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Although similar developmental regulatory networks can produce diverse phenotypes, different networks can also produce the same phenotype. In theory, as long as development can produce an acceptable end phenotype, the details of the process could be shielded from selection, leading to the possibility of developmental system drift, where the developmental mechanisms underlying a stable phenotype continue to evolve. Many examples exist of divergent developmental genetics underlying conserved traits. However, studies that elucidate how these differences arose and how other features of development accommodated them are rarer. In Caenorhabditis elegans, six GATA-type transcription factors (GATA factors) comprise the zygotic part of the endoderm specification network. Here we show that the core of this network - five of the genes - originated within the genus during a brief but explosive radiation of this gene family and that at least three of them evolved from a single ancestral gene with at least two different spatio-temporal expression patterns. Based on analyses of their evolutionary history, gene structure, expression, and sequence, we explain how these GATA factors were integrated into this network. Our results show how gene duplication fueled the developmental system drift of the endoderm network in a phylogenetically brief period in developmentally canalized worms.

Transcription factors are defined by their DNA-binding domains (DBDs). The binding affinities and specificities of a transcription factor to its DNA binding sites can be used by an organism to fine-tune gene regulation and so are targets for evolution. Here we investigate the evolution of GATA-type transcription factors (GATA factors) in the Caenorhabditis genus. Based upon comparisons of their DBDs, these proteins form 13 distinct groups. This protein family experienced a burst of gene duplication in several of these groups along two short branches in the species tree, giving rise to subclades with very distinct complements of GATA factors. By comparing extant gene structures, DBD sequences, genome locations, and selection pressures we reconstructed how these duplications occurred. Although the paralogs have diverged in various ways, the literature shows that at least eight of the DBD groups bind to similar G-A-T-A DNA sequences. Thus, despite gene duplications and divergence among DBD sequences, most Caenorhabditis GATA factors appear to have maintained similar binding preferences, which could create the opportunity for developmental system drift. We hypothesize that this limited divergence in binding specificities contributes to the apparent disconnect between the extensive genomic evolution that has occurred in this genus and the absence of significant anatomical changes.

The cyanobacterium Synechococcus elongatus is a model organism for the study of circadian rhythms. It is naturally competent for transformation—that is, it takes up DNA from the environment, but the underlying mechanisms are unclear. Here, we use a genome-wide screen to identify genes required for natural transformation in S. elongatus, including genes encoding a conserved Type IV pilus, genes known to be associated with competence in other bacteria, and others. Pilus biogenesis occurs daily in the morning, while natural transformation is maximal when the onset of darkness coincides with the dusk circadian peak. Thus, the competence state in cyanobacteria is regulated by the circadian clock and can adapt to seasonal changes of day length., The cyanobacterium Synechococcus elongatus is a model organism for the study of circadian rhythms, and is naturally competent for transformation. Here, Taton et al. identify genes required for natural transformation in this organism, and show that the coincidence of circadian dusk and darkness regulates the competence state in different day lengths.

Behavioral phenotyping of model organisms has played an important role in unravelling the complexities of animal behavior. Techniques for classifying behavior often rely on easily identified changes in posture and motion. However, such approaches are likely to miss complex behaviors that cannot be readily distinguished by eye (e.g., behaviors produced by high dimensional dynamics). To explore this issue, we focus on the model organism Caenorhabditis elegans, where behaviors have been extensively recorded and classified. Using a dynamical systems lens, we identify high dimensional, nonlinear causal relationships between four basic shapes that describe worm motion (eigenmodes, also called “eigenworms”). We find relationships between all pairs of eigenmodes, but the timescales of the interactions vary between pairs and across individuals. Using these varying timescales, we create “interaction profiles” to represent an individual’s behavioral dynamics. As desired, these profiles are able to distinguish well-known behavioral states: i.e., the profiles for foraging individuals are distinct from those of individuals exhibiting an escape response. More importantly, we find that interaction profiles can distinguish high dimensional behaviors among divergent mutant strains that were previously classified as phenotypically similar. Specifically, we find it is able to detect phenotypic behavioral differences not previously identified in strains related to dysfunction of hermaphrodite-specific neurons., Author summary A primary reason for studying C. elegans is the possibility of achieving an end-to-end understanding of animal behaviour—one that mechanistically links genes to behaviour through neurobiology. With the genome and connectome fully mapped, techniques for measuring the behavior of C. elegans have traditionally relied on easily identified changes in posture and motion. Such approaches, however, can miss complex and more subtle behaviors that cannot be readily distinguished by eye. To explore this issue, we develop an attractor-based network approach to show that the four basic shapes classically used to describe worm position can be related to each other in a network that represents how the shapes interact with each other over time. That is, we study not just worm poses, but how the poses interact. Importantly, we find that these interaction networks can distinguish subtle high dimensional behaviors among mutant strains previously classified as phenotypically similar, thereby making genetic distinctions not previously possible. Thus, the value of this attractor-based network approach is its ability to detect subtle differences that can discriminate macroscopic behavior at the gene-mutation level. We anticipate these methods will be of interest to behaviorists, neuroscientists, and geneticists studying the connection between behavior and genes.

Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterize behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded ‘attractor’ of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach – defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error – may be of broad interest and relevance to behavioural researchers working with video-derived time series.

The rapid evolution of a trait in a clade of organisms can be explained by the sustained action of natural selection or by a high mutational variance, that is the propensity to change under spontaneous mutation. The causes for a high mutational variance are still elusive. In some cases, fast evolution depends on the high mutation rate of one or few loci with short tandem repeats. Here, we report on the fastest evolving cell fate among vulva precursor cells in Caenorhabditis nematodes, that of P3.p. We identify and validate causal mutations underlying P3.p's high mutational variance. We find that these positions do not present any characteristics of a high mutation rate, are scattered across the genome and the corresponding genes belong to distinct biological pathways. Our data indicate that a broad mutational target size is the cause of the high mutational variance and of the corresponding fast phenotypic evolutionary rate., eLife digest Heritable characteristics or traits of a group of organisms, for example the large brain size of primates or the hooves of a horse, are determined by genes, the environment, and by the interactions between them. Traits can change over time and generations when enough mutations in these genes have spread in a species to result in visible differences. However, some traits, such as the large brain of primates, evolve faster than others, but why this is the case has been unclear. It could be that a few specific genes important for that trait in question mutate at a high rate, or, that many genes affect the trait, creating a lot of variation for natural selection to choose from. Here, Besnard, Picao-Osorio et al. studied the roundworm Caenorhabditis elegans to better understand the causes underlying the different rates of trait evolution. These worms have a short life cycle and evolve quickly over many generations, making them an ideal candidate for studying mutation rates in different traits. Previous studies have shown that one of C. elegans’ six cells of the reproductive system evolves faster than the others. To investigate this further, Besnard, Picao-Osorio et al. analysed the genetic mutations driving change in this cell in 250 worm generations. The results showed that five mutations in five different genes – all responsible for different processes in the cells – were behind the supercharged evolution of this particular cell. This suggests that fast evolution results from natural selection acting upon a collection of genes, rather than one gene, and that many genes and pathways shape this trait. In conclusion, these results demonstrate that how traits are coded at the molecular level, in one gene or many, can influence the rate at which they evolve.

Although mRNA is a necessary precursor to protein, several studies have argued that the relationship between mRNA and protein levels is often weak. This claim undermines the functional relevance of conclusions based on quantitative analyses of mRNA levels, which are ubiquitous in modern biology from the single gene to the whole genome scale. Furthermore, if post-translational processes vary between strains and species, then comparative studies based on mRNA alone would miss an important driver of diversity. However, gene expression is dynamic, and most studies examining relationship between mRNA and protein levels at the genome scale have analyzed single timepoints. We measure yeast gene expression after pheromone exposure and show that, for most genes, protein timecourses can be predicted from mRNA timecourses through a simple, gene-specific, generative model. By comparing model parameters and predictions between strains, we find that while mRNA variation often leads to protein differences, evolution also manipulates protein-specific processes to amplify or buffer transcriptional regulation.

The recurrent pattern of light and darkness generated by Earth’s axial rotation has profoundly influenced the evolution of organisms, selecting for both biological mechanisms that respond acutely to environmental changes and circadian clocks that program physiology in anticipation of daily variations. The necessity to integrate environmental responsiveness and circadian programming is exemplified in photosynthetic organisms such as cyanobacteria, which depend on light-driven photochemical processes. The cyanobacterium Synechococcus elongatus PCC 7942 is an excellent model system for dissecting these entwined mechanisms. Its core circadian oscillator, consisting of three proteins KaiA, KaiB, and KaiC, transmits time-of-day signals to clock-output proteins, which reciprocally regulate global transcription. Research performed under constant light facilitates analysis of intrinsic cycles separately from direct environmental responses, but does not provide insight into how these regulatory systems are integrated during light-dark cycles. Thus, we sought to identify genes that are specifically necessary in a day-night environment. We screened a dense bar-coded transposon library in both continuous light and daily cycling conditions and compared the fitness consequences of loss of each nonessential gene in the genome. Although the clock itself is not essential for viability in light-dark cycles, the most detrimental mutations revealed by the screen were those that disrupt KaiA. The screen broadened our understanding of light-dark survival in photosynthetic organisms, identified unforeseen clock-protein interaction dynamics, and reinforced the role of the clock as a negative regulator of a night-time metabolic program that is essential for S. elongatus to survive in the dark.SignificanceUnderstanding how photosynthetic bacteria respond to and anticipate natural light–dark cycles is necessary for predictive modeling, bioengineering, and elucidating metabolic strategies for diurnal growth. Here, we identify the genetic components that are important specifically under light-dark cycling conditions and determine how a properly functioning circadian clock prepares metabolism for darkness, a starvation period for photoautotrophs. This study establishes that the core circadian clock protein KaiA is necessary to enable rhythmic de-repression of a night-time circadian program.

