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Nanorough titanium surfaces reduce adhesion of Escherichia coli and Staphylococcus aureus via nano adhesion points
Accepted Manuscript Title: Nanorough titanium surfaces reduce adhesion of Escherichia coli and Staphylococcus aureus via nano adhesion points Author: Claudia Ludecke ¨ Martin Roth Wenqui Yu Uwe Horn J¨org Bossert Klaus D. Jandt PII: DOI: Reference:
S0927-7765(16)30381-2 http://dx.doi.org/doi:10.1016/j.colsurfb.2016.05.049 COLSUB 7898
To appear in:
Colloids and Surfaces B: Biointerfaces
Received date: Revised date: Accepted date:
9-2-2016 26-4-2016 17-5-2016
Please cite this article as: Claudia Ludecke, ¨ Martin Roth, Wenqui Yu, Uwe Horn, J¨org Bossert, Klaus D.Jandt, Nanorough titanium surfaces reduce adhesion of Escherichia coli and Staphylococcus aureus via nano adhesion points, Colloids and Surfaces B: Biointerfaces http://dx.doi.org/10.1016/j.colsurfb.2016.05.049 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Nanorough titanium surfaces reduce adhesion of Escherichia coli and Staphylococcus aureus via nano adhesion points
Claudia Lüdecke a,b,d, Martin Roth b,d, Wenqui Yu c, Uwe Horn b,d, Jörg Bossert a,d, Klaus D. Jandt a,d #
a
Chair of Materials Science (CMS), Otto Schott Institute of Materials Research (OSIM), Faculty of
Physics and Astronomy, Friedrich Schiller University Jena, Löbdergraben 32, 07743 Jena, Germany b
Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI),
Bio Pilot Plant, Adolf-Reichwein-Str. 23, 07745 Jena, Germany c
Microbial Genetics, University Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
d
Excellence Graduate School “Jena School für Microbial Communication (JSMC)”, Friedrich Schiller
University Jena, Neugasse 23, 07743 Jena, Germany
#
Corresponding author: Phone: +49-3641-947730; Fax: +49-3641-947732
E-mail address: [email protected] (Klaus D. Jandt)
Running title: Microbial adhesion via nano adhesion points
Words Abstract: 222 Words Manuscript (incl. Title page, Text, Acknowledgement, References): 5639 Number of Figures: 6 Number of Tables: 2
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Graphical abstract
Highlights
Nanorough titanium surfaces were produced with physical vapor deposition. Microbial adhesion was reduced up to 55.6 % on the nanorough titanium. Direct insight into the microbe-titanium- interface is given using FIB-SEM. Initial microbial adhesion on nanorough surfaces is controlled via nano adhesion points. Late microbial adhesion on nanorough surfaces is controlled by the attachment area.
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ABSTRACT Microbial adhesion to natural and synthetic materials surfaces is a key issue e. g. in food industry, sewage treatment and most importantly in the biomedical field. The current development and progress in nanoscale structuring of materials surfaces to control microbial adhesion requires an improved understanding of the microbe-material-interaction. This study aimed to investigate the nanostructure of the microbe-material-interface and link it to microbial adhesion kinetics as function of titanium surface nanoroughness to gain new insight into controlling microbial adhesion via materials` surface nanoroughness. Adhesion of Escherichia coli and Staphylococcus aureus was statistically significantly reduced (p ≤ 0.05) by 55.6 % and 40.5 %, respectively, on physical vapor deposited titanium thin films with a nanoroughness of 6 nm and the lowest surface peak density compared to 2 nm with the highest surface peak density. Cross-sectioning of the microbial cells with a focused ion beam (FIB) and SEM imaging provided for the first time direct insight into the titanium-microbe-interface. High resolution SEM micrographs gave evidence that the surface peaks are the loci of initial contact between the microbial cells and the material`s surface. In a qualitative model we propose that the initial microbial adhesion on nanorough surfaces is controlled by the titanium surface peak density via nano adhesion points. These new understanding will help towards the design of materials surfaces for controlling microbial adhesion.
Key Words nanoroughness, microbial adhesion, titanium, SEM, focused ion beam
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1. INTRODUCTION Microbial adhesion to natural and synthetic materials surfaces is a key issue e. g. in food industry, sewage treatment and most importantly in the biomedical field. The current development and progress in nanoscale structuring of materials surfaces to control microbial adhesion requires an improved understanding of the microbe-material-interaction [1]. On the macro and micro scale it has been shown that topographical surface features of materials significantly affect the initial adhesion of microorganisms [2]. In particular, the impact of surface roughnesses on microbial adhesion has been extensively studied [3-8]. The findings have established the common knowledge that as a rule of thumb microbial adhesion increases with increasing surface roughness. Researchers assume that microbes adherent to macro- or micrometer rough surfaces are protected against abrasion from shear stress [7-11]. In addition, materials with surface structures of the size of the microbial cells may provide a maximum contact area between microbe and material and, thus, promote adhesion [12-15]. Consequently, one current strategy for reducing microbial adhesion on materials` surfaces is preparing surfaces as smooth as possible. Recently, nanostructured materials gained interest because of their effect on microbial adhesion [16-24]. In particular titanium as most often used material in the biomedical field was in the focus of several studies. Singh et al. [20] observed for example a significantly increased adhesion of Escherichia coli and Pseudomonas aeruginosa on supersonic cluster beam deposited titanium thin films with a surface nanoroughness of 21.7 nm compared to 16.2 nm. The authors assume, that differences in the wettability of the surfaces may have caused this increase. Ivanova et al. [17-18] reported contradicting results. They found that the surface coverage of magnetron sputtered titanium thin films with Staphylococcus aureus was reduced with increasing surface roughness from 0.2 nm to 0.5 nm and 0.7 nm to 1.6 nm, respectively. These authors, as well, assumed that a change in surface wettability at least partly caused the differences in microbial adhesion. Summarizing, to date there is no consensus in the current literature how nanoroughness affects microbial adhesion and results are contradictory. Moreover, standard roughness values (such as 4
the average roughness or the root mean square roughness) as used in these studies for description of the surface topography are vertical surface parameter not considering the spatial/horizontal distribution of structure elements e. g. grains/peaks and valleys on the surface. To better understand the effect of surface nanoroughness on microbial adhesion, it is, thus, important to include additional surface parameters such as peak-to-peak distance or peak density in the analysis. To fill this gap in knowledge, the aim of the current study was [i] to investigate the microbial adhesion kinetics of S. aureus and E. coli as function of titanium surface nanoroughness in particular considering next to vertical also spatial surface parameters, [ii] to elucidate the unknown nanostructure of the microbe-titanium-interface and [iii] to derive a qualitative model of microbial adhesion to materials surfaces as a function of nanoroughness and time. In a previous study, we found that with increasing stochastic nanoroughness of physical vapor deposited titanium thin films from 2 nm to 6 nm, the distance between the topographical surface peaks increased threefold [21]. Thus, with increasing roughness the relative number of titanium surface peaks, i.e., the peak density, decreased. Based on these results and based on a previous simulation study [25], we hypothesize that these surface peaks may be the points of first contact between the microbial cell and the titanium surface during initial stage adhesion. Accordingly, with increasing surface nanoroughness, less points for first contact are available between the microbial cell and the material which might result in reduced adhesion. The hypothesis tested in the present study was, therefore, that with increasing surface nanoroughness and decreasing peak density, microbial adhesion is reduced. 2. MATERIALS AND METHODS 2.1. Titanium thin film preparation Titanium thin films (Ti-TF) were deposited on glass slides (diameter 15 mm; Borofloat® B33; Jena 4 H Engineering GmbH, Jena, Germany) using the PVD method as described elsewhere [26]. Titanium surface roughness was adjusted by varying the deposition rate and the film thickness 5
with 0.1 nm/s and 100 nm (sample group A), 0.5 nm/s and 200 nm (sample group B), 0.5 nm/s and 500 nm (sample group C), and 1.0 nm/s and 500 nm (sample group D). Samples were sterilized in the autoclave for 20 min at 121 °C before investigating microbial adhesion. 2.2. Titanium surface characterization An atomic force microscope (Dimension 3100, Digital Instruments, Santa Barbara, CA, USA), equipped with a standard silicon tip (tip diameter 5 nm) was used to characterize the topography of the Ti-TFs surfaces. The AFM was operated in tapping mode with a scan rate of 2 Hz, an image resolution of 512 × 512 points and a scan size of 1 µm × 1 µm. AFM image processing and calculation of titanium surface parameters were performed with Gwyddion 2.25, a free SPM data analysis software [27] according to the protocol described elsewhere [21]. The roughness parameters were calculated according to the standard ISO 42871997. For the calculation of the average peak-to-peak distances and the peak densities of the titanium surfaces, the coordinates of the local maxima of the AFM height images were estimated using ImageJ (National Institutes of Health NIH, Bethesda, Maryland, USA). Based on a Delaunay triangulation algorithm, the distances between the neighboring maxima were calculated using MATLAB (MathWorks, Natick, Massachusetts, USA). The contact angle measurements were performed with the dynamic drop method using doubledistilled water drops (advancing angles: increase of drop volume from 5 µL to 7 µL with a rate of 10 µL/min) (DSA 10Mk2 drop shape analysis system, Krüss, Hamburg, Germany) on n = 3 replicates of each Ti-TF surface. 10 images of each increasing drop on the materials surfaces were recorded by a camera and analyzed using the software supplied by the manufacturer according to the circle fitting method. 2.3. Microbial strains S. aureus and E. coli both common pathogens relevant e.g. in the biomedical field were used as test organisms in this study. The microbial strains E. coli EC042, producing the green fluorescent 6
