Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes.

Heterogeneity in early language development in autism spectrum disorder (ASD) is clinically important and may reflect neurobiologically distinct subtypes. Here, we identified a large-scale association between multiple coordinated blood leukocyte gene coexpression modules and the multivariate functional neuroimaging (fMRI) response to speech. Gene coexpression modules associated with the multivariate fMRI response to speech were different for all pairwise comparisons between typically developing toddlers and toddlers with ASD and poor versus good early language outcome. Associated coexpression modules were enriched in genes that are broadly expressed in the brain and many other tissues. These coexpression modules were also enriched in ASD-associated, prenatal, human-specific, and language-relevant genes. This work highlights distinctive neurobiology in ASD subtypes with different early language outcomes that is present well before such outcomes are known. Associations between neuroimaging measures and gene expression levels in blood leukocytes may offer a unique in vivo window into identifying brain-relevant molecular mechanisms in ASD

Weighted gene coexpression network analysis strategies applied to mouse weight

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F2 mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm

Simultaneous Clustering of Multiple Gene Expression and Physical Interaction Datasets

Many genome-wide datasets are routinely generated to study different aspects of biological systems, but integrating them to obtain a coherent view of the underlying biology remains a challenge. We propose simultaneous clustering of multiple networks as a framework to integrate large-scale datasets on the interactions among and activities of cellular components. Specifically, we develop an algorithm JointCluster that finds sets of genes that cluster well in multiple networks of interest, such as coexpression networks summarizing correlations among the expression profiles of genes and physical networks describing protein-protein and protein-DNA interactions among genes or gene-products. Our algorithm provides an efficient solution to a well-defined problem of jointly clustering networks, using techniques that permit certain theoretical guarantees on the quality of the detected clustering relative to the optimal clustering. These guarantees coupled with an effective scaling heuristic and the flexibility to handle multiple heterogeneous networks make our method JointCluster an advance over earlier approaches. Simulation results showed JointCluster to be more robust than alternate methods in recovering clusters implanted in networks with high false positive rates. In systematic evaluation of JointCluster and some earlier approaches for combined analysis of the yeast physical network and two gene expression datasets under glucose and ethanol growth conditions, JointCluster discovers clusters that are more consistently enriched for various reference classes capturing different aspects of yeast biology or yield better coverage of the analysed genes. These robust clusters, which are supported across multiple genomic datasets and diverse reference classes, agree with known biology of yeast under these growth conditions, elucidate the genetic control of coordinated transcription, and enable functional predictions for a number of uncharacterized genes

Innovation in health economic modelling of service improvements for longer-term depression: demonstration in a local health community

Background The purpose of the analysis was to develop a health economic model to estimate the costs and health benefits of alternative National Health Service (NHS) service configurations for people with longer-term depression. Method Modelling methods were used to develop a conceptual and health economic model of the current configuration of services in Sheffield, England for people with longer-term depression. Data and assumptions were synthesised to estimate cost per Quality Adjusted Life Years (QALYs). Results Three service changes were developed and resulted in increased QALYs at increased cost. Versus current care, the incremental cost-effectiveness ratio (ICER) for a self-referral service was £11,378 per QALY. The ICER was £2,227 per QALY for the dropout reduction service and £223 per QALY for an increase in non-therapy services. These results were robust when compared to current cost-effectiveness thresholds and accounting for uncertainty. Conclusions Cost-effective service improvements for longer-term depression have been identified. Also identified were limitations of the current evidence for the long term impact of services

Discovering Dysfunction of Multiple MicroRNAs Cooperation in Disease by a Conserved MicroRNA Co-Expression Network

MicroRNAs, a new class of key regulators of gene expression, have been shown to be involved in diverse biological processes and linked to many human diseases. To elucidate miRNA function from a global perspective, we constructed a conserved miRNA co-expression network by integrating multiple human and mouse miRNA expression data. We found that these conserved co-expressed miRNA pairs tend to reside in close genomic proximity, belong to common families, share common transcription factors, and regulate common biological processes by targeting common components of those processes based on miRNA targets and miRNA knockout/transfection expression data, suggesting their strong functional associations. We also identified several co-expressed miRNA sub-networks. Our analysis reveals that many miRNAs in the same sub-network are associated with the same diseases. By mapping known disease miRNAs to the network, we identified three cancer-related miRNA sub-networks. Functional analyses based on targets and miRNA knockout/transfection data consistently show that these sub-networks are significantly involved in cancer-related biological processes, such as apoptosis and cell cycle. Our results imply that multiple co-expressed miRNAs can cooperatively regulate a given biological process by targeting common components of that process, and the pathogenesis of disease may be associated with the abnormality of multiple functionally cooperative miRNAs rather than individual miRNAs. In addition, many of these co-expression relationships provide strong evidence for the involvement of new miRNAs in important biological processes, such as apoptosis, differentiation and cell cycle, indicating their potential disease links

