Performance Characteristics of Next-Generation Sequencing for the Detection of Antimicrobial Resistance Determinants in Escherichia coli Genomes and Metagenomes

Genomic perspectives on NDM Salmonella Typhi, and a case report from India

BACKGROUND

Carbapenem resistance in Enterobacterales is a growing public health concern, primarily driven by carbapenemase enzymes such as OXA-48, VIM, NDM, and IMP. Among these, New Delhi Metallo-β-lactamase (NDM) has disseminated widely across various Enterobacterales species, including Salmonella Typhi, though reports remain rare.

CASE PRESENTATION

We report an 11-year-old boy from Bangalore with a 10-day history of high-grade fever, chills, rigors, and cough. Laboratory investigations revealed elevated CRP, normal CBC, and microcytic hypochromic anemia. A respiratory panel detected Human Rhinovirus/ Enterovirus. Blood cultures grew non-lactose fermenting gram-negative bacilli, identified as Salmonella spp. via Vitek ID/AST. The isolate exhibited resistance to ampicillin, ciprofloxacin, ceftriaxone, tetracycline, and meropenem but remained susceptible to azithromycin, chloramphenicol, and Co-trimoxazole. Serotyping confirmed the serotype as Salmonella Typhi. Whole-genome sequencing (Illumina) revealed blaNDM-5 and aac(6')-Ia, InCX3 plasmid, and the fluoroquinolone resistance-associated gyrAS83Y mutation. Phylogenetic analysis placed the isolate (IOB-SWH-01) within a cluster of recently sequenced S. Typhi strains from India belonging to the H58 haplotype.

DISCUSSION AND CONCLUSION

To date, NDM-producing S. Typhi has been reported only once, from Pakistan. This is the first documented case in India. The presence of blaNDM-5 in S. Typhi poses a serious clinical and public health threat, given its multidrug-resistant nature and potential for interspecies transmission. Continued genomic surveillance is crucial to monitor its spread and guide treatment strategies.

Evaluation of DNA extraction kits for long-read shotgun metagenomics using Oxford Nanopore sequencing for rapid taxonomic and antimicrobial resistance detection

During a bacterial infection or colonization, the detection of antimicrobial resistance (AMR) is critical, but slow due to culture-based approaches for clinical and screening samples. Culture-based phenotypic AMR detection and confirmation require up to 72 hours (h) or even weeks for slow-growing bacteria. Direct shotgun metagenomics by long-read sequencing using Oxford Nanopore Technologies (ONT) may reduce the time for bacterial species and AMR gene identification. However, screening swabs for metagenomics is complex due to the range of Gram-negative and -positive bacteria, diverse AMR genes, and host DNA present in the samples. Therefore, DNA extraction is a critical initial step. We aimed to compare the performance of different DNA extraction protocols for ONT applications to reliably identify species and AMR genes using a shotgun long-read metagenomic approach. We included three different sample types: ZymoBIOMICS Microbial Community Standard, an in-house mock community of ESKAPE pathogens including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Escherichia coli (ESKAPE Mock), and anonymized clinical swab samples. We processed all sample types with four different DNA extraction kits utilizing different lysis (enzymatic vs. mechanical) and purification (spin-column vs. magnetic beads) methods. We used kits from Qiagen (QIAamp DNA Mini and QIAamp PowerFecal Pro DNA) and Promega (Maxwell RSC Cultured Cells and Maxwell RSC Buccal Swab DNA). After extraction, samples were subject to the Rapid Barcoding Kit (RBK004) for library preparation followed by sequencing on the GridION with R9.4.1 flow cells. The fast5 files were base called to fastq files using Guppy in High Accuracy (HAC) mode with the inbuilt MinKNOW software. Raw read quality was assessed using NanoPlot and human reads were removed using Minimap2 alignment against the Hg38 genome. Taxonomy identification was performed on the raw reads using Kraken2 and on assembled contigs using Minimap2. The AMR genes were identified using Minimap2 with alignment against the CARD database on both the raw reads and assembled contigs. We identified all bacterial species present in the Zymo Mock Community (8/8) and ESKAPE Mock (6/6) with Qiagen PowerFecal Pro DNA kit (chemical and mechanical lysis) at read and assembly levels. Enzymatic lysis retrieved fewer aligned bases for the Gram-positive species (Staphylococcus aureus and Enterococcus faecium) from the ESKAPE Mock on the assembly level compared to the mechanical lysis. We detected the AMR genes from Gram-negative and -positive species in the ESKAPE Mock with the QIAamp PowerFecal Pro DNA kit on reads level with a maximum median time of 1.9 h of sequencing. Long-read metagenomics with ONT may reduce the turnaround time in screening for AMR genes. Currently, the QIAamp PowerFecal Pro DNA kit (chemical and mechanical lysis) for DNA extraction along with the Rapid Barcoding Kit for the ONT sequencing captured the best taxonomy and AMR identification for our specific use case.

