Rapid absolute quantification of pathogens and ARGs by nanopore sequencing

A mica filter enables bacterial enrichment from large volumes of natural water for sensitive monitoring of pathogens by nanopore sequencing

Nanopore sequencing is extremely promising for the high-throughput detection of pathogenic bacteria in natural water; these bacteria may be transmitted to humans and cause waterborne infectious diseases. However, the concentration of pathogenic bacteria in natural water is too low to be detected directly by nanopore sequencing. Herein, we developed a mica filter to enrich over 85% of bacteria from > 10 L of natural water in 100 min, which led to a 102-fold improvement in the assay limits of the MinION sequencer for assessing pathogenic bacteria. Correspondingly, the sequencing time of S. Typhi detection at a concentration as low as 105 CFU/L was reduced from traditional 48 h to 3 h. The bacterial adsorption followed pseudo-first-order kinetics and the successful adsorption of bacteria to the mica filter was confirmed by scanning electron microscopy and Fourier infrared spectroscopy et al. The mica filter remained applicable to a range of water samples whose quality parameters were within the EPA standard limits for freshwater water. The mica filter is thus an effective tool for the sensitive and rapid monitoring of pathogenic bacteria by nanopore sequencing, which can provide timely alerts for waterborne transmission events.

High-resolution and real-time wastewater viral surveillance by Nanopore sequencing

Wastewater genomic sequencing stands as a pivotal complementary tool for viral surveillance in populations. While long-read Nanopore sequencing is a promising platform to provide real-time genomic data, concerns over the sequencing accuracy of the earlier Nanopore versions have somewhat restrained its widespread application in wastewater analysis. Here, we evaluate the latest improved version of Nanopore sequencing (R10.4.1), using SARS-CoV-2 as the model infectious virus, to demonstrate its effectiveness in wastewater viral monitoring. By comparing amplicon lengths of 400 bp and 1200 bp, we revealed that shorter PCR amplification is more suitable for wastewater samples due to viral genome fragmentation. Utilizing mock wastewater samples, we validated the reliability of Nanopore sequencing for variant identification by comparing it with Illumina sequencing results. The strength of Nanopore sequencing in generating real-time genomic data for providing early warning signals was also showcased, indicating that as little as 0.001 Gb of data can provide accurate results for variant prevalence. Our evaluation also identified optimal alteration frequency cutoffs (>50 %) for precise mutation profiling, achieving >99 % precision in detecting single nucleotide variants (SNVs) and insertions/deletions (indels). Monitoring two major wastewater treatment plants in Hong Kong from September 2022 to April 2023, covering over 4.5 million population, we observed a transition in dominant variants from BA.5 to XBB lineages, with XBB.1.5 being the most prevalent variants. Mutation detection also highlighted the potential of wastewater Nanopore sequencing in uncovering novel mutations and revealed links between signature mutations and specific variants. This study not only reveals the environmental implications of Nanopore sequencing in SARS-CoV-2 surveillance but also underscores its potential in broader applications including environmental health monitoring of other epidemic viruses, which could significantly enhance the field of wastewater-based epidemiology.

Sensitivity and consistency of long- and short-read metagenomics and epicPCR for the detection of antibiotic resistance genes and their bacterial hosts in wastewater

Wastewater surveillance is a powerful tool to assess the risks associated with antibiotic resistance in communities. One challenge is selecting which analytical tool to deploy to measure risk indicators, such as antibiotic resistance genes (ARGs) and their respective bacterial hosts. Although metagenomics is frequently used for analyzing ARGs, few studies have compared the performance of long-read and short-read metagenomics in identifying which bacteria harbor ARGs in wastewater. Furthermore, for ARG host detection, untargeted metagenomics has not been compared to targeted methods such as epicPCR. Here, we 1) evaluated long-read and short-read metagenomics as well as epicPCR for detecting ARG hosts in wastewater, and 2) investigated the host range of ARGs across the wastewater treatment plant (WWTP) to evaluate host proliferation. Results highlighted long-read revealed a wider range of ARG hosts compared to short-read metagenomics. Nonetheless, the ARG host range detected by long-read metagenomics only represented a subset of the hosts detected by epicPCR. The ARG-host linkages across the influent and effluent of the WWTP were characterized. Results showed the ARG-host phylum linkages were relatively consistent across the WWTP, whereas new ARG-host species linkages appeared in the WWTP effluent. The ARG-host linkages of several clinically relevant species found in the effluent were identified.

