Background: Accurate classification of different Burkholderia cepacia complex (BCC) species is essential for therapy, prognosis assessment and research. The taxonomic status of BCC remains problematic and an improved knowledge about the classification of BCC is in particular needed. Methods: We compared phylogenetic trees of BCC based on 16S rRNA, recA, hisA and MLSA (multilocus sequence analysis). Using the available whole genome sequences of BCC, we inferred a species tree based on estimated single-copy orthologous genes and demarcated species of BCC using dDDH/ANI clustering. Results: We showed that 16S rRNA, recA, hisA and MLSA have limited resolutions in the taxonomic study of closely related bacteria such as BCC. Our estimated species tree and dDDH/ANI clustering clearly separated 116 BCC strains into 36 clusters. With the appropriate reclassification of misidentified strains, these clusters corresponded to 22 known species as well as 14 putative novel species. Conclusions: This is the first large-scale and systematic study of the taxonomic status of the BCC and could contribute to further insights into BCC taxonomy. Our study suggested that conjunctive use of core phylogeny based on single-copy orthologous genes, as well as pangenome-based dDDH/ANI clustering would provide a preferable framework for demarcating closely related species. Reviewer: This article was reviewed by Dr. Xianwen Ren.

Some Brucella spp. are important pathogens. According to the latest prokaryotic taxonomy, the Brucella genus consists of facultative intracellular parasitic Brucella species and extracellular opportunistic or environmental Brucella species. Intracellular Brucella species include classical and nonclassical types, with different species generally exhibiting host preferences. Some classical intracellular Brucella species can cause zoonotic brucellosis, including B. melitensis, B. abortus, B. suis, and B. canis. Extracellular Brucella species comprise opportunistic or environmental species which belonged formerly to the genus Ochrobactrum and thus nowadays renamed as for example Brucella intermedia or Brucella anthropi, which are the most frequent opportunistic human pathogens within the recently expanded genus Brucella. The cause of the diverse phenotypic characteristics of different Brucella species is still unclear. To further investigate the genetic evolutionary characteristics of the Brucella genus and elucidate the relationship between its genomic composition and prediction of phenotypic traits, we collected the genomic data of Brucella from the NCBI Genome database and conducted a comparative genomics study. We found that classical and nonclassical intracellular Brucella species and extracellular Brucella species exhibited differences in phylogenetic relationships, horizontal gene transfer and distribution patterns of mobile genetic elements, virulence factor genes, and antibiotic resistance genes, showing the close relationship between the genetic variations and prediction of phenotypic traits of different Brucella species. Furthermore, we found significant differences in horizontal gene transfer and the distribution patterns of mobile genetic elements, virulence factor genes, and antibiotic resistance genes between the two chromosomes of Brucella, indicating that the two chromosomes had distinct dynamics and plasticity and played different roles in the survival and evolution of Brucella. These findings provide new directions for exploring the genetic evolutionary characteristics of the Brucella genus and could offer new clues to elucidate the factors influencing the phenotypic diversity of the Brucella genus.

Antigen peptides that are presented by a major histocompatibility complex (MHC) and recognized by a T cell receptor (TCR) have an essential role in immunotherapy. Although substantial progress has been made in predicting MHC presentation, accurately predicting the binding interactions between antigen peptides, MHCs and TCRs remains a major computational challenge. In this paper, we propose a unified deep framework (called UniPMT) for peptide, MHC and TCR binding prediction to predict the binding between the peptide and the CDR3 of TCR β in general, presented by class I MHCs. UniPMT is comprehensively validated by a series of experiments and achieved state-of-the-art performance in the peptide–MHC–TCR, peptide–MHC and peptide–TCR binding prediction tasks with up to 15% improvements in area under the precision–recall curve taking the peptide–MHC–TCR binding prediction task as an example. In practical applications, UniPMT shows strong predictive power, correlates well with T cell clonal expansion and outperforms existing methods in neoantigen-specific binding prediction with up to 17.62% improvements in area under the precision–recall curve on experimentally validated datasets. Moreover, UniPMT provides interpretable insights into the identification of key binding sites and the quantification of peptide–MHC–TCR binding probabilities. In summary, UniPMT shows great potential to serve as a useful tool for antigen peptide discovery, disease immunotherapy and neoantigen vaccine design.

Influenza A virus (IAV) genomes are composed of eight single-stranded RNA segments. Genetic exchange through reassortment of the segmented genomes often endows IAVs with new genetic characteristics, which may affect transmissibility and pathogenicity of the viruses. However, a comprehensive understanding of the reassortment history of IAVs remains lacking. To this end, we assembled 40,296 whole-genome sequences of IAVs for analysis. Using a new clustering method based on Mean Pairwise Distances in the phylogenetic trees, we classified each segment of IAVs into clades. Correspondingly, reassortment events among IAVs were detected by checking the segment clade compositions of related genomes under specific environment factors and time period. We systematically identified 1,927 possible reassortment events of IAVs and constructed their reassortment network. Interestingly, minimum spanning tree of the reassortment network reproved that swine act as an intermediate host in the reassortment history of IAVs between avian species and humans. Moreover, reassortment patterns among related subtypes constructed in this study are consistent with previous studies. Taken together, our genome-wide reassortment analysis of all the IAVs offers an overview of the leaping evolution of the virus and a comprehensive network representing the relationships of IAVs.

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.