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In addition to central functions in cell adhesion signalling, integrin-associated proteins have wider roles at sites distal to adhesion receptors. In experimentally defined adhesomes, we noticed that there is clear enrichment of proteins that localise to the nucleus, and conversely, we now report that nuclear proteomes contain a class of adhesome components that localise to the nucleus. We here define a nucleo-adhesome, providing experimental evidence for a remarkable scale of nuclear localisation of adhesion proteins, establishing a framework for interrogating nuclear adhesion protein functions. Adding to nuclear FAK’s known roles in regulating transcription, we now show that nuclear FAK regulates expression of many adhesion-related proteins that localise to the nucleus and that nuclear FAK binds to the adhesome component and nuclear protein Hic-5. FAK and Hic-5 work together in the nucleus, co-regulating a subset of genes transcriptionally. We demonstrate the principle that there are subcomplexes of nuclear adhesion proteins that cooperate to control transcription.

Eukaryotic life is possible due to the multitude of complex and precise phenomena that take place in the cell. Essential processes like gene transcription, mRNA translation, cell growth, and proliferation, or membrane traffic, among many others, are strictly regulated to ensure functional success. Such systems or vital processes do not work and adjusts independently of each other. It is required to ensure coordination among them which requires communication, or crosstalk, between their different elements through the establishment of complex regulatory networks. Distortion of this coordination affects, not only the specific processes involved, but also the whole cell fate. However, the connection between some systems and cell fate, is not yet very well understood and opens lots of interesting questions. In this review, we focus on the coordination between the function of the three nuclear RNA polymerases and cell cycle progression. Although we mainly focus on the model organism Saccharomyces cerevisiae, different aspects and similarities in higher eukaryotes are also addressed. We will first focus on how the different phases of the cell cycle affect the RNA polymerases activity and then how RNA polymerases status impacts on cell cycle. A good example of how RNA polymerases functions impact on cell cycle is the ribosome biogenesis process, which needs the coordinated and balanced production of mRNAs and rRNAs synthesized by the three eukaryotic RNA polymerases. Distortions of this balance generates ribosome biogenesis alterations that can impact cell cycle progression. We also pay attention to those cases where specific cell cycle defects generate in response to repressed synthesis of ribosomal proteins or RNA polymerases assembly defects.

Sphingolipids (SLs) are bioactive lipids involved in various important physiological functions. The SL pathway has been shown to be affected in several brain-related disorders, including Alzheimer’s disease (AD). Recent evidence suggests that epigenetic dysregulation plays an important role in the pathogenesis of AD as well. Here, we use an integrative approach to better understand the relationship between epigenetic and transcriptomic processes in regulating SL function in the middle temporal gyrus of AD patients. Transcriptomic analysis of 252 SL-related genes, selected based on GO term annotations, from 46 AD patients and 32 healthy age-matched controls, revealed 103 differentially expressed SL-related genes in AD patients. Additionally, methylomic analysis of the same subjects revealed parallel hydroxymethylation changes in PTGIS, GBA, and ITGB2 in AD. Subsequent gene regulatory network-based analysis identified three candidate genes, i.e. SELPLG, SPHK1 and CAV1 whose alteration holds the potential to revert the gene expression program from a diseased towards a healthy state. Together, this epigenomic and transcriptomic approach highlights the importance of SL-related genes in AD, and may provide novel biomarkers and therapeutic alternatives to traditionally investigated biological pathways in AD.

A number of bacteria are known to differentiate into cells with distinct phenotypic traits during processes such as biofilm formation or the development of reproductive structures. These cell types, by virtue of their specialized functions, embody a division of labor. However, how bacteria build spatial patterns of differentiated cells is not well understood. Here, we examine the factors that drive spatial patterns in divisions of labor in colonies of Streptomyces coelicolor, a multicellular bacterium capable of synthesizing an array of antibiotics and forming complex reproductive structures (e.g., aerial hyphae and spores). Using fluorescent reporters, we demonstrate that the pathways for antibiotic biosynthesis and aerial hypha formation are activated in distinct waves of gene expression that radiate outwards in S. coelicolor colonies. We also show that the spatiotemporal separation of these cell types depends on a key activator in the developmental pathway, AdpA. Importantly, when we manipulated local gradients by growing competing microbes nearby, or through physical disruption, expression in these pathways could be decoupled and/or disordered, respectively. Finally, the normal spatial organization of these cell types was partially restored with the addition of a siderophore, a public good made by these organisms, to the growth medium. Together, these results indicate that spatial divisions of labor in S. coelicolor colonies are determined by a combination of physiological gradients and regulatory network architecture, key factors that also drive patterns of cellular differentiation in multicellular eukaryotic organisms.IMPORTANCEStreptomyces coelicolor is a multicellular bacterium that differentiates into specialized cell types and produces a diverse array of natural products. While much is known about the genetic networks that regulate development and antibiotic biosynthesis in S. coelicolor, what drives the spatial organization of these activities within a co

Eukaryotic life is possible due to the multitude of complex and precise phenomena that take place in the cell. Essential processes like gene transcription, mRNA translation, cell growth, and proliferation, or membrane traffic, among many others, are strictly regulated to ensure functional success. Such systems or vital processes do not work and adjusts independently of each other. It is required to ensure coordination among them which requires communication, or crosstalk, between their different elements through the establishment of complex regulatory networks. Distortion of this coordination affects, not only the specific processes involved, but also the whole cell fate. However, the connection between some systems and cell fate, is not yet very well understood and opens lots of interesting questions. In this review, we focus on the coordination between the function of the three nuclear RNA polymerases and cell cycle progression. Although we mainly focus on the model organism Saccharomyces cerevisiae, different aspects and similarities in higher eukaryotes are also addressed. We will first focus on how the different phases of the cell cycle affect the RNA polymerases activity and then how RNA polymerases status impacts on cell cycle. A good example of how RNA polymerases functions impact on cell cycle is the ribosome biogenesis process, which needs the coordinated and balanced production of mRNAs and rRNAs synthesized by the three eukaryotic RNA polymerases. Distortions of this balance generates ribosome biogenesis alterations that can impact cell cycle progression. We also pay attention to those cases where specific cell cycle defects generate in response to repressed synthesis of ribosomal proteins or RNA polymerases assembly defects.

