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Adv Sci (Weinh) ; 8(23): e2102593, 2021 12.
Article in English | MEDLINE | ID: covidwho-1559092


Fast and accurate identification of microbial pathogens is critical for the proper treatment of infections. Traditional culture-based diagnosis in clinics is increasingly supplemented by metagenomic next-generation-sequencing (mNGS). Here, RNA/cDNA-targeted sequencing (meta-transcriptomics using NGS (mtNGS)) is established to reduce the host nucleotide percentage in clinic samples and by combining with Oxford Nanopore Technology (ONT) platforms (meta-transcriptomics using third-generation sequencing, mtTGS) to improve the sequencing time. It shows that mtNGS improves the ratio of microbial reads, facilitates bacterial identification using multiple-strategies, and discovers fungi, viruses, and antibiotic resistance genes, and displaying agreement with clinical findings. Furthermore, longer reads in mtTGS lead to additional improvement in pathogen identification and also accelerate the clinical diagnosis. Additionally, primary tests utilizing direct-RNA sequencing and targeted sequencing of ONT show that ONT displays important potential but must be further developed. This study presents the potential of RNA-targeted pathogen identification in clinical samples, especially when combined with the newest developments in ONT.

Bronchoalveolar Lavage Fluid/microbiology , High-Throughput Nucleotide Sequencing/methods , Infections/genetics , Metagenomics/methods , RNA/genetics , Sequence Analysis, RNA/methods , Aged , Bronchoalveolar Lavage/methods , Female , Humans , Male , Metagenome/genetics , Middle Aged
China CDC Wkly ; 3(46): 967-972, 2021 Nov 12.
Article in English | MEDLINE | ID: covidwho-1513532


INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a recently emergent coronavirus of natural origin and caused the coronavirus disease (COVID-19) pandemic. The study of its natural origin and host range is of particular importance for source tracing, monitoring of this virus, and prevention of recurrent infections. One major approach is to test the binding ability of the viral receptor gene ACE2 from various hosts to SARS-CoV-2 spike protein, but it is time-consuming and labor-intensive to cover a large collection of species. METHODS: In this paper, we applied state-of-the-art machine learning approaches and created a pipeline reaching >87% accuracy in predicting binding between different ACE2 and SARS-CoV-2 spike. RESULTS: We further validated our prediction pipeline using 2 independent test sets involving >50 bat species and achieved >78% accuracy. A large-scale screening of 204 mammal species revealed 144 species (or 61%) were susceptible to SARS-CoV-2 infections, highlighting the importance of intensive monitoring and studies in mammalian species. DISCUSSION: In short, our study employed machine learning models to create an important tool for predicting potential hosts of SARS-CoV-2 and achieved the highest precision to our knowledge in experimental validation. This study also predicted that a wide range of mammals were capable of being infected by SARS-CoV-2.