The Trans-omics Landscape of COVID-19

The Trans-omics Landscape of COVID-19 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Trans-omics Landscape of COVID-19 Peng Wu, Dongsheng Chen, Wencheng Ding, Ping Wu, Hongyan Hou, and 65 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-59060/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jul, 2021 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract The outbreak of coronavirus disease 2019 (COVID-19) has been causing a global health emergency. Although previous studies investigated COVID-19 at different omics levels, the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here, we presented a trans-omics landscape for COVID-19 based on integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles from blood samples of 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Notably, we found neutrophils heterogeneity existed between asymptomatic and critically ill patients. Expression discordance of inflammatory cytokines at mRNA and protein levels in asymptomatic patients could possibly be explained by post-transcriptional regulation by RNA binding proteins (RBPs) and microRNAs. Neutrophils over-activation, induced arginine depletion, and tryptophan metabolites accumulation contributed to T/NK cell dysfunction in critical patients. Anti-virus interferons were gradually suppressed along with disease severity. Overall, our study systematically revealed multi-omics characteristics of COVID-19, and the data we generated could hopefully help illuminate COVID-19 pathogenesis and provide valuable clues about potential therapeutic strategies for COVID-19. Immunology Infectious Diseases COVID-19 trans-omics landscape neutrophils heterogeneity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Coronavirus disease 2019 (COVID-19), a newly emerged respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has recently become a pandemic 1 . The disease is now found in almost all countries, totaling 15,785,641 confirmed cases and 640,016 deaths worldwide as of July 27th, 2020 2 . The symptoms of COVID-19 vary dramatically, ranging from asymptomatic to critical. Several studies have reported on confirmed patients who exhibit no symptoms (i.e., asymptomatic) 3-6 . Since such individuals are not routinely tested, the proportion of asymptomatic patients is not precisely known, but appears to range from 13% in children 7 to 50% in the testing of contact tracing evaluation 8 . Of the COVID-19 patients with symptoms, 80% are classified as mild to moderate, 13.8% as severe, and 6.2% are classified as critical 1, 9 . Some confounding factors appeared to be associated with COVID-19 progress and prognosis. For example, preliminary evidence suggests that comorbidities such as hypertension, diabetes, cardiovascular disease, and respiratory disease result in a worsened prognosis of COVID-19 10 , and dramatically increases the mortality rate 11 . Furthermore, death due to COVID-19 is found to be significantly more common in older patients (i.e.,≥65 years old), possibly due to the decline in immune response with age 10, 12 . Thus, although the overall mortality rate of diagnosed cases was estimated to be ~3.4% 2 , the rate varies from 0.2% to 22.7% depending on the age groups and other health issues of patients 13, 14 . So far, most studies have focused on the relationship between the disease and clinical characteristics, sequencing of virus genomes 15 and identifying the structure of the SARS-CoV-2 spike glycoprotein 16, 17 . There has also been some work on integrated multi-omics signatures. For example, meta-transcriptome sequencing was conducted on the bronchoalveolar lavage fluid of SARS-CoV-2 infected patients 18 . Proteomic and metabolomic analyses of the serum from COVID-19 patients have also been investigated 19-21 . However, from the data so far, it remains difficult to determine which parameters are due to infection from the virus and which to comorbidities as no systematic study of the disease have been published thus far. In our study we selected 231 COVID-19 cases with different clinical severity and without comorbidities to investigate the sole effect of SARS-CoV-2 infection on disease severity. We performed trans-omics analysis, including genomic, transcriptomic, proteomic, metabolomic, and lipidomic analytes, to better understand the associations between the genetic and molecular mechanisms of consecutively severe COVID-19 symptoms. We proposed a novel mechanism for inflammatory cytokine regulation at the post-transcriptional level. Neutrophils were excessively activated in critical patients. Cytokine storm, arginine, tryptophan metabolites, and T/NK cell dysfunction cooperatively contribute to the severity of COVID-19. Results Patient enrollment To gain a comprehensive insight into the molecular characteristics of COVID-19 in patients characterized with different disease severities, a cohort of 231 out of 1432 COVID-19 patients were selected based on stringent criteria for the trans-omics study ( Extended Data Fig. 1 ). Given that older age and comorbidities appear to have effects on disease progression and prognosis 22 , 23 , participants without comorbidities and aged between 20 and 70 years old (mean ± SD, 46.7 ± 13.5) were selected. Detailed information about the enrolled patients, including sampling date and basic clinical information, are shown in Extended Data Fig. 2 , and Supplementary Tables 1–2 . Among our enrolled 231 COVID-19 patients, 64 were asymptomatic, 90 were mild, 55 were severe, and 22 were critical. Trans-omics profiling for COVID-19 In-depth multi-omics profiling was performed, including whole-genome sequencing (203 samples) and transcriptome sequencing (RNA-seq and miRNA-seq of 178 samples) of whole blood. Concurrently, liquid chromatography–mass spectrometry (LC-MS) was performed to capture the proteomic, metabolomic, and lipidomic features of COVID-19 patient sera (161 samples) (Fig. 1 a). After data pre-processing and annotation, the final dataset contained a total of 25882 analytes including 18245 mRNAs, 240 miRNAs, 5207 lncRNAs, 634 proteins, 814 metabolites, and 742 complex lipids (Fig. 1 b, Extended Data Fig. 3 and Supplementary Table 3.1 ). To quantify the molecular profiles in relation to disease severity, we conducted pairwise comparisons between the four severity groups for each omics-level (see Methods ). Results indicated extensive changes across all omics levels (Fig. 1 b, Extended Data Fig. 4 and Supplementary Tables 3.2–3.5 ). We first found profound differences between asymptomatic and symptomatic patients at all omics levels, suggesting a shared specific molecular feature in asymptomatic patients. Second, the changes in analytes between mild and severe groups were subtle at all omics levels except for proteins, indicating marked molecular similarities between these two severities, even in the presence of differences in clinical manifestations. Third, differences between the critical group and other groups were extremely high, implying a sudden and dramatic change from severe to critical disease. Genomic architecture of COVID-19 patients After data quality control based on whole-genome sequencing of 203 unrelated patients, 15.3 million bi-allelic single nucleotide polymorphisms (SNPs) were used for single-variant based association tests to investigate the connections among common variants (MAF > 0.05) and the diversity of clinical manifestations ( Extended Data Figs. 5 a-j and Supplementary Table 4.1 ). We first compared the generalized severe group (severe and critical, n = 65) with the mild group (asymptomatic and mild, n = 138) ( Extended Data Figs. 6 a-b), then compared the asymptomatic group (n = 63) with all other symptomatic patients (n = 140) ( Extended Data Figs. 6 c-d, Supplementary Table 4.2 ). In general, no signal showed genome-wide significance ( P < 5e − 8 ) in these comparisons. A suggestive signal ( P < 1e − 6 ) associated with the absence of symptoms was found on chromosome 20q13.13, which comprised of six SNPs, the most significant being SNP rs235001 (Supplementary Table 4.3) . Locus zoom identified two protein coding genes, B4GALT5 and PTGIS , in the region spanning ± 50 k of the SNP ( Extended Data Fig. 6 e). As a member of β-1, 4 galactosyltransferase family, B4GALT5 may participate in the glycosylation process of the membrane protein as well as the viral protein. A study in porcine showed that pB4GALT5 may play immunological protection roles in porcine respiratory syndrome virus (PRRSV) infection 24 . Together with the reported ABO gene (also glycosyl transferase), the altered glycoprotein modification may greatly affect the immunogenicity and host immune recognition process, resulting in the difference in susceptibility and severity. PTGIS encodes the enzyme for the synthesis of prostaglandin I2, a potent inhibitor of platelet aggregation, inhibiting platelet adherence to vessel walls. Additionally, PTGIS possesses anti-inflammatory properties by modulating the expression of IL-1, IL-6, IL-10, which may be associated with the COVID-19 severity 25 . We also assessed two loci, rs657152 at locus 9q34.2 and rs11385942 at locus 3p21.31, which have been found to be associated with COVID-19 patients with severe respiratory failure in Spanish and Italian populations 26 . For rs657152, the overall frequency of the protective allele C was 0.5468 (222/406) in our data, with the lowest rate found in the critical group (AF = 0.382, 13/34, Fisher’s exact test P = 0.04896). For rs11385942, the risk allele GA was not detected in any patient in our study, as this variant was rare in Chinese people 27 ( Supplementary Table 4.4 ), consistent with previously reported global distribution 26 . Quantitative trait locus (QTL) analysis has been widely applied to infer the contribution of genetic variations to complex phenotypes 28 . Here, QTL analysis was performed to explore the correlations of proteomic, metabolomic, and lipidomic features with genetic variations, resulting in 1328 mRNAs, 76 proteins, 195 metabolites and 4 lipids significantly associated with a variety of QTL ( P ≤ 5e − 8 ) ( Supplementary Table 5 ). Transcriptomic hallmark of COVID-19 To characterize progressive transcriptional changes through the four disease severities of COVID-19, we conducted unsupervised clustering of mRNAs that were differentially expressed in at least three of the six comparison groups Supplementary Table 3.2) . Three expression patterns were identified across patients with different disease severities (Fig. 2 a, Supplementary Table 6.1 ). Intriguingly, genes in cluster 1 increased both in asymptomatic and critically ill patients in comparison to mild and severe patients. The extend of upregulation was greater in asymptomatic cases. GO analysis showed these genes to be related to neutrophil activation, inflammatory response, granulocyte chemotaxis, and IL2, IL-6, IL-8 production (Fig. 2 a, Supplementary Table 6.2 ). Consistently, digital cytometry CIBRSORTx 29 , a widely used machine learning method estimated cell type abundances from bulk transcriptomes, revealing a dramatic increase of neutrophils in asymptomatic and critically ill patients (Fig. 2 b). Key chemokines ( CXCL8 , CXCR1 , CXCR2 ) for neutrophil activation and accumulation, as well as inflammatory responses genes ( TLR4 and TLR6 ) associated with toll-like receptors, and several key inflammatory response genes ( MMP8 , MMP9 , S100A12 , S100A8 , UBE2E3 ) shared this expression pattern (Fig. 2 c), suggesting a highly activated innate immune and pro-inflammatory response both in asymptomatic and critically ill patients than that in mild and severe patients at the transcriptomic level. Genes in cluster 2 were enriched in T cell activation, leukocyte-mediated cytotoxicity, NK cell-mediated immunity, and interferon-gamma production (Fig. 2 a ) . The expression levels of these genes were specifically decreased in critical patients compared to that of the other three severities. Important genes for T cell activation, such as CD28 , LCK , and ZAP70 , as well as key transcript factors for interferon-gamma production ( GATA3 , EOMES and IL23A ), showed this expression pattern (Fig. 2 c). Moreover, digital cytometry estimation revealed lower numbers of T and NK cells in critically ill patients (Fig. 2 b). Thus, although innate immune responses were activated in both asymptomatic and critically ill patients, T cell mediated adaptive immune response was specifically suppressed in critical COVID-19 patients. Cluster 3 contained genes primarily involved in protein polyubiquitination and autophagy. The expression of genes in this cluster gradually increased from the asymptomatic to mild/severe and then peaked at the critical group (Fig. 2 a). An important transcript factor encoding gene for autophagy, FOXO3 , displayed this expression pattern (Fig. 2 c). Genes in cluster 3 reflected the increasing tissue damage and cell death along with disease severity. Next, we investigated the post-transcriptional regulatory network associated with the genes in Fig. 2 c. miR-25-3p , miR-486-5p and miR-93-5p was uncovered to be negatively correlated with 11 genes about inflammatory response, and neutrophil activation (Fig. 2 d, Supplementary Table 7 ). Meanwhile, many lncRNAs were strongly and negatively correlated with FOXO3 , which plays a critical role in autophagy (Fig. 2 d, Supplementary Table 8 ). In view of the fact that the expression of FOXO3 , a negative regulator of the antiviral response, elevated along with the aggravation of the patient's condition, lncRNA differential accumulation may play a role in autophagy and antiviral response dysregulation in critically ill COVID-19 patients (Fig. 2 c-d) 30 . Landscape of proteins, metabolites and lipids in COVID-19 All proteins, metabolites, and lipids were classified into seven clusters with four progressive severities. Increasing patterns include the gradually increasing cluster C2 and the sharply increasing cluster C3. Decreasing patterns were composed of gradually decreasing cluster C6 and sharply decreasing cluster C1. C4, C5 and C7 belonged to the U-shaped patterns, mild specific, and critical specific patterns respectively (Fig. 3 a, Extended Data Fig. 7 and Supplementary Table 9 ). To systematically characterize the interaction networks among proteins, metabolites, and lipids within each cluster, we conducted co-expression network analysis using ranked spearman correlation coefficient (see Methods ), resulting in a systematic multi-omics network for each cluster ( Extended Data Fig. 8, Supplementary Tables 10.1 – 10.2) . Overall, we revealed putative dynamic interactions within each network, connecting immunity proteins (CSF1, C1S etc. ) to specific groups of metabolites (phenylalanine, tryptophan etc. ) and lipids (phosphatidylethanolamine, triglyceride etc. ). Protein circuits in COVID-19 Notably, a variety of biological pathways were found to be specifically enriched in the different clusters ( Fig. 3 b, Supplementary Table 9.2) . Consistent with transcription analysis (Fig. 2 a), a variety of proteins (BID, ILK, ADAMTSL4 etc .) related to the positive regulation of apoptotic processes were preferentially present in critical COVID-19 patients (C2, C3). However, inconsistent with mRNA expression patterns, proteins associated with positive regulation of inflammatory response and macrophage migration (S100A8, S100A12, C5, LBP, DDT etc. ) were gradually or sharply increased (C2, C3) (Fig. 3 c). Platelet degranulation and blood coagulation proteins were gradually increased (C2), or gradually increased (C6) respectively (Fig. 3 c), supporting the observed thrombocytopenia and coagulopathy in critically ill patients. Metabolites turnover in COVID-19 Metabolites showed distinct profiles in the different clusters. In particular, phenylalanine and tryptophan metabolism increased sharply (C3) in critical patients (Figs. 3 d-e, Supplementary Table 9.3 ). Tryptophan metabolism was considered a biomarker and therapeutic target of inflammation 31 , and changes in tryptophan metabolism were reported to be correlated with serum interleukin-6 (IL-6) levels 32 . Consistently, IL-6 levels were highest in critical patients ( Supplementary Table 2 ). Furthermore, compared to other severities, arginine gradually deceased along with disease severity (C6) (Figs. 3 d-e). Arginine is metabolized by myeloid cells (neutrophils, macrophages, granulocytes) by arginase 33 , further supporting activation of neutrophils and macrophages in symptomatic patients, especially in the critical. “Lipid