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

Abstract

Multidrug resistant organisms are a serious threat to human health1,2. Fast, accurate antibiotic susceptibility testing (AST) is a critical need in addressing escalating antibiotic resistance, since delays in identifying multidrug resistant organisms increase mortality3,4 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 patients with infection 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 h faster than standard workflows, with <4 h assay time on a pilot instrument for hybridization-based multiplexed RNA detection implemented directly from positive blood cultures.

Conflict of interest statement

Competing Interests

R.P.B., P.M., J.Livny, and D.T.H. are co-inventors on subject matter in US provisional application No. 62/723,417 filed by the Broad Institute directed to RNA signatures for AST, as described in this manuscript. L.W., R.B., R.K., and J.B. are employees at NanoString, Inc., the company that manufactures the RNA detection platforms used in this manuscript. NanoString, Inc. has licensed the intellectual property for RNA-based AST from the Broad Institute. V.M.P. received research funds from SeLux Diagnostics, Inc. for work on an unrelated project.

Figures

Extended Data Figure 1.. Differential gene expression upon antibiotic exposure distinguishes susceptible and resistant strains.

(a) RNA-Seq data from two susceptible (left panels) or two resistant (right panels) clinical isolates of E. coli or A. baumannii treated with meropenem (60 min), ciprofloxacin (30 min), or gentamicin (60 min) at CLSI breakpoint concentrations are presented as MA plots. Statistical significance was determined by a two-sided Wald test with the Benjamini-Hochberg correction for multiple hypothesis testing, using the DESeq2 package. (b) Heatmaps of normalized, log-transformed fold-induction of top 10 antibiotic-responsive transcripts from 24 clinical isolates of E. coli or A. baumannii treated at CLSI breakpoint concentrations with meropenem, ciprofloxacin, or gentamicin. Gene identifiers are listed at right, along with gene names if available. CLSI classifications of each strain based on broth microdilution are shown below. * = strains with large inoculum effects in meropenem MIC; x = strains discordant by more than one dilution. (c) GoPhAST-R predictions of probability of resistance from a random forest model trained on NanoString data from the derivation cohort and tested on the validation cohort (y-axis) are compared with standard CLSI classification based on broth microdilution MIC (x-axis) for E. coli (top) or A. baumannii isolates treated with meropenem, ciprofloxacin, and gentamicin. Horizontal dashed lines indicate 50% probability of resistance. Vertical dashed lines indicate the CLSI breakpoint between susceptible and not susceptible (i.e. intermediate/resistant). Numbers in each quadrant indicate concordant and discordant classifications between GoPhAST-R and broth microdilution. Carbapenemase (square outline) and select ESBL (diamond outline) gene content as detected by GoPhAST-R are also displayed on the meropenem plot.

Extended Data Figure 2.. Timecourse of RNA-Seq data upon antibiotic exposure reveals differential gene expression between susceptible and resistant clinical isolates.

Susceptible (left panels) or resistant (right panels) clinical isolates of K. pneumoniae, E. coli, or A. baumannii treated with meropenem, ciprofloxacin, or gentamicin at CLSI breakpoint concentrations for the indicated times. Data are presented as MA plots, Statistical significance was determined by a two-sided Wald test with the Benjamini-Hochberg correction for multiple hypothesis testing, using the DESeq2 package.

Extended Data Figure 3.. Phylogenetic trees highlight the diversity of strains used in this study.

Phylogenetic trees of all sequenced isolates deposited in NCBI for (a)K. pneumoniae, (b)E. coli, (c)A. baumannii, and (d)P. aeruginosa, with all sequenced isolates used in this study indicated by colored arrowheads around the periphery. See Supplemental Methods for details.

Extended Data Figure 4.. NanoString data from dozens of antibiotic-responsive genes distinguish susceptible from resistant isolates.

Heatmaps of normalized, log-transformed fold-induction of antibiotic-responsive transcripts from clinical isolates of K. pneumoniae (24, 18, and 26 independent clinical isolates for the three antibiotics, respectively), E. coli (24 independent clinical isolates for each antibiotic), or A. baumannii (24 clinical isolates for each antibiotic) treated at CLSI breakpoint concentrations with meropenem, ciprofloxacin, or gentamicin. CLSI classifications are shown below. All antibiotic-responsive transcripts chosen as described from RNA-Seq data are shown here; the subset of these chosen by reliefF as the 10 most discriminating transcripts are shown in Fig. 1b or Supplemental Fig. 1b. * = strains with large inoculum effects in meropenem MIC; + = one-dilution errors; x = strains discordant by more than one dilution.