Sphingolipids (SLs) are bioactive lipids involved in various important physiological functions. The SL pathway has been shown to be affected in several brain-related disorders, including Alzheimer’s disease (AD). Recent evidence suggests that epigenetic dysregulation plays an important role in the pathogenesis of AD as well. Here, we use an integrative approach to better understand the relationship between epigenetic and transcriptomic processes in regulating SL function in the middle temporal gyrus of AD patients. Transcriptomic analysis of 252 SL-related genes, selected based on GO term annotations, from 46 AD patients and 32 healthy age-matched controls, revealed 103 differentially expressed SL-related genes in AD patients. Additionally, methylomic analysis of the same subjects revealed parallel hydroxymethylation changes in PTGIS, GBA, and ITGB2 in AD. Subsequent gene regulatory network-based analysis identified three candidate genes, i.e. SELPLG, SPHK1 and CAV1 whose alteration holds the potential to revert the gene expression program from a diseased towards a healthy state. Together, this epigenomic and transcriptomic approach highlights the importance of SL-related genes in AD, and may provide novel biomarkers and therapeutic alternatives to traditionally investigated biological pathways in AD.

Eukaryotic life is possible due to the multitude of complex and precise phenomena that take place in the cell. Essential processes like gene transcription, mRNA translation, cell growth, and proliferation, or membrane traffic, among many others, are strictly regulated to ensure functional success. Such systems or vital processes do not work and adjusts independently of each other. It is required to ensure coordination among them which requires communication, or crosstalk, between their different elements through the establishment of complex regulatory networks. Distortion of this coordination affects, not only the specific processes involved, but also the whole cell fate. However, the connection between some systems and cell fate, is not yet very well understood and opens lots of interesting questions. In this review, we focus on the coordination between the function of the three nuclear RNA polymerases and cell cycle progression. Although we mainly focus on the model organism Saccharomyces cerevisiae, different aspects and similarities in higher eukaryotes are also addressed. We will first focus on how the different phases of the cell cycle affect the RNA polymerases activity and then how RNA polymerases status impacts on cell cycle. A good example of how RNA polymerases functions impact on cell cycle is the ribosome biogenesis process, which needs the coordinated and balanced production of mRNAs and rRNAs synthesized by the three eukaryotic RNA polymerases. Distortions of this balance generates ribosome biogenesis alterations that can impact cell cycle progression. We also pay attention to those cases where specific cell cycle defects generate in response to repressed synthesis of ribosomal proteins or RNA polymerases assembly defects.

Sphingolipids (SLs) are bioactive lipids involved in various important physiological functions. The SL pathway has been shown to be affected in several brain-related disorders, including Alzheimer’s disease (AD). Recent evidence suggests that epigenetic dysregulation plays an important role in the pathogenesis of AD as well. Here, we use an integrative approach to better understand the relationship between epigenetic and transcriptomic processes in regulating SL function in the middle temporal gyrus of AD patients. Transcriptomic analysis of 252 SL-related genes, selected based on GO term annotations, from 46 AD patients and 32 healthy age-matched controls, revealed 103 differentially expressed SL-related genes in AD patients. Additionally, methylomic analysis of the same subjects revealed parallel hydroxymethylation changes in PTGIS, GBA, and ITGB2 in AD. Subsequent gene regulatory network-based analysis identified three candidate genes, i.e. SELPLG, SPHK1 and CAV1 whose alteration holds the potential to revert the gene expression program from a diseased towards a healthy state. Together, this epigenomic and transcriptomic approach highlights the importance of SL-related genes in AD, and may provide novel biomarkers and therapeutic alternatives to traditionally investigated biological pathways in AD.

Sphingolipids (SLs) are bioactive lipids involved in various important physiological functions. The SL pathway has been shown to be affected in several brain-related disorders, including Alzheimer’s disease (AD). Recent evidence suggests that epigenetic dysregulation plays an important role in the pathogenesis of AD as well. Here, we use an integrative approach to better understand the relationship between epigenetic and transcriptomic processes in regulating SL function in the middle temporal gyrus of AD patients. Transcriptomic analysis of 252 SL-related genes, selected based on GO term annotations, from 46 AD patients and 32 healthy age-matched controls, revealed 103 differentially expressed SL-related genes in AD patients. Additionally, methylomic analysis of the same subjects revealed parallel hydroxymethylation changes in PTGIS, GBA, and ITGB2 in AD. Subsequent gene regulatory network-based analysis identified three candidate genes, i.e. SELPLG, SPHK1 and CAV1 whose alteration holds the potential to revert the gene expression program from a diseased towards a healthy state. Together, this epigenomic and transcriptomic approach highlights the importance of SL-related genes in AD, and may provide novel biomarkers and therapeutic alternatives to traditionally investigated biological pathways in AD.

