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See detailDEVELOPING INDIVIDUAL-BASED GUT MICROBIOME METABOLIC MODELS FOR THE INVESTIGATION OF PARKINSON’S DISEASE-ASSOCIATED INTESTINAL MICROBIAL COMMUNITIES
Baldini, Federico UL

Doctoral thesis (2019)

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 ... [more ▼]

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. [less ▲]

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See detailSystematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease
Heinken, Almut Katrin UL; Ravcheev, Dmitry UL; Baldini, Federico UL et al

in Microbiome (2019)

Background The human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their ... [more ▼]

Background The human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their subsequent deconjugation and transformation by the gut microbiome. To understand these system-level host-microbe interactions, a mechanistic, multi-scale computational systems biology approach that integrates the different types of omic data is needed. Here, we use a systematic workflow to computationally model bile acid metabolism in gut microbes and microbial communities. Results Therefore, we first performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes and expanded 232 curated genome-scale microbial metabolic reconstructions with the corresponding reactions (available at https://vmh.life). We then predicted the bile acid biotransformation potential of each microbe and in combination with other microbes. We found that each microbe could produce maximally six of the 13 secondary bile acids in silico, while microbial pairs could produce up to 12 bile acids, suggesting bile acid biotransformation being a microbial community task. To investigate the metabolic potential of a given microbiome, publicly available metagenomics data from healthy Western individuals, as well as inflammatory bowel disease patients and healthy controls, were mapped onto the genomes of the reconstructed strains. We constructed for each individual a large-scale personalized microbial community model that takes into account strain-level abundances. Using flux balance analysis, we found considerable variation in the potential to deconjugate and transform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric inflammatory bowel disease patients were significantly depleted in their bile acid production potential compared with that of controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between inflammatory bowel disease patients and controls. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model. Conclusions This large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between the host and gut microbiomes in health and diseases states. Our models and tools are freely available to the scientific community. [less ▲]

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See detailIntegrated Analyses of Microbiome and Longitudinal Metabolome Data Reveal Microbial-Host Interactions on Sulfur Metabolism in Parkinson’s Disease
Hertel, Johannes; Harms, Amy C.; Heinken, Almut et al

in Cell Reports (2019), 29(7), 1767-1777

Parkinson’s disease (PD) exhibits systemic effects on human metabolism with emerging roles for the gut microbiome. Here, we integrated longitudinal metabolome data from 30 drug-naïve, de-novo PD patients ... [more ▼]

Parkinson’s disease (PD) exhibits systemic effects on human metabolism with emerging roles for the gut microbiome. Here, we integrated longitudinal metabolome data from 30 drug-naïve, de-novo PD patients and 30 matched controls with constraint-based modeling of gut microbial communities derived from an independent, drug-naïve PD cohort, and prospective data from a general population. Our key results are i) longitudinal trajectory of metabolites associated with the interconversion of methionine and cysteine via cystathionine differed between PD patients and controls, ii) dopaminergic medication showed strong lipidomic signatures, iii) taurine-conjugated bile acids correlated with the severity of motor symptoms, while low levels of sulfated taurolithocholate were associated with incident PD in the general population, and iv) computational modeling predicted changes in sulfur metabolism, driven by A. muciniphila and B. wadsworthia, consistent with the changed metabolome. In conclusion, the multi-omics integration revealed PD-specific patterns in microbial-host sulfur co-metabolism that may contribute to PD severity. [less ▲]

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See detailThe Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities
Baldini, Federico UL; Heinken, Almut Katrin UL; Heirendt, Laurent UL et al

in Bioinformatics (2018)

The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. To address this gap, we created a ... [more ▼]

The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. To address this gap, we created a comprehensive toolbox to model i) microbe-microbe and host-microbe metabolic interactions, and ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the COBRA Toolbox. The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox. [less ▲]

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See detailBacArena: Individual-Based Metabolic Modeling of Heterogeneous Microbes in Complex Communities
Bauer, Eugen UL; Zimmermann, Johannes; Baldini, Federico UL et al

in PLoS Computational Biology (2017)

Recent advances focusing on the metabolic interactions within and between cellular populations, have emphasized the importance of microbial communities for human health. Constraint-based modeling, with ... [more ▼]

Recent advances focusing on the metabolic interactions within and between cellular populations, have emphasized the importance of microbial communities for human health. Constraint-based modeling, with flux balance analysis in particular, has been established as a key approach for studying microbial metabolism, whereas individual-based modeling has been commonly used to study complex dynamics between interacting organisms. In this study, we combine both techniques into the R package BacArena (https://cran.r-project.org/package=BacArena), to generate novel biological insights into Pseudomonas aeruginosa biofilm formation as well as a seven species model community of the human gut. For our P. aeruginosa model, we found that cross-feeding of fermentation products cause a spatial differentiation of emerging metabolic phenotypes in the biofilm over time. In the human gut model community, we found that spatial gradients of mucus glycans are important for niche formations, which shape the overall community structure. Additionally, we could provide novel hypothesis concerning the metabolic interactions between the microbes. These results demonstrate the importance of spatial and temporal multi-scale modeling approaches such as BacArena. [less ▲]

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