Genetic method to identify hundreds of pathogens holds 'promise', study finds

In the search for accurate diagnoses for diseases, physicians have traditionally used several methods – including the tradition of patient specimens on a wide variety of media, l reviewing countless medical records and analyzing clinical data using complex mathematical algorithms – in an attempt to identify the bacteria, virus, fungus or other pathogen responsible for an infection. The hunt is often slow and laborious, and the processes used may not have a broad enough scope to find specific pathogens.

A solution might be next-generation sequencing (NGS), according to the findings of a recent study by researchers at Johns Hopkins Medicine. NGS allows clinicians to simultaneously sequence multiple strands of DNA found in patient samples and use this analysis to quickly and accurately identify a single pathogen, among hundreds of suspects.

In a short article published for the first time online on June 13 2022 in the American Culture for Microbiology’s Journal of Scientific Microbiology, researchers compared the pathogen detection capability of an NGS system – the Respiratory Pathogen Infectious Ailments/Antimicrobial Resistance Panel (RPIP) – with a previously studied NGS system and standard of care (SOC) diagnostic methods for specimens obtained with bronchoalveolar lavage. This is where a bronchoscope is passed through the mouth or nose into the lungs, followed by a lavage fluid which is collected for examination.

Researchers believe that their study is among the first to compare NGS and SOC diagnostics for respiratory pathogens.

“We evaluated both methods of NGS diagnostics, one of which was RPIP , and found that in both cases, the ability of NGS to identify specific pathogen brokers was almost similar to the battery of diagnostic tests that clinicians have been using for decades,” the study explains. lead author Patricia Simner, Ph.DMSc. associate professor of pathology at Johns Hopkins University College of Medicine. “While this holds great promise for RPIP and NGS diagnostics in general, we believe more work is needed to further refine the technology before NGS can be considered equal or better than current SOC methods.”

In their study, Simner and colleagues first evaluated the diagnostic capability of metagenomic NGS, a previously studied workflow process in which all DNA obtained from a bronchoalveolar lavage is sequenced — including the individual’s unique genetic material (the “host reads” or “human readout”) and the pathogen of interest (the “microbial readout”). Removing the host DNA allows clinicians to focus their search on the remaining genetic material to hopefully find the microbial readout and ultimately identify the cause of the affected person’s disease.

In the second part of their experiment, the researchers evaluated a different NGS approach using the RPIP system called targeted NGS. In this method, everything in the client’s breath sample is sequenced as with metagenomic NGS, but capture probes – tiny, easy-stranded DNA fragments that structurally match the DNA of specific pathogens – are used to improve research capacity.

“The use of NGS to find the genetic signatures of ‘pathogens is akin to searching for information on a specific topic in a library with a huge number of books,” says study lead author David Gaston, MD, a Ph.D. former pathology researcher at Johns Hopkins University School of Medicine now at Vanderbilt University Medical Center. “With metagenomic NGS, you have to read all the books to find out which ones refer to the subject. But with targeted NGS, you first ask the librarian to pull the volumes most likely to include the subject, and then conduct more targeted, more promising research.”

The researchers found that the effectiveness of metagenomic and targeted NGS varied depending on the type of organism sought. They report that both NGS methods were successful in identifying viruses, with herpes viruses being the most easily detected. Results for bacteria and mycobacteria (which include the causative organism of tuberculosis) approached the level of SOC diagnoses, but dropped as the number of organisms decreased – even with the use of capture probes in the targeted NGS. Neither of the two NGS methods detected the fungi well.

Overall, the researchers found that the targeted workflow of RIPP was in agreement with traditional diagnostics 66 % time. Specifically, they noted 46% agreement for targeted NGS to detect clinically important pathogens and 86% to show pathogens were absent.

In addition to its potential for more accurate identification of 300 pathogenic organisms from bronchoalveolar lavage, researchers believe that targeted NGS also holds great promise to be able to reveal in about 1 day 200 genetic markers in pathogens that indicate which organisms are most likely to be resistant to antibiotics..

“Overall, the current accuracy of NGS metagenomics and targeted approaches to that of current diagnostic procedures and this is a major element of our study,” says Gaston. “We found that NGS can detect a large number of pathogens, but not all, and that in some cases both NGS methods could identify pathogen brokers that traditional diagnostics would have missed.”

“There are currently advantages and disadvantages for the use of NGS as a microbiological diagnostic tool”, explains Simner. “For example, the targeted RPIP workflow requires more time and reagents but requires less bioinformatics analysis of the resulting data. On the other hand, metagenomic NGS is less technically demanding but requires more complex analysis.”

Based on their findings, the researchers believe that metagenomic workflows and targeted NGS can currently be considered complements, but not yet replacements, of SOC diagnostic methods. With further refinement, they believe, NGS systems could one day become the standard for diagnosing respiratory pathogens.

With Gaston and Simner, team members Johns Hopkins Medicine study participants are Karen Carroll, John Fissel, Ethan Gough, Emily Jacobs, Eili Klein, Heather Miller and Jaijun Wu.

The study was supported by the Sherrilyn and Ken Fisher Middle for Environmental Infectious Foods in the Division of Infectious Diseases at Johns Hopkins College Faculty of Drugs. Study materials – including the RPIP panel and the automated Explify system used for bioinformatics analysis of RPIP-targeted NGS data – were provided by their respective manufacturers, Illumina and IDbyDNA.

However, the researchers report that these entities had no role in the design of the study, the collection and interpretation of the data, or the decision to submit the work for publication.

The authors of the study report no conflict of interest.

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