Workshop 3: Robustness in Biological Systems: Titles & Abstracts
Screens monitoring the effects of deletion, knock-down or over expression of regulatory genes on the expression of their target genes are critical for deciphering the organization of complex regulatory networks. However, since perturbation assays cannot distinguish direct from indirect effects, the derived networks are significantly more complex than the true underlying one. Discovery of the true network organization is a long-standing challenge and several approaches have been developed to infer regulatory networks based on gene expression data. Recent studies indicate [ref] that information obtained from perturbation screens is critical for the ability to identify network structure accurately. This information is typically incorporated into network inference algorithms in two ways: first, the strength of the perturbation effect is translated into interaction confidence values and, second, topological analysis of the experimental networks is performed to find interactions that are most important to preserve network structure and, hence, are more likely to be biological. This underscores importance of having accurate methodology for accurate inference of network topology. In this work we present Exigo, an approach for systematic analysis of network topology. Exigo provides the means to identify core network structure for an input network of any topology with an arbitrary number of activating and inhibiting interactions. We further show that Exigo allows for significant improvement in the network inference. To illustrate this, we constructed a chimeric network inference method that incorporates Exigo into existing inference pipeline [ref], benchmarked it against DREAM challenge networks and found significant improvement in network inference compared to DREAM top performers.
Evolution is the movement of populations through a space of genotypes. This space can be modeled as an undirected network connecting genotypes that can be reached through mutation. In this view, the mutational robustness of a genotype is the proportion of its mutational neighbors that are viable. Robustness can facilitate the exploration of genotype networks, or evolvability. Sexual reproduction is also widely believed to promote evolvability, for two reasons. First, because it allows long jumps through genotype space. Second, because it selects for mutational robustness. Here, I show that, depending on the structure of the genotype network, sexual reproduction may not select for the highest mutational robustness, and can actually reduce evolvability.
An immune response involves the release of many cytokines with various immunomodulatory functions. Its efficacy depends, in part, on the types of cytokines secreted by activated T cells and their corresponding kinetic profiles. Through serial microengraving [1], the Love Lab is able to quantify single and multiple T cell cytokine secretion dynamics, offering a unique multidimensional perspective to study time-dependent functional differences specific to immunophenotypes. We have employed dynamical systems strategies to these data to investigate the temporal evolution of specific cytokine responses that are indicative of a healthy immune system. Further experiments using defined stimulatory perturbations provide additional insight on the regulatory mechanisms that control T cell function. As a result, we are able to better resolve and predict the nonlinear functional dynamics governing qualitatively different T-cell responses. This systems-level methodology should enable the identification of unique time-dependent cytokine signatures indicative of productive immune function, and may offer metrics to design more effective and personalized treatment strategies.
[1] Qing Han et al., Lab Chip, 2010, 10: 1391-1400.
The polyamine molecules putrescine, spermidine and spermine are involved in a number of important cellular processes such as transcriptional silencing, translation, protection from reactive oxygen species and coenzyme A synthesis. Components of the polyamine pathway are also potential targets for cancer therapeutics, as unregulated polyamine synthesis can trigger uncontrolled cell proliferation. Conversely, polyamine depletion can cause apoptosis, and during development, defects leading to mental retardation in humans (Snyder-Robinson Syndrome). Controlling polyamine concentrations is thus a significant regulatory challenge for the cell, because there are multiple cellular requirements for polyamines as well as a need to homeostatically maintain their concentration within a certain non-toxic range. In the cell, polyamine concentrations are regulated by multiple mechanisms, the most important of which is a negative feedback control loop involving Spe1 (the enzyme catalysing the first step in the polyamine biosynthesis pathway) and the protein antizyme, which is synthesised via a +1 ribosomal frameshift during translation of the antizyme mRNA. In this talk I will present the first predictive model of the polyamine feedback controller, which has been developed and validated using a Systems Biology approach incorporating enzyme kinetics, control engineering and experimental molecular biology.
Previously, we used computational modelling to explore the roles of mechanics and evolution in the generation of biological forms that withstand genetic and mechanical perturbations. Here I will present more recent work from my group in which we have used the dorsal thorax of the fly as an experimental system in which to ask similar questions during the refinement of a tissue prior to hatching. This has revealed a set of stochastic and noisy cell biological processes that contribute to the development of a well-ordered tissue from messy beginnings. I will briefly discuss the implications of these findings for our understanding of tissue homeostasis and disease.
