Genomic analysis of gene regulation complexity

With multiple metazoan genomes in each family being sequenced promoter analysis is becoming a useful tool in genomic analysis. Aligning the promoter regions in the DNA of C. elegans and C. briggsae identifies conserved promoter elements. While not all promoter elements are conserved and not all conserved regions are promoter elements, we find that conservation is a useful method for determining promoter complexity. Promoter complexity identifies which genes have particularly interesting regulation, identifying gene groups with a strong promoter complexity signal and cases where a gene\u27s promoter complexity differs from the group\u27s promoter complexity. We identify potential promoter sequence by several local sequence alignment methods. Instead of studying individual promoter elements we are looking at patterns of promoter complexity; the total conserved sequence for each gene gives us a measure for promoter complexity. Monte Carlo random sampling is used to identify Gene Ontology and KEGG Pathway annotated gene groups that appear to have significantly low or high complexity. Developmental genes were found to have low complexity while growth genes have high complexity. Other groups that we expected to have high significance show none at all or had low promoter complexity. Genes contributing to the extracellular region scored high in promoter complexity while basal transcription factors often scored low in complexity. Genes annotated with GO terms transcription factors, signalling genes, genes with multiple alternative splice products, and developmental genes had significant promoter scores. We examined gene expression in the published C. elegans microarray experiments and found a strong positive correlation between gene group expression variation and promoter complexity. Promoter complexity tends to be an accurate predictor of the complexity of a gene\u27s pattern of expression and also gives us another tool to find anomalous genes

Transcriptional Regulation: a Genomic Overview

The availability of the Arabidopsis thaliana genome sequence allows a comprehensive analysis of transcriptional regulation in plants using novel genomic approaches and methodologies. Such a genomic view of transcription first necessitates the compilation of lists of elements. Transcription factors are the most numerous of the different types of proteins involved in transcription in eukaryotes, and the Arabidopsis genome codes for more than 1,500 of them, or approximately 6% of its total number of genes. A genome-wide comparison of transcription factors across the three eukaryotic kingdoms reveals the evolutionary generation of diversity in the components of the regulatory machinery of transcription. However, as illustrated by Arabidopsis, transcription in plants follows similar basic principles and logic to those in animals and fungi. A global view and understanding of transcription at a cellular and organismal level requires the characterization of the Arabidopsis transcriptome and promoterome, as well as of the interactome, the localizome, and the phenome of the proteins involved in transcription

Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions

Genetic regulatory networks (GRNs) have been widely studied, yet there is a lack of understanding with regards to the final size and properties of these networks, mainly due to no network currently being complete. In this study, we analyzed the distribution of GRN structural properties across a large set of distinct prokaryotic organisms and found a set of constrained characteristics such as network density and number of regulators. Our results allowed us to estimate the number of interactions that complete networks would have, a valuable insight that could aid in the daunting task of network curation, prediction, and validation. Using state-of-the-art statistical approaches, we also provided new evidence to settle a previously stated controversy that raised the possibility of complete biological networks being random and therefore attributing the observed scale-free properties to an artifact emerging from the sampling process during network discovery. Furthermore, we identified a set of properties that enabled us to assess the consistency of the connectivity distribution for various GRNs against different alternative statistical distributions. Our results favor the hypothesis that highly connected nodes (hubs) are not a consequence of network incompleteness. Finally, an interaction coverage computed for the GRNs as a proxy for completeness revealed that high-throughput based reconstructions of GRNs could yield biased networks with a low average clustering coefficient, showing that classical targeted discovery of interactions is still needed.Comment: 28 pages, 5 figures, 12 pages supplementary informatio

Topological Analysis of Metabolic Networks Integrating Co-Segregating Transcriptomes and Metabolomes in Type 2 Diabetic Rat Congenic Series

Background: The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus is caused by complex organ-specific cellular mechanisms contributing to impaired insulin secretion and insulin resistance. Methods: We used systematic metabotyping by 1H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualise shortest paths between metabolites and genes significantly associated with each genomic block. Results: Despite strong genomic similarities (95-99%) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific metabotypes (mQTL) and genome-wide expression traits (eQTL). Variation in key metabolites like glucose, succinate, lactate or 3-hydroxybutyrate, and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing shortest path length drove prioritization of biological validations by gene silencing. Conclusions: These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulations and to characterize novel functional roles for genes determining tissue-specific metabolism

The hardwiring of development: Organization and function of genomic regulatory systems

The gene regulatory apparatus that directs development is encoded in the DNA, in the form of organized arrays of transcription factor target sites. Genes are regulated by interactions with multiple transcription factors and the target sites for the transcription factors required for the control of each gene constitute its cis-regulatory system. These systems are remarkably complex. Their hardwired internal organization enables them to behave as genomic information processing systems. Developmental gene regulatory networks consist of the cis-regulatory systems of all the relevant genes and the regulatory linkages amongst them. Though there is yet little explicit information, some general properties of genomic regulatory networks have become apparent. The key to understanding how genomic regulatory networks are organized, and how they work, lies in experimental analysis of cis-regulatory systems at all levels of the regulatory network

