Jeroen Wackers
email: jeroen.wackers@ec-nantes.fr

Riccardo Pellegrini

Andrea Serani

Matteo Diez

Michel Visonneau

MULTI-FIDELITY ACTIVE LEARNING FOR SHAPE OPTIMIZATION PROBLEMS AFFECTED BY NOISE

Keywords: Active learning, Multi-fidelity, Shape optimization, Mesh adaptation, Simulation noise

The efficiency of simulation-driven design optimization based on surrogate models, depends strongly on the suitability of the surrogate model for the simulation data on which it is based. We investigate adaptive surrogate modelling methods that maximize the efficiency and the robustness for any optimization problem. Specific techniques include: adaptive sampling, noise filtering by metamodel tuning, and small initial datasets to give maximum freedom to the adaptation. These methodological advancements are demonstrated for an analytical test problem, as well as the shape optimization of the DTMB 5415 ship model for calm-water resistance.

INTRODUCTION

In naval engineering, simulation-driven design optimization (SDDO) is continuously growing in popularity, because it allows the designers to explore innovative designs and to better assess the designs' performance in the intended operational environment. The effectiveness of SDDO comes from the tight integration of (a) mathematical models, from analytical models to numerical simulations, to evaluate the design performance, (b) highly controllable shape deformation techniques, and (c) effective optimization algorithms (global/local and derivativebased/derivative free algorithms). And yet, accurate performance predictions for innovative configurations or off-design conditions require high-fidelity physics-based models, such as CFD simulations. Furthermore, the number of iterations required by an optimization algorithm to converge, may imply performing a significant number of simulations, especially if a global optimum is desired. As a consequence, the computational requirements (such as hardware and/or computational time) may easily become unaffordable for most users. The cost associated with SDDO may be reduced by supervised machine learning via surrogate modeling methods. Here, a surrogate model is trained on the responses from a limited number of simulations and the optimization is then performed over the surrogate model, which is cheap to evaluate. Among these methods, multi-fidelity (MF) approaches are gaining attention, due to their capability to combine the accuracy of high-fidelity solvers with the computational cost of low-fidelity solvers.

Different fidelities for the input data may be defined based for example on the physical model [START_REF] Anselma | Multidisciplinary design optimization for hybrid electric vehicles: component sizing and multi-fidelity frontal crashworthiness[END_REF] or spatial and/or temporal discretization [START_REF] Song | Multi-fidelity local surrogate model for computationally efficient microwave component design optimization[END_REF]. These data are combined into one surrogate model, by correcting a low-fidelity model using suitable error metamodels (or bridge functions) which represent the difference between fidelity levels, ordered following a given hierarchy. The resulting MF surrogate is then explored by the optimization algorithm.

The efficiency and the robustness of a multi-fidelity active learning approach depend strongly on its suitability for the specific problem being modeled. Since surrogate models have many free parameters and modelling choices, a practically useable method requires automated, adaptive tuning and selection of these parameters for each specific design optimization, see e.g. [START_REF] Volpi | Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification[END_REF][START_REF] Rumpfkeil | Multi-fidelity, gradient-enhanced, and locally optimized sparse polynomial chaos and kriging surrogate models applied to test problems[END_REF]. The present authors' work on MF active learning and model auto-tuning includes adaptive sampling of the datapoints [START_REF] Serani | Adaptive multi-fidelity sampling for CFD-based optimisation via radial basis function metamodels[END_REF], noise reduction through tuned least-squares fitting of the surrogate models [START_REF] Wackers | Multi-fidelity machine learning from adaptive-and multi-grid RANS simulations[END_REF], the use of more than two fidelity levels [START_REF] Wackers | Adaptive N-fidelity metamodels for noisy CFD data[END_REF], and reduced initial datasets to provide maximum freedom to the adaptation [START_REF] Pellegrini | Towards automatic parameter selection for multifidelity metamodels[END_REF].

The objective of the paper is to present the authors' current adaptive multi-fidelity active learning approach. The performance of the method is evaluated using an analytical test and a SDD case based on computational fluid dynamics (CFD) simulations, namely the shape optimization of the DTMB 5415 ship model, for which different fidelity levels are provided by CFD with adaptive grid refinement. Under the assumption of a limited budget of function evaluations, the proposed MF method shows better performance in comparison with less sophisticated MF models.

MULTI-FIDELITY ACTIVE LEARNING METHOD

Multi-fidelity setting

Consider x ∈ R D as a design variables vector of dimension D. Let the true merit function to be optimized f (x), be assessed by N fidelity levels: the highest-fidelity level is f 1 (x), the lowest-fidelity is f N (x), and the intermediate fidelity levels are {f i } N -1 i=2 (x). Using • to denote surrogate model prediction and • for multi-fidelity prediction, the MF approximation f i (x) of f i (x) (i = 1, . . . , N -1) is the sum of the lowest-fidelity surrogate and surrogates of the errors (inter-level errors or bridge-functions, ε) between subsequent levels

f i (x) = f N (x) + N -1 k=i ε k (x). (1)
For each i-th fidelity level the training set is T i = {x j , f i (x j )} J i j=1 , with J i the training set size. The resulting inter-level error training set is defined as

E i = {x j , ε i (x j )} J i j=1
, where

ε i (x j ) = f i (x j ) -f i+1 (x j ). (2)
The surrogate models are based on stochastic radial basis functions (SRBF) which provide both the prediction and its associated uncertainty [START_REF] Volpi | Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification[END_REF]. If the uncertainty U f N of the lowestfidelity prediction is uncorrelated with the uncertainty U ε k of the inter-level error predictions, the uncertainty U f i of the MF prediction can be evaluated as (i = 1, . . . , N -1)

U f i (x) = U 2 f N (x) + N -1 k=i U 2 ε k (x). (3)

Stochastic radial basis functions with least squares approximation

Given a (single-fidelity) training set T = {x i , f (x i )} J i=1 , the SRBF surrogate model prediction f (x) is computed as the expected value (EV) over a stochastic tuning parameter of the surrogate model [START_REF] Volpi | Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification[END_REF], τ ∼ unif [START_REF] Anselma | Multidisciplinary design optimization for hybrid electric vehicles: component sizing and multi-fidelity frontal crashworthiness[END_REF][START_REF] Ficini | Assessing the performance of an adaptive multi-fidelity gaussian process with noisy training data: A statistical analysis[END_REF]

f (x) = EV [g (x, τ )] τ , g (x, τ ) = EV [f ] + M j=1 w j ||x -c j || τ , (4)
= (A T A) -1 A T (f -EV [f ]
). The optimal number of stochastic RBF centers (M ⋆ ) is defined by minimizing a leave-one-out cross-validation (LOOCV) metric [START_REF] Wackers | Multi-fidelity machine learning from adaptive-and multi-grid RANS simulations[END_REF].

Initial training set and bounded surrogate model

The reduced training set (RS) of [START_REF] Pellegrini | Towards automatic parameter selection for multifidelity metamodels[END_REF] is used: except on the lowest fidelity level, where the domain center and the centers of the boundary faces are sampled, the metamodels are initialized with only a point in the domain center. For the error metamodels, this requires a SRBF surrogate which can handle extrapolation. Therefore, a bounded surrogate model prediction and uncertainty (both identified with the B subscript) for the error metamodels are defined as described in Algorithm 1. The definition of U ε Bi (x) stems from the consideration that the error surrogates represent errors in the MF approximation f . Therefore the average error can be used as reference for the surrogate model prediction uncertainty when an extrapolation is performed.

In Algorithm 1 a sigmoid-like function s(r) is used to provide a smooth transition between the SRBF prediction and the bounded prediction

s(r) = 1 1 + e α(r-γ) , (5)
where, for the present work, α = -75 and γ = 0.2. To define r, the smallest hyperrectangle (whose edges are parallel to the Cartesian coordinated axis) containing the training points is defined and r is the Euclidean distance of x from the hyperrectangle boundaries.

Active learning method

The MF surrogate model is dynamically updated by adding new training points. First, a new training point x ⋆ is identified based on the aggregate-criteria active learning (ACAS, see Fig.

