Evolution of bow-tie architectures in biology

Single-cell phylotranscriptomics of developmental and cell type evolution

Single-cell phylotranscriptomics is an emerging tool to reveal the molecular and cellular mechanisms of evolution. We summarize its utility in studying the hourglass pattern of ontogenetic evolution and for understanding the evolutionary history of cell types. The developmental hourglass model suggests that the mid-embryonic stage is the most conserved period of development across species, which is supported by morphological and molecular studies. Single-cell phylotranscriptomic analysis has revealed previously underappreciated heterogeneity in transcriptome ages among lineages and cell types throughout development, and has identified the lineages and tissues that drive the whole-organism hourglass pattern. Single-cell transcriptome age analyses also provide important insights into the origin of germ layers, the different selective forces on tissues during adaptation, and the evolutionary relationships between cell types.

The HmrABCX pathway regulates the transition between motile and sessile lifestyles in Caulobacter crescentus by a HfiA-independent mechanism

Through its cell cycle, the bacterium Caulobacter crescentus switches from a motile, free-living state, to a sessile surface-attached cell. During this coordinated process, cells undergo irreversible morphological changes, such as shedding of their polar flagellum and synthesis of an adhesive holdfast at the same pole. In this work, we used genetic screens to identify genes involved in the regulation of the motile to sessile lifestyle transition. We identified a predicted hybrid histidine kinase that inhibits biofilm formation and activates the motile lifestyle: HmrA (Holdfast and motility regulator A). Genetic screens and genomic localization led to the identification of additional genes that regulate the proportion of cells harboring an active flagellum or a holdfast and that form a putative phosphorelay pathway with HmrA. Further genetic analysis indicates that the Hmr pathway is independent of the holdfast synthesis regulator HfiA and may impact c-di-GMP synthesis through the diguanylate cyclase DgcB pathway. Finally, we provide evidence that the Hmr pathway is involved in the regulation of sessile-to-motile lifestyle as a function of environmental stresses, namely excess copper and non-optimal temperatures.

Hierarchical network structure as the source of hierarchical dynamics (power-law frequency spectra) in living and non-living systems: How state-trait continua (body plans, personalities) emerge from first principles in biophysics

Living systems are hierarchical control systems that display a small world network structure. In such structures, many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a 'power-law' cluster size distribution (a mereology). Just like their structure, the dynamics of living systems shows fractal-like qualities: the timeseries of inner message passing and overt behavior contain high frequencies or 'states' (treble) that are nested within lower frequencies or 'traits' (bass), producing a power-law frequency spectrum that is known as a 'state-trait continuum' in the behavioral sciences. Here, we argue that the power-law dynamics of living systems results from their power-law network structure: organisms 'vertically encode' the deep spatiotemporal structure of their (anticipated) environments, to the effect that many small clusters near the base of the hierarchy produce high frequency signal changes and fewer larger clusters at its top produce ultra-low frequencies. Such ultra-low frequencies exert a tonic regulatory pressure that produces morphological as well as behavioral traits (i.e., body plans and personalities). Nested-modular structure causes higher frequencies to be embedded within lower frequencies, producing a power-law state-trait continuum. At the heart of such dynamics lies the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.q., earthquakes, stock market fluctuations). Since hierarchical structure produces hierarchical dynamics, the development and collapse of hierarchical structure (e.g., during maturation and disease) should leave specific traces in system dynamics (shifts in lower frequencies, i.e. morphological and behavioral traits) that may serve as early warning signs to system failure. The applications of this idea range from (bio)physics and phylogenesis to ontogenesis and clinical medicine.

The Yeast Protein Kinase Sch9 Functions as a Central Nutrient-Responsive Hub That Calibrates Metabolic and Stress-Related Responses

Yeast cells are equipped with different nutrient signaling pathways that enable them to sense the availability of various nutrients and adjust metabolism and growth accordingly. These pathways are part of an intricate network since most of them are cross-regulated and subject to feedback regulation at different levels. In yeast, a central role is played by Sch9, a protein kinase that functions as a proximal effector of the conserved growth-regulatory TORC1 complex to mediate information on the availability of free amino acids. However, recent studies established that Sch9 is more than a TORC1-effector as its activity is tuned by several other kinases. This allows Sch9 to function as an integrator that aligns different input signals to achieve accuracy in metabolic responses and stress-related molecular adaptations. In this review, we highlight the latest findings on the structure and regulation of Sch9, as well as its role as a nutrient-responsive hub that impacts on growth and longevity of yeast cells. Given that most key players impinging on Sch9 are well-conserved, we also discuss how studies on Sch9 can be instrumental to further elucidate mechanisms underpinning healthy aging in mammalians.

Bow-tie architectures in biological and artificial neural networks: Implications for network evolution and assay design

Modern artificial neural networks (ANNs) have long been designed on foundations of mathematics as opposed to their original foundations of biomimicry. However, the structure and function of these modern ANNs are often analogous to real-life biological networks. We propose that the ubiquitous information-theoretic principles underlying the development of ANNs are similar to the principles guiding the macro-evolution of biological networks and that insights gained from one field can be applied to the other. We generate hypotheses on the bow-tie network structure of the Janus kinase - signal transducers and activators of transcription (JAK-STAT) pathway, additionally informed by the evolutionary considerations, and carry out ANN simulation experiments to demonstrate that an increase in the network's input and output complexity does not necessarily require a more complex intermediate layer. This observation should guide novel biomarker discovery-namely, to prioritize sections of the biological networks in which information is most compressed as opposed to biomarkers representing the periphery of the network.

Information Theory as an Experimental Tool for Integrating Disparate Biophysical Signaling Modules

There is a growing appreciation in the fields of cell biology and developmental biology that cells collectively process information in time and space. While many powerful molecular tools exist to observe biophysical dynamics, biologists must find ways to quantitatively understand these phenomena at the systems level. Here, we present a guide for the application of well-established information theory metrics to biological datasets and explain these metrics using examples from cell, developmental and regenerative biology. We introduce a novel computational tool named after its intended purpose, calcium imaging, (CAIM) for simple, rigorous application of these metrics to time series datasets. Finally, we use CAIM to study calcium and cytoskeletal actin information flow patterns between Xenopus laevis embryonic animal cap stem cells. The tools that we present here should enable biologists to apply information theory to develop a systems-level understanding of information processing across a diverse array of experimental systems.

