Global
- 1College of Natural Resources and Environment, Northwest A&F University, Shaanxi, 712100, China
- 2State Key Laboratory of Soil Pollution Control and Safety, Zhejiang University, Hangzhou, 310058, China; College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
- 3College of Natural Resources and Environment, Northwest A&F University, Shaanxi, 712100, China; Corresponding author.
Abstract
Livestock farming environments are major reservoirs of antimicrobial resistance (AMR), yet scalable genetic indicators that quantitatively capture AMR risk remain limited. The class 1 integrase gene intI 1 is a hallmark of class 1 integrons (CL1s), which couple gene capture with horizontal transfer capacity and have emerged as leading candidates. However, their suitability as a direct proxy for livestock-associated AMR risk has not been rigorously tested at scale. Here we show that the abundance of intI 1 functions as a robust quantitative indicator of livestock-associated AMR risk. Using a custom Class 1 Integrase Database expanded by 63.5% and integrating 4017 livestock metagenomes, 9625 livestock-derived isolate genomes, and approximately 1.2 million human clinical isolate genomes, we demonstrate that intI 1 abundance tracks host- and geography-dependent risk patterns, that livestock CL1s carry compact, conserved resistance-cassette arrays matching clinical spectra, and that nearly all are plasmid-borne, with their efficient dissemination facilitated by Tn402, ISCR, and IS110 family elements. A random-forest model trained on these data predicts global intI 1 abundance and associated risk with high accuracy ( R 2 = 0.93), revealing persistent hotspots across Asia, sub-Saharan Africa, and South America over two decades. These findings establish intI 1 as a practical, single-platform proxy that can be incorporated into One Health surveillance and early-warning systems.
BibTeX
@article{yao2026global,
title = {{Global}},
author = {Chengcheng Yao and Liusheng Lei and Wenyan Wang and Lan Jiang and Wenshan Yang and Yaqi Sheng and Zishu Liu and Xun Qian},
journal = {Environmental Science and Ecotechnology},
year = {2026},
month = sep,
doi = {10.1016/j.ese.2026.100759},
url = {https://doi.org/10.1016/j.ese.2026.100759},
note = {CC BY},
}
RIS
TY - JOUR
TI - Global
AU - Chengcheng Yao
AU - Liusheng Lei
AU - Wenyan Wang
AU - Lan Jiang
AU - Wenshan Yang
AU - Yaqi Sheng
AU - Zishu Liu
AU - Xun Qian
PY - 2026
DA - 2026/09/01
JO - Environmental Science and Ecotechnology
DO - 10.1016/j.ese.2026.100759
UR - https://doi.org/10.1016/j.ese.2026.100759
AB - Livestock farming environments are major reservoirs of antimicrobial resistance (AMR), yet scalable genetic indicators that quantitatively capture AMR risk remain limited. The class 1 integrase gene intI 1 is a hallmark of class 1 integrons (CL1s), which couple gene capture with horizontal transfer capacity and have emerged as leading candidates. However, their suitability as a direct proxy for livestock-associated AMR risk has not been rigorously tested at scale. Here we show that the abundance of intI 1 functions as a robust quantitative indicator of livestock-associated AMR risk. Using a custom Class 1 Integrase Database expanded by 63.5% and integrating 4017 livestock metagenomes, 9625 livestock-derived isolate genomes, and approximately 1.2 million human clinical isolate genomes, we demonstrate that intI 1 abundance tracks host- and geography-dependent risk patterns, that livestock CL1s carry compact, conserved resistance-cassette arrays matching clinical spectra, and that nearly all are plasmid-borne, with their efficient dissemination facilitated by Tn402, ISCR, and IS110 family elements. A random-forest model trained on these data predicts global intI 1 abundance and associated risk with high accuracy ( R 2 = 0.93), revealing persistent hotspots across Asia, sub-Saharan Africa, and South America over two decades. These findings establish intI 1 as a practical, single-platform proxy that can be incorporated into One Health surveillance and early-warning systems.
SN - 2666-4984
ER -