Bacterial diversity and the antimicrobial resistome in the southwestern highlands of Saudi Arabia

High-throughput profiling of antibiotic resistance genes in the Yellow River of Henan Province, China

Profiling antibiotic resistance genes (ARGs) in the Yellow River of China's Henan Province is essential for understanding the health risks of antibiotic resistance. The profiling of ARGs was investigated using high-throughput qPCR from water samples in seven representative regions of the Yellow River. The absolute and relative abundances of ARGs and moble genetic elements (MGEs) were higher in summer than in winter (ANOVA, p < 0.001). The diversity and abundance of ARGs were higher in the Yellow River samples from PY and KF than the other sites. Temperature (r = 0.470 ~ 0.805, p < 0.05) and precipitation (r = 0.492 ~ 0.815, p < 0.05) positively influenced the ARGs, while pH had a negative effect (r = - 0.462 ~ - 0.849, p < 0.05). Network analysis indicated that the pathogenic bacteria Rahnella, Bacillus, and Shewanella were the possible hub hosts of ARGs, and tnpA1 was the potential MGE hub. These findings provide insights into the factors influencing ARG dynamics and the complex interaction among the MGEs, pathogenic bacteria and environmental parameters in enriching ARGs in the Yellow River of Henan Province.

Antibiotic resistance genes prevalence prediction and interpretation in beaches affected by urban wastewater discharge

Background

The annual death toll of over 1.2 million worldwide is attributed to infections caused by resistant bacteria, driven by the significant impact of antibiotic misuse and overuse in spreading these bacteria and their associated antibiotic resistance genes (ARGs). While limited data suggest the presence of ARGs in beach environments, efficient prediction tools are needed for monitoring and detecting ARGs to ensure public health safety. This study aims to develop interpretable machine learning methods for predicting ARGs in beach waters, addressing the challenge of black-box models and enhancing our understanding of their internal mechanisms.

Methods

In this study, we systematically collected beach water samples and subsequently isolated bacteria from these samples using various differential and selective media supplemented with different antibiotics. Resistance profiles of bacteria were determined by using Kirby-Bauer disk diffusion method. Further, ARGs were enumerated by using the quantitative polymerase chain reaction (qPCR) to detect and quantify ARGs. The obtained qPCR data and hydro-meteorological were used to create an ML model with high prediction performance and we further used two explainable artificial intelligence (xAI) model-agnostic interpretation methods to describe the internal behavior of ML model.

Results

Using qPCR, we detected blaCTX-M, blaNDM, blaCMY, blaOXA, blatetX, blasul1, and blaaac(6'-Ib-cr) in the beach waters. Further, we developed ML prediction models for blaaac(6'-Ib-cr), blasul1, and blatetX using the hydro-metrological and qPCR-derived data and the models demonstrated strong performance, with R2 values of 0.957, 0.997, and 0.976, respectively.

Conclusions

Our findings show that environmental factors, such as water temperature, precipitation, and tide, are among the important predictors of the abundance of resistance genes at beaches.

Impacts of soybean agriculture on the resistome of the Amazonian soil

The soils of the Amazon are complex environments with different organisms cohabiting in continuous adaptation processes; this changes significantly when these environments are modified for the development of agricultural activities that alter the chemical, macro, and microbiological compositions. The metagenomic variations and the levels of the environmental impact of four different soil samples from the Amazon region were evaluated, emphasizing the resistome. Soil samples from the organic phase from the different forest, pasture, and transgenic soybean monocultures of 2–14 years old were collected in triplicate at each site. The samples were divided into two groups, and one group was pre-treated to obtain genetic material to perform sequencing for metagenomic analysis; another group carried out the chemical characterization of the soil, determining the pH, the content of cations, and heavy metals; these were carried out in addition to identifying with different databases the components of the microbiological communities, functional genes, antibiotic and biocide resistance genes. A greater diversity of antibiotic resistance genes was observed in the forest soil. In contrast, in monoculture soils, a large number of biocide resistance genes were evidenced, highlighting the diversity and abundance of crop soils, which showed better resistance to heavy metals than other compounds, with a possible dominance of resistance to iron due to the presence of the acn gene. For up to 600 different genes for resistance to antibiotics and 256 genes for biocides were identified, most of which were for heavy metals. The most prevalent was resistance to tetracycline, cephalosporin, penam, fluoroquinolone, chloramphenicol, carbapenem, macrolide, and aminoglycoside, providing evidence for the co-selection of these resistance genes in different soils. Furthermore, the influence of vegetation cover on the forest floor was notable as a protective factor against the impact of human contamination. Regarding chemical characterization, the presence of heavy metals, different stress response mechanisms in monoculture soils, and the abundance of mobile genetic elements in crop and pasture soils stand out. The elimination of the forest increases the diversity of genes for resistance to biocides, favoring the selection of genes for resistance to antibiotics in soils.