RNA signatures allow rapid identification of pathogens and antibiotic susceptibilities

Clinical pilot of bacterial transcriptional profiling as a combined genotypic and phenotypic antimicrobial susceptibility test

Antimicrobial resistance is a growing health threat, but standard methods for determining antibiotic susceptibility are slow and can delay optimal treatment, which is especially consequential in severe infections such as bacteremia. Novel approaches for rapid susceptibility profiling have emerged that characterize either bacterial response to antibiotics (phenotype) or detect specific resistance genes (genotype). Genotypic and Phenotypic AST through RNA detection (GoPhAST-R) is a novel assay, performed directly on positive blood cultures, that integrates rapid transcriptional response profiling with the detection of key resistance gene transcripts, thereby providing simultaneous data on both phenotype and genotype. Here, we performed the first clinical pilot of GoPhAST-R on 42 positive blood cultures: 26 growing Escherichia coli, 15 growing Klebsiella pneumoniae, and 1 with both. An aliquot of each positive blood culture was exposed to nine different antibiotics, lysed, and underwent rapid transcriptional profiling on the NanoString platform; results were analyzed using an in-house susceptibility classification algorithm. GoPhAST-R achieved 95% overall agreement with standard antimicrobial susceptibility testing methods, with the highest agreement for beta-lactams (98%) and the lowest for fluoroquinolones (88%). Epidemic resistance genes including the extended spectrum beta-lactamase blaCTX-M-15 and the carbapenemase blaKPC were also detected within the population. This study demonstrates the clinical feasibility of using transcriptional response profiling for rapid resistance determination, although further validation with larger and more diverse bacterial populations will be essential in future work. GoPhAST-R represents a promising new approach for rapid and comprehensive antibiotic susceptibility testing in clinical settings.IMPORTANCEExposure to antibiotics causes differential transcriptional signatures in susceptible vs resistant bacteria. These differences can be leveraged to rapidly predict resistance profiles of Escherichia coli and Klebsiella pneumoniae in clinically positive blood cultures.

Electrochemical aptasensors based on porous carbon derived from graphene oxide/ZIF-8 composites for the detection of Erwinia cypripedii

In this research, self-screening aptamer and MOFs-derived nanomaterial have been combined to construct electrochemical aptasensor for environmental detection. By utilizing the large specific surface area of reduced graphene oxide (rGO), ZIF-8 was grown in situ on surface of rGO, and the composites was pyrolyzed to obtain MOFs-derived porous carbon materials (rGO-NCZIF). Thanks to the synergistic effect between rGO and NCZIF, the complex exhibits remarkable characteristics, including a high electron transfer rate and electrocatalytic activity. In addition, the orderly arrangement of imidazole ligands within ZIF-8 facilitated the uniform doping of nitrogen elements into the porous carbon, thereby significantly enhancing its electrochemical performance. After carboxylation, rGO-NCZIF was functionalized with self-screening aptamer for fabricating electrochemical aptasensor, which can be used to detect Erwinia cypripedii, a kind of quarantine plant bacteria, with detection limit of 4.92 × 103 cfu/mL. Due to the simplicity and speed, the aptasensor is suitable for rapid customs inspection and quarantine. Additionally, the universality of this sensing strategy was verified through exosomes detection by changing the aptamer. The results indicated that the rGO-NCZIF-based electrochemical aptasensor had practical value in the environmental and medical fields.

Clinical Pilot of Bacterial Transcriptional Profiling as a Combined Genotypic and Phenotypic Antimicrobial Susceptibility Test

Antimicrobial resistance is a growing health threat, but standard methods for determining antibiotic susceptibility are slow and can delay optimal treatment, which is especially consequential in severe infections such as bacteremia. Novel approaches for rapid susceptibility profiling have emerged that characterize either bacterial response to antibiotics (phenotype) or detect specific resistance genes (genotype). GoPhAST-R is a novel assay, performed directly on positive blood cultures, that integrates rapid transcriptional response profiling with detection of key resistance gene transcripts, thereby providing simultaneous data on both phenotype and genotype. Here, we performed the first clinical pilot of GoPhAST-R on 42 positive blood cultures: 26 growing Escherichia coli, 15 growing Klebsiella pneumoniae, and 1 with both. An aliquot of each positive blood culture was exposed to 9 different antibiotics, lysed, then underwent rapid transcriptional profiling on the NanoString® platform; results were analyzed using an in-house susceptibility classification algorithm. GoPhAST-R achieved 95% overall agreement with standard antimicrobial susceptibility testing methods, with the highest agreement for beta-lactams (98%) and the lowest for fluoroquinolones (88%). Epidemic resistance genes including the extended spectrum beta-lactamase bla CTX-M-15 and the carbapenemase bla KPC were also detected within the population. This study demonstrates the clinical feasibility of using transcriptional response profiling for rapid resistance determination, although further validation with larger and more diverse bacterial populations will be essential in future work. GoPhAST-R represents a promising new approach for rapid and comprehensive antibiotic susceptibility testing in clinical settings.

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism Mycobacterium tuberculosis (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Transcriptomic responses to antibiotic exposure in Mycobacterium tuberculosis

Transcriptional responses in bacteria following antibiotic exposure offer insights into antibiotic mechanism of action, bacterial responses, and characterization of antimicrobial resistance. We aimed to define the transcriptional antibiotic response (TAR) in Mycobacterium tuberculosis (Mtb) isolates for clinically relevant drugs by pooling and analyzing Mtb microarray and RNA-seq data sets. We generated 99 antibiotic transcription profiles across 17 antibiotics, with 76% of profiles generated using 3-24 hours of antibiotic exposure and 49% within one doubling of the WHO antibiotic critical concentration. TAR genes were time-dependent, and largely specific to the antibiotic mechanism of action. TAR signatures performed well at predicting antibiotic exposure, with the area under the receiver operating curve (AUC) ranging from 0.84-1.00 (TAR <6 hours of antibiotic exposure) and 0.76-1.00 (>6 hours of antibiotic exposure) for upregulated genes and 0.57-0.90 and 0.87-1.00, respectfully, for downregulated genes. This work desmonstrates that transcriptomics allows for the assessment of antibiotic activity in Mtb within 6 hours of exposure.

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths annually and 10 million new cases reported each year1. The causative organism, Mycobacterium tuberculosis (Mtb) can take nearly 40 days to culture2,3, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification of Mtb and rapid antibiotic susceptibility testing (AST) are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the MtB complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and in patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all 5 BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5000. We show how this instrument and our machine learning model enables combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Relative quantification of the recA gene for antimicrobial susceptibility testing in response to ciprofloxacin for pathogens of concern

Antimicrobial resistance (AR) is one of the greatest threats to global health and is associated with higher treatment costs, longer hospital stays, and increased mortality. Current gold standard antimicrobial susceptibility tests (AST) rely on organism growth rates that result in prolonged time-to-answer for slow growing organisms. Changes in the cellular transcriptome can be rapid in the presence of stressors such as antibiotic pressure, providing the opportunity to develop AST towards transcriptomic signatures. Here, we show that relative quantification of the recA gene is an indicator of pathogen susceptibly when select species are challenged with relevant concentrations of ciprofloxacin. We demonstrate that ciprofloxacin susceptible strains of Y. pestis and B. anthracis have significant increases in relative recA gene expression after 15 min of exposure while resistant strains show no significant differences. Building upon this data, we designed and optimized seven duplex RT-qPCR assays targeting the recA and 16S rRNA gene, response and housekeeping genes, respectively, for multiple biothreat and ESKAPE pathogens. Final evaluation of all seven duplex assays tested against 124 ciprofloxacin susceptible and resistant strains, including Tier 1 pathogens, demonstrated an overall categorical agreement compared to microbroth dilution of 97% using a defined cutoff. Testing pathogen strains commonly associated with urinary tract infections in contrived mock sample sets demonstrated an overall categorical agreement of 96%. These data indicate relative quantification of a single highly conserved gene accurately determines susceptibility for multiple bacterial species in response to ciprofloxacin.