Algorithm 1: Bounding of the SRBF prediction and the associated uncertainty for the error metamodels.

if 1) presented in [START_REF] Serani | Adaptive multi-fidelity sampling for CFD-based optimisation via radial basis function metamodels[END_REF], which samples points with large prediction uncertainty and small objective function value:

J i = 1, i = 1, . . . , N -1 then // One training point available ε Bi (x) = ε(x 1 ) ; U ε Bi (x) = ε(x 1 ) ; else if J i > 1, i = 1, . . . , N -1 then // J i training points available ε Bi (x) = ε i (x) [1 -s i (r)] + EV[ε i ]s i (r) ; U ε Bi (x) = min(U ε i , EVε i ]) ; end a) 0.0 0.2 0.4 0.6 0.8 1.0 x [-]
x ⋆ = argmin x f (x) -U f (x) , (6)
Once x ⋆ is identified, the fidelity to be evaluated is selected. The new training point is added to the k-th training set T k and to the lower-fidelity sets from k +1 up to N , where k = maxloc(ϕ) and the elements of ϕ for i = 1, . . . , N -1 are defined as

ϕ i =    U 2 ε i -MSE i β i , if MSE i < U 2 ε i , U ε i β i , if MSE i ≥ U 2 ε i , (7)
and for i = N as

ϕ N =      U 2 f N -MSE N β N if MSE N < U 2 f N , U f N β N if MSE N ≥ U 2 f N . ( 8
)
Here β i = c i /c 1 with c i the computational cost associated to the i-th level and c 1 the computa-tional cost of the highest-fidelity. MSE i is the mean-squared error computed as

MSE i =    1 J i J i j=1 [ε i (y j ) -ε i (y j )] 2 if i < N, 1 J N J N j=1 f N (y j ) -f N (y j ) 2 if i = N. (9)
This approach aims to better distribute the available budget of function evaluations among the fidelity levels. Indeed, when the least-squares regression is accurate for a fidelity and the surrogate prediction uncertainty decreases towards the average noise variance in the training set, it is not interesting to continue sampling that fidelity level, since the surrogate prediction is already accurate compared with the noise affecting the training set. Thus, adding more (noisy) training points will not improve the metamodel accuracy.

HYDRODYNAMIC FLOW SOLVER

Simulations to create the input data are performed with the Navier-Stokes solver ISIS-CFD developed at ECN -CNRS, available in the FINE™/Marine computing suite from Cadence Design Systems. ISIS-CFD is an incompressible unstructured finite-volume solver for multifluid flow. The velocity field is obtained from the momentum conservation equations and the pressure field is extracted from the mass conservation constraint transformed into a pressure equation. Free-surface flow is simulated with a conservation equation for the volume fraction of water, discretized with specific compressive discretization schemes. A detailed description of the solver is given by [START_REF] Queutey | An interface capturing method for free-surface hydrodynamic flows[END_REF].

The computational grids are created through adaptive grid refinement [START_REF] Wackers | Can adaptive grid refinement produce grid-independent solutions for incompressible flows[END_REF]. For the MF optimization, grid adaptation is used to take into account the need for several fidelities. The interest of this procedure is that different fidelity results can be obtained by running the same simulations and simply changing the refinement threshold, a parameter which determines the global mesh fineness. Thus, it is straightforward to automate the MF simulations. The grids for the simulation of different geometries are obtained through grid deformation [START_REF] Durand | Light and flexible Fluid/Structure Interaction, application to sailing boats[END_REF].

OPTIMIZATION PROBLEMS

For the numerical tests, the MF SRBF method presented here (labeled RS-MSE) is compared with a method (labeled RS) where the correction with the MSE is removed from Eqs. ( 7) and ( 8) and with one (FS) where furthermore, the RS is replaced by a full startset on all levels. The assessment of these methods is based on an analytical test and a CFD-based design optimization problem, with design space dimension D = 2. The optimization is performed with a fixed budget of function evaluations: considering a normalized computational cost of a highest-fidelity evaluation (equal to 1), the overall computational cost CC is proportional to the training set sizes J l : CC = J 1 + N l=2 β l J l .

Analytical test problem

This is an analytical test problem affected by artificial numerical noise with D = 2, defined as

minimize f (x) subject to l ≤ x ≤ u, (10)
where l i = 0.3 and u i = 1 (for i = 1, . . . , D) are the lower and upper bound for x, respectively, and f (x) is approximated by N = 2 fidelity levels (f 1 (x) and s 2 (x)) as The computational cost of the analytical test problem is negligible, therefore an artificial computational cost is defined as β 1 = 1 and β 2 = 0.2. A computational budget equal to 100D is used. Since the noise is synthetically added to the analytical functions by a numerical generator of random numbers, a statistical analysis [START_REF] Ficini | Assessing the performance of an adaptive multi-fidelity gaussian process with noisy training data: A statistical analysis[END_REF] is performed varying the seed of the random number generator for 25 repetitions.

f 1 (x) = sin 1 x 1 x 2 , f 2 (x) =f 1 (x) -9A 2 cos 1 x 1 x 2 , s 2 (x) =f 2 (x) + N (x), (11)

DTMB 5415 model

The shape of the DTMB 5415 destroyer is optimized for minimal resistance R T . The optimization problem reads

minimize f (x) = R T (x) subject to L pp (x) = L pp,0 and to l ≤ x ≤ u, (12)
where L pp,0 = 5.72 m (model scale) is the original length between perpendiculars. The ship is at even keel, with Froude number Fr = 0.30 and Re = 1.18 • 10 7 . The L pp constraint is automatically satisfied by the shape modification method. The modified geometries (g) are produced by the linear superposition of D orthonormal basis functions (ψ) on the original geometry (g 0 ), as follows

g(ξ, x) = g 0 (ξ) + δ(ξ, x), (13)
with

δ(ξ, x) = D k=1 x k ψ k (ξ), (14)
where ξ are the geometry Cartesian coordinates, whereas -1.25 ≤ {x k } D k=1 ≤ 1.25 and {ψ k } D k=1 are the reduced design variables and the eigenfunctions, respectively, provided by the designspace augmented dimensionality reduction (ADR) procedure described in [START_REF] Serani | Stochastic shape optimization via design-space augmented dimensionality reduction and RANS computations[END_REF]. In the current work, two design variables are used. The simulation setup for the DTMB 5415 is provided in [START_REF] Wackers | Adaptive N-fidelity metamodels for noisy CFD data[END_REF]; the cost functions are taken as β 2 = 0.21 and β 3 = 0.06.

NUMERICAL RESULTS

The results for the analytical problem are assessed by two error metrics [START_REF] Serani | Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems[END_REF]. Knowing the position of the global optimum x, these metrics characterize the normalized error in the design space and the objective function space, respectively:

E x ≡ ∥x ⋆ -x∥, E f ≡ f (x ⋆ ) -f (x) R 1 , (15)
where x ⋆ is the location of the approximated optimum (normalized in unit hypercube), and R 1 is the range of the highest-fidelity level computed considering the initial FS training set. The equation for E f uses an evaluation of the objective with the highest fidelity level at the point x ⋆ identified by the surrogate as the global optimum.

For the DTMB 5415 model problem the reference optimum is not available, therefore a different set of design-sensitive metrics are employed. These metrics quantify design point location and objective function, respectively:

∆ x ≡ ∥x ⋆ -x 0 ∥ √ D , ∆ f ≡ f (x ⋆ ) -f (x 0 ) f (x 0 ) , (16)
where x 0 is the original objective function value, meaning that ∆ x evaluates the distance of the global optimum position from the original design in the design variable space, whereas ∆ f provides the objective function variation with respect to the parent design. Additionally, a prediction error is used to quantify the error of the surrogate model in predicting the minimum value:

E p = f (x ⋆ ) -f (x ⋆ ) R 1 . ( 17
)

Analytical test problem

The challenge of this optimization test is twofold (Fig. 2): first, it has multiple optima located on the loci x 1 x 2 = 2/(3π) and x 1 x 2 = 2/(7π), and second, the LF and HF optima are in different locations, which is a danger for multi-fidelity methods which rely too heavily on LF data.

Considering the sampling approach of our three methods, in Fig. 3a-b the training set sizes are discussed using box plots. The box plot shows the q 1 , q 2 (median), and q 3 quartiles, while the lower and upper whiskers are given by q 1 -1.5IQR and q 3 + 1.5IQR, respectively, with IQR = q 3q 1 the interquantile range. Since nested training sets are used, the number of low-fidelity samples is equal to the number of active learning iterations minus five (for the initialization). Thus, the RS approach without the forced HF sampling in the startset leads to a higher number of iterations with respect to FS, allowing a potential better exploration of the design space. Differently, the RS-MSE approach performs a lower number of iterations but uses a higher number of high-fidelity samples in comparison with FS and RS, since LF sampling is discouraged once the LF uncertainty approaches the noise level. The evolution of the sample sizes (Fig. 5c) shows the higher number of LF samples at the start (below CC = 10) for both RS and RS-MSE, indicating that the early exploration is performed with low-fidelity samples only. This is followed by a larger increase of HF samples for RS-MSE. Still, RS and RS-MSE have larger whiskers than FS, showing a significant variability and less consistency in the results.

Figure 4 shows the box plots of the three error metrics. The RS and RS-MSE approaches achieve lower median values than the FS approach, while RS-MSE performs the best; its median result is an almost exact optimum. Finally, the FS approach achieves the smallest IQR. Thus, FS is consistent, but it is consistently wrong: the information in the large initial sample set forces the optimization into a fixed, but suboptimal direction. RS with its greater freedom performs better, but its reliance on LF data also leads to some bad results, since the LF optimum does not correspond to the HF one; hence the large IQR for this approach. Finally, RS-MSE with its initial LF sampling and final emphasis on HF samples provides more consistently good results.

In Fig. 5 the placement of HF samples with respect to x 1 x 2 is studied: the quantity c i is the number of elements in each bin of width 0.017. The figure shows that FS requests most of its high-fidelity training point close to the x 1 x 2 = 2/(3π) locus and only a small quantity in the neighborhood of the x 1 x 2 = 2/(7π) locus, negatively affecting the final performance.

(a) Ex (b) E f
The RS approach request almost the same quantity of high-fidelity training points between the two loci, in the position of the LF optimum. The RS-MSE approach requests for the highest number of high-fidelity training points in the x 1 x 2 = 2/(3π) locus and almost the same number of high-fidelity samples in the second locus as the RS approach. This explains why the RS-MSE approach achieves the best performance overall.

DTMB 5415 SDDO problem

Since this optimization of 3D free-surface flows is characterized by high computational costs, the problem is solved only with the RS-MSE approach. The comparison with the FS approach is provided taking the results presented in [START_REF] Wackers | Multi-fidelity machine learning from adaptive-and multi-grid RANS simulations[END_REF]. Since both the adaptive metamodelling strategy and the CFD simulations have changed, this does not provide a detailed assessment of one topic; rather, the comparison globally shows the progress that has been achieved in the last two years. Figure 6a-b present the multi-fidelity surrogate models at the last iteration of the active learning approach, the MF datasets, and the predicted optima. The sampling strategies for the two approaches are radically different: RS-MSE performed an exploration of the domain using only low-fidelity samples, correctly identifying the minimum region. The precision in this region is then increased using mainly medium-fidelity evaluations; only two high-fidelity points are sampled, one of which is almost in the optimum location. Near the end of the sampling, most points are added around the optimum. FS on the other hand, uses more HF points spread around the parameter space. Not all these points are useful; note for example the set of points in the top left corner, where a second minimum was suspected in the initial stages of the sampling. And while the data points are clustered, none are placed directly around the optimum.

These differences are also reflected in the x ⋆ convergence (Fig. 6c). The CC of evaluating the startset is 7.35 for FS and only 1.24 for RS-MSE which, combined with the efficient initial LF exploration, means that RS-MSE has globally identified the optimum before FS finishes half its startset. The subsequent RS-MSE convergence is fast and without oscillations, as medium-and high-fidelity points are added around the optimum. The optimization has converged around CC = 15. The FS convergence is much more irregular, as it identifies two incorrect optima before finally settling on the correct one around CC = 24. Table 1 summarizes the performance of the FS and RS-MSE approaches. Although the CC is lower, the RS-MSE approach correctly identified the region of the minimum, using more low-and medium-fidelity data than the FS approach. The prediction error is twice lower, which confirms that the metamodel is accurate around the optimum. The ∆ x % value is larger for RS-MSE than for FS, meaning that the exploration for the identification of the minimum moved further. Finally, the RS-MSE approach achieves a lower resistance than the FS approach. Altogether, the RS-MSE optimization produces a similar optimum as the old FS result, in a more robust manner, for a total wall clock time that has reduced from 25 to about 11 days.

CONCLUSIONS

This paper focuses on assessing the efficiency of an adaptive multi-fidelity method for simulation-driven design optimization. Notably, the tests concern the reduced startset (RS) with one single point for all the fidelities except the lowest one, and the improved fidelity sampling, by subtracting the mean-squared error (MSE) between the training set and the least-squares regression, from the uncertainty on each fidelity level while selecting the fidelity to sample.

Numerical results show that the RS approach is effective in improving the identification of the minima and better distributing the high-fidelity samples in interesting regions of the domain. It encourages early exploration with low-fidelity samples, unconstrained by high-fidelity data which may wrongly indicate sub-optimal regions. This early exploration leads to a reliable identification of the minimum region. Later on, thanks to the limits imposed on the extrapolated high-fidelity uncertainty, sampling of high-fidelity data in the observed minimum region is encouraged, which efficiently increases the precision in the minimum region.

For the analytical test case, the RS-MSE approach achieves even better results than the RS approach, since its formulation is effective in forcing the fidelity selection towards the highfidelity after a large number of low-fidelity evaluations have been performed. This is necessary for this case, since the low-and high-fidelity optima are in different locations. Finally, the DTMB 5415 SDDO problem shows that the RS-MSE method is highly effective in identifying the region of the minimum, performing high-and medium-fidelity evaluations almost exclusively in this region. Although few high-fidelity points are added, it is likely that the sampling with (accurate yet cost-effective) medium-fidelity points is encouraged by the RS-MSE.

In conclusion, the test results are promising, showing that the two developments tested specifically here, have a beneficial effect on the metamodel performance. Similar tests in our earlier papers have indicated the same for the other aspects of our surrogate modeling approach, such as the ACAS adaptive sampling and the LS-RBF noise-filtered metamodel. Thus, all these methods contribute to our eventual goal of achieving fully adaptive automatic multi-fidelity surrogate modelling.

Figure 1 :

 1 Figure 1: Example of the active learning method using one fidelity: (a) shows the initial surrogate model with the associated prediction uncertainty and training set; (b) shows the position of the new training point and the new surrogate model prediction and its uncertainty.

with A 2 Figure 2 :

 22 Figure 2: Analytical test problem without artificial numerical noise. The dashed lines show the two loci with the lowest f 1 (x) value (the x 1 x 2 = 2/(7π) locus is in the neighborhood of the bottom-right corner).

Figure 3 :

 3 Figure 3: Analytical test, box plot of the training sets size and training sets sizes versus computational cost.

Figure 4 :

 4 Figure 4: Analytical test, box plots of the E x and E f metrics.

Figure 5 :

 5 Figure 5: Analytical test, histogram of the x 1 x 2 quantity for the high-fidelity training set (with c i the number of elements in each bin).

Figure 6 :

 6 Figure 6: DTMB 5415 SDDO problem, multi-fidelity surrogate model prediction and x ⋆ convergence for the FS and RS-MSE approaches.

  where w j are unknown coefficients, || • || is the Euclidean norm and c j are the RBF centers, with j = 1, . . . , M and M ≤ J. Noise reduction in the training set is achieved by choosing a number of RBF centers M smaller than the number of training points J, and c j coordinates are defined via k-means clustering[START_REF] Lloyd | Least squares quantization in PCM[END_REF] of the training points coordinates. w j are determined with least squares regression by solving w

Table 1 :

 1 DTMB 5415 SDDO problem, summary of the results.

	Approach CC FS 24.0 0.5506 0.1330 26.2 x 1 x 2 ∆ x % ∆ f % |E p |% J 1 J 2 J 3 -4.5 1.73 16 18 72
	RS-MSE	18.4 0.5043 0.1525 37.2	-4.9	0.87	3	44 103

ACKNOWLEDGMENTS CNR-INM is partially supported by the Office of Naval Research through NICOP grant N62909-21-1-2042, administered by Dr. Elena McCarthy and by Dr. Woei-Min Lin of the Office of Naval Research Gobal and the Office of Naval Research, respectively. The work is conducted in collaboration with NATO STO AVT task group on "Goal-driven, multi-fidelity approaches for military vehicle system-level design" (AVT-331).


Angéline Duval

Thierry Sayd

Sylvie Blinet

Laurent Aubry

Vinciane Mosconi

Vincenza Ferraro

V Santé-Lhoutellier

Mosconi V Ferraro

Nutritional evaluation of meal-scale supplementation for the elderly Authors

Meal composition

Digesta 1 INRAE, QuaPA UR 370, BPM, F-63122 Saint Genès Champanelle, France *veronique.sante-lhoutellier@inrae.fr Two meals (enriched with a protein mix S or not C) produced by the company RESTORIA were studied. Each meal included 5 dishes (starter: vegetable terrine, meat: beef bourguignon, side dish: gratin dauphinois (French dish of sliced potatoes baked in milk or cream), cheese, dessert: cake and custard). The quantity adapted to the nursing home resident Opposition of control vs supplemented meal on axis 1, with higher peptides release for the supplemented meal Axis 1 corresponded to time digestion aligning with free amino acids Opposition of control vs supplemented meal on axis 2. In the supplemented meal, more protein hydrolysis occured with some essential amino acids in larger quantity.

The undigested proteins was reduced by 20% in the supplemented meal

  ), total sugars (Dubois) , macro & micro minerals (Ca, K, Na, Mg; Cu, Fe, Zn (ICP AES), vitamins A, B9, C, D2, D3) biplot performed on the gastric phase kinetic data. 18 individuals control meal :C1, C2, C3 corresponding to 3 digestion repetitions, supplemented meal : S1, S2, S3 corresponding to 3 digestion repetitions at 3 digestion times (30 min, 60 min, 90 min) 4 variables: glucose content, Direct Detect® (DD), antioxidant activity: DPPH and ABTS PCA biplot performed on the intestinal phase kinetic data. 18 individuals: control meal : C1, C2, C3 corresponding to 3 digestion repetitions, supplemented meal : S1, S2, S3 corresponding to 3 digestion repetitions) at 3 digestion times : 90 min, 150 min, 240 min 25 variables: glucose level, Direct Detect® (DD), antioxidant activity: DPPH and ABTS, and 20 amino acids.

Acknowledgements

We gratefully acknowledge the FUI AAginoV project and BPI France for financial support

RESULTS

5

MATERIALS AND METHODS


M.D P D Veith

D G Glew

E C Gorasia

E C Reynolds
email: e.reynolds@unimelb.edu.au

Cascales

M D Glew

D G Gorasia

E Cascales

The Type IX Secretion System and Its Role in Bacterial Function and Pathogenesis

Keywords: Periodontal disease(s)/periodontitis, Bacterial virulence, Host pathogen interactions, Ultrastructure, Microbiology

Severe Periodontitis: A T9SS driven dysbiosis?

Periodontitis is a prevalent, chronic inflammatory disease associated with a dysbiotic subgingival plaque that results in the destruction of the tooth's supporting tissues. The global prevalence of severe periodontitis has been estimated at 11.2% and the disease is ranked the 6 th most prevalent disease worldwide [START_REF] Tonetti | Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: A call for global action[END_REF]. The economic burden of periodontitis in the US and Europe has been estimated as US$342 billion [START_REF] Botelho | Economic burden of periodontitis in the United States and Europe -an updated estimation[END_REF]. Over the last 15 years evidence has accumulated that periodontitis is linked with a range of systemic diseases like diabetes, cardiovascular diseases, certain cancers and dementia, suggesting that periodontal health is vital for good general health [START_REF] Hajishengallis | Local and systemic mechanisms linking periodontal disease and inflammatory comorbidities[END_REF]. The identification of bacterial species that characterize a subgingival plaque dysbiotic signature started with the seminal cross-sectional study by [START_REF] Socransky | Microbial complexes in subgingival plaque[END_REF]. This study was the first to show a close association of a specific bacterial consortium with severe periodontitis [START_REF] Socransky | Microbial complexes in subgingival plaque[END_REF]. The consortium comprising Porphyromonas gingivalis, Treponema denticola and Tannerella forsythia was referred to as the red complex as these species were found together in deep periodontal pockets and they were the only bacteria of the 40 bacterial taxa studied that closely associated with disease.

The technology for identification of bacterial species in dental plaque has progressed significantly since these earlier studies such that now using Next-Generation Sequencing (NGS) techniques over 200 different taxa can be identified in the microbial community of a plaque sample. There are now many cross-sectional plaque microbiome studies published comparing periodontal health with disease using NGS. These microbiome studies have identified bacterial species that seem preferentially associated with health or disease, and largely they have confirmed the close association of the red complex bacteria in deep periodontal pockets from patients with severe periodontitis where these three species P. gingivalis, T. denticola and T. forsythia can be found in abundance within a complex microbial community [START_REF] Kirst | Dysbiosis and alterations in predicted functions of the subgingival microbiome in chronic periodontitis[END_REF][START_REF] Lenartova | The oral microbiome in periodontal health[END_REF][START_REF] Shi | The subgingival microbiome associated with periodontitis in type 2 diabetes mellitus[END_REF].

A limitation with cross-sectional analyses of subgingival plaque samples is the dynamic nature of periodontitis where sites may be inactive at any single sampling time hence prospective clinical studies are required to better link the microbial composition with disease progression. One prospective study by [START_REF] Byrne | Progression of chronic periodontitis can be predicted by the levels of Porphyromonas gingivalis and Treponema denticola in subgingival plaque[END_REF] was designed to determine the level of certain bacterial species in subgingival plaque of patients with moderate to severe periodontitis at three monthly intervals during a maintenance program [START_REF] Byrne | Progression of chronic periodontitis can be predicted by the levels of Porphyromonas gingivalis and Treponema denticola in subgingival plaque[END_REF].

The levels of these species at sites that progressed by losing 2 mm or greater clinical attachment could then be compared with their levels at sites that remained stable. This study confirmed the finding that the red complex bacteria were strongly associated with disease severity but importantly extended the finding by showing that the emergence of these pathogens, particularly P. gingivalis preceded attachment loss, implying a direct link with disease progression. In fact, the study showed that the level of P. gingivalis was an excellent biomarker for disease progression at severe periodontitis sites.

These pathogenic species associated with severe periodontitis display mutualistic symbioses in metabolism, growth and biofilm formation as well as synergistic virulence in animal models of infection [START_REF] Tan | Porphyromonas gingivalis and Treponema denticola exhibit metabolic symbioses[END_REF]. In fact, animal model studies have shown that P. gingivalis can drive dysbiosis and that the proteases called gingipains are the major virulence factors of the bacterium. These studies have resulted in P. gingivalis being referred to as a keystone pathogen in periodontitis [START_REF] Hajishengallis | The keystone-pathogen hypothesis[END_REF]. Further evidence of the importance of these bacterial species in severe disease is their temporospatial relationship in the periodontal pocket. Studies investigating the architecture and temporospatial distribution of pathogens in subgingival plaque have demonstrated that species from the genera Porphyromonas, Tannerella, Prevotella and Treponema (e.g. P. gingivalis, T. forsythia, Prevotella intermedia and T. denticola) are found at the base of deep pockets in severe periodontitis patients and therefore are referred to as site specialists [START_REF] Kigure | Distribution of Porphyromonas gingivalis and Treponema denticola in human subgingival plaque at different periodontal pocket depths examined by immunohistochemical methods[END_REF][START_REF] Welch | Biogeography of the oral microbiome: The site-specialist hypothesis[END_REF]. Metagenomic analyses of these bacteria demonstrates an expanded functionality or over-representation of genes encoding proteases, adhesins, iron acquisition systems and secretion systems. In fact, it has been suggested that even though the microbiomes of severe periodontitis sites may be taxonomically heterogeneous they are functionally congruent through the expression and secretion of functionally similar virulence factors [START_REF] Altabtbaei | Anna Karenina and the subgingival microbiome associated with periodontitis[END_REF][START_REF] Shi | The subgingival microbiome associated with periodontitis in type 2 diabetes mellitus[END_REF]. Interestingly a common characteristic of P. gingivalis, T forsythia and P. intermedia is the newly identified T9SS which these species use to load their cell surface with an array of virulence factors including proteases, adhesins, haemolysins and internalins [START_REF] Veith | Type IX secretion: the generation of bacterial cell surface coatings involved in virulence, gliding motility and the degradation of complex biopolymers[END_REF]. These virulence factors are actively released on outer membrane vesicles (OMVs) which can then penetrate the pocket epithelium to dysregulate the host defenses [START_REF] O'brien-Simpson | Porphyromonas gingivalis RgpA-Kgp proteinase-adhesin complexes penetrate gingival tissue and induce proinflammatory cytokines or apoptosis in a concentration-dependent manner[END_REF]. The best example of this process is the secretion of the major virulence factors of P. gingivalis, the gingipains, by the T9SS.

