Signaling Factor Interactions with Polysaccharide Aggregates of

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Signaling Factor Interactions with Polysaccharide Aggregates of Bacterial Biofilms Stephen C. Desalvo, Yating Liu, Geetika Sanjay Choudhary, Dacheng Ren, Shikha Nangia, and Radhakrishna Sureshkumar Langmuir, Just Accepted Manuscript • DOI: 10.1021/la504721b • Publication Date (Web): 21 Jan 2015 Downloaded from http://pubs.acs.org on January 31, 2015

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Signaling Factor Interactions with Polysaccharide Aggregates of Bacterial Biofilms Stephen C. DeSalvo,† Yating Liu,† Geetika Sanjay Choudhary†§, Dacheng Ren†§Ψ¥, Shikha Nangia,† and Radhakrishna Sureshkumar*,†,‡ † Department of Biomedical and Chemical Engineering, ΨDepartment of Civil and Environmental Engineering, ¥Department of Biology, ‡Department of Physics, §Syracuse Biomaterials Institute, Syracuse University, Syracuse, NY 13244 KEYWORDS Polysaccharide, Lyase, Chain Length, Potential of Mean Force, Coarse-Graining, Molecular Dynamics, Dextran, Biofilm, Signaling Factor, Lipid Bilayer, TNF-α, GM-CSF ABSTRACT Biofilms are surface-attached colonies of bacteria embedded in an extracellular polymeric substance (EPS). Inside the eukaryotic hosts, bacterial biofilms interact with the host cells through signaling factors (SFs). These signaling processes play important roles in the interaction between bacteria and host cells and the outcome of infections and symbiosis. However, how host immune factors diffuse through biofilms are not well understood. Here we describe synergistic molecular dynamics and experimental approaches for studying the translocation of signaling factors through polysaccharide chain aggregates present in the extracellular matrix of bacterial biofilms. The effect of polysaccharide chain degradation on the energetics of SF-EPS interactions was examined by simulating an EPS consisting of various polysaccharide chain lengths. It is shown that the SF stabilization energy, defined as the average potential of mean force difference between the environments outside and within the matrix, increases linearly with decreasing chain length. This effect has been explained based on the changes in the polysaccharide configurations around the SF. Specifically, shorter chains are packed tightly around the SF promoting favorable SF-EPS interactions while longer chains are packed loosely resulting in screening of interactions with neighboring chains. We further investigated the translocation of SFs through the host cell membrane using molecular dynamics simulations. Further, simulations predict the existence of energy barriers greater than 1000 kJ mol-1 associated with the translocation of the signaling factors necrosis factor-alpha (TNF-α) and granulocyte macrophage colony stimulating factor (GM-CSF) across the lipid bilayer. The agreement of computational and experimental findings motivates future computational studies using a more detailed description of the EPS, aimed at understanding the role of the extracellular matrix on biofilm drug resistance.

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INTRODUCTION

It is well documented that bacteria can form drug tolerant multicellular structures, known as biofilms, which are aggregates of bacteria surrounded by an extracellular polymeric substance (EPS) attached to both biotic and abiotic surfaces such as blood vessels, medical implants, and surgical devices.1,2 Due to the protection of EPS and slow growth, biofilms are highly tolerant to antibiotics and disinfectants. After treatment, the surviving bacterial cells, especially the dormant subpopulation, called the persister cells, can repopulate the biofilm causing the relapse of infection.3 Biofilms and their extracellular matrices, composed predominantly of water and polysaccharides, along with smaller fractions of protein and DNA fragments, serve as a protective barrier for such persister cells.3 Resultantly, bacterial attachment and biofilm formation have proven critical to the persistence of bacterial infection in sepsis and cystic fibrosis patients.4,5 It has been suggested that better biofilm therapeutics may thrive on mechanisms that induce biofilm detachment from adherent surfaces.6-8 The fate of a bacterial biofilm within a living tissue strongly depends on its interaction with host cells through molecules called signaling factors (SFs). The signaling factors are relatively small polypeptides or proteins that could influence the cellular activities of the host. Thus, better understanding of how SFs interact with biofilm matrix and embedded bacterial cells could offer vital clues to combating the formation and irreversible attachment of undesired biofilm colonies onto the host tissues. However, studying the bacteria-host signaling process is quite complex, and our present knowledge of the interactions between the SF and the host cell is primitive. An improved understanding of protein-exopolysaccharide interactions is essential to identifying signaling pathways/barriers.9 Since inhibition of cell-to-cell signals could aid in the treatment of infections caused by biofilms,10 insights into the interaction between the SF and biofilms may


