestimmung nach Rudisher in Fleisch und Fleischwaren[END_REF] and water content was calculated from the weight loss of a 5 g sample after drying at 1058C for 24 h. FAC of muscle lipids was determined by gas chromatography of methyl esters as above. Total lipids were extracted according to [START_REF] Maxwell | Determination of total fat in meat and meat products by a rapid, dry column method[END_REF]. Methyl esters of fatty acids were prepared using a modification of the method proposed by [START_REF] Pelick | Lipid derivatives and gas liquid chromatography[END_REF] with petroleum ether and hexane replacing benzene and diethyl ether, respectively.

Lipid and water contents were expressed as gram per 100 g of fresh tissue. The traits analysed to account for FAC of fat and muscle lipids were:

> proportions of the major fatty acids (C14:0, C16:0, C16:1, C18:0, C18:1, C18:2, C18:3 and C20:4), expressed as percentages of total identified fatty acids, > percentages of saturated fatty acids (SFA), monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA), taking into account the above-cited major fatty acids and all identified minor fatty acids of the three classes (i.e. C10:0, C12:0, C15:0, C17:0 and C20:0 for SFA; C17:1 and C20:1 for MUFA; C20:2, C20:3, C22:4, C22:5 and C22:6 for PUFA), > coefficient of unsaturation of lipids (UNSAT), defined as the average number of double bounds of unsaturated fatty acids, and > ratio of polyunsaturated to saturated fatty acids (P/S).

Statistical analyses

Table 1 gives the means and s.d. of the tissue compositional traits under study. Breed means were estimated using an ANOVA model including the fixed effects of breed and contemporary group and the slaughter weight as a covariate. The average number of animals per contemporary group was 17.9 for PF traits and 19.8 for BF and LM traits.

Variance-covariance components were estimated using the restricted maximum likelihood methodology applied to a multiple-trait animal model. Analyses were performed with the VCE software package (Neumaier and Groeneveld, Sellier, Maignel and Bidanel 1998). The mixed linear model comprised the same fixed effects and covariate as above, the random effect of litter of birth and the random additive genetic effect of the animal. The number of animals in the pedigree file ranged from 950 to 7653 depending on the group of traits under analysis. Phenotypic correlations among traits (r p ) were calculated from the sums of litter, animal and residual components of variances and covariances, that is on a within breed, batch and slaughter weight basis. Approximate standard errors of estimated heritabilities (h 2 ) and genetic correlations (r a ) were obtained from an approximation of the Hessian matrix when convergence was reached.

Results and discussion

Differences between tissues

As could be expected, large differences in chemical composition were found between the three tissues dealt with in this study (Table 1), and these differences are in general agreement with the findings of previous studies (e.g. [START_REF] Malmfors | Fatty acid composition of porcine back fat and muscle lipids as affected by sex, weight and anatomical location[END_REF][START_REF] Wood | The chemical composition of fat tissues in the pig: effects of castration and feeding treatment[END_REF][START_REF] Monziols | Comparison of the lipid content and fatty acid composition of intermuscular and subcutaneous adipose tissues in pig carcasses[END_REF]. PF as compared with BF contains more lipids, and these lipids exhibit a lower ratio of MUFA to SFA. The high lipid content displayed by PF is likely to be partly because of the higher activities of lipogenic enzymes in this adipose tissue compared with subcutaneous fat [START_REF] Anderson | Lipogenic enzyme activities and cellularity of porcine adipose tissue from various anatomical locations[END_REF], although the preferential sites of lipid storage could differ from preferential sites of lipid synthesis according to [START_REF] Mourot | Comparative study of in vitro lipogenesis in various adipose tissues in the growing pig (Sus domesticus)[END_REF].

The average value of intramuscular fat content (1.23%) was low in this study compared with other studies dealing with the LM. This fact may be partly explained by the anatomical location of muscle sampling (13th to 14th rib level) where the minimal marbling within the LM is exhibited according to [START_REF] Faucitano | Distribution of intramuscular fat content and marbling within the longissimus muscle in pigs[END_REF].

