key: cord-0889632-ad7nrdax authors: Li, Y.; Schneider, A. M.; Mehta, A.; Sade-Feldman, M.; Kays, K. R.; Gentili, M.; Charland, N. C.; Gonye, A. L.; Gushterova, I.; Khanna, H. K.; LaSalle, T. J.; Lavin-Parsons, K. M.; Lilley, B. M.; Lodenstein, C. L.; Manakongtreecheep, K.; Margolin, J. D.; McKaig, B. N.; Parry, B. A.; Rojas-Lopez, M.; Russo, B. C.; Sharma, N.; Tantivit, J.; Thomas, M. F.; Regan, J.; Flynn, J. P.; Villani, A.-C.; Hacohen, N.; Goldberg, M. B.; Filbin, M. R.; Li, J. Z. title: SARS-CoV-2 Viremia is Associated with Distinct Proteomic Pathways and Predicts COVID-19 Outcomes date: 2021-02-26 journal: medRxiv : the preprint server for health sciences DOI: 10.1101/2021.02.24.21252357 sha: 482a3841ad68650bb0f90e6c945d230aff3deb3c doc_id: 889632 cord_uid: ad7nrdax Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) plasma viremia has been associated with severe disease and death in coronavirus disease 2019 (COVID-19) in small-scale cohort studies. The mechanisms behind this association remain elusive. Methods: We evaluated the relationship between SARS-CoV-2 viremia, disease outcome, inflammatory and proteomic profiles in a cohort of COVID-19 emergency department participants. SARS-CoV-2 viral load was measured using qRT-PCR based platform. Proteomic data were generated with Proximity Extension Assay (PEA) using the Olink platform. Results: Three hundred participants with nucleic acid test-confirmed COVID-19 were included in this study. Levels of plasma SARS-CoV-2 viremia at the time of presentation predicted adverse disease outcomes, with an adjusted odds ratio (aOR) of 10.6 (95% confidence interval [CI] 4.4, 25.5, P<0.001) for severe disease (mechanical ventilation and/or 28-day mortality) and aOR of 3.9 (95%CI 1.5, 10.1, P=0.006) for 28-day mortality. Proteomic analyses revealed prominent proteomic pathways associated with SARS-CoV-2 viremia, including upregulation of SARS-CoV-2 entry factors (ACE2, CTSL, FURIN), heightened markers of tissue damage to the lungs, gastrointestinal tract, endothelium/vasculature and alterations in coagulation pathways. Conclusions: These results highlight the cascade of vascular and tissue damage associated with SARS-CoV-2 plasma viremia that underlies its ability to predict COVID-19 disease outcomes. With coronavirus disease-2019 (COVID-19) causing over two million deaths globally by early 2021 1 , 50 there remains an urgent need to elucidate disease pathogenesis to improve clinical management and 51 treatment. There is increasing evidence that COVID-19, caused by the severe acute respiratory 52 syndrome coronavirus 2 (SARS-CoV-2) virus, frequently manifests pathology beyond the pulmonary 53 tract 2-4 . In both immunocompromised and immunocompetent hosts, SARS-CoV-2 nucleic acids have 54 been detected across a broad range of extrapulmonary sites, including spleen, heart, liver, and 55 intestinal tract 5-9 . In addition, endothelial cells are known to express ACE-2 and some reports have 56 suggested that direct infection of endothelial cells may be leading to a hypercoagulable state with 57 vascular and downstream organ damage. Furthermore, viremia has been implicated in transplacental 58 transmission 7,10 . These reports suggest that dissemination of infection outside of the respiratory tract 59 into the circulatory system may be a critical step for COVID-19 pathogenesis. 60 We and others have previously demonstrated that SARS-CoV-2 plasma viremia in hospitalized 61 patients is associated with severe disease and death [11] [12] [13] [14] . However, these studies have been limited by 62 sampling late during the disease course and relatively small sample sizes. Here, we performed plasma 63 SARS-CoV-2 viral load quantification, proteomic analysis, and assessed neutralizing antibody titers in a 64 large cohort of emergency department (ED) patients enrolled at the time of initial presentation. We 65 evaluated whether levels of SARS-CoV-2 viremia could predict COVID-19 disease outcomes after 66 adjusting for multiple potential confounders. We also performed proteomic analysis to reveal prominent 67 pathways that are upregulated in the setting of plasma viremia and determined the relationship 68 between plasma SARS-CoV-2 viral load and levels of neutralizing antibodies. 