codes” in COVID-19 We investigated the dynamics of lipids among the different severities. Phosphatidylethanolamine (PE), Lysophosphatidyliositol (LPI), and ceramides (Cer) were gradually increased (C2) (Fig. 3 f). A previous study suggested that RNA virus replication was dependent on the enrichment of PE distributed at the replication sites of subcellular membranes 34 , implying that the increase of PE in critical COVID-19 patients might facilitate the replication of viruses. LPI and Cer were found to increase in symptomatic groups. LPI is an endogenous agonist for GPR55 whose activation regulates several pro-inflammatory cytokines 35 . Ceramide induction has been thought as a strategy to inhibit T cell cytoskeletal reorganization in measles virus immunosuppression 36 and could increase the efficiency of pathogen uptake into dendritic cells 37 . Lysophosphatidylcholine (LPC) was sharply decreased in critical patients (C1) (Fig. 3 f). It has been reported that LPC levels decreased with the onset of sepsis and strongly predictive power for sepsis-related mortality 38 . LPC has shown therapeutic effects in experimental sepsis and microbial infections by enhancing H 2 O 2 production in neutrophils in vitro 39 and by inhibiting endotoxin-induced release of a late proinflammatory cytokine, HMGB1 as well. Therefore, the significant diminished LPC level may partly explain the more severe illness and worser clinical outcomes of critical patients. Intriguingly, LPA, which could enhance the secretion of IFNγ by activated NK cells 40 , were significantly enriched in asymptotic COVID-19 patients (C6) (Fig. 3 f). Overall, our study suggests that lipidome changes may play important and complex roles in COVID-19 disease development. Distinct neutrophils status within asymptomatic and critically ill COVID-19 patients Neutrophils are the first-responders of immune defense, and play critical roles in many forms of airway infection, including in antiviral immunity 41 . However, excessive neutrophils activation cause tissue damage. Especially during severe viral infections, neutrophils may abnormally differentiate to pathological low-density neutrophils (LDNs) with an enhanced capacity to release neutrophil extracellular traps (NETs) 42 . Excessive NETs release cause endothelium damage, promote thrombosis, and contribute to mortality in COVID-19 43 . As we showed in Fig. 2 a, transcriptional analysis indicated neutrophil were massively enriched in asymptomatic patients and mildly increased in critically ill patients (Fig. 4 a). However, most of these proteins (20 genes were detectable in proteomics data), including genes involved in activated inflammatory pathways (CHI3L1, S100A8, S100A9, S100A11, and S100A12), neutrophil degranulation (LFT, ANXA3, FGL2, LRG1, PGLYRP1, DEFA1B, SLPI), and NETs (MPO, and ELANE) were extremely low in asymptomatic patients, and then progressively increased along with disease severity (Fig. 4 b). This discrepancy implied that heterogeneous neutrophils, that are “beneficial” or “detrimental” depending on their functional protein expression, exist between asymptomatic and critically ill patients. Further, we analyzed expression correlations in all genes with available paired mRNA and proteins levels. 93 genes showed the expression pattern with highest transcripts but lowest protein levels in asymptomatic patients, and their protein levels were gradually increased and reached the peaks in critically ill patients (Figs. 4 c-d). Impressively, myeloid leukocytes activation and degranulation pathways were enriched in these genes (Fig. 4 d), which further supported various neutrophil status may contribute to the disease severity. “Cytokine paradox” in asymptomatic COVID-19 patients Pro-inflammatory pathway and inflammatory cytokines were unexpected transcriptionally activated in asymptomatic patients (Figs. 2 a and 4 e). However, consistent with a recent report 44 , secretion of inflammatory cytokines such as IL-6, IL-8, IL-2R, and IL-10 was extremely low in sera from the asymptomatic population (Fig. 4 f ) . In contrast, critically ill patients were characterized with excessive inflammatory cytokine production, whereas their transcription levels were only modestly elevated (Fig. 4 e ) . Typically, inflammatory cytokine production is tightly regulated both transcriptionally and post-transcriptionally 45 , 46 . Post-transcription of inflammation-related mRNAs is mainly regulated by RNA-binding proteins (RBPs) and microRNAs. Interestingly, RBPs (HNRNPD, TTP, ZC3H12A, ILF3, ZNF692, ZCCHC11, FXR1, ELAVL1, and BRF1/2) and microRNAs (miR-181a, miR-10a, miR-23b, miR-222, and miR-21), which are involved in the degradation and destabilization of inflammatory cytokines 47 , were highly expressed in asymptomatic patients but showed extremely low expression in critical patients (Figs. 4 g-h ) . Tryptophan and arginine metabolism perturbations contribute to T cells dysfunction in critically ill COVID-19 patients T cells play a critical role in antiviral immunity against SARS-CoV-2 48 , but their functional state and contribution to COVID-19 severity remain largely unknown. T/NK cell- mediated adaptive immune response were defected in critically ill patients (Fig. 2 a). Interestingly, tryptophan (Trp) metabolism was gradually accelerated along with disease severity (Figs. 3 e, and 5 a). Tryptophan degradation products depleted T cells, increased Th and NK cells apoptosis, and promoted T cell exhaustion 49 , 50 . Moreover, L-arginine is important for T-cell proliferation and function. The release of Arginase (ARG1/2) from activated neutrophils inhibit T-cell activation by inducing L-Arginine and Glutamine depletion 51 . Here we found ARG1 and ARG2 levels were upregulated in critical patients. Consistently, L-arginine, N-acetylornithine, and L-glutamine were lowest in critical patients (Fig. 5 b). Phenotypically, in addition to the dramatically decreased T cells in critically ill patients (Fig. 2 b), we noticed a significant upregulation of exhaustion markers: e.g ., PD-1 , CTLA4 , TIM3 , ICOS , and BTLA in T cells (Fig. 5 c). Impaired interferon response in critically ill COVID-19 patients An effective interferon (IFN) response eliminate viral infection including SARS-CoV-2 52 . Insufficient activation of IFN signaling may contribute to severe cases of COVID-19 53, 54 . As such, we compared the pathways of anti-viral IFN responses in the different severities of COVID-19 patients. Intriguingly, we found that critically ill patients failed to launch a robust IFN response compared with the highly activated IFN response observed in asymptomatic patients by measuring the expression of interferon-stimulated genes (ISGs) (Figs. 6 a-b). Furthermore, IFN receptors were specifically upregulated in asymptomatic patients while most IFN transcripts were undetectable in blood (Fig. 6 c). Third, multiple IFN upstream molecules, including TLR3, IRF1, IRF7, MAVS, DDX58, TBK1, JAK1 , and STAT2 were also increased in asymptomatic patients (Fig. 6 d). Furthermore, we performed the reverse engineering of gene regulatory network (GRN) to explore the transcriptional regulation network of IFN pathway in patients with various severity ( Supplementary Table 11 ). In asymptomatic patients, transcription factors including STAT5B, STAT3, STAT6, E2F3, NFYC, FLI1, ATF6, TFEB, and ARID3A firmly connected with IFN or IFN receptors (Fig. 6 e). Given the nature of GRN, decreasing counts of edges indicates reduced regulatory relationship between genes. Gradual loss of connectivity in the regulatory network of IFN were observed in symptomatic groups, especially in the critical patients (Fig. 6 e), which may contribute greatly to the dysregulation of IFN pathway in critically ill patients. Discussion To the best of our knowledge, this is the first trial designed to systematically analyze trans-omics data of COVID-19 patients with grade of clinical severity. Thought comprehensive multi-omics analysis, we revealed high neutrophils counts, low inflammatory cytokines and enhanced interferon anti-virus response in asymptomatic patients. In contrast, critically ill patients were characterized by neutrophils over-activation, cytokine storm, and interferon mediated innate immune or T/NK mediated adaptive immune defection. Asymptomatic patients have drawn great attention as these silent spreaders are hard to identify and cause difficulties in epidemic control 44 . Through our study, we observed an unexpected expression discordance exist with extreme transcriptional activation but low inflammatory cytokines secretion in asymptomatic patients. Typically, inflammatory cytokine production is elegantly regulated both transcriptionally and post-transcriptionally. By recognizing inflammatory cytokine mRNA with stem-loop structures, RBPs can degrade or decay inflammatory cytokine mRNA. microRNAs have also emerged as fine-tune regulators to control inflammation. The balance of these actions controls inflammation intensity 47 . For instance, AUF1 (HNRNPD), TTP (ZFP36) attenuates inflammation by destabilizing mRNAs encoding inflammatory cytokines, including IL-2, IL-6, TNF and IL-1β 55 – 57 . Regnase-1 (ZCH12A) has a wide antiviral spectrum and efficiently inhibits the influenza A virus. Furthermore, Regnase-1 restrains inflammation by negatively regulating IL6 and IL17 mRNA stabilization 58 , 59 . Thus, Regnase-1 depletion facilitates severe systemic inflammation and virus replication. miR-10a, and miR-21 has been shown to negatively regulate IL-6 and TNF 60 . Accordingly, we propose that the observed discrepancy between cytokine mRNA and protein levels could be attributed to post-transcriptional mRNA stabilization mediated by RBPs or microRNAs. Our data suggests a novel mechanism for inflammatory cytokine regulation at the post-transcriptional level, which explains the molecular mechanism of various clinical symptoms and suggests that RBPs could be a potential therapeutic target in COVID-19. However, additional functional researches will be required to ascertain their contribution towards the development of COVID-19. Neutrophils play a protective role in antiviral immunity, whose depletion led to viral replication and increased lethality in mice infected with the influenza virus 61 . Neutrophils exhibit a strong ability to mediate virus elimination not only by direct phagocytic activity, but also in cooperation with B cells and also modulate dendritic cell (DC), macrophage, and T-cell activities 62 . However, excessively activated neutrophils form neutrophil extracellular traps (NETs) and lead to tissue damage, which is termed NETosis, as we found in critically ill COVID-19 patients. NETs are closely related to the severity of influenza, Ebola virus infection, and COVID-19 43, 63 . These observations hint at a prominent role of neutrophils in COVID-19. So, the determinants of neutrophil transition from beneficial to detrimental effects deserves additional investigation. Furthermore, considering the major role excess NETs play in COVID-19 severity, targeting NETs formation by directly inhibiting critical molecules required for NET (neutrophil elastase (NE), PAD4, and gasdermin D 64 – 66 provide a promising therapeutic choice to reduce the clinical severity of COVID-19. T cell depletion in critically ill patients is in line with the clinically observed T cell lymphopenia, which was also negatively correlated with COVID-19 severity 67 . Recent research has demonstrated that SARS-CoV-2 dramatically reduces T cells, and up-regulates exhaustion markers PD-1, and Tim-3, especially in critically ill patients 67 . Mechanistically, various clinical evidences show that T-cell counts are negatively associated with serum IL-6, IL-10, and TNF-alpha concentrations 22 , uncontrolled cytokine release may prompt the depletion and exhaustion of T cells. Second, it is well known that accelerated Trp metabolism by rate-limiting enzymes, i.e., indoleamine 2,3-dioxygenases (IDO1 and IDO2) mediates T cell dysfunction. Trp catabolite production, KYN, 3-HAA, and Quin inhibits adaptive T cell immunity, block expansion and proliferation of conventional CD4 + helper T cells and effector CD8 + T cells, and potentiate CD4 + regulatory T (Treg) cell function 68 . Third, L-Arginine depletion owing to hyper-activated neutrophils inhibited T-cell function. Thus, in addition to the loss of T cell counts in critical patients, T cells become metabolically exhausted and dysfunctional. The impaired IFN response in the critical could be responsible for the loss of viral replication control in these patients 67 . Moreover, highly accumulated PE lipids (Fig. 3 f), which are important for RNA virus replication 34 , further enhanced SARS-CoV-2 replication in critically ill patients. Consequently, uncontrolled viral replication can result in the orchestration of a much stronger inflammatory response in critically ill patients, characterized by cytokine storms and immunopathogenesis. Conversely, the sufficient IFN response in asymptomatic patients could help to defend against viral infections. It is possible that biological crosstalk exists among the cytokine storm, Trp metabolism, and T cell dysfunction processes. First, considering the essential role of Trp metabolism in blocking the expansion and proliferation of conventional CD4 + helper T cells and effector CD8 + T cells as well as in potentiating CD4 + regulatory T (Treg) cell function 68 , the accumulated Trp catabolite production would inhibit adaptive T cell immunity. Second, Trp directly stimulates immune checkpoint expression levels, such as CTLA4 and PD-1 69 . Third, in addition to the direct effects on T cell dysfunction, proinflammatory cytokines, e.g., IL-1β, IFN-γ, and IL-6, can lead to a robust elevation in circulating Kyn levels by up regulation of IDO/TDO 70 , which synergistically worsen T cell dysfunction. Fourth, adaptive T cell immunity plays an unexpected role in tempering the initial innate response 71 , T cells defection in critically ill patients could in turn exacerbate an uncontrolled innate immune response. Therapeutically, considering the essential effects of arginine, tryptophan, IDO, and T cell function on COVID-19 severity, bolstering the immune system by restoring exhausted T cells may be a promising strategy for disease treatment. Direct arginine supplement, targeting Trp catabolism by indoximod, or targeting IDO1/TDO2 by navoximod (NLG919) 72 , BMS-986205 73 , or PF-06840003 74 could metabolically restore T cell function. Furthermore, immune checkpoint blockage with PD1/PD-L1 or CTLA4 antibody has been shown to increase T cell numbers and restore T cell function 75 , which may be a potential strategy for the treatment of critically ill patients. It may therefore be worthwhile to test if the aforementioned immune-boosting strategies are effective in COVID-19 clinical trials. In conclusion, our study presented a trans-omics landscape of blood samples within a large cohort of COVID-19 patients with various severities from asymptomatic to critically ill. Overall, we uncovered multiple novel insights and therapeutic targets relevant to COVID-19. Our data provided valuable clues for deciphering COVID-19 and the underlying mechanism warrant for further pursuits. Materials and Methods Patients Enrollment and Sample Preparation Blood samples for 231 COVID-19 patients without any comorbidities were collected from Tongji Hospital and Union Hospital of Huazhong University of Science and Technology, Xiangyang Central Hospital, Hubei University of Arts and Science and Hubei Dazhong Hospital of Chinese Traditional Medicine from 19th February, 2020 to 26th April, 2020. Flowchart of patient selection for this study were shown in Extended Data Fig. 1 . The demographic data and laboratory indicators were shown in Supplementary Tables 1-2 . The mean age of the patients was 46.7 years old (Standard Deviation=13.5), and the ratio of male to female was 1.12:1. All these patients were diagnosed following the guidelines for COVID-19 diagnosis and treatment (Trial Version 7) released by the National Health Commission of the People’s Republic of China. The patients were classified into four groups according to their disease severity: critical, severe, mild, and asymptomatic. The critical disease was defined as fulfilling at least one of the following conditions: (1) acute respiratory distress syndrome (ARDS) requiring mechanical ventilation, (2) shock, (3) combining with other organ failure requiring ICU admission. Severe disease met at least one of the following conditions: (1) respiratory rate ≥ 30 times/min, (2) oxygen saturation ≤93% at resting state, (3) arterial partial pressure of oxygen (PaO2)/fraction of inspired oxygen (FiO2) ≤300 mmHg, (4) pulmonary imaging examination showed that the lesions significantly progressed by more than 50% within 24-48 hours. Mild patients were defined as having fever, respiratory symptoms, lung imaging evidence of pneumonia. The patients with normal body temperature, without any respiratory symptoms were defined as asymptomatic. The definition of each severity was consistent with the previous article 76 . All Ethylenediaminetetraacetic acid disodium salt (EDTA-2Na)-anticoagulated venous blood samples were separated by centrifuge at 3,000 rpm, room temperature for 7min after standard diagnostic tests, the whole blood cells were stored at -80°C, 200 μL aliquot of serum were added 800μL ice-cold methanol, mixed well and stored at -80°C, another 200 μL aliquot of serum were added 800μL ice-cold isopropanol, mixed well and stored at -80°C. Nucleic Acid Extraction A 200 μL aliquot of each thawed whole blood cells was used to extract DNA using QIAamp DNA Blood Mini Kit (51304, Qiagen), following the manufacturer’s instructions. Total RNA was extracted from another 200 μL aliquot of blood cells using QIAGEN miRNeasy Mini Kit (217004，Qiagen) according to the manufacturer’s protocol. All the extraction was performed under Level III protection in the biosafety III laboratory. Sequencing Library Construction and Data Generation The whole genome data was generated through the following steps: 1) DNA was randomly fragmented by Covaris. The fragmented genomic DNA were selected by Magnetic beads to an average size of 200-400bp. 2) Fragments were end repaired and then 3’ adenylated. Adaptors were ligated to the ends of these 3’ adenylated fragments. 