A comprehensive analysis of systematically screened laboratory tests: based on a COVID-19 cohort.

INTRODUCTION: The study aimed at screening indicators with differential diagnosis values and investigating the characteristics of laboratory tests in COVID-19 patients. METHODOLOGY: All the laboratory tests from COVID-19 patients and non-COVID-19 patients in this cohort were included. Test values from the groups during the course, days 1-7, and days 8-14 were analyzed. Mann-Whitney U test, univariate logistic regression analysis, and multivariate regression analysis were performed. Regression models were established to verify the diagnostic performance of indicators. RESULTS: 302 laboratory tests were included in this cohort, and 115 indicators were analyzed; the values of 61 indicators had significant differences (p < 0.05) between groups, and 23 indicators were independent risk factors of COVID-19. During days 1-7, the values of 40 indicators had significant differences (p < 0.05) between groups, while 20 indicators were independent risk factors of COVID-19. During days 8-14, the values of 45 indicators had significant differences (p < 0.05) between groups, and 23 indicators were independent risk factors of COVID-19. About 10, 12, and 12 indicators showed significant differences (p < 0.05) in multivariate regression analysis in different courses respectively, and the diagnostic performance of the model from them was 74.9%, 80.3%, and 80.8% separately. CONCLUSIONS: The indicators obtained through systematic screening have preferable differential diagnosis values. Compared with non-COVID-19 patients, the screened indicators indicated that COVID-19 patients had more severe inflammatory responses, organ damage, electrolyte and metabolism disturbance, and coagulation disorders. This screening approach could find valuable indicators from a large number of laboratory test indicators.

Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells.

Corona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyocytes can lead to cardiovascular diseases. In this study, we sought to explain the influence of respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the gene expression levels of vascular endothelial cells and cardiomyocytes. We designed an advanced machine learning-based workflow to analyze the gene expression profile data of vascular endothelial cells and cardiomyocytes from patients with COVID-19 and healthy controls. An incremental feature selection method with a decision tree was used in building efficient classifiers and summarizing quantitative classification genes and rules. Some key genes, such as MALAT1, MT-CO1, and CD36, were extracted, which exert important effects on cardiac function, from the gene expression matrix of 104,182 cardiomyocytes, including 12,007 cells from patients with COVID-19 and 92,175 cells from healthy controls, and 22,438 vascular endothelial cells, including 10,812 cells from patients with COVID-19 and 11,626 cells from healthy controls. The findings reported in this study may provide insights into the effect of COVID-19 on cardiac cells and further explain the pathogenesis of COVID-19, and they may facilitate the identification of potential therapeutic targets.

Genetic variety of ORF3a shapes SARS-CoV-2 fitness through modulation of lipid droplet.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection leads to the accumulation of lipid droplets (LD), the central hubs of the lipid metabolism, in vitro or in type II pneumocytes and monocytes from coronavirus disease 19 (COVID-19) patients and blockage of LD formation by specific inhibitors impedes SARS-CoV-2 replication. Here, we showed that ORF3a is necessary and sufficient to trigger LD accumulation during SARS-CoV-2 infection, leading to efficient virus replication. Although highly mutated during evolution, ORF3a-mediated LD modulation is conserved in most SARS-CoV-2 variants except the Beta strain and is a major difference between SARS-CoV and SARS-CoV-2 that depends on the genetic variations on the amino acid position 171, 193, and 219 of ORF3a. Importantly, T223I substitution in recent Omicron strains (BA.2-BF.8) impairs ORF3a-Vps39 association and LD accumulation, leading to less efficient replication and potentially contributing to lower pathogenesis of the Omicron strains. Our work characterized how SARS-CoV-2 modulates cellular lipid homeostasis to benefit its replication during virus evolution, making ORF3a-LD axis a promising drug target for the treatment of COVID-19.

Characterizing the cellular and molecular variabilities of peripheral immune cells in healthy recipients of BBIBP-CorV inactivated SARS-CoV-2 vaccine by single-cell RNA sequencing.

