Ongoing positive selection drives the evolution of SARS-CoV-2 genomes.

SARS-CoV-2 is a new RNA virus affecting humans and spreads extensively through world populations since its first outbreak in December, 2019. Whether the transmissibility and pathogenicity of SARS-CoV-2 in humans after zoonotic transfer are actively evolving, and driven by adaptation to the new host and environments is still under debate. Understanding the evolutionary mechanism underlying epidemiological and pathological characteristics of COVID-19 is essential for predicting the epidemic trend, and providing guidance for disease control and treatments. Interrogating novel strategies for identifying natural selection using within-species polymorphisms and 3,674,076 SARS-CoV-2 genome sequences of 169 countries as of December 30, 2021, we demonstrate with population genetic evidence that during the course of SARS-CoV-2 pandemic in humans, (i) SARS-CoV-2 genomes are overall conserved under purifying selection, especially for the 14 genes related to viral RNA replication, transcription, and assembly; (ii) Ongoing positive selection is actively driving the evolution of 6 genes (e.g., S, ORF3a, and N) that play critical roles in molecular processes involving pathogen-host interactions, including viral invasion into and egress from host cells, viral inhibition, or evasion of host immune response, possibly leading to high transmissibility and mild symptom in SARS-CoV-2 evolution. According to an established haplotype phylogenetic relationship of 138 viral clusters, a spatial and temporal landscape of 556 critical mutations is constructed based on their divergence among viral haplotype clusters or repeatedly increase in frequency within at least 2 clusters, of which multiple mutations potentially conferring alterations in viral transmissibility, pathogenicity, and virulence of SARS-CoV-2 are highlighted, warranting attentions.

McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks.

Haplotype networks are graphs used to represent evolutionary relationships between a set of taxa and are characterized by intuitiveness in analyzing genealogical relationships of closely related genomes. We here propose a novel algorithm termed McAN that considers mutation spectrum history (mutations in ancestry haplotype should be contained in descendant haplotype), node size (corresponding to sample count for a given node) and sampling time when constructing haplotype network. We show that McAN is two orders of magnitude faster than state-of-the-art algorithms without losing accuracy, making it suitable for analysis of a large number of sequences. Based on our algorithm, we developed an online web server and offline tool for haplotype network construction, community lineage determination, and interactive network visualization. We demonstrate that McAN is highly suitable for analyzing and visualizing massive genomic data and is helpful to enhance the understanding of genome evolution. Availability: Source code is written in C/C++ and available at https://github.com/Theory-Lun/McAN and https://ngdc.cncb.ac.cn/biocode/tools/BT007301 under the MIT license. Web server is available at https://ngdc.cncb.ac.cn/bit/hapnet/. SARS-CoV-2 dataset are available at https://ngdc.cncb.ac.cn/ncov/. Contact: songshh@big.ac.cn (Song S), zhaowm@big.ac.cn (Zhao W), baoym@big.ac.cn (Bao Y), zhangzhang@big.ac.cn (Zhang Z), ybxue@big.ac.cn (Xue Y).

Genomic Perspectives on the Emerging SARS-CoV-2 Omicron Variant.

A new variant of concern for SARS-CoV-2, Omicron (B.1.1.529), was designated by the World Health Organization on November 26, 2021. This study analyzed the viral genome sequencing data of 108 samples collected from patients infected with Omicron. First, we found that the enrichment efficiency of viral nucleic acids was reduced due to mutations in the region where the primers anneal to. Second, the Omicron variant possesses an excessive number of mutations compared to other variants circulating at the same time (median: 62 vs. 45), especially in the Spike gene. Mutations in the Spike gene confer alterations in 32 amino acid residues, more than those observed in other SARS-CoV-2 variants. Moreover, a large number of nonsynonymous mutations occur in the codons for the amino acid residues located on the surface of the Spike protein, which could potentially affect the replication, infectivity, and antigenicity of SARS-CoV-2. Third, there are 53 mutations between the Omicron variant and its closest sequences available in public databases. Many of these mutations were rarely observed in public databases and had a low mutation rate. In addition, the linkage disequilibrium between these mutations was low, with a limited number of mutations concurrently observed in the same genome, suggesting that the Omicron variant would be in a different evolutionary branch from the currently prevalent variants. To improve our ability to detect and track the source of new variants rapidly, it is imperative to further strengthen genomic surveillance and data sharing globally in a timely manner.

