S Le Roy

T T N Vu

G Teyssedre

Modelling charge transport in a HVDC cable using different softwares: from fluid models to macroscopic model

Keywords: fluid model, macroscopic model, cable, HVDC

Modelling charge transport in a solid organic insulation has been actively developed this last decade, using homemade models or commercial ones for the case of fluid models, or macroscopic models based on the Maxwell-Wagner theory. Until now, no comparison has been performed to understand the advantages and drawbacks of each type of modelling. Moreover, the availability of 2D or 3D space charge measurements makes it now possible, in theory, to develop the available models in 2D or 3D. Going in this direction, two different fluid models, using either the finite volume method or the finite element method, and simulating charge transport in a model cable have been developed. A third model, macroscopic, based on the variation of the conductivity as a function of the electric field and the temperature has also been developed in parallel. We first compare the simulated results obtained using these models for the case of a polyethylene-insulated cable, i.e. a cylindrical geometry where the electric field and the temperature are not homogeneous along the insulation. The comparison shows the differences between the macroscopic model and the fluids ones, due to the fact that injection and bulk trapping of charges are not treated in the macroscopic model. A special attention is also paid to the comparison between the different fluid models, and to show the difficulty to handle the commercial software.

I. INTRODUCTION

Since the first works of Alison et al [1], Kaneko et al [2], and Fukuma et al [START_REF] Fukuma | Proc. 4th International Conference on Properties and Applications of Dielectric Materials[END_REF] on charge transport modelling in solid organic insulators using a time and space dependent advection equation, a large number of studies has been published on this subject [START_REF] Xia | Numerical analysis of packetlike charge behavior in low-density polyethylene by a Gunn Effectlike model[END_REF][START_REF] Hoang | Charge transport in LDPE nanocomposites Part II -Computational approach[END_REF][START_REF] Min | Numerical analysis of space charge accumulation and conduction properties in LDPE nanodielectrics[END_REF] mostly on polyethylene based materials. These models have been mostly developed in a 1D geometry, function of the thickness of the sample. Few attempts have been made to reproduce the space charge behaviour in a cable geometry for transport of electricity under direct current (HVDC) [START_REF] Roy | Modelling space charge in a cable geometry[END_REF]. This has been done in 1D axisymmetric geometry. No attempts have been done to model the real 2D system, where charge transport is not only in one direction. One of the reason is the difficulty of development of such model, that are time consuming, complex to implement when working with 'home made' models, and requiring for more parameters that are not known. On the numeric side, commercial softwares, using finite element method as an example, can be used for developing such complex models, and seem easier to handle at first sight. Looking carefully at these models, the task is however not straightforward, and even risky if not carefully implemented.

Moreover, other types of model are available in the literature, and are developed in 2D. Macroscopic models, based on the variation of the conductivity with electric field and temperature, cannot predict the space charge generation and transport, but can simulate the electric field distribution in complex situations such as cylindrical multilayers systems, and predict the charge density due to the conductivity gradient. These simulation results can be compared to simulated results arising from the fluid models. In the present paper, after describing each model, we present the electric field distribution in a polyethylene-insulated cable, arising from a macroscopic model and from fluid models. We also compare a 'home made' 1D axisymmetric model solved using Fortran, to a 1D axisymmetric model using commercial software.

II. MATERIALS DESCRIPTION

A. Polyethylene cable system

A MV cable, made from cross-linked polyethylene (XLPE) of thickness 4.5 mm is simulated using the different models. The inner insulation radius is 5 mm, the outer radius is 9.5mm. A temperature gradient can be applied on the cable. When the temperature gradient is dT=16°C, the inner electrode is at T=57°C and the outer at 41°C. The applied voltage at the inner electrode is -80 kV. The outer electrode is grounded. Fig. 1 represents the geometric electric field distribution and the temperature distribution inside the cable for the example of a cable polarized at -80 kV and a dT=16°C.

III. MODELS DESCRIPTION

A. Charge transport models bipolar model has already been presented elsewhere [START_REF] Roy | Modelling space charge in a cable geometry[END_REF] and features the bipolar injection of electronic carriers at the electrodes, conduction between shallow traps via a field and temperature dependent mobility (hopping), trapping into deep traps, detrapping and recombination, function of the carriers mobility (Langevin) (Fig. 2). For sake of simplification, internal generation (dissociation) is not taken into account in the simulations. The equations to solve are of the form, neglecting diffusion: j a (r, t) = n a,µ (r, t)µ a (r, t)E(r, t)

Transport ( 1
)
where a refers to the type of charge, i.e. electron or hole and r is the cable radius. j is the conduction current density for each kind of carriers, n aµ is the mobile density of charge. µ refers to the mobility, E is the electric field and t the time.

dE(r, t) dr = ρ(r, t) ε 0 ε r Poisson equation (2) ∂n a (r, t) ∂t + ∂j a (r, t) ∂r = s a (r, t) convection-diffusion (3)
ρ is the net charge density, 0 ε the vacuum permittivity, and r ε the dielectric permittivity. s a are the source terms, which encompass the changes in local density by processes other than transport (trapping, detrapping, etc). Charge generation is only by injection at each electrode, and follows the modified Schottky law:

2 0 0 ( , ) ( , ) ( ) exp exp 1 ( ) ( ) 4 inj inj i b b r ew e eE r t j r t AT r k T r k T r πε ε ⎧ ⎫ ⎛ ⎞ - ⎛ ⎞ ⎪ ⎪ = - ⎜ ⎟ ⎨ ⎬ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠⎪ ⎪ ⎝ ⎠ ⎩ ⎭ (4)
where A is the Richardson constant, r i0 corresponds to the interface coordinate, and w inj is the injection barrier height. k B is the Boltzmann constant, T the temperature, and E(r,t) the electric field at the electrode at position r. The extraction of charges is possible at each electrode, and follows an ohmic law. The temperature distribution within the cable is calculated using the following equation:

T(r) = T(r i ) - ln r r i " # $ % & ' T(r i ) -T(r o ) ( ) ln r o r i " # $ % & ' (5)
where T(r i ) and T(r o ) are the temperature at the inner and the outer electrode, respectively. For all the simulations, the parameters taken into account are the one of [START_REF] Roy | Modelling space charge in a cable geometry[END_REF] for LDPE based materials. No attempt has been made to optimize the set of parameters to fit to XLPE results. The only requirement was to have a mean hopping mobility (electrons-holes) of the same order of magnitude as the one fitted by the macroscopic model, for a given electric field, which was the case with the parameters used.