Over 3 billion doses of inactivated vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been administered globally. However, our understanding of the immune cell functional transcription and T cell receptor (TCR)/B cell receptor (BCR) repertoire dynamics following inactivated SARS-CoV-2 vaccination remains poorly understood. Here, we performed single-cell RNA and TCR/BCR sequencing on peripheral blood mononuclear cells at four time points after immunization with the inactivated SARS-CoV-2 vaccine BBIBP-CorV. Our analysis revealed an enrichment of monocytes, central memory CD4+ T cells, type 2 helper T cells and memory B cells following vaccination. Single-cell TCR-seq and RNA-seq comminating analysis identified a clonal expansion of CD4+ T cells (but not CD8+ T cells) following a booster vaccination that corresponded to a decrease in the TCR diversity of central memory CD4+ T cells and type 2 helper T cells. Importantly, these TCR repertoire changes and CD4+ T cell differentiation were correlated with the biased VJ gene usage of BCR and the antibody-producing function of B cells post-vaccination. Finally, we compared the functional transcription and repertoire dynamics in immune cells elicited by vaccination and SARS-CoV-2 infection to explore the immune responses under different stimuli. Our data provide novel molecular and cellular evidence for the CD4+ T cell-dependent antibody response induced by inactivated vaccine BBIBP-CorV. This information is urgently needed to develop new prevention and control strategies for SARS-CoV-2 infection. (ClinicalTrials.gov Identifier: NCT04871932).

High-intensity vector signals for detecting SARS-CoV-2 RNA using CRISPR/Cas13a couple with stabilized graphene field-effect transistor.

False detection of SARS-CoV-2 is detrimental to epidemic prevention and control. The scalar nature of the detected signal and the imperfect target recognition property of developed methods are the root causes of generating false signals. Here, we reported a collaborative system of CRISPR-Cas13a coupling with the stabilized graphene field-effect transistor, providing high-intensity vector signals for detecting SARS-CoV-2. In this collaborative system, SARS-CoV-2 RNA generates a "big subtraction" signal with a right-shifted feature, whereas any untargets cause the left-shifted characteristic signal. Thus, the false detection of SARS-CoV-2 is eliminated. High sensitivity with 0.15 copies/µL was obtained. In addition, the wide concerned instability of the graphene field-effect transistor for biosensing in solution environment was solved by the hydrophobic treatment to its substrate, which should be a milestone in advancing it's engineering application. This collaborative system characterized by the high-intensity vector signal and amazing stability significantly advances the accurate SARS-CoV-2 detection from the aspect of signal nature.

Predicting In Vitro and In Vivo Anti-SARS-CoV-2 Activities of Antivirals by Intracellular Bioavailability and Biochemical Activity.

Cellular drug response (concentration required for obtaining 50% of a maximum cellular effect, EC50) can be predicted by the intracellular bioavailability (F ic) and biochemical activity (half-maximal inhibitory concentration, IC50) of drugs. In an ideal model, the cellular negative log of EC50 (pEC50) equals the sum of log F ic and the negative log of IC50 (pIC50). Here, we measured F ic's of remdesivir, favipiravir, and hydroxychloroquine in various cells and calculated their anti-SARS-CoV-2 EC50's. The predicted EC50's are close to the observed EC50's in vitro. When the lung concentrations of antiviral drugs are higher than the predicted EC50's in alveolar type 2 cells, the antiviral drugs inhibit virus replication in vivo, and vice versa. Overall, our results indicate that both in vitro and in vivo antiviral activities of drugs can be predicted by their intracellular bioavailability and biochemical activity without using virus. This virus-free strategy can help medicinal chemists and pharmacologists to screen antivirals during early drug discovery, especially for researchers who are not able to work in the high-level biosafety lab.

PddCas: A Polydisperse Droplet Digital CRISPR/Cas-Based Assay for the Rapid and Ultrasensitive Amplification-Free Detection of Viral DNA/RNA.

Clustered regularly interspaced short palindromic repeats (CRISPR)-based assays have been an emerging diagnostic technology for pathogen diagnosis. In this work, we developed a polydisperse droplet digital CRISPR-Cas-based assay (PddCas) for the rapid and ultrasensitive amplification-free detection of viral DNA/RNA with minimum instruments. LbaCas12a and LbuCas13a were used for the direct detection of viral DNA and RNA, respectively. The reaction mixtures were partitioned with a common vortex mixer to generate picoliter-scale polydisperse droplets in several seconds. The limit of detection (LoD) for the target DNA and RNA is approximately 100 aM and 10 aM, respectively, which is about 3 × 104-105 fold more sensitive than corresponding bulk CRISPR assays. We applied the PddCas to successfully detect severe acute respiratory syndrome coronavirus (SARS-CoV-2) and human papillomavirus type 18 (HPV 18) in clinical samples. For the 23 HPV 18-suspected cervical epithelial cell samples and 32 nasopharyngeal swabs for SARS-CoV-2, 100% sensitivity and 100% specificity were demonstrated. The dual-gene virus detection with PddCas was also established and verified. Therefore, PddCas has potential for point-of-care application and is envisioned to be readily deployed for frequent testing as part of an integrated public health surveillance program.

