Paolo Ghensi, V Heidrich, D Bazzani, F Asnicar (@fasnicar.bsky.social), F Armanini, A Bertelle, F Dell'Acqua, E Dellasega, R Waldner, D Vicentini, M Bolzan, L Trevisiol, C Tomasi, E Pasolli (@epasolli.bsky.social), Nicola Segata

We also used machine learning to show that diseased implants could be identified from microbiome profiles alone, reinforcing the potential of assessing the peri-implant microbiome in the clinical setting as a supportive diagnostic tool

These included many functional and taxonomic unknowns, but also the well-studied for its role in periodontitis Fusobacterium nucleatum, with specific F. nuc clades associated differentially with mucositis and peri-implantitis

Here we expanded our previous cohort (Ghensi et al. 2020) and used improved metagenomic profiling tools to identify strong microbiome markers of implant health and disease (mucositis or peri-implantitis)

Full-text: t.co/G8zk3j9Vtm

Finally, we discuss the impact that microbiome transmission can have on human health and disease, and how it is possible to leverage this knowledge for the design of effective microbiome-targeting strategies

www.nature.com/articles/s41...

Have a nice read!
@cibiocm.bsky.social

We also list the social, host-related, and microbial factors potentially associated with human microbiome transmission, and talk about the methodological challenges associated with microbiome transmission inference

Since this includes microbial exchange not only with the main fount of our microbes - other humans, we dissect all the potential sources of human microbiome strains, including animals and foods

We discuss how the building blocks of our microbiome - single microbial strains - are dynamically exchanged with the surroundings, leading to the continued acquisition and transmission of microbiome members

Wrong figure here, right one below

Link to the publication: rdcu.be/eajx6

@fackelmeister.bsky.social @raj09.bsky.social @cengoni.bsky.social @cibiocm.bsky.social

In conclusion, we hope our results and the better ability to metagenomically profile oral samples from dogs enabled by our study will encourage further efforts to characterize the microbiomes of pets (7/7)

This finding provides some support for the continued use of dogs as models of periodontal conditions in humans and, more interestingly, hints at the possibility of the acquisition of oral pathogens from companion animals (6/7)

This is probably linked to the very different anatomy, environmental conditions, and hygiene levels found in each host species' oral cavity. Despite this limited overlap, we noticed that species found in both hosts were often periodontal pathobionts, such as Campylobacter rectus (5/7)

We found the dog plaque microbiome is more diverse than the human plaque microbiome and that there is very little microbial overlap between the dental plaque of dogs and humans (only ~6% of species found in both host species) (4/7)

We then added these new dog plaque species to the database of MetaPhlAn 4 (github.com/biobakery/Me...) to be able to quantify their abundance in metagenomes. This allowed us to evaluate the overlap between the plaque microbiome of dogs and humans (3/7)

Through metagenomic sequencing and assembly, we reconstructed genomes for 347 dog plaque microbial species (spanning one archaeal and 17 bacterial phyla), 265 of which had never been identified before (2/7)