Multidrug-resistant extraintestinal pathogenic Escherichia coli of sequence type ST131 in animals and foods.

Multidrug-resistant Escherichia colt sequence type 131 (51131) has recently emerged as a globally distributed cause of extraintestinal infections in humans. Diverse factors have been investigated as explanations for ST131's rapid and successful dissemination, including transmission through animal contact and consumption of food, as suggested by the detection of ST131 in a number of nonhuman species. For example, ST131 has recently been identified as a cause of clinical infection in companion animals and poultry, and both host groups have been confirmed as faecal carriers of ST131. Moreover, a high degree of similarity has been shown among certain ST131 isolates from humans, companion animals, and poultry based on resistance characteristics and genomic background and human and companion animal ST131 isolates tend to exhibit similar virulence genotypes. However, most ST131 isolates from poultry appear to possess specific virulence genes that are typically absent from human and companion animal isolates, including genes associated with avian pathogenic E. coli. Since the number of reported animal and food-associated ST131 isolates is quite small, the role of nonhuman host species in the emergence, dissemination, and transmission of ST131 to humans remains unclear. Nevertheless, given the profound public health importance of the emergent ST131 clonal group, even the limited available evidence indicates a pressing need for further careful study of this significant question.

Soil carbon stock in the tropical rangelands of Australia: Effects of soil type and grazing pressure, and determination of sampling requirement.

On-going, high-profile public debate about climate change has focussed attention on how to monitor the soil organic carbon stock (C(s)) of rangelands (savannas). Unfortunately, optimal sampling of the rangelands for baseline C(s) - the critical first step towards efficient monitoring - has received relatively little attention to date. Moreover, in the rangelands of tropical Australia relatively little is known about how C(s) is influenced by the practice of cattle grazing. To address these issues we used linear mixed models to: (i) unravel how grazing pressure (over a 12-year period) and soil type have affected C(s) and the stable carbon isotope ratio of soil organic carbon (delta(13)C) (a measure of the relative contributions of C(3) and C(4) vegetation to C(s)); (ii) examine the spatial covariation of C(s) and delta(13)C; and, (iii) explore the amount of soil sampling required to adequately determine baseline C(s). Modelling was done in the context of the material coordinate system for the soil profile, therefore the depths reported, while conventional, are only nominal. Linear mixed models revealed that soil type and grazing pressure interacted to influence C(s) to a depth of 0.3 m in the profile. At a depth of 0.5 m there was no effect of grazing on C(s), but the soil type effect on C(s) was significant. Soil type influenced delta(13)C to a soil depth of 0.5 m but there was no effect of grazing at any depth examined. The linear mixed model also revealed the strong negative correlation of C(s) with delta(13)C, particularly to a depth of 0.1 m in the soil profile. This suggested that increased C(s) at the study site was associated with increased input of C from C(3) trees and shrubs relative to the C(4) perennial grasses; as the latter form the bulk of the cattle diet, we contend that C sequestration may be negatively correlated with forage production. Our baseline C(s) sampling recommendation for cattle-grazing properties of the tropical rangelands of Australia is to: (i) divide the property into units of apparently uniform soil type and grazing management; (ii) use stratified simple random sampling to spread at least 25 soil sampling locations about each unit, with at least two samples collected per stratum. This will be adequate to accurately estimate baseline mean C(s) to within 20% of the true mean, to a nominal depth of 0.3 m in the profile.

Characterisation of chicken Campylobacter jejuni isolates using resolution optimised single nucleotide polymorphisms and binary gene markers

The principal objective of this study was to determine if Campylobacter jejuni genotyping methods based upon resolution optimised sets of single nucleotide polymorphisms (SNPs) and binary genetic markers were capable of identifying epidemiologically linked clusters of chicken-derived isolates. Eighty-eight C. jejuni isolates of known flaA RFLP type were included in the study. They encompassed three groups of ten isolates that were obtained at the same time and place and possessed the same flaA type. These were regarded as being epidemiologically linked. Twenty-six unlinked C. jejuni flaA type I isolates were included to test the ability of SNP and binary typing to resolve isolates that were not resolved by flaA RFLP. The remaining isolates were of different flaA types. All isolates were typed by real-time PCR interrogation of the resolution optimised sets of SNPs and binary markers. According to each typing method, the three epidemiologically linked clusters were three different clones that were well resolved from the other isolates. The 26 unlinked C. jejuni flaA type I isolates were resolved into 14 SNP-binary types, indicating that flaA typing can be unreliable for revealing epidemiological linkage. Comparison of the data with data from a fully typed set of isolates associated with human infection revealed that abundant lineages in the chicken isolates that were also found in the human isolates belonged to clonal complex (CC) -21 and CC-353, with the usually rare C-353 member ST-524 being especially abundant in the chicken collection. The chicken isolates selected to be diverse according to flaA were also diverse according to SNP and binary typing. It was observed that CC-48 was absent in the chicken isolates, despite being very common in Australian human infection isolates, indicating that this may be a major cause of human disease that is not chicken associated.