3) PCR and Circularization. 4) After library construction and sample quality control, whole genome sequencing was conducted on MGI2000 PE100 platform with 100bp paired end reads. Transcriptome RNA data was generated through the following steps: 1) rRNA was removed by using RNase H method, 2) QAIseq FastSelect RNA Removal Kit was used to remove the Globin RNA, 3) The purified fragmented cDNA was combined with End Repair Mix, then add A-Tailing Mix, mix well by pipetting, incubation, 4) PCR amplification, 5) Library quality control and pooling cyclization, 6) The RNA library was sequenced by MGI2000 PE100 platform with 100bp paired-end reads. Small RNA data was generated through the following steps: 1) Small RNA enrichment and purification, 2) Adaptor ligation and Unique molecular identifiers (UMI) labeled Primer addition, 3) RT-PCR, Library quantitation and pooling cyclization, 4) Library quality control, 5) Small RNAs were sequenced by BGI500 platform with 50bp single-end reads resulting in at least 20M reads for each sample. Cytokine detection We detected cytokines including IL-6, IL-8, IL-10, IL-2R in serum samples of patients. Assays were conducted by using an automated analyzer (Cobas e602, Roche Diagnostics, Germany or Immulite 1000, DiaSorin Liaison, Italy) as described in the manufacturer’s instructions. IL-6 kit (#05109442190) was obtained from Roche Diagnostics (Mannheim, Germany). IL-8 kit (#LK8P1), IL-10 kit (#LKXP1), IL-2R kit (#LKIP1) were obtained from DiaSorin (Vercelli, Italy). WGS data analysis and joint variant calling Whole genome sequencing data was processed using the Sentieon Genomics software (version: sentieon-genomics-201911) 77 . Pipeline was built according to the best practice’s workflows for germline short variant discovery described in https://gatk.broadinstitute.org/. Sequencing reads were mapped to hg38 reference genome using BWA algorithm 78 . After duplicates marking, InDel realignment and base quality score recalibration (BQSR), per-sample variants were called using the Haplotyper algorithm in the GVCF mode. Then the GVCFtyper algorithm was used to perform joint-calling and generate cohort VCF. Variant Quality Score Recalibration was performed using Genome Analysis Toolkit (GATK version 4.1.2) 79 . The truth-sensitivity-filter-level were set as 99.0 for both the SNPs and the Indels. Finally, variants with PASS flag and quality score ≥ 100 were selected for further analysis. Genotype-Phenotype Association Analysis PCA was performed using PLINK (v1.9) 80 . Bi-allelic SNPs were selected based on the following criteria: minor allele frequency (MAF) ≥ 5%; genotyping rate ≥ 90%; LD prune (window = 50, step = 5 and r2 ≥ 0.5). A subset of 605,867 SNPs was used to perform PCA on the 203 unrelated individuals. We used rvtest 81 to perform genotype-phenotype association analysis for 5,082,104 bi-allelic common SNPs with MAF > 5%. Gender, age and top 10 principal components were used as covariates for all the association tests. The qqman 82 and CMplot R packages 83 were applied to generate the Manhattan plot and quantile-quantile plot. We defined genome-wide significance for single variant association test as 5e -8 , suggestive significance as 1e -6 . QTL Analysis We obtained matched proteomics, lipidomics, metabolomics, gene expression and SNP genotyping data for COVID-19 patients (n = 132). For the genotyping data, we removed outlier SNPs with MAF < 0.05. The QTL analysis (cis-eQTL analysis [local, distance < 10kb] for gene expression data, QTL analysis for proteomics, lipidomics, metabolomics data) was conducted using linear regression as implemented in MatrixEQTL 84 . In this analysis, age and gender (1 for male and 2 for female) were considered as covariates. Associations with a p value less than 0.001 were kept, followed by FDR estimation using the Benjamini-Hochberg procedure as implemented in Matrix-QTL. QTL associations with an FDR-corrected p value < 5e -8 were considered significant 85 . Gene Expression Analysis RNA-seq raw sequencing reads were filtered by SOAPnuke 86 to remove reads with sequencing adapter, with low-quality base ratio (base quality 20%, and with unknown base ('N' base) ratio > 5%. Reads aligned to rRNA by Bowtie2 (v2.2.5) 87 were removed. Then, the clean reads were mapped to the reference genome using HISAT2 88 . Bowtie2 (v2.2.5) was applied to align the clean reads to the transcriptome. Then the gene expression level (FPKM) was determined by RSEM 89 . Genes with FPKM > 0.1 in at least one sample were retained. Differential expression analysis was performed using DESeq2 (v1.4.5) with gender and age as confounders. Differential expressed genes were defined as those with Benjamini Hochberg adjusted p value 2. GO enrichment analysis was performed using clusterProfiler 90 . GO BP terms with an FDR adjusted p value threshold of 0.05 were considered as significant 91 . Small RNA raw sequencing reads with low quality tags (which have more than four bases whose quality is less than ten, or have more than six bases with a quality less than thirteen.), the reads with poly A tags, and the tags without 3' primer or tags shorter than 18nt were removed. After data filtering, the clean reads were mapped to the reference genome and other sRNA database including miRbase, siRNA, piRNA and snoRNA using Bowtie2 87 . Particularly, cmsearch 92 was performed for Rfam mapping. The small RNA expression level was calculated by counting absolute numbers of molecules using unique molecular identifiers (UMI, 8-10nt). MiRNA with UMI count lager than 1 in at least one sample were considered as expressed. Differential expression analysis was performed using DESeq2 (v1.4.5) 93 with gender and age as confounders to control for the additional variation and the detection cutoff was set as adjusted P < 0.05 and log2 of fold change ≥ 1. Construction of mRNA-miRNA and mRNA-lncRNA Network To investigate the post-transcriptional regulation, spearman correlation coefficients of mRNA-miRNA ( Supplementary Table 7 ) and mRNA-lncRNA were calculated ( Supplementary Table 8 ). Correlation pairs with coefficients < -0.5 in mRNA-miRNA or < -0.6 in mRNA-lncRNA were retained. MultiMiR was used to confirm the top pairs of mRNA-miRNA by performing miRNA target prediction 94 . The mRNA-miRNA and mRNA-lncRNA networks were visualized using Cytoscape ( Fig. 2d ) 95 . Proteomics Analysis The sera samples were inactivated at 56°C water bath for 30min and followed by processing with the Cleanert PEP 96-well plate (Agela, China). According to the manufacturer’s instructions, high-abundance proteins under a denaturing condition were removed 96 . The Bradford protein assay kit (Bio-Rad, USA) was used to determine the final protein concentration. The proteins were extracted by the 8M urea and subsequently reduced by a final concentration of 10mM Dithiothreitol at 37°C water bath for 30min and alkylated to a final concentration of 55mM iodoacetamide at room temperature for 30min in the darkroom. The extracted proteins were digested by trypsin (Promega, USA) in 10 KD FASP filter (Sartorious, U.K.) with a protein-to-enzyme ratio of 50:1 and eluded with 70% acetonitrile (ACN), dried in the freeze dryer. DIA (Data Independent Acquisition) strategy was performed by Q Exactive HF mass spectrometer (Thermo Scientific, San Jose, USA) coupled with an UltiMate 3000 UHPLC liquid chromatography (Thermo Scientific, San Jose, USA). The 1μg peptides mixed with iRT (Biognosys, Schlieren, Switzerland) were injected into the liquid chromatography (LC) and enriched and desalted in trap column. Then peptides were separated by self-packed analytical column (150μm internal diameter, 1.8μm particle size, 35cm column length) at the flowrate of 500 nL/min. The mobile phases consisted of (A) H 2 O/ACN (98/2,v/v) (0.1% formic acid); and (B) ACN/H 2 O (98/2,v/v) (0.1% formic acid) with 120 min elution gradient (min, %B): 0, 5; 5, 5; 45, 25; 50, 35; 52, 80; 55, 80; 55.5, 5; 65, 5. For HF settings, the ion source voltage was 1.9kV; MS1 range was 400-1250m/z at the resolution of 120,000 with the 50 ms max injection time(MIT). 400-1250 m/z was equally divided into 45 continuous windows MS2 scans at 30,000 resolution with the automatic MIT and automatic gain control (AGC) of 1E6. MS2 normalized collision energy was distributed to 22.5, 25, 27.5. The raw data was analyzed by Spectronaut software (12.0.20491.14.21367) with the default settings against the self-built plasma spectral library which achieved deeper proteome quantification. The FDR cutoff for both peptide and protein level were set as 1%. Next, the R package MSstats 97 finished log2 transformation, normalization, and p-value calculation. Metabolomics Analysis The 100μl sera of each sample were transferred into the 96-well plate and mixed with 10μl SPLASH LipidoMixTM Internal Standard (Avanti Polar Lipids, USA) and 10μl home-made Internal Standard mixture containing D3-L-Methionine (100 ppm, TRC, Canada), 13C9-Phenylalanine (100ppm, CIL, USA), D6-L-2-Aminobutyric Acid(100ppm, TRC, Canada), D4-L-Alanine (100ppm, TRC, Canada), 13C4-L-Threonine (100ppm, CIL, USA), D3-L-Aspartic Acid (100ppm, TRC, Canada), and 13C6-L-Arginine (100ppm, CIL, USA). The 300μl pre-chilled extraction buffer of methanol/ACN (67/33, v/v) was added to the plasma sample then vortexed for 1 min and incubated at -20°C for 2 hours. After centrifugation at 4000 RPM for 20 min, 300ul supernatants were taken and dried in the freeze dryer. The metabolites were dissolved in 150μl buffer of methanol/ACN (50/50, v/v) and centrifuged at 4000 RPM for 30min. Supernatants were injected into mass spectrometer. Metabolomics data acquisition was completed using a same spectrometer, LC, and settings were set as lipidomics except for following parameters: the mobile phases of positive mode were (A) H2O (0.1% formic acid) and (B) methanol (0.1% formic acid). The mobile phases of negative mode were (A) H2O (10mM NH4HCO2) and (B) methanol /H2O (95/5, v/v) (10 mM NH4HCO2). Both positive and negative models used the same gradient (min, %B): 0, 2; 1, 2; 9, 98; 12, 98; 12.1, 2; 15, 2. The temperature of column was set at 45°C. MS1 range set as 70-1050m/z. MS2 stepped normalized collision energy was distributed to 20, 40, 60. The raw data was searched by Compound Discoverer 3.1 software (Thermo Fisher Scientific, USA) with different libraries including our self-built BGI library containing more than 3000 metabolites with corresponding detailed mass spectrum data. After quantification, subsequent processing steps were finished by metaX as same as lipidomics analysis. Lipidomics Analysis The 100 μl sera of each sample was transferred into the 96-well plate and mixed with 10 μl SPLASH LipidoMixTM Internal Standard (Avanti Polar Lipids, USA). The 300μl pre-chilled Isopropanol (IPA) was added to the plasma sample and vortex for 1 min and incubated at -20°C overnight. Then samples were centrifuged at 4000 RPM for 20min while proteins precipitated. The supernatants were used for MS analysis. Lipidomics analysis was performed using Q Exactive mass spectrometer (Thermo Scientific, San Jose, USA) coupled with Waters 2D UPLC (waters, USA). The CSH C18 column (1.7μm 2.1*100mm, Waters, USA) was used for separation with following elution gradient (min, %B) consisted of (A) ACN/H2O (60/40, v/v) (10 mM NH4HCO2 and 0.1% formic acid) and (B) IPA/ACN (90/10, v/v) (10 mM NH4HCO2 and 0.1% formic acid): 0, 40; 2, 43; 2.1, 50; 7, 54; 7.1, 70; 13, 99; 13.1, 40; 15, 40. The temperature of column was set as 55°C, the injection value was set as 5μL, and the flowrate was set as 0.35mL/min. For HF settings, the samples were scanned twice in both positive and negative modes. The positive spray voltage was set as 3.80 kV and negative spray voltage was set as 3.20 kV. MS1 range was 200-2000m/z at the resolution of 70,000 with the 100ms MIT and AGC of 3e6. The top3 precursors were set as trigger MS2 scans at the resolution of 17,500 with the 50ms MIT and AGC of 1E5. MS2 stepped normalized collision energy was distributed to 15, 30, 45. The sheath gas flow rate was set as 40 and the aux gas flow rate was set as 10. The raw data was analyzed by Lipidsearch software Version 4.1 (Thermo Fisher Scientific, USA) which finished feature detection, identification and alignment. The following settings were applied: tolerance of mass shift, 5ppm; identification grade, A-D; filters, top rank; all isomer peak, FA priority, M-score, 5; c-score, 2.0; The export quantitative data from Lipidsearch was analyzed by R package metaX 98 which finished the normalization, correction of batch effect, and imputation of missing value. For each patient in the cohort, we computed intensity for a given lipid complex class by summing up intensity of each lipid in the class. For each lipid complex class, the intensity value of each patient was further scaled by median value of intensity from mild patient group. We applied Mann-Whitney U-test (multiple comparisons correction with Bonferroni) to test statistically significant difference of scaled intensity of each lipid complex class between severity groups. Differential Expression of Proteins, Metabolites and Lipids Expression data was first adjusted using robust linear model (RLM) for gender and age. The residuals following RLM were analyzed by Two-sided Mann-Whitney rank test for each pair of comparing group and p values were adjusted using Benjamini & Hochberg. Differentially expressed proteins, metabolites or lipids were defined using the criteria of adjusted P value 1.5. Clustering Clustering was performed using the R package ‘Mfuzz’ after log2-transformation and Z-score scaling of the data. For mRNA from whole blood, genes differentially expressed in at least three out of the six comparison groups were clustered. For proteins, metabolites, lipids from sera, all the three analytes were clustered together. Pathway analysis To annotate the proteins and metabolites in 7 clusters, gene ontology (GO) enrichment analysis were performed to obtain the enriched GO Biological Process terms of proteins in different clusters by clusterProfiler 90 . And the 7 lists of metabolites in KEGG ID were classified into pathways by the Kyoto encyclopedia of genes and genomes (KEGG) database. The KEGG annotation was finished using in-house software. Correlation Network Analysis Pairwise Spearman’s rank correlations were calculated using the r package ‘Hmisc’ and weighted, undirected networks were plotted with Cytoscape. Correlations with Bonferroni adjusted P values 0.4 ( Supplementary Table 8 ) were included and displayed via the Fruchterman-Reingold method. Nodes color indicate analytes type and their size represent the degree of the node. Construction of gene regulatory network The ARACNe-AP 99 was employed to construct