Isolation and expression analysis of multiple isoforms of putative farnesoic acid O-methyltransferase in several crustacean species

Farnesoic acid O-methyltransferase (FaMeT) is the enzyme responsible for the conversion of farnesoic acid (FA) to methyl farnesoate (MF) in the final step of MF synthesis. Multiple isoforms of putative FaMeT were isolated from six crustacean species belonging to the families Portunidae, Penaeidae, Scyllaridae and Parastacidae. The portunid crabs Portunus pelagicus and Scylla serrata code for three forms: short, intermediate and long. Two isoforms (short and long) were isolated from the penaeid prawns Penaeus monodon and Fenneropenaeus merguiensis. Two isoforms were also identified in the scyllarid Thenus orientalis and parastacid Cherax quadricarinatus. Putative FaMeT sequences were also amplified from the genomic DNA of P. pelagicus and compared to the putative FaMeT transcripts expressed. Each putative FaMeT cDNA isoform was represented in the genomic DNA, indicative of a multi-gene family. Various tissues from P. pelagicus were individually screened for putative FaMeT expression using PCR and fragment analysis. Each tissue type expressed all three isoforms of putative FaMeT irrespective of sex or moult stage. Protein domain analysis revealed the presence of a deduced casein kinase II phosphorylation site present only in the long isoform of putative FaMeT.

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.

Time-dependent instantaneous mortality rates from multiple tagging experiments with exact times of release and recovery

Instantaneous natural mortality rates and a nonparametric hunting mortality function are estimated from a multiple-year tagging experiment with arbitrary, time-dependent fishing or hunting mortality. Our theory allows animals to be tagged over a range of times in each year, and to take time to mix into the population. Animals are recovered by hunting or fishing, and death events from natural causes occur but are not observed. We combine a long-standing approach based on yearly totals, described by Brownie et al. (1985, Statistical Inference from Band Recovery Data: A Handbook, Second edition, United States Fish and Wildlife Service, Washington, Resource Publication, 156), with an exact-time-of-recovery approach originated by Hearn, Sandland and Hampton (1987, Journal du Conseil International pour l'Exploration de la Mer, 43, 107-117), who modeled times at liberty without regard to time of tagging. Our model allows for exact times of release and recovery, incomplete reporting of recoveries, and potential tag shedding. We apply our methods to data on the heavily exploited southern bluefin tuna (Thunnus maccoyii).

How accurate are the marker orders in crop linkage maps generated from large marker datasets?

Marker ordering during linkage map construction is a critical component of QTL mapping research. In recent years, high-throughput genotyping methods have become widely used, and these methods may generate hundreds of markers for a single mapping population. This poses problems for linkage analysis software because the number of possible marker orders increases exponentially as the number of markers increases. In this paper, we tested the accuracy of linkage analyses on simulated recombinant inbred line data using the commonly used Map Manager QTX (Manly et al. 2001: Mammalian Genome 12, 930-932) software and RECORD (Van Os et al. 2005: Theoretical and Applied Genetics 112, 30-40). Accuracy was measured by calculating two scores: % correct marker positions, and a novel, weighted rank-based score derived from the sum of absolute values of true minus observed marker ranks divided by the total number of markers. The accuracy of maps generated using Map Manager QTX was considerably lower than those generated using RECORD. Differences in linkage maps were often observed when marker ordering was performed several times using the identical dataset. In order to test the effect of reducing marker numbers on the stability of marker order, we pruned marker datasets focusing on regions consisting of tightly linked clusters of markers, which included redundant markers. Marker pruning improved the accuracy and stability of linkage maps because a single unambiguous marker order was produced that was consistent across replications of analysis. Marker pruning was also applied to a real barley mapping population and QTL analysis was performed using different map versions produced by the different programs. While some QTLs were identified with both map versions, there were large differences in QTL mapping results. Differences included maximum LOD and R-2 values at QTL peaks and map positions, thus highlighting the importance of marker order for QTL mapping

