Classification Of Bacterial Species Research Articles

Use of polymers as wavenumber calibration standards in deep-UVRR

Deep-UV resonance Raman spectroscopy (UVRR) allows the classification of bacterial species with high accuracy and is a promising tool to be developed for clinical application. For this attempt, the optimization of the wavenumber calibration is required to correct the overtime changes of the Raman setup. In the present study, different polymers were investigated as potential calibration agents. The ones with many sharp bands within the spectral range 400–1900 cm−1 were selected and used for wavenumber calibration of bacterial spectra. Classification models were built using a training cross-validation dataset that was then evaluated with an independent test dataset obtained after 4 months. Without calibration, the training cross-validation dataset provided an accuracy for differentiation above 99 % that dropped to 51.2 % after test evaluation. Applying the test evaluation with PET and Teflon calibration allowed correct assignment of all spectra of Gram-positive isolates. Calibration with PS and PEI leads to misclassifications that could be overcome with majority voting. Concerning the very closely related and similar in genome and cell biochemistry Enterobacteriaceae species, all spectra of the training cross-validation dataset were correctly classified but were misclassified in test evaluation. These results show the importance of selecting the most suitable calibration agent in the classification of bacterial species and help in the optimization of the deep-UVRR technique.

Machine learning classification of bacterial species using mix-and-match reagents on paper microfluidic chips and smartphone-based capillary flow analysis.

Traditionally, specific bioreceptors such as antibodies have rapidly identified bacterial species in environmental water samples. However, this method has the disadvantages of requiring an additional process to conjugate or immobilize bioreceptors on the assay platform, which becomes unstable at room temperature. Here, we demonstrate a novel mix-and-match method to identify bacteria species by loading the bacterial samples with simple bacteria interacting components (not bioreceptors), such as lipopolysaccharides, peptidoglycan, and bovine serum albumin, and carboxylated particles, all separately on multiple channels. Neither covalent conjugation nor surface immobilization was necessary. Interactions between bacteria and the above bacteria interacting components resulted in varied surface tension and viscosity, leading to various flow velocities of capillary action through the paper fibers. The smartphone camera and a custom Python code recorded multiple channel flow velocity, each loaded with different bacteria interacting components. A multi-dimensional data set was obtained for a given bacterial species and concentration and used as a machine learning training model. A support vector machine was applied to classify the six bacterial species: Escherichia coli, Salmonella Typhimurium, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecium, and Bacillus subtilis. Under optimized conditions, the training model predicts the bacterial species with an accuracy of > 85% of the six bacteria species.

In-Depth Analysis of Bacillus anthracis 16S rRNA Genes and Transcripts Reveals Intra- and Intergenomic Diversity and Facilitates Anthrax Detection.

ABSTRACTAnalysis of 16S rRNA (rRNA) genes provides a central means of taxonomic classification of bacterial species. Based on presumed sequence identity among species of the Bacillus cereus sensu lato group, the 16S rRNA genes of B. anthracis have been considered unsuitable for diagnosis of the anthrax pathogen. With the recent identification of a single nucleotide polymorphism in some 16S rRNA gene copies, specific identification of B. anthracis becomes feasible. Here, we designed and evaluated a set of in situ, in vitro, and in silico assays to assess the unknown 16S state of B. anthracis from different perspectives. Using a combination of digital PCR, fluorescence in situ hybridization, long-read genome sequencing, and bioinformatics, we were able to detect and quantify a unique 16S rRNA gene allele of B. anthracis (16S-BA-allele). This allele was found in all available B. anthracis genomes and may facilitate differentiation of the pathogen from any close relative. Bioinformatics analysis of 959 B. anthracis SRA data sets inferred that abundances and genomic arrangements of the 16S-BA-allele and the entire rRNA operon copy numbers differ considerably between strains. Expression ratios of 16S-BA-alleles were proportional to the respective genomic allele copy numbers. The findings and experimental tools presented here provide detailed insights into the intra- and intergenomic diversity of 16S rRNA genes and may pave the way for improved identification of B. anthracis and other pathogens with diverse rRNA operons.IMPORTANCE For severe infectious diseases, precise pathogen detection is crucial for antibiotic therapy and patient survival. Identification of Bacillus anthracis, the causative agent of the zoonosis anthrax, can be challenging when querying specific nucleotide sequences such as in small subunit rRNA (16S rRNA) genes, which are commonly used for typing of bacteria. This study analyzed on a broad genomic scale a cryptic and hitherto underappreciated allelic variant of the bacterium’s 16S rRNA genes and their transcripts using a set of in situ, in vitro, and in silico assays and found significant intra- and intergenomic heterogeneity in the distribution of the allele and overall rRNA operon copy numbers. This allelic variation was uniquely species specific, which enabled sensitive pathogen detection on both DNA and transcript levels. The methodology used here is likely also applicable to other pathogens that are otherwise difficult to discriminate from their less harmful relatives.

Fuzzy characterization and classification of bacteria species detected at single-cell level by surface-enhanced Raman scattering

Advanced chemometric methods, such as fuzzy c-means, a semi-supervised clustering method, and fuzzy linear discriminant analysis (FLDA), a new robust supervised classification method in combination with principal component analysis (PCA), namely PCA-FLDA, have been successfully applied for characterization and classification of bacterial species detected at single-cell level by surface-enhanced Raman scattering (SERS) spectroscopy. SERS spectra of three species (S. aureus, E. faecalis and P. aeruginosa) were recorded in an original fashion, using in situ laser induced silver spot as metallic substrate. The detection process of bacteria was isolated inside a hermetically sealed in-house built microfluidic device, connected to a syringe pump for injecting the analytes and a portable Raman spectrometer as detection tool. The obtained results (fuzzy partitions) and spectra of the prototypes (robust fuzzy spectra mean corresponding to each fuzzy partition) clearly demonstrated the efficiency and information power of the advanced fuzzy methods in bacteria characterization and classification based on SERS spectra, and allowed a rationale assigning to a specific group. Also, this powerful detection and classification methodology generates the premises for future investigations of Raman and other spectroscopic data obtained for various samples.

Unified Classification of Bacterial Colonies on Different Agar Media Based on Hyperspectral Imaging and Machine Learning.

A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive classification of three kinds of bacterial colonies (Escherichia coli, Staphylococcus aureus and Salmonella) cultured on three kinds of agar media (Luria–Bertani agar (LA), plate count agar (PA) and tryptone soy agar (TSA)). Based on the extracted spectral data, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were employed to established classification models. The parameters of SVM models were optimized by comparing genetic algorithm (GA), particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). The best classification model was GOA-SVM, where the overall correct classification rates (OCCRs) for calibration and prediction of the full-wavelength GOA-SVM model were 99.45% and 98.82%, respectively, and the Kappa coefficient for prediction was 0.98. For further investigation, the CARS, SPA and GA wavelength selection methods were used to establish GOA-SVM simplified model, where CARS-GOA-SVM was optimal in model accuracy and stability with the corresponding OCCRs for calibration and prediction and the Kappa coefficients of 99.45%, 98.73% and 0.98, respectively. The above results demonstrated that it was feasible to classify bacterial colonies on different agar media and the unified model provided a continent and accurate way for bacterial classification.

Genome-Based Reclassification of Fusobacterium nucleatum Subspecies at the Species Level.

