Rust fungal effectors mimic host transit peptides to translocate into chloroplasts

Parasite effector proteins target various host cell compartments to alter host processes and promote infection. How effectors cross membrane‐rich interfaces to reach these compartments is a major question in effector biology. Growing evidence suggests that effectors use molecular mimicry to subvert host cell machinery for protein sorting. We recently identified chloroplast‐targeted protein 1 (CTP1), a candidate effector from the poplar leaf rust fungus Melampsora larici‐populina that carries a predicted transit peptide and accumulates in chloroplasts and mitochondria. Here, we show that the CTP1 transit peptide is necessary and sufficient for accumulation in the stroma of chloroplasts. CTP1 is part of a Melampsora‐specific family of polymorphic secreted proteins. Two members of that family, CTP2 and CTP3, also translocate in chloroplasts in an N‐terminal signal‐dependent manner. CTP1, CTP2 and CTP3 are cleaved when they accumulate in chloroplasts, while they remain intact when they do not translocate into chloroplasts. Our findings reveal that fungi have evolved effector proteins that mimic plant‐specific sorting signals to traffic within plant cells.     

Identification of Polymorphisms in the Rabbit Growth Hormone Receptor (GHR) Gene and Association with Finishing Weight in a Commercial Meat Rabbit Line

A shortcut to identify DNA markers associated with economic traits is to use a candidate gene approach that is still useful in livestock species in which molecular tools and resources are not advanced or not well developed. Mutations in the growth hormone receptor (GHR) gene associated with production traits have been already described in several livestock species. For this reason GHR could be an interesting candidate gene in the rabbit. In this study we re-sequenced all exons and non-coding regions of the rabbit GHR gene in a panel of 10 different rabbits and identified 10 single nucleotide polymorphisms (SNPs). One of them (g.63453192C>G or c.106C>G), located in exon 3 was a missense mutation (p.L36V) substituting an amino acid in a highly conserved position across all mammals. This mutation was genotyped in 297 performance tested rabbits of a meat male line and association analysis showed that the investigated SNP was associated with weight at 70 days (P < 0.05). The most frequent genotype (GG) was in animals with higher weight at this age, suggesting that the high directional selection pressure toward this trait since the constitution of the genotyped line might have contributed to shape allele frequencies at this polymorphic site.     

Variation and inheritance of the Xanthomonas raxX-raxSTAB gene cluster required for activation of XA21-mediated immunity

The rice XA21-mediated immune response is activated on recognition of the RaxX peptide produced by the bacterium Xanthomonas oryzae pv. oryzae (Xoo). The 60-residue RaxX precursor is post-translationally modified to form a sulfated tyrosine peptide that shares sequence and functional similarity with the plant sulfated tyrosine (PSY) peptide hormones. The 5-kb raxX-raxSTAB gene cluster of Xoo encodes RaxX, the RaxST tyrosylprotein sulfotransferase, and the RaxA and RaxB components of a predicted type I secretion system. To assess raxX-raxSTAB gene cluster evolution and to determine its phylogenetic distribution, we first identified rax gene homologues in other genomes. We detected the complete raxX-raxSTAB gene cluster only in Xanthomonas spp., in five distinct lineages in addition to X. oryzae. The phylogenetic distribution of the raxX-raxSTAB gene cluster is consistent with the occurrence of multiple lateral (horizontal) gene transfer events during Xanthomonas speciation. RaxX natural variants contain a restricted set of missense substitutions, as expected if selection acts to maintain peptide hormone-like function. Indeed, eight RaxX variants tested all failed to activate the XA21-mediated immune response, yet retained peptide hormone activity. Together, these observations support the hypothesis that the XA21 receptor evolved specifically to recognize Xoo RaxX.     

