60 research outputs found

    Genetic support for a quantitative trait nucleotide in the ABCG2 gene affecting milk composition of dairy cattle

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    <p>Abstract</p> <p>Background</p> <p>Our group has previously identified a quantitative trait locus (QTL) affecting fat and protein percentages on bovine chromosome 6, and refined the QTL position to a 420-kb interval containing six genes. Studies performed in other cattle populations have proposed polymorphisms in two different genes (<it>ABCG2 </it>and <it>OPN</it>) as the underlying functional QTL nucleotide. Due to these conflicting results, we have included these QTNs, together with a large collection of new SNPs produced from PCR sequencing, in a dense marker map spanning the QTL region, and reanalyzed the data using a combined linkage and linkage disequilibrium approach.</p> <p>Results</p> <p>Our results clearly exclude the <it>OPN </it>SNP (<it>OPN_3907</it>) as causal site for the QTL. Among 91 SNPs included in the study, the <it>ABCG2 </it>SNP (<it>ABCG2_49</it>) is clearly the best QTN candidate. The analyses revealed the presence of only one QTL for the percentage traits in the tested region. This QTL was completely removed by correcting the analysis for <it>ABCG2_49</it>. Concordance between the sires' marker genotypes and segregation status for the QTL was found for <it>ABCG2_49 </it>only. The C allele of <it>ABCG2_49 </it>is found in a marker haplotype that has an extremely negative effect on fat and protein percentages and positive effect on milk yield. Of the 91 SNPs, <it>ABCG2_49 </it>was the only marker in perfect linkage disequilibrium with the QTL.</p> <p>Conclusion</p> <p>Based on our results, OPN_3907 can be excluded as the polymorphism underlying the QTL. The results of this and other papers strongly suggest the [A/C] mutation in <it>ABCG2_49 </it>as the causal mutation, although the possibility that <it>ABCG2_49 </it>is only a marker in perfect LD with the true mutation can not be completely ruled out.</p

    What traits are carried on mobile genetic elements, and why?

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    Although similar to any other organism, prokaryotes can transfer genes vertically from mother cell to daughter cell, they can also exchange certain genes horizontally. Genes can move within and between genomes at fast rates because of mobile genetic elements (MGEs). Although mobile elements are fundamentally self-interested entities, and thus replicate for their own gain, they frequently carry genes beneficial for their hosts and/or the neighbours of their hosts. Many genes that are carried by mobile elements code for traits that are expressed outside of the cell. Such traits are involved in bacterial sociality, such as the production of public goods, which benefit a cell's neighbours, or the production of bacteriocins, which harm a cell's neighbours. In this study we review the patterns that are emerging in the types of genes carried by mobile elements, and discuss the evolutionary and ecological conditions under which mobile elements evolve to carry their peculiar mix of parasitic, beneficial and cooperative genes

    In Vivo Dynamics of the Musculoskeletal System Cannot Be Adequately Described Using a Stiffness-Damping-Inertia Model

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    Background: Visco-elastic properties of the (neuro-)musculoskeletal system play a fundamental role in the control of posture and movement. Often, these properties are described and identified using stiffness-damping-inertia (KBI) models. In such an approach, perturbations are applied to the (neuro-)musculoskeletal system and subsequently KBI-model parameters are optimized to obtain a best fit between simulated and experimentally observed responses. Problems with this approach may arise because a KBI-model neglects critical aspects of the real musculoskeletal system. Methodology/Principal Findings: The purpose of this study was to analyze the relation between the musculoskeletal properties and the stiffness and damping estimated using a KBI-model, to analyze how this relation is affected by the nature of the perturbation and to assess the sensitivity of the estimated stiffness and damping to measurement errors. Our analyses show that the estimated stiffness and damping using KBI-models do not resemble any of the dynamical parameters of the underlying system, not even when the responses are very accurately fitted by the KBI-model. Furthermore, the stiffness and damping depend non-linearly on all the dynamical parameters of the underlying system, influenced by the nature of the perturbation and the time interval over which the KBI-model is optimized. Moreover, our analyses predict a very high sensitivity of estimated parameters to measurement errors. Conclusions/Significance: The results of this study suggest that the usage of stiffness-damping-inertia models t

    Postal survey of physicians and laboratories: Practices and perceptions of molecular oncology testing

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    <p>Abstract</p> <p>Background</p> <p>Molecular oncology testing (MOT) to detect genomic alterations underlying cancer holds promise for improved cancer care. Yet knowledge limitations regarding the delivery of testing services may constrain the translation of scientific advancements into effective health care.</p> <p>Methods</p> <p>We conducted a cross-sectional, self-administered, postal survey of active cancer physicians in Ontario, Canada (N = 611) likely to order MOT, and cancer laboratories (N = 99) likely to refer (i.e., referring laboratories) or conduct (i.e., testing laboratories) MOT in 2006, to assess respondents' perceptions of the importance and accessibility of MOT and their preparedness to provide it.</p> <p>Results</p> <p>54% of physicians, 63% of testing laboratories and 60% of referring laboratories responded. Most perceived MOT to be important for treatment, diagnosis or prognosis now, and in 5 years (61% – 100%). Yet only 45% of physicians, 59% of testing labs and 53% of referring labs agreed that patients in their region were receiving MOT that is indicated as a standard of care. Physicians and laboratories perceived various barriers to providing MOT, including, among 70% of physicians, a lack of clear guidelines regarding clinical indications, and among laboratories, a lack of funding (73% – 100%). Testing laboratories were confident of their ability to determine whether and which MOT was indicated (77% and 82% respectively), and perceived that key elements of formal and continuing education were helpful (75% – 100%). By contrast, minorities of physicians were confident of their ability to assess whether and which MOT was indicated (46% and 34% respectively), and while majorities considered various continuing educational resources helpful (68% – 75%), only minorities considered key elements of formal education helpful in preparing for MOT (17% – 43%).</p> <p>Conclusion</p> <p>Physicians and laboratory professionals were enthusiastic about the value of MOT for cancer care but most did not believe patients were gaining adequate access to clinically necessary testing. Further, our results suggest that many were ill equipped as individual stakeholders, or as a coordinated system of referral and interpretation, to provide MOT. These challenges should inspire educational, training and other interventions to ensure that developments in molecular oncology can result in optimal cancer care.</p

    A naturally protective epitope of limited variability as an influenza vaccine target

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    Current antigenic targets for influenza vaccine development are either highly immunogenic epitopes of high variability or conserved epitopes of low immunogenicity. This requires continuous update of the variable epitopes in the vaccine formulation or boosting of immunity to invariant epitopes of low natural efficacy. Here we identify a highly immunogenic epitope of limited variability in the head domain of the H1 haemagglutinin protein. We show that a cohort of young children exhibit natural immunity to a set of historical influenza strains which they could not have previously encountered and that this is partially mediated through the epitope. Furthermore, vaccinating mice with these epitope conformations can induce immunity to human H1N1 influenza strains that have circulated since 1918. The identification of epitopes of limited variability offers a mechanism by which a universal influenza vaccine can be created; these vaccines would also have the potential to protect against newly emerging influenza strains

    Purification of cross-linked RNA-protein complexes by phenol-toluol extraction

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    Recent methodological advances allowed the identification of an increasing number of RNA-binding proteins (RBPs) and their RNA-binding sites. Most of those methods rely, however, on capturing proteins associated to polyadenylated RNAs which neglects RBPs bound to non-adenylate RNA classes (tRNA, rRNA, pre-mRNA) as well as the vast majority of species that lack poly-A tails in their mRNAs (including all archea and bacteria). We have developed the Phenol Toluol extraction (PTex) protocol that does not rely on a specific RNA sequence or motif for isolation of cross-linked ribonucleoproteins (RNPs), but rather purifies them based entirely on their physicochemical properties. PTex captures RBPs that bind to RNA as short as 30 nt, RNPs directly from animal tissue and can be used to simplify complex workflows such as PAR-CLIP. Finally, we provide a global RNA-bound proteome of human HEK293 cells and the bacterium Salmonella Typhimurium

    Affinity Proteomic Analysis of the Human Exosome and Its Cofactor Complexes

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    In humans, the RNA exosome consists of an enzymatically inactive nine-subunit core, with ribonucleolytic activity contributed by additional components. Several cofactor complexes also interact with the exosome-these enable the recruitment of, and specify the activity upon, diverse substrates. Affinity capture coupled with mass spectrometry has proven to be an effective means to identify the compositions of RNA exosomes and their cofactor complexes: here, we describe a general experimental strategy for proteomic characterization of macromolecular complexes, applied to the exosome and an affiliated adapter protein, ZC3H18
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