Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022.

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.

Did variants in inborn errors of immunity genes contribute to the extinction of Neanderthals?

BACKGROUND: Neanderthals were a species of archaic humans that became extinct around 40,000 years ago. Modern humans have inherited 1-6% of Neanderthal DNA as a result of interbreeding. These inherited Neanderthal genes have paradoxical influences, while some can provide protection to viral infections, some others are associated with autoimmune/auto-inflammatory diseases. OBJECTIVE: We aim to investigate whether genetic variants with strong detrimental effects on the function of the immune system could have potentially contributed to the extinction of the Neanderthal population. METHODS: We used the publically available genome information from an Altai Neanderthal and filtered for potentially damaging variants present in genes associated with inborn errors of immunity (IEI) and checked whether these variants were present in the genomes of the Denisovan, Vindija and Chagyrskaya Neanderthals. RESULTS: We identified 24 homozygous variants and 15 heterozygous variants in IEI-related genes in the Altai Neanderthal. Two homozygous variants in the UNC13D gene and one variant in the MOGS gene were present in all archaic genomes. Defects in the UNC13D gene are known to cause a severe and often fatal disease called hemophagocytic lymphohistiocystosis (HLH). One of these variants p.(N943S) has been reported in patients with HLH. Variants in MOGS are associated with glycosylation defects in the immune system affecting the susceptibility for infections. CONCLUSIONS: Although the exact functional impact of these three variants needs further elucidation, we speculate that they could have resulted in an increased susceptibility to severe diseases and may have contributed to the extinction of Neanderthals after exposure to specific infections.

Human Genome Polymorphisms and Computational Intelligence Approach Revealed a Complex Genomic Signature for COVID-19 Severity in Brazilian Patients.

We present a genome polymorphisms/machine learning approach for severe COVID-19 prognosis. Ninety-six Brazilian severe COVID-19 patients and controls were genotyped for 296 innate immunity loci. Our model used a feature selection algorithm, namely recursive feature elimination coupled with a support vector machine, to find the optimal loci classification subset, followed by a support vector machine with the linear kernel (SVM-LK) to classify patients into the severe COVID-19 group. The best features that were selected by the SVM-RFE method included 12 SNPs in 12 genes: PD-L1, PD-L2, IL10RA, JAK2, STAT1, IFIT1, IFIH1, DC-SIGNR, IFNB1, IRAK4, IRF1, and IL10. During the COVID-19 prognosis step by SVM-LK, the metrics were: 85% accuracy, 80% sensitivity, and 90% specificity. In comparison, univariate analysis under the 12 selected SNPs showed some highlights for individual variant alleles that represented risk (PD-L1 and IFIT1) or protection (JAK2 and IFIH1). Variant genotypes carrying risk effects were represented by PD-L2 and IFIT1 genes. The proposed complex classification method can be used to identify individuals who are at a high risk of developing severe COVID-19 outcomes even in uninfected conditions, which is a disruptive concept in COVID-19 prognosis. Our results suggest that the genetic context is an important factor in the development of severe COVID-19.

Using generative adversarial networks for genome variant calling from low depth ONT sequencing data.

Genome variant calling is a challenging yet critical task for subsequent studies. Existing methods almost rely on high depth DNA sequencing data. Performance on low depth data drops a lot. Using public Oxford Nanopore (ONT) data of human being from the Genome in a Bottle (GIAB) Consortium, we trained a generative adversarial network for low depth variant calling. Our method, noted as LDV-Caller, can project high depth sequencing information from low depth data. It achieves 94.25% F1 score on low depth data, while the F1 score of the state-of-the-art method on two times higher depth data is 94.49%. By doing so, the price of genome-wide sequencing examination can reduce deeply. In addition, we validated the trained LDV-Caller model on 157 public Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) samples. The mean sequencing depth of these samples is 2982. The LDV-Caller yields 92.77% F1 score using only 22x sequencing depth, which demonstrates our method has potential to analyze different species with only low depth sequencing data.

SARS-CoV-2 RNA elements share human sequence identity and upregulate hyaluronan via NamiRNA-enhancer network.

