Variants in SARS-CoV-2 associated with mild or severe outcome

Structure and dynamics of whole-sequence homology model of ORF3a protein of SARS-CoV-2: An insight from microsecond molecular dynamics simulations

The ORF3a is a large accessory protein in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which plays an important role in virulence and viral replication; especially in inflammasome activation and apoptosis. However,, the existing cryo-EM structure of SARS-CoV-2 ORF3a is incomplete, . making it challenging to understand its structural and functional features. The aim of this study is to investigate the dynamic behaviors of the full-sequence homology model of ORF3a and compare it with the cryo-EM structure using microsecond molecular dynamics simulations. The previous studies indicated that the unresolved residues of the cryo-EM structure are not only involved in the pathogenesis of the SARS-CoV-2 but also exhibit a significant antigenicity. The dynamics scenario of homology model revealed higher RMSD, Rg, and SASA values with stable pattern when compared to the cryo-EM structure. Moreover, the RMSF analysis demonstrated higher fluctuations at specific positions (1-43, 97-110, 172-180, 219-243) in the model structure, whereas the cryo-EM structure displayed lower overall drift (except 1-43) in comparison to the model structure.Secondary structural features indicated that a significant unfolding in the transmembrane domains and β-strand at positions 166 to 172, affecting the stability and compactness of the cryo-EM structure , whereas the model exhibited noticeable unfolding in transmembrane domains and small-coiled regions in the N-terminal. , The results from molecular docking and steered molecular dynamics investigations showed the model structure had a greater number of non-bonding interactions, leading to enhanced stability when compared to the cryo-EM structure. Consequently, higher forces were necessary for unbinding of the baricitinib and ruxolitinib inhibitors from the model structure.. Our findings can help better understanding of the significance of unresolved residues at the molecular level. Additionally, this information can guide researchers for experimental endeavors aimed at completing the full-sequence structure of the ORF3a.Communicated by Ramaswamy H. Sarma.

Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm

Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.

COVID-19 and Cardiomyopathy in African Americans: An Early Single-Center Experience

Introduction The 2019 coronavirus pandemic has taken a toll on our society. Although most patients report minimal symptoms, a small proportion of patients have reported significant respiratory symptoms that led to admission to the inpatient medical ward or even the intensive care unit. Complications and long-term sequela of COVID-19 are still being reported and studied. The presence of cardiomyopathy, whether established or new-onset and its effect on inpatient mortality, admission to the intensive care unit or length of stay hasn't been studied.  Methods All inpatient hospitalizations in our database between March 1, 2020, and April 30, 2020, due to COVID-19 were reviewed. Patients who had at least a limited echocardiogram during this time were included in the study if they were above the age of 18. Patients were then assigned to three groups. The first group had patients with normal left ventricular systolic function. The second group had established cardiomyopathy that persisted throughout admission. The third group had patients who were found to have new-onset cardiomyopathy during admission.  Results The inpatient mortality, although high and variable, wasn't significantly different between the three groups. Also, there was no significant difference between admission to the intensive care unit, disposition at discharge, or oxygenation status at 24 hours between the three groups. The length of stay in the established cardiomyopathy group was markedly lower, and we suspect that could be due to more aggressive discussions about end-of-life care.  Conclusion Early COVID-19 experience at our center revealed a relatively high mortality rate that was primarily due to respiratory failure. The presence of established or new cardiomyopathy didn't appear to alter the outcomes significantly early in the pandemic.

Drug Cocktail Formulation via Circuit Design

Electronic circuits intuitively visualize and quantitatively simulate biological systems with nonlinear differential equations that exhibit complicated dynamics. Drug cocktail therapies are a powerful tool against diseases that exhibit such dynamics. We show that just six key states, which are represented in a feedback circuit, enable drug-cocktail formulation: 1) healthy cell number; 2) infected cell number; 3) extracellular pathogen number; 4) intracellular pathogenic molecule number; 5) innate immune system strength; and 6) adaptive immune system strength. To enable drug cocktail formulation, the model represents the effects of the drugs in the circuit. For example, a nonlinear feedback circuit model fits measured clinical data, represents cytokine storm and adaptive autoimmune behavior, and accounts for age, sex, and variant effects for SARS-CoV-2 with few free parameters. The latter circuit model provided three quantitative insights on the optimal timing and dosage of drug components in a cocktail: 1) antipathogenic drugs should be given early in the infection, but immunosuppressant timing involves a tradeoff between controlling pathogen load and mitigating inflammation; 2) both within and across-class combinations of drugs have synergistic effects; 3) if they are administered sufficiently early in the infection, anti-pathogenic drugs are more effective at mitigating autoimmune behavior than immunosuppressant drugs.

Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity

Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data.

Statistical analysis supports UTR (untranslated region) deletion theory in SARS-CoV-2

It was noticed that the mortality rate of SARS-CoV-2 infection experienced a significant declination in the early stage of the epidemic. We suspect that the sharp deterioration of virus toxicity is related to the deletion of the untranslated region (UTR) of the virus genome. It was found that the genome length of SARS-CoV-2 engaged a significant truncation due to UTR deletion after a mega-sequence analysis. Sequence similarity analysis further indicated that short UTR strains originated from its long UTR ancestors after an irreversible deletion. A good correlation was discovered between genome length and mortality, which demonstrated that the deletion of the virus UTR significantly affected the toxicity of the virus. This correlation was further confirmed in a significance analysis of the genetic influence on the clinical outcomes. The viral genome length of hospitalized patients was significantly more extensive than that of asymptomatic patients. In contrast, the viral genome length of asymptomatic was considerably longer than that of ordinary patients with symptoms. A genome-level mutation scanning was performed to systematically evaluate the influence of mutations at each position on virulence. The results indicated that UTR deletion was the primary driving force in alternating virus virulence in the early evolution. In the end, we proposed a mathematical model to explain why this UTR deletion was not continuous.

Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning

Epidemiological studies show that COVID-19 variants-of-concern, like Delta and Omicron, pose different risks for severe disease, but they typically lack sequence-level information for the virus. Studies which do obtain viral genome sequences are generally limited in time, location, and population scope. Retrospective meta-analyses require time-consuming data extraction from heterogeneous formats and are limited to publicly available reports. Fortuitously, a subset of GISAID, the global SARS-CoV-2 sequence repository, includes "patient status" metadata that can indicate whether a sequence record is associated with mild or severe disease. While GISAID lacks data on comorbidities relevant to severity, such as obesity and chronic disease, it does include metadata for age and sex to use as additional attributes in modeling. With these caveats, previous efforts have demonstrated that genotype-patient status models can be fit to GISAID data, particularly when country-of-origin is used as an additional feature. But are these models robust and biologically meaningful? This paper shows that, in fact, temporal and geographic biases in sequences submitted to GISAID, as well as the evolving pandemic response, particularly reduction in severe disease due to vaccination, create complex issues for model development and interpretation. This paper poses a potential solution: efficient mixed effects machine learning using GPBoost, treating country as a random effect group. Training and validation using temporally split GISAID data and emerging Omicron variants demonstrates that GPBoost models are more predictive of the impact of spike protein mutations on patient outcomes than fixed effect XGBoost, LightGBM, random forests, and elastic net logistic regression models.

SARS-CoV-2 Amino Acid Mutations Detection in Greek Patients Infected in the First Wave of the Pandemic

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel virus that belongs to the Coronoviridae family, emerged in December 2019, causing the COVID-19 pandemic in March 2020. Unlike previous SARS and Middle East respiratory syndrome (MERS) outbreaks, this virus has a higher transmissibility rate, albeit a lower case fatality rate, which results in accumulation of a significant number of mutations and a faster evolution rate. Genomic studies on the mutation rate of the virus, as well as the identification of mutations that prevail and their impact on disease severity, are of great importance for pandemic surveillance and vaccine and drug development. Here, we aim to identify mutations on the SARS-CoV-2 viral genome and their effect on the proteins they are located in, in Greek patients infected in the first wave of the pandemic. To this end, we perform SARS-CoV-2 amplicon-based NGS sequencing on nasopharyngeal swab samples from Greek patients and bioinformatic analysis of the results. Although SARS-CoV-2 is considered genetically stable, we discover a variety of mutations on the viral genome. In detail, 18 mutations are detected in total on 10 SARS-CoV-2 isolates. The mutations are located on ORF1ab, S protein, M protein, ORF3a and ORF7a. Sixteen are also detected in patients from other regions around the world, and two are identified for the first time in the present study. Most of them result in amino acid substitutions. These substitutions are analyzed using computational tools, and the results indicate minor or major impact on the proteins’ structural stability, which could probably affect viral transmissibility and pathogenesis. The correlation of these variations with the viral load levels is examined, and their implication for disease severity and the biology of the virus are discussed.

SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load

The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be "severe" (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the "mild" category (severity probability <0.5) had an average Ct of 20.4 (P=0.0017). We also found a nontrivial correlation between predicted severity probability and cycle threshold (r = -0.199). Finally, when divided into severity probability quartiles, the group most likely to experience severe illness (≥75% probability) had a Ct of 16.6 (n = 10), whereas the group least likely to experience severe illness (<25% probability) had a Ct of 21.4 (n = 350) (P=0.0045). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to clinical diagnostic tests and that relative severity may be inferred from diagnostic values.

Impact of SARS-CoV-2 Mutations on PCR Assay Sequence Alignment

Real-time reverse transcription polymerase chain reaction (RT-PCR) assays are the most widely used molecular tests for the detection of SARS-CoV-2 and diagnosis of COVID-19 in clinical samples. PCR assays target unique genomic RNA regions to identify SARS-CoV-2 with high sensitivity and specificity. In general, assay development incorporates the whole genome sequences available at design time to be inclusive of all target species and exclusive of near neighbors. However, rapid accumulation of mutations in viral genomes during sustained growth in the population can result in signature erosion and assay failures, creating situational blind spots during a pandemic. In this study, we analyzed the signatures of 43 PCR assays distributed across the genome against over 1.6 million SARS-CoV-2 sequences. We present evidence of significant signature erosion emerging in just two assays due to mutations, while adequate sequence identity was preserved in the other 41 assays. Failure of more than one assay against a given variant sequence was rare and mostly occurred in the two assays noted to have signature erosion. Assays tended to be designed in regions with statistically higher mutations rates. in silico analyses over time can provide insights into mutation trends and alert users to the emergence of novel variants that are present in the population at low proportions before they become dominant. Such routine assessment can also potentially highlight false negatives in test samples that may be indicative of mutations having functional consequences in the form of vaccine and therapeutic failures. This study highlights the importance of whole genome sequencing and expanded real-time monitoring of diagnostic PCR assays during a pandemic.

Mutations in the genome of severe acute respiratory syndrome coronavirus 2: implications for COVID-19 severity and progression

Coronaviridae is a large family of enveloped, positive-strand RNA viruses that has plagued the world since it was discovered in humans in the 1960s. The recent severe acute respiratory syndrome coronavirus (SARS-CoV)-2 pandemic has already exceeded the number of combined cases and deaths witnessed during previous SARS-CoV and Middle East respiratory syndrome-CoV epidemics in the last two decades. This narrative review focuses on genomic mutations in SARS-CoV-2 and their impact on the severity and progression of COVID-19 in light of reported data in the literature. Notable SARS-CoV-2 mutations associated with open reading frames, the S glycoprotein, and nucleocapsid protein, currently circulating globally, are discussed along with emerging mutations such as those in the SARS-CoV-2 VUI 202012/01 variant in the UK and other European countries, the 484K.V2 and P.1 variants in Brazil, the B.1.617 variant in India, and South African variants 501Y.V2 and B.1.1.529 (omicron). These variants have the potential to influence the receptor binding domain, host-virus fusion, and SARS-CoV-2 replication. Correlating these mutations with disease dynamics could help us understand their pathogenicity and design appropriate therapeutics.

Conserved Molecular Signatures in the Spike, Nucleocapsid, and Polymerase Proteins Specific for the Genus Betacoronavirus and Its Different Subgenera

