influence of hydrodynamic conditions on bacteria physiology and behavior

Faculty of Engineering of University of Porto University of Sheffield Influence of hydrodynamic conditions on bacteria physiology and behavior by Leonam Vieira Gonçalves Dissertation held under the Master Degree in Bioengineering – Major in Biological Engineering Advisors: Prof. Manuel Simões; Prof. Lucia Simões and Prof. Catherine Biggs Supervisor: Dr. Esther Karanukaran September 13th 2013 II © Copyright by Vieira Gonçalves, Leonam, 2013 III Abstract The hydrodynamic conditions have been shown to have a significant impact on bacteria swimming, collective motion and cohesive structure formation. Those are notable aspects of microbial dynamics and were shown to be relevant for wide range of applications. Nevertheless, the need for a deeper insight about the direct influence of hydrodynamics on microbial behavior and physiology at planktonic state remained latent. Aiming to overcome that, the present study focused on the characterization of bacterial growth and physicochemical properties of cell surface grown under different agitations speeds. The influence of distinct hydrodynamic conditions on growth of Acinetobacter calcoaceticus was evaluated by growth and substrate consumption curve elaboration. It was observed that cells under slower agitation speed presented a significant lower growth rate with higher biomass yield in terms of reducing sugars (substrate) consumption. Moreover, the metabolic activity of microbial cultures was evaluated by respirometry, which showed higher total respiratory activity in mid-exponential growth phase was achieved for cells grown under higher agitation speed. Thus, it was concluded that observed differences in terms of cell growth and respiratory activity across different agitation speeds could be the result of distinct cellular dynamics, which are the product of an interplay between physical (mass transfer issues) and biological (energy uncoupling between anabolism and catabolism) phenomena. In terms of cell surface properties, suspensions of A. calcoaceticus cultivated in distinct hydrodynamic conditions were examined by Microbial Adhesion to Hydrocarbons (MATH) assay, electrophoretic mobility measurement, Fourier transform infrared spectroscopy (FTIRS) and X-ray photoelectron spectroscopy (XPS). These experiments showed that bacteria cells cultivated under distinct agitation speeds possess different cell surface chemistry as well as hydrophobicity and cell surface charge differences. The influence of hydrodynamics on those properties is important as cell-cell and cell-abiotic surface interactions were reported to rely on it. For instance, it was found in this study that microbial cells at lower agitation speed had a higher trend to auto-aggregate, which demonstrates enhancement at the level of cell-cell interactions. It was also shown that polysaccharides and proteins account for most changes at cell surface level across different agitation speed, where higher content of those macromolecules were attained for IV bacterial cells cultivated under higher agitation speeds. It was hypothesized that lipopolysaccharides (LPS) with long O-antigen structure could be more abundant at surface of cells cultivated under higher agitation speeds. This was explained by LPS physicochemical properties like hydrophilicity and cell mechanical stability enhancement. Since the influence of hydrodynamics on A. calcoaceticus dynamics was the main goal of the present study, proteomic studies through tandem mass spectrophotometry (LCMS/MS) were carried out to verify differences in terms of outer membrane protein (OMP) expression. It was reported that cells cultivated at higher agitation speed had a higher number of proteins linked to transport and receptor mechanisms. Hence, it was concluded that cells under higher agitation speed may have a more complete apparatus to sense the environment, and may present higher metabolic response to environmental changes. Besides, differences at the level of iron metabolism and pathogenicity factors were reported. That suggests cells cultivated at higher agitation speeds may present higher cellular adaptation capability in different hosts. The present study showed that hydrodynamics can be regarded as an important cell cultivation parameter at planktonic state as it was reported that cell surface chemistry and growth dynamics seem to be affected. The differences observed for cells at planktonic state help to understand differences found in the literature in terms of bacteria collective behavior and coherent structure formation across different hydrodynamic conditions. V Acknowledgements First of all, I would like to deeply thank my advisor in the University of Porto, Prof. Manuel Simões for all the scientific advices, constructive suggestions and for believing I would be capable of achieving interesting results throughout the project by means not only of hard work, but by positive thinking as well. Of course, I would also like to deeply thank my advisor in the University of Sheffield, Prof. Catherine Biggs, for advising and giving me the opportunity to work in such a great environment like the Department of Chemical and Biological Engineering (CBE). Thanks to her I was able to contact and perform a wider range of experiments, which made this work to become even more interesting to me. I would also like to extend my gratitude to my supervisor, Dr. Esther Karunakaran, who guided me through the many experiments (even in holidays) and helped me to overcome a lot of problems with patience. Thanks to her I was introduced to great people like Dr. Narciso Couto, Dr. Henriette Jensen and Dr. Bharathi Ramalingam, who also gave me valuable advices throughout the project and showed me that a lab can also be a fun place. I believe my adaptation and accommodation in Sheffield would not be possible without the help of two people who were my first true friends in United Kingdom, Jennifer Dick and Simon Vardy. They were really nice to me even when I was unfairly complaining about the city. Last, but not least, I would like to thank and dedicate this dissertation to my beloved parents, José Manuel and Francisca for the unconditional support, love and care throughout the entire course. I owe them every challenge I was able to overcome. VI VII Table of Contents Abstract ......................................................................................................................................... III Acknowledgements ........................................................................................................................ V Table of Contents ........................................................................................................................ VII List of Figures ............................................................................................................................... IX List of Table .................................................................................................................................. XI Symbols and Abbreviations ...................................................................................................... XIII 1. Introduction ............................................................................................................................ 1 1.1 Background information ................................................................................................ 1 1.2 Physical aspects: Long-range hydrodynamics .............................................................. 3 1.2.1 Bacteria movement and cell-surface interaction ........................................................ 4 1.2.2 Cell-cell interaction and near-field analysis .............................................................. 7 1.3 Hydrodynamic impact on cohesive structures: biofilm ............................................... 9 1.3.1 Mechanism of biofilm formation and cell adhesion ................................................ 10 1.3.2 Hydrodynamics impact on biofilm structure and mechanical properties ................ 11 1.3.3 Mass transfer and signaling events .......................................................................... 13 1.3.4 Cell metabolism ....................................................................................................... 14 1.4 Motivation and objectives ............................................................................................. 16 2. Materials and methods ......................................................................................................... 19 2.1 Bacteria isolation and identification ................................................................................ 19 2.2 Bacteria growth and distinct hydrodynamic conditions ................................................ 19 2.3 Kinetic studies .................................................................................................................... 20 2.4 Respiratory activity assay ................................................................................................. 21 2.5 Physicochemical characterization of cell surface ........................................................... 22 2.5.1 Hydrophobicity assessment by Microbial Adhesion to Hydrocarbons (MATH) assay .............................................................................................................................................. 22 2.5.2 Cell surface charge by electrophoretic mobility (EPM) measurement ........................ 23 2.5.3 Cell surface functional groups analysis through Attenuated total reflectance Fourier Transform Infrared Spectroscopy – ATR-FTIRS ................................................................. 23 2.5.4 Cell surface chemistry analysis through X-ray photoelectron spectroscopy (XPS) .... 24 2.6 Auto-aggregation analysis ................................................................................................ 25 2.6.1 Visual auto-aggregation assay ..................................................................................... 25 2.6.2 Auto-aggregation quantification .................................................................................. 26 VIII 2.7 Outer membrane proteins analysis – Proteomic study .................................................. 26 2.7.1 Outer membrane protein isolation and analysis by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) ............................................................... 26 2.7.2 In-gel digestion of proteins into smaller peptides ....................................................... 27 2.7.3 Peptide ion sequencing of OMP by LC-MS/MS and protein identification ................ 28 2.8 Statistical Analysis ............................................................................................................. 29 3. Results and Discussion ......................................................................................................... 31 3.1 Growth and respiratory activity ...................................................................................... 31 3.2 Influence of hydrodynamics on cell hydrophobicity ...................................................... 36 3.3 Hydrodynamic condition influence on cell electrophoretic mobility (EPM) ............... 38 3.4 Influence of hydrodynamics on outer membrane chemistry – ATR-FTIR spectroscopy ................................................................................................................................................... 42 3.5 Influence of hydrodynamics on outer membrane chemistry – XPS analysis ............... 46 3.6 Hydrodynamics influence on auto-aggregation capability ............................................ 52 3.7 Outer membrane protein (OMP) variations with distinct hydrodynamic conditions. 54 4. Conclusions and future work perspectives ......................................................................... 59 5.References .................................................................................................................................. 61 Appendix A – Statistical analysis .................................................................................................. 69 A.1 Analysis on specific growth rate difference .................................................................. 69 A.2 Analysis on total respiratory activity difference ............................................................ 70 A.3 Analysis on hydrophobicity index difference ................................................................ 70 A.4 Analysis on electrophoretic mobility medians at pH 7 difference ................................ 71 A.4.1 Analysis on electrophoretic mobility medians at pH 2 and 6 in mid-exponential growth phase ........................................................................................................................ 71 A.5 Analysis of auto aggregation index difference .............................................................. 72 Appendix B – Figures .................................................................................................................... 73 Appendix C - Tables ...................................................................................................................... 76 C.1 Identified OMP from A. calcoaceticus cultivated under 100 rpm agitation speed ........ 76 C.2 Identified OMP from A. calcoaceticus cultivated under 220 rpm agitation speed ........ 78 C.3 Identified OMP from A. calcoaceticus cultivated under 375 rpm agitation speed ........ 80 C.4 Factor analysis associated to Principal Component Analysis (PCA) ............................ 82 Appendix D - Equations ................................................................................................................ 92 IX List of Figures FIGURE 1-1: BACTERIAL MODEL AND SHAPE PARAMETERS COMMONLY USED FOR FLOW FIELD AND HYDRODYNAMIC MODELING. ACCORDING TO THIS MODEL, BACTERIA SWIMS BY PERFORMING A HELICAL WAVE DOWN ITS FLAGELLUM, WHERE H IS THE WAVE AMPLITUDE AND K THE WAVE NUMBER (ADAPTED FROM ISHIKAWA ET AL., 2007) ............................................................................................. 5 FIGURE 1-2: REPRESENTATION OF DIFFERENT CASES OF BACTERIAL SWIMMING AND FORCES DEVELOPED AS IT MOVES IN A FLUID. (A) REPRESENTS THE FLOW-FIELD AROUND BACTERIUM AS A FORCE DIPOLE; (B) THE CELL SURFACE HYDRODYNAMIC INTERACTION AND DEVELOPMENT OF IMAGE SYSTEM AND (C) SWIMMING TRAJECTORY CHANGES AND HYDRODYNAMIC INTERACTION WITH NO-SLIP SURFACE (ADAPTED FROM BERKE ET AL. (2008)) ................................................................................................. 5 FIGURE 1-3: DYNAMICS OF BIOFILM FORMATION AS A CYCLE WITH THE MOST RELEVANT ASPECTS (ADAPTED FROM O'TOOLE ET AL., 2000) .............................................................................................................. 10 FIGURE 3-1: A. CALCOACETICUS GROWTH PROFILE AT DISTINCT AGITATION SPEEDS. CELLS WERE CULTIVATED IN TSB MEDIUM AT 30ºC IN BATCH MODE. ERRORS BARS STAND FOR STANDARD DEVIATION. ............ 31 FIGURE 3-2: A. CALCOACETICUS REDUCING SUGARS CONSUMPTION AT DISTINCT AGITATION SPEEDS. ERRORS BARS STAND FOR STANDARD DEVIATION. ............................................................................................ 32 FIGURE 3-3: TOTAL RESPIRATORY ACTIVITY ACROSS DIFFERENT AGITATION SPEEDS AT DISTINCT GROWTH PHASES. (A) REFERS TO MID-EXPONENTIAL PHASE AND (B) MID-STATIONARY PHASE. ERROR BARS REPRESENT STANDARD DEVIATION. ..................................................................................................... 35 FIGURE 3-4: ELECTROPHORETIC MOBILITY OF A. CALCOACETICUS ACROSS PH RANGE OF 2,0 TO 7,0 FOR DISTINCT AGITATION SPEEDS. (A) REFERS TO MID-EXPONENTIAL PHASE AND (B) STATIONARY PHASE. GREY BARS CORRESPOND TO STANDARD ERRORS. .............................................................................. 39 FIGURE 3-5: ATR-FTIR SPECTRA FOR A. CALCOACETICUS GROWN UNDER DISTINCT AGITATION SPEEDS AT STATIONARY GROWTH PHASE. BLUE LINE REPRESENTS CELLS GROWN AT 100 RPM; RED LINE CELLS AT 220 RPM AND GREEN LINE CELLS AT 375 RPM. ................................................................................. 