An All-in-One Highly Multiplexed Diagnostic Assay for Rapid, Sensitive, and Comprehensive Detection of Intraocular Pathogens

PURPOSE

Intraocular infections are sight-threatening conditions that can lead to vision loss. Rapid identification of the etiologies plays a key role in early initiation of effective therapy to save vision. However, current diagnostic modalities are time consuming and lack sensitivity and inclusiveness. We present here a newly developed comprehensive ocular panel designed to improve diagnostic yields and provide a tool for rapid and sensitive pathogen detection.

DESIGN

Experimental laboratory investigation.

METHODS

A panel containing 46 pathogens and 2 resistance/virulence markers that are commonly detected in intraocular infections was developed. Genomic targets were scrutinized for stretches predicted to be specific for a particular species while being conserved across different strains. A set of primers for sample enrichment, and two 50mer NanoString compatible probes were then designed for each target. Probe-target hybrids were detected and quantified using the NanoString nCounter SPRINT Profiler. Diagnostic feasibility was assessed in a pilot clinical study testing samples from infectious retinitis (n = 15) and endophthalmitis (n = 12) patients, for which the etiologies were confirmed by polymerase chain reaction (PCR) or culture.

RESULTS

Analytical studies demonstrated highly sensitive detection of a broad spectrum of pathogens, including bacteria, viruses, and parasites, with limits of detection being as low as 2.5 femtograms per reaction. We also found excellent target specificity, with minimal cross-reactivity detected. The custom-designed NanoString ocular panel correctly identified the causative agent from all clinical specimens positive for a variety of pathogens.

CONCLUSION

This highly multiplexed panel for pathogen detection offers a sensitive, comprehensive, and uniform assay run directly on ocular fluids that could significantly improve diagnostics of sight-threatening intraocular infections.

Nanozyme-catalyzed cascade reaction enables a highly sensitive detection of live bacteria

The accurate and timely detection of bacteria is critically important for human health as it helps to determine the original source of bacterial infections and prevent disease spread. Herein, gold nanoparticles (AuNPs) were synthesized using polyoxometalates (POMs) as the stabilizing agent. Since AuNPs have glucose oxidase (GOx)-like activity and POMs possess peroxidase (HRP)-like activity, the as-prepared Au@POM nanoparticles have double enzyme-like activities and facilitate cascade reaction. As known, glucose is required as an energy resource during bacterial metabolism, the concentration of glucose decreases with the increase of bacteria content in a system with bacteria and glucose. Therefore, when we use Au@POM nanozymes to trigger the cascade catalysis of glucose and 3,3',5,5'-tetramethylbenzidine (TMB), the concentration of glucose and bacteria can be sensitively detected using the absorbance intensity at 652 nm in the visible spectrum. As demonstration, S. aureus and E. coli were used as model bacteria. The experimental results show that the present method has a good linear relationship in the bacterial concentration range of 1 to 7.5 × 10⁷ colony-forming units (CFU) mL^-1 with a detection limit of 5 CFU mL^-1. This study shows a great promise of nanozyme cascade reactions in the construction of biosensors and clinical detections.

A novel rRNA hybridization-based approach to rapid, accurate Candida identification directly from blood culture

Invasive fungal infections are increasingly common and carry high morbidity and mortality, yet fungal diagnostics lag behind bacterial diagnostics in rapidly identifying the causal pathogen. We previously devised a fluorescent hybridization-based assay to identify bacteria within hours directly from blood culture bottles without subculture, called phylogeny-informed rRNA-based strain identification (Phirst-ID). Here, we adapt this approach to unambiguously identify 11 common pathogenic Candida species, including C. auris, with 100% accuracy from laboratory culture (33 of 33 strains in a reference panel, plus 33 of 33 additional isolates tested in a validation panel). In a pilot study on 62 consecutive positive clinical blood cultures from two hospitals that showed yeast on Gram stain, Candida Phirst-ID matched the clinical laboratory result for 58 of 59 specimens represented in the 11-species reference panel, without misclassifying the 3 off-panel species. It also detected mixed Candida species in 2 of these 62 specimens, including the one discordant classification, that were not identified by standard clinical microbiology workflows; in each case the presence of both species was validated by both clinical and experimental data. Finally, in three specimens that grew both bacteria and yeast, we paired our prior bacterial probeset with this new Candida probeset to detect both pathogen types using Phirst-ID. This simple, robust assay can provide accurate Candida identification within hours directly from blood culture bottles, and the conceptual approach holds promise for pan-microbial identification in a single workflow.

LAY SUMMARY

Candida bloodstream infections cause considerable morbidity and mortality, yet slow diagnostics delay recognition, worsening patient outcomes. We develop and validate a novel molecular approach to accurately identify Candida species directly from blood culture one day faster than standard workflows.

Host respiratory transcriptome signature associated with poor outcome in children with influenza-Staphylococcus aureus pneumonia

Respiratory coinfection of influenza with Staphylococcus aureus often causes severe disease; methicillin resistant S. aureus (MRSA) coinfection is frequently fatal. Understanding disease pathogenesis may inform therapies. We aimed to identify host and pathogen transcriptomic (mRNA) signatures from the respiratory compartment of patients with influenza-S. aureus coinfection (ISAC) critical illness that predict worse outcomes. mRNA extracted from endotracheal aspirates was evaluated for S. aureus and host transcriptomic biosignatures. Influenza-MRSA outcomes were worse, but of 190 S. aureus virulence-associated genes, 6 were differentially expressed between MRSA- versus methicillin-susceptible S. aureus coinfected patients and none discriminated outcome. Host gene expression in ISAC patients was compared to influenza infection alone. Patients with poor clinical outcomes (death or prolonged multi-organ dysfunction) had relatively reduced expression of interferons and down-regulation of interferon gamma-induced immune cell chemoattractants CXCL10 and CXCL11. In influenza-S. aureus respiratory coinfection, airway host but not pathogen gene expression profiles predicted worse clinical outcomes.

