Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes

<p>Abstract</p> <p>Background</p> <p>A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.</p> <p>Recently, several methods for the identification of genes with bimodal distributions have been introduced. A straightforward approach is to cluster the expression values and score the distance between the two distributions. Other scores directly measure properties of the distribution. The kurtosis, e.g., measures divergence from a normal distribution. An alternative is the outlier-sum statistic that identifies genes with extremely high or low expression values in a subset of the samples.</p> <p>Results</p> <p>We compare and discuss scores for bimodality for expression data. For the genome-wide identification of bimodal genes we apply all scores to expression data from 194 patients with node-negative breast cancer. Further, we present the first comprehensive genome-wide evaluation of the prognostic relevance of bimodal genes. We first rank genes according to bimodality scores and define two patient subgroups based on expression values. Then we assess the prognostic significance of the top ranking bimodal genes by comparing the survival functions of the two patient subgroups. We also evaluate the global association between the bimodal shape of expression distributions and survival times with an enrichment type analysis.</p> <p>Various cluster-based methods lead to a significant overrepresentation of prognostic genes. A striking result is obtained with the outlier-sum statistic (<it>p </it>< 10^-12). Many genes with heavy tails generate subgroups of patients with different prognosis.</p> <p>Conclusions</p> <p>Genes with high bimodality scores are promising candidates for defining prognostic patient subgroups from expression data. We discuss advantages and disadvantages of the different scores for prognostic purposes. The outlier-sum statistic may be particularly valuable for the identification of genes to be included in prognostic signatures. Among the genes identified as bimodal in the breast cancer data set several have not yet previously been recognized to be prognostic and bimodally expressed in breast cancer.</p

Diet-related chronic disease in the northeastern United States: a model-based clustering approach

Background: Obesity and diabetes are global public health concerns. Studies indicate a relationship between socioeconomic, demographic and environmental variables and the spatial patterns of diet-related chronic disease. In this paper, we propose a methodology using model-based clustering and variable selection to predict rates of obesity and diabetes. We test this method through an application in the northeastern United States. Methods: We use model-based clustering, an unsupervised learning approach, to find latent clusters of similar US counties based on a set of socioeconomic, demographic, and environmental variables chosen through the process of variable selection. We then use Analysis of Variance and Post-hoc Tukey comparisons to examine differences in rates of obesity and diabetes for the clusters from the resulting clustering solution. Results: We find access to supermarkets, median household income, population density and socioeconomic status to be important in clustering the counties of two northeastern states. The results of the cluster analysis can be used to identify two sets of counties with significantly lower rates of diet-related chronic disease than those observed in the other identified clusters. These relatively healthy clusters are distinguished by the large central and large fringe metropolitan areas contained in their component counties. However, the relationship of socio-demographic factors and diet-related chronic disease is more complicated than previous research would suggest. Additionally, we find evidence of low food access in two clusters of counties adjacent to large central and fringe metropolitan areas. While food access has previously been seen as a problem of inner-city or remote rural areas, this study offers preliminary evidence of declining food access in suburban areas. Conclusions: Model-based clustering with variable selection offers a new approach to the analysis of socioeconomic, demographic, and environmental data for diet-related chronic disease prediction. In a test application to two northeastern states, this method allows us to identify two sets of metropolitan counties with significantly lower diet-related chronic disease rates than those observed in most rural and suburban areas. Our method could be applied to larger geographic areas or other countries with comparable data sets, offering a promising method for researchers interested in the global increase in diet-related chronic disease

Environmental Influences on Mate Preferences as Assessed by a Scenario Manipulation Experiment

Many evolutionary psychology studies have addressed the topic of mate preferences, focusing particularly on gender and cultural differences. However, the extent to which situational and environmental variables might affect mate preferences has been comparatively neglected. We tested 288 participants in order to investigate the perceived relative importance of six traits of an ideal partner (wealth, dominance, intelligence, height, kindness, attractiveness) under four different hypothetical scenarios (status quo/nowadays, violence/post-nuclear, poverty/resource exhaustion, prosperity/global well-being). An equal number of participants (36 women, 36 men) was allotted to each scenario; each was asked to allocate 120 points across the six traits according to their perceived value. Overall, intelligence was the trait to which participants assigned most importance, followed by kindness and attractiveness, and then by wealth, dominance and height. Men appraised attractiveness as more valuable than women. Scenario strongly influenced the relative importance attributed to traits, the main finding being that wealth and dominance were more valued in the poverty and post-nuclear scenarios, respectively, compared to the other scenarios. Scenario manipulation generally had similar effects in both sexes, but women appeared particularly prone to trade off other traits for dominance in the violence scenario, and men particularly prone to trade off other traits for wealth in the poverty scenario. Our results are in line with other correlational studies of situational variables and mate preferences, and represent strong evidence of a causal relationship of environmental factors on specific mate preferences, corroborating the notion of an evolved plasticity to current ecological conditions. A control experiment seems to suggest that our scenarios can be considered as realistic descriptions of the intended ecological conditions

