Interindividual variation in epigenomic phenomena in humans

Our knowledge of regulatory mechanisms of gene expression and other chromosomal processes related to DNA methylation and chromatin state is continuing to grow at a rapid pace. Understanding how these epigenomic phenomena vary between individuals will have an impact on understanding their broader role in determining variation in gene expression and biochemical, physiological, and behavioural phenotypes. In this review we survey recent progress in this area, focusing on data available from humans. We highlight the role of obligatory (sequence-dependent) epigenomic variation as an important mechanism for generating interindividual variation that could impact our understanding of the mechanistic basis of complex trait architecture.     

Faster-X Evolution of Gene Expression in Drosophila

DNA sequences on X chromosomes often have a faster rate of evolution when compared to similar loci on the autosomes, and well articulated models provide reasons why the X-linked mode of inheritance may be responsible for the faster evolution of X-linked genes. We analyzed microarray and RNA–seq data collected from females and males of six Drosophila species and found that the expression levels of X-linked genes also diverge faster than autosomal gene expression, similar to the “faster-X” effect often observed in DNA sequence evolution. Faster-X evolution of gene expression was recently described in mammals, but it was limited to the evolutionary lineages shortly following the creation of the therian X chromosome. In contrast, we detect a faster-X effect along both deep lineages and those on the tips of the Drosophila phylogeny. In Drosophila males, the dosage compensation complex (DCC) binds the X chromosome, creating a unique chromatin environment that promotes the hyper-expression of X-linked genes. We find that DCC binding, chromatin environment, and breadth of expression are all predictive of the rate of gene expression evolution. In addition, estimates of the intraspecific genetic polymorphism underlying gene expression variation suggest that X-linked expression levels are not under relaxed selective constraints. We therefore hypothesize that the faster-X evolution of gene expression is the result of the adaptive fixation of beneficial mutations at X-linked loci that change expression level in cis. This adaptive faster-X evolution of gene expression is limited to genes that are narrowly expressed in a single tissue, suggesting that relaxed pleiotropic constraints permit a faster response to selection. Finally, we present a conceptional framework to explain faster-X expression evolution, and we use this framework to examine differences in the faster-X effect between Drosophila and mammals.     

Variable number tandem repeats – Their emerging role in sickness and health

Understanding the mechanisms regulating tissue specific and stimulus inducible regulation is at the heart of understanding human biology and how this translates to wellbeing, the ageing process, and disease progression. Polymorphic DNA variation is superimposed as an extra layer of complexity in such processes which underpin our individuality and are the focus of personalized medicine. This review focuses on the role and action of repetitive DNA, specifically variable number tandem repeats and SINE-VNTR-Alu domains, highlighting their role in modification of gene structure and gene expression in addition to their polymorphic nature being a genetic modifier of disease risk and progression. Although the literature focuses on their role in disease, it illustrates their potential to be major contributors to normal physiological function. To date, these elements have been under-reported in genomic analysis due to the difficulties in their characterization with short read DNA sequencing methods. However, recent advances in long read sequencing methods should resolve these problems allowing for a greater understanding of their contribution to a host of genomic and functional mechanisms underlying physiology and disease.     

Gene expression and the evolution of phenotypic diversity in social wasps

Background  

Organisms are capable of developing different phenotypes by altering the genes they express. This phenotypic plasticity provides a means for species to respond effectively to environmental conditions. One of the most dramatic examples of phenotypic plasticity occurs in the highly social hymenopteran insects (ants, social bees, and social wasps), where distinct castes and sexes all arise from the same genes. To elucidate how variation in patterns of gene expression affects phenotypic variation, we conducted a study to simultaneously address the influence of developmental stage, sex, and caste on patterns of gene expression in Vespula wasps. Furthermore, we compared the patterns found in this species to those found in other taxa in order to investigate how variation in gene expression leads to phenotypic evolution.     

Synthetic circuits reveal how mechanisms of gene regulatory networks constrain evolution

Phenotypic variation is the raw material of adaptive Darwinian evolution. The phenotypic variation found in organismal development is biased towards certain phenotypes, but the molecular mechanisms behind such biases are still poorly understood. Gene regulatory networks have been proposed as one cause of constrained phenotypic variation. However, most pertinent evidence is theoretical rather than experimental. Here, we study evolutionary biases in two synthetic gene regulatory circuits expressed in Escherichia coli that produce a gene expression stripe—a pivotal pattern in embryonic development. The two parental circuits produce the same phenotype, but create it through different regulatory mechanisms. We show that mutations cause distinct novel phenotypes in the two networks and use a combination of experimental measurements, mathematical modelling and DNA sequencing to understand why mutations bring forth only some but not other novel gene expression phenotypes. Our results reveal that the regulatory mechanisms of networks restrict the possible phenotypic variation upon mutation. Consequently, seemingly equivalent networks can indeed be distinct in how they constrain the outcome of further evolution.     

The common ground of genomics and systems biology

The rise of systems biology is intertwined with that of genomics, yet their primordial relationship to one another is ill-defined. We discuss how the growth of genomics provided a critical boost to the popularity of systems biology. We describe the parts of genomics that share common areas of interest with systems biology today in the areas of gene expression, network inference, chromatin state analysis, pathway analysis, personalized medicine, and upcoming areas of synergy as genomics continues to expand its scope across all biomedical fields.     

Reshaping of global gene expression networks and sex-biased gene expression by integration of a young gene

New genes originate frequently across diverse taxa. Given that genetic networks are typically comprised of robust, co-evolved interactions, the emergence of new genes raises an intriguing question: how do new genes interact with pre-existing genes? Here, we show that a recently originated gene rapidly evolved new gene networks and impacted sex-biased gene expression in Drosophila. This 4–6 million-year-old factor, named Zeus for its role in male fecundity, originated through retroposition of a highly conserved housekeeping gene, Caf40. Zeus acquired male reproductive organ expression patterns and phenotypes. Comparative expression profiling of mutants and closely related species revealed that Zeus has recruited a new set of downstream genes, and shaped the evolution of gene expression in germline. Comparative ChIP-chip revealed that the genomic binding profile of Zeus diverged rapidly from Caf40. These data demonstrate, for the first time, how a new gene quickly evolved novel networks governing essential biological processes at the genomic level.     

Harnessing gene expression to identify the genetic basis of drug resistance

The advent of cost‐effective genotyping and sequencing methods have recently made it possible to ask questions that address the genetic basis of phenotypic diversity and how natural variants interact with the environment. We developed Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), a statistical method that integrates genotype, gene expression and phenotype data to automatically build models that both predict complex quantitative phenotypes and identify genes that actively influence these traits. Camelot integrates genotype and gene expression data, both generated under a reference condition, to predict the response to entirely different conditions. We systematically applied our algorithm to data generated from a collection of yeast segregants, using genotype and gene expression data generated under drug‐free conditions to predict the response to 94 drugs and experimentally confirmed 14 novel gene–drug interactions. Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug.