Jihane Amarir-Bouhram

Me ´lodie Goin

Marie-Agne `s Petit
email: marie-agnes.petit@jouy.inra.fr

Low Efficiency of Homology-Facilitated Illegitimate Recombination during Conjugation in Escherichia coli

Homology-facilitated illegitimate recombination has been described in three naturally competent bacterial species. It permits integration of small linear DNA molecules into the chromosome by homologous recombination at one end of the linear DNA substrate, and illegitimate recombination at the other end. We report that homology-facilitated illegitimate recombination also occurs in Escherichia coli during conjugation with small non-replicative plasmids, but at a low frequency of 3610 210 per recipient cell. The fate of linear DNA in E. coli is either RecBCD-dependent degradation, or circularisation by ligation, and integration into the chromosome by single crossing-over. We also report that the observed single crossingovers are recA-dependent, but essentially recBCD, and recFOR independent. This suggests that other, still unknown, proteins may act as mediator for the loading of RecA on DNA during single crossing-over recombination in E. coli.

Introduction

Homology-facilitated illegitimate recombination (HFIR) is a hybrid recombination reaction whereby a linear DNA molecule integrates into the bacterial chromosome during natural transformation. Up to now, it has been reported in the three naturally competent bacterial species Streptococcus pneumoniae [START_REF] Prudhomme | Homologous recombination at the border: insertion-deletions and the trapping of foreign DNA in Streptococcus pneumoniae[END_REF], Acinetobacter baylyi [START_REF] De Vries | Integration of foreign DNA during natural transformation of Acinetobacter sp. by homology-facilitated illegitimate recombination[END_REF] and Pseudomonas stutzeri [START_REF] Meier | Mechanisms of homology-facilitated illegitimate recombination for foreign DNA acquisition in transformable Pseudomonas stutzeri[END_REF]. In HFIR, one end only of the linear incoming DNA molecule shares homology with the resident chromosome. Homologous recombination at this end is associated with an illegitimate recombination event within the nonhomologous region of the linear fragment (see Figure 1A). In the recombination product, a deletion of a size similar to the inserted foreign DNA is generally observed (on average 1 kb insertions and 1 kb deletions). In most cases, a 3 to 10 bp micro-homology is found at the illegitimate recombination junction. Compared to the efficiency of homologous recombination, HFIR is 100 times less frequent in S. pneumoniae [START_REF] Prudhomme | Homologous recombination at the border: insertion-deletions and the trapping of foreign DNA in Streptococcus pneumoniae[END_REF], 10 5 times less frequent in A. baylyi [START_REF] De Vries | Integration of foreign DNA during natural transformation of Acinetobacter sp. by homology-facilitated illegitimate recombination[END_REF], and 10 6 times less frequent in P. stutzeri. HFIR is always more frequent than strictly illegitimate recombination, and in all cases the reaction is RecA-dependent. Moreover, in A. baylyi, HFIR efficiency is 20 fold increased in a recJ background [START_REF] Harms | The RecJ DNase strongly suppresses genomic integration of short but not long foreign DNA fragments by homology-facilitated illegitimate recombination during transformation of Acinetobacter baylyi[END_REF]. RecJ is a 59 to 39 single-strand-specific DNA exonuclease [START_REF] Lovett | Identification and purification of a singlestranded-DNA-specific exonuclease encoded by the recJ gene of Escherichia coli[END_REF][START_REF] Lovett | Nucleotide sequence of the Escherichia coli recJ chromosomal region and construction of recJ-overexpression plasmids[END_REF], which might degrade the 59 single strand extremity required for the illegitimate recombination event in A. baylyi.

We recently performed a systematic analysis of the variable DNA segments found upon multiple alignments of Escherichia coli genomes, which revealed that a majority of them are small in size (less than 500 bp), and correspond to insertions coupled with deletions [START_REF] Touzain | Small variable segments constitute a major type of diversity of bacterial genomes at the species level[END_REF], as presented in Figure 1C. This situation could formally originate from HFIR events (Figure 1A andB). However, such recombination events have never been reported in E. coli, which is not naturally competent. We reasoned that other means of horizontal transfer might provide substrates similar to those used in HFIR in naturally competent cells. For instance, during DNA conjugation, singlestranded DNA is transferred, converted into double-stranded DNA, and may remain linear (and therefore a substrate of HFIR), if the DNA transfer is interrupted before completion.

We report the development of a genetic screen to detect HFIR events in E. coli, following DNA conjugation with small, nonreplicative plasmids. We show that HFIR occurs once in every 3610 10 cells involved in conjugation, a frequency a million times lower than homologous recombination, and which might be too low to impact on genome evolution of E. coli. We also found evidence that linear DNA has two principal fates in E. coli: it is either degraded by RecBCD, or ligated and integrated into the chromosome by single crossing-over (SCO). This led us to enquire into the E. coli genetic requirements SCO between a nonreplicative circular molecule and the bacterial chromosome. We show that SCO is clearly RecA-dependent, but only slightly dependent on the RecBCD and the RecF pathways. We also observed that SCO is inhibited by RuvABC, and is RecGindependent. Finally, SCO events were inhibited by a factor of 20 by the UvrD helicase. We conclude that the factors mediating the loading of RecA during SCO events, if they exist in E. coli, are not RecFOR nor RecBCD, and are unknown at present. Homologous DNA recombination, which has been studied in depth for years in E. coli, still has some aspects that remain to be elucidated.

Materials and Methods

Strains

All strains are listed Table 1. Strain b2163 [START_REF] Demarre | A new family of mobilizable suicide plasmids based on broad host range R388 plasmid (IncW) and RP4 plasmid (IncPalpha) conjugative machineries and their cognate Escherichia coli host strains[END_REF] is a MG1655 derivative hosting RP4-2-Tc::Mu in the glvB gene of its chromosome [START_REF] Ferrieres | Silent mischief: bacteriophage Mu insertions contaminate products of Escherichia coli random mutagenesis performed using suicidal transposon delivery plasmids mobilized by broad-host-range RP4 conjugative machinery[END_REF]. RP4-2-Tc::Mu contains the transfer functions of the conjugative plasmid RP4, together with a kanamycine resistance gene, and a Mu-prophage inserted in its tetracycline resistance gene. In addition, this strain contains the pir gene and erythromycine resistance gene integrated at the dap locus, so that it requires 0.3 mM diaminopimelate for growth. An EcoK restriction and modification mutant derivative of strain b2163 was constructed (strain MAC1306), in which the two genes hsdR hsdM were replaced by a phleomycin resistance (phleo R ) gene. Like strain b2163, MAC1306 contains the Mu prophage [START_REF] Ferrieres | Silent mischief: bacteriophage Mu insertions contaminate products of Escherichia coli random mutagenesis performed using suicidal transposon delivery plasmids mobilized by broad-host-range RP4 conjugative machinery[END_REF]. We verified that Mu did not interfere with the recombination assay (not shown). The single mutant DhsdR::FRT derivative of b2163 was also constructed (strain MAC1308), to use as a donor strain in conjugation experiments when the plasmid had to be protected from EcoK restriction in the recipient strain.

The MAC1306 or MAC1308 strains containing the various plasmids described below were used as a donor strain in the conjugation experiments. Recipient strains were either MG1655 (wild type strain) or MG1655 mutant derivatives listed in Table 1, containing plasmid pISceI. Mutant phenotypes were verified by UV sensitivity tests (for recA, recB, recF, recB recF, recO, recR, recG, recJ and ruvA mutants), T4gp2 sensitivity (recD allele), mutator phenotype (uvrD), and sensitivity to EcoK restriction of phage Lambda grown on non-modifying strains (hsdR and hsdM phenotypes).

Plasmid constructions

To place DNA in a situation in which HFIR may occur, a system allowing plasmid delivery with high efficiency, and maintaining this plasmid linear in the recipient cell for as long as possible was designed. To reach high efficiency of plasmid entry, we set up a genetic system based on conjugation in E. coli, taking advantage of the mobilizable suicide plasmid pSW23T [START_REF] Demarre | A new family of mobilizable suicide plasmids based on broad host range R388 plasmid (IncW) and RP4 plasmid (IncPalpha) conjugative machineries and their cognate Escherichia coli host strains[END_REF]. This plasmid carries the oriT transfer origin of RP4, and relies for conjugation on the RP4 conjugation genes provided in trans (RP4-2-Tc::Mu is chromosomally integrated in the donor strain). It also contains the vegetative replication origin of plasmid R6K, which relies on the Pir replication protein provided in trans in the donor strain. This system is conceived such that, once mobilized and introduced in a recipient strain, pSW23T no longer replicates and can only survive by recombining with the recipient chromosome.

