Transforming clinical microbiology with bacterial genome sequencing

Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning

The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity within many bacterial species. Awareness of this genetic heterogeneity has corresponded with a greater appreciation of intraspecies variation in virulence. A number of comparative genomic strategies have been developed to link these genotypic and pathogenic differences with the aim of discovering novel virulence factors. Here, we review recent advances in comparative genomic approaches to identify bacterial virulence determinants, with a focus on genome-wide association studies and machine learning.

Predominance of CTX-M-15-producing ST131 strains among ESBL-producing Escherichia coli isolated from asylum seekers in the Netherlands

Objectives

Numerous studies show increased prevalence of MDR bacteria amongst asylum seekers, but data on the molecular profiles of such strains are limited. We aimed to evaluate the molecular profiles of ESBL-producing Escherichia coli (ESBL-E. coli) strains isolated from asylum seekers and investigate their phylogenetic relatedness.

Methods

WGS data of ESBL-E. coli isolates from asylum seekers, retrieved from 1 January to 31 December 2016, were analysed to assess MLST STs, fim types, phylogroups and resistance genes. Fifty-two ESBL-E. coli isolates from the Dutch–German border region were used for genome comparison purposes as a control group.

Results

Among 112 ESBL-E. coli isolates from asylum seekers, originating mostly from Syria (n = 40) and Iraq (n = 15), the majority belonged to ST131 (21.4%) and ST10 (17.0%). The predominant gene for β-lactam resistance was bla_CTX-M-15 (67.9%), followed by the often co-detected bla_TEM-1B (39.3%). No mcr or carbapenemase genes were detected. The majority of the strains belonged to phylogroups B2 (38.4%) and A (32.1%), carrying fimH27 (25%) and fimH30 (19.6%). A core genome MLST minimum spanning tree did not reveal clusters containing strains from the asylum seekers and the control group. Five clusters were formed within the asylum seeker group, by strains isolated from people originating from different countries.

Conclusions

The most frequently isolated clones in this study were isolated on a regular basis within the Dutch population before the increase in the asylum seeker population. No mcr- or carbapenemase-producing clones were detected among the asylum seeker population. Minor clustering was observed amongst the asylum seeker strains.

Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review

Background

Antimicrobial resistance (AMR) is a rising health threat with 10 million annual casualties estimated by 2050. Appropriate treatment of infectious diseases with the right antibiotics reduces the spread of antibiotic resistance. Today, clinical practice relies on molecular and PCR techniques for pathogen identification and culture-based antibiotic susceptibility testing (AST). Recently, WGS has started to transform clinical microbiology, enabling prediction of resistance phenotypes from genotypes and allowing for more informed treatment decisions. WGS-based AST (WGS-AST) depends on the detection of AMR markers in sequenced isolates and therefore requires AMR reference databases. The completeness and quality of these databases are material to increase WGS-AST performance.

Methods

We present a systematic evaluation of the performance of publicly available AMR marker databases for resistance prediction on clinical isolates. We used the public databases CARD and ResFinder with a final dataset of 2587 isolates across five clinically relevant pathogens from PATRIC and NDARO, public repositories of antibiotic-resistant bacterial isolates.

Results

CARD and ResFinder WGS-AST performance had an overall balanced accuracy of 0.52 (±0.12) and 0.66 (±0.18), respectively. Major error rates were higher in CARD (42.68%) than ResFinder (25.06%). However, CARD showed almost no very major errors (1.17%) compared with ResFinder (4.42%).

Conclusions

We show that AMR databases need further expansion, improved marker annotations per antibiotic rather than per antibiotic class and validated multivariate marker panels to achieve clinical utility, e.g. in order to meet performance requirements such as provided by the FDA for clinical microbiology diagnostic testing.

Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research

Antimicrobial resistance (AMR) remains one of the most challenging phenomena of modern medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the development of algorithms that learn how to accurately predict outcome variables using large sets of predictor variables that are typically not hand selected and are minimally curated. Models are parameterized using a training data set and then applied to a test data set on which predictive performance is evaluated. The application of ML algorithms to the problem of AMR has garnered increasing interest in the past 5 years due to the exponential growth of experimental and clinical data, heavy investment in computational capacity, improvements in algorithm performance, and increasing urgency for innovative approaches to reducing the burden of disease. Here, we review the current state of research at the intersection of ML and AMR with an emphasis on three domains of work. The first is the prediction of AMR using genomic data. The second is the use of ML to gain insight into the cellular functions disrupted by antibiotics, which forms the basis for understanding mechanisms of action and developing novel anti-infectives. The third focuses on the application of ML for antimicrobial stewardship using data extracted from the electronic health record. Although the use of ML for understanding, diagnosing, treating, and preventing AMR is still in its infancy, the continued growth of data and interest ensures it will become an important tool for future translational research programs.

Cationic Tricyclic AIEgens for Concomitant Bacterial Discrimination and Inhibition

New ionic compounds with aggregation-induced emission (AIE) feature has been widely studied. These AIE-based luminogens (AIEgens) not only effectively resolve aggregation-caused quenching (ACQ) problems that are encountered for most of conventional fluorescent dyes, but also exhibit promising applications in biological imaging, potentially for a wide variety of diseases. However, such an AIE system needs to be further developed. In this work, a series of novel cationic AIEgens that are comprised of tricyclic 2-aminopyridinium derivatives with seven-membered rings are designed and synthesized via a simple, multicomponent reaction. Notably, these AIEgens exhibit the ability to specifically stain gram-positive bacteria. Moreover, a specific AIEgen, BMTAP-7, possesses highly efficient bacteriostatic ability for Staphylococcus aureus (S. aureus) in both liquid medium and solid agar plates, which have a minimum inhibitory concentration (MIC) between 4 and 8 µg mL^-1 . Using live-cell imaging and a wash-free process, it is observed that hydrophilic AIEgens are localized to mitochondria, whereas lipophilic AIEgens display specific staining of lysosomes. These AIEgens with bacteriostatic activity hold great promise for distinguishing between bacterial types and inhibiting bacterial infections in situ.

Genome analysis of Spiroplasma citri strains from different host plants and its leafhopper vectors

BACKGROUND

Spiroplasma citri comprises a bacterial complex that cause diseases in citrus, horseradish, carrot, sesame, and also infects a wide array of ornamental and weed species. S. citri is transmitted in a persistent propagative manner by the beet leafhopper, Neoaliturus tenellus in North America and Circulifer haematoceps in the Mediterranean region. Leafhopper transmission and the pathogen's wide host range serve as drivers of genetic diversity. This diversity was examined in silico by comparing the genome sequences of seven S. citri strains from the United States (BR12, CC-2, C5, C189, LB 319, BLH-13, and BLH-MB) collected from different hosts and times with other publicly available spiroplasmas.

RESULTS

Phylogenetic analysis using 16S rRNA sequences from 39 spiroplasmas obtained from NCBI database showed that S. citri strains, along with S. kunkelii and S. phoeniceum, two other plant pathogenic spiroplasmas, formed a monophyletic group. To refine genetic relationships among S. citri strains, phylogenetic analyses with 863 core orthologous sequences were performed. Strains that clustered together were: CC-2 and C5; C189 and R8-A2; BR12, BLH-MB, BLH-13 and LB 319. Strain GII3-3X remained in a separate branch. Sequence rearrangements were observed among S. citri strains, predominantly in the center of the chromosome. One to nine plasmids were identified in the seven S. citri strains analyzed in this study. Plasmids were most abundant in strains isolated from the beet leafhopper, followed by strains from carrot, Chinese cabbage, horseradish, and citrus, respectively. All these S. citri strains contained one plasmid with high similarity to plasmid pSci6 from S. citri strain GII3-3X which is known to confer insect transmissibility. Additionally, 17 to 25 prophage-like elements were identified in these genomes, which may promote rearrangements and contribute to repetitive regions.

