Can Extensive Reticulation and Concerted Evolution Result in a Cladistically Structured Molecular Data Set?

Hierarchy is the main criterion for informativeness in a data set, even if no explicit reference to evolution as a causal process is provided. Sequence data (nuclear ribosomal DNA ITS) from Armeria (Plumbaginaceae) contains a certain amount of hierarchical structure as suggested by data decisiveness and distribution of tree lengths. However, ancillary evidence suggests that extensive gene flow and biased concerted evolution in these multicopy regions have significantly shaped the ITS data set. This argument is discussed using parsimony analysis of four data sets, constructed by combining wild sequences with those from different generations of artificial hybrids (wild + F₁, F₂, and backcrosses; wild + backcrosses; wild + F₁; wild + F₂). Compared to the F₁ hybrids, F₂ show a certain degree of homogenization in polymorphic sites. This effect reduces topological disruption caused by F₁ and is considered to be illustrative of how extensive gene flow and biased concerted evolution may have modeled the wild ITS data. The possibility that hierarchy has arisen as a result of—or despite a significant contribution from—those two such potentially perturbing forces raises the question of what kind of signal are we recovering from this molecular data set.     

Distribution of Clostridium difficile strains from a North American,European and Australian trial of treatment for C. difficile infections: 2005–2007

Clostridium difficile is a widely distributed pathogen with multiple strain types as determined by restriction endonuclease analysis (REA) and by PCR ribotyping, two well-characterized typing systems. In this study, REA typing was performed on 894 C. difficile isolates from patients enrolled from 16 countries on three continents in two large, recently conducted clinical treatment trials of C. difficile infection. REA group BI (Ribotype 027) isolates were the most common strains identified and were widely distributed throughout North America, but restricted to three of thirteen countries in Europe. REA group J (Ribotype 001) isolates were the most common strains identified in Europe and non-specific REA groups (historically less frequent) were the most common strains identified in Australia. REA groups BI, J, G and CF correlated with specific PCR ribotypes whereas more than one ribotype was found within REA groups Y, BK, and K. International surveillance of C. difficile strains is important to document the changing epidemiology of this enteric pathogen that continues to cause healthcare facility outbreaks and sporadic infections in other settings.     

Characterization of Cimetidine–Piroxicam Coprecipitate Interaction Using Experimental Studies and Molecular Dynamic Simulations

The crystalline states of cimetidine and piroxicam, when coprecipitated from solvents containing 1:1 mole ratio, were transformed to amorphous states as observed using powder X-ray diffraction (PXRD). Amorphous forms of drugs generally exhibit higher water solubility than crystalline forms. It is therefore interesting to investigate the interactions that cause the transformation of both the crystalline drugs. Intermolecular interactions between the drugs were determined using Fourier-transform infrared spectroscopy (FTIR) and solid-state ¹³C CP/MAS NMR. Molecular dynamic (MD) simulation was performed for the first time for this type of study to indicate the specific groups involved in the interactions based on radial distribution function (RDF) analyses. RDF is a useful tool to describe the average density of atoms at a distance from a specified atom. FTIR spectra revealed a shift of the C≡N stretching band of cimetidine. The ¹³C CP/MAS NMR spectra indicated downfield shifts of C₁₁, C₁₅ and C₇ of piroxicam. RDF analyses indicated that intermolecular interactions occurred between the amide oxygen atom as well as the pyridyl nitrogen of piroxicam and H-N₃ of cimetidine. The hydrogen atom (O–H) at C₇ interacts with the N₁ of cimetidine. Since the MD simulation results are consistent with, and complementary to the experimental analyses, such simulations could provide a novel strategy for investigating specific interacting groups of drugs in coprecipitates, or in amorphous mixtures.     

Assessing Coverage of Protein Interaction Data Using Capture–Recapture Models

Protein interaction networks comprise thousands of individual binary links between distinct proteins. Whilst these data have attracted considerable attention and been the focus of many different studies, the networks, their structure, function, and how they change over time are still not fully known. More importantly, there is still considerable uncertainty regarding their size, and the quality of the available data continues to be questioned. Here, we employ statistical models of the experimental sampling process, in particular capture–recapture methods, in order to assess the false discovery rate and size of protein interaction networks. We uses these methods to gauge the ability of different experimental systems to find the true binary interactome. Our model allows us to obtain estimates for the size and false-discovery rate from simple considerations regarding the number of repeatedly interactions, and provides suggestions as to how we can exploit this information in order to reduce the effects of noise in such data. In particular our approach does not require a reference dataset. We estimate that approximately more than half of the true physical interactome has now been sampled in yeast.     

