Lettuce cultivar mediates both phyllosphere and rhizosphere activity of Escherichia coli O157:H7

Plant roots and leaves can be colonized by human pathogenic bacteria, and accordingly some of the largest outbreaks of foodborne illness have been associated with salad leaves contaminated by E. coli O157. Integrated disease management strategies often exploit cultivar resistance to provide a level of protection from economically important plant pathogens; however, there is limited evidence of whether the genotype of the plant can also influence the extent of E. coli O157 colonization. To determine cultivar-specific effects on colonization by E. coli O157, we used 12 different cultivars of lettuce inoculated with a chromosomally lux-marked strain of E. coli O157:H7. Lettuce seedlings grown gnotobiotically in vitro did exhibit a differential cultivar-specific response to E. coli O157 colonization, although importantly there was no relationship between metabolic activity (measured as bioluminescence) and cell numbers. Metabolic activity was highest and lowest on the cultivars Vaila-winter gem and Dazzle respectively, and much higher in endophytic and tightly bound cells than in epiphytic and loosely bound cells. The cultivar effect was also evident in the rhizosphere of plants grown in compost, which suggests that cultivar-specific root exudate influences E. coli O157 activity. However, the influence of cultivar in the rhizosphere was the opposite to that in the phyllosphere, and the higher number and activity of E. coli O157 cells in the rhizosphere may be a consequence of them not being able to gain entry to the plant as effectively. If metabolic activity in the phyllosphere corresponds to a more prepared state of infectivity during human consumption, leaf internalization of E. coli O157 may pose more of a public health risk than leaf surface contamination alone.     

Kappa agonist CovX-Bodies

Small peptidic kappa agonists were covalently linked to the reactive lysine of the CovX antibody to create compounds having potent activity at the kappa receptor with greatly extended half-life when compared to the parent peptide as exemplified by compound 20.     

Multistate approaches in computational protein design

Computational protein design (CPD) is a useful tool for protein engineers. It has been successfully applied towards the creation of proteins with increased thermostability, improved binding affinity, novel enzymatic activity, and altered ligand specificity. Traditionally, CPD calculations search and rank sequences using a single fixed protein backbone template in an approach referred to as single-state design (SSD). While SSD has enjoyed considerable success, certain design objectives require the explicit consideration of multiple conformational and/or chemical states. Cases where a "multistate" approach may be advantageous over the SSD approach include designing conformational changes into proteins, using native ensembles to mimic backbone flexibility, and designing ligand or oligomeric association specificities. These design objectives can be efficiently tackled using multistate design (MSD), an emerging methodology in CPD that considers any number of protein conformational or chemical states as inputs instead of a single protein backbone template, as in SSD. In this review article, recent examples of the successful design of a desired property into proteins using MSD are described. These studies employing MSD are divided into two categories-those that utilized multiple conformational states, and those that utilized multiple chemical states. In addition, the scoring of competing states during negative design is discussed as a current challenge for MSD.     

Genomic islands of divergence in hybridizing Heliconius butterflies identified by large-scale targeted sequencing

Heliconius butterflies represent a recent radiation of species, in which wing pattern divergence has been implicated in speciation. Several loci that control wing pattern phenotypes have been mapped and two were identified through sequencing. These same gene regions play a role in adaptation across the whole Heliconius radiation. Previous studies of population genetic patterns at these regions have sequenced small amplicons. Here, we use targeted next-generation sequence capture to survey patterns of divergence across these entire regions in divergent geographical races and species of Heliconius. This technique was successful both within and between species for obtaining high coverage of almost all coding regions and sufficient coverage of non-coding regions to perform population genetic analyses. We find major peaks of elevated population differentiation between races across hybrid zones, which indicate regions under strong divergent selection. These 'islands' of divergence appear to be more extensive between closely related species, but there is less clear evidence for such islands between more distantly related species at two further points along the 'speciation continuum'. We also sequence fosmid clones across these regions in different Heliconius melpomene races. We find no major structural rearrangements but many relatively large (greater than 1 kb) insertion/deletion events (including gain/loss of transposable elements) that are variable between races.     

