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Welcome to the October 1, 2021 installment of Research Roundup, a recurring snapshot of recent studies published by scientists at the ӳ����ý and their collaborators.

Tackling diabetes from every angle

Learn in this ӳ����ý story how the Diabetes Research Group is taking different approaches and using a variety of skill sets and expertise in a collaborative way to learn about the biological basis of diabetes, and ultimately to find a cure. The group is pursuing new drug strategies, using genetic information to define more subtypes of diabetes, and further pinpointing what happens in liver cells and insulin-producing beta cells at the molecular level to bring about the disease. "When I retire, I want to see that the practice of diabetes changed because we had access to the genome and used this information in smart ways," says institute member Jose Florez, the group’s leader and co-director of the Metabolism Program.

Neural networking predicts drug synergy for COVID-19 

Antiviral therapies are often more effective when used in combination. Deep learning promises to identify new and effective drug combinations, but these approaches require large amounts of data on previous infections, which are not often available for emerging diseases like COVID-19.  Postdoctoral associate Wengong Jin in the Data Sciences Platform and his colleagues developed ComboNet, a neural network that, instead of relying on previous infection data, predicts synergy between antiviral drugs by analyzing their structure and targets as well as their antiviral activity when delivered alone. ComboNet identified two drug combinations with increased antiviral activity against SARS-CoV-2 in vitro. Read more in and in an . 

Understanding of how cancer spreads 

Metastatic cancers arise from cells released by tumors into the bloodstream. Bashar Hamza (MIT), Alex Miller (Koch Institute), associate member Scott Manalis of the Cancer Program, and colleagues have now developed a blood-exchange method that measures the generation rate of these circulating tumor cells (CTCs) in mice, and quantifies how long CTCs survive once released into the bloodstream. This approach helps demonstrate the relationship between CTC characteristics and metastasis, thus providing a better understanding of how different types of cancers spread through the body. Read more in and this .

Scrutinizing signaling systems with SigPath

Proteins pass signals within the cell in part through phosphorylation (adding and removing phosphate at specific amino acid positions, or phosphosites). Methods that simultaneously read multiple phosphosites on multiple proteins could help researchers better understand how phosphorylation patterns reflect disease biology and treatment responses. Hasmik Keshishian, institute scientist and senior director of proteomics Steven Carr, and several collaborators in the ӳ����ý's Proteomics Platform and beyond have developed SigPath, a multiplexed, targeted mass spectrometry assay measuring a curated panel of 284 biologically-relevant phosphosites in 200 proteins from cell lines, preclinical models, and clinical samples. SigPath could be an attractive tool for target discovery, verification, and for preclinical studies. Learn more in and on .

Robotics make UbiFast faster

In early 2020, a Proteomics Platform team led by Namrata Udeshi, Deepak Mani, and Carr introduced , a method for rapid measurement of more than 10,000 protein ubiquitylation sites in as little as half a milligram of sample from cell lines or clinical tissues. In , Udeshi, Carr, Keith Rivera, and colleagues now report that by automating UbiFast they have doubled the number of sites measured per sample, increased the technique's throughput (to 96 samples/day), and improved its reproducibility while reducing variability. The improvements make UbiFast suitable for studying ubiquitylation in large sample sets. Learn more in a by Rivera.

Therapeutic approach for bladder cancer 

Approximately 25 percent of bladder cancer cases are muscle-invasive bladder cancer (MIBC), an aggressive but potentially curable disease state. Cancer Program associate member Kent Mouw, postdoctoral scholar Raie Bekele, and colleagues have identified a subset of MIBCs with focal amplification of the RAF1 gene. Their research further demonstrated that RAF1-amplified tumors rely on RAF1 activity for survival, and that RAF1-activated cell lines and patient-derived models are sensitive to RAF inhibitors. The findings highlight a novel therapeutic approach for a molecularly defined subset of bladder tumors. Read more in the  and this from Kent.

An almanac for navigating cancer treatment

A tool developed by researchers at ӳ����ý and the Dana Farber Cancer Institute (DFCI) could help guide precision cancer medicine. Created by Brendan Reardon, Cancer Program associate member Eliezer Van Allen, and colleagues, the Molecular Oncology Almanac () incorporates different kinds of molecular data from patients and tumors to identify features connected to disease prognosis and treatment resistance or sensitivity. When tested, MOAlmanac identified about two therapeutic strategies per patient, and provided more clinical hypotheses than traditional algorithms. MOAlmanac is available on , , , and through . Learn more in and a ӳ����ý story and DFCI video.

A single-cell look at glioma

A new multi-omics single-cell analysis of patients’ brain tumors provides a deeply detailed look at glioma. A team including Ronan Chaligne, Federico Gaiti, and Joshua Schiffman of Weill Cornell Medicine, along with postdoctoral associate Dana Silverbush, institute member Mario Suvà in the Epigenomics Program, and former postdoctoral researcher Dan Landau (now at Weill Cornell and New York Genome Center), recorded gene mutations, gene activity, and DNA methylation within individual cells. They mapped distinct tumor states in gliomas and identified key programming marks associated with state shifts, marks that could one day be targeted therapeutically. The approach may be useful for studying the development of other kinds of tumors or mutations that arise with age in healthy tissues. Read more in , a , and a .

Devising different decoders

Quadruplet codons enable researchers to expand the genetic code, but can suffer from poor efficiencies and context dependence. A team including Erika DeBenedictis (MIT), Gavriela Carver, Christina Chung (Yale), Dieter Söll (Yale), and former ӳ����ý fellow Ahmed Badran (now at Scripps Research Institute) explored these issues by developing quadruplet-decoding transfer RNA phage-assisted continuous evolution (qtRNA-PACE). Using the UAGA as a testbed, they recovered qtRNA variants that were up to 80-fold more efficient in translating quadruplet codons. They also nominated scaffolds for robust qtRNA expression in E. coli, enabling up to four decoding events in a single protein. Read more in and this .