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prove vital to the treatment or prevention of bacterial infections. Specifically, identifying the translocation mechanisms of SFs through the exopolysaccharide matrix of the biofilm as well as the host cell membrane is essential to understanding the signaling process between the biofilm and host cell. Earlier studies have demonstrated that EPS elimination may allow antibiotics to kill bacteria in biofilms.2 Polysaccharides are a major component of the biofilm matrix, and the role of polysaccharides in the structure and function of EPS is well established.9 In this work we performed experiments with alginate, an important polysaccharide in the EPS of bacterium Pseudomonas aeruginosa, and show that alginate lyase effectively degrades the alginate and promotes the diffusion of GM-CSF. Earlier studies have shown that alginate lyase causes bacterial detachment and ultimately, biofilm deterioration.5,6,8,11,12 A broad range of polysaccharases, including alginate lyase, can be found in biofilm matrices, and their effects on the structure and function of the biofilm vary greatly.11 As such, analysis of the effect of polysaccharide chain degradation through simulation of the actions of polysaccharase may provide useful insight for devising anti-biofilm therapeutics. In this work, we present a coarse-grained molecular dynamics (MD) simulation framework for studying SF-EPS interactions and SF-host cell membrane and its applicability to predicting quantitative thermodynamic free energy barriers and membrane damage. Figure 1 provides a schematic representation of the interactions that are being studied. As a model SF, we chose tumor necrosis factor-alpha (TNF-α), which has been found in high levels in sepsis patients, especially non-survivors,13 and granulocyte macrophage colony-stimulating factor (GM-CSF), a pro- and anti-inflammatory cytokine implicated in numerous diseases, including rheumatoid arthritis.14 TNF-α is known to produce a necessary inflammatory response to signal immune


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Figure 1. Schematic representation of the (A) signaling-factor membrane and (B) signaling factor-EPS matrix interactions studied in this work.

system defense mechanisms to attack pathogens. In fact, TNF-α has also been cited as a key regulator of GM-CSF production.14 Furthermore, TNF-α exists in both the transmembrane, insoluble state and the soluble signaling state, with soluble TNF being cleaved from the insoluble form.15-19 We analyze the potential of mean force (PMF) associated with the interaction of TNFα and GM-CSF with aggregates of a ubiquitous polysaccharide matrix. Simulations are conducted to study the effect of polysaccharide chain length on the PMF by varying the chain molecular weight. Further, analyze the PMF for TNF-α and GM-CSF interaction with a model cell membrane consisting of distearoylphosphatidylcholine (DSPC) lipids. Synergistically, we


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designed experiments to study the effect of polysaccharide chain length on GM-SCF translocation through the extracellular polysaccharide matrix described in detail below. The complexity of biofilms presents a prohibitive bottleneck to perform simulations with any reasonably sized system over sufficiently long periods of time if an all-atom description is used. Despite the limitation of being semi-quantitative, coarse graining provides a computationally affordable methodology. We use the MARTINI framework20,21 of coarse graining that maps four-heavy (non-hydrogen) atoms into a single CG bead. Compared to an all-atom system, coarse graining has several advantages, including a reduction in the number of degrees of freedom, ability to use much larger time-steps in the MD simulations, and consequently, low computational cost. The MARTINI framework allows one to perform MD simulations of relatively large systems while preserving essential chemical details, as shown in our recent work on shape and phase transitions in surfactant micelles,22,23 and effect of shape anisotropy on the translocation of nanoparticles through cell-membranes.24