Nanopore adaptive sampling of a metagenomic sample derived from a human monkeypox case

In 2022, a series of human monkeypox cases in multiple countries led to the largest and most widespread outbreak outside the known endemic areas. Setup of proper genomic surveillance is of utmost importance to control such outbreaks. To this end, we performed Nanopore (PromethION P24) and Illumina (NextSeq. 2000) Whole Genome Sequencing (WGS) of a monkeypox sample. Adaptive sampling was applied for in silico depletion of the human host genome, allowing for the enrichment of low abundance viral DNA without a priori knowledge of sample composition. Nanopore sequencing allowed for high viral genome coverage, tracking of sample composition during sequencing, strain determination, and preliminary assessment of mutational pattern. In addition to that, only Nanopore data allowed us to resolve the entire monkeypox virus genome, with respect to two structural variants belonging to the genes OPG015 and OPG208. These SVs in important host range genes seem stable throughout the outbreak and are frequently misassembled and/or misannotated due to the prevalence of short read sequencing or short read first assembly. Ideally, standalone standard Illumina sequencing should not be used for Monkeypox WGS and de novo assembly, since it will obfuscate the structure of the genome, which has an impact on the quality and completeness of the genomes deposited in public databases and thus possibly on the ability to evaluate the complete genetic reason for the host range change of monkeypox in the current pandemic.

Comparison of qPCR and metagenomic sequencing methods for quantifying antibiotic resistance genes in wastewater

Surveillance methods of circulating antibiotic resistance genes (ARGs) are of utmost importance in order to tackle what has been described as one of the greatest threats to humanity in the 21st century. In order to be effective, these methods have to be accurate, quickly deployable, and scalable. In this study, we compare metagenomic shotgun sequencing (TruSeq DNA sequencing) of wastewater samples with a state-of-the-art PCR-based method (Resistomap HT-qPCR) on four wastewater samples that were taken from hospital, industrial, urban and rural areas. ARGs that confer resistance to 11 antibiotic classes have been identified in these wastewater samples using both methods, with the most abundant observed classes of ARGs conferring resistance to aminoglycoside, multidrug-resistance (MDR), macrolide-lincosamide-streptogramin B (MLSB), tetracycline and beta-lactams. In comparing the methods, we observed a strong correlation of relative abundance of ARGs obtained by the two tested methods for the majority of antibiotic classes. Finally, we investigated the source of discrepancies in the results obtained by the two methods. This analysis revealed that false negatives were more likely to occur in qPCR due to mutated primer target sites, whereas ARGs with incomplete or low coverage were not detected by the sequencing method due to the parameters set in the bioinformatics pipeline. Indeed, despite the good correlation between the methods, each has its advantages and disadvantages which are also discussed here. By using both methods together, a more robust ARG surveillance program can be established. Overall, the work described here can aid wastewater treatment plants that plan on implementing an ARG surveillance program.

A global perspective on microbial risk factors in effluents of wastewater treatment plants

Effective monitoring and management of microbial risk factors in wastewater treatment plants (WWTPs) effluents require a comprehensive investigation of these risks. A global survey on microbial risk factors in WWTP effluents could reveal important insights into their risk features. This study aims to explore the abundance and types of antibiotic resistance genes (ARGs), virulence factor genes (VFGs), the vector of ARG/VFG, and dominant pathogens in global WWTP effluents. We collected 113 metagenomes of WWTP effluents from the Sequence Read Archive of the National Center for Biotechnology Information and characterized the microbial risk factors. Our results showed that multidrug resistance was the dominant ARG type, while offensive virulence factors were the most abundant type of VFGs. The most dominant types of ARGs in the vector of plasmid and phage were both aminoglycoside resistance, which is concerning as aminoglycosides are often a last resort for treating multi-resistant infections. Acinetobacter baumannii was the most dominant pathogen, rather than Escherichia coli, and a weak negative correlation between Escherichia coli and two other dominant pathogens (Acinetobacter baumannii and Bacteroides uniformis) suggests that using Escherichia coli as a biological indicator for all pathogens in WWTP effluents may not be appropriate. The Getah virus was the most dominant virus found in global WWTP effluents. Our study presents a comprehensive global-scale investigation of microbial risk factors in WWTP effluents, providing valuable insights into the potential risks associated with WWTP effluents and contributing to the monitoring and control of these risks.