BACKGROUND: Since late 2019, SARS-CoV-2 infection has resulted in COVID-19 accompanied by diverse clinical manifestations. However, the underlying mechanism of how SARS-CoV-2 interacts with host and develops multiple symptoms is largely unexplored. METHODS: Bioinformatics analysis determined the sequence similarity between SARS-CoV-2 and human genomes. Diverse fragments of SARS-CoV-2 genome containing Human Identical Sequences (HIS) were cloned into the lentiviral vector. HEK293T, MRC5 and HUVEC were infected with laboratory-packaged lentivirus or transfected with plasmids or antagomirs for HIS. Quantitative RT-PCR and chromatin immunoprecipitation assay detected gene expression and H3K27ac enrichment, respectively. UV-Vis spectroscopy assessed the interaction between HIS and their target locus. Enzyme-linked immunosorbent assay evaluated the hyaluronan (HA) levels of culture supernatant and plasma of COVID-19 patients. FINDINGS: Five short sequences (24-27 nt length) sharing identity between SARS-CoV-2 and human genome were identified. These RNA elements were highly conserved in primates. The genomic fragments containing HIS were predicted to form hairpin structures in silico similar to miRNA precursors. HIS may function through direct genomic interaction leading to activation of host enhancers, and upregulation of adjacent and distant genes, including cytokine genes and hyaluronan synthase 2 (HAS2). HIS antagomirs and Cas13d-mediated HIS degradation reduced HAS2 expression. Severe COVID-19 patients displayed decreased lymphocytes and elevated D-dimer, and C-reactive proteins, as well as increased plasma hyaluronan. Hymecromone inhibited hyaluronan production in vitro, and thus could be further investigated as a therapeutic option for preventing severe outcome in COVID-19 patients. INTERPRETATION: HIS of SARS-CoV-2 could promote COVID-19 progression by upregulating hyaluronan, providing novel targets for treatment. FUNDING: The National Key R&D Program of China (2018YFC1005004), Major Special Projects of Basic Research of Shanghai Science and Technology Commission (18JC1411101), and the National Natural Science Foundation of China (31872814, 32000505).

Whole-genome sequencing reveals host factors underlying critical COVID-19.

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2-4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease.

Inflammatory activation and immune cell infiltration are main biological characteristics of SARS-CoV-2 infected myocardium.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) can target cardiomyocytes (CMs) to directly invade the heart resulting in high mortality. This study aims to explore the biological characteristics of SARS-CoV-2 infected myocardium based on omics by collecting transcriptome data and analyzing them with a series of bioinformatics tools. Totally, 86 differentially expressed genes (DEGs) were discovered in SARS-CoV-2 infected CMs, and 15 miRNAs were discovered to target 60 genes. Functional enrichment analysis indicated that these DEGs were mainly enriched in the inflammatory signaling pathway. After the protein-protein interaction (PPI) network was constructed, several genes including CCL2 and CXCL8 were regarded as the hub genes. SRC inhibitor saracatinib was predicted to potentially act against the cardiac dysfunction induced by SARS-CoV-2. Among the 86 DEGs, 28 were validated to be dysregulated in SARS-CoV-2 infected hearts. Gene Set Enrichment Analysis (GSEA) analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that malaria, IL-17 signaling pathway, and complement and coagulation cascades were significantly enriched. Immune infiltration analysis indicated that 'naive B cells' was significantly increased in the SARS-CoV-2 infected heart. The above results may help to improve the prognosis of patients with COVID-19.

Human genetic risk of treatment with antiviral nucleoside analog drugs that induce lethal mutagenesis: The special case of molnupiravir.

This review considers antiviral nucleoside analog drugs, including ribavirin, favipiravir, and molnupiravir, which induce genome error catastrophe in SARS-CoV or SARS-CoV-2 via lethal mutagenesis as a mode of action. In vitro data indicate that molnupiravir may be 100 times more potent as an antiviral agent than ribavirin or favipiravir. Molnupiravir has recently demonstrated efficacy in a phase 3 clinical trial. Because of its anticipated global use, its relative potency, and the reported in vitro "host" cell mutagenicity of its active principle, ß-d-N4-hydroxycytidine, we have reviewed the development of molnupiravir and its genotoxicity safety evaluation, as well as the genotoxicity profiles of three congeners, that is, ribavirin, favipiravir, and 5-(2-chloroethyl)-2'-deoxyuridine. We consider the potential genetic risks of molnupiravir on the basis of all available information and focus on the need for additional human genotoxicity data and follow-up in patients treated with molnupiravir and similar drugs. Such human data are especially relevant for antiviral NAs that have the potential of permanently modifying the genomes of treated patients and/or causing human teratogenicity or embryotoxicity. We conclude that the results of preclinical genotoxicity studies and phase 1 human clinical safety, tolerability, and pharmacokinetics are critical components of drug safety assessments and sentinels of unanticipated adverse health effects. We provide our rationale for performing more thorough genotoxicity testing prior to and within phase 1 clinical trials, including human PIG-A and error corrected next generation sequencing (duplex sequencing) studies in DNA and mitochondrial DNA of patients treated with antiviral NAs that induce genome error catastrophe via lethal mutagenesis.

PAGEANT: personal access to genome and analysis of natural traits.