II.A. COMPUTATIONAL METHODS All coarse grained molecular dynamics simulations were performed using the GROMACS package, versions 4.0.3 and 4.5.4.25 Periodic boundary conditions were used in the x-, y-, and zdimensions for each system. Short-range van der Waals and electrostatic interactions were calculated using shifted potentials. Lennard-Jones (LJ) interactions were shifted to zero in the range of 0.9 to 1.2 nm, and electrostatics were shifted to zero from 0.0 to 1.2 nm. These cutoffs are consistent with the recommended settings based on the MARTINI force field parameterization.21,26 At the cutoff distance, the energy and force vanish in order to prevent


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undesired noise. Pressure coupling was applied isotropically using the Parrinello-Rahman barostat and temperature coupling was applied using the Nosé-Hoover thermostat, unless otherwise stated. Figures 2 and 3 illustrate the interaction of TNF-α and GM-CSF signaling factors, respectively, with the DSPC lipid bilayer at three different points during a center-of-mass (COM) pull simulation. COM pulling is a steered molecular dynamics (SMD) technique frequently employed in molecular dynamics simulations to explore phenomena occurring on prohibitively long timescales through the use of a biasing force. SMD simulations have been performed successfully in numerous applications, including the study of protein-protein interactions,27-29 lipid extraction from membranes,30 nanoparticle-membrane interactions,24 and protein-membrane interactions.31 In numerous instances, pull simulations have been performed to allow qualitative assessment of molecular interactions and the creation of a trajectory for producing umbrella sampling configurations.24,27,31-33

Figure 2. PMF profile (red) associated with the translocation of TNF-α across a DPSC lipid bilayer showing an energy barrier of 1341 kJ mol-1. Images of TNF-α (yellow) interacting with the DSPC lipid bilayer (green, dark blue, red) during the pull simulation with black box representing the periodic boundary, and coarse-grained water (light blue).


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Figure 3. PMF profile (red) associated with the translocation of GM-CSF across a DPSC lipid bilayer showing an energy barrier of 1227 kJ mol-1. Images of GM-CSF (orange) interacting with the DSPC lipid bilayer (green, dark blue, red) during the pull simulation with black box representing the periodic boundary, and coarse-grained water (light blue).

Umbrella sampling is a technique used in MD simulations to calculate the free energy change, ∆G, associated with a particular process occurring along a specified order parameter, ξ.34-36 In umbrella sampling, a trajectory is described by many individual configurations, or windows, and equilibration and MD simulations are conducted within each. Configurations are typically manually created, but some have found it useful to use SMD to create a trajectory that can be split into useful snapshots.27 The analysis is concluded with use of the weighted histogram analysis method (WHAM)37 to achieve a potential of mean force (PMF) curve describing the free energy landscape along the order parameter. DSPC Lipid Bilayer Simulations A DSPC molecule lipid bilayer was self-assembled in box of dimensions 15×15×6 nm3 in the presence of CG water. In some instances, self-assembled lipid bilayers may be preferred to


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manually crafted bilayers given that the former is expected to be closer to thermodynamic equilibrium than the latter.38 The self-assembly process was initiated by packing as many CG lipids as possible into a box of the noted dimensions using the genbox utility in GROMACS and solvating the box with CG water. At this time, a total of 527 lipid molecules were packed into the box. Steepest descent energy minimization was performed to ensure minimal forces on each atom. Position restraints of 1000 kJ mol-1 nm-2 were defined in the z-dimension on selected atoms of each lipid. The NPT (isobaric-isothermal) ensemble was employed to relax the system using semi-isotropic pressure coupling and velocity rescaling temperature coupling of water and DSPC at 300 K for 40 ns of simulation time. Following equilibration, an adequately selfassembled lipid bilayer was formed, and all position restraints and water molecules were removed from the system. The box was resized to 11×16.2×25 nm3, causing lipids outside of the newly defined boundaries to be deleted. The final self-assembled DSPC lipid bilayer contained 493 lipids, with 249 lipids in the upper leaflet and 244 lipids in the lower leaflet. It has been noted that self-assembled lipid bilayers frequently contain unequal numbers of lipid molecules in each leaflet, creating a slightly asymmetrical bilayer.38 Tumor Necrosis Factor-Alpha Simulations The initial protein structure for TNF-α was obtained from PDB entry 1TNF from the RCSB Protein Data Bank,39 and coarse-grained to 984 atoms according to MARTINI force field specifications. An elastic network was placed over the protein to prevent its three component polypeptide chains from separating using a force constant of 500 kJ mol-1 nm-2.40 The elastic network allows for control of the protein conformation while maintaining an accurate chemical description. The TNF-α molecule was combined with the DSPC lipid bilayer in a box of dimensions 11×16.2×25 nm3, and the box was solvated with 38,634 coarse-grained water