Infant apnea, defined as a pause in breathing for more than 20 seconds, can lead to oxygen desaturation and the need for resuscitation and assisted ventilation. A recent study has demonstrated that continuously applied stochastic (randomly fluctuating) somatosensory stimulation stabilizes breathing patterns in preterm infants and can reduce apnea by approximately 65%. I hypothesize that stochastic inputs to the respiratory central pattern generator (CPG) increase the dynamic range of the breathing rhythm in neonates. In this talk, I will discuss a proposal to test this hypothesis through a combination of in vitro electrophysiology and computational modeling to understand the role of noise in the immature respiratory CPG.
In multicellular organisms, the appropriate control of apoptosis, or cell death, is essential to homeostasis and health. There is a striking variability in behavior as cells respond to death-inducing ligands: even within a clonal population of cancer cells, cells die at different times and some cells survive. In addition, some dying cells require a signal transduction pathway involving the permeabilization of the mitochondrial outer membrane (MOMP), while others can commit to apoptosis without that pathway. Using a combination of single-cell measurements and analysis of computational models of the relevant protein signaling networks, we are uncovering the relationships between multiple factors determining the timing of ligand-induced cell death and the requirement for MOMP. Although the cell death leads to an inert state, the cell death decision process is a dynamic process which is not amenable to steady-state analysis. This talk will focus on our use of Lyapunov analysis and phase-diagrams to explore which initial cellular states result in cell death or survival following treatment with death ligands and which regions of phase-space show homogeneous (robust) or heterogeneous (sensitive) cell behavior.
Fruit flies are models for understanding the genetic regulation involved in specifying the complex body plans of higher animals. The head-to-tail (anterior-posterior) axis of the fly (Drosophila) is established in the first hours of development. Maternally supplied factors form concentration gradients which direct embryonic (zygotic) genes where to be activated to express proteins. These protein patterns specify the positions and cell types of the body's tissues. Recent research has shown, comparing between embryos, that the zygotic gene products are much more precisely positioned than the maternal gradients, indicating an embryonic error reduction mechanism. Within embryos, there is the additional aspect that DNA and mRNA operate at very low copy number, and the associated high relative noise has the potential to strongly affect protein expression patterns. In recent work, we have focused on the noise aspects of positional specification within individual embryos, and what molecular mechanisms confer robustness to the process.
We simulate activation of hunchback (hb), a primary target of the maternal Bicoid (Bcd) protein gradient, which forms an expression pattern dividing the embryo into anterior and posterior halves. We use a master equation approach to simulate the stochastic dynamics of hb regulation, including the binding/unbinding of Bcd molecules at the hb promoter (the portion of DNA regulating hb transcription), hb transcription, subsequent translation to Hb protein, binding/unbinding of Hb at the promoter (self-regulation), and diffusion of the Bcd and Hb proteins. Model parameters were set by deterministically matching large scale pattern features for a series of experimental expression patterns: wild-type (WT) embryos; hb mutants lacking self-regulation; and constructs in which portions of the hb promoter were used to express a reporter gene (lacZ). The model was then solved stochastically to predict the noise output in these different experiments. In subsequent noise measurements we experimentally corroborated several predictions: including that mRNA is noisier than protein and that Hb self-regulation reduces noise.
Results indicate that WT (self-regulatory) Hb output noise is predominantly dependent on the transcription and translation dynamics of its own expression, and is uncorrelated with Bcd fluctuations. In the constructs and mutant, which lack self-regulation, we find that increasing the number and strength of Bcd binding sites (there are 6 in the core hb promoter) provides a rudimentary level of noise reduction. We have recently incorporated a known inhibitor of hb, Krüppel (Kr), into the model – preliminary indications are that the Hb-Kr activator-inhibitor dynamics increase precision at the mid-embryo boundary.
The analysis of hb shows common modes of gene regulation (e.g. multiple regulatory sites, self-regulation) that are involved in noise reduction, which can be applied generally to reproducibility and determinacy of spatial patterning in other developmental phenomena.