The T9SS loads the outer membrane with virulence factors for a mobilised vesicle army

The T9SS is a nanomachine that secretes cargo proteins from the periplasm to the cell surface of Gram-negative bacteria. The T9SS is not ubiquitous and is restricted to species belonging to the Bacteroidetes-Chlorobi-Fibrobacteres superphylum. All T9SS cargo proteins are translocated across the inner membrane (IM) by the Sec translocon due to their N-terminal signal sequence and are then recruited by the T9SS for secretion across the outer membrane (OM) by their structurally conserved C-terminal domain (CTD) sequence [START_REF] Lasica | The type IX secretion system (T9SS): Highlights and recent insights into its structure and function[END_REF][START_REF] Veith | Type IX secretion: the generation of bacterial cell surface coatings involved in virulence, gliding motility and the degradation of complex biopolymers[END_REF]. The cargo proteins are then attached to the cell surface via covalent linkage to A-LPS to form a virulence coat, surrounding both cells and released OMVs. The genes required for these processes fall into two broad groups (Table 1). The first group of genes code for components of the T9SS machinery, including IM proteins (PorL and PorM), OM associated proteins (PorE, PorF, PorG, PorK, PorN, PorP, PorQ, PorT, PorU, PorV, PorW, PorZ and Sov) [START_REF] Naito | PGN_0297 is an essential component of the type IX secretion system (T9SS) in Porphyromonas gingivalis: Tn-seq analysis for exhaustive identification of T9SS-related genes[END_REF][START_REF] Sato | A protein secretion system linked to Bacteroidete gliding motility and pathogenesis[END_REF]) and components involved in signal transduction and gene regulation (PorA, PorX, PorY and SigP) [START_REF] Yukitake | PorA, a conserved C-terminal domain-containing protein, impacts the PorXY-SigP signaling of the type IX secretion system[END_REF].

Besides PorA, all of these proteins have orthologs in T. forsythia and P. intermedia (Table 1). Deletion of these genes prevents the secretion, maturation and attachment of cargo to the cell surface resulting in non-pigmented colonies of P. gingivalis on a blood agar plate [START_REF] Sato | A protein secretion system linked to Bacteroidete gliding motility and pathogenesis[END_REF]).

The second group include genes involved in the biosynthesis of A-LPS. The polysaccharide portion of A-LPS is proposed to consist of an anionic phosphorylated branched mannan [START_REF] Paramonov | Identification of the linkage between A-polysaccharide and the core in the A-lipopolysaccharide of Porphyromonas gingivalis W50[END_REF], however accumulating evidence suggests that A-LPS includes a more conventional polysaccharide requiring four glycosyltransferases specific for A-LPS (WbaP, GtfC, VimF and GtfF) and two further glycosyltransferases (GtfE and GtfB) which are also required for the synthesis of O-polysaccharide in "O-LPS" [START_REF] Shoji | Identification of genes encoding glycosyltransferases involved in lipopolysaccharide synthesis in Porphyromonas gingivalis[END_REF]). The novel A-LPS linking sugar which is bonded to the cargo proteins was recently identified to be an 2-N-seryl-3-N-acetylglucuronamide in P. gingivalis and 2-N-glycyl-3-Nacetylmannuronic acid in T. forsythia that is synthesized by the novel Wbp/Vim pathway comprising WbpA, UgdA, WbpB, WbpE [also known as PorR], WbpD, WbpS, VimE and VimA [START_REF] Veith | Type IX secretion system cargo proteins are glycosylated at the C terminus with a novel linking sugar of the Wbp/Vim pathway[END_REF]. Deletion of A-LPS biosynthetic genes results in loss of surface attachment, with the cargo released into the growth medium [START_REF] Gorasia | Porphyromonas gingivalis type IX secretion substrates are cleaved and modified by a sortase-like mechanism[END_REF] and nonpigmented colonies on blood agar [START_REF] Shoji | Identification of genes encoding glycosyltransferases involved in lipopolysaccharide synthesis in Porphyromonas gingivalis[END_REF]. P. intermedia does not have orthologs for several enzymes in the Wbp/Vim pathway (Table 1) suggesting it may utilize a different type of linking sugar to attach its cargo to the cell surface.

Since T9SS cargo proteins are greatly enriched in P. gingivalis OMVs and comprise approximately 60% of the protein content [START_REF] Veith | Porphyromonas gingivalis outer membrane vesicles exclusively contain outer membrane and periplasmic proteins and carry a cargo enriched with virulence factors[END_REF][START_REF] Veith | Outer membrane vesicle proteome of Porphyromonas gingivalis is differentially modulated relative to the outer membrane in response to heme availability[END_REF]), OMVs can be considered an extension of the T9SS, delivering cargo into the periodontal tissues. OMVs play important roles in adaptation to stress, biofilm formation, host immunomodulation, and adherence to host cells and dysbiosis [START_REF] Cecil | Outer membrane vesicle-host cell interactions[END_REF][START_REF] Veith | Outer membrane vesicle proteome of Porphyromonas gingivalis is differentially modulated relative to the outer membrane in response to heme availability[END_REF][START_REF] Zhang | The role of Porphyromonas gingivalis outer membrane vesicles in periodontal disease and related systemic diseases[END_REF].

There are many mechanisms by which OMVs are generated involving peptidoglycan biosynthesis, envelope stress response, nutrient response such as to iron-limitation, regulation of OM proteins binding to peptidoglycan and regulation of the VacJ/Yrb phospholipid (PL) import system [START_REF] Roier | A novel mechanism for the biogenesis of outer membrane vesicles in Gram-negative bacteria[END_REF][START_REF] Schwechheimer | Outer-membrane vesicles from Gram-negative bacteria: biogenesis and functions[END_REF][START_REF] Zhang | The role of Porphyromonas gingivalis outer membrane vesicles in periodontal disease and related systemic diseases[END_REF]. P. gingivalis uses its T9SS to secrete and anchor many characterized virulence proteins to the surface of the cell as recently reviewed [START_REF] Lasica | The type IX secretion system (T9SS): Highlights and recent insights into its structure and function[END_REF][START_REF] Veith | Type IX secretion: the generation of bacterial cell surface coatings involved in virulence, gliding motility and the degradation of complex biopolymers[END_REF]. Some of these virulence proteins are discussed below and include the abundant cysteine proteases known as gingipains (Kgp, RgpA, RgpB) which are the bacterium's major virulence factors that degrade host proteins and extract peptides for growth. The gingipains also play a major role in disease pathogenesis through dysregulation of host defense and inflammatory responses [START_REF] Guo | Dichotomy of gingipains action as virulence factors: from cleaving substrates with the precision of a surgeon's knife to a meat chopper-like brutal degradation of proteins[END_REF]. Other proteases are PepK, CPG70, PrtT and periodontain.

Peptidylarginine deiminase (PPAD) is linked to rheumatoid arthritis [START_REF] Gully | Porphyromonas gingivalis peptidylarginine deiminase, a key contributor in the pathogenesis of experimental periodontal disease and experimental arthritis[END_REF].

PG0350 and PG1374 are leucine-rich repeat containing proteins with homology to internalin J from Listeria monocytogenes and may facilitate invasion of host cells and affect mono-and heterotypic-species biofilm formation. Mfa5 is one of three ancillary tip proteins essential for assembly of the tip fimbrillins of MfaI fimbriae. The T9SS and Mfa5 in particular have been shown to be essential for the recently discovered "surface translocation" of P. gingivalis [START_REF] Moradali | Amino acids as wetting agents: surface translocation by Porphyromonas gingivalis[END_REF].

P. gingivalis is auxotrophic for heme and the acquisition of iron plays an essential role in its growth and virulence [START_REF] Smalley | Heme acquisition mechanisms of Porphyromonas gingivalisstrategies used in a polymicrobial community in a heme-limited host environment[END_REF]. The availability of iron in the host is extremely low due to strong sequestration by host hemin binding proteins such as the haemoglobin-haptoglobin complex, hemopexin and albumin, therefore P. gingivalis has evolved very efficient systems for the extraction, capture and transportation of heme [START_REF] Rangarajan | Hemin binding by Porphyromonas gingivalis strains is dependent on the presence of A-LPS[END_REF][START_REF] Smalley | Heme acquisition mechanisms of Porphyromonas gingivalisstrategies used in a polymicrobial community in a heme-limited host environment[END_REF]). An additional role of the gingipains is the extraction of heme from host proteins, converting it to the black pigmented heme dimer, µ-oxo bisheme, and efficiently storing it on the cell surface bound to A-LPS. P. gingivalis has a 10-fold greater specific hemin binding affinity and greater heme storage capacity than the accessory pathogen P. intermedia [START_REF] Socransky | Microbial complexes in subgingival plaque[END_REF][START_REF] Tompkins | Detection and comparison of specific hemin binding by Porphyromonas gingivalis and Prevotella intermedia[END_REF].