CONCLUSIONS

The genome of seven S. citri strains were found to contain a single circularized chromosome, ranging from 1.58 Mbp to 1.74 Mbp and 1597-2232 protein-coding genes. These strains possessed a plasmid similar to pSci6 from the GII3-3X strain associated with leafhopper transmission. Prophage sequences found in the S. citri genomes may contribute to the extension of its host range. These findings increase our understanding of S. citri genetic diversity.

Recent advances in rapid antimicrobial susceptibility testing systems

INTRODUCTION

Until recently antimicrobial susceptibility testing (AST) methods based on the demonstration of phenotypic susceptibility in 16-24 h remained largely unchanged.

AREAS COVERED

Advances in rapid phenotypic and molecular-based AST systems.

EXPERT OPINION

AST has changed over the past decade, with many rapid phenotypic and molecular methods developed to demonstrate phenotypic or genotypic resistance, or biochemical markers of resistance such as β-lactamases associated with carbapenem resistance. Most methods still require isolation of bacteria from specimens before both legacy and newer methods can be used. Bacterial identification by MALDI-TOF mass spectroscopy is now widely used and is often key to the interpretation of rapid AST results. Several PCR arrays are available to detect the most frequent pathogens associated with bloodstream infections and their major antimicrobial resistance genes. Many advances in whole-genome sequencing of bacteria and fungi isolated by culture as well as directly from clinical specimens have been made but are not yet widely available. High cost and limited throughput are the major obstacles to uptake of rapid methods, but targeted use, continued development and decreasing costs are expected to result in more extensive use of these increasingly useful methods.

Centralised or Localised Pathogen Whole Genome Sequencing: Lessons Learnt From Implementation in a Clinical Diagnostic Laboratory

Whole genome sequencing (WGS) has had widespread use in the management of microbial outbreaks in a public health setting. Current models encompass sending isolates to a central laboratory for WGS who then produce a report for various levels of government. This model, although beneficial, has multiple shortcomings especially for localised infection control interventions and patient care. One reason for the slow rollout of WGS in clinical diagnostic laboratories has been the requirement for professionally trained personal in both wet lab techniques and in the analysis and interpretation of data, otherwise known as bioinformatics. A further bottleneck has been establishment of regulations in order to certify clinical and technical validity and demonstrate WGS as a verified diagnostic test. Nevertheless, this technology is far superior providing information that would normally require several diagnostic tests to achieve. An obvious barrier to informed outbreak tracking is turnaround time and requires isolates to be sequenced in real-time to rapidly identify chains of transmission. One way this can be achieved is through onsite hospital sequencing with a cumulative analysis approach employed. Onsite, as opposed to centralised sequencing, has added benefits including the increased agility to combine with local infection control staff to iterate through the data, finding links that aide in understanding transmission chains and inform infection control strategies. Our laboratory has recently instituted a pathogen WGS service within a diagnostic laboratory, separate to a public health laboratory. We describe our experience, address the challenges faced and demonstrate the advantages of de-centralised sequencing through real-life scenarios.

A global resource for genomic predictions of antimicrobial resistance and surveillance of Salmonella Typhi at pathogenwatch

As whole-genome sequencing capacity becomes increasingly decentralized, there is a growing opportunity for collaboration and the sharing of surveillance data within and between countries to inform typhoid control policies. This vision requires free, community-driven tools that facilitate access to genomic data for public health on a global scale. Here we present the Pathogenwatch scheme for Salmonella enterica serovar Typhi (S. Typhi), a web application enabling the rapid identification of genomic markers of antimicrobial resistance (AMR) and contextualization with public genomic data. We show that the clustering of S. Typhi genomes in Pathogenwatch is comparable to established bioinformatics methods, and that genomic predictions of AMR are highly concordant with phenotypic susceptibility data. We demonstrate the public health utility of Pathogenwatch with examples selected from >4,300 public genomes available in the application. Pathogenwatch provides an intuitive entry point to monitor of the emergence and spread of S. Typhi high risk clones.

Whole genome sequencing data are increasingly becoming routinely available but generating actionable insights is challenging. Here, the authors describe Pathogenwatch, a web tool for genomic surveillance of S. Typhi, and demonstrate its use for antimicrobial resistance assignment and strain risk assessment.

Array-based microbial identification upon extracellular aminoglycoside residue sensing

Sensitive and rapid identification of pathogenic microorganisms is of great importance for clinical diagnosis and treatment. In this study, we developed an ultrasensitive colorimetric sensor array (CSA) based on the interactions between aminoglycoside antibiotics (AMGs) and Ag nanoparticles decorated with β-cyclodextrin (AgNPs@β-CD) to discriminate microorganisms quickly and accurately. Microorganisms can absorb different amounts of AMGs after incubation. Upon the addition of AgNPs@β-CD, the corresponding extracellular AMG residues will bind to AgNPs@β-CD, leading to color changes due to the modifications in localized surface plasmon resonance. The array was developed using 4 AMGs as sensing elements and AgNPs@β-CD as the colorimetric probe to generate a unique colorimetric response pattern for each microorganism. Standard chemometric methods indicated excellent discrimination among 20 microorganisms at low concentrations of 2 × 10⁶ CFU/mL. Therefore, this ultrasensitive CSA can be used for microbial discrimination portably and efficiently. Importantly, the concentration of microbial discrimination by our array is much lower than that of prior CSAs. This method of extracellular residue sensing also provided a new strategy to improve the sensitivity of conventional CSA in the discrimination of microorganisms, to measure the amount of intercellular uptake of AMGs by microorganisms, and to screen drugs that can easily be accumulated by the pathogenic microorganisms.

Combining Functional Genomics and Whole-Genome Sequencing to Detect Antibiotic Resistance Genes in Bacterial Strains Co-Occurring Simultaneously in a Brazilian Hospital

(1) Background: The rise of multi-antibiotic resistant bacteria represents an emergent threat to human health. Here, we investigate antibiotic resistance mechanisms in bacteria of several species isolated from an intensive care unit in Brazil. (2) Methods: We used whole-genome analysis to identify antibiotic resistance genes (ARGs) and plasmids in 34 strains of Gram-negative and Gram-positive bacteria, providing the first genomic description of Morganella morganii and Ralstonia mannitolilytica clinical isolates from South America. (3) Results: We identified a high abundance of beta-lactamase genes in resistant organisms, including seven extended-spectrum beta-lactamases (OXA-1, OXA-10, CTX-M-1, KPC, TEM, HYDRO, BLP) shared between organisms from different species. Additionally, we identified several ARG-carrying plasmids indicating the potential for a fast transmission of resistance mechanism between bacterial strains. Furthermore, we uncovered two pairs of (near) identical plasmids exhibiting multi-drug resistance. Finally, since many highly resistant strains carry several different ARGs, we used functional genomics to investigate which of them were indeed functional. In this sense, for three bacterial strains (Escherichia coli, Klebsiella pneumoniae, and M. morganii), we identified six beta-lactamase genes out of 15 predicted in silico as those mainly responsible for the resistance mechanisms observed, corroborating the existence of redundant resistance mechanisms in these organisms. (4) Conclusions: Systematic studies similar to the one presented here should help to prevent outbreaks of novel multidrug-resistant bacteria in healthcare facilities.

Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture

Data on comprehensive population-based surveillance of antimicrobial resistance is lacking. In low- and middle-income countries, the challenges are high due to weak laboratory capacity, poor health systems governance, lack of health information systems, and limited resources. Developing countries struggle with political and social dilemma, and bear a high health and economic burden of communicable diseases. Available data are fragmented and lack representativeness which limits their use to advice health policy makers and orientate the efficient allocation of funding and financial resources on programs to mitigate resistance. Low-quality data means soaring rates of antimicrobial resistance and the inability to track and map the spread of resistance, detect early outbreaks, and set national health policy to tackle resistance. Here, we review the barriers and limitations of conducting effective antimicrobial resistance surveillance, and we highlight multiple incremental approaches that may offer opportunities to strengthen population-based surveillance if tailored to the context of each country.

