PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 16083299

Parallel profiling of active transcription factors using an oligonucleotide array-based transcription factor assay (OATFA).

Wei Shao·Hua-Jiang Wei·Ji-Ying Qiao·Yong-Chao Zhao·Yi-Min Sun·Yu-Xiang Zhou·Jing Cheng
View original source ↗Export metadata ↓

Abstract

Cell/tissue-specific gene expression are tightly regulated by various combinations of multiple transcription factors (TFs). Here, we present an oligonucleotide array-based transcription factor assay (OATFA), which allows the simultaneous assay of multiple active TFs. In this proof-of-principle work, both purified TFs and cell extracts were analyzed using OATFA and further antibody-based validation confirmed the chip data. This method could simplify the assay of multiple TFs and may facilitate high-throughput profiling of large numbers of TFs.

Explore related subjects

Biological AssayCell NucleusHeLa CellsHumansOligonucleotide Array Sequence AnalysisReproducibility of ResultsTranscription FactorsJournal ArticleResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wei Shao, Hua-Jiang Wei, Ji-Ying Qiao, Yong-Chao Zhao, Yi-Min Sun, Yu-Xiang Zhou, Jing Cheng. Parallel profiling of active transcription factors using an oligonucleotide array-based transcription factor assay (OATFA).. https://doi.org/10.1021/pr050053l

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

The statistical performance of an MCF-7 cell culture assay evaluated using generalized linear mixed models and a score test.

Biological assays often utilize experimental designs where observations are replicated at multiple levels, and where each level represents a separate component of the assay's overall variance. Statistical analysis of such data usually ignores these design effects, whereas more sophisticated methods would improve the statistical power of assays. This report evaluates the statistical performance of an in vitro MCF-7 cell proliferation assay (E-SCREEN) by identifying the optimal generalized linear mixed model (GLMM) that accurately represents the assay's experimental design and variance components. Our statistical assessment found that 17beta-oestradiol cell culture assay data were best modelled with a GLMM configured with a reciprocal link function, a gamma error distribution, and three sources of design variation: plate-to-plate; well-to-well, and the interaction between plate-to-plate variation and dose. The gamma-distributed random error of the assay was estimated to have a coefficient of variation (COV) = 3.2 per cent, and a variance component score test described by X. Lin found that each of the three variance components were statistically significant. The optimal GLMM also confirmed the estrogenicity of five weakly oestrogenic polychlorinated biphenyls (PCBs 17, 49, 66, 74, and 128). Based on information criteria, the optimal gamma GLMM consistently out-performed equivalent naive normal and log-normal linear models, both with and without random effects terms. Because the gamma GLMM was by far the best model on conceptual and empirical grounds, and requires only trivially more effort to use, we encourage its use and suggest that naive models be avoided when possible.

Biological Assay↗
PubMed2007

Reactivation of Kaposi's sarcoma-associated herpesvirus by natural products from Kaposi's sarcoma endemic regions.

Kaposi's sarcoma (KS) and its causative agent, Kaposi's sarcoma associated herpesvirus (KSHV/HHV-8), a gamma2 herpesvirus, have distinctive geographical distributions that are largely unexplained. We propose the "oncoweed" hypothesis to explain these differences, namely that environmental cofactors present in KS endemic regions cause frequent reactivation of KSHV in infected subjects, leading to increased viral shedding and transmission leading to increased prevalence of KSHV infection as well as high viral load levels and antibody titers. Reactivation also plays a role in the pathogenesis of KSHV-associated malignancies. To test this hypothesis, we employed an in vitro KSHV reactivation assay that measured increases in KSHV viral load in KSHV infected primary effusion lymphoma (PEL) cells and screened aqueous natural product extracts from KS endemic regions. Of 4,842 extracts from 38 countries, 184 (5%) caused KSHV reactivation. Extracts that caused reactivation came from a wide variety of plant families, and extracts from Africa, where KSHV is highly prevalent, caused the greatest level of reactivation. Time course experiments were performed using 28 extracts that caused the highest levels of reactivation. The specificity of the effects on viral replication was examined using transcriptional profiling of all viral mRNAs. The array data indicated that the natural extracts caused an ordered cascade of lytic replication similar to that seen after induction with synthetic activators. These in vitro data provide support for the "oncoweed" hypothesis by demonstrating basic biological plausibility.

Biological Assay↗
PubMed2007

Techniques for radiation measurements: microdosimetry and dosimetry.

Experimental microdosimetry is concerned with the determination of radiation quality and how this can be specified in terms of the distribution of energy deposition arising from the interaction of a radiation field with a particular target site. This paper discusses various techniques that have been developed to measure radiation energy deposition over the three orders of magnitude of site-size; nanometer, micrometer and millimetre, which radiation biology suggests is required to fully account for radiation quality. Inevitably, much of the discussion will concern the use of tissue-equivalent proportional counters and variants of this device, but other technologies that have been studied, or are under development, for their potential in experimental microdosimetry are also covered. Through an examination of some of the quantities used in radiation metrology and dosimetry the natural link with microdosimetric techniques will be shown and the particular benefits of using microdosimetric methods for dosimetry illustrated.

Biological Assay↗