Antibiotic resistance drivers, brain tumor therapy targets, base editors for pooled screening, and more
Research Roundup: February 19, 2021
Welcome to the February 19, 2021 installment of Research Roundup, a recurring snapshot of recent studies published by scientists at the Broad Institute and their collaborators.
New genes driving antibiotic resistance
Antibiotic resistance is associated with a limited number of bacterial genes. To broaden the search for more of these genes, Allison Lopatkin (now at Columbia University), Sarah Bening, Abigail Manson, Jonathan Stokes, Ahmed Badran, Ashlee Earl, Jim Collins, all in the Infectious Disease and Microbiome Program, and colleagues sequenced and analyzed E. coli with varying levels of antibiotic resistance. They found various genes not typically linked to antibiotic resistance, such as ones involved in carbon and energy metabolism. Mutations in these genes resulted in decreased cell respiration, which allows bacteria to escape the metabolic toxicity induced by several different antibiotics. The findings shed light on how antibiotics work, and suggest potential new avenues for developing drugs that could enhance existing antibiotics. Read more in Science and a story on the Broad site.
New immunotherapy targets for malignant brain tumors
Little is known about the gene expression patterns of cancer-fighting T-cells in malignant brain tumors. Associate member Kai Wucherpfennig of the Klarman Cell Observatory, institute member Mario Suvà of the Epigenomics Program, core institute member (on leave) Aviv Regev (now at Genentech), David Reardon (DFCI), Nathan Mathewson, Orr Ashenberg, Itay Tirosh (Weizmann Institute), Simon Gritsch, Elizabeth Perez, Sascha Marx (DFCI), and colleagues used single-cell RNA sequencing to chart the gene expression and clonal landscape of tumor-infiltrating T cells across 31 patients with malignant brain tumors. The researchers identified CD161, an inhibitory receptor molecule that suppresses the cancer-fighting activity of immune T cells and other natural killer cell receptors –– as new immunotherapy targets. Learn more in Cell and a Dana Farber news story.
Repurposing drugs for COVID-19
In response to the COVID-19 pandemic, Anastasiya Belyaeva, Louis Cammarata (Harvard), Adityanarayanan Radhakrishnan, and associate member Caroline Uhler in the Cell Circuits Program and of MIT developed a machine learning-based approach to identify existing approved drugs that might be repurposed to treat COVID-19, especially for higher risk older patients. Their system accounts for changes in gene expression in lung cells due to both the disease and aging. Reporting in Nature Communications, the scientists identified the protein RIPK1 as a promising target and uncovered three approved drugs that alter cellular levels of the protein. Read more in MIT News.
Getting the blur out of transcriptome maps
Spatial transcriptomics (ST) lets scientists examine individual cells' states and identities within a tissue while retaining information about each cell's original position. However, individual ST measurements may actually reflect more than one cell, blurring cell-type-specific expression and localization patterns. In Nature Biotechnology, Dylan Cable, Evan Murray, Luli Zou, Aleksandrina Goeva, institute member Evan Macosko, core institute member Fei Chen, and Rafael Irizarry (DFCI) introduce RCTD, a computational method that sifts cell type mixtures in ST data using scRNA-seq profiles, allowing the team to identify genes with spatially-dependent expression patterns, uncover principles of cellular organization within tissues, and more.
Pooled screening meets base editors
Characterizing the impact of different genetic variants in a cell remains a major challenge in genomics. A team led by graduate student Ruth Hanna, institute scientist and Genetic Perturbation Platform (GPP) associate director John Doench, and colleagues in the GPP has developed a pooled screening approach using cytosine base editors, enabling researchers to make tens of thousands of single-base edits to mammalian cells at scale and assess their effects. The team tested their approach by identifying known loss-of-function mutations in BRCA1 and BRCA2, point mutations in MCL1, BCL2L1, and PARP1 that confer drug sensitivity or resistance, and loss-of-function variants in numerous DNA damage repair genes. Learn more in Cell.