Showing papers by "Matej Lexa published in 2016"

Effects of Isoflavone-Enriched Feed on the Rumen Microbiota in Dairy Cows

Abstract: In this study, we compared the effects of two diets containing different isoflavone concentrations on the isoflavone transfer from feed into milk and on the rumen microbiota in lactating dairy cows. The on-farm experiment was conducted on twelve lactating Czech Fleckvieh x Holstein cows divided into two groups, each with similar mean milk yield. Twice daily, cows were individually fed a diet based on maize silage, meadow hay and supplemental mixture. Control group (CTRL) received the basal diet while the experimental group (EXP) received the basal diet supplemented with 40% soybean isoflavone extract. The average daily isoflavone intake in the EXP group (16 g/day) was twice as high as that in the CTRL group (8.4 g/day, P<0.001). Total isoflavone concentrations in milk from the CTRL and EXP groups were 96.89 and 276.07 μg/L, respectively (P<0.001). Equol concentrations in milk increased from 77.78 μg/L in the CTRL group to 186.30 μg/L in the EXP group (P<0.001). The V3-4 region of bacterial 16S rRNA genes was used for metagenomic analysis of the rumen microbiome. The experimental cows exhibited fewer OTUs at a distance level of 0.03 compared to control cows (P<0.05) and reduced microbial richness compared to control cows based on the calculated Inverse Simpson and Shannon indices. Non-metric multidimensional scaling analysis showed that the major contributor to separation between the experimental and control groups were changes in the representation of bacteria belonging to the phyla Bacteroidetes, Proteobacteria, Firmicutes, and Planctomycetes. Surprisingly, a statistically significant positive correlation was found only between isoflavones and the phyla Burkholderiales (r = 0.65, P<0.05) and unclassified Betaproteobacteria (r = 0.58, P<0.05). Previous mouse and human studies of isoflavone effects on the composition of gastrointestinal microbial populations generally report similar findings.

p53 Specifically Binds Triplex DNA In Vitro and in Cells

Abstract: Triplex DNA is implicated in a wide range of biological activities, including regulation of gene expression and genomic instability leading to cancer. The tumor suppressor p53 is a central regulator of cell fate in response to different type of insults. Sequence and structure specific modes of DNA recognition are core attributes of the p53 protein. The focus of this work is the structure-specific binding of p53 to DNA containing triplex-forming sequences in vitro and in cells and the effect on p53-driven transcription. This is the first DNA binding study of full-length p53 and its deletion variants to both intermolecular and intramolecular T.A.T triplexes. We demonstrate that the interaction of p53 with intermolecular T.A.T triplex is comparable to the recognition of CTG-hairpin non-B DNA structure. Using deletion mutants we determined the C-terminal DNA binding domain of p53 to be crucial for triplex recognition. Furthermore, strong p53 recognition of intramolecular T.A.T triplexes (H-DNA), stabilized by negative superhelicity in plasmid DNA, was detected by competition and immunoprecipitation experiments, and visualized by AFM. Moreover, chromatin immunoprecipitation revealed p53 binding T.A.T forming sequence in vivo. Enhanced reporter transactivation by p53 on insertion of triplex forming sequence into plasmid with p53 consensus sequence was observed by luciferase reporter assays. In-silico scan of human regulatory regions for the simultaneous presence of both consensus sequence and T.A.T motifs identified a set of candidate p53 target genes and p53-dependent activation of several of them (ABCG5, ENOX1, INSR, MCC, NFAT5) was confirmed by RT-qPCR. Our results show that T.A.T triplex comprises a new class of p53 binding sites targeted by p53 in a DNA structure-dependent mode in vitro and in cells. The contribution of p53 DNA structure-dependent binding to the regulation of transcription is discussed.

Semi-automatic mining of correlated data from a complex database: Correlation network visualization

Abstract: In previous work we have addressed the issue of frequent ad-hoc queries in deeply-structured databases. We wrote a library of functions AutodenormLib.py for issuing proper JOIN commands to denormalize an arbitrary subset of stored data for downstream processing. This may include statistical analysis, visualization or machine learning. Here, we visualize the content of the Thalamoss biomedical database as a correlation network. The network is created by calculating pairwise correlations through all pairs of variables, whether they be numerical, ordinal or nominal. We subsequently construct the network over the entire set of variables, clustering variables with similar effects to discover group relationships between the various biomedical characteristics. We use a semi-automatic procedure that makes the selection of all pairs possible and discuss issues of dealing with different types of variables. This is done either by limiting the analysis to numerical and ordinal ones, or by binning their values into intervals of values. Knowledge extracted from the data in this mode can be used to select variables for statistical models, or as markers of medically interesting conditions.