Benjamin C. Davis

Background: Metagenomic next-generation sequencing (mNGS) enables broad, untargeted detection of pathogens and microbial signals across complex sample types. However, the diversity of operational contexts, from regulatory enforcement to exploratory discovery, challenges the defining of analytical or interpretive standards appropriate across all applications. Variability in laboratory practices, bioinformatic methods, and reporting conventions continues to limit consistency and decision-maker confidence in mNGS results. Objectives: We introduce Structured Framework for Reporting, Assessment, and Translational Utility of Metagenomics (STRATUM), a use-case–stratified framework that aligns QA, metadata reporting, and interpretive standards with the consequence and intended use of metagenomic sequencing outputs. Methods: STRATUM is organized around five representative biosurveillance use cases spanning public health, food safety, environmental monitoring, synthetic biology detection, and national security. A three-tier interpretive model calibrates analytical rigor, validation expectations, and reporting requirements to decision consequence; from high-consequence regulatory and clinical determinations (Tier 1), through operational surveillance (Tier 2), to exploratory and hypothesis-generating contexts (Tier 3). Results: The framework provides graduated guidance across key domains including sample preparation, sequencing design, controls and contamination governance, reference database curation, bioinformatics reproducibility, and multi-factor signal validation. Cross-cutting principles include explicit documentation of evidentiary bases, transparency in database and pipeline provenance, and defined escalation pathways when results transition between interpretive tiers. Conclusion: Realizing the operational potential of mNGS requires evidentiary standards responsive to decision context rather than fixed across applications. STRATUM offers a consequence-tiered model for quality and reporting in applied metagenomics, supporting reproducible, transparent, and defensible sequencing-based surveillance across public health and biodefense domains. Highlights: Introduction of STRATUM, a consequence-tiered evidentiary framework for applied metagenomics. Structured calibration of analytical rigor and reporting standards to decision context. Cross-use-case guidance spanning surveillance, regulatory, and national security applications.

Metagenomics holds promise as a comprehensive, nontargeted tool for environmental monitoring. However, one key limitation is that the quantitative capacity of metagenomics is not well-defined. Here, we demonstrated a quantitative metagenomic technique and benchmarked the approach for wastewater-based surveillance of antibiotic resistance genes. To assess the variability of low-abundance oligonucleotide detection across sample matrices, we spiked DNA reference standards (meta sequins) into replicate wastewater DNA extracts at logarithmically decreasing mass-to-mass percentages (m/m%). Meta sequin ladders exhibited strong linearity at input concentrations as low as 2 × 10^-3 m/m% (R² > 0.95), with little to no reference length or GC bias. At a mean sequencing depth of 94 Gb, the limits of quantification (LoQ) and detection were calculated to be 1.3 × 10³ and 1 gene copy per μL DNA extract, respectively. In wastewater influent, activated sludge, and secondary effluent samples, 27.3, 47.7, and 44.3% of detected genes were ≤LoQ, respectively. Volumetric gene concentrations and log removal values were statistically equivalent between quantitative metagenomics and ddPCR for 16S rRNA, intI1, sul1, CTX-M-1, and vanA. The quantitative metagenomics benchmark here is a key step toward establishing metagenomics for high-throughput, nontargeted, and quantitative environmental monitoring.

Antimicrobial resistance (AMR) has long been framed as an epidemiological and public health concern. Its impacts on the environment are unclear. Yet, the basis for AMR is altered cell physiology. Just as this affects how microbes interact with antimicrobials, it can also affect how they interact with their own species, other species, and their non-living environment. Moreover, if the microbes are globally notorious for causing landscape-level environmental issues, then these effects could alter biodiversity and ecosystem function on a grand scale. To investigate these possibilities, we compiled peer-reviewed literature from the past 20 years regarding AMR in toxic freshwater cyanobacterial harmful algal blooms (HABs). We examined it for evidence of AMR affecting HAB frequency, severity, or persistence. Although no study within our scope was explicitly designed to address the question, multiple studies reported AMR-associated changes in HAB-forming cyanobacteria (and co-occurring microbes) that pertained directly to HAB timing, toxicity, and phase, as well as to the dynamics of HAB-afflicted aquatic food webs. These findings highlight the potential for AMR to have far-reaching environmental impacts (including the loss of biodiversity and ecosystem function) and bring into focus the importance of confronting complex interrelated issues such as AMR and HABs in concert, with interdisciplinary tools and perspectives.

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. Graphical Abstract

Antimicrobial resistance (AMR) is a grand societal challenge with important dimensions in the water environment that contribute to its evolution and spread. Environmental monitoring could provide vital information for mitigating the spread of AMR; this includes assessing antibiotic resistance genes (ARGs) circulating among human populations, identifying key hotspots for evolution and dissemination of resistance, informing epidemiological and human health risk assessment models, and quantifying removal efficiencies by domestic wastewater infrastructure. However, standardized methods for monitoring AMR in the water environment will be vital to producing the comparable data sets needed to address such questions. Here we sought to establish scientific consensus on a framework for such standardization, evaluating the state of the science and practice of AMR monitoring of wastewater, recycled water, and surface water, through a literature review, survey, and workshop leveraging the expertise of academic, governmental, consulting, and water utility professionals.

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. Graphical abstract

Comprehensive surveillance approaches are needed to assess sources, clinical relevance, and mobility of antibiotic resistance genes (ARGs) in watersheds. Here, we examined metrics derived from shotgun metagenomic sequencing and relationship to human fecal markers (HFMs; crAssphage, enterococci) and anthropogenic antibiotic resistance markers (AARMs; intI1, sul1) in three distinct Puerto Rican watersheds as a function of adjacent land use and wastewater treatment plant (WWTP) input 6 months after Hurricane Maria, a category V storm. Relative abundance and diversity of total ARGs increased markedly downstream of WWTP inputs, with ARGs unique to WWTP and WWTP-impacted river samples predominantly belonging to the aminoglycoside and β-lactam resistance classes. WWTP and other anthropogenic inputs were similarly associated with elevated resistome risk scores and mobility incidence (M%). Contig analysis indicated a wide variety of mobile β-lactam ARGs associated with pathogens downstream of WWTP discharge that were consistent with regional clinical concern, e.g., Klebsiella pneumoniae contigs containing KPC-2 within an ISKpn6-like transposase. HFMs and AARMs correlated strongly with the absolute abundance of total ARGs, but AARMs better predicted the majority of ARGs in general (85.4 versus <2%) and β-lactam ARGs in particular. This study reveals sensitive, quantitative, mobile, clinically relevant, and comprehensive targets for antibiotic resistance surveillance in watersheds.