Illuminating the Blueprint of Disease
Amid a pandemic, Chaz Langelier decodes genetic patterns that can improve diagnoses
Treating respiratory illness involves more unknowns than we might like to imagine. Consider a patient who comes into an emergency room with symptoms of pneumonia: fever, cough, difficulty breathing, chest pain. The underlying cause could be anything from asthma to a heart attack—and now, COVID-19. Does the patient have an infection, and if so, what kind? The answer isn’t always clear-cut.
The cells of patients with infectious diseases harbor patterns of gene expression that can reveal far more about the illness than conventional diagnostic tests. Chaz Langelier ’00 is decoding these patterns with a technique called metagenomic sequencing at the University of California, San Francisco (UCSF), where he is a practicing physician and associate professor of medicine.
“There’s this huge unmet need for better approaches to diagnosing infectious diseases,” Langelier said. Nowhere is this more clear than in the current pandemic. Most of the existing tests for COVID-19 are challenged by both low accuracy and an inability to predict infectiousness or disease severity.
Metagenomic sequencing, on the other hand, can reveal not only the presence of a pathogen but also its exact genetic composition and details about how the patient’s immune system is responding, all from a single sample of blood or respiratory fluid.
“If the genome for a pathogen exists in any database in the world, then we can identify it using this approach,” Langelier said. “We also get outstanding information on the immune response of the person to the infection.”
An infection is not just the result of a pathogen entering the body. It’s a complex interplay between the pathogen, the host, and the host’s immune response. While typical diagnostic tests look for the presence of a limited set of microbes, metagenomic sequencing offers a more holistic view.
In the case of an emergency room patient with respiratory distress, a doctor might prescribe antibiotics as a precaution—but if it turns out the patient has a virus, the medicine is not only unnecessary but potentially harmful.
“People are given broad-spectrum antibiotics to cover the most likely possibilities, but not necessarily tailored to what’s causing the problem,” Langelier said. “That phenomenon has really contributed to the rise of drug-resistant, bacterial infections throughout the world.”
More than 20,000 genes could be involved in a given immune response to an infection. … Machine learning—a process through which computer models can be “taught” to identify patterns based on received data—refines thousands of genes into a handful that most reliably point to a specific ailment.
More than 20,000 genes could be involved in a given immune response to an infection. With metagenomic sequencing, an instrument decodes all of the genetic sequences in a biological sample, providing the ability to see which ones have been turned on or off within the body. Machine learning—a process through which computer models can be “taught” to identify patterns based on received data—refines thousands of genes into a handful that most reliably point to a specific ailment.
The genetic signatures spotted through this technique can be used not just to diagnose infections, Langelier said, but also to track transmissions between individuals and manage outbreaks. For instance, if multiple people are suspected to be involved in an outbreak of methicillin-resistant Staphylococcus aureus, the potentially deadly infection known as MRSA, sequencing can determine whether the bacterial strains causing the infection are genetically identical and, therefore, originating from the same source.
Similarly, Langelier and colleagues at UCSF are using sequencing to track the transmission of COVID-19 infections in communities and hospitals to complement public health contact tracing and surveillance testing efforts in California.
As a chemistry/biochemistry major at Colby, Langelier was inspired by his professors, naming Whitney King and Frank Fekete in particular. “They really got me interested in, and passionate about, doing science,” he said.
While studying medicine and biochemistry in an M.D.-Ph.D. program at the University of Utah, his primary interest was the health impacts of exposure to environmental hazards such as air pollution. That changed after he spent three months volunteering at an HIV-AIDS clinic in Uganda during his first year of medical school.
“That experience really made a significant impact on me,” Langelier said. “I was seeing so many people who were suffering from infectious diseases—not only HIV and AIDS and the opportunistic infections that result, but also malaria and diarrheal illness.”
He began his medical residency in 2014 at UCSF, learning for the first time about metagenomic sequencing through Joseph DeRisi, a biochemist who has pioneered the concept. UCSF is the first research facility in the country to bring sequencing into the clinic, using it to diagnose meningitis.
Broader use of the technique, Langelier says, is a couple of years away: The testing is expensive, and it requires specialized equipment. But costs are coming down, and it’s easy to imagine a hospital’s investment in a sequencing machine paying off, he adds, if it means figuring out the cause of a patient’s infection in 24 hours instead of three to five days.
As part of his role at UCSF, Langelier teaches courses at the medical school when he isn’t treating patients or conducting research. During his training, people advised him against tackling both research and clinical work, saying it was too difficult to do both well. But seeing patients inspires and motivates him, he said, to better understand the many questions that surround infectious disease.
“Honestly, every time that I am an attending [physician] on the infectious disease service, it reemphasizes the need to develop better diagnostic tests,” he said. “You have to see both sides of the picture.”