Characterization of disease resistance gene candidates of the nucleotide binding site (NBS) type from banana and correlation of a transcriptional polymorphism with resistance to Fusarium oxysporum f.sp. cubense race 4

Most plant disease resistance (R) genes encode proteins with a nucleotide binding site and leucine-rich repeat structure (NBS-LRR). In this study, degenerate primers were used to amplify genomic NBS-type sequences from wild banana (Musa acuminata ssp. malaccensis) plants resistant to the fungal pathogen Fusarium oxysporum formae specialis (f. sp.) cubense (FOC) race 4. Five different classes of NBS-type sequences were identified and designated as resistance gene candidates (RGCs). The deduced amino acid sequences of the RGCs revealed the presence of motifs characteristic of the majority of known plant NBS-LRR resistance genes. Structural and phylogenetic analyses grouped the banana RGCs within the non-TIR (homology to Toll/interleukin-1 receptors) subclass of NBS sequences. Southern hybridization showed that each banana RGC is present in low copy number. The expression of the RGCs was assessed by RT-PCR in leaf and root tissues of plants resistant or susceptible to FOC race 4. RGC1, 3 and 5 showed a constitutive expression profile in both resistant and susceptible plants whereas no expression was detected for RGC4. Interestingly, RGC2 expression was found to be associated only to FOC race 4 resistant lines. This finding could assist in the identification of a FOC race 4 resistance gene.

Determination of management units for grey mackerel fisheries in northern Australia.

