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1. Alpha to Omicron: Disease Severity and Clinical Outcomes of Major SARS-CoV-2 Variants

   https://doi.org/10.1093/infdis/jiac411  

   Esper, Frank P. ; Adhikari, Thamali M. ; Tu, Zheng Jin ; Cheng, Yu-Wei ; El-Haddad, Kim ; Farkas, Daniel H. ; Bosler, David ; Rhoads, Daniel ; Procop, Gary W. ; Ko, Jennifer S. ; et al ( October 2022 , The Journal of Infectious Diseases)

   Four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants predominated in the United States since 2021. Understanding disease severity related to different SARS-CoV-2 variants remains limited.

   Viral genome analysis was performed on SARS-CoV-2 clinical isolates circulating March 2021 through March 2022 in Cleveland, Ohio. Major variants were correlated with disease severity and patient outcomes.

   In total 2779 patients identified with either Alpha (n = 1153), Gamma (n = 122), Delta (n = 808), or Omicron variants (n = 696) were selected for analysis. No difference in frequency of hospitalization, intensive care unit (ICU) admission, and death were found among Alpha, Gamma, and Delta variants. However, patients with Omicron infection were significantly less likely to be admitted to the hospital, require oxygen, or admission to the ICU (χ2 = 12.8, P < .001; χ2 = 21.6, P < .002; χ2 = 9.6, P = .01, respectively). In patients whose vaccination status was known, a substantial number had breakthrough infections with Delta or Omicron variants (218/808 [26.9%] and 513/696 [73.7%], respectively). In breakthrough infections, hospitalization rate was similar regardless of variant by multivariate analysis. No difference in disease severity was identified between Omicron subvariants BA.1 and BA.2.

   Disease severity associated with Alpha, Gamma, and Delta variants is comparable while Omicron infections are significantly less severe. Breakthrough disease is significantly more common in patients with Omicron infection.

    

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2. Severe Acute Respiratory Syndrome Coronavirus 2 Delta Vaccine Breakthrough Transmissibility in Alachua County, Florida

   https://doi.org/10.1093/cid/ciac197  

   Rife Magalis, Brittany ; Rich, Shannan ; Tagliamonte, Massimiliano S. ; Mavian, Carla ; Cash, Melanie N. ; Riva, Alberto ; Marini, Simone ; Amador, David Moraga ; Zhang, Yanping ; Shapiro, Jerne ; et al ( March 2022 , Clinical Infectious Diseases)

   Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant has caused a dramatic resurgence in infections in the United Sates, raising questions regarding potential transmissibility among vaccinated individuals.

   Between October 2020 and July 2021, we sequenced 4439 SARS-CoV-2 full genomes, 23% of all known infections in Alachua County, Florida, including 109 vaccine breakthrough cases. Univariate and multivariate regression analyses were conducted to evaluate associations between viral RNA burden and patient characteristics. Contact tracing and phylogenetic analysis were used to investigate direct transmissions involving vaccinated individuals.

   The majority of breakthrough sequences with lineage assignment were classified as Delta variants (74.6%) and occurred, on average, about 3 months (104 ± 57.5 days) after full vaccination, at the same time (June-July 2021) of Delta variant exponential spread within the county. Six Delta variant transmission pairs between fully vaccinated individuals were identified through contact tracing, 3 of which were confirmed by phylogenetic analysis. Delta breakthroughs exhibited broad viral RNA copy number values during acute infection (interquartile range, 1.2-8.64 Log copies/mL), on average 38% lower than matched unvaccinated patients (3.29-10.81 Log copies/mL, P < .00001). Nevertheless, 49% to 50% of all breakthroughs, and 56% to 60% of Delta-infected breakthroughs exhibited viral RNA levels above the transmissibility threshold (4 Log copies/mL) irrespective of time after vaccination.

   Delta infection transmissibility and general viral RNA quantification patterns in vaccinated individuals suggest limited levels of sterilizing immunity that need to be considered by public health policies. In particular, ongoing evaluation of vaccine boosters should specifically address whether extra vaccine doses curb breakthrough contribution to epidemic spread.