Sphingolipids (SLs) are bioactive lipids involved in various important physiological functions. The SL pathway has been shown to be affected in several brain-related disorders, including Alzheimer’s disease (AD). Recent evidence suggests that epigenetic dysregulation plays an important role in the pathogenesis of AD as well. Here, we use an integrative approach to better understand the relationship between epigenetic and transcriptomic processes in regulating SL function in the middle temporal gyrus of AD patients. Transcriptomic analysis of 252 SL-related genes, selected based on GO term annotations, from 46 AD patients and 32 healthy age-matched controls, revealed 103 differentially expressed SL-related genes in AD patients. Additionally, methylomic analysis of the same subjects revealed parallel hydroxymethylation changes in PTGIS, GBA, and ITGB2 in AD. Subsequent gene regulatory network-based analysis identified three candidate genes, i.e. SELPLG, SPHK1 and CAV1 whose alteration holds the potential to revert the gene expression program from a diseased towards a healthy state. Together, this epigenomic and transcriptomic approach highlights the importance of SL-related genes in AD, and may provide novel biomarkers and therapeutic alternatives to traditionally investigated biological pathways in AD.

Sphingolipids (SLs) are bioactive lipids involved in various important physiological functions. The SL pathway has been shown to be affected in several brain-related disorders, including Alzheimer’s disease (AD). Recent evidence suggests that epigenetic dysregulation plays an important role in the pathogenesis of AD as well. Here, we use an integrative approach to better understand the relationship between epigenetic and transcriptomic processes in regulating SL function in the middle temporal gyrus of AD patients. Transcriptomic analysis of 252 SL-related genes, selected based on GO term annotations, from 46 AD patients and 32 healthy age-matched controls, revealed 103 differentially expressed SL-related genes in AD patients. Additionally, methylomic analysis of the same subjects revealed parallel hydroxymethylation changes in PTGIS, GBA, and ITGB2 in AD. Subsequent gene regulatory network-based analysis identified three candidate genes, i.e. SELPLG, SPHK1 and CAV1 whose alteration holds the potential to revert the gene expression program from a diseased towards a healthy state. Together, this epigenomic and transcriptomic approach highlights the importance of SL-related genes in AD, and may provide novel biomarkers and therapeutic alternatives to traditionally investigated biological pathways in AD.

A number of bacteria are known to differentiate into cells with distinct phenotypic traits during processes such as biofilm formation or the development of reproductive structures. These cell types, by virtue of their specialized functions, embody a division of labor. However, how bacteria build spatial patterns of differentiated cells is not well understood. Here, we examine the factors that drive spatial patterns in divisions of labor in colonies of Streptomyces coelicolor, a multicellular bacterium capable of synthesizing an array of antibiotics and forming complex reproductive structures (e.g., aerial hyphae and spores). Using fluorescent reporters, we demonstrate that the pathways for antibiotic biosynthesis and aerial hypha formation are activated in distinct waves of gene expression that radiate outwards in S. coelicolor colonies. We also show that the spatiotemporal separation of these cell types depends on a key activator in the developmental pathway, AdpA. Importantly, when we manipulated local gradients by growing competing microbes nearby, or through physical disruption, expression in these pathways could be decoupled and/or disordered, respectively. Finally, the normal spatial organization of these cell types was partially restored with the addition of a siderophore, a public good made by these organisms, to the growth medium. Together, these results indicate that spatial divisions of labor in S. coelicolor colonies are determined by a combination of physiological gradients and regulatory network architecture, key factors that also drive patterns of cellular differentiation in multicellular eukaryotic organisms.IMPORTANCE Streptomyces coelicolor is a multicellular bacterium that differentiates into specialized cell types and produces a diverse array of natural products. While much is known about the genetic networks that regulate development and antibiotic biosynthesis in S. coelicolor, what drives the spatial organization of these activities within a c

Eukaryotic life is possible due to the multitude of complex and precise phenomena that take place in the cell. Essential processes like gene transcription, mRNA translation, cell growth, and proliferation, or membrane traffic, among many others, are strictly regulated to ensure functional success. Such systems or vital processes do not work and adjusts independently of each other. It is required to ensure coordination among them which requires communication, or crosstalk, between their different elements through the establishment of complex regulatory networks. Distortion of this coordination affects, not only the specific processes involved, but also the whole cell fate. However, the connection between some systems and cell fate, is not yet very well understood and opens lots of interesting questions. In this review, we focus on the coordination between the function of the three nuclear RNA polymerases and cell cycle progression. Although we mainly focus on the model organism Saccharomyces cerevisiae, different aspects and similarities in higher eukaryotes are also addressed. We will first focus on how the different phases of the cell cycle affect the RNA polymerases activity and then how RNA polymerases status impacts on cell cycle. A good example of how RNA polymerases functions impact on cell cycle is the ribosome biogenesis process, which needs the coordinated and balanced production of mRNAs and rRNAs synthesized by the three eukaryotic RNA polymerases. Distortions of this balance generates ribosome biogenesis alterations that can impact cell cycle progression. We also pay attention to those cases where specific cell cycle defects generate in response to repressed synthesis of ribosomal proteins or RNA polymerases assembly defects.

The expression and regulation of matrisome genes—the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors—is of paramount importance for many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding of the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of “landmark” matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.

Pbx genes encode transcription factors that belong to the TALE (three-amino-acid loop extension) superclass of homeodomain proteins. We have witnessed a surge in information about the roles of this gene family as leading actors in the transcriptional control of development. PBX proteins represent a clear example of how transcription factors can regulate developmental processes by combinatorial properties, acting within multimeric complexes to implement activation or repression of transcription depending on their interaction partners. Here, we revisit long-emphasized functions of PBX transcription factors as cofactors for HOX proteins, major architects of the body plan. We further discuss new knowledge on roles of PBX proteins in different developmental contexts as upstream regulators of Hox genes-as factors that interact with non-HOX proteins and can work independently of HOX-as well as potential pioneer factors. Committed to building a perfect body, PBX proteins govern regulatory networks that direct essential morphogenetic processes and organogenesis in vertebrate development. Perturbations of PBX-dependent networks can cause human congenital disease and cancer.

