Classification Of Bacterial Pathogens Research Articles

Design of Rapid Bacterial Identification System Based on Scattering of Laser Light and Classification of Binned Plots.

The rapid detection and classification of bacterial pathogens have great medical utility, and widespread applications in clinical labs, intensive care units, and infectious disease departments. The Rapid Bacterial Identification System (RBIS) is based on the principle of variation in light scattering when the laser beam passes through bacterial microbes. The use of laser light scattering and classification of binned plots algorithm gives a new dimension for real-time identification of different pathogens without any biochemical processing. Bacterium identification device consists of an assembly of photodetectors surrounded by the bacterial sample at different angles. The photodetectors acquire the scattered light in form of peaks when the laser beam passes by the sample. The acquired peak values were used to create 3D histograms to evaluate the frequency of occurrence. However, the identification is based on creating two dimensional binned plots with the help of the frequency of occurrence of peak values across two photodetectors. The algorithm of the system consists of two parts: Library files and the Comparator. Library files contain data of bacterial species in form of binned plots, while comparator compares the data of test sample with library files. The classification of sample depends on the maximum resemblance of the number of binned plots with library files. The classification of Enterococcus faecalis, Staphylococcus aureus, and Escherichia coli gives mean accuracies of 81.8%, 70.9%, and 71.4 %, respectively. The proposed system can be applied to future real-time intelligent theranostic systems for diagnosis and treatment of pathogenic diseases.

Looking Into the Binary Interactome of Enterobacteriaceae Family of Bacteria

Protein-protein interactions (PPIs) regulate most of the biological activities within a cell. A set of pairwise PPIs in seven genera of bacterial pathogens (Salmonella, Escherichia, Shigella, Yersinia, Klebsiella, Photorhabdus, and Pantoea) of Enterobacteriaceae family was analysed. At genotypic level, the correlation coefficient analysis of the mutation spectra of the ten sets of directly interacting protein partners in Escherichia coli recognised all the ‘interacting partners' in Escherichia coli. Extending the correlation analysis to include strains from the rest of the bacterial genera decreased the recognition efficiency providing quantitative evidence that binary interactome have incomplete superposition across species. At phenotype level, a reliable classification of bacterial pathogens was obtained by measuring PPI variations in terms of between phylogenetic distance correlation distances among ten sets of proteins partners. This forces us to rethink upon the possibility of PPI rewiring with a consequent change in physiological role of the same protein.

Steady-state and fluorescence lifetime spectroscopy for identification and classification of bacterial pathogens

This study focuses on identification and classification of Staphylococcus aureus, Pseudomonas aeruginosa, Salmonella typhi and Klebsiella pneumoniae based on steady-state and fluorescence lifetime spectroscopy. Bacterial samples were cultured under controlled conditions and diluted in saline to 4 × 10 6 cell/ml for analysis. Two excitations at 250 nm (aromatic amino acids and nucleic acids, AAA + NA) and 280 nm (tryptophan) were used for steady-state while fluorescence lifetime decays were acquired at 280 nm excitation. A small range of difference was obtained for tryptophan lifetime from the four bacteria for the fast lifetime component (1.61-1.73 ns) and the slow lifetime component (5.25-5.44 ns). The application of principal component analysis (PCA) to steady-state fluorescence spectra resulted in the successful classification of the four bacteria and highest percentage of good classification was obtained for tryptophan (λexc = 280 nm). Our results demonstrated that steady-state fluorescence spectroscopy coupled to PCA is a powerful tool for identification and classification of bacterial pathogens at genus level.

Phylogenetic incongruence in E. coli O104: understanding the evolutionary relationships of emerging pathogens in the face of homologous recombination.