Antifungal activity of amino-alcohols based cationic surfactants and in silico, homology modeling, docking and molecular dynamics studies against lanosterol 14-α-demethylase enzyme

Fungi are being responsible for causing serious infections in humans and animals. The opportunistic microorganisms provoke environmental contaminations in health and storage facilities to represent a serious concern to health security. The present work investigates the antifungal activity of two amino-alcohols based cationic surfactants such as C_nEtOH, C_nPrOH (with n = 14 and 16 are the carbon numbers of alkyl chain and EtOH = Ethanol and PrOH = Propanol) against a collection of different Candida species (Candida tropicalis, Candida albicans, Candida auris, Cyberlindnera jadinii, Candida parapsilosis, Candida glabrata and Candida rugosa) respectively. The amino-alcohols based cationic surfactants exhibited good antifungal activity against all Candida strains tested with minimum inhibitory concentrations (MIC) ranging from 0.002 to 0.30 mM. The MIC evaluation shows an increase as a function of the hydrophobicity of all inhibitors against the majority of the Candida strains tested. The different location of the alcoholic OH function in the polar head shows the influence on the availability of N⁺ responsible for electrostatic interactions with the candidate's cell walls, which remains a very important step in the mode of action of quaternary ammonium cationic surfactants. Hence, a 3D structure of lanosterol 14-α-demethylase enzyme from C. auris was constructed by homology modeling using an online SWISS-MODEL server. The predicted model was analyzed by serval servers. Furthermore, a molecular docking study was carried out to better understand the binding mechanism of lanosterol homologous protein with surfactant ligands. Then, the docked complexes lanosterol-surfactants were refined by the molecular dynamic simulation to analyze their interaction behavior during the simulation.Communicated by Ramaswamy H. Sarma.

Nanomotion Detection-Based Rapid Antibiotic Susceptibility Testing

Rapid antibiotic susceptibility testing (AST) could play a major role in fighting multidrug-resistant bacteria. Recently, it was discovered that all living organisms oscillate in the range of nanometers and that these oscillations, referred to as nanomotion, stop as soon the organism dies. This finding led to the development of rapid AST techniques based on the monitoring of these oscillations upon exposure to antibiotics. In this review, we explain the working principle of this novel technique, compare the method with current ASTs, explore its application and give some advice about its implementation. As an illustrative example, we present the application of the technique to the slowly growing and pathogenic Bordetella pertussis bacteria.

Digital Insights Into Nucleotide Metabolism and Antibiotic Treatment Failure

Nucleotide metabolism plays a central role in bacterial physiology, producing the nucleic acids necessary for DNA replication and RNA transcription. Recent studies demonstrate that nucleotide metabolism also proactively contributes to antibiotic-induced lethality in bacterial pathogens and that disruptions to nucleotide metabolism contributes to antibiotic treatment failure in the clinic. As antimicrobial resistance continues to grow unchecked, new approaches are needed to study the molecular mechanisms responsible for antibiotic efficacy. Here we review emerging technologies poised to transform understanding into why antibiotics may fail in the clinic. We discuss how these technologies led to the discovery that nucleotide metabolism regulates antibiotic drug responses and why these are relevant to human infections. We highlight opportunities for how studies into nucleotide metabolism may enhance understanding of antibiotic failure mechanisms.

An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis

OBJECTIVE

Tuberculosis is the leading cause of death from a single infectious agent. The emergence of antimicrobial resistant Mycobacterium tuberculosis strains makes the problem more severe. Pyrazinamide (PZA) is an important component for short-course treatment regimens and first- and second-line treatment regimens. This research aims for fast diagnosis of M. tuberculosis resistance to PZA and identification of genetic features causing resistance.

MATERIALS AND METHODS

We use clinically collected genomic data of M. tuberculosis that are resistant or susceptible to PZA. A machine learning platform is built to diagnose PZA resistance using the whole genome sequence data, and to identify resistance genes and mutations. The platform consists of a deep convolutional neural network (DCNN) model for resistance diagnosis and a support vector machine (SVM) model as a surrogate to identify resistance genes and mutations.

RESULTS

The DCNN model achieves a PZA resistance diagnosis accuracy of 93%. Each prediction takes less than a second. The SVM has revealed 2 novel genes, embB and gyrA, besides the well-known pncA gene, and 9 mutations that harbor PZA resistance.

DISCUSSION

The DCNN and SVM machine learning platform, if used together with the real-time genome sequencing machines, could allow for rapid PZA diagnosis, allowing for critical time to ensure good patient outcomes, and preventing outbreaks of deadly infections. Furthermore, identifying pertinent resistance genes and mutations will help researchers better understand the biological mechanisms behind resistance.

CONCLUSIONS

Machine learning can be used to achieve high-accuracy resistance prediction, and identify genes and mutations causing the resistance.

Approaches for characterizing and tracking hospital-associated multidrug-resistant bacteria

Hospital-associated infections are a major concern for global public health. Infections with antibiotic-resistant pathogens can cause empiric treatment failure, and for infections with multidrug-resistant bacteria which can overcome antibiotics of "last resort" there exists no alternative treatments. Despite extensive sanitization protocols, the hospital environment is a potent reservoir and vector of antibiotic-resistant organisms. Pathogens can persist on hospital surfaces and plumbing for months to years, acquire new antibiotic resistance genes by horizontal gene transfer, and initiate outbreaks of hospital-associated infections by spreading to patients via healthcare workers and visitors. Advancements in next-generation sequencing of bacterial genomes and metagenomes have expanded our ability to (1) identify species and track distinct strains, (2) comprehensively profile antibiotic resistance genes, and (3) resolve the mobile elements that facilitate intra- and intercellular gene transfer. This information can, in turn, be used to characterize the population dynamics of hospital-associated microbiota, track outbreaks to their environmental reservoirs, and inform future interventions. This review provides a detailed overview of the approaches and bioinformatic tools available to study isolates and metagenomes of hospital-associated bacteria, and their multi-layered networks of transmission.

Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo

Comparing the pathogen genomes from several cases of an infectious disease has the potential to help us understand and control outbreaks. Many methods exist to reconstruct a phylogeny from such genomes, which represents how the genomes are related to one another. However, such a phylogeny is not directly informative about transmission events between individuals. TransPhylo is a software tool implemented as an R package designed to bridge the gap between pathogen phylogenies and transmission trees. TransPhylo is based on a combined model of transmission between hosts and pathogen evolution within each host. It can simulate both phylogenies and transmission trees jointly under this combined model. TransPhylo can also reconstruct a transmission tree based on a dated phylogeny, by exploring the space of transmission trees compatible with the phylogeny. A transmission tree can be represented as a coloring of a phylogeny where each color represents a different host of the pathogen, and TransPhylo provides convenient ways to plot these colorings and explore the results. This article presents the basic protocols that can be used to make the most of TransPhylo. © 2021 The Authors. Basic Protocol 1: First steps with TransPhylo Basic Protocol 2: Simulation of outbreak data Basic Protocol 3: Inference of transmission Basic Protocol 4: Exploring the results of inference.

Clin-mNGS: Automated Pipeline for Pathogen Detection from Clinical Metagenomic Data

Background:

Since bacteria are the earliest known organisms, there has been significant interest in their variety and biology, most certainly concerning human health. Recent advances in Metagenomics sequencing (mNGS), a culture-independent sequencing technology, have facilitated an accelerated development in clinical microbiology and our understanding of pathogens.