43 FIGURE 3-6: PRINCIPAL COMPONENT ANALYSIS (PCA) FOR A. CALCOACETICUS (AT STATIONARY GROWTH PHASE) ATR-FTIR SPECTRA. .............................................................................................................. 44 FIGURE 3-7: CELL SURFACE MOLECULAR COMPOSITION IN TERMS OF CELL FRACTION OCCUPIED BY CELL WALL MAIN CONSTITUENTS. (A) REFERS TO MID-EXPONENTIAL PHASE AND (B) STATIONARY PHASE. 51 FIGURE 3-8: NUMBER OF OMP WITH RESPECTIVE FUNCTION IDENTIFIED FOR A. CALCOACETICUS GROWN UNDER DIFFERENT AGITATION SPEED AT STATIONARY PHASE. ............................................................ 55 FIGURE B.1: ATR-FTIR SPECTRA FOR A. CALCOACETICUS GROWN UNDER DISTINCT AGITATION SPEEDS AT MID-EXPONENTIAL GROWTH PHASE. BLUE LINE REPRESENTS CELLS GROWN AT 100 RPM; RED LINE CELLS AT 220 RPM AND GREEN LINE CELLS AT 375 RPM. .................................................................. 73 FIGURE B.2: SDS-PAGE ANALYSIS OF OUTER MEMBRANE PROTEINS EXTRACTED FROM A. CALCOACETICUS CULTIVATED UNDER DISTINCT AGITATION SPEEDS AT STATIONARY GROWTH PHASE. NUMBERS ON THE LEFT SIDE REPRESENT MOLECULAR WEIGTH (IN KDA). ........................................................................ 74 2 The velocity field of the fluid in contact with microbial layer and the shear stress developed under certain flow regimes influence formation, structure and stability of biofilms. Those parameters are interlinked and may influence diffusion rate of cells and nutrients as well as formation of detachment force. For instance, it is known that biofilms which are grown under turbulent flow have a distinct architecture in such way they are thinner, denser and have higher content of extracellular polymeric substances (EPS) (Pereira et al., 2002). It is expected that microbial metabolism will be affected at some level, since density and thickness will influence resistance to diffusion of nutrients and affect microenvironment surrounding cells (Liu and Tay, 2001a). Despite the influence of hydrodynamics on biofilm structure has been attributed in the literature mainly to physical phenomenon, biological phenomenon has played a role as there are evidences that microbial cells can respond to hydraulic shear by changing morphology, growth rate, cell size and metabolic activity (Chen and Huang, 2000; Simões et al., 2007). For instance, it was reported that as shear stress increases, cell growth yield reduces and 2-(p-iodophenyl)-3-(p-nitrophenyl)-5-phenyl tetrazolium chloride (INT) dehydrogenase activity increases concomitantly. Hence, shear stress influenced metabolism at both catabolic and anabolic levels (Liu and Tay, 2001b). Besides, Stoodley et al. (1999) showed that hydrodynamic conditions have greater impact on biofilm formation and structure than cell signaling events (known as quorum sensing). Although those evidences about the effect of hydrodynamic conditions on biofilm development and cell metabolism are experimentally verified, the mechanisms by which those phenomena occur are not fully understood yet and models that describe the influence of hydrodynamic parameters on cell behavior are still required. Large-scale coherence processes such as biofilm formation and co-aggregation has an initial step composed by physical movements to initiate bacterium-to-bacterium contact and/or bacterium-to-surface contact, which involves mainly cell mobility and hydrodynamic force (Pratt and Kolter, 1998). Thus, several theoretical models at microscopic level focusing physical aspects of bacterial dynamics in suspension have been developed and they are based on flow field around a bacterium. It is believed that long-range hydrodynamic interactions are important for cell-cell/surface interactions (Najafi and Golestanian, 2010). Despite that, Drescher et al. (2011) observed that stochasticity (mainly associated to thermoand chemotaxis) drowns the effects of longrange hydrodynamic for cell-cell interactions. However, they discovered that hydrodynamics play a role on cell-surface interaction when bacterium is within few 3 microns of the surface, which may influence the collective behavior (Drescher et al., 2011). A macroscopic evidence of this phenomenon was achieved by Lecuyer et al. (2011) that showed shear stress increased significantly the residence time of Pseudomonas aeruginosa adhesion, where there is formation of specific (known as catchbonds) and non-specific bonds. In order to have a deeper insight on the influence of hydrodynamics at a micro- (interaction between single cells), meso- (collective motion) and macroscopic levels (biofilm formation) and predict cell distribution as well as steady-state profile of adsorption, models that describe the physical phenomenon of attraction have been developed (Berke et al., 2008). These models can assume bacteria as non-Brownian particles for study of near-field interactions and describe bacteria movement and noise of the system (mainly associated to collision and roughness of surfaces) (Metzger and Butler, 2010). Hence, these models are useful analytical tools at different levels since they tend to describe bacteria collective motion and behavior, which can be used to obtain important parameters for development of models that account for hydrodynamic effect on biofilm formation for example. In this context, the present section aims to review the current state-of-the-art on the influence of hydrodynamics on microbial cell behavior with emphasis on biofilm formation and metabolic induced changes. Furthermore, hydrodynamic models at microscopic scale and description of bacterium flow field will be presented. 1.2 Physical aspects: Long-range hydrodynamics As discussed earlier, bacterial collective motion and behavior at microscopic scale are triggered by biochemical and physical events where hydrodynamics may play a major role. An example of biochemical event is the quorum-sensing phenomenon, where quorum-sensing bacteria produce and release auto-inducers which can be detected by other bacteria and alter their gene expression and behavior (Waters and Basslers, 2005). On the other hand, long-range hydrodynamic forces and far-field hydrodynamic interactions represent physical events related to bacterial dynamics (Najafi and Golestanian, 2010). For instance, Ishikawa and Hota (2006) showed that hydrodynamic forces instead of biological reactions mainly induced changes in direction between two swimming cells, which may impact collective motion. 4 In order to better comprehend the role of these interactions in bacterial collective behavior, models have been developed to predict bacteria walk, diffusion and collision with surfaces. Unlike chemoand thermotaxis, which considers bacterial movement as random and net movement caused by gradients of nutrient concentration and temperature (Adler, 1966), the models for long-range hydrodynamics are deterministic and have been successful to describe experimental data (Drescher et al., 2011; Ishikawa and Hota, 2006). These models are continuum, since microorganism is generally much smaller than the flow field under analysis and variables are mainly volume-averaged quantities (Metcalfe et al., 2004). Despite the fact hydrodynamic coupling for cell-cell and cell-surface interaction has been almost exclusively described in terms of far-field interactions; Ishikawa et al. (2006) found out that near-field interactions also play a role on the stability of swimming motions and trajectories. Thus far-field and near-field interactions should be analytically described. 1.2.1 Bacteria movement and cell-surface interaction Bacteria locomotion often arises from flagella rotation, which is capable to proper them forward or promote changes in direction. Basically, the flagella rotation is driven by an inward ion current across the cell membrane through an embedded motor which can lead to bidirectional rotation (clockwise and counter-clockwise) and changes in direction. Some bacteria can change their direction by variation of flagella rotation speed (Armitage and Schmitt, 1997). In terms of modeling, one of the most commonly used bacterial models is the one presented by Phan-Thien et al. (1987). It was developed by means of boundary-element analysis of flagellar propulsion for spherical and ellipsoidal bodies that allowed attaining optimum aspect ratios. Figure 1-1 represents the bacterial model commonly used and respective shape parameters: 5 Figure 1-1: Bacterial model and shape parameters commonly used for flow field and hydrodynamic modeling. According to this model, bacteria swims by performing a helical wave down its flagellum, where h is the wave amplitude and k the wave number (adapted from Ishikawa et al., 2007) For modeling purposes, it is assumed that a single bacterium is a neutrally buoyant particle due to the fact sedimentation velocity is much less than swimming speed. In addition, it is assumed that center of buoyancy associated to the bacterium is the same as the geometric center, which makes it to be force and torque free (Ramia et al., 1993). The most relevant assumption underlying hydrodynamic models is that self-propelled bacterium can be seen as a force dipole, since the drag force exerted by the cell body and flagellum is balanced by a rearward flagellar thrust (see Figure 1-2 A) (Pedley and Kessler, 1992). Figure 1-2: Representation of different cases of bacterial swimming and forces developed as it moves in a fluid. (A) Represents the flow-field around bacterium as a force dipole; (B) the cell surface hydrodynamic interaction and development of image system and (C) Swimming trajectory changes and hydrodynamic interaction with no-slip surface (adapted from Berke et al. (2008)) The flow around a single bacterium can be approximated by force dipole and a derivation of the Stokes flow field gives rise to Equation 1-2 which relates fluid velocity (u) with 6 swimming parameters like dipole strength (ps), swimming direction (e) and distance to the dipole (r), as follows: ) ) (Equation 1-2) It is important to take into account that this model is only valid for distances significantly larger than the length of swimming cell. Thus, it is classified as a far-field model (Berke et al., 2008). As bacterium gets closer to a surface, the flow field induced by the cell is the net result of a superposition of a force dipole plus image flow field generated by an image system. Since bacterium is assumed to promote a force dipole parallel to a no-slip surface (condition at which fluid possess zero velocity relative to the boundary), the image system for this type of force is mainly composed by force dipole, force quadrupole and source quadrupole (Blake, 1971). The resultant of these forces induces a velocity component towards surface, which is directly proportional to dipole strength and inversely proportional to the square of distance between bacterium and surface (see Figure 1-2 B). The generated velocity component can be seen as an explanation in physical terms for attraction phenomenon (Hernandez-Ortiz et al., 2005). The image system represents a symmetric dipole on the other side of the surface and therefore, dipole-dipole attraction takes place. Although dipole-dipole attraction describes a physical mechanism by which cells interact with surfaces, it is not ideal since collisions and Brownian motion may disturb the alignment of bacteria trajectory and nearby surface states. Consequently, force dipole is not aligned with no-slip surface and attraction can turn into repulsion, thus it must be considered in the model. In the case of Figure 1-2 C, a vertical velocity (uy) away from the surface is formed and can be computed through Equation 1-3, as follows: ) ) (Equation 1-3) Where is angle of dipole position with respect to the vertical direction. From Equation 1-3, repulsion (positive uy) is verified for very small angles. The image system on the no-slip surface does not only induce attractive and/or repulsive velocities, but it also has nonzero velocity gradients that promote rotation of the cells. The rotation rate (Ω) is computed by the following set of equations, as follows: (Equation 1-4) ) (Equation 1-5) 7 ( ) ) (Equation 1-6) Where is the vorticity of flow field, E is the rate of strain of the flow field due to the image system and γ is the aspect ratio of the cell according to the bacterial model. The Equation 1-6 shows that cell rotates at half the vorticity induced by the image system plus a term, which takes into account shape parameter and flow field strain (Kim and Karilla, 1991). Allison et al. (2008) demonstrated that far-field hydrodynamic interactions of swimming cells with solid surfaces lead to cell reorientation in a plane parallel to the surface as well as attraction by the nearest surface. Despite that evidence, Drescher et al. (2011) reported that noise due to Brownian motion and intrinsic swimming stochasticity drown long-range hydrodynamic effects between two bacteria beyond small length scale of few microns. This evidence implies that hydrodynamic effects between cells are only relevant in dense suspensions. On the other hand, they discovered that near-field hydrodynamic effects might play a role within few microns of the surface, which may lead to bacteria trapping onto surface. Lecuyer et al. 2011 showed that shear stress increases residence time of adhesion of Pseudomonas aeruginosa and that none of the bacterial surface features (flagellum and extracellular matrix production capability) is responsible for the observed trend. They also hypothesized that longer residence time is a direct consequence of less motile cells due to high shear stress. Therefore, physical interactions rather than chemical ones may be responsible for the increased residence time. Nevertheless, the role of cell appendages and surface chemistry on the adhesion process and the influence of hydrodynamics are not fully understood yet. 1.2.2 Cell-cell interaction and near-field analysis The cell-cell interaction is an important factor for cohesive structure formation like granules (through aggregation phenomena) and biofilm. Additionally, the effects of hydrodynamic interactions between two bacteria on the rheology and diffusivity of suspensions have been investigated (Ishikawa et al., 2007; Hernandes-Ortiz, 2005; Ishikawa et al., 2006b). By modifying suspension fluid mechanics, individual cell and collective behavior may be altered. According to Ishikawa et al. (2007), stability of swimming motion dominates the length and timescales of these coherent structures formation. The near-field hydrodynamic interactions play a major role on the stability of swimming motion and cell-surface interaction within few micrometers. Thus, it must be 8 treated precisely and included in hydrodynamic models (Ishikawa et al., 2006a; Drescher et al., 2011). For discussing far-field hydrodynamic interactions, models can assume bacteria as a single point force or stresslet, since moments decay rapidly when distance between particles is significantly great. Nevertheless, multipole moments must be taken into account in the context of near-field interaction analysis, which makes models at this level to have different assumptions. For instance, bacteria cannot be seen as particles with Brownian motion due to their size (between 2-10 μm), i.e., they are significantly large for Brownian motion to be considered in near-field interaction. Besides, the swimming trajectory for near-field interaction analysis is only seven times the body length, which makes hydrodynamic interactions to be dominant over Brownian motion (Brennen and Winet, 1977). The hydrodynamic interactions between two swimming bacteria are computed in an infinite fluid and since the flow field around bacteria can be described as a Stokes flow, the flow field around bacteria that interacts with another one can be given by Equation 1-7, as follows (Ishikawa et al., 2007; Vicsek et al., 1995): ) ∑∫ ) ) (Equation 1-7) Where Sm stands for bacterium surface; J the Oseen tensor and tj is the traction force (Youngren and Acrivos, 1975). Since it is assumed that bacteria possess the center of buoyancy at the same position as the center of gravity, a force and torque free model is developed and the velocity field on the surface of the body (including spherical body plus flagellum) is given by the following set of equations which includes translation velocity (U) and rotation velocity (Ω) (Ishikawa et al., 2007): ) { ) (Equation 1-8) Where f is an angular velocity. These equations are normally solved numerically by using the boundary element method. Ishikawa et al. (2007) observed that there is an unstable parallel motion of bacteria by applying the mentioned boundary element method, which breaks down easily in three-dimensional space. Besides, as two bacteria approach each other near-field interactions like lubrication forces can change their orientations. Although Ishikawa and Hota (2006) already verified experimentally that hydrodynamic interaction play a role on the cell-cell interaction, the effect of near-field lubrication forces remains to be observed experimentally. In this context, Drescher et al. 2011 verified experimentally 9 that collision models are more suitable than long-range hydrodynamics, but hydrodynamic interactions may still play a role in near-field. In order to obtain the complete physic picture of the bacterium dynamics and influence of hydrodynamics, models should include both far-field and near-field interactions as well as the impact of biochemical events and cell surface features. Lega and Passot (2003) developed a hydrodynamic model, which integrates continuum equations involving reaction and diffusion of nutrients and bacteria with velocity field of bacteria-water mixture. The model was able to describe successfully the dynamics of microorganisms within colony and a mechanism for collective motion towards fresh nutrients. Their study shows that incorporation of reaction-diffusion equations for nutrient concentration (like in chemotaxis phenomenon) may be useful for development of more reliable hydrodynamic models. 