Sara Villeneuve

Diane Cyr

Elsebeth Lynge

Laurent Orsi

Svend Sabroe

Franco Merletti

Giuseppe Gorini

Maria Morales-Suarez-Varela

Wolfgang Ahrens

Cornelia Baumgardt-Elms

Linda Kaerlev

Mikael Eriksson

Lennart Hardell

Pascal Gu

Occupation and occupational exposure to endocrine disrupting chemicals in male breast cancer: a case-control study in Europe

Keywords: Case-Control Studies, Occupations, Occupational Exposures, Breast Neoplasms,Male, Endocrine Disruptors

Objectives

Male breast cancer is a rare disease of largely unknown etiology. Besides genetic or hormone-related risk factors, a large number of environmental chemicals are suspected to play a role in breast cancer. The identification of occupations or occupational exposures associated with an increased incidence of breast cancer in men may help to identify mammary carcinogens in the environment. Methods Occupational risk factors of male breast cancer were investigated in a multi-centre case-control study conducted in 8 European countries, including 104 cases and 1901 controls. Lifetime work history was obtained during in-person interviews. Occupational exposures to endocrine disrupting chemicals (alkylphenolic compounds, phthalates, PCBs and dioxins) were assessed on a case-by-case basis from expert judgment. Results Male breast cancer incidence was more particularly increased in motor vehicle mechanics (OR 2.1, CI 1.0 4.4) with a dose-effect = relationship with duration employment. It was also increased in paper makers and painters, and in workers in forestry and logging, health and social work, and manufacture of furniture. The odds ratio for exposure to alkylphenolic compounds above median was 3.8 (CI 1.5 9.5). This association persisted after adjustment for occupational exposures to other environmental estrogens.

-

Conclusion

These findings suggest that some environmental chemicals are possible mammary carcinogens. Gasoline, organic petroleum solvents or PAH can be suspected from the consistent elevated risk of male breast cancer observed in motor vehicle mechanics. Endocrine disruptors such as alkylphenolic compounds may play a role in breast cancer.

INTRODUCTION

Male breast cancer is a rare disease, representing less than 1 of all breast cancers, with incidence rates of approximately 1 per % 100,000 men per year in Europe.

Factors linked to hormonal or reproductive function in women have been studied extensively as [START_REF] Curado | Cancer Incidence in Five Continents[END_REF] etiologic factors in female breast cancers, but little is known about the etiology of male breast cancer. The risk factors reported for this disease include mutations of the BRCA2 gene, the presence of an extra X chromosome (Klinefelter syndrome), infertility, cryptorchidism, mumps orchitis, or alcohol drinking. [START_REF] Weiss | Epidemiology of male breast cancer[END_REF] Certain environmental exposures, such as organic solvents or endocrine disrupting chemicals that interfere with hormonally [START_REF] Labreche | Exposure to organic solvents and breast cancer in women: a hypothesis[END_REF] mediated processes and can stimulate the growth of breast cancer cells in laboratory studies are also suspected risk factors for breast cancer.

Many studies on female breast cancer risk in the community have been conducted in relation to a few endocrine disrupting [4 -6 ] chemicals such as DDT or PCBs with mainly negative results. , However, occupational studies on female breast cancer where [7 8 ] exposures are higher and better characterized compared with community settings have rarely been conducted. These studies have shown associations between female breast cancer and exposure to certain organic solvents, , pesticides, , PCBs or other [ endocrine disruptors.

There is also growing evidence that disruption of circadian rhythm during night shift work increases the risk of [START_REF] Aschengrau | Occupational exposure to estrogenic chemicals and the occurrence of breast cancer: an exploratory analysis[END_REF] female breast cancer.

For the purpose of identifying mammary carcinogens in occupational settings, the study of male breast cancer [START_REF] Stevens | Light-at-night, circadian disruption and breast cancer: assessment of existing evidence[END_REF] offers several advantages over female breast cancer for identifying environmental mammary carcinogens, because exposures to the occupational agents of interest, such as PAHs, occur more frequently in jobs held by men than by women. In addition, confounding from female reproductive risk factors is not a concern in men. Occupational studies have inconsistently reported associations between male breast cancer and exposure to heat, gasoline, PAHs or combustion products, , and electromagnetic fields. , [16 -19 ] [ 19 20 ] [19 21 -23 ] None of these studies has specifically investigated the role of exposure to endocrine disrupting chemicals.

The first objective of this study was to examine the risk of male breast cancer by occupation to give possible clues about potential occupational carcinogens. A second objective was to test the hypothesis that exposure to environmental estrogens present in occupational settings , increased the risk of male breast cancer. More specifically, we investigated occupational exposures to alkylphenolic

MATERIAL AND METHODS

We conducted a European multi-centre case-control study for 7 sites of rare cancers including male breast (gallbladder and extra-hepatic bile ducts, small intestine, bone, eye melanoma, thymus and mycosis fungoides). Cases and controls were recruited in selected areas of 8 European countries. The recruitment was population-based in Denmark, France, Germany, Italy and Sweden, and hospital-based in Latvia, Portugal and Spain. Findings on alcohol drinking in relation to male breast cancer have been reported previously for the countries that used a population-based design.

The study design and the procedures of data collection have been described in [START_REF] Guenel | Alcohol drinking may increase risk of breast cancer in men: a European population-based case-control study[END_REF] detail earlier and are summarized below.

[30 ]

Recruitment of cases and controls

Cases were men living in the participating study areas who were diagnosed with a breast cancer between January 1 , 1995 and June st 30 , 1997. Only men aged 35 70 years at diagnosis were eligible for the study. Inclusion was restricted to this age range because the th study focused on occupations and because occupational exposures that occurred in early periods among older subjects may be hard to recall, and difficult to assess by study investigators due to changes in work processes. Case ascertainment was based on regular contacts with clinical and pathology departments in each study area. For each case an expert pathologist reviewed the pathology report and one slide representative of the tumor. In total 122 male breast cancer patients were eligible for the study. Eighteen cases could not be contacted because the doctor did not give permission or the patient refused to participate. In total 104 cases (85 ) were interviewed and were % available for the analysis ( ). table 1 Controls were selected randomly during case recruitment and were frequency-matched to the cases by year of birth (5-year strata), sex, and study area. Controls were selected from population registers in Denmark, Italy and Sweden, from electoral rolls in France and from municipality registers in Germany. Colon cancer patients were regarded as appropriate alternatives to population controls in some cases, as no occupational exposure to chemicals is known to play a role in colon cancer. Hospital-based cancer controls were selected randomly among incident colon cancer patients in Latvia and Spain, and among the colon and a few stomach cancer patients in Portugal. The controls served as a common pool of controls for each of the seven groups of rare cancer cases included in the European study. For the present study we selected only control men in the study areas where at least one male breast cancer patient was included. The participation rate among population controls was high in France (81 ) and Italy (74 ) but it was lower (<60 ) in Denmark, Germany, and Sweden. In % % % countries using a hospital-based design, the participation rate among cancer controls was above 95 . The overall participation rate in % controls was 67 , and 1901 controls were available for the analysis ( ).

% table 1

Data collection

A structured questionnaire was first developed in English, translated into the language of each participating country and then back-translated into English for quality control. This questionnaire was used during a face to face or a telephone (Denmark, Sweden) interview by a trained interviewer. We obtained information on socio-demographic characteristics, previous medical conditions, lifestyle factors, anthropometric characteristics, alcohol and tobacco consumption. A detailed occupational questionnaire was used for each job held for more than 6 consecutive months during lifetime. Questionnaires specific to 27 work tasks such as welding or painting were also used whenever relevant to search for particular occupational exposures.

Coding of jobs and occupational exposures

Each job held for more than 6 month was coded for occupation and industry by trained coders. Occupation was coded according to the International Standard Classification of Occupation (ISCO) of the International Labor Office, 1968 revision. Industry was coded according to the Classification of Activities in the European Community (NACE), 1996 revision.

