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Abstract Background Y. enterocolitica biotype (BT) 1A strains are often isolated from human clinical samples but their contribution to disease has remained a controversial topic. Variation and the population structure among the clinical Y. enterocolitica BT 1A isolates have been poorly characterized. We used multi-locus sequence typing (MLST), 16S rRNA gene sequencing, PCR for ystA and ystB, lipopolysaccharide analysis, phage typing, human serum complement killing assay and analysis of the symptoms of the patients to characterize 298 clinical Y. enterocolitica BT 1A isolates in order to evaluate their relatedness and pathogenic potential. Results A subset of 71 BT 1A strains, selected based on their varying LPS patterns, were subjected to detailed genetic analyses. The MLST on seven house-keeping genes (adk, argA, aroA, glnA, gyrB, thrA, trpE) conducted on 43 of the strains discriminated them into 39 MLST-types. By Bayesian analysis of the population structure (BAPS) the strains clustered conclusively into two distinct lineages, i.e. Genetic groups 1 and 2. The strains of Genetic group 1 were more closely related (97% similarity) to the pathogenic bio/serotype 4/O:3 strains than Genetic group 2 strains (95% similarity). Further comparison of the 16S rRNA genes of the BT 1A strains indicated that altogether 17 of the 71 strains belong to Genetic group 2. On the 16S rRNA analysis, these 17 strains were only 98% similar to the previously identified subspecies of Y. enterocolitica. The strains of Genetic group 2 were uniform in their pathogenecity-related properties: they lacked the ystB gene, belonged to the same LPS subtype or were of rough type, were all resistant to the five tested yersiniophages, were largely resistant to serum complement and did not ferment fucose. The 54 strains in Genetic group 1 showed much more variation in these properties. The most commonly detected LPS types were similar to the LPS types of reference strains with serotypes O:6,30 and O:6,31 (37%), O:7,8 (19%) and O:5 (15%). Conclusions The results of the present study strengthen the assertion that strains classified as Y. enterocolitica BT 1A represent more than one subspecies. Especially the BT 1A strains in our Genetic group 2 commonly showed resistance to human serum complement killing, which may indicate pathogenic potential for these strains. However, their virulence mechanisms remain unknown.

Abstract Background Worldwide effort on sampling and characterization of molecular variation within a large number of human and animal pathogens has lead to the emergence of multi-locus sequence typing (MLST) databases as an important tool for studying the epidemiology and evolution of pathogens. Many of these databases are currently harboring several thousands of multi-locus DNA sequence types (STs) enriched with metadata over traits such as serotype, antibiotic resistance, host organism etc of the isolates. Curators of the databases have thus the possibility of dividing the pathogen populations into subsets representing different evolutionary lineages, geographically associated groups, or other subpopulations, which are defined in terms of molecular similarities and dissimilarities residing within a database. When combined with the existing metadata, such subsets may provide invaluable information for assessing the position of a new set of isolates in relation to the whole pathogen population. Results To enable users of MLST schemes to query the databases with sets of new bacterial isolates and to automatically analyze their relation to existing curated sequences, we introduce here a Bayesian model-based method for semi-supervised classification of MLST data. Our method can use an MLST database as a training set and assign simultaneously any set of query sequences into the earlier discovered lineages/populations, while also allowing some or all of these sequences to form previously undiscovered genetically distinct groups. This tool provides probabilistic quantification of the classification uncertainty and is highly efficient computationally, thus enabling rapid analyses of large databases and sets of query sequences. The latter feature is a necessary prerequisite for an automated access through the MLST web interface. We demonstrate the versatility of our approach by anayzing both real and synthesized data from MLST databases. The introduced method for semi-supervised classification of sets of query STs is freely available for Windows, Mac OS X and Linux operative systems in BAPS 5.4 software which is downloadable at http://web.abo.fi/fak/mnf/mate/jc/software/baps.html. The query functionality is also directly available for the Staphylococcus aureus database at http://www.mlst.net and shortly will be available for other species databases hosted at this web portal. Conclusions We have introduced a model-based tool for automated semi-supervised classification of new pathogen samples that can be integrated into the web interface of the MLST databases. In particular, when combined with the existing metadata, the semi-supervised labeling may provide invaluable information for assessing the position of a new set of query strains in relation to the particular pathogen population represented by the curated database. Such information will be useful both for clinical and basic research purposes.

Abstract Background Since the introduction of large-scale genotyping methods that can be utilized in genome-wide association (GWA) studies for deciphering complex diseases, statistical genetics has been posed with a tremendous challenge of how to most appropriately analyze such data. A plethora of advanced model-based methods for genetic mapping of traits has been available for more than 10 years in animal and plant breeding. However, most such methods are computationally intractable in the context of genome-wide studies. Therefore, it is hardly surprising that GWA analyses have in practice been dominated by simple statistical tests concerned with a single marker locus at a time, while the more advanced approaches have appeared only relatively recently in the biomedical and statistical literature. Results We introduce a novel Bayesian modeling framework for association mapping which enables the detection of multiple loci and their interactions that influence a dichotomous phenotype of interest. The method is shown to perform well in a simulation study when compared to widely used standard alternatives and its computational complexity is typically considerably smaller than that of a maximum likelihood based approach. We also discuss in detail the sensitivity of the Bayesian inferences with respect to the choice of prior distributions in the GWA context. Conclusions Our results show that the Bayesian model averaging approach which explicitly considers gene-gene interactions may improve the detection of disease associated genetic markers in two respects: first, by providing better estimates of the locations of the causal loci; second, by reducing the number of false positives. The benefits are most apparent when the interacting genes exhibit no main effects. However, our findings also illustrate that such an approach is somewhat sensitive to the prior distribution assigned on the model structure.

