The pharmaceutical R&D enterprise presents unique challenges for information technologists and computer scientists. The diversity and complexity of

Introduction 14.1

The goal of this chapter is to present the WISDOM initiative, which is one of the main accomplishments in the use of grids for biomedical sciences achieved on grid infrastructures in Europe. Researchers in life sciences are among the most active scientifi c communities on the EGEE infrastructure. As a consequence, the biomedical virtual organization stands fourth in terms of resources consumed in 2007, with an average of 7000 jobs submitted every day to the grid and more than 4 million hours of CPU consumed in the last 12 months. Only three experiments on the CERN Large Hadron Collider have used more resources. Compared to particle physics, the use of resources is much less centralized as about 40 different scientifi c applications are now currently deployed on EGEE. Each of them requires an amount of CPU which ranges from a few to a few hundred CPU years. Thanks to the 20,000 processors available to the users of the biomedical virtual organization, crunching factors in the hundreds are witnessed routinely. Such performances were already achieved on supercomputers but at the cost of reservation and long delays in the access to resources. On the contrary, grid infrastructures are constantly open to the user communities.

Such changes in the scale of the computing resources made continuously available to the researchers in biomedical sciences open opportunities for exploring new fi elds or changing the approach to existing challenges. In this chapter, we would like to show the potential impact of grids in the fi eld of drug discovery through the example of the WISDOM initiative. the information required to arrive at well-founded decisions based on both scientifi c and business criteria is remarkable and well recognized in the industry.

Drug discovery is the process by which drugs are discovered and/or designed. Drug candidates are inputs to the drug development process. Recent progress in genomics, transcriptomics, proteomics, high throughput screening, combinatorial chemistry, molecular biology, and pharmacogenomics has radically changed the traditional physiology-based approach to drug discovery where the organism is seen as a black box. In silico drug discovery contributes to increasing biological system knowledge. The effi ciency gains of such an integrated knowledge system could correspond to save 35% costs, or about US$300 million, and 15% time, or two years of development time per drug.

In silico drug discovery is one of the most promising strategies to speed-up the drug discovery process. It is important to know and control the in silico process, which is described below. Figure 14.1 shows the different phases of a drug discovery process with their approximate duration, their success rate and the corresponding in silico contributions.

A target is a cellular or genetic molecule which is believed to be associated with a desired change in the behavior of diseased cells and on which drugs usually act. The target identifi cation and validation aims to isolate and select it. In silico drug discovery contributes to the target discovery by gene expression analysis, target function prediction and target threedimensional (3D) structure prediction for postprocessing.

To identify a lead compound, a substance affecting the target selected in a drug-like way, two different in silico pipelines can be used which speed up the process and reduce costs avoiding useless in vitro tests: de novo design and virtual screening. De novo design builds iteratively a compound from the structure of a protein active site. Virtual screening selects in silico the best compound from a molecule database. Lead optimization addresses the development from the most promising lead compounds to a safe and effective drug. Instead of expensive and longer in vitro and in vivo tests, evaluation of basic chemical properties can be achieved by virtual screening and quantitative structure activity relationship (QSAR). QSAR is a quantitative correlation process of chemical structure with well-defi ned methods, such as optimization for pharmaceutical properties [absorption, distribution, metabolism, excretion and toxicity (ADMET)] or effi cacy against the target organism.

In silico drug discovery contributes to increasing biological system knowledge, to managing data in a collaboration space, to speeding up analysis and consequently increasing the low success rate of the traditional "wet" approach. The effi ciency gains of such an integrated knowledge system could correspond to save 35% costs, or about US$300 million, and 15% time, or two years of development time per drug.

Nevertheless, in spite of increasing levels of investment in in silico techniques, there is a steady decline in the number of new molecules that enter clinical development and reach the market. Many factors have changed over the past ten years, particularly the domination of the target-based drug discovery paradigm, favoring screening and rational drug discovery programs. A new approach aims to integrate rational drug discovery with a strong physiology and disease focus.

Requirements 14.2.1.1

Reducing the research time and cost in the discovery stage and enhancing information about the leads are key priorities for pharmaceutical companies worldwide [START_REF]Creating Biomedical R&D Leadership for Europe to Benefi t Patients and Society[END_REF]. To achieve this goal, in silico drug discovery must meet the following requirements: The • in silico drug discovery process includes the management of a large variety and quantity of scientifi c data; for example, images, sequences, models, and databases. Data integration is thus a challenge to increase knowledge discovery but also to ease the complex workfl ow. This implies data format standardization, datafl ow defi nition in a distributed system, infrastructure and software providers for data storage, services for data and metadata registration, data manipulation, and database updates.

The •

in silico drug discovery process also includes the management of a large variety and quantity of software. Software integration is another challenge to build effi cient and complex workfl ows and to ease data management and data mining. Software can be provided in a distributed environment such as a Web server on the Internet. Different experts are absolutely necessary to maintain and update software and workfl ows to propose new methods or pipelines, to use remote services, exploit outputs, and fi nally to propose compounds for assay. A software workfl ow

Q1

will assist the scientist and the decision-maker in organizing their work in a fl exible manner, and in delivering the information and knowledge to the organization.

Deploying intensive computing is a challenge for • in silico drug discovery. For instance, computing 1 million docking probabilities or modeling 1000 compounds on one target protein requires in the order of a few TFlops in one day. Very large computing resources are also needed to describe accurately protein structure models by computational methods based on all-atom physicsbased force fi elds including implicit solution. Computing power is also required for bioinformatics resource centers where server access is saturated by the large number of short tasks requested by users.

Joining new information technologies with life sciences to enable • in silico drug discovery requires strong remote collaboration between different public and private experts when addressing neglected and emerging infectious diseases. It also involves strong sharing of resources: data and knowledge, software and workfl ow, and infrastructures such as computing, storage, and networks.

The collaboration space needs experts to maintain the resources.

Having tools and data accessible to everyone in collaboration requires intuitive interfaces that need to be maintained. These interfaces reduce the development time of new methods. They also help the integration of data and software from in silico drug discovery but also from experimental processes.

Security is a key challenge for pharmaceutical industries but also • for academic institutes in most cases. Effective protection of intellectual properties and sensitive information requires, for instance, authentication of users from different institutions, mechanisms for management of user accounts, and privileges and support for resource owners to implement and enforce access control policies.

In summary, the main requirements to develop in silico drug discovery are data and software integration, intensive computing deployment, remote collaboration and resources sharing, and, of course, security. Thus, there is a need for a powerful and secured environment sharing and integrating remote resources such as tools, data, computing, and storage.

Grid Added Value 14.2.1.2

The grid added value in the development of in silico drug discovery for neglected and emerging infectious diseases has multiple dimensions: Grids offer unprecedented opportunities for resource sharing • and collaboration.

Grids open exciting perspectives for handling information fl ows. • Grids provide the resources to speed up the execution of time-• consuming software.

Grids offer unprecedented opportunities for resource sharing and collaboration:

The sharing of resources in a cross-organizational collaboration • space between the pharmaceutical industry and academic rese arch institutions, and between developed and least developed countries

The creation of as a virtual laboratory for the different actors,

• increasing cooperation and communication between partners

The mobilizing of resources routinely or in an emergency Access to computing resources by a single effi cient path • Grids are unique tools for collecting and sharing information, networking experts, mobilizing resources routinely or in an emergency. A grid is thus an appropriate environment to develop in silico drug discovery.

Grid-Enabled Virtual Screening 14.2.2

Virtual screening is about selecting in silico the best candidate drugs acting on a given target protein [START_REF] Lyne | Structure-based virtual screening: An overview[END_REF]. Screening can be done in vitro but it is very expensive as there are now millions of chemicals that can be synthesized [START_REF] Congreve | Structural biology and drug discovery[END_REF]. A reliable way of in silico screening could reduce the number of molecules required for in vitro and then in vivo testing from a few millions to a few hundreds. Docking is only the fi rst step of virtual screening since the docking output data has to be processed further [START_REF] Ghosh | Structure-based virtual screening of chemical libraries for drug discovery[END_REF].

However, in silico virtual screening requires intensive computing, in the order of a few TFlops per day, to compute 1 million docking probabilities or for the molecular modeling of 1000 compounds on one target protein. Access to very large computing resources is therefore needed for successful high throughput virtual screening [START_REF] Chien | Grid technologies empowering drug discovery[END_REF].

