Detection of antibiotic resistance genes in clinical samples through bioinformatic approaches

By Dr. Sanchari Sinha Dutta, Ph.D.Reviewed by Lily Ramsey, LLMJul 13 2023

A team of scientists from China has explored the utility of metagenomic next-generation sequencing alignment and assembly methods to identify bacterial populations with antibiotic resistance genes in clinical samples collected from patients with pulmonary diseases.

The study is currently available on the Research Square preprint server while under consideration for publication in BMC Antimicrobial Resistance and Infection Control.

Study: Sequencing methods to study the microbiome with antibiotic resistance genes in patients with pulmonary infections. Image Credit: bluesroad/Shutterstock.com

*Important notice: Research Square publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Background

Pulmonary infection is among the most frequent hospital-acquired infections associated with significant morbidity and mortality. Overuse or misuse of antibiotic treatment is the major factor responsible for persistent pulmonary infections.

Conventional microbial cultures are not always sufficient to detect bacterial infections, and physicians often prescribe antibiotics empirically to patients even without a positive test result. This leads to the emergence of antibiotic-resistant pathogens that are difficult to manage with existing antibiotics.

Metagenomic next-generation sequencing (mNGS) is a powerful diagnostic method capable of rapidly detecting potential pathogens and antibiotic resistance genes (ARGs) directly from clinical samples. However, one major disadvantage of this method is false-positive predictions.   

The mNGS alignment and assembly methods are promising bioinformatics approaches for detecting genetic determinants of antibiotic resistance.

The alignment method works by identifying ARGs from low-abundance pathogens present in complex communities. This method largely depends on antibiotic resistance databases containing all reference gene variants.

The assembly method, on the other hand, identifies ARGs that are more divergent from the known sequences in the reference databases. Thus, the assembly method can potentially assist the alignment method to determine positive predictions and attributions between ARGs and bacterial populations.  

In the current study, scientists have employed mNGS alignment and assembly methods to identify antibiotic-resistant bacteria (ARB) from clinical samples derived from patients with pulmonary infections and determine the distributions of ARGs and ARB in clinical samples.  

Study design

One hundred fifty-one clinical samples were collected from 144 patients with pulmonary infections. The samples were analyzed by mNGS alignment and assembly methods and conventional microbial detection methods for identifying bacteria with ARGs.

The attributions between ARGs and bacteria were determined by co-occurring ARG – ARB network analysis.

Important observations

The mNGS analysis of 151 samples resulted in the identification of 114 and 52 ARB-positive samples by the alignment and assembly methods, respectively.

A total of 52 samples identified as ARB positive by alignment and assembly methods were selected for the comprehensive analysis. Of 165 ARB detected by both methods, 142 were specifically detected by the alignment method, and 48 were detected by the assembly method.

A significantly higher number of ARB detected by the alignment method could be due to false-positive predictions.

The comparison of mNGS method with conventional microbial detection methods revealed that mNGS has significantly higher efficiency in detecting pathogens and ARB than conventional methods.

Notably, the alignment method showed a significantly higher ARB false-positive detection rate than the conventional method.

Specifically, assembly and alignment methods were found to have predictive capabilities of 46% and 13%, respectively. The assembly method could assist the alignment method for true ARB detection in clinical samples.

ARG – ARB network analysis

The ARG-ARB network analysis by mNGS alignment and assembly methods revealed the main ARGs in predominant ARB. 361 ARGs were detected, primarily belonging to multidrug and beta-lactam antibiotic classes.

Specifically, 101 ARGs detected by both methods and 34 ARGs detected only by the assembly method achieved a clear ARG–bacteria attribution, which could potentially optimize reference antibiotic resistance databases.

Furthermore, both methods effectively detected predominant ARB and their corresponding ARGs and drug classes. Both methods, including Acinetobacter baumannii, Pseudomonas aeruginosa, Klebsiella pneumoniae, Stenotrophomonas maltophilia, and Corynebacterium striatum, detected a total of 48 predominant ARB.

Study significance

The study indicates that mNGS alignment and assembly methods are efficient and useful for detecting bacterial populations with ARGs in clinical samples.

To reduce false-positive ARB prediction by the alignment method, scientists have set a cut-off over 10⁷ effective reads, which might be suitable for both methods to detect ARGs.

*Important notice: Research Square publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.