Recently, we have shown that A-LPS can be exchanged between different P. gingivalis cells through close contact or through fusion with OMVs with the potential for transfer of the bound µ-oxo bisheme [START_REF] Glew | Complementation in trans of Porphyromonas gingivalis lipopolysaccharide biosynthetic mutants demonstrates lipopolysaccharide exchange[END_REF]). This has important consequences in terms of altering the oral microbiota and may help explain why P. gingivalis has such a key role in pathogenesis [START_REF] Hajishengallis | The keystone-pathogen hypothesis[END_REF]. The T9SS by loading OMVs with µ-oxo bisheme can enable P. gingivalis to affect the availability of iron for other periodontitis-related bacterial species that require hemin for growth. Thus, LPS-hemin exchange with P. gingivalis, particularly with its OMVs, may contribute to polymicrobial synergy in the development of a dysbiotic community. Transponson sequencing (TnSeq) technology has enabled the genome-wide determination of P. gingivalis essential genes for growth and fitness. These strategies have compared the populations of saturated transposon mutagenized genes before and after planktonic growth in rich medium [START_REF] Hutcherson | Comparison of inherently essential genes of Porphyromonas gingivalis identified in two transposon-sequencing libraries[END_REF][START_REF] Klein | Identification of essential genes of the periodontal pathogen Porphyromonas gingivalis[END_REF], after colonisation of oral epithelial cells (TIGK), after infection in a murine abscess model [START_REF] Miller | Genes contributing to Porphyromonas gingivalis fitness in abscess and epithelial cell colonization environments[END_REF] or after exposure to cigarette smoke extract [START_REF] Hutcherson | Porphyromonas gingivalis genes conferring fitness in a tobacco-rich environment[END_REF]. Essential genes for growth are "inherently essential" and cannot be considered for assessment for fitness in defined environments which are "conditionally essential". Table 1 summarises the P. gingivalis TnSeq data for T9SS-related genes that are inherently essential or required for fitness under three conditions. Sixty-five percent of the T9SS components and 58% of the genes specific for the biosynthesis of A-LPS were found to be essential for growth. In contrast, only 23% of T9SS cargo genes were inherently essential but 77% were required for fitness under one to three conditions (Table 1). These TnSeq analyses show an important difference between T9SS component and cargo protein genes. The majority of T9SS components are essential for growth but the majority of the T9SS cargo proteins are important for fitness which exemplifies the importance of the T9SS-related genes for P. gingivalis survival and fitness. It also reinforces our understanding of the T9SS cargo proteins as important virulence factors.

The T9SS Nanomachine: Molecular architecture and function

A fully assembled T9SS has not been isolated yet, suggesting it is a dynamic system. Nevertheless, full or partial structures and functions of many components have been elucidated. Structures of major sub-complexes including the motor complex and the translocon have been solved using cryo-electron microscopy (cryo-EM), and protein interactome studies are helping to form a cohesive functional picture of the T9SS as a whole as detailed below.

The T9SS translocon

The identity of the components that form the OM translocon was unknown until recently.

Sov/SprA, a 267 kDa, protein was hypothesized to form the secretion channel in the OM based on its large size and its retention in a minimal T9SS found in some members of the Bacteroidetes phylum [START_REF] Lauber | Type 9 secretion system structures reveal a new protein transport mechanism[END_REF]). Accordingly, these authors tagged and isolated SprA containing complexes from Flavobacterium johnsoniae and solved their structures by cryo-EM. Two complexes were solved, one with SprA bound to the PorV OM β-barrel protein and the other with SprA bound to the Plug protein [START_REF] Lauber | Type 9 secretion system structures reveal a new protein transport mechanism[END_REF]. SprA forms an extremely large, 36-stranded transmembrane β-barrel with an external cap, a lateral opening above the membrane and an internal solvent-filled pore of approximately 70 Å, large enough to allow the secretion of folded cargo proteins. This is the largest single polypeptide transmembrane β-barrel identified to date. When bound to PorV, the lateral opening of SprA faces the extracellular loops of PorV, while in the Plug-SprA complex, the lateral opening is free, but the periplasmic entrance is blocked by the Plug protein. The authors proposed a model for the T9SS translocon mechanism whereby the two ends of the channel are alternately gated. In this model, the SprA-PorV complex represents the "open-state" and allows uptake of the T9SS substrates from the periplasm while the SprA-Plug complex represents the state after the T9SS substrates have been released, leading to unidirectional transport of substrates through the translocon.

The shuttle protein

The most abundant T9SS component is PorV, also called LptO, a 14-stranded OM β-barrel protein related to the family of FadL transporters [START_REF] Chen | The outer membrane protein LptO is essential for the O-deacylation of LPS and the co-ordinated secretion and attachment of A-LPS and CTD proteins in Porphyromonas gingivalis[END_REF]Gorasia et al. 2020b). As mentioned above, PorV associates with the T9SS translocon and additionally, PorV binds to T9SS substrates harboring a CTD signal [START_REF] Glew | PorV is an outer membrane shuttle protein for the type IX secretion system[END_REF]. Given that the only known similarity amongst the T9SS cargo is the presence of the CTD signal, it was suggested that PorV binds to the CTD signal enabling it to collect substrates from the translocon and shuttle them to the attachment complexes (discussed below) [START_REF] Glew | PorV is an outer membrane shuttle protein for the type IX secretion system[END_REF]). This theory was elegantly supported by the work of Lauber et al, where a long extracellular loop of PorV was found to penetrate inside the lumen of the SprA pore [START_REF] Lauber | Type 9 secretion system structures reveal a new protein transport mechanism[END_REF] suggesting PorV collects the T9SS substrates directly from the translocon. However, it is unclear whether substrate bound PorV moves through the OM to reach an attachment complex or whether the T9SS substrate gets passed along a chain of PorV molecules on the cell surface.

The attachment complex

Most of the T9SS cargo proteins in P. gingivalis are attached to the cell surface. It is known that T9SS substrates are modified with A-LPS explaining cell attachment, however the exact barrels, was identified using blue native gel electrophoresis [START_REF] Glew | PorV is an outer membrane shuttle protein for the type IX secretion system[END_REF]. Both PorU and PorZ possess a CTD signal and utilize the T9SS to reach the cell surface. However, rather than being processed and conjugated to A-LPS [START_REF] Glew | PG0026 is the C-terminal signal peptidase of a novel secretion system of Porphyromonas gingivalis[END_REF][START_REF] Lasica | Structural and functional probing of PorZ, an essential bacterial surface component of the type-IX secretion system of human oralmicrobiomic Porphyromonas gingivalis[END_REF], PorU and PorZ are anchored to the OM through their binding to PorV and PorQ respectively [START_REF] Glew | PorV is an outer membrane shuttle protein for the type IX secretion system[END_REF]. The crystal structure of PorZ showed that it consists of two beta-propeller domains in addition to its CTD [START_REF] Lasica | Structural and functional probing of PorZ, an essential bacterial surface component of the type-IX secretion system of human oralmicrobiomic Porphyromonas gingivalis[END_REF]. The proposed function of PorZ was to bind carbohydrate/A-LPS [START_REF] Glew | PorV is an outer membrane shuttle protein for the type IX secretion system[END_REF][START_REF] Lasica | Structural and functional probing of PorZ, an essential bacterial surface component of the type-IX secretion system of human oralmicrobiomic Porphyromonas gingivalis[END_REF]) which was confirmed by detecting the specific binding of recombinant PorZ to A-LPS [START_REF] Madej | PorZ, an essential component of the type IX secretion system of Porphyromonas gingivalis, delivers anionic lipopolysaccharide to the PorU sortase for transpeptidase processing of T9SS cargo proteins[END_REF].

Furthermore, PorZ interacts with PorU in an A-LPS-dependent manner supporting a role for PorZ in presenting the A-LPS substrate to PorU [START_REF] Madej | PorZ, an essential component of the type IX secretion system of Porphyromonas gingivalis, delivers anionic lipopolysaccharide to the PorU sortase for transpeptidase processing of T9SS cargo proteins[END_REF]. The catalytic component of the attachment system, PorU uses a sortase-like mechanism to cleave the CTD signal at the cell surface, and covalently attach the A-LPS linking sugar (see above) to the new C-terminus of the cargo proteins via a peptide bond [START_REF] Glew | PG0026 is the C-terminal signal peptidase of a novel secretion system of Porphyromonas gingivalis[END_REF][START_REF] Gorasia | Porphyromonas gingivalis type IX secretion substrates are cleaved and modified by a sortase-like mechanism[END_REF][START_REF] Veith | Type IX secretion system cargo proteins are glycosylated at the C terminus with a novel linking sugar of the Wbp/Vim pathway[END_REF]. This results in permanent anchorage of the T9SS substrates to the cell surface.

