# Tunable simultaneous propulsion by two distinct motility motors drives surface motility in bacteria

Anna Mas,âm Mignot, Marcelo Nollmann, Antoine Le Gall

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Anna Mas,âm Mignot, Marcelo Nollmann, Antoine Le Gall. Tunable simultaneous propulsion by two distinct motility motors drives surface motility in bacteria. 2025. hal-05361664

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# Tunable simultaneous propulsion by two distinct motility motors drives surface motility in bacteria

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Anna Mas¹, Âtém Mignot², Marcelo Nollmann¹,* , Antoine Le Gall¹,*

¹ Centre de Biologie Structurale, CNRS UMR 5048, INSERM U1054, Université de Montpellier, 60 rue de Navacelles, 34090, Montpellier, France

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²Laboratoire de Chimie Bactérienne, Marseille, France.

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* Corresponding authors: marcelo.nollmann@cbs.cnrs.fr, antoine.legall@cbs.cnrs.fr

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## ABSTRACT

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Motility enables cells to adapt to environmental cues, explore surfaces, and coordinate collective behaviors. Many organisms encode multiple motility systems, but whether these act independently or can operate together within the same cell has remained unclear. The predatory bacterium *Myxococcus xanthus* has long been thought to segregate its two propulsion systems: focal adhesions for gliding (A-motility) and type IV pili for twitching (S-motility), with each acting in distinct contexts. Here we overturn this view by providing the first direct demonstration that both systems are co-deployed and active within individual cells. Using dual-color high-resolution imaging, quantitative single-cell tracking, and agent-based simulations, we show that calcium, a key regulator of pili activity, tunes their relative engagement, shifting cells from adhesion- to pili-dominated propulsion with intermediate states combining both. This dynamic co-propulsion allows wild-type cells to move faster and explore more effectively than single-motility mutants, revealing a synergistic mode of motility integration shaped by environmental context.

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**Keywords:** *Myxococcus xanthus*; motility; gliding; twitching; type IV pili; focal adhesion complexes; co-propulsion; single-cell tracking; calcium; agent-based modeling; environmental adaptation; synergy

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## INTRODUCTION

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Motility is a fundamental property of living cells, enabling them to explore their environment, coordinate collective behaviors, and adapt to changing conditions. While some cells rely on a single motility system, many organisms, including bacteria, archaea, protists, and higher eukaryotes, possess multiple, genetically distinct propulsion mechanisms ¹. For example, bacteria such as *Pseudomonas aeruginosa* can move through flagellar swimming or through type IV pili-dependent twitching motility ². Similarly, the archaeon *Sulfolobus acidocaldarius* utilizes its archaella for swimming and pili for surface motility ³. Among eukaryotic microbes, *Salpingoeca rosetta* alternates between a flagellum-powered swimming form and an amoeboid crawling form ⁴. This coexistence of distinct motility systems is therefore widespread, raising the question of whether they function independently or interact in coordinated ways to modulate behavior.
1 The soil bacterium *Myxococcus xanthus* provides a powerful model to address this question.

2 *M. xanthus* is a predatory bacterium that glides across surfaces to locate and consume prey, often coordinating its movements across large multicellular groups 5-8. To do so, it uses two

3 mechanistically distinct motility systems 5,9. A-motility (adventurous gliding) depends on two

4 envelope-spanning modules: the proton-motive-force-driven AglRQS motor and the force-
5 transducing Glt machinery 10. These membrane assemblies connect to the cytosolic scaffold

6 AglZ, which organizes motor assembly, and engage the CglB adhesin to form focal-
8 adhesion-like sites along the cell body 11,12. Proton flux through the Agl module drives traction

9 through the Glt module, anchoring via CglB and pulling the cell forward. Instead, S-motility (social twitching) relies on type IV pili: PilA subunits are polymerized into extracellular filaments by the PilB ATPase at the leading pole, attach to surfaces, and then retract via the PilT ATPase to drag the cell forward 13,14. Both systems are regulated by the small G-protein

13 MglA: in its GTP-bound form MglA localizes to a cell pole and promotes assembly of both A- and S-motility complexes, and reversal of cell direction is triggered by the Frz chemosensory pathway, which relocalizes MglA (and hence both machines) to the opposite pole 15,16. This

16 shared regulation suggests that both machines could be deployed together within the same
cell. However, the prevailing view has been that the two systems act separately: A-motility predominates on stiff substrates and was classically attributed to isolated “scout” cells at the colony edge, while S-motility is favored on softer surfaces and was associated with collective

20 swarming behind the colony front 17,18. Whether A- and S-motility truly operate independently
21 in different contexts or can function concurrently within individual cells has remained an open
22 question.

23 In a previous study, we challenged the view that A- and S-motility operate in strictly separate
24 contexts by showing that mutants lacking either system intermingle within both solitary cells
25 and multicellular groups during predation, and that neither motility system is restricted to one
26 of these collective arrangements 19. Moreover, we observed that solitary “scout” cells can
27 join swarms, and conversely that swarm members can disperse into solitary cells, indicating
28 that these collective states are dynamically interchangeable. These findings suggested that
29 wild-type cells, which retain both motility systems, are capable of transitioning between
30 solitary and collective modes, but how the two systems are deployed and coordinated within
31 individual cells has remained unresolved. Despite over four decades of work on *M. xanthus*
32 motility 5,17, direct evidence for simultaneous use within the same cell had remained elusive
33 until now. A full understanding has been impeded by two key challenges. First, although A-
34 motility can be visualized by genetically tagging focal-adhesion components, S-motility
35 demands high sensitivity and specificity labeling and imaging of type IV pili, making
36 simultaneous visualization of both systems technically challenging. Second, simply seeing
37 pili does not prove they power movement: they may also be present for other functions,
38 including DNA uptake, surface sensing, etc. 20. Therefore, new technological developments
39 coupling dual-label imaging with quantitative assays are needed to show that each machine
40 genuinely drives propulsion.

40 genuinely drives propulsion.
39 coupling dual-label imaging with quantitative assays are needed to show that each machine
41 Here, we addressed this challenge by combining multi-modal high-resolution imaging,
42 single-cell trajectory analysis, and agent-based simulations to dissect how A- and S-motility
43 systems are deployed and contribute to movement in *M. xanthus*. We show that both
44 systems can operate simultaneously within the same cell and jointly drive propulsion. By
45 systematically varying calcium concentration, a key modulator of pili function, we reveal that
46 wild-type cells dynamically adjust the relative engagement of each motility system to external
47 conditions. This flexibility allows them to switch from predominantly A-motility-like movement
48 at low calcium, to S-motility-like movement at high calcium, and to an intermediate state with
49 features of both modes. Our findings show that the behavior of wild-type cells cannot be
50 explained by an additive contribution of the two motility systems, but instead exhibit an
51 emergent, integrated mode of motility shaped by environmental conditions.

51 emergent, integrated mode of motility shaped by environmental conditions.
49 features of both modes. Our findings show that the behavior of wild-type cells cannot be
50 explained by an additive contribution of the two motility systems, but instead exhibit an
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# 1 RESULTS

# 2 A-Motility Is Propulsive and Widespread Across Biofilm Cell Populations

Previous models proposed that A-motility and S-motility are segregated at the population level, with A-motility driving solitary "scout" cells at the colony edge and S-motility dominating collective swarms behind the front. To test whether both motility systems can in fact be deployed within the same cells, we considered three possible modes of integration: cells where only one motility system is present and that can therefore move only by one or the other system (mutually exclusive deployment model), cells where both motility systems are present but where only one is active at any given time (co-deployment with alternating propulsion model), or cells where both motility systems are present and can be active (co-deployment with simultaneous co-propulsion model) (Fig. 1a). To distinguish between these models, we developed a multi-modal high-resolution, dual-color imaging strategy (Fig. 1b, Fig. S1a). Phase-contrast imaging was used to detect and track the individual cells positions at high spatiotemporal resolution (Fig. 1b, Fig.S1b), while a combined TIRF/HILO fluorescence imaging system allowed us to monitor multiple fluorescent reporters simultaneously to detect the presence and function of A and S-motility systems in single cells (Fig. 1b; Fig. S1a, Movie S1).

A-motility was classically associated with isolated "scout" cells, therefore we expect A-motile cells to be present predominantly at the colony edge. To directly monitor A-motility activity, we tagged AglZ, a cytoplasmic component of the Agl-Glt machinery, with YFP and visualized it in TIRF mode to reveal substrate-anchored focal adhesions; static AglZ-YFP clusters mark propulsive adhesion sites<sup>11,12</sup>. In a predation assay (Fig. 1c), wild-type cells expressing AglZ-YFP were plated with prey on nutrient-poor agar and imaged after 48h of co-incubation. As expected, AglZ-YFP clusters were detected in isolated cells at the colony front (Fig. 1d). Strikingly, such clusters were also present in small swarms (Fig. 1e) and in large swarms behind the front (Fig. 1f). To assess whether these clusters were propulsive, we examined multiple cells from each population type: kymographs of AglZ-YFP intensity consistently exhibited vertical streaks (Fig. 1g-i), the hallmark of static, substrate-anchored adhesions. Quantitative tracking confirmed that over 41% remained immobile relative to the substrate (Fig. 1j). Together, these data demonstrate that A-motility is actively deployed and propulsive in all cell contexts, including the high-density swarms where S-motility was thought to dominate. While we expect that S-motility will contribute to, or even drive, collective cell motion within high-density swarms, high cell densities prevented direct visualization of S-motility structures within these dense structures.

