Prime editing times two, SARS-CoV-2 mutations, a single-cell view of pancreatic cancer, and more
Research Roundup: December 10, 2021
Welcome to the December 10, 2021 installment of Research Roundup, a recurring snapshot of recent studies published by scientists at the Broad Institute and their collaborators.
Twin prime editing
Prime editing can precisely make small substitutions, insertions, and deletions in cells, but editing sequences longer than tens of base pairs is challenging. Now, Andrew Anzalone, Xin Gao, Christopher Podracky, core institute member and Merkin Institute for Transformative Technologies in Healthcare director David Liu, and colleagues have developed twin prime editing (twinPE). TwinPE uses a prime editor protein and two prime editing guide RNAs to create two complementary DNA strands between two single-stranded nicks, enabling edits spanning hundreds of base pairs. When paired with a site-specific recombinase, twinPE can be used to precisely insert or invert DNA segments thousands of base pairs long at therapeutically relevant sites in the genome. Read more in Nature Biotechnology, a tweetorial by David, and a Broad news story.
Predicting SARS-CoV-2’s evasive maneuvers
Researchers have found that the SARS-CoV-2 receptor binding domain (RBD) can tolerate a larger number of mutations than observed in previous variants of concern. Associate member Jonathan Abraham in the Infectious Disease and Microbiome Program and colleagues studied the virus’s spike protein with different RBD changes that were found in an infected immunocompromised patient and in a global database of SARS-CoV-2 genomes. They found that viral pseudotypes with up to seven RBD mutations can better evade vaccine-induced immunity and therapeutic antibodies, compared to those with fewer mutations. The findings suggest continued emergence of escape variants of SARS-CoV-2. Read more in Science and in a Harvard Medical School story.
The cell states of pancreatic cancer
Certain gene expression states have emerged as potential clinical biomarkers in pancreatic ductal adenocarcinoma (PDAC), but their drivers and links to therapeutic response are unknown. Srivatsan Raghavan, Peter Winter, Andrew Navia, Hannah Williams; institute member William Hahn and associate member Andrew Aguirre in the Cancer Program; institute member Alex Shalek, and colleagues systematically profiled metastatic PDAC biopsies and matched organoid models at a single-cell resolution. They identified a new intermediate transcriptional state and found that signals from a tumor's microenvironment shape cell state, plasticity, and drug responses. The study provides a framework for assessing cell states, identifying drivers of transcriptional plasticity, and developing new therapeutic strategies to target both. Read more in Cell and a story from MIT News.
Machine learning eyes the liver
High levels of liver fat, called hepatic steatosis, is a major liver and heart disease risk factor. Liver fat is not typically quantified in clinical practice, however, making screening and diagnosis difficult. Using data from nearly 37,000 UK Biobank participants, Mary Haas, James Pirruccello, Sam Friedman, Cardiovascular Disease Initiative associate member Amit Khera, and colleagues developed a machine learning method for precisely quantifying liver fat by MRI, and identified eight common genetic variants associated with liver fat, as well as rare variants that highlight a unique steatosis subtype. They also used their common variant data to develop a polygenic score that predicts advanced liver disease risk. Learn more in Cell Genomics and a tweetorial by Amit.
Autoimmune impacts of Th17 cells
Intestinal Th17 cells help maintain intestinal homeostasis and contribute to autoimmune tissue inflammation. To examine this dichotomy, a team including Alexandra Schnell, Linglin Huang (DFCI/HSPH), Cell Circuits Program associate member Meromit Singer, core member (on leave) Aviv Regev, and institute member Vijay Kuchroo in the Klarman Cell Observatory combined single-cell RNA and T cell receptor sequencing with fate-mapping studies to examine 84,124 tissue Th17 cells at homeostasis and during central nervous system autoimmunity, identifying a transition of stem-like intestinal to pathogenic Th17 cells in experimental autoimmune encephalomyelitis. The work provides mechanistic insights that may apply to various autoimmune contexts, and suggests that targeting the stem-like intestinal Th17 population could be useful in treating autoimmune conditions. Read more in Cell.