The proposed T9SS powertrain

PorK, PorL, PorM and PorN together form a trans-envelope complex [START_REF] Sato | A protein secretion system linked to Bacteroidete gliding motility and pathogenesis[END_REF][START_REF] Vincent | Characterization of the Porphyromonas gingivalis type IX secretion trans-envelope PorKLMNP core complex[END_REF]) which appears to be anchored to the peptidoglycan layer via the PorP-PorE complex (Gorasia et al. 2020a;Gorasia et al. 2020c). The PorP component was shown to interact with the trans-envelope complex [START_REF] Vincent | Characterization of the Porphyromonas gingivalis type IX secretion trans-envelope PorKLMNP core complex[END_REF], while PorE was shown to associate with the peptidoglycan [START_REF] Trinh | Crystal structure of Type IX secretion system PorE C-terminal domain from Porphyromonas gingivalis in complex with a peptidoglycan fragment[END_REF]. Cryo-EM analysis of a purified complex containing PorK, PorN and PorG showed that these three proteins form large 50 nm ring-like structures [START_REF] Gorasia | Structural insights into the PorK and PorN components of the Porphyromonas gingivalis type IX secretion system[END_REF]. Biochemical evidence suggested that the PorK/N ring was localised in the periplasm and tethered to the OM, a location supported by recent in-situ images from cryo-EM (Gorasia et al. 2020a). Although the PorK/N ring and PorL/M motor are purified as separate complexes, PorM is known to interact with the ring proteins [START_REF] Vincent | Characterization of the Porphyromonas gingivalis type IX secretion trans-envelope PorKLMNP core complex[END_REF].

The structure of the molecular motor that drives secretion and gliding motility was solved by cryo-EM and found to be composed of a PorL pentamer associated with a PorM dimer. In total, ten α-helices from PorL form the 'stator' which is membrane-embedded and surrounds the two trans-membrane helices of the PorM 'rotor' and causes them to rotate by using energy from the proton motive force (Hennell [START_REF] James | Structure and mechanism of the proton-driven motor that powers type 9 secretion and gliding motility[END_REF]. The periplasmic domains of PorM form a bent shaft which traverses the periplasm and at the tip is calculated to rotate around a circle of 18 nm where it may drive processes at the OM through its contact with the PorK/N rings (Hennell [START_REF] James | Structure and mechanism of the proton-driven motor that powers type 9 secretion and gliding motility[END_REF][START_REF] Sato | PorM, a core component of bacterial type IX secretion system, forms a dimer with a unique kinked-rod shape[END_REF]). The porKLMN transcription unit is well conserved amongst bacteria which use the T9SS supporting the proposed flow of energy from the motor complex to the PorK/N ring.

While the motor has five subunits of PorL and two subunits of PorM, the PorK/N ring has 32-36 subunits of each component [START_REF] Gorasia | Structural insights into the PorK and PorN components of the Porphyromonas gingivalis type IX secretion system[END_REF]. Given the relative whole cell abundance of these proteins is approximately 1 PorK to 3 PorL to 1 PorM to 1PorN (Glew et al. 2017;Gorasia et al. 2020b), there is likely to be a ring of motors directly underneath the PorK/N ring to form a symmetrical funnel-shaped cage in the periplasm (Fig. 1).

Until recently, the Sov translocon had no known connection to the PorKLMN system, such that the pathway of energy provision for secretion was unclear. Recently we provided evidence that PorW connects these two disparate complexes in P. gingivalis and have now confirmed direct interactions between PorW and PorN and also between PorW and Sov (Gorasia et al. 2020a); (Gorasia and Lunar Silva et al. unpublished). For the first time these interactions map out a potential pathway for energy transduction to the Sov translocon.

Additionally, this same interactome maps out a pathway for the delivery of cargo to the translocon since the CTD signal of multiple cargo proteins have been shown to interact with PorM, PorN and PorW [START_REF] Vincent | A conserved motif of Porphyromonas Type IX secretion effectors C-terminal secretion signal specifies interactions with the PorKLMN core complex[END_REF]; (Lunar Silva et al. unpublished).

The emerging design suggests that cargo proteins are recruited by the rotating PorM shaft and transferred to PorN. PorN may in turn be physically moved around the fixed circular track composed of PorK and deliver the cargo to PorW, like slides are delivered to the projection window in a slide carousel. PorW would then transfer the cargo to the secretion pore Sov (Fig. 2). This design enables cargo to be recruited from a large catchment volume and be delivered to the secretion pore in potentially high concentrations. This concentration effect alone may be the "energy transduction" that drives secretion, or else PorN may additionally transfer mechanical energy through its interaction with PorW. This scheme may also accommodate gliding motility which has been shown to use a helical track along which motility adhesins flow [START_REF] Nakane | Helical flow of surface protein required for bacterial gliding motility[END_REF]. To convert the circular track into a helical one it may be sufficient just to add an additional component to the track. The gliding-specific GldJ lipoprotein which shares homology with GldK/PorK is a perfect candidate for this role and has already been suggested to form the helical track in F. johnsoniae [START_REF] Braun | Flavobacterium johnsoniae GldJ is a lipoprotein that is required for gliding motility[END_REF].

Formation of the virulence coat

An intriguing and unique element of the T9SS is the attachment of numerous cargo proteins to the cell surface via A-LPS to produce a 'virulence coat' around the cell. We initially thought that both the translocon and the attachment complexes would be located within the PorK/N rings, which would make the work of the PorV shuttle protein more efficient by limiting its distance of diffusion (Gorasia et al. 2020a). However, with the PorK/N rings implicated in energy transduction and substrate delivery, the barrier theory is no longer required to explain their function. Furthermore, it is known that PorV and other attachment complex members are highly enriched in OMVs, whereas PorK, PorN, PorW and Sov are not [START_REF] Veith | Porphyromonas gingivalis outer membrane vesicles exclusively contain outer membrane and periplasmic proteins and carry a cargo enriched with virulence factors[END_REF], and therefore for the barrier theory to be correct, OMVs would need to be formed preferentially from the OM immediately above and inside the PorK/N rings, which we have found no evidence for and seems unlikely. It is likely then, that Sov together with PorV and the attachment system are located outside the PorK/N rings (Fig. 1, Fig. 2). Furthermore, we have developed a mechanistic model for the cargo shuttle process which greatly extends the cell surface area serviced by the attachment system (Fig. 3). The PorV barrels are proposed to interact laterally to form branching chains that interconnect each Sov translocon with multiple attachment complexes. The cargo proteins are passed along the PorV chains until they reach an attachment complex where they are anchored to A-LPS and released (Fig. 3). The basis for this idea is three-fold. Firstly, lateral interactions between OM β-barrels is common, for example in porin trimers in other species [START_REF] Ma | Dynamics of OmpF trimer formation in the bacterial outer membrane of Escherichia coli[END_REF]) and in the RagAB peptide transporter of P. gingivalis [START_REF] Madej | Structural and functional insights into oligopeptide acquisition by the RagAB transporter from Porphyromonas gingivalis[END_REF]) making the idea of lateral interactions between adjacent PorV molecules plausible. Secondly, lateral interactions involving PorV are already known to occur for the Sov-PorV complex [START_REF] Lauber | Type 9 secretion system structures reveal a new protein transport mechanism[END_REF] and lateral interactions between PorV and PorQ in the attachment complexes also seem likely to help hold the complexes together. And thirdly, passing substrates along a PorV chain obviates the need for an unlikely bi-directional highway of PorV molecules diffusing through the membrane to load and off-load the cargo. Our model also considers the relative whole cell stoichiometry recently shown to be approximately 1 PorK/N ring to 8 attachment complexes to 100 PorV molecules [START_REF] Glew | PorV is an outer membrane shuttle protein for the type IX secretion system[END_REF]Gorasia et al. 2020b). and PorN molecules which is estimated to be 32-36 per ring based on cryo-EM images [START_REF] Gorasia | Structural insights into the PorK and PorN components of the Porphyromonas gingivalis type IX secretion system[END_REF]. Since there are only two PorM molecules per motor complex, we predict that many motors (possibly 16-18 of them) form a ring in the IM with their bent shafts forming a dynamic funnel-shaped cage in the periplasm. One PorW subunit forms a bridge to the single translocon (Sov) which is shown bound to the PorV shuttle protein (Gorasia et al. 2020a). The delivery of cargo from the translocon (Sov) to the attachment complex comprised of PorU, PorV, PorZ and PorQ is an outstanding question of considerable interest. This figure is a speculative model that assumes that this process occurs outside of the PorK/N rings (A) Once secreted and bound to PorV, the cargo (pink) are proposed to be passed along a series of PorV molecules until they reach an attachment complex where they are processed to become permanently attached to the OM via conjugation to A-LPS. The PorV chain may be continuous (as depicted) or it may have gaps requiring short diffusion distances to be covered. In this latter case, the PorV-PorV interactions may be dynamic. (B) In the context of a whole cell, we envisage a tree-like arrangement of PorV highways that connect a small number of translocation systems to a much larger number of attachment complexes at a ratio of approximately 1:8 [START_REF] Glew | PorV is an outer membrane shuttle protein for the type IX secretion system[END_REF]. The major outer membrane proteins of Gramnegative bacteria are known to be organized into "Omp islands" which are areas of extremely low diffusion [START_REF] Chavent | How nanoscale protein interactions determine the mesoscale dynamic organisation of bacterial outer membrane proteins[END_REF]. The T9SS shuttle system would be located outside of these areas where there is higher diffusion and where the OM has more freedom to bulge out and form OMVs.

