Bat ASC2 suppresses inflammasomes and ameliorates inflammatory diseases.

Bats are special in their ability to live long and host many emerging viruses. Our previous studies showed that bats have altered inflammasomes, which are central players in aging and infection. However, the role of inflammasome signaling in combating inflammatory diseases remains poorly understood. Here, we report bat ASC2 as a potent negative regulator of inflammasomes. Bat ASC2 is highly expressed at both the mRNA and protein levels and is highly potent in inhibiting human and mouse inflammasomes. Transgenic expression of bat ASC2 in mice reduced the severity of peritonitis induced by gout crystals and ASC particles. Bat ASC2 also dampened inflammation induced by multiple viruses and reduced mortality of influenza A virus infection. Importantly, it also suppressed SARS-CoV-2-immune-complex-induced inflammasome activation. Four key residues were identified for the gain of function of bat ASC2. Our results demonstrate that bat ASC2 is an important negative regulator of inflammasomes with therapeutic potential in inflammatory diseases.

Gut microbiota in COVID-19: key microbial changes, potential mechanisms and clinical applications.

The gastrointestinal tract is involved in coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The gut microbiota has important roles in viral entry receptor angiotensin-converting enzyme 2 (ACE2) expression, immune homeostasis, and crosstalk between the gut and lungs, the 'gut-lung axis'. Emerging preclinical and clinical studies indicate that the gut microbiota might contribute to COVID-19 pathogenesis and disease outcomes; SARS-CoV-2 infection was associated with altered intestinal microbiota and correlated with inflammatory and immune responses. Here, we discuss the cutting-edge evidence on the interactions between SARS-CoV-2 infection and the gut microbiota, key microbial changes in relation to COVID-19 severity and host immune dysregulations with the possible underlying mechanisms, and the conceivable consequences of the pandemic on the human microbiome and post-pandemic health. Finally, potential modulatory strategies of the gut microbiota are discussed. These insights could shed light on the development of microbiota-based interventions for COVID-19.

Data-driven analytics of COVID-19 'infodemic'.

The rampant of COVID-19 infodemic has almost been simultaneous with the outbreak of the pandemic. Many concerted efforts are made to mitigate its negative effect to information credibility and data legitimacy. Existing work mainly focuses on fact-checking algorithms or multi-class labeling models that are less aware of the intrinsic characteristics of the language. Nor is it discussed how such representations can account for the common psycho-socio-behavior of the information consumers. This work takes a data-driven analytical approach to (1) describe the prominent lexical and grammatical features of COVID-19 misinformation; (2) interpret the underlying (psycho-)linguistic triggers in terms of sentiment, power and activity based on the affective control theory; (3) study the feature indexing for anti-infodemic modeling. The results show distinct language generalization patterns of misinformation of favoring evaluative terms and multimedia devices in delivering a negative sentiment. Such appeals are effective to arouse people's sympathy toward the vulnerable community and foment their spreading behavior.

Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome.

Our knowledge of the role of the gut microbiome in acute coronavirus disease 2019 (COVID-19) and post-acute COVID-19 is rapidly increasing, whereas little is known regarding the contribution of multi-kingdom microbiota and host-microbial interactions to COVID-19 severity and consequences. Herein, we perform an integrated analysis using 296 fecal metagenomes, 79 fecal metabolomics, viral load in 1378 respiratory tract samples, and clinical features of 133 COVID-19 patients prospectively followed for up to 6 months. Metagenomic-based clustering identifies two robust ecological clusters (hereafter referred to as Clusters 1 and 2), of which Cluster 1 is significantly associated with severe COVID-19 and the development of post-acute COVID-19 syndrome. Significant differences between clusters could be explained by both multi-kingdom ecological drivers (bacteria, fungi, and viruses) and host factors with a good predictive value and an area under the curve (AUC) of 0.98. A model combining host and microbial factors could predict the duration of respiratory viral shedding with 82.1% accuracy (error ± 3 days). These results highlight the potential utility of host phenotype and multi-kingdom microbiota profiling as a prognostic tool for patients with COVID-19.

Faecal microbiome-based machine learning for multi-class disease diagnosis.

