Force-induced repolarization of an active crawler

We develop a quantitative model of mechanical repolarization in a contraction-driven gel layer mimicking a crawling cell. We show that the force-velocity relations for such active crawlers exhibit multi-valuedness and hysteresis under both force and velocity control. The model predicts steady oscillations of cells attached to an elastic environment and offers a self-consistent mechanical explanation for all experimentally observed outcomes of cell collision tests.

Introduction
To perform individual tasks, for instance, chase an intruder, or to act collectively, as in morphogenetic flows, crawling cells are able to switch their direction of motion in response to external stimuli. They do so by reorganizing their cytoskeleton [1], an internal meshwork of biopolymers held together by passive crosslinkers and actively contracted by molecular motors. Among the various physical cues that can induce such repolarization [2,3], mechanical contact forces are particularily important when cells operate in crowded environments.
Building reliable links between repolarization mechanisms and measurable biophysical parameters is fundamental for the control of development, integrity and regeneration of living organisms [4][5][6][7][8][9][10][11][12][13]. A broadly accepted mechanism of force-induced repolarization relies on a reaction-diffusion instability involving several biochemical agents, in particular, Rho-GTPases, which control the motor activity in the cytoskeleton [14][15][16]. In this paper we propose an alternative mechanism by showing that the application of an external force can lead to mechanical repolarization involving relocation of molecular motors, without invoking any biochemical signal transduction.
We build our theoretical construction upon the recent advances in the modeling of individual cell migration [17][18][19][20][21][22]. As a proof of principle, we use an analytically transparent one-dimensional model of an active gel representing the cytoskeleton as a contraction driven viscous layer [23][24][25][26]. Such prototypical modeling is appropriate for the case of a cell crawling on a 1D track [27] or inside a capillary [28]. Interestingly, it has been recently argued that motility confined to 1D objects may be closer to the physiological 3D motility along fibers of the extra-cellular matrix than 2D motility on a flat substrate [29,30].
To make quantitative predictions, we consider two basic problems: the oscillatory behavior of an elastically tethered cell and the head-on collision of two initially polarized self propelling cells. Our results highlight several important qualitative features of the proposed model. First of all, at a fixed value of the external force the model supports two coexisting steady regimes: dissipative, when the active object is dragged by the force, and antidissipative, when it is pulling cargo. The fact that the cytoskeleton can spontaneously self-organize from one of these steady states to another through a hysteresis loop offers a purely mechanical explanation for cell repolarization. However, this would not be enough, to ensure the adequate description of cell collision. Here we use another important feature of the model, that a steady regime with a given velocity can take place under applied forces of opposite signs, corresponding to either an attached cargo or an external engine. In particular, our active segment can be stalled by two forces that are equal in magnitude but have different directions. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.
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The importance of the study of spontaneous activity-induced mechanical oscillations stems from their ubiquitous presence in living systems at various length-scales [31,32]. Contractility due to the presence of molecular motors has been identified as one of the main mechanisms behind this phenomenon [33,34]. In particular, contractility-driven center of mass oscillations have been recently reported for cells constrained to move along one-dimensional fibers [35]. Here we show that our simple model predicts such steady state oscillations in the case of tethered cells when both the contractility and the stiffness of the effective spring are within a physiological range. We construct a phase diagram in the space of measurable parameters which distinguishes the oscillatory regimes from the regimes when the cell is either static, unable to polarize, or fully polarized but stalled by the contact force.
An important test for any theory of cell motility is its ability to capture adequately the repolarization caused by the mechanical interaction between colliding cells. Experiments show that collision of two cells can result in four basic outcomes [36,37]: velocity reversal, representing a quasi-elastic collision with symmetric repolarization, two quasiinelastic pairing scenarios with the formation of a cell doublet that can be motile (train) or static (stall) and finally, a bypass regime, when cells advance over each other [16]. The reversal and pairing regimes can be linked to the phenomenon of contact inhibition of locomotion (CIL) [38]. Being a driver of cell dispersion and collective motility, CIL is essential for biological tissues and its loss is usually associated with pathological processes, including cancer [38]. Motivated by experimental observations which suggest that a fundamental building block of CIL is motorinduced contractility [39], we simulated cell collision tests using our active layer model. It is quite remarkable that all four known cell collision scenarios could be accessed in this purely mechanical setting by tuning a single nondimensional parameter describing cell contractility. The obtained phase diagram reveals the physical conditions ensuring the failure of CIL, which have so far remained largely unknown.
To emphasize the importance of the force-induced repolarization in problems involving multiple cell collisions, we developed a reduced model of active segments viewing them as active particles with polarity serving as an internal parameter. Despite the extreme simplicity of such a scaled-down model, which reduces to a system of two ODEs (instead of the original system of PDEs), we have shown that it captures all the steady and nonsteady regimes which we have identified in the original active layer model.

Model of a crawling segment
We represent a cell crawling on a straight track by a segment of viscous contractile gel of length L, see the inset in figure 1.
To make the problem analytically tractable, we assume that the length of the active segment L is fixed. The associated size-control mechanisms are discussed in [40]. While length changes are known to be involved in oscillatory motion of cells [41] and may play some role during the initiation of motility [42], we have chosen to (blue, gray). The darker background indicates the region where the V-F curve is anti-dissipative. neglect these effects in the present paper in order to study the mechanical repolarization phenomenon in its most pure form. We remark, however, that in the special case of fish keratocytes, the cell length is tightly controlled during the motion and the stiffness of an effective 'spring', ensuring such a control, is much larger than the stresses involved in the rearrangements of the cytoskeleton [40,43].
The time dependent free boundaries of the segment are then x f (t) for the front and x r (t)=x f (t)−L for the rear. Using the position of its geometric center S(t)=(x r (t)+x f (t))/2 as a reference, we can introduce the comoving coordinate y(x, t)=x−S(t)ä[−L/2, L/2]. Given that the segment boundaries are impermeable, we can write where V is the macroscopic velocity of the segment and w(y, t) is the microscopic velocity of the cytoskeleton in the co-moving frame of reference. Linear momentum balance for the cytoskeleton requires that where σ(y, t) is the axial stress field and ξ is the external friction coefficient. The constitutive model of the active gel reduces in this 1D setting to a relation where η is the bulk viscosity, χ is the contractility and c(y, t) is the concentration of motors generating the active stress.
Following [44], we assume that the symmetry is broken by the applied force F(t) which enters the model through the boundary condition Note that condition (4) does not depend on the exact configuration/partition of the applied forces at the two boundaries of the segment. Indeed, if we denote q ± the tractions at the ±L/2 and introduce q 0 the (kinematic) residual stress associated with the length constraint, we obtain two conditions σ(±L/2, t)=q ± +q 0 . If we now eliminate q 0 we obtain (4) with F=q +q − . Following [26,45], we further assume that the organization of the molecular motors in the segment is governed by the drift-diffusion equation which is equipped with no flux boundary conditions ∂ y c(±L/2, t)=0, ensuring that the total amount of motors M c y d To non-dimensionalize the problem we introduce the characteristic length The ensuing problem depends on three dimensionless parameters, representing, respectively, the normalized, length of the segment, traction force applied to the system and contractility of the motors. In the context of fish keratocytes, available data lead to the following basic estimates [40]: 10 ~and 2 ~, however, these numbers can also vary in different experimental conditions [45].
In the obtained model the flow velocity w at point y is induced by the presence in another point z of an active force dipole, represented by a motor concentration-dependent active stress [23,24,46]. It is also affected by the external force F. If we combine the momentum balance with the constitutive relation we can obtain a single relation Here we introduced the convolution operator c y t y z c z t z , ,d .