Rapid Detection of MCR-Mediated Colistin Resistance in Escherichia coli

Colistin is one of the last-resort antibiotics for infections caused by multidrug-resistant Gram-negative bacteria. However, the wide spread of novel plasmid-carrying colistin resistance genes mcr-1 and its variants substantially compromise colistin's therapeutic effectiveness and pose a severe danger to public health. To detect colistin-resistant microorganisms induced by mcr genes, rapid and reliable antibiotic susceptibility testing (AST) is imminently needed. In this study, we identified an RNA-based AST (RBAST) to discriminate between colistin-susceptible and mcr-1-mediated colistin-resistant bacteria. After short-time colistin treatment, RBAST can detect differentially expressed RNA biomarkers in bacteria. Those candidate mRNA biomarkers were successfully verified within colistin exposure temporal shifts, concentration shifts, and other mcr-1 variants. Furthermore, a group of clinical strains were effectively distinguished by using the RBAST approach during the 3-h test duration with over 93% accuracy. Taken together, our findings imply that certain mRNA transcripts produced in response to colistin treatment might be useful indicators for the development of fast AST for mcr-positive bacteria.

IMPORTANCE The emergence and prevalence of mcr-1 and its variants in humans, animals, and the environment pose a global public health threat. There is a pressing urgency to develop rapid and accurate methods to identify MCR-positive colistin-resistant bacteria in the clinical samples, providing a basis for subsequent effective antibiotic treatment. Using the specific mRNA signatures, we develop an RNA-based antibiotic susceptibility testing (RBAST) for effectively distinguishing colistin-susceptible and mcr-1-mediated colistin-resistant strains. Meanwhile, the detection efficiency of these RNA biomarkers was evidenced in other mcr variants-carrying strains. By comparing with the traditional AST method, the RBAST method was verified to successfully characterize a set of clinical isolates during 3 h assay time with over 93% accuracy. Our study provides a feasible method for the rapid detection of colistin-resistant strains in clinical practice.

Rapid Antimicrobial Susceptibility Testing by Stimulated Raman Scattering Imaging of Deuterium Incorporation in a Single Bacterium

To slow and prevent the spread of antimicrobial resistant infections, rapid antimicrobial susceptibility testing (AST) is in urgent need to quantitatively determine the antimicrobial effects on pathogens. It typically takes days to complete the AST by conventional methods based on the long-time culture, and they do not work directly for clinical samples. Here, we report a rapid AST method enabled by stimulated Raman scattering (SRS) imaging of deuterium oxide (D2O) metabolic incorporation. Metabolic incorporation of D2O into biomass and the metabolic activity inhibition upon exposure to antibiotics at the single bacterium level are monitored by SRS imaging. The single-cell metabolism inactivation concentration (SC-MIC) of bacteria upon exposure to antibiotics can be obtained after a total of 2.5 h of sample preparation and detection. Furthermore, this rapid AST method is directly applicable to bacterial samples in complex biological environments, such as urine or whole blood. SRS metabolic imaging of deuterium incorporation is transformative for rapid single-cell phenotypic AST in the clinic.

Paving the Way to Overcome Antifungal Drug Resistance: Current Practices and Novel Developments for Rapid and Reliable Antifungal Susceptibility Testing

The past year has established the link between the COVID-19 pandemic and the global spread of severe fungal infections; thus, underscoring the critical need for rapid and realizable fungal disease diagnostics. While in recent years, health authorities, such as the Centers for Disease Control and Prevention, have reported the alarming emergence and spread of drug-resistant pathogenic fungi and warned against the devastating consequences, progress in the diagnosis and treatment of fungal infections is limited. Early diagnosis and patient-tailored therapy are established to be key in reducing morbidity and mortality associated with fungal (and cofungal) infections. As such, antifungal susceptibility testing (AFST) is crucial in revealing susceptibility or resistance of these pathogens and initiating correct antifungal therapy. Today, gold standard AFST methods require several days for completion, and thus this much delayed time for answer limits their clinical application. This review focuses on the advancements made in developing novel AFST techniques and discusses their implications in the context of the practiced clinical workflow. The aim of this work is to highlight the advantages and drawbacks of currently available methods and identify the main gaps hindering their progress toward clinical application.

Rapid and Accurate Antibiotic Susceptibility Determination of tet(X)-Positive E. coli Using RNA Biomarkers

The emergence and prevalence of novel plasmid-mediated tigecycline resistance genes, namely, tet(X) and their variants, pose a serious threat to public health worldwide. Rapid and accurate antibiotic susceptibility testing (AST) that can simultaneously detect the genotype and phenotype of tet(X)-positive bacteria may contribute to the deployment of an effective antibiotic arsenal, mortality reduction, and a decrease in the use of broad-spectrum antimicrobial agents. However, current bacterial growth-based AST methods, such as broth microdilution, are time consuming and delay the prompt treatment of infectious diseases. Here, we developed a rapid RNA-based AST (RBAST) assay to effectively distinguish tet(X)-positive and -negative strains. RBAST works by detecting specific mRNA expression signatures in bacteria after short-term tigecycline exposure. As a proof of concept, a panel of clinical isolates was characterized successfully by using the RBAST method, with a 3-h assay time and 87.9% accuracy (95% confidence interval [CI], 71.8% to 96.6%). Altogether, our findings suggest that RNA signatures upon antibiotic exposure are promising biomarkers for the development of rapid AST, which could inform early antibiotic choices.

IMPORTANCE Infections caused by multidrug-resistant (MDR) Gram-negative pathogens are an increasing threat to global health. Tigecycline is one of the last-resort antibiotics for the treatment of these complicated infections; however, the emergence of plasmid-encoded tigecycline resistance genes, namely, tet(X), severely diminishes its clinical efficacy. Currently, there is a lack of rapid and accurate antibiotic susceptibility testing (AST) for the detection of tet(X)-positive bacteria. In this study, we developed a rapid and robust RNA-based antibiotic susceptibility determination (RBAST) assay to effectively distinguish tet(X)-negative and -positive strains using specific RNA biomarkers in bacteria after tigecycline exposure. Using this RBAST method, we successfully characterized a set of clinical strains in 3 h. Our data indicate that the RBAST assay is useful for identifying tet(X)-positive Escherichia coli.

Ultrasensitive Detection of Bacteria Using a 2D MOF Nanozyme-Amplified Electrochemical Detector

Bacterial infection is one of the major causes of human death worldwide. To prevent bacterial infectious diseases from spreading, it is of critical importance to develop convenient, ultrasensitive, and cost-efficient methods for bacteria detection. Here, an electrochemical detector of a functional two-dimensional (2D) metal-organic framework (MOF) nanozyme was developed for the sensitive detection of pathogenic Staphylococcus aureus. A dual recognition strategy consisting of vancomycin and anti-S. aureus antibody was proposed to specifically anchor S. aureus. The 2D MOFs with excellent peroxidase-like activity can efficiently catalyze o-phenylenediamine to 2,2-diaminoazobenzene, which is an ideal electrochemical signal readout for monitoring the bacteria concentration. Under optimal conditions, the present bioassay provides a wide detection range of 10-7.5 × 10⁷ colony-forming units CFU/mL with a detection limit of 6 CFU/mL, which is better than most of the previous reports. In addition, the established electrochemical sensor can selectively and accurately identify S. aureus in the presence of other bacteria. The present work provides a new pathway for sensitive and selective detection of S. aureus and presents a promising potential in the realm of clinical diagnosis.

