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Antimicrobial resistance is one of the greatest public health threats spanning the One Health continuum (humans, animals and the environment). Antibiotics are of invaluable public health importance and are used on a daily basis worldwide to save and ease the suffering of millions of human and animal lives. However, their extensive and often uncontrolled use has led to the global spread of resistance in bacteria of medical and veterinary importance to an unprecedented level. This is threatening the ways we practice medicine and our ability to care for the sickest patients including those in need of life-saving treatments such as organ transplantation or cancer chemotherapy, and those in intensive care units. Antibiotic resistance is now recognised by the WHO as one of the greatest threats to human health and is increasingly topical within medical, veterinary and lay organisations of national and global reach. Enterococcus faecium, a bacterium carried harmlessly in the gut of humans and animals, has emerged as a leading cause of infections in critically ill and severely immunocompromised patients in hospitals. It has a propensity to accumulate and disseminate multiple antibiotic resistance determinants. Our previous work using a bacterial DNA fingerprinting technique called short-read whole-genome sequencing (WGS) established that E. faecium causing infections in hospital belongs to distinct strains from those found in livestock. In addition, we found different types of antibiotic resistance genes predominating in the two reservoirs. However, we also found instances of identical resistance genes, including to classes of antibiotics that are important in human medicine. Short-read WGS has limitations when trying to reconstruct the hierarchical levels of transmission units responsible for the spread of antibiotic resistance, which range from the whole bacterial strains, to consecutively smaller layers of mobile genetic elements known as plasmids and transposons down to the gene level. In order to decipher this "Russian doll" model, a different technique known as long-read WGS is required. Here, we propose to carefully select isolates for long-read WGS to allow us to quantify and understand the architectural context of shared antibiotic resistance genes between human and animal strains of E. faecium. Antibiotic susceptibility testing is a technique used daily in laboratories around the world to establish if antibiotics are still effective at treating bacterial strains of interest (i.e. ensuring the strains have not developed resistance). Resistance to antibiotics is mediated by genetic changes, hence whole genome sequencing has emerged as an attractive technology to characterise the full repertoire of known genetic changes that cause resistance and predict from the bacterial DNA if antibiotics are still effective. However, a complete understanding of the genetics governing resistance to antibiotics is required before WGS can be adopted to inform antibiotic prescribing. Our previous research has shown that WGS is very good at predicting the effectiveness of most antibiotics in E.faecium, except for 3 last-resort antibiotics used against the most resistant strains: daptomycin, tigecycline and linezolid. Here, we aim to redress this shortcoming by generating additional laboratory tests and sequencing data and to apply state-of-the art population genomic methods to improve predictions.

Canada

Oats can claim to be the only 'European' cereal. Previous work indicated that its closest wild relatives are found around Turkey near sites where wheat and barley were first domesticated, but archaeological evidence points to first use as a crop only late in the development of agriculture and then in Central Europe, rather than in its original environment. A plausible explanation for this transfer is that a population of the wild relative (Avena sterilis), which colonises broken ground, had become adapted to life as an agricultural weed. The new weed (A. fatua) is less able to spread in the wild but is very successful at colonising tilled fields of cereals such as barley. A. fatua could have spread across Europe with those crops and then become a crop in its own right in hard times where others failed. Indeed, oats have traditionally been grown on poor ground and at the end of rotations when soil fertility is low, a possible legacy of this secondary domestication. At some stage completely non-shattering oats would have been selected for easier grain harvest and storage, and greater pressure would have been applied for full domestication (loss of awns, larger grain size and so on). A complication for this model is that oats appear to have been domesticated twice. Most cultivars now resemble 'white' oats (A. sativa), sown in the spring and most closely related to wild populations in central Turkey. However, around the Mediterranean, traditional landraces are often 'red' oats (A. byzantina), sown in autumn and most closely related to wild populations in south western Turkey. Modern breeding programmes are largely based on crosses between these two types, and the UK's unique winter oat cultivars may be particularly dependent on A. byzantina traits. We have a strong interest in understanding the origin of both red and white oats, and the role of A. fatua. This is not only driven by curiosity about the development of agriculture but also by the need to add new variation to the modern crop. Each domestication step has created genetic bottlenecks where potentially valuable germplasm has been lost. Better understanding of these bottlenecks, and of natural variation in wild populations, will help find useful variation to incorporate into breeding programmes. Genotyping-by-sequence (GbS) is a recent high throughput method to reveal genetic variation across entire genomes. It samples short sequences adjacent to specific restriction enzyme sites, and does not require prior knowledge of which variants are present. Hundreds of samples may be processed, making it ideal for diversity screens. We have panels of over 500 weedy, wild and landrace accessions already screened by GbS. Unfortunately, the oat genome is almost as large and complex as that of wheat, and no reference is yet available to associate GbS tags with neighbouring genes. Even when the first references become available (expected in 2018), they will have been derived from modern cultivars which may differ from the ancestral stocks we are studying. In this project we will bridge the gap between GbS and genome references to assess gene variation either directly or by improving the references available. In the first step we will use a newly developed sequencing approach to cut the cost of building a red oat reference. In the second we will work with a German barley expert who developed exome capture, a method that samples and sequences only targeted gene regions, and with a Canadian partner who has created an exome capture for oats, based in part on our genome data. We will use an improved design to recover comprehensive collections of gene sequences from 220 key GbS panel accessions. Finally we will obtain sequence from progenitors that will allow comparisons of gene content and order with modern cultivars, and will enhance analysis of exome and GbS data. Working with a Polish partner who has created hybrid populations, we will identify key genes underpinning domestication.

