Modeling the dynamics of transcriptional gene regulatory networks for animal development

Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators

The transcriptional regulatory network (TRN) is a graph framework that helps understand the complex transcriptional regulation mechanisms in the transcription process. Identifying the phenotype-specific transcription regulators is vital to reveal the functional roles of transcription elements in associating the specific phenotypes. Although many methods have been developed towards detecting the phenotype-specific transcription elements based on the static TRN in the past decade, most of them are not satisfactory for elucidating the phenotype-related functional roles of transcription regulators in multiple levels, as the dynamic characteristics of transcription regulators are usually ignored in static models. In this study, we introduce a novel framework called DTGN to identify the phenotype-specific transcription factors (TFs) and pathways by constructing dynamic TRNs. We first design a graph autoencoder model to integrate the phenotype-oriented time-series gene expression data and static TRN to learn the temporal representations of genes. Then, based on the learned temporal representations of genes, we develop a statistical method to construct a series of dynamic TRNs associated with the development of specific phenotypes. Finally, we identify the phenotype-specific TFs and pathways from the constructed dynamic TRNs. Results from multiple phenotypic datasets show that the proposed DTGN framework outperforms most existing methods in identifying phenotype-specific TFs and pathways. Our framework offers a new approach to exploring the functional roles of transcription regulators that associate with specific phenotypes in a dynamic model.

Alternative splicing coupled to nonsense-mediated decay coordinates downregulation of non-neuronal genes in developing mouse neurons

BACKGROUND

The functional coupling between alternative pre-mRNA splicing (AS) and the mRNA quality control mechanism called nonsense-mediated decay (NMD) can modulate transcript abundance. Previous studies have identified several examples of such a regulation in developing neurons. However, the systems-level effects of AS-NMD in this context are poorly understood.

RESULTS

We developed an R package, factR2, which offers a comprehensive suite of AS-NMD analysis functions. Using this tool, we conducted a longitudinal analysis of gene expression in pluripotent stem cells undergoing induced neuronal differentiation. Our analysis uncovers hundreds of AS-NMD events with significant potential to regulate gene expression. Notably, this regulation is significantly overrepresented in specific functional groups of developmentally downregulated genes. Particularly strong association with gene downregulation is detected for alternative cassette exons stimulating NMD upon their inclusion into mature mRNA. By combining bioinformatic analyses with CRISPR/Cas9 genome editing and other experimental approaches we show that NMD-stimulating cassette exons regulated by the RNA-binding protein PTBP1 dampen the expression of their genes in developing neurons. We also provided evidence that the inclusion of NMD-stimulating cassette exons into mature mRNAs is temporally coordinated with NMD-independent gene repression mechanisms.

CONCLUSIONS

Our study provides an accessible workflow for the discovery and prioritization of AS-NMD targets. It further argues that the AS-NMD pathway plays a widespread role in developing neurons by facilitating the downregulation of functionally related non-neuronal genes.

Differential sensitivity to longitudinal and transverse stretch mediates transcriptional responses in mouse neonatal ventricular myocytes

To identify how cardiomyocyte mechanosensitive signaling pathways are regulated by anisotropic stretch, micropatterned mouse neonatal cardiomyocytes were stretched primarily longitudinally or transversely to the myofiber axis. Four hours of static, longitudinal stretch induced differential expression of 557 genes, compared with 30 induced by transverse stretch, measured using RNA-seq. A logic-based ordinary differential equation model of the cardiac myocyte mechanosignaling network, extended to include the transcriptional regulation and expression of 784 genes, correctly predicted measured expression changes due to anisotropic stretch with 69% accuracy. The model also predicted published transcriptional responses to mechanical load in vitro or in vivo with 63-91% accuracy. The observed differences between transverse and longitudinal stretch responses were not explained by differential activation of specific pathways but rather by an approximately twofold greater sensitivity to longitudinal stretch than transverse stretch. In vitro experiments confirmed model predictions that stretch-induced gene expression is more sensitive to angiotensin II and endothelin-1, via RhoA and MAP kinases, than to the three membrane ion channels upstream of calcium signaling in the network. Quantitative cardiomyocyte gene expression differs substantially with the axis of maximum principal stretch relative to the myofilament axis, but this difference is due primarily to differences in stretch sensitivity rather than to selective activation of mechanosignaling pathways.NEW & NOTEWORTHY Anisotropic stretch applied to micropatterned neonatal mouse ventricular myocytes induced markedly greater acute transcriptional responses when the major axis of stretch was parallel to the myofilament axis than when it was transverse. Analysis with a novel quantitative network model of mechanoregulated cardiomyocyte gene expression suggests that this difference is explained by higher cell sensitivity to longitudinal loading than transverse loading than by the activation of differential signaling pathways.

Understanding molecular signaling cascades in neural disease using multi-resolution models

If the genome defines the program for the operations of a cell, signaling networks execute it. These cascades of chemical, cell-biological, structural, and trafficking events span milliseconds (e.g., synaptic release) to potentially a lifetime (e.g., stabilization of dendritic spines). In principle almost every aspect of neuronal function, particularly at the synapse, depends on signaling. Thus dysfunction of these cascades, whether through mutations, local dysregulation, or infection, leads to disease. The sheer complexity of these pathways is matched by the range of diseases and the diversity of their phenotypes. In this review, we discuss how to build computational models, how these models are essential to tackle this complexity, and the benefits of using families of models at different levels of detail to understand signaling in health and disease.

Cis-Regulatory Logic Produces Gene-Expression Noise Describing Phenotypic Heterogeneity in Bacteria

Gene transcriptional process is random. It occurs in bursts and follows single-molecular kinetics. Intermittent bursts are measured based on their frequency and size. They influence temporal fluctuations in the abundance of total mRNA and proteins by generating distinct transcriptional variations referred to as “noise”. Noisy expression induces uncertainty because the association between transcriptional variation and the extent of gene expression fluctuation is ambiguous. The promoter architecture and remote interference of different cis-regulatory elements are the crucial determinants of noise, which is reflected in phenotypic heterogeneity. An alternative perspective considers that cellular parameters dictating genome-wide transcriptional kinetics follow a universal pattern. Research on noise and systematic perturbations of promoter sequences reinforces that both gene-specific and genome-wide regulation occur across species ranging from bacteria and yeast to animal cells. Thus, deciphering gene-expression noise is essential across different genomics applications. Amidst the mounting conflict, it is imperative to reconsider the scope, progression, and rational construction of diversified viewpoints underlying the origin of the noise. Here, we have established an indication connecting noise, gene expression variations, and bacterial phenotypic variability. This review will enhance the understanding of gene-expression noise in various scientific contexts and applications.

