==== Front Intensive Care Med Exp Intensive Care Med Exp Intensive Care Medicine Experimental 2197-425X Springer International Publishing Cham 33284413 362 10.1186/s40635-020-00362-8 Research Articles Endotoxemia and circulating bacteriome in severe COVID-19 patients Sirivongrangson Phatadon 123 Kulvichit Win 123 Payungporn Sunchai 45 Pisitkun Trairak 567 Chindamporn Ariya 8 Peerapornratana Sadudee 1239 Pisitkun Prapaporn 10 Chitcharoen Suwalak 411 Sawaswong Vorthon 411 Worasilchai Navaporn 8 Kampunya Sarinya 567 Putcharoen Opass 12 Thawitsri Thammasak 13 Leelayuwatanakul Nophol 14 Kongpolprom Napplika 14 Phoophiboon Vorakamol 14 Sriprasart Thitiwat 14 Samransamruajkit Rujipat 15 Tungsanga Somkanya 1 Tiankanon Kanitha 1 Lumlertgul Nuttha 123 Leelahavanichkul Asada 16 Sriphojanart Tueboon 17 Tantawichien Terapong 1218 Thisyakorn Usa 18 Chirathaworn Chintana 818 Praditpornsilpa Kearkiat 1 Tungsanga Kriang 1 Eiam-Ong Somchai 1 Sitprija Visith 19 Kellum John A. 20 http://orcid.org/0000-0002-8544-8132 Srisawat Nattachai drnattachai@yahoo.com 123182021 1 grid.411628.8 0000 0000 9758 8584 Division of Nephrology, Department of Medicine, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Bangkok, 10330 Thailand 2 grid.411628.8 0000 0000 9758 8584 Excellence Center for Critical Care Nephrology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand 3 grid.7922.e 0000 0001 0244 7875 Critical Care Nephrology Research Unit, Chulalongkorn University, Bangkok, Thailand 4 grid.7922.e 0000 0001 0244 7875 Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330 Thailand 5 grid.7922.e 0000 0001 0244 7875 Center of Excellence in Systems Biology, Chulalongkorn University (CUSB), Bangkok, 10330 Thailand 6 grid.7922.e 0000 0001 0244 7875 Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330 Thailand 7 grid.279885.9 0000 0001 2293 4638 Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD USA 8 grid.7922.e 0000 0001 0244 7875 Department of Microbiology, Chulalongkorn University, Bangkok, Thailand 9 grid.7922.e 0000 0001 0244 7875 Department of Laboratory Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand 10 grid.10223.32 0000 0004 1937 0490 Division of Allergy Immunology and Rheumatology, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand 11 grid.7922.e 0000 0001 0244 7875 Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, 10330 Thailand 12 grid.7922.e 0000 0001 0244 7875 Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand 13 grid.7922.e 0000 0001 0244 7875 Deparment of Anesthesiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand 14 grid.7922.e 0000 0001 0244 7875 Division of Pulmonary and Critical Care, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand 15 grid.7922.e 0000 0001 0244 7875 Critical Care Excellence Center, King Chulalongkorn Memorial Hospital and Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand 16 grid.7922.e 0000 0001 0244 7875 Center of Excellence in Immunology and Immune-Mediated Diseases, Department of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand 17 grid.10223.32 0000 0004 1937 0490 Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand 18 grid.7922.e 0000 0001 0244 7875 Tropical Medicine Cluster, Chulalongkorn University, Bangkok, Thailand 19 Queen Saovabha Memorial Institute, Thai Red Cross Society, Bangkok, Thailand 20 grid.21925.3d 0000 0004 1936 9000 Center for Critical Care Nephrology, The CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh, School of Medicine, Pittsburgh, PA USA 21 Academy of Science, Royal Society of Thailand, Bangkok, Thailand 7 12 2020 7 12 2020 12 2020 8 721 9 2020 25 11 2020 © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Background When severe, COVID-19 shares many clinical features with bacterial sepsis. Yet, secondary bacterial infection is uncommon. However, as epithelium is injured and barrier function is lost, bacterial products entering the circulation might contribute to the pathophysiology of COVID-19. Methods We studied 19 adults, severely ill patients with COVID-19 infection, who were admitted to King Chulalongkorn Memorial Hospital, Bangkok, Thailand, between 13th March and 17th April 2020. Blood samples on days 1, 3, and 7 of enrollment were analyzed for endotoxin activity assay (EAA), (1 → 3)-β-d-glucan (BG), and 16S rRNA gene sequencing to determine the circulating bacteriome. Results Of the 19 patients, 13 were in intensive care and 10 patients received mechanical ventilation. We found 8 patients with high EAA (≥ 0.6) and about half of the patients had high serum BG levels which tended to be higher in later in the illness. Although only 1 patient had a positive blood culture, 18 of 19 patients were positive for 16S rRNA gene amplification. Proteobacteria was the most abundant phylum. The diversity of bacterial genera was decreased overtime. Conclusions Bacterial DNA and toxins were discovered in virtually all severely ill COVID-19 pneumonia patients. This raises a previously unrecognized concern for significant contribution of bacterial products in the pathogenesis of this disease. Keywords COVID-19 Critically ill Endotoxemia Circulating bacteriome Acute respiratory distress syndrome Sepsis http://dx.doi.org/10.13039/501100004776 Faculty of Medicine, Chulalongkorn University issue-copyright-statement© The Author(s) 2020 ==== Body Background While most cases coronavirus disease 2019 (COVID-19) are mild, severe COVID-19 pneumonia can occur with a mortality rate as high as 50% [1]. It is unclear why some patients develop clinical features of sepsis/septic shock with multiple organ dysfunction [2]. The majority of bacterial cultures from severe COVID-19 patients are negative [3], and although empiric antibiotics are commonly used [3–5], they are not recommended [6]. However, while the respiratory tract is the principle site of infection for COVID-19, the disease has been shown to involve the GI tract as well and symptoms such as diarrhea