Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota

By Yongfei Hu, Xi Yang, J. Qin, Na Lu, Gong Cheng, Na Wu, Yuanlong Pan, Jing Li, Liying Zhu, Xin Wang and 14 more. Nature Communications, 2013

733 citations recorded by the source corpus.

Subfields: Infectious Diseases, Molecular Biology, Molecular Medicine

View Paper: https://www.semanticscholar.org/paper/4e1062ebdc5c60028b7b09e8d92c5fa21651708c

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Citations in the corpus

The healthy human microbiome (2016, background): “Species such as Bacteroides coprocola and Prevotella copri show the greatest differences [73] and strain-level variants in antibiotic resistance genes spanning populations [75].”
Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes (2015, methodology): “Additionally, the Mann–Whitney test was implemented to compare whether the medians of the ARG abundances among various environments are significantly different (Hu et al., 2013).”
Dynamics and Stabilization of the Human Gut Microbiome during the First Year of Life. (2015, background): “Here we observed the presence of antibiotic resistance genes already in the newborn microbiome (Figures S4A–S4C), possibly a consequence of the relative high abundance of Proteobacteria DNA, whose genomes contain high levels of antibiotic resistance genes (Hu et al., 2013; Li et al., 2014).”
High-Specificity Targeted Functional Profiling in Microbial Communities with ShortBRED (2015, background): “As previously observed [17, 18], resistance to the tetracyclines was the most widespread AR function in the human gut microbiome, with at least one tetracycline resistance mechanism being identified in 99% of individuals across all three studies' global populations (97% ribosomal protection; 87% eff”
Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology (2014, methodology): “For example, as was observed with functional selections, we found no significant difference in the percentage of total AR functions encoded in microbial genomes between habitats (Supplementary Figure S4), contradicting previous predictions using pairwise sequence alignment annotation methods (Hu et ”
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights (2016, background): “More recently, shotgun metagenomics [19] provided expanded resolution to the level of microbial species [20–22] and strain [23], to the fungal and viral kingdoms [24], and to the level of individual genes across the metagenome [25,26].”