# 37 Motility Systems Are Co-Deployed At The Single-Cell Level

To overcome this limitation and directly monitor motility system deployment at the single-cell level, we performed high-resolution imaging of isolated cells deposited on a 0.75 % agar pad (an intermediate concentration chosen to promote both systems), covered with a coverslip. To directly detect if both motility systems are present in single cells, we imaged wild-type cells co-expressing AglZ-YFP and fluorescently labeled pili. For the latter, PilA, the major type IV pilin, was labeled with Atto 565 and imaged under HILO illumination to detect pili
within ~1 µm of the surface²¹. Remarkably, in wild-type cells, focal adhesions and Atto 565-labeled pili were often detected in the same single cells (Fig. 1k; Movies S3-S4), directly demonstrating that A- and S-motility systems are co-deployed. Kymograph and cluster tracking of AglZ-YFP (Fig. 1l-m, Fig. S1c) confirmed that adhesion sites in these wild-type cells were propulsive. To verify that each motility system can function on its own, we also imaged motility mutants where either A-motility (A+S+) or S-motility (A+S−) were inactive. In the A+S− mutant (Δ*pilA*), AglZ-YFP clusters were clearly visible and associated with cell propulsion, confirming that A-motility remained fully deployed and functional under these conditions (Fig. 1k; Fig. S1d). In contrast, the A+S+ mutant (Δ*aglQ*) deployed pili at its leading pole but did not move, indicating that S-motility machinery can be deployed without necessarily driving propulsion in isolated cells (Movie S5). Notably, wild-type cells (A+S+) moved slightly faster than A+S− mutants, with no obvious differences in the relative movements of the leading and lagging poles, a proxy for “cell pole behaviors” that will be analyzed in detail below (Fig. S1e-g). This observation raised the possibility that pili may augment propulsion when A-motility is active, though with only minimal effect on overall cell movement on agar. In summary, these experiments establish that both motility systems can be deployed within the same single cell. Whether they also operate simultaneously to drive propulsion remains however unresolved, as S-motile cells are poorly motile under these conditions.

## Co-Propulsion by A- and S-Motility Enables Distinct Behaviors and Faster Movement Within Single Cells

To directly test whether both motilities can be active in single cells, we implemented a setup where both A+S− and A+S+ mutants are able to move processively. Specifically, we employed a microfluidic chamber with coverslips coated with chitosan, a polysaccharide that promotes A-motility²². Under these conditions, both motility systems remain co-deployed (Movie S6) as in agar, and both A+S− and A+S+ mutants were able to move (Fig. S2a), confirming that chitosan-coated surfaces support processive motility by either system. This provided the conditions needed to ask whether A+S+ cells can engage both motility systems simultaneously within the same cell.

Notably, joint detection of AglZ-YFP foci and of pili in A+S+ cells indicate that both motilities are simultaneously deployed in chitosan-coated coverslips (Fig. 2a, Movie S7). To determine if A-motility remains propulsive on this substrate, we analyzed AglZ-YFP clusters using kymographs (Fig. 2b) and cluster tracking (Fig. 2c). These analyses showed that many AglZ-YFP foci remained stationary relative to the substrate, consistent with active focal adhesions throughout the A+S+ population. Fluorescently-labeled pili localized at the leading pole of the same cells displaying static AglZ-YFP clusters, demonstrating that both A and S motilities can be co-deployed. Interestingly, quantification of the number of static AglZ-YFP clusters per cell was similar in A+S+ and A+S− strains (Fig. 2d; Fig. S2b), indicating that pili does not affect the assembly of focal adhesion complexes under these conditions.

To determine whether pili also contribute to propulsion in A+S+ cells, one would need to correlate individual pilus extension-retraction events with cell movement. In practice, however, pilus fluorescence is dim and photobleaches rapidly, and its fast kinetics of retraction demand both high frame rates and light doses that lead to phototoxicity. These constraints make it impractical to directly link pilus retraction events to cell propulsion, so we instead sought a robust signature of active pilus retraction. By first examining the trajectories of the leading pole, the cell center, and the lagging pole of A+S+ cells, we noticed pronounced deflections of the leading pole relative to the cell axis (Fig. 2e; Fig. S2c, Movie S8), suggesting that pili might pull the leading pole off-axis even in the presence of
1 stationary focal adhesions. To minimize the confounding effect of cellular reversals, we
2 tracked the same three trajectory positions in ΔfrzE backgrounds. In A+S⁻ (Δ*pilA*, ΔfrzE)
3 cells, both the leading and lagging poles remained tightly aligned with the cell center,
4 yielding straight trajectories (Fig. 2f, Movie S9). By contrast, A⁻S⁺ (Δ*aglQ*, ΔfrzE) cells
5 displayed pronounced leading-pole deflections while their lagging poles stayed aligned,
6 confirming that pilus activity alone can shift the leading pole off-axis (Fig. 2g, Movie S9).
7 Remarkably, A+S⁺ (ΔfrzE) cells also displayed large leading-pole fluctuations while
8 maintaining lagging-pole alignment (Fig. 2h, Movie S9).

9 To quantify these deflections, we measured the angle distributions between the leading pole
10 and the cell axis. In A+S⁺ cells, these angles reached up to ~45° (Fig. 2i, Fig. S2d). In
11 contrast, the leading poles of A+S⁻ cells fluctuated in a considerably narrower range (~20°,
12 Figs. 2j, S2d), consistent with the activity of pili being responsible for these deflections. As
13 expected, the leading pole of A+S⁺ cells deflected with similar amplitudes to A+S⁺ cells (Fig.
14 2k, Fig. S2d), in agreement with large off-axis deviations of the pole being driven by pilus
15 activity. Altogether, these data show coexisting stationary AglZ-YFP foci and pronounced
16 pole fluctuations, therefore supporting the view that focal adhesions and pili are not only co-
17 deployed but also jointly power single-cell propulsion across the A+S⁺ population.

18 Next, we investigated how co-deployment of both systems impacts single-cell movement by
19 extracting 16 local motility descriptors, such as tortuosity, local radius of gyration, directional
20 autocorrelation, and angular dispersion, along each of the leading-pole, cell-center, and
21 lagging-pole trajectories (48 features in total, see Supplementary Material). To remove any
22 time dependence, all trajectories were first rediscretized at uniform distance intervals. These
23 descriptors therefore strictly captured how each tracked positions of the cell sample its local
24 environment in terms of path geometry, directionality, and area explored. Importantly, none
25 of the 16 features included time-derivative information such as velocity or acceleration,
26 ensuring that these features focused strictly on how cells explore space rather than how fast
27 they move.

28 To visualize and compare these complex, high-dimensional behavioral profiles, we projected
29 the features into two dimensions using Uniform Manifold Approximation and Projection
30 (UMAP), a nonlinear dimensionality reduction method that preserves local structure ²³. In this
31 projected "behavioral space," each point corresponds to a segment of a cell trajectory
32 described by its local motility features, so that proximity in the plot reflects similarity in
33 movement patterns. Data points from A+S⁻, A⁻S⁺, and A+S⁺ cells formed largely distinct
34 clusters (Fig. 2l; Fig. S2e-h), with only minimal overlap between strains. Notably, this
35 segregation emerged without providing any information about the cell genotype, indicating
36 that strain-specific behaviors can be reliably distinguished based solely on local spatial
37 features of cell motion. A+S⁺ cells occupied a clearly separate region of the behavioral space,
38 distinct from both A+S⁻ and A⁻S⁺ mutants, confirming that they exhibit a unique motility
39 signature that differs from either single-motility system alone.

40 Finally, we compared the speeds of the three strains to evaluate the functional impact of co-
41 existing motility systems. Remarkably, A+S⁺ cells moved approximately twice as fast as
42 either single-motility mutant (Fig. 2m; Fig. S2i), indicating that co-propulsion results not only
43 in behavioral diversification but also in enhanced locomotion capacity. This raised the
44 question of why A+S⁺ cells move faster. Does co-propulsion simply sum forces from both
45 systems, or is their activity regulated?

46 Motility Behavior and Propulsion Dynamics Are Modulated by
47 Environmental Conditions
1 To test if A- and S-motility systems may be regulated, we next examined how environmental
cues influence their relative activity. We varied extracellular CaCl₂ which is known to
3 modulate pili activity via PilY1-dependent assembly and stabilization<sup>24,25</sup>, while noting that
4 calcium may also affect A-motility<sup>26</sup>.

