An overview of bioinformatics technology application in the study of novel coronavirus

Bioinformatics technology has many applications in the research of novel coronavirus SARS-CoV-2. By sequencing the whole genome of the collected samples, the target genetic code can be obtained. The analysis of sample virus strains on the genetic information level could help study the mutation degree and evolution process of the virus, providing help for tracing the source of the virus, and finally guiding the prevention and control of the epidemic situation. However, the variants of SARS-CoV-2 have evolved rapidly, which has brought challenges to vaccine research and development, as well as epidemic prevention and control. In this paper, the bioinformatics technology applied in the relevant research of novel coronavirus is comprehensively described. Researchers can provide effective information for virus tracing by using second-generation and third-generation sequencing and nucleic acid analysis. © 2023 SPIE.

Chronological development of in-patient oncology in times of COVID-19: a retrospective analysis of hospitalized oncology and COVID-19 patients of a German University Hospital.

PURPOSE: The goal of this study is to examine the chronological development of hospitalized oncology and COVID-19 patients, and compare effects on oncology sub-disciplines for pre-pandemic (2017-19) and pandemic (2020-21) years in the setting of a German university maximum care provider. METHODS: Data were retrospectively retrieved from the hospital performance controlling system for patient collectives with oncological main (nOnco) and COVID-19 secondary diagnosis (nCOVID-19). Data analysis is based on descriptive statistical assessment. RESULTS: The oncology patient collective (nOnco = 27,919) shows a decrease of hospitalized patients for the whole pandemic (- 4% for 2020 and - 2,5% for 2021 to 2019). The number of hospitalized COVID-19 patients increases from first to second pandemic year by + 106.71% (nCOVID-19 = 868). Maximum decline in monthly hospitalized oncology patients amounts to - 19% (May 2020) during the first and - 21% (December 2020) during the second lockdown. Relative monthly hospitalization levels of oncology patients reverted to pre-pandemic levels from February 2021 onwards. CONCLUSION: The results confirm a decline in hospitalized oncology patients for the entire pandemic in the setting of a maximum care provider. Imposed lockdown and contact restrictions, rising COVID-19 case numbers, as well as discovery of new virus variants have a negative impact on hospitalized treated oncological patients.

The Relationship between the Transmission of Different SARS-CoV-2 Strains and Air Quality: A Case Study in China.

Coronavirus Disease 2019 (COVID-19) has been a global public health concern for almost three years, and the transmission characteristics vary among different virus variants. Previous studies have investigated the relationship between air pollutants and COVID-19 infection caused by the original strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, it is unclear whether individuals might be more susceptible to COVID-19 due to exposure to air pollutants, with the SARS-CoV-2 mutating faster and faster. This study aimed to explore the relationship between air pollutants and COVID-19 infection caused by three major SARS-CoV-2 strains (the original strain, Delta variant, and Omicron variant) in China. A generalized additive model was applied to investigate the associations of COVID-19 infection with six air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3). A positive correlation might be indicated between air pollutants (PM2.5, PM10, and NO2) and confirmed cases of COVID-19 caused by different SARS-CoV-2 strains. It also suggested that the mutant variants appear to be more closely associated with air pollutants than the original strain. This study could provide valuable insight into control strategies that limit the concentration of air pollutants at lower levels and would better control the spread of COVID-19 even as the virus continues to mutate.

Virus variant-specific clinical performance of SARS coronavirus two rapid antigen tests in point-of-care use, from November 2020 to January 2022.