are reported in about a third of cases [7]. Enterocytes in ileum and colon express the ACE2 receptor and virus has been detected in stool. Thus, there is a possibility that bacterial translocation from the GI tract might complicate severe COVID-19 disease [8]. Endotoxin, a part of the cell wall of Gram-negative bacteria, has been extensively investigated and acknowledged as one of the key triggers of lethal shock during severe sepsis and also one of the primary drivers of the cytokine storm [9–11]. Serum (1→ 3)-b-d-glucan (BG) has been evaluated as a potential marker of intestinal barrier dysfunction. Serum BG was tested in several mouse models of gut leakage, including dextran sulfate solution administration, endotoxin injection, and cecal ligation and puncture sepsis [12]. However, the presence of endotoxemia and serum BG in severe COVID-19 has never been examined. Not only bacterial toxin, but also direct bacterial invasion might play role in severe COVID-19. Exploring circulating bacteriome in severe COVID-19 may allow us to test the presence of any bacterial invasion during critical illness. Thus, we designed this investigation to determine whether bacterial products were present in the blood of severe COVID-19 pneumonia patients and whether their source was likely to be the gut as evidenced by serum BG. We also sought to characterize the circulating bacteriome in COVID-19 pneumonia. Methods Study population, setting, and data collection This was a prospective observational study in COVID-19 pneumonia patients admitted to King Chulalongkorn Memorial Hospital, Bangkok, Thailand, between 13th March and 17th April 2020. Our inclusion criteria included (1) age > 18 years, (2) confirmed COVID-19 pneumonia, and (3) had leftover blood samples. The first day of enrollment was the day that patients fulfilled inclusion criteria. The study was reviewed and approved by Faculty of Medicine, Chulalongkorn University ethics committee (IRB no. 336/63). The informed consent was waived due to the observational nature of the study. The study was designed and conducted according to the STROBE guideline [13]. We obtained demographic data, information on clinical presentations, laboratory investigations, and radiography at the time of presentation, and during intensive care unit (ICU) admission. We collected blood samples that were left over on day 1, day 3, and day 7 after enrollment. All laboratory tests and radiologic assessments, including standard chest radiographs and chest computed tomography, were performed at the discretion of the treating physician. Endotoxin activity assay (EAA), cytokines, and serum BG were measured on day 1, 3, and 7 of enrollment. We assessed clinical outcomes on day 28 after enrollment, including mechanical ventilation, ventilator-free day, vasopressor, prone position, extracorporeal membrane oxygenation (ECMO), acute kidney injury (AKI), renal replacement therapy (RRT), and successful extubation. Study definitions A confirmed case of COVID-19 was defined by a positive result of a reverse transcriptase-polymerase chain reaction (RT-PCR) assay of a specimen collected from a nasopharyngeal swab. We defined COVID-19 pneumonia as a COVID-19 case who showed the evidence of pulmonary infiltration from chest radiography or chest computer tomography. We defined severe COVID-19 as a COVID-19 case who was admitted in ICU. Acute kidney injury was defined based on serum creatinine and urine output criteria according to the Kidney Disease Improving Global Outcome 2012 guideline [14]. We defined patient with high endotoxin by EAA ≥ 0.6 on day 1. Standard of care treatment Treating physicians performed thorough evaluations and managed COVID-19 patients with standard care including volume status assessment, hemodynamic and respiratory support according to Surviving Sepsis Campaign: Guidelines on the Management of Critically Ill Adults with Coronavirus Disease 2019 (COVID-19) [6]. Although, currently, there is no specific antiviral treatment for COVID-19, antiviral therapy was given to all patients with confirmed COVID-19 pneumonia as recommended by the Department of Medical Service, Ministry of Public Health of Thailand. The treatment consists of a combination of (1) favipiravir, (2) lopinavir/ritonavir or darunavir, (3) hydroxychloroquine, and (4) azithromycin. Other treatments including intravenous fluid administration and supportive medication were given in discretion of attending physicians. Laboratory procedures COVID-19 test confirmation COVID-19 tests were performed by qRT-PCR technique using cobas® SARS-CoV-2 qualitative test with the cobas®6800 platform (Roche Diagnostics, Indianapolis, IN). We followed the manufacturer’s instructions for testing. The samples were obtained by nasopharyngeal swab and preserved in viral transport cases before sending to analysis. The qRT-PCR tests provided cycle threshold (Ct) value for each test. These values represent the number of cycles required for the positive fluorescent signal. Therefore, the lower Ct values correlated with the higher viral load. The assay is designed to detect ORF1 genes and N genes of SARS-CoV-2. The result was considered positive when the Ct values of both target genes were < 40, negative when they were both > 40. If only one of the target genes had a Ct value < 40, the tests were confirmed by another RT-PCR machine, CFX96 Touch qPCR Detection System (Bio-Rad, Hercules, CA) with detection of 3 genes (ORF1 gene, ORF3 gene, and N gene). All procedures were performed in a biosafety level 2 laboratory. Endotoxin activity assay We performed the chemiluminescent-based endotoxin activity assay (EAA; Spectral Diagnostics, Ontario, Canada) as described elsewhere [15]. This assay is based on the detection of enhanced respiratory burst activity in neutrophils following their priming by complexes of endotoxin and a specific anti-endotoxin antibody. Briefly, 40 μL of whole blood were