The Virtual Screening Pipeline 14.2.2.1

Screening of chemical compounds against a target is an important step in the drug discovery process. Virtual screening is the process that screens the chemical compounds in silico against a target. The prerequisite to set up a virtual screening experiment is knowledge on the target, against which the screening has to be performed, and on the chemical compound libraries. Most of the information related to the targets is available in the literature, whether it is digital or paper-based. Docking is the method of fi rst choice for rapid in silico screening of large ligand databases for drug research, since it is based on a rational physical model. Basically, protein-compound docking is about computing the binding energy of a protein target to a library of potential drugs using a scoring algorithm. The target is typically a protein which plays a pivotal role in a pathological process; for example, the biological cycles of a given pathogen (parasite, virus, bacteria, etc.). The goal is to identify which molecules could dock on the protein active sites in order to inhibit its action and therefore interfere with the molecular processes essential for the pathogen. Libraries of compound 3D structures are made openly available by chemistry companies which can produce them. Many docking software are available either open-source or licensed.

However, there is very often a compromise between speed and accuracy of results (in terms of the actual binding mode as well as the calculated affi nity values) concerning the best scoring docking solutions. Docking methods usually generate a number of possible orientations of ligands in the binding site of the receptor, and the "correct" one (e.g. the orientation observed in the crystal structure) may not necessarily be ranked among the fi rst docking solutions. This is due to defi ciencies in orientation sampling and to the approximate nature of the scoring functions. Thus, it seems reasonable to subject screening results to postdocking refi nement; for example, using molecular dynamics or similar methods that can describe biomolecular structure and energy in more details.

An example of workfl ow to deploy virtual screening that additionally includes post-docking refi nement goes through the following steps:

Step 1 Selection of the target, the chemical compound database and the docking software.

Step 2 Preparation. If the selected target is a X-ray crystal structure with a bound ligand, then it requires preparing the binding site of the protein by taking 6-8 Angstroms from the cocrystallized ligand, taking care the signifi cant amino acids for the activity are included in the binding site. Information on the signifi cant amino acids can be obtained either from the literature or from the Brookhaven protein database. Regarding the preparation of the compounds to dock, open source database providing ready to dock molecules are made available on internet by the companies selling the compounds, but both target and compound have to be prepared according to the needs of the software.

Step 3 Docking. Access to data analysis and visualization software is required at this point.

Step 4 Postdocking analysis. Results are analyzed based on the docking energy score and binding mode of the compound inside the binding site.

Q2

Step 5 Refi nement of docking orientations using molecular dynamics and reranking of the best compounds with more accurate scoring functions.

Step 6 Postmolecular dynamics. analysis. Access to data analysis and visualization software is required at this point.

Step 7 Selection of a few hundreds of compounds for in vitro testing.

Figure 14.2 illustrates this workfl ow with some popular software packages.

Virtual Screening on the Grid: The WISDOM Initiative 14.3

Historical Perspective 14.3.1

The WISDOM initiative was born from discussions that took place in July 2003 at the PharmaGrid conference in Welwyn, UK. Organized yearly since 2001 by the PRISM forum [START_REF]PRISM forum[END_REF], the PharmaGrid cycle of conferences was aimed at steering exchanges between the leaders of the IT departments of the pharmaceutical laboratories and the developers of the grid technology. During the conference in Welwyn, the idea was discussed to identify a few topics where private and public partners could work together on the development and deployment of pilot projects on the grid. One of the topics was in silico drug discovery for neglected diseases.

Proposed by Martin Hofmann (SCAI, Fraunhofer Institute) and Vincent Breton, the idea raised interest from Manuel Peitsch who was at that time Global Head of Informatics and Knowledge Management at Novartis. After the conference, Novartis started a project focused on fi nding in silico new hits against dengue in partnership with University of Basel. As it was turning out diffi cult for other partners to contribute to this dengue project, the idea to launch a second project on an open-grid infrastructure emerged. In view of previous experience, we decided to start the project without involving a pharmaceutical laboratory. Discussions started in the spring of 2004 with Nicolas Jacq, who was a PhD student at that time at LPC Clermont-Ferrand. The fi rst decision was to choose a disease on which to focus our efforts. We did not have any a priori so we decided to ask one of our friends, the African pastor Joany Bazemo from Burkina-Faso, what was the worst plague in Africa. When asked, he did not hesitate in replying that malaria was the worst plague because it was killing children in hundreds of thousands. Once the disease was chosen, the next decision was to choose a biological target. This was entrusted to Vinod Kasam, a masters student at SCAI Fraunhofer, who studied the literature and identifi ed the family of plasmepsins as a new potentially promising target. Plasmepsins are involved in the degradation of hemoglobin by the malaria vector, Plasmodium falciparum. Structures for plasmepsin II and IV were available in the Protein Data Base so all the ingredients were there for launching our fi rst large virtual screening deployment on the grid. Large-scale deployments are called data challenges in the EGEE jargon.

First Data Challenge on Malaria 14.3.2 Preparation 14.3.2.1

Having identifi ed the disease and the targets, there was still a lot of work to do in order to prepare the data challenge. On the biochemical side, three ingredients were required:

3D structures of targets • A docking software •

A database of drug-like molecules to dock against the targets • We had already identifi ed the 3D structures of interest in the PDB [START_REF] Berman | The Protein Data Bank[END_REF] database.

The docking software initially selected was Autodock [START_REF] Morris | Automated docking using a Lamarckian genetic algorithm and empirical binding free energy function[END_REF], an open-source algorithm developed by the Scripps Research Institute. SCAI Fraunhofer is known worldwide for developing one of the best docking algorithms, FlexX [START_REF] Rarey | Predicting receptor-ligand interactions by an incremental construction algorithm[END_REF] but FlexX could not be deployed on multiple grid sites because it is licensed software. We had planned, however, that the best compounds selected with Autodock would be reprocessed on Fraunhofer cluster using the FlexX algorithm to compare Autodock and FlexX scores.

Libraries of compound 3D structures are made available open source by chemistry companies which can produce them. ZINC is one of these open source databases of compounds [START_REF] Irwin | ZINC-a free database of commercially available compounds for virtual screening[END_REF] which is constantly growing and was already providing the 3D structures of more than 3.4 million compounds in 2005.

Tests performed in December 2004 showed that docking the whole of ZINC against one target using Autodock represented about 35,000 jobs of approximately 20 hours, corresponding to about 80 years CPU. It soon appeared relevant from a biochemical perspective to perform docking not only on one but rather on three plasmepsin II structures (1lee, 1lf2, 1lf3) and one plasmepsin IV structure (1ls5) obtained from the Brookhaven Protein Data Base. In view of this increase of the number of targets, it was decided to focus on a subset of ZINC, the ChemBridge database collecting "only" 500,000 compounds. Finally, thanks to our collaborators from SCAI Fraunhofer, the BioSolveIT company distributing the FlexX software made graciously available up to 3000 free licenses of the software for deployment on the grid. This was extremely important as we had only a limited experience with Autodock.

Preparation of the data challenge involved on the biochemical side a number of steps in relation to the preparation of the targets and the validation of the docking procedure. On the grid side, this was the fi rst time a biomedical application was going to require about one century of CPU time. At that time, only the LHC experiments had been deploying data challenges and the know-how for such large-scale deployments was shared by only a few experts. Preparation of the deployment included the development of an environment for job submission and output data collection. This environment had to be able to handle the submission of about 70,000 15-hour long jobs and the collection of the output data. A major issue was to handle job resubmission whenever a job failed for any reason, as the grid success rate was typically of the order of 80-85%. Large-scale tests were made on the French regional grid AuverGrid to validate the environment and to identify potential issues and bottlenecks. Other issues were raised by the data challenge, like the usage of licensed software on the grid or the need for a high throughput job submission scheme.

Based on the experience acquired during the testing phase on AuverGrid, the WISDOM production system (see Figure 14.3) [START_REF] Jacq | Grid enabled virtual screening against malaria[END_REF] was developed in Perl, except the multithreaded job submission tool in Java. Two packages, wisdom_install and wisdom_test, were developed for installing the application components on the resources and for testing these components, together with the resources and grid services.