Core Antibiotic-Induced Transcriptional Signatures Reflect Susceptibility to All Members of an Antibiotic Class

Current growth-based antibiotic susceptibility testing (AST) is too slow to guide early therapy. We previously developed a diagnostic approach that quantifies antibiotic-induced transcriptional signatures to distinguish susceptible from resistant isolates, providing phenotypic AST 24 to 36 h faster than current methods. Here, we show that 10 transcripts optimized for AST of one fluoroquinolone, aminoglycoside, or beta-lactam reflect susceptibility when the organism is exposed to other members of that class. This finding will streamline development and implementation of this strategy, facilitating efficient antibiotic deployment.

Feasibility and potential significance of rapid in vitro qualitative phenotypic antimicrobial susceptibility testing of gram-negative bacilli with the ProMax system

The emergence and evolution of antibiotic resistance has been accelerated due to the widespread use of antibiotics and a lack of timely diagnostic tests that guide therapeutic treatment with adequate sensitivity, specificity, and antimicrobial susceptibility testing (AST) accuracy. Automated AST instruments are extensively used in clinical microbiology labs and provide a streamlined workflow, simplifying susceptibility testing for pathogenic bacteria isolated from clinical samples. Although currently used commercial systems such as the Vitek2 and BD Phoenix can deliver results in substantially less time than conventional methods, their dependence on traditional AST inoculum concentrations and optical detection limit their speed somewhat. Herein, we describe the GeneFluidics ProMax lab automation system intended for a rapid 3.5-hour molecular AST from clinical isolates. The detection method described utilizes a higher starting inoculum concentration and automated molecular quantification of species-specific 16S rRNA through the use of an electrochemical sensor to assess microbiological responses to antibiotic exposure. A panel of clinical isolates consisting of species of gram-negative rods from the CDC AR bank and two hospitals, New York-Presbyterian Queens and Medical College of Wisconsin, were evaluated against ciprofloxacin, gentamicin, and meropenem in a series of reproducibility and clinical studies. The categorical agreement and reproducibility for Citrobacter freundii, Enterobacter cloacae, Escherichia coli, Klebsiella aerogenes, Klebsiella oxytoca, Klebsiella pneumoniae, and Pseudomonas aeruginosa were 100% and 100% for ciprofloxacin, 98.7% and 100% for gentamicin and 98.5% and 98.5% for meropenem, respectively.

Recent Development of Rapid Antimicrobial Susceptibility Testing Methods through Metabolic Profiling of Bacteria

Due to the inappropriate use and overuse of antibiotics, the emergence and spread of antibiotic-resistant bacteria are increasing and have become a major threat to human health. A key factor in the treatment of bacterial infections and slowing down the emergence of antibiotic resistance is to perform antimicrobial susceptibility testing (AST) of infecting bacteria rapidly to prescribe appropriate drugs and reduce the use of broad-spectrum antibiotics. Current phenotypic AST methods based on the detection of bacterial growth are generally reliable but are too slow. There is an urgent need for new methods that can perform AST rapidly. Bacterial metabolism is a fast process, as bacterial cells double about every 20 to 30 min for fast-growing species. Moreover, bacterial metabolism has shown to be related to drug resistance, so a comparison of differences in microbial metabolic processes in the presence or absence of antimicrobials provides an alternative approach to traditional culture for faster AST. In this review, we summarize recent developments in rapid AST methods through metabolic profiling of bacteria under antibiotic treatment.

Analysis of Staphylococcus aureus Transcriptome in Pediatric Soft Tissue Abscesses and Comparison to Murine Infections

A comprehensive understanding of how Staphylococcus aureus adapts to cause infections in humans can inform development of diagnostic, therapeutic, and preventive approaches. Expression analysis of clinical strain libraries depicts in vitro conditions that differ from those in human infection, but low bacterial burden and the requirement for reverse transcription or nucleic acid amplification complicate such analyses of bacteria causing human infection. We developed methods to evaluate the mRNA transcript signature of S. aureus in pediatric skin and soft tissue infections (SSTI) directly ex vivo Abscess drainage from 47 healthy pediatric patients undergoing drainage of a soft tissue infection was collected, and RNA was extracted from samples from patients with microbiologically confirmed S. aureus abscesses (42% due to methicillin-resistant S. aureus [MRSA]). Using the NanoString platform and primers targeting S. aureus mRNA transcripts encoding surface-expressed or secreted proteins, we measured direct counts of 188 S. aureus mRNA transcripts in abscess drainage. We further evaluated this mRNA signature in murine models of S. aureus SSTI and nasal colonization where the kinetics of the transcriptome could be determined. Heat maps of the S. aureus mRNA signatures from pediatric abscesses demonstrated consistent per-target expression across patients. While there was significant overlap with the profiles from murine SSTI and nasal colonization, important differences were noted, which can inform efforts to develop therapeutic and vaccine approaches.

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.

The History of Colistin Resistance Mechanisms in Bacteria: Progress and Challenges

Since 2015, the discovery of colistin resistance genes has been limited to the characterization of new mobile colistin resistance (mcr) gene variants. However, given the complexity of the mechanisms involved, there are many colistin-resistant bacterial strains whose mechanism remains unknown and whose exploitation requires complementary technologies. In this review, through the history of colistin, we underline the methods used over the last decades, both old and recent, to facilitate the discovery of the main colistin resistance mechanisms and how new technological approaches may help to improve the rapid and efficient exploration of new target genes. To accomplish this, a systematic search was carried out via PubMed and Google Scholar on published data concerning polymyxin resistance from 1950 to 2020 using terms most related to colistin. This review first explores the history of the discovery of the mechanisms of action and resistance to colistin, based on the technologies deployed. Then we focus on the most advanced technologies used, such as MALDI-TOF-MS, high throughput sequencing or the genetic toolbox. Finally, we outline promising new approaches, such as omics tools and CRISPR-Cas9, as well as the challenges they face. Much has been achieved since the discovery of polymyxins, through several innovative technologies. Nevertheless, colistin resistance mechanisms remains very complex.

Mining the Unmapped Reads in Bovine RNA-Seq Data Reveals the Prevalence of Bovine Herpes Virus-6 in European Dairy Cows and the Associated Changes in Their Phenotype and Leucocyte Transcriptome

Microbial RNA is detectable in host samples by aligning unmapped reads from RNA sequencing against taxon reference sequences, generating a score proportional to the microbial load. An RNA-Seq data analysis showed that 83.5% of leukocyte samples from six dairy herds in different EU countries contained bovine herpes virus-6 (BoHV-6). Phenotypic data on milk production, metabolic function, and disease collected during their first 50 days in milk (DIM) were compared between cows with low (1–200 and n = 114) or high (201–1175 and n = 24) BoHV-6 scores. There were no differences in milk production parameters, but high score cows had numerically fewer incidences of clinical mastitis (4.2% vs. 12.2%) and uterine disease (54.5% vs. 62.7%). Their metabolic status was worse, based on measurements of IGF-1 and various metabolites in blood and milk. A comparison of the global leukocyte transcriptome between high and low BoHV-6 score cows at around 14 DIM yielded 485 differentially expressed genes (DEGs). The top pathway from Gene Ontology (GO) enrichment analysis was the immune system process. Down-regulated genes in the high BoHV-6 cows included those encoding proteins involved in viral detection (DDX6 and DDX58), interferon response, and E3 ubiquitin ligase activity. This suggested that BoHV-6 may largely evade viral detection and that it does not cause clinical disease in dairy cows.

