Introduction

Microswimmers are single-celled bodies powered by one or more flagella and widespread across the tree of life. In aqueous environments they exploit their motility to find mating partners or sustenance. To do so, they must be able to sense and interpret environmental signals to steer towards their goal. Due to their relatively small size, many microswimmers are considered unable to spatially recognize signal gradients, and must discriminate gradient direction by scanning their environment [1]. One of the most prominent search strategies applied by microswimmers, and extensively studied in the bacterium Escherichia coli [2], is the run-and-tumble motion, where straight swimming stretches are alternated by rapid turns. Adapting their tumbling frequency to increasing concentrations of the attractant allows these bacteria to move up the gradient, a process termed klinokinesis [3]. Run-and-tumble behaviour is not only omnipresent in prokaryotes, eukaryotic microswimmers such as the alga Chlamydomonas reinhardtii employ this type of motility as well [4].

Oomycetes of the genus Phytophthora can cause devastating diseases on a wide variety of crops and considerable damage in forests and natural ecosystems. Notorious species are Phytophthora infestans on potato [5], Phytophthora palmivora on cocoa and oil palm [6], Phytophthora capsici on peppers and cucurbits [7], and Phytophthora sojae on soybean [8]. Under conducive circumstances they can nullify crop yields [9] and even with intensive pesticide spraying regimes [10], damage is still substantial. P. infestans for example, remains responsible for destroying 15–20% of global potato yield [11]. The ever-heavy disease pressure of Phytophthora is often attributed to its short asexual reproduction cycle, with a massive release of dispersal agents, i.e. spores [12]. As opposed to most of their morphologically similar but phylogenetically distinct fungal counterparts, oomycetes also produce motile dispersal agents termed zoospores [13]. These wall-less, flagellated mononuclear cells are typical examples of microswimmers. As outlined in a recent review [14] Phytophthora zoospores show a multitude of taxes including chemotaxis and are thought to use their motility and sensing capacity to propel themselves through saturated soils and water films on leaves to actively target their host. This behaviour known as ‘homing’ [14], is evident from their localized accumulation on plant roots [15], stomates [16,17] and wounds [18]. Zoospore aggregation is induced by a variety of plant derived components, such as amino acids, sugars, alcohols and flavonoids in root exudates. An example of such a root exudate component is the amino acid glutamic acid (Glu), which induces zoospore aggregation not only in all studied plant pathogenic oomycetes [14], but even in the oomycete fish pathogen Saprolegnia diclina [19].

In eukaryotes G-protein mediated signalling plays a crucial role in sensing environmental signals. In the canonical G-protein signalling pathway membrane-spanning G-protein-coupled receptors (GPCRs) are activated by a very diverse range of ligands such as, for example, neurotransmitters, pheromones and volatiles. This activation triggers dissociation of the heterotrimeric G-protein complex releasing the Gα subunit from Gβγ as initiators for further downstream signalling. Zoospores of P. infestans and P. sojae transformants lacking the Gα subunit show aberrant swimming patterns and do not aggregate at Glu sources [20,21]. Moreover, the P. sojae transformants no longer aggregate to daidzein, an isoflavone secreted by soybean in the rhizosphere as sensor for beneficial microbes but hijacked by pathogens [21]. Likewise, P. sojae transformants in which a non-canonical GPCR gene is silenced show defects in zoospore swimming behaviour and aggregation suggesting a role for this GPCR as chemoreceptor [21,22]. Notably, besides canonical GPCRs oomycetes have a remarkable repertoire of unique non-canonical GPCRs but as yet their functions are unknown [23].

Although zoospores have been described to actively approach attractant sources [24], the manner in which they do so remains unclear. In a recent review [14], we summarized studies on aggregation and behaviour of zoospores in response to chemoattractants. We noted that earlier reports on zoospore behaviour are limited to descriptions of observations, occasionally supported by hand-drawn tracings [25–27] or long exposure-images [27] of zoospore trajectories, using terms such as ‘excited’ [24], ‘jerky’ [28] and ‘milling’ [29] to define a zoospore’s response to attractants. It appears that various authors use different terminology to describe the same behavioural sequence displayed by zoospores. In an attempt to harmonize the terminology we coined the following terms for the successive stages in the homing response: (i) reorientation, (ii) approaching, (iii) retention and (iv) settling. Recent studies exploited more advanced tools for direct visualization of zoospore trajectories, allowing for increased quantification and precise characterization of swimming behaviour. As a result, meticulous analyses of Phytophthora parasitica zoospores [30] enabled identification of the parameters controlling the distinct stages of the zoospore homing response [31,32].