The genus Betacoronavirus, consisting of four main subgenera (Embecovirus, Merbecovirus, Nobecovirus, and Sarbecovirus), encompasses all clinically significant coronaviruses (CoVs), including SARS, MERS, and the SARS-CoV-2 virus responsible for current COVID-19 pandemic. Very few molecular characteristics are known that are specific for the genus Betacoronavirus or its different subgenera. In this study, our analyses of the sequences of four essential proteins of CoVs, viz., spike, nucleocapsid, envelope, and RNA-dependent RNA polymerase (RdRp), identified ten novel molecular signatures consisting of conserved signature indels (CSIs) in these proteins which are specific for the genus Betacoronavirus or its subgenera. Of these CSIs, two 14-aa-conserved deletions found within the heptad repeat motifs 1 and 2 of the spike protein are specific for all betacoronaviruses, except for their shared presence in the highly infectious avian coronavirus. Six additional CSIs present in the nucleocapsid protein and one CSI in the RdRp protein are distinctive characteristics of either the Merbecovirus, Nobecovirus, or Sarbecovirus subgenera. In addition, a 4-aa insert is present in the spike protein, which is uniquely shared by all viruses from the subgenera Merbecovirus, Nobecovirus, and Sarbecovirus, but absent in Embecovirus and all other genera of CoVs. This molecular signature provides evidence that viruses from the three subgenera sharing this CSI are more closely related to each other, and they evolved after the divergence of embecoviruses and other CoVs. As all CSIs specific for different groups of CoVs are flanked by conserved regions, their sequences provide novel means for identifying the above groups of CoVs and for developing novel diagnostic tests. Furthermore, our analyses of the structures of the spike and nucleocapsid proteins show that all identified CSIs are localized in the surface-exposed loops of these protein. It is postulated that these surface loops, through their interactions with other cellular proteins/ligands, play important roles in the biology/pathology of these viruses.

Rapid SARS-CoV-2 Intra-Host and Within-Household Emergence of Novel Haplotypes

In February 2020, the municipality of Vo', a small town near Padua (Italy) was quarantined due to the first coronavirus disease 19 (COVID-19)-related death detected in Italy. To investigate the viral prevalence and clinical features, the entire population was swab tested in two sequential surveys. Here we report the analysis of 87 viral genomes, which revealed that the unique ancestor haplotype introduced in Vo' belongs to lineage B, carrying the mutations G11083T and G26144T. The viral sequences allowed us to investigate the viral evolution while being transmitted within and across households and the effectiveness of the non-pharmaceutical interventions implemented in Vo'. We report, for the first time, evidence that novel viral haplotypes can naturally arise intra-host within an interval as short as two weeks, in approximately 30% of the infected individuals, regardless of symptom severity or immune system deficiencies. Moreover, both phylogenetic and minimum spanning network analyses converge on the hypothesis that the viral sequences evolved from a unique common ancestor haplotype that was carried by an index case. The lockdown extinguished both the viral spread and the emergence of new variants.

SARS-CoV-2 and Influenza A virus Co-infections in Ferrets

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and seasonal influenza viruses are co-circulating in the human population. However, only a few cases of viral co-infection with these two viruses have been documented in humans with some people having severe disease and others mild disease. In order to examine this phenomenon, ferrets were co-infected with SARS-CoV-2 and human seasonal influenza A viruses (IAVs) (H1N1 or H3N2) and were compared to animals that received each virus alone. Ferrets were either immunologically naïve to both viruses or vaccinated with the 2019-2020 split-inactivated influenza virus vaccine. Co-infected naive ferrets lost significantly more body weight than ferrets infected with each virus alone and induced more severe inflammation in both the nose and lungs than ferrets single-infected with each virus. Co-infected naïve animals had predominantly higher IAV titers than SARS-CoV-2 titers, and IAVs efficiently transmitted to the co-housed ferrets by direct contact. Comparatively, SARS-CoV-2 failed to transmit to the ferrets that co-housed with co-infected ferrets by direct contact. Moreover, vaccination significantly reduced IAVs virus titers and shortened the viral shedding, but did not completely block influenza virus direct contact transmission. Notably, vaccination significantly ameliorated the influenza associated disease by protecting vaccinated animals from severe morbidity after IAV single infection or IAV and SARS-CoV-2 co-infection, suggesting that seasonal influenza virus vaccination is pivotal to prevent severe disease induced by IAVs and SARS-CoV-2 co-infection during the COVID-19 pandemic.

Importance

Influenza A viruses cause severe morbidity and mortality during each influenza virus season. The emergence of SARS-CoV-2 infection in the human population offers the opportunity to potential co-infections of both viruses. The development of useful animal models to asses pathogenesis, transmission, and viral evolution of these viruses as the co-infect a host is of critical importance for the development of vaccines and therapeutics. The ability to prevent the most severe effects of viral co-infections can be studied using effect co-infection ferret models described in this report.

