The Trans-omics Landscape of COVID-19

Wu, Peng; Chen, Dongsheng; Ding, Wencheng; Wu, Ping; Hou, Hongyan; Bai, Yong; Zhou, Yuwen; Li, Kezhen; Xiang, Shunian; Liu, Panhong; Ju, Jia; Guo, Ensong; Liu, Jia; Yang, Bin; Fan, Junpeng; He, Liang; Sun, Ziyong; Li, Feng; Wang, Jian; Wu, Tangchun; Wang, Hao; Cheng, Jin; Xing, Hui; Meng, Yifan; Li, Yongsheng; Zhang, Yuanliang; Luo, Hongbo; Xie, Gang; Lan, Xianmei; Tao, Ye; Yuan, Hao; Huang, Kang; Sun, Wan; Qian, Xiaobo; Li, Zhichao; Huang, Mingxi; Ding, Peiwen; Wang, Haoyu; Qiu, Jiaying; Wang, Fei‐Yue; Wang, Shiyou; Zhu, Jiacheng; Ding, Xiangning; Chai, Chaochao; Liang, Langchao; Wang, Xiaoling; Luo, Lihua; Sun, Yuzhe; Yang, Ying; Zhuang, Zhenkun; Li, Tao; Tian, Lei; Zhang, Shaoqiao; Zhu, Linnan; Chen, Lei; Wu, Youmei; Ma, Xiaoyan; Chen, Fang; Ren, Yan; Xu, Xun; Liu, Siqi; Yang, Huanming; Wang, Lin; Sun, Chaoyang; Ma, Ding; Jin, Xin; Chen, Gang

doi:10.1101/2020.07.17.20155150

Cited by 8 publications

(16 citation statements)

References 90 publications

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“…Actually, we observed a severity‐dependent decrease in PC species with long and polyunsaturated acyl chains, as shown in Figure 3F,G. PC and LPC reflected and predicted the maximum disease severity, which is concordant with the results of previous studies 26,27 . Regarding the LPS/PS axis, although the PS levels could not be properly evaluated using serum samples (Figure S1), some LPS levels, such as 16:0 LPS and 18:0 LPS, decreased during the early phase, while 22:6 LPS increased during the late phase (Figure S8B,C and Figure 4E).…”

Section: Discussionsupporting

confidence: 89%

“…Regarding glycerophospholipids, for the LPC/PC axis, we observed a decrease in LPC in COVID‐19 subjects with mild disease, while an increase in PC was seen in COVID‐19 subjects with severe disease. Previous studies reported lower LPC levels, 17,23,26 while several reports have shown higher LPC levels 20,21 . Some reports showed that subjects with severe disease reportedly had lower LPC levels, 17,26 whereas one paper reported the opposite 21 .…”

Section: Discussionmentioning

confidence: 93%

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Dynamic modulations of sphingolipids and glycerophospholipids in COVID‐19

Kurano

Okamoto

Jubishi

et al. 2022

Clinical & Translational Med

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Background A heterogeneous clinical phenotype is a characteristic of coronavirus disease 2019 (COVID‐19). Therefore, investigating biomarkers associated with disease severity is important for understanding the mechanisms responsible for this heterogeneity and for developing novel agents to prevent critical conditions. This study aimed to elucidate the modulations of sphingolipids and glycerophospholipids, which have been shown to possess potent biological properties. Methods We measured the serum sphingolipid and glycerophospholipid levels in a total of 887 samples from 215 COVID‐19 subjects, plus 115 control subjects without infectious diseases and 109 subjects with infectious diseases other than COVID‐19. Results We observed the dynamic modulations of sphingolipids and glycerophospholipids in the serum of COVID‐19 subjects, depending on the time course and severity. The elevation of C16:0 ceramide and lysophosphatidylinositol and decreases in C18:1 ceramide, dihydrosphingosine, lysophosphatidylglycerol, phosphatidylglycerol and phosphatidylinositol were specific to COVID‐19. Regarding the association with maximum severity, phosphatidylinositol and phosphatidylcholine species with long unsaturated acyl chains were negatively associated, while lysophosphatidylethanolamine and phosphatidylethanolamine were positively associated with maximum severity during the early phase. Lysophosphatidylcholine and phosphatidylcholine had strong negative correlations with CRP, while phosphatidylethanolamine had strong positive ones. C16:0 ceramide, lysophosphatidylcholine, phosphatidylcholine and phosphatidylethanolamine species with long unsaturated acyl chains had negative correlations with D‐dimer, while phosphatidylethanolamine species with short acyl chains and phosphatidylinositol had positive ones. Several species of phosphatidylcholine, phosphatidylethanolamine and sphingomyelin might serve as better biomarkers for predicting severe COVID‐19 during the early phase than CRP and D‐dimer. Compared with the lipid modulations seen in mice treated with lipopolysaccharide, tissue factor, or histone, the lipid modulations observed in severe COVID‐19 were most akin to those in mice administered lipopolysaccharide. Conclusion A better understanding of the disturbances in sphingolipids and glycerophospholipids observed in this study will prompt further investigation to develop laboratory testing for predicting maximum severity and/or novel agents to suppress the aggravation of COVID‐19.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 93%

Dynamic modulations of sphingolipids and glycerophospholipids in COVID‐19

Kurano

Okamoto

Jubishi

et al. 2022

Clinical & Translational Med

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“…patient OMICS data, including proteomics and transcriptomics regulation of whole blood, serum or plasma of mostly inpatients (10; 12; 13; 22; 57; 63; 71; 95)…”