Pbx genes encode transcription factors that belong to the TALE (three-amino-acid loop extension) superclass of homeodomain proteins. We have witnessed a surge in information about the roles of this gene family as leading actors in the transcriptional control of development. PBX proteins represent a clear example of how transcription factors can regulate developmental processes by combinatorial properties, acting within multimeric complexes to implement activation or repression of transcription depending on their interaction partners. Here, we revisit long-emphasized functions of PBX transcription factors as cofactors for HOX proteins, major architects of the body plan. We further discuss new knowledge on roles of PBX proteins in different developmental contexts as upstream regulators of Hox genes-as factors that interact with non-HOX proteins and can work independently of HOX-as well as potential pioneer factors. Committed to building a perfect body, PBX proteins govern regulatory networks that direct essential morphogenetic processes and organogenesis in vertebrate development. Perturbations of PBX-dependent networks can cause human congenital disease and cancer.

Queen pheromones are chemical signals that mediate reproductive division of labor in eusocial animals. Remarkably, queen pheromones are composed of identical or chemically similar compounds in some ants, wasps and bees, even though these taxa diverged > 150MYA and evolved queens and workers independently. Here, we measure the transcriptomic consequences of experimental exposure to queen pheromones in workers from two ant and two bee species (genera: Lasius, Apis, Bombus), and test whether they are similar across species. Queen pheromone exposure affected transcription and splicing at many loci. Many genes responded consistently in multiple species, and the set of pheromone-sensitive genes was enriched for functions relating to lipid biosynthesis and transport, olfaction, production of cuticle, oogenesis, and histone (de)acetylation. Pheromone-sensitive genes tend to be evolutionarily ancient, positively selected, peripheral in the gene coexpression network, hypomethylated, and caste-specific in their expression. Our results reveal how queen pheromones achieve their effects, and suggest that ants and bees use similar genetic modules to achieve reproductive division of labor.

[Abstract] Cell fate determination, the process through which cells commit to differentiated states is commonly mediated by gene regulatory motifs with mutually exclusive expression states. The classical deterministic picture for cell fate determination includes bistability and hysteresis, which enables the persistence of the acquired cellular state after withdrawal of the stimulus, ensuring a robust cellular response. However, stochasticity inherent to gene expression dynamics is not compatible with hysteresis, since the stationary solution of the governing Chemical Master Equation does not depend on the initial conditions. We provide a quantitative description of a transient hysteresis phenomenon reconciling experimental evidence of hysteretic behaviour in gene regulatory networks with inherent stochasticity: under sufficiently slow dynamics hysteresis is transient. We quantify this with an estimate of the convergence rate to the equilibrium and introduce a natural landscape capturing system’s evolution that, unlike traditional cell fate potential landscapes, is compatible with coexistence at the microscopic level.

Pbx genes encode transcription factors that belong to the TALE (three-amino-acid loop extension) superclass of homeodomain proteins. We have witnessed a surge in information about the roles of this gene family as leading actors in the transcriptional control of development. PBX proteins represent a clear example of how transcription factors can regulate developmental processes by combinatorial properties, acting within multimeric complexes to implement activation or repression of transcription depending on their interaction partners. Here, we revisit long-emphasized functions of PBX transcription factors as cofactors for HOX proteins, major architects of the body plan. We further discuss new knowledge on roles of PBX proteins in different developmental contexts as upstream regulators of Hox genes-as factors that interact with non-HOX proteins and can work independently of HOX-as well as potential pioneer factors. Committed to building a perfect body, PBX proteins govern regulatory networks that direct essential morphogenetic processes and organogenesis in vertebrate development. Perturbations of PBX-dependent networks can cause human congenital disease and cancer.

Over the past decade, genetic studies have recognized hundreds of polymorphic DNA loci called response QTLs (reQTLs) as potential contributors to interindividual variation in transcriptional responses to stimulations. Such reQTLs commonly affect the transduction of signals along the regulatory network that controls gene transcription. Identifying the pathways through which reQTLs perturb the underlying network has been a major challenge. Here, we present GEVIN ("Genome-wide Embedding of Variation In Networks"), a methodology that simultaneously identifies a reQTL and the particular pathway in which the reQTL affects downstream signal transduction along the network. Using synthetic data, we show that this algorithm outperforms existing pathway identification and reQTL identification methods. We applied GEVIN to the analysis of murine and human dendritic cells in response to pathogenic components. These analyses revealed significant reQTLs together with their perturbed Toll-like receptor signaling pathways. GEVIN thus offers a powerful framework that renders a comprehensive picture of disease-related DNA loci and their molecular functions within regulatory networks.

Actinomycetes are a diverse family of filamentous bacteria that produce a plethora of natural products relevant for agriculture, biotechnology and medicine, including the majority of the antibiotics we use in the clinic. Rather than as free-living bacteria, many actinomycetes have evolved to live in symbiosis with among others plants, fungi, insects and sponges. As a common theme, these organisms profit from the natural products and enzymes produced by the actinomycetes, for example, for protection against pathogenic microbes, for growth promotion or for the degradation of complex natural polymers such as lignocellulose. At the same time, the actinomycetes benefit from the resources of the hosts they interact with. Evidence is accumulating that these interactions control the expression of biosynthetic gene clusters and have played a major role in the evolution of the high chemical diversity of actinomycete-produced secondary metabolites. Many of the biosynthetic gene clusters for antibiotics are poorly expressed under laboratory conditions, but they are likely expressed in response to host-specific demands. Here, we review the environmental triggers and cues that control natural product formation by actinomycetes and provide pointers as to how these insights may be harnessed for drug discovery.