Networks of Neuronal Genes Affected by Common and Rare Variants in Autism Spectrum Disorders

Autism spectrum disorders (ASD) are neurodevelopmental disorders with phenotypic and genetic heterogeneity. Recent studies have reported rare and de novo mutations in ASD, but the allelic architecture of ASD remains unclear. To assess the role of common and rare variations in ASD, we constructed a gene co-expression network based on a widespread survey of gene expression in the human brain. We identified modules associated with specific cell types and processes. By integrating known rare mutations and the results of an ASD genome-wide association study (GWAS), we identified two neuronal modules that are perturbed by both rare and common variations. These modules contain highly connected genes that are involved in synaptic and neuronal plasticity and that are expressed in areas associated with learning and memory and sensory perception. The enrichment of common risk variants was replicated in two additional samples which include both simplex and multiplex families. An analysis of the combined contribution of common variants in the neuronal modules revealed a polygenic component to the risk of ASD. The results of this study point toward contribution of minor and major perturbations in the two sub-networks of neuronal genes to ASD risk

Environmental Influences on Mate Preferences as Assessed by a Scenario Manipulation Experiment

Many evolutionary psychology studies have addressed the topic of mate preferences, focusing particularly on gender and cultural differences. However, the extent to which situational and environmental variables might affect mate preferences has been comparatively neglected. We tested 288 participants in order to investigate the perceived relative importance of six traits of an ideal partner (wealth, dominance, intelligence, height, kindness, attractiveness) under four different hypothetical scenarios (status quo/nowadays, violence/post-nuclear, poverty/resource exhaustion, prosperity/global well-being). An equal number of participants (36 women, 36 men) was allotted to each scenario; each was asked to allocate 120 points across the six traits according to their perceived value. Overall, intelligence was the trait to which participants assigned most importance, followed by kindness and attractiveness, and then by wealth, dominance and height. Men appraised attractiveness as more valuable than women. Scenario strongly influenced the relative importance attributed to traits, the main finding being that wealth and dominance were more valued in the poverty and post-nuclear scenarios, respectively, compared to the other scenarios. Scenario manipulation generally had similar effects in both sexes, but women appeared particularly prone to trade off other traits for dominance in the violence scenario, and men particularly prone to trade off other traits for wealth in the poverty scenario. Our results are in line with other correlational studies of situational variables and mate preferences, and represent strong evidence of a causal relationship of environmental factors on specific mate preferences, corroborating the notion of an evolved plasticity to current ecological conditions. A control experiment seems to suggest that our scenarios can be considered as realistic descriptions of the intended ecological conditions

Deconstructing transcriptional heterogeneity in pluripotent stem cells

SUMMARY Pluripotent stem cells (PSCs) are capable of dynamic interconversion between distinct substates, but the regulatory circuits specifying these states and enabling transitions between them are not well understood. We set out to characterize transcriptional heterogeneity in PSCs by single-cell expression profiling under different chemical and genetic perturbations. Signaling factors and developmental regulators show highly variable expression, with expression states for some variable genes heritable through multiple cell divisions. Expression variability and population heterogeneity can be influenced by perturbation of signaling pathways and chromatin regulators. Strikingly, either removal of mature miRNAs or pharmacologic blockage of signaling pathways drives PSCs into a low-noise ground state characterized by a reconfigured pluripotency network, enhanced self-renewal, and a distinct chromatin state, an effect mediated by opposing miRNA families acting on the c-myc / Lin28 / let-7 axis. These data illuminate the nature of transcriptional heterogeneity in PSCs

Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors.

Colorectal carcinoma represents a heterogeneous entity, with only a fraction of the tumours responding to available therapies, requiring a better molecular understanding of the disease in precision oncology. To address this challenge, the OncoTrack consortium recruited 106 CRC patients (stages I-IV) and developed a pre-clinical platform generating a compendium of drug sensitivity data totalling >4,000 assays testing 16 clinical drugs on patient-derived in vivo and in vitro models. This large biobank of 106 tumours, 35 organoids and 59 xenografts, with extensive omics data comparing donor tumours and derived models provides a resource for advancing our understanding of CRC. Models recapitulate many of the genetic and transcriptomic features of the donors, but defined less complex molecular sub-groups because of the loss of human stroma. Linking molecular profiles with drug sensitivity patterns identifies novel biomarkers, including a signature outperforming RAS/RAF mutations in predicting sensitivity to the EGFR inhibitor cetuximab