The prevalence of extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli (E. coli) is increasing rapidly in both hospital environments and animal farms. A lot of animal manure has been directly applied into arable fields in developing countries. But the impact of ESBL-positive bacteria from animal manure on the agricultural fields is sparse, especially in the rural regions of Tai'an, China. Here, we collected 29, 3, and 10 ESBL-producing E. coli from pig manure, compost, and soil samples, respectively. To track ESBL-harboring E. coli from agricultural soil, these isolates of different sources were analyzed with regard to antibiotic resistance profiles, ESBL genes, plasmid replicons, and enterobacterial repetitive intergenic consensus (ERIC)-polymerase chain reaction (PCR) typing. The results showed that all the isolates exhibited multi-drug resistant (MDR). CTX-M gene was the predominant ESBL gene in the isolates from pig farm samples (30/32, 93.8%) and soil samples (7/10, 70.0%), but no SHV gene was detected. Twenty-five isolates contained the IncF-type replicon of plasmid, including 18 strains (18/32, 56.3%) from the pig farm and 7 (7/10, 70.0%) from the soil samples. ERIC-PCR demonstrated that 3 isolates from soil had above 90% genetic similarity with strains from pig farm samples. In conclusion, application of animal manure carrying drug-resistant bacteria on agricultural fields is a likely contributor to antibiotic resistance gene spread.

The dissemination of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (E. coli) from food-producing animals to the surrounding environment has attracted much attention. To determine the emissions of ESBL-producing E. coli from pig farms to the surrounding environment, fecal and environmental samples from six pig farms were collected. In total, 119 ESBL-producing E. coli were isolated from feces, air samples, water, sludge and soil samples. Antibiotic susceptibility testing showed that the ESBL-producing isolates were resistant to multiple antibiotics and isolates of different origin within the same farm showed similar resistance phenotypes. Both CTX-M and TEM ESBL-encoding genes were detected in these isolates. CTX-M-14 and CTX-M-15 were the predominant ESBL genes identified. ESBL producers from feces and environmental samples within the same farm carried similar CTX-M types. The results indicated that the ESBL-producing E. coli carrying multidrug resistance could readily disseminate to the surrounding environment.

The dissemination of drug-resistant bacteria from animal farms to aquatic environments can pose a potential threat to public health. In this study, antimicrobial resistance, resistance genes, and genetic similarity of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli of different origins (chicken feces and upstream and downstream river waters) were analyzed to track the spread of drug-resistant bacteria of animals. The results showed that a total of 29 ESBL-producing E. coli were obtained from 258 samples, and isolation rates of the ESBL-producing E. coli from chicken feces and upstream and downstream waters were 10.7% (16/150), 3.7% (1/27), and 14.8% (12/81), respectively. The ESBL-producing E. coli from upstream water was resistant to 7 antibiotics, but isolates from feces and downstream water had a higher resistance rate. In 29 ESBL-producing E. coli, the most common gene was CTX-M and the SHV gene was not detected. Five ESBL-producing isolates from downstream water showed >90% similarity with the fecal isolates, while the only one isolate from upstream water had <70% similarity with fecal isolates. The results suggest that animal farms' effluent, especially the untreated wastewater, could contribute to the spread of resistance genes.