Rapid, Selective, and Ultra-Sensitive Field Effect Transistor-Based Detection of Escherichia coli

Escherichia coli (E. coli) was among the first organisms to have its complete genome published (Genome Sequence of E. coli 1997 Science). It is used as a model system in microbiology research. E. coli can cause life-threatening illnesses, particularly in children and the elderly. Possible contamination by the bacteria also results in product recalls, which, alongside the potential danger posed to individuals, can have significant financial consequences. We report the detection of live Escherichia coli (E. coli) in liquid samples using a biosensor based on a field-effect transistor (FET) biosensor with B/N co-coped reduced graphene oxide (rGO) gel (BN-rGO) as the transducer material. The FET was functionalized with antibodies to detect E. coli K12 O-antigens in phosphate-buffered saline (PBS). The biosensor detected the presence of planktonic E. coli bacterial cells within a mere 2 min. The biosensor exhibited a limit of detection (LOD) of 10 cells per sample, which can be extrapolated to a limit of detection at the level of a single cell per sample and a detection range of at least 10-108 CFU/mL. The selectivity of the biosensor for E. coli was demonstrated using Bacillus thuringiensis (B. thuringiensis) as a sample contaminant. We also present a comparison of our functionalized BN-rGO FET biosensor with established detection methods of E. coli k12 bacteria, as well as with state-of-the-art detection mechanisms.

Molecular rapid diagnostic testing for bloodstream infections: Nanopore targeted sequencing with pathogen-specific primers

BACKGROUND

Nanopore sequencing, known for real-time analysis, shows promise for rapid clinical infection diagnosis but lacks effective assays for bloodstream infections (BSIs).

METHODS

We prospectively assessed the performance of a novel nanopore targeted sequencing (NTS) assay in identifying pathogens and predicting antibiotic resistance in BSIs, analyzing 387 blood samples from December 2021 to April 2023.

RESULTS

The positivity rate for NTS (69.5 %, 269/387) nearly matches that of metagenomic next-generation sequencing (mNGS) (74.7 %, 289/387; p = 0.128) and surpasses the positivity rate of conventional blood culture (BC) (33.9 %, 131/387; p < 0.01). Frequent pathogens detected by NTS included Klebsiella pneumoniae (n = 54), Pseudomonas aeruginosa (n = 36), Escherichia coli (n = 36), Enterococcus faecium(n = 30), Acinetobacter baumannii(n = 26), Staphylococcus aureus(n = 23), and Human cytomegalovirus (n = 37). Against a composite BSI diagnostic standard, NTS demonstrated a sensitivity and specificity of 84.0 % (95 % CI 79.5 %-87.7 %) and 90.1 % (95 % CI 81.7 %-88.5 %), respectively. The concordance between NTS and mNGS results (the percentage of total cases where both either detected BSI-related pathogens or were both negative) was 90.2 % (359/387), whereas the consistency between NTS and BC was only 60.2 % (233/387). In 80.6 % (50/62) of the samples with identical pathogens identified by both NTS tests and BCs, the genotypic resistance identified by NTS correlated with culture-confirmed phenotypic resistance. Using NTS, 95 % of samples can be tested and analyzed in approximately 7 h, allowing for early patient diagnosis.

CONCLUSIONS

NTS is rapid, sensitive, and efficient for detecting BSIs and drug-resistant genes, making it a potential preferred diagnostic tool for early infection identification in critically ill patients.

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.

Analysis of Antimicrobial Resistance in Bacterial Pathogens Recovered from Food and Human Sources: Insights from 639,087 Bacterial Whole-Genome Sequences in the NCBI Pathogen Detection Database

Understanding the role of foods in the emergence and spread of antimicrobial resistance necessitates the initial documentation of antibiotic resistance genes within bacterial species found in foods. Here, the NCBI Pathogen Detection database was used to query antimicrobial resistance gene prevalence in foodborne and human clinical bacterial isolates. Of the 1,843,630 sequence entries, 639,087 (34.7%) were assigned to foodborne or human clinical sources with 147,788 (23.14%) from food and 427,614 (76.88%) from humans. The majority of foodborne isolates were either Salmonella (47.88%), Campylobacter (23.03%), Escherichia (11.79%), or Listeria (11.3%), and the remaining 6% belonged to 20 other genera. Most foodborne isolates were from meat/poultry (95,251 or 64.45%), followed by multi-product mixed food sources (29,892 or 20.23%) and fish/seafood (6503 or 4.4%); however, the most prominent isolation source varied depending on the genus/species. Resistance gene carriage also varied depending on isolation source and genus/species. Of note, Klebsiella pneumoniae and Enterobacter spp. carried larger proportions of the quinolone resistance gene qnrS and some clinically relevant beta-lactam resistance genes in comparison to Salmonella and Escherichia coli. The prevalence of mec in S. aureus did not significantly differ between meat/poultry and multi-product sources relative to clinical sources, whereas this resistance was rare in isolates from dairy sources. The proportion of biocide resistance in Bacillus and Escherichia was significantly higher in clinical isolates compared to many foodborne sources but significantly lower in clinical Listeria compared to foodborne Listeria. This work exposes the gaps in current publicly available sequence data repositories, which are largely composed of clinical isolates and are biased towards specific highly abundant pathogenic species. We also highlight the importance of requiring and curating metadata on sequence submission to not only ensure correct information and data interpretation but also foster efficient analysis, sharing, and collaboration. To effectively monitor resistance carriage in food production, additional work on sequencing and characterizing AMR carriage in common commensal foodborne bacteria is critical.

