Guilhem Sempéré
email: guilhem.sempere@cirad.fr

Florian Philippe

Alexis Dereeper

Manuel Ruiz

Gautier Sarah

Pierre Larmande

Gigwa-Genotype investigator for genomewide analyses

Keywords: Genomic variations, VCF, HapMap, NoSQL, MongoDB, SNP, INDEL, Web interface Findings

Background: Exploring the structure of genomes and analyzing their evolution is essential to understanding the ecological adaptation of organisms. However, with the large amounts of data being produced by next-generation sequencing, computational challenges arise in terms of storage, search, sharing, analysis and visualization. This is particularly true with regards to studies of genomic variation, which are currently lacking scalable and user-friendly data exploration solutions.

Description: Here we present Gigwa, a web-based tool that provides an easy and intuitive way to explore large amounts of genotyping data by filtering it not only on the basis of variant features, including functional annotations, but also on genotype patterns. The data storage relies on MongoDB, which offers good scalability properties. Gigwa can handle multiple databases and may be deployed in either single-or multi-user mode. In addition, it provides a wide range of popular export formats.

Conclusions:

The Gigwa application is suitable for managing large amounts of genomic variation data. Its user-friendly web interface makes such processing widely accessible. It can either be simply deployed on a workstation or be used to provide a shared data portal for a given community of researchers.

Background

With the advent of next-generation sequencing (NGS) technology, thousands of new genomes of both plant and animal organisms have recently become available. Whole exome and genome sequencing, genotyping-bysequencing and restriction site-associated DNA sequencing (RADseq) are all becoming standard methods to detect single-nucleotide polymorphisms (SNPs) and insertions/deletions (indels), in order to identify causal mutations or study the associations between genetic variations and functional traits [START_REF] Gheyas | Functional classification of 15 million SNPs detected from diverse chicken populations[END_REF][START_REF] Li | Joint genomewide association study for milk fatty acid traits in Chinese and Danish Holstein populations[END_REF][START_REF] Shinada | Quantitative trait loci for rice blast resistance detected in a local rice breeding population by genome-wide association mapping[END_REF][START_REF] Marcotuli | Genome wide association mapping for arabinoxylan content in a collection of tetraploid wheats[END_REF]. As a result, huge amounts of gene sequence variation data are accumulating in numerous species-oriented projects, such as 3000 rice genomes [START_REF]The 3000 rice genomes project. The 3,000 rice genomes project[END_REF] or 1001 Arabidopsis genomes [START_REF] Ossowski | Sequencing of natural strains of Arabidopsis thaliana with short reads[END_REF][START_REF] Cao | Wholegenome sequencing of multiple Arabidopsis thaliana populations[END_REF]. In this context, the Variant Call Format (VCF) [START_REF] Danecek | The variant call format and VCFtools[END_REF] has become a convenient and standard file format for storing variants identified by NGS approaches.

VCF files may contain information on tens of millions of variants, for thousands of individuals. Having to manage such significant volumes of data involves considerations of efficiency with regard to the following aspects:

1. Filtering features. Such data can be processed by applications like VCFtools [START_REF] Danecek | The variant call format and VCFtools[END_REF], GATK [START_REF] Mckenna | The genome analysis toolkit: a MapReduce framework for analyzing nextgeneration DNA sequencing data[END_REF], PyVCF [START_REF] Casbon | PyVCF -A Variant Call Format Parser for Python[END_REF], VariantAnnotation [START_REF] Obenchain | VariantAnnotation: a bioconductor package for exploration and annotation of genetic variants[END_REF] or WhopGenome [START_REF] Wittelsburger | WhopGenome: high-speed access to whole-genome variation and sequence data in R[END_REF] to query, filter and extract expertized datasets for day-to-day research. However, these tools are limited to command line or programmatic application programming interfaces (APIs) targeted at experienced users, and are not suitable for nonbioinformaticians. 2. Storage performance. Working with flat files is not an optimal solution in cases where scientists need to establish comparisons across projects and/or take metadata into account. The use of relational databases is still widely prevalent within the range of more integrated approaches. However, such solutions have limitations when managing big data [START_REF] Bach | Innovative control systems for tracked vehicle platforms[END_REF]. In computational environments with large amounts of heterogeneous data, the NoSQL database technology [START_REF] Gajendran | A survey on NoDQL databases[END_REF][START_REF] Moniruzzaman | Nosql database: New era of databases for big data analytics-classification, characteristics and comparison[END_REF] has emerged as an alternative to traditional relational database management systems. NoSQL refers to nonrelational database management systems designed for large-scale data storage and massively parallel data processing. During the past 5 years, a number of bioinformatics projects have been developed based on NoSQL databases such as HBase [START_REF] O'connor | SeqWare query engine: storing and searching sequence data in the cloud[END_REF][START_REF] Wang | High dimensional biological data retrieval optimization with NoSQL technology[END_REF],

Hadoop [START_REF] Langmead | Searching for SNPs with cloud computing[END_REF][START_REF] Afgan | CloudMan as a platform for tool, data, and analysis distribution[END_REF][START_REF] Schatz | CloudBurst: highly sensitive read mapping with MapReduce[END_REF], Persevere [START_REF] Russ | Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case[END_REF], Cassandra [START_REF] Ye | Arequest skewaware heterogeneous distributed storage systembased on Cassandra[END_REF] and CouchDB [START_REF] Manyam | Relax with CouchDB -Into the non-relational DBMS era of bioinformatics[END_REF]. been developed for the graphical visualization of genomic variation datasets. Some of these have been integrated into data portals associated with specific projects (e.g. OryzaGenome [START_REF] Ohyanagi | OryzaGenome : Genome Diversity Database of Wild Oryza Species Special Online Collection -Database Paper[END_REF], SNP-Seek [START_REF] Alexandrov | SNP-Seek database of SNPs derived from 3000 rice genomes[END_REF]) and are, therefore, only relevant to a particular community. Generic tools also exist (e.g. vcf.iobio [START_REF] Miller | Iobio: a Web-based, real-time, sequence alignment file inspector[END_REF][START_REF] Sera | iobio-A visually driven variant data inspector and real-time analysis web application[END_REF], JBrowse [START_REF] Skinner | JBrowse: a nextgeneration genome browser[END_REF]) and may be built upon to create more versatile applications.

The Gigwa application, the name of which stands for 'Genotype investigator for genome-wide analyses' , aims to take account of all of these aspects. It is a web-based, platform-independent solution that feeds a MongoDB [29] NoSQL database with VCF or HapMap files containing billions of genotypes, and provides a web interface to filter data in real time. In terms of visualization, the first version includes only an online density chart generator. However, Gigwa supplies the means to export filtered data in several popular formats, thus facilitating connectivity with many existing visualization engines.

Application description

A single instance of the Gigwa application is able to display data from multiple databases, which can be chosen from a drop-down menu at the top of the page. A database may syndicate any source of genotyping data as long as the variant positions are provided on the same reference assembly. Gigwa supports work on a single project at a time (although a project may be divided into several runs, in which case new data connected to existing individuals are seen as additional samples). Project selection may be changed from within the action panel (Fig. 1) that sits to the right-hand side of the screen. This panel also enables the launch of searches, toggles the availability of browsing and exporting functions (because limiting the initial approach to the counting of results saves time), configures and launches the export, checks the progress of ongoing operations, and can terminate them if required.