FVM 'homemade' model

The description of the 'homemade' model has already been published in [START_REF] Roy | Modelling space charge in a cable geometry[END_REF]. It is a Finite Volume explicit code, using a third order accuracy in space and fourth order in time. The grid to discretize the insulating material is divided into 301 elements, non-uniform, having a smaller size when approaching the electrodes. A flux limiter (ULTIMATE) is used to correct the charge flux within the dielectric. The time steps follow the CFL condition.

FEM 1D axisymmetric model

The model, developed with COMSOL Multiphysics® uses the Transport of Diluted Species (TDS) module to solve the convection-diffusion equation for each kind of carrier [START_REF] Xia | Numerical analysis of packetlike charge behavior in low-density polyethylene by a Gunn Effectlike model[END_REF]. The advantage of this module is that it already includes solutions to stabilize the solution and to prevent oscillations (i.e. negative densities). The mathematic module is used to couple these equations to the Poisson equation. Backward Difference F solver is used for the time integration (maximum order 2, and minimum order 1). The discretization grid is divided into 9000 elements of 0.5 µm each.

B. Macroscopic model

The input data used in the macroscopic approach are issued from conductivity measurements realized in a previous work [START_REF] Vu | Maxwell-Wagner effect in multi-layered dielectrics: interfacial charge measurement and modelling[END_REF]. For each of the materials, the conductivity was estimated after 1 h charging time as a function of temperature (20 to 90 °C) and field (from 2 up to 30 kV/mm), on 500 µm-thick samples metallized with gold electrodes. These experimental data were fitted with a conductivity law of the type: Conduction is by free charges in the transport levels, associated with a hopping mobility between shallow traps. Deep trapping is taken into account through the existence of a single trapping level for each kind of carriers, electrons and holes. Si, Bi and Di are recombination (Langevin), trapping and de-trapping coefficients respectively. ni is the carrier density in transport and trapping levels. Indexes e and h refer to electrons and holes; µ ant t refer to mobile and trapped charges.

σ(T, E) = A exp - E a k B T # $ % & ' (sinh B(T).E ( )E α (6)
where is the field, T is the temperature, kB is the Boltzmann's constant and the other symbols are coefficients with values taken from [START_REF] Vu | Maxwell-Wagner effect in multi-layered dielectrics: interfacial charge measurement and modelling[END_REF]. In this model, two different modules of COMSOL Multiphysics® (Heat transfer and electric currents) have been coupled. Heat transfer consists in the exchange of thermal energy between physical systems. The heat transfer rate is dependent on the systems temperatures and on the properties of the involved medium through which the heat is transferred. The electric currents module allows solving the current equation considering the electrical potential as a reference under DC or AC stress.

IV. RESULTS

A. Comparison between FVM and FEM results

Simulations have been performed for a MV cable polarized at -80 kV and a dT=16°C, for 48h, using the FVM model and the FEM model, with the same parameters. Figure 3 presents the comparison of the simulated electric fields as a function of the radius of the cable at different times. At times below 5 hours, both models give globally the same electric field distributions. Some differences already appear, on the location of the maximum of the electric field, and the maximal value. This difference is less than 1% in position, and around 2% in value for 5hours. However, when increasing the simulation time, the difference between FEM and FVM becomes substantial, leading to a maximum of electric field located in the middle of the dielectric for the case of FEM, whereas it is next to the outer electrode for the FVM method. Net charge density as a function of radius is also presented (Figure 4) for the same times of simulation. It seems that the FVM model smoothes the net charge density compared to the FEM model, which is surprising, as the scheme used is not known for having a high numerical diffusion. On the contrary, the FEM results show oscillations, which are not due to physical processes (packets).

Simulations have been performed for plane/parallel geometry for each model, leading to similar results for each model. This validates the FEM model development, the FVM model having already been validated in different publications. The specific case of cable geometry, with a non-uniform electric field and temperature, increases the differences, notably at the inner interface, where charge generation is enhanced by the high temperature and field. A small difference in this boundary condition has a huge impact on the simulated data. Other causes could be found (discretization errors, diffusion, meshing...), without knowing at the moment which one has the most significant impact on the simulation results.

B. Comparison between fluid and macroscopic models

Electric field distribution vs. radius has been simulated for a XLPE material using the macroscopic model (Figure 5) [START_REF] Vu | Maxwell-Wagner effect in multi-layered dielectrics: interfacial charge measurement and modelling[END_REF].

These results are to be compared to those of Figure 3, issued from fluid models. Different processes are at play in the bipolar fluid models, with variable characteristic times for each process. At short times, injection is the most important process, while trapping/detrapping becomes more important at longer times. On the other side, only a mean conductivity drives the charge dynamics in the macroscopic model. We then choose to compare the electric field distributions at stationary states for each model, not regarding the transient results. Both models (macroscopic and fluid) are able to predict the change in the location of the maximal field that changes from the inner electrode (Laplacien Field) to the outer electrode with time. The maximal and minimal values are however overestimated in the fluid models compared to the macroscopic model, the maximal electric field reaching 22.5 kV/mm in the case of the fluid model (FVM), whereas the maximal value in the case of the macroscopic model is less than 20 kV/mm. Moreover, both fluid models (FVM and FEM) predict an electric field being almost 0 at the inner electrode, which is not the case in the macroscopic model, where the electric field minimum is of the order of 17 kV/mm. Fluid models, taking into account charge injection and accumulation (homocharges in this case) allow having such a variation of the electric field whereas the macroscopic model results will remain in the limits fixed by the conductivity variation linked to the thermal and/or electrical gradient. However, the parameterization for the fluid models, the physics behind, is not straightforward to implement. Besides, it requires a long computation time, compared to macroscopic models. C. Comparison between experimental and simulated electric field distribution Space charge measurements have also been performed for this specific on MV cables polarized at -80 kV for a temperature gradient of dT=16°C [START_REF] Vu | Field distribution under temperature gradient in polymeric MV-HVDC model cable: simulation and space charge measurements[END_REF]. The Poisson equation (2) allows obtaining the measured field distribution as a function of cable radius for different times (Figure 6). The maximal value of the electric field, initially located at the inner electrode, is observed at the outer electrode after 2h of applied voltage. The maximal value is around 22 kV/mm after 3 hours, while another peak appears at the inner electrode, due to positive heterocharges accumulating there. When comparing the simulated results to the experimental ones, the location of the maximal electric field is predicted. The value of this electric field is however underestimated in the case of the macroscopic model, while the fluid models prediction is consistent. However, the charge dynamics is excessively in the fluid models (due a non optimization of the parameters for XLPE among other possible explanations). Macroscopic and fluid models fail to reproduce the increase in the electric field at the inner electrode with time. The macroscopic model will never be able to predict it, as only based on conductivity variation. The fluid models could predict this peak if either barrier to extraction or ionic species were considered in as physical processes.