Exploring the effects of antimicrobial treatment on the gut and oral microbiomes and resistomes from elderly long-term care facility residents via shotgun DNA sequencing

Monitoring antibiotic-resistant bacteria (ARB) and understanding the effects of antimicrobial drugs on the human microbiome and resistome are crucial for public health. However, no study has investigated the association between antimicrobial treatment and the microbiome-resistome relationship in long-term care facilities, where residents act as reservoirs of ARB but are not included in the national surveillance for ARB. We conducted shotgun metagenome sequencing of oral and stool samples from long-term care facility residents and explored the effects of antimicrobial treatment on the human microbiome and resistome using two types of comparisons: cross-sectional comparisons based on antimicrobial treatment history in the past 6 months and within-subject comparisons between stool samples before, during and 2-4 weeks after treatment using a single antimicrobial drug. Cross-sectional analysis revealed two characteristics in the group with a history of antimicrobial treatment: the archaeon Methanobrevibacter was the only taxon that significantly increased in abundance, and the total abundance of antimicrobial resistance genes (ARGs) was also significantly higher. Within-subject comparisons showed that taxonomic diversity did not decrease during treatment, suggesting that the effect of the prescription of a single antimicrobial drug in usual clinical treatment on the gut microbiota is likely to be smaller than previously thought, even among very elderly people. Additional analysis of the detection limit of ARGs revealed that they could not be detected when contig coverage was <2.0. This study is the first to report the effects of usual antimicrobial treatments on the microbiome and resistome of long-term care facility residents.

Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools

BACKGROUND

Although the spread of antimicrobial resistance (AMR) through food and its production poses a significant concern, there is limited research on the prevalence of AMR bacteria in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance targets within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches.

RESULTS

We simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Kraken2/Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2/Bracken indicated presence of organisms not included in the synthetic metagenomes. Metaphlan3/Metaphlan4 analysis of community composition was more specific but with lower sensitivity than the Kraken2/Bracken analysis. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 (which allows read to map to multiple targets) falsely reported presence of distantly related ARGs at all isolate genome coverage levels. The presence of background microbiota in metagenomes influenced the accuracy of ARG detection by KMA, resulting in mcr-1 detection at 0.1X isolate coverage in the lettuce but not in the beef metagenome.

CONCLUSIONS

This study demonstrates accurate detection of ARGs in synthetic metagenomes using various bioinformatic methods, provided that reads from the ARG-encoding organism exceed approximately 5X isolate coverage (i.e. 0.4% of a 40 million read metagenome). While lowering thresholds for target gene detection improved sensitivity, this led to the identification of alternative ARG-alleles, potentially confounding the identification of critical ARGs in the resistome. Further advancements in sequencing technologies providing increased coverage depth or extended read lengths may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking clinically important ARGs in agri-food samples.

A microbial consortium alters intestinal Pseudomonadota and antimicrobial resistance genes in individuals with recurrent Clostridioides difficile infection

Intestinal colonization with pathogens and antimicrobial-resistant organisms (AROs) is associated with increased risk of infection. Fecal microbiota transplant (FMT) has successfully been used to cure recurrent Clostridioides difficile infection (rCDI) and to decolonize intestinal AROs. However, FMT has significant practical barriers to safe and broad implementation. Microbial consortia represent a novel strategy for ARO and pathogen decolonization, with practical and safety advantages over FMT. We undertook an investigator-initiated analysis of stool samples collected from previous interventional studies of a microbial consortium, microbial ecosystem therapeutic (MET-2), and FMT for rCDI before and after treatment. Our aim was to assess whether MET-2 was associated with decreased Pseudomonadota (Proteobacteria) and antimicrobial resistance gene (ARG) burden with similar effects to FMT. Participants were selected for inclusion if baseline stool had Pseudomonadota relative abundance ≥10%. Pre- and post-treatment Pseudomonadota relative abundance, total ARGs, and obligate anaerobe and butyrate-producer relative abundances were determined by shotgun metagenomic sequencing. MET-2 administration had similar effects to FMT on microbiome outcomes. The median Pseudomonadota relative abundance decreased by four logs after MET-2 treatment, a greater decrease than that observed after FMT. Total ARGs decreased, while beneficial obligate anaerobe and butyrate-producer relative abundances increased. The observed microbiome response remained stable over 4 months post-administration for all outcomes. IMPORTANCE Overgrowth of intestinal pathogens and AROs is associated with increased risk of infection. With the rise in antimicrobial resistance, new therapeutic strategies that decrease pathogen and ARO colonization in the gut are needed. We evaluated whether a microbial consortium had similar effects to FMT on Pseudomonadota abundances and ARGs as well as obligate anaerobes and beneficial butyrate producers in individuals with high Pseudomonadota relative abundance at baseline. This study provides support for a randomized, controlled clinical trial of microbial consortia (such as MET-2) for ARO decolonization and anaerobe repletion.

SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning

The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen .