Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi-omic analyses

in Microbiome (2022)

Background: Alterations to the gut microbiome have been linked to multiple chronic diseases. However, the drivers of such changes remain largely unknown. The oral cavity acts as a major route of exposure ... [more ▼]

Background: Alterations to the gut microbiome have been linked to multiple chronic diseases. However, the drivers of such changes remain largely unknown. The oral cavity acts as a major route of exposure to exogenous factors including pathogens, and processes therein may affect the communities in the subsequent compartments of the gastrointestinal tract. Here, we perform strain‑resolved, integrated meta‑genomic, transcriptomic, and proteomic analyses of paired saliva and stool samples collected from 35 individuals from eight families with multiple cases of type 1 diabetes mellitus (T1DM). Results: We identified distinct oral microbiota mostly reflecting competition between streptococcal species. More specifically, we found a decreased abundance of the commensal Streptococcus salivarius in the oral cavity of T1DM individuals, which is linked to its apparent competition with the pathobiont Streptococcus mutans. The decrease in S. salivarius in the oral cavity was also associated with its decrease in the gut as well as higher abundances in facultative anaerobes including Enterobacteria. In addition, we found evidence of gut inflammation in T1DM as reflected in the expression profiles of the Enterobacteria as well as in the human gut proteome. Finally, we were able to follow transmitted strain‑variants from the oral cavity to the gut at the individual omic levels, highlighting not only the transfer, but also the activity of the transmitted taxa along the gastrointestinal tract. Conclusions: Alterations of the oral microbiome in the context of T1DM impact the microbial communities in the lower gut, in particular through the reduction of “mouth‑to‑gut” transfer of Streptococcus salivarius. Our results indicate that the observed oral‑cavity‑driven gut microbiome changes may contribute towards the inflammatory processes involved in T1DM. Through the integration of multi‑omic analyses, we resolve strain‑variant “mouth‑to‑gut” transfer in a disease context. [less ▲]

An archaeal compound as a driver of Parkinson’s disease pathogenesis

E-print/Working paper (2022)

Patients with Parkinson’s disease (PD) exhibit differences in their gut microbiomes compared to healthy individuals. Although differences have most commonly been described in the abundances of bacterial ... [more ▼]

Patients with Parkinson’s disease (PD) exhibit differences in their gut microbiomes compared to healthy individuals. Although differences have most commonly been described in the abundances of bacterial taxa, changes to viral and archaeal populations have also been observed. Mechanistic links between gut microbes and PD pathogenesis remain elusive but could involve molecules that promote α-synuclein aggregation. Here, we show that 2-hydroxypyridine (2-HP) represents a key molecule for the pathogenesis of PD. We observe significantly elevated 2-HP levels in faecal samples from patients with PD or its prodrome, idiopathic REM sleep behaviour disorder (iRBD), compared to healthy controls. 2-HP is correlated with the archaeal species Methanobrevibacter smithii and with genes involved in methane metabolism, and it is detectable in isolate cultures of M. smithii. We demonstrate that 2-HP is selectively toxic to transgenic α-synuclein overexpressing yeast and increases α-synuclein aggregation in a yeast model as well as in human induced pluripotent stem cell derived enteric neurons. It also exacerbates PD-related motor symptoms, α-synuclein aggregation, and striatal degeneration when injected intrastriatally in transgenic mice overexpressing human α-synuclein. Our results highlight the effect of an archaeal molecule in relation to the gut-brain axis, which is critical for the diagnosis, prognosis, and treatment of PD. [less ▲]

Mantis: flexible and consensus-driven genome annotation

in GigaScience (2021), 10(6),

The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously ... [more ▼]

The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources.We implemented a protein annotation tool, Mantis, which uses database identifiers intersection and text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for the customization of reference data and execution parameters, and is reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which significantly improved annotation performance compared to sequence-wide annotation. The parallelized implementation of Mantis results in short runtimes while also outputting high coverage and high-quality protein function annotations.Mantis is a protein function annotation tool that produces high-quality consensus-driven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license at https://github.com/PedroMTQ/mantis. [less ▲]