Ph.D William S Oetting
email: oetti001@umn.edu

Christophe Béroud

Steven E Brenner

Marc S Greenblatt

Rachel Karchin

Sean D Mooney

Methods and Tools for Assessing the Impact of Genetic Variations: The 2017 Annual Scientific Meeting of the Human Genome Variation Society

Keywords: HGVS, meeting report, genetic variants, methodology

Introduction

The 2017 Annual Scientific Meeting of the Human Genome Variation Society (HGVS; www.hgvs.org) was held on the 17 th of October in Orlando, Florida, USA with the theme of "Methods & Tools for Assessing the Impact of Genetic Variations". The meeting was opened by Marc Greenblatt of the University of Vermont Medical Center, Burlington, Vermont. This is an exciting time in genetic variation analysis. In the beginning of the Human Genome Variation Society (HGVS; http://www.hgvs.org), only a few individuals were interested in creating tools to predict the functional impact of genetic variants, but this has now become a mainstream portion of the genetics world. Both researchers and clinicians are awash in full exome and genome sequences, with the myriad of variants they harbor. For some purposes, well-studied variants provide research insight and clinical resolutions. But more often, the variants' roles are not conclusively known from previous studies, and therefore methods are necessary to help predict their phenotypic impact. In this meeting cutting-edge research and practical approaches involving methods for interpreting human genetic variants were presented.

Session 1

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3

Session 1 was chaired by Marc Greenblatt. The first invited presentation was given by Mark Gerstein from the Program in Computational Biology and Bioinformatics at Yale University, Connecticut, who spoke on "Prioritizing somatic variants". Personal genomics will increasingly play an essential role in providing precision medicine to the general public through identification of functional genetic variants, both in the germline and somatic tissues.

This is becoming apparent in individualizing cancer therapy. A tumor typically contains 2 to 8 driver mutations which confer a selective growth advantage to the tumor cell.

Unfortunately the tumor also contains thousands of passenger mutations which have no obvious direct or an indirect effect on tumor progression. It is important to differentiate between these two classes of variants to better enable more precise diagnostics and targeted therapies. A number of approaches have been created to identifying key variants either by predicting the functional impact of the variant, or by considering the recurrence of a mutation in tumors. Two programs, Annotation of Loss-of-Function Transcripts (ALoFT;

ALoFT.gersteinlab.org) and Frustration (github.com/gersteinlab/Frustration) provide a means to predict the functionality of a specific coding variant. A major problem which can confound this type of analysis is the existence of multiple transcripts from a single locus.

The Variant Annotation Tool (VAT; http://VAT.gersteinlab.org) was created to take into account transcript isoforms when determining the functional consequences of a variant on protein function. Together with VAT, ALoFT or Frustration can characterize the functionality of variants and help identify the drivers of tumors. ALoFT creates an impact score, along with a confidence score, to determine if a variant is benign or deleterious. This method uses annotation of functional domains and conservation of sequence to derive the ALoFT score. Frustration attempts to determine the effect of a coding variant by predicting its effect on protein folding by evaluating localized frustration changes due to amino acid substitutions and their effect on changes in localized energies of protein folding. Aix-Marseille University, France, who spoke on "Predicting the impact of mutations on splicing signals". Intronic variants which affect transcript splicing account for a significant number of mutations associated with disease. Additionally up to 30-50% of exonic variants might also impact transcript splicing. Because we will be collecting millions of variants through next-generation DNA sequencing (NGS), we need efficient methods to identify those that affect splicing. The role of splicing is to remove introns from pre-messenger RNA and is a complex process requiring the recognition of multiple RNA degenerated splicing motifs.

Thus, the prediction of a variants' impact on those signals is difficult to assess and vary according to the splicing signal (splice sites, branch point or auxiliary sequences). We therefore need prediction tools able to predict the impact of variants on 5'splice site and 3'splice site motifs as well as branch point sites yet taking into account exonic splice site

This article is protected by copyright. All rights reserved. 8 motifs such as exonic splicing enhancers and silencers (ESE and ESS). To this end various approaches and tools have been created. These include Human Splicing Finder, MaxEntScan, NNSplice and SplicePort. Human Splicing Finder (HSF; http://www.umd.be/HSF3) uses a position weight matrices (PWM) approach. In testing pathogenic and non-pathogenic splice sites mutations from BRCA1 and BRCA2, HSF had a positive predictive value (PPV) of 0.986 and a negative predictive value (NPV) of 1.0. This was more accurate for this set of mutations than MaxEntScan (PPV=0.714 and NPV=0.750).

The HSF system also successfully predicts the impact of mutations on branch point sequences as all such mutations have been accurately predicted. Moreover, HSF now contains specific matrices for non-canonical splice sites and can be very helpful for VUS reevaluation. Additional motifs which affect transcript splicing are ESEs and ESSs and intronic splicing enhancers (ISE) and silencers (ISS). Predicting the impact of mutations on ESE and ESS motifs is more difficult because of the paucity of experimental data.

Nevertheless, HSF now provides an improved accuracy. Methods like HSF are now available for accurate interpretation of variants which affect splicing and will aid in helping select functional variants from the large number of variants identified in NGS data.

The topic of accurately identifying variants which affect splicing was continued by Gabe Rudy of Golden Helix, Bozeman, Montana, who presented his talk "Rethinking the 5 splice site algorithms used in clinical genomics". There are several algorithms for splice site prediction. In this report, five methods were compared for their accuracy in predicting the consequences of variation at splicing motifs: Human Splice Finder-like, SpliceSiteFinderlike, MaxEntScan, NNSplice and GeneSplicer. The test set consisted of 20,000 known splice sites extracted from the human GRCh38 reference sequence using exon boundaries specified by NCBI RefSeq Genes. Additionally, 20,000 false splice sites from the HS3D splice site

This article is protected by copyright. All rights reserved. 9

dataset were added to the test set. For acceptor splice site results all methods did very well.

For donor splice sites, GeneSplicer had the best performance for all metrics. MaxEntScan also performed well. SpliceSiteFinder-like and HumanSpliceFinder-like both had poor precision due to high false positive rates. This was due to not utilizing information in the dependencies between bases in which PWM based methods fail to capture because they that treat each base independently. It should be noted that the "-like" tools did not embody the full array of methods in the original authors' implementation. While these methods can provide useful in silico predictions to help prioritize and provide supplemental evidence when interpretation a variant in a clinical context, they cannot be used on their own to show that a variant disrupts splicing in a clinical context.

The second invited speaker in this session was Rachel Karchin of the Institute for Computational Medicine, Johns Hopkins Biomedical Engineering and Oncology Departments, Baltimore, Maryland, who spoke on "Evaluating the evaluation of cancer driver genes". Cancer is an evolutionary process driven by somatic alterations in driver genes and cancer cells are positively selected due to numerous alterations that result in an increased rate of clonal expansion compared to normal cells. The number of driver gene mutations in tumors is small compared to the number of benign passenger mutations. Identifying driver genes is important for precision cancer treatment but their identification is challenging.

Informatics/statistical methods are critical for discriminating drivers and passengers. A number of different methods have been proposed to identify driver genes in tumors. A machine learning-based ratiometric approach was presented as an example (github.com/KarchinLab/2020plus). Ratiometric methods assess the composition of mutations in a gene normalized by total number of mutations in all genes. Several driver gene preditors identify significantly mutated genes by modeling the background somatic

This article is protected by copyright. All rights reserved. 10 mutation rate for all genes and then identifying genes exhibiting an elevated mutation rate.

However, there can be difficulties in estimating the background mutation rate, because it is highly variable for many reasons that are not completely understood. Tumors with a high mutation rate can be particularly problematic for identification of "significantly mutated" Data input for VEP can be variant coordinates, a variant call file (VCF), HGVS nomenclature or variant IDs. The VEP returns detailed annotation for predicted effects of variants on transcripts, proteins and regulatory regions, including functional consequences, pathogenicity predictions and HGVS notations relative to the transcript and protein sequences. For known or overlapping variants, allele frequencies, phenotype information and literature citations can also be retrieved from the Ensembl databases. Predicted consequences for regulatory regions can be limited to specific cell types using data from the Ensembl Regulatory Build.

Pathogenicity predictions are accomplished using different algorithms such as SIFT,

This article is protected by copyright. All rights reserved. 11

PolyPhen, FATHMM and MutationTaster. Additionally, the Haplosaurus is a recently developed VEP-like tool that uses phased genotype data to predict the consequences of multiple variants. This can help identify cryptic stop gained codons or protein truncating variants (PTV) created by multiple variants as well as variants which rescue PTVs and frameshifts. This is particularly important for variants which frame rescue small in/dels or multiple missense variants in cis.

Session 3

Session 3 was chaired by Christophe Béroud. The first invited speaker was Sean Tavtigian of Oncological Sciences, Huntsman Cancer Institute, Salt Lake City, Utah, who spoke on "Validating and calibrating computational and functional approaches in BRCA and MMR genes". Identification of mutations driving tumors is critical to personalized treatment.

Classification models attempt to score these variants on a continuum from 5 (pathogenic;

posterior-probability >0.95) to 1 (neutral; posterior-probability of pathogenicity <0.001).

Unfortunately, many somatic variants are classified as of unknown significance (UV/VUS; posterior-probability between 0.05 and 0.95). Additional information can update prior probability of their classification to (hopefully) a more accurate posterior classification using Bayes rule using likelihood ratios or odds ratios of causality. The updated in silico models from the Breast Cancer Information Core (BIC) to classify variants include co- with predicted effects on protein-protein binding was done using yeast 2 hybrid experiments.

