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.

Single kernel NIR assessment for QTL identification

Development and evaluation of a single kernel NIR assessment method for improving baley malting quality QTL identification.

Flaws in the current method for calculating methane emissions during dairy manure management in New Zealand

New Zealand's Greenhouse Gas Inventory (the NZ Inventory) currently estimates methane (CH4) emissions from anaerobic dairy effluent ponds by: (1) determining the total pond volume across New Zealand; (2) dividing this volume by depth to obtain the total pond surface area; and (3) multiplying this area by an observational average CH4 flux. Unfortunately, a mathematically erroneous determination of pond volume has led to an imbalanced equation and a geometry error was made when scaling-up the observational CH4 flux. Furthermore, even if these errors are corrected, the nationwide estimate still hinges on field data from a study that used a debatable method to measure pond CH4 emissions at a single site, as well as a potentially inaccurate estimation of the amount of organic waste anaerobically treated. The development of a new methodology is therefore critically needed.