Method for Evaluating Multiple Mediators: Mediating Effects of Smoking and COPD on the Association between the CHRNA5-A3 Variant and Lung Cancer Risk

A mediation model explores the direct and indirect effects between an independent variable and a dependent variable by including other variables (or mediators). Mediation analysis has recently been used to dissect the direct and indirect effects of genetic variants on complex diseases using case-control studies. However, bias could arise in the estimations of the genetic variant-mediator association because the presence or absence of the mediator in the study samples is not sampled following the principles of case-control study design. In this case, the mediation analysis using data from case-control studies might lead to biased estimates of coefficients and indirect effects. In this article, we investigated a multiple-mediation model involving a three-path mediating effect through two mediators using case-control study data. We propose an approach to correct bias in coefficients and provide accurate estimates of the specific indirect effects. Our approach can also be used when the original case-control study is frequency matched on one of the mediators. We employed bootstrapping to assess the significance of indirect effects. We conducted simulation studies to investigate the performance of the proposed approach, and showed that it provides more accurate estimates of the indirect effects as well as the percent mediated than standard regressions. We then applied this approach to study the mediating effects of both smoking and chronic obstructive pulmonary disease (COPD) on the association between the CHRNA5-A3 gene locus and lung cancer risk using data from a lung cancer case-control study. The results showed that the genetic variant influences lung cancer risk indirectly through all three different pathways. The percent of genetic association mediated was 18.3% through smoking alone, 30.2% through COPD alone, and 20.6% through the path including both smoking and COPD, and the total genetic variant-lung cancer association explained by the two mediators was 69.1%.     

Sexy sons from re-mating do not recoup the direct costs of harmful male interactions in the Drosophila melanogaster laboratory model system

The empirical foundation for sexual conflict theory is the data from many different taxa demonstrating that females are harmed while interacting with males. However, the interpretation of this keystone evidence has been challenged because females may more than counterbalance the direct costs of interacting with males by the indirect benefits of obtaining higher quality genes for their offspring. A quantification of this trade-off is critical to resolve the controversy and is presented here. A multi-generation fitness assay in the Drosophila melanogaster laboratory model system was used to quantify both the direct costs to females due to interactions with males and indirect benefits via sexy sons. We specifically focus on the interactions that occur between males and nonvirgin females. In the laboratory environment of our base population, females mate soon after eclosion and store sufficient sperm for their entire lifetime, yet males persistently court these nonvirgin females and frequently succeed in re-mating them. Females may benefit from these interactions despite direct costs to their lifetime fecundity if re-mating allows them to trade-up to mates of higher genetic quality and thereby secure indirect benefits for their offspring. We found that direct costs of interactions between males and nonvirgin females substantially exceeded indirect benefits through sexy sons. These data, in combination with past studies of the good genes route of indirect benefits, demonstrate that inter-sexual interactions drive sexually antagonistic co-evolution in this model system.     

Nonparametric pathway-based regression models for analysis of genomic data

High-throughout genomic data provide an opportunity for identifying pathways and genes that are related to various clinical phenotypes. Besides these genomic data, another valuable source of data is the biological knowledge about genes and pathways that might be related to the phenotypes of many complex diseases. Databases of such knowledge are often called the metadata. In microarray data analysis, such metadata are currently explored in post hoc ways by gene set enrichment analysis but have hardly been utilized in the modeling step. We propose to develop and evaluate a pathway-based gradient descent boosting procedure for nonparametric pathways-based regression (NPR) analysis to efficiently integrate genomic data and metadata. Such NPR models consider multiple pathways simultaneously and allow complex interactions among genes within the pathways and can be applied to identify pathways and genes that are related to variations of the phenotypes. These methods also provide an alternative to mediating the problem of a large number of potential interactions by limiting analysis to biologically plausible interactions between genes in related pathways. Our simulation studies indicate that the proposed boosting procedure can indeed identify relevant pathways. Application to a gene expression data set on breast cancer distant metastasis identified that Wnt, apoptosis, and cell cycle-regulated pathways are more likely related to the risk of distant metastasis among lymph-node-negative breast cancer patients. Results from analysis of other two breast cancer gene expression data sets indicate that the pathways of Metalloendopeptidases (MMPs) and MMP inhibitors, as well as cell proliferation, cell growth, and maintenance are important to breast cancer relapse and survival. We also observed that by incorporating the pathway information, we achieved better prediction for cancer recurrence.     

