Inferring the hosts of coronavirus using dual statistical models based on nucleotide composition

Utilizing machine learning and hemagglutinin sequences to identify likely hosts of influenza H3Nx viruses

Influenza is a disease that represents both a public health and agricultural risk with pandemic potential. Among the subtypes of influenza A virus, H3 influenza virus can infect many avian and mammalian species and is therefore a virus of interest to human and veterinary public health. The primary goal of this study was to train and validate classifiers for the identification of the most likely host species using the hemagglutinin gene segment of H3 viruses. A five-step process was implemented, which included training four machine learning classifiers, testing the classifiers on the validation dataset, and further exploration of the best-performing model on three additional datasets. The gradient boosting machine classifier showed the highest host-classification accuracy with a 98.0 % (95 % CI [97.01, 98.73]) correct classification rate on an independent validation dataset. The classifications were further analyzed using the predicted probability score which highlighted sequences of particular interest. These sequences were both correctly and incorrectly classified sequences that showed considerable predicted probability for multiple hosts. This showed the potential of using these classifiers for rapid sequence classification and highlighting sequences of interest. Additionally, the classifiers were tested on a separate swine dataset composed of H3N2 sequences from 1998 to 2003 from the United States of America, and a separate canine dataset composed of canine H3N2 sequences of avian origin. These two datasets were utilized to look at the applications of predicted probability and host convergence over time. Lastly, the classifiers were used on an independent dataset of environmental sequences to explore the host identification of environmental sequences. The results of these classifiers show the potential for machine learning to be used as a host identification technique for viruses of unknown origin on a species-specific level.

A game of resistance: War between bacteria and phages and how phage cocktails can be the solution

While phages hold promise as an antibiotic alternative, they encounter significant challenges in combating bacterial infections, primarily due to the emergence of phage-resistant bacteria. Bacterial defence mechanisms like superinfection exclusion, CRISPR, and restriction-modification systems can hinder phage effectiveness. Innovative strategies, such as combining different phages into cocktails, have been explored to address these challenges. This review delves into these defence mechanisms and their impact at each stage of the infection cycle, their challenges, and the strategies phages have developed to counteract them. Additionally, we examine the role of phage cocktails in the evolving landscape of antibacterial treatments and discuss recent studies that highlight the effectiveness of diverse phage cocktails in targeting essential bacterial receptors and combating resistant strains.

Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review

Introduction

Predicting which species are susceptible to viruses (i.e., host range) is important for understanding and developing effective strategies to control viral outbreaks in both humans and animals. The use of machine learning and bioinformatic approaches to predict viral hosts has been expanded with advancements in in-silico techniques. We conducted a scoping review to identify the breadth of machine learning methods applied to influenza and coronavirus genome data for the identification of susceptible host species.

Methods

The protocol for this scoping review is available at https://hdl.handle.net/10214/26112. Five online databases were searched, and 1,217 citations, published between January 2000 and May 2022, were obtained, and screened in duplicate for English language and in-silico research, covering the use of machine learning to identify susceptible species to viruses.

Results

Fifty-three relevant publications were identified for data charting. The breadth of research was extensive including 32 different machine learning algorithms used in combination with 29 different feature selection methods and 43 different genome data input formats. There were 20 different methods used by authors to assess accuracy. Authors mostly used influenza viruses (n = 31/53 publications, 58.5%), however, more recent publications focused on coronaviruses and other viruses in combination with influenza viruses (n = 22/53, 41.5%). The susceptible animal groups authors most used were humans (n = 57/77 analyses, 74.0%), avian (n = 35/77 45.4%), and swine (n = 28/77, 36.4%). In total, 53 different hosts were used and, in most publications, data from multiple hosts was used.

Discussion

The main gaps in research were a lack of standardized reporting of methodology and the use of broad host categories for classification. Overall, approaches to viral host identification using machine learning were diverse and extensive.

Machine Learning to Identify Critical Biomarker Profiles in New SARS-CoV-2 Variants

The global dissemination of SARS-CoV-2 resulted in the emergence of several variants, including Alpha, Alpha + E484K, Beta, and Omicron. Our research integrated the study of eukaryotic translation factors and fundamental components in general protein synthesis with the analysis of SARS-CoV-2 variants and vaccination status. Utilizing statistical methods, we successfully differentiated between variants in infected individuals and, to a lesser extent, between vaccinated and non-vaccinated infected individuals, relying on the expression profiles of translation factors. Additionally, our investigation identified common causal relationships among the translation factors, shedding light on the interplay between SARS-CoV-2 variants and the host's translation machinery.

Predictive phage therapy for Escherichia coli urinary tract infections: Cocktail selection for therapy based on machine learning models

This study supports the development of predictive bacteriophage (phage) therapy: the concept of phage cocktail selection to treat a bacterial infection based on machine learning (ML) models. For this purpose, ML models were trained on thousands of measured interactions between a panel of phage and sequenced bacterial isolates. The concept was applied to Escherichia coli associated with urinary tract infections. This is an important common infection in humans and companion animals from which multidrug-resistant (MDR) bloodstream infections can originate. The global threat of MDR infection has reinvigorated international efforts into alternatives to antibiotics including phage therapy. E. coli exhibit extensive genome-level variation due to horizontal gene transfer via phage and plasmids. Associated with this, phage selection for E. coli is difficult as individual isolates can exhibit considerable variation in phage susceptibility due to differences in factors important to phage infection including phage receptor profiles and resistance mechanisms. The activity of 31 phage was measured on 314 isolates with growth curves in artificial urine. Random Forest models were built for each phage from bacterial genome features, and the more generalist phage, acting on over 20% of the bacterial population, exhibited F1 scores of >0.6 and could be used to predict phage cocktails effective against previously untested strains. The study demonstrates the potential of predictive ML models which integrate bacterial genomics with phage activity datasets allowing their use on data derived from direct sequencing of clinical samples to inform rapid and effective phage therapy.

SARS-CoV-2 Outbreaks on Mink Farms-A Review of Current Knowledge on Virus Infection, Spread, Spillover, and Containment

Many studies have been conducted to explore outbreaks of SARS-CoV-2 in farmed mink and their intra-/inter-species spread and spillover to provide data to the scientific community, protecting human and animal health. Studies report anthropozoonotic introduction, which was initially documented in April 2020 in the Netherlands, and subsequent inter-/intra-species spread of SARS-CoV-2 in farmed mink, likely due to SARS-CoV-2 host tropism capable of establishing efficient interactions with host ACE2 and the mink hosts' ability to enhance swift viral transmission due to their density, housing status, and occupational contacts. Despite the rigorous prevention and control measures adopted, transmission of the virus within and between animal species was efficient, resulting in the development of mink-associated strains able to jump back and forth among the mink hosts and other animal/human contacts. Current knowledge recognizes the mink as a highly susceptible animal host harboring the virus with or without clinical manifestations, furthering infection transmission as a hidden animal reservoir. A One Health approach is, thus, recommended in SARS-CoV-2 surveillance and monitoring on mink farms and of their susceptible contact animals to identify and better understand these potential animal hosts.

Predicting zoonotic potential of viruses: where are we?