OBJECTIVES: Antigen rapid diagnostic tests (RDTs) for SARS coronavirus 2 (SARS-CoV-2) are quick, widely available, and inexpensive. Consequently, RDTs have been established as an alternative and additional diagnostic strategy to quantitative reverse transcription polymerase chain reaction (RT-qPCR). However, reliable clinical and large-scale performance data specific to a SARS-CoV-2 virus variant of concern (VOC) are limited, especially for the Omicron VOC. The aim of this study was to compare RDT performance among different VOCs. METHODS: This single-centre prospective performance assessment compared RDTs from three manufacturers (NADAL, Panbio, MEDsan) with RT-qPCR including deduced standardized viral load from oropharyngeal swabs for detection of SARS-CoV-2 in a clinical point-of-care setting from November 2020 to January 2022. RESULTS: Among 35 479 RDT/RT-qPCR tandems taken from 26 940 individuals, 164 of the 426 SARS-CoV-2 positive samples tested true positive with an RDT corresponding to an RDT sensitivity of 38.50% (95% CI, 34.00-43.20%), with an overall specificity of 99.67% (95% CI, 99.60-99.72%). RDT sensitivity depended on viral load, with decreasing sensitivity accompanied by descending viral load. VOC-dependent sensitivity assessment showed a sensitivity of 42.86% (95% CI, 32.82-53.52%) for the wild-type SARS-CoV-2, 43.42% (95% CI, 32.86-54.61%) for the Alpha VOC, 37.67% (95% CI, 30.22-45.75%) for the Delta VOC, and 33.67% (95% CI, 25.09-43.49%) for the Omicron VOC. Sensitivity in samples with high viral loads of ≥106 SARS-CoV-2 RNA copies per mL was significantly lower in the Omicron VOC (50.00%; 95% CI, 36.12-63.88%) than in the wild-type SARS-CoV-2 (79.31%; 95% CI, 61.61-90.15%; p 0.015). DISCUSSION: RDT sensitivity for detection of the Omicron VOC is reduced in individuals infected with a high viral load, which curtails the effectiveness of RDTs. This aspect furthert: limits the use of RDTs, although RDTs are still an irreplaceable diagnostic tool for rapid, economic point-of-care and extensive SARS-CoV-2 screening.

Understanding the mechanisms for COVID-19 vaccine's protection against infection and severe disease.

INTRODUCTION: Multiple COVID-19 vaccines have been approved and employed in the fight against the pandemic. However, these vaccines have limited long-term effectiveness against severe cases and a decreased ability to prevent mild disease. AREAS COVERED: This review discusses the relevant factors influencing the efficacy of the vaccines against mild and severe infection, analyzes the possible underlying mechanisms contributing to the different outcomes in terms of vaccine function and disease progression, and proposes improvements for the next generation of vaccines. EXPERT OPINION: The reduced efficacy of the COVID-19 vaccine in the prevention of viral infection is closely related to the emergence of novel SARS-CoV-2 variants and their rapid transmission ability. Fundamentally, the immune responses induced by COVID-19 vaccines cannot effectively halt virus replication in the upper respiratory tract because only a limited number of specific antibodies reach these areas and decrease in concentration over time. However, the established immune response can provide sufficient protection against severe diseases by blocking viral infection of the lower respiratory tract or lung owing to sufficient antibody repertoires and memory responses. Considering this situation, future COVID-19 vaccines should have the potential to replenish the mucosal immune response in the respiratory tract to prevent viral infection.

Coronavirus disease 2019 scenarios for a long-term strategy under fundamental uncertainty.

Early on, scientists have pointed out that coronavirus disease 2019 is most likely here to stay, although its course and development are uncertain. This requires a long-term strategy of living with the virus. However, the urgency of new waves of infection and the emergence of new variants have invoked an approach of acute crisis management over and over, hindering the design of a structural approach for the long term. Exploratory scenarios can provide scientific strategic guidance to policy processes to be better prepared in this situation of fundamental uncertainty. We have therefore developed five scenarios, which describe the possible long-term development of the pandemic from an epidemiological, virological, and broader societal perspective. These scenarios are based on four driving forces that are both important and uncertain: immunity, vaccination, mutations, and human behavior. The scenarios are (1) return to normal, (2) flu+, (3) external threat, (4) continuous struggle, and (5) worst case. Working with scenarios is crucial for appropriate public communication and provides guidance for anticipating the various conceivable possibilities for the further course of the pandemic.

Real-Time Analysis of Predictors of COVID-19 Infection Spread in Countries in the European Union Through a New Tool.

