GOODBYO - Multi-commodities microbial-driven BiOrefinery based on food-processing industry wastes, biogenic CO2 and bioprocess wastewaters (HORIZON-JU-CBE-2023)

GoodByO aims to develop a new generation of biorefinery system that integrate agri-food waste, biogenic CO₂ and bioprocess wastewater to produce bioproducts.

The project will implement this visionary concept at the bio-plant of Dutch company ChainCraft BV, in order to exploit gaseous and liquid residues as sustainable raw materials at zero cost. Thanks to the metabolic diversity of microbial catalysts, GoodByO will develop and validate four different microbial factories at TRL5.

The project will also design an in silico renewable energy model capable of supporting the variable energy demand of the entire system integrated over time, exploiting the biomethanation process as an energy grid balancing system. GoodByO will produce bio-octanoic acid, bio-hexanol, carotenoids, microbial proteins and organic fertilisers in order to meet growing market demand at competitive prices compared to benchmarks.

The ultimate goal is to encourage end-user companies to replace fossil oil and palm oil-based products with bio-based ones, helping to increase the EU’s global leadership in the manufacturing industry and also advancing the biotechnology sectors for CO₂ capture and utilisation.

Sheep-TreeSeq: Scalable analysis of sheep genome diversity using tree sequences

Sheep-TreeSeq will perform scalable analysis of genomic diversity of sheep global populations using the novel tree sequence data format and methodology. Technological advances in agritech have increased the availability of genomic data, leading to massive datasets (“big data”) which pose challenges for storage, processing and analysis, e.g. the sheer volume of the data, the rapid generation of new data (updating results, expanding training populations, streaming applications), and the heterogeneity of data sources (integration of data from multiple sequencing and genotyping platforms). The tree sequence algorithm offers an excellent way to address such challenges, by providing lossless compression and novel representation of the data. As an example, using tree sequences on the 1000 Bull Genome Project data a 90% lossless compression was obtained, reducing the data size from ~800 GB to 45 GB. For the Sheep TreeSeq project we will use around 3,500 sheep whole genome sequences and over 50,000 genotypes (~10 TB of data). Our plan is to apply the tree sequence approach to compress the data and obtain a data representation highly suited for population genetics and demographic analysis: (i) principal component and genealogical nearest neighbour clustering; (ii) fixation index measuring genetic differentiation; (iii) deep neural network based clustering methods; iv) detection of runs of homozygosity (ROH) and heterozygosity-rich regions (HRR). This is the first time that this approach is applied to sheep genomics.

SCALA-MEDI Improving sustainability and quality of Sheep and Chicken production by leveraging the Adaptation potential of LocAl breeds in the MEDIterranean area

The SCALA-MEDI project will optimize the sustainable use and conservation of local sheep and poultry genetic resources in the Mediterranean region, focusing on adaptation to climate conditions and consumer preferences.

The experience and data from previous EU projects will be extended to the genetic and epigenetic characterization of local resources and their adaptation to different production environments in three North African countries: Tunisia, Algeria, and Morocco.

Tools and strategies will be developed to improve local breeds for sustainable production. The application of these tools will be demonstrated to farmers in different Mediterranean production systems.

PRIN AGRITECH (SPOKE 1) - Plant, animal and microbial genetic resources and adaptation to climatic changes

Plant, animal and microbial genetic resources and adaptation to climatic changes

The resilience of agricultural and forest ecosystems under stressful conditions generated by climate change requires the enhancement and exploitation of genetic resources through state-of-the-art conservation strategies, combined with in-depth genome characterization and high-throughput phenotyping.

Activities include large-scale sequencing of accessions/breeds/strains, extensive marker-based genome descriptions, elucidation of the (pan)genome, deep phenotyping, and multi-omics characterization.

The resulting information, processed through advanced methods for the analysis, interpretation, archiving, and management of complex data, will highlight superior alleles/haplotypes, identify beneficial interactions across a variety of conditions, and define conservation units.

DeepMicroCore: Harnessing the power of artificial intelligence to identify the core microbiome

In DeepMicroCore we will develop DNN models to predict the substrate (species, tissue, food), time (e.g. developmental/physiological stage, experimental timepoint) and place (geographical location) of origin from microbiome data. We refer (and will use) specifically to 16S/18S/ITS rRNA-gene/amplicon sequencing data (metataxonomics). These predictive models will be optimized to maximise the accuracy of predictions in an unbiased way through cross-validation. With these working models, we will then be able to estimate variable importance and retrieve interesting features, e.g. the most relevant ASV/OTU for their impact on the prediction accuracy, or combinations and functions of ASV/OTU, or between-sample distances from embeddings. The features extracted from the DNN predictive models will then help us define and identify the core microbiome. For example, given microbiome data from multiple human/animal tissues (e.g. gut, skin, milk) sampled in different geographical locations, we will build a DNN model able to accurately predict to which tissue and location each sample belongs. Then, by extracting the relevant features from this model we will be able to identify the gut/skin/milk core microbiome. The core microbiome will be composed of those microbial taxa that discriminate between different tissues: some taxa may overlap between different microbiomes, and these will be identified by specific predictive models (e.g. one vs all predictions).

PRIN 2022 MISS.GO – Microbiota and Immune SignatureS of Graves’ disease and Orbitopathy

MISSGO addresses Graves’ disease and its main complication, Graves’ orbitopathy, an inflammatory condition that can cause severe functional and aesthetic impairment. As predictive models for identifying patients at risk of developing orbitopathy are still lacking, the project aims to clarify the underlying biological mechanisms.

MISSGO investigates the interaction between the gut microbiota and the immune system, with a particular focus on the imbalance between pro-inflammatory and regulatory T cells. Using advanced shotgun sequencing techniques integrated with multi-omics approaches, the project analyses microbiota composition and immune responses in patients with Graves’ disease, Graves’ orbitopathy, and healthy controls.

A key innovative aspect is the study of immune cells directly sampled from the thyroid and lymph nodes, enabling a disease-specific characterization. By integrating immunological and microbiological data, MISSGO aims to develop a predictive model for early identification of patients at risk, opening new perspectives for timely diagnosis and targeted therapies