1.3 Hydrodynamic impact on cohesive structures: biofilm Biofilm is defined as a complex coherent structure of surface-attached bacteria plus cellular products or inert content such exopolysaccharides (EPS). It can be comprised by single or multiple species and is found ubiquitously in many natural, medical and industrial settings (O'Toole et al., 2000). Biofilms can be formed in any hydrated environment with proper nutrient conditions and are also able to develop on a variety of abiotic hydrophobic and hydrophilic surfaces. Due to the wide range of surfaces where it can develop, biofilm formation is often associated to "biofouling" (Melo and Flemming, 2010) and bacterial infections as well as contamination of food preparations (Costerton and Stewart, 2001). Nevertheless, biofilms have been successfully employed in the biotechnology field for wastewater treatment processes in bioreactors (Lazarova and Manem, 1994; Kwok et al., 1998). Biofilm development, structure and population phenotype are strongly influenced by intrinsic biological properties and environmental factors like hydrodynamic conditions. The latter has major impacts on biofilm architecture, heterogeneity (Rochex et al., 2008) and metabolism of cells within (Simões et al., 2008). Hydrodynamic conditions are shown to strongly influence mass transport of cells and nutrients to the biofilm surface as well as quorum-sensing molecules by advection (Kirisits et al., 2007). Turbulent regimes are often associated to high shear stress and compression forces, which lead to structure 10 detachment. Besides, Simões et al. (2004) reported that biofilms formed under turbulent regime were more active and compact. 1.3.1 Mechanism of biofilm formation and cell adhesion It was verified that bacteria primarily exist in association with surfaces rather than at planktonic state (Watnick and Colter, 2000). Biofilms represents a protection mechanism for microorganisms and allows them to survive and growth in hostile conditions. Additionally, it enables bacteria to have dynamics similar to multicellular organisms through signaling events (Waters and Bassler, 2005). Cells within biofilms possess distinct phenotype characteristics when compared to planktonic counterparts. Simões et al. (2003) noticed that Pseudomonas fluorescens cells at planktonic state were inactivated by antimicrobial agents at higher extent than when the cells were within biofilm. Besides that, they observed that biofilms formed under laminar conditions were more easily inactivated by antimicrobial agents, which suggests the coherent structure resilience. The formation of biofilms can be described by the following five-step process: i) formation of a conditioning film on a given surface; ii) transport of microorganisms from the fluid to the surface, iii) adhesion of microorganisms at the interface between solid and liquid phases; iv) transport of nutrients and consumption which leads to cell replication and EPS matrix production and v) biofilm detachment as consequence of the liquid shear stress, which results in detachment force (Vieira et al., 2003). The general mechanism of biofilm formation is depicted as a cycle in Figure 1-3. Figure 1-3: Dynamics of biofilm formation as a cycle with the most relevant aspects (adapted from O'Toole et al., 2000) 11 Biofilms may spread to uninfected areas depending on environmental conditions by detachment process, where detached cells re-enter a planktonic state and may adhere to another surface giving rise to a novel biofilm (O’Toole et al., 2000). The ability of the cell to attach to a given surface is carried out by specific cell surface proteins (e.g. appendages) and non-specific interactions (Saini, 2010). Interfacial forces like Van der Waals, electrostatic and Lewis acid-base interactions are shown to play a role on bacterial adhesion processes and are related to cell surface properties (mainly chargeand chemical composition related) (Van Oss, 1994). Basically, bacteria are theorized as colloid particles and can be included in the Derjaguin, Landau, Verwey and Overbeek (DLVO) theory, which states that bacteria attachment is the balance between the Van der Waals (attractive) and electrostatic (repulsive) forces (Goulter et al., 2009). Additionally, the self-propelling capability of bacteria through flagella is associated to biofilm forming ability (Pratt and Kolter, 1998). In general, physical (e.g. hydrodynamic and diffusion forces), biochemical (eg. surface dehydration) and chemical interactions (e.g. hydrogen bonding) are essential for adhesion and biofilm three-dimensional structure build-up (Liu et al., 2000; Calleja, 1984; Marshal, 1971). Nevertheless, the influence of hydrodynamics on cell surface properties and the consequence on interactions between cells and abiotic surfaces has not been fully understood yet. Despite hydrodynamic conditions represent an important environmental factor, nutrient availability was also shown to influence biofilm growth, development and detachment behavior (Telgmann et al., 2004). Thus, the impact of each factor must be evaluated separately. 1.3.2 Hydrodynamics impact on biofilm structure and mechanical properties Hydrodynamic shear force has a significant influence on biofilm steady state structures. Chang et al. (1991) observed that biofilm density increases in linear fashion with increasing shear stress, whilst biofilm thickness decreases linearly. In other words, more dense and compact biofilms are generated under higher shear stress. Pereira et al. (2002) proposed that lower thickness and higher density at higher hydrodynamic stresses are due to need of microorganisms to remain active and to be protected from external aggressions, thus microorganisms produce more EPS per unit volume which leads to an effective adhesion and reduction of void spaces. Hence, biofilms with greater cohesion are 18 19 2. Materials and methods In this chapter, a brief description of the procedures and apparatus associated to the methods employed throughout the experimental work will be given. The set of experiments were defined in order to evaluate the hypothesis that changes in hydrodynamic environment will promote a distinct cell behavior as well as changes at the cell surface level. As described earlier, the main goals of the present study were to verify the influence of different hydrodynamic conditions on growth and collective behavior as well as on cell surface properties at the planktonic state of a selected bacterium. In order to evaluate the effects on growth, experiments involving growth curve under different agitation speeds and study of associated kinetics were performed. The impact on collective behavior was analyzed by means of auto-aggregation evaluation. On the other hand, cell surface properties were determined by a set of experiments that allowed the surface characterization at physicochemical and protein expression levels. 2.1 Bacteria isolation and identification Acinetobacter calcoaceticus was the microorganism selected for the present study. It was isolated from a model laboratory drinking water distribution system as described by Simões et al. (2006). The bacterium was stored at -80 ºC in 50% (v/v) glycerol solution and colonies of A. calcoaceticus were obtained by plating on Luria-Bertani medium with Agar medium (LB-A). 2.2 Bacteria growth and distinct hydrodynamic conditions Bacterial cells were grown in batch mode overnight at 30 ºC in Tryptic Soy Broth –TSB medium(Sigma-Aldrich, UK) in 220 mL culture medium with 280 mL hold-up (500 mL total volume flask). In order to create distinct hydrodynamic environments, cells 20 on each assay were grown under distinct agitation speeds. The agitation speeds employed throughout the present work were 100 rpm, 220 rpm and 375 rpm and culture flasks were agitated in a shaker/incubator (MaxQ 4450, Thermo Scientific, USA). Rotation speeds were determined by the incubator built-in tachometer. Inoculum cultures were grown under the similar conditions at 220 rpm agitation speed. 2.3 Kinetic studies In order to observe the influence of different agitation speeds on growth behavior, growth kinetics in terms of biomass formation and reducing sugar (substrate) consumption over time were studied. Growth curve was attained for each agitation speed through spectrophotometry at 640 nm as carried out by Tam (2002). Inoculum was diluted in sterile TSB medium to obtain an initial optical density (OD) of 0,200±0,0 determined by spectrophotometer (V1200, VWR, Germany) in 220 mL as total volume. Following that, 2 ml aliquots were taken for OD at 640 nm measurements from 30 to 30 min in the first 2 hours and at every completed hour until total 11 hours of growth. On the following day, the same procedure was applied until stationary phase was reached. Measurements were performed in biological triplicates for each agitation speed. A relationship between optical density at 640 nm and colony-forming units (CFU) per ml was obtained for a stock culture cultivated at similar conditions under 220 rpm. Each increase of 0,1 OD unit corresponds to an increase of 2,5×107 CFU.mL-1. Based on the growth curve obtained for each agitation speed, cell harvesting time points representing mid-exponential and stationary phases were defined for further experiments and are presented in Table 2-1. Table 2-1: Time points for harvesting and respective growth phase for each experimental condition. Agitation speed (rpm) Growth phase Time point for harvesting 100 Mid-exponential 12 h post-inoculation Stationary 25 h post-inoculation 220 Mid-exponential 5 h post-inoculation Stationary 20 h post-inoculation 375 Mid-exponential 4 h post-inoculation Stationary 15 h post-inoculation 21 Simultaneously, substrate consumption in terms of reducing sugars content was assessed by the 3,5 dinitro salicylic acid – DNS – (Sigma-Aldrich, UK) colorimetric method in a similar manner as performed by Gusakov et al. (2011). Prior analysis, cells were spinned down by centrifugation at 4500×g for 10 min. From 2 mL aliquots, 500 μL of sample was mixed with 500 μL DNS in a tube, which is placed in 100 ºC bath for 5 min in order to promote oxidation of aldehyde groups present in reducing sugars (Breuil and Saddler, 1985). The reaction leads to solution colour modification, which is analyzed by spectrophotometry. Absorbances at 540 nm were measured by the same spectrophotometer in biological triplicates for each condition and measurements were performed every 2 hours until 12 hours of growth were reached. Relationship between optical density at 540 nm and reducing sugars concentration was obtained, where each decrease of 0,1 OD corresponds to 225 mg reducing sugars.L-1 consumed. Finally, specific growth rate (μ) and yield of biomass in terms of reducing sugars consumption (YX/S) were computed through Equation 2-1 and 2-2, respectively (Bailey and Ollis, 1986). ⁄ (Equation 2-1) (Equation 2-2) Where Xf represents biomass concentration at the end of exponential phase (CFU.mL-1); Xi biomass concentration at beginning of exponential phase (CFU.mL-1); t is time until end of exponential phase (h); Sf reducing sugars concentration at the end of exponential phase (mg.mL-1) and Si reducing sugars concentration at beginning of exponential phase (mg.mL-1). The kinetic parameters were obtained to describe the exponential growth phase for each condition. 2.4 Respiratory activity assay The respiratory activity of bacterial cells was assessed by measurement of oxygen uptake rate through a biological oxygen monitor (Model 53, Yellow Spring Instruments Inc., USA) as described by Simões et al. (2005). Firstly, cells cultivated in TSB were harvested at specific time points (see Table 2-1) by centrifugation at 4000×g for 15 min at 25 ºC. Following that, cells were washed and resuspended in 100 mM potassium chloride (VWR, Germany) until final OD at 610 nm of 0,4 was reached. Afterwards, 30 mL of 22 suspension were aerated for 10 min to assure oxygen saturation. After aeration, culture was placed in temperature-controlled vessels at 25 ºC attached to the biological oxygen monitor and oxygen concentration decrease was monitored over 10 min, where after initial 5 minutes 10 μL of 5 g.L-1 glucose (Sigma-Aldrich, Germany) was injected. According to Simões et al. (2005), the total respiration rate corresponds to the slope associated to the linear decrease of oxygen concentration in cell suspension after glucose injection. In order to compute the total respiratory activity (mg O2.dry bacterial mass-1.min-1), dry bacterial mass by total volatile solids measurement and oxygen solubility in water at 25 ºC were obtained. Dry bacterial mass determination was performed as described by APHA, AWWA and WEF (1989). This experiment was performed in biological triplicates with technical duplicates. The experiment was performed for each agitation speed condition and growth phase (mid-exponential and stationary). 2.5 Physicochemical characterization of cell surface 2.5.1 Hydrophobicity assessment by Microbial Adhesion to Hydrocarbons (MATH) assay The cell surface hydrophobicity was assessed by MATH assay developed by Rosenberg et al. (1980) with modifications proposed by Mukherjee et al. (2011). Briefly, cells were harvested by centrifugation at 4500×g for 10 min at 4ºC and washed twice with 100 mM potassium chloride. Following that, cells were suspended in a high ionic strength solution (150 mM potassium chloride at pH 7) to a final concentration of 1,000 OD at 600 nm. According to Rosenberg (2006), aqueous phase with high ionic strength tends to diminish electrostatic forces that would affect cell interaction with the organic phase, which makes cell adhesion to that phase to rely only on hydrophobic interactions. Following that, 200 μL of n-hexadecane 99% (Sigma-Aldrich, Germany) were added to 1mL of suspension. Control suspension was prepared without addition of nhexadecane. The mixture was vortexed for 90 s and organic phase was allowed to separate from the bacterial phase by settling the mixture for 15 min. Finally, OD at 600 nm is obtained for the aqueous phase and hydrophobicity index (H%) is computed by Equation 2-3 as follows (Mukherjee et al., 2011) 23 ) (Equation 2-3) Where AC stands for OD at 600 nm of the control suspension and AM is the OD at 600 nm of test suspension after mixing with n-hexadecane. This experiment was performed with 4 biological replicates for each tested agitation speed condition at mid-exponential and stationary phase. 