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molecules. The total system was then energy minimized using a steepest descent algorithm to reduce the forces on any given atom in the system to the specified minimum, and an NPT equilibration was performed for 40 ns with temperature coupling of TNF-α and DSPC lipids with water molecules at 300 K using the velocity rescaling thermostat, as well as semi-isotropic pressure coupling. The canonical ensemble was utilized as additional system equilibration and relaxation with temperature coupling of TNF-α and DSPC lipids with water molecules at 300 K using the velocity rescaling thermostat. MD simulation using the Berendsen barostat and temperature coupling at 300 K was performed for 20 ns in order to introduce initial interactions between the atoms and to provide additional equilibration to the system. A center-of-mass pull simulation was performed for 2.7 ns over a distance of 25 nm by applying a force constant of 1000 kJ mol-1 nm-2 to the COM of the protein. This spring force constant parallels that used by other researchers in biomolecular MD studies.24,27,32 121 umbrella sampling windows were created along the trajectory over a 16 nm distance, with each window spaced by approximately 0.2 nm from a distance of 8 nm to 4.2 nm above the lipid bilayer COM, and from a distance of 4 nm to 8 nm below the bilayer COM, all measured with respect to the TNF-α COM. From 4.2 nm above the lipid bilayer COM to 4 nm below the bilayer COM, measured with respect to the TNF-α COM, the window spacing was approximately 0.1 nm. Within each window, NPT equilibration was performed for 1 ns to relax the structure at the particular configuration, and a molecular dynamics simulation was performed in each window for 100 ns to simulate the interactions at that location along the trajectory. The g_wham utility of GROMACS version 4.5.4 was utilized to remove the biasing potential from umbrella sampling using the weighted histogram analysis method, and to extract a PMF curve and sampling histogram for the translocation process.41


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GM-CSF Simulations The initial protein structure for GM-CSF was obtained from PDB entry 2GMF from the RCSB Protein Data Bank,42 and coarse-grained to 540 atoms according to MARTINI force field specifications. The DSPC lipid bilayer was reassembled with semi-isotropic pressure coupling at 300 K for 30 ns of simulation time. The GM-CSF molecule was combined with the DSPC lipid bilayer in a box of dimensions 10×15×30 nm3, and the box was solvated with 32,028 coarsegrained water molecules. Eight sodium ions were added to the water to neutralize the negative charge on the protein. The methodologies used for energy minimization, equilibration, and umbrella sampling MD simulations were identical to those described above for the TNF-α case. The experiment included 145 umbrella sampling windows along the pull trajectory. Dextran Simulations The density of the EPS for a typical biofilm has been estimated as 75 g carbon / liter.43 This represents a low-density matrix with wide dispersion of polysaccharides. Systems of dextran polymer matrices were created to parallel this characterization with matrix densities of approximately 74 g carbon/liter. Therefore, a system was created of fourteen 100-mer coarsegrained dextran chains of α-D-glucopyronosyl residues with (1,6) linkages. Each glucopyronosyl residue was mapped into three CG beads with polar P2 bead type to maintain the polarity of the hydroxyl groups. The equilibrium bond distances and angles for the glucose units were determined from atomistic simulation using the GROMOS96 45a3 force field. The structural features of 100-mer CG dextran chains were benchmarked against the atomistic pair distribution functions, persistence length, and radius of gyration.


