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Welcome to the January 28, 2022 installment of Research Roundup, a recurring snapshot of recent studies published by scientists at the Ó³»­´«Ã½ and their collaborators.

A massive-scale approach to analyzing coding variants

Analyzing the cellular impact of the millions of known coding variants in a scalable, unbiased, information-rich way is a major challenge in functional genomics. In , Oana Ursu and core institute member (on leave) Aviv Regev of the Klarman Cell Observatory, J.T. Neal and Jesse Boehm of the Cancer Program, and colleagues unveiled sc-eVIP, an approach that combines DNA-barcoded overexpression constructs and single-cell RNA sequencing in a pooled format and provides a detailed view of mutations' cellular effects at massive scale. In proof-of-concept experiments, the team screened 200 cancer-associated TP53 and KRAS mutations, and found that the practice of dividing cancer mutations into "drivers" and "passengers" may be overly simplistic. Learn more in a Ó³»­´«Ã½ story.

COVID-19 insights from a mathematical model

Mathematical modeling can aid in understanding the complex interactions between injury and immune response in critical illness. A team supervised by Ó³»­´«Ã½ associate member Rakesh Jain and colleagues from Massachusetts General Hospital, Harvard Medical School, and the University of Cyprus has developed a systems biology model of COVID-19 to explore the relationships between patient characteristics, predictive biomarkers, and treatment outcomes. The model has the potential to help clinicians provide optimal care for diverse patients and efficiently plan clinical trials. Check out the full story in and a from MGH.

Containing infectious disease outbreaks on college campuses

College campuses have been hit hard by COVID-19, but learning from past infectious disease outbreaks could help. In , institute member Pardis Sabeti of the Infectious Disease and Microbiome Program (IDMP), Andres Colubri, Mirai Shah (Harvard), Gabby Ferra (Brown), and colleagues used data from a 2016 mumps outbreak at Harvard University to investigate which university-level interventions were most effective. They created a computational model of disease spread through which they could simulate various interventions and observe the predicted outcomes. Using their model, the researchers were able to identify best practices for quarantine and diagnosis protocols. Even with vaccines, outbreaks of infectious disease can and do occur, so identifying the best intervention protocols is imperative.

Understanding a gene's double (or triple, or quadruple) meanings

A common assumption is that every gene has a single function, but functional genomics teaches us that disrupting a single gene can impact many cellular pathways independently, a principle known as pleiotropy. In , Josh Pan, Marinka Zitnik, James McFarland, and institute member William Hahn of the Cancer Program, along with the and teams, present a computational framework for modeling pleiotropy in genome-wide functional screens. Dubbed , the method uses high-dimensional cell fitness data to explore the consequences of perturbing a gene in different cell types, creating a dictionary of biological functions that helps explain a gene's "meanings" (a.k.a. roles) in various contexts. Learn more in a of by Josh.

Markers of SARS-CoV-2 reinfection

Detecting hotspots of SARS-CoV-2 reinfection is important for pandemic response, so as to lower the risk of variants that escape immunity. Sameed Siddiqui, IDMP associate member Galit Alter and colleagues have found simple, rapid immune biomarkers of SARS-CoV-2 reinfection in a rhesus macaque model, including IgG3 antibody levels against nucleocapsid and FcγR2A receptor binding activity of anti-RBD antibodies that are recapitulated in human re-infection cases. These could be cost-effective, scalable markers of reinfection that provide increased resolution and resilience against new outbreaks. Read more in .

A polygenic risk score for obesity

Although both genetic and lifestyle risk are known to influence obesity, scientists have largely focused on these factors in healthy population cohorts. In , Hassan Dashti, associate member Richa Saxena of the Program in Medical and Population Genetics, and colleagues have examined this interaction using the Mass General Brigham Biobank, which includes patients with a range of comorbidities less common in population-based studies. They showed that a polygenic risk score for obesity was associated with measured BMI, illustrating the transferability of genetic findings from healthy populations to clinical studies. The team also observed significant interaction between genetic and lifestyle risk factors, suggesting that lifestyle can modify genetic predispositions to obesity.

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