Background Rabbit wool traits are important in fiber production and for model organism research on hair growth, but their genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a breed well-known for the quality of its wool. Considering the cost to generate population-scale sequence data and the biased detection of variants using chip data, developing an effective genotyping strategy using low-coverage whole-genome sequencing (LCS) data is necessary to conduct genetic analyses. Results Different genotype imputation strategies (BaseVar + STITCH, Bcftools + Beagle4, and GATK + Beagle5), sequencing coverages (0.1X, 0.5X, 1.0X, 1.5X, and 2.0X), and sample sizes (100, 200, 300, 400, 500, and 600) were compared. Our results showed that using BaseVar + STITCH at a sequencing depth of 1.0X with a sample size larger than 300 resulted in the highest genotyping accuracy, with a genotype concordance higher than 98.8% and genotype accuracy higher than 0.97. We performed multivariate genome-wide association studies (GWAS), followed by conditional GWAS and estimation of the confidence intervals of quantitative trait loci (QTL) to investigate the genetic architecture of wool traits. Six QTL were detected, which explained 0.4 to 7.5% of the phenotypic variation. Gene-level mapping identified the fibroblast growth factor 10 (FGF10) gene as associated with fiber growth and diameter, which agrees with previous results from functional data analyses on the FGF gene family in other species, and is relevant for wool rabbit breeding. Conclusions We suggest that LCS followed by imputation can be a cost-effective alternative to array and high-depth sequencing for assessing common variants. GWAS combined with LCS can identify new QTL and candidate genes that are associated with quantitative traits. This study provides a cost-effective and powerful method for investigating the genetic architecture of complex traits, which will be useful for genomic breeding applications.

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Duck hepatitis A virus 1 (DHAV-1) is a highly contagious etiological agent that causes acute hepatitis in young ducklings. MicroRNAs (miRNAs) play important regulatory roles in response to pathogens. However, the interplay between DHAV-1 infection and miRNAs remains ambiguous. We characterized and compared miRNA and mRNA expression profiles in duck embryo fibroblasts cells (DEFs) infected with DHAV-1. In total, 36 and 96 differentially expressed (DE) miRNAs, and 4110 and 2595 DE mRNAs, were identified at 12 and 24 h after infection. In particular, 126 and 275 miRNA–mRNA pairs with a negative correlation were chosen to construct an interaction network. Subsequently, we identified the functional annotation of DE mRNAs and target genes of DE miRNAs enriched in diverse Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, which may be important for virus resistance, cell proliferation, and metabolism. Moreover, upregulated miR-222a could negatively regulate DHAV-1 replication in DEFs and downregulate integrin subunit beta 3 (ITGB3) expression by targeting the 3′ untranslated region (3′UTR), indicating that miR-222a may modulate DHAV-1 replication via interaction with ITGB3. In conclusion, the results reveal changes of mRNAs and miRNAs during DHAV-1 infection and suggest miR-222a as an antiviral factor against DHAV-1.

The Angora rabbit, a well-known breed for fiber production, has been undergoing traditional breeding programs relying mainly on phenotypes. Genomic selection (GS) uses genomic information and promises to accelerate genetic gain. Practically, to implement GS in Angora rabbit breeding, it is necessary to evaluate different marker densities and GS models to develop suitable strategies for an optimized breeding pipeline. Considering a lack in microarray, low-coverage sequencing combined with genotype imputation was used to boost the number of SNPs across the rabbit genome. Here, in a population of 629 Angora rabbits, a total of 18,577,154 high-quality SNPs were imputed (imputation accuracy above 98%) based on low-coverage sequencing of 3.84X genomic coverage, and wool traits and body weight were measured at 70, 140 and 210 days of age. From the original markers, 0.5K, 1K, 3K, 5K, 10K, 50K, 100K, 500K, 1M and 2M were randomly selected and evaluated, resulting in 50K markers as the baseline for the heritability estimation and genomic prediction. Comparing to the GS performance of single-trait models, the prediction accuracy of nearly all traits could be improved by multi-trait models, which might because multiple-trait models used information from genetically correlated traits. Furthermore, we observed high significant negative correlation between the increased prediction accuracy from single-trait to multiple-trait models and estimated heritability. The results indicated that low-heritability traits could borrow more information from correlated traits and hence achieve higher prediction accuracy. The research first reported heritability estimation in rabbits by using genome-wide markers, and provided 50K as an optimal marker density for further microarray design, genetic evaluation and genomic selection in Angora rabbits. We expect that the work could provide strategies for GS in early selection, and optimize breeding programs in rabbits.

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