The Genome and Methylome of a Subsocial Small Carpenter Bee, Ceratina calcarata

Understanding the evolution of animal societies, considered to be a major transition in evolution, is a key topic in evolutionary biology. Recently, new gateways for understanding social evolution have opened up due to advances in genomics, allowing for unprecedented opportunities in studying social behavior on a molecular level. In particular, highly eusocial insect species (caste-containing societies with nonreproductives that care for siblings) have taken center stage in studies of the molecular evolution of sociality. Despite advances in genomic studies of both solitary and eusocial insects, we still lack genomic resources for early insect societies. To study the genetic basis of social traits requires comparison of genomes from a diversity of organisms ranging from solitary to complex social forms. Here we present the genome of a subsocial bee, Ceratina calcarata. This study begins to address the types of genomic changes associated with the earliest origins of simple sociality using the small carpenter bee. Genes associated with lipid transport and DNA recombination have undergone positive selection in C. calcarata relative to other bee lineages. Furthermore, we provide the first methylome of a noneusocial bee. Ceratina calcarata contains the complete enzymatic toolkit for DNA methylation. As in the honey bee and many other holometabolous insects, DNA methylation is targeted to exons. The addition of this genome allows for new lines of research into the genetic and epigenetic precursors to complex social behaviors

A New Advanced Backcross Tomato Population Enables High Resolution Leaf QTL Mapping and Gene Identification.

Quantitative Trait Loci (QTL) mapping is a powerful technique for dissecting the genetic basis of traits and species differences. Established tomato mapping populations between domesticated tomato (Solanum lycopersicum) and its more distant interfertile relatives typically follow a near isogenic line (NIL) design, such as the S. pennellii Introgression Line (IL) population, with a single wild introgression per line in an otherwise domesticated genetic background. Here, we report on a new advanced backcross QTL mapping resource for tomato, derived from a cross between the M82 tomato cultivar and S. pennellii This so-called Backcrossed Inbred Line (BIL) population is comprised of a mix of BC2 and BC3 lines, with domesticated tomato as the recurrent parent. The BIL population is complementary to the existing S. pennellii IL population, with which it shares parents. Using the BILs, we mapped traits for leaf complexity, leaflet shape, and flowering time. We demonstrate the utility of the BILs for fine-mapping QTL, particularly QTL initially mapped in the ILs, by fine-mapping several QTL to single or few candidate genes. Moreover, we confirm the value of a backcrossed population with multiple introgressions per line, such as the BILs, for epistatic QTL mapping. Our work was further enabled by the development of our own statistical inference and visualization tools, namely a heterogeneous hidden Markov model for genotyping the lines, and by using state-of-the-art sparse regression techniques for QTL mapping

The modern versus extended evolutionary synthesis : sketch of an intra-genomic gene's eye view for the evolutionary-genetic underpinning of epigenetic and developmental evolution

Studying the phenotypic evolution of organisms in terms of populations of genes and genotypes, the Modern Synthesis (MS) conceptualizes biological evolution in terms of 'inter-organismal' interactions among genes sitting in the different individual organisms that constitute a population. It 'black-boxes' the complex 'intra-organismic' molecular and developmental epigenetics mediating between genotypes and phenotypes. To conceptually integrate epigenetics and evo-devo into evolutionary theory, advocates of an Extended Evolutionary Synthesis (EES) argue that the MS's reductive gene-centrism should be abandoned in favor of a more inclusive organism-centered approach. To push the debate to a new level of understanding, we introduce the evolutionary biology of 'intra-genomic conflict' (IGC) to the controversy. This strategy is based on a twofold rationale. First, the field of IGC is both ‘gene-centered’ and 'intra-organismic' and, as such, could build a bridge between the gene-centered MS and the intra-organismic fields of epigenetics and evo-devo. And second, it is increasingly revealed that IGC plays a significant causal role in epigenetic and developmental evolution and even in speciation. Hence, to deal with the ‘discrepancy’ between the ‘gene-centered’ MS and the ‘intra-organismic’ fields of epigenetics and evo-devo, we sketch a conceptual solution in terms of ‘intra-genomic conflict and compromise’ – an ‘intra-genomic gene’s eye view’ that thinks in terms of intra-genomic ‘evolutionarily stable strategies’ (ESSs) among numerous and various DNA regions and elements – to evolutionary-genetically underwrite both epigenetic and developmental evolution, as such questioning the ‘gene-de-centered’ stance put forward by EES-advocates