2.5.2 Cell surface charge by electrophoretic mobility (EPM) measurement This experiment was carried out in a similar manner as performed by Mukherjee et al. (2011). The bacterial electrical properties for all the agitation speed conditions at midexponential and stationary growth phases were attained by electrophoretic mobility measurements. The measurements were performed in a zeta potential analyzer (ZetaPALS, Brookhaven Instruments, UK) in 2 to 7 pH range. Prior analysis, the cells were washed with 100 mM potassium chloride and resuspended in the same solution until a final OD at 600 nm of 1,000 was reached. Three hundred microliters cell suspensions were diluted into 1200 μL potassium chloride solution over pH ranging from 2 to 7. Measurements were carried out with an associated electrical field of 2,5 V.cm-1 at 2,0 Hz frequency and 5 runs with 40 cycles each were performed for each assay. Each agitation speed condition was analysed in biological triplicates for both mid-exponential and stationary growth phase. 2.5.3 Cell surface functional groups analysis through Attenuated total reflectance Fourier Transform Infrared Spectroscopy – ATRFTIRS The ATR-FTIRS was one of the techniques employed in the present study to characterize surface chemistry of cells grown under distinct agitation speeds (at midexponential and stationary growth phase) in terms of functional groups. The technique was performed in a similar manner as described by Mukherjee et al. (2011) and Jiang et al. (2004). Briefly, cells were washed twice with 0,9% sodium chloride solution (Sigma, UK) and small portion of pellet was allowed to air dry for 1h on the ATR attachment 24 (Pike Technologies, USA) associated to the FTIR spectrophotometer (IR Prestige-21, Shimadzu, UK). The Happ-Genzel apodisation function was used over 64 scans with resolution set to 4 cm-1 to obtain a FTIR spectrum between 600 and 4000 wave numbers. Spectrum processing was performed using the software IR solution provided by equipment supplier. In terms of data processing, atmospheric and baseline corrections were done to remove noise caused mainly by atmospheric water vapor and carbon dioxide. Normalization of the spectrum to intensity of the peak at 2924 cm-1 (related to the assymmetric stretching of C-H in –CH2 according to Nauman (2006)) was carried out in order to compensate for different cell numbers loaded on the equipment across different tested conditions. That normalization procedure allowed direct comparison of spectra. For each condition, three biological replicates with three technical replicates each were performed. Principal Component Analysis (PCA) was the multivariate statistical analysis used to attest for difference significance between spectra of cells grown under different agitation speeds. The analysis was executed by XLSTAT software (version 2013.2, Addinsoft). 2.5.4 Cell surface chemistry analysis through X-ray photoelectron spectroscopy (XPS) The XPS analysis of the cell surface was performed in a similar manner as described by Mukherjee et al. (2011) and Ojeda et al. (2008) with slight alterations. Bacterial cells were washed three times with sterile distilled water, then 250 μL aliquot were taken and flash frozen in liquid nitrogen. Following that, cells were freeze-dried and submitted to XPS analysis. The equipment used for the referred analysis was an Ultra Photoelectron spectrometer (Kratos Axis 165, Shimadzu, UK) at 10 kV and 20 mA with an Al Kα X-ray source. Two biological replicates were obtained for each tested hydrodynamic condition at mid-exponential and stationary growth phase. For each assay, survey scan with an associated step-size of 1,0 eV and a high resolution scan with a step-size of 0,1 eV were performed. Both scans were carried out with pass energy of 20 eV. Spectra analysis and deconvolution was performed by CasaXPS software (2.3.15 version, Casa Software). The binding energies of the survey scan peaks were assessed using the carbon (C) peak at 285 25 eV. On the other hand, the binding energies determination associated to high resolution scan peaks were computed using C1s peak of 284,6 eV. The prediction of molecular composition was carried out by usage of a set of equations described by Rouxhet et al. (1994) (see Appendix D). 2.6 Auto-aggregation analysis In order to evaluate the influence of distinct hydrodynamic conditions on autoaggregation capability of A. calcoaceticus, a quantitative method based on spectrophotometry and a semi-quantitative one (visual assay) were employed for cells cultivated under distinct agitation speed at stationary phase. 2.6.1 Visual auto-aggregation assay The visual auto-aggregation assay was performed as described by Simões et al. (2008) with minor alterations. Cells were harvested by centrifugation at 5000×g for 20 min and washed twice with sterile 100 mM potassium chloride through centrifugation cycles of 4500×g for 10 min. Following that, cells were resuspended in sterile 100 mM potassium chloride at pH 7 until final OD at 600 nm of 1,500 was attained. Then, 4 mL suspension was transferred to a test tube. After vortexing for 10 s and rolling the tube for 30 s, degree of auto-aggregation was visually assessed according to the score scheme developed by Cisar et al. (1979) at 0 h, 2 h, 24 h and 48 h after suspension preparation. The score criteria employed throughout the assay are described as follows: value 0 for suspensions with no evidence of auto-aggregation; value 1 for finely dispersed flocs; value 2 for aggregates that form immediately but remain suspended in turbid background; value 3 for aggregates that settled giving rise to slightly cloud supernatant and value 4 for rapid and complete settling of large aggregates giving rise to a clear supernatant. The experiment was performed in biological duplicates for each hydrodynamic condition tested. 26 2.6.2 Auto-aggregation quantification The quantitative analysis of the auto-aggregation phenomenon was carried out as described by Phuong et al. (2008) with some alterations. Cells were washed and resuspended in 100 mM potassium chloride at pH 7 as previously described for the visual assay. Following that, 4 mL of suspension were placed in a test tube at room temperature and OD at 600 nm was measured at 0 h and 1 h after suspension preparation. The autoaggregation index (A%) was computed by Equation 2-4 as follows: ) (Equation 2-4) Where AB stands for OD at 600 nm at 0 h after suspension preparation and AA represents the OD at 600 nm at 1 h after suspension preparation. The auto-aggregation quantification was performed in biological triplicates for each hydrodynamic condition. 2.7 Outer membrane proteins analysis – Proteomic study In order to analyse the influence of different hydrodynamic conditions on the expression of proteins at the outer membrane of A. calcoaceticus, a bottom-up approach for proteins identification was carried out (Chen and Pramanik, 2009). Proteins associated to the outer membrane of cells cultivated under different agitation speeds were extracted, digested into smaller peptides and submitted to Liquid Chromatography coupled with Tandem Mass Spectrometry (LC-MS/MS) for further data processing and comparison with protein data base. The study was performed for cells at stationary phase. 2.7.1 Outer membrane protein isolation and analysis by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) The outer membrane proteins (OMP) were isolated according to the method described by Mukherjee et al. (2011) with slight alterations. After cell harvesting, cells were resuspended in 30 mL of tris(hydroxymethyl) aminomethane-hydrochloric acid (Tris-HCl) buffer adjusted to pH 7,5 with final OD at 600 nm of 1,000. Cells were washed twice with 50 mM Tris-HCl buffer and resuspended in 5 mL of the same buffer. Cell lysis occurred by sonication in ice bath comprising 6 cycles of 60 s each executed by an 27 ultrasonic cell disruptor (Branson 450, Emerson, UK). Non-lysed cells were removed by centrifugation at 5000×g for 10 min and obtained supernatant was submitted to centrifugation at 21000×g for 60 min. The pellet (mainly cell debris) was resuspended in 5 mL of Tris-HCl buffer and N-lauroylsarcosine sodium salt (Sigma-Aldrich, UK) was added to give final concentration of 1,5 % (v/v) in order to solubilize inner membrane proteins. The suspension is incubated for 20 min at room temperature. After incubation, suspension is centrifuged at 20000×g for 90 min. Resultant pellet containing OMP was resuspended in 1mL deionized water and stored at -20 ºC until required. Protein concentration prior SDS-PAGE analysis was assessed by NanoDrop UV-Vis spectrophotometer (2000c, Thermo Scientific, USA). The SDS-PAGE analysis with 12% (w/v) polyacrylamide was performed as described by Laemmli (1970). Twenty micrograms of protein per sample were electrophoresed with voltages of 80V and 180V for 20 min and 45 min, respectively. The applied molecular-weight protein marker was the SigmaMarker S8445 (Sigma-Aldrich, UK), which presents molecular weights ranging from 6,5 kDa to 200 kDa. Finally, staining process was carried out by addition of Bio-Safe Coomassie blue stain (Bio-Rad, UK) to the gel. For imaging purposes, gel was destained by successive washing steps with distilled water. 2.7.2 In-gel digestion of proteins into smaller peptides The in-gel protein digestion carried out in the present study was performed according to the method described by Shevchenko et al. (2006). Protein bands were excised, removed from the gel and placed in siliconized tube. Afterwards, protein bands were incubated with 200 μL of 100 mM ammonium bicarbonate (VWR, UK) for 30 min at room temperature with occasional vortexing and resultant supernatant was discarded. Following that, gel bands were incubated with 200 μL of 100 mM ammonium bicarbonate in 50% (v/v) acetonitrile for 30 min and that was repeated three times. This step was followed by incubation with 500 μL acetonitrile for 10 min at room temperature. Then, reduction process was performed by incubation with 100 μL of 10 mM dithiothreitol for 45 min at 56 ºC and resultant supernatant was discarded. Following an incubation with 500 μL acetonitrile for 10 min, alkylation process was carried out by incubating gel pieces with 100 μL iodoacetamide (55 mM iodoacetamide in 100 mM ammonium bicarbonate) 34 Table 3-1: Kinetic parameters associated to A. calcoaceticus cultivation in TSB at distinct agitation speeds, where μ and YX/S are specific growth rate and biomass yield in terms of substrate consumption, respectively. Speed (rpm) μ (h-1) YX/S (CFU.g-1 × 1011) 100 0,098 (±0,004) 30,80 (±1,52) 220 0,132 (±0,005) 3,54 (±0,04) 375 0,322 (±0,032) 4,11 (±0,06) Table 3-1 shows that bacteria tend to grow faster as agitation speed increases. Specific growth at lowest agitation speed is significant different (p<0,05) from the one obtained at highest agitation speed. Cutter and Stroot (2008) obtained a specific growth rate of 0,381 h-1 for the same organism cultivated at 250 rpm which is a comparable value to the ones obtained in the present study. Differences are probably due to different growth conditions like medium and aeration system. Regarding biomass yield, a reduction of around 8 times is observed from the lowest to the highest agitation speed. The values are in the same order of magnitude for 220 and 375 rpm assays. Assuming that higher agitation speeds leads to higher shear stress, it can be stated that the results are in agreement with the literature, since it was observed that remarkable biomass yield reductions are achieved by increasing shear stress (Lertpocasombut, 1991; Liu and Tay, 2001). It was hypothesized that hydrodynamic shear force may have an impact on cell metabolic pathway as it may regulate the substrate flux flowing between catabolism and anabolism (Liu and Tay, 2002). Aiming to verify the catabolic activity of cells grown under different agitation speeds, respirometry assays were carried out and results are presented in Figure 3-3. 35 Figure 3-3: Total respiratory activity across different agitation speeds at distinct growth phases. (A) refers to mid-exponential phase and (B) mid-stationary phase. Error bars represent standard deviation. According to Simões et al. (2005), the respirometry is a reliable method for assessing the metabolic state and respiratory activity in terms of oxygen consumption for heterotrophic aerobic bacteria. Comparison of respiratory activity at mid-exponential phase shows that bacteria grown under 100 rpm had a significantly lower metabolic activity than at higher agitation speeds (p<0,05). Biomass yield and specific growth rate were obtained taking into account data obtained at exponential phase, which makes correlation with total respiratory activity at the same growth phase to be possible. Therefore, it is possible to affirm that low agitation speed, consequently, lower shear stress led to high biomass yield with concomitant low respiratory activity. These results seem to be in agreement with the literature (Liu and Tay, 2002; Simões et al., 2007). The differences between cell growth and metabolic activities across different agitation speed may be the result of interplay between physical and biological phenomena. The first is composed by mass transfer which involves bioconvection and diffusion of essential nutrients (sugars and oxygen) that could increase consumption rate in an environment with proper mixing, whereas the latter can be characterized by a stimulation of catabolism over anabolism (energy uncoupling) as shown in the literature 0,00 0,40 0,80 1,20 1,60 2,00 A B Total respiratory activity (mgO2.g biomass.min-1) 100 RPM 220 RPM 375 RPM 36 by proton translocation activity experiments (Liu and Tay, 2001). Moreover, Russell and Cook (1995) showed that an excess of proton translocation cycles at the cell membrane under high agitation speeds takes place when there is an imbalance between catabolic and anabolic rates and that it could be responsible for energy spilling and, consequently, lower growth efficiency. It is noteworthy that total respiratory activities from 220 rpm to 375 rpm at stationary phase are significant different, which shows the enhancement of total respiratory activity for higher agitation speed. A significant increase of activity is also observed for the experiment at 100 rpm in stationary growth phase. It may be explained by the fact that at stationary phase bacteria does not grow any longer and substrate is channelled exclusively for maintenance purposes, i.e. only catabolism occurs. On the other hand, the anabolic process appears to be more active at exponential phase 3.2 Influence of hydrodynamics on cell hydrophobicity Hydrophobicity at cell surface has been shown in the literature to play a major role on bacterial adhesion potential, which can be crucial for its physiology, cell mobility, colonization and formation of cohesive structures like granules and biofilms (Breiner et al., 2006). In terms of methods for cell hydrophobicity evaluation, MATH assay is one of the most commonly employed methods due to its relative simplicity and relation with adhesion (Phuong et al., 2009; Rosenberg and Doyle, 1990). Therefore, this method was carried out in the present study to evaluate influence of different hydrodynamic environments on bacterial cell surface hydrophobicity. The results are presented in Table 3-2. 