How subtle changes in 3D structure can create large changes in transcription

Animal genomes are organized into topologically associated domains (TADs). TADs are thought to contribute to gene regulation by facilitating enhancer-promoter (E-P) contacts within a TAD and preventing these contacts across TAD borders. However, the absolute difference in contact frequency across TAD boundaries is usually less than 2-fold, even though disruptions of TAD borders can change gene expression by 10-fold. Existing models fail to explain this hypersensitive response. Here, we propose a futile cycle model of enhancer-mediated regulation that can exhibit hypersensitivity through bistability and hysteresis. Consistent with recent experiments, this regulation does not exhibit strong correlation between E-P contact and promoter activity, even though regulation occurs through contact. Through mathematical analysis and stochastic simulation, we show that this system can create an illusion of E-P biochemical specificity and explain the importance of weak TAD boundaries. It also offers a mechanism to reconcile apparently contradictory results from recent global TAD disruption with local TAD boundary deletion experiments. Together, these analyses advance our understanding of cis-regulatory contacts in controlling gene expression and suggest new experimental directions.

Addressing Non-linear System Dynamics of Single-Strand RNA Virus-Host Interaction

Positive single-strand ribonucleic acid [(+)ssRNA] viruses can cause multiple outbreaks, for which comprehensive tailored therapeutic strategies are still missing. Virus and host cell dynamics are tightly connected, generating a complex dynamics that conveys in virion assembly to ensure virus spread in the body. Starting from the knowledge of relevant processes in (+ss)RNA virus replication, transcription, translation, virions budding and shedding, and their respective energy costs, we built up a systems thinking (ST)-based diagram of the virus-host interaction, comprehensive of stocks, flows, and processes as well-described in literature. In ST approach, stocks and flows are expressed by a proxy of the energy embedded and transmitted, respectively, whereas processes are referred to the energy required for the system functioning. In this perspective, healthiness is just a particular configuration, in which stocks relevant for the system (equivalent but not limited to proteins, RNA, DNA, and all metabolites required for the survival) are constant, and the system behavior is stationary. At time of infection, the presence of additional stocks (e.g., viral protein and RNA and all metabolites required for virion assembly and spread) confers a complex network of feedbacks leading to new configurations, which can evolve to maximize the virions stock, thus changing the system structure, output, and purpose. The dynamic trajectories will evolve to achieve a new stationary status, a phenomenon described in microbiology as integration and symbiosis when the system is resilient enough to the changes, or the system may stop functioning and die. Application of external driving forces, acting on processes, can affect the dynamic trajectories adding a further degree of complexity, which can be captured by ST approach, used to address these new configurations. Investigation of system configurations in response to external driving forces acting is developed by computational analysis based on ST diagrams, with the aim at designing novel therapeutic approaches.

Identifying Transcription Factor Combinations to Modulate Circadian Rhythms by Leveraging Virtual Knockouts on Transcription Networks

The mammalian circadian systems consist of indigenous, self-sustained 24-h rhythm generators. They comprise many genes, molecules, and regulators. To decode their systematic controls, a robust computational approach was employed. It integrates transcription-factor-occupancy and time-series gene-expression data as input. The model equations were constructed and solved to determine the transcriptional regulatory logics in the mouse transcriptome network. This hypothesizes to explore the underlying mechanisms of combinatorial transcriptional regulations for circadian rhythms in mouse. We reconstructed the quantitative transcriptional-regulatory networks for circadian gene regulation at a dynamic scale. Transcriptional-simulations with virtually knocked-out mutants were performed to estimate their influence on networks. The potential transcriptional-regulators-combinations modulating the circadian rhythms were identified. Of them, CLOCK/CRY1 double knockout preserves the highest modulating capacity. Our quantitative framework offers a quick, robust, and physiologically relevant way to characterize the druggable targets to modulate the circadian rhythms at a dynamic scale effectively.

A mathematical model exhibiting the effect of DNA methylation on the stability boundary in cell-fate networks

Cell-fate networks are traditionally studied within the framework of gene regulatory networks. This paradigm considers only interactions of genes through expressed transcription factors and does not incorporate chromatin modification processes. This paper introduces a mathematical model that seamlessly combines gene regulatory networks and DNA methylation (DNAm), with the goal of quantitatively characterizing the contribution of epigenetic regulation to gene silencing. The 'Basin of Attraction percentage' is introduced as a metric to quantify gene silencing abilities. As a case study, a computational and theoretical analysis is carried out for a model of the pluripotent stem cell circuit as well as a simplified self-activating gene model. The results confirm that the methodology quantitatively captures the key role that DNAm plays in enhancing the stability of the silenced gene state.

Effects of β-HgS on cell viability and intracellular oxidative stress in PC-12 cells

Traditional Tibetan medicines containing β-HgS have been used to treat chronic ailments for thousands of years. The effects were studied of β-HgS on cell viability and intracellular oxidative stress in PC-12 cells.

Connecting the molecular function of microRNAs to cell differentiation dynamics

MicroRNAs form a class of short, non-coding RNA molecules which are essential for proper development in tissue-based plants and animals. To help explain their role in gene regulation, a number of mathematical and computational studies have demonstrated the potential canalizing effects of microRNAs. However, such studies have typically focused on the effects of microRNAs on only one or a few target genes. Consequently, it remains unclear how these small-scale effects add up to the experimentally observed developmental outcomes resulting from microRNA perturbation at the whole-genome level. To answer this question, we built a general computational model of cell differentiation to study the effect of microRNAs in genome-scale gene regulatory networks. Our experiments show that in large gene regulatory networks, microRNAs can control differentiation time without significantly changing steady-state gene expression profiles. This temporal regulatory role cannot be naturally replicated using protein-based transcription factors alone. While several microRNAs have been shown to regulate differentiation time in vivo, our findings provide a new explanation of how the cumulative molecular actions of individual microRNAs influence genome-scale cellular dynamics. Taken together, these results may help explain why tissue-based organisms exclusively depend on miRNA-mediated regulation, while their more primitive counterparts do not.

Effect of temperature on Pi54-mediated leaf blast resistance in rice

Assessment of temperature effect on plant resistance against diseases has become essential under climate change scenario as temperature rise is anticipated to modify host resistance. To determine temperature influence on resistance gene, a pair of near-isogenic rice lines differing for the Pi54 resistance gene was assessed against leaf blast. Blast resistance was determined as the extent of infection efficiency (IE) and sporulation (SP) at suboptimal (22 °C and 32 °C) and optimal temperature (27 °C) of pathogen aggressiveness. Relative resistance for IE and SP was higher at suboptimal temperature as compared to that of optimal temperature. Maximum level of resistance was at 22 °C where higher levels of expression of Pi54 and defence-regulatory transcription factor WRKY45 were also noted. At 32 °C, although some level of resistance noted, but level of Pi54 and WRKY45 expression was too low, suggesting that resistance recorded at higher temperature was due to reduced pathogen aggressiveness. At the optimal temperature for pathogen aggressiveness, comparatively lower levels of Pi54 and WRKY45 expression suggest possible temperature-induced interruption of the defence processes. The variation in resistance patterns modulated by temperature is appeared to be due to pathogen's sensitivity to temperature that leads to varying levels of Pi54 gene activation. Quick and violent activity of the pathogen at optimal temperature came into sight for the interruption of defence process activated by Pi54 gene. Evaluation of blast resistance genes under variable temperature conditions together with weather data could be applied in screening rice genotypes for selection of resistance having resilience to temperature rise.

