Adaptive immune responses to SARS-CoV-2 infection in severe versus mild individuals

The global Coronavirus disease 2019(COVID-19)pandemic caused by SARS-CoV-2 has affected more than eight million people.There is an urgent need to investigate how the adaptive immunity is established in COVID-19 patients.In this study,we proled adaptive immune cells of PBMCs from recovered COVID-19 patients with varying disease severity using single-cell RNA and TCR/BCR V(D)J sequencing.The sequencing data revealed SARS-CoV-2-specic shufing of adaptive immune repertories and COVID-19-induced remodeling of peripheral lymphocytes.Characterization of variations in the peripheral T and B cells from the COVID-19 patients revealed a positive correlation of humoral immune response and T-cell immune memory with disease severity.Sequencing and functional data revealed SARS-CoV-2-specic T-cell immune memory in the convalescent COVID-19 patients.Furthermore,we also identied novel antigens that are responsive in the convalescent patients.Altogether,our study reveals adaptive immune repertories underlying pathogenesis and recovery in severe versus mild COVID-19 patients,providing valuable information for potential vaccine and therapeutic development against SARS-CoV-2 infection.

PHISDetector:A Tool to Detect Diverse In Silico Phage–host Interaction Signals for Virome Studies

Phage-microbe interactions are appealing systems to study coevolution,and have also been increasingly emphasized due to their roles in human health,disease,and the development of novel therapeutics.Phage-microbe interactions leave diverse signals in bacterial and phage genomic sequences,defined as phage-host interaction signals(PHISs),which include clustered regularly interspaced short palindromic repeats(CRISPR)targeting,prophage,and protein-protein interaction signals.In the present study,we developed a novel tool phage-host interaction signal detector(PHISDetector)to predict phage-host interactions by detecting and integrating diverse in silico PHISs,and scoring the probability of phage-host interactions using machine learning models based on PHIS features.We evaluated the performance of PHISDetector on multiple benchmark datasets and application cases.When tested on a dataset of 758 annotated phage-host pairs,PHISDetector yields the prediction accuracies of 0.51 and 0.73 at the species and genus levels,respectively,outperforming other phage-host prediction tools.When applied to 125,842 metagenomic viral contigs(mVCs)derived from 3042 geographically diverse samples,a detection rate of 54.54% could be achieved.Furthermore,PHISDetector could predict infecting phages for 85.6% of 368 multidrug-resistant(MDR)bacteria and 30% of 454 human gut bacteria obtained from the National Institutes of Health(NIH)Human Microbiome Project(HMP).The PHISDetector can be run either as a web server(http://www.microbiome-bigdata.com/PHISDetector/)for general users to study individual inputs or as a stand-alone version(https://github.com/HITImmunologyLab/PHISDetector)to process massive phage contigs from virome studies.

Metagenomic analysis reveals oropharyngeal microbiota alterations in patients with COVID-19

COVID-19 remains a serious emerging global health problem,and little is known about the role of oropharynx commensal microbes in infection susceptibility and severity.Here,we present the oropharyngeal microbiota characteristics identified by shotgun metagenomic sequencing analyses of oropharynx swab specimens from 31 COVID-19 patients,29 influenza B patients,and 28 healthy controls.Our results revealed a distinct oropharyngeal microbiota composition in the COVID-19 patients,characterized by enrichment of opportunistic pathogens such as Veillonella and Megasphaera and depletion of Pseudopropionibacterium,Rothia,and Streptococcus.Based on the relative abundance of the oropharyngeal microbiome,we built a microbial classifier to distinguish COVID-19 patients from flu patients and healthy controls with an AUC of 0.889,in which Veillonella was identified as the most prominent biomarker for COVID-19 group.Several members of the genus Veillonella,especially Veillonella parvula which was highly enriched in the oropharynx of our COVID-19 patients,were also overrepresented in the BALF of COVID-19 patients,indicating that the oral cavity acts as a natural reservoir for pathogens to induce co-infections in the lungs of COVID-19 patients.We also found the increased ratios of Klebsiella sp.,Acinetobacter sp.,and Serratia sp.were correlated with both disease severity and elevated systemic inflammation markers(neutrophil-lymphocyte ratio,NLR),suggesting that these oropharynx microbiota alterations may impact COVID-19 severity by influencing the inflammatory response.Moreover,the oropharyngeal microbiome of COVID-19 patients exhibited a significant enrichment in amino acid metabolism and xenobiotic biodegradation and metabolism.In addition,all 26 drug classes of antimicrobial resistance genes were detected in the COVID-19 group,and were significantly enriched in critical cases.In conclusion,we found that oropharyngeal microbiota alterations and functional differences were associated with COVID-19 severity.