The variant-filtering interface (top of Fig. 2) is both compact and intuitive. In the top-left corner of this panel, three lists allow multiple-item selection of variation types (e.g. SNPs, indels, structural variants), individuals and reference sequences. More specific filters can be incorporated to refine searches using combinations of the following parameters: 'Genotypes' -this filter makes it possible to retrieve only variant positions that respect a specified genotyping pattern when considering selected individuals. If no individuals are selected, the application takes them all into account. A dozen predefined options (e.g. all same, at least one heterozygous) are available, covering those cases that are most frequently meaningful. 'Minimum per-sample genotype quality' and 'Minimum per-sample read depth' -these individual-based filters may be used, in the case of data from VCF files, to set thresholds on the quality (GQ) and depth (DP) fields assigned to genotypes [30]. Individuals that do not meet these criteria are subsequently treated as missing data. ' Authorized missing data ratio' -this filter allows a maximum threshold of acceptable missing data among selected individuals to be defined. Its default value is 100 %, that is, accepting all data. 'Minor allele frequency' (MAF) -this filter retains only the variant positions for which the MAF calculated on selected individuals falls in the SnpEff widgets -these allow additional filtering on variant effects and gene names for data originating from VCF files that have been annotated with SnpEff [START_REF] Cingolani | A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of drosophila melanogaster strain w 1118; iso-2; iso-3[END_REF]. The application automatically detects such additional data and is able to handle both types of annotation field, that is, 'EFF' (SnpEff versions prior to 4.1) and ' ANN' (SnpEff versions from 4.1 onwards).

Matching variants are displayed in paginated form (see bottom of Fig. 2) after application of the filters. Results are listed in a sortable table that provides the main attributes, namely ID (when provided in the input file), reference sequence, start and stop positions, alleles, variant effect and gene name (the latter two only being displayed if available). In addition, the user can focus on a specific position and display variant details, including selected individuals' genotypes, using the magnifier at the end of each row. These details appear in a dialogue (Fig. 3) that, for each run in the selected project, provides:

additional variant-level attributes or annotations (global attributes related to the variant), on the left of the screen;

Data export and visualization

The Gigwa application offers seven standardized formats (VCF, Eigenstrat, GFF3, BED, HapMap, DARwin and PLINK) in which to export filtered results in compressed files. Export is individual-based. Thus, if the data selection includes several samples that belong to the same individual, only one genotype per variant is exported. If these genotypes are inconsistent, the one most frequently found is selected. If there is no most frequently found genotype, one is picked at random.

Where data that originated from VCF files is being reexported in the same format, the application takes phased genotypes into account: a procedure was implemented to maintain phasing information (i.e. haplotype estimation) in the database and recalculate it at export time, even if intermediate positions had been filtered out.

Exports can be directed either to the client computer or to a temporary URL on a web server, thus making the dataset instantly shareable, for example, with Galaxy [START_REF] Giardine | Galaxy: a platform for interactive large-scale genome analysis[END_REF]. Such links remain active for a week. In addition, when applied to the VCF format, this 'export to URL' feature provides the means for users to view selected variants in their genomic context in a running instance of the Integrative Genomics Viewer (IGV) [START_REF] Thorvaldsdóttir | Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration[END_REF].

The current selection may also be directed to an online, interactive, density chart viewer. The variant distribution of each sequence may then be observed, with the ability to filter on variation type, and these figures can also be exported in various file formats (i.e. PNG, JPEG, PDF and SVG).

Technical insights

Third-party software involved MongoDB [29] was chosen as the storage layer for several reasons: its complex query support, its scalability, its open-source nature and its proactive support community. The server application was developed in Java and takes advantage of several Spring Framework modules [START_REF]Java Spring Framework[END_REF] (e.g. Spring Data). The client interface was designed using Java Server Pages (JSP) and jQuery [START_REF]The jQuery Foundation[END_REF]. Some import and export procedures make use of the SAMtools HTSJDK API v1.143 [START_REF]SamTools API[END_REF]. The density visualization tool was implemented using the HighCharts Javascript library [START_REF]Highcharts API[END_REF].

Data structure

The data model for storing genotyping information, defined using Spring Data documents, is shared with the WIDDE application [START_REF] Russ | Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case[END_REF] and allows a single database to hold genotypes from multiple runs of multiple projects. This model is marker-oriented and mainly relies on two basic document types: VariantData, which embeds variant-level information (e.g. position, marker type); VariantRunData, which contains genotyping data along with possible metadata.

A collection named taggedVariants is not tied to a model object because its documents only contain variant IDs. However, it serves an important purpose by providing dividers ('landmarks') that partition the entire collection of variants into evenly sized chunks. These chunks are then used when querying directly on the Varian-tRunData collection (i.e. without a preliminary filter on variant features) to split the query into several subqueries, which confers several advantages (see Querying strategy below).

Less significant model objects include GenotypingProject, which keeps track of elements used to rapidly build the interface (e.g. distinct lists of sequence names and variant types involved in the project), and DBVCFHeader, which simply stores the contents of headers for runs imported in VCF format.

Querying strategy

When the Search button is clicked, the values selected in the search-interface widgets are passed to the server application. They may then be used to count and/or browse matching variants.

The first time a given combination of filters is invoked, the count procedure is launched to establish the number of variants that match the combination. This result is then cached in a dedicated collection so that whenever a user subsequently repeats the same search, the result will be available instantly.

Once the count result has been displayed to the user, if the 'Enable browsing and exporting' box is checked, a second request is sent to the server, invoking the find procedure that eventually provides paginated, detailed variant information in the form of a comprehensive table.

In general, serving such requests (count or find) may be divided into two consecutive steps: a simple, preliminary query of variant features (variant type, sequence, start position), which is applied to indexed fields and therefore executes quickly; the main aggregation query, which is split into several partial queries aimed at running in simultaneous threads on evenly sized variant chunks of the VariantRunData collection. This technique not only improves performance, but also allows MongoDB configurations gave response times showing high degrees of heterogeneity. In order to establish more distinction between them, these benchmarks were therefore executed 12 times. In all cases, average response times were calculated and then reported through graphical plots. The caching system implemented in Gigwa was disabled for the duration of the benchmarking.

Benchmark results

In the case of the location-based filter benchmark (Fig. 4), the MySQL solution was the fastest, with response times that were negligible on the smallest dataset, and never more than 0.05 s on the largest. In comparison, Gigwa queries were less responsive but still remained fairly fast, never taking more than 0.3 s on the largest dataset. However, VCFtools proved so much slower than all the other alternatives benchmarked that we had to exclude its last record for the plot to remain readable. This difference can be explained by the fact that database engines typically take advantage of prebuilt indexes that lead directly to results, whereas VCFtools has to scan the entire file. Relational databases are usually the most efficient for this kind of simple query because their indexing mechanisms have been optimized over decades.

In the case of the MAF filter benchmark (Fig. 5), the fastest solution was VCFtools, followed by the two most compressed MongoDB databases (Gigwa-WT-zlib and Gigwa-WT-snappy), and then by the two least compressed MongoDB databases (Gigwa-MMapv1 and Gigwa-WT-none). The MySQL engine performed so poorly here that it was considered unnecessary to run the longest query on it. In practice, the type of analysis involved in this particular benchmark requires that all stored positions be scanned. VCFtools excels here because it is a C++ program working on flat files, which means that the time needed to access each record is negligible, whereas database engines need to obtain/deflate objects before manipulating them. In contrast to the situation seen in the first filter benchmark, a significant difference in performance emerged here between the various storage solutions offered by MongoDB. There is more room in this benchmark for performance distinctions because memory consumption becomes more crucial when executing a multi-step aggregation pipeline rather than a simple index count. WiredTiger applies compression to indexes, which leaves more memory available for other tasks, thus increasing performance. In addition, WiredTiger is known to perform better than MMapv1 on multi-threaded queries, which are being used by Gigwa.

