Toward Accurate and Robust Environmental Surveillance Using Metagenomics

Building a pathway to One Health surveillance and response in Asian countries

To detect and respond to emerging diseases more effectively, an integrated surveillance strategy needs to be applied to both human and animal health. Current programs in Asian countries operate separately for the two sectors and are principally concerned with detection of events that represent a short-term disease threat. It is not realistic to either invest only in efforts to detect emerging diseases, or to rely solely on event-based surveillance. A comprehensive strategy is needed, concurrently investigating and managing endemic zoonoses, studying evolving diseases which change their character and importance due to influences such as demographic and climatic change, and enhancing understanding of factors which are likely to influence the emergence of new pathogens. This requires utilisation of additional investigation tools that have become available in recent years but are not yet being used to full effect. As yet there is no fully formed blueprint that can be applied in Asian countries. Hence a three-step pathway is proposed to move towards the goal of comprehensive One Health disease surveillance and response.

Optimized bacterial community characterization through full-length 16S rRNA gene sequencing utilizing MinION nanopore technology

BACKGROUND

Accurate identification of bacterial communities is crucial for research applications, diagnostics, and clinical interventions. Although 16S ribosomal RNA (rRNA) gene sequencing is a widely employed technique for bacterial taxonomic classification, it often results in misclassified or unclassified bacterial taxa. This study sought to refine the full-length 16S rRNA gene sequencing protocol using the MinION sequencer, focusing on the V1-V9 regions. Our methodological enquiry examined several factors, including the number of PCR amplification cycles, choice of primers and Taq polymerase, and specific sequence databases and workflows employed. We used a microbial standard comprising eight bacterial strains (five gram-positive and three gram-negative) in known proportions as a validation control.

RESULTS

Based on the MinION protocol, we employed the microbial standard as the DNA template for the 16S rRNA gene amplicon sequencing procedure. Our analysis showed that an elevated number of PCR amplification cycles introduced PCR bias, and the selection of Taq polymerase and primer sets significantly affected the subsequent analysis. Bacterial identification at genus level demonstrated Pearson correlation coefficients ranging from 0.73 to 0.79 when assessed using BugSeq, Kraken-Silva and EPI2ME-16S workflows. Notably, the EPI2ME-16S workflow exhibited the highest Pearson correlation with the microbial standard, minimised misclassification, and increased alignment accuracy. At the species taxonomic level, the BugSeq workflow was superior, with a Pearson correlation coefficient of 0.92.

CONCLUSIONS

These findings emphasise the importance of careful selection of PCR settings and a well-structured analytical framework for 16S rRNA full-length gene sequencing. The results showed a robust correlation between the predicted and observed bacterial abundances at both the genus and species taxonomic levels, making these findings applicable across diverse research contexts and with clinical utility for reliable pathogen identification.

Environmental determinants and demographic influences on global urban microbiomes, antimicrobial resistance and pathogenicity

Urban microbiome plays crucial roles in human health and are related to various diseases. The MetaSUB Consortium has conducted the most comprehensive global survey of urban microbiomes to date, profiling microbial taxa/functional genes across 60 cities worldwide. However, the influence of environmental/demographic factors on urban microbiome remains to be elucidated. We collected 35 environmental and demographic characteristics to examine their effects on global urban microbiome diversity/composition by PERMANOVA and regression models. PM10 concentration was the primary determinant factor positively associated with microbial α-diversity (observed species: p = 0.004, β = 1.66, R2 = 0.46; Fisher's alpha: p = 0.005, β = 0.68, R2 = 0.43), whereas GDP per capita was negatively associated (observed species: p = 0.046, β = -0.70, R2 = 0.10; Fisher's alpha: p = 0.004, β = -0.34, R2 = 0.22). The β-diversity of urban microbiome was shaped by seven environmental characteristics, including Köppen climate type, vegetation type, greenness fraction, soil type, PM2.5 concentration, annual average precipitation and temperature (PERMANOVA, p < 0.001, R2 = 0.01-0.06), cumulatively accounted for 20.3% of the microbial community variance. Canonical correspondence analysis (CCA) identified microbial species most strongly associated with environmental characteristic variation. Cities in East Asia with higher precipitation showed an increased abundance of Corynebacterium metruchotii, and cities in America with a higher greenness fraction exhibited a higher abundance of Corynebacterium casei. The prevalence of antimicrobial resistance (AMR) genes were negatively associated with GDP per capita and positively associated with solar radiation (p < 0.005). Total pathogens prevalence was positively associated with urban population and negatively associated with average temperature in June (p < 0.05). Our study presents the first comprehensive analysis of the influence of environmental/demographic characteristics on global urban microbiome. Our findings indicate that managing air quality and urban greenness is essential for regulating urban microbial diversity and composition. Meanwhile, socio-economic considerations, particularly reducing antibiotic usage in regions with lower GDP, are paramount in curbing the spread of antimicrobial resistance in urban environments.