Fusobacterium nucleatum is classified as four subspecies, subsp. nucleatum, polymorphum, vincentii, and animalis, based on DNA-DNA hybridization (DDH) patterns, phenotypic characteristics, and/or multilocus sequence analysis (MLSA). The gold standards for classification of bacterial species are DDH and 16S ribosomal RNA gene (16S rDNA) sequence homology. The thresholds of DDH and 16S rDNA similarity for delineation of bacterial species have been suggested to be >70 and 98.65%, respectively. Average nucleotide identity (ANI) and genome-to-genome distance (GGD) analysis based on genome sequences were recently introduced as a replacement for DDH to delineate bacterial species with ANI (95-96%) and GGD (70%) threshold values. In a previous study, F. hwasookii was classified as a new species based on MLSA and DDH results. 16S rDNA similarity between F. hwasookii type strain and F. nucleatum subspecies type strains was higher than that between F. nucleatum subspecies type strains. Therefore, it is possible that the four F. nucleatum subspecies can be classified as Fusobacterium species. In this study, we performed ANI and GGD analyses using the genome sequences of 36 F. nucleatum, five F. hwasookii, and one Fusobacterium periodonticum strain to determine whether the four F. nucleatum subspecies could be classified as species using OrthoANI and ANI web-based softwares provided by ChunLab and Kostas lab, respectively, and GGD calculator offered by German Collection of Microorganisms and Cell Cultures. ANI values calculated from OrthoANI and ANI calculators between the type strains of F. nucleatum subspecies ranged from 89.80 to 92.97 and from 90.40 to 91.90%, respectively. GGD values between the type strains of F. nucleatum subspecies ranged from 42.3 to 46.0%. ANI and GGD values among strains belonging to the same F. nucleatum subspecies, subsp. nucleatum, subsp. polymorphum, subsp. vincentii, and subsp. animalis were >96 and >68.2%, respectively. These results strongly suggest that F. nucleatum subsp. nucleatum, subsp. polymorphum, subsp. vincentii, and subsp. animalis should be classified as F. nucleatum, F. polymorphum, F. vincentii, and F. animalis, respectively.

Molecular typing and diagnosis of Anaplasma spp. closely related to Anaplasma phagocytophilum in ruminants from Tunisia

Accurate diagnosis of animal and zoonotic diseases, such as granulocytic anaplasmosis, is crucial to estimate risk during control programs. In this study, 16S rRNA nested PCR and RFLP assay were combined to investigate the presence of Anaplasma phagocytophilum and genetically related strains (namely A. phagocytophilum-like 1 and 2) in 936 Tunisian ruminants. By using this method, A. phagocytophilum was not detected in any of the tested animals, while A. phagocytophilum-like 1 and A. phagocytophilum-like 2 were detected at variable prevalence rates in sheep, goats and cattle at coinfection rates respectively of 3.9, 2.5 and 0.5%. Sequence analysis validated RFLP data, and confirmed the co-occurrence of two potentially novel species closely related to A. phagocytophilum in Tunisian ruminants. Phylogeny indicated the presence of genetic variants shared by different ruminant species for each type of A. phagocytophilum-like strains. Results raise concern on the use and interpretation of indirect and direct tests traditionally employed for detecting pathogenic A. phagocytophilum strains in ruminants and in other vertebrates’ species, and provide additional background to improve classification of bacterial species closely related to A. phagocytophilum, and to reconstruct their evolutionary history.

Phylogenomic Analyses and Reclassification of Species within the Genus Tsukamurella: Insights to Species Definition in the Post-genomic Era

Owing to the highly similar phenotypic profiles, protein spectra and 16S rRNA gene sequences observed between three pairs of Tsukamurella species (Tsukamurella pulmonis/Tsukamurella spongiae, Tsukamurella tyrosinosolvens/Tsukamurella carboxy-divorans, and Tsukamurella pseudospumae/Tsukamurella sunchonensis), we hypothesize that and the six Tsukamurella species may have been misclassified and that there may only be three Tsukamurella species. In this study, we characterized the type strains of these six Tsukamurella species by tradition DNA–DNA hybridization (DDH) and “digital DDH” after genome sequencing to determine their exact taxonomic positions. Traditional DDH showed 81.2 ± 0.6% to 99.7 ± 1.0% DNA–DNA relatedness between the two Tsukamurella species in each of the three pairs, which was above the threshold for same species designation. “Digital DDH” based on Genome-To-Genome Distance Calculator and Average Nucleotide Identity for the three pairs also showed similarity results in the range of 82.3–92.9 and 98.1–99.1%, respectively, in line with results of traditional DDH. Based on these evidence and according to Rules 23a and 42 of the Bacteriological Code, we propose that T. spongiae Olson et al. 2007, should be reclassified as a later heterotypic synonym of T. pulmonis Yassin et al. 1996, T. carboxydivorans Park et al. 2009, as a later heterotypic synonym of T. tyrosinosolvens Yassin et al. 1997, and T. sunchonensis Seong et al. 2008 as a later heterotypic synonym of T. pseudospumae Nam et al. 2004. With the advancement of genome sequencing technologies, classification of bacterial species can be readily achieved by “digital DDH” than traditional DDH.

