How the method works

It all started with one milliliter of blood from each sample from 17 patients in the pilot clinical study. The samples were collected at the same time as samples for blood cultures from infants and toddlers. 

Researchers perfected DNA isolation and machine learning methods to reduce or eliminate signals from human DNA compared to pathogen DNA in the samples.“Since human DNA significantly outnumbers pathogen DNA, this allows us to better detect the ‘needle in the haystack’ that is the pathogen,” Fraley said. 

Mridu Sinha, one of Fraley’s former Ph.D. students and now CEO of Melio, the startup company they cofounded, optimized a machine learning algorithm to reliably detect the difference between melt curves from the pathogens and background noise. The algorithm matches the curves to a database of known DNA melt curves. It’s also able to detect curves created by organisms that are not in this database, which could show up in a sample if it contains rare or emerging pathogens.  

The results not only matched exactly the results from blood cultures from the same blood samples; they also did not produce any false positives. By contrast, other types of tests relying on nucleic acid amplification and next-generation DNA sequencing databases will amplify any DNA present, leading to false positives. Often, DNA gets into the sample from the environment, test tubes, reagents, skin and more. Sample contamination can cause issues with knowing how to interpret the test results. 

“Our test has incorporated sample preparation processes, assay design techniques, and algorithms that ensure we only detect DNA from intact organisms, which is clinically relevant,” Sinha said. 

Next steps include conducting a broader clinical study, as well as expanding the method to adult patients. 

Fraley and Sinha licensed the technology from UC San Diego and cofounded startup Melio to commercialize their method. 

“We want to give doctors the ability to treat their patients based not on aggregate data, but with precise, accurate individual data, enabling truly personalized medicine,” Fraley said.

What is DNA melting?

The DNA in the blood samples is heated, causing it to melt at temperatures between 50 to 90 degrees Celsius–about 120 to 190 degrees Fahrenheit.

As the DNA double-helix melts, the bonds holding together the DNA strands break. Depending on the DNA’s sequence, the bonds have different strengths, and that changes the way the strands unwind from each other. This creates a unique sequence-dependent fingerprint, which researchers can detect using a special dye. The dye causes the unwinding process to give off fluorescent light, creating what researchers call a melting curve—a unique signature for each type of pathogen.

In the past, DNA melting has produced simple curves that were used primarily to confirm that a PCR reaction worked, but this new approach advances melting to generate complex melt curve signatures that are unique to gene sequences. 

Quote from Dr. Karen Mestan, Chief of the Division of Neonatology at Rady Children's Hospital and the UC San Diego Department of Pediatrics:
“The findings of this study fill an important need in pediatrics, especially for critically ill infants and small children in which clinical signs of bacteremia are extremely difficult to decipher.  In settings of early subclinical sepsis, and also overwhelming septic shock, the bacterial pathogen is often challenging to identify accurately and in a timely manner. A test that provides higher reliability and shorter turnaround than current practice is urgently needed. Eventual clinical application of U-dHRM will lead to a reduction in unnecessary antibiotic exposure, prevention of untoward side effects and global antibiotic resistance, better antibiotic stewardship, improved and faster diagnostic accuracy, and overall improved pediatric outcomes. In cases of serious bloodstream infection, it could save lives.”

The work was funded by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (award number R01AI134982 to S.I.F), a Burroughs Wellcome Fund Career Award at the Scientific Interface (award number 1012027 to S.I.F.), and UCSD Clinical Translational Research Institute and UCSD Accelerating Innovations to Market pilot grants.

Universal digital high resolution melt analysis for the diagnosis of bacteremia

April Aralar,a Tyler Goshia,a Nanda Ramchandar,b,c Shelley M. Lawrence,d Aparajita Karmakar,e Ankit Sharma,e Mridu Sinha,e David T. Pride,f Peiting Kuo,f Khrissa Lecrone,f Megan Chiu,f Karen Mestan,g Eniko Sajti,g Michelle Vanderpool,h Sarah Lazar,g Melanie Crabtree,g Yordanos Tesfai,g Stephanie I. Fraleya*#

aDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA

bDepartment of Pediatrics, Naval Medical Center San Diego, San Diego, CA, USA

cDepartment of Pediatrics, Division of Infectious Diseases, University of California, San Diego, La Jolla, CA, USA

dDepartment of Pediatrics, Division of Neonatology, The University of Utah, Salt Lake City, UT, USA

eMelio, Inc, Santa Clara, CA, USA

fDepartment of Pathology, University of California, San Diego, La Jolla, CA, USA

gDepartment of Pediatrics, Division of Neonatology, University of California, San Diego, La Jolla, CA, USA

hDepartment of Pathology and Laboratory Medicine, Rady Children’s Hospital – San Diego, San Diego, San Diego, CA, USA