Olivier Corby

Alban Gaignard

Catherine Faron Zucker

Johan Montagnat

Catherine Faron Zucker

KGRAM Versatile Inference and Query Engine for the Web of Linked Data

Keywords: Knowledge graphs, Data Mediation, Distributed Query Processing, Modular architecture

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I. INTRODUCTION

An increasing number of data sources becomes available over the Internet, which either adopt graph-based knowledge representation (e.g. RDF and RDFS standards), or comply to various inherited representations, such as relational data. Pushed by the need to federate such data sources, the concept of a Web of Linked Data has emerged, where data entities are uniquely identified and relations between entities are semantically well defined. Nowadays, a growing number of applications expect to manipulate the Linked Data seamlessly available over the Web. Conversely, Linked Data opens new opportunities to enrich existing applications by exploiting enlarged, joined data and knowledge repositories. This momentum creates the need for new tools able to query, join, and manipulate the heterogeneous and distributed data sources composing the Web of Linked Data.

The objective of this work is the design of a versatile Query System that supports (i) multiple Query Languages for the Web of Linked Data (especially, but not limited to, the SPARQL 1.0 and 1.1 standards); (ii) query-based federation of multiple, possibly remote, data sources; (iii) mediation of a wide range of heterogeneous data models encountered on the Web; and (iv) adaptability to various data manipulation use cases. This paper introduces a semantic query distribution algorithm relying on a modular mediation software architecture to implement efficient dynamic querying over distributed heterogeneous data sources To simultaneously address these objectives, this Query System abstracts both the query language used to select and manipulate data entities, and the data models of the connected data sources. A pivot Abstract Knowledge Graph (AKG) model is used internally to represent data entities and the relations between them (see Section II). The core Query Engine, described in Section III, is founded on an Abstract Query Language (AQL) that provides an abstract and extended representation of the SPARQL 1.1 language (see Section III-A). The system provides unifying reasoning mechanisms for querying various knowledge graph models [START_REF] Corby | The KGRAM Abstract Machine for Knowledge Graph Querying[END_REF], hence its name: Knowledge Graph Abstract Machine (KGRAM). A novel query distribution (Section III-B) and optimization (III-C) algorithm is introduced in KGRAM. It relies on the KGRAM modular software suite based on welldefined abstract interfaces that is described in Section IV. KGRAM software modules can easily be combined to architect and deploy dedicated platforms tackling specific data query use cases. The system design integrates optimization concerns to deliver high query performance and improve end-users experience. A reference implementation that covers the SPARQL 1.1 standard query language is exemplified and evaluated in Section V.

II. KGRAM PRINCIPLES

KGRAM is an Abstract Machine in the sense that its core is a generic query and inference engine abstracted from the data model and query language used. It processes Knowledge Graphs through a graph-based knowledge representation model (AKG) and an associated graph-based query language (AQL) designed for abstracting and manipulating data entities extracted from heterogeneous data sources [START_REF] Baget | GRIWES: Generic Model and Preliminary Specifications for a Graph-Based Knowledge Representation Toolkit[END_REF]. Indeed, typed graph structures, which nodes and edges are labeled with types that can support inferences, are multiplying on the Web. They not only support logical reasoning but can also be seen as metric spaces to pilot approximate reasoning and querying, as indexes of knowledge in distributed environments, as models to make interfaces more intelligible to end-users, or as new frameworks for social structures analysis. The mapping of knowledge graphs (in particular conceptual hypergraphs and RDF graphs) to the AKG representation is straightforward by design. Other commonly encountered data models such as relational databases or 1. KGRAM is decomposed into two main components: a Query Engine (QE), capable of handling different query languages, planning queries over multiple data sources and joining data entities retrieved from multiple sources; and a Data Producer capable of interfacing to heterogeneous data sources. The QE internally uses AQL to decompose incoming queries from any (supported) Query Language and performs query planning through query rewriting. Input queries can thus be transformed into as many subqueries as deemed necessary to efficiently query multiple and heterogeneous data sources. AQL subqueries are transferred to a Data Producer that acts as an abstraction for a native data source. It rewrites inbound AQL queries into the native query language of its associated data source, and conversely transforms the native data entities retrieved (e.g. triples or tuples) into KGRAM's AKG homogeneous representation. The AKGs retrieved while handling a query are postprocessed by KGRAM's QE, which may filter out and join the received AKGs to produce the final AKG query result.

B. Remote Data Federation

To enable multiple and remote data sources querying, KGRAM's Producers may need to be architected according to different concerns, as illustrated in Figure 2. Multiple sources querying (top) requires rewriting inbound AQL subqueries into different native Query Languages. The data entities retrieved are transformed into pivot AKG representations and can be joined before being delivered to the QE. Remote sources querying (bottom) requires the integration of a client, a server and a communication channel. The inbound AQL subqueries are serialized for transfer between the client and the server. On the server side, the query rewriting and results transforming actions are undertaken, before AKG resulting entities are serialized for delivery to the client.

Depending on the precise data querying infrastructure needed, both multiple-and remote-querying capabilities may be needed. KGRAM is architected as a versatile toolbox facilitating the design of a specific query infrastructure by deployment of tailored generic software components. III. QUERY ENGINE KGRAM query engine relies on AQL to decompose input queries into homogeneous subqueries and a planner to efficiently evaluate query parts and join results.

A. Abstract Query Language (AQL)

To query and manipulate knowledge graphs, KGRAM searches for homomorphisms in oriented labelled hypergraphs through a comparator of graph node and edge labels. AQL, which grammar and semantics are detailed in [START_REF] Corby | The KGRAM Abstract Machine for Knowledge Graph Querying[END_REF], is a SPARQL 1.1 extension. SELECT-WHERE clauses extended to n-ary relations are represented through labelled Nodes, n-ary Edges, Filters defining constraints on the searched nodes, Paths of binary relations between two nodes in a graph, as well as conjunction, disjunction, options, negation and existence checking of the formers. AQL models nested sub-queries, SPARQL's aggregates, and SERVICE clauses. The engine supports entailment regimes and builds new structures in the query result AKG to model CONSTRUCT, UPDATE and INSERT clauses. Note however that updates and insertions cause changes to the in-memory result graph, but they are not materialized in the remote data sources. An heterogeneous, distributed implementation of UPDATE and INSERT clauses would imply modifying the data sources through data producers able to transform new/changed structures into the native data sources representation and the associated native query language. AQL encompasses various existing query languages such as the Simple Conceptual Graph model [START_REF] Chein | Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs[END_REF] or conceptual graphs with constraints [START_REF] Baget | Extensions of Simple Conceptual Graphs: the Complexity of Rules and Constraints[END_REF].

Figure 3 gives an AQL example querying authors and titles of documents linked through edges labelled with 'hasCreated' and 'hasTitle'. The result of this query evaluation is the binding of node variables '?author' and '?title' with matching values in the queried graph.

B. Query Planning and Processing

The core of KGRAM is its evaluation function. The operationalization of AQL involves the invocation of the connected Data Producer to fetch AKG-formatted candidate nodes and edges. Candidates are processed through a customizable Matcher implementing search of homomorphisms on labelled edges, possibly with Filter constraints.

1) Query evaluation algorithm: Algorithm 1 provides a simplified representation of the KGRAM query evaluator. Input queries are parsed and transformed into a tree representation which nodes contain elementary AQL expressions. The tree structure is flatten into a queryArray so that the recursive eval function (lines 11 and 17) can browse the query structure depth first and iteratively process each expression. NODE and EDGE expressions in particular invoke the data producer connected to fetch the graph nodes and edges to match (lines 8 and 14). The candidate AKG nodes and edges returned by the data producer are matched against the query expression by the matcher. The evaluation of filters (constraints) is delegated to an abstract filter.test function. All the other AQL expressions have been implemented by specific blocks integrated to the backbone of this algorithm.

eval is initially called with an index i pointing to the first query expression (i ← 0) and an empty environment env. All along the query evaluation, KGRAM stores node bindings and keeps track of all (partial) homomorphisms found between the AQL expressions and the data graph in env. The eval function processes each expression in the context of the known environment, which is progressively enriched so that partial matches found are taken into account when evaluating subsequent elementary expressions in the expressions tree. At the end of the evaluation, the homomorphisms are complete and returned as a list of bindings from the environment (line 3). Consider the query given in Figure 3 as an example. The query engine would first search for relations between authors and documents they wrote, returning a number of bindings for the pair of nodes authordocument in the environment. It would then query for relations between the only documents found in the previous step and their title. In case a partial homomorphism represented by the current environment cannot be completed, a backtrack in the evaluation function invocation stack reverses to a previous level in the expressions tree to enumerate new candidates and filter them in an updated environment.

Algorithm 1: Simplified KGRAM query algorithm.

