Nanopore ultra-long read sequencing technology for antimicrobial resistance detection in Mannheimia haemolytica

Laboratory tools for the direct detection of bacterial respiratory infections and antimicrobial resistance: a scoping review

Rapid laboratory tests are urgently required to inform antimicrobial use in food animals. Our objective was to synthesize knowledge on the direct application of long-read metagenomic sequencing to respiratory samples to detect bacterial pathogens and antimicrobial resistance genes (ARGs) compared to PCR, loop-mediated isothermal amplification, and recombinase polymerase amplification. Our scoping review protocol followed the Joanna Briggs Institute and PRISMA Scoping Review reporting guidelines. Included studies reported on the direct application of these methods to respiratory samples from animals or humans to detect bacterial pathogens ±ARGs and included turnaround time (TAT) and analytical sensitivity. We excluded studies not reporting these or that were focused exclusively on bioinformatics. We identified 5,636 unique articles from 5 databases. Two-reviewer screening excluded 3,964, 788, and 784 articles at 3 levels, leaving 100 articles (19 animal and 81 human), of which only 7 studied long-read sequencing (only 1 in animals). Thirty-two studies investigated ARGs (only one in animals). Reported TATs ranged from minutes to 2 d; steps did not always include sample collection to results, and analytical sensitivity varied by study. Our review reveals a knowledge gap in research for the direct detection of bacterial respiratory pathogens and ARGs in animals using long-read metagenomic sequencing. There is an opportunity to harness the rapid development in this space to detect multiple pathogens and ARGs on a single sequencing run. Long-read metagenomic sequencing tools show potential to address the urgent need for research into rapid tests to support antimicrobial stewardship in food animal production.

Antibiotic resistance in plant growth promoting bacteria: A comprehensive review and future perspectives to mitigate potential gene invasion risks

Plant growth-promoting bacteria (PGPB) are endowed with several attributes that can be beneficial for host plants. They opened myriad doors toward green technology approach to reduce the use of chemical inputs, improve soil fertility, and promote plants’ health. However, many of these PGPB harbor antibiotic resistance genes (ARGs). Less attention has been given to multi-resistant bacterial bioinoculants which may transfer their ARGs to native soil microbial communities and other environmental reservoirs including animals, waters, and humans. Therefore, large-scale inoculation of crops by ARGs-harboring bacteria could worsen the evolution and dissemination of antibiotic resistance and aggravate the negative impacts on such ecosystem and ultimately public health. Their introduction into the soil could serve as ARGs invasion which may inter into the food chain. In this review, we underscore the antibiotic resistance of plant-associated bacteria, criticize the lack of consideration for this phenomenon in the screening and application processes, and provide some recommendations as well as a regulation framework relating to the development of bacteria-based biofertilizers to aid maximizing their value and applications in crop improvement while reducing the risks of ARGs invasion.

Evaluating the potential of third generation metagenomic sequencing for the detection of BRD pathogens and genetic determinants of antimicrobial resistance in chronically ill feedlot cattle

Background

Bovine respiratory disease (BRD) is an important cause of morbidity and mortality and is responsible for most of the injectable antimicrobial use in the feedlot industry. Traditional bacterial culture can be used to diagnose BRD by confirming the presence of causative pathogens and to support antimicrobial selection. However, given that bacterial culture takes up to a week and early intervention is critical for treatment success, culture has limited utility for informing rapid therapeutic decision-making. In contrast, metagenomic sequencing has the potential to quickly resolve all nucleic acid in a sample, including pathogen biomarkers and antimicrobial resistance genes. In particular, third-generation Oxford Nanopore Technology sequencing platforms provide long reads and access to raw sequencing data in real-time as it is produced, thereby reducing the time from sample collection to diagnostic answer. The purpose of this study was to compare the performance of nanopore metagenomic sequencing to traditional culture and sensitivity methods as applied to nasopharyngeal samples from segregated groups of chronically ill feedlot cattle, previously treated with antimicrobials for nonresponsive pneumonia or lameness.

Results

BRD pathogens were isolated from most samples and a variety of different resistance profiles were observed across isolates. The sequencing data indicated the samples were dominated by Moraxella bovoculi, Mannheimia haemolytica, Mycoplasma dispar, and Pasteurella multocida, and included a wide range of antimicrobial resistance genes (ARGs), encoding resistance for up to seven classes of antimicrobials. Genes conferring resistance to beta-lactams were the most commonly detected, while the tetH gene was detected in the most samples overall. Metagenomic sequencing detected the BRD pathogens of interest more often than did culture, but there was limited concordance between phenotypic resistance to antimicrobials and the presence of relevant ARGs.