  and Prevotella species found in severe periodontitis use the Type IX Secretion System (T9SS) to load their outer membrane surface with an array of virulence factors. These virulence factors are then released on outer membrane vesicles (OMVs) which penetrate the host to dysregulate the immune response to establish a positive feedback loop of chronic, inflammatory destruction of the tooth's supporting tissues. In this review we present the latest information on the molecular architecture of the T9SS and provide mechanistic insight into its role in secretion and attachment of cargo proteins to produce a virulence coat on cells and OMVs. The recent molecular structures of the T9SS motor comprising PorL and PorM as well as the secretion pore Sov, together with advances in the overall interactome have provided insight into the possible mechanisms of secretion. We propose the presence of PorL/M motors arranged in a circle at the inner membrane with bent periplasmic rotors interacting with the PorN protein. At the outer membrane, we envisage a slide carousel model where the PorN protein is driven around a circular track composed of PorK. Cargo proteins are transported by PorN to PorW and the Sov translocon just as slides are rotated to the projection window. Secreted proteins are proposed to then be shuttled along highways consisting of the PorV shuttle protein to an array of attachment complexes distributed around the cell. The cell surface attachment of cargo is a hallmark of the T9SS and in Porphyromonas gingivalis and Tannerella forsythia, this attachment is achieved via covalent bonding to a linking sugar synthesised by the Wbp/Vim pathway. The cell-surface attached cargo are enriched on OMVs which are then released from the cell.

  this occurred was unclear until recently. The 440 kDa attachment complex, comprising the PorU and PorZ surface proteins and the PorV and PorQ OM β-

  of periodontitis can be explained by the temporospatial emergence of periodontal pathogens at the base of a periodontal pocket. These pathogens secrete virulence factors using the T9SS and then release them into host tissue on OMVs to dysregulate the immune defense to generate a destructive, chronic, inflammatory response in a positive feedback loop to progress disease(Fig 4). The mechanisms by which the T9SS loads virulence factors onto the surface of cells and OMVs appears to involve a novel circular conveyor belt to deliver energy and cargo proteins (virulence factors) from the motor to the secretion pore and then to the surface for attachment.

  no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figure 1 :

 1 FIGURE LEGENDS

Figure 2 :

 2 Figure 2: Proposed cargo delivery from PorM to PorV

Figure

  Figure 3. Proposed mechanism and pathway of cargo delivery to the attachment complexes

Figure 4 .

 4 Figure 4. A working model of pathogenic mechanisms mediated by OMVs loaded with virulence factors secreted by the T9SS

Figure 1 :Figure 3 .

 13 Figure 1: Multiple PorL/M motors form a funnel-shaped cage in the periplasm 165x91mm (300 x 300 DPI)

Table 1 . T9SS-related genes that are essential for growth or fitness identified by TnSeq

 1

	Gene Name	33277 Locus Tag	W83 Locus Tag	Ortholog in Pi or Tf	Protein Description	Inherently Essential for Growth a	Essential for Fitness b
	T9SS components
	plug	PGN_0144	PG2092	Pi, Tf	Plugs periplasmic opening of Sov	-	-
	porA c,d	PGN_0123	PG2172	-, -	Proposed T9SS regulatory signal receptor	-	-
	porE	PGN_1296	PG1058	Pi, Tf	PorP-associated lipoprotein; peptidoglycan	+	-
					binding domain
	porF	PGN_1437	PG0534	Pi, Tf	Predicted TonB-dependent receptor	+	-
	porG	PGN_0297	PG0189	Pi, Tf	Predicted 8-stranded OM β-barrel	+	-
					associated with PorK/N
	porK	PGN_1676	PG0288	Pi, Tf	OM lipoprotein, forms large periplasmic	-	-
					rings with PorN
	porL	PGN_1675	PG0289	Pi, Tf	Motor stator	+	-
	porM	PGN_1674	PG0290	Pi, Tf	Motor rotor and shaft	+	-
	porN	PGN_1673	PG0291	Pi, Tf	Forms large periplasmic rings in association	+	-
					with PorK
	porP	PGN_1677	PG0287	Pi, Tf	Predicted 14-stranded OM β-barrel, binds to	+	-
					PorE.
	porQ	PGN_0645	PG0602	Pi, Tf	Attachment complex, 14-stranded OM β-	+	-
					barrel, binds to PorZ
	porT	PGN_0778	PG0751	Pi, Tf	Predicted 8-stranded OM β-barrel protein	-	-
	porU c	PGN_0022	PG0026	Pi, Tf	Attachment complex, T9SS sortase, binds to	+	-
					PorV
	porV	PGN_0023	PG0027	Pi, Tf	T9SS shuttle protein and attachment	+	-
					complex member, 14-stranded OM β-barrel,
					binds to PorU, Sov, PorA and other cargo
	porW	PGN_1877	PG1947	Pi, Tf	OM lipoprotein, forms bridge between PorN	-	-
					and Sov
	porX	PGN_1019	PG0928	Pi, Tf	Two-component response regulator,	+	-
					activates SigP
	porY	PGN_2001	PG0052	Pi, Tf	Two-component histidine kinase, activates	+	-
					PorX
	porZ c,d	PGN_0509	PG1604	Pi, Tf	Attachment complex, binds to PorQ, and A-	-	-
					LPS.
	sigP	PGN_0274	PG0162	Pi, Tf	Extracytoplasmic function sigma factor, σ P	-	-
	sov	PGN_0832	PG0809	Pi, Tf	T9SS translocon, gated by its binding to	+	-
					PorV or Plug
	A-LPS biosynthesis
	ugdA	PGN_0613	PG1277	Pi, Tf	UDP-D-GlcNAc 6-dehydrogenase	+	E, M
	wbpA	PGN_1243	PG1143	Pi, Tf	UDP-D-GlcNAc 6-dehydrogenase	+	E, M
	wbpB	PGN_0168	PG2119	-, Tf	C3-dehydrogenase	-	E, M, C
	wbpD	PGN_0002	PG0002	-, Tf	Acetyltransferase	-	E, M, C
	wbpE	PGN_1236	PG1138	Pi, Tf	Aminotransferase	-	-
	wbpS	PGN_1234	PG1136	-, -	Amidotransferase	+	-
	vimE	PGN_1055	PG0883	-, Tf	Deacetylase	-	-

http://mc.manuscriptcentral.com/jdr Journal of Dental Research

ACKNOWLEDGEMENTS

This work was supported by the Australian National Health and Medical Research Council grants ID 1123866 and 1193647 and the Australian Research Council grant DP200100914.

AUTHOR CONTRIBUTIONS

P.D.Veith, contributed to conception and design, contributed to acquisition, analysis, and interpretation, drafted manuscript, critically revised manuscript; M.D. Glew, contributed to conception and design, contributed to acquisition, analysis, and interpretation, drafted manuscript, critically revised manuscript; D.G. Gorasia, contributed to conception and design, contributed to acquisition, analysis, and interpretation, drafted manuscript, critically revised manuscript; E. Cascales, contributed to acquisition, analysis, and interpretation, critically revised manuscript; E.C. Reynolds, contributed to conception and design, contributed to acquisition, analysis, and interpretation, drafted manuscript, critically revised manuscript. All authors gave final approval and agree to be accountable for all aspects of the work.

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