Periodic Reporting for period 1 - ExpoBiome (Deciphering the impact of exposures from the gut microbiome-derived molecular complex in human health and disease)
Reporting period: 2020-11-01 to 2022-04-30
The diverse ecology of the human gut microbiome is vital to human physiology. Numerous inflammatory-based chronic disorders, such as autoimmune and neurological diseases, are linked to changes in the microbiome. Chronic disorders, by definition, develop over longer time and require continued medical attention. This directly or indirectly reduces the quality of life of the affected individuals and possibly of their families and friends. The presence or imbalance of specific microorganisms (“who is there”) are not the only disease-related changes, however. High amounts of effector molecules produced by the microbiome, such as nucleic acids, (poly)peptides, and metabolites, are found in the gut. These molecules have thus far not been thoroughly studied. This information gap restricts the mechanistic understanding of the microbiome's functional impact on chronic disorders including Parkinson's disease and rheumatoid arthritis. The ExpoBiome project aims to, for the first time, comprehensively identify the components of this effector molecule complex and their effects on the human immune system. The project will develop and apply cutting-edge molecular approaches on microbiome samples taken from healthy people and those who have just been diagnosed with PD or RA. The resulting data will be integrated using existing and newly developed computational biology and machine learning approaches to put it into existing context and generate new insights on microbial factors in health and disease. A model clinical intervention (fasting) with the aim to reduce inflammation and thereby improving health will be applied and the resulting changes of the microbiome will be reviewed. Newly identified anti-inflammatory compounds will be specifically studied using a gut-on-chip model. ExpoBiome will thus lead to key advances to better understand how microbiome shifts provide benefits in the context of inflammatory-based chronic disordered, how specific molecules can be used to improve health, and to forecast treatment outcomes, thereby substantially contributing to the development of future diagnostic and therapeutic applications.
In the period covering the first financial report, the organizational and scientific foundations for the project have been successfully established. Activities for the sample collection from healthy people and those who have just been diagnosed with Parkinson’s disease and rheumatoid arthritis have been initiated and have been largely completed, with most longitudinal study participants having been recruited and being followed up for 12 months. A thorough and systematic sample collection is key to ensure proper establishment of a baseline (Objective 1; Identify) and follow-up (Objective 2; Intervene). Protocols for sample processing have been developed and published. A knowledge base compiling the body of knowledge on interactions of microbial taxa, microbial molecules, human immune pathways, and diseases was developed and is provided as an interactive web-based interface at https://expobiome.lcsb.uni.lu/minerva/. This knowledge base serves to explore and expand our current understanding of the role of the gut microbiome in human health and disease.
In the next phase of the ExpoBiome project, the collected samples will be processed using high-throughput molecular techniques to generate high-resolution meta-omic data to resolve the composition, functional potential, and activity of the gut microbiome as well as the effector molecules produced. This will represent an unprecedented dataset of microbiota dynamics as well as systemic effects in Parkinson’s disease and rheumatoid arthritis compared to healthy controls and how fasting affects these dynamics as well as the human host. The meta-omic data will be combined with the collected metadata, e.g. clinical lab readouts or dietary habits, and integrated using existing and newly developed computational approaches. Using machine learning techniques, biomarkers will be identified at baseline and selected biomarkers will be followed over time in individuals that underwent a model clinical intervention. We will use our in-house developed gut-on-a-chip, HuMiX, to create personalised models and will screen novel anti-inflammatory compounds (Objective 3; Validate).