Plasmid pSW23T was engineered further to maintain it in a linear form in the recipient strain, and thus facilitate HFIR detection. For this, a ''cut cassette'' composed of two EcoK restriction sites flanking an I-sceI restriction site was cloned into pSW23T, giving pJA1 (see Figure 2). EcoK is a type I restriction enzyme which loads DNA on the AAC(N6)GTGC sequence, then tracks along the DNA from its loading site, by pulling it from both sides [START_REF] Loenen | Tracking EcoKI and DNA fifty years on: a golden story full of surprises[END_REF]. Cutting occurs at random positions, upon collision with a second EcoK/DNA complex. Therefore, a minimum of two EcoK restriction sites are needed on a plasmid to provoke cutting. An internal segment of the ycgN gene of Bacillus subtilis, which contains two EcoK sites 450 bp apart was PCR amplified, using primers j1 and j2 (Table 2), containing at their 59 extremities a Chi site oriented such that it protects pJA2 from RecBCD degradation, once linearised in the cut cassette. A linker composed of the IsceI cutting site (TAGGGA-TAACAGGGTAAT) and compatible ends (oligonucleotides j7 and j8, Table 2) was then ligated into the ycgN PCR fragment, between the ClaI and BclI sites. Finally, this synthetic cassette was cloned at the SmaI site of pSW23T, giving plasmid pJA1. The presence of all critical elements of this cassette was verified by DNA sequencing. This cassette allows the production, in the recipient strain, of a linear plasmid flanked by two Chi sites, properly oriented to protect linear DNA from RecBCD degradation.

In a further step, to provide the conjugative plasmid with homology to the recipient chromosome, a 1 kb segment corresponding to the 39 terminal part of the lacZ ORF was amplified with primers j3 and j4, and cloned into pJA1 between the BamHI and SacII sites, giving pJA2 (Figure 2). Linearization of pJA2 at the cut cassette produces a molecule with 1 kb of homology to the lacZ chromosomal gene at one end (except for a few hundreds of terminal bp corresponding to the distance between the cutting site and the region of lacZ homology), whereas the other end has no homology with the recipient chromosome.

As a control for the efficiency of linearization, a pJA3 plasmid was derived from pJA2, containing the 59 end of the lacZ ORF. This segment of lacZ was amplified with primers j5 and j6, and cloned between the PstI and SalI sites of pJA2. Linearization of pJA3 produces a molecule with 1 kb lacZ sequences at both ends, which are correctly oriented to integrate the plasmid by double crossing-over recombination in the lacZ gene in the E. coli chromosome, and produce a Lac 2 Cm R ex-conjugant (Figure 2). If on the contrary pJA3 integrates into the chromosome as a circular form, by a single-crossing over recombination in either of the two lacZ segments present on pJA3, an intact lacZ ORF is restored, and a Lac + Cm R phenotype is expected.

Conjugations

Conjugations were performed between the MAC1306 or MAC1308 donor strains containing plasmid pJA1, pJA2 or pJA3, and various derivatives of the wild type strain MG1655 as recipient. The EcoK restriction system is active in MG1655. Plasmid pIsceI is a pUC derivative encoding the cI 857 thermosensitive repressor of bacteriophage Lambda, and the gene encoding the IsceI endonuclease cloned downstream of the Lambda pL promoter. Transcription of the endonuclease is induced by a shift from 37uC to 41uC for 1h30 prior to conjugation. Conjugations were done on filters at 41uC, unless otherwise stated, for two hours, starting from exponentially growing cells of the donor and recipient strains (OD 600 of 0.3), and using a ratio of 2.5 recipient per donor cell. After conjugation, filters were taken, cells resuspended by vortexing in LB, and various dilutions of the mixtures were then plated at 37uC on LB rich medium devoid of diaminopimelate, to counterselect the donor cells, and to count recipient cells. Cm R recombinants, both Lac + and Lac 2 , were detected on LB plates supplemented with 20 mg/ml of chloramphenicol, 0.5 mM isopropyl-b-D-1-thiogalactopyranoside (IPTG) and 40 mg/ml of 5-bromo-4-chloro-3-indolyl-beta-D-galactopyranoside (X-gal).

Results

Experimental set-up to test HFIR

To detect HFIR during conjugation in E. coli, we used plasmid pJA2, and its two controls pJA1 and pJA3, presented in Figure 2. All plasmids encode a gene conferring resistance to chloramphenicol (Cm R ), carry the oriT transfer origin of RP4, and the vegetative replication origin of plasmid R6K. Once mobilized and introduced in a recipient strain, such plasmids no longer replicate (nor conjugate), and can only survive by recombining with the recipient chromosome. To maintain the plasmid in a linear form in the recipient strain, the three plasmids contain a 'cut cassette', composed of sites cut by the two endonucleases EcoK and IsceI, framed by two Chi sites. Plasmid pJA2 has 1 kb of homology at the 39end of lacZ, so that the linear pJA2 has homology to the chromosome at one end, and no homology at the other. With such a set up, HFIR is expected to produce Cm R Lac 2 exconjugants as represented Figure 3 (central part), which can easily be distinguished from the Cm R Lac + ex-conjugants formed by single crossing-over due to possible residual circular molecules (Figure 3, left part). Plasmid pJA3 is a control plasmid derived from pJA2 (see Figure 2), which permits measuring the efficiency of linearization. It contains a second 1 kb region of homology with lacZ (the 59 end of the lacZ ORF). Finally, pJA1 has no homology to the chromosome.

Estimation of the efficiency of linearization of the incoming plasmids after conjugation

All conjugative plasmids used in this study enter as single strand, linear DNA in the recipient cell. However, the plasmid recircularizes once entry is completed, and converts into double strand (ds) DNA. This conversion to ds DNA is probably rapid, as recombination of pJA2 into the chromosome is sensitive to EcoK and IsceI ds endonucleases (see below and Figure 3). In order to maintain the incoming ds DNA molecules linear, we made use of the resident restriction enzyme EcoK, and added an additional IsceI endonuclease provided by plasmid pIsceI. The efficiency of cutting by each system was estimated using pJA3. If all pJA3 molecules are kept linear, 100% of Lac 2 recombinant clones are expected, because all Cm R exconjugants will result from double crossingover events (DCO) involving the two lacZ fragments present on the plasmid. If some of the molecules remain circular on the contrary, they will generate recombinants by single crossing-overs (SCO), which will reconstitute the full length lacZ ORF, and be Lac + . Finally, when no restriction is provided (i.e., the donor strain is Modification + for EcoK, and the recipient does not have plasmid pIsceI), the proportion of Lac 2 clones will indicate how many DCO are generated starting from a circular substrate.

Conjugations with MAC1306 (EcoK Modification 2 ) or MAC1308 (EcoK Modification + ) donor cells containing pJA3 and recipient MG1655 wild-type cells, expressing or not expressing the IsceI site specific nuclease, were carried out on filters for 2 hours. Cells were then resuspended and appropriate dilutions were plated either on LB medium lacking DAP, to counterselect donor cells and count recipient cells only, or on the same plates supplemented with Xgal, IPTG and chloramphenicol, to count recombinant recipients and distinguish Lac + from Lac 2 . Results are reported in Figure 4, left panel. We found that the background proportion of Lac 2 clones obtained with pJA3 when no restriction is provided was 61%. Providing EcoK cutting or IsceI cutting increased the level of Lac 2 clones to 86% and 91%, respectively. Finally, when both endonucleases were active, Lac 2 clones were 98.7% of all ex-conjugants. We conclude that both cutting systems are efficient, even though 1.3% of exconjugants remain Lac+ when both systems are acting together, suggesting that a maximum of 1.3% of the plasmid molecules have remained circular (we suppose here that circular and linear molecules recombine with similar efficiencies).

Recombination with linear pJA2 is decreased compared to linear pJA3

Next, conjugations with MAC1306 or MAC1308 donor cells containing pJA2 and recipient MG1655 wild-type cells, expressing or not the IsceI site-specific endonuclease, were carried out. Results are reported in Figure 4, middle panel. Efficiency of recombination with pJA2 decreased 10 fold when one endonuclease was active on the substrate, and 50 fold when both were active. The decrease in recombination yield for the linear molecules, compared to pJA3, is most likely due to the lack of homology at one end of the linear DNA. Despite the use of two endonuclease systems, EcoK and I-sceI, to linearise the incoming plasmids, a background ratio of 4610 26 Lac + Cm R per recipient cells was obtained, which represents 2% of the recombinants obtained when the incoming plasmid is not cut. This is a ratio similar to that observed for pJA3 (1.3%), and corresponds to the inescapable background production of Lac+ clones resulting from single crossing-overs. We nevertheless detected 4610 29 Lac 2 Cm R clones in the ''double cut'' context. No Lac 2 Cm R clones were scored when either one of the cutting system was used alone. We cannot rule out that this absence of Lac2 is due to the technical difficulty of screening such clones among a wealth of Lac+ colonies. Alternatively, it may be that the sticky ends produced by each single restriction system reseal more easily when the two restriction enzymes are not combined.

A control experiment with plasmid pJA1 (Figure 4, right panel) sharing no homology with the E. coli chromosome showed a marked, 1000 decrease in the yield of Cm R Lac + clones compared to pJA2 or pJA3 when no cleavage was applied, as expected. These most likely were produced by illegitimate recombination. Interestingly, their yield was also reduced 10 to 100 fold upon cleavage. These clones were not analysed further.

Detection of HFIR events with pJA2

We next proceeded to analyse the few Lac 2 Cm R recipients produced with pJA2 when both cutting systems were active, as these were expected to correspond to HFIR events (see first section of the results). Lac 2 Cm R recipients were found at a frequency of 5.8 (+/24.0)610 29 recombinants per recipient cells (60 Lac 2 clones recovered over a total of 31 conjugation experiments, 0 to 7 Lac 2 clones per experiment). All 60 Lac 2 clones were analysed by PCR to test whether the integration of pJA2 into the chromosome had generated a deletion of part of lacZ as predicted for HFIR (Figure 3, middle). In most cases, unexpectedly, a duplication of part of lacZ, rather than a deletion, was observed (Figure 3, right part). The PCR test diagnostic for duplication use oligonucleotides j19 and j18, with j19 being placed at the limit of the lacZ end segment cloned into pJA2, and j18 facing j19, next to the Cm R sequence (see Figure 3 lower panel for the positions of the oligonucleotides). In case of an HFIR event, these two oligonucleotides do not face each other (Fig. 3, middle) and no product is expected. In 56 of the 60 clones, a PCR product was found, whose size varied between 500 and 2 kb. Sequencing of 14 such clones confirmed the presence of a duplication of part of the lacZ-end homologous locus, with a size between 109 to 950 bp. We concluded that this category of Lac 2 recombinants resulted from a single crossing-over between a pJA2 circular molecule lacking some of the lacZ-end fragment and the chromosome (see Figure 3, right part). Loss of some lacZ sequences in pJA2 could result from pre-existing plasmid deletion, or linearization by EcoK or IsceI followed by partial, RecBCD dependent degradation of lacZ-end and re-circularisation. The results below, showing that a recD mutation decreased the yield of Lac2 clones, strongly suggests that the second alternative is more likely.

The remaining 4 of the 60 Cm R Lac 2 clones appeared to be the result of HFIR. To detect the new junction expected, a set of primers upstream from the lacZ 59 locus (j17, j5 and j16, all 1 kb apart from each other, Figure 3, lower part) were tested in combination with j18, the pJA2-specific primer oriented towards the new junction. PCR products of different lengths were detected in all clones (one with the oligonucleotides combination j17-j18, three with j5-j18). Table 3 summarizes the deletion/integration length and the presence of homology at the junction for these clones. For 3 of the 4 clones, no homology was found at the new junction. We conclude that HFIR is detectable during conjugation in E. coli, albeit at a very low frequency. Based on the fact that 27 conjugations among 31 did not produce any HFIR clone, and using the first term of the Poisson distribution, we can estimate a frequency of HFIR of 3.5610 210 , which is 10 6 fold below the frequency of homologous recombination events.

SCO are nearly independent of RecBCD and RecFOR, and inhibited by RuvA

To better understand recombination in our assay, experiments were performed in various mutants. RecA is the central actor of all homologous recombination reactions, while RecBCD and Re-cFOR are mediator proteins facilitating access of RecA to singlestranded DNA. The RecBCD complex is specialised in promoting RecA loading at the extremity of linear ds DNA substrates, whereas RecFOR act on single-stranded gaps. The RuvABC complex resolves Holliday junctions into recombination products at the last step of recombination, and RecG is involved in a redundant pathway of resolution (for a review, see [START_REF] Persky | Mechanisms of recombination: lessons from E. coli[END_REF]). RecJ is a 59 to 39 single-strand exonuclease acting in the RecF pathway, and has been reported to prevent HFIR in S. pneumoniae [START_REF] Harms | The RecJ DNase strongly suppresses genomic integration of short but not long foreign DNA fragments by homology-facilitated illegitimate recombination during transformation of Acinetobacter baylyi[END_REF], and UvrD displaces toxic RecA filaments [START_REF] Veaute | UvrD helicase, unlike Rep helicase, dismantles RecA nucleoprotein filaments in Escherichia coli[END_REF].

Before testing recombination in strains permitting the linearization of pJA2, where several events are taking place (SCO and HFIR), we first examined the genetics of SCO, using donor and recipient strains that do not lead to pJA2 linearization. Efficiencies of recombination, measured as Cm R /Recipient cells, are reported in Table 4. Recombination was found to be recA dependent, but recB, recD and recF independent. Mutants for recD, recO and recR were not affected either. The double recB recF mutant yielded to a 2 fold reduction in recombination efficiency. Finally, recombination was increased by a factor of 17 in the ruvA mutant, and 22 in a uvrD mutant.

Intrigued by the limited effects of the recB and recF mutations on SCO during conjugation, we asked whether the efficiency of SCO during electroporation was also unaffected by these mutations. The same bacteria were prepared for electroporation, and transformed with pJA2. As a control for transformation efficiency, 1 ng of the replicative plasmid pACYC184 was used. Recombination yields were measured relative to the transformation efficiency. In the MG1655 wild type strain, a yield of 1.4610 24 Cm R recombinants per transformed cells was obtained. In the recB and recF derivatives, yields of 4.8 and 4.3610 25 respectively were found, meaning once again that SCO recombination is barely affected (3 fold) by defects in the recF or recB pathways.

We finally measured recombination efficiencies in mutants with linear pJA2, i. e. under conditions of cleavage by both EcoK and IsceI. Both Lac + Cm R (SCO products) and Lac 2 Cm R (essentially pJA2 trimming, followed by SCO) are reported in Table 5. Compared to circular pJA2, similar effects of the recA, ruvA and recJ mutations were observed. A slight change was found for the recB, recF and recG mutations, as a small but significant 4 to 5 fold reduction in the yield of recombinants was observed, compared to the wild type situation. This means that with the linear pJA2 substrate, ,1/5th of the recombinants produced by RecA occur in a RecB, RecF and RecG independent manner. The recombination assay with linear pJA2 could not be performed in uvrD mutant, due to the instability of plasmid pIsceI in this context, nor in the recB recF double mutant due to its low viability.

In general, the mutations tested had a similar effect on the yield of Lac+ and Lac2 clones, compared to the wild type strain. An interesting exception is the recD mutation. The recD mutation inactivates the dsDNA exonuclease (ExoV) activity of the RecBCD complex, while maintaining its recombination potential. The overall yield of Cm R Lac + recombinants was increased by 4 fold, The joint is composed of the concatenation of the two bold sequences from each parent. doi:10.1371/journal.pone.0028876.t003

but the proportion of Cm R Lac 2 was decreased 14 fold. These two effects are those expected for the exonuclease mutant: the 4-fold overall increase in recombination is due to the higher amount of linear DNA substrate that escapes ExoV degradation. It suggests that in a RecBCD+ background, with 4 fold less recombinants compared to the recD mutant, only J of the linear molecules resist degradation, i.e. L of the linear molecules are degraded. This degradation would have been even more pronounced if pJA2 had not been protected by its two Chi sites (see Methods). Therefore in the recD background, pJA2 linear molecules remain undegraded, can close up and integrate by SCO (see Figure 3: increase of the right pathway). The lower yield of Lac 2 Cm R is also due to an escape from ExoV-mediated degradation of the lacZend region: the lacZ segment remains complete and gives rise more often to a Lac + recombinant (Figure 3, decrease of the left pathway). Actually, sequencing the junction in the three Lac 2 clones obtained in the recD mutant revealed that none of them had suffered lacZend degradation. These 3 clones were not produced by HFIR either, but rather resulted from a SCO between an undeleted pJA2 and the chromosome. Their Lac 2 phenotype was due to spontaneous mutations elsewhere in lacZ, either a 16 or 59 bp long deletion, or a 5 bp insertion in lacZ, but away from the junction (not shown). More generally, a PCR analysis of the Lac2 clones obtained in the various genetic backgrounds was done, to discriminate between true HFIR events and a lacZ duplication (oligonucleotides j18 and j19). Results are reported in Table 5, last column. We conclude that HFIR events were not drastically enriched in any of these backgrounds, as most events observed were again lacZ duplications.

The most striking conclusion of this genetics study is that SCO are almost independent of both the RecFOR and the RecBCD pathway, and inhibited by RuvA.

Discussion

During plasmid conjugation, in E. coli, HFIR at the lacZ locus occurs at a low frequency of 3610 210 , it is therefore not a frequent by-product of conjugation events. We can also exclude that HFIR is not detected due to the competition with a more efficient process removing the appropriate linear substrate. Indeed, the various steps Table 4. Recombination frequencies with circular pJA2 in E. coli mutant derivatives.

MAC1308 Donor6

Recipient strain (1) Relevant of this study allow drawing a picture of the fate of linear DNA in E. coli: the pJA2 linear molecules are first degraded by RecBCD, and our measurements in the recD background suggest a frequency of degradation ''D'' of 75% of the molecules (this degradation would have been much higher in the absence of Chi sites). Among the molecules escaping degradation, a ligation step occurs, at a frequency ''L'' which we can estimate to be 10%, because the yield of recombinants with linear pJA2, in the recD background (2610 25 , Table 5), is 10% that of recombinants with circular pJA2 in the wild type background (3.3610 24 , Table 4). Finally, some of the circular molecules enter the chromosome by SCO, at a frequency ''H'' of 3610 24 as deduced from the number of recombinants with circular pJA2 in the wild type background (Table 4, first lane). An even smaller fraction integrates the chromosome by illegitimate recombination (frequency I = 5610 27 , Figure 4 panel pJA1, value for uncut DNA). Therefore 22.5% of the linear molecules should remain linear and not degraded, providing sufficient substrate for HFIR. The remaining pJA2 molecules that do not recombine with the chromosome, both linear and circular, are probably diluted across generations, being non replicative.