A consensus genetic map of sorghum that integrates multiple component maps and high-throughput Diversity Array Technology (DArT) markers

Background: Sorghum genome mapping based on DNA markers began in the early 1990s and numerous genetic linkage maps of sorghum have been published in the last decade, based initially on RFLP markers with more recent maps including AFLPs and SSRs and very recently, Diversity Array Technology (DArT) markers. It is essential to integrate the rapidly growing body of genetic linkage data produced through DArT with the multiple genetic linkage maps for sorghum generated through other marker technologies. Here, we report on the colinearity of six independent sorghum component maps and on the integration of these component maps into a single reference resource that contains commonly utilized SSRs, AFLPs, and high-throughput DArT markers. Results: The six component maps were constructed using the MultiPoint software. The lengths of the resulting maps varied between 910 and 1528 cM. The order of the 498 markers that segregated in more than one population was highly consistent between the six individual mapping data sets. The framework consensus map was constructed using a "Neighbours" approach and contained 251 integrated bridge markers on the 10 sorghum chromosomes spanning 1355.4 cM with an average density of one marker every 5.4 cM, and were used for the projection of the remaining markers. In total, the sorghum consensus map consisted of a total of 1997 markers mapped to 2029 unique loci ( 1190 DArT loci and 839 other loci) spanning 1603.5 cM and with an average marker density of 1 marker/0.79 cM. In addition, 35 multicopy markers were identified. On average, each chromosome on the consensus map contained 203 markers of which 58.6% were DArT markers. Non-random patterns of DNA marker distribution were observed, with some clear marker-dense regions and some marker-rare regions. Conclusion: The final consensus map has allowed us to map a larger number of markers than possible in any individual map, to obtain a more complete coverage of the sorghum genome and to fill a number of gaps on individual maps. In addition to overall general consistency of marker order across individual component maps, good agreement in overall distances between common marker pairs across the component maps used in this study was determined, using a difference ratio calculation. The obtained consensus map can be used as a reference resource for genetic studies in different genetic backgrounds, in addition to providing a framework for transferring genetic information between different marker technologies and for integrating DArT markers with other genomic resources. DArT markers represent an affordable, high throughput marker system with great utility in molecular breeding programs, especially in crops such as sorghum where SNP arrays are not publicly available.

Detection and use of QTL for complex traits in multiple environments.

QTL mapping methods for complex traits are challenged by new developments in marker technology, phenotyping platforms, and breeding methods. In meeting these challenges, QTL mapping approaches will need to also acknowledge the central roles of QTL by environment interactions (QEI) and QTL by trait interactions in the expression of complex traits like yield. This paper presents an overview of mixed model QTL methodology that is suitable for many types of populations and that allows predictive modeling of QEI, both for environmental and developmental gradients. Attention is also given to multi-trait QTL models which are essential to interpret the genetic basis of trait correlations. Biophysical (crop growth) model simulations are proposed as a complement to statistical QTL mapping for the interpretation of the nature of QEI and to investigate better methods for the dissection of complex traits into component traits and their genetic controls.

Characterising insect plant host relationships facilitates understanding multiple host use

