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Antibiotics have been widely used in animal husbandry, which leads to high risk of food-borne transfer of antibiotic resistance genes (ARGs). The present study investigated the distribution of β -lactamase resistance genes (β -RGs) on dairy farm in the Songnen Plain of western Heilongjiang Province, China, to provide mechanistic insights into food-borne transmission of β -RGs through "meal-to-milk" chain under practically relevant circumstances. The results demonstrated that the abundance of β -RGs (91%) was much higher than that of other ARGs in the livestock farms. The blaTEM exhibited the content as high as 94.55% among all ARGs, and higher than 98% blaTEM was detected in meal, water and milk sample. The metagenomic taxonomy analysis indicated that the blaTEM should be carried by tnpA-04 (7.04%) and tnpA-03 (1.48%) hosted in Pseudomonas genus (15.36%) and Pantoea (29.02%) genus. Both tnpA-04 and tnpA-03 in the milk sample were identified to be the key mobile genetic elements (MGEs) responsible for transferring blaTEM along the "meal-manure-soil-surface water-milk" chain. The ARGs transfer across ecological boundaries underscored the need to evaluate potential dissemination of high-risk Proteobacteria and Bacteroidetes carried by humans and animals. They were capable of producing expanded-spectrum β -lactamases (ESBLs) and destroying commonly used antibiotics, leading to possible risk of food-borne horizontal transmission of ARGs. This study not only has important environmental implications for identifying the pathway for ARGs transfer, but also highlights the demand for appropriate policy toward safe regulation of dairy farm and husbandry products. [Display omitted] • β -lactamase resistance genes (β -RGs) are prevalent and much higher than other antibiotic resistance genes (ARGs). • Meal-to-milk transfer of β -RGs occurs on diary farm. • tnpA-04 and tnpA-03 in milk are the key MGEs to β -RGs transfer. • Proteobacteria and Bacteroidetes carried by humans and animals have high risk. [ABSTRACT FROM AUTHOR]

Antibiotics have been widely used in animal husbandry, which leads to high risk of food-borne transfer of antibiotic resistance genes (ARGs). The present study investigated the distribution of β -lactamase resistance genes (β -RGs) on dairy farm in the Songnen Plain of western Heilongjiang Province, China, to provide mechanistic insights into food-borne transmission of β -RGs through "meal-to-milk" chain under practically relevant circumstances. The results demonstrated that the abundance of β -RGs (91%) was much higher than that of other ARGs in the livestock farms. The blaTEM exhibited the content as high as 94.55% among all ARGs, and higher than 98% blaTEM was detected in meal, water and milk sample. The metagenomic taxonomy analysis indicated that the blaTEM should be carried by tnpA-04 (7.04%) and tnpA-03 (1.48%) hosted in Pseudomonas genus (15.36%) and Pantoea (29.02%) genus. Both tnpA-04 and tnpA-03 in the milk sample were identified to be the key mobile genetic elements (MGEs) responsible for transferring blaTEM along the "meal-manure-soil-surface water-milk" chain. The ARGs transfer across ecological boundaries underscored the need to evaluate potential dissemination of high-risk Proteobacteria and Bacteroidetes carried by humans and animals. They were capable of producing expanded-spectrum β -lactamases (ESBLs) and destroying commonly used antibiotics, leading to possible risk of food-borne horizontal transmission of ARGs. This study not only has important environmental implications for identifying the pathway for ARGs transfer, but also highlights the demand for appropriate policy toward safe regulation of dairy farm and husbandry products. [Display omitted] • β -lactamase resistance genes (β -RGs) are prevalent and much higher than other antibiotic resistance genes (ARGs). • Meal-to-milk transfer of β -RGs occurs on diary farm. • tnpA-04 and tnpA-03 in milk are the key MGEs to β -RGs transfer. • Proteobacteria and Bacteroidetes carried by humans and animals have high risk. [ABSTRACT FROM AUTHOR]

To accurately visualize the spatial distribution of heavy metal pollution and provide basic information on soil remediation in dairy farm, Geographic Information System (GIS) is used for optimization of sample collection and data analysis. Based on GIS technology, dairy manure, 10 cm-depth surface soil, 50 cm-depth sub soil, and surface water samples were collected from dairy farm in Dulbert Mongolian Autonomous County, Daqing City, Heilongjiang Province in China. The spatial distribution and assessment of heavy metals were performed by using GIS inverse distance weighted interpolation and pollution index method. The single factor pollution index value of As element in the soil was found to indicate the class of extreme contamination, whereas Ni in both surface water inside and outside the farm, and Sb in the cow drinking water were assigned to the level of moderate contamination. The comprehensive pollution index implied serious contamination for soil samples, slight contamination for water samples and safety for manure samples, respectively. Comprehensive score for heavy metal elements followed the orders of As>Zn>Cr>Ni>Cu>Pb>Cd>Hg. The horizontal pollution that mainly occurred in the middle and east regions was increased from north to south, and west to east district. Historically, the dairy farm belonged to heavily polluted saline-alkali soil, where the heavy metals might enter the food chain through transportation from soil to water, the cows, and eventually to the milk and human body. Visualizing spatial distribution of heavy metal contamination by using GIS technology will be of significance to provide useful information for soil remediation of dairy farm. Image 1 • Black soil in North China is precious mollisol in the world, to protect heavy metal contamination entry food chain. • GIS used to intuitively obtain pollutant location and concentration, which was important to precision soil remediation. • As in the soil was extremely contaminated class, soil was seriously polluted, water was slightly polluted level. • Accurate pollutant analysis is Abdujappar et al., 2018; Bhattacharya et al., 2007; Cheng et al., 2007; China National Environmental Monitoring Center, 1990; Clemens et al., 2018; Cui et al., 2018; Draggan and Antimony, 2008; Du, 2018; Förstner et al., 1990; Gu et al., 2014; Hou et al., 2017; Huang et al., 2016, 2018; Jia et al., 2016; Li et al., 2018; Lin et al., 2017; Lucas-Borja et al., 2019; Nanos and Martín, 2012; Salman et al., 2019; Stafilov et al., 2010; Suriyagoda et al., 2018; Tang and Han, 2017; Thomas et al., 2018; Wilding, 1985; Yang et al., 2019; Ye et al., 2019; Yu et al., 2018; Yu and Zhang, 2003; P. Y. Zhang et al., 2018; X. L. Zhang et al., 2018; Y. Zhang et al., 2018; Zhu et al., 2019 important for correct technology choosing, save the time and cost of soil remediation. [ABSTRACT FROM AUTHOR]