Conclusions

Metagenomic sequencing can reduce the time from sampling to results, detect pathogens missed by bacterial culture, and identify genetically encoded determinants of resistance. Increasing sequencing coverage of target organisms will be an essential component of improving the reliability of this technology, such that it can be better used for the surveillance of pathogens of interest, genetic determinants of resistance, and to inform diagnostic decisions.

Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective

Antimicrobial resistance (AMR) is a global health crisis that poses a great threat to modern medicine. Effective prevention strategies are urgently required to slow the emergence and further dissemination of AMR. Given the availability of data sets encompassing hundreds or thousands of pathogen genomes, machine learning (ML) is increasingly being used to predict resistance to different antibiotics in pathogens based on gene content and genome composition. A key objective of this work is to advocate for the incorporation of ML into front-line settings but also highlight the further refinements that are necessary to safely and confidently incorporate these methods. The question of what to predict is not trivial given the existence of different quantitative and qualitative laboratory measures of AMR. ML models typically treat genes as independent predictors, with no consideration of structural and functional linkages; they also may not be accurate when new mutational variants of known AMR genes emerge. Finally, to have the technology trusted by end users in public health settings, ML models need to be transparent and explainable to ensure that the basis for prediction is clear. We strongly advocate that the next set of AMR-ML studies should focus on the refinement of these limitations to be able to bridge the gap to diagnostic implementation.

Bovine Respiratory Disease: Conventional to Culture-Independent Approaches to Studying Antimicrobial Resistance in North America

Numerous antimicrobial resistance (AMR) surveillance studies have been conducted in North American feedlot cattle to investigate the major bacterial pathogens of the bovine respiratory disease (BRD) complex, specifically: Mannheimia haemolytica, Pasteurella multocida, Histophilus somni, and Mycoplasma bovis. While most bacterial isolates recovered from healthy cattle are susceptible to a repertoire of antimicrobials, multidrug resistance is common in isolates recovered from cattle suffering from BRD. Integrative and conjugative elements (ICE) have gained increasing notoriety in BRD-Pasteurellaceae as they appear to play a key role in the concentration and dissemination of antimicrobial resistant genes. Likewise, low macrolide susceptibility has been described in feedlot isolates of M. bovis. Horizontal gene transfer has also been implicated in the spread of AMR within mycoplasmas, and in-vitro experiments have shown that exposure to antimicrobials can generate high levels of resistance in mycoplasmas via a single conjugative event. Consequently, antimicrobial use (AMU) could be accelerating AMR horizontal transfer within all members of the bacterial BRD complex. While metagenomics has been applied to the study of AMR in the microbiota of the respiratory tract, the potential role of the respiratory tract microbiome as an AMR reservoir remains uncertain. Current and prospective molecular tools to survey and characterize AMR need to be adapted as point-of-care technologies to enhance prudent AMU in the beef industry.

The Methods of Digging for "Gold" within the Salt: Characterization of Halophilic Prokaryotes and Identification of Their Valuable Biological Products Using Sequencing and Genome Mining Tools

Halophiles, the salt-loving organisms, have been investigated for at least a hundred years. They are found in all three domains of life, namely Archaea, Bacteria, and Eukarya, and occur in saline and hypersaline environments worldwide. They are already a valuable source of various biomolecules for biotechnological, pharmaceutical, cosmetological and industrial applications. In the present era of multidrug-resistant bacteria, cancer expansion, and extreme environmental pollution, the demand for new, effective compounds is higher and more urgent than ever before. Thus, the unique metabolism of halophilic microorganisms, their low nutritional requirements and their ability to adapt to harsh conditions (high salinity, high pressure and UV radiation, low oxygen concentration, hydrophobic conditions, extreme temperatures and pH, toxic compounds and heavy metals) make them promising candidates as a fruitful source of bioactive compounds. The main aim of this review is to highlight the nucleic acid sequencing experimental strategies used in halophile studies in concert with the presentation of recent examples of bioproducts and functions discovered in silico in the halophile's genomes. We point out methodological gaps and solutions based on in silico methods that are helpful in the identification of valuable bioproducts synthesized by halophiles. We also show the potential of an increasing number of publicly available genomic and metagenomic data for halophilic organisms that can be analysed to identify such new bioproducts and their producers.