The kernel
y y H y y sinh 2 2 sinh 2 cosh , where H(y) is the Heaviside function, is an odd function which ensures that an even distribution of force dipoles does not generate a directional flow [47].

Velocity-force relation
Under the action of a constant external force F, the active layer reaches a steady velocity V which can be found by solving the stationary drift-diffusion equation (∂ t c=0) with the nonlocal closure relation (5). The ensuing velocity-force (V-F) relations are illustrated in figure 1 where qualitatively different regimes are delineated by critical thresholds. The  dependence of the critical thresholds is shown in figure 2. When  is smaller than c   ( ), the V-F relations are single-valued and dissipative, in the sense that V F>0. In these regimes, when the velocity V is large and F   , the effective drag coefficient (tangential viscosity) is always positive and  independent When V is small, the drag coefficient depends on , in particular, ). Overall, the increase of contractility reduces the effective drag until μ 0 vanishes at c   = . Similar effect of activity on friction has also been reported for several other systems [48,49].
When c   > , the V-F relation develops a domain of bi-stability which spreads over the range Fä[−F t , F t ]. Within this range, the stationary velocity can take three values, V V V ; 0 0 0 * * * < < the metastable branches V F 0 *( ) and V F 0 **( ) are connected through an unstable branch V 0 (F). Note that between the two turning points F=±F t one of the metastable dynamic regimes is necessarily anti-dissipative in the sense that VF 0  . In such regimes molecular motors overcome the passive frictional and viscous dissipation.
To clarify this point, consider the energy balance relation: ( ) /c y d 0 < . Therefore, the positivity of FV indicates that the dissipation (friction, viscosity) dominates the anti-dissipative power supply by the molecular motors, see [26,44] for further details.
We remark that similar bi-directionality was also observed in other active systems [50][51][52]. The striking feature of the present model is the existence of two other thresholds m   ( )and s   ( ).  boundaries were absent, while the role of V was played by the angular velocity of the flow and the applied torque served as the analog of our F [53].
The importance of the revealed complexity of the V-F relations is demonstrated below through the solution of the two prototypical problems: active oscillations and active collision.

Active oscillations
Consider first an active segment connected to a fixed support by a spring of stiffness k p such that  (see [40]) is realistic in view of the experimental data reported in [35].
The phase diagram, shown in figure 4, reveals that the transition from static to oscillatory state is controlled not only by the contractility level, as it has been found experimentally [35], but also by the environmental stiffness. In stiff environments the increase of contractility can at most put the system in a stall state. Instead, in soft environments, oscillations become possible at elevated contractility with a subsequent discontinuous  transition from oscillatory to stall state at even larger levels of contractility (between m  and s  ). We note that all three dynamic phases shown in figure 4 are accessible in soft environments by tuning contractility only, which suggests that cells in these conditions can potentially actively vary their dynamic state.

Active collision
As a second illustration, consider two identical but differently polarized active segments moving towards each other. We use the subscripts mto differentiate the segment coming from the left (minus sign) from the segment coming from the right (plus sign), see figure 5. Following [10,13], we assume that the colliding cells interact through the force F originating from a repulsion potential which penalizes the overlap of the two cells: The force F + is applied at the x r + boundary and F − is symmetrically applied at the x fboundary. The magnitude of the force depends on the degree of separation: t x t x t r f  = -+ -( ) | ( ) ( )|, see figure 5. In (7) we introduced a characteristic size of a cell-cell contact c    and defined F c as the scale of the repulsive force. Note that F c is finite which allows the segments to pass each other. We imply that the the adhesive clusters between the two cells are only transient and that the effective spring, which engages upon the contact, is always under compression and cannot bear any significant tensile load [38]. As cells pass each other, this spring is stretched until it disengages making the interaction force negligible at c    . While the account of an attractive part of the interaction potential would increase the possibility of forming cell doublets and, for many cell types, would be necessary to capture the formation of a stable tissue, we leave these issues for a separate study.
In our numerical experiments we explored the whole range of contractility levels above the motility initiation threshold c   > . Similar to what was observed in experiment [36,37], our simple mechanical model predicts four possible outcomes of a collision test: reversal, pairing, which can be motile (train) or static (stall), and bypass. Our quantitative results are summarized in figure 6 showing the regimes diagram on the F , c  ( ) plane. The typical trajectories of the colliding active agents for each of the four regime and the corresponding configurations of molecular motors are illustrated in figure 7. Similar behavior was also obtained using a much more detailed biochemical model in [16].
In the reversal regimes, the active segments repolarize during collision as a result of being exposed to large contact forces. The outcome of such 'quasi-elastic' collision is that the colliding agents change the sign of their polarities but not the magnitude of their velocities.
In the bypass regimes, the agents go past each other as our model allows for mutual overlap when the repulsion contact force is not sufficient to impede the propulsive machinery. While such non-one-dimensional outcomes would have to be confirmed in detailed 3D simulations, we anticipate that our prototypical model captures them adequately when the cell-cell friction is negligible compared to the cell-substrate friction.
Finally, in the pairing regimes the two initially mobile agents first get immobilized and push against each other as both of them reach transiently stall conditions. Such regimes can be stable (forming a robust stall phase) only for s   > when there exists a steady stall state. For s   < , in the train regimes, one of the two active agents eventually sweeps along the other one by forcefully repolarizing its internal configuration and afterwards they continue traveling together as a cell doublet. One can show that in such regimes, stall configurations also exist but are unstable and are destroyed by infinitesimal perturbations.

Active particle model
To highlight the idea that the applied force affects not only the spatial location but also the polarity of a moving cell, we now construct a reduced, coarse grained model by interpreting our active segment as an active particle.  In addition to its spatial location S(t) such particle should also have an internal degree of freedom C(t) representing polarity. A simple reduced model can be formulated in terms of two ordinary differential equations: is an arbitrary polarity measure and is a Landau type potential. The first equation in (8) can be derived by summing the values of velocity (5) computed at the two boundaries 2   and using relations (1). The relation between c(y, t) and C(t) then follows from the averaging over the boundaries. This equation shows the competition between the external force F and the active force C  competing in determining the direction of the motion. The second equation in (8) is obtained phenomenologically as a way to describe spontaneous polarization at zero applied force when c   = and to capture the simplest linear biasing of the polarity by the applied force.
It is remarkable that by adjusting the parameters k S , k C and α of this almost naive model, one can not only quantitatively emulate the hysteretic V-F relations presented in figure 1 but also reproduce all non-stationary regimes shown in figures 3 and 6 (see insets). The ability of the active particle model (8) to capture the mechanical response of the active segment model points to the idea that the coupling between the polarity variable and the applied force should be an essential element of any model seeking to describe mechanical interaction of active particles.

