From microarray to biology: an integrated experimental, statistical and in silico analysis of how the extracellular matrix modulates the phenotype of cancer cells

A statistically robust and biologically-based approach for analysis of microarray data is described that integrates independent biological knowledge and data with a global F-test for finding genes of interest that minimizes the need for replicates when used for hypothesis generation. First, each microarray is normalized to its noise level around zero. The microarray dataset is then globally adjusted by robust linear regression. Second, genes of interest that capture significant responses to experimental conditions are selected by finding those that express significantly higher variance than those expressing only technical variability. Clustering expression data and identifying expression-independent properties of genes of interest including upstream transcriptional regulatory elements (TREs), ontologies and networks or pathways organizes the data into a biologically meaningful system. We demonstrate that when the number of genes of interest is inconveniently large, identifying a subset of "beacon genes" representing the largest changes will identify pathways or networks altered by biological manipulation. The entire dataset is then used to complete the picture outlined by the "beacon genes." This allow construction of a structured model of a system that can generate biologically testable hypotheses. We illustrate this approach by comparing cells cultured on plastic or an extracellular matrix which organizes a dataset of over 2,000 genes of interest from a genome wide scan of transcription. The resulting model was confirmed by comparing the predicted pattern of TREs with experimental determination of active transcription factors

Disease-associated pathophysiologic structures in pediatric rheumatic diseases show characteristics of scale-free networks seen in physiologic systems: implications for pathogenesis and treatment

<p>Abstract</p> <p>Background</p> <p>While standard reductionist approaches have provided some insights into specific gene polymorphisms and molecular pathways involved in disease pathogenesis, our understanding of complex traits such as atherosclerosis or type 2 diabetes remains incomplete. Gene expression profiling provides an unprecedented opportunity to understand complex human diseases by providing a global view of the multiple interactions across the genome that are likely to contribute to disease pathogenesis. Thus, the goal of gene expression profiling is not to generate lists of differentially expressed genes, but to identify the physiologic or pathogenic processes and structures represented in the expression profile.</p> <p>Methods</p> <p>RNA was separately extracted from peripheral blood neutrophils and mononuclear leukocytes, labeled, and hybridized to genome level microarrays to generate expression profiles of children with polyarticular juvenile idiopathic arthritis, juvenile dermatomyositis relative to childhood controls. Statistically significantly differentially expressed genes were identified from samples of each disease relative to controls. Functional network analysis identified interactions between products of these differentially expressed genes.</p> <p>Results</p> <p><it>In silico </it>models of both diseases demonstrated similar features with properties of scale-free networks previously described in physiologic systems. These networks were observable in both cells of the innate immune system (neutrophils) and cells of the adaptive immune system (peripheral blood mononuclear cells).</p> <p>Conclusion</p> <p>Genome-level transcriptional profiling from childhood onset rheumatic diseases suggested complex interactions in two arms of the immune system in both diseases. The disease associated networks showed scale-free network patterns similar to those reported in normal physiology. We postulate that these features have important implications for therapy as such networks are relatively resistant to perturbation.</p

Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available

Temporal Asynchrony of Trophic Status Between Mainstream and Tributary Bay Within a Giant Dendritic Reservoir: The Role of Local-Scale Regulators

Limnologists have regarded temporal coherence (synchrony) as a powerful tool for identifying the relative importance of local-scale regulators and regional climatic drivers on lake ecosystems. Limnological studies on Asian reservoirs have emphasized that climate and hydrology under the influences of monsoon are dominant factors regulating seasonal patterns of lake trophic status; yet, little is known of synchrony or asynchrony of trophic status in the single reservoir ecosystem. Based on monthly monitoring data of chlorophyll a, transparency, nutrients, and nonvolatile suspended solids (NVSS) during 1-year period, the present study evaluated temporal coherence to test whether local-scale regulators disturb the seasonal dynamics of trophic state indices (TSI) in a giant dendritic reservoir, China (Three Gorges Reservoir, TGR). Reservoir-wide coherences for TSICHL, TSISD, and TSITP showed dramatic variations over spatial scale, indicating temporal asynchrony of trophic status. Following the concept of TSI differences, algal productivity in the mainstream of TGR and Xiangxi Bay except the upstream of the bay were always limited by nonalgal turbidity (TSICHL−TSISD <0) rather than nitrogen and phosphorus (TSICHL−TSITN <0 and TSICHL−TSITP <0). The coherence analysis for TSI differences showed that local processes of Xiangxi Bay were the main responsible for local asynchrony of nonalgal turbidity limitation levels. Regression analysis further proved that local temporal asynchrony for TSISD and nonalgal turbidity limitation levels were regulated by local dynamics of NVSS, rather than geographical distance. The implications of the present study are to emphasize that the results of trophic status obtained from a single environment (reservoir mainstream) cannot be extrapolated to other environments (tributary bay) in a way that would allow its use as a sentinel site

Climate Change and Developing-Country Cities: Implications For Environmental Health and Equity

Climate change is an emerging threat to global public health. It is also highly inequitable, as the greatest risks are to the poorest populations, who have contributed least to greenhouse gas (GHG) emissions. The rapid economic development and the concurrent urbanization of poorer countries mean that developing-country cities will be both vulnerable to health hazards from climate change and, simultaneously, an increasing contributor to the problem. We review the specific health vulnerabilities of urban populations in developing countries and highlight the range of large direct health effects of energy policies that are concentrated in urban areas. Common vulnerability factors include coastal location, exposure to the urban heat-island effect, high levels of outdoor and indoor air pollution, high population density, and poor sanitation. There are clear opportunities for simultaneously improving health and cutting GHG emissions most obviously through policies related to transport systems, urban planning, building regulations and household energy supply. These influence some of the largest current global health burdens, including approximately 800,000 annual deaths from ambient urban air pollution, 1.2 million from road-traffic accidents, 1.9 million from physical inactivity, and 1.5 million per year from indoor air pollution. GHG emissions and health protection in developing-country cities are likely to become increasingly prominent in policy development. There is a need for a more active input from the health sector to ensure that development and health policies contribute to a preventive approach to local and global environmental sustainability, urban population health, and health equity

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards

Changing Patterns of Microhabitat Utilization by the Threespot Damselfish, Stegastes planifrons, on Caribbean Reefs

Background: The threespot damselfish, Stegastes planifrons (Cuvier), is important in mediating interactions among corals, algae, and herbivores on Caribbean coral reefs. The preferred microhabitat of S. planifrons is thickets of the branching staghorn coral Acropora cervicornis. Within the past few decades, mass mortality of A. cervicornis from white-band disease and other factors has rendered this coral a minor ecological component throughout most of its range. Methodology/Principal Findings: Survey data from Jamaica (heavily fished), Florida and the Bahamas (moderately fished), the Cayman Islands (lightly to moderately fished), and Belize (lightly fished) indicate that distributional patterns of S. planifrons are positively correlated with live coral cover and topographic complexity. Our results suggest that speciesspecific microhabitat preferences and the availability of topographically complex microhabitats are more important than the abundance of predatory fish as proximal controls on S. planifrons distribution and abundance. Conclusions/Significance: The loss of the primary microhabitat of S. planifrons—A. cervicornis—has forced a shift in the distribution and recruitment of these damselfish onto remaining high-structured corals, especially the Montastraea annulari

Comparison of Marine Spatial Planning Methods in Madagascar Demonstrates Value of Alternative Approaches

The Government of Madagascar plans to increase marine protected area coverage by over one million hectares. To assist this process, we compare four methods for marine spatial planning of Madagascar's west coast. Input data for each method was drawn from the same variables: fishing pressure, exposure to climate change, and biodiversity (habitats, species distributions, biological richness, and biodiversity value). The first method compares visual color classifications of primary variables, the second uses binary combinations of these variables to produce a categorical classification of management actions, the third is a target-based optimization using Marxan, and the fourth is conservation ranking with Zonation. We present results from each method, and compare the latter three approaches for spatial coverage, biodiversity representation, fishing cost and persistence probability. All results included large areas in the north, central, and southern parts of western Madagascar. Achieving 30% representation targets with Marxan required twice the fish catch loss than the categorical method. The categorical classification and Zonation do not consider targets for conservation features. However, when we reduced Marxan targets to 16.3%, matching the representation level of the “strict protection” class of the categorical result, the methods show similar catch losses. The management category portfolio has complete coverage, and presents several management recommendations including strict protection. Zonation produces rapid conservation rankings across large, diverse datasets. Marxan is useful for identifying strict protected areas that meet representation targets, and minimize exposure probabilities for conservation features at low economic cost. We show that methods based on Zonation and a simple combination of variables can produce results comparable to Marxan for species representation and catch losses, demonstrating the value of comparing alternative approaches during initial stages of the planning process. Choosing an appropriate approach ultimately depends on scientific and political factors including representation targets, likelihood of adoption, and persistence goals