Escherichia coli ST2797 Is Abundant in Wastewater and Might Be a Novel Emerging Extended-Spectrum Beta-Lactamase E. coli.

The increasing prevalence of antibiotic-resistant bacteria is an emerging threat to global health. The analysis of antibiotic-resistant enterobacteria in wastewater can indicate the prevalence and spread of certain clonal groups of multiresistant bacteria. In a previous study of Escherichia coli that were isolated from a pump station in Norway over 15 months, we found a recurring E. coli clone that was resistant to trimethoprim, ampicillin, and tetracycline in 201 of 3,123 analyzed isolates (6.1%). 11 representative isolates were subjected to whole-genome sequencing and were found to belong to the MLST ST2797 E. coli clone with plasmids carrying resistance genes, including blaTEM-1B, sul2, dfrA7, and tetB. A phenotypic comparison of the ST2797 isolates with the uropathogenic ST131 and ST648 that were repeatedly identified in the same wastewater samples revealed that the ST2797 isolates exhibited a comparable capacity for temporal survival in wastewater, greater biofilm formation, and similar potential for the colonization of mammalian epithelial cells. ST2797 has been isolated from humans and has been found to carry extended spectrum ß-lactamase (ESBL) genes in other studies, suggesting that this clonal type is an emerging ESBL E. coli. Collectively, these findings show that ST2797 was more ubiquitous in the studied wastewater than were the infamous ST131 and ST648 and that ST2797 may have similar abilities to survive in the environment and cause infections in humans. IMPORTANCE The incidence of drug-resistant bacteria found in the environment is increasing together with the levels of antibiotic-resistant bacteria that cause infections. The COVID-19 pandemic has shed new light on the importance of monitoring emerging threats and finding early warning systems. Therefore, to mitigate the antimicrobial resistance burden, the monitoring and early identification of antibiotic-resistant bacteria in hot spots, such as wastewater treatment plants, are required to combat the occurrence and spread of antibiotic-resistant bacteria. Here, we applied a PhenePlate system as a phenotypic screening method for genomic surveillance and discovered a dominant and persistent E. coli clone ST2797 with a multidrug resistance pattern and equivalent phenotypic characteristics to those of the major pandemic lineages, namely, ST131 and ST648, which frequently carry ESBL genes. This study highlights the continuous surveillance and report of multidrug resistant bacteria with the potential to spread in One Health settings.

High HBV-DNA serum levels are associated with type 2 diabetes in adults with positive HBsAg: An observational study.

Background: The prevalence of diabetes is higher in hepatitis B virus (HBV)-infected population. We aimed to examine the relationship between different serum HBV-DNA levels and type 2 diabetes in adults with positive HBV surface antigen (HBsAg). Methods: We conducted cross-sectional analyses of data obtaining from the Clinical Database System of Wuhan Union Hospital. Diabetes was defined by self-report of type 2 diabetes, fasting plasma glucose (FPG) ≥7mmol/L, or glycated hemoglobin (HbA1c) ≥6.5%. Binary logistic regression analyses were performed to investigate the factors associated with diabetes. Results: Among 12,527 HBsAg-positive adults, 2,144 (17.1%) were diabetic. Patients with serum HBV-DNA <100, 100-2000, 2000-20000 and ≥20000 IU/mL accounted for 42.2% (N=5,285), 22.6% (N=2,826), 13.3% (N=1,665) and 22.0% (N=2,751), respectively. The risk of type 2 diabetes, FPG ≥7mmol/L and HbA1c ≥6.5% in individuals with highly elevated serum HBV-DNA level (≥20000 IU/mL) were 1.38 (95% confidence interval [CI]: 1.16 to 1.65), 1.40 (95% CI: 1.16 to 1.68) and 1.78 (95% CI: 1.31 to 2.42) times relative to those with negative or lowly elevated serum HBV-DNA (<100 IU/mL). However, the analyses showed no association of moderately (2000-20000 IU/mL) to slightly (100-2000 IU/mL) raised serum HBV-DNA levels with type 2 diabetes (OR=0.88, P=0.221; OR=1.08, P=0.323), FPG ≥7mmol/L (OR=1.00, P=0.993; OR=1.11, P=0.250) and HbA1c ≥6.5% (OR=1.24, P=0.239; OR=1.17, P=0.300). Conclusion: In HBsAg-positive adults, highly elevated level rather than moderately to slightly raised levels of serum HBV-DNA is independently associated with an increased risk of type 2 diabetes.

