The complex genetics of same-sex sexual behavior, the "quiet" genome of a childhood cancer, and a metabolic angle on antibiotics' activity.
Research Roundup: August 30, 2019
Welcome to the August 30, 2019 installment of Research Roundup, a recurring snapshot of recent studies published by scientists at the Broad Institute and their collaborators.
The complex genetics of same-sex sexual behavior
In a study published in Science, an international team led in part by Andrea Ganna and institute member Benjamin Neale of the Program in Medical and Population Genetics examined existing data from nearly 500,000 UK Biobank and 23andMe participants to look for genetic variants associated with same-sex sexual behavior. They found evidence that there are likely thousands of genetic variants contributing to same-sex sexual behavior, but each has a small influence (similar to what is seen for many other complex traits); that it’s not possible to predict same-sex behavior through genetics; and that same-sex sexual behavior is a natural part of our diversity overall. Learn more about the study in a Broad resource page and on the study website. Because the research raises important social, ethical, and scientific issues that are worth considering, the Broad invited members of our community to provide their thoughts on the study, the process, the implications, and lessons we might learn in a series of perspective pieces. Follow coverage of the study and these discussions in The New York Times, NPR, The Washington Post, and elsewhere.
A quiet genome speaks up
Some cancers, including pediatric rhabdoid tumors (RTs), have “quiet” genomes lacking recurrent, targetable mutations. To find potential targets, core institute member Stuart Schreiber, Charles Roberts (now at St. Jude), Elaine Oberlick, Matthew Rees, Jake Bieber, and colleagues in the Cancer Program, the Chemical Biology and Therapeutics Sciences Program, and the Pediatric Dependencies working group performed a high-throughput small-molecule screen and a genome-scale CRISPR-Cas9 screen in RT and control cell lines. Described in Cell Reports, the work points to receptor tyrosine kinases as potential targets and demonstrates that large-scale perturbational screening can uncover vulnerabilities in quiet genomes.
A better predictor of antibiotic lethality
Researchers have thought that antibiotics are better at killing bacteria that are fast-growing versus slow-growing ones. But Allison Lopatkin and institute member James Collins of the Infectious Disease and Microbiome Program and colleagues have found that bacterial metabolic state at the time of treatment better predicts antibiotic lethality than growth rate. The team measured growth and metabolism in various Gram-positive and Gram-negative species, across a range of conditions including nine different antibiotics, to determine the relative contribution of growth and metabolism to antibiotic lethality. The researchers write that the findings could influence the development of new antibacterial drugs and suggest ways of boosting the efficacy of existing drugs. Read more in Nature Microbiology.