Molecular classification of cutaneous malignant melanoma by gene expression profiling

The most common human cancers are malignant neoplasms of the skin(1,2). Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease(3,4). Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm(2,3). Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities(2). Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas(5). Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

Evolving noisy oscillatory dynamics in genetic regulatory networks

We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.

Evolving genetic regulatory networks using an artificial genome

Boolean models of genetic regulatory networks (GRNs) have been shown to exhibit many of the characteristic dynamics of real GRNs, with gene expression patterns settling to point attractors or limit cycles, or displaying chaotic behaviour, depending upon the connectivity of the network and the relative proportions of excitatory and inhibitory interactions. This range of behaviours is only apparent, however, when the nodes of the GRN are updated synchronously, a biologically implausible state of affairs. In this paper we demonstrate that evolution can produce GRNs with interesting dynamics under an asynchronous update scheme. We use an Artificial Genome to generate networks which exhibit limit cycle dynamics when updated synchronously, but collapse to a point attractor when updated asynchronously. Using a hill climbing algorithm the networks are then evolved using a fitness function which rewards patterns of gene expression which revisit as many previously seen states as possible. The final networks exhibit “fuzzy limit cycle” dynamics when updated asynchronously.

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

Dynamics of gene expression in an artificial genome

Complex systems techniques provide a powerful tool to study the emergent properties of networks of interacting genes. In this study we extract models of genetic regulatory networks from an artificial genome, represented by a sequence of nucleotides, and analyse how variations in the connectivity and degree of inhibition of the extracted networks affects the resulting classes of behaviours. For low connectivity systems were found to be very stable. Only with higher connectivity was a significant occurrence of chaos found. Most interestingly, the peak in occurrence of chaos occurs perched on the edge of a phase transition in the occurrence of attractors.

Network lifetime maximization with cross-layer design in wireless sensor networks

This paper investigates a cross-layer design approach for minimizing energy consumption and maximizing network lifetime (NL) of a multiple-source and single-sink (MSSS) WSN with energy constraints. The optimization problem for MSSS WSN can be formulated as a mixed integer convex optimization problem with the adoption of time division multiple access (TDMA) in medium access control (MAC) layer, and it becomes a convex problem by relaxing the integer constraint on time slots. Impacts of data rate, link access and routing are jointly taken into account in the optimization problem formulation. Both linear and planar network topologies are considered for NL maximization (NLM). With linear MSSS and planar single-source and single-sink (SSSS) topologies, we successfully use Karush-Kuhn-Tucker (KKT) optimality conditions to derive analytical expressions of the optimal NL when all nodes are exhausted simultaneously. The problem for planar MSSS topology is more complicated, and a decomposition and combination (D&C) approach is proposed to compute suboptimal solutions. An analytical expression of the suboptimal NL is derived for a small scale planar network. To deal with larger scale planar network, an iterative algorithm is proposed for the D&C approach. Numerical results show that the upper-bounds of the network lifetime obtained by our proposed optimization models are tight. Important insights into the NL and benefits of cross-layer design for WSN NLM are obtained.

Activation of vascular endothelial growth factor receptor-1 sustains angiogenesis and Bcl-2 expression via the phosphatidylinositol 3-kinase pathway in endothelial cells

Vascular insufficiency and retinal ischemia precede many proliferative retinopathies and stimulate secretion of various vasoactive growth factors, including vascular endothelial growth factor (VEGF) and placenta growth factor (PlGF). It is unclear, however, how PlGF, which is elevated in proliferative diabetic retinopathy and is a VEGF homolog that binds only to VEGF receptor (VEGFR)-1, promotes pathological angiogenesis. When primary microvascular endothelial cells were grown on collagen gels, PlGF-containing ligands upregulated Bcl-2 expression and stimulated the formation of capillary-like tube networks that were retained for up to 14 days in culture. The inhibition of VEGFR-1 results in a dramatic decrease in the number of capillary connections, indicating that VEGFR-1 ligands promote branching angiogenesis. In contrast, VEGF-induced tube formations and Bcl-2 expression were significantly decreased at the end of this period. Flow cytometry analysis of annexin-V/propidium iodide-stained cells revealed that PlGF and PlGF/VEGF heterodimer inhibited apoptosis in serum-deprived endothelial cells. These two growth factors stimulated a survival signaling pathway phosphatidylinositol 3-kinase (PI3K), as identified by increased Akt phosphorylation and because blocking PI3K signalling by adenovirus-mediated overexpression of wild-type phosphatase and tensin homolog on chromosome 10 (PTEN) disrupted angiogenesis and decreased Bcl-2 expression by PlGF and PlGF/VEGF heterodimer, whereas a dominant-negative PTEN mutant enhanced endothelial sprout formation and Bcl-2 expression. Together, these findings indicate that PlGF-containing ligands contribute to pathological angiogenesis by prolonging cell survival signals and maintaining vascular networks.