It has been known for a long time that the degree of saturation of fatty acids increases from the outside to the inside of the body. This point is well illustrated by the compared FAC of subcutaneous BF and internal PF. Regarding the markedly lower C18:1 content of PF, the lower activity of D9 fatty acid desaturase (stearoyl-CoA-desaturase) in this adipose tissue [START_REF] Thompson | Relationship between stearic acid desaturase and fatty acid composition[END_REF]) is primarily involved. The difference in temperature between the subcutaneous area and internal body cavity could play a role as the activity of D9 fatty acid desaturase is negatively linked to temperature (Kouba et al., 1999). As far as extrinsic factors are concerned, the wide range observed among studies for the average linoleic acid content of fat depots is to be ascribed to differences in slaughter weight and gender/castration status of the animals (e.g. [START_REF] Malmfors | Fatty acid composition of porcine back fat and muscle lipids as affected by sex, weight and anatomical location[END_REF][START_REF] Wood | The chemical composition of fat tissues in the pig: effects of castration and feeding treatment[END_REF] and, predominantly, to differences in dietary linoleic acid level (e.g. [START_REF] Brooks | Fatty acid composition of pork lipids as affected by basal diet, fat source and fat level[END_REF].

The comparatively high-PUFA content of intramuscular lipids is essentially linked to the phospholipid fraction of total muscle lipids. The PUFA percentage of phospholipids (major components of cell membranes) is indeed 35% to 45% compared with 7% to 15% for triacylglycerols in the pig, as reviewed by [START_REF] Smet | Meat fatty acid composition as affected by fatness and genetic factors: a review[END_REF]. This increase in PUFA percentage is essentially made at the expense of MUFA percentage, which is around 20% in phospholipids and 50% in triacylglycerols [START_REF] Sharma | Comparative lipid composition of porcine muscles at different anatomical locations[END_REF].

It may also be noted from Table 1 that s.d. of FAC traits are consistently larger in muscle than in fat depots. The most likely reason lies in the strong dependence of FAC of muscle lipids upon intramuscular lipid content (see below) and the high coefficient of variation (37%) of the latter trait.

Breed differences

Differences between LW and LR breeds were in general of small-to-moderate magnitude, whereas being statistically significant for a number of traits (data not shown). LTGR was higher in LW (377 g/day) than in LR pigs (352 g/day), owing to higher performance levels for both daily weight gain (120 g/day) and carcass lean content (11.3 percentage units). Minor breed differences were found for lipid and water contents in the three tissues studied. Regarding FAC, the prominent feature is that the proportion of MUFA was greater in all three tissues in LR compared with LW pigs (e.g. 48.5 6 0.13 and 47.5 6 0.09, respectively, in BF). The higher proportion of MUFA was mainly caused by more C18:1 in LR than in LW pigs. Our findings are in accordance with the results reported by Gue ´blez et al. (1993) for BF in the same two populations. A greater activity of the D9 fatty acid desaturase in the LR could be at the origin of the breed difference in concentration of C18:1. Heritabilities Heritability estimates found here for production traits (from 0.39 to 0.67) were within the usual range of literature Composition of fat and muscle tissues in pigs values. Our h 2 estimate for LTGR (0.65 6 0.04) was, however, greater than the previously reported values of around 0.4 to 0.5, because of the relatively high heritability estimates for both ADG and killing out percentage in this study.

Table 2 shows h 2 estimates for compositional traits of BF, PF and LM. Water and lipid contents of the three tissues were moderately to highly heritable (range of h 2 estimates: 0.26 to 0.65). Regarding the heritability of water content of BF, [START_REF] Cameron | Genetic and phenotypic parameters for carcass traits, meat and eating quality traits in pigs[END_REF] reported a value (0.27), which is lower than our estimate while being comparable with our h 2 estimate for water content of PF. The value of 0.46 found here for the heritability of intramuscular fat content is very close to the average literature value quoted by [START_REF] Sellier | Genetics of meat and carcass traits[END_REF]. Furthermore, our h 2 estimate for water content of LM was of the same order as those reported by [START_REF] Cameron | Genetic and phenotypic parameters for carcass traits, meat and eating quality traits in pigs[END_REF], [START_REF] Lo | Genetic analyses of growth, real-time ultrasound, carcass, and pork quality traits in Duroc and Landrace pigs: II. Heritabilities and correlations[END_REF] and [START_REF] Larzul | Phenotypic and genetic parameters for longissimus muscle fiber characteristics in relation to growth, carcass, and meat quality traits in Large White pigs[END_REF].