For the user submitting jobs, the entry point was a simple command line tool. Its users during the data challenge were members of the Biomedical Task Force, which gathers a team of engineers with recognized expertise in application development and deployment. This software environment was developed to allow the submission and monitoring of job sets which were called instances. The different jobs of a given instance have the same target input and docking software. They only differ by the molecules of the compound library which are docked. Tasks needed to submit an instance were automatically executed by the WISDOM execution system. The user, authenticated by a proxy certifi cate, had to start his or her instance execution following a precise submission schedule to avoid too much competition between the computation participants, which would lead to a grid overload. Once the computation had started, the WISDOM environment took care of monitoring jobs and registering results. The user only had to check regularly if the process ran correctly, up to the end of all the jobs belonging to the instance. The overall process progression could be monitored through an output fi le for follow-up messages and an error fi le in case of any problems.

For each instance, a confi guration fi le contained the instance information (software, target, database, parameter settings) and the grid parameters (number of jobs for the instance, resource brokers, computing elements, storage elements). A shell script and a job description language fi le were created for each job and used by the submission tool.

On the worker node, after the environment was confi gured, the shell script downloaded the database fi le from a storage element chosen by the information system using the LCG API. Binaries were then called with the target and parameter settings transferred with the job. The compressed result was stored on a storage element and registered in the grid fi le catalog. A backup copy was also generated on another storage element. For the sake of simplifi cation, the most relevant metadata relative to the output (software, parameter settings, compounds database, etc.) were stored in the name of the output itself. Output, errors and accounting messages were transferred on the user interface.

Execution of FlexX jobs required handling fl oating licenses. Each job using FlexX software was contacting the Flexlm server at the beginning of the job and asked for a license, namely an ASCII fi le with specifi c keys generated for this server. Then the job was able to run without connection to the license server. Accessing fl oating licenses on the grid behind fi rewalls required known IP and the opening of two specifi c ports for institutes hosting worker nodes.

Deployment 14.3.2.2

The deployment took place in July and August 2005. During this period, ten users launched jobs from fi ve user interfaces, monitoring the process with the help of the WISDOM environment and interacting with the user support of the EGEE project and the nodes administrators. For a total of 80 CPU years, 72,751 jobs were launched, producing 1 TB of data (500 GB, doubled for the back-up). Detailed analysis of the deployment can be found in [START_REF] Jacq | Grid enabled virtual screening against malaria[END_REF] from which Table 14 The data challenge was very useful to identify the limitations and bottlenecks of the EGEE infrastructure. The WISDOM production system developed to submit the jobs on the grid accounted for a small fraction of the failures as well as the grid management system. On the other hand, the resource brokers were observed to signifi cantly limit the rate at which the jobs could be submitted. Another signifi cant source of ineffi ciency came from the diffi culty for the grid information system to provide all the relevant information to the resource brokers when they distributed the jobs on the grid. As a consequence, job scheduling was a time-consuming task for the WISDOM users during all the data challenge due to the encountered limitations of the information system, the computing elements, and the resource brokers.

Results

14.3.2.3

Postprocessing of the huge amount of data generated was a very demanding task as millions of docking scores had to be compared. At the end of the large-scale docking deployment, the best 1000 compounds based on scoring were selected thanks to postprocessing ranking jobs deployed on the grid. They were inspected individually. Several strategies were employed to reduce the number of false positives. A further 100 compounds were selected for postprocessing. These compounds had been selected based on the docking score, the binding mode of the compound inside the binding pocket, and the interactions of the compounds to key residues of the protein.

Several scaffolds were identifi ed in the 100 compounds selected for postprocessing. The urea, thiourea, and guanidino scaffolds were the most frequently observed in the top 1000 compounds. Some of the compounds identifi ed were similar to already known plasmepsin inhibitors, like the urea analogs which were already established as micromolar inhibitors for plasmepsins (Walter Reed compounds) [START_REF] Silva | Structure and inhibition of plasmepsin II, A haemoglobin degrading enzyme from Plasmodium falciparum[END_REF]. These results were already an indication that the overall approach was sensible and that large-scale docking on computational grids had the potential to identify new inhibitors, such as the guanidino analogs. To confi rm these results, a new step had to be implemented: the refi nement of the compound selection using molecular dynamics computations.

Molecular Dynamics on the Grid 14.3.3

Introduction 14.3.3.1

While docking methods have been signifi cantly improved in the last few years by including more thorough compound orientation searches, additional energy contributions, and/or refi ned parameters in the force fi eld, it is generally agreed that docking results need to be postprocessed with more accurate modeling tools before biological tests are undertaken. Molecular dynamics (MD) [START_REF] Lamb | Computational approaches to molecular recognition[END_REF] has great potential at this stage: fi rst, it enables a fl exible treatment of the compound/target complexes at room temperature for a given simulation time, and therefore is able to refi ne compound orientations by fi nding more stable complexes; second, it partially solves conformation and orientation search defi ciencies which might arise from docking; third, it allows the reranking of molecules based on more accurate scoring functions.

Just at the time when the results of the fi rst WISDOM data challenge were being analyzed at SCAI Fraunhofer by Vinod Kasam, the BioinfoGRID project was launched in January 2006. LPC Clermont-Ferrand contribution to the project focused on the deployment of MD computations on the grid.

Deployment 14.3.3.2

Work started on the identifi cation of the relevant molecular dynamics software and on the choice of the grid infrastructure on which to deploy the computations. Contacts were established with two groups at SCAI Fraunhofer and University of Modena which expressed interest to rerank the best hits coming out of the fi rst WISDOM data challenge. Both groups were using the Amber software for molecular modeling which raised again the problem of deploying a licensed software on a grid. Contacts were established with the institution distributing Amber regarding the license policy on the grid.

The outcome of the negotiation was that we were allowed to deploy Amber on the grid under the following conditions:

Each cluster deploying Amber had to have at least one license. • Grid users allowed to use Amber had to come from one of the • laboratories owning an Amber license.

Grid users allowed to use Amber under the conditions described • above could deploy their computations on all the grid clusters.

Regarding the choice of infrastructure, contacts were established with the EGEE biomedical virtual organization and several groups involved in DEISA. A clear preference for deployment on EGEE was expressed by the groups collaborating with us at University of Modena and SCAI Fraunhofer. Both had developed MD procedures which were well fi tted for running on clusters. As a consequence, we focused our efforts on deploying these procedures on EGEE.

From September 2006, we started to investigate in details the deployment of the MD procedure designed by Giulio Rastelli (University of Modena and Reggio Emilia) on EGEE and based on the Amber software package. Amber [START_REF] Case | The Amber biomolecular simulation programs[END_REF] is a suite of different tools that carry out molecular dynamics simulations. The simulations in Amber can be divided into three phases and different programs of the Amber distribution are responsible for performing these steps:

Preparatory phase •

Simulatory phase • Analysis phase •

Encoding these steps in separate programs has some important advantages. First, it allows individual pieces to be upgraded or replaced with minimal impact on other parts of the program suite. Second, it allows different programs to be written with different coding practices: LEAP is written in C using X-window libraries, ptraj and antechamber are textbased C codes, mm-pbsa is implemented in Perl, and the main simulation programs are coded in Fortran 90. Third, this separation often eases porting to new computing platforms: only the principal simulation codes (sander and pmemd) need to be coded for parallel operations or need to know about optimized (perhaps vendor-supplied) libraries. The preparation and analysis programs are carried out on local machines on a user's desktop, whereas time-consuming simulation tasks are sent to a batch system on a remote machine; having stable and well-defi ned fi le formats for these interfaces facilitates this mode of operation.

Molecular Dynamics Refi nement and Rescoring Procedure 14.3.3.3

An automated multistep procedure for the refi nement and rescoring of docking screening results was designed and validated at University of Modena in Giulio Rastelli team [START_REF] Ferrari | Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors[END_REF]. The workfl ow, based on the Amber package, is able to automatically and effi ciently refi ne docking poses, which sometimes may not be accurate, and rank the compounds based on more accurate scoring functions. The procedure requires as input a pdb fi le containing the structure of the protein and a mol2 fi le containing the coordinates of the docked ligands. The coordinates of the docked ligand and target structure are merged to create the complex.

