Teagasc - The Irish Agriculture and Food Development Authority

Low pH foods can attenuate the glycemic response to starch-rich foods. It has been demonstrated that lemon juice, due to its low pH (pH≈2.3), inhibited key digestive enzymes thereby interrupting gastric digestion of starch in vitro. This effect can significantly reduce the glycemic response in humans. In particular, adding lemon juice to a starch rich meal reduced the mean blood glucose concentration peak by 30%. Considering the panoply of food options available, it is likely that other combinations have similar effects but no work has been conducted to develop a consolidated knowledge base to exploit this strategy. GlucoMatchMaker will go beyond the state-of-the art by addressing this knowledge gap. The main goal is to develop and test the real-life effectiveness of the first mobile app to guide individuals on how to mix and match starchy foods with other foods/beverages to attenuate glycemic responses. The research work will employ multidisciplinary knowledge and methodologies and is divided into 4 parts (1) Selection and characterization of starch-rich foods, low-pH foods/beverages and of how their combination influences starch digestion in vitro (WP1). (2) Determination of the conditions of effectiveness of these combinations (in silico models) (WP2). (3) Development of the first mobile app that will integrate this knowledge to guide the user on how to mix and match starch-rich foods with others to lower their glycemic impact (WP3). (4) Test the effectiveness of the developed strategy in a real-life context (WP4). This project addresses the United Nations and EU target to reduce premature mortality from non-communicable diseases by one third as part of the 2030 Agenda for Sustainable Development. The research plan was developed in the framework of “H2020 Work Programme - Health, demographic change and wellbeing”, specifically the aim to “translate new knowledge into innovative applications and accelerate large-scale uptake and deployment”.

Breeding for improved perennial ryegrass (PRG) cultivars to support pastoral based production systems for milk and meat is a critically important goal. However, genetic gains for traits such as forage yield and quality have very much lagged behind genetic gain for agronomic traits in cereals. One reason for this is the long breeding cycle in a typical PRG breeding programme, where a single cycle of selection can take 5-6 years. Genomic selection (GS) is a form of marker assisted selection that simultaneously estimates all loci, haplotype, or marker effects across the entire genome to calculate Genomic Estimated Breeding Values (GEBVs). The main advantage that GS could offer PRG breeding is to enable multiple cycles of selection to be achieved in the same time it takes to do a single cycle of conventional selection, thereby increasing the rate of genetic gain. Improving digestibility of the forage leads to an increase in animal performance, and is therefore an important target trait for forage breeders. Furthermore, it has already been shown that increases in organic matter digestibility can reduce methane emissions. Reducing methane emissions is a key target of the EUs climate and energy policy. In this action I will focus on developing and validating GS equations for feed parameters that are being used as model inputs into the Cornell Net Carbohydrate and Protein System (CNCPS). This CNCPS is currently being adapted to predict nutritional value to the grazing animal in pasture based production systems, and it is envisaged that it will be able to identify feed parameters limiting milk-solid production and thereby direct future forage breeding efforts. The work of this action will lead to a novel and innovative forage breeding programme that can select for multiple feed parameters to develop the ideal forage cultivars for pasture production systems.

The human gastrointestinal tract harbors a complex community of microorganisms that confer metabolic, immunological and neurological benefits to the host. This assemblage is known as the Gut Microbiome and has received increased attention over the last decade. Scientists have begun to uncover the importance of these bacterial inhabitants and expand investigations to consider how site-specific microbiomes affect host physiology. While more than one million cholecystectomies (gallbladder removal surgeries) are performed throughout Europe each year, the bacterial communities associated with the human gallbladder and its disease states remain unknown. Studies are lacking that characterize the effects of cholecystectomies on the gut microbiome. Without the ability to regulate bile entering the duodenum during food intake, it is expected that gallbladder removal will lead to downstream changes in the intestinal population. Here, the microbial composition of human bile, gallbladder mucosa, and biopsies of surgically removed healthy gallbladders (adherent and non-adherent microbiota) will be investigated using 16S rRNA metagenomics. The profiles will be compared to samples of a second cohort undergoing emergency cholecystectomies, in order to identify possible biomarkers for gallbladder disease. Once the gallbladder microbiome has been elucidated, the impact of its removal on the gut microbiome will be assessed. Using molecular and cultivation based techniques, on stool samples (collected during the recovery period) and analyzed for community composition, metabolomics, bile, fat and energy content. GallBiome will form the basis for establishing relationships between gallbladder microbiota, gut microbiota, and human health with a view to informing future development of diagnostics and therapeutics. Ultimately, characterization of the core gallbladder microbiome has important biological and medical implications with potential to lower the risk and incidence of cholelithiasis.

Potato breeding is a 10-year process that involves combining over 40 characteristics to produce varieties that have improved sustainability, utilisation and consumer characteristics. Genomic and marker assisted selection (GS and MAS) can make breeding faster/more efficient, increasing the rate of genetic gain and the ability to combine multiple traits. Strategies for GS to date have tended to employ many thousands of markers; however, the economic burden of deploying such approaches on thousands of plants annually in a breeding programme may restrict the adoption of GS. The objective of this action is to develop a novel, low cost, genome-scanning marker platform for use in simultaneous GS and MAS for multiple traits in potato breeding, by combining existing knowledge on the patterns of linkage disequilibrium in potato with recent advances in genotyping-by-sequencing approaches based on massively multiplex PCR. The marker system will target clusters of SNPs whose aggregate profile over distances covered by paired-end next generation sequencing (NGS) reads will allow them to be used as haplotags. Unlike bi-allelic SNPs, haplotags can discriminate multiple allelic variants in tetraploid potato. Approximately 400 loci at placed at 1Mb spacing throughout the euchromatic portion of the genome, and a further 100 SNPs linked to specific traits, will be targeted and assayed using an approach called GT-Seq, which uses combinatorial barcoding to allow multiplexing of thousands of samples in a single NGS run. The sequencing platform will be utilised in conjunction with 2 training populations to develop genomic prediction equations for yield and fry colour of potato. Subsequently, it will be deployed for combined GS and MAS on 1000 plants from the second field generation of a commercial potato breeding programme. The research and training activities in the fellowship proposal will equip a young postdoctoral researcher with the skills of a "next generation plant breeder".