The prospect of identifying high-risk viruses and designing interventions to pre-empt their emergence into human populations is enticing, but controversial, particularly when used to justify large-scale virus discovery initiatives. We review the current state of these efforts, identifying three broad classes of predictive models that have differences in data inputs that define their potential utility for triaging newly discovered viruses for further investigation. Prospects for model predictions of public health risk to guide preparedness depend not only on computational improvements to algorithms, but also on more efficient data generation in laboratory, field and clinical settings. Beyond public health applications, efforts to predict zoonoses provide unique research value by creating generalisable understanding of the ecological and evolutionary factors that promote viral emergence.

DYRK1A promotes viral entry of highly pathogenic human coronaviruses in a kinase-independent manner

Identifying host genes essential for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has the potential to reveal novel drug targets and further our understanding of Coronavirus Disease 2019 (COVID-19). We previously performed a genome-wide CRISPR/Cas9 screen to identify proviral host factors for highly pathogenic human coronaviruses. Few host factors were required by diverse coronaviruses across multiple cell types, but DYRK1A was one such exception. Although its role in coronavirus infection was previously undescribed, DYRK1A encodes Dual Specificity Tyrosine Phosphorylation Regulated Kinase 1A and is known to regulate cell proliferation and neuronal development. Here, we demonstrate that DYRK1A regulates ACE2 and DPP4 transcription independent of its catalytic kinase function to support SARS-CoV, SARS-CoV-2, and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) entry. We show that DYRK1A promotes DNA accessibility at the ACE2 promoter and a putative distal enhancer, facilitating transcription and gene expression. Finally, we validate that the proviral activity of DYRK1A is conserved across species using cells of nonhuman primate and human origin. In summary, we report that DYRK1A is a novel regulator of ACE2 and DPP4 expression that may dictate susceptibility to multiple highly pathogenic human coronaviruses.

Managing the deluge of newly discovered plant viruses and viroids: an optimized scientific and regulatory framework for their characterization and risk analysis

The advances in high-throughput sequencing (HTS) technologies and bioinformatic tools have provided new opportunities for virus and viroid discovery and diagnostics. Hence, new sequences of viral origin are being discovered and published at a previously unseen rate. Therefore, a collective effort was undertaken to write and propose a framework for prioritizing the biological characterization steps needed after discovering a new plant virus to evaluate its impact at different levels. Even though the proposed approach was widely used, a revision of these guidelines was prepared to consider virus discovery and characterization trends and integrate novel approaches and tools recently published or under development. This updated framework is more adapted to the current rate of virus discovery and provides an improved prioritization for filling knowledge and data gaps. It consists of four distinct steps adapted to include a multi-stakeholder feedback loop. Key improvements include better prioritization and organization of the various steps, earlier data sharing among researchers and involved stakeholders, public database screening, and exploitation of genomic information to predict biological properties.

The relevant information about the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using the five-question approach (when, where, what, why, and how) and its impact on the environment

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is regarded as a threat because it spreads quickly across the world without requiring a passport or establishing an identity. This tiny virus has wreaked havoc on people's lives, killed people, and created psychological problems all over the world. The viral spike protein (S) significantly contributes to host cell entry, and mutations associated with it, particularly in the receptor-binding protein (RBD), either facilitate the escape of virus from neutralizing antibodies or enhance its transmission by increasing the affinity for cell entry receptor, angiotensin-converting enzyme 2 (ACE2). The initial variants identified in Brazil, South Africa, and the UK have spread to various countries. On the other hand, new variants are being detected in India and the USA. The viral genome and proteome were applied for molecular detection techniques, and nanotechnology particles and materials were utilized in protection and prevention strategies. Consequently, the SARS-CoV-2 pandemic has resulted in extraordinary scientific community efforts to develop detection methods, diagnosis tools, and effective antiviral drugs and vaccines, where prevailing academic, governmental, and industrial institutions and organizations continue to engage themselves in large-scale screening of existing drugs, both in vitro and in vivo. In addition, COVID-19 pointed on the possible solutions for the environmental pollution globe problem. Therefore, this review aims to address SARS-CoV-2, its transmission, where it can be found, why it is severe in some people, how it can be stopped, its diagnosis and detection techniques, and its relationship with the environment.

Herbs-derived phytochemicals - a boon for combating COVID-19

The recent pandemic, the novel coronavirus (COVID-19), has put the whole world on alert with the threat of the virus that targets the human respiratory system. The disease has affected more than 633.6 million people globally and caused 6.5 million deaths since November 18, 2022. About 12.94 billion people are vaccinated as of November 18, 2022. Due to varied climatic conditions, SARS-CoV-2 has shown rapid mutation in recent years. Because of the lack of appropriate therapeutic drugs, inadequate diagnostic mechanisms, life-supporting medical facilities, and lack of awareness, the spread of SARS-CoV-2 has become severe. Thus, the most efficient strategy to control this disease is to follow preventive measures. However, treating SARS-CoV-2 cases in Wuhan using traditional Chinese herbs has set an example to show how traditional health can contribute to treating this novel virus. Medicinal herbs are known for their antimicrobial, antibacterial, antiviral, immunomodulatory, immunoadjuvant, and anti-inflammatory properties. These medicinal herbs are used during cooking and consumed regularly worldwide. In this view, medicinal herbs gained evident attention. These herbs can serve as a potential and economical remedy for combating the lethal effects of COVID-19. The present review highlights the phytochemicals and their mechanisms of action in preventing SARS-CoV-2.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42535-023-00601-9.

Preparation of a new monoclonal antibody against nucleocapsid protein of swine acute diarrhea syndrome coronavirus and identification of its linear antigenic epitope

Swine acute diarrhea syndrome coronavirus (SADS-CoV), which causes severe diarrhea in newborn piglets, was first identified in Southern China in 2017. Since the Nucleocapsid (N) protein in SADS-CoV is highly conserved and plays a key role in virus replication, it is often used as a target protein in scientific research. In this study, the N protein of SADS-CoV was successfully expressed, and a new monoclonal antibody (mAb), 5G12, against the protein was generated successfully. The mAb 5G12 can be used to detect SADS-CoV strains by indirect immunofluorescence assay (IFA) and western blotting. The mAb 5G12 epitope was located to amino acids 11 EQAESRGRK 19 by evaluating the antibody for reactivity with a series of truncated N protein segments. The biological information analysis showed that the antigenic epitope had a high antigenic index and conservation. This study will help further understand the protein structure and function of SADS-CoV and in the establishment of specific SADS-CoV detection methods.

PREHOST: Host prediction of coronaviridae family using machine learning

Coronavirus, a zoonotic virus capable of transmitting infections from animals to humans, emerged as a pandemic recently. In such circumstances, it is essential to understand the virus's origin. In this study, we present a novel machine-learning pipeline PreHost for host prediction of the family, Coronaviridae. We leverage the complete viral genome and sequences at the protein level (spike protein, membrane protein, and nucleocapsid protein). Compared with the current state-of-the-art approaches, the random forest model attained high accuracy and recall scores of 99.91% and 0.98, respectively, for genome sequences. In addition to the spike protein sequences, our study shows membrane and nucleocapsid protein sequences can be utilized to predict the host of viruses. We also identified important sites in the viral sequences that help distinguish between different host classes. The host prediction pipeline PreHost will cater as a valuable tool to take effective measures to govern the transmission of future viruses.