37 Table 3-2: Hydrophobicity across different agitation speeds and growth phases obtained by MATH method. Speed (rpm) Growth phase Hydrophobicity Index (%) 100 Mid-exponential 16,2 (±1,7) Mid-stationary 6,1(±1,6) 220 Mid-exponential 11,8(±1,2) Mid-stationary 1,9(±0,7) 375 Mid-exponential 7,8(±0,6) Mid-stationary 3,3(±0,5) Since operational parameters applied throughout the experiment minimize electrostatic interactions effects (Rosenberg, 2006), obtained hydrophobicity index can be regarded mainly to hydrophobicity at cell surface. According to Tahmourespour et al. (2008), cell surface is regarded as hydrophobic when hydrophobicity index exceeds 50%. Hence, A. calcoaceticus presented mainly hydrophilic surface at all tested agitation speeds. It is shown that hydrophobicity at mid-exponential phase tends to decrease significantly (p<0,05) with increasing agitation speeds. It is important to notice that higher heterogeneity is observed for bacteria cultivated at 100 rpm when comparing standard deviations. Hydrophobic interactions represent the attractive force component of the Lewis acid-base interactions, which is mainly based on electron acceptor-electron donor interactions between polar moieties (Van Oss, 1994). Taking that into account, it is plausible to affirm that the differences observed in terms of hydrophobicity along hydrodynamic condition changes may be a reflect of different chemical polar moieties at the cell surface. Hydrophobicity plays a role on cell interaction with the surrounding environment, namely cell-cell interactions and self-aggregation phenomenon. For instance, the higher hydrophobicity obtained for the lowest agitation speed would cause decrease of excess Gibbs energy associated to the cell surface. That way, cell-cell interactions would be favored in a thermodynamic point of view. Furthermore, it was reported in the literature that adhesion of cells to hydrocarbons may occur via proteins as it was shown that A. calcoaceticus RAG-1 adhesion occurs via fimbriae (Rosenberg et al., 1982) and Acinetobacter spp. A3 occurs through two proteins of 26,5 kDa and 65 kDa (Hanson et al., 1994). Based on that, it can be hypothesized that different agitation speeds 38 may also induce changes at cell membrane proteins expression level, which will be discussed later in this section. Controversially, Tay et al. (2001) found that hydrophobicity of cell surface is improved by increasing shear force and that significant difference was observed prior and after granulation. The reason for the differences between these results and the ones obtained in the present work may be mainly associated to granulation extension differences (higher for Tay and Liu, 2001), different experimental set-up (aerated vs nonaerated system; media composition) and distinct operational parameters for MATH assay. The latter has been shown to be significantly sensitive to operational parameters like hydrocarbon-aqueous phase volume ratio, vortex duration and buffer ionic strength (Saini, 2010). Besides, it is possible that the magnitude of applied shear forces is distinct between two studies. Finally, a hydrophobicity decrease from mid-exponential to mid-stationary phase was observed for all the assays, where less pronounced decrease was attained at 375 rpm (ca. 58% difference). Palmgren et al. (1998) demonstrated that oxygen limitation is generally responsible for cell surface hydrophobicity reduction. At stationary phase, dissolved oxygen is probably scarce, especially at lower agitation speeds where mixing can be affected, which may be causing the observed hydrophobicity lowering. 3.3 Hydrodynamic condition influence on cell electrophoretic mobility (EPM) It is well known that bacteria interfacial physiology is very important for organism viability, since it tends to channel portions of its metabolic energy for synthesis and maintenance of essential macromolecules (Beveridge and Graham, 1991). The latter have an impact on the net electronegativity associated to cell surface, which has influence on cell polarity and interaction with substrates in surrounding environment (Wilson et al., 2001). The charge at cell surface is the result of pH dependent ionization of macromolecules functional groups, thus analysis of surface charge gives a hint about the chemistry on cellular interface (Hong and Brown, 2008). Due to a significant heterogeneity at the cell surface and the fact that evaluation of electrostatic charge of colloids (such as bacteria) is a challenging task, indirect methods 39 like electrophoretic light scattering, which relates to bacterial electrophoretic mobility, were developed (Lytle et al., 1999). In the present study, the influence of different agitation speeds on cell interface chemical moieties and charge were investigated and results are presented in Figure 3-4 for mid-exponential and stationary phase as follows: Figure 3-4: Electrophoretic mobility of A. calcoaceticus across pH range of 2,0 to 7,0 for distinct agitation speeds. (A) refers to mid-exponential phase and (B) stationary phase. Grey bars correspond to standard errors. A B 40 From the literature, net cell surface charge is proportional to its electrophoretic mobility (Marshal, 1976). So, it can be stated that a surface charge increase occurred for all the conditions. Higher surface charge is attained at pH 7 for every condition, except for 375 rpm at stationary phase condition, which is significantly different (p<0,05, see Appendix A) from the ones obtained for other agitation speeds (see Figure 3-4). Assuming that experimental conditions apart from pH (e.g. ionic strength, cell density, washing step etc.) were kept constant throughout the experiment and the fact the organism under study is Gram-negative, the observed decreasing trend of electrophoretic mobility along pH can be mainly attributed to high presence of ionized phosporyl and carboxylate functional groups (characterized as anionic with acidic pKa) of lipopolyssacharides (LPS) located in the outer membrane (Rijnaarts et al. 1995; Beveridge and Graham, 1991). Significant differences between electrophoretic mobilities (p<0,05) were found by comparison of 100 rpm and 375 rpm agitation speeds at pH 2 and 6 in mid-exponential phase. On the other hand, significant differences were obtained across different agitation speeds at pH 7 with cells in stationary phase. Those variations may be mainly associated to subtle differences in terms of functional group moieties type and quantity at cell surface (Hong and Brown, 2008). Moreover, interactions between charged moieties play a role as pKa of an anion is increased by anionic-anionic interactions and decreased by anionic-cationic interactions (Rijnaarts et al., 1995; Soon et al., 2011). For instance, differences found at pH 7 for cells in stationary phase can be caused by an increase of functional groups with a basic pKa (such as amines) on surface of cells grown under 375 rpm. Amines can be associated to proteins, thus observed distinct electrophoretic mobility values suggest differences in terms of protein content associated to the cell outer membrane. The cells were submitted to washing steps so extracellular polymeric substances (EPS) at the surface would be removed. Therefore, it can be assumed that differences observed were exclusively due to distinct functional groups at cell interface. From these results, it is plausible to affirm that different hydrodynamic conditions seem to have an impact on functional groups moieties, which in turn are shown to influence cell surface charge. The electron transport system activity is strongly related to catabolic processes of microorganisms; and for aerobic organisms it has been shown that respiratory activity is coupled to proton translocation phenomenon with consequent oxygen reduction (Babcock 41 and Wikstrom, 1992). Basically, higher respiratory activities lead to a higher extension of protons translocated through cell membrane and acidification of the medium (Chen and Huang, 2000). Bearing that in mind, the higher accumulation of positive charge observed for bacterial cells under 375 rpm in stationary phase may also be associated to a higher proton translocation through cell membrane. This translocation phenomenon can be attributed to the stimulation of total respiratory activity as observed for bacterial cells cultivated at 375 rpm (see Figure 3-3). Difference of mobilities at pH 7 between growth phases was also evident and it can be a consequence of lipids and lipopolysaccharide conformation and composition changes (El-Khani and Stretton, 1981; Bravo et al., 2008). For example, molecular modeling suggested that LPS assumes a “bent” conformation in such manner O-antigen region covers other adjacent surface-bound molecules which could shield charged regions at the surface (Kastowsky et al., 1992; Walker et al., 2004). It is highly likely that differences at pH 7 across agitation speeds can be related to distinct LPS moieties at cell surface, which will be discussed later. Hong and Brown (2009) showed that proton motive force across membrane and adenosine triphosphate (ATP) formation are altered by a variation of proton concentration at cell surface caused by charge regulation effects, which occurs between ionizable molecules containing surface. They reported that as bacterium approaches to a positively charged surface, pH at interface (distinct from pH at the bulk) increases and leads to an inverse pH gradient reducing the proton motive force, which would eventually cause cell death and detachment. Considering bacteria as a biotic surface, it can be hypothesized that hydrodynamic stress may also have an influence on cell adhesion potential as charge regulating phenomenon between cells and surfaces are altered by increasing proton concentration on cell interface, which in turn is promoted by high agitation speed. Lastly, the isoelectric point (pH at which cell surface net charge is null) was not attained in the present work. Nevertheless, it can be seen that the isoelectric point is probably lower than 2,0, which suggests significant amounts of phosphate moieties at cell surface. 42 3.4 Influence of hydrodynamics on outer membrane chemistry – ATRFTIR spectroscopy Biophysical methods such as FTIR spectroscopy have been successfully used for identification and classification of bacteria as well as cell surface analysis (Naumann et al., 1991; Jing et al., 1994). Among the innumerous advantages of this method, costand time-effectivity, sensitivity and low biomass requirements are noteworthy (Becker et al., 2006). This method is performed at the mid-IR electromagnetic spectrum region (4004000 cm-1) and based on passage of IR radiation through a sample where specific wavelengths are absorbed causing chemical bonds vibration (stretching, bending and contracting). Since functional groups absorb in the same wavenumber in an independently manner compared to the rest of the macromolecules, correlation between band positions and functional groups in the molecule is possible (Smith, 1996). IR spectra obtained for cells are complex and represent many superpositions of contributions from biomolecules present in a bacterial cell, which makes spectra to look similar for all bacteria but with subtle differences that are consequence of a different molecule composition often used for bacteria identification (Davis and Mauer, 2010). Besides, Jiang et al. (2004) concluded that different spectra obtained through ATR-FTIR spectroscopy are due to different properties of the cell wall and not interior of cell, which validates the method as physicochemical analysis of cell surface. Peak assignment to functional groups is a crucial step in ATR-FTIR spectroscopy analysis. Naumann (2000) described major absorbance regions for biological samples, which are highlighted in Table 3-3. Table 3-3: Assigment of band regions commonly found in biological sample IR spectra. Adapted from Naumann (2000). Wave number region (cm-1) Assigment 1800-1500 Conformation-sensitive amide I and amide II bands Around 1400 −COOfunctional groups associated to aminoacids and free fatty acids 1500-1300 >CH2 and −CH3 bending associated to proteins and lipids 1200-900 Symmetric stretching of ; C−O−C and C−O−P stretching vibration associated to oligoand polysaccharides 43 In this context, ATR-FTIR spectroscopy was performed in the present study to identify specific functional groups and physicochemical properties variations associated to the cell surface in order to have a deeper insight about the influence of hydrodynamic conditions on bacteria. The spectra of A. calcoaceticus at stationary phase under distinct agitation speeds are shown in Figure 3-5. Figure 3-5: ATR-FTIR spectra for A. calcoaceticus grown under distinct agitation speeds at stationary growth phase. Blue line represents cells grown at 100 RPM; red line cells at 220 RPM and green line cells at 375 RPM. From the spectra, it can be seen that cell surface differences across agitation speed are especially in amide I and amide II bands (from 1500 to 1800 cm-1) and polysaccharide-associated ether functional groups (1200 to 900 cm-1). Additionally, slight differences are observed in the region between 1300 and 1400 cm-1, which denotes different moieties of –CH2 and –CH3 associated to lipids and proteins as well as amino acid side-chains and free fatty acids (Naumann, 2000). In order to evaluate the statistical significance of the observed differences, PCA was carried out and the results are depicted in Figure 3-6. PCA is a multivariate statistical analysis tool that reduces multidimensionality of data into most relevant components and highlights variations between samples (Rodriguez-Saona et al., 2004). Over the range 0 1 2 3 4 900100011001200130014001500160017001800 Absorbance units Wavenumber (1/cm) 100 RPM 220 RPM 375 RPM 50  tends to be similar within 100 to 220 rpm range and decreases at 375 rpm (see Table 3-4). Thus, obtained results for XPS seems to be in agreement with the ones obtained by EPM measurements as less negative cell surface at pH 7 was attained at 375 rpm on stationary growth phase. Moreover, it is an evidence of contribution of distinct carboxyl group moieties at cell surface, which is induced by different hydrodynamic environments, to differences in terms of observed electrophoretic mobility. The cell hydrophobicity has been associated to certain functional groups at the surface and correlation between     ));( ) and ) with hydrophobicity was established in the literature (Mozes et al., 1998; Hamadi et al., 2008). Whilst the latter has been found to be directly correlated with hydrophobicity, the first two functional groups were inversely correlated owing it to the presence of O and N, which favours hydrogen bonds. Taking that into account, it can be stated that results obtained by XPS are in agreement with the ones obtained by MATH assay in the present study. For example, increasing     )) functional group concentration with agitation speed observed at both growth phases can explain the decreasing hydrophobicity. Despite Marshal et al. (1994) warning regarding the errors that may be associated to relationships between XPS data and cell surface properties for Gram-negative cells, obtained results are in agreement with previous experiments. Rouxhet et al. (1994) established that results obtained by XPS for chemical functions can be worked out further in order to predict the molecular composition associated to the cell surface. Basically, they developed a set of equations (see Appendix D) in which parameters obtained by XPS are represented as function of model constituents, such as protein (Prot), polysaccharide (Poly) and hydrocarbon-like compounds (Hydroc). By using a set of equations proposed by Rouxhet et al. (1994), the results regarding bonding environment gave rise to ratios of protein, hydrocarbon-like compounds and polysaccharides with respect to total carbon were assessed and results at distinct growth phases are shown as follows. 51 Figure 3-7: Cell surface molecular composition in terms of cell fraction occupied by cell wall main constituents. (A) refers to mid-exponential phase and (B) stationary phase. It can be seen in Figure 3-7 that macromolecules profile is different across agitation speeds. At mid-exponential phase, the hydrocarbon-like compounds are the predominant constituents of cell surface, except at 220 rpm, which possess higher amounts of proteins. On the other hand, protein levels are much higher at stationary phase and tend to increase with agitation speed. It is noteworthy that polysaccharides tend to increase with increasing agitation speeds for both growth stages and confirms previous results presented in this section. Walker et al. (2004) reported that polysaccharides 0,00 0,10 0,20 0,30 0,40 0,50 0,60 C(Prot)/C C(Poly)/C C(Hydroc)/C A 100 RPM 220 RPM 375 RPM 0,00 0,10 0,20 0,30 0,40 0,50 0,60 C(Prot)/C C(Poly)/C C(Hydroc)/C B 100 RPM 220 RPM 375 RPM 52 exposed on the outer membrane represent an important factor controlling bacterial adhesion and transport, thus it can be suggested from these results that hydrodynamic environment may have an impact on cell adhesion processes not only by physical component (detachment and attachment) or force interaction with lectin-like adhesin FimH (Thomas et al., 2002), but through biological modifications in terms of macromolecules types at the surface. Similarly to these results, Tay et al. (2001b) reported that high shear stress stimulated production of cellular polysaccharide, which were shown to be associated to granulation phenomenon. Although main constituents could be predicted by this method, it was not possible to differentiate phospholipids from other lipid compounds nor polysaccharides and lipopolysaccharides. Furthermore, it should be noticed that the cell surface composition prediction may not reflect real surfaces, since cells were submitted to freeze-drying which may change the cell surface or even lead to cell disruption. 