Giulia Menconi
email: menconi@mail.dm.unipi.it.

Aldamaria Puliti

Isabella Sbrana

Valerio Conti

Roberto Marangoni

A top-down linguistic

Keywords: 68R15, 62P10, 92B05

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Genomes, and in particular eukaryotic genomes, are far to be homogeneous set of sequences, as they host several elements of different structure, functional role and even origin (e.g. exogenous elements). The development of strategies to recognize and classify these different kind of sequences is then a challenge for contemporary bioinformatics. This task can be resembled to a reverse engineering of an unknown operating system that sizes 3.3 gigabytes and host data, routines, re-assembled file fragments, etc., as in the very bright analogy suggested by Robbins [START_REF] Robbins | Challenges in the Human genome project[END_REF].

The first step towards such a reverse engineering, is to get a (evenly rough) classification of the different elements existing on a genomic sequence: i.e., to distinguish between biological features carried by different sub-sequences (also called segments or words) of the DNA. Experimental methods have discovered a wide set of qualitatively different elements: coding regions, noncoding regions, introns, exons, promoters, enhancers, transcription factor binding sites, etc. Since experimental methods are very slow in adding new information, computational methods are searched in order to screen the whole genome searching for these features. Current computational approaches to discover functionally-associated elements along the genome are commonly based on a bottom-up philosophy (we could call it also inductive methods): on the basis of a known set of sequences that belong to a given class, suitable algorithms are designed to recognize unknown element. With suitable algorithm we refer to any approach belonging to approximate searches (e.g. consensus searches) or to machine learning strategies, as neural networks, support vector machines and similar.

Such bottom-up approaches are the most widely used and very useful, they anyhow soffer of a common problem: their efficiency is strictly bound to the composition of the training set. Since many classes show a very low common similarity, most of the classifications obtained by means of these methods are continuosly subject to revision.

From an opposite philosophy there are the so-called top-down methods (which could also be called deductive methods) where abstract tools are used to extract features from genomic sequences, without using any experimental data already known [START_REF] Arvey | Detection of genomic islands via segmental genome heterogeneity[END_REF] . The importance of such approaches is clear: if we found an abstract formula able to correctly recognize some functional features in genomic sequence, this would represent a great advance in understanding DNA logic.

As of today, top-down approaches have grown slowly and less efficiently, if compared with bottom-up method, as the main problem is to recognize a good theoretical criterion to be applied to biological data.

The most important first attempt to do this, is represented by the application of general-purpose linguistic approaches on genomic sequences: the authors of [START_REF] Brendel | Linguistics of nucleotide sequences: morphology and comparison of vocabulary[END_REF] introduced the concept of "meaningful words" as an element of an organism-specific vocabulary in the DNA language. Since this pioneeristic work, several other papers have been produced where linguistic approaches have been applied to understand a wide variety of characteristics in genomes, from the identification of active genes to the large scale comparison [START_REF] Kirzhner | A large-scale comparison of genomic sequences: one promising approach[END_REF], [START_REF] Stern | Discovering patterns in Plasmodium falciparium genomic DNA[END_REF], [START_REF] Corá | Identification of candidate regulatory sequences in mammalian 3' UTRs by statistical analysis of oligonucleotide distributions[END_REF].

In recent times, great importance is tributed to compression algorithms, as they provide, at the same time, both a linguistic tool to analyze sequence, and a method to store large sequences saving space. In fact, due to the exponential growing of biological databanks, a compression method able to efficently compress and allowing the sequence analysis directly on the compressed data is actively searched [START_REF] Ferragina | Compression-based classification of biological sequences and structures via the Universal Similarity Metrix: experimental assessment[END_REF].

Dictionary-based compression algorithms, like those of the Lempel-Ziv family have been already used in the past to have an automatic selector of over-represented words, in order to select repeats along a genomic sequence [START_REF] Stern | Discovering patterns in Plasmodium falciparium genomic DNA[END_REF] [18] [START_REF] Gabrielian | Sequence complexity and DNA curvature[END_REF], or to classify coding/non-coding sequences on the basis of the compression factor or similar indexes [START_REF] Menconi | A compression-based approach for coding sequences identification I. Prokaryotic genomes[END_REF].

The analysis we are going to perform is general purpose and it is not focused on a specific biological feature. This makes it necessary to apply this kind of investigation on a well known case study, where biological features, structural characteristics and even phylogenetic relationships have been already described, being a first-glance genomic wide study almost impossible. Among the possible choices, we selected the glutamate metabotropic receptors genes 1 and 5 in the Human and in the Mouse. This selection has been based on multiple criteria: first, even if these sequences are referred to single genes, they are very long (∼400 Kbp), showing long intronic sequences and, likely, they host most of the structural/functional characteristics found in the genome. Moreover, they exist in different subtypes, expressed in different organisms, and expressed not only in the nervous system but also in many other tissues, then it is possible to infer common features. Last, they have a great importance in cellular and synaptic activity, plasticity, cell death and survival, learning and memory, pain perception, and motor activity, and seem to have a role in the development of many complex pathologies. These genes are either paralogous or orthologous each other and this allows us to study the eventual relationship between homology and over-expressed words.

Approach

The use of compression algorithms for genome data mining has been previously explored; in a previous work some of us proved that a discrimination between coding and non-coding regions in bacteria genomic sequences can be obtained a priori by studing the information content of a sequence [START_REF] Menconi | A compression-based approach for coding sequences identification I. Prokaryotic genomes[END_REF].

The work is organised as follows.

A first information analysis exploits a compression on the genes and provides a dictionary of recurrent words. It is clear that recurrent subsequences share a symmetry in AT/CG content, which suggests an ad hoc deeper investigation. Second, we perform a statistical linguistic analysis on the complete gene sequences. Finally, we show whether and what the relationships are of the above results with experimentally found or computationally predicted local biological properties.

Materials and Methods

Metabotropic glutamate receptors 1 and 5

The mGlu1 and 5 receptors belong to the group I of metabotropic glutamate receptors which represent a family of eight G-protein coupled receptors distinguished on the basis of sequence diversity, expression profiles and pharmacology. The gene encoding for the mGlu1 receptor (locus name: GRM 1 in humans and Grm1 in other species) has been mapped to chromosome 6q24 in humans, and chromosome 10, band 10a1, in mice, while the gene encoding for the mGlu5 receptor (locus name: GRM 5 in humans and Grm5 in other species) has been mapped to chromosome 11 in humans and chromosome 7 in mice [28]. Exon/intron boundaries reveals that the human GRM 1 spans about 410 kilobase pairs and consists of 10 exons and 9 introns. Exons vary from 85 (exon IX) to 3724 bp (exon X) in size, whereas intron sizes range from 149 to 1.3 kilobase pairs. The 10 different exons generate, by alternative splicing, more than 6 different splice variants [START_REF] Ferraguti | Metabotropic glutamate 1 receptor: current concepts and perspectives[END_REF]. Different protein isoforms have been described both in human and murine GRM1, among which the alpha and beta, of 1199 and 906 amino acids respectively, are the longest variants and represent the major forms expressed in the central nervous system. Comparison of the genomic structures of GRM1 with GRM5 reveals a high degree of similarity in terms of exon/intron arrangement, both in human and mouse, which strongly suggests that group I mGlu receptors have been generated by gene duplication from a common ancestor. Analogies and/or diversities in their genomic sequence organization may reveal some biological features that the paralogous genes may share.

Concerning transcriptional regulation, functional studies indicate that the mGlu1 receptor gene, both in humans and mice, is driven by at least two alternative promoters located upstream from exons I and II, with the latter encoding the transcription initiation codon [START_REF] Crepaldi | Transcriptional Activators and Repressors for the Neuron-specific Expression of a Metabotropic Glutamate Receptor[END_REF]. Functional analyses reveal the presence of a 57-bp core promoter from the first transcription initiation site, and two silencing elements, located between exons Ib and Ic, and the regulatory factor for X-box element found upstream from exon II [START_REF] Crepaldi | Transcriptional Activators and Repressors for the Neuron-specific Expression of a Metabotropic Glutamate Receptor[END_REF]. Both silencing elements have a strong suppressive role in non-neuronal cells.