Characterization of plasticity, robustness, and evolvability is an important issue in biology. First, proportionality among evolution speed, phenotypic plasticity, and isogenic phenotypic fluctuation is derived by borrowing and extending fluctuation-response relationship in physics. Following an evolutionary stability hypothesis we derive a general proportionality relationship between the phenotypic fluctuations of epigenetic and genetic origins; The former is given by the variance of phenotype due to noise in developmental process, and the latter due to genetic mutation. The relationship suggests a link between robustness to noise and to mutation, since robustness can be defined by the sharpness of the distribution of phenotype. Second, the proportionality between the variances is demonstrated to hold also over different phenotypic traits, when the system acquires robustness through the evolution. Third, adaptation to environmental variation is studied, which is shown to require a certain degree of phenotypic fluctuations. Indeed, the highest adaptability is achieved when the system is near the transition point to lose the robustness. Here, change in phenotypes (i.e., in the gene expression pattern) induced by environmental change is reduced later as a result of genetic evolution. All the obtained relationships are confirmed in models of gene expression dynamics, as well as in laboratory experiments. Based on our results, we revisit Waddington's canalization and genetic assimilation, and discuss how consistency between evolutionary and developmental scales constrains developmental process and leads to universal laws on phenotypic fluctuations.
References: K.K. Life: An Introduction to Complex Systems Biology, Springer (2006); PLoS One(2007) 2 e434, J Biosci.34 (2009) 529: BMC Evolutionary Biology 11(2011) 27; Ito et al., Mol. Sys. Biol. 5 (2009) 264
There are at least three classes of question we can ask about the robustness of biological systems. "What are the strategies for robustness (i.e. what mechanisms make systems robust)?", "How does robustness evolve (i.e. what evolutionary mechanisms drive biological systems to become robust)?", and "What are the consequences of robustness (i.e. how does robustness, or the evolutionary paths that lead to it, constrain the organization and behavior of biological systems)? My talk concerns the last of these classes of question which, arguably, has been the least well explored. Work from several groups indicates that robustness may constrain biological systems in at least two ways: through trade-offs (whereby the need to overcome interference between strategies for achieving different types of robustness constrains system organization and drives the evolution of complexity), and through inadvertent selection for epistasis (whereby the evolution of robustness drives a net increase in the nonlinearity of interactions between system components). From a practical standpoint, it is not enough that we know that such constraints arise; we'd like to know when and where in any given system they will arise, and how large the effects will be. To this end, we have been building and analyzing models of developmental systems, exploring tradeoffs in pattern formation and growth control engendered by interference among strategies for parametric robustness, "microenvironmental" robustness, robustness to somatic mutation, etc. We have also been exploring models in which selection for robustness drives increased synergistic epistasis, in order to learn how the combinatorial fragility that such an effect produces is distributed among the different components of a network. Among other things, the results of such studies have direct bearing on our expectations for the kinds of genetic interactions and allele frequencies that underlie human genetic disease.
Making genes into gene products is subject to predictable errors, each with a phenotypic effect that depends on a normally cryptic sequence. The distribution of fitness effects of these cryptic sequences, like that of new mutations, is bimodal. For example, a cryptic sequence might be strongly deleterious if it causes protein misfolding, or it might have only a minor effect if it preserves the protein fold and tweaks function. Few sequences have effect sizes that fall in between.
Strongly deleterious sequences can be subject to some selection even while they are cryptic, and expressed only at low levels that depend on a molecular error. Robustness to the potentially deleterious effects of cryptic sequences can be achieved globally by avoiding making errors (e.g., via proofreading machinery) or locally by ensuring that each cryptic sequence has a relatively benign effect. The local solution requires powerful selection acting on every cryptic site, and so evolves only in large populations. Small populations with less effective selection evolve global proofreading solutions. However, we also find that for a large range of realistic intermediate population sizes, the evolutionary dynamics are bistable and either solution may result. The local solution, which does not occur in very small populations, facilitates the co-option of cryptic sequences and therefore substantially increases evolvability. This can occur even in genetically uniform populations, illustrating that neighbourhood richness or "quality" in genotype space can be more important to evolvability than the quantity of genotypes in a population spread across that space.