The broadly conserved signaling nucleotide cyclic di-adenosine monophosphate (c-di-AMP) is essential for viability in most bacteria where it has been studied. However, characterization of the cellular functions and metabolism of c-di-AMP has largely been confined to the class Bacilli, limiting our functional understanding of the molecule among diverse phyla. We identified the cyclase responsible for c-di-AMP synthesis and characterized the molecule’s role in survival of darkness in the model photosynthetic cyanobacterium Synechococcus elongatus PCC 7942. In addition to the use of traditional genetic, biochemical, and proteomic approaches, we developed a high-throughput genetic interaction screen (IRB-Seq) to determine pathways where the signaling nucleotide is active. We found that in S. elongatus c-di-AMP is produced by an enzyme of the diadenylate cyclase family, CdaA, which was previously unexplored experimentally. A cdaA-null mutant experiences increased oxidative stress and death during the nighttime portion of day-night cycles, in which potassium transport is implicated. These findings suggest that c-di-AMP is biologically active in cyanobacteria and has non-canonical roles in the phylum including oxidative stress management and day-night survival. The pipeline and analysis tools for IRB-Seq developed for this study constitute a quantitative high-throughput approach for studying genetic interactions., Author summary Cyclic di-adenosine monophosphate (c-di-AMP) is a molecule that has significant roles in many microorganisms. This work shows the existence of c-di-AMP for the first time in photosynthetic microorganisms, cyanobacteria, and demonstrates its role in survival during the light-to-dark shifts that occur in day-night cycles. Despite the obvious importance of adaptation to these daily cycles for organisms that are fundamentally reliant on light, such as cyanobacteria, understanding of diurnal physiology is lacking because most cyanobacterial research is conducted during growth in constant light. To identify other players in c-di-AMP’s function we developed a low-cost and efficient method for finding interactions between genes. The technique combines one mutation, in this case for the gene that encodes the enzyme for synthesis of c-di-AMP, with thousands of other individual mutations to find pairwise interactions that affect fitness of the resulting mutants. Mutants are tagged with DNA barcodes to allow their survival to be easily tracked in a population of cells. The method enables us to place the function of c-di-AMP within the context of pathways previously known to be involved in day-night survival. Taken together, this work expands the known roles of c-di-AMP, improves our understanding of cyanobacterial survival in day-night cycles, and presents an improved approach for determining genetic interactions.

When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell timecourses of the inducers Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of GAL inducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection -a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.One Sentence SummarySingle-cell galactose induction timecourses reveal that cellular resources and stochastic events determine which yeast cells outcompete their peers.

Heritable variation in gene expression patterns plays a fundamental role in trait variation and evolution, making understanding the mechanisms by which genetic variation acts on gene expression patterns a major goal for biology. Both theoretical and empirical work have largely focused on variation in steady-state mRNA levels and mRNA synthesis rates, particularly of protein-coding genes. Yet in order for this variation to affect higher order traits it must lead to differences at the protein level. Variation in protein-specific processes including protein synthesis rates and protein decay rates could amplify, mask, or even reverse effects transmitted from the transcript level, but the extent to which this happens is unclear. Moreover, mechanisms that underlie protein expression variation under dynamic conditions have not been examined. To address this challenge, we analyzed how mRNA and protein expression dynamics covary between two strains ofSaccharomyces cerevisiaeduring mating pheromone response. Although divergentsteady-statemRNA expression levels explained divergentsteady-stateprotein levels for four out of five genes in our study, the same was true for only one out of five genes for expressiondynamics. By integrating decay rate and allele-specific protein expression analyses, we resolved that expression divergence for Fig1p was caused by genetic variation acting intranson protein synthesis rate, expression divergence for Ina1p was caused bycis-by-transepistatic effects on transcript level and protein synthesis rate, and expression divergence for Fus3p and Tos6p were caused by divergence in protein synthesis rates. Our study demonstrates that steady-state analysis of gene expression is insufficient to understand the impact of genetic variation on gene expression variation. An integrated and dynamic approach to gene expression analysis - comparing mRNA levels, protein levels, protein decay rates, and allele-specific protein expression - allows for a detailed analysis of the genetic mechanisms underlying protein expression divergences.

Cis-regulatory elements (CREs) are crucial links in developmental gene regulatory networks, but in many cases, it can be difficult to discern whether similar CREs are functionally equivalent. We found that despite similar conservation and binding capability to upstream activators, different GATA cis-regulatory motifs within the promoter of the C. elegans endoderm regulator elt-2 play distinctive roles in activating and modulating gene expression throughout development. We fused wild-type and mutant versions of the elt-2 promoter to a gfp reporter and inserted these constructs as single copies into the C. elegans genome. We then counted early embryonic gfp transcripts using single-molecule RNA FISH (smFISH) and quantified gut GFP fluorescence. We determined that a single primary dominant GATA motif located -527 bp upstream of the elt-2 start codon was necessary for both embryonic activation and later maintenance of transcription, while nearby secondary GATA motifs played largely subtle roles in modulating postembryonic levels of elt-2. Mutation of the primary activating site increased low-level spatiotemporally ectopic stochastic transcription, indicating that this site acts repressively in non-endoderm cells. Our results reveal that CREs with similar GATA factor binding affinities in close proximity can play very divergent context-dependent roles in regulating the expression of a developmentally critical gene in vivo.

The phenotypic differences between individual organisms can often be ascribed to underlying genetic and environmental variation. However, even genetically identical organisms in homogeneous environments vary, indicating that randomness in developmental processes such as gene expression may also generate diversity. To examine the consequences of gene expression variability in multicellular organisms, we studied intestinal specification in the nematode Caenorhabditis elegans in which wild-type cell fate is invariant and controlled by a small transcriptional network. Mutations in elements of this network can have indeterminate effects: some mutant embryos fail to develop intestinal cells, whereas others produce intestinal precursors. By counting transcripts of the genes in this network in individual embryos, we show that the expression of an otherwise redundant gene becomes highly variable in the mutants and that this variation is subjected to a threshold, producing an ON/OFF expression pattern of the master regulatory gene of intestinal differentiation. Our results demonstrate that mutations in developmental networks can expose otherwise buffered stochastic variability in gene expression, leading to pronounced phenotypic variation., National Institutes of Health (U.S.). Pioneer Award, Mathematical Sciences Postdoctoral Research Fellowships (DMS-0603392), National Institutes of Health (U.S.). Ruth L. Kirschstein National Research Service Award (5F32GM080966)

Background Recent techniques for tagging and visualizing single molecules in fixed or living organisms and cell lines have been revolutionizing our understanding of the spatial and temporal dynamics of fundamental biological processes. However, fluorescence microscopy images are often noisy, and it can be difficult to distinguish a fluorescently labeled single molecule from background speckle. Results We present a computational pipeline to distinguish the true signal of fluorescently labeled molecules from background fluorescence and noise. We test our technique using the challenging case of wide-field, epifluorescence microscope image stacks from single molecule fluorescence in situ experiments on nematode embryos where there can be substantial out-of-focus light and structured noise. The software recognizes and classifies individual mRNA spots by measuring several features of local intensity maxima and classifying them with a supervised random forest classifier. A key innovation of this software is that, by estimating the probability that each local maximum is a true spot in a statistically principled way, it makes it possible to estimate the error introduced by image classification. This can be used to assess the quality of the data and to estimate a confidence interval for the molecule count estimate, all of which are important for quantitative interpretations of the results of single-molecule experiments. Conclusions The software classifies spots in these images well, with >95% AUROC on realistic artificial data and outperforms other commonly used techniques on challenging real data. Its interval estimates provide a unique measure of the quality of an image and confidence in the classification. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0534-z) contains supplementary material, which is available to authorized users.

An experiment in twelve different strains of Drosophila suggests that naturally occurring mutations generate a large amount of gene expression variation at a surprisingly rapid rate. After only 200 generations of allowing natural mutations (with weak or no ill effects) to accumulate, gene expression evolution was detected in 40% of all genes. That variation has the potential to fuel evolution, yet comparison of the observed evolutionary rates with differences between species suggests that natural variation is constrained by physical, developmental, or some other factors. Mutation is the ultimate source of biological diversity because it generates the variation that fuels evolution1. Gene expression is the first step by which an organism translates genetic information into developmental change. Here we estimate the rate at which mutation produces new variation in gene expression by measuring transcript abundances across the genome during the onset of metamorphosis in 12 initially identical Drosophila melanogaster lines that independently accumulated mutations for 200 generations2. We find statistically significant mutational variation for 39% of the genome and a wide range of variability across corresponding genes. As genes are upregulated in development their variability decreases, and as they are downregulated it increases, indicating that developmental context affects the evolution of gene expression. A strong correlation between mutational variance and environmental variance shows that there is the potential for widespread canalization3. By comparing the evolutionary rates that we report here with differences between species4,5, we conclude that gene expression does not evolve according to strictly neutral models. Although spontaneous mutations have the potential to generate abundant variation in gene expression, natural variation is relatively constrained.