Conclusion
We showed that a contraction-driven active gel segment exhibits fundamental bi-stability with two dynamic regimes representing opposite polarities. The associated (V-F) relations are generically doubly hysteretic, allowing for both force and velocity induced repolarization of an active object. Dynamic bi-stability was shown to play a crucial role in self-induced oscillations of a tethered active objects and to be essential for capturing the experimentally observed outcomes of the cell collision tests. Our study suggests that even the reduced model of an active particle with self-adjusting polarity is capable of describing complex mechanical cell-cell interactions. Due to its ultimate simplicity, it should prove useful for the development of the kinetic theory of tissues driven by internal cellular motion.
A major prediction of our model is that cell contractility can serve as a purely mechanical regulator of CIL without the need to involve complex biochemical mechano-transduction pathways. This rather unexpected result would have to be checked experimentally through drug treatments [55] and optogenetics [56].


extfieldsofstudy: Medicine Biology

s2fieldsofstudy: Biology Engineering

text:
Molecular analysis of the endobronchial stent microbial biofilm reveals bacterial communities that associate with stent material and frequent fungal constituents

Endobronchial stents are increasingly used to treat airway complications in multiple conditions including lung transplantation but little is known about the biofilms that form on these devices. We applied deep sequencing to profile luminal biofilms of 46 endobronchial stents removed from 20 subjects primarily with lung transplantation-associated airway compromise. Microbial communities were analyzed by bacterial 16S rRNA and fungal ITS marker gene sequencing. Corynebacterium was the most common bacterial taxa across biofilm communities. Clustering analysis revealed three bacterial biofilm types: one low diversity and dominated by Corynebacterium; another was polymicrobial and characterized by Staphylococcus; and the third was polymicrobial and associated with Pseudomonas, Streptococcus, and Prevotella. Biofilm type was significantly correlated with stent material: covered metal with the Staphylococcus-type biofilm, silicone with the Corynebacterium-dominated biofilm, and uncovered metal with the polymicrobial biofilm. Subjects with sequential stents had frequent transitions between community types. Fungal analysis found Candida was most prevalent, Aspergillus was common and highly enriched in two of three stents associated with airway anastomotic dehiscence, and fungal taxa not typically considered pathogens were highly enriched in some stents. Thus, molecular analysis revealed a complex and dynamic endobronchial stent biofilm with three bacterial types that associate with stent material, a central role for Corynebacterium, and that both expected and unexpected fungi inhabit this unique niche. The current work provides a foundation for studies to investigate the relationship between stent biofilm composition and clinical outcomes, mechanisms of biofilm establishment, and strategies for improved stent technology and use in airway compromise.

Introduction
Endobronchial stents are frequently implanted to treat airway complications, with lung transplantation being a particularly common setting for indications including dehiscence, stenosis, and malacia [1,2]. Stents are composed of various materials, including uncovered metal, covered metal, and silicone [3]. Despite their value in treating medical conditions, implanted biomaterials typically become colonized via biofilms. In other biomedical devices, biofilms have been linked to device failure, especially in immunosuppressed and critically ill hosts [4]. Bacterial biofilms may also contain potential pathogens, and organisms within biofilms are more resistant to host immunity and antimicrobials than free-floating planktonic forms [5]. Biofilms on central venous catheters, intra-cardiac devices, and orthopedic devices has been explored extensively [4,5]. Understanding the nature of biofilms that form on implanted devices is an essential first step in assessing biofilm impact on outcome and/or optimizing such devices.
Remarkably little is known about biofilms on endobronchial stents, however.
Emerging culture-independent molecular tools have revolutionized our ability to understand the composition of microbial communities [6][7][8]. Such sequence-based approaches can comprehensively define entire communities, are highly quantitative, and do not depend on a priori knowledge of suspected constituents or the ability to culture individual organisms. These methods have recently been applied to oral [9][10][11] and endotracheal tube biofilms [12][13][14][15][16]. Here we present the first sequence-based analysis of luminal biofilms of endobronchial stents used for clinical treatment of critical airway compromise, most of which were in the setting of lung transplant-associated airway complications.

Subjects and sample collection
Endobronchial stents were collected from March 2014 through November 2016 from subjects who were undergoing clinically-indicated bronchoscopic removal due to need for stent replacement or to resolution of the disease process. Stents were initially placed in the airway and subsequently retrieved through rigid bronchoscopy by a team of interventional pulmonary specialists. Following removal, stents were placed in sterile specimen cups containing sterile saline for transport to the lab (15-30 minutes) and then gently rinsed again to dissociate poorly adherent material. The lumen was then vigorously swabbed circumferentially (Copan Diagnostics), and swabs kept at -80˚C. In four cases, bronchoalveolar lavage (BAL) was available from the same bronchoscopy. Clinical data was extracted from the electronic medical record. All subjects gave written informed consent under protocols approved by the University of Pennsylvania IRB (protocol #823558, #812748, #817513, #820073).

Bacterial and fungal amplification, sequencing and analysis
DNA extraction, PCR amplification, and sequencing details are included in supplemental materials. Briefly, DNA was extracted from swabs by combined chemical and mechanical (BioSpec Mini-Beadbeater-16) methods using PowerSoil DNA isolation kit (MoBio) with an additional 95˚C incubation to improve DNA recovery from fungi. Extracted DNA was amplified using barcode-labelled primers (27F forward and 338R reverse) directed at the bacterial 16S rRNA gene V1-V2 and fungal ITS-1 regions (ITS1F forward and ITS2 reverse), and sequenced on the Illumina MiSeq platform with 250-bp paired-end reads [17]. ITS amplification was assessed using automated electrophoresis with Agilent 22000 TapeStation system (Agilent Technologies, Santa Clara, CA) and if no detectable amplification occurred, or if the amplification was less than that of primer dimers, these samples were not pooled for sequencing. As a control for environmental background, sterile swabs exposed to saline in collection cups were analyzed in parallel.
Bacterial 16S rRNA gene sequences were clustered into de novo operational taxonomic units (OTUs) at 97% sequence similarity using UCLUST in the QIIME 1.91 pipeline [18], and then aligned to the GreenGenes reference database (v13_8) using PyNAST. Samples with more than 1000 reads (which included all stent swab samples) and OTU's with 2 or more hits across samples were kept for downstream analysis. Fungal ITS sequence data were processed with the PIPITS pipeline, which uses ITSx software that employs hidden Markov models to extract known ITS sequences from the amplicons. This was followed by annotation of 97% percent identity OTU representative sequences with BROCC and manual BLASTn results [19,20]. Bacterial analysis was based on relative abundances, except when comparing BAL with bronchoscopic pre-wash samples where we also examined absolute read counts so that taxa within low microbial biomass pre-wash samples are not exaggerated [6,21]. Fungal analysis was carried out using absolute read numbers rather than relative abundances given the high variability in fungal quantity between samples, also to avoid exaggeration of taxa with high proportions in specimens with very low fungal content [22]. Sequence data are available in the NCBI Short Read Archives (Project SRP154880) and our code and metadata are available at: https://github.com/johnmcginniss/stent/.

Statistical analysis
All figures and statistical tests were conducted in R (v.3.4.4; http://www.r-project.org). Alpha diversity (richness, Shannon index) was calculated using the vegan package. Beta diversity (UniFrac distances) was calculated in the QIIME pipeline (v.1.91; http://qiime.org) and used to perform principal coordinate analysis (PCoA) [23]. Clinical data were summarized using range, median, and interquartile range. Pairwise Wilcoxon rank sum test was used to compare between-group differences with Bonferroni-Holm correction for multiple testing. Fisher's exact test was used to analyze categorical data. Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was used to calculate group differences in weighted UniFrac distances. We used the hclust function from the R stats package for hierarchical clustering and the pam function from the cluster package for partitioning around medoids analysis (PAM) of weighted UniFrac data [24], with further details in the S1 File.

Subjects and stent samples
We analyzed the luminal biofilm of 46 airway stents from 20 subjects (Table 1). Nineteen were treated for airway complication following lung transplantation: 3 for anastomotic dehiscence, and 14 for stenosis or dynamic airway collapse (indication was not available for 2 subjects). All transplant subjects were maintained on an immunosuppression regimen of multiple agents (generally tacrolimus, mycophenolate, and prednisone). One subject had non-transplant-associated dynamic airway collapse. Stents were silicone (n = 20), uncovered metal (n = 18) or covered metal (n = 8).

Bacterial 16S rRNA gene sequence analysis of biofilm communities
Stent biofilm samples were subject to bacterial 16S rRNA gene amplification using V1-V2 primers followed by Illumina sequencing. After quality filtering we had approximately 6.88x10 6 reads in total, with a median read count per stent biofilm sample of 53,471 (IQR 46,252-64,614). After 97% clustering this yielded 13,769 de novo OTUs in the swab samples. For each sample, the OTU-based taxa were collapsed into the lowest taxonomic assignment available in our analysis. These were then ranked on absolute read count.
Reported are the top two taxa per sample. If the cultured organism was not in the top two it is reported with its respective rank. D Culture is from bronchoalveolar lavage or tissue. NA denotes no culture taken at the time of stent removal. E + denotes sample that underwent ITS sequencing; samples without sufficient ITS amplicon for sequencing are indicated as below threshold (BT).
The amount of 16S amplicon, as a surrogate for bacterial biomass [22,25], was markedly greater in stent biofilms compared to several types of controls (p<0.001 comparing biofilm samples to laboratory background and bronchoscope prewash; S1 Fig Subjects transplanted for suppurative lung disease had lower within-sample diversity (alpha-diversity as measured by the Shannon index) compared to those transplanted for ILD (p = 0.0134) or COPD (p = 0.0033) ( Fig 1A). In contrast, alpha-diversity did not differ across the three stent materials ( Fig 1B).
We examined between-sample diversity (beta-diversity) using a principal coordinate analysis (PCoA) of weighted UniFrac distances (Fig 2). UniFrac compares communities based on , and within-sample bacterial diversity was calculated using the Shannon Index. Each dot represents a sample, the boxplot represents the median, 75th and 25th percentiles, and points outside the whiskers are outliers. Subjects with underlying suppurative lung disease (cystic fibrosis (CF) and non-CF bronchiectasis) had lower alpha diversity compared to COPD and ILD (p < 0.05, pairwise comparison Wilcoxon rank sum test). Stent material did not impact alpha diversity.
https://doi.org/10.1371/journal.pone.0217306.g001 shared phylogenetic lineages of constituent taxa, and the weighted approach accounts for relative abundances of constituent taxa. The taxa present in greater >5% mean relative abundance driving the distribution on the PCoA were Corynebacterium, Staphylococcus, Pseudomonas, Prevotella, and Streptococcus. There was a statistically significant relationship between stent material and composition on the weighted UniFrac analysis (Fig 2B; R 2 = 0.09, p = 0.016; PER-MANOVA;). In contrast, there was only a nonsignificant trend towards clustering by diagnosis (Fig 2A; R To examine whether alternative analytical approaches might change the nature of these results, we carried out the same analysis after raising the threshold for inclusion of an OTU to �10 reads across all samples (reducing the total to 3199 OTUs) and excluding OTUs that could potentially be derived from contamination sources [26] (leaving 3167 OTUs). We also applied rarefaction to 10,000 reads per sample. However, as shown in S3 Fig, this alternative analysis did not change the associations between biofilm diversity and clinical or stent features, nor impact the ordination on the PCoA plot. Thus, these findings are robust to different methodological approaches. Subsequent analyses therefore employed the more complete dataset.
When we examined taxa at the 1% mean relative abundance level (S4 Fig), Corynebacterium continued to define one dimension, suggesting a dominant effect on these communities, whereas multiple additional taxa contributed to other dimensions of the PCoA plot. We repeated this analysis without 10 specimens from one highly sampled subject, #0099, which revealed a similar pattern (S5 Fig), indicating that this relationship is not driven by subject oversampling.
Within the Corynebacterium genus there were 865 de novo OTUs with more than 10 reads assigned. However, >90% of reads were assigned to two OTUs, accounting for 71.8% and 18.9%, respectively. The top hits by BLASTn search of the NCBI 16S rRNA database for these OTUs were C. striatum (99% coverage and 100% identity) and C. xerosis (99% coverage and 99% identity), respectively; however, there is a high degree of homology in this region among Corynebacterium, and other species also had slightly lower but still >97% coverage and identity for these two OTU's, thus precluding definitive assignment at the species level. Within the Staphylococcus genus there were 3 de novo OTUs that accounted for >95% of reads. The two most prevalent Staphylococcal OTUs could not be assigned at the genus level, while the top five BLASTn hits for the third most prevalent OTU (21% of Staphylococcal reads) were to S. aureus (100% coverage and 99% sequence identity), with other Staphylococcal species having lower coverage and/or identity.

Temporal dynamics and subject-level factors influencing biofilms
We asked whether stent biofilms characteristics were linked to the duration that stents were in place. Duration of stent was not related to biofilm total bacterial biomass (S6 Fig; r = 0.18, p = 0.24), nor PCoA axes 1 or 2 (axis 1: r = 0.06, p = 0.69; axis 2: r = -0.01, p = 0.97). This suggests that factors other than stent duration determine community composition.
Seven subjects had �2 stents available, including two with contemporaneous bilateral stents (Fig 3A). Sequential stents often showed consistent biofilm composition, but sometimes this was interrupted by gradual (#0991) or abrupt (#0099, #0988) compositional changes. In contrast, when bilateral stents were present, the communities were highly concordant (e.g. #0099 days 143, 151, 253, 262; #0988 day 85). We also visualized the 4 subjects with �3 stents on the PCoA plot (Fig 3B). Most showed considerable change. Together these results indicate that subject-level variables such as pre-transplant lung disease, and temporal factors such as time post-transplant or stent duration, did not have a primary role in determining stent biofilm composition. type 2 was clearly distinct, while types 1 and 3 reflected more of a continuum between community types (Fig 4A). The three PAM biofilm types had similar bacterial biomass based on 16S amplicon concentrations and were markedly greater than background (S1B Fig), with the Corynebacterium-dominated type 2 being slightly but non-significantly greater (p = 0.07, ANOVA). This result indicates that none of the biofilms reflect background or contamination. We queried the taxa present above 1% mean relative abundance within each PAM biofilm type (Table 2). Corynebacterium was widely present. Furthermore, Corynebacterium dominated biofilm type 2, accounting for 86% of all reads. In contrast, PAM types 1 and 3 had more taxa above 1%. Concordantly, biofilm type 2 had lower diversity than the other types (Fig 4B). Stent material was significantly associated with biofilm type (Fig 4C; p = 0.008, Fisher's exact test). Specifically, covered metal was associated with type 1 (Staphylococcus), silicone with type 2 (Corynebacterium), and uncovered metal with type 3 (polymicrobial). In contrast, biofilm type did not correlate with pre-transplant diagnosis.

Stent biofilm relationship to the bronchoalveolar lavage (BAL) microbiome and environmental controls
Four subjects had BAL performed during the stent removal bronchoscopy. We compared the stent biofilm, BAL, and washes of the bronchoscope working channel prior to the procedure (S7 Fig). The stent biofilm and BAL bacterial composition were similar, and distinct from the environmental controls. In biofilms with high Corynebacterium, this bacterium was abundant in the BAL but not in environmental controls, indicating it was authentically present in the stent biofilm and lower respiratory tract, and not derived from environmental contamination.

Fungal microbiome in stent biofilms
We used ITS sequencing to identify fungal taxa in the stent biofilm. Of the 46 specimens, 21 had no definitive fungal amplification using ITS primers and were not pooled for sequencing. We first visualized fungal taxa in the positive samples on a heat map (Fig 5). Because the presence of fungi is highly variable in lower respiratory tract specimens [22,27], we analyzed fungi by read count numbers rather than relative abundances, which can exaggerate the appearance of taxa in samples with few fungal reads [22].
Several stents yielded high numbers of ITS reads aligning to Aspergillus and Candida, and in some samples, Scedosporium, Stereum, and Sarcinomyces (Fig 5, Table 3). Candida species included C. albicans, C. glabrata, and C. tropicalis. Among samples with Aspergillus, three were confidently assigned to A. fumigatus, while the remainder could only be assigned to Aspergillus at the genus level. Scedosporium is occasionally recognized as an opportunistic pathogen. However, Stereum and Sarcinomyces were unexpected because they are typically considered environmental contaminants, yet they were present at high read numbers within biofilms and absent from our environmental controls. Environmental control specimens had very low numbers of fungal reads, suggesting that fungi identified at high read count in stent biofilms are authentic constituents rather than contamination-derived.
In an analysis of bacterial/fungal co-variation, Candida showed a significant inverse correlation with the most abundant bacterial genus, Corynebacterium (S8 Fig; ρ = -0.48, p = 0.017, Spearman's rank correlation). In contrast, there was no relationship between Candida and Streptococcus or other prevalent taxa.

Relationship between biofilm composition, airway cultures, and clinical features
Ten subjects had airway cultures concurrent with stent removal; six cultures revealed respiratory pathogens (Table 1). In five out of the six positive cultures, the cultured bacteria were identified in the stent biofilm. Importantly, the respiratory pathogen was not the most abundant 16S rRNA sequence, but rather was part of a community that included anaerobic or upper respiratory-type bacteria that are not usually cultured. One subject (#0895) grew Mycobacterium avium-intracellulare (MAI), which was not identified by sequencing. This suggests that either MAI does not participate in stent biofilm communities even if present in the lower respiratory tract, or these molecular methods are less sensitive than culture for mycobacteria [27], even though our previous work suggests that this pipeline can detect mycobacteria [17].
Conversely, we detected Enterococcus by sequencing but not culture (#0099, day 267) five days after a prior positive airway culture for this organism.
Three subjects had stents placed for anastomotic dehiscence, and two of these had the highest numbers of Aspergillus reads in our study (#0777, #0988; Fig 5 and Table 3). Subject 0077 (day 98) had a contemporaneous fungal culture, which was positive for Aspergillus. Candida spp. were detected by sequencing in many stents, including several at high levels. Candida spp. are typically reported in respiratory cultures as yeast with no further identification by our clinical microbiology lab. The stents that had Scedosporium, Stereum, and Sarcinomyces did not have contemporaneous BAL cultures. Twenty-five biofilm swab samples and seven control samples had fungal ITS amplification sufficient for sequencing. In the heat map, rows reflect fungal taxa at the lowest level assignment possible (down to the species level). Each column is a unique biofilm sample, grouped by subject. The name is in the format of: subject ID-side of stent-day post-transplant. Because the total fungal biomass is highly variable among samples, the color scale reflects absolute read counts rather than relative abundances to avoid exaggeration of taxa in low fungal content specimens. Taxa with fewer than 10 reads in any sample are excluded. https://doi.org/10.1371/journal.pone.0217306.g005 Endobronchial stent biofilm microbiome Taxa with �3000 reads in a sample are shown, along with the number of reads. All but one subject was on at least one antibiotic at the time of stent removal and most were also exposed to anti-fungal medications (S1 Table). The ubiquity of the exposure and between-subject heterogeneity limits statistical analysis of the impact of specific antimicrobials on biofilm composition, but indicates that, overall, biofilm formation is robust to antimicrobial treatment. While the goal of this study was to understand the composition of luminal biofilms established, we also queried the relationship to clinical outcome, data for which was available in 13 subjects. We asked if biofilm type (limited to the final stent in subjects with >1 stent) was related to outcome categorized as resolution of the airway problem motivating stent placement, presence of excessive granulation tissue, and stent mucus plugging. Within these categories, there was no significant relationship between stent biofilm type and outcomes (p = 1, Fisher's Exact Test), though the number of subjects in any individual outcome group was small in this cohort. Thus, the impact of biofilm types identified here on outcomes will require future prospective studies.

Discussion
Here we report the first molecular investigation of the airway stent biofilm. In this predominantly post-transplant cohort, we identified three bacterial community types-one low diversity and dominated by Corynebacterium, while the other two have greater diversity and are characterized by Staphylococcus or by a mixed population including Pseudomonas, Prevotella, and Streptococcus. Biofilm type correlated with stent material. When individuals had serial stents, the biofilm communities frequently shifted, suggesting that device-related, procedural and/or contemporaneous clinical factors, but not pre-transplant lung disease, influence biofilm formation. Fungal sequences were frequently detected, sometimes at high read counts.
Corynebacterium was the most prevalent bacteria. It dominated biofilm type 2, and was present at substantial albeit lower abundance in other biofilm types. Corynebacteria are common skin flora and are generally not considered pathogens in the respiratory tract [6,28], although there have been reports of C. striatum respiratory tract infection in immunocompromised hosts[29-32]. One previous study identified Corynebacterium in airway cultures from 4.6% of patients after lung transplantation, and within this population the presence of airway stents was associated with Corynebacterium persistence [33]. Our data suggests a potentially important role for Corynebacterium is its interactions and competition with other bacteria and fungi in the biofilm. Supporting this notion, Corynebacterium spp. have been shown to antagonize Staphylococcus aureus and Streptococcus pneumoniae in the anterior nares [34][35][36]. Furthermore, Corynebacterium spp. have recently been identified as foundational taxa in dental biofilms, serving as a nidus of nucleation for other bacteria in dental plaque [11]. Thus, in the post-transplant stent biofilm Corynebacterium may be derived from the oral cavity and thrive within the biofilm, provide a scaffold for some taxa to persist while competing with others, and play a central role in endobronchial stent biofilm structure and composition.
Despite their importance for treating airway compromise, stents may themselves increase risk of lower respiratory tract complications, including infection, granulation tissue formation and mucous plugging [37,38]. Stent colonization has been postulated to contribute to these consequences [39][40][41], and so understanding biofilms that form on these devices could be important to optimizing their development and use. We found stent material was significantly correlated with biofilm type, with silicone stents favoring the Corynebacterium-dominated biofilms, covered metal favoring Staphylococcus-type biofilms, and uncovered metal associated with polymicrobial biofilms. Although the association was imperfect and explained only part of the biofilm composition, this information if further substantiated could help guide stent design or selection for the purposes of minimizing infectious and inflammatory complications.
Such knowledge could be important in reducing the risk of colonization by potential pathogens. Future studies could address how antimicrobial-impregnated stent materials impact biofilm development.
Fungi were common within stent biofilms, with Candida being the most frequently identified, including pathogenic species C. albicans, C. glabrata, and C. tropicalis. While Candida are often considered contaminants in respiratory cultures, they have a propensity to establish biofilms [4,42,43]. Our observation of high read numbers of Candida spp. suggest they are bona fide inhabitants of these post-transplant airway stent biofilms. Aspergillus is often suspected as a cause of anastomotic complications requiring stent insertion [1], and we found a preponderance of Aspergillus reads in dehiscence cases. The presence of Aspergillus in these biofilms is not only consistent with such an association, but raises the possibility that the biofilm might also contribute to fungal persistence. Several other fungi were found in individual samples at high abundance, including Scedosporium, Stereum, and Sarcinomyces. Scedosporium is recognized as an opportunistic pathogen. In contrast, the latter two fungi are generally not considered human pathogens. Further study is needed to determine whether these biofilm inhabitants are linked to anastomotic tissue infections, either a cause or a consequence, or might perpetuate inflammation, impede healing, or cause stent/airway complications.
The fact that stent biofilms were markedly higher in bacterial biomass and differed in composition from environmental controls (S1 and S2 Figs) indicates that these findings authentically reflect biofilm and not contamination. In addition, the major findings are robust to different analytical approaches. Nevertheless, our study has several limitations. The study was observational so there was inherent heterogeneity in exposures, although accurately reflects clinical practice in complex patients. Sampling was done at the time of stent removal, so it is difficult to make inferences into the how the stent microbiome changed in situ. We investigated a relatively large number of stents (n = 46), but the number of any single stent material, underlying lung disease, and outcome group was modest. Finally, our study was done in a predominantly post-transplant population, which has distinct exposures to antibiotics and immunosuppression that may limit generalizability to stent biofilms that form in non-transplant populations.
In summary, this is the first study to comprehensive, unbiased molecular methods to interrogate the biofilm of airway stents. We identify a central role for Corynebacterium in the stent biofilm, three broad types of biofilm communities, evidence for unexpected uncultured fungi and fungal/bacterial community co-variation, a possible link between fungi and anastomotic dehiscence, and an association between stent material and biofilm type. Understanding the nature and composition of airway stent biofilms sets the stage for future studies to determine how luminal biofilms influence outcomes, identification of low-risk or high-risk biofilms and better understanding of mechanisms involved in their establishment. This knowledge will offer opportunities for improved stent technology and decision-making around stent selection and management. Four subjects had bronchoalveolar lavage (BAL) carried out contemporaneous with stent removal, with samples shown as heatmaps, along with matched bronchoscope pre-wash samples as a background control. Each column is a sample, each row is a bacterial taxon, and each group represents a different subject. The left-hand panel shows the number of matched reads per sample; because the environmental controls are low biomass samples we used absolute read counts rather than relative abundances so taxa with low numbers of reads within a low microbial biomass sample are not exaggerated. The right-hand panel shows the relative abundances within each sample. Within each subject grouping, the first column is a BAL sample (B), second is a stent biofilm (S), and third is a bronchoscope pre-wash environmental control (P). (TIF) S8 Fig. Fungal-bacterial covariation. For all samples with at least 500 fungal reads (n = 22), the absolute reads on a log10 scale of Candida within the ITS dataset (x-axis) were plotted against the proportion Corynebacterium and Streptococcus (y-axis). The line is fit with a general linear regression model with 95% confidence intervals shown in gray. There was a significant inverse relationship between Candida and Corynebacterium (ρ = -0.48, p = 0.017, Spearman's rank correlation) but no correlation with Streptococcus relative abundance. The regression line is derived from a generalized linear regression model with 95% confidence interval in gray. (TIF) S1 Table. Anti-microbial exposure at the time of stent removal. (DOCX)


extfieldsofstudy: Biology Medicine

text:
Genetic characterization of Cryptosporidium spp. and Giardia duodenalis in dogs and cats in Guangdong, China

Background There are only limited number of reports on molecular epidemiology of Cryptosporidium spp. and Giardia duodenalis in dogs and cats in China. This study was conducted to assess the infection rates, genetic identity, and public health potential of these parasites in dogs and cats in Guangdong, China. Methods PCR and sequence analyses were used to identify and genotype Cryptosporidium spp. and G. duodenalis in fecal samples from 641 dogs and 418 cats in Guangdong. Chi-square test and odds ratio analysis were used to compare the occurrence rates of these pathogens and identify risk factors for infection. Results The overall infection rates of Cryptosporidium spp. and G. duodenalis were 6.9% (44/641) and 9.4% (60/641) in dogs, and 6.2% (26/418) and 3.6% (15/418) in cats. Purebred cats (12.4%; χ2 = 5.110, OR = 2.8, P = 0.024) and dogs (10.8%; χ2 = 5.597, OR = 4.8, P = 0.018) were more likely to be infected by Cryptosporidium spp. and G. duodenalis, respectively. Dogs (12.0%; χ2 = 7.589, OR = 2.6, P = 0.006) and cats (13.6%; χ2 = 8.235, OR = 3.5, P = 0.004) under 6 months had significantly higher infection rates of Cryptosporidium spp. than older animals. Household (13.9%; χ2 = 10.279, OR = 2.6, P = 0.008) and pet shop dogs (11.0%; χ2 = 7.182, OR = 2.0, P = 0.048) had higher occurrence of Cryptosporidium spp., as was the case for G. duodenalis occurrence in experimental dogs (13.4%; χ2 = 9.223, OR = 1.9, P = 0.017). Cryptosporidium canis (n = 42), C. muris (n = 1) and Cryptosporidium rat genotype IV (n = 1) were identified in dogs, while C. felis (n = 21), C. parvum (n = 3), C. muris (n = 1) and Cryptosporidium rat genotype IV (n = 1) were identified in cats. In contrast, the canine-specific assemblages C (n = 27) and D (n = 26) and the feline-specific assemblage F (n = 14) were almost exclusively the only genotypes of G. duodenalis in dogs and cats, respectively. There was no significant difference in infection rates of Cryptosporidium spp. and G. duodenalis between diarrheal and non-diarrheal pets. Conclusions While domestic pets in Guangdong are infected with zoonotic Cryptosporidium species, they are mainly infected with host-specific G. duodenalis genotypes. Risk factors for infections differ between Cryptosporidium spp. and G. duodenalis and between dogs and cats.