Thus, Gigwa configured with WiredTiger-snappy (or WiredTiger-zlib in the case of constraints on disk space) appears to be an excellent compromise, being the only solution that responds in a reasonable time to both kinds of query. Furthermore, although it was beyond the scope of this benchmark, we should mention that the greatly reduced storage space required by both WiredTiger-snappy and WiredTiger-zlib, when compared with that required by MMapv1, provides an additional justification for choosing WiredTiger in most cases.

Conclusions

We developed Gigwa to manage large genomic variation data derived from NGS analyses or high-throughput genotyping. The application aims to provide a userfriendly web interface that makes real-time filtering of such data, based on variant features and individuals' genotypes, widely accessible. Gigwa can be deployed either in single-user mode or in multi-user mode, with credentials and permissions allowing fine-grained control of access to connected databases.

We ran benchmarks on two kinds of queries -variantoriented and genotype-oriented -to compare Gigwa's performance with that of both VCFtools and a standard MySQL model. Each of these latter tools performed best in one benchmark but by far the worst in the other. Gigwa, when configured with the WiredTiger storage engine and either the snappy or zlib compression level, appeared as an excellent compromise, performing almost as well as the best solution in both benchmarks.

Future versions of Gigwa will include a RESTful API to allow external applications to interact with Gigwa and query data in a standardized manner, as well as additional visualization tools and a Docker [40] package aimed at distributing the tool as a solution capable of functioning in platform-as-a-service (PaaS) [START_REF]Platform as a Service[END_REF] mode. Further benchmarks will be conducted to evaluate the application's performance in a distributed environment using MongoDB's sharding functionality.

Fig. 1

 1 Fig. 1 Action panel enabling project selection, progress indication, abort and export functionalities

Fig. 2 Fig 3

 23 Fig. 2 Variant-filtering interface, allowing search criteria definition and result browsing

Fig. 4

 4 Fig. 4 Response-time plot by tool for first benchmarked query (location-based filter). VCFtools is by far the slowest option

Acknowledgements

This project was funded by UMR DIADE and Agropolis Fondation under the reference ID ARCAD 0900-001. The authors thank the South Green Platform team for technical support, Sébastien Ravel for testing the annotation filters, Francois Sabot and Mathieu Rouard for meaningful advice, and Aravind Venktesan and Jean-Marc Mienville for careful reading that helped improve the manuscript.

Availability and requirements

Project name: Gigwa Project home page: http://www.southgreen.fr/ content/gigwa Operating system(s): Platform-independent Programming language: Java & MongoDB Requirements: Java 7 or higher, Tomcat 7 or higher, MongoDB 3 or higher License: GNU GPLv3 Restrictions to use for non-academics: None Availability of supporting data Gigwa's source code is available in South Green's public GitHub repository [42]. Supplementary data, benchmarking material and installation archives can be found in the GigaScience GigaDB repository [START_REF] Sempere | Supporting information for "Gigwa -Genotype Investigator for Genome Wide Analyses[END_REF].

Gigwa to provide a progress indicator and the facility to terminate a run before it has finished. The method used for dividing the main query depends on whether or not a preliminary filter was executed beforehand. If it was, the application holds a subset of variant IDs as a consequence, which it uses to split the data using MongoDB's $in operator in each subquery. Otherwise, the contents of the taggedVariants collection are used, in conjunction with the $lte (less than or equal) and $gt (greater than) operators, to define the limits of each sub-query's chunk.

Summary of features

Gigwa's value resides in the following features: Support for large genotyping files with up to several million variants Responsive queries even in the case of a local deployment Intuitive graphical user interface allowing the definition of precise queries in a few clicks Filtering on functional annotations Ability to abort running queries Display of query progress Support of multiple data sources for a single instance A multi-user mode which enables both public and private access to databases to be defined Support for incremental data loading Support for seven different export formats Easy connection with IGV for integration within a consistent genomic context No loss of phasing information when provided (VCF format only) Support for haploid, diploid and polyploid data Online variant density viewing.

The Gigwa application therefore represents a very efficient, versatile and user-friendly tool for users with standard levels of expertise in web navigation to explore large amounts of genotyping data, identify variants of interest and export subsets of data in a convenient format for further analysis. We believe that its large panel of undoubtedly useful features will make Gigwa an essential tool in the increasingly complex field of genomics.

Benchmarking

In order to assess Gigwa's performance, we conducted benchmarks against comparable applications.

Hardware used

All tests were run on an IBM dx360 M2 server with: two quad-core CPUs (Core-i7 L5520 at 2.26 GHz); 36 GB RAM (DDR3 at 1333 MHz); 250-GB SATA2 hard drive.

Dataset selection

As our base dataset, we chose to use the CoreSNP dataset from the 3000 Rice Genomes Project [START_REF]The 3000 rice genomes project. The 3,000 rice genomes project[END_REF][START_REF]Rice genomes datasets[END_REF], which at the time of download (v2.1) contained genotypes for 3000 individuals on 365,710 SNPs. This dataset was first converted to a 4.09-GB VCF file using VCFtools, from which three progressively smaller datasets were then generated by successively dividing the number of variants by ten, i.e. resulting in datasets of 36,571, 3658 and 366 SNPs, respectively.

Benchmark comparisons

We considered it appropriate to compare Gigwa's performance with that of:

1. VCFtools (v0.1.13) [START_REF] Wittelsburger | WhopGenome: high-speed access to whole-genome variation and sequence data in R[END_REF]; 2. A MySQL (v5.6.28) [START_REF]MySQL[END_REF] implementation of a standard relational database model with indexes on appropriate fields. Corresponding queries were implemented as stored procedures, and both these and the database schema are provided as supplementary material within the supporting data.

In addition, the opportunity was taken to evaluate the relative performance of the currently available storage solutions offered by MongoDB v3.0.6, i.e. the newly introduced WiredTiger (WT) storage engine, configured with three different compression levels (none, snappy and zlib), and the original MMapv1 storage engine.

Therefore, each of the benchmarking plots generated contains six series: VCFtools, MySQL, Gigwa-MMapv1, Gigwa-WT-none, Gigwa-WT-snappy and Gigwa-WTzlib. MongoDB queries were launched via the Gigwa interface because, internally, the application splits them into a number of partial, concurrent queries.

Benchmark queries

Two kinds of queries that we considered representative were executed as benchmarks on each dataset for each tool:

Location-based query: a query counting variants located in a defined region of a chromosome (chromosome 3, 1 Mbp to 5 Mbp). Genotype-based query: a query counting variants exhibiting a given MAF range (10 to 30 %) on the first 2000 individuals (out of 3000).

All benchmarks were executed three times, except for the MAF queries in Gigwa where the different

Additional file

Additional file 1: Gigwa, Genotype investigator for genome-wide analyses. Provides the MySQL scripts used for benchmarking and guidelines on how to import data into Gigwa and configure its access for existing users. (DOCX 95 kb) Authors' contributions MR provided the original idea. GSempéré designed the application logic. GSempéré and FP implemented the software. AD and GSarah generated the benchmark datasets. PL and GSempéré conducted the benchmarks. AD and MR performed beta-testing. PL, GSempéré and AD drafted the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.