The rapid advances in high-throughput biotechnologies such as DNA microarrays and mass spectrometry have generated vast amounts of data ranging from gene expression to proteomics data. The large size and complexity involved in analyzing such data demand a significant amount of computing power. High-performance computation (HPC) is an attractive and increasingly affordable approach to help meet this challenge. There is a spectrum of techniques that can be used to achieve computational speedup with varying degrees of impact in terms of how drastic a change is required to allow the software to run on an HPC platform. This paper describes a high- productivity/low-maintenance (HP/LM) approach to HPC that is based on establishing a collaborative relationship between the bioinformaticist and HPC expert that respects the former's codes and minimizes the latter's efforts. The goal of this approach is to make it easy for bioinformatics researchers to continue to make iterative refinements to their programs, while still being able to take advantage of HPC. The paper describes our experience applying these HP/LM techniques in four bioinformatics case studies: (1) genome-wide sequence comparison using Blast, (2) identification of biomarkers based on statistical analysis of large mass spectrometry data sets, (3) complex genetic analysis involving ordinal phenotypes, (4) large-scale assessment of the effect of possible errors in analyzing microarray data. The case studies illustrate how the HP/LM approach can be applied to a range of representative bioinformatics applications and how the approach can lead to significant speedup of computationally intensive bioinformatics applications, while making only modest modifications to the programs themselves.

Motivation: A gene expression trajectory moves through a high dimensional space where each axis represents the mRNA abundance of a different gene. Genome wide gene expression has a dynamic structure, especially in studies of development and temporal response. Both visualization and analyses of such data require an explicit attention to the temporal structure. Results: Using three cell cycle trajectories from Saccharomyces cerevisiae to illustrate, we present several techniques which reveal the geometry of the data. We import phase-delay time plots from chaotic systems theory as a dynamic data visualization device and show how these plots capture important aspects of the trajectories. We construct an objective function to find an optimal two-dimensional projection of the cell cycle, demonstrate that the system returns to this plane after three different initial perturbations, and explore the conditions under which this geometric approach outperforms standard approaches such as singular value decomposition and Fourier analysis. Finally, we show how a geometric analysis can isolate distinct parts of the trajectories, in this case the initial perturbation versus the cell cycle. Contact: junhyong.kim@yale.edu * To whom correspondence should be addressed.

A microarray experiment gives a snapshot of the state of an organism in terms of the relative abundances of its mRNA transcripts, locating the organism at a point in a high dimensional state space where each axis represents the relative expression level of a single gene. Multiple experiments generate a cloud of points in this gene expression space. We present a geometric approach to analyzing the covariational properties of such a cloud and use a dataset from Saccharomyces cerevisiae as an illustration. In particular, we use singular value decomposition to identify significant linear sub-structures in the data and analyze the contributions of both individual genes and functional classes of genes to these major directions of variation. Analyzing the publicly available yeast expression data, we show that under all experimental conditions the variation in expression is limited to a small number of linear dimensions. Projections of individual gene axes onto the significant dimensions can order the contribution of individual genes to variation in expression within an experiment. We show that no particular groups of genes characterize particular experimental conditions. Instead, the particular structure of the coordinated expression of the entire genome characterizes a particular experiment.

Metamorphosis is an integrated set of developmental processes controlled by a transcriptional hierarchy that coordinates the action of hundreds of genes. In order to identify and analyze the expression of these genes, high-density DNA microarrays containing several thousand Drosophila melanogaster gene sequences were constructed. Many differentially expressed genes can be assigned to developmental pathways known to be active during metamorphosis, whereas others can be assigned to pathways not previously associated with metamorphosis. Additionally, many genes of unknown function were identified that may be involved in the control and execution of metamorphosis. The utility of this genome-based approach is demonstrated for studying a set of complex biological processes in a multicellular organism.

A method for probing a target sequence of messenger ribonucleic acid molecules (mRNA's) in a fixed, permeabilized cell, said target sequence including at least 30 non- overlapping probe binding regions of 15-100 nucleotides, comprising immersing said cell in an excess of at least 30 nucleic acid hybridization probes, each singly labeled with the same fluorescent label and each containing a nucleic acid sequence that is complementary to a different probe binding region of said target sequence; washing said fixed cell to remove unbound probes; and detecting fluorescence from said probes.

The processes by which genetic variation in complex traits is generated and maintained in populations has for a long time been treated in abstract and statistical terms. As a consequence, quantitative genetics has provided limited insights into our understanding of the molecular bases of quantitative trait variation. With the developing technological and conceptual tools of systems biology, cellular and molecular processes are being described in greater detail. While we have a good description of how signaling and other molecular networks are organized in the cell, we still do not know how genetic variation affects these pathways, because systems and molecular biology usually ignore the type and extent of genetic variation found in natural populations. Here we discuss the quantitative genetics and systems biology approaches for the study of complex trait architecture and discuss why these two disciplines would synergize with each other to answer questions that neither of the two could answer alone.