Molecular Typing of MRSA and of Clinical Staphylococcus aureus Isolates from Ia?i,Romania

Romania is one of the countries with the highest prevalence of methicillin-resistant Staphylococcus aureus (MRSA) in the world. To obtain data on affiliation of MRSA to strains and clonal complexes and on the population of methicillin susceptible S. aureus (MSSA), clinical isolates from bloodstream infections, skin and soft tissue infections as well as from screening swabs were collected at hospitals in Ia?i, a city in the North-Eastern part of Romania. Isolates were characterised by microarray hybridisation. Nearly half of all isolates (47%), and about one third (34%) of bloodstream isolates were MRSA. The prevalence of the Panton-Valentine leukocidin (PVL) was also high (31% among MRSA, 14% among MSSA). The most common MRSA strain was a PVL-negative CC1-MRSA-IV that might have emerged locally, as a related MSSA was also common. PVL-positive CC8-MRSA-IV (“USA300”) and PVL-negative ST239-like MRSA-III were also frequently found while other MRSA strains were only sporadically detected. Among MSSA, PVL-positive CC121 as well as PVL-negative CC1, CC22 and CC45 predominated. Although this study provides only a snapshot of S. aureus/MRSA epidemiology in Romania, it confirms the high burden of MRSA and PVL on Romanian healthcare settings.     

Determination of the Backbone Mobility of Ribonuclease T1 and its 2′GMP Complex Using Molecular Dynamics Simulations and NMR Relaxation Data

Abstract

The results of 1-nanosecond molecular dynamics simulations of the enzyme ribonuclease T1 and its 2′GMP complex in water are presented. A classification of the angular reorientations of the backbone amide groups is achieved via a transformation of NH-vector trajectories into several coordinate frames, thus unravelling contributions of NH-bond librations and backbone dihedral angle fluctuations.

The former turned out to be similar for all amides, as characterized by correlation times of librational motions in a subpicosecond scale, angular amplitudes of about 10–12° for out-of-peptide-plane displacements of the NH-bond and 3–5° for the in-plane displacements, whereas the contributions of much slower backbone dihedral angle fluctuations strongly depend on the secondary structure. Correlation functions relevant for NMR were obtained and analyzed utilizing the ‘model-free’ approach (Lipari, G. and Szabo, A. (1982) J. Am. Chem. Soc. 104, 4546–4559,4559-4570; Clore et al., (1990) J. Am. Chem. Soc. 112, 4989–4991). The dependence of the amplitude of local motion on the residue location in the backbone is in good agreement with the results of NMR relaxation measurements and X-ray data. The protein dynamics is characterized by a highly restricted local motion of those parts of the backbone with defined secondary structure as well as by a high flexibility in loop regions. The comparison of results derived from different periods of the trajectory (of 50 ps and 1 ns duration, 1000 points sampled) reveals a dependence of the observed dynamic picture on the characteristic time scale of the experimental method used. Comparison of the MD data for the free and liganded enzyme clearly indicates a restriction of the mobility within certain regions of the backbone upon inhibitor binding.     

Prevalence of Clostridium spp. and Clostridium difficile in children with acute diarrhea in São Paulo city,Brazil

Species of Clostridium are widely distributed in the environment, inhabiting both human and animal gastrointestinal tracts. Clostridium difficile is an important pathogen associated with outbreaks of pseudomembranous colitis and other intestinal disorders, such as diarrhea. In this study, the prevalence of Clostridium spp. and C. difficile, from hospitalized children with acute diarrhea, was examined. These children were admitted to 3 different hospitals for over 12 months. Eighteen (20%) and 19 (21%) stool specimens from children with (90) and without (91) diarrhea respectively, were positive to clostridia. Only 10 C. difficile strains were detected in 5.5% of the stool samples of children with diarrhea. None healthy children (without diarrhea) harbored C. difficile. From these 10 C. difficile, 9 were considered as toxigenic and genotyped as tcdA+/tcdB+ or tcdA-/tcdB+, and 1 strain as nontoxigenic (tcdA-/tdcB-). They were detected by the citotoxicity on VERO cells and by the multiplex-polymerase chain reaction. Thirty clinical fecal extracts produced minor alterations on VERO cells. The presence of C. difficile as a probable agent of acute diarrhea is suggested in several countries, but in this study, the presence of these organisms was not significant. More studies will be necessary to evaluate the role of clostridia or C. difficile in diarrhoeal processes in children.     