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The dextran system was solvated with CG water, and steepest descent energy minimization was performed to ensure that a minimal force of less than 50 kJ mol-1 nm-2 was present on each atom. The canonical ensemble was employed to relax the system with velocity rescale temperature coupling of water and matrix at 300 K for 5 ns of simulation time. The box was expanded to dimensions 15×15×25 nm3 in the presence of CG water with 12% CG antifreeze water, and a CG TNF-α molecule with the elastic network intact was placed 1.5 nm above the dextran matrix. The methodologies used for system energy minimization and equilibration were identical to those described above for the TNF-α and GM-CSF cases. A COM pull simulation was performed for 2.66 ns over a distance of approximately 16 nm by applying a constant force of 1000 kJ mol-1 nm-2 to the COM of the TNF-α protein. The zcoordinate was defined as the order parameter (ξ). As shown in the Supporting Information, 81 umbrella sampling windows spaced approximately 0.2 nm apart were created along the trajectory from a distance 8 nm above the dextran chains COM, to a distance 8 nm below the dextran chains COM, all with respect to the TNF-α COM. In order to analyze the effect of chain length, all system attributes must be held constant except for dextran chain length, including the pull path. However, performing umbrella sampling for each of the four systems created with four unique pull simulations inherently introduces variability in pull paths. Therefore, following the creation of all 81 umbrella sampling configurations, three new systems were created by effectively chopping the 100-mer dextran chains into 50-mer, 20-mer and 10-mer chains. All systems originated from one pull simulation, contain the same number of dextran and water atoms, and only vary in dextran chain length. This methodology for computing the difference in potential of mean forcefor translocation of TNF-α through low-density dextran matrices of various chain length is new to our knowledge, and allows for the pull path to be held constant


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across all simulations. The interaction of TNF-α signaling factor with the four matrices of various length dextran chains of α-D-glucopyronosyl residues with (1,6) linkages is depicted in Figure 4.

Figure 4. Images of TNF-α (yellow) interacting with the dextran chains of α-D-glucopyronosyl residues with (1,6) linkages (multicolor by chain) of various chain length during the pull simulation; (a) 10mer, (b) 20mer, (c) 50mer and (d) 100mer system configurations.

NPT equilibration with isotropic pressure coupling was performed for 1 ns for the 100-mer dextran chain system, and 5 ns for each of the other three systems, to relax the structure at the particular configuration. The 10-mer, 20-mer and 50-mer dextran chain systems were simulated under NPT for longer than the 100-mer dextran chain system due to the additional equilibration necessary for the “chopped” bonds of the polymer matrix. The methodologies used for energy minimization and equilibration in each window for each system were identical to those described above for the DPSC lipid bilayer and TNF-α case. The umbrella sampling MD simulation time was reduced to 20 ns in each window given that the 100 ns simulation time used in the lipid bilayer umbrella sampling simulations was found to be excessive. Membrane Deformation Analysis


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Membrane deformation was analyzed using the free GRIDMAT-MD program developed by Allen, Lemkul and Bevan.44 The x- and y- box vectors were scaled to fit on a grid of 30 points by 30 points. This means that the actual vectors along the x- and y-dimensions were sliced into 30 sections for analysis by the GRIDMAT-MD program. GRIDMAT-MD analyzed the distance from each PO4 and NC3 atom of each lipid in the top leaflet of the lipid bilayer to its nearest neighbor in the bottom leaflet. This was repeated from the bottom leaflet upward to the top leaflet. The average of these two calculations was also evaluated by the program. II.B. EXPERIMENTAL METHOD Polysaccharide alginate layers were developed on transwell inserts (0.4 µm pore size) by adding 100 µL of 0.3% w/v alginate (Sigma-Aldrich, St. Louis, MO, USA) in deionized water to each well. The transwell inserts were kept in a desiccator for 15 min under vacuum to remove water. Then 50 ng of GM-CSF (R&D systems, Minneapolis, MN) in 100 µL deionized water was added to each well. For the treatment samples, 100 µg/mL alginate lyase (in 100 µL deionized water) was added along with GM-CSF. Then the transwells were transferred to a 12-well plate with 1 mL deionized water in each well to submerge the transwell insert. GM-CSF was allowed to diffuse at 37ºC over 2 h. The samples (10 µL each) were taken from transwell insert (labeled as “top”) and the well underneath (labeled as “bottom”) at 0, 1, and 2 h to determine the amount of GM-CSF using Western blotting. For Western blotting, the protein samples were first separated with electrophoresis using 10% acrylamide gels. Fifty ng GM-CSF was loaded as the positive control sample. After electrophoresis, the gels were transferred to blotting chambers for Western blotting (to PVDF transfer membranes at 250 mA for 2 h) and GM-CSF was detected using mouse-derived anti-