Olfactory perireceptor and receptor events in moths: a kinetic model revised

Modelling reveals that within about 3 ms after entering the sensillum lymph, 17% of total pheromone is enzymatically degraded while 83% is bound to the pheromone-binding protein (PBP) and thereby largely protected from enzymatic degradation. The latter proceeds within minutes, 20,000-fold more slowly than with the free pheromone. In vivo the complex pheromone–PBP interacts with the receptor molecule. At weak stimulation the half-life of the active complex is 0.8 s due to the postulated pheromone deactivation. Most likely this process is enzymatically catalysed; it changes the PBP into a scavenger form, possibly by interference with the C-terminus. The indirectly determined PBP concentration (3.8 mM) is close to direct measurements. The calculated density of receptor molecules within the plasma membrane of the receptor neuron reaches up to 6,000 units per μm2. This is compared with the estimated densities of the sensory-neuron membrane protein and of ion channels. The EC50 of the model pheromone–PBP complex interacting with the receptor molecules is 6.8 μM, as compared with the EC50 = 1.5 μM of bombykol recently determined using heterologous expression. A possible mechanism widening the range of stimulus intensities covered by the dose–response curve of the receptor-potential is proposed

Evolution of a Signaling Nexus Constrained by Protein Interfaces and Conformational States

Heterotrimeric G proteins act as the physical nexus between numerous receptors that respond to extracellular signals and proteins that drive the cytoplasmic response. The Gα subunit of the G protein, in particular, is highly constrained due to its many interactions with proteins that control or react to its conformational state. Various organisms contain differing sets of Gα-interacting proteins, clearly indicating that shifts in sequence and associated Gα functionality were acquired over time. These numerous interactions constrained much of Gα evolution; yet Gα has diversified, through poorly understood processes, into several functionally specialized classes, each with a unique set of interacting proteins. Applying a synthetic sequence-based approach to mammalian Gα subunits, we established a set of seventy-five evolutionarily important class-distinctive residues, sites where a single Gα class is differentiated from the three other classes. We tested the hypothesis that shifts at these sites are important for class-specific functionality. Importantly, we mapped known and well-studied class-specific functionalities from all four mammalian classes to sixteen of our class-distinctive sites, validating the hypothesis. Our results show how unique functionality can evolve through the recruitment of residues that were ancestrally functional. We also studied acquisition of functionalities by following these evolutionarily important sites in non-mammalian organisms. Our results suggest that many class-distinctive sites were established early on in eukaryotic diversification and were critical for the establishment of new Gα classes, whereas others arose in punctuated bursts throughout metazoan evolution. These Gα class-distinctive residues are rational targets for future structural and functional studies

Activation of MEK1 or MEK2 isoform is sufficient to fully transform intestinal epithelial cells and induce the formation of metastatic tumors

