Changes in antibiotic resistance of Escherichia coli during the broiler feeding cycle.

The purpose of this study was to investigate the drug-resistant phenotypes and genes of Escherichia coli in animal, environmental, and human samples before and after antibiotic use at a large-scale broiler farm to understand the respective effects on E. coli resistance during the broiler feeding cycle. The antibiotic use per broiler house was 143.04 to 183.50 mg/kg, and included tilmicosin, florfenicol, apramycin, and neomycin. All strains isolated on the first day the broilers arrived (T1; day 1) were antibiotic-resistant bacteria. E. coli strains isolated from animal samples were resistant to ampicillin, tetracycline, and sulfamethoxazole (100%), and those isolated from environmental samples were resistant to 5 different drugs (74.07%, 20 of 27). E. coli strains isolated on the last day before the broilers left (T2; day 47) had a higher resistance rate to florfenicol (100%, 36 of 36) than at T1 (P < 0.05). Multidrug resistance increased from T1 (84.21%, 32 of 38) to T2 (97.22%, 35 of 36). Most strains were resistant to 5 classes of antibiotics, and 2 strains were resistant to 6 classes of antibiotics. Among 13 identified drug resistance genes, 11 and 13 were detected at T1 and T2, respectively. NDM-1 was detected in 4 environmental samples and 1 animal sample. In conclusion, the use of antibiotics during breeding increases E. coli resistance to antibacterial drugs. Drug-resistant bacteria in animals and the environment proliferate during the feeding cycle, leading to the widespread distribution of drug resistance genes and an increase in the overall resistance of bacteria.

Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets.

BACKGROUND: Shotgun metagenomics based on untargeted sequencing can explore the taxonomic profile and the function of unknown microorganisms in samples, and complement the shortage of amplicon sequencing. Binning assembled sequences into individual groups, which represent microbial genomes, is the key step and a major challenge in metagenomic research. Both supervised and unsupervised machine learning methods have been employed in binning. Genome binning belonging to unsupervised method clusters contigs into individual genome bins by machine learning methods without the assistance of any reference databases. So far a lot of genome binning tools have emerged. Evaluating these genome tools is of great significance to microbiological research. In this study, we evaluate 15 genome binning tools containing 12 original binning tools and 3 refining binning tools by comparing the performance of these tools on chicken gut metagenomic datasets and the first CAMI challenge datasets. RESULTS: For chicken gut metagenomic datasets, original genome binner MetaBat, Groopm2 and Autometa performed better than other original binner, and MetaWrap combined the binning results of them generated the most high-quality genome bins. For CAMI datasets, Groopm2 achieved the highest purity (> 0.9) with good completeness (> 0.8), and reconstructed the most high-quality genome bins among original genome binners. Compared with Groopm2, MetaBat2 had similar performance with higher completeness and lower purity. Genome refining binners DASTool predicated the most high-quality genome bins among all genomes binners. Most genome binner performed well for unique strains. Nonetheless, reconstructing common strains still is a substantial challenge for all genome binner. CONCLUSIONS: In conclusion, we tested a set of currently available, state-of-the-art metagenomics hybrid binning tools and provided a guide for selecting tools for metagenomic binning by comparing range of purity, completeness, adjusted rand index, and the number of high-quality reconstructed bins. Furthermore, available information for future binning strategy were concluded.