Airborne antibiotic resistome and human health risk in railway stations during COVID-19 pandemic.

Antimicrobial resistance is recognized as one of the greatest public health concerns. It is becoming an increasingly threat during the COVID-19 pandemic due to increasing usage of antimicrobials, such as antibiotics and disinfectants, in healthcare facilities or public spaces. To explore the characteristics of airborne antibiotic resistome in public transport systems, we assessed distribution and health risks of airborne antibiotic resistome and microbiome in railway stations before and after the pandemic outbreak by culture-independent and culture-dependent metagenomic analysis. Results showed that the diversity of airborne antibiotic resistance genes (ARGs) decreased following the pandemic, while the relative abundance of core ARGs increased. A total of 159 horizontally acquired ARGs, predominantly confering resistance to macrolides and aminoglycosides, were identified in the airborne bacteria and dust samples. Meanwhile, the abundance of horizontally acquired ARGs hosted by pathogens increased during the pandemic. A bloom of clinically important antibiotic (tigecycline and meropenem) resistant bacteria was found following the pandemic outbreak. 251 high-quality metagenome-assembled genomes (MAGs) were recovered from 27 metagenomes, and 86 genera and 125 species were classified. Relative abundance of ARG-carrying MAGs, taxonomically assigned to genus of Bacillus, Pseudomonas, Acinetobacter, and Staphylococcus, was found increased during the pandemic. Bayesian source tracking estimated that human skin and anthropogenic activities were presumptive resistome sources for the public transit air. Moreover, risk assessment based on resistome and microbiome data revealed elevated airborne health risks during the pandemic.

Lucidenic acid A inhibits the binding of hACE2 receptor with spike protein to prevent SARS-CoV-2 invasion.

High infection caused by mutations of SARS-CoV-2 calls for new prevention strategy. Ganoderma lucidum known as a superior immunoenhancer exhibits various antiviral effects, whether it can resist SARS-CoV-2 remains unclear. Herein, virtual screening combined with in vitro hACE2 inhibition assays were used to investigate its anti SARS-CoV-2 effect. Potential 54 active components, 80 core targets and 20 crucial pathways were identified by the component-target-pathway network. The binding characters of these components to hACE2 and its complexes with spike protein including omicron variant was analyzed by molecular docking. Lucidenic acid A was selected as the top molecule with high affinity to all receptors by forming hydrogen bonds. Molecular dynamics simulation showed it had good binding stability with the receptor proteins. Finally, in vitro FRET test demonstrated it inhibited the hACE2 activity with IC50 2 µmol/mL. Therefore, lucidenic acid A can prevent the virus invasion by blocking hACE2 binding with SARS-CoV-2.

Improved drug-target interaction prediction with intermolecular graph transformer.

The identification of active binding drugs for target proteins (referred to as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieve better performance than molecular docking, existing models often neglect topological or spatial of intermolecular information, hindering prediction performance. We recognize this problem and propose a novel approach called the Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art (SoTA) approaches by 9.1% and 20.5% over the second best option for binding activity and binding pose prediction, respectively, and exhibits superior generalization ability to unseen receptor proteins than SoTA approaches. Furthermore, IGT exhibits promising drug screening ability against severe acute respiratory syndrome coronavirus 2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. Source code and datasets are available at https://github.com/microsoft/IGT-Intermolecular-Graph-Transformer.

Adsorption of uremic toxins using biochar for dialysate regeneration.