Causal Gene Regulatory Network Modeling and Genomics: Second-Generation Challenges

Gene regulatory network modeling has played a major role in advancing the understanding of developmental systems, by crystallizing structures of relevant extant information, by formally posing hypothetical functional relationships between network elements, and by offering clear predictive tests to improve understanding of the mechanisms driving developmental progression. Both ordinary differential equation (ODE)-based and Boolean models have also been highly successful in explaining dynamics within subcircuits of more complex processes. In a very small number of cases, gene regulatory network models of much more global scope have been proposed that successfully predict the dynamics of the processes establishing most of an embryonic body plan. Can such successes be expanded to very different developmental systems, including post-embryonic mammalian systems? This perspective discusses several problems that must be solved in more quantitative and predictive theoretical terms, to make this possible. These problems include: the effects of cellular history on chromatin state and how these affect gene accessibility; the dose dependence of activities of many transcription factors (a problem for Boolean models); stochasticity of some transcriptional outputs (a problem for simple ODE models); response timing delays due to epigenetic remodeling requirements; functionally different kinds of repression; and the regulatory syntax that governs responses of genes with multiple enhancers.

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

BACKGROUND

Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.

OBJECTIVE

Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.

METHODS

Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.

RESULTS

GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.

CONCLUSION

In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

Encounters across networks: Windows into principles of genomic regulation

Gene regulatory networks account for the ability of the genome to program development in complex multi-cellular organisms. Such networks are based on principles of gene regulation by combinations of transcription factors that bind to specific cis-regulatory DNA sites to activate transcription. These cis-regulatory regions mediate logic processing at each network node, enabling progressive increases in organismal complexity with development. Gene regulatory network explanations of development have been shown to account for patterning and cell type diversification in fly and sea urchin embryonic systems, where networks are characterized by fast coupling between transcriptional inputs and changes in target gene transcription rates, and crucial cis-regulatory elements are concentrated relatively close to the protein coding sequences of the target genes, thus facilitating their identification. Stem cell-based development in post-embryonic mammalian systems also depends on gene networks, but differs from the fly and sea urchin systems. First, the number of regulatory elements per gene and the distances between regulatory elements and the genes they control are considerably larger, forcing searches via genome-wide transcription factor binding surveys rather than functional assays. Second, the intrinsic timing of network state transitions can be slowed considerably by the need to undo stem-cell chromatin configurations, which presumably add stability to stem-cell states but retard responses to transcription factor changes during differentiation. The dispersed, partially redundant cis-regulatory systems controlling gene expression and the slow state transition kinetics in these systems already reveal new insights and opportunities to extend understanding of the repertoire of gene networks and regulatory system logic.

Methods for the experimental and computational analysis of gene regulatory networks in sea urchins

The discovery of gene regulatory networks (GRNs) has opened a gate to access the genomic mechanisms controlling development. GRNs are systems of transcriptional regulatory circuits that control the differential specification of cell fates during development by regulating gene expression. The experimental analysis of GRNs involves a collection of methods, each revealing aspects of the overall control process. This review provides an overview of experimental and computational methods that have been successfully applied for solving developmental GRNs in the sea urchin embryo. The key in this approach is to obtain experimental evidence for functional interactions between transcription factors and regulatory DNA. In the second part of this review, a more generally applicable strategy is discussed that shows a path from experimental evidence to annotation of regulatory linkages to the generation of GRN models.

Robust Gene Circuit Control Design for Time-Delayed Genetic Regulatory Networks Without SUM Regulatory Logic

This paper investigates the gene circuit control design problem of time-delayed genetic regulatory networks. In the genetic regulatory networks, the time delays are unknown constants, and the genetic regulatory is not conventional SUM regulatory logic and can be modeled to be an unknown nonlinear function of the time-delayed states of the other genes in a cell. By Lyapunov stability, a novel adaptive gene circuit control design approach is proposed for the genetic regulatory networks, where the unknown time delays are estimated online by adaptive algorithms and the unknown regulatory functions are approximated by neural networks. The design approach in this paper is delay-dependent and has less conservatism than the delay-independent approach. From theoretical analysis, the closed-loop system is asymptotically stable and all the signals in the system converge to an adjustable neighborhood of the origin. Finally, a numerical example is given to show the effectiveness of the new design approach.

Regulatory states in the developmental control of gene expression

A growing body of evidence shows that gene expression in multicellular organisms is controlled by the combinatorial function of multiple transcription factors. This indicates that not the individual transcription factors or signaling molecules, but the combination of expressed regulatory molecules, the regulatory state, should be viewed as the functional unit in gene regulation. Here, I discuss the concept of the regulatory state and its proposed role in the genome-wide control of gene expression. Recent analyses of regulatory gene expression in sea urchin embryos have been instrumental for solving the genomic control of cell fate specification in this system. Some of the approaches that were used to determine the expression of regulatory states during sea urchin embryogenesis are reviewed. Significant developmental changes in regulatory state expression leading to the distinct specification of cell fates are regulated by gene regulatory network circuits. How these regulatory state transitions are encoded in the genome is illuminated using the sea urchin endoderm-mesoderms cell fate decision circuit as an example. These observations highlight the importance of considering developmental gene regulation, and the function of individual transcription factors, in the context of regulatory states.

An integrative method to decode regulatory logics in gene transcription

Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF–TF interactions that form TF logics in regulating target genes. By combining cis-regulatory logics and transcriptional kinetics into one single model framework, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA-binding signals in order to identify the TF logics and to reconstruct the underlying TRNs. We evaluated the newly developed methodology using simulation, comparison and application studies, and the results not only show their consistence with existing knowledge, but also demonstrate its ability to accurately reconstruct TRNs in biological complex systems.

Existing transcriptional regulatory networks models fall short of deciphering the cooperation between multiple transcription factors on dynamic gene expression. Here the authors develop an integrative method that combines gene expression and transcription factor-DNA binding data to decode transcription regulatory logics.

Cell differentiation defines acute and chronic infection cell types in Staphylococcus aureus

A central question to biology is how pathogenic bacteria initiate acute or chronic infections. Here we describe a genetic program for cell-fate decision in the opportunistic human pathogen Staphylococcus aureus, which generates the phenotypic bifurcation of the cells into two genetically identical but different cell types during the course of an infection. Whereas one cell type promotes the formation of biofilms that contribute to chronic infections, the second type is planktonic and produces the toxins that contribute to acute bacteremia. We identified a bimodal switch in the agr quorum sensing system that antagonistically regulates the differentiation of these two physiologically distinct cell types. We found that extracellular signals affect the behavior of the agr bimodal switch and modify the size of the specialized subpopulations in specific colonization niches. For instance, magnesium-enriched colonization niches causes magnesium binding to S. aureusteichoic acids and increases bacterial cell wall rigidity. This signal triggers a genetic program that ultimately downregulates the agr bimodal switch. Colonization niches with different magnesium concentrations influence the bimodal system activity, which defines a distinct ratio between these subpopulations; this in turn leads to distinct infection outcomes in vitro and in an in vivo murine infection model. Cell differentiation generates physiological heterogeneity in clonal bacterial infections and helps to determine the distinct infection types.

