How P. aeruginosa cells with diverse stator composition collectively swarm

ABSTRACT Swarming is a macroscopic phenomenon in which surface bacteria organize into a motile population. The flagellar motor that drives swarming in Pseudomonas aeruginosa is powered by stators MotAB and MotCD. Deletion of the MotCD stator eliminates swarming, whereas deletion of the MotAB stator enhances swarming. Interestingly, we measured a strongly asymmetric stator availability in the wild-type (WT) strain, with MotAB stators produced at an approximately 40-fold higher level than MotCD stators. However, utilization of MotCD stators in free swimming cells requires higher liquid viscosities, while MotAB stators are readily utilized at low viscosities. Importantly, we find that cells with MotCD stators are ~10× more likely to have an active motor compared to cells uses the MotAB stators. The spectrum of motility intermittency can either cooperatively shut down or promote flagellum motility in WT populations. In P. aeruginosa, transition from a static solid-like biofilm to a dynamic liquid-like swarm is not achieved at a single critical value of flagellum torque or stator fraction but is collectively controlled by diverse combinations of flagellum activities and motor intermittencies via dynamic stator utilization. Experimental and computational results indicate that the initiation or arrest of flagellum-driven swarming motility does not occur from individual fitness or motility performance but rather related to concepts from the “jamming transition” in active granular matter. IMPORTANCE It is now known that there exist multifactorial influences on swarming motility for P. aeruginosa, but it is not clear precisely why stator selection in the flagellum motor is so important. We show differential production and utilization of the stators. Moreover, we find the unanticipated result that the two motor configurations have significantly different motor intermittencies: the fraction of flagellum-active cells in a population on average with MotCD is active ~10× more often than with MotAB. What emerges from this complex landscape of stator utilization and resultant motor output is an intrinsically heterogeneous population of motile cells. We show how consequences of stator recruitment led to swarming motility and how the stators potentially relate to surface sensing circuitry.


Supplemental Methods
Modeling of cell populations with heterogeneous motor outputs.
Using discrete element method simulations, we study the collective motion of an ensemble of active 2D circulo-lines.The ciculo-line shape accounts for the elongated geometry of bacteria.Each circulo-line i is defined by the endpoints,  !"   #" , length  " , and thickness, 2 " .A ciruclo-line is defined as the collection of points that are equidistant from the line connecting the vertices  !" and  #" .The aspect ratio of the circulo-line is , set to 4 for these simulations.Cicrulo-lines i and j interact via the pairwise, purely repulsive linear spring potential: where k is the spring constant, (. ) is the Heaviside step function, which prevents interactions between circulo-lines that are not in contact, rij is the closest distance between interacting circulo-lines, and  "& −  "& is the magnitude of the overlap between the two particles.Following the procedure described previously (1), we ensure that the potential energy and forces,  ⃗ " = ∇ : :⃗ " , are continuous when the circulo-lines come in and out of contact.
We initialize the simulations by placing 324 ciruclo-lines with random positions and orientations in a square box with side length L = 1, periodic boundary conditions, and low packing fraction φ= , " = 0.2.We quasistatically compress the system in small steps ∆ until we reach the final packing fraction φ0=0.96.
Each simulation contained a fixed fraction of active ( 3 >0) and inactive bacteria ( 3 =0), see Movies S5-8.The relative magnitude of the active force is measured compared to the repulsive interaction coefficient, k in equation (1).The flagellar motor is usually not uniformly pushing in one direction over long times, it may perform temporary stochastic events of motor halt or directionality reversal (2,3).Hence, to design more biologically relevant model we introduced stochasticity to the system by randomly selecting 0.1 fraction of the flagellum active particles ( 3 >0) to halt, and another 0.1 fraction to reverse direction of  3 .The states are updated every 4x10 -4  9 for 12  9 where is measured in the low-density limit.Finally, flagella are not always aligned with the cell body (2), hence the active force's direction is defined as: where were  is random angle between the cell body axis and the flagella as defined previously (2) with centered at /2 and standard deviation /8, restricted to values between 0 and .
The translational friction coefficients acting on each ciculo-line are calculated as described in (4): ) were  : denotes the viscosity of the solvent.
We also consider the rotational dynamics for each circulo-line following the Langevin equation: where , ,  ; ,  = , are moment of inertia, angular velocity, rotational friction coefficient, and torque from interaction with other circulo-lines.The rotational friction coefficients acting on each ciculo-line are calculated as described in (4): The values for the rotational and translational damping coefficients are set according to equation (2).The mean square displacement (MSD) for time ~ 5  9 was calculated as follows: = 1  D| " () −  " ( = 0)| # ?"@! ( 8 )