69 This cohort consisted of 306 participants with a molecular diagnosis of COVID-19, of which 300 72 participants had successful plasma SARS-CoV-2 viral load quantification and thus were included in this 73 current analysis. Baseline characteristics were reported in our prior study 15 and summarized in Table 74 1. Thirty-nine percent of participants were 65 years or older and about half of participants were female. 75 Eleven percent of participants had morbid obesity (body mass index [BMI] ≥40 kg/m 2 ), 47% had a 76 diagnosis of hypertension and 36% with diabetes. Fifty-three out of 300 participants (18%, Figure 1A ) 77 had a baseline SARS-CoV-2 viral load above the limit of quantification (2 log 10 copies/ml). Individuals 78 with quantifiable SARS-CoV-2 viral load at the time of ED presentation were of older age, had higher 79 rates of diabetes, and had clinical laboratory values consistent with higher disease severity, including 80 lower lymphocyte count, and higher creatinine, C-reactive protein (CRP), and troponin (Table 1) . 81 Median time between symptom onset and ED presentation was 7 days (interquartile range [IQR] , 4, 11) 82 and comparable between individuals with viral load above and below the limit of quantification ( Figure 83 1B and Supplementary Figure S1 ). Quantified SARS-CoV-2 viral load at the time of ED presentation 84 was correlated with older age, lower lymphocyte count, higher inflammatory markers including CRP, D 85 dimer, Lactate dehydrogenase (LDH), and with both renal and liver dysfunction ( Figure 1C) . Elevated SARS-CoV-2 viremia ≥2 log 10 copies/ml at the time of ED presentation was a strong predictor 90 of maximal COVID-19 disease acuity within 28 days of enrollment. Those with elevated viral load were 91 significantly more likely to have severe disease (82% vs. 26%, P<0.001, Figure 2A ), which included 92 those who died or required invasive mechanical ventilation. Participants with SARS-CoV-2 viral loads 93 <2 log 10 copies/mL were further categorized into those with detectable viral load below the limit of 94 quantification or with undetectable viral load (aviremic). This revealed a dose-dependent effect of 95 viremia on adverse outcomes ( Figure 2B ). Higher levels of SARS-CoV-2 viremia upon ED presentation 96 were associated with increased severity at all timepoints measured -days 0, 3, 7, and 28 97 (Supplementary Figure S2) . 28-day mortality was 32% in the high viral load group and 9.7% in the low 98 viral load group (P<0.001). Higher plasma viral load was also consistently associated with higher risk of 99 severe disease and death across age groups (Supplementary Figure S3) . 100 We also assessed the impact of SARS-CoV-2 by univariate and multiple logistic regression for severe 102 disease. Viremia ≥2 log 10 copies/ml had an OR of 12.6 (95% CI 6.0, 26.5, P<0.001) in univariate logistic 103 regression for severe disease (Table 2) . After adjusting for other baseline variables with a P value <0.1 104 in univariate analyses, viremia remained significantly associated with severe disease, with an adjusted 105 OR (aOR) of 10.6 (95% CI 4.4, 25.5, P<0.001). Similarly, viremia ≥2 log 10 copies/ml was strongly 106 associated with death within 28 days (Table 2) , with an aOR of 3.9 (95% CI 1.5, 10.1, P=0.006) in 107 multivariate analysis. The results were consistent when viral load was categorized into 3 strata (2 log 10 , 108 detectable below 2 log 10 and aviremic) and when analyzed as a continuous variable (Supplementary 109 Table S1 ). Each log 10 increase in viral load was associated with an aOR 2.49 of severe disease 110 (P<0.001) and aOR 1.46 of death (P=0.01). Finally, higher viral load was also associated with higher 111 risk of death at day 28 by Cox proportional hazard modelling (adjusted hazard ratio [aHR] 4.0, 95% CI 112 1.9, 8.7, P<0.001, Supplementary Figure S4 ). We performed logistic regression to evaluate 113 demographic and laboratory variables associated