A complex systems approach to aging biology

Having made substantial progress understanding molecules, cells, genes and pathways, aging biology research is now moving toward integration of these parts, attempting to understand how their joint dynamics may contribute to aging. Such a shift of perspective requires the adoption of a formal complex systems framework, a transition being facilitated by large-scale data collection and new analytical tools. Here, we provide a theoretical framework to orient researchers around key concepts for this transition, notably emergence, interaction networks and resilience. Drawing on evolutionary theory, network theory and principles of homeostasis, we propose that organismal function is accomplished by the integration of regulatory mechanisms at multiple hierarchical scales, and that the disruption of this ensemble causes the phenotypic and functional manifestations of aging. We present key examples at scales ranging from sub-organismal biology to clinical geriatrics, outlining how this approach can potentially enrich our understanding of aging.

Patterns of selection across gene regulatory networks

Gene regulatory networks (GRNs) are the core engine of organismal development. If we would like to understand the origin and diversification of phenotypes, it is necessary to consider the structure of GRNs in order to reconstruct the links between genetic mutations and phenotypic change. Much of the progress in evolutionary developmental biology, however, has occurred without a nuanced consideration of the evolution of functional relationships between genes, especially in the context of their broader network interactions. Characterizing and comparing GRNs across traits and species in a more detailed way will allow us to determine how network position influences what genes drive adaptive evolution. In this perspective paper, we consider the architecture of developmental GRNs and how positive selection strength may vary across a GRN. We then propose several testable models for these patterns of selection and experimental approaches to test these models.

Neuroinflammation Following Traumatic Brain Injury: Take It Seriously or Not

Traumatic brain injury (TBI) is associated with high mortality and disability, with a substantial socioeconomic burden. With the standardization of the treatment process, there is increasing interest in the role that the secondary insult of TBI plays in outcome heterogeneity. The secondary insult is neither detrimental nor beneficial in an absolute sense, among which the inflammatory response was a complex cascade of events and can thus be regarded as a double-edged sword. Therefore, clinicians should take the generation and balance of neuroinflammation following TBI seriously. In this review, we summarize the current human and animal model studies of neuroinflammation and provide a better understanding of the inflammatory response in the different stages of TBI. In particular, advances in neuroinflammation using proteomic and transcriptomic techniques have enabled us to identify a functional specific delineation of the immune cell in TBI patients. Based on recent advances in our understanding of immune cell activation, we present the difference between diffuse axonal injury and focal brain injury. In addition, we give a figurative profiling of the general paradigm in the pre- and post-injury inflammatory settings employing a bow-tie framework.

The hidden simplicity of metabolic networks is revealed by multireaction dependencies

Understanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceived from their structure alone.

GDF15, an emerging key player in human aging

Growth differentiation factor 15 (GDF15) is recently emerging not only as a stress-related mitokine, but also as a key player in the aging process, being one of the most up-regulated protein with age and associated with a variety of age-related diseases (ARDs). Many data indicate that GDF15 has protective roles in several tissues during different stress and aging, thus playing a beneficial role in apparent contrast with the observed association with many ARDs. A possible detrimental role for this protein is then hypothesized to emerge with age. Therefore, GDF15 can be considered as a pleiotropic factor with beneficial activities that can turn detrimental in old age possibly when it is chronically elevated. In this review, we summarize the current knowledge on the biology of GDF15 during aging. We also propose GDF15 as a part of a dormancy program, where it may play a role as a mediator of defense processes aimed to protect from inflammatory damage and other stresses, according to the life history theory.

Gene regulatory network inference in long-lived C. elegans reveals modular properties that are predictive of novel aging genes

We design a “wisdom-of-the-crowds” GRN inference pipeline and couple it to complex network analysis to understand the organizational principles governing gene regulation in long-lived glp-1/Notch Caenorhabditis elegans. The GRN has three layers (input, core, and output) and is topologically equivalent to bow-tie/hourglass structures prevalent among metabolic networks. To assess the functional importance of structural layers, we screened 80% of regulators and discovered 50 new aging genes, 86% with human orthologues. Genes essential for longevity—including ones involved in insulin-like signaling (ILS)—are at the core, indicating that GRN's structure is predictive of functionality. We used in vivo reporters and a novel functional network covering 5,497 genetic interactions to make mechanistic predictions. We used genetic epistasis to test some of these predictions, uncovering a novel transcriptional regulator, sup-37, that works alongside DAF-16/FOXO. We present a framework with predictive power that can accelerate discovery in C. elegans and potentially humans.

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Gene-regulatory inference provides global network of long-lived animals

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The large-scale topology of the network has an hourglass structure

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Membership to the core of the hourglass is a good predictor of functionality

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Discovered 50 novel aging genes, including sup-37, a DAF-16 dependent gene

Feedback regulation and coordination of the main metabolism for bacterial growth and metabolic engineering for amino acid fermentation

Living organisms such as bacteria are often exposed to continuous changes in the nutrient availability in nature. Therefore, bacteria must constantly monitor the environmental condition, and adjust the metabolism quickly adapting to the change in the growth condition. For this, bacteria must orchestrate (coordinate and integrate) the complex and dynamically changing information on the environmental condition. In particular, the central carbon metabolism (CCM), monomer synthesis, and macromolecular synthesis must be coordinately regulated for the efficient growth. It is a grand challenge in bioscience, biotechnology, and synthetic biology to understand how living organisms coordinate the metabolic regulation systems. Here, we consider the integrated sensing of carbon sources by the phosphotransferase system (PTS), and the feed-forward/feedback regulation systems incorporated in the CCM in relation to the pool sizes of flux-sensing metabolites and αketoacids. We also consider the metabolic regulation of amino acid biosynthesis (as well as purine and pyrimidine biosyntheses) paying attention to the feedback control systems consisting of (fast) enzyme level regulation with (slow) transcriptional regulation. The metabolic engineering for the efficient amino acid production by bacteria such as Escherichia coli and Corynebacterium glutamicum is also discussed (in relation to the regulation mechanisms). The amino acid synthesis is important for determining the rate of ribosome biosynthesis. Thus, the growth rate control (growth law) is further discussed on the relationship between (p)ppGpp level and the ribosomal protein synthesis.

A weighted network analysis framework for the hourglass effect-And its application in the C. elegans connectome

Understanding hierarchy and modularity in natural as well as technological networks is of utmost importance. A major aspect of such analysis involves identifying the nodes that are crucial to the overall processing structure of the network. More recently, the approach of hourglass analysis has been developed for the purpose of quantitatively analyzing whether only a few intermediate nodes mediate the information processing between a large number of inputs and outputs of a network. We develop a new framework for hourglass analysis that takes network weights into account while identifying the core nodes and the extent of hourglass effect in a given weighted network. We use this framework to study the structural connectome of the C. elegans and identify intermediate neurons that form the core of sensori-motor pathways in the organism. Our results show that the neurons forming the core of the connectome show significant differences across the male and hermaphrodite sexes, with most core nodes in the male concentrated in sex-organs while they are located in the head for the hermaphrodite. Our work demonstrates that taking weights into account for network analysis framework leads to emergence of different network patterns in terms of identification of core nodes and hourglass structure in the network, which otherwise would be missed by unweighted approaches.