Objective:

For the implementation of mNGS in routine clinical practice to become feasible, a practical and scalable strategy for the study of mNGS data is essential. This study presents a robust automated pipeline to analyze clinical metagenomic data for pathogen identification and classification.

Method:

The proposed Clin-mNGS pipeline is an integrated, open-source, scalable, reproducible, and user-friendly framework scripted using the Snakemake workflow management software. The implementation avoids the hassle of manual installation and configuration of the multiple commandline tools and dependencies. The approach directly screens pathogens from clinical raw reads and generates consolidated reports for each sample.

Results:

The pipeline is demonstrated using publicly available data and is tested on a desktop Linux system and a High-performance cluster. The study compares variability in results from different tools and versions. The versions of the tools are made user modifiable. The pipeline results in quality check, filtered reads, host subtraction, assembled contigs, assembly metrics, relative abundances of bacterial species, antimicrobial resistance genes, plasmid finding, and virulence factors identification. The results obtained from the pipeline are evaluated based on sensitivity and positive predictive value.

Conclusion:

Clin-mNGS is an automated Snakemake pipeline validated for the analysis of microbial clinical metagenomics reads to perform taxonomic classification and antimicrobial resistance prediction.

Full-length 16S rRNA gene amplicon analysis of human gut microbiota using MinION™ nanopore sequencing confers species-level resolution

Background

Species-level genetic characterization of complex bacterial communities has important clinical applications in both diagnosis and treatment. Amplicon sequencing of the 16S ribosomal RNA (rRNA) gene has proven to be a powerful strategy for the taxonomic classification of bacteria. This study aims to improve the method for full-length 16S rRNA gene analysis using the nanopore long-read sequencer MinION™. We compared it to the conventional short-read sequencing method in both a mock bacterial community and human fecal samples.

Results

We modified our existing protocol for full-length 16S rRNA gene amplicon sequencing by MinION™. A new strategy for library construction with an optimized primer set overcame PCR-associated bias and enabled taxonomic classification across a broad range of bacterial species. We compared the performance of full-length and short-read 16S rRNA gene amplicon sequencing for the characterization of human gut microbiota with a complex bacterial composition. The relative abundance of dominant bacterial genera was highly similar between full-length and short-read sequencing. At the species level, MinION™ long-read sequencing had better resolution for discriminating between members of particular taxa such as Bifidobacterium, allowing an accurate representation of the sample bacterial composition.

Conclusions

Our present microbiome study, comparing the discriminatory power of full-length and short-read sequencing, clearly illustrated the analytical advantage of sequencing the full-length 16S rRNA gene.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02094-5.

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.

Halo-fluorescein for photodynamic bacteria inactivation in extremely acidic conditions

Aciduric bacteria that can survive in extremely acidic conditions (pH < 4.0) are challenging to the current antimicrobial approaches, including antibiotics and photodynamic bacteria inactivation (PDI). Here, we communicate a photosensitizer design concept of halogenation of fluorescein for extremely acidic PDI. Upon halogenation, the well-known spirocyclization that controls the absorption of fluorescein shifts to the acidic pH range. Meanwhile, the heavy atom effect of halogens boosts the generation of singlet oxygen. Accordingly, several photosensitizers that could work at even pH < 2.0 were discovered for a broad band of aciduric bacteria families, with half maximal inhibitory concentrations (IC50) lower than 1.1 μM. Since one of the discovered photosensitizers is an FDA-approved food additive (2',4',5',7'-tetraiodofluorescein, TIF), successful bacteria growth inhibition in acidic beverages was demonstrated, with greatly extended shelf life from 2 days to ~15 days. Besides, the in vivo PDI of Candidiasis with TIF under extremely acidic condition was also demonstrated.

Phylogenetic Methods for Genome-Wide Association Studies in Bacteria

Genome-wide association studies in bacteria have great potential to deliver a better understanding of the genetic basis of many biologically important phenotypes, including antibiotic resistance, pathogenicity, and host adaptation. Such studies need however to account for the specificities of bacterial genomics, especially in terms of population structure, homologous recombination, and genomic plasticity. A powerful way to tackle this challenge is to use a phylogenetic approach, which is based on long-standing methodology for the evolutionary analysis of bacterial genomic data. Here we present both the theoretical and practical aspects involved in the use of phylogenetic methods for bacterial genome-wide association studies.

How quickly can we predict trimethoprim resistance using alchemical free energy methods?

The emergence of antimicrobial resistance threatens modern medicine and necessitates more personalized treatment of bacterial infections. Sequencing the whole genome of the pathogen(s) in a clinical sample offers one way to improve clinical microbiology diagnostic services, and has already been adopted for tuberculosis in some countries. A key weakness of a genetics clinical microbiology is it cannot return a result for rare or novel genetic variants and therefore predictive methods are required. Non-synonymous mutations in the S. aureus dfrB gene can be successfully classified as either conferring resistance (or not) by calculating their effect on the binding free energy of the antibiotic, trimethoprim. The underlying approach, alchemical free energy methods, requires large numbers of molecular dynamics simulations to be run. We show that a large number (N = 15) of binding free energies calculated from a series of very short (50 ps) molecular dynamics simulations are able to satisfactorily classify all seven mutations in our clinically derived testset. A result for a single mutation could therefore be returned in less than an hour, thereby demonstrating that this or similar methods are now sufficiently fast and reproducible for clinical use.

Taking the next-gen step: Comprehensive antimicrobial resistance detection from Burkholderia pseudomallei

BACKGROUND

Antimicrobial resistance (AMR) poses a major threat to human health. Whole-genome sequencing holds great potential for AMR identification; however, there remain major gaps in accurately and comprehensively detecting AMR across the spectrum of AMR-conferring determinants and pathogens.

METHODS

Using 16 wild-type Burkholderia pseudomallei and 25 with acquired AMR, we first assessed the performance of existing AMR software (ARIBA, CARD, ResFinder, and AMRFinderPlus) for detecting clinically relevant AMR in this pathogen. B. pseudomallei was chosen due to limited treatment options, high fatality rate, and AMR caused exclusively by chromosomal mutation (i.e. single-nucleotide polymorphisms [SNPs], insertions-deletions [indels], copy-number variations [CNVs], inversions, and functional gene loss). Due to poor performance with existing tools, we developed ARDaP (Antimicrobial Resistance Detection and Prediction) to identify the spectrum of AMR-conferring determinants in B. pseudomallei.

FINDINGS

CARD, ResFinder, and AMRFinderPlus failed to identify any clinically-relevant AMR in B. pseudomallei; ARIBA identified AMR encoded by SNPs and indels that were manually added to its database. However, none of these tools identified CNV, inversion, or gene loss determinants, and ARIBA could not differentiate AMR determinants from natural genetic variation. In contrast, ARDaP accurately detected all SNP, indel, CNV, inversion, and gene loss AMR determinants described in B. pseudomallei (n≈50). Additionally, ARDaP accurately predicted three previously undescribed determinants. In mixed strain data, ARDaP identified AMR to as low as ~5% allelic frequency.

INTERPRETATION

Existing AMR software packages are inadequate for chromosomal AMR detection due to an inability to detect resistance conferred by CNVs, inversions, and functional gene loss. ARDaP overcomes these major shortcomings. Further, ARDaP enables AMR prediction from mixed sequence data down to 5% allelic frequency, and can differentiate natural genetic variation from AMR determinants. ARDaP databases can be constructed for any microbial species of interest for comprehensive AMR detection.

FUNDING

National Health and Medical Research Council (BJC, EPP, DSS); Australian Government (DEM, ES); Advance Queensland (EPP, DSS).

