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2.
Front Microbiol ; 12: 712081, 2021.
Article in English | MEDLINE | ID: covidwho-1497098

ABSTRACT

COVID-19 is mainly associated with respiratory distress syndrome, but a subset of patients often present gastrointestinal (GI) symptoms. Imbalances of gut microbiota have been previously linked to respiratory virus infection. Understanding how the gut-lung axis affects the progression of COVID-19 can provide a novel framework for therapies and management. In this study, we examined the gut microbiota of patients with COVID-19 (n = 47) and compared it to healthy controls (n = 19). Using shotgun metagenomic sequencing, we have identified four microorganisms unique in COVID-19 patients, namely Streptococcus thermophilus, Bacteroides oleiciplenus, Fusobacterium ulcerans, and Prevotella bivia. The abundances of Bacteroides stercoris, B. vulgatus, B. massiliensis, Bifidobacterium longum, Streptococcus thermophilus, Lachnospiraceae bacterium 5163FAA, Prevotella bivia, Erysipelotrichaceae bacterium 6145, and Erysipelotrichaceae bacterium 2244A were enriched in COVID-19 patients, whereas the abundances of Clostridium nexile, Streptococcus salivarius, Coprococcus catus, Eubacterium hallii, Enterobacter aerogenes, and Adlercreutzia equolifaciens were decreased (p < 0.05). The relative abundance of butyrate-producing Roseburia inulinivorans is evidently depleted in COVID-19 patients, while the relative abundances of Paraprevotella sp. and the probiotic Streptococcus thermophilus were increased. We further identified 30 KEGG orthology (KO) modules overrepresented, with 7 increasing and 23 decreasing modules. Notably, 15 optimal microbial markers were identified using the random forest model to have strong diagnostic potential in distinguishing COVID-19. Based on Spearman's correlation, eight species were associated with eight clinical indices. Moreover, the increased abundance of Bacteroidetes and decreased abundance of Firmicutes were also found across clinical types of COVID-19. Our findings suggest that the alterations of gut microbiota in patients with COVID-19 may influence disease severity. Our COVID-19 classifier, which was cross-regionally verified, provides a proof of concept that a set of microbial species markers can distinguish the presence of COVID-19.

4.
Nat Metab ; 3(7): 909-922, 2021 07.
Article in English | MEDLINE | ID: covidwho-1279905

ABSTRACT

Exosomes represent a subtype of extracellular vesicle that is released through retrograde transport and fusion of multivesicular bodies with the plasma membrane1. Although no perfect methodologies currently exist for the high-throughput, unbiased isolation of pure plasma exosomes2,3, investigation of exosome-enriched plasma fractions of extracellular vesicles can confer a glimpse into the endocytic pathway on a systems level. Here we conduct high-coverage lipidomics with an emphasis on sterols and oxysterols, and proteomic analyses of exosome-enriched extracellular vesicles (EVs hereafter) from patients at different temporal stages of COVID-19, including the presymptomatic, hyperinflammatory, resolution and convalescent phases. Our study highlights dysregulated raft lipid metabolism that underlies changes in EV lipid membrane anisotropy that alter the exosomal localization of presenilin-1 (PS-1) in the hyperinflammatory phase. We also show in vitro that EVs from different temporal phases trigger distinct metabolic and transcriptional responses in recipient cells, including in alveolar epithelial cells, which denote the primary site of infection, and liver hepatocytes, which represent a distal secondary site. In comparison to the hyperinflammatory phase, EVs from the resolution phase induce opposing effects on eukaryotic translation and Notch signalling. Our results provide insights into cellular lipid metabolism and inter-tissue crosstalk at different stages of COVID-19 and are a resource to increase our understanding of metabolic dysregulation in COVID-19.


Subject(s)
COVID-19/metabolism , COVID-19/virology , Extracellular Vesicles/metabolism , Lipidomics , Metabolomics , SARS-CoV-2 , Biological Transport , COVID-19/epidemiology , Cell Fractionation , Cell Membrane/metabolism , Chemical Fractionation , Cluster Analysis , Computational Biology/methods , Exosomes/metabolism , Host-Pathogen Interactions , Humans , Lipidomics/methods , Metabolome , Metabolomics/methods , Retrospective Studies , SARS-CoV-2/genetics , SARS-CoV-2/immunology
6.
Clin Infect Dis ; 71(15): 778-785, 2020 07 28.
Article in English | MEDLINE | ID: covidwho-1217823