Global connectivity in genome-scale metabolic networks revealed by comprehensive FBA-based pathway analysis

Background

Graph-based analysis (GBA) of genome-scale metabolic networks has revealed system-level structures such as the bow-tie connectivity that describes the overall mass flow in a network. However, many pathways obtained by GBA are biologically impossible, making it difficult to study how the global structures affect the biological functions of a network. New method that can calculate the biologically relevant pathways is desirable for structural analysis of metabolic networks.

Results

Here, we present a new method to determine the bow-tie connectivity structure by calculating possible pathways between any pairs of metabolites in the metabolic network using a flux balance analysis (FBA) approach to ensure that the obtained pathways are biologically relevant. We tested this method with 15 selected high-quality genome-scale metabolic models from BiGG database. The results confirmed the key roles of central metabolites in network connectivity, locating in the core part of the bow-tie structure, the giant strongly connected component (GSC). However, the sizes of GSCs revealed by GBA are significantly larger than those by FBA approach. A great number of metabolites in the GSC from GBA actually cannot be produced from or converted to other metabolites through a mass balanced pathway and thus should not be in GSC but in other subsets of the bow-tie structure. In contrast, the bow-tie structural classification of metabolites obtained by FBA is more biologically relevant and suitable for the study of the structure-function relationships of genome scale metabolic networks.

Conclusions

The FBA based pathway calculation improve the biologically relevant classification of metabolites in the bow-tie connectivity structure of the metabolic network, taking us one step further toward understanding how such system-level structures impact the biological functions of an organism.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02357-1.

Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure

Uncovering the general principles that govern the structure of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries can help illuminate this problem by enabling the exploration of chemical reaction universes that are constrained by general mathematical rules. Here, we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We developed a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET), to study string chemistries using tools from the field of stoichiometric constraint-based modeling. In addition to exploring the topological characteristics of different string chemistry networks, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental nutrients. We found that the composition of these minimal metabolic networks was influenced more strongly by the metabolites in the biomass reaction than the identities of the environmental nutrients. This finding has important implications for the reconstruction of organismal metabolic networks and could help us better understand the rise and evolution of biochemical organization. More generally, our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.

Dimensional reduction in complex living systems: Where, why, and how

The unprecedented prowess of measurement techniques provides a detailed, multi-scale look into the depths of living systems. Understanding these avalanches of high-dimensional data-by distilling underlying principles and mechanisms-necessitates dimensional reduction. We propose that living systems achieve exquisite dimensional reduction, originating from their capacity to learn, through evolution and phenotypic plasticity, the relevant aspects of a non-random, smooth physical reality. We explain how geometric insights by mathematicians allow one to identify these genuine hallmarks of life and distinguish them from universal properties of generic data sets. We illustrate these principles in a concrete example of protein evolution, suggesting a simple general recipe that can be applied to understand other biological systems.

Bow-tie architecture of gene regulatory networks in species of varying complexity

The gene regulatory network (GRN) architecture plays a key role in explaining the biological differences between species. We aim to understand species differences in terms of some universally present dynamical properties of their gene regulatory systems. A network architectural feature associated with controlling system-level dynamical properties is the bow-tie, identified by a strongly connected subnetwork, the core layer, between two sets of nodes, the in and the out layers. Though a bow-tie architecture has been observed in many networks, its existence has not been extensively investigated in GRNs of species of widely varying biological complexity. We analyse publicly available GRNs of several well-studied species from prokaryotes to unicellular eukaryotes to multicellular organisms. In their GRNs, we find the existence of a bow-tie architecture with a distinct largest strongly connected core layer. We show that the bow-tie architecture is a characteristic feature of GRNs. We observe an increasing trend in the relative core size with species complexity. Using studied relationships of the core size with dynamical properties like robustness and fragility, flexibility, criticality, controllability and evolvability, we hypothesize how these regulatory system properties have emerged differently with biological complexity, based on the observed differences of the GRN bow-tie architectures.

A Weighted Network Analysis Framework for the Hourglass Effect — and its Application in the C. Elegans Connectome.. [DOI: 10.1101/2021.03.19.436224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Understanding hierarchy and modularity in natural as well as technological networks is of utmost importance. A major aspect of such analysis involves identifying the nodes that are crucial to the overall processing structure of the network. More recently, the approach of hourglass analysis has been developed for the purpose of quantitatively analyzing whether only a few intermediate nodes mediate the information processing between a large number of inputs and outputs of a network. We develop a new framework for hourglass analysis that takes network weights into account while identifying the core nodes and the extent of hourglass effect in a given weighted network. We use this framework to study the structural connectome of theC. elegansand identify intermediate neurons that form the core of sensori-motor pathways in the organism. Our results show that the neurons forming the core of the connectome show significant differences across the male and hermaphrodite sexes, with most core nodes in the male concentrated in sex-organs while they are located in the head for the hermaphrodite. Our work demonstrates that taking weights into account for network analysis framework leads to emergence of different network patterns in terms of identification of core nodes and hourglass structure in the network, which otherwise would be missed by unweighted approaches.

How higher goals are constructed and collapse under stress: A hierarchical Bayesian control systems perspective

In this paper, we show that organisms can be modeled as hierarchical Bayesian control systems with small world and information bottleneck (bow-tie) network structure. Such systems combine hierarchical perception with hierarchical goal setting and hierarchical action control. We argue that hierarchical Bayesian control systems produce deep hierarchies of goal states, from which it follows that organisms must have some form of 'highest goals'. For all organisms, these involve internal (self) models, external (social) models and overarching (normative) models. We show that goal hierarchies tend to decompose in a top-down manner under severe and prolonged levels of stress. This produces behavior that favors short-term and self-referential goals over long term, social and/or normative goals. The collapse of goal hierarchies is universally accompanied by an increase in entropy (disorder) in control systems that can serve as an early warning sign for tipping points (disease or death of the organism). In humans, learning goal hierarchies corresponds to personality development (maturation). The failure of goal hierarchies to mature properly corresponds to personality deficits. A top-down collapse of such hierarchies under stress is identified as a common factor in all forms of episodic mental disorders (psychopathology). The paper concludes by discussing ways of testing these hypotheses empirically.