Bibliometric analysis of papers on antibiotic resistance genes in aquatic environments on a global scale from 2012 to 2022: Evidence from universality, development and harmfulness

Antibiotic resistance genes (ARGs), emerging pollutants, are widely distributed in aquatic environments, and are tightly linked to human health. However, the research progress and trends in recent years on ARGs of aquatic environments are still unclear. This paper made a comprehensive understanding of the research advance, study trends and key topics of 1592 ARGs articles from 2012 to 2022 by bibliometrics. Publications on ARGs increased rapidly from 2012 to 2022, and scholars paid closer attention to the field of Environmental Sciences & Ecology. The most influential country and institution was mainly China and Chinese Academy of Sciences, respectively. The most articles (14.64 %) were published in the journal Science of the total environment. China and USA had the most cooperation, and USA was more inclined to international cooperation. PCR-based methods for water ARG research were the most widely used, followed by metagenomics. The most studied ARG types were sulfonamides, tetracyclines. Moreover, ARGs from wastewater and rivers were popularly concerned. Current topics mainly included pollution investigation, characteristics, transmission, reduction and risk identification of ARGs. Additionally, future research directions were proposed. Generally, by bibliometrics, this paper reviews the research hotspots and future directions of ARGs on a global scale, and summarizes the more important categories of ARGs, the pollution degree of ARGs in the relevant water environment and the research methods, which can provide a more comprehensive information for the future breakthrough of resistance mechanism, prevention and control standard formulation of ARGs.

Nanopore Sequencing Assessment of Bacterial Pathogens and Associated Antibiotic Resistance Genes in Environmental Samples

As seen in earlier and present pandemics, monitoring pathogens in the environment can offer multiple insights on their spread, evolution, and even future outbreaks. The present paper assesses the opportunity to detect microbial pathogens and associated antibiotic resistance genes, in relation to specific pathogen sources, by using nanopore sequencing in municipal waters and wastewaters in Romania. The main results indicated that waters collecting effluents from a meat processing facility exhibit altered communities' diversity and abundance, with reduced values (101-108 and 0.86-0.91) of Chao1 and, respectively, Simpson diversity indices and Campylobacterales as main order, compared with other types of municipal waters where the same diversity index had much higher values of 172-214 and 0.97-0.98, and Burkholderiaceae and Pseudomonadaceae were the most abundant families. Moreover, the incidence and type of antibiotic resistance genes were significantly influenced by the proximity of antibiotic sources, with either tetracycline (up to 45% of total reads) or neomycin, streptomycin and tobramycin (up to 3.8% total reads) resistance incidence being shaped by the sampling site. As such, nanopore sequencing proves to be an easy-to-use, accessible molecular technique for environmental pathogen surveillance and associated antibiotic resistance genes.

Application of high-throughput sequencing technologies and analytical tools for pathogen detection in urban water systems: Progress and future perspectives

The ubiquitous presence of pathogenic microorganisms, such as viruses, bacteria, fungi, and protozoa, in urban water systems poses a significant risk to public health. The emergence of infectious waterborne diseases mediated by urban water systems has become one of the leading global causes of mortality. However, the detection and monitoring of these pathogenic microorganisms have been limited by the complexity and diversity in the environmental samples. Conventional methods were restricted by long assay time, high benchmarks of identification, and narrow application sceneries. Novel technologies, such as high-throughput sequencing technologies, enable potentially full-spectrum detection of trace pathogenic microorganisms in complex environmental matrices. This review discusses the current state of high-throughput sequencing technologies for identifying pathogenic microorganisms in urban water systems with a concise summary. Furthermore, future perspectives in pathogen research emphasize the need for detection methods with high accuracy and sensitivity, the establishment of precise detection standards and procedures, and the significance of bioinformatics software and platforms. We have compiled a list of pathogens analysis software/platforms/databases that boast robust engines and high accuracy for preference. We highlight the significance of analyses by combining targeted and non-targeted sequencing technologies, short and long reads technologies, sequencing technologies, and bioinformatic tools in pursuing upgraded biosafety in urban water systems.