The processes by which genetic variation in complex traits is generated and maintained in populations has for a long time been treated in abstract and statistical terms. As a consequence, quantitative genetics has provided limited insights into our understanding of the molecular bases of quantitative trait variation. With the developing technological and conceptual tools of systems biology, cellular and molecular processes are being described in greater detail. While we have a good description of how signaling and other molecular networks are organized in the cell, we still do not know how genetic variation affects these pathways, because systems and molecular biology usually ignore the type and extent of genetic variation found in natural populations. Here we discuss the quantitative genetics and systems biology approaches for the study of complex trait architecture and discuss why these two disciplines would synergize with each other to answer questions that neither of the two could answer alone.

In the past several years, a host of new technologies have made it possible to visualize single molecules within cells and organisms (Raj et al., Nat Methods 5:877-879, 2008; Paré et al., Curr Biol 19:2037-2042, 2009; Lu and Tsourkas, Nucleic Acids Res 37:e100, 2009; Femino et al., Science 280:585-590, 1998; Rodriguez et al., Semin Cell Dev Biol 18:202-208, 2007; Betzig et al., Science 313:1642-1645, 2006; Rust et al., Nat Methods 3:793-796, 2006; Fusco et al., Curr Biol 13:161-167, 2003). Many of these are based on fluorescence, either fluorescent proteins or fluorescent dyes coupled to a molecule of interest. In many applications, the fluorescent signal is limited to a few pixels, which poses a classic signal processing problem: how can actual signal be distinguished from background noise? In this chapter, I present a MATLAB (MathWorks (2010) MATLAB. Retrieved from http://www.mathworks.com) software suite designed to work with these single-molecule visualization technologies (Rifkin (2010) spotFinding Suite. http://www.biology.ucsd.edu/labs/rifkin/software.html). It takes images or image stacks from a fluorescence microscope as input and outputs locations of the molecules. Although the software was developed for the specific application of identifying single mRNA transcripts in fixed specimens, it is more general than this and can be used and/or customized for other applications that produce localized signals embedded in a potentially noisy background. The analysis pipeline consists of the following steps: (a) create a gold-standard dataset, (b) train a machine-learning algorithm to classify image features as signal or noise depending upon user defined statistics, (c) run the machine-learning algorithm on a new dataset to identify mRNA locations, and (d) visually inspect and correct the results.

Expression quantitative trait loci (eQTL) mapping studies have become a widely used tool for identifying genetic variants that affect gene regulation. In these studies, expression levels are viewed as quantitative traits, and gene expression phenotypes are mapped to particular genomic loci by combining studies of variation in gene expression patterns with genome-wide genotyping. Results from recent eQTL mapping studies have revealed substantial heritable variation in gene expression within and between populations. In many cases, genetic factors that influence gene expression levels can be mapped to proximal (putatively cis) eQTLs and, less often, to distal (putatively trans) eQTLs. Beyond providing great insight into the biology of gene regulation, a combination of eQTL studies with results from traditional linkage or association studies of human disease may help predict a specific regulatory role for polymorphic sites previously associated with disease.

Identifying the properties of gene networks that influence their evolution is a fundamental research goal. However, modes of evolution cannot be inferred solely from the distribution of natural variation, because selection interacts with demography and mutation rates to shape polymorphism and divergence. We estimated the effects of naturally occurring mutations on gene expression while minimizing the effect of natural selection. We demonstrate that sensitivity of gene expression to mutations increases with both increasing trans-mutational target size and the presence of a TATA box. Genes with greater sensitivity to mutations are also more sensitive to systematic environmental perturbations and stochastic noise. These results provide a mechanistic basis for gene expression evolvability that can serve as a foundation for realistic models of regulatory evolution.

Changes in genetic regulation contribute to adaptations in natural populations and influence susceptibility to human diseases. Despite their potential phenotypic importance, the selective pressures acting on regulatory processes in general and gene expression levels in particular are largely unknown. Studies in model organisms suggest that the expression levels of most genes evolve under stabilizing selection, although a few are consistent with adaptive evolution. However, it has been proposed that gene expression levels in primates evolve largely in the absence of selective constraints. In this article, we discuss the microarray-based observations that led to these disparate interpretations. We conclude that in both primates and model organisms, stabilizing selection is likely to be the dominant mode of gene expression evolution. An important implication is that mutations affecting gene expression will often be deleterious and might underlie many human diseases.

Interspecies comparisons of gene expression levels will increase our understanding of the evolution of transcriptional mechanisms and help to identify targets of natural selection. This approach holds particular promise for apes, as many human-specific adaptations are thought to result from differences in gene expression rather than in coding sequence. To date, however, all studies directly comparing interspecies gene expression have been performed on single-species arrays, so that it has been impossible to distinguish differential hybridization due to sequence mismatches from underlying expression differences. To evaluate the severity of this potential problem, we constructed a new multiprimate cDNA array using probes from human, chimpanzee, orangutan, and rhesus. We find a large effect of sequence divergence on hybridization signal, even in the closest pair of species, human and chimpanzee. By comparing single-species array analyses with results from multispecies arrays, we examine how estimates of differential gene expression are affected by sequence divergence. Our results indicate that naive use of single-species arrays in direct interspecies comparisons can yield spurious results.

