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Streptococcus pneumoniae is the most common human respiratory pathogen, and β-lactam antibiotics have been employed to treat infections caused by S. pneumoniae for decades. β-lactam resistance is steadily increasing in pneumococci and is mainly associated with the alteration in penicillin-binding proteins (PBPs) that reduce binding affinity of antibiotics to PBPs. However, the high variability of PBPs in clinical isolates and their mosaic gene structure hamper the predication of resistance level according to the PBP gene sequences. In this study, we developed a systematic strategy for applying supervised machine learning to predict S. pneumoniae antimicrobial susceptibility to β-lactam antibiotics. We combined published PBP sequences with minimum inhibitory concentration (MIC) values as labelled data and the sequences from NCBI database without MIC values as unlabelled data to develop an approach, using only a fragment from pbp2x (750 bp) and a fragment from pbp2b (750 bp) to predicate the cefuroxime and amoxicillin resistance. We further validated the performance of the supervised learning model by constructing mutants containing the randomly selected pbps and testing more clinical strains isolated from Chinese hospital. In addition, we established the association between resistance phenotypes and serotypes and sequence type of S. pneumoniae using our approach, which facilitate the understanding of the worldwide epidemiology of S. pneumonia. [ABSTRACT FROM AUTHOR]

Background: Extranodal natural killer (NK) cell/T‐cell lymphoma (NKTCL), a rare type of non‐Hodgkin's lymphoma, has strongly been associated with Epstein‐Barr virus (EBV) infection. However, there are no EBV genomes isolated from NKTCL, and the roles the variations of EBV strains play in the pathogenesis of NKTCL are still unclear. Materials and Methods: In this study, whole EBV genomes from eight primary NKTCL biopsy specimens were obtained using next‐generation sequencing, designated NKTCL‐EBV1 to NKTCL‐EBV8. Results: Compared with the six mostly referenced EBV strains, NKTCL‐EBVs closely resemble the GD1 strain but still harbor 2,072 variations, including 1,938 substitutions, 58 insertions, and 76 deletions. The majority of nonsynonymous mutations were located in latent and tegument genes. Moreover, the results from phylogenetic analysis of whole NKTCL genomes and specific genes demonstrated that all the NKTCL‐EBVs were related to Asian EBV strains. Based on the amino acid changes in certain residues of latent membrane protein 1 (LMP1) and EBV‐determined nuclear antigen 1 (EBNA1), all the NKTCL‐EBVs were sorted to China 1 and V‐val subtype, respectively. Furthermore, changes in CD4+ and CD8+ T‐cell epitopes of EBNA1 and LMP1 may affect the efficacy for a cytotoxic T lymphocyte (CTL)‐based therapy. Conclusion: This is the first large study to our knowledge to obtain EBV genomes isolated from NKTCL and show the diversity of EBV genomes in a whole genome level by phylogenetic analysis. Implications for Practice: In this study, the full‐length sequence of Epstein‐Barr virus (EBV) isolated from eight patients with nasal natural killer/T‐cell lymphoma (NKTCL) was determined and further compared with the sequences previously reported isolated from other malignancies. Phylogenetic analysis showed that NKTCL‐EBV strains are close to other Asian subtypes instead of non‐Asian ones, leading to the conclusion that EBV infections are more likely affected by different geographic regions rather than particular EBV‐associated malignancies. Therefore, these data have implications for the development of effective prophylactic and therapeutic vaccine approaches targeting the personalized or geographic‐specific EBV antigens in these aggressive diseases. Extranodal natural killer cell/T cell lymphoma (NKTCL) has been associated with Epstein‐Barr virus (EBV) infection. This article describes the distribution of genetic variation in EBV genomes isolated from NKTCL biopsy samples and evaluates genomic diversity in the EBV strains. [ABSTRACT FROM AUTHOR]