The topology fi les are created using antechamber. The ligand atoms are described with GAFF (general Amber force-fi eld) atom types and AM1-BCC charges. In order to avoid the time-consuming procedure of charge calculation, atomic charges of the ligand are read from the original mol2 fi le and not computed during the procedure; this choice adds the advantage that charge calculation of ligands can be performed only once, and obtained mol2 fi les used for many other target proteins. The ligand charges are calculated using antechamber by means of a separate script. Interestingly, the same set of AM1-BCC charges can also be exploited for automated ligand docking and MD. Missing gaff force fi eld parameters for the ligand are automatically assigned by parmcheck. The ligand, receptor, and complex topologies are written using leap utility (Amber 9). Minimization, MD, and fi nal reminimization of the complexes are performed using sander with a distance-dependent dielectric constant e = 4r. For each of these steps, the procedure enables the user to set ad hoc refi nement options depending on the application. After refi nement of the complex, a pdb fi le is generated as output and the fi nal coordinates of the ligand, receptor, and complex are updated and used to compute the binding free energy evaluation using Amber MM-PBSA and MM-GBSA. The free energy results (DG MM , DG solv , and DG' bind ) are written to an output fi le and compounds are ranked on the basis of their binding free energy [START_REF] Ferrari | Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors[END_REF].

This workfl ow was implemented in the EGEE grid using the WISDOM production environment designed for the large-scale docking experiments to deploy the MD procedure on the grid. The wide CPU availability of EGEE grid allowed the submission of a large subset of best docking complexes to the MD refi nement and rescoring procedure despite its high computational demand.

The fi rst successful deployment of the MD procedure was achieved in December 2006 on fake data. At preparation stage, all the required input fi les and Amber executables were stored on the storage element (SE) of the grid. At execution stage, jobs lasting approximately 20 hours were submitted corresponding to 50 compound subsets. Each subset with 50 compounds was submitted on one worker node along with the Amber executables, target structure, and the main script controlling the MD refi nement and rescoring operations. After the jobs were fi nished the result fi les (structures and energy scores) were copied and stored on the storage element.

Following the successful test, the 5000 best compounds coming out of the fi rst data challenge on plasmepsin II were refi ned and reranked. Computing resources from the EGEE Biomed virtual organization were used exclusively; 100 CPUs were used in parallel, all of them belonged to our local cluster at Clermont Ferrand, due to licensing issues. One single simula tion was consuming ~20 CPU minutes on an Intel Xeon 3.05 machine. The estimated CPU time if the simulations were to be performed on one machine was therefore expected to be 124 days. By using EGEE infrastructure the simulation time was signifi cantly brought down to 7 days.

Results

14.3.3.4

After rescoring the 5000 best docking results by Molecular Dynamics with Amber and MM-PBSA and MM-GBSA, the next step was to select the best compounds in the perspective of in vitro tests. The starting points were the two ranked lists of compounds, one according to MM-PBSA and the other according to MM-GBSA free energies. One hundred complexes of each list were analyzed manually. Each complex was visualized in 3D with UCSF Chimera software in order to determine the molecular interactions between protein and ligand in the complex. The major criteria for selection were the ligand-making interactions to the two catalytic residues of plasmesin II, Asp 34 and Asp214. Second, the interaction with other key amino acids was checked: Gly36, Val78, Gly216, Ser79. The complexes with no interaction with at least one of the two amino acids of the catalytic dyad were rejected. The complexes which were kept had at least one main interaction to amino acids of catalytic dyad. In total, 30 out of 200 compounds were selected for in vitro tests.

In Vitro 14.3.4 Tests

The 30 compounds selected for testing showed submicromolar or nanomolar IC 50 values against recombinant P. falciparum plasmepsin II, using the inhibition assay based on FRET substrate degradation, which is well documented in the literature [START_REF] Luker | Kinetic analysis of plasmepsin I and II aspartic protease of the Plasmodium falciparum digestive vacuole[END_REF][START_REF] Matayoshi | Novel fl uorogenic substrates for assaying retroviral proteases by resonance energy transfer[END_REF]. In the parasite, plasmepsin II is translated as an inactive zymogen containing a 124 amino acid-long N-terminal prosequence that has a membrane-spanning domain. Within the food vacuole the prosequence is removed by a calpain-like maturase, and active plasmepsin II is released [START_REF] Banerjee | Food vacuole plasmepsins are processed at a conserved site by an acidic convertase activity in Plasmodium falciparum[END_REF]. Plasmepsin II was expressed well from a pET3d construct that contained Glu124 after initiator Met, and inclusion body of the protein was refolded and purifi ed to near homogeneity as judged by SDS-PAGE. Based on molecular mass standards, recombinant plasmepsin II migrates at about 37 kDa. Pepstatin A, a general inhibitor of aspartic proteases of microbial origin [START_REF] Morishima | The structure of pepstatin[END_REF], was also reported to inhibited hemoglobin degradation by extracts of digestive vacuoles of P. falciparum [START_REF] Gluzman | Order and specifi city of the Plasmodium falciparum hemoglobin degradation pathway[END_REF]. In the in vitro inhibition test, the recombinant plasmepsin II activity without inhibitor was used as negative control and pepstatin A was used as a positive control. Pepstatin A showed inhibition (IC 50 80 nM) against the recombinant plasmepsin II activity. Among 30 compounds tested, six compounds had IC 50 values below 80 nM. These results are extremely encouraging and suggest that the overall approach used to select the candidates is sensible for discovery of new plasmepsin inhibitors. The six compounds are currently being evaluated for their antimalarial activities in vitro.

Second Data Challenge on Malaria 14.4 Introduction 14.4.1

With the success achieved by the fi rst data challenge both on the computation and biological sides, several scientifi c groups around the world proposed targets implicated in malaria which led to the second assault on malaria. The WISDOM-II project dealt with several targets, which were both X-ray crystal structures and homology models. Targets from different classes of proteins were also being tested; reductases such as malarial dihydrofolate reductase (DHFR) and transferases such as GST as can be seen from Table 14.2. The same procedure described previously for the fi rst data challenge was applied for target preparation. We had again the privilege to be able to use the FlexX software thanks to the generous support of BioSolveIT.

DHFR and dihydropteroate synthase (DHPS) are two enzymes that belong to the folate biosynthetic pathway. The antifolates are the most exploited class of antimalarials, to which belong well-known molecules like pyrimethamine and cycloguanil. To date, the most widely used antifolate is a combination of pyrimethamine, a DHFR inhibitor, and sulfadoxin, a DHPS inhibitor. Nevertheless, their synergic action that results in enhanced activity is seriously compromised by drug resistance and hypersensibilization. For example, drug resistance is due to point mutations of various amino acids in the DHFR and DHPS (P. falciparum and P. vivax) active sites, and severely decreases drug effi cacy. While most antimalarial research has been conducted on P. falciparum DHFR, there is growing interest on P. vivax DHFR, a less-studied target that is getting increasingly important because mixed falciparum and vivax infections are increasing, and parasites have developed resistance to both. Therefore, there remains a pressing need of new molecules apt to selectively bind these targets. To date, six different malarial DHFR crystal structures are available in the Protein Data Bank. These are the structures of wild-type P. falciparum DHFR (PDB code 1J3I), and of its C59R + S108N (1J3J) and N51I + C59R + S108N + I164L (1J3K) highly resistant mutants. In addition, four crystal structures of P. vivax DHFR are available, the wild-type structure (2BL9, 2BLB) and the S58R + S117N resistant mutant structures (2BLA, 2BLC). Hence, we thought it was a great opportunity to make use of these available resources to develop technology-based in silico screening on these targets. We chose P. falciparum DHFR (wt and the highly resistant quadruple mutant) and P. vivax DHFR (wt and the double mutant) structures (Table 14.2).

Evolution of the Production Environment 14.4.2

Following the experience acquired during the fi rst data challenge on malaria and the fi rst data challenge on avian fl u [START_REF] Lee | Grid enabled high throughput in silico screening against infl uenza A neuraminidase[END_REF], the WISDOM environment was reorganized in two different and independent tasks:

The submission of the jobs •

The follow-up of the jobs, and eventually their resubmission, as • well as the collection of the job status and their publication on a Web site These two processes can be started and run simultaneously, the second one being fed from the information provided by the fi rst one.

As seen during the deployment against avian fl u [21], we removed the automatic resubmission of jobs in case of a job failure, because we thought that the overload generated by this automatic resubmission was a major cause for the poor reliability we observed during the fi rst WISDOM deployment. Removing the automatic resubmission of jobs helped indeed a lot to improve overall reliability, but this induced a large amount of work for the users, who had to handle manual resubmissions of the instances after all the jobs had fi nished. The environment was further modifi ed and enhanced to become more a "launch and forget" system, to carry all the tedious tasks and relieve the users whenever possible, such as automatic resubmission of jobs, automatic storage of job results in a relational database, automatic and real-time update of the experiment statistics and status, viewable through a Web site, and so on. As a consequence, the WISDOM environment was easily customized, tested and successfully used by the EELA [22] and EUChinaGRID [23] virtual organization.