Variables: producer data producer interfaced to KGRAM engine matcher graph homomorphisms matcher f ilter filter evaluator used Arguments: queryArray array of all query elements to be evaluated iteratively, i index of the query element currently evaluated, env environment Result: list of bindings found 2) Querying multiple data sources: The KGRAM query evaluation algorithm makes it easy to query multiple data sources through multiple data producers. A meta-producer, interfaced to KGRAM's QE on the one side and to multiple data producers on the other side, just forwards the graph node and edge queries of Algorihm 1 (lines 8 and 14) to all attached producers before merging all resulting bindings received into the QE's environment.

3) Remoting Producers: The implementation of the data producer and its capability to return only the necessary AKG components to the QE is critical regarding the performance of KGRAM, especially in a distributed environment were data sources are remote and the AKG components are communicated over the network. In a naive implementation, a remote producer can passively deliver all AKG nodes and edges corresponding to its entire data base for matching and filtering by the QE. However, this strategy is fairly inefficient in case a large number of the AKG components sent to the QE are later on discarded by the matcher or the filter evaluator. Consequently, the interface to the producer also includes parameters to transfer the known bindings and the AQL filters to it. This allows for the implementation of advanced operations such as source data filtering and partial matching inside data producers.

C. Query optimizations

KGRAM implements various strategies to minimize the query processing time. Some performance optimizations are structural, related to the AQL design and the query algorithm parallelization. Some are related to the QE planning capability. Others are related to the work locally performed by the Data Producers. KGRAM's QE internally implements optimizations and its interface was designed to allow for advanced and asynchronous Data Producer implementations.

1) Nodes search: The ability of KGRAM to evaluate homomorphisms both by node search and edge search allows for optimizing the process of query languages which make use of the NODE expression.

2) Exploiting partial query results: Partial query results may be exploited to improve the processing of subqueries that have not yet been performed. In particular, KGRAM implements bind joins. The QE manages an index of already known mappings between variables and values in its environment. These already known bindings are transferred to the data producer at query runtime for exploitation when querying the native data source. As a consequence, the order in which subqueries are evaluated may have a significant impact on performance. KGRAM favors evaluation of selective queries which return few candidates first to reduce the amount of data that will be transferred and joined at the level of the QE. The heuristics consists in considering first, edges with fewer candidates through a pre-analysis of the graph edges cardinality. Edges are also reordered in such a way that, whenever possible, two consecutive edges share at least a node.

3) Backtracking: When a partial homomorphism cannot be completed, the recursive evaluation function call stack is backtracked, restoring former states of the corresponding environment. A backtrack optimization called backjump was implemented, enabling several backtrack levels in the query stack and the associated environment simultaneously. A backjump returns directly to an expression whose evaluation provides a new binding for at least one query node from the last expression that just failed.

4) Asynchronous querying: Concurrent querying of multiple data sources is an obvious optimization when querying multiple data sources, especially if these are remotely located and each source query is processed by different computing units. A parallel meta-producer that implements a meta-producer connected to data sources queried simultaneously rather than sequentially was added to the KGRAM software suite.

Algorithm 2 shows the simplified distributed query processor. The algorithm consists in iterating over each NODE or EDGE expression in the initial AQL query (line 1) and, for each one, in concurrently querying all remote Producers (line 3). The meta-producer then waits for all connected endpoints to finish through a synchronization barrier (line 4). Results are finally accumulated for the current NODE or EDGE expression (lines 5 and 6) and the iteration over the next expression can be processed (line 1). To soften the synchronization barrier a pipelining approach was adopted, in which a synchronized blocking queue allows the metaproducer to post-process results as soon as they become available. Due to space limitation, the pipelining algorithm is not detailed.

5) Data sources selection: KGRAM is using a cache index dynamically created with SPARQL ASK queries, which prevents from unnecessary communications. For each AQL edge expression, this index stores the identified data sources providing candidate AKG edges and thus potentially contributing to the result set. By using its index, KGRAM is able to send remote edge requests only to data sources hosting candidates. This strategy is specially adapted for Linked Data querying scenarios where each remote data source is specialized into one kind of data.

IV. REFERENCE IMPLEMENTATION KGRAM principles have been operationalized in several java implementations. As a proof of concept, a first implementation was first provided with the existing Corese1 search engine, based on the Conceptual Graphs model. A Jenabased2 [START_REF] Mcbride | Jena: A semantic web toolkit[END_REF] implementation has also been experimented by creating lightweight KGRAM API adaptors for Jena objects. The last version of KGRAM is a reference implementation available from the Web 3 , based on an optimized specific implementation of the AKG model. The KGRAM software suite is architected as a modular and versatile toolbox aimed at covering many use cases such as heterogeneous data sources manipulation, access to remote data stores, data source querying and inference processing (RDFS entailments and rule-based inferences) on data sources. KGRAM's modules deployment can easily be tailored to address different data querying and federation scenarios. The reference implementation particularly focuses on SPARQL 1.1, which is the de facto query language for knowledge graphs models like RDF.

A. Modular Software Architecture

The KGRAM software architecture is modular with clearly defined, decoupled interfaces and extensible components. KGRAM's QE and Data Producers (see Section II) are implemented as decoupled modules communicating through a Producer interface to exchange AQL queries and AKG results. The QE adapts to different input Query Languages through a query language Parser interface. The AQL language can be finely customized through the Matcher and Filter interfaces. A Matcher computes edge homomorphisms. Depending on its implementation, labels comparison may e.g. consist in simple string equality, or take into account class and property subsumption, compute approximate matches, etc. A Filter evaluates query constraints by implementing the filtering dialect of the target language.

Figure 4 diagrams a KGRAM QE. The KGRAM API defines abstract interfaces for the different KGRAM modules to interface with each others. The engine has 4 required interfaces and it is completely defined only when the 4 corresponding modules are connected to these interfaces: a data source interfaced through the Producer API, a query parser interfaced through the Query API, a graph matcher interfaced through the Matcher API and a constraint evaluator interfaced through the Filter API. Internally, the KGRAM query engine manipulates a Query Stack used to decompose complex queries into elementary AQL expressions, and an Environment Manager used to accumulate partial AKG results as detailed in section III-B.

B. KGRAM Software Suite

The core QE included in the KGRAM software suite comes with a Parser implementing the SPARQL 1.1 query language, a Matcher exploiting RDFS entailments, and a Filter implementing SPARQL operators and XML Schema datatypes.

Several base Producer implementations are included in the software suite (Figure 5a): for RDF datasets in raw RDF/XML files, N3 text files or Jena databases. The bottom row of Figure 5 also shows three other specific Producer implementations available. The Meta-Producer (5b) is a module enabling the connection to multiple data sources. It exposes a Producer interface and requires multiple other Producer components to be connected through the same interface. It simply forwards input AQL queries to the connected Producers and conversely merges all AKG results delivered by all subsequent Producers before delivering them to the QE. The Producer client (5c) is an interface to any SPARQL remote endpoint. It transforms inbound AQL queries into SPARQL queries that are sent over HTTP to any SPARQL-compliant endpoint. The entities returned by the endpoint are then transformed into AKG results. Since AQL extends SPARQL 1.1, KGRAM can connect to any SPARQL endpoint. However, some AQL queries may not be fully interpreted by a regular SPARQL endpoint as they contain non-SPARQL expressions. KGRAM software suite therefore contains a KGRAM-enabled endpoint as illustrated in Figure (5d). The producer client communicates queries to the KGRAM endpoint using AQL rather than SPARQL. The endpoint uses a local KGRAM instance to parse and evaluate the inbound AQL queries.

KGRAM interfaces are designed in order to minimize the glue code necessary to interface to external software components. As a result, Corese's and Jena's portings to KGRAM required very few source lines of code. Corese's porting was almost immediate because KGRAM was partly designed as an abstraction of Corese principles. Jena's porting has required less than 1000 lines of source code.

C. KGRAM Extensibility Examples

Many different deployment scenarios can be implemented taking advantage of the KGRAM component-based architecture. Both the query language and the knowledge representation language can be customized to different needs through the implementation of various Parsers, data Filters and graph Matchers. Furthermore, any kind of data source can be queried simultaneously, given that a Producer interface can be developed for it. The meta-producer combined with the scalable query algorithm allow for the integration of multiple data sources. To illustrate the versatility of KGRAM architecture, two complex deployments inspired by real data federation platforms are presented below.