protein (GFPuv) [28], and S. aureus HG003, producing the red fluorescent protein mCherry [29], were used for microbial adhesion tests. Preparation of the fluorescent strains based on transformation is described in the supplemental material (Text S1). 2.4. Cultivation of the microorganisms and adhesion test A recently described in vitro device [26] was used to investigate microbial adhesion as a function of the titanium surface nanoroughness. This in vitro system allows for the investigation of microbial adhesion on materials surfaces at reproducible and constant conditions throughout the experiment. E. coli and S. aureus were each cultivated in a separate continuous culture (chemostat) which was used for inoculation of a biofilm reactor (non-constant depth film fermenter; nCDFF) containing the materials samples. A complete description of the system can be found elsewhere [26]. Experimental details of the cultivation of the microorganisms are provided as supplemental material (Text S2). Based on the results of pre-experiments in culture well plates (data not shown), the sampling time points were set to 1 h, 3 h, 5 h, 7 h, 9 h and 11 h, respectively, with n = 3 replicates for analysis with CLSM. For cross-sectioning of adherent cells and high resolution SEM, additional Ti-TFs were sampled after 3 h and 9 h, respectively, with n = 2 replicates. 2.5. Confocal laser scanning microscopy Using confocal laser scanning microscopy (CLSM), the adhesion kinetics of E. coli and S. aureus were investigated as a function of the nanoroughness of physical vapor deposited titanium thin films. Samples for CLSM imaging were prepared as described previously [26]. A confocal laser scanning microscope (Zeiss LSM 510 Meta, Carl Zeiss MicroImaging, Jena, Germany) equipped with an Argon laser (488 nm) and Helium-Neon laser (633 nm) and a 63× NA 1.4 oil immersion lens objective (Zeiss PLANAPOCHROMAT®) was used for fluorescence imaging of the microorgansims. The materials` surface coverage with the microbes was calculated based on five single plane CLSM images per sample for each sampling time point using the free software bioImage_L v.2.1 [30]. For image analysis, a factor of 0.03 was applied for noise reduction. 7
Microbial adhesion rates were calculated as percent increase of titanium surface coverage with the microbes per hour [increase coverage % per h] based on a standard linear correlation function. Initial microbial adhesion rates were calculated between 1 h and 3 h and fastest adhesion rates were defined as the highest increase in surface coverage between the two time points with. 2.6. Cross-sectioning of microbial cells and scanning electron microscopy Cross-sectioning of the adherent microbial cells and high resolution scanning electron microscopy (SEM) were used for direct visualization of the nanostructure of the microbe-material-interface. Microbial cells were cross-sectioned after 3 h (initial stage adhesion) and 9 h (late stage adhesion) of incubation on the surfaces with a focused ion beam and imaged with SEM. Initial stage adhesion was defined as the state of adhesion were contact between the microbial cells and the materials surface is predominately mediated by physical interaction, whereas late stage adhesion is noticeably influenced by biological factors, such as the production of extracellular polymeric substances. Titanium samples with adherent microbes were fixed, dehydrated and critical point dried according to standard procedures as described elsewhere [21,36]. Samples were not sputter-coated to avoid the coverage of structures sized on the very low nanometer scale. An AURIGA 60 CrossBeam® FIB-SEM scanning electron microscope (Carl Zeiss AG, Oberkochen, Germany) equipped with a focused ion beam apparatus was used at magnifications from 50.000 to 150.000 operated at 5 kV and a working distance of 5 mm. The focused ion beam was operated with a beam current of 1 pA resulting in the smallest possible beam diameter of 3 nm at 30 kV. 2.7. Statistical analysis For statistical analysis (Statgraphics Centurion XV software; StatPoint Inc., Warrenton, USA), an analysis of variance (ANOVA) based on a Scheffé t-test with a 95 % confidence interval (p ≤ 0.05) was performed. With a one-factorial (one-way) ANOVA, the statistical differences among the Ti-TFs (sample groups A - D) with regard to microbial coverage were tested for each sampling time point and for the two microbial species. In a multi-factorial (multi-way) ANOVA, 8
the impact of the (independent) categorical factors titanium surface topography, microbial species and time on the dependent (response) variable surface coverage was statistically tested following the questions [a] if any of the factors have a statistically significant effect on the dependent variable and [b] which is the most significant factor, i.e., which factor influences the dependent variable most. 3. RESULTS 3.1. Titanium surface characterization With increasing PVD deposition rate and increasing film thickness, R a and Rq increased from 1.58 ± 0.10 nm to 4.82 ± 0.18 nm and 2.00 ± 0.12 nm to 6.13 ± 0.19 nm, respectively. The skewness values of all surfaces were in the same range (Table 1), thus, the principal shapes of the profiles were similar to each other’s and independent of the increase of surface nanoroughness (Fig. 1). The surface area difference Rsa giving the percentage difference between the specific surface area and the projected area, i.e., the scan size used for AFM, decreased from 6.4 ± 1.8 % to 3.8 ± 0.2 % with increasing surface roughness. The peak density decreased with increasing roughness one order of magnitude. The peak-to-valley distance and peak-to-peak distance of the profiles increased with increasing roughness from 16.9 ± 1.7 nm to 45.2 ± 2.8 nm and from 13.1 ± 6.4 nm to 42.2 ± 23.2 nm, respectively. The small standard deviations of the peak-to-valley distances indicate that the titanium surface profiles were very uniform with similar heights of all titanium grains present at the surface. The high standard deviations of the peak-to-peak distances in the range of 50 % of the mean values indicate higher variations in diameter of the titanium surface grains. All titanium surfaces were moderately hydrophilic (Table 1). The very slight increase in surface hydrophobicity can be explained by the increased surface nanoroughness [31]. 3.2. Microbial adhesion on nanorough titanium surfaces The surface coverage with E. coli was reduced with increasing titanium nanoroughness (Fig. 2, Table S1). The degree of reduction on the roughest surface compared to the smoothest surface 9
was similar for all investigated time points with 61.4 % after 1 h and 55.6 % after 11 h. S. aureus also showed an adhesion reduced by 40.5 % after 11h on the roughest surface compared to the smoothest. However, the overall highest reduction for S. aureus was observed on sample type B (Rq = 2.3 nm) with 63.6 % after 11 h. The surface peak density and the Rsa were correlated with the Ti-TF nanoroughnesses as described in section 3.1. Thus, adhesion of E. coli decreased with decreasing surface peak density and decreasing Rsa. The microbes showed a slow initial adhesion followed by a faster adhesion (Fig. S1). For E. coli, the initial adhesion rates and the fastest adhesion rates decreased with increasing roughness from 2.2 % per h (sample type A) to 0.7 % per h (sample type D) and 8.1 % per h (sample type A) to 3.0 % per h (sample type D), respectively. For S. aureus, the fastest adhesion rates as well decreased with increasing roughness from 12.5 % per h to 3.6 % per h, whereas the initial adhesion rate was the lowest on sample type B (Rq = 2.3 nm). S. aureus formed cell aggregates on the samples with higher surface roughnesses, whereas E. coli cells were separately attached as single cells independently of the surface roughness (Fig. 3). 3.3. One and multi way analyses of variance Results of the one-way ANOVA are presented in Table 2 with all crosses (×) beneath each other in one column indicating homogeneous groups with no statistically significant differences and crosses not in the same column showing significant differences with p ≤ 0.05. For E. coli, at all investigated time points, the surface coverage was statistically significantly higher on the smoothest titanium surfaces (sample group A) compared to the roughest surfaces (sample group D). With exception of time point 1 h, for all time points between 3 h and 11 h, the surface coverage with S. aureus on the roughest surface (D) was statistically significantly reduced compared to the smoothest surface (A), as also observed for E. coli. The multi-way ANOVA revealed that all tested factors (titanium surface topography, time, microbial species) had a significant influence on the surface coverage (Table S2). The factor time 10
influenced the surface coverage most with p ≤ 0.001. For all time points between 1 h and 11 h, the surface coverage differed statistically significantly. The second most important factor influencing the surface coverage was the titanium surface topography. The surface coverage of the Ti-TF sample group A significantly differed from the surface coverage of the thin films B, C and D. Also the factor microbial species significantly influenced the surface coverage. With a p-value of ≤ 0.005, this factor was from the statistical point of view the least important factor influencing the surface coverage. 3.4. Nanostructure of the microbe-titanium-interface After 3 h of adhesion (initial stage adhesion), E. coli and S. aureus were attached to the nanorough titanium surface at a few points only (Fig. 4 B-C, E-F). For E. coli it was observed, that these adhesion respectively contact points were formed by small strands most likely consisting of extracellular polymeric substances (EPS) which were directly attached to the topographical titanium surface peaks. After 9 h of adhesion (late stage adhesion), the SEM images clearly show the production of considerable amounts of EPS by E. coli almost covering the complete titanium surface (Fig. 5 A) and fewer amounts of EPS produced by S. aureus (Fig. 5 C). Independently of the amount of produced EPS, both E. coli and S. aureus were along the visible contact line in complete contact with the nanorough titanium surface (Fig. 5 B, D). 4. DISCUSSION The current development and progress in nanoscale structuring of materials surfaces to control microbial adhesion requires an improved understanding of the microbe-material-interaction [1]. In this study the nanostructure of the microbe-material-interface was investigated and link to microbial adhesion kinetics as function of titanium surface nanoroughness to gain new insight into controlling microbial adhesion via materials` surface nanoroughness. In the literature, the influence of titanium surface nanoroughness on microbial adhesion was often discussed to be a result of a changed surface wettability [17-18,20] or microbe-specific surface properties such as the zeta potential which may lead to an electrostatic repulsion [23-24]. 11
