Classification of Clinical Isolates of Klebsiella pneumoniae Based on Their in vitro Biofilm Forming Capabilities and Elucidation of the Biofilm Matrix Chemistry With Special Reference to the Protein Content

Klebsiella pneumoniae is a human pathogen, capable of forming biofilms on abiotic and biotic surfaces. The limitations of the therapeutic options against Klebsiella pneumoniae is actually due to its innate capabilities to form biofilm and harboring determinants of multidrug resistance. We utilized a newer approach for classification of biofilm producing Klebsiella pneumoniae isolates and subsequently we evaluated the chemistry of its slime, more accurately its biofilm. We extracted and determined the amount of polysaccharides and proteins from representative bacterial biofilms. The spatial distribution of sugars and proteins were then investigated in the biofilm matrix using confocal laser scanning microscopy (CLSM). Thereafter, the extracted matrix components were subjected to sophisticated analysis incorporating Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, one-dimensional gel-based electrophoresis (SDS-PAGE), high performance liquid chromatography (HPLC), and MALDI MS/MS analysis. Besides, the quantification of its total proteins, total sugars, uronates, total acetyl content was also done. Results suggest sugars are not the only/major constituent of its biofilms. The proteins were harvested and subjected to SDS-PAGE which revealed various common and unique protein bands. The common band was excised and analyzed by HPLC. MALDI MS/MS results of this common protein band indicated the presence of different proteins within the biofilm. The 55 different proteins were identified including both cytosolic and membrane proteins. About 22 proteins were related to protein synthesis and processing while 15 proteins were identified related to virulence. Similarly, proteins related to energy and metabolism were 8 and those related to capsule and cell wall synthesis were 4. These results will improve our understanding of Klebsiella biofilm composition and will further help us design better strategies for controlling its biofilm such as techniques focused on weakening/targeting certain portions of the slime which is the most common building block of the biofilm matrix

<i>In silico</i> Platform for Prediction of N-, O- and C-Glycosites in Eukaryotic Protein Sequences

<div><p>Glycosylation is one of the most abundant and an important post-translational modification of proteins. Glycosylated proteins (glycoproteins) are involved in various cellular biological functions like protein folding, cell-cell interactions, cell recognition and host-pathogen interactions. A large number of eukaryotic glycoproteins also have therapeutic and potential technology applications. Therefore, characterization and analysis of glycosites (glycosylated residues) in these proteins is of great interest to biologists. In order to cater these needs a number of <i>in silico</i> tools have been developed over the years, however, a need to get even better prediction tools remains. Therefore, in this study we have developed a new webserver GlycoEP for more accurate prediction of N-linked, O-linked and C-linked glycosites in eukaryotic glycoproteins using two larger datasets, namely, standard and advanced datasets. In case of standard datasets no two glycosylated proteins are more similar than 40%; advanced datasets are highly non-redundant where no two glycosites’ patterns (as defined in methods) have more than 60% similarity. Further, based on our results with several algorihtms developed using different machine-learning techniques, we found Support Vector Machine (SVM) as optimum tool to develop glycosite prediction models. Accordingly, using our more stringent and non-redundant advanced datasets, the SVM based models developed in this study achieved a prediction accuracy of 84.26%, 86.87% and 91.43% with corresponding MCC of 0.54, 0.20 and 0.78, for N-, O- and C-linked glycosites, respectively. The best performing models trained on advanced datasets were then implemented as a user-friendly web server GlycoEP (<a href="http://www.imtech.res.in/raghava/glycoep/" target="_blank">http://www.imtech.res.in/raghava/glycoep/</a>). Additionally, this server provides prediction models developed on standard datasets and allows users to scan sequons in input protein sequences.</p></div

Performances of various models on standard datasets in term of ROC, for N-, O- and C-linked glycosites (Panel A, B and C, respectively) in eukaryotic proteins.

<p>Performances of various models on standard datasets in term of ROC, for N-, O- and C-linked glycosites (Panel A, B and C, respectively) in eukaryotic proteins.</p

Flowchart showing process for creating various datasets used for developing GlycoEP models.

<p>Flowchart showing process for creating various datasets used for developing GlycoEP models.</p

The performance of sequon (motifs) detection in N-linked glycosylation using five independent glycoproteins on GlycoEP server.

<p>The performance of sequon (motifs) detection in N-linked glycosylation using five independent glycoproteins on GlycoEP server.</p

The performance of models developed on advanced datasets for predicting N-linked, O-linked and C-linked glycosites.

<p>As well as performance on balanced patterns of advanced datasets (results with standard deviation of five fold).</p

Comparative performances of existing method with our model developed on standard datasets.

<p>Note: GlycoEP -<a href="http://www.imtech.res.in/raghava/glycoep/" target="_blank">http://www.imtech.res.in/raghava/glycoep/</a>, 1- <a href="http://www.comp.chem.nottingham.ac.uk/glyco/" target="_blank">http://www.comp.chem.nottingham.ac.uk/glyco/</a>, 2- <a href="http://www.turing.cs.iastate.edu/EnsembleGly/" target="_blank">http://www.turing.cs.iastate.edu/EnsembleGly/</a>, 3- <a href="http://www.cbs.dtu.dk/services/NetNGlyc/" target="_blank">http://www.cbs.dtu.dk/services/NetNGlyc/</a>, 4- <a href="http://www.cbs.dtu.dk/services/NetOGlyc" target="_blank">http://www.cbs.dtu.dk/services/NetOGlyc</a>.</p

The performance of models on an independent datasets, these models were developed on standard datasets.

<p>The performance of models on an independent datasets, these models were developed on standard datasets.</p

The process of creating of overlapping patterns in a glycoproteins and assigning glycosylated and non-glycosylated patterns.

<p>The process of creating of overlapping patterns in a glycoproteins and assigning glycosylated and non-glycosylated patterns.</p