Motif-guided sparse decomposition of gene expression data for regulatory module identification

Inhibition of GPR137 expression reduces the proliferation and colony formation of malignant glioma cells

GPR137 are ubiquitously expressed in the central nervous system. However, the role o f GPR137 in human malignant glioma is still poorly known. In the present study, we firstly detected the expression of GPR137 in 29 human glioma tissue specimens by immunohistochemistry and in 5 malignant glioma cell lines by quantitative RT-PCR. The expression of GPR137 was much stronger in high-grade gliomas than in low-grade gliomas. Lentivirus-mediated small interfering RNAs (siRNAs) were employed to knock down GPR137 expression in glioma cells. Inhibition of GPR137 expression by RNAi significantly inhibited the proliferation and colony-forming capacity of U251, A172 and U373 cells. Moreover, flow cytometry analysis showed that knockdown of GPR137 led to the cell-cycle arrest at the S phase. Our results indicated that GPR137 is involved in the progression of human glioma, suggesting GPR137 as a potential oncogene of glioma cells.

mAPC-GibbsOS: an integrated approach for robust identification of gene regulatory networks

Background

Identification of cooperative gene regulatory network is an important topic for biological study especially in cancer research. Traditional approaches suffer from large noise in gene expression data and false positive connections in motif binding data; they also fail to identify the modularized structure of gene regulatory network. Methods that are capable of revealing underlying modularized structure and robust to noise and false positives are needed to be developed.

Results

We proposed and developed an integrated approach to identify gene regulatory networks, which consists of a novel clustering method (namely motif-guided affinity propagation clustering (mAPC)) and a sampling based method (called Gibbs sampler based on outlier sum statistic (GibbsOS)). mAPC is used in the first step to obtain co-regulated gene modules by clustering genes with a similarity measurement taking into account both gene expression data and binding motif information. This clustering method can reduce the noise effect from microarray data to obtain modularized gene clusters. However, due to many false positives in motif binding data, some genes not regulated by certain transcription factors (TFs) will be falsely clustered with true target genes. To overcome this problem, GibbsOS is applied in the second step to refine each cluster for the identification of true target genes. In order to evaluate the performance of the proposed method, we generated simulation data under different signal-to-noise ratios and false positive ratios to test the method. The experimental results show an improved accuracy in terms of clustering and transcription factor identification. Moreover, an improved performance is demonstrated in target gene identification as compared with GibbsOS. Finally, we applied the proposed method to two breast cancer patient datasets to identify cooperative transcriptional regulatory networks associated with recurrence of breast cancer, as supported by their functional annotations.

Conclusions

We have developed a two-step approach for gene regulatory network identification, featuring an integrated method to identify modularized regulatory structures and refine their target genes subsequently. Simulation studies have shown the robustness of the method against noise in gene expression data and false positives in motif binding data. The proposed method has been applied to two breast cancer gene expression datasets to infer the hidden regulation mechanisms. The experimental results demonstrate the efficacy of the method in identifying key regulatory networks related to the progression and recurrence of breast cancer.

Reconstruction of transcriptional regulatory networks by stability-based network component analysis

Reliable inference of transcription regulatory networks is a challenging task in computational biology. Network component analysis (NCA) has become a powerful scheme to uncover regulatory networks behind complex biological processes. However, the performance of NCA is impaired by the high rate of false connections in binding information. In this paper, we integrate stability analysis with NCA to form a novel scheme, namely stability-based NCA (sNCA), for regulatory network identification. The method mainly addresses the inconsistency between gene expression data and binding motif information. Small perturbations are introduced to prior regulatory network, and the distance among multiple estimated transcript factor (TF) activities is computed to reflect the stability for each TF's binding network. For target gene identification, multivariate regression and t-statistic are used to calculate the significance for each TF-gene connection. Simulation studies are conducted and the experimental results show that sNCA can achieve an improved and robust performance in TF identification as compared to NCA. The approach for target gene identification is also demonstrated to be suitable for identifying true connections between TFs and their target genes. Furthermore, we have successfully applied sNCA to breast cancer data to uncover the role of TFs in regulating endocrine resistance in breast cancer.