The process that warrants the generation of self tolerant peripheral T cells is called the thymocyte selection. During this maturation process, the overtly self reactive as well as unresponsive thymocytes are deleted from the cell population. The thymocytes equipped with T cell receptors (TCRs), capable of responding moderately to the self peptides are allowed to survive. Recently water soluble second messenger, inositol(1,3,4,5) tetrakisphosphate (IP4), has been implicated to play a crucial role in thymocyte positive selection (Huang et al.). It has been suggested that these IP4 molecules regulate the transient activation of the Tec- family protein tyrosine kinase Itk through a competing positive and negative feedbacks. The exact molecular mechanism involved in this feedback is however unclear. It is possible to con- struct more than one model with contrasting molecular mechanisms to explain the present body of experimental observations. This calls for criteria to choose among these models.
Robustness in face of the variation of the parameters in a model has been ubiquitously used as a criterion for model discrimination. Here we have used the maximum entropy, calculated with the constraints imposed by the experiments as a measure of robustness. Our data indicates that the models which are maximally robust share a cooperative allosteric mode of Itk regulation involving dimeric PH domains.
Joint work with Stephanie Rigaud, S. Seok, Agnieszka Prochenka, Guo Fu, Michael Dworkin, Nicholas R. J. Gascoigne, Veronika J. Vieland, Karsten Sauer, and Jayajit Das.
Plants and pathogens are engaged in ongoing battles of detection and evasion, which are recognized to be crucial in driving their coevolution. Simple genetic models of plant-pathogen interactions have been examined for decades, but recent experimental results point to a rich interplay involving partially redundant pathogen effector repertoires, sector-switching plant defense networks, convergent evolution of pathogen effector targeting, and interactions among guards, guardees and decoys. Robustness and evolvability of biological systems are increasingly recognized to be supported in part by nontrivial geometries in high-dimensional configuration spaces that span the many levels of organization from genotype to phenotype. Building on some previous work in characterizing the shapes of such spaces and their implications for biological function and evolution, I am exploring the geometric structure of detection and evasion in models of plant-pathogen interactions, and its ramifications for robustness, redundancy, and plant-pathogen coevolution.
Biochemical processes typically involve huge numbers of individual reversible steps, each with its own dynamical rate constants. For example, kinetic proofreading processes rely upon numerous sequential reactions in order to guarantee the precise construction of specific macromolecules. I will present a characterization of the first passage (completion) time distributions for such processes. I will argue that, for a wide class of biochemical kinetics systems related to kinetic proofreading, the completion time time behavior simplifies as the system size grows: it becomes either deterministic or exponentially distributed, with a very narrow transition between the two regimes. In both regimes, the dynamical complexity of the full system is trivial compared to its apparent structural complexity. This robust simplification of completion time distributions is independent of many microscopic details of the signaling systems and can be utilized for efficient control of cellular response properties. Even further simplifications are possible when one considers dynamics of many coupled kinetic proofreading enabled receptors, which can attain low activation noise with robust mean time to activation.
The focus of the talk will be on stochastic effects for spatial dynamics of complex biology systems. Through modeling and simulations, we will study several general principles underlining noise attenuation and robustness in multiscale systems involving both intracellular and extracellular components. For several cases, existing and new experimental evidences that support our theoretical and computational results will also be presented.
Cells accomplish a great diversity of biochemical and molecular functions and many of these must be performed simultaneously. Metabolic systems are subject to large and irregular hourly and daily fluctuations in inputs, and are subject to continually changing demands for particular metabolites. Since many metabolic pathways share enzymes, metabolites and cofactors, there must be mechanisms that ensure that large variation in a particular region of a pathway does not compromise functions elsewhere.
We study these questions using a physiologically-based mathematical model for folate-mediated one-carbon metabolism. This complex network provides the first steps in nucleotide synthesis, and the synthesis of S-adenosylmethionine, the universal donor of methyl groups for a host of methyl transfer reactions such as DNA and histone methylation, and the synthesis of glutathione, the universal antioxidant in animals.
