Klebsiella pneumoniae is a common human commensal and opportunistic pathogen. In recent years, the clinical isolation and resistance rates of K. pneumoniae have shown a yearly increase, leading to a special interest in mobile genetic elements. Prophages are a representative class of mobile genetic elements that can carry host-friendly genes, transfer horizontally between strains, and coevolve with the host’s genome. In this study, we identified 15,946 prophages from the genomes of 1437 fully assembled K. pneumoniae deposited in the NCBI database, with 9755 prophages on chromosomes and 6191 prophages on plasmids. We found prophages to be notably diverse and widely disseminated in the K. pneumoniae genomes. The K. pneumoniae prophages encoded multiple putative virulence factors and antibiotic resistance genes. The comparison of strain types with prophage types suggests that the two may be related. The differences in GC content between the same type of prophages and the genomic region in which they were located indicates the alien properties of the prophages. The overall distribution of GC content suggests that prophages integrated on chromosomes and plasmids may have different evolutionary characteristics. These results suggest a high prevalence of prophages in the K. pneumoniae genome and highlight the effect of prophages on strain characterization.

Throughout history, Influenza A viruses (IAVs) have caused significant harm and catastrophic pandemics. The presence of host barriers results in viral host tropism, where infected hosts are subject to strict restrictions due to the hindered spread of viruses across hosts. Therefore, the identification of host tropism of IAVs, particularly in humans, is crucial to preventing the cross-host transmission of avian viruses and their outbreaks in humans. Nevertheless, efficiently and effectively identifying host tropism, especially for early host susceptibility warnings based on viral genome sequences during outbreak onset, remains challenging. To address this challenge, we propose Flu-CNN, a deep neural network model based on classical character-level convolutional networks. By analyzing the genomic segments of IAVs, Flu-CNN can accurately identify the host tropism, with a particular focus on avian influenza viruses that may infect humans. According to our experimental evaluations, Flu-CNN achieved an accuracy of 99% in identifying virus hosts via only a single genomic segment, even for subtypes with a relatively small number of viral strains such as H5N1, H7N9, and H9N2. The superiority of Flu-CNN demonstrates its effectiveness in screening for critical amino acid mutations, which is important to host adaptation, and zoonotic risk prediction of viral strains. Flu-CNN is a valuable tool for identifying evolutionary characterization, monitoring potential outbreaks, and preventing epidemical spreads of IAVs, which contribute to the effective surveillance of influenza A viruses.