Numerous studies have shown that patients with COVID-19 have a high incidence of renal dysfunction. However, the dialysis supplies, including dialysates, are also severely inadequate in hospitals at the pandemic centers. Therefore, there is an urgent need to develop materials that can efficiently and rapidly remove toxins and thus regenerate dialysate to make this vital resource remains readily available. In this work, by simple carbonization and activation treatment, the porous activated carbon from waste rubber seed shell (RAC) was prepared. The adsorption results showed that the maximum adsorption capacities of the obtained RAC for creatinine and uric acid were 430 mg/g and 504 mg/g, respectively. Significantly, the adsorption process can be close to the equilibrium state within 0.5 h, which proved the ultra-fast adsorption response capacity of RAC. Further, the thermodynamics analysis results showed that both the creatinine and uric acid adsorption processes were monolayer, exothermic, and spontaneous. The adsorption kinetics results indicated that the adsorption process of the two uremic toxins followed the pseudo-second-order rate model and was dominated by chemisorption. The instrument analysis results reflected the efficient adsorption of the RAC for the above uremic toxins which might be due to the dipole-dipole interaction between the dipolar oxygen-containing groups of the surface of RAC and the dipoles of the toxins. Moreover, the formed hydrogen bonds between the oxygen groups and the toxins also played an important role. In all, the as-prepared RAC has the potential to efficiently remove major toxins from the dialysate and can be used in in vitro dialysis of numerous patients during the current COVID-19 pandemic.

The trans-omics landscape of COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.

Dynamic Antibody Responses in Patients with Different Severity of COVID-19: A Retrospective Study.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic is a serious public health event and poses a global health threat. To study the specific antibody responses would contribute to a better understanding of COVID-19. METHODS: We collected complete follow-up data from 777 patients with pathogen-confirmed COVID-19 with corresponding immunoglobulin G and M (IgG/IgM) testing results. RESULTS: Overall, the positive rates of IgG and IgM in severe patients were slightly higher than those in non-severe patients. In addition, higher IgG levels were detected in severe patients compared to non-severe patients (P = 0.026). Through further analysis, differences in IgG were only significant in serum samples taken in the first 14 days of disease onset (P < 0.001). On the basis of analysis of antibody expression levels at different time points in 74 patients who had undergone more than three detection tests, we found that the differences in IgG levels between the severe/non-severe patients were more pronounced than those of IgM. On multivariate logistic regression, after adjusting for cofactors, the higher anti-SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) IgG levels observed in the first 14 days of disease onset were independently associated with severe COVID-19 disease (odds ratio (OR) = 1.368, 95% confidence interval (CI) 1.138-1.645). CONCLUSION: We observed differences in antibody responses among patients with different severity of COVID-19. A high IgG level in the first 14 days of disease may be positively associated with disease severity.

Dynamics and Correlation Among Viral Positivity, Seroconversion, and Disease Severity in COVID-19 : A Retrospective Study.

BACKGROUND: The understanding of viral positivity and seroconversion during the course of coronavirus disease 2019 (COVID-19) is limited. OBJECTIVE: To describe patterns of viral polymerase chain reaction (PCR) positivity and evaluate their correlations with seroconversion and disease severity. DESIGN: Retrospective cohort study. SETTING: 3 designated specialty care centers for COVID-19 in Wuhan, China. PARTICIPANTS: 3192 adult patients with COVID-19. MEASUREMENTS: Demographic, clinical, and laboratory data. RESULTS: Among 12 780 reverse transcriptase PCR tests for severe acute respiratory syndrome coronavirus 2 that were done, 24.0% had positive results. In 2142 patients with laboratory-confirmed COVID-19, the viral positivity rate peaked within the first 3 days. The median duration of viral positivity was 24.0 days (95% CI, 18.9 to 29.1 days) in critically ill patients and 18.0 days (CI, 16.8 to 19.1 days) in noncritically ill patients. Being critically ill was an independent risk factor for longer viral positivity (hazard ratio, 0.700 [CI, 0.595 to 0.824]; P < 0.001). In patients with laboratory-confirmed COVID-19, the IgM-positive rate was 19.3% in the first week, peaked in the fifth week (81.5%), and then decreased steadily to around 55% within 9 to 10 weeks. The IgG-positive rate was 44.6% in the first week, reached 93.3% in the fourth week, and then remained high. Similar antibody responses were seen in clinically diagnosed cases. Serum inflammatory markers remained higher in critically ill patients. Among noncritically ill patients, a higher proportion of those with persistent viral positivity had low IgM titers (<100 AU/mL) during the entire course compared with those with short viral positivity. LIMITATION: Retrospective study and irregular viral and serology testing. CONCLUSION: The rate of viral PCR positivity peaked within the initial few days. Seroconversion rates peaked within 4 to 5 weeks. Dynamic laboratory index changes corresponded well to clinical signs, the recovery process, and disease severity. Low IgM titers (<100 AU/mL) are an independent risk factor for persistent viral positivity. PRIMARY FUNDING SOURCE: None.

