Genomic and Metagenomic Approaches for Predictive Surveillance of Emerging Pathogens and Antibiotic Resistance

TGC-ARG: Anticipating Antibiotic Resistance via Transformer-Based Modeling and Contrastive Learning

In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) often require substantial time and effort and are limited in accuracy. Moreover, the decentralized nature of existing data repositories complicates comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce a novel computational framework named TGC-ARG designed to predict potential ARGs. This framework takes protein sequences as input, utilizes SCRATCH-1D for protein secondary structure prediction, and employs feature extraction techniques to derive distinctive features from both sequence and structural data. Subsequently, a Siamese network is employed to foster a contrastive learning environment, enhancing the model's ability to effectively represent the data. Finally, a multi-layer perceptron (MLP) integrates and processes sequence embeddings alongside predicted secondary structure embeddings to forecast ARG presence. To evaluate our approach, we curated a pioneering open dataset termed ARSS (Antibiotic Resistance Sequence Statistics). Comprehensive comparative experiments demonstrate that our method surpasses current state-of-the-art methodologies. Additionally, through detailed case studies, we illustrate the efficacy of our approach in predicting potential ARGs.

Metagenomic analysis of sewage for surveillance of bacterial pathogens: A release experiment to determine sensitivity

Accurate monitoring of gastro-enteric and other diseases in large populations poses a challenge for public health management. Sewage represents a larger population, is freely obtainable and non-subject to ethical approval. Metagenomic sequencing offers simultaneous, multiple-target analysis. However, no study has demonstrated the sensitivity of metagenomics for detecting bacteria in sewage. In this study, we spot-released 1013 colony-forming units (CFU) of Staphyloccus hyicus (non-pathogenetic strain 842J-88). The strain was flushed down a toilet into the sewer in the catchment area of a public wastewater treatment plant (WWTP), serving a population of 36,000 people. Raw sewage was continuously sampled at the WWTP's inlet over 30- and 60-minute intervals for a total period of seven hours. The experiment was conducted twice with one week in-between release days and under comparable weather conditions. For the metagenomics analyses, the pure single isolate of S. hyicus was sequenced, assembled and added to a large database of bacterial reference sequences. All sewage samples were analyzed by shotgun metagenome sequencing and mapped against the reference database. S. hyicus was identified in duplicate samples at both of two release days and these sequence fragment counts served as a proxy to estimate the minimum number of sick people or sensitivity required in order to observe at least one sick person at 95% probability. We found the sensitivity to be in the range 41-140 and 16-36 sick people at release days 1 and 2, respectively. The WWTP normally serves 36,000 people giving a normalized sensitivity in the range of one in 257 to 2,250 persons.

Promising bioprocesses for the efficient removal of antibiotics and antibiotic-resistance genes from urban and hospital wastewaters: Potentialities of aerobic granular systems

The use, overuse, and improper use of antibiotics have resulted in higher levels of antibiotic-resistant bacteria (ARB) and antibiotic-resistance genes (ARGs), which have profoundly disturbed the equilibrium of the environment. Furthermore, once antibiotic agents are excreted in urine and feces, these substances often can reach wastewater treatment plants (WWTPs), in which improper treatments have been highlighted as the main reason for stronger dissemination of antibiotics, ARB, and ARGs to the receiving bodies. Hence, achieving better antibiotic removal capacities in WWTPs is proposed as an adequate approach to limit the spread of antibiotics, ARB, and ARGs into the environment. In this review, we highlight hospital wastewater (WW) as a critical hotspot for the dissemination of antibiotic resistance due to its high level of antibiotics and pathogens. Hence, monitoring the composition and structure of the bacterial communities related to hospital WW is a key factor in controlling the spread of ARGs. In addition, we discuss the advantages and drawbacks of the current biological WW treatments regarding the antibiotic-resistance phenomenon. Widely used conventional activated sludge technology has proved to be ineffective in mitigating the dissemination of ARB and ARGs to the environment. However, aerobic granular sludge (AGS) technology is a promising technology-with broad adaptability and excellent performance-that could successfully reduce antibiotics, ARB, and ARGs in the generated effluents. We also outline the main operational parameters involved in mitigating antibiotics, ARB, and ARGs in WWTPs. In this regard, WW operation under long hydraulic and solid retention times allows better removal of antibiotics, ARB, and ARGs independently of the WW technology employed. Finally, we address the current knowledge of the adsorption and degradation of antibiotics and their importance in removing ARB and ARGs. Notably, AGS can enhance the removal of antibiotics, ARB, and ARGs due to the complex microbial metabolism within the granular biomass.