Weed biocontrol relies on host specificity testing, usually carried out under quarantine conditions to predict the future host range of candidate control agents. The predictive power of host testing can be scrutinised directly with Aconophora compressa, previously released against the weed Lantana camara L. (lantana) because its ecology in its new range (Australia) is known and includes the unanticipated use of several host species. Glasshouse based predictions of field host use from experiments designed a posteriori can therefore be compared against known field host use. Adult survival, reproductive output and egg maturation were quantified. Adult survival did not differ statistically across the four verbenaceous hosts used in Australia. Oviposition was significantly highest on fiddlewood (Citharexylum spinosum L.), followed by lantana, on which oviposition was significantly higher than on two varieties of Duranta erecta (‘‘geisha girl’’ and ‘‘Sheena’s gold’’; all Verbenaceae). Oviposition rates across Duranta varieties were not significantly different from each other but were significantly higher than on the two non-verbenaceous hosts (Jacaranda mimosifolia D. Don: Bignoneaceae (jacaranda) and Myoporum acuminatum R. Br.: Myoporaceae (Myoporum)). Production of adult A. compressa was modelled across the hosts tested. The only major discrepancy between model output and their relative abundance across hosts in the field was that densities on lantana in the field were much lower than predicted by the model. The adults may, therefore, not locate lantana under field conditions and/or adults may find lantana but leave after laying relatively few eggs. Fiddlewood is the only primary host plant of A. compressa in Australia, whereas lantana and the others are used secondarily or incidentally. The distinction between primary, secondary and incidental hosts of a herbivore species helps to predict the intensity and regularity of host use by that herbivore. Populations of the primary host plants of a released biological control agent are most likely to be consistently impacted by the herbivore, whereas secondary and incidental host plant species are unlikely to be impacted consistently. As a consequence, potential biocontrol agents should be released only against hosts to which they have been shown to be primarily adapted.

QTL mapping of multiple foliar disease and root-lesion nematode resistances in wheat.

A genetic linkage map, based on a cross between the synthetic hexaploid CPI133872 and the bread wheat cultivar Janz, was established using 111 F1-derived doubled haploid lines. The population was phenotyped in multiple years and/or locations for seven disease resistance traits, namely, Septoria tritici blotch (Mycosphaeralla graminicola), yellow leaf spot also known as tan spot (Pyrenophora tritici-repentis), stripe rust (Puccinia striiformis f. sp. tritici), leaf rust (Puccinia triticina), stem rust (Puccinia graminis f. sp. tritici) and two species of root-lesion nematode (Pratylenchyus thornei and P. neglectus). The DH population was also scored for coleoptile colour and the presence of the seedling leaf rust resistance gene Lr24. Implementation of a multiple-QTL model identified a tightly linked cluster of foliar disease resistance QTL in chromosome 3DL. Major QTL each for resistance to Septoria tritici blotch and yellow leaf spot were contributed by the synthetic hexaploid parent CPI133872 and linked in repulsion with the coincident Lr24Sr24/ locus carried by parent Janz. This is the first report of linked QTL for Septoria tritici blotch and yellow leaf spot contributed by the same parent. Additional QTL for yellow leaf spot were detected in 5AS and 5BL. Consistent QTL for stripe rust resistance were identified in chromosomes 1BL, 4BL and 7DS, with the QTL in 7DS corresponding to the Yr18Lr34/ region. Three major QTL for P. thornei resistance (2BS, 6DS, 6DL) and two for P. neglectus resistance (2BS, 6DS) were detected. The recombinants combining resistance to Septoria tritici blotch, yellow leaf spot, rust diseases and root-lesion nematodes from parents CPI133872 and Janz constitute valuable germplasm for the transfer of multiple disease resistance into new wheat cultivars.

Multiple host use by a sap-sucking membracid: population consequences of nymphal development on primary and secondary host plant species

Aconophora compressa is a gregarious, sap-sucking insect that uses multiple host plant species. Nymphal host plant species (and variety) significantly affected nymphal survival, nymphal development rate and the subsequent size and fecundity of adults, with fiddlewood ( Citharexylum spinosum ) being significantly best in all respects. Nymphs that developed on a relatively poor host ( Duranta erecta var “geisha girl”) and which were moved to fiddlewood as adults laid significantly fewer eggs (mean ± SE = 836 ± 130) than those that developed solely on fiddlewood (1,329 ± 105). Adults on geisha girl, regardless of having been reared as nymphs on fiddlewood or geisha girl, laid significantly fewer eggs (342 ± 83 and 317 ± 74, respectively) than adults on fiddlewood. A simple model that incorporates host plant related survival, development rate and fecundity suggests that the population dynamics of A. compressa are governed mainly by fiddlewood, the primary host. The results have general implications for understanding the population dynamics of herbivores that use multiple host plant species, and also for the way in which weed biological control host testing methods should be conducted.