    

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3. Host-Genome Similarity Characterizes the Adaption of SARS-CoV-2 to Humans

   https://doi.org/10.3390/biom12070972  

   Sun, Weitao ( July 2022 , Biomolecules)

   The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a high mutation rate and many variants have emerged in the last 2 years, including Alpha, Beta, Delta, Gamma and Omicron. Studies showed that the host-genome similarity (HGS) of SARS-CoV-2 is higher than SARS-CoV and the HGS of open reading frame (ORF) in coronavirus genome is closely related to suppression of innate immunity. Many works have shown that ORF 6 and ORF 8 of SARS-CoV-2 play an important role in suppressing IFN-β signaling pathway in vivo. However, the relation between HGS and the adaption of SARS-CoV-2 variants is still not clear. This work investigates HGS of SARS-CoV-2 variants based on a dataset containing more than 40,000 viral genomes. The relation between HGS of viral ORFs and the suppression of antivirus response is studied. The results show that ORF 7b, ORF 6 and ORF 8 are the top 3 genes with the highest HGS. In the past 2 years, the HGS values of ORF 8 and ORF 7B of SARS-CoV-2 have increased greatly. A remarkable correlation is discovered between HGS and inhibition of antivirus response of immune system, which suggests that the similarity between coronavirus and host gnome may be an indicator of the suppression of innate immunity. Among the five variants (Alpha, Beta, Delta, Gamma and Omicron), Delta has the highest HGS and Omicron has the lowest HGS. This finding implies that the high HGS in Delta variant may indicate further suppression of host innate immunity. However, the relatively low HGS of Omicron is still a puzzle. By comparing the mutations in genomes of Alpha, Delta and Omicron variants, a commonly shared mutation ACT > ATT is identified in high-HGS strain populations. The high HGS mutations among the three variants are quite different. This finding strongly suggests that mutations in high HGS strains are different in different variants. Only a few common mutations survive, which may play important role in improving the adaptability of SARS-CoV-2. However, the mechanism for how the mutations help SARS-CoV-2 escape immunity is still unclear. HGS analysis is a new method to study virus–host interaction and may provide a way to understand the rapid mutation and adaption of SARS-CoV-2. 

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4. Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platform

   https://doi.org/10.1186/s40537-021-00423-z  

   Villanustre, Flavio ; Chala, Arjuna ; Dev, Roger ; Xu, Lili ; LexisNexis, Jesse Shaw ; Furht, Borko ; Khoshgoftaar, Taghi ( December 2021 , Journal of Big Data)

   Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper. 

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5. Site specific N- and O-glycosylation mapping of the spike proteins of SARS-CoV-2 variants of concern

   https://doi.org/10.1038/s41598-023-33088-0  

   Shajahan, Asif ; Pepi, Lauren E. ; Kumar, Bhoj ; Murray, Nathan B. ; Azadi, Parastoo ( June 2023 , Scientific Reports)

   The glycosylation on the spike (S) protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, modulates the viral infection by altering conformational dynamics, receptor interaction and host immune responses. Several variants of concern (VOCs) of SARS-CoV-2 have evolved during the pandemic, and crucial mutations on the S protein of the virus have led to increased transmissibility and immune escape. In this study, we compare the site-specific glycosylation and overall glycomic profiles of the wild type Wuhan-Hu-1 strain (WT) S protein and five VOCs of SARS-CoV-2: Alpha, Beta, Gamma, Delta and Omicron. Interestingly, both N- and O-glycosylation sites on the S protein are highly conserved among the spike mutant variants, particularly at the sites on the receptor-binding domain (RBD). The conservation of glycosylation sites is noteworthy, as over 2 million SARS-CoV-2 S protein sequences have been reported with various amino acid mutations. Our detailed profiling of the glycosylation at each of the individual sites of the S protein across the variants revealed intriguing possible association of glycosylation pattern on the variants and their previously reported infectivity. While the sites are conserved, we observed changes in the N- and O-glycosylation profile across the variants. The newly emerged variants, which showed higher resistance to neutralizing antibodies and vaccines, displayed a decrease in the overall abundance of complex-type glycans with both fucosylation and sialylation and an increase in the oligomannose-type glycans across the sites. Among the variants, the glycosylation sites with significant changes in glycan profile were observed at both theN-terminal domain and RBD of S protein, with Omicron showing the highest deviation. The increase in oligomannose-type happens sequentially from Alpha through Delta. Interestingly, Omicron does not contain more oligomannose-type glycans compared to Delta but does contain more compared to the WT and other VOCs. O-glycosylation at the RBD showed lower occupancy in the VOCs in comparison to the WT. Our study on the sites and pattern of glycosylation on the SARS-CoV-2 S proteins across the VOCs may help to understand how the virus evolved to trick the host immune system. Our study also highlights how the SARS-CoV-2 virus has conserved bothN- andO- glycosylation sites on the S protein of the most successful variants even after undergoing extensive mutations, suggesting a correlation between infectivity/ transmissibility and glycosylation.

    

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