Junbai Wang

Hotspots of transcription factor colocalization in the genome of Drosophila melanogaster

Regulation of gene expression is a highly complex process that requires the concerted action of many proteins, including sequence-specific transcription factors, cofactors, and chromatin proteins. In higher eukaryotes, the interplay between these proteins and their interactions with the genome still is poorly understood. We systematically mapped the in vivo binding sites of seven transcription factors with diverse physiological functions, five cofactors, and two heterochromatin proteins at ≈1-kb resolution in a 2.9 Mb region of the Drosophila melanogaster genome. Surprisingly, all tested transcription factors and cofactors show strongly overlapping localization patterns, and the genome contains many “hotspots” that are targeted by all of these proteins. Several control experiments show that the strong overlap is not an artifact of the techniques used. Colocalization hotspots are 1–5 kb in size, spaced on average by ≈50 kb, and preferentially located in regions of active transcription. We provide evidence that protein–protein interactions play a role in the hotspot association of some transcription factors. Colocalization hotspots constitute a previously uncharacterized type of feature in the genome of Drosophila, and our results provide insights into the general targeting mechanisms of transcription regulators in a higher eukaryote.

Array Comparative Genomic Hybridization Reveals Distinct DNA Copy Number Differences between Gastrointestinal Stromal Tumors and Leiomyosarcomas

Leiomyosarcomas are spindle cell tumors showing smooth muscle differentiation. Until recently, most gastrointestinal stromal tumors (GIST) were also classified as smooth muscle tumors, but now GISTs are recognized as a separate entity, defined as spindle cell and/or epithelioid tumors localized in the gastrointestinal tract. Using microarray-based comparative genomic hybridization (array CGH), we have created a detailed map of DNA copy number changes for 7 GISTs and 12 leiomyosarcomas. Considerable gains and losses of chromosomal segments were observed in both tumor types. The most frequent aberration observed in GISTs was loss of chromosomes 14 and 22, with minimal recurrent regions in 14q11.2-q32.33 (71% of the tumors) and 22q12.2-q13.31 (100%). In leiomyosarcomas, frequent loss of chromosome 10 and 13q was observed, with minimal recurrent regions in 10q21.3 (75%) and 13q14.2-q14.3 (75%). Recurrent high-level amplification of 17p13.1-p11.2 was detected in leiomyosarcomas. Expression profiling using cDNA microarrays revealed four candidate genes in this region with high expression (AURKB, SREBF1, MFAP4, and FLJ10847). Altered expression of AURKB and SREBF1 has been observed previously in other malignancies. Hierarchical clustering of all samples separated GISTs and leiomyosarcomas into two distinct clusters. Statistical analysis identified six chromosomal regions, 1p36.11-p13.1, 9q21.11-9q34.3, 14q11.2-q23.2, 14q31.3-q32.33, 15q24.3-q26.3, and 22q11.21-q13.31, which were significantly different in copy number between GISTs and leiomyosarcomas. Our results show the potential of using array comparative genomic hybridization to classify histologically similar tumors such as GISTs and leiomyosarcomas.

Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data

BackgroundUsing DNA microarrays, we have developed two novel models for tumor classification and target gene prediction. First, gene expression profiles are summarized by optimally selected Self-Organizing Maps (SOMs), followed by tumor sample classification by Fuzzy C-means clustering. Then, the prediction of marker genes is accomplished by either manual feature selection (visualizing the weighted/mean SOM component plane) or automatic feature selection (by pair-wise Fishers linear discriminant).ResultsThe proposed models were tested on four published datasets: (1) Leukemia (2) Colon cancer (3) Brain tumors and (4) NCI cancer cell lines. The models gave class prediction with markedly reduced error rates compared to other class prediction approaches, and the importance of feature selection on microarray data analysis was also emphasized.ConclusionsOur models identify marker genes with predictive potential, often better than other available methods in the literature. The models are potentially useful for medical diagnostics and may reveal some insights into cancer classification. Additionally, we illustrated two limitations in tumor classification from microarray data related to the biology underlying the data, in terms of (1) the class size of data, and (2) the internal structure of classes. These limitations are not specific for the classification models used.

