Developmental evolution in social insects: regulatory networks from genes to societies

The evolution and development of complex phenotypes in social insect colonies, such as queen-worker dimorphism or division of labor, can, in our opinion, only be fully understood within an expanded mechanistic framework of Developmental Evolution. Conversely, social insects offer a fertile research area in which fundamental questions of Developmental Evolution can be addressed empirically. We review the concept of gene regulatory networks (GRNs) that aims to fully describe the battery of interacting genomic modules that are differentially expressed during the development of individual organisms. We discuss how distinct types of network models have been used to study different levels of biological organization in social insects, from GRNs to social networks. We propose that these hierarchical networks spanning different organizational levels from genes to societies should be integrated and incorporated into full GRN models to elucidate the evolutionary and developmental mechanisms underlying social insect phenotypes. Finally, we discuss prospects and approaches to achieve such an integration.     

Growing genetic regulatory networks from seed genes

MOTIVATION: A number of models have been proposed for genetic regulatory networks. In principle, a network may contain any number of genes, so long as data are available to make inferences about their relationships. Nevertheless, there are two important reasons why the size of a constructed network should be limited. Computationally and mathematically, it is more feasible to model and simulate a network with a small number of genes. In addition, it is more likely that a small set of genes maintains a specific core regulatory mechanism. RESULTS: Subnetworks are constructed in the context of a directed graph by beginning with a seed consisting of one or more genes believed to participate in a viable subnetwork. Functionalities and regulatory relationships among seed genes may be partially known or they may simply be of interest. Given the seed, we iteratively adjoin new genes in a manner that enhances subnetwork autonomy. The algorithm is applied using both the coefficient of determination and the Boolean-function influence among genes, and it is illustrated using a glioma gene-expression dataset. AVAILABILITY: Software for the seed-growing algorithm will be available at the website for Probabilistic Boolean Networks: http://www2.mdanderson.org/app/ilya/PBN/PBN.htm     

EMERGE: a flexible modelling framework to predict genomic regulatory elements from genomic signatures

Regulatory DNA elements, short genomic segments that regulate gene expression, have been implicated in developmental disorders and human disease. Despite this clinical urgency, only a small fraction of the regulatory DNA repertoire has been confirmed through reporter gene assays. The overall success rate of functional validation of candidate regulatory elements is low. Moreover, the number and diversity of datasets from which putative regulatory elements can be identified is large and rapidly increasing. We generated a flexible and user-friendly tool to integrate the information from different types of genomic datasets, e.g. ATAC-seq, ChIP-seq, conservation, aiming to increase the ease and success rate of functional prediction. To this end, we developed the EMERGE program that merges all datasets that the user considers informative and uses a logistic regression framework, based on validated functional elements, to set optimal weights to these datasets. ROC curve analysis shows that a combination of datasets leads to improved prediction of tissue-specific enhancers in human, mouse and Drosophila genomes. Functional assays based on this prediction can be expected to have substantially higher success rates. The resulting integrated signal for prediction of functional elements can be plotted in a build-in genome browser or exported for further analysis.     

Genetics of autism: from genome scans to candidate genes

The autistic disorder was firstly described by Leo Kanner sixty years ago. This complex developmental disability is characterized by social and communicative impairments and repetitive and stereotyped behaviours and interests. The prevalence of autism in the general population is about 1 in 1,000, with four males affected for one female. In approximately 15% of the cases, autism is associated with known genetic disorders, such as fragile X syndrome, tuberous sclerosis or Rett syndrome. Nevertheless, a recognised medical etiology can only be identified in a minority of cases. A higher recurrence risk in families with autistic subjects (45 times greater than the prevalence in the general population) and higher concordance for autism among monozygotic (60-90%) than dizygotic (0-10%) twins argue for a genetic predisposition to idiopathic autism. The past decade has been marked by an increased interest in the genetic basis of autism, with a series of multiple independent whole genome scans and chromosomal abnormalities studies. These analyses have pointed out several candidate regions on chromosomes 2q, 7q, 6q, 15q and sex chromosomes. These regions possess candidate genes that have been screened for mutations or association with autism. However, a clear involvement of a major susceptibility gene (or genes) in autism remains far from clear. The results from linkage studies and the clear drop in the concordance rates between monozygotic and dizygotic twins suggests that the genetic aetiology of autism is certainly heterogeneous (different genes in different families) and polygenic (more than one affected gene per individual). The almost finished sequence of the human genome and the generation of haplotype maps will shed light on the inter-individual genetic variability and will certainly increase the power and reliability of association studies for complex traits, such as autism.     

Reconstructing gene regulatory networks: from random to scale-free connectivity

The manipulation of organisms using combinations of gene knockout, RNAi and drug interaction experiments can be used to reveal regulatory interactions between genes. Several algorithms have been proposed that try to reconstruct the underlying regulatory networks from gene expression data sets arising from such experiments. Often these approaches assume that each gene has approximately the same number of interactions within the network, and the methods rely on prior knowledge, or the investigator's best guess, of the average network connectivity. Recent evidence points to scale-free properties in biological networks, however, where network connectivity follows a power-law distribution. For scale-free networks, the average number of regulatory interactions per gene does not satisfactorily characterise the network. With this in mind, a new reverse engineering approach is introduced that does not require prior knowledge of network connectivity and its performance is compared with other published algorithms using simulated gene expression data with biologically relevant network structures. Because this new approach does not make any assumptions about the distribution of network connections, it is suitable for application to scale-free networks.     

Deconvoluting lung evolution: from phenotypes to gene regulatory networks

Speakers in this symposium presented examples of respiratoryregulation that broadly illustrate principles of evolution fromwhole organ to genes. The swim bladder and lungs of aquaticand terrestrial organisms arose independently from a commonprimordial "respiratory pharynx" but not from each other. Pathwaysof lung evolution are similar between crocodiles and birds buta low compliance of mammalian lung may have driven the developmentof the diaphragm to permit lung inflation during inspiration.To meet the high oxygen demands of flight, bird lungs have evolvedseparate gas exchange and pump components to achieve unidirectionalventilation and minimize dead space. The process of "screening"(removal of oxygen from inspired air prior to entering the terminalunits) reduces effective alveolar oxygen tension and potentiallyexplains why nonathletic large mammals possess greater pulmonarydiffusing capacities than required by their oxygen consumption.The "primitive" central admixture of oxygenated and deoxygenatedblood in the incompletely divided reptilian heart is actuallyco-regulated with other autonomic cardiopulmonary responsesto provide flexible control of arterial oxygen tension independentof ventilation as well as a unique mechanism for adjusting metabolicrate. Some of the most ancient oxygen-sensing molecules, i.e.,hypoxia-inducible factor-1alpha and erythropoietin, are up-regulatedduring mammalian lung development and growth under apparentlynormoxic conditions, suggesting functional evolution. Normalalveolarization requires pleiotropic growth factors acting viahighly conserved cell–cell signal transduction, e.g.,parathyroid hormone-related protein transducing at least partlythrough the Wingless/int pathway. The latter regulates morphogenesisfrom nematode to mammal. If there is commonality among thesediverse respiratory processes, it is that all levels of organization,from molecular signaling to structure to function, co-evolveprogressively, and optimize an existing gas-exchange framework.     

Tracking of time-varying genomic regulatory networks with a LASSO-Kalman smoother

It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. In this paper, we refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth. We propose a novel outlook on the inference of time-varying genetic networks, from a limited number of noisy observations, by formulating the network estimation as a target tracking problem. We overcome the limited number of observations (small n large p problem) by performing tracking in a compressed domain. Assuming linear dynamics, we derive the LASSO-Kalman smoother, which recursively computes the minimum mean-square sparse estimate of the network connectivity at each time point. The LASSO operator, motivated by the sparsity of the genetic regulatory networks, allows simultaneous signal recovery and compression, thereby reducing the amount of required observations. The smoothing improves the estimation by incorporating all observations. We track the time-varying networks during the life cycle of the Drosophila melanogaster. The recovered networks show that few genes are permanent, whereas most are transient, acting only during specific developmental phases of the organism.     

Comparative genomic analysis of Ralstonia solanacearum reveals candidate genes for host specificity

Background

Ralstonia solanacearum is a vascular soil-borne plant pathogen with an unusually broad host range. This economically destructive and globally distributed bacterium has thousands of distinct lineages within a heterogeneous and taxonomically disputed species complex. Some lineages include highly host-adapted strains (ecotypes), such as the banana Moko disease-causing strains, the cold-tolerant potato brown rot strains (also known as R3bv2) and the recently emerged Not Pathogenic to Banana (NPB) strains.