Roadmap to tackle antibiotic resistance in the environment under the One Health framework

Antibiotic resistance has been recognized as a major challenge worldwide for humans. "One Health" has been recognized as a key concept for containment of antibiotic resistance. Under the framework, the role of the environment in the development of antibiotic resistance genes (ARGs) has become increasingly obvious. Despite numerous efforts, response to antibiotic resistance is considered to be inadequate, which is probably due to the lack of a clear roadmap. Here, we propose a "One Health" roadmap to combat antibiotic resistance in the environment through (1) understanding environmental resistome. The environmental gene pool has long been recognized as the single largest reservoir of both known and novel ARGs. (2) Standardizing ARG quantification. Systematic joint efforts based on standardized quantification are urgently needed to understand the true tempospatial profiles of the environmental resistome. (3) Identifying mechanisms of resistome development. Horizontal gene transfer and co-selection have been recognized as the two main mechanisms contributing to the environmental resistome. (4) Establishing a risk-assessment framework. The first critical step for large-scale cost-effective targeted ARG management in the environment is the risk assessment to identify the priority ARGs for control. (5) Formulating regulatory standards. By correlating the environmental ARG profile with public health, we may identify the indicator ARGs that can be integrated into current environmental quality standards. (6) Developing control strategies. Systematic analysis of available control technologies is required to identify the most feasible ones to curtail the spread of ARGs in the environment. The proposed roadmap under the "One Health" framework provides a guide to tackle antibiotic resistance in the environment.

Toward a Universal Unit for Quantification of Antibiotic Resistance Genes in Environmental Samples

Surveillance of antibiotic resistance genes (ARGs) has been increasingly conducted in environmental sectors to complement the surveys in human and animal sectors under the "One-Health" framework. However, there are substantial challenges in comparing and synthesizing the results of multiple studies that employ different test methods and approaches in bioinformatic analysis. In this article, we consider the commonly used quantification units (ARG copy per cell, ARG copy per genome, ARG density, ARG copy per 16S rRNA gene, RPKM, coverage, PPM, etc.) for profiling ARGs and suggest a universal unit (ARG copy per cell) for reporting such biological measurements of samples and improving the comparability of different surveillance efforts.

QMRA of beach water by Nanopore sequencing-based viability-metagenomics absolute quantification

The majority of the current regulatory practices for routine monitoring of beach water quality rely on the culture-based enumeration of faecal indicator bacteria (FIB) to develop criteria for promoting the general public's health. To address the limitations of culture methods and the arguable reliability of FIB in indicating health risks, we developed a Nanopore metagenomic sequencing-based viable cell absolute quantification workflow to rapidly and accurately estimate a broad range of microbes in beach waters by a combination of propidium monoazide (PMA) and cellular spike-ins. Using the simple synthetic bacterial communities mixed with viable and heat-killed cells, we observed near-complete relic DNA removal by PMA with minimal disturbance to the composition of viable cells, demonstrating the feasibility of PMA treatment in profiling viable cells by Nanopore sequencing. On a simple mock community comprised of 15 prokaryotic species, our results showed high accordance between the expected and estimated concentrations, suggesting the accuracy of our method in absolute quantification. We then further assessed the accuracy of our method for counting viable Escherichia coli and Vibrio spp. in beach waters by comparing to culture-based method, which were also in high agreement. Furthermore, we demonstrated that 1 Gb sequences obtained within 2 h would be sufficient to quantify a species having a concentration of ≥ 10 cells/mL in beach waters. Using our viability-resolved quantification workflow to assess the microbial risk of the beach water, we conducted (1) screening-level quantitative microbial risk assessment (QMRA) to investigate human illness risk and site-specific risk patterns that might guide risk management efforts and (2) metagenomics-based resistome risk assessment to evaluate another layer of risk caused by difficult illness treatment due to antimicrobial resistance (AMR). In summary, our metagenomic workflow for the rapid absolute quantification of viable bacteria demonstrated its great potential in paving new avenues toward holistic microbial risk assessment.

Metagenomic sequencing combined with flow cytometry facilitated a novel microbial risk assessment framework for bacterial pathogens in municipal wastewater without cultivation

A workflow that combined metagenomic sequencing with flow cytometry was developed. The absolute abundance of pathogens was accurately estimated in mock communities and real samples. Metagenome-assembled genomes binned from metagenomic data set is robust in phylogenetic analysis and virulence profiling.

