Characterization of the SARS-CoV-2 genomes in Egypt in first and second waves of infection

Analysis of the Spread and Evolution of COVID-19 Mutations in Ecuador Using Open Data

Currently, the analyses of and prediction using COVID-19-related data extracted from patient information repositories compiled by hospitals and health organizations are of paramount importance. These efforts significantly contribute to vaccine development and the formulation of contingency techniques, providing essential tools to prevent resurgence and to effectively manage the spread of the disease. In this context, the present research focuses on analyzing the biological information of the SARS-CoV-2 viral gene sequences and the clinical data of COVID-19-affected patients using publicly accessible data from Ecuador. This involves considering variables such as age, gender, and geographical location to understand the evolution of mutations and their distributions across Ecuadorian provinces. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology is applied for data analysis. Various data preprocessing and statistical analysis techniques are employed, including Pearson correlation, the chi-square test, and analysis of variance (ANOVA). Statistical diagrams and charts are used to facilitate a better visualization of the results. The results illuminate the genetic diversity of the virus and its correlation with clinical variables, offering a comprehensive understanding of the dynamics of COVID-19 spread in Ecuador. Critical variables influencing population vulnerability are highlighted, and the findings underscore the significance of mutation monitoring and indicate a need for global expansion of the research area.

VIPERA: Viral Intra-Patient Evolution Reporting and Analysis

Viral mutations within patients nurture the adaptive potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during chronic infections, which are a potential source of variants of concern. However, there is no integrated framework for the evolutionary analysis of intra-patient SARS-CoV-2 serial samples. Herein, we describe Viral Intra-Patient Evolution Reporting and Analysis (VIPERA), a new software that integrates the evaluation of the intra-patient ancestry of SARS-CoV-2 sequences with the analysis of evolutionary trajectories of serial sequences from the same viral infection. We have validated it using positive and negative control datasets and have successfully applied it to a new case, which revealed population dynamics and evidence of adaptive evolution. VIPERA is available under a free software license at https://github.com/PathoGenOmics-Lab/VIPERA.

Comparative genotyping of SARS-CoV-2 among Egyptian patients: near-full length genomic sequences versus selected spike and nucleocapsid regions

Several tools have been developed for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genotyping based on either whole genome or spike sequencing. We aimed to highlight the molecular epidemiological landscape of SARS-CoV-2 in Egypt since the start of the pandemic, to describe discrepancies between the 3 typing tools: Global Initiative on Sharing Avian Influenza Data (GISAID), Nextclade, and Phylogenetic Assignment of Named Global Outbreak Lineages (PANGOLIN) and to assess the fitness of spike and nucleocapsid regions for lineage assignment compared to the whole genome. A total of 3935 sequences isolated from Egypt (March 2020-2023) were retrieved from the GISAID database. A subset of data (n = 1212) with high coverage whole genome was used for tool discrimination and agreement analyses. Among 1212 sequences, the highest discriminatory power was 0.895 for PANGOLIN, followed by GISAID (0.872) and Nextclade (0.866). There was a statistically significant difference (p = 0.0418) between lineages assigned via spike (30%) and nucleocapsid (46%) compared to their whole genome-assigned lineages. The first 3 pandemic waves were dominated by B.1, followed by C.36 and then C.36.3, while the fourth to sixth waves were dominated by the B.1.617.2, BA, and BA.5.2 lineages, respectively. Current shift in lineage typing to recombinant forms. The 3 typing tools showed comparable discrimination among SARS-CoV-2 lineages. The nucleocapsid region could be used for lineage assignment.

Understanding COVID-19-related myocarditis: pathophysiology, diagnosis, and treatment strategies

Coronavirus disease 2019 (COVID-19) disease has infected nearly 600 million people, resulting in > 6 million deaths, with many of them dying from cardiovascular diseases. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is caused by a combination of the virus surface spike protein and the human angiotensin-converting enzyme 2 (ACE2) receptor. In addition to being highly expressed in the lungs, ACE2 is widely distributed in the heart, mainly in myocardial cells and pericytes. Like other types of viruses, SARS-CoV-2 can cause myocarditis after infecting the myocardial tissue, which is attributed to the direct damage of the virus and uncontrolled inflammatory reactions. Patients with chest tightness, palpitation, abnormal electrocardiogram, and cardiac troponin elevation, should be suspected of myocarditis within 1-3 weeks of COVID-19 infection. When the hemodynamics change rapidly, fulminant myocarditis should be suspected. Cardiac ultrasound, myocardial biopsy, cytokine detection, cardiac magnetic resonance imaging, 18F-fluorodeoxyglucose positron emission tomography, and other examination methods can assist in the diagnosis. Although scientists and clinicians have made concerted efforts to seek treatment and prevention measures, there are no clear recommendations for the treatment of COVID-19-related myocarditis. For most cases of common myocarditis, general symptomatic and supportive treatments are used. For COVID-19-related fulminant myocarditis, it is emphasized to achieve "early identification, early diagnosis, early prediction, and early treatment" based on the "life support-based comprehensive treatment regimen." Mechanical circulatory support therapy can rest the heart, which is a cure for symptoms, and immune regulation therapy can control the inflammatory storms which is a cure for the disease. Furthermore, complications of COVID-19-related myocarditis, such as arrhythmia, thrombosis, and infection, should be actively treated. Herein, we summarized the incidence rate, manifestations, and diagnosis of COVID-19-related myocarditis and discussed in detail the treatment of COVID-19-related myocarditis, especially the treatment strategy of fulminant myocarditis.

