A high-density consensus map of barley linking DArT markers to SSR, RFLP and STS loci and agricultural traits

Background: Molecular marker technologies are undergoing a transition from largely serial assays measuring DNA fragment sizes to hybridization-based technologies with high multiplexing levels. Diversity Arrays Technology (DArT) is a hybridization-based technology that is increasingly being adopted by barley researchers. There is a need to integrate the information generated by DArT with previous data produced with gel-based marker technologies. The goal of this study was to build a high-density consensus linkage map from the combined datasets of ten populations, most of which were simultaneously typed with DArT and Simple Sequence Repeat (SSR), Restriction Enzyme Fragment Polymorphism (RFLP) and/or Sequence Tagged Site (STS) markers. Results: The consensus map, built using a combination of JoinMap 3.0 software and several purpose-built perl scripts, comprised 2,935 loci (2,085 DArT, 850 other loci) and spanned 1,161 cM. It contained a total of 1,629 'bins' (unique loci), with an average inter-bin distance of 0.7 ± 1.0 cM (median = 0.3 cM). More than 98% of the map could be covered with a single DArT assay. The arrangement of loci was very similar to, and almost as optimal as, the arrangement of loci in component maps built for individual populations. The locus order of a synthetic map derived from merging the component maps without considering the segregation data was only slightly inferior. The distribution of loci along chromosomes indicated centromeric suppression of recombination in all chromosomes except 5H. DArT markers appeared to have a moderate tendency toward hypomethylated, gene-rich regions in distal chromosome areas. On the average, 14 ± 9 DArT loci were identified within 5 cM on either side of SSR, RFLP or STS loci previously identified as linked to agricultural traits. Conclusion: Our barley consensus map provides a framework for transferring genetic information between different marker systems and for deploying DArT markers in molecular breeding schemes. The study also highlights the need for improved software for building consensus maps from high-density segregation data of multiple populations.

Decoration of Pasteurella multocida lipopolysaccharide with phosphocholine is important for virulence

Phosphocholine (PCho) is an important substituent of surface structures expressed by a number of bacterial pathogens. Its role in virulence has been investigated in several species, in which it has been shown to play a role in bacterial adhesion to mucosal surfaces, in resistance to antimicrobial peptides, or in sensitivity to complement-mediated killing. The lipopolysaccharide (LPS) structure of Pasteurella multocida strain Pm70, whose genome sequence is known, has recently been determined and does not contain PCho. However, LPS structures from the closely related, virulent P. multocida strains VP161 and X-73 were shown to contain PCho on their terminal galactose sugar residues. To determine if PCho was involved in the virulence of P. multocida, we used subtractive hybridization of the VP161 genome against the Pm70 genome to identify a four-gene locus (designated pcgDABC) which we show is required for the addition of the PCho residues to LPS. The proteins predicted to be encoded by pcgABC showed identity to proteins involved in choline uptake, phosphorylation, and nucleotide sugar activation of PCho. We constructed a P. multocida VP161 pcgC mutant and demonstrated that this strain produces LPS that lacks PCho on the terminal galactose residues. This pcgC mutant displayed reduced in vivo growth in a chicken infection model and was more sensitive to the chicken antimicrobial peptide fowlicidin-1 than the wild-type P. multocida strain

NEESTIMATOR v2: re-implementation of software for the estimation of contemporary effective population size (N-e) from genetic data

NeEstimator v2 is a completely revised and updated implementation of software that produces estimates of contemporary effective population size, using several different methods and a single input file. NeEstimator v2 includes three single-sample estimators (updated versions of the linkage disequilibrium and heterozygote-excess methods, and a new method based on molecular coancestry), as well as the two-sample (moment-based temporal) method. New features include the following: (i) an improved method for accounting for missing data; (ii) options for screening out rare alleles; (iii) confidence intervals for all methods; (iv) the ability to analyse data sets with large numbers of genetic markers (10000 or more); (v) options for batch processing large numbers of different data sets, which will facilitate cross-method comparisons using simulated data; and (vi) correction for temporal estimates when individuals sampled are not removed from the population (Plan I sampling). The user is given considerable control over input data and composition, and format of output files. The freely available software has a new JAVA interface and runs under MacOS, Linux and Windows.