Costanza Pizzi
email: costanza.pizzi@lshtm.ac.uk

Bianca De Stavola

Franco Merletti

Rino Bellocco

Isabel Dos

Santos Silva

Neil Pearce

Lorenzo Richiardi

Sample selection and validity of exposure-disease association estimates in cohort studies

Keywords: Directed Acyclical Graphs, Selection bias, Confounding, Monte Carlo Simulations 3,277, Abstract: 240, References: 24, Figures: 2, Tables: 1, Supplementary Tables: 1

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Introduction

Selection of study subjects from restricted source populations according to prespecified criteria is an approach that is frequently used in cohort studies. The purposes of such restrictions are to enhance study feasibility and to increase the prevalence of exposure or the completeness of follow-up, thereby increasing study validity and precision. Typically this may involve recruiting participants from a subgroup of the general population, rather than sampling directly from the entire general population. Such subgroups may be defined on the basis of occupation, gender, geographical area, birth cohort, etc. The British Doctors Study [START_REF] Doll | Mortality in relation to smoking: 50 years' observations on male British doctors[END_REF] and the Nurses' Health Study, [START_REF] Colditz | The Nurses' Health Study: lifestyle and health among women[END_REF] occupational cohorts, [START_REF] Magnani | Cancer risk after cessation of asbestos exposure: a cohort study of Italian asbestos cement workers[END_REF] follow-up of participants in specific events, [START_REF] Lagerros | Measures of physical activity and their correlates: the Swedish National March Cohort[END_REF] analyses restricted to specific subgroups of the population, such as nonsmokers, [START_REF] Vineis | Environmental tobacco smoke and risk of respiratory cancer and chronic obstructive pulmonary disease in former smokers and never smokers in the EPIC prospective study[END_REF] ancillary analyses of non-randomized exposures in randomized studies, [START_REF] Beattie | Endogenous sex hormones, breast cancer risk, and tamoxifen response: an ancillary study in the NSABP Breast Cancer Prevention Trial (P-1)[END_REF] follow-up studies of screening attendants [7] are all examples of cohort studies based on restricted samples.

Undoubtedly, restriction of the source population may introduce problems of generalizability of the study findings, but this also applies to studies that are based in the general population (e.g., most cardiovascular epidemiology involves cohort studies in specific communities rather than true general population samples). We will therefore not consider issues of generalizability here; rather, our focus is on whether using a restricted source population may affect the validity of the exposure-disease associations. [START_REF] Glymour | Using causal diagrams to understand common problems in social epidemiology[END_REF][START_REF] Hernan | A structural approach to selection bias[END_REF] In particular, bias will be introduced if a risk factor for disease is not associated with exposure in the general population but is associated with exposure in the study population, as a result of the selection process. Such biases can be represented using Directed Acyclical Graphs (DAGs). [START_REF] Glymour | Using causal diagrams to understand common problems in social epidemiology[END_REF][START_REF] Greenland | Causal diagrams for epidemiologic research[END_REF][START_REF] Hernan | Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology[END_REF][START_REF] Pearl | Causal diagrams for empirical research[END_REF][START_REF] Pearl | Causality: Models, Reasoning, and Inference[END_REF] The example depicted in Figure 1a, represents a population in which there is no association between an exposure (E) and a disease (D); there is another risk factor (R) for the disease, but this is not a source of confounding as it is not associated with the exposure. However, E and R both affect the likelihood of being selected (S=1) into the study.

When analyses are restricted to the selected subjects, there is an inherent conditioning on S (as represented by a square around S in Figure 1b), which leads to a spurious association between E and R (represented by a dashed line). Under this scenario, even if E has no causal effect on D, the backdoor path E-R-D is opened and the estimated associational relative risk between the exposure and the disease (ARR DE ) may differ from the causal relative risk (CRR DE ). This could, for example, be the situation in a cohort study of the effect of obesity (E) on breast cancer (D) based on breast cancer screening participants (the restricted source population). In this example, obese women (E) are less likely to attend the screening programs [START_REF] Ferrante | Cancer screening in women: body mass index and adherence to physician recommendations[END_REF] while women with a family history of breast cancer (R) are more likely to participate. Among those who attend screening (i.e. conditioning on those with S=1), obesity (E) and family history of breast cancer (R) become positively correlated. In fact an obese woman is more likely to have a family history of breast cancer within the selected sample than in the general population, because otherwise she may not participated in the screening program. As a result family, history of breast cancer is a confounder of the obesity-breast cancer association if studied among screening attendees but not -or to a less extent -in the general population. This type of bias has been extensively discussed in the causal inference literature from a theoretical point of view. [START_REF] Glymour | Using causal diagrams to understand common problems in social epidemiology[END_REF][START_REF] Hernan | A structural approach to selection bias[END_REF] Hernan and colleagues' 2004 paper on selection bias provides the conceptual framework and indicates that if the risk factor associated with the selection process is known and measured it is possible to adjust for selection bias, whereas if the risk factor is unmeasured, the effect estimates may be biased. However, although the theoretical basis of selection bias is clear, there have been few attempts to quantify the likely strength of such biases. One exception is that of Greenland, [START_REF] Greenland | Quantifying biases in causal models: classical confounding vs colliderstratification bias[END_REF] who studied the setting of Figure 1b with dichotomous exposure and outcome variables, employing methods originally developed to quantify the impact of unmeasured confounding. [START_REF] Yanagawa | Case-Control Studies: Assessing the Effect of a Confounding Factor[END_REF] He calculated the likely maximum strength of the bias in the estimation of the E-D association in the S=1 stratum as a function of the odds ratios (ORs) corresponding to the true associations depicted in Figure 1b (i.e. OR SE , OR SR , OR DR ). However, it is not clear how these results apply to cohort studies. Because of the increasing frequency of cohort studies based on selected populations, such as the internet-based birth cohort studies based in Italy (NINFEA cohort) and New-Zealand (ELFS), [17] quantifying the potential biases involved in analysing such data is timely and relevant.

Our aim is therefore to study the extent of these biases. We use simulations to mimic a variety of cohort restrictions and disease settings and examine the consequent bias in the estimated exposure hazard (or rate) ratio (HR) of disease. We then discuss these results in terms of whether, and under what circumstances, the resulting selection bias is serious enough to strongly bias the exposure effect estimates. For simplicity, we will assume throughout the paper that there is negligible random variation, that all variables are measured without error, and that there is uninformative censoring.

Sample selection and disease risk factors

As previously recognised, [START_REF] Hernan | A structural approach to selection bias[END_REF]18] a fundamental characteristic of selection bias in restricted cohort studies is that the selection process makes a disease risk factor, which may not be associated with the exposure in the general population, become associated with the exposure among the study population and therefore act as a confounder. Confounders in the general population and risk factors that become confounders in a restricted source population are usually indistinguishable when the study is analysed.

Although typically some disease risk factors (i.e. potential confounders) are known a priori, it is seldom known whether these are associated with the exposure of interest in the specific population in which the study will be carried out. Both in general population-based and restricted cohorts, therefore, researchers attempt to collect information on all known and suspected important risk factors of the disease in the population that they are studying, regardless of their expectations about whether these are associated with the exposure or not.

The example of the association between smoking and socioeconomic position (SEP) illustrates well this point. Depending on the population and the calendar period, SEP can be positively or negatively associated, or not associated at all, with smoking. Researchers aiming to estimate the association between smoking and mortality will always attempt to collect information on SEP and, in most instances, will control for it, irrespective of whether the confounding effect of SEP is due to a real association between SEP and smoking in the general population or a spurious association caused by the sample selection process.

Another possible consequence of the selection mechanism is a change in magnitude, and in extreme cases direction, of the confounding effect of a risk factor. This may occur if the strength of the association between the risk factor and the exposure in the selected sample differs from that originally present in the general population. For example, when two (parent) variables influence a third (child) variable in the same direction, conditioning on the child variable likely leads to a negative association between the parent variables. [START_REF] Glymour | Using causal diagrams to understand common problems in social epidemiology[END_REF] Thus, if an exposure and a confounder influence the selection process in the same direction, the original association between exposure and confounder will be reduced in the subset of those who participate if they were originally positively associated, or increased if their original association was negative. For example, in many populations smoking and physical exercise are negatively associated. In a hypothetical study restricted to blood donors, who typically have a healthy lifestyle and thus smoke less and exercise more than the average individual in the general population, the sample selection would add a positive association between smoking and physical exercise. Therefore, the original negative association present in the general population would be, if anything, attenuated among blood donors.

In the next section, we use simulations to quantify the likely extent of selection bias arising from the use of restricted cohorts.

Quantification of the bias

Methods

We conducted Monte Carlo simulations of alternative settings corresponding to the scenario of Figure 1b to quantify the resulting bias in the estimation of the E-D effect when conditioning on S=1 and not adjusting for R. [START_REF] Burton | The design of simulation studies in medical statistics[END_REF] The generation process of the four variables of Figure 1b is described below.

We generated E and R as marginally independent binary variables, with prevalence, respectively P E and P R , initially set equal to 0.5 in the source population. They were later allowed to decrease to 0.25 for P E and to 0.1 for P R , in order to investigate scenarios more frequently addressed by epidemiologists.

The binary variable S was generated using a logistic regression model with baseline prevalence, P S , equal to 0.5 and with the ORs for the explanatory binary variables E and R taking values 0.25, 0.33, 0.50, 2, 3 and 4. Specifically, with α S indicating the log(odds) of S=1 among the non-exposed, β SE indicating the log(OR) corresponding to exposure E and β SR indicating the log(OR) corresponding to R, the generating model was:

logit(S=1)= α S + β SE E+ β SR R (1)
A more complex model that included an interaction term between E and R was also considered:

logit(S=1)= α S + β SE E+ β SR R + β inter E*R (2)
with OR inter , corresponding to exp(β inter ), set at values 0.5 or 2. The interaction term was introduced to examine more realistic selection settings. For example, in the first empirical demonstration of the Berkson's bias, Roberts and colleagues found that not only chronic conditions increase the chance of hospitalization, but often they also interact more than multiplicatively. [START_REF] Roberts | An empirical demonstration of Berkson's bias[END_REF] We generated time to the outcome D assuming a constant rate λ, i.e. we assumed that time to event followed an exponential distribution. [START_REF] Bender | Generating survival times to simulate Cox proportional hazards models[END_REF] The baseline rate λ 0 was set equal to 0.01, 0.03, or 0.06 events/year, with administrative censoring time set at 5 years. The rate λ was allowed to be affected only by R, with hazard ratios HR DR taking values 0.25, 0.33, 0.50, 2, 3 and 4, while we assumed no E-D association, i.e. HR DE =1. Specifically, with β DE indicating the log(HR) of D for the exposure E and β DR indicating the log(HR) of D for the risk factor R, the log rate function for D, log(λ), was defined as:

log(λ)=log λ 0 + β DE E + β DR R (3)
with β DE fixed at 0.

We generated a total of 1,000 Monte Carlo simulated datasets of 5,000 subjects for each combination of the parameters described above. We also used a size of 2,500 subjects, increasing the number of simulations (n=2,000), to deal with the greater impact of random variation.