Claudia Almeida

Antonin Lamazière

Angélique Filleau

Yohann Corvis

Philippe Espeau

Jesus Ayala-Sanmartin
email: jesus.ayala-sanmartin@upmc.fr

Membrane re-arrangements and rippled phase stabilisation by the cell penetrating peptide penetratin

Keywords: (lipids packing, membrane organisation, penetratin, rippled phase) Chol: cholesterol, CPP: cell penetrating peptide, DMPC:1, 2-Dimyristoyl-sn-glycero-3phosphocholine, DMPG:1, 2-Dimyristoyl-sn-glycero-3-phosphoglycerol, DPPC:1, 2 differential scanning calorimetry, GUV: giant unilamellar vesicle, LUV: large unilamellar vesicle, MLV: multilamellar vesicle, PC:L-α-phosphatidylcholine

come   L'archive ouverte pluridisciplinaire

Introduction

Cell penetrating peptides (CPPs) are potential therapeutic vectors for delivery of molecules inside eukaryotic cells. Such peptides (i.e. TAT, penetratin) are usually rich in basic amino acid residues. Penetratin, a peptide derived from the homeodomain transcription factor Antennapaedia was described to successfully carry active molecules inside cells and is one of the most studied CPPs [START_REF] Joliot | Antennapedia homeobox peptide regulates neural morphogenesis[END_REF][START_REF] Perez | Rab3A and Rab3B carboxy-terminal peptides are both potent and specific inhibitors of prolactin release by rat cultured anterior pituitary cells[END_REF][START_REF] Derossi | The third helix of the Antennapedia homeodomain translocates through biological membranes[END_REF].

Membrane binding and cell penetration of CPPs are modulated by the physicochemical properties of both the membrane and the peptide [START_REF] Ziegler | Thermodynamic studies and binding mechanisms of cell-penetrating peptides with lipids and glycosaminoglycans[END_REF]. Cell penetration is known to use two pathways: the receptor-and metabolic energy-independent pathway, and the endocytic pathway [START_REF] Drin | Studies on the internalization mechanism of cationic cell-penetrating peptides[END_REF][START_REF] Duchardt | A comprehensive model for the cellular uptake of cationic cell-penetrating peptides[END_REF]. However, to reach the cytosol and the nucleus after cellular or "physical" endocytosis, the peptides must cross the plasma membrane or the endosomal membrane barrier. Therefore, a direct interaction with membrane lipids seems to be the most important factor for their translocation into the cytosolic or nuclear compartments.

Several mechanisms for CPP membrane translocation have been proposed including an "electroporation-like" mechanism [START_REF] Binder | Charge-dependent translocation of the Trojan peptide penetratin across lipid membranes[END_REF], neutralisation of arginine residues by guanidiniumphosphate complex formation [START_REF] Rothbard | Role of membrane potential and hydrogen bonding in the mechanism of translocation of guanidinium-rich peptides into cells[END_REF], inverted micelles [START_REF] Derossi | Cell internalization of the third helix of the Antennapedia homeodomain is receptorindependent[END_REF] and also a direct translocation through the membrane bilayer [START_REF] Su | Asymmetric insertion of membrane proteins in lipid bilayers by solid-state NMR paramagnetic relaxation enhancement: A cell-penetrating peptide example[END_REF] (for reviews see [START_REF] Dietz | Delivery of bioactive molecules into the cell: the Trojan horse approach[END_REF][START_REF] Fischer | Break on through to the other sidebiophysics and cell biology shed light on cell-penetrating peptides[END_REF][START_REF] Mae | Cell-penetrating peptides as vectors for peptide, protein and oligonucleotide delivery[END_REF][START_REF] Murriel | Influence of protein transduction domains on intracellular delivery of macromolecules[END_REF][START_REF] Di Pisa | Translocation mechanism(s) of cell-penetrating peptides: biophysical studies using artificial membrane bilayers[END_REF]. Experiments with model membranes showed that membrane translocation in large unilamellar vesicles (LUVs) is dependent on membrane potential including pH gradient and is modulated by the lipid composition [START_REF] Terrone | Penetratin and related cell-penetrating cationic peptides can translocate across lipid bilayers in the presence of a transbilayer potential[END_REF][START_REF] Magzoub | Modeling the endosomal escape of cell-penetrating peptides: transmembrane pH gradient driven translocation across phospholipid bilayers[END_REF][START_REF] Madani | Liposome Model Systems to Study the Endosomal Escape of Cell-Penetrating Peptides: Transport across Phospholipid Membranes Induced by a Proton Gradient[END_REF][START_REF] Bjorklund | Real-time transmembrane translocation of penetratin driven by light-generated proton pumping[END_REF].

However, one report showed that membrane potential is not strictly necessary for translocation in LUVs [START_REF] Swiecicki | The efficacies of cell-penetrating peptides in accumulating in large unilamellar vesicles depend on their ability to form inverted micelles[END_REF], and in giant unilamellar vesicles (GUVs), membrane translocation had also shown to be membrane potential-independent [START_REF] Thoren | The antennapedia peptide penetratin translocates across lipid bilayers -the first direct observation[END_REF]. Membrane curvature and/or membrane tension which are higher in LUVs than in GUVs could be responsible of these differences.

Using membrane models, we have previously shown that penetratin and different basic peptides induce membrane invaginations, which results in the formation of tubular structures [START_REF] Lamaziere | Nonmetabolic membrane tubulation and permeability induced by bioactive peptides[END_REF][START_REF] Lamaziere | The homeodomain derived peptide Penetratin induces curvature of fluid membrane domains[END_REF][START_REF] Lamaziere | Transduction peptides: structural-functional analyses in model membranes[END_REF]. Therefore, membrane negative curvature induced by basic peptides could be crucial for the mechanism of internalisation by tube formation ("physical endocytosis") [START_REF] Lamaziere | Nonmetabolic membrane tubulation and permeability induced by bioactive peptides[END_REF] and inverted micelles. Moreover, the capacity of peptides to induce tubes and both negative or positive curvature was also observed in membranes of biological origin [START_REF] Maniti | Metabolic energyindependent mechanism of internalization for the cell penetrating peptide penetratin[END_REF][START_REF] Maniti | Basic cell penetrating peptides induce plasma membrane positive curvature, lipid domain separation and protein redistribution[END_REF]. Besides the capacity of peptides to modify the arrangements of membrane phospholipids, peptide structural changes might be important for membrane translocation. Penetratin has been extensively studied showing to acquire α-helix, β-sheet and coil conformations in different conditions [START_REF] Bellet-Amalric | Interaction of the third helix of Antennapedia homeodomain and a phospholipid monolayer, studied by ellipsometry and PM-IRRAS at the air-water interface[END_REF][START_REF] Caesar | Membrane interactions of cell-penetrating peptides probed by tryptophan fluorescence and dichroism techniques: correlations of structure to cellular uptake[END_REF][START_REF] Christiaens | Tryptophan fluorescence study of the interaction of penetratin peptides with model membranes[END_REF][START_REF] Magzoub | Conformational states of the cell-penetrating peptide penetratin when interacting with phospholipid vesicles: effects of surface charge and peptide concentration[END_REF][START_REF] Magzoub | Interaction and structure induction of cell-penetrating peptides in the presence of phospholipid vesicles[END_REF][START_REF] Persson | Vesicle membrane interactions of penetratin analogues[END_REF][START_REF] Persson | Penetratin-induced aggregation and subsequent dissociation of negatively charged phospholipid vesicles[END_REF][START_REF] Maniti | Distinct behaviour of the homeodomain derived cell penetrating peptide penetratin in interaction with different phospholipids[END_REF].