Identification of DISE-inducing shRNAs by monitoring cellular responses

Off-target effects (OTE) are an undesired side effect of RNA interference (RNAi) caused by partial complementarity between the targeting siRNA and mRNAs other than the gene to be silenced. The death receptor CD95 and its ligand CD95L contain multiple sequences that when expressed as either si- or shRNAs kill cancer cells through a defined OTE that targets critical survival genes. Death induced by survival gene elimination (DISE) is characterized by specific morphological changes such as elongated cell shapes, senescence-like enlarged cells, appearance of large intracellular vesicles, release of mitochondrial ROS followed by activation of caspase-2, and induction of a necrotic form of mitotic catastrophe. Using genome-wide shRNA lethality screens with eight different cancer cell lines, we recently identified 651 genes as critical for the survival of cancer cells. To determine whether the toxic shRNAs targeting these 651 genes contained shRNAs that kill cancer cell through DISE rather than by silencing their respective target genes, we tested all shRNAs in the TRC library derived from a subset of these genes targeting tumor suppressors (TS). We now report that only by monitoring the responses of cancer cells following expression of shRNAs derived from these putative TS it was possible to identify DISE-inducing shRNAs in five of the genes. These data indicate that DISE in general is not an undefined toxic response of cells caused by a random OTE but rather a specific cellular response with shared features that points at a specific biological function involving multiple genes in the genome.     

The facilitation of the native bluegill sunfish by the invasive bighead carp

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Prioritization of candidate cancer genes--an aid to oncogenomic studies

The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies.     

Enrichment Analysis Identifies Functional MicroRNA-Disease Associations in Humans

Substantial evidence has shown that microRNAs (miRNAs) may be causally linked to the occurrence and progression of human diseases. Herein, we conducted an enrichment analysis to identify potential functional miRNA-disease associations (MDAs) in humans by integrating currently known biological data: miRNA-target interactions (MTIs), protein-protein interactions, and gene-disease associations. Two contributing factors to functional miRNA-disease associations were quantitatively considered: the direct effects of miRNA that target disease-related genes, and indirect effects triggered by protein-protein interactions. Ninety-nine miRNAs were scanned for possible functional association with 2223 MeSH-defined human diseases. Each miRNA was experimentally validated to target ≥ 10 mRNA genes. Putative MDAs were identified when at least one MTI was confidently validated for a disease. Overall, 19648 putative MDAs were found, of which 10.0% was experimentally validated. Further results suggest that filtering for miRNAs that target a greater number of disease-related genes (n ≥ 8) can significantly enrich for true MDAs from the set of putative associations (enrichment rate = 60.7%, adjusted hypergeometric p = 2.41×10⁻⁹¹). Considering the indirect effects of miRNAs further elevated the enrichment rate to 72.6%. By using this method, a novel MDA between miR-24 and ovarian cancer was found. Compared with scramble miRNA overexpression of miR-24 was validated to remarkably induce ovarian cancer cells apoptosis. Our study provides novel insight into factors contributing to functional MDAs by integrating large quantities of previously generated biological data, and establishes a feasible method to identify plausible associations with high confidence.     

Immediate and delayed life history effects caused by food deprivation early in life in a short‐lived lizard

Detailed studies of the mechanisms driving life history effects of food availability are of prime importance to understand the evolution of phenotypic plasticity and the capacity of organisms to produce better adapted phenotypes. Food availability may influence life history trajectories through three nonexclusive mechanisms: (i) immediate and long‐lasting effects on individual quality, and indirect delayed effects on (ii) intracohort and (iii) intercohort interactions. Using the common lizard (Zootoca vivipara), we tested whether a food deprivation during the two‐first months of life influence life history (growth, survival, reproduction) and performance traits (immunocompetence, locomotor performances) until adulthood. We investigated the underlying mechanisms and their possible interactions by manipulating jointly food availability in a birth cohort and in cohorts of older conspecifics. Food deprivation had direct immediate negative effects on growth but positive long‐lasting effects on immunocompetence. Food deprivation had also indirect delayed effects on growth, body size, early survival and reproduction mediated by an interaction between its direct effects on individual quality and its delayed effects on the intensity of intercohort social interactions combined with density dependence on body size. These results demonstrate that interactions between direct and socially mediated effects of past environments influence life history evolution in size‐structured and stage‐structured populations.     

