(117f) Modeling the Ability of Lubricin to Reduce Bacterial Proliferation On the Surfaces of Medical Devices

Authors: 
Aninwene, G. II, Northeastern University
Webster, T. J., Northeastern University


Abstract:

Infection of medical devices is a major clinical issue. This study used mathematical modeling to describe and quantify the ability of lubricin, an anti-adhesive glycoprotein found in synovial fluid, to inhibit the proliferation of bacteria on the surfaces of medical devices. In a previous experiment, bacterial growth trials were performed with Staphylococcus aureus and Staphylococcus epidermidis on tissue cultures with either lubricin, vitronectin, or mucin to investigate how lubricin and protein sub-regions of lubricin may reduce bacterial adhesion and proliferation. Mathematical models were created with the resulting data and were used to describe the growth dynamics of these bacteria in cultures with each of these proteins. The effect on lag time (λ), maximum specific growth rate (μm), and the asymptote reached during the stationary phase (A) were determined and compared. Lubricin was found to significantly increase λ of S. aureus and S. epidermidis and significantly decreased both the μm of S. aureus and A of S. aureus and S. epidermidis.

Introduction:

The attachment and proliferation of bacteria on the surface of medical devices threatens the success of these devices. It can lead to a reduction or loss of device function as well as infection, inflammation, and/or damage to the surrounding tissue[1]Staphylococcus aureus and Staphylococcus epidermidis colonize a large percentage of the human population[2] and are two of the major culprits of biofouling, the proliferation of unwanted biological material on the surfaces of an implanted device. Following attachment to surfaces, they can form biofilms which are resistant to antimicrobials and difficult for the host’s immune system to combat[3].

This study examined the ability of lubricin (LUB), a glycoprotein found in the synovial fluid that has lubricating and anti-adhesive properties, to decrease bacteria functions. LUB has a mucin-like center domain and vitronectin-like tail domains[4]. It is thought that LUB can serve as an effective non-immune opsonification agent for resisting bacteria colonization[5]. If bacteria are unable to colonize a surface and form a biofilm, it will be much easier for the immune system and antimicrobial agents to clear the bacteria before they can cause an infection.

The ability of LUB to inhibit bacterial proliferation is quantified in this study by determining the effect that LUB and protein sub-regions of LUB have on the growth curve of each bacterium. This curve is generally separated into several phases including a lag phase, during which the bacteria are adapting to the conditions of their environment and not dividing, an exponential phase during which exponential growth is seen, and a stationary phase during which the growth and death rate are equal[6].  The quantities determined by our modeling were the lag time (the length of the lag phase), the maximum specific growth rate (the greatest growth rate during the acceleration of the exponential phase), and the asymptote (a value directly correlated to the number of organisms in the stationary phase where the growth and death rates are in equilibrium). These values were determined by fitting the growth data to two sigmoid growth functions: logistic and Gompertz.

Methods:

In a previous experiment, 96-well polystyrene plates were treated with either lubricin (LUB), bovine submaxillary mucin (BSM), or bovine vitronectin (VTN). BSM was used to assess the effect of the central mucin-like domain in LUB, and VTN to assess the effect of the globular vitronectin-like tail domains[4]. LUB used in this study was extracted from bovine synovial fluid (Pel-Freez)[7]. BSM and VTN were obtained from Sigma Aldrich. LUB and BSM were used at a concentration of 200µg/mL. VTN was used at a concentration of 50μg/mL. 50μL of each protein solution were dried on the polystyrene well surface overnight.

Staphylococcus aureus (ATCC (25923)) and Staphylococcus epidermidis (ATCC (35984)) obtained from the ATCC (25923) were cultured in tryptic soy broth (TSB) (Sigma Aldrich) for 18 hours to reach stationary phase and were then diluted to a density of 107 bacteria/mL. The bacterial cell culture procedure involved inoculating approximately 3 mL of sterile TSB inoculated with one colony of desired bacteria, then incubating the solution on a shaker set to 200 rpm inside an incubator maintained at 37°C. Bacteria were seeded into the wells and were allowed to incubate for 15 minutes in a stationary incubator maintained at 37°C. After 15 minutes the bacterial solution was removed, the plates were rinsed with sterile PBS, and the wells were filled with 200μL of fresh TSB. Optical density measurements were taken every 4 minutes for 24 hours.