the gene regulatory networks (GRNs) for each group. The variability of genes expression traits was evaluated by Median Absolute Deviation (MAD), and the top half of genes were recruited in the network. Mutual information 100 was introduced to represent the strength of the regulatory relationship between TFs and target genes, and only significant pairs are kept (P<1×10 -8 ).We also executed 100 bootstraps and applied a Data Processing Inequality tolerance filter 101 . The consensus network of each group was combined by statistically significant edges across all bootstrap networks (p<0.05, Bonferroni corrected), based on Poisson distribution. The degree was used to evaluate the centrality of genes in the network. To ensure the robustness of our remodeled GRN, we applied Chip-X Enrichment Analysis Version 3(ChEA3) 102 to identify TFs that target to IFN and IFN receptors, and those unrecognized were eliminated. Quantification of cell fractions from bulk RNAseq profiles The estimation of abundances of immune cell types in blood tissue was performed using CIBERSORTx 103 based on blood RNAseq data. Protein interaction network construction and function enrichment analysis Interaction network construction and biological process GO term enrichment for protein lists were conducted using STRING 104 database with default parameters. Declarations Competing Interest The authors declare no competing interests. Data Availability Data for this project will be available upon request. The data that support the findings of this study, including the genome-wide association test summary statistics, expression matrices for multi-omics have been deposited in CNSA (China National GeneBank Sequence Archive) in Shenzhen, China with accession number CNP0001126 ( https://db.cngb.org/cnsa/ ) and will be released to the public after the manuscript is accepted for publication. Besides, processed data and scripts will be released at http://120.79.46.200:81/COVID19 . Code Availability Custom scripts for data analysis in this study were present in https://github.com/DongshengChen-TY/COVID19 . Ethics Statement This study was reviewed and approved by the Institutional Review Board of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20200405). All the enrolled patients signed an informed consent form, and all the blood samples were collected using the rest of the standard diagnostic tests, with no burden to the patients. Author Contributions D.M, X.J, G.C, C.S, L.W, P.W contributed to project conceptualization. D.M, X.J, X.X, S.L, J.W, H.Y contributed to the supervision. P.W, W.D, P.W, H.H, K.L, E.G, J.L, B.Y, J.F, L.H, Z.S, L.F, J.W, T.W, H.W, J.C, H.X, Y.M, Y.L contributed to sample collection. P.W, D.C, W.D, P.W, H.H, Y.B, Y.Z, K.L contributed to data analysis coordination. Y.R, Y.Z, K.H, W.S, Y.Z, H.L contributed to WGS, RNA-seq, LC-MS experiments. S.X, J.J, P.D, H.W, J.Q, F.W, J.Z, S.W, X.W, X.D, L.L, L.L, C.C, Z.Z contributed to RNA-seq analysis M.H, Y.S contributed to miRNA-mRNA, lncRNA-mRNA interaction networks. Y.R, Y.Z, K.H, W.S, P.D, H.W, J.Q, F.W, J.Z, S.W, X.W, X.D, L.L, L.L, C.C contributed to proteomic analysis. Y.R, Y.Z, K.H, W.S, P.D, H.W, J.Q, F.W, J.Z, S.W, X.W, X.D, L.L, L.L, C.C metabolites analysis Y.Y, Y.R, Y.Z, K.H, W.S, P.D, H.W, J.Q, F.W, J.Z, S.W, X.W, X.D, L.L, L.L, C.C contributed to lipids analysis. Y.T, P.D, H.W, J.Q, F.W, J.Z, S.W, X.W, X.D, L.L, L.L, C.C, A.C contributed to data visualization. Y.S, Y.Y, Z.Z, T.L, L.T, S.Z, L.Z, L.C, Y.W, X.M, F.C contributed to data interpretation. Y.Z contributed to data deposition. D.M, X.J, P.W, D.C, W.D, P.W, H.H, Y.B, Y.Z, K.L, L.W, C.S, G.C, A.C contributed to writing the original draft. Acknowledgements The study was supported by funding from National University Basic Scientific Research Special Foundation (2020kfyXGYJ00), China National GeneBank (CNGB) and Guangdong Provincial Key Laboratory of Genome Read and Write (No. 2017B030301011), Natural Science Foundation of Guangdong Province (2017A030306026), Funds for Distinguished Young Scholar of South China University of China (2017JQ017). We would like to thank Shangbo Xie, Yuying Zeng, Chengcheng Sun, Wendi Wu, Yan Li, Siyang Liu from BGI for helpful discussions of the results and advices. References WHO (2020). Worldometers Coronavirus (COVID-19) Mortality Rate. Last updated: May 14. (2020). Lu, X. et al. SARS-CoV-2 Infection in Children. N Engl J Med 382, 1663–1665 (2020). Pan, X. et al. Asymptomatic cases in a family cluster with SARS-CoV-2 infection. Lancet Infect Dis 20, 410–411 (2020). Chan, J.F. et al. 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Supplementary Files SupplementaryFiguresNC.docx Supplementary Figures SupplementaryTable1.xlsx Supplementary Table 1 SupplementaryTable2.xlsx Supplementary Table 2 SupplementaryTable3.xlsx Supplementary Table 3 SupplementaryTable4.xlsx Supplementary Table 4 SupplementaryTable5.xls Supplementary Table 5 SupplementaryTable6.xlsx Supplementary Table 6 SupplementaryTable7.xlsx Supplementary Table 7 SupplementaryTable8.xlsx Supplementary Table 8 SupplementaryTable9.xlsx Supplementary Table 9 SupplementaryTable10.xlsx Supplementary Table 10 SupplementaryTable11.xlsx Supplementary Table 11 Cite Share Download PDF Status: Published Journal Publication published 27 Jul, 2021 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ding","middleName":"","lastName":"Ma","suffix":""},{"id":1486446,"identity":"d724ab0e-f4f0-4921-bda3-b7672d91944b","order_by":68,"name":"Xin Jin","email":"","orcid":"","institution":"BGI-Shenzhen, Shenzhen, China.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Jin","suffix":""},{"id":1486447,"identity":"7581bd14-755c-4e29-9847-29dac242f977","order_by":69,"name":"Gang Chen","email":"","orcid":"","institution":"Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2020-08-13 18:01:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-59060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-59060/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-021-24482-1","type":"published","date":"2021-07-27T10:50:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":2167326,"identity":"acf31985-c93e-4f22-b64c-668d7185fc3a","added_by":"auto","created_at":"2020-08-31 17:22:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168135,"visible":true,"origin":"","legend":"Patient enrollment, study design and trans-omics profiling of COVID-19. a, Overview of COVID-19 patient enrollment criteria and the study design including multi-omics profiling from blood samples of COVID-19 patients spanning four disease severities covering asymptomatic (asym), mild, severe, and critical. Venn diagram shows the overlapping of samples profiled using WGS, RNAseq and LC-MS. b, Bar plot showing the numbers of significantly differentially expressed mRNAs, proteins, metabolites, and lipids in six groups of comparison (Increased: adjusted P value \u003c 0.05 and fold change \u003e 1.5, Decreased: adjusted P value \u003c 0.05 and fold change \u003c -1.5).","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-59060/v1/Fig1.png"},{"id":2167327,"identity":"3cb6f145-b17c-4082-9846-707d1ee951a2","added_by":"auto","created_at":"2020-08-31 17:22:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":283533,"visible":true,"origin":"","legend":"Transcriptomic hallmark of COVID-19. a, Bubble plot showing reprehensive biological process (BP) GO terms enriched in each expression patterns across four disease severity groups. Red colors and blue colors represent genes up-regulated or down-regulated in investigated group compared to mild group respectively (median of log2 (fold-changes)). The dot size represents -log2 (adjusted P values), which were determined using DEseq2 and adjusted for multiple test using Benjamini-Hochberg correction. b, Estimated immune cells abundance using CIBERSORTx for various COVID-19 severity groups. c, Boxplot of representative genes associated with regulation of inflammatory response, regulation of inflammatory response, T cell activation, interferon-gamma production, protein K48-linked ubiquitination and autophagy across four severity groups (for Fig. 2b, and Fig. 2c, the statistical significance was calculated by Wilcox Test. The symbol ns means adjusted P value \u003e0.05,* means adjusted P value ≤0.05，** means adjusted P value≤0.01，*** means adjusted P value≤0.001 and **** means P≤0.0001). d, mRNA-miRNA and mRNA-lncRNA interaction networks for genes mentioned in Fig. 2c. ","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-59060/v1/Fig2.png"},{"id":2167328,"identity":"329bb1b4-77a6-4d58-8433-49a0f55bd998","added_by":"auto","created_at":"2020-08-31 17:22:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":419445,"visible":true,"origin":"","legend":"Landscape of proteins, metabolites and lipids in COVID-19. a, Expression patterns of COVID-19 plasma analytes including proteins, metabolites, and lipids across four disease severity groups. b, Representative biological process (BP) GO terms enriched for proteins in seven patterns (P values were calculated using hypergeometric test). c, Heatmap representing protein expressions in five functional categories. Each column indicates a COVID-19 patient sample and each row represents a protein. Colors of each cell shows Z-score of log2 protein abundance in that sample. d, Enriched KEGG pathway for metabolites in seven patterns (P values were calculated using hypergeometric test). e, Heatmap representing metabolites expression in phenylalanine, tryptophan metabolism and arginine biosynthesis pathway. f, Lipid expression changes across four disease severity groups. The symbol ns means P value\u003e0.05,* means P value≤0.05，** means P value≤0.01，*** means P value≤0.001 and **** means P value≤0.0001)","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-59060/v1/Fig3.png"},{"id":2167329,"identity":"cead6305-e271-4dd7-989d-8f7bbda63ad7","added_by":"auto","created_at":"2020-08-31 17:22:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":280778,"visible":true,"origin":"","legend":"Distinct neutrophils status and “cytokine paradox” within asymptomatic and critically ill COVID-19 patients. a, Heatmap of mRNA abundance for genes in the neutrophil activation pathway across four disease severity groups. b, Heatmap of protein abundance in the neutrophil activation pathway across four disease severity groups. c, Dynamic changes of representative genes that showed discordance between protein and mRNA expression. The abundance of mRNA and protein were scaled by median expression. d, The protein-protein interaction network (PPIN) of genes that showed discrepancy pattern in abundance of mRNA and protein across four disease severity groups. e, The variation patterns of gene expression of inflammatory cytokines across four disease severity. f, Quantification of IL-6, IL-8, IL-10 (pg/mL) and IL-2R(u/L) in each group (detected using ELISA from serum samples of COVID-19 patients). g, Heatmap showing the mRNA abundance of RBPs across four disease severity groups. h, Heatmap showing the miRNA abundance across four disease severity groups.","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-59060/v1/Fig4.png"},{"id":2167330,"identity":"30a4b865-525c-4382-9fb3-26f3bc5ceef4","added_by":"auto","created_at":"2020-08-31 17:22:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":188426,"visible":true,"origin":"","legend":"Tryptophan and arginine metabolism perturbations contribute to T cells dysfunction in critically ill COVID-19 patients. a, Summary of tryptophan metabolism pathways. (IDO, indoleamine 2,3-dioxygenase; KAT, kynurenine aminotransferase; MAO, monoamine oxidase; TDO, tryptophan 2,3-dioxygenase). Box plots in this panel showed the expression level change (log2(x+1)-scaled original value) of selected regulated metabolites across four disease severities. b, Boxplots of mRNA for (ARG1, ARG2) and metabolic abundance of arginine metabolism pathway components (L−arginine, N−acetylornithine, L−glutamine). c, Relative expression abundance of exhaustion marker genes CTLA4, BTLA, HAVCR2, ICOS and PDCD1 in T cells. The relative expression abundance of the exhaustion marker genes was defined as their expression levels dividing the expression level of T cell marker gene CD3E. The statistical significance was calculated by Wilcox Test. The symbol ns means adjusted P value \u003e0.05,* means adjusted P value ≤0.05，** means adjusted P value≤0.01，*** means adjusted P value≤0.001 and **** means adjusted P value≤0.0001.","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-59060/v1/Fig5.png"},{"id":2167331,"identity":"8caec60d-407f-464e-a5f1-f284e582be0c","added_by":"auto","created_at":"2020-08-31 17:22:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":224321,"visible":true,"origin":"","legend":"Impaired interferon response in critically ill COVID-19 patients. a, Heatmap demonstrating the expression levels of Interferon-stimulated gene (ISG) across four disease severity groups. b, Quantification of ISG scores (measured by the mean expression of genes mentioned in Fig. 6a) in four disease severities. c, Heatmap of mRNA of IFN and IFN receptors across four disease severity groups. d, Heatmap of mRNA of upstream regulators of IFN signaling across four disease severity groups. e, The gene regulatory sub-network of interferon and interferon receptors. Nodes were colored based on mRNA expression abundance, which were scaled in different groups, and the size of nodes corresponds to their degree centrality. The color and size of edges represent whether the regulation relationship of each pair exists in different groups.","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-59060/v1/Fig6.png"},{"id":13586609,"identity":"54766af4-9b0f-4b8e-8365-85ac9e6d77b0","added_by":"auto","created_at":"2021-09-17 04:47:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2786221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-59060/v1/430720db-4c4e-4152-aca2-2419afcee98d.pdf"},{"id":2167333,"identity":"41cc864d-cd26-4d4c-8e41-e260eeb6e126","added_by":"auto","created_at":"2020-08-31 17:22:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2880431,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"SupplementaryFiguresNC.docx","url":"https://assets-eu.researchsquare.com/files/rs-59060/v1/SupplementaryFiguresNC.docx"},{"id":2167334,"identity":"678422a9-6a6f-42dd-9919-3c8bb5ef9e67","added_by":"auto","created_at":"2020-08-31 17:22:30","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16930,"visible":true,"origin":"","legend":"Supplementary Table 1","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-59060/v1/SupplementaryTable1.xlsx"},{"id":2167335,"identity":"6b2ba1cb-7f48-4f81-9222-e6dfb0fd72c8","added_by":"auto","created_at":"2020-08-31 17:22:30","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17177,"visible":true,"origin":"","legend":"Supplementary Table 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The disease is now found in almost all countries, totaling 15,785,641 confirmed cases and 640,016 deaths worldwide as of July 27th, 2020 \u003csup\u003e2\u003c/sup\u003e. The symptoms of COVID-19 vary dramatically, ranging from asymptomatic to critical. Several studies have reported on confirmed patients who exhibit no symptoms (i.e., asymptomatic) \u003csup\u003e3-6\u003c/sup\u003e. Since such individuals are not routinely tested, the proportion of asymptomatic patients is not precisely known, but appears to range from 13% in children \u003csup\u003e7\u003c/sup\u003e to 50% in the testing of contact tracing evaluation \u003csup\u003e8\u003c/sup\u003e. Of the COVID-19 patients with symptoms, 80% are classified as mild to moderate, 13.8% as severe, and 6.2% are classified as critical \u003csup\u003e1, 9\u003c/sup\u003e. Some confounding factors appeared to be associated with COVID-19 progress and prognosis. For example, preliminary evidence suggests that comorbidities such as hypertension, diabetes, cardiovascular disease, and respiratory disease result in a worsened prognosis of COVID-19 \u003csup\u003e10\u003c/sup\u003e, and dramatically increases the mortality rate \u003csup\u003e11\u003c/sup\u003e. Furthermore, death due to COVID-19 is found to be significantly more common in older patients (i.e.,\u0026ge;65 years old), possibly due to the decline in immune response with age \u003csup\u003e10, 12\u003c/sup\u003e. Thus, although the overall mortality rate of diagnosed cases was estimated to be ~3.4% \u003csup\u003e2\u003c/sup\u003e, the rate varies from 0.2% to 22.7% depending on the age groups and other health issues of patients \u003csup\u003e13, 14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSo far, most studies have focused on the relationship between the disease and clinical characteristics, sequencing of virus genomes \u003csup\u003e15\u003c/sup\u003e and identifying the structure of the SARS-CoV-2 spike glycoprotein \u003csup\u003e16, 17\u003c/sup\u003e. There has also been some work on integrated multi-omics signatures. For example, meta-transcriptome sequencing was conducted on the bronchoalveolar lavage fluid of SARS-CoV-2 infected patients \u003csup\u003e18\u003c/sup\u003e. Proteomic and metabolomic analyses of the serum from COVID-19 patients have also been investigated \u003csup\u003e19-21\u003c/sup\u003e. However, from the data so far, it remains difficult to determine which parameters are due to infection from the virus and which to comorbidities as no systematic study of the disease have been published thus far.