Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID platform

Abstract

Background: Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next-generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences.

Results: To address this need, we developed the Chan Zuckerberg ID (CZ ID) AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and single-isolate whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes from Illumina data. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and single-isolate WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections.

Conclusions: In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and single-isolate WGS data. Its development represents an important step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR.

Keywords: Antimicrobial resistance; CZ ID; Chan Zuckerberg ID; Metagenomics; Whole-genome sequencing.

Fig. 1

High-level flow diagram highlighting the integrated AMR and mNGS modules within the CZ ID platform. A more detailed diagram is provided in Additional file 1: Fig. S1

Fig. 2

Examples of CZ ID web tool sample reports. A The report in the AMR module with a filter of Number of Reads ≥ 5 and Reads/Contig percent coverage ≥ 10% applied to the detected AMR genes. B The report in the mNGS module showing the list of detected species (top) and the coverage visualization for one species (bottom). Details about report metrics are discussed in the main text and CZ ID help center https://help.czid.org/

Fig. 3

Combining pathogen detection and AMR gene profiling of mNGS and single-isolate WGS data to investigate Klebsiella pneumoniae transfusion-related sepsis. A Abundance and genome coverage of Klebsiella pneumoniae from direct mNGS of plasma or serum samples versus WGS of cultured bacterial isolates. B AMR genes detected in each sample. * denotes AMR gene(s) for which resistance originates due to point mutations (as opposed to presence/absence of the gene); these were detected by the “protein variant model” in CARD and the gene name shown is a representative reference gene containing the mutations known to lead to resistance. Legend: NT rPM = reads mapping to pathogen in the NCBI NT database per million reads sequenced. Contig = contiguous sequence. Strict/Perfect/Nudged refers to RGI’s alignment stringency threshold. If one gene was detected through multiple contigs or reads, the highest alignment confidence among them is shown on the plot (see methods in Additional file 1). “pt1” = patient 1, “pt2” = patient 2. “pre” = pre-transfusion, “post” = post-transfusion

Fig. 4

Outbreak investigation pairing WGS of methicillin susceptible Staphylococcus aureus isolates and mNGS of surveillance skin swabs from babies in a neonatal intensive care unit. A Unsupervised clustering of AMR gene profiles from single-isolate WGS data reveals a cluster of related isolates indicated by the dashed-line box. B Matrix of single nucleotide polymorphism (SNP) distances between each sequenced isolate confirms the genetic relatedness of this cluster, which is highlighted by a dashed-line box

Fig. 5

Bacterial genera and AMR gene families detected by mNGS of skin swabs from babies in a neonatal intensive care unit. A mNGS of swab samples demonstrated a diversity of genera in both samples from patients within an outbreak cluster of genetically related S. aureus, as well as in those from patients outside of the cluster. B mNGS analysis revealed a greater number and type of AMR gene families versus those identified by WGS of S. aureus isolated in culture from the swabs in Fig. 4A. Selected AMR gene families of high public health concern are highlighted in red with the specific genes (shown using CARD ARO terms) detected in parenthesis

Fig. 6

Co-detection of microbes and AMR genes in patients with critical bacterial infections. A Relative abundance (reads per million, rpM) of the eight most abundant taxa in the lower respiratory tract detected by RNA mNGS of tracheal aspirate from a patient with Serratia marcescens pneumonia. The dominant microbe is highlighted in blue. B AMR genes and their species prediction by the AMR module. Columns indicate the species these AMR genes and their variants are found in according to CARD Resistomes, Variants, & Prevalence database, and those found in the dominant species as in A are colored in blue. AMR genes that are further associated with the dominant species by the pathogen-of-origin analysis are colored in purple. C Relative abundance (rpM) of the eight most abundant taxa detected by plasma DNA mNGS in a patient with sepsis due to MRSA bloodstream infection. The dominant microbe is highlighted in blue. D AMR genes and their species prediction by the AMR module. Columns indicate the species these AMR genes and their variants are found in according to CARD Resistomes, Variants, & Prevalence database, and those found in the dominant species as in C are colored in blue. AMR genes that are further associated with the dominant species by the pathogen-of-origin analysis are colored in purple

Fig. 7

Longitudinal profiling of pathogen and AMR gene abundance in a patient hospitalized for severe respiratory syncytial virus (RSV) infection who developed Pseudomonas aeruginosa ventilator-associated pneumonia (VAP). A Relative abundance in reads per million (rpM) of RSV and P. aeruginosa. B AMR genes detected in the lower respiratory tract microbiome at each time point. Perfect or strict AMR alignments from contigs are highlighted in yellow, while those nudged are orange. Short read alignments are in red. AMR genes mapping to Pseudomonas aeruginosa or any Pseudomonas species are highlighted in blue and purple, respectively. *Sample from day 12 did not have enough sequencing reads but was plotted to maintain even scaling on the x-axis

Fig. 8

AMR surveillance from environmental water samples. AMR gene families identified from global surveillance of surface or wastewater samples from Boston, USA and Vellore, India. AMR genes found by contigs that passed Perfect or Strict cutoff are included in heatmap. Gene families of high public health concern are highlighted in red