GWASs have identified numerous genetic variants associated with a wide variety of diseases, yet despite the wide availability of genetic testing the insights that would enhance the interpretability of these results are not widely available to members of the public. As a proof of concept and demonstration of technological feasibility, we developed PAGEANT (Personal Access to Genome & Analysis of Natural Traits), usable through Graphical User Interface or command line-based version, aiming to serve as a protocol and prototype that guides the overarching design of genetic reporting tools. PAGEANT is structured across five core modules, summarized by five Qs: (i) quality assurance of the genetic data; (ii) qualitative assessment of genetic characteristics; (iii) quantitative assessment of health risk susceptibility based on polygenic risk scores and population reference; (iv) query of third-party variant databases (e.g. ClinVAR and PharmGKB) and (v) quick Response code of genetic variants of interest. Literature review was conducted to compare PAGEANT with academic and industry tools. For 2504 genomes made publicly available through the 1000 Genomes Project, we derived their genomic characteristics for a suite of qualitative and quantitative traits. One exemplary trait is susceptibility to COVID-19, based on the most up-to-date scientific findings reported.

AI for the collective analysis of a massive number of genome sequences: various examples from the small genome of pandemic SARS-CoV-2 to the human genome.

In genetics and related fields, huge amounts of data, such as genome sequences, are accumulating, and the use of artificial intelligence (AI) suitable for big data analysis has become increasingly important. Unsupervised AI that can reveal novel knowledge from big data without prior knowledge or particular models is highly desirable for analyses of genome sequences, particularly for obtaining unexpected insights. We have developed a batch-learning self-organizing map (BLSOM) for oligonucleotide compositions that can reveal various novel genome characteristics. Here, we explain the data mining by the BLSOM: an unsupervised AI. As a specific target, we first selected SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) because a large number of viral genome sequences have been accumulated via worldwide efforts. We analyzed more than 0.6 million sequences collected primarily in the first year of the pandemic. BLSOMs for short oligonucleotides (e.g., 4-6-mers) allowed separation into known clades, but longer oligonucleotides further increased the separation ability and revealed subgrouping within known clades. In the case of 15-mers, there is mostly one copy in the genome; thus, 15-mers that appeared after the epidemic started could be connected to mutations, and the BLSOM for 15-mers revealed the mutations that contributed to separation into known clades and their subgroups. After introducing the detailed methodological strategies, we explain BLSOMs for various topics, such as the tetranucleotide BLSOM for over 5 million 5-kb fragment sequences derived from almost all microorganisms currently available and its use in metagenome studies. We also explain BLSOMs for various eukaryotes, including fishes, frogs and Drosophila species, and found a high separation ability among closely related species. When analyzing the human genome, we found enrichments in transcription factor-binding sequences in centromeric and pericentromeric heterochromatin regions. The tDNAs (tRNA genes) could be separated according to their corresponding amino acid.

Genome-wide DNA methylation profiling of peripheral blood reveals an epigenetic signature associated with severe COVID-19.

The global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic RNA virus causing coronavirus disease 2019 (COVID-19) in humans. Although most patients with COVID-19 have mild illness and may be asymptomatic, some will develop severe pneumonia, acute respiratory distress syndrome, multi-organ failure, and death. RNA viruses such as SARS-CoV-2 are capable of hijacking the epigenetic landscape of host immune cells to evade antiviral defense. Yet, there remain considerable gaps in our understanding of immune cell epigenetic changes associated with severe SARS-CoV-2 infection pathology. Here, we examined genome-wide DNA methylation (DNAm) profiles of peripheral blood mononuclear cells from 9 terminally-ill, critical COVID-19 patients with confirmed SARS-CoV-2 plasma viremia compared with uninfected, hospitalized influenza, untreated primary HIV infection, and mild/moderate COVID-19 HIV coinfected individuals. Cell-type deconvolution analyses confirmed lymphopenia in severe COVID-19 and revealed a high percentage of estimated neutrophils suggesting perturbations to DNAm associated with granulopoiesis. We observed a distinct DNAm signature of severe COVID-19 characterized by hypermethylation of IFN-related genes and hypomethylation of inflammatory genes, reinforcing observations in infection models and single-cell transcriptional studies of severe COVID-19. Epigenetic clock analyses revealed severe COVID-19 was associated with an increased DNAm age and elevated mortality risk according to GrimAge, further validating the epigenetic clock as a predictor of disease and mortality risk. Our epigenetic results reveal a discovery DNAm signature of severe COVID-19 in blood potentially useful for corroborating clinical assessments, informing pathogenic mechanisms, and revealing new therapeutic targets against SARS-CoV-2.

Large-scale genomic study reveals robust activation of the immune system following advanced Inner Engineering meditation retreat.