The requirement for Queensland, Northern Territory and Western Australian jurisdictions to ensure sustainable harvest of fish resources and their optimal use relies on robust information on the resource status. For grey mackerel (Scomberomorus semifasciatus) fisheries, each of these jurisdictions has their own management regime in their corresponding waters. The lack of information on stock structure of grey mackerel, however, means that the appropriate spatial scale of management is not known. As well, fishers require assurance of future sustainability to encourage investment and long-term involvement in a fishery that supplies lucrative overseas markets. These management and fisher-unfriendly circumstances must be viewed in the context of recent 3-fold increases in catches of grey mackerel along the Queensland east coast, combined with significant and increasing catches in other parts of the species' northern Australian range. Establishing the stock structure of grey mackerel would also immensely improve the relevance of resource assessments for fishery management of grey mackerel across northern Australia. This highlighted the urgent need for stock structure information for this species. The impetus for this project came from the strategic recommendations of the FRDC review by Ward and Rogers (2003), "Northern mackerel (Scombridae: Scomberomorus): current and future research needs" (Project No. 2002/096), which promoted the urgency for information on the stock structure of grey mackerel. In following these recommendations this project adopted a multi-technique and phased sampling approach as carried out by Buckworth et al (2007), who examined the stock structure of Spanish mackerel, Scomberomorus commerson, across northern Australia. The project objectives were to determine the stock structure of grey mackerel across their northern Australian range, and use this information to define management units and their appropriate spatial scales. We used multiple techniques concurrently to determine the stock structure of grey mackerel. These techniques were: genetic analyses (mitochondrial DNA and microsatellite DNA), otolith (ear bones) isotope ratios, parasite abundances, and growth parameters. The advantage of using this type of multi-technique approach was that each of the different methods is informative about the fish’s life history at different spatial and temporal scales. Genetics can inform about the evolutionary patterns as well as rates of mixing of fish from adjacent areas, while parasites and otolith microchemistry are directly influenced by the environment and so will inform about the patterns of movement during the fishes lifetime. Growth patterns are influenced by both genetic and environmental factors. Due to these differences the use of these techniques concurrently increases the likelihood of detecting different stocks where they exist. We adopted a phased sampling approach whereby sampling was carried out at broad spatial scales in the first year: east coast, eastern Gulf of Carpentaria (GoC), western GoC, and the NW Northern Territory (NW NT). By comparing the fish samples from each of these locations, and using each of the techniques, we tested the null hypothesis that grey mackerel were comprised of a single homogeneous population across northern Australia. Having rejected the null hypothesis we re-sampled the 1st year locations to test for temporal stability in stock structure, and to assess stock structure at finer spatial scales. This included increased spatial coverage on the east coast, the GoC, and WA. From genetic approaches we determined that there at least four genetic stocks of grey mackerel across northern Australia: WA, NW NT (Timor/Arafura), the GoC and the east Grey mackerel management units in northern Australia ix coast. All markers revealed concordant patterns showing WA and NW NT to be clearly divergent stocks. The mtDNA D-loop fragment appeared to have more power to resolve stock boundaries because it was able to show that the GoC and east coast QLD stocks were genetically differentiated. Patterns of stock structure on a finer scale, or where stock boundaries are located, were less clear. From otolith stable isotope analyses four major groups of S. semifasciatus were identified: WA, NT/GoC, northern east coast and central east coast. Differences in the isotopic composition of whole otoliths indicate that these groups must have spent their life history in different locations. The magnitude of the difference between the groups suggests a prolonged separation period at least equal to the fish’s life span. The parasite abundance analyses, although did not include samples from WA, suggest the existence of at least four stocks of grey mackerel in northern Australia: NW NT, the GoC, northern east coast and central east coast. Grey mackerel parasite fauna on the east coast suggests a separation somewhere between Townsville and Mackay. The NW NT region also appears to comprise a separate stock while within the GoC there exists a high degree of variability in parasite faunas among the regions sampled. This may be due to 1. natural variation within the GoC and there is one grey mackerel stock, or 2. the existence of multiple localised adult sub-stocks (metapopulations) within the GoC. Growth parameter comparisons were only possible from four major locations and identified the NW NT, the GoC, and the east coast as having different population growth characteristics. Through the use of multiple techniques, and by integrating the results from each, we were able to determine that there exist at least five stocks of grey mackerel across northern Australia, with some likelihood of additional stock structuring within the GoC. The major management units determined from this study therefore were Western Australia, NW Northern Territory (Timor/Arafura), the Gulf of Carpentaria, northern east Queensland coast and central east Queensland coast. The management implications of these results indicate the possible need for management of grey mackerel fisheries in Australia to be carried out on regional scales finer than are currently in place. In some regions the spatial scales of management might continue as is currently (e.g. WA), while in other regions, such as the GoC and the east coast, managers should at least monitor fisheries on a more local scale dictated by fishing effort and assess accordingly. Stock assessments should also consider the stock divisions identified, particularly on the east coast and for the GoC, and use life history parameters particular to each stock. We also emphasise that where we have not identified different stocks does not preclude the possibility of the occurrence of further stock division. Further, this study did not, nor did it set out to, assess the status of each of the stocks identified. This we identify as a high priority action for research and development of grey mackerel fisheries, as well as a management strategy evaluation that incorporates the conclusions of this work. Until such time that these priorities are addressed, management of grey mackerel fisheries should be cognisant of these uncertainties, particularly for the GoC and the Queensland east coast.

A phylogenetic and taxonomic re-evaluation of the Bipolaris - Cochliobolus - Curvularia Complex

Three genera, Cochliobolus, Bipolaris and Curvularia form a complex that contains many plant pathogens, mostly on grasses (Poaceae) with a worldwide distribution. The taxonomy of this complex is confusing as frequent nomenclatural changes and refinements have occurred. There is no clear morphological boundary between the asexual genera Bipolaris and Curvularia, and some species show intermediate morphology. We investigated this complex based on a set of ex-type cultures and collections from northern Thailand. Combined gene analysis of rDNA ITS (internal transcribed spacer), GPDH (glyceraldehyde 3-phosphate dehydrogenase), LSU (large subunit) and EF1-α (translation elongation factor 1-α) shows that this generic complex divides into two groups. Bipolaris and Cochliobolus species clustered in Group 1 along with their type species, whereas Curvularia species (including species named as Bipolaris, Cochliobolus and Curvularia) clustered in Group 2, with its generic type. The nomenclatural conflict in this complex is resolved giving priority to the more commonly used established generic names Bipolaris and Curvularia. Modern descriptions of the genera Bipolaris and Curvularia are provided and species resolved in this study are transferred to one of these genera based on their phylogeny. © 2012 Mushroom Research Foundation.