Systemic characterisation of the human faecal microbiome provides the opportunity to develop non-invasive approaches in the diagnosis of a major human disease. However, shared microbial signatures across different diseases make accurate diagnosis challenging in single-disease models. Herein, we present a machine-learning multi-class model using faecal metagenomic dataset of 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, colorectal adenomas, Crohn's disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular disease, post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 microbial species derived from 14.3 terabytes of sequence. The trained model achieves an area under the receiver operating characteristic curve (AUROC) of 0.90 to 0.99 (Interquartile range, IQR, 0.91-0.94) in predicting different diseases in the independent test set, with a sensitivity of 0.81 to 0.95 (IQR, 0.87-0.93) at a specificity of 0.76 to 0.98 (IQR 0.83-0.95). Metagenomic analysis from public datasets of 1,597 samples across different populations observes comparable predictions with AUROC of 0.69 to 0.91 (IQR 0.79-0.87). Correlation of the top 50 microbial species with disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease model has potential clinical application in disease diagnostics and treatment response monitoring and warrants further exploration.

Antibiotics and probiotics impact gut antimicrobial resistance gene reservoir in COVID-19 patients.

Dysbiosis of gut microbiota is well-described in patients with coronavirus 2019 (COVID-19), but the dynamics of antimicrobial resistance genes (ARGs) reservoir, known as resistome, is less known. Here, we performed longitudinal fecal metagenomic profiling of 142 patients with COVID-19, characterized the dynamics of resistome from diagnosis to 6 months after viral clearance, and reported the impact of antibiotics or probiotics on the ARGs reservoir. Antibiotic-naive patients with COVID-19 showed increased abundance and types, and higher prevalence of ARGs compared with non-COVID-19 controls at baseline. Expansion in resistome was mainly driven by tetracycline, vancomycin, and multidrug-resistant genes and persisted for at least 6 months after clearance of SARS-CoV-2. Patients with expanded resistome exhibited increased prevalence of Klebsiella sp. and post-acute COVID-19 syndrome. Antibiotic treatment resulted in further increased abundance of ARGs whilst oral probiotics (synbiotic formula, SIM01) significantly reduced the ARGs reservoir in the gut microbiota of COVID-19 patients during the acute infection and recovery phase. Collectively, these findings shed new insights on the dynamic of ARGs reservoir in COVID-19 patients and the potential role of microbiota-directed therapies in reducing the burden of accumulated ARGs.

[Relationship between blood electrolytes and prognosis of patients with severe coronavirus disease 2019].

OBJECTIVE: To analyze the relationship between blood electrolytes and the prognosis of patients with severe coronavirus disease 2019 (COVID-19) and to provide assistance for clinical decision-making. METHODS: The clinical data of patients with severe COVID-19 admitted to intensive care unit (ICU) of the Wuhan Third Hospital by the Shanghai aid-Hubei medical team from January 21 to March 4, 2020 were collected. Excluding ineligible patients, 110 patients were finally enrolled. The patients' gender, age, temperature, heart rate, systolic and diastolic blood pressure, clinical symptoms at admission, time of symptom onset, duration of fever, and relevant indicators at admission to ICU (including blood potassium, chloride, sodium, calcium, phosphorus, and magnesium, etc.) and prognosis were analyzed. The patients were grouped by blood potassium or calcium levels or blood potassium/calcium ratio. The Kaplan-Meier survival curves were used to analyze the survival of patients in each group. The relationship between the potassium/calcium ratio and the prognosis was analyzed using restricted cubic spline plots. The relationship between each index in the different models and the prognosis was analyzed using Cox regression models. RESULTS: Among 110 severe COVID-19 patients, 78 cases survived, and 32 cases died. Compared with the surviving group, patients in the death group had higher blood potassium levels [mmol/L: 4.25 (3.80, 4.65) vs. 3.90 (3.60, 4.20), P < 0.05] and lower blood calcium levels (mmol/L: 2.00±0.14 vs. 2.19±0.18, P < 0.05). The Kaplan-Meier survival curves showed that patients in the potassium > 4.2 mmol/L group had a worse prognosis than the potassium < 3.8 mmol/L group and the potassium 3.8-4.2 mmol/L group (P = 0.011), patients in the calcium > 2.23 mmol/L group had a better prognosis than the calcium < 2.03 mmol/L group and the calcium 2.03-2.23 mmol/L group, and the lower calcium group had a worse prognosis (P = 0.000 15). Cox regression analysis showed that the hazard ratio (HR) of blood potassium and calcium were 2.08 and 0.01, respectively, in model 1 (single blood potassium or calcium) and in model 2 (model 1 plus age and gender), the HR of blood potassium and calcium were 1.98 and 0.01 respectively, which were significantly associated with patient prognosis (all P < 0.05). Patients in the group with the potassium/calcium ratio > 1.9 had higher blood potassium levels and a higher proportion of mechanical ventilation, lower calcium levels and lower proportion of survival, and longer time of ICU admission compared with the groups with the potassium/calcium ratio < 1.7 and 1.7-1.9. The Kaplan-Meier survival curves showed that the survival rate of the potassium/calcium ratio > 1.9 group was the lowest (P < 0.000 1), and there was no statistically significant difference in survival between the potassium/calcium ratio < 1.7 group and the potassium/calcium ratio 1.7-1.9 group. A restricted cubic spline plot corrected for age and gender showed that patients in the potassium/calcium ratio > 1.8 group had HR values > 1. Cox regression analysis corrected for other indicators showed that the potassium/calcium ratio was still associated with patient prognosis (HR = 4.85, P = 0.033). CONCLUSION: Blood potassium, calcium, and the potassium/calcium ratio at ICU admission are related to the prognosis of patients with severe COVID-19, and the potassium/calcium ratio is an independent risk factor for the death of patients. The higher the potassium/calcium ratio, the worse the prognosis of patients.

Data-driven analytics of COVID-19 ‘infodemic’

The rampant of COVID-19 infodemic has almost been simultaneous with the outbreak of the pandemic. Many concerted efforts are made to mitigate its negative effect to information credibility and data legitimacy. Existing work mainly focuses on fact-checking algorithms or multi-class labeling models that are less aware of the intrinsic characteristics of the language. Nor is it discussed how such representations can account for the common psycho-socio-behavior of the information consumers. This work takes a data-driven analytical approach to (1) describe the prominent lexical and grammatical features of COVID-19 misinformation;(2) interpret the underlying (psycho-)linguistic triggers in terms of sentiment, power and activity based on the affective control theory;(3) study the feature indexing for anti-infodemic modeling. The results show distinct language generalization patterns of misinformation of favoring evaluative terms and multimedia devices in delivering a negative sentiment. Such appeals are effective to arouse people’s sympathy toward the vulnerable community and foment their spreading behavior.

Public Health Measures During the COVID-19 Pandemic Reduce the Spread of Other Respiratory Infectious Diseases.

Background: Public health measures (such as wearing masks, physical distancing, and isolation) have significantly reduced the spread of the coronavirus disease-2019 (COVID-19), but the impact of public health measures on other respiratory infectious diseases is unclear. Objective: To assess the correlation between public health measures and the incidence of respiratory infectious diseases in China during the COVID-19 pandemic. Methods: We collected the data from the National Health and Construction Commission in China on the number of patients with six respiratory infectious diseases (measles, tuberculosis, pertussis, scarlet fever, influenza, and mumps) from 2017 to 2020 and assessed the correlation between public health measures and the incidence of respiratory infectious diseases. Finally, we used the data of the six respiratory infectious diseases in 2021 to verify our results. Results: We found public health measures significantly reduced the incidence of measles (p = 0.002), tuberculosis (p = 0.002), pertussis (p = 0.004), scarlet fever (p = 0.002), influenza (p = 0.034), and mumps (p = 0.002) in 2020, and prevented seasonal peaks. Moreover, the effects of public health measures were most marked during the peak seasons for these infections. Of the six respiratory infectious diseases considered, tuberculosis was least affected by public health measures. Conclusion: Public health measures were very effective in reducing the incidence of respiratory infectious diseases, especially when the respiratory infectious diseases would normally have been at their peak.

Comparison of clinical and serological features of RT-PCR positive and negative COVID-19 patients.

BACKGROUND: In December 2019, an outbreak of coronavirus disease 2019 (COVID-19) began in Wuhan, China, and led to a global epidemic. We aimed to compare the clinical and serological features of COVID-19 patients with positive and negative reverse transcriptase polymerase chain reaction (RT-PCR) tests. METHODS: This was a retrospective cohort study conducted from 9 February to 4 April 2020. COVID-19 patients at Leishenshan Hospital in Wuhan, China (125 total cases; 87 RT-PCR positive and 38 RT-PCR negative) were included. COVID-19 serology was assessed by colloidal gold assay. All cases were analyzed for demographic, clinical, and serological features. RESULTS: There were no significant differences in most demographic features, clinical symptoms, complications or treatments of RT-PCR positive and negative COVID-19 patients. Serum IgM/IgG was positive in 82 (94%) and 33 (87%) RT-PCR positive and negative cases, respectively. IgM was detectable as early as 3 days after symptom onset and was undetectable 60 days after symptom onset. By contrast, IgG could be detected only 10 days after symptom onset and reached its peak 60 days after symptom onset. CONCLUSIONS: Serological tests performed during the appropriate time window of disease progression could be valuable auxiliary methods to RT-PCR in COVID-19 patients.