Objectives: Real-time data analysis during a pandemic is crucial. This paper aims to introduce a novel interactive tool called Covid-Predictor-Tracker using several sources of COVID-19 data, which allows examining developments over time and across countries. Exemplified here by investigating relative effects of vaccination to non-pharmaceutical interventions on COVID-19 spread. Methods: We combine >100 indicators from the Global COVID-19 Trends and Impact Survey, Johns Hopkins University, Our World in Data, European Centre for Disease Prevention and Control, National Centers for Environmental Information, and Eurostat using random forests, hierarchical clustering, and rank correlation to predict COVID-19 cases. Results: Between 2/2020 and 1/2022, we found among the non-pharmaceutical interventions "mask usage" to have strong effects after the percentage of people vaccinated at least once, followed by country-specific measures such as lock-downs. Countries with similar characteristics share ranks of infection predictors. Gender and age distribution, healthcare expenditures and cultural participation interact with restriction measures. Conclusion: Including time-aware machine learning models in COVID-19 infection dashboards allows to disentangle and rank predictors of COVID-19 cases per country to support policy evaluation. Our open-source tool can be updated daily with continuous data streams, and expanded as the pandemic evolves.

From COVID-19 to the Central Dogma: Investigating the SARS-CoV-2 Spike Protein

Students often struggle with visualizing protein structures when working with two-dimensional textbook and lecture materials, so introducing them to 3D visualization software developed by and for structural biologists offers them a unique opportunity to work with authentic data while furthering their spatial reasoning skills and understanding of molecular structure and function. This article presents an active learning virtual laboratory in which students use authentic structural biology data to investigate the effects of both hypothetical and real-world SARS-CoV-2 mutations on the virus’s ability to bind to human ACE2 receptors and infect a host, causing COVID-19. Through this activity, introductory-level college students or advanced high school students gain a better understanding of applied biology, such as how vaccines and treatments are designed, as well as strengthening their understanding of core disciplinary concepts, such as the relationship between protein structure and function and the central dogma of molecular biology. While there were challenges during the pilot phase of activity development due to COVID-19 restrictions, students in the pilot groups came away from the activity with deeper understanding of the relationship between proteins and amino acid sequences and a new appreciation for the ways researchers design treatments for and study viruses.

Comparison of Variations between the Waves in This COVID-19 Pandemic: Past, Present and Future

The COVID-19 pandemic started by the SARS-CoV-2 virus from China hit different parts of the world and caused till now the first, second and third waves at different time periods from March 2020 to December 2021. Virus variants emerged to cause these waves with altered behaviour and severity of the disease and difficulty in the management of the pandemic. An unexpected upsurge happened during these waves due to social reasons and policies. In this article, we discuss the variations in the waves from a few geographic locations which will give us a better understanding of regional effects and precautions needed for the future. © 2022 Society for Biomaterials and Artificial Organs - India. All rights reserved.

Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models.

The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Synergistic Antiviral Activity of Pamapimod and Pioglitazone against SARS-CoV-2 and Its Variants of Concern.

The SARS-CoV-2 pandemic remains a major public health threat, especially due to newly emerging SARS-CoV-2 Variants of Concern (VoCs), which are more efficiently transmitted, more virulent, and more able to escape naturally acquired and vaccine-induced immunity. Recently, the protease inhibitor Paxlovid® and the polymerase inhibitor molnupiravir, both targeting mutant-prone viral components, were approved for high-risk COVID-19 patients. Nevertheless, effective therapeutics to treat COVID-19 are urgently needed, especially small molecules acting independently of VoCs and targeting genetically stable cellular pathways which are crucial for viral replication. Pamapimod is a selective inhibitor of p38 Mitogen-Activated Protein Kinase alpha (p38 MAPKα) that has been extensively clinically evaluated for the treatment of rheumatoid arthritis. Signaling via p38 has recently been described as a key pathway for the replication of SARS-CoV-2. Here, we reveal that the combination of pamapimod with pioglitazone, an anti-inflammatory and approved drug for the treatment of type 2 diabetes, possesses potent and synergistic activity to inhibit SARS-CoV-2 replication in vitro. Both drugs showed similar antiviral potency across several cultured cell types and similar antiviral activity against SARS-CoV-2 Wuhan type, and the VoCs Alpha, Beta, Gamma, Delta, and Omicron. These data support the combination of pamapimod and pioglitazone as a potential therapy to reduce duration and severity of disease in COVID-19 patients, an assumption currently evaluated in an ongoing phase II clinical study.

Conserved 3' UTR of Severe Acute Respiratory Syndrome Coronavirus 2: Potential Therapeutic Targets.

Our previous paper showed that microRNAs (miRNAs) present within human placental or mesenchymal stem cell-derived extracellular vesicles (EVs) directly interacted with the RNA genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), inhibiting viral replication. In this paper, we analyzed whether these miRNAs could exert antiviral activity against other variants of SARS-CoV-2. We downloaded compete SARS-CoV-2 genome data submitted to the National Center for Biotechnology Information for each SARS-CoV-2 variant, aligned the data to the reference SARS-CoV-2 genome sequence, and then confirmed the presence of 3' untranslated region (UTR) mutations. We identified one type of 3' UTR mutation in the Alpha variant, four in the Beta variant, four in the Gamma variant, three in the Delta variant, and none in the Omicron variant. Our findings indicate that 3' UTR mutations rarely occur as persistent mutations. Interestingly, we further confirmed that this phenomenon could suppress virus replication in the same manner as the previously discovered interaction of placental-EV-derived miRNA with 3' UTRs of SARS-CoV-2. Because the 3' UTR of the SARS-CoV-2 RNA genome has almost no mutations, it is expected to be an effective therapeutic target regardless of future variants. Thus, a therapeutic strategy targeting the 3' UTR of SARS-CoV-2 is likely to be extremely valuable, and such an approach is also expected to be applied to all RNA-based virus therapeutics.

Effectiveness of Naturally Acquired and Vaccine-Induced Immune Responses to SARS-CoV-2 Mu Variant.

SARS-CoV-2 Mu variant emerged in Colombia in 2021 and spread globally. In 49 serum samples from vaccinees and COVID-19 survivors in Colombia, neutralization was significantly lower (p<0.0001) for Mu than a parental strain and variants of concern. Only the Omicron variant of concern demonstrated higher immune evasion.

Monitoring the evolution of SARS-CoV-2 on a Spanish university campus through wastewater analysis: A pilot project for the reopening strategy.

Wastewater surveillance is a fast and cost-effective tool that enables tracing of both symptomatic and asymptomatic transmission of SARS-CoV-2. In this paper, a pilot program carried out at the University Jaume I for monitoring the trends of the presence of SARS-CoV-2 in wastewater. To the best of our knowledge, this is the first such project conducted on a university campus in Spain. Wastewater samples (n = 838) were collected when students returned to campus, from October 2020 until August 2021, at a confluence sewer point and at the building level including different academic departments and services, the library, administration offices and the university student residence. It has been observed that the probability of SARS-CoV-2 RNA detection in wastewater depended on COVID-19 incidence on campus and visitors/occupants of the buildings i.e., high-, or low-traffic buildings with high or low frequency of potential contacts. Moreover, the third wave in Spain (after Christmas 2020) and an outbreak that occurred at the university student's residence could be carefully followed, allowing confirmation of the end of the outbreak. In addition, viral variants (i.e., mutations and linages) from selected time points were detected by sequencing and gave an indication of the evolution of the virus over time. The results illustrate the potential of wastewater-based epidemiology to provide an early warning for SARS-CoV-2 within the university, especially in buildings with low traffic and more defined populations, like the student residence. The strategy and experience gathered in this study will allow for implementation of improvements for reliable monitoring in the future.

Neuroinvasion and Neurotropism by SARS-CoV-2 Variants in the K18-hACE2 Mouse.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) not only affects the respiratory tract but also causes neurological symptoms such as loss of smell and taste, headache, fatigue or severe cerebrovascular complications. Using transgenic mice expressing human angiotensin-converting enzyme 2 (hACE2), we investigated the spatiotemporal distribution and pathomorphological features in the CNS following intranasal infection with SARS-CoV-2 variants, as well as after prior influenza A virus infection. Apart from Omicron, we found all variants to frequently spread to and within the CNS. Infection was restricted to neurons and appeared to spread from the olfactory bulb mainly in basally oriented regions in the brain and into the spinal cord, independent of ACE2 expression and without evidence of neuronal cell death, axonal damage or demyelination. However, microglial activation, microgliosis and a mild macrophage and T cell dominated inflammatory response was consistently observed, accompanied by apoptotic death of endothelial, microglial and immune cells, without their apparent infection. Microgliosis and immune cell apoptosis indicate a potential role of microglia for pathogenesis and viral effect in COVID-19 and the possible impairment of neurological functions, especially in long COVID. These data may also be informative for the selection of therapeutic candidates and broadly support the investigation of agents with adequate penetration into relevant regions of the CNS.