Section: Methodsmentioning

confidence: 99%

“…We recapitulate human RBPs which are predicted or experimentally determined to binding to SARS-CoV-2 by previous studies and identify novel host RBP candidates with no previously reported binding to SARS-CoV-2. We integrate knowledge of these proteins across other pathogens and highlight RBPs with clinical relevance, by annotating those that were found among SARS-CoV-2-associated genes from Genome Wide Association Studies (GWAS) (64), CRISPR studies (24; 30; 70; 91), physical binding experiments (18; 69; 89), or patient OMICS data from blood serum and plasma (10; 12; 13; 22; 57; 63; 71; 95). Finally, we perform extensive in silico single-nucleotide perturbations across the SARS-CoV-2 genome to identify variants that would lead to gain and/or disruption of RBP binding sites and thus may alter viral fitness.…”

Section: Introductionmentioning

confidence: 99%

Computational Mapping of the Human-SARS-CoV-2 Protein-RNA Interactome

Horlacher

Oleshko

et al. 2021

Preprint

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It is well known that viruses make extensive use of the host cell's machinery, hijacking it for the purpose of viral replication and interfere with the activity of master regulatory proteins - including RNA binding proteins (RBPs). RBPs recognize and bind RNA molecules to control several steps of cellular RNA metabolism, such as splicing, transcript stability, translation and others, and recognize their targets by means of sequence or structure motifs. Host RBPs are critical factors for viral replication, especially for RNA viruses, and have been shown to influence viral RNA stability, replication and escape of host immune response. While current research efforts have been centered around identifying mechanisms of host cell-entry, the role of host RBPs in the context of SARS-CoV-2 replication remains poorly understood. Few experimental studies have started mapping the SARS-CoV-2 RNA-protein interactome in infected human cells, but they are limited in the resolution and exhaustivity of their output. On the other hand, computational approaches enable screening of large numbers of human RBPs for putative interactions with the viral RNA, and are thus crucial to prioritize candidates for further experimental investigation. Here, we investigate the role of RBPs in the context of SARS-CoV-2 by constructing a first single-nucleotide \textit{in silico} map of human RBP / viral RNA interactions by using deep learning models trained on RNA sequences. Our framework is based on Pysster and DeepRiPe, two deep learning method which use a convolutional neural network to learn sequence-structure preferences of a specific RBP. Models were trained using eCLIP and PAR-CLIP datasets for >150 RBP generated on human cell lines and applied cross-species to predict the propensity of each RBP to bind the SARS-CoV-2 genome. After extensive validation of predicted binding sites, we generate RBP binding profiles across different SARS-CoV-2 variants and 6 other betacoronaviruses. We address the questions of (1) conservation of binding between pathogenic betacoronaviruses, (2) differential binding across viral strains and (3) gain and loss of binding events in novel mutants which can be linked to disease severity and spread in the population. In addition, we explore the specific pathways hijacked by the virus, by integrating host factors linked to these virus-binding RBPs through protein-protein interaction networks or genome wide CRISPR screening. We believe that identifying viral RBP binding sites will give valuable insights into the mechanisms of host-virus interaction, thus giving us a deeper understanding of the life cycle of SARS-CoV-2 but also opening new avenues for the development of new therapeutics.

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“…Chaoyang Sun 1,2 Yong Bai 3 Dongsheng Chen 3 Liang He 1,2 Jiacheng Zhu 3,4 Xiangning Ding 3,4 Lihua Luo 3,4 Yan Ren 3 Hui Xing 5 Xin Jin 3,6,7 Gang Chen…”

Section: O N F L I C T O F I N T E R E S Tmentioning

confidence: 99%

Accurate classification of COVID‐19 patients with different severity via machine learning

Sun

Bai

Chen

et al. 2021

Clinical & Translational Med

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Infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could cause dramatic response in coronavirus disease 2019 (COVID-19) patients at multi-omics level, 1-3 thus it is essential to systematically assess the pathogenesis of COVID-19. In our previous study, we presented the first trans-omics landscape of 236 COVID-19 patients with 4 clinical severity groups (including asymptomatic, mild, severe and critically ill cases) and found that the mild and severe COVID-19 patients shared several similar characteristics. 4 However, it is crucial to discriminate mild from severe COVID-19 patients to prevent the latter from the progression of disease by facilitating early intervention. Herein, we developed an extreme gradient boosting (XGBoost) machine-learning model to predict the COVID-19 severities by leveraging multi-omics data. Briefly, we randomly stratified samples for the training set (80%) and the independent testing set (20%) (Figure 1A, see Methods in the Supporting Information). After normalization, a total of 297 multi-omics features were preliminarily selected by applying a hybrid method (see Methods in the Supporting Information). The XGBoost model was trained on the training set with the preliminarily selected features, achieving mean micro-average AUROC (area under the receiver operating characteristic curve) and mean micro-average AUPR (area under the precisionrecall curve) of 0.9715 (95% CI, 0.9497-0.9932) and 0.9495 (95% CI, 0.9086-0.9904), respectively (Figure 1B and C). This showed strong generalizable discrimination among the four severities based on fivefold cross-validation over 100 iterations. The multi-omics features were prioritized and ranked by the XGBoost model and the SHAP (SHapley Additive exPlanations, see Methods in the Supporting Information) value. Top 60 important features were further selected consisting of 19 proteins, 11 metabolites, 7 lipids, and 23 mRNAs (Figure 1D, Figures S1-S4). With the top 60 features, the XGBoost model was retrained and val-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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The Trans-omics Landscape of COVID-19

Cited by 8 publications

References 90 publications

Dynamic modulations of sphingolipids and glycerophospholipids in COVID‐19

Dynamic modulations of sphingolipids and glycerophospholipids in COVID‐19

Computational Mapping of the Human-SARS-CoV-2 Protein-RNA Interactome

Accurate classification of COVID‐19 patients with different severity via machine learning

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