The positive impact of meditation on human well-being is well documented, yet its molecular mechanisms are incompletely understood. We applied a comprehensive systems biology approach starting with whole-blood gene expression profiling combined with multilevel bioinformatic analyses to characterize the coexpression, transcriptional, and protein-protein interaction networks to identify a meditation-specific core network after an advanced 8-d Inner Engineering retreat program. We found the response to oxidative stress, detoxification, and cell cycle regulation pathways were down-regulated after meditation. Strikingly, 220 genes directly associated with immune response, including 68 genes related to interferon signaling, were up-regulated, with no significant expression changes in the inflammatory genes. This robust meditation-specific immune response network is significantly dysregulated in multiple sclerosis and severe COVID-19 patients. The work provides a foundation for understanding the effect of meditation and suggests that meditation as a behavioral intervention can voluntarily and nonpharmacologically improve the immune response for treating various conditions associated with excessive or persistent inflammation with a dampened immune system profile.

The European Bioinformatics Institute (EMBL-EBI) in 2021.

The European Bioinformatics Institute (EMBL-EBI) maintains a comprehensive range of freely available and up-to-date molecular data resources, which includes over 40 resources covering every major data type in the life sciences. This year's service update for EMBL-EBI includes new resources, PGS Catalog and AlphaFold DB, and updates on existing resources, including the COVID-19 Data Platform, trRosetta and RoseTTAfold models introduced in Pfam and InterPro, and the launch of Genome Integrations with Function and Sequence by UniProt and Ensembl. Furthermore, we highlight projects through which EMBL-EBI has contributed to the development of community-driven data standards and guidelines, including the Recommended Metadata for Biological Images (REMBI), and the BioModels Reproducibility Scorecard. Training is one of EMBL-EBI's core missions and a key component of the provision of bioinformatics services to users: this year's update includes many of the improvements that have been developed to EMBL-EBI's online training offering.

Mapping the proteo-genomic convergence of human diseases.

Characterization of the genetic regulation of proteins is essential for understanding disease etiology and developing therapies. We identified 10,674 genetic associations for 3892 plasma proteins to create a cis-anchored gene-protein-disease map of 1859 connections that highlights strong cross-disease biological convergence. This proteo-genomic map provides a framework to connect etiologically related diseases, to provide biological context for new or emerging disorders, and to integrate different biological domains to establish mechanisms for known gene-disease links. Our results identify proteo-genomic connections within and between diseases and establish the value of cis-protein variants for annotation of likely causal disease genes at loci identified in genome-wide association studies, thereby addressing a major barrier to experimental validation and clinical translation of genetic discoveries.

The reactome pathway knowledgebase 2022.

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied ('dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.

Genome-Wide Profiling Reveals Alternative Polyadenylation of Innate Immune-Related mRNA in Patients With COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has caused many deaths worldwide. To date, the mechanism of viral immune escape remains unclear, which is a great obstacle to developing effective clinical treatment. RNA processing mechanisms, including alternative polyadenylation (APA) and alternative splicing (AS), are crucial in the regulation of most human genes in many types of infectious diseases. Because the role of APA and AS in response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remains unknown, we performed de novo identification of dynamic APA sites using a public dataset of human peripheral blood mononuclear cell (PBMC) RNA-Seq data in COVID-19 patients. We found that genes with APA were enriched in innate immunity -related gene ontology categories such as neutrophil activation, regulation of the MAPK cascade and cytokine production, response to interferon-gamma and the innate immune response. We also reported genome-wide AS events and enriched viral transcription-related categories upon SARS-CoV-2 infection. Interestingly, we found that APA events may give better predictions than AS in COVID-19 patients, suggesting that APA could act as a potential therapeutic target and novel biomarker in those patients. Our study is the first to annotate genes with APA and AS in COVID-19 patients and highlights the roles of APA variation in SARS-CoV-2 infection.

Identification of Cellular Factors Required for SARS-CoV-2 Replication.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the recently emerged virus responsible for the COVID-19 pandemic. Clinical presentation can range from asymptomatic disease and mild respiratory tract infection to severe disease with lung injury, multiorgan failure, and death. SARS-CoV-2 is the third animal coronavirus to emerge in humans in the 21st century, and coronaviruses appear to possess a unique ability to cross borders between species and infect a wide range of organisms. This is somewhat surprising as, except for the requirement of host cell receptors, cell-pathogen interactions are usually species-specific. Insights into these host-virus interactions will provide a deeper understanding of the process of SARS-CoV-2 infection and provide a means for the design and development of antiviral agents. In this study, we describe a complex analysis of SARS-CoV-2 infection using a genome-wide CRISPR-Cas9 knock-out system in HeLa cells overexpressing entry receptor angiotensin-converting enzyme 2 (ACE2). This platform allows for the identification of factors required for viral replication. This study was designed to include a high number of replicates (48 replicates; 16 biological repeats with 3 technical replicates each) to prevent data instability, remove sources of bias, and allow multifactorial bioinformatic analyses in order to study the resulting interaction network. The results obtained provide an interesting insight into the replication mechanisms of SARS-CoV-2.