The membrane domains and raft microdomains concepts have been developed in the last fifteen years, and the role of lipid arrangements in cellular membranes functions and organisation have been demonstrated [START_REF] Simons | Revitalizing membrane rafts: new tools and insights[END_REF]. The role of membrane microdomains in CPP binding and uptake is not well understood. Moreover, the role of membrane lipid charges in the penetratin binding ability has been a subject of controversy. Several studies have suggested that negatively charged phospholipids are required for binding [START_REF] Christiaens | Tryptophan fluorescence study of the interaction of penetratin peptides with model membranes[END_REF][START_REF] Magzoub | Conformational states of the cell-penetrating peptide penetratin when interacting with phospholipid vesicles: effects of surface charge and peptide concentration[END_REF][START_REF] Persson | Application of a novel analysis to measure the binding of the membrane-translocating peptide penetratin to negatively charged liposomes[END_REF], but others have shown that zwitterionic phospholipids are also able to bind the peptide [START_REF] Bellet-Amalric | Interaction of the third helix of Antennapedia homeodomain and a phospholipid monolayer, studied by ellipsometry and PM-IRRAS at the air-water interface[END_REF][START_REF] Andersson | Diffusion and dynamics of penetratin in different membrane mimicking media[END_REF]. It has also been shown that cholesterol (Chol) (an essential component of the so called membrane "rafts" microdomains) modulates CPP binding to phosphatidylcholine/phosphatidylglycerol (PC/PG) membranes [START_REF] Caesar | Membrane interactions of cell-penetrating peptides probed by tryptophan fluorescence and dichroism techniques: correlations of structure to cellular uptake[END_REF]. Moreover, we demonstrated that membranes in a liquid ordered raft-like phase seems to block or reduce the peptide-induced effect on membrane curvature in comparison with membranes in the liquid disordered phase [START_REF] Lamaziere | The homeodomain derived peptide Penetratin induces curvature of fluid membrane domains[END_REF][START_REF] Lamaziere | Tubular structures in heterogeneous membranes induced by the cell penetrating peptide penetratin[END_REF].The mechanisms involved in the peptide interaction with different membrane domains and the modifications of membrane lipids organisation by CPPs are therefore important factors for the development of vector-mediated therapeutic strategies at both, the peptide (vector) and the lipid membrane (target) levels. Therefore, we have investigated the influence of the CPP penetratin on membranes with different phospholipid composition mimicking different microdomains of the eukaryotic plasma membranes. The influence of the membrane state on penetratin induced phospholipid organisation was studied by X-Ray diffraction, laurdan fluorescence and differential scanning calorimetry (DSC). The results indicate that penetratin is able to induce rearrangements of membrane lipids that favour phase separation and membrane heterogeneity as shown specifically by the stabilisation of the membrane rippled phase. The implications of the data in penetratin membrane activities are discussed.

Materials and Methods

Materials

Egg yolk L-α-phosphatidylcholine (PC), egg yolk L-α-phosphatidyl-DL-glycerol (PG), sphingomyeline (SM) and cholesterol (Chol) were purchased from Sigma. 1,2-Dimyristoylsn-glycero-3-phosphocholine (DMPC), 1,2-Dipalmitoyl-sn-glycero-3-phosphocholine (DPPC),1,2-Dimyristoyl-sn-glycero-3-phosphoglycerol (DMPG ) and 1,2-Dipalmitoyl-snglycero-3-phosphoglycerol (DPPG) were purchased from Genezyme. Penetratin (RQIKIWFQNRRMKWKK) was synthesised using Boc solid phase strategy and was purified by HPLC as previously described [START_REF] Lamaziere | Nonmetabolic membrane tubulation and permeability induced by bioactive peptides[END_REF]. Laurdan was from molecular probes.

Preparation of membranes

Multilamellar vesicles (MLVs) for X-ray diffraction were obtained as previously described [START_REF] Lamaziere | The homeodomain derived peptide Penetratin induces curvature of fluid membrane domains[END_REF]. Briefly, by dissolving 20 mg of lipids in the appropriate proportions in a mixture of chloroform and methanol, 2/1 (v/v), followed by solvent evaporation under nitrogen. Final traces of solvent were removed in a vacuum chamber attached to a liquid nitrogen trap for 3-4 hours. Lipid films were hydrated with 25 µl of buffer10 mM HEPES pH 7.4 containing 2 mg of penetratin and mixed extensively. The lipid dispersion was thoroughly stirred, sealed under argon and kept until examination at 4°C. Large unilamellar vesicles (100 nm diameter LUVs) were prepared by extrusion in 0.5 mM HEPES buffer (pH 7.4) as described in [START_REF] Zibouche | The N-terminal domain of annexin 2 serves as a secondary binding site during membrane bridging[END_REF]. The lipids were added at the desired proportions to obtain a suspension of 1 mg ml -1 LUVs in buffer. Laurdan was present at 0.1% lipid weight ratio.

MLVs for DSC experiments were performed as follows: 2 mg of lipids on chloroform were dried and subjected to vacuum for 10 minutes. Then, 2 ml of buffer were added in the presence or the absence of penetratin at the desired Peptide/Lipid Molar Ratio (P/L) (1/50 or 1/10). The samples were strongly stirred to obtain the suspension of MLVs (1 mg ml -1 ) ready to use for the CSC calorimeter. For the experiments performed with the DSC 822 calorimeter, the MLV suspensions (2 mg ml -1 ) were centrifuged in a Beckman centrifuge at 100 000g. The MLV pellets were recovered in a final volume of 40 μl (MLV final concentration of 50 mg ml -1 ).

Fluorescence spectroscopy

Fluorescence measurements were performed with a Jasco fluorimeter as previously described [START_REF] Illien | Lipid organization regulates annexin A2 Ca(2+)-sensitivity for membrane bridging and its modulator effects on membrane fluidity[END_REF]. The excitation and emission band-pass were set at 5 nm. Spectra were recorded 7 min after addition of penetratin to LUVs in 0.5 mM HEPES buffer (pH 7.4). Laurdan emission spectra were recorded from 400 to 650 nm using a 365 nm excitation wavelength in the absence or presence of penetratin at different penetratin/lipid molar ratios. The temperature was regulated with a Peltier device.

The generalised polarisation (GP) was calculated with the equation:

GP = (I430-I485)/(I430+I485)
And the ΔGP was defined as GP final (after penetratin addition) -GP initial (before penetratin addition).

X-ray diffraction

Small angle and wide angle X-ray diffraction (SAXD, WAXD) measurements were performed at the Synchrotron Radiation Source of Spring8 (Japan) following the protocol described in [START_REF] Lamaziere | The homeodomain derived peptide Penetratin induces curvature of fluid membrane domains[END_REF]. Briefly, the samples (20 l) were deposited between two thin mica windows and mounted on a programmable temperature stage. Samples were exposed for 30 seconds recordings. Spacing distances were determined from circular integration of 2-D images using FIT2D program (Andy Hammersley) and silver behenate was used as the x-axis calibration standard. Peak analysis was performed with Origin and PeakFit 4 programs.

Differential scanning calorimetry (DSC)

Calorimetry was performed on a high-sensitivity Differential Scanning Calorimeter (Calorimetry Sciences Corporation, CSC) and a Mettler-Toledo DSC 822 calorimeter. A scan rate of 1 C min -1 was used and a delay of 10 min between sequential scans in a series was set for thermal equilibration. Indium and zinc (purity higher than 99.9%) were used for temperature and enthalpy calibration of the DSC device. Data analyses were performed with the programs provided by the constructors. The total lipid concentrations used were 1 and 50 mg ml -1 for the CSC and the Mettler-Toledo calorimeters, respectively. For peptide concentrations corresponding to peptide/lipid molar ratio (P/L) 1:10, no thermal events were observed over the temperature range of 0-100C. This indicates that the endothermic events observed in this study arise solely from phase transitions of the phospholipids vesicles. For the CSC calorimeter a minimum of three heating and cooling scans were performed.

Statistical analysis

The statistical significance of differences between experiments was determined by the Student's t-tests with GraphPad Prism software. For differences before and after peptide addition we used paired test, and for differences between different conditions the unpaired test was performed. Differences were considered statistically significant at P < 0.05. Graphs are given as bar plots ± standard error (SE).