However, the significant reduction of microbial adhesion with increasing titanium nanoroughness in the current study can not be explained with the only slight increase of surface hydrophobicity (Table 1; increase of approximately 5 °) of the Ti-TFs. Wettability and zeta potential were shown to be generally not a predictive parameter for microbial adhesion [32]. Microbial adhesion is generally known as first step of biofilm formation and can be described as two-stage process. The very initial adhesion respectively contact of the microbes with the materials surface (first stage) is controlled by solely physical interaction of the microbe with the materials surface. This process is most often described in literature by the DLVO theory (Derjaguin-Landau-Verwey-Overbeek) of colloid stability [33] or the extend DLVO theory considering, as well, Lewis acid base interactions. Comprehensive overview about forces involved in microbial adhesion ca be found elsewhere [34]. With increasing adhesion time, the adhesion process is not only determined by physical forces, but also biological factors (second stage) [35]. Adhering microorganisms may react to membrane stress arising from small deformations due to the adhesion forces [35]. E. coli for example is known to have mechanosensitive membrane channels [36]. The transition from the first to the second adhesion stage is marked by a change in gene expression, i. e. the switch from the planktonic life style to the sessile life style. As such, genes for EPS formation including polysaccharides, proteins, nucleic acids and lipids [37] are up regulated. According to this two-stage definition, in the current study, it was roughly differed between initial stage adhesion and late stage adhesion, which was based on microscopy and visible changes of the physiology of the microbes, e. g. EPS formation through which they embed themselves in a protective matrix. The time point of visible switch indicated by EPS production was approximately after 6 hours of adhesion. At different study conditions (e.g. different available nutrients, other microorganisms or used materials as well as at in vivo conditions), this switching time point may be earlier or later. FIB-SEM imaging of the nanostructure of the microbe-material-interface revealed that the reduced adhesion on the rougher surfaces can be explained by a lower number of points available 12
for the first contact between the cells and the titanium surface, respectively, the topographical surface peaks (initial stage adhesion). These peaks correspond to the points of the shortest distance between the microbial cell and the titanium surface (indicated in Fig. 4 C, F with arrows) where long-range adhesive forces (mainly Lifshitz-Van der Waals interactions) and, in the following when the distance becomes smaller, short-range adhesive forces (mainly Lewis acidbase-interactions) initially occur [34]. The total interaction force between one microbial cell and the materials surface is mainly determined by the adhesive forces at each of these points of closest distance. For the late stage adhesion, the reduced coverage on the roughest surface may be an effect of the already initially reduced adhesion in the initial stage of adhesion. Moreover, the specific surface area of the rougher titanium was significantly reduced compared to the smoother surfaces (Table 1). After 9 h, a considerable amount of EPS was produced by the microbes (Fig. 5). This EPS mediated a complete contact between the microbial cells and the nanorough surface. The reduced specific surface area with increased titanium nanoroughness was associated with a reduced total area for contact between the cells and the titanium. This may have led to reduced interaction forces and, in consequence, to a reduced surface coverage with the microbes. A recent study found a decreased adhesion of E. coli, S. aureus and P. aeruginosa with a decreasing effective surface area of polished glass [38]. However, in the current study most likely both effects played a role during late stage adhesion. In a recent simulation-based study, Siegismund et al. [25] showed that on the nanometer scale (average roughness Ra < 70 nm) the surface peak density exerts a significant influence on the interaction between microorganisms and materials surfaces during initial stage adhesion. The authors found a decreased interaction energy between a microbial cell and a nanorough surface with increasing peak density which they assumed results in a reduced microbial adhesion. The authors hypothesized that the surface peaks act like spacers between the microbial cell and the surface. Thus, a decreased peak density, i.e., an increased spatial distance between the peaks, allows vice versa for an increased interaction between cells and surface and, thus, leads to a 13
higher interaction energy (microbe/valley interaction). These results are partly contrary to the findings presented here. Our results indicate that despite decreasing peak density (e. g. on titanium samples D with Rq = 6.1 nm), there is no relevant interaction of the cells with the topographical valleys during early adhesion (after 3 h, see for example Fig. 4 C). So our experimental data showed that initial microbial adhesion on nanorough surfaces is, independently of the peak density, determined by a microbe-peak interaction. S. aureus showed a different adhesion behavior on the nanorough surfaces compared to the results reported in the literature and compared to that of E. coli reported here. Microbial adhesion was reduced most on the titanium surfaces with a roughness of 2.3 nm (sample group B). Based on the proposed mechanisms of microbial adhesion on nanorough materials surfaces via nano adhesion points, we conclude that the surface with the highest nanoroughness and the lowest peak density is the most unfavorable for S. aureus, too, since the fewest number of points for adhesion are available. In general, S. aureus cells tend to form cell aggregates due to specific cell surface associated molecules, such as the polysaccharide intercellular adhesins (PIA) [39]. So on these surfaces, S. aureus cells might have naturally preferred to adhere to other cells that have previously successfully attached to the surface. In that way, aggregates were formed rather than a flat layer of randomly distributed cells. This aggregate formation, in turn, may have caused an artificially increase in the calculated surface coverage observed with increasing titanium nanoroughness. This explanation is also supported by the fastest adhesion rates calculated for S. aureus. The fastest adhesion rates of both E. coli and S. aureus decreased with increasing surface nanoroughness. This indicates that for both microbial species, independently if Gram-negative or Gram-positive, rod-shaped or spherical-shaped, a higher surface nanoroughness with a lower peak density impairs microbial adhesion. Summarizing, the hypothesis tested in the current study, that microbial adhesion is reduced with increasing surface nanoroughness and decreasing peak density, was fully confirmed for E. coli and, with some limitation, also for S. aureus.
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Based on our results reported and discussed above, we propose a simple qualitative model of microbial adhesion as a function of the titanium surfaces nanoroughness (visualized in Fig. 6): Microbial adhesion on nanorough titanium surfaces is initially controlled by the surface peak density, i.e., the relative number of nano adhesion points between a microbial cell and the surface. These topographical surface peaks mediate the first contact to the microbial cell. A decreased number of these nano adhesion points is associated with decreased total interaction forces between the microbial cell and the materials` surface resulting in a reduced adhesion. With increasing adhesion time, biological factors, such as the production of EPS influence microbial adhesion as well. These EPS cause a complete contact between microbial cell and titanium surface by filling the voids of the nanorough surface. For late stage adhesion, microbial adhesion on nanorough titanium surfaces is, thus, additionally controlled by the specific materials surface area available for contact (attachment area) between the cells and the material. A decreased attachment area with increased nanoroughness results, too, in decreased total adhesive forces and, thus, in a reduced microbial adhesion. For further verification of the current results and our qualitative model, next steps should include the investigation of microbial adhesion on other types of nanorough titanium as well as on other nanorough materials with varying surface peak density. Based on these results, a quantitative adhesion model might be derived. 5. CONCLUSIONS Our simple model of microbial adhesion as function of surface nanoroughness extends the in the macro- and micrometer range widely accepted theory of “most contact” [12-15] to the nanometer scale. We conclude that the fundamental mechanisms how surface roughness impacts microbial adhesion are principally the same for the micro- and macrometer scale and for the nanometer scale but with opposite effects. Microbial adhesion is controlled by the possible contact with the materials` surface. On the nanometer scale, adhesion is reduced with increasing roughness associated with a decreased number of adhesion points and a reduced attachment area, 15
respectively, between the cells and the material. On the micro- and macrometer scale, adhesion is increased with increasing surface roughness associated with a higher contact area between the cells and the material`s surface. Our study, moreover, demonstrated that a focused ion beam for cross-sectioning of microorganisms adherent to material surfaces is a powerful tool to gain direct insight into the microbe-material-interface. The results, furthermore, emphasize the importance of time-dependent studies. In that way, for the first time, the aggregate formation of S. aureus cells induced by surfaces nanoroughnesses was observed. ACKNOWLEDGEMENTS The authors thank F. Götz (Microbial Genetics, University of Tübingen, Tübingen, Germany) for providing the S. aureus HG003 strain, D. Femerling for construction of the E. coli EC042 strain, R. Wagner for support during microscopy, M. Cyrulies for help with the setup of the in vitro test system, M. Beyer and S. Maenz for introduction to statistics and the Excellence Graduate School “Jena School for Microbial Communication” which is funded by the German Excellence Initiative for financial support. Furthermore, KDJ gratefully acknowledges the partial financial support of the Deutsche Forschungsgemeinschaft (DFG), grant reference INST 275/241-1 FUGG, and the TMBWK, grant reference 62-4264 925/1/10/1/01.
