Multi-omics analysis reveals the genetic and environmental factors in shaping the gut resistome of a keystone rodent species

Understanding the emergence and spread of antibiotic resistance genes(ARGs)in wildlife is critical for the health of humans and animals from a“One Health”perspective.The gut microbiota serve as a reservoir for ARGs;however,it remains poorly understood how environmental and host genetic factors influence ARGs by affecting the gut microbiota.To elucidate this,we analyzed whole-genome resequencing data from 79 individuals of Brandt’s vole in two geographic locations with different antibiotics usage,together with metabolomic data and shotgun sequencing data.A high diversity of ARGs(851 subtypes)was observed in vole’s gut,with a large variation in ARG composition between individuals from Xilingol and Hulunbuir in China.The diversity and composition of ARGs were strongly correlated with variations in gut microbiota community structure.Genome-wide association studies revealed that 803 loci were significantly associated(P<5.05×10^(−9))with 31 bacterial species,and bipartite networks identified 906 bacterial species-ARGs associations.Structural equation modeling analysis showed that host genetic factors,air temperature,and presence of pollutants(Bisphenol A)significantly affected gut microbiota community structure,which eventually regulated the diversity of ARGs.The present study advances our understanding of the complex host-environment interactions that underlie the spread of ARGs in the natural environments.

SARS-CoV-2 shows a much earlier divergence in the world than in the Chinese mainland

Currently,the origin and early transmission pattern of SARS-CoV-2 remain unknown.The Time to the Most Recent Common Ancestor(TMRCA)of SARS-CoV-2 has been suggested to be mid-October or November of 2019(Pekar et al.,2021;van Dorp et al.,2020)by using a small number of early samples which may not cover full lineages of its evolutionary tree.Available methods of estimating TMRCA of viruses(Drummond et al.,2003)are often time-consuming and thus constrained by using big data.

Modeling analysis revealed the distinct global transmission patterns of influenza A viruses and their influencing factors

Influenza A virus has caused huge damage to human health and poultry production worldwide,but its global transmission patterns and influencing factors remain unclear.Here,by using the Nearest Genetic Distance Approach with genetic sequences data,we reconstructed the global transmission patterns of 4 most common subtypes of influenza A virus(H1N1,H3N2,H5N1,and H7N9)and analyzed associations of transmission velocity of these influenza viruses with environmental factors.We found that the transmission patterns of influenza viruses and their associations with environmental factors were closely related to their host properties.H1N1 and H3N2,which are mainly held by humans,are transmitted between regions at high velocity and over long distances,which may be due to human transportation via airplane;while H5N1 and H7N9,which are mainly carried by animals,are transmitted locally at short distances and at low velocity,which may be facilitated by poultry transportation via railways or high ways.H1N1 and H3N2 spread faster in cold seasons,while H5N1 spread faster in both cold and warm seasons,and H7N9 spread faster in wet seasons.H1N1,H3N2,and H5N1 spread faster in places with both high and low human densities.Our study provided novel insights into the global transmission patterns,processes,and management strategies for influenza under accelerated global change.