Figure 6 illustrates an implementation that was experimented in the context of the ISICIL project 4 . In this scenario, RDF data is distributed over three servers, each of them in charge of inferences on a specific type of data:

(1) social network and user profiles, online communities, activity tracking and trust model; (2) tag model, document metadata, terminologies, thesaurus; (3) Web resource model with low level data such as MIME type, production context, format, duration, etc. Some of the Web applications developed in this context require to query data distributed over these three servers. A KGRAM core component is therefore instantiated with a SPARQL Parser, a Matcher exploiting RDFS entailments, and a Filter supporting XSD datatypes. A Meta-Producer component is connected to this engine to interface to the three data sources, each of them interfaced through an instance of RDF Producer. Another complex deployment, inspired by the NeuroLOG collaborative platform for neurosciences 5 , is illustrated in Figure 7. The use case is the joint querying of two heterogeneous data sources (an SQL and an RDF repository) located at different places, using the SPARQL query language. Similarly to the previous case, a SPARQL-enabled KGRAM query engine interfaced to a meta-producer is deployed. To access remote data sources, KGRAM endpoints and their associated Producer clients are used. Each endpoint is connected to a specific Producer (SQL or RDF data producer) adapting to the site data source.

V. EVALUATION

There are several facets of the KGRAM query system to consider for evaluation. First, the strict compliance of the reference implementation to standards is evaluated with the W3C SPARQL 1.1 Query Test case 6 (with 4 failures over 465 queries). Second, AQL's and AKG's high expressiveness is illustrated (V-A). Third, KGRAM's performance is evaluated by comparison to a high-performance relational databases federation product (V-B). Finally, KGRAM's ability to implement complex Linked Data scenarios is demonstrated (V-C).

A. Expressiveness

The seminal NeuroLOG platform (see Section IV-C) was federating relational databases from 5 neuroscience centers in France. Its data federation layer was based on the high-performance DataFederator commercial tool 7 . The platform was re-deployed using KGRAM for data federation. KGRAM can access NeuroLOG relational databases either directly through an ad-hoc SQL Producer, or indirectly through its RDF Producer after conversion of the relational database into an RDF repository with the METAMorphoses mapping tool 8 . For this experiment, the SQL Producer is a simple component implementing query-specific mappings allowing to retrieve SQL entities matching the query and transform them into AKG entities.

Query Q 1 from Figure 8 is a real use-case that searches for image datasets (acquired with Gadolinium contrast agent) associated to patients (join in line 4) in the context of multicentric studies addressing the Multiple Sclerosis pathology. During the evaluation of this query, all types of medical images are searched in order to match a "GADO" tag associated to the use of the Gadolinium contrast agent. The evaluation of this query can be considerably enhanced by exploiting clinical knowledge represented in an RDFS vocabulary. Indeed Gadolinium is used in the context of magnetic resonance (MR) acquisitions (T1 or T2 weighted MRIs for instance) but generally not in the context of any other modality (Ultrasound for instance). By exploiting this domain knowledge, the query time can be significantly reduced since all non-MR datasets (such as Ultrasound datasets) are excluded. In other words, when provided with an implementation of its Matcher interface supporting RDFS entailments, KGRAM performs inferences which enables it to produce better query results.

When provided with a SPARQL 1.1 Parser, KGRAM provides a mean to handle the intelligent distribution of ?dataset linguistic-expression:has-for-name ?dsName .

3

?patient examination-subject:has-for-subject-identifier ?clinID .

4

?patient iec:is-referred-to-by ?dataset .

5

?study study:involves-as-patient ?patient . highly expressive and graph-based semantic queries. For instance, let us consider again query Q 1 . Line 2 and 4 of Figure 8 could be merged with the following PATH expression:

?patient iec:is-referred-to-by / linguistic-exp:has-for-name ?dsName.

KGRAM thus allows to perform this kind of graph-based information retrieval which cannot be easily expressed in SQL.

B. Performance Evaluation

The query processing time using the seminal NeuroLOG relational databases federator was experimentally compared against KGRAM's heterogeneous platform deployment (federating both RDF-converted and original SQL data sources). Two queries are considered below: Q 1 , introduced in Section V-A, and Q 2 , a query for datasets acquired through the T2-weighted MRI modality. It is a very selective query, leading to 5 results only, on the data sources considered. To be robust against variability observed in real distributed systems, results are averaged over three query runs. The average query execution time ± one standard deviation is displayed in the following table. For Q 1 , leading to 336 remote invocations, the query times are better with the optimized SQL federation engine than with KGRAM, but it remains in the same order of magnitude. For the very selective query Q 9 illustrates a SPARQL query which is evaluated by KGRAM by first listing all NeuroLEX subclasses of MRI protocol and their associated labels, and then exploiting these labels to search for relevant medical images, and their corresponding patients provided by the distributed NeuroLOG data providers. There exist many neuroscience ontologies with different modeling objectives. Some may focus on particular pathologies while others may focus on neuro-data processing. In that context, and beyond the neuroscience area, the KGRAM query system provides a transparent mean to query distributed and heterogeneous data sources while potentially benefiting from several ontologies. This is possible by implementing the Producer associated to each data source and its ontology with a Matcher exploiting RDFS entailment.

VI. RELATED WORKS

The recent W3C working draft [START_REF] Prud'hommeaux | SPARQL 1.1 Federated Query[END_REF] addresses the federated querying of distributed knowledge bases through a set of language extensions for SPARQL 1.1 (SERVICE and VAL-UES clauses). These clauses help implementing distributed query processing when the content of data sources is well partitioned and known at query design time. However, this assumption does not hold in many real use cases. This approach is not suitable in the context of dynamic knowledge base federations in which pre-designed SPARQL 1.1 queries must be adapted to take into account the data source availability.

Transparent federating approaches generally address performance issues. For instance, DARQ [START_REF] Quilitz | Querying distributed rdf data sources with sparql[END_REF], SPLENDID [START_REF] Görlitz | SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions[END_REF] or FedX [START_REF] Schwarte | Fedx: optimization techniques for federated query processing on linked data[END_REF] propose a set of static and dynamic optimizations. KGRAM implements similar strategies to exploit value constraints at query rewriting time and to enhance the querying at runtime through bind joins. Moreover, in line with FEDX, KGRAM also exploits the parallelism of both distributed data sources and modern multi-core CPUs with a multithreaded implementation. However, there remain several optimization opportunities for KGRAM. Query planning techniques implementing data sources selection could be applied. Block bind joins introduced by FEDX also seem to be an interesting optimization.

Addressing the SPARQL querying of traditional relational databases through a declarative language, Bizer et al. highlight in [START_REF] Bizer | D2rq -lessons learned[END_REF] the user needs for both weird mappings, and real data federation (not directly addressed through D2RQ). In line with these observations, KGRAM could benefit from this approach by letting legacy database experts populate remote producers with relational to semantic RDF mappings.

To the best of our knowledge, none of the state-of-the art transparent federating approaches address both the issues of efficient and heterogeneous distributed querying. The strength of the KGRAM model is to provide a versatile and extensible framework allowing to efficiently and dynamically access to multiple data sources internally using different data representations.

VII. CONCLUSION

The KGRAM software suite is architected following a modular approach to ease the integration of multiple, heterogeneous and distributed data sources. Its modules can easily be combined to architect and deploy dedicated platforms tackling specific data query use cases while delivering high query performance. KGRAM's versatility makes it a strong candidate to tackle the challenges raised by the Web of Linked Data.

KGRAM is an abstract machine for querying knowledge graph models. Its Abstract Query Language covers, but is not limited to, the SPARQL 1.1 standard graph-based query language promoted by the W3C. It also enables querying and reasoning through other forms of graph pattern matching such as Conceptual Graphs Homomorphism. Through AQL, KGRAM can easily adapt to future language evolutions and provides an extensible framework in which new language constructs can be experimented.

Although most SPARQL clauses, including SELECT-WHERE and CONSTRUCT, are supported while querying distributed heterogeneous data sources, the UPDATE clause is more challenging and its implementation covers local RDF sources only. This opens new data coherency challenges to tackle when considering knowledge graph insertions, deletions, or modifications in a distributed setup.

Figure 3 .

 3 Figure 3. Query example.

Algorithm 2 : 4 wait for scheduler ; 5 foreach

 245 Simplified parallel distributed query processing. Data: P roducers set of data source endpoints, Exp set of node and edge expressions from the AQL query, scheduler a thread pool allowing for parallel execution. Result: Results the set of AQL query results. 1 foreach (e ∈ Exp) do 2 foreach (p ∈ P roducers) do in parallel 3 scheduler.submit(p.matchingExp(e)) ; (task ∈ scheduler.getFinished()) do 6 Results ← task.getResults() ;

Figure 4 .Figure 5 .

 45 Figure 4. KGRAM core query engine

Figure 6 .

 6 Figure 6. ISICIL data query architecture federating 3 RDF stores.

6 FILTERFigure 8 .

 68 Figure 8. Sample SPARQL query Q 1 : search for patient, study and dataset information in the context of the Multiple Sclerosis disease.

4 ? 5 ? 6 ?Figure 9 .

 4569 Figure 9. Sample SPARQL query exploiting the NeuroLex taxonomy for medical image modalities to search images for the NeuroLOG platform

Figure

  Figure9illustrates a SPARQL query which is evaluated by KGRAM by first listing all NeuroLEX subclasses of MRI protocol and their associated labels, and then exploiting these labels to search for relevant medical images, and their corresponding patients provided by the distributed NeuroLOG data providers. There exist many neuroscience ontologies with different modeling objectives. Some may focus on particular pathologies while others may focus on neuro-data processing. In that context, and beyond the neuroscience area, the KGRAM query system provides a transparent mean to query distributed and heterogeneous data sources while potentially benefiting from several ontologies. This is possible by implementing the Producer associated to each data source and its ontology with a Matcher exploiting RDFS entailment.

  parts, 151 spinal cord parts and 30 other parts of the nervous system). It aims at delivering a standard lexicon for neuroscience entities, covering their meaning and their classification, to address data integration issues generally faced in the neuroscience area.In this Linked Data experiment, neuroimaging data from the NeuroLOG federation is linked with neuroscience "open" knowledge capitalized through the NeuroLex initiative, allowing neuroscientists involved in the NeuroLOG federation to benefit from the NeuroLex lexicon and its semantic wiki interface. Thanks to KGRAM versatility, the NeuroLOG platform is easily extended with a new data source that exposes the NeuroLex ontology. To bridge the two knowledge bases, an ad-hoc semantic alignment was implemented, in which all NeuroLOG datasets are annotated with the NeuroLex Label property corresponding to their medical image modality.

				which describes 18,490 neuroscience terms (287 neurons
				and 952 brain
		2 , we observe comparable query times for
	both environments. Query	KGRAM relational federator
	Q 1	11.76 s ± 0.05	3.03 s ± 0.25
	Q 2	1.53 s ± 0.14	1.52 s ± 0.62
	C. Linked Data Scenario
	NeuroLex [7], supported by The Neuroscience Infor-
	mation Framework and the International Neuroinformatics
	Coordinating Facility, is a dynamic Neuroscience Lexicon

1 SELECT ?patient ?dataset WHERE { 2 ?t property:Label \"MRI protocol\"ˆˆxsd:string .

3

?s rdfs:subClassOf * ?t .

Corese: http://wimmics.inria.fr/corese

Jena: http://jena.sourceforge.net

KGRAM: http://wimmics.inria.fr/corese

ISICIL: http://isicil.inria.fr

NeuroLOG: http://neurolog.polytech.unice.fr

W3C tests: http://www.w3.org/2009/sparql/docs/tests

SAP DataFederator: http://www.sap.com/solutions/sapbusinessobjects/ large/eim/datafederator/index.epx

ACKNOWLEDGMENT

This work is partly funded by the French National Agency for Research under grant ANR-09-COSI-03 "VIP".


Alvaro O Ardiles

Cheril C Tapia-Rojas

Madhuchhanda Mandal

Frédéric Alexandre

Alfredo Kirkwood
email: kirkwood@jhu.edu

Nibaldo C Inestrosa

Adrian G Palacios
email: adrian.palacios@uv.cl

Álvaro O Ardiles

Frederic Alexandre

Postsynaptic dysfunction is associated with spatial and object recognition memory loss in a

/body

Alzheimer's disease (AD) is an age-related neurodegenerative disorder characterized by the accumulation of abnormally processed proteins in neurofibrillary tangles (NFTs) and senile plaques [START_REF] Selkoe | Alzheimer's disease: genes, proteins, and therapy[END_REF]. These lesions are present in both familial and sporadic forms of AD. Familial AD is linked to inherited mutations in AD-related genes and represents a small percentage of AD cases, while sporadic AD represents the vast majority of cases and is not inherited.

Results from transgenic mice bearing mutations in APP, Psen1/2, and Tau show synaptic dysfunction in early stages of AD, before overt neurodegeneration [START_REF] Gotz | Animal models of Alzheimer's disease and frontotemporal dementia[END_REF][START_REF] Philipson | Animal models of amyloid-beta-related pathologies in Alzheimer's disease[END_REF]. More recent studies have underscored a critical role for soluble Aβ oligomers as an early trigger for AD, and they have been associated with memory and neural plasticity loss [START_REF] Jacobsen | Early-onset behavioral and synaptic deficits in a mouse model of Alzheimer's disease[END_REF][START_REF] Selkoe | Soluble oligomers of the amyloid beta-protein impair synaptic plasticity and behavior[END_REF][START_REF] Klein | Targeting small Abeta oligomers: the solution to an Alzheimer's disease conundrum?[END_REF][START_REF] Lesne | A specific amyloid-beta protein assembly in the brain impairs memory[END_REF][START_REF] Shankar | Amyloid-beta protein dimers isolated directly from Alzheimer's brains impair synaptic plasticity and memory[END_REF].

Although transgenic mice have been extremely useful in elucidating the pathological mechanisms of AD, there have been substantial limitations. Several examples are the absence of tau mutations linked to AD, except for a triple transgenic mouse 3xTg-AD, bearing mutations for APP, Psen1/2, and Tau [START_REF] Oddo | Triple-transgenic model of Alzheimer's disease with plaques and tangles: intracellular Abeta and synaptic dysfunction[END_REF], the inability to develop the whole spectrum of the disease, the overexpression of transgenes into a non physiological scenario, and the fact that the manipulated genes represent only familial, and not sporadic forms of AD [START_REF] Duff | Transgenic mouse models of Alzheimer's disease: how useful have they been for therapeutic development?[END_REF][START_REF] Dodart | Does my mouse have Alzheimer's disease?[END_REF]. It would be highly desirable to have a non-transgenic model of AD to complement those that already exist. Several species naturally develop features of AD with age; however, their usefulness is still limited as they do not exhibit the full spectrum ADrelated alterations [START_REF] Johnstone | Conservation of the sequence of the Alzheimer's disease amyloid peptide in dog, polar bear and five other mammals by cross-species polymerase chain reaction analysis[END_REF][START_REF] Sarasa | Natural non-trasgenic animal models for research in Alzheimer's disease[END_REF][START_REF] Braidy | Recent rodent models for Alzheimer's disease: clinical implications and basic research[END_REF]. For example the Aβ peptide sequences of Cavia porcellus (guinea pig) and Microcebus murinus are similar to that of human [START_REF] Beck | Guinea pigs as a nontransgenic model for APP processing in vitro and in vivo[END_REF][START_REF] Bons | Microcebus murinus: a useful primate model for human cerebral aging and Alzheimer's disease?[END_REF], but the first fails to develop senile plaques and NFTs [START_REF] Beck | Guinea pigs as a nontransgenic model for APP processing in vitro and in vivo[END_REF] and experiments examining synaptic function and memory have not been carried out in such models. A promising candidate model for 4 sporadic AD is the rodent Octodon degus (degus) which naturally develops the histochemical hallmarks of AD, including intra and extracellular accumulation of amyloid plaques, tau deposition in NFT [START_REF] Inestrosa | Human-like rodent amyloid-beta-peptide determines Alzheimer pathology in aged wild-type Octodon degu[END_REF], along with hippocampal disconnection and brain parenchyma pathology [START_REF] Van Groen | Age-related brain pathology in Octodon degu: blood vessel, white matter and Alzheimer-like pathology[END_REF]. Prompted by these preliminary observations, we examined the neuropathological spectrum of AD in degus. Here we report that degus exhibits an agerelated accumulation of soluble Aβ oligomers and tau protein phosphorylation that correlates with cognitive decline in spatial (T-maze) and object recognition (ORM) memory, as well as synaptic and neural plasticity dysfunction. Based on these findings we propose that i) Aβ dodecamers (Aβ*56), may associate with phosphorylated tau proteins, constituting an early candidate for the neural toxicity and synaptic dysfunction that occurs before the appearance of fibrillar forms of Aβ, which are common to familial and sporadic forms of AD and ii) degus is a suitable non-transgenic model of sporadic AD.

Results

The degree of neuropathology was evaluated at the behavioral, synaptic, and molecular level in degus at different ages. This approach allowed us to establish interpretative correlations to identify those animals suffering from AD (See Table S2).

Age-related cognitive decline during aging in Octodon degus

We first evaluated memory capacity with ORM and T-maze tests in 6, 12, 36, and 60 month old (mo) degus. The results show no significant difference between 6 and 12 mo or 36 and 60 mo degus and therefore animals were considered as young (6 and 12 mo) and aged (36 and 60 mo) (Fig. 1B andD). In the T-maze, the aged degus showed a decreased performance compared to young degus (Fig. 1A-B; ANOVA, p < 0.0001). In the ORM, aged degus explored less (total time and number of visits) and had longer latency than younger animals (Table S1). Moreover, unlike young degus, aged degus did not show a preference between new and familiar objects (Fig. 1C-D; t-test p < 0.0001). In general, we observed an age dependent decline in memory performance beginning at 36 mo and maintained through 60 mo (Fig. 1B).