High Resolution Detection and Analysis of CpG Dinucleotides Methylation Using MBD-Seq Technology

Methyl-CpG binding domain protein sequencing (MBD-seq) is widely used to survey DNA methylation patterns. However, the optimal experimental parameters for MBD-seq remain unclear and the data analysis remains challenging. In this study, we generated high depth MBD-seq data in MCF-7 cell and developed a bi-asymmetric-Laplace model (BALM) to perform data analysis. We found that optimal efficiency of MBD-seq experiments was achieved by sequencing ∼100 million unique mapped tags from a combination of 500 mM and 1000 mM salt concentration elution in MCF-7 cells. Clonal bisulfite sequencing results showed that the methylation status of each CpG dinucleotides in the tested regions was accurately detected with high resolution using the proposed model. These results demonstrated the combination of MBD-seq and BALM could serve as a useful tool to investigate DNA methylome due to its low cost, high specificity, efficiency and resolution.

Splenic marginal zone lymphoma with villous lymphocytes shows on-going immunoglobulin gene mutations.

Splenic marginal zone lymphoma (also splenic lymphoma with villous lymphocytes) is a B-cell non-Hodgkins lymphoma with a characteristic morphology and phenotype. We studied the pattern of somatic hypermutation of the rearranged immunoglobulin heavy chain genes on 23 cases and have correlated these data with survival as well as immunophenotypic and genetic characteristics of the cases. Two-thirds of the cases show immunoglobulin gene mutations, half of which show evidence of antigen selection, whereas one-third of the cases show no significant mutations. On-going mutation, a feature characteristic of follicular lymphoma, was demonstrated in all six cases randomly selected for this analysis, including one case with a low number of mutations (<2%). No statistical significant correlation was found between immunoglobulin mutation status and clinical, immunophenotypic, or genetic characteristics. Our results demonstrate that on-going somatic hypermutation is a prominent feature of splenic marginal zone lymphoma with circulating villous lymphocytes. On-going somatic hypermutation has previously been demonstrated in extra-nodal and nodal marginal zone lymphoma. Our results indicate that marginal zone lymphomas at different anatomical localizations may derive from a similar B-cell subset.

M-CGH: Analysing microarray-based CGH experiments

BackgroundMicroarray-based comparative genomic hybridisation (array CGH) is a technique by which variation in relative copy numbers between two genomes can be analysed by competitive hybridisation to DNA microarrays. This technology has most commonly been used to detect chromosomal amplifications and deletions in cancer. Dedicated tools are needed to analyse the results of such experiments, which include appropriate visualisation, and to take into consideration the physical relation in the genome between the probes on the array.ResultsM-CGH is a MATLAB toolbox with a graphical user interface designed specifically for the analysis of array CGH experiments, with multiple approaches to ratio normalization. Specifically, the distributions of three classes of DNA copy numbers (gains, normal and losses) can be estimated using a maximum likelihood method. Amplicon boundaries are computed by either the fuzzy K-nearest neighbour method or a wavelet approach. The program also allows linking each genomic clone with the corresponding genomic information in the Ensembl database http://www.ensembl.org.ConclusionsM-CGH, which encompasses the basic tools needed for analysing array CGH experiments, is freely available for academics http://www.uio.no/~junbaiw/mcgh, and does not require any other MATLAB toolbox.

MGraph: graphical models for microarray data analysis

UNLABELLED This paper introduces a MATLAB toolbox, MGraph, which applies graphical models as a natural environment to formulate and solve problems in microarray data analysis. MGraph with its graphical interface allows the user to predict genetic regulatory networks by a graphical gaussian model (GGM), and to quantify the effects of different experimental treatment conditions on gene expression profiles by a graphical log-linear model (GLM). The power of graphical models was explored and illustrated through two example applications. First, four MAPK pathways in yeast were meaningfully reconstructed through GGM. Second, GLM was used to quantify the contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. This application may provide a valuable aid in the prediction of genetic regulatory networks, as well as in investigations of various experimental conditions that affect global gene expression profiles. AVAILABILITY The MATLAB program MGraph is freely available at http://www.uio.no/~junbaiw/mgraph/mgraph.html for academics.

