Biology subjects

Laudes, M.

Publications and source records attributed to Laudes, M..

3 recordsLinked to original sources

bioRxiv2025

Genome-scale metabolic models predict diet- and lifestyle-driven shifts of ecological interactions in the gut microbiome

1.Microbiomes and their host environments form complex, interconnected ecosystems. The microbial species within a microbiome, on the one hand, compete for resources, while on the other hand, they exchange vital metabolites to support their survival. These interactions are influenced by the microbial genetic repertoire, environmental conditions, and the availability of nutrients. We developed EcoGS (http://www.github.com/KaletaLab/EcoGS), a metabolic modelling tool designed to predict ecological interactions between pairs of microbes. Applying EcoGS to the microbiomes of two distinct human cohorts revealed a shift from collaborative to exploitative ecological interactions associated with higher dietary intake of simple sugars (glucose and fructose), in diabetic individuals and those living in industrialised lifestyles. On the other hand, the consumption of Cobalamin (vitamin B12), phylloquinone (vitamin K1) and biotin (vitamin B7), among other compounds, was associated with increased collaboration in the gut microbiome. We conclude that the abundance of simple sugars as an energy source reduces the necessity for microbes to cooperate, thereby increasing competition and hostility among microbiome members. Moreover, our study proposes multiple compounds, such as urate, deoxyadenosine, deoxyguanosine, and hypoxanthine, for in vitro validation tests as dietary interventions that have the potential to restore the ecological balance amongst the community. EcoGS serves as a valuable tool for exploring microbiome dynamics and their connections to environmental changes and disease.

systems biology↗

bioRxiv2025

Degradation of rutin and genistein and the effect on human bacterial fecal populations of obese and non-obese

This study re-evaluates the microbial degradation of rutin and genistein, emphasizing health-related metabotypes and novel degradation and transformation products of these compounds. In silico predictions and calculations were made to determine the molecular formulae of metabolites derived from precursor molecules by methylation, sulfation, and dehydroxylation. To validate these, anaerobic, ex vivo experiments were conducted with pooled fecal samples of obese and non-obese test persons (n=7 per group) over 48 hours, followed by 16S rRNA gene sequencing and semi-targeted high-resolution mass spectrometry analysis. We identified 46 rutin and 23 genistein metabolites, including novel methylated and sulfated derivatives. Microbial analysis revealed that the plant compounds influenced 34 bacterial families and 83 genera. Rutin inhibited obesity-associated genera while promoting butyrate-producing bacteria in BMI >40 samples, but reduced health-associated bifidobacteria and increased enterobacteria. Both compounds stimulated Eggerthella. These findings highlight significant microbial and metabolic shifts induced by plant-derived compounds, suggesting potential health implications.

microbiology↗

bioRxiv2023

Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics

1.The microbiome is increasingly receiving attention as an important modulator of host health and disease. However, while numerous mechanisms through which the microbiome influences its host have been identified, there is still a lack of approaches that allow to specifically modulate the abundance of individual microbes or microbial functions of interest. Moreover, current approaches for microbiome manipulation such as fecal transfers often entail a non-specific transfer of entire microbial communities with potentially unwanted side effects. To overcome this limitation, we here propose the concept of precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In a first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we present a metabolic modeling network framework that allows us to define precision prebiotics for a two-member C. elegans microbiome model community comprising the immune-protective Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. Thus, we predicted compounds that specifically boost the abundance of the host-beneficial MYb11, four of which were experimentally validated in vitro (L-serine, L-threonine, D-mannitol, and {gamma}-aminobutyric acid). L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that constraint-based metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.

systems biology↗