with SARS-CoV-2 viremia. In multivariate analysis, 114 only diabetes and CRP>100mg/dl were associated with viremia (Supplementary Table S2) . To identify differentially expressed proteins between viremic and aviremic participants, we created 128 linear models to fit each of the proteins at Day 0 with viremia status as a main effect and adjusted for 129 age, demographics, and key comorbidities ( Figure 3B ). A number of prominent proteomic pathways 130 were associated with higher plasma viral load. First, viremic participants demonstrated higher 131 inflammatory protein 3 alpha (MIP3A), CXCL10/Interferon gamma-induced protein 10 (IP-10), 134 CXCL9/monokine induced by gamma interferon (MIG), CXCL8/IL8, interferon lambda 1 (IFNL1), 135 CCL2/MCP1, CCL19/MIP3B, CCL3/MIP1A, CXCL11, IL15, and IL18 ( Figure 3C ). Nicotinamide 136 phosphoribosyl transferase (NAMPT), an important regulator upstream to IL6 production 16 , was also 137 upregulated in the viremic group. Second, viremia was associated with elevation of tissue damage 138 markers 17 , including gastrointestinal (GI) tract/pancreas/liver markers (e.g. REG3A, REG1B, AGR2, 139 GP2, MUC13, FABP1, PLA2G1B, PLA2G10, SPINK1, EPCAM, IGFBP1), lung markers especially 140 surfactant proteins (SFTPD, SFTPA1/2, AGER, LAMP3), and cardiac markers (Troponin I3/TNNI3, 141 NTproBNP, MB, CDH2). KRT18, KRT19, and RUVBL1 which are widely expressed in a variety of 142 tissue types, including GI tract, pancreas, lungs, urinary system, and adipose tissue, are also 143 significantly elevated in viremic participants, serving as markers of pan-tissue damage. It is worth 144 mentioning that some of these proteins are also likely playing an important role in tissue fibrosis, In addition to proteins related to tissue injury, fibrosis and repair, we noted significant elevation of 166 certain monocytes/dendritic cells (i.e., CD14, CD163) and plasmablasts (i.e. CD138/SDC1, TXNDC5) 167 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. To assess the longitudinal impact of viremia, we focused on 103 hospitalized participants with complete 195 proteomic data from Day 0, 3 and 7 (acuity level from A1 to A4). We first looked at the trajectory of 196 those proteins identified in the Day 0 analysis (Figure 3 ). Viremic participants had persistently higher 197 levels of proinflammatory markers beyond day 0, especially those related to monocyte activation. For 198 some inflammatory markers (e.g., TNF, IL18, and CD14), differences between groups became highly 199 divergent over time with hyper-accentuated inflammatory responses in viremic participants ( Figure 4A ). 200 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 26, 2021. ; Longitudinal proteomic analysis also demonstrated the persistent elevation of proteomic pathways 201 reflecting organ damage, endothelial damage, and a hypercoagulable state. Certain complement 202 pathway related proteins and entry-related factors were also persistently elevated in the viremic group 203 ( Figure 4A ). 204 We next fit linear mixed models (LMMs) for each protein with time and viremia status as main 205 effects and adjusted for age, demographics, and key comorbidities to identify proteins that were 206 significant for the interaction between viremia and time ( Figure 4B ). We further noted an uptrend in 207 monocyte-related proteins in the viremic group at later time points, followed by neutrophil and B-208 cell/plasmablast related proteins ( Figure 4C ). Many of these markers were significantly elevated even 209 after adjustment for severe disease (labeled in bold font). We also noted an association between 210 viremia and persistent, yet uptrending tissue damage levels, especially those from GI system. 211 Finally, we evaluated the relationship between SARS-CoV-2 viremia and neutralization level. We 219 included participants with neutralization data available at baseline and at least one follow-up time point. 