Main functions of mGlu1 and 5 receptors are in the regulation of neuronal excitability, synaptic plasticity, synapse selection, and neurotransmitter release, which are important for brain development and mechanisms of learning and neuroprotection. For their functions both mGlu1 and 5 receptors have been implicated in the pathophysiology of several neurological and psychiatric disorders, and represent possible targets for new therapeutic approaches. For all these reasons, a better comprension of mechanisms regulating GRM1 and GRM5 gene structures, activities and expression may be instrumental for the achievement of these goals.

DNA sequences

Human GRM1 and GRM5 sequences were from NCBI Build 36.1 and mouse sequences Grm1 and Grm5 from Build 37 (UCSC Genome Bioinformatics [START_REF]dna range=chr6:146385472-146805427[END_REF]). We based our analysis of human and mouse GRM1 genes on the genomic structures obtained from UCSC data for all reported gene isoforms (obtained by [START_REF] Crepaldi | Transcriptional Activators and Repressors for the Neuron-specific Expression of a Metabotropic Glutamate Receptor[END_REF]). The DNA strand that has been analysed is that indicated by the UCSC browser as coding strand (plus strand). The analysed sequence includes the 5 5kb upstream to the first exon, and the 5kb downstream the end of the last 3 exons. All exons, including 5 and 3 UTR exons, were taken in consideration to get the final sequence to be analysed. Some statistical features of the genes are shown in Table 1. Genes GRM1 and GRM5 in Homo sapiens and Grm1 and Grm5 in Mus musculus all share a low GC content.

This work aims at achieving a better understanding of oligonucleotide repetitive structures shared by the four genes.

Algorithm and dictionaries

The proposed method is based on the use of CASToRe, a fast dictionarybased compression algorithm of the Lempel-Ziv family. We remark that definitions and indices may be equivalently defined for any reversible compression algorithm. We shall use CASToRe since it is useful in fast identification of some repeats.

The algorithm CASToRe selects a dictionary by exact matches and parses the input sequence σ in some variable-length recurrent words. Each new parsed word is the one that can be made with the longest prefix and the longest suffix already parsed. The input sequence is parsed in subwords belonging to the final dictionary relative to the sequence:

Dict(σ) = {φ 1 , . . . , φ t }.
For instance, the input sequence on alphabet {A, C, G, T }:

σ = AACACGCACGT CCGAGT CT GT C (1)
has the following final dictionary after parsing:

Dict(σ) = {A.A, C.A, C.G, CA.

CG, T.C, CG.A, G.T C, T.GT C}

where prefix and suffix are separated by a dot. The main properties of the algorithm are shown in ref. [START_REF] Bonanno | Computational Information for the logistic map at the chaos threshold[END_REF].

We analysed each word in the final dictionary Dict(σ) = {φ 1 , . . . , φ t } by calculating the word score as follows. Each word φ j is made of a prefix ρ p (j) and a suffix ρ s (j) both belonging to {φ 1 , . . . , φ j-1 }.

Then, even if the φ j 's are pairwise distinct (with the possible exception of the last one, φ t ), each φ j occurs occ(j) ≥ 1 times within the sequence σ when used as a prefix or a suffix of a subsequent word (with the possible exception of φ t ). For instance, given the sequence σ in above example (1), the words in the dictionary occur differently: occ(A) = 6, occ(CG) = 3, occ(GT C) = 2, etc.

Results and discussion

Word usage

First steps concern the compression of CASToRe algorithm on the complete gene sequences. The dictionaries resulting from that compression have been analysed and compared in order to extract common features to be helpful as a preliminary filter in the statistical linguistic investigation. We remark that this analysis is completely biologically blind, therefore it highlights structures whose importance (in recurrence, length, etc) is given by intrinsic features, typical of the sequence and not derived from external knowledge.

In our genes the most frequent word length is = 6bp and the words statistics is meaningful about up to length = 10 -11bp: longer words occur only 2-3 times. It is remarkable that when the algorithm has parsed coding regions, words are mostly of length 3 and codons.

A crucial remark concerns the structure of words in the dictionaries of GRMs. The parsed words show some peculiar recurrences in the {A, T } -{G, C} content. This naturally leads to the linguistic analysis we have performed. From the final dictionary relative to the complete sequences of the four genes, we extracted some interesting words as the words that have length ranging from 12 to 36 nt and occur from 5 to 25 times within the dictionary. Such words are well-known microsatellites (see part of them in Table 2 for Grm1, but the same happens for the other genes). This supports the idea that such an analysis may be extremely helpful in mining gene linguistics. Moreover, all the selected patterns belong to nonexon regions within the gene and show an evident symmetry between the occurrence of weak and strong chemical bonds among nucleotides.

Over-represented oligonucleotides

Due to the manifest symmetry of interesting words in AT/GC bonds in the dictionary of complete genes and since such words belong to nonexon se-quences, we built the nucleotide supersequence Nex(g) made of only nonexon fragments inside each gene g and took under consideration the binary filtering of the nucleotide sequence, based on the Weak/Strong bond:

[A, T ] = w, [G, C] = s.
Let us assume the following null hypothesis: the {w, s} nucleotides are distributed following a Bernoullian distribution of parameter p = f w , that is the frequency of symbol 'w'. Then an oligonucleotide x of length n (n-mer, in the following) containing exactly k w-bases should appear with probability

p(x) = n k p k (1 -p) n-k (2)
Any n-mer whose frequency exceeds that estimated probability by 5×10 -3 is defined as over-represented. The threshold value is optimized in order to keep over-represented words both in common among genes and sufficiently long. Notice that over-represented n-mers may not be the most frequent n-mers, since being over-represented is related to a comparison with random expectation, not with frequency. The n-mer containing exactly k w-nucleotides and h = (nk) s-nucleotides shall be denoted by n(k, h). We shall extract the over-represented {w, s}-oligonucleotides of length 4 to 24 in Nex sequences and selct only the ones shared by the four genes, then we shall analyse only the selected 24-mers corresponding onto the 4 bases alphabet {A, C, G, T } on complete sequences.

Linguistic analysis

From the analysis we exploited on n-mers frequency for n = 4, 5, . . . , 24, we may observe that for n ≥ 7 the over represented m-mers contain the over represented (m -1)-mers and also omo-m-mers become frequent.

The over-representation chain is shown in Table 3. At each length n, we selected only the over-represented n-mers that were in common for the 4 genes.

The over-represented 24-mers under the binary filter {w, s} belong to 6 different classes, which may be roughly distinguished into two categories: from 1 3 to 1 2 of w =AT-content and more than 3 4 of AT-content. We remark that some classes also include some words found in the dictionary build by CASToRe (see Table 2, last two rows).

We shall now investigate such 24-mers in the light of the {A, C, G, T } alphabet. For each of the 6 above {w, s} classes, we performed the same statistical analysis on the complete gene sequences and on the 4 bases alphabet {A, C, G, T }. Again, we used a multinomial null hypothesis and the overrepresentation is defined with threshold 2.2 × 10 -4 (that is the order of the empirical frequecies of 24-mers over the 4-bases alphabet). They are grouped w.r.t. the (former) {w, s} content and denoted by (n A , n C , n G , n T ).

The over-represented common 24-mers on {A, C, G, T } alphabet are given by two groups: balanced and omoWeak. In table 4 we show the list of omoWeak and balanced over-represented 24-mers on {A, C, G, T } alphabet shared by the four genes.