Background
Cryptosporidium spp. and Giardia duodenalis are important protozoan parasites that inhabit the gastrointestinal tract of humans and other vertebrates. Diarrhea is the main clinical symptom of cryptosporidiosis and giardiasis. Humans acquire these two pathogens through contact with infected persons and animals, or consuming contaminated food or water [1,2]. Among the ~40 known Cryptosporidium species, C. hominis, C. parvum, C. meleagridis, C. canis and C. felis are the most common species in humans [3]. Similarly, among the eight common genotypes (A to H) of G. duodenalis, only assemblages A and B are major human pathogens [2].
Cryptosporidium spp. and G. duodenalis are commonly detected in dogs and cats worldwide [4,5]. Cryptosporidium canis and C. felis are major Cryptosporidium species in dogs and cats respectively, but C. hominis, C. parvum, C. muris and C. ubiquitum have been occasionally detected in these animals [6][7][8][9][10]. Similarly, dog-adapted assemblages C and D, and cat-adapted assemblage F are the dominant G. duodenalis genotypes in these animals, although zoonotic assemblages A and B have been identified in some studies [2,11].
Limited data are available on the transmission of Cryptosporidium spp. and G. duodenalis in dogs and cats in China. The reported infection rates of Cryptosporidium spp. range from 1.6% to 10.5%, with C. canis and C. felis being identified as the dominant Cryptosporidium species in dogs and cats, respectively. In contrast, the infection rates of G. duodenalis were reported to range from 1.9 to 26.2%, with assemblages A, B, C, D and E being identified in dogs and assemblage F in cats [9,[11][12][13][14][15][16][17][18][19][20][21]. The risk factors involved in the acquisition of cryptosporidiosis and giardiasis have rarely been examined in these studies.
Guangdong Province has the largest populations of humans (111.69 million in 2017) [22] and pets (10.62% of the > 100 million pets in the country in 2015 were in Guangdong [23] in China. The subtropical climate and abundant rainfall provide a favorable environment for the transmission of waterborne pathogens such as Cryptosporidium spp. and G. duodenalis. Both cryptosporidiosis and giardiasis are known to be common in AIDS patients and diarrheic children in Guangdong, China [24,25]. Several studies have also reported the prevalence of G. duodenalis in dogs and cats in the province [12,15,18]. Thus far, there are no systematic studies of Cryptosporidium spp. in dogs and cats in the province. As children in China are sometimes infected with several zoonotic Cryptosporidium species (C. canis and C. felis) that are traditionally associated with pets [26,27], we examined in this study the occurrence and identity of Cryptosporidium spp. and G. duodenalis in dogs and cats in Guangdong for the assessment of the zoonotic potential of these pathogens. Li et al. Parasites Vectors (2019) 12:571

Sample collection
From July 2017 to August 2018, 1059 fecal samples were collected from dogs and cats in five cities of Guangdong (Fig. 1). Among them, 641 were from dogs of various living settings, including households (n = 79), veterinary clinics (n = 109), pet shelters (n = 134), pet shops (n = 118) and a research center (n = 201). Simultaneously, 418 fecal samples were collected from cats in households (n = 49), veterinary clinics (n = 130), pet shelters (n = 132), pet shops (n = 27), and strays (n = 80) in these cities. The animals were divided into two age groups: ≤ 6 months (125 dogs and 66 cats); and > 6 months (402 dogs and 299 cats), with 114 dogs and 53 cats of unknown age. In addition, we recorded information on the sex (291 and 129 female dogs and cats, 191 and 163 male dogs and cats, respectively, and 159 dogs and 126 cats of unknown sex), breed (446 and 89 purebred dogs and cats, 82 and 187 mixed-breed dogs and cats, respectively, and 113 dogs and 142 cats of unknown breeds) and clinical signs (17 and 19 diarrheic dogs and cats, and 624 and 399 non-diarrheic dogs and cats, respectively) of the animals as conditions permitted. Each fecal sample was placed into a 50 ml plastic centrifuge tube with 2.5% potassium dichromate, transferred to the laboratory, and stored at 4 °C for less than two weeks before DNA extraction.

DNA extraction and PCR analysis
Each fecal sample was washed twice with distilled water by centrifugation. DNA was extracted from the washed fecal materials using a Fast DNA SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA, USA). The extracted genomic DNA was stored at -20 °C until use. A nested PCR targeting the small subunit (SSU) rRNA gene was employed to detect Cryptosporidium spp. [28], while PCR assays targeting the β-giardin (bg) [29], glutamate dehydrogenase (gdh) [30] and triosephosphate isomerase (tpi) [31] genes were employed to detect G. duodenalis. An ~850-bp fragment of 60 kDa glycoprotein (gp60) gene was amplified to identify the subtype of C. parvum [32]. Each sample was analyzed at least twice by PCR at each genetic locus, with both negative and positive controls being included in each PCR analysis. The secondary PCR products were analyzed by 1.5% agarose electrophoresis.

Sequence analysis
All secondary PCR products of the expected size were sequenced on an ABI3730 autosequencer by the Sangon Biotech (Shanghai, China) in both directions using the PCR primers. The DNA sequences obtained were assembled using ChromasPro 1.5 (http://www.Techn elysi um.com.au/Chrom asPro .html) and edited using BioEdit 7.1.3.0 (http://www.mbio.ncsu.edu/BioEd it/bioed it.html). They were aligned with reference sequences of each locus downloaded from GenBank using Clustal X 2.1 (http://www.clust al.org/) to determine the identity of Cryptosporidium species and G. duodenalis genotypes.

Statistical analysis
Differences in infection rates of Cryptosporidium spp. and G. duodenalis in dogs and cats were compared between sexes, breeds, age groups, living conditions and clinical signs using the Chi-square test implemented in SPSS 20.0 version (IBM Inc., Chicago, IL, USA). Odds ratios (OR) and their 95% confidence intervals (95% CI) were calculated to identify risk factors involved in the acquisition of these pathogens. Differences were considered significant at P < 0.05.

