Gateway RFP-fusion vectors for high-throughput functional analysis of genes

There is an increasing demand for high throughput (HTP) methods for gene analysis on a genome-wide scale. However, the current repertoire of HTP detection methodologies allows only a limited range of cellular phenotypes to be studied. We have constructed two HTP-optimized expression vectors generated from the red fluorescent reporter protein (RFP) gene. These vectors produce RFP-tagged target proteins in a multiple expression system using gateway cloning technology (GCT). The RFP tag was fused with the cloned genes, thereby allowing us localize the expressed proteins in mammalian cells. The effectiveness of the vectors was evaluated using an HTP-screening system. Sixty representative human C2 domains were tagged with RFP and overexpressed in HiB5 neuronal progenitor cells, and we studied in detail two C2 domains that promoted the neuronal differentiation of HiB5 cells. Our results show that the two vectors developed in this study are useful for functional gene analysis using an HTP-screening system on a genome-wide scale.We appreciate the helpful advice of Dr. Tobias Meyer and Dr. Won Do Heo of Stanford University in the construction of the set of entry clones of human C2 domains. This work was supported by a grant from the Basic Research Program of the Korea Science and Engineering Foundation (R01-2002- 000-00128-0), and a Korea Research Foundation Grant (KRF- 2006-005-J04204)

Tristetraprolin inhibits the growth of human glioma cells through downregulation of urokinase plasminogen activator/urokinase plasminogen activator receptor mRNAs

Urokinase plasminogen activator (uPA) and urokinase plasminogen activator receptor (uPAR) play a major role in the infiltrative growth of glioblastoma. Downregulatoion of the uPA and uPAR has been reported to inhibit the growth glioblastoma. Here, we demonstrate that tristetraprolin (TTP) inhibits the growth of U87MG human glioma cells through downregulation of uPA and uPAR. Our results show that expression level of TTP is inversely correlated with those of uPA and uPAR in human glioma cells and tissues. TTP binds to the AU-rich elements within the 3' untranslated regions of uPA and uPAR and overexpression of TTP decreased the expression of uPA and uPAR through enhancing the degradation of their mRNAs. In addition, overexpression of TTP inhibited the growth and invasion of U87MG cells. Our findings implicate that TTP can be used as a promising therapeutic target to treat human glioma

The use of plants in the traditional management of diabetes in Nigeria: Pharmacological and toxicological considerations

Ethnopharmacological relevance: The prevalence of diabetes is on a steady increase worldwide and it is now identified as one of the main threats to human health in the 21st century. In Nigeria, the use of herbal medicine alone or alongside prescription drugs for its management is quite common. We hereby carry out a review of medicinal plants traditionally used for diabetes management in Nigeria. Based on the available evidence on the species׳ pharmacology and safety, we highlight ways in which their therapeutic potential can be properly harnessed for possible integration into the country׳s healthcare system. Materials and methods: Ethnobotanical information was obtained from a literature search of electronic databases such as Google Scholar, Pubmed and Scopus up to 2013 for publications on medicinal plants used in diabetes management, in which the place of use and/or sample collection was identified as Nigeria. ‘Diabetes’ and ‘Nigeria’ were used as keywords for the primary searches; and then ‘Plant name – accepted or synonyms’, ‘Constituents’, ‘Drug interaction’ and/or ‘Toxicity’ for the secondary searches. Results: The hypoglycemic effect of over a hundred out of the 115 plants reviewed in this paper is backed by preclinical experimental evidence, either in vivo or in vitro. One-third of the plants have been studied for their mechanism of action, while isolation of the bioactive constituent(s) has been accomplished for twenty three plants. Some plants showed specific organ toxicity, mostly nephrotoxic or hepatotoxic, with direct effects on the levels of some liver function enzymes. Twenty eight plants have been identified as in vitro modulators of P-glycoprotein and/or one or more of the cytochrome P450 enzymes, while eleven plants altered the levels of phase 2 metabolic enzymes, chiefly glutathione, with the potential to alter the pharmacokinetics of co-administered drugs. Conclusion: This review, therefore, provides a useful resource to enable a thorough assessment of the profile of plants used in diabetes management so as to ensure a more rational use. By anticipating potential toxicities or possible herb–drug interactions, significant risks which would otherwise represent a burden on the country׳s healthcare system can be avoided

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe