Coexistence of antibiotic resistance genes, fecal bacteria, and potential pathogens in anthropogenically impacted water.

Microbial indicators are often used to monitor microbial safety of aquatic environments. However, information regarding the correlation between microbial indicators and ecotoxicological factors such as potential pathogens and antibiotic resistance genes (ARGs) in anthropogenically impacted waters remains highly limited. Here, we investigated the bacterial community composition, potential pathogens, ARGs diversity, ARG hosts, and horizontal gene transfer (HGT) potential in urban river and wastewater samples from Chaohu Lake Basin using 16S rRNA and metagenomic sequencing. The composition of the microbial community and potential pathogens differed significantly in wastewater and river water samples, and the total relative abundance of fecal indicator bacteria was positively correlated with the total relative abundance of potential pathogens (p < 0.001 and Pearson's r = 0.758). Network analysis indicated that partial ARG subtypes such as dfrE, sul2, and PmrE were significantly correlated with indicator bacteria (p < 0.05 and Pearson's r > 0.6). Notably, Klebsiella was the indicator bacteria significantly correlated with 4 potential pathogens and 14 ARG subtypes. ARGs coexisting with mobile gene elements were mainly found in Thauera, Pseudomonas, Escherichia, and Acinetobacter. Next-generation sequencing (NGS) can be used to conduct preliminary surveys of environmental samples to access potential health risks, thereby facilitating water resources management.

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.

Simultaneous differentiation and diagnosis of goose parvovirus and astrovirus in clinical samples with duplex SYBR Green I real-time PCR.

Two pairs of primers were designed to bind conserved genomic regions of goose parvovirus (GPV) and goose astrovirus (GAstV) to establish a simple, sensitive, and highly specific duplex quantitative PCR (qPCR) method to simultaneously detect the two viruses. The duplex qPCR can distinguish GPV (melting point: 82.1 °C) and GAstV (melting point: 79.8 °C) by the peaks of their individual melting curves. Mixed testing with other waterfowl viruses produced no nonspecific peaks. The established standard curves showed good linear relationships (R2 > 0.997) and the limits of detection (LOD) for GPV and GAstV were 5.74 × 101 and 6.58 × 101 copies/µL, respectively. Both intra- and inter-assay coefficients of variation were <2%, indicating that the method has good repeatability. Twenty tissue samples from diseased geese were examined with the duplex qPCR assay and conventional PCR. Duplex qPCR showed positive rates of 25% for GPV and 45% for GAstV, and the positive rate for GPV and GAstV coinfection was 15%, slightly higher than the results for conventional PCR. These results indicated that this duplex qPCR method is highly sensitive, specific, and reproducible, and is suitable for epidemiological studies to effectively control the transmission of GPV and GAstV.

A TaqMan-based quantitative real-time PCR assay for identification of the goose circovirus.

Goose circovirus (GoCV) is a potential immunosuppressive virus that poses a great hazard to the goose industry and has been shown to be widely distributed throughout China. We have established a fast, sensitive and highly specific TaqMan real-time quantitative PCR detection method for this virus. Specific primers and probes were designed against the conserved regions of the genomic GoCV Rep gene. The results showed that the assay was highly specific and sensitive for GoCV and did not cross-react with other non-targeted waterfowl viruses. The established method will be helpful for epidemiological detection and may be effective in the prevention and control of the disease.