ABSTRACT
The pandemic caused by SARS-CoV-2 is the most widely spread disease in the 21st century. Due to the continuous emergence of variants across the world, it is necessary to expand our understanding of host-virus interactions and explore new agents against SARS-CoV-2. In this study, it was found exopolysaccharides (EPSs) from halophilic archaeon Haloarcula hispanica ATCC33960 can bind to the spike protein of SARS-CoV-2 with the binding constant KD of 2.23 nM, block the binding of spike protein to Vero E6 and bronchial epithelial BEAS-2B cells, and inhibit pseudovirus infection. However, EPSs from the gene deletion mutant â³HAH_1206 almost completely lost the antiviral activity against SARS-CoV-2. A significant reduction of glucuronic acid (GlcA) and the sulfation level in EPSs of â³HAH_1206 was clearly observed. Our results indicated that sulfated GlcA in EPSs is possible for a main structural unit in their inhibition of binding of SARS-CoV-2 to host cells, which would provide a novel antiviral mechanism and a guide for designing new agents against SARS-CoV-2.
ABSTRACT
Wastewater-based genomic surveillance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus shows promise to complement genomic epidemiology efforts. Multiplex tiling PCR is a desirable approach for targeted genome sequencing of SARS-CoV-2 in wastewater due to its low cost and rapid turnaround time. However, it is not clear how different multiplex tiling PCR primer schemes or wastewater sample matrices impact the resulting SARS-CoV-2 genome coverage. The objective of this work was to assess the performance of three different multiplex primer schemes, consisting of 150-bp, 400-bp, and 1,200-bp amplicons, as well as two wastewater sample matrices, influent wastewater and primary sludge, for targeted genome sequencing of SARS-CoV-2. Wastewater samples were collected weekly from five municipal wastewater treatment plants (WWTPs) in the Metro Vancouver region of British Columbia, Canada during a period of increased coronavirus disease 19 (COVID-19) case counts from February to April 2021. RNA extracted from clarified influent wastewater provided significantly higher genome coverage (breadth and median depth) than primary sludge samples across all primer schemes. Shorter amplicons appeared to be more resilient to sample RNA degradation but were hindered by greater primer pool complexity in the 150-bp scheme. The identified optimal primer scheme (400 bp) and sample matrix (influent) were capable of detecting the emergence of mutations associated with genomic variants of concern, for which the daily wastewater load significantly correlated with clinical case counts. Taken together, these results provide guidance on best practices for implementing wastewater-based genomic surveillance and demonstrate its ability to inform epidemiology efforts by detecting genomic variants of concern circulating within a geographic region. IMPORTANCE Monitoring the genomic characteristics of the SARS-CoV-2 virus circulating in a population can shed important insights into epidemiological aspects of the COVID-19 outbreak. Sequencing every clinical patient sample in a highly populous area is a difficult feat, and thus sequencing SARS-CoV-2 RNA in municipal wastewater offers great promise to augment genomic surveillance by characterizing a pooled population sample matrix, particularly during an escalating outbreak. Here, we assess different approaches and sample matrices for rapid targeted genome sequencing of SARS-CoV-2 in municipal wastewater. We demonstrate that the optimal approach is capable of detecting the emergence of SARS-CoV-2 genomic variants of concern, with strong correlations to clinical case data in the province of British Columbia. These results provide guidance on best practices on, as well as further support for, the application of wastewater genomic surveillance as a tool to augment current genomic epidemiology efforts.
ABSTRACT
Healthcare workers at the frontline are facing a substantial risk of respiratory tract infection during the COVID-19 outbreak due to an extremely stressful work schedule and public health event. A well-established first-line defense on oropharyngeal microbiome could be a promising strategy to protect individuals from respiratory tract infections including COVID-19. The most thoroughly studied oropharyngeal probiotic product which creates a stable upper respiratory tract microbiota capable of preventing upper respiratory tract infections was chosen to evaluate the safety and efficacy on reducing episodes of upper respiratory tract infections for COVID-19 healthcare workers. To our knowledge to date, this is the very first study describing the beneficial effects of oropharyngeal probiotic been administered by healthcare workers during the COVID-19 pandemic. In this randomized controlled trial, we provided the probiotics to frontline medical staff who work in the hospitals in Wuhan and had been in close contact with hospitalized COVID-19 patients for prophylactic use on a daily basis. Our finding suggests that oropharyngeal probiotic administration significantly reduced the incidence of respiratory tract infections by 64.8%, reduced the time experiencing respiratory tract infections and oral ulcer symptoms by 78%, shortened the days absent from work by 95.5%, and reduced the time under medication where there is no record of antibiotic and anti-viral drug intake in the probiotic group. Furthermore, medical staff treated with Bactoblis experienced sustained protection from respiratory tract infections since the 10th day of oropharyngeal probiotic administration resulting in an extremely low incidence rate of respiratory tract infections.
ABSTRACT
With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.
Subject(s)
Algorithms , Computational Biology/methods , Data Mining/methods , High-Throughput Nucleotide Sequencing/methods , Natural Language Processing , Software , Datasets as Topic , Deep Learning , Humans , Models, TheoreticalABSTRACT
To explore the value, and influencing factors, of D-dimer on the prognosis of patients with COVID-19. A total of 1,114 patients with confirmed COVID-19 who were admitted to three designated COVID-19 hospitals in Wuhan, China from January 18, 2020, to March 24, 2020, were included in this study. We examined the relationship between peripheral blood levels of D-dimer, and clinical classification and prognosis, as well as its related influencing factors. D-dimer levels were found to be related to the clinical classification and the prognosis of clinical outcome. D-dimer levels were more likely to be abnormal in severely and critically ill patients compared with mild and ordinary cases, while D-dimer levels of patients who had died were significantly higher than those of surviving patients according to the results of the first and last lab tests. The results from ROC analyses for mortality risk showed that the AUCs of D-dimer were 0.909, YI was 0.765 at the last lab test, and a D-dimer value of 2.025 mg/L was regarded to be the optimal probability cutoff for a prognosis of death. In addition, we found that patients with advanced age, male gender, dyspnea symptoms, and some underlying diseases have a higher D-dimer value (p < 0.05). In short, D-dimer is related to the clinical classification and can be used to evaluate the prognosis of COVID-19 patients. The D-dimer value of 2.025 mg/L was the optimal probability cutoff for judging an outcome of death. Advanced age, male gender, dyspnea symptoms, and some underlying diseases are influencing factors for D-dimer levels, which impacts the prognosis of patients.