Molecular Phylogeny of Section Parrya of Pinus （Pinaceae） Based on Chloroplast matK Gene Sequence Data

The molecular phylogenetics of sect. Parrya Myre of Pinus L. was analyzed based onchloroplast matKgene sequence data. The section was resolved as paraphyletic because members of thesect. Strobus were nested within a clade composed by the Asian members of the section, including theVietnamese P. krempfii Lecomte, which was strongly supported with a bootstrap value of 92%. [n thistopology, the three sampled species of sect. Strobus formed a strongly supported monophyletic group,while their relationships of Asian species of sect. Parrya were not clear. P. krempfii was grouped with P.gerardiana Wall. ex D. Don with low bootstrap support. The relationships among the Asian members of thesect. Parrya, i.e.P, bungeana Zucc. ex Loud., P. gerardiana and the recently described endangered pine, P.squarnata X. W. Li, was not resolved, although the monophyly of the three pines was strongly supported inthe combined analysis of four cpDNA sequences. The topology of the neighbor joining tree revealed anassemblage of the American members of the section, which also appeared in the majority rule tree withweak bootstrap support. However, this assemblage was not resolved in the consensus tree of theparsimonious analysis. The American subsect. Ba/fourianae Engelm. formed a weakly supported groupincluding P. aristata Engelm., while the relationships among and within the other two American subsections,Cembroides Englem. and tTzedowskianae Carv., were not resolved, as the members of them formed apolytomy in the consensus tree of the parsimonious analysis. The biogeographical implications of theresults are also discussed in this paper.     

Molecular Epidemiology of Clostridium difficile at a Medical Center in Taiwan: Persistence of Genetically Clustering of A?B+ Isolates and Increase of A+B+ Isolates

Introduction

We investigated the changing trend of various toxigenic Clostridium difficile isolates at a 3 500-bed hospital in Taiwan. Genetic relatedness and antimicrobial susceptibility of toxigenic C. difficile isolates were also examined.

Methods

A total of 110 non-repeat toxigenic C. difficile isolates from different patients were collected between 2002 and 2007. Characterization of the 110 toxigenic isolates was performed using agar dilution method, multilocus variable-number tandem-repeat analysis (MLVA) genotyping, tcdC genotyping, and toxinotyping.

Results

Among the 110 toxigenic isolates studied, 70 isolates harbored tcdA and tcdB (A⁺B⁺) and 40 isolates harbored tcdB only (A⁻B⁺). The annual number of A⁺B⁺ isolates considerably increased over the 6-year study (P = 0.055). A total of 109 different MLVA genotypes were identified, in which A⁺B⁺ isolates and A⁻B⁺ isolates were differentiated into two genetic clusters with similarity of 17.6%. Twenty-four (60%) of the 40 A⁻B⁺ isolates formed a major cluster, MLVA-group 1, with a similarity of 85%. Seven (6.4%) resistant isolates were identified, including two metronidazole-resistant and five vancomycin-resistant isolates.

Conclusions

This study indicated a persistence of a MLVA group 1 A⁻B⁺ isolates and an increase of A⁺B⁺ isolates with diverse MLVA types. Moreover, C. difficile isolates with antimicrobial resistance to metronidazole or vancomycin were found to have emerged. Continuous surveillance is warranted to understand the recent situation and control the further spread of the toxigenic C. difficile isolates, especially among hospitalized patients.     

Effect of Cigarette Smoking and Passive Smoking on Hearing Impairment: Data from a Population–Based Study

Objectives

In the present study, we aimed to determine the effect of both active and passive smoking on the prevalence of the hearing impairment and the hearing thresholds in different age groups through the analysis of data collected from the Korea National Health and Nutrition Examination Survey (KNHANES).

Study Design

Cross-sectional epidemiological study.

Methods

The KNHANES is an ongoing population study that started in 1998. We included a total of 12,935 participants aged ≥19 years in the KNHANES, from 2010 to 2012, in the present study. Pure-tone audiometric (PTA) testing was conducted and the frequencies tested were 0.5, 1, 2, 3, 4, and 6 kHz. Smoking status was categorized into three groups; current smoking group, passive smoking group and non-smoking group.

