Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning.

Introduction: Coronaviruses (CoVs) are naturally found in bats and can occasionally cause infection and transmission in humans and other mammals. Our study aimed to build a deep learning (DL) method to predict the adaptation of bat CoVs to other mammals. Methods: The CoV genome was represented with a method of dinucleotide composition representation (DCR) for the two main viral genes, ORF1ab and Spike. DCR features were first analyzed for their distribution among adaptive hosts and then trained with a DL classifier of convolutional neural networks (CNN) to predict the adaptation of bat CoVs. Results and discussion: The results demonstrated inter-host separation and intra-host clustering of DCR-represented CoVs for six host types: Artiodactyla, Carnivora, Chiroptera, Primates, Rodentia/Lagomorpha, and Suiformes. The DCR-based CNN with five host labels (without Chiroptera) predicted a dominant adaptation of bat CoVs to Artiodactyla hosts, then to Carnivora and Rodentia/Lagomorpha mammals, and later to primates. Moreover, a linear asymptotic adaptation of all CoVs (except Suiformes) from Artiodactyla to Carnivora and Rodentia/Lagomorpha and then to Primates indicates an asymptotic bats-other mammals-human adaptation. Conclusion: Genomic dinucleotides represented as DCR indicate a host-specific separation, and clustering predicts a linear asymptotic adaptation shift of bat CoVs from other mammals to humans via deep learning.

Preparation of the luciferase-labeled antibody for improving the detection sensitivity of viral antigen.

BACKGROUND: Viral antigen detection test is the most common method used to detect viruses in the field rapidly. However, due to the low sensitivity, it can only be used as an auxiliary diagnosis method for virus infection. Improving sensitivity is crucial for developing more accurate viral antigen tests. Nano luciferase (Nluc) is a sensitive reporter that has not been used in virus detection. RESULTS: In this study, we produced an intracellularly Nluc labeled detection antibody (Nluc-ch2C5) and evaluated its ability to improve the detection sensitivity of respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens. Compared with the traditional horse-radish peroxidase (HRP) labeled antibody (HRP-ch2C5), Nluc-ch2C5 was 41 times more sensitive for inactivated SARS-CoV-2 virus by sandwich chemiluminescence ELISA. Then we applied Nluc-ch2C5 to establish an automatic magnet chemiluminescence immune assay (AMCA) for the SARS-CoV-2 viral spike protein, the limit of detection was 68 pfu/reaction. The clinical sensitivity and specificity reached 75% (24/32) and 100% (48/48) using 32 PCR-positive and 48 PCR-negative swab samples for clinical evaluation, which is more sensitive than the commercial ELSA kit and colloid gold strip kit. CONCLUSIONS: Here, monoclonal antibody ch2C5 served as a model antibody and the SARS-CoV-2 served as a model pathogen. The Nluc labeled detecting antibody (Nluc-ch2C5) significantly improved the detection sensitivity of SARS-CoV-2 antigen. This labeling principle applies to other viral infections, so this labeling and test format could be expected to play an important role in detecting other virus antigens.

Prussian Blue@Zeolitic Imidazolate Framework Composite Toward Solar-triggered Biodecontamination

Metal-organic frameworks (MOFs) featuring composition and bandstructure diversity, are an emerging class of photoresponsive disinfectants. In this study, we demonstrated the superiority of core-shell arranged photoactive MOFs (prussian blue (PB) and zeolitic imidazolate framework (ZIF-8)) for pathogen inactivation in terms of biocidal efficiency and broad-spectrum sensitivity. Reactive oxygen species (ROS) production was significantly promoted after the integration of PB due to the photosensitization effect and initiation of in situ Fenton reaction. Favorably, another inactivation channel was also opened owing to the unique photothermal effect of PB. Attributed to the facilitated ROS intracellular penetration by heat, the composite outperforms not only individual component but anatase TiO2 in pathogen elimination. Specifically, the Staphylococcus aureus (S. aureus) inactivation efficiency of the composite (6.6 log) is 2, 1.8 and 5.1 times higher than that of PB (3.3 log), ZIF-8 (3.7 log) and TiO2 (1.3 log) over 45 min of simulated sunlight illumination. Significantly, the infectivity of Bacillus anthracis and murine coronavirus in droplets on composite-coated filter surface could be greatly reduced (approximately 3 log reduction in colony number/coronavirus titer) within few minutes of solar exposure, indicative of the great potential of MOF composites toward life-threatening microbial infection prevention.