Our h 2 estimates for the variables pertaining to FAC of the two fat depots were medium-to-high, with most estimates in the range from 0.40 to 0.70. This pattern of h 2 values is in good agreement with that reported by Schwo ¨rer et al. (1988), [START_REF] Cameron | Genetic and phenotypic parameters for carcass traits, meat and eating quality traits in pigs[END_REF] and [START_REF] Suzuki | Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs[END_REF]. Standard errors of the h 2 estimates relating to PF are rather large in this study but there is a consistent trend for higher h 2 of linoleic acid content, PUFA percentage, UNSAT and P/S in PF compared with BF. Four different fat depots (inner and outer layers of BF, PF and belly fat) were investigated by Schwo ¨rer et al. ( 1988) and h 2 estimates for concentrations of major fatty acids were found to be globally comparable between these sites of fat deposition. A similar situation was found by [START_REF] Suzuki | Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs[END_REF] for h 2 estimates relating to the fatty acid profile of intermuscular fat and inner and outer layers of BF.

Most of h 2 estimates reported in Table 2 for fatty acid percentages of intramuscular lipids are below 0.40 and, therefore, tend to be lower than those of PF and, to a lesser extent, BF. About the same pattern of h 2 estimates as in this study were found in the few genetic studies dealing with the FAC of intramuscular lipids. Previous studies referred to LR and Duroc pigs [START_REF] Cameron | Fatty acid composition of lipid in Longissimus dorsi muscle of Duroc and British Landrace pigs and its relationship with eating quality[END_REF], Duroc pigs (Suzuki et al., 2006) and crossbred pigs having an LW 3 LR 3 Pietrain genetic background [START_REF] Colman | Genetic parameters for intramuscular fatty acid composition and metabolism in pork[END_REF]. Unweighted means of the h 2 estimates found in the three above-cited studies and the current one amount to 0.33, 0.37, 0.35, 0.37, 0.40 and 0.24 for palmitic, stearic, palmitoleic, oleic, linoleic and arachidonic acid contents of total muscle lipids, respectively. Furthermore, [START_REF] Colman | Genetic parameters for intramuscular fatty acid composition and metabolism in pork[END_REF] stated that genetic variation in FAC of muscle lipids is partly independent from intramuscular lipid content.

Within-tissue relationships among compositional traits Table 3 gives estimates of phenotypic and genetic correlations among some compositional traits of the same tissue. The opposition between lipid content and water content was stronger for adipose depots than for muscle in this study. It should, however, be pointed out that [START_REF] Cameron | Genetic and phenotypic parameters for carcass traits, meat and eating quality traits in pigs[END_REF] and [START_REF] Larzul | Phenotypic and genetic parameters for longissimus muscle fiber characteristics in relation to growth, carcass, and meat quality traits in Large White pigs[END_REF] reported a genetic correlation amounting to around 20.7 between lipid and water contents of LM.

Phenotypic correlations of lipid content with UNSAT and P/S were significantly negative in all tissues and ranged from 20.16 to 20.39. Corresponding estimates of genetic correlations were also negative, but lipid content and FAC of PF were poorly correlated at the genetic level. The strongly negative phenotypic (20.50) and genetic (20.90) correlations reported by [START_REF] Suzuki | Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs[END_REF] between lipid content and C18:2 content in LM from Duroc pigs denote a closer association between the two traits than in our study. The genetic trends observed in a five-generation selection experiment for increased intramuscular content in Duroc pigs also showed a substantial correlated response consisting of a decrease in linoleic acid content in loin muscle, whereas other fatty acids were essentially unaffected [START_REF] Burkett | Correlated response in fatty acid composition of selection for intramuscular fat in Duroc pigs[END_REF]. This antagonistic relation between intramuscular lipid content and PUFA percentage is to be connected with the augmentation of triacylglycerols in comparison to phospholipids in muscle lipids from animals having higher levels of intramuscular fat. Phospholipid content indeed shows a low variability among individuals in a given muscle. [START_REF] Sharma | Comparative lipid composition of porcine muscles at different anatomical locations[END_REF] reported a mean of 0.6 g (s.d. 5 0.1 g) per 100 g of wet tissue for phospholipids v. a mean of 1.4 g (s.d. 5 1.0 g) for triacylglycerols in the LM of LW pigs. As a consequence, the variation among individuals in intramuscular fat content merely results from the more or less pronounced accumulation of triacylglycerols.

