Darwin to DNA, molecules to morphology: the end of classical population genetics and the road ahead

Natural allelic variation defines a role for ATMYC1: trichome cell fate determination

The molecular nature of biological variation is not well understood. Indeed, many questions persist regarding the types of molecular changes and the classes of genes that underlie morphological variation within and among species. Here we have taken a candidate gene approach based on previous mapping results to identify the gene and ultimately a polymorphism that underlies a trichome density QTL in Arabidopsis thaliana. Our results show that natural allelic variation in the transcription factor ATMYC1 alters trichome density in A. thaliana; this is the first reported function for ATMYC1. Using site-directed mutagenesis and yeast two-hybrid experiments, we demonstrate that a single amino acid replacement in ATMYC1, discovered in four ecotypes, eliminates known protein–protein interactions in the trichome initiation pathway. Additionally, in a broad screen for molecular variation at ATMYC1, including 72 A. thaliana ecotypes, a high-frequency block of variation was detected that results in >10% amino acid replacement within one of the eight exons of the gene. This sequence variation harbors a strong signal of divergent selection but has no measurable effect on trichome density. Homologs of ATMYC1 are pleiotropic, however, so this block of variation may be the result of natural selection having acted on another trait, while maintaining the trichome density role of the gene. These results show that ATMYC1 is an important source of variation for epidermal traits in A. thaliana and indicate that the transcription factors that make up the TTG1 genetic pathway generally may be important sources of epidermal variation in plants.

Among the goals of modern evolutionary biology is to identify the molecular genetic sources of natural variation. Although genetic mapping has led to an increased understanding of the genetic architecture of natural variation, there are surprisingly few cases where the molecular source of the variation has been identified. Here, we utilize previous mapping results to identify the gene and ultimately a polymorphism that underlies natural variation for a dynamic trait in Arabidopsis thaliana: trichome density. We show that plants carrying a knock-out of the bHLH transcription factor ATMYC1 have a reduced trichome density phenotype; this is the first reported function for ATMYC1. Using traditional and molecular genetic approaches, we identify a single base change in natural alleles of ATMYC1, which leads to an amino acid replacement that qualitatively alters protein–protein interactions with known partners, presumably altering the trichome cell fate pathway. In a broad screen for molecular variation in ATMYC1, we identify a dense block of amino acid replacements that differentiates two high-frequency allele types. Although this block of variation does not appear to affect trichome density, based on paralogs of ATMYC1, we propose that this variation has arisen due to directional selection on another epidermal trait.

Applications of genome-scale metabolic reconstructions

The availability and utility of genome-scale metabolic reconstructions have exploded since the first genome-scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high-throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis-driven discovery, (4) interrogation of multi-species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome-scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.

Molecular approach for assessing responses of microbial-feeding nematodes to burning and chronic nitrogen enrichment in a native grassland

A substantial proportion of the primary productivity in grassland ecosystems is allocated belowground, sustaining an abundant and diverse community of microbes and soil invertebrates. These belowground communities drive many important ecosystem functions and are responsive to a variety of environmental changes. Nematodes, an abundant and diverse component of grassland soil communities, are particularly responsive to altered environmental conditions, such as those associated with reduced fire frequency and nitrogen enrichment, with the most consistent responses displayed by microbial-feeding nematodes. However, much of the available research characterizing nematode responses to environmental change has been carried out at the taxonomic level of family or by broad trophic categories (e.g. fungivores, bacterivores). The extent to which differential responses to environmental change occurs at the genus level or below is unclear. Therefore, the objective of this study was to use molecular methods to quantify the response of microbial-feeding nematodes, at the lowest levels of taxonomic resolution, to nitrogen enrichment and changes in fire frequency. Using sequencing and quantitative polymerase chain reaction (PCR) probes for the 18S ribosomal RNA gene and the ITS1 region, we identified 19 microbial-feeding nematode taxa across four families. When nematodes were sampled across treatments, we found that some nematode taxa within a family responded similarly to nitrogen and burning treatments, while other taxa within the same family respond quite differently. Additionally, although nematodes from different families on average responded differently to nitrogen enrichment and burning, similar responses were seen in nematode taxa that span three taxonomic families. Thus, if nematodes are to be used as indicators of environmental change, care should be taken to assess the response at the lowest taxonomic level possible.