Modern approach to the identification and elimination of periodontal infection

In global terms, periodontitis is the leading cause of tooth loss, and causes serious aesthetic and functional problems. There are numerous scientific evidence that power periopathogenic microorganisms may have influence on some systemic conditions and diseases. If we add the fact that periimplantitis are caused by identical periopathogenes, management or effective identification and elimination of periodontal inflammation set as imperative in successful periodontal treatment and possible complications locally or systemic, as well as resistance and lifetime of the implants. As a method for quantifying and classification of bacterial species are recommended monitoring of the bacterial micro flora through PCR (polymerase chain reaction) technique. Laser therapy as a supplementary method to classic treatment is strongly emphasized as a new therapeutic option that uses the photo-thermal effect to fight bacterial infection. Air flow - an original method that uses water, air and powder help to destroy the biofilm and remove plaque. Oral application of LAD (Light activated disinfection) therapy influence nascent oxygen breaks down the interaction in biofilm and makes the microorganisms more sensitive to further damage. Finally, the role and use of probiotics as a link between the beneficial oral bacteria species and inflammation.

ABOid: A Software for Automated Identification and Phyloproteomics Classification of Tandem Mass Spectrometric Data

We have developed suite of bioinformatics algorithms for automated identification and classification of microbes based on comparative analysis of protein sequences. This application uses sequence information of microbial proteins revealed by mass spectrometry-based proteomics for identification and phyloproteomics classification. The algorithms transforms results of searching product ion spectra of peptide ions against a protein database, performed by commercially available software (e.g. SEQUEST), into a taxonomically meaningful and easy to interpret output. To achieve this goal we constructed a custom protein database composed of theoretical proteomes derived from all fully sequenced bacterial genomes (1204 microorganisms as of August 25th, 2010) in a FASTA format. Each protein sequence in the database is supplemented with information on a source organism and chromosomal position of each protein coding open reading frame (ORF) is embedded into the protein sequence header. In addition this information is linked with a taxonomic position of each database bacterium. ABOid analyzes SEQUEST search results files to provide the probabilities that peptide sequence assignments to a product ion mass spectrum (MS/MS) are correct and uses the accepted spectrum–to-sequence matches to generate a sequence-to-organism (STO) matrix of assignments. Because peptide sequences are differentially present or absent in various strains being compared this allows for the classification of bacterial species in a high throughput manner. For this purpose, STO matrices of assignments, viewed as assignment bitmaps, are next analyzed by a ABOid module that uses phylogenetic relationships between bacterial species as a part of decision tree process, and by applying multivariate statistical techniques (principal component and cluster analysis), to reveal relationship of the analyzed unknown sample to the database microorganisms. Our bacterial classification and identification algorithm uses assignments of an analyzed organism to taxonomic groups based on an organized scheme that begins at the phylum level and follows through classes, orders, families and genus down to strain level.