A metagenomic analysis for combination therapy of multiple classes of antibiotics on the prevention of the spread of antibiotic-resistant genes

Antibiotics used systemically to treat infections may have off-target effects on the gut microbiome, potentially resulting in the emergence of drug-resistant bacteria or selection of pathogenic species. These organisms may present a risk to the host and spread to the environment with a risk of transmission in the community. To investigate the risk of emergent antibiotic resistance in the gut microbiome following systemic treatment with antibiotics, this metagenomic analysis project used next-generation sequencing, a custom-built metagenomics pipeline, and differential abundance analysis to study the effect of antibiotics (ampicillin, ciprofloxacin, and fosfomycin) in monotherapy and different combinations at high and low doses, to determine the effect on resistome and taxonomic composition in the gut of Balb/c mice. The results showed that low-dose monotherapy treatments showed little change in microbiome composition but did show an increase in expression of many antibiotic-resistant genes (ARGs) posttreatment. Dual combination treatments allowed the emergence of some conditionally pathogenic bacteria and some increase in the abundance of ARGs despite a general decrease in microbiota diversity. Triple combination treatment was the most successful in inhibiting emergence of relevant opportunistic pathogens and completely suppressed all ARGs after 72 h of treatment. The relative abundances of mobile genetic elements that can enhance transmission of antibiotic resistance either decreased or remained the same for combination therapy while increasing for low-dose monotherapy. Combination therapy prevented the emergence of ARGs and decreased bacterial diversity, while low-dose monotherapy treatment increased ARGs and did not greatly change bacterial diversity.

Risk assessment and dissemination mechanism of antibiotic resistance genes in compost

In recent years, the excessive of antibiotics in livestock and poultry husbandry, stemming from extensive industry experience, has resulted in the accumulation of residual antibiotics and antibiotic resistance genes (ARGs) in livestock manure. Composting, as a crucial approach for the utilization of manure resources, has the potential to reduce the levels of antibiotics and ARGs in manure, although complete elimination is challenging. Previous studies have primarily focused on the diversity and abundance of ARGs in compost or have solely examined the correlation between ARGs and their carriers, potentially leading to a misjudgment of the actual risk associated with ARGs in compost. To address this gap, this study investigated the transfer potential of ARGs in compost and their co-occurrence with opportunistic pathogenic bacteria by extensively analyzing metagenomic sequencing data of compost worldwide. The results demonstrated that the potential risk of ARGs in compost was significantly lower than in manure, suggesting that composting effectively reduces the risk of ARGs. Further analysis showed that the microbes shifted their life history strategy in manure and compost due to antibiotic pressure and formed metabolic interactions dominated by antibiotic-resistant microbes, increasing ARG dissemination frequency. Therefore, husbandry practice without antibiotic addition was recommended to control ARG evolution, dissemination, and abatement both at the source and throughout processing.

A First Insight into the Microbial and Viral Communities of Comau Fjord—A Unique Human-Impacted Ecosystem in Patagonia (42∘ S)

While progress has been made in surveying the oceans to understand microbial and viral communities, the coastal ocean and, specifically, estuarine waters, where the effects of anthropogenic activity are greatest, remain partially understudied. The coastal waters of Northern Patagonia are of interest since this region experiences high-density salmon farming as well as other disturbances such as maritime transport of humans and cargo. Here, we hypothesized that viral and microbial communities from the Comau Fjord would be distinct from those collected in global surveys yet would have the distinctive features of microbes from coastal and temperate regions. We further hypothesized that microbial communities will be functionally enriched in antibiotic resistance genes (ARGs) in general and in those related to salmon farming in particular. Here, the analysis of metagenomes and viromes obtained for three surface water sites showed that the structure of the microbial communities was distinct in comparison to global surveys such as the Tara Ocean, though their composition converges with that of cosmopolitan marine microbes belonging to Proteobacteria, Bacteroidetes, and Actinobacteria. Similarly, viral communities were also divergent in structure and composition but matched known viral members from North America and the southern oceans. Microbial communities were functionally enriched in ARGs dominated by beta-lactams and tetracyclines, bacitracin, and the group macrolide–lincosamide–streptogramin (MLS) but were not different from other communities from the South Atlantic, South Pacific, and Southern Oceans. Similarly, viral communities were characterized by exhibiting protein clusters similar to those described globally (Tara Oceans Virome); however, Comau Fjord viromes displayed up to 50% uniqueness in their protein content. Altogether, our results indicate that microbial and viral communities from the Comau Fjord are a reservoir of untapped diversity and that, given the increasing anthropogenic impacts in the region, they warrant further study, specifically regarding resilience and resistance against antimicrobials and hydrocarbons.