220 Neutralization levels between viremic and aviremic groups were not significantly different at days 0, 3, 221 and 7 ( Figure 5A ). In the subset of participants with neutralization data available beyond day 7, no clear 222 difference was observed between viremic and aviremic groups ( Figure 5B ). At the time of ED 223 presentation, levels of SDC1/CD138, a cardinal and specific marker for plasmablasts 26,28 , was 224 significantly correlated with neutralization level, irrespective of the presence of viremia ( Figure 5C ). We 225 also conducted an analysis including a subgroup of participants with available viral load at Day 3 (n=49) 226 and Day 7 (n=39). Undetectable viral load at Day 3 or Day 7 was not associated with higher 227 neutralizing antibody titers (Supplementary Figure S9) . 228 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 26, 2021. ; In this study, we report a comprehensive analysis of SARS-CoV-2 viremia and its associations with 230 disease outcomes and proteomic pathways from a cohort of ED patients with COVID-19. To our 231 knowledge, this is the largest longitudinal cohort to explore this topic. The results demonstrate that 232 SARS-CoV-2 plasma viremia at the time of ED presentation predicts maximal COVID-19 disease 233 severity and mortality within 28 days. In addition, we for the first time uncovered proteomic signatures 234 upregulated in the setting of SARS-CoV-2 viremia, including prominent pathways highlighting lung and 235 systemic tissue damage, tissue fibrosis and repair, a pronounced proinflammatory response, perturbed 236 hemostasis, and upregulation of viral entry factors. 237 It is now clear that SARS-CoV-2 infection extends outside the respiratory system 2 , and the detection of 239 plasma viremia represents the "link" for extrapulmonary multiorgan involvement and adverse outcomes. 240 Systemic invasion from the respiratory tract is not unique to SARS-CoV-2, as viremia has also been 241 described for other respiratory viruses including SARS-CoV-1 31 , influenza virus 32 , respiratory syncytial 242 virus 33 , and adenovirus 34 . We and others have previously demonstrated that SARS-CoV-2 viremia is 243 more commonly detected in critically ill populations 11,12,14,35 , and is correlated with cardinal 244 proinflammatory markers, including IL6 11,14 , IP10/CXCL10 36 , CCL2/MCP1 36 and markers of 245 endothelial damage 36 . These studies were limited by a lack of true viral load quantification, small 246 sample sizes that could not account for confounders, and/or the evaluation of hospitalized patients only 247 late in their disease course. Here, we report the largest study to date of plasma SARS-CoV-2 plasma 248 viremia using a quantitative viral load assay that allowed for the confirmation of the previous findings 249 11,14 even after adjustment of multiple potential confounding variables. A particular strength of our study 250 was the ability to enroll all acutely ill patients upon ED arrival and thereby minimize selection bias. Our 251 results demonstrate that at the time of ED presentation, plasma SARS-CoV-2 viral load levels 252 independently predicted, in a dose-dependent manner, severe disease and death within the next 28 253 days. SARS-CoV-2 viremia was associated with clinical markers associated with disease severity, 254 including elevated CRP and lymphopenia. 255 256 Our proteomic analysis represents another strength of this study, which demonstrates unique pathways 257 in patients with plasma viremia that together orchestrate a "perfect storm". Viremic individuals displayed 258 a proteomic pattern of broad tissue damage, highlighted by severe lung damage, GI damages, 259 persistent proinflammatory markers elevation, endovascular damage, and tissue fibrosis. While 260 previous studies have reported the elevation of certain nonspecific tissue damage markers in viremic 261 individuals, especially LDH 36,37 , our study allows a far more precise evaluation and demonstrates that 262 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 26, 2021. ; could also be that the level of plasma viremia is a reflection of the extent of tissue-based infection and 296 less a reflection of the current level of neutralizing antibody titers. 