Biology subjects

Marinos, G.

Publications and source records attributed to Marinos, G..

5 recordsLinked to original sources

bioRxiv2024

Metabolic modelling reveals increased autonomy and antagonism in type 2 diabetic gut microbiota

1.Type 2 diabetes presents a growing global health concern, with emerging evidence highlighting the pivotal role of the human gut microbiome in metabolic diseases. This study employs metabolic modelling to elucidate changes in host-microbiome interactions in type 2 diabetes. Glucose levels, dietary intake, 16S sequences and metadata were estimated and collected for a cohort of 1,866 individuals. In addition, microbial community models, as well as ecological interactions were simulated for the gut microbiomes of the cohort participants. Our findings revealed a significant decrease in the fluxes of metabolites provided by the host to the microbiome through the diet in patients with type 2 diabetes, accompanied by an increase in within-community exchanges. Moreover, the diabetic microbial community shifts towards increased exploitative ecological interactions among its member species at the expense of collaborative interactions. The reduced butyrate flux from the community to the host and reduced tryptophan acquired by the microbiome from the hosts diet further highlight the dysregulation in microbial-host interactions in diabetes. Additionally, microbiomes of type 2 diabetes patients exhibit enrichment in energy metabolism pathways, indicative of increased metabolic activity and antagonism. This study provides insights into the metabolic dynamics of the diabetic gut microbiome, shedding light on its increased autonomy and altered ecological interactions accompanying diabetes, and provides candidate metabolic targets for intervention studies and experimental validations, such as butyrate, tryptophan, H2S, several nucleotides, amino acids, and B vitamins.

systems biology↗

bioRxiv2024

Virtual Colon: Spatiotemporal modelling of metabolic interactions in a computational colonic environment

1.Host-microbial metabolic interactions have been recognised as an essential factor in host health and disease. Genome-scale metabolic modelling approaches have made important contributions to our understanding of the interactions in such communities. One particular such modelling approach is BacArena in which metabolic models grow, reproduce, and interact as independent agents in a spatiotemporal metabolic environment. Here, we present a modelling application of BacArena, a virtual colonic environment, which reveals spatiotemporal metabolic interactions in a computational colonic environment. This environment resembles the crypt space together with the mucus layers, the lumen and fluid dynamics. Our proof-of-principle experiments include mono-colonisation simulations of context-specific colonic cells and simulations of context-specific colonic cells with the SIHUMIx minimal model microbiome. Our simulations propose host-microbial and microbial-microbial interactions that can be verified based on the literature. Most importantly, the Virtual Colon offers visualisation of interactions through time and space, adding another dimension to the genome-scale metabolic modelling approaches. Lastly, like BacArena, it is freely available and can be easily adapted to model other spatially structured environments (http://www.github.com/maringos/VirtualColon).

systems biology↗

bioRxiv2024

Metabolic modeling reveals the aging-associated decline of host-microbiome metabolic interactions in mice

Aging is the predominant cause of morbidity and mortality in industrialized countries. The specific molecular mechanisms that drive aging are poorly understood, especially the contribution of the microbiota in these processes. Here, we combined multi-omics with metabolic modeling in mice to comprehensively characterize host-microbiome interactions and how they are affected by aging. Our findings reveal a complex dependency of host metabolism on microbial functions, including previously known as well as novel interactions. We observed a pronounced reduction in metabolic activity within the aging microbiome, which we attribute to reduced beneficial interactions in the microbial community and a reduction in its metabolic output. These microbial changes coincided with a corresponding downregulation of key host pathways predicted by our model to be dependent on the microbiome that are crucial for maintaining intestinal barrier function, cellular replication, and homeostasis. Our results elucidate microbiome-host interactions that potentially influence host aging processes, focusing on microbial nucleotide metabolism as a pivotal factor in aging dynamics. These pathways could serve as future targets for the development of microbiome-based therapies against aging. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/587009v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@a8e7faorg.highwire.dtl.DTLVardef@115f35aorg.highwire.dtl.DTLVardef@1bbf32org.highwire.dtl.DTLVardef@1a58c7f_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Closely related Bacteroides of the murine intestinal microbiota affect each other's growth positively or negatively

The mammalian intestine is a unique ecosystem for thousands of bacterial species and strains. How naturally coexisting bacteria of the microbiota interact with each other is not yet fully understood. Here, we isolated formerly coexisting, closely related strains of the genus Bacteroides from the intestines of healthy, wild-derived mice. The effect of one strain on another strains growth was tested in 169 pairs in vitro. We find a vast diversity of growth promoting and growth inhibiting activities. A strong positive effect was observed between two strains with differing metabolisms. Growth inhibition among a subset of strains was associated with the known bacterial toxin bacteroidetocin B. Across all strains, we observed growth promotion more often than growth inhibition. The effects were independent of two strains belonging to the same or different species. In some cases, one species differed in its effect on another according to host origin. These findings on obligate host-associated bacteria demonstrate that closely related and naturally coexisting strains have the potential to affect each others growth positively or negatively. These results have implications for our basic understanding of host-associated microbes and the design of synthetic microbial communities.

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↗