5 We leveraged the ability of our microfluidic system to deliver buffers to the same cell cohort
6 to progressively expose the same cells to increasing CaCl₂ concentrations (from 0 to 4 mM)
7 while measuring the impact to cell velocities and behavior. In the A⁺S⁻ mutant, cells
8 remained motile at all CaCl₂ levels, with the leading/lagging poles and the cell center tracing
9 straight, tightly aligned paths (Fig. 3a; Movie S10). At 0 mM CaCl₂, cells displayed short
10 trajectories, which lengthened at moderate calcium concentrations before shortening again
11 at higher levels, suggesting a non-linear effect of calcium on A-motility. By contrast, the A⁻S⁺
12 mutant was immotile below ~0.7 mM CaCl₂ and then displayed a sharp activation of motility,
13 exhibiting processive movement and growing pole fluctuations at higher CaCl₂
14 concentrations (Fig. 3b, Movie S11). Overall, these results indicate that calcium activates S-
15 motility in line with previous studies<sup>25</sup>, but also confirm that CaCl₂ modulates A-motility
16 behavior<sup>26</sup>.

17 Remarkably, A⁺S⁺ cells combined both behaviors: they glided with no detectable pili activity
18 signature at 0 mM [CaCl₂], acquired leading-pole fluctuations from 0.4 mM onward, and at
19 ≥0.9 mM the lagging pole progressively lost alignment with the cell center, indicating that the
20 entire cell increasingly follows pilus-driven fluctuations initiated at the leading pole (Fig. 3c,
21 Movie S11). Together, these observations suggest that wild-type cells gradually shift from
22 adhesion-dominated to pili-dominated propulsion as calcium concentration increases, rather
23 than relying on one system exclusively.

24 To better capture how motility patterns change with calcium, we summarized the 48 motility
25 features into a two-dimensional “behavioral space” using UMAP (Fig. 3d; Fig. S3a-h). In this
26 space, proximity reflects similarity in movement patterns. A⁺S⁻ and A⁻S⁺ mutants each
27 progressed smoothly through distinct regions as [CaCl₂] increased, consistent with gradual
28 modulation of a single motility system. By contrast, A⁺S⁺ cells occupied three separate
29 regimes. At 0 mM [CaCl₂] they overlapped with the A⁺S⁻ cluster, consistent with pure gliding
30 (Fig. 3d, left). At 4 mM [CaCl₂] they not only entered but extended beyond the A⁻S⁺ region,
31 indicating amplified twitching dynamics (Fig. 3d, right). At intermediate concentrations (0.4-2
32 mM), A⁺S⁺ cells traced a clear transition zone: around 0.4 mM their profiles closely matched
33 A⁺S⁻ cells at 0.9 mM, while already showing partial overlap with A⁻S⁺ cells at the same
34 concentration; as [CaCl₂] rose toward 2 mM, they progressively entered the A⁻S⁺ region
35 defined at higher calcium. This trajectory demonstrates that wild-type cells do not simply
36 blend gliding and twitching behaviors but instead switch their dominant motility mode in
37 response to calcium.

38 To assess the functional impact of these behavioral transitions, we next measured cell
39 speeds across calcium concentrations. A⁺S⁻ cells maintained a rather constant speed profile
40 peaking around 2mM [CaCl₂] (Fig. S3i), whereas A⁻S⁺ cells were immotile below ~0.7 mM
41 and then showed a sharp increase in velocity with rising calcium. These patterns confirm
42 that calcium independently modulates both motility systems: it subtly tunes adhesion-based
43 gliding while strongly activating and amplifying pili-driven twitching. A⁺S⁺ cells combined
44 features of both mutants: matching pure gliding speeds at zero calcium and ultimately
45 surpassing each single-motor strain across the entire CaCl₂ range (Fig. 3e; Fig. S3i-k).
46 Strikingly, at 4 mM [CaCl₂] A⁺S⁺ cells even outpaced the summed maximal speeds of the
47 mutants, demonstrating a synergistic interaction between A- and S-motility.
1 Together, these data indicate that calcium modulates each motility system individually but,
2 more importantly, enhances their simultaneous engagement in the same cell. This co-
3 propulsion produces behaviors and speeds that neither system achieves alone under the
4 same conditions. We therefore propose that mechanical interactions between focal
5 adhesion- and pili-based motility underlie this emergent synergy.

6 Agent-based Model Reproduces A<sup>+</sup>S<sup>-</sup> and A<sup>-</sup>S<sup>+</sup> Motility Dynamics

7 To test different hypotheses about how each motility system contributes to movement, we
8 developed a biophysical simulation framework that models bacterial motility based on the
9 underlying mechanisms. For this, we implemented an agent-based model in which the
10 bacterium is represented as a deformable body, a linear chain of six disks connected by
11 Hookean springs, with bending penalties to enforce shape persistence and velocity-
12 proportional damping to capture surface friction and viscous drag (Methods) 27-29. This
13 flexible architecture preserves overall cell integrity while allowing local deformations. Disk
14 positions are updated using Verlet integration to ensure numerical stability. Importantly, all
15 core mechanical parameters and nearly all motility-specific parameters (described hereafter)
16 were drawn from the literature or directly constrained by our experimental data
17 (Supplementary Table 3).

18 A-motility is implemented via focal adhesion complexes modeled as static substrate anchors
19 that are deposited whenever the leading disk advances beyond a threshold distance from
20 the previous anchor. Each focal adhesion complex generates a force aligned with the local
21 axis of the cell and acts as a spring-like tether on any disk that overlaps with its position.
22 This dual role captures both the directional propulsion and substrate anchoring characteristic
23 of focal adhesion-based motility (Fig. 4a). To reflect intrinsic and cell-to-cell variability we
24 varied key focal adhesion parameters (force magnitude, spring stiffness, deposition distance
25 threshold) with noise within and between simulation runs. We did not include calcium
26 dependence here because our experiments detected only modest changes in A-motility
27 speed across the 0-4 mM CaCl₂ range (Fig. 3e). This simulation produced straight, tightly
28 aligned trajectories for the leading pole, center, and lagging pole (Fig. 4b, Movie S13), and
29 instantaneous-speed distributions that resemble the experimental data across CaCl₂ levels
30 (Fig. 4c).

31 We next modeled A⁻S⁺ cells by modeling type IV pili activity at the leading pole (Fig. 4d). In
32 this model, pili stochastically bind the substrate within a defined angular range and retract at
33 0.5 µm/s velocity ¹⁴. In a similar way to A⁺S⁻ modeling, we varied key pili parameters
34 (binding rate, retraction rate, maximum pilus count, length, stall force, angular binding range)
35 with noise within and between simulation runs to reflect intrinsic and cell-to-cell variability. In
36 these simulations, we did not consider variations in pilus length or number with [CaCl₂] as
37 fluorescent labeling of pili revealed no consistent change in pilus length or number across
38 calcium concentrations (Fig. S4a). Instead, we varied pilus binding lifetime as a proxy for
39 calcium-mediated stabilization. Simulated trajectories closely resembled experimental
40 patterns (Fig. 3b), with characteristic center-lagging pole alignment and enhanced leading
41 pole fluctuations (Fig. 4e, Movie S14). Moreover, instantaneous speed distributions closely
42 matched the experimental data (Fig. 4f). The only exception was below 0.4 mM CaCl₂,
43 where experimental A⁻S⁺ cells were largely immobile while simulated cells remained motile,
44 suggesting a potential biological activation threshold or regulatory mechanism that limits S-
45 motility under low-calcium conditions in vivo, possibly linked to calcium-binding affinity.

46 A-Motility Enhances S-Motility Independently of Focal Adhesions
1 We then asked whether simulations combining A+S⁻ and A⁻S⁺ models could recapitulate
2 experimental A+S⁺ motility patterns (Fig. 4g). To address this question, we first constructed
3 an “additive” A+S⁺ model without synergy (where both systems acted independently from
4 each other). The simulated trajectories resembled those of experimental A+S⁺ cells at 2-4
5 mM CaCl₂ (Fig. 4g), with similar leading pole fluctuations and center-lagging pole
6 coordination (Fig. 4h vs. 3c, Movie S15). However, below 0.9 mM the simulated cells
7 showed reduced leading-pole fluctuations compared to the experiments, indicating they lack
8 the dynamic leading pole deflections of true A+S⁺ cells. Next, we compared instantaneous
9 speeds. Across most CaCl₂ levels (0-2 mM), the additive model tracked the experimental
10 speed curves faithfully (Fig. 4i). Yet at 4 mM CaCl₂ the simulated cells showed statistically
11 lower speeds than experiments. Together, these results show that, within the confines of our
12 simplified model, simply summing A- and S-motility without synergy fails to account for both
13 leading pole fluctuation dynamics and velocities across the whole CaCl₂ range. This points
14 to additional cross-modulation or mechanical coupling between the two systems as the basis
15 for the emergent co-propulsive synergy in A+S⁺ cells.