While in hospital, patients can be unwittingly exposed to bacteria that can cause disease. These hospital-associated bacteria can lead to potentially life-threatening infections that may also complicate the treatment of the patients’ existing medical conditions. Staphylococcus aureus is one such bacterium, and it can cause several types of infection including pneumonia, blood infections and long-term infections of prosthetic devices.

It is thought that S. aureus is able to cause so many different types of infection because it is capable of colonizing distinct tissues and organs in various parts of the body. Understanding the biological processes that drive the different infections is crucial to improving how these infections are treated.

S. aureus lives either as an independent, free-swimming cell or as part of a community known as a biofilm. These different lifestyles dictate the type of infection the bacterium can cause, with free-swimming cells producing toxins that contribute to intense, usually short-lived, infections and biofilms promoting longer-term infections that are difficult to eradicate. However, it is not clear how a population of S. aureus cells chooses to adopt a particular lifestyle and whether there are any environmental signals that influence this decision.

Here, Garcia-Betancur et al. found that S. aureus populations contain small groups of cells that have already specialized into a particular lifestyle. These groups of cells collectively influence the choice made by other cells in the population. While both lifestyles will be represented in the population, environmental factors influence the numbers of cells that initially adopt each type of lifestyle, which ultimately affects the choice made by the rest of the population. For example, if the bacteria colonize a tissue or organ that contains high levels of magnesium ions, the population is more likely to form biofilms.

In the future, the findings of Garcia-Betancur et al. may help us to predict how an infection may develop in a particular patient, which may help to diagnose the infection more quickly and allow it to be treated more effectively.

Deconvolution of Gene Expression Noise into Spatial Dynamics of Transcription Factor-Promoter Interplay

Gene expression noise is not only the mere consequence of stochasticity, but also a signal that reflects the upstream physical dynamics of the cognate molecular machinery. Soil bacteria facing recalcitrant pollutants exploit noise of catabolic promoters to deploy beneficial phenotypes such as metabolic bet-hedging and/or division of biochemical labor. Although the role of upstream promoter-regulator interplay in the origin of this noise is little understood, its specifications are probably ciphered in flow cytometry data patterns. We studied Pm promoter activity of the environmental bacterium Pseudomonas putida and its cognate regulator XylS by following expression of Pm-gfp fusions in single cells. Using mathematical modeling and computational simulations, we determined the kinetic properties of the system and used them as a baseline code to interpret promoter activity in terms of upstream regulator dynamics. Transcriptional noise was predicted to depend on the intracellular physical distance between regulator source (where XylS is produced) and the target promoter. Experiments with engineered bacteria in which this distance is minimized or enlarged confirmed the predicted effects of source/target proximity on noise patterns. This approach allowed deconvolution of cytometry data into mechanistic information on gene expression flow. It also provided a basis for selecting programmable noise levels in synthetic regulatory circuits.

A mathematical model of mechanotransduction reveals how mechanical memory regulates mesenchymal stem cell fate decisions

BACKGROUND

Mechanical and biophysical properties of the cellular microenvironment regulate cell fate decisions. Mesenchymal stem cell (MSC) fate is influenced by past mechanical dosing (memory), but the mechanisms underlying this process have not yet been well defined. We have yet to understand how memory affects specific cell fate decisions, such as the differentiation of MSCs into neurons, adipocytes, myocytes, and osteoblasts.

RESULTS

We study a minimal gene regulatory network permissive of multi-lineage MSC differentiation into four cell fates. We present a continuous model that is able to describe the cell fate transitions that occur during differentiation, and analyze its dynamics with tools from multistability, bifurcation, and cell fate landscape analysis, and via stochastic simulation. Whereas experimentally, memory has only been observed during osteogenic differentiation, this model predicts that memory regions can exist for each of the four MSC-derived cell lineages. We can predict the substrate stiffness ranges over which memory drives differentiation; these are directly testable in an experimental setting. Furthermore, we quantitatively predict how substrate stiffness and culture duration co-regulate the fate of a stem cell, and we find that the feedbacks from the differentiating MSC onto its substrate are critical to preserve mechanical memory. Strikingly, we show that re-seeding MSCs onto a sufficiently soft substrate increases the number of cell fates accessible.

CONCLUSIONS

Control of MSC differentiation is crucial for the success of much-lauded regenerative therapies based on MSCs. We have predicted new memory regions that will directly impact this control, and have quantified the size of the memory region for osteoblasts, as well as the co-regulatory effects on cell fates of substrate stiffness and culture duration. Taken together, these results can be used to develop novel strategies to better control the fates of MSCs in vitro and following transplantation.

A resource dependent protein synthesis model for evaluating synthetic circuits

Reliable in silico design of synthetic gene networks necessitates novel approaches to model the process of protein synthesis under the influence of limited resources. We present such a novel protein synthesis model which originates from the Ribosome Flow Model and among other things describes the movement of RNA-polymerase and ribosomes on mRNA and DNA templates, respectively. By analyzing the convergence properties of this model based upon geometric considerations, we present additional insights into the dynamic mechanisms of the process of protein synthesis. Further, we demonstrate how this model can be used to evaluate the performance of synthetic gene circuits under different loading scenarios.

A spectrum of modularity in multi-functional gene circuits

A major challenge in systems biology is to understand the relationship between a circuit's structure and its function, but how is this relationship affected if the circuit must perform multiple distinct functions within the same organism? In particular, to what extent do multi‐functional circuits contain modules which reflect the different functions? Here, we computationally survey a range of bi‐functional circuits which show no simple structural modularity: They can switch between two qualitatively distinct functions, while both functions depend on all genes of the circuit. Our analysis reveals two distinct classes: hybrid circuits which overlay two simpler mono‐functional sub‐circuits within their circuitry, and emergent circuits, which do not. In this second class, the bi‐functionality emerges from more complex designs which are not fully decomposable into distinct modules and are consequently less intuitive to predict or understand. These non‐intuitive emergent circuits are just as robust as their hybrid counterparts, and we therefore suggest that the common bias toward studying modular systems may hinder our understanding of real biological circuits.