Balanced over-represented 24-mers are 24 [START_REF] Corti | Gene Structure of the Human Metabotropic Glutamate Receptor 5 and Functional Analysis of Its Multiple Promoters in Neuroblastoma and Astroglioma Cells[END_REF][START_REF] Couronne | Strategies and Tools for Whole-Genome Alignments[END_REF], whose ratio AT/GC OmoWeak over-represented 24-mers are 24 [START_REF] Menconi | A compression-based approach for coding sequences identification I. Prokaryotic genomes[END_REF][START_REF] Bray | AVID: A Global Alignment Program[END_REF], 24 [START_REF] Puliti | Low-copy repeats on chromosome 22q11.2 show replication timing switches, DNA flexibility peaks and stress inducible asynchrony, sharing instability features with fragile sites[END_REF][START_REF] Bonanno | Computational Information for the logistic map at the chaos threshold[END_REF] and 24 [START_REF] Robbins | Challenges in the Human genome project[END_REF][START_REF] Barski | High-resolution profiling of histone methylations in the human genome[END_REF]. Within such three subclasses, several combinatorial structures occur. Notice that for some of them, also the reverse complement one is over-represented: in 24 [START_REF] Menconi | A compression-based approach for coding sequences identification I. Prokaryotic genomes[END_REF][START_REF] Bray | AVID: A Global Alignment Program[END_REF], [START_REF] Bultrini | Pentamer vocabularies characterizing introns and intron-like intergenic tracts from C. elegans and D. melanogaster[END_REF][START_REF] Bonanno | Computational Information for the logistic map at the chaos threshold[END_REF][START_REF] Arazo-Bravo | Sequencedependent conformational energy of DNA derived from molecular dynamics simulations: toward understanding the indirect readout mechanism in protein-DNA recognition[END_REF][START_REF] Couronne | Strategies and Tools for Whole-Genome Alignments[END_REF] and [START_REF] Bray | AVID: A Global Alignment Program[END_REF][START_REF] Barski | High-resolution profiling of histone methylations in the human genome[END_REF][START_REF] Arvey | Detection of genomic islands via segmental genome heterogeneity[END_REF][START_REF] Ferragina | Compression-based classification of biological sequences and structures via the Universal Similarity Metrix: experimental assessment[END_REF] and in 24 [START_REF] Puliti | Low-copy repeats on chromosome 22q11.2 show replication timing switches, DNA flexibility peaks and stress inducible asynchrony, sharing instability features with fragile sites[END_REF][START_REF] Bonanno | Computational Information for the logistic map at the chaos threshold[END_REF], [START_REF] Brendel | Linguistics of nucleotide sequences: morphology and comparison of vocabulary[END_REF][START_REF] Barski | High-resolution profiling of histone methylations in the human genome[END_REF][START_REF] Arazo-Bravo | Sequencedependent conformational energy of DNA derived from molecular dynamics simulations: toward understanding the indirect readout mechanism in protein-DNA recognition[END_REF][START_REF] Couronne | Strategies and Tools for Whole-Genome Alignments[END_REF]. For the others this is not true.

As a first remark, please note that, since the gene sequences are definitely AT-rich (more than 60%), the results regarding the over-representation of balanced (GC-rich) 24-mers are anyway surprising. We located the above two classes of over-represented acgt-24-mers on the complete sequence of each gene and investigated some of their features in comparison with known biological structures.

We are aware of a recent oligonucleotide analysis concerning pyknons [START_REF] Tsirigos | Human and mouse introns are linked to the same processes and functions through each genomes most frequent nonconserved motifs[END_REF]. The authors identified recurrent variable-length sequences (most of them is 16nt long) in human and mouse genomes and linked them to properties of intronic regions. Only a list of human and mouse pyknons is available and no information is given about occurrences or location of each pyknon. Therefore, the only way to make a comparison seemed to match each pyknon to our words. We found that only around 10% of balanced words had a match in (shorter) pyknons, while the fraction was about 2-4% for omoWeak words.

Anyway, we followed a further step, by observing that the selected words frequently overlap onto eachother and they result to be clustered in overlap intervals.

From now on, we shall focus on those collections of consecutive overlapping words (not on individual words) and denote them as -either balanced or omoWeak-segments.

The extent of segments is on average around 30 nt for every class of segments, while the longest segments reach 10 2 nt in the case of omoWeak and an average of 90 nt for balanced segments.

Some quantitative results are summarized on Table 5. In particular, the fraction of gene sequence covered by the segments of each class is a conserved property, expecially for balanced segments.

Gene organization and conservation

First of all, we located the segments on the gene sequences, w.r.t. introns and exons, making reference to the genomic sequence available on UCSC. Exons cover from 0.7% to 1% of the genes.

For what concerns balanced segments, about less than 2.5% of the segments intersect an exon in human and mouse GRM1, while about 1.3% in GRM5. Finally, omoweak segments located within exons do not exceed 0.10% in the four genes.

The vast majority of segments are completely contained in noncoding regions. When analysing what the dispersion is of each class of segments within each UCSC intronic region, we have that for the four genes the distribution of segments is almost uniform, according to the relative length of the introns (see supplementary material, section 1). Longer introns contain most of all segments. Anyway, a few exceptions are notable. For GRM1, there is a slight Second, we co-localized segments w.r.t. homologous regions, in order to investigate how much such segments (which have been selected as the ones shared by the four GRM genes) are influenced by the homologies among the genomic sequences under study.

We took under consideration homology level by calculating identity by means of GenomeVista [START_REF] Couronne | Strategies and Tools for Whole-Genome Alignments[END_REF][START_REF] Bray | AVID: A Global Alignment Program[END_REF]. Results among paralogous genes are 16% identity for GRM1 /GRM5 and 13% identity for Grm1 /Grm5 (where results are read w.r.t. GRM1 ) and homology among orthologous genes ranges from 18% identity forGRM1 /Grm1 to 12% identity for GRM5 /Grm5 (where results are read w.r.t. human genes). An example of the performed analysis is shown in figure 1.

The fraction of balanced segments localized in homology regions (w.r.t. all balanced segments) ranges from around 45% for paralogous genes to 12 -15% for orthologous genes. The fraction of omoWeak segments localized in homology regions ranges from 50% to 70% (for paralogous genes, more concentrated in human genes; for orthologous genes, more concentrated in GRM1 genes).

We also compared the expected fraction Exp of genomic sequence covered by the balanced/omoWeak segments localized in homology regions to the observed value Obs. The value Exp was calculated as Exp = C × f , where C is the fraction of conserved regions in the reference gene (i.e., the fraction of GRM1 with homology w.r.t. GRM5 ) and f is the fraction of reference gene covered by balanced/omoWeak segments (taken from Table 5). The value Obs is the fraction of reference gene covered by balanced/omoWeak segments localized within homology regions.

This analysis resulted in an overall coincidence of the two measures (see figures on Table 6), which supports the idea of the absence of an explicit bias of the sequence conservation on the segments' distribution. Then, we investigated whether there are any relationships among the balanced and omoWeak segments and the biological features.

We took under consideration some physical features of the DNA helix and experimentally found or computationally predicted biological properties of these inter-exon sequences.

DNA sequence and chromatin structure

DNA sequence shows different patterns throughout genomes associated with alternative conformations and diversely able to sustain chemical interactions.

In the following tables, we shall show the results by focusing on the colocalization of balanced/omoWeak segments with the features listed above. To this aim, we shall denote (for instance) the balanced segments co-localizing to some feature (say, Z-DNA configurations) by label "bal-IN-feature" and the features co-localizing to some balanced segments by label "feature-INbal". Therefore, Zdna-IN-bal is the fraction of Z-DNA configurations (for each gene) co-localizing to balanced segments is w.r.t. total number of configurations, while the fraction of balanced segments co-localizing to Z-DNA configurations w.r.t. total amount of segments is the fraction bal-IN-Zdna.

Flexibility

The torsional flexibility of DNA helix is a sequence-dependent property expressed as fluctuations of twist angle; the potential local variations in the DNA structure may be estimated by analysing the dinucleotide degree values calculated by algorithm stabflex [29] based on work by Sarai and coll. [START_REF] Arazo-Bravo | Sequencedependent conformational energy of DNA derived from molecular dynamics simulations: toward understanding the indirect readout mechanism in protein-DNA recognition[END_REF] in overlapping windows, summed and averaged by the window length (we used windows 100nt-long and overlapping for 99nt). Flexible regions are known to mediate DNA-protein binding processes and are suggested to play a role in specific interactions of DNA metabolism; evidence has been reported on a relationship with DNA replication origin, DNA repair, DNase I cleavage and regulatory elements binding. DNA flexibility is involved in chromosome instability [START_REF] Puliti | Low-copy repeats on chromosome 22q11.2 show replication timing switches, DNA flexibility peaks and stress inducible asynchrony, sharing instability features with fragile sites[END_REF].

We selected the flexibility regions as follows. We selected the measures of deviation of the twist angle which are not lower than a threshold. We used θ = mean value + 2 • stand dev which ranges from 12.27 to 12.46 for the different genes. Finally, we aggregate such windows (when overlapping) into wider flexible regions to which the results refer. Balanced segments show negligible matches to regions with high flexibility. Differently, flexible regions are significantly covered by omoWeak segments (see Table 7), as expected by their frequent association to AT-rich regions; more than 70% of flexible regions match some omoWeak segment, with the only exception of Grm1 where this phenomenon is more diluted.