Preliminary work was also shown regarding differences of functional variants between populations. It was found that common sequence variants affect molecular function more often than rare variants. There is optimism that the effect of variants can be accurately determined, but there is still a long way to go in the quest of fully interpreting one's genome.

The final talk of the session and the meeting was by Steven Brenner who spoke on "Findings from CAGI, the Critical Assessment of Genome Interpretation," a community experiment to evaluate phenotype prediction. All individuals have many variants in their DNA sequence.

Current computational methods to predict the impact of these variants are not accurate enough to evaluate all genomic variants for clinical use, with most producing many false positives. Each GAGI edition has about a dozen challenges to assess different prediction methods. In one challenge, variants within the α-N-acetylglucosaminidase (NAGLU) gene, associated with San Filippo disease (mucopolysacchraidosis type IIIB), were used as a test dataset. A total of 165 missense variants with known functional effects were presented to predictors. The 10 research groups participating in this challenge used 17 methods.

Independent assessment of this challenge has found that top missense prediction methods are highly statistically significant, but individual variant accuracy is limited. Missense methods also tend to correlate better with each other than with experiment (for reasons that may reflect biases in the predictive methods but also in the experimental assays). Although overall

This article is protected by copyright. All rights reserved. 14 missense accuracy is limited, there is a subset of variants where methods may be sufficiently reliable to providing strong evidence for clinical use. Variants with a nearly complete loss of activity were predicted most accurately. Newer approaches employed in CAGI often enhance performance compared to more established methods. In a challenge using clinical data, predictors were able to identify causal variants that were overlooked in the clinical analysis; these variants were then clinically confirmed. The results have also highlighted possible diagnostic ambiguities. Additionally, the results suggest that running multiple uncalibrated methods and considering their consensus may result in undue confidence in impact assignment. Some of the lessons learned from CAGI were: 1) In general predictions are statistically significant. 2) However, predictive accuracy for specific variants is low. 3)

Bespoke approaches often enhance performance. 4) Missense methods tend to correlate better with each other than with experiments. 5) Predictors were able to identify causal variants overlooked by a clinical laboratory. 6) Interpretation of non-coding variants is not at the level of missense variants. 7) Use of multiple uncalibrated missense variants impact prediction methods and is not advised. To further the assessment of these predictive models, CAGI 5 has been launched (http://genomeinterpretation.org). In closing, Dr. Brenner also mentioned the comprehensive special issue on CAGI that was recently published (http://dx.doi.org/10.1002/humu.2017.38.issue-9).

Closing Remarks

The meeting concluded with an appreciation for how far the field has progressed in recent years, and optimism that the tools that were discussed will lead to the advances that will be required to interpret and manage the large volume of data that will be arriving as genome sequencing expands.

  The functional effects of non-coding variants can be predicted using FunSeq (http://FunSeq.gersteinlab.org) which integrates evidence from multiple datasets. The program uses annotation of transcriptional factor binding sites, open chromatin domains and between benign and disease producing variants than other methods when tested on 1,953 pathogenic and 2,406 benign variants from ClinVar, or 935 disease mutations from SwissVar and 141,051 neutral variants. REVEL was also more accurate for interpreting rare variants (MAF<0.5%). Pre-computed REVEL scores for >80 million theoretically possible human missense variants, along with the sensitivity and specificity for a wide range of cutpoints, can be found at sites.google.com/site/revelgenomics.

	from over 3,300 studies. The methodology used to obtain data for GRASP include: (1) which were associated with disease or on observed functionality the programs MutPred and
	controlled vocabulary searches, (2) review of tens of thousands of abstracts for inclusion MutPred2 (mutpred.mutdb.org) were tested and compared. MutPred2 uses conservation of
	criteria, (3) extraction of all associations with a p<0.05, and (4) associations with given SNP sequence, structural and functional properties of protein and other factors within a neural
	sequence conservation. This is an entropy based method for weighing many genomic having a clearly distinct phenotype. Following QC, each record is associated with study-network ensemble. Testing three tasks: 1) Identifying variants associated with disease (MRN
	features which may impact transcription. Variation in somatic mutations is closely associated level information including phenotype, sample size, ancestry and publication information. It and CHEK2 and breast cancer), 2) Prediction of in vitro enzyme activity (N-acetyl-
	with chromatin structure (topologically associated domains; TADs) and replication timing should be noted that eQTLs and associations in the MHC locus are over-represented among glucosaminidase glycolyase, NAGLU) and 3) Prediction of effects on CBS-dependent cellular
	which will alter the background mutation rate. MrTADFinder published significant results. GRASP contains more results for specific sub-phenotypes per growth using as determined by a yeast complementation assay, MutPred2 was consistently
	(http://github.com/gersteinlab/MrTADfinder) helps identify replication TAD boundaries as SNP compared to the NHGRI-EBI catalog. This increased specificity may more accurately more accurate than MutPred. It was also found that predictors tend to perform better when
	reflect GWAS study designs. The GRASP v3 update will expand clinically relevant results biochemical and functional evidence is available.
	for chronic diseases that challenge public health.
	Variants in noncoding Annotations (LARVA; http://LARVA.gersteinlab.org), were designed The second invited speaker was Weiva Sieh, of Population Health Science and Policy and of
	to analyze the probable functionality of a variant based on its reoccurrence in tumors. These Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, New York,
	methods require well curated databases. These different methods can help identify the driver
	mutations and better characterize (and treat) tumors.
	The second talk of this session was by Ben Rodriguez of the Population Sciences Branch,
	NHLBI, Framingham, Massachusetts, who spoke on "GRASP v3: An updated GWAS
	catalog and contrast to similar catalogs". Mining GWAS data drives scientific discovery and
	research productivity. GRASP (Genome-wide Repository of Associations between SNPs and
	Phenotypes; http://grasp.nhlbi.nih.gov) is a leading repository of published single nucleotide
	polymorphism (SNP) associations with human traits, including methylation and expression
	quantitative trait loci (eQTL). GRASP began as an open access database in 2008 initially
	with results from 118 studies (56,000 data points) obtained from different cohorts including
	diabetes related, cardiovascular and neurological studies. GRASP v3.0 will contain results
	7

possible locations for functional non-coding variants. Mutation recurrence is another method to determine functionality of variants in tumors. Two methods, Mutations Overburdening Annotations Tool (MOAT; http://MOAT.gersteinlab.org) and Large-scale Analysis of who spoke on "Predicting the pathogenicity of rare missense variants". Rare coding variants play an important role in disease but are often overlooked. Healthy individuals carry several hundred non-synonymous variants. Predicting which are functional is important to help identify disease-causing variants and tailor prevention and treatment according to each person's genetic risks. There are many in silico prediction tools for predicting the pathogenicity of variants, but they often disagree. Ensemble methods that combine scores from multiple tools often perform better. Of these methodologies, few focus on rare variants. This was the motivation for the creation of the Rare Exome Variant Ensemble Learner (REVEL; https://sites.google.com/site/revelgenomics/). The method was developed by combining 18 scores from 13 individual tools including MutPred, VEST, PolyPhen, SIFT and MutationTaster. The method was trained using recently reported HGMD disease mutations and rare neutral variants, and evaluated using two independent test sets. REVEL was more This article is protected by copyright. All rights reserved. 6 accurate for differentiating The last speaker of this session was Vikas Pejaver from the Department of Biomedical Informatics and Medical Education and the eScience Institute, University of Washington, Seattle, who presented his talk on "Probabilistic prediction of the different notions of missense variant impact". Sources of evidence for the functionality of a genetic variant incudes conservation of the amino acid between different species, co-segregation of the variant with disease, effect of the variant on protein structure and other functional properties and effects in model organism studies. Different computational tools are being used to predict functionality in silico based on the above criteria. More recently, machine learning approaches are being trained on either variants experimentally shown to alter protein function, or on variants shown to be associated with disease. But function altering variants are not always disease causing. In some cases reported functional effects are determined in other species and may not accurately predict their function in humans. Additionally, predicted changes, especially 'small' changes, in protein function may not result in a change in the phenotype. Despite this, previous studies have suggested that such predictors do predict disease-associated variants accurately. The converse question was presented, "Can predictors trained on binary labels of 'pathogenic' and 'benign' generalize to the prediction of real-valued functional effects of missense variants (e.g., protein function, cellular phenotype)?" Using CAGI experiment (https://genomeinterpretation.org) based variants This article is protected by copyright. All rights reserved. Session 2 Session 2 was chaired by Steven Brenner of the University of California, Berkeley. The first invited speaker of this session was Christophe Béroud of the Genetics and Bioinformatics,

  segregation and co-occurrence with disease, summary family history and tumor histopathology to classify variants in breast cancer. This can be further enhanced by the knowledge that most functional missense mutations occur in specific protein domains the ring domain and BRCTs of BRCA1 and the DNA binding domain and PALB2 interaction and RAD51 dissasembly domains of BRCA2. Calibration with these methods improves the accuracy of variant classification. The creation of large datasets for validation and replication of the methodology are necessary for this. Test sets of variants from Myriad (68,000) and Ambry (100,000) were used to this end. In the case of mismatch repair (MMR) genes, variants were classified using a cell free in vitro assay for MMR activity. Analysis addressed the calibration of the assay, comparing the functional assay outputs with prior classifications that were independent of in vitro data. A final calibration curve successfully separated functional from non-functional variants. A 2-component classification, incorporating previously determined in silico scores of pathogenicity with functional assay results, could help classify MMR gene variants with only a small amount of additional clinical data. Using the American College of Medical Genetics (ACMG) combining rules provide better supporting evidence for pathogenicity scores. Improvement of these models should allow for more accurate computational scoring of variants. 13 consequences of amino acid substitutions. The program was applied to identifying effects of de novo variants in a cohort of individuals with autism and to subsequently predict autism genes. Controls were unaffected sibs. The most significant signal was from variants found in proteins which altered macromolecular binding sites, with catalytic activity and phosphorylation also showing statistical significance. Experimental validation of variants

	The second invited speaker was Predrag Radivojac of the Department of Computer Science,
	Indiana University Bloomington, who presented his talk on "Predicting the molecular
	mechanisms of genetic disease for protein coding variants". There are several tools which
	predict whether a variant is pathogenic or not, but they do not predict the underlying
	mechanisms of disease. There is a need to include additional types of information to predict
	the functional consequences of variants. However, functional analysis of variants can be
	complex because of multiple functional sites in a single protein. For example, in the tumor
	suppressor protein p53, the p.Arg175His variant likely alters metal binding, the p.Val143Ala
	may alter stability, the p.Lys120Arg affects an acetylation site, and several others impact
	macromolecular binding. Additionally, a specific amino acid substitution can result in a loss

of function or gain of function (e.g., a disease-associated variant p.Asp374Tyr in PCSK9 increases catalytic activity 10-fold). MutPred2 was created to predict these different types of This article is protected by copyright. All rights reserved.

This article is protected by copyright. All rights reserved.

Acknowledgements

The Scientific Program Committee of Christophe Béroud, Steven E. Brenner, Anthony J.

Brookes, Marc S. Greenblatt, Rachel Karchin and Sean D. Mooney would like to thank Rania Horaitis for her professional help in running this HGVS annual scientific meeting. Dr.

Brenner's participation was supported by NIH U41 HG007346. This meeting of the HGVS was chaired by Marc Greenblatt, Steven Brenner, and Christophe Béroud. The authors would like to thank the speakers for their help in the preparation of this report.


Christophe Hausswirth

Sébastien Argentin

François Bieuzen

Yann Le Meur

Antoine Couturier

Jeanick Brisswalter

; Argentin

; Bieuzen

; Le Meur

Yann

; Couturier

; Brisswalter

Endurance and strength training effects on

Keywords: Freely chosen cadence, concurrent training, fatigue, cycling, prolonged exercice

INTRODUCTION

The studies focusing on long distance cycling exercises have shown a discrepancy between the freely chosen cycling cadence (FCC) and the energetically optimal cadence (EOC), i.e. the cadence at which the energy expenditure represented by the oxygen uptake ( V .

O 2 ) is minimal [START_REF] Brisswalter | Energetically optimal cadence vs. freely-chosen cadence during cycling: effect of exercise duration[END_REF][START_REF] Lepers | Evidence of neuromuscular fatigue after prolonged cycling exercise[END_REF]. For exercise lasting more than 30 minutes in duration, several authors have observed significant variations in FCC [START_REF] Argentin | Relation between preferred and optimal cadences during two hours of cycling in triathletes[END_REF][START_REF] Lepers | Evidence of neuromuscular fatigue after prolonged cycling exercise[END_REF]Vercruyssen et al., 2001). In this framework, [START_REF] Lepers | Evidence of neuromuscular fatigue after prolonged cycling exercise[END_REF] reported that during a 2-hour constant exercise at 65% of maximal aerobic power (MAP) within a group of well-trained triathletes, the decrease in the FCC was higher with the increase in the exercise duration. According to these authors, the inability of the subjects to maintain a constant cycling cadence could be related to the emergence of neuromuscular fatigue, resulting in a change of neural and contractile properties of the quadriceps muscle such as alterations of the M-wave and isometric muscular twitch [START_REF] Lepers | Evidence of neuromuscular fatigue after prolonged cycling exercise[END_REF]. [START_REF] Argentin | Relation between preferred and optimal cadences during two hours of cycling in triathletes[END_REF] have also observed a significant decrease in FCC (87 to 68 rpm) over two hours of cycling at 65% MAP. This pedalling cadence decrease is associated with a significant drop in maximal voluntary isometric contraction values (isoMVC), -13.5% for Vastus Lateralis (VL), and -9.6% for the Gastrocnemius Lateralis (GL), as well as with isometric electromyographic values (isoEMG, expressed in % RMS) of 8.3% for VL and 14,1% for GL between "before" and "after" the 2-hour cycling test. At the end of a 30-min cycling exercise at an intensity corresponding to 80% of maximal oxygen uptake ( V . O 2max ) followed by four 60-s periods at 120% of V . O 2max , [START_REF] Bentley | Muscle activation of the knee extensors following high intensity endurance exercise in cyclists[END_REF] also observed a significant decrease in EMGi measured during an isoMVC preformed immediately after and also six hours later. For these authors, the level of exercise stress administered in this study was sufficient to impair the central and peripheral mechanisms of force generation in knee extensors for a period of 6 hours and athletes engaged in concurrent training (strength and endurance) should consider this effect in exercise programming.

Indeed, to improve their specific endurance performance, many competitive endurance athletes such as triathletes and cyclists included strength sessions in their classical endurance training. Accordingly, [START_REF] Paavolainen | Explosive-strength training improves 5-km running time by improving running economy and muscle power[END_REF] showed that simultaneous explosive strength and endurance training produced a significant increase in a 5-km running performance without changes in V . O 2 max or other aerobic power variables in well-trained athletes. This could be due to the enhancement in muscular characteristics which were transferred into improvement in muscle power and running economy. The effects of strength training can also have different consequences. Muscular strength can be increased by changes in the cross-sectional area of muscle from protein synthesis and by neural adaptations that enhance motor unit activation [START_REF] Sale | Neural adaptations to strength training[END_REF]. Variations in training intensity and protocol appeared to elicit different neuromuscular adaptations. High loads (3 to 6 maximal repetitions [MR]) and lower volumes were associated with neural adaptations while muscle hypertrophy was produced with lower loads (8 to 12 MR) higher volume resistance training occurred through increased protein synthesis in muscle fibres [START_REF] Docherty | A proposed model for examining the interference phenomenon between concurrent aerobic and strength training[END_REF]. According to these authors, studies using various experimental protocols have shown that concurrent strength and endurance training resulted in compromised strength gains [START_REF] Hennessey | The interference effects of training for strength and endurance simultaneously[END_REF][START_REF] Hickson | Interference of strength development by simultaneously training for strength and endurance[END_REF] or uncompromised strength gains [START_REF] Hunter | Development of strength and maximum oxygen uptake during simultaneous training for strength and endurance[END_REF][START_REF] Mccarthy | Compatibility of adaptive responses with combining strength and endurance training[END_REF] while no decrease in endurance capacity was observed, specifically over longer periods of concurrent training or in trained athletes [START_REF] Hennessey | The interference effects of training for strength and endurance simultaneously[END_REF]. Muscular characteristics may be important for endurance performance [START_REF] Paavolainen | Explosive-strength training improves 5-km running time by improving running economy and muscle power[END_REF]. Heavy resistance strength training has improved the endurance performance of previously untrained subjects [START_REF] Hickson | Potential for strength and endurance training to amplify endurance performance[END_REF][START_REF] Marcinik | Effects of strength training on lactate threshold and endurance performance[END_REF][START_REF] Mccarthy | Compatibility of adaptive responses with combining strength and endurance training[END_REF] as well as the running economy of female distance runners [START_REF] Johnston | Strength training in female distance runners[END_REF], without changes in maximal oxygen uptake ( Indeed, it is well-known that the stabilization of both cadence and power during a cycling stage could improve the subsequent running performance.

MATERIALS AND METHODS

Subjects. 14 well-trained male triathletes currently competing at the regional and national levels participated in the study (See Table 1). Seven of these subjects were randomly assigned to the endurance-strength training group (ES; N=7) and were studied before and after a strength training programme during 5 weeks (three times per week). The remaining seven subjects were assigned to the endurance-only training group (E; N=7). Before participating in this study, subjects were fully informed about the protocol modalities (except for the special interest in pedal cadence), and a written consent was given to all subjects before all testing, according to local ethical committee guidelines.

Training Design. The training period lasted 5 weeks and was carried out during the autumn period when the subjects put an end to their competitive season, and when they were not so involved in competition. Cycling Ergometer. All experimental cycling tests were conducted on an electromagnetically braked ergocycle (Lode, type Excalibur, Gröningen, The Nederland) at the self-selected cadence. The ergometer allowed subjects to maintain the power output constant independent of cycling cadence.

EMG signal measurements. Surface EMG activity was continuously recorded for the following 2 muscles of the right lower limb: rectus femoris (RF) and vastus lateralis (VL). A pair of surface pre-gelled Ag/AgCl electrodes (Medicotest, type Blue Sensor, Denmark, Sensor) was attached to the skin with a 20-mm inter-electrode distance. The electrodes were placed longitudinally with respect to the underlying muscle fibre arrangement and located according to the recommendations by SENIAM (Surface EMG for Non-Invasive Assessment of Muscles) [START_REF] Hermens | Development of recommendations for SEMG sensors and sensor placement procedures[END_REF][START_REF] Rainoldi | Geometrical factors in surface EMG of the vastus medialis and lateralis muscles[END_REF]. A common reference electrode was placed over the anterior superior spine of the iliac crest. Prior to electrode application, the skin was shaved and cleaned with alcohol in order to minimize impedance (The impedance was checked, and only values below 1000 Ω were accepted). The signal was preamplified (Gain x 600), band pass filtered with cut-off frequency of 5/500 Hz and saved on a computer. EMG data were collected with an acquisition card, digitised using a 1 kHz sampling rate and stored on computer (software Origin 6.1). Subsequently, from the raw EMG of each muscle a linear envelope of the muscle activity was created using a 4th order Butterworth digital filter with a cutoff frequency of 5 Hz. A preliminary assessment of the EMG data conidered cutoff frequencies between 3 and 15 Hz, and the maximal efforts were considered similarly. It was concluded that 5 Hz provided sufficient smoothing of the raw data to obtain mean data for both groups without attenuating significant characteristics of the EMG signals. From the linear envelopes the integrated EMG signal was calculated (iEMG) and all iEMG data were normalised (normalised iEMG) by dividing the value of each muscle by its own EMG value obtained during the maximal voluntary contraction (MVC) (period of 500 ms) performed before starting the 2-hour cycling exercise. Consequently, iEMG values were analysed between the 5th and the 20th minutes (period 1: P 1 ), between the 55th and the 70th minutes (period 2: P 2 ) and between the 105th minutes and the end of the test (period 3: P 3 ). Then, the iEMG was calculated at 20-s intervals for a period of 15 min. During the isometric strength measurement test, EMG data were respectively collected from the VL and RF muscles during extension movement of the right lower limb. O 2 , expiratory flow ( V . E ), respiratory exchange ratio (RER) and heart rate (HR) were continuously recorded breath by breath and averaged every 10 seconds using a telemetric system collecting gas exchanges (Cosmed K4b 2 , Roma, Italy). The criteria used for the determination of V .

Maximal oxygen uptake (

O2max were a plateau in V .

O2 despite an increase in power output, a RER above 1.1 or a HR over 90% of the predicted maximal HR [START_REF] Howley | Criteria for maximal oxygen uptake: review and commentary[END_REF]. The V .

O 2max was defined as the highest V .

O 2 attained in three successive 10 sec [START_REF] Billat | Intermittent runs at the velocity associated with maximal oxygen uptake enables subjects to remain at maximal oxygen uptake for a longer time than intense but submaximal runs[END_REF]. In addition, ventilatory thresholds (VT 1 and VT 2 ) were determined by using the method described by [START_REF] Wasserman | Anaerobic threshold and respiratory gas exchange during exercise[END_REF] and [START_REF] Reinhard | Determination of anaerobic threshold by the ventilation equivalent in normal individuals[END_REF] based on the identification of breakpoints in the evolution of the ventilatory equivalents for O 2 and CO 2 . Visual evaluation to determine VT was carried out independently by three experienced investigators. Gross Efficiency (GE) was calculated as the ratio of work accomplished to energy expended (expressed in percentage), using the V .

O 2 and energy equivalent for oxygen corresponding to each respiratory exchange ratio value from the tables by [START_REF] Péronnet | Table of nonprotein respiratory quotient: an update[END_REF] (cf. [START_REF] Chavarren | Cycling efficiency and pedalling frequency in road cyclists[END_REF].

Maximal concentric lower-limb strength measurements. The evaluation of maximum strength, represented by the load lifted up one and only one time (1 RM), was achieved using a lower limb press with a 45° inclination (Hand press, Multi-form, La Roque d'Anthéor, France). The position of the feet on the platform was adjusted so that the joint angle between the leg and the thigh was 90°. From this initial position, the subjects were asked to perform an extension with their lower limbs, with standard conditions of performance.

Before the test began, the subjects were familiarised with the machine and specific technical moves [START_REF] Stone | Weight training : A scientific approach[END_REF]. After a standardised warm-up, the subject's near maximal resistance was estimated. The resistance was then gradually increased until the subject was only able to lift the resistance once (1MR) and not twice. This was recorded as the subject's 1 RM (Mero et al., 1989). There was at least 5 minutes' recovery between each trial.

Isometric strength measurements. Before starting the 2-hour cycling test, subjects were placed in a seated position and were securely strapped into the test chair to perform a right isometric knee extension using an isometric ergometer (Type: Schnell Trainingsgesäte GmbH, Peutenhausen, Deutschland). Subjects performed three maximal isometric contractions of short duration (2-3 sec) using the knee extensor muscles. The maximal force under extension movement (starting position corresponded to thigh/shank angle of 90°) was measured using a strength sensor and the best performance consecutive to the three trials was selected as the maximal isometric voluntary contraction (isoMVC).

Rating of perceived exertion (RPE): Subjects were provided with a typewritten set of standardised directions for the use of Ratings of Perceived Exertion [START_REF] Borg | Perceived exertion as an indicator of somatic stress[END_REF]. Subjects were instructed to give an overall RPE immediately at the end of each measurement) period during the 2-hour cycling test.

2h cycling test: Before the study, few attempts of intensity manipulation were performed in order to target the precise power output. Accordingly, the VT 1 + 5% intensity was defined as too high in respect to the accomplishment of the 2-hr cycling task. O 2 , GE, V .

E , RER, HR, RPE, iEMG and the pedalling cadence) were recorded and analysed between the 5th and the 20th minutes (period 1: P 1 ), between the 55th and the 70th minutes (period 2: P 2 ) and between the 105th minutes and the end of the test (period 3: P 3 ); this was done in order to give the cyclists a maximal amount of respiratory comfort and to enable them to drink during the test. Cycling cadence was continuously recorded and heart rate was monitored using a cardio frequency meter (Polar, X Vantage, Finland) connected with the telemetric device of the Cosmed K4b 2 .

The EMG parameters of the VL and RF muscles were recorded during the last 3 minutes of the 3 periods (P 1 , P 2 and P 3 ). No feedback was given to the subjects concerning their self-

RESULTS

The main characteristics of the triathletes are presented in along, during the 2-hour task (-6.9 ± 4.1% from P 1 to P 2 ; P<0.05 and -11.2 ± 8.9% form P 2 to P 3 , P<0.01). [START_REF] Bigard | Adaptations biochimiques et structurales du muscle à l'entraînement en force[END_REF][START_REF] Docherty | A proposed model for examining the interference phenomenon between concurrent aerobic and strength training[END_REF][START_REF] Moritani | Neural factors versus hypertrophy in the time course of muscle strength gain[END_REF][START_REF] Narici | Change in force, cross-sectional area, and neural activation during strength training and detraining of the human quadriceps[END_REF][START_REF] Sale | Neural adaptation to resistance training[END_REF][START_REF] Sale | Neural adaptations to strength training[END_REF][START_REF] Thorstensson | Effect of strength training on enzyme activities and fiber characteristics in human skeletal muscle[END_REF], we can suppose that our 5-week strength training protocol did not induce muscular hypertrophy. We have also observed that the isoMVC values recorded before the 2-hour cycling test for ES group were significantly more important than in the post test as opposed to pre test (+ 8,1% for VL, p<0.01 and + 7,1% for RF, p<0.01). The increase in isoMVC obtained at the end of the 5 weeks of combined training was associated with an increase in EMG activity for VL (+ 7,7%, p<0.05) as well as for RF (+ 8,8%, p<0.01). These results are in agreement with the study by [START_REF] Gabriel | Neural adaptations to resistive exercise: mechanisms and recommendations for training practices[END_REF] who emphasizes the fact that strength gains in the early phase of a strength training program are associated with an increase in the amplitude of EMG activity.

Therefore, the strength gained by the ES group is probably due to central adaptations (increased activation, more efficient recruitment, more efficient motor unit synchronisation, more efficient excitability of the α-motor neurons, and decreased Golgi tendon organ inhibition) [START_REF] Sale | Neural adaptations to strength training[END_REF]. Indeed, variations in training intensity and volume appeared to elicit different neuromuscular adaptations. High load and lower volume (i.e. three to five sets to failure of 3-5 repetitions, with a load > 90% 1MR, like in our study) are associated with an increase in force generation without an increase in muscle size, and are related to neural adaptations [START_REF] Docherty | A proposed model for examining the interference phenomenon between concurrent aerobic and strength training[END_REF]. The gains in 1MR were lower than the improvements showed in previous studies [22% for 1 RM squat [START_REF] Mccarthy | Compatibility of adaptive responses with combining strength and endurance training[END_REF];

22.5%for 1 RM squat, [START_REF] Bishop | The effects of strength training on endurance performance and muscle characteristics[END_REF]; 25% in the half squat and 17% in the calf raise, [START_REF] Millet | Effects of concurrent endurance and strength training on running economy and VO 2 kinetics[END_REF]; 19.5% in the leg press [START_REF] Kraemer | Compatibility of high-intensity strength and endurance training on hormonal and skeletal muscla adaptations[END_REF]; 27% in parallel squat [START_REF] Hickson | Interference of strength development by simultaneously training for strength and endurance[END_REF]]. These observations have to be related with the period of time of our study: ). These values correspond to those observed in isometric conditions (-13%) by [START_REF] Lepers | Evidence of neuromuscular fatigue after prolonged cycling exercise[END_REF] after a similar 2-hour cycling test performed at 65% of Pmax but lower than those registered by [START_REF] Sahlin | Effects of prolonged exercise on the contractile properties of human quadriceps muscle[END_REF] after 85 minutes of cycling at a fixed cadence in non expert cyclists (-34%) who were not familiarised with the cycling task.

CHANGE IN PHYSIOLOGICAL VARIABLES. V .
O 2max , HR max , P max and the V .

