fimA-folD genes linkage in Salmonella identifies a putative junctional site of chromosomal rearrangement in the enterobacterial genome

Abstract Analysis of the Salmonella chromosomal region located upstream of the fimA gene (coding for the major type 1 fimbrial subunit) showed a close linkage of this gene to the folD gene (coding for the enzyme 5,10-methylenetetrahydrofolate dehydrogenase/5, 10-methenyltetrahydrofolate cyclohydrolase), indicating that the fim gene cluster of Salmonella , unlike that of Escherichia coli , has no regulatory genes located upstream of fimA and apparently terminates with this gene. The respective locations of the fim and folD genes in the E. coli and Salmonella genetic maps suggests that the fimA-folD intergenic region of Salmonella encompasses a junctional site of a genetic rearrangement that probably originated from the different chromosomal location of the fim genes in these species.     

Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology

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The intersectional hybrid between Weigela hortensis and W. maximowiczii (Caprifoliaceae)

Morphologically intermediate plants between Weigela hortensis (Siebold & Zucc.) K.Koch and W. maximowiczii (S.Moore) Rehder have been found in Miyagi and Yamagata Pref., northern Japan. Quantitative character analyses of flowers, pollen stainability and molecular analyses indicated that the intermediate plants were hybrids of those two species. This is the first record of an intersectional hybrid with W. maximowiczii (sect. Weigelastrum ) as one of the parent species. The morphological differences among hybrid individuals imply the possibility of backcrosses or formation of second or later generations of hybrids, although those may be quite rare because of a low frequency of viable pollen grains. Causes of hybridization between two distantly-related species in Weigela are discussed. © 2002 The Linnean Society of London, Botanical Journal of the Linnean Society , 2002, 138 , 369–380.     

MV-mimicking micelles loaded with PEG-serine-ACP nanoparticles to achieve biomimetic intra/extra fibrillar mineralization of collagen in vitro

Since their discovery, matrix vesicles (MVs) containing minerals have received considerable attention for their role in the mineralization of bone, dentin and calcified cartilage. Additionally, MVs' association with collagen fibrils, which serve as the scaffold for calcification in the organic matrix, has been repeatedly highlighted. The primary purpose of the present study was to establish a MVs–mimicking model (PEG-S-ACP/micelle) in vitro for studying the exact mechanism of MVs-mediated extra/intra fibrillar mineralization of collagen in vivo. In this study, high-concentration serine was used to stabilize the amorphous calcium phosphate (S-ACP), which was subsequently mixed with polyethylene glycol (PEG) to form PEG-S-ACP nanoparticles. The nanoparticles were loaded in the polysorbate 80 micelle through a micelle self-assembly process in an aqueous environment. This MVs–mimicking model is referred to as the PEG-S-ACP/micelle model. By adjusting the pH and surface tension of the PEG-S-ACP/micelle, two forms of minerals (crystalline mineral nodules and ACP nanoparticles) were released to achieve the extrafibrillar and intrafibrillar mineralization, respectively. This in vitro mineralization process reproduced the mineral nodules mediating in vivo extrafibrillar mineralization and provided key insights into a possible mechanism of biomineralization by which in vivo intrafibrillar mineralization could be induced by ACP nanoparticles released from MVs. Also, the PEG-S-ACP/micelle model provides a promising methodology to prepare mineralized collagen scaffolds for repairing bone defects in bone tissue engineering.     

Nearest Neighbor Classification Rule for Mixtures of Discrete and Continuous Random Variables

In this paper very simple nonparametric classification rule for mixtures of discrete and continuous random variables is described. It is based on the method of nearest neighbor proposed by Cover and Hart (1967). The bounds on the limit of the nearest neighbor rule risks are given. Both lower and upper bound depend on the Bayes risk and the loss function. Finally the method is compared with other existing methods on some practical data set.     

An in-depth Comparison of the Pediatric and Adult Urinary N-glycomes

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Highlights

• •N-glycan patterns are distinct in pediatric and adult urine.
• •Sex differences of N-glycans are much larger in adults.
• •Pediatric urine has almost no sex differences in N-glycan levels.
• •In adults, the majority of N-glycans were more abundant in males.

     

Application of fluorescence internal transcribed spacer-PCR (f-ITS) for the cluster analysis of bacteria isolated from air and deteriorated fresco surfaces

The aim of this work was the evaluation of fluorescence ITS-PCR (f-ITS) as a molecular tool to analyze the microbial community involved in the biodeterioration of cultural heritage surfaces. As a case study we analyzed by f-ITS ninety-two bacterial strains isolated from a medieval fresco and the surrounding air environment. The internal transcribed spacer between the 16S and 23S rRNA genes was amplified, and then the fluorescently labeled PCR products were separated by capillary electrophoresis. Bacterial strains were identified by 16S rDNA sequencing. The f-ITS electropherograms showed different profiles coherent with the affiliation of the strains at the genus and species levels. Among the isolates obtained from the fresco surface, those belonging to the genus Bacillus were the most prevailing exhibiting 8 different f-ITS profiles. The airborne bacilli exhibited only 2 of these 8 profiles. Staphylococcus were mostly isolated from air and produced 4 different profiles. Pseudomonas isolates presented 3 different profiles, and one of them was typical of Pseudomonas putida. Members of the other genera produced their distinctive profiles. Our results show that f-ITS is a promising molecular tool for the rapid selection and clustering of strains isolated from different sources.     

Association between FABP2 Ala54Thr polymorphisms and type 2 diabetes mellitus risk: a HuGE Review and Meta‐Analysis

Many studies have examined the association between the FABP2 (rs1799883) Ala54Thr gene polymorphism and type 2 diabetes mellitus risk (T2DM) in various populations, but their results have been inconsistent. To assess this relationship more precisely, A HuGE review and meta‐analysis were performed. The PubMed and CNKI database was searched for case‐control studies published up to April 2014. Data were extracted and pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated. Ultimately, 13 studies, comprising 2020 T2DM cases and 2910 controls were included. Overall, for the Thr carriers (Ala/Thr and Thr/Thr) versus the wild‐type homozygotes (Ala/Ala), the pooled OR was 1.18 (95% CI = 1.04–1.34, P = 0.062 for heterogeneity), for Thr/Thr versus Ala/Ala the pooled OR was 1.17 (95% CI = 1.05–1.41 P = 0.087 for heterogeneity). In the stratified analysis by ethnicity, the significantly risks were found among Asians but not Caucasians. This meta‐analysis suggests that the FABP2 (rs1799883) Ala54Thr polymorphisms are associated with increased susceptibility to T2DM risk among Asians but not Caucasians.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.