Genomic Variation, Evolvability, and the Paradox of Mental Illness

Twentieth-century genetics was hard put to explain the irregular behavior of neuropsychiatric disorders. Autism and schizophrenia defy a principle of natural selection; they are highly heritable but associated with low reproductive success. Nevertheless, they persist. The genetic origins of such conditions are confounded by the problem of variable expression, that is, when a given genetic aberration can lead to any one of several distinct disorders. Also, autism and schizophrenia occur on a spectrum of severity, from mild and subclinical cases to the overt and disabling. Such irregularities reflect the problem of missing heritability; although hundreds of genes may be associated with autism or schizophrenia, together they account for only a small proportion of cases. Techniques for higher resolution, genomewide analysis have begun to illuminate the irregular and unpredictable behavior of the human genome. Thus, the origins of neuropsychiatric disorders in particular and complex disease in general have been illuminated. The human genome is characterized by a high degree of structural and behavioral variability: DNA content variation, epistasis, stochasticity in gene expression, and epigenetic changes. These elements have grown more complex as evolution scaled the phylogenetic tree. They are especially pertinent to brain development and function. Genomic variability is a window on the origins of complex disease, neuropsychiatric disorders, and neurodevelopmental disorders in particular. Genomic variability, as it happens, is also the fuel of evolvability. The genomic events that presided over the evolution of the primate and hominid lineages are over-represented in patients with autism and schizophrenia, as well as intellectual disability and epilepsy. That the special qualities of the human genome that drove evolution might, in some way, contribute to neuropsychiatric disorders is a matter of no little interest.

Environmental flexibility does not explain metabolic robustness

Cells show remarkable resilience against genetic and environmental perturbations. However, its evolutionary origin remains obscure. In order to leverage methods of systems biology for examining cellular robustness, a computationally accessible way of quantification is needed. Here, we present an unbiased metric of structural robustness in genome-scale metabolic models based on concepts prevalent in reliability engineering and fault analysis. The probability of failure (PoF) is defined as the (weighted) portion of all possible combinations of loss-of-function mutations that disable network functionality. It can be exactly determined if all essential reactions, synthetic lethal pairs of reactions, synthetic lethal triplets of reactions etc. are known. In theory, these minimal cut sets (MCSs) can be calculated for any network, but for large models the problem remains computationally intractable. Herein, we demonstrate that even at the genome scale only the lowest-cardinality MCSs are required to efficiently approximate the PoF with reasonable accuracy. Building on an improved theoretical understanding, we analysed the robustness of 489 E. coli, Shigella, Salmonella, and fungal genome-scale metabolic models (GSMMs). In contrast to the popular "congruence theory", which explains the origin of genetic robustness as a byproduct of selection for environmental flexibility, we found no correlation between network robustness and the diversity of growth-supporting environments. On the contrary, our analysis indicates that amino acid synthesis rather than carbon metabolism dominates metabolic robustness.

Working together for the family: determination of HER oncogene co-amplifications in breast cancer

HER2 is a well-studied tyrosine kinase (TK) membrane receptor which functions as a therapeutic target in invasive ductal breast carcinomas (IDC). The standard of care for the treatment of HER2-positive breast is the antibody trastuzumab. Despite specific treatment unfortunately, 20% of primary and 70% of metastatic HER2 tumors develop resistance. HER2 belongs to a gene family, with four members (HER1-4) and these members could be involved in resistance to anti-HER2 therapies. In this study we designed a probemix to detect the amplification of the four HER oncogenes in a single reaction. In addition, we developed a protocol based on the combination of MLPA with ddPCR to detect the tumor proportion of co-amplified HERs. On 111 IDC, the HER2 MLPA results were validated by FISH (Adjusted r ² = 0,91, p < 0,0001), CISH (Adjusted r ² = 0,938, p < 0,0001) and IHC (Adjusted r ² = 0,31, p < 0,0001). HER1-4 MLPA results were validated by RT-qPCR assays (Spearman Rank test p < 0,05). Of the 111 samples, 26% presented at least one HER amplified, of which 23% showed co-amplifications with other HERs. The percentage of cells with HER2 co-amplified varied among the tumors (from 2-72,6%). Independent in-silico findings show that the outcome of HER2+ patients is conditioned by the status of HER3 and HER4. Our results encourage further studies to investigate the relationship with patient's response to single or combined treatment. The approach could serve as proof of principle for other tumors in which the HER oncogenes are involved.

The Regulatory Role of Key Metabolites in the Control of Cell Signaling

Robust biological systems are able to adapt to internal and environmental perturbations. This is ensured by a thick crosstalk between metabolism and signal transduction pathways, through which cell cycle progression, cell metabolism and growth are coordinated. Although several reports describe the control of cell signaling on metabolism (mainly through transcriptional regulation and post-translational modifications), much fewer information is available on the role of metabolism in the regulation of signal transduction. Protein-metabolite interactions (PMIs) result in the modification of the protein activity due to a conformational change associated with the binding of a small molecule. An increasing amount of evidences highlight the role of metabolites of the central metabolism in the control of the activity of key signaling proteins in different eukaryotic systems. Here we review the known PMIs between primary metabolites and proteins, through which metabolism affects signal transduction pathways controlled by the conserved kinases Snf1/AMPK, Ras/PKA and TORC1. Interestingly, PMIs influence also the mitochondrial retrograde response (RTG) and calcium signaling, clearly demonstrating that the range of this phenomenon is not limited to signaling pathways related to metabolism.

Regulating cellular cyclic adenosine monophosphate: "Sources," "sinks," and now, "tunable valves"

A number of hormones and growth factors stimulate target cells via the second messenger pathways, which in turn regulate cellular phenotypes. Cyclic adenosine monophosphate (cAMP) is a ubiquitous second messenger that facilitates numerous signal transduction pathways; its production in cells is tightly balanced by ligand-stimulated receptors that activate adenylate cyclases (ACs), that is, "source" and by phosphodiesterases (PDEs) that hydrolyze it, that is, "sinks." Because it regulates various cellular functions, including cell growth and differentiation, gene transcription and protein expression, the cAMP signaling pathway has been exploited for the treatment of numerous human diseases. Reduction in cAMP is achieved by blocking "sources"; however, elevation in cAMP is achieved by either stimulating "source" or blocking "sinks." Here we discuss an alternative paradigm for the regulation of cellular cAMP via GIV/Girdin, the prototypical member of a family of modulators of trimeric GTPases, Guanine nucleotide Exchange Modulators (GEMs). Cells upregulate or downregulate cellular levels of GIV-GEM, which modulates cellular cAMP via spatiotemporal mechanisms distinct from the two most often targeted classes of cAMP modulators, "sources" and "sinks." A network-based compartmental model for the paradigm of GEM-facilitated cAMP signaling has recently revealed that GEMs such as GIV serve much like a "tunable valve" that cells may employ to finetune cellular levels of cAMP. Because dysregulated signaling via GIV and other GEMs has been implicated in multiple disease states, GEMs constitute a hitherto untapped class of targets that could be exploited for modulating aberrant cAMP signaling in disease states. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Biological Mechanisms > Cell Signaling.