The co-localized flexible-omoWeak regions are distributed along the whole gene, independently of level of sequence identity.

Since the omoWeak segments co-localizing are around 20%, we investigated whether some specific omoWeak words (w.r.t. table 4) co-localize with flexible regions. More than 40% of the omoWeak21 words (21(10,1,2,11) only) match flexible regions (in GRM1, 47%); they represent around the 10% out of all co-localizing omoWeak words. The other combinatorial structures within omoWeak19 and omoWeak20 classes are represented for a smaller fraction.

The co-localized flexible-omoWeak regions are distributed along the whole gene, independently of the level of sequence identity.

Non-B DNA conformations

We consider Z-DNA and G-quadruplex structures. Z-DNA patterns, whose potential is predictable by the algorithm zhunt [30], occur at sequences with alternating pyrimidines and purines, -such as (CG:CG)n and (CA:TG)n-that may wind the double helix into a left-handed zigzag pattern. Therefore, unlike B-form DNA, which possesses one major groove and one minor groove, Z-DNA has only one deep and narrow groove with 12 bp per helical turn. G-quadruplex DNA is a four-stranded structure consisting of a square co-planar array of four guanines formed by a stretch of guanine-rich DNA. Each guanine acts as a donor and acceptor of Hoogsteen hydrogen bonds in a cyclic arrangement involving N-1, N-2, O-6, and N-7. G-quadruplex conformations have been predicted by quadparser algorithm [31]. Non-B DNA conformations show non random distributions in genomes and association with unstable regions.

Resulting data on configurations are poor (with the exception of murine Z-DNA), therefore also co-localization is weak, especially for Z-DNA conformations. Table 8 do not show figures for bal-IN-and omoW-IN-since the fraction is negligible.

Histones' modifications

DNA binding proteins define functional domains and chromatin activity. A high resolution map for histone modification distribution and CTCF bind- ing sites is available for human genome, provided by Barski et al. [START_REF] Barski | High-resolution profiling of histone methylations in the human genome[END_REF] and Wang et al. [START_REF] Wang | Combinatorial patterns of histone acetylations and methylations in the human genome[END_REF]. The available data are 21 signals for human histone methylations and 18 for human histone acetylations. Each signal contains a list of peaks coming from the experimental outputs. The data has been compressed by the authors with a minor loss in resolution: for instance, if the worst case is 0.0625, the signals contained only values bigger than 1.

Methylation files range from around 200 to around 10 3 data per signal, while acetylation from 200 to 700 data per signal.

We checked whether such peaks intersect balanced or omoWeak segments. We shall show the average results over the complete data sets provided by Barski et collaborators. The histone methylation patterns in GRM genes under study are comparable to those reported by Barsky for the transcriptional regions of active genes; high levels of H3K27me1 e H3K36me3 are present and also, even if more limited, of H3K4me1, H3K4me2, H3K9me1, H2BK5me1 e H4K20me1. The same is true for results concerning acetylation.

A limited co-localization of histone modifications with both balanced and omoWeak segments is detectable (table 9); it indicates that segments have an histone-independent pattern. [START_REF] Barski | High-resolution profiling of histone methylations in the human genome[END_REF].

CTCF target sites generally cluster at boundaries of chromatin domains and are scantily represented inside gene sequences; this occurs even in our genes, where low signals are present. By considering all of them for the correlation analysis with balanced and omoWeak words we found multiple matches (table 10) and a slight excess of CTCF binding sites in omoWeak (Figure 2). This may be representative of an interaction between them.

Repeats

We refer to the interspersed repeat databases screened by RepeatMasker that are based on the repeat databases (Repbase Update) copyrighted by the Genetic Information Research Institute [27]. We considered repeats classified on both the DNA strands, since we disregard the relationships with the transciption activity, focusing our interest only on the genomic features. than 50%, on average for omoWeak segments. This means that we select only a few part of known repeats. Moreover, segments not matching to any known repeat range from 30% to 70% of the whole collection, more evident for omoWeak segments.

In supplementary material (section 3) pictures can be found, showing the composition of the co-localizing RepeatMasker sequences for the GRM genes for the complete collection of repeat classes: SINE, LINE, LTR, low complexity, simple repeats and other repeats. Some comments are due. First, it is a common behaviour that balanced classes are extremely similar to each other, with the only exception of Grm5. The dispersion of RepeatMasker classes w.r.t. omoWeak matching segments seems to follow rather a specie-specific trend than to be a conserved property, expecially for the different role played by simple repeats in mouse and human genes.

Final remarks

We investigated what a top-down analysis of four genes, linked by either paralogy or orthology relationhips, may suggest about their biological Summing up what the relation is between some properties and omoWeak/balanced segments. When the relation is co-localization (resp. anti-localization), we mean that the fraction of feature-IN-omoWeak/bal is definitely greater (resp. lower) than 50%. The arrow is an additional tool to visualize such relation. When figures are around 50% we say that the feature and the linguistic class are indifferent to each other, meaning that there are no suggestions of any existing bias among the two occurrences. Starting from some combinatorial hints on recurrent words given by a preliminary compression analysis, we built a collection of DNA segments almost uniformly located along the genes, that were over-represented with respect to some biologically blind rule. We found that they are concentrated on non-exon sequences. We compared the balanced and omoWeak segments in the {w, s} alphabet, to some physical features of the DNA helix and experimentally found biological properties of these inter-exon sequences.

The resulting matches show that more than half of the complete collection of over-represented segments may be related to some already described biological property. The reverse is challenging: part of the collection shows (in human genes) meaningful relations with histone methylation and acethylation signals, whose biological interpretation remains vague since the mea-surement values for the studied genes are extremely low.

As a general evaluation, we observe that the non negligible relationship between these segments and several biological properties, very different each other, along with their statistical significance, suggest that these segments should have a role in the functional information content of the genome. This conjecture is strongly supported by the conservation we have found for these over-expressed words in our comparisons between orthologous and paralogous genes. Even though the very low similarity in their sequences (less than 20% in the average case) the conservation of the over-expressed words is very high.

The over-expressed words would play some structural/functional role which is important and then conserved beyond the plain sequence preservation. The very similar outcome of co-localization analysis for omoWeak with CTCF signals and flexible regions would suggest a relationship of these words with some aspects of chromatin structure. Indeed CTCF binding sites regulate chromatin domains activity [START_REF] Burke | CTCF binding and higher order chromatin structure of the H19 locus are maintained in mitotic chromatin[END_REF] whilst flexible regions mediate DNAprotein binding processes and specific interactions of DNA metabolism [START_REF] Grove | Localized DNA Flexibility Contributes to Target Site Selection by DNA-bending Proteins[END_REF]. Interestingly, both these sequence-dependent parameters that are not related to specific sequences, colocalize with words along the whole genes independently of sequence identity. These findings may be suggestive of a conserved long-range structure, with a possible functional role, and of a higher-order organization. The impossibility of further focusing the words role is probably due to the present lacking of more detailed biological knowledge.

This paper would represent a suggestion directed to genomists to further investigate, from an experimental point of view, the possible role of such segments.

Supplementary material

We provide some additional data about gene organization, co-localization w.r.t. CpG islands and w.r.t. individual classes of repeats.

2 . 4

 24 There are only three combinatorial structure of these words: they are A 3 C 9 G 4 T 8 and its reverse complement A 8 C 4 G 9 T 3 and two singletons A

Figure 1 :

 1 Figure 1: Example of results for GRM1 gene: homology w.r.t. Grm1; gene organization; flexible regions; localization of omoWeak segments; omoWeak segments co-localizing with torsional flexibility measures; localization of balanced segments; CTCF methylation data; Z-DNA sequences; repeat classes. The coordinates of single segments and of flexible regions have been included in BED files and displayed as custom tracks on the UCSC browser.

Figure 2 :

 2 Figure 2: Example of results for 15kb within GRM1 gene displayed as custom tracks on the UCSC browser (chr6:146,388,224-146,403,223): flexible regions; localization of omoWeak segments; CTCF methylation data.