Results

These distinct ecotypes offer a robust model to study host adaptation and the emergence of ecotypes because the polyphyletic Moko strains include lineages that are phylogenetically close to the monophyletic brown rot and NPB strains. Draft genomes of eight new strains belonging to these three model ecotypes were produced to complement the eleven publicly available R. solanacearum genomes. Using a suite of bioinformatics methods, we searched for genetic and evolutionary features that distinguish ecotypes and propose specific hypotheses concerning mechanisms of host adaptation in the R. solanacearum species complex. Genome-wide, few differences were identified, but gene loss events, non-synonymous polymorphisms, and horizontal gene transfer were identified among type III effectors and were associated with host range differences.

Conclusions

This extensive comparative genomics analysis uncovered relatively few divergent features among closely related strains with contrasting biological characteristics; however, several virulence factors were associated with the emergence of Moko, NPB and brown rot and could explain host adaptation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1474-8) contains supplementary material, which is available to authorized users.     

Predicting biological networks from genomic data

Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate to high-throughput experimental methods.     

The CanOE strategy: integrating genomic and metabolic contexts across multiple prokaryote genomes to find candidate genes for orphan enzymes

Of all biochemically characterized metabolic reactions formalized by the IUBMB, over one out of four have yet to be associated with a nucleic or protein sequence, i.e. are sequence-orphan enzymatic activities. Few bioinformatics annotation tools are able to propose candidate genes for such activities by exploiting context-dependent rather than sequence-dependent data, and none are readily accessible and propose result integration across multiple genomes. Here, we present CanOE (Candidate genes for Orphan Enzymes), a four-step bioinformatics strategy that proposes ranked candidate genes for sequence-orphan enzymatic activities (or orphan enzymes for short). The first step locates "genomic metabolons", i.e. groups of co-localized genes coding proteins catalyzing reactions linked by shared metabolites, in one genome at a time. These metabolons can be particularly helpful for aiding bioanalysts to visualize relevant metabolic data. In the second step, they are used to generate candidate associations between un-annotated genes and gene-less reactions. The third step integrates these gene-reaction associations over several genomes using gene families, and summarizes the strength of family-reaction associations by several scores. In the final step, these scores are used to rank members of gene families which are proposed for metabolic reactions. These associations are of particular interest when the metabolic reaction is a sequence-orphan enzymatic activity. Our strategy found over 60,000 genomic metabolons in more than 1,000 prokaryote organisms from the MicroScope platform, generating candidate genes for many metabolic reactions, of which more than 70 distinct orphan reactions. A computational validation of the approach is discussed. Finally, we present a case study on the anaerobic allantoin degradation pathway in Escherichia coli K-12.     

Evolution of disease response genes in loblolly pine: insights from candidate genes

Background

Host-pathogen interactions that may lead to a competitive co-evolution of virulence and resistance mechanisms present an attractive system to study molecular evolution because strong, recent (or even current) selective pressure is expected at many genomic loci. However, it is unclear whether these selective forces would act to preserve existing diversity, promote novel diversity, or reduce linked neutral diversity during rapid fixation of advantageous alleles. In plants, the lack of adaptive immunity places a larger burden on genetic diversity to ensure survival of plant populations. This burden is even greater if the generation time of the plant is much longer than the generation time of the pathogen.

Methodology/Principal Findings

Here, we present nucleotide polymorphism and substitution data for 41 candidate genes from the long-lived forest tree loblolly pine, selected primarily for their prospective influences on host-pathogen interactions. This dataset is analyzed together with 15 drought-tolerance and 13 wood-quality genes from previous studies. A wide range of neutrality tests were performed and tested against expectations from realistic demographic models.

Conclusions/Significance

Collectively, our analyses found that axr (auxin response factor), caf1 (chromatin assembly factor) and gatabp1 (gata binding protein 1) candidate genes carry patterns consistent with directional selection and erd3 (early response to drought 3) displays patterns suggestive of a selective sweep, both of which are consistent with the arm-race model of disease response evolution. Furthermore, we have identified patterns consistent with diversifying selection at erf1-like (ethylene responsive factor 1), ccoaoemt (caffeoyl-CoA-O-methyltransferase), cyp450-like (cytochrome p450-like) and pr4.3 (pathogen response 4.3), expected under the trench-warfare evolution model. Finally, a drought-tolerance candidate related to the plant cell wall, lp5, displayed patterns consistent with balancing selection. In conclusion, both arms-race and trench-warfare models seem compatible with patterns of polymorphism found in different disease-response candidate genes, indicating a mixed strategy of disease tolerance evolution for loblolly pine, a major tree crop in southeastern United States.