Biosafety and Health最新文献

In 2008, China launched a national surveillance system for hand-foot-and-mouth disease (HFMD). Several million cases of HFMD are reported every year, coxsackievirus A16 (CVA16) was the leading cause of HFMD epidemic in Yantai city, China in recent years, but the information of epidemiology and molecular characterization of CVA16 in Yantai is limited. The aim of this study is to investigate the epidemiological characteristics and pathogenic spectrum of HFMD, and most importantly, the molecular characterization of CVA16 in Yantai from 2018 to 2021. A total of 2,000 clinical samples were collected in Yantai city from 2018 to 2021 and the enterovirus typing was performed using real-time reverse transcriptase–polymerase chain reaction (qRT-PCR). VP1 coding regions of 41 CVA16 isolates were amplified and Sanger sequenced, and phylogenetic analysis was performed. During the study period, HFMD became prevalent from May to August each year. It peaked in June and declined in September. The incidence was highest in children aged 1 to 5 years, while more common in males than females. 1,617 out of 2,000 clinical collection of samples were tested positive for enterovirus. Among them, 614 were identified as CVA16, 45 were enterovirus A71 (EV A17), and 958 were other enterovirus serotypes. All 41 CVA16 strains belonged to the Bla and B1b genotypes. Homology analysis showed that 41 CVA16 isolates shared 83.2%–100% nucleotide and 93.7%–100% amino acid similarity among themselves. The results of this study update molecular epidemiology of CVA16 and provide a reference for HFMD prevention and control.

Aedes aegypti (Ae. aegypti) is a major vector of dengue virus (DENV) and Zika virus (ZIKV). Understanding the complex interaction mechanisms between mosquito vectors and arboviruses is essential to interrupt virus transmission. This study constructed CYP4C21 knockout (KO) Aag2 cells (Ae. aegypti cells) and confirmed that CYP4C21 KO reduced DENV2 and ZIKV copies in Aag2 cells, which suggests that CYP4C21 may play an important role in mosquito infection with arboviruses. Furthermore, it is the first report of the CYP4 family related to viral infection, which lays the foundation for exploring the role of the CYP4C21 in the interaction of Ae. aegypti and arbovirus and provides novel insights into the function of cytochrome family proteins.

Metagenomic next-generation sequencing (mNGS) has been widely applied to identify pathogens associated with infectious diseases. However, limited studies have explored the use of mNGS-based dynamic pathogen monitoring in intensive care unit patients with severe pneumonia. Here, we present a clinical case of an 86-year-old male patient with severe pneumonia caused by a fungal infection. During the clinical treatment, four mNGS analyses were performed within two consecutive weeks. Various respiratory fungal pathogens, including Candida orthopsilosis, Candida albicans, and Aspergillus fumigatus were detected by mNGS of bronchoalveolar lavage fluid (BALF). Based on conventional pathogen identification and clinical symptoms, the patient was diagnosed with severe pneumonia caused by a fungal infection. The abundance of fungal species decreased gradually in response to antifungal and empirical therapies, and the fungal infections were effectively controlled. In summary, our results demonstrated that mNGS could effectively identify pathogens in patients with severe pneumonia. Additionally, dynamic pathogen monitoring based on mNGS could assist in the precise diagnosis of complex infections and may facilitate rapid induction of the most appropriate therapy.

Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and blocking these PPIs contribute to controlling and preventing viruses. However, wet-lab experiment-based identification of human-virus PPIs is usually expensive, labor-intensive, and time-consuming, which presents the need for computational methods. Many machine-learning methods have been proposed recently and achieved good results in predicting human-virus PPIs. However, most methods are based on protein sequence features and apply manually extracted features, such as statistical characteristics, phylogenetic profiles, and physicochemical properties. In this work, we present an embedding-based neural framework with convolutional neural network (CNN) and bi-directional long short-term memory unit (Bi-LSTM) architecture, named Emvirus, to predict human-virus PPIs (including human–SARS-CoV-2 PPIs). In addition, we conduct cross-viral experiments to explore the generalization ability of Emvirus. Compared to other feature extraction methods, Emvirus achieves better prediction accuracy.

