Cellular decision-making bias: the missing ingredient in cell functional diversity

Cell functional diversity is a significant determinant on how biological processes unfold. Most accounts of diversity involve a search for sequence or expression differences. Perhaps there are more subtle mechanisms at work. Using the metaphor of information processing and decision-making might provide a clearer view of these subtleties. Understanding adaptive and transformative processes (such as cellular reprogramming) as a series of simple decisions allows us to use a technique called cellular signal detection theory (cellular SDT) to detect potential bias in mechanisms that favor one outcome over another. We can apply method of detecting cellular reprogramming bias to cellular reprogramming and other complex molecular processes. To demonstrate scope of this method, we will critically examine differences between cell phenotypes reprogrammed to muscle fiber and neuron phenotypes. In cases where the signature of phenotypic bias is cryptic, signatures of genomic bias (pre-existing and induced) may provide an alternative. The examination of these alternates will be explored using data from a series of fibroblast cell lines before cellular reprogramming (pre-existing) and differences between fractions of cellular RNA for individual genes after drug treatment (induced). In conclusion, the usefulness and limitations of this method and associated analogies will be discussed.Comment: 18 pages; 6 figures, 2 tables, 4 supplemental figure

Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes

Even within a defined cell type, the expression level of a gene differs in individual samples. The effects of genotype, measured factors such as environmental conditions, and their interactions have been explored in recent studies. Methods have also been developed to identify unmeasured intermediate factors that coherently influence transcript levels of multiple genes. Here, we show how to bring these two approaches together and analyse genetic effects in the context of inferred determinants of gene expression. We use a sparse factor analysis model to infer hidden factors, which we treat as intermediate cellular phenotypes that in turn affect gene expression in a yeast dataset. We find that the inferred phenotypes are associated with locus genotypes and environmental conditions and can explain genetic associations to genes in trans. For the first time, we consider and find interactions between genotype and intermediate phenotypes inferred from gene expression levels, complementing and extending established results

Gene × environment interactions for ADHD: synergistic effect of 5HTTLPR genotype and youth appraisals of inter-parental conflict

<p>Abstract</p> <p>Background</p> <p>Serotonin genes have been hypothesized to play a role in the etiology of attention-deficit hyperactivity disorder (ADHD); prior work suggests that serotonin may interact with psychosocial stressors in ADHD, perhaps via mechanisms involved in emotional dysregulation. Because the development of behavioral and emotional regulation depends heavily both on the child's experience within the family context and the child's construals of that experience, children's appraisals of inter-parental conflict are a compelling candidate potentiator of the effects of variation within the serotonin transporter gene promoter polymorphism (5HTTLPR) on liability for ADHD.</p> <p>Method</p> <p>304 youth from the local community underwent a multi-informant diagnostic assessment procedure to identify ADHD cases and non-ADHD controls. Youth also completed the Children's Perception of Inter-Parental Conflict (CPIC) scale to assess appraisals of self-blame in relation to their parents' marital disputes. The trialleic configuration of 5HTTLPR (long/short polymorphism with A> G substitution) was genotyped and participants were assigned as having high (La/La N = 78), intermediate (La/Lg, La/short, N = 137), or low (Lg/Lg, Lg/short, short/short, N = 89) serotonin transporter activity genotypes. Teacher reported behavior problems were examined as the target outcome to avoid informant overlap for moderator and outcome measures.</p> <p>Results</p> <p>Hierarchical linear regression analyses indicated significant 5HTTLPR × self-blame interactions for ADHD symptoms. Examination of the interactions indicated positive relations between reports of self-blame and ADHD symptoms for those with the high and low serotonin activity genotypes. There was no relation between self-blame and ADHD for those with intermediate activity 5HTTLPR genotypes.</p> <p>Conclusion</p> <p>Both high and low serotonergic activity may exert risk for ADHD when coupled with psychosocial distress such as children's self-blame in relation to inter-parental conflict. Results are discussed in relation to the role of serotonin in the etiology of the ADHD and related externalizing behaviors.</p

Probabilistic Latent Variable Models in Statistical Genomics

In this thesis, we propose different probabilistic latent variable mod- els to identify and capture the hidden structure present in commonly studied genomics datasets. We start by investigating how to cor- rect for unwanted correlations due to hidden confounding factors in gene expression data. This is particularly important in expression quantitative trait loci (eQTL) studies, where the goal is to identify associations between genetic variants and gene expression levels. We start with a na¨ ıve approach, which estimates the latent factors from the gene expression data alone, ignoring the genetics, and we show that it leads to a loss of signal in the data. We then highlight how, thanks to the formulation of our model as a probabilistic model, it is straightforward to modify it in order to take into account the specific properties of the data. In particular, we show that in the na¨ ıve ap- proach the latent variables ”explain away” the genetic signal, and that this problem can be avoided by jointly inferring these latent variables while taking into account the genetic information. We then extend this, so far additive, model to additionally detect interactions between the latent variables and the genetic markers. We show that this leads to a better reconstruction of the latent space and that it helps dis- secting latent variables capturing general confounding factors (such as batch effects) from those capturing environmental factors involved in genotype-by-environment interactions. Finally, we investigate the effects of misspecifications of the noise model in genetic studies, show- ing how the probabilistic framework presented so far can be easily ex- tended to automatically infer non-linear monotonic transformations of the data such that the common assumption of Gaussian distributed residuals is respected

Using regulatory variants to detect gene-gene interactions identifies networks of genes linked to cell immortalization

The extent to which the impact of regulatory genetic variants may depend on other factors, such as the expression levels of upstream transcription factors, remains poorly understood. Here we report a framework in which regulatory variants are first aggregated into sets, and using these as estimates of the total cis-genetic effects on a gene we model their non-additive interactions with the expression of other genes in the genome. Using 1220 lymphoblastoid cell lines across platforms and independent datasets we identify 74 genes where the impact of their regulatory variant-set is linked to the expression levels of networks of distal genes. We show that these networks are predominantly associated with tumourigenesis pathways, through which immortalised cells are able to rapidly proliferate. We consequently present an approach to define gene interaction networks underlying important cellular pathways such as cell immortalisation

Mediation Analysis Demonstrates That Trans-eQTLs Are Often Explained by Cis-Mediation:A Genome-Wide Analysis among 1,800 South Asians

A large fraction of human genes are regulated by genetic variation near the transcribed sequence (cis-eQTL, expression quantitative trait locus), and many cis-eQTLs have implications for human disease. Less is known regarding the effects of genetic variation on expression of distant genes (trans-eQTLs) and their biological mechanisms. In this work, we use genome-wide data on SNPs and array-based expression measures from mononuclear cells obtained from a population-based cohort of 1,799 Bangladeshi individuals to characterize cis- and trans-eQTLs and determine if observed trans-eQTL associations are mediated by expression of transcripts in cis with the SNPs showing trans-association, using Sobel tests of mediation. We observed 434 independent trans-eQTL associations at a false-discovery rate of 0.05, and 189 of these transeQTLs were also cis-eQTLs (enrichment P</p

Towards the identification of the loci of adaptive evolution.

1. Establishing the genetic and molecular basis underlying adaptive traits is one of the major goals of evolutionary geneticists in order to understand the connection between genotype and phenotype and elucidate the mechanisms of evolutionary change. Despite considerable effort to address this question, there remain relatively few systems in which the genes shaping adaptations have been identified. 2. Here, we review the experimental tools that have been applied to document the molecular basis underlying evolution in several natural systems, in order to highlight their benefits, limitations and suitability. In most cases, a combination of DNA, RNA and functional methodologies with field experiments will be needed to uncover the genes and mechanisms shaping adaptation in nature.This work was supported by BBSRC grant number BB/K019945/1 to CJ.This is the final published version of the article. It was originally published online in Methods in Ecology and Evolution, 2015

Gene-environment interactions in sarcoidosis

Susceptibility to most human diseases is polygenic, with complex interactions between functional polymorphisms of single genes governing disease incidence, phenotype, or both. In this context, the contribution of any discrete gene is generally modest for a single individual, but may confer substantial attributable risk on a population level. Environmental exposure can modify the effects of a polymorphism, either by providing a necessary substrate for development of human disease or because the effects of a given exposure modulate the effects of the gene. In several diseases, genetic polymorphisms have been shown to be context-dependent, i.e. the effects of a genetic variant are realized only in the setting of a relevant exposure. Since sarcoidosis susceptibility is dependent on both genetic and environmental modifiers, the study of gene-environment interactions may yield important pathogenetic information and will likely be crucial for uncovering the range of genetic susceptibility loci. However, the complexity of these relationships implies that investigations of geneenvironment interactions will require the study of large cohorts with carefully-defined exposures and similar clinical phenotypes. A general principle is that the study of gene-environment interactions requires a sample size at least several-fold greater than for either factor alone. To date, the presence of environmental modifiers has been demonstrated for one sarcoidosis susceptibility locus, HLADQB1, in African-American families. This article reviews general considerations obtaining for the study of gene-environment interactions in sarcoidosis. It also describes the limited current understanding of the role of environmental influences on sarcoidosis susceptibility genes. Originally published Clinics in Dermatology, Vol. 25, No. 3, May-June 200

Ecological Complex Systems

Main aim of this topical issue is to report recent advances in noisy nonequilibrium processes useful to describe the dynamics of ecological systems and to address the mechanisms of spatio-temporal pattern formation in ecology both from the experimental and theoretical points of view. This is in order to understand the dynamical behaviour of ecological complex systems through the interplay between nonlinearity, noise, random and periodic environmental interactions. Discovering the microscopic rules and the local interactions which lead to the emergence of specific global patterns or global dynamical behaviour and the noises role in the nonlinear dynamics is an important, key aspect to understand and then to model ecological complex systems.Comment: 13 pages, Editorial of a topical issue on Ecological Complex System to appear in EPJ B, Vol. 65 (2008