A way to check whether estimations for D, L and H are correct, is to calculate the predicted frequency ''F'' of Lac + clones obtained with pJA2 after linearization by double cutting: it should be the product of the probabilities ''1-D'' of not being degraded, ''L'' of being ligated, and ''H'' of recombining by SCO:

F~(1-D)|L|H,
This gives F = (0.25)6(0.1)6(3610 24 ) = 7.5610 26 , a value not far from the observed value of 4.2610 26 (Table 5).

We also detected events resulting from a partial RecBCDdependent degradation of linear molecules, followed by ligation and SCO (Figure 3, right panel), which led to the very same phenotype as the HFIR events, and were 10 times more frequent than HFIR. The frequency of this event (Lac 2 Cm R ) being 4610 29 , we can deduce that partial degradation occurs at a frequency D9 such that:

D'|L|H~4|10 {9
Using the values estimated for L and H, this gives D9 = 1.3610 24 . This illustrates the reported high processivity of RecBCD is on its dsDNA substrate [START_REF] Roman | Processivity of the DNA helicase activity of Escherichia coli recBCD enzyme[END_REF], once loaded, it does not unload frequently.

Our initial bioinformatics observation of abundant insertions coupled with deletions of short DNA fragments (also called dimorphic loci) among E. coli genomes is therefore unlikely to be accounted for by HFIR as suggested initially, and these rearrangements might be formed by more complex events [START_REF] Touzain | Small variable segments constitute a major type of diversity of bacterial genomes at the species level[END_REF]. Visual inspection of 47 dimorphic loci revealed that in 70% of the cases, the two supposedly different variable segments detected by multiple genomes alignment had in fact some level of similarity. Homeologous recombination between distantly related DNA sequences could thus explain such situations [START_REF] Touzain | Small variable segments constitute a major type of diversity of bacterial genomes at the species level[END_REF]. The low efficiency of HFIR in E. coli during conjugation suggests that it is a process essentially restricted to naturally competent species, where the activities that process the incoming linear DNA might promote their efficient insertion coupled with a deletion in the chromosome. The observations reported in this work may even suggest that during transformation of these species, some protein might prevent the circularisation of linear double-strand DNA, to permit HFIR, or that DNA enters the chromosome in a singlestrand state.

In the process of this work, we nevertheless came across a surprising observation, apparently unnoticed previously, that the exact mechanism by which SCO is taking place in E. coli is unknown at present. We report here that it is almost independent of both RecBCD and RecF pathways. Rather than supposing that the DNA substrate needed to initiate the recombination process is present on the incoming plasmid molecule, it may be that it is present on the recipient chromosome. For instance, a gap might be present in the process of some DNA repair (mismatch repair or nucleotide excision repair) or simply at a replication fork. Indeed, several reports point to the possible presence of RecA at stalled replication fork, with the need of a helicase such as UvrD to remove RecA from such forks [START_REF] Veaute | UvrD helicase, unlike Rep helicase, dismantles RecA nucleoprotein filaments in Escherichia coli[END_REF][START_REF] Flores | A fork-clearing role for UvrD[END_REF][START_REF] Lestini | UvrD controls the access of recombination proteins to blocked replication forks[END_REF]. In line with this hypothesis, we also report that SCO is increased in a uvrD background. However, RecA loading at the fork is thought to be recF dependent, an observation incompatible with the data reported here. The absence of any recF, recO or recR phenotype in our experimental set up remains puzzling, and suggest that RecA may use still other mediators, or none at all, to load onto certain substrates. We also report that RuvABC apparently inhibits SCO. This had already been reported in a different recombination assay based on replicative plasmids [START_REF] Lovett | Crossing over between regions of limited homology in Escherichia coli. RecAdependent and RecA-independent pathways[END_REF]. The proposed interpretation was that Ruv-mediated resolution might favour the non-cross-over products. Besides its role in resolution, RuvAB is also known to reverse some types of stalled replication forks [START_REF] Baharoglu | RuvAB is essential for replication forks reversal in certain replication mutants[END_REF]. It may be that by doing so, it removes the substrate with which circular plasmids usually interact for recombination. In conclusion, this work raises the interesting question of how SCO recombinants are produced in E. coli.

Figure 1 .

 1 Figure 1. HFIR may account for the occurrence of insertions coupled with deletions, observed upon multiple genomes alignments. A. During Homology-Assisted Illegitimate Recombination (HFIR), a linear fragment sharing homology with the chromosome (black part) enters into the recipient chromosome by an atypical double crossing-over event, with homologous recombination (HR) at the leftend of the molecule, and illegitimate recombination (IR) at the other end. B. Alignment of the resulting recombinant with the ancestral genome will produce two small ''variable regions'' flanked by the backbone sequences, common to both strains. C. Diversification of strains produced by HFIR, leads to the detection of insertions (variable segment VS2 in genomes G3-G5) coupled with deletions (variable segment VS1 in genomes G1-G2) upon multiple genome alignments of various E. coli strains (here 5 strains). doi:10.1371/journal.pone.0028876.g001

Figure 2 .

 2 Figure 2. Maps of plasmids pJA1, pJA2 and pJA3 after linearization in the cut cassette. The 1 kb 59 part of lacZ is shown with a grey box, and the 1 kb 39 part of lacZ (lacZend) by a white arrow. The cat gene encoding resistance to chloramphenicol (Cm R ) is shown as a stripped arrow. The cut cassette is indicated by a small black box, linearization of pJA plasmids in vivo might happen either at the IsceI recognition sequence, or between the two EcoK sites. The cut cassette map is shown in details, with two grey boxes indicating the Chi sites. Below the three plasmids, the E. coli chromosome with which plasmids might recombine is shown (wavy line) in the lacZ region (grey to white arrow). doi:10.1371/journal.pone.0028876.g002

Figure 3 .

 3 Figure 3. Inferred chromosomal recombination products obtained with plasmid pJA2. Left part: Single crossing-over (SCO) between circular pJA2 and the chromosome, as produced if pJA2 remains uncut (or if pJA2 is cut and resealed). The resulting exconjugants are Lac + Cm R . Central part: HFIR between linear pJA2 and the chromosome. Exconjugants are Lac 2 Cm R , and therefore distinguishable from the previous SCO events. Right part: If pJA2 is cut, degraded up into the lacZ sequence, and recircularized, a SCO between a circular, deleted (wavy bold line) pJA2 molecule and the chromosome will give rise to Lac 2 Cm R exconjugants. White arrow, 39 part of the lacZ gene. Grey box, remaining part of the lacZ gene. Wavy lines, other sequences of the E. coli chromosome. Straight line, pJA2 plasmid sequences. Black box, ''cut cassette''. Primers j18, j19, j17, j5, j16 complementary to the plasmid pJA2 and the chromosome, are shown above each chromosomal recombination product. doi:10.1371/journal.pone.0028876.g003

Figure 4 .

 4 Figure 4. Efficiency of post-conjugative recombination with plasmids pJA3, pJA2 and pJA1. The Lac+ (blue bars) and Lac2 (white bars, when present) Cm R ex-conjugants reported to the viable recipient cells, are scored separately. doi:10.1371/journal.pone.0028876.g004

Table 1 .

 1 Strains and plasmids used in this study.

	Strains
	Name	Relevant genotype (all strains are MG1655 derivatives)	Source, construction
	b2163	RP4-2-Tc::Mu DdapA:(Erm-pir)	[8]
	MAC1306	RP4-2-Tc::Mu DdapA:(Erm-pir) DhsdR-hsdM::phleo R	This work

doi:10.1371/journal.pone.0028876.t001

Table 2 .

 2 Sequence of the oligonucleotides used in this study.

	Name	Sequence
	j1	AACTGCAGCTGGTGGAAACACAGTGGTTCTGAAACC
	j2	AATACCCGGGCTGGTGGCAGGTCCCATATAAACATCT
	j3	ATACCTCCGCGGGCAACTCTGGCTCACAGTAC
	j4	ATAGGATCCTTATTTTTGACACCAGACCA
	j5	TTACTGCAGATGACCATGATTACGGATTC
	j6	TTAGTCGACAAACCGACATCGCAGGCTTC
	j7	CGATAGGGATAACAGGGTAAT
	j8	GATCATTACCCTGTTATCCCTAT
	j16	CCCAACCGCGTGGCACAACA
	j17	GATTGAAAATGGTCTGCTGC
	j18	AATGCCTCAAAATGTTCTTTACGA
	j19	GCAACTCTGGCTCACAGTAC

doi:10.1371/journal.pone.0028876.t002

Table 3 .