Evaluation of the Diagnostic Performance of mNGS in Detecting Intra-Abdominal Infections of the Emergency Department Patients

Purpose

Intra-abdominal infections (IAI) are gradually becoming common in the emergency department, though the incidence is low and the prognosis is fair, as the symptoms are similar to other intra-abdominal diseases, rapid and accurate diagnosis of the causative agents is essential for clinical management. This study aimed to evaluate the diagnostic performance of metagenomic next-generation sequencing (mNGS) in detecting IAI in the emergency department.

Patients and Methods

This was a retrospective, single-centered study including patients admitted to the emergency department from January 1st, 2021 to August 31st, 2022 with diagnosis of IAI. The comparison between mNGS and microbial culture using paracentesis fluid samples was performed to evaluate the diagnostic performance of mNGS for IAI. Meanwhile, paracentesis fluid and peripheral blood mNGS were compared to explore the sample specificity. Further, the microbial community structure of the patients with pyogenic liver abscesses (PLA) was analyzed.

Results

Thirty-four IAI patients including 23 with pyogenic liver abscesses (PLA), 3 with parapancreatic abscesses, and 8 with other IAI were included in this study. Compared with the conventional microbial culture of paracentesis fluid, mNGS using paracentesis fluid detected more positive cases of IAI (93.75% vs 81.25%), and identified more species of pathogens, especially in obligate anaerobes and viral pathogens. Peripheral blood mNGS presented a relatively high consistency with the paracentesis fluid mNGS (91% mutual positive). The microbial community structure of PLA patients with diabetes is less diverse than that of those without diabetes. Patients with diabetes are at high risk of PLA caused by Klebsiella pneumonia.

Conclusion

mNGS has advantages in detecting IAI in the emergency department, and peripheral blood mNGS can be a non-invasive choice for early diagnosis.

A Metagenomic Investigation of Spatial and Temporal Changes in Sewage Microbiomes across a University Campus

Wastewater microbial communities are not static and can vary significantly across time and space, but this variation and the factors driving the observed spatiotemporal variation often remain undetermined. We used a shotgun metagenomic approach to investigate changes in wastewater microbial communities across 17 locations in a sewer network, with samples collected from each location over a 3-week period. Fecal material-derived bacteria constituted a relatively small fraction of the taxa found in the collected samples, highlighting the importance of environmental sources to the sewage microbiome. The prokaryotic communities were highly variable in composition depending on the location within the sampling network, and this spatial variation was most strongly associated with location-specific differences in sewage pH. However, we also observed substantial temporal variation in the composition of the prokaryotic communities at individual locations. This temporal variation was asynchronous across sampling locations, emphasizing the importance of independently considering both spatial and temporal variation when assessing the wastewater microbiome. The spatiotemporal patterns in viral community composition closely tracked those of the prokaryotic communities, allowing us to putatively identify the bacterial hosts of some of the dominant viruses in these systems. Finally, we found that antibiotic resistance gene profiles also exhibit a high degree of spatiotemporal variability, with most of these genes unlikely to be derived from fecal bacteria. Together, these results emphasize the dynamic nature of the wastewater microbiome, the challenges associated with studying these systems, and the utility of metagenomic approaches for building a multifaceted understanding of these microbial communities and their functional attributes. IMPORTANCE Sewage systems harbor extensive microbial diversity, including microbes derived from both human and environmental sources. Studies of the sewage microbiome are useful for monitoring public health and the health of our infrastructure, but the sewage microbiome can be highly variable in ways that are often unresolved. We sequenced DNA recovered from wastewater samples collected over a 3-week period at 17 locations in a single sewer system to determine how these communities vary across time and space. Most of the wastewater bacteria, and the antibiotic resistance genes they harbor, were not derived from human feces, but human usage patterns did impact how the amounts and types of bacteria and bacterial genes we found in these systems varied over time. Likewise, the wastewater communities, including both bacteria and their viruses, varied depending on location within the sewage network, highlighting the challenges and opportunities in efforts to monitor and understand the sewage microbiome.