Exposure to endocrine disrupting chemicals was assessed for each job held during the subjects work history by an expert in ' occupational hygiene. Exposure assessment was conducted by individually reviewing the job-specific questionnaires to collect information on the production of the plant, the work tasks performed, or the type of equipment and chemical products used by the worker. In order to decrease the total number of jobs to be reviewed by the expert, we made a preliminary selection of the job questionnaires based on their ISCO and NACE codes and discarded the jobs where exposure to endocrine disrupting chemicals had no chance to occur. Based on the expert s judgment and a literature review, exposure to alkylphenolic compounds, phthalates, PCBs and dioxins was coded using airborne concentration or dermal contact (1: low; 2: medium; 3: high). Coding of exposure was blind as to case-control status. Exposure to pesticides was assessed in the same way, but a detailed assessment by type of pesticide was beyond the scope of the present paper.

We calculated an exposure score for each job in the subject s work history as the product of exposure probability, frequency and ' intensity and the duration of the job period in years. The lifetime cumulative exposure of each study subject consisted of the sum of the job-specific exposure scores over the entire work history.

Statistical analysis

Odds ratios were calculated for workers ever employed in an occupation or industry using workers who were never employed in that occupation or industry as a reference group. Occupations and industries were defined by the 2-or 3-digit level of the ISCO code and by the 2-digit level of the NACE code, respectively. Only job groups with at least 5 cases are presented.

In the analyses by occupational exposure to endocrine disrupting chemicals, we categorized ever-exposed subjects into 2 groups according to the median of the cumulative exposure distribution among controls, and used never exposed subjects as the reference category. For pesticides, exposed workers were divided according to exposure tertiles.

All analyses were conducted using unconditional logistic regressions using the SAS software package (version 9, Cary, NC). Odds ® ratios were adjusted for age (5-year age group) and country, the stratification variables, as well as for potential confounders coded as categorical variables, including alcohol drinking, body mass index and education.

We also conducted several rounds of analyses (i) restricting to countries that used a population-based design and no colon cancer controls; (ii) restricting to the cases with know estrogen positive receptor status (48 out of 54 cases with known status); and for the occupational exposure analyses (iii) calculating cumulative exposure scores with a lag time of 5, 10, or 15 before diagnosis or interview for the controls; (iv) excluding jobs with low exposure probability from the cumulative exposure scores. These sensitivity analyses did not modify our findings and are not shown.

RESULTS

Study sample

The distribution of cases and controls by age, alcohol consumption, and education are presented in . The age distribution of Table 2 cases and controls did not differ significantly. As reported earlier, alcohol consumption was strongly associated with male breast cancer.[ Lower education was associated with some increase in risk, although the odds ratios did not reach statistical significance. [START_REF] Guenel | Alcohol drinking may increase risk of breast cancer in men: a European population-based case-control study[END_REF] statistically significant excess risk was observed in any BMI category. Because BMI was weakly associated with male breast cancer in some countries, we adjusted for this variable in further analyses.

[29 ]

Occupations and industries

Odds ratios for male breast cancer were increased in wood preparation workers and paper makers (OR 2.4, CI 0.9 6.5), motor vehicle electrical machinery (OR 2.0, CI 0.8 5.3), and manufacture of furniture (OR 1.8, CI 0.9 3.7), were also increased but did not reach the

= - = -
level of statistical significance.

Occupational exposures

In , the odds ratio adjusted for non-occupational risk factors was elevated for alkylphenolic compounds above median (ORa table 5 = 3.8, CI 1.5 9.5), and a dose-response trend was apparent (p<0.01). The odds ratio for exposure to PCBs and dioxin above the median was also increased (ORb 2.1, CI 1.0 4.5) and at the limit of statistical significance. When adjusting further for exposures to other endocrine

= -
disruptors (ORb in ), i.e. including all occupational exposure variables in a single model, male breast cancer incidence remained table 5 associated with alkylphenolic compounds (OR 3.3, CI 1.1 9.9 exposure above median), but the dose-response trend was not statistically = significant. No increased risk was observed for phthalates, and for PCBs and dioxins. Exposure to pesticides was not associated with male breast cancer in either model. Confounding between occupational exposures is thus apparent from , indicating that multiple table 5 exposures to endocrine disruptors occurred among workers in our data.

DISCUSSION

We found that the incidence of male breast cancer was increased in wood preparation workers and paper makers, in motor vehicle mechanics, and in painters as well as in workers in the industry of forestry and logging, in health and social work, in the manufacture of furniture, and in sale and repair of motor vehicles. We also investigated occupational exposures to endocrine disrupting chemicals, and found that male breast cancer incidence was more particularly associated with alkylphenolic compounds.

Study strengths and limitations

This is a relatively large study on male breast cancer conducted in 8 European countries. Despite the large study base, the high participation rate among male breast cancer patients and the large number of controls, some analyses were based on relatively low [START_REF] Lynge | European multi-centre case-control study on risk factors for rare cancers of unknown aetiology[END_REF] statistical power. We calculated for example that a 2-fold increased odds ratio was detectable with a statistical power of 80 (alpha 5 ,

% = %
two-sided) if the exposure prevalence among control was at least 15 , a condition that was not met in all calculations. Chance findings % due to multiple testing are also possible. Nevertheless, if the observed associations are real, they may have been difficult to detect in epidemiological studies on female breast cancer based on higher numbers of cases but much lower exposure prevalence.

The overall participation rate among controls was 67 , with large disparities across countries. A selection bias, e.g. according to % socioeconomic status or education, was thus possible, but should be controlled for, at least in part, by adjusting for education. Restricting to countries with high participation rates among controls such as France and Italy did not alter the findings, giving reassurance that selection bias does not constitute a major problem.

Using colon cancer controls in countries with a hospital-based design could have biased our results if colon cancer was associated with the occupational risk factors of interest. However, with the exception of low physical activity, there is no recognized occupational risk [START_REF] Chow | Occupational risks for colon cancer in Sweden[END_REF] factor for colon cancer. Our results were unchanged when the analyses were conducted separately in countries that did not use colon cancer controls.

Occupational exposures to estrogenic chemicals were assessed by expert judgment reviewing the job-specific questionnaires blindly as to the case-control status. This case-by-case exposure assessment method may lead to non-differential exposure misclassification errors, but it is considered as more efficient than a job-exposure matrix, since the exposure is evaluated for individual workers rather than for groups of workers with the same job. Exposures to estrogenic chemicals arising from the general environment could not be evaluated [START_REF] Siemiatycki | Costs and statistical power associated with five methods of collecting occupation exposure information for population-based casecontrol studies[END_REF] in our study. This should not be a major problem considering that environmental exposures occur at lower levels than in occupational settings, and that they should be equally distributed among occupationally and non-occupationally exposed subjects.

Occupations and industries

Few studies have investigated male breast cancer risk in relation to occupational risk factors, but most of them have several drawbacks, including low statistical power, the use of a mortality rather than incidence register to recruit male breast cancer [16 -18 ] patients, or information on occupation known from census data at only one point in time instead of lifetime occupational history. , [START_REF] Cocco | Case-control study of occupational exposures and male breast cancer[END_REF] [20 , 23 33 ] We found a 2-fold increased incidence of male breast cancer in motor vehicle mechanics, with an indication of a dose-response relationship with duration of employment (OR 5.9 in men employed for 10 or more years in that occupation). Similarly, a registry-based = case-control study in Denmark reported an increased incidence of male breast cancer in workers employed in service stations, vehicle [START_REF] Hansen | Elevated risk for male breast cancer after occupational exposure to gasoline and vehicular combustion products[END_REF] maintenance, wholesale trade of gasoline, or car repair shops. Exposure to solvents, gasoline and vehicle combustion products, containing suspected mammary carcinogens such as benzene and polycyclic aromatic hydrocarbons (PAH) may be causal explanations for these associations, , and previous studies on female breast cancer provided some evidence of a link with exposure to solvents. , This [3 34 ] [9 10 ] link is of interest and needs to be investigated further in male and in female breast cancer.

