CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning.

BACKGROUND: Literature about SARS-CoV-2 widely discusses the effects of variations that have spread in the past 3 years. Such information is dispersed in the texts of several research articles, hindering the possibility of practically integrating it with related datasets (e.g., millions of SARS-CoV-2 sequences available to the community). We aim to fill this gap, by mining literature abstracts to extract-for each variant/mutation-its related effects (in epidemiological, immunological, clinical, or viral kinetics terms) with labeled higher/lower levels in relation to the nonmutated virus. RESULTS: The proposed framework comprises (i) the provisioning of abstracts from a COVID-19-related big data corpus (CORD-19) and (ii) the identification of mutation/variant effects in abstracts using a GPT2-based prediction model. The above techniques enable the prediction of mutations/variants with their effects and levels in 2 distinct scenarios: (i) the batch annotation of the most relevant CORD-19 abstracts and (ii) the on-demand annotation of any user-selected CORD-19 abstract through the CoVEffect web application (http://gmql.eu/coveffect), which assists expert users with semiautomated data labeling. On the interface, users can inspect the predictions and correct them; user inputs can then extend the training dataset used by the prediction model. Our prototype model was trained through a carefully designed process, using a minimal and highly diversified pool of samples. CONCLUSIONS: The CoVEffect interface serves for the assisted annotation of abstracts, allowing the download of curated datasets for further use in data integration or analysis pipelines. The overall framework can be adapted to resolve similar unstructured-to-structured text translation tasks, which are typical of biomedical domains.

COVID-19 Variant Detection with a High-Fidelity CRISPR-Cas12 Enzyme.

Laboratory tests for the accurate and rapid identification of SARS-CoV-2 variants can potentially guide the treatment of COVID-19 patients and inform infection control and public health surveillance efforts. Here, we present the development and validation of a rapid COVID-19 variant DETECTR assay incorporating loop-mediated isothermal amplification (LAMP) followed by CRISPR-Cas12 based identification of single nucleotide polymorphism (SNP) mutations in the SARS-CoV-2 spike (S) gene. This assay targets the L452R, E484K/Q/A, and N501Y mutations, at least one of which is found in nearly all major variants. In a comparison of three different Cas12 enzymes, only the newly identified enzyme CasDx1 was able to accurately identify all targeted SNP mutations. An analysis pipeline for CRISPR-based SNP identification from 261 clinical samples yielded a SNP concordance of 97.3% and agreement of 98.9% (258 of 261) for SARS-CoV-2 lineage classification, using SARS-CoV-2 whole-genome sequencing and/or real-time RT-PCR as test comparators. We also showed that detection of the single E484A mutation was necessary and sufficient to accurately identify Omicron from other major circulating variants in patient samples. These findings demonstrate the utility of CRISPR-based DETECTR as a faster and simpler diagnostic method compared with sequencing for SARS-CoV-2 variant identification in clinical and public health laboratories.

Graphene Oxide Nanosheets Interact and Interfere with SARS-CoV-2 Surface Proteins and Cell Receptors to Inhibit Infectivity.

Nanotechnology can offer a number of options against coronavirus disease 2019 (COVID-19) acting both extracellularly and intracellularly to the host cells. Here, the aim is to explore graphene oxide (GO), the most studied 2D nanomaterial in biomedical applications, as a nanoscale platform for interaction with SARS-CoV-2. Molecular docking analyses of GO sheets on interaction with three different structures: SARS-CoV-2 viral spike (open state - 6VYB or closed state - 6VXX), ACE2 (1R42), and the ACE2-bound spike complex (6M0J) are performed. GO shows high affinity for the surface of all three structures (6M0J, 6VYB and 6VXX). When binding affinities and involved bonding types are compared, GO interacts more strongly with the spike or ACE2, compared to 6M0J. Infection experiments using infectious viral particles from four different clades as classified by Global Initiative on Sharing all Influenza Data (GISAID), are performed for validation purposes. Thin, biological-grade GO nanoscale (few hundred nanometers in lateral dimension) sheets are able to significantly reduce copies for three different viral clades. This data has demonstrated that GO sheets have the capacity to interact with SARS-CoV-2 surface components and disrupt infectivity even in the presence of any mutations on the viral spike. GO nanosheets are proposed to be further explored as a nanoscale platform for development of antiviral strategies against COVID-19.

Profiling SARS-CoV-2 mutation fingerprints that range from the viral pangenome to individual infection quasispecies.

BACKGROUND: The genome of SARS-CoV-2 is susceptible to mutations during viral replication due to the errors generated by RNA-dependent RNA polymerases. These mutations enable the SARS-CoV-2 to evolve into new strains. Viral quasispecies emerge from de novo mutations that occur in individual patients. In combination, these sets of viral mutations provide distinct genetic fingerprints that reveal the patterns of transmission and have utility in contact tracing. METHODS: Leveraging thousands of sequenced SARS-CoV-2 genomes, we performed a viral pangenome analysis to identify conserved genomic sequences. We used a rapid and highly efficient computational approach that relies on k-mers, short tracts of sequence, instead of conventional sequence alignment. Using this method, we annotated viral mutation signatures that were associated with specific strains. Based on these highly conserved viral sequences, we developed a rapid and highly scalable targeted sequencing assay to identify mutations, detect quasispecies variants, and identify mutation signatures from patients. These results were compared to the pangenome genetic fingerprints. RESULTS: We built a k-mer index for thousands of SARS-CoV-2 genomes and identified conserved genomics regions and landscape of mutations across thousands of virus genomes. We delineated mutation profiles spanning common genetic fingerprints (the combination of mutations in a viral assembly) and a combination of mutations that appear in only a small number of patients. We developed a targeted sequencing assay by selecting primers from the conserved viral genome regions to flank frequent mutations. Using a cohort of 100 SARS-CoV-2 clinical samples, we identified genetic fingerprints consisting of strain-specific mutations seen across populations and de novo quasispecies mutations localized to individual infections. We compared the mutation profiles of viral samples undergoing analysis with the features of the pangenome. CONCLUSIONS: We conducted an analysis for viral mutation profiles that provide the basis of genetic fingerprints. Our study linked pangenome analysis with targeted deep sequenced SARS-CoV-2 clinical samples. We identified quasispecies mutations occurring within individual patients and determined their general prevalence when compared to over 70,000 other strains. Analysis of these genetic fingerprints may provide a way of conducting molecular contact tracing.

Lung organoid simulations for modelling and predicting the effect of mutations on SARS-CoV-2 infectivity.

The global pandemic caused by the SARS-CoV-2 virus continues to spread. Infection with SARS- CoV-2 causes COVID-19, a disease of variable severity. Mutation has already altered the SARS-CoV-2 genome from its original reported sequence and continued mutation is highly probable. These mutations can: (i) have no significant impact (they are silent), (ii) result in a complete loss or reduction of infectivity, or (iii) induce increase in infectivity. Physical generation, for research purposes, of viral mutations that could enhance infectivity are controversial and highly regulated. The primary purpose of this project was to evaluate the ability of the DeepNEU machine learning stem-cell simulation platform to enable rapid and efficient assessment of the potential impact of viral loss-of-function (LOF) and gain-of-function (GOF) mutations on SARS-CoV-2 infectivity. Our data suggest that SARS-CoV-2 infection can be simulated in human alveolar type lung cells. Simulation of infection in these lung cells can be used to model and assess the impact of LOF and GOF mutations in the SARS-CoV2 genome. We have also created a four- factor infectivity measure: the DeepNEU Case Fatality Rate (dnCFR). dnCFR can be used to assess infectivity based on the presence or absence of the key viral proteins (NSP3, Spike-RDB, N protein, and M protein). dnCFR was used in this study, not to only assess the impact of different mutations on SARS-CoV2 infectivity, but also to categorize the effects of mutations as loss of infectivity or gain of infectivity events.

The impact of oxidative stress damage induced by the environmental stressors on COVID-19.

The ongoing pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a substantial stressor that is greatly impacting environmental sustainability. Besides, the different pre-existing environmental stressors and coronavirus disease-2019 (COVID-19)-related stressors are further worsening the effects of the viral disease by inducing the generation of oxidative stress. The generated oxidative stress results in nucleic acid damage associated with viral mutations, that could potentially reduce the effectiveness of COVID-19 management, including the vaccine approach. The current review is aimed to overview the impact of the oxidative stress damage induced by various environmental stressors on COVID-19. The available data regarding the COVID-19-related stressors and the effects of oxidative stress damage induced by the chronic stress, exposure to free radicals, and malnutrition are also analyzed to showcase the promising options, which could be investigated further for sustainable control of the pandemic.