In each simulated dataset, we estimated two main parameters in the stratum S=1

(which sample size varies as a consequence of the selected parameters for the selection process): the association between E and R (OR ER ) and the association between E and D (HR DE ) which is induced by the selection process. The estimate of HR DE was obtained fitting a Cox proportional hazards regression model with no adjustment for R. [START_REF] Clayton | Statistical Models in Epidemiology[END_REF] We then calculated the bias in the E-D association as the difference between zero, i.e. the true value of β DE , and the logarithm of the estimated HR DE . For each scenario, we summarised the bias, and the estimated values of β DE , in terms of means, standard deviation, and 5 th and 95 th percentiles.

Results

We first considered the situation with prevalence of E and R both equal to 0.5, OR inter =1 (i.e. no multiplicative interaction), and λ 0 =0.03 (the "reference scenario" in Table 1). As expected, the size of the bias in the estimation of OR DE depended on: i) the induced association between the exposure and the risk factor (OR ER ), which increased in absolute terms with the absolute size of OR SR and OR SE ; and ii) the magnitude of the association between the risk factor and the disease (HR DR ). The largest values of the bias in the log odds ratio were ±0.15 (Table 1, "reference scenario"), which were reached when OR SE , OR SR and HR DR were furthest from the null value (i.e. equal to 0.25 or 4). Note that in Table 1 the range for log(OR ER|S=1 ) is not symmetrical because the magnitude of the association induced by the selection between E and R also depends on the prevalence of S in the population (P s ), with the strongest association obtained when P s =0.5. The complete results for all combinations of the values of OR SE , OR SR and HR DR are reported in Supplementary Table 1 .

The mean bias decreased from ±0.15 to just ±0.02 when the three ORs/HRs were equal to 2 or 0.5.

When an interaction term between E and R was included in the model generating S, the induced E-R association increased considerably (Figure 2), up to a log(OR) of -0.98 (Table 1, row 2) when OR SE and OR SR were equal to 0.25 and the OR inter was 0.5. The bias increased accordingly, ranging from -0.24 to 0.27 (Table 1, row 2). This situation is equivalent, in terms of induced bias, to those involving very strong marginal associations with selection. It is clear, from Figure 2, that the impact of the interaction is not the same for all the parameter combinations, as the magnitude of the induced E-R association is strengthened or reduced according to the sign of the interaction term but also to the size of the stratum of subjects exposed to both E and R.

Neither the prevalence of the exposure E (Table 1 row 3) nor the baseline rate for the disease D (Table 1 rows 4-5), or the sample size (Table 1, row 6) affected the extent of the bias. Conversely, the prevalence of R, which becomes a confounder of the E-D association when S=1, had a non-marginal effect. For a given value of the induced E-R association, the bias reached its peak when the prevalence of R among the selected subjects (S=1 stratum) was 0.5. For this reason when the population prevalence of R was set equal to 0.1 instead of 0.5, the range of the mean bias decreased to (-0.12; 0.07) (Table 1, row 7).

Discussion

Conducting cohort studies in a restricted sample of the general population may offer several advantages, including more precise measurement of the exposure, higher exposure prevalence, enhanced feasibility of the study, better control of confounding, increased sample size, higher recruitment rates, and a higher completeness of follow-up. These advantages should be balanced against issues of validity.

In this paper we have shown, via simulations, that the possible bias introduced by restriction of the source population, is usually weak when internal comparisons are carried out within the cohort, with a maximum bias in the log(HR) of ±0.15.

These results are in agreement with those of Greenland, [START_REF] Greenland | Quantifying biases in causal models: classical confounding vs colliderstratification bias[END_REF] who used an analytical approach to quantify the maximum selection bias in settings where the outcome risk is rare so that the analysis of cohort data can be performed using logistic regression. Our simulations add further insight to these results as we examined a wide range of disease and selection parameters, including exposure and risk factor prevalence, which highlighted their individual role in influencing the extent of the bias. Further, we considered settings where exposure and risk factor interact when influencing the selection process. Some additional points are warranted.

First, the bias is necessarily small when the association between the exposure of interest and the selection process is relatively weak (i.e. 0.5 < OR < 2). In particular when the exposure-selection OR is equal to 2 or 0.5, while the risk factor-selection OR and the risk factor-disease HR are allowed to take values up to 4 or down to 0.25, the maximum bias in the estimated exposure-disease association is within the ± 0.07 range (on the log hazard scale). For example consider the Million Women Study, a cohort nested within the breast screening programme in the UK.

[7] From the study carried out to compare the characteristics of the study participants with the rest of the population (women who attended the screening but did not join the study plus not attendants), [START_REF] Banks | Comparison of various characteristics of women who do and do not attend for breast cancer screening[END_REF] the participation OR for current use of hormone replacement therapy (HRT), which is the main exposure of interest of the study, was derived. This estimated OR was about 1.6. On the basis of this information it is possible to assume that, in this cohort, the bias introduced by the baseline selection on the estimates of the HRT's effect on the outcome of interest would be negligible.

Second, selection must be associated with one or more unmeasured or unknown disease risk factors in order to introduce bias. However, unknown or unmeasured disease risk factors can introduce bias whether or not the cohort is based on the general population or a restricted source population; in the latter case, the sample selection can either increase or decrease the overall bias with a magnitude and direction difficult to predict if there are multiple risk factors involved. [START_REF] Vanderweele | Causal directed acyclic graphs and the direction of unmeasured confounding bias[END_REF] Third, we have shown that even when all of the associations involved in the selection and outcome mechanisms are reasonably large (e.g., all ORs/HRs of 4.0 or 0.25), the prevalence of the risk factor R is about 50% and there is no adjustment for R, the resulting bias is relatively weak (i.e. ±0.15 on the log scale). This is reassuring, as this scenario is rather extreme and very unlikely to occur in practice. Besides, a disease risk factor with a 50% prevalence and a disease hazard ratio of 4.0 would have an attributable fraction of 60% and is therefore unlikely not to have been known and measured when a study is planned.

The scenarios considered in our simulations were restricted to binary exposure and binary risk factor and assumed no association between the exposure and the risk factor in the general population. A limitation is that we examined only the case of a single unmeasured determinant of the disease that also influences the selection process. However we believe it is unlikely that multiple and independent important disease risk factors would affect the sample selection. It is indeed reasonable to consider R as a vector resulting from the combination of a set of correlated risk factors, all moderately associated with S. Finally, we only showed the findings derived from the analyses based on the assumption of a null causal association between the exposure and the outcome of interest; however choosing a true associational value, β DE , different from zero would not modify the simulation results and therefore our conclusions.

We conclude that using a restricted source population for a cohort study will, under a range of sensible scenarios, produce only weak bias in estimates of the exposure-disease associations. On the other hand, the use of such restrictions may increase the response rate and the exposure prevalence, as well as being the only feasible approach in many circumstances. Results From 1,000 Simulations

Figure 1 :Figure 2 :

 12 Figure 1: Diagram of a cohort based on a selected sample. a) In the population the exposure of interest (E) is not associated with the disease of interest (D) that is caused by a risk factor (R). Both E and R affect the probability of being selected (S) as a member of the cohort. b) The study is carried out in the selected sample and, therefore, there is an inherent conditioning on S (a box around a variable means conditioning for that variable) which generated an induced association between E and R (represented by a dashed line)

Table 1 : Bias in the Crude Estimation of the E-D Association by Selected Values of the Data Generating Parameters: Results From 1,000 Simulations

 1

	N		p(E=1)	p(R=1)	Baseline rate of D	Interaction	Mean β ER Range a	Mean E-D Bias Range b
	Reference scenario
	5000	0.5	0.5	0.03	NO	-0.31 ; 0.45	-0.15 ; 0.15
	Alternative scenarios
	5000	0.5	0.5	0.03	YES	-0.98 ; 0.74	-0.24 ; 0.27
	5000	0.25	0.5	0.03	NO	-0.31 ; 0.45	-0.15 ; 0.15
	5000	0.5	0.5	0.01	NO	-0.31 ; 0.45	-0.15 ; 0.16
	5000	0.5	0.5	0.06	NO	-0.31 ; 0.45	-0.14 ; 0.15
	2500	c	0.5	0.5	0.03	NO	-0.32 ; 0.45	-0.15 ; 0.15
	5000	0.5	0.1	0.03	NO	-0.33 ; 0.45	-0.12 ; 0.07

E, exposure; R, risk factor; D, disease a β ER expressed as log(OR) b β DE expressed as log(HR) c Results from 2,000 simulations

Richiardi L, Baussano I, Vizzini L, et al. Feasibility of recruiting a birth cohort through the Internet: the experience of the NINFEA cohort. Eur J Epidemiol 2007;22:831-7.

Competing Interest: None declared.

Funding

The study was conducted within projects partially funded by Compagnia SanPaolo/FIRMS, the Piedmont Region, the Italian Ministry of University and Research (MIUR), the Italian

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Bleton

Jean Pierre

Marie

Bourdain * Frédéric

Ducorps Antoine

Schwartz Denis

Delmaire Christine

Lehéricy Stéphane

Renault Bernard

Garnero Line

Meunier Sabine

Marie Pr

Vidailhet
email: marie.vidailhet@psl.aphp.fr

Somatosensory cortical remodeling after rehabilitation and clinical benefit of in writer's cramp

Keywords:

ABSTRACT

In order to explore the pathophysiological basis of a new rehabilitation therapy in writer's cramp (WC), healthy controls, untreated WC and WC who recovered a legible handwriting after rehabilitation were explored using magnetoencephalography and the somatosensory evoked fields of fingers I, II, III and V in the sensory cortex were studied. In the cortex controlling the dystonic limb, the size of the hand representation in the trained patients was similar to that of healthy controls, and significantly different from that of untrained patients. Trained patients exhibited "super-normal" reorganization of the finger maps. In the cortex controlling the non-dystonic limb, there was little difference between trained and untrained patients and the hand representation was enlarged and disorganized. We hypothesize that prolonged tailored rehabilitation in WC may induce long term plasticity phenomena, lateralized to the cortex controlling the dystonic hand.

Writer's cramp (WC) is a task-specific disorder associated with abnormal postures of the upper limb during writing. Injections of botulinum toxin can be partially effective in a sub-group of patients but may be disappointing on the long term. [START_REF] Kruisdijk | Botulinum toxin for writer's cramp: a randomised, placebo-controlled trial and 1-year follow-up[END_REF] A more recent approach is based on neurorehabilitation to target underlying pathophysiological processes such as degraded cortical and subcortical representations, 2 impaired sensori-motor integration and maladaptative plasticity. [START_REF] Vidailhet | Pathophysiology of dystonia[END_REF][START_REF] Breakefield | The pathophysiological basis of dystonias[END_REF] In keeping with the view that abnormal plasticity may underlie the dystonic symptoms, we developed a rehabilitation program using targeted repetitive peripheral sensory stimulations during the execution of skilled manual tasks, a crucial process to promote the development of LTP/LTD in the M1 cortex. [START_REF] Kaelin-Lang | Modulation of human corticomotor excitability by somatosensory input[END_REF][START_REF] Quartarone | Task-specific hand dystonia: can too much plasticity be bad for you?[END_REF] Moreover, as an aberrant sensory-cued motor program with inappropriately scaled and gated input from the S1 cortex may lead to inadequately patterned activation/deactivation of the M1 cortex [START_REF] Lemon | Functional properties of monkey motor cortex neurones receiving afferent input from the hand and fingers[END_REF] , we also used a stepwise, fragmented and individually tailored motor training. We hypothesize that behavioral treatments of dystonia may modify the functional organization of central somatosensory networks. Our study was aimed to explore plasticity phenomena of the cortical map associated with rehabilitation in WC. Therefore, we specifically selected WC who had recovered near-normal handwriting after rehabilitation. We studied them once their writing performance had stabilized to ensure that the physiological correlates we examined were not related to short term plasticity rapidly induced by manipulation of somatosensory inputs 8 but to a relearning process. Finger representation in the S1 cortex, mapped by using magnetoencephalography (MEG), was compared with those of untreated WC (who had undergone neither rehabilitation nor botulinum toxin injections).