<p>Abstract</p> <p>Background</p> <p>The Ras-dependent ERK1/2 MAP kinase signaling pathway plays a central role in cell proliferation control and is frequently activated in human colorectal cancer. Small-molecule inhibitors of MEK1/MEK2 are therefore viewed as attractive drug candidates for the targeted therapy of this malignancy. However, the exact contribution of MEK1 and MEK2 to the pathogenesis of colorectal cancer remains to be established.</p> <p>Methods</p> <p>Wild type and constitutively active forms of MEK1 and MEK2 were ectopically expressed by retroviral gene transfer in the normal intestinal epithelial cell line IEC-6. We studied the impact of MEK1 and MEK2 activation on cellular morphology, cell proliferation, survival, migration, invasiveness, and tumorigenesis in mice. RNA interference was used to test the requirement for MEK1 and MEK2 function in maintaining the proliferation of human colorectal cancer cells.</p> <p>Results</p> <p>We found that expression of activated MEK1 or MEK2 is sufficient to morphologically transform intestinal epithelial cells, dysregulate cell proliferation and induce the formation of high-grade adenocarcinomas after orthotopic transplantation in mice. A large proportion of these intestinal tumors metastasize to the liver and lung. Mechanistically, activation of MEK1 or MEK2 up-regulates the expression of matrix metalloproteinases, promotes invasiveness and protects cells from undergoing anoikis. Importantly, we show that silencing of MEK2 expression completely suppresses the proliferation of human colon carcinoma cell lines, whereas inactivation of MEK1 has a much weaker effect.</p> <p>Conclusion</p> <p>MEK1 and MEK2 isoforms have similar transforming properties and are able to induce the formation of metastatic intestinal tumors in mice. Our results suggest that MEK2 plays a more important role than MEK1 in sustaining the proliferation of human colorectal cancer cells.</p

Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants

<p>Abstract</p> <p>Background</p> <p>One of the eminent opportunities afforded by modern genomic technologies is the potential to provide a mechanistic understanding of the processes by which genetic change translates to phenotypic variation and the resultant appearance of distinct physiological traits. Indeed much progress has been made in this area, particularly in biomedicine where functional genomic information can be used to determine the physiological state (e.g., diagnosis) and predict phenotypic outcome (e.g., patient survival). Ecology currently lacks an analogous approach where genomic information can be used to diagnose the presence of a given physiological state (e.g., stress response) and then predict likely phenotypic outcomes (e.g., stress duration and tolerance, fitness).</p> <p>Results</p> <p>Here, we demonstrate that a compendium of genomic signatures can be used to classify the plant abiotic stress phenotype in <it>Arabidopsis </it>according to the architecture of the transcriptome, and then be linked with gene coexpression network analysis to determine the underlying genes governing the phenotypic response. Using this approach, we confirm the existence of known stress responsive pathways and marker genes, report a common abiotic stress responsive transcriptome and relate phenotypic classification to stress duration.</p> <p>Conclusion</p> <p>Linking genomic signatures to gene coexpression analysis provides a unique method of relating an observed plant phenotype to changes in gene expression that underlie that phenotype. Such information is critical to current and future investigations in plant biology and, in particular, to evolutionary ecology, where a mechanistic understanding of adaptive physiological responses to abiotic stress can provide researchers with a tool of great predictive value in understanding species and population level adaptation to climate change.</p

Regulators of AWC-Mediated Olfactory Plasticity in Caenorhabditis elegans

While most sensory neurons will adapt to prolonged stimulation by down-regulating their responsiveness to the signal, it is not clear which events initiate long-lasting sensory adaptation. Likewise, we are just beginning to understand how the physiology of the adapted cell is altered. Caenorhabditis elegans is inherently attracted to specific odors that are sensed by the paired AWC olfactory sensory neurons. The attraction diminishes if the animal experiences these odors for a prolonged period of time in the absence of food. The AWC neuron responds acutely to odor-exposure by closing calcium channels. While odortaxis requires a Gα subunit protein, cGMP-gated channels, and guanylyl cyclases, adaptation to prolonged odor exposure requires nuclear entry of the cGMP-dependent protein kinase, EGL-4. We asked which candidate members of the olfactory signal transduction pathway promote nuclear entry of EGL-4 and which molecules might induce long-term adaptation downstream of EGL-4 nuclear entry. We found that initiation of long-term adaptation, as assessed by nuclear entry of EGL-4, is dependent on G-protein mediated signaling but is independent of fluxes in calcium levels. We show that long-term adaptation requires polyunsaturated fatty acids (PUFAs) that may act on the transient receptor potential (TRP) channel type V OSM-9 downstream of EGL-4 nuclear entry. We also present evidence that high diacylglycerol (DAG) levels block long-term adaptation without affecting EGL-4 nuclear entry. Our analysis provides a model for the process of long-term adaptation that occurs within the AWC neuron of C. elegans: G-protein signaling initiates long-lasting olfactory adaptation by promoting the nuclear entry of EGL-4, and once EGL-4 has entered the nucleus, processes such as PUFA activation of the TRP channel OSM-9 may dampen the output of the AWC neuron