Design and characterization of novel SARS-CoV-2 fusion inhibitors with N-terminally extended HR2 peptides.

Development of potent and broad-spectrum antivirals against SARS-CoV-2 remains one of top priorities, especially in the case of that current vaccines cannot effectively prevent viral transmission. We previously generated a group of fusion-inhibitory lipopeptides, with one formulation being evaluated under clinical trials. In this study, we dedicated to characterize the extended N-terminal motif (residues 1161-1168) of the so-called spike (S) heptad repeat 2 (HR2) region. Alanine scanning analysis of this motif verified its critical roles in S protein-mediated cell-cell fusion. Using a panel of HR2 peptides with the N-terminal extensions, we identified a peptide termed P40, which contained four extended N-terminal residues (VDLG) and exhibited improved binding and antiviral activities, whereas the peptides with further extensions had no such effects. Then, we developed a new lipopeptide P40-LP by modifying P40 with cholesterol, which exhibited dramatically increased activities in inhibiting SARS-CoV-2 variants including divergent Omicron sublineages. Moreover, P40-LP displayed a synergistic effect with IPB24 lipopeptide that was designed containing the C-terminally extended residues, and it could effectively inhibit other human coronaviruses, including SARS-CoV, MERS-CoV, HCoV-229E, and HCoV-NL63. Taken together, our results have provided valuable insights for understanding the structure-function relationship of SARS-CoV-2 fusion protein and offered novel antiviral strategies to fight against the COVID-19 pandemic.

A computational map of the human-SARS-CoV-2 protein-RNA interactome predicted at single-nucleotide resolution.

RNA-binding proteins (RBPs) are critical host factors for viral infection, however, large scale experimental investigation of the binding landscape of human RBPs to viral RNAs is costly and further complicated due to sequence variation between viral strains. To fill this gap, we investigated the role of RBPs in the context of SARS-CoV-2 by constructing the first in silico map of human RBP-viral RNA interactions at nucleotide-resolution using two deep learning methods (pysster and DeepRiPe) trained on data from CLIP-seq experiments on more than 100 human RBPs. We evaluated conservation of RBP binding between six other human pathogenic coronaviruses and identified sites of conserved and differential binding in the UTRs of SARS-CoV-1, SARS-CoV-2 and MERS. We scored the impact of mutations from 11 variants of concern on protein-RNA interaction, identifying a set of gain- and loss-of-binding events, as well as predicted the regulatory impact of putative future mutations. Lastly, we linked RBPs to functional, OMICs and COVID-19 patient data from other studies, and identified MBNL1, FTO and FXR2 RBPs as potential clinical biomarkers. Our results contribute towards a deeper understanding of how viruses hijack host cellular pathways and open new avenues for therapeutic intervention.

Applications of nanobodies in the prevention, detection, and treatment of the evolving SARS-CoV-2.

Global health and economy are deeply influenced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its newly emerging variants. Nanobodies with nanometer-scale size are promising for the detection and treatment of SARS-CoV-2 and its variants because they are superior to conventional antibodies in terms of cryptic epitope accessibility, tissue penetration, cost, formatting adaptability, and especially protein stability, which enables their aerosolized specific delivery to lung tissues. This review summarizes the progress in the prevention, detection, and treatment of SARS-CoV-2 using nanobodies, as well as strategies to combat the evolving SARS-CoV-2 variants. Generally, highly efficient generation of potent broad-spectrum nanobodies targeting conserved epitopes or further construction of multivalent formats targeting non-overlapping epitopes can promote neutralizing activity against SARS-CoV-2 variants and suppress immune escape.

Chimeric mRNA-based COVID-19 vaccine induces protective immunity against Omicron and Delta variants.

The emerging SARS-CoV-2 variants of concern (VOCs) exhibit enhanced transmission and immune escape, reducing the effectiveness of currently approved mRNA vaccines. To achieve wider coverage of VOCs, we first constructed a cohort of mRNAs harboring a furin cleavage mutation in the spike (S) protein of predominant VOCs, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2). The mutation abolished the cleavage between the S1 and S2 subunits. Systematic evaluation in vaccinated mice discovered that individual VOC mRNAs elicited strong neutralizing activity in a VOC-specific manner. In particular, the neutralizing antibodies (nAb) produced by immunization with Beta-Furin and Washington (WA)-Furin mRNAs showed potent cross-reactivity with other VOCs. However, neither mRNA elicited strong neutralizing activity against the Omicron variant. Hence, we further developed an Omicron-specific mRNA vaccine that restored protection against the original Omicron variant and some sublineages. Finally, to broaden the protection spectrum of the new Omicron mRNA vaccine, we engineered an mRNA-based chimeric immunogen by introducing the receptor-binding domain of Delta variant into the entire S antigen of Omicron. The resultant chimeric mRNA induced potent and broadly nAbs against Omicron and Delta, which paves the way to developing new vaccine candidates to target emerging variants in the future.

