Complete Genomic Characterisation and Mutation Patterns of Iraqi SARS-CoV-2 Isolates.

This study was performed for molecular characterisation of the SARS-CoV-2 strains in Iraq and reveal their variants, lineages, clades, and mutation patterns. A total of 912 Iraqi sequences were retrieved from GISAID, which had been submitted from the beginning of the SARS-CoV-2 pandemic to 26 September 2022, along with 12 samples that were collected during the third and fifth waves of the SARS-CoV-2 pandemic. Next-generation sequencing was performed using an Illumina MiSeq system, and phylogenetic analysis was performed for all the Iraqi sequences retrieved from GISAID. Three established global platforms GISAID, Nextstrain, and PANGO were used for the classification of isolates into distinct clades, variants, and lineages. Analysis of the isolates of this study showed that all the sequences from the third wave were clustered in the GK clades and the 21J (Delta) clade according to the GISAID and Nextclade systems, while the PANGO system revealed that six sequences were B.1.617.2 and four sequences were of the AY.33 lineage. Furthermore, the latest e wave in the summer of 2022 was due to thpredominance of the BA.5.2 lineage of the 22B (Omicron) clade in Iraq. Our study revealed patterns of circulation and dominance of SARS-CoV-2 clades and their lineages in the subsequent pandemic waves in the country.

Using primary literature on SARS-CoV-2 to promote student learning about evolution.

The ongoing COVID-19 pandemic caused by SARS-CoV-2 has caused widespread deaths, illnesses, and societal disruption. I describe here how I pivoted a discussion-based senior biology capstone course to include a multiweek module surrounding one primary literature paper on the evolution of SARS-CoV-2 and the subsequent scientific discourse about the paper. Using a gradual reveal of the paper following the CREATE method (consider, read, elucidate, and think of the next experiment), I challenged students to learn new evolutionary principles and critically analyze the data surrounding the evolution and transmission of SARS-CoV-2 presented in the paper. I also provide general advice for implementing this module in future courses.

Analysis of Genomic Characteristics of SARS-CoV-2 in Italy, 29 January to 27 March 2020.

We performed next-generation sequencing (NGS), phylogenetic analysis, gene flows, and N- and O-glycosylation prediction on SARS-CoV-2 genomes collected from lab-confirmed cases from different Italian regions. To this end, a total of 111 SARS-CoV-2 genomes collected in Italy between 29 January and 27 March 2020 were investigated. The majority of the genomes belonged to lineage B.1, with some descendant lineages. The gene flow analysis showed that the spread occurred mainly from the north to the center and to the south of Italy, as confirmed by epidemiological data. The mean evolutionary rate estimated here was 8.731 × 10-4 (95% highest posterior density, HPD intervals 5.809 × 10-4 to 1.19 × 10-3), in line with values reported by other authors. The dated phylogeny suggested that SARS-CoV-2 lineage B.1 probably entered Italy between the end of January and early February 2020. Continuous molecular surveillance is needed to trace virus circulation and evolution.

A Pre-Vaccination Baseline of SARS-CoV-2 Genetic Surveillance and Diversity in the United States.

COVID-19 vaccines were first administered on 15 December 2020, marking an important transition point for the spread of SARS-CoV-2 in the United States (U.S.). Prior to this point in time, the virus spread to an almost completely immunologically naïve population, whereas subsequently, vaccine-induced immune pressure and prior infections might be expected to influence viral evolution. Accordingly, we conducted a study to characterize the spread of SARS-CoV-2 in the U.S. pre-vaccination, investigate the depth and uniformity of genetic surveillance during this period, and measure and otherwise characterize changing viral genetic diversity, including by comparison with more recently emergent variants of concern (VOCs). In 2020, SARS-CoV-2 spread across the U.S. in three phases distinguishable by peaks in the numbers of infections and shifting geographical distributions. Virus was genetically sampled during this period at an overall rate of ~1.2%, though there was a substantial mismatch between case rates and genetic sampling nationwide. Viral genetic diversity tripled over this period but remained low in comparison to other widespread RNA virus pathogens, and although 54 amino acid changes were detected at frequencies exceeding 5%, linkage among them was not observed. Based on our collective observations, our analysis supports a targeted strategy for worldwide genetic surveillance as perhaps the most sensitive and efficient means of detecting new VOCs.