Rapid Determination of Antimicrobial Susceptibility by Stimulated Raman Scattering Imaging of D2O Metabolic Incorporation in a Single Bacterium

Rapid antimicrobial susceptibility testing (AST) is urgently needed for treating infections with appropriate antibiotics and slowing down the emergence of antibiotic-resistant bacteria. Here, a phenotypic platform that rapidly produces AST results by femtosecond stimulated Raman scattering imaging of deuterium oxide (D2O) metabolism is reported. Metabolic incorporation of D2O into biomass in a single bacterium and the metabolic response to antibiotics are probed in as short as 10 min after culture in 70% D2O medium, the fastest among current technologies. Single-cell metabolism inactivation concentration (SC-MIC) is obtained in less than 2.5 h from colony to results. The SC-MIC results of 37 sets of bacterial isolate samples, which include 8 major bacterial species and 14 different antibiotics often encountered in clinic, are validated by standard minimal inhibitory concentration blindly measured via broth microdilution. Toward clinical translation, stimulated Raman scattering imaging of D2O metabolic incorporation and SC-MIC determination after 1 h antibiotic treatment and 30 min mixture of D2O and antibiotics incubation of bacteria in urine or whole blood is demonstrated.

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of the qnrS1 gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 93% of the test samples’ MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work does not treat machine learning as a black box model concept, but also identifies the genomic features that determine susceptibility. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement.

Entropy of a bacterial stress response is a generalizable predictor for fitness and antibiotic sensitivity

Current approaches explore bacterial genes that change transcriptionally upon stress exposure as diagnostics to predict antibiotic sensitivity. However, transcriptional changes are often specific to a species or antibiotic, limiting implementation to known settings only. While a generalizable approach, predicting bacterial fitness independent of strain, species or type of stress, would eliminate such limitations, it is unclear whether a stress-response can be universally captured. By generating a multi-stress and species RNA-Seq and experimental evolution dataset, we highlight the strengths and limitations of existing gene-panel based methods. Subsequently, we build a generalizable method around the observation that global transcriptional disorder seems to be a common, low-fitness, stress response. We quantify this disorder using entropy, which is a specific measure of randomness, and find that in low fitness cases increasing entropy and transcriptional disorder results from a loss of regulatory gene-dependencies. Using entropy as a single feature, we show that fitness and quantitative antibiotic sensitivity predictions can be made that generalize well beyond training data. Furthermore, we validate entropy-based predictions in 7 species under antibiotic and non-antibiotic conditions. By demonstrating the feasibility of universal predictions of bacterial fitness, this work establishes the fundamentals for potentially new approaches in infectious disease diagnostics.

Single-cell RNA-sequencing reports growth-condition-specific global transcriptomes of individual bacteria

Bacteria respond to changes in their environment with specific transcriptional programmes, but even within genetically identical populations these programmes are not homogenously expressed¹. Such transcriptional heterogeneity between individual bacteria allows genetically clonal communities to develop a complex array of phenotypes¹, examples of which include persisters that resist antibiotic treatment and metabolically specialized cells that emerge under nutrient-limiting conditions². Fluorescent reporter constructs have played a pivotal role in deciphering heterogeneous gene expression within bacterial populations³ but have been limited to recording the activity of single genes in a few genetically tractable model species, whereas the vast majority of bacteria remain difficult to engineer and/or even to cultivate. Single-cell transcriptomics is revolutionizing the analysis of phenotypic cell-to-cell variation in eukaryotes, but technical hurdles have prevented its robust application to prokaryotes. Here, using an improved poly(A)-independent single-cell RNA-sequencing protocol, we report the faithful capture of growth-dependent gene expression patterns in individual Salmonella and Pseudomonas bacteria across all RNA classes and genomic regions. These transcriptomes provide important reference points for single-cell RNA-sequencing of other bacterial species, mixed microbial communities and host-pathogen interactions.

Modern Tools for Rapid Diagnostics of Antimicrobial Resistance

Fast, robust, and affordable antimicrobial susceptibility testing (AST) is required, as roughly 50% of antibiotic treatments are started with wrong antibiotics and without a proper diagnosis of the pathogen. Validated growth-based AST according to EUCAST or CLSI (European Committee on Antimicrobial Susceptibility Testing, Clinical Laboratory Standards Institute) recommendations is currently suggested to guide the antimicrobial therapy. Any new AST should be validated against these standard methods. Many rapid diagnostic techniques can already provide pathogen identification. Some of them can additionally detect the presence of resistance genes or resistance proteins, but usually isolated pure cultures are needed for AST. We discuss the value of the technologies applying nucleic acid amplification, whole genome sequencing, and hybridization as well as immunodiagnostic and mass spectrometry-based methods and biosensor-based AST. Additionally, we evaluate the potential of integrated systems applying microfluidics to integrate cultivation, lysis, purification, and signal reading steps. We discuss technologies and commercial products with potential for Point-of-Care Testing (POCT) and their capability to analyze polymicrobial samples without pre-purification steps. The purpose of this critical review is to present the needs and drivers for AST development, to show the benefits and limitations of AST methods, to introduce promising new POCT-compatible technologies, and to discuss AST technologies that are likely to thrive in the future.

Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies

Clinical microbiology is experiencing revolutionary advances in the deployment of molecular, genome sequencing-based, and mass spectrometry-driven detection, identification, and characterization assays. Laboratory automation and the linkage of information systems for big(ger) data management, including artificial intelligence (AI) approaches, also are being introduced. The initial optimism associated with these developments has now entered a more reality-driven phase of reflection on the significant challenges, complexities, and health care benefits posed by these innovations. With this in mind, the ongoing process of clinical laboratory consolidation, covering large geographical regions, represents an opportunity for the efficient and cost-effective introduction of new laboratory technologies and improvements in translational research and development. This will further define and generate the mandatory infrastructure used in validation and implementation of newer high-throughput diagnostic approaches. Effective, structured access to large numbers of well-documented biobanked biological materials from networked laboratories will release countless opportunities for clinical and scientific infectious disease research and will generate positive health care impacts. We describe why consolidation of clinical microbiology laboratories will generate quality benefits for many, if not most, aspects of the services separate institutions already provided individually. We also define the important role of innovative and large-scale diagnostic platforms. Such platforms lend themselves particularly well to computational (AI)-driven genomics and bioinformatics applications. These and other diagnostic innovations will allow for better infectious disease detection, surveillance, and prevention with novel translational research and optimized (diagnostic) product and service development opportunities as key results.

Differential DNA accessibility to polymerase enables 30-minute phenotypic β-lactam antibiotic susceptibility testing of carbapenem-resistant Enterobacteriaceae

The rise in carbapenem-resistant Enterobacteriaceae (CRE) infections has created a global health emergency, underlining the critical need to develop faster diagnostics to treat swiftly and correctly. Although rapid pathogen-identification (ID) tests are being developed, gold-standard antibiotic susceptibility testing (AST) remains unacceptably slow (1-2 d), and innovative approaches for rapid phenotypic ASTs for CREs are urgently needed. Motivated by this need, in this manuscript we tested the hypothesis that upon treatment with β-lactam antibiotics, susceptible Enterobacteriaceae isolates would become sufficiently permeabilized, making some of their DNA accessible to added polymerase and primers. Further, we hypothesized that this accessible DNA would be detectable directly by isothermal amplification methods that do not fully lyse bacterial cells. We build on these results to develop the polymerase-accessibility AST (pol-aAST), a new phenotypic approach for β-lactams, the major antibiotic class for gram-negative infections. We test isolates of the 3 causative pathogens of CRE infections using ceftriaxone (CRO), ertapenem (ETP), and meropenem (MEM) and demonstrate agreement with gold-standard AST. Importantly, pol-aAST correctly categorized resistant isolates that are undetectable by current genotypic methods (negative for β-lactamase genes or lacking predictive genotypes). We also test contrived and clinical urine samples. We show that the pol-aAST can be performed in 30 min sample-to-answer using contrived urine samples and has the potential to be performed directly on clinical urine specimens.