For this study, the growth data (optical density vs time) obtained from these bacterial trials were fit to modified forms of the logistic model and the Gompertz model using MATLAB. The model equations used were modified from their generic forms to contain coefficients for the three bacterial growth parameters lag time (λ), maximum specific growth rate (µm), and asymptote of the stationary phase (A) by deriving formulas to represent the mathematical parameters in terms of the bacterial growth parameters[8]. The goodness-of-fit was determined using R-square and sum of squares for error (SSE) and the models with the best fit were used to determine the value of the three parameters.

Results and Discussion:

The Gompertz and logistic models both fit the bacterial growth data very closely. The Gompertz model had a slightly better fit for 8 out of 10 data sets, based on the R-square and SSE.

S. aureus: LUB, BSM, and VTN all significantly increased the duration of the lag phase (λ) compared to the PBS soaked control. On average, LUB showed a 27% increase (52 minutes), VTN showed a 43% increase (82 minutes), and BSM showed a 29% increase (55 minutes). LUB and BSM decreased the maximum specific growth rate (μm) while VTN increased it. VTN and LUB also decreased the upper asymptote (A). The lag time increases seen with LUB, BSM, and VTN indicate that these proteins were making it more difficult for S. aureus to adapt itself to the environment and start growing exponentially. The decrease in μm seen with LUB and BSM shows that these proteins are reducing the acceleration of the growth phase. Though the acceleration was reduced, BSM reached a similar asymptote in the stationary phase as the PBS soaked control, while LUB also led to a much larger reduction in the asymptote. Interestingly, VTN increased the μm, but still led to a decrease in the asymptote. It was suggested that vitronectin can enhance adhesion in some bacteria[9], which may explain the increase in μm.

S. epidermidis: LUB and BSM significantly increased the lag time while VTN decreased it compared to the PBS soaked control. There was a 35% increase (105 minutes) in lag time with LUB, a 30% increase with BSM (89 minutes), and a 2% decrease (6 minutes) with VTN. LUB and BSM increased the maximum specific growth rate while VTN decreased it and increased the asymptote while VTN decreased it. Though the increase in asymptote caused by LUB is statistically significant, the difference is so small as to not indicate a biological or clinical significance. Once again, a significant increase in lag time is seen with both LUB and BSM indicating that these proteins inhibited S. epidermidis from adapting to the environment and slowed the entry into the exponential phase.

Conclusions:

Lubricin significantly inhibited the ability of both S. aureus and S. epidermidis to attach and proliferate on surfaces. The most striking finding was the large increase in lag time. This is particularly significant because it may give the immune system and antimicrobials more time to clear the bacteria before an infection is established.

References:

[1] a) S. D. Puckett, E. Taylor, T. Raimondo and T. J. Webster, Biomaterials 2010, 31, 706-713; b) D. J. Weber, K. L. Hoffman, R. A. Thoft and A. S. Baker, Review of Infectious Diseases 1986, 8, 12-20.

[2] S. R. Gill, D. E. Fouts, G. L. Archer, E. F. Mongodin, R. T. DeBoy, J. Ravel, I. T. Paulsen, J. F. Kolonay, L. Brinkac, M. Beanan, R. J. Dodson, S. C. Daugherty, R. Madupu, S. V. Angiuoli, A. S. Durkin, D. H. Haft, J. Vamathevan, H. Khouri, T. Utterback, C. Lee, G. Dimitrov, L. Jiang, H. Qin, J. Weidman, K. Tran, K. Kang, I. R. Hance, K. E. Nelson and C. M. Fraser, J. Bacteriol. 2005, 187, 2426-2438.

[3] J. W. Costerton, P. S. Stewart and E. P. Greenberg, Science 1999, 284, 1318-1322.

[4] D. P. Chang, N. I. Abu-Lail, F. Guilak, G. D. Jay and S. Zauscher, Langmuir 2008, 24, 1183-1193.

[5] G. E. I. Aninwene, E. Taylor, D. Hall, A. Mei, G. D. Jay and T. J. Webster, MRS Online Proceedings Library 2012, 1417.

[6] M. L. Shuler and F. Kargi, Bioprocess Engineering: Basic Concepts (Second Edition), Prentice Hall PTR, Upper Saddle River, NJ, 2002, p.

[7] G. D. Jay, D. A. Harris and C.-J. Cha, Glycoconjugate Journal 2001, 18, 807-815.

[8] M. H. Zwietering, I. Jongenburger, F. M. Rombouts and K. Vantriet, Applied and Environmental Microbiology 1990, 56, 1875-1881.

[9] B. Singh, Y. C. Su and K. Riesbeck, Mol Microbiol 2010, 78, 545-560.

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