16 To explore whether the two motility systems influence one another beyond simple co-
17 deployment, we quantified focal adhesion complexes (FACS) across CaCl₂ levels. In A⁺S⁻
18 cells, the mean number of FACS decreased with rising [CaCl₂] and stabilized at roughly one
19 adhesion per cell at high calcium (Fig. 4j; Fig. S4b,d, Movie S16), consistent with A-motility
20 being the sole propulsion mechanism in this background and indicating that CaCl₂ also
21 impacts the assembly of focal adhesions. In A⁺S⁺ cells, the mean number of FACS
22 complexes per cell displayed a similar decrease with [CaCl₂] below 0.9 mM. In contrast, the
23 mean number of FACS was lower than one per cell above 2 mM (Fig. 4j; Fig. S4c,e, Movie
24 S17). This is a surprising observation given that A+S⁺ speeds continued to increase with
25 [CaCl₂] above 2mM. These observations can be explained by a considerable proportion of
26 A+S⁺ cells moving without detectable adhesions at high CaCl₂ concentrations. To test this
27 hypothesis, we analyzed how the mean number of FACS varies with instantaneous cell
28 speed. In A⁺S⁻ cells, the mean number of FACS remain above one up to the maximum
29 speed of the cells (~ 2 µm/min) (Fig. 4k). Instead, in A⁺S⁺ cells the mean number of FACS
30 per cell decreased steadily with speed, and on average, vanished above ~6 µm/min (Fig. 4k,
31 Fig. S4f-g). These observations demonstrate that, at high CaCl₂ levels, wild-type *M. xanthus*
32 cells can achieve rapid, focal adhesion-independent movement. Yet A⁺S⁺ cells still outpace
33 A⁻S⁺ mutants under the same conditions, suggesting that the A-system enhances S-motility
34 through a mechanism other than direct traction by focal adhesions.

35 To test this directly, we examined an A-motility mutant lacking the surface adhesin but
36 retaining a complete A-motor complex (ΔcglB ΔfrzE, hereafter A<sup>ΔcglB</sup>S<sup>+</sup>). This strain thus
37 retains pili-based propulsion and a non-anchored A-system with a functional A-motor activity.
38 Kymographs of AgIZ-YFP in A<sup>ΔcglB</sup>S<sup>+</sup> cells confirmed the presence of dynamic A-motility
39 complexes without any static adhesions (Fig. S4h, Movie S19). In absence of CaCl<sub>2</sub>, A<sup>ΔcglB</sup>S<sup>+</sup>
40 cells were immotile, consistent with the absence of functional A- and S-motilities in this
41 condition (Fig. 4l, Movie S18). In contrast, above 0.4 mM [CaCl<sub>2</sub>], A<sup>ΔcglB</sup>S<sup>+</sup> cells displayed
42 increasing leading-pole oscillations and lagging-pole decoupling, closely mirroring the CaCl<sub>2</sub>
43 response of A<sup>+</sup>S<sup>+</sup> cells. Strikingly, at 0.4 mM [CaCl<sub>2</sub>] A<sup>ΔcglB</sup>S<sup>+</sup> cells no longer occupy the
44 gliding-associated region seen in A<sup>+</sup>S<sup>+</sup> cells but instead map onto the twitching-like sector
45 characteristic of A<sup>-</sup>S<sup>+</sup> behavior, indicating early pili engagement before the A<sup>-</sup>S<sup>+</sup> mutant even
46 moves, suggesting that the A-system facilitates or accelerates the onset of pili-based
47 motility. At calcium levels above 0.7 mM, A<sup>ΔcglB</sup>S<sup>+</sup> trajectories overlapped extensively with
48 A<sup>+</sup>S<sup>+</sup> cells, yet remained distinct from A<sup>-</sup>S<sup>+</sup> cells (Fig. 4m; Fig. S4i-l). Speed measurements
49 echoed this pattern: A<sup>ΔcglB</sup>S<sup>+</sup> cells matched the velocities of A<sup>+</sup>S<sup>+</sup> cells up to 2 mM and
50 reached similar speeds at 4 mM (Fig. 4n; Fig. S4m). Notably, these findings demonstrate
1 that a non-anchored A-system can boost S-motility by a mechanism that does not require

2 focal adhesion-driven activity.

3 Finally, we sought to rationalize how the A-system could facilitate S-motility without engaging
4 substrate adhesion using simulations. First, we hypothesized that A<sup>ΔcglB</sup>S<sup>+</sup> cells may have
5 larger pili conferring them the ability to move faster than A-S<sup>+</sup> cells. However, increasing the
6 length of pili from 5 µm to 10 µm failed to reproduce the experimental speeds of A<sup>ΔcglB</sup>S<sup>+</sup>
7 cells, particularly underestimating instantaneous velocities at low CaCl<sub>2</sub> levels and remaining
8 below experimental values even at high calcium concentrations (Fig. S4n). Second, we
9 hypothesized that A<sup>ΔcglB</sup>S<sup>+</sup> cells may have a higher number of pili. Doubling pilus number
10 brought simulated velocities much closer to the data across the CaCl<sub>2</sub> concentration range,
11 though it still underestimated speeds at intermediate CaCl<sub>2</sub> levels (Fig. S4o). Consistent with
12 these in silico predictions, we observed no consistent differences in pilus length or number
13 when we observed fluorescently labeled pili in A<sup>ΔcglB</sup>S<sup>+</sup> across a range of [CaCl<sub>2</sub>], from 0.7 to
14 4 mM (Fig. S4p). Third, we tested if variations in pilus retraction rate, maximal stall force,
15 and mechanical damping with [CaCl<sub>2</sub>] can explain the behavior of A<sup>ΔcglB</sup>S<sup>+</sup> cells. Simulations
16 where we increased the retraction rate from 30 µm/min to 60 µm/min failed to improve the
17 speed profiles, with simulated velocities consistently underestimated across all calcium
18 concentrations (Fig. S4q). In contrast, either raising the stall-force limit (from 70 to 200 pN)
19 or reducing the damping coefficient (from 8 to 2.5 pN·min/µm) yielded simulated trajectories
20 that closely match the experimental patterns (Fig. 4o, Movie S20) and produced speed
21 distributions that nearly superimpose on the experimental curves of A<sup>ΔcglB</sup>S<sup>+</sup> cells across
22 calcium concentrations (Fig. 4p; Fig. S4r). These results suggest that the A-motility system,
23 even when unanchored, may facilitate S-motility by increasing pilus strength or reducing
24 resistance to cellular motion.

25 DISCUSSION

26 Many motile cells, from bacteria to eukaryotes, harbor more than one motility system, but
27 whether these systems are simultaneously active in single cells, and to what functional end,
28 remains poorly understood. In this study, we investigated how A-motility and S-motility
29 contribute within single *Myxococcus xanthus* cells. We demonstrate that these motility
30 systems are not only both deployed in single cells but can contribute in multiple ways: jointly
31 propelling cells, with one system facilitating the other or, under certain conditions, operating
32 independently. These findings establish that dual-motility usage produces emergent
33 behaviors not achievable by either system alone, providing new insight into how distinct
34 bacterial motility motors integrate.

35 Cells carrying both motility systems exhibited behaviors distinct from either A<sup>+</sup>S<sup>-</sup> or A<sup>-</sup>S<sup>+</sup>
36 mutants: they moved faster and displayed movement patterns that could not be reproduced
37 by simply summing single-motor behaviors. Strikingly, even when A-motility's focal-adhesion
38 complexes are absent, as in a ΔcglB mutant, cells still behave as A<sup>+</sup>S<sup>+</sup> trajectories under
39 high CaCl<sub>2</sub> concentration conditions. This shows that the A-system can facilitate S-motility
40 even without generating traction through adhesions. This effect cannot be explained solely
41 by the loss of CglB-mediated adhesion, since wild-type cells retain CglB yet move even
42 faster than A<sup>-</sup>S<sup>+</sup> at high Ca<sup>2+</sup> when focal adhesions vanish. Thus, facilitation requires A-
43 motor activity rather than simple release of a CglB 'brake'. Simulations suggest that this
44 facilitation may arise either from amplification of pilus-generated forces or from reduction in
45 mechanical resistance. In *M. xanthus*, theoretical work has proposed that slime secretion
46 alters cell-substrate interactions, potentially acting as a lubricant or coupling agent <sup>30</sup>,
47 although this remains to be demonstrated experimentally. Notably, recent genetic evidence
48 implicates the type II secretion secretin GspD in the release of the A-motility-associated
1 exopolymeric slime in *M. xanthus* and that their loss impairs A-motility ³¹, consistent with a

2 functional link between slime production and A-system activity.

3 Moreover, we show that CaCl₂ within environmentally relevant concentrations (0-4 mM

4 CaCl₂; within the broader soil range of ~0.01-14 mM ³²) progressively shifts cells from

5 gliding- to pili-dominated motility. At low CaCl₂ concentration, highly directional A-motility

6 dominates; as CaCl₂ concentration rises, pilus-driven pulling becomes more prominent while

7 the number of focal adhesions decreases, to the point that in fast-twitching cells adhesions

8 are largely absent. Yet A-motility remains functional under the same conditions, as shown by

9 motility of A+S⁻ cells, indicating that high pilus activity can suppress adhesion engagement,

10 consistent with a mechanical limit where focal adhesions cannot keep up with the rapid, pili-

11 driven motion. Calcium has previously been reported to promote pili assembly and adhesion

12 ²⁴,²⁵ and to modulate A-motility ²⁶, but our results reveal for the first time how calcium

13 oppositely biases the two systems within the same cells, uncovering a mechanical coupling

14 between them.