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

Large Scale Genome-Centric Metagenomic Data from the Gut Microbiome of Food-Producing Animals and Humans

The One Health concept is a global strategy to study the relationship between human and animal health and the transfer of pathogenic and non-pathogenic species between these systems. However, to the best of our knowledge, no data based on One Health genome-centric metagenomics are available in public repositories. Here, we present a dataset based on a pilot-study of 2,915 metagenome-assembled genomes (MAGs) of 107 samples from the human (N = 34), cattle (N = 28), swine (N = 15) and poultry (N = 30) gut microbiomes. Samples were collected from the five Brazilian geographical regions. Of the draft genomes, 1,273 were high-quality drafts (≥90% of completeness and ≤5% of contamination), and 1,642 were medium-quality drafts (≥50% of completeness and ≤10% of contamination). Taxonomic predictions were based on the alignment and concatenation of single-marker genes, and the most representative phyla were Bacteroidota, Firmicutes, and Proteobacteria. Many of these species represent potential pathogens that have already been described or potential new families, genera, and species with potential biotechnological applications. Analyses of this dataset will highlight discoveries about the ecology and functional role of pathogens and uncultivated Archaea and Bacteria from food-producing animals and humans. Furthermore, it also represents an opportunity to describe new species from underrepresented taxonomic groups.

Measurement(s)	Metagenome
Technology Type(s)	Illumina Sequencing

Smart Multifunctional Polymer Systems as Alternatives or Supplements of Antibiotics To Overcome Bacterial Resistance

In recent years, infectious diseases have again become a critical threat to global public health largely due to the challenges posed by antimicrobial resistance. Conventional antibiotics have played a crucial role in combating bacterial infections; however, their efficacy is significantly impaired by widespread drug resistance. Natural antimicrobial peptides (AMPs) and their polymeric mimics demonstrate great potential for killing bacteria with low propensity of resistance as they target the microbial membrane rather than a specific molecular target, but they are also toxic to the host eukaryotic cells. To minimize antibiotics systemic spread and the required dose that promote resistance and to advocate practical realization of the promising activity of AMPs and polymers, smart systems to target bacteria are highly sought after. This review presents bacterial recognition by various specific targeting molecules and the delivery systems of active components in supramolecules. Bacteria-induced activations of antimicrobial-based nanoformulations are also included. Recent advances in the bacteria targeting and delivery of synthetic antimicrobial agents may assist in developing new classes of highly selective antimicrobial systems which can improve bactericidal efficacy and greatly minimize the spread of bacterial resistance.

Identification of Protein Drug Targets of Biofilm Formation and Quorum Sensing in Multidrug Resistant Enterococcus faecalis

Abstract:

Enterococcus faecalis (E. faecalis) is an opportunistic multidrug-resistant (MDR) pathogen found in the guts of humans and farmed animals. Due to the occurrence of (MDR) strain there is an urgent need to look for an alternative treatment approach. E. faecalis is a Gram-positive bacterium, which is among the most prevalent multidrug resistant hospital pathogens. Its ability to develop quorum sensing (QS) mediated biofilm formation further exacerbates the pathogenicity and triggers lifethreatening infections. Therefore, developing a suitable remedy for curing E. faecalis mediated enterococcal infections is an arduous task. Several putative virulence factors and proteins are involved in the development of biofilms in E. faecalis. Such proteins often play important roles in virulence, disease, and colonization by pathogens. The elucidation of the structure-function relationship of such protein drug targets and the interacting compounds could provide an attractive paradigm towards developing structure-based drugs against E. faecalis. This review provides a comprehensive overview of the current status, enigmas that warrant further studies, and the prospects toward alleviating the antibiotic resistance in E. faecalis. Specifically, the role of biofilm and quorum sensing (QS) in the emergence of MDR strains had been elaborated along with the importance of the protein drug targets involved in both the processes.

Monitoring antibiotic resistance genes in wastewater environments: The challenges of filling a gap in the One-Health cycle

Antibiotic resistance (AR) is a global problem requiring international cooperation and coordinated action. Global monitoring must rely on methods available and comparable across nations to quantify AR occurrence and identify sources and reservoirs, as well as paths of AR dissemination. Numerous analytical tools that are gaining relevance in microbiology, have the potential to be applied to AR research. This review summarizes the state of the art of AR monitoring methods, considering distinct needs, objectives and available resources. Based on the overview of distinct approaches that are used or can be adapted to monitor AR, it is discussed the potential to establish reliable and useful monitoring schemes that can be implemented in distinct contexts. This discussion places the environmental monitoring within the One-Health approach, where two types of risk, dissemination across distinct environmental compartments, and transmission to humans, must be considered. The plethora of methodological approaches to monitor AR and the variable features of the monitored sites challenge the capacity of the scientific community and policy makers to reach a common understanding. However, the dialogue between different methods and the production of action-oriented data is a priority. The review aims to warm up this discussion.

Profile of Bacteria with ARGs Among Real-World Samples from ICU Admission Patients with Pulmonary Infection Revealed by Metagenomic NGS

Background

Treatment of pulmonary infections in the intensive care unit (ICU) represents a great challenge, especially infections caused by antibiotic resistance pathogens. A thorough and up-to-date knowledge of the local spectrum of antibiotic resistant bacteria can improve the antibiotic treatment efficiency. In this study, we aimed to reveal the profile of bacteria with antibiotic resistance genes (ARGs) in real-world samples from ICU admission patients with pulmonary infection in Mainland, China, by metagenomic next-generation sequencing (mNGS).

Methods

A total of 504 different types of clinical samples from 452 ICU admission patients with pulmonary infection were detected by mNGS analysis.

Results

A total of 485 samples from 434 patients got successful mNGS results. Among 434 patients, one or more bacteria with ARGs were detected in 192 patients (44.24%, 192/434), and ≥2 bacteria with ARGs were detected in 85 (19.59%, 85/434) patients. The predominant detected bacteria were Corynebacterium striatum (C. striatum) (11.76%, 51/434), Acinetobacter baumannii (A. baumannii) (11.52%, 50/434) and Enterococcus faecium (E. faecium) (8.99%, 39/434). ermX conferred resistance to MSL_B and cmx to phenicol were the only two ARGs detected in C. striatum; in A. baumannii, most of ARGs were resistance-nodulation-division (RND)-type efflux pumps genes, which conferred resistance to multi-drug; ermB conferred resistance to MSL_B and efmA to multi-drug were the predominant ARGs in E. faecium. Bacteria with ARGs were detected in 50% (140/280) bronchoalveolar lavage fluid (BALF) and 50.5% (48/95) sputum samples, which were significantly higher than in blood and cerebrospinal fluid (CSF) samples.

Conclusion

High level of bacteria with ARGs was observed in clinical samples, especially BALF and sputum samples from ICU admission patients with pulmonary infection in Mainland, China. And C. striatum resistant to MSL_B and/or phenicol, multi-drug resistance A. baumannii and E. faecium were the lead bacteria.

Phage therapeutics: from promises to practices and prospectives

The rise in multi-drug resistant bacteria and the inability to develop novel antibacterial agents limits our arsenal against infectious diseases. Antibiotic resistance is a global issue requiring an immediate solution, including the development of new antibiotic molecules and other alternative modes of therapy. This article highlights the mechanism of bacteriophage treatment that makes it a real solution for multidrug-resistant infectious diseases. Several case reports identified phage therapy as a potential solution to the emerging challenge of multi-drug resistance. Bacteriophages, unlike antibiotics, have special features, such as host specificity and do not impact other commensals. A new outlook has also arisen with recent advancements in the understanding of phage immunobiology, where phages are repurposed against both bacterial and viral infections. Thus, the potential possibility of phages in COVID-19 patients with secondary bacterial infections has been briefly elucidated. However, significant obstacles that need to be addressed are to design better clinical studies that may contribute to the widespread use of bacteriophage therapy against multi-drug resistant pathogens. In conclusion, antibacterial agents can be used with bacteriophages, i.e. bacteriophage-antibiotic combination therapy, or they can be administered alone in cases when antibiotics are ineffective.