ARGem: a new metagenomics pipeline for antibiotic resistance genes: metadata, analysis, and visualization

Antibiotic resistance is of crucial interest to both human and animal medicine. It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user's configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available at https://github.com/xlxlxlx/ARGem.

RNA-based amplicon sequencing is ineffective in measuring metabolic activity in environmental microbial communities

BACKGROUND

Characterization of microbial activity is essential to the understanding of the basic biology of microbial communities, as the function of a microbiome is defined by its biochemically active ("viable") community members. Current sequence-based technologies can rarely differentiate microbial activity, due to their inability to distinguish live and dead sourced DNA. As a result, our understanding of microbial community structures and the potential mechanisms of transmission between humans and our surrounding environments remains incomplete. As a potential solution, 16S rRNA transcript-based amplicon sequencing (16S-RNA-seq) has been proposed as a reliable methodology to characterize the active components of a microbiome, but its efficacy has not been evaluated systematically. Here, we present our work to benchmark RNA-based amplicon sequencing for activity assessment in synthetic and environmentally sourced microbial communities.

RESULTS

In synthetic mixtures of living and heat-killed Escherichia coli and Streptococcus sanguinis, 16S-RNA-seq successfully reconstructed the active compositions of the communities. However, in the realistic environmental samples, no significant compositional differences were observed in RNA ("actively transcribed - active") vs. DNA ("whole" communities) spiked with E. coli controls, suggesting that this methodology is not appropriate for activity assessment in complex communities. The results were slightly different when validated in environmental samples of similar origins (i.e., from Boston subway systems), where samples were differentiated both by environment type as well as by library type, though compositional dissimilarities between DNA and RNA samples remained low (Bray-Curtis distance median: 0.34-0.49). To improve the interpretation of 16S-RNA-seq results, we compared our results with previous studies and found that 16S-RNA-seq suggests taxon-wise viability trends (i.e., specific taxa are universally more or less likely to be viable compared to others) in samples of similar origins.

CONCLUSIONS

This study provides a comprehensive evaluation of 16S-RNA-seq for viability assessment in synthetic and complex microbial communities. The results found that while 16S-RNA-seq was able to semi-quantify microbial viability in relatively simple communities, it only suggests a taxon-dependent "relative" viability in realistic communities.  Video Abstract.

Managing the Transition to Widespread Metagenomic Monitoring: Policy Considerations for Future Biosurveillance

The technological possibilities and future public health importance of metagenomic sequencing have received extensive attention, but there has been little discussion about the policy and regulatory issues that need to be addressed if metagenomic sequencing is adopted as a key technology for biosurveillance. In this article, we introduce metagenomic monitoring as a possible path to eventually replacing current infectious disease monitoring models. Many key enablers are technological, whereas others are not. We therefore highlight key policy challenges and implementation questions that need to be addressed for "widespread metagenomic monitoring" to be possible. Policymakers must address pitfalls like fragmentation of the technological base, private capture of benefits, privacy concerns, the usefulness of the system during nonpandemic times, and how the future systems will enable better response. If these challenges are addressed, the technological and public health promise of metagenomic sequencing can be realized.

An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics

BACKGROUND

Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples, (2) they do not distinguish between viable and nonviable organisms, and (3) they do not reveal the microbial load quantitatively. An additional practical challenge towards a robust pipeline is the inability to efficiently allocate sequencing resources a priori. Assessment of sequencing depth is generally practiced post hoc, if at all, for most microbiome studies, regardless of the sample type. This practice is inefficient at best, and at worst, poor sequencing depth jeopardizes the interpretation of study results. To address these challenges, we present a workflow for metagenomics-based environmental surveillance that is appropriate for low-biomass samples, distinguishes viability, is quantitative, and estimates sequencing resources.

RESULTS

The workflow was developed using a representative microbiome sample, which was created by aggregating 120 surface swabs collected from a medical intensive care unit. Upon evaluating and optimizing techniques as well as developing new modules, we recommend best practices and introduce a well-structured workflow. We recommend adopting liquid-liquid extraction to improve DNA yield and only incorporating whole-cell filtration when the nonbacterial proportion is large. We suggest including propidium monoazide treatment coupled with internal standards and absolute abundance profiling for viability assessment and involving cultivation when demanding comprehensive profiling. We further recommend integrating internal standards for quantification and additionally qPCR when we expect poor taxonomic classification. We also introduce a machine learning-based model to predict required sequencing effort from accessible sample features. The model helps make full use of sequencing resources and achieve desired outcomes. Video Abstract CONCLUSIONS: This workflow will contribute to more accurate and robust environmental surveillance and infection prevention. Lessons gained from this study will also benefit the continuing development of methods in relevant fields.

