Characterization of Antimicrobial Susceptibility of Bacterial Biofilms on Biological Tissues

abstract: Prosthetic joint infection (PJI) is a devastating complication associated with total joint arthroplasty that results in high cost and patient morbidity. There are approximately 50,000 PJIs per year in the US, imposing a burden of about $5 billion on the healthcare system. PJI is especially difficult to treat because of the presence of bacteria in biofilm, often highly tolerant to antimicrobials. Treatment of PJI requires surgical debridement of infected tissues, and local, sustained delivery of antimicrobials at high concentrations to eradicate residual biofilm bacteria. However, the antimicrobial concentrations required to eradicate biofilm bacteria grown in vivo or on tissue surfaces have not been measured. In this study, an experimental rabbit femur infection model was established by introducing a variety of pathogens representative of those found in PJIs [Staphylococcus Aureus (ATCC 49230, ATCC BAA-1556, ATCC BAA-1680), Staphylococcus Epidermidis (ATCC 35984, ATCC 12228), Enterococcus Faecalis (ATCC 29212), Pseudomonas Aeruginosa (ATCC 27853), Escherichia Coli (ATCC 25922)]. Biofilms of the same pathogens were grown in vitro on biologic surfaces (bone and muscle). The ex vivo and in vitro tissue minimum biofilm eradication concentration (MBEC; the level required to eradicate biofilm bacteria) and minimum inhibitory concentration (MIC; the level required to inhibit planktonic, non-biofilm bacteria) were measured using microbiological susceptibility assays against tobramycin (TOB) and vancomycin (VANC) alone or in 1:1 weight combination of both (TOB+VANC) over three exposure durations (6 hour, 24 hour, 72 hour). MBECs for all treatment combinations (pathogen, antimicrobial used, exposure time, and tissue) were compared against the corresponding MIC values to compare the relative susceptibility increase due to biofilm formation. Our data showed median in vitro MBEC to be 100-1000 times greater than the median MIC demonstrating the administration of local antimicrobial doses at MIC level would not kill the persisting bacteria in biofilm. Also, administering dual agent (TOB+VANC) showed median MBEC values to be comparable or lower than the single agents (TOB or VANC)Dissertation/ThesisMasters Thesis Bioengineering 201

Comparative Proteomic Characterization of Protein Disorder distribution across Eukaryota

<p>An analysis of the complexity of proteomes using SLiMPred, a method to detect short linear motifs in protein sequences.</p

De Novo Short linear motif discovery: Implications for disease understanding and drug discovery in Malaria

<p>Short linear motifs in proteins of 3-8 residues in length play key roles in protein-protein interactions, frequently binding specifically to peptide-binding domains within interacting proteins. These may provide novel drug targets for peptides or small molecules. However, their tendency to be found in disordered segments of proteins has meant that they have often been overlooked. Here, we present SLiMPred (Short Linear Motif Predictor) [1], the first general de novo method to computationally predict such regions in proteins. This is based on ensembles of bidirectional recurrent neural networks, trained in five-fold cross-validation on a non-redundant protein dataset containing known motif instances from the Eukaryotic Linear Motif (ELM)<br>database.</p

Distribution of 4-class (4%, 25% and 50% exposed thresholds) solvent accessibility prediction accuracy as a function of quality of the best hit in PSI-BLAST templates

<p><b>Copyright information:</b></p><p>Taken from "Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information"</p><p>http://www.biomedcentral.com/1471-2105/8/201</p><p>BMC Bioinformatics 2007;8():201-201.</p><p>Published online 14 Jun 2007</p><p>PMCID:PMC1913928.</p><p></p> Quality measured as Resolution+Rfactor/20. The blue bars represent predictions using templates, the red bars template-less predictions. See text for details

An example of prediction by Porter H compared to Porter, DSSP assignments, and best template

<p><b>Copyright information:</b></p><p>Taken from "Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information"</p><p>http://www.biomedcentral.com/1471-2105/8/201</p><p>BMC Bioinformatics 2007;8():201-201.</p><p>Published online 14 Jun 2007</p><p>PMCID:PMC1913928.</p><p></p> Best template sequence similarity is 22%. Porter_H correctly identifies the first helix (from the template – strand in Porter), but does not follow the template and assigns correctly the second strand (helix in the template)

Fight Malaria@Home Boinc Project

<p>FightMalaria@Home<br><br> Crowd-sourcing anti-malarial drug discovery</p

De novo protein motif prediction: Implications for disease understanding

<p>Poster Presented at the Quantitative Bioinformatics Conference organised by Queen's University Belfast 2011.</p> <p> </p> <p>The study of protein-protein interactions (PPI) has dominated the last decade of research into the cell. These experimentally derived catalogues of protein protein associations need to be understood in terms of the functional sites within proteins that mediate their association. Short (less than 10 residues) linear motifs (SLiM) are sequences in the protein that confer the specificity of the association. From post-translational modifications to subcellular localisation signals SLiMs are responsible for a wide range of cellular activities. Here we present work on the identification and prediction of SLiMs using profile and machine learning methods.</p> <p>The pace of technological advancement has outstripped our ability to annotate the information. Bioinformatic approaches to predict and synthesis the information generated are required, allowing for the first time the de novo prediction of SLiMs facilitating a change in the rate at which it is possible to interpret protein association networks. This will lead to a better understanding of fundamental biological processes such as signal transduction, localization and regulation.</p> <p>We carried out genome wide predictions to discover and analyse protein motifs in a range of species. These relied on machine learning methods to predict protein motifs based on the protein sequence and profile (or alignment) based methods that provide a sensitive representation of motif characteristics suitable for inclusion into automated motif discovery pipelines. Our results show the power of our methods to discover novel motifs and will help provide context when searching for instances of known motifs. An application to the characterization of RVxP ciliar localization motif illustrates the potential of our approach. We will also show how these motifs may be used to prioritise experimental validation of oligopeptides with potential bioactivity in modulating disease processes including thrombosis and cancer formation.</p

Distribution of secondary structure prediction accuracy as a function of quality of the best hit in PSI-BLAST templates

<p><b>Copyright information:</b></p><p>Taken from "Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information"</p><p>http://www.biomedcentral.com/1471-2105/8/201</p><p>BMC Bioinformatics 2007;8():201-201.</p><p>Published online 14 Jun 2007</p><p>PMCID:PMC1913928.</p><p></p> Quality measured as Resolution+Rfactor/20. The blue bars represent predictions using templates, the red bars template-less predictions (Porter). See text for details

Distribution of 4-class (4%, 25% and 50% exposed thresholds) solvent accessibility prediction accuracy as a function of sequence similarity to the best hit in PSI-BLAST templates

<p><b>Copyright information:</b></p><p>Taken from "Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information"</p><p>http://www.biomedcentral.com/1471-2105/8/201</p><p>BMC Bioinformatics 2007;8():201-201.</p><p>Published online 14 Jun 2007</p><p>PMCID:PMC1913928.</p><p></p> The blue bars represent predictions using templates (maximal sequence similarity allowed is 95%), the red bars template-less predictions. See text for details

Distribution of best-hit (blue) and average (red) sequence similarity in the PSI-BLAST templates for the S2171 set

<p><b>Copyright information:</b></p><p>Taken from "Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information"</p><p>http://www.biomedcentral.com/1471-2105/8/201</p><p>BMC Bioinformatics 2007;8():201-201.</p><p>Published online 14 Jun 2007</p><p>PMCID:PMC1913928.</p><p></p> Hits above 95% sequence similarity excluded