METHODS

Eleven patients with previously disabling WC (group R: 8 women, 3 men; age 43.3 ± 14 years, (22-64); mean ± SD, range, (years), who recovered fluent writing with close to normal posture after rehabilitation (subjective improvement and writing score on the Burke-Fahn-Marsden disability scale (BFM), [START_REF] Burke | Validity and reliability of a rating scale for the primary torsion dystonias[END_REF] were compared with 10 untreated WC (group D: 6 women, 4 men; age 48.7 ± 8 (28-60 years), and with 11 healthy controls (group C: 7 women, 4 men; age 43 ± 13 (27-71 years). Groups D and R were matched for the duration of illness (R: 5.6 ± 5.4 years (1-13); D: mean 4.1 ± 3.2 years (1-10), Mann-Whitney U test p = 0.7) movement score on BFM (R before rehabilitation, mean 2.2 ± 0.7 (1-3); D, mean 1.9 ± 0.7 (1-3), Mann-Whitney U test p = 0.4).

All patients were evaluated by the same neurologists (MV, FB) at the time of the study and before and after rehabilitation. The rehabilitation program was a re-learning process with weekly 30 minutes individual sessions with a physiotherapist (JPB) and daily similar home program, for several months (average 5.5 months). It included relaxation techniques, correction of elementary movement not directly pertaining to writing activity, training of finger dexterity and ergonomic postures, sensory control (pressure, position on the pen), synergies between proximal and distal muscles, progression from simple to complex graphic exercises drawing and writing) and fractionated epochs of work with rest and relaxation.

Right and left hand representations were acquired by MEG and 3D-MRI 3D brain reconstructions as previously described 2 and data analysis was done using the same quantitative measures, Dm and Dmax. A somatotopy index (SI) allowed us to assess semi quantitatively the topographic order of the fingers. The study was approved by the Ethical Committee of the Salpêtrière Hospital, Paris, France.

Statistical analysis

The effects of the group (C/D/R), the side (dominant/non dominant hand) and their interaction were assessed by using 3-group analysis of variance (ANOVA) with Dmax, Dm and SI as dependant variables. Post-hoc comparisons were done using Fisher's test. If there was a significant effect of side, the effect of the group was tested separately for each hand by ANOVA. The effect of rehabilitation on the clinical scores was tested by repeated ANOVA with the two values of the clinical scores (before and after rehabilitation) forming the repeats.

RESULTS

Clinical assessment:

With rehabilitation, there was an improvement in the BFM movement (from 2.36 ± 0.7 to 0.64 ± 0.7; repeated measure ANOVA, p < 0.0001) and disability (from1.82 ± 0.7 to 0.18 ± 0.4, p < 0.0001) scores. Patients in group R were trained for a mean of 5.5 months (range 2-12 months).

Improvement of handwriting is illustrated in the Table. The degree of improvement was not influenced by the time (1 month to 1 year) elapsed since the end of rehabilitation (r2= 0.007).

Quantitative assessment of hand representation in S1 (Table)

Dm was similar in rehabilitated patients (R) and controls (C), and was significantly larger in the untreated WC (D) (ANOVA: effect of group p < 0.0012, effect of side p < 0.015, group*side interaction p = 0.5 (post-hoc Fisher tests: D versus R p < 0.0004, R versus D p < 0.006, R versus C p ns). This was found in both hemispheres (Fisher test, dominant hemisphere C versus R p = 0.6, C versus D p < 0.02, D versus R p < 0.05; non-dominant hemisphere C versus R p = 0.4, C versus D p < 0.01, D versus R p < 0.04).

There was a trend towards significance for Dmax among the 3 groups (ANOVA effect of group p= 0.06). Dmax was significantly different between the right and left side (effect of side p < 0.01, interaction side*group p ns). We thus tested each hemisphere separately. In the non-dominant hemisphere, non rehabilitated patients had a larger Dmax than healthy volunteers and rehabilitated patients (D versus C p < 0.008, D versus R p < 0.05, R versus C p ns).

Topographic order of the fingers (Figure, Table) SI differed between groups C, D and R, with a side*group interaction (ANOVA group p < 0.05, group*side p <0.02). This interaction was due to a higher SI index in the dominant hemisphere of the rehabilitated patients versus control and WC (Fisher test: D versus R p < 0.0005, C versus R p < 0.003, C versus D p = 0.5). The SI index in the left post-central gyrus of the group R patients was close to 3 (SI = 2.7) as the order of the fingers fitted the Penfield homuncular organization. In controls, the left post-central gyrus order of finger representations was more variable (SI = 1.7), with frequent inversions of two adjacent finger representations, as previously described. [START_REF] Meunier | Human brain mapping in dystonia reveals both endophenotypic traits and adaptive reorganization[END_REF] . SI did not differ between the three groups in the non-dominant hemisphere.

Discussion

In specifically selected WC who recovered near-normal handwriting after prolonged rehabilitation and had stabilized their writing performance, the MEG somatosensory map of the cortex controlling the dystonic hand is "normalized" as the size of hand representation of these rehabilitated WC was similar to that of healthy controls and significantly different from that of untreated WC. Moreover, the order of the fingers fitted the Penfield homuncular organization, creating a "super normal" somatotopic order of the fingers (in normal subjects, the order of finger representations could be slightly variable with inversions of two adjacent finger representations). [START_REF] Meunier | Human brain mapping in dystonia reveals both endophenotypic traits and adaptive reorganization[END_REF] In line with these results, we hypothesize that prolonged tailored rehabilitation in WC may induce long term plasticity phenomena, lateralized to the cortex controlling the dystonic hand. Moreover, it may persist over time as the patients were studied long after the end of the retraining process. In addition, the endophenotype of dystonia 2 would still be detectable in the cortex controlling the non dystonic hand, with a disruption of the MEG somatosensory map of the hand, similar in rehabilitated and untreated patients.

Several studies, with various methods of clinical evaluation, reported some beneficial effects of somatosensory training, [START_REF] Zeuner | Sensory training for patients with focal hand dystonia[END_REF][START_REF] Tinazzi | Sensory functions in dystonia: insights from behavioral studies[END_REF] sensori-motor retuning, [START_REF] Candia | Effective behavioral treatment of focal hand dystonia in musicians alters somatosensory cortical organization[END_REF][START_REF] Zeuner | Motor training as treatment in focal hand dystonia[END_REF][START_REF] Zeuner | Abnormal reorganization in focal hand dystonia--sensory and motor training programs to retrain cortical function[END_REF] and learning-based sensori-motor training [START_REF] Schenk | Does training improve writer's cramp? An evaluation of a behavioral treatment approach using kinematic analysis[END_REF][START_REF] Baur | Effects of modified pen grip and handwriting training on writer's cramp[END_REF][START_REF] Byl | Focal hand dystonia: effectiveness of a home program of fitness and learning-based sensorimotor and memory training[END_REF] in WC [START_REF] Byl | Effect of sensory discrimination training on structure and function in patients with focal hand dystonia: a case series[END_REF][START_REF] Mckenzie | Differences in physical characteristics and response to rehabilitation for patients with hand dystonia: musicians' cramp compared to writers' cramp[END_REF] or musician's dystonia. [START_REF] Candia | Effective behavioral treatment of focal hand dystonia in musicians alters somatosensory cortical organization[END_REF][START_REF] Byl | Effect of sensory discrimination training on structure and function in patients with focal hand dystonia: a case series[END_REF][START_REF] Mckenzie | Differences in physical characteristics and response to rehabilitation for patients with hand dystonia: musicians' cramp compared to writers' cramp[END_REF][START_REF] Candia | Changing the brain through therapy for musicians' hand dystonia[END_REF] Few studies have examined the physiological correlates of rehabilitation strategies WC. In most of them, the training program was brief and patients were tested shortly after. No change in motor or sensory cortical excitability were observed after 4 weeks of motor training. [START_REF] Zeuner | Motor training as treatment in focal hand dystonia[END_REF] Changes in short-interval-intracorticalinhibitition were found after 15 minutes of proprioceptive training. [START_REF] Rosenkranz | Sensorimotor reorganization by proprioceptive training in musician's dystonia and writer's cramp[END_REF] The somatosensory map of the hand before and immediately after 8 consecutive days of sensorimotor retraining therapy in musicians was studied using MEG recordings in one study. [START_REF] Candia | Effective behavioral treatment of focal hand dystonia in musicians alters somatosensory cortical organization[END_REF] After rehabilitation, finger representations contralateral to the dystonic side became similar to those of the non affected side, whereas they differed before treatment. As this effect was observed immediately after the end of a short training period this may mainly reflect short term plasticity. In contrast, we may have explored long term plasticity phenomena as we examined cortical maps up to 1 year after the end of a prolonged (5.5 months) rehabilitation program. The lack of MEG mapping prior to rehabilitation is a limitation in our study. Ideally, larger groups of patients would have allowed to compare patients who have been through similar rehabilitation program and had various amount of improvement and to study the correlations between clinical improvement and finger cortical representations. Our work may be considered as a pilot, preliminary study, contributing to the field of neuro-rehabilitation with a new rehabilitation program leading to sustained clinical, benefit potentially reflecting long-term plasticity.

Figure .

 . Figure. Finger somatotopy in the primary sensory cortex and sample of handwriting

Table -

 - Quantitative assessment of hand area in primary sensory cortex Table -Quantitative assessment of hand area in primary sensory cortex

			Dominant hemisphere			Nondominant hemisphere
		C	R	D	p	C	R	D	p
	Dmax (cm) 1.38 ± 0.6	1.5 ± 0.7	1,57 ± 0,65	0,7	1,45 ± 0,6	2,17 ± 1	2,37 ± 0,9	0,06
	p					└───0.07───┘
						└──────── 0.02 ────────┘
	Dm (cm)	0.72 ± 0.2	0.79 ± 0.4	1.12 ± 0.4	<0,04	0.88 ± 0.4	1.06 ± 0.6	1.6 ± 0.7	<0.02
	p		└──<0.05──┘			└──0.04──┘
		└────────0.02────────┘		└─────── 0.008 ────────┘
	SI	1.7 ± 0.8	2.7 ± 0.5	1,5 ± 0.8	<0.001	2 ± 0.6	1.9 ± 0.9	2 ± 0.8	0,9
	p	└─<0.003──┘ └─<0.0005──┘

Acknowledgements

This work was supported by the INSERM National Dystonia Network,GIS-Maladies Rares and the patients' association AMADYS. Dr F Bourdain was supported by a grant from Société Française de Neurologie. Dr Sabine Meunier was supported by grants from AP-HP, INSERM and MESR (Unité de recherche mixte U731 INSERM/UPMC), IRME and Institut Garches. We are indebted to Drs Sangla and Trocello for patient recruitment and to Pr. Dormont for his help in MRI acquisitions.

