Infectious Diseases

Diagnosing a disease quickly and accurately is essential for patients with acute infections. As many infectious diseases present similar signs and symptoms, laboratory tests have been developed to detect particular microbes that cause a patient’s illness. Physicians commonly test the blood, urine, and other fluids and subject the patient’s sample to microbiology laboratory tests for specific disease agents based on the patient’s symptoms and history, such as recent travels. With tens of thousands of possible disease agents known, a physician must prioritize which disease agents are more likely to be responsible and select the appropriate diagnostic tests.

Infectious agents like bacteria, viruses, fungi, and parasites come in many forms and are therefore only detected by different diagnostic tests. Despite advances in diagnostics, many patients with suspected infections are prescribed antimicrobial therapies, such as antibiotics, rather than thoroughly tested, diagnosed, and treated for the underlying, specific disease agent. As a result, the current stock of available antimicrobial therapies are overused, increasing the risk of antimicrobial resistance.

For critically ill patients, when initial diagnostic tests do not reveal the cause of illness, additional testing is often costly and time-consuming, and may only provide vague or contradictory results. Health care decisions for hospitalized patients must therefore often be made on the basis of limited and imperfect information, with an increased likelihood of death as many therapeutics may be tried without success.

In California, it has been estimated that 240,000 infections are annually contracted from hospital stays alone, costing the health care system approximately $3.1 billion. Many more infections are acquired in community settings.

Metagenomic Next Generation Sequencing

To help address this problem, the research team pioneered an approach that has the potential to identify thousands of disease agents in a single minimally invasive test. This innovative genome analysis is called metagenomic next generation sequencing (mNGS), where 'meta' represents all organisms, 'genome' encapsulates the entire genetic (DNA) material of an organism, and 'sequencing' refers to genome analysis.

The technique can detect almost all known infectious agents in a single sample from a patient, quickly revealing the cause of bacterial, viral, fungal, and parasitic infections, many of which routinely elude physicians. Such information is also highly useful for ruling out an infection, as in the case of an autoimmune disease that may present similar symptoms.

This data-driven precision medicine test works by analyzing the genomic information present in a patient’s sample, such as cerebrospinal fluid (CSF) or blood. Because bacteria, viruses, fungi, and parasites also have DNA-based genomes and can be recognized by unique genetic signatures, infectious agents may be detected among total genomic information in a sample, which is mostly human.

DNA sequencing technologies have advanced at an exponential pace, dramatically lowering both speed and cost. While a genome analysis approach for diagnosing infections has long been theoretically possible, the process would have taken months and cost millions of dollars to diagnose a single patient one decade ago. The research team has so far optimized their system to offer the mNGS diagnostic test for $2,200, providing results within a few days of sample collection. Just prior to the awarding of funds, the research team and its collaborators had obtained evidence that supported the life-saving potential of the technique for critically ill patients. The mNGS test radically sped up the diagnostic process and led to a diagnosis when conventional tests had failed. For example, the team was presented with a case of a 14-year-old boy who was near death with brain inflammation that remained undiagnosed despite months of lab tests, expensive imaging technologies, and invasive procedures, such as brain biopsy. The mNGS test was able to identify the infectious agent, and the boy quickly recovered after targeted treatment.



The research team previously developed a genome-based test for infectious disease diagnostics and obtained evidence that this comprehensive test can accelerate identification of infectious agents in previously difficult-to-diagnose cases. The test has the potential to save the lives of critically ill patients by enabling appropriate treatments in a timely fashion.

The goals of this project were to expand the use of metagenomic next generation sequencing mNGS for diagnostic tests of meningitis, encephalitis, sepsis, and pneumonia from a research lab to a routine clinical setting and to collect evidence for its clinical and economic utility as compared to conventional testing.

Moving a diagnostic test into clinical practice is a complex process that includes 1) developing procedures that ensure accurate and reliable test results and confirming the performance of the test in a licensed diagnostic laboratory that is certified to federal standards (Clinical Laboratory Improvement Amendments, CLIA); 2) determining the test’s clinical validity, that is, how well it identifies, measures, or predicts the presence or absence of a clinical condition; 3) creating processes for ordering, billing, and reimbursing the test; 4) educating the medical profession about the utility and use of the test; and 5) scaling the test to allow for large volume testing.

Toward their ultimate goal of ensuring accessibility, the team has also initiated a cost effectiveness analysis and made progress on multiple technical, logistical, and regulatory steps.

With additional third-party funding in place, the team is well positioned to complete the project and, if successful, transform clinical diagnosis for infectious diseases.

Supplemental Project

With supplemental funds in awarded in 2017, the team completed the CLIA standard confirmation of the test for diagnosing blood infections and initiated a clinical study for patients with blood infections. They also increased the speed of the tests, studied the determinants of antibiotic resistance, and harnessed AI to better diagnose infectious or non-infectious causes of illness.

Research Team and Collaborators

Research Team

  • University of California, San Francisco
    • Charles Chiu, MD, PhD
    • Steve Miller, MD, PhD
    • Joseph DeRisi, PhD
    • Eric Chow, PhD
    • Steven Hauser, MD
    • Michael Wilson, MD
    • Michael Geschwind, MD, PhD
    • Jeffrey Gelfand, MD
    • Felicia Chow, MD
    • Jacob Appelbaum, MD, PhD
    • Chaz Langelier, MD, PhD
    • Kristen McCaleb, PhD
  • University of California, Los Angeles
    • Romney Humphries, PhD. D(ABMM)
    • Jeffrey Klausner, MD, MPH
    • Tara Vijayan, MD
  • University of California, Davis
    • Christopher Polage, MD
    • Stuart Cohen, MD
  • University of California, Berkeley
    • Brent Fulton, PhD, MBA
  • California Department of Public Health
    • James Watt, MD
    • Dongxiang Xia, MD, PhD
    • Sharon Messenger, PhD
    • Debra Wadford, PhD


  • Synapse, Inc.
    • Jonathan Hirsch, PhD
    • Laurie Gomer, MBA
  • DNAnexus, Inc.
    • Davis Shaywitz, MD, PhD
    • Omar Serang, BS
  • Quest Diagnostics, Inc.
    • Rick Pesano, MD, PhD
    • John Leake, MD, MPH
  • Illumina, Inc.
    • Mostafa Ronaghi, PhD
  • Google, Inc.
    • Sri Madabushi, PhD