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[24] V.K. Truong, R. Lapovok, Y.S. Estrin, S. Rundell, J.Y. Wang, C.J. Fluke, et al., The influence of nano-scale surface roughness on bacterial adhesion to ultrafine-grained titanium, Biomaterials 31 (20109 3674-3683. [25] D. Siegismund, A. Undisz, S. Germeroth, S. Schuster, M. Rettenmayr, Quantification of the interaction between biomaterial surfaces and bacteria by 3-D-modeling, Acta Biomaterialia 10 (2014) 267-275. [26] C. Lüdecke, K.D. Jandt, D. Siegismund, M.J. Kujau, E. Zang, M. Rettenmayr, et al., Reproducible biofilm cultivation of chemostat-grown Escherichia coli and investigation of bacterial adhesion on biomaterials using a nonconstant-depth film fermenter, PLoS ONE 9 (2014) e84837. [27] D. Necas, P. Klapetek, Gwyddion: an open-source software for SPM data analysis, Central European Journal of Physics 10 (2012) 181-188. [28] A. Crameri, E.A. Whitehorn, E. Tate, W.P.C. Stemmer, Improved green fluorescent protein by molecular evolution using DNA shuffling, Nature Biotechnology 14 (1996) 315-319. [29] N.C. Shaner, R.E. Campbell, P.A. Steinbach, B.N.G. Giepmans, A.E. Palmer, R.Y. Tsien, Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein, Nature Biotechnology 22 (2004) 1567-1572. [30] L.C. de Paz, Image Analysis Software Based on Color Segmentation for Characterization of Viability and Physiological Activity of Biofilms, Applied and Environmental Microbiology 75 (2009) 1734-1739. [31] P.A. Tran, T.J. Webster, Understanding the wetting properties of nanostructured selenium coatings: the role of nanostructured surface roughness and air-pocket formation, International Journal of Nanomedicine 8 (2013) 20012009. [32] G.M. Bruinsma, H.C. van der Mei, H.J. Busscher, Bacterial adhesion to surface hydrophilic and hydrophobic contact lenses, Biomaterials 22 (2001) 3217-3224. [33] B. Derjaguin, L. Landau, Theory of the stability of strongly charged lyophobic sols and of the adhesion of strongly charged particles in solutions of electrolytes, Progress in Surface Science 43 (1993) 30-59. [34] H.J. Busscher, W. Norde, P.K. Sharma, H.C. van der Mei, Interfacial re-arrangement in initial microbial adhesion to surfaces, Current Opinion in Colloid & Interface Science 15 (2010) 510-517. [35] H.J. Busscher, H.C. van der Mei, How Do Bacteria Know They Are on a Surface and Regulate Their Response to an Adhering State? PLoS Pathogens 2012 8(1): e1002440. [36] I. Iscla, R. Wray, P. Blount, The oligomeric state of the truncated mechanosensitive channel of large conductance shows no variance in vivo, Protein Science 20 (2011) 1638-1642. [37] H.C. Flemming, J. Wingender, The biofilm matrix. Nature Reviews Microbiology 8 (2010) 623-633. [38] K. Bohinc, G. Drazic, R. Fink, M. Oder, M. Jevsnik, D. Nipic, et al., Available surface dictates microbial adhesion capacity, International Journal of Adhesion & Adhesives 50 (2014) 265-272. [39] C. Vuong, J.M. Voyich, E.R. Fischer, K.R. Braughton, A.R. Whitney, F.R. DeLeo, M. Otto, Polysaccharide intercellular adhesin (PIA) protects Staphylococcus epidermidis against major components of the human innate immune system, Cell Microbiol 6 (2004) 269-275.
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Figure 1 AFM height images and surface profiles of nanorough titanium thin films. Physical vapor deposited titanium thin films with different surface nanoroughnesses Rq of 2.00 nm (sample group A), 2.28 nm (sample group B), 2.96 nm (sample group C), and 6.13 nm (sample group D); the distance between two height graduation steps on the vertical axis of the profiles indicates 5 nm; the profile heights are elevated for better visualization.
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Figure 2 Microbial adhesion on nanorough titanium thin films. Surface coverage of the titanium thin films with E. coli and S. aureus over time as function of nanoroughness; the most relevant statistically significant differences are indicated with stars (p ≤ 0.05); reduction of surface coverage after 11 h of adhesion between the smoothest and roughest surface is given in percent; coverages are given as mean values ± standard deviation with n = 3 samples and 5 images for each sample resulting in 15 different locations of analysis per microbial species, per time point and per roughness; detailed results of statistical variance analysis are given in Table 2 and Table S2.
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Figure 3 CLSM images of E. coli and S. aureus adherent to nanorough titanium thin films after 5 and 11 hours of incubation. Coverage with E. coli decreased with increasing surface nanoroughness. Coverage with S. aureus decreased from Rq = 2.00 nm to Rq = 2.28 nm and, further, increased with increasing surface roughness (see also Fig. 2). With increasing surface nanoroughness, for S. aureus an increasing formation of cell aggregates was observed compared to E. coli with no formation of cell aggregates.
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Figure 4 Nanostructure of the microbe-titanium-interface after 3 hours of incubation (initial stage adhesion). Scanning electron microscopy images of E. coli (A - C) and S. aureus (D - F) on titanium thin films with a surface roughness R q of 2.00 nm after 3 h of incubation (initial stage adhesion). Cells were cross-sectioned with a focused-ion beam (C, E), with (F) as magnification of (E), for visualization of the interface between microbial cell and nanorough titanium surface. Points of closest distance, respectively, contact between the cells and the titanium surface are indicated with arrows.
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Figure 5 Nanostructure of the microbe-titanium-interface after 9 hours of incubation (late stage adhesion). Scanning electron microscopy images of E. coli (A - B) and S. aureus (C - D) on titanium thin films with a surface roughness Rq of 2.00 nm after 9 h of incubation (late stage adhesion). Cells were cross-sectioned with a focused-ion beam (B diagonal section, D) for visualization of the interface between microbial cell and nanorough titanium surface. Arrows on A and C indicate the formation of extracellular polymeric substances clearly visible after 9 h of incubation of the cells on the surfaces. Arrows on B and D indicate the contact interface between microbial cells and nanorough titanium surface. On image D, the cutting plane of the second cell on the right was behind the contact interface between cell and titanium.
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Figure 6 Qualitative model of microbial adhesion on nanorough titanium surfaces via nano adhesion points. Microbial adhesion on titanium surfaces with different nanoroughnesses is initially controlled by the biomaterial`s surface peak density. The surface peaks mediate the first contact to the microbial cell (nano adhesion points). Late microbial adhesion is additionally controlled by the specific titanium`s surface area available for contact (attachment area) between the EPS-producing cells and the material`s surface. A decreased number of nano adhesion points and attachment area result in decreased adhesive forces and, thus, in reduced microbial adhesion.
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Table 1 Characterization of the physical vapor deposited titanium thin film surfaces prepared with different deposition rates and film thicknesses (sample groups A - D). Surface roughness values Ra and Rq, skewness, surface area difference Rsa, peak density, maximum vertical peak-to-valley distance and average horizontal peak-to-peak distance were caluclated based on AFM imaging with scan size 1 µm × 1 µm; in addition, water contact angles were estimated; n = 3; values given as mean ± standard deviation (SD).
