Application 1: Identification of AMR genes from WGS and mNGS data.
To demonstrate the CZ ID AMR module’s utility for detecting bacterial pathogens and their AMR genes in both WGS and mNGS data, we leveraged data from a recent investigation of transfusion-related sepsis22. In this study, two immunocompromised patients received platelet units originating from a single donor. Both developed septic shock within hours after the transfusion, with blood cultures from Patient 1, who did not survive, returning positive for Klebsiella pneumoniae. Patient 2, who was receiving prophylactic antibiotic therapy at the time of the transfusion, survived, but had negative blood cultures. Direct mNGS of post-transfusion blood samples from both patients revealed a large increase in reads mapping to Klebsiella pneumoniae, a pathogen which was later also identified from culture of residual material from the transfused platelet bag (Fig. 3A)22. While blood mNGS data yielded less coverage of the K. pneumoniae genome compared to WGS of the cultured isolates, mNGS of patient 1’s post-transfusion plasma sample recovered all the AMR genes found by WGS of cultured isolates (Fig. 3B). Even in patient 2, whose blood sample had fewer reads mapping to K. pneumoniae, most AMR genes found in the cultured isolates were still able to be identified using the RGI “Nudged” threshold.
Application 2: Comprehensive metagenomic and WGS profiling of pathogens and AMR genes in the setting of a hospital outbreak.
To demonstrate how the CZ ID AMR module can facilitate deeper insights into pathogen and AMR transmission in hospitals, we evaluated WGS and mNGS data from surveillance skin swabs collected from 40 babies in a neonatal intensive care unit (NICU). The swabs were collected to evaluate for suspected transmission of methicillin-susceptible Staphylococcus aureus (MSSA) between patients. WGS of the MSSA isolates followed by implementation of the AMR module demonstrated many shared AMR genes, and revealed a cluster of nine samples with identical AMR profiles (Fig. 4A). Subsequent phylogenetic assessment using split k-mer analysis with SKA223, revealed that samples within this cluster differed by less than 11 single nucleotide polymorphisms (SNP) across their genomes, consistent with an outbreak involving S. aureus transmission between patients (Fig. 4B).
Within this cluster of patients, we considered whether other bacterial species in the microbiome were also being exchanged in addition to the S. aureus. Intriguingly, mNGS analysis of the direct swab samples from which the S. aureus isolates were selectively cultured revealed a diversity of bacterial taxa, many of which were more abundant than S. aureus. These included several established healthcare-associated pathogens that were never identified using the selective culture-based approach, such as Enterobacter, Citrobacter, Klebsiella and Enterococcus species. mNGS also demonstrated that each sample had a distinct microbial community composition even among samples from the cluster, indicating that only S. aureus and potentially a subset of other species were actually exchanged between babies, rather than the entire skin microbiome (Fig. 5A).
Further analysis of mNGS data using the AMR module also revealed a diversity of AMR genes conferring resistance to several drug classes, and commonly associated with nosocomial pathogens. These included genes encoding ampC-type inducible beta-lactamases (e.g., CKO, CMY, SST), extended spectrum beta-lactamases (e.g., SHV), and the recently emerged MCR class of AMR genes, which confer plasmid-transmissible colistin resistance24.
The AMR gene profiles varied greatly across the samples, both within the cluster and outside of the cluster, consistent with the observed taxonomic diversity (Fig. 5B). Together, these results revealed both inter-patient MSSA transmission in the NICU, and the acquisition of AMR genes associated with nosocomial pathogens within the first months of life.
Application 3: Correlating pathogen identification with AMR gene detection.
Next, we aimed to integrate results from the CZ ID mNGS and AMR modules by analyzing mNGS data from critically ill patients with bacterial infections. In Patient 35025, who was hospitalized for Serratia marcescens pneumonia, metagenomic RNA sequencing (RNA-seq) of a lower respiratory tract sample identified Serratia marcescens as the single most dominant species within the lung microbiome (Fig. 6A)25. Among the detected AMR genes, based on the Resistomes & Variants information from CARD, SRT-2 and SST-1 are found exclusively in Serratia marcescens (Fig. 6B in blue). Further analysis by the pathogen-of-origin feature in the AMR module matched the k-mers from reads and contigs containing rsmA, AAC(6’)-Ic, and CRP to Serratia marcescens (Fig. 6B in purple).
In Patient 1182726, who was hospitalized for sepsis due to a methicillin-resistant Staphylococcus aureus (MRSA) blood stream infection, analysis of plasma mNGS data demonstrated that Staphylococcus aureus was the dominant species present in the blood sample (Fig. 6C)26. Among the detected AMR genes, based on Resistome & Variants information from CARD, Staphylococcus aureus norA, Staphylococcus aureus LmrS, arlS, mepA, tet(38), mecR1, mecA are found exclusively in staph species (Fig. 6D in blue). Pathogen-of-origin analysis further matched k-mers from the reads containing sdrM to S. aureus (Fig. 6D in purple).
Application 4: Profiling the longitudinal dynamics of pathogens and AMR genes.
To demonstrate the utility of the CZID mNGS and AMR modules for studying the longitudinal dynamics of infection, we analyzed serially-collected lower respiratory RNA-seq data from a critically ill patient with respiratory syncytial virus (RSV) infection who subsequently developed ventilator-associated pneumonia (VAP) due to Pseudomonas aeruginosa27,28. Analysis of microbial mNGS data using the CZ ID pipeline highlighted the temporal dynamics of RSV abundance, which decreased over time. Following viral clearance, we noted an increase in reads mapping to P. aeruginosa on day 9, correlating with a subsequent clinical diagnosis of VAP and bacterial culture positivity (Fig. 7A)27,28. Analysis using the CZ ID AMR module demonstrated that P. aeruginosa-associated AMR genes were also detected, and their prevalence tracked with the relative abundance of the nosocomial bacterial pathogen (Fig. 7B).
Application 5: AMR gene detection from environmental surveillance samples.
Lastly, to highlight the application of the CZ ID AMR module for environmental surveillance of AMR pathogens, we analyzed publicly-available short-read mNGS data from a wastewater surveillance study comparing Boston, USA to Vellore, India29. In this study, municipal wastewater, hospital wastewater, and surface water samples were collected from each city and underwent DNA mNGS. From AMR gene alignments at the contig level, we observed a total 22 AMR gene families in Boston samples versus 30 from Vellore (Fig. 8). Several AMR genes of high public health concern such as the KPC and NDM plasmid-transmissible carbapenemase genes were only present in hospital effluent, reflecting the fact that hospitals frequently serve as reservoirs of AMR pathogens30 .