Advances in the computational analysis of SARS-COV2 genome

SARS-CoV2 Nsp3 protein triggers cell death and exacerbates amyloid β42-mediated neurodegeneration

Infection caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus, responsible for the coronavirus disease 2019 (COVID-19) pandemic, induces symptoms including increased inflammatory response, severe acute respiratory syndrome (SARS), cognitive dysfunction like brain fog, and cardiovascular defects. Long-term effects of SARS-CoV2 COVID-19 syndrome referred to as post-COVID-19 syndrome on age-related progressive neurodegenerative disorders such as Alzheimer’s disease remain understudied. Using the targeted misexpression of individual SARS-CoV2 proteins in the retinal neurons of the Drosophila melanogaster eye, we found that misexpression of nonstructural protein 3 (Nsp3), a papain-like protease, ablates the eye and generates dark necrotic spots. Targeted misexpression of Nsp3 in the eye triggers reactive oxygen species production and leads to apoptosis as shown by cell death reporters, terminal deoxynucleotidyl transferase (TdT) dUTP Nick-end labeling (TUNEL) assay, and dihydroethidium staining. Furthermore, Nsp3 misexpression activates both apoptosis and autophagy mechanism(s) to regulate tissue homeostasis. Transient expression of SARS-CoV2 Nsp3 in murine neuroblastoma, Neuro-2a cells, significantly reduced the metabolic activity of these cells and triggers cell death. Misexpression of SARS-CoV2 Nsp3 in an Alzheimer’s disease transgenic fly eye model (glass multiple repeats [GMR]>amyloid β42) further enhances the neurodegenerative rough eye phenotype due to increased cell death. These findings suggest that SARS-CoV2 utilizes Nsp3 protein to potentiate cell death response in a neurodegenerative disease background that has high pre-existing levels of neuroinflammation and cell death.

High-throughput isolation of SARS-CoV-2 nucleocapsid antibodies for improved antigen detection

SARS-CoV-2 nucleocapsid protein (NP) is the main target for COVID-19-diagnostic PCR and antigen rapid diagnostic tests (Ag-RDTs). Ag-RDTs are more convenient than PCR tests for point-of-care testing or self-testing to identify the SARS-CoV-2 antigen. The sensitivity and specificity of this method depends mainly on the affinity and specificity of NP-binding antibodies; therefore, antigen-antibody binding is key elements for the Ag-RDTs. Here, we applied the high-throughput antibody isolation platform that has been utilized to isolate therapeutic antibodies against rare epitopes. Two NP antibodies were identified to recognize non-overlapping epitopes with high affinity. One antibody specifically binds to SARS-CoV-2 NP, and the other rapidly and tightly binds to SARS-CoV-2 NP with cross-reactivity to SARS-CoV NP. Furthermore, these antibodies were compatible with a sandwich enzyme-linked immunosorbent assay that exhibited enhanced sensitivity for NP detection compared to the previously isolated NP antibodies. Thus, the NP antibody pair is applicable to more sensitive and specific Ag-RDTs, highlighting the utility of a high-throughput antibody isolation platform for diagnostics development.

Quantification of the Diversity in Gene Structures Using the Principles of Polarization Mapping

Results of computational analysis and visualization of differences in gene structures using polarization coding are presented. A two-dimensional phase screen, where each element of which corresponds to a specific basic nucleotide (adenine, cytosine, guanine, or thymine), displays the analyzed nucleotide sequence. Readout of the screen with a coherent beam characterized by a given polarization state forms a diffracted light field with a local polarization structure that is unique for the analyzed nucleotide sequence. This unique structure is described by spatial distributions of local values of the Stokes vector components. Analysis of these distributions allows the comparison of nucleotide sequences for different strains of pathogenic microorganisms and frequency analysis of the sequences. The possibilities of this polarization-based technique are illustrated by the model data obtained from a comparative analysis of the spike protein gene sequences for three different model variants (Wuhan, Delta, and Omicron) of the SARS-CoV-2 virus. Various modifications of polarization encoding and analysis of gene structures and a possibility for instrumental implementation of the proposed method are discussed.

Modified SIQR model for the COVID-19 outbreak in several countries

In this paper, we propose a modified Susceptible-Infected-Quarantine-Recovered (mSIQR) model, for the COVID-19 pandemic. We start by proving the well-posedness of the model and then compute its reproduction number and the corresponding sensitivity indices. We discuss the values of these indices for epidemiological relevant parameters, namely, the contact rate, the proportion of unknown infectious, and the recovering rate. The mSIQR model is simulated, and the outputs are fit to COVID-19 pandemic data from several countries, including France, US, UK, and Portugal. We discuss the epidemiological relevance of the results and provide insights on future patterns, subjected to health policies.

Complexity of COVID-19 Dynamics

With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. Although enormous efforts have been made to understand the spread of COVID-19, our knowledge of the COVID-19 dynamics still remains limited. The present study employs the concepts of chaos theory to examine the temporal dynamic complexity of COVID-19 around the world. The false nearest neighbor (FNN) method is applied to determine the dimensionality and, hence, the complexity of the COVID-19 dynamics. The methodology involves: (1) reconstruction of a single-variable COVID-19 time series in a multi-dimensional phase space to represent the underlying dynamics; and (2) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the COVID-19 series. For implementation, COVID-19 data from 40 countries/regions around the world are studied. Two types of COVID-19 data are analyzed: (1) daily COVID-19 cases; and (2) daily COVID-19 deaths. The results for the 40 countries/regions indicate that: (1) the dynamics of COVID-19 cases exhibit low- to medium-level complexity, with dimensionality in the range 3 to 7; and (2) the dynamics of COVID-19 deaths exhibit complexity anywhere from low to high, with dimensionality ranging from 3 to 13. The results also suggest that the complexity of the dynamics of COVID-19 deaths is greater than or at least equal to that of the dynamics of COVID-19 cases for most (three-fourths) of the countries/regions. These results have important implications for modeling and predicting the spread of COVID-19 (and other infectious diseases), especially in the identification of the appropriate complexity of models.