OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based bject, Predicate, Object> triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based bject, Predicate, Object> triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the bute, Value> triple structure recommended for annotations in these works (which is isomorphic to the bject, Predicate, Object> triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

Análisis genómico y funcional de los sistemas de Quorum Sensing en Rhizobium leguminosarum

Rhizobium leguminosarum (Rl) es una alfa-proteobacteria capaz de establecer una simbiosis diazotrófica con distintas leguminosas. A pesar de la importancia de esta simbiosis en el balance global del ciclo del nitrógeno, muy pocos genomas de rhizobios han sido secuenciados, que aporten nuevos conocimientos relacionados con las características genéticas que contribuyen a importantes procesos simbióticos. Únicamente tres secuencias completas de Rl han sido publicadas: Rl bv. viciae 3841 y dos genomas de Rl bv. trifolii (WSM1325 y WSM2304), ambos simbiontes de trébol. La secuencia genómica de Rlv UPM791 se ha determinado por medio de secuenciación 454. Este genoma tiene un tamaño aproximado de 7.8 Mb, organizado en un cromosoma y 5 replicones extracromosómicos, que incluyen un plásmido simbiótico de 405 kb. Este nuevo genoma se ha analizado en relación a las funciones simbióticas y adaptativas en comparación con los genomas completos de Rlv 3841 y Rl bv. trifolii WSM1325 y WSM2304. Mientras que los plásmidos pUPM791a y b se encuentran conservados, el plásmido simbiótico pUPM791c exhibe un grado de conservación muy bajo comparado con aquellos descritos en las otras cepas de Rl. Uno de los factores implicados en el establecimiento de la simbiosis es el sistema de comunicación intercelular conocido como Quorum Sensing (QS). El análisis del genoma de Rlv UPM791 ha permitido la identificación de dos sistemas tipo LuxRI mediados por señales de tipo N-acyl-homoserina lactonas (AHLs). El análisis mediante HPLC-MS ha permitido asociar las señales C6-HSL, C7-HSL y C8-HSL al sistema rhiRI, codificado en el plásmido simbiótico; mientras que el sistema cinRI, localizado en el cromosoma, produce 3OH-C14:1-HSL. Se ha identificado una tercera sintasa (TraI) codificada en el plásmido simbiótico, pero su regulador correspondiente se encuentra truncado debido a un salto de fase. Adicionalmente, se han encontrado tres reguladores de tipo LuxR-orphan que no presentan una sintasa LuxI asociada. El efecto potencial de las señales tipo AHL se ha estudiado mediante una estrategia de quorum quenching, la cual interfiere con los sistemas de QS de la bacteria. Esta estrategia está basada en la introducción del gen aiiA de Bacillus subtilis, que expresa constitutivamente una enzima lactonasa degradadora de AHLs. Para llevar a cabo el análisis en condiciones simbióticas, se ha desarrollado un sistema de doble marcaje que permite la identificación basado en los marcadores gusA y celB, que codifican para una enzima β–glucuronidasa y una β–galactosidasa termoestable, respectivamente. Los resultados obtenidos indican que Rlv UPM791 predomina sobre la cepa Rlv 3841 para la formación de nódulos en plantas de guisante. La baja estabilidad del plásmido que codifica para aiiA, no ha permitido obtener una conclusión definitiva sobre el efecto de la lactonasa AiiA en competitividad. Con el fin de analizar el significado y la regulación de la producción de moléculas señal tipo AHL, se han generado mutantes defectivos en cada uno de los dos sistemas de QS. Se ha llevado a cabo un análisis detallado sobre la producción de AHLs, formación de biofilm y simbiosis con plantas de guisante, veza y lenteja. El efecto de las deleciones de los genes rhiI y rhiR en Rlv UPM791 es más drástico en ausencia del plásmido pUPM791d. Mutaciones en cinI o cinRIS muestran tanto ausencia de señales, como producción exclusivamente de las de bajo peso molecular, respectivamente, producidas por el sistema rhiRI. Estas mutaciones mostraron un efecto importante en simbiosis. El sistema rhiRI se necesita para un comportamiento simbiótico normal. Además, mutantes cinRIS generaron nódulos blancos e ineficientes, mientras que el mutante cinI fue incapaz de producir nódulos en ninguna de las leguminosas utilizadas. Dicha mutación resultó en la inestabilización del plasmido simbiótico por