V. CONCLUSION Two fluid models, a Finite Volume code and a Finite Element code, have been developed and compared as regards a real situation, i.e. a MV cable geometry with a thermal gradient. Comparison between FEM and FVM model shows discrepancy in the electric field distribution that increases with the simulation time. Even for steady state, the simulation results are not totally consistent. This shows at first the need to take care of the numerical resolution of the model. On this area, more work needs to be done to improve the FEM model. Comparing fluid models to macroscopic ones by means of electric field distribution shows the strengths and drawbacks of each approach, i.e. easy implementation of the conductivity and short time simulations in the case of macroscopic models, while limited when charge accumulation comes at play. On the other side, the complex physics/complex parameterization/long lasting simulation drawbacks are counterbalanced by an efficient description of the charge behaviour. Fig. 6. Measured (PEA) electric field distribution vs. radius for different times, for a MV cable of 4.5mm polarized at -80 kV, and a temperature gradient of ΔT=16°C.

Fig. 1 .

 1 Fig. 1. Temperature distribution and electric field distribution in a MV cable of 4.5mm polarized at -80 kV, with a temperature gradient of ΔT=16°C.

Fig. 2 .

 2 Fig. 2. Schematic representation of the two-level transport model.Conduction is by free charges in the transport levels, associated with a hopping mobility between shallow traps. Deep trapping is taken into account through the existence of a single trapping level for each kind of carriers, electrons and holes. Si, Bi and Di are recombination (Langevin), trapping and de-trapping coefficients respectively. ni is the carrier density in transport and trapping levels. Indexes e and h refer to electrons and holes; µ ant t refer to mobile and trapped charges.

Fig. 3 .

 3 Fig. 3. Electric field distribution vs. radius for different times of simulation, for the FVM model (lines), and for the FEM model (dotted lines) for a MV cable of 4.5mm thick insulation polarized at -80 kV, and a temperature gradient of ΔT=16°C.

Fig. 4 .

 4 Fig. 4. Net charge density vs. radius for different times of simulation, for the FVM model (lines), and for the FEM model (dotted lines) for a MV cable of 4.5mm thick insulation polarized at -80 kV, and a temperature gradient of ΔT=16°C.

Fig. 5 .

 5 Fig. 5. Electric field distribution vs. radius for different times of simulation, for the macroscopic model and for a MV cable of 4.5mm thick polarized at -80 kV, and a temperature gradient of ΔT=16°C.

ACKNOWLEDGEMENTS

We would like to acknowledge the C.N.R.S. for financial support from the PICS project ModHVDC n° 07965


Nguyen-Hung Le

Marie Locard-Paulet

Alexandre Stella

Nicolas Tomas

Virginie Molle

Odile Burlet-Schiltz

Mamadou Daffé

Hedia Marrakchi
email: hedia.marrakchi@ipbs.fr

PknB kinase regulates biosynthesis and trafficking of mycolic acids in mycobacteria

Keywords: Mycobacteria, cell wall, mycolic acid, drug target, regulation, protein kinases, proteomics, phosphorylation, Fatty acid/Biosynthesis, lipids

Mycobacterium tuberculosis, a causative agent of tuberculosis, remains one of the most wide-spread and deadliest pathogens in the world. A distinguishing feature of mycobacteria that sets them apart from other bacteria is the unique architecture of the cell wall characterized by the presence of various species-specific lipids, most notably mycolic acids (MAs). Therefore, targeted inhibition of enzymes involved in MA biosynthesis, transport and assembly has been extensively explored in drug discovery. Additionally, more recent evidence suggests that many enzymes in the MA biosynthesis pathway are regulated by kinasemediated phosphorylation, thus opening additional drug development opportunities. However, how phosphorylation regulates mycolic acid production remains unclear. Here, we employed genetic strategies combined with lipidomics and phosphoproteomics approaches to investigate the role of phosphorylation in Mycobacterium. Our results revealed that PknB regulates export of MA and remodeling of the mycobacterial cell envelope. In particular, we identified the essential Mycobacterial membrane protein Large 3 (MmpL3) as a new substrate of PknB, thus directly linking kinase activity with MA trafficking. Taken together, our study documents the essential role of phosphorylation/dephosphorylation in regulating MA biosynthesis pathway and provides a blueprint for future anti-mycobacterial drug discovery.

INTRODUCTION

Mycobacterium tuberculosis (Mtu), a causative agent of tuberculosis (TB), remains one of the most wide-spread and deadliest pathogens in the world. Currently available treatment regimens for patients with active TB are significantly more complex than other bacterial infections and require the use of multiple antibiotics taken over the course of 6 to 9 months. The length, complexity and side effects of this chemotherapy may have contributed to the emergence of multi drug-resistant (MDR) and extensively drugresistant (XDR) Mtu strains due to low compliance. This highlights the need for continued development of additional TB treatment options as well as further research into Mtu biological features that set it apart from other bacterial pathogens. One such distinguishing feature of mycobacteria is one-of-a-kind architecture of their cell wall, which provides a protective layer and renders the pathogen recalcitrant to chemical damage, dehydration and antibiotic treatment. This cell wall contains various species-specific lipids, most notably mycolic acids (MAs) that are unique to mycobacteria and play key roles in mycobacterial physiology and fitness (1) as well as in Mtu virulence and persistence within infected cells (2). MAs are very long-chain branched, -hydroxylated fatty acids ranging from C70-C90 [START_REF] Daffé | Mycolic Acids: From Chemistry to Biology[END_REF]. MAs represent the main constituents of the cell wall outer membrane (OM or mycomembrane) wherein they are anchored. MAs are transported across the plasma (inner) membrane as trehalose monomycolates (TMMs) and then are either covalently linked to the arabinogalactan-peptidoglycan to form the mycoloyl arabinogalactan peptidoglycan (mAGP) complex or incorporated into trehalose dimycolates (TDM), found in the outermost leaflet of the mycomembrane.

The outer leaflet also contains other noncovalently associated lipids, such as phthiocerol dimycocerosates and sulfolipids [START_REF] Daffé | [END_REF].

MAs are synthesized in the cytoplasm via a highly conserved and well-characterized mixed fatty acid synthase (FAS)/polyketide synthase (PKS) biosynthetic pathway that has been extensively studied (5,6). Additionally, a molecular picture of cell-wall biogenesis process, which in Mtu involves 13 mycobacterial Membrane protein Large (MmpL) transporters that shuttle lipids across the membrane (7), has also emerged (8,9). However, intracellular regulation of cell wall biosynthesis still remains poorly understood. Recently, phosphorylation mediated by Ser/Thr protein kinases (STPKs) has begun to emerge as a major regulation mechanism for many biological processes controlling mycobacterial infection and persistence (10)(11)(12). The Mtu genome encodes 11 STPKs named PknA, PknB and PknD-to-L. These enzymes are responsible for responding to environmental signals and coordinating cellular responses to ensure growth and survival. Recently, STPK-mediated phosphorylation has been reported to inhibit many enzymes involved in the MA biosynthesis, including proteins involved in MA chain elongation within the FAS-II system (13)(14)(15)(16), mycolic chain modification (17), as well as the enzyme that specifically activates the meromycolic acid during mycolic condensation (18).