Distribution and migration of antibiotic resistance genes, as well as their correlation with microbial communities in swine farm and its surrounding environments

The prevalence and correlation of antibiotic resistance genes (ARGs) in pig farm wastewater treatment plants (WWTPs) and surrounding environment were investigated using metagenomics and real time quantitative PCR (q-PCR). The hosts of ARGs were also studied in this study. The abundance of ARGs decreased significantly in the anoxic/oxic (A/O) process and disinfection tank of WWTPs. New ARGs emerged in wastewater that passed though the anaerobic reactor. The abundances of ARGs in the soils and water near pig farm were 10- and 35-fold higher than those in the control, respectively. The abundance of ARGs in wells near pig farm were an order of magnitude higher than that in the control. Similarly, a high abundance of ARGs was detected in swine manure. After composting, most of the ARGs were eliminated, but sul1 increased 10.5-fold. A high-throughput analysis revealed that the pig farm altered the microbial community structure in the surrounding environment, with 52% and 37% of the operational taxonomic units (OTUs) endemic to the soil and water samples near pig farm in comparison with these data in the control, respectively. The phyla Proteobacteria, Choroflexi, and Actinobacteriota dominated the water and soil samples. In addition, three pathogenic genera were found in the surrounding soil and water samples. A metagenomic analysis identified 14 types of ARGs (>1%), with the highest proportion of multidrug ARGs at 47%. A total of 28 subtypes of ARGs were detected (>1%), with macB the most prevalent. The correlation analysis revealed that several key phyla, including Proteobacteria, Actinobacteria and Acidobacteria, were the main potential hosts and posed a positive correlation with the ARGs. Efflux pumps (60-66%) were the primary resistance mechanism, and each resistance mechanism was distributed in similar proportions in the microbial community.

Analysis of metatranscriptomic methods to enable wastewater-based biosurveillance of all infectious diseases

Introduction

Wastewater-based surveillance emerged during the COVID-19 pandemic as an efficient way to quickly screen large populations, monitor infectious disease transmission over time, and identify whether more virulent strains are becoming more prevalent in the region without burdening the health care system with individualized testing. Ohio was one of the first states to implement wastewater monitoring through its Ohio Coronavirus Wastewater Monitoring Network (OCWMN), originally tracking the prevalence of COVID-19 by quantitative qPCR from over 67 sites across the state. The OCWMN evolved along with the pandemic to include sequencing the SARS-CoV-2 genome to assess variants of concern circulating within the population. As the pandemic wanes, networks such as OCWMN can be expanded to monitor other infectious diseases and outbreaks of interest to the health department to reduce the burden of communicable diseases. However, most surveillance still utilizes qPCR based diagnostic tests for individual pathogens, which is hard to scale for surveillance of multiple pathogens.

Methods

Here we have tested several genomic methods, both targeted and untargeted, for wastewater-based biosurveillance to find the most efficient procedure to detect and track trends in reportable infectious diseases and outbreaks of known pathogens as well as potentially novel pathogens or variants on the rise in our communities. RNA extracts from the OCWMN were provided weekly from 10 sites for 6 weeks. Total RNA was sequenced from the samples on the Illumina NextSeq and on the MinION to identify pathogens present. The MinION long read platform was also used to sequence SARS-CoV-2 with the goal of reducing the complexity of variant calling in mixed populations as occurs with short Illumina reads. Finally, a targeted hybridization approach was tested for compatibility with wastewater RNA samples.

Results and discussion

The data analyzed here provides a baseline assessment that demonstrates that wastewater is a rich resource for infectious disease epidemiology and identifies technology gaps and potential solutions to enable this resource to be used by public health laboratories to monitor the infectious disease landscape of the regions they serve.

Topography of respiratory tract and gut microbiota in mice with influenza A virus infection

Introduction

Influenza A virus (IAV)-induced dysbiosis may predispose to severe bacterial superinfections. Most studies have focused on the microbiota of single mucosal surfaces; consequently, the relationships between microbiota at different anatomic sites in IAV-infected mice have not been fully studied.

Methods

We characterized respiratory and gut microbiota using full-length 16S rRNA gene sequencing by Nanopore sequencers and compared the nasopharyngeal, oropharyngeal, lung and gut microbiomes in healthy and IAV-infected mice.

