| Reference : DEVELOPING INDIVIDUAL-BASED GUT MICROBIOME METABOLIC MODELS FOR THE INVESTIGATION OF ... |
| Dissertations and theses : Doctoral thesis | |||
| Life sciences : Microbiology Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others | |||
| Systems Biomedicine | |||
| http://hdl.handle.net/10993/41182 | |||
| DEVELOPING INDIVIDUAL-BASED GUT MICROBIOME METABOLIC MODELS FOR THE INVESTIGATION OF PARKINSON’S DISEASE-ASSOCIATED INTESTINAL MICROBIAL COMMUNITIES | |
| English | |
| [en] DEVELOPING INDIVIDUAL-BASED GUT MICROBIOME METABOLIC MODELS FOR THE INVESTIGATION OF PARKINSON’S DISEASE-ASSOCIATED INTESTINAL MICROBIAL COMMUNITIES | |
Baldini, Federico  [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >] | |
| 12-Dec-2019 | |
| University of Luxembourg, Belvaux, Lussemburgo | |
| Federico Baldini | |
| 141 | |
| Krüger, Rejko | |
| [en] systems biology ; metabolic modeling ; human gut microbiota ; Parkinson's disease ; bioinformatics | |
| [en] 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. | |
| Researchers ; Professionals ; Students ; General public | |
| http://hdl.handle.net/10993/41182 |
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