کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
2577408 | 1561368 | 2006 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Genotyping of group B streptococcus and risk assessment for invasive infection
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
بیوشیمی، ژنتیک و زیست شناسی مولکولی
زیست شناسی مولکولی
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چکیده انگلیسی
We have developed a group B streptococcus (GBS) fingerprinting system that identifies (a) molecular serotypes (MS) and subtypes (msst); (b) surface protein gene profiles (pgp); (c) mobile genetic elements (mge); (d) antibiotic resistance (AR) related markers and (e) sequence types of Cβ protein gene (bac). Previously, we described differences in the distribution of MS/msst, between GBS from different types of invasive disease and vaginal colonization and showed that the most common MS/msst generally correspond with certain multilocus sequence types (ST) namely: III-1 with ST-19; Ia-1 with ST-23; V-1 with ST-1; and III-2 with ST-17. In this study we amplified and sequenced bac from 255 GBS isolates. The mean sequence length of bac-PCR amplicons was significantly less for invasive than colonizing isolates, because of a greater frequency of 18 bp deletions. We also characterised 796 GBS isolates from patients in Australia and New Zealand-including 695 from normally sterile sites and 101 from vaginal swabs-according to genotype and AR markers. We confirmed significant associations between: MS V and adult disease; msst III-2 and late onset (LO) neonatal disease; msst III-1 and vaginal colonization. MS II was negatively associated with LO. Three macrolide/lincosamine/streptogramin B (MLSB) resistance markers were each found in 2-3% of isolates; mef(A/E) was significantly more common in Australia than New Zealand. The integrase gene int-Tn was significantly more common among colonizing than invasive isolates. The distribution of all AR markers, except mef(A/E), differed significantly between MS; erm(A/TR) was significantly more common in MS V. We postulate that analysis of molecular, clinical and epidemiological data using machine-learning algorithms will provide the basis for risk assessment in GBS carriers.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Congress Series - Volume 1289, April 2006, Pages 66-70
Journal: International Congress Series - Volume 1289, April 2006, Pages 66-70
نویسندگان
Gwendolyn L. Gilbert, Xianyu Zeng, Zuotao Zhao, Heather Gidding, Fanrong Kong,