Structural insights into the activity regulation of full-length non-structural protein 1 from SARS-CoV-2

Non-structural protein 1 (Nsp1) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major virulence factor and thus an attractive drug target. The last 33 amino acids of Nsp1 have been shown to bind within the mRNA entry tunnel of the 40S ribosomal subunit, shutting off host gene expression. Here, we report the solution-state structure of full-length Nsp1, which features an α/β fold formed by a six-stranded, capped β-barrel-like globular domain (N-terminal domain [NTD]), flanked by short N-terminal and long C-terminal flexible tails. The NTD has been found to be critical for 40S-mediated viral mRNA recognition and promotion of viral gene expression. We find that in free Nsp1, the NTD mRNA-binding surface is occluded by interactions with the acidic C-terminal tail, suggesting a mechanism of activity regulation based on the interplay between the folded NTD and the disordered C-terminal region. These results are relevant for drug-design efforts targeting Nsp1.

An Automated Strategy to Handle Antigenic Variability in Immunisation Protocols, Part I: Nanopore Sequencing of Infectious Agent Variants

Infectious agents often challenge therapeutics, from antibiotics resistance to antigenic variability affecting inoculation measures. Over the last decades, genome sequencing arose as an important ally to address such challenges. In bacterial infection, whole-genome-sequencing (WGS) supports tracking pathogenic alterations affecting the human microbiome. In viral infection, the analysis of the relevant sequence of nucleotides helps with determining historical variants of a virus and elucidates details about infection clusters and their distribution. Additionally, genome sequencing is now an important step in inoculation protocols, isolating target genes to design more robust immunisation assays. Ultimately, genetic engineering has empowered repurposing at scale, allowing long-lasting repeating clinical trials to be automated within a much shorter time-frame, by adjusting existing protocols. This is particularly important during sanitary emergencies as the ones caused by the 2014 West African Ebola outbreak, the Zika virus rapid spread in both South and North America in 2015, followed by Asia in 2016, and the pandemic caused by the SARS-CoV-2, which has infected more than 187 million people and caused more than 4 million deaths, worldwide, as per July 2021 statistics. In this scenery, this chapter presents a novel fully automated strategy to handle antigenic variability in immunisation protocols. The methodology comprises of two major steps (1) nanopore sequencing of infectious agent variants - the focus is on the SARS-CoV-2 and its variants; followed by (2) mRNA vector design for immunotherapy. This chapter presents the nanopore sequencing step and Chapter 17 introduces a protocol for mRNA vector design.

Effect of COVID-19 on healthcare workers' morbidity and mortality compared to the general population in Kohgiluyeh and Boyer-Ahmad Province, Iran

During the Covid-19 pandemic, the adverse effects of recent coronaviruses on healthcare professionals cannot be ignored. This study compared the admission rates due to Covid-19 and characteristics of hospitalized healthcare workers with the general population of Kohgiluyeh and Boyer-Ahmad (K.B) province. 18546 hospitalized patients infected with Covid-19 in hospitals in four cities of K.B province were enrolled in this study; of them, 236 (1.27%) patients were healthcare workers. Demographic and clinical data of hospitalized cases due to Covid-19 infection were collected from August 2020 to September 2021. The underlying diseases were also considered in this study. According to our findings, 55.5% of the hospitalized healthcare workers were male, and 44.5% were female; their mean age was 41.41 years. However, in the general population, hospitalization rates were higher for women than for men (51.2% and 48.8%, respectively). Although the SARS-CoV-2 infectivity rate was higher in healthcare workers compared to the general population (68.6% vs. 56.1%), the mortality rate was significantly lower in them (1.7% vs. 3.8%). Fever, cough, Acute Respiratory Distress Syndrome, headache, and myalgia were the most prevalent symptoms in both groups. Among the cases examined in this study, inpatient ones aged 30-40 years and the general population aged over 60 seemed to be more likely to be hospitalized for Covid-19. The hospitalization rate of healthcare workers during the pandemic follows the same pattern as the general population, but since the start of vaccination, this rate has decreased among healthcare workers compared to the general population of KB province.

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

BACKGROUND

Emerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant data remain comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone.

RESULTS

We present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that it can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and visualize the landscape of fungal pathogenicity. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats.

CONCLUSIONS

The neural networks trained using our data collection enable accurate detection of novel fungal pathogens. A curated set of over 1400 genomes with host and pathogenicity metadata supports training of machine-learning models and sequence comparison, not limited to the pathogen detection task.

AVAILABILITY AND IMPLEMENTATION

The data, models and code are hosted at https://zenodo.org/record/5846345, https://zenodo.org/record/5711877 and https://gitlab.com/dacs-hpi/deepac.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Advances of Broad-Spectrum and Hot Anti-Coronavirus Drugs

Coronaviruses, mainly including severe acute respiratory syndrome virus, severe acute respiratory syndrome coronavirus 2, Middle East respiratory syndrome virus, human coronavirus OC43, chicken infectious bronchitis virus, porcine infectious gastroenteritis virus, porcine epidemic diarrhea virus, and murine hepatitis virus, can cause severe diseases in humans and livestock. The severe acute respiratory syndrome coronavirus 2 is infecting millions of human beings with high morbidity and mortality worldwide, and the multiplicity of swine epidemic diarrhea coronavirus in swine suggests that coronaviruses seriously jeopardize the safety of public health and that therapeutic intervention is urgently needed. Currently, the most effective methods of prevention and control for coronaviruses are vaccine immunization and pharmacotherapy. However, the emergence of mutated viruses reduces the effectiveness of vaccines. In addition, vaccine developments often lag behind, making it difficult to put them into use early in the outbreak. Therefore, it is meaningful to screen safe, cheap, and broad-spectrum antiviral agents for coronaviruses. This review systematically summarizes the mechanisms and state of anti-human and porcine coronavirus drugs, in order to provide theoretical support for the development of anti-coronavirus drugs and other antivirals.

The Evolutionary Dance between Innate Host Antiviral Pathways and SARS-CoV-2

Compared to what we knew at the start of the SARS-CoV-2 global pandemic, our understanding of the interplay between the interferon signaling pathway and SARS-CoV-2 infection has dramatically increased. Innate antiviral strategies range from the direct inhibition of viral components to reprograming the host’s own metabolic pathways to block viral infection. SARS-CoV-2 has also evolved to exploit diverse tactics to overcome immune barriers and successfully infect host cells. Herein, we review the current knowledge of the innate immune signaling pathways triggered by SARS-CoV-2 with a focus on the type I interferon response, as well as the mechanisms by which SARS-CoV-2 impairs those defenses.

Novel deep learning approach to model and predict the spread of COVID-19

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally, producing new variants and has become a pandemic. People have lost their lives not only due to the virus but also because of the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop robust artificial intelligence techniques to predict the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models are trained and tested on publicly available novel coronavirus dataset. The proposed models are evaluated by using Mean Absolute Error and compared with the existing methods for the prediction of the spread of COVID-19. Our experimental results demonstrate the superior prediction performance of the proposed models. The proposed DSPM and NRM achieve MAEs of 388.43 (error rate 1.6%) and 142.23 (0.6%), respectively compared to 6508.22 (27%) achieved by baseline SVM, 891.13 (9.2%) by Time-Series Model (TSM), 615.25 (7.4%) by LSTM-based Data-Driven Estimation Method (DDEM) and 929.72 (8.1%) by Maximum-Hasting Estimation Method (MHEM).

SARS-CoV-2 host prediction based on virus-host genetic features

The genetic diversity of the Coronaviruses gives them different biological abilities, such as infect different cells and/or organisms, a wide spectrum of clinical manifestations, their different routes of dispersion, and viral transmission in a specific host. In recent decades, different Coronaviruses have emerged that are highly adapted for humans and causing serious diseases, leaving their host of unknown origin. The viral genome information is particularly important to enable the recognition of patterns linked to their biological characteristics, such as the specificity in the host-parasite relationship. Here, based on a previously computational tool, the Seq2Hosts, we developed a novel approach which uses new variables obtained from the frequency of spike-Coronaviruses codons, the Relative Synonymous Codon Usage (RSCU) to shed new light on the molecular mechanisms involved in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) host specificity. By using the RSCU obtained from nucleotide sequences before the SARS-CoV-2 pandemic, we assessed the possibility of know the hosts capable to be infected by these new emerging species, which was first identified infecting humans during 2019 in Wuhan, China. According to the model trained and validated using sequences available before the pandemic, bats are the most likely the natural host to the SARS-CoV-2 infection, as previously suggested in other studies that searched for the host viral origin.

Polymeric nanoparticles and nanomicelles of hydroxychloroquine co-loaded with azithromycin potentiate anti-SARS-CoV-2 effect

The outbreak of coronavirus (COVID-19) has put the world in an unprecedented scenario. To reestablish the world routine as promote the effective treatment of this disease, the world is looking for new (and old) drug that can efficiently kill the virus. In this study, we have developed two nanosystems: polymeric nanoparticles and nanomicelles-based on hydroxychloroquine and azithromycin. The nanosystem was fully characterized by AFM and DLS techniques. Also, the nanosystems were radiolabeled with 99mTc and pulmonary applied (installation) in vivo to evaluate the biological behavior. The toxicity of both nanosystem were evaluated in primary cells (FGH). Finally, both nanosystems were evaluated in vitro against the SARS-CoV-2. The results demonstrated that the methodology used to produce the nanomicelles and the nanoparticle was efficient, the characterization showed a nanoparticle with a spherical shape and a medium size of 390 nm and a nanomicelle also with a spherical shape and a medium size of 602 nm. The nanomicelles were more efficient (~ 70%) against SARS-CoV-2 than the nanoparticles. The radiolabeling process with 99mTc was efficient (> 95%) in both nanosystems and the pulmonary application demonstrated to be a viable route for both nanosystems with a local retention time of approximately, 24 h. None of the nanosystems showed cytotoxic effect on FGH cells, even in high doses, corroborating the safety of both nanosystems. Thus, claiming the benefits of the nanotechnology, especially with regard the reduced adverse we believe that the use of nanosystems for COVID-19 treatment can be an optimized choice.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40097-022-00476-3.

Antidepressant and Antipsychotic Drugs Reduce Viral Infection by SARS-CoV-2 and Fluoxetine Shows Antiviral Activity Against the Novel Variants in vitro

Repurposing of currently available drugs is a valuable strategy to tackle the consequences of COVID-19. Recently, several studies have investigated the effect of psychoactive drugs on SARS-CoV-2 in cell culture models as well as in clinical practice. Our aim was to expand these studies and test some of these compounds against newly emerged variants. Several antidepressants and antipsychotic drugs with different primary mechanisms of action were tested in ACE2/TMPRSS2-expressing human embryonic kidney cells against the infection by SARS-CoV-2 spike protein-dependent pseudoviruses. Some of these compounds were also tested in human lung epithelial cell line, Calu-1, against the first wave (B.1) lineage of SARS-CoV-2 and the variants of concern, B.1.1.7, B.1.351, and B.1.617.2. Several clinically used antidepressants, including fluoxetine, citalopram, reboxetine, imipramine, as well as antipsychotic compounds chlorpromazine, flupenthixol, and pimozide inhibited the infection by pseudotyped viruses with minimal effects on cell viability. The antiviral action of several of these drugs was verified in Calu-1 cells against the B.1 lineage of SARS-CoV-2. By contrast, the anticonvulsant carbamazepine, and novel antidepressants ketamine, known as anesthetic at high doses, and its derivatives as well as MAO and phosphodiesterase inhibitors phenelzine and rolipram, respectively, showed no activity in the pseudovirus model. Furthermore, fluoxetine remained effective against pseudoviruses with common receptor binding domain mutations, N501Y, K417N, and E484K, as well as B.1.1.7 (alpha), B.1.351 (beta), and B.1.617.2 (delta) variants of SARS-CoV-2. Our study confirms previous data and extends information on the repurposing of these drugs to counteract SARS-CoV-2 infection including different variants of concern, however, extensive clinical studies must be performed to confirm our in vitro findings.

COVID-19 in Africa: rethinking the tools to manage future pandemics

Corona virus disease 2019 (COVID-19) remains an incurable, progressive pneumonia-like illness characterized by fever, dry cough, fatigue, and headache during its early stages. COVID-19 has ultimately resulted in mortality in at least 2 million people worldwide. Millions of people globally have already been affected by this disease, and the numbers are expected to increase, perhaps until an effective cure or vaccine is identified. Although Africa was initially purported by the World Health Organization (WHO) to be severely hit by the pandemic, Africa recorded the least number of cases during the first wave, with lowest rates of infections, compared to Asia, Europe, and the Americas. This statistic might be attributed to the low testing capacity, existing public health awareness and lessons learnt during Ebola epidemic. Nonetheless, the relatively low rate of infection should be an opportunity for Africa to be better prepared to overcome this and future epidemics. In this paper, the authors provide insights into the dynamics and transmission of the severe acute respiratory syndrome corona virus (SARS-CoV-2) during the first wave of the pandemic; possible explanations into the relatively low rates of infection recorded in Africa; with recommendations for Africa to continue to fight Covid-19; and position itself to effectively manage future pandemics.