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GM-CSF (1:2000 dilution) as the primary antibody and anti-mouse IgG conjugated with alkaline phosphatase (1:20,000 dilution) as the secondary antibody. BCIP (5-bromo-4-chloro-3-indolylphosphate) and NBT (nitro blue tetrazolium) were used (30 min of incubation with the membranes) to detect the alkaline phosphatase activity of conjugated secondary antibody. This experiment was conducted in triplicate and consistent results were obtained. III.

RESULTS AND DISCUSSION

A potential of mean force (PMF) curve, shown in Figure 2, was extracted from the umbrella sampling process described above for the translocate of TNF-α across a DSPC lipid bilayer. The PMF curve for GM-CSF translocation across a DSPC lipid bilayer is shown in Figure 3. The potential of mean force curve

is computed over all configurations of the system along

translocation path ξ.. The energy barrier associated with the PMF shown in Figure 2 is 1341 kJ mol-1, and the barrier shown in Figure 3 is 1227 kJ mol-1. These are high energy barriers for TNF-α and GM-CSF to translocate passively through the membrane and enter the host cell. It has been previously proposed that adenosine triphosphate (ATP) molecules may work in the presence of a protein to overcome the energy barrier associated with translocation of a molecule across a membrane.31 The computed barrier of 1341 kJ mol-1 suggests that about 38 ATP molecules would be necessary to translocate TNF-α across a DSPC lipid bilayer, and about 34 ATP molecules to translocate GM-CSF across a DSPC lipid bilayer. The average thickness of the model cell membrane has been analyzed for three different windows of the TNF-α translocation simulation. The average lipid bilayer thickness at any given


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point for the analyzed structure is shown along the z-axis in Figure 5. As demonstrated by Fig. 5,

Figure 5. Images of TNF- α interacting with a DSPC lipid bilayer and the corresponding 3D surface contour plot of lipid bilayer thickness for a) TNF-α above the bilayer; b) TNF-α within the bilayer; c) TNF-α having passed through the bilayer.


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as TNF-α (the protein) passes through the lipid bilayer, the thickness of the cell membrane visibly decreases in the area around the protein, and this deformation to the membrane is depicted in the bilayer thickness plots. The contour maps projected onto the xy-plane of the plots in Figure 5 provide additional visual representation of the rate of change of the lipid bilayer thickness at any given point, with each line representing a constant lipid bilayer thickness. This analysis of membrane deformation demonstrates that pore formation is associated with the signaling factor translocation across the lipid bilayer. PMF curves, shown in Figure 6, were extracted from the umbrella sampling process described previously for the translocation of TNF-α through 100-mer, 50-mer, 20-mer and 10-mer dextran chains of α-D-glucopyronosyl residues with (1,6) linkages. TNF-α movement in Figure 6 is from left to right toward the center of the dextran chain model matrices. The results are only shown up to 0 nm, representing the alignment of the TNF-α center of mass with the center of mass of the dextran matrix with respect to the order parameter. The potential of mean force for values less than 0 nm, or distances traversed by the TNF-α SF past the alignment of the center of masses, is theoretically a mirror image of the potential of mean force as the signaling factor approaches the zero point. However, deformation of the dextran matrix during the pull simulation causes numerical artifacts past the zero point alignment of the center of masses.


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Figure 6. PMF profile associated with the translocation of TNF-α through α-D-glucopyronosyl residues with (1,6) linkages of length 100mer (blue), 50mer (green), 20mer (red) and 10mer (purple) showing an energetically favorable process. The PMF curves show an increasing energetic favorability for translocation with decreasing dextran chain length. It is clear from Figure 6 that the energy descent along the TNF-α trajectory is steepest for the 10mer dextran chains, and that a small energy barrier forms along the trajectory as one moves to the 100mer dextran chain curve. It has been proposed that the EPS matrix could serve as a sink for the sequestration of signaling factors.45 The results presented in Figure 6 appear to agree with this proposal. In this case, TNF-α SF would readily translocate through the dextran matrix, but would immediately encounter an energy barrier for exiting once the minimum energy has been reached. To quantify the energy difference between the environments outside and within the matrix, or the stabilization energy, associated with translocation of TNF-α through the dextran chains of various monomer lengths, several calculations were completed on the pull simulation performed. In order to improve the statistical convergence, the 20 ns molecular dynamics simulations in each window along ξ was sliced into several overlapping sets to maximize insight from the limited amount of data available for this CPU time-intensive simulation. Using the GROMACS


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g_wham tool, 10 ns of the total 20 ns simulations was analyzed for each chain length using different initial and final times for conducting a weighted histogram analysis. For the data sets numbered 1 through 4, bootstrap analysis46 was conducted from 5 to 15 ns, 6 to 16 ns, 7 to 17 ns, and 8 to 18 ns. From this sampling setup, it was possible to maximize data extraction from the single simulation that was completed. The average potential of mean force differences, or stabilization energies, (∆PMFav) for all four sets of analyses are summarized for each monomer chain length in Table 1. The results in Table 1 are indicative of a clear relationship between polysaccharide chain length and the stabilization energy. Specifically, as the dextran chain length Table 1. Average stabilization energies (kJ mol-1) for TNF-α and GM-CSF translocation through dextran chains. Chain length

Signaling factors

10mer

20mer

50mer

100mer

TNF-α GM-CSF

71.19±8.11 59.21±4.90

63.42±4.26 55.42±5.31

53.21±5.60 40.20±3.82

21.23±3.13 14.62±2.18

increases, the stabilization energy decreases. A plot of this data depicted in Figure 7 shows a linear relationship between ∆Eav and the polysaccharide chain length, N for both the signaling factors.


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Figure 7. Linear relationship between the TNF-α (blue) and GM-CSF (red) signaling factor stabilization energy, ∆Eav as a function of polysaccharide chain length, N-mer.

Further, we obtained experimental evidence that degradation of the alginate layer promotes the diffusion of GM-CSF. As shown in Figure 8, for samples without alginate lyase, there was no detectable level of GM-CSF below the alginate layer after 2 h of incubation, indicating that no significant diffusion occurred. In contrast, when alginate lyase was added along with GM-CSF, substantial amount of GM-CSF diffused across the alginate layer within 1 h. The results discussed here are consistent with the observations that chain length deterioration within biofilm matrices caused by polysaccharases greatly affect biofilm matrix integrity and properties.5,6,8,11,12


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Figure 8. Western blotting results of GM-CSF diffusion across alginate layers. GM-CSF was added to the top of transwells and the titers of GM-CSF above and below the alginate layers during the 2 h of intubation were compared. The bands indicate GM-CSF detected by anti-GM-CSF (primary antibody) followed by anti-mouse IgG (secondary antibody). A conformational analysis of the dextran polymer matrix-TNF-α signaling factor complex was performed quantitatively by evaluating the radius of gyration and radial distribution functions. Figure 9(a) shows the ratio of the radius of gyration (rg) of the dextran chains to that of the TNFα signaling factor as a function of N. Equation 1 below quantifies this relationship: rg ,dextran rg , protein

= 0.11N 0.51 , where rg, protein ≈ 2.13 nm

(1)

An exponent of ≈ ½ is consistent with mesoscale (Rouse) theory of polymer dynamics under theta solvent conditions. The radial distribution functions of dextran monomers, g(r), are given as a function of the distance from the TNF-α COM for the 10mer, 20mer, and 50mer systems in Figure 9(b). The results from the g(r) calculations indicate that as polymer chain length increases, g(r) becomes broader suggesting the existence of polymer conformations in which substantial portions of the chains are well-separated from the SF resulting in an SF-matrix


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interaction that is weaker overall. Figure 10 shows snapshots of the top and side views of the TNF-α SF within the dextran polymer matrix for the 10mer and 50mer systems. There are more

(a)

(b)

Figure 9. (a) Ratio of the average radius of gyration of the dextran chains in the matrix to the radius of gyration of the TNF-α signaling factor as a function of chain length, and (b) radial distribution function as taken from the center of mass of the TNF-α matrix to the dextran chains within the matrix for the 10mer (purple), 20mer (red), and 50mer (green) length dextran chain systems.