297 Our study also has a few notable limitations. Although quite comprehensive, our proteomic database 299 does not cover all the cytokines and proteins of interest in COVID-19 pathogenesis. We rely on a pre-300 existing proteomic database 17 and peripheral blood databases 26,28 to infer the origin of differentially 301 In summary, we report the largest study to date that demonstrates SARS-CoV-2 viremia predicts 307 severe COVID-19 disease outcomes and the likely role of systemic viral dissemination in mediating 308 tissue damage, tissue fibrosis, hypercoagulable state, persistent elevation of proinflammatory markers, 309 and higher viral entry factor expression. Our findings provide key insights into SARS-CoV-2 310 pathogenesis and identify potential therapeutic targets to mitigate COVID-19 disease severity. 311 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Participant enrollment was described in our prior report 15 . Briefly, participants were enrolled in the 314 Emergency Department (ED) from Massachusetts General Hospital, Boston MA, from 3/24/2020 to 315 4/30/2020 during the first peak of the COVID-19 surge, with an institutional IRB-approved waiver of 316 informed consent. Symptomatic participants of 18 years or older with nucleic acid tests confirmed of 317 SARS-CoV-2 infection were included in this current study. Clinical course was followed to 28 days post-318 enrollment, or until hospital discharge if that occurred after 28 days. 319 320 Enrolled participants who were SARS-CoV-2 positive (N=306) were categorized into five 321 outcome/acuity groups: 1) A1, Death within 28 days, 2) A2, Requiring mechanical ventilation and 322 survival to 28 days, 3) A3, Requiring hospitalization on supplemental oxygen within 28 days, 4) A4, 323 Requiring hospitalization without needing supplemental oxygen, and 5) A5, Discharge from ED and not 324 subsequently requiring hospitalization within 28 days. Severe disease was defined as belonging to 325 group A1 or A2. In this current analysis, we only included participants with available plasma SARS-326 CoV-2 viral load (n=300). 327 328 The primary endpoint of this study is severe COVID-19 within 28 days of enrollment (intubation and/or 330 death). Secondary endpoints include 28-day mortality and SARS-CoV-2 viremia. 331 332 Plasma SARS-CoV-2 viral load measurement was reported in our previous study 11 with the following 334 modifications. Briefly, RNA was extracted from 300μL of RPMI-1640 diluted ethylenediaminetetraacetic 335 acid (EDTA)-preserved plasma sample (RPMI-1640: Plasma 2:1 dilution) 15 using TRIzol TM -based 336 method (Thermo Fisher Scientific, Waltham, MA). SARS-CoV-2 viral load was quantified using the US 337 CDC 2019-nCoV_N1 primers and probe set 11 . The lower limit of SARS-CoV-2 N gene quantification 338 was 100 copies/mL. Samples with a positive signal but viral load <100 copies/mL were denoted as 339 detectable but unquantifiable. 340 341 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 26, 2021. ; Proteomic analyses were described in a prior report 15 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 26, 2021. ; One day before neutralization experiment, 293T ACE2/TMPRSS2 cells were seeded at 5 x 10 3 cells in 374 100 μl per well in 96-well plates. On the day of lentiviral harvest, 100 μl SARS-CoV-2 S pseudotyped 375 lentivirus was incubated with 50 μl of plasma diluted in medium to a final concentration of 1:100. Cytoflex LX (Beckman Coulter), and data was analyzed with FlowJo. Neutralization rate was defined as 382 1-(GFP% pseudovirus+plasma /GFP% pseudovirus alone ). 383 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 26, 2021. ; We summarized continuous variables using median and interquartile ranges (IQRs). For clinical 385 variables, we used the Wilcoxon rank-sum test to compare continuous variables from two different 386 categorical groups and Dunn's test for three or more groups. Categorical variables were evaluated 387 using the χ 2 test or Fisher's exact test. We used Spearman's rank correlation coefficient to evaluate 388 correlation between different continuous variables. To evaluate the association of plasma SARS-CoV-2 389 viral load and clinical outcomes, we used logistic regression analyses to calculate odds ratio (OR) and 390 95% confidence intervals (CI). Both univariate and multivariate logistic regression analyses were 391 performed. In multivariate analyses, factors with a P value <0.10 from univariate models were included. 