15 In *M. xanthus*, calcium binds PilY1.1's EF-hand-like and DUF4114 domains to prime type

16 IVa pilus assembly and strengthen pilus-EPS adhesion ²⁵,³³. By strengthening these pilus-

17 matrix contacts, calcium promotes EPS-dependent twitching. In other bacteria, such as

18 *Pseudomonas aeruginosa* and *Xylella fastidiosa*, environmental calcium similarly promotes

19 early biofilm development by stabilizing adhesin activity and increasing matrix production

20 ³⁴,³⁵. By analogy, in *M. xanthus* calcium-enhanced twitching could favor initial clustering of

21 cells, while subsequent re-engagement of gliding could disperse cells to colonize new

22 territory, positioning calcium as a switch between aggregation and dispersal during surface

23 colonization.

24 We also note that the two systems confer distinct mechanical advantages: A-motility

25 produces unidirectional gliding ideal for long-distance "scouting" without steering, whereas

26 S-motility's rapid pilus retraction allows cells to turn more frequently which favours local

27 exploration. Wild-type cells combine these modes, keeping both engines primed and

28 engaging them according to external conditions, achieving speeds beyond either system

29 alone. Although our analysis focused on single-cell trajectories, the fact that A-motility forms

30 FACS in large and small swarms suggests it may do more than drive isolated "scouts".

31 Indeed, Rombouts et al. observed that wild-type swarms exhibit higher directional

32 persistence than A-S⁺ swarms ¹⁹, implying that A-motility could bias collective motion toward

33 a shared direction, thereby providing directional persistence.

34 Interestingly, Faure et al. demonstrated that A-motility generates significant rotational torque

35 on the cell body ¹². At the same time, type IV pili exert strong, unidirectional pulling forces,

36 raising the question of how these contrasting mechanics coexist within a single cell. One

37 possibility is that pilus extension-retraction cycles are much faster than the cell's rotational

38 period during gliding, so individual pili never build up prohibitive torsional stress. Additionally,

39 compliant linkages in the pilus machinery, or transient mechanical uncoupling between the

40 A- and S-systems, could absorb residual torque, allowing both motors to function together.

41 In *M. xanthus*, the MglA-Frz system regulates the assembly and reversals of both A and S

42 systems ¹⁵,¹⁶. The fact that they are both present and active in single cells implies that MglA-

43 Frz need to regulate both systems simultaneously. In its GTP-bound form, MglA recruits both

44 Agl-Glt adhesions and the PilB/PilT pilus apparatus to one pole via GltJ and SgmX ²¹,³⁶, while

45 Frz periodically flips MglA to trigger reversals. MglA-GTP must therefore partition to

46 assemble both complexes at the same pole. The motility systems' relative engagement is

47 then likely determined by downstream effectors or by mechanical coupling between pili and

48 focal adhesions. For instance, when pili generate stronger traction, focal adhesions tend to
1 dissipate. Elucidating how Ca²⁺-sensitive effectors (e.g. PilY1, CglB) or other downstream

2 regulators bias this interplay will be key to understanding how one small G-protein switch

3 integrates two propulsion modules into a coherent, context-responsive motor.

4 More broadly, our findings illustrate how multiple motility systems can combine in ways that

5 go beyond redundancy or task specialization. Rather than acting independently, the A- and

6 S-motility systems of *M. xanthus* exhibit a form of mechanical and functional synergy³⁷,³⁸.

7 This principle may extend beyond bacteria: many eukaryotic cells also rely on multiple

8 motility mechanisms³⁹ (e.g., actin-driven crawling and cilia-based swimming), and

9 understanding how these are integrated may reveal general strategies for achieving adaptive

10 movement.

11 Taken together, our work shows that the coexistence of motility systems can be an

12 opportunity for emergent coordination. In *M. xanthus*, this coordination allows motility

13 patterns to shift with environmental conditions, producing behaviors that neither system

14 achieves alone. These findings establish a framework for exploring how physical and

15 functional interactions between motility systems contribute to cellular adaptability across

16 diverse biological systems. Similar principles may apply in other microbial contexts where

17 cells combine appendage-based and surface-dependent motility, such as flagella and pili

18 ¹,⁴⁰,⁴¹, or shift between motile and sessile states ⁴²⁻⁴⁴. Motility integration may thus represent a

19 widespread strategy for maximizing behavioral flexibility in fluctuating environments.

20

21 Acknowledgments

22 This project was funded by the French National Research Agency (grant ID ANR-23-CE13-

23 0036) and the CBS2 doctoral school (A.M. scholarship). The CBS is a member of the

24 France-Biolmaging, a national infrastructure supported by the French National Research

25 Agency (ANR-10-INBS-04-01). We further thank Emmanuel Margeat (CBS) for providing

26 maleimide fluorophores for Type IV pili labeling, Laetitia My for her help in strain construction

27 and Flora Honore (LCB) for sharing her experimental protocol for studying *M. xanthus* in

28 Ibidi-chitosan microfluidics chambers.

29

30 Author Contributions

31 A.L.G., A.M., M.N., and T.M. conceived the study and the design. A.M. built new strains,

32 implemented the experimental protocols, and acquired the data. A.L.G. and A.M. analyzed

33 the data. A.L.G. built the microscope. A.L.G. and A.M. wrote the software. A.L.G. and A.M.

34 performed simulations. A.L.G., A.M., and M.N. interpreted the data. A.L.G. and A.M. wrote

35 the first manuscript draft. A.L.G., A.M., M.N., and T.M. contributed to manuscript editing.

36 A.L.G., M.N., and T.M. supervised the study and acquired funding.

37

38 Competing interests statement

39 The authors declare no competing interests.
bioRxiv preprint doi: https://doi.org/10.1101/2025.10.29.685086; this version posted October 29, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

<figure><img src="image_2.png" /><figcaption>Figure 1</figcaption></figure>
1 **Figure 1 : A-Motility Is Propulsive in Biofilms and Co-Deployed with S-Motility in**
2 **Individual Cells**

3 **(a)** Models for the deployment and usage of motility systems in *Myxococcus xanthus* cells.
4 Top: Exclusive deployment model, cells deploy and propel using only one motility system at
5 a time. Middle: Co-deployment with switchable propulsion model, both systems are deployed
6 within the same cell, but only one is propulsive at a time. Bottom: Co-deployment and co-
7 propulsion model, both systems are deployed and simultaneously contribute to propulsion
8 within the same cell. Green: Agl-Glt complexes (transient on the left; fixed on the substrate
9 on the right), Orange: Type IV pili.

10 **(b)** Top row: Phase-contrast imaging & single-cell tracking. A rapid time series (left) is
11 segmented to identify individual cells (middle), whose leading pole (red), lagging pole
12 (purple), and center (cyan) are tracked over time (right). Representative trajectory overlays
13 are shown for 120 s and 240 s, as well as the full 0-240 s interval. Middle row: TIRF imaging
14 of AglZ-YFP clusters & kymograph analysis. Cells expressing AglZ-YFP are imaged by TIRF
15 (488 nm) (left) and cluster fluorescence along each cell body is extracted into automated
16 kymographs (middle), where the x-axis corresponds to distance along the cell and the y-axis
17 to time. Clusters are then classified as static (magenta) or mobile (green) by tracking their
18 intensity peaks over successive frames (right). Bottom row: HILO detection of prey and pili
19 (left). A simultaneous HILO time series (561 nm) detects fluorescently labeled *E. coli* prey
20 (middle) in predation assays or type IV pili (yellow arrowheads) in motility assays emanating
21 from *M. xanthus* cells (right).

22 **(c)** Schematic of the predation assay where A+S+ *M. xanthus* cells expressing AglZ-YFP
23 (green) are spotted next to *E. coli* HU-mCherry prey on 1.5% agar. After 48 h of co-
24 incubation, distinct populations, large swarms, small swarms, and individual cells, were
25 identified and imaged for downstream cluster analysis.

26 **(d-f)** Fluorescence images of A+S+ AglZ-YFP (green clusters) and *E. coli* HU-mCherry (red) in the three depicted zones of panel (c) of the predation assay: isolated cells (d), small swarms (e), and large swarms (f).

29 **(g-i)** Automated kymographs of AglZ-YFP signal in A+S+ cells extracted from panels (d-f).
30 Two example cells are shown for each condition (d: individual cells, e: small swarms, f: large
31 swarms).

32 **(j)** Bar chart showing the percentage of AglZ-YFP clusters classified as static (magenta) or
33 mobile (green) across the entire field of view in each population type.

34 **(k)** Time-lapse HILO/TIRF of pili and AglZ-YFP dynamics in different genetic backgrounds
35 on 0.75% agar pads. Top: A+S<sup>-</sup> (ΔpilA AglZ-YFP) Middle: A+S<sup>+</sup> (ΔaglQ PilA<sup>D71C</sup> Atto 565)
36 Bottom: A+S<sup>+</sup> (AglZ-YFP, PilA<sup>D71C</sup> Atto 565). Yellow arrowheads indicate AglZ-YFP clusters
37 (top), fluorescent pili (middle), or both (bottom).