Surfactant-enhanced DNA accessibility to nuclease accelerates phenotypic β-lactam antibiotic susceptibility testing of Neisseria gonorrhoeae

Rapid antibiotic susceptibility testing (AST) for Neisseria gonorrhoeae (Ng) is critically needed to counter widespread antibiotic resistance. Detection of nucleic acids in genotypic AST can be rapid, but it has not been successful for β-lactams (the largest antibiotic class used to treat Ng). Rapid phenotypic AST for Ng is challenged by the pathogen's slow doubling time and the lack of methods to quickly quantify the pathogen's response to β-lactams. Here, we asked two questions: (1) Is it possible to use nucleic acid quantification to measure the β-lactam susceptibility phenotype of Ng very rapidly, using antibiotic-exposure times much shorter than the 1- to 2-h doubling time of Ng? (2) Would such short-term antibiotic exposures predict the antibiotic resistance profile of Ng measured by plate growth assays over multiple days? To answer these questions, we devised an innovative approach for performing a rapid phenotypic AST that measures DNA accessibility to exogenous nucleases after exposure to β-lactams (termed nuclease-accessibility AST [nuc-aAST]). We showed that DNA in antibiotic-susceptible cells has increased accessibility upon exposure to β-lactams and that a judiciously chosen surfactant permeabilized the outer membrane and enhanced this effect. We tested penicillin, cefixime, and ceftriaxone and found good agreement between the results of the nuc-aAST after 15-30 min of antibiotic exposure and the results of the gold-standard culture-based AST measured over days. These results provide a new pathway toward developing a critically needed phenotypic AST for Ng and additional global-health threats.

ShinyOmics: collaborative exploration of omics-data

BACKGROUND

Omics-profiling is a collection of increasingly prominent approaches that result in large-scale biological datasets, for instance capturing an organism's behavior and response in an environment. It can be daunting to manually analyze and interpret such large datasets without some programming experience. Additionally, with increasing amounts of data; management, storage and sharing challenges arise.

RESULTS

Here, we present ShinyOmics, a web-based application that allows rapid collaborative exploration of omics-data. By using Tn-Seq, RNA-Seq, microarray and proteomics datasets from two human pathogens, we exemplify several conclusions that can be drawn from a rich dataset. We identify a protease and several chaperone proteins upregulated under aminoglycoside stress, show that antibiotics with the same mechanism of action trigger similar transcriptomic responses, point out the dissimilarity in different omics-profiles, and overlay the transcriptional response on a metabolic network.

CONCLUSIONS

ShinyOmics is easy to set up and customize, and can utilize user supplied metadata. It offers several visualization and comparison options that are designed to assist in novel hypothesis generation, as well as data management, online sharing and exploration. Moreover, ShinyOmics can be used as an interactive supplement accompanying research articles or presentations.

Simultaneous detection of genotype and phenotype enables rapid and accurate antibiotic susceptibility determination

Multidrug resistant organisms (MDROs) are a serious threat to human health^1,2. Fast, accurate antibiotic susceptibility testing (AST) is a critical need in addressing escalating antibiotic resistance, since delays in identifying MDROs increase mortality^3,4 and use of broad-spectrum antibiotics, further selecting for resistant organisms. Yet current growth-based AST assays, such as broth microdilution⁵, require several days before informing key clinical decisions. Rapid AST would transform the care of infected patients while ensuring that our antibiotic arsenal is deployed as efficiently as possible. Growth-based assays are fundamentally constrained in speed by doubling time of the pathogen, and genotypic assays are limited by the ever-growing diversity and complexity of bacterial antibiotic resistance mechanisms. Here, we describe a rapid assay for combined Genotypic and Phenotypic AST through RNA detection, GoPhAST-R, that classifies strains with 94–99% accuracy by coupling machine learning analysis of early antibiotic-induced transcriptional changes with simultaneous detection of key genetic resistance determinants to increase accuracy of resistance detection, facilitate molecular epidemiology, and enable early detection of emerging resistance mechanisms. This two-pronged approach provides phenotypic AST 24–36 hours faster than standard workflows, with <4 hour assay time on a pilot instrument for hybridization-based multiplexed RNA detection implemented directly from positive blood cultures.

Nanoarray Digital Polymerase Chain Reaction with High-Resolution Melt for Enabling Broad Bacteria Identification and Pheno-Molecular Antimicrobial Susceptibility Test

Toward combating infectious diseases caused by pathogenic bacteria, there remains an unmet need for diagnostic tools that can broadly identify the causative bacteria and determine their antimicrobial susceptibilities from complex and even polymicrobial samples in a timely manner. To address this need, a microfluidic and machine-learning-based platform that performs broad bacteria identification (ID) and rapid yet reliable antimicrobial susceptibility testing (AST) is developed. Specifically, this platform builds on "pheno-molecular AST", a strategy that transforms nucleic acid amplification tests (NAATs) into phenotypic AST through quantitative detection of bacterial genomic replication, and utilizes digital polymerase chain reaction (PCR) and digital high-resolution melt (HRM) to quantify and identify bacterial DNA molecules. Bacterial species are identified using integrated experiment-machine learning algorithm via HRM profiles. Digital DNA quantification allows for rapid growth measurement that reflects susceptibility profiles of each bacterial species within only 30 min of antibiotic exposure. As a demonstration, multiple bacterial species and their susceptibility profiles in a spiked-in polymicrobial urine specimen were correctly identified with a total turnaround time of ∼4 h. With further development and clinical validation, this platform holds the potential for improving clinical diagnostics and enabling targeted antibiotic treatments.

Impact of Species Diversity on the Design of RNA-Based Diagnostics for Antibiotic Resistance in Neisseria gonorrhoeae

Quantitative assessment of antibiotic-responsive RNA transcripts holds promise for a rapid point-of-care (POC) diagnostic tool for antimicrobial susceptibility testing. These assays aim to distinguish susceptible and resistant isolates by transcriptional differences upon drug exposure. However, an often-overlooked dimension of designing these tests is that the genetic diversity within a species may yield differential transcriptional regulation independent of resistance phenotype. Here, we use a phylogenetically diverse panel of Neisseria gonorrhoeae and transcriptome profiling coupled with reverse transcription-quantitative PCR to test this hypothesis, to identify azithromycin responsive transcripts and evaluate their potential diagnostic value, and to evaluate previously reported diagnostic markers for ciprofloxacin resistance (porB and rpmB). Transcriptome profiling confirmed evidence of genetic distance and population structure impacting transcriptional response to azithromycin. Taking this into account, we found azithromycin-responsive transcripts overrepresented in susceptible strains compared to resistant strains and selected four candidate diagnostic transcripts (rpsO, rplN, omp3, and NGO1079) that were the most significantly differentially regulated between phenotypes across drug exposure. RNA signatures for these markers categorically predicted resistance in 19/20 cases, with the one incorrect categorical assignment for an isolate at the threshold of reduced susceptibility. Finally, we found that porB and rpmB expression were not uniformly diagnostic of ciprofloxacin resistance in a panel of isolates with unbiased phylogenetic sampling. Overall, our results suggest that RNA signatures as a diagnostic tool are promising for future POC diagnostics; however, development and testing should consider representative genetic diversity of the target pathogen.