In this study, we design a setup for high-speed microscopy of microswimmers and use this to track Phytophthora zoospores over time when exposed to the chemoattractant Glu or the non-attractant amino acid phenylalanine (Phe). We show that in the absence of an attractant, zoospores swim in a run-and-tumble pattern, where straight swimming stretches are alternated by rapid turns. In the presence of Glu, we find that the tumbling frequency increases in a dose-dependent manner while the swimming velocity and flagellar beating frequency are unaltered. We show that the increased tumbling frequency is sufficient to explain chemotaxis through stochastic aggregation. Zoospores produced by a P. infestans Gα subunit mutant [20] show an increased tumbling frequency even in the absence of an attractant, but still respond to Glu in the same fashion as the wildtype does.

Results

Phytophthora zoospores run and tumble

To investigate Phytophthora zoospore swimming, we use high spatio-temporal resolution microscopy to track zoospore movement over time. Four different species were examined, i.e., P. palmivora, P. sojae, P. capsici and P. infestans. From the obtained trajectories (Figs 1A and S1), we extract parameters characterizing zoospore behaviour, i.e., velocity v, curvature k and moving direction θ (Fig 1A). The results show that zoospores from all four species perform straight runs typically lasting less than a second (S2 Fig) before being interrupted by reorientations through tumbling. These tumbles typically occur within a second (S2 Fig) and, as shown for P. infestans zoospores, through flagellar desynchronization (S3 Fig). Tumbling occurs at a wide range of angles anywhere between 30° and 180° (S4 Fig). Velocity plummets during tumbling, after which it recovers to a constant run velocity (Fig 1A). This suggests a zoospore’s average velocity (S2 Fig) to be dictated by tumble frequency rather than adaptable running velocity. The difference in basal velocity and tumbling frequencies between Phytophthora species dictates how efficiently they explore, a parameter we express as active diffusion. High-velocity, low-tumbling P. sojae zoospores show a much higher basal active diffusion than low-velocity, high-tumbling P. infestans zoospores (Fig 2B).

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Fig 1. Swimming trajectories of Phytophthora zoospores.

Trajectories are deduced from high-speed microscopy images of P. infestans, P. palmivora, P. sojae and P. capsici zoospores swimming in water in microchambers. Minimum number of zoospores imaged per species is 3251. (a) Representative trajectories. Curvature (k) in 1 μm^-1, moving direction (θ) in ° and velocity (v) in μm s^-1 are plotted over time (t) in seconds (s). (b) Kinetic fingerprints of swimming zoospores in which each dot depicts the mean velocity for a trajectory of a single zoospore (Y axis) and the logarithm of the mean curvature for that trajectory (X axis). On the right an overlay of kinetic fingerprints of the four species.

https://doi.org/10.1371/journal.ppat.1012577.g001

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Fig 2. Swimming trajectories of Phytophthora zoospores in response to glutamic acid (Glu).

Minimum number of zoospores imaged per treatment is 1672. (a) Representative trajectories of P. infestans zoospores swimming in microchambers in the absence (0) or presence of Glu at 0.05, 0.5 and 50 mM. Curvature (k) in 1 μm^-1, moving direction (θ) in ° and velocity (v) in μm s^-1 are plotted over time (t) in seconds (s). (b) Trajectories of zoospores of four Phytophthora species in the absence of Glu (top) and in 5 mM Glu (bottom) depicted as overlays with the starting point of each trajectory in the centre. Black circles indicate the average net distance covered (active diffusion) by zoospores in 1 second. (c) Kinetic fingerprints of swimming zoospores in which each dot depicts the mean velocity for a trajectory of a single zoospore (Y axis) and the logarithm of the mean curvature for that trajectory (X axis) in the absence (0) or presence of Glu in concentrations up to 100 mM. (d) Effect of different concentrations of Glu (x-axis) on run velocity (top), percentage of the time spent in tumbles (middle) and active diffusion (bottom).

https://doi.org/10.1371/journal.ppat.1012577.g002

Tumble tuning is sufficient to induce aggregation at a source

Adapting tumbling frequencies in response to an attractant is reminiscent of klinokinesis, the mechanism bacteria use to move up a gradient. To investigate whether the adaptative tumbling frequencies that we observe are sufficient to explain aggregation at a source, we developed a simulation model of non-interacting run-and-tumble swimmers using input parameters (run velocity and tumble rate) estimated from our experiments on P. infestans zoospores responding to Glu. We model an attractant source in the centre of a square simulation box which diffuses according to root exudate profiles described by Fickian diffusion [39] (S7 Fig), and trajectories of randomly distributed zoospores (Fig 3). With increasing source concentrations (S7B Fig), we find zoospores to increasingly aggregate at the source, showing that our measured increase in tumbling frequency in response to a chemoattractant is sufficient to explain aggregation at a source. Phytophthora thus has access to a positive klinokinetic aggregation strategy to target its host.

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Fig 3. Simulation model of zoospores reacting to a diffusive chemoattractant.

(left) A chemoattractant diffuses from a point source of 50 mM in a square chamber. (middle) Zoospore trajectories in response to the chemoattractant. Simulated trajectories of two individual zoospores (red and black) are plotted using our observed parameters on run velocity and adaptation of tumbling frequency in response to glutamic acid. (right) Predicted zoospore density after a 1000 zoospores were allowed to ‘swim’ for 1 hour. Zoospore density given as a time fraction of zoospore presence.

https://doi.org/10.1371/journal.ppat.1012577.g003

Disabling the Gα subunit affects swimming behaviour but not Glu perception

Based on previous studies in P. infestans and P. sojae showing that silencing the Gα subunit gene results in mutants with defects in zoospore swimming behavior and chemotactic responses towards Glu [20,21], it was hypothesized that Glu perception is mediated by a GPCR that functions as chemoreceptor. An alternative hypothesis, however, is that this inability to swim towards the chemoattractant Glu is due to a defect in controlling swimming direction. To investigate this we used the high speed camera setup to analyze zoospore movement of one of the Phytophthora Gα mutants in more detail and in absence and presence of Glu. P. infestans gs2 is a transformant in which the G⍺-subunit gene Pigpa1 is silenced and PGA1 is not detectable [20]. In control settings (no Glu), we found gs2 zoospores to indeed show strongly aberrant swimming patterns as compared to the wildtype (Fig 4A), exhibiting a higher tumbling frequency and a severely decreased active diffusion (Fig 4B and 4D). Although flagellar beating patterns did not seem to be affected, Fourier analysis of of trajectory velocity fluctuations did indicate gs2 to tumble regularly, at about 1.4 Hz, whereas no such tumble regularity was found in Fourier analysis of wildtype zoospores (S6B Fig). When exposed to Glu, gs2 zoospores respond in the same manner as wildtype zoospores: Their tumbling frequency (Fig 4D) and mean velocity (S2 Fig) are decreased, while run velocity (Fig 4D) is not affected. Glu does not decrease active diffusion of gs2 zoospores. Rather, in the gs2 mutant active diffusion without Glu is already severely reduced, comparable to the levels displayed by the wildtype when exposed to high concentrations of Glu (Fig 4D). This shows that the loss of chemotaxis to Glu in gs2 zoospores is not due to a defect in Glu perception but likely to alterations in their swimming patterns, where increased and regular tumbles drastically decrease their basal active diffusion (Fig 4D).

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Fig 4. Swimming trajectories of P. infestans Gα-silenced (gs2) zoospores in response to glutamic acid (Glu).