Dynamics of the Interaction between Cotton Bollworm Helicoverpa armigera and Nucleopolyhedrovirus as Revealed by Integrated Transcriptomic and Proteomic Analyses

Over the past decades, Helicoverpa armigera nucleopolyhedrovirus (HearNPV) has been widely used for biocontrol of cotton bollworm, which is one of the most destructive pest insects in agriculture worldwide. However, the molecular mechanism underlying the interaction between HearNPV and host insects remains poorly understood. In this study, high-throughput RNA-sequencing was integrated with label-free quantitative proteomics analysis to examine the dynamics of gene expression in the fat body of H. armigera larvae in response to challenge with HearNPV. RNA sequencing-based transcriptomic analysis indicated that host gene expression was substantially altered, yielding 3,850 differentially expressed genes (DEGs), whereas no global transcriptional shut-off effects were observed in the fat body. Among the DEGs, 60 immunity-related genes were down-regulated after baculovirus infection, a finding that was consistent with the results of quantitative real-time RT-PCR. Gene ontology and functional classification demonstrated that the majority of down-regulated genes were enriched in gene cohorts involved in energy, carbohydrate, and amino acid metabolic pathways. Proteomics analysis identified differentially expressed proteins in the fat body, among which 76 were up-regulated, whereas 373 were significantly down-regulated upon infection. The down-regulated proteins are involved in metabolic pathways such as energy metabolism, carbohydrate metabolism (CM), and amino acid metabolism, in agreement with the RNA-sequence data. Furthermore, correlation analysis suggested a strong association between the mRNA level and protein abundance in the H. armigera fat body. More importantly, the predicted gene interaction network indicated that a large subset of metabolic networks was significantly negatively regulated by viral infection, including CM-related enzymes such as aldolase, enolase, malate dehydrogenase, and triose-phosphate isomerase. Taken together, transcriptomic data combined with proteomic data elucidated that baculovirus established systemic infection of host larvae and manipulated the host mainly by suppressing the host immune response and down-regulating metabolism to allow viral self-replication and proliferation. Therefore, this study provided important insights into the mechanism of host-baculovirus interaction.

Placental transcriptomes in the common aneuploidies reveal critical regions on the trisomic chromosomes and genome-wide effects

OBJECTIVE

Chromosomal aberrations are frequently associated with birth defects and pregnancy losses. Trisomy 13, Trisomy 18 and Trisomy 21 are the most common, clinically relevant fetal aneusomies. This study used a transcriptomics approach to identify the molecular signatures at the maternal-fetal interface in each aneuploidy.

METHODS

We profiled placental gene expression (13-22 weeks) in T13 (n = 4), T18 (n = 4) and T21 (n = 8), and in euploid pregnancies (n = 4).

RESULTS

We found differentially expressed transcripts (≥2-fold) in T21 (n = 160), T18 (n = 80) and T13 (n = 125). The majority were upregulated and most of the misexpressed genes were not located on the relevant trisomic chromosome, suggesting genome-wide dysregulation. A smaller number of the differentially expressed transcripts were encoded on the trisomic chromosome, suggesting gene dosage. In T21, <10% of the genes were transcribed from the Down syndrome critical region (21q21-22), which contributes to the clinical phenotype. In T13, 15% of the upregulated genes were on the affected chromosome (13q11-14), and in T18, the percentage increased to 24% (18q11-22 region).

CONCLUSION

The trisomic placental (and possibly fetal) phenotypes are driven by the combined effects of genome-wide phenomena and increased gene dosage from the trisomic chromosome. © 2016 John Wiley & Sons, Ltd.

Mature maternal mRNAs are longer than zygotic ones and have complex degradation kinetics in sea urchin

Early in embryogenesis, maternally deposited transcripts are degraded and new zygotic transcripts are generated during the maternal to zygotic transition. Recent works have shown that early zygotic transcripts are short compared to maternal transcripts, in zebrafish and Drosophila species. The reduced zygotic transcript length was attributed to the short cell cycle in these organisms that prevents the transcription of long primary transcripts (intron delay). Here we study the length of maternal mRNAs and their degradation kinetics in two sea urchin species to further the understanding of maternal gene usage and processing. Early zygotic primary transcripts and mRNAs are shorter than maternal ones in the sea urchin, Strongylocentrotus purpuratus. Yet, while primary transcripts length increases when cell cycle lengthens, typical for intron delay, the relatively short length of zygotic mRNAs is consistent. The enhanced mRNA length is due to significantly longer maternal open reading frames and 3'UTRs compared to the zygotic lengths, a ratio that does not change with developmental time. This implies unique usage of both coding sequences and regulatory information in the maternal stage compared to the zygotic stages. We extracted the half-lifetimes due to maternal and zygotic degradation mechanisms from high-density time course of a set of maternal mRNAs in Paracentrotus lividus. The degradation rates due to maternal and zygotic degradation mechanisms are not correlated, indicating that these mechanisms are independent and relay on different regulatory information. Our studies illuminate specific structural and kinetic properties of sea urchin maternal mRNAs that might be broadly shared by other organisms.

Stepwise Progression of Embryonic Patterning

It is long established that the graded distribution of Dorsal transcription factor influences spatial domains of gene expression along the dorsoventral (DV) axis of Drosophila melanogaster embryos. However, the more recent realization that Dorsal levels also change with time raises the question of whether these dynamics are instructive. An overview of DV axis patterning is provided, focusing on new insights identified through quantitative analysis of temporal changes in Dorsal target gene expression from one nuclear cycle to the next ('steps'). Possible roles for the stepwise progression of this patterning program are discussed including (i) tight temporal regulation of signaling pathway activation, (ii) control of gene expression cohorts, and (iii) ensuring the irreversibility of the patterning and cell fate specification process.

Eric Davidson: Steps to a gene regulatory network for development

Eric Harris Davidson was a unique and creative intellectual force who grappled with the diversity of developmental processes used by animal embryos and wrestled them into an intelligible set of principles, then spent his life translating these process elements into molecularly definable terms through the architecture of gene regulatory networks. He took speculative risks in his theoretical writing but ran a highly organized, rigorous experimental program that yielded an unprecedentedly full characterization of a developing organism. His writings created logical order and a framework for mechanism from the complex phenomena at the heart of advanced multicellular organism development. This is a reminiscence of intellectual currents in his work as observed by the author through the last 30-35 years of Davidson's life.

p85α promotes nucleolin transcription and subsequently enhances EGFR mRNA stability and EGF-induced malignant cellular transformation

p85α is a regulatory subunit of phosphatidylinositol 3-kinase (PI3K) that is a key lipid enzyme for generating phosphatidylinositol 3, 4, 5-trisphosphate, and subsequently activates signaling that ultimately regulates cell cycle progression, cell growth, cytoskeletal changes, and cell migration. In addition to form a complex with the p110 catalytic subunit, p85α also exists as a monomeric form due to that there is a greater abundance of p85α than p110 in many cell types. Our previous studies have demonstrated that monomeric p85α exerts a pro-apoptotic role in UV response through induction of TNF-α gene expression in PI3K-independent manner. In current studies, we identified a novel biological function of p85α as a positive regulator of epidermal growth factor receptor (EGFR) expression and cell malignant transformation via nucleolin-dependent mechanism. Our results showed that p85α was crucial for EGFR and nucleolin expression and subsequently resulted in an increase of malignant cellular transformation by using both specific knockdown and deletion of p85α in its normal expressed cells. Mechanistic studies revealed that p85α upregulated EGFR protein expression mainly through stabilizing its mRNA, whereas nucleolin (NCL) was able to bind to egfr mRNA and increase its mRNA stability. Consistently, overexpression of NCL in p85α-/- cells restored EGFR mRNA stabilization, protein expression and cell malignant transformation. Moreover, we discovered that p85α upregulated NCL gene transcription via enhancing C-Jun activation. Collectively, our studies demonstrate a novel function of p85α as a positive regulator of EGFR mRNA stability and cell malignant transformation, providing a significant insight into the understanding of biomedical nature of p85α protein in mammalian cells and further supporting that p85α might be a potential target for cancer prevention and therapy.