incubated with zymosan and anti-endotoxin antibody. The endotoxin activity level of ≥ 0.60 was considered as high activity level (Additional file 1). Serum (1 → 3)-b-d-glucan (BG) We analyzed serum for BG with Fungitell® assay (Associates of Cape Cod, Falmouth, MA) following manufacturer instructions. Briefly, 5 mL of serum was mixed with an alkaline pretreatment reagent (0.125 M KOK/0.6 M KCl) and incubated at 37 °C for 10 min. One hundred microliters of the reconstituted Fungitell reagent was added to each well and the reaction monitored at A405nm–A490nm for 40 min. The Fungitell assay detects BG through the activation of factor G, a protease zymogen which activates a second protease zymogen, pro-clotting enzyme, that cleaves a chromophore from a chromogenic peptide resulting in light absorbance at 405 nm. Serum BG > 60 pg/mL was used as positive cut-off [16]. BG values at < 7.8 pg/mL and > 523.4 pg/mL were recorded as 0 and 523 pg/mL, respectively (Additional file 2). Cytokines We measured a panel of cytokines including interleukin-1 beta (IL-1β), interferon-alpha-2 (IFN-α2), interferon gamma (IFN-γ), tumor necrosis factor-α (TNF-α), monocyte chemoattractant protein-1 (MCP-1), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10), interleukin-12, p70 (IL-12 p70), interleukin-17A (IL-17A, interleukin-18 (IL-18), interleukin-23 (IL-23), and interleukin-33 (IL-33) at the same time points using LEGENDplex™ Human Th Cytokine Panel (BioLegend, San Diego, CA, USA) according to manufacturer’s protocol. 16S rDNA high-throughput sequencing Genomic DNA was extracted from 300 µl of whole blood using the GenUp™ gDNA kit (Biotechrabbit, Germany). The amplification of the bacterial 16S rDNA was performed in total volume 25 µl consisting of Taq DNA polymerase (0.5U) (Biotechrabbit, Germany), 1.5 mM MgCl2, 0.2 mM dNTPs, 0.2 mM forward primer 5′-ACTCCTACGGRAGGCAGCAG-3′ and 0.2 mM reverse primer 5′-CCGTCAATTYYTTTRAGTTT-3′. The PCR product was re-amplified within V4 region of 16S rDNA by using phasing adaptor primers following from [17]. Amplified PCR products (~ 430 bp) were purified by using the QIAquick PCR Purification Kit (QIAGEN, Germany) and quantified by KAPA library quantification kits for Illumina platforms (Kapa Biosystems, USA). The DNA libraries were pooled at equal amount and paired-end (2 × 250 cycles) sequenced on an Illumina MiSeq platform with a MiSeq Reagent Kit V2 (Illumina, USA). Statistical analyses Raw sequencing data were demultiplexed by MiSeq reporter software (version 2.6.2.3). The FASTQ files were analyzed by QIIME2 pipeline (version 2019.7) [18]. The paired-end sequences were merged and trimmed based on quality score (< Q30). Then, merged reads were deduplicated and clustered with 99% similarity by using VSEARCH [19]. UCHIME algorithm was used for filtering out the chimeric sequences [20]. The filtered reads were classified based on 16S Greengene database [21] using VSEARCH algorithm. The alpha diversity was analyzed by implemented function in QIIME2. Differential abundance analysis was conducted by linear discriminant analysis effect size (LEfSe) [22]. Wilcoxon matched pairs test were analyzed using GraphPad Prism version 6.01. Statistical comparisons for continuous and categorical data were performed using Chi-square/Fisher exact test and Mann–Whitney U test/Kruskal–Wallis test. Data are reported as counts (percentages) for categorical and median with interquartile range for continuous data. No imputation was performed on missing data. All statistical analyses were performed using Stata version 15.1 (STATA Corp, TX). P value of less than 0.05 was considered as statistical significance for all tests performed. Results A total of 147 patients were recruited. Of these, 53 (34.6%) patients were diagnosed COVID-19 pneumonia. Among patients with COVID-19 pneumonia, only 19 patients fulfilled the inclusion criteria and 13 (68.4%) patients were admitted in the ICU (Fig. 1). Male sex was predominant in our cohort. Markers of inflammations including ferritin, C-reactive protein (CRP), and IL-6 were markedly high (Table 1). Detailed clinical data and the outcomes of 19 patients are shown in Table 2 and Additional file 3: Table S1.Fig. 1 Study cohort Table 1 Clinical characteristics of COVID-19 pneumonia at enrollment Characteristic Total (N = 19) EAA ≥ 0.6 (N = 8) EAA < 0.6 (N = 11) Age, years 54 (43, 59) 49 (40, 59) 57 (48, 58) Male 15 (79%) 6 (75) 9 (82) APACHE II score 8 (4, 11) N = 13 9 (5, 10) N = 5 7.5 (4.0, 11.5) N = 8 SOFA score 6 (3, 10) 5 (3, 11) N = 5 6 (3.5, 9) N = 8 PaO2/FiO2 ratio at enrollment 233 (186, 261) N = 13 246 (186, 293) N = 5 223 (187, 254) N = 8 Underlying disease  Hypertension 6 (32) 1 (13) 5 (45)  Diabetes 8 (42) 2 (25) 6 (55)  Chronic kidney disease 2 (11) 0 2 (18)  Time from onset of fever to hospital admission (days) 5 (3, 7) 3.5 (2, 5) 7 (5, 8)  Time from onset of fever to 1st EAA measurement (days) 9 (9, 11) 9 (6, 12.5) 9 (9, 11)  ICU admission (%) 13 (68.4) 5 (62.5) 8 (72.7)  Serum creatinine, mg/dL 0.90 (0.80, 1.16) 0.82 (0.69, 1.07) 0.91 (0.86, 1.28)  EAA level 0.57 (0.47, 0.86) 0.86 (0.80, 0.90) 0.48 (0.42, 0.57)  Cycle threshold 23.9 (18.3, 27.7) 23.7 (15.8, 27.0) 24.9 (18.3, 30.1)  Ferritin 872 (638, 2409) 740 (417, 2047) 1057 (750, 2771)  CRP 127 (27.5, 1457) 38 (27.5, 130) 137 (70, 175)  Procalcitonin 0.16 (0.07, 0.55) 0.11 (0.03, 2.34) 0.24 (0.09, 0.43) Treatment  Darunavir 9 (47) 6 (75) 3 (27)  Ritonavir 9 (47) 6 (75) 3 (27)  Favipiravir 18 (95) 7 (88) 11 (100)  Antimalarial drug 18 (95) 8 (100) 10 (91)  Azithromycin 13 (68) 6 (75) 7 (64)  Corticosteroid 4 (21) 3 (38) 1 (9)  Antibiotic 13 (68) 5 (63) 8 (73) Data shown as counts (%) or median (IQR) APACHE II Acute Physiology and Chronic Health Evaluation II, CRP C-reactive protein, EAA endotoxin activity assay, FiO2 fractional inspired oxygen, IL-6 interleukin-6, PaO2 partial pressure of arterial oxygen, SOFA Sequential Organ Failure Assessment