Screening of Severe Acute Respiratory Syndrome Coronavirus 2 RNA-Dependent RNA Polymerase Inhibitors Using Computational Approach

The detrimental effect of coronavirus disease 2019 (COVID-19) pandemic has manifested itself as a global crisis. Currently, no specific treatment options are available for COVID-19, so therapeutic interventions to tackle the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection must be urgently established. Therefore, cohesive and multidimensional efforts are required to identify new therapies or investigate the efficacy of small molecules and existing drugs against SARS-CoV-2. Since the RNA-dependent RNA Polymerase (RdRP) of SARS-CoV-2 is a promising therapeutic target, this study addresses the identification of antiviral molecules that can specifically target SARS-CoV-2 RdRP. The computational approach of drug development was used to screen the antiviral molecules from two antiviral libraries (Life Chemicals [LC] and ASINEX) against RdRP. Here, we report six antiviral molecules (F3407-4105, F6523-2250, F6559-0746 from LC and BDG 33693278, BDG 33693315, LAS 34156196 from ASINEX), which show substantial interactions with key amino acid residues of the active site of SARS-CoV-2 RdRP and exhibit higher binding affinity (>7.5 kcalmol-1) than Galidesivir, an Food and Drug Administration-approved inhibitor of the same. Further, molecular dynamics simulation and Molecular Mechanics Poisson-Boltzmann Surface Area results confirmed that identified molecules with RdRP formed higher stable RdRP-inhibitor(s) complex than RdRP-Galidesvir complex. Our findings suggest that these molecules could be potential inhibitors of SARS-CoV-2 RdRP. However, further in vitro and preclinical experiments would be required to validate these potential inhibitors of SARS-CoV-2 protein.

A brief molecular insight of COVID-19: epidemiology, clinical manifestation, molecular mechanism, cellular tropism and immuno-pathogenesis

In December 2019, the emergence and expansion of novel and infectious respiratory virus SARS-CoV-2 originated from Wuhan, China caused an unprecedented threat to the public health and became a global pandemic. SARS-CoV-2 is an enveloped, positive sense and single stranded RNA virus belonging to genera betacoronavirus, of Coronaviridae family. The viral genome sequencing studies revealed 75-80% similarity with SARS-CoV. SARS-CoV-2 mainly affects the lower respiratory system and may progress to pneumonia and Acute Respiratory Distress Syndrome (ARDS). Apart from life-threatening situations and burden on the global healthcare system, the COVID-19 pandemic has imposed several challenges on the worldwide economics and livelihood. The novel pathogen is highly virulent, rapidly mutating and has a tendency to cross the species boundaries such as from bats to humans through the evolution and natural selection from intermediate host. In this review we tried to summarize the overall picture of SARS-CoV-2 including origin/ emergence, epidemiology, pathogenesis, genome organization, comparative analysis with other CoVs, infection and replication mechanism along with cellular tropism and immunopathogenesis which will provide a brief panoramic view about the virus and disease.

Positive selection as a key player for SARS-CoV-2 pathogenicity: Insights into ORF1ab, S and E genes

The human β-coronavirus SARS-CoV-2 epidemic started in late December 2019 in Wuhan, China. It causes Covid-19 disease which has become pandemic. Each of the five-known human β-coronaviruses has four major structural proteins (E, M, N and S) and 16 non-structural proteins encoded by ORF1a and ORF1b together (ORF1ab) that are involved in virus pathogenicity and infectivity. Here, we performed detailed positive selection analyses for those six genes among the four previously known human β-coronaviruses and within 38 SARS-CoV-2 genomes to assess signatures of adaptive evolution using maximum likelihood approaches. Our results suggest that three genes (E, S and ORF1ab genes) are under strong signatures of positive selection among human β-coronavirus, influencing codons that are located in functional important protein domains. The E protein-coding gene showed signatures of positive selection in two sites, Asp 66 and Ser 68, located inside a putative transmembrane α-helical domain C-terminal part, which is preferentially composed by hydrophilic residues. Such Asp and Ser sites substitutions (hydrophilic residues) increase the stability of the transmembrane domain in SARS-CoV-2. Moreover, substitutions in the spike (S) protein S1 N-terminal domain have been found, all of them were located on the S protein surface, suggesting their importance in viral transmissibility and survival. Furthermore, evidence of strong positive selection was detected in three of the SARS-CoV-2 nonstructural proteins (NSP1, NSP3, NSP16), which are encoded by ORF1ab and play vital roles in suppressing host translation machinery, viral replication and transcription and inhibiting the host immune response. These results are insightful to assess the role of positive selection in the SARS-CoV-2 encoded proteins, which will allow to better understand the virulent pathogenicity of the virus and potentially identifying targets for drug or vaccine strategy design.

In Silico Mutagenesis-Based Remodelling of SARS-CoV-1 Peptide (ATLQAIAS) to Inhibit SARS-CoV-2: Structural-Dynamics and Free Energy Calculations

The prolific spread of COVID-19 caused by a novel coronavirus (SARS-CoV-2) from its epicenter in Wuhan, China, to every nook and cranny of the world after December 2019, jeopardize the prevailing health system in the world and has raised serious concerns about human safety. Multi-directional efforts are made to design small molecule inhibitors, and vaccines and many other therapeutic options are practiced, but their final therapeutic potential is still to be tested. Using the old drug or vaccine or peptides could aid this process to avoid such long experimental procedures. Hence, here, we have repurposed a small peptide (ATLQAIAS) from the previous study, which reported the inhibitory effects of this peptide. We used in silico mutagenesis approach to design more peptides from the native wild peptide, which revealed that substitutions (T2W, T2Y, L3R, and A5W) could increase the binding affinity of the peptide towards the 3CLpro. Furthermore, using MD simulation and free energy calculation confirmed its dynamics stability and stronger binding affinities. Per-residue energy decomposition analysis revealed that the specified substitution significantly increased the binding affinity at the residue level. Our wide-ranging analyses of binding affinities disclosed that our designed peptide owns the potential to hinder the SARS-CoV-2 and will reduce the progression of SARS-CoV-2-borne pneumonia. Our research strongly suggests the experimental and clinical validation of these peptides to curtail the recent corona outbreak.

Update on the Phylodynamics of SADS-CoV

Coronaviruses are known to be harmful and heterogeneous viruses, able to infect a large number of hosts. Among them, SADS-CoV (Swine Acute Diarrhea Syndrome Coronavirus), also known as PEAV (Porcine Enteric Alphacoronavirus), or SeA-CoV (Swine Enteric Alphacoronavirus), is the most recent Alphacoronavirus discovered, and caused several outbreaks reported in Chinese swine herds between late 2016 and 2019. We performed an upgraded phylodinamic reconstruction of SADS-CoV based on all whole genomes available on 21 June 2021. Results showed a very close relationship between SADS-CoV and HKU2-like CoV, which may represent the evolutionary intermediate step towards the present SADS-CoV. The direct progenitor of SADS-CoV is so far unknown and, although it is well known that horseshoe bats are reservoirs for Rhinolophus bat coronavirus HKU2-like (HKU2-like CoVs), the transmission path from bats to pigs is still unclear. The discrepancies in the phylogenetic position of rodent CoV, when different molecular markers were considered, corroborate the recombination hypothesis, suggesting that wild rats, which are frequent in farms, may have played a key role. The failure of the attempt at molecular dating, due to the lack of a clock signal, also corroborates the occurrence of a recombination event hypothesis. Zoonotic infections originating in wildlife can easily become a significant threat for human health. In such a context, due to the high recombination and cross-species capabilities of Coronavirus, SADS-CoV represents a possible high-risk pathogen for humans which needs a constant molecular monitoring.

COVID-19 challenges and its therapeutics

COVID-19, an infectious disease, has emerged as one of the leading causes of death worldwide, making it one of the severe public health issues in recent decades. nCoV, the novel SARS coronavirus that causes COVID-19, has brought together scientists in the quest for possible therapeutic and preventive measures. The development of new drugs to manage COVID-19 effectively is a challenging and time-consuming process, thus encouraging extensive investigation of drug repurposing and repositioning candidates. Several medications, including remdesivir, hydroxychloroquine, chloroquine, lopinavir, favipiravir, ribavirin, ritonavir, interferons, azithromycin, capivasertib and bevacizumab, are currently under clinical trials for COVID-19. In addition, several medicinal plants with considerable antiviral activities are potential therapeutic candidates for COVID-19. Statistical data show that the pandemic is yet to slow down, and authorities are placing their hopes on vaccines. Within a short period, four types of vaccines, namely, whole virus, viral vector, protein subunit, and nucleic acid (RNA/DNA), which can confer protection against COVID-19 in different ways, were already in a clinical trial. SARS-CoV-2 variants spread is associated with antibody escape from the virus Spike epitopes, which has grave concerns for viral re-infection and even compromises the effectiveness of the vaccines. Despite these efforts, COVID-19 treatment is still solely based on clinical management through supportive care. We aim to highlight the recent trends in COVID-19, relevant statistics, and clinical findings, as well as potential therapeutics, including in-line treatment methods, preventive measures, and vaccines to combat the prevalence of COVID-19.

Non-RBM Mutations Impaired SARS-CoV-2 Spike Protein Regulated to the ACE2 Receptor Based on Molecular Dynamic Simulation

The emergence of novel coronavirus mutants is a main factor behind the deterioration of the epidemic situation. Further studies into the pathogenicity of these mutants are thus urgently needed. Binding of the spinous protein receptor binding domain (RBD) of SARS-CoV-2 to the angiotensin-converting enzyme 2 (ACE2) receptor was shown to initiate coronavirus entry into host cells and lead to their infection. The receptor-binding motif (RBM, 438-506) is a region that directly interacts with ACE2 receptor in the RBD and plays a crucial role in determining affinity. To unravel how mutations in the non-RBM regions impact the interaction between RBD and ACE2, we selected three non-RBM mutant systems (N354D, D364Y, and V367F) from the documented clinical cases, and the Q498A mutant system located in the RBM region served as the control. Molecular dynamics simulation was conducted on the mutant systems and the wild-type (WT) system, and verified experiments also performed. Non-RBM mutations have been shown not only to change conformation of the RBM region but also to significantly influence its hydrogen bonding and hydrophobic interactions. In particular, the D364Y and V367F systems showed a higher affinity for ACE2 owing to their electrostatic interactions and polar solvation energy changes. In addition, although the binding free energy at this point increased after the mutation of N354D, the conformation of the random coil (Pro384-Asp389) was looser than that of other systems, and the combined effect weakened the binding free energy between RBD and ACE2. Interestingly, we also found a random coil (Ala475-Gly485). This random coil is very sensitive to mutations, and both types of mutations increase the binding free energy of residues in this region. We found that the binding loop (Tyr495-Tyr505) in the RBD domain strongly binds to Lys353, an important residue of the ACE2 domain previously identified. The binding free energy of the non-RBM mutant group at the binding loop had positive and negative changes, and these changes were more obvious than that of the Q498A system. The results of this study elucidate the effect of non-RBM mutation on ACE2-RBD binding, and provide new insights for SARS-CoV-2 mutation research.

Clinical manifestation, laboratory and radiology finding, treatment and outcomes of COVID-19: A systematic review and meta-analysis

BACKGROUND

Since December 2019, coronavirus (COVID-19) spread throughout the world. The high rate of infection and its unknown nature led specialists to report the condition of patients. The aim of this study is to systematically review of symptoms, laboratory and radiologic findings, treatment, and outcomes of patients with COVID-19.

MATERIALS AND METHODS

Databases such as PubMed, Embase, Scopus, Web of Science, Google Scholar, and Cochrane were searched. Finally, 46 articles were appropriate for the aim of the study. After quality evaluation, the necessary data were extracted and meta-analysis was performed.

RESULTS

4858 articles were retrieved until March 30, 2020. After screening, the full-text of 46 articles was assessed. Of the reported cases, 31.7% had no comorbidities, 21.4% had high blood pressure, 70.6% had fever, and lymphopenia was reported in 55.2% of patients. For 16% bilateral patchy shadowing in radiography and for 51% ground-glass opacity was reported. Outcomes were remarkable for recover to death.

CONCLUSION

COVID-19 leads to healthcare problems for countries. Nonspecific symptoms have made it difficult for differential diagnoses without computed tomography-scan or corona Test, but they are not available in many countries. Therefore, this systematic review can help health care staff to make decisions based on symptoms, treatments, and outcomes..

Application of machine learning in bacteriophage research

Phages are one of the key components in the structure, dynamics, and interactions of microbial communities in different bins. It has a clear impact on human health and the food industry. Bacteriophage characterization using in vitro approaches are time/cost consuming and laborious tasks. On the other hand, with the advent of new high-throughput sequencing technology, the development of a powerful computational framework to characterize the newly identified bacteriophages is inevitable for future research. Machine learning includes powerful techniques that enable the analysis of complex datasets for knowledge discovery and pattern recognition. In this study, we have conducted a comprehensive review of machine learning methods application using different types of features were applied in various aspects of bacteriophage research including, automated curation, identification, classification, host species recognition, virion protein identification, and life cycle prediction. Moreover, potential limitations and advantages of the developed frameworks were discussed.

Molecular biology of the SARs-CoV-2 spike protein: A review of current knowledge

The global coronavirus disease 2019 (COVID‐19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), has led to an unprecedented worldwide public health emergency. Despite the concerted efforts of the scientific field, by April 25, 2021, SARS‐CoV‐2 had spread to over 192 countries/regions, causing more than 146 million confirmed cases including 31 million deaths. For now, an established treatment for patients with COVID‐19 remains unavailable. The key to tackling this pandemic is to understand the mechanisms underlying its infectivity and pathogenicity. As a predominant focus, the coronavirus spike (S) protein is the key determinant of host range, infectivity, and pathogenesis. Thereby comprehensive understanding of the sophisticated structure of SARS‐CoV‐2 S protein may provide insights into possible intervention strategies to fight this ongoing global pandemic. Herein, we summarize the current knowledge of the molecular structural and functional features of SARS‐CoV‐2 S protein as well as recent updates on the cell entry mechanism of the SARS‐CoV‐2, paving the way for exploring more structure‐guided strategies against SARS‐CoV‐2.