Decreased body weight in young Osterix-Cre transgenic mice results in delayed cortical bone expansion and accrual

Conditional gene inactivation using the Cre/loxP system has lead to significant advances in our understanding of the function of genes in a wide range of disciplines. It is becoming increasingly apparent in the literature, that Cre transgenic mice may themselves have a phenotype. In the following study we describe the bone phenotype of a commonly used Cre transgenic mouse line to study osteoblasts, the Osx-GFP::Cre (Osx-Cre) mice. Cortical and trabecular bone parameters were determined in the femurs of Osx-Cre mice at 6 and 12?weeks of age by microtomography (μCT). At 6?weeks of age, Osx-Cre mice had reduced body weight by 22% (P?相似文献   

Genome-wide association study of three-dimensional facial morphology identifies a variant in PAX3 associated with nasion position

Craniofacial morphology is highly heritable, but little is known about which genetic variants influence normal facial variation in the general population. We aimed to identify genetic variants associated with normal facial variation in a population-based cohort of 15-year-olds from the Avon Longitudinal Study of Parents and Children. 3D high-resolution images were obtained with two laser scanners, these were merged and aligned, and 22 landmarks were identified and their x, y, and z coordinates used to generate 54 3D distances reflecting facial features. 14 principal components (PCs) were also generated from the landmark locations. We carried out genome-wide association analyses of these distances and PCs in 2,185 adolescents and attempted to replicate any significant associations in a further 1,622 participants. In the discovery analysis no associations were observed with the PCs, but we identified four associations with the distances, and one of these, the association between rs7559271 in PAX3 and the nasion to midendocanthion distance (n-men), was replicated (p = 4 × 10(-7)). In a combined analysis, each G allele of rs7559271 was associated with an increase in n-men distance of 0.39 mm (p = 4 × 10(-16)), explaining 1.3% of the variance. Independent associations were observed in both the z (nasion prominence) and y (nasion height) dimensions (p = 9 × 10(-9) and p = 9 × 10(-10), respectively), suggesting that the locus primarily influences growth in the yz plane. Rare variants in PAX3 are known to cause Waardenburg syndrome, which involves deafness, pigmentary abnormalities, and facial characteristics including a broad nasal bridge. Our findings show that common variants within this gene also influence normal craniofacial development.     

Analysis of doramectin in the serum of repeatedly treated pastured cattle used to predict the probability of cattle fever ticks (Acari: Ixodidae) feeding to repletion

Analysis of doramectin concentration in blood serum of pastured cattle injected repeatedly (12 treatments) at two different dosage rates and 28-day intervals throughout the year was used to predict the probability that cattle fever ticks could successfully feed to repletion during the interval between any two consecutive treatments. Treatment at ~270 μg/kg indicated that serum doramectin concentration dropped below the baseline concentration estimated for tick survival (8 ppb) in 7 of the 12 treatments. However, the longest period between any two treatments during which the doramectin concentration remained below the 8 ppb baseline level for successful tick feeding was 15 days, making it virtually impossible for any ticks to reach ovipositional status prior to a subsequent treatment. At a dosage rate of ~540 μg/kg, the concentration dropped below the baseline tick survival level (8 ppb) only once, following the initial treatment, and the duration during which the concentration remained below the baseline level prior to the subsequent treatment was only 6 days. Thus, at the high dosage rate results indicated, with absolute certainty, that no ticks could successfully feed to repletion between any two consecutive treatments. Based on the data obtained in the study it was concluded that analysis of doramectin concentration in serum of treated animals would be a reliable predictor for assessing the probability that ticks could successfully develop to repletion. More importantly, results demonstrated that the trial policy, instituted by the Cattle Fever Tick Eradication Program, of repeatedly treating cattle with doramectin injections at 25–28 day intervals for eliminating cattle fever ticks would produce little or no risk of any viable ticks developing to repletion and re-infesting the field between treatment applications.     

Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments

Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that “all genes affect every complex trait” complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and potential pleiotropic pathways involved.     

A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard

ABSTRACT: BACKGROUND: Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers. RESULTS: We compared three classifiers of protein subcellular localisation, and evaluated our estimates of sensitivity and specificity against estimates obtained using a gold standard. The method overestimated sensitivity and specificity with only a small discrepancy, and correctly ranked the classifiers. Diagnostic tests for swine flu were then compared on a small data set. Lastly, classifiers for a genome-wide association study of macular degeneration with 541094 SNPs were analysed. In all cases, run times were feasible, and results precise. The optimal logical combination of classifiers was also determined for all three data sets. Code and data are available from http://bioinformatics.monash.edu.au/downloads/. CONCLUSIONS: The examples demonstrate the methods are suitable for both small and large data sets, applicable to the wide range of bioinformatics classification problems, and robust to dependence between classifiers. In all three test cases, the globally optimal logical combination of the classifiers was found to be their union, according to three out of four ranking criteria. We propose as a general rule of thumb that the union of classifiers will be close to optimal.