We reported an increased incidence of male breast cancer in painters (OR 2.3, CI 1.0 5.3) suggesting a potential adverse effect of = solvents again, or paint additives. The elevated incidence in wood preparation workers and paper makers, and in men employed in forestry and logging, also suggests that chemicals with endocrine disrupting properties, such as volatile organic compounds in wood, could [START_REF] Mellanen | Wood-Derived Estrogens: Studies in Vitro with Breast Cancer Cell Lines and in Vivo in Trout[END_REF] play a role in breast cancer.

Conversely, no increased incidence of male breast cancer was detected in our study among metal processors (ISCO 7-2, ) or table 3 among men employed in the metal manufacturing industry (NACE 27,

). These findings do not confirm the elevated risks of male table 4 breast cancer reported in workers employed in blast furnaces, steel work and rolling mills, and hence do not support the [16 -19 ] hypothesis that exposure to heat which is common in these industries, may be carcinogenic to the breast. Our results do not point [START_REF] Thomas | Breast Cancer in Men -Risk Factors with Hormonal Implications[END_REF] either toward a role of electromagnetic fields in male breast cancer, as suggested by other investigators, , , since occupations [21 37 38 ] with potentially high exposures such as electricians or welders were not at increased risk in our data.

Occupational exposures to endocrine disrupting chemicals

This is the first study examining a possible association between male breast cancer and exposure to endocrine disrupting chemicals.

Many hormonally active compounds suspected to affect breast cancer due to their estrogenic or anti-estrogenic properties were identified in laboratory studies , but only a few have been investigated in epidemiological studies. We examined estrogenic compounds that can [START_REF] Brody | Environmental pollutants and breast cancer[END_REF] be found in occupational settings, , and excluded chemicals with very low exposure prevalence in the study such as bisphenol A or [24 25 ] parabens.

Exposure to alkylphenolic compounds was associated with male breast cancer in our data. Alkylphenol polyethoxylates surfactants and alkylphenols are used in a broad range of occupations and industries, but are mainly used as detergents, in plastics, rubber products or in cosmetics, , and in the textile industry.

The workers exposed to alkylphenolic compounds in the present study can be better

[24 25 ] [40 ]
characterized by their industry, and were mainly employed in finishing of textiles, in the manufacture of leather clothes, in the industry of pulp and paper, plastics, paints and varnishes, soap and detergents, and rubber products. To our knowledge, only one study in women has examined the relation between exposure to alkylphenolic compound and breast cancer.

In this population-based case-control study, the [START_REF] Aschengrau | Occupational exposure to estrogenic chemicals and the occurrence of breast cancer: an exploratory analysis[END_REF] possibility of exposure to 18 xenoestrogens was determined using a job-exposure matrix derived from the US National Occupational Exposure Survey database. Probable exposure to 4-octylphenol, an alkylphenolic compound, was associated with an odds ratio of 2.9 (0.8-10.8) after adjustment for potential confounders. Overall, our findings provide some support to the hypothesis that alkylphenolic compounds may play a role in breast cancer incidence. It should be noted however that multiple exposures to endocrine disruptors occurred in our data, and that confounding made it difficult to disentangle the effects of individual compounds. It is thus possible that the main association observed between male breast cancer and alkylphenolic compounds should be accounted for multiple exposure to different classes of estrogenic compounds, which can have synergistic effects. [START_REF] Diamanti-Kandarakis | Endocrine-disrupting chemicals: an Endocrine Society scientific statement[END_REF] The odds ratio associated with exposure to dioxins and PCBs above median was increased 2-fold, although this association was not apparent after adjusting for alkylphenolic compounds. PCBs and dioxins are two groups of persistent organochlorine compounds. PCBs were used as dielectric fluids in electrical capacitors, but also for miscellaneous uses in adhesives, oils or paint . Most studies on the [START_REF] Broadhurst | Use and replaceability of polychlorinated biphenyls[END_REF] association between breast cancer and environmental exposure to PCBs, based on measurements in the blood or fat of cases or controls, reported no increased risk of female breast cancer.

In a cohort study of women employed in capacitor facilities, no overall increase of [START_REF] Negri | Environmental exposure to polychlorinated biphenyls (PCBs) and breast cancer: a systematic review of the epidemiological evidence[END_REF] breast cancer incidence was detected after exposure to PCBs, as estimated from a job-exposure matrix. Several studies, however, point [START_REF] Silver | Occupational exposure to polychlorinated biphenyls and risk of breast cancer[END_REF] to a possible role of PCBs in breast cancer for the subgroup of potentially predisposed women who carry a genetic variant in the CYP1A1 gene.

There also remain uncertainties concerning the role of certain PCB subgroups, such as dioxin-like PCBs.

Dioxins have

[42 -45 ] [8 ]
been investigated less frequently in relation to breast cancer. In the cohort of women exposed to 2,3,7,8-TCDD -the main dioxinfollowing the explosion of a trichlorophenol manufacturing plant near Seveso, Italy, an increase in female breast cancer incidence was observed in the most contaminated areas and among women with higher TCDD serum levels.

Increased mortality from breast [START_REF] Pesatori | Cancer incidence in the population exposed to dioxin after the Seveso accident : twenty years of follow-up[END_REF] [47 ]

cancer in both women and men was also reported in an international cohort of workers exposed to phenoxy herbicides contaminated with dioxins.

Overall these data provide some support to the hypothesis that some PCBs and dioxins may increase the risk of breast cancer, [START_REF] Kogevinas | Cancer incidence and mortality in women occupationally exposed to chlorophenoxy herbicides, chlorophenols, and dioxins[END_REF] and requires further attention.

Phthalates have been widely used as plasticizers in soft plastics or in cosmetics, but they have never been investigated thoroughly [49 ] in relation to breast cancer incidence in an epidemiological study. We found no significant association of male breast cancer with exposure to phthalates possibly because of confounding by alkylphenolic compounds. Because of their widespread use, their effects in hormone-related cancers would deserve further attention. The role of environmental exposures to persistent organochlorine pesticides, such as DDT, was investigated in many studies with mainly negative findings . Other studies in female farmers reported increased breast [START_REF] Lopez-Cervantes | Dichlorodiphenyldichloroethane burden and breast cancer risk: a meta-analysis of the epidemiologic evidence[END_REF] cancer risk in women likely exposed to pesticides, or living closer to pesticide application areas.

Our data provide no support for [11 ] [12 ] an association between pesticide exposure and breast cancer in men, but the lack of information on specific pesticides may account for these negative findings.

Conclusion

The elevated risk of male breast cancer in certain occupations provided some clues about possible environmental mammary carcinogens. The elevated risk of male breast cancer among motor vehicle mechanics, points to a role of PAH and gasoline or petroleum solvents in breast carcinogenesis, that needs to be investigated further in studies of male or female breast cancer. For the first time in male breast cancer, we have shown that endocrine disrupting chemicals could affect breast cancer risk. These results support the growing evidence that breast cancer may be linked to exposure to environmental pollutants and should encourage further studies on this issue.