Results

The oropharyngeal, lung and gut microbiota of healthy mice were dominated by Lactobacillus spp., while nasopharyngeal microbiota were comprised primarily of Streptococcus spp. However, the oropharyngeal, nasopharyngeal, lung, and gut microbiota of IAV-infected mice were dominated by Pseudomonas, Escherichia, Streptococcus, and Muribaculum spp., respectively. Lactobacillus murinus was identified as a biomarker and was reduced at all sites in IAV-infected mice. The microbiota composition of lung was more similar to that of the nasopharynx than the oropharynx in healthy mice.

Discussion

These findings suggest that the main source of lung microbiota in mice differs from that of adults. Moreover, the similarity between the nasopharyngeal and lung microbiota was increased in IAV-infected mice. We found that IAV infection reduced the similarity between the gut and oropharyngeal microbiota. L. murinus was identified as a biomarker of IAV infection and may be an important target for intervention in post-influenza bacterial superinfections.

Nanopore long-read-only metagenomics enables complete and high-quality genome reconstruction from mock and complex metagenomes

BACKGROUND

The accurate and comprehensive analyses of genome-resolved metagenomics largely depend on the reconstruction of reference-quality (complete and high-quality) genomes from diverse microbiomes. Closing gaps in draft genomes have been approaching with the inclusion of Nanopore long reads; however, genome quality improvement requires extensive and time-consuming high-accuracy short-read polishing.

RESULTS

Here, we introduce NanoPhase, an open-source tool to reconstruct reference-quality genomes from complex metagenomes using only Nanopore long reads. Using Kit 9 and Q20+ chemistries, we first evaluated the feasibility of NanoPhase using a ZymoBIOMICS gut microbiome standard (including 21 strains), then sequenced the complex activated sludge microbiome and reconstructed 275 MAGs with median completeness of ~ 90%. As a result, NanoPhase improved the MAG contiguity (median MAG N50: 735 Kb, 44-86X compared to conventional short-read-based methods) while maintaining high accuracy, allowing for a full and accurate investigation of target microbiomes. Additionally, leveraging these high-contiguity reference-quality genomes, we identified 165 prophages within 111 MAGs, with 5 as active prophages, indicating the prophage was a neglected source of genetic diversity within microbial populations and influencer in shaping microbial composition in the activated sludge microbiome.

CONCLUSIONS

Our results demonstrated that NanoPhase enables reference-quality genome reconstruction from complex metagenomes directly using only Nanopore long reads. Furthermore, besides the 16S rRNA genes and biosynthetic gene clusters, the generated high-accuracy and high-contiguity MAGs improved the host identification of critical mobile genetic elements, e.g., prophage, serving as a genomic blueprint to investigate the microbial potential and ecology in the activated sludge ecosystem. Video Abstract.

A rapid bacterial pathogen and antimicrobial resistance diagnosis workflow using Oxford nanopore adaptive sequencing method

Metagenomic sequencing analysis (mNGS) has been implemented as an alternative approach for pathogen diagnosis in recent years, which is independent of cultivation and is able to identify all potential antibiotic resistance genes (ARGs). However, current mNGS methods have to deal with low amounts of prokaryotic deoxyribonucleic acid (DNA) and high amounts of host DNA in clinical samples, which significantly decrease the overall microbial detection resolution. The recently released nanopore adaptive sampling (NAS) technology facilitates immediate mapping of individual nucleotides to a given reference as each molecule is sequenced. User-defined thresholds allow for the retention or rejection of specific molecules, informed by the real-time reference mapping results, as they are physically passing through a given sequencing nanopore. We developed a metagenomics workflow for ultra-sensitive diagnosis of bacterial pathogens and ARGs from clinical samples, which is based on the efficient selective 'human host depletion' NAS sequencing, real-time species identification and species-specific resistance gene prediction. Our method increased the microbial sequence yield at least 8-fold in all 21 sequenced clinical Bronchoalveolar Lavage Fluid (BALF) samples (4.5 h from sample to result) and accurately detected the ARGs at species level. The species-level positive percent agreement between metagenomic sequencing and laboratory culturing was 100% (16/16) and negative percent agreement was 100% (5/5) in our approach. Further work is required for a more robust validation of our approach with large sample size to allow its application to other infection types.