Regarding the relationships among the three families of fatty acids (SFA, MUFA and PUFA), genetic correlations were generally of the same order as phenotypic correlations. It appears that MUFA shows a stronger opposition to SFA than to PUFA in BF and, to a lesser extent, in PF. This pattern of relationships was also observed for subcutaneous fat of Iberian pigs by [START_REF] Fernandez | Genetic parameters for meat and fat quality and carcass composition traits in Iberian pigs[END_REF]. By contrast, MUFA reveals to be more opposed to PUFA than to SFA in intramuscular fat, for which the opposition of PUFA to both SFA and MUFA is prevalent. Phenotypic correlations found by [START_REF] Cameron | Fatty acid composition of lipid in Longissimus dorsi muscle of Duroc and British Landrace pigs and its relationship with eating quality[END_REF] among the major fatty acids of intramuscular fat (C16:0, C18:0, C18:1 and C18:2) show the same associations as above, with strongly negative correlations of C18:2 with both C18:1 and C16:0. 'Between-tissue' relationships among homologous compositional traits Phenotypic and genetic correlations between homologous compositional traits of the three tissues are reported in Table 4.

Significant positive phenotypic correlations 'between' back and PF depots were found for lipid content, water content and FAC, but they were far from being unity. For example, there was a correlation of around 0.50, at both phenotypic and genetic levels, between the coefficients of unsaturation of the two fat tissues. Positive genetic correlations of equivalent or even higher magnitude were reported by [START_REF] Suzuki | Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs[END_REF] between homologous fatty acid contents of subcutaneous fat and intermuscular fat.

Phenotypic correlations between the characteristics of adipose depots and the homologous characteristics of muscle were found to be low in this study. A safe interpretation of the corresponding genetic correlations is made difficult by the large standard errors of most estimates. Positive phenotypic correlations 'between' LM and inner BF layer were reported by [START_REF] Cameron | Fatty acid composition of lipid in Longissimus dorsi muscle of Duroc and British Landrace pigs and its relationship with eating quality[END_REF] for contents of major fatty acids, with closer correlations for saturated fatty acids (0.4 to 0.5) than for unsaturated fatty acids (0.2 to 0.3). Interestingly, [START_REF] Suzuki | Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs[END_REF] found that genetic correlations 'between' subcutaneous or intermuscular adipose tissues and LM ranged from 0.4 to 0.8 for C16:0, C18:0 and C18:1 concentrations but fell to less than 0.2 for C18:2 concentration. The latter result for C18:2 is in fairly good agreement with our own results for both UNSAT and P/S that are very closely linked to C18:2 content.

As a general rule, few quantitative trait loci (QTL) have been so far found to simultaneously affect the FAC of different fat depots, which is consistent with the fairly moderate genetic relationships 'between' tissues for that trait. However, [START_REF] Nii | Quantitative trait loci mapping for fatty acid composition traits in perirenal and back fat using a Japanese wild boar 3 Large White intercross[END_REF] reported that two QTL located on pig chromosomes (SSC) 4 and 5 influence C18:2 and longchain PUFA content, respectively, in both PF and inner layer of BF. A SSC7 QTL affecting C18:2 content and P/S in both intramuscular and abdominal (belly) fat depots has also been detected by [START_REF] Guo | Quantitative trait loci for fatty acid composition in longissimus dorsi and abdominal fat: results from a White Duroc 3 Erhualian intercross F 2 population[END_REF].