Results

Membrane lipids rearrangements studied by X-ray diffraction.

The penetratin effects on phospholipid phase separation and its thermal stability were studied by Small Angle X-ray diffraction (SAXD) in model membranes mimicking different domains of cellular plasma membranes. PC mimics the liquid disordered (Ld) membrane domains. PC/PG (9/1) mimics Ld domains but in the presence of a small proportion of negative lipids. This domains are present in several cancer cells. SM/Chol (1/1) mimics the liquid ordered (raft) domains. SM/Chol/PG (4/5/1) mimics the raft domains in the presence of negative charges, and finally, PC/SM/Chol (1/1/1) membrane mimics the overall heterogeneous plasma membrane with coexisting liquid ordered and liquid disordered domains. We previously showed that penetratin was able to induce phase separation in fluid membranes but not in liquid ordered membranes. Here we studied in more detail the temperature dependent variations on d-spacing, i.e. the distance between two membranes including the water layer (Figure 1). The variation of the two first diffraction orders of diffractograms for PC as well as the variation of the corresponding d-spacing distances are represented as a function of the temperature in Figure 1. As can be seen, for PC in the absence of penetratin, the d-spacing corresponding to one lamellar structure (L1) decreases with increasing the temperature. When penetratin is added to PC, diffraction peaks with higher d-spacing corresponding to two different lamellar structures (L2 and L3) were then observed (Figure 1B). As previously reported [START_REF] Lamaziere | Lipid domain separation, bilayer thickening and pearling induced by the cell penetrating peptide penetratin[END_REF], the electron density profiles show that the two SAXD peaks correspond to two different lamellar phases and not only to an increase of the hydration layer due to peptide accumulation. The L2 of similar thickness as the L1, but the L3 layer being thicker by about 0.5 nm [START_REF] Lamaziere | Lipid domain separation, bilayer thickening and pearling induced by the cell penetrating peptide penetratin[END_REF]. This indicates that a real phase separation took place. As can be seen, the peak width is broadened. As for pure PC (L1), when the temperature increases, the d-spacing of the L2 phase decrease. On the contrary, for the L3 phase a thermal stability was observed (Fig. 1B and1C).

The d-spacing obtained from SAXD experiments are gathered in Table 1 for the 5 MLV of different composition at 25 °C with and without penetratin.

In SM/Chol and PC/SM/Chol membranes, penetratin showed only moderate effect in phase separation (PC/SM/Chol as published previously [START_REF] Lamaziere | Lipid domain separation, bilayer thickening and pearling induced by the cell penetrating peptide penetratin[END_REF]) and thermal stability (SM/Chol in Figure 1C). However, as for PC, PC/PG and SM/Chol/PG, penetratin showed different lamellar structures with an increase in d-spacing. For comparison, table 1 shows the d-spacing at 25°C. It is evident the increase of d-spacing in liquid disordered phases and in the presence of PG as well as phase separation. After Penetratin addition the observed tendency was that the peptide did not modify the thermal behaviour of the thinner phase (L2), but induced thermal stability of the thickest phase (L3, d-spacing of about 8.7 nm).

The penetratin effect on the lateral phospholipid organisation and on their thermal stability was studied by Wide Angle X-ray Diffraction (WAXD). In the absence of penetratin, for the five different MLVs studied (three examples are shown in Figure 2), the WAXD peaks were very symmetric and shifted to higher d-spacing with the increase of the temperature. At 25°C the spacing distances were in the range of 4.39 to 4.63 Å (Figure 2A-C and Table 1S of supplementary material). With the exception of SM/Chol liquid ordered MLVs (Figure 2F and Table 1S), the presence of penetratin induced, in all cases, the splitting of the symmetric WAXD peak into a signal composed by two or three different contributions (Figures 2D,E and Table 1S). The analysis of WAXD signal for PC in the presence of penetratin at 25 °C showed to be formed by the contribution of three peaks at 4.72, 4.42 and 4.15 Å and for PC/SM/Chol two peaks at 4.70 and 4.47 Å. This fact reveals a rearrangement of head groups in interaction with penetratin as previously suggested [START_REF] Zhang | Mechanism of penetration of Antp(43-58) into membrane bilayers[END_REF]. These distances could be consistent with lipid organisation in a quasi hexagonal or a distorted triangular lattice [START_REF] Harlos | Pretransitions in the hydrocarbon chains of phosphatidylethanolamines. A wide angle X-ray diffraction study[END_REF][START_REF] Heimburg | A model for the lipid pretransition: coupling of ripple formation with the chainmelting transition[END_REF][START_REF] Rappolt | New evidence for gel-liquid crystalline phase coexistence in the ripple phase of phosphatidylcholines[END_REF][START_REF] Williams | A combined SAXS/WAXS investigation of the phase behaviour of di-polyenoic membrane lipids[END_REF]. Moreover (with the exception of SM/Chol), the position (spacing) of peaks remained thermally more stable compared to the peaks in the absence of the peptide, especially for the contribution around 4.7 Å (Figures 2G,H,I). Two particular cases were observed: the spacing changes at low temperature from 5 to 15°C for PC and from 5 to 25°C for PC/SM/Chol (Figures 2G and2H) and at higher temperature the spacing remains stabilised by the peptide.

Overall, these data show that penetratin induces stronger changes in liquid disordered membranes compared to the liquid ordered (raft) domains.

Changes in membrane lipids packing induced by penetratin.

In order to study the effect of penetratin on membrane lipids compactness we used the environment probe laurdan. We showed previously that penetratin was able to induce phospholipid rigidity in PG and PE but not in PC membranes [START_REF] Maniti | Distinct behaviour of the homeodomain derived cell penetrating peptide penetratin in interaction with different phospholipids[END_REF]. Herein, and in order to better understand the observed effect of penetratin on head group organisation (WAXD data) we studied the interaction of penetratin on well characterised DMPC membranes which organise in different phases: at 9 °C (gel phase), at 19 °C (rippled phase) and 30 °C (liquid phase) in the absence or in the presence of a negatively charged phospholipid (DMPC and DMPC/DMPG (9/1) respectively). Figure 3A shows an example of laurdan spectra of DMPC/DMPG LUVs at 30°C in the presence and in the absence of penetratin. In DMPC membranes, penetratin showed a moderate effect in lipid packing depending on the lipid phases. As shown in figure 3B at low P/L ratio (1/50), penetratin only showed a decrease in lipids packing in the liquid phase (30 °C) and at higher P/L ratio (1/10), the effect in lipid compactness was stronger in the gel (9 °C), rippled (19 °C) and liquid (30 °C) phases.

In the presence of DMPG, a negative charged lipid that increases the affinity of penetratin to membranes, the penetratin effect in lipid packing was stronger. As shown in figure 3C, the decrease in compactness was observed for both P/L ratios but it was clear that the peptideinduced changes are stronger in liquid phase and are smaller in the rippled phase. In order to further study the effect of penetratin on lipid organisation, we characterised the thermal lipid phase transitions: gel-rippled and rippled-liquid transition temperatures by DSC.

Penetratin induced changes in thermal transitions of the membrane.