Pandemic strategies with computational and structural biology against COVID-19: A retrospective

The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life since of 2020. Research studies on the virus and exploration of therapeutic and preventive strategies has been moving at rapid rates to control the pandemic. In the field of bioinformatics or computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.

Association between prognostic factors and the outcomes of patients infected with SARS-CoV-2 harboring multiple spike protein mutations

The outcome of SARS-CoV-2 infection is determined by multiple factors, including the viral, host genetics, age, and comorbidities. This study investigated the association between prognostic factors and disease outcomes of patients infected by SARS-CoV-2 with multiple S protein mutations. Fifty-one COVID-19 patients were recruited in this study. Whole-genome sequencing of 170 full-genomes of SARS-CoV-2 was conducted with the Illumina MiSeq sequencer. Most patients (47%) had mild symptoms of COVID-19 followed by moderate (19.6%), no symptoms (13.7%), severe (4%), and critical (2%). Mortality was found in 13.7% of the COVID-19 patients. There was a significant difference between the age of hospitalized patients (53.4 ± 18 years) and the age of non-hospitalized patients (34.6 ± 19) (p = 0.001). The patients' hospitalization was strongly associated with hypertension, diabetes, and anticoagulant and were strongly significant with the OR of 17 (95% CI 2-144; p = 0.001), 4.47 (95% CI 1.07-18.58; p = 0.039), and 27.97 (95% CI 1.54-507.13; p = 0.02), respectively; while the patients' mortality was significantly correlated with patients' age, anticoagulant, steroid, and diabetes, with OR of 8.44 (95% CI 1.5-47.49; p = 0.016), 46.8 (95% CI 4.63-472.77; p = 0.001), 15.75 (95% CI 2-123.86; p = 0.009), and 8.5 (95% CI 1.43-50.66; p = 0.019), respectively. This study found the clade: L (2%), GH (84.3%), GR (11.7%), and O (2%). Besides the D614G mutation, we found L5F (18.8%), V213A (18.8%), and S689R (8.3%). No significant association between multiple S protein mutations and the patients' hospitalization or mortality. Multivariate analysis revealed that hypertension and anticoagulant were the significant factors influencing the hospitalization and mortality of patients with COVID-19 with an OR of 17.06 (95% CI 2.02-144.36; p = 0.009) and 46.8 (95% CI 4.63-472.77; p = 0.001), respectively. Moreover, the multiple S protein mutations almost reached a strong association with patients' hospitalization (p = 0.07). We concluded that hypertension and anticoagulant therapy have a significant impact on COVID-19 outcomes. This study also suggests that multiple S protein mutations may impact the COVID-19 outcomes. This further emphasized the significance of monitoring SARS-CoV-2 variants through genomic surveillance, particularly those that may impact the COVID-19 outcomes.

Conceptual causal framework to assess the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients

BACKGROUND

SARS-CoV-2 strains evolve continuously and accumulate mutations in their genomes over the course of the pandemic. The severity of a SARS-CoV-2 infection could partly depend on these viral genetic characteristics. Here, we present a general conceptual framework that allows to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients.

METHODS

A causal model is defined and visualized using a Directed Acyclic Graph (DAG), in which assumptions on the relationship between (confounding) variables are made explicit. Various DAGs are presented to explore specific study design options and the risk for selection bias. Next, the data infrastructure specific to the COVID-19 surveillance in Belgium is described, along with its strengths and weaknesses for the study of clinical impact of variants.

DISCUSSION

A well-established framework that provides a complete view on COVID-19 disease severity among hospitalized patients by combining information from different sources on host factors, viral factors, and healthcare-related factors, will enable to assess the clinical impact of emerging SARS-CoV-2 variants and answer questions that will be raised in the future. The framework shows the complexity related to causal research, the corresponding data requirements, and it underlines important limitations, such as unmeasured confounders or selection bias, inherent to repurposing existing routine COVID-19 data registries.

TRIAL REGISTRATION

Each individual research project within the current conceptual framework will be prospectively registered in Open Science Framework (OSF identifier: https://doi.org/10.17605/OSF.IO/UEF29 ). OSF project created on 18 May 2021.