Population Dynamics and Structural Effects at Short and Long Range Support the Hypothesis of the Selective Advantage of the G614 SARS-CoV-2 Spike Variant.

SARS-CoV-2 epidemics quickly propagated worldwide, sorting virus genomic variants in newly established propagules of infections. Stochasticity in transmission within and between countries or an actual selective advantage could explain the global high frequency reached by some genomic variants. Using statistical analyses, demographic reconstructions, and molecular dynamics simulations, we show that the globally invasive G614 spike variant 1) underwent a significant demographic expansion in most countries explained neither by stochastic effects nor by overrepresentation in clinical samples, 2) increases the spike S1/S2 furin-like site conformational plasticity (short-range effect), and 3) modifies the internal motion of the receptor-binding domain affecting its cross-connection with other functional domains (long-range effect). Our results support the hypothesis of a selective advantage at the basis of the spread of the G614 variant, which we suggest may be due to structural modification of the spike protein at the S1/S2 proteolytic site, and provide structural information to guide the design of variant-specific drugs.

The coSIR model predicts effective strategies to limit the spread of SARS-CoV-2 variants with low severity and high transmissibility.

Multiple new variants of SARS-CoV-2 have been identified as the COVID-19 pandemic spreads across the globe. However, most epidemic models view the virus as static and unchanging and thus fail to address the consequences of the potential evolution of the virus. Here, we built a competitive susceptible-infected-removed (coSIR) model to simulate the competition between virus strains of differing severities or transmissibility under various virus control policies. The coSIR model predicts that although the virus is extremely unlikely to evolve into a "super virus" that causes an increased fatality rate, virus variants with less severe symptoms can lead to potential new outbreaks and can cost more lives over time. The present model also demonstrates that the protocols restricting the transmission of the virus, such as wearing masks and social distancing, are the most effective strategy in reducing total mortality. A combination of adequate testing and strict quarantine is a powerful alternative to policies such as mandatory stay-at-home orders, which may have an enormous negative impact on the economy. In addition, building Mobile Cabin Hospitals can be effective and efficient in reducing the mortality rate of highly infectious virus strains. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11071-021-06705-8.

Lung organoid simulations for modelling and predicting the effect of mutations on SARS-CoV-2 infectivity.

The global pandemic caused by the SARS-CoV-2 virus continues to spread. Infection with SARS- CoV-2 causes COVID-19, a disease of variable severity. Mutation has already altered the SARS-CoV-2 genome from its original reported sequence and continued mutation is highly probable. These mutations can: (i) have no significant impact (they are silent), (ii) result in a complete loss or reduction of infectivity, or (iii) induce increase in infectivity. Physical generation, for research purposes, of viral mutations that could enhance infectivity are controversial and highly regulated. The primary purpose of this project was to evaluate the ability of the DeepNEU machine learning stem-cell simulation platform to enable rapid and efficient assessment of the potential impact of viral loss-of-function (LOF) and gain-of-function (GOF) mutations on SARS-CoV-2 infectivity. Our data suggest that SARS-CoV-2 infection can be simulated in human alveolar type lung cells. Simulation of infection in these lung cells can be used to model and assess the impact of LOF and GOF mutations in the SARS-CoV2 genome. We have also created a four- factor infectivity measure: the DeepNEU Case Fatality Rate (dnCFR). dnCFR can be used to assess infectivity based on the presence or absence of the key viral proteins (NSP3, Spike-RDB, N protein, and M protein). dnCFR was used in this study, not to only assess the impact of different mutations on SARS-CoV2 infectivity, but also to categorize the effects of mutations as loss of infectivity or gain of infectivity events.

Phylogenetic network analysis of SARS-CoV-2 genomes.

In a phylogenetic network analysis of 160 complete human severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) genomes, we find three central variants distinguished by amino acid changes, which we have named A, B, and C, with A being the ancestral type according to the bat outgroup coronavirus. The A and C types are found in significant proportions outside East Asia, that is, in Europeans and Americans. In contrast, the B type is the most common type in East Asia, and its ancestral genome appears not to have spread outside East Asia without first mutating into derived B types, pointing to founder effects or immunological or environmental resistance against this type outside Asia. The network faithfully traces routes of infections for documented coronavirus disease 2019 (COVID-19) cases, indicating that phylogenetic networks can likewise be successfully used to help trace undocumented COVID-19 infection sources, which can then be quarantined to prevent recurrent spread of the disease worldwide.