Ackowledgements:

DENMARK

'

  semi-quantitative indicators for exposure probability (0: not exposed; 1: possibly exposed; 2: probably exposed; 3: certainly exposed), exposure frequency (1: < 30 of working hours; 2: 30 70 ; 3: > 70 ) and exposure intensity, based on the expert judgment about % -% %

table 3 for 10

 310 or more years as motor vehicle mechanics (OR 5.9, CI 2.4 14.6) (not shown). Conversely, a non-significantly decreased odds ratio = was observed for farmers and agricultural workers.presents odds ratios associated with specific industries. Increased odds ratios were observed in forestry and logging (OR 2.4,

	=	-

Table 4 =

 4 CI 1.0 5.6), health and social work (OR 2.3, CI 1.1 5.1), and sale and repair of motor vehicles (OR 1.8, CI 1.0 3.2). This later increase

	-	=	-	=	-
	(NACE 50) was explained by the increased odds ratio in motor vehicle mechanics (ISCO 8 43), with 9 cases and 57 controls being -
	classified in both NACE 50 and ISCO 8 43. Odds ratios in the manufacture of rubber and plastic (OR 1.9, CI 0.8 4.6), manufacture of -= -

European Study Group on Occupational Causes of Rare Cancers:

  : Herman Autrup, Henrik Kolstad, Linda Kaerlev, The Elsebeth Lynge, Jorn Olsen, Lisbeth Norum Pedersen, Svend Sabroe, Preben Johansen, Stein Poulsen, Peter Stubbe Teglbjaerg, Mogens Vyberg. FRANCE: Pascal Gu nel, Jo lle F votte, Diane Cyr and the members of the FRANCIM association: Patrick Arveux, Antoine Education, Science, Research and Technology (BMBF), grant no. 01-HP-684/8. Italy: MURST, Region Piedmont, Ministry of Labour, Italian Association for Cancer Research, Compagnia SanPaolo/FIRMS. Portugal: Junta Nacional de Investidac o Cientifica e Tecnol gica, Praxis Clinico-Epidemiol gica, Hospital Dr. Peset. Generalitet Valenciana; Departmento de Sanidad y Consumo, Gobierno Vasco; Fondo de Investigaci n de la Sanitaria (FIS), Ministerio de Sanidad y Consumo, Ayuda a la Investigaci n del Departamento de Salud del Gobierno de Navarra. Sweden: Swedish Council for Work Life Research, Research Foundation of the Department of Oncology in Ume , Swedich Society å of Medicine, Lund University Hospital Research Foundation, Gunnar, Arvid and Elisabeth Nilsson Cancer Foundation, rebro County Ö Council Research Committee, rebro Medical Center Research Foundation, John and Augusta Persson Foundation for Scientific Medical

	é	ë	é
				ó	ó	í
	nez Gonz lez, Francisco Guill n Grima, Rosa Guarch, Agustin Llopis Gonz lez, Blanca Mar n, Amparo Marquina, Mar a M Morales Su á é á í í	á
	é	" é	é	"
				ä	ó
	XXI, no2/2.1/SAU/1178/95. Spain: Fondo de Investigaci n de la Sanitarie, Ministerio de Sanidad y Consumo, Unidad de Investigaci n ó ó
	ó			ó

Buemi, Paule-Marie Carli, Gilles Chaplain, Jean-Pierre Daur s, Jean Faivre, Pascale Grosclaude, Anne-Val rie Guizard, Michel Henry-Amar,

è é

Guy Launoy, Francois M n goz, Nicole Raverdy, Paul Schaeffer. GERMANY: Wolfgang Ahrens, Cornelia Baumgardt-Elms, Sibylle é é Gotthardt, Ingeborg Jahn, Karl-Heinz J ckel, Hiltrud Merzenich, Andreas Stang, Christa Stegmaier, Antje Timmer, Hartwig Ziegler. ITALY: ö Terri Ballard, Franco Bertoni, Giuseppe Gorini, Sandra Gostinicchi, Giovanna Masala, Enzo Merler, Franco Merletti, Lorenzo Simonato, Paola Zambon. LATVIA: Irena Rogovska, Galina Sharkova, Aivars Stengrevics. LITHUANIA: Jolita Gibaviciene, Laimonas Jazukevicius, Juozas Kurtinaitis, Poma Pociute. PORTUGAL: Noemia Alfonso, Altamiro Costa-Pereira, Sonia Doria, Carlos Lopes, Jos Manuel Lopes, é Ana Miranda, Cristina Santos. SPAIN: M Adela Sanz Aguado, Juan J Aurrekoetxea, Concepci n Brun, Alicia C rdoba, Miguel Angel Mart rez-Varela, In s Aguinaga Ontoso, JM Mart nez Pe uela, Ana Puras, Francisco Vega, Maria Aurora Villanueva Guardia. SWEDEN: Mikael é í ñ Eriksson, Lennart Hardell, Irene Larsson, Hakan Olson, Monica Sandstr m, Gun Wingren. SWITZERLAND: Jean-Michel Lutz. UNITED ö KINGDOM: Janine Bell, Ian Cree, Tony Fletcher, Alex JE Foss. The study Occupational risk factors for rare cancers of unknown etiology was financially supported by the European Commission, Funding: " " DGXII, grants no BMH1 CT 931630 and ERB CIPD CT 940285, and national funding agencies. Denmark: The Strategic Environment Programme. France: Ligue Nationale contre le Cancer, Fondation de France (grant No 955368), Institut National de la Sant et de la é Recherche M dicale (INSERM) (grant R seau en Sant Publique ), French Ministry for Environment. Germany: Federal Ministry for ó Ö research, Berta Kamprad Foundation for Cancer Research.

Footnotes:

: All authors declare they have no potential competing financial interest Competing interests

Table 1

 1 Number of men eligible for the study and number of respondents by country European study on male breast cancer

	-
	Cases

Table 2

 2 Comparison of cases and controls for selected characteristics European study on male breast cancer

	-
	cases		controls
	N 104 =	%	N 1901 =	%	OR *	95 CI %

* OR adjusted for age and country

Table 4

 4 Odds ratios by industry (NACE code ) European study on male breast cancer

*

† -

[START_REF] Engel | Pesticide use and breast cancer risk among farmers wives in the agricultural health study[END_REF]

[START_REF] Engel | Pesticide use and breast cancer risk among farmers wives in the agricultural health study[END_REF]

[START_REF] Engel | Pesticide use and breast cancer risk among farmers wives in the agricultural health study[END_REF]

[START_REF] Engel | Pesticide use and breast cancer risk among farmers wives in the agricultural health study[END_REF]

[START_REF] Engel | Pesticide use and breast cancer risk among farmers wives in the agricultural health study[END_REF]

[START_REF] Engel | Pesticide use and breast cancer risk among farmers wives in the agricultural health study[END_REF]

[START_REF] Engel | Pesticide use and breast cancer risk among farmers wives in the agricultural health study[END_REF]


Miguel A San-Miguel

Colin Robinson

P Mark Rodger

SECONDARY STRUCTURE SIMULATIONS OF TWIN-ARGININE SIGNAL PEPTIDES IN DIFFERENT ENVIRONMENTS

Keywords: Tat system, molecular dynamics, TFE, SufI Tat system, MD, TFE, SufI

The twin-arginine translocation (Tat) system transports folded proteins across bacterial plasma membranes and the chloroplast thylakoid membrane. A twin arginine motif in the signal peptide sequence plays a key role in the signal process. In this article we report the results of molecular dynamics simulations on a typical E. coli RR-signal peptide and two mutant variants in both aqueous and trifluoroethanol (TFE) solutions. It has been found that the peptide switches between two distinct states: random coil in water and some helical content in TFE. Our simulations demonstrate that the wild-type peptide is considerably more flexible than either of the mutants in both the solvents investigated. The twin arginine motif was found to provide a nucleation point for the formation of an α-helix in water, but also appears to destabilise α-helices in other regions of the peptide when dissolved in TFE.

INTRODUCTION

Protein translocation processes across biological membranes have attracted considerable attention from scientists, particularly during the last two decades [1][START_REF] Jungnickel | Protein translocation: common themes from bacteria to man[END_REF][START_REF] Izard | Signal peptides: exquisitely designed transport promoters[END_REF][START_REF] Schatz | Common principles of protein translocation across membranes[END_REF][START_REF] Heins | The protein transport apparatus of chloroplast envelopes[END_REF][START_REF] Dalbey | Protein translocation into and across the bacterial plasma membrane and the plant thylakoid membrane[END_REF][START_REF] Hermann | Protein transport into mitochondria[END_REF]. Because membrane bilayers are tightly sealed, and in many cases impermeable even to inorganic ions, sophisticated systems have evolved to accomplish the difficult process of transporting large globular proteins in a highly specific manner. It had been believed that the translocation process required the proteins to unfold -either prior to, or during, translocation across the membrane. However, within the last decade studies on thylakoid protein targeting identified a novel protein translocase, which facilitates translocation in the folded state [START_REF] Clark | A folded protein can be transported across the chloroplast envelope and thylakoid membranes[END_REF][START_REF] Hynds | The Sec-independent twin-arginine translocation system can transport both tightly folded and malfolded proteins across the thylakoid membrane[END_REF]. This translocation mechanism has since been shown to operate in a wide variety of bacteria, chloroplasts and plant mitochondria [START_REF] Robinson | Protein targeting by the twin-arginine translocation pathway[END_REF]. The system recognises proteins bearing an aminoterminal signal peptide containing a twin-arginine (RR) motif and has been termed the twin-arginine translocase, or Tat system. Several mutagenesis studies have demonstrated that this RR signal peptide contains all of the information required for precise recognition by the Tat system, and have identified certain essential features of the sequence. Of particular importance is the presence of adjacent arginines at the junction between an N-terminal basic domain and hydrophobic core domain, together with the presence of a strongly hydrophobic residue at the second or third position after the twin arginine [START_REF] Chaddock | A new type of signal peptide: central role of a twin-arginine motif in transfer signals for the ∆pH-dependent thylakoidal protein translocase[END_REF][START_REF] Brink | Targeting of thylakoid proteins by the ∆pH-driven twin-arginine translocation pathway requires a specific signal in the hydrophobic domain in conjunction with the twin-arginine motif[END_REF][START_REF] Stanley | The twin arginine consensus motif of Tat signal peptides is involved in Sec-independent protein targeting in Escherichia coli[END_REF]. However, while much is known about the required primary structure there is still little knowledge of the secondary structure. In a previous study [START_REF] San-Miguel | An Escherichia coli twin-arginine signal peptide switches between helical and unstructured conformations depending on the hydrophobicity of the environment[END_REF] we analysed circular dichroism (CD) spectra of a typical Escherichia coli RR-signal peptide and two inactive mutant variants in different environments. It was found that in aqueous solvent the peptides are unstructured, whereas some α-helix character (up to ca. 40%) emerged in hydrophobic solvents. The CD showed no significant differences in percentage helical content for the active and inactive sequences.

However, analogous molecular dynamics (MD) simulations [START_REF] San-Miguel | An Escherichia coli twin-arginine signal peptide switches between helical and unstructured conformations depending on the hydrophobicity of the environment[END_REF] suggested that there were differences in the location of the α-helix between the wild-type and the mutants, with only the wild-type peptide showing helix formation in the vicinity of the RR motif. From those results it was clear that the peptide structure switches between two distinct states according to the environment, and that the Tat motif appeared to effect rather subtle changes in the secondary structure associated with these two states. However, a more detailed study of the Formatted: English U.K.

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Deleted: (Blobel et al, 1975;[START_REF] Jungnickel | Protein translocation: common themes from bacteria to man[END_REF][START_REF] Izard | Signal peptides: exquisitely designed transport promoters[END_REF][START_REF] Schatz | Common principles of protein translocation across membranes[END_REF][START_REF] Heins | The protein transport apparatus of chloroplast envelopes[END_REF][START_REF] Dalbey | Protein translocation into and across the bacterial plasma membrane and the plant thylakoid membrane[END_REF][START_REF] Hermann | Protein transport into mitochondria[END_REF] Deleted: [START_REF] Clark | A folded protein can be transported across the chloroplast envelope and thylakoid membranes[END_REF][START_REF] Hynds | simulation of the antimicrobial salivary peptide histatin-5 in water and in trifluoroethanol: a microscopic description of the water destructuring effect[END_REF] Deleted: [START_REF] Robinson | Protein targeting by the twin-arginine translocation pathway[END_REF] Deleted: [START_REF] Chaddock | A new type of signal peptide: central role of a twin-arginine motif in transfer signals for the ∆pH-dependent thylakoidal protein translocase[END_REF]Brink et al, 1998;[START_REF] Stanley | The twin arginine consensus motif of Tat signal peptides is involved in Sec-independent protein targeting in Escherichia coli[END_REF] Deleted In this paper we present the results of a detailed MD study of the structural and dynamical properties of an E. coli RR-signal peptide together with two mutations known to render the peptides inactive in translocation. The three peptides have been simulated in water and in trifluoroethanol (TFE) solution (an apolar environment that mimics the membrane bilayer).

Different isotropic solvents have been chosen in molecular modelling in the literature to create a hydrophobic environment surrounding the peptide similar to that experienced when inserted in the membrane. Some solvents used have been chloroform [START_REF] Peter | Peptides of aminoxy acids: a molecular dynamics simulation study of conformational equilibria under various conditions[END_REF], methane [START_REF] Olivella | Influence of the environment in the conformation of α-helices studied by protein database search and molecular dynamics simulations[END_REF], octane [START_REF] Tieleman | Molecular dynamics simulations of antimicrobial peptides: from membrane binding to trans-membrane channels[END_REF], hexane [START_REF] Chipot | Early events in the folding of an amphipathic peptide: a multinanosecond molecular dynamics study[END_REF], and methanol [START_REF] Tieleman | Alamethicin helices in a bilayer and in solution: molecular dynamics simulations[END_REF].

However, TFE has gained popularity because for more than three decades has been used in CD and NMR studies of peptides in solution showing that it is a α-helix promoting solvent [START_REF] Roccatano | Mechanism by which 2,2,2trifluoroethanol/water mixtures stabilize secondary structure formation in peptides: a molecular dynamics study[END_REF][START_REF] Iovino | Molecular dynamics simulation of the antimicrobial salivary peptide histatin-5 in water and in trifluoroethanol: a microscopic description of the water destructuring effect[END_REF][START_REF] Fioroni | Solvation phenomena of a tetrapeptide in water/trifluoroethanol and water/ethanol mixtures: a diffusion NMR, intermolecular NOE, and molecular dynamics study[END_REF][START_REF] Diaz | Evidence of complete hydrophopic coating of bombesin by trifluoroethanol in aqueous solution: an NMR spectroscopic and molecular dynamics study[END_REF][START_REF] De Loof | Molecular dynamics simulation of galanin in aqueous and nonaqueous solution[END_REF][START_REF] Sticht | Molecular dynamics simulation of equine infectious anemia virus Tat protein in water and in 40% trifluoroethanol[END_REF][START_REF] Van Buuren | Molecular dynamics simulation of the stability of a 22-residue α-helix and 30% trifluoroethanol[END_REF][START_REF] Chitra | A comparison of the properties of 2,2,2-trifluoroethanol and 2,2,2-trifluoroethanol/water mixtures using different force fields[END_REF].

More recently, Bemporad et al have analysed in detail the behaviour of different solutes inside a lipid membrane and they concluded that the membrane core does not behave like a simple liquid solvent [START_REF] Bemporad | Computer simulation of small molecule permeation across a lipid bilayer: dependence on bilayer properties and solute volume, size, and cross-sectional area[END_REF].

The use of explicit membrane has also been extended to numerous studies although only when the protein structure is known. Since the dynamics is slower and the number of atoms involved use to be larger than in isotropic fluids, therefore the simulations become highly time consuming [START_REF] Fowler | Monotopic enzymes and lipid bilayers: a comparative study[END_REF][START_REF] Tieleman | Analysis and evaluation of channel models: simulations of alamethicin[END_REF][START_REF] Tieleman | Voltage-dependent insertion of alamethicin at phospholipid/water and octane/water interfaces[END_REF][START_REF] Forrest | Exploring models of the influenza A M2 channel: MD simulations in a phospholipids bilayer[END_REF].

In this paper fully atomistic models of the solvent have been used in each case. The dynamical behaviour of the peptide, in particular, is found to differ substantially between the wild-type and the two inactive mutants, suggesting that a propensity for forming an α-helix on binding to a membrane may be a key factor in determining Tat activity. In next section we describe the different peptides and solvent models used, and give details of the simulation methodology. Thereafter, we focus on the results obtained from the dynamics when the peptides are inserted into water, TFE or a vacuum; some of the implications of these results are also discussed. In the final section the main conclusions are briefly summarized.

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Deleted: modeling

Deleted: [START_REF] Peter | Peptides of aminoxy acids: a molecular dynamics simulation study of conformational equilibria under various conditions[END_REF] Deleted: [START_REF] Olivella | Influence of the environment in the conformation of α-helices studied by protein database search and molecular dynamics simulations[END_REF] Deleted: (Tieleman et al., 2001) Deleted: [START_REF] Chipot | Early events in the folding of an amphipathic peptide: a multinanosecond molecular dynamics study[END_REF] Deleted: (Tieleman et al., 1999) Deleted: Very Deleted: . (Bemporad et al, 2004) Deleted: . [START_REF] Domene | Filter flexibility and distortion in a bacterial inward rectifier K + channel: simulation studies of KirBac1.1[END_REF][START_REF] Tieleman | Analysis and evaluation of channel models: simulations of alamethicin[END_REF]Tieleman and Sansom, 2001;[START_REF] Forrest | Exploring models of the influenza A M2 channel: MD simulations in a phospholipids bilayer[END_REF][START_REF] Rocca | Simulation studies of the interaction of antimicrobial peptides and lipid bilayers[END_REF]Tieleman et al., 1999)

PEPTIDES

Molecular dynamics (MD) simulations have been performed in order to investigate the influence of the twin arginine motif on both the secondary structure and dynamical behaviour of SufI peptides in two different environments: aqueous and trifluoroethanol (TFE) solutions.

The latter was chosen as it is known to be a membrane mimicking solvent.

The wild type sequence (SufI-RR) is SufI-RR MSLSRRQFIQASGIALCAGAVPLKASA while the two mutants chosen for this study (SufI-KK) and SufI-AA) are SufI-KK MSLSKKQFIQASGIALCAGAVPLKASA SufI-AA MSLSRRQAAQASGIALCAGAVPLKASA Secondary structure predictions were carried out using a number of different protein structure prediction servers: PSIPRED [START_REF] Mcguffin | The PSIPRED protein structure prediction server[END_REF], JPRED [START_REF] Cuff | Evaluation and improvement of multiple sequence methods for protein secondary structure prediction[END_REF], Prof [START_REF] Ouali | Cascaded multiple classifiers for secondary structure prediction[END_REF] and PHD [START_REF] Rost | PHD: predicting one-dimensional protein structure by profile-based neural networks[END_REF]. All predicted an α-helix in the central region, although each predicted a slightly different length.

The PHD method gave what was essentially a consensus result and so was used in this work to construct the initial configurations for subsequent MD simulations. The coordinates and force field for all three peptides were constructed by using Quanta/CHARMm version 28 [START_REF]QUANTA/CHARMm, Version 28[END_REF] and then exported to DL_POLY [START_REF] Smith | DL_POLY: application to molecular simulation[END_REF] format for the MD simulations. All the sequences included a right-handed α-helix with a length of 16 amino acid residues, as predicted by the PHD method. Initial structures were optimised using the conjugate gradient method.

SOLVATED STRUCTURES

Each peptide conformation was then inserted into a solvent box (previously equilibrated at 300 K, 1 atm) and all solvent molecules that overlapped with the peptide were removed; three additional solvent molecules were converted to Cl -ions to compensate for the charge of +3e on these peptides. The resulting system contained 3173 water molecules, or 990 TFE molecules, in a periodic truncated octahedral simulation box of maximum length 65 Å. The system was then relaxed by (i) performing a 5 ps MD simulation at 300 K, 1 atm in which the peptide was treated as a rigid body, and (ii) performing a 2 ps MD simulation with Formatted: English U.K.

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Deleted: methods

Deleted: (McGuffin et al, 2000)

Deleted: [START_REF] Cuff | Evaluation and improvement of multiple sequence methods for protein secondary structure prediction[END_REF] Deleted: [START_REF] Ouali | Cascaded multiple classifiers for secondary structure prediction[END_REF] Deleted: (Rost, 1996) Deleted: (Accelerys Inc.)

Deleted: [START_REF] Smith | DL_POLY: application to molecular simulation[END_REF] Deleted: 65 Å,; a fully flexible peptide at 2 K; these stages served to remove any strain introduced on solvation without destroying the initial secondary structure. A further 1 ns simulation in the water solvated systems and 6 ns simulation in the TFE system were then accumulated to study the evolution of the secondary structure. As will be shown in the next section, this time scale was sufficiently long for the water systems to converge to a steady random coiled state.

Secondary structure was analysed using STRIDE [START_REF] Frishman | Knowledge-based protein secondary structure assignment[END_REF].

This study does not intend to analyse the secondary structure of the sequences during extended periods of time, but the propensity of the peptides to change the initial secondary structure from different initial conditions during the first stage of exposition to two different environments.

SIMULATION PROTOCOL

All MD simulations were performed at constant temperature and pressure (NPT) using the Nosé-Hoover method [START_REF] Nosé | A molecular dynamics method for simulations in the canonical ensemble[END_REF][START_REF] Hoover | Canonical dynamics: equilibrium phase-space distributions[END_REF] with thermostat and barostat relaxation constants of 0.5 ps and 1.0 ps, respectively. A time step of 2 fs was used in all calculations and the equations of motion were integrated using the leap frog algorithm [START_REF] Allen | Computer Simulation of Liquids[END_REF]. Configurations were stored every 2 ps for statistical analysis.

The peptide was modelled with the CHARMM-22 force field [START_REF] Mackerell | All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins[END_REF], water with the rigid SPC model [START_REF] Berendsen | Interaction models for water in relation to protein hydration[END_REF] and TFE using the Model 5 reported by Chitra and Smith [START_REF] Chitra | A comparison of the properties of 2,2,2-trifluoroethanol and 2,2,2-trifluoroethanol/water mixtures using different force fields[END_REF];

previous studies had shown the CHARMM potential for TFE to underestimate the stability of α-helices [START_REF] San-Miguel | An Escherichia coli twin-arginine signal peptide switches between helical and unstructured conformations depending on the hydrophobicity of the environment[END_REF][START_REF] Rodziewicz-Motowidio | Conformational solution studies of neuropeptide gamma using CD and NMR spectroscopy[END_REF]. Long range forces were evaluated using the reaction field method, with the explicit solvent sphere around each site having radius 15 Å and a relative dielectric constant of 80 and 27 for water and TFE, respectively; all other non-bonded interactions were also truncated at a cutoff atom radius of 15 Å.

SIMULATIONS IN VACUUM

In addition to the solvated systems, we have carried out simulations of the three sequences in vacuum. These have been used both to identify significant intramolecular interactions and to compare with solvent effects. Since the primary purpose of these vacuum simulations was to identify peptide configurations that were intrinsically stable, and not to simulate equilibrium properties, a temperature of 500 K was used to speed up the exploration of configuration space. Trajectories of 48 ns were generated and configurations saved every 2

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Deleted: [START_REF] Frishman | Knowledge-based protein secondary structure assignment[END_REF] Deleted: (Nosé, 1984;Hoover, 1985) Deleted: (Allen and Tildesley, 1987) Deleted: ps for statistical analysis. All charge-charge interactions were truncated at 15 Å based on an atom-atom scheme within a direct coulombic approach.

