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ABSTRACT: To discuss the clinic effect and surgical experience of complicated deflection of nasal septum with endoscopic surgery.
Fifty-two patients with complicated deflection of nasal septum were diagnosed by nasal endoscopy and CT scan,such as posterior segment deflection, huge processus spinosus, multi-abnormal curvature, nasal septum fracture dislocation and second operation for deflection of nasal septum. All case were followed-up for six months,
All case had satisfactory surgical results. All had an unobstructed nasal cavity and no perforation of nasal septum, nasal synechia and bridge of nose collapsed.
Endoscopic surgery in complicated deflection of nasal septum have the good advantages of vision and accuracy. So the endoscopic surgery is a minimally invasive surgery and benefit to the nasal physiological function.
Lin chuang er bi yan hou ke za zhi = Journal of clinical otorhinolaryngology 04/2012; 26(8):367-9.
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ABSTRACT: Androgen receptor (AR) is a hormone-activated transcription factor that plays important roles in prostate development and function, as well as malignant transformation. The downstream pathways of AR, however, are incompletely understood. AR has been primarily known as a transcriptional activator inducing prostate-specific gene expression. Through integrative analysis of genome-wide AR occupancy and androgen-regulated gene expression, here we report AR as a globally acting transcriptional repressor. This repression is mediated by androgen-responsive elements (ARE) and dictated by Polycomb group protein EZH2 and repressive chromatin remodeling. In embryonic stem cells, AR-repressed genes are occupied by EZH2 and harbor bivalent H3K4me3 and H3K27me3 modifications that are characteristic of differentiation regulators, the silencing of which maintains the undifferentiated state. Concordantly, these genes are silenced in castration-resistant prostate cancer rendering a stem cell-like lack of differentiation and tumor progression. Collectively, our data reveal an unexpected role of AR as a transcriptional repressor inhibiting non-prostatic differentiation and, upon excessive signaling, resulting in cancerous dedifferentiation.
Genome Research 12/2011; 22(2):322-31. · 13.61 Impact Factor
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ABSTRACT: RNA sequencing (RNA-Seq) is a powerful new technology for mapping and quantifying transcriptomes using ultra high-throughput next-generation sequencing technologies. Using deep sequencing, gene expression levels of all transcripts including novel ones can be quantified digitally. Although extremely promising, the massive amounts of data generated by RNA-Seq, substantial biases and uncertainty in short read alignment pose challenges for data analysis. In particular, large base-specific variation and between-base dependence make simple approaches, such as those that use averaging to normalize RNA-Seq data and quantify gene expressions, ineffective.
In this study, we propose a Poisson mixed-effects (POME) model to characterize base-level read coverage within each transcript. The underlying expression level is included as a key parameter in this model. Since the proposed model is capable of incorporating base-specific variation as well as between-base dependence that affect read coverage profile throughout the transcript, it can lead to improved quantification of the true underlying expression level. Availability and implementation: POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html.
yuzhu@purdue.edu; zhaohui.qin@emory.edu
Supplementary data are available at Bioinformatics online.
Bioinformatics 11/2011; 28(1):63-8. · 5.47 Impact Factor
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Jung H Kim,
Saravana M Dhanasekaran,
John R Prensner,
Xuhong Cao,
Daniel Robinson,
Shanker Kalyana-Sundaram,
Christina Huang,
Sunita Shankar,
Xiaojun Jing,
Matthew Iyer, [......],
Lee Sam,
Catherine Grasso,
Christopher A Maher,
Nallasivam Palanisamy,
Rohit Mehra,
Hal D Kominsky,
Javed Siddiqui,
Jindan Yu,
Zhaohui S Qin,
Arul M Chinnaiyan
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ABSTRACT: Beginning with precursor lesions, aberrant DNA methylation marks the entire spectrum of prostate cancer progression. We mapped the global DNA methylation patterns in select prostate tissues and cell lines using MethylPlex-next-generation sequencing (M-NGS). Hidden Markov model-based next-generation sequence analysis identified ∼68,000 methylated regions per sample. While global CpG island (CGI) methylation was not differential between benign adjacent and cancer samples, overall promoter CGI methylation significantly increased from ~12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues, respectively (P-value < 2 × 10(-16)). We found distinct patterns of promoter methylation around transcription start sites, where methylation occurred not only on the CGIs, but also on flanking regions and CGI sparse promoters. Among the 6691 methylated promoters in prostate tissues, 2481 differentially methylated regions (DMRs) are cancer-specific, including numerous novel DMRs. A novel cancer-specific DMR in the WFDC2 promoter showed frequent methylation in cancer (17/22 tissues, 6/6 cell lines), but not in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested an epigenetic mechanism for alternate transcription start site utilization, and these modifications segregated into distinct regions when present on the same promoter. Finally, we observed differences in repeat element methylation, particularly LINE-1, between ERG gene fusion-positive and -negative cancers, and we confirmed this observation using pyrosequencing on a tissue panel. This comprehensive methylome map will further our understanding of epigenetic regulation in prostate cancer progression.