Extended Data Figure 5.. One-dimensional projection of NanoString data distinguishes susceptible from resistant isolates and reflects MIC.

(a) Phase 1 NanoString data from Extended Data Fig. 2 (i.e., normalized, log-transformed fold-induction for each responsive transcript), analyzed as described to generate squared projected distance (SPD) metrics (y-axes) for each strain (see Supplemental Methods), are binned by CLSI classifications (x-axes), for clinical isolates of K. pneumoniae (24, 18, and 26 independent clinical isolates for the three antibiotics, respectively), E. coli (24 independent clinical isolates for each antibiotic), or A. baumannii (24 clinical isolates for each antibiotic) treated at CLSI breakpoint concentrations with meropenem, ciprofloxacin, or gentamicin (the same isolates shown in Fig. 1b–c and Extended Data Fig. 1b–c). By definition, an SPD of 0 indicates a transcriptional response to antibiotic equivalent to that of an average susceptible strain, while an SPD of 1 indicates a response equivalent to that of an average resistant strain. See Supplemental Methods for details. Data are summarized as box-and-whisker plots, where boxes extend from the 25th to 75th percentile for each category, with a line at the median, and whiskers extend from the minimum to the maximum. Note that for A. baumannii and meropenem, the clustering of the majority of susceptible strains by this simple metric (aside from one outlier which is misclassified as resistant by GoPhAST-R) underscores the true differences in transcription between susceptible and resistant isolates, despite the more subtle-appearing differences in heatmaps for this combination (Extended Data Fig. 1b), which is largely caused by one strain with an exaggerated transcriptional response (seen here as the strain with a markedly negative SPD) that affects scaling of the heatmap. (b) The same SPD data (y-axes) plotted against broth microdilution MICs (x-axes) reveal that the magnitude of the transcriptional response to antibiotic exposure correlates with MIC. In both (a) and (b), strains with a large inoculum effect upon meropenem treatment are displayed in red and enlarged. Vertical dashed line indicates the CLSI breakpoint between susceptible and not susceptible (i.e., intermediate or resistant).

Extended Data Figure 6.. RNA-Seq and NanoString data reveal differential gene expression that distinguishes susceptible from resistant clinical isolates for S. aureus + levofloxacin and P. aeruginosa + ciprofloxacin.

(a) RNA-Seq data from two susceptible or two resistant clinical isolates of each species treated with the indicated fluoroquinolone at 1 mg/L for 60 minutes are presented as MA plots. Statistical significance was determined by a two-sided Wald test with the Benjamini-Hochberg correction for multiple hypothesis testing, using the DESeq2 package. (b) Heatmaps of normalized, log-transformed fold-induction of antibiotic-responsive transcripts from 24 independent clinical isolates of each species treated with the indicated fluoroquinolone at 1 mg/L for 60 minutes. For each species, NanoString data from all candidate transcripts are shown at left, and top the 10 transcripts selected from Phase 1 testing are shown at right. (c) GoPhAST-R predictions of probability of resistance from a random forest model trained on Phase 1 NanoString data from the derivation cohort and tested on the validation cohort (y-axis) compared with standard CLSI classification based on broth microdilution MIC (x-axis). Horizontal dashed lines indicate 50% probability of resistance. Vertical dashed lines indicate the CLSI breakpoint between susceptible and not susceptible (i.e. intermediate/resistant). Numbers in each quadrant indicate concordant and discordant classifications between GoPhAST-R and broth microdilution.

Extended Data Figure 7.. Schematic of data analysis scheme, including “two-phase” machine learning approach to feature selection and strain classification.