Molecular epidemiology analysis of early variants of SARS-CoV-2 reveals the potential impact of mutations P504L and Y541C (NSP13) in the clinical COVID-19 outcomes.

Since severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused global pandemic with alarming speed, comprehensively analyzing the mutation and evolution of early SARS-CoV-2 strains contributes to detect and prevent such virus. Here, we explored 1962 high-quality genomes of early SARS-CoV-2 strains obtained from 42 countries before April 2020. The changing trends of genetic variations in SARS-CoV-2 strains over time and country were subsequently identified. In addition, viral genotype mapping and phylogenetic analysis were performed to identify the variation features of SARS-CoV-2. Results showed that 57.89% of genetic variations involved in ORF1ab, most of which (68.85%) were nonsynonymous. Haplotype maps and phylogenetic tree analysis showed that amino acid variations in ORF1ab (p.5828P > L and p.5865Y > C, also NSP13: P504L and NSP13: Y541C) were the important characteristics of such clade. Furthermore, these variants showed more significant aggregation in the United States (P = 2.92E-66, 95%) than in Australia or Canada, especially in strains from Washington State (P = 1.56E-23, 77.65%). Further analysis demonstrated that the report date of the variants was associated with the date of increased infections and the date of recovery and fatality rate change in the United States. More importantly, the fatality rate in Washington State was higher (4.13%) and showed poorer outcomes (P = 4.12E-21 in fatality rate, P = 3.64E-29 in death and recovered cases) than found in other states containing a small proportion of strains with such variants. Using sequence alignment, we found that variations at the 504 and 541 sites had functional effects on NSP13. In this study, we comprehensively analyzed genetic variations in SARS-CoV-2, gaining insights into amino acid variations in ORF1ab and COVID-19 outcomes.

Longitudinal multi-omics transition associated with fatality in critically ill COVID-19 patients.

PURPOSE: Critically ill COVID-19 patients have significantly increased risk of death. Although several circulating biomarkers are thought to be related to COVID-19 severity, few studies have focused on the characteristics of critically ill patients with different outcomes. The objective of this study was to perform a longitudinal investigation of the potential mechanisms affecting the prognosis of critically ill COVID-19 patients. METHODS: In addition to clinical data, 113 whole blood samples and 85 serum samples were collected from 33 severe and critical COVID-19 patients without selected comorbidities. Multi-omics analysis was then performed using longitudinal samples. RESULTS: Obvious transcriptional transitions were more frequent in critical survivors than in critical non-survivors, indicating that phase transition may be related to survival. Based on analysis of differentially expressed genes during transition, the erythrocyte differentiation pathway was significantly enriched. Furthermore, clinical data indicated that red blood cell counts showed greater fluctuation in survivors than in non-survivors. Moreover, declining red blood cell counts and hemoglobin levels were validated as prognostic markers of poor outcome in an independent cohort of 114 critical COVID-19 patients. Protein-metabolite-lipid network analysis indicated that tryptophan metabolism and melatonin may contribute to molecular transitions in critical COVID-19 patients with different outcomes. CONCLUSIONS: This study systematically and comprehensively depicted the longitudinal hallmarks of critical COVID-19 patients and indicated that multi-omics transition may impact the prognosis. TAKE HOME MESSAGE: Frequent transcriptional phase transitions may contribute to outcome in critically ill COVID-19 patients. Furthermore, fluctuation in red blood cell and hemoglobin levels may relate to poor prognosis. The biological function of melatonin was suppressed in COVID-19 non-survivors, which may provide a potential theoretical basis for clinical administration.