ABSTRACT

BACKGROUND: The emergence of coronavirus disease 2019 (COVID-19) is a major healthcare threat. The current method of detection involves a quantitative polymerase chain reaction (qPCR)-based technique, which identifies the viral nucleic acids when present in sufficient quantity. False-negative results can be achieved and failure to quarantine the infected patient would be a major setback in containing the viral transmission. We aim to describe the time kinetics of various antibodies produced against the 2019 novel coronavirus (SARS-CoV-2) and evaluate the potential of antibody testing to diagnose COVID-19. METHODS: The host humoral response against SARS-CoV-2, including IgA, IgM, and IgG response, was examined by using an ELISA-based assay on the recombinant viral nucleocapsid protein. 208 plasma samples were collected from 82 confirmed and 58 probable cases (qPCR negative but with typical manifestation). The diagnostic value of IgM was evaluated in this cohort. RESULTS: The median duration of IgM and IgA antibody detection was 5 (IQR, 3-6) days, while IgG was detected 14 (IQR, 10-18) days after symptom onset, with a positive rate of 85.4%, 92.7%, and 77.9%, respectively. In confirmed and probable cases, the positive rates of IgM antibodies were 75.6% and 93.1%, respectively. The detection efficiency by IgM ELISA is higher than that of qPCR after 5.5 days of symptom onset. The positive detection rate is significantly increased (98.6%) when combining IgM ELISA assay with PCR for each patient compared with a single qPCR test (51.9%). CONCLUSIONS: The humoral response to SARS-CoV-2 can aid in the diagnosis of COVID-19, including subclinical cases.


Subject(s)
Betacoronavirus/immunology , Coronavirus Infections/diagnosis , Coronavirus Infections/immunology , Immunity, Humoral/immunology , Pneumonia, Viral/diagnosis , Pneumonia, Viral/immunology , Adult , Amino Acid Sequence , Antibodies, Viral/immunology , COVID-19 , Child , Child, Preschool , Coronavirus Infections/virology , Enzyme-Linked Immunosorbent Assay/methods , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/virology , Polymerase Chain Reaction/methods , SARS-CoV-2
7.
Microbes Infect ; 23(4-5): 104806, 2021.
Article in English | MEDLINE | ID: covidwho-1120151

ABSTRACT

This study aimed to investigate the frequency and characteristics of respiratory co-infections in COVID-19 patients in the intensive care unit (ICU). In this retrospective observational study, pathogens responsible for potential co-infections were detected by the bacterial culture, real-time polymerase chain reaction (RT-PCR), or serological fungal antigen tests. Demographic and clinical characteristics, as well as microbial results, were analyzed. Bacterial culture identified 56 (58.3%) positive samples for respiratory pathogens, with the most common bacteria being Burkholderia cepacia (18, 18.8%). RT-PCR detected 38 (76.0%) and 58 (87.9%) positive results in the severe and critical groups, respectively. Most common pathogens detected were Stenotrophomonas maltophilia (28.0%) and Pseudomonas aeruginosa (28.0%) in the severe group and S. maltophilia (45.5%) in the critical group. P. aeruginosa was detected more during the early stage after ICU admission. Acinetobacter baumannii and Staphylococcus aureus were more frequently identified during late ICU admission. Fungal serum antigens were more frequently positive in the critical group than in the severe group, and the positive rate of fungal serum antigens frequency increased with prolonged ICU stay. A high frequency of respiratory co-infections presented in ICU COVID-19 patients. Careful examinations and necessary tests should be performed to exclude these co-infections.


Subject(s)
Bacterial Infections/epidemiology , COVID-19/epidemiology , Coinfection/epidemiology , Mycoses/epidemiology , Adult , Aged , Aged, 80 and over , Bacterial Infections/virology , COVID-19/microbiology , China/epidemiology , Coinfection/microbiology , Coinfection/virology , Female , Humans , Intensive Care Units , Male , Middle Aged , Mycoses/virology , Respiratory Tract Infections/epidemiology
8.
Commun Biol ; 3(1): 780, 2020 12 11.
Article in English | MEDLINE | ID: covidwho-975030

ABSTRACT

Coronavirus Disease 2019 (COVID-19) has caused a global pandemic. Here we profiled the humoral response against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by measuring immunoglobulin (Ig) A, IgM, and IgG against nucleocapsid and spike proteins, along with IgM and IgG antibodies against receptor-binding domain (RBD) of the spike protein and total neutralizing antibodies (NAbs). We tested 279 plasma samples collected from 176 COVID-19 patients who presented and enrolled at different stages of their disease. Plasma dilutions were optimized and based on the data, a single dilution of plasma was used. The mean absorbance at 450 nm was measured for Ig levels and NAbs were measured using geometric mean titers. We demonstrate that more severe cases have a late-onset in the humoral response compared to mild/moderate infections. All the antibody titers continue to rise in patients with COVID-19 over the disease course. However, these levels are mostly unrelated to disease severity. The appearance time and titers of NAbs showed a significant positive correlation to the antibodies against spike protein. Our results suggest the late onset of antibody response as a risk factor for disease severity, however, there is a limited role of antibody titers in predicting disease severity of COVID-19.