GENOME TREES AND THE NATURE OF GENOME EVOLUTION

Genome trees are a means to capture the overwhelming amount of phylogenetic information that is present in genomes. Different formalisms have been introduced to reconstruct genome trees on the basis of various aspects of the genome. On the basis of these aspects, we separate genome trees into five classes: (a) alignment-free trees based on statistic properties of the genome, (b) gene content trees based on the presence and absence of genes, (c) trees based on chromosomal gene order, (d) trees based on average sequence similarity, and (e) phylogenomics-based genome trees. Despite their recent development, genome tree methods have already had some impact on the phylogenetic classification of bacterial species. However, their main impact so far has been on our understanding of the nature of genome evolution and the role of horizontal gene transfer therein. An ideal genome tree method should be capable of using all gene families, including those containing paralogs, in a phylogenomics framework capitalizing on existing methods in conventional phylogenetic reconstruction. We expect such sophisticated methods to help us resolve the branching order between the main bacterial phyla.

Evaluation of the Petrifilm™ Culture System for the Identification of Mastitis Bacteria as Compared to Standard Bacteriological Methods

The largest cost associated with treating clinical mastitis during lactation is the discarded milk during and after therapy. To justify this cost, one should ensure that the present infection will respond to antibiotic therapy. Most intramammary antibiotics target gram-positive cocci organisms, and are not particularly effective against Mycoplasma spp, yeasts and mild infections caused by gram-negative pathogens. Similarly, research has demonstrated that other organisms, such as Streptococcus uberis, are highly responsive to intramammary antibiotics, or that the cure rates achieved by various intramammary treatments are not significantly better than no treatment. Thus, there has been an increasing demand for research towards the development of on-farm mastitis culturing media for early identification and accurate classification of bacterial species. Petrifilm™ plates are selective culture media products, which are used for rapid bacteriological isolation and enumeration from food products. The 3M Petrifilm™ products are small, playing card size, sample-ready plates that will allow users to easily and efficiently perform on-site microbial identification. Petriflim™ plates that are potentially useful for mastitis diagnoses are the Aerobic Count Plates, Coliform Count Plates, and Staph Express Count Plates, due to their ease of use, and the ability to reach a diagnosis in as little as 24 hours. The usefulness of the Petrifilm™ culture system for the diagnosis of mastitis has been previously reported.1 The objective of the current project was to determine the test characteristics of Petrifilm™ to characterize the causative organism in producer-defined cases of clinical mastitis.

Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis

Robust computer algorithms are required to interpret the vast amounts of proteomic data currently being produced and to generate generalized models which are applicable to 'real world' scenarios. One such scenario is the classification of bacterial species. These vary immensely, some remaining remarkably stable whereas others are extremely labile showing rapid mutation and change. Such variation makes clinical diagnosis difficult and pathogens may be easily misidentified. We applied artificial neural networks (Neuroshell 2) in parallel with cluster analysis and principal components analysis to surface enhanced laser desorption/ionization (SELDI)-TOF mass spectrometry data with the aim of accurately identifying the bacterium Neisseria meningitidis from species within this genus and other closely related taxa. A subset of ions were identified that allowed for the consistent identification of species, classifying >97% of a separate validation subset of samples into their respective groups. Neuroshell 2 is commercially available from Ward Systems.

Molecular and evolutionary relationships among enteric bacteria

Classification of bacterial species into genera has traditionally relied upon variation in phenotypic characteristics. However, these phenotypes often have a multifactorial genetic basis, making unambiguous taxonomic placement of new species difficult. By designing evolutionarily conserved oligonucleotide primers, it is possible to amplify homologous regions of genes in diverse taxa using the polymerase chain reaction and determine their nucleotide sequences. We have constructed a phylogeny of some enteric bacteria, including five species classified as members of the genus Escherichia, based on nucleotide sequence variation at the loci encoding glyceraldehyde-3-phosphate dehydrogenase and outer membrane protein 3A, and compared this genealogy with the relationships inferred by biotyping. The DNA sequences of these genes defined congruent and robust phylogenetic trees indicating that they are an accurate reflection of the evolutionary history of the bacterial species. The five species of Escherichia were found to be distantly related and, contrary to their placement in the same genus, do not form a monophyletic group. These data provide a framework which allows the relationships of additional species of enteric bacteria to be inferred. These procedures have general applicability for analysis of the classification, evolution, and epidemiology of bacterial taxa.