Table 2 Outcomes at 28 days after enrollment Characteristic Total (N = 19) EAA ≥ 0.6 (N = 8) EAA < 0.6 (N = 11) Subsequent bacterial infection 5 (26%) 3 (38%) 2 (18%) Total mechanical ventilation 10 (52.6) 5 (62.5) 5 (45.5) Successful extubation 8/10 (80%) 4/5 (80%) 4/5 (80%) Ventilator-free day (days) 24 (20, 28) 23.5 (15, 28) 28 (21.5, 28) Vasopressor 7 (36.8) 2 (25.0) 5 (45.5) Prone position 2 (10.5) 2 (25.0) 0 ECMO 1 (5.3) 1 (12.5) 0 AKI 8 (42.1) 2 (25.0) 6 (54.5) RRT 3 (15.8) 2 (25.0) 1 (9.1) Mortality 0 0 0 Data shown as counts (%) or median (IQR) AKI acute kidney injury, EAA endotoxin activity assay, ECMO extracorporeal membrane oxygenation, RRT renal replacement therapy Endotoxin activity and BG Overall median (IQR) EAA on day 1, day 3, and day 7 were 0.57 (0.47, 0.86), 0.65 (0.49, 0.96), and 0.57 (0.43, 0.74), respectively. There were 8 (42.1%) patients with day 1 EAA ≥ 0.60 (Table 1). Distributions of EAA on day 1, day 3, and day 7 are shown in Fig. 2a.Fig. 2 a EAA distribution of COVID-19 pneumonia. b Serum BG distribution of COVID-19 pneumonia Figure 2b demonstrates the distributions of serum BG following day of enrollment. During 7 days of observation, 8 patients (42.1%) had high BG levels (defined as BG > 60 pg/mL), and 37.5% of patients with high BG also had EAA > 0.6. The level of BG increased following the day of enrollment (Fig. 2b). The median BG levels were higher in patients with high EAA compared those with low EAA [day1 (25 VS 12 pg/mL), day 3 (42 vs 10 pg/mL), and day 7 (75 vs 26 pg/mL)]. Circulating bacteriome Forty-nine of 50 samples (98.3%) from 19 COVID-19 pneumonia patients had presence of bacterial DNA in serum. Rarefaction curves are shown in Additional file 4: Figure S1. Bacterial classification revealed that the relative abundances of bacteria were different among patients and days of illness (Additional file 4: Figure S2). Taxonomy composition phyla abundances revealed that the Proteobacteria phylum was the predominant phylum at every time point (Fig. 3, Additional file 4: Figure S3). Interestingly, Gram-negative bacterial genera including Sphingomonas, Bradyrhizobium, Enhydrobacter, Phyllobacterium, Agrobacterium, Comamonas, Sediminibacterium, Acinetobacter, and Pseudomonas were most likely found in different days of illness (Additional file 4: Figure S2). Biodiversity of bacteria, demonstrated by the Chao1 richness, on day 1 was significantly higher than on day 3 (Additional file 4: Figures S4, S5). The bacterial genera including Sphingomonas and Sediminibacterium were significantly (P < 0.05) higher on day 3 compared to day 1; whereas Comamonas, Acinetobacter, and Pseudomonas were significantly (P < 0.05) decreased on day 3 (Additional file 4: Figure S6).Fig. 3 Overall summary of dynamic bacterial community profiles in COVID-19 patients on day 1, 3, and 7. Proteobacteria were observed to be the dominant bacterial phyla followed by Bacteroidetes, Actinobacteria and Firmicutes Cytokines A heat map of cytokine levels on day 1, 3, and 7 is depicted in Fig. 4. Most of COVID-19 pneumonia had elevated of cytokines. On day 1, MCP-1, IL-6, IL-8, IL-10 in COVID-19 pneumonia who were admitted in ICU were significantly higher than COVID-19 pneumonia who were not admitted in ICU [(848 (410, 1782) vs 285 (215, 300), P = 0.003; 45 (16, 334) vs 12 (7, 25), P = 0.023; 73 (47, 128) vs 24 (20, 41), P = 0.009; 23 (19, 56) vs 8 (2, 13), P = 0.022.] (Additional file 3: Table S2).Fig. 4 Cytokine heat map on day 1, day 3, and day 7 Clinical characteristics at baseline and outcomes Comparing clinical features between patients with high (≥ 0.6 on day 1) and low EAA (< 0.6 on day 1) revealed that those with high EAA had lower median age, and higher severity scores (Table 1). Patients with high EAA levels sought medical attention earlier than patients with low EAA levels [median time from onset of fever to hospital admission [3.5 (2, 5) vs 7 (5, 8)]. Regarding clinical outcomes, there were five patients who had subsequent infection. All of them had positive bacterial culture from endotracheal suctions or sputum. Three patients had Acinetobacter baumanmii pneumonia around 7 days after ICU admission. One patient had Pseudomonas pneumonia 3 days after admission. One patient had Klebsiella pneumoniae pneumonia 3 days after admission. Patients with high EAA were more likely to develop subsequent bacterial infection within 28 days after enrollment (38% vs 18%). Patients with high EAA tended to need more mechanical ventilation support than the low-EAA group (62.5% vs 45.5%). The proportion of patients requiring vasopressors, prone position, and ECMO did not differ between EAA groups (Table 2). The 28-day outcomes are shown in Fig. 5. The overall rate of AKI (any stage) in our study was 42.1%. Three (15.8%) patients required renal replacement therapy (RRT). When compared to patients with low EAA, patients with high EAA tended to have lower ventilator-free days [23.5 (15, 28) vs 28 (21.5, 28)] and successful extubation rate (20% vs 60%). No patient died within 28 days after enrollment.Fig. 5 Clinical outcomes in study cohort We have compared EAA and BG level of our COVID-19 patients with the data from our previous study in 136 severe sepsis or septic shock patients (Additional file 2: Figure S7a and S7b) [23, 24]. This demonstrated that COVID-19 patients had EAA and BG levels comparable to severe sepsis patients. Discussion In this cohort of COVID-19 pneumonia, we show that nearly 90% and 40% of patients had endotoxemia (defined as a moderate to high level of EAA), and high levels of BG, a measure of gut permeability (Fig. 2). Using next-generation sequencing (NGS), we could also demonstrate the dominant bacterial DNA came from Proteobacteria, a phylum of Gram-negative bacteria which includes several pathogens that can cause sepsis (Fig. 3). In animals, high endotoxin activity in viral infection has been demonstrated to be associated