Networks of Causal Linkage Between Eigenmodes Characterize Behavioral Dynamics of Caenorhabditis elegans.

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.

Tracking changes in behavioural dynamics using prediction error.

Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterise 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.

A larger target leads to faster evolution.

The speed at which a cell fate decision in nematode worms evolves is due to the number of genes that control the decision, rather than to a high mutation rate.

The circadian clock and darkness control natural competence in cyanobacteria.

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.

Genome-wide fitness assessment during diurnal growth reveals an expanded role of the cyanobacterial circadian clock protein KaiA.

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 nighttime metabolic program that is essential for S. elongatus to survive in the dark.

High-throughput interaction screens illuminate the role of c-di-AMP in cyanobacterial nighttime survival.

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.

A living vector field reveals constraints on galactose network induction in yeast.

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. Cellular memory of long-term glucose exposure delays GAL induction and makes it highly variable with in a cell population, while other nutrient histories lead to rapid, uniform responses. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell time courses of the proteins Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of these GAL transducers 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.

Mutagenesis of GATA motifs controlling the endoderm regulator elt-2 reveals distinct dominant and secondary cis-regulatory elements.

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 527bp 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.

MED GATA factors promote robust development of the C. elegans endoderm.

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.

Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images.

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.

The yeast galactose network as a quantitative model for cellular memory.

Recent experiments have revealed surprising behavior in the yeast galactose (GAL) pathway, one of the preeminent systems for studying gene regulation. Under certain circumstances, yeast cells display memory of their prior nutrient environments. We distinguish two kinds of cellular memory discovered by quantitative investigations of the GAL network and present a conceptual framework for interpreting new experiments and current ideas on GAL memory. Reinduction memory occurs when cells respond transcriptionally to one environment, shut down the response during several generations in a second environment, then respond faster and with less cell-to-cell variation when returned to the first environment. Persistent memory describes a long-term, arguably stable response in which cells adopt a bimodal or unimodal distribution of induction levels depending on their preceding environment. Deep knowledge of how the yeast GAL pathway responds to different sugar environments has enabled rapid progress in uncovering the mechanisms behind GAL memory, which include cytoplasmic inheritance of inducer proteins and positive feedback loops among regulatory genes. This network of genes, long used to study gene regulation, is now emerging as a model system for cellular memory.

Ubiquitin-mediated response to microsporidia and virus infection in C. elegans.

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.

The genotype-phenotype maps of systems biology and quantitative genetics: distinct and complementary.

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.

Identifying fluorescently labeled single molecules in image stacks using machine learning.

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.

Variability in gene expression underlies incomplete penetrance.

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.

Imaging individual mRNA molecules using multiple singly labeled probes.

We describe a method for imaging individual mRNA molecules in fixed cells by probing each mRNA species with 48 or more short, singly labeled oligonucleotide probes. This makes each mRNA molecule visible as a computationally identifiable fluorescent spot by fluorescence microscopy. We demonstrate simultaneous detection of three mRNA species in single cells and mRNA detection in yeast, nematodes, fruit fly wing discs, and mammalian cell lines and neurons.

Revealing the architecture of gene regulation: the promise of eQTL studies.

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.

Genetic properties influencing the evolvability of gene expression.

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.