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Figure 10. Visualizations of TNF-α (orange) in a dextran polymer matrix of 10mer chains with a (a) side view and (b) top view, as well as of 50mer chains with a (c) side view and (d) top view. Each chain is colored uniquely and the endpoints are represented as black van der Waals spheres.

polymer endpoint attachments to the SF when the chain length is smaller. The conformation of longer dextran chains are more complex, probing a larger volume around the SF and thus causing a screening effect that blocks adjoining chains from approaching closer to the surface. This phenomenon is observed in polymer brushes formed by the attachment of one end of polymers to a surface or interface while the other ends stretch away from the surface for relatively short


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chains.47,48 As the chain length is increased, the polymer-surface interactions become more complicated: for instance, a long flexible chain could extend away from the surface, fold and attach to the surface at an intermediate position along the chain contour, thereby preventing a second chain from interacting with the surface. Short chains result in a tighter arrangement around the SF and promote stronger dextran-SF interactions and SF stabilization.

IV. CONCLUSIONS Through umbrella sampling molecular dynamics simulations of signaling factor interactions with a DSPC lipid bilayer, the energy barriers associated with TNF-α and GM-CSF translocation across the lipid bilayer have been determined. The process was found to be energetically unfavorable, as expected. Bilayer deformation analysis showed clear pore formation during the course of the TNF-α translocation process. The experimental and simulation results of signaling factor interactions with polysaccharide matrices of various chain lengths demonstrate that the favorability GM-CSF translocation increases with decreasing chain length. The results suggest that increased communication between signaling factors, bacteria, and the host cell can be expected with deterioration of the polysaccharides within the EPS of the biofilm. Specifically, a linear relationship between the stabilization energy, ∆PMFav, and the dextran chain length, N, was shown to exist. Additional analyses of the radius of gyration and radial distribution functions of dextran monomers for different polymer chain lengths were used to explain the stabilization energy variations with respect to dextran configurations around the SF. Specifically, shorter chains are packed tightly around the SF interface promoting favorable interactions between the SF and EPS, while longer chains are packed loosely, causing screening of interactions with neighboring chains. These findings motivate future computational studies using a more detailed


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description of the EPS, aimed at understanding the role of the extracellular matrix on biofilm drug resistance.


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AUTHOR INFORMATION Corresponding Author *Email: [email protected]

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Funding Sources National Science Foundation grants CMMI-1049489 and EFRI-1137186 ACKNOWLEDGMENT We gratefully acknowledge National Science Foundation grants CMMI-1049489 and EFRI1137186 for supporting this research.

ABBREVIATIONS ATP, adenosine triphosphate; CG, coarse-grained; COM, center of mass; DSPC, distearoylphosphatidylcholine; EPS, extracellular polymeric substance; GM-CSF, granulocyte macrophage colony stimulating factor; MD, molecular dynamics; NPT, constant particle number, pressure, and temperature; NVT, constant particle number, volume, and temperature; PMF, potential of mean force; SF, signaling factor; TNF-α, tumor necrosis factor – alpha.


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Table of Contents Graphic

The figure shows that translocation of signaling factors TNF-α (blue) and GM-CSF (red) through extracellular polymeric substances found in bacterial biofilms is enhanced when the polymer chain length is decreased, i.e., signaling factor stabilization energy, ∆Eav increases linearly with decreasing polysaccharide chain length. These predictions obtained from Molecular Dynamics simulations are supported by experimental findings reported in this article that diffusion of GMCSF through EPS is enhanced by the addition of enzymes that cleave the polymer chains.


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Signaling Factor Interactions with Polysaccharide Aggregates of

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