O 2 , HR and power values measured to VT 1 and VT 2 remained unchanged with training in the two groups during the incremental cycling test to exhaustion. The results concerning trained subjects are in agreement with the current literature [START_REF] Bishop | The effects of strength training on endurance performance and muscle characteristics[END_REF][START_REF] Hickson | Interference of strength development by simultaneously training for strength and endurance[END_REF][START_REF] Hickson | Potential for strength and endurance training to amplify endurance performance[END_REF][START_REF] Millet | Effects of concurrent endurance and strength training on running economy and VO 2 kinetics[END_REF]. Only [START_REF] Paavolainen | Explosive-strength training improves 5-km running time by improving running economy and muscle power[END_REF] O 2 [START_REF] Bishop | The influence of resistance training on the critical power function & time to fatigue at critical power[END_REF][START_REF] Hickson | Potential for strength and endurance training to amplify endurance performance[END_REF]Marcinick et al., 1991) O 2 , oxidative enzyme activity or fibre composition. [START_REF] Marcinik | Effects of strength training on lactate threshold and endurance performance[END_REF] also showed that a strength training programme (3 times per week during 12 weeks) could increase endurance performance on an ergocycle independently of an increase in V . O 2max .

According to these authors, increases in leg strength, rather than metabolic adaptations, were responsible for the improvements in endurance performance after strength training.

Nevertheless, in our study, the increase in the maximal strength of the lower limbs did not trigger any increase in endurance performance. No modification was noticed after the five weeks of combined training regarding the various physiological parameters registered during the 2-hour cycling test. The gains in strength measured in our study were much lower than those measured in the two previous studies referenced below [+27% in squat and +25% in leg extension [START_REF] Hickson | Potential for strength and endurance training to amplify endurance performance[END_REF]; + 30% in leg extension, [START_REF] Marcinik | Effects of strength training on lactate threshold and endurance performance[END_REF]], and were not efficient enough to induce an increase in endurance performance.

The 2-hour cycling task elicited an increased in V .

O 2 (P<0.05), which is an indicator of metabolic efficiency and cycling economy when power output is constant. In agreement with the study by [START_REF] Lepers | Evidence of neuromuscular fatigue after prolonged cycling exercise[END_REF], all the other parameters such as RER, V .

E , RPE and heart rate were also significantly higher during P 2 and P 3 than during P 1 (Table 3) and remained unchanged with training. An increase in energy cost with exercise duration had already been observed for prolonged exercises such as running [START_REF] Davies | Physiological responses to prolonged exercise in ultramarathon athletes[END_REF] or triathlon [START_REF] Hausswirth | Variability in energy cost of running at the end of a triathlon and a marathon[END_REF], but relatively few data exist concerning prolonged cycling exercise. The progressive and continuous increase in V .

O 2 values during the 2h task can be linked to several factors: a drift in the temperature of the subjects [START_REF] Saltin | Circulatory response to prolonged severe exercise[END_REF], thermoregulation [START_REF] Galloway | Effects of ambient temperature on the capacity to perform prolonged cycle exercise in man[END_REF] and an increase in the sudoral gland activity combined with a decrease in the phosphagene way efficiency.

The use of lipids as substrates is also known to increase the V .

O 2 requirement of exercise [START_REF] Hausswirth | Variability in energy cost of running at the end of a triathlon and a marathon[END_REF]. Therefore, RER was found to decline significantly (P<0.01), which suggested that the energy source was gradually switched from carbohydrate to fat substrates during the 2-hour exercise.

CHANGE IN THE EVOLUTION OF FCC:

The changes over time of the pedalling cadence during the 2-hour cycling task changed after five weeks of concurrent training. Before training, our results were close to those obtained in previous studies that emphasize a significant drop in the pedalling cadence due to the prolonged exercise. For exercises lasting over 30 minutes, several authors reported a significant decrease in the FCC of 12.3% (P<0.01) after 1h pedalling (Vercruyssen et al., 2001) and a decrease ranging from 19 to 21% (P<0.01) after 2-hour pedalling [START_REF] Argentin | Relation between preferred and optimal cadences during two hours of cycling in triathletes[END_REF][START_REF] Lepers | Evidence of neuromuscular fatigue after prolonged cycling exercise[END_REF]. The decrease in FCC relative to the duration of the exercise is specific to triathletes and was characterised by a new organisation of the pattern movement linked to an optimisation of the metabolic cost [START_REF] Lepers | Evidence of neuromuscular fatigue after prolonged cycling exercise[END_REF].

In contrast to the performance of an isolated cycle task, the triathlete may save some energy during the cycle session of a triathlon in anticipation of the succeeding run [START_REF] Argentin | Relation between preferred and optimal cadences during two hours of cycling in triathletes[END_REF]. According to the progressive decrease (P<0.01) in FCC and the progressive increase (P<0.01) of EMGi (VL) and EMGi (RF) throughout the 2-hour cycling at constant power, the muscular strategy which consisted of greater forces applied to the pedals with a reduction in the pedalling rate [START_REF] Patterson | Bicycle pedalling forces as a function of pedalling rate and power output[END_REF] as the exercise duration increased, appeared to contradict results regarding a decrease in post exercise maximal muscular strength. A change in muscular type fibre recruitment during the 2-hour cycling exercise is often put forward.

According to [START_REF] Ahlquist | The effect of pedaling frequency on glycogen depletion rates in type I and II quadriceps muscle fibers during submaximal cycling exercise[END_REF], a decrease in FCC observed with exercise duration in the present study could indicate that type II fibres were increasingly recruited. [START_REF] Woledge | Possible effects of fatigue on muscle efficiency[END_REF] mentioned the change in recruitment from Type I to Type II fibres during prolonged exercise.

It could explain changes in V .

O 2 with exercise duration in association with a decrease in FCC.

This may lead to a decrease in thermodynamic muscle efficiency and consequently an increase in metabolic cost. In relation to our results and to others [START_REF] Argentin | Relation between preferred and optimal cadences during two hours of cycling in triathletes[END_REF][START_REF] Lepers | Evidence of neuromuscular fatigue after prolonged cycling exercise[END_REF], one hypothesis relates to the increase in V .

O 2 from P 1 to P 3 to the additional recruitment of type II muscle fibres.

After five weeks of concurrent training, the progressive and continuous decrease in O 2 [START_REF] Argentin | Relation between preferred and optimal cadences during two hours of cycling in triathletes[END_REF][START_REF] Lepers | Evidence of neuromuscular fatigue after prolonged cycling exercise[END_REF][START_REF] Woledge | Possible effects of fatigue on muscle efficiency[END_REF]. In this study, although the stabilisation of FCC is not associated with a stabilisation of physiological parameters such as V .

E or V .

O 2 , all these parameters have not been modified by concurrent training as such ; it appears that the stabilisation of the EMGi (VL) values could explain the stabilisation of cadence.

In conclusion, the results of this study confirm the decrease in FCC during a 2-hour constant cycling task. This decrement could be mainly related to the occurrence of muscular fatigue, resulting into a change in neural and contractile properties in the quadriceps muscle.

One interesting finding in this study is that a combined endurance and strength training improve the level of maximal isometric force in triathletes and prevent the decrease in the cadence mostly observed due to exercise duration.

Volume of training during the 5 weeks between pre-and post-assessment

Swim training (h week -1 ) 1.5 ± 1.4 1.9 ± 1.4 c Bike training (h week -1 ) 6.9 ± 2.6 C 6.7 ± 3.2 C Run training (h week -1 ) 3.3 ± 0.9 c 3.3 ± 0.8 c Amount training (h week -1 ) 11.7 ± 3.7 C 11.9 ± 3.1 C

Values are means ± SD. Pre, pre-training; post, post-training; 1 RM, one repetition maximal on leg press. C P < 0.01; c P < 0.01 for differences between pre-and posttraining.

  . Findings by Mc Carthy et al. (1995) indicate that 3 days per week of concurrent performance in both strength and endurance training does not impair adaptations in strength, muscle hypertrophy, nor in neural activation induced by strength training alone. Significant increases in maximal isometric knee-extension torque were accompanied by non significant (P≤ 0.07) increases in root mean squared EMG amplitude of the quadriceps musculature for both S (high-intensity strength training) and CC (concurrent strength and endurance training) groups. Recently, Hansen et al. (2007) have also shown that strength training increased the 1MR in both squat and leg curl and reduced the freely chosen pedal rate during submaximal cycling in healthy subjects. To the best of our knowledge, no data are available focusing on the effect of this type of concurrent training on cycling performance in well-trained triathletes. Therefore, the purpose of the present study was to examine the effects of a regime of maximal strength training in combination with a usual endurance training programme on the physiological and muscular parameters during a 2-hour constant cycling test in well-trained triathletes. The main hypothesis tested is that a 5-week period of strength training allowed triathletes to maintain the freely-chosen-cadence in cycling during a long duration ergocycle test at a steady-state.