Key points

• AMR, a consequence of antibiotic generated menace globally, has led to the resurgence of phage therapy as an effective and sustainable solution without any side effects and high specificity against refractory MDR bacterial infections.

• Bacteriophages have fewer adverse reactions and can thus be used as monotherapy as well as in conjunction with antibiotics.

• In the context of the COVID-19 pandemic, phage therapy may be a viable option.

Antibiotic resistance: Time of synthesis in a post-genomic age

Antibiotic resistance has been highlighted by international organizations, including World Health Organization, World Bank and United Nations, as one of the most relevant global health problems. Classical approaches to study this problem have focused in infected humans, mainly at hospitals. Nevertheless, antibiotic resistance can expand through different ecosystems and geographical allocations, hence constituting a One-Health, Global-Health problem, requiring specific integrative analytic tools. Antibiotic resistance evolution and transmission are multilayer, hierarchically organized processes with several elements (from genes to the whole microbiome) involved. However, their study has been traditionally gene-centric, each element independently studied. The development of robust-economically affordable whole genome sequencing approaches, as well as other -omic techniques as transcriptomics and proteomics, is changing this panorama. These technologies allow the description of a system, either a cell or a microbiome as a whole, overcoming the problems associated with gene-centric approaches. We are currently at the time of combining the information derived from -omic studies to have a more holistic view of the evolution and spread of antibiotic resistance. This synthesis process requires the accurate integration of -omic information into computational models that serve to analyse the causes and the consequences of acquiring AR, fed by curated databases capable of identifying the elements involved in the acquisition of resistance. In this review, we analyse the capacities and drawbacks of the tools that are currently in use for the global analysis of AR, aiming to identify the more useful targets for effective corrective interventions.

High-throughput molecular analyses of microbiomes as a tool to monitor the wellbeing of aquatic environments

Aquatic environments are the recipients of many sources of environmental stress that trigger both local and global changes. To evaluate the associated risks to organisms and ecosystems more sensitive and accurate strategies are required. The analysis of the microbiome is one of the most promising candidates for environmental diagnosis of aquatic systems. Culture-independent interconnected meta-omic approaches are being increasing used to fill the gaps that classical microbial approaches cannot resolve. Here, we provide a prospective view of the increasing application of these high-throughput molecular technologies to evaluate the structure and functional activity of microbial communities in response to changes and disturbances in the environment, mostly of anthropogenic origin. Some relevant topics are reviewed, such as: (i) the use of microorganisms for water quality assessment, highlighting the incidence of antimicrobial resistance as an increasingly serious threat to global public health; (ii) the crucial role of microorganisms and their complex relationships with the ongoing climate change, and other stress threats; (iii) the responses of the environmental microbiome to extreme pollution conditions, such as acid mine drainage or oil spills. Moreover, protists and viruses, due to their huge impacts on the structure of microbial communities, are emerging candidates for the assessment of aquatic environmental health.

Critical evaluation of short, long, and hybrid assembly for contextual analysis of antibiotic resistance genes in complex environmental metagenomes

In the fight to limit the global spread of antibiotic resistance, the assembly of environmental metagenomes has the potential to provide rich contextual information (e.g., taxonomic hosts, carriage on mobile genetic elements) about antibiotic resistance genes (ARG) in the environment. However, computational challenges associated with assembly can impact the accuracy of downstream analyses. This work critically evaluates the impact of assembly leveraging short reads, nanopore MinION long-reads, and a combination of the two (hybrid) on ARG contextualization for ten environmental metagenomes using seven prominent assemblers (IDBA-UD, MEGAHIT, Canu, Flye, Opera-MS, metaSpades and HybridSpades). While short-read and hybrid assemblies produced similar patterns of ARG contextualization, raw or assembled long nanopore reads produced distinct patterns. Based on an in-silico spike-in experiment using real and simulated reads, we show that low to intermediate coverage species are more likely to be incorporated into chimeric contigs across all assemblers and sequencing technologies, while more abundant species produce assemblies with a greater frequency of inversions and insertion/deletions (indels). In sum, our analyses support hybrid assembly as a valuable technique for boosting the reliability and accuracy of assembly-based analyses of ARGs and neighboring genes at environmentally-relevant coverages, provided that sufficient short-read sequencing depth is achieved.