One-step synthesis of triethanolamine-capped Pt nanoparticle for colorimetric and electrochemiluminescent immunoassay of SARS-CoV spike proteins.

Platinum nanoparticles (PtNPs) have been attracted worldwide attention due to their versatile application potentials, especially in the catalyst and sensing fields. Herein, a facile synthetic method of triethanolamine (TEOA)-capped PtNPs (TEOA@PtNP) for electrochemiluminescent (ECL) and colorimetric immunoassay of SARS-CoV spike proteins (SARS-CoV S-protein, a target detection model) is developed. Monodisperse PtNPs with an average diameter of 2.2 nm are prepared by a one-step hydrothermal synthesis method using TEOA as a green reductant and stabilizer. TEOA@PtNPs can be used as a nanocarrier to combine with antigen by the high-affinity antibody, which leads to a remarkable inhibition of electron transfer efficiency and mass transfer processes. On the basis of its peroxidase-like activity and easy-biolabeling property, the TEOA@PtNP can be used to establish a colorimetric immunosensor of SARS-CoV S-protein thought catalyzing the reaction of H2O2 and 3,3',5,5'-tetramethylbenzidine (TMB). Especially, the Ru(bpy)3 2+ ECL reaction is well-achieved with the TEOA@PtNPs due to their great conductivity and loading abundant TEOA co-reactants, resulting in an enhancing ECL signal in immunoassay of SARS-CoV S-protein. As a consequence, two proposed methods could achieve sensitive detection of SARS-CoV S-protein in wide ranges, the colorimetric and ECL detection limits were as low as 8.9 fg /mL and 4.2 fg /mL (S/N = 3), respectively. We believe that the proposed colorimetric and ECL immunosesors with high sensitivity, good reproducibility, and good stability will be a promising candidate for a broad spectrum of applications.

Physical Activity and Interpersonal Adaptation in Chinese Adolescents After COVID-19: The Mediating Roles of Self-Esteem and Psychological Resilience.

Previous studies have implied that physical activity profoundly influences interpersonal adaptation. However, this effect and its mechanisms have not been directly verified, especially for adolescents. This study examines the association between physical activity and interpersonal adaptation in adolescents through self-esteem and psychological resilience after the coronavirus disease 2019 (COVID-19) pandemic. Participants included 542 Chinese adolescents (aged 13-18 years; 242 boys and 300 girls). Adolescents in China anonymously completed a series of questionnaires, including the PARS-3 Scale of PE Activity Grade (PARS-3), the Self-esteem Scale (SES), the Resilience Scale for Adolescents (RSCA), and the Interpersonal Adaptation Scale. The results showed that physical activity positively correlated with self-esteem, psychological resilience, and interpersonal adaptation. Additionally, self-esteem and psychological resilience serially mediated the impact of physical activity on interpersonal adaptation. The findings highlight the positive impact of physical activity on adolescent interpersonal adaptation by strengthening positive psychological resources in the post-pandemic era.

Resistance profile and mechanism of severe acute respiratory syndrome coronavirus-2 variants to LCB1 inhibitor targeting the spike receptor-binding motif.

LCB1 is a 56-mer miniprotein computationally designed to target the spike (S) receptor-binding motif of SARS-CoV-2 with potent in vitro and in vivo inhibitory activities (Cao et al., 2020; Case et al., 2021). However, the rapid emergence and epidemic of viral variants have greatly impacted the effectiveness of S protein-targeting vaccines and antivirals. In this study, we chemically synthesized a peptide-based LCB1 inhibitor and characterized the resistance profile and underlying mechanism of SARS-CoV-2 variants. Among five variants of concern (VOCs), we found that pseudoviruses of Beta, Gamma, and Omicron were highly resistant to the LCB1 inhibition, whereas the pseudoviruses of Alpha and Delta as well as the variant of interest (VOI) Lambda only caused mild resistance. By generating a group of mutant viruses carrying single or combination mutations, we verified that K417N and N501Y substitutions in RBD critically determined the high resistance phenotype of VOCs. Furthermore, a large panel of 85 pseudoviruses with naturally occurring RBD point-mutations were generated and applied to LCB1, which identified that E406Q, K417N, and L455F conferred high-levels of resistance, when Y505W caused a ∼6-fold resistance fold-change. We also showed that the resistance mutations could greatly weaken the binding affinity of LCB1 to RBD and thus attenuated its blocking capacity on the interaction between RBD and the cell receptor ACE2. In conclusion, our data have provided crucial information for understanding the mechanism of SARS-CoV-2 resistance to LCB1 and will guide the design strategy of novel LCB1-based antivirals against divergent VOCs and evolutionary mutants.