Feature

Table 1 :

 1 The four grm genes under study.

	Gene	specie	Chromosome	length	GC-content
	GRM1	H. sapiens	chr 6	412965 bp	37%
	GRM5	H. sapiens	chr 11	563148 bp	36%
	Grm1 M. musculus	chr 10	398962 bp	39%
	Grm5 M. musculus	chr 7	552292 bp	37%

Table 2 :

 2 Part of interesting words selected as the longest and recurrent in the dictionary gene Grm1 after CASToRe analysis.

	atcctcctgactgcagcagtgtgaaggtggaggagt	cctgactgcagcagtgtg
	aaggtggaggagtatcctcctgactgcagcagtgtg	aaaatatt
	aattgaaaat	cccgagaaggaaag
	tatcctcctgactgcagcagtgtg	aacacagggcccccaatggagaag

Table 3 :

 3 Over-represented n-mers (n = 4, . . . , 24) shared by the 4 genes under binary filter. .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

	n		over-represented n-mers
	4		4(2,2)
	5		5(2,3)
	6		6(3,3)
	7		7(3,4)
	8	8(8,0)
	9	9(9,0)	9(4,5)
	10	10(10,0)
	11	11(10,1)	11(5,6)
	12	12(11,1)
	13	13(12,1) 13(11,2)	13(6,7)
	14	14(13,1) 14(12,2)
	...
	24	24(9,15) 24(10,14)	24(11,13)	24(19,5) 24(20,4) 24(21,3)

. . . . . . . . . . . . . . . . . . . . . .

Table 4 :

 4 Over-represented 24-mers on {A, C, G, T } alphabet shared by the 4 genes. The nucleotide content is shown as n A , n C , n G , n T .

		balanced
	24(11,13)	(3, 9, 4, 8) (8, 4, 9, 3)	(4,9,4,7) (4,10,3,7)
		omoWeak
		(6, 4, 1, 13)		(5, 3, 2, 14)
		(13, 1, 4, 6)		(14, 2, 3, 5)
	24(19,5)	(4,2,3,15)	(6,2,3,13)	(5,2,3,14)

Table 5 :

 5 Classes of over-represented 24-mers on {A, C, G, T } alphabet shared by the 4 genes: words found, segments, and relative sequence fraction covered.

	Gene	# balanced words # segments % covered
	GRM1	1167	542	3.51%
	Grm1	1210	572	3.82%
	GRM5	1391	669	3.14%
	Grm5	1285	592	2.88%
	Gene # omoWeak words # segments % covered
	GRM1	6066	1532	11.15%
	Grm1	3887	972	7.28%
	GRM5	8050	2149	11.34%
	Grm5	7182	1879	10.16%

excess of balanced segments within intron II w.r.t. intron fraction over the complete gene (1.10% of segments against relative intron length of 0.33%). For Grm5, its balanced segments are not contained in intron V (about 10 4 nt long) and for Grm1 its omoWeak segments are not contained in intron IX (about 1.4 × 10 3 nt long). The distribution within the other introns is almost uniform.

Table 6 :

 6 Expected and observed genomic sequence covered by segments in conserved regions. Paralogy figures must be read w.r.t. GRM1 genes, orthology figures w.r.t. human genes.

	Homology	Exp(bal) Obs(bal) Exp(omoW) Obs(omoW)
	GRM1 /GRM5	0.56%	1.60%	1.78%	2.63%
	Grm1 /Grm5	0.50%	1.57%	0.95%	1.81%
	GRM1 /Grm1	0.63%	0.54%	2.00%	2.59%
	GRM5 /Grm5	0.38%	0.30%	1.36%	1.30%

Table 7 :

 7 Segments co-localizing with flexibility peaks.

	Gene	# flexible regions	flex-IN-bal flex-IN-omoW
	GRM1	275	2.2%	72.36%
	Grm1	206	3.88%	56.80%
	GRM5	327	3.80%	72.48%
	Grm5	284	3.88%	71.48%
	Gene		bal-IN-flex omoW-IN-flex
	GRM1		1.11%	21.02%
	Grm1		1.57%	18.11%
	GRM5		1.35%	18.43%
	Grm5		1.86%	18.68%

Table 8 :

 8 Segments co-localizing with non-B DNA configurations. We set 0% even when the fraction is not greater than 10 -3 %.

	Gene	# Zdna regions	Zdna-IN-bal	Zdna-IN-omoW
	GRM1	33	0%	3.03%
	Grm1	110	4.54%	2.73%
	GRM5	68	1.47%	10.29%
	Grm5	93	2.15%	2.15%
	Gene	# Gquadr. regions	Gquadr-IN-bal Gquadr-IN-omoW
	GRM1	24	12%	0%
	Grm1	60	13.33%	0%
	GRM5	28	14.29%	0%
	Grm5	88	12.5%	1.14%

Table 9 :

 9 Segments co-localizing with histone methylation and acetylation data (average results). DNA elements defining different chromatin domains. Thus it plays an important role in chromatin remodeling. It can dimerize when it is bound to different DNA sequences, mediating long-range chromatin looping. It mediates interchromosomal association and may direct distant DNA segments to a common transcription factor. It causes local loss of histone acetylation and gain of histone methylation. When bound to chromatin, it provides an anchor point for nucleosomes positioning. Experimental data are taken from

	Gene meth-IN-bal meth-IN-omoW
	GRM1	28.29%	47.50%
	GRM5	24.71%	51.75%
	Gene bal-IN-meth omoW-IN-meth
	GRM1	41.26%	31.30%
	GRM5	29.19%	22.56%
	Gene	acet-IN-bal acet-IN-omoW
	GRM1	24.66%	48.25%
	GRM5	25.95%	50.62%
	Gene	bal-IN-acet omoW-IN-acet
	GRM1	12.80%	11.10%
	GRM5	19.00%	14.06%
	4.4.4. CTCF
	CTCF is a multi-zinc finger protein in vertebrates that binds the insula-
	tors, i.e.

Table 10 :

 10 Segments co-localizing with CTCF.

	Gene CTCF-IN-bal CTCF-IN-omoW
	GRM1	39.19%	72.89%
	GRM5	33.74%	75.50%
	Gene bal-IN-CTCF omoW-IN-CTCF
	GRM1	28.60%	29.76%
	GRM5	20.24%	22.21%

Table 11 :

 11 Segments co-localizing with repeats: cumulative results.We compared the segments to the repeats listed in the database and we show in table 11 the quantitative results taking into account all the repeats as a whole, w.r.t. balanced/omoWeak segments. For each segment, multiple matches can be found and viceversa. The fraction of repeats-IN is around 25%, on average for balanced segments but ranges from around 30% to more

	Gene repeats-IN-bal repeats-IN-omoW
	GRM1	28.33%	49.40%
	Grm1	23.05%	28.14%
	GRM5	25.32%	53.40%
	Grm5	24.11%	46.99%
	Gene bal-IN-repeats omoW-IN-repeats
	GRM1	48.16%	41.09%
	Grm1	41.08%	29.02%
	GRM5	60.54%	45.97%
	Grm5	64.87%	37.47%

Table 12 :

 12

ACKNOWLEDGEMENTS

The work of G.M. was supported by a post-doc research scholarship "Compagnia di San Paolo" awarded by the Istituto Nazionale di Alta Matematica "F. Severi". The Authors want to thank Andrea Bedini for having made available the software used to compute DNA flexibility and Francesco Morandin for useful suggestions.


David J Kelly
email: djkelly@tcd.ie

Leigh A L Corner

Eamonn Gormley

Denise Murphy

Eamon Costello

Frank E Aldwell

Nicola M Marples

Evaluation of attractant flavours for use in oral vaccine baits for badgers (Meles meles)

Keywords: Attractants, Carob, European badger, Meles meles, Oral vaccine, Bovine tuberculosis

European badgers (Meles meles) are a wildlife reservoir for Mycobacterium bovis infection (tuberculosis) in Ireland and the UK and are implicated in the transmission of infection to livestock. Vaccination of badgers with the human BCG vaccine (Bacille Calmette Guerin) is considered as an important strategy to reduce the burden of disease in this species, and a pragmatic approach is likely to involve oral vaccination. In this study, we evaluated nine different flavours for use as attractants in a prototype oral vaccine bait for European badgers (M. meles): aniseed, apple, cocoa powder, carob powder, curry, fish, garlic, peanut and strawberry. The bait matrix was composed of a natural lipid formulation, developed as a vehicle for oral vaccination against tuberculosis in wildlife. A 'food for work' paradigm was employed during the trials to ensure the animals were actively seeking the baits. The trials showed carob and cocoa powders were equally attractive and more attractive than any of the other candidates. Carob and cocoa show potential as bait attractants for badgers and might form part of a novel vaccine delivery system.