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study we selected 231 COVID-19 cases with different clinical severity and without comorbidities to investigate the sole effect of SARS-CoV-2 infection on disease severity. We performed trans-omics analysis, including genomic, transcriptomic, proteomic, metabolomic, and lipidomic analytes, to better understand the associations between the genetic and molecular mechanisms of consecutively severe COVID-19 symptoms. We proposed a novel mechanism for inflammatory cytokine regulation at the post-transcriptional level. Neutrophils were excessively activated in critical patients. Cytokine storm, arginine, tryptophan metabolites, and T/NK cell dysfunction cooperatively contribute to the severity of COVID-19.\u003c/p\u003e"},{"header":"Results","content":" \u003ch2\u003ePatient enrollment\u003c/h2\u003e \u003cp\u003eTo gain a comprehensive insight into the molecular characteristics of COVID-19 in patients characterized with different disease severities, a cohort of 231 out of 1432 COVID-19 patients were selected based on stringent criteria for the trans-omics study (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Given that older age and comorbidities appear to have effects on disease progression and prognosis \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, participants without comorbidities and aged between 20 and 70\u0026nbsp;years old (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, 46.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5) were selected. Detailed information about the enrolled patients, including sampling date and basic clinical information, are shown in \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and \u003cb\u003eSupplementary Tables\u0026nbsp;1\u0026ndash;2\u003c/b\u003e. Among our enrolled 231 COVID-19 patients, 64 were asymptomatic, 90 were mild, 55 were severe, and 22 were critical.\u003c/p\u003e \n\n\u003ch2\u003eTrans-omics profiling for COVID-19\u003c/h2\u003e \u003cp\u003eIn-depth multi-omics profiling was performed, including whole-genome sequencing (203 samples) and transcriptome sequencing (RNA-seq and miRNA-seq of 178 samples) of whole blood. Concurrently, liquid chromatography\u0026ndash;mass spectrometry (LC-MS) was performed to capture the proteomic, metabolomic, and lipidomic features of COVID-19 patient sera (161 samples) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). After data pre-processing and annotation, the final dataset contained a total of 25882 analytes including 18245 mRNAs, 240 miRNAs, 5207 lncRNAs, 634 proteins, 814 metabolites, and 742 complex lipids (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;3.1\u003c/b\u003e). To quantify the molecular profiles in relation to disease severity, we conducted pairwise comparisons between the four severity groups for each omics-level (see \u003cb\u003eMethods\u003c/b\u003e). Results indicated extensive changes across all omics levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cb\u003eSupplementary Tables\u0026nbsp;3.2\u0026ndash;3.5\u003c/b\u003e). We first found profound differences between asymptomatic and symptomatic patients at all omics levels, suggesting a shared specific molecular feature in asymptomatic patients. Second, the changes in analytes between mild and severe groups were subtle at all omics levels except for proteins, indicating marked molecular similarities between these two severities, even in the presence of differences in clinical manifestations. Third, differences between the critical group and other groups were extremely high, implying a sudden and dramatic change from severe to critical disease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003ch2\u003eGenomic architecture of COVID-19 patients\u003c/h2\u003e \u003cp\u003eAfter data quality control based on whole-genome sequencing of 203 unrelated patients, 15.3\u0026nbsp;million bi-allelic single nucleotide polymorphisms (SNPs) were used for single-variant based association tests to investigate the connections among common variants (MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and the diversity of clinical manifestations (\u003cb\u003eExtended Data\u003c/b\u003e Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-j and \u003cb\u003eSupplementary Table\u0026nbsp;4.1\u003c/b\u003e). We first compared the generalized severe group (severe and critical, n\u0026thinsp;=\u0026thinsp;65) with the mild group (asymptomatic and mild, n\u0026thinsp;=\u0026thinsp;138) (\u003cb\u003eExtended Data\u003c/b\u003e Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-b), then compared the asymptomatic group (n\u0026thinsp;=\u0026thinsp;63) with all other symptomatic patients (n\u0026thinsp;=\u0026thinsp;140) (\u003cb\u003eExtended Data\u003c/b\u003e Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec-d, \u003cb\u003eSupplementary Table\u0026nbsp;4.2\u003c/b\u003e). In general, no signal showed genome-wide significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5e\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) in these comparisons. A suggestive signal (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1e\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) associated with the absence of symptoms was found on chromosome 20q13.13, which comprised of six SNPs, the most significant being SNP rs235001 \u003cb\u003e(Supplementary Table\u0026nbsp;4.3)\u003c/b\u003e. Locus zoom identified two protein coding genes, \u003cem\u003eB4GALT5\u003c/em\u003e and \u003cem\u003ePTGIS\u003c/em\u003e, in the region spanning\u0026thinsp;\u0026plusmn;\u0026thinsp;50\u0026nbsp;k of the SNP (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). As a member of β-1, 4 galactosyltransferase family, \u003cem\u003eB4GALT5\u003c/em\u003e may participate in the glycosylation process of the membrane protein as well as the viral protein. A study in porcine showed that pB4GALT5 may play immunological protection roles in porcine respiratory syndrome virus (PRRSV) infection \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Together with the reported \u003cem\u003eABO\u003c/em\u003e gene (also glycosyl transferase), the altered glycoprotein modification may greatly affect the immunogenicity and host immune recognition process, resulting in the difference in susceptibility and severity. \u003cem\u003ePTGIS\u003c/em\u003e encodes the enzyme for the synthesis of prostaglandin I2, a potent inhibitor of platelet aggregation, inhibiting platelet adherence to vessel walls. Additionally, PTGIS possesses anti-inflammatory properties by modulating the expression of IL-1, IL-6, IL-10, which may be associated with the COVID-19 severity \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. We also assessed two loci, rs657152 at locus 9q34.2 and rs11385942 at locus 3p21.31, which have been found to be associated with COVID-19 patients with severe respiratory failure in Spanish and Italian populations \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. For rs657152, the overall frequency of the protective allele C was 0.5468 (222/406) in our data, with the lowest rate found in the critical group (AF\u0026thinsp;=\u0026thinsp;0.382, 13/34, Fisher\u0026rsquo;s exact test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04896). For rs11385942, the risk allele GA was not detected in any patient in our study, as this variant was rare in Chinese people \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary Table\u0026nbsp;4.4\u003c/b\u003e), consistent with previously reported global distribution \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Quantitative trait locus (QTL) analysis has been widely applied to infer the contribution of genetic variations to complex phenotypes \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Here, QTL analysis was performed to explore the correlations of proteomic, metabolomic, and lipidomic features with genetic variations, resulting in 1328 mRNAs, 76 proteins, 195 metabolites and 4 lipids significantly associated with a variety of QTL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;5e\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e\n\n\u003ch2\u003eTranscriptomic hallmark of COVID-19\u003c/h2\u003e \u003cp\u003eTo characterize progressive transcriptional changes through the four disease severities of COVID-19, we conducted unsupervised clustering of mRNAs that were differentially expressed in at least three of the six comparison groups \u003cb\u003eSupplementary Table\u0026nbsp;3.2)\u003c/b\u003e. Three expression patterns were identified across patients with different disease severities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cb\u003eSupplementary Table\u0026nbsp;6.1\u003c/b\u003e). Intriguingly, genes in cluster 1 increased both in asymptomatic and critically ill patients in comparison to mild and severe patients. The extend of upregulation was greater in asymptomatic cases. GO analysis showed these genes to be related to neutrophil activation, inflammatory response, granulocyte chemotaxis, and IL2, IL-6, IL-8 production (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cb\u003eSupplementary Table\u0026nbsp;6.2\u003c/b\u003e). Consistently, digital cytometry CIBRSORTx \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, a widely used machine learning method estimated cell type abundances from bulk transcriptomes, revealing a dramatic increase of neutrophils in asymptomatic and critically ill patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Key chemokines (\u003cem\u003eCXCL8\u003c/em\u003e, \u003cem\u003eCXCR1\u003c/em\u003e, \u003cem\u003eCXCR2\u003c/em\u003e) for neutrophil activation and accumulation, as well as inflammatory responses genes (\u003cem\u003eTLR4\u003c/em\u003e and \u003cem\u003eTLR6\u003c/em\u003e) associated with toll-like receptors, and several key inflammatory response genes (\u003cem\u003eMMP8\u003c/em\u003e, \u003cem\u003eMMP9\u003c/em\u003e, \u003cem\u003eS100A12\u003c/em\u003e, \u003cem\u003eS100A8\u003c/em\u003e, \u003cem\u003eUBE2E3\u003c/em\u003e) shared this expression pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), suggesting a highly activated innate immune and pro-inflammatory response both in asymptomatic and critically ill patients than that in mild and severe patients at the transcriptomic level.\u003c/p\u003e \u003cp\u003eGenes in cluster 2 were enriched in T cell activation, leukocyte-mediated cytotoxicity, NK cell-mediated immunity, and interferon-gamma production (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. The expression levels of these genes were specifically decreased in critical patients compared to that of the other three severities. Important genes for T cell activation, such as \u003cem\u003eCD28\u003c/em\u003e, \u003cem\u003eLCK\u003c/em\u003e, and \u003cem\u003eZAP70\u003c/em\u003e, as well as key transcript factors for interferon-gamma production (\u003cem\u003eGATA3\u003c/em\u003e, \u003cem\u003eEOMES\u003c/em\u003e and \u003cem\u003eIL23A\u003c/em\u003e), showed this expression pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Moreover, digital cytometry estimation revealed lower numbers of T and NK cells in critically ill patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Thus, although innate immune responses were activated in both asymptomatic and critically ill patients, T cell mediated adaptive immune response was specifically suppressed in critical COVID-19 patients.\u003c/p\u003e \u003cp\u003eCluster 3 contained genes primarily involved in protein polyubiquitination and autophagy. The expression of genes in this cluster gradually increased from the asymptomatic to mild/severe and then peaked at the critical group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). An important transcript factor encoding gene for autophagy, \u003cem\u003eFOXO3\u003c/em\u003e, displayed this expression pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Genes in cluster 3 reflected the increasing tissue damage and cell death along with disease severity.\u003c/p\u003e \u003cp\u003eNext, we investigated the post-transcriptional regulatory network associated with the genes in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec. \u003cem\u003emiR-25-3p\u003c/em\u003e, \u003cem\u003emiR-486-5p\u003c/em\u003e and \u003cem\u003emiR-93-5p\u003c/em\u003e was uncovered to be negatively correlated with 11 genes about inflammatory response, and neutrophil activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, \u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). Meanwhile, many lncRNAs were strongly and negatively correlated with \u003cem\u003eFOXO3\u003c/em\u003e, which plays a critical role in autophagy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, \u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e). In view of the fact that the expression of \u003cem\u003eFOXO3\u003c/em\u003e, a negative regulator of the antiviral response, elevated along with the aggravation of the patient's condition, lncRNA differential accumulation may play a role in autophagy and antiviral response dysregulation in critically ill COVID-19 patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-d) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \n\n\n\u003ch2\u003eLandscape of proteins, metabolites and lipids in COVID-19\u003c/h2\u003e \u003cp\u003eAll proteins, metabolites, and lipids were classified into seven clusters with four progressive severities. Increasing patterns include the gradually increasing cluster C2 and the sharply increasing cluster C3. Decreasing patterns were composed of gradually decreasing cluster C6 and sharply decreasing cluster C1. C4, C5 and C7 belonged to the U-shaped patterns, mild specific, and critical specific patterns respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cb\u003eExtended Data Fig.\u0026nbsp;7\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;9\u003c/b\u003e). To systematically characterize the interaction networks among proteins, metabolites, and lipids within each cluster, we conducted co-expression network analysis using ranked spearman correlation coefficient (see \u003cb\u003eMethods\u003c/b\u003e), resulting in a systematic multi-omics network for each cluster (\u003cb\u003eExtended Data Fig.\u0026nbsp;8, Supplementary Tables\u0026nbsp;10.1\u003c/b\u003e\u0026ndash;\u003cb\u003e10.2)\u003c/b\u003e. Overall, we revealed putative dynamic interactions within each network, connecting immunity proteins (CSF1, C1S \u003cem\u003eetc.\u003c/em\u003e) to specific groups of metabolites (phenylalanine, tryptophan \u003cem\u003eetc.\u003c/em\u003e) and lipids (phosphatidylethanolamine, triglyceride \u003cem\u003eetc.\u003c/em\u003e).\u003c/p\u003e \n\n\n\u003ch2\u003eProtein circuits in COVID-19\u003c/h2\u003e \u003cp\u003eNotably, a variety of biological pathways were found to be specifically enriched in the different clusters \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, \u003cb\u003eSupplementary Table\u0026nbsp;9.2)\u003c/b\u003e. Consistent with transcription analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), a variety of proteins (BID, ILK, ADAMTSL4 \u003cem\u003eetc\u003c/em\u003e.) related to the positive regulation of apoptotic processes were preferentially present in critical COVID-19 patients (C2, C3). However, inconsistent with mRNA expression patterns, proteins associated with positive regulation of inflammatory response and macrophage migration (S100A8, S100A12, C5, LBP, DDT \u003cem\u003eetc.\u003c/em\u003e) were gradually or sharply increased (C2, C3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Platelet degranulation and blood coagulation proteins were gradually increased (C2), or gradually increased (C6) respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), supporting the observed thrombocytopenia and coagulopathy in critically ill patients.