Project description:A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor alpha (ERalpha) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A). RESULTS: The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC), Student's t-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete variable (X), it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. CONCLUSION: CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the association predicted by CID are applicable to the construction of transcriptional regulatory networks. This study shows how information from different data sources and learning algorithms can be integrated to investigate whether relevant regulatory mechanisms identified in cell models can also be partially re-identified in clinical samples of breast cancers. AVAILABILITY: the implementation of CID in R codes can be freely downloaded from (http://homepage.ntu.edu.tw/~lyliu/BC/).

Project description:The centromere-specific Histone H3-variant CENH3 (also known as CENP-A) is considered to be an epigenetic mark for establishment and propagation of centromere identity. Pulse-induction of CENH3 (Drosophila CID) in Schneider S2 cells incorporates into noncentromeric regions and generates CID islands that resist clearing from chromosome arms for multiple cell generations. We demonstrate that CID islands represent functional ectopic kinetochores, which are non-randomly distributed on the chromosome and display a preferential localization near telomeres and pericentric heterochromatin in transcriptionally silent, intergenic chromatin domains. Although overexpression of heterochromatin protein 1 (HP1) or increasing Histone acetylation interferes with CID islands formation on a global scale, induction of a locally defined region of synthetic heterochromatin by targeting HP1-LacI fusions to stably integrated Lac Operator arrays produces a proximal hotspot for CID islands formation. These data suggest that the characteristics of regions bordering heterochromatin promote de novo kinetochore assembly and thereby contribute to centromere identity. ArrayExpress Release Date: 2011-07-15 Person Roles: submitter Person Last Name: Diehl Person First Name: Sarah Person Mid Initials: Person Email: diehl@immunbio.mpg.de Person Phone: (+49) 761 5108 795 Person Address: Stuebeweg 51, 79108 Freiburg im Breisgau, Germany Person Affiliation: Max-Planck-Institute for Immunobiology and Epigenetics Person Roles: investigator Person Last Name: Heun Person First Name: Patrick Person Mid Initials: Person Email: heun@immunbio.mpg.de Person Phone: (+49) 761 5108 717 Person Address: Stuebeweg 51, 79108 Freiburg im Breisgau, Germany Person Affiliation: Max-Planck-Institute for Immunobiology and Epigenetics Publication Title: Heterochromatin boundaries are hotspots for de novo kinetochore formation. Publication Author List: Agata Olszak, Dominic van Essen, Antonio J. Pereira, Sarah Diehl, Thomas Manke, Helder Maiato, Simona Saccani and Patrick Heun

Project description:A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor alpha (ERalpha) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A). RESULTS: The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC), Student's t-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete variable (X), it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. CONCLUSION: CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the association predicted by CID are applicable to the construction of transcriptional regulatory networks. This study shows how information from different data sources and learning algorithms can be integrated to investigate whether relevant regulatory mechanisms identified in cell models can also be partially re-identified in clinical samples of breast cancers. AVAILABILITY: the implementation of CID in R codes can be freely downloaded from (http://homepage.ntu.edu.tw/~lyliu/BC/). Experiment Overall Design: Total 48 clinical arrays (48A) used in this study can be found in GSE9309. We designed the experiments using a given breast cancer population with clear status of estrogen receptor alpha (ER), which were confirmed by immunochemical staining (If Â³10% immunopositive stain is found at tumor section, we designate it as ER(+). Otherwise, it is ER(-). ) in this study. 48A consist of 36A with positive in ER status and of 12A with negative in ER status.

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