Nucleic Acids Analysis

Nucleic acids are natural biopolymers of nucleotides that store, encode, transmit and express genetic information, which play central roles in diverse cellular events and diseases in living things. The analysis of nucleic acids and nucleic acids-based analysis have been widely applied in biological studies, clinical diagnosis, environmental analysis, food safety and forensic analysis. During the past decades, the field of nucleic acids analysis has been rapidly advancing with many technological breakthroughs. In this review, we focus on the methods developed for analyzing nucleic acids, nucleic acids-based analysis, device for nucleic acids analysis, and applications of nucleic acids analysis. The representative strategies for the development of new nucleic acids analysis in this field are summarized, and key advantages and possible limitations are discussed. Finally, a brief perspective on existing challenges and further research development is provided.

Comparative Genome Analysis Reveals the Molecular Basis of Niche Adaptation of Staphylococcus epidermidis Strains

Staphylococcus epidermidis is one of the most commonly isolated species from human skin and the second leading cause of bloodstream infections. Here, we performed a large-scale comparative study without any pre-assigned reference to identify genomic determinants associated with the diversity and adaptation of S. epidermidis strains to various environments. Pan-genome of S. epidermidis was open with 435 core proteins and had a pan-genome size of 8,034 proteins. Genome-wide phylogenetic tree showed high heterogeneity and suggested that routine whole genome sequencing was a powerful tool for analyzing the complex evolution of S. epidermidis and for investigating the infection sources. Comparative genome analyses demonstrated a range of antimicrobial resistance (AMR) genes, especially those within mobile genetic elements. The complicated host-bacterium and bacterium-bacterium relationships help S. epidermidis to play a vital role in balancing the epithelial microflora. The highly variable and dynamic nature of the S. epidermidis genome may contribute to its success in adapting to broad habitats. Genes related to biofilm formation and cell toxicity were significantly enriched in the blood and skin, demonstrating their potentials in identifying risk genotypes. This study gave a general landscape of S. epidermidis pan-genome and provided valuable insights into mechanisms for genome evolution and lifestyle adaptation of this ecologically flexible species.

Clinical perspectives in integrating whole-genome sequencing into the investigation of healthcare and public health outbreaks - hype or help?

Outbreaks pose a significant risk to patient safety as well as being costly and time consuming to investigate. The implementation of targeted infection prevention and control measures relies on infection prevention and control teams having access to rapid results that detect resistance accurately, and typing results that give clinically useful information on the relatedness of isolates. At present, determining whether transmission has occurred can be a major challenge. Conventional typing results do not always have sufficient granularity or robustness to define strains unequivocally, and sufficient epidemiological data are not always available to establish links between patients and the environment. Whole-genome sequencing (WGS) has emerged as the ultimate genotyping tool, but has not yet fully crossed the divide between research method and routine clinical diagnostic microbiological technique. A clinical WGS service was officially established in 2014 as part of the Scottish Healthcare Associated Infection Prevention Institute to confirm or refute outbreaks in hospital settings from across Scotland. This article describes the authors' experiences with the aim of providing new insights into practical application of the use of WGS to investigate healthcare and public health outbreaks. Solutions to overcome barriers to implementation of this technology in a clinical environment are proposed.

Higher genome mutation rates of Beijing lineage of Mycobacterium tuberculosis during human infection

Mycobacterium tuberculosis (Mtb) strains of Beijing lineage have caused great concern because of their rapid emergence of drug resistance and worldwide spread. DNA mutation rates that reflect evolutional adaptation to host responses and the appearance of drug resistance have not been elucidated in human-infected Beijing strains. We tracked and obtained an original Mtb isolate of Beijing lineage from the 1999 tuberculosis outbreak in Japan, as well as five other isolates that spread in humans, and two isolates from the patient caused recurrence. Three isolates were from patients who developed TB within one year after infection (rapid-progressor, RP), and the other three isolates were from those who developed TB more than one year after infection (slow-progressor, SP). We sequenced genomes of these isolates and analyzed the propensity and rate of genomic mutations. Generation time versus mutation rate curves were significantly higher for RP. The ratio of oxidative versus non-oxidation damages induced mutations was higher in SP than RP, suggesting that persistent Mtb are exposed to oxidative stress in the latent state. Our data thus demonstrates that higher mutation rates of Mtb Beijing strains during human infection is likely to account for the higher adaptability and an emergence ratio of drug resistance.

In silico characterisation of stand-alone response regulators of Streptococcus pyogenes

Bacterial “stand-alone” response regulators (RRs) are pivotal to the control of gene transcription in response to changing cytosolic and extracellular microenvironments during infection. The genome of group A Streptococcus (GAS) encodes more than 30 stand-alone RRs that orchestrate the expression of virulence factors involved in infecting multiple tissues, so causing an array of potentially lethal human diseases. Here, we analysed the molecular epidemiology and biological associations in the coding sequences (CDSs) and upstream intergenic regions (IGRs) of 35 stand-alone RRs from a collection of global GAS genomes. Of the 944 genomes analysed, 97% encoded 32 or more of the 35 tested RRs. The length of RR CDSs ranged from 297 to 1587 nucleotides with an average nucleotide diversity (π) of 0.012, while the IGRs ranged from 51 to 666 nucleotides with average π of 0.017. We present new evidence of recombination in multiple RRs including mga, leading to mga-2 switching, emm-switching and emm-like gene chimerization, and the first instance of an isolate that encodes both mga-1 and mga-2. Recombination was also evident in rofA/nra and msmR loci with 15 emm-types represented in multiple FCT (fibronectin-binding, collagen-binding, T-antigen)-types, including novel emm-type/FCT-type pairings. Strong associations were observed between concatenated RR allele types, and emm-type, MLST-type, core genome phylogroup, and country of sampling. No strong associations were observed between individual loci and disease outcome. We propose that 11 RRs may form part of future refinement of GAS typing systems that reflect core genome evolutionary associations. This subgenomic analysis revealed allelic traits that were informative to the biological function, GAS strain definition, and regional outbreak detection.

Facile Coupling of Droplet Magnetofluidic-Enabled Automated Sample Preparation for Digital Nucleic Acid Amplification Testing and Analysis

Digital nucleic acid amplification testing (dNAAT) and analysis techniques, such as digital polymerase chain reaction (PCR), have become useful clinical diagnostic tools. However, nucleic acid (NA) sample preparation preceding dNAAT is generally laborious and performed manually, thus creating the need for a simple sample preparation technique and a facile coupling strategy for dNAAT. Therefore, we demonstrate a simple workflow which automates magnetic bead-based extraction of NAs with a one-step transfer to dNAAT. Specifically, we leverage droplet magnetofluidics (DM) to automate the movement of magnetic beads between small volumes of reagents commonly employed for NA extraction and purification. Importantly, the buffer typically used to elute the NAs off the magnetic beads is replaced by a carefully selected PCR solution, enabling direct transfer from sample preparation to dNAAT. Moreover, we demonstrate the potential for multiplexing using a digital high-resolution melt (dHRM) after the digital PCR (dPCR). The utility of this workflow is demonstrated with duplexed detection of bacteria in a sample imitating a coinfection. We first purify the bacterial DNA into a PCR solution using our DM-based sample preparation. We then transfer the purified bacterial DNA to our microfluidic nanoarray to amplify 16S rRNA using dPCR and then perform dHRM to identify the two bacterial species.

Wound healing: cellular mechanisms and pathological outcomes

Wound healing is a complex, dynamic process supported by a myriad of cellular events that must be tightly coordinated to efficiently repair damaged tissue. Derangement in wound-linked cellular behaviours, as occurs with diabetes and ageing, can lead to healing impairment and the formation of chronic, non-healing wounds. These wounds are a significant socioeconomic burden due to their high prevalence and recurrence. Thus, there is an urgent requirement for the improved biological and clinical understanding of the mechanisms that underpin wound repair. Here, we review the cellular basis of tissue repair and discuss how current and emerging understanding of wound pathology could inform future development of efficacious wound therapies.