Estimating abdominal adipose tissue with DXA and anthropometry

Objective: To identify an anatomically defined region of interest (ROI) from DXA assessment of body composition that when combined with anthropometry can be used to accurately predict intra‐abdominal adipose tissue (IAAT) in overweight/obese individuals. Research Methods and Procedures: Forty‐one postmenopausal women (age, 49 to 66 years; BMI, 26 to 37 kg/m²) underwent anthropometric and body composition assessments. ROI were defined as quadrilateral boxes extending 5 or 10 cm above the iliac crest and laterally to the edges of the abdominal soft tissue. A single‐slice computed tomography (CT) scan was measured at the L3 to L4 intervertebral space, and abdominal skinfolds were taken. Results: Forward step‐wise regression revealed the best predictor model of IAAT area measured by CT (r² = 0.68, standard error of estimate = 17%) to be: IAAT area (centimeters squared) = 51.844 + DXA 10‐cm ROI (grams) (0.031) + abdominal skinfold (millimeters) (1.342). Interobserver reliability for fat mass (r = 0.994; coefficient of variation, 2.60%) and lean mass (r = 0.986, coefficient of variation, 2.67%) in the DXA 10‐cm ROI was excellent. Discussion: This study has identified a DXA ROI that can be reliably measured using prominent anatomical landmarks, in this case, the iliac crest. Using this ROI, combined with an abdominal skinfold measurement, we have derived an equation to predict IAAT in overweight/obese postmenopausal women. This approach offers a simpler, safer, and more cost‐effective method than CT for assessing the efficacy of lifestyle interventions aimed at reducing IAAT. However, this warrants further investigation and validation with an independent cohort.     

Subspace projection approaches to classification and visualization of neural network-level encoding patterns

Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA), Principal Components Analysis (PCA) and Artificial Neural Networks (ANN), and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD). Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD.     

Multimetal resistance and tolerance in microbial biofilms

Geochemical cycling and industrial pollution have made toxic metal ions a pervasive environmental pressure throughout the world. Biofilm formation is a strategy that microorganisms might use to survive a toxic flux in these inorganic compounds. Evidence in the literature suggests that biofilm populations are protected from toxic metals by the combined action of chemical, physical and physiological phenomena that are, in some instances, linked to phenotypic variation among the constituent biofilm cells. Here, we propose a multifactorial model by which biofilm populations can withstand metal toxicity by a process of cellular diversification.     

Quantification of urinary zwitterionic organic acids using weak-anion exchange chromatography with tandem MS detection

A rapid and accurate quantitative method was developed and validated for the analysis of four urinary organic acids with nitrogen containing functional groups, formiminoglutamic acid (FIGLU), pyroglutamic acid (PYRGLU), 5-hydroxyindoleacetic acid (5-HIAA), and 2-methylhippuric acid (2-METHIP) by liquid chromatography tandem mass spectrometry (LC/MS/MS). The chromatography was developed using a weak anion-exchange amino column that provided mixed-mode retention of the analytes. The elution gradient relied on changes in mobile phase pH over a concave gradient, without the use of counter-ions or concentrated salt buffers. A simple sample preparation was used, only requiring the dilution of urine prior to instrumental analysis. The method was validated based on linearity (r2>or=0.995), accuracy (85-115%), precision (C.V.<12%), sample preparation stability (相似文献   

Analysis of NVMe over fabrics with SCinet DTN-as-a-Service

Supporting transfers of science big data over Wide Area Networks (WANs) with Data Transfer Nodes (DTNs) requires optimizing multiple parameters within the underlying infrastructure. New solutions for such data movement require new paradigms and technologies, such as NVMe over Fabrics, which provides high-performance data movement with direct remote NVMe device access over traditional fabrics. However, recent NVMe over Fabrics studies have been limited to local storage fabrics. To support increasing demands for the large volume of science data movement during Supercomputing (SC) conferences, we proposed a SCinet DTN-as-a-Service framework orchestrating the desired optimization to meet users, applications, and providers’ requirements. Furthermore, we extend the SCinet DTN-as-a-Service framework to incorporate new techniques, solve optimization issues in data-intensive science and evaluate NVMe over Fabrics with multiple WAN testbeds to examine its performance and discover new opportunities for optimization.