Sexual conflict is not counterbalanced by good genes in the laboratory Drosophila melanogaster model system

Sexual conflict theory is based on the observation that females of many species are harmed through their interactions with males. Direct harm to females, however, can potentially be counterbalanced by indirect genetic benefits, where females make up for a reduction in offspring quantity by an increase in offspring quality through a generic increase in offspring fitness (good genes) and/or one restricted to the context of sexual selection (sexy sons). Here, we quantify the magnitude of the good genes mechanism of indirect benefits in a laboratory-adapted population of Drosophila melanogaster. We find that despite high-standing genetic variance for fitness, females gain at most only a modest benefit through the good genes form of indirect benefits--far too little to counterbalance the direct cost of male-induced harm.     

Phenotypic and Evolutionary Consequences of Social Behaviours: Interactions among Individuals Affect Direct Genetic Effects

Traditional quantitative genetics assumes that an individual''s phenotype is determined by both genetic and environmental factors. For many animals, part of the environment is social and provided by parents and other interacting partners. When expression of genes in social partners affects trait expression in a focal individual, indirect genetic effects occur. In this study, we explore the effects of indirect genetic effects on the magnitude and range of phenotypic values in a focal individual in a multi-member model analyzing three possible classes of interactions between individuals. We show that social interactions may not only cause indirect genetic effects but can also modify direct genetic effects. Furthermore, we demonstrate that both direct and indirect genetic effects substantially alter the range of phenotypic values, particularly when a focal trait can influence its own expression via interactions with traits in other individuals. We derive a function predicting the relative importance of direct versus indirect genetic effects. Our model reveals that both direct and indirect genetic effects can depend to a large extent on both group size and interaction strength, altering group mean phenotype and variance. This may lead to scenarios where between group variation is much higher than within group variation despite similar underlying genetic properties, potentially affecting the level of selection. Our analysis highlights key properties of indirect genetic effects with important consequences for trait evolution, the level of selection and potentially speciation.     

Direct and indirect ecosystem effects of evolutionary adaptation in the Trinidadian guppy (Poecilia reticulata)

Ecological and evolutionary processes may interact on the same timescale, but we are just beginning to understand how. Several studies have examined the net effects of adaptive evolution on ecosystem properties. However, we do not know whether these effects are confined to direct interactions or whether they propagate further through indirect ecological pathways. Even less well understood is how the combination of direct and indirect ecological effects of the phenotype promotes or inhibits evolutionary change. We coupled mesocosm experiments and ecosystem modeling to evaluate the ecological effects of local adaptation in Trinidadian guppies (Poecilia reticulata). The experiments show that guppies adapted to life with and without predators alter the ecosystem directly through differences in diet. The ecosystem model reveals that the small total indirect effect of the phenotype observed in the experiments is likely a combination of several large indirect effects that act in opposing directions. The model further suggests that these indirect effects can reverse the direction of selection that direct effects alone exert back on phenotypic variation. We conclude that phenotypic divergence can have major effects deep in the web of indirect ecological interactions and that even small total indirect effects can radically change the dynamics of adaptation.     

A novel survival multifactor dimensionality reduction method for detecting gene–gene interactions with application to bladder cancer prognosis

