Analysis of the dynamics of Staphylococcus aureus binding to white blood cells using whole blood assay and geno-to-pheno mapping.

Given that binding and internalization of bacteria to host cells promotes infections and invasion, we aimed at characterizing how various S. aureus isolates adhere to and are internalized by different white blood cells. In particular, the role of genetic determinants on the association kinetics should be unveiled. A flow cytometric (FACS) whole blood assay with fluorescently labelled isolates was applied to 56 clinical S. aureus isolates. This phenotypic data was then linked to previously obtained genotyping data (334 genes) with the help of a redescription mining algorithm. Professional phagocytes showed a time-dependent increase of bacterial adhesion and internalization. Isolates showing higher affinity to granulocytes were associated with lower binding to monocytes. In contrast binding activity between S. aureus and lymphocytes could be subdivided into two phases. Preliminary binding (phase 1) was highest directly after co-incubation and was followed by S. aureus detachment or by sustained binding of a small lymphocyte subset (phase 2). Strain-dependent low granulocyte binding was observed for clonal complex 5 (CC5) isolates (MRSA), as compared to CC30 and CC45 (MSSA). S. aureus isolates associated with low granulocyte phagocytosis were characterized by the presence (cap8, can) and the absence (sasG, lukD, isdA, splA, setC) of specific hybridization signals.

Detecting Staphylococcus aureus Virulence and Resistance Genes: a Comparison of Whole-Genome Sequencing and DNA Microarray Technology.

Staphylococcus aureusis a major bacterial pathogen causing a variety of diseases ranging from wound infections to severe bacteremia or intoxications. Besides host factors, the course and severity of disease is also widely dependent on the genotype of the bacterium. Whole-genome sequencing (WGS), followed by bioinformatic sequence analysis, is currently the most extensive genotyping method available. To identify clinically relevant staphylococcal virulence and resistance genes in WGS data, we developed anin silicotyping scheme for the software SeqSphere(+)(Ridom GmbH, Münster, Germany). The implemented target genes (n= 182) correspond to those queried by the IdentibacS. aureusGenotyping DNA microarray (Alere Technologies, Jena, Germany). Thein silicoscheme was evaluated by comparing the typing results of microarray and of WGS for 154 humanS. aureusisolates. A total of 96.8% (n= 27,119) of all typing results were equally identified with microarray and WGS (40.6% present and 56.2% absent). Discrepancies (3.2% in total) were caused by WGS errors (1.7%), microarray hybridization failures (1.3%), wrong prediction of ambiguous microarray results (0.1%), or unknown causes (0.1%). Superior to the microarray, WGS enabled the distinction of allelic variants, which may be essential for the prediction of bacterial virulence and resistance phenotypes. Multilocus sequence typing clonal complexes and staphylococcal cassette chromosomemecelement types inferred from microarray hybridization patterns were equally determined by WGS. In conclusion, WGS may substitute array-based methods due to its universal methodology, open and expandable nature, and rapid parallel analysis capacity for different characteristics in once-generated sequences.

A geospatial analysis of flies and the spread of antimicrobial resistant bacteria.

Livestock is often colonized with ESBL-producing Enterobacteriaceae (ESBL-E) and Staphylococcus aureus. There is a risk that flies spread antimicrobial resistant bacteria from livestock to humans. Here, we screened flies from urban and rural areas near the city of Münster, Germany, for the presence of ESBL-E and S. aureus and compared molecular characteristics of these isolates with those isolated from humans in the same region. In total, 1346 single flies were grinded and cultured overnight in BHI-broth. The broth was cultured on Columbia blood agar and selective media for the detection of S. aureus and ESBL-E. Human isolates from routine diagnostics at the University Hospital Münster were used for comparison. Antimicrobial susceptibility, phylogroups (Escherichia coli), spa types/multilocus sequence types (S. aureus) and selected antimicrobial resistance genes were determined for each isolate. GPS data of the sampling sites were used to map flies carrying ESBL-E and S. aureus. Overall, Serratia fonticola (123/1346; 9.1%) was the most prevalent ESBL-E in flies, followed by E. coli (44/1346; 3.3%). A high proportion of flies was positive for ESBL-producing E. coli (15.0-22.2%) in a rural area. CTX-M-1 was the most prevalent beta-lactamase in E. coli (38.6%). One livestock-associated methicillin resistant S. aureus (LA-MRSA, t011/ST398) was found in the city centre of Münster. Overall, a substantial part of ESBL-producing E. coli and S. aureus from flies and humans showed a similar genetic background. In conclusion, flies can carry ESBL-E and LA-MRSA in urban and rural areas. The similar genetic background of isolates from humans and flies points towards a common source.

DNA co-methylation analysis suggests novel functional associations between gene pairs in breast cancer samples.

Localized promoter hypermethylation and overall DNA hypomethylation have been associated with the presence of tumor in humans. Yet, despite the large amount of recently produced epigenetic data, there is still a lack of understanding on how several genes behave in tumor cells with respect to their epigenetic alterations such as DNA methylation. Here we performed a novel type of analysis that measures the correlation of DNA methylation levels between two genes across many samples. We linked this so-called co-methylation to the genomic distance of these genes, their functional similarity and their expression levels. Co-methylation analysis of more than 300 breast cancer samples from the TCGA portal yielded 187 pairs of genes showing Pearson correlation coefficients |r| ≥ 0.75. These pairs were formed by 133 genes. Less than half of these pairs are located on the same chromosome. For these, we found that the level of co-methylation is weakly anti-correlated with genomic distance (r = -0.29). Linking co-methylation with the functional similarity of genes showed that genes with r ≥ 0.8 tend to have similar molecular function and to be involved in the same biological process as described in the Gene Ontology project. Clustering of highly co-methylated genes identified four enriched KEGG pathways. Hence we have introduced co-methylation as a new indicator to discover functional associations between gene pairs in breast cancer and furthermore to discover new candidate genes that should be inspected more closely in the context of the studied disease.

