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COVID-19 GPH|COVID-19 Genomics and Precision Public Health Weekly Update|PHGKB
Publication Date: Nov 24, 2021
Pathogen and Human Genomics Studies

Myocardial Infarction, Stroke, and Pulmonary Embolism After BNT162b2 mRNA COVID-19 Vaccine in People Aged 75 Years or Older
MJ Jabagi et al, JAMA,November 22,2021>

The BNT162b2 mRNA vaccine (Pfizer-BioNTech) was the first SARS-CoV-2 vaccine authorized and most widely used in older persons in France. Although no increases in cardiovascular events were reported in the phase 3 trials, questions emerged once the vaccine was used on a large scale because older people were underrepresented in the trials. In this nationwide study involving persons aged 75 years or older in France, no increase in the incidence of acute myocardial infarction, stroke, and pulmonary embolism was detected 14 days following each BNT162b2 mRNA vaccine dose.
Antibody titers before and after booster doses of SARS-CoV-2 mRNA vaccines in healthy adults
MR Demonbreun et al,EDRXIV, November 21,2021>
We measured anti-receptor binding domain (RBD) IgG and surrogate virus neutralization of the interaction between SARS-CoV-2 spike protein and the human angiotensin-converting enzyme (ACE2) receptor, before and after boosters in N=33 healthy adults. We document large antibody responses 6-10 days after booster, with antibody levels that exceed levels documented after natural infection with COVID-19, after two doses of vaccine, or after both natural infection and vaccination. Surrogate neutralization of B.1.617.2 is high but reduced in comparison with wild-type SARS-CoV-2.
Global Mutational Sweep of SARS-CoV-2: from Chaos to Order
X Wang et al, BIORXIV, November 17, 2021>
Analysis of large-scale genome sequences demonstrates the mutation of SARS-CoV-2 has been undergoing significant sweeps. Driven by emerging variants, global sweeps are accelerated and purified over time. This may prolong the pandemic with repeating epidemics, presenting challenges to the control and prevention of SARS-CoV-2.
Organ-specific genome diversity of replication-competent SARS-CoV-2.
Van Cleemput Jolien et al. Nature communications 2021 11 (1) 6612>
We report a detailed virological analysis of thirteen postmortem coronavirus disease 2019 (COVID-19) cases that provides proof of viremia and presence of replication-competent SARS-CoV-2 in extrapulmonary organs of immunocompromised patients, including heart, kidney, liver, and spleen (NCT04366882). In parallel, we identify organ-specific SARS-CoV-2 genome diversity and mutations of concern N501Y, T1027I, and Y453F, while the patient had died long before reported emergence of VOCs. These mutations appear in multiple organs and replicate in Vero E6 cells, highlighting their infectivity


Non-Genomics Precision Health Studiess

Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests.
Çubukçu Hikmet Can et al. American journal of clinical pathology 2021 11

We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results. The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%).
The impact of weather condition and social activity on COVID-19 transmission in the United States.
Zhang Xinxuan et al. Journal of environmental management 2021 11 (Pt B) 114085
The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables,


News, Reviews and Commentaries

Dissecting the early COVID-19 cases in Wuhan.

Worobey Michael et al. Science (New York, N.Y.) 2021 11 eabm4454
Wastewater surveillance to infer COVID-19 transmission: A systematic review.

Shah Shimoni et al. The Science of the total environment 2021 11 150060

Of 763 studies identified, 92 studies distributed across 34 countries were shortlisted for qualitative synthesis. A total of 26,197 samples were collected between January 2020 and May 2021 from various locations serving population ranging from 321 to 11,400,000 inhabitants. Overall sample positivity was moderate at 29.2% in all examined settings with the spike (S) gene having maximum rate of positive detections and nucleocapsid (N) gene being the most targeted. Wastewater signals preceded confirmed cases by up to 63 days, with 13 studies reporting sample positivity before the first cases were detected in the community.
SARS-CoV-2 genomics as a springboard for future disease mitigation in LMICs.