Genome-wide scan reveals that genetic variation for transcriptional plasticity in yeast is biased towards multi-copy and dispensable genes

One of the most important aspects of the evolution of development and physiology is the interplay between gene expression and the environment, by which traits become altered in response to environmental triggers. This feature is known as phenotypic plasticity. When different genotypes show different levels of plasticity for a trait, then they show genotype-by-environment interaction, or GEI. It is now clear that gene expression plays an important role in organismic-level phenotypic plasticity, but we know very little about whether gene expression itself is subject to genetic variation for phenotypic plasticity (GEI). Given that gene regulation is likely to have evolved to respond to environmental changes, it is of central importance to understand how environmental and genetic variation interact to produce variation in gene expression. Here we investigate genetic variation for phenotypic plasticity in the yeast transcriptome for the whole genome. Six strains of Saccharomyces cerevisiae were grown in four different environments representing a continuum of rich and poor natural conditions. Using DNA-microarray data and an ANOVA analysis with a stringent criterion of significance, we found significant genetic variation for transcriptional plasticity (GEI) among strains for approximately 5% of the genes in the genome. There are about twice as many genes that show genetic variation for phenotypic plasticity as show genetic variation in transcription level independent of the environment. We also found that genes with genetic variation for plasticity were less likely to be essential and were significantly biased towards genes that have paralogs.

Recasting developmental evolution in terms of genetic pathway and network evolution … and the implications for comparative biology

The morphological features of complex organisms are the outcomes of developmental processes. Developmental processes, in turn, reflect the genetic networks that underlie them. Differences in morphology must ultimately, therefore, reflect differences in the underlying genetic networks. A mutation that affects a developmental process does so by affecting either a gene whose product acts as an upstream controlling element, an intermediary connecting link, or as a downstream output of the network that governs the trait's development. Although the immense diversity of gene networks in the animal and plant kingdoms would seem to preclude any general "rules" of network evolution, the material discussed here suggests that the patterns of genetic pathway and network evolution actually fall into a number of discrete modes. The potential utility of this conceptual framework in reconstructing instances of developmental evolution and for comparative neurobiology will be discussed.

Designing experiments using spotted microarrays to detect gene regulation differences within and among species

Comparative studies of genome-wide gene expression must account for variation not only among species, but also within species. Such studies are necessarily large in scale, because they incorporate experiments on multiple individuals of multiple species in multiple developmental stages in multiple environmental conditions. If the experiments are carefully designed and performed, the data they provide are worth the effort. We describe the utility of spotted microarrays for these studies and highlight experimental design criteria that will maximize inferential and statistical power. We conclude with a discussion of experimental protocols that are designed for investigations of differential gene expression and their pitfalls.

The evolution of signaling complexity suggests a mechanism for reducing the genomic search space in human association studies

The size complexity of the human genome has been traditionally viewed as an obstacle that frustrates efforts aimed at identifying the genetic correlates of complex human phenotypes. As such complex phenotypes are attributed to the combined action of numerous genomic loci, attempts to identify the underlying multi-locus interactions may produce a combinatorial sum of false positives that drown out the real signal. Faced with such grim prospects for successfully identifying the genetic basis of complex phenotypes, many geneticists simply disregard epistatic interactions altogether. However, the emerging picture from systems biology is that the cellular programs encoded by the genome utilize nested signaling hierarchies to integrate a number of loosely coupled, semiautonomous, and functionally distinct genetic networks. The current view of these modules is that connections encoding inter-module signaling are relatively sparse, while the gene-to-gene (protein-to-protein) interactions within a particular module are typically denser. We believe that each of these modules is encoded by a finite set of discontinuous, sequence-specific, genomic intervals that are functionally linked to association rules, which correlate directly to features in the environment. Furthermore, because these environmental association rules have evolved incrementally over time, we explore theoretical models of cellular evolution to better understand the role of evolution in genomic complexity. Specifically, we present a conceptual framework for (1) reducing genomic complexity by partitioning the genome into subsets composed of functionally distinct genetic modules and (2) improving the selection of coding region SNPs, which results in an increased probability of identifying functionally relevant SNPs. Additionally, we introduce the notion of 'genomic closure,' which provides a quantitative measure of how functionally insulated a specific genetic module might be from the influence of the rest of the genome. We suggest that the development and use of theoretical models can provide insight into the nature of biological systems and may lead to significant improvements in computational algorithms designed to reduce the complexity of the human genome.