The plasticity of NBS resistance genes in sorghum is driven by multiple evolutionary processes

Background Increased disease resistance is a key target of cereal breeding programs, with disease outbreaks continuing to threaten global food production, particularly in Africa. Of the disease resistance gene families, the nucleotide-binding site plus leucine-rich repeat (NBS-LRR) family is the most prevalent and ancient and is also one of the largest gene families known in plants. The sequence diversity in NBS-encoding genes was explored in sorghum, a critical food staple in Africa, with comparisons to rice and maize and with comparisons to fungal pathogen resistance QTL. Results In sorghum, NBS-encoding genes had significantly higher diversity in comparison to non NBS-encoding genes and were significantly enriched in regions of the genome under purifying and balancing selection, both through domestication and improvement. Ancestral genes, pre-dating species divergence, were more abundant in regions with signatures of selection than in regions not under selection. Sorghum NBS-encoding genes were also significantly enriched in the regions of the genome containing fungal pathogen disease resistance QTL; with the diversity of the NBS-encoding genes influenced by the type of co-locating biotic stress resistance QTL. Conclusions NBS-encoding genes are under strong selection pressure in sorghum, through the contrasting evolutionary processes of purifying and balancing selection. Such contrasting evolutionary processes have impacted ancestral genes more than species-specific genes. Fungal disease resistance hot-spots in the genome, with resistance against multiple pathogens, provides further insight into the mechanisms that cereals use in the “arms race” with rapidly evolving pathogens in addition to providing plant breeders with selection targets for fast-tracking the development of high performing varieties with more durable pathogen resistance.

QTL analysis in multiple sorghum populations facilitates the dissection of the genetic and physiological control of tillering

Tillering in sorghum can be associated with either the carbon supply–demand (S/D) balance of the plant or an intrinsic propensity to tiller (PTT). Knowledge of the genetic control of tillering could assist breeders in selecting germplasm with tillering characteristics appropriate for their target environments. The aims of this study were to identify QTL for tillering and component traits associated with the S/D balance or PTT, to develop a framework model for the genetic control of tillering in sorghum. Four mapping populations were grown in a number of experiments in south east Queensland, Australia. The QTL analysis suggested that the contribution of traits associated with either the S/D balance or PTT to the genotypic differences in tillering differed among populations. Thirty-four tillering QTL were identified across the populations, of which 15 were novel to this study. Additionally, half of the tillering QTL co-located with QTL for component traits. A comparison of tillering QTL and candidate gene locations identified numerous coincident QTL and gene locations across populations, including the identification of common non-synonymous SNPs in the parental genotypes of two mapping populations in a sorghum homologue of MAX1, a gene involved in the control of tiller bud outgrowth through the production of strigolactones. Combined with a framework for crop physiological processes that underpin genotypic differences in tillering, the co-location of QTL for tillering and component traits and candidate genes allowed the development of a framework QTL model for the genetic control of tillering in sorghum.

Isolation of multiple novel paramyxoviruses from pteropid bat urine

Bats have been found to harbor a number of new emerging viruses with zoonotic potential and there has been a great deal of interest in identifying novel bat pathogens to determine risk to human and animal health. Many groups have identified novel viruses in bats by detection of viral nucleic acid, however virus isolation is still a challenge and there are few reports of viral isolates from bats. In recent years, our group has developed optimized procedures for virus isolation from bat urine, including the use of primary bat cells. In previous reports we have described the isolation of Hendra virus, Menangle virus and Cedar virus, in Queensland, Australia. Here, we report the isolation of four additional novel bat paramyxoviruses from urine collected from beneath pteropid bat (flying fox) colonies in Queensland and New South Wales during 2009-2011.