MALDI-TOFMS compared with other polyphasic taxonomy approaches for the identification and classification of Bacillus pumilus spores

To verify the efficacy of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) protein profiling for identifying and differentiating bacterial species, several strains of Bacillus pumilus were examined in a thorough taxonomic study incorporating a polyphasic approach. Sixteen isolates of putative B. pumilus isolated from spacecraft assembly facilities, the Mars Odyssey spacecraft, and the International Space Station, were characterized for their biochemical and molecular profiles using the Biolog system, DNA techniques, and MALDI-TOFMS protein profiling. MALDI-TOFMS protein profiling was more accurate than Biolog metabolic profiling, more discriminating than 16S rDNA sequence analysis, and complemented the results of gyrB sequence analysis and DNA-DNA hybridization for the identification of the B. pumilus spores. This is the first report whereby MALDI-TOFMS generated protein profiles from a set of microbes is compared directly with DNA-DNA hybridization yielding a positive correlation. Unique, cluster-specific biomarker peaks have been identified in the spores of the B. pumilus examined in this study. MALDI-TOFMS protein profiling is a rapid and simple analysis and has been demonstrated as a useful taxonomic tool for differentiating spores of the genus Bacillus. For practical purposes, it would be ideal (and necessary) to have a publicly available, standardized MALDI profile database, to facilitate the use of the technique as a diagnostic method to differentiate bacterial species.     

A new approach to the species classification problem in floristic analysis

A new method of species (inverse) classification of vegetation data, i.e. classification of species into groups with similar ecological tolerances, is presented which overcomes the problems of species abundance distorting the results. The algorithm TWO-STEP is based on the use of an asymmetric measure of dissimilarity: where i, j are species, h is the stand, n is the total number of stands, and x_ih is the amount of species i in stand h. The algorithm uses the rows of the asymmetric dissimilarity matrix generated as above to form a second symmetric dissimilarity matrix using the measure: where m is the number of species and k the species. Flexible sorting is applied to generate a species classification. Comparison of results after applying the TWO-STEP algorithm and a standard alternative to an artificial data set demonstrates its efficacy. TWO-STEP also shows considerable advantages over previous analyses for a Queensland rainforest data set (quantitative) and an English heath (qualitative) data set. Normalization of species data appears advantageous for quantitative data only.     

A metabolomics approach to characterise and identify various Mycobacterium species

We investigated the potential use of gas chromatography mass spectrometry (GC-MS), in combination with multivariate statistical data processing, to build a model for the classification of various tuberculosis (TB) causing, and non-TB Mycobacterium species, on the basis of their characteristic metabolite profiles. A modified Bligh-Dyer extraction procedure was used to extract lipid components from Mycobacterium tuberculosis, Mycobacterium avium, Mycobacterium bovis, and Mycobacterium kansasii cultures. Principle component analyses (PCA) of the GC-MS generated data showed a clear differentiation between all the Mycobacterium species tested. Subsequently, the 12 compounds best describing the variation between the sample groups were identified as potential metabolite markers, using PCA and partial least-squares discriminant analysis (PLS-DA). These metabolite markers were then used to build a discriminant classification model based on Bayes' theorem, in conjunction with multivariate kernel density estimation. This model subsequently correctly classified 2 "unknown" samples for each of the Mycobacterium species analysed, with probabilities ranging from 72 to 100%. Furthermore, Mycobacterium species classification could be achieved in less than 16 h, and the detection limit for this approach was 1×10(3)bacteriamL(-1). This study proves the capacity of a GC-MS, metabolomics pattern recognition approach for its possible use in TB diagnostics and disease characterisation.     

A single PCR-restriction endonuclease analysis for rapid identification of Malassezia species

AIMS: The present study describes a system based on PCR and restriction endonuclease analysis (REA) to distinguish the seven currently recognized Malassezia species. METHODS AND RESULTS: Fifty-five representative yeast isolates were examined. A single primer pair was designed to amplify the large subunit ribosomal RNA (LSU rRNA) gene of the seven Malassezia species, and identification was achieved by digestion of the PCR products with three restriction endonucleases: BanI, HaeII and MspI. A specific restriction endonuclease analysis pattern was determined for each species investigated. Moreover, PCR-REA allowed the detection and characterization of mixtures of several Malassezia species. CONCLUSION: PCR-REA of only the LSU rRNA gene is a reliable and rapid method to distinguish all Malassezia species. SIGNIFICANCE AND IMPACT OF THE STUDY: PCR-REA represents a considerable saving in time over currently available identification procedures. This method should be evaluated on clinical material directly.     