Comparative Study of Transcriptomics-Based Scoring Metrics for the Epithelial-Hybrid-Mesenchymal Spectrum

The Epithelial-mesenchymal transition (EMT) is a cellular process implicated in embryonic development, wound healing, and pathological conditions such as cancer metastasis and fibrosis. Cancer cells undergoing EMT exhibit enhanced aggressive behavior characterized by drug resistance, tumor-initiation potential, and the ability to evade the immune system. Recent in silico, in vitro, and in vivo evidence indicates that EMT is not an all-or-none process; instead, cells can stably acquire one or more hybrid epithelial/mesenchymal (E/M) phenotypes which often can be more aggressive than purely E or M cell populations. Thus, the EMT status of cancer cells can prove to be a critical estimate of patient prognosis. Recent attempts have employed different transcriptomics signatures to quantify EMT status in cell lines and patient tumors. However, a comprehensive comparison of these methods, including their accuracy in identifying cells in the hybrid E/M phenotype(s), is lacking. Here, we compare three distinct metrics that score EMT on a continuum, based on the transcriptomics signature of individual samples. Our results demonstrate that these methods exhibit good concordance among themselves in quantifying the extent of EMT in a given sample. Moreover, scoring EMT using any of the three methods discerned that cells can undergo varying extents of EMT across tumor types. Separately, our analysis also identified tumor types with maximum variability in terms of EMT and associated an enrichment of hybrid E/M signatures in these samples. Moreover, we also found that the multinomial logistic regression (MLR)-based metric was capable of distinguishing between "pure" individual hybrid E/M vs. mixtures of E and M cells. Our results, thus, suggest that while any of the three methods can indicate a generic trend in the EMT status of a given cell, the MLR method has two additional advantages: (a) it uses a small number of predictors to calculate the EMT score and (b) it can predict from the transcriptomic signature of a population whether it is comprised of "pure" hybrid E/M cells at the single-cell level or is instead an ensemble of E and M cell subpopulations.

Comparative Study of Transcriptomics-Based Scoring Metrics for the Epithelial-Hybrid-Mesenchymal Spectrum

The Epithelial-mesenchymal transition (EMT) is a cellular process implicated in embryonic development, wound healing, and pathological conditions such as cancer metastasis and fibrosis. Cancer cells undergoing EMT exhibit enhanced aggressive behavior characterized by drug resistance, tumor-initiation potential, and the ability to evade the immune system. Recent in silico, in vitro, and in vivo evidence indicates that EMT is not an all-or-none process; instead, cells can stably acquire one or more hybrid epithelial/mesenchymal (E/M) phenotypes which often can be more aggressive than purely E or M cell populations. Thus, the EMT status of cancer cells can prove to be a critical estimate of patient prognosis. Recent attempts have employed different transcriptomics signatures to quantify EMT status in cell lines and patient tumors. However, a comprehensive comparison of these methods, including their accuracy in identifying cells in the hybrid E/M phenotype(s), is lacking. Here, we compare three distinct metrics that score EMT on a continuum, based on the transcriptomics signature of individual samples. Our results demonstrate that these methods exhibit good concordance among themselves in quantifying the extent of EMT in a given sample. Moreover, scoring EMT using any of the three methods discerned that cells can undergo varying extents of EMT across tumor types. Separately, our analysis also identified tumor types with maximum variability in terms of EMT and associated an enrichment of hybrid E/M signatures in these samples. Moreover, we also found that the multinomial logistic regression (MLR)-based metric was capable of distinguishing between "pure" individual hybrid E/M vs. mixtures of E and M cells. Our results, thus, suggest that while any of the three methods can indicate a generic trend in the EMT status of a given cell, the MLR method has two additional advantages: (a) it uses a small number of predictors to calculate the EMT score and (b) it can predict from the transcriptomic signature of a population whether it is comprised of "pure" hybrid E/M cells at the single-cell level or is instead an ensemble of E and M cell subpopulations.

MyD88 Death-Domain Oligomerization Determines Myddosome Architecture: Implications for Toll-like Receptor Signaling

Toll-like receptors (TLRs) are pivotal in triggering the innate immune response to pathogen infection. Ligand binding induces receptor dimerization which facilitates the recruitment of other post-receptor signal transducers into a complex signalosome, the Myddosome. Central to this process is Myeloid differentiation primary response 88 (MyD88), which is required by almost all TLRs, and signaling is thought to proceed via the stepwise, sequential assembly of individual components. Here, we show that the death domains of human MyD88 spontaneously and reversibly associate to form helical filaments in vitro. A 3.1-Å cryoelectron microscopy structure reveals that the architecture of the filament is identical to that of the 6:4 MyD88-IRAK4-IRAK2 hetero-oligomeric Myddosome. Additionally, the death domain of IRAK4 interacts with the filaments to reconstitute the non-stoichiometric 6:4 MyD88-IRAK4 complex. Together, these data suggest that intracellularly, the MyD88 scaffold may be pre-formed and poised for recruitment of IRAKs on receptor activation and TIR engagement.

The hourglass organization of the Caenorhabditis elegans connectome

We approach the C. elegans connectome as an information processing network that receives input from about 90 sensory neurons, processes that information through a highly recurrent network of about 80 interneurons, and it produces a coordinated output from about 120 motor neurons that control the nematode's muscles. We focus on the feedforward flow of information from sensory neurons to motor neurons, and apply a recently developed network analysis framework referred to as the "hourglass effect". The analysis reveals that this feedforward flow traverses a small core ("hourglass waist") that consists of 10-15 interneurons. These are mostly the same interneurons that were previously shown (using a different analytical approach) to constitute the "rich-club" of the C. elegans connectome. This result is robust to the methodology that separates the feedforward from the feedback flow of information. The set of core interneurons remains mostly the same when we consider only chemical synapses or the combination of chemical synapses and gap junctions. The hourglass organization of the connectome suggests that C. elegans has some similarities with encoder-decoder artificial neural networks in which the input is first compressed and integrated in a low-dimensional latent space that encodes the given data in a more efficient manner, followed by a decoding network through which intermediate-level sub-functions are combined in different ways to compute the correlated outputs of the network. The core neurons at the hourglass waist represent the information bottleneck of the system, balancing the representation accuracy and compactness (complexity) of the given sensory information.