Regulation of stability in this network has features that resemble physiological homeostasis. Homeostasis of critical biological functions requires a surprisingly diverse set of dynamic biochemical regulatory mechanisms which cannot be deduced from examination of the biochemical reaction diagram. We have elucidated ten different homeostatic mechanisms that operate simultaneously to stabilize function against fluctuations in input and demand. The homeostasis of critical biological functions (e.g., methylation capacity, nucleotide synthesis, response to oxidative stress) is not the consequence of a stable steady-state, but instead requires continuous large, adaptive changes in some fluxes and metabolites.
Work done in collaboration with Mike Reed.
Genetically identical cells grown in the same culture display striking cell-to-cell heterogeneity in gene expression and other traits. A crucial challenge is to understand how much of this heterogeneity reflects the noise tolerance of a robust system and how much serves a biological function. In some circumstances, heterogeneous traits might be favored over robust ones. For example, in bacteria cell-to-cell heterogeneity can serve as a bet-hedging mechanism, allowing a few cells to survive acute antibiotic stress while the others perish. Where a population of organisms falls on the continuum from uniformity to bet-hedging depends on the environmental regime it experiences. We describe a bet-hedging phenomenon in the yeast Saccharomyces cerevisiae, which occupies a range of natural and human-associated environments. We use a novel, high-throughput microscopy assay that monitors variable protein expression, growth rate and survival outcomes of tens of thousands of yeast microcolonies simultaneously. Clonal yeast populations display broad distributions of growth rates, and slow growth predicts resistance to acute heat stress. Expression of Tsl1, a trehalose-synthesis regulator, marks slow-growing cells and contributes to this resistance. I will present these results and discuss them in the context of the evolutionary forces shaping robust and heterogeneous traits.
Widespread robustness of cellular networks, in principle, could make the systems biology task of identifying and characterizing cellular networks difficult because it implies potentially large uncertainties in parameters or topologies of mathematical models that quantitatively capture the network behavior. Yet, robustness may be exploited in systems identification as an additional criterion for the plausibility of a given model. In both cases, quantitative characterizations of system robustness to perturbations are needed. This talk focuses on implications of robustness on systems identification with a focus on metabolic networks. Using the network structure and assumptions on optimal rejection of perturbations alone, we developed a method to analyze sensitivities in metabolic reaction networks that can be directly linked to, for instance, variability in gene expression. In an application to global data sets on the reaction of Bacillus subtilis to changes in nutrient source, the method revealed qualitatively different, large-scale responses to apparently simple, symmetric perturbations.
Model-based analysis enabled us to interpret the qualitative control scheme in an evolutionary context, and to reason about optimality of the bacterium's responses. In extending the sensitivity-based approach, we propose a method that infers possible structures of control mechanisms for metabolic networks directly by analyzing trade-offs between robust control and its associated costs, to yield experimentally testable predictions on transcription factor - gene interactions. General methods for quantification of robustness, in addition, are becoming feasible by using probabilistic approaches, which may ultimately provide principled insight into the role of robustness for the identification of cellular systems.
Daily behaviors of mammals are regulated by the so-called "master" circadian clock in the hypothalamic suprachiasmatic nucleus. It is composed of thousands of neurons, each of which contains the transcriptional feedback loops that are the clockworks. Isolated, these neurons show low-amplitude or no oscillations. In the tissue, they show high-amplitude, coordinated rhythms. The intercellular signaling network transforms the behavior of neurons from sloppy to precise oscillators. For the clock to function, individual cells must correctly adjust their phase and amplitude to signals and the tissue as a whole must arrive at a predictable consensus of in-phase oscillations at a period of nearly 24 hours. Thus, robust oscillations of the clock as a whole depend on features both of the cells and of the network.
In this talk, I will present analyses of data taken from SCN slices and relate it to simulation and analyses of mathematical (ordinary differential equation) models of the SCN. I will discuss oscillator-specific measures of sensitivity and relate them to network behaviors. For example, when the phase response magnitude of each cell is dynamic (higher magnitude when the cell is lower amplitude), networks of cells synchronize quickly.