SARS-CoV-2: An Overview of Virus Genetics, Transmission, and Immunopathogenesis

The human population is currently facing the third and possibly the worst pandemic caused by human coronaviruses (CoVs). The virus was first reported in Wuhan, China, on 31 December 2019 and spread within a short time to almost all countries of the world. Genome analysis of the early virus isolates has revealed high similarity with SARS-CoV and hence the new virus was officially named SARS-CoV-2. Since CoVs have the largest genome among all RNA viruses, they can adapt to many point mutation and recombination events; particularly in the spike gene, which enable these viruses to rapidly change and evolve in nature. CoVs are known to cross the species boundaries by using different cellular receptors. Both animal reservoir and intermediate host for SARS-CoV-2 are still unresolved and necessitate further investigation. In the current review, different aspects of SARS-CoV-2 biology and pathogenicity are discussed, including virus genetics and evolution, spike protein and its role in evolution and adaptation to novel hosts, and virus transmission and persistence in nature. In addition, the immune response developed during SARS-CoV-2 infection is demonstrated with special reference to the interplay between immune cells and their role in disease progression. We believe that the SARS-CoV-2 outbreak will not be the last and spillover of CoVs from bats will continue. Therefore, establishing intervention approaches to reduce the likelihood of future CoVs spillover from natural reservoirs is a priority.

Immunogenomics guided design of immunomodulatory multi-epitope subunit vaccine against the SARS-CoV-2 new variants, and its validation through in silico cloning and immune simulation

Reports of the novel and more contagious strains of SARS-CoV-2 originating in different countries have further aggravated the pandemic situation. The recent substitutions in spike protein may be critical for the virus to evade the host's immune system and therapeutics that have already been developed. Thus, this study has employed an immunoinformatics pipeline to target the spike protein of this novel strain to construct an immunogenic epitope (CTL, HTL, and B cell) vaccine against the new variant. Our investigation revealed that 12 different epitopes imparted a critical role in immune response induction. This was validated by an exploration of physiochemical properties and experimental feasibility. In silico and host immune simulation confirmed the expression and induction of both primary and secondary immune factors such as IL, cytokines, and antibodies. The current study warrants further lab experiments to demonstrate its efficacy and safety.

Exploring SARS-COV-2 structural proteins to design a multi-epitope vaccine using immunoinformatics approach: An in silico study

In December 2019, a new virus called SARS-CoV-2 was reported in China and quickly spread to other parts of the world. The development of SARS-COV-2 vaccines has recently received much attention from numerous researchers. The present study aims to design an effective multi-epitope vaccine against SARS-COV-2 using the reverse vaccinology method. In this regard, structural proteins from SARS-COV-2, including the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins, were selected as target antigens for epitope prediction. A total of five helper T lymphocytes (HTL) and five cytotoxic T lymphocytes (CTL) epitopes were selected after screening the predicted epitopes for antigenicity, allergenicity, and toxicity. Subsequently, the selected HTL and CTL epitopes were fused via flexible linkers. Next, the cholera toxin B-subunit (CTxB) as an adjuvant was linked to the N-terminal of the chimeric structure. The proposed vaccine was analyzed for the properties of physicochemical, antigenicity, and allergenicity. The 3D model of the vaccine construct was predicted and docked with the Toll-like receptor 4 (TLR4). The molecular dynamics (MD) simulation was performed to evaluate the stable interactions between the vaccine construct and TLR4. The immune simulation was also conducted to explore the immune responses induced by the vaccine. Finally, in silico cloning of the vaccine construct into the pET-28 (+) vector was conducted. The results obtained from all bioinformatics analysis stages were satisfactory; however, in vitro and in vivo tests are essential to validate these results.

Fundamental and Advanced Therapies, Vaccine Development against SARS-CoV-2

Coronavirus disease (COVID-19) caused by the SARS-CoV-2 virus has been affecting the world since the end of 2019. The severity of the disease can range from an asymptomatic or mild course to acute respiratory distress syndrome (ARDS) with respiratory failure, which may lead to death. Since the outbreak of the pandemic, scientists around the world have been studying the genome and molecular mechanisms of SARS-CoV-2 infection to develop effective therapies and prevention. In this review, we summarize the progressive development of various treatments and vaccines as they have emerged, a year after the outbreak of the pandemic. Initially for COVID-19, patients were recommended drugs with presumed antiviral, anti-inflammatory, and antimicrobial effects that were previously used to treat other diseases. Thereafter, therapeutic interventions were supplemented with promising approaches based on antibodies, peptides, and stem cells. However, licensed COVID-19 vaccines remain the most effective weapon in combating the pandemic. While there is an enormous effort to enhance the vaccination rate to increase the entire population immunity, the production and delivery of vaccines is becoming limited in several countries. In this regard, there are new challenges needing to be addressed by combining non-pharmacological intervention with effective therapies until vaccination is accessible to all.

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.

Application of Bayesian phylogenetic inference modelling for evolutionary genetic analysis and dynamic changes in 2019-nCoV

The novel coronavirus (2019-nCoV) has recently caused a large-scale outbreak of viral pneumonia both in China and worldwide. In this study, we obtained the entire genome sequence of 777 new coronavirus strains as of 29 February 2020 from a public gene bank. Bioinformatics analysis of these strains indicated that the mutation rate of these new coronaviruses is not high at present, similar to the mutation rate of the severe acute respiratory syndrome (SARS) virus. The similarities of 2019-nCoV and SARS virus suggested that the S and ORF6 proteins shared a low similarity, while the E protein shared the higher similarity. The 2019-nCoV sequence has similar potential phosphorylation sites and glycosylation sites on the surface protein and the ORF1ab polyprotein as the SARS virus; however, there are differences in potential modification sites between the Chinese strain and some American strains. At the same time, we proposed two possible recombination sites for 2019-nCoV. Based on the results of the skyline, we speculate that the activity of the gene population of 2019-nCoV may be before the end of 2019. As the scope of the 2019-nCoV infection further expands, it may produce different adaptive evolutions due to different environments. Finally, evolutionary genetic analysis can be a useful resource for studying the spread and virulence of 2019-nCoV, which are essential aspects of preventive and precise medicine.

Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changes

There are currently limited Food and Drug Administration (FDA)-approved drugs and vaccines for the treatment or prevention of Coronavirus Disease 2019 (COVID-19). Enhanced understanding of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and pathogenesis is critical for the development of therapeutics. To provide insight into viral replication, cell tropism, and host-viral interactions of SARS-CoV-2, we performed single-cell (sc) RNA sequencing (RNA-seq) of experimentally infected human bronchial epithelial cells (HBECs) in air-liquid interface (ALI) cultures over a time course. This revealed novel polyadenylated viral transcripts and highlighted ciliated cells as a major target at the onset of infection, which we confirmed by electron and immunofluorescence microscopy. Over the course of infection, the cell tropism of SARS-CoV-2 expands to other epithelial cell types including basal and club cells. Infection induces cell-intrinsic expression of type I and type III interferons (IFNs) and interleukin (IL)-6 but not IL-1. This results in expression of interferon-stimulated genes (ISGs) in both infected and bystander cells. This provides a detailed characterization of genes, cell types, and cell state changes associated with SARS-CoV-2 infection in the human airway.