A study on level of mental health and influencing factors of medical staff in the makeshift hospital during COVID-19 in Shanghai

Objective　To analyze the mental health status of medical staff in the Fourth Branch of National Convention and Exhibition Center Makeshift Hospital during the COVID-19 epidemic in Shanghai to lay a theoretical foundation for the mental health and psychological intervention of medical staff in COVID-19 and other public health emergencies. Methods　An online questionnaire survey was conducted with the generalized anxiety disorder scale (GAD-7), patient health questionnaire (PHQ-9), and Athens insomnia scale (AIS) before medical staff entering the makeshift hospital and one month later. Results　The detection rates of anxiety, depression and insomnia were 18.4%, 22.1% and 27.0% respectively before entering the makeshift hospital, and 28.8%, 59.3% and 64.2% respectively during the follow-up period one month later. The GAD-7, PHQ-9 and AIS scores of medical staff after working in the makeshift hospital for one month increased significantly compared with those at the baseline period (P<0.01). Female and previous history of using sedative and hypnotic drugs were risk factors for increased depression level among medical staff in the makeshift hospital. Conclusions　The anxiety, depression and insomnia levels of the medical staff in Shanghai increased after working in the makeshift hospital for one month. It is of great significance for the front-line support work to identify the medical staff with serious psychological problems and carry out psychological intervention in the early stage.

High-intensity vector signals for detecting SARS-CoV-2 RNA using CRISPR/Cas13a couple with stabilized graphene field-effect transistor

False detection of SARS-CoV-2 is detrimental to epidemic prevention and control. The scalar nature of the detected signal and the imperfect target recognition property of developed methods are the root causes of generating false signals. Here, we reported a collaborative system of CRISPR-Cas13a coupling with the stabilized graphene field-effect transistor, providing high-intensity vector signals for detecting SARS-CoV-2. In this collaborative system, SARS-CoV-2 RNA generates a “big subtraction” signal with a right-shifted feature, whereas any untargets cause the left-shifted characteristic signal. Thus, the false detection of SARS-CoV-2 is eliminated. High sensitivity with 0.15 copies/μL was obtained. In addition, the wide concerned instability of the graphene field-effect transistor for biosensing in solution environment was solved by the hydrophobic treatment to its substrate, which should be a milestone in advancing it's engineering application. This collaborative system characterized by the high-intensity vector signal and amazing stability significantly advances the accurate SARS-CoV-2 detection from the aspect of signal nature.

Probe-Free Identification of RNA Virus Variants with Point Mutations by Surface-Enhanced Raman Spectroscopy.

As observed in the COVID-19 pandemic, RNA viruses continue to rapidly evolve through mutations. In the absence of effective therapeutics, early detection of new severely pathogenic viruses and quarantine of infected people are critical for reducing the spread of the viral infections. However, conventional detection methods require a substantial amount of time to develop probes specific to new viruses, thereby impeding immediate response to the emergence of viral pathogens. In this study, we identified multiple types of viruses by obtaining the spectral fingerprint of their surface proteins with probe-free surface-enhanced Raman scattering (SERS). In addition, the SERS-based method can remarkably distinguish influenza virus variants with several surface protein point mutations from their parental strain. Principal component analysis (PCA) of the SERS spectra systematically captured the key Raman bands to distinguish the variants. Our results show that the combination of SERS and PCA can be a promising tool for rapid detection of newly emerging mutant viruses without a virus-specific probe.

Emotion Analysis of COVID-19 Vaccines Based on a Fuzzy Convolutional Neural Network.