Wastewater and marine bioindicators surveillance to anticipate COVID-19 prevalence and to explore SARS-CoV-2 diversity by next generation sequencing: One-year study.

This study presents the results of SARS-CoV-2 surveillance in sewage water of 11 municipalities and marine bioindicators in Galicia (NW of Spain) from May 2020 to May 2021. An integrated pipeline was developed including sampling, pre-treatment and biomarker quantification, RNA detection, SARS-CoV-2 sequencing, mechanistic mathematical modeling and forecasting. The viral load in the inlet stream to the wastewater treatment plants (WWTP) was used to detect new outbreaks of COVID-19, and the data of viral load in the wastewater in combination with data provided by the health system was used to predict the evolution of the pandemic in the municipalities under study within a time horizon of 7 days. Moreover, the study shows that the viral load was eliminated from the treated sewage water in the WWTP, mainly in the biological reactors and the disinfection system. As a result, we detected a minor impact of the virus in the marine environment through the analysis of seawater, marine sediments and, wild and aquacultured mussels in the final discharge point of the WWTP.

COVID-WideNet-A capsule network for COVID-19 detection.

Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been proved to provide encouraging results with higher accuracy as compared to the conventional method of RT-PCR testing. Chest radiography, particularly using X-ray images, is a prime imaging modality for detecting the suspected COVID-19 patients. However, the performance of these approaches still needs to be improved. In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the COVIDx dataset. In particular, COVID-WideNet performs better than any other CNN based approaches for diagnosis of COVID-19 infected patients. Further, the proposed COVID-WideNet has the number of trainable parameters that is 20 times less than that of other CNN based models. This results in fast and efficient diagnosing COVID-19 symptoms and with achieving the 0.95 of Area Under Curve (AUC), 91% of accuracy, sensitivity and specificity respectively. This may also assist radiologists to detect COVID and its variant like delta.

Modelling the COVID-19 epidemic and the vaccination campaign in Italy by the SUIHTER model.

Several epidemiological models have been proposed to study the evolution of COVID-19 pandemic. In this paper, we propose an extension of the SUIHTER model, to analyse the COVID-19 spreading in Italy, which accounts for the vaccination campaign and the presence of new variants when they become dominant. In particular, the specific features of the variants (e.g. their increased transmission rate) and vaccines (e.g. their efficacy to prevent transmission, hospitalization and death) are modeled, based on clinical evidence. The new model is validated comparing its near-future forecast capabilities with other epidemiological models and exploring different scenario analyses.

Critical View on the Importance of Host Defense Strategies on Virus Distribution of Bee Viruses: What Can We Learn from SARS-CoV-2 Variants?

Bees, both wild and domesticated ones, are hosts to a plethora of viruses, with most of them infecting a wide range of bee species and genera. Although viral discovery and research on bee viruses date back over 50 years, the last decade is marked by a surge of new studies, new virus discoveries, and reports on viral transmission in and between bee species. This steep increase in research on bee viruses was mainly initiated by the global reports on honeybee colony losses and the worldwide wild bee decline, where viruses are regarded as one of the main drivers. While the knowledge gained on bee viruses has significantly progressed in a short amount of time, we believe that integration of host defense strategies and their effect on viral dynamics in the multi-host viral landscape are important aspects that are currently still missing. With the large epidemiological dataset generated over the last two years on the SARS-CoV-2 pandemic, the role of these defense mechanisms in shaping viral dynamics has become eminent. Integration of these dynamics in a multi-host system would not only greatly aid the understanding of viral dynamics as a driver of wild bee decline, but we believe bee pollinators and their viruses provide an ideal system to study the multi-host viruses and their epidemiology.