The governance of online expression in a networked world:free-speech, surveillance and censorship

In recent years, we have witnessed the mushrooming of pro- democracy and protest movements not only in the Arab world, but also within Europe and the Americas. Such movements have ranged from popular upheavals, like in Tunisia and Egypt, to the organization of large- scale demonstrations against unpopular policies, as in Spain, Greece and Poland. What connects these different events are not only their democratic aspirations, but also their innovative forms of communication and organization through online means, which are sometimes considered to be outside of the State’s control. At the same time, however, it has become more and more apparent that countries are attempting to increase their understanding of, and control over, their citizens’ actions in the digital sphere. This involves striving to develop surveillance instruments, control mechanisms and processes engineered to dominate the digital public sphere, which necessitates the assistance and support of private actors such as Internet intermediaries. Examples include the growing use of Internet surveillance technology with which online data traffic is analysed, and the extensive monitoring of social networks. Despite increased media attention, academic debate on the ambivalence of these technologies, mechanisms and techniques remains relatively limited, as is discussion of the involvement of corporate actors. The purpose of this edited volume is to reflect on how Internet-related technologies, mechanisms and techniques may be used as a means to enable expression, but also to restrict speech, manipulate public debate and govern global populaces.

Self-aware and self-expressive camera networks

Smart cameras perform on-board image analysis, adapt their algorithms to changes in their environment, and collaborate with other networked cameras to analyze the dynamic behavior of objects. A proposed computational framework adopts the concepts of self-awareness and self-expression to more efficiently manage the complex tradeoffs among performance, flexibility, resources, and reliability. The Web extra at http://youtu.be/NKe31-OKLz4 is a video demonstrating CamSim, a smart camera simulation tool, enables users to test self-adaptive and self-organizing smart-camera techniques without deploying a smart-camera network.

Impacts of seawater acidification on mantle gene expression patterns of the Baltic Sea blue mussel: implications for shell formation and energy metabolism, link to supplementary material

Marine organisms have to cope with increasing CO2 partial pressures and decreasing pH in the oceans. We elucidated the impacts of an 8-week acclimation period to four seawater pCO2 treatments (39, 113, 243 and 405 Pa/385, 1,120, 2,400 and 4,000 µatm) on mantle gene expression patterns in the blue mussel Mytilus edulis from the Baltic Sea. Based on the M. edulis mantle tissue transcriptome, the expression of several genes involved in metabolism, calcification and stress responses was assessed in the outer (marginal and pallial zone) and the inner mantle tissues (central zone) using quantitative real-time PCR. The expression of genes involved in energy and protein metabolism (F-ATPase, hexokinase and elongation factor alpha) was strongly affected by acclimation to moderately elevated CO2 partial pressures. Expression of a chitinase, potentially important for the calcification process, was strongly depressed (maximum ninefold), correlating with a linear decrease in shell growth observed in the experimental animals. Interestingly, shell matrix protein candidate genes were less affected by CO2 in both tissues. A compensatory process toward enhanced shell protection is indicated by a massive increase in the expression of tyrosinase, a gene involved in periostracum formation (maximum 220-fold). Using correlation matrices and a force-directed layout network graph, we were able to uncover possible underlying regulatory networks and the connections between different pathways, thereby providing a molecular basis of observed changes in animal physiology in response to ocean acidification.

