Browsing by Author "Center for Computational Biology and Bioinformatics, School of Medicine"

Now showing 1 - 6 of 6
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    A dynamic time order network for time-series gene expression data analysis
    (Springer Nature, 2012) Zhang, Pengyue; Mourad, Raphaël; Xiang, Yang; Huang, Kun; Huang, Tim; Nephew, Kenneth; Liu, Yunlong; Li, Lang; Center for Computational Biology and Bioinformatics, School of Medicine
    Background: Typical analysis of time-series gene expression data such as clustering or graphical models cannot distinguish between early and later drug responsive gene targets in cancer cells. However, these genes would represent good candidate biomarkers. Results: We propose a new model - the dynamic time order network - to distinguish and connect early and later drug responsive gene targets. This network is constructed based on an integrated differential equation. Spline regression is applied for an accurate modeling of the time variation of gene expressions. Then a likelihood ratio test is implemented to infer the time order of any gene expression pair. One application of the model is the discovery of estrogen response biomarkers. For this purpose, we focused on genes whose responses are late when the breast cancer cells are treated with estradiol (E2). Conclusions: Our approach has been validated by successfully finding time order relations between genes of the cell cycle system. More notably, we found late response genes potentially interesting as biomarkers of E2 treatment.
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    A modulator based regulatory network for ERα signaling pathway
    (Springer Nature, 2012) Wu, Heng-Yi; Zheng, Pengyue; Jiang, Guanglong; Liu, Yunlong; Nephew, Kenneth P.; Huang, Tim H. M.; Li, Lang; Center for Computational Biology and Bioinformatics, School of Medicine
    Background: Estrogens control multiple functions of hormone-responsive breast cancer cells. They regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. ERα requires distinct co-regulator or modulators for efficient transcriptional regulation, and they form a regulatory network. Knowing this regulatory network will enable systematic study of the effect of ERα on breast cancer. Methods: To investigate the regulatory network of ERα and discover novel modulators of ERα functions, we proposed an analytical method based on a linear regression model to identify translational modulators and their network relationships. In the network analysis, a group of specific modulator and target genes were selected according to the functionality of modulator and the ERα binding. Network formed from targets genes with ERα binding was called ERα genomic regulatory network; while network formed from targets genes without ERα binding was called ERα non-genomic regulatory network. Considering the active or repressive function of ERα, active or repressive function of a modulator, and agonist or antagonist effect of a modulator on ERα, the ERα/modulator/target relationships were categorized into 27 classes. Results: Using the gene expression data and ERα Chip-seq data from the MCF-7 cell line, the ERα genomic/non-genomic regulatory networks were built by merging ERα/ modulator/target triplets (TF, M, T), where TF refers to the ERα, M refers to the modulator, and T refers to the target. Comparing these two networks, ERα non-genomic network has lower FDR than the genomic network. In order to validate these two networks, the same network analysis was performed in the gene expression data from the ZR-75.1 cell. The network overlap analysis between two cancer cells showed 1% overlap for the ERα genomic regulatory network, but 4% overlap for the non-genomic regulatory network. Conclusions: We proposed a novel approach to infer the ERα/modulator/target relationships, and construct the genomic/non-genomic regulatory networks in two cancer cells. We found that the non-genomic regulatory network is more reliable than the genomic regulatory network.
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    An assignment of intrinsically disordered regions of proteins based on NMR structures
    (Elsevier, 2013) Ota, Motonori; Koike, Ryotaro; Amemiya, Takayuki; Tenno, Takeshi; Romero, Pedro R.; Hiroaki, Hidekazu; Dunker, A. Keith; Fukuchi, Satoshi; Center for Computational Biology and Bioinformatics, School of Medicine
    Intrinsically disordered proteins (IDPs) do not adopt stable three-dimensional structures in physiological conditions, yet these proteins play crucial roles in biological phenomena. In most cases, intrinsic disorder manifests itself in segments or domains of an IDP, called intrinsically disordered regions (IDRs), but fully disordered IDPs also exist. Although IDRs can be detected as missing residues in protein structures determined by X-ray crystallography, no protocol has been developed to identify IDRs from structures obtained by Nuclear Magnetic Resonance (NMR). Here, we propose a computational method to assign IDRs based on NMR structures. We compared missing residues of X-ray structures with residue-wise deviations of NMR structures for identical proteins, and derived a threshold deviation that gives the best correlation of ordered and disordered regions of both structures. The obtained threshold of 3.2Å was applied to proteins whose structures were only determined by NMR, and the resulting IDRs were analyzed and compared to those of X-ray structures with no NMR counterpart in terms of sequence length, IDR fraction, protein function, cellular location, and amino acid composition, all of which suggest distinct characteristics. The structural knowledge of IDPs is still inadequate compared with that of structured proteins. Our method can collect and utilize IDRs from structures determined by NMR, potentially enhancing the understanding of IDPs.