COVID-19 created immense global challenges in 2020, and the world will live under its threat indefinitely. Much of the information on social media supported the government in addressing this major public health event. On January 9, to control the virus, the Chinese government announced universal vaccinations. However, due to a range of varied interpretations, people held different attitudes towards vaccination. Therefore, the success of the mass immunization strategy greatly depended on the public perception of the COVID-19 vaccine. This article explores the changes in people's emotional attitudes towards vaccines and the reasons behind them in the context of the global pandemic in an effort to help mankind overcome this ongoing crisis. For this article, microblogs from January to September containing Chinese people's responses to the COVID-19 vaccines were collected. Based on fuzzy logic and deep learning, we advance the hypothesis that fuzzy vector adaptive improvements will make it possible to better express language emotion and that fuzzy emotion vectors can be integrated into deep learning models, thus making these models more interpretable. Based on this assumption, we design a deep learning model with a fuzzy emotion vector. The experimental results show the positive effect of this model. By applying the model in analyses of people's attitudes towards vaccines, we can obtain people's attitudes towards vaccines in different time periods. We discovered that the most negative emotions about the vaccine appeared in April and that the most positive emotions about the vaccine appeared in February. Combined with word cloud technology and the LDA model, we can effectively explore the reasons for the changes in vaccine attitudes. Our findings show that people's negative emotions about the vaccine are always higher than their positive emotions about the vaccine and that people's attitudes towards the vaccine are closely related to the progress of the epidemic. There is also a certain relationship between people's attitudes towards the vaccine and those towards the vaccination.

Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods.

The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy.

Association of delayed chemoradiotherapy with elevated Epstein-Barr virus DNA load and adverse clinical outcome in nasopharyngeal carcinoma treatment during the COVID-19 pandemic: a retrospective study.

BACKGROUND: To summarize the impact of radiotherapy (RT) and chemotherapy delays on patients with nasopharyngeal carcinoma (NPC) during the COVID-19 pandemic. METHODS: We retrospectively included 233 patients with stage II-IVa NPC treated with RT and chemotherapy between December 11, 2019 and March 11, 2020. The outcomes were elevation in the EBV DNA load between two adjacent cycles of chemotherapy or during RT, and 1-year disease-free survival (DFS). RESULTS: RT delay occurred in 117 (50%) patients, and chemotherapy delay occurred in 220 (94%) patients. RT delay of ≥ 6 days was associated with a higher EBV DNA elevation rate (20.4% vs. 3.6%, odds ratio [OR] = 6.93 [95% CI = 2.49-19.32], P < 0.001), and worse 1-year DFS (91.2% vs. 97.8%, HR = 3.61 [95% CI = 1.37-9.50], P = 0.006), compared with on-schedule RT or delay of < 6 days. Chemotherapy delay of ≥ 10 days was not associated with a higher EBV DNA elevation rate (12.5% vs. 6.8%, OR = 1.94 [95% CI = 0.70-5.40], P = 0.20), or worse 1-year DFS (93.8% vs. 97.1%, HR = 3.73 [95% CI = 0.86-16.14], P = 0.059), compared with delay of < 10 days. Multivariable analyses showed RT delay of ≥ 6 days remained an independent adverse factor for both EBV DNA elevation and DFS. CONCLUSION: To ensure treatment efficacy for patients with nonmetastatic NPC, initiation of RT should not be delayed by more than 6 days; the effect of chemotherapy delay requires further investigation.

Impact of the COVID-19 pandemic on the treatment and prognosis of acute myocardial infarction in Xuzhou, China.

INTRODUCTION: The treatment of acute myocardial infarction (AMI) during the COVID-19 pandemic has been affected to varying degrees. This study is the first to explore the impact of COVID-19 on the treatment and prognosis of rural and urban AMI in developing countries. METHODOLOGY: A total of 128 patients with AMI in our hospital during the COVID-19 pandemic were enrolled. A total of 197 patients diagnosed with AMI before the COVID-19 pandemic were selected as the control group and one year of follow-up was performed. RESULTS: Hospital stay and the proportion of Killip class ≥ 2 patients were increased among rural AMI patients in the 'during COVID-19' group, compared with the 'before COVID-19' group. Among ST-segment elevation myocardial infarction (STEMI) total and rural STEMI patients, the treatment time in the during-COVID-19 group was longer than that in the before-COVID-19 group, whereas only the symptom to door (S to D) total and door to balloon (D to B) were extended in urban STEMI patients. In AMI total and rural AMI patients, major adverse cardiovascular events (MACEs) and all-cause mortality were increased in the during-COVID-19 group compared with the before-COVID-19 group. Kaplan-Meier analysis revealed that the survival and occurrence of MACEs in AMI total and rural AMI patients were significantly higher in the during-COVID-19 group. CONCLUSIONS: The COVID-19 pandemic led to delayed treatment and worse prognosis in AMI patients. Rural areas appear to be at a greater risk.

Structural communication fingerprinting and dynamic investigation of RBD-hACE2 complex from BA.1 × AY.4 recombinant variant (Deltacron) of SARS-CoV-2 to decipher the structural basis for enhanced transmission.

The BA.1 × AY.4 recombinant variant (Deltacron) continues to inflict chaos globally due to its rapid transmission and infectivity. To decipher the mechanism of pathogenesis by the BA.1 × AY.4 recombinant variant (Deltacron), a protein coupling, protein structural graphs (PSG), residue communication and all atoms simulation protocols were used. We observed that the bonding network is altered by this variant; engaging new residues that helps to robustly bind. HADDOCK docking score for the wild type has been previously reported to be -111.8 ± 1.5 kcal/mol while the docking score for the Deltacron variant was calculated to be -128.3 ± 2.5 kcal/mol. The protein structural graphs revealed variations in the hub residues, number of nodes, inter and intra residues communities, and path communication perturbation caused by the acquired mutations in the Deltacron-RBD thus alter the binding approach and infectivity. Moreover, the dynamic behaviour reported a highly flexible structure with enhanced residues flexibility particularly by the loops required for interaction with ACE2. It was observed that these mutations have altered the secondary structure of the RBD mostly transited to the loops thus acquired higher flexible dynamics than the native structure during the simulation. The total binding free energy for each of these complexes, that is, WT-RBD and Deltacron-RBD were reported to be -61.38 kcal/mol and -70.47 kcal/mol. Protein's motion revealed a high trace value in the Deltacron variant that clearly depict more structural flexibility. The broad range of phase space covered by the Deltacron variant along PC1 and PC2 suggests that these mutations are important in contributing conformational heterogeneity or flexibility that consequently help the variant to bind more efficiently than the wild type. The current study provides a basis for structure-based drug designing against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Decomposition of residential electricity-related CO2 emissions in China, a spatial-temporal study.

The COVID-19 pandemic brings a surge in household electricity consumption, thereby enabling extensive research interest on residential carbon emissions as one of the hot topics in carbon reduction. However, research on spatial-temporal driving forces for the increase of residential CO2 emissions between regions still remains unknown in terms of emissions mitigation in post-pandemic era. Therefore, we studied the residential CO2 emissions from the electricity consumption of China during the period 1997-2019. Afterward, the regional specified production emission factors, combining with electricity use pattern, living standard and household size, were modelled to reveal the spatial-temporal driving forces at national and provincial scales. We observed that the national residential electricity-related CO2 increased from 1997 to 2013, before fluctuating to a peak in 2019. Guangdong, Shandong and Jiangsu, from East China were the top emitters with 27% of the national scale. The decomposition results showed that the income improvement was the primary driving force behind the emission increase in most provinces, while the household size and production emission effects were the main negative effects. For the spatial decomposition, differences in the total households between regions further widen the gaps of total emissions. At the provincial scale of temporal decomposition, eastern developed regions exhibited the most significant decrease in production emissions. In contrast, electricity intensity effect showed negative emission influences in the east and central regions, and positive in north-eastern and western China. The research identified the different incremental patterns of residential electricity-related CO2 emissions in various Chinese provinces, thereby providing scientific ways to save energy and reduce emissions.

Fear of progression in patients with mild or common type COVID-19.

AIM: To investigate the current condition and degree of fear of disease progression and associated factors in patients with mild or common type COVID-19. BACKGROUND: At the end of 2019, COVID-19 spread from Wuhan in Hubei Province throughout China. Confirmed cases and deaths have since been reported in many countries around the world. However, fear of progression in these patients has been poorly explored. METHODS: During February 2020, we recruited 114 patients with mild or common type COVID-19 admitted to a Fangcang shelter hospital. We assessed patients' degree of fear using the simplified Fear of Progression Questionnaire (Chinese version). Multiple regression analysis was applied to explore potential factors. RESULTS: The fear of disease progression scores of patients with mild or common COVID-19 was at the low-to-moderate level. Current unemployment, disease duration of 28 days or more and not having a spouse diagnosed with COVID-19 were factors potentially associated with fear of progression. CONCLUSION: With a high prevalence of fear of disease progression in patients with COVID-19, the risk of psychological effects from the pandemic is significant and fear of progression is one of the manifestations. The need for psychological support services for patients should be included in all pandemic and disaster planning.

Identification of DNA Methylation Signature and Rules for SARS-CoV-2 Associated with Age.