 3 Properties of the HFIR clones obtained.

	Strain	Size of lacZ deletion,	Size of the	Homology at
	name	upstream of lacZ-end	pJA2 insert	the junction	Sequence of the joint a
	MAC1343	1081 bp	1747 bp	None	59ATGTCGGTTTCCGCGAGGTG 39lacZ
					59TATCGATACCGTCGACGCCG 39pJA2
	JAC233	1357 bp	1750 bp	None	59TTTGCCGACCGCACGCCGCA 39lacZ
					59GCGTTCGAAAATTCTCGTAC 39pJA2
	JAC228	245 bp	1989 bp	None	59GCGCTGTACTGGAGGCTGAA 39lacZ
					59CTTATCGATACCGTCGACGC 39pJA2
	JAC227	1385 bp	1974 bp	1 nt	59GCCACTCGCTTTAATGATGA 39lacZ
					59CGTACCAACTTCTCTTGATC 39pJA2
	a

Table 5 .

 5 Recombination frequencies with linear pJA2 in E. coli mutant derivatives.

		genotype	Nb of		Fold, relative
		of recipient	experiments	Av. Cm R /recipient	to WT (2)
	MG1655	WT	5	3.3 (+/21.1)610 24	1
	JAC21	recA	4	2.7 (+/22.3)610 27	0.0006 **
	MAC1394	recB	4	2.7 (+/20.9)610 24	0.58
	MAC1354	recD	4	6.2 (+/22.7)610 24	1.9
	MAC1470	recF	4	4.8 (+/21.6)610 24	1.45
	MAC1503	recO	4	5.1 (+/22.8)610 24	1.5
	MAC1507	recR	4	3.5 (+/21.6)610 24	1.04
	MAC1350	recJ	4	4.0 (+/22.0)610 24	1.2
	MAC1511	recB recF	4	1.8 (+/20.5)610 24	0.54 *
	MAC1497	ruvA	4	5.6 (+/24.4)610 23	17 *
	MAC1476	recG	4	2.7 (+/20.8)610 24	0.8
	MAC1662	uvrD	4	7.2 (+/22.9)610 23	22 *
	(1) Conjugations performed at 37uC.
	(2) *Significant below 5%, ** Significant below1% with a Student test.
	doi:10.1371/journal.pone.0028876.t004

(1)

*Significant below 5%, ** Significant below1% with a Student test.

(2)

Frequencies calculated with the first term of a Poisson law. doi:10.1371/journal.pone.0028876.t005

PLoS ONE | www.plosone.org

December 2011 | Volume 6 | Issue 12 | e28876

Acknowledgments

We thank Didier Mazel for providing the initial material for all conjugation experiments, and Marina Elez and Ariel Lindner for the gift of the pIsceI plasmid. We are indebted to Franc ¸ois-Xavier Barre for decisive discussions at the onset of this work, and Colin R. Tinsley and Marianne de Paepe for critical reading of the manuscript prior to publication.

Dr. Amarir-Bouhram PhD grant is funded by the French Ministry of Research. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

MAC 1308 RP4-2-Tc::Mu DdapA:(Erm-pir) DhsdR::FRT This work JAC7 pIsceI (Amp R ) This work JAC21 recA306 srl::Tn10 This work MAC1348 recA306 srl::Tn10 pIsceI (Amp R ) This work MAC1394 recB268::Tn10 This work MAC1397 recB268::Tn10 pIsceI (Amp R ) This work MAC1470 recF400:Tn5 This work MAC1473 recF400:Tn5 pIsceI (Amp R ) This work MAC1497 ruvA60::Tn10 This work MAC1357 ruvA60::Tn10 pIsceI (Amp R ) This work MAC1476 recG::Tet6200 This work MAC1479 recG::Tet6200 pIsceI (Amp R ) This work MAC1354 recD::Tn10 This work MAC1352 recD::Tn10 pIsceI (Amp R ) This work MAC1500 recJ::PhleoR This work MAC1350 recJ::PhleoR pIsceI (Amp R ) This work MAC1503 recO::Tn5 This work MAC1507 recR::Tn5 This work

Author Contributions

Conceived and designed the experiments: JAB MAP. Performed the experiments: JAB MG MAP. Analyzed the data: JAB MG MAP. Contributed reagents/materials/analysis tools: JAB MAP. Wrote the paper: MAP.


Justyna J Gorzecka
email: justyna.gorzecka@agrsci.dk

Marius C M C Codrea
email: marius.codrea@agrsci.dk

Nicolas N C Friggens
email: nicolas.friggens@agroparistech.fr

Henrik H Callesen
email: henrik.callesen@agrsci.dk

Marius Cosmin Codrea

Nicolas C Friggens

Progesterone profiles around the time of

Keywords:

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

Introduction

Progesterone measurements have been used in cattle reproduction for more than 30 years for diagnosis of pregnancy [START_REF] Dobson | Clinical application of the progesteronein-milk test[END_REF], monitoring of ovarian activity post-partum, assessing the reproductive status of the cow [START_REF] Bulman | Milk progesterone levels in relation to conception, repeat breeding and factors influencing acyclicity in dairy cows[END_REF], and detec-tion of some reproductive diseases. Because of the high, positive correlation between progesterone concentration in blood and milk [START_REF] Dobson | Relationship between progesterone concentrations in milk and plasma during the bovine oestrous cycle[END_REF], milk samples can be used for measuring progesterone. The recent development of automated in-line systems for measuring progesterone in milk [START_REF] Friggens | Improved detection of reproduction status in dairy cows using milk progesterone[END_REF] gives new opportunities for progesterone as a fertility management tool. Combining identification of cows ready for insemination with an index of "good quality progesterone profile" would help to improve fertility.

Existing definitions of abnormal progesterone profiles are useful for describing the types of resumption of post-partum ovarian activity. These include the length of post-partum anestrus, the length of luteal phase and intervals between cycles. However, this only addresses one aspect of estrous cycle quality, the overall temporal pat-tern. Another aspect about which there is a relative lack of information is the actual progesterone concentration in different stages of the cycle. The latter aspect has become particularly relevant with respect to modern dairy cows that may have lower concentrations of progesterone, associated with high dry matter intake and high milk yield [START_REF] Hommeida | Luteal function and conception in lactating cows and some factors influencing luteal function after first insemination[END_REF][START_REF] Lucy | Reproductive loss in high-producing dairy cattle: where will it end?[END_REF][START_REF] Wiltbank | Changes in reproductive physiology of lactating dairy cows due to elevated steroid metabolism[END_REF]. This is important in the context of modern high-yielding dairy herds, especially in the Holstein breed, with increased percentages of abnormal progesterone profiles [START_REF] Royal | Declining fertility in dairy cattle: changes in traditional and endocrine parameters of fertility[END_REF] and decreased expression of visible signs of estrus [START_REF] Lopez | Relationship between level of milk production and estrous behavior of lactating dairy cows[END_REF] being reported.

A good estrus cycle may be described as a cycle where successful ovulation and fertilization are followed by maintenance of early pregnancy. There is currently increasing evidence of an association between embryo survival and the systemic concentrations of progesterone both in the cycle preceding ovulation and in the early luteal phase following insemination [START_REF] Diskin | Embryo survival in dairy cows managed under pastoral conditions[END_REF][START_REF] Erb | Profiles of reproductive hormones associated with fertile and nonfertile inseminations of dairy cows[END_REF][START_REF] Morris | Effect of progesterone on embryo survival[END_REF][START_REF] Rosenberg | Seasonal variations in post-partum plasma progesterone levels and conception in primiparous and multiparous dairy cows[END_REF][START_REF] Stronge | Post-insemination milk progesterone concentration and embryo survival in dairy cows[END_REF]. Another candidate for progesterone profile features may be the concentration of progesterone during ovulation [START_REF] Båge | Suprabasal progesterone levels in repeat breeder heifers during the pro-and oestrous period[END_REF][START_REF] Waldmann | Progesterone concentrations in milk fat at first insemination -effects on non-return and repeat-breeding[END_REF]. Gorzecka et al. (in press) also found some features of progesterone profile length and shape (luteal peak and the rate of upward and downward slope) associated with clinical findings of uterine inflammation. In the present study, we examined these features in context of the outcome of inseminations. The aim of the study was to identify a complex of progesterone profile features associated with successful insemination.

Materials and methods

The data for this study were collected in the dairy herd at the Danish Cattle Research Center in Foulum, Denmark. This herd included three breeds (Danish Holstein, Danish Jersey, and Danish Red), kept in a loose housing system and fed ad libitum with a total mixed ration containing 50% roughage (approximately fifty/fifty corn silage and grass silage) and 50% standard dairy concentrate. The cows were milked by VMS (Voluntary Milking System, DeLaval, Tumba, Sweden) and the average milk yield was 9640 kg ECM (energy corrected milk yield). Cows had on average 90 open days and conception rate 36%. Average estrus detection was 80%, by means of observations and pedometers.

Animals

The data collection were done from 2002 to 2009. After removal of incomplete progesterone profiles (see below), the resulting data represented information during 785 lactations from 554 cows: 240 Danish Holstein, 178 Danish Red and 136 Jersey, in parities 1-5.