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    D2P2: database of disordered protein predictions
    (Oxford University Press, 2013) Oates, Matt E.; Romero, Pedro; Ishida, Takashi; Ghalwash, Mohamed; Mizianty, Marcin J.; Xue, Bin; Dosztányi, Zsuzsanna; Uversky, Vladimir N.; Obradovic, Zoran; Kurgan, Lukasz; Dunker, A. Keith; Gough, Julian; Center for Computational Biology and Bioinformatics, School of Medicine
    We present the Database of Disordered Protein Prediction (D(2)P(2)), available at http://d2p2.pro (including website source code). A battery of disorder predictors and their variants, VL-XT, VSL2b, PrDOS, PV2, Espritz and IUPred, were run on all protein sequences from 1765 complete proteomes (to be updated as more genomes are completed). Integrated with these results are all of the predicted (mostly structured) SCOP domains using the SUPERFAMILY predictor. These disorder/structure annotations together enable comparison of the disorder predictors with each other and examination of the overlap between disordered predictions and SCOP domains on a large scale. D(2)P(2) will increase our understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history. The parsed data are made available in a unified format for download as flat files or SQL tables either by genome, by predictor, or for the complete set. An interactive website provides a graphical view of each protein annotated with the SCOP domains and disordered regions from all predictors overlaid (or shown as a consensus). There are statistics and tools for browsing and comparing genomes and their disorder within the context of their position on the tree of life.
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    Induced Mist1 Expression Promotes Remodeling of Mouse Pancreatic Acinar Cells
    (Elsevier, 2012) Direnzo, Daniel; Hess, David A.; Damsz, Barbara; Hallett, Judy E.; Marshall, Brett; Goswami, Chirayu; Liu, Yunlong; Deering, Tye; Macdonald, Raymond J.; Konieczny, Stephen F.; Center for Computational Biology and Bioinformatics, School of Medicine
    Background & aims: Early embryogenesis involves cell fate decisions that define the body axes and establish pools of progenitor cells. Development does not stop once lineages are specified; cells continue to undergo specific maturation events, and changes in gene expression patterns lead to their unique physiological functions. Secretory pancreatic acinar cells mature postnatally to synthesize large amounts of protein, polarize, and communicate with other cells. The transcription factor MIST1 is expressed by only secretory cells and regulates maturation events. MIST1-deficient acinar cells in mice do not establish apical-basal polarity, properly position zymogen granules, or communicate with adjacent cells, disrupting pancreatic function. We investigated whether MIST1 directly induces and maintains the mature phenotype of acinar cells. Methods: We analyzed the effects of Cre-mediated expression of Mist1 in adult Mist1-deficient (Mist1(KO)) mice. Pancreatic tissues were collected and analyzed by light and electron microscopy, immunohistochemistry, real-time polymerase chain reaction analysis, and chromatin immunoprecipitation. Primary acini were isolated from mice and analyzed in amylase secretion assays. Results: Induced expression of Mist1 in adult Mist1(KO) mice restored wild-type gene expression patterns in acinar cells. The acinar cells changed phenotypes, establishing apical-basal polarity, increasing the size of zymogen granules, reorganizing the cytoskeletal network, communicating intercellularly (by synthesizing gap junctions), and undergoing exocytosis. Conclusions: The exocrine pancreas of adult mice can be remodeled by re-expression of the transcription factor MIST1. MIST1 regulates acinar cell maturation and might be used to repair damaged pancreata in patients with pancreatic disorders.
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    Secondary Structure, a Missing Component of Sequence-Based Minimotif Definitions
    (Public Library of Science, 2012) Sargeant, David P.; Gryk, Michael R.; Maciejewski, Mark W.; Thapar, Vishal; Kundeti, Vamsi; Rajasekaran, Sanguthevar; Romero, Pedro; Dunker, Keith; Li, Shun-Cheng; Kaneko, Tomonori; Schiller, Martin R.; Center for Computational Biology and Bioinformatics, School of Medicine
    Minimotifs are short contiguous segments of proteins that have a known biological function. The hundreds of thousands of minimotifs discovered thus far are an important part of the theoretical understanding of the specificity of protein-protein interactions, posttranslational modifications, and signal transduction that occur in cells. However, a longstanding problem is that the different abstractions of the sequence definitions do not accurately capture the specificity, despite decades of effort by many labs. We present evidence that structure is an essential component of minimotif specificity, yet is not used in minimotif definitions. Our analysis of several known minimotifs as case studies, analysis of occurrences of minimotifs in structured and disordered regions of proteins, and review of the literature support a new model for minimotif definitions that includes sequence, structure, and function.