3.6 Hydrodynamics influence on auto-aggregation capability It is well known that A. calcoaceticus is an organism capable of autoand coaggregate as its bridging function for biofilm formation was reported (Simões et al. 2008). Auto-aggregation and co-aggregation phenomena have been shown to be important processes on coherent structure formation like biofilms and represent an important part of bacteria collective behavior. Hence, the influence of different hydrodynamic environments on aggregation feature of A. calcoaceticus at stationary phase was investigated through quantitative (spectrophotometric assay) and semi-quantitative (visual assay) methods. The results are shown in Table 3-6. Table 3-6: Auto aggregation index for A. calcoaceticus at stationary phase cultivated under different agitation speeds along with co aggregation scores over time obtained by visual assay. Speed (rpm) Auto-aggregation after 1h (%) Score after 2h Score after 24h Score after 48h 100 3,40 (±1,06) 2 4 4 220 2,71 (±0,74) 1 3 4 375 0,62 (±0,31) 0 2 3 53 It can be noted that auto-aggregation was affected by different agitation speed, since auto-aggregation index tends to reduce considerably when agitation speed is increased from 220 rpm to 375 rpm. Nevertheless, differences of auto-aggregation index among distinct agitation speeds were not significant different (p>0,05). The visual assay corroborates the results obtained for the auto-aggregation index as faster settleability was achieved at lower agitation speed (see also Fig B.3 in Appendix B). Although visual assay has been associated to lack of sensitivity (Elliot et al., 2006), reproducible results were obtained and are in agreement with auto-aggregation trend revealed by the spectrophotometric assay. The capability of cells to autoand co-aggregate is associated to its adhesion potential, which depends mainly on Van der Waal (attractive), electrostatic (repulsive) forces and Lewis acid-base interactions (hydrophobic and steric interactions) (Olofsson et al., 1998; Saini, 2010). Additionally, specific interactions that involve adhesins and complementary receptors, more specifically lectin-saccharide interactions, were shown to be crucial for the auto-aggregation process (Buswell et al., 1997). In the literature, hydrophobic interactions have been emphasized as a driving-force for cells to initiate bacterium-bacterium contact out of the liquid phase and have been correlated directly with aggregation phenomena (Liu and Tay, 2002; Phuong et al., 2009). In this context, the obtained result agrees very well with one obtained by MATH assay as cell grown at slowest agitation speed presented higher hydrophobicity and auto-aggregation index, suggesting that hydrophobicity was the predominant interaction upon auto-aggregation. It was shown by XPS that cells grown in different hydrodynamic conditions possess different surface chemistry with different amounts of proteins, polysaccharides and even hydrocarbon-like compounds. Since specific interactions between lectin and saccharide are important to auto-aggregation as well as the densities of those ligands on cell surface (Simões et al., 2008; Buswell et al., 1997), it is possible to infer that hydrodynamic environment may have an impact on the moieties and distribution of those specific ligands at cell surface. The autoand co-aggregation collective behavior has clear advantages to microorganisms, like ease transfer of chemical signals, exchange of genetic information and higher metabolic cooperation (Wimpenny and Colasanti, 2004). Lower agitation speed is associated to lower mass transfer of important metabolites and formation of nutrient gradients. Therefore, it can be hypothesized that higher auto-aggregation 54 extension for cells cultivated at 100 rpm is part of mechanism by which cells undergo in order to overcome mass transfer issues. 3.7 Outer membrane protein (OMP) variations with distinct hydrodynamic conditions Proteome is part of a dynamic system by which cells use to adapt to different environments. Proteins represent cellular building blocks that show potential function of genes through cellular processes ranging from enzymatic catalysis to molecular signaling (Yates et al., 2009). In this study, the outer membrane proteins (OMP) of cells grown under different agitation speeds at stationary phase were separated and identified by LC/MS in bottom-up approach. Extracted proteins were submitted to SDS-PAGE analysis prior separation and result is presented in Figure B.2 (in Appendix B). It is noteworthy that protein profile is comparable among agitation speeds, where major differences are only in terms of band intensity in the region ranging from 29 kDa to 20 kDa. Since lanes represent duplicates for each condition, differences between duplicates within a condition may represent certain heterogeneity in terms of protein expression, in this case, higher heterogeneity was attained for cells cultivated at 100 rpm. Further analysis through LC/MS separation and direct search against A. calcoaceticus protein database allowed identification of extracted proteins. Identified OMP with respective accession number for each tested condition are found in Appendix C. For 100 rpm, 220 rpm and 375 rpm conditions, 17%, 26% and 27% of total identified proteins were OMP, respectively. This result seems to agree well with the prediction carried out through XPS analysis as at 100 RPM less proteins were found at the cell surface. The number of OMP and respective functions were obtained for each condition and are presented in Figure 3-8. 55 Figure 3-8: Number of OMP with respective function identified for A. calcoaceticus grown under different agitation speed at stationary phase. Apart from uncharacterized proteins, it is noticeable that major differences among agitation speeds rely on proteins that have transport and receptor functions. Bacterial cells cultivated at higher agitation speeds showed a higher number of those proteins compared to 100 rpm condition. It is known that transport processes play an important role in cellular metabolism, since uptake of nutrients, excretion of toxic metabolites and maintenance of ion concentration depend on it (Burkovski and Kraemer, 2002). Receptor proteins bind to specific molecules, which lead to conformation changes and control of a multitude of cellular activities, i.e., receptor proteins represent a connection between cell internal and external environments (Mitchel, 2013). Therefore, it seems that cells grown under higher agitation speeds have more complete apparatus to sense the environment, which may yield to rapid metabolic responses to environmental stress. TonB-dependent receptor (F0KNU8_ACICP); putative hemolysin activator (F0KPM4_ACICP); putative outer membrane copper receptor (F0KJZ1_ACICP) were receptor/transport proteins only found for 220 and 375 rpm conditions. The first protein is associated to TonB-dependent transport, which involves a TonB complex that transduces proton motive forces of the cytoplasm to energize transport of rare nutrients such as vitamin B12, iron and nickel transport across the outer membrane (Schauer et al., 2008; Schauer et al., 2007). This complex is often associated to pathogenicity factor because 0 2 4 6 8 10 OMP Number 100 RPM 220 RPM 375 RPM 56 bacteria survival chances in their hosts will rely on the ability to compete for micronutrients (Miethke and Marahiel, 2007). Apart from TonB role on molecules transportation, it was found that TonB complex is also involved in recognition and binding of cells to fibronectin, which allows effective colonization of niches in a given host (Pauer et al., 2009). Putative hemolysin activator is a protein predicted by bioinformatics tools, which are thought to be involved in transport mechanism that is associated to transcription activation of hemolysin, prototype of homologous RTX family of toxins secreted by Gram-negative bacteria (UniProt Consortium, 2013; Langston et al., 2004). The putative outer membrane copper receptor was also predicted to be part of TonB complex and can be regarded as fibronectin binding protein, which has been shown to be crucial for bacteria adhesion to fibronectin surfaces and pathogenicity of Acinetobacter baumanii (Smani et al., 2012). In general, it is plausible to affirm that those proteins are strongly involved with cell pathogenicity, adaptation and survival. Hence, it corroborates results found in the literature where higher activity and more diverse metabolic mechanisms were suggested for cell cultivated under turbulent regimes (Liu and Tay, 2002; Simões et al. 2007). Zhan et al. (2011) reported that A. calcoaceticus PHEA-2 acquired many genes associated to cell adaptation through horizontal gene transfer. Furthermore, Hendrickx et al. (2000) showed that A. calcoaceticus BD413 subjected to high shear stress displayed higher transformation frequency in terms of ratio of cells transformed to recipient cells. Bearing that in mind, it can be hypothesized that lack of most transport/receptor proteins at 100 rpm may be associated to low horizontal gene transfer due to low shear stress and mass transfer effects and clearly it has an impact on cell survival dynamics. In terms of oxireductases, two proteins were identified at 100 rpm and 375 rpm and none at 220 rpm. Electron transfer flavoprotein alpha-subunit (F0KL87_ACICP) and Malate dehydrogenase (F0KPX6_ACICP) were the identified proteins. The first is a soluble heterodimeric FAD-containing protein which functions as electron carrier between diverse flavoprotein dehydrogenases and are strongly associated to membranebound respiratory activity (Toogood et al. 2007). The latter is an enzyme involved in the reversible catalysis of reversible oxidation of malate to oxaloacetate, which is part of tricarboxylic acid cycle associated to aerobic respiration pathway (Eprintsev et al. 2004). According to Trémoulet et al. (2002), malate dehydrogenase is highly regulated in order to adapt to different growth conditions and is involved in biofilm growth. Shin et al. (2009) demonstrated that proteins involved in metabolism like malate dehydrogenase are 57 up-regulated when Acinetobacter baumannii 1656-2 undergo biofilm formation. Therefore, the fact that those proteins were only identified for 100 rpm and 375 rpm conditions (both regarded as stress conditions) may be due to an up-regulation of those proteins caused by cell trend to form biofilm. In this case, a biofilm formation study in a flow cell reactor with hydrodynamic environments similar to the ones applied in this study would represent a huge asset. Interestingly, the presence of those proteins involved in cellular respiration is in agreement with results obtained for respirometry assay (see Figure 3-3) because higher respiration activities were attained at stationary phase for 100 RPM and 375 rpm conditions (as opposed to mid-exponential phase). Proteins exclusive to each condition were identified and are presented in Table 3-7 as follows. Table 3-7: Proteins identified exclusively for each condition. Speed (rpm) Accession number Protein PSORTb score for outermembrane location 100 F0KLX8_ACICP Succinyl-CoA ligase [ADP-forming] subunit beta 0,05 220 F0KPM3_ACICP Putative member of ShlA/HecA/FhaA exoprotein family 9,95 F0KGT9_ACICP Ferric enterobactin receptor 10 375 F0KHB5_ACICP Putative ferric siderophore receptor 10 It is shown that proteins identified for 220 rpm and 375 rpm are associated to iron metabolism of cells as Gram-negative bacteria acquire iron through elaboration of chelating agents (e.g. ferric siderophores) and uptake of the ferric complex by specific receptors, which are TonB-dependent outer membrane receptor proteins (Cae et al., 2000; Payne et al. 1997). Differences in terms of iron metabolism can be inferred, which seems to be influenced by the agitation speed. The iron metabolism is often associated to pathogenicity of bacteria, thus it is highly probable that bacteria cultivated under higher speed agitations are more pathogenic and, consequently, more metabolically active. On the other hand, the protein identified only for 100 rpm condition catalyzes a reversible reaction of succinyl-CoA to succinate, which is important for substrate-level 58 phosphorylation step of tricarboxylic acid cycle (Nishimura, 1986). Despite being known as cytoplasmic protein, Thomas (1974) observed that diglyceride kinase activity of isolated E. coli vesicles at cell membrane is mediated by succinyl CoA synthetase, which can appear at the inner membrane. Diglyceride kinase is an enzyme involved in the biosynthesis of membrane phospholipids (Raetz and Newman, 1979). It can be suggested that cells at 100 rpm may also differ from the ones grown at higher agitation speeds in terms of inner membrane phospholipids formation dynamics, which can even contribute to distinct cell mechanical stability. In order to have a clearer picture about the influence of different hydrodynamic conditions on bacteria proteome and their metabolic pathways, it would be interesting to perform quantitative proteomic studies not only at the level of outer membrane, but at cytoplasmic level as well. 59 4. Conclusions and future work perspectives The outcomes of hydrodynamic conditions on bacteria behavior have been extensively studied in the literature mainly at cohesive structure formation level. In order to have a clear picture about hydrodynamics impact on cell growth, physiology and dynamics, the present study focused on the influence of different agitation speeds on bacteria at planktonic state growth and metabolic activity as well as on cell surface properties. In terms of growth kinetics and metabolic activity, it was observed that cells grown under lower agitation speed had lower growth rate with lower reducing consumption and total respiratory activity. Those results were in agreement with the ones found in the literature and confirm that hydrodynamics have influence on cell catabolism and anabolism, as microbial cells cultivated under 100 rpm seemed to direct carbon mainly to anabolic pathways, which gives rise to higher biomass yield in terms of reducing sugars consumption with concomitant lower respiratory activity. The observed uncoupling between anabolic and catabolic processes can be regarded as one of the evidences that attest for biological component associated to hydrodynamics. Microbial cell surface represents a crucial interaction interface between cells and biotic/abiotic surfaces and as such, possess important roles on microbial cell growth dynamics. This study showed that cell surface present different chemistry for different hydrodynamic environments, which may contribute to observed differences in terms of cell surface hydrophobicity and surface charge. Since those properties are assumed to be relevant for cell interactions, it can be concluded that cells may present distinct interactions with its surroundings depending on the hydrodynamic conditions. Differences in terms of macromolecules moieties at the surface were mainly associated to polysaccharides, which were shown to increase significantly with increasing agitation speed conditions. Due to the fact LPS is a major constituent of Gram-negative cells outer membrane, differences in polysaccharide content can be associated to different moieties of LPS at outer membrane, which may impart different features to cell surface. 