Microbiome And The Eye

Author : Aaron Barton Sullivan
Publisher :
ISBN 13 :
Total Pages : 91 pages
Book Rating : 4.:/5 (919 download)

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Download or read book P. Aeruginosa Interactions with the Ocular Surface Epithelia written by Aaron Barton Sullivan and published by . This book was released on 2015 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microbial keratitis, a sight-destroying disease that affects wearers of contact lenses, is caused by microbes in the cornea. To succeed in causing disease, the microbe (e.g. bacteria, fungus or virus) must adhere to, penetrate, and traverse the corneal epithelium and enter the stroma. Once they are inside the stroma, the inflammatory response caused by infiltrating erythrocytes, proteins, and fluid results in opacity of the cornea. (Niederkorn, Kaplan et al. 2007) The most common strategy for researching microbial keratitis is to inject bacteria under the epithelium or to scratch the epithelium down to the stroma before application of the bacteria, because the epithelium has barrier functions that prevent both bacterial adhesion and penetration of the cornea. Therefore, the role of the corneal epithelium in this disease has been understudied. Further study of the epithelium is important to find out how bacteria attempt to exploit vulnerabilities in our protective barriers, and even more so to identify the critical barriers providing the protection we need to stay healthy and free from disease. My research focuses on the initial stages of infection and how the bacteria cross the corneal epithelial barrier to begin the process. To that end, I have been studying two virulence systems of Pseudomonas aeruginosa, namely the Type II and Type III secretion systems, which are known to be involved in corneal infections (Hobden 2002, Coburn, Sekirov et al. 2007). The Type II secretion system (T2SS) is an extracellular system that secretes several proteases (including LasA, LasB and AprA) and other virulence factors outside the bacteria to interact with the surface of the host cell. The Type III secretion system (T3SS) is an injection system that uses a needle apparatus to inject virulence factors directly into the host cell. Previous research has shown that a knockout of the entire T3SS stops traversal completely, but which virulence factor is responsible has not yet been identified. Both systems have been shown to be important in bacterial virulence and are associated with severe disease. It is suspected that these systems help penetrate and traverse the corneal epithelium, which is the initial stage in microbial keratitis not caused by deep injury. My first finding reported in this study is that LasB from the T2SS is the protease involved in bacterial traversal of the corneal epithelium. Knockout bacteria lacking the LasB protease are unable to traverse the corneal epithelium to the basal lamina, whereas, in a rescue experiment in which LasB encoded on a plasmid is reintroduced to bacteria lacking LasA and LasB, the bacteria regain the ability to traverse. Though the T3SS is important, the role of individual secreted factors in this system remains unclear. I have used knockout bacteria for all of the secreted effectors (ExoS, ExoT, ExoU, and ExoY) but still observe epithelial disruption and bacterial traversal to the stroma. Further knockouts of the translocon (PopB, PopD) also failed to prevent this outcome. Only knockouts of the T3SS structural proteins pertaining to the needle (PscC and PscD) or to the entire secretion system (ExsA, the master regulator) were able to prevent this process. Therefore, we cannot establish whether any secreted factor is involved in this process, but it is possible that a fourth previously unknown effector is involved, or that the T3SS structural proteins are involved in virulence. I addressed quantification of the relative position of 1μm bacteria within the corneal epithelium in order to quantify bacterial positions and epithelial damage. By means of 3D modeling of confocal microscopy images taken at increasing depths through an eye, the apical surface was reconstructed in Bitplane Imaris software. Then, using a distance transform algorithm in MatLab that I developed, both the distance of individual bacteria from the basement membrane and the epithelial thickness were measured. This allowed quantification and statistical analysis of damage to the layers of the cornea, which offers an advantage over qualitative analysis of images using pathology scoring in terms of both accuracy of object identification and flexibility of application. Pathology scores are subjective with very few criteria by which to score results; therefore, small changes are difficult to quantify in this subjective method of analysis. Using 3D reconstruction methods and advanced distance measurement algorithms, we are able to accurately describe any 3D situation in a variety of ways to suit the data. These methods can also be automated, eliminating the subjective variability provided by observational assessments and allowing both higher throughput of data analysis and replication by multiple users. Several null infection models are discussed in this study. A null infection model is a model where the normal outcome is no disease; the initial stages of disease can be studied through interactions with these models. In addition to evaluating current null infection methods, I discovered a new approach by exposing the corneal surface to 5% FBS. (Fetal Bovine Serum, a common growth medium derived from fetal bovine blood serum) However, the null infection models differed with regard to which bacterial factors were needed for epithelial barrier disruption and bacterial traversal. These differences point to different susceptibilities caused by the models, which may relate to the barriers of infection. Thus, virulence factors used by P. aeruginosa to traverse the corneal epithelium depend upon how host defenses are compromised. I also used models to investigate whether or not a stable microbiome exists on the ocular surface and therefore possibly contributes to protecting the eye, as has been found with microbiomes in other areas of the body. Microbiome analysis uses DNA evidence to identify microbes in a very sensitive assay not dependent on culture techniques that may be unable to grow bacteria with special needs. Our initial analysis of interactions between bacteria found in microbiome analysis and the ocular surface indicate that it is very difficult for bacteria to survive on the ocular surface or within the conjunctival mucosal membrane of the eye, and that this is true for both gram-positive and gram-negative ocular pathogens. Staining experiments, designed to enable a live look at bacteria in the eye, were promising in their ability to stain bacteria but, unfortunately, stained epithelial tissue as well. Human subjects whose ocular washes were collected to grow in multiple nonstandard conditions were equally unable to produce results, indicating that the bacteria implicated in microbiome DNA analysis, some culturable even under standard conditions, may not be actually present- at least not in a live form. There still might be a stable microbiome on the ocular surface that I was unable to find, but current results suggest that this possibility is remote. Further investigation with a murine model is currently underway to determine whether the use of DNA evidence to identify living colonies of microbes can be achieved. Taken together, the research presented in this dissertation advances our understanding of how the ocular surface remains healthy despite exposure to the barrage of potentially pathogenic microbes that exist in our environment. The data continue to support the notion that the healthy ocular surface harbors only transient microbes due to its capacity to rapidly clear even large inocula. They also show that when corneal surface defenses are compromised severely enough to allow bacteria to traverse the epithelial barrier, multiple virulence bacteria factors can contribute, with the details depending on the nature of the corneal compromise and the state of the bacteria. While implicating specific host and bacterial factors, these findings also highlight the importance of mimicking conditions allowing health or susceptibility in animal models, and the need to monitor variability among bacterial isolates from different sources, even for the same strain.