Findings

Neutralizing Zika to Prevent Birth Defects

An unusual type of antibody that even at miniscule levels neutralizes the Zika virus and renders the infection undetectable in preclinical models has been identified by a team led by Weill Cornell Medicine, NewYork-Presbyterian and NIH investigators.

Because Zika can cause birth defects when passed from a pregnant person to their fetus, this discovery could lead to the development of therapies to protect babies from the potentially devastating effects of the disease.

In research published in Cell, the investigators isolated an ultrapotent immunoglobulin M antibody — a five-armed immune protein that latches onto the virus — using blood cells taken from pregnant people infected with Zika. In experiments with mice, they determined that the antibody not only protected the animals from otherwise lethal infections, but also suppressed the virus to the point that it could not be detected in their blood.

At this point, doctors have no approved vaccines or treatments to offer patients. With further research, this antibody has the potential to help fill that gap, according to co-senior author Dr. Sallie Permar, the Nancy C. Paduano Professor in Pediatrics and chair of pediatrics at Weill Cornell Medicine and pediatrician-in-chief at NewYork-Presbyterian/Weill Cornell Medical Center and NewYork-Presbyterian Komansky Children’s Hospital. “There are two potential ways it could be used: to quickly reduce levels of Zika in the blood of pregnant people who have become infected,” she says, “or as a preventative measure given to those at risk of contracting the virus during an outbreak.” 

Dr. Bruce Schackman, the Saul P. Steinberg Distinguished Professor of Population Health Sciences at Weill Cornell Medicine and the principal investigator of a five-year, $8.1 million grant from the National Institutes of Health to help stem the national opioid epidemic. One major challenge in reducing opioid deaths has been difficulty collecting and analyzing data to enhance the capacity to implement evidence-based services, Dr. Schackman says. He and his colleagues will use the grant to forecast the cost and value of opioid interventions using simulation modeling that integrates available data. They also hope to fuel future research priorities and offer real-time estimates that help policymakers decide next steps.

Christopher E. Mason, a professor of physiology and biophysics and co-director of the WorldQuant Initiative for Quantitative Prediction at Weill Cornell Medicine, describing research, published in Microbiome, showing that a type of bacteria known as Acinetobacter pittii is evolving to become more resistant to antibiotics and is finding ways to survive in the harsh environment of the International Space Station.

Surprising New Ways to Suppress Tumor Cells

By analyzing key enzymes in a new way, an international team led by researchers at Weill Cornell Medicine has discovered how a well-known signaling molecule can either stimulate or suppress tumor growth depending on where it’s produced. The work, published in Cell Reports, reveals a new aspect of tumor cell biology and points to a promising strategy for treating many types of cancer.

“We think this is going to have a lot of applications in a variety of different tissue systems and clinical contexts,” says co-senior author Dr. Jonathan H. Zippin, vice chair of research and an associate professor of dermatology and pharmacology at Weill Cornell Medicine, director of dermatology precision medicine at the Englander Institute for Precision Medicine, and a dermatologist at NewYork-Presbyterian/Weill Cornell Medical Center.

The number of major subtypes of post-COVID syndrome, identified by different clusters of symptoms, that were elucidated in a recent study led by Weill Cornell Medicine researchers.

“Looking at how cases cluster can profoundly impact the prognosis and care of patients,” says Dr. Rainu Kaushal, senior author of the study, which was published in Nature Medicine and was the largest of its kind to examine long COVID. Researchers used a machine learning algorithm to spot symptom patterns in the health records of nearly 35,000 U.S. patients who tested positive for SARS-CoV-2 infection and later developed lingering long-COVID-type symptoms.

The research is part of a one-year $9.8 million NIH grant focusing on electronic health record cohort studies, spearheaded by principal investigator Dr. Kaushal, senior associate dean for clinical research and chair of the Department of Population Health Sciences at Weill Cornell Medicine.