Minimum number of zoospores imaged per treatment is 219. (a) Representative trajectories of gs2 zoospores swimming in microchambers at 0, 0.05, 0.5 and 50 mM Glu. Curvature (k) in 1 μm^-1, moving direction (θ) in ° and velocity (v) in μm s^-1 are plotted over time (t) in seconds (s). (b) Zoospore trajectories in the absence of Glu (left) and in 5 mM Glu (right) depicted as overlays with the starting point of each trajectory in the centre. Black circles indicate the average effective distance travelled (active diffusion) by zoospores in 1 second. (c) Overlay of kinetic fingerprints in which each dot depicts the mean velocity for a trajectory of a single zoospore (Y axis) and the logarithm of the mean curvature for that trajectory (X axis) in the absence of Glu (0) and in the presence of Glu in concentrations up to 100 mM. (d) Effect of Glu on run velocity (left), percentage of time spent in tumbles (middle) and active diffusion (right).

https://doi.org/10.1371/journal.ppat.1012577.g004

Discussion

Phytophthora zoospore aggregation at chemoattractant sources is well documented, but reports on how they accomplish this address qualitative rather than quantitative parameters [14]. Tran et al. [30] showed how high-speed microscopy allows for direct and quantitative monitoring of zoospore behaviour. In this study we use this tool to investigate zoospore behaviour in multiple Phytophthora species. We also monitor and quantify changes in their swimming behaviour in response to a chemoattractant and investigate how a P. infestans transformant impaired in G-protein signaling [20] is affected in those aspects.

We found zoospores of P. infestans, P. palmivora, P. capsici and P. sojae to swim in patterns of straight stretches alternated by sudden changes of direction (Fig 2). This behaviour is known as run-and-tumble swimming, which Tran et al. [30] likewise ascribed to P. parasitica zoospores. Earlier work on Phytophthora zoospore behaviour also recognizes the alternation between runs and tumbles: our findings are in line with reports on P. infestans [20,25] and P. palmivora [40], and similar observations have been reported for Phytophthora megasperma [41] and Phytophthora cinnamomi [29,42]. Why would zoospores adapt such swimming patterns? Native run-and-tumbling (i.e., in the absence of attractants) is used by bacteria to pick up on host-derived signals [43]. Through run-and-tumble swimming, bacteria adopt exploration patterns called Lévy walks, allowing them to identify scarce nutrient sources [43]. Alternatively, run-and-tumbling could improve soil navigation: bacteria need to tumble in order to prevent getting stuck while navigating complex geometries [44]. Tumbling to prevent getting stuck might also allow oomycete zoospores to successfully migrate through the complex geometry of the soil [44–46], as exemplified by a zoospore’s tendency to tumble upon collision with a surface [29].

Klinokinesis is a stochastic navigation mechanism whereby attractant concentrations determine tumbling frequency, thereby biasing the movement of microswimmers up the gradient. Bacteria typically perform negative klinokinesis, where attractants decrease tumbling frequency thereby promoting straight stretches toward the source [2]. In contrast, we found Phytophthora zoospores to increase tumble frequencies in response to increasing concentrations of the chemoattractant Glu (Fig 2D). This suggests that zoospores use positive rather than negative klinokinesis to aggregate, a mechanism that according to our simulation models should be sufficient for aggregation of zoospores at a point source. Positive klinokinesis was likely also observed by others in earlier experiments, but not coined as such. For example, Phytophthora and Pythium zoospores have been known to increase their tumbling frequency when nearing attractant sources, as well as in biological settings, i.e. when zoospores approach roots (as reviewed in [14]). Often, the process is described as ‘trapping’, with zoospores initially being drawn in by directed movement toward the source [47], a behaviour also reported in brown algae when attracted by sex pheromones [48,49].

We found zoospores to respond to Glu in the mM-range. How likely is a zoospore to encounter such concentrations in the phytosphere? The microbiome ensures that most plant exudates diffuse not further than only a couple of millimetres from the root [39], and it is thus likely that zoospores only perceive plant-derived glutamic acid (and other attractive primary metabolites) at what is basically the surface of plant wounds and root tips [18]. This arrival might be by pure chance or alternatively, by compounds suited to long-range attraction (like isoflavonoids [50]). Irrespective of their mode of arrival, we show how Glu can retain zoospores at the plant surface: Positive klinokinesis ensures zoospore net movement is greatly reduced, and thus results in aggregation at the site of interest. We therefore suggest that at least some components of the plant exudate, rather than inducing directional swimming, induce zoospore aggregation by retention.

