Xianghong Jasmine Zhou

Mining coherent dense subgraphs across massive biological networks for functional discovery

MOTIVATION The rapid accumulation of biological network data translates into an urgent need for computational methods for graph pattern mining. One important problem is to identify recurrent patterns across multiple networks to discover biological modules. However, existing algorithms for frequent pattern mining become very costly in time and space as the pattern sizes and network numbers increase. Currently, no efficient algorithm is available for mining recurrent patterns across large collections of genome-wide networks. RESULTS We developed a novel algorithm, CODENSE, to efficiently mine frequent coherent dense subgraphs across large numbers of massive graphs. Compared with previous methods, our approach is scalable in the number and size of the input graphs and adjustable in terms of exact or approximate pattern mining. Applying CODENSE to 39 co-expression networks derived from microarray datasets, we discovered a large number of functionally homogeneous clusters and made functional predictions for 169 uncharacterized yeast genes. AVAILABILITY http://zhoulab.usc.edu/CODENSE/

Joint Genome-Wide Profiling of miRNA and mRNA Expression in Alzheimer's Disease Cortex Reveals Altered miRNA Regulation

Although microRNAs are being extensively studied for their involvement in cancer and development, little is known about their roles in Alzheimers disease (AD). In this study, we used microarrays for the first joint profiling and analysis of miRNAs and mRNAs expression in brain cortex from AD and age-matched control subjects. These data provided the unique opportunity to study the relationship between miRNA and mRNA expression in normal and AD brains. Using a non-parametric analysis, we showed that the levels of many miRNAs can be either positively or negatively correlated with those of their target mRNAs. Comparative analysis with independent cancer datasets showed that such miRNA-mRNA expression correlations are not static, but rather context-dependent. Subsequently, we identified a large set of miRNA-mRNA associations that are changed in AD versus control, highlighting AD-specific changes in the miRNA regulatory system. Our results demonstrate a robust relationship between the levels of miRNAs and those of their targets in the brain. This has implications in the study of the molecular pathology of AD, as well as miRNA biology in general.

Dense Chromatin Activates Polycomb Repressive Complex 2 to Regulate H3 Lysine 27 Methylation

Maintaining Repression The Polycomb Repressive Complex 2 (PRC2) plays a critical role in gene silencing in metazoans, methylating histone H3 on lysine 27 (H3K27) to generate a repressive chromatin mark. The catalytic subunit E(z)/Ezh2 requires the presence of two other subunits—ESC/EED and Su(z)12—for enzyme activity. Yuan et al. (p. 971; see the Perspective by Pirrotta) show that both a fragment of the histone H3 N-terminal tail, and histone H1 stimulated PRC2 enzyme activity on poor, low-density chromatin substrates, indicating that that PRC2 is regulated by the density and compaction states of chromatin. The histone H3 fragment binds to the Su(z)12 subunit of PRC2 to stimulate E(z)/Ezh2. Local chromatin compaction preceded establishment of histone H3K27 methylation indicating how PRC2 might maintain the repressed state. The density and compaction state of chromatin directly regulates the activity of a transcription repressor protein complex. Polycomb repressive complex 2 (PRC2)–mediated histone H3 lysine 27 (H3K27) methylation is vital for Polycomb gene silencing, a classic epigenetic phenomenon that maintains transcriptional silencing throughout cell divisions. We report that PRC2 activity is regulated by the density of its substrate nucleosome arrays. Neighboring nucleosomes activate the PRC2 complex with a fragment of their H3 histones (Ala31 to Arg42). We also identified mutations on PRC2 subunit Su(z)12, which impair its binding and response to the activating peptide and its ability in establishing H3K27 trimethylation levels in vivo. In mouse embryonic stem cells, local chromatin compaction occurs before the formation of trimethylated H3K27 upon transcription cessation of the retinoic acid–regulated gene CYP26a1. We propose that PRC2 can sense the chromatin environment to exert its role in the maintenance of transcriptional states.

Functional annotation and network reconstruction through cross-platform integration of microarray data

The rapid accumulation of microarray data translates into a need for methods to effectively integrate data generated with different platforms. Here we introduce an approach, 2nd-order expression analysis, that addresses this challenge by first extracting expression patterns as meta-information from each data set (1st-order expression analysis) and then analyzing them across multiple data sets. Using yeast as a model system, we demonstrate two distinct advantages of our approach: we can identify genes of the same function yet without coexpression patterns and we can elucidate the cooperativities between transcription factors for regulatory network reconstruction by overcoming a key obstacle, namely the quantification of activities of transcription factors. Experiments reported in the literature and performed in our lab support a significant number of our predictions.