PknB is proposed to be one of the master regulators of Ser/Thr phosphorylation-mediated signaling in Mtu (19,20). It is essential and regulates the activity of a large repertoire of substrates, notably those involved in cell wall synthesis. Modulation of its expression levels has been shown to impact cellular morphology and survival (21,22), which suggests that the expression and activity of this kinase must be critically fine-tuned inside the bacterium. Its gene is localized in an operon containing two other essential genes involved in Ser/Thr phosphorylation: pknA and pstP. Interestingly, pstP is the only gene of Mtu known to encode a Ser/Thr phosphatase that likely forms a functional pair with PknB to control mycobacterial cell growth via modulating phosphorylation/dephosphorylation levels on substrate proteins (20,23). The dual regulation has only been reported for a few Mtu proteins, such as acyl-AMP ligase activity of FadD32 involved in the MA pathway (18). Despite these efforts, the full scope of STPK substrates remains unknown and the mechanistic and system-level link between intracellular phosphorylation/dephosphorylation events and cell wall biogenesis is not well understood. To begin to address this issue, we performed lipidomic and quantitative phosphoproteomic analysis of M. smegmatis (Msm) strains engineered to exhibit altered Ser/Thr phosphorylation through genetic manipulation. We depleted PstP to globally increase Ser/Thr phosphorylation levels, and combined with overexpression of pknB and its kinase-inactive mutant to identify PknB-dependent phosphorylations and their impact on cell wall composition and integrity through distinct downstream changes in lipid profiles. We identify MmpL3 as the substrate of PknB, and this phosphorylation event as a major regulatory node in MA trafficking and cell wall biosynthesis.

MATERIALS AND METHODS

Plasmid construction. To produce PknB in M. smegmatis mc²155, the pknB gene was cloned into pLD1, an IPTG-inducible mycobacterial expression vector previously described (18). The pknB gene was PCRamplified using a donor vector and primers (PknB_F and PknB_R, listed in Table S3) containing ClaI and NdeI restriction sites, respectively. To create the vector pLD1-pknB_K40M, the DNA portion of pknB containing the K40M mutation was transferred into pLD1-pknB using Gibson Assembly method. Briefly, fragment 1 was amplified from pLD1-pknB using the primers pair Frag1_F and Frag1_R, and fragment 2 was amplified from a donor vector using the primers pair Frag2_F and Frag2_R (Table S3). The 2 fragments were incubated together for 1h at 50°C using Gibson Assembly Master Mix (New England Biolab). E. coli DH5α competent cells were used to select successful assembly products. All the inserted sequences and mutation were confirmed by DNA sequencing (Eurofins Genomics, France).

Bacterial strains and culture conditions. The plasmids pLD1-null, pLD1-pknB and pLD1-pknB_K40M were electroporated into M. smegmatis mc 2 155. Transformants were selected on 7H10/0.05% Glycerol agar plates with Hygromycin B (100 µg/mL). Starter cultures were grown for 30h and then diluted in 7H9 medium (Difco) supplemented with Glycerol (2 g/L), Tyloxapol (0.025%) and Hygromycin B (100 µg/mL).

The resulting cultures were incubated under shaking (180 RPM) at 37°C until reaching OD600 of 0.6. PknB overexpression was induced with IPTG (0.5 mM final) (Euromedex, France). Cells were harvested 4h after IPTG induction, washed with 50 mM PBS, pelleted by centrifugation, flash-frozen and kept at -80°C. Starter cultures of M. smegmatis mc²-cd-pstP were grown for 30h in 7H9 medium (Difco) supplemented with Glycerol (2 g/L), Tyloxapol (0.025%) and Kanamycin (50 µg/mL). Cultures were initiated at OD600 of 0,1 and grown in the absence or presence of anhydrotetracyclin (ATc) for 12h. The resulting cultures were incubated under shaking (200 RPM) at 37°C until reaching an OD600 of 1. Cells were harvested, pelleted and kept at -80°C.

Immunoblotting. Cell pellets were thawed and resuspended in lysis buffer (50 mM HEPES pH7.5, 10% glycerol (v/v), 500 mM NaCl, and 2 mM 4-(2-Aminoethyl) benzenesulfonyl fluoride (AEBSF, Euromedex).

The suspension was lysed with a cell disruptor (One Shot Model -Constant System Ltd., France) at 2.6 kbar. Total cell lysates were fractionated by SDS-PAGE, and then transferred to 0.4 µm Nitrocellulose membrane using the Transblot Turbo semi-dry transfer method (Biorad). Following the transfer, His-tagged PknB and PknB_K40M were detected using mouse monoclonal anti-polyHis antibodies (Sigma Aldrich) diluted 1/5000 or rat monoclonal anti-PknB antibodies (1652A rat antibody raised against the catalytic domain, gift from the STATENS SERUM INSTITUT); HRP-conjugated goat antibodies against mouse (Biorad, diluted 1/10000) or rat (Biorad, diluted 1/20000) were used as secondary antibodies. For mc²-cd-pstP cell pellets were thawed and resuspended in lysis buffer (50 mM Tris pH8, 8% glycerol (v/v), 500 mM NaCl, and 2 mM 4-(2-Aminoethyl) benzenesulfonyl fluoride (AEBSF, Euromedex). The suspension was lysed with a cell disruptor (One Shot Model -Constant System Ltd., France) at 2.6 kbar. Total cell lysates were fractionated by SDS-PAGE, and then transferred to 0.4 µm Nitrocellulose membrane using the Transblot Turbo semi-dry transfer method (Biorad). PstP-FLAG were detected using mouse monoclonal anti-FLAG antibodies HRP-conjugated (Sigma) diluted 1/10000. Detections were performed with Amersham Tm ECL Prime Western Blotting Reagents Kit (GE Healthcare). Immunoreactive bands were revealed and measured with Chemidoc Imaging System (Biorad).

Lipid radiolabeling, extraction and analysis.

To radiolabel all the lipids, 1,5 µL of [1-14 C] acetic acid (92,67x103 Bq, Amersham) were added to 5.4 mL of each M. smegmatis culture 3h post-induction. The cultures were incubated again for 1h at 37°C under shaking (150 RPM). Radiolabeled bacteria cultures were divided in two aliquots, and then harvested by centrifugation for 10 min at 2900 x g. Cell pellets were stored at -20°C.

To analyze labeled whole cell fatty acids, wet cell pellets were saponified with KOH (40%)/2methoxyethanol (1/7; v/v) for 3 h at 110°C. FAs and MAs acids were extracted 3 times with diethyl ether and the resulting fractions acids were methylated using TMS-diazomethane (Sigma) and analyzed by thin layer chromatography (TLC) with CH2Cl2 as developing solvent.

The total extractable lipids (TEL) were obtained from wet bacterial pellets by two successive extractions using distinct mixtures of CHCl3/CH3OH (1/2 and 2/1, v/v). The fractions were pooled, washed with water, and dried. TLC bands of each radiolabeled lipid were quantified by PhosphorImaging and the ImageQuant software (Variable Mode Imager Typhoon TRIO, Amersham Biosciences). As reference, non-labeled lipid profiles were also analyzed.

For unlabeled lipid analysis, equivalent weights of total lipid fraction from each strain were spotted onto a HPTLC silica gel 60 plate (Merck) with a Camag ATS4 apparatus. The plates were developed in the indicated solvent system using a Camag ADC2 device and stained by immersion using a Camag CID3 apparatus with primuline (10% (w/v) in acetone/H2O 84/16) then revealed and quantified with Chemidoc Imaging System (Biorad).

Sample preparation for label-free proteomics and phosphoproteomics analysis. Flash-frozen cell pellets

were resuspended in Urea lysis buffer (Urea 8 M, 50 mM Tris-HCl pH 8.0, Sodium Orthonovanadate 1 mM, supplemented with Complete Ultra tablets and Phosstop Phosphatase Inhibitor Cocktail tablets) and lysed with a cell disruptor (One Shot Model -Constant System Ltd., France) at 2.6 kbar. Non-lysed bacteria and cell debris were eliminated by centrifugation at 9000 x g. Protein concentrations of clarified lysates were quantified with Biorad DC Assay kit (Biorad). Equal amounts of lysate (3.1 mg) from each condition wer e reduced and alkylated with 5 mM Dithiothreitol (DTT) and 20 mM Iodoacetamide, respectively. Urea concentration was reduced to 1 mM by 8-fold dilution with Ammonium Bicarbonate 50 mM. The samples were then incubated overnight at 37°C with Trypsin at a ratio 1/100 (w/w, trypsin/proteins). After digestion, peptides were desalted using Sep-Pak columns (Waters; tC18 6cc 500 mg) following the provider instructions. Peptides were then dried down under vacuum and kept at -20°C.

Phospho-peptide enrichment. Titansphere TiO2 beads (5 µm; GL Sciences 5020 75000) were pre-washed in TiO2 loading buffer (80% MeCN, 5% Trifluoroacetic acid (TFA), 1 M glycolic acid). Peptide pellets (500 µg per sample) were suspended in TiO2 loading buffer (1 µg/µL), and incubated with conditioned TiO2 beads (6 mg of beads per mg of peptides) for 20 min under agitation at room temperature. Beads were then washed with washing buffer (60% acetonitrile 1% TFA) and loaded onto a home-made column (200 µL tip with an EMPORE C18 filter). Packed TiO2 beads were washed once with washing buffer and phosphopeptides were eluted twice with 1% NH4OH. Pooled eluates were acidified with TFA before being dried down under vacuum.

NanoLC-MS/MS Analysis.

Peptides and phospho-peptides were analyzed by nanoLC-MS/MS using an UltiMate 3000 RSLCnano system (Dionex, Amsterdam, The Netherlands) coupled to a Q-ExactivePlus mass spectrometer (ThermoScientific, Bremen, Germany). Peptides were suspended in 5% acetonitrile, 0.05% TFA spiked with iRT (Biognosis 1X). 25% of each sample were loaded onto a C-18 precolumn (300µm inner diameter x 5 mm, Dionex) at 20 µl/min in 5% acetonitrile, 0.05% TFA. After 5 min of desalting, the precolumn was switched online with the analytical C-18 column (75 μm inner diameter × 50 cm; inhouse packed with Reprosil C18) equilibrated in 95% solvent A (5% acetonitrile, 0.2% formic acid) and 5% solvent B (80% acetonitrile, 0.2% formic acid). The peptides were eluted using a 4% to 40% gradient of solvent B at 300 nl/min flow rate for 240 min. The mass spectrometer was operated in data-dependent acquisition mode with the XCalibur software. Survey MS scans were acquired in the Orbitrap on the 400-1600 m/z range with a resolution of 70000, the 10 most intense ions per survey scan were selected for HCD fragmentation and resulting fragments were analyzed at a resolution of 17500 in the Orbitrap. The mass spectrometry proteomics data have been deposited to the ProteomeXchange (24) Consortium via the PRIDE (25) partner repository with the dataset identifier PXD012553.

Protein identification and quantification. Raw MS files were analyzed by MaxQuant version 1.5.5.1. Data were searched with the Andromeda search engine against M. smegmatis entries of the Swissprot protein database (strain ATCC 700084 / mc(2)155 Uniprot IDs UP000000757 and UP000006158 with 6602 and 6585 entries, respectively). In case of discrepancies in the residue numbering between the two combined databases of Msm, both phosphorylation site positions were reported, separated by the symbol "|". The search included methionine oxidation, serine, threonine and tyrosine phosphorylation as variable modifications, and carbamidomethylation of cysteine as a fixed modification. Validation was performed through a false discovery rate set to 1% at protein and peptide spectral match (PSM) level determined by target-decoy search in MaxQuant. Specificity of trypsin digestion was set for cleavage after lysine or arginine, and up to two missed cleavages were allowed. The precursor mass tolerance was set to 20 ppm for the first search and 4.5 ppm for the main Andromeda database search. The mass tolerance in MS/MS mode was set to 20 ppm. For label-free relative quantification of the samples, the "match between runs" option of MaxQuant was enabled to allow cross-assignment of MS features detected in the different runs.

Statistical analysis.

The quantitative phosphoproteomic analysis was performed using the statistical package R (R Development Core Team, 2012; http://www.R-project.org/) and R scripts related to the analysis can be found online (https://github.com/mlocardpaulet/PhosphoSmegPKNB). Phosphorylation site relative quantification was performed with the intensities from the mono-phosphorylated peptides of the "Phospho (STY)" tables of MaxQuant with a localisation score ≥ 75%. Intensities were first normalized for instrument variation using spiked-in standards. Protein entries identified as potential contaminants by MaxQuant were eliminated from the analysis, as well as PSMs with a PEP value ≤ 0.01. Log2-transformed values of technical repeats were averaged. Each phosphorylation site relative quantities across the conditions were normalized to its cognate protein relative quantities determined by its LFQ values in the proteome (peptides analysis before phospho-enrichment). Then, missing values of the phosphorylation sites with < 1 data point across the 3 experiments were replaced for each condition (empty vector, PknB-K40M, PknB-WT) with the 1% quantile of the entire data set. In order to identify the PknB-dependent phosphorylation sites, we systematically performed two-sided unpaired Welch t-tests followed by Benjamini-Hochberg corrections of the p-value between the conditions. Phosphorylation sites were considered statistically significant when they presented a corrected p-value ≤ 0.05 and a minimum absolute log2-transformed fold change ≥ 1 (corresponds to a two-fold change) (see Table S1 for the results of the statistical analysis).

Drug susceptibility testing. Colorimetric tetrazolium microplate assay was used to determine drug susceptibility of M. smegmatis. Non-frozen cell pellets from 30 mL cultures were vortexed with 2 mm glass beads and resuspended with 2 mL 7H9/0.02% Glycerol broth. The suspensions were then centrifuged at 100

x g to eliminate glass beads and bacterial clumps. The supernatants were used to prepare mycobacterial suspension at OD600 of 0.1. In a 96-well microplate (Nunc), 2 µL was added from two-fold dilution series of antibiotics in DMSO. For no inhibition control wells, 2 µL of DMSO was added instead. Next, 100 µL of 7H9/0.02% Glycerol broth was added into all wells, followed by 100 µL of mycobacteria suspension.

The microplates were incubated at 37°C with moderate shaking (120 RPM). After 24h, 50 µL of Thiazolyl Blue Tetrazolium Bromide solution (Sigma) at 1 mg/mL was added to each well and re-incubated for 4h. In living bacteria, tetrazolium salts were reduced to insoluble formazan, which were solubilized by adding 50 µL of SDS 20%. When the formazan crystals were totally dissolved, absorbance at 570 nm was read with a microplate reader Clariostar (BMG Labtech, Germany). The inhibition curves (log10-transformed inhibitor concentration vs. Normalized response) were plotted using Graph Pad Prism 5.0.

RESULTS

The Msm Ser/Thr phosphatase PstP modulates cell envelope mycolates.

Given that PstP is the only known mycobacterial Ser/Thr protein phosphatase, we hypothesized that decrease of PstP activity would result in systemic perturbation of phosphorylation balance, and result in a phenotypic change in cell wall content. To test this hypothesis, we first examined the impact of PstP depletion on the different MA-containing lipids produced by Msm. We used a Msm mutant strain where the essential pstP gene was placed under the Tet-OFF promoter, which allowed us to induce PstP expression in the presence of anhydrotetracyclin (ATc) as described previously (26). The phosphatase depletion was confirmed by immunoblot (Fig. 1A). Following saponification of whole bacterial cells and lipid extraction, analysis of the resulting fatty acid (FA) and MA content showed no difference in their respective amounts between the wild-type (PstP + ) and PstP-depleted (PstP -) strains (Table S1). However, when total bacterial lipids were solvent-extracted to yield the "total extractable lipid" fraction (triglycerides, glycopeptidolipids, phospholipids, trehalose-linked mycolic acids, etc.), we observed a significant (+90%) increase of the amount of TMM present in the PstP -strain compared to PstP + (Table S1, Fig. 1B and1C). TMMs are the mycoloyl donors of the major mycolate-containing lipids of the mycomembrane (TDM and mycoloyl arabinogalactan-peptidoglycan). Thus, their increase notably impacts the TMM/TDM balance, with a ratio enhanced in the PstP -strain by 1.7 fold.

Our results show that PstP depletion is responsible for a significant increase of TMM abundance in Msm.

This indicates that synthesis, transport or transfer of TMM may be regulated by phospho-Ser/Thr levels.

Knowing that MA biosynthesis is regulated by Pkn kinase activities and given the essential and ubiquitous nature of PknB, we hypothesized that PknB could play a major role in MA metabolism, thereby participating in maintaining cell wall integrity. We decided to verify this hypothesis in a more controlled system through overexpression of PknB.

PknB plays a key role in maintaining cell envelope integrity.

To assess the role of PknB-mediated phosphorylation on the cell wall constituents, full length PknB from Mtu was overproduced with a N-terminal His-tag in Msm. As a control, we also expressed the mutant Mtu pknB_K40M, which is known to be enzymatically inactive (21). This ensured that the observed phenotypic differences between the strains expressing pknB or pknB_K40M were specific to PknB kinase activity, and not due to changes in protein quantity. The induction of Mtu pknB overexpression reduced Msm growth rate compared to wild type Msm or Msm expressing Mtu pknB_K40M 4h after induction (Fig. 2A, inset), which is in agreement with previously published data (22). We thus decided to perform all the experiments presented here (lipidomics, proteomics, drug sensitivity, etc) 4h following pknB variant induction. Indeed, at this time point the different strains have a similar growth rate, comparable biomass and total lipid/protein contents (Fig. 2), which allowed us to compare the phosphoproteomes and quantity of MA-containing lipids.

Western blot analysis of the protein extracts from the different strains confirmed the overproduction of PknB and PknB_K40M (Fig. 2B), and their relative quantification by mass spectrometry indicated that they were expressed in similar amounts (Fig. S1).

In order to evaluate the possible impact of PknB kinase activity on the cell envelope integrity and/or remodeling, we compared the impact of large (rifampicin (RIF) and vancomycin (VAN)) compounds that enter the cells by diffusion through the lipid domain of the MM, and hydrophilic (isoniazid (INH) and ethambutol (EMB)) compounds that enter via pore-forming proteins (porins) (27,28), on the Msm strains expressing pknB or its inactive K40M mutant. Interestingly, the strain expressing Mtu pknB was specifically more sensitive to both large molecules, RIF (hydrophobic) and VAN (hydrophilic) (Fig. 2C,2D), compared to the control and the mutant variant. This might be explained by a higher fluidity of the mycomembrane, permitting a better diffusion of the drugs. On the other hand, no significant change in the susceptibility to the small hydrophilic anti-TB drugs EMB and INH was measured in strains overexpressing PknB versus PknB_K40M. Altogether, these results indicate a measurable alteration of the cell wall properties resulting from induction of PknB kinase activity.

PknB activity regulates synthesis and transport of mycolic acids.

