SOURCE-LINKED INTELLIGENCE

Informed ecological rewiring of gut microbiome for dysbiosis-associated disorders

CORDIS · observation · Publication date unknown

pproach, moving from observational/in-silico data to experimental in-vitro and in-vivo outputs to clinical validation. In Aim1 I will assess the microbiome of PI-IBS patients, identify target taxa by machine learning, build ecological models to predict recovery strategies (e.g. competition, pathobiont suppression, antagonism, mutualism, symbiont growth, full rewiring). Based on such strategies, in Aim2 I will use a gut simulator to test the effect of microbial therapeutics (antibiotics, prebiotics, microbial consortia, targeted fecal transplant) on patient microbiota, narrowing them down to build an ecological framework for microbial restoration. In parallel I will rig up known PI-IBS mouse models by colonizing germ-free and C. rodentium-infected mice with patient microbiota or target taxa. Then I will test down-selected interventions in such mice. In Aim3 I will validate this metagenome-informed, targeted framework for microbiome rewiring in a randomized trial of PI-IBS patients. My strategy may offer a notable clinical benefit to PI-IBS and other dysbiosis-associated diseases. By d

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recordType

award

status

SIGNED

region

EU

value

1499557

unit

EUR