The widespread use of high-throughput methods of single nucleotide polymorphism (SNP) genotyping has created a number of computational and statistical challenges. The problem of identifying SNP–SNP interactions in case–control studies has been studied extensively, and a number of new techniques have been developed. Little progress has been made, however, in the analysis of SNP–SNP interactions in relation to time-to-event data, such as patient survival time or time to cancer relapse. We present an extension of the two class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP–SNP interactions in the context of survival analysis. The proposed Survival MDR (Surv-MDR) method handles survival data by modifying MDR’s constructive induction algorithm to use the log-rank test. Surv-MDR replaces balanced accuracy with log-rank test statistics as the score to determine the best models. We simulated datasets with a survival outcome related to two loci in the absence of any marginal effects. We compared Surv-MDR with Cox-regression for their ability to identify the true predictive loci in these simulated data. We also used this simulation to construct the empirical distribution of Surv-MDR’s testing score. We then applied Surv-MDR to genetic data from a population-based epidemiologic study to find prognostic markers of survival time following a bladder cancer diagnosis. We identified several two-loci SNP combinations that have strong associations with patients’ survival outcome. Surv-MDR is capable of detecting interaction models with weak main effects. These epistatic models tend to be dropped by traditional Cox regression approaches to evaluating interactions. With improved efficiency to handle genome wide datasets, Surv-MDR will play an important role in a research strategy that embraces the complexity of the genotype–phenotype mapping relationship since epistatic interactions are an important component of the genetic basis of disease.     

Predator-Mediated Indirect Effects of Snowshoe Hares on Dall's Sheep in Alaska

ABSTRACT Indirect interactions among species can strongly influence population dynamics and community structure but are often overlooked in management of large mammals. We estimated survival of Dall's sheep (Ovis dalli) in the central Alaska Range, USA, during years of differing snowshoe hare (Lepus americanus) abundance to test whether indirect interactions with a cyclic hare population affect Dall's sheep either negatively, by subsidizing predators (apparent competition), or positively, by diverting predation (apparent commensalism). Annual survival of adult female sheep was consistently high (0.85 for all yr and age classes combined). In contrast, annual estimates of lamb survival ranged from 0.15 to 0.63. The main predators of lambs were coyotes (Canis latrans) and golden eagles (Aquila chrysaetos), which rely on hares as their primary food and prey on lambs secondarily. Coyotes and eagles killed 78% of 65 radiocollared lambs for which cause of death was known. Lamb survival was negatively related to hare abundance during the previous year, and lamb survival rates more than doubled when hare abundance declined, supporting the hypothesis of predator-mediated apparent competition between hares and sheep. However, stage-specific predation and delays in predator responses to changes in hare numbers led to a positive relationship between abundance of adult Dall's sheep and hares. Lacking reliable estimates of survival, a manager might erroneously conclude that hares benefit sheep. Thus, support for different indirect effects can be obtained from different types of data, which demonstrates the need to determine the mechanisms that create indirect interactions. Long-term survey data suggest that predation by coyotes is limiting this sheep population below levels typical when coyotes were rare or absent. Understanding the nature of indirect interactions is necessary to effectively manage complex predator–prey communities.     

Target Inhibition Networks: Predicting Selective Combinations of Druggable Targets to Block Cancer Survival Pathways

A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced prediction accuracies in cross validation as well as significant reduction in computation times. Such cost-effective computational-experimental design strategies have the potential to greatly speed-up the drug testing efforts by prioritizing those interventions and interactions warranting further study in individual cancer cases.     

RhoGDIα-dependent balance between RhoA and RhoC is a key regulator of cancer cell tumorigenesis

RhoGTPases are key signaling molecules regulating main cellular functions such as migration, proliferation, survival, and gene expression through interactions with various effectors. Within the RhoA-related subclass, RhoA and RhoC contribute to several steps of tumor growth, and the regulation of their expression affects cancer progression. Our aim is to investigate their respective contributions to the acquisition of an invasive phenotype by using models of reduced or forced expression. The silencing of RhoC, but not of RhoA, increased the expression of genes encoding tumor suppressors, such as nonsteroidal anti-inflammatory drug-activated gene 1 (NAG-1), and decreased migration and the anchorage-independent growth in vitro. In vivo, RhoC small interfering RNA (siRhoC) impaired tumor growth. Of interest, the simultaneous knockdown of RhoC and NAG-1 repressed most of the siRhoC-related effects, demonstrating the central role of NAG-1. In addition of being induced by RhoC silencing, NAG-1 was also largely up-regulated in cells overexpressing RhoA. The silencing of RhoGDP dissociation inhibitor α (RhoGDIα) and the overexpression of a RhoA mutant unable to bind RhoGDIα suggested that the effect of RhoC silencing is indirect and results from the up-regulation of the RhoA level through competition for RhoGDIα. This study demonstrates the dynamic balance inside the RhoGTPase network and illustrates its biological relevance in cancer progression.     