In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively

Actinomycetes are a diverse family of filamentous bacteria that produce a plethora of natural products relevant for agriculture, biotechnology and medicine, including the majority of the antibiotics we use in the clinic. Rather than as free-living bacteria, many actinomycetes have evolved to live in symbiosis with among others plants, fungi, insects and sponges. As a common theme, these organisms profit from the natural products and enzymes produced by the actinomycetes, for example, for protection against pathogenic microbes, for growth promotion or for the degradation of complex natural polymers such as lignocellulose. At the same time, the actinomycetes benefit from the resources of the hosts they interact with. Evidence is accumulating that these interactions control the expression of biosynthetic gene clusters and have played a major role in the evolution of the high chemical diversity of actinomycete-produced secondary metabolites. Many of the biosynthetic gene clusters for antibiotics are poorly expressed under laboratory conditions, but they are likely expressed in response to host-specific demands. Here, we review the environmental triggers and cues that control natural product formation by actinomycetes and provide pointers as to how these insights may be harnessed for drug discovery.

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration.

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration.

BackgroundThe size and complexity of published biochemical network reconstructions are steadily increasing, expanding the potential scale of derived computational models. However, the construction of large biochemical network models is a laborious and error-prone task. Automated methods have simplified the network reconstruction process, but building kinetic models for these systems is still a manually intensive task. Appropriate kinetic equations, based upon reaction rate laws, must be constructed and parameterized for each reaction. The complex test-and-evaluation cycles that can be involved during kinetic model construction would thus benefit from automated methods for rate law assignment.ResultsWe present a high-throughput algorithm to automatically suggest and create suitable rate laws based upon reaction type according to several criteria. The criteria for choices made by the algorithm can be influenced in order to assign the desired type of rate law to each reaction. This algorithm is implemented in the software package SBMLsqueezer 2. In addition, this program contains an integrated connection to the kinetics database SABIO-RK to obtain experimentally-derived rate laws when desired.ConclusionsThe described approach fills a heretofore absent niche in workflows for large-scale biochemical kinetic model construction. In several applications the algorithm has already been demonstrated to be useful and scalable. SBMLsqueezer is platform independent and can be used as a stand-alone package, as an integrated plugin, or through a web interface, enabling flexible solutions and use-case scenarios.

Quantitative trait loci (QTL) mapping studies enable identification of loci that are part of regulatory networks controlling various phenotypes. Detailed investigations of genes within these loci are required to ultimately understand the function of individual genes and how they interact with other players in the network. In this study, we use transgenic plants in combination with natural variation to investigate the regulatory role of the AOP3 gene found in GS-AOP locus previously suggested to contribute to the regulation of glucosinolate defense compounds. Phenotypic analysis and QTL mapping in F2 populations with different AOP3 transgenes support that the enzymatic function and the AOP3 RNA both play a significant role in controlling glucosinolate accumulation. Furthermore, we find different loci interacting with either the enzymatic activity or the RNA of AOP3 and thereby extend the regulatory network controlling glucosinolate accumulation.

In dieser Arbeit werden zwei intrazelluläre Signalwege, betreffend den Tumorsuppressor p53 und das Signalmolekül Ca2+ , diskutiert und modelliert. Einzelzellmessungen des Tumorsuppressors p53 zeigen pulsatile Antwor- ten nach Zufügung von DNA Doppelstrangbrüchen (DSBs). Außer für sehr hohe Schadensdosen, ist das zeitliche auftreten dieser Pulse unregelmäßig. Mithilfe eines Wavelet basierten Pulsdetektors werden die einzelzell Trajek- torien untersucht und die inter-Puls Intervall (IPI) Verteilungen extrahiert. Diese weisen auf nicht-oszillatorische Regime in den Daten hin. Die Theorie der anregbaren Systeme angewendet auf regulatorische Netzwerke ermöglicht dieses komplexe Verhalten mathematisch zu beschreiben. Die Kopplung von Schadens-Sensor-Kinase Dynamik mit dem kanonischen p53 negativen feedback loop, ergibt ein anregbares p53 Modell. Detaillier- te Bifurkationsanalysen zeigen ein robustes anregbares Regime, welches durch ein starkes Schadenssignal auch in Oszillationen überführt werden kann. Treibt man das p53 Modell mit einem stochastischen DNA-Schadens-Prozess, kann sowohl das oszillatorische Verhalten nach hohem Schaden, als auch das unregelmäßige pulsatile Verhalten ohne äußere Stimulation reproduziert werden. Intrazelluläre Ca 2+ Spikes entstehen durch eine hierarchische Kaskade stochastischer prozesse. Die Anwendung einer semi-markovschen Beschreibung führt zu praktischen analytischen Lösungen des erstpassagezeiten Problems. Eine hierbei entdeckte Zeitskalenseparation ermöglicht ein neues allgemeines Ca2+ -Modell. Dieses erklärt auf äußerst prägnante Weise viele wesentliche experimentelle Ergebnisse, insbesondere die Momentenbeziehungen der inter-Spike Intervall Verteilungen. Schließlich erlaubt die hier vorgestellte Theorie Berechnungen der Stimulus-Enkodierung, also die Adaption des Ca 2+ Signals auf veränderliche extrazelluläre Stimuli. Die Vorhersage einer fold change Enkodierung kann durch Experimente gestützt werden., In this work two signaling pathways, involving the tumor suppressor p53 and the second messenger Ca2+ , are to be discussed and modelled. The tumor suppressor p53 shows a pulsatile response in single cells after induction of DNA double strand breaks (DSBs). Except for very high amounts of damage, these pulses appear at irregular times. The concept of excitable systems is employed as a convenient way to model such observed dynamics. An application to biomolecular reaction networks shows the need for a positive feedback within the p53 regulatory network. Exploiting the reported ultrasensitive dynamics of the upstream damage sensor kinases, leads to a simplified excitable kinase-phosphatase model. Coupling that to the canonical negative feedback p53 regulatory loop, is the core idea behind the construction of the excitable p53 model. A detailed bifurcation analysis of the model establishes a robust excitable regime, which can be switched to oscillatory dynamics via a strong DNA damage signal. Driving the p53 model with a stochastic DSB process yields pulsatile dynamics which reflect different experimental scenarios. Intracellular Ca 2+ concentration spikes arise from a hierarchic cascade of stochastic events. An analytical solution strategy, employing a semi-Markovian description and involving Laplace transformations, is devised and successfully applied to a specific Ca2+ model. The new gained insights are then used, to construct a new generic Ca2+ model, which elegantly captures many known features of Ca2+ signaling. In particular the experimentally observed relations between the average and the standard deviation of the inter spike intervals (ISIs) can be explained in a concise way. Finally, the theoretical considerations allow to calculate the stimulus encoding relation, which governs the adaption of the Ca 2+ signals to varying extracellular stimuli. This is predicted to be a fold change response and new experimental results display a strong support of this idea.