Schematic representation of major data analysis steps in identifying antibiotic-responsive transcriptional signatures from RNA-Seq data, validating and optimizing these signatures using NanoString in two phases, and using these signatures to classify strains of unknown MIC, also in two phases. First, candidate antibiotic-responsive and control transcripts were chosen from RNA-Seq data using custom scripts built around the DESeq2 package, and conserved regions of these transcripts were identified for targeting in a hybridization assay. In phase 1 (implemented for all pathogen-antibiotic pairs), these candidate transcripts were quantitated on the NanoString assay platform, and the resulting data were partitioned by strain into training and testing cohorts. Ten transcripts that best distinguish susceptible from resistant strains within the training cohort were then selected (step 1A) using the reliefF feature selection algorithm (implemented via the CORElearn package), then used to train an ensemble classifier (step 1B) on the same training cohort using a random forest algorithm (implemented via the caret package). This trained classifier was then used to predict susceptibilities of strains in the testing cohort (step 1C), and accuracy was assessed by comparing with broth microdilution results (Supplementary Table 4). In phase 2 (implemented for K. pneumoniae + meropenem and ciprofloxacin), the same process was repeated, but the phase 1 training and testing cohorts were combined into a single, larger training cohort for feature selection (step 2A) and classifier training (step 2B), and a new set of strains were obtained as a testing cohort. The 10 genes selected from the phase 2 training cohort were measured from this phase 2 testing cohort, and the trained classifier was used for AST on these new strains (step 2C), with accuracy again assessed by comparison with broth microdilution (Supplementary Table 4). See Supplemental Methods for detailed descriptions of each of these analysis steps.

Extended Data Figure 8.. GoPhAST-R accurately classifies K. pneumoniae isolates tested in phase 2.

(a) Heatmaps of normalized, log-transformed fold-induction of top 10 antibiotic-responsive transcripts from K. pneumoniae treated at CLSI breakpoint concentrations with meropenem (31 independent clinical isolates) or ciprofloxacin (25 independent clinical isolates). CLSI classifications are shown below. * = strain with large inoculum effects in meropenem MIC; + = one-dilution error; x = strain discordant by more than one dilution. Note that the 10 responsive transcripts shown are the only 10 tested for this second phase of GoPhAST-R implementation. (b) GoPhAST-R predictions of probability of resistance from a random forest model trained on all Phase 1 NanoString data the independent Phase 2 cohort (y-axis) compared with standard CLSI classification based on broth microdilution MIC (x-axis). Horizontal dashed lines indicate 50% probability of resistance. Vertical dashed lines indicate the CLSI breakpoint between susceptible and not susceptible (i.e. intermediate/resistant). Numbers in each quadrant indicate concordant and discordant classifications between GoPhAST-R and broth microdilution. * = strain with large inoculum effects in meropenem MIC.

Extended Data Figure 9.. GoPhAST-R accurately classifies AST and detects key resistance elements directly from simulated positive blood culture bottles in

(a) Heatmaps of normalized, log-transformed fold-induction NanoString data from the top 10 antibiotic-responsive transcripts directly from 12 simulated positive blood culture bottles for each indicated pathogen-antibiotic combination reveal antibiotic-responsive transcription in susceptible but not resistant isolates. For meropenem, results of carbapenemase / ESBL gene detection are also displayed as a normalized, background-subtracted, log-transformed heatmap above. CLSI classifications of isolates, which were blinded until analysis was complete, are displayed below each heatmap. (b) Probability of resistance from a random forest model trained by leave-one-out cross-validation on NanoString data from (a) (y-axis) compared with standard CLSI classification based on broth microdilution MIC (x-axis) for each isolate. Horizontal dashed lines indicate 50% chance of resistance based on random forest model. Vertical dashed lines indicate CLSI breakpoint between susceptible and resistant. Carbapenemase (square outline) and select ESBL (diamond outline) gene content as detected by GoPhAST-R are also displayed on meropenem plots. See Supplementary Methods for details of spike-in protocol.

Fig. 1.. Differential gene expression upon antibiotic exposure distinguishes susceptible and resistant strains.