Emerging Analytical Techniques for Rapid Pathogen Identification and Susceptibility Testing

In the face of looming threats from multi-drug resistant microorganisms, there is a growing need for technologies that will enable rapid identification and drug susceptibility profiling of these pathogens in health care settings. In particular, recent progress in microfluidics and nucleic acid amplification is pushing the boundaries of timescale for diagnosing bacterial infections. With a diverse range of techniques and parallel developments in the field of analytical chemistry, an integrative perspective is needed to understand the significance of these developments. This review examines the scope of new developments in assay technologies grouped by key enabling domains of research. First, we examine recent development in nucleic acid amplification assays for rapid identification and drug susceptibility testing in bacterial infections. Next, we examine advances in microfluidics that facilitate acceleration of diagnostic assays via integration and scale. Lastly, recentdevelopments in biosensor technologies are reviewed. We conclude this review with perspectives on the use of emerging concepts to develop paradigm-changing assays.

Molecular typing of Bluetongue virus using the nCounter® analysis system platform

Bluetongue virus (BTV) is a segmented double-stranded RNA virus, existing in multiple serotypes, belonging to the genus Orbivirus of the family Reoviridae. BTV causes Bluetongue (BT), a major OIE-listed disease of ruminants. Identification of BTV serotype is accomplished using multiple typing assays and tends to be executed based on the known epidemiological situation within a given country. Samples containing multiple serotypes, particularly those containing novel introductions, may therefore be missed. The aim of this work was to optimize the nCounter® Analysis System Microarray platform (NanoString technologies), that would simultaneously identify all BTV serotypes and co-infections in analyzed samples. Probes were designed according to all Seg-2 sequences, coding for VP2 proteins which determine serotype specificity, available on line. A specific BTV CodeSet of probes was optimized. Experiments were performed with 30 BTV isolates and with 46 field samples previously shown to be infected with BTV by classical molecular assays. All BTV isolates were correctly identified and the expected BTV serotype was recognized in 35 field samples with C_T values between 22.0-33.0. In turn, it was unable to identify 11 samples with C_T values between 29.0-38.0. Although specificity of the assay needs to be further investigated against a larger panel of BTVs collected worldwide, RNA loads, which are normally detected in blood samples during the acute phase of infection, are within the range of C_T values detectable by the BTV CodeSet. We propose the NanoString RNA microarray as a first-line molecular diagnostic tool for identification and typing of BTV. Once identification of the index cases is performed, diagnosis of the following samples may be performed by specific, more sensitive and cheaper PCR-based tools.

Sequence- And Structure-Specific Probing of RNAs by Short Nucleobase-Modified dsRNA-Binding PNAs Incorporating a Fluorescent Light-up Uracil Analog

RNAs are emerging as important biomarkers and therapeutic targets. The strategy of directly targeting double-stranded RNA (dsRNA) by triplex-formation is relatively underexplored mainly due to the weak binding at physiological conditions for the traditional triplex-forming oligonucleotides (TFOs). Compared to DNA and RNA, peptide nucleic acids (PNAs) are chemically stable and have a neutral peptide-like backbone, and thus, they show significantly enhanced binding to natural nucleic acids. We have successfully developed nucleobase-modified dsRNA-binding PNAs (dbPNAs) to facilitate structure-specific and selective recognition of dsRNA over single-stranded RNA (ssRNA) and dsDNA regions at near-physiological conditions. The triplex formation strategy facilitates the targeting of not only the sequence but also the secondary structure of RNA. Here, we report the development of novel dbPNA-based fluorescent light-up probes through the incorporation of A-U pair-recognizing 5-benzothiophene uracil (^btU). The incorporation of ^btU into dbPNAs does not affect the binding affinity toward dsRNAs significantly, in most cases, as evidenced by our nondenaturing gel shift assay data. The blue fluorescence emission intensity of ^btU-modified dbPNAs is sequence- and structure-specifically enhanced by dsRNAs, including the influenza viral RNA panhandle duplex and HIV-1-1 ribosomal frameshift-inducing RNA hairpin, but not ssRNAs or DNAs, at 200 mM NaCl, pH 7.5. Thus, dbPNAs incorporating ^btU-modified and other further modified fluorescent nucleobases will be useful biochemical tools for probing and detecting RNA structures, interactions, and functions.

Rapid identification and phylogenetic classification of diverse bacterial pathogens in a multiplexed hybridization assay targeting ribosomal RNA

Rapid bacterial identification remains a critical challenge in infectious disease diagnostics. We developed a novel molecular approach to detect and identify a wide diversity of bacterial pathogens in a single, simple assay, exploiting the conservation, abundance, and rich phylogenetic content of ribosomal RNA in a rapid fluorescent hybridization assay that requires no amplification or enzymology. Of 117 isolates from 64 species across 4 phyla, this assay identified bacteria with >89% accuracy at the species level and 100% accuracy at the family level, enabling all critical clinical distinctions. In pilot studies on primary clinical specimens, including sputum, blood cultures, and pus, bacteria from 5 different phyla were identified.

Cross-Domain and Viral Interactions in the Microbiome

The importance of the microbiome to human health is increasingly recognized and has become a major focus of recent research. However, much of the work has focused on a few aspects, particularly the bacterial component of the microbiome, most frequently in the gastrointestinal tract. Yet humans and other animals can be colonized by a wide array of organisms spanning all domains of life, including bacteria and archaea, unicellular eukaryotes such as fungi, multicellular eukaryotes such as helminths, and viruses. As they share the same host niches, they can compete with, synergize with, and antagonize each other, with potential impacts on their host. Here, we discuss these major groups making up the human microbiome, with a focus on how they interact with each other and their multicellular host.

Targeting Antibiotic Tolerance, Pathogen by Pathogen

Antibiotic tolerance, the capacity of genetically susceptible bacteria to survive the lethal effects of antibiotic treatment, plays a critical and underappreciated role in the disease burden of bacterial infections. Here, we take a pathogen-by-pathogen approach to illustrate the clinical significance of antibiotic tolerance and discuss how the physiology of specific pathogens in their infection environments impacts the mechanistic underpinnings of tolerance. We describe how these insights are leading to the development of species-specific therapeutic strategies for targeting antibiotic tolerance and highlight experimental platforms that are enabling us to better understand the complexities of drug-tolerant pathogens in in vivo settings.