Laboratory experiments with RNA bacteriophage phi-6 were used to examine the evolution of mutational robustness (phenotypic constancy in the face of mutation input), and the relationship between robustness and evolvability (adaptive potential). We previously showed that selection for robustness was relaxed under virus co-infection, because complementation between virus genotypes automatically buffers mutational effects. Recent results showed that lineages founded by robust viruses evolved faster than those initiated by their brittle (non-robust) counterparts, in novel environments where heat-shock survival is an adaptive challenge; these data demonstrated a positive link between robustness and evolvability. Here we show that the phenomenon is bi-directional: prior selection for heat-shock survival caused viruses to evolve robustness as a correlated trait. This tight relationship between robustness and heat-shock evolvability may be explained by a single amino-acid substitution in the lysis enzyme that governs virus entry/exit from the cell. A combination of structural-biology, crystallography and fitness data suggested that the mutant enzyme was favored under heat shock, but deleterious under benign conditions. Using a systems-biology approach, we discuss why the phi-6 lysis-enzyme gene may be a global robustness regulator for the virus.
When natural selection is acting at the level of the the individual phenotype, we expect selection to favor more robust phenotypes. However, selection acting at the level of the gene can undermine adaptation of the individual organism, and lead to the fixation of suboptimal traits. Genomic imprinting, the phenomenon where the pattern of expression of an allele depends on its parental origin, is thought to result from and evolutionary intragenomic conflict, where maternally and paternally inherited alleles favor different optimal phenotypes. Using a simple model, I will illustrate how this can lead to the systematic unravelling of phenotypic robustness.
Poster Presentations
Mutational robustness, defined as the proportion of mutations which are neutral, seems to be the opposite of evolvability. But while more robust populations produce fewer selectable mutations, they also contain greater neutral genetic variation. If this variation is epistatic, then this cryptic diversity may help robust populations adapt faster. Here we study adaptation in two models of neutral epistasis – mutations which are neutral when they arise, but may interact with subsequent mutations. We show that these interactions link robustness to adaptation and provide new insights into the dynamics of adapting populations. Our results also reflect the surprising effects of epistasis on foundational results in evolutionary theory, such as Kimura's famous derivation of the rate of neutral molecular evolution.
Understanding of stem cell differentiation and the characterization of the difference between stem cells and differentiated cells is an importance issue in developmental biology. To address the problem, we have carried out extensive simulations of a model of interacting cells with intracellular gene-expression dynamics and cell divisions. From more than hundred million gene-regulation networks, we selected those networks that show stemness i.e., both proliferation and cell-differentiation. We found that model stem cells always exhibited oscillatory expression dynamics, and the differentiation frequency of such stem cells was regulated, resulting in a robust number distribution. Moreover, we uncovered the common regulatory motifs for stem cell differentiation, in which a combination of regulatory motifs that generated oscillatory expression dynamics and stabilized distinct cellular states played an essential role. These findings may explain the recently observed heterogeneity and dynamic equilibrium in cellular states of stem cells, and can be used to predict regulatory networks responsible for differentiation in stem cell systems.
Finally we then propose a hypothesis that cancer cells lie outside the normal stable states attracted through normal cell differentiation, which explains why cancer cells accumulate mutations, to which they are not robust, and why these mutations create a new stable state far from the normal gene expression profile space.
[1] Oscillatory protein expression dynamics endows stem cells with robust differentiation potential. Suzuki N, Furusawa C, Kaneko K (2011) PLoS One 6(11):e27232.
[2] "Characterization of stem cells and cancer cells on the basis of gene expression profile stability, plasticity and robustness", Kaneko K. (2011) Bioessays 2011 33: 403–413
A fundamental question for evolutionary biology is why different proteins evolve at dramatically different rates. In particular, it is controversial to what degree the functional importance of a protein constrains its evolutionary rate, in part because functional importance is typically measured crudely, using knock-outs. Here we leverage biochemically-detailed systems biology models to develop a novel measure of functional importance. We define a protein domain's dynamical influence to be the integrated sensitivity of network dynamics to changes in the rate constants for reactions that domain participates in. We show that protein domains with greater dynamical influence (lower network robustness) typically evolve more slowly, suggesting that functional importance and network robustness do constrain protein evolution. We also show that this relationship is not due to a correlation between dynamical influence and knock-out essentiality or expression level. More broadly, our work shows that detailed simulation models can offer insight not only into how a system functions, but also how it evolves.
