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

BACKGROUND: The 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. RESULTS: In 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. CONCLUSION: Using 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

DiseaseConnect: a comprehensive web server for mechanism-based disease–disease connections

The DiseaseConnect (http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms. Connecting diseases with similar pathological mechanisms could inspire novel strategies on the effective repositioning of existing drugs and therapies. Although there have been several studies attempting to generate disease connectivity networks, they have not yet utilized the enormous and rapidly growing public repositories of disease-related omics data and literature, two primary resources capable of providing insights into disease connections at an unprecedented level of detail. Our DiseaseConnect, the first public web server, integrates comprehensive omics and literature data, including a large amount of gene expression data, Genome-Wide Association Studies catalog, and text-mined knowledge, to discover disease–disease connectivity via common molecular mechanisms. Moreover, the clinical comorbidity data and a comprehensive compilation of known drug–disease relationships are additionally utilized for advancing the understanding of the disease landscape and for facilitating the mechanism-based development of new drug treatments

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

Bone Marrow Mononuclear Cells Up-Regulate Toll-Like Receptor Expression and Produce Inflammatory Mediators in Response to Cigarette Smoke Extract

Several reports link cigarette smoking with leukemia. However, the effects of cigarette smoke extract (CSE) on bone marrow hematopoiesis remain unknown. The objective of this study was to elucidate the direct effects of cigarette smoke on human bone marrow hematopoiesis and characterize the inflammatory process known to result from cigarette smoking. Bone marrow mononuclear cells (BMCs) from healthy individuals when exposed to CSE had significantly diminished CFU-E, BFU-E and CFU-GM. We found increased nuclear translocation of the NF-κB p65 subunit and, independently, enhanced activation of AKT and ERK1/2. Exposure of BMCs to CSE induced IL-8 and TGF-β1 production, which was dependent on NF-κB and ERK1/2, but not on AKT. CSE treatment had no effect on the release of TNF-α, IL-10, or VEGF. Finally, CSE also had a significant induction of TLR2, TLR3 and TLR4, out of which, the up-regulation of TLR2 and TLR3 was found to be dependent on ERK1/2 and NF-κB activation, but not AKT. These results indicate that CSE profoundly inhibits the growth of erythroid and granulocyte-macrophage progenitors in the bone marrow. Further, CSE modulates NF-κB- and ERK1/2-dependent responses, suggesting that cigarette smoking may impair bone marrow hematopoiesis in vivo as well as induce inflammation, two processes that proceed malignant transformation

Shifting the limits in wheat research and breeding using a fully annotated reference genome

Introduction: Wheat (Triticum aestivum L.) is the most widely cultivated crop on Earth, contributing about a fifth of the total calories consumed by humans. Consequently, wheat yields and production affect the global economy, and failed harvests can lead to social unrest. Breeders continuously strive to develop improved varieties by fine-tuning genetically complex yield and end-use quality parameters while maintaining stable yields and adapting the crop to regionally specific biotic and abiotic stresses. Rationale: Breeding efforts are limited by insufficient knowledge and understanding of wheat biology and the molecular basis of central agronomic traits. To meet the demands of human population growth, there is an urgent need for wheat research and breeding to accelerate genetic gain as well as to increase and protect wheat yield and quality traits. In other plant and animal species, access to a fully annotated and ordered genome sequence, including regulatory sequences and genome-diversity information, has promoted the development of systematic and more time-efficient approaches for the selection and understanding of important traits. Wheat has lagged behind, primarily owing to the challenges of assembling a genome that is more than five times as large as the human genome, polyploid, and complex, containing more than 85% repetitive DNA. To provide a foundation for improvement through molecular breeding, in 2005, the International Wheat Genome Sequencing Consortium set out to deliver a high-quality annotated reference genome sequence of bread wheat. Results: An annotated reference sequence representing the hexaploid bread wheat genome in the form of 21 chromosome-like sequence assemblies has now been delivered, giving access to 107,891 high-confidence genes, including their genomic context of regulatory sequences. This assembly enabled the discovery of tissue- and developmental stage–related gene coexpression networks using a transcriptome atlas representing all stages of wheat development. The dynamics of change in complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. Aspects of the future value of the annotated assembly for molecular breeding and research were exemplarily illustrated by resolving the genetic basis of a quantitative trait locus conferring resistance to abiotic stress and insect damage as well as by serving as the basis for genome editing of the flowering-time trait. Conclusion: This annotated reference sequence of wheat is a resource that can now drive disruptive innovation in wheat improvement, as this community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding. Importantly, the bioinformatics capacity developed for model-organism genomes will facilitate a better understanding of the wheat genome as a result of the high-quality chromosome-based genome assembly. By necessity, breeders work with the genome at the whole chromosome level, as each new cross involves the modification of genome-wide gene networks that control the expression of complex traits such as yield. With the annotated and ordered reference genome sequence in place, researchers and breeders can now easily access sequence-level information to precisely define the necessary changes in the genomes for breeding programs. This will be realized through the implementation of new DNA marker platforms and targeted breeding technologies, including genome editing

Transitive functional annotation by shortestpath analysis of gene expression data

Current methods for the functional analysis of microarray gene expression data make the implicit assumption that genes with similar expression profiles have similar functions in cells. However, among genes involved in the same biological pathway, not all gene pairs show high expression similarity. Here, we propose that transitive expression similarity among genes can be used as an important attribute to link genes of the same biological pathway. Based on large-scale yeast microarray expression data, we use the shortestpath analysis to identify transitive genes between two given genes from the same biological process. We find that not only functionally related genes with correlated expression profiles are identified but also those without. In the latter case, we compare our method to hierarchical clustering, and show that our method can reveal functional relationships among genes in a more precise manner. Finally, we show that our method can be used to reliably predict the function of unknown genes from known genes lying on the same shortest path. We assigned functions for 146 yeast genes that are considered as unknown by the Saccharomyces Genome Database and by the Yeast Proteome Database. These genes constitute around 5% of the unknown yeast ORFome