Genome Research 07/2011; 21(7):1028-41. · 13.61 Impact Factor
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Jindan Yu,
Jianjun Yu,
Ram-Shankar Mani,
Qi Cao,
Chad J Brenner,
Xuhong Cao,
Xiaoju Wang,
Longtao Wu,
James Li, Ming Hu, [......],
Sunita Shankar,
Yong Li,
Saravana M Dhanasekaran,
Roger Morey,
Terrence Barrette,
Robert J Lonigro,
Scott A Tomlins,
Sooryanarayana Varambally,
Zhaohui S Qin,
Arul M Chinnaiyan
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ABSTRACT: Chromosomal rearrangements fusing the androgen-regulated gene TMPRSS2 to the oncogenic ETS transcription factor ERG occur in approximately 50% of prostate cancers, but how the fusion products regulate prostate cancer remains unclear. Using chromatin immunoprecipitation coupled with massively parallel sequencing, we found that ERG disrupts androgen receptor (AR) signaling by inhibiting AR expression, binding to and inhibiting AR activity at gene-specific loci, and inducing repressive epigenetic programs via direct activation of the H3K27 methyltransferase EZH2, a Polycomb group protein. These findings provide a working model in which TMPRSS2-ERG plays a critical role in cancer progression by disrupting lineage-specific differentiation of the prostate and potentiating the EZH2-mediated dedifferentiation program.
Cancer cell 05/2010; 17(5):443-54. · 25.29 Impact Factor
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ABSTRACT: Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein-DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic 'greedy' search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation.
Nucleic Acids Research 04/2010; 38(7):2154-67. · 8.03 Impact Factor
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ABSTRACT: Protein-DNA interaction constitutes a basic mechanism for the genetic regulation of target gene expression. Deciphering this mechanism has been a daunting task due to the difficulty in characterizing protein-bound DNA on a large scale. A powerful technique has recently emerged that couples chromatin immunoprecipitation (ChIP) with next-generation sequencing, (ChIP-Seq). This technique provides a direct survey of the cistrom of transcription factors and other chromatin-associated proteins. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed to analyze the massive amount of data generated by this method.
Here we introduce HPeak, a Hidden Markov model (HMM)-based Peak-finding algorithm for analyzing ChIP-Seq data to identify protein-interacting genomic regions. In contrast to the majority of available ChIP-Seq analysis software packages, HPeak is a model-based approach allowing for rigorous statistical inference. This approach enables HPeak to accurately infer genomic regions enriched with sequence reads by assuming realistic probability distributions, in conjunction with a novel weighting scheme on the sequencing read coverage.
Using biologically relevant data collections, we found that HPeak showed a higher prevalence of the expected transcription factor binding motifs in ChIP-enriched sequences relative to the control sequences when compared to other currently available ChIP-Seq analysis approaches. Additionally, in comparison to the ChIP-chip assay, ChIP-Seq provides higher resolution along with improved sensitivity and specificity of binding site detection. Additional file and the HPeak program are freely available at http://www.sph.umich.edu/csg/qin/HPeak.
BMC Bioinformatics 01/2010; 11:369. · 2.75 Impact Factor
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ABSTRACT: In microarray gene expression data analysis, it is often of interest to identify genes that share similar expression profiles with a particular gene such as a key regulatory protein. Multiple studies have been conducted using various correlation measures to identify co-expressed genes. While working well for small datasets, the heterogeneity introduced from increased sample size inevitably reduces the sensitivity and specificity of these approaches. This is because most co-expression relationships do not extend to all experimental conditions. With the rapid increase in the size of microarray datasets, identifying functionally related genes from large and diverse microarray gene expression datasets is a key challenge. We develop a model-based gene expression query algorithm built under the Bayesian model selection framework. It is capable of detecting co-expression profiles under a subset of samples/experimental conditions. In addition, it allows linearly transformed expression patterns to be recognized and is robust against sporadic outliers in the data. Both features are critically important for increasing the power of identifying co-expressed genes in large scale gene expression datasets. Our simulation studies suggest that this method outperforms existing correlation coefficients or mutual information-based query tools. When we apply this new method to the Escherichia coli microarray compendium data, it identifies a majority of known regulons as well as novel potential target genes of numerous key transcription factors.
PLoS ONE 02/2009; 4(2):e4495. · 4.09 Impact Factor
