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Emma Allen-Vercoe
Researcher at University of Guelph
Publications - 143
Citations - 28476
Emma Allen-Vercoe is an academic researcher from University of Guelph. The author has contributed to research in topics: Microbiome & Biology. The author has an hindex of 42, co-authored 117 publications receiving 24132 citations. Previous affiliations of Emma Allen-Vercoe include Queen's University & Veterinary Laboratories Agency.
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Journal ArticleDOI
Drivers of human gut microbial community assembly: coadaptation, determinism and stochasticity
TL;DR: Stochasticity had the largest influence on the species structure when substrate concentrations varied, whereas habitat filtering greatly impacted the metabonomic output, suggesting overall that polysaccharide utilization by Firmicutes is dependent on cooperation.
Book ChapterDOI
Invasions of Host-Associated Microbiome Networks
Carmen Lía Murall,Jessica L. Abbate,M. Puelma Touzel,Emma Allen-Vercoe,Samuel Alizon,Rémy Froissart,Kevin S. McCann +6 more
TL;DR: The elucidation of principles underlying the invasibility of WH networks will ultimately help in the development of medical applications and help shape the understanding of human health and disease.
Journal ArticleDOI
Rebooting the microbiome
Sean Munoz,Mabel Guzman-Rodriguez,Jun Sun,Yong Guo Zhang,Curtis Noordhof,Shu Mei He,Emma Allen-Vercoe,Erika C. Claud,Elaine O. Petrof +8 more
TL;DR: Differences in the findings between the Salmonella colitis and DSS colitis models are discussed, speculation as to which bacteria may be important in the protective effects of MET-1 is provided, and potential implications for other GI diseases such as IBD are discussed.
Journal ArticleDOI
Protease-dependent excitation of nodose ganglion neurons by commensal gut bacteria.
TL;DR: In this article, the secretory products of commensal gut bacteria can modulate the excitability of vagal afferent neurons with cell bodies in nodose ganglia, which may in turn impact on autonomic reflexes and behaviour.
Journal ArticleDOI
Phylogenetic Clustering of Genes Reveals Shared Evolutionary Trajectories and Putative Gene Functions.
TL;DR: This work proposes a new approach that uses a coevolutionary method defined by Pagel to account for the phylogenetic relationships amongst target organisms, and a hierarchical-clustering approach to define sets of genes with common distributions across the organisms.