  After a 48-hour restriction upon strenuous physical activity, each of the 14 subjects performed a continuous, incremental cycling test on the electromagnetically braked ergocycle (Lode, type Excalibur, Gröningen, The Netherlands). The test began with a warmup of 100 W lasting 6 min, then) the power output was increased by 30 W a minute until volitional exhaustion. During the protocol, V .

  selected cadences during the entire experiment. Indoor air temperatures ranged from 19 to 21°C. Two fans were placed in front of the ergocycle to reduce sweating during cycling and to keep the triathletes cool. Subjects drank on average 1000 mL of water (H 2 0) per hour during the experiment.Statistical analysis: All data were expressed as mean ± Standard Deviation (SD).Kolmogorv-Smirnov tests were conducted before statistical analysis and confirmed that all data were normally distributed. Subsequently, a two-factor (training group x period) ANOVA with repeated measures was performed to compare the following variables measured before and after the 5-week training period: , HR, RPE, EMGi and the pedalling cadence during the 2h-cycling test. A two-factor (training group x time) ANOVA with repeated measures was performed to compare the pre post isoMVC VL and isoMVC RF values before and after the 2-hour cycling exercise. Post-hoc analyses (Newman-Keuls) were used to test differences among pairs of means when appropriate. The accepted significant level of difference was set at P < 0.05 for all tests. Statistical analyses were performed using GraphPad InStat software for Windows (GraphPad InStat, version 3.06, GraphPad Software, San Diego California, USA).

  pre-tests were performed by subjects who were fit immediately after the competitive season, as opposed to the post-tests performed after five weeks of low intensity endurance training (i.e. a low percentage of maximal aerobic power, lower than 75% V . O 2max ). The decrease in 1RM observed for the control group (E) illustrated that endurance training alone, performed at low intensity, was not efficient enough to maintain the initial level of performance of the subjects. Nevertheless, adding three sessions of strength training per week to this endurance training programme enabled the ES group to improve isoMVC (VL) (+8.4% ; P<0.01) and isoMVC (RF) (+7.1% ; P<0.01) together with an increase in the maximal strength level ; therefore, enabling them to maintain their performance during the post-tests. Depending on the group (ES or E), and whatever the test period (pre or post training), our results showed a decrease (P<0.01) in the isometric maximal voluntary contraction of the VL and RF muscles after a 2-hour cycling test (9 and 16.7%, respectively

FCC

  observed in pre-training is replaced by a steady FCC for the ES group during the second hour of exercise, from P 2 (70.6 ± 9.8 rpm) to P 3 (69.1 ± 7.5 rpm). The upholding of a constant pedalling cadence during the second hour of the task was observed with a stabilisation of the EMGi (VL) values, in accordance with Patterson and Moreno studies (1990). Alquist et al. (1992) suggested that force development, as opposed to velocity contraction, determines the degree of type II fibre recruitment. In the present study, according to this hypothesis, a stabilisation of FCC observed during post-training 2-hour cycling exercise could indicate that recruitment of type II fibres were stabilised. Progressive recruitment of type II fibres all along the exercise triggers a progressive increase in V .

  The intensity of VT 1 + 3% was then accepted as a definitive intensity for the experimental procedure (ES: 244.1 ± 14.1W and E: 237.3 ± 36.5W). The test consisted in sustaining this constant power output for 2-hour (excluding a 10 min warm-up at 33% of Pmax) at a free chosen cadence. The interval between the calculations of respiratory parameters was set at 10 s (breath by breath) using the Cosmed K4b 2 . Data ( V .

Table 1

 1 In our study, the pedalling cadence was the main result that we were able to observe during the continuous 2-hour test (Figure1). The analyses of variance show a significant period effect (P<0.01) for ES and E in pre and post training. If the cadence registered by the

	. No difference was

. O 2 , HR and power values measured to VT 1 and VT 2 did not differ between the two groups before training and remained unchanged with training in the two groups.

Table 4

 4

	Post-training isoMVC VL (before: 100.3 ± 17.9; after: 91.9 ± 14.9 N.m) and isoMVC RF (before:
	99.2 ± 16.4; after: 89.2 ± 11.6 N.m) were significantly (P<0.05) higher than pre-training
	isoMVC VL (before: 92.5 ± 16.4; after: 83.9 ± 14.6 N.m) and isoMVC RF (before: 92.6 ± 14.8;
	after: 81.1 ± 8.6 N.m) for ES. The isoMVC VL and isoMVC RF values did not differ significantly
	between ES and E in pre-training. However, after training, the test values before the 2-hour
	constant cycling did not differ significantly while those measured after are significantly higher
	(P<0.05) than ES as opposed to E, as well as for isoMVC VL (91.9 ± 14.9 vs. 76.6 ± 7.2 N.m)
	and for isoMVC RF (89.2 ± 11.6 vs. 76.5 ± 9.6 N.m).
	The changes over time of the integrated EMG signal of VL (EMGi (VL) ) and RF (EMGi
	(RF) ) for the two groups are presented in Figure 2 and Figure 3. ANOVA shows a significant

shows the effects of training on the electromyographic variables measured before and after the 2-hour constant cycling test. Both isoMVC VL and isoMVC RF measured before the 2-hour constant cycling test were significantly (P<0.01), lower than isoMVC VL and isoMVC RF measured after the 2-hour cycling test, in pre-and post-training for the two groups. period effect (P<0.01) for ES and E in pre-and post-training. Values did not differ between the two groups before and after training and remained unchanged with training in E. However, E , HR) and perceived exertion values during a two-hour constant cycling task performed in laboratory conditions by well-trained triathletes. CHANGE IN MAXIMAL STRENGTH WITH TRAINING. In the present study, the triathletes of the ES group achieved significant gains in 1 RM leg press after 5 weeks of concurrent training (+6.6 ± 3.9% ; P<0.01) whereas the E group presented a significant decrease (-4.1 ± 3.0%; P<0.01) in maximal strength after the training period. Nevertheless, no variation in body mass was observed in subjects of either group, after the five weeks of training. Supporting the existing literature

Table 1 . Main characteristics of the endurance-strength (ES) and endurance-only (E) triathletes.

 1

	ES (N = 7)	E (N = 7)

ACKNOWLEDGEMENTS

The authors would like to thank firsltly the French Triathlon Federation and all French triathletes who participated in this experiment, and secondly to Patricia Vandewalle for her help in the correction of this manuscript.


Amandine Conte-Daban

Maryline Beyler

Raphaël Tripier

Christelle Hureau
email: christelle.hureau@lcc-toulouse.fr

Kinetics are crucial when targeting copper ions to fight Alzheimer's disease: an illustration with azamacrocyclic ligands

Keywords: copper, peptide, bioinorganic, chelator, azamacrocycles Aβ, Amyloid-, Abs, absorbance, AD, Alzheimer's disease, Asc, ascorbate, EPR: Electron Paramagnetic resonance, XANES (X-ray Absorption Near-Edge Structure), HEPES, 4-(2-hydroxyethyl)piperazine-1-ethanesulfonic acid, ROS, Reactive Oxygen Species, ThT : Thioflavine T, TEM: Transmission Electronic Microscopy

Introduction

AD is the most common neurodegenerative disorder characterized by the formation of extracellular senile plaques. [1] They are found in AD brains and contain high levels of Cu 2+ and Zn 2+ ions embedded with aggregates of the amyloid- (A) peptide. [2] Aggregates of A can be of various size and morphologies with distinct cell toxicity. [3] While metal ions can effect aggregation pathways and final species, neither the mechanism nor the resulting type of aggregates is clear. Cu ions are essential and play key biological roles, [4] but due to their ill-controlled redox ability they are harmful in AD. [5] Actually, A-bound Cu ions in the presence of a reductant such as ascorbate (Asc) present at 200-400 µM in the synaptic cleft and of dioxygen, produce Reactive Oxygen Species (ROS) that participate in the oxidative stress observed in AD and damage neighboring biomolecules. [5b] This is one of the main reason why molecules targeting copper ions, mainly Cu 2+ , have been developed as drug candidates within the context of AD. Such ligands can remove Cu 2+ from A (chelator) and optionally redistribute it intracellularly (metallophore) and many of them have been reported in the last years. [6] They fulfill several requirements, including having a higher affinity for Cu 2+ than the A peptide, the ability to redox silence the Cu 2+ ion and a very high Cu 2+ over Zn 2+ selectivity. [7] However, because the synaptic cleft where the Cu-Aβ interaction occurs is proposed to be a quite reducing environment, [8] Cu 2+ might not be the most appropriate redox state of copper to target. Thus, a first possible strategy is to target Cu + , [9] or both redox states. [10] A second one is to design Cu 2+ drug candidates that are able to remove Cu 2+ " fast enough" from the Aβ peptide. Indeed during ROS production the copper ion switches between the +I and +II redox state; hence an efficient Cu 2+ ligand should remove the transient Cu 2+ from the Aβ peptide and redox-silent it faster than Asc can reduce it (Scheme 1).

Scheme 1. Two approaches to stop Cu-Aβ ROS production. On the one hand, Cu  is targeted and the complex Cu  LI is inert toward O2 oxidation (shaded part of the scheme). On the other hand, Cu  is targeted and the complex Cu  LII is inert toward Asc reduction. Two possibilities occur: (i) if LII is able to remove the Cu Tetraazamacrocycles are ligands that fulfill the thermodynamic criteria stated above and are well known for the kinetic inertness and the thermodynamic stability of the complexes counterparts. In particular, 1,4,7,10-tetraazacyclododecane (cyclen) and 1,4,8,11tetrazacyclotetradecane (cyclam) have been previously tested in the AD context with mitigating results. [11] They are also the scaffolds of many chelators used for radiopharmaceutical purposes that bind 64/67 Cu 2+ , respectively for positron emission tomography and/or therapy and Gd 3+ for magnetic resonance imaging, for instance. [12] As a general trend, Cu 2+ complexation with unsubstituted tetraazamacrocycles is quite slow and pending arms are added to improve the rate of Cu 2+ complexation, [13] as it was also observed with other cations. [14] Inspired by these works, we report here the use of two families of tetraazamacrocycles based on the cyclen or cyclam scaffold as potential Cu 2+ chelators against AD and illustrate the importance of kinetic issues in the removal of Cu 2+ from the A peptide ‡ and subsequent ROS arrest.

Scheme 2. Representation of the six different azaligands studied.

Results and Discussion

Scheme 2 shows the structure of the six ligands used in the present study. In addition to the non-functionalized cyclen and cyclam ligands, four macrocycles based on cyclen and cyclam ligands and bearing one (do1pa and te1pa, respectively) or two (do2pa and te2pa, respectively) methylpicolinate pendant arms were studied. Te2pa is new and as for its three analogues, has been obtained following the regiospecific N-functionalization of the macrocycle via the bisaminal chemistry (synthetic scheme S1). A view of the structure of the Cu 2+ corresponding complex is shown in Figure 1, while bond distances and angles of the metal coordination environment are given in the caption (see also Tables S1-S2). The central Cu 2+ cation is six-coordinated in a highly elongated octahedral geometry, with the four nitrogen atoms of the macrocycle in the equatorial plane and the two nitrogen atoms of the picolinate arms at the apical positions. The Cu-NH (Cu-N2) bond are shorter than the Cu-N1 bond (1.9974(13) versus 2.0462(13) Å) whereas the apical Cu-N3 bonds are much longer (2.7910( 16) Å) due to the Jahn-Teller distortion. The carboxylate functions do not participate to the coordination of the metal center as observed for te1pa. [15] This six-coordinated Cu 2+ complex adopts the usually observed trans-III configuration (R,R,S,S conformation). The thermodynamic protonation constants of te2pa and its stability constants with Cu 2+ were determined in 0.10 M KNO3 at 25°C using potentiometric titrations. The stepwise constants (log(K)) and the overall constants (log(β)) are collected in Table S3, while the speciation diagrams are showed in Figures S1-S2. The conditional affinity constant (corresponding to the absolute affinity constant at a given pH) has been calculated from the log(K) and log(β) values and equals 10 14.9 at pH 7.1, a weaker value compared to affinity constants of the other five ligands under study (Table 1). [15][16] XANES and EPR signatures of the six Cu 2+ -L (L is any of the ligand studied) complexes in frozen solution are shown in Figures S3-S4 and corresponding EPR parameters are listed in Table 1. EPR signatures and parameters are consistent with an elongated octahedral geometry and a 4N binding set in the equatorial plane. [17] XANES fingerprints of Cu 2+ -te2pa is reminiscent of those of the parent Cu 2+ -cyclam complex in line with the X-ray structure (Figure 1) that indicates an extremely long apical distance.

The cyclic voltammograms of the six complexes (Figure S5) indicate the possibility of reduction of the Cu 2+ ion but at very low potentials (below -0.5 V versus SCE). As a general trend, addition of methylpicolinate arm(s) makes the reduction easier, i.e. the reduction occurs at higher potential (Table 1). Given the potential values of the cathodic process observed for all complexes , reduction by Asc is thermodynamically disfavored. [10] Table 1

     No incubation Cu Cu-A ~     ~      
[a] Not determined, [b] "Rev" indicates a electrochemically reversible or quasi-reversible process, "Irrev" indicates an electrochemically irreversible process, [c] =total arrest, =no arrest, ~=incomplete arrest of ROS production.

In line with the electrochemical measurements, once the Cu 2+ complexes are formed with any of the six ligands, they can't be reduced by Asc as probed by the absence of Asc consumption monitored by UV-Vis (Figure S6, Panels A and B). Similarly, Asc consumption was not observed when Cu 2+ -A16 complex was pre-incubated with the ligand (Figure 2, Panels A and B). This is in line with the higher Cu 2+ affinity values of the ligands compared to A (Table 1) and the resistance to Asc reduction of the resulting Cu 2+ -L complexes. Hence, the six macrocyclic ligands appear as good candidates for Cu 2+ chelatotherapeutic purposes. In order to go further and challenge these ligands under more biologically relevant conditions, the Asc consumption assay was performed differently: Cu 2+ (Figure S6, Panels C and D) or Cu 2+ -A (Figure 2, Panels C and D when A= A16 and Figure S6, panels E and F when A= A40) was first reacted with Asc and then the ligands were added. Such conditions better mimic the brain environment, which is at the same time rich in Asc and dioxygen. [20] Under such conditions, the arrest of Asc consumption depends on the ligand: the arrest is total with do1pa, te1pa and te2pa, quasitotal with do2pa and absent for both cyclam and cyclen. It is worth noting the Asc consumption experiments mirror the formation of ROS, in particular H2O2 and HO°, [21] and is a robust way to document the ability of ligand in the removal and redox silencing of Cu-A species. [10,22] Here, we also directly monitor the formation of HO° using a fluorescent assay (Figure S7) and the results perfectly match those of the Asc consumption experiment. The different behavior of the six ligands with respect to the arrest of Asc consumption may be linked to the rate of Cu 2+ extraction from Cu 2+ -A during the redox cycling. Indeed, in the course of the Asc consumption, there is a competition between Cu 2+ -A reduction (and thus Asc consumption) and Cu 2+ extraction by the ligands. If the Cu 2+ extraction is not fast enough, the Cu 2+ -A keeps on redox cycling and consuming Asc. Based on the properties of nonsubstituted and substituted ligands with respect to the rate of Cu 2+ complexation, [13] it is anticipated that the addition of methylpicolinate arm(s) accelerates Cu 2+ removal from A. Such assumption has been probed by the measurements of the rate of Cu 2+ binding to the six ligands without or with pre-incubation with the A peptide (Figure 3, panels A-C and B-D, respectively). Several comments can be made: (i) for all the ligands, the kinetic of Cu 2+ removal from A is slower than the Cu 2+ binding. This suggests that to remove Cu 2+ from A, the ligand either acts on unbound Cu 2+ in pre-equilibrium with peptide-bound Cu 2+ or forms a ternary species with Cu 2+ -A, the creation and/or destruction of which is the rate limiting step; (ii) in line with previous reports, cyclen binds Cu 2+ more rapidly than cyclam does, [15] (iii) as anticipated, ligands with methylpicolinate arms bind Cu 2+ and remove it from A faster than the unsubstituted parent ligands and (iv) within the series of methylpicolinate ligands, the trend is as follows: do2pa < te1pa do1pa te2pa (Figure S9 for the ordering of the three later ligands). This trend is in agreement with the ability of the ligand to stop Cu 2+ -A catalyzed ROS formation (Table 1 andFigure 2). As modulation of A aggregation by Cu 2+ ions could potentiate the toxicity of the A aggregates via the formation of other morphological structures, [22][23] we also test the six ligands for the ability to prevent formation of Cu 2+ -type aggregates and to restore the formation of apo-like fibrils. All six ligands are able to do so, as evidenced by the monitoring of kinetic of fibrils formation using the classical ThT assay (Figure 4). More precisely, the formation of apo-type fibrils follows the sigmoid-type curve as expected for the nucleationelongationequilibrium three-step aggregation process. The t1/2 value corresponding to the time at which the ThT fluorescence equals half of the maximum fluorescence, is about 20 hours. In contrast, in presence of Cu 2+ ion, a weak increase of ThT fluorescence is observed in the first hours of the aggregation, followed by a stationary phase and then a sigmoid-like increase with a t1/2 of about 60 hours. In presence of Cu 2+ , the final ThT fluorescence intensity is about twice weaker than for the apo-peptide. Upon addition of any of the six ligands, the first weak increase of ThT fluorescence is lost and a pure sigmoid-like curve recovered with t1/2 values spanning from 15 to 30 hours, a divergence from the t1/2 value of the apo-peptide that is in the acceptable range of error bars of aggregation experiments. The kinetic data are confirmed by TEM (Figure 5). Apo-type fibrils consists in bold intertwisted fibrils made of two to three protofilaments while the Cu 2+ -type fibrils are longer and thinner mainly made of one unique protofilament. In presence of te2pa on the Aβ40 + Cu 2+ sample, the morphology of apo-type fibrils is recovered. The identical activity of the six ligands observed here is linked to the time scale of the aggregation experiments (several hours compared to the seconds time scale of the ROS experiments), which allows Cu 2+ to be removed from the A peptide by any of them.

Conclusions

In the present article, we report the study of six macrocyclic ligands to demonstrate that kinetic issues are important in the design of new chelator/metallophore in the context of AD. The study was performed with the Aβ peptide starting at position 1, considered as the most deleterious with respect to ROS production. More recently, indeed, N-terminally truncated peptides starting at position 4 were reported to be present in significant amount in the synaptic cleft and amyloid plaques, [24] but their Cu 2+ complexes showed much more less propensity to produce ROS. [25] Here the objective is to influence the future design of drug candidates against AD and other diseases involving Cu-induced oxidative stress, such as Parkinson disease. [26] We show that kinetic parameters can be as important as thermodynamic or structural ones. This is particularly obvious when comparing the te2pa and the parent cyclam ligand: while the Cu 2+ environments are very close in both Cu 2+ complexes (the two picolinate arms are indeed very far from the Cu 2+ center in the Cu 2+ -te2pa complex) and the affinity of the latter is higher by about 5 orders of magnitude, the te2pa can remove Cu 2+ faster from A and is active against ROS production in contrast to the parent cyclam. No general trend can be drawn within the family of azamacrocyclic ligands, since for instance in the cyclen series, the addition of a sole picolinate arm leads to a faster Cu(II) removal from A than with the addition of two picolinate arms while in the cyclam series, this is the opposite. To draw a structureactivity relationship the series of ligands studied has to be enlarged. More generally, having ligands able to remove copper from A fast enough may be one overlooked requirement to take into account in the design of drug-candidates targeting Cu ions to fight AD. It adds to other better-known requirements such as being able to cross the blood brain barrier, having Cu 2+ affinity higher than A, not removing Cu 2+ ions from essential enzymes and being selective for Cu 2+ against Zn 2+ . [27] The kinetic counterpart of the latter point, that is to say, the effect of Zn 2+ ions on kinetic processes related to AD is currently under study and will be highlighted in further forthcoming works.

In a period where the deleterious intervention of metal ions and A peptides in AD are disputed, [28] due to the failure of the first generation of metallophores, one might wonder whether targeting copper ions is still a viable approach. With the present study, we show that an alternative way of questioning the metal hypothesis is to ask ourselves whether all requirements have been taken into account in the design of drug candidates targeting metal ions.