un mecanismo dependiente de cinI que no ha sido aclarado. En general, los resultados obtenidos indican la existencia de un modelo de regulación dependiente de QS significativamente distinto a los que se han descrito previamente en otras cepas de R. leguminosarum, en las cuales no se había observado ningún fenotipo relevante en simbiosis. La regulación de la producción de AHLs Rlv UPM791 es un proceso complejo que implica genes situados en los plásmidos UPM791c y UPM791d, además de la señal 3-OH-C14:1-HSL. Finalmente, se ha identificado un transportador de tipo RND, homologo a mexAB-oprM de P. aeruginosa e implicado en la extrusión de AHLs de cadena larga. La mutación he dicho transportador no tuvo efectos apreciables sobre la simbiosis. ABSTRACT Rhizobium leguminosarum (Rl) is a soil alpha-proteobacterium that establishes a diazotrophic symbiosis with different legumes. Despite the importance of this symbiosis to the global nitrogen cycling balance, very few rhizobial genomes have been sequenced so far which provide new insights into the genetic features contributing to symbiotically relevant processes. Only three complete sequences of Rl strains have been published: Rl bv. viciae 3841, harboring six plasmids (7.75 Mb) and two Rl bv. trifolii (WSM1325 and WSM2304), both clover symbionts, harboring 5 and 4 plasmids, respectively (7.41 and 6.87 Mb). The genomic sequence of Rlv UPM791 was undertaken by means of 454 sequencing. Illumina and Sanger reads were used to improve the assembly, leading to 17 final contigs. This genome has an estimated size of 7.8 Mb organized in one chromosome and five extrachromosomal replicons, including a 405 kb symbiotic plasmid. Four of these plasmids are already closed, whereas there are still gaps in the smallest one (pUPM791d) due to the presence of insertion elements and repeated sequences, which difficult the assembly. The annotation has been carried out thanks to the Manatee pipeline. This new genome has been analyzed as regarding symbiotic and adaptive functions in comparison to the Rlv 3841 complete genome, and to those from Rl bv. trifolii strains WSM1325 and WSM2304. While plasmids pUPM791a and b are conserved, the symbiotic plasmid pUPM791c exhibited the lowest degree of conservation as compared to those from the other Rl strains. One of the factors involved in the symbiotic process is the intercellular communication system known as Quorum Sensing (QS). This mechanism allows bacteria to carry out diverse biological processes in a coordinate way through the production and detection of extracellular signals that regulate the transcription of different target genes. Analysis of the Rlv UPM791 genome allowed the identification of two LuxRI-like systems mediated by N-acyl-homoserine lactones (AHLs). HPLC-MS analysis allowed the adscription of C6-HSL, C7-HSL and C8-HSL signals to the rhiRI system, encoded in the symbiotic plasmid, whereas the cinRI system, located in the chromosome, produces 3OH-C14:1-HSL, previously described as “bacteriocin small”. A third synthase (TraI) is encoded also in the symbiotic plasmid, but its cognate regulator TraR is not functional due to a fameshift mutation. Three additional LuxR orphans were also found which no associated LuxI-type synthase. The potential effect of AHLs has been studied by means of a quorum quenching approach to interfere with the QS systems of the bacteria. This approach is based upon the introduction into the strains Rl UPM791 and Rl 3841 of the Bacillus subtilis gene aiiA expressing constitutively an AHL-degrading lactonase enzyme which led to virtual absence of AHL even when AiiA-expressing cells were a fraction of the total population. No significant effect of AiiA-mediated AHL removal on competitiveness for growth in solid surface was observed. For analysis under symbiotic conditions we have set up a two-label system to identify nodules produced by two different strains in pea roots, based on the markers gusA and celB, encoding a β–glucuronidase and a thermostable β–galactosidase enzymes, respectively. The results obtained show that Rlv UPM791 outcompetes Rlv 3841 for nodule formation in pea plants, and that the presence of the AiiA plasmid does not significantly affect the relative competitiveness of the two Rlv strains. However, the low stability of the pME6863 plasmid, encoding aiiA, did not lead to a clear conclusion about