Recent findings have provided increasing evidence that lipid metabolism and other biological features displayed by skeletal muscle adipocytes differ from those displayed by subcutaneous, intermuscular or visceral adipocytes in the pig (e.g. [START_REF] Gardan | Lipid metabolism and secretory function of porcine intramuscular adipocytes compared with subcutaneous and perirenal adipocytes[END_REF][START_REF] Gondret | Regional differences in porcine adipocytes isolated from skeletal muscle and adipose tissues as identified by a proteomic approach[END_REF].

Relationships between tissue compositional traits and production traits Table 5 gives estimates of phenotypic and genetic correlations between three production traits (ADG, LEAN and LTGR) and four tissue compositional traits (lipid and water contents, UNSAT and P/S). BF 5 backfat; PF 5 perirenal fat; LM 5 longissimus muscle; r p 5 phenotypic correlation; r a 5 genetic correlation; UNSAT 5 coefficient of unsaturation of lipids; P/S 5 ratio of polyunsaturated to saturated fatty acids.

Average daily gain. Phenotypic and genetic correlations of ADG with lipid and water contents of adipose depots and

Composition of fat and muscle tissues in pigs LM were close to zero or of small magnitude. De [START_REF] Vries | Genetic parameters of pork quality and production traits in Yorkshire populations[END_REF] also reported that intramuscular fat content is genetically independent from ADG. Nevertheless, moderately positive genetic correlations between the two traits were found by [START_REF] Hovenier | Genetic parameters of pig meat quality traits in a halothane negative population[END_REF], [START_REF] Lo | Genetic analyses of growth, real-time ultrasound, carcass, and pork quality traits in Duroc and Landrace pigs: II. Heritabilities and correlations[END_REF] and in early studies reviewed by [START_REF] Schwo ¨rer | Genetic parameters of intramuscular fat content and fatty acids of pork fat[END_REF].

Relationships between ADG and FAC traits were of lowto-moderate magnitude, both phenotypically and genetically, in this study. A higher ADG tended to be genetically associated with a lower unsaturation degree of adipose depots, in accordance with the negative values (20.18 to 20.51) reported by [START_REF] Suzuki | Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs[END_REF] between ADG and palmitoleic and linoleic acid contents in various fat depots. It should be kept in mind that a similar association occurs when variation in ADG is induced by system of feeding (ad libitum v. restricted feed allocation) as shown by [START_REF] Cameron | Fatty acid composition of lipid in Longissimus dorsi muscle of Duroc and British Landrace pigs and its relationship with eating quality[END_REF] for muscle lipids and by [START_REF] Affentranger | Growth and carcass characteristics as well as meat and fat quality of three types of pigs under different feeding regimens[END_REF] for BF lipids. As for the genetic correlations of ADG with and P/S values of muscle lipids, the moderately negative values found here agree with the results reported for C18:2 content by [START_REF] Suzuki | Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs[END_REF].

Carcass leanness. Relationships implying LEAN were much closer than those implying ADG and were globally consistent for the three tissues dealt with in this study.

Higher carcass leanness is genetically associated with lower lipid content, higher water content and increased unsaturation of adipose depots, which is a well-established feature in the pig (e.g. [START_REF] Scott | Fatty acid composition of adipose tissue from lean and obese swine[END_REF][START_REF] Schwo ¨rer | Genetic parameters of fatty acids of pork fat[END_REF][START_REF] Cameron | Genetic and phenotypic parameters for carcass traits, meat and eating quality traits in pigs[END_REF][START_REF] Suzuki | Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs[END_REF]. This pattern of within-population genetic relationships between carcass leanness and chemical composition of fat depots is also found at the betweenpopulation level in the frame of breed comparisons (e.g. Bout et al., 1989; Warriss et al., 1990). More generally, any factor, genetic or non-genetic, contributing to a reduced whole fatness of pig carcasses generates correlated modifications of the properties of fat depots, namely an augmentation of water to lipid ratio and unsaturation of constituent fatty acids and, consequently, a decreased firmness and an increased risk of oxidation. The changes in fatty acid profile in fat depots of leaner animals results from a smaller contribution of de novo synthesised fatty acids (free from C18.2 and other PUFA in the pig) and, conversely, a greater contribution of dietary fatty acids, which are of vegetable origin in standard pig diets and, hence, contain C18:2 and other PUFA [START_REF] Scott | Fatty acid composition of adipose tissue from lean and obese swine[END_REF].