(a) RNA-Seq data from two susceptible (left panels) or two resistant (right panels) clinical isolates of K. pneumoniae treated with meropenem (60 min), ciprofloxacin (30 min), or gentamicin (60 min) at CLSI breakpoint concentrations are presented as MA plots. Statistical significance was determined by a two-sided Wald test with the Benjamini-Hochberg correction for multiple hypothesis testing, using the DESeq2 package. (b) Heatmaps of normalized, log-transformed fold-induction of top 10 antibiotic-responsive transcripts from K. pneumoniae treated at CLSI breakpoint concentrations with meropenem (left, 24 independent clinical isolates), ciprofloxacin (center, 18 independent clinical isolates), or gentamicin (right, 26 independent clinical isolates). Gene identifiers are listed at right, along with gene names if available. CLSI classifications of each strain based on broth microdilution are shown below. + = strains with one-dilution errors in classification. (c) GoPhAST-R predictions of probability of resistance from a random forest model trained on NanoString data from the derivation cohort and tested on the validation cohort (y-axis) are compared with standard CLSI classification based on broth microdilution MIC (x-axis) for K. pneumoniae isolates treated with meropenem, ciprofloxacin, and gentamicin. Horizontal dashed lines indicate 50% probability of resistance. Vertical dashed lines indicate the CLSI breakpoint between susceptible and not susceptible (i.e. intermediate/resistant). Numbers in each quadrant indicate concordant and discordant classifications between GoPhAST-R and broth microdilution. Carbapenemase (square outline) and select ESBL (diamond outline) gene content as detected by GoPhAST-R are also displayed on the meropenem plot. Arrow indicates a strain with high-level meropenem resistance, but no carbapenemase.

Fig. 2.. GoPhAST-R detects carbapenemase and ESBL gene content from tested strains.

Known carbapenemase and select ESBL transcript content based on WGS data (left panels) are compared with heatmaps of GoPhAST-R results (right panels) for all (a)K. pneumoniae, (b)E. coli (, and (c)A. baumannii isolates tested for meropenem susceptibility for which WGS data was available (42, 20, and 13 independent clinical isolates, respectively). Heatmap intensity reflects normalized, background-subtracted, log-transformed NanoString data from probes for the indicated gene families. Vertical dashed line separates carbapenemases (left) from ESBL genes (right). Phenotypic AST classification by broth microdilution and GoPhAST-R is shown at right (“S” = susceptible, “I” = intermediate, “R” = resistant; “tr.” = strain used in training cohort, thus not classified by GoPhAST-R). * = strains with large inoculum effects in meropenem MIC; x = strain discordant by more than one dilution.

Fig. 3.. GoPhAST-R detects antibiotic-responsive transcripts directly from positive blood culture bottles.

Heatmaps of normalized, log-transformed fold-induction of the top 10 ciprofloxacin-responsive transcripts from 8 positive blood culture bottles that grew either E. coli (6 independent bottles, A-F) or K. pneumoniae (2 independent bottles, G-H). CLSI classifications of isolates performed by the clinical microbiology laboratory, which were blinded until analysis was complete, are displayed below each heatmap.

Fig. 4.. GoPhAST-R workflow with the NanoString Hyb & Seq™ platform distinguishes phenotypically susceptible from resistant strains and detects genetic resistance determinants in

(a) The GoPhAST-R workflow on the Hyb & Seq detection platform begins once growth is detected in a blood culture bottle. Pathogen identification could either be done prior to this process, or in parallel by multiplexing mRNA targets from multiple organisms (see Supplementary Text). (b) Hyb & Seq hybridization scheme: probe pairs targeting each RNA transcript are hybridized in crude lysate. Each probe A contains a unique barcode sequence (green) for detection and a shared 3’ capture sequence; each probe B contains a biotin group (gray circle) for surface immobilization and a shared 5’ capture sequence. (c) Hyb & Seq detection strategy: immobilized, purified ternary probe-target complexes undergo sequential cycles of multi-step imaging for spatially resolved single-molecule detection. Each cycle consists of reporter probe binding and detection, UV cleavage, a second round of reporter probe binding and detection, and a low-salt wash to regenerate the unbound probe-target complex. 5 Hyb & Seq cycles were used to generate the data shown. See Supplemental Methods for details. (d) Pilot studies for accelerated meropenem susceptibility testing of 6 clinical K. pneumoniae isolates. Above: heatmaps of normalized, log-transformed fold-induction of top 10 meropenem-responsive transcripts measured using this Hyb & Seq workflow, with strains arranged in order of MIC for each antibiotic. CLSI classifications are shown below. Below: heatmaps of normalized, background-subtracted, log-transformed NanoString data from carbapenemase (“CPase”) and select ESBL transcripts measured in the same Hyb & Seq assay.