38 **(l)** Automated kymographs of the A+S+ cell shown in panel (k).

39 **(m)** Cluster classification of static (magenta) or mobile (green) clusters of AglZ-YFP from the
40 same A+S+ cell, overlaid on the kymograph trace (white outline).

41

42
bioRxiv preprint doi: https://doi.org/10.1101/2025.10.29.685086; this version posted October 29, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

<figure><img src="image_3.png" /><figcaption>**Figure 2**</figcaption></figure>
1 **Figure 2 : Co-Propulsion by A- and S-Motility Generates Distinct Behaviors and faster**

2 movement within Single Cells

3 (a) Combined TIRF (488 nm) and HILO (561 nm) fluorescence images of A<sup>+S<sup>+</sup></sup> *M. xanthus*

4 coexpressing AglZ-YFP and PilA<sup>D71C</sup> Atto 565 on a chitosan surface with 0.9 mM CaCl<sub>2</sub>. Top

5 row: HILO channel showing labeled pili (yellow arrowheads). Middle row: TIRF channel

6 showing AglZ-YFP clusters. Bottom row: merged phase-contrast and AglZ-YFP for cell

7 outline.

8 (b) Automated kymograph of AglZ-YFP intensity extracted along the long axis of the same

9 A<sup>+S<sup>+</sup></sup> cell. Magenta arrowheads mark static clusters.

10 (c) Cluster classification for the cell in (A-B): static AglZ-YFP clusters (magenta) versus

11 mobile clusters (green) over the 0-1000 s interval, overlaid on the kymograph trace (white

12 outline).

13 (d) Distribution of static AglZ-YFP clusters per cell per image for A<sup>+S<sup>-</sup></sup> (ΔpilA, red, N = 2

14 replicates, n = 28 cells) and A<sup>+S<sup>+</sup></sup> (green, N = 2 replicates, n = 123 cells) strains. Bars show

15 counts; solid lines are kernel density estimates.

16 (e) Trajectories of the leading pole (red), lagging pole (purple), and cell center (cyan) for the

17 A<sup>+S<sup>+</sup></sup> cell from panels (a-c) over 0-1000 s. Gray region indicates the cell contour projected

18 through time.

19 (f-h) Full-field trajectories (top, over 597 s) color-coded by time and single-cell examples

20 (bottom, over 400 s) showing leading pole (red), lagging pole (purple), and center (cyan)

21 paths for: (f) A<sup>+S<sup>-</sup></sup> (ΔpilA ΔfrzE), (g) A<sup>-S<sup>+</sup></sup> (ΔaglQ ΔfrzE), (h) A<sup>+S<sup>+</sup></sup> (ΔfrzE). Cell outlines (light

22 gray) at each timepoint highlight contour evolution.

23 (i) Polar density plot of instantaneous cell speed (radial axis, µm/min) versus angular

24 deviation of the leading pole (0° = forward; ±90° = lateral) for A<sup>+S<sup>+</sup></sup> (ΔfrzE; N=4, n=159).

25 Color scale indicates point density.

26 (j-k) Polar density plots as in (i) for: (j) A<sup>+S<sup>-</sup></sup> (ΔpilA ΔfrzE; N=3, n=61), (k) A<sup>-S<sup>+</sup></sup> (ΔaglQ ΔfrzE;

27 N=3, n=67).

28 (l) UMAP projections of 48 spatial behavior features (excluding any speed metrics),

29 computed for the leading pole, center, and lagging pole over sliding time windows, shown as

30 a two-dimensional density map. Above each density map is a schematic of a single-cell

31 leading-pole trajectory (orange) with a shaded time window, illustrating that each "behavior

32 segment" corresponds to one such window along the leading-pole path, from which 16

33 spatial features are extracted (48 total when including the center and lagging pole, not

34 shown). Each small square (bin) in the map represents a region of UMAP space, and its

35 color indicates how many "behavior segments" fall into that bin (lighter = more segments).

36 Projections are shown separately for A<sup>+S<sup>-</sup></sup> (ΔpilA ΔfrzE, red), A<sup>-S<sup>+</sup></sup> (ΔaglQ ΔfrzE, blue), and

37 A<sup>+S<sup>+</sup></sup> (ΔfrzE, green), with the far-right panel overlaying all three densities. Iso-contours in the

38 first three panels indicate the combined occupancy across strains.

39 (m) Kernel density distribution of the median instantaneous speed per cell for A<sup>+S<sup>+</sup></sup> (ΔfrzE, N

40 = 4 replicates, n = 167 cells), A<sup>+S<sup>-</sup></sup> (ΔpilA ΔfrzE, N = 3, n = 61 cells), and A<sup>-S<sup>+</sup></sup> (ΔaglQ ΔfrzE,

41 N = 3, n = 67 cells) strains. Dashed line indicates population medians.

42

43
Figure 3

bioRxiv preprint doi: https://doi.org/10.1101/2025.10.29.685086; this version posted October 29, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>
1 **Figure 3 :** Motility Behavior and Propulsion Dynamics Are Modulated by
2 Environmental Conditions

3 **(a-c)** Representative single-cell trajectories of the leading pole (red), lagging pole (purple),
4 and cell center (cyan) under increasing CaCl₂ concentrations (0, 0.4, 0.7, 0.9, 2, 4 mM) for:
5 (a) A⁺S⁻ (*ΔpilA* *ΔfrzE*), (b) A⁻S⁺ (*ΔaglQ* *ΔfrzE*), (c) A⁺S⁺ (*ΔfrzE*). Light-gray outlines show cell
6 contours at each timepoint (light gray).

7 **(d)** UMAP embeddings of the same 48 spatial behavior features introduced in Figure 2l, here
8 shown as density maps for each strain across the CaCl₂ series. From top to bottom: A⁺S⁻
9 (*ΔpilA* *ΔfrzE*, red), A⁻S⁺ (*ΔaglQ* *ΔfrzE*, blue), and A⁺S⁺ (*ΔfrzE*, green), and merged. The
10 shifting density clouds illustrate how each strain's repertoire of behaviors moves through
11 feature space as calcium increases. Iso-contours represent the combined behavioral density
12 of all three strains pooled at the calcium concentration of 0.9 mM.

13 **(e)** Median instantaneous speeds per cell across CaCl₂ concentrations for A⁺S⁺ (*ΔfrzE*,
14 green), A⁻S⁻ (*ΔpilA* *ΔfrzE*, red), and A⁻S⁺ (*ΔaglQ* *ΔfrzE*, blue). Boxes extend from the 25th to
15 75th percentile, whiskers show 1.5× the interquartile range, and dots represent individual
16 cells. Sample sizes across the six calcium conditions were: A⁺S⁺ (4 replicates): 36, 140, 249,
17 167, 271, and 164 cells; A⁺S⁻ (3 replicates): 19, 32, 31, 61, 45, and 34 cells; A⁻S⁺ (3
18 replicates): −, −, 29, 67, 147, and 199 cells.

19
20
<caption>a</caption>

A-motility

<figure><img src="image_10.png" /></figure>

Propulsion
along the body

<figure><img src="image_11.png" /><figcaption>d</figcaption></figure>

S-motility

<figure><img src="image_12.png" /></figure>

Propulsion
from the head

<figure><img src="image_13.png" /><figcaption>g</figcaption></figure>

A+S+
A + S motility

<figure><img src="image_14.png" /></figure>

Propulsion from
the head and
along the body

<figure><img src="image_15.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>h</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>i</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>j</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>k</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>l</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>m</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>n</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>o</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>p</figcaption></figure>

<figure><img src="image_28.png" /></figure>
1 **Figure 4 : Model Reproduces A⁺S⁻ and A⁻S⁺ Behaviors and Supports a Non-Additive Role of A-Motility in A⁺S⁺**

3

4 **(a-c)** Modeling gliding (A⁺S⁻) mutants. (a) Schematic of our A⁺S⁻ (Δ*pilA* Δ*frzE*) model: the cell is represented as six disks (grey) linked by springs, with a constant propulsive force along its length (red). (b) Examples of simulated trajectories of leading pole (red), lagging pole (purple), and cell center (cyan) used to compare with experimental data under increasing CaCl₂ in the following panels. Final disks configuration is outlined in gray. (c) Box plots of instantaneous speeds for experimental (red) versus simulated (pink) A⁺S⁻ cells across CaCl₂ (n = 100 simulated trajectories per condition). Boxes: 25th-75th percentiles; whiskers: 1.5× interquartile range.

12 **(d-f)** Modeling twitching (A⁻S⁺) mutants. (d) Schematic of our A⁻S⁺ (Δ*aglQ* Δ*frzE*) model: six disks with propulsion on the leading disk only, driven by pili whose binding lifetime varies with CaCl₂. (e) Examples of simulated trajectories for A⁻S⁺ driven by pili (green lines) under the same CaCl₂ series, where each concentration corresponds to a different binding lifetime. (f) Instantaneous speed box plots for experimental (blue) and simulated (light blue) A⁻S⁺ cells across CaCl₂/binding lifetimes (n = 100 simulated trajectories per condition).

18 **(g-i)** Modeling A⁺S⁺. (g) Schematic of the combined A⁺S⁺ (Δ*frzE*) additive model, merging A⁺S⁻ and A⁻S⁺ modules with the same parameter sets. (h) Simulated A⁺S⁺ trajectories across CaCl₂/binding lifetimes. (i) Instantaneous speed box plots for experimental (green) and simulated (light green) A⁺S⁺ across CaCl₂/binding lifetimes (n = 100 simulated trajectories per condition).

23 **(j)** Mean number of static AglZ-YFP clusters per cell across calcium concentration for experimental A⁺S⁻ (Δ*pilA*, red) and A⁺S⁺ (green) strains. Error bars = ±SD across replicates. Sample sizes across the six calcium conditions were: A⁺S⁻ (2 replicates): 13, 23, 24, 28, 16, and 17 cells; A⁺S⁺ (2 replicates): 7, 108 127, 123, 91, and 53 cells.

27 **(k)** Mean static AglZ-YFP clusters count versus instantaneous cell speed for the same strains (A⁺S⁻ n = 122; A⁺S⁺ n = 509, 2 replicates each). Error bars = ±SD across replicates.

29 **(l-n)** Experimental results in a Δ*cgIB* background. (l) Trajectories of the leading pole (red), lagging pole (purple), and center (cyan) for representative A*ΔcgIBS⁺* (Δ*cgIB* Δ*frzE*) cells across calcium concentrations. Cell contours are shown at each timepoint (light gray). (m) UMAP density maps for experimental A⁺S⁺ (Δ*aglQ* Δ*frzE*, blue), A⁺S⁺ (Δ*frzE*, green), and A*ΔcgIBS⁺* (Δ*cgIB* Δ*frzE*, orange), each pooled over CaCl₂ (0-4 mM). (n) Median instantaneous speeds per cell versus CaCl₂ for A⁺S⁺ (Δ*frzE*, green), A⁺S⁻ (Δ*frzE*, red), A⁻S⁺ (Δ*aglQ* Δ*frzE*, blue), and A*ΔcgIBS⁺* (Δ*cgIB* Δ*frzE*, orange). Sample sizes for A⁺S⁺, and A⁻S⁺ are the same as in Figure 3e. Sample size for A*ΔcgIBS⁺* across the six calcium conditions were (N=4 replicates) –, 155, 253, 245, 342, and 390 cells.

38 **(o-p)** Reduced-damping simulations. (o) Representative simulated A*ΔcgIBS⁺* trajectories with reduced damping across CaCl₂/binding lifetimes. (p) Instantaneous speed box plots for experimental A*ΔcgIBS⁺* (orange) and A*ΔcgIBS⁺* low-damping simulations (peach) across CaCl₂/binding lifetimes, n = 100 simulated trajectories per condition.

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# 1 METHODS

## 2 Predation assay

3 M. xanthus strains were grown in CYE (10 mM MOPS pH 7.6; 10 g/L Bacto Casitone peptone; 5 g/L yeast extract; 1 g/L MgSO₄·7H₂O; q.s. to 1 L with Milli-Q water) medium at 32 °C with shaking (0.42 x g) overnight in the dark. Cells were centrifuged at 2 500 × g for 5 min, the supernatant discarded, and cells washed twice in CF buffer (10 mM MOPS pH 7.6; 1 mM KH₂PO₄; 8 mM MgSO₄; 0.02 % (NH₄)₂SO₄; 0.2 % sodium citrate; 0.015 % casitone, q.s. to 1 L with Milli-Q water) before being resuspended in CF buffer to an OD₆₀₀ of 5. E. coli prey cells were grown overnight in LB at 37 °C with shaking (0.42 x g) in the dark. The culture was then diluted 1:10 into fresh LB, incubated for 2 h, and centrifuged for 5 min at 2500 × g. The supernatant was discarded, and cells were resuspended in CF buffer to an OD₆₀₀ of 0.005. CF agar pads (0.75 % or 1.5 %) were prepared on microscope slides. On each pad, 1 µL of M. xanthus suspension was spotted, and 1 µL of E. coli suspension was spotted ~0.5 mm away. Pads were then placed onto the surface of CF agar (1.5 %) in standard Petri dishes to maintain humidity, and incubated for 48 h at 32 °C. For imaging, slides were carefully removed, a coverslip was applied, and predation was observed by microscopy.

## 19 Microfluidic chitosan device

20 M. xanthus cells were grown overnight in CYE medium at 32 °C with shaking (0.42 × g) in the dark. Cultures were centrifuged for 5 min at 2 500 × g, the supernatant discarded, and cells resuspended in CF buffer to an OD₆₀₀ of 0.05. The microfluidic device comprised an Ibidi sticky-slide (Ibidi VI 0.4) assembled with a chitosan-coated coverslip (Idylle). Before use, the chitosan surface was rehydrated by incubating with Milli-Q water for 15 min. To prepare the channel, it was first flushed once with M. xanthus suspension to remove any residual fluid, and then a second flush (90 µL) was used to establish the final cell suspension for imaging. To promote firm cell adhesion to the chitosan surface, the assembled slide was centrifuged for 1 min at 50 × g. For calcium-gradient experiments, the 90 µL of culture used for the final suspension was first removed, leaving the adhered cells on the substrate. Then, CF buffer supplemented with CaCl₂ (90 µL) was introduced sequentially into the same channel at final concentrations of 0.4, 0.7, 1.0, 2.0, and 4.0 mM. Each CF CaCl₂ solution was incubated for 2 min before imaging; the solution was then removed and replaced with the next concentration.

## 36 Type-IV pili labelling and visualization

37 Pili were labeled using an adapted protocol based on 21,45. Strains of M. xanthus carrying pSWU19-*P<sub>pilA</sub>-*pilA*<sup>D71C</sup> or pSWU30-*P<sub>pilA</sub>-*pilA*<sup>D71C</sup> (see Supplementary Table 1) were grown overnight in CYE at 32 °C with shaking (0.42 × g) in the dark. Cultures were centrifuged (2 min at 4 930 × g), the supernatant discarded, and cells resuspended at an OD₆₀₀ of 0.3 in CF buffer containing CaCl₂ at the desired concentration and 25 µg.mL⁻¹ of Atto 565-maleimide (Atto-tec) or Alexa Fluor™ 488 C₅ maleimide (Thermo Fisher). Labeling proceeded in the dark for 1 h at room temperature. After incubation, cells were centrifuged (2 min at 4 830 × g) and washed seven times in CF CaCl₂ (same calcium concentration) to
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## 23 High-speed acquisition

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For construction of AM1 (DZ2 att<sub>Mx8</sub>::pSWU30-*P<sub>pilA</sub>-pilA<sup>D71C</sup> AgIZ-YFP) and AM8 (DZ2 ΔagIQ att<sub>Mx8</sub>::pSWU30-*P<sub>pilA</sub>-pilA<sup>D71C</sup>), a pSWU30-based plasmid encoding the *P<sub>pilA</sub>-pilA<sup>D71C</sup>* variant was integrated at the Mx8 phage attB site in TM9 (DZ2 agIZ-YFP) and TM146 (DZ2 ΔagIQ), respectively, via site-specific recombination. Plasmids were introduced by electroporation, and transformants were selected on CYE medium containing 5 µg/mL tetracycline. Correct insertion was confirmed by whole-genome sequencing (Plasmidsaurus) of DNA prepared with the Quick-DNA Miniprep Plus Kit (Zymo Research). Phenotypic motility assays were then performed on CYE agar (0.5% and 1.5%) by spotting 10 µL of OD<sub>600</sub> = 5 cultures and incubating for 48 h, using wild-type strain and known motility mutants as controls. Finally, the presence of pili was verified by microscopy.

Imaging was performed on a custom-built microscope based on a Nikon Eclipse Ti inverted stand, using a Nikon CFI Plan Apochromat Lambda 100×/1.45 NA oil-immersion objective (Nikon Instruments). For phase-contrast imaging, a ring-shaped LED illuminator (Amazon) was used in place of a traditional condenser to illuminate the sample from above. A PH3 phase ring, mounted in a plane conjugate to the objective's back focal plane, was aligned to overlap with the LED ring image to generate phase contrast images on an Andor iXon Ultra 897 EMCCD (Andor Technology). For fluorescence excitation, two laser lines, 488 nm (Coherent CUBE) and 561 nm (Coherent Sapphire), were combined via dichroic mirrors and their intensities computer-controlled by an acousto-optic tunable filter (AOTF). The selected beam was then directed through a dichroic set FF496-SDi01 (Semrock) to switch between total internal reflection fluorescence (TIRF) and highly inclined laminated optical sheet (HILO) excitation paths. Fluorescence signal was then separated from excitation light using a dichroic ZT 405/488/561/633rpc (Chroma Technology) and separated from LED illumination channel using a second dichroic FF484-FDi01 (Semrock) transmitting fluorescence signal onto a second Andor iXon Ultra 897 EMCCD camera. Emitted light was filtered through a 525/50 nm band-pass for YFP/Alexa 488 or a 605/70 nm band-pass for mCherry/Atto 565 (Chroma Technology). The effective pixel size of both phase contrast and fluorescence cameras at the sample plane was 0.120 µm. All illumination pulses were controlled by a DAQ card and synchronized to the cameras integration windows: LED exposures were 400 ms, 488 nm laser pulses 10 or 30 ms, and 561 nm pulses 10 ms. Pulses were time-shifted to avoid overlap, and the three exposures cycled once per second to generate a complete illumination sequence of typically 16 minutes. An OBIS 785 nm infrared
1 laser autofocus module (Coherent) maintained focus by monitoring back-reflections during acquisitions. All acquisitions were performed at room temperature (22-25 °C).