392 We also used Cox proportional model to evaluate the correlation between viremia and 28-day mortality 393 by calculating the hazard ratio (HR). Clinical data analyses, logistic regression and Cox proportion 394 regression were performed on Stata (version 13.1) and figures were generated by Stata and GraphPad 395 (Prism, version 9.0). R (version 4.0.2) was used to analyze proteomic data. 396 397 Linear regression models were fit independently to each protein using the lm package in R with protein 399 values (NPX for Olink data) as the dependent variable. The models included a term for viremia and 400 covariates for age, sex, ethnicity, heart disease, diabetes, hypertension, hyperlipidemia, pulmonary 401 disease, kidney disease, immunocompromised status to control for any potential confounding. P-values 402 were adjusted to control the false discovery rate (FDR) at 5% using the Benjamini-Hochberg method 403 implemented in the emmeans package in R. 404 405 Linear mixed effects models (LMMs) were fit independently to each protein using the lme4 package in 407 R with protein values (NPX for Olink data) as the dependent variable. The model for viremia included a 408 main effect of time, a main effect of viremia, the interaction between these two terms, and a random 409 effect of patient ID to account for the correlation between samples coming from the same patient. 410 Covariates for age, sex, ethnicity, heart disease, diabetes, hypertension, hyperlipidemia, pulmonary 411 disease, kidney disease, and immuno-compromised status were included in the model to control for 412 any potential confounding effects. Details were reported in our recent study 15 . 413 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 26, 2021. ; All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 26, 2021. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. was used to evaluate the correlation between SDC1/CD138 NPX and neutralizing rates. 574 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. BMI, body mass index; CRP, C reactive protein; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; IQR, interquartile range; N/A, not applicable. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 26, 2021. ; (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 26, 2021. ; https://doi.org/10.1101/2021.02.24.21252357 doi: medRxiv preprint COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins 443 University (JHU) COVID-19): A Review Liver Fibrosis Index FIB-4 Is Associated With Mortality in COVID-19. Hepatol Commun Increased Prevalence of Myocardial Injury in Patients with SARS-CoV-2 Viremia Persistence and Evolution of SARS-CoV-2 in an Immunocompromised Host Multiorgan and Renal Tropism of SARS-CoV-2 Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19 Association of Cardiac Infection With SARS-CoV-2 in Confirmed COVID-19 Autopsy 457 Cases Histopathological findings and viral tropism in UK patients with severe fatal COVID-19: a 459 post-mortem study Transplacental transmission of SARS-CoV-2 infection SARS-CoV-2 viral load is associated with increased disease severity and mortality High Frequency of SARS-CoV-2 RNAemia and Association With Severe Disease Relationship Between serum SARS-CoV-2 nucleic acid(RNAemia) and Organ Damage in 466 COVID-19 Patients: A Cohort Study Detectable Serum Severe Acute Respiratory Syndrome Coronavirus 2 Viral Load 468 (RNAemia) Is Closely Correlated With Drastically Elevated Interleukin 6 Level in Critically Ill Patients With 469 Plasma proteomics reveals tissue-specific cell death and mediators of cell-cell Extracellular Nampt promotes macrophage survival via a nonenzymatic interleukin-6/STAT3 We want to thank all the participants in this study. We thank the all the clinical staff who made sample 415 collection possible. 416