66 Pereira, M.; Kuehn, M.; Wuertz, S. et al. (2002). Effect of flow regime on the architecture of a Pseudomonas fluorescens biofilm. Biotechnology and Bioengineering. Vol. 78, pp.: 164-171. Phan-Thien, N.; Tran-Cong, T. and Ramia, M. (1987). A boundary-element analysis of flagellar propulsion. Journal of Fluid Mechanics. Vol. 184, pp.: 533-549. Phuong, K.; Kakii, K. and Nikata, T. (2009). Intergeneric coaggregation of non-flocculating Acinetobacter spp. isolates with other sludge-constituting bacteria. Journal of Bioscience and Bioengineering. Vol. 107, pp: 394-400. Phuong, K.; Kakii,K. and Nikata, T. (2009). Intergeneric coaggregation of non-flocculating Acinetobacter spp. isolates with other sludge-constituting bacteria. Journal of Bioscience and Bioengineering. Vol. 107, pp: 394-400. Pratt L.; Kolter, R. (1998). Genetic analysis of E. coli biofilm formation: roles of flagella, motility, chemotaxis and type I pili. Molecular Microbiology. Vol. 30, pp.:285–293. Purevdorj, B.; Costerton, J. and Stoodley, P. (2002). The influence of hydrodynamics and cell signaling on the Structure Behavior of Pseudomonas aeruginosa Biofilms. Applied and Environmental Microbiology. Vol. 68, pp.: 4457-4464. Raetz, C. and Newman, K. (1979). Diglyceride kinase mutants of Escherichia coli: inner membrane association of 1,2Diglyceride and its relation to synthesis of membrane-derived oligosaccharides. Journal of Bacteriology. Vol. 137, pp: 860-868. Ramia, M.; Tullock, D. and Phan-Thien, N. (1993). The role of hydrodynamic interaction in the locomotion of microorganisms. Biophysics Journal. Vol. 65, pp.: 755-778. Ramstedt, M.; Nakao, R.; Nyunt Wai, S. et al. (2010). Monitoring surface chemical changes in the bacterial cell wall. Journal of Biological Chemistry. Vol. 286, pp: 12389-12396. Rijnaarts, H.; Norde, W.; Lyklema, J. et al. (1995). The isoelectric point of bacteria as an indicator for the presence of cell surface polymers that inhibit adhesion. Colloids and Surfaces B: Biointerfaces. Vol. 4, pp: 191-197. Rochex, A., Godon, J.; Bernet, N. et al. (2008). Role of shear stress on composition, diversity and dynamics of biofilm bacterial communities. Water research. Vol. 42, pp.: 4915-4922. Rodriguez-Saona, L.; Khambaty, F.; Fry, F. et al. (2004). Detection and identification of bacteria in a juice matrix with Fourier transform-near infrared spectroscopic and multivariate analysis. Journal of Food Protection. Vol. 67, pp: 25552559. Rosenberg, M and Doyle, R. (1990). Microbial cell surface hydrophobicity: history, measurement and significance. ASM. Washington DC, United States of America. Rosenberg, M; Bayer, E.; Delarea, J. et al. (1982). Role of thin fimbriae in adherence and growth of Acinetobacter calcoaceticus RAG-1 on hexadecane. Applied and Environmental Microbiology. Vol. 44, pp: 929-937. Rosenberg, M. (2006). Microbial adhesion to hydrocarbons: twenty-five years of doing MATH. FEMS Microbiology Letters. Vol. 262, pp: 129-134. Rosenberg, M.; Gutnick, D. and Rosenberg, E. (1980). Adherence of bacteria to hydrocarbons: a simple method for measuring cell-surface hydrophobicity. FEMS Microbiology Letters. Vol. 9, pp: 29-33. Rouxhet P.; Mozes N. (1990). Physical chemistry of the interaction between attached microorganisms and their support. Water Science Technology. Vol. 22, pp.: 1–16. Rouxhet, P.; Mozes, N.; Dengis, P. et al. (1994). Application of X-ray photoelectron spectroscopy to microorganisms. Colloids and Surfaces B. Bionterfaces. Vol. 2, pp: 347-369. Russell, J. and Cook, G. (1995). Energetics of bacterial growth: balance of anabolic and catabolic reactions. Microbiology Review. Vol. 59, pp: 48-62. Saini, G. (2010). Bacterial hydrophobicity: Assessment techniques, applications and extension to colloids. Oregon State University. Oregon, United States of America. Schauer, K.; Gouget, B.; Carrière, M. et al. (2007). Novel nickel transport mechanism across the bacterial outer membrane energized by the TonB/ExbB/ExbD machinery. Molecular Microbiology. Vol. 63, pp: 1054-1068. Schauer, K.; Rodionov, D. and de Reuse, H. (2008). New substrate for TonB-dependent transport: do we only see the “tip of the iceberg”?. Trends in Biochemical Science. pp: 1-9. 67 Shevchenko, A.; Tomas, H.; Havlis, J. et al. (2006). In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nature Protocols. Vol. 1, pp: 2856-2860. Shin, J.; Lee, H.; Kim, S. et al. (2009). Proteomic analysis of Acinetobacter baumannii in biofilm and planktonic growth mode. The Journal of Microbiology. Vol. 47, pp: 728-735. Simões, L.; Azevedo, N.; Pacheco, A. et al. (2006). Drinking water biofilm assessment of total and culturable bacteria under different operating conditions. Biofouling. Vol. 22, pp: 91-99. Simões, L.; Simões, M. and Vieira, M. (2008). Intergeneric coaggregation among drinking water bacteria: evidence of a role for Acinetobacter calcoaceticus as a bridging bacterium. Applied and Environmental Microbiology. Vol. 74, pp: 1259-1263. Simões, L.; Simões, M.; Oliveira, R. et al. (2007). Potential of the adhesion of bacteria isolated from drinking water to materials. Journal of Basic Microbiology. Vol. 47, pp: 174-183. Simões, M.; Pereira, M. and Vieira, J. (2005). Validation of respirometry as a short-term method to assess the efficacy of biocides. Biofouling. Vol. 21, pp: 9-17. Simões, M.; Pereira, M. and Vieira, M. (2004). Cellular changes due to biofilm formation - influence of flow regime. International Conference: Biofilms 2004. Las Vegas, United States of America, pp.: 105-109. Simões, M.; Pereira, M.; Sillankorva, S.; et al. (2007). The effect of hydrodynamic conditions on the phenotype of Pseudomonas fluorescens biofilms. Biofouling. Vol. 23, pp.: 249-258. Simões, M.; Pereira, M.and Vieira, M. (2003). Monitoring the effects of biocide treatment of Pseudomonas fluorescens formed under different flow regimes. Water Science Technology. Vol. 47, pp.: 217-223. Simões, M.; Simões, L. and Vieira, M. (2008). Physiology and behavior of Pseudomonas fluorescens single and dual strain biofilms under diverse hydrodynamic stresses. International Journal of Food Microbiology.Vol. 128, pp.: 309316. Smani, Y.; McConnell, M. and Pachón, J. (2012). Role of fibronectin in the adhesion of Acinetobacter baumanii to host cells. PLOS One. Vol. 7, pp: 1-7. Smith, B. (1996). Fundamentals of Fourier transform infrared spectroscopy. CRC Press. Washington DC, United States of America. Soon, R.; Nation, R.; Cockram, S. et al. (2011). Different surface charge of colistin-susceptible and –resistant Acinetobacter baumanii cells measured with zeta potential as a function of growth phase and colistin treatment. Journal of Antimicrobial Chemotherapy. Vol. 66, pp: 126-133. Stewart, P. (2012). Mini-review: Convection around biofilms. Biofouling. Vol. 28, pp.: 187-198. Stoodley, P.; Jorgensen, F.; Williams, P.; et al. (1999). The Role of hydrodynamics and AHL signaling molecules as determinants of the structure of Pseudomonas aeruginosa biofilms. "Biofilms: The Good, The Bad, and The Ugly" - 4th meeting of the Biofilm Club. Gregynog, United Kingdom. Tam, K. (2002). Removal of multiple substrates in a mixed culture process for the treatment of brewery wastewater. McGill University. Montréal, Canada. Tay, J.; Liu, Q. and Liu, Y. (2001). The effects of shear force on the formation, structure and metabolism of aerobic granules. Applied Microbiology and Technology. Vol. 57, pp: 227-233. Tay, J.; Liu, Q. and Liu, Y. (2001b). The role of cellular polysaccharides in the formation and stability of aerobic granules. Letters in Applied Microbiology. Vol. 33, pp: 222-226. Tay, J.; Xu, H. and Teo, K. (2000). Molecular mechanism of granulation. I: H+ translocation-dehydration theory. Journal of Environmental Engineering. Vol. 126, pp.: 403-410. Telgmann, U.; Horn, H. and Morgenroth, E. (2004). Influence of growth history on sloughing and erosion from biofilms. Water Research. Vol. 38, pp.: 3671-3684. Teo, K.; Xu, H.; Tay, J. (2000). Molecular mechanism of granulation. II: Proton translocating activity. Journal of Environmental Engineering. Vol. 126, pp.: 411-418. Teodósio, J.; Simões, M.; Melo, L. and Mergulhão, F. (2011). Flow cell hydrodynamics and their effects on E. coli biofilm formation under different nutrient conditions and turbulent flow. Biofouling. Vol 27, pp.: 1-11. 68 Thomas, E. (1974). Studies on the metabolism of ATP by isolated bacterial membranes: role of succinyl CoA synthetase in diglyceride kinase activity. Archives of Biochemistry and Biophysics. Vol. 163, pp: 530-536. Thomas, W.; Trintchina, E.; Forero, M. et al. (2002). Bacterial adhesion to target cells enhanced by shear force. Cell. Vol. 109, pp: 913-923. Toogood, H.; Leys, D. and Scrutton, N. (2007). Dynamics driving function – new insights from electron transferring flavoproteins and partner complexes. The FEBS Journal. Vol. 274, pp: 5481-5504. Trémoulet, F.; Duché, O.; Namane, A. et al. (2002). A proteomic study of Escherichia coli O157:H7 NCTC12900 cultivated in biofilm or in planktonic growth mode. FEMS Microbiology Letters. Vol. 215, pp: 7-14. Uniprot Consortium. (2013). www.uniprot.org. Date of access: July-2013. Van Denmark, P. and Batzing, B. (1987). The Microbes: An Introduction to Their Nature and Importance. Benjamin/Cummings Publishing. California, United States of America. Van Loosdrecht, M.; Picioreanu, C. and Heijnen, J. (1997). A more unifying hypothesis for the structure of microbial biofilms. FEMS Microbiology Ecology. Vol. 24, pp.: 181-183. Van Oss, C. (1994). Interfacial forces in aqueous media. Marcel Dekker Inc.. Florida, United States of America. Vaughan, B.; Smith, B. and Chopp, D. (2010). The Influence of Fluid Flow on Modeling Quorum Sensing in Bacterial Biofilms. Bulletin of Mathematical Biology. Vol. 72, pp.: 1143-1165. Vicsek, T.; Czirok, A.; Ben-Jacob, E. et al. (1995). Novel type of phase transition in a system of self-driven particles. Physics Review Letters. Vol. 75, pp.: 1226-1229. Vieira, M.; Melo, L. and Pinheiro, M. (1993). Biofilm formation: Hydrodynamic effects on internal diffusion and structure. Vol. 7, pp.: 67-80. Walker, S.; Redman, J. and Elimelech, M. (2004). Role of cell surface lipopolysaccharides in Escherichia coli K12 adhesion and transport. Langmuir. Vol. 20, pp: 7736-7746. Waters CM, Bassler BL (2005) Quorum sensing: cell-to-cell communication in bacteria. Annu Rev Cell Dev Biol 21:319–346. Waters, C. and Bassler, B. (2005). Quorum sensing: cell-to-cell communication in bacteria. Annual Review of Cell and Developmental Biology. Vol. 21, pp.: 319-346. Watnick, P. and Kolter, R. (2000). Biofilm, city of microbes. Journal of Bacteriology. Vol. 182, pp.: 2675-2679. Wentzel, M.; Loetter, L.; Loewenthal, R. et al. (1986). Metabolic behavior of Acinetobacter spp. in enhanced biological phosphorous removal – a biochemical model. Water SA. Vol. 12, pp: 209-224. Wessa, P. (2013). Free Statistics Software. Office for Research, development and education, URL http://www.wessa.net/ Wilson, W.; Wade, M.; Holman, S. et al. (2001). Status of methods for assessing bacterial cell surface charge properties based on zeta potential measurements. Journal of Microbiological Methods. Vol. 43, pp: 153-164. Wimpenny, J. and Colasanti, R. (2004). A simple automaton model for coaggregation. Biofilms. Vol. 1, pp: 369-375. Yates, J.; Ruse, C. and Nakorchevsky, A. (2009). Proteomics by mass spectrometry: approaches, advances, and applications. Annual Review of Biomedical Engineering. Vol. 11, pp: 49-79. Youngren, G. and Acrivos, A. (1975). Stokes flow past a particle of arbitrary shape: a numerical method of solution. Journal of Fluid Mechanics. Vol. 69, pp.: 377-403. Zhan, Y.; Yan, Y.; Zhang, W. et al. (2011). Comparative analysis of the completgenome of an Acinetobacter calcoaceticus strain adapted to a phenol-polluted environment. Research in Microbiology. pp: 1-8. 69 Appendix A – Statistical analysis The Wicoxon-Mann-Whitney test was the statistical test applied throughout the present study to evaluate the statistical significance associated to differences between two independent samples. It is a non-parametric test, which is commonly applied as an alternative to t-Student for samples with small dimension, heterogeneous variances and non-normal distributions. The statistical hypothesis associated to the bilateral test are described as follows: i) null hypothesis (H0): differences between two independent samples are not significant (p>0,05) and ii) alternative hypothesis (H1): differences between two independent samples are significant (p<0,05) (Wessa, 2013). The probe values (95% confidence) associated to experiments results, for which Wicoxon-Mann-Whitney test was relevant, are shown as follows. A.1 Analysis on specific growth rate difference Table A.1: Probe values associated to non-parametric test for analysis of specific growth rate differences between agitation speeds conditions. Samples under analysis P-value 100 RPM vs 220 RPM 0,0014 220 RPM vs 375 RPM 0,0075 100 RPM vs 375 RPM 0,0060 70 A.2 Analysis on total respiratory activity difference Table A.2: Probe values associated to non-parametric test for analysis of total respiration activity differences between agitation speeds conditions in mid-exponential and stationary growth phases. Growth phase Samples under analysis P-value Mid-exponential 100 RPM vs 220 RPM 0,0471 220 RPM vs 375 RPM 0,400 100 RPM vs 375 RPM 0,0286 Stationary 100 RPM vs 220 RPM 0,1714 220 RPM vs 375 RPM 0,0286 100 RPM vs 375 RPM 0,3143 A.3 Analysis on hydrophobicity index difference Table A.3.1: Probe values associated to non-parametric test for hydrophobicity index differences between agitation speeds conditions at mid-exponential and stationary growth phases. Growth phase Samples under analysis P-value Mid-exponential 100 RPM vs 220 RPM 0,1623 220 RPM vs 375 RPM 0,1320 100 RPM vs 375 RPM 0,0022 Stationary 100 RPM vs 220 RPM 0,0022 220 RPM vs 375 RPM 0,0087 100 RPM vs 375 RPM 0,0021 Table A.3.2: Probe values associated to non-parametric test for hydrophobicity index differences between distinct growth phases across different agitation speeds conditions. Samples under analysis P-value 100 RPM exponential vs 100 RPM stationary 0,0022 220 RPM exponential vs 220 RPM stationary 0,0023 375 RPM exponential vs 375 RPM stationary 0,0022 71 A.4 Analysis on electrophoretic mobility medians at pH 7 difference Table A.4: Probe values associated to non-parametric test for electrophoretic mobility differences between agitation speeds conditions at pH 7 in mid-exponential and stationary growth phases. Growth phase Samples under analysis P-value Mid-exponential 100 RPM vs 220 RPM 0,9918 220 RPM vs 375 RPM 0,6750 100 RPM vs 375 RPM 0,6453 Stationary 100 RPM vs 220 RPM 0,3833 220 RPM vs 375 RPM 0,0458 100 RPM vs 375 RPM 0,0089 A.4.1 Analysis on electrophoretic mobility medians at pH 2 and 6 in mid-exponential growth phase Table A.4.1: Probe values associated to non-parametric test for electrophoretic mobility differences between agitation speeds conditions at pH 2 and 6 in mid-exponential growth phases. pH Samples under analysis P-value 6 100 RPM vs 220 RPM 0,0463 220 RPM vs 375 RPM 0,4925 100 RPM vs 375 RPM 0,0416 2 100 RPM vs 220 RPM 0,1339 220 RPM vs 375 RPM <0,0001 100 RPM vs 375 RPM 0,0013 72 A.5 Analysis of auto aggregation index difference Table A.5: Probe values associated to non-parametric test for analysis of auto aggregation index differences between agitation speeds conditions in stationary growth phase. Samples under analysis P-value 100 RPM vs 220 RPM 0,400 220 RPM vs 375 RPM 0,100 100 RPM vs 375 RPM 0,100 73 Appendix B – Figures Figure B.1: ATR-FTIR spectra for A. calcoaceticus grown under distinct agitation speeds at midexponential growth phase. Blue line represents cells grown at 100 rpm; red line cells at 220 rpm and green line cells at 375 rpm. 0 1 2 3 4 900100011001200130014001500160017001800 Absorbance units Wavenumber (1/cm) 100 RPM 220 RPM 375 RPM 74 Figure B.2: SDS-PAGE analysis of outer membrane proteins extracted from A. calcoaceticus cultivated under distinct agitation speeds at stationary growth phase. Numbers on the left side represent molecular weigth (in kDa). 75 Figure B.3: Auto aggregation analysis by visual assay of A. calcoaceticus cultivated under different agitation speeds at stationary phase. Numbers on the upper part represent time elapsed after auto aggregation suspension preparation. 