Belman Sophie et al. Nature reviews. Microbiology 2021 11

This Genome Watch highlights how the SARS-CoV-2 pandemic laid the groundwork for continued use of real-time genomic epidemiology for public health responses in low-and-middle-income countries. Until recently, the use of pathogen sequencing to track emerging epidemics was mainly the domain of university research and public health laboratories in high-income countries. The SARS-CoV-2 pandemic has been pivotal in the expansion of real-time genomic epidemiology globally.
COVID-19 vaccines for children

JS Gerber et al, Science, November 19, 2021

Although it is true that most children experience asymptomatic or mild disease, some will get quite sick, and a small number will die. It’s why children are vaccinated against influenza, meningitis, chickenpox, and hepatitis—none of which, even before vaccines were available, killed as many as SARS-CoV-2 per year. Some parents are understandably hesitant to vaccinate their young children. However, a choice not to get a vaccine is not a risk-free choice; rather, it’s a choice to take a different and more serious risk. The biomedical community must strive to make this clear to the public. It could be one of the most important health decisions a parent will make.
Prevention of Cardiovascular Burden in COVID-19 Patients Suffering from Familial Hypercholesterolemia: A Global Challenge.

Vuorio Alpo et al. Cardiology and therapy 2021 11

A recent meta-analysis of over 20,000 individuals showed that hospitalized COVID-19 patients with acute myocardial injury had more than fourfold higher mortality than those without such injury. Since the COVID-19 pandemic exacerbates already existing health inequalities, there is an urgent need to create measures to protect the most vulnerable patient groups, including those with a pre-existing increased risk of atherosclerotic cardiovascular disease (ASCVD). A typical example is familial hypercholesterolemia (FH), a common genetic disease affecting over 30 million individuals worldwide. If left untreated or undertreated.
Vaccine Design by Reverse Vaccinology and Machine Learning.

Ong Edison et al. Methods in molecular biology (Clifton, N.J.) 2021 11 1-16

Reverse vaccinology (RV) is the state-of-the-art vaccine development strategy that starts with predicting vaccine antigens by bioinformatics analysis of the whole genome of a pathogen of interest. Vaxign is the first web-based RV vaccine prediction method based on calculating and filtering different criteria of proteins. Vaxign-ML is a new Vaxign machine learning (ML) method that predicts vaccine antigens based on extreme gradient boosting with the advance of new technologies and cumulation of protective antigen data.
mRNA vaccines against COVID-19: a showcase for the importance of microbial biotechnology.

Brüssow Harald et al. Microbial biotechnology 2021 11
The role of microRNAs in solving COVID-19 puzzle from infection to therapeutics: A mini-review.

Paul Sujay et al. Virus research 2021 11 198631

MicroRNAs (miRNAs) are small (20-24 nucleotides), non-coding RNA molecules that regulate post-transcriptional gene expression. Recently, it has been demonstrated that both host and viral-encoded miRNAs are crucial for the successful infection of SARS-CoV-2. For instance, dysregulation of miRNAs that modulate multiple genes expressed in COVID-19 patients with comorbidities (e.g., type 2 diabetes, lung adenocarcinoma, and cerebrovascular disorders) could affect the severity of the disease. Thus, altered expression levels of circulating miRNAs might be helpful to diagnose this illness and forecast whether a COVID-19 patient could develop a severe state of the disease.
Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies.

Napolitano Francesco et al. Briefings in bioinformatics 2021 11

SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contribution, we collected, categorized and prioritized over 17 000 COVID-19-related research articles including both peer-reviewed and preprint publications that make a relevant use of computational approaches. Using machine learning methods, we identified six broad application areas i.e. Molecular Pharmacology and Biomarkers, Molecular Virology, Epidemiology, Healthcare, Clinical Medicine and Clinical Imaging.


About Weekly Scan

This weekly update contains the latest information and publications on the impact of genomics and precision health technologies on the investigation and control of COVID-19. Items are selected by staff from the CDC Office of Genomics and Precision Public Health daily from the COVID-19 GPH.


Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.

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