The main objective was also to improve the fault-tolerance of the system, in implementing, for instance, a persistent environment that can be stopped and restarted at any time without risking of losing important information. This also proved to be also very useful as it enabled the whole maintenance of the scripts and code and improved the interactivity with the user, as the user could also manage jobs in fi ne detail; for instance, force the cancellation and resubmission of a scheduled job. Along with this, we tried to minimize the cost of the environment in terms of disk space and CPU consumption for the user interface. While a 500 MB quota account was just enough to handle a single instance during the deployment on avian fl u [START_REF] Lee | Grid enabled high throughput in silico screening against infl uenza A neuraminidase[END_REF], it was now enough to handle no less than 20 instances simultaneously. Most of the job fi les were now generated dynamically which allowed also the user to modify on the fl y the confi guration of the resource brokers and the job requirements. This way, the user was sure that the next submissions would take these modifi cations into account. Figure 14.4 describes the overall architecture of the environment and the deployment process:

The user is interacting with the system through the two main scripts • (wisdom_submit and wisdom_status) deployed on the user interface. These scripts will take care automatically of job fi les generation, submission, status follow-up, and eventually resubmission.

The jobs are submitted directly to the grid workload management • system, and are executed on the grid computing elements. As soon as it is running, a job transfers all the fi les stored on the storage elements via the data management system of the grid, and the FlexX software, which asks a fl oating license for the FlexLm license server, can start to process the dockings.

During the job lifetime the status is retrieved from the user inter-• face and some statistics are generated and collected to a remote server which hosts a relational database and outputs these statistics through a Web site.

Once the job is fi nished, the outputs are stored back on the grid stor-• age elements via the data management system and the useful docking results are inserted directly from the grid to a relational database where they can later be more easily queried and analyzed.

Data Challenge Deployment 14.4.3

The deployment was performed on several grid infrastructures (Auvergrid [24], EELA, EGEE, EUChinaGrid) and involved at least one manager to oversee the process on each of them. The three groups of targets (GST, P. vivax and P. falciparum DHFR) were docked against the whole ZINC database (4.3 million ligands). The database was actually cut into 2422 chunks of 1800 ligands each. This splitting was chosen because we wanted to have an approximated processing time ranging from 20 to 30 hours for each job (one docking process takes from 40 seconds to 1 minute depending on the CPU power). The subsets had to be stored on the grid infrastructures. They were basically copied on a storage element and registered on the grid fi le catalog (LFC) and were also replicated on several locations whenever possible to improve fault-tolerance. We defi ned a WISDOM instance as being a target structure docked against the whole ZINC database, with a given parameter set. stored like all the inputs on the grid storage elements and were installed on the fl y on each worker node at the beginning of the job.

As the average duration of a job was around 30 hours, we submitted one instance per day, with a delay of 30 seconds between each submission. As one instance was submitted in about 20 hours, the submission process was quite continuous during the fi rst month of deployment. The jobs were submitted to 15 resource brokers in a round-robin order. At the end of a job, the results were stored on the grid storage elements, and directly into a relational database.

Figure 14.6 shows the repartition of the jobs on the different grid federations. It also shows the AuverGrid, EuChinaGRID, and EELA infrastructures contribution. Each of these three infrastructures ran one single instance which corresponds to 3% of the total 32 instances.

Postdocking Analysis and MD Refinement 14.4.4

Analysis of the docking results was carried out on the four targets docked. The University of Modena took charge and analyzed results obtained on DHFR targets from P. vivax and P. falciparum. CSIR, in collaboration with LPC Clermont-Ferrand, analyzed results on GST. Two criteria were employed for result analysis: statistical analysis based on scoring values and analysis of compounds making interactions to key residues of the receptor. Based on these two criteria, the best compounds coming out of the docking step were selected and refi ned using the MD procedure described previously.

The results refi ned with MD at the University of Modena for wild-type P. falciparum DHFR showed that the identifi cation of novel families of antimalarials whose structures are not related to classical antifolates is indeed possible. For wild-type P. falciparum DHFR, the best 15,000 compounds resulting from the docking screening (FlexX) have been refi ned with molecular dynamics procedure, and the molecules have been rescored using MM-PBSA and MM-GBSA. Hydrogen bond analyses of the 15,000 ligands interacting at the active site revealed a signifi cant enrichment of hydrogen bonds with DHFR key residues Asp54, Ile14, and Ile164 (as well as their combination) in the fi rst ranking positions. This fi nding may validate the approach, because interaction with these residues is known to be required for biological activity.

In perspective, MD refi nement will also be performed on the results obtained with the quadruple mutant DHFR structure and the results generated with P. vivax DHFRs. Once the analyses are complete, a fi nal selection of potential inhibitors will be performed and the best candidates will be tested in vitro at Mahidol University (Thailand) in Worachart Sirawaraporn's laboratory. MD refi nement of the results obtained on GST has been performed in collaboration with the University of Modena and the results of this rescoring step are presently under analysis.

Conclusion and Perspectives 14.5

The WISDOM initiative has demonstrated how the grid can signifi cantly change the approach to screening which is a mandatory step on the road to drug discovery. In the last couple of years, a number of targets have been screened in silico using grid infrastructure resources. The molecules that have been selected through this process have shown a signifi cant inhibition activity in vitro and some of them are under a patenting process. The grid added value has been clearly demonstrated by the remarkable increase of the virtual screening throughput up to 100,000 docked compounds per hour using 5000 computers on the EGEE grid.

Following this success, the WISDOM initiative, started from discussions between Martin Hofmann and Vincent Selection of ligands can signifi cantly reduce the number of compounds docked. It can be based on toxicity or on the properties of the target binding site. We are presently investigating existing open-source software packages to fi lter the ligands before their docking on the grid.

FIGURE 14 . 1

 141 FIGURE 14.1 Representation of the different phases of the drug discovery process with their duration, their success rate and the corresponding in silico contributions.

FIGURE 14 . 2

 142 FIGURE 14.2 Example of a virtual screening workfl ow (credit: A. Da Costa).

Q3 TABLE 14 . 2

 142 Structural Features of all the Targets Used in the Second WISDOM Data Challenge

  Design of the WISDOM production system.

	Installer	Tester	User
	wisdom_install	wisdom_test	Instance
			wisdom_execution
			Workload definition
	BIOMEDICAL VO LCG components EGEE resources Application components	Job submission Job monitoring Job bookkeeping Fault tracking Fault fixing	Superviser
			Job resubmission
	License server	Accounting data
			wisdom_collect
			wisdom_db	wisdom_site
	FIGURE 14.3

TABLE 14 . 1

 141 .1 is extracted.Main Metrics from WISDOM First Data Challenge

	Q3

Table 14 .

 14 [START_REF] Congreve | Structural biology and drug discovery[END_REF] shows the instances deployed on the different infrastructures. Schematic representation of the automated multistep refi nement and rescoring procedure of the ligand-target complexes based on Amber. (From Luker, K.E. et al.,Molecular and Biochemical Parasitology, 79, 1996, 71-78.)

	Q4

  Distribution of jobs on the different grid federations.

	1% 2%	2%	3%	3%
				3%	3%	EGEE Germany Switzerland EGEE Asia Pacific
	38%				5%	EGEE Russia
					AuverGrid
					6%	EuChinaGRID
					EELA
					EGEE South Western Europe
					EGEE Central Europe
					EGEE Northern Europe
					EGEE Italy
					7%	EGEE South Eastern Europe
					EGEE France
					EGEE UKI
				12%
	15%
	FIGURE 14.6

  Breton at the PharmaGrid conference in Welwin City in July 2003, has turned into a collaboration which has seven partners today: CNRS in Clermont-Ferrand (http://clrpcsv.in2p3.fr) with exper-• tise in grid technology and Web services for life sciences SCAI Fraunhofer institute in Bonn, with expertise in bio-and • chemoinformatics, in text-mining technology and author of the FlexX docking software University of Modena, with expertise in molecular dynamics for • virtual screening and drug design on malaria CNR-ITB Institute for Biomedical Technologies in Milano, with • expertise in high performance and GRID computing applications for bioinformatics in life science EMBRACE partners in Europe. Each grid job consisted of 20 proteins that would run for 20 hours approximately on queues of 72 hours and were managed using the WISDOM production environment. More than 90% of the total of 17 years of CPU time was fi nished in two months. The 18,738 rerefi nements were completed in four months. The rerefi ned structure models are available from http://www.cmbi.ru.nl/pdb_redo/.