Distribution of Cryptosporidium species
The secondary PCR products from all 44 Cryptosporidium-positive canine and 26 Cryptosporidium-positive feline samples were sequenced successfully. Among the canine samples, 42 were identified as positive for C. canis, and one each for C. muris and the Cryptosporidium rat genotype IV. Among the feline samples, 21 were identified as positive for C. felis, three for C. parvum, and one each for C. muris and Cryptosporidium rat genotype IV (Table 3). Within C. canis, the nucleotide sequences of the SSU rRNA gene obtained from 21 samples were identical to the GenBank reference sequence KJ776591, while nucleotide sequences from the remaining 21 C. canis samples had minor differences from the reference sequence, including one single nucleotide polymorphism (SNP) in 20 samples (T to C substitution at position 627 of KJ776591), four SNPs in one sample (A to G substitution at positions 293 and 341, and T to C substitution at positions 561 and 627 of KJ776591). Within C. felis, the nucleotide sequences obtained from 18 samples were identical to the GenBank reference sequence KM977642, while those from the remaining three samples were identical to the reference sequence AF159113. Within C. parvum, two nucleotide sequences were identical to the reference sequence AB968048, whereas the third one had two SNPs compared to the reference sequence (T to C substitution at position 102, and G to A substitution at position 586 of AB968048). The nucleotide sequence from C. muris in the feline sample was identical to KM870575, while the one from the canine sample had two SNPs (C to G substitution at position 112, and G to A substitution at position 196). Within the Cryptosporidium rat genotype IV, the sequence obtained from the canine sample had two SNPs (T to C substitution at positions 360 and 418) compared to AY737582, while the other one from the feline sample had one SNP (A to G substitution at position 427) (Additional file 1: Table S1).

Distribution of G. duodenalis assemblages
Fifty-eight of the 60 G. duodenalis-positive samples from dogs and all 15 G. duodenalis-positive samples from cats were sequenced successfully. There were some differences in the PCR detection rates among the bg, tpi, and gdh loci (Additional file 1: Table S2). Giardia duodenalis assemblages C, D and concurrence of both were detected in 27, 26 and 5 dogs, respectively. In contrast, assemblages A and F were found in 1 and 14 cats, respectively ( Table 3). The assemblage A was identified as A1 (GenBank: L40509) at the gdh locus, A5 (GenBank: AB469365) at the bg locus, and A4 (Gen-Bank: GQ329677) at the tpi locus. For assemblage F, the nucleotide sequences from two samples were identical to the GenBank reference sequence KX960131, and the remaining 12 samples were identical to KM977659 at the bg locus. The nucleotide sequences from eight samples were identical to KJ194112, and the remaining six samples were identical to AB569374 at the gdh locus. The nucleotide sequences from seven samples were identical to KM977655, one sample was identical to KP866788, while the remaining six samples failed in genotyping at the tpi locus. The nucleotide sequence differences within G. duodenalis assemblages C and D at the bg, gdh and tpi loci are shown in Additional file 1: Table S3.

Concurrent infections of Cryptosporidium spp. and G. duodenalis
Co-infection of Cryptosporidium spp. and G. duodenalis was found in 13 dogs and 1 cat. Among them, 9 dogs had co-infection of C. canis and assemblage C, 4 dogs had co-infections of C. canis and assemblage D, and one cat had co-infection of C. parvum and assemblage A. The coinfection rate in household dogs (6.3%) was significantly higher than in pet shelters (0.75%; χ 2 = 5.659, P = 0.017).

Discussion
We have shown in the present study a common occurrence of Cryptosporidium spp. and G. duodenalis in dogs and cats in five cities in Guangdong. Young age  A (1); F (14) was identified as the main risk factor for the transmission of Cryptosporidium spp. in these animals. The finding of higher infection rates of Cryptosporidium spp. in dogs and cats under 6 months is consistent with previous studies conducted elsewhere [14,33].
Results of the present study suggest that Cryptosporidium spp. and G. duodenalis have different transmission characteristics between dogs and cats. For example, young age was identified as a risk factor for the transmission of Cryptosporidium spp., but not for G. duodenalis for both dogs and cats; pure breed was a risk factor for Cryptosporidium spp. in cats and G. duodenalis in dogs; and the female and male sexes were risk factors for G. duodenalis in dogs and cats, respectively. Previous studies had shown that pedigree pets were more susceptible to infectious diseases [34,35]. Consistent with these observations, household and pet shop dogs had higher infection rate of Cryptosporidium spp. than dogs in research centers, while the opposite was observed for G. duodenalis. Some of the differences are attributable to the different life styles between dogs and cats; domestic cats mostly stay indoors and have little chance of contact with other pets or contaminated environment. In contrast, pet owners in urban areas often exercise their dogs in parks, where dogs frequently have contact with other pets and contaminated soil, increasing the risk of transmission of these parasites in household dogs [36,37]. The higher infection rates of these parasites in pet shop and experimental dogs are expected. These places are often overcrowded with young animals and have inadequate sanitary control, which may provide a favorable environment for the fecal-oral transmission of Cryptosporidium spp. and G. duodenalis [38,39].
Results of the present study support the suggestion that C. canis and C. felis are the most common Cryptosporidium species in dogs and cats, respectively [10]. As members of the five most common human-pathogenic Cryptosporidium species, C. canis and C. felis have been detected in humans worldwide [40][41][42], sometimes in both pets and their owners [43,44]. The other zoonotic species C. parvum detected in three cats in the present study had been reported in pets previously [9,45]. Subtype analysis had identified the common C. parvum subtypes IIaA15G2R1 and IIaA17G2R1 in urban companion animals in Great Britain [46,47]. We have failed to subtype the C. parvum in the present study, thus cannot exclude the possibility of the transient passage of the parasite without established infection. The occasional infections of C. muris in pets are expected, as this Cryptosporidium species is common in rodents in China [48]. Dogs and cats are in frequent contact with rodents either in pet shops or in the wild.
Giardia duodenalis assemblages C, D, and F were the most prevalent genotypes in this study. These results are consistent with the observation in previous studies that these assemblages are the most common genotypes in dogs and cats [49]. In addition, assemblage A infection was detected in one cat from a veterinary clinic, and this genotype was previously found in cats in Guangzhou [18]. Even though assemblages A and B are the main zoonotic genotypes, assemblages C, D, and F have been identified in a few human cases [50][51][52][53].

Conclusions
Results of this study suggest a common occurrence of Cryptosporidium spp. and G. duodenalis in dogs and cats in Guangdong, China, and young age, certain sex, pure breed and some living conditions could be risk factors for infections. Most Cryptosporidium species detected in the study, namely C. canis, C. felis, C. parvum and C. muris, are known zoonotic parasites while almost all of the G. duodenalis genotypes in dogs and cats are host-adapted ones. Further studies with sampling of humans and pets in the same area and characterization of zoonotic Cryptosporidium spp. at the subtype level are needed for improved understanding of zoonotic transmission of Cryptosporidium spp. in humans due to contact with pets.
Additional file 1: Table S1. Nucleotide substitutions in partial sequences of the SSU rRNA gene of Cryptosporidium species/genotypes obtained from dogs and cats in Guangdong. Table S2. Occurrence rates of G. duodenalis by PCR analyses of the β-giardin, glutamate dehydrogenase, and triosephosphate isomerase genes in dogs and cats. Table S3. Nucleotide substitutions in partial sequences of the β-giardin, glutamate dehydrogenase, and triosephosphate isomerase genes of G. duodenalis assemblages obtained from dogs in Guangdong.