Keystone Species and Modularity in Microbial Hydrocarbon Degradation Uncovered by Network Analysis and Association Rule Mining

Natural microbial communities in soils are highly diverse, allowing for rich networks of microbial interactions to unfold. Identifying key players in these networks is difficult as the distribution of microbial diversity at the local scale is typically non-uniform, and is the outcome of both abiotic environmental factors and microbial interactions. Here, using spatially resolved microbial presence-absence data along an aquifer transect contaminated with hydrocarbons, we combined co-occurrence analysis with association rule mining to identify potential keystone species along the hydrocarbon degradation process. Derived co-occurrence networks were found to be of a modular structure, with modules being associated with specific spatial locations and metabolic activity along the contamination plume. Association rules identify species that never occur without another, hence identifying potential one-sided cross-feeding relationships. We find that hub nodes in the rule network appearing in many rules as targets qualify as potential keystone species that catalyze critical transformation steps and are able to interact with varying partners. By contrasting analysis based on data derived from bulk samples and individual soil particles, we highlight the importance of spatial sample resolution. While individual inferred interactions are hypothetical in nature, requiring experimental verification, the observed global network patterns provide a unique first glimpse at the complex interaction networks at work in the microbial world.

The Ultimate Guide to Bacterial Swarming: An Experimental Model to Study the Evolution of Cooperative Behavior

Cooperation has fascinated biologists since Darwin. How did cooperative behaviors evolve despite the fitness cost to the cooperator? Bacteria have cooperative behaviors that make excellent models to take on this age-old problem from both proximate (molecular) and ultimate (evolutionary) angles. We delve into Pseudomonas aeruginosa swarming, a phenomenon where billions of bacteria move cooperatively across distances of centimeters in a matter of a few hours. Experiments with swarming have unveiled a strategy called metabolic prudence that stabilizes cooperation, have showed the importance of spatial structure, and have revealed a regulatory network that integrates environmental stimuli and direct cooperative behavior, similar to a machine learning algorithm. The study of swarming elucidates more than proximate mechanisms: It exposes ultimate mechanisms valid to all scales, from cells in cancerous tumors to animals in large communities.

Insights into the molecular features of the von Hippel-Lindau-like protein

We present an in silico characterization of the von Hippel-Lindau-like protein (VLP), the only known human paralog of the von Hippel-Lindau tumor suppressor protein (pVHL). Phylogenetic investigation showed VLP to be mostly conserved in upper mammals and specifically expressed in brain and testis. Structural analysis and molecular dynamics simulations show VLP to be very similar to pVHL three-dimensional organization and binding dynamics. In particular, conservation of elements at the protein interfaces suggests VLP to be a functional pVHL homolog potentially possessing multiple functions beyond HIF-1α-dependent binding activity. Our findings show that VLP may share at least seven interactors with pVHL, suggesting novel functional roles for this understudied human protein. These may occur at precise hypoxia levels where functional overlap with pVHL may permit a finer modulation of pVHL functions.

Microbial life cycles link global modularity in regulation to mosaic evolution

Microbes are exposed to changing environments, to which they can respond by adopting various lifestyles such as swimming, colony formation or dormancy. These lifestyles are often studied in isolation, thereby giving a fragmented view of the life cycle as a whole. Here, we study lifestyles in the context of this whole. We first use machine learning to reconstruct the expression changes underlying life cycle progression in the bacterium Bacillus subtilis, based on hundreds of previously acquired expression profiles. This yields a timeline that reveals the modular organization of the life cycle. By analysing over 380 Bacillales genomes, we then show that life cycle modularity gives rise to mosaic evolution in which life stages such as motility and sporulation are conserved and lost as discrete units. We postulate that this mosaic conservation pattern results from habitat changes that make these life stages obsolete or detrimental. Indeed, when evolving eight distinct Bacillales strains and species under laboratory conditions that favour colony growth, we observe rapid and parallel losses of the sporulation life stage across species, induced by mutations that affect the same global regulator. We conclude that a life cycle perspective is pivotal to understanding the causes and consequences of modularity in both regulation and evolution.

Environment-to-phenotype mapping and adaptation strategies in varying environments

Biological organisms exhibit diverse strategies for adapting to varying environments. For example, a population of organisms may express the same phenotype in all environments ("unvarying strategy") or follow environmental cues and express alternative phenotypes to match the environment ("tracking strategy"), or diversify into coexisting phenotypes to cope with environmental uncertainty ("bet-hedging strategy"). We introduce a general framework for studying how organisms respond to environmental variations, which models an adaptation strategy by an abstract mapping from environmental cues to phenotypic traits. Depending on the accuracy of environmental cues and the strength of natural selection, we find different adaptation strategies represented by mappings that maximize the long-term growth rate of a population. The previously studied strategies emerge as special cases of our model: The tracking strategy is favorable when environmental cues are accurate, whereas when cues are noisy, organisms can either use an unvarying strategy or, remarkably, use the uninformative cue as a source of randomness to bet hedge. Our model of the environment-to-phenotype mapping is based on a network with hidden units; the performance of the strategies is shown to rely on having a high-dimensional internal representation, which can even be random.

Modular design: Implementing proven engineering principles in biotechnology

Modular design is at the foundation of contemporary engineering, enabling rapid, efficient, and reproducible construction and maintenance of complex systems across applications. Remarkably, modularity has recently been discovered as a governing principle in natural biological systems from genes to proteins to complex networks within a cell and organism communities. The convergent knowledge of natural and engineered modular systems provides a key to drive modern biotechnology to address emergent challenges associated with health, food, energy, and the environment. Here, we first present the theory and application of modular design in traditional engineering fields. We then discuss the significance and impact of modular architectures on systems biology and biotechnology. Next, we focus on the very recent theoretical and experimental advances in modular cell engineering that seeks to enable rapid and systematic development of microbial catalysts capable of efficiently synthesizing a large space of useful chemicals. We conclude with an outlook towards theoretical and practical opportunities for a more systematic and effective application of modular cell engineering in biotechnology.