Genomic context and expression noise shape eukaryotic gene expression patterns, necessitating new experimental and analytic approaches to characterize the genotype-phenotype relationship. Human retroviruses, including human immunodeficiency virus-1 (HIV), which integrate their genomes into their host's genome, provide an important model system where genomic context and noise-driven expression variability may significantly impact the viral fate choice between replication and latency. Using an in vitro model of the HIV Tat-mediated positive-feedback loop, we had previously demonstrated that stochastic fluctuations in protein levels of the viral transactivator Tat generate integration-site-dependent, stochastically-driven phenotypes, in which non-expressing and highly-expressing cells can switch (activate or inactivate) with a delay. Here, we extend this model, and design a forward genetic screen to systematically identify genetic elements in the HIV LTR promoter that control the distribution of stochastic phenotypes over genomic integrations. Our screen identified mutations in core promoter regions, including Sp1 and TATA transcription factor binding sites, which increase the fraction of genomic integrations that specify Switching phenotypes. Analysis of strongly selected mutations in Sp1 site III and the TATA box revealed that both mutations decrease the efficiency of Tat amplification in the viral expression circuit, but only the selected Sp1 mutation modestly affects LTR basal expression dynamics in the absence of Tat. Computational analysis suggests that, while such changes in basal expression may partially account for the enrichment of stochastic phenotypes for the Sp1 mutant, both mutations likely control the phenotype distribution by altering Tat-mediated noise amplification in the transactivation circuit. The observation that the selected mutations demonstrate modest or no effect on basal expression dynamics suggests that perhaps the WT LTR has already been optimized to generate high levels of basal expression noise, and that enhancement of variegated phenotypes must come at the expense of transactivated expression for the HIV LTR.
Overall, our study demonstrates a methodology for identifying promoter elements that affect context-dependent stochastic phenotypes in a biomedically relevant system, lays the foundations for identifying the underlying mechanisms involved, and may also further our understanding of how mutational selection can control the frequency of latent HIV infection.
Joint work with Ron Skupsky, Priya S. Shah, Adam P. Arkin, and David V. Schaffer
Nested canalyzing functions (NCFs) are biologically relevant regulatory rules. This poster presents the concept of layer numbers for NCFs which are good indicators of the stability of NCF networks. Results concerning the effect of intrinsic noise in the stability of NCF networks with different layer numbers will be presented.
While high-throughput NGS (Illumina paired-end, 75 bp) has been demonstrated to be a rapid and cost-effective method to sequencing transcriptomes of model organisms, its application in examining expression and annotation in non-model organisms has been less explored. We used the Fluorescent Protein (FP) gene family as an example due to its unique biophysical properties and the extensive literature surrounding this molecule. Using a combination of NGS, de novo transcriptome assembly, gene annotation and analysis and synthetic gene construction, we show that novel gene sequences can be obtained, some which are nearly twice the length of other FPs. This method can perhaps shed new light on missing domains that may affect protein function, and can facilitate functional characterization of any new protein family. We generated a de novo transcriptome assembly of two Scleractinian coral samples of the same genus, Favia sp. (termed Fav23 and Fav62), from the Northern Red Sea. We elected to focus on fluorescent proteins (FPs) as a model gene family, because of their unique characteristics including small size (~238 amino acids) and ease of visualization. The FP chromophore is generated by the autocatalytic, posttranslational cyclization and oxidation of the tripeptide 65SYG67 sequence, a unique process which does not involve any external co-factors to generate biofluorescence. The assembly and annotation of these two samples yielded around 7,023 orthologous protein clusters (E-value 2e-30) with homology to Nematostella vectensis (Startlet sea anemone). Screening the abovementioned clusters led to identification of 11 new full-length fluorescent sequences. Out of these 11 sequences, seven belonging to Fav23 sample and four belonging to Fav62 sample are homologous to N. vectensis (JGI Protein ID: 205348). These 11 novel fluorescent protein genes are phylogenetically classified into five different clades. Strikingly, one of the newly identified fluorescent proteins is185 amino acids longer than the consensus length of sequences reported in NCBI (GFP from Aequorea victoria is 236 amino acids). This makes it the longest sequence of any FP reported. The two other ones are 49 and 41 amino acids longer. Synthetic gene construction of these three sequences validated our de novo assembly method. Furthermore, using short-read data we ranked their expression level in each coral sample.