We dedicate this work to Dr Line Garnero †, PhD, late Director of the MEG centre.

the Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence on a worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if accepted) to be published in the Journal of Neurology, Neurosurgery & Psychiatry editions and any other BMJPGL products to exploit all subsidiary rights, as set out in our licence (http://jnnp.bmjjournals.com/ifora/licence.pdf).

Legends:

Figure: Finger somatotopy in the primary sensory cortex and sample of handwriting prior and after rehabilitation

The equivalent current dipole locations corresponding to each finger (thumb-I (yellow dot), index-II (green dot) middle-III (blue dot) and little-V fingers (red dot)) are superimposed on 3D brain reconstructions in a control subject (A), a non-rehabilitated dystonic patient (B) and a rehabilitated dystonic patient (C). Sample of handwriting and drawing before and 2.5 months after rehabilitation (D). WC: writer's cramp Table : Quantitative assessment of hand area in primary sensory cortex R: rehabilitated patients with writer's cramp. D: untrained patients with writer's cramp. C: normal control subjects. Dm: mean Euclidean distance between the equivalent current dipole (ECD) locations of two adjacent fingers in the inferior-superior direction in the sagittal plane; Dmax: Euclidean distance between the ECD locations of the two fingers most distant from one another. SI: somatotopy index indicating the correct topographic order of adjacent fingers. The SI was 3 if the finger ECDs hada correct topographic order in the inferior-superior direction in the sagittal plane (I, II, III, V from bottom to top); The SI was 2 if there was an inversion between the ECDs of adjacent fingers; and the SI was 1 if there were 2 or 3 inversions, or an inversion between two nonadjacent fingers. Values are expressed as mean + SD.


Christine Aldridge

Kritchai Poonchareon

Supreet Saini

Thomas Ewen

Alexandra Solovyova

Christopher V Rao

Katsumi Imada

Tohru Minamino

Phillip D Aldridge
email: p.d.aldridge@ncl.ac.uk

Alexandra Soloyva

The interaction dynamics of a

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Salmonella enterica serovar Typhimurium utilizes flagella, self-assembled nanomachines anchored in the cell envelope, to swim through liquids and swarm over surfaces. Approximately 60 flagellar genes, organized into 13 operons, are involved in flagellar assembly, motility and chemotaxis. The expression of the flagellar genes follows a strict hierarchy resulting in the subdivision of the flagellar operons into three classes based on the promoters that drive their transcription: P class1 , P class2 and P class3 [START_REF] Chilcott | Coupling of Flagellar Gene Expression to Flagellar Assembly in Salmonella enterica Serovar Typhimurium and Escherichia coli[END_REF]. There is only one P class1 promoter that drives transcription of the genes flhD and flhC. FlhD and FlhC form a hetero-hexameric complex, FlhD 4 C 2 , which is the master regulator of the enteric flagellar systems [START_REF] Wang | Structure of the Escherichia coli FlhDC complex, a prokaryotic heteromeric regulator of transcription[END_REF]. FlhD 4 C 2 activates transcription itself through a direct interaction with the alpha subunit of RNA polymerase, allowing σ 70 to activate transcription from FlhD 4 C 2 specific promoters [START_REF] Liu | The C-terminal region of the alpha subunit of Escherichia coli RNA polymerase is required for transcriptional activation of the flagellar level II operons by the FlhD/FlhC complex[END_REF].

Previous studies of the FlhD 4 C 2 have shown that the DNA binding component is primarily directed through FlhC. FlhD has been proposed to stabilize the FlhD 4 C 2 :DNA complex [START_REF] Claret | Functions of the subunits in the FlhD(2)C(2) transcriptional master regulator of bacterial flagellum biogenesis and swarming[END_REF]. There are eight FlhD 4 C 2 -dependent flagellar-specific P class2 promoters. One of these promoters drives the transcription of the fliA gene, encoding an alternative sigma factor, σ 28 , required for transcription from the eight P class3 promoters [START_REF] Ohnishi | Gene fliA encodes an alternative sigma factor specific for flagellar operons in Salmonella typhimurium[END_REF]. The gene products of flhD, flhC and fliA are essential for the expression of the flagellar genes.

Along with fliA, a further regulatory component transcribed from both P class2 and P class3 promoters is flgM. The gene product of flgM is an anti-sigma factor that directly interacts with σ 28 in the cell, preventing σ 28 from activating transcription from the P class3 promoters during HBB assembly (Gillen and Hughes, 1991a;Gillen and Hughes, 1991b).

Upon HBB completion P class3 activation is coupled to flagellar assembly through the [START_REF] Hughes | Sensing structural intermediates in bacterial flagellar assembly by export of a negative regulator[END_REF][START_REF] Kutsukake | Role of the FliA-FlgM regulatory system on the transcriptional control of the flagellar regulon and flagellar formation in Salmonella typhimurium[END_REF].

The coupling of assembly and gene expression suggests that some gene products are only expressed at a given stage of the assembly pathway. However, one limitation of this model is that it only considers a single flagellum. S. enterica does not produce a single flagellum per cell. Rather, it produces many, approximately four to eight per cell [START_REF] Iino | Assembly of Salmonella flagellin in vitro and in vivo[END_REF]. Thus, in a growing cell with more than one flagellum, all components required to assemble a complete flagellum are in theory present. Therefore, the flagellar system likely requires additional levels control in the presence of growing or assembled flagella that allow it to coordinate and adjust flagellar gene expression accordingly. Specifically, the presence of an upper limit in flagella number suggests that a negative feedback loop, sensitive to the number of flagella present, exists to modulate the activity of the regulators FlhD 4 C 2 and σ 28 . For σ 28 :FlgM, the physical secretion of FlgM itself through completed HBB structures provides a given level of control for σ 28 . We have recently shown that changing the rate of FlgM secretion modulates σ 28 activity [START_REF] Brown | The rate of protein secretion dictates the temporal dynamics of flagellar gene expression[END_REF]. Ideally, a level of negative feedback similar to the σ 28 :FlgM circuit would regulate the activity of the master regulator FlhD 4 C 2 in response to flagellar assembly. As FlhD 4 C 2 is at the top of the transcriptional hierarchy, any changes in its activity will have the net affect of reducing both P class2 activity and σ 28 -dependent activation of P class3 . Other than FlgM and σ 28 , there are two regulatory proteins that modulate flagellar gene transcription: FliT and FliZ [START_REF] Kutsukake | Two novel regulatory genes, fliT and fliZ, in the flagellar regulon of Salmonella[END_REF][START_REF] Saini | FliZ Is a posttranslational activator of FlhD 4 C 2 -dependent flagellar gene expression[END_REF][START_REF] Yamamoto | FliT acts as an anti-FlhD 2 C 2 factor in the transcriptional control of the flagellar regulon in Salmonella enterica serovar Typhimurium[END_REF]. The necessary negative feedback required to regulate FlhD 4 C 2 activity to secretion is the anti-FlhD 4 C 2 factor, FliT [START_REF] Yamamoto | FliT acts as an anti-FlhD 2 C 2 factor in the transcriptional control of the flagellar regulon in Salmonella enterica serovar Typhimurium[END_REF]. FliT is known to inhibit FlhD 4 C 2 -binding to DNA through its direct interaction with FlhC [START_REF] Yamamoto | FliT acts as an anti-FlhD 2 C 2 factor in the transcriptional control of the flagellar regulon in Salmonella enterica serovar Typhimurium[END_REF], while it also interacts with the filament-capping protein FliD and two soluble proteins, FliI and FliJ, of the flagellar T3S apparatus [START_REF] Evans | An escort mechanism for cycling of export chaperones during flagellum assembly[END_REF][START_REF] Fraser | Substrate-specific binding of hookassociated proteins by FlgN and FliT, putative chaperones for flagellum assembly[END_REF][START_REF] Imada | Completion of the hook-basal body complex of the Salmonella typhimurium flagellum is coupled to FlgM secretion and fliC transcription[END_REF].

FliJ enhances FliT/FliD interactions with the C-terminal cytoplasmic domain of FlhA, a key protein of the flagellar-specific type III secretion apparatus (Bange et al.; Saijo-Hamano et al.). The interactions of FliT with FliD, FlhA and FliJ are thought to facilitate the secretion of FliD upon completion of the HBB. With respect to its interaction with FlhC, FliT has been defined as an anti-FlhD 4 C 2 factor and FliD an anti-anti-FlhD 4 C 2 factor [START_REF] Yamamoto | FliT acts as an anti-FlhD 2 C 2 factor in the transcriptional control of the flagellar regulon in Salmonella enterica serovar Typhimurium[END_REF]. In this regulatory circuit, FlhD 4 C 2 would regulate flagellar assembly in response to the rate of FliD secretion.

FliT consists of four α-helices (α1, α2, α3, and α4) [START_REF] Imada | Completion of the hook-basal body complex of the Salmonella typhimurium flagellum is coupled to FlgM secretion and fliC transcription[END_REF]. A truncation of the C-terminal α4-helix, leading to FliT94, increases the binding affinity for the FlhD 4 C 2 complex, suggesting that α4 plays an autoregulatory role in modulating FliT activity. A surface exposed residue Lys-79 in α3 is critical for the interaction with FliD. The α4 helix binds to the hydrophobic cleft formed by α2 and α3. As a truncation of α4 does not affect the interaction with FliD or FlhD 4 C 2 per se, it is proposed that the interaction between α4 and the hydrophobic cleft modulates the binding affinity for the FlhD 4 C 2 complex (Imada et al., 2010).

In this work, we investigate the role of FliT regulation of FlhD 4 C 2 during continual flagellar assembly. We show that FliT regulation of FlhD 4 C 2 activity allows the system to efficiently respond to bacterial growth rates. In addition, unlike the all-or-nothing response observed with respect to the deletion of fliA (σ 28 ) or flgM, genetic analysis of the FliT circuit suggests FliT tunes the activity of the FlhD 4 C 2 , but never switches it off. To investigate the mechanism behind this behavior, we performed a biochemical characterization of the

Results

The assembly of flagella in S. enterica responds to changes in growth rates.

Recently a two-fold change in flhDC gene expression was shown to increase flagellar numbers by two-fold (Erhardt and Hughes). This study used, in part, a chromosomally encoded, functional FliM-GFP fusion to numerate flagella by the number of HBB per cell (Aldridge et al., 2006a). A significant number of studies over the past 5 decades monitor S. enterica flagellar assembly in LB. We asked if the growth conditions were changed, would a similar flagella distribution or a growth dependent phenotype be observed. Using the FliM-GFP construct we counted the number of HBB per cell of S. enterica grown in four conditions. We used a minimal medium base (Minimal E plus 0.2% Glucose) containing increasing amounts of Yeast Extract (YE) (0.04 g/L, 0.2 g/L, 1 g/L and 3 g/L). Yeast extract was chosen, as it could increase growth rates to that equivalent of LB (3 g/L YE (data not shown). Growth was performed at 30°C for consistency with previous experiments performed [START_REF] Brown | The rate of protein secretion dictates the temporal dynamics of flagellar gene expression[END_REF]. The rationale of adding rich nutrients to a base medium was similar to seminal growth experiments performed by Schaechter et al., to observe changes in growth mass in S. enterica at different growth rates [START_REF] Schaechter | Dependency on medium and temperature of cell size and chemical composition during balanced grown of Salmonella typhimurium[END_REF].