BACKGROUND: COVID-19 displays an increased mortality rate and higher risk of severe symptoms with increasing age, which is thought to be a result of the compromised immunity of elderly patients. However, the underlying mechanisms of aging-associated immunodeficiency against Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains unclear. Epigenetic modifications show considerable changes with age, causing altered gene regulations and cell functions during the aging process. The DNA methylation patterns among patients with coronavirus 2019 disease (COVID-19) who had different ages were compared to explore the effect of aging-associated methylation modifications in SARS-CoV-2 infection. METHODS: Patients with COVID-19 were divided into three groups according to age. Boruta was used on the DNA methylation profiles of the patients to remove irrelevant features and retain essential signature sites to identify substantial aging-associated DNA methylation changes in COVID-19. Next, these features were ranked using the minimum redundancy maximum relevance (mRMR) method, and the feature list generated by mRMR was processed into the incremental feature selection method with decision tree (DT), random forest, k-nearest neighbor, and support vector machine to obtain the key methylation sites, optimal classifier, and decision rules. RESULTS: Several key methylation sites that showed distinct patterns among the patients with COVID-19 who had different ages were identified, and these methylation modifications may play crucial roles in regulating immune cell functions. An optimal classifier was built based on selected methylation signatures, which can be useful to predict the aging-associated disease risk of COVID-19. CONCLUSIONS: Existing works and our predictions suggest that the methylation modifications of genes, such as NHLH2, ZEB2, NWD1, ELOVL2, FGGY, and FHL2, are closely associated with age in patients with COVID-19, and the 39 decision rules extracted with the optimal DT classifier provides quantitative context to the methylation modifications in elderly patients with COVID-19. Our findings contribute to the understanding of the epigenetic regulations of aging-associated COVID-19 symptoms and provide the potential methylation targets for intervention strategies in elderly patients.

SARS-CoV-2 nucleocapsid protein triggers hyperinflammation via protein-protein interaction-mediated intracellular Cl- accumulation in respiratory epithelium.

SARS-CoV-2, the culprit pathogen of COVID-19, elicits prominent immune responses and cytokine storms. Intracellular Cl- is a crucial regulator of host defense, whereas the role of Cl- signaling pathway in modulating pulmonary inflammation associated with SARS-CoV-2 infection remains unclear. By using human respiratory epithelial cell lines, primary cultured human airway epithelial cells, and murine models of viral structural protein stimulation and SARS-CoV-2 direct challenge, we demonstrated that SARS-CoV-2 nucleocapsid (N) protein could interact with Smad3, which downregulated cystic fibrosis transmembrane conductance regulator (CFTR) expression via microRNA-145. The intracellular Cl- concentration ([Cl-]i) was raised, resulting in phosphorylation of serum glucocorticoid regulated kinase 1 (SGK1) and robust inflammatory responses. Inhibition or knockout of SGK1 abrogated the N protein-elicited airway inflammation. Moreover, N protein promoted a sustained elevation of [Cl-]i by depleting intracellular cAMP via upregulation of phosphodiesterase 4 (PDE4). Rolipram, a selective PDE4 inhibitor, countered airway inflammation by reducing [Cl-]i. Our findings suggested that Cl- acted as the crucial pathological second messenger mediating the inflammatory responses after SARS-CoV-2 infection. Targeting the Cl- signaling pathway might be a novel therapeutic strategy for COVID-19.

Identifying Methylation Signatures and Rules for COVID-19 With Machine Learning Methods.

The occurrence of coronavirus disease 2019 (COVID-19) has become a serious challenge to global public health. Definitive and effective treatments for COVID-19 are still lacking, and targeted antiviral drugs are not available. In addition, viruses can regulate host innate immunity and antiviral processes through the epigenome to promote viral self-replication and disease progression. In this study, we first analyzed the methylation dataset of COVID-19 using the Monte Carlo feature selection method to obtain a feature list. This feature list was subjected to the incremental feature selection method combined with a decision tree algorithm to extract key biomarkers, build effective classification models and classification rules that can remarkably distinguish patients with or without COVID-19. EPSTI1, NACAP1, SHROOM3, C19ORF35, and MX1 as the essential features play important roles in the infection and immune response to novel coronavirus. The six significant rules extracted from the optimal classifier quantitatively explained the expression pattern of COVID-19. Therefore, these findings validated that our method can distinguish COVID-19 at the methylation level and provide guidance for the diagnosis and treatment of COVID-19.