Milk collection and progesterone analysis

Proportional whole milk samples were collected from the cows during their milking in a robotic milking system with free traffic (mean no. of visits/day = 2.4). One progesterone measure was programmed daily during the first 120 days from calving, and every second day for the remainder of lactation. Actual average intervals between progesterone samples before and after 120 days from calving were 1.4 and 2.4 days, respectively. (SD = 2.0 and 3.6 days, respectively). The discrepancies between planned and actual sampling intervals were due to some missed samples resulting from technical issues.

Progesterone was analyzed using the Ridgeway ELISAkit (Ridgeway Science Ltd., Gloucestershire, UK). Milk samples were pipetted, diluted and distributed using a Biomek 2000 ©(Laboratory Automation workstation, Beckman Coulter, Fullerton, CA, USA). Milk samples (25 l, diluted 1:2 with water) were handled according to manufacturer's instructions, except for the incubation R4 which was increased to 1 h and 30 min. Plates were read using a spectrophotometer/fluorometer at 575 nm (Fluostar©, BMG Labtechnologies, Offenburg, Germany). Analyses were performed in 96-well plates; two sets of seven standards (0-30 ng/ml), locally made using milk from an ovariectomized cow and ethanolic progesterone solutions, and two sets of two control samples were used for every analysis and plate. For the low and high controls, the intra-assay precision (CV%) was 14.9 and 1.4, respectively; the inter-assay precision (CV%) was 32.7 and 20.1, respectively; and the average inaccuracy (bias) was ±0.82 and 0.60 ng/ml, respectively.

The time series of milk progesterone measurements were examined for gaps longer than 14 days with no progesterone values and profiles with such gaps were excluded. Further, the entire lactation was excluded if there were insufficient data to identify the end of the postpartum anestrus period (i.e. the time series did not start at calving and there were less than five measurements before the cow was detected to have elevated progesterone and resumed estrous cycles).

Extraction of progesterone profiles and their features

The progesterone profiles were extracted by fitting a spline curve to the data and then using the 1st and 2nd derivatives of the spline function (hereafter referred to as velocity and acceleration, respectively) to identify the peaks and troughs of the progesterone profile, and the points with greatest rates of decrease or increase in the profile. The smoothing spline was a 5th order polynomial Bspline with knots placed at each data point and a roughness penalty imposed on the 2nd derivative.

Smoothing was carried out using the functional data analysis approach of [START_REF] Ramsay | Functional Data Analysis[END_REF] implemented in R (R Development Core Team, 2005). The value of the roughness penalty, , used was 50, this provides sufficient flexibility in the function to obtain a close fit to the data. Interpolation between progesterone measurements was made by calculating the values of the spline function at intervals of 0.1 day. An example of a smoothed profile is shown in Fig. 1.

At the peaks and the troughs of the smoothed profiles, velocity, i.e. the rate of change in progesterone, is zero and when the velocity is at a local maximum or minimum, the acceleration is zero. In a first step, all points were identified where velocity = 0 and velocity had exceeded a lower or upper velocity threshold of ±1 ng/ml/d in the interval since the preceding zero velocity was identified (the open and solid circles in Fig. 1). The velocity threshold was imposed to filter out 0 velocity values associated with trivial undulations in the profile. Likewise, points of zero acceleration were identified using an acceleration threshold of ±0.35 ng/ml/d/d (the open and solid squares in Fig. 1).

In a second step, these points on the smoothed profiles were used to identify progesterone profile features using the eight following definitions:

(i) Luteal phase peaks (progesterone peak) were defined as a point of zero velocity crossing from positive to negative velocity values and a smoothed progesterone value of >8 ng/ml (the solid circles in Fig. 1).

When more than one such point occurred in a luteal phase (the interval between two estrus marks), the last occurrence was chosen as the luteal phase peak as this marks the end of the luteal phase. In these cases, the requirement of having exceeded a velocity threshold was relaxed as the later peaks are not always preceded by a sharp rise in the progesterone profile. The same procedure was adopted if no luteal phase peak was identified in a luteal phase. In the last period of any given profile (usually the pregnancy period) the first such point was chosen. (ii) Estrus marks were defined as a point of zero velocity with velocity crossing from negative to positive values and a smoothed progesterone value of <8 ng/ml (the open circles in Fig. 1). Where repeated estrus marks occurred without an intervening luteal phase peak, i.e. some form of prolonged follicular phase, then the last occurrence was chosen as the estrus mark. (iii) The end of the post-partum anestrus period was defined as the first point on the smoothed profile >3 ng/ml immediately prior to the first two consecutive progesterone measurements >3 ng/ml (around day 35 in Fig. 1). (iv) The pre-estrus maximum rate of decline in progesterone (down rate) was defined as the velocity of the smoothed profile at the point of zero acceleration crossing from negative to positive acceleration in the period from luteal peak to estrus mark (the open squares in Fig. 1). If there was more than one such point, then the point with the minimum (i.e. most negative) velocity was chosen.

(v) The post-estrus maximum rate of increase in progesterone (up rate) was defined as the velocity of the smoothed profile at the point of zero acceleration crossing from positive to negative acceleration in the period from an estrus mark to a luteal peak (the solid squares in Fig. 1). If there was more than 1 such point, then the point with the maximum (i.e. most positive) velocity was chosen. In the last period (usually pregnancy) after the luteal peak mark acceleration marks were excluded. (vi) The length of the cycles was calculated as the number of days between two subsequent estrus marks. (vii) The length of luteal phase was equal to number of days between last estrus time and time of luteal peak. (viii) The length of follicular phase was equal to number of days between time of luteal peak and time of subsequent estrus.

The "estrus-unit"

One analyzed "estrus-unit" consisted of several progesterone features derived from the estrous cycle preceding and following the particular ovulation and insemination. The features from the estrous cycle preceding the ovulation were: last estrus progesterone concentration, last maximum rate of upward slope, luteal phase peak progesterone concentration, maximum rate of downward slope and the length of follicular and luteal phase.

The features from the estrous cycle following insemination were: estrus progesterone concentration, maximum rate of upward slope and number of days between estrus and maximum rate of upward slope following the insemination.

Groups of estruses

Only estruses in which insemination occurred within an acceptable time from estrus were chosen, in order to eliminate bad timing of insemination as a reason of pregnancy failure. This time interval was calculated using the group of 145 cows with confirmed pregnancy, the average interval for these cows was 0.96 days, the SD was 0.99 days. Inseminations occurring within the range from the average interval ±2 SD, i.e. -1.02 days before estrus to 2.94 days after estrus, were accepted.

The estruses were classified as:

Group 1 (estruses resulting in a confirmed pregnancy) consisted of estruses that resulted in full-term pregnancy or, for these cows not completing gestation within the time-frame of the data, having a positive pregnancy determination and consistently high progesterone concentrations (never dropping below 8 ng/ml). Group 2 (estruses not resulting in pregnancy) consisted of estruses where the insemination was followed by an estrous cycle not longer than 25 days, with a luteal a phase not longer than 18 days [START_REF] Lamming | The use of milk progesterone profiles to characterise components of subfertility in milked dairy cows[END_REF].

Group 3 (estruses resulting in lost pregnancy) consisted of estruses with high levels of progesterone (never dropping below 8 ng/ml) until 25 days after insemination and followed by reduced progesterone levels between 26

and 49 days post-insemination. This group includes late embryonic losses (LEM) between 16 and 42 days after insemination [START_REF] Sreenan | Embryo survival rate in cattle: a major limitation to the achievement of high fertility[END_REF], with the assumption that progesterone still remains high for a couple of days after losing the embryo (up to 49 days).

Creation of groups

In order to ensure the homogeneity of the two main groups (1 = pregnant, 2 = not pregnant), estruses were selected in pairs (87 pairs) in relation to breed, parity, estrus number and days from calving to the particular estrus (when insemination occurred). First post-partum estruses were excluded from the study, because there was a lack of information concerning the preceding cycle.

Estruses which had pregnancy loss in the preceding cycle were excluded from the analysis, because information concerning the preceding cycle would be biased.

According to the recent study [START_REF] Friggens | Probability of pregnancy as affected by oestrus number and days to first oestrus in dairy cows of three breeds and parities[END_REF] there was a significant increase in probability of pregnancy from first to second estrus, but no significant change in probability of pregnancy thereafter. Independent of the effect of estrus number, there was a significant increase in probability of pregnancy with increasing days from calving to first estrus up to 100 days from calving. Consequently in the present study, in relation to estrus number, pairs of estruses were matched as second estruses and third estruses, and thereafter matching was less precise and ranged between 4th and 6th estrus numbers.

Days from calving to the estrus were matched with the difference of maximum 10 days up to 100 days from calving and maximum 30 days after 100 days post-partum. The distribution of calendar dates within the resulting groups was checked for possible bias due to season. There were no marked differences between the distributions. Therefore, season was not included in the subsequent analyses.

Group 3 consisted of 39 estruses, which were not matched to the other groups, because of too small number of cases of LEM in the database.

