A new smartphone-based genetic testing device can rapidly identify pathogens that cause healthcare-associated infections, researchers say.
In a pilot clinical test, the new system, polarization anisotropy diagnostics (PAD), proved comparable to that of bacterial culture, report Ki Soo Park, PhD, from Harvard Medical School in Boston, Massachusetts, and colleagues.
"In contrast to the culture, the PAD assay was fast (~2 hours), multiplexed, and cost-effective (<2$ per assay)," they write in an article published in the May 6 issue of Science Advances.
Every day, 1 in 25 hospitalized patients acquires an infection, the researchers note. It can be hard to figure out which bacteria are to blame, and therefore which treatment to prescribe.
The traditional method requires culturing patient samples in agar for up to several days.
Polymerase chain reaction (PCR) amplification of bacterial nucleotides works faster, but is more expensive and can be complicated to use. Existing automated systems are bulky, according to the authors, and the technique is susceptible to false-positives resulting from contamination, so the pre- and post-PCR areas have to be kept in separate places.
Therefore, the researchers set out to invent a faster, less expensive, more mobile system. They came up with PAD, which measures changes in fluorescence anisotropy when detection probes recognize target bacterial nucleic acids.
The system is ratiometric and independent of fluorescence intensity, limiting its susceptibility to environmental noise.
It consists of a compact device with a disposable cartridge for sample preparation and multiwell detection. It is optimized to perform nucleic acid amplification and detection without washing steps. The team also integrated a contamination control into the assay protocol. The user extracts genetic material from a sample, such as fluid from an infection. The device amplifies this material using asymmetric reverse transcription in a disposable plastic cube, measuring about 2 × 2 cm.
The nucleic acid probes detect genetic sequences that uniquely define a disease-causing bacterium. The inventors have so far created more than 35 sequence-specific probes to assess bacterial burden, types, antibiotic resistance, and virulence.
A reporter probe attached to a fluorescent tag generates a light signal. When the device detects a sample with copies of a target bacterial sequence, it measures the light signal and sends it to the electronic base station, where it gets digitized and transmitted to a smartphone with a PAD application.
This app converts the data into a report with a time stamp and global positional coordinates. The entire process lasts less than 2 hours.
To test it out, the inventors applied PAD to detect gram-negative Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa and Gram-positive Staphylococcus aureus.
They defined the PAD output as Δr = r 0 − r, where r is the fluorescence anisotropy of the sample and r 0 is the fluorescence anisotropy of control samples containing DNA polymerase and the reporter only.
Across different bacterial species, the authors observed consistent Δr values in concentration-matched samples, with P = .8857. This result supported the use of the PAD in estimating total bacterial load.
They next performed titration experiments with serially diluted bacterial samples. The PAD assay's dynamic range spanned more than 104 colony-forming units (CFUs); the limit of detection was down to single-digit CFUs.
Next the authors tested the efficacy of the contamination control system, which works by substituting deoxythymidine triphosphate with deoxyuridine triphosphate during PCR to render all amplicons to have a uracil-containing DNA backbone.
They spiked samples with deoxyuridine triphosphate containing PCR products. The negative control with no bacterial targets showed false-positives, but this signal was eliminated with the addition of uracil-DNA glycosylase, which cleaved these amplicons, selectively destroying the contaminants while keeping bona fide DNA templates. Only those samples containing the true bacteria had high Δr.
To differentiate healthcare-acquired infection-causing pathogens, the authors used keys that target the hypervariable regions of the 16s rRNA in different bacterial species. They found that these achieved high specificity. For example, the Escherichia key showed a high Δr only with its intended target.
The inventors were equally successful in using keys that target bacterial genes, making them antibiotic or highly virulent. They were able to detect genes that confer multidrug resistance on Staphylococcus aureus and virulence factors that contribute to its pathogenicity.
Patient Samples Compare With Laboratory Culture
Finally, the researchers tested the device in abdominal fluid or nephrostomy samples from nine patients and compared the findings with those of a clinical pathology laboratory. The laboratory used both culture and quantitative PCR to detect pathogens in the same samples.
The PAD and the pathology laboratory both detected no infections in three of the patients. In three others, they both detected Escherichia, and in another, they both detected Escherichia as well as Klebsiella.
In another patient, the PAD and laboratory both detected Staphylococcus with virulence factors.
In one patient, the PAD detected an infection but could not match it to a pathogen. The laboratory reported infection with Providencia, which was not among the bacteria the PAD was set up to identify.
This work shows that the PAD is scalable for comprehensive screening, the inventors report. One common fluorescence reporter can be used for all detection targets, and the incremental cost of additional targets is only about $0.01, they write.
Despite the high concordance with the laboratory reports, the researchers noted some limitations. First, none of the clinical samples were found to contain drug-resistant strains, so the PAD's capacity to perform this detection remains untested.
Second, the PAD may show ambiguous results when target nucleic acids overlap. For example, the current keys could not distinguish community-acquired MRSA from the mixture of healthcare-acquired multidrug-resistant S aureus and methicillin-susceptible S aureus. They expect this problem to resolve as more bacterial genomic data are produced through whole-genome sequencing.
The report drew praise from Francis Collins, MD, PhD, director of the National Institutes of Health, which partially funded the research.
"The rise of antibiotic resistant bacteria is a particularly urgent public health concern," he wrote on his blog.
"There's a critical need for new and better tools for detecting bacterial infections and their resistance to particular antibiotic treatments early. This new technology, and others like it, provide hope that we can win this important battle."
The study was funded by the National Institutes of Health, the Department of Defense, the National Research Foundation of Korea, and the Korean Ministry of Science, ICT and Future Planning. The authors have disclosed no relevant financial relationships.
Sci Adv. 2016;2:e1600300. Full text
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Cite this: Smartphone-Based Genetic Testing Identifies Hospital Pathogens - Medscape - May 30, 2016.
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