The Impact of SARS-CoV-2 on the Human Immune System and Microbiome

A recent outbreak of coronavirus disease 2019 (COVID-19) caused by the single-stranded enveloped RNA virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has developed into a global pandemic, after it was first reported in Wuhan in December 2019. SARS-CoV-2 is an emerging virus, and little is known about the basic characteristics of this pathogen, the underlying mechanism of infection, and the potential treatments. The immune system has been known to be actively involved in viral infections. To facilitate the development of COVID-19 treatments, the understanding of immune regulation by this viral infection is urgently needed. This review describes the mechanisms of immune system involvement in viral infections and provides an overview of the dysregulation of immune responses in COVID-19 patients in recent studies. Furthermore, we emphasize the role of gut microbiota in regulating immunity and summarized the impact of SARS-CoV-2 infection on the composition of the microbiome. Overall, this review provides insights for understanding and developing preventive and therapeutic strategies by regulating the immune system and microbiota.

Digestive system involvement of infections with SARS-CoV-2 and other coronaviruses: Clinical manifestations and potential mechanisms

Although coronavirus (CoV) infection is often characterized by respiratory symptoms, the virus can also result in extrapulmonary symptoms, especially the symptoms related to the digestive system. The outbreak of coronavirus disease 2019 (COVID-19) is currently the world's most pressing public health threat and has a significant impact on civil societies and the global economy. The occurrence of digestive symptoms in patients with COVID-19 is closely related to the development and prognosis of the disease. Moreover, thus far, there are no specific antiviral drug or vaccine approved for the treatment or prevention of COVID-19. Therefore, we elaborate on the effects of CoVs on the digestive system and the potential underlying mechanisms.

Genome-wide in silico identification and characterization of Simple Sequence Repeats in diverse completed SARS-CoV-2 genomes

Simple sequence repeats (SSRs) or, Microsatellites are short repeat sequences that have been extensively studied in eukaryotic (plants) and prokaryotic (bacteria) organisms. Compared to other organisms, the presence and incidence of SSR on viral genomes are less studied. With the emergence of novel infectious viruses over the past few decades, it is imperative to study the genetic diversity in such viruses to predict their evolutionary and functional changes over time. Following the emergence of SARS-CoV-2, we have assembled 121 complete genomes reported from 31 countries across the six continents for the identification and characterization of SSR repeats. Using two independent SSR identification tools, we have found remarkable consistency in the diversity of microsatellites pattern (38–42 per genome) found in the 121 analyzed SARS-CoV-2 genomes indication their important role for genome stability. Among the identified motifs, trinucleotide and hexanucleotide repeats were found to be the most abundant form followed by mono- and di-nucleotide. There were no tetra- or penta-nucleotide repeats in the analyzed SARS-CoV-2 genomes. The discovery of microsatellites in SARS-CoV-2 genomes may become useful for the population genetics, evolutionary analysis, strain identification and genetic variation.

Genome-wide CRISPR Screens Reveal Host Factors Critical for SARS-CoV-2 Infection

Identification of host genes essential for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may reveal novel therapeutic targets and inform our understanding of coronavirus disease 2019 (COVID-19) pathogenesis. Here we performed genome-wide CRISPR screens in Vero-E6 cells with SARS-CoV-2, Middle East respiratory syndrome CoV (MERS-CoV), bat CoV HKU5 expressing the SARS-CoV-1 spike, and vesicular stomatitis virus (VSV) expressing the SARS-CoV-2 spike. We identified known SARS-CoV-2 host factors, including the receptor ACE2 and protease Cathepsin L. We additionally discovered pro-viral genes and pathways, including HMGB1 and the SWI/SNF chromatin remodeling complex, that are SARS lineage and pan-coronavirus specific, respectively. We show that HMGB1 regulates ACE2 expression and is critical for entry of SARS-CoV-2, SARS-CoV-1, and NL63. We also show that small-molecule antagonists of identified gene products inhibited SARS-CoV-2 infection in monkey and human cells, demonstrating the conserved role of these genetic hits across species. This identifies potential therapeutic targets for SARS-CoV-2 and reveals SARS lineage-specific and pan-CoV host factors that regulate susceptibility to highly pathogenic CoVs.

Recent Developments in SARS-CoV-2 Neutralizing Antibody Detection Methods

The ongoing Coronavirus disease 19 pandemic has likely changed the world in ways not seen in the past. Neutralizing antibody (NAb) assays play an important role in the management of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak. Using these tools, we can assess the presence and duration of antibody-mediated protection in naturally infected individuals, screen convalescent plasma preparations for donation, test the efficacy of immunotherapy, and analyze NAb titers and persistence after vaccination to predict vaccine-induced protective effects. This review briefly summarizes the various methods used for the detection of SARS-CoV-2 NAbs and compares their advantages and disadvantages to facilitate their development and clinical application.

Structural Characterization of SARS-CoV-2: Where We Are, and Where We Need to Be

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread in humans in almost every country, causing the disease COVID-19. Since the start of the COVID-19 pandemic, research efforts have been strongly directed towards obtaining a full understanding of the biology of the viral infection, in order to develop a vaccine and therapeutic approaches. In particular, structural studies have allowed to comprehend the molecular basis underlying the role of many of the SARS-CoV-2 proteins, and to make rapid progress towards treatment and preventive therapeutics. Despite the great advances that have been provided by these studies, many knowledge gaps on the biology and molecular basis of SARS-CoV-2 infection still remain. Filling these gaps will be the key to tackle this pandemic, through development of effective treatments and specific vaccination strategies.

Clinical characteristics of 41 patients with pneumonia due to 2019 novel coronavirus disease (COVID-19) in Jilin, China

BACKGROUND

The clinical characteristics of patients with confirmed 2019 novel coronavirus disease (COVID-19) in Jilin Province, China were investigated.

METHODS

Clinical, laboratory, radiology, and treatment data of 41 hospitalized patients with confirmed COVID-19 were retrospectively collected. The population was stratified by disease severity as mild, moderate, or severe, based on guidelines of the National Health and Medical Commission of China.

RESULTS

The 41 hospitalized patients with COVID-19 were studied, and the median age was 45 years (interquartile range [IQR], 31-53; range, 10-87 years) and 18 patients (43.9%) were female. All of the patients had recently visited Wuhan or other places (ie, Beijing, Thailand) or had Wuhan-related exposure. Common symptoms included fever (32[78%]) and cough (29[70.7%]). All patients were without hepatitis B/C virus hepatitis. CRP (C-reactive protein, 11.3 mg/L [interquartile range {IQR}, 2.45-35.2]) was elevated in 22 patients (53.7%), and cardiac troponin I (1.5 ng/mL [IQR, 0.8-5.0]) was elevated in 41 patients (100%). Chest computed tomographic scans showed bilateral ground glass opacity (GGO) or GGO with consolidation in the lungs of 27(65.9%) patients. 31(75.6%) patients had an abnormal electrocardiograph (ECG). Comparing the three groups, the levels of CRP and cardiac troponin I, GGO distribution in bilateral lungs, and electrocardiogram changes were statistically significant (p < 0.05). Cardiac troponin I had a strong positive correlation with CRP (r = 0.704, p = 0.042) and LDH (r = 0.738, p = 0.037).

CONCLUSION

Significant differences among the groups suggest that several clinical parameters may serve as biomarkers of COVID-19 severity at hospital admission. Elevated cTnI could be considered as a predictor of severe COVID-19, reflecting the prognosis of patients with severe COVID-19. The results warrant further inspection and confirmation.