The results from the laurdan fluorescence and X-ray diffraction reveal that penetratin induces a different organisation in local regions of the phospholipid membranes. Thus, to clarify the penetratin effect on thermal stability on different lipid phases, we performed calorimetric experiments with DMPC, and DMPC/DMPG (9/1) membranes in the absence and in the presence of penetratin at 1/10 and 1/50 peptide/lipid molar ratios. The thermograms showed the typical two transition peaks (Fig 4A). The temperature and enthalpy of transition values are reported in Table 2. For DMPC MLVs, a small peak for the pre-transition temperature (Tpre) (transition from Lβ' gel to the rippled Pβ' phases) was observed starting at 12.5 °C, and for the transition temperature (Tm) (Pβ' to Lα liquid state) at 23.6 °C, values in agreement with several reports [START_REF] Alves | Membrane interaction and perturbation mechanisms induced by two cationic cell penetrating peptides with distinct charge distribution[END_REF][START_REF] Janiak | Nature of the Thermal pretransition of synthetic phospholipids: dimyristolyl-and dipalmitoyllecithin[END_REF][START_REF] Jobin | The enhanced membrane interaction and perturbation of a cell penetrating peptide in the presence of anionic lipids: toward an understanding of its selectivity for cancer cells[END_REF]. After the addition of penetratin, there was no significant variation for the transition temperatures at low peptide concentration. However, for higher concentration, penetratin induced a significant shift in the pre-transition temperature to a lower value (11.7 °C). To figure out the effect of a negative charge in the membranes, MLVs were prepared with DMPC/DMPG (9/1). The transition temperatures in the absence of the peptide remained stable as compared with DMPC alone. Nevertheless, the penetratin effect was similar but more pronounced compared to DMPC membranes. The peptide induced a stronger shift of the pre-transition peak resulting in a decrease of the pre-transition temperature (10.5 °C) (Figure 4B and table 2). In addition, for DMPC/DMPG membranes we observed a very small but constant and significant increase in the transition temperature (Figure 4B). These effects were observed at low and high concentration of penetratin, but were stronger at higher penetratin concentration (1/10 peptide/lipid molar ratio) with a temperature lowering of 1.9 °C for the Lβ' -Pβ' phase transition.

Concerning the enthalpy, we observed a significant decrease for MLVs with penetratin at high concentration during the pre-transition. This decrease in enthalpy is probably due to the increase in the peptide-induced heterogeneity of the sample by the introduction of different lamellar structures into the system.

In order to further study the peptide effect in the membranes, we prepared DPPC/DPPG membranes since the presence of DMPG induced stronger effects with DM-lipids. The results presented in Table 2 show a similar behaviour for DPPC/DPPG compared with DMPC/DMPG MLVs. Penetratin at higher concentration reduce the pre-transition temperature of 2.1 °C. We observed as well a lowering in enthalpy for the Lβ' -Pβ' phase transition.

Discussion

Studies of CPP-membrane interaction present a high appositeness in pharmaceutical application. To better elucidate the CPP capacity to translocate through membranes and then promote an appropriate biological response, the understanding of membrane lipid rearrangement induced by CPPs is relevant. This work is focused on penetratin peptide, which have been shown to induce membrane invaginations resulting in the formation of tubular structures [START_REF] Lamaziere | Nonmetabolic membrane tubulation and permeability induced by bioactive peptides[END_REF][START_REF] Lamaziere | The homeodomain derived peptide Penetratin induces curvature of fluid membrane domains[END_REF][START_REF] Lamaziere | Tubular structures in heterogeneous membranes induced by the cell penetrating peptide penetratin[END_REF]. The peptide induces negative curvature of membrane, which could be crucial for cellular internalisation by tube and inverted micelles mechanisms [START_REF] Lamaziere | Nonmetabolic membrane tubulation and permeability induced by bioactive peptides[END_REF]. Penetratin also had shown to acquire α-helix, β-sheet and coil conformations in different conditions [27-34, 50, 51].

To get more insight into the lipid rearrangements induced by penetratin, we analysed the physicochemical properties of membrane lipids after the addition of penetratin. Specifically, we characterised lipid rearrangements by X-ray diffraction, lipid packing by laurdan fluorescence and lipid phase transition by differential scanning calorimetry.

To perform the X-ray diffraction study, we chose different membranes. PC to mimic liquid disordered external leaflet domains, PC/PG (9/1) to study the effect of negative charge and SM/Chol (1/1), SM/Chol/PG (4/5/1) and PC/SM/Chol (1/1/1) to study pure raft, and heterogeneous cholesterol containing membranes. We observed, with the exception of SM/Chol membranes (raft), that penetratin induced membrane phase separation. This fact confirmed the precedent data showing that penetratin was unable to induce membrane tubulation in liquid ordered membranes [START_REF] Lamaziere | The homeodomain derived peptide Penetratin induces curvature of fluid membrane domains[END_REF]. A second interesting point from these experiments was the observation that after Penetratin addition, the resulting lamellar phases showed different temperature dependence. Particularly the lamellar phase of higher d-spacing showed to be more resistant to change the d-spacing in function of temperature. These penetratin effects were confirmed by WAXD analyses. The peptide induced a change in the WAXD signal from one single peak (probably a hexagonal lattice) to a peak containing three different contributions suggesting a distorted hexagonal organisation. Since WAXD experiment reflects the head polar group arrangement of phospholipids, we demonstrated that penetratin induce a re-organisation of lipids within the membranes. As for SAXD we observed that after a temperature increase, the lipid distances for one of the contributions induced by penetratin (around 4.7 Å) were more stable than the two others. In summary, the results from WAXD and SAXD experiments are correlated because after the penetratin addition, there were appearance of thermally stable peak in both, the polar head group distance (WAXD) and the lipid bilayer thickness (SAXD).

To study the lipid organisation thoroughly, we measured the compactness of the membrane lipids by laurdan fluorescence. Synthetic lipids were used here (DMPC and DMPC/DMPG) to obtain data of penetratin interaction on specific well-characterised membrane phases, which are stable at different temperatures (gel Lβ, rippled Pβ' and fluid Lα phases). The laurdan environment polarity increases with the increase of the polar head groups distance, producing a red shifted spectrum. Our data showed that penetratin induce a quite small change on DMPC membranes. In fact, the peptide decreases compactness only at a high peptide/lipid ratio. The changes were very small on the gel and rippled phases but stronger for the liquid phase. In the presence of 10% DMPG, which increases the affinity of penetratin for the membrane, the effect of penetratin on lipid packing was clear. We observed that penetratin induces a strong reduction of the lipids packing in the liquid phase. On the other hand, the effects were moderate for the gel phase and even smaller for the rippled phase. These results allow us to suggest that the interaction of penetratin with the Pβ' phase is naturally occurring.

In other words penetratin adapts easily to this organisation inducing only a small change in lipid packing, since in the other phases penetratin trigger stronger changes in membrane compactness when interacting with them.

Then to better understand the penetratin effect on the different lamellar phases (gel, rippled and liquid), we performed DSC experiments, which allowed the study of the influence of the peptide on the transition temperatures between these different phases.

As shown in table 2, the peptide decreased considerably the pre-transition temperature and slightly increased the transition temperature. As for the laurdan experiments, this perturbations in temperature were stronger for DMPC/DMPG than for DMPC membranes. These results were also observed for DPPC/DPPG membranes. Therefore, the peptide clearly favours the transition through the rippled phase in a way to stabilise it. A very similar behaviour was also observed for the peptide S4(13)-PV with DPPC membranes [START_REF] Cardoso | S4(13)-PV cellpenetrating peptide induces physical and morphological changes in membrane-mimetic lipid systems and cell membranes: implications for cell internalization[END_REF].

The whole data presented here shows that penetratin is able to separate lamellar phases in a membrane and that these new phases acquire new thermal properties. Penetratin induces thermal stability of the "the novo" formed phases and the de-packing of saturated lipids.

