About
9
Publications
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452
Citations
Introduction
I am a Marie Skłodowska Curie postdoctoral fellow investigating the evolution of the human microbiome in the era of plastics at the Segata Lab of Computational Metagenomics, University of Trento, Italy.
I completed my fast-track PhD program at Ulm University in Germany, where I was also the recipient of a doctoral scholarship from the German Academic Scholarship Foundation.
I've lived in the United States (VT), Germany, Austria, and Italy.
Current institution
Additional affiliations
February 2018 - December 2022
Position
- Fast-Track PhD Student
Description
- PhD thesis: Anthropogenic effects on microbiome communities in wildlife and associated health implications
March 2016 - April 2016
February 2016 - March 2016
Education
February 2018 - December 2022
August 2014 - August 2017
Publications
Publications (9)
Understudied pet-associated microbiomes represent a rich source for the discovery of microbial taxa important for pet and human health. From a cohort of 23 dogs, we sampled and metagenomically sequenced 64 dental plaque microbiomes, generating 1945 metagenome-assembled genomes spanning 347 microbial species, including 277 undercharacterized species...
As plant-based diets gain traction, interest in their impacts on the gut microbiome is growing. However, little is known about diet-pattern-specific metagenomic profiles across populations. Here we considered 21,561 individuals spanning 5 independent, multinational, human cohorts to map how differences in diet pattern (omnivore, vegetarian and vega...
Complex microbiomes are part of the food we eat and influence our own microbiome, but their diversity remains largely unexplored. Here, we generated the open access curatedFoodMetagenomicData (cFMD) resource by integrating 1,950 newly sequenced and 583 public food metagenomes. We produced 10,899 metagenome-assembled genomes spanning 1,036 prokaryot...
Background
Human encroachment into nature and the accompanying environmental changes are a big concern for wildlife biodiversity and health. While changes on the macroecological scale, i.e. species community and abundance pattern, are well documented, impacts on the microecological scale, such as the host’s microbial community, remain understudied....
Microplastics contaminate environments worldwide and are ingested by numerous species, whose health is affected in multiple ways. A key dimension of health that may be affected is the gut microbiome, but these effects are relatively unexplored. Here, we investigated if microplastics are associated with changes in proventricular and cloacal microbio...
Human habitat disturbance affects both species diversity and intraspecific genetic diversity, leading to correlations between these two components of biodiversity (termed species–genetic diversity correlation, SGDC). However, whether SGDC predictions extend to host‐associated communities, such as the intestinal parasite and gut microbial diversity,...
Parasitic infections disturb gut microbial communities beyond their natural range of variation, possibly leading to dysbiosis. Yet it remains underappreciated that most infections are accompanied by one or more co-infections and their collective impact is largely unexplored. Here we developed a framework illustrating changes to the host gut microbi...
In the Anthropocene, humans, domesticated animals, wildlife, and their environments are interconnected, especially as humans advance further into wildlife habitats. Wildlife gut microbiomes play a vital role in host health. Changes to wildlife gut microbiomes due to anthropogenic disturbances, such as habitat fragmentation, can disrupt natural gut...
As small pieces of plastics known as microplastics pollute even the remotest parts of Earth, research currently focuses on unveiling how this pollution may affect biota. Despite increasing awareness, one potentially major consequence of chronic exposure to microplastics has been largely neglected: the impact of the disruption of the symbiosis betwe...
Questions
Question (1)
Dear all,
My questions concern the Silva and Greengenes databases to assign taxonomy in 16S rRNA gene sequencing studies. I recognize that each database has its merits and drawbacks and as such I am evaluating which of these two databases is best suited for my data. Therefore, I used both to assign taxonomy and analyzed for which percentage of reads each database was unable to assign taxonomy to each of the taxonomic ranks (from domain to species). In other words, for Greengenes, I counted the blank cells at the domain, phylum, class, etc. rank. For Silva, I counted not only the blank cells, but also cells containing any of the following: ambiguous taxa, uncultured bacterium, and uncultured. The output yielded the following, which includes the absolute number of unassigned cells as well as their respective relative proportions:
Rank | Silva_absolute_unassigned | gg_absolute_unassigned
Domain | 0 | 0
Phylum | 446 | 447
Class | 936 | 682
Order | 2637 | 1778
Family | 4398 | 7969
Genus | 10573 | 14200
Species | 18044 | 17734
Rank | Silva_relative_unassigned | gg_relative_unassigned
Domain | 0.000000 | 0.000000
Phylum | 2.460282 | 2.467160
Class | 5.163283 | 3.764212
Order | 14.546558 | 9.813445
Family | 24.260812 | 43.983883
Genus | 58.324139 | 78.375097
Species | 99.536628 | 97.880561
Here, Silva and Greengenes assigned similar proportions of features at the phylum level (note that domain is 0 because I filtered the reads to obtain bacteria only), then Greengenes assigned more for the class and order ranks, after which Silva assigned a greater proportion of features at the family and genus ranks. Since Silva does not curate its database to include the species level, it makes sense why Greengenes assigned more features here. Has anyone else observed this pattern that Greengenes assigns more features than Silva at the class and order ranks? This seems somewhat counterintuitive considering that Silva is the larger of the two databases.
I have looked into the publication SILVA, RDP, Greengenes, NCBI and OTT — how do these taxonomies compare? by Monika Balvočiūtė and Daniel Huson (2017) for answers and noticed that in the Venn diagrams depicted in Figure 3, the amount of unique taxa in the Greengenes database increases until the order rank and begins to decrease from family onwards, mirroring my observations. However, I am not sure if there is a concrete reason for this similarity or if I'm seeing this pattern simply because I am searching for an answer. So, in short, my questions are: Why does Greengenes assign more features at the class and order ranks than Silva? Based on the table provided, which database is better suited for my analysis?
Many thanks in advance!
Gloria