Conclusions/Significance: We have shown that de novo assembly of short-read transcriptome data is a rapid and useful method for novel full-length protein identification. It offers the possibility of discovering longer sequences not usually identified using traditional methods. Additionally, the longer sequences may represent a common characteristic of the FP superfamily. This offers additional clues to binding interfaces and even new insights into this heavily examined protein, listed in over 30,000 publications (The Web of Science).
- City University of New York, The Graduate Center, Molecular, Cellular and Developmental Biology, New York, NY 10065, USA
- American Museum of Natural History, Sackler Institute of Comparative Genomics
- Department of Psychiatry and Human Behavior, Division of Biology and Medicine, Warren Alpert Medical School, Brown University Providence, RI 02912, USA
- John B. Pierce Laboratory, Cellular and Molecular Physiology, Yale University, New Haven, CT 06519, USA
- Marine Biology Department, The Leon H. Charney School of Marine Sciences, University of Haifa, Mount Carmel, Haifa 31905, Israel
- Department of Natural Sciences, City University of New York, Baruch College, Box A-0506, 17 Lexington Avenue, New York, New York 10010, USA
Joint work with Rob DeSalle, Hung-Teh Kao, Apurva Narechania, Zhou Han, Dan Tchernov, Vincent Pieribone, and David F. Gruber
The genetic architecture of quantitative traits has significant consequences for the variational properties of traits and for the detection of their molecular determinants. Nevertheless, it remains poorly understood how the most basic components of the genetic architecture (\textit{e.g.} the number of contributing loci) of a trait should evolve, or how it should vary across different traits. Here we document a striking relationship between the strength of selection on a trait and the number of loci that control it: \textit{Saccharomyces cerevisae} transcripts of intermediate abundance are controlled by many loci, whereas transcripts of either low or high abundance are controlled by few loci. We develop a simple, population-genetic model for the evolution of genetic architecture that provides an intuitive explanation for this empirical observation. The explanation depends on the action of compensatory evolution, which favors duplications over deletions in genes that contribute to traits under intermediate selection.
The pathogenicity of P. falciparum, the dominant cause of malaria, has been attributed to the PfEMP1 protein, which is expressed on the surface of infected red blood cells and causes them to adhere to the microvasculature, preventing circulation and clearance by the spleen. Because of its prominent location on the surface of the infected RBCs, PfEMP1 is thought to be the primary target of the human immune response. PfEMP1 is encoded by a set of 50-60 hyper-variable multi-copy genes, known as var genes, that undergo coordinated and mutually exclusive expression. This is a well-studied phenomenon termed "antigenic variation", and it serves to lengthen the duration of infection. The immense variation of var gene sequences within and between parasite genomes accounts for, respectively, the remarkable persistence and recurrence of P. falciparum infections within individual hosts. Because var gene variation is generated primarily through recombination, we are developing network-based methods to analyze large datasets of field-collected sequences to elucidate the evolutionary dynamics that generate the variation. We are also using an agent-based modeling approach to track some of the simpler dynamics responsible for generating and maintaining var gene diversity. One overarching aim is to understand what determines the extent of var gene diversity in a given population, since this has been shown to vary considerably by location. Another overarching aim is to understand whether there are different functional classes of var genes, and if so, whether they are distributed equally among parasite genomes. The extent and structure of PfEMP1 sequence variation has crucial implications for intervention strategies—e.g., for designing vaccines against this antigen, for determining the long-term impact of temporary reductions in transmission rate, and for predicting age-specific responses.
The mammalian circadian master clock is a network of thousands of interacting oscillators. Biological experimentation and mathematical modeling have shown that inter-cellular coupling is critical to precise, synchronized, high amplitude oscillations. When cells are decoupled, they show low amplitude or no rhythms. Further, different cells have different intrinsic periods, and therefore drift out of phase with each other when decoupled. Cells in tissue send and receive signals, adjusting both their phases and their amplitudes, resulting in strong, coordinated oscillations. It is likely that there is an interaction between each cell's current amplitude and its phase responsiveness and, further, that this interaction allows efficient recovery from environmental changes. In this poster, we present a model that allows us to study the effects of phase and amplitude responsiveness as well as their interactions.