Escherichia coli O104:H4 was identified as an emerging pathogen during the spring and summer of 2011 and was responsible for a widespread outbreak that resulted in the deaths of 50 people and sickened over 4075. Traditional phenotypic and genotypic assays, such as serotyping, pulsed field gel electrophoresis (PFGE), and multilocus sequence typing (MLST), permit identification and classification of bacterial pathogens, but cannot accurately resolve relationships among genotypically similar but pathotypically different isolates. To understand the evolutionary origins of E. coli O104:H4, we sequenced two strains isolated in Ontario, Canada. One was epidemiologically linked to the 2011 outbreak, and the second, unrelated isolate, was obtained in 2010. MLST analysis indicated that both isolates are of the same sequence type (ST678), but whole-genome sequencing revealed differences in chromosomal and plasmid content. Through comprehensive phylogenetic analysis of five O104:H4 ST678 genomes, we identified 167 genes in three gene clusters that have undergone homologous recombination with distantly related E. coli strains. These recombination events have resulted in unexpectedly high sequence diversity within the same sequence type. Failure to recognize or adjust for homologous recombination can result in phylogenetic incongruence. Understanding the extent of homologous recombination among different strains of the same sequence type may explain the pathotypic differences between the ON2010 and ON2011 strains and help shed new light on the emergence of this new pathogen.

Fluorescence Spectroscopy for Rapid Detection and Classification of Bacterial Pathogens

This study deals with the rapid detection and differentiation of Escherichia coli, Salmonella, and Campylobacter, which are the most commonly identified commensal and pathogenic bacteria in foods, using fluorescence spectroscopy and multivariate analysis. Each bacterial sample cultured under controlled conditions was diluted in physiologic saline for analysis. Fluorescence spectra were collected over a range of 200-700 nm with 0.5 nm intervals on the PerkinElmer Fluorescence Spectrometer. The synchronous scan technique was employed to find the optimum excitation (lambda(ex)) and emission (lambda(em)) wavelengths for individual bacteria with the wavelength interval (Deltalambda) being varied from 10 to 200 nm. The synchronous spectra and two-dimensional plots showed two maximum lambda(ex) values at 225 nm and 280 nm and one maximum lambda(em) at 335-345 nm (lambda(em) = lambda(ex) + Deltalambda), which correspond to the lambda(ex) = 225 nm, Deltalambda = 110-120 nm, and lambda(ex) = 280 nm, Deltalambda = 60-65 nm. For all three bacterial genera, the same synchronous scan results were obtained. The emission spectra from the three bacteria groups were very similar, creating difficulty in classification. However, the application of principal component analysis (PCA) to the fluorescence spectra resulted in successful classification of the bacteria by their genus as well as determining their concentration. The detection limit was approximately 10(3)-10(4) cells/mL for each bacterial sample. These results demonstrated that fluorescence spectroscopy, when coupled with PCA processing, has the potential to detect and to classify bacterial pathogens in liquids. The methodology is rapid (>10 min), inexpensive, and requires minimal sample preparation compared to standard analytical methods for bacterial detection.

Label-free detection of multiple bacterial pathogens using light-scattering sensor

Technologies for rapid detection and classification of bacterial pathogens are crucial for securing the food supply. This report describes a light-scattering sensor capable of real-time detection and identification of colonies of multiple pathogens without the need for a labeling reagent or biochemical processing. Bacterial colonies consisting of the progeny of a single parent cell scatter light at 635 nm to produce unique forward-scatter signatures. Zernike moment invariants and Haralick descriptors aid in feature extraction and construction of the scatter-signature image library. The method is able to distinguish bacterial cultures at the genus and species level for Listeria, Staphylococcus, Salmonella, Vibrio, and Escherichia with an accuracy of 90–99% for samples derived from food or experimentally infected animal. Varied amounts of exopolysaccharide produced by the bacteria causes changes in phase modulation distributions, resulting in strikingly different scatter signatures. With the aid of a robust database the method can potentially detect and identify any bacteria colony essentially instantaneously. Unlike other methods, it does not destroy the sample, but leaves it intact for other confirmatory testing, if needed, for forensic or outbreak investigations.