Selective postsynaptic dysfunction induces impairments in synaptic transmission and plasticity.

We next examined the synaptic basis of these learning deficits by evaluating the strength and plasticity of the CA3-CA1 synapses in hippocampal slices prepared from behaviorally tested individuals. Basal excitatory synaptic transmission was reduced in aged degus compared to young degus (Fig. 2A1; ANOVA, p < 0.0001). Importantly, the fEPSP slopes, but not fiber volley amplitude, were significantly reduced in aged animals (Fig. 2A2-A3; repeated measures ANOVA, p < 0.0001). This is consistent with findings reported in AD mouse models overexpressing mutant forms of APP [START_REF] Fitzjohn | Age-related impairment of synaptic transmission but normal long-term potentiation in transgenic mice that overexpress the human APP695SWE mutant form of amyloid precursor protein[END_REF][START_REF] Hsia | Plaque-independent disruption of neural circuits in Alzheimer's disease mouse models[END_REF] (see [START_REF] Wang | Consequences of inhibiting amyloid precursor protein (APP) processing enzymes on synaptic function and plasticity[END_REF] for a review). The reduced transmission could be due to a reduced postsynaptic responsiveness or to a reduced probability of neurotransmitter release. We ruled out the latter possibility because no difference in the paired-pulse facilitation ratio was observed (Fig. 2B1-B2; 2-way ANOVA, p > 0.05). To confirm the likely postsynaptic basis for the reduced fEPSPs, we recorded AMPA receptor (AMPAR)-mediated miniature EPSCs (mEPSCs). Consistent with previous reports in AD models [START_REF] Chang | AMPA receptor downscaling at the onset of Alzheimer's disease pathology in double knockin mice[END_REF][START_REF] Ting | Amyloid precursor protein overexpression depresses excitatory transmission through both presynaptic and postsynaptic mechanisms[END_REF], we found that the amplitude but not the frequency of mEPSCs was significantly reduced in aged degus (Fig. 2C1-C3; t-test, p < 0.01), suggesting a change in AMPAR function or number. The electrophysiological analysis indicated that aging preferentially affects postsynaptic processes in degus. Therefore, we further assessed the integrity of pre-and postsynaptic elements by measuring the levels of critical synaptic proteins extracted from hippocampal slices by Western blot. We found a selective reduction in PSD-95, AMPAR subunit GluR2, and NMDA receptor (NMDAR) subunit NR2B expression (Fig. 3A-B; ANOVA, p < 0.05), but not of synaptophysin in accordance with the observation that measures of presynaptic function (FV and PPF) are not affected in aged degus (Fig. 2A2 andB1). These data support the observation that postsynaptic integrity is more vulnerable in aged degus.

Next we assessed whether synaptic plasticity was affected at the CA3-CA1 synapses of the aged degus. We measured long-term potentiation (LTP) induced with theta-burst stimulation (TBS) and long-term depression (LTD) induced with paired pulse-low frequency stimulation (ppLFS). The LTP amplitude in aged degus was significantly decreased after 60 min compared to that of young degus (Fig. 4A; 2-way ANOVA, p < 0.0001), whereas LTD was slightly but significantly increased in aged degus compared to young (Fig. 4B, 2-way ANOVA, p < 0.0001). Prompted by the fact that neural plasticity was similar in the 36 and 60 mo age groups, suggesting a decrease in the progression of neurodegeneration, we examined an additional group of 72 mo degus. At this age, LTP continues to decrease and LTD to increase (Fig. S1A) indicating a further progression of the neuropathology associated with AD. Synaptic plasticity measurements were recorded in behaviorally characterized degus, raising the possibility that synaptic modifications induced during the learning tasks might have affected the subsequent induction of LTP and LTD in slices [START_REF] Middei | Learning discloses abnormal structural and functional plasticity at hippocampal synapses in the APP23 mouse model of Alzheimer's disease[END_REF]. However, the magnitude of LTP and LTD were similar (Fig. S4C-D) in slices from naïve and trained degus, indicating that the effects of previous behavioral testing were minimal.

Increased soluble Aβ oligomers and tau phosphorylation in aged degus.

Growing evidence establishes that soluble Aβ oligomers are involved in synapse and memory impairments in AD [START_REF] Selkoe | Soluble oligomers of the amyloid beta-protein impair synaptic plasticity and behavior[END_REF][START_REF] Klein | Targeting small Abeta oligomers: the solution to an Alzheimer's disease conundrum?[END_REF][START_REF] Lesne | A specific amyloid-beta protein assembly in the brain impairs memory[END_REF]. To identify the nature of different types of Aβ species in hippocampal extracts from young and aged degus, we used a specific mouse anti-Aβ peptide 4G8, followed by immunoblotting [START_REF] Lesne | A specific amyloid-beta protein assembly in the brain impairs memory[END_REF]. Both young and aged degus show similar forms of small Aβ peptides, such as trimers (∼12 KDa), tetramers (∼16 KDa), and hexamers (∼27 kDa), while aged degus displayed higher levels of Aβ dodecamers (∼56 kDa) (Fig. 5A1-A2). The presence of these Aβ dodecamers in the hippocampus was confirmed by a slot blot assay using the specific antioligomeric antibody A11, which detects soluble amyloid assemblies larger than 40 kDa [START_REF] Kayed | Common structure of soluble amyloid oligomers implies common mechanism of pathogenesis[END_REF]. Aged degus showed greater levels of larger Aβ oligomers (A11) when compared to young degus (Fig. 5B).

The number and localization of NFT, but not senile plaques, has been correlated with levels of dementia in AD patients [START_REF] Arriagada | Neurofibrillary tangles but not senile plaques parallel duration and severity of Alzheimer's disease[END_REF]. In order to search for the presence of NFTs, we measured tau phosphorylation at sites known to be present in paired helical filaments (PHFs). The PHF-1 antibody detects tau phosphorylation at serine residues 396 and 404 [START_REF] Otvos | Monoclonal antibody PHF-1 recognizes tau protein phosphorylated at serine residues 396 and 404[END_REF]. Aged degus show an increase in the phosphorylation levels detected by this antibody compared to young degus (Fig. 5C). A similar result was obtained by using AT8 antibody, which detects tau phosphorylation at serine 202 and threonine 205 [START_REF] Goedert | Monoclonal antibody AT8 recognises tau protein phosphorylated at both serine 202 and threonine 205[END_REF], Tau phosphorylation at these sites was only slightly greater in aged degus, including 72 mo degus (Fig. S1B-C), compared to young degus (Fig. 5C). The increase in tau phosphorylation was not simply due to an increase in tau protein levels. Fig. S1 (B-C) shows that the total tau level (Tau-5) remains unchanged up to 72 mo. Furthermore, pathological phosphorylated tau begins to appear first in the cortex and then in the hippocampus, where neurons containing hyperphosphorylated tau were preferentially detected in aged degus (Fig. 7 and S5, also see

Increased levels of soluble Aβ oligomers and tau phosphorylation correlate with behavioral impairment and reduced synaptic plasticity.

Table S2 shows that the 12mer dodecamer (Aβ*56) is highly negatively correlated with LTP magnitude (r 2 = 0.31, p = 0.0012; Fig. 6A), and performance in Tmaze (r 2 = 0.24, p = 0.0048; Fig. 6B), and ORM (r 2 = 0.34, p = 0.0006; Fig. 6C) tasks. Similarly, higher levels of PHF-1 correlate with lower levels of LTP (r 2 = 0.28, p = 0.0020, Fig. 6D), ORM (r 2 = 0.37, p = 0.0003; Fig. 6F), and to a lesser degree with T-maze (r 2 = 0.19, p = 0.0138; Fig. 6E) performance. Comparable correlations were obtained when A11 and AT8 were compared with LTP, T-maze, and ORM performance (Fig. S2). Further analysis using Principal Component Analysis is presented in supplementary information.

Although these results suggest a similar time course and a plausible causal link between the increase in soluble Aβ oligomers, phosphorylated tau, and the incidence of synaptic and cognitive alterations, we cannot rule out the possibility that insoluble amyloid fibrils also participate in this cascade of neurotoxic events. For this reason, we analyzed the presence of amyloid deposition by immunohistochemistry and Thioflavine S (ThS) staining (Fig. 7 and S5). Amyloid plaques were almost absent in young degus and began to appear in the cortex of aged degus at 36-60 mo, whereas plaques did not appear in the hippocampus until over 60 mo (Fig. S5). Just as with ThS-positive plaques, extracellular Aβ immunoreactivity (Fig. 7I) and pathological phosphorylated tau was first observed in the cortex at 36 mo, and later in the hippocampus after 60 mo (Fig. 7J).

Results here indicate that during aging, degus develop a neurodegenerative AD-like condition. In the initial stages, increased A oligomers and phosphorylated tau levels could explain the impairments in learning, memory, and neural plasticity capacities; whereas in later stages (after 72 mo), progressive deposition of plaques and tangles, likely leading to neurodegeneration, occurs which could aggravate or accelerate the symptomatology of AD in degus (Fig. 7,S1,S5).

Throughout this study we noted that the behavioral, functional, and molecular alterations did not proceed at a uniform pace during aging. Rather, most of the changes occurred between 12 and 36 mo. To identify the most critical factors involve in the progression of AD, we performed a Principal Component Analysis (PCA) (Table S2; variables with correlation ≥ 0.50: LTP, Tmaze, ORM, Aβ*56, PHF1, GluR2, and NR2b). Interestingly, the plot in the space of the first two principal components (PC1 vs PC2) clearly segregates two different classes of individuals: young and aged degus (Table S4, Fig. S3), supporting the importance of the variables examined here. Finally, it is important to note that although most of the deficits develop between 12 and 36 months, the changes are not homogeneous in this population. About 25% of individuals aged 36-months exhibit "unimpaired" performance either in the spatial or the object recognition memory test [START_REF] Ponce | Aging and Spatial Memory in the rodent Octodon degus[END_REF]. This variability would be expected for a group of non-transgenic animals where the genetic background comes from a controlled but natural population.

Discussion

Synaptic and cognitive dysfunction in AD models has revealed significant impairments before neurodegeneration becomes evident [START_REF] Fitzjohn | Age-related impairment of synaptic transmission but normal long-term potentiation in transgenic mice that overexpress the human APP695SWE mutant form of amyloid precursor protein[END_REF][START_REF] Hsia | Plaque-independent disruption of neural circuits in Alzheimer's disease mouse models[END_REF][START_REF] Chang | AMPA receptor downscaling at the onset of Alzheimer's disease pathology in double knockin mice[END_REF][START_REF] Ting | Amyloid precursor protein overexpression depresses excitatory transmission through both presynaptic and postsynaptic mechanisms[END_REF]. We show here a clear correlation between high levels of A *56 oligomers and tau phosphorylation with the reduction of synaptic strength and plasticity (Fig. 6 andS3). However, the exact mechanism by which A oligomers or phosphorylated tau might impair synaptic function is still under debate. Some studies have reported that the addition of A oligomers is sufficient to impact synaptic function in vitro [START_REF] Selkoe | Soluble oligomers of the amyloid beta-protein impair synaptic plasticity and behavior[END_REF][START_REF] Palop | Amyloid-beta-induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks[END_REF][START_REF] Larson | Soluble Abeta oligomer production and toxicity[END_REF] as well as alter cognitive function [START_REF] Lesne | A specific amyloid-beta protein assembly in the brain impairs memory[END_REF][START_REF] Cleary | Natural oligomers of the amyloid-beta protein specifically disrupt cognitive function[END_REF]. Other studies provide evidence that chemical reactions occurring during the process of A aggregation produce toxic species like reactive oxygen species [START_REF] De Felice | Abeta oligomers induce neuronal oxidative stress through an N-methyl-D-aspartate receptor-dependent mechanism that is blocked by the Alzheimer drug memantine[END_REF][START_REF] Cenini | Generation of reactive oxygen species by beta amyloid fibrils and oligomers involves different intra/extracellular pathways[END_REF]. In that context, degus could provide a valuable "natural model" to test toxicity mechanisms.

Recent evidence suggests that soluble A oligomers (also referred to as ADDLs) may induce synaptic failure as an early event related to memory deficits in AD [START_REF] Klein | Targeting small Abeta oligomers: the solution to an Alzheimer's disease conundrum?[END_REF][START_REF] Lacor | Abeta oligomer-induced aberrations in synapse composition, shape, and density provide a molecular basis for loss of connectivity in Alzheimer's disease[END_REF]. Indeed, it has been observed that soluble Aβ particular affects synapses by selectively targeting postsynaptic components [START_REF] Lacor | Abeta oligomer-induced aberrations in synapse composition, shape, and density provide a molecular basis for loss of connectivity in Alzheimer's disease[END_REF][START_REF] Lacor | Synaptic targeting by Alzheimer's-related amyloid beta oligomers[END_REF]. More recently it has been reported that cellular prion protein (PrP C ) functions as a receptor for Aβ oligomers [START_REF] Lauren | Cellular prion protein mediates impairment of synaptic plasticity by amyloid-beta oligomers[END_REF]. Once bound to the membrane, these oligomers tend to accumulate at excitatory synapses forming clusters with metabotropic glutamate receptors (mGluR5) causing impairments in synaptic plasticity [START_REF] Renner | Deleterious effects of amyloid beta oligomers acting as an extracellular scaffold for mGluR5[END_REF]. Many different lengths and conformational states of Aβ peptide are generated during its biosynthesis, including highly mobile soluble A oligomers, pre-fibrillar, and fibrillar aggregates. These diverse assemblies have been associated with the disruption of memory and synaptic plasticity. For instance, dimers [START_REF] Shankar | Amyloid-beta protein dimers isolated directly from Alzheimer's brains impair synaptic plasticity and memory[END_REF][START_REF] Walsh | Naturally secreted oligomers of amyloid beta protein potently inhibit hippocampal long-term potentiation in vivo[END_REF], trimers [START_REF] Townsend | Effects of secreted oligomers of amyloid beta-protein on hippocampal synaptic plasticity: a potent role for trimers[END_REF] and dodecamers [START_REF] Lesne | A specific amyloid-beta protein assembly in the brain impairs memory[END_REF], derived from diverse sources (including chemical synthesis, transfected cells, and mouse and human AD brains), potently impair synaptic plasticity and memory. However, it has been showed that Aβ*56, compared to others assemblies, has the greatest effect on memory [START_REF] Lesne | A specific amyloid-beta protein assembly in the brain impairs memory[END_REF][START_REF] Cheng | Accelerating amyloid-beta fibrillization reduces oligomer levels and functional deficits in Alzheimer disease mouse models[END_REF][START_REF] Reed | Cognitive effects of cell-derived and synthetically derived Abeta oligomers[END_REF] and has been proposed to be the key neurotoxic non-fibrillar assembly in AD because it is highly stable and prone to aggregate [START_REF] Bernstein | Amyloid-beta protein oligomerization and the importance of tetramers and dodecamers in the aetiology of Alzheimer's disease[END_REF]. In the same way, we observed in degus a stronger inverse relationship between increasing levels of Aβ*56 and LTP, T-maze, and ORM performance, which becomes more severe during aging (Fig. 6). Strikingly, aged degus also show an increase in the levels of phosphorylated tau (Fig. 5C,S5), suggesting a functional link between Aβ processing and tau phosphorylation, since phosphorylated tau residues also negatively correlate with LTP and cognitive impairments (Fig. 6). Several reports have shown that in the hippocampus of mice, an increase in soluble Aβ by local administration increases the level of phosphorylated tau proteins, producing cognitive impairments [START_REF] Pavia | Alzheimer's disease: relationship between muscarinic cholinergic receptors, beta-amyloid and tau proteins[END_REF][START_REF] Oddo | Reduction of soluble Abeta and tau, but not soluble Abeta alone, ameliorates cognitive decline in transgenic mice with plaques and tangles[END_REF][START_REF] Rank | Direct interaction of soluble human recombinant tau protein with Abeta 1-42 results in tau aggregation and hyperphosphorylation by tau protein kinase II[END_REF]. Furthermore when both proteins decrease, a recovery of cognitive abilities was observed [START_REF] Oddo | Reduction of soluble Abeta and tau, but not soluble Abeta alone, ameliorates cognitive decline in transgenic mice with plaques and tangles[END_REF]. Interestingly, direct interaction between tau proteins and Aβ peptides induces tau aggregation and hyperphosphorylation [START_REF] Rank | Direct interaction of soluble human recombinant tau protein with Abeta 1-42 results in tau aggregation and hyperphosphorylation by tau protein kinase II[END_REF]. Furthermore, it has recently been shown that A oligomers cause missorting and phosphorylation of tau [START_REF] Zempel | Abeta oligomers cause localized Ca(2+) elevation, missorting of endogenous Tau into dendrites, Tau phosphorylation, and destruction of microtubules and spines[END_REF], microtubule destabilization, and axonal transport disruption [START_REF] Decker | Amyloid-beta peptide oligomers disrupt axonal transport through an NMDA receptor-dependent mechanism that is mediated by glycogen synthase kinase 3beta in primary cultured hippocampal neurons[END_REF]. On the other hand, Aβinduced impairments in LTP are mediated by tau phosphorylation suggesting that tau proteins are one element required for the synaptotoxic effects of Aβ oligomers [START_REF] Shipton | Tau protein is required for amyloid beta-induced impairment of hippocampal long-term potentiation[END_REF].