Flagellar numbers were counted at late exponential phase of standard shaken cultures prior to the transition into stationary phase (Figure 1A, Figure S1 and Table 1). This time point is suggested to coincide with maximal flagellar gene expression [START_REF] Pruss | A regulator of the flagellar regulon of Escherichia coli, flhD, also affects cell division[END_REF][START_REF] Pruss | Cell cycle regulation of flagellar genes[END_REF]. We observed an unexpected growth dependent phenotype that went against our perception of flagella utilisation. In our nutrient limiting conditions (0.04 g/L YE) we observed a significant reduction in flagellar numbers compared to 3 g/L YE. Changing carbon source or using a defined amino acid mix did not 8 alter this response (data not shown). On average, growth in 0.04 g/L YE produced 0.3 flagella/cell, while 3 g/L YE produce 3.74 flagella/cell (Table 1). No cells were observed for 0.04 g/L YE with more than 2 foci (Figure 1A). In contrast to growth in 0.04 g/L that exhibited an exponential distribution of flagellar numbers, 3 g/L YE growth produced a Gaussian (or normal) distribution of flagellar numbers. For the 3 g/L YE population, 90% of cells observed possessed one or more foci (Table 1). This suggests that only when nutrients are perceived will flagellar numbers increase, dependent on nutrient availability, to achieve optimal swimming behavior and therefore chemotaxis. These observations are consistent with previous reports on the influence of media composition on the flagellation of E. coli [START_REF] Adler | The effect of environmental conditions on the motility of Escherichia coli[END_REF].

FliT regulation is required to allow the system to respond accurately to external signals

The response of the S. enterica flagellar system to growth conditions suggests the system is flexible enough to respond to external cues, even though motility is defined as a robust phenotype [START_REF] Barkai | Robustness in simple biochemical networks[END_REF]. The majority of external signals are transduced into the flagellar system through changes in flhDC expression or FlhD 4 C 2 stability, observable as a change in FlhD 4 C 2 activity [START_REF] Soutourina | Regulation cascade of flagellar expression in Gram-negative bacteria[END_REF]. [START_REF] Yamamoto | FliT acts as an anti-FlhD 2 C 2 factor in the transcriptional control of the flagellar regulon in Salmonella enterica serovar Typhimurium[END_REF] have shown that FlhD 4 C 2 activity is negatively regulated via a direct interaction with FliT. This suggests that FlhD 4 C 2 activity is regulated by internal signals from the flagellar system. In their work, it was proposed that FliD regulates the ability of FliT to inhibit FlhD 4 C 2 activity [START_REF] Yamamoto | FliT acts as an anti-FlhD 2 C 2 factor in the transcriptional control of the flagellar regulon in Salmonella enterica serovar Typhimurium[END_REF]. Therefore, FliT regulation of FlhD 4 C 2 activity sensitises the system to flagellar assembly via FliD secretion rates [START_REF] Brown | The rate of protein secretion dictates the temporal dynamics of flagellar gene expression[END_REF]. As a growth dependent phenotype for flagella numbers was observed, we asked which level of regulation was dominant, the external regulation of FlhD 4 C 2 or the endogenous regulation through FliT. We therefore compared FliM-GFP foci numbers in our growth conditions for ∆fliT and ∆fliD mutants to wild type (Figure 1B andC).

We observed a significant increase in flagellar numbers at all growth rates for the ∆fliT mutant compared to wild type (Figure 1B and Figure S1). The percentage of cells with foci also significantly increased for the ∆fliT mutant compared to wild type. Grown in 0.04 g/L YE 60% of cells from the ∆fliT mutant population contained FliM-GFP foci as compared to 24% in wild type. The average number of FliM-GFP foci in the ∆fliT mutant also increased in all conditions (Table 1). Consistently, a reciprocal drop in flagellar numbers was observed for the ∆fliD mutant (Figure 1C, Figure S1, andTable 1). Even for 3g/L YE the 54% of foci containing cells of the ∆fliD mutant had only one or two foci (16/50 cells) with only a small proportion having 3 foci (4 cells), 4 foci (3 cells), 5 foci (2 cells) or 6 foci (2 cells) (Figure 1C). Importantly, the ∆fliT and ∆fliD phenotypes were observed at all growth rates, suggesting that the level of regulation exerted through FliT on FlhD 4 C 2 is independent of growth. Furthermore the data suggests that for the system to respond to external cues efficiently, endogenous regulation of FlhD 4 C 2 by FliT is required.

Overexpression of fliT does not halt flagellar gene expression completely.

Consistent with the flagellar numbers of the ∆fliT and ∆fliD mutants, plasmid-based expression of fliT or fliD resulted in the expected change. An increase of full-length fliT concentration by approximately 4 fold (data not shown) was unable to completely inhibit movement as assayed using motility agar plates (Figure 2A). This level of increased fliT expression resulted in only 2 % of cells possessing one FliM-GFP foci in comparison to 90 % in wild type. The recent description of the FliT structure suggested that a C-terminal αhelix modulates FliT activity [START_REF] Imada | Completion of the hook-basal body complex of the Salmonella typhimurium flagellum is coupled to FlgM secretion and fliC transcription[END_REF]. Deletion of this α-helix, producing the 10 variant fliT94, resulted in a further reduction in motility on motility agar after 8 hours incubation when fliT94 was expressed from a high copy number plasmid (pSE-fliT94) or as a GST fusion protein (pGST-fliT94 (pMMT002) -Figure 2A) [START_REF] Imada | Completion of the hook-basal body complex of the Salmonella typhimurium flagellum is coupled to FlgM secretion and fliC transcription[END_REF]. In comparison, overexpression of either flgM or fliA from pSE380 did not reduce motility in soft agar plates significantly (Figure 2A). This suggests that the σ 28 :FlgM and FliT regulatory circuits play distinct roles in regulating flagellar assembly, albeit that they are interlinked by σ 28 -dependent transcription of the fliDST operon.

We have recently established a flagellar gene expression assay that monitors flagellar gene expression through a Fla -to Fla + transition [START_REF] Brown | The rate of protein secretion dictates the temporal dynamics of flagellar gene expression[END_REF]. The motility phenotypes of plasmid-based expression of fliT and fliT94 suggest that a basal level of flagellar gene expression is still present. To confirm this, flagellar gene expression dynamics were measured for plasmid-based fliT or fliT94 expression from pSE380 (Figure 2B). As compared to wild type, plasmid-based expression of fliT and fliT94 resulted in a significant delay in activation of P class2 and P class3 promoters. However, the flagellar transcriptional hierarchy was still evident in all strains (Figure 2B). When expressing fliT, P flgA activity peaked at 1672 RLU at 91 minutes, a 4.9 fold reduction compared to its vector control that peaked at 8219 at 119 minutes. For fliT94 P flgA activity reached a maximum activity of 1067 RLU at 98 minutes, a 7.7 fold reduction in activity. For the P class3 promoter P fliC , fliT and fliT94 expression resulted in maximum activity of 130 and 118 RLU respectively compared to 15339 RLU for the vector control. These observations are consistent with the proposed function of α4 modulating FliT activity by binding into a cleft formed by helices α2 and α3. Importantly, both constructs were unable to completely inhibit flagellar gene expression. Instead, flagellar gene expression is significantly reduced to a basal level that can still sustain some motility. Therefore, this suggests that for the

FliT disrupts the free FlhD 4 C 2 complex

The in vivo data on FliT regulation suggests that even in excess, FliT is unable to completely inhibit FlhD 4 C 2 . Instead, the data suggests that FliT acts to significantly reduce FlhD 4 C 2 activity. However, the in vivo data does not offer an explanation to how this level of regulation by FliT is achieved or why it exists. We therefore investigated the biochemical characteristics of the FliT regulatory circuit in vitro to determine if the nature of the interactions between FliT, FliD, FlhD 4 C 2 and the FlhD 4 C 2 DNA binding site could aid our understanding of FliT regulation in vivo. [START_REF] Yamamoto | FliT acts as an anti-FlhD 2 C 2 factor in the transcriptional control of the flagellar regulon in Salmonella enterica serovar Typhimurium[END_REF] were able to show that FliT interacts with FlhC of the FlhD 4 C 2 complex. All in vitro analysis in this work focussed on FliT regulation of the FlhD 4 C 2 complex rather than FlhC itself. The rationale for this approach argued that to understand the in vivo data, how the FlhD 4 C 2 complex behaves in the presence of FliT rather than FlhC alone was important. We began by asking whether we could isolate an FlhD 4 C 2 :FliT complex. FlhD 4 C 2 and FliT were purified separately, mixed at different molar ratios and subjected to gel filtration. No high molecular weight species characteristic of a stable FliT:FlhD 4 C 2 complex was observed (data not shown). The analyses of the FliT:FlhD 4 C 2 interaction using surface plasma resonance (SPR) and isothermal calorimetry were unable to add any value to understanding the gel filtration data. We therefore utilised analytical ultra-centrifugation (AUC) to characterise the FliT:FlhD 4 C 2 interaction further (Figure 3). FliT exists in equilibrium between its monomeric and dimeric form in solution, as judged by sedimentation equilibrium AUC measurements [START_REF] Imada | Completion of the hook-basal body complex of the Salmonella typhimurium flagellum is coupled to FlgM secretion and fliC transcription[END_REF]. Analysis of FlhD 4 C 2 , by sedimentation velocity AUC, produced a single protein species with a mass of 94.9 kDa. This is in good agreement with the theoretical mass of the FlhD 4 C 2 complex (96.7 kDa) [START_REF] Wang | Structure of the Escherichia coli FlhDC complex, a prokaryotic heteromeric regulator of transcription[END_REF]. Addition of FliT to FlhD 4 C 2 at a 1:1 molar ratio using 8 µM FlhD 4 C 2 did not produce a high molecular weight species larger than 94.9 kDa. Instead the FlhD 4 C 2 peak was reduced in size and a long shoulder that tapered towards the left of the FlhD 4 C 2 peak appeared. Higher ratios of FliT:FlhD 4 C 2 were then analysed and resulted in the shoulder becoming a defined peak with a predicted mass of 61-63 kDa. According to the size of this new protein species we assume that it could either be the dissociated FlhD 4 tetramer or a FliT:FlhC 2 complex. This data suggests that on interaction of FliT with FlhD 4 C 2 , the net result is the disassociation of the FlhD 4 C 2 complex through the interaction of FliT with FlhC. This is in agreement with a previous report that the amount of FlhD coeluted with GST-FliT94 from a GST column is much lower than that of FlhC [START_REF] Imada | Completion of the hook-basal body complex of the Salmonella typhimurium flagellum is coupled to FlgM secretion and fliC transcription[END_REF].

Inhibition of FlhD 4 C 2 :DNA complex formation requires excess FliT

Within the FliT regulatory circuit, the key interaction is FlhD 4 C 2 -binding to its DNA binding site. Based on our AUC results, we speculated that excess amounts of FliT would provide increased inhibition of FlhD 4 C 2 :DNA binding. To test our hypothesis, we chose a SPR approach. We immobilized 350-400 RU of double stranded 80-mers, biotinylated on one strand, to a streptavidin sensor chip containing the FlhD 4 C 2 DNA binding site from flgB (P flgB ) and flhB (P flhB ) and an intergenic region of fliC coding sequence as a control DNA.