The role transition of radiotherapy for the treatment of liver cancer in the COVID-19 era.

The uncontrollable COVID-19 crises in the SARS-CoV-2 high-prevalence areas have greatly disrupted the routine treatment of liver cancer and triggered a role transformation of radiotherapy for liver cancer. The weight of radiotherapy in the treatment algorithm for liver cancer has been enlarged by the COVID-19 pandemic, which is helpful for the optimal risk-benefit profile.

Potent inhibition of diverse Omicron sublineages by SARS-CoV-2 fusion-inhibitory lipopeptides.

The emergence and rapid spreading of SARS-CoV-2 variants of concern (VOCs) have posed a great challenge to the efficacy of vaccines and therapeutic antibodies, calling for antivirals that can overcome viral evasion. We recently reported that SARS-CoV-2 fusion-inhibitory lipopeptides, IPB02V3 and IPB24, possessed the potent activities against divergent VOCs, including Alpha, Beta, Gamma, Delta, and the initial Omicron strain (B.1.1.529); however, multiple Omicron sublineages have emerged and BA.4/5 is now becoming predominant globally. In this study, we focused on characterizing the functionality of the spike (S) proteins derived from Omicron sublineages and their susceptibility to the inhibition of IPB02V3 and IPB24. We first found that the S proteins of BA.2, BA.2.12.1, BA.3, and BA.4/5 exhibited significantly increased cell fusion capacities compared to BA.1, whereas the pseudoviruses of BA.2.12.1, BA.3, and BA.4/5 had significantly increased infectivity relative to BA.1 or BA.2. Next, we verified that IPB02V3 and IPB24 also maintained their very high potent activities in inhibiting diverse Omicron sublineages, even with enhanced potencies relative to the inhibition on ancestral virus. Moreover, we demonstrated that evolved Omicron mutations in the inhibitor-binding heptad repeat 1 (HR1) site could impair the S protein-driven cell fusogenicity and infectivity, but none of single or combined mutations affected the antiviral activity of IPB02V3 and IPB24. Therefore, we believe that viral fusion inhibitors possess high potential to be developed as effective drugs for fighting SARS-CoV-2 variants including diverse Omicron sublineages.

The role transition of radiotherapy for the treatment of liver cancer in the COVID-19 era

The uncontrollable COVID-19 crises in the SARS-CoV-2 high-prevalence areas have greatly disrupted the routine treatment of liver cancer and triggered a role transformation of radiotherapy for liver cancer. The weight of radiotherapy in the treatment algorithm for liver cancer has been enlarged by the COVID-19 pandemic, which is helpful for the optimal risk-benefit profile.

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection.

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

Advances in application of single-cell RNA sequencing in cardiovascular research.

Single-cell RNA sequencing (scRNA-seq) provides high-resolution information on transcriptomic changes at the single-cell level, which is of great significance for distinguishing cell subtypes, identifying stem cell differentiation processes, and identifying targets for disease treatment. In recent years, emerging single-cell RNA sequencing technologies have been used to make breakthroughs regarding decoding developmental trajectories, phenotypic transitions, and cellular interactions in the cardiovascular system, providing new insights into cardiovascular disease. This paper reviews the technical processes of single-cell RNA sequencing and the latest progress based on single-cell RNA sequencing in the field of cardiovascular system research, compares single-cell RNA sequencing with other single-cell technologies, and summarizes the extended applications and advantages and disadvantages of single-cell RNA sequencing. Finally, the prospects for applying single-cell RNA sequencing in the field of cardiovascular research are discussed.

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

Who are the 'silent spreaders'?: contact tracing in spatio-temporal memory models.

The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (ART), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent-based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission.