Periodic Reporting for period 2 - 3D-omics (Three-dimensional holo’omic landscapes to unveil host-microbiota interactions shaping animal production)
Período documentado: 2023-03-01 hasta 2024-08-31
The world's population continues to grow - but the Earth's surface does not. This urges us to ensure that the associated need for increased food production is performed in a sustainable fashion, because optimising food production is not only of commercial interest for companies, but also of critical importance for humanity and biodiversity. In this regard, understanding the interplay between animals and microorganisms associated with them has been recognised as an essential step by the European Commission for improving and optimising animal health, welfare, and production worldwide.
To understand the truly relevant biomolecular interactions that impact production processes, researchers are implementing novel strategies based on the analysis of animal genomes, the metagenomes of the associated microorganisms, and the different omic layers interconnecting them — the so-called holo’omic framework.
However, these approaches do not yet capture spatial properties of feeds, microorganisms, and host epithelial tissue, which are known to differ across intestinal sections, time points, and individuals. Neither conventional multi-omics nor histology provide information on how gene expression and biomolecule production of such cells is triggered by proximity of specific bacterial cells and metabolites, and vice versa. Current methods provide us information analogous to what would be learned from studying the functioning of the Amazon rainforest through mixing all living organisms in the jungle in a big pot and quantifying their relative abundances without acknowledging, for instance, which bird nests or which mycorrhizal fungi are associated with which tree.
Now, we are finally capable of breaking these barriers and boosting animal-microbiota research with a groundbreaking technological advancement. In recent years, there has been a rapid development of techniques that, separately, have enabled the generation of multiple omics data, including both microbial and host components, processing very small amounts of biological material, generating 3D reconstructions of biological elements, and analysing complex microbial communities. Hence, the technology required for 3D multi-omics are finally in place and we will use it to develop, implement, and assess a new methodological framework that will revolutionise animal-microbiota research in animal science and beyond. Ultimately, we foresee that our new framework will open new research avenues to improve the generation of animal breeds with enhanced microbiota-related genetic features, probiotics, microbiota - and host-tailored feeds, animal health treatments, and management practices that will enable increasing production efficiency while decreasing environmental impact and improving animal welfare.
To understand the truly relevant biomolecular interactions that impact production processes, researchers are implementing novel strategies based on the analysis of animal genomes, the metagenomes of the associated microorganisms, and the different omic layers interconnecting them — the so-called holo’omic framework.
However, these approaches do not yet capture spatial properties of feeds, microorganisms, and host epithelial tissue, which are known to differ across intestinal sections, time points, and individuals. Neither conventional multi-omics nor histology provide information on how gene expression and biomolecule production of such cells is triggered by proximity of specific bacterial cells and metabolites, and vice versa. Current methods provide us information analogous to what would be learned from studying the functioning of the Amazon rainforest through mixing all living organisms in the jungle in a big pot and quantifying their relative abundances without acknowledging, for instance, which bird nests or which mycorrhizal fungi are associated with which tree.
Now, we are finally capable of breaking these barriers and boosting animal-microbiota research with a groundbreaking technological advancement. In recent years, there has been a rapid development of techniques that, separately, have enabled the generation of multiple omics data, including both microbial and host components, processing very small amounts of biological material, generating 3D reconstructions of biological elements, and analysing complex microbial communities. Hence, the technology required for 3D multi-omics are finally in place and we will use it to develop, implement, and assess a new methodological framework that will revolutionise animal-microbiota research in animal science and beyond. Ultimately, we foresee that our new framework will open new research avenues to improve the generation of animal breeds with enhanced microbiota-related genetic features, probiotics, microbiota - and host-tailored feeds, animal health treatments, and management practices that will enable increasing production efficiency while decreasing environmental impact and improving animal welfare.
Over the first 36 months of the project, we have made significant progress towards achieving the operational objectives of 3D'omics.
The primary objective is to develop the 3D’omics technology to reconstruct three-dimensional, multi-omic intestinal landscapes from micro-scale genomic, transcriptomic, metabolomic, and imaging data. In the first reporting period, we successfully developed and benchmarked protocols for sample embedding, slicing, and microdissection to generate micro-scale multi-omic data. In the second reporting period, we advanced by refining molecular procedures to produce high-quality sequencing data from tiny microsamples containing picograms of DNA. We validated the accuracy of these methods by comparing molecular data with fluorescence in situ hybridization (FISH) imaging. Additionally, we scaled up data generation by automating processes with liquid handlers, improving throughput and reproducibility, while optimising reagent use to reduce costs.
For the second objective—demonstrating the 3D’omics technology in two monogastric animal systems, poultry and swine—we conducted four animal experiments in the second reporting period, building on the three proof-of-principle trials from the first period. In poultry, we successfully completed the Salmonella (bacterial challenge) and Histomonas (protozoa challenge) trials, advancing our goal to explore micro-scale pathogen-microbiota-host interactions and improve animal health. In swine, we conducted two scheduled experiments: one focused on protein digestibility and the other on fibre utilisation. Using hybrid sequencing, we generated extensive bacterial genome catalogues for all experiments, enabling us to analyse microbiota composition and activity dynamics across time and treatments using multi-omic data.
To achieve the third objective—integrating 3D’omics data into the models used by breeding and feeding industries to analyse phenotypic variability—we engaged our industrial partners in discussions on result interpretation. We also began outlining procedures for implementing this new knowledge into production practices.
The fourth objective, which focuses on assessing the technical, economic, and societal impact of 3D’omics, was initiated toward the end of the second reporting period, when large-scale micro-scale data first became available.
Finally, for the fifth objective, which aims to establish strong collaborations with related microbiome research initiatives, we actively pursued new partnerships by participating in numerous public and scientific communication events and exploring future collaborative endeavours. Employing the platform of a EU Horizon booster grant, joint outreach material has been developed together with four H2020 sister-projects and the network Joint Dissemination Network "EcoGen" was founded. We also co-organised the second Applied Hologenomics Conference, maximising international exposure for 3D'omics.
The primary objective is to develop the 3D’omics technology to reconstruct three-dimensional, multi-omic intestinal landscapes from micro-scale genomic, transcriptomic, metabolomic, and imaging data. In the first reporting period, we successfully developed and benchmarked protocols for sample embedding, slicing, and microdissection to generate micro-scale multi-omic data. In the second reporting period, we advanced by refining molecular procedures to produce high-quality sequencing data from tiny microsamples containing picograms of DNA. We validated the accuracy of these methods by comparing molecular data with fluorescence in situ hybridization (FISH) imaging. Additionally, we scaled up data generation by automating processes with liquid handlers, improving throughput and reproducibility, while optimising reagent use to reduce costs.
For the second objective—demonstrating the 3D’omics technology in two monogastric animal systems, poultry and swine—we conducted four animal experiments in the second reporting period, building on the three proof-of-principle trials from the first period. In poultry, we successfully completed the Salmonella (bacterial challenge) and Histomonas (protozoa challenge) trials, advancing our goal to explore micro-scale pathogen-microbiota-host interactions and improve animal health. In swine, we conducted two scheduled experiments: one focused on protein digestibility and the other on fibre utilisation. Using hybrid sequencing, we generated extensive bacterial genome catalogues for all experiments, enabling us to analyse microbiota composition and activity dynamics across time and treatments using multi-omic data.
To achieve the third objective—integrating 3D’omics data into the models used by breeding and feeding industries to analyse phenotypic variability—we engaged our industrial partners in discussions on result interpretation. We also began outlining procedures for implementing this new knowledge into production practices.
The fourth objective, which focuses on assessing the technical, economic, and societal impact of 3D’omics, was initiated toward the end of the second reporting period, when large-scale micro-scale data first became available.
Finally, for the fifth objective, which aims to establish strong collaborations with related microbiome research initiatives, we actively pursued new partnerships by participating in numerous public and scientific communication events and exploring future collaborative endeavours. Employing the platform of a EU Horizon booster grant, joint outreach material has been developed together with four H2020 sister-projects and the network Joint Dissemination Network "EcoGen" was founded. We also co-organised the second Applied Hologenomics Conference, maximising international exposure for 3D'omics.
3D’omics is still immersed in the first part of the project focused on developing and optimising molecular biology techniques to reconstruct microbial landscapes in animal intestines. All preliminary experiments that produced samples for technology optimisation were concluded successfully, and the methodologies for collecting and preserving samples for generating micro-scale multi-omic data have been established. The consortium is now pushing the limits of nucleic acid sequencing and mass spectrometry methods to identify detectability limits and assess the robustness of the methods under scrutiny. Once the technology is ready, we will use it to unveil animal-microbe and microbe-microbe interactions in bird and swine intestines, aiming at addressing different challenges. In the case of poultry, we will focus on pathogen challenges and will identify interactions and mechanistic causal processes between pathogens, microorganisms, animal cells and phenotypes, and how these vary within and between breeds, sexes, management practices, diet and environmental conditions. The swine system will focus on nutrition, through analysing protein deposition and fibre degradation. We will perform trials with pigs with distinct genetic backgrounds at multiple developmental stages, which will enable us to identify the way functional animal-microbiota interactions change responding to feeding and management practices. The gained knowledge will allow us to include data on microbial ecosystems in the models used to analyse phenotypic variability and to perform genetic evaluations, contributing to the efforts to improve resource use and environmental impact of terrestrial livestock production.