Erythromycin loaded by tetrahedral framework nucleic acids are more antimicrobial sensitive against Escherichia coli (E. coli)

Erythromycin is a commonly used broad-spectrum antibiotic, but resistance to this antibiotic makes its use less effective. Considerable efforts, beside finding alternatives, are needed to enhance its antimicrobial effect and stability against bacteria. Tetrahedral framework nucleic acids (tFNAs), a novel delivery vehicle with a three-dimensional nanostructure, have been studied as a carrying platform of antineoplastic drugs. In this study, the use of tFNAs in delivering erythromycin into Escherichia coli (E. coli) was investigated for the first time. The tFNAs vehicle increased the bacterial uptake of erythromycin and promoted membrane destabilization. Moreover, it increased the permeability of the bacterial cell wall, and reduced drug resistance by improving the movement of the drug across the membrane. The tFNAs-based delivery system enhanced the effects of erythromycin against E. coli. It may therefore provide an effective delivery vehicle for erythromycin in targeting antibiotic-resistant bacteria with thick cell wall.

Genomic Prediction of Antimicrobial Resistance: Ready or Not, Here It Comes!

BACKGROUND

Next-generation sequencing (NGS) technologies are being used to predict antimicrobial resistance. The field is evolving rapidly and transitioning out of the research setting into clinical use. Clinical laboratories are evaluating the accuracy and utility of genomic resistance prediction, including methods for NGS, downstream bioinformatic pipeline components, and the clinical settings in which this type of testing should be offered.

CONTENT

We describe genomic sequencing as it pertains to predicting antimicrobial resistance in clinical isolates and samples. We elaborate on current methodologies and workflows to perform this testing and summarize the current state of genomic resistance prediction in clinical settings. To highlight this aspect, we include 3 medically relevant microorganism exemplars: Mycobacterium tuberculosis, Staphylococcus aureus, and Neisseria gonorrhoeae. Last, we discuss the future of genomic-based resistance detection in clinical microbiology laboratories.

SUMMARY

Antimicrobial resistance prediction by genomic approaches is in its infancy for routine patient care. Genomic approaches have already added value to the current diagnostic testing landscape in specific circumstances and will play an increasingly important role in diagnostic microbiology. Future advancements will shorten turnaround time, reduce costs, and improve our analysis and interpretation of clinically actionable results.

Rapid Methods for Antimicrobial Resistance Diagnostics

Antimicrobial resistance (AMR) is one of the most challenging threats in public health; thus, there is a growing demand for methods and technologies that enable rapid antimicrobial susceptibility testing (AST). The conventional methods and technologies addressing AMR diagnostics and AST employed in clinical microbiology are tedious, with high turnaround times (TAT), and are usually expensive. As a result, empirical antimicrobial therapies are prescribed leading to AMR spread, which in turn causes higher mortality rates and increased healthcare costs. This review describes the developments in current cutting-edge methods and technologies, organized by key enabling research domains, towards fighting the looming AMR menace by employing recent advances in AMR diagnostic tools. First, we summarize the conventional methods addressing AMR detection, surveillance, and AST. Thereafter, we examine more recent non-conventional methods and the advancements in each field, including whole genome sequencing (WGS), matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectrometry, Fourier transform infrared (FTIR) spectroscopy, and microfluidics technology. Following, we provide examples of commercially available diagnostic platforms for AST. Finally, perspectives on the implementation of emerging concepts towards developing paradigm-changing technologies and methodologies for AMR diagnostics are discussed.

Oxford nanopore sequencing in clinical microbiology and infection diagnostics

Extended turnaround times and large economic costs hinder the usage of currently applied screening methods for bacterial pathogen identification (ID) and antimicrobial susceptibility testing. This review provides an overview of current detection methods and their usage in a clinical setting. Issues of timeliness and cost could soon be circumvented, however, with the emergence of detection methods involving single molecule sequencing technology. In the context of bringing diagnostics closer to the point of care, we examine the current state of Oxford Nanopore Technologies (ONT) products and their interaction with third-party software/databases to assess their capabilities for ID and antimicrobial resistance (AMR) prediction. We outline and discuss a potential diagnostic workflow, enumerating (1) rapid sample prep kits, (2) ONT hardware/software and (3) third-party software and databases to improve the cost, accuracy and turnaround times for ID and AMR. Multiple studies across a range of infection types support that the speed and accuracy of ONT sequencing is now such that established ID and AMR prediction tools can be used on its outputs, and so it can be harnessed for near real time, close to the point-of-care diagnostics in common clinical circumstances.

