Snapshots of native pre-50S ribosomes reveal a biogenesis factor network and evolutionary specialization

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Improved Detection of Extended Spectrum Beta-Lactamase (ESBL)-Producing Escherichia coli in Input and Output Samples of German Biogas Plants by a Selective Pre-Enrichment Procedure

The presence of extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli was investigated in input (manure from livestock husbandry) and output samples of six German biogas plants in 2012 (one sampling per biogas plant) and two German biogas plants investigated in an annual cycle four times in 2013/2014. ESBL-producing Escherichia coli were cultured by direct plating on CHROMagar ESBL from input samples in the range of 10⁰ to 10⁴ colony forming units (CFU) per g dry weight but not from output sample. This initially indicated a complete elimination of ESBL-producing E. coli by the biogas plant process. Detected non target bacteria were assigned to the genera Acinetobacter, Pseudomonas, Bordetella, Achromobacter, Castellaniella, and Ochrobactrum. A selective pre-enrichment procedure increased the detection efficiency of ESBL-producing E. coli in input samples and enabled the detection in five of eight analyzed output samples. In total 119 ESBL-producing E. coli were isolated from input and 46 from output samples. Most of the E. coli isolates carried CTX-M-type and/or TEM-type beta lactamases (94%), few SHV-type beta lactamase (6%). Sixty-four bla _CTX-M genes were characterized more detailed and assigned mainly to CTX-M-groups 1 (85%) and 9 (13%), and one to group 2. Phylogenetic grouping of 80 E. coli isolates showed that most were assigned to group A (71%) and B1 (27%), only one to group D (2%). Genomic fingerprinting and multilocus sequence typing (MLST) showed a high clonal diversity with 41 BOX-types and 19 ST-types. The two most common ST-types were ST410 and ST1210. Antimicrobial susceptibility testing of 46 selected ESBL-producing E. coli revealed that several isolates were additionally resistant to other veterinary relevant antibiotics and some grew on CHROMagar STEC but shiga-like toxine (SLT) genes were not detected. Resistance to carbapenems was not detected. In summary the study showed for the first time the presence of ESBL-producing E. coli in output samples of German biogas plants.     

Analysis of residuals from enzyme kinetic and protein folding experiments in the presence of correlated experimental noise

Experimental data from continuous enzyme assays or protein folding experiments often contain hundreds, or even thousands, of densely spaced data points. When the sampling interval is extremely short, the experimental data points might not be statistically independent. The resulting neighborhood correlation invalidates important theoretical assumptions of nonlinear regression analysis. As a consequence, certain goodness-of-fit criteria, such as the runs-of-signs test and the autocorrelation function, might indicate a systematic lack of fit even if the experiment does agree very well with the underlying theoretical model. A solution to this problem is to analyze only a subset of the residuals of fit, such that any excessive neighborhood correlation is eliminated. Substrate kinetics of the HIV protease and the unfolding kinetics of UMP/CMP kinase, a globular protein from Dictyostelium discoideum, serve as two illustrative examples. A suitable data-reduction algorithm has been incorporated into software DYNAFIT [P. Kuzmi?, Anal. Biochem. 237 (1996) 260-273], freely available to all academic researchers from http://www.biokin.com.     

Simultaneous Statistical Inference for Epigenetic Data

Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.