Homozygous deletion mapping in myeloma samples identifies genes and an expression signature relevant to pathogenesis and outcome.

PURPOSE: Myeloma is a clonal malignancy of plasma cells. Poor-prognosis risk is currently identified by clinical and cytogenetic features. However, these indicators do not capture all prognostic information. Gene expression analysis can be used to identify poor-prognosis patients and this can be improved by combination with information about DNA-level changes. EXPERIMENTAL DESIGN: Using single nucleotide polymorphism-based gene mapping in combination with global gene expression analysis, we have identified homozygous deletions in genes and networks that are relevant to myeloma pathogenesis and outcome. RESULTS: We identified 170 genes with homozygous deletions and corresponding loss of expression. Deletion within the "cell death" network was overrepresented and cases with these deletions had impaired overall survival. From further analysis of these events, we have generated an expression-based signature associated with shorter survival in 258 patients and confirmed this signature in data from two independent groups totaling 800 patients. We defined a gene expression signature of 97 cell death genes that reflects prognosis and confirmed this in two independent data sets. CONCLUSIONS: We developed a simple 6-gene expression signature from the 97-gene signature that can be used to identify poor-prognosis myeloma in the clinical environment. This signature could form the basis of future trials aimed at improving the outcome of poor-prognosis myeloma.

HNF1B variants associate with promoter methylation and regulate gene networks activated in prostate and ovarian cancer

Two independent regions within HNF1B are consistently identified in prostate and ovarian cancer genome-wide association studies (GWAS); their functional roles are unclear. We link prostate cancer (PC) risk SNPs rs11649743 and rs3760511 with elevated HNF1B gene expression and allele-specific epigenetic silencing, and outline a mechanism by which common risk variants could effect functional changes that increase disease risk: functional assays suggest that HNF1B is a pro-differentiation factor that suppresses epithelial-to-mesenchymal transition (EMT) in unmethylated, healthy tissues. This tumor-suppressor activity is lost when HNF1B is silenced by promoter methylation in the progression to PC. Epigenetic inactivation of HNF1B in ovarian cancer also associates with known risk SNPs, with a similar impact on EMT. This represents one of the first comprehensive studies into the pleiotropic role of a GWAS-associated transcription factor across distinct cancer types, and is the first to describe a conserved role for a multi-cancer genetic risk factor.

Bayesian Methods for Inferring Gene Regulatory Networks

Thesis (Ph.D.)--University of Washington, 2016-08

Whole Genome Analysis of Gene Expression Reveals Coordinated Activation of Signaling and Metabolic Pathways during Pollen-Pistil Interactions in Arabidopsis

Plant reproduction depends on the concerted activation of many genes to ensure correct communication between pollen and pistil. Here, we queried the whole transcriptome of Arabidopsis (Arabidopsis thaliana) in order to identify genes with specific reproductive functions. We used the Affymetrix ATH1 whole genome array to profile wild-type unpollinated pistils and unfertilized ovules. By comparing the expression profile of pistils at 0.5, 3.5, and 8.0 h after pollination and applying a number of statistical and bioinformatics criteria, we found 1,373 genes differentially regulated during pollen-pistil interactions. Robust clustering analysis grouped these genes in 16 time-course clusters representing distinct patterns of regulation. Coregulation within each cluster suggests the presence of distinct genetic pathways, which might be under the control of specific transcriptional regulators. A total of 78% of the regulated genes were expressed initially in unpollinated pistil and/or ovules, 15% were initially detected in the pollen data sets as enriched or preferentially expressed, and 7% were induced upon pollination. Among those, we found a particular enrichment for unknown transcripts predicted to encode secreted proteins or representing signaling and cell wall-related proteins, which may function by remodeling the extracellular matrix or as extracellular signaling molecules. A strict regulatory control in various metabolic pathways suggests that fine-tuning of the biochemical and physiological cellular environment is crucial for reproductive success. Our study provides a unique and detailed temporal and spatial gene expression profile of in vivo pollen-pistil interactions, providing a framework to better understand the basis of the molecular mechanisms operating during the reproductive process in higher plants.

Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks

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