Preparation of the luciferase-labeled antibody for improving the detection sensitivity of viral antigen

Viral antigen detection test is the most common method used to detect viruses in the field rapidly. However, due to the low sensitivity, it can only be used as an auxiliary diagnosis method for virus infection. Improving sensitivity is crucial for developing more accurate viral antigen tests. Nano luciferase (Nluc) is a sensitive reporter that has not been used in virus detection. In this study, we produced an intracellularly Nluc labeled detection antibody (Nluc-ch2C5) and evaluated its ability to improve the detection sensitivity of respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens. Compared with the traditional horse-radish peroxidase (HRP) labeled antibody (HRP-ch2C5), Nluc-ch2C5 was 41 times more sensitive for inactivated SARS-CoV-2 virus by sandwich chemiluminescence ELISA. Then we applied Nluc-ch2C5 to establish an automatic magnet chemiluminescence immune assay (AMCA) for the SARS-CoV-2 viral spike protein, the limit of detection was 68 pfu/reaction. The clinical sensitivity and specificity reached 75% (24/32) and 100% (48/48) using 32 PCR-positive and 48 PCR-negative swab samples for clinical evaluation, which is more sensitive than the commercial ELSA kit and colloid gold strip kit. Here, monoclonal antibody ch2C5 served as a model antibody and the SARS-CoV-2 served as a model pathogen. The Nluc labeled detecting antibody (Nluc-ch2C5) significantly improved the detection sensitivity of SARS-CoV-2 antigen. This labeling principle applies to other viral infections, so this labeling and test format could be expected to play an important role in detecting other virus antigens.

Convolutional Neural Networks Based on Sequential Spike Predict the High Human Adaptation of SARS-CoV-2 Omicron Variants.

The COVID-19 pandemic has frequently produced more highly transmissible SARS-CoV-2 variants, such as Omicron, which has produced sublineages. It is a challenge to tell apart high-risk Omicron sublineages and other lineages of SARS-CoV-2 variants. We aimed to build a fine-grained deep learning (DL) model to assess SARS-CoV-2 transmissibility, updating our former coarse-grained model, with the training/validating data of early-stage SARS-CoV-2 variants and based on sequential Spike samples. Sequential amino acid (AA) frequency was decomposed into serially and slidingly windowed fragments in Spike. Unsupervised machine learning approaches were performed to observe the distribution in sequential AA frequency and then a supervised Convolutional Neural Network (CNN) was built with three adaptation labels to predict the human adaptation of Omicron variants in sublineages. Results indicated clear inter-lineage separation and intra-lineage clustering for SARS-CoV-2 variants in the decomposed sequential AAs. Accurate classification by the predictor was validated for the variants with different adaptations. Higher adaptation for the BA.2 sublineage and middle-level adaptation for the BA.1/BA.1.1 sublineages were predicted for Omicron variants. Summarily, the Omicron BA.2 sublineage is more adaptive than BA.1/BA.1.1 and has spread more rapidly, particularly in Europe. The fine-grained adaptation DL model works well for the timely assessment of the transmissibility of SARS-CoV-2 variants, facilitating the control of emerging SARS-CoV-2 variants.

Convolutional Neural Networks Based on Sequential Spike Predict the High Human Adaptation of SARS-CoV-2 Omicron Variants

The COVID-19 pandemic has frequently produced more highly transmissible SARS-CoV-2 variants, such as Omicron, which has produced sublineages. It is a challenge to tell apart high-risk Omicron sublineages and other lineages of SARS-CoV-2 variants. We aimed to build a fine-grained deep learning (DL) model to assess SARS-CoV-2 transmissibility, updating our former coarse-grained model, with the training/validating data of early-stage SARS-CoV-2 variants and based on sequential Spike samples. Sequential amino acid (AA) frequency was decomposed into serially and slidingly windowed fragments in Spike. Unsupervised machine learning approaches were performed to observe the distribution in sequential AA frequency and then a supervised Convolutional Neural Network (CNN) was built with three adaptation labels to predict the human adaptation of Omicron variants in sublineages. Results indicated clear inter-lineage separation and intra-lineage clustering for SARS-CoV-2 variants in the decomposed sequential AAs. Accurate classification by the predictor was validated for the variants with different adaptations. Higher adaptation for the BA.2 sublineage and middle-level adaptation for the BA.1/BA.1.1 sublineages were predicted for Omicron variants. Summarily, the Omicron BA.2 sublineage is more adaptive than BA.1/BA.1.1 and has spread more rapidly, particularly in Europe. The fine-grained adaptation DL model works well for the timely assessment of the transmissibility of SARS-CoV-2 variants, facilitating the control of emerging SARS-CoV-2 variants.

`Development of a rapid neutralizing antibody test for SARS-CoV-2 and its application for neutralizing antibody screening and vaccinated serum testing

Background : Since the outbreak of coronavirus disease (COVID-19), the high infection rate and mutation frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent, have contributed to the ongoing global pandemic. Vaccination has become the most effective means of controlling COVID-19. Traditional neutralizing tests of sera are complex and labor-intensive, therefore, a rapid test for detecting neutralizing antibodies and antibody status post-immunization is needed. Methods : Based on the fact that antibodies exhibit neutralizing activity by blocking the binding of the S protein receptor-binding domain (S-RBD) to ACE2, we developed a rapid neutralizing antibody test, ACE2-Block-ELISA. To evaluate the sensitivity and specificity, we used 54 positive and 84 negative serum samples. We also tested the neutralizing activities of monoclonal antibodies (mAbs) and 214 sera samples from healthy individuals immunized with the inactivated SARS-CoV-2 vaccine. Results : The sensitivity and specificity of the ACE2-Block ELISA were 96.3% and 100%, respectively. For neutralizing mAb screening, ch-2C5 was selected for its ability to block the ACE2–S-RBD interaction. A plaque assay confirmed that ch-2C5 neutralized SARS-CoV-2, with NT50 values of 4.19, 10.63, and 1.074 μg/mL against the SARS-CoV-2 original strain, and the Beta and Delta variants, respectively. For the immunized sera samples, the neutralizing positive rate dropped from 82.14% to 32.16% within 4 months post-vaccination. Conclusions : This study developed and validated an ACE2-Block-ELISA to test the neutralizing activities of antibodies. As a rapid, inexpensive and easy-to-perform method, this ACE2-Block-ELISA has potential applications in rapid neutralizing mAb screening and SARS-CoV-2 vaccine evaluation.

Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants.

Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype-genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA viruses and applied a three-dimensional convolutional neural network (3D CNN) analysis to learn the human adaptation of other existing coronaviruses (CoVs) and predict the adaptation of SARS-CoV-2 variants of concern (VOCs). A markedly separable, linear DCR distribution was observed in two major genes-receptor-binding glycoprotein and RNA-dependent RNA polymerase (RdRp)-of six families of single-stranded (ssRNA) viruses. Additionally, there was a general host-specific distribution of both the spike proteins and RdRps of CoVs. The 3D CNN based on spike DCR predicted a dominant type II adaptation of most Beta, Delta and Omicron VOCs, with high transmissibility and low pathogenicity. Type I adaptation with opposite transmissibility and pathogenicity was predicted for SARS-CoV-2 Alpha VOCs (77%) and Kappa variants of interest (58%). The identified adaptive determinants included D1118H and A570D mutations and local DNTs. Thus, the 3D CNN model based on DCR features predicts SARS-CoV-2, a major type II human adaptation and is qualified to predict variant adaptation in real time, facilitating the risk-assessment of emerging SARS-CoV-2 variants and COVID-19 control.

Simultaneous Detection of Seven Human Coronaviruses by Multiplex PCR and MALDI-TOF MS

Human coronaviruses (HCoVs) are associated with a range of respiratory symptoms. The discovery of severe acute respiratory syndrome (SARS)-CoV, Middle East respiratory syndrome, and SARS-CoV-2 pose a significant threat to human health. In this study, we developed a method (HCoV-MS) that combines multiplex PCR with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), to detect and differentiate seven HCoVs simultaneously. The HCoV-MS method had high specificity and sensitivity, with a 1–5 copies/reaction detection limit. To validate the HCoV-MS method, we tested 163 clinical samples, and the results showed good concordance with real-time PCR. Additionally, the detection sensitivity of HCoV-MS and real-time PCR was comparable. The HCoV-MS method is a sensitive assay, requiring only 1 μL of a sample. Moreover, it is a high-throughput method, allowing 384 samples to be processed simultaneously in 30 min. We propose that this method be used to complement real-time PCR for large-scale screening studies.

Development of an automatic integrated gene detection system for novel severe acute respiratory syndrome-related coronavirus (SARS-CoV2).

In December 2019, Wuhan, China suffered a serious outbreak of a novel coronavirus infectious disease (COVID) caused by novel severe acute respiratory syndrome-related coronavirus (SARS-CoV 2). To quickly identify the pathogen, we designed and screened primer sets, and established a sensitive and specific qRT-PCR assay for SARS-CoV 2; the lower limit of detection (LOD) was 14.8 (95% CI: 9.8-21) copies per reaction. We combined this qRT-PCR assay with an automatic integration system for nucleic acid extraction and amplification, thereby establishing an automatic integrated gene detection system (AIGS) for SARS-CoV 2. Cross reactive analysis performed in 20 other respiratory viruses and 37 nasopharyngeal swabs confirmed a 100% specificity of the assay. Using two fold diluted SARS-CoV 2 culture, the LOD of AIGS was confirmed to be 365 copies/ml (95% CI: 351-375), which was Comparable to that of conventional q RT-PCR (740 copies/ml, 95% CI: 689-750). Clinical performances of AIGS assay were assessed in 266 suspected COVID-19 clinical respiratory tract samples tested in parallel with a commercial kit. The clinical sensitivity of the AIGS test was 97.62% (95% CI: 0.9320-0.9951) based on the commercial kit test result, and concordance analysis showed a high agreement in SARS-CoV-2 detection between the two assays, Pearson R was 0.9623 (95% CI: 0.9523-0.9703). The results indicated that this AIGS could be used for rapid detection of SARS-CoV 2. With the advantage of simple operation and less time consuming, AIGS could be suitable for SARS-CoV2 detection in primary medical institutions, thus would do a great help to improve detection efficiency and control the spread of COVID-19.