Age-related reductions in spontaneous and evoked AMPAR-mediated currents have also been observed in AD models, which are attributed to a reduction in the number of these receptors [START_REF] Chang | AMPA receptor downscaling at the onset of Alzheimer's disease pathology in double knockin mice[END_REF][START_REF] Ting | Amyloid precursor protein overexpression depresses excitatory transmission through both presynaptic and postsynaptic mechanisms[END_REF]. We did not observe differences in the frequency of mEPSCs, suggesting that in 60 mo degus the number of functional synapses is not reduced. However, a possible explanation for the observed decrease in mEPSCs amplitude is that the content of AMPARs available for trafficking is impaired in aged degus. Certainly, Aβ peptides prevent recruitment and anchoring of AMPARs to the postsynaptic compartment by reducing CAMKII activation and distribution as well as PSD-95 levels [START_REF] Gu | {beta}-Amyloid impairs AMPA receptor trafficking and function by reducing Ca2+/calmodulin-dependent protein kinase II synaptic distribution[END_REF][START_REF] Almeida | Beta-amyloid accumulation in APP mutant neurons reduces PSD-95 and GluR1 in synapses[END_REF][START_REF] Kamenetz | APP processing and synaptic function[END_REF]. Together, these results suggest that postsynaptic compartments are more susceptible to the effects of Aβ peptides than are presynaptic elements, considering that both PSD-95 and glutamate receptors were reduced in aged degus (Fig. 2 and4). However, presynaptic mechanisms cannot be totally excluded since APP [START_REF] Ting | Amyloid precursor protein overexpression depresses excitatory transmission through both presynaptic and postsynaptic mechanisms[END_REF], Aβ [START_REF] Lazarov | Evidence that synaptically released beta-amyloid accumulates as extracellular deposits in the hippocampus of transgenic mice[END_REF], and presenilin (1/2) (53) have been reported to be localized presynaptically.

An interesting comparison can be made between degus and the 3xTg-AD mice [START_REF] Oddo | Triple-transgenic model of Alzheimer's disease with plaques and tangles: intracellular Abeta and synaptic dysfunction[END_REF]. In both models, the decline in synaptic plasticity can be associated with an increase in Aβ peptides before an increase in tau phosphorylation occurs. Since this process occurs naturally in degus, it represents a unique opportunity to test for physiological mechanism and rescue therapies during sporadic AD.

Another motivating comparison here is with Microcebus murinus, a small nocturnal primate living 8-14 years old in captivity in which 20% of elderly adults show neurodegenerative hallmarks of spontaneous AD, including brain amyloid plaques, tau pathology, decrease of ACh neurons, and behavioral failures with loss of sensory and cognitive functions (review in ( 16)). As in microcebus, 72 mo degus develops a discrete number of Aβ deposits, which are first detectable in the cortex and subsequently in the hippocampus (Fig. 7 and S5, also see Fig. 1 in ( 17)). We have also established changes in neural plasticity (LTP, LTD) in degus that correlate with the presence of soluble Aβ oligomers and phosphorylated tau proteins.

A recurrent and difficult question to address while studying neurodegeneration is its close association with the natural aging process. For example, aged rats show a modest decrease in basal synaptic transmission, NMDAR-mediated response, and deficits in synaptic plasticity [START_REF] Boric | Potential adaptive function for altered long-term potentiation mechanisms in aging hippocampus[END_REF][START_REF] Lee | NMDA receptorindependent long-term depression correlates with successful aging in rats[END_REF][START_REF] Rosenzweig | Impact of aging on hippocampal function: plasticity, network dynamics, and cognition[END_REF] similar to the decreases in both synaptic transmission and plasticity (Fig. 2 and3) seen in degus. However, aged rats do not show a decrease in spatial (T-maze) [START_REF] Rosenzweig | Impact of aging on hippocampal function: plasticity, network dynamics, and cognition[END_REF] or ORM performance [START_REF] Willig | Short-term memory, exploration and locomotor activity in aged rats[END_REF][START_REF] Lukaszewska | Object recognition is not impaired in old rats[END_REF], which is clearly different from the performance observed in degus (Fig. 1).

Together, our findings suggest that degus provides a strong and naturalistic model to study early neurodegenerative process associated with sporadic AD. It is worth mentioning that degus can live around 9-10 years in captivity, so the present work represents only half of a degus lifespan. Finally, while the precise mechanism remains unclear, our data are consistent with the idea that soluble Aβ oligomers at pre-fibrillar stages can act as toxic ligands at postsynaptic compartments, driving the synaptic and memory disruption observed in early AD models.

Materials and methods

See SI Material and Methods for detailed information about all techniques used here. Electrophysiology: Extracellular and whole-cell patch-clamp recordings were performed in hippocampal slices from behaviorally characterized degus as described [START_REF] Boric | Potential adaptive function for altered long-term potentiation mechanisms in aging hippocampus[END_REF][START_REF] Lee | NMDA receptorindependent long-term depression correlates with successful aging in rats[END_REF].

Animals

Immunoblotting: Proteins were run on gradient, denaturing gels, blotted, and probed with appropriate antibodies.

Histology: PFA-fixed brains were sectioned into 20µm slices and free-floating slides were processed following immunohistochemical and ThS staining procedures.

Statistics: All data are presented as mean ± standard error or deviation of the mean (S.E.M. or S.D.). Data analysis was carried out using Prism software (GraphPad Software Inc). degus. A-D: Immunoreactivity for A peptides using the specific antibody 6E10 showed extracellular (black arrows) staining in the hippocampus and the cerebral cortex from degus. A greater number of extracellular insoluble deposits were observed in aged (72 mo, C, D) compared to young (12 mo, A, B) degus. E-H: Immunodetection of pathological phosphorylated tau using the specific antibody AT8 in the hippocampus and cerebral cortex from degus. A greater number of positively stained somas (black arrows) were observed in aged (G, H) compared to young (E, F) degus. I-J: Quantification of A burden (I, percentage of area occupied by 6E10-positive plaques) and the number of AT8-positive cells (J, number of neurons per area) for 6 mo (white, n=2), 12 mo (gray, n=2), 36 mo (blue, n=2) 60 mo (red, n=2) and 72 mo (black, n=2) revealed a significant increase after 36 mo in cortex and after 60 mo in hippocampus. One-way ANOVA (F (4,88) = 238.8, *p < 0.0001 for 6E10-Hip), (F (4,88) = 646.4, *p < 0.0001 for 6E10-Cx), (F (4,88) = 266.0, *p < 0.0001 for AT8-Hip), and (F (4,88) = 134.9, *p < 0.0001 for AT8-Cx) followed by Tukey´s post-hoc test (p < 0.05) compared to 6 mo.

Fig. 1

 1 Fig. 1 in (17)).

:

  Octodon degus were obtained from a breeding colony at the Animal facility of the Universidad de Valparaiso (Approved Animal Welfare Assurance NIH A5823-01). Protocols: We submitted the different groups of degus to a complete characterization including, behavioral tests, electrophysiological recordings and biochemical measures. All of animals, regardless of age, received the same manipulations: before evaluating their cognitive capacity, we first habituate the animals to an open field over 5 consecutive days; next, degus were submitted to an object recognition memory test for 5 days and then to a Tmaze test for 17 days. Once the behaviorally characterization was complete, degus were sacrificed to obtain hippocampal slices from which to study synaptic transmission and plasticity. After completion of the electrophysiological experiments, hippocampal slices were immediately frozen for biochemical characterization. We later collected all the tissue to obtain homogenates from the hippocampus to quantify different proteins levels by immunoblotting. Behavioral tests. Object recognition memory (ORM) was assessed with an open field arena made of black plexiglass (50 cm × 40 cm × 63 cm) during 5 days. Each ORM session consisted of three phases (180 sec each one): (i) Familiarization where degus explored a pair of identical objects; (ii) Retention where the degus is removed for object cleaning and changing; (iii) Recognition where degus explored a pair of different objects: a familiar object (FO) (extra copy of familiar object) and a novel object (NO). To quantitate OMR, a preference index (PI) was calculated by computing PI = NO / NO+FO. Spatial working memory was assessed in a T-maze task using a training protocol to search for a reward during 12 consecutive days. Each session consisted of 10 trials composed of three parts (60 sec each): (i) forced choice memory learning, with no food reward and where one arm remained closed; (ii) retention time, where the animal was removed for cleaning and the closed arm was opened; (iii) free choice for memory recognition, where the two arms are open and the reward was placed in the previously closed arm. A correct response corresponded to a visit to the closed arm during (i), in which case the animal was rewarded with a sunflower seed.

Fig. 1 .

