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Shotgun metagenomic sequencing of the collective genomic information carried across microbial communities is emerging as a powerful approach for monitoring antibiotic resistance in environmental matrices. Metagenomics is advantageous in that known and putative antibiotic resistance genes (ARGs) (i.e., the resistome) can be screened simultaneously without a priori selection of targets. Additionally, as new ARGs are discovered and catalogued, stored sequencing data can be reanalyzed to assess the prevalence of emerging genes or pathogens. However, best practices for metagenomic data generation and processing are needed to support comparability across space and time. To support reproducible downstream analysis, guidance is first needed with respect to sampling design, sample preservation and storage, DNA extraction, library preparation, sequencing depth, and experimental controls. Here we conducted a systematic review to assess current practices for the application of metagenomics for AR profiling of wastewater, recycled water, and surface water and to offer recommendations to support comparability in the collection, production, and analysis of resulting data. Based on integrated analysis of findings and data reported across 95 articles identified, a field to benchtop metagenomic workflow is discussed for optimizing the representativeness and comparability of generated data. Through the reanalysis of 1474 publicly-available metagenomes, appropriate sequencing depths per environment and uniform normalization strategies are provided. Further, there is opportunity to harness the quantitative capacity of metagenomics more overtly through inclusion of sequencing controls. The recommendations will amplify the overall value of the metagenomic data generated to support within and between study comparisons, now and in the future. [ABSTRACT FROM AUTHOR]

Water environments are increasingly recognized as a conduit for the spread of antibiotic resistance, but there is need to standardize antibiotic resistance monitoring protocols to ensure comparability across studies. Quantitative polymerase chain reaction (qPCR) is attractive as a sensitive means of quantifying antibiotic resistance genes (ARGs) and has been applied broadly over the past two decades to various water matrices. QPCR avoids challenges and biases associated with culture-based methods, providing a reproducible and highly sensitive measure of ARGs carried across a bacterial community. However, there are numerous quality assurance and other aspects of protocols that need to be addressed to ensure that measurements are representative and comparable across studies. Here we conducted a critical review to identify gene targets that are most commonly measured by qPCR to quantify antibiotic resistance in surface water, recycled water, and wastewater and to assess corresponding protocols. Identified targets monitored in water samples included sul1, tetA, and intI1, given their abundance and tendency to correlate with anthropogenic inputs, and vanA and blaCTX-M, as more rarely detected, but highly clinically-relevant targets. We identified 117 peer-reviewed studies meeting search criteria for application of these assays to water matrices of interest and systematically assessed the corresponding protocols, including sample collection and concentration, DNA extraction, primer/probe specificity, amplification conditions, amplicon length, PCR inhibition evaluation, and limit of detection/quantification. Gene copy numbers reported across studies were further compared by assay and water matrix. Based on this comprehensive evaluation, we recommend assays, standardized workflows, and reporting for the five target genes. [ABSTRACT FROM AUTHOR]

Pesticides have a pervasive presence in aquatic ecosystems throughout the world. While pesticides are intended to control fungi, insects, and other pests, their mechanisms of action are often not specific enough to prevent unintended effects, such as on non-target microbial populations. Microorganisms, including algae and cyanobacteria, protozoa, aquatic fungi, and bacteria, form the basis of many food webs and are responsible for crucial aspects of biogeochemical cycling; therefore, the potential for pesticides to alter microbial community structures must be understood to preserve ecosystem services. This review examines studies that focused on direct population-level effects and indirect community-level effects of pesticides on microorganisms. Generally, insecticides, herbicides, and fungicides were found to have adverse direct effects on algal and fungal species. Insecticides and fungicides also had deleterious direct effects in the majority of studies examining protozoa species, although herbicides were found to have inconsistent direct effects on protozoans. Our synthesis revealed mixed or no direct effects on bacterial species among all pesticide categories, with results highly dependent on the target species, chemical, and concentration used in the study. Examination of community-level, indirect effects revealed that all pesticide categories had a tendency to reduce higher trophic levels, thereby diminishing top-down pressures and favoring lower trophic levels. Often, indirect effects exerted greater influence than direct effects. However, few studies have been conducted to specifically address community-level effects of pesticides on microorganisms, and further research is necessary to better understand and predict the net effects of pesticides on ecosystem health. [ABSTRACT FROM PUBLISHER]