Consecutive injections of FlhD 4 C 2 in a dilution series to P flgB DNA exhibited a concentration dependent response (Figure 4A). Addition of FliT in a 1:1 ratio with FlhD 4 C 2 prior to 13 injection resulted in a significant reduction in FlhD 4 C 2 :DNA interaction but did not completely inhibit it (Figure 4A). Analysis of the data showed that V max for FlhD 4 C 2 alone to be 3933 compared to 560 for FlhD 4 C 2 and FliT (Table S2). Binding to P flhB and fliC DNA (data not shown) showed consistency with previous studies on the FlhD 4 C 2 :DNA interaction. Stafford et al. showed that FlhD 4 C 2 could bind to DNA without a defined FlhD 4 C 2 binding site [START_REF] Stafford | Binding and transcriptional activation of nonflagellar genes by the Escherichia coli flagellar master regulator FlhD 2 C 2[END_REF][START_REF] Wang | Structure of the Escherichia coli FlhDC complex, a prokaryotic heteromeric regulator of transcription[END_REF]. Therefore, the observation that FlhD 4 C 2 bound to a coding region of fliC is not that surprising.

Our AUC data suggested that increasing molar ratios of FliT:FlhD 4 C 2 would significantly disrupt the FlhD 4 C 2 complex. Using our SPR assay the ability of increasing FlhD 4 C 2 :FliT ratios to inhibit FlhD 4 C 2 to bind its DNA binding site were tested. Increasing amounts of FliT were added to 100 nM FlhD 4 C 2 giving the ratios of 1:1, 1:1.5, 1:2.5 and 1:4 prior to injection over P flgB (Figure 4B). Increasing amounts of FliT significantly reduced the ability of FlhD 4 C 2 to bind to its binding site (Table S2). Molar ratios higher than 1:4 gave similar results (data not shown) suggesting that the maximal level of inhibition in our assays of the FlhD 4 C 2 to DNA interaction is a ratio of approximately 1:4.

A FlhD 4 C 2 :DNA complex is resistant to FliT regulation

The above observations from our AUC and initial SPR analysis are sufficient to explain the observed phenotypes of the ∆fliT and ∆fliD mutants and fliT overexpression with respect to FliM-GFP foci. Our data suggests that to completely inhibit FlhD 4 C 2 a high ratio of FliT to FlhD 4 C 2 is required. Previous studies characterising the FlhD 4 C 2 :DNA interaction have established that the FlhD 4 C 2 :DNA complex is very stable with a half-life of approximately 40 minutes [START_REF] Claret | Functions of the subunits in the FlhD(2)C(2) transcriptional master regulator of bacterial flagellum biogenesis and swarming[END_REF]. Thus, irrespective of the level of free FliT, its regulation of FlhD 4 C 2 will potentially encounter FlhD 4 C 2 bound to DNA. Therefore we 14 asked what effect would FliT have upon an FlhD 4 C 2 :DNA complex using our SPR assay (Figure 5). The injection of PBS running buffer did not show any effect, as expected.

Unexpectantly, FliT would not disrupt the already existing FlhD 4 C 2 :DNA complex (Figure 5A -addition of a 100 nM solution of FliT to bound FlhD 4 C 2 complexes is shown). In these assays a constant concentration of 100 nM FlhD 4 C 2 was first bound to the immobilised DNA. Even a 100-fold excess of FliT (10 µM) could not disrupt the FlhD 4 C 2 :DNA complex (data not shown). In contrast and as a control to show that FlhD 4 C 2 was bound and could be disassociated, injecting 100 nM free P flgB DNA resulted in disassociation of the FlhD 4 C 2 :DNA complex from all 3 DNA species tested (Figure 5B). Importantly, this control also showed that the interaction dynamics of FlhD 4 C 2 with the three DNA species behaved in agreement with a previous work [START_REF] Stafford | Binding and transcriptional activation of nonflagellar genes by the Escherichia coli flagellar master regulator FlhD 2 C 2[END_REF]. These results, therefore, suggest that FliT can prevent FlhD 4 C 2 from binding DNA but if FlhD 4 C 2 is already bound to DNA, FlhD 4 C 2 is resistant to FliT regulation.

FliD acts as an anti-anti-FlhD 4 C 2 factor

The phenotype of a ∆fliD mutant with respect to changes in flagellar gene expression and that FliD directly interacts with FliT led [START_REF] Yamamoto | FliT acts as an anti-FlhD 2 C 2 factor in the transcriptional control of the flagellar regulon in Salmonella enterica serovar Typhimurium[END_REF] to propose that FliD acts as an anti-anti-FlhD 4 C 2 factor. However, this hypothesis has not been experimentally explored. We therefore used the SPR-based assay of FlhD 4 C 2 binding DNA to observe what effect FliD would have upon FliT inhibition of FlhD 4 C 2 .

As FliD:FliT interact in a 1:1 molar ratio, a 1:1:1 ratio of FliD:FliT:FlhD 4 C 2 was assayed. Addition of FliD to FlhD 4 C 2 , in the absence of FliT, did not alter the ability of FlhD 4 C 2 to bind DNA (Figure 6). This was in agreement with gel filtration and bacterial twohybrid analysis that did not identify a FliD:FlhD 4 C 2 interaction (data not shown). In binding DNA (Figure 6). A similar response to the addition of FliD to a 1:1 mix of FliT94:FlhD 4 C 2 was also observed (Figure S2). Therefore we can confirm Yamamoto and Kutsukake's proposal that FliD does act in an anti-anti-FlhD 4 C 2 manner. Interestingly, a complete inhibition of FliT regulation was not observed.

The FliD/FlhD 4 C 2 binding sites on FliT overlap

The data above suggests that FliD interaction with FliT when all proteins are at a 1:1:1 ratio is not sufficient to completely inhibit the regulation of FlhD 4 C 2 by FliT. One explanation for this is that the binding sites for FliD and FlhD 4 C 2 are on distinct surfaces of FliT. As a result even in the presence of FliD, FliT could disrupt the FlhD 4 C 2 complex. An alternative explanation is that the sites of interaction overlap and the observed response is due to FliD/FlhD 4 C 2 competition for FliT. To distinguish between these two models, a genetic analysis of fliT was performed. Our intent was to determine if fliT point mutations would exist that could bind FliD but not interact with FlhD 4 C 2 and vice versa. The P class1 flhDC promoter is negatively autoregulated by FlhD 4 C 2 (Figure 7). Deletion of fliT although observed to have increased P class2 activity, exhibits reduced P class1 activity (Figure 7). This suggests that FliT regulation effects not only P class2 activity but also FlhD 4 C 2 levels through regulation of the autoregulatory control of the P class1 promoter region by FlhD 4 C 2 . Our screen used the drop in P class1 activity associated with loss of FliT regulation to isolate fliT mutations exhibiting reduced activity against FlhD 4 C 2 . Mutations in fliT were isolated using random PCR mutagenesis (Aldridge et al., 2006b). From 2000 colonies, 17 colonies with reduced P class1 activity were isolated randomly and sequenced. From these 9 had single point mutations, 3 had two point mutations, 2 had triple mutations, one had a C-terminal extension of 25 amino acids due to the mutation of the stop codon and one did not contain Interestingly, one mutation N74D was at the same residue as had been identified to show weaker FliD interaction [START_REF] Imada | Completion of the hook-basal body complex of the Salmonella typhimurium flagellum is coupled to FlgM secretion and fliC transcription[END_REF]. This suggests that the FliD and FlhD 4 C 2 binding sites on FliT may in fact overlap.

To further investigate whether the FliD/FlhD 4 C 2 binding sites on FliT overlap, we assayed alanine substitution mutants in the C-terminal portion of α3 in motility assays, an FlhC pull-down assay [START_REF] Imada | Completion of the hook-basal body complex of the Salmonella typhimurium flagellum is coupled to FlgM secretion and fliC transcription[END_REF] and the SPR-based assay. Motility assays of these mutations introduced into fliT94 showed that five exhibited a strong reduction in the ability of fliT94 to inhibit motility: I68A, N74A, E75A, K79A and L82A (Figure 8A).

Consistently, in a pull-down assay one of these mutations (E75A) showed a significant drop in its ability to interact with FlhC compared to those that showed a similar phenotype to wild type fliT94 (Figure 8B).

Four substitutions showing motility were also assayed for their ability to inhibit the FlhD 4 C 2 :DNA interaction. In agreement with the motility and pull-down assays FliT94(E75A) exhibited the weakest inhibition of the DNA interaction (Figure 8C and Table S2). In contrast, the substitutions K79A and L82A behaved in a similar fashion showing inhibition at ratios greater than 1:2 of FlhD 4 C 2 :FliT94, while N74A exhibited a slight drop in inhibition at all concentrations. Importantly, all alanine substitutions still retained the ability to bind to FlhC to some extent and therefore inhibited wild-type motility (Figure 8).

Therefore, these mutants strongly suggest that the FliD/FlhD 4 C 2 interface on FliT somehow overlap and the observed response in the SPR analysis is the result of competition for FliT.

Discussion

A key factor often overlooked with respect to flagellar assembly is how the cell coordinates the synthesis of multiple flagella during bacterial growth. Here we have presented evidence suggesting that enteric bacteria are able to sense the extent of their flagellation via an endogenous regulatory circuit that modulates the activity of FlhD 4 C 2 . Furthermore this internal regulation is key to allow the system to respond accurately to extracellular environmental, internal metabolic or growth dependent signals.

Two key observations from this study showed that i) loss of FlhD 4 C 2 regulation by FliT disrupted an efficient response to changes in growth conditions and ii) if bound to DNA, FlhD 4 C 2 was insensitive to FliT regulation. Consistent with both observations was the finding that deleting fliD or expressing full length or a truncated fliT variant from a plasmid was unable to completely inhibit FlhD 4 C 2 activity in any growth condition. That when bound to DNA, FlhD 4 C 2 was insensitive to FliT regulation is an intriguing twist to an already complex regulatory network that couples flagellar gene expression to the assembly pathway.

Hughes and co-workers predicted that FlhC was the major DNA binding component of the FlhD 4 C 2 complex [START_REF] Claret | Functions of the subunits in the FlhD(2)C(2) transcriptional master regulator of bacterial flagellum biogenesis and swarming[END_REF]. [START_REF] Yamamoto | FliT acts as an anti-FlhD 2 C 2 factor in the transcriptional control of the flagellar regulon in Salmonella enterica serovar Typhimurium[END_REF] when describing the FliT:FlhD 4 C 2 interaction were able to show that FliT was interacting with FlhC. Our data are in agreement with these observations. The resistance of the FlhD 4 C 2 :DNA complex to FliT suggests that the FliT binding site on FlhC overlaps the FlhC DNA binding domain. This is different than the model presented by [START_REF] Wang | Structure of the Escherichia coli FlhDC complex, a prokaryotic heteromeric regulator of transcription[END_REF] when FlhD 4 C 2 was crystallised. [START_REF] Wang | Structure of the Escherichia coli FlhDC complex, a prokaryotic heteromeric regulator of transcription[END_REF] proposed a configuration that shows FlhD being the major DNA binding component. However, when discussing this model, they 18 conceded that the data available were insufficient to rule out other plausibility's, such as FlhC making the majority of the DNA:protein contacts [START_REF] Wang | Structure of the Escherichia coli FlhDC complex, a prokaryotic heteromeric regulator of transcription[END_REF]. Our data, with that of the co-workers of Hughes and Kutsukake suggests that the proposed alternative is more feasible but still requires direct biochemical or structural evidence to confirm the correct orientation.

