Effects of chronic exposure of antibiotics on microbial community structure and functions in hyporheic zone sediments

Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.

Antibiotics are widely used in humans and animals, but there is a big concern about their negative impacts on ecosystem and human health after use. So far there is a lack of information on emission inventory and environmental fate of antibiotics in China. We studied national consumption, emissions, and multimedia fate of 36 frequently detected antibiotics in China by market survey, data analysis, and level III fugacity modeling tools. Based on our survey, the total usage for the 36 chemicals was 92700 tons in 2013, an estimated 54000 tons of the antibiotics was excreted by human and animals, and eventually 53800 tons of them entered into the receiving environment following various wastewater treatments. The fugacity model successfully predicted environmental concentrations (PECs) in all 58 river basins of China, which are comparable to the reported measured environmental concentrations (MECs) available in some basins. The bacterial resistance rates in the hospitals and aquatic environments were found to be related to the PECs and antibiotic usages, especially for those antibiotics used in the most recent period. This is the first comprehensive study which demonstrates an alarming usage and emission of various antibiotics in China.

Exploring large environmental datasets generated by high-throughput DNA sequencing technologies requires new analytical approaches to move beyond the basic inventory descriptions of the composition and diversity of natural microbial communities. In order to investigate potential interactions between microbial taxa, network analysis of significant taxon co-occurrence patterns may help to decipher the structure of complex microbial communities across spatial or temporal gradients. Here, we calculated associations between microbial taxa and applied network analysis approaches to a 16S rRNA gene barcoded pyrosequencing dataset containing >160 000 bacterial and archaeal sequences from 151 soil samples from a broad range of ecosystem types. We described the topology of the resulting network and defined operational taxonomic unit categories based on abundance and occupancy (that is, habitat generalists and habitat specialists). Co-occurrence patterns were readily revealed, including general non-random association, common life history strategies at broad taxonomic levels and unexpected relationships between community members. Overall, we demonstrated the potential of exploring inter-taxa correlations to gain a more integrated understanding of microbial community structure and the ecological rules guiding community assembly.