Global agricultural production is required to double by 2050 to meet the demands of an increasing population and the challenges of a changing climate. Changing climatic conditions, including increasing temperatures, more variable precipitation, and drought are likely to put pressure on maintaining both high crop yields and a steady supply of food. On the other hand, assuming other factors are not limiting, rising atmospheric CO2 levels may lead to increased crop productivity, as the increased availability of carbon dioxide can promote enhanced rates of plant photosynthesis. The varying abilities of different crops or cultivars to adapt to water, temperature or nutrient pressures signifies the inherent resilience of a given agricultural system, and the likelihood and the degree to which they will be impacted by climate change. Understanding how current and future plant growth conditions affect crop yield is a major priority for ensuring food security, for adapting crop selection and management strategies and for guiding crop breeding programmes. The key challenge here is linking plant behaviour that can be measured at the leaf-level in the laboratory, to plant behaviour at the national or global scale, and predicting future behaviour under forecasted climate conditions. As environmental drivers operate and interact at multiple temporal and spatial scales, addressing this challenge will require transforming how we understand, monitor and predict plant responses to stress. Observations from satellites have revolutionised spatial ecology in recent years; making it possible to monitor ecological trends over large spatial scales, and to scale from the plant to the globe. Increasingly sophisticated instruments and techniques allow scientists to examine changing vegetation trends in response to climate change from satellites at unprecedented levels of accuracy. These advances have been made possible by sensor developments, an increasing archive of legacy satellite data, and new and emerging techniques such as solar-induced chlorophyll fluorescence, which has been shown to be closely related to plant productivity. Whilst still in its infancy, solar-induced chlorophyll fluorescence has shown potential to remotely monitor crop growth, using drones through to satellites. However, these remote sensing techniques must first be underpinned by a process-based understanding of the connections between the remote sensing signal and plant characteristics. In this research, controlled laboratory experiments will be used to understand how plant stress manifests in changes to the leaf biochemical and structural properties, and in turn, how optical reflectance signatures, can be used to measure these changes. These optical markers will then be used to 'scale up' our observations, first using drone technology at the field scale, and then and at national and global scales using satellite data. This remote sensing data on crop health will be used within sophisticated biosphere models to predict plant performance under current conditions and forecasted future conditions. These approaches in combination will provide a technological basis for a complete picture at different scales, to fully exploit the resources available for crop improvement. The overarching goal of the research is to assess the ability of nationally and globally important agricultural crops to maintain their growth and performance under different environmental stresses. This research will deploy a cutting-edge, cross-disciplinary approach using controlled growth chambers, novel remote sensing techniques and plant science methods to scale from the leaf to the globe, and provide a step-change understanding in the future pressures that crops may face in light of a changing climate and their underlying resilience.