对几起疑似野生动物肉食品的分子遗传学分析

长期以来,非法捕猎是威胁物种多样性及保护的一个国际性难题.由于偷猎对象往往涉及一些国家保护的物种,因此在执法过程中需要对偷猎对象进行准确的物种鉴定,以作为执法的依据.     

A simple method for the rapid removal of Bacillus anthracis spores from DNA preparations

This study establishes a filtration method for the safe removal of Bacillus anthracis spores which may contaminate DNA preparations. Centrifugal filtration with 0.1-microm filter units can be used following extraction of DNA from B. anthracis spores to render samples safe without compromising the sensitivity of diagnostic real-time PCR assays for B. anthracis.     

Nuclear and Wolbachia-based multimarker approach for the rapid and accurate identification of tsetse species

Background

Tsetse flies (Diptera: Glossinidae) are solely responsible for the transmission of African trypanosomes, causative agents of sleeping sickness in humans and nagana in livestock. Due to the lack of efficient vaccines and the emergence of drug resistance, vector control approaches such as the sterile insect technique (SIT), remain the most effective way to control disease. SIT is a species-specific approach and therefore requires accurate identification of natural pest populations at the species level. However, the presence of morphologically similar species (species complexes and sub-species) in tsetse flies challenges the successful implementation of SIT-based population control.

Results

In this study, we evaluate different molecular tools that can be applied for the delimitation of different Glossina species using tsetse samples derived from laboratory colonies, natural populations and museum specimens. The use of mitochondrial markers, nuclear markers (including internal transcribed spacer 1 (ITS1) and different microsatellites), and bacterial symbiotic markers (Wolbachia infection status) in combination with relatively inexpensive techniques such as PCR, agarose gel electrophoresis, and to some extent sequencing provided a rapid, cost effective, and accurate identification of several tsetse species.

Conclusions

The effectiveness of SIT benefits from the fine resolution of species limits in nature. The present study supports the quick identification of large samples using simple and cost effective universalized protocols, which can be easily applied by countries/laboratories with limited resources and expertise.

     

Development and application of CRISPR-based genetic tools in Bacillus species and Bacillus phages

Recently, the clustered regularly interspaced short palindromic repeats (CRISPR) system has been developed into a precise and efficient genome editing tool. Since its discovery as an adaptive immune system in prokaryotes, it has been applied in many different research fields including biotechnology and medical sciences. The high demand for rapid, highly efficient and versatile genetic tools to thrive in bacteria-based cell factories accelerates this process. This review mainly focuses on significant advancements of the CRISPR system in Bacillus subtilis, including the achievements in gene editing, and on problems still remaining. Next, we comprehensively summarize this genetic tool's up-to-date development and utilization in other Bacillus species, including B. licheniformis, B. methanolicus, B. anthracis, B. cereus, B. smithii and B. thuringiensis. Furthermore, we describe the current application of CRISPR tools in phages to increase Bacillus hosts' resistance to virulent phages and phage genetic modification. Finally, we suggest potential strategies to further improve this advanced technique and provide insights into future directions of CRISPR technologies for rendering Bacillus species cell factories more effective and more powerful.     

Small-scale DNA preparation for rapid genetic identification of Campylobacter species without radioisotope

A simplified and rapid genetic identification method for Campylobacter species without radioisotope was established. Three different amounts of DNA (200, 50, and 12.5 ng) extracted from each type strain of Campylobacter species with standard Marmur's procedure were spotted on a nitrocellulose filter. DNA obtained from one ml bacterial suspension at a concentration of McFarland standard turbidity No. 1 of Campylobacter fetus, C. jejuni, C. coli, and C. pylori isolates were sufficiently labeled with photo-biotin within 15 min and clearly hybridized with the type strain of the corresponding species within four to six hours. Hybridized spots were visualized with alkaline-phosphatase-conjugated streptavidin color-detection method. The reaction was usually stopped within 30 min. Atypical clinical isolates such as a nitrate-negative C. jejuni, two nalidixic acid-resistant C. jejuni, and two strains of C. fetus able to grow at 42 C, which were tentatively identified as such, were definitely identified by the simplified DNA hybridization method presented here. This method will be applicable routinely for the definite identification of atypical strains of Campylobacter species and other gram-negative bacteria difficult to identify biochemically.     

A novel approach for nontargeted data analysis for metabolomics. Large-scale profiling of tomato fruit volatiles

To take full advantage of the power of functional genomics technologies and in particular those for metabolomics, both the analytical approach and the strategy chosen for data analysis need to be as unbiased and comprehensive as possible. Existing approaches to analyze metabolomic data still do not allow a fast and unbiased comparative analysis of the metabolic composition of the hundreds of genotypes that are often the target of modern investigations. We have now developed a novel strategy to analyze such metabolomic data. This approach consists of (1) full mass spectral alignment of gas chromatography (GC)-mass spectrometry (MS) metabolic profiles using the MetAlign software package, (2) followed by multivariate comparative analysis of metabolic phenotypes at the level of individual molecular fragments, and (3) multivariate mass spectral reconstruction, a method allowing metabolite discrimination, recognition, and identification. This approach has allowed a fast and unbiased comparative multivariate analysis of the volatile metabolite composition of ripe fruits of 94 tomato (Lycopersicon esculentum Mill.) genotypes, based on intensity patterns of >20,000 individual molecular fragments throughout 198 GC-MS datasets. Variation in metabolite composition, both between- and within-fruit types, was found and the discriminative metabolites were revealed. In the entire genotype set, a total of 322 different compounds could be distinguished using multivariate mass spectral reconstruction. A hierarchical cluster analysis of these metabolites resulted in clustering of structurally related metabolites derived from the same biochemical precursors. The approach chosen will further enhance the comprehensiveness of GC-MS-based metabolomics approaches and will therefore prove a useful addition to nontargeted functional genomics research.     

POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis

Metabolomics and proteomics, like other omics domains, usually face a data mining challenge in providing an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the most difficult challenges and it is critical in the subsequent biological interpretation of the results. Because of this, combined with the computational programming skills needed for this type of analysis, several bioinformatic tools aimed at simplifying metabolomics and proteomics data analysis have emerged. However, sometimes the analysis is still limited to a few hidebound statistical methods and to data sets with limited flexibility. POMAShiny is a web-based tool that provides a structured, flexible and user-friendly workflow for the visualization, exploration and statistical analysis of metabolomics and proteomics data. This tool integrates several statistical methods, some of them widely used in other types of omics, and it is based on the POMA R/Bioconductor package, which increases the reproducibility and flexibility of analyses outside the web environment. POMAShiny and POMA are both freely available at https://github.com/nutrimetabolomics/POMAShiny and https://github.com/nutrimetabolomics/POMA, respectively.     

A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data

In this paper, we propose to use probabilistic neural networks (PNNs) for classification of bacterial growth/no-growth data and modeling the probability of growth. The PNN approach combines both Bayes theorem of conditional probability and Parzen's method for estimating the probability density functions of the random variables. Unlike other neural network training paradigms, PNNs are characterized by high training speed and their ability to produce confidence levels for their classification decision. As a practical application of the proposed approach, PNNs were investigated for their ability in classification of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most frequently used traditional statistical method based on logistic regression and multilayer feedforward artificial neural network (MFANN) trained by error backpropagation was also carried out. The PNN-based models were found to outperform linear and nonlinear logistic regression and MFANN in both the classification accuracy and ease by which PNN-based models are developed.     

Do-it-yourself statistics: A computer-assisted likelihood approach to analysis of data from genetic crosses

Graduate school programs in genetics have become so full that courses in statistics have often been eliminated. In addition, typical introductory statistics courses for the "statistics user" rather than the nascent statistician are laden with methods for analysis of measured variables while genetic data are most often discrete numbers. These courses are often seen by students and genetics professors alike as largely irrelevant cookbook courses. The powerful methods of likelihood analysis, although commonly employed in human genetics, are much less often used in other areas of genetics, even though current computational tools make this approach readily accessible. This article introduces the MLIKELY.PAS computer program and the logic of do-it-yourself maximum-likelihood statistics. The program itself, course materials, and expanded discussions of some examples that are only summarized here are available at http://www.unisi. it/ricerca/dip/bio_evol/sitomlikely/mlikely.h tml.     

A network approach for inferring species associations from co‐occurrence data

Positive and negative associations between species are a key outcome of community assembly from regional species pools. These associations are difficult to detect and can be caused by a range of processes such as species interactions, local environmental constraints and dispersal. We integrate new ideas around species distribution modeling, covariance matrix estimation, and network analysis to provide an approach to inferring non‐random species associations from local‐ and regional‐scale occurrence data. Specifically, we provide a novel framework for identifying species associations that overcomes three challenges: 1) correcting for indirect effects from other species, 2) avoiding spurious associations driven by regional‐scale distributions, and 3) describing these associations in a multi‐species context. We highlight a range of research questions and analyses that this framework is able to address. We show that the approach is statistically robust using simulated data. In addition, we present an empirical analysis of > 1000 North American tree communities that gives evidence for weak positive associations among small groups of species. Finally, we discuss several possible extensions for identifying drivers of associations, predicting community assembly, and better linking biogeography and community ecology.     