the AiiA lactonase effect on competitiveness. In order to further analyze the significance and regulation of the production of AHL signal molecules, mutants deficient in each of the two QS systems were constructed. A detailed analysis of the effect of these mutations on AHL production, biofilm formation and symbiosis with pea, vetch and lentil plants has been carried out. The effect of deletions on Rlv UPM791 rhiI and rhiR genes is more pronounced in the absence of plasmid pUPM791d, as no signal is detected in UPM791.1, lacking this plasmid. Mutations in cinI or cinRIS show either no signals, or only the small ones produced by the rhiRI system, suggesting that cinR might be regulating the rhiRI system. These mutations had a strong effect on symbiosis. Analysis of rhi mutants revealed that rhiRI system is required for normal symbiotic performance, as a drastic reduction of symbiotic fitness is observed when rhiI is deleted, and rhiR is essential for nitrogen fixation in the absence of plasmid pUPM791d. Furthermore, cinRIS mutants resulted in white and inefficient nodules, whereas cinI mutant was unable to form nodules on any legume tested. The latter mutation is associated to the instabilization of the symbiotic plasmid through a mechanism still uncovered. Overall, the results obtained indicate the existence of a model of QS-dependent regulation significantly different to that previously described in other R. leguminosarum strains, where no relevant symbiotic phenotype had been observed. The regulation of AHL production in Rlv UPM791 is a complex process involving the symbiotic plasmid (pUPM791c) and the smallest plasmid (pUPM791d), with a key role for the 3-OH-C14:1-HSL signal. Finally, we made a search for potential AHL transporters in Rlv UPM791 genome. These signals diffuse freely across membranes, but in the case of the long-chain AHLs an active efflux system might be required, as it has been described for C12-HSL in the case of Pseudomonas aeruginosa. We have identified a putative AHL transporter of the RND family homologous to P. aeruginosa mexAB-oprM. A mutant strain deficient in this transporter has been generated, and TLC analysis shows absence of 3OH-C14:1-HSL in its supernatant. This deficiency was complemented by the reintroduction of an intact copy of the genes via plasmid transfer. The mutation in mexAB genes had no significant effects on the symbiotic performance of R. leguminosarum bv. viciae.

Influence of glycerin and lecithin inclusion in the diet on liver characteristics and lipid fraction in the serum of brownegg laying hens at 55 week of age

The effects of the inclusion of raw glycerin (GLYC) and raw lecithin, in the diet (23 to 55 wk) on liver characteristics and various serum lipid fractions were studied in brown egg-laying hens at 55 wk of age. The control diets were based on corn, soybean meal, and 4% supplemental fat and contained 2,750 kcal AMEn/kg, 16.5% CP, and 0.73% digestible Lys. The diets were arranged as a 2 × 3 factorial with 2 levels of GLYC (0 and 7%) and 3 animal fat to lecithin ratios (4:0, 2:2, and 0:4%). Each treatment was replicated 8 times and the experimental unit was a cage with 10 hens. At 55 wk of age, 2 hens per cage replicate were randomly selected, weighed individually, and slaughtered by CO2 inhalation. Liver was immediately removed and weighed and the color recorded by spectrophotometry. In addition, blood samples from one bird per replicate were collected from the wing vein and the concentration of total cholesterol, low and high density lipoprotein cholesterol, and triglycerides were determined. The data were analyzed as a completely randomized design and the main effects of GLYC and lecithin content of the diet and the interactions were determined. No interactions between GLYC and lecithin content of the diets were detected for any of the variables studied. Liver characteristics and serum lipid traits were not affected by the inclusion of GLYC in the diet. The substitution of animal fat by lecithin, however, reduced the redness (a* 14.9 to 13.8) and yellowness (b* 8.60 to 7.20) values of the liver (P < 0.05) but did not affect the content of serum lipid fractions. It is concluded that the inclusion of GLYC and lecithin in the diet did not affect liver size or serum lipid fraction. However, the inclusion of lecithin reduced the a* and b* value of the liver