Simulating the evolution of glyphosate resistance in grains farming in northern Australia.

Background and Aims: The evolution of resistance to herbicides is a substantial problem in contemporary agriculture. Solutions to this problem generally consist of the use of practices to control the resistant population once it evolves, and/or to institute preventative measures before populations become resistant. Herbicide resistance evolves in populations over years or decades, so predicting the effectiveness of preventative strategies in particular relies on computational modelling approaches. While models of herbicide resistance already exist, none deals with the complex regional variability in the northern Australian sub-tropical grains farming region. For this reason, a new computer model was developed. Methods: The model consists of an age- and stage-structured population model of weeds, with an existing crop model used to simulate plant growth and competition, and extensions to the crop model added to simulate seed bank ecology and population genetics factors. Using awnless barnyard grass (Echinochloa colona) as a test case, the model was used to investigate the likely rate of evolution under conditions expected to produce high selection pressure. Key Results: Simulating continuous summer fallows with glyphosate used as the only means of weed control resulted in predicted resistant weed populations after approx. 15 years. Validation of the model against the paddock history for the first real-world glyphosate-resistant awnless barnyard grass population shows that the model predicted resistance evolution to within a few years of the real situation. Conclusions: This validation work shows that empirical validation of herbicide resistance models is problematic. However, the model simulates the complexities of sub-tropical grains farming in Australia well, and can be used to investigate, generate and improve glyphosate resistance prevention strategies.

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.

Complete genome sequence and intracellular protein localization of Datura yellow vein nucleorhabdovirus

A limited number of plant rhabdovirus genomes have been fully sequenced, making taxonomic classification, evolutionary analysis and molecular characterization of this virus group difficult. We have for the first time determined the complete genome sequence of 13,188 nucleotides of Datura yellow vein nucleorhabdovirus (DYVV). DYVV genome organization resembles that of its closest relative, Sonchus yellow net virus (SYNV), with six ORFs in antigenomic orientation, separated by highly conserved intergenic regions and flanked by complementary 3′ leader and 5′ trailer sequences. As is typical for nucleorhabdoviruses, all viral proteins, except the glycoprotein, which is targeted to the endoplasmic reticulum, are localized to the nucleus. Nucleocapsid (N) protein, matrix (M) protein and polymerase, as components of nuclear viroplasms during replication, have predicted strong canonical nuclear localization signals, and N and M proteins exclusively localize to the nucleus when transiently expressed as GFP fusions. As in all nucleorhabdoviruses studied so far, N and phosphoprotein P interact when co-expressed, significantly increasing P nuclear localization in the presence of N protein. This research adds to the list of complete genomes of plant-infecting rhabdoviruses, provides molecular tools for further characterization and supports classification of DYVV as a nucleorhabdovirus closely related to but with some distinct differences from SYNV.