A system-wide network reconstruction of gene regulation and metabolism in Escherichia coli

Genome-scale metabolic models have become a fundamental tool for examining metabolic principles. However, metabolism is not solely characterized by the underlying biochemical reactions and catalyzing enzymes, but also affected by regulatory events. Since the pioneering work of Covert and co-workers as well as Shlomi and co-workers it is debated, how regulation and metabolism synergistically characterize a coherent cellular state. The first approaches started from metabolic models, which were extended by the regulation of the encoding genes of the catalyzing enzymes. By now, bioinformatics databases in principle allow addressing the challenge of integrating regulation and metabolism on a system-wide level. Collecting information from several databases we provide a network representation of the integrated gene regulatory and metabolic system for Escherichia coli, including major cellular processes, from metabolic processes via protein modification to a variety of regulatory events. Besides transcriptional regulation, we also take into account regulation of translation, enzyme activities and reactions. Our network model provides novel topological characterizations of system components based on their positions in the network. We show that network characteristics suggest a representation of the integrated system as three network domains (regulatory, metabolic and interface networks) instead of two. This new three-domain representation reveals the structural centrality of components with known high functional relevance. This integrated network can serve as a platform for understanding coherent cellular states as active subnetworks and to elucidate crossover effects between metabolism and gene regulation.

Representing the Process of Inflammation as Key Events in Adverse Outcome Pathways

Inflammation is an important biological process involved in many target organ toxicities. However, there has been little consensus on how to represent inflammatory processes using the adverse outcome pathway (AOP) framework. In particular, there were concerns that inflammation was not being represented in a way that it would be recognized as a highly connected, central node within the global AOP network. The consideration of salient features common to the inflammatory process across tissues was used as a basis to propose 3 hub key events (KEs) for use in AOP network development. Each event, "tissue resident cell activation", "increased pro-inflammatory mediators", and "leukocyte recruitment/activation," is viewed as a hallmark of inflammation, independent of tissue, and can be independently measured. Using these proposed hub KEs, it was possible to link together a series of AOPs that previously had no shared KEs. Significant challenges remain with regard to accurate prediction of inflammation-related toxicological outcomes even if a broader and more connected network of inflammation-centered AOPs is developed. Nonetheless, the current proposal addresses one of the major hurdles associated with representation of inflammation in AOPs and may aid fit-for-purpose evaluations of other AOPs operating in a network context.

A predictive computational model reveals that GIV/girdin serves as a tunable valve for EGFR-stimulated cyclic AMP signals

Cellular levels of the versatile second messenger cyclic (c)AMP are regulated by the antagonistic actions of the canonical G protein → adenylyl cyclase pathway that is initiated by G-protein–coupled receptors (GPCRs) and attenuated by phosphodiesterases (PDEs). Dysregulated cAMP signaling drives many diseases; for example, its low levels facilitate numerous sinister properties of cancer cells. Recently, an alternative paradigm for cAMP signaling has emerged in which growth factor–receptor tyrosine kinases (RTKs; e.g., EGFR) access and modulate G proteins via a cytosolic guanine-nucleotide exchange modulator (GEM), GIV/girdin; dysregulation of this pathway is frequently encountered in cancers. In this study, we present a network-based compartmental model for the paradigm of GEM-facilitated cross-talk between RTKs and G proteins and how that impacts cellular cAMP. Our model predicts that cross-talk between GIV, Gα_s, and Gα_i proteins dampens ligand-stimulated cAMP dynamics. This prediction was experimentally verified by measuring cAMP levels in cells under different conditions. We further predict that the direct proportionality of cAMP concentration as a function of receptor number and the inverse proportionality of cAMP concentration as a function of PDE concentration are both altered by GIV levels. Taking these results together, our model reveals that GIV acts as a tunable control valve that regulates cAMP flux after growth factor stimulation. For a given stimulus, when GIV levels are high, cAMP levels are low, and vice versa. In doing so, GIV modulates cAMP via mechanisms distinct from the two most often targeted classes of cAMP modulators, GPCRs and PDEs.

MoVE identifies metabolic valves to switch between phenotypic states

Metabolism is highly regulated, allowing for robust and complex behavior. This behavior can often be achieved by controlling a small number of important metabolic reactions, or metabolic valves. Here, we present a method to identify the location of such valves: the metabolic valve enumerator (MoVE). MoVE uses a metabolic model to identify genetic intervention strategies which decouple two desired phenotypes. We apply this method to identify valves which can decouple growth and production to systematically improve the rate and yield of biochemical production processes. We apply this algorithm to the production of diverse compounds and obtained solutions for over 70% of our targets, identifying a small number of highly represented valves to achieve near maximal growth and production. MoVE offers a systematic approach to identify metabolic valves using metabolic models, providing insight into the architecture of metabolic networks and accelerating the widespread implementation of dynamic flux redirection in diverse systems.

Metabolic Determinants of Enzyme Evolution in a Genome-Scale Bacterial Metabolic Network

Different genes and proteins evolve at very different rates. To identify the factors that explain these differences is an important aspect of research in molecular evolution. One such factor is the role a protein plays in a large molecular network. Here, we analyze the evolutionary rates of enzyme-coding genes in the genome-scale metabolic network of Escherichia coli to find the evolutionary constraints imposed by the structure and function of this complex metabolic system. Central and highly connected enzymes appear to evolve more slowly than less connected enzymes, but we find that they do so as a by-product of their high abundance, and not because of their position in the metabolic network. In contrast, enzymes catalyzing reactions with high metabolic flux-high substrate to product conversion rates-evolve slowly even after we account for their abundance. Moreover, enzymes catalyzing reactions that are difficult to by-pass through alternative pathways, such that they are essential in many different genetic backgrounds, also evolve more slowly. Our analyses show that an enzyme's role in the function of a metabolic network affects its evolution more than its place in the network's structure. They highlight the value of a system-level perspective for studies of molecular evolution.

Rapid sample preparation with Lyse-It® for Listeria monocytogenes and Vibrio cholerae

Sample preparation is a leading bottleneck in rapid detection of pathogenic bacteria. Here, we use Lyse-It^® for bacterial cellular lysis, genomic DNA fragmentation, and protein release and degradation for both Listeria monocytogenes and Vibrio cholerae. The concept of Lyse-It^® employs a conventional microwave and Lyse-It^® slides for intensely focused microwave irradiation onto the sample. High microwave power and a <60 second irradiation time allow for rapid cellular lysis and subsequent intracellular component release. The pathogenic bacteria are identified by quantitative polymerase chain reaction (qPCR), which subsequently demonstrates the viability of DNA for amplification post microwave-induced lysis. Intracellular component release, degradation, and detection of L. monocytogenes and V. cholerae has been performed and shown in this paper. These results demonstrate a rapid, low-cost, and efficient way for bacterial sample preparation on both food and water-borne Gram-positive and -negative organisms alike.