Sample group
Rate [nm/s]/ film thickness [nm]
Ra ± SD [nm]a
Rq ± SD [nm]a
Skewness ± SD
Rsa ± SD [%]b
Peak density ± SD [peaks/µm²]
Peak-to-valley ± SD [nm]a
Peak-to-peak ± SD [nm]a
Water contact angle [°]
A
0.1/100
1.58 ± 0.10*
2.00 ± 0.12*
0.47 ± 0.02
6.42 ± 1.80
2240 ± 901*
16.94 ± 1.72*
13.12 ± 6.43
70.5 ± 2.8
B
0.5/200
1.81 ± 0.01*
2.28 ± 0.01*
0.31 ± 0.02
5.37 ± 1.78
1185 ± 462*
19.93 ± 1.21
17.87 ± 8.05
73.6 ± 1.8
C
0.5/500
2.35 ± 0.14*
2.96 ± 0.19*
0.33 ± 0.06
3.25 ± 0.57
500 ± 47*
24.66 ± 6.49
26.40 ± 11.33
73.1 ± 1.8
D
1.0/500
4.82 ± 0.18*
6.13 ± 0.19*
0.36 ± 0.21
3.82 ± 0.22
213 ± 64*
45.17 ± 2.84*
42.17 ± 23.20
76.1 ± 2.0
a
Data published previously by Lüdecke et al. [21] Surface area difference Rsa is defined as the increase in surface area caused by the topography, i.e., the percentage difference between the actual/specific surface area and the projected area * Indicates a statistically significant difference of a sample group compared to the other 3 sample groups within each surface parameter with p ≤ 0.05 b
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Table 2 Results of the one-way ANOVA considering the impact of the factor titanium surface topography on the materials surface coverage with E. coli and S. aureus at each sampling time point. Crosses (×) in the same column indicate homogenous groups with no statistically significant differences between the groups. Crosses not beneath each other indicate statistically significant differences between the groups with p ≤ 0.05. Ti surface topography Sample group/ roughness Rq
Homogeneous groups for each time point
Escherichia coli
1h
3h
5h
A / 2.0 nm B / 2.3 nm C / 3.0 nm D / 6.1 nm
× × ×
× ×
×
Staphylococcus aureus
1h
3h
A / 2.0 nm B / 2.3 nm C / 3.0 nm D / 6.1 nm
×
×
7h × ×
× ×
× × × ×
× ×
× 5h
× ×
9h
× ×
11 h
× ×
× × ×
11 h
× × × × × × × × × × × × × ×
7h
× ×
9h
× ×
× × × × ×
× × ×
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S0927-7765(16)30381-2 http://dx.doi.org/doi:10.1016/j.colsurfb.2016.05.049 COLSUB 7898
To appear in:
Colloids and Surfaces B: Biointerfaces
Received date: Revised date: Accepted date:
9-2-2016 26-4-2016 17-5-2016
Please cite this article as: Claudia Ludecke, ¨ Martin Roth, Wenqui Yu, Uwe Horn, J¨org Bossert, Klaus D.Jandt, Nanorough titanium surfaces reduce adhesion of Escherichia coli and Staphylococcus aureus via nano adhesion points, Colloids and Surfaces B: Biointerfaces http://dx.doi.org/10.1016/j.colsurfb.2016.05.049 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Nanorough titanium surfaces reduce adhesion of Escherichia coli and Staphylococcus aureus via nano adhesion points
Claudia Lüdecke a,b,d, Martin Roth b,d, Wenqui Yu c, Uwe Horn b,d, Jörg Bossert a,d, Klaus D. Jandt a,d #
a
Chair of Materials Science (CMS), Otto Schott Institute of Materials Research (OSIM), Faculty of
Physics and Astronomy, Friedrich Schiller University Jena, Löbdergraben 32, 07743 Jena, Germany b
Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI),
Bio Pilot Plant, Adolf-Reichwein-Str. 23, 07745 Jena, Germany c
Microbial Genetics, University Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
d
Excellence Graduate School “Jena School für Microbial Communication (JSMC)”, Friedrich Schiller
University Jena, Neugasse 23, 07743 Jena, Germany
#
Corresponding author: Phone: +49-3641-947730; Fax: +49-3641-947732
E-mail address: [email protected] (Klaus D. Jandt)
Running title: Microbial adhesion via nano adhesion points
Words Abstract: 222 Words Manuscript (incl. Title page, Text, Acknowledgement, References): 5639 Number of Figures: 6 Number of Tables: 2
1
Graphical abstract
Highlights
Nanorough titanium surfaces were produced with physical vapor deposition. Microbial adhesion was reduced up to 55.6 % on the nanorough titanium. Direct insight into the microbe-titanium- interface is given using FIB-SEM. Initial microbial adhesion on nanorough surfaces is controlled via nano adhesion points. Late microbial adhesion on nanorough surfaces is controlled by the attachment area.
2
ABSTRACT Microbial adhesion to natural and synthetic materials surfaces is a key issue e. g. in food industry, sewage treatment and most importantly in the biomedical field. The current development and progress in nanoscale structuring of materials surfaces to control microbial adhesion requires an improved understanding of the microbe-material-interaction. This study aimed to investigate the nanostructure of the microbe-material-interface and link it to microbial adhesion kinetics as function of titanium surface nanoroughness to gain new insight into controlling microbial adhesion via materials` surface nanoroughness. Adhesion of Escherichia coli and Staphylococcus aureus was statistically significantly reduced (p ≤ 0.05) by 55.6 % and 40.5 %, respectively, on physical vapor deposited titanium thin films with a nanoroughness of 6 nm and the lowest surface peak density compared to 2 nm with the highest surface peak density. Cross-sectioning of the microbial cells with a focused ion beam (FIB) and SEM imaging provided for the first time direct insight into the titanium-microbe-interface. High resolution SEM micrographs gave evidence that the surface peaks are the loci of initial contact between the microbial cells and the material`s surface. In a qualitative model we propose that the initial microbial adhesion on nanorough surfaces is controlled by the titanium surface peak density via nano adhesion points. These new understanding will help towards the design of materials surfaces for controlling microbial adhesion.
Key Words nanoroughness, microbial adhesion, titanium, SEM, focused ion beam
3
1. INTRODUCTION Microbial adhesion to natural and synthetic materials surfaces is a key issue e. g. in food industry, sewage treatment and most importantly in the biomedical field. The current development and progress in nanoscale structuring of materials surfaces to control microbial adhesion requires an improved understanding of the microbe-material-interaction [1]. On the macro and micro scale it has been shown that topographical surface features of materials significantly affect the initial adhesion of microorganisms [2]. In particular, the impact of surface roughnesses on microbial adhesion has been extensively studied [3-8]. The findings have established the common knowledge that as a rule of thumb microbial adhesion increases with increasing surface roughness. Researchers assume that microbes adherent to macro- or micrometer rough surfaces are protected against abrasion from shear stress [7-11]. In addition, materials with surface structures of the size of the microbial cells may provide a maximum contact area between microbe and material and, thus, promote adhesion [12-15]. Consequently, one current strategy for reducing microbial adhesion on materials` surfaces is preparing surfaces as smooth as possible. Recently, nanostructured materials gained interest because of their effect on microbial adhesion [16-24]. In particular titanium as most often used material in the biomedical field was in the focus of several studies. Singh et al. [20] observed for example a significantly increased adhesion of Escherichia coli and Pseudomonas aeruginosa on supersonic cluster beam deposited titanium thin films with a surface nanoroughness of 21.7 nm compared to 16.2 nm. The authors assume, that differences in the wettability of the surfaces may have caused this increase. Ivanova et al. [17-18] reported contradicting results. They found that the surface coverage of magnetron sputtered titanium thin films with Staphylococcus aureus was reduced with increasing surface roughness from 0.2 nm to 0.5 nm and 0.7 nm to 1.6 nm, respectively. These authors, as well, assumed that a change in surface wettability at least partly caused the differences in microbial adhesion. Summarizing, to date there is no consensus in the current literature how nanoroughness affects microbial adhesion and results are contradictory. Moreover, standard roughness values (such as 4
the average roughness or the root mean square roughness) as used in these studies for description of the surface topography are vertical surface parameter not considering the spatial/horizontal distribution of structure elements e. g. grains/peaks and valleys on the surface. To better understand the effect of surface nanoroughness on microbial adhesion, it is, thus, important to include additional surface parameters such as peak-to-peak distance or peak density in the analysis. To fill this gap in knowledge, the aim of the current study was [i] to investigate the microbial adhesion kinetics of S. aureus and E. coli as function of titanium surface nanoroughness in particular considering next to vertical also spatial surface parameters, [ii] to elucidate the unknown nanostructure of the microbe-titanium-interface and [iii] to derive a qualitative model of microbial adhesion to materials surfaces as a function of nanoroughness and time. In a previous study, we found that with increasing stochastic nanoroughness of physical vapor deposited titanium thin films from 2 nm to 6 nm, the distance between the topographical surface peaks increased threefold [21]. Thus, with increasing roughness the relative number of titanium surface peaks, i.e., the peak density, decreased. Based on these results and based on a previous simulation study [25], we hypothesize that these surface peaks may be the points of first contact between the microbial cell and the titanium surface during initial stage adhesion. Accordingly, with increasing surface nanoroughness, less points for first contact are available between the microbial cell and the material which might result in reduced adhesion. The hypothesis tested in the present study was, therefore, that with increasing surface nanoroughness and decreasing peak density, microbial adhesion is reduced. 2. MATERIALS AND METHODS 2.1. Titanium thin film preparation Titanium thin films (Ti-TF) were deposited on glass slides (diameter 15 mm; Borofloat® B33; Jena 4 H Engineering GmbH, Jena, Germany) using the PVD method as described elsewhere [26]. Titanium surface roughness was adjusted by varying the deposition rate and the film thickness 5
with 0.1 nm/s and 100 nm (sample group A), 0.5 nm/s and 200 nm (sample group B), 0.5 nm/s and 500 nm (sample group C), and 1.0 nm/s and 500 nm (sample group D). Samples were sterilized in the autoclave for 20 min at 121 °C before investigating microbial adhesion. 2.2. Titanium surface characterization An atomic force microscope (Dimension 3100, Digital Instruments, Santa Barbara, CA, USA), equipped with a standard silicon tip (tip diameter 5 nm) was used to characterize the topography of the Ti-TFs surfaces. The AFM was operated in tapping mode with a scan rate of 2 Hz, an image resolution of 512 × 512 points and a scan size of 1 µm × 1 µm. AFM image processing and calculation of titanium surface parameters were performed with Gwyddion 2.25, a free SPM data analysis software [27] according to the protocol described elsewhere [21]. The roughness parameters were calculated according to the standard ISO 42871997. For the calculation of the average peak-to-peak distances and the peak densities of the titanium surfaces, the coordinates of the local maxima of the AFM height images were estimated using ImageJ (National Institutes of Health NIH, Bethesda, Maryland, USA). Based on a Delaunay triangulation algorithm, the distances between the neighboring maxima were calculated using MATLAB (MathWorks, Natick, Massachusetts, USA). The contact angle measurements were performed with the dynamic drop method using doubledistilled water drops (advancing angles: increase of drop volume from 5 µL to 7 µL with a rate of 10 µL/min) (DSA 10Mk2 drop shape analysis system, Krüss, Hamburg, Germany) on n = 3 replicates of each Ti-TF surface. 10 images of each increasing drop on the materials surfaces were recorded by a camera and analyzed using the software supplied by the manufacturer according to the circle fitting method. 2.3. Microbial strains S. aureus and E. coli both common pathogens relevant e.g. in the biomedical field were used as test organisms in this study. The microbial strains E. coli EC042, producing the green fluorescent 6