"Day 25": a temporal indicator of stabilization of mortality risk among COVID-19 patients with high viral load

INTRODUCTION

The relationship between SARS-CoV-2 viral load and hospitalization and mortality among COVID-19 patients has been established. However, the estimation of the duration of time after which the risk of mortality of these patients stops escalating was not extensively discussed earlier. Stratifying patients according to their risk of mortality would optimize healthcare services and costs and reduce mortality.

METHODOLOGY

In this retrospective observational study, hospital records were used to collect data of 519 COVID-19 patients from May through November 2020. Data included the clinical condition of patients, their viral loads, their admission chest computed tomography results (CO-RAD scale), and the duration of their hospitalization. A Kaplan-Meier analysis was constructed to estimate mortality risk concerning viral load.

RESULTS

By the end of the study, 20.42% of patients were deceased. The cumulative mortality was: 36.1% (75/208) among patients with high viral load, 12.6% (28/222) in those with moderate viral load, and 3.4% (3/89) among those with low viral load. Predictors of mortality were: older age [adjusted hazard ratio (aHR) = 1.02, 95% CI: [1.00-1.03], (p = 0.05)], "being female" [aHR = 1.53 with 95% CI: [1.03-2.26], (p = 0.031), "high CO-RAD scale" [aHR = 1.32 (1.06-1.64), p = 0.013], "high viral load" [aHR = 4.59 (2.38-20.92), p = 0.017, ICU admission [aHR = 15.95; 95%CI:7.22-35.20, p < 0.001] and lymphocytosis [aHR = 1.89 45;95%CI:1.04-3.45, p = 0.036]. In the ICU-admitted patients, the median survival was 19 days and mortality stabilized at "day 25". For patients with high viral load, mortality rates stabilized at "day 25 post-admission" after which the risks of mortality did not change until day 40, while patients with low and moderate viral loads reached the peak and stabilized at day "20 post-admission".

CONCLUSIONS

Initial high SARS-CoV-2 viral load might be used as an indicator of a delayed stabilization of mortality risk among COVID-19 patients.

Analysis of the mutation dynamics of SARS-CoV-2 genome in the samples from Georgia State of the United States

BACKGROUND

The COVID-19 is caused by a novel coronavirus SARS-CoV-2, which started from China. It spread rapidly throughout the world and was later declared a pandemic by the WHO. Over the course of time, SARS-CoV-2 has mutated for survival advantages, and this led to multiple variants. Multiple studies on mutations identification in SARS-CoV2 have been published covering extensive sample areas. The purpose of this study was to limit the sample area to the Georgia state in the U.S. and to analyze the genome sequences for mutation profiling across the genome and origin of variants.

METHODS

The genome sequences (n = 3,970) were obtained from the NCBI database as of June 12, 2021, with the filter of being complete sequenced genomes, homo-sapiens host, and only from Georgia State of the U.S. NextClade, an online tool was used for the analysis of the sequences using Wuhan-Hu-1/2019 as a reference genome. The algorithm was sequence alignment, translation, mutation calling, phylogenetic placement, clade assignment, and quality control (QC). Thirty-six samples with bad QC were removed from the mutational analysis.

RESULTS

A total 117,743 mutations in the nucleotides were identified (averaging 31.5 mutations per sample). The mutations A23403G, C3037T, C241T, and C14408T were detected in 98% of the samples. Also, a total of 75,517 mutations in the amino acid were identified (averaging 20.2 mutations per sample). The mutations D614G and P314L were identified in >97% samples whereas R203K, G204R, P681H, and N501Y were detected in >50% samples. Analysis also revealed 16 different clades with 20I (49.6%). Clades 20G (24.2%) and 20A (5.5%) being the most abundant, showed that SARS-CoV-2 in the Georgia State originated mainly from Southeast England, other parts of the U.S., and several countries in Western Europe.

CONCLUSION

Looking at the three most common variants in Georgia State of the U.S., we could determine the primary locations of transmission or origin for the virus, and our analyses indicates that majority of the cases originated from Southeast England (Clade 20I), the U.S. itself (Clade 20G), and from Western Europe (Clade 20C).

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern-particularly Alpha, Beta, Delta, and Omicron-on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century.