The genetic relationships of LEAN with muscle compositional characteristics followed about the same pattern as above, though the relationships were less close. There exist many literature estimates of the genetic correlation between carcass lean to fat ratio and intramuscular content, as reviewed by [START_REF] Schwo ¨rer | Genetic parameters of intramuscular fat content and fatty acids of pork fat[END_REF] and [START_REF] Sellier | Genetics of meat and carcass traits[END_REF]. Our estimate of the genetic correlation between LEAN and lipid content of LM (20.44 6 0.08) is slightly higher, in absolute value, than most estimates previously reported. As for the association of LEAN with the unsaturation degree of muscle fatty acids, our positive r a estimates are generally in line with the findings of [START_REF] Cameron | Fatty acid composition of lipid in Longissimus dorsi muscle of Duroc and British Landrace pigs and its relationship with eating quality[END_REF], [START_REF] Suzuki | Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs[END_REF] and [START_REF] Colman | Genetic parameters for intramuscular fatty acid composition and metabolism in pork[END_REF]. This association is likely to operate mainly through the variation in intramuscular lipid content, a trait which is genetically linked to the same extent to both LEAN and UNSAT (or P/S), and the above-mentioned correlative change in the ratio of phospholipids to triacylglycerols among total muscle lipids.

Lean tissue growth rate. LTGR is a compound trait of interest as it roughly corresponds to the major selection objective of past and current pig breeding schemes in the area of growing animal characters [START_REF] Nguyen | Genetic selection strategies for efficient lean growth in pigs[END_REF].

Phenotypic and genetic correlations between LTGR and chemical composition traits of adipose and muscle tissues were low-to-moderate in this study. The traits being most closely associated with LTGR at the genetic level referred to subcutaneous BF with absolute values of r a estimates ranging from 0.20 to 0.33. On this basis, breeding for LTGR would therefore lead to an increase in water to lipid ratio It is worth mentioning here that the realised genetic trends resulting from 20 years of selection mainly based on ADG and BF thickness (hence, approximately for better LTGR) in the French LW breed [START_REF] Tribout | Estimation, par utilisation de semence congele ´e, du progre `s ge ´ne ´tique re ´alise ´en France entre 1977 et 1998 dans la race porcine Large White: re ´sultats pour quelques caracte `res de production et de qualite ´des tissus gras et maigres[END_REF] were the followings: 77 6 27 g for ADG (P , 0.01), 8.6 6 1.7% for LEAN (P , 0.001), 4.6 6 1.0% for water content of BF (P , 0.001), 0.033 6 0.013 for UNSAT of BF (P , 0.05), 2.6 6 0.8% for PUFA content of BF (P , 0.01), 22.3 6 0.8% for SFA content of BF (P , 0.01), 0.07 6 0.28% for water content of LM (P 5 0.82), and 0.19 6 0.63% for lipid content of LM (P 5 0.63). The genetic trends pertaining to tissue compositional traits are in fairly good agreement, in sign and in compared magnitude, with the present r a estimates involving LTGR, except for the lack of significant change in intramuscular fat content. The latter discrepancy could partly result from the fact that the anatomical location of LM sampling differed between the two studies (13th to 14th v. 6th to 7th rib level).

