DEVELOPING INDIVIDUAL-BASED GUT MICROBIOME METABOLIC MODELS FOR THE INVESTIGATION OF PARKINSON’S DISEASE-ASSOCIATED INTESTINAL MICROBIAL COMMUNITIES

The human phenotype is a result of the interactions of environmental factors with genetic
ones. Some environmental factors such as the human gut microbiota composition and the
related metabolic functions are known to impact human health and were put in correlation
with the development of different diseases. Most importantly, disentangling the metabolic
role played by these factors is crucial to understanding the pathogenesis of complex and multifactorial
diseases, such as Parkinson’s Disease. Microbial community sequencing became
the standard investigation technique to highlight emerging microbial patterns associated with
different health states. However, even if highly informative, such technique alone is only
able to provide limited information on possible functions associated with specific microbial
communities composition. The integration of a systems biology computational modeling
approach termed constraint-based modeling with sequencing data (whole genome sequencing,
and 16S rRNA gene sequencing), together with the deployment of advanced statistical
techniques (machine learning), helps to elucidate the metabolic role played by these environmental
factors and the underlying mechanisms.
The first goal of this PhD thesis was the development and deployment of specific methods
for the integration of microbial abundance data (coming from microbial community sequencing)
into constraint-based modeling, and the analysis of the consequent produced data. The
result was the implementation of a new automated pipeline, connecting all these different
methods, through which the study of the metabolism of different gut microbial communities
was enabled. Second, I investigated possible microbial differences between a cohort a
Parkinson’s disease patients and controls. I discovered microbial and metabolic changes in
Parkinson’s disease patients and their relative dependence on several physiological covariates,
therefore exposing possible mechanisms of pathogenesis of the disease.Overall, the work presented in this thesis represents method development for the investigation
of before unexplored functional metabolic consequences associated with microbial
changes of the human gut microbiota with a focus on specific complex diseases such as Parkinson’s
disease. The consequently formulated hypothesis could be experimentally validated
and could represent a starting point to envision possible clinical interventions.