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A collection of 399 “Streptococcus milleri” strains were identified to the species level by the use of a line blot assay. Their PCR-amplified partial 16S rRNA gene sequences were hybridized with species-specific 5′-biotinylated oligonucleotide probes homologous to the bp 213 to 231 regions of the 16S rRNA gene sequences of the type strains Streptococcus anginosus ATCC 33397, Streptococcus constellatus ATCC 27823, and Streptococcus intermedius ATCC 27335. The hybridization results were compared with the reference phenotypic identification method data (R. A. Whiley, H. Fraser, J. M. Hardie, and D. Beighton, J. Clin. Microbiol. 28:1497-1501, 1990). Most strains (357 of 399 [89.5%]) reacted unambiguously with only one probe. However, 42 of the 399 strains (10.5%) reacted with both the S. constellatus- and S. intermedius-specific probes; 41 of them were phenotypically identified as S. constellatus. These dually reactive strains hybridized with a 5′-biotinylated probe based on the bp 213 to 231 region of the 16S rRNA gene sequence of one of two species. Analysis of the 5′ ends of the 16S rRNA gene sequences (487 bp) demonstrated that the dually reactive strains represent a distinct rRNA population sharing 98.1% sequence similarity with S. constellatus. Phenotypic consistency between the dually reactive strains and the S. constellatus strains was not demonstrated. Line blot hybridization proved to be a simple and inexpensive method to screen large numbers of strains for genetic relatedness, and it allowed the detection of a distinct 16S rRNA type within the “S. milleri” group.
Rapid species identification of "Streptococcus milleri" strains by line blot hybridization: identification of a distinct 16S rRNA population closely related to Streptococcus constellatus.
J A Jacobs, C S Schot, A E Bunschoten, and L M Schouls
Streptococcus intermedius is known to cause periodontitis and pyogenic infections in the brain and liver. Here we report the complete genome sequence of strain TYG1620 (genome size, 2,006,877 bp; GC content, 37.6%; 2,020 predicted open reading frames [ORFs]) isolated from a brain abscess in an infant. Comparative analysis of S. intermedius genome sequences suggested that TYG1620 carries a notable type VII secretion system (T7SS), two long repeat regions, and 19 ORFs for cell wall-anchored proteins (CWAPs). To elucidate the genes responsible for the pathogenicity of TYG1620, transcriptome analysis was performed in a murine subcutaneous abscess model. The results suggest that the levels of expression of small hypothetical proteins similar to phenol-soluble modulin β1 (PSMβ1), a staphylococcal virulence factor, significantly increased in the abscess model. In addition, an experiment in a murine subcutaneous abscess model with random transposon (Tn) mutant attenuation suggested that Tn mutants with mutations in 212 ORFs in the Tn mutant library were attenuated in the murine abscess model (629 ORFs were disrupted in total); the 212 ORFs are putatively essential for abscess formation. Transcriptome analysis identified 37 ORFs, including paralogs of the T7SS and a putative glucan-binding CWAP in long repeat regions, to be upregulated and attenuated in vivo. This study provides a comprehensive characterization of S. intermedius pathogenicity based on the complete genome sequence and a murine subcutaneous abscess model with transcriptome and Tn mutagenesis, leading to the identification of pivotal targets for vaccines or antimicrobial agents for the control of S. intermedius infections.
brain abscess, genomics, murine model, Streptococcus, transposon mutagenesis, whole-genome sequence
Characterization of the Pathogenicity of Streptococcus intermedius TYG1620 Isolated from a Human Brain Abscess Based on the Complete Genome Sequence with Transcriptome Analysis and Transposon Mutagenesis in a Murine Subcutaneous Abscess Model
Noriko Hasegawa,a,b Tsuyoshi Sekizuka,a Yutaka Sugi,a Nobuhiro Kawakami,c Yumiko Ogasawara,a Kengo Kato,a Akifumi Yamashita,a Fumihiko Takeuchi,a and Makoto Kurodacorresponding authora
This study aimed to evaluate Chinese tertiary hospital nurses’ research output, research ability, and their related training needs regarding scientific research methodology and analyze the relations among them.
A nationwide survey was conducted in China on a large sample of tertiary hospital nurses (n = 27,335) recruited from 22 provinces, autonomous regions, and municipalities. A validated, self-designed questionnaire, consisted of a common questionnaire, the Science Research Skills Self-Rating Questionnaire (SRSQ) and the Scientific Research Training Needs Questionnaire (SRTNQ) were used to assess nurses’ research output, self-rated research skills and research-training needs.
The nurses’ scientific research participation rates (with 4.13%, 7.85%, 5.35%, and 2.04% in research projects, research attendance, papers published, and patent, respectively) and their self-rated research skills 25.00 (12.50, 37.50) were very low. However, the research training needs were relatively high 53.12(37.50, 75.00). Significant differences in research participation rates (research projects, research attendance, papers published, and patent), scientific research skills, and research-training needs were determined by age, highest education level, nursing experience, employment, technical title, administrative post, and clinical tutoring experience (P < 0.05). Female and male nurses had different research participation rates (only research projects and studies published) and scientific research skills (P < 0.05). Positive correlations were observed among research output, scientific research skills, and research-training needs (P < 0.01). Conclusions Nurses’ scientific research participation and self-rated research ability were below the optimal despite that they had relatively high research-training needs. Nurses should be provided further research training with tailored content to their characteristics and capacity.
Nursing staff, Hospital, Nursing research, Needs assessment
Research-training needs of clinical nurses: A nationwide study among tertiary hospitals in China
Xue Wu,a,1 Xinjuan Wu,b,1 Yanhong Gao,c Limin Wang,a Jingfen Jin,d Yinglan Li,e Shouzhen Cheng,f Xianxiu Wen,g Aiping Wang,h Qingyin Li,i and Shaomei Shanga,∗
2019 Jul 10;