Statistical analysis

Exploratory one-way analysis of variance of the effect of pregnancy status (pregnant vs. not pregnant) on each of the progesterone profile features separately (i.e. ignoring correlations between features) was performed in Minitab 9 software.

Subsequently, discriminant analysis of progesterone profile features was performed in Minitab 9 software (www.minitab.com). The purpose of discriminant analysis is to assign an unknown subject to one or more groups on the basis of multivariate observations (i.e. accounting for the correlations between features). This analysis calculates the probability of a given observation belonging to one of the groups [START_REF] Lachenbruch | Discriminant analysis[END_REF]. Discriminant analysis of progesterone features was performed first on all three pregnancy status groups and there on the matched groups 1 (pregnant) and 2 (not pregnant) only.

Finally, we tested the effect of feature selection on the discriminant analysis for the two-group classification.

Although in this study the features were carefully selected to capture important physiological characteristics of the estrous cycle, it is not known a-priory whether this set of features can predict the pregnancy status. Proper feature selection can improve the recognition performance by including only those characteristics that carry information relevant for the given classification task (see [START_REF] Saeys | A review of feature selection techniques in bioinformatics[END_REF] for a recent review). The advantages can be twofold: (1) "noisy" features which may obscure the classifier are excluded and (2) smaller feature spaces are better covered by the same number of samples and thus, better explained statistically. Because the initial set of features was small (9 features), it was feasible to actually examine all possible combinations. Computations were done using the lda procedure from the MASS R package by [START_REF] Venables | Modern Applied Statistics with S[END_REF].

Results

The average raw and smoothed progesterone profiles for pregnant and not pregnant cows are shown in Fig. 2.

One-way analysis of variance

The analysis of variance of the individual progesterone profile features revealed two features which were significantly different between groups of pregnant and not pregnant cows. These features were: the length of follicular phase and the time from estrus to maximum velocity artificial insemination (Table Pregnant cows had a shorter follicular phase at the previous estrous cycle and shorter time from inseminated estrus to maximum velocity.

Discriminant analysis

Discriminant analysis of 3 groups of estruses (1 = pregnant, 2 = not pregnant, 3 = lost pregnancy) revealed a low proportion (0.521) of correctly classified estruses (Table 2). Coefficients for the linear discriminant function of progesterone profile features are presented in Table 3. Discriminant analysis of 2 matched groups of estruses (1 = pregnant, 2 = not pregnant) revealed a higher proportion (0.672) of correctly classified estruses (Table 4). Coefficients for the linear discriminant function of progesterone profile features are presented in Table 5. As can be seen in Table 4, there were no substantial differences between pregnancy status groups in the weights attached to the progesterone profile features. Consequently the correct classification proportion is not very high.

Discriminant analysis and features selection

The univariate analysis of variance indicated that specific progesterone features (e.g., last estrus progesterone) have very little power in classifying the pregnant/not pregnant states. Multivariate classifiers can benefit from using only a small number of features and therefore, in order to test the potential gain in performance we performed an exhaustive experiment. Since the original number of features was relatively small (9), we systematically tested all possible combinations of features (2 9 -1 = 511). The classification accuracy was estimated by leave-one-out cross-validation procedure. The results are shown in Fig. 3, where each row of the image represents one of the 511 possible subsets of features. The black/white hit-map denotes the inclusion (white) and exclusion (black) each of the 9 features. The heat-map denotes the sensitivity (TP -true pregnant), specificity (TN -true not pregnant) and the mean classification accuracy (M -mean) of the discriminant analysis when using the corresponding subsets of features.

In general, no strong pattern can be observed among the feature subsets and the accuracy measures (TP, TN and M) smoothly span continuous shades of gray, indicating that there is no specific subset that outperforms the others. The slight tiling effect of the heat-map, most visible in panels B and C, is due to the discrete sequence of feature subsets.

However, it is noticeable that not using features viii (length of follicular phase) and ix (time from estrus to maximum velocity) gives the least sensitivity as highlighted in panel A. A similar trend can be observed in the same region of panel C, indicating the strength of these two features, despite the fact that the use of feature ix gives low specificity (panel B). On the other hand, for example, feature i (last estrus progesterone) is distributed almost uniformly in panel C suggesting it is ineffective, but virtually harmless. These results are in agreement with the outcome of the univariate analysis of variance.

A summary of the best results of the feature selection investigation is given in Table 6. A top sensitivity of 0.874 was recorded but the corresponding specificity of 0.356 is not acceptable (panel A). Similarly, the best specificity (0.701) is accompanied by a poor sensitivity of 0.46 (panel B). The best accuracy was 0.678 when using the features i (last estrus progesterone) and viii (length of follicular phase) and the second best performance was 0.666 when using features viii (length of follicular phase) and ix (time 1, 3 and5). The heat-map (columns TP -true pregnant, TN -true not pregnant and M -mean) denotes the sensitivity, specificity and the mean classification accuracy (in percent, scaled to [0,1]) of the discriminant analysis when using the corresponding subsets of features. The three panels show the feature subset patterns when sorting in descending order from top to bottom by the three performance measures TP, TN and M in turn. The encircled region highlights that not using features viii (length of follicular phase) and ix (time from estrus to maximum velocity) yields the least sensitivity (TP).

Table 6

A summary of the best results of the feature selection procedure. Results are given in terms of sensitivity (TP), specificity (TN) and the mean classification accuracy (M). from estrus to maximum velocity) (panel C). In all, the results are only marginally different from the full feature set discriminant analysis with a sensitivity of ∼0.75, specificity of ∼0.58 and mean accuracy of ∼0.67.

Discussion

The aim of this study was to identify a complex of progesterone profile features associated with a successful insemination. The main finding is that there were only minor differences between the progesterone profiles of pregnant and not pregnant cows in the estrous cycle preceding insemination, and the rate of rise in progesterone following insemination.

Exploratory one-way analysis of variance of individual progesterone features was performed in the two main groups of estruses. The analysis of variance resulted in finding two significantly different features between groups of pregnant and not pregnant cows. These features were: the length of follicular phase and the time from estrus to maximum velocity following artificial insemination. The strength of these two features was confirmed by the features selection procedure. In the pregnant group the length of follicular phase was shorter and progesterone reached the maximum velocity of upward slope one day faster than in not pregnant cows. This is in agreement with [START_REF] Starbuck | Timing of follicular phase events and the postovulatory progesterone rise following synchronisation of oestrus in cows[END_REF] where both the intervals from luteolysis to ovulation (corresponding to follicular phase in our study) and from ovulation to significant progesterone rise (corresponding to time from estrus to maximum velocity in our study) were longer in cows with late progesterone rise post ovulation. The late rise in progesterone may be caused by a delay in ovulation or by a delay in the post-ovulatory luteinisation. The results of [START_REF] Starbuck | Timing of follicular phase events and the postovulatory progesterone rise following synchronisation of oestrus in cows[END_REF] suggest that poor post-ovulatory development was a more serious problem for cow fertility than a delay in the time of ovulation. Earlier, it has been hypothesized that the animals with delayed onset of luteal function are less likely to become pregnant or to maintain pregnancy [START_REF] Larson | Reduced fertility associated with low progesterone postbreeding and increased milk urea nitrogen in lactating cows[END_REF][START_REF] Mann | Relationship between maternal endocrine environment, early embryo development and inhibition of the luteolytic mechanism in cows[END_REF][START_REF] Starbuck | The detection and treatment of post insemination progesterone insufficiency in dairy cows[END_REF]. This is also consistent with the previous results indicating that suboptimal progesterone support during the early luteal phase is likely to affect embryo viability [START_REF] Mcneill | Associations between milk progesterone concentration on different days and with embryo survival during the early luteal phase in dairy cows[END_REF][START_REF] Stronge | Post-insemination milk progesterone concentration and embryo survival in dairy cows[END_REF]. Direct and indirect effects of progesterone on embryo survival have previously been described by [START_REF] Morris | Effect of progesterone on embryo survival[END_REF].

In previous studies it has been shown by using the logistic regression, that on days 5, 6 and 7 after insemination, too low and too high progesterone concentrations were associated with reduced embryo survival [START_REF] Starbuck | The detection and treatment of post insemination progesterone insufficiency in dairy cows[END_REF][START_REF] Stronge | Post-insemination milk progesterone concentration and embryo survival in dairy cows[END_REF]. [START_REF] Stronge | Post-insemination milk progesterone concentration and embryo survival in dairy cows[END_REF] also found a significant linear and quadratic relationship between the rate of change in the progesterone concentration from days 4 to 7 and subsequent embryo survival. Therefore, in our study, we decided not to examine concentrations of progesterone in the early luteal phase, but its maximum rate of change (maximum velocity) post-insemination. However, we did not find significant differences between groups in pregnant and non-pregnant cows and as mentioned before, the time to achieve the maximum velocity was more important than maximum velocity value itself.

Given the above literature, we were surprised to not find greater differences in the progesterone profiles of the cycle preceding, and the rise following, insemination. The possibility exists that the method used to select and smooth progesterone profiles contributed to this lack of marked difference by removing extreme profiles. A preselection was made to remove incomplete profiles (gaps or too few measures), but these types of problem were due to milk sampling equipment malfunction and were in no way systematically associated with insemination patterns. The farm did not use the progesterone profiles to decide when a cow should be inseminated. As with all smoothing methods, there were a few cases where the features identified were not realistic since the raw profiles were extremely atypical, but this resulted in only a very few abnormal cycles being excluded from the study. By its nature smoothing implies removal of outliers; however, as seen in Fig. 2, both raw and smoothed average values of progesterone were very similar in the groups of pregnant and not pregnant cows. This suggests that the method of smoothing of progesterone curves did not influence markedly the results of the analysis. Another possibility lies in the selection of estrous cycles to compare. We selected cycles from cows with similar biological states (breed, parity, estrous cycle number, and days from calving) in the two groups (within the limits imposed by the size of the dataset). We also excluded from the study estruses in which insemination occurred in a non-reasonable time interval, and this way we avoided bad timing of insemination as a reason for pregnancy failure. Evidently, the latter affected the not pregnant group more than the pregnant group, but this filtering was based exclusively on the timing of the insemination and not on the progesterone profile itself.

The most plausible reason for a potential bias in the data relates to the fact that not all of the pregnancy failures in the not pregnant cycles are a result of a poor estrous cycle. It is clear that there is a significant proportion of early embryo loss related to other factors such as the inherent viability of the embryo [START_REF] Zavy | Embryonic mortality in cattle[END_REF]. In these cases, there would be "good" progesterone profiles in the not pregnant group. Conversely, the probability of "bad" progesterone profiles occurring in the pregnant group is much less.

The discriminant analysis of nine progesterone features revealed a low proportion of correct classification of estruses between all groups of estruses. The average probability of correct classification was higher in the discriminant analysis of two main groups (pregnant vs. not pregnant, 0.672) than when all three groups were considered (0.521). When using only the two main groups of estruses, the sensitivity (proportion of pregnant cows detected as pregnant) was 0.75, compared to specificity (proportion of non-pregnant cows detected as non-pregnant), which was much lower: 0.60 (Table 3). This shows that, on the basis of these progesterone profile features, the model was much better in predicting true pregnant animals.

A sensitivity of 0.75 does not at first sight seem high; however, it would not be expected to get a correct proportion of 1.00. Even with perfect hormonal conditions, there are a number of animals with fertilization failure or pregnancy losses that are independent of hormonal environment. Estimates of non-fertilization are of the order of 10% [START_REF] Sreenan | The extent and timing of embryonic mortality in cattle[END_REF]. Further, if we assume a 10% loss due to poor innate (i.e., not linked to the prior progesterone profile) embryo viability, then it would not be reasonable to expect sensitivity -based on progesterone measures alone -greater than 0.80.

Viewed in this light, and following further research to extend these findings, it is possible to envisage providing information to help the farmer to choose whether or not to inseminate a cow (using only pre-estrus parameters). This requires that progesterone profiles are measured sufficiently frequently to identify, in real-time, the key features. This has recently become possible on-farm [START_REF] Friggens | Improved detection of reproduction status in dairy cows using milk progesterone[END_REF].

Generally, the usefulness of progesterone profiles in inline, on-farm systems may depend on the reproductive efficiency of the herd. The herd which we examined had good average estrus detection (80%) and reproductive efficiency (days open = 90, conception rate = 36%). [START_REF] Ostergaard | Technical and economic effects of an inline progesterone indicator in a dairy herd estimated by stochastic simulation[END_REF] have investigated the economic effects of an in-line progesterone indicator estimated by stochastic simulations. The highest economical benefits were found in the herd with initial poor reproductive efficiency. It is envisaged that in such herds the differences in progesterone profiles between pregnant and not pregnant cows would be greater, due to e.g., less precise estrus detection or management factors affecting ovarian activity and being reflected in progesterone profiles.

Conclusions

We have not found clear differences between progesterone profiles of pregnant and not pregnant dairy; however, the examined complex of progesterone profile features indicated 75% of true pregnant animals and could be used to some extent as a predictive tool for improving reproductive performance in dairy cows.

Individual analysis of progesterone profile features revealed that a shorter follicular phase preceding insemination is associated with proper timing of post-ovulatory luteinisation and therefore is more likely to result in pregnancy.

Fig. 1 .

 1 Fig. 1. An example of a smoothed progesterone profile. The upper panel shows the raw data (x) and the smoothed fit (solid line) derived using a B-spline function with a roughness penalty as described in the text. The dotted line indicates a progesterone concentration of 8 ng/ml. The circles (open and solid) indicate points of zero velocity. The squares (open and solid) indicate points of zero acceleration. The middle panel shows the velocity (ng/ml/d) of the smoothed fit and the dotted line indicates a velocity threshold of ±1 ng/ml/d. Solid circles indicate point of crossing from positive to negative velocity, open circles indicate point of crossing from negative to positive velocity. The lower panel shows the acceleration (ng/ml/d/d) of the smoothed fit and the dotted line indicates an acceleration threshold of ±0.35 ng/ml/d/d. Solid squares indicate point of crossing from positive to negative acceleration, open squares indicate point of crossing from negative to positive acceleration.

Fig. 2 .

 2 Fig. 2. Plot of raw and smoothed progesterone values showing how similar average progesterone profiles in groups of pregnant and not pregnant animals are. The upper panel presents average raw values of progesterone in the pregnant group. The middle panel presents average row values of progesterone in the not pregnant group. The lower panel presents average smoothed progesterone profiles in the pregnant (bold line) and not pregnant group (dotted line).

Fig. 3 .

 3 Fig.3. The black/white hit-map denotes the inclusion (white) and exclusion (black) of each of the features (i-ix; see names of the features in Tables1, 3 and 5). The heat-map (columns TP -true pregnant, TN -true not pregnant and M -mean) denotes the sensitivity, specificity and the mean classification accuracy (in percent, scaled to [0,1]) of the discriminant analysis when using the corresponding subsets of features. The three panels show the feature subset patterns when sorting in descending order from top to bottom by the three performance measures TP, TN and M in turn. The encircled region highlights that not using features viii (length of follicular phase) and ix (time from estrus to maximum velocity) yields the least sensitivity (TP).

Table 1

 1 Analysis of variance of progesterone profile features in matched groups of estruses: 1 = pregnant and 2 = not pregnant.

	Feature	Group	Mean	SD	P
	(i) Last estrus progesterone	1 2	1.65 1.61	2.03 2.39	0.895
	(ii) Last Up Rate	1 2	2.68 2.82	0.72 0.83	0.214
	(iii) Progesterone peak	1 2	22.52 22.22	4.73 5.25	0.701
	(iv) Down Rate	1 2	-3.83 -3.63	0.96 0.93	0.170
	(v) Estrus progesterone	1 2	1.57 1.28	1.91 2.10	0.345
	(vi) Up Rate	1 2	2.92 2.90	0.78 0.83	0.922
	(vii) Length of luteal phase	1 2	13.64 13.12	4.09 4.04	0.399
	(viii) Length of follicular phase	1 2	9.06 9.74	1.46 1.88	0.009
	(ix) Time from estrus to maximum velocity	1 2	4.65 5.63	2.07 2.06	0.013

Table 2

 2 Summary of classification for groups of estruses: 1 = pregnant, 2 = not pregnant, 3 = lost pregnancy.

	True group

Table 4

 4 Summary of classification for groups of estruses: 1 = pregnant, 2 = not pregnant. Sensitivity is the proportion of pregnant cows, detected by the model as pregnant. Specificity is the proportion of non-pregnant cows, detected by the model as non-pregnant.

		True	Group		Total
	Recognized as 1	2
	1	65	35		100
	2	2 2	5 2		7 4
	Total N	87	87		174
	N Correct	65	52		117
	Proportion	0.747 sensitivity	0.598 specificity	0.672
	Table 5
	Coefficients of linear discriminant function for groups of estruses:
	1 = pregnant, 2 = not pregnant.
			1	2	Ratio
	Constant		-67.83	-68.73	-
	(i) Last estrus progesterone	0.72	0.89	1.24
	(ii) Last up rate		7.38	8.13	1.10
	(iii) Progesterone peak	-5.18	-5.08	0.98
	(iv) Down rate		-27.12	-25.81	0.95
	(v) Estrus progesterone	5.89	5.66	0.96
	(vi) Up rate		6.18	6.37	1.03
	(vii) Length of luteal phase	1.37	1.39	1.01
	(viii) Length of follicular phase	8.12	8.15	1.00
	(ix) Time from estrus to	1.26	1.37	1.09
	maximum velocity

Acknowledgments

The paper is a part of a PhD project funded by the Danish Research Council, Aarhus University, the Faculty of Agricultural Sciences and Lattec I/S. The authors would like to thank Kurt Myrup Pedersen for helpful comments and general support. We would also like to thank Martin Bjerring, the staff of the KFC farm and the staff of the milk laboratory for the collection and analysis of milk progesterone.

Conflict of interest statement

The authors report a financial relationship with Lattec I/S. We declare that Lattec I/S had no involvement in the conception, implementation or reporting of this work.