Estimating the heritability of SARS-CoV-2 susceptibility and COVID-19 severity.

SARS-CoV-2 has infected over 340 million people, prompting therapeutic research. While genetic studies can highlight potential drug targets, understanding the heritability of SARS-CoV-2 susceptibility and COVID-19 severity can contextualize their results. To date, loci from meta-analyses explain 1.2% and 5.8% of variation in susceptibility and severity respectively. Here we estimate the importance of shared environment and additive genetic variation to SARS-CoV-2 susceptibility and COVID-19 severity using pedigree data, PCR results, and hospitalization information. The relative importance of genetics and shared environment for susceptibility shifted during the study, with heritability ranging from 33% (95% CI: 20%-46%) to 70% (95% CI: 63%-74%). Heritability was greater for days hospitalized with COVID-19 (41%, 95% CI: 33%-57%) compared to shared environment (33%, 95% CI: 24%-38%). While our estimates suggest these genetic architectures are not fully understood, the shift in susceptibility estimates highlights the challenge of estimation during a pandemic, given environmental fluctuations and vaccine introduction.

Using machine learning probabilities to identify effects of COVID-19.

Coronavirus disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has had extensive economic, social, and public health impacts in the United States and around the world. To date, there have been more than 600 million reported infections worldwide with more than 6 million reported deaths. Retrospective analysis, which identified comorbidities, risk factors, and treatments, has underpinned the response. As the situation transitions to an endemic, retrospective analyses using electronic health records will be important to identify the long-term effects of COVID-19. However, these analyses can be complicated by incomplete records, which makes it difficult to differentiate visits where the patient had COVID-19. To address this issue, we trained a random Forest classifier to assign a probability of a patient having been diagnosed with COVID-19 during each visit. Using these probabilities, we found that higher COVID-19 probabilities were associated with a future diagnosis of myocardial infarction, urinary tract infection, acute renal failure, and type 2 diabetes.

Effective Communication of Personalized Risks and Patient Preferences During Surgical Informed Consent Using Data Visualization: Qualitative Semistructured Interview Study With Patients After Surgery.

BACKGROUND: There is no consensus on which risks to communicate to a prospective surgical patient during informed consent or how. Complicating the process, patient preferences may diverge from clinical assumptions and are often not considered for discussion. Such discrepancies can lead to confusion and resentment, raising the potential for legal action. To overcome these issues, we propose a visual consent tool that incorporates patient preferences and communicates personalized risks to patients using data visualization. We used this platform to identify key effective visual elements to communicate personalized surgical risks. OBJECTIVE: Our main focus is to understand how to best communicate personalized risks using data visualization. To contextualize patient responses to the main question, we examine how patients perceive risks before surgery (research question 1), how suitably the visual consent tool is able to present personalized surgical risks (research question 2), how well our visualizations convey those personalized surgical risks (research question 3), and how the visual consent tool could improve the informed consent process and how it can be used (research question 4). METHODS: We designed a visual consent tool to meet the objectives of our study. To calculate and list personalized surgical risks, we used the American College of Surgeons risk calculator. We created multiple visualization mock-ups using visual elements previously determined to be well-received for risk communication. Semistructured interviews were conducted with patients after surgery, and each of the mock-ups was presented and evaluated independently and in the context of our visual consent tool design. The interviews were transcribed, and thematic analysis was performed to identify major themes. We also applied a quantitative approach to the analysis to assess the prevalence of different perceptions of the visualizations presented in our tool. RESULTS: In total, 20 patients were interviewed, with a median age of 59 (range 29-87) years. Thematic analysis revealed factors that influenced the perception of risk (the surgical procedure, the cognitive capacity of the patient, and the timing of consent; research question 1); factors that influenced the perceived value of risk visualizations (preference for rare event communication, preference for risk visualization, and usefulness of comparison with the average; research question 3); and perceived usefulness and use cases of the visual consent tool (research questions 2 and 4). Most importantly, we found that patients preferred the visual consent tool to current text-based documents and had no unified preferences for risk visualization. Furthermore, our findings suggest that patient concerns were not often represented in existing risk calculators. CONCLUSIONS: We identified key elements that influence effective visual risk communication in the perioperative setting and pointed out the limitations of the existing calculators in addressing patient concerns. Patient preference is highly variable and should influence choices regarding risk presentation and visualization.

Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions.

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

Shotgun Transcriptome and Isothermal Profiling of SARS-CoV-2 Infection Reveals Unique Host Responses, Viral Diversification, and Drug Interactions.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.

Mechanical confinement via a PEG/Collagen interpenetrating network inhibits behavior characteristic of malignant cells in the triple negative breast cancer cell line MDA.MB.231.

To decouple the effects of collagen fiber density and network mechanics on cancer cell behavior, we describe a highly tunable in vitro 3D interpenetrating network (IPN) consisting of a primary fibrillar collagen network reinforced by a secondary visible light-mediated thiol-ene poly(ethylene glycol) (PEG) network. This PEG/Collagen IPN platform is cytocompatible, inherently bioactive via native cellular adhesion sites, and mechanically tunable over several orders of magnitude-mimicking both healthy and cancerous breast tissue. Furthermore, we use the PEG/Collagen IPN platform to investigate the effect of mechanical confinement on cancer cell behavior as it is hypothesized that cells within tumors that have yet to invade into the surrounding tissue experience mechanical confinement. We find that mechanical confinement via the IPN impairs behavior characteristic of malignant cells (i.e., viability, proliferation, and cellular motility) in the triple negative breast cancer cell line MDA.MB.231, and is more effective than removal of soluble growth signals. The PEG/Collagen IPN platform is a useful tool for studying mechanotransductive signaling pathways and motivates further investigation into the role of mechanical confinement in cancer progression. STATEMENT OF SIGNIFICANCE: In this study, we have developed, optimized, and applied a novel 3D in vitro cell culture platform composed of an interpenetrating network (IPN) that is both mechanically tunable and inherently bioactive. The IPN consists of a primary fibrillar collagen type-1 network reinforced by a secondary thiol-ene poly(ethylene glycol) (PEG) network. The IPNs are formed via a novel strategy in which cell-laden collagen gels are formed first, and soluble PEG monomers are added later and crosslinked via visible light. This approach ensures that the collagen gels contain a fibrillar architecture similar to the collagen architecture present in vivo. We applied our IPN platform to study the effect of mechanical confinement on cancer cell behavior and found that it inhibits malignant-like behavior.