3

4 **Data preprocessing : Acquisition output, alignment, drift correction, and preprocessing**

6 After microscope acquisition, a phase-contrast TIFF stack was generated, and zero, one, or two fluorescence stacks were produced depending on the experiment. When both 488 nm and 561 nm channels were acquired, they were stored in a single interleaved TIFF and then split into two separate stacks. When AgIZ-YFP was imaged (488 nm channel), that fluorescence stack was denoised with the Aydin software ⁴⁶ with the "Noise2SelfFGR-cb" model. Then each fluorescence channel was spatially aligned to the phase-contrast reference by estimating a pure translation via frequency-domain cross-correlation and applying this subpixel shift to the image stack, ensuring precise overlap between channels.

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To correct for any sample drift, the phase-contrast reference stack was temporally smoothed with a moving-average filter and intensity-clipped; OpenCV's phase-correlation then measures frame-to-frame drift within a sliding window, and the inverse shifts were applied to each stack. Finally, the drift-corrected phase-contrast stack was processed frame by frame with an unsharp-mask filter to enhance edges.

19

20 **Segmentation**

21 For each frame of the preprocessed phase-contrast stack, we ran the "bact_phase_omni"
22 Cellpose model to generate an labeled instance mask that assigns every cell an integer label
23 ⁴⁷. The resulting per-frame masks were concatenated into a single TIFF stack, with each
24 pixel's value indicating the mask ID for that cell in that frame.

25

26 **Single-cell tracking**

27 Single-cell tracks were reconstructed from the labeled phase-contrast TIFF stack using our custom Python pipeline (see Supplementary Data). Briefly, we tracked each cell's centroid across frames using LapTrack's cost-based mask matching ⁴⁸, reconnected any fragmented trajectories to fill gaps, and discarded tracks that were too short or touched image borders.
28
29
30 After finalizing track continuity, we relabelled each track with consecutive IDs and updated the labeled TIFF stack so that pixel values reflected these corrected labels. We then extracted per-cell morphological and kinetic features from each track's masks, identified cell poles via skeleton-and-curvature analysis, and appended pole coordinates to the existing centroid tracks to obtain pole trajectories. Next, we smoothed all trajectories using a Savitzky-Golay filter to reduce high-frequency noise and computed signed displacements to distinguish leading versus lagging poles. Finally, we performed manual inspection in Napari 31
32
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37
38 ⁴⁹ and applied morphological and kinetic filters to retain only high-quality single-cell trajectories for downstream analysis.
39
40

40

41 **Instantaneous speed calculation**

42 Instantaneous speeds were computed directly on the non-rediscretized center trajectories by
43 measuring the Euclidean distance between each pair of successive (x,y) positions and
44 dividing by the acquisition interval (1 s), and converted to µm/min using the pixel size (0.120
45 µm pixel⁻¹).

45

46
1 **Trajectory discretization**

2 To decouple spatial sampling from cell speed, we performed spatial trajectory

3 rediscretization using Traja⁵⁰ to each center and pole track, placing new points at uniform

4 0.025-pixel intervals.

5

6 **Cell features**

7 For each spatially rediscretized cell trajectory, comprising its center, leading pole, and

8 lagging pole, we computed a suite of 16 local morphodynamic features. Fifteen of these

9 were derived via sliding fixed-size windows over the (x, y, time) data, and one (distances

10 between coordinate pairs) was calculated instantaneously at each frame. Each feature

11 quantified a specific aspect of cell shape or motion. Detailed implementation is provided in

12 the Supplementary Data.

13

14 **UMAP embedding**

15 UMAP embedding was computed on the preprocessed 48-dimensional feature matrix (see

16 Supplementary Data for preprocessing procedure), comprising only our local

17 morphodynamic descriptors (excluding dynamic features such as instantaneous speed),

18 using the umap-learn package with the following settings: n_neighbors=50, min_dist=0.9,

19 metric="euclidean", init="spectral", n_components=2. The resulting two-dimensional layout

20 was rendered with Datashader, color-coding points by strain or calcium concentration, to

21 reveal phenotype clusters across conditions. To compare global distributions, we overlaid

22 iso-density contours computed via Gaussian kernel density estimation on a 2D grid, allowing

23 visual comparison of population structure across conditions.

24

25 **Polar density plot**

26 We visualized the relationship between angular deviation and their corresponding speeds by

27 constructing a polar density plot. First, we selected the leading pole angular deviations (in

28 degrees) and leading pole speeds (µm/min) for the strain and condition of interest, then

29 determined a radial limit (rmax) as the 95th percentile of all speeds to avoid distortion by

30 extreme outliers. We binned the data into a two-dimensional histogram over a regular grid of

31 angles (-180° to +180°) and speeds (0 to rmax), applied a small Gaussian filter to smooth

32 the resulting counts, and plotted the smoothed density in polar coordinates. In this view,

33 each direction around the circle corresponds to a turning angle (0° at top, increasing

34 clockwise to the right and counterclockwise to the left), and the distance from the center

35 encodes speed. High-density regions thus reveal the predominant movement directions and

36 speeds of the leading pole under the chosen experimental conditions.

37

38 **Automated kymographs generation**

39 For experiments imaging AglZ-YFP, time-lapse sequences were acquired every 1-5 s with a

40 30 ms exposure. Kymographs were computed on the fluorescence TIFF stack that had been

41 drift-corrected, denoised, and rigidly registered to the phase-contrast channel. Because cells

42 often bend, we computed their medial axis for each one of them in each frame by

43 skeletonizing their segmented masks and fitting that skeleton with a smooth spline to define

44 the long axis. We then sampled fluorescence intensity along each cell length by, at each

45 spline point, drawing a short line segment of fixed width perpendicular to the spline and

46 averaging the pixel values along it, producing a raw kymograph (intensity vs. position) for

47 each timepoint. Cell motion correction was then applied by projecting each frame-to-frame

48 cell-centroid displacement onto the long-axis direction and shifting the corresponding

49 kymograph profile by that amount, thereby fixing the kymograph in the substrate reference

50 frame so that fluorescence features stationary on the surface remain static over time.

51
# 1 Clusters of AglZ-YFP classification

2 AglZ clusters were first detected using the Spotiflow probabilistic spot detector and refined
3 by 2D Gaussian fitting <sup>51</sup>. These detections were then linked into tracks with TrackPy <sup>52</sup>, gap-
4 filled by linear interpolation, and tracks shorter than five frames were discarded. The
5 remaining trajectories were smoothed with a Savitzky-Golay filter, spatially rediscretized, and
6 a suite of morphodynamic features was computed. We then used a seven-dimensional
7 Gaussian Mixture Model to classify every spot-timepoint as "static" or "mobile." Finally, we
8 assigned each cluster track to its nearest cell mask and retained only those clusters located
9 more than 5 px away from the leading pole, thereby selecting for adhesion complexes that
10 move along the cell's basal surface within the substrate-proximal zone for downstream
11 analysis.

12

13 Simulations

14 To model bacterial motility, we implemented a two-dimensional agent-based simulation in
15 which each cell was represented as a chain of six interconnected disks. Disks were linked by
16 Hookean springs and subjected to bending and damping forces to capture the mechanical
17 properties of the cell body. Bending penalties enforced shape persistence, while damping
18 simulated resistance from surface friction and viscous drag. Disk positions were updated
19 using Verlet integration to ensure numerical stability. Two propulsion systems were modeled:
20 type IV pili and focal adhesion complexes (FACS). Pili extended from the leading disk and
21 bound stochastically to the substrate within a defined angular range. Retraction generated
22 pulling forces limited by a motor saturation threshold. Binding durations were randomly
23 sampled around a user-defined lifetime, independent of force feedback. FACS were
24 modeled as transient, static adhesive sites deposited when the leading disk advanced
25 beyond a threshold distance from the previous anchor. Each FACS acted as a spring-like
26 tether that exerted a restoring force on any overlapping disk and simultaneously applied a
27 directional force aligned with the local cell axis, defined by the vector from disk *i*-1 to *i*+1. To
28 capture biological heterogeneity, core pili parameters (e.g., rest length, binding rate,
29 retraction rate, number, motor force, angular range), and FACS parameters (e.g., force
30 magnitude, spring stiffness, anchoring threshold) were independently perturbed using
31 multiplicative log-normal noise (σ = 50%) in each simulation. Full simulation and processing
32 details are provided in the Supplementary Methods.

33

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36
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