The microbiome's underlying dynamics play an important role in regulating the behavior and health of its host. In order to explore the details of these interactions, we created an in silico model of a living microbiome, engineered with synthetic biology, that interfaces with a biomimetic, robotic host. By analytically modeling and computationally simulating engineered gene networks in these commensal communities, we reproduced complex behaviors in the host. We observed that robot movements depended upon programmed biochemical network dynamics within the microbiome. These results illustrate the model's potential utility as a tool for exploring inter-kingdom ecological relationships. These systems could impact fields ranging from synthetic biology and ecology to biophysics and medicine.

Mobile transcription factors play many roles in plant development. Here, we compare the use of mobile transcription factors as signals with some canonical signal transduction processes in prokaryotes and eukaryotes. After an initial survey, we focus on the SHORT-ROOT pathway in Arabidopsis roots to show that, despite the simplicity of the concept of mobile transcription factor signalling, many lines of evidence reveal a surprising complexity in control mechanisms linked to this process. We argue that these controls bestow precision, robustness, and versatility on mobile transcription factor signalling.

The microbiome's underlying dynamics play an important role in regulating the behavior and health of its host. In order to explore the details of these interactions, we created an in silico model of a living microbiome, engineered with synthetic biology, that interfaces with a biomimetic, robotic host. By analytically modeling and computationally simulating engineered gene networks in these commensal communities, we reproduced complex behaviors in the host. We observed that robot movements depended upon programmed biochemical network dynamics within the microbiome. These results illustrate the model's potential utility as a tool for exploring inter-kingdom ecological relationships. These systems could impact fields ranging from synthetic biology and ecology to biophysics and medicine.

The microbiome's underlying dynamics play an important role in regulating the behavior and health of its host. In order to explore the details of these interactions, we created an in silico model of a living microbiome, engineered with synthetic biology, that interfaces with a biomimetic, robotic host. By analytically modeling and computationally simulating engineered gene networks in these commensal communities, we reproduced complex behaviors in the host. We observed that robot movements depended upon programmed biochemical network dynamics within the microbiome. These results illustrate the model's potential utility as a tool for exploring inter-kingdom ecological relationships. These systems could impact fields ranging from synthetic biology and ecology to biophysics and medicine.

In dieser Arbeit werden zwei intrazelluläre Signalwege, betreffend den Tumorsuppressor p53 und das Signalmolekül Ca2+ , diskutiert und modelliert. Einzelzellmessungen des Tumorsuppressors p53 zeigen pulsatile Antwor- ten nach Zufügung von DNA Doppelstrangbrüchen (DSBs). Außer für sehr hohe Schadensdosen, ist das zeitliche auftreten dieser Pulse unregelmäßig. Mithilfe eines Wavelet basierten Pulsdetektors werden die einzelzell Trajek- torien untersucht und die inter-Puls Intervall (IPI) Verteilungen extrahiert. Diese weisen auf nicht-oszillatorische Regime in den Daten hin. Die Theorie der anregbaren Systeme angewendet auf regulatorische Netzwerke ermöglicht dieses komplexe Verhalten mathematisch zu beschreiben. Die Kopplung von Schadens-Sensor-Kinase Dynamik mit dem kanonischen p53 negativen feedback loop, ergibt ein anregbares p53 Modell. Detaillier- te Bifurkationsanalysen zeigen ein robustes anregbares Regime, welches durch ein starkes Schadenssignal auch in Oszillationen überführt werden kann. Treibt man das p53 Modell mit einem stochastischen DNA-Schadens-Prozess, kann sowohl das oszillatorische Verhalten nach hohem Schaden, als auch das unregelmäßige pulsatile Verhalten ohne äußere Stimulation reproduziert werden. Intrazelluläre Ca 2+ Spikes entstehen durch eine hierarchische Kaskade stochastischer prozesse. Die Anwendung einer semi-markovschen Beschreibung führt zu praktischen analytischen Lösungen des erstpassagezeiten Problems. Eine hierbei entdeckte Zeitskalenseparation ermöglicht ein neues allgemeines Ca2+ -Modell. Dieses erklärt auf äußerst prägnante Weise viele wesentliche experimentelle Ergebnisse, insbesondere die Momentenbeziehungen der inter-Spike Intervall Verteilungen. Schließlich erlaubt die hier vorgestellte Theorie Berechnungen der Stimulus-Enkodierung, also die Adaption des Ca 2+ Signals auf veränderliche extrazelluläre Stimuli. Die Vorhersage einer fold change Enkodierung kann durch Experimente gestützt werden., In this work two signaling pathways, involving the tumor suppressor p53 and the second messenger Ca2+ , are to be discussed and modelled. The tumor suppressor p53 shows a pulsatile response in single cells after induction of DNA double strand breaks (DSBs). Except for very high amounts of damage, these pulses appear at irregular times. The concept of excitable systems is employed as a convenient way to model such observed dynamics. An application to biomolecular reaction networks shows the need for a positive feedback within the p53 regulatory network. Exploiting the reported ultrasensitive dynamics of the upstream damage sensor kinases, leads to a simplified excitable kinase-phosphatase model. Coupling that to the canonical negative feedback p53 regulatory loop, is the core idea behind the construction of the excitable p53 model. A detailed bifurcation analysis of the model establishes a robust excitable regime, which can be switched to oscillatory dynamics via a strong DNA damage signal. Driving the p53 model with a stochastic DSB process yields pulsatile dynamics which reflect different experimental scenarios. Intracellular Ca 2+ concentration spikes arise from a hierarchic cascade of stochastic events. An analytical solution strategy, employing a semi-Markovian description and involving Laplace transformations, is devised and successfully applied to a specific Ca2+ model. The new gained insights are then used, to construct a new generic Ca2+ model, which elegantly captures many known features of Ca2+ signaling. In particular the experimentally observed relations between the average and the standard deviation of the inter spike intervals (ISIs) can be explained in a concise way. Finally, the theoretical considerations allow to calculate the stimulus encoding relation, which governs the adaption of the Ca 2+ signals to varying extracellular stimuli. This is predicted to be a fold change response and new experimental results display a strong support of this idea.

Natural and synthetic triterpenoids have been shown to kill cancer cells via multiple mechanisms. The therapeutic effect and underlying mechanism of the synthetic triterpenoid bardoxolone methyl (C-28 methyl ester of 2-cyano-3,12-dioxoolean-1,9-dien-28-oic acid; CDDO-Me) on esophageal cancer are unclear. Herein, we aimed to investigate the anticancer effects and underlying mechanisms of CDDO-Me in human esophageal squamous cell carcinoma (ESCC) cells. Our study showed that CDDO-Me suppressed the proliferation and arrested cells in G2/M phase, and induced apoptosis in human ESCC Ec109 and KYSE70 cells. The G2/M arrest was accompanied with upregulated p21Waf1/Cip1 and p53 expression. CDDO-Me significantly decreased B-cell lymphoma-extra large (Bcl-xl), B-cell lymphoma 2 (Bcl-2), cleaved caspase-9, and cleaved poly ADP ribose polymerase (PARP) levels but increased the expression level of Bcl-2-associated X (Bax). Furthermore, CDDO-Me induced autophagy in both Ec109 and KYSE70 cells via suppression of the phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/Akt/mTOR) signaling pathway. There were interactions between the autophagic and apoptotic pathways in Ec109 and KYSE70 cells subject to CDDO-Me treatment. CDDO-Me also scavenged reactive oxygen species through activation of the nuclear factor (erythroid-derived 2)-related factor 2 (Nrf2) pathway in Ec109 and KYSE70 cells. CDDO-Me inhibited cell invasion, epithelial-mesenchymal transition, and stemness in Ec109 and KYSE70 cells. CDDO-Me significantly downregulated E-cadherin but upregulated Snail, Slug, and zinc finger E-box-binding homeobox 1 (TCF-8/ZEB1) in Ec109 and KYSE70 cells. CDDO-Me significantly decreased the expression of octamer-4, sex determining region Y-box 2 (Sox-2), Nanog, and B lymphoma Mo-MLV insertion region 1 homolog (Bmi-1), all markers of cancer cell stemness, in Ec109 and KYSE70 cells. Taken together, these results indicate that CDDO-Me is a promising anticancer agent a

Background: The size and complexity of published biochemical network reconstructions are steadily increasing, expanding the potential scale of derived computational models. However, the construction of large biochemical network models is a laborious and error-prone task. Automated methods have simplified the network reconstruction process, but building kinetic models for these systems is still a manually intensive task. Appropriate kinetic equations, based upon reaction rate laws, must be constructed and parameterized for each reaction. The complex test-and-evaluation cycles that can be involved during kinetic model construction would thus benefit from automated methods for rate law assignment. Results: We present a high-throughput algorithm to automatically suggest and create suitable rate laws based upon reaction type according to several criteria. The criteria for choices made by the algorithm can be influenced in order to assign the desired type of rate law to each reaction. This algorithm is implemented in the software package SBMLsqueezer 2. In addition, this program contains an integrated connection to the kinetics database SABIO-RK to obtain experimentally-derived rate laws when desired. Conclusions: The described approach fills a heretofore absent niche in workflows for large-scale biochemical kinetic model construction. In several applications the algorithm has already been demonstrated to be useful and scalable. SBMLsqueezer is platform independent and can be used as a stand-alone package, as an integrated plugin, or through a web interface, enabling flexible solutions and use-case scenarios.

Quantitative trait loci (QTL) mapping studies enable identification of loci that are part of regulatory networks controlling various phenotypes. Detailed investigations of genes within these loci are required to ultimately understand the function of individual genes and how they interact with other players in the network. In this study, we use transgenic plants in combination with natural variation to investigate the regulatory role of the AOP3 gene found in GS-AOP locus previously suggested to contribute to the regulation of glucosinolate defense compounds. Phenotypic analysis and QTL mapping in F2 populations with different AOP3 transgenes support that the enzymatic function and the AOP3 RNA both play a significant role in controlling glucosinolate accumulation. Furthermore, we find different loci interacting with either the enzymatic activity or the RNA of AOP3 and thereby extend the regulatory network controlling glucosinolate accumulation.