Introduction

Mycobacterium bovis infection is endemic in wildlife populations in many countries, e.g. brushtail possums (Trichosurus vulpecula) in New Zealand [START_REF] Morris | The epidemiology of Mycobacterium bovis infections[END_REF], white-tailed deer (Odocoileus virginianus) in USA [START_REF] Schmitt | Bovine tuberculosis in free-ranging white-tailed deer from Michigan[END_REF], wild boar (Sus scrofa) in Spain [START_REF] Naranjo | Evidence of the role of European wild boar as a reservoir of tuberculosis due to Mycobacterium tuberculosis complex[END_REF]) and badgers (Meles meles) in Ireland and the UK [START_REF] Wilesmith | Mycobacterium bovis in the European badger (Meles meles): epidemiological findings in tuberculous badgers from a naturally infected population[END_REF][START_REF] Gormley | Tuberculosis and badgers: new approaches to diagnosis and control[END_REF][START_REF] Griffin | The impact of badger removal on the control of tuberculosis in cattle herds in Ireland[END_REF].

Bovine tuberculosis (M. bovis infection in animals, BTB) is a significant disease of cattle and wildlife in many countries, and as a zoonotic infection, M. bovis has the potential for transmission across species barriers to susceptible hosts including humans [START_REF] Ali | Bovine TB zoonoses; a review[END_REF][START_REF] Doran | An outbreak of tuberculosis affecting cattle and people on an Irish dairy farm, following the consumption of raw milk[END_REF]. Although the badger is protected under Irish and European law, where an epidemiological investigation implicates badgers in the transmission of infection to cattle, they can be culled in the vicinity of affected farms [START_REF] More | The tuberculosis eradication programme in Ireland: a review of scientific and policy advances since 1988[END_REF][START_REF] O'keeffe | Description of a medium term national strategy towards eradication of tuberculosis in cattle in Ireland[END_REF]. It is unlikely that limited culling alone will eradicate infection from the Irish badger population, and it is not considered an ecologically sustainable, long-term approach [START_REF] Griffin | The impact of badger removal on the control of tuberculosis in cattle herds in Ireland[END_REF]).

An oral vaccination program may offer an alternative control strategy for BTB in badgers [START_REF] Gormley | The development of wildlife control strategies for eradication of tuberculosis in cattle in Ireland[END_REF]. Vaccination of badgers with BCG, the only vaccine licensed for tuberculosis in humans, has been shown to induce immune protection in badgers when delivered by a variety of routes [START_REF] Corner | Experimental tuberculosis in the European, badger (Meles meles) after endobronchial inoculation of Mycobacterium bovis: I. Pathology and bacteriology[END_REF][START_REF] Lesellier | Immunological responses and protective immunity in BCG vaccinated badgers following endobronchial infection with Mycobacterium bovis[END_REF], and it has been proven to be safe in captive badger studies [START_REF] Lesellier | The safety and immunogenicity of Bacillus Calmette-Guerin (BCG) vaccine in European badgers (Meles meles)[END_REF]). If vaccination is shown to be effective in wild populations, it may be incorporated as a key element in a long-term control strategy [START_REF] Gormley | The development of wildlife control strategies for eradication of tuberculosis in cattle in Ireland[END_REF]. Oral vaccination is an attractive proposition as it is a relatively inexpensive technique and relies on a simple delivery system [START_REF] Pouwels | Lactic acid bacteria as antigen delivery vehicles for oral immunization purposes[END_REF]). Oral baits have been used to deliver vaccines [START_REF] Slate | Oral rabies vaccination in North America: opportunities, complexities, and challenges[END_REF], contraceptives [START_REF] Cooper | Immunocontraception of mammalian wildlife: ecological and immunogenetic issues[END_REF] and toxic chemicals [START_REF] Mcdowell | Application of pharmaceutical drug delivery for biological control of the common brushtail possum in New Zealand: a review[END_REF] to wildlife.

Given the difficulties in trying to achieve high levels of vaccine coverage in wildlife populations, numerous studies have been conducted that have incorporated attractants into baits for wildlife to increase levels of consumption.

A wide range of products have been used to increase the attractiveness of baits for wild mammals; strawberry (cotton wool impregnated with a strawberry flavouring) has been used successfully as an attractant for feral swine [START_REF] Campbell | Strawberry-flavored baits for pharmaceutical delivery to feral swine[END_REF], cinnamon-truffle powder (as a bait additive) for feral swine piglets [START_REF] Ballesteros | Evaluation of baits for oral vaccination of European wild boar piglets[END_REF], apple (both apple juice and apple odour) for white-tailed deer [START_REF] Bean | Attractiveness of liquid baits containing natural and artificial sweeteners to white-tailed deer[END_REF], marshmallow (as a bait flavouring) for gray foxes [START_REF] Steelman | Bait delivery for oral rabies vaccine to gray foxes[END_REF] and cinnamon oil and anise oil (as bait additives) for brushtail possums [START_REF] Morgan | Behavioural response of brushtail possums, Trichosurus vulpecula, to baits used in pest control[END_REF]Aldwell et al. 2003a). Fish products have worked well as attractants for raccoons [START_REF] Linhart | A field-evaluation of baits for delivering oral rabies vaccines to raccoons (Procyon lotor)[END_REF], skunks [START_REF] Jojola | Oral rabies vaccine (ORV) bait uptake by captive striped skunks[END_REF]), mongooses [START_REF] Creekmore | Field evaluation of baits and baiting strategies for delivering oral vaccine to mongooses in Antigua, West Indies[END_REF]) and red foxes [START_REF] Smith | Acceptance of baits, designed to carry oral rabies vaccines, by foxes in Britain[END_REF], whereas offal has proved a useful attractant for dogs [START_REF] Estrada | Acceptability of local made baits for oral vaccination of dogs against rabies in the Philippines[END_REF]) and coyotes [START_REF] Stolzenburg | Activation of liquid bait devices by coyotes in southern New Mexico[END_REF]. Peanuts, often mixed with syrup, have frequently been used to bait badger traps [START_REF] Southey | Sulfadimethoxine and rhodamine B as oral biomarkers for European badgers (Meles meles)[END_REF]. Although meat-based baits have been used to attract mustelids and carnivores [START_REF] Matter | Test of three bait types for oral immunization of dogs against rabies in Tunisia[END_REF][START_REF] Estrada | Acceptability of local made baits for oral vaccination of dogs against rabies in the Philippines[END_REF][START_REF] Smith | Acceptance of baits, designed to carry oral rabies vaccines, by foxes in Britain[END_REF], their use has been discontinued due to concerns relating to transmissible spongiform encephalopathies (TSEs) [START_REF] Novakofski | Prion biology relevant to bovine spongiform encephalopathy[END_REF].

Uptake of baits is influenced both by their attractiveness and their palatability: attractiveness will bring animals to the baits and palatability will ensure consumption. Attractiveness and palatability can be influenced by the presentation, construction and composition of the baits. Recent developments in an oral vaccine for tuberculosis (Aldwell et al. 2003a, b;[START_REF] Cross | Lipid-formulated BCG as an oral-bait vaccine for tuberculosis: vaccine stability, efficacy, and palatability to brushtail possums (Trichosurus vulpecula) in New Zealand[END_REF][START_REF] Tompkins | Oral vaccination reduces the incidence of tuberculosis in free-living brushtail possums[END_REF] have resulted in the production of solid lipid baits that can be used to encapsulate live BCG bacilli. When delivered by the oral route, lipid-formulated BCG has been shown to induce protection in brushtail possums [START_REF] Tompkins | Oral vaccination reduces the incidence of tuberculosis in free-living brushtail possums[END_REF]) and badgers [START_REF] Corner | Oral vaccination of badgers (Meles meles) with BCG and protective immunity against endobronchial challenge with Mycobacterium bovis[END_REF]. We used lipid baits in the current study with captive badgers to test the relative attractiveness of nine flavours when presented in a prototype oral lipid bait formulation.

Methods

Study area

The studies with captive badgers were conducted in a compound designed for holding badgers for bovine tuberculosis research. Our studies ran between May and June 2008.