A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds

Single nucleotide polymorphisms (SNPs) able to describe population differences can be used for important applications in livestock, including breed assignment of individual animals, authentication of mono-breed products and parentage verification among several other applications. To identify the most discriminating SNPs among thousands of markers in the available commercial SNP chip tools, several methods have been used. Random forest (RF) is a machine learning technique that has been proposed for this purpose. In this study, we used RF to analyse PorcineSNP60 BeadChip array genotyping data obtained from a total of 2737 pigs of 7 Italian pig breeds (3 cosmopolitan-derived breeds: Italian Large White, Italian Duroc and Italian Landrace, and 4 autochthonous breeds: Apulo-Calabrese, Casertana, Cinta Senese and Nero Siciliano) to identify breed informative and reduced SNP panels using the mean decrease in the Gini Index and the Mean Decrease in Accuracy parameters with stability evaluation. Other reduced informative SNP panels were obtained using Delta, Fixation index and principal component analysis statistics, and their performances were compared with those obtained using the RF-defined panels using the RF classification method and its derived Out Of Bag rates and correct prediction proportions. Therefore, the performances of a total of six reduced panels were evaluated. The correct assignment of the animals to its breed was close to 100% for all tested approaches. Porcine chromosome 8 harboured the largest number of selected SNPs across all panels. Many SNPs were included in genomic regions in which previous studies identified signatures of selection or genes (e.g. ESR1, KITL and LCORL) that could contribute to explain, at least in part, phenotypically or economically relevant traits that might differentiate cosmopolitan and autochthonous pig breeds. Random forest used as preselection statistics highlighted informative SNPs that were not the same as those identified by other methods. This might be due to specific features of this machine learning methodology. It will be interesting to explore if the adaptation of RF methods for the identification of selection signature regions could be able to describe population-specific features that are not captured by other approaches.     

The application of whole-cell protein electrophoresis for the classification and identification of basidiomycetous yeast species

The relationships among 65 basidiomycetous yeast strains were determined by one-dimensional electrophoresis of SDS-solubilized whole-cell proteins. Protein profiles were compared by the Pearson product moment correlation coefficient (r). The strains investigated represented species from the generaCystofilobasidium, Filobasidium, Filobasidiella, Kondoa, Leucosporidium, Mrakia andRhodosporidium. Except for the genusMrakia, all species constituted separate protein electrophoretic clusters. The species of the genusMrakia (M. frigida, M. gelida, M. nivalis andM. stokesii) show highly similar protein patterns, suggesting that these four species may be synonymous. Strains of two varieties ofFilobasidiella neoformans, F. neoformans var.neoformans andF. neoformans var.bacillispora, could not be differentiated by protein electrophoresis.For the delineation of the protein electrophoretic clusters of the yeasts studied, literature data relying on other criteria, such as DNA base composition, carbon source utilization patterns, enzymatic protein electrophoregrams, ubiquinone systems, DNA-DNA homology and rRNA sequence data were used. It was demonstrated that a database of SDS-protein patterns provides a valuable tool for the identification of yeasts.     

A new meta-ANOVA approach for synthesizing information under signal-heterogeneity setting with application to nuclear magnetic resonance spectroscopic data

A new synthesizing statistical methodology is proposed to resolve issues of signal-heterogeneity in data sets collected through high-resolution ¹H nuclear magnetic resonance (NMR) spectroscopy. This signal-heterogeneity is typically caused by subjective operations for processing spectral profiles and measuring peak areas, non-homogeneous biological phases of experimental subjects, and variations of systems in multi-center. All these causes are likely to simultaneously impact signals of metabolic changes and their precision in a nonlinear fashion. As a combined effect, signal-heterogeneity chiefly manifests through non-homomorphic patterns of standardized treatment mean deviations spanning all experiments, and makes most remedial statistical models with linearity structure invalid. By avoiding a huge and very complex model, we develop a simple meta-ANOVA approach to synthesize many one-way-layout ANOVA analyses from individual experiments. A scale-invariant F-ratio statistic is taken as the summarizing sufficient statistic of a non-centrality parameter that supposedly captures the information about metabolic change from each experiment. Then a joint-likelihood function of a common non-centrality is constructed as the basis for maximum likelihood estimation and Chi-square likelihood ratio testing for statistical inference. We apply the meta-ANOVA to detect metabolic changes of three metabolites identified through pattern recognition on NMR spectral profiles obtained from muscle and liver tissues. We also detect effect differences among different treatments via meta-ANOVA multiple comparison.