Whole-genome sequencing of SARS-CoV-2 reveals diverse mutations in circulating Alpha and Delta variants during the first, second, and third waves of COVID-19 in South Kivu, east of the Democratic Republic of the Congo

OBJECTIVES

We used whole-genome sequencing of SARS-CoV-2 to identify variants circulating in the Democratic Republic of the Congo and obtain molecular information useful for diagnosis, improving treatment, and general pandemic control strategies.

METHODS

A total of 74 SARS-CoV-2 isolates were sequenced using Oxford Nanopore platforms. Generated reads were processed to obtain consensus genome sequences. Sequences with more than 80% genome coverage were used for variant calling, phylogenetic analysis, and classification using Pangolin lineage annotation nomenclature.

RESULTS

Phylogenetic analysis based on Pangolin classification clustered South Kivu sequences into seven lineages (A.23.1, B.1.1.6, B.1.214, B.1.617.2, B.1.351, C.16, and P.1). The Delta (B.1.617.2) variant was the most dominant and responsible for outbreaks during the third wave. Based on the Wuhan reference genome, 289 distinct mutations were detected, including 141 missenses, 123 synonymous, and 25 insertions/deletions when our isolates were mapped to the Wuhan reference strain. Most of these point mutations were located within the coding sequences of the SARS-CoV-2 genome that includes spike, ORF1ab, ORF3, and nucleocapsid protein genes. The most common mutation was D614G (1841A>G) observed in 61 sequences, followed by L4715L (14143 C>T) found in 60 sequences.

CONCLUSION

Our findings highlight multiple introductions of SARS-CoV-2 into South Kivu through different sources and subsequent circulation of variants in the province. These results emphasize the importance of timely monitoring of genetic variation and its effect on disease severity. This work set a foundation for the use of genomic surveillance as a tool for future global pandemic management and control.

SARS-CoV-2 genome variations and evolution patterns in Egypt: a multi-center study

A serious global public health emergency emerged late November 2019 in Wuhan City, China, by a new highly pathogenic virus, SARS-CoV-2. The virus evolution spread has been tracked by three developing databases: GISAID, Nextstrain and PANGO to understand its circulating variants. In this study, 110 diagnosed positive COVID-19 patient's samples, were collected from Kasr Al-Aini Hospital and the Children Cancer Hospital Egypt 57357 between May 2020 and January 2021, with clinical severity ranging from mild to severe. The viral genomes were sequenced by next generation sequencing, and phylogenetic analysis was performed to understand viral transmission dynamics. According to Nextstrain clades, most of our sequenced samples belonged to clades 20A and 20D, which in addition to clade 20B were present from the beginning of sample collection in May 2020. Clades 19A and 19B, on the other hand, appeared in the mid and late 2020 respectively, followed by the disappearance of clade 20B at the end of 2020. We identified a relatively high prevalence of the D614G spike protein variant and novel patterns of mutations associated together and with different clades. We also identified four mutations, spike H49Y, ORF3a H78Y, ORF8 E64stop and nucleocapsid E378V, associated with higher disease severity. Altogether, our study contributes genetic, phylogenetic, and clinical correlation data about the spread of the SARS-CoV-2 pandemic in Egypt.

Determinants of the Development of SARS-CoV-2 Anti-Spike Immune-Response after Vaccination among Healthcare Workers in Egypt

BACKGROUND

Understanding the factors affecting humoral immune response to COVID-19 vaccines among healthcare workers (HCWs) is essential to predict their level of protection. Vaccination elicits antibodies against SARS-CoV-2 spike protein (anti-S).

AIM

To investigate the factors associated with the presence of SARS-CoV-2 anti-S antibodies among vaccinated HCWs.

METHODS

This cross-sectional study included 143 vaccinated HCWs, with or without a history of previous COVID-19 infection (clinically, radiologically, or by laboratory results) from different departments. Socio-demographic, clinical, as well as vaccine-related data, were recorded. Serum samples were collected and tested for SARS-CoV-2 spike antibodies.

RESULTS

Vaccination provoked an immunogenic response, where the overall anti-S positivity was 83.9% (95% CI: 77.8-90.0%). The response was not affected either by the age or gender of HCWs. Out of the 143 HCWs, 46 (32.1%; 95% CI: 24.4-39.9%) reported a previous history of COVID-19 infection, and seropositivity was significantly higher among them (p = 0.002), and it was associated with the frequency of infection (p = 0.044) and duration since diagnosis of COVID-19 infection (p = 0.065). They had higher median anti-S titers (111.8 RU/mL) than those without infection (39.8 RU/mL). Higher seropositivity was observed with Oxford/AstraZeneca vaccine (AZD1222) (88.9%; 95% CI: 83.1-95.0%) than Sinopharm (BBIBP-CorV) (67.7%; 95% CI: 50.3-85.2%), and with receiving two doses of vaccine (92.3%; 95% CI: 87.1-97.5%).

CONCLUSIONS

Antibody positivity was significantly affected by the previous history of COVID-19 infection, type of vaccine, the number of doses received, and duration since vaccination.