BackgroundThe size and complexity of published biochemical network reconstructions are steadily increasing, expanding the potential scale of derived computational models. However, the construction of large biochemical network models is a laborious and error-prone task. Automated methods have simplified the network reconstruction process, but building kinetic models for these systems is still a manually intensive task. Appropriate kinetic equations, based upon reaction rate laws, must be constructed and parameterized for each reaction. The complex test-and-evaluation cycles that can be involved during kinetic model construction would thus benefit from automated methods for rate law assignment.ResultsWe present a high-throughput algorithm to automatically suggest and create suitable rate laws based upon reaction type according to several criteria. The criteria for choices made by the algorithm can be influenced in order to assign the desired type of rate law to each reaction. This algorithm is implemented in the software package SBMLsqueezer 2. In addition, this program contains an integrated connection to the kinetics database SABIO-RK to obtain experimentally-derived rate laws when desired.ConclusionsThe described approach fills a heretofore absent niche in workflows for large-scale biochemical kinetic model construction. In several applications the algorithm has already been demonstrated to be useful and scalable. SBMLsqueezer is platform independent and can be used as a stand-alone package, as an integrated plugin, or through a web interface, enabling flexible solutions and use-case scenarios.

The duplication of transcription regulators can elicit major regulatory network rearrangements over evolutionary timescales. However, few examples of duplications resulting in gene network expansions are understood in molecular detail. Here we show that four Candida albicans transcription regulators that arose by successive duplications have differentiated from one another by acquiring different intrinsic DNA-binding specificities, different preferences for half-site spacing, and different associations with cofactors. The combination of these three mechanisms resulted in each of the four regulators controlling a distinct set of target genes, which likely contributed to the adaption of this fungus to its human host. Our results illustrate how successive duplications and diversification of an ancestral transcription regulator can underlie major changes in an organism's regulatory circuitry.

Nowadays, with large amounts of data becoming available, solving biological quests is becoming more and more a data-driven activity. To support this, there is a need for tools that enable the integration of the many sources of data. This thesis presents several avenues that can be taken, showing how integration can support research in the life sciences. Biological data can be integrated at several levels. We present a categorization of these different strategies within the context of the Data-Information-Knowledge-Wisdom paradigm. We argue that bioinformatics research should not only concentrate on the individual levels, but also on the transitions between these domains. Throughout the thesis we present possible solutions for a number of these different transformations. One of these transformations bridges the gap that exists between the two lowest levels of data integration: data representations (e.g. databases) and data analysis (e.g. pattern recognition, statistical analysis). The wealth of different data sources, each describing a different aspect of the molecular system (such as gene expressions, locations on genome, physical binding partners etc.), have driven data representation approaches towards flat and flexible formats. In contrast, data analysis prefers structured multi-dimensional array-based data formats. We argue that this gap cannot be closed, but rather needs to be bridged through the use of novel query systems. As a solution, we introduce the tool IBIDAS, which allows one to easily handle not only tables, but also more complexly structured data, making it a flexible tool for the exploration and analysis of data. Another question concerns at what point different data sources need to be integrated. One strategy (`late' integration) is to analyse each data source separately, after which the results are integrated. This strategy however prevents the discovery of connections that transcend the individual data sources. The alternative `early' integration, Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

The architecture of tomato inflorescence strongly affects flower production and subsequent crop yield. To understand the genetic activities involved, insight into the underlying network of genes that initiate and control the sympodial growth in the tomato is essential. In this paper, we show how the structure of this network can be derived from available data of the expressions of the involved genes. Our approach starts from employing biological expert knowledge to select the most probable gene candidates behind branching behavior. To find how these genes interact, we develop a stepwise procedure for computational inference of the network structure. Our data consists of expression levels from primary shoot meristems, measured at different developmental stages on three different genotypes of tomato. With the network inferred by our algorithm, we can explain the dynamics corresponding to all three genotypes simultaneously, despite their apparent dissimilarities. We also correctly predict the chronological order of expression peaks for the main hubs in the network. Based on the inferred network, using optimal experimental design criteria, we are able to suggest an informative set of experiments for further investigation of the mechanisms underlying branching behavior

Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic errors in prediction uncertainty estimation. We argue that prediction uncertainty assessment must therefore be performed on a per-prediction basis using a full computational uncertainty analysis. In practice this is feasible by providing a model with a sample or ensemble representing the distribution of its parameters. Within a Bayesian framework, such a sample may be generated by a Markov Chain Monte Carlo (MCMC) algorithm that infers the parameter distribution based on experimental data. Matlab code for generating the sample (with the Differential Evolution Markov Chain sampler) and the subsequent uncertainty analysis using such a sample, is supplied as Supplemental Information.

Aromatic biodegradation pathways of environmental bacteria are vast sources of matching trios of enzymes, substrates and regulators that can be refactored to run logic operations through cell-to-cell communication. As a proof of concept, the connection between two Pseudomonas putida strains using benzoic acid as the wiring molecule is presented. In this system, a sender strain harboring the TOL pathway for biodegradation of aromatics processed toluene as input and generated benzoate as the output signal. Diffusion of such metabolic intermediate to the medium was then sensed by a second strain (the receiver) that used benzoate as input for a new logic gate producing a visual output (i.e., light emission). The setup was functional irrespective of whether sender and receiver cells were in direct contact or in liquid culture. These results highlight the potential of environmental metabolic pathways as sources of building blocks for the engineering of multicellular logic in prokaryotic systems. © 2013 American Chemical Society.