Coexistence of antibiotic resistance genes, fecal bacteria, and potential pathogens in anthropogenically impacted water

Microbiome-based classification models for fresh produce safety and quality evaluation

Small sample sizes and loss of sequencing reads during the microbiome data preprocessing can limit the statistical power of differentiating fresh produce phenotypes and prevent the detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored a machine learning-based k-mer hash analysis strategy to identify DNA signatures predictive of produce safety (PS) and produce quality (PQ) and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step and ASV-based taxonomy strategy. Random forest-based classifiers for PS and PQ using 7-mer hash data sets had significantly higher classification accuracy than those using the ASV data sets. We also demonstrated that the proposed combination of integrating multiple data sets and leveraging a 7-mer hash strategy leads to better classification performance for PS and PQ compared to the ASV method but presents lower PS classification accuracy compared to the feature-selected ASV-based taxonomy strategy. Due to the current limitation of generating taxonomy using the 7-mer hash strategy, the ASV-based taxonomy strategy with remarkably less computing time and memory usage is more efficient for PS and PQ classification and applicable for important taxa identification. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing data sets for the application of machine learning in the area of microbial safety and quality of food.

IMPORTANCE

Identification of generalizable indicators for produce safety (PS) and produce quality (PQ) improves the detection of produce contamination and quality decline. However, effective sequencing read loss during microbiome data preprocessing and the limited sample size of individual studies restrain statistical power to identify important features contributing to differentiating PS and PQ phenotypes. We applied machine learning-based models using individual and integrated k-mer hash and amplicon sequence variant (ASV) data sets for PS and PQ classification and evaluated their classification performance and found that random forest (RF)-based models using integrated 7-mer hash data sets achieved significantly higher PS and PQ classification accuracy. Due to the limitation of taxonomic analysis for the 7-mer hash, we also developed RF-based models using feature-selected ASV-based taxonomic data sets, which performed better PS classification than those using the integrated 7-mer hash data set. The RF feature selection method identified 480 PS indicators and 263 PQ indicators with a positive contribution to the PS and PQ classification.

Mapping the spread and mobility of antibiotic resistance in wastewater due to COVID-19 surge

Large amounts of antibiotics have been discharged into wastewater during the COVID-19 pandemic due to overuse and misuse of antibiotics to treat patients. Wastewater-based surveillance can be used as an early warning for antibiotic resistance (AR) emergence. The present study analyzed municipal wastewater corresponding to the major pandemic waves (WW1, WW2, and WW3) in India along with hospital wastewater (Ho) taken as a benchmark for AR. Commonly prescribed antibiotics during a pandemic, azithromycin and cefixime residues, were found in the range of 2.1-2.6 μg/L in Ho and WW2. Total residual antibiotic concentration was less in WW2; however, the total antibiotic resistance gene (ARG) count was 1065.6 ppm compared to 85.2 ppm in Ho. Metagenome and RT-qPCR analysis indicated a positive correlation between antibiotics and non-corresponding ARGs (blaOXA, aadA, cat, aph3, and ere), where 7.2-7.5% was carried by plasmid in the bacterial community of WW1 and WW2. Moreover, as the abundance of the dfrA and int1 genes varied most among municipal wastewater, they can be suggested as AR markers for the pandemic. The common pathogens Streptococcus, Escherichia, Shigella, and Aeromonas were putative ARG hosts in metagenome-assembled genomes. The ARG profile and antibiotic levels varied between municipal wastewaters but were fairly similar for WW2 and Ho, suggesting the impact of the pandemic in shaping the resistome pattern. The study provides insights into the resistome dynamic, AR markers, and host-ARG association in wastewater during the COVID-19 surge. Continued surveillance and identification of intervention points for AR beyond the pandemic are essential to curbing the environmental spread of ARGs in the near future.

Effect of composted pig manure, biochar, and their combination on antibiotic resistome dissipation in swine wastewater-treated soil

The prevalence of antibiotic resistance genes (ARGs), owing to irrigation using untreated swine wastewater, in vegetable-cultivated soils around swine farms poses severe threats to human health. Furthermore, at the field scale, the remediation of such soils is still challenging. Therefore, here, we performed field-scale experiments involving the cultivation of Brassica pekinensis in a swine wastewater-treated soil amended with composted pig manure, biochar, or their combination. Specifically, the ARG and mobile genetic element (MGE) profiles of bulk soil (BS), rhizosphere soil (RS), and root endophyte (RE) samples were examined using high-throughput quantitative polymerase chain reaction. In total, 117 ARGs and 22 MGEs were detected. Moreover, we observed that soil amendment using composted pig manure, biochar, or their combination decreased the absolute abundance of ARGs in BS and RE after 90 days of treatment. However, the decrease in the abundance of ARGs in RS was not significant. We also observed that the manure and biochar co-application showed a minimal synergistic effect. To clarify this observation, we performed network and Spearman correlation analyses and used structure equation models to explore the correlations among ARGs, MGEs, bacterial composition, and soil properties. The results revealed that the soil amendments reduced the abundances of MGEs and potential ARG-carrying bacteria. Additionally, weakened horizontal gene transfer was responsible for the dissipation of ARGs. Thus, our results indicate that composted manure application, with or without biochar, is a useful strategy for soil nutrient supplementation and alleviating farmland ARG pollution, providing a justification for using an alternative to the common agricultural practice of treating the soil using only untreated swine wastewater. Additionally, our results are important in the context of soil health for sustainable agriculture.