protein (GFPuv) [28], and S. aureus HG003, producing the red fluorescent protein mCherry [29], were used for microbial adhesion tests. Preparation of the fluorescent strains based on transformation is described in the supplemental material (Text S1). 2.4. Cultivation of the microorganisms and adhesion test A recently described in vitro device [26] was used to investigate microbial adhesion as a function of the titanium surface nanoroughness. This in vitro system allows for the investigation of microbial adhesion on materials surfaces at reproducible and constant conditions throughout the experiment. E. coli and S. aureus were each cultivated in a separate continuous culture (chemostat) which was used for inoculation of a biofilm reactor (non-constant depth film fermenter; nCDFF) containing the materials samples. A complete description of the system can be found elsewhere [26]. Experimental details of the cultivation of the microorganisms are provided as supplemental material (Text S2). Based on the results of pre-experiments in culture well plates (data not shown), the sampling time points were set to 1 h, 3 h, 5 h, 7 h, 9 h and 11 h, respectively, with n = 3 replicates for analysis with CLSM. For cross-sectioning of adherent cells and high resolution SEM, additional Ti-TFs were sampled after 3 h and 9 h, respectively, with n = 2 replicates. 2.5. Confocal laser scanning microscopy Using confocal laser scanning microscopy (CLSM), the adhesion kinetics of E. coli and S. aureus were investigated as a function of the nanoroughness of physical vapor deposited titanium thin films. Samples for CLSM imaging were prepared as described previously [26]. A confocal laser scanning microscope (Zeiss LSM 510 Meta, Carl Zeiss MicroImaging, Jena, Germany) equipped with an Argon laser (488 nm) and Helium-Neon laser (633 nm) and a 63× NA 1.4 oil immersion lens objective (Zeiss PLANAPOCHROMAT®) was used for fluorescence imaging of the microorgansims. The materials` surface coverage with the microbes was calculated based on five single plane CLSM images per sample for each sampling time point using the free software bioImage_L v.2.1 [30]. For image analysis, a factor of 0.03 was applied for noise reduction. 7
Microbial adhesion rates were calculated as percent increase of titanium surface coverage with the microbes per hour [increase coverage % per h] based on a standard linear correlation function. Initial microbial adhesion rates were calculated between 1 h and 3 h and fastest adhesion rates were defined as the highest increase in surface coverage between the two time points with. 2.6. Cross-sectioning of microbial cells and scanning electron microscopy Cross-sectioning of the adherent microbial cells and high resolution scanning electron microscopy (SEM) were used for direct visualization of the nanostructure of the microbe-material-interface. Microbial cells were cross-sectioned after 3 h (initial stage adhesion) and 9 h (late stage adhesion) of incubation on the surfaces with a focused ion beam and imaged with SEM. Initial stage adhesion was defined as the state of adhesion were contact between the microbial cells and the materials surface is predominately mediated by physical interaction, whereas late stage adhesion is noticeably influenced by biological factors, such as the production of extracellular polymeric substances. Titanium samples with adherent microbes were fixed, dehydrated and critical point dried according to standard procedures as described elsewhere [21,36]. Samples were not sputter-coated to avoid the coverage of structures sized on the very low nanometer scale. An AURIGA 60 CrossBeam® FIB-SEM scanning electron microscope (Carl Zeiss AG, Oberkochen, Germany) equipped with a focused ion beam apparatus was used at magnifications from 50.000 to 150.000 operated at 5 kV and a working distance of 5 mm. The focused ion beam was operated with a beam current of 1 pA resulting in the smallest possible beam diameter of 3 nm at 30 kV. 2.7. Statistical analysis For statistical analysis (Statgraphics Centurion XV software; StatPoint Inc., Warrenton, USA), an analysis of variance (ANOVA) based on a Scheffé t-test with a 95 % confidence interval (p ≤ 0.05) was performed. With a one-factorial (one-way) ANOVA, the statistical differences among the Ti-TFs (sample groups A - D) with regard to microbial coverage were tested for each sampling time point and for the two microbial species. In a multi-factorial (multi-way) ANOVA, 8
the impact of the (independent) categorical factors titanium surface topography, microbial species and time on the dependent (response) variable surface coverage was statistically tested following the questions [a] if any of the factors have a statistically significant effect on the dependent variable and [b] which is the most significant factor, i.e., which factor influences the dependent variable most. 3. RESULTS 3.1. Titanium surface characterization With increasing PVD deposition rate and increasing film thickness, R a and Rq increased from 1.58 ± 0.10 nm to 4.82 ± 0.18 nm and 2.00 ± 0.12 nm to 6.13 ± 0.19 nm, respectively. The skewness values of all surfaces were in the same range (Table 1), thus, the principal shapes of the profiles were similar to each other’s and independent of the increase of surface nanoroughness (Fig. 1). The surface area difference Rsa giving the percentage difference between the specific surface area and the projected area, i.e., the scan size used for AFM, decreased from 6.4 ± 1.8 % to 3.8 ± 0.2 % with increasing surface roughness. The peak density decreased with increasing roughness one order of magnitude. The peak-to-valley distance and peak-to-peak distance of the profiles increased with increasing roughness from 16.9 ± 1.7 nm to 45.2 ± 2.8 nm and from 13.1 ± 6.4 nm to 42.2 ± 23.2 nm, respectively. The small standard deviations of the peak-to-valley distances indicate that the titanium surface profiles were very uniform with similar heights of all titanium grains present at the surface. The high standard deviations of the peak-to-peak distances in the range of 50 % of the mean values indicate higher variations in diameter of the titanium surface grains. All titanium surfaces were moderately hydrophilic (Table 1). The very slight increase in surface hydrophobicity can be explained by the increased surface nanoroughness [31]. 3.2. Microbial adhesion on nanorough titanium surfaces The surface coverage with E. coli was reduced with increasing titanium nanoroughness (Fig. 2, Table S1). The degree of reduction on the roughest surface compared to the smoothest surface 9
was similar for all investigated time points with 61.4 % after 1 h and 55.6 % after 11 h. S. aureus also showed an adhesion reduced by 40.5 % after 11h on the roughest surface compared to the smoothest. However, the overall highest reduction for S. aureus was observed on sample type B (Rq = 2.3 nm) with 63.6 % after 11 h. The surface peak density and the Rsa were correlated with the Ti-TF nanoroughnesses as described in section 3.1. Thus, adhesion of E. coli decreased with decreasing surface peak density and decreasing Rsa. The microbes showed a slow initial adhesion followed by a faster adhesion (Fig. S1). For E. coli, the initial adhesion rates and the fastest adhesion rates decreased with increasing roughness from 2.2 % per h (sample type A) to 0.7 % per h (sample type D) and 8.1 % per h (sample type A) to 3.0 % per h (sample type D), respectively. For S. aureus, the fastest adhesion rates as well decreased with increasing roughness from 12.5 % per h to 3.6 % per h, whereas the initial adhesion rate was the lowest on sample type B (Rq = 2.3 nm). S. aureus formed cell aggregates on the samples with higher surface roughnesses, whereas E. coli cells were separately attached as single cells independently of the surface roughness (Fig. 3). 3.3. One and multi way analyses of variance Results of the one-way ANOVA are presented in Table 2 with all crosses (×) beneath each other in one column indicating homogeneous groups with no statistically significant differences and crosses not in the same column showing significant differences with p ≤ 0.05. For E. coli, at all investigated time points, the surface coverage was statistically significantly higher on the smoothest titanium surfaces (sample group A) compared to the roughest surfaces (sample group D). With exception of time point 1 h, for all time points between 3 h and 11 h, the surface coverage with S. aureus on the roughest surface (D) was statistically significantly reduced compared to the smoothest surface (A), as also observed for E. coli. The multi-way ANOVA revealed that all tested factors (titanium surface topography, time, microbial species) had a significant influence on the surface coverage (Table S2). The factor time 10
influenced the surface coverage most with p ≤ 0.001. For all time points between 1 h and 11 h, the surface coverage differed statistically significantly. The second most important factor influencing the surface coverage was the titanium surface topography. The surface coverage of the Ti-TF sample group A significantly differed from the surface coverage of the thin films B, C and D. Also the factor microbial species significantly influenced the surface coverage. With a p-value of ≤ 0.005, this factor was from the statistical point of view the least important factor influencing the surface coverage. 3.4. Nanostructure of the microbe-titanium-interface After 3 h of adhesion (initial stage adhesion), E. coli and S. aureus were attached to the nanorough titanium surface at a few points only (Fig. 4 B-C, E-F). For E. coli it was observed, that these adhesion respectively contact points were formed by small strands most likely consisting of extracellular polymeric substances (EPS) which were directly attached to the topographical titanium surface peaks. After 9 h of adhesion (late stage adhesion), the SEM images clearly show the production of considerable amounts of EPS by E. coli almost covering the complete titanium surface (Fig. 5 A) and fewer amounts of EPS produced by S. aureus (Fig. 5 C). Independently of the amount of produced EPS, both E. coli and S. aureus were along the visible contact line in complete contact with the nanorough titanium surface (Fig. 5 B, D). 4. DISCUSSION The current development and progress in nanoscale structuring of materials surfaces to control microbial adhesion requires an improved understanding of the microbe-material-interaction [1]. In this study the nanostructure of the microbe-material-interface was investigated and link to microbial adhesion kinetics as function of titanium surface nanoroughness to gain new insight into controlling microbial adhesion via materials` surface nanoroughness. In the literature, the influence of titanium surface nanoroughness on microbial adhesion was often discussed to be a result of a changed surface wettability [17-18,20] or microbe-specific surface properties such as the zeta potential which may lead to an electrostatic repulsion [23-24]. 11
However, the significant reduction of microbial adhesion with increasing titanium nanoroughness in the current study can not be explained with the only slight increase of surface hydrophobicity (Table 1; increase of approximately 5 °) of the Ti-TFs. Wettability and zeta potential were shown to be generally not a predictive parameter for microbial adhesion [32]. Microbial adhesion is generally known as first step of biofilm formation and can be described as two-stage process. The very initial adhesion respectively contact of the microbes with the materials surface (first stage) is controlled by solely physical interaction of the microbe with the materials surface. This process is most often described in literature by the DLVO theory (Derjaguin-Landau-Verwey-Overbeek) of colloid stability [33] or the extend DLVO theory considering, as well, Lewis acid base interactions. Comprehensive overview about forces involved in microbial adhesion ca be found elsewhere [34]. With increasing adhesion time, the adhesion process is not only determined by physical forces, but also biological factors (second stage) [35]. Adhering microorganisms may react to membrane stress arising from small deformations due to the adhesion forces [35]. E. coli for example is known to have mechanosensitive membrane channels [36]. The transition from the first to the second adhesion stage is marked by a change in gene expression, i. e. the switch from the planktonic life style to the sessile life style. As such, genes for EPS formation including polysaccharides, proteins, nucleic acids and lipids [37] are up regulated. According to this two-stage definition, in the current study, it was roughly differed between initial stage adhesion and late stage adhesion, which was based on microscopy and visible changes of the physiology of the microbes, e. g. EPS formation through which they embed themselves in a protective matrix. The time point of visible switch indicated by EPS production was approximately after 6 hours of adhesion. At different study conditions (e.g. different available nutrients, other microorganisms or used materials as well as at in vivo conditions), this switching time point may be earlier or later. FIB-SEM imaging of the nanostructure of the microbe-material-interface revealed that the reduced adhesion on the rougher surfaces can be explained by a lower number of points available 12
for the first contact between the cells and the titanium surface, respectively, the topographical surface peaks (initial stage adhesion). These peaks correspond to the points of the shortest distance between the microbial cell and the titanium surface (indicated in Fig. 4 C, F with arrows) where long-range adhesive forces (mainly Lifshitz-Van der Waals interactions) and, in the following when the distance becomes smaller, short-range adhesive forces (mainly Lewis acidbase-interactions) initially occur [34]. The total interaction force between one microbial cell and the materials surface is mainly determined by the adhesive forces at each of these points of closest distance. For the late stage adhesion, the reduced coverage on the roughest surface may be an effect of the already initially reduced adhesion in the initial stage of adhesion. Moreover, the specific surface area of the rougher titanium was significantly reduced compared to the smoother surfaces (Table 1). After 9 h, a considerable amount of EPS was produced by the microbes (Fig. 5). This EPS mediated a complete contact between the microbial cells and the nanorough surface. The reduced specific surface area with increased titanium nanoroughness was associated with a reduced total area for contact between the cells and the titanium. This may have led to reduced interaction forces and, in consequence, to a reduced surface coverage with the microbes. A recent study found a decreased adhesion of E. coli, S. aureus and P. aeruginosa with a decreasing effective surface area of polished glass [38]. However, in the current study most likely both effects played a role during late stage adhesion. In a recent simulation-based study, Siegismund et al. [25] showed that on the nanometer scale (average roughness Ra < 70 nm) the surface peak density exerts a significant influence on the interaction between microorganisms and materials surfaces during initial stage adhesion. The authors found a decreased interaction energy between a microbial cell and a nanorough surface with increasing peak density which they assumed results in a reduced microbial adhesion. The authors hypothesized that the surface peaks act like spacers between the microbial cell and the surface. Thus, a decreased peak density, i.e., an increased spatial distance between the peaks, allows vice versa for an increased interaction between cells and surface and, thus, leads to a 13
higher interaction energy (microbe/valley interaction). These results are partly contrary to the findings presented here. Our results indicate that despite decreasing peak density (e. g. on titanium samples D with Rq = 6.1 nm), there is no relevant interaction of the cells with the topographical valleys during early adhesion (after 3 h, see for example Fig. 4 C). So our experimental data showed that initial microbial adhesion on nanorough surfaces is, independently of the peak density, determined by a microbe-peak interaction. S. aureus showed a different adhesion behavior on the nanorough surfaces compared to the results reported in the literature and compared to that of E. coli reported here. Microbial adhesion was reduced most on the titanium surfaces with a roughness of 2.3 nm (sample group B). Based on the proposed mechanisms of microbial adhesion on nanorough materials surfaces via nano adhesion points, we conclude that the surface with the highest nanoroughness and the lowest peak density is the most unfavorable for S. aureus, too, since the fewest number of points for adhesion are available. In general, S. aureus cells tend to form cell aggregates due to specific cell surface associated molecules, such as the polysaccharide intercellular adhesins (PIA) [39]. So on these surfaces, S. aureus cells might have naturally preferred to adhere to other cells that have previously successfully attached to the surface. In that way, aggregates were formed rather than a flat layer of randomly distributed cells. This aggregate formation, in turn, may have caused an artificially increase in the calculated surface coverage observed with increasing titanium nanoroughness. This explanation is also supported by the fastest adhesion rates calculated for S. aureus. The fastest adhesion rates of both E. coli and S. aureus decreased with increasing surface nanoroughness. This indicates that for both microbial species, independently if Gram-negative or Gram-positive, rod-shaped or spherical-shaped, a higher surface nanoroughness with a lower peak density impairs microbial adhesion. Summarizing, the hypothesis tested in the current study, that microbial adhesion is reduced with increasing surface nanoroughness and decreasing peak density, was fully confirmed for E. coli and, with some limitation, also for S. aureus.
14
Based on our results reported and discussed above, we propose a simple qualitative model of microbial adhesion as a function of the titanium surfaces nanoroughness (visualized in Fig. 6): Microbial adhesion on nanorough titanium surfaces is initially controlled by the surface peak density, i.e., the relative number of nano adhesion points between a microbial cell and the surface. These topographical surface peaks mediate the first contact to the microbial cell. A decreased number of these nano adhesion points is associated with decreased total interaction forces between the microbial cell and the materials` surface resulting in a reduced adhesion. With increasing adhesion time, biological factors, such as the production of EPS influence microbial adhesion as well. These EPS cause a complete contact between microbial cell and titanium surface by filling the voids of the nanorough surface. For late stage adhesion, microbial adhesion on nanorough titanium surfaces is, thus, additionally controlled by the specific materials surface area available for contact (attachment area) between the cells and the material. A decreased attachment area with increased nanoroughness results, too, in decreased total adhesive forces and, thus, in a reduced microbial adhesion. For further verification of the current results and our qualitative model, next steps should include the investigation of microbial adhesion on other types of nanorough titanium as well as on other nanorough materials with varying surface peak density. Based on these results, a quantitative adhesion model might be derived. 5. CONCLUSIONS Our simple model of microbial adhesion as function of surface nanoroughness extends the in the macro- and micrometer range widely accepted theory of “most contact” [12-15] to the nanometer scale. We conclude that the fundamental mechanisms how surface roughness impacts microbial adhesion are principally the same for the micro- and macrometer scale and for the nanometer scale but with opposite effects. Microbial adhesion is controlled by the possible contact with the materials` surface. On the nanometer scale, adhesion is reduced with increasing roughness associated with a decreased number of adhesion points and a reduced attachment area, 15
respectively, between the cells and the material. On the micro- and macrometer scale, adhesion is increased with increasing surface roughness associated with a higher contact area between the cells and the material`s surface. Our study, moreover, demonstrated that a focused ion beam for cross-sectioning of microorganisms adherent to material surfaces is a powerful tool to gain direct insight into the microbe-material-interface. The results, furthermore, emphasize the importance of time-dependent studies. In that way, for the first time, the aggregate formation of S. aureus cells induced by surfaces nanoroughnesses was observed. ACKNOWLEDGEMENTS The authors thank F. Götz (Microbial Genetics, University of Tübingen, Tübingen, Germany) for providing the S. aureus HG003 strain, D. Femerling for construction of the E. coli EC042 strain, R. Wagner for support during microscopy, M. Cyrulies for help with the setup of the in vitro test system, M. Beyer and S. Maenz for introduction to statistics and the Excellence Graduate School “Jena School for Microbial Communication” which is funded by the German Excellence Initiative for financial support. Furthermore, KDJ gratefully acknowledges the partial financial support of the Deutsche Forschungsgemeinschaft (DFG), grant reference INST 275/241-1 FUGG, and the TMBWK, grant reference 62-4264 925/1/10/1/01.