On the Relationship of Protein and mRNA Dynamics in Vertebrate Embryonic Development

A biochemical explanation of development from the fertilized egg to the adult requires an understanding of the proteins and RNAs expressed over time during embryogenesis. We present a comprehensive characterization of protein and mRNA dynamics across early development in Xenopus. Surprisingly, we find that most protein levels change little and duplicated genes are expressed similarly. While the correlation between protein and mRNA levels is poor, a mass action kinetics model parameterized using protein synthesis and degradation rates regresses protein dynamics to RNA dynamics, corrected for initial protein concentration. This study provides detailed data for absolute levels of ∼10,000 proteins and ∼28,000 transcripts via a convenient web portal, a rich resource for developmental biologists. It underscores the lasting impact of maternal dowry, finds surprisingly few cases where degradation alone drives a change in protein level, and highlights the importance of transcription in shaping the dynamics of the embryonic proteome.

Dynamical modeling of uncertain interaction-based genomic networks

BACKGROUND

Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental design purposes in the laboratory. The first approach requires a comprehensive knowledge of the parameters involved in all biological processes a priori, whereas the results from the second method may not have a biological correspondence and thus cannot be tested in the laboratory. Moreover, the current methods cannot readily utilize existing curated knowledge databases and do not consider uncertainty in the knowledge. Therefore, a new methodology is needed that can generate a dynamical model based on available biological data, assuming uncertainty, while the results from experimental design can be examined in the laboratory.

RESULTS

We propose a new methodology for dynamical modeling of genomic networks that can utilize the interaction knowledge provided in public databases. The model assigns discrete states for physical entities, sets priorities among interactions based on information provided in the database, and updates each interaction based on associated node states. Whenever uncertainty in dynamics arises, it explores all possible outcomes. By using the proposed model, biologists can study regulation networks that are too complex for manual analysis.

CONCLUSIONS

The proposed approach can be effectively used for constructing dynamical models of interaction-based genomic networks without requiring a complete knowledge of all parameters affecting the network dynamics, and thus based on a small set of available data.

Comparative Study of Regulatory Circuits in Two Sea Urchin Species Reveals Tight Control of Timing and High Conservation of Expression Dynamics

Accurate temporal control of gene expression is essential for normal development and must be robust to natural genetic and environmental variation. Studying gene expression variation within and between related species can delineate the level of expression variability that development can tolerate. Here we exploit the comprehensive model of sea urchin gene regulatory networks and generate high-density expression profiles of key regulatory genes of the Mediterranean sea urchin, Paracentrotus lividus (Pl). The high resolution of our studies reveals highly reproducible gene initiation times that have lower variation than those of maximal mRNA levels between different individuals of the same species. This observation supports a threshold behavior of gene activation that is less sensitive to input concentrations. We then compare Mediterranean sea urchin gene expression profiles to those of its Pacific Ocean relative, Strongylocentrotus purpuratus (Sp). These species shared a common ancestor about 40 million years ago and show highly similar embryonic morphologies. Our comparative analyses of five regulatory circuits operating in different embryonic territories reveal a high conservation of the temporal order of gene activation but also some cases of divergence. A linear ratio of 1.3-fold between gene initiation times in Pl and Sp is partially explained by scaling of the developmental rates with temperature. Scaling the developmental rates according to the estimated Sp-Pl ratio and normalizing the expression levels reveals a striking conservation of relative dynamics of gene expression between the species. Overall, our findings demonstrate the ability of biological developmental systems to tightly control the timing of gene activation and relative dynamics and overcome expression noise induced by genetic variation and growth conditions.

Is the Cell Nucleus a Necessary Component in Precise Temporal Patterning?

One of the functions of the cell nucleus is to help regulate gene expression by controlling molecular traffic across the nuclear envelope. Here we investigate, via stochastic simulation, what effects, if any, does segregation of a system into the nuclear and cytoplasmic compartments have on the stochastic properties of a motif with a negative feedback. One of the effects of the nuclear barrier is to delay the nuclear protein concentration, allowing it to behave in a switch-like manner. We found that this delay, defined as the time for the nuclear protein concentration to reach a certain threshold, has an extremely narrow distribution. To show this, we considered two models. In the first one, the proteins could diffuse freely from cytoplasm to nucleus (simple model); and in the second one, the proteins required assistance from a special class of proteins called importins. For each model, we generated fifty parameter sets, chosen such that the temporal profiles they effectuated were very similar, and whose average threshold time was approximately 150 minutes. The standard deviation of the threshold times computed over one hundred realizations were found to be between 1.8 and 7.16 minutes across both models. To see whether a genetic motif in a prokaryotic cell can achieve this degree of precision, we also simulated five variations on the coherent feed-forward motif (CFFM), three of which contained a negative feedback. We found that the performance of these motifs was nowhere near as impressive as the one found in the eukaryotic cell; the best standard deviation was 6.6 minutes. We argue that the significance of these results, the fact and necessity of spatio-temporal precision in the developmental stages of eukaryotes, and the absence of such a precision in prokaryotes, all suggest that the nucleus has evolved, in part, under the selective pressure to achieve highly predictable phenotypes.

Discerning mechanistically rewired biological pathways by cumulative interaction heterogeneity statistics

Changes in response of a biological pathway could be a consequence of either pathway rewiring, changed input, or a combination of both. Most pathway analysis methods are not designed for mechanistic rewiring such as regulatory element variations. This limits our understanding of biological pathway evolution. Here we present a Q-method to discern whether changed pathway response is caused by mechanistic rewiring of pathways due to evolution. The main innovation is a cumulative pathway interaction heterogeneity statistic accounting for rewiring-specific effects on the rate of change of each molecular variable across conditions. The Q-method remarkably outperformed differential-correlation based approaches on data from diverse biological processes. Strikingly, it also worked well in differentiating rewired chaotic systems, whose dynamics are notoriously difficult to predict. Applying the Q-method on transcriptome data of four yeasts, we show that pathway interaction heterogeneity for known metabolic and signaling pathways is indeed a predictor of interspecies genetic rewiring due to unbalanced TATA box-containing genes among the yeasts. The demonstrated effectiveness of the Q-method paves the way to understanding network evolution at the resolution of functional biological pathways.