The majority of the currently known receptors that govern chemotaxis are GPCRs, a large family of very diverse seven transmembrane spanning receptors that are widespread in eukaryotes. They act as gateways; upon sensing extracellular signals they activate heterotrimeric G-proteins and downstream intracellular signalling cascades to accomplish the desired response. G-protein mediated signalling has been shown to be involved in chemotaxis in P. infestans and P. sojae [20,21]. Our findings show that zoospores of a G⍺-deficient P. infestans mutant that have lost the ability to aggregate and to swim toward Glu, show higher tumbling rates (Fig 4D). It appears however, that they are still capable to sense Glu: they respond in the same manner as zoospores of the wildtype, and hence their capacity to sense and recognize Glu is not affected (Fig 4D). Lack of chemotaxis [20,21] therefore seems to be due to aberrant swimming behaviour, rather than loss of chemoreception. Through investigation of swimming behaviour, we have thus been able for the first time to distinguish between the capacity to respond to an attractant, i.e, chemotaxis, and the capacity to sense an attractant, i.e., chemoreception. Further research into identifying receptors involved in the Phytophthora homing response should thus carefully design experiments to distinguish chemotaxis from chemoreception.

The regularity (S6 Fig) and increase (Fig 4D) of tumbling of zoospores of the G⍺-deficient P. infestans mutant hints to involvement of G⍺ in tumble suppression. We show that tumbling is the result of flagellar desynchronization (S3 Fig) in line with observations on P. parasitica zoospores [30]. For other microswimmers, such as C. reinhardtii and sperm cells, it has been shown that flagellar beating is governed by calcium fluxes [51–53], and that reorientation of the flagella is correlated with increased calcium influx rates [54]. As in other eukaryotes, calcium is a key second messenger in oomycetes [55]. There are clues that show calcium to play a role in zoospore chemotaxis and autotaxis [28], and calcium fluxes are associated with encystment [56] and the perception of chemoattractants [57]. Moreover, manipulation of extracellular and internal calcium levels by calcium modulators has shown that calcium plays a role in controlling swimming patterns, with different modulators invoking distinct changes such as perpetual circular or straight swimming, slow spiral ‘skidding’, or irregular jerky movements [26]. The tumbling increase we observe in the G⍺-deficient mutant could well be the result of disturbance of calcium homeostasis, with regular influxes of calcium inducing consistent tumbling. Notability, analyses of P. sojae G⍺-deficient mutants pointed to G⍺ as potential negative regulator of intermediates in calcium signalling such as calmodulin and calmodulin-dependent protein kinases [21]. In general evidence for involvement of G-protein signalling components in maintaining calcium oscillations is accumulating [58,59] but to what extent they influence flagellar beating patterns in oomycetes in unknown. Thus, beyond roles in chemoreception, it is worthwhile to investigate the interplay between G-protein mediated signalling and calcium oscillations and influx, and how this affects flagellar beating.

In conclusion, this study demonstrates the power of high speed microscopy to monitor the behaviour of zoospores in real time in the presence of a chemoattractant. By combining high speed microscopy, automatic tracing of zoospore swimming trajectories and intricate quantification of trajectory parameters, we collected a large body of data on zoospore behaviour in an unprecedented manner. By exploring these data and by simulation modelling we were able to show that Phytophthora zoospores use positive klinokinesis as a mechanism to remain at the host surface once arriving there, a phase in the homing response coined as retention [14]. Moreover, we showed that our analyses allow us to distinguish between defects in chemotaxis and chemoreception, a prerequisite when identifying candidate receptors for chemoattractants. The methods and insights on zoospore swimming behaviour presented in this study can be instrumental in exploring strategies for Phytophthora disease control, for example, for screening efficacy of soil dispersed particle traps laden with (a cocktail of) proven chemoattractants, in ‘fishing’ out wandering zoospores before arriving at the root, or for identification of chemoreceptor inhibitors or antagonists that disturb the homing response and disable zoospores in finding their targets.

Materials & methods

Supporting information

Acknowledgments

We thank the Wageningen Light Microscopy Centre (WU) for the use of their facilities, and dr. Martin Lankheet and Remco Pieterse (Laboratory of Experimental Zoology, WU) for technical support and advice.