QTL analysis of resistance to Fusarium head blight in Swiss winter wheat ( Triticum aestivum L.)

Fusarium head blight (FHB) of wheat is a widespread and destructive disease which occurs in humid and semi-humid areas. FHB epidemics can cause serious yield and quality losses under favorable climatic conditions, but the major concern is the contamination of grains with mycotoxins. Resistance to FHB is quantitatively inherited and greatly influenced by the environment. Its evaluation is costly and time-consuming. The genetic basis of FHB resistance has mainly been studied in spring wheat. The objective of this study was to map quantitative trait loci (QTLs) for resistance to FHB in a population of 240 recombinant inbred lines (RILs) derived from a cross between the two Swiss winter wheat cultivars Arina (resistant) and Forno (susceptible). The RILs were genotyped with microsatellite and RFLP markers. The resulting genetic map comprises 380 loci and spans 3,086cM. The 240 RILs were evaluated for resistance to FHB in six field trials over 3 years. Composite interval mapping (CIM) analyses carried out on FHB AUDPC (i.e. mean values across six environments) revealed eight QTLs which altogether explained 47% of the phenotypic variance. The three main QTLs were mapped on the long arms of chromosomes 6D (R 2=22%), 5B (R 2=14%) and 4A (R 2=10%). The QTL detected on 5B originated from the susceptible parent Forno. Other QTLs with smaller effects on FHB resistance were detected on chromosomes 2AL, 3AL, 3BL, 3DS and 5A

Detection of QTLs for Stagonospora glume blotch resistance in Swiss winter wheat

Stagonospora nodorum is the causal agent of the Stagonospora glume blotch disease in hexaploid wheat. The Swiss winter bread wheat cv. 'Arina' has a highly effective, durable and quantitative glume blotch resistance. We studied 240 single seed descent (SSD)-derived lines of an 'Arina × Forno' F5:7 population to identify and map quantitative trait loci (QTLs) for glume blotch resistance under natural infestation. Using composite interval mapping (CIM) and LOD>4.5, we detected two chromosomal regions on chromosome arms 3BS and 4BL which were specifically associated with glume blotch resistance. These identified QTLs were designated QSng.sfr-3BS and QSng.sfr-4BL, respectively. QSng.sfr-3BS peaked at the locus Xgwm389 in the telomeric region of the short arm of chromosome 3B and explained 31.2% of the observed phenotypic variance for the resistance within the population. The responsible QSng.sfr-3BS allele originated from the resistant parent 'Arina'. The QTL QSng.sfr-4BL (19.1%) mapped to chromosome arm 4BL ('Forno' allele) very close to two known genes, TaMlo and a catalase (Cat). Both QTL alleles combined could enhance the resistance level by about 50%. Additionally, they showed significant epistatic effects (4.4%). We found PCR-based microsatellite markers closely linked to QSng.sfr-3BS (gwm389) and QSng.sfr-4BL (gwm251) which make marker-assisted selection (MAS) for Stagonospora glume blotch resistance feasible. We also found one resistance QTL, QSng.sfr-5BL, on the long arm of chromosome 5B which overlapped with QTLs for plant height as well as heading tim

Dissection of quantitative and durable leaf rust resistance in Swiss winter wheat reveals a major resistance QTL in the Lr34 chromosomal region

The Swiss winter bread wheat cv. ‘Forno' has a highly effective, durable and quantitative leaf rust (Puccinia triticina Eriks.) resistance which is associated with leaf tip necrosis (LTN). We studied 240 single seed descent lines of an ‘Arina×Forno' F5:7 population to identify and map quantitative trait loci (QTLs) for leaf rust resistance and LTN. Percentage of infected leaf area (%) and the response to infection (RI) were evaluated in seven field trials and were transformed to the area under the disease progress curves (AUDPC). Using composite interval mapping and LOD>4.4, we identified eight chromosomal regions specifically associated with resistance. The largest and most consistent leaf rust resistance locus was identified on the short arm of chromosome 7D (32.6% of variance explained for AUDPC_% and 42.6% for AUDPC_RI) together with the major QTL for LTN (R 2=55.6%) in the same chromosomal region as Lr34 (Xgwm295). A second major leaf rust resistance QTL (R 2=28% and 31.5%, respectively) was located on chromosome arm 1BS close to Xgwm604 and was not associated with LTN. Additional minor QTLs for LTN (2DL, 3DL, 4BS and 5AL) and leaf rust resistance were identified. These latter QTLs might correspond to the leaf rust resistance genes Lr2 or Lr22 (2DS) and Lr14a (7BL

An integrative genetic linkage map of winter wheat ( Triticum aestivum L.)

