Predicting appropriate GHG mitigation strategies based on modelling variables that contribute to ruminant environmental impact.
Ruminant production is responsible for ~ 9% of anthropogenic CO2 emission and 37% of CH4 emissions. Release of methane results in 6-12% less energy being available to the animal. Ruminants also contribute towards NO2 within the environment, a persistent gas in the atmosphere which has 296 times more warming potential than CO2.
RumenPredict brings together members of the international Rumen
Microbial Genomics network (led by IBERS, AU), of which the Hungate 1000
(focussed on sequencing 1000 rumen microbes) and the Rumen Census
(focussed on evaluating effects of diet, host genetics and geographical
location on the rumen microbiome) are projects within.
RumenPredict brings together key members of the RMG network to generate the necessary data to link rumen microbiome information to host genetics and phenotype and develop feed based mitigation strategies. This will enhance innovative capacity and allow integration of new knowledge with that previously generated to devise geographic and animal-specific solutions to reduce the environmental impact of livestock ruminants. The project members have access to recent data/tools resulting from an array of projects, and RumenPredict will build upon and enhance the integration of knowledge generated from these projects whilst providing innovation through further testing and validation of key hypotheses resulting from the previously obtained data. RumenPredict will provide a platform for predicting how host genetics, feed additives or microbiome may affect emission phenotypes and develop genetic/diet/prediction technologies further for implementation to improve nitrogen use efficiency whilst decreasing environmental impact of ruminants.
Natural Secreted Nano Vesicles as a Source ofNovel Biomass Products for Circular Economy
This BioFuture2025 project targets the nano-and micro vesiclesthat are called collectively here as the exosomes. The exosomes represent a new humoral, systemic layer thatcontrolshomeostasis. Since the exosomes are around the size of viruses and that they are also present in saliva, the exosomes may function as a novel bio aerosolclass. The exosomes transmit various types of relevant cellular biomolecules such as proteins, RNA/DNA and the metabolites. Due to these reasons the exosomes may offer openings to target (biological) drugs, image tissues and organs in vivoand ways to develop even non–invasivesurgery therapies at the end. The exosomes can be expected to offer fundamental opportunitiesfor disease diagnostics. Individual exosomes maythemselves serve as biological drugs when produced in mass quantitiesfor medical practise. In summary the exosomes offer important opportunities to develop significant bio economicallyvaluableproducts. In the project we will enrich exosomes from the air, milk and certain other biological fluids. We will define the composition of the exosomes, their nucleic acids and proteins. We will develop better ways to purify the exosomes and to methods to define theirmolecular signatures. With the identified molecular tools we aim to enrich specific types of exosomes. We will then use the enriched exosomes in assays to learn more about their cellular functions and mechanisms of action. We will use nano levelfilters to analyseair and to study if the exosomes may serve as a novel way to characterize qualityof air. We will develop technologiesto enrich and characterize exosomes from milk. We will go on to target theroles of the milk-derivedexosomes in wealth in defined model assay systems. The aim is to reveal the mode of their cellular entry and roles in metabolic control. Moreoverwe will study hownutrition may reflect to the composition of the exosomes and quality of milk and if the milk offers ways to obtain large amounts of exosomes and to generate custom made exosomes for the different sectors of bio economy. Form the obtained data sets we will generate a data bank.
Identification of functionally active genomic features relevant to phenotypic diversity and plasticity in cattle
Despite the revolution in functional genome analysis a wide gap in understanding associations between the (epi)genome and complex phenotypes of interest currently remains and impedes efficient use of annotated genomes for precision breeding. The BovReg consortium will provide a comprehensive map of functionally active genomic features in cattle and how their (epi)genetic variation in beef and dairy breeds translates into phenotypes. This constitutes key knowledge for biology-driven genomic prediction needed by scientific and industry livestock communities. The BovReg brings together a critical mass of experts in ruminant research and beyond encompassing bioinformatics, molecular and quantitative genetics, animal breeding, reproductive physiology, ethics and social science. Our 20 partners from the EU, Canada and Australia form a global interdisciplinary team, which builds on previous and running national and EU-funded projects and many established industry cooperations. In BovReg we will generate functional genome data based on FAANG core assays from representative bovine tissues and newly established cell lines covering different ontological stages and phenotypes applying novel bioinformatic pipelines. We will establish detailed knowledge on traits related to robustness, health and biological efficiency in cattle. Data, knowledge and protocols will be deposited in European biological archives, aiming to set up and maintain a knowledge hub and establish gold standards. Long-term availability of data and targeted dissemination and communication activities are guaranteed by EMBL-EBI, FAANG and EAAP. Our biology-driven genomic prediction tools will integrate biological knowledge on regulatory genomic variation into genomic selection schemes for local and global cattle populations. This improved knowledge will be useful for re-focussing cattle production, fully taking into account societal awareness, environmental and animal-welfare aspects and bio-efficiency.