Conclusions

This study confirms that the chemical composition of porcine fat and muscle tissues show a moderate-to-high within-breed additive genetic variation. Genetic correlations between these traits and growth rate are globally of small magnitude. On the contrary, tissue compositional traits are significantly correlated at the genetic level with carcass leanness. Breeding for leaner pigs is, therefore, expected to cause an increase in the ratio of water to lipids and the unsaturation degree of lipids, thus leading to a higher occurrence of the 'soft fat' condition and to a greater risk of lipid oxidation. The positive relationship between carcass leanness and lipid unsaturation mainly operates through the underlying variation in the ratio of de novo synthesised to dietary fatty acids in subcutaneous and visceral fats and through the respective importance of the phospholipid and triacylglycerol fractions among muscle lipids. Moreover, the highest genetic correlation 'between' back and perirenal sites of fat deposition for the same compositional traits does not exceed 0.6 and is therefore far from being unity. This finding supports the view that fat is not a unique tissue throughout the body. The lack of actually close relationships 'between' tissues for the unsaturation degree of constituent lipids is still more evident when separable depot fats, on one hand, and intramuscular fat, in contrast, are considered. Further research is needed for investigating muscles other than the widely studied LM in order to dispose of an 'across-muscle' knowledge of the fat component of muscle tissue in the pig.

Table 1

 1 Means and residual standard deviations for tissue compositional traits a

		BF		PF			LM
	Compositional trait Mean r.s.d. Mean r.s.d. Mean r.s.d
	% Lipids	81.6	4.9	87.0	4.8	1.23 0.46
	% Water	9.3	2.2	8.2	2.7	74.5	0.9
	% C14:0	1.2	0.1	1.2	0.2	1.1	0.3
	% C16:0	25.3	1.2	26.3	1.4	24.9	2.0
	% C16:1	1.8	0.5	1.9	0.4	3.2	0.8
	% C18:0	15.3	1.7	22.3	2.0	13.7	1.7
	% C18:1	44.8	2.1	37.3	2.3	42.8	4.0
	% C18:2	8.6	1.0	8.3	1.8	9.1	3.3
	% C18:3	0.6	0.2	0.6	0.2	0.3	0.2
	% C20:4	,0.1	-,0.1	-	2.1	1.5
	% SFA	42.4	2.3	50.5	2.5	40.4	3.1
	% MUFA	47.8	2.1	39.9	2.4	47.0	4.3
	% PUFA	9.8	1.3	9.5	2.0	12.6	5.0
	UNSAT	1.183 0.024	1.210 0.042 1.295 0.136
	P/S	0.232 0.038	0.187 0.044 0.319 0.140

BF 5 backfat; PF 5 perirenal fat; LM 5 longissimus muscle; r.s.d. 5 residual standard deviation; SFA 5 saturated fatty acids; MUFA 5 monounsaturated fatty acids; PUFA 5 polyunsaturated fatty acids; UNSAT 5 coefficient of unsaturation of lipids; P/S 5 ratio of polyunsaturated to saturated fatty acids. a r.s.d. were calculated from the error mean square of the ANOVA model with breed and batch as fixed effects and slaughter BW as a covariate.

Table 2

 2 Heritability estimates (s.e. in brackets) for tissue compositional traits

	Trait	BF	PF	LM
	% Lipids	0.26 (0.10)	0.65 (0.14)	0.47 (0.09)
	% Water	0.59 (0.11)	0.32 (0.13)	0.31 (0.10)
	C14:0	0.75 (0.11)	0.44 (0.16)	0.01 (0.18)
	C16:0	0.60 (0.11)	0.65 (0.14)	0.52 (0.17)
	C16:1	0.72 (0.10)	0.66 (0.14)	0.16 (0.14)
	C18:0	0.42 (0.10)	0.27 (0.14)	0.15 (0.10)
	C18:1	0.55 (0.10)	0.69 (0.13)	0.26 (0.11)
	C18:2	0.47 (0.10)	0.81 (0.11)	0.35 (0.12)
	C18:3	0.03 (0.05)	0.13 (0.21)	n.e.
	C20:4	n.e.	n.e	0.15 (0.18)
	SFA	0.50 (0.10)	0.66 (0.15)	0.43 (0.14)
	MUFA	0.59 (0.10)	0.63 (0.12)	0.14 (0.11)
	PUFA	0.44 (0.10)	0.70 (0.10)	0.33 (0.15)
	UNSAT	0.38 (0.09)	0.61 (0.15)	0.29 (0.10)
	P/S	0.45 (0.08)	0.61 (0.16)	0.36 (0.14)

BF 5 backfat; PF 5 perirenal fat; LM 5 longissimus muscle; SFA 5 saturated fatty acids; MUFA 5 monounsaturated fatty acids; PUFA 5 polyunsaturated fatty acids; UNSAT 5 coefficient of unsaturation of lipids; P/S 5 ratio of polyunsaturated to saturated fatty acids; n.e. 5 not estimable.