Attractant selection

From the analysis of results generated in other studies (including studies on mammal species other than badgers), we selected a range of nine attractants representing animal, vegetable and spice flavours: anise (Aldwell et al. 2003b), apple [START_REF] Bean | Attractiveness of liquid baits containing natural and artificial sweeteners to white-tailed deer[END_REF], carob [START_REF] Craig | Caffeine and theobromine levels in cocoa and carob products[END_REF], cocoa [START_REF] Cross | Lipid-formulated BCG as an oral-bait vaccine for tuberculosis: vaccine stability, efficacy, and palatability to brushtail possums (Trichosurus vulpecula) in New Zealand[END_REF], curry [START_REF] Morgan | Responses of captive and wild possums to lures used in poison baiting[END_REF], fish [START_REF] Smith | Acceptance of baits, designed to carry oral rabies vaccines, by foxes in Britain[END_REF], garlic [START_REF] Marsh | Bait additives as a means of improving acceptance by rodents[END_REF], peanut [START_REF] Southey | Sulfadimethoxine and rhodamine B as oral biomarkers for European badgers (Meles meles)[END_REF] and strawberry [START_REF] Takeuchi | Avoidance of badger (Meles meles) damage to a small strawberry field[END_REF].

Bait preparation

The attractants used were: liquid aniseed flavouring1 , liquid apple flavouring (Richworth Baits, Surrey, UK), carob powder (Hambledon Herbs, Wiltshire, UK), cocoa powder (Cadbury Ireland Ltd, Coolock, Dublin), liquid curry flavouring (Richworth Baits, Surrey, UK), blended fish oil (Nutrabaits, www.nutrabits.com), garlic oil (Nutrabaits, www.nutrabits.com), liquid peanut flavouring (Richworth Baits, Surrey, UK) and liquid strawberry flavour (Richworth Baits, Surrey, UK). The attractants were mixed into the molten (38°C), edible lipid matrix (Lipid-PK, Aldwell et al. 2003b[START_REF] Aldwell | Oral vaccination of mice with lipid-encapsulated Mycobacterium bovis BCG: anatomical sites of bacterial replication and immune activity[END_REF]) at the following rates: liquid flavourings, 1.2% v/v; fish oil, 1.2% v/v; garlic oil, 0.4% v/v; powders, 0.8% w/w. The bait formulations were mixed thoroughly, refrigerated and cut into small blocks (∼1.2 cm 3 ) once they had solidified. These baits were stored at 6-8°C.

Study animals

The badgers (n=17) were held in seven outdoor pens (each ∼200 m 2 with earthen floors covered in grass and shrubs) in groups of 1-4 animals. They had been in the pens for several months prior to the start of the presentations. The pens were constructed with standard chain-link fencing, buried to a depth of 0.5 m. The fences had an overhanging rim along the interior surface to prevent the badgers from climbing out of the pens but were otherwise uncovered. The badgers were fed daily on a diet of high energy biscuits (Racer™ Connolly's Red Mills, Goresbridge, Ireland) and shelled peanuts (Murtagh & Sons, Ashbourne, Ireland) (total mixed ration ∼450 g per badger per day). Access to water was ad libitum.

Animal care

All work with the captive badgers was carried out under license from the Department of Health and Children (B100/ 3187) under the regulations of the Cruelty To Animals Act, 1876, as amended by European Communities Regulations: S.I. 2002/566, 2005/613. Ethical approval for working with captive badgers was obtained from the Animal Research Ethics Committee (University College Dublin).

Study design

Data were recorded at the level of pens, as it was not possible to record the behaviour of individual animals. A preliminary study (see below), where all pens were offered the same three baits under novel tiles, was conducted to establish the viability of the experimental presentation format. This study provided clear evidence that badgers were willing to seek out food placed under tiles. In the main study, designed to test the comparative attractiveness of nine different odours, an incomplete Latin square design was used to present the attractants (Table 1). The attractants were presented in groups of three (each under a separate tile). Each attractant was offered to a pen on three separate occasions (once during each iteration). Different combinations of attractants were presented to each pen at each iteration, such that no two attractants were offered together on more than one occasion.

Presentation of attractants

Preliminary study This study used all of the animals subsequently included in the main study. Badgers were presented with shelled peanuts, raisins or broken biscuits, placed under ceramic tiles (area ∼0.06 m 2 , weight ∼2.3 kg). The tiles were considered to be sufficiently heavy to prevent access from non-target species (e.g. birds (Corvus spp.) and rats (Rattus norvegicus)). Movement of the tiles was considered to be evidence of badger activity.

Main study At each presentation, each pen was offered three different attractants (two pieces of bait covered by a tile) and a control tile (a tile covering a paper marker without bait); giving a total of four tiles per pen at each presentation. This allowed a comparison of the behaviour of the social group in each pen towards the attractants, as well as towards the tiles.

In order to offer each pen all nine attractants, three separate presentations were made (see Fig. 1). These presentations were made on alternate days to limit habituation of the animals. Each set of three presentations was termed an iteration. The trial consisted of three such iterations. Iterations were conducted on alternate weeks to limit habituation. The baits were placed in position in the late evening as badgers are nocturnal feeders, and the tiles were examined in the early morning.

Assessment of behaviour

The morning after a presentation, all tiles were examined for evidence of badger activity. The bait was deemed to have been investigated if the tile had been moved from its original position and, after careful examination of the ground underneath, and in the vicinity of the tile, the baits were deemed to have been removed (by badgers) if no baits were present.

Statistical analysis

The blocked design was such that different pens received the baits in different orders (Table 1). To include this information in the analyses, a variable, order, was added. Investigation for each attractant at each presentation was scored as either 1 or 0. The investigation data were analysed with a linear mixed effects model using a logit function with the assumption that the observed data were binomially distributed. The analyses were run in R v2.9.1 (R Foundation for Statistical Computing, Vienna, Austria) using the function lmer contained in the package lme4. The variables pen and time (iteration and/or order) were included as random factors, while attractant was included as a fixed factor. Time effects were investigated in a nested manner (nesting cycle within order; a facility within the lmer function) as well as independently. Attractant (using reactions towards the fish attractant for reference) was used as a fixed factor. Fish was chosen as the reference attractant because it was the attractant with the highest average investigation score after carob and cocoa. Parameter effects in the mixed effects model are presented as P values. As the sample size was relatively large (a total of 189 presentations), AIC (Akaike's Information Criterion) values, rather than the small sample-corrected alternatives (AICc), were used in this instance. A similar lmer model was used to analyse removal of the baits.

A third lmer model was used to analyse the frequency with which badgers investigated the control tiles. In this analysis, pen, order and experience were used as variables. Experience was an integer; 1=first encounter with control tiles, 2=second encounter, et cetera. Iteration was not used in this analysis, as control tiles were presented on multiple occasions in a single iteration of the trial (i.e. each pen was presented with a control tile in addition to the three attractants during each iteration). In this case, the model consisted of a relatively small sample size (a total of 42 presentations), so AICc were used for comparisons.

Results

Preliminary study

This study demonstrated that badgers were willing to move the tiles to gain access to the food items beneath. On a number of occasions (4 of 62 presentations), the tiles were moved, but not all the food was removed from beneath it.

Control tiles (placed outside the badger enclosures) were placed across obvious rat runs or in prominent positions (to encourage access by crows). None of the food presented under control tiles showed indications of investigation or removal.

Main study

There were three broad levels of investigation of the attractants (Fig. 1). These investigation scores are considered to be a good proxy for the attractiveness of the baits. Carob and cocoa baits were the most frequently investigated, with an average score of 0.76, approximately 50% greater than the overall average investigation score (0.47) for all baits. Curry, fish and peanut baits had lower levels of investigation with average scores of 0.48, and the remaining attractants all had average scores of ≤0.38, at least 33% lower than the overall investigation score. The maximal model for attractant investigation gave primary importance to "pen" and "iteration", although sub-maximal models (with AIC weights >0.1) also included "order". Comparisons of the attractants in the maximal model showed carob and cocoa baits to have been more frequently investigated than fish baits (P<0.05 in both cases; AIC=221.4, AIC weight=0.62; Table 2).

Like investigation, there were three broad levels of removal of the attractants (Fig. 2). The grouping for removal scores was similar to that for investigation scores, with carob and cocoa baits being the most frequently removed (0.76). Removal scores were generally lower than investigation scores. The maximal model for attractant removal gave primary importance to "pen" and "iteration", although sub-maximal models (with AIC weights >0.1) also included "order". Comparisons of the attractants in the maximal model showed carob and cocoa baits to be removed more frequently than fish baits and strawberry baits to be removed less frequently than fish baits (P<0.05 in all cases; AIC=223.7, AIC weight=0.54; Table 3).