(

RESULTS

SUFIS IN WATER

CD measurements of the SufI and the mutant peptides in water indicated that all sequences are random coils in water [START_REF] San-Miguel | An Escherichia coli twin-arginine signal peptide switches between helical and unstructured conformations depending on the hydrophobicity of the environment[END_REF]. Our MD simulations are in good agreement with this evidence. Figure 1 shows the number of aminoacid residues participating in an α-helix at any given time. The starting configurations contain a 16-residue helix, but for each peptide this rapidly degrades to give a random coil structure. Some differences in the dynamics of the three peptide sequences are evident. The initial α-helix is seen to decay 4-5 times faster in SufI-RR than in the inactive mutants, persisting for only 30 ps in SufI-RR, but for about 110-150 ps in the two mutants. At the same time, SufI-RR shows a much greater propensity to reform the helices subsequently, with transient 6-residue α-helices forming on at least 7 separate occasions during the time interval 400-800 ps. In contrast, subsequent helix reformation in SufI-KK was much less frequent (3 events) and gave shorter helices, while no subsequent secondary structure was observed in SufI-AA. Close inspection of these transient configurations showed that, for SufI-RR, the helix always appeared in the fragment between Ser( 2) and Arg [START_REF] Dalbey | Protein translocation into and across the bacterial plasma membrane and the plant thylakoid membrane[END_REF] and between Leu(3) and Gln [START_REF] Hermann | Protein transport into mitochondria[END_REF], i.e. in the region of the key motif for the Tat translocation. In contrast, the transient helices in SufI-KK involved Ile( 14) at ca. 250 ps and Ala( 18) at 750 ps.

The origin of these helices is the formation of strong intramolecular hydrogen bonds and so we have computed the number of intramolecular H-bonds within each peptide as a function of time; the results are plotted in Figure 2. As can be seen, the number of H-bonds fluctuates significantly, with most of the data indicating only short excursions during which more than one intra-molecular H-bond is found. The main exception to this is for SufI-RR between 550 and 650 ps. During this time interval, SufI-RR consistently shows two intramolecular H-bonds, and frequently as many as 4-5. This is precisely the time interval during which transient helices are seen to form in SufI-RR. (Figure 1)

The root mean square atomic displacements from the first configuration have also been calculated and are presented in Figure 3. Plots indicate that SufI-RR and SufI-AA change much more rapidly at the beginning than SufI-KK, which increases monotonically and does not reach a stable plateau region on this time scale. SufI-RR and SufI-AA appear to Formatted: English U.K.

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SUFIS IN TFE

MD simulations of all three peptides in TFE have been carried out, each with a total duration of 6 ns, and the time dependence of the resultant helix lengths is shown in Figure 4.

In this case the mutant sequences retain most of the original secondary structure throughout In our previous work we reported results from CD measurements where we found that the helical content in TFE was around 45% for all the peptides, although slightly higher for SufI-AA and slightly lower for SufI-KK than for SufI-RR. The simulations presented in this work predict a variation in helical content: 59% for SufI-AA, 48% for SufI-KK and 25% for SufI-RR. The results for SufI-AA and SufI-KK would seem to be in line with experiment, while the results for SufI-RR give too little helical content. In fact, both mutants and in particular SufI-AA, show almost no conformational changes on the simulation timescale keeping their initial helical content.

One point that emerges clearly from Figure 4 is that the wild-type peptide shows much greater flexibility than either of the mutants, and develops many different configurations with different helical content during the course of the simulation.

The assignment of secondary structure for each configuration in a trajectory also allows one to calculate the probability of finding each amino acid within an α-helix, P α . The results are depicted in Figure 5. The plot for SufI-AA emphasises the lack of conformational change noted above, with unit probability of finding residues 3-17 forming an α-helix.

Similar stability is seen in residues 7-15 of SufI-KK, although some breakdown of the αhelix is seen on either side of these residues. In particular, there is substantial dip in P α at the 6 th residue, i.e. at the second lysine mutation, that is not seen in the presence of the RR motif.

SufI-KK also shows a small decrease in P α for amino acids 16-17, corresponding to the changes evident at ca. 3.5 ns in Figure 4. For SufI-RR, two different helix regions are identified. The first of these (which we shall denote by H1) is 4-6 residues long, and is centred on three amino acids (Arg-Arg-Gln) that include the twin arginine motif required for translocation. The second helix (H2) is centred on residues 15-17 (Ala-Leu-Cys), with transient involvement from the residues on either side of this region.

The time evolution of the helix distributions, P α , has been calculated by finding the average probabilities during successive 1 ns segments of the simulation. The results for SufI-RR are shown in Figure 6. Analogous plots are not presented for the mutants since they showed no significant variation with time. The two helix regions (H1 and H2) are clearly defined at all times. The H1 region appears to have converged to an equilibrium distribution after ca. 2 ns, with the helix present about 50% of the time. The secondary structure in the H2 region becomes more stable as the simulation continues, reaching a plateau of about 70% by 4-6 ns.

Visual inspection of the trajectory indicates that a very stable interaction emerges between Arg( 6) and Gln [START_REF] Robinson | Protein targeting by the twin-arginine translocation pathway[END_REF], and this probably stabilises the H1 region. A typical conformation is shown in Figure 7 and shows the presence of a second, intra-residue, H-bond within Arg [START_REF] Dalbey | Protein translocation into and across the bacterial plasma membrane and the plant thylakoid membrane[END_REF] in addition to the one between Arg( 6) and Gln [START_REF] Robinson | Protein targeting by the twin-arginine translocation pathway[END_REF]. A long-lived H-bond is also seen in the H2 region, between a hydrogen atom in Cys( 17) and oxygen atom in Ile( 14) (Figure 7); this H-bond persisted throughout the whole simulation. In general, the total number of intra-peptide H-bonds was consistent with stability of the α-helices, as noted above (Figure 8). No long-term changes are evident in the total number of H-bonds. SufI-RR presents the lowest number of H-bonds (8±4), whereas SufI-KK, which is the most stable structure, has 12±3 and SufI-AA 10±3.

The decreased stability, or increased flexibility, of the wild-type sequence relative to the two mutants is also seen in the atomic RMSDs (Figure 9 and Figure 10). SufI-RR shows the largest deviations for the whole peptide from the initial structure at all times. (Figure 9)

The deviations can be assigned mainly to the atoms in the N-terminal and secondly to those ones in the H-domain. (Figure 10)

The number of dihedral transitions for the backbone angles φ and ψ have also been computed for every aminoacid residue in each peptide in TFE and they have been plotted in significant for SufI-RR, indicating a higher flexibility for this peptide. This fact can be related to the presence of the twin-arginine motif in the N-terminal region where the number of φ and ψ transitions is much higher than for the mutants.

SUFIS IN VACUUM

The dynamics of solvated proteins is a competition between intra-peptide forces and solute-solvent interactions. The absence of solvent in vacuum simulations leaves just the intra-peptide interactions to govern the dynamical behaviour, and so provides a useful comparison with the solvated calculations. We have therefore performed additional simulations of the various peptides in vacuum at 500 K in vacuum. Trajectories of 48 ns were generated and configurations stored every 2 ps for statistical analysis.

Figure 12 shows the helix length as a function of time. Very different behaviour is exhibited by the three sequences. The starting helix in SufI-RR decays to just four residues in the first nanosecond and then vanishes completely after 24 ns, with a transient reappearance after 42 ns. The mutants, on the other hand, exhibit substantial helix content during most of the trajectory, albeit with large, dynamic fluctuations in the helix length. The location of these α-helices also shows interesting variations between the three peptides (see Figure 13).