Identifying context-specific transcription factor targets from prior knowledge and gene expression data

Numerous methodologies, assays, and databases presently provide candidate targets of transcription factors (TFs). However, TFs rarely regulate their targets universally. The context of activation of a TF can change the transcriptional response of targets. Direct multiple regulation typical to mammalian genes complicates direct inference of TF targets from gene expression data. We present a novel statistic that infers context-specific TF regulation based upon the CoGAPS algorithm, which infers overlapping gene expression patterns resulting from coregulation. Numerical experiments with simulated data showed that this statistic correctly inferred targets that are common to multiple TFs, except in cases where the signal from a TF is negligible relative to noise level and signal from other TFs. The statistic is robust to moderate levels of error in the simulated gene sets, identifying fewer false positives than false negatives. Significantly, the regulatory statistic refines the number of TF targets relevant to cell signaling in gastrointestinal stromal tumors (GIST) to genes consistent with the phosphorylation patterns of TFs identified in previous studies. As formulated, the proposed regulatory statistic has wide applicability to inferring set membership in integrated datasets. This statistic could be naturally extended to account for prior probabilities of set membership or to add candidate gene targets.

Cancer Gene Expression Data Analysis Using Rough Based Symmetrical Clustering

Identification of cancer subtypes is the central goal in the cancer gene expression data analysis. Modified symmetry-based clustering is an unsupervised learning technique for detecting symmetrical convex or non-convex shaped clusters. To enable fast automatic clustering of cancer tissues (samples), in this chapter, the authors propose a rough set based hybrid approach for modified symmetry-based clustering algorithm. A natural basis for analyzing gene expression data using the symmetry-based algorithm is to group together genes with similar symmetrical patterns of microarray expressions. Rough-set theory helps in faster convergence and initial automatic optimal classification, thereby solving the problem of unknown knowledge of number of clusters in gene expression measurement data. For rough-set-theoretic decision rule generation, each cluster is classified using heuristically searched optimal reducts to overcome overlapping cluster problem. The rough modified symmetry-based clustering algorithm is compared with another newly implemented rough-improved symmetry-based clustering algorithm and existing K-Means algorithm over five benchmark cancer gene expression data sets, to demonstrate its superiority in terms of validity. The statistical analyses are also performed to establish the significance of this rough modified symmetry-based clustering approach.

Antiestrogen Resistance and the Application of Systems Biology

Understanding the molecular changes that drive an acquired antiestrogen resistance phenotype is of major clinical relevance. Previous methodologies for addressing this question have taken a single gene/pathway approach and the resulting gains have been limited in terms of their clinical impact. Recent systems biology approaches allow for the integration of data from high throughput "-omics" technologies. We highlight recent advances in the field of antiestrogen resistance with a focus on transcriptomics, proteomics and methylomics.

High consistency between replicate 454 pyrosequencing analyses of ectomycorrhizal plant root samples

In this methodological study, we compare 454 sequencing and a conventional cloning and Sanger sequencing approach in their ability to characterize fungal communities PCR amplified from four root systems of the ectomycorrhizal plant Bistorta vivipara. To examine variation introduced by stochastic processes during the laboratory work, we replicated all analyses using two independently obtained DNA extractions from the same root systems. The ITS1 region was used as DNA barcode and the sequences were clustered into OTUs as proxies for species using single linkage clustering (BLASTC: lust) and 97% sequence similarity cut-off. A relatively low overlap in fungal OTUs was observed between the 454 and the clone library datasets - even among the most abundant OTUs. In a non-metric multidimensional scaling analysis, the samples grouped more according to methodology compared to plant. Some OTUs frequently detected by 454, most notably those OTUs with taxonomic affinity to Glomales, were not detected in the Sanger dataset. Likewise, a few OTUs, including Cenococcum sp., only appeared in the clone libraries. Surprisingly, we observed a significant relationship between GC/AT content of the OTUs and their proportional abundances in the 454 versus the clone library datasets. Reassuringly, a very good consistency in OTU recovery was observed between replicate runs of both sequencing methods. This indicates that stochastic processes had little impact when applying the same sequencing technique on replicate samples.

Matrix Factorization for Transcriptional Regulatory Network Inference

Inference of Transcriptional Regulatory Networks (TRNs) provides insight into the mechanisms driving biological systems, especially mammalian development and disease. Many techniques have been developed for TRN estimation from indirect biochemical measurements. Although successful when initially tested in model organisms, these regulatory models often fail when applied to data from multicellular organisms where multiple regulation and gene reuse increase dramatically. Non-negative matrix factorization techniques were initially introduced to find non-orthogonal patterns in data, making them ideal techniques for inference in cases of multiple regulation. We review these techniques and their application to TRN analysis.