Community-Associated Staphylococcus aureus from Sub-Saharan Africa and Germany: A Cross-Sectional Geographic Correlation Study.

Clonal clusters and gene repertoires of Staphylococcus aureus are essential to understand disease and are well characterized in industrialized countries but poorly analysed in developing regions. The objective of this study was to compare the molecular-epidemiologic profiles of S. aureus isolates from Sub-Saharan Africa and Germany. S. aureus isolates from 600 staphylococcal carriers and 600 patients with community-associated staphylococcal disease were characterized by DNA hybridization, clonal complex (CC) attribution, and principal component (PCA)-based gene repertoire analysis. 73% of all CCs identified representing 77% of the isolates contained in these CCs were predominant in either African or German region. Significant differences between African versus German isolates were found for alleles encoding the accessory gene regulator type, enterotoxins, the Panton-Valentine leukocidin, immune evasion gene cluster, and adhesins. PCA in conjunction with silhouette analysis distinguished nine separable PCA clusters, with five clusters primarily comprising of African and two clusters of German isolates. Significant differences between S. aureus lineages in Africa and Germany may be a clue to explain the apparent difference in disease between tropical/(so-called) developing and temperate/industrialized regions. In low-resource countries further clinical-epidemiologic research is warranted not only for neglected tropical diseases but also for major bacterial infections.

BEclear: Batch Effect Detection and Adjustment in DNA Methylation Data.

Batch effects describe non-natural variations of, for example, large-scale genomic data sets. If not corrected by suitable numerical algorithms, batch effects may seriously affect the analysis of these datasets. The novel array platform independent software tool BEclear enables researchers to identify those portions of the data that deviate statistically significant from the remaining data and to replace these portions by typical values reconstructed from neighboring data entries based on latent factor models. In contrast to other comparable methods that often use some sort of global normalization of the data, BEclear avoids changing the apparently unaffected parts of the data. We tested the performance of this approach on DNA methylation data for various tumor data sets taken from The Cancer Genome Atlas and compared the results to those obtained with the existing algorithms ComBat, Surrogate Variable Analysis, RUVm and Functional normalization. BEclear constantly performed at par with or better than these methods. BEclear is available as an R package at the Bioconductor project http://bioconductor.org/packages/release/bioc/html/BEclear.html.

AKSmooth: enhancing low-coverage bisulfite sequencing data via kernel-based smoothing.

Whole-genome bisulfite sequencing (WGBS) is an approach of growing importance. It is the only approach that provides a comprehensive picture of the genome-wide DNA methylation profile. However, obtaining a sufficient amount of genome and read coverage typically requires high sequencing costs. Bioinformatics tools can reduce this cost burden by improving the quality of sequencing data. We have developed a statistical method Ajusted Local Kernel Smoother (AKSmooth) that can accurately and efficiently reconstruct the single CpG methylation estimate across the entire methylome using low-coverage bisulfite sequencing (Bi-Seq) data. We demonstrate the AKSmooth performance on the low-coverage (~ 4 ×) DNA methylation profiles of three human colon cancer samples and matched controls. Under the best set of parameters, AKSmooth-curated data showed high concordance with the gold standard high-coverage sample (Pearson 0.90), outperforming the popular analogous method. In addition, AKSmooth showed computational efficiency with runtime benchmark over 4.5 times better than the reference tool. To summarize, AKSmooth is a simple and efficient tool that can provide an accurate human colon methylome estimation profile from low-coverage WGBS data. The proposed method is implemented in R and is available at https://github.com/Junfang/AKSmooth.

Matched-cohort DNA microarray diversity analysis of methicillin sensitive and methicillin resistant Staphylococcus aureus isolates from hospital admission patients.

As genotyping of S. aureus is important for epidemiologic research and for hygiene management, methods are required for standardized fast and easily applicable evaluation of closely related epidemic strains with high prevalence in hospitals. In this single centre matched control study we compared a new commercially available DNA microarray (IdentiBAC) with standard spa-typing for S. aureus genotyping. Included in the study was a subgroup of 46 MRSA and matched 46 MSSA nasal isolates of the Saarland University Medical Center collected during a state-wide admission prevalence screening. Microarray (MA) and also spa-typing could easily differentiate the genetically diverse MSSA group. However, due to the predominance of CC5/t003 in the MRSA group a sufficient subtyping required analysis of more complex genetic profiles as was shown here by the MA comprising a total number of 334 different hybridization probes. The genetic repertoire of the MRSA group was characterized by more virulence genes as compared to the MSSA group. The standard evaluation of MA results by the original software into CCs, agr-, SCCmec- and capsule-types was substituted in the present study by implementation of multivariate subtyping of closely related CC5 isolates using three different bioinformatic methods (splits graph, cluster dendrogram, and principal component analysis). Each method used was applicable for standardized and highly discriminative subtyping with high concordance. We propose that the identified S. aureus subtypes with characteristic virulence gene profiles are presumably associated also with virulence and pathogenicity in vivo; however, this remains to be analyzed in future studies. MA was superior to spa-typing for epidemiologic and presumably also provide functional respectively virulence associated characterization of S. aureus isolates. This is of specific importance for the hospital setting. In future, MA could become a new standard test for S. aureus typing in combination with multivariate bioinformatic analysis.