Development of sequencing-based methodologies to distinguish viable from non-viable cells in a bovine milk matrix: A pilot study

Although high-throughput DNA sequencing-based methods have been of great value for determining the composition of microbial communities in various environments, there is the potential for inaccuracies arising from the sequencing of DNA from dead microorganisms. In this pilot study, we compared different sequencing-based methods to assess their relative accuracy with respect to distinguishing between viable and non-viable cells, using a live and heat-inactivated model community spiked into bovine milk. The methods used were shotgun metagenomics with and without propidium monoazide (PMA) treatment, RNA-based 16S rRNA sequencing and metatranscriptomics. The results showed that methods were generally accurate, though significant differences were found depending on the library types and sequencing technologies. Different molecular targets were the basis for variations in the results generated using different library types, while differences in the derived composition data from Oxford Nanopore Technologies-and Illumina-based sequencing likely reflect a combination of different sequencing depths, error rates and bioinformatics pipelines. Although PMA was successfully applied in this study, further optimisation is required before it can be applied in a more universal context for complex microbiomes. Overall, these methods show promise and represent another important step towards the ultimate establishment of approaches that can be applied to accurately identify live microorganisms in milk and other food niches.

The Application of Metagenomics to Study Microbial Communities and Develop Desirable Traits in Fermented Foods

The microbial communities present within fermented foods are diverse and dynamic, producing a variety of metabolites responsible for the fermentation processes, imparting characteristic organoleptic qualities and health-promoting traits, and maintaining microbiological safety of fermented foods. In this context, it is crucial to study these microbial communities to characterise fermented foods and the production processes involved. High Throughput Sequencing (HTS)-based methods such as metagenomics enable microbial community studies through amplicon and shotgun sequencing approaches. As the field constantly develops, sequencing technologies are becoming more accessible, affordable and accurate with a further shift from short read to long read sequencing being observed. Metagenomics is enjoying wide-spread application in fermented food studies and in recent years is also being employed in concert with synthetic biology techniques to help tackle problems with the large amounts of waste generated in the food sector. This review presents an introduction to current sequencing technologies and the benefits of their application in fermented foods.

Shotgun-metagenomics based prediction of antibiotic resistance and virulence determinants in Staphylococcus aureus from periprosthetic tissue on blood culture bottles

Shotgun-metagenomics may give valuable clinical information beyond the detection of potential pathogen(s). Identification of antimicrobial resistance (AMR), virulence genes and typing directly from clinical samples has been limited due to challenges arising from incomplete genome coverage. We assessed the performance of shotgun-metagenomics on positive blood culture bottles (n = 19) with periprosthetic tissue for typing and prediction of AMR and virulence profiles in Staphylococcus aureus. We used different approaches to determine if sequence data from reads provides more information than from assembled contigs. Only 0.18% of total reads was derived from human DNA. Shotgun-metagenomics results and conventional method results were consistent in detecting S. aureus in all samples. AMR and known periprosthetic joint infection virulence genes were predicted from S. aureus. Mean coverage depth, when predicting AMR genes was 209 ×. Resistance phenotypes could be explained by genes predicted in the sample in most of the cases. The choice of bioinformatic data analysis approach clearly influenced the results, i.e. read-based analysis was more accurate for pathogen identification, while contigs seemed better for AMR profiling. Our study demonstrates high genome coverage and potential for typing and prediction of AMR and virulence profiles in S. aureus from shotgun-metagenomics data.

Relating sinking and suspended microbial communities in the California Current Ecosystem: digestion resistance and the contributions of phytoplankton taxa to export

We used 16S, 18S, plastid and internal transcribed spacer (for Synechococcus strains) sequencing to quantify relative microbial abundances in water-column samples and on sediment-trap-collected particles across an environmental gradient in the California Current Ecosystem (CCE) spanning a > 60-fold range of surface chlorophyll. Most mixed-layer dominant eukaryotes and prokaryotes were consistently underrepresented on sinking particles. Diatoms were the only phototrophic taxa consistently overrepresented. Even within this class, however, one genus (Thalassiosira) was a particle-enriched dominant, while a similarly abundant species was poorly represented. Synechococcus was significantly enriched on sinking particles at only one of four sites, but clade I was disproportionately abundant on sinking particles throughout the region compared with clade IV, the euphotic-zone co-dominant. The most abundant microbes on particles across the CCE were organisms with distributional maxima close to the sediment-trap depth (rhizarians), microbes associated with metazoans or sinking particles as a nutritional habitat (certain alveolates, Gammaproteobacteria) and organisms that resist digestive degradation of their DNA (Thalassiosira, Synechococcus). For assessing taxon contributions of phytoplankton to carbon export, our results highlight the need for sequence-based quantitative approaches that can be used to integrate euphotic-zone abundances, compute rates and account for taxon differences in preservation of sequence markers through trophic processing.