Supervised harvesting of expression trees

Background

We propose a new method for supervised learning from gene expression data. We call it 'tree harvesting'. This technique starts with a hierarchical clustering of genes, then models the outcome variable as a sum of the average expression profiles of chosen clusters and their products. It can be applied to many different kinds of outcome measures such as censored survival times, or a response falling in two or more classes (for example, cancer classes). The method can discover genes that have strong effects on their own, and genes that interact with other genes.

Results

We illustrate the method on data from a lymphoma study, and on a dataset containing samples from eight different cancers. It identified some potentially interesting gene clusters. In simulation studies we found that the procedure may require a large number of experimental samples to successfully discover interactions.

Conclusions

Tree harvesting is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worthy of further investigation.     

Temperature alters the relative abundance and population growth rates of species within the Dendroctonus frontalis (Coleoptera: Curculionidae) community

Temperature has strong effects on metabolic processes of individuals and demographics of populations, but effects on ecological communities are not well known. Many economically and ecologically important pest species have obligate associations with other organisms; therefore, effects of temperature on these species might be mediated by strong interactions. The southern pine beetle (Dendroctonus frontalis Zimmermann) harbors a rich community of phoretic mites and fungi that are linked by many strong direct and indirect interactions, providing multiple pathways for temperature to affect the system. We tested the effects of temperature on this community by manipulating communities within naturally infested sections of pine trees. Direct effects of temperature on component species were conspicuous and sometimes predictable based on single-species physiology, but there were also strong indirect effects of temperature via alteration of species interactions that could not have been predicted based on autecological temperature responses. Climatic variation, including directional warming, will likely influence ecological systems through direct physiological effects as well as indirect effects through species interactions.     

Hessian fly (Mayetiola destructor) attack causes a dramatic shift in carbon and nitrogen metabolism in wheat

Carbon and nitrogen (C/N) metabolism and allocation within the plant have important implications for plant-parasite interactions. Many plant parasites manipulate the host by inducing C/N changes that benefit their own survival and growth. Plant resistance can prevent this parasite manipulation. We used the wheat-Hessian fly (Mayetiola destructor) system to analyze C/N changes in plants during compatible and incompatible interactions. The Hessian fly is an insect but shares many features with plant pathogens, being sessile during feeding stages and having avirulence (Avr) genes that match plant resistance genes in gene-for-gene relationships. Many wheat genes involved in C/N metabolism were differentially regulated in plants during compatible and incompatible interactions. In plants during compatible interactions, the content of free carbon-containing compounds decreased 36%, whereas the content of free nitrogen-containing compounds increased 46%. This C/N shift was likely achieved through a coordinated regulation of genes in a number of central metabolic pathways, including glycolysis, the tricarboxylic acid cycle, and amino-acid synthesis. Our data on plants during compatible interactions support recent findings that Hessian fly larvae create nutritive cells at feeding (attack) sites and manipulate host plants to enhance their own survival and growth. In plants during incompatible interactions, most of the metabolic genes examined were not affected or down-regulated.     

The relative importance of direct and indirect effects of hunting mortality on the population dynamics of brown bears

There is increasing evidence of indirect effects of hunting on populations. In species with sexually selected infanticide (SSI), hunting may decrease juvenile survival by increasing male turnover. We aimed to evaluate the relative importance of direct and indirect effects of hunting via SSI on the population dynamics of the Scandinavian brown bear (Ursus arctos). We performed prospective and retrospective demographic perturbation analyses for periods with low and high hunting pressures. All demographic rates, except yearling survival, were lower under high hunting pressure, which led to a decline in population growth under high hunting pressure (λ = 0.975; 95% CI = 0.914–1.011). Hunting had negative indirect effects on the population through an increase in SSI, which lowered cub survival and possibly also fecundity rates. Our study suggests that SSI could explain 13.6% of the variation in population growth. Hunting also affected the relative importance of survival and fecundity of adult females for population growth, with fecundity being more important under low hunting pressure and survival more important under high hunting pressure. Our study sheds light on the importance of direct and indirect effects of hunting on population dynamics, and supports the contention that hunting can have indirect negative effects on populations through SSI.