A kinetic model identifies phosphorylated estrogen receptor-α (ERα) as a critical regulator of ERα dynamics in breast cancer

Receptor levels are a key mechanism by which cells regulate their response to stimuli. The levels of estrogen receptor-α (ERα) impact breast cancer cell proliferation and are used to predict prognosis and sensitivity to endocrine therapy. Despite the clinical application of this information, it remains unclear how different cellular processes interact as a system to control ERα levels. To address this question, experimental results from the ERα-positive human breast cancer cell line (MCF-7) treated with 17-β-estradiol or vehicle control were used to develop a mass-action kinetic model of ERα regulation. Model analysis determined that RNA dynamics could be captured through phosphorylated ERα (pERα)-dependent feedback on transcription. Experimental analysis confirmed that pERα-S118 binds to the estrogen receptor-1 (ESR1) promoter, suggesting that pERα can feedback on ESR1 transcription. Protein dynamics required a separate mechanism in which the degradation rate for pERα was 8.3-fold higher than nonphosphorylated ERα. Using a model with both mechanisms, the root mean square error was 0.078. Sensitivity analysis of this combined model determined that while multiple mechanisms regulate ERα levels, pERα-dependent feedback elicited the strongest effect. Combined, our computational and experimental results identify phosphorylation of ERα as a critical decision point that coordinates the cellular circuitry to regulate ERα levels.

Division time-based amplifiers for stochastic gene expression

While cell-to-cell variability is a phenotypic consequence of gene expression noise, sources of this noise may be complex – apart from intrinsic sources such as the random birth/death of mRNA and stochastic switching between promoter states, there are also extrinsic sources of noise such as cell division where division times are either constant or random.

Divergent transcriptional patterns are related to differences in hypoxia tolerance between the intertidal and the subtidal sculpins

Transcriptionally mediated phenotypic plasticity as a mechanism of modifying traits in response to an environmental challenge remains an important area of study. We compared the transcriptional responses to low oxygen (hypoxia) of the hypoxia-tolerant intertidal fish, the tidepool sculpin (Oligocottus maculosus) with the closely related hypoxia-intolerant subtidal fish, the silverspotted sculpin (Blepsias cirrhosus) to determine whether these species use different mechanisms to cope with hypoxia. Individuals from each species were exposed to environmental O(2) tensions chosen to yield a similar level of tissue hypoxia, and gene transcription was assessed in the liver over time. There was an effect of time in hypoxia, where the greatest transcriptional change in the silverspotted sculpin occurred between 3 and 24 h in contrast to the tidepool sculpin where the largest transcriptional change occurred between 24 and 72 h of hypoxia. A number of genes showed similar hypoxia-induced transcription patterns in both species (e.g. genes associated with glycolysis and apoptosis) suggesting they are involved in a conserved hypoxia response. A large set of genes showed divergent transcriptional patterns in the two species, including fatty acid oxidation and oxidative phosphorylation, suggesting that these biological processes may contribute to explaining variation in hypoxia tolerance in these species. When both species were exposed to a single environmental O(2) tension, large transcriptional responses were seen in the hypoxia-intolerant silverspotted sculpin while almost no response was observed in the hypoxia-tolerant tidepool sculpin. Overall, divergent transcription patterns in response to both magnitude and duration of hypoxia provide insights into the processes that may determine an animal's capacity to tolerate frequent bouts of hypoxia in the wild.

Analytic approaches to stochastic gene expression in multicellular systems

Deterministic thermodynamic models of the complex systems, which control gene expression in metazoa, are helping researchers identify fundamental themes in the regulation of transcription. However, quantitative single cell studies are increasingly identifying regulatory mechanisms that control variability in expression. Such behaviors cannot be captured by deterministic models and are poorly suited to contemporary stochastic approaches that rely on continuum approximations, such as Langevin methods. Fortunately, theoretical advances in the modeling of transcription have assembled some general results that can be readily applied to systems being explored only through a deterministic approach. Here, I review some of the recent experimental evidence for the importance of genetically regulating stochastic effects during embryonic development and discuss key results from Markov theory that can be used to model this regulation. I then discuss several pairs of regulatory mechanisms recently investigated through a Markov approach. In each case, a deterministic treatment predicts no difference between the mechanisms, but the statistical treatment reveals the potential for substantially different distributions of transcriptional activity. In this light, features of gene regulation that seemed needlessly complex evolutionary baggage may be appreciated for their key contributions to reliability and precision of gene expression.

Developmental gene regulatory network evolution: insights from comparative studies in echinoderms

One of the central concerns of Evolutionary Developmental biology is to understand how the specification of cell types can change during evolution. In the last decade, developmental biology has progressed toward a systems level understanding of cell specification processes. In particular, the focus has been on determining the regulatory interactions of the repertoire of genes that make up gene regulatory networks (GRNs). Echinoderms provide an extraordinary model system for determining how GRNs evolve. This review highlights the comparative GRN analyses arising from the echinoderm system. This work shows that certain types of GRN subcircuits or motifs, i.e., those involving positive feedback, tend to be conserved and may provide a constraint on development. This conservation may be due to a required arrangement of transcription factor binding sites in cis regulatory modules. The review will also discuss ways in which novelty may arise, in particular through the co-option of regulatory genes and subcircuits. The development of the sea urchin larval skeleton, a novel feature that arose in echinoderms, has provided a model for study of co-option mechanisms. Finally, the types of GRNs that can permit the great diversity in the patterns of ciliary bands and their associated neurons found among these taxa are discussed. The availability of genomic resources is rapidly expanding for echinoderms, including genome sequences not only for multiple species of sea urchins but also a species of sea star, sea cucumber, and brittle star. This will enable echinoderms to become a particularly powerful system for understanding how developmental GRNs evolve.

Multicellular computing using conjugation for wiring

Recent efforts in synthetic biology have focussed on the implementation of logical functions within living cells. One aim is to facilitate both internal "re-programming" and external control of cells, with potential applications in a wide range of domains. However, fundamental limitations on the degree to which single cells may be re-engineered have led to a growth of interest in multicellular systems, in which a "computation" is distributed over a number of different cell types, in a manner analogous to modern computer networks. Within this model, individual cell type perform specific sub-tasks, the results of which are then communicated to other cell types for further processing. The manner in which outputs are communicated is therefore of great significance to the overall success of such a scheme. Previous experiments in distributed cellular computation have used global communication schemes, such as quorum sensing (QS), to implement the "wiring" between cell types. While useful, this method lacks specificity, and limits the amount of information that may be transferred at any one time. We propose an alternative scheme, based on specific cell-cell conjugation. This mechanism allows for the direct transfer of genetic information between bacteria, via circular DNA strands known as plasmids. We design a multi-cellular population that is able to compute, in a distributed fashion, a Boolean XOR function. Through this, we describe a general scheme for distributed logic that works by mixing different strains in a single population; this constitutes an important advantage of our novel approach. Importantly, the amount of genetic information exchanged through conjugation is significantly higher than the amount possible through QS-based communication. We provide full computational modelling and simulation results, using deterministic, stochastic and spatially-explicit methods. These simulations explore the behaviour of one possible conjugation-wired cellular computing system under different conditions, and provide baseline information for future laboratory implementations.

A single molecule view on Dbp5 and mRNA at the nuclear pore

Numerous molecular details of intracellular mRNA processing have been revealed in recent years. However, the export process of single native mRNA molecules, the actual translocation through the nuclear pore complex (NPC), could not yet be examined in vivo. The problem is observing mRNA molecules without interfering with their native behavior. We used a protein-based labeling approach to visualize single native mRNPs in live salivary gland cells of Chironomus tentans, an iconic system used for decades to study the mRNA life cycle. Recombinant hrp36, the C. tentans homolog of mammalian hnRNP A1, was fluorescence labeled and microinjected into living cells, where it was integrated into nascent mRNPs. Intranuclear trajectories of single mRNPs, including their NPC passage, were observed with high space and time resolution employing a custom-built light sheet fluorescence microscope. We analyzed the kinetics and dynamics of mRNP export and started to study its mechanism and regulation by measuring the turnover-kinetics of single Dbp5 at the NPC.

Gene regulatory control in the sea urchin aboral ectoderm: spatial initiation, signaling inputs, and cell fate lockdown

The regulation of oral-aboral ectoderm specification in the sea urchin embryo has been extensively studied in recent years. The oral-aboral polarity is initially imposed downstream of a redox gradient induced by asymmetric maternal distribution of mitochondria. Two TGF-β signaling pathways, Nodal and BMP, are then respectively utilized in the generation of oral and aboral regulatory states. However, a causal understanding of the regulation of aboral ectoderm specification has been lacking. In this work control of aboral ectoderm regulatory state specification was revealed by combining detailed regulatory gene expression studies, perturbation and cis-regulatory analyses. Our analysis illuminates a dynamic system where different factors dominate at different developmental times. We found that the initial activation of aboral genes depends directly on the redox sensitive transcription factor, hypoxia inducible factor 1α (HIF-1α). Two BMP ligands, BMP2/4 and BMP5/8, then significantly enhance aboral regulatory gene transcription. Ultimately, encoded feedback wiring lockdown the aboral ectoderm regulatory state. Our study elucidates the different regulatory mechanisms that sequentially dominate the spatial localization of aboral regulatory states.

Cell division curtails helper phenotype plasticity and expedites helper T-cell differentiation

Following activation by antigen, helper T cells differentiate into one of many effector phenotypes. Formulating mechanistic mathematical models combining regulatory networks at the transcriptional, translational and epigenetic level, we study how individual helper T cells may adopt their different phenotypes. For each cytokine phenotype, for example, T helper type 1 (Th1) and type 2 (Th2) cells, we find that the intracellular molecular network allows a cell to adopt one of the three states, which we interpret as naive, active and memory states. Cell division markedly speeds up the differentiation into a particular memory state because of DNA demythelation. In a memory state, cells readily resume production of the same cytokine they produced before. Using stochastic models we show that helper T-cell plasticity (that is, the ability to switch phenotype) is low during clonal expansion. Although most memory cells rapidly secrete the original cytokine upon restimulation, some adopt another phenotype and produce different cytokines, allowing for considerable diversity in the phenotypes that are adopted during a memory response. In summary, we show that helper T-cell division expedites cell differentiation by increasing DNA demethylation. We also show that plasticity is low during the clonal expansion phase, but that helper T cells may adopt alternative phenotypes during a memory response.

Bioinformatics and systems biology

Delivering personalized therapeutic options to cancer patients based on the genetic and molecular aberrations of the tumor offers great promise to improve the outcomes of cancer therapy. Significant progress in biotechnology has allowed the measurement of tens of thousands of "omic" data points across multiple levels (DNA, RNA protein, metabolomics) from a single tumor biopsy sample in a reasonable time frame for making clinical decisions. With this data in hand, the challenge from the bioinformatics and systems biology point of view is how does one convert data into information and knowledge that can improve the delivery of personalized therapy to the patient.

Analysis of early C2C12 myogenesis identifies stably and differentially expressed transcriptional regulators whose knock-down inhibits myoblast differentiation

Myogenesis is a tightly controlled process involving the transcriptional activation and repression of thousands of genes. Although many components of the transcriptional network regulating the later phases of myogenesis have been identified, relatively few studies have described the transcriptional landscape during the first 24 h, when myoblasts commit to differentiate. Through dense temporal profiling of differentiating C2C12 myoblasts, we identify 193 transcriptional regulators (TRs) whose expression is significantly altered within the first 24 h of myogenesis. A high-content shRNA screen of 77 TRs involving 427 stable lines identified 42 genes whose knockdown significantly inhibits differentiation of C2C12 myoblasts. Of the TRs that were differentially expressed within the first 24 h, over half inhibited differentiation when knocked down, including known regulators of myogenesis (Myod1, Myog, and Myf5), as well as 19 TRs not previously associated with this process. Surprisingly, a similar proportion (55%) of shRNAs targeting TRs whose expression did not change also inhibited C2C12 myogenesis. We further show that a subset of these TRs inhibits myogenesis by downregulating expression of known regulatory and structural proteins. Our findings clearly illustrate that several TRs critical for C2C12 myogenesis are not differentially regulated, suggesting that approaches that focus functional studies on differentially-expressed transcripts will fail to provide a comprehensive view of this complex process.

Precise cis-regulatory control of spatial and temporal expression of the alx-1 gene in the skeletogenic lineage of s. purpuratus

Deployment of the gene-regulatory network (GRN) responsible for skeletogenesis in the embryo of the sea urchin Strongylocentrotus purpuratus is restricted to the large micromere lineage by a double negative regulatory gate. The gate consists of a GRN subcircuit composed of the pmar1 and hesC genes, which encode repressors and are wired in tandem, plus a set of target regulatory genes under hesC control. The skeletogenic cell state is specified initially by micromere-specific expression of these regulatory genes, viz. alx1, ets1, tbrain and tel, plus the gene encoding the Notch ligand Delta. Here we use a recently developed high throughput methodology for experimental cis-regulatory analysis to elucidate the genomic regulatory system controlling alx1 expression in time and embryonic space. The results entirely confirm the double negative gate control system at the cis-regulatory level, including definition of the functional HesC target sites, and add the crucial new information that the drivers of alx1 expression are initially Ets1, and then Alx1 itself plus Ets1. Cis-regulatory analysis demonstrates that these inputs quantitatively account for the magnitude of alx1 expression. Furthermore, the Alx1 gene product not only performs an auto-regulatory role, promoting a fast rise in alx1 expression, but also, when at high levels, it behaves as an auto-repressor. A synthetic experiment indicates that this behavior is probably due to dimerization. In summary, the results we report provide the sequence level basis for control of alx1 spatial expression by the double negative gate GRN architecture, and explain the rising, then falling temporal expression profile of the alx1 gene in terms of its auto-regulatory genetic wiring.