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News / 05.10.19

Research Roundup: May 10, 2019

Erik Jacobs
Credit : Erik Jacobs
By Broad Communications

Better targeting for base editors, an expanding encyclopedia of cancer cell lines, molecular messengers in IBD, and more.

Welcome to the May 10, 2019 installment of Research Roundup, a recurring snapshot of recent studies published by scientists at the Broad Institute and their collaborators.

Keeping base editors on target

Adenine base editors (ABEs) are good at hitting their DNA targets, but it was unclear how often they might unintentionally alter cellular RNA. Because adenosine-to-inosine RNA changes have been linked to disease, a team led by Holly Rees and core institute member and Merkin Institute for Transformative Technologies in Healthcare director David Liu examined the RNA changes made by ABEmax, the most efficient ABE yet, and found it induces widespread low levels of A-to-I editing. As they describe in Science Advances and GenomeWeb, they developed ABEmax mutants that minimize RNA edits while still conferring specific and efficient DNA editing. The new ABE variants could be useful for biological studies or therapeutic development efforts.

A cellular survey of inflamed joints

Defining the cell types underlying rheumatoid arthritis could highlight new therapeutic targets. A new study in Nature Immunology used single-cell proteomic and transcriptomic methods to survey all the cells in samples of synovial tissue from inflamed joints. Led in part by Kamil Slowikowski, Chamith Fonseka, Fan Zhang, and institute member Soumya Raychaudhuri (all of the Program in Medical and Population Genetics), the study was part of the Accelerating Medical Partnerships program, which engaged the Broad and many other institutions throughout the country. The work revealed 18 unique cell populations, including T cell, B cell, monocyte, and stromal populations, that were expanded in rheumatoid arthritis cases.

Cancer cell collection gives up more secrets

The Cancer Cell Line Encyclopedia — a large-scale collection of cancer cell lines compiled by the Cancer Program and Novartis starting in 2008 — is a crucial resource for mapping cancer cells' genetic dependencies and finding new target opportunities. Researchers first characterized the collection's genetic features in 2012, and now two teams led by Broad alumni Mahmoud Ghandi and Franklin Huang, Chemical Biology and Therapeutics Science graduate student Haoxin Li, institute scientist and Metabolomics Platform senior director Clary Clish, Cancer Program institute member on leave Levi Garraway, and core institute member William Sellers have greatly expanded that compendium of information to include whole genome and exome, transcriptomic, proteomic, epigenetic, and metabolomic data. Learn more in Nature, Nature Medicine, and a Broad news story.

Gut bacteria send signals about health and disease

Sphingolipids, a signaling molecule and structural component of both bacterial and mammalian cell membranes, play a central role in regulating inflammation, immunity, growth, and cell survival. However, the specific roles of bacterial sphingolipids in regulating innate immunity or metabolism in the mammalian gut were largely unknown. Reporting in Cell Host & Microbe, Eric Brown, Hera Vlamakis, and core institute member Ramnik Xavier of the Infectious Disease and Microbiome Program; Clary Clish; and colleagues describe sphingolipid metabolite alterations in stool as a defining signature in inflammatory bowel disease in humans and further demonstrate in mice that bacterial sphingolipid production plays a significant role in the host’s gut health and disease.

Machine learning points to biological mechanism

With new technologies and analytical tools, scientists can confidently identify associations between biological signals and cellular or clinical phenotypes in large datasets — yet pinning down the causal relationships between these elements is still a challenge. Jason Yang, institute member James Collins, and colleagues have developed a new approach that combines biochemical screening, network modeling, and machine learning to tackle this problem. In Cell, they apply their system to reveal how antibiotics alter bacterial metabolism and kill Escherichia coli. Learn more in an MIT News story.

To learn more about research conducted at the Broad, visit broadinstitute.org/publications, and keep an eye on broadinstitute.org/news.