Clinical Relevance of Multidrug Resistance Gene Expression in Ovarian Serous Carcinoma Effusions

The presence of tumor cells in effusions within serosal cavities is a clinical manifestation of advanced-stage cancer and is generally associated with poor survival. Identifying molecular targets may help to design efficient treatments to eradicate these aggressive cancer cells and improve patient survival. Using a state-of-the-art TaqMan-based qRT-PCR assay, we investigated the multidrug resistance (MDR) transcriptome of 32 unpaired ovarian serous carcinoma effusion samples obtained at diagnosis or at disease recurrence following chemotherapy. MDR genes were selected a priori based on an extensive curation of the literature published during the last three decades. We found three gene signatures with a statistically significant correlation with overall survival (OS), response to treatment [complete response (CR) vs other], and progression free survival (PFS). The median log-rank p-values for the signatures were 0.023, 0.034, and 0.008, respectively. No correlation was found with residual tumor status after cytoreductive surgery, treatment (with or without chemotherapy) and stage defined according to the International Federation of Gynecology and Obstetrics. Further analyses demonstrated that gene expression alone can effectively predict the survival outcome of women with ovarian serous carcinoma (OS, log-rank p = 0.0000; and PFS, log-rank p = 0.002). Interestingly, the signature for overall survival is the same in patients at first presentation and those who had chemotherapy and relapsed. This pilot study highlights two new gene signatures that may help in optimizing the treatment for ovarian carcinoma patients with effusions.

Interleukin-33 Drives a Proinflammatory Endothelial Activation That Selectively Targets Nonquiescent Cells

Objective—Interleukin (IL)-33 is a nuclear protein that is released from stressed or damaged cells to act as an alarmin. We investigated the effects of IL-33 on endothelial cells, using the prototype IL-1 family member, IL-1&bgr;, as a reference. Methods and Results—Human umbilical vein endothelial cells were stimulated with IL-33 or IL-1&bgr;, showing highly similar phosphorylation of signaling molecules, induction of adhesion molecules, and transcription profiles. However, intradermally injected IL-33 elicited significantly less proinflammatory endothelial activation when compared with IL-1&bgr; and led us to observe that quiescent endothelial cells (ppRblowp27high) were strikingly resistant to IL-33. Accordingly, the IL-33 receptor was preferentially expressed in nonquiescent cells of low-density cultures, corresponding to selective induction of adhesion molecules and chemokines. Multiparameter phosphoflow cytometry confirmed that signaling driven by IL-33 was stronger in nonquiescent cells. Manipulation of nuclear IL-33 expression by siRNA or adenoviral transduction revealed no functional link between nuclear, endogenous IL-33, and exogenous IL-33 responsiveness. Conclusion—In contrast to other inflammatory cytokines, IL-33 selectively targets nonquiescent endothelial cells. By this novel concept, quiescent cells may remain nonresponsive to a proinflammatory stimulus that concomitantly triggers a powerful response in cells that have been released from contact inhibition.

A new framework for identifying combinatorial regulation of transcription factors: A case study of the yeast cell cycle

By integrating heterogeneous functional genomic datasets, we have developed a new framework for detecting combinatorial control of gene expression, which includes estimating transcription factor activities using a singular value decomposition method and reducing high-dimensional input gene space by considering genomic properties of gene clusters. The prediction of cooperative gene regulation is accomplished by either Gaussian Graphical Models or Pairwise Mixed Graphical Models. The proposed framework was tested on yeast cell cycle datasets: (1) 54 known yeast cell cycle genes with 9 cell cycle regulators and (2) 676 putative yeast cell cycle genes with 9 cell cycle regulators. The new framework gave promising results on inferring TF-TF and TF-gene interactions. It also revealed several interesting mechanisms such as negatively correlated protein-protein interactions and low affinity protein-DNA interactions that may be important during the yeast cell cycle. The new framework may easily be extended to study other higher eukaryotes.