Antimicrobial resistant (AMR) Escherichia coli (E. coli) isolated from animals may lead to antibiotic treatment failure and economic losses to farmers. The co-existence of antimicrobial resistant genes (ARGs) in the same isolate presents a major challenge for the prevention and control of infection in multidrug-resistant (MDR) Gram-negative organisms. There have been a lot of studies on the antibiotic resistance of E. coli in livestock and poultry, but few of them have focused on clinical pathogens. Objective: The aim of this study was to explore the genetic characteristics, co-occurrence, and correlations between ARGs of E. coli isolated from the pathological tissues of livestock and poultry in Shandong Province, East China during 2015–2020. Methods: A total of 158 E. coli strains were collected and subjected to antimicrobial susceptibility testing and sequencing by whole-genome Next Generation Sequencing (NGS). Results: MDR strains accounted for 46.20% of the 158 E. coli strains with the highest resistant rate of ciprofloxacin (71.52%). In addition, strains with bla_NDM-5/mcr-1.1 and mcr-1.1/mcr-3.24 were found in chickens, while three strains with Tet(X4) were found in pigs. In addition, the most common serotypes detected were the O serotype (76/158) and H serotype (36/158). Moreover, seventy-one STs were found and the most common STs were ST10 (6.33%), ST155 (6.33%), and ST101 (5.69%). The genetic environment analysis of the phylogroups revealed that E. coli belonging to phylogroup B1, phylogroup A, and phylogroup C constituted 39.87%, 27.85%, and 15.19%, respectively. Through the correlation analysis, mcr genes were observed to have certain relationships with ARGS such as bla_TEM, floR, catA/B, and oqx. Conclusions: This study demonstrates the high prevalence and gene diversity of MDR E. coli isolated from a clinic in Shandong Province, East China. We predicted the transmission risk of animal-borne Tet(X4)-bearing and mcr-harboring E. coli to public health and provided insight into the relationship of co-existence or co-transfer between mcr with ARGS. These relationships present a great challenge for the infection control of MDR Gram-negative organisms.

Chronic bee paralysis virus (CBPV) is a serious pathogen of honey bees and has expanded across major beekeeping regions in recent years. Understanding viral connectivity between apiaries is therefore essential for disease control. However, consensus-based phylogenetic analysis has limited power to resolve epidemiological links between closely managed colonies. To address this, we applied virome sequencing and intra-host single-nucleotide variant (iSNV) profiling to investigate shared variation and possible viral exchange between two apiaries across two consecutive years. One apiary was translocated ~80 km away each spring and returned each winter to within ~1 km of a stationary apiary. This seasonal translocation mirrors the long-distance movements and winter aggregations common in commercial beekeeping operations in China and the United States, providing a natural microcosm of the migratory practices that drive large-scale viral connectivity. Parallel 16S rRNA gene profiling of the midgut microbiota revealed no detectable association between bacterial community composition and CBPV infection status. iSNV analysis identified extensive shared low-frequency variants between the two apiaries across both years, including formerly private variants that became detectable in both sites after winter co-location. In contrast, consensus phylogenies placed two co-located apiaries in separate clades, suggesting that standard surveillance may underestimate cryptic viral connectivity. We cannot rule out the possibility that the observed shared iSNVs reflect shared ancestry, earlier contact, or common external sources. Furthermore, the direction of viral exchange cannot be determined from observational data with limited replication and uneven sequencing depth. These results show that finer-scale iSNV profiling highlights patterns of viral connectivity in managed bee systems, providing a model for high-resolution surveillance of RNA viruses in mobile managed host populations where conventional methods underestimate connectivity, though validation in controlled experimental settings is needed to establish causal transmission pathways.