Quantitative and synthetic biology approaches to combat bacterial pathogens

Antibiotic resistance is one of the biggest threats to public health. The rapid emergence of resistant bacterial pathogens endangers the efficacy of current antibiotics and has led to increasing mortality and economic burden. This crisis calls for more rapid and accurate diagnosis to detect and identify pathogens, as well as to characterize their response to antibiotics. Building on this foundation, treatment options also need to be improved to use current antibiotics more effectively and develop alternative strategies that complement the use of antibiotics. We here review recent developments in diagnosis and treatment of bacterial pathogens with a focus on quantitative biology and synthetic biology approaches.

Profiling of Bacterial and Fungal Microbial Communities in Cystic Fibrosis Sputum Using RNA

Here, we report an approach to detect diverse bacterial and fungal taxa in complex samples by direct analysis of community RNA in one step using NanoString probe sets. We designed rRNA-targeting probe sets to detect 42 bacterial and fungal genera or species common in cystic fibrosis (CF) sputum and demonstrated the taxon specificity of these probes, as well as a linear response over more than 3 logs of input RNA. Culture-based analyses correlated qualitatively with relative abundance data on bacterial and fungal taxa obtained by NanoString, and the analysis of serial samples demonstrated the use of this method to simultaneously detect bacteria and fungi and to detect microbes at low abundance without an amplification step. Compared at the genus level, the relative abundances of bacterial taxa detected by analysis of RNA correlated with the relative abundances of the same taxa as measured by sequencing of the V4V5 region of the 16S rRNA gene amplified from community DNA from the same sample. We propose that this method may complement other methods designed to understand dynamic microbial communities, may provide information on bacteria and fungi in the same sample with a single assay, and with further development, may provide quick and easily interpreted diagnostic information on diverse bacteria and fungi at the genus or species level.IMPORTANCE Here we demonstrate the use of an RNA-based analysis of specific taxa of interest, including bacteria and fungi, within microbial communities. This multiplex method may be useful as a means to identify samples with specific combinations of taxa and to gain information on how specific populations vary over time and space or in response to perturbation. A rapid means to measure bacterial and fungal populations may aid in the study of host response to changes in microbial communities.

RNA markers enable phenotypic test of antibiotic susceptibility in Neisseria gonorrhoeae after 10 minutes of ciprofloxacin exposure

Antimicrobial-resistant Neisseria gonorrhoeae is an urgent public-health threat, with continued worldwide incidents of infection and rising resistance to antimicrobials. Traditional culture-based methods for antibiotic susceptibility testing are unacceptably slow (1-2 days), resulting in the use of broad-spectrum antibiotics and the further development and spread of resistance. Critically needed is a rapid antibiotic susceptibility test (AST) that can guide treatment at the point-of-care. Rapid phenotypic approaches using quantification of DNA have been demonstrated for fast-growing organisms (e.g. E. coli) but are challenging for slower-growing pathogens such as N. gonorrhoeae. Here, we investigate the potential of RNA signatures to provide phenotypic responses to antibiotics in N. gonorrhoeae that are faster and greater in magnitude compared with DNA. Using RNA sequencing, we identified antibiotic-responsive transcripts. Significant shifts (>4-fold change) in transcript levels occurred within 5 min of antibiotic exposure. We designed assays for responsive transcripts with the highest abundances and fold changes, and validated gene expression using digital PCR. Using the top two markers (porB and rpmB) we correctly determined the antibiotic susceptibility and resistance of 49 clinical isolates after 10 min exposure to ciprofloxacin. RNA signatures are therefore promising as an approach on which to build rapid AST devices for N. gonorrhoeae at the point-of-care, which is critical for disease management, surveillance, and antibiotic stewardship efforts.

Rapid pathogen-specific phenotypic antibiotic susceptibility testing using digital LAMP quantification in clinical samples

Rapid antimicrobial susceptibility testing (AST) is urgently needed for informing treatment decisions and preventing the spread of antimicrobial resistance resulting from the misuse and overuse of antibiotics. To date, no phenotypic AST exists that can be performed within a single patient visit (30 min) directly from clinical samples. We show that AST results can be obtained by using digital nucleic acid quantification to measure the phenotypic response of Escherichia coli present within clinical urine samples exposed to an antibiotic for 15 min. We performed this rapid AST using our ultrafast (~7 min) digital real-time loop-mediated isothermal amplification (dLAMP) assay [area under the curve (AUC), 0.96] and compared the results to a commercial (~2 hours) digital polymerase chain reaction assay (AUC, 0.98). The rapid dLAMP assay can be used with SlipChip microfluidic devices to determine the phenotypic antibiotic susceptibility of E. coli directly from clinical urine samples in less than 30 min. With further development for additional pathogens, antibiotics, and sample types, rapid digital AST (dAST) could enable rapid clinical decision-making, improve management of infectious diseases, and facilitate antimicrobial stewardship.

FAST: Rapid determinations of antibiotic susceptibility phenotypes using label-free cytometry

Sepsis, a life-threatening immune response to blood infections (bacteremia), has a ∼30% mortality rate and is the 10th leading cause of US hospital deaths. The typical bacterial loads in adult septic patients are ≤100 bacterial cells (colony forming units, CFU) per ml blood, while pediatric patients exhibit only ∼1000 CFU/ml. Due to the low numbers, bacteria must be propagated through ∼24-hours blood cultures to generate sufficient CFUs for diagnosis and further analyses. Herein, we demonstrate that, unlike other rapid post-blood culture antibiotic susceptibility tests (ASTs), our phenotypic approach can drastically accelerate ASTs for the most common sepsis-causing gram-negative pathogens by circumventing long blood culture-based amplification. For all blood isolates of multi-drug resistant pathogens investigated (Escherichia coli, Klebsiella pneumoniae, and Acinetobacter nosocomialis), effective antibiotic(s) were readily identified within the equivalent of 8 hours from initial blood draw using <0.5 mL of adult blood per antibiotic. These methods should drastically improve patient outcomes by significantly reducing time to actionable treatment information and reduce the incidence of antibiotic resistance. © 2018 International Society for Advancement of Cytometry.

Gene detection and expression profiling of Neisseria meningitidis using NanoString nCounter platform

Detection of bacterial gene transcripts in low density mucosal samples is challenging. We evaluated the NanoString nCounter system for transcript detection in Neisseria meningitidis (Nm) cultures. The method was sensitive, reproducible (R² = 0.99) and demonstrated changes in gene expression. Studying Nm transcripts from pharyngeal samples may be feasible using this approach.

Continuous and High-Throughput Electromechanical Lysis of Bacterial Pathogens Using Ion Concentration Polarization

Electrical lysis of mammalian cells has been a preferred method in microfluidic platforms because of its simple implementation and rapid recovery of lysates without additional reagents. However, bacterial lysis typically requires at least a 10-fold higher electric field (∼10 kV/cm), resulting in various technical difficulties. Here, we present a novel, low-field-enabled electromechanical lysis mechanism of bacterial cells using electroconvective vortices near ion selective materials. The vortex-assisted lysis only requires a field strength of ∼100 V/cm, yet it efficiently recovers proteins and nucleic acids from a variety of pathogenic bacteria and operates in a continuous and ultrahigh-throughput (>1 mL/min) manner. Therefore, we believe that the electromechanical lysis will not only facilitate microfluidic bacterial sensing and analysis but also various high-volume applications such as the energy-efficient recovery of valuable metabolites in biorefinery pharmaceutical industries and the disinfection of large-volume fluid for the water and food industries.