We hypothesized that the increase of Msm sensitivity to RIF and VAN upon induction of PknB kinase activity could result from an alteration of the cell envelope composition. To investigate the impact of PknB kinase activity on FA and MA content, bacteria were harvested after induction of the pknB variants expression. In order to monitor MA production, and improve the sensitivity of detection of fine changes in metabolism, cells were harvested 3h post-induction and labelled with 14 C-acetate for 1h. Whole bacterial cells were either saponified (to obtain total MAs and FAs) (Fig. 3A), or extracted with organic solvents (CHCl3/CH3OH) to yield total non-covalently linked extractable lipids (TEL) (Fig. 3B). Samples were then resolved and detected by radio-thin layer chromatography (Fig. 3; Table S2). The incorporation of labelled acetate into total FAs and MAs dramatically decreased in Msm overexepressing Mtu PknB (-34% and -60%, respectively), compared to the control strain harboring the empty vector and the strain expressing the inactive K40M isoform. Thus, MA production was reduced upon induction of PknB kinase activity. In parallel, in the non-covalently linked (organic solvent-extracted) lipids, mycolates in the form of TMMs notably accumulated (+42%) while TDM quantities significantly decreased (-58%) (Fig. 3B; Table S2) in the PknB-overexpression strain. Previously, this imbalance of TMM/TDM ratio in favor of TMM (>3-fold increase) was observed upon inhibition of MmpL3, either using small molecule inhibitors (29,30) or in conditional mycobacterial mmpL3 mutants (31). Interestingly, triglyceride quantification showed no difference in the incorporation of radioactivity between the three strains (Fig. S2), whereas incorporation of radioactivity into FAs, precursors of phospholipids and MA chains, also decreased (Fig. S2 and Table S2).

Taken together, our data suggest that PknB kinase activity impacts not only FA and MA synthesis, but the transport of MAs to the cell wall as well, an insight that has not been documented before.

PknB kinase overexpression identifies proteins of the cell envelope as major substrates

To explore the possibility that PknB affects MA transport via direct phosphorylation of protein(s) involved in this process, we performed a large-scale quantitative phoshophoproteomic analysis of the different Msm strains. Three independent cultures of Msm transformed with empty vector, pknB and pknB_K40M were prepared. Bacteria were lysed, proteins were trypsin-digested and phosphorylated peptides were then enriched using titanium dioxide beads. Peptides before and after phospho-enrichment were analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) to determine relative abundance of protein and phosphorylation sites across samples, respectively (Fig. 4A). We then performed two-sided unpaired t-tests followed by a Benjamini-Hotchberg correction of the p-values to determine the proteins and phosphorylation sites regulated upon induction of PknB kinase activity. We identified four times as many phosphorylation sites upon Mtu PknB overproduction compared to both controls (Fig. S3 panel A). The phosphorylation sites in both control conditions exhibited similar relative abundance, as expected (Fig. S3 panel B). Kinase activity of Mtu PknB increased both the number of phosphorylated sites and their intensity in Msm, as all phosphopeptides detected upon induction of Mtu PknB were more abundant than in any of the control conditions (Fig. S3 panels C andD).

Interestingly, while phosphoproteomics data indicate a systems-wide increase in phosphorylation upon induction of PknB activity, less than 1% of the Msm proteome was significantly altered upon expression of Mtu PknB compared to the controls (Dataset S1). This suggests that the differences observed between the three strains are more likely due to the phosphorylation status of protein substrates, rather than to differences in protein expression.

Moreover, number and nature of significantly regulated phosphorylation sites (Fig. S3 panel E) indicate prevalence of phospho-threonines over phospho-serines and phospho-tyrosines, which is in agreement with the reported "bias" of mycobacterial STPK towards threonines (32).

Our analysis identified 2256 phosphorylation sites on 1194 proteins (Dataset S1), amongst which 1385 phosphorylation sites (from 811 proteins) were differentially regulated between the strains displaying enhanced PknB kinase activity compared to the inactive PknB_K40M (Fig. S3A). Differentially regulated sites were identified on proteins known to be regulated by PknB, such as PknB itself, GarA (a FHA domaincontaining protein), PtsP (Ppp) and Wag31 (DivIVA), an essential protein organizing cell wall biosynthesis at the growing cellular poles (11) (Fig. S4). In addition to confirming results from previous studies (32,33), we identified a significant number of previously unreported phosphorylated proteins in the MA pathway, thus expanding the mycobacterial phosphoproteome. Notably, we document that threonine 22 (T22) on de novo fatty acid synthase FAS-I displayed differential PknB activity-dependent phosphorylation, as well as proteins involved in the biosynthetic pathway of MAs: dehydratase HadA, -ketoacyl-ACP reductase MabA (FabG), acyl-CoA carboxylase subunits AccA3 (T533) and AccD5 (T2, S8 and T29), and the mycolic condensing enzyme Pks13 (T14, T1445, T1450) (Fig. 4B). Some of our data recapitulates previously reported link between PknB activity on substrates such as adenylating enzyme FadD32 and Pks13 (Fig. 4B; (32,34). Moreover, we also describe sites which were not previously known to be subject to PknB phosphorylation such as those on ACCases.

Importantly, among the highly regulated phosphorylation sites, we identified threonine 984 (T984) on MmpL3, further supporting the proposed role of PknB activity in regulation of MA trafficking (Fig. 4B).

Collectively, our lipidomic and phosphoproteomic analyses reveal that PknB plays a global regulatory role, not only on the production of MAs, but on their export to the mycomembrane as well, thus participating in the remodeling of the cell envelope (Fig. 5).

DISCUSSION

Mycobacterial STPKs have emerged as critical regulators of viability and survival, for example by phosphorylating substrate proteins involved in MA biosynthesis known to be critical for Mtu cell wall adaptation. Such mechanisms rely on a tight and reversible balance of Ser/Thr phosphorylation/dephosphorylation. In Mtu, and closely related Msm, PknB is one of the best characterized kinases that is central for regulation of mycobacterial signaling, although additional kinases are relevant as well given that Mtu genome codes for 11 STPKs, while PstP is the only phosphatase identified to date.

Therefore, the interplay between PknB and PstP activities is expected to be critically important for mycobacterial physiology. While we were preparing this work, two independent studies characterizing PstP were published, both suggesting that this phosphatase plays a central role in regulating cell division (26,35), as well as cell wall biosynthesis and global phosphorylation levels (35). Here, we provide more granular evidence that PstP plays an important role in regulating MA biosynthesis based on the observation that PstP depletion significantly increased the quantities of TMM, a precursor for mycolic acids.

To further investigate this phenotype, we produced the ubiquitous and essential protein kinase PknB from Mtu and its enzymatically inactive K40M mutant in Msm, to study the importance of PknB-dependent phosphorylation in regulating the MA metabolism. Although induction of Mtu PknB overexpression had little impact on the Msm proteome, it caused a major change in the permeability of the cells to the large-size drugs RIF and VAN, indicating remodeling of the envelope. As expected (22), a specific reduction of the growth rate of pknB-expressing Msm was observed after 4h of induction. Accordingly, labeling of bacterial cells with 14 C-acetate following PknB-induction showed a drastic decrease of MA synthesis and a specific and dramatic change in the balance of trehalose mycolates in the overexpressing strain. An accumulation of TMM was observed, with a decrease of the quantities of TDM, the final acceptor of MAs. These observations were dependent on the kinase activity of PknB since no effect was observed in Msm overexpressing the inactive K40M mutant, and comparable to those obtained upon inhibition of trehalose monomycolate transport from the cytosol to the cell wall by MmpL3.

Consistently, a comparative analysis of the phosphoproteomes of these mycobacterial strains demonstrated the phosphorylation of several proteins involved in both the synthesis and transport of MAs, especially MmpL3 (Fig. 4). In fact, phosphorylation of MmpL3 pheno-copied both mycobacterial depletion of this protein and inhibition of the cell wall lipid TMM export by MmpL3 inhibitors. This identifies for the first time the negative modulation of MmpL3 activity by STPK-regulation, and adds MmpL3 as a new "piece" in the landscape of cell wall biogenesis proteins negatively-regulated by STPK phosphorylation (36) (Fig. 5).

Altogether, our results not only confirm previously known PknB-dependent phosphorylation sites on proteins involved in MA biosynthesis, but also identified new phosphorylated proteins involved in MA biosynthesis and trafficking, thus expanding the mycobacterial phosphoproteome. These data brought to light the phospho-dependent regulation of Msm cell envelope integrity through regulation of enzymes involved in FA and MA metabolism (Fig 4B). These findings support a working model in which phosphorylation of FAS-II enzymes reduce the production of MAs, which transport by MmpL3 is in turn negatively regulated by phosphorylation, thus generating the accumulation of TMM and the reduction of their final acceptors, i.e. TDM and cell-wall arabinogalactan (Fig. 5).

Cell envelope biogenesis -and in particular MA synthesis-is a validated pathway for anti-TB drug discovery. The present study provides a set of phosphorylation sites that are under control of PknB kinase activity. These include known PknB substrates (FadD32, HadA, MabA), as well as new sites potentially involved in drug resistance and adaptation to the host (20,[START_REF] Baros | Molecular & cellular proteomics : MCP 38[END_REF]. These data, together with our lipidomics analysis of cell wall remodeling upon Ser/Thr phosphorylation, indicate that cell wall biogenesis relies on phosphorylation of proteins involved in MA synthesis and transport, including MmpL3, an essential mycolate transporter and the proposed target of many anti-mycobacterial inhibitors under development (29)(30)(31)38). These findings are in line with a recent study pointing to mycobacterial cell envelope lipid alteration and the decreased phosphorylation of MmpL3 upon treatment with PknA and PknB inhibitors (39). Overall, these data highlight the critical role of MA regulation by STPKs and, as such, confirm that phospho-regulation of the essential cell wall metabolism represents a promising target for antimycobacterial drug discovery. Schematic workflow of sample preparation and data analysis. Three independent cultures of Msm transformed with empty vector, pknB-or pknB_K40M-expressing vectors were prepared. Bacteria were lysed, proteins were trypsin-digested and phospho-containing peptides (pSTY) were then enriched using titanium dioxide (TiO 2 ) beads. Peptides before and after phospho-enrichment were analyzed by LC-MS/MS to determine the protein and phosphorylation site relative quantities across samples, respectively. Phosphorylation site relative quantities were normalized using their cognate protein relative quantities in order to correct for variations in protein expression across conditions. The normalized data were then Phosphorylation of MpmL3 impairs its TMM export to the outer membrane, leading to cytoplasmic TMM accumulation and severe decrease in TDM. TMM, the mycoloyl donor, is transferred by the mycoloyltransferases (Ag85 complex) to the biologically active mycolate-containing compounds (TDM, glucose monomycolates, glycerol monomycolates), as well as to arabinogalactan to form the mAGP complex. The negative control of key enzymes in MA biogenesis pathway, in addition to modulation of TMM amounts, result in overall cell wall remodeling. Red asterisks: PknB-regulated proteins identified and/or confirmed in this study.

Fig. 1 .

 1 Fig. 1. Depletion of PstP in M. smegmatis affects mycolic acid content. (A) Immunoblot analysis showing

Fig. 2 .

 2 Fig. 2. Overexpression of PknB affects M. smegmatis growth and enhances mycobacterial

Fig. 3 .

 3 Fig. 3. PknB kinase activity impacts production of cell wall mycolic acids. Radio-TLC profiles of whole

Fig. 4 .

 4 Fig. 4. Label-free quantitative analysis of PknB-dependent phosphoproteome in M. smegmatis. (A)

  subjected to statistical analysis to determine PknB-dependent phosphorylation events. (B) Heatmap of log2transformed MS signal obtained for phosphorylation sites of interest across the 3 triplicate samples. The log2-transformed fold changes (PknB Vs PknB_K40M) are represented with the horizontal bar plot on the right-hand side. Fold changes in red highlight the phosphorylation sites that passed our statistical thresholds (PknB Vs PknB_K40M, corrected p-value of two-sided unpaired Welch t-test ≤ 0.05 and minimum 2-fold change). Missing values are in grey.

Fig. 5 .

 5 Fig. 5. Proposed model of cell wall remodeling by multistep STPK-regulation of mycolic acid

Novo Nordisk Foundation Center for Protein ResearchThe center is supported financially by the Novo Nordisk Foundation (Grant agreement NNF14CC0001).

DATA AVAILABILITY STATEMENT

All the data described are contained within this manuscript.

ACKNOWLEDGEMENTS/GRANT SUPPORT

We are grateful to Dr Vinay Nandicoori for his gift of the M. smegmatis pstP strain. N.L. was supported by a fellowship from the French Foreign Office (Ministère des Affaires Etrangères). We acknowledge funding from the Agence Nationale de la Recherche (FASMY, grant ANR-14-CE16-0012). For O.B-S., this work was supported in part by the Région Midi-Pyrénées, European funds (Fonds Européens de Développement Régional, FEDER), Toulouse Métropole, and the French Ministry of Research with the Investissement d'Avenir Infrastructures Nationales en Biologie et Santé program (ProFI, Proteomics French Infrastructure project, ANR-10-INBS-08).

AUTHOR CONTRIBUTIONS

HL, MD and HM designed the project; HL, MLP, NT, AS, HM designed and performed experiments and analyzed the data, HL, MLP, NT and HM prepared the figures. HL, VM, MLP, MD and HM wrote the manuscript. All authors reviewed the manuscript.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest with the contents of this article.