The maximal model for control tile investigation (Fig. 3) gave primary importance to "experience" and "pen" (AICc = 41.39, AICc weight = 0.82, n = 42) and showed control tiles were investigated more frequently on their first encounter than on their second encounter (P< 0.05). No control tiles were investigated during the fifth and sixth encounters.

Discussion

Of the nine candidate attractants tested in this study, carob and cocoa were the most frequently investigated and removed. The baits containing carob and cocoa attractants were investigated and removed on over 75% of the presentations across the three iterations of the study. The next highest scoring attractant bait (fish oil) was investigated and removed on only 48% of presentations.

A successful bait attractant will encourage the target animal to investigate the area where it is located and may well contribute towards the consumption of the bait, once found. Carob and cocoa were the most successful attractants in this investigation. There were a number of attractants which showed a disparity between the investigation and removal scores. It is unclear if these uneaten baits were disturbed while the badgers were searching for more preferred flavours. However, there were clearly times when baits were uncovered by badgers but left uneaten.

While we have been careful to record the baits as removed, rather than consumed by the badgers, the evidence from the control tiles in the preliminary study suggests that non-target animals were not gaining access to the baits. The disparity between investigation and removal scores suggests that the lipid baits were not considered attractive by non-target species. While we have no photographic record of the activity of the badgers in the enclosures, we believe that all removed baits were removed and consumed by badgers. Subsequent presentation studies with wild badgers (unpublished data) have shown lipid baits to be treated as valuable prey items.

We observed variation in the investigation patterns of badgers to the different attractants across iterations (Fig. 1). Some attractants were investigated consistently (carob, cocoa and curry), while others showed a changing pattern of investigation. Several attractants were investigated and/ or removed to a progressively decreasing extent. This is a pattern indicative of learning and memory by the badgers. Such a pattern was also clearly apparent in the investigation of the control tiles (Fig. 3).

All badgers learned, after four presentations, that the lack of an attractant cue (presumably odour) signalled the absence of a food reward. There were no signs of investigation of control tiles on their fifth and sixth presentations (Fig. 3). Where this pattern of declining investigation was found for baits containing attractants, it suggests that those baits were being classified as unattractive or were failing to provide an adequate food reward. By extension, it is possible that those baits showing higher levels of investigation over the three iterations had been learned and remembered as preferred food rewards. Apple, garlic and strawberry attractants all showed steeply decreasing patterns of investigation across the three iterations of the study (Fig. 1). This was unexpected, as apples and strawberries are known to be consumed by wild badgers [START_REF] Boesi | Diet of the Eurasian badger Meles meles (Linnaeus, 1758) in the Natural Reserve of Lago di Piano, northern Italy[END_REF][START_REF] Takeuchi | Avoidance of badger (Meles meles) damage to a small strawberry field[END_REF]. Baits were freshly prepared for each iteration, so neither chemical dispersal nor degeneration can explain this effect. The concentrations of the attractants used in this study were selected on the basis of a review of the literature and communications with those conducting similar studies elsewhere. Concentrations chosen were considered as moderate. We noted the experience of UK researchers, who reported rejection by badgers of baits flavoured with aniseed oil where the concentration of the oil (1% w/w) was ten times that favoured by New Zealand workers (0.1% w/w) working with brushtail possums (FE Aldwell, personal communication). It is unclear whether stronger or weaker concentrations of either carob or cocoa would have altered their attractiveness as observed in this study. Investigations into concentration effects may be undertaken in future studies.

Meat baits using mammalian tissues have traditionally been used for carnivores. However, they are now considered unacceptable because of concerns regarding TSEs. As an alternative, fish-meal baits have proved attractive for a range of carnivores: foxes [START_REF] Smith | Acceptance of baits, designed to carry oral rabies vaccines, by foxes in Britain[END_REF], raccoons [START_REF] Roscoe | Efficacy of an oral vaccinia-rabies glycoprotein recombinant vaccine in controlling epidemic raccoon rabies in New Jersey[END_REF], dogs [START_REF] Corn | Evaluation of baits for delivery of oral rabies vaccine to dogs in Guatemala[END_REF], skunks [START_REF] Jojola | Oral rabies vaccine (ORV) bait uptake by captive striped skunks[END_REF]) and badgers [START_REF] Southey | Sulfadimethoxine and rhodamine B as oral biomarkers for European badgers (Meles meles)[END_REF]. In this study, fish oil was not as attractive to badgers as either carob or cocoa.

Although equally as attractive as carob, concerns have been raised regarding the potential toxicity of cocoa. If badgers consume very large quantities of chocolate (cocoa), the constituent methylxanthines may induce fatal intoxication [START_REF] Jansson | Theobromine intoxication in a red fox and a European badger in Sweden[END_REF]. As methylxanthines pose similar dangers to other wildlife [START_REF] Gunning | Theobromine poisoning in ducks due to the feeding of cacao waste products[END_REF][START_REF] Gartrell | Death by chocolate: a fatal problem for an inquisitive wild parrot[END_REF] and pets [START_REF] Gans | Effects of shortterm and long-term theobromine administration to male dogs[END_REF][START_REF] Bonagura | Kirk's current veterinary therapy[END_REF], cocoa may be an unsuitable constituent for oral vaccine baits. As carob contains few or no methylxanthines, it does not pose a similar risk [START_REF] Craig | Caffeine and theobromine levels in cocoa and carob products[END_REF]. Therefore, carob might be a more suitable additive for an oral vaccine bait for badgers. Despite the clearly attractive nature of carob, its inclusion in oral vaccine baits will depend on how it affects BCG viability if it is in direct contact with the live vaccine [START_REF] Tassou | Weak antimicrobial effect of carob (Ceratonia siliqua) extract against food-related bacteria in culture media and model food systems[END_REF].

Nevertheless, the present study has clearly demonstrated the potential of carob as an effective attractant for badgers.

Fig. 1

 1 Fig. 1 Investigation scores of baits by seven pens of captive badgers. Values given are the totals of the seven pens for each iteration as well as the average value for the three iterations. The dotted line indicates the mean investigation score for all baits across all iterations (0.47)

Fig. 3

 3 Fig. 3 Investigation scores for the control tiles (unbaited) by seven pens of captive badgers. Values given are the percentages of the pens showing evidence of investigation at each presentation

Fig. 2

 2 Fig.2Removal scores of baits by seven pens of captive badgers. Values given are the totals of the seven pens for each iteration as well as the average value for the three iterations. The dotted line indicates the mean investigation score for all baits across all iterations (0.41)

Table 2

 2 Maximal model for investigation of nine attractants by seven pens of badgers, using fish oil as the control

		Estimate	SE	P value
	Intercept (fish)	-0.204	0.91	-
	Aniseed	-0.551	0.73	-
	Apple	-1.138	0.76	-
	Carob	1.826	0.81	<0.05
	Cocoa	1.826	0.81	<0.05
	Curry	0.000	0.72	-
	Garlic	-0.551	0.73	-
	Peanut	0.000	0.72	-
	Strawberry	-1.463	0.78	-

Parameter estimates (expressed as log-odds), associated standard error of the estimates and P values are given for each treatment (N=189)

Table 3

 3 Maximal model for removal of nine attractants by seven pens of badgers, using fish oil as the control

		Estimate	SE	P value
	Intercept (fish)	-0.193	0.77	-
	Aniseed	-1.342	0.75	-
	Apple	-1.037	0.72	-
	Carob	1.697	0.77	<0.05
	Cocoa	1.697	0.77	<0.05
	Curry	-0.491	0.70	-
	Garlic	-0.747	0.71	-
	Peanut	-0.491	0.70	-
	Strawberry	-1.684	0.78	<0.05

Parameter estimates (expressed as log-odds), associated standard error of the estimates and P values are given for each treatment (N=189)

Flavourings were not essential oils and had water-based and oil-based components.

Acknowledgments We thank A. L. Jackson and C. J. Tanner for advice on statistical techniques and two anonymous referees for their comments on an earlier draft of this manuscript. DJK was supported by a grant from the Irish Department of Agriculture, Fisheries and Food. The contributions of EG, LALC and DM were funded by FP7 TB-Step (Strategies for the Eradication of Bovine Tuberculosis).