with poor outcome. Inoculation of lipopolysaccharides (LPS) in mice with influenza infection resulted in the activation of local pulmonary inflammatory responses and may lead to secondary bacterial pneumonia [25]. LPS binds with toll-like receptor 4 (TLR4) and activates transcription factors activating protein-1 (AP-1), nuclear factor kappa B (NF-kB) and interferon regulatory factor 3 (IRF3) through myeloid differentiation factor 88 (MyD88) and TIR-domain-containing adapter-inducing interferon-β (TRIF)-dependent pathways. This leads to the induction of proinflammatory cytokines and interferons [26]. BG is a key structural polysaccharide of the cell wall of most fungi including Candida spp., and has been used in the diagnosis of invasive fungal infection [16, 27]. Leelahavanichkul et al. reported the use of serum BG as a biomarker of gut permeability in human sepsis [12]. In our study, the increase of serum BG level later in the course of illness (Fig. 2b) might reflect overgrowth of Candida spp. in the gastrointestinal tract from antibiotics exposure [28]. Intestinal Candida overgrowth alone did not increase serum BG. Hence, the detection of BG in serum implied gut-permeability defect in these patients. In addition, BG also induced proinflammatory responses through Dectin-1 signaling [12]. With NGS, a highly sensitive technology, we could detect bacteria which were unable to grow using standard culture methods. From previous reports, bacterial DNA and RNA were discovered between 4 and 100% in blood of healthy individual [29–33]. Our data agree with Gosiewski et al. which showed increased abundance of proteobacteria in sepsis patients compared to the healthy population (60.1% vs 16.4%) [34]. This raises the possibility that COVID-19 could cause a sepsis-like syndrome with the same dominant bacteria phylum as in sepsis patients. This phylum contains many genera of bacteria. The most predominant bacteria were Sphingomonas, Bradyrhizobium, and Enhydrobacter. Sphingomonas paucimobilis is an opportunistic pathogen that can cause hospital-associated infections from environmental exposure [35]. Bradyrhizobium enterica were found in patients with colitis [36]. Enhydrobacter aerosaccus can be detected from a patient with Hemophagocytic lymphohistiocytosis (HLH) with concomitant corticosteroid use [37]. This is particularly of interest due to the growing evidence of HLH syndrome in severe COVID-19 patients [38]. This hypothesis was supported by a recently published study. Arunachalam et al. demonstrated that severe COVID-19 patients had significant increase plasma LPS and bacterial 16S rRNA gene product when compared to healthy adults [39]. From these data, we proposed that both number and diversity of bacteria might contribute to severity of COVID-19 (Additional file 4: Figures S1–S7). Patients with COVID-19 have been found to have high levels of proinflammatory cytokines such as IL-6, IL-1β, IP10, and MCP-1 [4]. Similar findings were also reported in patients with SARS [40] and MERS-CoV [41]. This has prompted several authors to discuss so-called “Cytokine Storm” in viral respiratory infection that is a cause of multiple organ failure. However, our results suggest that bacterial products might be another possible contributor to the cytokine storm rather than only the virus itself (Additional file 4: Figures S1–S7). The source of bacterial toxin, and bacterial DNA in the blood of patients with COVID-19 pneumonia is unclear. It is possible that viremia results in capillary leakage syndrome similar to bacterial sepsis which causes interstitial edema and induces dysfunction of the lung and intestinal barrier which may facilitate bacterial toxin and live bacteria translocation into the circulation. These bacterial products then induce the release of proinflammatory cytokines. In our study, many patients (47.3%) ultimately developed subsequent bacterial infection within 28 days. Hanada et al. proposed the mechanism of viral-induced susceptibility of secondary bacterial infection involving local and systemic immune response which results in alterations in respiratory and gut microbiomes and impaired pulmonary immune response [42]. However, the GI tract appears to be another target of COVID-19. Similar to respiratory tract, various cells in the GI tract also express ACE2 and TMPRSS2 which are crucial for fusion of viral particles with host cells [43, 44]. Biopsies from severe cases have revealed involvement of COVID-19 throughout the GI tract from esophagus to colon [45]. Thus, we propose that loss of gut barrier function might be one of the mechanisms that contribute to the presence of bacterial toxin and bacterial DNA in the blood of patients with severe COVID-19 (Fig. 6).Fig. 6 Hypothetical pathogenesis of endotoxemia in COVID-19 pneumonia. At early stage, SARS-CoV-2 primarily infects type 2 pneumocytes in the lungs and causes pneumonia which can progress to ARDS and induces susceptibility of secondary bacterial infection by impairing the pulmonary immune response. The virus can enter the bloodstream causing viremia targeting organs with high ACE2 expression including the gut. SARS-CoV-2 infection of enterocytes causes inflammatory response of gastrointestinal tract which results in alteration of the intestinal microenvironment including epithelial hyperpermeability, attenuated local immune system, and dysbiosis of the microbiome. The perturbations of the intestinal microenvironment allow the pathogenic bacteria from the gut lumen to translocate to the bloodstream. Hence, we propose the sources of endotoxin to be majorly from the gut and minorly from the secondary bacterial infection of the lungs. COVID-19, the coronavirus disease 2019; SAR-CoV-2, severe acute respiratory syndrome coronavirus 2; ARDS, acute respiratory distress syndrome; ACE2, angiotensin converting enzyme 2; MOF, multiple organ failure To our knowledge, this is the first study to