Biology subjects

Hertel, J.

Publications and source records attributed to Hertel, J..

5 recordsLinked to original sources

bioRxiv2022

Longitudinal flux balance analyses of a patient with Crohn's disease highlight microbiome metabolic alterations

Inflammatory bowel diseases (IBD) are characterised by episodic inflammation of the gastrointestinal tract. Gut microbial dysbiosis characterises the pathoetiology, but its role remains understudied. We report the first use of constraint-based microbial community modelling on a single individual with IBD, covering seven dates over 16 months, enabling us to identify a number of time-correlated microbial species and metabolites. We find that the individuals dynamical microbial ecology in the disease state drives time-varying in silico overproduction, compared to healthy controls, of more than 24 biologically important metabolites, including oxygen, methane, thiamine, formaldehyde, trimethylamine N-oxide, folic acid, serotonin, histamine, and tryptamine. A number of these metabolites may yield new biomarkers of disease progression. The microbe-metabolite contribution analysis revealed that some genus Dialister species changed metabolic pathways according to the disease phases. At the first time point, characterised by the highest levels of blood and faecal inflammation biomarkers, they produced L-serine or formate. The production of the compounds, through a cascade effect, was mediated by the interaction with pathogenic Escherichia coli strains and Desulfovibrio piger. We integrated the microbial community metabolic models of each time point with a male whole-body, organ-resolved model of human metabolism to track the metabolic consequences of dysbiosis at different body sites. The presence of D. piger in the gut microbiome influenced the sulphur metabolism with a domino effect affecting the liver. These results underline the importance of tracking an individuals gut microbiome along a time course, creating a new analysis framework for self-quantified medicine. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/520975v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@136ef4forg.highwire.dtl.DTLVardef@1993042org.highwire.dtl.DTLVardef@2f795forg.highwire.dtl.DTLVardef@c95a6d_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2021

Causal inference on microbiome-metabolome relations via in silico in vivo association pattern analyses

The effects of the microbiome on the hosts metabolism are core to understanding the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, entailing a methodology to combine microbiome metabolome association studies with in silico constraint-based microbial community modelling. By dissecting confounding and causal paths, we show that in silico in vivo association pattern analyses allows for causal inference on microbiome-metabolome relations in observational data. Then, we demonstrate the feasibility and validity of our approach on a published multi-omics dataset (n=346), demonstrating causal microbiome-metabolite relations for 43 out of 53 metabolites from faeces. Finally, we utilise the identified in silico in vivo association pattern to estimate the microbial component of the faecal metabolome, revealing that the retrieved metabolite prediction scores correlate with the measured metabolite concentrations, and they also reflect the multivariate structure of the faecal metabolome. Concluding, we integrate with hypothesis free screening association studies and knowledge-based in silico modelling two major paradigms of systems biology, generating a promising new paradigm for causal inference in metabolic host-microbe interactions.

systems biology↗

bioRxiv2020

AGORA2: Large scale reconstruction of the microbiome highlights wide-spread drug-metabolising capacities.

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs, yet comprehensive systems-level approaches to interrogate drug-microbiome interactions are lacking. Here, we present a computational resource of human microbial genome-scale reconstructions, deemed AGORA2, which accounts for 7,206 strains, includes microbial drug degradation and biotransformation, and was extensively curated based on comparative genomics and literature searches. AGORA2 serves as a knowledge base for the human microbiome and as a metabolic modelling resource. We demonstrate the latter by mechanistically modelling microbial drug metabolism capabilities in single strains and pairwise models. Moreover, we predict the individual-specific drug conversion potential in a cohort of 616 colorectal cancer patients and controls. This analysis reveals that some drug activation capabilities are present in only a subset of individuals, moreover, drug conversion potential correlate with clinical parameters. Thus, AGORA2 paves the way towards personalised, predictive analysis of host-drug-microbiome interactions.

systems biology↗

bioRxiv2020

Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacteria species on community butyrate production

Integrating constraint-based community modelling with population statistics, we introduce new theoretical concepts for interrogating the metabolic functions of the microbiome, applying them to a public metagenomic dataset consisting of 365 colorectal cancer cases (CRC) and 251 healthy controls. We found that 1) glutarate production capability was significantly enriched in CRC microbiomes and mechanistically linked to lysine fermentation in Fusobacteria species, 2) acetate and butyrate production potentials were lowered in CRC, 3) Fusobacteria presence had large negative ecological effects on community butyrate production in CRC and healthy controls. Validating the model predictions against faecal metabolomics, our in silico frameworks correctly predicted in vivo species metabolite correlations with high accuracy. In conclusion, highlighting the value of combining statistical association studies with in silico modelling, this study delivers insights on the metabolic role of Fusobacteria in the gut, while providing a proof of concept for the validity of constraint-based community modelling.

systems biology↗

bioRxiv2019

Parkinson’s disease-associated alterations of the gut microbiome can invoke disease-relevant metabolic changes

Parkinsons disease (PD) is a systemic disease clinically defined by the degeneration of dopaminergic neurons in the brain. While alterations in the gut microbiome composition have been reported in PD, their functional consequences remain unclear. Herein, we first analysed the gut microbiome of patients and healthy controls by 16S rRNA gene sequencing of stool samples from the Luxembourg Parkinsons study (n=147 typical PD cases, n=162 controls). All individuals underwent detailed clinical assessment, including neurological examinations and neuropsychological tests followed by self-reporting questionnaires. Second, we predicted the potential secretion for 129 microbial metabolites through personalised metabolic modelling using the microbiome data and genome-scale metabolic reconstructions of human gut microbes. Our key results include: 1. eight genera and nine species changed significantly in their relative abundances between PD patients and healthy controls. 2. PD-associated microbial patterns statistically depended on sex, age, BMI, and constipation. The relative abundances of Bilophila and Paraprevotella were significantly associated with the Hoehn and Yahr staging after controlling for the disease duration. In contrast, dopaminergic medication had no detectable effect on the PD microbiome composition. 3. Personalised metabolic modelling of the gut microbiomes revealed PD-associated metabolic patterns in secretion potential of nine microbial metabolites in PD, including increased methionine and cysteinylglycine. The microbial pantothenic acid production potential was linked to the presence of specific non-motor symptoms and attributed to individual bacteria, such as Akkermansia muciniphila and Bilophila wardswarthia. Our results suggest that PD-associated alterations of gut microbiome could translate into functional differences affecting host metabolism and disease phenotype.

microbiology↗