The Future of Livestock Management: A Review of Real-Time Portable Sequencing Applied to Livestock

Oxford Nanopore Technologies' MinION has proven to be a valuable tool within human and microbial genetics. Its capacity to produce long reads in real time has opened up unique applications for portable sequencing. Examples include tracking the recent African swine fever outbreak in China and providing a diagnostic tool for disease in the cassava plant in Eastern Africa. Here we review the current applications of Oxford Nanopore sequencing in livestock, then focus on proposed applications in livestock agriculture for rapid diagnostics, base modification detection, reference genome assembly and genomic prediction. In particular, we propose a future application: 'crush-side genotyping' for real-time on-farm genotyping for extensive industries such as northern Australian beef production. An initial in silico experiment to assess the feasibility of crush-side genotyping demonstrated promising results. SNPs were called from simulated Nanopore data, that included the relatively high base call error rate that is characteristic of the data, and calling parameters were varied to understand the feasibility of SNP calling at low coverages in a heterozygous population. With optimised genotype calling parameters, over 85% of the 10,000 simulated SNPs were able to be correctly called with coverages as low as 6×. These results provide preliminary evidence that Oxford Nanopore sequencing has potential to be used for real-time SNP genotyping in extensive livestock operations.

High quality genome assemblies of Mycoplasma bovis using a taxon-specific Bonito basecaller for MinION and Flongle long-read nanopore sequencing

BACKGROUND

Implementation of Third-Generation Sequencing approaches for Whole Genome Sequencing (WGS) all-in-one diagnostics in human and veterinary medicine, requires the rapid and accurate generation of consensus genomes. Over the last years, Oxford Nanopore Technologies (ONT) released various new devices (e.g. the Flongle R9.4.1 flow cell) and bioinformatics tools (e.g. the in 2019-released Bonito basecaller), allowing cheap and user-friendly cost-efficient introduction in various NGS workflows. While single read, overall consensus accuracies, and completeness of genome sequences has been improved dramatically, further improvements are required when working with non-frequently sequenced organisms like Mycoplasma bovis. As an important primary respiratory pathogen in cattle, rapid M. bovis diagnostics is crucial to allow timely and targeted disease control and prevention. Current complete diagnostics (including identification, strain typing, and antimicrobial resistance (AMR) detection) require combined culture-based and molecular approaches, of which the first can take 1-2 weeks. At present, cheap and quick long read all-in-one WGS approaches can only be implemented if increased accuracies and genome completeness can be obtained.

RESULTS

Here, a taxon-specific custom-trained Bonito v.0.1.3 basecalling model (custom-pg45) was implemented in various WGS assembly bioinformatics pipelines. Using MinION sequencing data, we showed improved consensus accuracies up to Q45.2 and Q46.7 for reference-based and Canu de novo assembled M. bovis genomes, respectively. Furthermore, the custom-pg45 model resulted in mean consensus accuracies of Q45.0 and genome completeness of 94.6% for nine M. bovis field strains. Improvements were also observed for the single-use Flongle sequencer (mean Q36.0 accuracies and 80.3% genome completeness).

CONCLUSIONS

These results implicate that taxon-specific basecalling of MinION and single-use Flongle Nanopore long reads are of great value to be implemented in rapid all-in-one WGS tools as evidenced for Mycoplasma bovis as an example.

New insights into Arabidopsis transcriptome complexity revealed by direct sequencing of native RNAs

Arabidopsis thaliana transcriptomes have been extensively studied and characterized under different conditions. However, most of the current ‘RNA-sequencing’ technologies produce a relatively short read length and demand a reverse-transcription step, preventing effective characterization of transcriptome complexity. Here, we performed Direct RNA Sequencing (DRS) using the latest Oxford Nanopore Technology (ONT) with exceptional read length. We demonstrate that the complexity of the A. thaliana transcriptomes has been substantially under-estimated. The ONT direct RNA sequencing identified novel transcript isoforms at both the vegetative (14-day old seedlings, stage 1.04) and reproductive stages (stage 6.00–6.10) of development. Using in-house software called TrackCluster, we determined alternative transcription initiation (ATI), alternative polyadenylation (APA), alternative splicing (AS), and fusion transcripts. More than 38 500 novel transcript isoforms were identified, including six categories of fusion-transcripts that may result from differential RNA processing mechanisms. Aided by the Tombo algorithm, we found an enrichment of m5C modifications in the mobile mRNAs, consistent with a recent finding that m5C modification in mRNAs is crucial for their long-distance movement. In summary, ONT DRS offers an advantage in the identification and functional characterization of novel RNA isoforms and RNA base modifications, significantly improving annotation of the A. thaliana genome.

