In silico and in vitro analysis of quorum quenching active phytochemicals from the ethanolic extract of medicinal plants against quorum sensing mediated virulence factors of Acinetobacter baumannii

Inhibition of quorum sensing called quorum quenching (QQ) is now extensively utilized in the prevention of bacterial infections. In the present study, in silico and in vitro analysis of quorum quenching (QQ) or anti-Quorum sensing (QS) activity of ethanolic extract of medicinal plants against QS mediated virulence factors of human pathogenic bacteria Acinetobacter baumannii has been investigated. The effect of plant extracts on QS by acyl homoserine lactone (AHL) has been carried out by quantification of secreted AHL by high-pressure liquid chromatography (HPLC). Measurement of QQ activity was determined by maximum inhibition of virulence factors and AHL production which was recorded in E. globules and A. indica extracts. In silico analysis was studied with possible bioactive compounds in the ethanolic extract of respective plant material that were characterized by gas chromatography equipped with mass spectroscopy (GCMS) against the enzyme responsible for the production of signaling molecule which mediates QS AHL synthase. Distinct reduction of all the QS-mediated virulence factors was recorded in the E. globules and A. indica. Among the different bioactive compounds, the ethanolic leaf extract of E. globules of GCMS analyzed compound, Hexadeconoic acid, 1-(hydroxymethyl), 1, 2-ethannediyl ester interacted with 1KZF protein (AHL synthase) and showed binding energy of −11.2 kcal/mol to MET 42 and TYR 54. Phytochemicals mediated inhibition of AHL synthase activity which was responsible for AHL production would suggest the possible utilization of plant extracts as an antibacterial agent to fight against disease-causing pathogenic bacteria

Can comprehensive background knowledge be incorporated into substitution models to improve phylogenetic analyses? A case study on major arthropod relationships

<p>Abstract</p> <p>Background</p> <p>Whenever different data sets arrive at conflicting phylogenetic hypotheses, only testable causal explanations of sources of errors in at least one of the data sets allow us to critically choose among the conflicting hypotheses of relationships. The large (28S) and small (18S) subunit rRNAs are among the most popular markers for studies of deep phylogenies. However, some nodes supported by this data are suspected of being artifacts caused by peculiarities of the evolution of these molecules. Arthropod phylogeny is an especially controversial subject dotted with conflicting hypotheses which are dependent on data set and method of reconstruction. We assume that phylogenetic analyses based on these genes can be improved further i) by enlarging the taxon sample and ii) employing more realistic models of sequence evolution incorporating non-stationary substitution processes and iii) considering covariation and pairing of sites in rRNA-genes.</p> <p>Results</p> <p>We analyzed a large set of arthropod sequences, applied new tools for quality control of data prior to tree reconstruction, and increased the biological realism of substitution models. Although the split-decomposition network indicated a high noise content in the data set, our measures were able to both improve the analyses and give causal explanations for some incongruities mentioned from analyses of rRNA sequences. However, misleading effects did not completely disappear.</p> <p>Conclusion</p> <p>Analyses of data sets that result in ambiguous phylogenetic hypotheses demand for methods, which do not only filter stochastic noise, but likewise allow to differentiate phylogenetic signal from systematic biases. Such methods can only rely on our findings regarding the evolution of the analyzed data. Analyses on independent data sets then are crucial to test the plausibility of the results. Our approach can easily be extended to genomic data, as well, whereby layers of quality assessment are set up applicable to phylogenetic reconstructions in general.</p

RNA-based phylogenetic methods: application to mammalian mitochondrial RNA sequences

The PHASE software package allows phylogenetic tree construction with a number of evolutionary models designed specifically for use with RNA sequences that have conserved secondary structure. Evolution in the paired regions of RNAs occurs via compensatory substitutions, hence changes on either side of a pair are correlated. Accounting for this correlation is important for phylogenetic inference because it affects the likelihood calculation. In the present study we use the complete set of tRNA and rRNA sequences from 69 complete mammalian mitochondrial genomes. The likelihood calculation uses two evolutionary models simultaneously for different parts of the sequence: a paired-site model for the paired sites and a single-site model for the unpaired sites. We use Bayesian phylogenetic methods and a Markov chain Monte Carlo algorithm is used to obtain the most probable trees and posterior probabilities of clades. The results are well resolved for almost all the important branches on the mammalian tree. They support the arrangement of mammalian orders within the four supra-ordinal clades that have been identified by studies of much larger data sets mainly comprising nuclear genes. Groups such as the hedgehogs and the murid rodents, which have been problematic in previous studies with mitochondrial proteins, appear in their expected position with the other members of their order. Our choice of genes and evolutionary model appears to be more reliable and less subject to biases caused by variation in base composition than previous studies with mitochondrial genomes.