Many cool-season grasses form permanent, mutualistic symbioses with asexual Epichloë endophytes. These fungal symbionts often perform a protective role within the association as many strains produce secondary metabolites that deter certain mammalian and invertebrate herbivores. Although initially a serious issue for agriculture, due to mammalian toxins that manifested in major animal health issues, selected strains that provide abiotic stress protection to plants with minimal ill effects to livestock are now commercialized and routinely used to enhance pasture performance in many farming systems. These fungal endophytes and their grass hosts have coevolved over millions of years, and it is now generally accepted that most taxonomic groupings of Epichloë are confined to forming compatible associations (i.e., symptomless associations) with related grass genera within a tribe. The most desired compounds associated with Epichloë festucae var. lolii, an endophyte species associated with perennial ryegrass, are peramine and epoxy-janthitrems. No other major secondary metabolites with invertebrate bioactivity have been identified within this association. However, other agriculturally beneficial compounds, such as lolines, have been discovered in related endophyte species that form associations with fescue grasses. A rationale therefore existed to develop novel grass-endophyte associations between loline-producing endophytes originally isolated from tall fescue with elite cultivars of perennial ryegrass to achieve a wider spectrum of insect bioactivity. A suitable loline-producing endophyte strain of Epichloë sp. FaTG-3 was selected and inoculated into perennial ryegrass. We hypothesed that endophyte transmission frequency, endophyte mycelial biomass and endophyte-derived alkaloid production would differ between the original tall fescue host and the artificial association. Consistent with our hypothesis, our data strongly suggest that plant species significantly affected the plant-endophyte association. This effect became more apparent for transmission frequency and endophyte biomass as the plants matured. Overall, the viable endophyte infection frequency was greater in the tall fescue host than in perennial ryegrass, at all sampling dates. Additionally, temperature was found to be a significant factor affecting endophyte transmission frequency, endophyte mycelial biomass and alkaloid production. Implications for the development of novel grass-endophyte associations are discussed.

Zoonotic diseases are a major cause of morbidity, and productivity losses in both human and animal populations. Identifying the source of food-borne zoonoses (e.g. an animal reservoir or food product) is crucial for the identification and prioritisation of food safety interventions. For many zoonotic diseases it is difficult to attribute human cases to sources of infection because there is little epidemiological information on the cases. However, microbial strain typing allows zoonotic pathogens to be categorised, and the relative frequencies of the strain types among the sources and in human cases allows inference on the likely source of each infection. We introduce sourceR, an R package for quantitative source attribution, aimed at food-borne diseases. It implements a Bayesian model using strain-typed surveillance data from both human cases and source samples, capable of identifying important sources of infection. The model measures the force of infection from each source, allowing for varying survivability, pathogenicity and virulence of pathogen strains, and varying abilities of the sources to act as vehicles of infection. A Bayesian non-parametric (Dirichlet process) approach is used to cluster pathogen strain types by epidemiological behaviour, avoiding model overfitting and allowing detection of strain types associated with potentially high “virulence”. sourceR is demonstrated using Campylobacter jejuni isolate data collected in New Zealand between 2005 and 2008. Chicken from a particular poultry supplier was identified as the major source of campylobacteriosis, which is qualitatively similar to results of previous studies using the same dataset. Additionally, the software identifies a cluster of 9 multilocus sequence types with abnormally high ‘virulence’ in humans. sourceR enables straightforward attribution of cases of zoonotic infection to putative sources of infection. As sourceR develops, we intend it to become an important and flexible resource for food-borne disease attribution studies.

Influenza virus type A (IAV) is a seasonal acute respiratory disease virus with severe symptoms, and an effective preventive measure is required. Despite many reports describing the potentially protective effects of lactic acid bacteria, few studies have investigated the effects of nutritional supplement combinations. This study reports the effect of the combined intake of heat-killed Lactobacillus brevis KB290 (KB290) and vitamin A (VA) on mice challenged with a sublethal dose of IAV. For 2 weeks, five groups of mice were fed either placebo, KB290, VA, or a combination of KB290 and VA (KB290+VA). After subsequent IAV challenge, bodyweight and general health were monitored for up to 2 weeks. Viral titres were determined in the lungs of animal subgroups euthanised at days 3, 7, and 14 after IAV challenge. A significant loss was observed in the bodyweights of IAV-infected animals from day 1 post-IAV challenge, whereas the mice fed KB290+VA did not lose any weight after IAV infection, indicating successful protection from the infection. Additionally, mice in the KB290+VA group showed the highest reduction in lung viral titres. In conclusion, the combination of KB290 and VA could be a useful food supplement relevant for protection against seasonal influenza virus infection in humans.

Summary: Floral nectar composition beyond common sugars shows great diversity but contributing genetic factors are generally unknown. Mānuka ( Leptospermum scoparium ) is renowned for the antimicrobial compound methylglyoxal in its derived honey, which originates from the precursor, dihydroxyacetone (DHA), accumulating in the nectar. Although this nectar trait is highly variable, genetic contribution to the trait is unclear. Therefore, we investigated key gene(s) and genomic regions underpinning this trait. We used RNAseq analysis to identify nectary‐associated genes differentially expressed between high and low nectar DHA genotypes. We also used a mānuka high‐density linkage map and quantitative trait loci (QTL) mapping population, supported by an improved genome assembly, to reveal genetic regions associated with nectar DHA content. Expression and QTL analyses both pointed to the involvement of a phosphatase gene, LsSgpp2 . The expression pattern of LsSgpp2 correlated with nectar DHA accumulation, and it co‐located with a QTL on chromosome 4. The identification of three QTLs, some of the first reported for a plant nectar trait, indicates polygenic control of DHA content. We have established plant genetics as a key influence on DHA accumulation. The data suggest the hypothesis of LsSGPP2 releasing DHA from DHA‐phosphate and variability in LsSgpp2 gene expression contributing to the trait variability.

Zoonotic diseases are a major cause of morbidity, and productivity losses in both humans and animal populations. Identifying the source of food-borne zoonoses (e.g. an animal reservoir or food product) is crucial for the identification and prioritisation of food safety interventions. For many zoonotic diseases it is difficult to attribute human cases to sources of infection because there is little epidemiological information on the cases. However, microbial strain typing allows zoonotic pathogens to be categorised, and the relative frequencies of the strain types among the sources and in human cases allows inference on the likely source of each infection. We introduce sourceR, an R package for quantitative source attribution, aimed at food-borne diseases. It implements a fully joint Bayesian model using strain-typed surveillance data from both human cases and source samples, capable of identifying important sources of infection. The model measures the force of infection from each source, allowing for varying survivability, pathogenicity and virulence of pathogen strains, and varying abilities of the sources to act as vehicles of infection. A Bayesian non-parametric (Dirichlet process) approach is used to cluster pathogen strain types by epidemiological behaviour, avoiding model overfitting and allowing detection of strain types associated with potentially high 'virulence'. sourceR is demonstrated using Campylobacter jejuni isolate data collected in New Zealand between 2005 and 2008. It enables straightforward attribution of cases of zoonotic infection to putative sources of infection by epidemiologists and public health decision makers. As sourceR develops, we intend it to become an important and flexible resource for food-borne disease attribution studies.