High-Throughput Metagenomics for Identification of Pathogens in the Clinical Settings

The application of sequencing technology is shifting from research to clinical laboratories owing to rapid technological developments and substantially reduced costs. However, although thousands of microorganisms are known to infect humans, identification of the etiological agents for many diseases remains challenging as only a small proportion of pathogens are identifiable by the current diagnostic methods. These challenges are compounded by the emergence of new pathogens. Hence, metagenomic next-generation sequencing (mNGS), an agnostic, unbiased, and comprehensive method for detection, and taxonomic characterization of microorganisms, has become an attractive strategy. Although many studies, and cases reports, have confirmed the success of mNGS in improving the diagnosis, treatment, and tracking of infectious diseases, several hurdles must still be overcome. It is, therefore, imperative that practitioners and clinicians understand both the benefits and limitations of mNGS when applying it to clinical practice. Interestingly, the emerging third-generation sequencing technologies may partially offset the disadvantages of mNGS. In this review, mainly: a) the history of sequencing technology; b) various NGS technologies, common platforms, and workflows for clinical applications; c) the application of NGS in pathogen identification; d) the global expert consensus on NGS-related methods in clinical applications; and e) challenges associated with diagnostic metagenomics are described.

Antimicrobial resistance in enteric bacteria: current state and next-generation solutions

Antimicrobial resistance is one of the largest threats to global health and imposes substantial burdens in terms of morbidity, mortality, and economic costs. The gut is a key conduit for the genesis and spread of antimicrobial resistance in enteric bacterial pathogens. Distinct bacterial species that cause enteric disease can exist as invasive enteropathogens that immediately evoke gastrointestinal distress, or pathobionts that can arise from established bacterial commensals to inflict dysbiosis and disease. Furthermore, various environmental reservoirs and stressors facilitate the evolution and transmission of resistance. In this review, we present a comprehensive discussion on circulating resistance profiles and gene mobilization strategies of the most problematic species of enteric bacterial pathogens. Importantly, we present emerging approaches toward surveillance of pathogens and their resistance elements as well as promising treatment strategies that can circumvent common resistance mechanisms.

Identification of a Novel Plasmid-Borne Gentamicin Resistance Gene in Nontyphoidal Salmonella Isolated from Retail Turkey

The spread of antibiotic-resistant bacteria presents a global health challenge. Efficient surveillance of bacteria harboring antibiotic resistance genes (ARGs) is a critical aspect to controlling the spread. Increased access to microbial genomic data from many diverse populations informs this surveillance but only when functional ARGs are identifiable within the data set. Current, homology-based approaches are effective at identifying the majority of ARGs within given clinical and nonclinical data sets for several pathogens, yet there are still some whose identities remain elusive. By coupling phenotypic profiling with genotypic data, these unknown ARGs can be identified to strengthen homology-based searches. To prove the efficacy and feasibility of this approach, a published data set from the U.S. National Antimicrobial Resistance Monitoring System (NARMS), for which the phenotypic and genotypic data of 640 Salmonella isolates are available, was subjected to this analysis. Six isolates recovered from the NARMS retail meat program between 2011 and 2013 were identified previously as phenotypically resistant to gentamicin but contained no known gentamicin resistance gene. Using the phenotypic and genotypic data, a comparative genomics approach was employed to identify the gene responsible for the observed resistance in all six of the isolates. This gene, grdA, is harbored on a 9,016-bp plasmid that is transferrable to Escherichia coli, confers gentamicin resistance to E. coli, and has never before been reported to confer gentamicin resistance. Bioinformatic analysis of the encoded protein suggests an ATP binding motif. This work demonstrates the advantages associated with coupling genomics technologies with phenotypic data for novel ARG identification.