 1 Fig. 1. Age-dependent decline in cognitive performance in O. degus. A: Average number of correct choices per day in the T-maze test. Two-way ANOVA (F (11,33) = 34.59, * p< 0.0001) followed by Bonferroni post-hoc test (p < 0.05) on the last 3 days for aged (36-60 mo) versus young (6-12 mo). B: Average correct choices at the end of the experimental phase from 6 mo (white), 12 mo (gray), 36 mo (blue) and 60 mo (red) degus. One-way ANOVA (F (3,26) = 6.509, *p = 0.002) followed by Tukey´s post-hoc test (p < 0.05). C: Preference index for object recognition. Paired two-tailed t-test (*p < 0.01) novel versus familiar objects. D: Average exploration time for novel vs familiar objects. Paired twotailed t-test (*p < 0.01) versus familiar objects. The values in parentheses indicate the number of animals.

Fig. 2 .

 2 Fig. 2. Altered synaptic transmission and postsynaptic deficits in the Schaffer Collateral-CA1 pathway. Representative traces of fEPSP at different stimulus intensities from 6 mo (white), 12 mo (gray), 36 mo (blue) and 60 mo (red) degus. Scale bars: 1mV, 10 ms. A1: AMPAR mediated input-output curves, from 6 mo (white), 12 mo (gray), 36 mo (blue) and 60 mo (red) degus. One-way ANOVA (F (3,58) = 157.3, *p < 0.0001) followed by Tukey´s post-hoc test (p < 0.05). A2: Relationship between FV amplitude or A3: fEPSP slope and stimulus intensity from 6 mo (white), 12 mo (gray), 36 mo (blue) and 60 mo (red) degus. Repeated measures ANOVA (F (15,45) = 61.06, *p < 0.0001) followed by Bonferroni post-hoc test (p < 0.05). B1: Normal paired-pulse facilitation between groups. B2: Representative traces at interstimulus intervals of 50 ms are shown. Scale bars: 1 mV, 20 ms. C1: Amplitudes and frequencies of AMPAR mediated mEPSCs from young (white) and aged (red) degus. C2: Cumulative probability plots for mEPSC amplitude size. Insert, representative superposed events, Calibration: 5pA, 20ms C3: Representative traces of mEPSCs are shown. Scale bars: 20pA, 2 s Calibration: 1mV, 10ms. Unpaired two-tailed ttest (*p< 0.01) young versus aged. The values in parentheses indicate the number of hippocampal slices (left) and the number of animals (right) used.

Fig. 3 .

 3 Fig. 3. Postsynaptic proteins affect synapse during degus aging. A: Representative blot of synaptic proteins from hippocampus extracts (age indicated above lanes). Arrows indicate respective migration positions. B: Relative levels of synaptophysin (SYP), PSD-95, GluR2-AMPAR subunit and NR2b-NMDAR subunit in the hippocampus from 6 mo (white), 12 mo (gray), 36 mo (blue) and 60 mo (red) degus. Mean values of synaptic proteins are relative to -tubulin levels. One-way ANOVA (F (3,20) = 6.626, *p = 0.0027 for PSD-95), (F (3,20) = 3.968, *p = 0.0227 for GluR2) and (F (3,21) = 2.662, *p = 0.0745 for NR2b) followed by Tukey´s post-hoc test (p < 0.05) for young (6-12 mo) vs aged (36-60 mo).

Fig. 4 .

 4 Fig. 4. Impaired hippocampal synaptic plasticity in aged Octodon degus. A: TBSinduced LTP in Schaffer Collateral-CA1 synapse. Traces shown to the right are representative fEPSPs recorded 1 min before (1) and 60 min after (2) TBS. LTP protocol was delivered at the time indicated by the arrow. Averaged LTP magnitude during the last ten minutes of recording for 6 mo (white), 12 mo (gray), 36 mo (blue) and 60 mo (red) degus is also shown. Two-way ANOVA (F (1,19) = 1841, *p < 0.0001) followed by Bonferroni post-hoc test (p < 0.05) on the last 10 min for aged versus young. B: ppLFSinduced LTD in Schaffer Collateral-CA1 synapse. Traces shown to the right are representative fEPSPs recorded 1 min before (1) and 60 min after (2) ppLFS. LTD protocol was delivered at the time indicated by the horizontal open bar. Averaged LTD magnitude during the last ten minutes of recording for 6 mo (white), 12 mo (gray), 36 mo (blue) and 60 mo (red) degus is also shown. Two-way ANOVA (F (1,19) = 435.6, *p < 0.0001) followed by Bonferroni post-hoc test (p < 0.05) on the last 10 min for aged versus young. Scale bars: 1mV, 10ms. The values in parentheses indicate the number of hippocampal slices (left) and the number of animals (right) used.

Fig. 5 .

 5 Fig. 5. Accumulation of large soluble A oligomers and tau phosphorylation during aging. A1: Representative blot for amyloid oligomers using anti-A peptide antibody 4G8. Arrows indicate respective migration positions of hexamers (6-mer), nonamers (9-mer) and dodecamers (12-mer). Synthetic A 42 peptide was used as size marker and positive control (right lane). A2: Identification and relative levels of different A oligomeric associations in hippocampal extracts from 6 mo (white), 12 mo (gray), 36 mo (blue) and 60 mo (red) degus. One-way ANOVA (F (3,21) = 17.21, *p < 0.0001 for 6-mer) and (F (3,19) = 5.439, *p=0.0026 for 12-mer) followed by Tukey´s post-hoc test (p < 0.05) for aged versus young. B: Relative levels of soluble A oligomers (Insert, representative slot blot from hippocampal extracts using anti-oligomeric antibody A11). One-way ANOVA (F (3,19) = 5.439, p = 0.0072) followed by Tukey´s post-hoc test (p < 0.05) for aged (36-60 mo) versus young (6-12 mo). C: Determination of phosphorylated tau protein levels using PHF-1 and AT8 antibodies. (Insert, representative blot for PHF-1, AT8 and -actin). One-way ANOVA (F (3,20) = 23.21, *p < 0.0001, for PHF-1) and (F (3,20) = 5.95, *p = 0.0059, for AT8) followed by Tukey´s post-hoc test (p < 0.05) for aged versus young. The values in parentheses indicate the number of animals used.

Fig. 6 .

 6 Fig. 6. Large A oligomers and tau phosphorylation correlate with LTP and memory impairments. Relationships between LTP magnitude (A), T-maze (B), ORM (C) and soluble dodecamer (A *56) levels for young and aged degus. Relationships between LTP magnitude (D), T-maze (E), and ORM (F) and PHF-1epitope tau phosphorylated levels for young and aged degus. A high correlation was observed for LTP and ORM. The values in parentheses indicate the number of animals used.

Fig. 7 .

 7 Fig. 7. Amyloid deposition and tau phosphorylation begins in the cerebral cortex of O. degus. A-D:Immunoreactivity for A peptides using the specific antibody 6E10 showed extracellular (black arrows) staining in the hippocampus and the cerebral cortex from degus. A greater number of extracellular insoluble deposits were observed in aged (72 mo, C, D) compared to young (12 mo, A, B) degus. E-H: Immunodetection of pathological phosphorylated tau using the specific antibody AT8 in the hippocampus and cerebral cortex from degus. A greater number of positively stained somas (black arrows) were observed in aged (G, H) compared to young (E, F) degus. I-J: Quantification of A burden (I, percentage of area occupied by 6E10-positive plaques) and the number of AT8-positive cells (J, number of neurons per area) for 6 mo (white, n=2), 12 mo (gray, n=2), 36 mo (blue, n=2) 60 mo (red, n=2) and 72 mo (black, n=2) revealed a significant increase after 36 mo in cortex and after 60 mo in hippocampus. One-way ANOVA (F (4,88) = 238.8, *p < 0.0001 for 6E10-Hip), (F (4,88) = 646.4, *p < 0.0001 for 6E10-Cx), (F (4,88) = 266.0, *p < 0.0001 for AT8-Hip), and (F (4,88) = 134.9, *p < 0.0001 for AT8-Cx) followed by Tukey´s post-hoc test (p < 0.05) compared to 6 mo.

ACKNOWLEDGMENTS.

We thank H.K. Lee and A. Megill (Hopkins U), J. Ewer (U Valparaiso), A. Chavez (A Einstein), A. Reichenbach (U Leipzig), L. Peichl (Max Planck) for discussion and comments, C. Elgueta (U Freiburg) for software assistance and T. Lee (U Michigan) for degus specimen in USA. This work was supported by NIH-FIRCA R03 TW007171-01A1 and R01AG034606 (AK), ANR-47 CONICYT (AP, FA), CONICYT-PFB 12/2007 (NI), CINV ICM P09-022-F (AP), Fellowship CONICYT AT-24091109 (AOA).