A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules

Motivation: It is well known that microRNAs (miRNAs) and genes work cooperatively to form the key part of gene regulatory networks. However, the specific functional roles of most miRNAs and their combinatorial effects in cellular processes are still unclear. The availability of multiple types of functional genomic data provides unprecedented opportunities to study the miRNA–gene regulation. A major challenge is how to integrate the diverse genomic data to identify the regulatory modules of miRNAs and genes. Results: Here we propose an effective data integration framework to identify the miRNA–gene regulatory comodules. The miRNA and gene expression profiles are jointly analyzed in a multiple non-negative matrix factorization framework, and additional network data are simultaneously integrated in a regularized manner. Meanwhile, we employ the sparsity penalties to the variables to achieve modular solutions. The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm. We apply the proposed method to integrate a set of heterogeneous data sources including the expression profiles of miRNAs and genes on 385 human ovarian cancer samples, computationally predicted miRNA–gene interactions, and gene–gene interactions. We demonstrate that the miRNAs and genes in 69% of the regulatory comodules are significantly associated. Moreover, the comodules are significantly enriched in known functional sets such as miRNA clusters, GO biological processes and KEGG pathways, respectively. Furthermore, many miRNAs and genes in the comodules are related with various cancers including ovarian cancer. Finally, we show that comodules can stratify patients (samples) into groups with significant clinical characteristics. Availability: The program and supplementary materials are available at http://zhoulab.usc.edu/SNMNMF/. Contact: xjzhou@usc.edu; zsh@amss.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Polycomb-Repressed Genes Have Permissive Enhancers that Initiate Reprogramming

Key regulatory genes, suppressed by Polycomb and H3K27me3, become active during normal differentiation and induced reprogramming. Using the well-characterized enhancer/promoter pair of MYOD1 as a model, we have identified a critical role for enhancers in reprogramming. We observed an unexpected nucleosome-depleted region (NDR) at the H3K4me1-enriched enhancer at which transcriptional regulators initially bind, leading to subsequent changes in the chromatin at the cognate promoter. Exogenous Myod1 activates its own transcription by binding first at the enhancer, leading to an NDR and transcription-permissive chromatin at the associated MYOD1 promoter. Exogenous OCT4 also binds first to the permissive MYOD1 enhancer but has a different effect on the cognate promoter, where the monovalent H3K27me3 marks are converted to the bivalent state characteristic of stem cells. Genome-wide, a high percentage of Polycomb targets are associated with putative enhancers in permissive states, suggesting that they may provide a widespread avenue for the initiation of cell-fate reprogramming.

Systematic discovery of functional modules and context-specific functional annotation of human genome

MOTIVATION The rapid accumulation of microarray datasets provides unique opportunities to perform systematic functional characterization of the human genome. We designed a graph-based approach to integrate cross-platform microarray data, and extract recurrent expression patterns. A series of microarray datasets can be modeled as a series of co-expression networks, in which we search for frequently occurring network patterns. The integrative approach provides three major advantages over the commonly used microarray analysis methods: (1) enhance signal to noise separation (2) identify functionally related genes without co-expression and (3) provide a way to predict gene functions in a context-specific way. RESULTS We integrate 65 human microarray datasets, comprising 1105 experiments and over 11 million expression measurements. We develop a data mining procedure based on frequent itemset mining and biclustering to systematically discover network patterns that recur in at least five datasets. This resulted in 143,401 potential functional modules. Subsequently, we design a network topology statistic based on graph random walk that effectively captures characteristics of a genes local functional environment. Function annotations based on this statistic are then subject to the assessment using the random forest method, combining six other attributes of the network modules. We assign 1126 functions to 895 genes, 779 known and 116 unknown, with a validation accuracy of 70%. Among our assignments, 20% genes are assigned with multiple functions based on different network environments. AVAILABILITY http://zhoulab.usc.edu/ContextAnnotation.

Discovery of multi-dimensional modules by integrative analysis of cancer genomic data

Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called ‘multi-dimensional genomic data’. Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module (md-module). Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DM, GE, and microRNA expression data of 385 ovarian cancer samples from the The Cancer Genome Atlas project. These md-modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional ‘omic’ data.

Integrative analysis of many weighted co-expression networks using tensor computation.

The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks.

An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer

BackgroundThe most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations.ResultsIn this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network.ConclusionUsing a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating co-expression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression.