Comparative analysis of single cell lung atlas of bat, cat, tiger and pangolin

Horseshoe bats (Rhinolophus sinicus) might help maintain coronaviruses severely affecting human health, such as SARS-CoV and SARS-CoV-2. It has long been suggested that bats may be more tolerant of viral infection than other mammals due to their unique immune system, but the exact mechanism remains to be fully explored. During the COVID-19 pandemic, multiple animal species were diseased by SARS-CoV-2 infection, especially in the respiratory system. Herein, single-cell transcriptomic data of the lungs of a horseshoe bat, a cat, a tiger, and a pangolin were generated. The receptor distribution of twenty-eight respiratory viruses belonging to fourteen viral families were characterized for the four species. Comparison on the immune-related transcripts further revealed limited cytokine activations in bats, which might explain the reason why bats experienced only mild diseases or even no symptoms upon virus infection. Our findings might increase our understanding of the immune background of horseshoe bats and their insensitivity to virus infections.

Screening of cell-virus, cell-cell, gene-gene interactions among kingdoms of life at single cell resolution

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) issued a significant and urgent threat to global health. The exact animal origin of SARS-CoV-2 remains obscure and understanding its host range is vital for preventing interspecies transmission. Previously, we have assessed the target cell profiles of SARS-CoV-2 in pets, livestock, poultry and wild animals. Herein, we expand this investigation to a wider range of animal species and viruses to provide a comprehensive source for large-scale screening of potential virus hosts. Single cell atlas for several mammalian species (alpaca, hamster, hedgehog, chinchilla etc.), as well as comparative atlas for lung, brain and peripheral blood mononuclear cells (PBMC) for various lineages of animals were constructed, from which we systemically analyzed the virus entry factors for 113 viruses over 20 species from mammalians, birds, reptiles, amphibians and invertebrates. Conserved cellular connectomes and regulomes were also identified, revealing the fundamental cell-cell and gene-gene cross-talks between these species. Overall, our study could help identify the potential host range and tissue tropism of SARS-CoV-2 and a diverse set of viruses and reveal the host-virus co-evolution footprints.

Viral receptor profiles of masked palm civet revealed by single-cell transcriptomics

Civets are small mammals belonging to the family Viverridae. The masked palm civets (Paguma larvata) served as an intermediate host in the bat-to-human transmission of severe acute respiratory syndrome coronavirus (SARS-CoV) in 20031. Because of their unique role in the SARS outbreak, civets were suspected as a potential intermediate host of SARS-CoV-2, the etiological pathogen of the COVID-19 pandemic. Besides their susceptibility to coronaviruses, civets can also be infected by other viruses, such as canine distemper viruses2, parvoviruses3, influenza viruses4, etc. Regarding the ecological and economical role of civets, it is vital to evaluate the potential threats from different pathogens to these animals. Receptor binding is a necessary step for virus entry into host cells. Understanding the distribution of receptors of various viruses provides hints to their potential tissue tropisms. Herein, we characterized the cell atlas of five important organs (the frontal lobe, lung, liver, spleen and kidney) of masked palm civets (Paguma larvata) and described the expression profiles of receptor associated genes of 132 viruses from 25 families, including 16 viruses from 10 families reported before that can attack civets and 116 viruses with little infection record.

The Trans-omics Landscape of COVID-19

System-wide molecular characteristics of COVID-19, especially in those patients without comorbidities, have not been fully investigated. We compared extensive molecular profiles of blood samples from 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Amongst the major findings, asymptomatic patients were characterized by highly activated anti-virus interferon, T/natural killer (NK) cell activation, and transcriptional upregulation of inflammatory cytokine mRNAs. However, given very abundant RNA binding proteins (RBPs), these cytokine mRNAs could be effectively destabilized hence preserving normal cytokine levels. In contrast, in critically ill patients, cytokine storm due to RBPs inhibition and tryptophan metabolites accumulation contributed to T/NK cell dysfunction. A machine-learning model was constructed which accurately stratified the COVID-19 severities based on their multi-omics features. Overall, our analysis provides insights into COVID-19 pathogenesis and identifies targets for intervening in treatment.

Single-cell screening of SARS-CoV-2 target cells in pets, livestock, poultry and wildlife

A few animals have been suspected to be intermediate hosts of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, a large-scale single-cell screening of SARS-CoV-2 target cells on a wide variety of animals is missing. Here, we constructed the single-cell atlas for 11 representative species in pets, livestock, poultry, and wildlife. Notably, the proportion of SARS-CoV-2 target cells in cat was found considerably higher than other species we investigated and SARS-CoV-2 target cells were detected in multiple cell types of domestic pig, implying the necessity to carefully evaluate the risk of cats during the current COVID-19 pandemic and keep pigs under surveillance for the possibility of becoming intermediate hosts in future coronavirus outbreak. Furthermore, we screened the expression patterns of receptors for 144 viruses, resulting in a comprehensive atlas of virus target cells. Taken together, our work provides a novel and fundamental strategy to screen virus target cells and susceptible species, based on single-cell transcriptomes we generated for domesticated animals and wildlife, which could function as a valuable resource for controlling current pandemics and serve as an early warning system for coping with future infectious disease threats.

Study on correlation between the level of serum uric acid and metabolic syndrome / 中国综合临床

Objective To explore the correlation between the level of serum uric acid and metabolic syndrome and associated factors including overweight,abnormality of blood glucose,blood lipid,and hypertension.Methods 654 cases of complete medical records,aging from 30-90with median age of 65 ,were completed were selected,Data base was established by using Excell software,meanwhile,single factor analysis and stepwise regression analysis were performed using SPSS 11.0 software package.Results The levels of serum uric acid of patients with overweight,high fasting blood glucose (FBG),hypertriglyceride (TG),high-density lipid cholesterol (HDL-C)and essential hypertension (EH)were higher than that of patients without overweight,normal fasting blood glucose,normal triglyceride,low high-density lipid cholesterol and without hypertension,respectively [respectively,(270.52 ±81.63 )μmol/L vs.(226.61 ± 67.42)μmol/L,t=-7.387,P=0.000;(265.71±73.50)μmol/Lvs.(235.03 ±75.00)μmol/L,t=-4.459,P=0.000;(262.80 ±74.45 )μmol/L vs.( 235.82±75.04)μmol/L,t=-3.927,P=0.000;(243.97 ±76.33)μmol/L vs.(212.78±57.60)μmol/L,t=-2.412,P=0.016;(282.87±64.79)μmol/L vs.( 180.22±41.60)μmol/L,t=22.59,P=0.000 ].The patients with metabolic syndrome (n=107)and the patients without metabolic syndrome (n=547)were included in our study.The level of serum uric acid of patients with metabolic syndrome was higher than that of patients without metabolic syndrome [(301.22±68.55 )μmol/L vs.(230.72±71.63)μmol/L,t=9.376,P=0.000].Stepwise regression analysis showed that there was positive correlation between systolic blood pressure (SBP),diastolic blood pressure (DBP),the body mass index (BMI),triglyceride (TG),high-density lipid cholesterol (HDL-C),fasting blood glucose (FBG)and the level of serum uric acid,respectively (t=3.409,13.401,6.979,2.943,3.514,4.706 respectively;P=0.000,0.001,0.000,0.003,0.000,0.000 respectively).Conclusion The level of serum uric acid in patients with metabolic syndrome is higher than those without metabolic syndrome.There is positive correlation between the level of serum uric acid and associated factors of metabolic syndrome including overweight,abnormality of blood glucose and blood lipid,and hypertension.