Acknowledgments

The authors would like to acknowledge the contribution from numerous collaborators to the research activities described in this chapter: we would like to particularly thank Vincent Bloch, Ana Da Costa, Gianluca Degliesposti, Matteo Diarena, Géraldine Fettahi, Nicolas Jacq, Yannick Legrè, Simon Nowak, and Jean Salzemann.

The work described in this chapter was partly supported by grants from the European Commission (BioinfoGRID, EGEE, Embrace), the French Miinistry of Research (AGIR, GWENDIA) and the regional authorities (Conseil Régional d'Auvergne, Conseil Général du Puy-de-Dôme, Conseil Général de l'Allier).

The Enabling Grids for E-sciencE (EGEE) project is cofunded by the European Commission under contract INFSO-RI-031688. The BioinfoGRID project is cofunded by the European Commission under contract INFSO-RI-026808. The EMBRACE project is cofunded by the European Commission under the thematic area "Life sciences, genomics and biotechnology for health," contract number LHSG-CT-2004-512092. The SHARE project is cofunded by the European Commission under contract number FP6-2005-IST-027694.

AuverGrid is a project funded by the Conseil Regional d'Auvergne. The AGIR and GWENDIA projects are supported by the French Ministry of Research.

A total number of 32 instances were deployed, corresponding to an overall workload of 77,504 jobs, and up to 140 million docking operations. As shown in Figure 14.5, the environment included a FlexLm server that was providing the fl oating licenses for the FlexX commercial software. During the fi rst WISDOM deployment in 2005, the license server was identifi ed as a potential bottleneck and point of failure because we had just one server available at this time. For WISDOM-II, we started the deployment with only one server as well. Very soon, up to three servers were made available at the SCAI Fraunhofer institute (http://www.scai.fraunhofer.de), with 3000 licenses available on each server. The FlexX software binaries were These very satisfactory results obtained both on the biochemical side and on the grid side of the project should not hide that very signifi cant progress can be achieved on both sides.

The virtual screening pipeline described in Figure 14.2 can be refi ned at several levels.

In terms of target preparation, it is well known that the PDB is full • of bugs, especially for the structures which have been downloaded many years ago.

In terms of ligand selection, our approach has been very naïve as • we have been docking all the compounds available in ZINC and Chembridge. Software packages exist to perform a fi rst selection of the ligands on physical and chemical criteria to reduce the number of dockings.

The availability of three-dimensional macromolecular coordinates is a prerequisite for many types of studies. PDB contains many anomalies ranging from proteins with small deviations from normal geometry, to structures that fi t their submitted experimental data very poorly.

Led by CMBI, a massive rerefi nement project was launched to obtain a better match between the experimental data and the atomic parameters (coordinates, B-factors) in the structure models while not compromising the geometric quality of the structure models [START_REF] Joosten | Re-refi nement of all X-ray structures in the PDB[END_REF]. The rerefi nement of such a vast number of structure models requires enormous amounts of computer power. The rerefi nement of 18,738 PDB fi les with 18,738 independent jobs requiring from 1 to 24 hours of CPU each is a very good example of a parallel project ideally suitable for deployment on a computing grid.

The rerefi nements of the structure models were done on a hybrid computer environment consisting of the Biomed and EMBRACE [26] virtual organizations on EGEE grid infrastructure as well as several clusters of


Ottavia Romoli
email: oromoli@pasteur-cayenne.fr

Mathilde Gendrin
email: mathilde.gendrin@pasteur.fr

Manipulating the mosquito microbiota to study its function

Keywords: Mosquito, microbiota, Aedes aegypti, development, gnotobiology, wing length

HAL is

Introduction

The mosquito Aedes aegypti is the main vector of several arboviruses, including yellow fever, chikungunya, Zika and dengue viruses. The distribution of this mosquito species throughout all tropical and sub-tropical regions of the world and the ease of rearing it in the laboratory makes it a commonlyused model for mosquito biology and vector competence studies.

The mosquito microbiota is known to have a significant impact on several factors determining disease transmission, including mosquito vector competence (i.e. capability to acquire and transmit pathogens) and different aspects of mosquito physiology. The larval microbiota has a critical effect on host physiology, since larval development is only possible in the presence of a living microbiota [START_REF] Coon | Mosquitoes rely on their gut microbiota for development[END_REF]. The rescue of development in axenic (i.e. germ-free) larvae can be achieved with specific diet supplementations, but with significant delay in larval growth [START_REF] Correa | Generation of axenic Aedes aegypti demonstrate live bacteria are not required for mosquito development[END_REF]. Exposure to different bacteria during larval development leads to significant differences in developmental rate, adult body size and egg production [START_REF] Dickson | Carryover effects of larval exposure to different environmental bacteria drive adult trait variation in a mosquito vector[END_REF][START_REF] Coon | Gut bacteria differentially affect egg production in the anautogenous mosquito Aedes aegypti and facultatively autogenous mosquito Aedes atropalpus (Diptera: Culicidae)[END_REF]. The adult microbiota composition also influences host physiological traits such as lifespan, mating behavior and vector competence [START_REF] Cirimotich | Natural microbe-mediated refractoriness to Plasmodium infection in Anopheles gambiae[END_REF][START_REF] Pike | Changes in the microbiota cause genetically modified Anopheles to spread in a population[END_REF].

The mosquito microbiota has a relatively low bacterial diversity, with four bacterial taxa representing on average 90% of the microbial population in the gut of a single mosquito [START_REF] Osei-Poku | Deep sequencing reveals extensive variation in the gut microbiota of wild mosquitoes from Kenya[END_REF]. Both culture-dependent and -independent analyses showed that the mosquito microbiota is mainly composed of Gram-negative bacteria, of which many have been cultured [START_REF] Osei-Poku | Deep sequencing reveals extensive variation in the gut microbiota of wild mosquitoes from Kenya[END_REF][START_REF] Ramirez | Reciprocal tripartite interactions between the Aedes aegypti midgut microbiota, innate immune system and dengue virus influences vector competence[END_REF][START_REF] Gendrin | The Anopheles mosquito microbiota and their impact on pathogen transmission[END_REF][START_REF] Charan | Comparative analysis of midgut bacterial communities in three aedine mosquito species from dengue-endemic and non-endemic areas of Rajasthan, India[END_REF]. Due to its culturability and simple composition, the mosquito microbiota can be manipulated in the laboratory and this represents a useful tool to study the role of the microbiota on mosquito physiology.

Here we describe a method to obtain gnotobiotic Aedes aegypti mosquitoes, i.e. mosquitoes that are colonized by a microbiota of known composition, in the present case composed of a single bacterial strain. In conventional rearing conditions, microbes are transmitted horizontally, between individuals sharing the same environment, or vertically, mostly via contamination of the surface of mosquito eggs by a female during egg laying. The egg cytoplasm is generally microbiologically sterile. When larvae hatch, they acquire their microbiota through ingestion of microorganisms present in the rearing water and/or on the egg envelope. Using this gnotobiology protocol, axenic Aedes aegypti larvae are obtained by surface sterilization of mosquito eggs (Figure 1). After addition of a bacterial culture to the rearing water, gnotobiotic larvae and mosquitoes are obtained and can be used to study the impact of a manipulated microbiota on mosquito development. Two different rearing methods are used depending on the parameters that need to be measured. Rearing larvae in a 24-well plate allows the follow-up of single individuals throughout development, while batch rearing in cell culture flasks is more timeefficient and therefore preferable when the measured parameters allow grouping. The experimental timeline of the protocol is indicated in Table 1.

Besides the rearing methodology, we provide an overview of different practices that are commonly used to estimate larval and mosquito development: larval length, duration of developmental stages, development success rate, adult sex ratio, adult wing length, and mosquito lifespan. Larval length is a parameter that is strictly dependent on larval rearing conditions. Notably, it varies significantly depending on larval density [START_REF] Jannat | Effects of larval density and feeding rates on larval life history traits in Anopheles gambiae s.s. (Diptera: Culicidae)[END_REF], rearing temperature [START_REF] Christiansen-Jucht | Temperature during larval development and adult maintenance influences the survival of Anopheles gambiae s.s[END_REF] and diet [START_REF] Linenberg | Larval diet affects mosquito development and permissiveness to Plasmodium infection[END_REF]. In addition to larval length, larval development can also be quantified in terms of duration of developmental stages and of developmental success rate, which both depend on nutrition and microbiota [START_REF] Dickson | Carryover effects of larval exposure to different environmental bacteria drive adult trait variation in a mosquito vector[END_REF][START_REF] Valzania | Both living bacteria and eukaryotes in the mosquito gut promote growth of larvae[END_REF]. The sex ratio of emerged adults is also influenced by larval rearing conditions [START_REF] Jannat | Effects of larval density and feeding rates on larval life history traits in Anopheles gambiae s.s. (Diptera: Culicidae)[END_REF]. In Aedes aegypti, sex is completely genetically determined [START_REF] Hall | A male-determining factor in the mosquito Aedes aegypti[END_REF] and males have a faster larval development than females. Therefore, although the reasons behind why rearing conditions affect sex ratio have not been determined, one can theorize that the presence of a larval slow killer could result in a higher proportion of adult males. Wing length is commonly used as a read-out of adult body size, which is dependent on larval life-history traits. The adult body size is an important parameter, as it is known to directly correlate with blood meal volume and fecundity [START_REF] Clements | Size alters susceptibility of vectors to dengue virus infection and dissemination[END_REF] and has been reported to be inversely correlated with dengue virus dissemination in the mosquito [17]. Finally, mosquito lifespan is a good indicator of the effect of the microbiota on the adult physiology. Considering that the transmitted pathogens develop for one to two weeks in the mosquito before being infectious for humans, mosquito lifespan is an important determinant of transmission.

Depending on the microbe and mosquito species used to generate gnotobiotic mosquitoes, this protocol should be adapted for the chosen host-microbe couple. In particular, culture media and microbial concentrations may vary from one microorganism to another and parameters of egg sterilization may vary between mosquito species. Here, we describe the materials and the procedures that we use to work with Escherichia coli in Ae. aegypti. However, several microbial strains have been used to produce gnotobiotic larvae of Ae. aegypti and Anopheles gambiae in independent laboratories [START_REF] Coon | Mosquitoes rely on their gut microbiota for development[END_REF][START_REF] Dickson | Carryover effects of larval exposure to different environmental bacteria drive adult trait variation in a mosquito vector[END_REF][START_REF] Valzania | Both living bacteria and eukaryotes in the mosquito gut promote growth of larvae[END_REF].

Materials

Live organisms:

1. Glycerol E. coli stock kept at -80°C. 2. Ae. aegypti eggs, kept on a dry paper for 4 days to 3 months (see Note 1).

Media/solutions:

1. Liquid Luria-Bertani (LB) medium: 10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl*. 2. Solid LB medium: 10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl, 1.5 g/L agar*.

Methods

3.1

The day before egg sterilization (day 0), revive the E. coli strain from the glycerol stock by streaking it onto an LB agar plate. Incubate the plate overnight at 37 °C.

3.2

Sterilization of Ae. aegypti eggs -day 1 (this procedure should be conducted inside a MSC) 1. Transfer Ae. aegypti eggs onto the filter of a vacuum filtration apparatus system. Since the sterilization process can reduce the percentage of hatched eggs, we suggest exceeding the amount of eggs with respect to the required number of larvae by 10-15%. Note that the filtration unit does not need to be sterile as it is only used to remove sterilizing solutions.

The same filtration unit can be used several times, as long as the filter is not visibly damaged.

2. From this point, proceed with egg sterilization without stopping between steps to avoid egg desiccation.

3. Add ~50 mL of 70% ethanol and incubate 5 minutes. During this incubation, mix the solution with a P1000 pipette tip to ensure that all eggs are well submerged in ethanol (see Note 3). Apply the vacuum to remove the ethanol.

4. Repeat the previous step with ~50 mL of 1% bleach (instead of ethanol) for 5 minutes, and then again with ~50 mL of 70% ethanol for another 5 minutes. The volumes of sterilizing solutions are approximate: the critical step is to ensure that the entirety of the egg surface is in contact with ethanol or bleach for the correct amount of time to ensure bactericidal activity.

5. Rinse eggs three times with abundant sterile deionized water, applying the vacuum each time to remove the water. This step is essential to remove any traces of sterilizing solutions.

6. Add sterile deionized water to the eggs and transfer them to a cell culture flask for larval emergence using a sterile plastic pipette. Keep the hatching larvae in their flask without any food until step 3.4.

7.

To check the efficiency of egg surface sterilization, transfer 10-20 eggs to a sterile 15 mL conical tube containing 3 mL of LB and incubate shaking at 37 °C overnight.

3.3

Inoculate a single E. coli colony into LB and incubate shaking at 37°C overnight (for ~16 h, see Notes 4 and 5) -day 1

Generation of gnotobiotic Ae. aegypti larvae -day 2

1. Centrifuge the E. coli culture for 30 minutes at 3200 x g. Resuspend the pellet in the same volume of sterile deionized water. Dilute this suspension 1:5 and check that the resulting suspension contains approximately 10 8 CFU/mL by plating on LB-agar. This bacterial suspension will be added to larvae (see Note 4).

2. 24-well plate set-up: transfer a single axenic larva to each well using a sterile plastic pipette. Add 2 mL of the bacterial suspension and 50 µL of the sterile diet suspension to each well.

3. Flask set-up: transfer 10 to 15 axenic larvae to each flask using a sterile plastic pipette. Add 15 mL of the bacterial suspension and 750 µL of the sterile diet suspension.

4. Keep an aliquot of axenic larvae in sterile water in a flask for a few days to ensure that larvae were not contaminated during manipulation. Axenic larvae are not able to grow without bacteria: these larvae will molt to the second instar (whether food was added or not) only if contamination has occurred during the plate/flask set-up.

5. Ensure that plates and/or flasks are closed to maintain sterility. Place larvae in a climatic chamber (30°C with LD 12:12 h cycle) or in an insectary compartment specifically dedicated to microbiota-manipulated mosquitoes.

Maintaining gnotobiotic Ae. aegypti mosquitoes -days 8-10

When the pupal stage is reached, transfer pupae to the autoclaved polypropylene boxes (Figure 2). Pupae are placed in the larger tube, while a sterile 10% sucrose solution is added to the small tube containing the sterile cotton roll.

3.6

Monitoring the effect of the manipulated microbiota on mosquito development and survival

1. As a control, use eggs from the same batch that were not subjected to the sterilization process and hatched in non-sterile water. Alternatively, use gnotobiotic eggs from the same batch and from the same sterilization process that are colonized with a control microbe.

The rearing conditions of non-sterile larvae should be as similar as possible to those used for the gnotobiotic rearing, using the same flasks or plates and the same sterilized diet. However, non-sterile controls should not be handled in microbiologically sterile conditions, i.e. on the bench rather than inside a MSC.

2. Larval length: 5 days post bacterial inoculation (day 7), larvae should have reached the fourth instar and body length can be used as a proxy for larval development (see Note 6).

For this analysis, larvae can be reared either on 24-well plates or on flasks. Fix larvae for 20 minutes at room temperature in PBS with 4% paraformaldehyde. Using a clean plastic pipette, place the desired number of larvae on a microscope slide and remove as much liquid as possible. Several larvae can be placed on the same slide, using clean forceps. Take a picture rapidly to avoid larval desiccation (see Note 7). The larval length is considered the distance between the anterior border of the head and the posterior border of the last abdominal segment, excluding the siphon (Figure 3A).

3. Duration of developmental stages: larvae placed in single wells of a 24-well plate are inspected daily, three times per day. For each larva, the moment at which they molt is noted.

In this way the duration of each larval instar and of the pupal stage can be determined for single individuals. A good indicator of larval molting is the presence and the size of exuviae in the well, therefore the larval stage can be deduced by counting the number of exuviae (1 exuvia = second instar, 2 exuviae = third instar, 3 exuviae = fourth instar). Since the first exuvia is often difficult to see, second instar larvae can also be distinguished from first instar larvae by their darker head.

4.

Wing length: Collect adult mosquitoes 2 days post emergence, as younger mosquitoes may not have completely spread or dried wings, while older mosquitoes might start to have damaged wings. Both rearing conditions, whether in flasks or in plates, are relevant for this analysis. For convenience, adult mosquitoes can be kept at -20°C until analysis. The measurement can be conducted on both wings or on one specific wing. It is however important to discriminate between male and female mosquitoes because of the significant difference in their wing sizes (Figure 3B and3C). Using dissection forceps, collect wings from several individuals and place them on a microscope slide. Use a coverslip to flatten samples and take a picture using the microscope camera. Wing length is defined as the distance between the alular notch and the radius 3 vein (Figure 3B and 3C).

5.

Percentage of developed larvae and sex ratio: larvae reared in 24-well plates are left in the plates until the adult stage. For each individual, mark the sex of the adult mosquito or if larvae/pupae are undeveloped or dead. In our hands, gnotobiotic larvae generally reach the adult stage at day 8-10 post bacterial inoculation. Since Ae. aegypti larvae might survive several days without developing, we set the end-point of the experiment at day 15 post bacterial inoculation, i.e. at least 5 days after observing the last pupation. This analysis allows operators to determine if the gnotobiotic rearing impacts the overall development of mosquitoes (see Note 8).

6. Survival: transfer pupae in autoclaved polypropylene boxes and provide sugar. Do not exceed 30 pupae per box. Inspect daily the boxes to count the number of dead and possibly determine their sex.

Notes

1. We use the New Orleans strain, but other Ae. aegypti strains have been used to produce gnotobiotic mosquitoes [START_REF] Dickson | Carryover effects of larval exposure to different environmental bacteria drive adult trait variation in a mosquito vector[END_REF][START_REF] Coon | Gut bacteria differentially affect egg production in the anautogenous mosquito Aedes aegypti and facultatively autogenous mosquito Aedes atropalpus (Diptera: Culicidae)[END_REF].

2. We use TetraMin Baby but we do not exclude that other types of diet can be used. Since TetraMin Baby is insoluble, after autoclaving the diet solution we prepare 5 mL aliquots that are easier to resuspend. This provides a standardized amount of diet to larvae.

3. The incubation time with ethanol and bleach should not exceed 5 minutes: a longer treatment will result in a decrease of the percentage of hatched eggs.

4. The volumes of the bacterial culture and of the diluted bacterial suspension should be adapted to the number of gnotobiotic larvae required and to the type of larval rearing: consider that 50 mL of bacterial suspension are needed for each 24-well plate (24 larvae), while 15 mL are sufficient for each flask (10 to 15 larvae). For E. coli we use a 5-fold diluted bacterial suspension, therefore we inoculate bacteria in a LB volume corresponding to 1:5 of the final bacterial suspension volume (e.g. 200 mL of LB for 1 L of diluted bacterial suspension). Use a pre-inoculum if the bacterial culture volume is larger than 50 mL. Bacterial concentrations should be adjusted for each type of bacterium as the minimal amount to rescue larval development varies between microorganisms [START_REF] Valzania | Both living bacteria and eukaryotes in the mosquito gut promote growth of larvae[END_REF].

5. Ideally bacteria should not be in the death phase when they are given to larvae. For E. coli we decided to inoculate the bacterial culture 16 h before adding bacteria to sterile larvae. This time schedule should be adjusted for each type of bacterium.

6. Other morphometric parameters can be measured to estimate larval development. For example, the head capsule width or the ratio between thorax width and head capsule width are commonly used [START_REF] Vogel | Transcriptome sequencing reveals large-scale changes in sxenic Aedes aegypti larvae[END_REF][START_REF] Timmermann | Larval growth and biosynthesis of reserves in mosquitoes[END_REF].

7. We use a dissecting microscope equipped with a HD color camera (Euromex). After acquiring the image, we use the Image Focus software (Euromex) to measure larval length. As an alternative, the open source software ImageJ can be used to measure larval/wing lengths.

8. To determine if a real effect on sex ratio is present, you may determine the sex of undeveloped larvae and pupae. This can be achieved by PCR using the primers for the male-determining Nix gene described in [START_REF] Hall | A male-determining factor in the mosquito Aedes aegypti[END_REF].

Figure captions

Figure 1. Experimental set-up to obtain gnotobiotic mosquitoes. Eggs are surface sterilized with three subsequent 5-minute washes in 70% ethanol, 1% bleach and 70% ethanol and rinsed in sterile water. A bacterial culture is added to axenic larvae to obtain gnotobiotic larvae and, subsequently, gnotobiotic adult mosquitoes. Gnotobiotic larvae can be reared in 24-well plates, if parameters on single individuals have to be monitored, or in cell culture flasks.

Figure 2 .

 2 Figure 2. Preparation of sterile boxes for gnotobiotic adult mosquitoes.(A) Material needed to setup the boxes: autoclavable polypropylene box for plant culture, larger autoclavable tube (typically used for urine collection), smaller autoclavable tube, cotton roll, gauze, filter paper cut to fit the bottom of the box. (B) The gauze is taped to cover the filter on the internal part of the lid to avoid mosquitoes being exposed to potential contaminants via their proboscis. (C) The filter paper is fixed to the bottom of the box with adhesive tape to collect mosquito excreta. The two tubes are also taped to the box sides: the larger one (red arrow) will allocate pupae, while in the smaller one (blue arrow) a cotton roll is placed for sugar feeding. (D) The prepared box is ready to be autoclaved: close the lid for approximately 3/4 of the surface, leaving one border of the lid opened. Close the lid immediately after sterilization. Alternatively, boxes can be autoclaved in autoclavable bags. After autoclave, let boxes dry inside a MSC. Before use, add the sterile 10% sucrose solution to the smaller tube with the cotton roll.

Figure 3 .

 3 Figure 3. Measuring larval and wing lengths as a proxy for development. (A) Measurement of larval length of 5-day old Ae. aegypti gnotobiotic larvae. Larval length is measured from the anterior border of the head to the posterior border of the last abdominal segment, excluding the siphon. (B, C) Measurement of wing length on female (A) and male (B) Ae. aegypti gnotobiotic mosquitoes. Wing length is defined as the distance between the alular notch and the radius 3 vein. Bars: 1 mm.

  3. Sterile deionized water*. 4. 70% ethanol, prepared with sterile deionized water. 5. 1% bleach, freshly prepared from a tablet into sterile deionized water (bleach concentration refers to the active chlorine). 6. Sterile 5% (w/v) fish food (TetraMin Baby) in deionized water (see Note 2)*. 7. 4% paraformaldehyde in autoclaved phosphate-buffered saline*: PBS, 8 g/L NaCl, 0.2 g/L

	2. Sterile 25 mL cell culture flasks with vented caps.
	3. Sterile petri dishes.
	4. Sterile 24-well plates.
	5. Sterile plastic pipettes.
	6. Sterile 15 mL tubes.
	7. Microscope slides.
	8. Coverslips.
	9. Autoclavable polypropylene boxes used for plant culture, equipped with a larger tube
	Other materials:
	1. Microbiological safety cabinet (MSC).
	2. Pump for vacuum filtration.
	3. Dissection forceps.
	4. Dissection microscope with camera.

KCl, 1.44 g/L Na2HPO4, 0.24 g/L KH2PO4. Add paraformaldehyde just before use (manipulate concentrated paraformaldehyde under a fume hood). 8. Sterile 10% (w/v) sucrose solution*. * autoclaved at 121°C for 30 minutes Disposable materials:

1. Disposable vacuum filtration unit (the material and the pore size of the filtering membrane are not important, we generally use nitrocellulose 0.2 µm membranes). (typically used for urine collection) for adult emergence and a smaller tube with a cotton roll for mosquito sugar feeding. Cover the filter on the top of the lid with gauze to avoid mosquito contamination. See Figure

2

for more details.

Table caption Table 1 . Experimental timeline of the gnotobiology protocol.

 caption1

	Table 1
	Day	Protocol step
	0	Revive the bacterial strain (3.1)
	1	Sterilize Ae. aegypti eggs (3.2) and inoculate the bacterium (3.3)
	2	Generate gnotobiotic larvae (3.4) 24-well plates	Culture flasks
	3
	4	Determine duration of
	5	developmental stages (3.6.3)
	6

Acknowledgements

We thank Verena Kircher and Charles Bobin for preliminary work on protocols, Jean-Géraud Issaly for egg production, Guillaume Lacour for training on wing length measurements, Siegfried Hapfelmeier for sharing of bacterial strain and Emma Wise for English proofreading of the manuscript. This work is funded by the French Government's Investissement d'Avenir program, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases" (grant no. ANR-10-LABX-62-IBEID) and by ANR JCJC Mosmi to MG (grant no. <ANR-18-CE15-0007>).