Table 3

 3 Within-tissue r p and r a correlations between some compositional traits

		BF		PF		LM
	Couple of traits	r p	r a (s.e.)	r p	r a (s.e.)	r p	r a (s.e.)

Table 4

 4 The r p and r a correlations between homologous traits of backfat, perirenal fat and longissimus muscle

		BF 3 PF			BF 3 LM		PF 3 LM
	Trait	r p	r a (s.e.)	r p	r a (s.e.)	r p	r a (s.e.)
	% Lipids	0.25**	0.14 (0.11)	0.13*	0.74 (0.15)	20.11	20.48 (0.31)
	% Water	0.34**	0.33 (0.06)	0.08	0.25 (0.18)	20.11	20.33 (0.16)
	UNSAT	0.48**	0.57 (0.05)	0.13*	0.22 (0.23)	0.08	0.08 (0.08)
	P/S	0.43**	0.40 (0.03)	0.09	0.15 (0.25)	0.19*	0.38 (0.29)

Table 5

 5 The r p and r a correlations between production traits and compositional traits of backfat, perirenal fat and longissimus muscle phenotypic correlation; r a 5 genetic correlation; ADG 5 average daily gain; LEAN 5 carcass lean percentage; LTGR 5 lean tissue growth rate; BF 5 backfat; PF 5 perirenal fat; LM 5 longissimus muscle; UNSAT 5 coefficient of unsaturation of lipids; P/S 5 ratio of polyunsaturated to saturated fatty acids.Sellier, Maignel and Bidanel and UNSAT in the BF depot. Moreover, we found a tendency to a negative genetic correlation (20.19 6 0.10) between LTGR and lipid accretion in the LM, whereas most estimates quoted by[START_REF] Schwo ¨rer | Genetic parameters of intramuscular fat content and fatty acids of pork fat[END_REF] are moderately positive.

				ADG		LEAN		LTGR
	Compositional trait	Tissue	r p	r a (s.e.)	r p	r a (s.e.)	r p	r a (s.e.)
	% Lipids	BF	0.01	0.02 (0.03)	20.50**	20.66 (0.14)	20.17**	20.31 (0.06)
		PF	0.00	0.01 (0.02)	20.26**	20.23 (0.06)	20.11	20.07 (0.04)
		LM	0.00	0.09 (0.10)	20.15**	20.44 (0.08)	20.07	20.19 (0.10)
	% Water	BF	20.01	20.02 (0.03)	0.39**	0.35 (0.05)	0.18**	0.20 (0.05)
		PF	20.03	20.01 (0.02)	0.37**	0.42 (0.07)	0.03	0.05 (0.03)
		LM	0.01	0.01 (0.09)	0.13**	0.05 (0.09)	0.06	0.01 (0.06)
	UNSAT	BF	20.04	20.15 (0.12)	0.48**	0.63 (0.06)	0.20**	0.33 (0.06)
		PF	20.22**	20.25 (0.04)	0.44**	0.50 (0.07)	0.18*	0.18 (0.09)
		LM	20.12	20.14 (0.02)	0.23**	0.27 (0.03)	0.02	0.03 (0.02)
	P/S	BF	20.09	20.27 (0.09)	0.57**	0.64 (0.04)	0.21**	0.31 (0.06)
		PF	20.03	20.04 (0.02)	0.58**	0.65 (0.05)	0.12	0.16 (0.04)
		LM	20.14*	20.37 (0.24)	0.20**	0.26 (0.20)	20.07	20.14 (0.18)
	r p 5

Acknowledgements

The authors are indebted to all the persons who have efficiently contributed, at different steps, for the achievement of this study.