Enterovirus A71 (EV-A71) is a significant hand-foot-mouth disease (HFMD) etiology. The inactivated EV-A71 vaccines were approved in China in 2016. However, the seroprevalence of EV-A71 after the vaccine application and its potential association with the EV-A71 epidemic in the population are rarely studied. In this study, we analyzed the incidence of EV-A71 infection and seroepidemiology in Guangzhou City, China. From 2019 to 2021, 167,920 clinically confirmed HFMD cases were reported in Guangzhou. In 6,868 enterovirus-positive samples, Coxsackievirus A6 and Coxsackievirus A16 were dominant genotypes, and only 3 EV-A71-positive samples were detected, highlighting the deficient epidemic activity of EV-A71. Microneutralization assay was performed on 1,000 representative serum samples. Notably, the seroprevalence and geometric mean titer (GMT) decreased significantly in 2020, and that in the < 3-year age group were increased and even higher than that in 3–5-year age group in 2019 and 2021, which was contrary to our previous surveillance result and other studies in Guangzhou. Furthermore, a moderate decline of GMT level was observed following the vaccination, but the seropositive serums were still detected for 49 months after second immunization, suggesting the long-term persistence of the immunity. Our seroepidemiology study revealed relatively higher neutralizing antibody activity in the susceptible population after the EV-A71 vaccine was adopted in 2016 in Guangzhou. It may be one of the reasons for the lower epidemic activity of EV-A71 in Guangzhou from 2019 to 2021.

Laboratory-acquired infection (LAI) is an important issue in laboratory biosafety for pathogenic microorganism, which aims to prevent the spread of infectious pathogens and protect laboratory personnel from potentially harmful microorganisms. Previous LAI reports provided a source of information for understanding the transmission routes and therapies helping to develop targeted prevention and response programs and to comprehensively ensure the biosafety of laboratories. In this study, from the perspective of the transmission routes of agents, the biosafety risks were discussed from four aspects: skin, eye, or mucous membrane exposure, contaminated sharp inoculation or bites from infected animals and arthropod vectors, ingestion or hand-to-mouth exposure, and inhalation of infectious aerosols. The development and evolution of LAI were reviewed, and appropriate countermeasures and suggestions were proposed accordingly.

Biological agents threats people's life through different ways, one of which lies in the impairment of cognition. It is believed cognitive decline may result from biological agents mediated neuron damage directly, or from the activation of the host immune response to eradicate the pathogen. However, direct linkage between infections and cognitive decline is very limited. Here we focus on the mechanisms of how different biological virus or they induced systemic and local inflammation link to the cognitive impairment, focusing on the roles of activated microglia and several molecular pathways mediated neurotoxicity.

Human enteroviruses (HEVs) include many different types that cause a wide range of diseases, and an effective method of genus-level identification has therefore significant clinical implications. However, quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR), the gold-standard method, still has shortfalls in diagnostic sensitivity and timeliness. Here we established a one-step real-time reverse-transcription recombinase-aided PCR assay (RT-RAP) to detect HEV fragment within an hour. The RT-RAP assay showed a detection limit of 5 copies/μL using recombinant plasmids and was extensively verified using 15 HEV strains. Among 15 types of HEV (species A-C), the sensitivity of RT-RAP was approximately 2–8 folds lower than that of the qRT-PCR in 9 types, and no-cross reaction with other viruses was observed. RT-RAP was further applied to analyze CSF and fecal specimens; the clinical performance demonstrated that the RT-RAP and the commercial qRT-PCR kit provided consistent results. These results indicated that RT-RAP assay may be a promising approach for rapid and sensitive detection of HEV.

The recent outbreak of the coronavirus disease 2019 (COVID-19) pandemic and the continuous evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have highlighted the significance of new detection methods for global monitoring and prevention. Although quantitative reverse transcription PCR (RT-qPCR), the current gold standard for diagnosis, performs excellently in genetic testing, its multiplexing capability is limited because of the signal crosstalk of various fluorophores. Herein, we present a highly efficient platform which combines 17-plex assays with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), enabling the targeting of 14 different mutation sites of the spike gene. Diagnosis using a set of 324 nasopharyngeal swabs or sputum clinical samples with SARS-CoV-2 MS method was identical to that with the RT-qPCR. The detection consistency of mutation sites was 97.9% (47/48) compared to Sanger sequencing without cross-reaction with other respiratory-related pathogens. Therefore, the MS method is highly potent to track and assess SARS-CoV-2 changes in a timely manner, thereby aiding the continuous response to viral variation and prevention of further transmission.

The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.