The α-helix in SufI-RR tends to include the twin arginine motif, with a fairly homogeneous distribution along the rest of the peptide. In contrast, both the mutants show a bimodal distribution, with probably the dominant helix-forming tendency arising from residues 15-20.

From these results, we conclude that the wild-type sequence is considerably more flexible than either of the mutants, with much more frequent-albeit transient-variations in secondary structure. The twin arginine motif does appear to provide a nucleation point for the formation of an α-helix, but does also appear to destabilise α-helices in the middle of the peptide when dissolved in TFE.

CONCLUDING REMARKS

In this report, we have investigated the dynamical behaviour of the Tat signal peptide in a hydrophilic (water) and hydrophobic (trifluoroethanol, TFE) solvent. A wild-type SufI peptide and two mutant variants known to be inactive were examined.

In our previous study [START_REF] San-Miguel | An Escherichia coli twin-arginine signal peptide switches between helical and unstructured conformations depending on the hydrophobicity of the environment[END_REF] circular dichroism measurements were performed and showed that the two non-functional peptides exhibit almost identical spectra to the wild-type SufI peptide in both solvents. In water, all the peptides were random coil structured, whereas in TFE, they showed a similar α-helical content (up to about 45%). In the same study our Formatted: English U.K.

Deleted: It can be seen that the higher flexibility for the SufI-RR can be related to a much higher number of transitions in the N-terminal region. and SufI-KK switch between two distinct states: random coil in water and helical structured in TFE. However, the α-helical content predicted was for both sequences about 20%, which is much lower than that one from the experiments. It was claimed that it might be caused by the TFE model used, which has been observed previously to underestimate the helical content. The fact that the helical extents were similar between the wild-type and the mutant lead us to conclude that the arginine sidechains, and not their contribution to the helical structure, are the critical factors in this class of peptide.

Deleted

In this work we have explored all these issues and extended our study to all the mutants. First of all, we have adopted a more accurate TFE model. We have also chosen a longer electrostatic cut-off radius (extended up to 15 Å, instead of 10 Å as in the previous work) to use in the reaction field method, taking into account explicitly a much higher number of interactions with neighbor residues and solvent molecules.

The simulations in water show that all the peptides rapidly lose the helical content during the first picoseconds. Surprisingly, transient α-helices structures involving the twinarginine motif can be observed during the trajectory for SufI-RR but not for any of the mutants.

The new TFE model seems to provide a better description of the system and the helical content predicted are higher than in the previous work. Our results show no significant conformational changes in the mutants during 6 ns. However, SufI-RR exhibits many different configurations during the same timescale, thus showing a much greater flexibility.

The analysis of its secondary structure demonstrates that all of these configurations contain α-helices that involve the twin-arginine motif. These studies do not intend to explore the conformational changes in much extended periods of time, but the response of the system to insertion in two different solvents in the initial stages. Different repeats in the same timescale have showed similar results and demonstrate the different behaviour between the wild-type sequence and the mutants, which can be assigned to the only difference between the peptides: the presence of the twin arginine motif.

Extended simulations were performed in vacuum up to much longer timescales (48 ns). SufI-RR again showed a flexible behaviour, with frequent conformational changes, and helices forming around the key twin-arginine motif. The mutants, on the other hand, exhibit substantial helix content in a bimodal distribution during most of the trajectory, with Figure 13. Probability of finding each amino acid residue within an α-helix structure for SufIs in vacuum.

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FIGURE 9

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FIGURE 11

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FIGURE 12

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  : (San Miguel et al, 2003) Deleted: hydrophopic Deleted:[START_REF] San-Miguel | An Escherichia coli twin-arginine signal peptide switches between helical and unstructured conformations depending on the hydrophobicity of the environment[END_REF] dynamics is required in order to characterise the nature of these variations.

  MacKerell et al, 1998) Deleted: ( Deleted: Berendsen et al, 1981) Deleted: (2001) Deleted: (Rodziewicz-Motowidio et al,2002;[START_REF] San-Miguel | An Escherichia coli twin-arginine signal peptide switches between helical and unstructured conformations depending on the hydrophobicity of the environment[END_REF]

  plateau region after 100 ps but SufI-RR shows larger fluctuations around the mean value than SufI-AA.

  the simulation: SufI-AA exhibits only minor fluctuations away from the 16 amino acid αhelix, while the helix in SufI-KK shortens slightly -initially to about 15 residues and then to 13 amino acids at about 3.5 ns. The response of the wild-type sequence to immersion in TFE contrasts strongly with the stable helices observed in the mutants. The helix reduces to around 10 amino acids during the first 30-40 ps, and then at about 1.5 ns shortens again up to just four residues. Thereafter there are frequent fluctuations in the length of the helix, with values in the range 4-8 amino acids throughout the rest of the simulation. The length of these simulations is long enough to indicate different behaviour between the wild-type peptide and the mutants.

4 Deleted: Figure 4 Deleted: Figure 5 Deleted: Figure 5 F

 4455

Figure 11 .

 11 Figure 11. It can be seen that the number of ψ transitions for most of the residues is only

  , in agreement with the experiments, indicated that SufI-RR

FormattedFigure 1 .

 1 FIGURES CAPTURE

Figure 2 .

 2 Figure 2. Number of intramolecular hydrogen bonds as a function of time for SufIs in aqueous solution.

Figure 3 .

 3 Figure 3. Atom positional root-mean-square distance (RMSD) variations with the first configuration during the simulation for SufIs in aqueous solution.

Figure 4 .

 4 Figure 4. Helix length (number of aminoacid residues) versus time for SufIs in TFE.

Figure 5 .

 5 Figure 5. Probability of finding each amino acid residue within an α-helix structure for SufIs in TFE.

Figure 6 .

 6 Figure 6. Probability of finding each amino acid residue within an α-helix structure calculated in portions of 1 ns for SufI-RR in TFE.

Figure 7 .

 7 Figure 7. Snapshot of SufI-RR. Arg[START_REF] Dalbey | Protein translocation into and across the bacterial plasma membrane and the plant thylakoid membrane[END_REF] and Gln[START_REF] Robinson | Protein targeting by the twin-arginine translocation pathway[END_REF] residues are shown in different colour in (a) and Cys[START_REF] Tieleman | Molecular dynamics simulations of antimicrobial peptides: from membrane binding to trans-membrane channels[END_REF] and Ile(14) in (b). The H-bonds are indicated in broken lines. The images were generated with VMD[START_REF] Humphrey | VMD: visual molecular dynamics[END_REF].

Figure 8 .

 8 Figure 8. Number of intramolecular hydrogen bonds as a function of time for SufIs in TFE.

Figure 9 .

 9 Figure 9. Atom positional root-mean-square distance (RMSD) variations for all atoms from the first configuration during the simulation for SufIs in TFE.

Figure 10 .

 10 Figure 10. Atom positional root-mean-square distance (RMSD) variations for atoms in the different peptide regions from the first configuration during the simulation for SufIs in TFE.

Figure 11 .

 11 Figure 11. Number of dihedral transitions for the backbone angles φ and ψ in each aminoacid residue for SufIs in TFE during the whole trajectory.

Figure 12 .

 12 Figure 12. Helix length (number of aminoacid residues) as a function of time for SufIs in vacuum.

  FIGURE 1

  Figure 10

  FIGURE[START_REF] Stanley | The twin arginine consensus motif of Tat signal peptides is involved in Sec-independent protein targeting in Escherichia coli[END_REF]

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ACKNOWLEDGEMENTS

This work was supported by Engineering and Physical Sciences Research Council grant GR/R36503.

Formatted: Font: Not Italic, English U.K.

Formatted: English U.K.

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