0 h 2 h 24 h 48 h 100 RPM 220 RPM 375 RPM 82 C.4 Factor analysis associated to Principal Component Analysis (PCA) Table C.4: Square cosines associated to Principal Component Analysis (PCA) of ATR-FTIR spectrum obtained for A. calcoaceticus at stationary growth phase. The values in bold represent variables to the factor for which squared cosine is the largest Wave number (1/cm) F1 F2 F3 F4 898,828256 0,006 0,213 0,564 0,000 900,757072 0,019 0,217 0,536 0,001 902,685888 0,079 0,208 0,517 0,001 904,614704 0,338 0,110 0,398 0,003 906,54352 0,577 0,027 0,289 0,006 908,472336 0,721 0,010 0,182 0,007 910,401152 0,801 0,004 0,128 0,006 912,329968 0,849 0,000 0,096 0,003 914,258784 0,860 0,003 0,081 0,002 916,1876 0,842 0,005 0,084 0,003 918,116416 0,793 0,005 0,114 0,003 920,045232 0,707 0,001 0,169 0,005 921,974048 0,535 0,006 0,273 0,006 923,902864 0,346 0,041 0,354 0,002 925,83168 0,206 0,082 0,409 0,000 927,760496 0,131 0,131 0,407 0,002 929,689312 0,061 0,139 0,453 0,009 931,618128 0,009 0,153 0,473 0,014 933,546944 0,002 0,150 0,485 0,012 935,47576 0,009 0,190 0,428 0,009 937,404576 0,013 0,293 0,334 0,007 939,333392 0,004 0,303 0,336 0,002 941,262208 0,000 0,358 0,318 0,000 943,191024 0,014 0,443 0,233 0,000 945,11984 0,037 0,512 0,181 0,000 947,048656 0,084 0,554 0,122 0,000 948,977472 0,121 0,566 0,101 0,001 950,906288 0,190 0,531 0,094 0,002 952,835104 0,281 0,477 0,078 0,004 954,76392 0,354 0,451 0,051 0,007 956,692736 0,418 0,418 0,034 0,007 958,621552 0,480 0,377 0,024 0,007 960,550368 0,506 0,362 0,018 0,008 962,479184 0,528 0,345 0,016 0,009 964,408 0,513 0,360 0,017 0,010 966,336816 0,486 0,388 0,016 0,011 968,265632 0,461 0,419 0,016 0,014 970,194448 0,431 0,464 0,014 0,015 972,123264 0,410 0,496 0,013 0,018 974,05208 0,394 0,523 0,012 0,019 975,980896 0,379 0,549 0,011 0,018 977,909712 0,369 0,569 0,010 0,016 83 979,838528 0,364 0,582 0,009 0,014 981,767344 0,369 0,582 0,008 0,012 983,69616 0,362 0,591 0,008 0,011 985,624976 0,351 0,602 0,009 0,009 987,553792 0,334 0,616 0,012 0,009 989,482608 0,314 0,632 0,014 0,008 991,411424 0,304 0,640 0,017 0,009 993,34024 0,318 0,626 0,018 0,009 995,269056 0,353 0,593 0,018 0,011 997,197872 0,403 0,547 0,017 0,013 999,126688 0,464 0,491 0,016 0,015 1001,055504 0,536 0,421 0,014 0,018 1002,98432 0,602 0,356 0,013 0,021 1004,913136 0,670 0,290 0,011 0,024 1006,841952 0,730 0,229 0,010 0,026 1008,770768 0,780 0,180 0,009 0,027 1010,699584 0,820 0,140 0,009 0,028 1012,6284 0,848 0,113 0,008 0,028 1014,557216 0,872 0,089 0,008 0,029 1016,486032 0,891 0,070 0,008 0,028 1018,414848 0,906 0,057 0,007 0,027 1020,343664 0,917 0,048 0,007 0,025 1022,27248 0,924 0,043 0,007 0,024 1024,201296 0,931 0,038 0,007 0,023 1026,130112 0,934 0,034 0,007 0,022 1028,058928 0,939 0,032 0,006 0,020 1029,987744 0,944 0,030 0,006 0,018 1031,91656 0,949 0,027 0,005 0,016 1033,845376 0,954 0,024 0,005 0,016 1035,774192 0,956 0,021 0,005 0,016 1037,703008 0,960 0,019 0,004 0,015 1039,631824 0,963 0,016 0,004 0,014 1041,56064 0,964 0,015 0,004 0,014 1043,489456 0,966 0,013 0,004 0,015 1045,418272 0,966 0,012 0,004 0,015 1047,347088 0,968 0,010 0,005 0,015 1049,275904 0,970 0,008 0,005 0,015 1051,20472 0,973 0,005 0,004 0,014 1053,133536 0,976 0,002 0,004 0,014 1055,062352 0,978 0,000 0,004 0,014 1056,991168 0,979 0,000 0,004 0,013 1058,919984 0,979 0,000 0,004 0,013 1060,8488 0,980 0,001 0,004 0,011 1062,777616 0,980 0,002 0,004 0,011 1064,706432 0,977 0,003 0,004 0,012 1066,635248 0,976 0,006 0,004 0,012 1068,564064 0,974 0,009 0,004 0,010 1070,49288 0,973 0,012 0,004 0,009 1072,421696 0,969 0,016 0,004 0,008 1074,350512 0,964 0,022 0,004 0,008 84 1076,279328 0,958 0,028 0,004 0,007 1078,208144 0,951 0,037 0,003 0,006 1080,13696 0,943 0,045 0,003 0,004 1082,065776 0,935 0,055 0,003 0,003 1083,994592 0,931 0,060 0,002 0,003 1085,923408 0,928 0,064 0,002 0,002 1087,852224 0,925 0,066 0,002 0,002 1089,78104 0,925 0,065 0,002 0,001 1091,709856 0,923 0,067 0,003 0,001 1093,638672 0,920 0,069 0,003 0,001 1095,567488 0,914 0,074 0,003 0,001 1097,496304 0,906 0,081 0,004 0,001 1099,42512 0,899 0,088 0,004 0,000 1101,353936 0,889 0,095 0,005 0,000 1103,282752 0,883 0,098 0,007 0,000 1105,211568 0,875 0,103 0,008 0,000 1107,140384 0,865 0,107 0,009 0,000 1109,0692 0,855 0,114 0,010 0,000 1110,998016 0,845 0,121 0,012 0,000 1112,926832 0,845 0,123 0,011 0,000 1114,855648 0,846 0,127 0,009 0,000 1116,784464 0,860 0,117 0,008 0,000 1118,71328 0,889 0,095 0,006 0,000 1120,642096 0,915 0,073 0,005 0,000 1122,570912 0,934 0,057 0,004 0,000 1124,499728 0,950 0,043 0,003 0,000 1126,428544 0,967 0,028 0,002 0,000 1128,35736 0,978 0,018 0,001 0,000 1130,286176 0,984 0,012 0,001 0,000 1132,214992 0,989 0,006 0,001 0,000 1134,143808 0,992 0,002 0,001 0,000 1136,072624 0,993 0,000 0,001 0,000 1138,00144 0,992 0,000 0,001 0,000 1139,930256 0,988 0,002 0,001 0,000 1141,859072 0,981 0,006 0,002 0,000 1143,787888 0,974 0,008 0,002 0,001 1145,716704 0,966 0,011 0,004 0,002 1147,64552 0,961 0,013 0,004 0,003 1149,574336 0,954 0,015 0,004 0,004 1151,503152 0,946 0,020 0,003 0,005 1153,431968 0,937 0,022 0,002 0,008 1155,360784 0,931 0,020 0,001 0,011 1157,2896 0,920 0,019 0,000 0,015 1159,218416 0,903 0,019 0,000 0,021 1161,147232 0,878 0,024 0,000 0,030 1163,076048 0,859 0,019 0,000 0,042 1165,004864 0,829 0,012 0,000 0,054 1166,93368 0,782 0,007 0,000 0,076 1168,862496 0,727 0,007 0,000 0,101 1170,791312 0,648 0,005 0,000 0,138 85 1172,720128 0,535 0,006 0,000 0,172 1174,648944 0,371 0,007 0,000 0,229 1176,57776 0,165 0,007 0,003 0,322 1178,506576 0,032 0,016 0,006 0,368 1180,435392 0,004 0,021 0,005 0,365 1182,364208 0,058 0,018 0,001 0,319 1184,293024 0,097 0,032 0,000 0,268 1186,22184 0,085 0,048 0,000 0,230 1188,150656 0,059 0,069 0,001 0,223 1190,079472 0,032 0,060 0,004 0,244 1192,008288 0,013 0,046 0,006 0,247 1193,937104 0,001 0,042 0,008 0,239 1195,86592 0,006 0,028 0,008 0,224 1197,794736 0,060 0,023 0,008 0,194 1199,723552 0,186 0,010 0,013 0,166 1201,652368 0,396 0,000 0,014 0,115 1203,581184 0,581 0,003 0,012 0,074 1205,51 0,693 0,008 0,012 0,053 1207,438816 0,746 0,017 0,010 0,040 1209,367632 0,781 0,027 0,010 0,030 1211,296448 0,804 0,039 0,006 0,019 1213,225264 0,820 0,052 0,006 0,017 1215,15408 0,820 0,070 0,007 0,017 1217,082896 0,828 0,079 0,005 0,014 1219,011712 0,836 0,080 0,005 0,013 1220,940528 0,846 0,080 0,004 0,011 1222,869344 0,867 0,066 0,003 0,007 1224,79816 0,879 0,057 0,002 0,006 1226,726976 0,885 0,054 0,001 0,007 1228,655792 0,892 0,050 0,002 0,009 1230,584608 0,897 0,045 0,002 0,010 1232,513424 0,898 0,041 0,002 0,012 1234,44224 0,894 0,042 0,002 0,016 1236,371056 0,901 0,035 0,001 0,020 1238,299872 0,907 0,024 0,001 0,024 1240,228688 0,907 0,020 0,002 0,024 1242,157504 0,913 0,011 0,001 0,026 1244,08632 0,915 0,003 0,002 0,032 1246,015136 0,913 0,000 0,002 0,038 1247,943952 0,901 0,007 0,004 0,039 1249,872768 0,878 0,028 0,004 0,037 1251,801584 0,833 0,068 0,005 0,039 1253,7304 0,775 0,124 0,005 0,039 1255,659216 0,700 0,203 0,005 0,038 1257,588032 0,618 0,288 0,005 0,032 1259,516848 0,544 0,372 0,003 0,023 1261,445664 0,460 0,452 0,005 0,028 1263,37448 0,391 0,513 0,009 0,036 1265,303296 0,323 0,587 0,008 0,032 1267,232112 0,266 0,647 0,008 0,030 86 1269,160928 0,210 0,710 0,006 0,026 1271,089744 0,162 0,756 0,006 0,028 1273,01856 0,130 0,769 0,011 0,044 1274,947376 0,101 0,776 0,018 0,061 1276,876192 0,067 0,798 0,020 0,069 1278,805008 0,041 0,822 0,018 0,072 1280,733824 0,022 0,834 0,020 0,076 1282,66264 0,011 0,829 0,021 0,089 1284,591456 0,003 0,844 0,016 0,087 1286,520272 0,001 0,873 0,008 0,067 1288,449088 0,000 0,881 0,005 0,062 1290,377904 0,000 0,849 0,011 0,089 1292,30672 0,001 0,817 0,014 0,113 1294,235536 0,004 0,808 0,014 0,120 1296,164352 0,009 0,815 0,011 0,111 1298,093168 0,021 0,782 0,012 0,128 1300,021984 0,037 0,745 0,014 0,144 1301,9508 0,060 0,706 0,013 0,158 1303,879616 0,091 0,679 0,010 0,158 1305,808432 0,130 0,659 0,007 0,147 1307,737248 0,169 0,650 0,002 0,127 1309,666064 0,203 0,624 0,001 0,121 1311,59488 0,223 0,660 0,006 0,063 1313,523696 0,211 0,688 0,016 0,044 1315,452512 0,240 0,674 0,007 0,040 1317,381328 0,229 0,673 0,028 0,032 1319,310144 0,261 0,639 0,021 0,037 1321,23896 0,271 0,614 0,000 0,076 1323,167776 0,277 0,576 0,001 0,105 1325,096592 0,265 0,580 0,002 0,110 1327,025408 0,253 0,596 0,001 0,105 1328,954224 0,242 0,614 0,001 0,099 1330,88304 0,244 0,615 0,001 0,100 1332,811856 0,256 0,598 0,001 0,104 1334,740672 0,272 0,626 0,001 0,064 1336,669488 0,278 0,579 0,098 0,004 1338,598304 0,212 0,591 0,162 0,000 1340,52712 0,268 0,592 0,096 0,007 1342,455936 0,353 0,448 0,003 0,139 1344,384752 0,379 0,368 0,006 0,178 1346,313568 0,370 0,422 0,002 0,153 1348,242384 0,319 0,508 0,001 0,125 1350,1712 0,276 0,570 0,000 0,109 1352,100016 0,230 0,629 0,000 0,099 1354,028832 0,184 0,704 0,000 0,074 1355,957648 0,137 0,756 0,000 0,069 1357,886464 0,098 0,808 0,001 0,058 1359,81528 0,053 0,876 0,010 0,029 1361,744096 0,034 0,890 0,036 0,009 1363,672912 0,017 0,913 0,025 0,010 87 1365,601728 0,004 0,905 0,008 0,042 1367,530544 0,003 0,903 0,001 0,028 1369,45936 0,008 0,890 0,000 0,022 1371,388176 0,003 0,900 0,011 0,009 1373,316992 0,000 0,869 0,064 0,001 1375,245808 0,001 0,849 0,069 0,001 1377,174624 0,009 0,833 0,044 0,002 1379,10344 0,022 0,782 0,055 0,001 1381,032256 0,008 0,733 0,100 0,005 1382,961072 0,002 0,659 0,148 0,009 1384,889888 0,033 0,566 0,254 0,001 1386,818704 0,123 0,405 0,355 0,003 1388,74752 0,200 0,339 0,339 0,000 1390,676336 0,258 0,195 0,311 0,003 1392,605152 0,416 0,102 0,381 0,001 1394,533968 0,387 0,099 0,403 0,003 1396,462784 0,417 0,068 0,418 0,005 1398,3916 0,453 0,048 0,400 0,004 1400,320416 0,596 0,008 0,289 0,000 1402,249232 0,550 0,018 0,220 0,001 1404,178048 0,516 0,008 0,306 0,004 1406,106864 0,369 0,000 0,234 0,002 1408,03568 0,276 0,051 0,162 0,000 1409,964496 0,162 0,020 0,152 0,000 1411,893312 0,067 0,005 0,107 0,002 1413,822128 0,023 0,010 0,079 0,008 1415,750944 0,041 0,341 0,267 0,009 1417,67976 0,012 0,452 0,196 0,003 1419,608576 0,011 0,541 0,039 0,001 1421,537392 0,110 0,465 0,004 0,004 1423,466208 0,119 0,589 0,002 0,000 1425,395024 0,359 0,312 0,090 0,005 1427,32384 0,377 0,249 0,151 0,015 1429,252656 0,258 0,478 0,054 0,020 1431,181472 0,267 0,475 0,047 0,027 1433,110288 0,260 0,442 0,046 0,038 1435,039104 0,148 0,552 0,010 0,082 1436,96792 0,153 0,504 0,018 0,125 1438,896736 0,211 0,316 0,074 0,173 1440,825552 0,213 0,008 0,102 0,360 1442,754368 0,151 0,003 0,081 0,434 1444,683184 0,126 0,012 0,022 0,416 1446,612 0,025 0,428 0,112 0,175 1448,540816 0,023 0,387 0,128 0,194 1450,469632 0,000 0,100 0,128 0,243 1452,398448 0,043 0,173 0,611 0,025 1454,327264 0,021 0,161 0,344 0,211 1456,25608 0,003 0,186 0,177 0,332 1458,184896 0,005 0,207 0,054 0,398 1460,113712 0,082 0,084 0,052 0,428 88 1462,042528 0,099 0,006 0,396 0,294 1463,971344 0,011 0,043 0,145 0,486 1465,90016 0,015 0,025 0,269 0,429 1467,828976 0,009 0,003 0,236 0,513 1469,757792 0,114 0,050 0,034 0,489 1471,686608 0,212 0,219 0,074 0,258 1473,615424 0,188 0,319 0,072 0,194 1475,54424 0,094 0,558 0,029 0,113 1477,473056 0,000 0,466 0,147 0,169 1479,401872 0,313 0,177 0,005 0,187 1481,330688 0,303 0,192 0,010 0,190 1483,259504 0,275 0,049 0,022 0,296 1485,18832 0,189 0,147 0,159 0,115 1487,117136 0,049 0,265 0,500 0,005 1489,045952 0,034 0,204 0,678 0,006 1490,974768 0,019 0,181 0,674 0,000 1492,903584 0,059 0,000 0,612 0,067 1494,8324 0,010 0,092 0,252 0,269 1496,761216 0,001 0,015 0,700 0,051 1498,690032 0,029 0,078 0,558 0,084 1500,618848 0,008 0,017 0,871 0,001 1502,547664 0,002 0,130 0,799 0,000 1504,47648 0,022 0,164 0,702 0,017 1506,405296 0,014 0,006 0,902 0,022 1508,334112 0,016 0,000 0,891 0,044 1510,262928 0,021 0,000 0,881 0,052 1512,191744 0,035 0,058 0,827 0,021 1514,12056 0,031 0,048 0,834 0,032 1516,049376 0,047 0,030 0,837 0,038 1517,978192 0,066 0,104 0,755 0,019 1519,907008 0,074 0,190 0,669 0,009 1521,835824 0,086 0,249 0,604 0,005 1523,76464 0,088 0,312 0,543 0,002 1525,693456 0,081 0,346 0,522 0,002 1527,622272 0,084 0,408 0,461 0,000 1529,551088 0,081 0,540 0,337 0,001 1531,479904 0,057 0,498 0,414 0,000 1533,40872 0,048 0,532 0,380 0,001 1535,337536 0,066 0,548 0,327 0,000 1537,266352 0,015 0,699 0,248 0,011 1539,195168 0,013 0,701 0,239 0,012 1541,123984 0,008 0,720 0,222 0,014 1543,0528 0,001 0,726 0,223 0,014 1544,981616 0,000 0,750 0,198 0,021 1546,910432 0,000 0,822 0,104 0,040 1548,839248 0,005 0,739 0,187 0,020 1550,768064 0,011 0,695 0,216 0,015 1552,69688 0,022 0,770 0,150 0,028 1554,625696 0,036 0,738 0,162 0,038 1556,554512 0,049 0,727 0,157 0,047 89 1558,483328 0,062 0,703 0,165 0,052 1560,412144 0,076 0,709 0,124 0,075 1562,34096 0,088 0,717 0,030 0,119 1564,269776 0,142 0,651 0,150 0,035 1566,198592 0,176 0,614 0,157 0,037 1568,127408 0,169 0,620 0,135 0,067 1570,056224 0,187 0,603 0,119 0,083 1571,98504 0,225 0,577 0,130 0,060 1573,913856 0,225 0,594 0,092 0,081 1575,842672 0,227 0,580 0,091 0,092 1577,771488 0,242 0,563 0,082 0,101 1579,700304 0,278 0,526 0,079 0,101 1581,62912 0,285 0,508 0,066 0,118 1583,557936 0,278 0,505 0,065 0,125 1585,486752 0,262 0,509 0,068 0,132 1587,415568 0,239 0,526 0,074 0,132 1589,344384 0,210 0,547 0,088 0,129 1591,2732 0,171 0,580 0,093 0,130 1593,202016 0,132 0,613 0,096 0,134 1595,130832 0,098 0,654 0,091 0,134 1597,059648 0,071 0,683 0,074 0,145 1598,988464 0,052 0,711 0,063 0,147 1600,91728 0,037 0,735 0,067 0,141 1602,846096 0,028 0,758 0,071 0,127 1604,774912 0,024 0,777 0,062 0,123 1606,703728 0,021 0,792 0,065 0,110 1608,632544 0,016 0,813 0,056 0,104 1610,56136 0,015 0,825 0,043 0,107 1612,490176 0,015 0,836 0,032 0,106 1614,418992 0,005 0,859 0,024 0,104 1616,347808 0,001 0,868 0,022 0,101 1618,276624 0,000 0,876 0,014 0,101 1620,20544 0,002 0,894 0,031 0,069 1622,134256 0,000 0,885 0,024 0,083 1624,063072 0,005 0,881 0,020 0,087 1625,991888 0,012 0,886 0,017 0,078 1627,920704 0,033 0,864 0,015 0,080 1629,84952 0,058 0,837 0,008 0,086 1631,778336 0,077 0,820 0,011 0,082 1633,707152 0,091 0,790 0,016 0,089 1635,635968 0,149 0,736 0,014 0,085 1637,564784 0,211 0,681 0,016 0,077 1639,4936 0,286 0,615 0,005 0,074 1641,422416 0,291 0,611 0,004 0,074 1643,351232 0,268 0,631 0,009 0,074 1645,280048 0,296 0,574 0,008 0,094 1647,208864 0,369 0,502 0,007 0,090 1649,13768 0,379 0,494 0,006 0,089 1651,066496 0,378 0,483 0,003 0,101 1652,995312 0,447 0,408 0,001 0,102 90 1654,924128 0,504 0,350 0,001 0,100 1656,852944 0,579 0,294 0,002 0,081 1658,78176 0,626 0,248 0,000 0,077 1660,710576 0,596 0,253 0,002 0,099 1662,639392 0,692 0,164 0,000 0,089 1664,568208 0,777 0,094 0,000 0,074 1666,497024 0,783 0,091 0,000 0,071 1668,42584 0,817 0,062 0,002 0,064 1670,354656 0,846 0,038 0,002 0,060 1672,283472 0,860 0,023 0,001 0,061 1674,212288 0,869 0,012 0,001 0,062 1676,141104 0,881 0,004 0,000 0,060 1678,06992 0,901 0,001 0,000 0,042 1679,998736 0,899 0,004 0,037 0,017 1681,927552 0,908 0,004 0,014 0,029 1683,856368 0,908 0,005 0,004 0,037 1685,785184 0,905 0,006 0,000 0,042 1687,714 0,917 0,020 0,008 0,018 1689,642816 0,914 0,039 0,003 0,008 1691,571632 0,925 0,017 0,001 0,022 1693,500448 0,872 0,017 0,005 0,061 1695,429264 0,882 0,023 0,000 0,053 1697,35808 0,891 0,023 0,000 0,047 1699,286896 0,878 0,027 0,001 0,054 1701,215712 0,852 0,027 0,005 0,070 1703,144528 0,873 0,024 0,003 0,058 1705,073344 0,872 0,018 0,000 0,063 1707,00216 0,843 0,012 0,013 0,081 1708,930976 0,869 0,009 0,011 0,050 1710,859792 0,840 0,002 0,017 0,066 1712,788608 0,729 0,015 0,000 0,169 1714,717424 0,559 0,071 0,004 0,264 1716,64624 0,398 0,129 0,036 0,330 1718,575056 0,272 0,154 0,111 0,355 1720,503872 0,190 0,135 0,267 0,314 1722,432688 0,121 0,114 0,320 0,347 1724,361504 0,048 0,086 0,302 0,439 1726,29032 0,002 0,100 0,342 0,448 1728,219136 0,007 0,269 0,136 0,470 1730,147952 0,007 0,327 0,004 0,517 1732,076768 0,021 0,309 0,019 0,522 1734,005584 0,023 0,276 0,036 0,540 1735,9344 0,018 0,245 0,101 0,524 1737,863216 0,038 0,214 0,070 0,558 1739,792032 0,049 0,122 0,018 0,645 1741,720848 0,079 0,010 0,151 0,508 1743,649664 0,026 0,117 0,728 0,000 1745,57848 0,004 0,165 0,691 0,012 1747,507296 0,001 0,227 0,643 0,007 1749,436112 0,004 0,253 0,583 0,001 91 1751,364928 0,002 0,261 0,356 0,097 1753,293744 0,006 0,080 0,080 0,330 1755,22256 0,000 0,292 0,126 0,150 1757,151376 0,010 0,208 0,104 0,234 1759,080192 0,001 0,063 0,060 0,358 1761,009008 0,040 0,071 0,060 0,305 1762,937824 0,050 0,018 0,011 0,337 1764,86664 0,127 0,024 0,000 0,346 1766,795456 0,118 0,078 0,105 0,197 1768,724272 0,125 0,071 0,150 0,152 1770,653088 0,135 0,044 0,122 0,153 1772,581904 0,142 0,040 0,090 0,143 1774,51072 0,158 0,030 0,064 0,163 1776,439536 0,190 0,030 0,050 0,152 1778,368352 0,204 0,082 0,072 0,121 1780,297168 0,157 0,161 0,142 0,082 1782,225984 0,158 0,128 0,068 0,130 1784,1548 0,152 0,200 0,063 0,109 1786,083616 0,130 0,032 0,169 0,284 1788,012432 0,291 0,063 0,006 0,145 1789,941248 0,084 0,497 0,136 0,013 1791,870064 0,080 0,515 0,121 0,017 1793,79888 0,089 0,496 0,089 0,031 1795,727696 0,014 0,717 0,110 0,001 1797,656512 0,028 0,565 0,167 0,002 1799,585328 0,057 0,550 0,134 0,010 1801,514144 0,058 0,553 0,129 0,012