\u003c/p\u003e \n\n\u003ch2\u003eMetabolites turnover in COVID-19\u003c/h2\u003e \u003cp\u003eMetabolites showed distinct profiles in the different clusters. In particular, phenylalanine and tryptophan metabolism increased sharply (C3) in critical patients (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed-e, \u003cb\u003eSupplementary Table\u0026nbsp;9.3\u003c/b\u003e). Tryptophan metabolism was considered a biomarker and therapeutic target of inflammation \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, and changes in tryptophan metabolism were reported to be correlated with serum interleukin-6 (IL-6) levels \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Consistently, IL-6 levels were highest in critical patients (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Furthermore, compared to other severities, arginine gradually deceased along with disease severity (C6) (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed-e). Arginine is metabolized by myeloid cells (neutrophils, macrophages, granulocytes) by arginase \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, further supporting activation of neutrophils and macrophages in symptomatic patients, especially in the critical.\u003c/p\u003e \u003ch2\u003e\u0026ldquo;Lipid codes\u0026rdquo; in COVID-19\u003c/h2\u003e \u003cp\u003eWe investigated the dynamics of lipids among the different severities. Phosphatidylethanolamine (PE), Lysophosphatidyliositol (LPI), and ceramides (Cer) were gradually increased (C2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). A previous study suggested that RNA virus replication was dependent on the enrichment of PE distributed at the replication sites of subcellular membranes \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, implying that the increase of PE in critical COVID-19 patients might facilitate the replication of viruses. LPI and Cer were found to increase in symptomatic groups. LPI is an endogenous agonist for GPR55 whose activation regulates several pro-inflammatory cytokines \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Ceramide induction has been thought as a strategy to inhibit T cell cytoskeletal reorganization in measles virus immunosuppression \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and could increase the efficiency of pathogen uptake into dendritic cells \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLysophosphatidylcholine (LPC) was sharply decreased in critical patients (C1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). It has been reported that LPC levels decreased with the onset of sepsis and strongly predictive power for sepsis-related mortality \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. LPC has shown therapeutic effects in experimental sepsis and microbial infections by enhancing H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e production in neutrophils \u003cem\u003ein vitro\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and by inhibiting endotoxin-induced release of a late proinflammatory cytokine, HMGB1 as well. Therefore, the significant diminished LPC level may partly explain the more severe illness and worser clinical outcomes of critical patients. Intriguingly, LPA, which could enhance the secretion of IFNγ by activated NK cells \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, were significantly enriched in asymptotic COVID-19 patients (C6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Overall, our study suggests that lipidome changes may play important and complex roles in COVID-19 disease development.\u003c/p\u003e \u003ch2\u003eDistinct neutrophils status within asymptomatic and critically ill COVID-19 patients\u003c/h2\u003e \u003cp\u003eNeutrophils are the first-responders of immune defense, and play critical roles in many forms of airway infection, including in antiviral immunity \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, excessive neutrophils activation cause tissue damage. Especially during severe viral infections, neutrophils may abnormally differentiate to pathological low-density neutrophils (LDNs) with an enhanced capacity to release neutrophil extracellular traps (NETs) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Excessive NETs release cause endothelium damage, promote thrombosis, and contribute to mortality in COVID-19 \u003csup\u003e43\u003c/sup\u003e. As we showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, transcriptional analysis indicated neutrophil were massively enriched in asymptomatic patients and mildly increased in critically ill patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). However, most of these proteins (20 genes were detectable in proteomics data), including genes involved in activated inflammatory pathways (CHI3L1, S100A8, S100A9, S100A11, and S100A12), neutrophil degranulation (LFT, ANXA3, FGL2, LRG1, PGLYRP1, DEFA1B, SLPI), and NETs (MPO, and ELANE) were extremely low in asymptomatic patients, and then progressively increased along with disease severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This discrepancy implied that heterogeneous neutrophils, that are \u0026ldquo;beneficial\u0026rdquo; or \u0026ldquo;detrimental\u0026rdquo; depending on their functional protein expression, exist between asymptomatic and critically ill patients. Further, we analyzed expression correlations in all genes with available paired mRNA and proteins levels. 93 genes showed the expression pattern with highest transcripts but lowest protein levels in asymptomatic patients, and their protein levels were gradually increased and reached the peaks in critically ill patients (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec-d). Impressively, myeloid leukocytes activation and degranulation pathways were enriched in these genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), which further supported various neutrophil status may contribute to the disease severity.\u003c/p\u003e \u003ch2\u003e\u0026ldquo;Cytokine paradox\u0026rdquo; in asymptomatic COVID-19 patients\u003c/h2\u003e \u003cp\u003ePro-inflammatory pathway and inflammatory cytokines were unexpected transcriptionally activated in asymptomatic patients (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). However, consistent with a recent report \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, secretion of inflammatory cytokines such as IL-6, IL-8, IL-2R, and IL-10 was extremely low in sera from the asymptomatic population (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef\u003cb\u003e)\u003c/b\u003e. In contrast, critically ill patients were characterized with excessive inflammatory cytokine production, whereas their transcription levels were only modestly elevated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e. Typically, inflammatory cytokine production is tightly regulated both transcriptionally and post-transcriptionally \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Post-transcription of inflammation-related mRNAs is mainly regulated by RNA-binding proteins (RBPs) and microRNAs. Interestingly, RBPs (HNRNPD, TTP, ZC3H12A, ILF3, ZNF692, ZCCHC11, FXR1, ELAVL1, and BRF1/2) and microRNAs (miR-181a, miR-10a, miR-23b, miR-222, and miR-21), which are involved in the degradation and destabilization of inflammatory cytokines \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, were highly expressed in asymptomatic patients but showed extremely low expression in critical patients (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg-h\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003ch2\u003eTryptophan and arginine metabolism perturbations contribute to T cells dysfunction in critically ill COVID-19 patients\u003c/h2\u003e \u003cp\u003eT cells play a critical role in antiviral immunity against SARS-CoV-2 \u003csup\u003e48\u003c/sup\u003e, but their functional state and contribution to COVID-19 severity remain largely unknown. T/NK cell- mediated adaptive immune response were defected in critically ill patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Interestingly, tryptophan (Trp) metabolism was gradually accelerated along with disease severity (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Tryptophan degradation products depleted T cells, increased Th and NK cells apoptosis, and promoted T cell exhaustion \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Moreover, L-arginine is important for T-cell proliferation and function. The release of Arginase (ARG1/2) from activated neutrophils inhibit T-cell activation by inducing L-Arginine and Glutamine depletion \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Here we found \u003cem\u003eARG1\u003c/em\u003e and \u003cem\u003eARG2\u003c/em\u003e levels were upregulated in critical patients. Consistently, L-arginine, N-acetylornithine, and L-glutamine were lowest in critical patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Phenotypically, in addition to the dramatically decreased T cells in critically ill patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), we noticed a significant upregulation of exhaustion markers: \u003cem\u003ee.g\u003c/em\u003e., \u003cem\u003ePD-1\u003c/em\u003e, \u003cem\u003eCTLA4\u003c/em\u003e, \u003cem\u003eTIM3\u003c/em\u003e, \u003cem\u003eICOS\u003c/em\u003e, and \u003cem\u003eBTLA\u003c/em\u003e in T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \n\n\n\u003ch2\u003eImpaired interferon response in critically ill COVID-19 patients\u003c/h2\u003e \u003cp\u003eAn effective interferon (IFN) response eliminate viral infection including SARS-CoV-2 \u003csup\u003e52\u003c/sup\u003e. Insufficient activation of IFN signaling may contribute to severe cases of COVID-19 \u003csup\u003e53, 54\u003c/sup\u003e. As such, we compared the pathways of anti-viral IFN responses in the different severities of COVID-19 patients. Intriguingly, we found that critically ill patients failed to launch a robust IFN response compared with the highly activated IFN response observed in asymptomatic patients by measuring the expression of interferon-stimulated genes (ISGs) (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-b). Furthermore, IFN receptors were specifically upregulated in asymptomatic patients while most IFN transcripts were undetectable in blood (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Third, multiple IFN upstream molecules, including \u003cem\u003eTLR3, IRF1, IRF7, MAVS, DDX58, TBK1, JAK1\u003c/em\u003e, and \u003cem\u003eSTAT2\u003c/em\u003e were also increased in asymptomatic patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Furthermore, we performed the reverse engineering of gene regulatory network (GRN) to explore the transcriptional regulation network of IFN pathway in patients with various severity (\u003cb\u003eSupplementary Table\u0026nbsp;11\u003c/b\u003e). In asymptomatic patients, transcription factors including STAT5B, STAT3, STAT6, E2F3, NFYC, FLI1, ATF6, TFEB, and ARID3A firmly connected with IFN or IFN receptors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). Given the nature of GRN, decreasing counts of edges indicates reduced regulatory relationship between genes. Gradual loss of connectivity in the regulatory network of IFN were observed in symptomatic groups, especially in the critical patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee), which may contribute greatly to the dysregulation of IFN pathway in critically ill patients.\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eTo the best of our knowledge, this is the first trial designed to systematically analyze trans-omics data of COVID-19 patients with grade of clinical severity. Thought comprehensive multi-omics analysis, we revealed high neutrophils counts, low inflammatory cytokines and enhanced interferon anti-virus response in asymptomatic patients. In contrast, critically ill patients were characterized by neutrophils over-activation, cytokine storm, and interferon mediated innate immune or T/NK mediated adaptive immune defection.\u003c/p\u003e \u003cp\u003eAsymptomatic patients have drawn great attention as these silent spreaders are hard to identify and cause difficulties in epidemic control \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Through our study, we observed an unexpected expression discordance exist with extreme transcriptional activation but low inflammatory cytokines secretion in asymptomatic patients. Typically, inflammatory cytokine production is elegantly regulated both transcriptionally and post-transcriptionally. By recognizing inflammatory cytokine mRNA with stem-loop structures, RBPs can degrade or decay inflammatory cytokine mRNA. microRNAs have also emerged as fine-tune regulators to control inflammation. The balance of these actions controls inflammation intensity \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. For instance, AUF1 (HNRNPD), TTP (ZFP36) attenuates inflammation by destabilizing mRNAs encoding inflammatory cytokines, including IL-2, IL-6, TNF and IL-1β \u003csup\u003e\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Regnase-1 (ZCH12A) has a wide antiviral spectrum and efficiently inhibits the influenza A virus. Furthermore, Regnase-1 restrains inflammation by negatively regulating IL6 and IL17 mRNA stabilization \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Thus, Regnase-1 depletion facilitates severe systemic inflammation and virus replication. miR-10a, and miR-21 has been shown to negatively regulate IL-6 and TNF \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Accordingly, we propose that the observed discrepancy between cytokine mRNA and protein levels could be attributed to post-transcriptional mRNA stabilization mediated by RBPs or microRNAs. Our data suggests a novel mechanism for inflammatory cytokine regulation at the post-transcriptional level, which explains the molecular mechanism of various clinical symptoms and suggests that RBPs could be a potential therapeutic target in COVID-19. However, additional functional researches will be required to ascertain their contribution towards the development of COVID-19.\u003c/p\u003e \u003cp\u003eNeutrophils play a protective role in antiviral immunity, whose depletion led to viral replication and increased lethality in mice infected with the influenza virus \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Neutrophils exhibit a strong ability to mediate virus elimination not only by direct phagocytic activity, but also in cooperation with B cells and also modulate dendritic cell (DC), macrophage, and T-cell activities \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. However, excessively activated neutrophils form neutrophil extracellular traps (NETs) and lead to tissue damage, which is termed NETosis, as we found in critically ill COVID-19 patients. NETs are closely related to the severity of influenza, Ebola virus infection, and COVID-19 \u003csup\u003e43, 63\u003c/sup\u003e. These observations hint at a prominent role of neutrophils in COVID-19. So, the determinants of neutrophil transition from beneficial to detrimental effects deserves additional investigation. Furthermore, considering the major role excess NETs play in COVID-19 severity, targeting NETs formation by directly inhibiting critical molecules required for NET (neutrophil elastase (NE), PAD4, and gasdermin D \u003csup\u003e\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e provide a promising therapeutic choice to reduce the clinical severity of COVID-19.\u003c/p\u003e \u003cp\u003eT cell depletion in critically ill patients is in line with the clinically observed T cell lymphopenia, which was also negatively correlated with COVID-19 severity \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Recent research has demonstrated that SARS-CoV-2 dramatically reduces T cells, and up-regulates exhaustion markers PD-1, and Tim-3, especially in critically ill patients \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Mechanistically, various clinical evidences show that T-cell counts are negatively associated with serum IL-6, IL-10, and TNF-alpha concentrations \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, uncontrolled cytokine release may prompt the depletion and exhaustion of T cells. Second, it is well known that accelerated Trp metabolism by rate-limiting enzymes, i.e., indoleamine 2,3-dioxygenases (IDO1 and IDO2) mediates T cell dysfunction. Trp catabolite production, KYN, 3-HAA, and Quin inhibits adaptive T cell immunity, block expansion and proliferation of conventional CD4\u003csup\u003e+\u003c/sup\u003e helper T cells and effector CD8\u003csup\u003e+\u003c/sup\u003e T cells, and potentiate CD4\u003csup\u003e+\u003c/sup\u003e regulatory T (Treg) cell function \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Third, L-Arginine depletion owing to hyper-activated neutrophils inhibited T-cell function. Thus, in addition to the loss of T cell counts in critical patients, T cells become metabolically exhausted and dysfunctional.\u003c/p\u003e \u003cp\u003eThe impaired IFN response in the critical could be responsible for the loss of viral replication control in these patients \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Moreover, highly accumulated PE lipids (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef), which are important for RNA virus replication \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, further enhanced SARS-CoV-2 replication in critically ill patients. Consequently, uncontrolled viral replication can result in the orchestration of a much stronger inflammatory response in critically ill patients, characterized by cytokine storms and immunopathogenesis. Conversely, the sufficient IFN response in asymptomatic patients could help to defend against viral infections.\u003c/p\u003e \u003cp\u003eIt is possible that biological crosstalk exists among the cytokine storm, Trp metabolism, and T cell dysfunction processes. First, considering the essential role of Trp metabolism in blocking the expansion and proliferation of conventional CD4\u003csup\u003e+\u003c/sup\u003e helper T cells and effector CD8\u003csup\u003e+\u003c/sup\u003e T cells as well as in potentiating CD4\u003csup\u003e+\u003c/sup\u003e regulatory T (Treg) cell function \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, the accumulated Trp catabolite production would inhibit adaptive T cell immunity. Second, Trp directly stimulates immune checkpoint expression levels, such as CTLA4 and PD-1 \u003csup\u003e69\u003c/sup\u003e. Third, in addition to the direct effects on T cell dysfunction, proinflammatory cytokines, e.g., IL-1β, IFN-γ, and IL-6, can lead to a robust elevation in circulating Kyn levels by up regulation of IDO/TDO \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, which synergistically worsen T cell dysfunction. Fourth, adaptive T cell immunity plays an unexpected role in tempering the initial innate response \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, T cells defection in critically ill patients could in turn exacerbate an uncontrolled innate immune response.\u003c/p\u003e \u003cp\u003eTherapeutically, considering the essential effects of arginine, tryptophan, IDO, and T cell function on COVID-19 severity, bolstering the immune system by restoring exhausted T cells may be a promising strategy for disease treatment. Direct arginine supplement, targeting Trp catabolism by indoximod, or targeting IDO1/TDO2 by navoximod (NLG919) \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, BMS-986205 \u003csup\u003e73\u003c/sup\u003e, or PF-06840003 \u003csup\u003e74\u003c/sup\u003e could metabolically restore T cell function. Furthermore, immune checkpoint blockage with PD1/PD-L1 or CTLA4 antibody has been shown to increase T cell numbers and restore T cell function \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e, which may be a potential strategy for the treatment of critically ill patients. It may therefore be worthwhile to test if the aforementioned immune-boosting strategies are effective in COVID-19 clinical trials.\u003c/p\u003e \u003cp\u003eIn conclusion, our study presented a trans-omics landscape of blood samples within a large cohort of COVID-19 patients with various severities from asymptomatic to critically ill. Overall, we uncovered multiple novel insights and therapeutic targets relevant to COVID-19. Our data provided valuable clues for deciphering COVID-19 and the underlying mechanism warrant for further pursuits.\u003c/p\u003e "},{"header":"Materials and Methods","content":"\u003ch2\u003ePatients Enrollment and Sample Preparation\u003c/h2\u003e\n\u003cp\u003eBlood samples for 231 COVID-19 patients without any comorbidities were collected from Tongji Hospital and Union Hospital of Huazhong University of Science and Technology, Xiangyang Central Hospital, Hubei University of Arts and Science and Hubei Dazhong Hospital of Chinese Traditional Medicine from 19th February, 2020 to 26th April, 2020. Flowchart of patient selection for this study were shown in \u003cstrong\u003eExtended Data Fig. 1\u003c/strong\u003e. The demographic data and laboratory indicators were shown in \u003cstrong\u003eSupplementary Tables 1-2\u003c/strong\u003e. The mean age of the patients was 46.7 years old (Standard Deviation=13.5), and the ratio of male to female was 1.12:1. All these patients were diagnosed following the guidelines for COVID-19 diagnosis and treatment (Trial Version 7) released by the National Health Commission of the People\u0026rsquo;s Republic of China. The patients were classified into four groups according to their disease severity: critical, severe, mild, and asymptomatic. The critical disease was defined as fulfilling at least one of the following conditions: (1) acute respiratory distress syndrome (ARDS) requiring mechanical ventilation, (2) shock, (3) combining with other organ failure requiring ICU admission. Severe disease met at least one of the following conditions: (1) respiratory rate \u0026ge; 30 times/min, (2) oxygen saturation \u0026le;93% at resting state, (3) arterial partial pressure of oxygen (PaO2)/fraction of inspired oxygen (FiO2) \u0026le;300 mmHg, (4) pulmonary imaging examination showed that the lesions significantly progressed by more than 50% within 24-48 hours. Mild patients were defined as having fever, respiratory symptoms, lung imaging evidence of pneumonia. The patients with normal body temperature, without any respiratory symptoms were defined as asymptomatic. The definition of each severity was consistent with the previous article \u003csup\u003e76\u003c/sup\u003e. All Ethylenediaminetetraacetic acid disodium salt (EDTA-2Na)-anticoagulated venous blood samples were separated by centrifuge at 3,000 rpm, room temperature for 7min after standard diagnostic tests, the whole blood cells were stored at -80\u0026deg;C, 200 \u0026mu;L aliquot of serum were added 800\u0026mu;L ice-cold methanol, mixed well and stored at -80\u0026deg;C, another 200 \u0026mu;L aliquot of serum were added 800\u0026mu;L ice-cold isopropanol, mixed well and stored at -80\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eNucleic Acid Extraction\u003c/h2\u003e\n\u003cp\u003eA 200 \u0026mu;L aliquot of each thawed whole blood cells was used to extract DNA using QIAamp DNA Blood Mini Kit (51304, Qiagen), following the manufacturer\u0026rsquo;s instructions. Total RNA was extracted from another 200 \u0026mu;L aliquot of blood cells using QIAGEN miRNeasy Mini Kit (217004，Qiagen) according to the manufacturer\u0026rsquo;s protocol. All the extraction was performed under Level III protection in the biosafety III laboratory.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSequencing Library Construction and Data Generation\u003c/h2\u003e\n\u003cp\u003eThe whole genome data was generated through the following steps: 1) DNA was randomly fragmented by Covaris. The fragmented genomic DNA were selected by Magnetic beads to an average size of 200-400bp. 2) Fragments were end repaired and then 3\u0026rsquo; adenylated. Adaptors were ligated to the ends of these 3\u0026rsquo; adenylated fragments. 3) PCR and Circularization. 4) After library construction and sample quality control, whole genome sequencing was conducted on MGI2000 PE100 platform with 100bp paired end reads.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTranscriptome RNA data was generated through the following steps: 1) rRNA was removed by using RNase H method, 2) QAIseq FastSelect RNA Removal Kit was used to remove the Globin RNA, 3) The purified fragmented cDNA was combined with End Repair Mix, then add A-Tailing Mix, mix well by pipetting, incubation, 4) PCR amplification, 5) Library quality control and pooling cyclization, 6) The RNA library was sequenced by MGI2000 PE100 platform with 100bp paired-end reads.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSmall RNA data was generated through the following steps: 1) Small RNA enrichment and purification, 2) Adaptor ligation and Unique molecular identifiers (UMI) labeled Primer addition, 3) RT-PCR, Library quantitation and pooling cyclization, 4) Library quality control, 5) Small RNAs were sequenced by BGI500 platform with 50bp single-end reads resulting in at least 20M reads for each sample.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCytokine detection\u003c/h2\u003e\n\u003cp\u003eWe detected cytokines including IL-6, IL-8, IL-10, IL-2R in serum samples of patients. Assays were conducted by using an automated analyzer (Cobas e602, Roche Diagnostics, Germany or Immulite 1000, DiaSorin Liaison, Italy) as described in the manufacturer\u0026rsquo;s instructions. IL-6 kit (#05109442190) was obtained from Roche Diagnostics (Mannheim, Germany). IL-8 kit (#LK8P1), IL-10 kit (#LKXP1), IL-2R kit (#LKIP1) were obtained from DiaSorin (Vercelli, Italy).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eWGS data analysis and joint variant calling\u003c/h2\u003e\n\u003cp\u003eWhole genome sequencing data was processed using the Sentieon Genomics software (version: sentieon-genomics-201911) \u003csup\u003e77\u003c/sup\u003e. Pipeline was built according to the best practice\u0026rsquo;s workflows for germline short variant discovery described in https://gatk.broadinstitute.org/. Sequencing reads were mapped to hg38 reference genome using BWA algorithm \u003csup\u003e78\u003c/sup\u003e. After duplicates marking, InDel realignment and base quality score recalibration (BQSR), per-sample variants were called using the Haplotyper algorithm in the GVCF mode. Then the GVCFtyper algorithm was used to perform joint-calling and generate cohort VCF. Variant Quality Score Recalibration was performed using Genome Analysis Toolkit (GATK version 4.1.2) \u003csup\u003e79\u003c/sup\u003e. The truth-sensitivity-filter-level were set as 99.0 for both the SNPs and the Indels. Finally, variants with PASS flag and quality score \u0026ge; 100 were selected for further analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eGenotype-Phenotype Association Analysis\u003c/h2\u003e\n\u003cp\u003ePCA was performed using PLINK (v1.9) \u003csup\u003e80\u003c/sup\u003e. Bi-allelic SNPs were selected based on the following criteria: minor allele frequency (MAF) \u0026ge; 5%; genotyping rate \u0026ge; 90%; LD prune (window = 50, step = 5 and r2 \u0026ge; 0.5). A subset of 605,867 SNPs was used to perform PCA on the 203 unrelated individuals. We used rvtest \u003csup\u003e81\u003c/sup\u003e to perform genotype-phenotype association analysis for 5,082,104 bi-allelic common SNPs with MAF \u0026gt; 5%. Gender, age and top 10 principal components were used as covariates for all the association tests. The qqman \u003csup\u003e82\u003c/sup\u003e and CMplot R packages \u003csup\u003e83\u003c/sup\u003e were applied to generate the Manhattan plot and quantile-quantile plot. We defined genome-wide significance for single variant association test as 5e\u003csup\u003e-8\u003c/sup\u003e, suggestive significance as 1e\u003csup\u003e-6\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eQTL Analysis\u003c/h2\u003e\n\u003cp\u003eWe obtained matched proteomics, lipidomics, metabolomics, gene expression and SNP genotyping data for COVID-19 patients (n = 132). For the genotyping data, we removed outlier SNPs with MAF \u0026lt; 0.05. The QTL analysis (cis-eQTL analysis [local, distance \u0026lt; 10kb] for gene expression data, QTL analysis for proteomics, lipidomics, metabolomics data) was conducted using linear regression as implemented in MatrixEQTL \u003csup\u003e84\u003c/sup\u003e. In this analysis, age and gender (1 for male and 2 for female) were considered as covariates. Associations with a p value less than 0.001 were kept, followed by FDR estimation using the Benjamini-Hochberg procedure as implemented in Matrix-QTL. QTL associations with an FDR-corrected p value \u0026lt; 5e\u003csup\u003e-8\u003c/sup\u003e were considered significant \u003csup\u003e85\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e\u0026nbsp;\u003c/h3\u003e\n\u003ch2\u003eGene Expression Analysis\u003c/h2\u003e\n\u003cp\u003eRNA-seq raw sequencing reads were filtered by SOAPnuke \u003csup\u003e86\u003c/sup\u003e to remove reads with sequencing adapter, with low-quality base ratio (base quality \u0026lt; 5) \u0026gt; 20%, and with unknown base ('N' base) ratio \u0026gt; 5%. Reads aligned to rRNA by Bowtie2 (v2.2.5) \u003csup\u003e87\u003c/sup\u003e were removed. Then, the clean reads were mapped to the reference genome using HISAT2 \u003csup\u003e88\u003c/sup\u003e. Bowtie2 (v2.2.5) was applied to align the clean reads to the transcriptome. Then the gene expression level (FPKM) was determined by RSEM \u003csup\u003e89\u003c/sup\u003e. Genes with FPKM \u0026gt; 0.1 in at least one sample were retained. Differential expression analysis was performed using DESeq2 (v1.4.5) with gender and age as confounders. Differential expressed genes were defined as those with Benjamini Hochberg adjusted p value \u0026lt; 0.05 and fold change \u0026gt; 2. GO enrichment analysis was performed using clusterProfiler \u003csup\u003e90\u003c/sup\u003e. GO BP terms with an FDR adjusted p value threshold of 0.05 were considered as significant \u003csup\u003e91\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSmall RNA raw sequencing reads with low quality tags (which have more than four bases whose quality is less than ten, or have more than six bases with a quality less than thirteen.), the reads with poly A tags, and the tags without 3' primer or tags shorter than 18nt were removed. After data filtering, the clean reads were mapped to the reference genome and other sRNA database including miRbase, siRNA, piRNA and snoRNA using Bowtie2 \u003csup\u003e87\u003c/sup\u003e. Particularly, cmsearch \u003csup\u003e92\u003c/sup\u003e was performed for Rfam mapping. The small RNA expression level was calculated by counting absolute numbers of molecules using unique molecular identifiers (UMI, 8-10nt). MiRNA with UMI count lager than 1 in at least one sample were considered as expressed. Differential expression analysis was performed using DESeq2 (v1.4.5) \u003csup\u003e93\u003c/sup\u003e with gender and age as confounders to control for the additional variation and the detection cutoff was set as adjusted P \u0026lt; 0.05 and log2 of fold change \u0026ge; 1.\u003c/p\u003e\n\u003ch3\u003e\u0026nbsp;\u003c/h3\u003e\n\u003ch2\u003eConstruction of mRNA-miRNA and mRNA-lncRNA Network\u003c/h2\u003e\n\u003cp\u003eTo investigate the post-transcriptional regulation, spearman correlation coefficients of mRNA-miRNA (\u003cstrong\u003eSupplementary Table 7\u003c/strong\u003e) and mRNA-lncRNA were calculated (\u003cstrong\u003eSupplementary Table 8\u003c/strong\u003e). Correlation pairs with coefficients \u0026lt; -0.5 in mRNA-miRNA or \u0026lt; -0.6 in mRNA-lncRNA were retained. MultiMiR was used to confirm the top pairs of mRNA-miRNA by performing miRNA target prediction \u003csup\u003e94\u003c/sup\u003e. The mRNA-miRNA and mRNA-lncRNA networks were visualized using Cytoscape (\u003cstrong\u003eFig. 2d\u003c/strong\u003e) \u003csup\u003e95\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eProteomics Analysis\u003c/h2\u003e\n\u003cp\u003eThe sera samples were inactivated at 56\u0026deg;C water bath for 30min and followed by processing with the Cleanert PEP 96-well plate (Agela, China). According to the manufacturer\u0026rsquo;s instructions, high-abundance proteins under a denaturing condition were removed \u003csup\u003e96\u003c/sup\u003e. The Bradford protein assay kit (Bio-Rad, USA) was used to determine the final protein concentration. The proteins were extracted by the 8M urea and subsequently reduced by a final concentration of 10mM Dithiothreitol at 37\u0026deg;C water bath for 30min and alkylated to a final concentration of 55mM iodoacetamide at room temperature for 30min in the darkroom. The extracted proteins were digested by trypsin (Promega, USA) in 10 KD FASP filter (Sartorious, U.K.) with a protein-to-enzyme ratio of 50:1 and eluded with 70% acetonitrile (ACN), dried in the freeze dryer.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDIA (Data Independent Acquisition) strategy was performed by Q Exactive HF mass spectrometer (Thermo Scientific, San Jose, USA) coupled with an UltiMate 3000 UHPLC liquid chromatography (Thermo Scientific, San Jose, USA). The 1\u0026mu;g peptides mixed with iRT (Biognosys, Schlieren, Switzerland) were injected into the liquid chromatography (LC) and enriched and desalted in trap column. Then peptides were separated by self-packed analytical column (150\u0026mu;m internal diameter, 1.8\u0026mu;m particle size, 35cm column length) at the flowrate of 500 nL/min. The mobile phases consisted of (A) H\u003csub\u003e2\u003c/sub\u003eO/ACN (98/2,v/v) (0.1% formic acid); and (B) ACN/H\u003csub\u003e2\u003c/sub\u003eO (98/2,v/v) (0.1% formic acid) with 120 min elution gradient (min, %B): 0, 5; 5, 5; 45, 25; 50, 35; 52, 80; 55, 80; 55.5, 5; 65, 5. For HF settings, the ion source voltage was 1.9kV; MS1 range was 400-1250m/z at the resolution of 120,000 with the 50 ms max injection time(MIT). 400-1250 m/z was equally divided into 45 continuous windows MS2 scans at 30,000 resolution with the automatic MIT and automatic gain control (AGC) of 1E6. MS2 normalized collision energy was distributed to 22.5, 25, 27.5.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe raw data was analyzed by Spectronaut software (12.0.20491.14.21367) with the default settings against the self-built plasma spectral library which achieved deeper proteome quantification. The FDR cutoff for both peptide and protein level were set as 1%. Next, the R package MSstats \u003csup\u003e97\u003c/sup\u003e finished log2 transformation, normalization, and p-value calculation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMetabolomics Analysis\u003c/h2\u003e\n\u003cp\u003eThe 100\u0026mu;l sera of each sample were transferred into the 96-well plate and mixed with 10\u0026mu;l SPLASH LipidoMixTM Internal Standard (Avanti Polar Lipids, USA) and 10\u0026mu;l home-made Internal Standard mixture containing D3-L-Methionine (100 ppm, TRC, Canada), 13C9-Phenylalanine (100ppm, CIL, USA), D6-L-2-Aminobutyric Acid(100ppm, TRC, Canada), D4-L-Alanine (100ppm, TRC, Canada), 13C4-L-Threonine (100ppm, CIL, USA), D3-L-Aspartic Acid (100ppm, TRC, Canada), and 13C6-L-Arginine (100ppm, CIL, USA). The 300\u0026mu;l pre-chilled extraction buffer of methanol/ACN (67/33, v/v) was added to the plasma sample then vortexed for 1 min and incubated at -20\u0026deg;C for 2 hours. After centrifugation at 4000 RPM for 20 min, 300ul supernatants were taken and dried in the freeze dryer. The metabolites were dissolved in 150\u0026mu;l buffer of methanol/ACN (50/50, v/v) and centrifuged at 4000 RPM for 30min. Supernatants were injected into mass spectrometer.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMetabolomics data acquisition was completed using a same spectrometer, LC, and settings were set as lipidomics except for following parameters: the mobile phases of positive mode were (A) H2O (0.1% formic acid) and (B) methanol (0.1% formic acid). The mobile phases of negative mode were (A) H2O (10mM NH4HCO2) and (B) methanol /H2O (95/5, v/v) (10 mM NH4HCO2). Both positive and negative models used the same gradient (min, %B): 0, 2; 1, 2; 9, 98; 12, 98; 12.1, 2; 15, 2. The temperature of column was set at 45\u0026deg;C. MS1 range set as 70-1050m/z. MS2 stepped normalized collision energy was distributed to 20, 40, 60.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe raw data was searched by Compound Discoverer 3.1 software (Thermo Fisher Scientific, USA) with different libraries including our self-built BGI library containing more than 3000 metabolites with corresponding detailed mass spectrum data. After quantification, subsequent processing steps were finished by metaX as same as lipidomics analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eLipidomics Analysis\u003c/h2\u003e\n\u003cp\u003eThe 100 \u0026mu;l sera of each sample was transferred into the 96-well plate and mixed with 10 \u0026mu;l SPLASH LipidoMixTM Internal Standard (Avanti Polar Lipids, USA). The 300\u0026mu;l pre-chilled Isopropanol (IPA) was added to the plasma sample and vortex for 1 min and incubated at -20\u0026deg;C overnight. Then samples were centrifuged at 4000 RPM for 20min while proteins precipitated. The supernatants were used for MS analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLipidomics analysis was performed using Q Exactive mass spectrometer (Thermo Scientific, San Jose, USA) coupled with Waters 2D UPLC (waters, USA). The CSH C18 column (1.7\u0026mu;m 2.1*100mm, Waters, USA) was used for separation with following elution gradient (min, %B) consisted of (A) ACN/H2O (60/40, v/v) (10 mM NH4HCO2 and 0.1% formic acid) and (B) IPA/ACN (90/10, v/v) (10 mM NH4HCO2 and 0.1% formic acid): 0, 40; 2, 43; 2.1, 50; 7, 54; 7.1, 70; 13, 99; 13.1, 40; 15, 40. The temperature of column was set as 55\u0026deg;C, the injection value was set as 5\u0026mu;L, and the flowrate was set as 0.35mL/min. For HF settings, the samples were scanned twice in both positive and negative modes. The positive spray voltage was set as 3.80 kV and negative spray voltage was set as 3.20 kV. MS1 range was 200-2000m/z at the resolution of 70,000 with the 100ms MIT and AGC of 3e6. The top3 precursors were set as trigger MS2 scans at the resolution of 17,500 with the 50ms MIT and AGC of 1E5. MS2 stepped normalized collision energy was distributed to 15, 30, 45. The sheath gas flow rate was set as 40 and the aux gas flow rate was set as 10.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe raw data was analyzed by Lipidsearch software Version 4.1 (Thermo Fisher Scientific, USA) which finished feature detection, identification and alignment. The following settings were applied: tolerance of mass shift, 5ppm; identification grade, A-D; filters, top rank; all isomer peak, FA priority, M-score, 5; c-score, 2.0; The export quantitative data from Lipidsearch was analyzed by R package metaX \u003csup\u003e98\u003c/sup\u003e which finished the normalization, correction of batch effect, and imputation of missing value.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor each patient in the cohort, we computed intensity for a given lipid complex class by summing up intensity of each lipid in the class. For each lipid complex class, the intensity value of each patient was further scaled by median value of intensity from mild patient group. We applied Mann-Whitney U-test (multiple comparisons correction with Bonferroni) to test statistically significant difference of scaled intensity of each lipid complex class between severity groups.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDifferential Expression of Proteins, Metabolites and Lipids\u003c/h2\u003e\n\u003cp\u003eExpression data was first adjusted using robust linear model (RLM) for gender and age. The residuals following RLM were analyzed by Two-sided Mann-Whitney rank test for each pair of comparing group and p values were adjusted using Benjamini \u0026amp; Hochberg. Differentially expressed proteins, metabolites or lipids were defined using the criteria of adjusted P value \u0026lt; 0.05 and absolute value of fold change \u0026gt; 1.5.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eClustering\u003c/h2\u003e\n\u003cp\u003eClustering was performed using the R package \u0026lsquo;Mfuzz\u0026rsquo; after log2-transformation and Z-score scaling of the data. For mRNA from whole blood, genes differentially expressed in at least three out of the six comparison groups were clustered. For proteins, metabolites, lipids from sera, all the three analytes were clustered together.\u003c/p\u003e\n\u003ch3\u003e\u0026nbsp;\u003c/h3\u003e\n\u003ch2\u003ePathway analysis\u003c/h2\u003e\n\u003cp\u003eTo annotate the proteins and metabolites in 7 clusters, gene ontology (GO) enrichment analysis were performed to obtain the enriched GO Biological Process terms of proteins in different clusters by clusterProfiler \u003csup\u003e90\u003c/sup\u003e. And the 7 lists of metabolites in KEGG ID were classified into pathways by the Kyoto encyclopedia of genes and genomes (KEGG) database. The KEGG annotation was finished using in-house software.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCorrelation Network Analysis\u003c/h2\u003e\n\u003cp\u003ePairwise Spearman\u0026rsquo;s rank correlations were calculated using the r package \u0026lsquo;Hmisc\u0026rsquo; and weighted, undirected networks were plotted with Cytoscape. Correlations with Bonferroni adjusted P values \u0026lt; 0.05 and absolute correlation coefficient \u0026gt;0.4 (\u003cstrong\u003eSupplementary Table 8\u003c/strong\u003e) were included and displayed via the Fruchterman-Reingold method. Nodes color indicate analytes type and their size represent the degree of the node.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConstruction of gene regulatory network\u003c/h2\u003e\n\u003cp\u003eThe ARACNe-AP \u003csup\u003e99\u003c/sup\u003e was employed to construct the gene regulatory networks (GRNs) for each group. The variability of genes expression traits was evaluated by Median Absolute Deviation (MAD), and the top half of genes were recruited in the network. Mutual information \u003csup\u003e100\u003c/sup\u003e was introduced to represent the strength of the regulatory relationship between TFs and target genes, and only significant pairs are kept (P\u0026lt;1\u0026times;10\u003csup\u003e-8\u003c/sup\u003e).We also executed 100 bootstraps and applied a Data Processing Inequality tolerance filter \u003csup\u003e101\u003c/sup\u003e. The consensus network of each group was combined by statistically significant edges across all bootstrap networks (p\u0026lt;0.05, Bonferroni corrected), based on Poisson distribution. The degree was used to evaluate the centrality of genes in the network. To ensure the robustness of our remodeled GRN, we applied Chip-X Enrichment Analysis Version 3(ChEA3) \u003csup\u003e102\u003c/sup\u003e to identify TFs that target to IFN and IFN receptors, and those unrecognized were eliminated.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eQuantification of cell fractions from bulk RNAseq profiles\u003c/h2\u003e\n\u003cp\u003eThe estimation of abundances of immune cell types in blood tissue was performed using CIBERSORTx \u003csup\u003e103\u003c/sup\u003e based on blood RNAseq data.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eProtein interaction network construction and function enrichment analysis\u003c/h2\u003e\n\u003cp\u003eInteraction network construction and biological process GO term enrichment for protein lists were conducted using STRING \u003csup\u003e104\u003c/sup\u003e database with default parameters.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e"},{"header":"Declarations","content":" \u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData for this project will be available upon request. The data that support the findings of this study, including the genome-wide association test summary statistics, expression matrices for multi-omics have been deposited in CNSA (China National GeneBank Sequence Archive) in Shenzhen, China with accession number CNP0001126 (\u003ca href=\"https://db.cngb.org/cnsa/\"\u003ehttps://db.cngb.org/cnsa/\u003c/a\u003e) and will be released to the public after the manuscript is accepted for publication. Besides, processed data and scripts will be released at \u003ca href=\"http://120.79.46.200:81/COVID19\"\u003ehttp://120.79.46.200:81/COVID19\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003eCode Availability\u003c/h2\u003e\n\u003cp\u003eCustom scripts for data analysis in this study were present in \u003ca href=\"https://github.com/DongshengChen-TY/COVID19\"\u003ehttps://github.com/DongshengChen-TY/COVID19\u003c/a\u003e.\u003c/p\u003e\u003ch2\u003eEthics Statement\u003c/h2\u003e \u003cp\u003eThis study was reviewed and approved by the Institutional Review Board of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20200405). All the enrolled patients signed an informed consent form, and all the blood samples were collected using the rest of the standard diagnostic tests, with no burden to the patients.\u003c/p\u003e \u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eD.M, X.J, G.C, C.S, L.W, P.W contributed to project conceptualization. D.M, X.J, X.X, S.L, J.W, H.Y contributed to the supervision. P.W, W.D, P.W, H.H, K.L, E.G, J.L, B.Y, J.F, L.H, Z.S, L.F, J.W, T.W, H.W, J.C, H.X, Y.M, Y.L contributed to sample collection. P.W, D.C, W.D, P.W, H.H, Y.B, Y.Z, K.L contributed to data analysis coordination. Y.R, Y.Z, K.H, W.S, Y.Z, H.L contributed to WGS, RNA-seq, LC-MS experiments. S.X, J.J, P.D, H.W, J.Q, F.W, J.Z, S.W, X.W, X.D, L.L, L.L, C.C, Z.Z contributed to RNA-seq analysis M.H, Y.S contributed to miRNA-mRNA, lncRNA-mRNA interaction networks. Y.R, Y.Z, K.H, W.S, P.D, H.W, J.Q, F.W, J.Z, S.W, X.W, X.D, L.L, L.L, C.C contributed to proteomic analysis. Y.R, Y.Z, K.H, W.S, P.D, H.W, J.Q, F.W, J.Z, S.W, X.W, X.D, L.L, L.L, C.C metabolites analysis Y.Y, Y.R, Y.Z, K.H, W.S, P.D, H.W, J.Q, F.W, J.Z, S.W, X.W, X.D, L.L, L.L, C.C contributed to lipids analysis. Y.T, P.D, H.W, J.Q, F.W, J.Z, S.W, X.W, X.D, L.L, L.L, C.C, A.C contributed to data visualization. Y.S, Y.Y, Z.Z, T.L, L.T, S.Z, L.Z, L.C, Y.W, X.M, F.C contributed to data interpretation. Y.Z contributed to data deposition. D.M, X.J, P.W, D.C, W.D, P.W, H.H, Y.B, Y.Z, K.L, L.W, C.S, G.C, A.C contributed to writing the original draft.\u003c/p\u003e \u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe study was supported by funding from National University Basic Scientific Research Special Foundation (2020kfyXGYJ00), China National GeneBank (CNGB) and Guangdong Provincial Key Laboratory of Genome Read and Write (No. 2017B030301011), Natural Science Foundation of Guangdong Province (2017A030306026), Funds for Distinguished Young Scholar of South China University of China (2017JQ017). We would like to thank Shangbo Xie, Yuying Zeng, Chengcheng Sun, Wendi Wu, Yan Li, Siyang Liu from BGI for helpful discussions of the results and advices.\u003c/p\u003e "},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO (2020).\u003c/li\u003e\n\u003cli\u003eWorldometers Coronavirus (COVID-19) Mortality Rate. Last updated: May 14. 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Although previous studies investigated COVID-19 at different omics levels, the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here, we presented a trans-omics landscape for COVID-19 based on integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles from blood samples of 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Notably, we found neutrophils heterogeneity existed between asymptomatic and critically ill patients. Expression discordance of inflammatory cytokines at mRNA and protein levels in asymptomatic patients could possibly be explained by post-transcriptional regulation by RNA binding proteins (RBPs) and microRNAs. Neutrophils over-activation, induced arginine depletion, and tryptophan metabolites accumulation contributed to T/NK cell dysfunction in critical patients. Anti-virus interferons were gradually suppressed along with disease severity. Overall, our study systematically revealed multi-omics characteristics of COVID-19, and the data we generated could hopefully help illuminate COVID-19 pathogenesis and provide valuable clues about potential therapeutic strategies for COVID-19.","manuscriptTitle":"The Trans-omics Landscape of COVID-19","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-08-31 16:33:28","doi":"10.21203/rs.3.rs-59060/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"062c00d7-4093-4372-a653-8ff79082bc5d","owner":[],"postedDate":"August 31st, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":388198,"name":"Immunology"},{"id":388199,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2021-07-27T10:50:47+00:00","versionOfRecord":{"articleIdentity":"rs-59060","link":"https://doi.org/10.1038/s41467-021-24482-1","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2021-07-27 10:50:47","publishedOnDateReadable":"July 27th, 2021"},"versionCreatedAt":"2020-08-31 16:33:28","video":"","vorDoi":"10.1038/s41467-021-24482-1","vorDoiUrl":"https://doi.org/10.1038/s41467-021-24482-1","workflowStages":[]},"version":"v1","identity":"rs-59060","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-59060","identity":"rs-59060","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}