Results

In the current smoking group, the prevalence of speech-frequency bilateral hearing impairment was increased in ages of 40−69, and the rate of high frequency bilateral hearing impairment was elevated in ages of 30−79. When we investigated the impact of smoking on hearing thresholds, we found that the current smoking group had significantly increased hearing thresholds compared to the passive smoking group and non-smoking groups, across all ages in both speech-relevant and high frequencies. The passive smoking group did not have an elevated prevalence of either speech-frequency bilateral hearing impairment or high frequency bilateral hearing impairment, except in ages of 40s. However, the passive smoking group had higher hearing thresholds than the non-smoking group in the 30s and 40s age groups.

Conclusion

Current smoking was associated with hearing impairment in both speech-relevant frequency and high frequency across all ages. However, except in the ages of 40s, passive smoking was not related to hearing impairment in either speech-relevant or high frequencies.     

Characterisation of interstitial duplications and triplications of chromosome 15q11–q13

Chromosome 15 is frequently involved in the formation of structural rearrangements. We report the molecular characterisation of 16 independent interstitial duplications, including those of one individual who carried a duplication on both of her chromosomes 15, and three interstitial triplications of the Prader-Willi/Angelman syndrome critical region (PWACR). In all probands except one, the rearrangement was maternal in origin. In one family, the duplication was paternal in origin, yet appeared to segregate in a sibship of three with an abnormal phenotype that included developmental delay and a behavioural disorder. Ten duplications were familial, five de novo and one unknown. All 16 duplications, including two not visible by routine G-banding, were of an almost uniform size and shared the common deletion breakpoints of Prader-Willi syndrome and Angelman syndrome. Like deletions, the formation of duplications can occur in both male and female meiosis and involve both inter- and intrachromosomal events. This implies that at least some deletions and duplications are the reciprocal products of each other. We observed no instances of meiotic instability in the transmission of a duplication, although recombination within the PWACR occurred in two members of the same family between the normal and the duplicated chromosome 15 homologues. All three triplications arose de novo and included alleles from both maternal chromosomes 15. Triplication breakpoints were more variable and extended distally beyond the PWACR. The molecular characteristics of duplications and triplications suggest that they are formed by different mechanisms.     

Mapping Plant Interactomes Using Literature Curated and Predicted Protein–Protein Interaction Data Sets

Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein–protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations.For well over two decades, plant scientists have studied protein interactions within plants using many different and evolving approaches. Their findings are represented by a large and growing corpus of peer-reviewed literature reflecting the increasing activity in this area of plant proteomic research. More recently, a number of predicted interactomes have been reported in plants and, while these predictions remain largely untested, they could act as a useful guide for future research. These studies have allowed researchers to better understand the function of protein complexes and to refine our understanding of protein function within the cell (Uhrig, 2006; Morsy et al., 2008). The extraction of protein interaction data from the literature and its standardized deposition and representation within publicly available databases remains a challenging task. Aggregating the data in databases allows researchers to leverage visualization, data mining, and integrative approaches to produce new insights that would be unachievable when the data are dispersed within largely inaccessible formats (Rodriguez et al., 2009).Currently, there are three databases that act as repositories of plant protein interaction data. These are IntAct (http://www.ebi.ac.uk/intact/; Aranda et al., 2010), The Arabidopsis Information Resource (TAIR; http://www.Arabidopsis.org/; Poole, 2007), and BioGRID (http://www.thebiogrid.org/; Breitkreutz et al., 2008). These databases curate experimentally established interactions available from the peer-reviewed literature (as opposed to predicted interactions, which will be discussed below). Each repository takes its own approach to the capture, storage, and representation of protein interaction data. TAIR focuses on Arabidopsis thaliana protein–protein interaction data exclusively; BioGRID currently focuses on the plant species Arabidopsis and rice (Oryza sativa), while IntAct attempts to capture protein interaction data from any plant species. Unlike the other repositories, IntAct follows a deep curation strategy that captures detailed experimental and biophysical details, such as binding regions and subcellular locations of interactions using controlled vocabularies (Aranda et al., 2010). While the majority of plant interaction data held by IntAct concern protein–protein interaction data in Arabidopsis, there is a small but growing content of interaction data relating to protein–DNA, protein–RNA, and protein–small molecule interactions, as well as interaction data from other plant species.Using the IntAct Arabidopsis data set as an example, we outline how the accumulating knowledge captured in these repositories can be used to further our understanding of the plant proteome. We compare the characteristics of predicted interactomes with the IntAct protein–protein interaction data set, which consists entirely of experimentally measured protein interactions, to gauge the predictive accuracy of these studies. Finally, we show how the IntAct data set can be used together with a recently developed Divide and Conquer k-Nearest Neighbors Method (DC-kNN; K. Lee et al., 2008) to predict the subcellular locations for most Arabidopsis proteins. This data set predicts high confidence subcellular locations for many unannotated Arabidopsis proteins and should act as a useful resource for future studies of protein function. Although this article focuses on the IntAct Arabidopsis protein–protein interaction data set, readers are also encouraged to explore the resources offered by our colleagues at TAIR and BioGRID.Each database employs its own system to report molecular interactions, as represented in the referenced source publications, and each avoids making judgments on interaction reliability or whether two participants in a complex have a direct interaction. Thus, the user should carefully filter these data sets for their specific purpose based on the full annotation of the data sets. In particular, the user should consider the experimental methods and independent observation of the same interaction in different publications when assessing the reliability and type of interaction of the proteins (e.g., direct or indirect). Confidence scoring schemes for interaction data are discussed widely in the literature (Yu and Finley, 2009).     

Quantitative Detection and Differentiation of Free-Living Amoeba Species Using SYBR Green–Based Real-Time PCR Melting Curve Analysis

Real-time polymerase chain reaction melting curve analysis (MCA) allows differentiation of several free-living amoebae species. Distinctive characteristics were found for Naegleria fowleri, N. lovaniensis, N. australiensis, N. gruberi, Hartmanella vermiformis, and Willaertia magna. Species specificity of the amplicons was confirmed using agarose gel electrophoresis and sequence-based approaches. Amplification efficiency ranged from 91% to 98%, indicating the quantitative potential of the assay. This MCA approach can be used for quantitative detection of free-living amoebae after cultivation but also as a culture-independent detection method.     

Molecular Bases of Cell–Cell Junctions Stability and Dynamics

Epithelial cell–cell junctions are formed by apical adherens junctions (AJs), which are composed of cadherin adhesion molecules interacting in a dynamic way with the cortical actin cytoskeleton. Regulation of cell–cell junction stability and dynamics is crucial to maintain tissue integrity and allow tissue remodeling throughout development. Actin filament turnover and organization are tightly controlled together with myosin-II activity to produce mechanical forces that drive the assembly, maintenance, and remodeling of AJs. In this review, we will discuss these three distinct stages in the lifespan of cell–cell junctions, using several developmental contexts, which illustrate how mechanical forces are generated and transmitted at junctions, and how they impact on the integrity and the remodeling of cell–cell junctions.Cell–cell junction formation and remodeling occur repeatedly throughout development. Epithelial cells are linked by apical adherens junctions (AJs) that rely on the cadherin-catenin-actin module. Cadherins, of which epithelial E-cadherin (E-cad) is the most studied, are Ca²⁺-dependent transmembrane adhesion proteins forming homophilic and heterophilic bonds in trans between adjacent cells. Cadherins and the actin cytoskeleton are mutually interdependent (Jaffe et al. 1990; Matsuzaki et al. 1990; Hirano et al. 1992; Oyama et al. 1994; Angres et al. 1996; Orsulic and Peifer 1996; Adams et al. 1998; Zhang et al. 2005; Pilot et al. 2006). This has long been attributed to direct physical interaction of E-cad with β-catenin (β-cat) and of α-catenin (α-cat) with actin filaments (for reviews, see Gumbiner 2005; Leckband and Prakasam 2006; Pokutta and Weis 2007). Recently, biochemical and protein dynamics analyses have shown that such a link may not exist and that instead, a constant shuttling of α-cat between cadherin/β-cat complexes and actin may be key to explain the dynamic aspect of cell–cell adhesion (Drees et al. 2005; Yamada et al. 2005). Regardless of the exact nature of this link, several studies show that AJs are indeed physically attached to actin and that cadherins transmit cortical forces exerted by junctional acto-myosin networks (Costa et al. 1998; Sako et al. 1998; Pettitt et al. 2003; Dawes-Hoang et al. 2005; Cavey et al. 2008; Martin et al. 2008; Rauzi et al. 2008). In addition, physical association depends in part on α-cat (Cavey et al. 2008) and additional intermediates have been proposed to represent alternative missing links (Abe and Takeichi 2008) (reviewed in Gates and Peifer 2005; Weis and Nelson 2006). Although further work is needed to address the molecular nature of cadherin/actin dynamic interactions, association with actin is crucial all throughout the lifespan of AJs. In this article, we will review our current understanding of the molecular mechanisms at work during three different developmental stages of AJs biology: assembly, stabilization, and remodeling, with special emphasis on the mechanical forces controlling AJs integrity and development.