Subject(s)
Antibodies, Neutralizing/immunology , Antibodies, Viral/immunology , COVID-19/immunology , Immunity, Humoral/immunology , SARS-CoV-2/immunology , Adolescent , Adult , Aged , Aged, 80 and over , Animals , Antibodies, Neutralizing/blood , Antibodies, Viral/blood , COVID-19/epidemiology , COVID-19/virology , China/epidemiology , Chlorocebus aethiops , Female , Humans , Kinetics , Male , Middle Aged , Pandemics , SARS-CoV-2/physiology , Severity of Illness Index , Spike Glycoprotein, Coronavirus/immunology , Vero Cells , Young Adult
9.
Open Forum Infect Dis ; 7(5): ofaa169, 2020 May.
Article in English | MEDLINE | ID: covidwho-623975

ABSTRACT

Background: There is currently a lack of nonspecific laboratory indicators as a quantitative standard to distinguish between the 2019 coronavirus disease (COVID-19) and an influenza A or B virus infection. Thus, the aim of this study was to establish a nomogram to detect COVID-19. Methods: A nomogram was established using data collected from 457 patients (181 with COVID-19 and 276 with influenza A or B infection) in China. The nomogram used age, lymphocyte percentage, and monocyte count to differentiate COVID-19 from influenza. Results: Our nomogram predicted probabilities of COVID-19 with an area under the receiver operating characteristic curve of 0.913 (95% confidence interval [CI], 0.883-0.937), greater than that of the lymphocyte:monocyte ratio (0.849; 95% CI, 0.812-0.880; P = .0007), lymphocyte percentage (0.808; 95% CI, 0.768-0.843; P < .0001), monocyte count (0.780; 95% CI, 0.739-0.817; P < .0001), or age (0.656; 95% CI, 0.610-0.699; P < .0001). The predicted probability conformed to the real observation outcomes of COVID-19, according to the calibration curves. Conclusions: We found that age, lymphocyte percentage, and monocyte count are risk factors for the early-stage prediction of patients infected with the 2019 novel coronavirus. As such, our research provides a useful test for doctors to differentiate COVID-19 from influenza.

11.
Clin Infect Dis ; 71(15): 793-798, 2020 07 28.
Article in English | MEDLINE | ID: covidwho-17963

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a public health emergency. The widely used reverse transcription-polymerase chain reaction (RT-PCR) method has limitations for clinical diagnosis and treatment. METHODS: A total of 323 samples from 76 COVID-19-confirmed patients were analyzed by droplet digital PCR (ddPCR) and RT-PCR based 2 target genes (ORF1ab and N). Nasal swabs, throat swabs, sputum, blood, and urine were collected. Clinical and imaging data were obtained for clinical staging. RESULTS: In 95 samples that tested positive by both methods, the cycle threshold (Ct) of RT-PCR was highly correlated with the copy number of ddPCR (ORF1ab gene, R2 = 0.83; N gene, R2 = 0.87). Four (4/161) negative and 41 (41/67) single-gene positive samples tested by RT-PCR were positive according to ddPCR with viral loads ranging from 11.1 to 123.2 copies/test. The viral load of respiratory samples was then compared and the average viral load in sputum (17 429 ±â€…6920 copies/test) was found to be significantly higher than in throat swabs (2552 ±â€…1965 copies/test, P < .001) and nasal swabs (651 ±â€…501 copies/test, P < .001). Furthermore, the viral loads in the early and progressive stages were significantly higher than that in the recovery stage (46 800 ±â€…17 272 vs 1252 ±â€…1027, P < .001) analyzed by sputum samples. CONCLUSIONS: Quantitative monitoring of viral load in lower respiratory tract samples helps to evaluate disease progression, especially in cases of low viral load.


Subject(s)
Betacoronavirus/genetics , Coronavirus Infections/diagnosis , Coronavirus Infections/virology , Pneumonia, Viral/diagnosis , Pneumonia, Viral/virology , Adult , COVID-19 , Diagnostic Tests, Routine/methods , False Negative Reactions , Female , Humans , Male , Middle Aged , Pandemics , Real-Time Polymerase Chain Reaction/methods , Respiratory System/virology , SARS-CoV-2 , Serologic Tests/methods , Sputum/virology , Viral Load/methods
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