Moreover, the lipid packing experiments showed that the peptide perturbs the less the rippled phase which is in agreement with the calorimetry studies showing that the peptide seems to favour the rippled phase. Two models have been suggested for the lipid organisation of the rippled phase. The first proposed that the lipids were tilted with respect to the normal of the membrane with periodic ripples [START_REF] Tardieu | Structure and Polymorphism of Hydrocarbon Chains of Lipids -Study of Lecithin-Water Phases[END_REF]. However, Tenchov [START_REF] Tenchov | Time-resolved x-ray diffraction and calorimetric studies at low scan rates: I. Fully hydrated dipalmitoylphosphatidylcholine (DPPC) and DPPC/water/ethanol phases[END_REF] reported data showing the existence of two different Pβ' phases, one being metastable. More recently, two groups published experimental and simulation data suggesting that the Pβ' phase could be organised as two structurally related structures, one typically in the tilted gel state coexisting with a second more fluid responsible of the ripples within the lamella [START_REF] Heimburg | A model for the lipid pretransition: coupling of ripple formation with the chainmelting transition[END_REF][START_REF] Rappolt | New evidence for gel-liquid crystalline phase coexistence in the ripple phase of phosphatidylcholines[END_REF]. Our data is consistent with this hypothesis since penetratin seems to prefer the rippled phase and at the same time induces de-packing and phase separation.The capacity of the penetratin to modify the organisation of membrane lipids could be an important factor involved in its capacity to cross the plasma membrane. Notice that the penetratin effect reported does not indicate that the rippled phase is important for membrane translocation (this phase is not present in cellular membranes). We suggest that the penetratin-induced membrane heterogeneity when interacting with an homogeneous membrane could be involved in cell penetration. The stabilisation of the rippled phase deduced by the low effect on membrane compactness and the effect on transition temperatures support this hypothesis on the importance of membrane heterogeneity, because the rippled phase is an heterogeneous structure in which gel and liquid contributions coexist. How this membrane heterogeneity would be involved in cellular peptide penetration? The penetratin would take advantage of the presence of mismatches, defects or discontinuities on the membrane between the pre-existent or the peptide-induced membrane phases (i.e. membrane domains) of different thickness and physical properties.

Further experiments with biological membranes will be necessary to test this interesting hypothesis.

Tables

Figure 1 .

 1 Figure 1. Penetratin effects on membrane phase separation and thermal stability by SAXD. A; diffractograms of PC MLVs at different temperatures (5°C continuous line. 15°C dotted line. 25°C dashed line. 35°C dotted-dashed line and 45°C bi-dotted-dashed line). Only the two first reflexions of the lamellar phase are shown. Notice the temperature-dependent dspacing shift of the lamellar phase (L1). B; Diffractograms of the PC MLVs in the presence of penetratin at different temperatures as in (A). Notice the two lamellar phases: one thermal sensitive shifting to the right (L2, horizontal arrow) and one thicker thermally stable (L3, vertical arrows). C; d-spacing of different lamellar phases in function of the temperature. (□)

Figure 2 .

 2 Figure 2. WAXD profile as a function of the temperature in the presence and absence of penetratin. A; PC. B; PC/SM/Chol.C; SM/Chol/PG. D; PC in the presence of penetratin. E; PC/SM/Chol in the presence of penetratin. F; SM/Chol in the presence of penetratin at different temperatures (5°C continuous line. 15°C dotted line. 25°C dashed line. 35°C dotteddashed line and 45°C bi-dotted-dashed line). Notice the presence of three different contributions (arrows in D and E). G.H and I; spacing of the different contributions as a function of temperature in the absence of penetratin (○. dotted line) and the presence of the peptide. peak a (■). peak b (▲) and peak c (▼). G; PC. H; PC/SM/Chol and I; SM/Chol/PG.

Figure 3 .

 3 Figure 3. Penetratin changes in membrane lipids compactness. A); Laurdan fluorescence spectrum of DMPC/DMPG (9/1) LUVs at 30 °C in the absence (continuous line) and the presence of penetratin at P/L ratio 1/10 (dotted line). B); Delta GPs of laurdan fluorescence spectra of DMPC. C); Delta GPs of laurdan fluorescence spectra of DMPC/PG (9/1). For B and C, membranes after penetratin addition at two different P/L (Peptide/Lipid) molar ratios and at three temperatures corresponding to the membranes in the gel (9 °C), rippled (19 °C) and liquid/phases (30 °C). The experiments were performed from 17 to 19 times. Bars represent the values with SE: *: p>0.05. **:p>0.01. ***: p>0.001. Stars at the base of the bars are related to the difference between GP before and after addition of penetratin. Stars with lines are related to the comparison between different conditions.

Figure 4 .

 4 Figure 4. Penetratin effects on the transition temperatures of DMPC/DMPG membranes. A); DSC profile of MLVs before and after addition of penetratin at P/L 1/50 molar ratio. B; Detail of the pre-transition temperatures (gel Lβ to rippled Pβ') of MLVs in the absence and the presence of penetratin at P/L 1/10. C; Detail of the transition temperatures (rippled Pβ' to liquid Lα) of MLVs in the absence and the presence of penetratin at P/L 1/50. MLVs before (continuous lines) and after penetratin addition (dotted lines). The endothermic signals are pointing up.

Table 1 .

 1 SAXD d-spacing from MLVs at 25°C in the absence and presence of penetratin. Spacing values were obtained as the average of 3 to 5 orders.

		d-spacing of MLVs	d-spacing of MLVs + penetratin (nm) a
		alone (nm) a
	MLVs	Phase L1	Phase L2	Phase L3
	PC	5.98	7.07	8.70
	SM/Chol (1/1)	6.59	6.77	-
	PC/SM/Chol (1/1/1)	6.71	6.69	5.96
	PC/PG (9/1)	6.11	6.91	8.73
	SM/Chol/PG (4/5/1)	6.41	-	8.20
	a;

Table 2 .

 2 Thermodynamic parameters obtained by DSC for the interaction of penetratin with different MLVs a .

	MLV	P/L	Tpre (°C)	ΔHpre (kJ/mol)	Tm (°C)	ΔHm (kJ/mol)
	DMPC	0	12.5 ± 0.37	2.7 ±0.19	23.6 ± 0.24	21.0 ± 1.6
		1/50	12.4 ± 0.38	2.6 ± 0.23	23.6 ± 0.24	22.0 ± 1.0
		1/10	11.7 ± 0.33 *	2.2 ± 0.18 *	23.5 ± 0.15	19.4 ± 1.4
	DMPC/DMPG	0	12.4 ± 0.19	1.9 ± 0.25	23.6 ± 0.15	18.0 ± 1.0
		1/50	11.5 ± 0.34 *	1.2 ± 0.16 *	23.7 ± 0.16 **	18.0 ± 1.4
		1/10	10.5 ± 0.34 *** 1.0 ± 0.18 **	23.7 ± 0.16 *	19.5 ± 1.6
	DPPC/DPPG	0	31.8 ±0.30	1.9 ± 0.12	40.6 ± 0.17	22.7 ±1.4
		1/50	32.0 ± 0.10	1.5 ±0.20 ***	41.0 ±0.10 *	21.7 ±1.7 *
		1/10	29.7 ±0.30 *** 1.1 ±0.06 ***	40.5 ±0.10	20.4 ±0.9 *
	a; Values are mean values ± standard error. Experiments were performed from 7 to 11 times
	for DMPC and DMPC/DMPG membranes and from 3 to 5 experiments for DPPC/DPPG
	MLVs. Statistical significance were performed by comparison of membranes alone and in the
	presence of penetratin. (*p<0.05. **p<0.01. ***p<0.001).

Acknowledgements

Claude Wolf, Cedric Tessier and Ofelia Maniti for useful discussions, X-ray diffraction facility at Spring8 (Japan). This work was supported by CNRS and an ANR-PCV grant.