Using microarray gene expression data from several Drosophila species and strains, we show that duplicated genes, compared with single-copy genes, significantly increase gene expression diversity during development. We show further that duplicate genes tend to cause expression divergences between Drosophila species (or strains) to evolve faster than do single-copy genes. This conclusion is also supported by data from different yeast strains.

Little is known about broad patterns of variation and evolution of gene expression during any developmental process. Here we investigate variation in genome-wide gene expression among Drosophila simulans, Drosophila yakuba and four strains of Drosophila melanogaster during a major developmental transition--the start of metamorphosis. Differences in gene activity between these lineages follow a phylogenetic pattern, and 27% of all of the genes in these genomes differ in their developmental gene expression between at least two strains or species. We identify, on a gene-by-gene basis, the evolutionary forces that shape this variation and show that, both within the transcriptional network that controls metamorphosis and across the whole genome, the expression changes of transcription factor genes are relatively stable, whereas those of their downstream targets are more likely to have evolved. Our results demonstrate extensive evolution of developmental gene expression among closely related species.

Microsporidia comprise a phylum of over 1400 species of obligate intracellular pathogens that can infect almost all animals, but little is known about the host response to these parasites. Here we use the whole-animal host C. elegans to show an in vivo role for ubiquitin-mediated response to the microsporidian species Nematocida parisii, as well to the Orsay virus, another natural intracellular pathogen of C. elegans. We analyze gene expression of C. elegans in response to N. parisii, and find that it is similar to response to viral infection. Notably, we find an upregulation of SCF ubiquitin ligase components, such as the cullin ortholog cul-6, which we show is important for ubiquitin targeting of N. parisii cells in the intestine. We show that ubiquitylation components, the proteasome, and the autophagy pathway are all important for defense against N. parisii infection. We also find that SCF ligase components like cul-6 promote defense against viral infection, where they have a more robust role than against N. parisii infection. This difference may be due to suppression of the host ubiquitylation system by N. parisii: when N. parisii is crippled by anti-microsporidia drugs, the host can more effectively target pathogen cells for ubiquitylation. Intriguingly, inhibition of the ubiquitin-proteasome system (UPS) increases expression of infection-upregulated SCF ligase components, indicating that a trigger for transcriptional response to intracellular infection by N. parisii and virus may be perturbation of the UPS. Altogether, our results demonstrate an in vivo role for ubiquitin-mediated defense against microsporidian and viral infections in C. elegans., Author Summary Microbial pathogens have two distinct lifestyles: some pathogens live outside of host cells, and others live inside of host cells and are called intracellular pathogens. Microsporidia are fungal-related intracellular pathogens that can infect all animals, but are poorly understood. We used the roundworm C. elegans as a host to show that ubiquitin pathways provide defense against both a natural microsporidian infection of C. elegans, as well as a natural viral infection. Our study shows that ubiquitin, the proteasome and autophagy components are all important to control intracellular infection in C. elegans, although microsporidia seem to partially evade this defense. We also show that SCF ubiquitin ligases help control both microsporidia and virus infection. Furthermore, we find that C. elegans upregulates expression of SCF ligases when ubiquitin-related degradation machinery is inhibited, indicating that C. elegans monitors the functioning of this core cellular process and upregulates ligase expression when it is perturbed. Altogether, our findings describe ubiquitin-mediated pathways that are involved in host response and defense against intracellular pathogens, and how this machinery is regulated by infection to increase defense against intracellular pathogens such as microsporidia and viruses.

Deletion of eight chromatin regulators and one transcription factor increases the variability in gene expression between two closely related yeast species, suggesting that large-scale regulators often buffer variations in gene expression. Similar analysis of metabolic enzymes indicates that, unlike regulators, these enzymes do not buffer gene expression variations., Biological systems are often robust to mutations—their outputs, (for example, gene expression profiles) remain stable in the face of mutations. This ensures that most individuals maintain the ‘correct' behavior, which has been shaped by million of years of evolution, despite a constant flux of mutations. How is robustness maintained, and in particular, which genes are required for it? Such questions have been studied for decades, yet there are no simple answers. Previous studies suggested that particular proteins, termed genetic capacitors, buffer the effects of mutations, thereby promoting robustness. The classical example of such a protein is Hsp90, whose activity as a chaperone has been proposed to aid the correct folding of mutant proteins and thus buffer the structural effects of mutations. The hallmark of a genetic capacitor is that its deletion reveals phenotypic differences between individuals or species, which are hidden (that is, buffered) in its presence. The example of Hsp90 may suggest that buffering is a property of only few proteins that carry particular catalytic functions such as chaperones. However, theoretical studies have instead suggested that many proteins serve as genetic capacitors and that buffering is not necessarily a consequence of their direct activity but rather emerges naturally during evolution of complex biological systems. Here, we show that eight chromatin regulators and one transcription factor buffer interspecies variations in gene expression. We deleted each of these nine regulators in two closely related yeast species and compared the extent of interspecies expression difference before and after each deletion. The results clearly show that deletion of these regulators tends to increase the extent of expression differences, indicating that they are normally buffering variations in gene expression, thus serving as genetic capacitors. Similar analysis of 11 metabolic enzymes showed that, unlike the regulators, deletion of these enzymes does not increase expression divergence. Thus, buffering may be a characteristic feature of large-scale regulators. Further analysis of the buffered variations suggested that these are often caused by mutations that affect regulatory proteins, presumably those involved in sensing the environment, and that buffered variations are found primarily in genes with distinctive promoter features that are associated with highly dynamic and responsive regulation. We believe, as others have previously proposed, that buffering emerged naturally during evolution of a complex system. More specifically, we propose that organisms accumulate many mutations that have no functional consequences through random drift, but that some of these mutations would in fact be functional if a certain regulatory protein is inactive. These mutations are often conditionally neutral because of their epistatic interactions with mutations in regulatory proteins. Such epistatic interactions may not reflect direct buffering activity (as proposed for Hsp90) but rather an inevitable consequence of the connectivity and complexity of biological systems. Note that the opposite case—mutations that are normally functional but become neutral when the regulatory protein is inactive—are also frequent, but these are presumed to be efficiently purged by natural selection. As a result, deletion of such regulatory proteins unleashes the effects of many ‘hidden' mutations and increases variations among individuals or species., Gene expression varies widely between closely related species and strains, yet the genetic basis of most differences is still unknown. Several studies suggested that chromatin regulators have a key role in generating expression diversity, predicting a reduction in the interspecies differences on deletion of genes that influence chromatin structure or modifications. To examine this, we compared the genome-wide expression profiles of two closely related yeast species following the individual deletions of eight chromatin regulators and one transcription factor. In all cases, regulator deletions increased, rather than decreased, the expression differences between the species, revealing hidden genetic variability that was masked in the wild-type backgrounds. This effect was not observed for individual deletions of 11 enzymes involved in central metabolic pathways. The buffered variations were associated with trans differences, as revealed by allele-specific profiling of the interspecific hybrids. Our results support the idea that regulatory proteins serve as capacitors that buffer gene expression against hidden genetic variability.

© 2015 Elsevier Inc. The MED-1,2 GATA factors contribute to specification of E, the progenitor of the Caenorhabditis elegans endoderm, through the genes end-1 and end-3, and in parallel with the maternal factors SKN-1, POP-1 and PAL-1. END-1,3 activate elt-2 and elt-7 to initiate a program of intestinal development, which is maintained by positive autoregulation. Here, we advance the understanding of MED-1,2 in E specification. We find that expression of end-1 and end-3 is greatly reduced in med-1,2(-) embryos. We generated strains in which MED sites have been mutated in end-1 and end-3. Without MED input, gut specification relies primarily on POP-1 and PAL-1. 25% of embryos fail to make intestine, while those that do display abnormal numbers of gut cells due to a delayed and stochastic acquisition of intestine fate. Surviving adults exhibit phenotypes consistent with a primary defect in the intestine. Our results establish that MED-1,2 provide robustness to endoderm specification through end-1 and end-3, and reveal that gut differentiation may be more directly linked to specification than previously appreciated. The results argue against an "all-or-none" description of cell specification, and suggest that activation of tissue-specific master regulators, even when expression of these is maintained by positive autoregulation, does not guarantee proper function of differentiated cells.

Recent techniques for tagging and visualizing single molecules in fixed or living organisms and cell lines have been revolutionizing our understanding of the spatial and temporal dynamics of fundamental biological processes. However, fluorescence microscopy images are often noisy, and it can be difficult to distinguish a fluorescently labeled single molecule from background speckle.We present a computational pipeline to distinguish the true signal of fluorescently labeled molecules from background fluorescence and noise. We test our technique using the challenging case of wide-field, epifluorescence microscope image stacks from single molecule fluorescence in situ experiments on nematode embryos where there can be substantial out-of-focus light and structured noise. The software recognizes and classifies individual mRNA spots by measuring several features of local intensity maxima and classifying them with a supervised random forest classifier. A key innovation of this software is that, by estimating the probability that each local maximum is a true spot in a statistically principled way, it makes it possible to estimate the error introduced by image classification. This can be used to assess the quality of the data and to estimate a confidence interval for the molecule count estimate, all of which are important for quantitative interpretations of the results of single-molecule experiments.The software classifies spots in these images well, with95% AUROC on realistic artificial data and outperforms other commonly used techniques on challenging real data. Its interval estimates provide a unique measure of the quality of an image and confidence in the classification.