Performance and Application of 16S rRNA Gene Cycle Sequencing for Routine Identification of Bacteria in the Clinical Microbiology Laboratory

This review provides a state-of-the-art description of the performance of Sanger cycle sequencing of the 16S rRNA gene for routine identification of bacteria in the clinical microbiology laboratory. A detailed description of the technology and current methodology is outlined with a major focus on proper data analyses and interpretation of sequences. The remainder of the article is focused on a comprehensive evaluation of the application of this method for identification of bacterial pathogens based on analyses of 16S multialignment sequences. In particular, the existing limitations of similarity within 16S for genus- and species-level differentiation of clinically relevant pathogens and the lack of sequence data currently available in public databases is highlighted. A multiyear experience is described of a large regional clinical microbiology service with direct 16S broad-range PCR followed by cycle sequencing for direct detection of pathogens in appropriate clinical samples. The ability of proteomics (matrix-assisted desorption ionization-time of flight) versus 16S sequencing for bacterial identification and genotyping is compared. Finally, the potential for whole-genome analysis by next-generation sequencing (NGS) to replace 16S sequencing for routine diagnostic use is presented for several applications, including the barriers that must be overcome to fully implement newer genomic methods in clinical microbiology. A future challenge for large clinical, reference, and research laboratories, as well as for industry, will be the translation of vast amounts of accrued NGS microbial data into convenient algorithm testing schemes for various applications (i.e., microbial identification, genotyping, and metagenomics and microbiome analyses) so that clinically relevant information can be reported to physicians in a format that is understood and actionable. These challenges will not be faced by clinical microbiologists alone but by every scientist involved in a domain where natural diversity of genes and gene sequences plays a critical role in disease, health, pathogenicity, epidemiology, and other aspects of life-forms. Overcoming these challenges will require global multidisciplinary efforts across fields that do not normally interact with the clinical arena to make vast amounts of sequencing data clinically interpretable and actionable at the bedside.

Application of metagenomic next-generation sequencing technology for difficult lung lesions in patients with haematological diseases

BACKGROUND

The purpose of this study was to evaluate the diagnostic value of combined virtual bronchoscopic navigation (Direct Path), radial endobronchial ultrasound with guide-sheath (EBUS), ultrathin bronchoscopy, rapid on-site evaluation of cytology (ROSE), and metagenomic next-generation sequencing (mNGS) for difficult lung lesions in patients with haematological diseases.

METHODS

In this study, lung specimens were obtained from patients with haematological diseases by transbronchial lung biopsy (TBLB) and bronchoalveolar lavage (BAL). The specimens were subjected to mNGS for sequencing of pathogenic microorganisms and sent to the laboratory for examination and pathological analysis. Additionally, the clinical data and pathogenic characteristics of the patients were analysed. The sensitivity and specificity of mNGS for sequencing pathogenic microorganisms were compared between TBLB and BAL specimens.

RESULTS

In this study, the diagnosis of infectious pneumonia mainly included cytomegalovirus pneumonia, Pneumocystis jirovecii pneumonia (PCP), pulmonary aspergillosis, and tuberculosis. Some patients had non-infectious pulmonary complications, and the clinical and therapeutic outcomes were diagnosed as graft-versus-host disease (GVHD), idiopathic pneumonia syndrome (IPS), and delayed pulmonary toxicity syndrome (DPTS). The sensitivity of mNGS for pathogenic microbes in lung tissue is better than that of alveolar lavage fluid, whereas compared with alveolar lavage fluid, its specificity is reduced.

CONCLUSIONS

The results of this study indicate that combined virtual bronchoscopic navigation (Direct Path), radial EBUS, ultrathin bronchoscopy, and ROSE of target control specimens reduce the risk of bleeding, and their combination with mNGS has high diagnostic value for difficult lung lesions in patients with haematological diseases, especially in the field of infection diagnosis. TBLB and BAL specimens have respective advantages in specificity and sensitivity for mNGS analysis.

Perceived utility and feasibility of pathogen genomics for public health practice: a survey among public health professionals working in the field of infectious diseases, Belgium, 2019

BACKGROUND

Pathogen genomics is increasingly being translated from the research setting into the activities of public health professionals operating at different levels. This survey aims to appraise the literacy level and gather the opinions of public health experts and allied professionals working in the field of infectious diseases in Belgium concerning the implementation of next-generation sequencing (NGS) in public health practice.

METHODS

In May 2019, Belgian public health and healthcare professionals were invited to complete an online survey containing eight main topics including background questions, general attitude towards pathogen genomics for public health practice and main concerns, genomic literacy, current and planned NGS activities, place of NGS in diagnostic microbiology pathways, data sharing obstacles, end-user requirements, and key drivers for the implementation of NGS. Descriptive statistics were used to report on the frequency distribution of multiple choice responses whereas thematic analysis was used to analyze free text responses. A multivariable logistic regression model was constructed to identify important predictors for a positive attitude towards the implementation of pathogen genomics in public health practice.

RESULTS

146 out of the 753 invited public health professionals completed the survey. 63% of respondents indicated that public health agencies should be using genomics to understand and control infectious diseases. Having a high level of expertise in the field of pathogen genomics was the strongest predictor of a positive attitude (OR = 4.04, 95% CI = 1.11 - 17.23). A significantly higher proportion of data providers indicated to have followed training in the field of pathogen genomics compared to data end-users (p < 0.001). Overall, 79% of participants expressed interest in receiving further training. Main concerns were related to the cost of sequencing technologies, data sharing, data integration, interdisciplinary working, and bioinformatics expertise.

CONCLUSIONS

Belgian health professionals expressed favorable views about implementation of pathogen genomics in their work activities related to infectious disease surveillance and control. They expressed the need for suitable training initiatives to strengthen their competences in the field. Their perception of the utility and feasibility of pathogen genomics for public health purposes will be a key driver for its further implementation.

Frequency of Antimicrobial Resistance Genes in Salmonella From Brazil by in silico Whole-Genome Sequencing Analysis: An Overview of the Last Four Decades

Salmonella is a leading human pathogen and a significant public health concern worldwide. Massive food production and distribution have contributed to this pathogen dissemination, which, combined with antimicrobial resistance (AMR), creates new control challenges in food safety. The development of AMR is a natural phenomenon and can occur in the bacterial evolutionary process. However, the overuse and the misuse of antimicrobial drugs in humans and in animals have increased AMR selective pressure. In Brazil, there is an accuracy lack in AMR frequency in Salmonella because too many isolates are under-investigated for genetic and phenotypic AMR by the Brazilian health authorities and the research community. This underreporting situation makes the comprehension of the real level of Salmonella AMR in the country difficult. The present study aimed to use bioinformatics tools for a rapid in silico screening of the genetic antimicrobial resistance profile of Salmonella through whole-genome sequences (WGS). A total of 930 whole-genome sequences of Salmonella were retrieved from the public database of the National Biotechnology Information Center (NCBI). A total of 65 distinct resistance genes were detected, and the most frequent ones were tet(A), sul2, and fosA7. Nine point mutations were detected in total, and parC at the 57 position (threonine → serine) was the highest frequent substitution (26.7%, 249/930), followed by gyrA at the 83 position (serine → phenylalanine) (20.0%, 186/930) and at the 87 position (aspartic acid → asparagine) (15.7%, 146/930). The in silico prediction of resistance phenotype showed that 58.0% (540/930) of the strains can display a multidrug resistance (MDR) profile. Ciprofloxacin and nalidixic acid were the antimicrobial drugs with the highest frequency rates of the predicted phenotype resistance among the strains. The temporal analysis through the last four decades showed increased frequency rates of antimicrobial resistance genes and predicted resistance phenotypes in the 2000s and the 2010s when compared with the 1980s and 1990s. The results presented herein contributed significantly to the understanding of the strategic use of WGS associated with in silico analysis and the predictions for the determination of AMR in Salmonella from Brazil.

Efflux pump activity potentiates the evolution of antibiotic resistance across S. aureus isolates

The rise of antibiotic resistance in many bacterial pathogens has been driven by the spread of a few successful strains, suggesting that some bacteria are genetically pre-disposed to evolving resistance. Here, we test this hypothesis by challenging a diverse set of 222 isolates of Staphylococcus aureus with the antibiotic ciprofloxacin in a large-scale evolution experiment. We find that a single efflux pump, norA, causes widespread variation in evolvability across isolates. Elevated norA expression potentiates evolution by increasing the fitness benefit provided by DNA topoisomerase mutations under ciprofloxacin treatment. Amplification of norA provides a further mechanism of rapid evolution in isolates from the CC398 lineage. Crucially, chemical inhibition of NorA effectively prevents the evolution of resistance in all isolates. Our study shows that pre-existing genetic diversity plays a key role in shaping resistance evolution, and it may be possible to predict which strains are likely to evolve resistance and to optimize inhibitor use to prevent this outcome.

An Observational Study of Sepsis in Takeo Province Cambodia: An in-depth examination of pathogens causing severe infections

The world's most consequential pathogens occur in regions with the fewest diagnostic resources, leaving the true burden of these diseases largely under-represented. During a prospective observational study of sepsis in Takeo Province Cambodia, we enrolled 200 patients over an 18-month period. By coupling traditional diagnostic methods such as culture, serology, and PCR to Next Generation Sequencing (NGS) and advanced statistical analyses, we successfully identified a pathogenic cause in 46.5% of our cohort. In all, we detected 25 infectious agents in 93 patients, including severe threat pathogens such as Burkholderia pseudomallei and viral pathogens such as Dengue virus. Approximately half of our cohort remained undiagnosed; however, an independent panel of clinical adjudicators determined that 81% of those patients had infectious causes of their hospitalization, further underscoring the difficulty of diagnosing severe infections in resource-limited settings. We garnered greater insight as to the clinical features of severe infection in Cambodia through analysis of a robust set of clinical data.

Next-generation sequencing capacity and capabilities within the National Animal Health Laboratory Network

With the cost of next-generation sequencing (NGS) decreasing, this technology is rapidly being integrated into the workflows of veterinary clinical and diagnostic laboratories nationwide. The mission of the U.S. Department of Agriculture-National Animal Health Laboratory Network (NAHLN) is in part to evaluate new technologies and develop standardized processes for deploying these technologies to network laboratories for improving detection and response to emerging and foreign animal diseases. Thus, in 2018, the NAHLN identified the integration of NGS into the network as a top priority. In order to assess the current state of preparedness across NAHLN laboratories and to identify which have the capability for performing NGS, a questionnaire was developed by the NAHLN Methods Technical Working Group and submitted to all NAHLN laboratories in December 2018. Thirty of 59 laboratories completed the questionnaire, of which 18 (60%) reported having some sequencing capability. Multiple sequencing platforms and reagents were identified, and limited standardized quality control parameters were reported. Our results confirm that NGS capacity is available within the NAHLN, but several gaps remain. Gaps include not having sufficient personnel trained in bioinformatics and data interpretation, lack of standardized methods and equipment, and maintenance of sufficient computing capacity to meet the growing demand for this technology.

Molecular epidemiology of predominant and emerging Clostridioides difficile ribotypes

There has been an increase in the incidence and severity of Clostridioides difficile infection (CDI) worldwide, and strategies to control, monitor, and diminish the associated morbidity and mortality have been developed. Several typing methods have been used for typing of isolates and studying the epidemiology of CDI; serotyping was the first typing method, but then was replaced by pulsed-field gel electrophoresis (PFGE). PCR ribotyping is now the gold standard method; however, multi locus sequence typing (MLST) schemes have been developed. New sequencing technologies have allowed comparing whole bacterial genomes to address genetic relatedness with a high level of resolution and discriminatory power to distinguish between closely related strains. Here, we review the most frequent C. difficile ribotypes reported worldwide, with a focus on their epidemiology and genetic characteristics.

One year cross-sectional study in adult and neonatal intensive care units reveals the bacterial and antimicrobial resistance genes profiles in patients and hospital surfaces

Several studies have shown the ubiquitous presence of bacteria in hospital surfaces, staff, and patients. Frequently, these bacteria are related to HAI (healthcare-associated infections) and carry antimicrobial resistance (AMR). These HAI-related bacteria contribute to a major public health issue by increasing patient morbidity and mortality during or after hospital stay. Bacterial high-throughput amplicon gene sequencing along with identification of AMR genes, as well as whole genome sequencing (WGS), are biotechnological tools that allow multiple-sample screening for a diversity of bacteria. In this paper, we used these methods to perform a one-year cross sectional profiling of bacteria and AMR genes in adult and neonatal intensive care units (ICU and NICU) in a Brazilian public, tertiary hospital. Our results showed high abundances of HAI-related bacteria such as S. epidermidis, S. aureus, K. pneumoniae, A. baumannii complex, E. coli, E. faecalis, and P. aeruginosa in patients and hospital surfaces. Most abundant AMR genes detected throughout ICU and NICU were mecA, blaCTX-M-1 group, blaSHV-like, and blaKPC-like. We found that NICU environment and patients were more widely contaminated with pathogenic bacteria than ICU. Patient samples, despite the higher bacterial load, have lower bacterial diversity than environmental samples in both units. Finally, we also identified contamination hotspots in the hospital environment showing constant frequencies of bacterial and AMR contamination throughout the year. Whole genome sequencing (WGS), 16S rRNA oligotypes, and AMR identification allowed a high-resolution characterization of the hospital microbiome profile.

Development of a rapid technique for extraction of viral DNA/RNA for whole genome sequencing directly from clinical liver tissues

Characterisation of the entire genome of Fowl aviadenoviruses (FAdV) requires isolation and propagation of the virus in chicken embryo liver or kidney cells, a process which is not only time consuming but may occasionally fail to result in viral growth. Furthermore, in a mixed infection, isolation in cell culture may result in the loss of viral strains. In this study, we optimised a FAdV DNA extraction technique directly from affected liver tissues using kaolin hydrated aluminium silicate treatment. The whole genome of FAdV was sequenced directly from extracted DNA without any targetted PCR based enrichment. The extraction method was also tested on avian liver tissues affected with the RNA virus Avian hepatitis E virus and demonstrated to yield sequencing grade RNA. Therefore, the method described here is a simple technique which is potentially useful for the extraction of sequencing grade DNA/RNA from tissues with high fat content.

Mycobacterium bovis: From Genotyping to Genome Sequencing

Mycobacterium bovis is the main pathogen of bovine, zoonotic, and wildlife tuberculosis. Despite the existence of programs for bovine tuberculosis (bTB) control in many regions, the disease remains a challenge for the veterinary and public health sectors, especially in developing countries and in high-income nations with wildlife reservoirs. Current bTB control programs are mostly based on test-and-slaughter, movement restrictions, and post-mortem inspection measures. In certain settings, contact tracing and surveillance has benefited from M. bovis genotyping techniques. More recently, whole-genome sequencing (WGS) has become the preferential technique to inform outbreak response through contact tracing and source identification for many infectious diseases. As the cost per genome decreases, the application of WGS to bTB control programs is inevitable moving forward. However, there are technical challenges in data analyses and interpretation that hinder the implementation of M. bovis WGS as a molecular epidemiology tool. Therefore, the aim of this review is to describe M. bovis genotyping techniques and discuss current standards and challenges of the use of M. bovis WGS for transmission investigation, surveillance, and global lineages distribution. We compiled a series of associated research gaps to be explored with the ultimate goal of implementing M. bovis WGS in a standardized manner in bTB control programs.