We constructed a genetic linkage map based on a cross between two Swiss winter wheat (Triticum aestivum L.) varieties, Arina and Forno. Two-hundred and forty F5 single-seed descent (SSD)-derived lines were analysed with 112 restriction fragment length polymorphism (RFLP) anonymous probes, 18 wheat cDNA clones coding for putative stress or defence-related proteins and 179 simple-sequence repeat (SSR) primer-pairs. The 309 markers revealed 396 segregating loci. Linkage analysis defined 27 linkage groups that could all be assigned to chromosomes or chromosome arms. The resulting genetic map comprises 380 loci and spans 3,086cM with 1,131cM for the A genome, 920cM for the B genome and 1,036cM for the D genome. Seventeen percent of the loci showed a significant (P < 0.05) deviation from a 1:1 ratio, most of them in favour of the Arina alleles. This map enabled the mapping of QTLs for resistance against several fungal diseases such as Stagonospora glume blotch, leaf rust and Fusarium head blight. It will also be very useful for wheat genetic mapping, as it combines RFLP and SSR markers that were previously located on separate map

MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe

Correction: AGAPE (Automated Genome Analysis PipelinE) for Pan-Genome Analysis of Saccharomyces cerevisiae

The characterization and public release of genome sequences from thousands of organisms is expanding the scope for genetic variation studies. However, understanding the phenotypic consequences of genetic variation remains a challenge in eukaryotes due to the complexity of the genotype-phenotype map. One approach to this is the intensive study of model systems for which diverse sources of information can be accumulated and integrated. Saccharomyces cerevisiae is an extensively studied model organism, with well-known protein functions and thoroughly curated phenotype data. To develop and expand the available resources linking genomic variation with function in yeast, we aim to model the pan-genome of S. cerevisiae. To initiate the yeast pan-genome, we newly sequenced or re-sequenced the genomes of 25 strains that are commonly used in the yeast research community using advanced sequencing technology at high quality. We also developed a pipeline for automated pan-genome analysis, which integrates the steps of assembly, annotation, and variation calling. To assign strain-specific functional annotations, we identified genes that were not present in the reference genome. We classified these according to their presence or absence across strains and characterized each group of genes with known functional and phenotypic features. The functional roles of novel genes not found in the reference genome and associated with strains or groups of strains appear to be consistent with anticipated adaptations in specific lineages. As more S. cerevisiae strain genomes are released, our analysis can be used to collate genome data and relate it to lineage-specific patterns of genome evolution. Our new tool set will enhance our understanding of genomic and functional evolution in S. cerevisiae, and will be available to the yeast genetics and molecular biology community

Expression variability of co-regulated genes differentiates Saccharomyces cerevisiae strains

Background: Saccharomyces cerevisiae (Baker’s yeast) is found in diverse ecological niches and is characterized by high adaptive potential under challenging environments. In spite of recent advances on the study of yeast genome diversity, little is known about the underlying gene expression plasticity. In order to shed new light onto this biological question, we have compared transcriptome profiles of five environmental isolates, clinical and laboratorial strains at different time points of fermentation in synthetic must medium, during exponential and stationary growth phases. Results: Our data unveiled diversity in both intensity and timing of gene expression. Genes involved in glucose metabolism and in the stress response elicited during fermentation were among the most variable. This gene expression diversity increased at the onset of stationary phase (diauxic shift). Environmental isolates showed lower average transcript abundance of genes involved in the stress response, assimilation of nitrogen and vitamins, and sulphur metabolism, than other strains. Nitrogen metabolism genes showed significant variation in expression among the environmental isolates. Conclusions: Wild type yeast strains respond differentially to the stress imposed by nutrient depletion, ethanol accumulation and cell density increase, during fermentation of glucose in synthetic must medium. Our results support previous data showing that gene expression variability is a source of phenotypic diversity among closely related organisms.Fundação para a Ciência e TecnologiaThe authors wish to thank Adega Cooperativa da Bairrada, Cantanhede, Portugal, for providing the commercial strains

Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells

Post-transcriptional modification of RNA nucleosides occurs in all living organisms. Pseudouridine, the most abundant modified nucleoside in non-coding RNAs, enhances the function of transfer RNA and ribosomal RNA by stabilizing the RNA structure. Messenger RNAs were not known to contain pseudouridine, but artificial pseudouridylation dramatically affects mRNA function—it changes the genetic code by facilitating non-canonical base pairing in the ribosome decoding centre. However, without evidence of naturally occurring mRNA pseudouridylation, its physiological relevance was unclear. Here we present a comprehensive analysis of pseudouridylation in Saccharomyces cerevisiae and human RNAs using Pseudo-seq, a genome-wide, single-nucleotide-resolution method for pseudouridine identification. Pseudo-seq accurately identifies known modification sites as well as many novel sites in non-coding RNAs, and reveals hundreds of pseudouridylated sites in mRNAs. Genetic analysis allowed us to assign most of the new modification sites to one of seven conserved pseudouridine synthases, Pus1–4, 6, 7 and 9. Notably, the majority of pseudouridines in mRNA are regulated in response to environmental signals, such as nutrient deprivation in yeast and serum starvation in human cells. These results suggest a mechanism for the rapid and regulated rewiring of the genetic code through inducible mRNA modifications. Our findings reveal unanticipated roles for pseudouridylation and provide a resource for identifying the targets of pseudouridine synthases implicated in human disease.American Cancer Society (Robbie Sue Mudd Kidney Cancer Research Scholar Grant RSG-13-396-01-RMC)National Institutes of Health (U.S.) (GM094303)National Institutes of Health (U.S.) (GM081399)American Cancer Society. New England Division (Ellison Foundation Postdoctoral Fellowship)American Cancer Society (Postdoctoral Fellowship PF-13-319-01-RMC)National Institutes of Health (U.S.) (Pre-doctoral Training Grant T32GM007287

Panorganismal metabolic response modeling of an experimental Echinostoma caproni infection in the mouse.

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