Computational Methods for Strain-Level Microbial Detection in Colony and Metagenome Sequencing Data

Metagenomic sequencing is a powerful tool for examining the diversity and complexity of microbial communities. Most widely used tools for taxonomic profiling of metagenomic sequence data allow for a species-level overview of the composition. However, individual strains within a species can differ greatly in key genotypic and phenotypic characteristics, such as drug resistance, virulence and growth rate. Therefore, the ability to resolve microbial communities down to the level of individual strains within a species is critical to interpreting metagenomic data for clinical and environmental applications, where identifying a particular strain, or tracking a particular strain across a set of samples, can help aid in clinical diagnosis and treatment, or in characterizing yet unstudied strains across novel environmental locations. Recently published approaches have begun to tackle the problem of resolving strains within a particular species in metagenomic samples. In this review, we present an overview of these new algorithms and their uses, including methods based on assembly reconstruction and methods operating with or without a reference database. While existing metagenomic analysis methods show reasonable performance at the species and higher taxonomic levels, identifying closely related strains within a species presents a bigger challenge, due to the diversity of databases, genetic relatedness, and goals when conducting these analyses. Selection of which metagenomic tool to employ for a specific application should be performed on a case-by case basis as these tools have strengths and weaknesses that affect their performance on specific tasks. A comprehensive benchmark across different use case scenarios is vital to validate performance of these tools on microbial samples. Because strain-level metagenomic analysis is still in its infancy, development of more fine-grained, high-resolution algorithms will continue to be in demand for the future.

Genomic surveillance of Escherichia coli and Klebsiella spp. in hospital sink drains and patients

Escherichia coli and Klebsiella spp. are important human pathogens that cause a wide spectrum of clinical disease. In healthcare settings, sinks and other wastewater sites have been shown to be reservoirs of antimicrobial-resistant E. coli and Klebsiella spp., particularly in the context of outbreaks of resistant strains amongst patients. Without focusing exclusively on resistance markers or a clinical outbreak, we demonstrate that many hospital sink drains are abundantly and persistently colonized with diverse populations of E. coli, Klebsiella pneumoniae and Klebsiella oxytoca, including both antimicrobial-resistant and susceptible strains. Using whole-genome sequencing of 439 isolates, we show that environmental bacterial populations are largely structured by ward and sink, with only a handful of lineages, such as E. coli ST635, being widely distributed, suggesting different prevailing ecologies, which may vary as a result of different inputs and selection pressures. Whole-genome sequencing of 46 contemporaneous patient isolates identified one (2 %; 95 % CI 0.05-11 %) E. coli urine infection-associated isolate with high similarity to a prior sink isolate, suggesting that sinks may contribute to up to 10 % of infections caused by these organisms in patients on the ward over the same timeframe. Using metagenomics from 20 sink-timepoints, we show that sinks also harbour many clinically relevant antimicrobial resistance genes including blaCTX-M, blaSHV and mcr, and may act as niches for the exchange and amplification of these genes. Our study reinforces the potential role of sinks in contributing to Enterobacterales infection and antimicrobial resistance in hospital patients, something that could be amenable to intervention. This article contains data hosted by Microreact.

Current Trends in Diagnostics of Viral Infections of Unknown Etiology

Viruses are evolving at an alarming rate, spreading and inconspicuously adapting to cutting-edge therapies. Therefore, the search for rapid, informative and reliable diagnostic methods is becoming urgent as ever. Conventional clinical tests (PCR, serology, etc.) are being continually optimized, yet provide very limited data. Could high throughput sequencing (HTS) become the future gold standard in molecular diagnostics of viral infections? Compared to conventional clinical tests, HTS is universal and more precise at profiling pathogens. Nevertheless, it has not yet been widely accepted as a diagnostic tool, owing primarily to its high cost and the complexity of sample preparation and data analysis. Those obstacles must be tackled to integrate HTS into daily clinical practice. For this, three objectives are to be achieved: (1) designing and assessing universal protocols for library preparation, (2) assembling purpose-specific pipelines, and (3) building computational infrastructure to suit the needs and financial abilities of modern healthcare centers. Data harvested with HTS could not only augment diagnostics and help to choose the correct therapy, but also facilitate research in epidemiology, genetics and virology. This information, in turn, could significantly aid clinicians in battling viral infections.