Whole genome sequencing (WGS) fails to detect antimicrobial resistance (AMR) from heteroresistant subpopulation of Salmonella enterica

Due to rapidly falling costs, whole genome sequencing (WGS) is becoming an essential tool in the surveillance of antimicrobial resistance (AMR) in Salmonella enterica. Although there have been many recent works evaluating the accuracy of WGS in predicting AMR from a large number of Salmonella isolates, little attention has been devoted to deciphering the underlying causes of disagreement between the WGS genotype and experimentally determined AMR phenotype. This study analyzed the genomes of six S. enterica isolates previously obtained from raw chicken which exhibited disagreements between WGS genotype and AMR phenotype. A total of five WGS false negative predictions toward ampicillin, amoxicillin/clavulanate, colistin, and fosfomycin resistance were presented in conjunction with their corresponding empirical phenotypic and/or genetic evidence of heteroresistance. A further case study highlighting the inherent limitations of WGS to detect the underlying genetic mechanisms of colistin heteroresistance was presented. These findings implicate heteroresistance as an underlying cause for false negative WGS-based AMR predictions in S. enterica and suggest that widespread use of WGS in the surveillance of AMR in food isolates might severely underestimate true resistance rates.

Gemini Peptide Amphiphiles with Broad-Spectrum Antimicrobial Activity and Potent Antibiofilm Capacity

To address the challenge from microbial resistance and biofilm, this work develops three gemini peptide amphiphiles with basic tetrapeptide spacers 12-(Arg)₄-12, 12-(Lys)₄-12, and 12-(His)₄-12 and finds that they exhibit varied antimicrobial/antibiofilm activities. 12-(Arg)₄-12 shows the best performance, possessing the broad-spectrum antimicrobial activity and excellent antibiofilm capacity. The antimicrobial and antibiofilm activities strongly depend on the membrane permeation and self-assembling structure of these peptide amphiphiles. Gemini peptide amphiphile with highly polar arginine as the spacer, 12-(Arg)₄-12, self-assembles into short rods that are prone to dissociate into monomers for permeating and lysing membrane , leading to its broad-spectrum antimicrobial activity and high efficiency in eradicating biofilm. Long rods formed by relatively weaker polar 12-(Lys)₄-12 are less prone to disassemble into monomers for further membrane permeation, which makes it selectively kill more negatively charged bacteria and endow it medium antibiofilm activity. Low polar 12-(His)₄-12 aggregates into long fibers, which are very difficult to dissociate and they mainly electrostatically bind on the negative microbial surface, resulting in its weakest antimicrobial and antibiofilm activity. This study reveals the effect of the antimicrobial peptide structure and aggregation on the antimicrobial activities and would be helpful for developing high-efficient antimicrobial peptides with antibiofilm activity.

One stone, three birds: one AIEgen with three colors for fast differentiation of three pathogens

Visually identifying pathogens favors rapid diagnosis at the point-of-care testing level. Here, we developed a microenvironment-sensitive aggregation-induced emission luminogen (AIEgen), namely IQ-Cm, for achieving fast discrimination of Gram-negative bacteria, Gram-positive bacteria and fungi by the naked-eye. With a twisted donor-acceptor and multi-rotor structure, IQ-Cm shows twisted intramolecular charge transfer (TICT) and AIE properties with sensitive fluorescence color response to the microenvironment of pathogens. Driven by the intrinsic structural differences of pathogens, IQ-Cm with a cationic isoquinolinium moiety and a membrane-active coumarin unit as the targeting and interacting groups selectively locates in different sites of three pathogens and gives three naked-eye discernible emission colors. Gram-negative bacteria are weak pink, Gram-positive bacteria are orange-red and fungi are bright yellow. Therefore, based on their distinctive fluorescence response, IQ-Cm can directly discriminate the three pathogens at the cell level under a fluorescence microscope. Furthermore, we demonstrated the feasibility of IQ-Cm as a visual probe for fast diagnosis of urinary tract infections, timely monitoring of hospital-acquired infection processes and fast detection of molds in the food field. This simple visualization strategy based on one single AIEgen provides a promising platform for rapid pathogen detection and point-of-care diagnosis.

Rumen Virus Populations: Technological Advances Enhancing Current Understanding

The rumen contains a multi-kingdom, commensal microbiome, including protozoa, bacteria, archaea, fungi and viruses, which enables ruminant herbivores to ferment and utilize plant feedstuffs that would be otherwise indigestible. Within the rumen, virus populations are diverse and highly abundant, often out-numbering the microbial populations that they both predate on and co-exist with. To date the research effort devoted to understanding rumen-associated viral populations has been considerably less than that given to the other microbial populations, yet their contribution to maintaining microbial population balance, intra-ruminal microbial lysis, fiber breakdown, nutrient cycling and genetic transfer may be highly significant. This review follows the technological advances which have contributed to our current understanding of rumen viruses and drawing on knowledge from other environmental and animal-associated microbiomes, describes the known and potential roles and impacts viruses have on rumen function and speculates on the future directions of rumen viral research.

Contaminant DNA in bacterial sequencing experiments is a major source of false genetic variability

Background

Contaminant DNA is a well-known confounding factor in molecular biology and in genomic repositories. Strikingly, analysis workflows for whole-genome sequencing (WGS) data commonly do not account for errors potentially introduced by contamination, which could lead to the wrong assessment of allele frequency both in basic and clinical research.

Results

We used a taxonomic filter to remove contaminant reads from more than 4000 bacterial samples from 20 different studies and performed a comprehensive evaluation of the extent and impact of contaminant DNA in WGS. We found that contamination is pervasive and can introduce large biases in variant analysis. We showed that these biases can result in hundreds of false positive and negative SNPs, even for samples with slight contamination. Studies investigating complex biological traits from sequencing data can be completely biased if contamination is neglected during the bioinformatic analysis, and we demonstrate that removing contaminant reads with a taxonomic classifier permits more accurate variant calling. We used both real and simulated data to evaluate and implement reliable, contamination-aware analysis pipelines.

Conclusion

As sequencing technologies consolidate as precision tools that are increasingly adopted in the research and clinical context, our results urge for the implementation of contamination-aware analysis pipelines. Taxonomic classifiers are a powerful tool to implement such pipelines.

ASA3P: An automatic and scalable pipeline for the assembly, annotation and higher-level analysis of closely related bacterial isolates

Whole genome sequencing of bacteria has become daily routine in many fields. Advances in DNA sequencing technologies and continuously dropping costs have resulted in a tremendous increase in the amounts of available sequence data. However, comprehensive in-depth analysis of the resulting data remains an arduous and time-consuming task. In order to keep pace with these promising but challenging developments and to transform raw data into valuable information, standardized analyses and scalable software tools are needed. Here, we introduce ASA3P, a fully automatic, locally executable and scalable assembly, annotation and analysis pipeline for bacterial genomes. The pipeline automatically executes necessary data processing steps, i.e. quality clipping and assembly of raw sequencing reads, scaffolding of contigs and annotation of the resulting genome sequences. Furthermore, ASA3P conducts comprehensive genome characterizations and analyses, e.g. taxonomic classification, detection of antibiotic resistance genes and identification of virulence factors. All results are presented via an HTML5 user interface providing aggregated information, interactive visualizations and access to intermediate results in standard bioinformatics file formats. We distribute ASA3P in two versions: a locally executable Docker container for small-to-medium-scale projects and an OpenStack based cloud computing version able to automatically create and manage self-scaling compute clusters. Thus, automatic and standardized analysis of hundreds of bacterial genomes becomes feasible within hours. The software and further information is available at: asap.computational.bio.