Determining antimicrobial resistance profiles and identifying novel mutations of Neisseria gonorrhoeae genomes obtained by multiplexed MinION sequencing

Gonorrhea is one of the most common sexually transmitted diseases worldwide. To cure infection and prevent transmission, timely and appropriate antimicrobial therapy is necessary. Unfortunately, Neisseria gonorrhoeae, the etiological agent of gonorrhea, has acquired nearly all known mechanisms of antimicrobial resistance (AMR), thereby compromising the efficacy of antimicrobial therapy. Treatment failure resulting from AMR has become a global public health concern. Whole-genome sequencing is an effective method to determine the AMR characteristics of N. gonorrhoeae. Compared with next-generation sequencing, the MinION sequencer (Oxford Nanopore Technologies (ONT)) has the advantages of long read length and portability. Based on a pilot study using MinION to sequence the genome of N. gonorrhoeae, we optimized the workflow of sequencing and data analysis in the current study. Here we sequenced nine isolates within one flow cell using a multiplexed sequencing strategy. After hybrid assembly with Illumina reads, nine integral circular chromosomes were obtained. By using the online tool Pathogenwatch and a BLAST-based workflow, we acquired complete AMR profiles related to seven classes of antibiotics. We also evaluated the performance of ONT-only assemblies. Most AMR determinants identified by ONT-only assemblies were the same as those identified by hybrid assemblies. Moreover, one of the nine assemblies indicated a potentially novel antimicrobial-related mutation located in mtrR which results in a frame-shift, premature stop codon, and truncated peptide. In addition, this is the first study using the MinION sequencer to obtain complete genome sequences of N. gonorrhoeae strains which are epidemic in China. This study shows that complete genome sequences and antimicrobial characteristics of N. gonorrhoeae can be obtained using the MinION sequencer in a simple and cost-effective manner, with hardly any knowledge of bioinformatics required. More importantly, this strategy provides us with a potential approach to discover new AMR determinants.

Simultaneous detection and comprehensive analysis of HPV and microbiome status of a cervical liquid-based cytology sample using Nanopore MinION sequencing

Human papillomavirus (HPV) is a major pathogen that causes cervical cancer and many other related diseases. HPV infection related cervical microbiome could be an induce factor of cervical cancer. However, it is uncommon to find a single test on the market that can simultaneously provide information on both HPV and the microbiome. Herein, a novel method was developed in this study to simultaneously detect HPV infection and microbiota composition promptly and accurately. It provides a new and simple way to detect vaginal pathogen situation and also provide valuable information for clinical diagnose. This approach combined multiplex PCR, which targeted both HPV16 E6E7 and full-length 16S rRNA, and Nanopore sequencing to generate enough information to understand the vagina condition of patients. One HPV positive liquid-based cytology (LBC) sample was sequenced and analyzed. After comparing with Illumina sequencing, the results from Nanopore showed a similar microbiome composition. An instant sequencing evaluation showed that 15 min sequencing is enough to identify the top 10 most abundant bacteria. Moreover, two HPV integration sites were identified and verified by Sanger sequencing. This approach has many potential applications in pathogen detection and can potentially aid in providing a more rapid clinical diagnosis.

Pan-Genomic and Polymorphic Driven Prediction of Antibiotic Resistance in Elizabethkingia

The Elizabethkingia are a genetically diverse genus of emerging pathogens that exhibit multidrug resistance to a range of common antibiotics. Two representative species, Elizabethkingia bruuniana and E. meningoseptica, were phenotypically tested to determine minimum inhibitory concentrations (MICs) for five antibiotics. Ultra-long read sequencing with Oxford Nanopore Technologies (ONT) and subsequent de novo assembly produced complete, gapless circular genomes for each strain. Alignment based annotation with Prokka identified 5,480 features in E. bruuniana and 5,203 features in E. meningoseptica, where none of these identified genes or gene combinations corresponded to observed phenotypic resistance values. Pan-genomic analysis, performed with an additional 19 Elizabethkingia strains, identified a core-genome size of 2,658,537 bp, 32 uniquely identifiable intrinsic chromosomal antibiotic resistance core-genes and 77 antibiotic resistance pan-genes. Using core-SNPs and pan-genes in combination with six machine learning (ML) algorithms, binary classification of clindamycin and vancomycin resistance achieved f1 scores of 0.94 and 0.84, respectively. Performance on the more challenging multiclass problem for fusidic acid, rifampin and ciprofloxacin resulted in f1 scores of 0.70, 0.75, and 0.54, respectively. By producing two sets of quality biological predictors, pan-genome genes and core-genome SNPs, from long-read sequence data and applying an ensemble of ML techniques, our results demonstrated that accurate phenotypic inference, at multiple AMR resolutions, can be achieved.