Identification of COVID-19-Associated DNA Methylation Variations by Integrating Methylation Array and scRNA-Seq Data at Cell-Type Resolution.

Single-cell transcriptome studies have revealed immune dysfunction in COVID-19 patients, including lymphopenia, T cell exhaustion, and increased levels of pro-inflammatory cytokines, while DNA methylation plays an important role in the regulation of immune response and inflammatory response. The specific cell types of immune responses regulated by DNA methylation in COVID-19 patients will be better understood by exploring the COVID-19 DNA methylation variation at the cell-type level. Here, we developed an analytical pipeline to explore single-cell DNA methylation variations in COVID-19 patients by transferring bulk-tissue-level knowledge to the single-cell level. We discovered that the methylation variations in the whole blood of COVID-19 patients showed significant cell-type specificity with remarkable enrichment in gamma-delta T cells and presented a phenomenon of hypermethylation and low expression. Furthermore, we identified five genes whose methylation variations were associated with several cell types. Among them, S100A9, AHNAK, and CX3CR1 have been reported as potential COVID-19 biomarkers previously, and the others (TRAF3IP3 and LFNG) are closely associated with the immune and virus-related signaling pathways. We propose that they might serve as potential epigenetic biomarkers for COVID-19 and could play roles in important biological processes such as the immune response and antiviral activity.

Identification of COVID-19-Associated DNA Methylation Variations by Integrating Methylation Array and scRNA-Seq Data at Cell-Type Resolution

Single-cell transcriptome studies have revealed immune dysfunction in COVID-19 patients, including lymphopenia, T cell exhaustion, and increased levels of pro-inflammatory cytokines, while DNA methylation plays an important role in the regulation of immune response and inflammatory response. The specific cell types of immune responses regulated by DNA methylation in COVID-19 patients will be better understood by exploring the COVID-19 DNA methylation variation at the cell-type level. Here, we developed an analytical pipeline to explore single-cell DNA methylation variations in COVID-19 patients by transferring bulk-tissue-level knowledge to the single-cell level. We discovered that the methylation variations in the whole blood of COVID-19 patients showed significant cell-type specificity with remarkable enrichment in gamma-delta T cells and presented a phenomenon of hypermethylation and low expression. Furthermore, we identified five genes whose methylation variations were associated with several cell types. Among them, S100A9, AHNAK, and CX3CR1 have been reported as potential COVID-19 biomarkers previously, and the others (TRAF3IP3 and LFNG) are closely associated with the immune and virus-related signaling pathways. We propose that they might serve as potential epigenetic biomarkers for COVID-19 and could play roles in important biological processes such as the immune response and antiviral activity.

C3D-UNET: A Comprehensive 3D Unet for Covid-19 Segmentation with Intact Encoding and Local Attention.

For COVID-19 prevention and treatment, it is essential to screen the pneumonia lesions in the lung region and analyze them in a qualitative and quantitative manner. Three-dimensional (3D) computed tomography (CT) volumes can provide sufficient information; however, extra boundaries of the lesions are also needed. The major challenge of automatic 3D segmentation of COVID-19 from CT volumes lies in the inadequacy of datasets and the wide variations of pneumonia lesions in their appearance, shape, and location. In this paper, we introduce a novel network called Comprehensive 3D UNet (C3D-UNet). Compared to 3D-UNet, an intact encoding (IE) strategy designed as residual dilated convolutional blocks with increased dilation rates is proposed to extract features from wider receptive fields. Moreover, a local attention (LA) mechanism is applied in skip connections for more robust and effective information fusion. We conduct five-fold cross-validation on a private dataset and independent offline evaluation on a public dataset. Experimental results demonstrate that our method outperforms other compared methods.

Genomic Epidemiology of SARS-CoV-2 in Pakistan.