Our genetic analysis of the FliT:FlhD 4 C 2 interaction suggests that it is not only the interaction sites of FliT and DNA on FlhC that overlap. Mutations in FliT itself, previously shown to reduce FliD interaction, were also found to have reduced activity in inhibiting FlhD 4 C 2 [START_REF] Imada | Completion of the hook-basal body complex of the Salmonella typhimurium flagellum is coupled to FlgM secretion and fliC transcription[END_REF]. A conclusion based on the analysis of alanine substitutions in the truncated variant FliT94. The genetic analysis, in conjunction with our biochemical analysis, suggests that a key factor in the way that FliT regulates FlhD 4 C 2 is the extent of competition between all components. As a result this circuit is sensitive to the availability of FliT to bind FliD, FliT to bind FlhC and the availability of FlhD 4 C 2 to bind DNA. A swing in the balance between these three reactions will alter the level of FlhD 4 C 2 output in terms of transcription activation.

In agreement with previous data we have shown that FlhD 4 C 2 can bind DNA unspecifically [START_REF] Stafford | Binding and transcriptional activation of nonflagellar genes by the Escherichia coli flagellar master regulator FlhD 2 C 2[END_REF]. The mode of binding may be different, as seen during the competition assays using unbiotinylated P flgB DNA to disassociate the FlhD 4 C 2 :DNA complexes (Figure 5B). The ability of FlhD 4 C 2 to bind DNA even without a defined binding site could potentially have an impact of FliT regulation as it means FlhD 4 C 2 does not only have to bind to its specific sites to become resistant to FliT.

What is the advantage of FlhD 4 C 2 being regulated by FliT only when not bound to DNA? A recent study using SPR, which our assays were based on, showed that the RelBE system behaves somewhat different to FlhD 4 C 2 regulation by FliT. The RelBE system is an toxin-antitoxin locus in which the toxin component can cleave mRNA associated with ribosomes but also autoregulates the transcription of the relBE locus [START_REF] Overgaard | Messenger interferase RelE controls relBE transcription by conditional cooperativity[END_REF]. If RelE interacts with a RelB:DNA complex the result is disassociation of the proteins from the DNA [START_REF] Overgaard | Messenger interferase RelE controls relBE transcription by conditional cooperativity[END_REF]. The flagellar and RelBE systems are very different with respect to their regulation. For the flagellar system there is an advantage for a given basal level of activity to exist even when flhDC expression is under significant negative influence from the long list of regulators that can influence its expression [START_REF] Soutourina | Regulation cascade of flagellar expression in Gram-negative bacteria[END_REF]. Both a P class2 and a P class3 promoter drive the transcription of fliT. Thus, even at low levels of flhDC expression some FliT can be made, even if no complete HBBs are present. The apparent competition between all components that our data suggests, predicts that even during HBB assembly, when FliD is not being secreted, a given level of FliT would be available to interact with free FlhD 4 C 2 .

Note also that activation of FlgM secretion allows σ 28 to increase transcription of fliT.

Therefore once one structure is complete the level of free FliT will increase due to changes in FliD and FlgM levels as a result of secretion. We propose the working model that under growth conditions that exhibit significant inhibition of flhDC expression, FliT regulation will control the extent of flagellation by further inhibiting any free FlhD 4 C 2 found in the cell. However, if the FlhD 4 C 2 :DNA interaction occurs first then bound FlhD 4 C 2 will be insensitive to FliT regulation.

Our data suggests that under certain environmental conditions a basal level of [START_REF] Adler | The effect of environmental conditions on the motility of Escherichia coli[END_REF] drew similar conclusions from their work to gain maximum motility of E. coli in a defined chemical medium. Interestingly, how efficient chemotaxis would be in E. coli or S. enterica cells possessing low flagella numbers has not been explored in detail and in the light of this work would be an interesting line of investigation [START_REF] Darnton | On torque and tumbling in swimming Escherichia coli[END_REF][START_REF] Turner | Real-time imaging of fluorescent flagellar filaments[END_REF].

Thus, in conclusion we have shown that the enteric flagellar system requires a given level of endogenous negative feedback regulation to respond to external signals. For enteric bacteria this regulation is via the negative regulation of FlhD 4 C 2 activity by FliT. We further conclude that FliT is unable to completely inhibit FlhD 4 C 2 from activating flagellar gene expression, thus preparing the population to respond, with respect to the flagellar system, accordingly to external signals. What is now of interest is to begin to question how non-FlhD 4 C 2 dependent systems control the same response.

Materials and Methods

Bacterial Strains, Plasmids and Cultivation

Strains and Plasmids used during this study are described in Table 2. Media used in this study included LB broth and Minimal E [START_REF] Davis | Advanced bacterial genetics: a manual for genetic engineering. Cold Spring Harbor Laboratory[END_REF][START_REF] Vogel | Acetylornithinase of Escherichia coli: partial purification and some properties[END_REF]. All plates contained 1.5% agar. Motility assays were performed using semi-solid motility agar containing 0.3% difco agar. Cultures were grown at either 37°C or 30°C with constant shaking at 180 rpm. Antibiotic concentrations have been described previously [START_REF] Bonifield | The flagellar hook protein, FlgE, of Salmonella enterica serovar Typhimurium is posttranscriptionally regulated in response to the stage of flagellar assembly[END_REF]. For the growth condition experiment Minimal E base salts were supplemented with 0.2% glucose and the stated concentration of Yeast extract from a stock solution of 25 g/L. For purification of FliT, FliD and FlhD 4 C 2 LB cultures were grown to an OD600 nm between 0.4 and 0.6 at 37°C. Induction of expression for 3 to 5 hours was achieved using a final concentration of 1 mM IPTG with incubation at 30°C. Cell pellets were harvested and stored at -80°C until needed.

DNA Manipulation

Throughout this study standard cloning and DNA manipulation techniques have been used. All plasmids were sequenced prior to use. Primer sequences used for constructs are available on request. A modified version of pET28a-mod was used in this study that reduced the size of the his-tag fused to FliT or FliD with a region of the multiple cloning site deleted (Table 2). For the purification of FlhD 4 C 2 the entire flhDC operon was subcloned into pET28a-mod. On induction this produced a complex that was his-FlhD 4 C 2 .

All protein identities were also confirmed by MALDI-TOF analysis using an in-house facility at Newcastle (PINNACLE). Random mutagenesis of fliT was performed using the natural error rate of Taq DNA polymerase. A PCR reaction generated using Taq DNA polymerase was recombined onto the chromosome of strain TPA993 selecting on Tetracycline-sensitive (Tet S ) plates (Aldridge et al., 2006b). After selection, crossing once more on to Tet S plates was used to isolate 2000 colonies. A pool of the 2000 colonies was then transduced with plasmid pRG38 (P flhDC ::luxABCDE) to screen for potential fliT mutants. To isolate colonies exhibiting reduced light production, images of the transduction plates were made using the chemiluminescent settings of an intelligent light box. Seventeen colonies were taken at random and purified to isolate phage free colonies before light production was confirmed using liquid cultures assayed in a BMG Fluostar microplate reader. The fliT-coding region was then sequenced from PCR products. Deletion of fliD in TPA2421 used previously described recombineering protocols [START_REF] Aldridge | The type III secretion chaperone FlgN regulates flagellar assembly via a negative feedback loop containing its chaperone substrates FlgK and FlgL[END_REF][START_REF] Datsenko | One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products[END_REF].

Epifluorescent Microscopy

To visualise FliM-GFP, cultures were grown as described above. At appropriate time points or OD600 nm values, one microlitre of culture was spotted onto a 1% agarose pad containing 1 x E-salts but no glucose in a Gene frame (AB gene). The agarose pad was sealed with a coverslip, a procedure sufficient to immobilize the motile bacteria. Images were acquired on a Zeiss Axiovert 200M system [START_REF] Murray | Dynamic control of the DNA replication initiation protein DnaA by Soj/ParA[END_REF]). An exposure time for all GFP images of 1.5 seconds was used and 100 milliseconds for a phase contrast image. Images were captured through METAMORPH (version V.6.2r6) and then processed using ImageJ (NIH).

To count FliM-GFP foci all cells were counted using the dot function of ImageJ.

From the counted cells a random number generator (www.randomizer.org) was used to identify 50 cells from each time point and strain. Using a stacked RGB composite image of the GFP and phase contrast images, the FliM-GFP foci were then counted manually in the identified cells. Histograms of the number distributions and statistical analysis were performed using Excel (Microsoft).

Protein Purification

All protein isolations were done using an Akta Prime Plus (GE Healthcare). Purification of FliT and FliD was performed using standard purification protocols. Briefly, cell pellets of induced cultures were resuspended in His-loading buffer: 50 mM HEPES, 150 mM NaCl, 20 mM Imidazole, pH 7.5 and sonicated for 5 min on ice. To remove all cell debris the sample was centrifuged at 31,500 x g for 30 min at 4°C prior to injection over a preequilibrated 5 ml his-trap column (GE Healthcare). Proteins were eluted using an 80% Imidazole gradient over 50 ml of His-elute buffer: 50 mM HEPES, 150 mM NaCl, 1 M Imidazole, pH 7.5. FlhD 4 C 2 purification was performed using a modified method described by [START_REF] Wang | Structure of the Escherichia coli FlhDC complex, a prokaryotic heteromeric regulator of transcription[END_REF]. Plasmid pPA158 was freshly transformed into BL21 and induced cell pellets isolated from up to 6 litres of culture and resuspended in Buffer A: 20 mM Tris, pH 7.9.

The cell suspension was sonicated for 5 min, centrifuged for 30 min at 31,500 x g at 4°C and injected over a 5 ml Heparin column (GE Healthcare). After injection, the column was washed with a 20% solution of Buffer B: 20 mM Tris, 1 M NaCl, pH 7.9. After the baseline reached a pre-loading level, elution of the FlhD 4 C 2 complex was achieved using a gradient of Buffer B up to 100% applied over 60 minutes. All protein fractions were checked by SDS page on 16.5% Tricine gels.

After purification FliT, FliD and FlhD 4 C 2 proteins were concentrated using VIVAspin columns (10 kDa cutoff) and subjected to gel filtration using a S200 column (GE Healthcare). The gel filtration buffer used was 20 mM Tris, 300 mM NaCl, 1 mM DTT, pH 7.9. Pull down assays by GST affinity chromatography have been previously described [START_REF] Imada | Completion of the hook-basal body complex of the Salmonella typhimurium flagellum is coupled to FlgM secretion and fliC transcription[END_REF].

