Restriction endonuclease analysis of varicella-zoster vaccine virus and wild-type DNAs

The DNA from several clinical isolates of varicella-zoster virus (VZV) were compared with the DNA from the vaccine strain VZV using three restriction endonucleases: BamHI, BgII, and HpaI. When electrophoresed through an agarose gel, the vaccine DNA digestion pattern was significantly different from the digestion patterns of the wild-type DNAs. Variations in the digestion pattern of the separate clinical isolates were also observed.     

Subtyping of Mycoplasma pneumoniae isolates based on extended genome sequencing and on expression profiles

Mycoplasma pneumoniae isolates from patients, collected over a period of 12 years in Germany, were characterized by various methods (parameters) including multilocus sequence typing, restriction fragment length polymorphisms, Western blotting with mono-specific antibodies directed against selected proteins or with polyspecific antibodies directed against the Triton X-114-soluble protein fraction, and two-dimensional gel electrophoresis. The results for 91 isolates from Germany, which were complemented with 14 isolates from the USA and 10 isolates from France, clearly showed that M. pneumoniae is a highly uniform species and that most of the isolates could be assigned to one of the two subtypes 1 and 2. The members of one subtype differ from the other with respect to the sequence of the P1 gene, the ORF6 gene, the P65 gene, and by a typical DNA restriction fragment pattern. We observed four isolates (variants), which seemed identical by the above mentioned criteria, but did not belong to either one of the two subtypes. They showed most of the subtype 2-specific features, but differed in the sequence of the P1 gene and showed a variation in the restriction fragment pattern. The appearance of subtype 1 or 2 over the last 12 years in Germany showed a dominance of subtype 1 between 1989 and 1996 and a dominance of subtype 2 between 1997 and 1998. The variant (neither subtype 1 nor subtype 2) was only detected in 1991 and 1995 but it had no epidemiological consequences.     

四川省2005年霍乱分子流行病学特征

目的：分析四川省2005年霍乱弧菌分子特征,以及霍乱暴发疫情分离的菌株与菌株之间,疫情分离的菌株与海、水产品监测分离的霍乱弧菌之间的遗传相关性,查找霍乱传染来源,为预测疫情和制定防治措施提供依据.方法：利用聚合酶链反应（PCR）检测O139群霍乱弧菌特异性编码脂多糖基因（LPS）和霍乱毒力基因（ctxAB）;脉冲场凝胶电泳对菌株进行分子分型,所得结果以BioNumerics V4.0软件UPGMA方法进行聚类分析.结果：所试16株霍乱弧菌14株LPS阳性,为O139群霍乱弧菌;另2株为O1群稻叶型霍乱弧菌.2株稻叶型霍乱弧菌和1株O139群霍乱弧菌ctxAB阴性,其余13株菌均具有ctxAB,为产毒株.对16株菌以NotⅠ酶切后PFGE可分为5个型别.O139群霍乱弧菌优势流行株与从甲鱼分离的O139群霍乱弧菌PFGE型别一致,且同为产毒株.结论：甲鱼等海、水产品被O139群霍乱弧菌污染情况严重,甲鱼中分离的O139群霍乱弧菌与霍乱疫情分离菌株之间高度同源,被污染的甲鱼可能是本年度食源性霍乱暴发的主要传染来源之一,海、水产品的监测是近期霍乱防控的重点.     

Semi-supervised learning of the electronic health record for phenotype stratification

Patient interactions with health care providers result in entries to electronic health records (EHRs). EHRs were built for clinical and billing purposes but contain many data points about an individual. Mining these records provides opportunities to extract electronic phenotypes, which can be paired with genetic data to identify genes underlying common human diseases. This task remains challenging: high quality phenotyping is costly and requires physician review; many fields in the records are sparsely filled; and our definitions of diseases are continuing to improve over time. Here we develop and evaluate a semi-supervised learning method for EHR phenotype extraction using denoising autoencoders for phenotype stratification. By combining denoising autoencoders with random forests we find classification improvements across multiple simulation models and improved survival prediction in ALS clinical trial data. This is particularly evident in cases where only a small number of patients have high quality phenotypes, a common scenario in EHR-based research. Denoising autoencoders perform dimensionality reduction enabling visualization and clustering for the discovery of new subtypes of disease. This method represents a promising approach to clarify disease subtypes and improve genotype-phenotype association studies that leverage EHRs.     

Subtyping of intraductal papillary mucinous neoplasms – pitfalls of MUC1 immunohistochemistry

Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions of pancreatic ductal adenocarcinoma (PDAC). Current edition of WHO Classification of Tumors of the Digestive System recognizes four different subtypes (gastric, intestinal, pancreatobiliary, and oncocytic) and recommends analysis of mucin expression (MUC1, MUC2, MUC5AC, MUC6) as well as evaluation of architectural and cell differentiation patterns for correct classification. However, there is no consensus on MUC1 expression of IPMN‐lesions in the literature. Current recommendations are based on studies where antibodies against the core MUC1 protein or sialylated MUC1 (tumor associated MUC1), not the fully glycosylated MUC1 were used. We have recently reported that MUC1 is strongly expressed in both gastric and intestinal types IPMN specimens from the cystic wall, obtained by endoscopic ultrasound guided microbiopsy procedure. We have used a commercial MUC1 antibody, validated and recommended for diagnostic use, which recognizes fully glycosylated MUC1. Based on the above, we propose a revision of the WHO Classification, specifying that antibodies against tumor associated MUC1 should be used for IPMN subtyping.     

Molecular methods for serovar determination of Salmonella

Abstract

Salmonella is a diverse foodborne pathogen, which has more than 2600 recognized serovars. Classification of Salmonella isolates into serovars is essential for surveillance and epidemiological investigations; however, determination of Salmonella serovars, by traditional serotyping, has some important limitations (e.g. labor intensive, time consuming). To overcome these limitations, multiple methods have been investigated to develop molecular serotyping schemes. Currently, molecular methods to predict Salmonella serovars include (i) molecular subtyping methods (e.g. PFGE, MLST), (ii) classification using serovar-specific genomic markers and (iii) direct methods, which identify genes encoding antigens or biosynthesis of antigens used for serotyping. Here, we reviewed reported methodologies for Salmonella molecular serotyping and determined the “serovar-prediction accuracy”, as the percentage of isolates for which the serovar was correctly classified by a given method. Serovar-prediction accuracy ranged from 0 to 100%, 51 to 100% and 33 to 100% for molecular subtyping, serovar-specific genomic markers and direct methods, respectively. Major limitations of available schemes are errors in predicting closely related serovars (e.g. Typhimurium and 4,5,12:i:-), and polyphyletic serovars (e.g. Newport, Saintpaul). The high diversity of Salmonella serovars represents a considerable challenge for molecular serotyping approaches. With the recent improvement in sequencing technologies, full genome sequencing could be developed into a promising molecular approach to serotype Salmonella.