demonstrate evidence of bacterial toxins including EAA, and the presence of circulating bacteriome in patients with COVID-19 pneumonia. We also showed the association of high level of EAA and the severity of COVID-19 pneumonia. Both the type of bacteria and the presence of BG in the serum suggest that the gut is the source. Our study had several limitations. First, our 16s RNA gene amplification technique could not demonstrate the absolute number of bacterial DNA. Therefore, we could not correlate the burden of circulating bacterial DNA to the severity of the patients. However, our study aimed to be a starting point for future investigation. Second, our study did not include respiratory tract or gastrointestinal specimens, so we cannot completely establish the source of bacterial products in our patients. Future study should explore the effect of organ crosstalk between the lung and the intestine during COVID-19 infection. Third, this was a single-center study and all patients had Thai ethnicity. Lastly, we did not use more specific invasive test such as urine sucralose tests for GI leakage due to unstable conditions of the patients. Instead, we used BG which is a non-invasive biomarker to demonstrate GI leakage. From previous study, BG was evaluated as a potential marker of intestinal barrier dysfunction [12]. Conclusions High levels of endotoxin activity and bacterial DNA can be found in the blood of patients with COVID-19 pneumonia. This previously unrecognized mechanism of hyperinflammation and organ failure in COVID-19 warrants further study. Supplementary Information Additional file 1. EAA laboratory specification manual. Additional file 2. BG laboratory specification manual. Additional file  3: Table S1. Clinical characteristics of COVID-19 pneumonia. Table S2. Cytokines level on day 1, day 3, and day 7 stratified by ICU admission status. Additional file 4: Figure S1. Rarefaction curve of pass-filter reads obtained from each sample. Figure S2. Dynamic bacterial community profiles on day 1, day 3, and day 7. Figure S3. Dynamic bacterial community profiles at the phylum level in COVID-19 patients. Figure S4. Shannon diversity index (A) and Chao1 richness (B) representing alpha diversity of bacterial community profiles in COVID-19 patients on day 1, 3, and 7. The Shannon diversity index was not significant differences among the date of onsets whereas the Chao1 richness was significant (P < 0.05) between the first and third date of onsets Figure S5. LEfSe analysis of bacteria on day 1 and day 3. Figure S6. Wilcoxon matched pairs test differential abundant of bacterial genera in each COVID-19 patient between day 1 and day 3. The bacterial genera including Sphingomonas and Sediminibacterium were significantly (P < 0.05) higher in the third date compared to the first of onset whereas Comamonas, Acinetobacter and Pseudomonas were significantly (P < 0.05) decreased during the third date of onsets. Figure S7. Comparison of EAA level (Figure S7a) and BG (Figure S7b) between COVID-19 and other type of infections using data from our previous studies in 136 severe sepsis or septic shock patients. Abbreviations AKI Acute kidney injury BG (1 → 3)-β-D-Glucan AP-1 Activates transcription factors activating protein-1 COVID-19 Coronavirus disease 2019 Ct Cycle threshold CRP C-reactive protein ECMO Extracorporeal membrane oxygenation EAA Endotoxin activity assay ICU Intensive care unit IFN-α2 Interferon alpha-2 IFN-γ Interferon gamma HLH Hemophagocytic lymphohistiocytosis IRF3 Interferon regulatory factor 3 IL-1β Interleukin-1 beta IL-6 Interleukin-6 IL-8 Interleukin-8 IL-10 Interleukin-10 IL-12 p70 Interleukin-12, p70 IL-17A Interleukin-17A IL-18 Interleukin-18 IL-23 Interleukin-23 IL-33 Interleukin-33 IQR Interquartile range LEfSe Linear discriminant analysis effect size LPS Lipopolysaccharides MCP-1 Monocyte chemoattractant protein-1 MyD88 Myeloid differentiation factor 88 NGS Next-generation sequencing NF-kB Nuclear factor kappa B RRT Renal replacement therapy RT-PCR Reverse transcriptase-polymerase chain reaction TLR4 Toll-like receptor 4 TNF-α Tumor necrosis factor-α TRIF TIR-domain-containing adapter-inducing interferon-β Supplementary Information Supplementary information accompanies this paper at 10.1186/s40635-020-00362-8. We would like to thank the staff, fellows, nurses, and research coordinators from the Excellence Center for Critical Care Nephrology (EC-CCN) and Emerging Infectious Disease (EID) unit, Faculty of Medicine, Chulalongkorn University. We also thank Miss Pimnara Peerawaranun for statistical analysis and Miss Sasipha Tachaboon medical technologist, for laboratory procedures. Authors’ contributions PS, WK, and NS were responsible for study concept and design. PS, WK, NS, TP, AC, SP, PP, SC, VS, NW, SK, OP, TT, NL, VP, NP, TS, RS, ST, KT, NLu, AL, TSri, TTa, UT, CC, KP, KTu, SE, JK, and NS were responsible for the acquisition, analysis, or interpretation of data. PS, WK and NS were responsible for drafting the manuscript. PS, WK and NS were responsible for statistical analysis. All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors interpreted the findings, contributed to writing the manuscript. All authors read and approved the final manuscript. Funding The investigator-initiated study was funded by the Excellence Center for Critical Care Nephrology, King Chulalongkorn Memorial Hospital. Availability of data and materials On reasonable request, data from this study are available from the corresponding author. Ethics approval and consent to participate The study was reviewed and approved by Faculty of Medicine, Chulalongkorn University ethics committee (IRB no. 336/63). The informed consent was waived due to the observational nature of the study. Consent for publication Not applicable. Competing interests Toray Industries provided endotoxin activity assay kits for use in this study. The company had no influence on the study design or analysis or on the comment of this article. None of the other authors have any disclosures. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Phatadon Sirivongrangson and Win Kulvichit contributed equally to this work ==== Refs References 1. Bhatraju PK Ghassemieh BJ Nichols M Kim R Jerome KR Nalla AK Greninger AL Pipavath S Wurfel MM Evans L Kritek PA West TE Luks A Gerbino A Dale CR Goldman JD O'Mahony S Mikacenic C Covid-19 in Critically Ill patients in the seattle region - case series N Engl J Med 2020 10.1056/NEJMoa2004500 32227758 2. Singer M Deutschman CS Seymour CW Shankar-Hari M Annane D Bauer M Bellomo R Bernard GR Chiche JD Coopersmith CM Hotchkiss RS Levy MM Marshall JC Martin GS Opal SM Rubenfeld GD van der Poll T Vincent JL Angus DC The third international consensus definitions for sepsis and septic shock (Sepsis-3) JAMA 2016 315 8 801 810 10.1001/jama.2016.0287 26903338 3. Zhou F Yu T Du R Fan G Liu Y Liu Z Xiang J Wang Y Song B Gu X Guan L Wei Y Li H Wu X Xu J Tu S Zhang Y Chen H Cao B Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study Lancet 2020 395 10229 1054 1062 10.1016/S0140-6736(20)30566-3 32171076 4. Huang C Wang Y Li X Ren L Zhao J Hu Y Zhang L Fan G Xu J Gu X Cheng Z Yu T Xia J Wei Y Wu W Xie X Yin W Li H Liu M Xiao Y Gao H Guo L Xie J Wang G Jiang R Gao Z Jin Q Wang J Cao B Clinical features of patients infected with 2019 novel coronavirus in Wuhan China Lancet 2020 395 10223 497 506 10.1016/s0140-6736(20)30183-5 31986264 5. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, Liu L, Shan H, Lei CL, Hui DSC, Du B, Li LJ, Zeng G, Yuen KY, Chen RC, Tang CL, Wang T, Chen PY, Xiang J, Li SY, Wang JL, Liang ZJ, Peng YX, Wei L, Liu Y, Hu YH, Peng P, Wang JM, Liu JY, Chen Z, Li G, Zheng ZJ, Qiu SQ, Luo J, Ye CJ, Zhu SY, Zhong NS, China Medical Treatment Expert Group for C clinical characteristics of coronavirus disease 2019 in China N Engl J Med 2020 10.1056/NEJMoa2002032 32109013 6. Alhazzani W, Møller MH, Arabi YM, Loeb M, Gong MN, Fan E, Oczkowski S, Levy MM, Derde L, Dzierba A, Du B, Aboodi M, Wunsch H, Cecconi M, Koh Y, Chertow DS, Maitland K, Alshamsi F, Belley-Cote E, Greco M, Laundy M, Morgan JS, Kesecioglu J, McGeer A, Mermel L, Mammen MJ, Alexander PE, Arrington A, Centofanti J, Citerio G, Baw B, Memish ZA, Hammond N, Hayden FG, Evans L (2020) Surviving Sepsis Campaign: Guidelines on the Management of Critically Ill Adults with Coronavirus Disease 2019 (COVID-19). ESICM 7. Luo S Zhang X Xu H Don't overlook digestive symptoms in patients with 2019 novel coronavirus disease (COVID-19) Clin Gastroenterol Hepatol 2020 10.1016/j.cgh.2020.03.043 32450364 8. Zhang H Kang Z Gong H Xu D Wang J Li Z Cui X Xiao J Meng T Zhou W Liu J Xu H The digestive system is a potential route of 2019-nCov infection: a bioinformatics analysis based on single-cell transcriptomes BioRxiv 2020 10.1101/2020.01.30.927806 33236014 9. Opal SM The host response to endotoxin, antilipopolysaccharide strategies, and the management of severe sepsis Int J Med Microbiol 2007 297 5 365 377 10.1016/j.ijmm.2007.03.006 17452016 10. Munford RS Sensing gram-negative bacterial lipopolysaccharides: a human disease determinant? Infect Immun 2008 76 2 454 465 10.1128/IAI.00939-07 18086818 11. Danner RL Elin RJ Hosseini JM Wesley RA Reilly JM Parillo JE Endotoxemia in human septic shock Chest 1991 99 1 169 175 10.1378/chest.99.1.169 1984950 12. Leelahavanichkul A Worasilchai N Wannalerdsakun S Jutivorakool K Somparn P Issara-Amphorn J Tachaboon S Srisawat N Finkelman M Chindamporn A Gastrointestinal leakage detected by serum (1–>3)-beta-d-glucan in mouse models and a pilot study in patients with sepsis Shock 2016 46 5 506 518 10.1097/SHK.0000000000000645 27172153 13. von Elm E Altman DG Egger M Pocock SJ Gotzsche PC Vandenbroucke JP Initiative S The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies Ann Intern Med 2007 147 8 573 577 10.7326/0003-4819-147-8-200710160-00010 17938396 14. KDIGO Clinical Practice Guideline for Acute Kidney Injury (2012). Kidney Int 2:1–138 15. Romaschin AD Harris DM Ribeiro MB Paice J Foster DM Walker PM Marshall JC A rapid assay of endotoxin in whole blood using autologous neutrophil dependent chemiluminescence J Immunol Methods 1998 212 2 169 185 10.1016/S0022-1759(98)00003-9 9672205 16. Worasilchai N Leelahavanichkul A Kanjanabuch T Thongbor N Lorvinitnun P Sukhontasing K Finkelman M Chindamporn A (1–>3)-beta-D-glucan and galactomannan testing for the diagnosis of fungal peritonitis in peritoneal dialysis patients, a pilot study Med Mycol 2015 53 4 338 346 10.1093/mmy/myv007 25851260 17. Wu L Wen C Qin Y Yin H Tu Q Van Nostrand JD Yuan T Yuan M Deng Y Zhou J Phasing amplicon sequencing on Illumina Miseq for robust environmental microbial community analysis BMC Microbiol 2015 15 125 10.1186/s12866-015-0450-4 26084274 18. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet C, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F (2018) QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. Peer J Preprints, 19. Rognes T Flouri T Nichols B Quince C Mahé F VSEARCH: a versatile open source tool for metagenomics PeerJ 2016 4 e2584 10.7717/peerj.2584 27781170 20. Edgar RC Haas BJ Clemente JC Quince C Knight R UCHIME improves sensitivity and speed of chimera detection Bioinformatics 2011 27 16 2194 2200 10.1093/bioinformatics/btr381 21700674 21. DeSantis TZ Hugenholtz P Larsen N Rojas M Brodie EL Keller K Huber T Dalevi D Hu P Andersen GL Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB Appl Environ Microbiol 2006 72 7 5069 5072 10.1128/AEM.03006-05 16820507 22. Segata N Izard J Waldron L Gevers D Miropolsky L Garrett WS Huttenhower C Metagenomic biomarker discovery and explanation Genome Biol 2011 12 6 R60 10.1186/gb-2011-12-6-r60 21702898 23. Srisawat N Kulvichit W Tungsanga S Peerapornratana S Vorasitchai S Tangkanakul C Lumlertgul N Komaenthammasophon C Praditpornsilpa K Tungsanga K Eiam-Ong S The role of neutrophil chemotaxis activity as an immunologic biomarker to predict mortality in critically-ill patients with severe sepsis J Crit Care 2020 56 215 221 10.1016/j.jcrc.2020.01.020 31982695 24. Srisawat N Tungsanga S Lumlertgul N Komaenthammasophon C Peerapornratana S Thamrongsat N Tiranathanagul K Praditpornsilpa K Eiam-Ong S Tungsanga K Kellum JA The effect of polymyxin B hemoperfusion on modulation of human leukocyte antigen DR in severe sepsis patients Crit Care 2018 22 1 279 279 10.1186/s13054-018-2077-y 30367647 25. Koch RM Diavatopoulos DA Ferwerda G Pickkers P de Jonge MI Kox M The endotoxin-induced pulmonary inflammatory response is enhanced during the acute phase of influenza infection Intensive Care Med Exp 2018 6 1 15 10.1186/s40635-018-0182-5 29978355 26. Lin E Lowry SF The human response to endotoxin Sepsis 1998 2 255 262 10.1023/A:1009890031293 27. Ruhnke M, Bohme A, Buchheidt D, Cornely O, Donhuijsen K, Einsele H, Enzensberger R, Hebart H, Heussel CP, Horger M, Hof H, Karthaus M, Kruger W, Maschmeyer G, Penack O, Ritter J, Schwartz S, Infectious Diseases Working Party in H, Oncology of the German Society for H, Oncology Diagnosis of invasive fungal infections in hematology and oncology–guidelines from the Infectious Diseases Working Party in Haematology and Oncology of the German Society for Haematology and Oncology (AGIHO) Ann Oncol 2012 23 4 823 833 10.1093/annonc/mdr407 21948809 28. Netea MG Joosten LA van der Meer JW Kullberg BJ van de Veerdonk FL Immune defence against Candida fungal infections Nat Rev Immunol 2015 15 10 630 642 10.1038/nri3897 26388329 29. Whittle E Leonard MO Harrison R Gant TW Tonge DP Multi-Method Characterization of the Human Circulating Microbiome Front Microbiol 2018 9 3266 10.3389/fmicb.2018.03266 30705670 30. Qiu J Zhou H Jing Y Dong C Association between blood microbiome and type 2 diabetes mellitus: a nested case-control study J Clin Lab Anal 2019 33 4 e22842 10.1002/jcla.22842 30714640 31. Li Q Wang C Tang C Zhao X He Q Li J Identification and characterization of blood and neutrophil-associated microbiomes in patients with severe acute pancreatitis using next-generation sequencing Front Cell Infect Microbiol 2018 8 5 10.3389/fcimb.2018.00005 29423379 32. Sato J Kanazawa A Ikeda F Yoshihara T Goto H Abe H Komiya K Kawaguchi M Shimizu T Ogihara T Tamura Y Sakurai Y Yamamoto R Mita T Fujitani Y Fukuda H Nomoto K Takahashi T Asahara T Hirose T Nagata S Yamashiro Y Watada H Gut dysbiosis and detection of "live gut bacteria" in blood of Japanese patients with type 2 diabetes Diabetes Care 2014 37 8 2343 2350 10.2337/dc13-2817 24824547 33. Panaiotov S Filevski G Equestre M Nikolova E Kalfin R Cultural isolation and characteristics of the blood microbiome of healthy individuals Adv Microbiol 2018 10.4236/aim.2018.85027 34. Gosiewski T Ludwig-Galezowska AH Huminska K Sroka-Oleksiak A Radkowski P Salamon D Wojciechowicz J Kus-Slowinska M Bulanda M Wolkow PP Comprehensive detection and identification of bacterial DNA in the blood of patients with sepsis and healthy volunteers using next-generation sequencing method—the observation of DNAemia Eur J Clin Microbiol Infect Dis 2017 36 2 329 336 10.1007/s10096-016-2805-7 27771780 35. Wasiura J Segal BH Mullin KM Cluster of Sphingomonas paucimobilis Bacteremias Linked to Diversion of Intravenous Hydromorphone N Engl J Med 2019 381 6 584 585 10.1056/NEJMc1902973 31390506 36. Bhatt AS Marty FM Meyerson M Bradyrhizobium enterica in cord colitis syndrome N Engl J Med 2013 369 19 1867 10.1056/NEJMc1311318 24195568 37. Mishra D Satpathy G Wig N Fazal F Ahmed NH Panda SK Evaluation of 16S rRNA broad range PCR assay for microbial detection in serum specimens in sepsis patients J Infect Public Health 2020 10.1016/j.jiph.2020.01.007 32061569 38. Mehta P McAuley DF Brown M Sanchez E Tattersall RS Manson JJ Collaboration HAS UK, COVID-19: consider cytokine storm syndromes and immunosuppression Lancet 2020 395 10229 1033 1034 10.1016/S0140-6736(20)30628-0 32192578 39. Arunachalam PS Wimmers F Mok CKP Perera R Scott M Hagan T Sigal N Feng Y Bristow L Tak-Yin Tsang O Wagh D Coller J Pellegrini KL Kazmin D Alaaeddine G Leung WS Chan JMC Chik TSH Choi CYC Huerta C Paine McCullough M Lv H Anderson E Edupuganti S Upadhyay AA Bosinger SE Maecker HT Khatri P Rouphael N Peiris M Pulendran B Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans Science 2020 10.1126/science.abc6261 32788292 40. Wong CK Lam CW Wu AK Ip WK Lee NL Chan IH Lit LC Hui DS Chan MH Chung SS Sung JJ Plasma inflammatory cytokines and chemokines in severe acute respiratory syndrome Clin Exp Immunol 2004 136 1 95 103 10.1111/j.1365-2249.2004.02415.x 15030519 41. Mahallawi WH Khabour OF Zhang Q Makhdoum HM Suliman BA MERS-CoV infection in humans is associated with a pro-inflammatory Th1 and Th17 cytokine profile Cytokine 2018 104 8 13 10.1016/j.cyto.2018.01.025 29414327 42. Hanada S Pirzadeh M Carver KY Deng JC Respiratory Viral Infection-Induced Microbiome Alterations and Secondary Bacterial Pneumonia Front Immunol 2018 9 2640 10.3389/fimmu.2018.02640 30505304 43. Jin X Lian JS Hu JH Gao J Zheng L Zhang YM Hao SR Jia HY Cai H Zhang XL Yu GD Xu KJ Wang XY Gu JQ Zhang SY Ye CY Jin CL Lu YF Yu X Yu XP Huang JR Xu KL Ni Q Yu CB Zhu B Li YT Liu J Zhao H Zhang X Yu L Guo YZ Su JW Tao JJ Lang GJ Wu XX Wu WR Qv TT Xiang DR Yi P Shi D Chen Y Ren Y Qiu YQ Li LJ Sheng J Yang Y Epidemiological, clinical and virological characteristics of 74 cases of coronavirus-infected disease 2019 (COVID-19) with gastrointestinal symptoms Gut 2020 10.1136/gutjnl-2020-320926 32345613 44. Lin L Jiang X Zhang Z Huang S Zhang Z Fang Z Gu Z Gao L Shi H Mai L Liu Y Lin X Lai R Yan Z Li X Shan H Gastrointestinal symptoms of 95 cases with SARS-CoV-2 infection Gut 2020 10.1136/gutjnl-2020-321013 33219048 45. Ng SC Tilg H COVID-19 and the gastrointestinal tract: more than meets the eye Gut 2020 2020 321195 10.1136/gutjnl-2020-321195