Surface Plasma Resonance

The majority of SPR experiments were performed using the BIAcore 2000. Experiments using FliT94 were performed on a Bio-Rad Proteon XPR36. DNA-protein interaction experiments were done using a streptavidin (SA) or neutravidin coated sensor chips (GE Healthcare and Bio-Rad). The running buffer for all experiments was phosphate buffered saline (PBS). For the DNA-protein experiments using the BIAcore 2000 365 resonance units (RU) of DNA were immobilized on the SA sensor chip, while for the XPR36 100 RU were immobilized. Prior to immobilization, 3 consecutive injections over all flow cells for 60 seconds at a flow rate of 10 µl / min with 1M NaCl then 0.05 M NaOH were performed to ensure no contamination of unspecific bound compounds. An empty flowcell was always used as a negative control for background subtraction and therefore had no DNA or protein bound to it. The DNA used was an 80 bp double-stranded oligonucleotide, with a 5'biotinylated forward strand of the flgB and flhB binding sites of FlhD 4 C 2 (Table 3) [START_REF] Stafford | Binding and transcriptional activation of nonflagellar genes by the Escherichia coli flagellar master regulator FlhD 2 C 2[END_REF]. Also as a negative control we used an intergenic region of fliC (Table 3). Annealing of the DNA was performed using a Biometra T3000 Thermocycler: 94°C for 2 min then ramp cooled to 25°C over 45 min then kept at 4°C until use. Annealing of samples was confirmed by native gel electrophoresis on 10% native gels. For all experiments FlhD 4 C 2 protein was applied for 60 seconds over all four flow cells.

Regeneration of the flow cells was done using a 10 second injection of 6 M guanidine-HCL. As a control for chip quality FlhD 4 C 2 alone was always run at the beginning, middle and end of an experimental set. For protein-protein interactions standard BIAcore

Analytical Ultracentrifugation

Sedimentation velocity (SV) experiments were carried out in a ProteomeLab XL-I analytical ultracentrifuge (Beckman Coulter, USA) using both absorbance (at 280 nm) and interference optics. All AUC runs were carried out at a rotation speed of 48,000 rpm and an experimental temperature of 4°C using an 8-hole rotor and double-sector aluminiumepon centrepieces. The sample volume was 400 µl for the SV experiments with sample concentrations between 0.2 and 1.2 mg/ml. The partial specific volumes ( v ) for the proteins were calculated from the protein amino acid sequence, using the program SEDNTERP and extrapolated to the experimental temperature. The density and viscosity of the buffer (10 mM Tris pH 7.5, 1 mM EDTA, 200 mM NaCl) at the experimental temperature was also calculated using SEDNTERP.

The distributions of sedimenting material were modelled as a distribution of Lamm equation solutions where the measured sedimentation boundary was modelled as an integral over differential concentration distributions c(s) as implemented in the program SEDFIT (http://www.analyticalultracentrifugation.com) [START_REF] Schuck | Sedimentation analysis of noninteracting and self-associating solutes using numerical solutions to the Lamm equation[END_REF]. The sedimentation velocity profiles were fitted using a maximum entropy regularisation parameter of p = 0.95.

The weight average sedimentation coefficient was calculated by integrating the differential sedimentation coefficient distribution [START_REF] Schuck | On the analysis of protein self-association by sedimentation velocity analytical ultracentrifugation[END_REF]. Sedimentation coefficients (s) were extrapolated to zero concentration and converted to standard conditions, i.e. those that would be measured at 20°C in water. The diffusion coefficient (D) corresponding to each sedimentation coefficient value was estimated from a weight-average frictional ratio (f/f 0 ) w F o r P e e r R e v i e w [START_REF] Schuck | Sedimentation analysis of noninteracting and self-associating solutes using numerical solutions to the Lamm equation[END_REF][START_REF] Schuck | On the analysis of protein self-association by sedimentation velocity analytical ultracentrifugation[END_REF]. Conversion to a molecular mass distribution from s and D was made using the Svedberg equation

) v 1 ( D sRT M ρ - = ,
where R is gas constant, T is temperature, v is a partial specific volume of solute and ρ is density of the buffer. The integration of the mass distribution c(M) was made similarly to the integration of c(s) distribution in order to determine the weight average molecular mass of each species. 32 weight of 28 kDa while the FlhD 4 C 2 complex (black solid line) was calculated to have a mass of 94.9 kDa in agreement with the published crystal structure [START_REF] Wang | Structure of the Escherichia coli FlhDC complex, a prokaryotic heteromeric regulator of transcription[END_REF].

F

Addition of FliT to FlhD 4 C 2 in a 1:1 ratio (black short dashed line) significantly reduced the FlhD 4 C 2 peak. Surprisingly no new species of greater mass was identified. Instead a shoulder to the left of the FlhD 4 C 2 peak was observed. When using greater FliT:FlhD 4 C 2 ratios at 5:1 (black long dashed line) or 10:1 (grey intermittent dashed line) this shoulder developed into an individual peak with a calculated mass of 61-63 kDa.

  FlgM via the flagellum, thus freeing σ 28 to activate transcription from P class3 promoters

  :FlhD 4 C 2 circuit. Using this approach, we show that FliT regulates FlhD 4 C 2dependent activation of P class2 by disrupting the FlhD 4 C 2 complex. However, FliTdependent inhibition is unable to disrupt the FlhD 4 C 2 -DNA complex. Based on our results, we propose the model that FliT regulation of FlhD 4 C 2 allows the flagellar system to respond efficiently to external regulatory inputs directed through changes in FlhD 4

  some aspect of the FliT:FlhD 4 C 2 regulatory circuit prevents FliT completely inhibiting FlhD 4 C 2 from activating flagellar gene expression.

  mix of FliD, FliT and FlhD 4 C 2 did alter the ability of FliT to inhibit FlhD 4 C 2 from

  the coding sequence of fliT (data not shown). Using the recently described structure of FliT the single point mutations were found to all cluster in α1 or α3.

FlhD 4 C

 4 2 activity is maintained via a balance between external regulation of flhDC expression and internal regulation through, for example, FliT inhibition of FlhD 4 C 2 . The result is that a small proportion of cells amongst the population will possess a low number of flagella. It is then the subpopulation of flagellated cells that are able to begin to move current location to seek more preferential growth conditions.

  using 300 RU of FliT immobilized on a CM5 sensor chip as ligand and either FliD or FlhD 4 C 2 as analyte.

Figure 1 :

 1 Figure 1: Effect of growth conditions on the distribution of FliM-GFP foci in wild type (A),

Figure 2 :

 2 Figure 2: Plasmid-based expression of FliT and FliT94 is unable to halt flagellar gene

Figure 3 :

 3 Figure 3: Analytical ultracentrifugation analysis of the interaction of FliT with FlhD 4 C 2 . In

Figure 4 :

 4 Figure 4: SPR analysis of FliT regulation of the FlhD 4 C 2 :DNA interaction. (A)

Figure 5 :

 5 Figure 5: A FlhD 4 C 2 :DNA complex is insensitive to FliT. (A) SPR profile of injection of

Figure 6 :

 6 Figure 6: FliD acts as an anti-anti-FlhD 4 C 2 factor. SPR profiles of the influence of FliD on

Figure 7 :

 7 Figure 7: Autoregulation of P class1 (P flhDC ) by FlhD 4 C 2 responds to FliT inhibition of

Figure 8 :

 8 Figure 8: The interaction interface of FliT for FliD and FlhD 4 C 2 overlap. (A) Motility assays

Table 1 :

 1 Statistics of the FliM-GFP foci distribution in wild type. ∆fliT and ∆fliD

	o r
	P
	e
	e r
	R
	e
	v i e
	w

Table 2 :

 2 Strains and Plasmids used in this study

	Strain	Strain/plasmid	Genotype	Reference
	No.	name
		LT2 BL21	Wild type strain F -ompT hsdSB dcm gal λ(DE3)	K. Hughes Promega
		DH5α	phi-80d lac∆m15 enda1 recA1 hsdR17	New England Biolabs
			supE44 thh-1 gyrA96 relA ∆lacU169
	TPA14		P flhDC 5451::Tn10[del-25]	(Karlinsey et al., 2000)
	TPA20		fliT::Km	(Kutsukake et al., 1999)
	TPA993		∆fliT7046::tetRA
	TPA1107 Wild type*	fliM5978-gfp (Mot+)	(Aldridge et al., 2006a)
	TPA2390 ∆fliT*	fliM5978-gfp fliT::Km
	TPA2421 ∆fliD*	fliM5978-gfp ∆fliD7097::FRT
		pET28a-mod	His-tag plasmid	Novagen
		pGEX6p-1	GST expression plasmid	Stratagene
		pMMT002	fliT94 in pGEX6p-1 (pGEX-fliT(2-94))	(Imada et al., 2010)
		pMMT002(I68I)	fliT94(I68A) in pGEX6p-1	(Imada et al., 2010)
		pMMT002(L72A)	fliT94(L72A) in pGEX6p-1	(Imada et al., 2010)
		pMMT002(N74A)	fliT94(N74A) in pGEX6p-1	(Imada et al., 2010)
		pMMT002(E75A)	fliT94(E75A) in pGEX6p-1	(Imada et al., 2010)
		pMMT002(K79A)	fliT94(K79A) in pGEX6p-1	(Imada et al., 2010)
		pMMT002(L81A)	fliT94(L81A) in pGEX6p-1	(Imada et al., 2010)
		pMMT002(L82A)	fliT94(L82A) in pGEX6p-1	(Imada et al., 2010)
		pMMT002(Q83A)	fliT94(Q83A) in pGEX6p-1	(Imada et al., 2010)
		pPA152	fliT in pET28a-mod
		pPA158	flhDC in pET28a-mod
		pPA159	fliD in pET28a-mod
		pRG38	P flhDC in pSB401	(Goodier and Ahmer,
				2001)
		pRG39::cat	P fliC in pSB401	(Brown et al., 2008)
		pRG52::cat	P flgB in pSB401	(Brown et al., 2008)
		pSE380		Invitrogen
		pSE-fliT	fliT in pSE380
		pSE-fliT94	fliT94 in pSE380
		pSE-flgM	flgM in pSE380
		pSE-fliA	fliA in pSE380
	* Names used in conjunction with Figure 1 and Table 1.

Table 3 :

 3 DNA Sequences used for SPR analysis.

	Name Sequence §
	P flgB	CCCGTGTGAAGCAAGCATCAACGCAATAAATAGCGACGCATTTTGCGTTTATTCCGGCGATAACGCGCGCGTGAAGGCAT
	P flhB	CGTATCCGGCTATCTCTATTAACGCCATAAACCCCGCCTTTTTTACCGCTTACTCTGCCTATTGGCGTAAAGCGGTTCTG
	fliC	TACGGGGGGAACTGGTAAAGATGGCTATTATGAAGTTTCCGTTGATAAGACGAACGGTGAGGTGACTCTTGCTGGCGGTG
	§ The FlhD 4 C 2 inverted repeat as defined by Stafford et al., (2005) is shown in bold.

Molecular Microbiology

Acknowledgements

We would like to thank all lab members and colleagues for critical reading of this manuscript. Special thanks goes to W. Vollmer for his input. We would also like to thank R. Daniel, H. Murray and J. Errington for use of their microscope suite and M. Kinoshita-Minamino for technical assistance. Thanks also go to Bob Lightowlers for access and advice on the use of the BIAcore 2000. BBSRC Grant No. BB/D015855/1 awarded to P.A. funded the majority of this work. K.P was awarded a PhD scholarship from Nareasuan University (Thailand). C.R. was partially supported by the National Science Foundation Grant CBET 0644744 and by Public Health Service Grants GM083601 and GM054365. Funding for K.I. and T.M is through the Targeted Proteins Research Program from the Ministry of Education, Science and Culture of Japan (K.I and T.M). An International Project Grant awarded jointly by the Diawa Foundation and Royal Society (UK) funded T.M. and P.A.