COVID-19 has swept globally and Pakistan is no exception. To investigate the initial introductions and transmissions of the SARS-CoV-2 in Pakistan, we performed the largest genomic epidemiology study of COVID-19 in Pakistan and generated 150 complete SARS-CoV-2 genome sequences from samples collected from March 16 to June 1, 2020. We identified a total of 347 mutated positions, 31 of which were over-represented in Pakistan. Meanwhile, we found over 1000 intra-host single-nucleotide variants (iSNVs). Several of them occurred concurrently, indicating possible interactions among them or coevolution. Some of the high-frequency iSNVs in Pakistan were not observed in the global population, suggesting strong purifying selections. The genomic epidemiology revealed five distinctive spreading clusters. The largest cluster consisted of 74 viruses which were derived from different geographic locations of Pakistan and formed a deep hierarchical structure, indicating an extensive and persistent nation-wide transmission of the virus that was probably attributed to a signature mutation (G8371T in ORF1ab) of this cluster. Furthermore, 28 putative international introductions were identified, several of which are consistent with the epidemiological investigations. In all, this study has inferred the possible pathways of introductions and transmissions of SARS-CoV-2 in Pakistan, which could aid ongoing and future viral surveillance and COVID-19 control.

The Global Landscape of SARS-CoV-2 Genomes, Variants, and Haplotypes in 2019nCoVR.

On January 22, 2020, China National Center for Bioinformation (CNCB) released the 2019 Novel Coronavirus Resource (2019nCoVR), an open-access information resource for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 2019nCoVR features a comprehensive integration of sequence and clinical information for all publicly available SARS-CoV-2 isolates, which are manually curated with value-added annotations and quality evaluated by an automated in-house pipeline. Of particular note, 2019nCoVR offers systematic analyses to generate a dynamic landscape of SARS-CoV-2 genomic variations at a global scale. It provides all identified variants and their detailed statistics for each virus isolate, and congregates the quality score, functional annotation, and population frequency for each variant. Spatiotemporal change for each variant can be visualized and historical viral haplotype network maps for the course of the outbreak are also generated based on all complete and high-quality genomes available. Moreover, 2019nCoVR provides a full collection of SARS-CoV-2 relevant literature on the coronavirus disease 2019 (COVID-19), including published papers from PubMed as well as preprints from services such as bioRxiv and medRxiv through Europe PMC. Furthermore, by linking with relevant databases in CNCB, 2019nCoVR offers data submission services for raw sequence reads and assembled genomes, and data sharing with NCBI. Collectively, SARS-CoV-2 is updated daily to collect the latest information on genome sequences, variants, haplotypes, and literature for a timely reflection, making 2019nCoVR a valuable resource for the global research community. 2019nCoVR is accessible at https://bigd.big.ac.cn/ncov/.

An online coronavirus analysis platform from the National Genomics Data Center.

Since the first reported severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in December 2019, coronavirus disease 2019 (COVID-19) has become a global pandemic, spreading to more than 200 countries and regions worldwide. With continued research progress and virus detection, SARS-CoV-2 genomes and sequencing data have been reported and accumulated at an unprecedented rate. To meet the need for fast analysis of these genome sequences, the National Genomics Data Center (NGDC) of the China National Center for Bioinformation (CNCB) has established an online coronavirus analysis platform, which includes de novoassembly, BLAST alignment, genome annotation, variant identification, and variant annotation modules. The online analysis platform can be freely accessed at the 2019 Novel Coronavirus Resource (2019nCoVR) (https://bigd.big.ac.cn/ncov/online/tools).

Evolutionary and structural analysis of SARS-CoV-2 specific evasion of host immunity.

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading fast worldwide. There is a pressing need to understand how the virus counteracts host innate immune responses. Deleterious clinical manifestations of coronaviruses have been associated with virus-induced direct dysregulation of innate immune responses occurring via viral macrodomains located within nonstructural protein-3 (Nsp3). However, no substantial information is available concerning the relationship of macrodomains to the unusually high pathogenicity of SARS-CoV-2. Here, we show that structural evolution of macrodomains may impart a critical role to the unique pathogenicity of SARS-CoV-2. Using sequence, structural, and phylogenetic analysis, we identify a specific set of historical substitutions that recapitulate the evolution of the macrodomains that counteract host immune response. These evolutionary substitutions may alter and reposition the secondary structural elements to create new intra-protein contacts and, thereby, may enhance the ability of SARS-CoV-2 to inhibit host immunity. Further, we find that the unusual virulence of this virus is potentially the consequence of Darwinian selection-driven epistasis in protein evolution. Our findings warrant further characterization of macrodomain-specific evolutionary substitutions in in vitro and in vivo models to determine their inhibitory effects on the host immune system.

Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images.

We propose a conceptually simple framework for fast COVID-19 screening in 3D chest CT images. The framework can efficiently predict whether or not a CT scan contains pneumonia while simultaneously identifying pneumonia types between COVID-19 and Interstitial Lung Disease (ILD) caused by other viruses. In the proposed method, two 3D-ResNets are coupled together into a single model for the two above-mentioned tasks via a novel prior-attention strategy. We extend residual learning with the proposed prior-attention mechanism and design a new so-called prior-attention residual learning (PARL) block. The model can be easily built by stacking the PARL blocks and trained end-to-end using multi-task losses. More specifically, one 3D-ResNet branch is trained as a binary classifier using lung images with and without pneumonia so that it can highlight the lesion areas within the lungs. Simultaneously, inside the PARL blocks, prior-attention maps are generated from this branch and used to guide another branch to learn more discriminative representations for the pneumonia-type classification. Experimental results demonstrate that the proposed framework can significantly improve the performance of COVID-19 screening. Compared to other methods, it achieves a state-of-the-art result. Moreover, the proposed method can be easily extended to other similar clinical applications such as computer-aided detection and diagnosis of pulmonary nodules in CT images, glaucoma lesions in Retina fundus images, etc.

Population Genetics of SARS-CoV-2: Disentangling Effects of Sampling Bias and Infection Clusters.

A novel RNA virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is responsible for the ongoing outbreak of coronavirus disease 2019 (COVID-19). Population genetic analysis could be useful for investigating the origin and evolutionary dynamics of COVID-19. However, due to extensive sampling bias and existence of infection clusters during the epidemic spread, direct applications of existing approaches can lead to biased parameter estimations and data misinterpretation. In this study, we first present robust estimator for the time to the most recent common ancestor (TMRCA) and the mutation rate, and then apply the approach to analyze 12,909 genomic sequences of SARS-CoV-2. The mutation rate is inferred to be 8.69 × 10-4 per site per year with a 95% confidence interval (CI) of [8.61 × 10-4, 8.77 × 10-4], and the TMRCA of the samples inferred to be Nov 28, 2019 with a 95% CI of [Oct 20, 2019, Dec 9, 2019]. The results indicate that COVID-19 might originate earlier than and outside of Wuhan Seafood Market. We further demonstrate that genetic polymorphism patterns, including the enrichment of specific haplotypes and the temporal allele frequency trajectories generated from infection clusters, are similar to those caused by evolutionary forces such as natural selection. Our results show that population genetic methods need to be developed to efficiently detangle the effects of sampling bias and infection clusters to gain insights into the evolutionary mechanism of SARS-CoV-2. Software for implementing VirusMuT can be downloaded at https://bigd.big.ac.cn/biocode/tools/BT007081.

The 2019 novel coronavirus resource.

An ongoing outbreak of a novel coronavirus infection in Wuhan, China since December 2019 has led to 31,516 infected persons and 638 deaths across 25 countries (till 16:00 on February 7, 2020). The virus causing this pneumonia was then named as the 2019 novel coronavirus (2019-nCoV) by the World Health Organization. To promote the data sharing and make all relevant information of 2019-nCoV publicly available, we construct the 2019 Novel Coronavirus Resource (2019nCoVR, https://bigd.big.ac.cn/ncov). 2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the Global Initiative on Sharing All Influenza Data, National Center for Biotechnology Information, China National GeneBank, National Microbiology Data Center and China National Center for Bioinformation (CNCB)/National Genomics Data Center (NGDC). It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains. Moreover, by linking seamlessly with related databases in CNCB/NGDC, 2019nCoVR offers virus data submission and sharing services for raw sequence reads and assembled sequences. In this report, we provide comprehensive descriptions on data deposition, management, release and utility in 2019nCoVR, laying important foundations in aid of studies on virus classification and origin, genome variation and evolution, fast detection, drug development and pneumonia precision prevention and therapy.