A Link Between Alzheimer's and Type II Diabetes Mellitus? Ca+2 -Mediated Signal Control and Protein Localization

We propose protein localization dependent signal activation (PLDSA) as a model to describe pre-existing protein partitioning between the cytosol, and membrane surface, as a means to modulate signal activation, specificity, and robustness. We apply PLDSA to explain possible molecular links between type II diabetes mellitus (T2DM) and Alzheimer's disease (AD) by describing Ca+2 -mediated interactions between the Src non-receptor tyrosine kinase and p52Shc adaptor protein. We suggest that these interactions may serve as a contributing factor to disease development and progression. In particular, we propose that signaling response is regulated, in part, by Ca+2 -mediated partitioning of lipid-bound and soluble forms of Src and p52shc. Thus, protein-protein interactions that drive signaling in response to extracellular ligand binding are also mediated by partitioning of signaling proteins between membrane-bound and soluble populations. We propose that PLDSA effects may explain, in part, the evolutionary basis of promiscuous protein interaction domains and their importance in cellular function.

Adverse outcome pathway networks II: Network analytics

Toxicological responses to stressors are more complex than the simple one-biological-perturbation to one-adverse-outcome model portrayed by individual adverse outcome pathways (AOPs). Consequently, the AOP framework was designed to facilitate de facto development of AOP networks that can aid in the understanding and prediction of pleiotropic and interactive effects more common to environmentally realistic, complex exposure scenarios. The present study introduces nascent concepts related to the qualitative analysis of AOP networks. First, graph theory-based approaches for identifying important topological features are illustrated using 2 example AOP networks derived from existing AOP descriptions. Second, considerations for identifying the most significant path(s) through an AOP network from either a biological or risk assessment perspective are described. Finally, approaches for identifying interactions among AOPs that may result in additive, synergistic, or antagonistic responses (or previously undefined emergent patterns of response) are introduced. Along with a companion article (part I), these concepts set the stage for the development of tools and case studies that will facilitate more rigorous analysis of AOP networks, and the utility of AOP network-based predictions, for use in research and regulatory decision-making. The present study addresses one of the major themes identified through a Society of Environmental Toxicology and Chemistry Horizon Scanning effort focused on advancing the AOP framework. Environ Toxicol Chem 2018;37:1734-1748. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

A Single-Domain Response Regulator Functions as an Integrating Hub To Coordinate General Stress Response and Development in Alphaproteobacteria

The alphaproteobacterial general stress response is governed by a conserved partner-switching mechanism that is triggered by phosphorylation of the response regulator PhyR. In the model organism Caulobacter crescentus, PhyR was proposed to be phosphorylated by the histidine kinase PhyK, but biochemical evidence in support of such a role of PhyK is missing. Here, we identify a single-domain response regulator, MrrA, that is essential for general stress response activation in C. crescentus We demonstrate that PhyK does not function as a kinase but accepts phosphoryl groups from MrrA and passes them on to PhyR, adopting the role of a histidine phosphotransferase. MrrA is phosphorylated by at least six histidine kinases that likely serve as stress sensors. MrrA also transfers phosphate to LovK, a histidine kinase involved in C. crescentus holdfast production and attachment, which also negatively regulates the general stress response. We show that LovK together with the response regulator LovR acts as a phosphate sink to redirect phosphate flux away from the PhyKR branch. In agreement with the biochemical data, an mrrA mutant is unable to activate the general stress response and shows a hyperattachment phenotype, which is linked to decreased expression of the major holdfast inhibitory protein HfiA. We propose that MrrA serves as a central phosphorylation hub that coordinates the general stress response with C. crescentus development and other adaptive behaviors. The characteristic bow-tie architecture of this phosphorylation network with MrrA as the central knot may expedite the evolvability and species-specific niche adaptation of this group of bacteria.IMPORTANCE Two-component systems (TCSs) consisting of a histidine kinase and a cognate response regulator are predominant signal transduction systems in bacteria. To avoid cross talk, TCSs are generally thought to be highly insulated from each other. However, this notion is based largely on studies of the HisKA subfamily of histidine kinases, while little information is available for the HWE and HisKA2 subfamilies. The latter have been implicated in the alphaproteobacterial general stress response. Here, we show that in the model organism Caulobacter crescentus an atypical FATGUY-type single-domain response regulator, MrrA, is highly promiscuous in accepting and transferring phosphoryl groups from and to multiple up- and downstream kinases, challenging the current view of strictly insulated TCSs. Instead, we propose that FATGUY response regulators have evolved in alphaproteobacteria as central phosphorylation hubs to broadly sample information and distribute phosphoryl groups between the general stress response pathway and other TCSs, thereby coordinating multiple cellular behaviors.

Calcium as a signal integrator in developing epithelial tissues

Decoding how tissue properties emerge across multiple spatial and temporal scales from the integration of local signals is a grand challenge in quantitative biology. For example, the collective behavior of epithelial cells is critical for shaping developing embryos. Understanding how epithelial cells interpret a diverse range of local signals to coordinate tissue-level processes requires a systems-level understanding of development. Integration of multiple signaling pathways that specify cell signaling information requires second messengers such as calcium ions. Increasingly, specific roles have been uncovered for calcium signaling throughout development. Calcium signaling regulates many processes including division, migration, death, and differentiation. However, the pleiotropic and ubiquitous nature of calcium signaling implies that many additional functions remain to be discovered. Here we review a selection of recent studies to highlight important insights into how multiple signals are transduced by calcium transients in developing epithelial tissues. Quantitative imaging and computational modeling have provided important insights into how calcium signaling integration occurs. Reverse-engineering the conserved features of signal integration mediated by calcium signaling will enable novel approaches in regenerative medicine and synthetic control of morphogenesis.

Green function of correlated genes in a minimal mechanical model of protein evolution

The function of proteins arises from cooperative interactions and rearrangements of their amino acids, which exhibit large-scale dynamical modes. Long-range correlations have also been revealed in protein sequences, and this has motivated the search for physical links between the observed genetic and dynamic cooperativity. We outline here a simplified theory of protein, which relates sequence correlations to physical interactions and to the emergence of mechanical function. Our protein is modeled as a strongly coupled amino acid network with interactions and motions that are captured by the mechanical propagator, the Green function. The propagator describes how the gene determines the connectivity of the amino acids and thereby, the transmission of forces. Mutations introduce localized perturbations to the propagator that scatter the force field. The emergence of function is manifested by a topological transition when a band of such perturbations divides the protein into subdomains. We find that epistasis-the interaction among mutations in the gene-is related to the nonlinearity of the Green function, which can be interpreted as a sum over multiple scattering paths. We apply this mechanical framework to simulations of protein evolution and observe long-range epistasis, which facilitates collective functional modes.