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[24] V.K. Truong, R. Lapovok, Y.S. Estrin, S. Rundell, J.Y. Wang, C.J. Fluke, et al., The influence of nano-scale surface roughness on bacterial adhesion to ultrafine-grained titanium, Biomaterials 31 (20109 3674-3683. [25] D. Siegismund, A. Undisz, S. Germeroth, S. Schuster, M. Rettenmayr, Quantification of the interaction between biomaterial surfaces and bacteria by 3-D-modeling, Acta Biomaterialia 10 (2014) 267-275. [26] C. Lüdecke, K.D. Jandt, D. Siegismund, M.J. Kujau, E. Zang, M. Rettenmayr, et al., Reproducible biofilm cultivation of chemostat-grown Escherichia coli and investigation of bacterial adhesion on biomaterials using a nonconstant-depth film fermenter, PLoS ONE 9 (2014) e84837. [27] D. Necas, P. Klapetek, Gwyddion: an open-source software for SPM data analysis, Central European Journal of Physics 10 (2012) 181-188. [28] A. Crameri, E.A. Whitehorn, E. Tate, W.P.C. Stemmer, Improved green fluorescent protein by molecular evolution using DNA shuffling, Nature Biotechnology 14 (1996) 315-319. [29] N.C. Shaner, R.E. Campbell, P.A. Steinbach, B.N.G. Giepmans, A.E. Palmer, R.Y. Tsien, Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein, Nature Biotechnology 22 (2004) 1567-1572. [30] L.C. de Paz, Image Analysis Software Based on Color Segmentation for Characterization of Viability and Physiological Activity of Biofilms, Applied and Environmental Microbiology 75 (2009) 1734-1739. [31] P.A. Tran, T.J. Webster, Understanding the wetting properties of nanostructured selenium coatings: the role of nanostructured surface roughness and air-pocket formation, International Journal of Nanomedicine 8 (2013) 20012009. [32] G.M. Bruinsma, H.C. van der Mei, H.J. Busscher, Bacterial adhesion to surface hydrophilic and hydrophobic contact lenses, Biomaterials 22 (2001) 3217-3224. [33] B. Derjaguin, L. Landau, Theory of the stability of strongly charged lyophobic sols and of the adhesion of strongly charged particles in solutions of electrolytes, Progress in Surface Science 43 (1993) 30-59. [34] H.J. Busscher, W. Norde, P.K. Sharma, H.C. van der Mei, Interfacial re-arrangement in initial microbial adhesion to surfaces, Current Opinion in Colloid & Interface Science 15 (2010) 510-517. [35] H.J. Busscher, H.C. van der Mei, How Do Bacteria Know They Are on a Surface and Regulate Their Response to an Adhering State? PLoS Pathogens 2012 8(1): e1002440. [36] I. Iscla, R. Wray, P. Blount, The oligomeric state of the truncated mechanosensitive channel of large conductance shows no variance in vivo, Protein Science 20 (2011) 1638-1642. [37] H.C. Flemming, J. Wingender, The biofilm matrix. Nature Reviews Microbiology 8 (2010) 623-633. [38] K. Bohinc, G. Drazic, R. Fink, M. Oder, M. Jevsnik, D. Nipic, et al., Available surface dictates microbial adhesion capacity, International Journal of Adhesion & Adhesives 50 (2014) 265-272. [39] C. Vuong, J.M. Voyich, E.R. Fischer, K.R. Braughton, A.R. Whitney, F.R. DeLeo, M. Otto, Polysaccharide intercellular adhesin (PIA) protects Staphylococcus epidermidis against major components of the human innate immune system, Cell Microbiol 6 (2004) 269-275.
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Figure 1 AFM height images and surface profiles of nanorough titanium thin films. Physical vapor deposited titanium thin films with different surface nanoroughnesses Rq of 2.00 nm (sample group A), 2.28 nm (sample group B), 2.96 nm (sample group C), and 6.13 nm (sample group D); the distance between two height graduation steps on the vertical axis of the profiles indicates 5 nm; the profile heights are elevated for better visualization.
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Figure 2 Microbial adhesion on nanorough titanium thin films. Surface coverage of the titanium thin films with E. coli and S. aureus over time as function of nanoroughness; the most relevant statistically significant differences are indicated with stars (p ≤ 0.05); reduction of surface coverage after 11 h of adhesion between the smoothest and roughest surface is given in percent; coverages are given as mean values ± standard deviation with n = 3 samples and 5 images for each sample resulting in 15 different locations of analysis per microbial species, per time point and per roughness; detailed results of statistical variance analysis are given in Table 2 and Table S2.
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Figure 3 CLSM images of E. coli and S. aureus adherent to nanorough titanium thin films after 5 and 11 hours of incubation. Coverage with E. coli decreased with increasing surface nanoroughness. Coverage with S. aureus decreased from Rq = 2.00 nm to Rq = 2.28 nm and, further, increased with increasing surface roughness (see also Fig. 2). With increasing surface nanoroughness, for S. aureus an increasing formation of cell aggregates was observed compared to E. coli with no formation of cell aggregates.
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Figure 4 Nanostructure of the microbe-titanium-interface after 3 hours of incubation (initial stage adhesion). Scanning electron microscopy images of E. coli (A - C) and S. aureus (D - F) on titanium thin films with a surface roughness R q of 2.00 nm after 3 h of incubation (initial stage adhesion). Cells were cross-sectioned with a focused-ion beam (C, E), with (F) as magnification of (E), for visualization of the interface between microbial cell and nanorough titanium surface. Points of closest distance, respectively, contact between the cells and the titanium surface are indicated with arrows.
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Figure 5 Nanostructure of the microbe-titanium-interface after 9 hours of incubation (late stage adhesion). Scanning electron microscopy images of E. coli (A - B) and S. aureus (C - D) on titanium thin films with a surface roughness Rq of 2.00 nm after 9 h of incubation (late stage adhesion). Cells were cross-sectioned with a focused-ion beam (B diagonal section, D) for visualization of the interface between microbial cell and nanorough titanium surface. Arrows on A and C indicate the formation of extracellular polymeric substances clearly visible after 9 h of incubation of the cells on the surfaces. Arrows on B and D indicate the contact interface between microbial cells and nanorough titanium surface. On image D, the cutting plane of the second cell on the right was behind the contact interface between cell and titanium.
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Figure 6 Qualitative model of microbial adhesion on nanorough titanium surfaces via nano adhesion points. Microbial adhesion on titanium surfaces with different nanoroughnesses is initially controlled by the biomaterial`s surface peak density. The surface peaks mediate the first contact to the microbial cell (nano adhesion points). Late microbial adhesion is additionally controlled by the specific titanium`s surface area available for contact (attachment area) between the EPS-producing cells and the material`s surface. A decreased number of nano adhesion points and attachment area result in decreased adhesive forces and, thus, in reduced microbial adhesion.
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Table 1 Characterization of the physical vapor deposited titanium thin film surfaces prepared with different deposition rates and film thicknesses (sample groups A - D). Surface roughness values Ra and Rq, skewness, surface area difference Rsa, peak density, maximum vertical peak-to-valley distance and average horizontal peak-to-peak distance were caluclated based on AFM imaging with scan size 1 µm × 1 µm; in addition, water contact angles were estimated; n = 3; values given as mean ± standard deviation (SD).
Sample group
Rate [nm/s]/ film thickness [nm]
Ra ± SD [nm]a
Rq ± SD [nm]a
Skewness ± SD
Rsa ± SD [%]b
Peak density ± SD [peaks/µm²]
Peak-to-valley ± SD [nm]a
Peak-to-peak ± SD [nm]a
Water contact angle [°]
A
0.1/100
1.58 ± 0.10*
2.00 ± 0.12*
0.47 ± 0.02
6.42 ± 1.80
2240 ± 901*
16.94 ± 1.72*
13.12 ± 6.43
70.5 ± 2.8
B
0.5/200
1.81 ± 0.01*
2.28 ± 0.01*
0.31 ± 0.02
5.37 ± 1.78
1185 ± 462*
19.93 ± 1.21
17.87 ± 8.05
73.6 ± 1.8
C
0.5/500
2.35 ± 0.14*
2.96 ± 0.19*
0.33 ± 0.06
3.25 ± 0.57
500 ± 47*
24.66 ± 6.49
26.40 ± 11.33
73.1 ± 1.8
D
1.0/500
4.82 ± 0.18*
6.13 ± 0.19*
0.36 ± 0.21
3.82 ± 0.22
213 ± 64*
45.17 ± 2.84*
42.17 ± 23.20
76.1 ± 2.0
a
Data published previously by Lüdecke et al. [21] Surface area difference Rsa is defined as the increase in surface area caused by the topography, i.e., the percentage difference between the actual/specific surface area and the projected area * Indicates a statistically significant difference of a sample group compared to the other 3 sample groups within each surface parameter with p ≤ 0.05 b
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Table 2 Results of the one-way ANOVA considering the impact of the factor titanium surface topography on the materials surface coverage with E. coli and S. aureus at each sampling time point. Crosses (×) in the same column indicate homogenous groups with no statistically significant differences between the groups. Crosses not beneath each other indicate statistically significant differences between the groups with p ≤ 0.05. Ti surface topography Sample group/ roughness Rq
Homogeneous groups for each time point
Escherichia coli
1h
3h
5h
A / 2.0 nm B / 2.3 nm C / 3.0 nm D / 6.1 nm
× × ×
× ×
×
Staphylococcus aureus
1h
3h
A / 2.0 nm B / 2.3 nm C / 3.0 nm D / 6.1 nm
×
×
7h × ×
× ×
× × × ×
× ×
× 5h
× ×
9h
× ×
11 h
× ×
× × ×
11 h
× × × × × × × × × × × × × ×
7h
× ×
9h
× ×
× × × × ×
× × ×
26