Abstract Background Campylobacter jejuni is the most common bacterial cause of human gastroenteritis worldwide. Due to the sporadic nature of infection, sources often remain unknown. Multilocus sequence typing (MLST) has been successfully applied to population genetics of Campylobacter jejuni and mathematical modelling can be applied to the sequence data. Here, we analysed the population structure of a total of 250 Finnish C. jejuni isolates from bovines, poultry meat and humans collected in 2003 using a combination of Bayesian clustering (BAPS software) and phylogenetic analysis. Results In the first phase we analysed sequence types (STs) of 102 Finnish bovine C. jejuni isolates by MLST and found a high diversity totalling 50 STs of which nearly half were novel. In the second phase we included MLST data from domestic human isolates as well as poultry C. jejuni isolates from the same time period. Between the human and bovine isolates we found an overlap of 72.2%, while 69% of the human isolates were overlapping with the chicken isolates. In the BAPS analysis 44.3% of the human isolates were found in bovine-associated BAPS clusters and 45.4% of the human isolates were found in the poultry-associated BAPS cluster. BAPS reflected the phylogeny of our data very well. Conclusions These findings suggest that bovines and poultry were equally important as reservoirs for human C. jejuni infections in Finland in 2003. Our results differ from those obtained in other countries where poultry has been identified as the most important source for human infections. The low prevalence of C. jejuni in poultry flocks in Finland could explain the lower attribution of human infection to poultry. Of the human isolates 10.3% were found in clusters not associated with any host which warrants further investigation, with particular focus on waterborne transmission routes and companion animals.

Abstract Background The human gastrointestinal (GI) tract microbiota is characterised by an abundance of uncultured bacteria most often assigned in phyla Firmicutes and Bacteroidetes. Diversity of this microbiota, even though approached with culture independent techniques in several studies, still requires more elucidation. The main purpose of this work was to study whether the genomic percent guanine and cytosine (%G+C) -based profiling and fractioning prior to 16S rRNA gene sequence analysis reveal higher microbiota diversity, especially with high G+C bacteria suggested to be underrepresented in previous studies. Results A phylogenetic analysis of the composition of the human GI microbiota of 23 healthy adult subjects was performed from a pooled faecal bacterial DNA sample by combining genomic %G+C -based profiling and fractioning with 16S rRNA gene cloning and sequencing. A total of 3199 partial 16S rRNA genes were sequenced. For comparison, 459 clones were sequenced from a comparable unfractioned sample. The most important finding was that the proportional amount of sequences affiliating with the phylum Actinobacteria was 26.6% in the %G+C fractioned sample but only 3.5% in the unfractioned sample. The orders Coriobacteriales, Bifidobacteriales and Actinomycetales constituted the 65 actinobacterial phylotypes in the fractioned sample, accounting for 50%, 47% and 3% of sequences within the phylum, respectively. Conclusion This study shows that the %G+C profiling and fractioning prior to cloning and sequencing can reveal a significantly larger proportion of high G+C content bacteria within the clones recovered, compared with the unfractioned sample in the human GI tract. Especially the order Coriobacteriales within the phylum Actinobacteria was found to be more abundant than previously estimated with conventional sequencing studies.

Abstract Background The versatility of DNA copy number amplifications for profiling and categorization of various tissue samples has been widely acknowledged in the biomedical literature. For instance, this type of measurement techniques provides possibilities for exploring sets of cancerous tissues to identify novel subtypes. The previously utilized statistical approaches to various kinds of analyses include traditional algorithmic techniques for clustering and dimension reduction, such as independent and principal component analyses, hierarchical clustering, as well as model-based clustering using maximum likelihood estimation for latent class models. Results While purely algorithmic methods are usually easily applicable, their suboptimal performance and limitations in making formal inference have been thoroughly discussed in the statistical literature. Here we introduce a Bayesian model-based approach to simultaneous identification of underlying tissue groups and the informative amplifications. The model-based approach provides the possibility of using formal inference to determine the number of groups from the data, in contrast to the ad hoc methods often exploited for similar purposes. The model also automatically recognizes the chromosomal areas that are relevant for the clustering. Conclusion Validatory analyses of simulated data and a large database of DNA copy number amplifications in human neoplasms are used to illustrate the potential of our approach. Our software implementation BASTA for performing Bayesian statistical tissue profiling is freely available for academic purposes at http://web.abo.fi/fak/mnf/mate/jc/software/basta.html

Abstract Background During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. Results We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. Conclusion The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at http://web.abo.fi/fak/mnf//mate/jc/software/baps.html.

Abstract Background We consider the discovery of recombinant segments jointly with their origins within multilocus DNA sequences from bacteria representing heterogeneous populations of fairly closely related species. The currently available methods for recombination detection capable of probabilistic characterization of uncertainty have a limited applicability in practice as the number of strains in a data set increases. Results We introduce a Bayesian spatial structural model representing the continuum of origins over sites within the observed sequences, including a probabilistic characterization of uncertainty related to the origin of any particular site. To enable a statistically accurate and practically feasible approach to the analysis of large-scale data sets representing a single genus, we have developed a novel software tool (BRAT, Bayesian Recombination Tracker) implementing the model and the corresponding learning algorithm, which is capable of identifying the posterior optimal structure and to estimate the marginal posterior probabilities of putative origins over the sites. Conclusion A multitude of challenging simulation scenarios and an analysis of real data from seven housekeeping genes of 120 strains of genus Burkholderia are used to illustrate the possibilities offered by our approach. The software is freely available for download at URL http://web.abo.fi/fak/mnf//mate/jc/software/brat.html.