Experimental Section

Synthesis. Reagents were purchased from ACROS Organics and from ALDRICH Chemical Co. Cyclam was purchased from Chematech (Dijon, France). Bisformyl-Cyclam (1) [29] and methyl 6-(chloromethyl)picolinate (2) [30] were synthesized as previously described (See Scheme S1). The solvents were freshly distilled prior to use and according to the standard methods. NMR spectra ( 1 H and 13 C) were recorded at the core facilities of the University of Brest, with Bruker Avance 500 (500 MHz) or Bruker AMX-3 300 (300 MHz) spectrometers. The HR-MS analyses were performed at the Institute of Analytic and Organic Chemistry, ICOA in Orléans. Reagents, except the different ligands, were commercially available and were used as received. All the solutions were prepared in milliQ water (resistance: 18.2 MΩ.cm).

The Cu 2+ ion source was CuSO4.5H2O, bought from Sigma-Aldrich.

HEPES buffer (sodium salt of 2- [4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid) was bought from Sigma-Aldrich. A stock solution was prepared at 500 mM, pH = 7.1.

Phosphate buffer was bought from Sigma-Aldrich. Two stock solutions, K2HPO4 and KH2PO4, were prepared at 500 mM, and they were mixed until to reach a stock solution at pH = 7.1.

Sodium ascorbate was bought from Sigma-Aldrich. A stock solution was prepared at 5 mM each day because of the quick degradation of the ascorbate.

Thioflavin T (ThT) was bought from Acros Organics. A stock solution of ThT at 250 μM was prepared in water without any further purification.

Peptides. Aβ16 (DAEFRHDSGYEVHHQK) was bought from Genecust. A stock solution of about 10 mM was prepared and titrated using the Tyr chromophore, with ε = 1410 cm -1 M -1 at acidic pH. The stock solution was stored at 4°C. Aβ16 is considered as a good model of the Aβ40 with respect to metal ions binding. [5a] Aβ40 (DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVV) was bought from Genecust. Around 6 mg were dissolved in approx. 400 µL of NaOH 50 mM. This solution was purified by FPLC, with a Superdex 75 column and NaOH 15 mM as eluent, with a flow rate at 0.9 mL min -1 . The collected fractions were titrated using the Tyr chromophore, with ε = 2400 cm -1 M -1 at basic pH. The stock solution was directly used for the ThT experiments.

Single crystal X-ray diffraction measurements. Single-crystal X-ray diffraction data were collected at 170 K on an X-CALIBUR-2 CCD 4-circle diffractometer (Oxford Diffraction) with graphite-monochromatized MoK radiation (λ = 0.71073). Crystal data and structure refinement details are given in Table 5. Unit-cell determination and data reduction, including interframe scaling, Lorentz, polarization, empirical absorption and detector sensitivity corrections, were carried out using attached programs of Crysalis software (Oxford Diffraction). [31] Structures were solved by direct methods and refined by full matrix least squares method on F2 with the SHELXL [32] suites of programs. The hydrogen atoms were identified at the last step and refined under geometrical restraints and isotropic U-constraints. [33] CCDC number 1540075 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge from the Cambridge Crystallographic Data Centre via www.ccdc.cam.ac.uk/data_request/cif.

UV-Visible spectrophotometry. UV-Vis ROS kinetic experiments were recorded on a spectrophotometer Agilent 8453 at 25°C in 1 cm path length quartz cuvette, with an 800 rpm stirring. The samples were prepared from stock solutions of ligand, peptide and Cu 2+ diluted down to 12, 12 and 10 µM respectively in 100mM HEPES solution, pH = 7.1. Ascorbate is diluted down to 100 µM. UV-Vis kinetic monitoring of the Cu 2+ binding or Cu 2+ removal from Aβ by the six ligands were recorded on a spectrophotometer Agilent 8453 at 25°C in 1 cm path length quartz cuvette, with an 800 rpm stirring. The samples were prepared from stock solutions of Cu 2+ , peptide and ligand diluted down to 50, 50 and 50 µM respectively in 100mM HEPES solution, pH = 7.1.

Fluorescence experiments.

ThT experiments were recorded on a FLUOstar OPTIMA BMG LABTECH at 37°C in a 384-well plate bought from Dutscher SAS. ThT was excited at 440 nm and the fluorescence was recorded at 490 nm. The gain was 1200. The samples were prepared from stock solutions of ligand, peptide and Cu 2+ diluted down to 20, 20 and 18 µM respectively in 100 mM HEPES buffer, pH = 7.1. ThT was added at a resulting concentration of 10 µM.

Transmission electron microscopy. Specimens were prepared for electron microscopy using the conventional negative staining procedure. 20 μL of solution was absorbed on Formvar-carbon-coated grids for 2 min, blotted, and negatively stained with uranyl acetate (1%) for 1 min. Grids were examined with a TEM (Jeol JEM-1400, JEOL Inc, Peabody, MA, USA) at 80 kV. Images were acquired using a digital camera (Gatan Orius, Gatan Inc, Pleasanton, CA, USA) at a x 25 000 magnification.

Supporting information. Details of potentiometry measurements and analysis, of EPR, UV-Vis, XANES and Fluorescence spectroscopy and TEM experiments are given in the Supporting Information.

2+from

  Aβ faster (bold solid line, LII= fast LII) than Asc reduces it, then it is efficient (); (ii) if not (dashed line, LII= slow LII) then it is inefficient () although it could be able to remove Cu 2+ from Aβ on longer time scales in absence of Asc.  and  refer to the notation used in

Figure 1 .

 1 Figure 1. X-ray crystal structure of Cu 2+ -te2pa with atom labelling; hydrogen atoms and water molecules are omitted for simplicity. The ORTEP plots is drawn at the 30% probability level. Cu-N(1)=2.0604(15), Cu-N(2)=1.9906(15), Cu-N(3)=2.7910(16), N(1)-Cu-N(2)=86.61(6), N(1)-Cu-N(2)=93.39(6), N(1)-Cu-N(3)=75.81(5), N(2)-Cu-N(3)=87.90(6), N(3)-Cu-N(1)=104.19. Symmetry transformations used to generate equivalent atoms: #1 -x+1, -y+1, -z+1.

Figure 2 .

 2 Figure 2. Kinetics of Asc consumption, followed by UV-visible spectroscopy at 265 nm with a background correction at 800 nm. Panel A. (a) Aβ16+Cu 2+ +Asc, (b) Aβ16+Cu 2+ +cyclen+Asc, (c) Aβ16+Cu 2+ +do1pa+Asc, (d) Aβ16+Cu 2+ +do2pa+ Asc. Panel B. (a) Aβ16+Cu 2+ +Asc, (b) Aβ16+Cu 2+ +cyclam+Asc, (c) Aβ16+Cu 2+ +te1pa+Asc, (d) Aβ16+Cu 2+ +te2pa+Asc. Panel C. (a) Asc+Aβ16+Cu 2+ , (b) Asc+Aβ16+Cu 2+ +cyclen, (c) Asc+Aβ16+Cu 2+ +do1pa, (d) Asc+Aβ16+Cu 2+ +do2pa. Panel D. (a) Asc+Aβ16+Cu 2+ , (b) Asc+Aβ16+Cu 2+ +cyclam, (c) Asc+Aβ16+Cu 2+ +te1pa, (d) Asc+Aβ16+Cu 2+ +te2pa. [L]=[Aβ16]=12µM, [Cu 2+ ]=10µM, [Asc]=10µM, [HEPES]=100mM, pH 7.1.

Figure 3 .

 3 Figure 3. Kinetics of Cu 2+ binding to the ligands (panels A and C) and after pre-incubation with Aβ (panels B and D) at the wavelength of maximum intensity. Panel A: cyclen (orange), do1pa (light green) or do2pa (dark green) +Cu 2+ ; Panel B: Cu 2+ -Aβ16+cyclen (orange), do1pa (light green) or do2pa (dark green); Panel C: cyclam (red), te1pa (light blue) or te2pa (dark blue) +Cu 2+ ; Panel D: Cu 2+ -Aβ16+cyclam (red), te1pa (light blue) or te2pa (dark blue). [L]=[Aβ16]=[Cu 2+ ]=50µM, [HEPES]=100 mM, pH 7.1. Additions are performed at t=100s and absorbace at the beginning of the kinetic is showed by a thin dotted horizontal line; see Figure S8 for the corresponding whole spectra plotted as a function of time.

Figure 4 .

 4 Figure 4. Aggregation curves of Aβ peptide. Panel A. Kinetics of ThT fluorescence of Aβ (black curve), Cu 2+ -Aβ (grey curve), Cu 2+ )-Aβ + cyclen (orange curve), Cu 2+ -Aβ + do1pa (light green curve), Cu 2+ -Aβ + do2pa (dark green curve).Panel B. Kinetics of ThT fluorescence of Aβ (black curve), Cu 2+ -Aβ (grey curve), Cu 2+ -Aβ + cyclam (red curve), Cu 2+ -Aβ + te1pa (light blue curve), Cu 2+ -Aβ + te2pa (dark blue curve). [L] = [Aβ40] = 20 µM, [Cu 2+ ] = 18 µM, [ThT] = 10 µM, [HEPES] = 50 mM, pH 7.1, T = 37°C.

Figure 5 .

 5 Figure 5. TEM images of Aβ40 (left image), Cu 2+ -Aβ (central image) and Aβ40 + Cu 2+ + te2pa (right image).

Table

Acknowledgements

The authors acknowledge the ESRF Synchrotron, proposal CRG-FAME-BM30B 30-02-1100 for providing beam time and for their help in recording the XANES spectra.