Biological Signaling Networks Research Articles

Photoswitchable Cascades for Allosteric and Bidirectional Control over Covalent Bonds and Assemblies.

Studies of complex systems and emerging properties to mimic biosystems are at the forefront of chemical research. Dynamic multistep cascades, especially those exhibiting allosteric regulation, are challenging. Herein, we demonstrate a versatile platform of photoswitchable covalent cascades toward remote and bidirectional control of reversible covalent bonds and ensuing assemblies. The relay of a photochromic switch, keto-enol equilibrium, and ring-chain equilibrium allows light-mediated reversible allosteric structural changes. The accompanying distinct reactivity further enables photoswitchable dynamic covalent bonding and release of substrates bidirectionally through alternating two wavelengths of light, essentially realizing light-mediated signaling cycles. The downfall of energy by covalent bond formation/scission upon photochemical reactions offers the driving force for the controlled direction of the cascade. To show the molecular diversity, photoswitchable on-demand assembly/disassembly of covalent polymers, including structurally reconfigurable polymers, was realized. This work achieves photoswitchable allosteric regulation of covalent architectures within dynamic multistep cascades, which has rarely been reported before. The results resemble allosteric control within biological signaling networks and should set the stage for many endeavors, such as dynamic assemblies, molecular motors, responsive polymers, and intelligent materials.

Physical exercise plays a role in rebalancing the bile acids of enterohepatic axis in non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is considered as one of the most common diseases of lipid metabolism disorders, which is closely related to bile acids disorders and gut microbiota disorders. Bile acids are synthesized from cholesterol in the liver, and processed by gut microbiota in intestinal tract, and participate in metabolic regulation through the enterohepatic circulation. Bile acids not only promote the consumption and absorption of intestinal fat but also play an important role in biological metabolic signaling network, affecting fat metabolism and glucose metabolism. Studies have demonstrated that exercise plays an important role in regulating the composition and function of bile acid pool in enterohepatic axis, which maintains the homeostasis of the enterohepatic circulation and the health of the host gut microbiota. Exercise has been recommended by several health guidelines as the first-line intervention for patients with NAFLD. Can exercise alter bile acids through the microbiota in the enterohepatic axis? If so, regulating bile acids through exercise may be a promising treatment strategy for NAFLD. However, the specific mechanisms underlying this potential connection are largely unknown. Therefore, in this review, we tried to review the relationship among NAFLD, physical exercise, bile acids, and gut microbiota through the existing data and literature, highlighting the role of physical exercise in rebalancing bile acid and microbial dysbiosis.

Cotton Ti-IMAC: Developing Phosphorylated Cotton as a Novel Platform for Phosphopeptide Enrichment.

Protein phosphorylation is an important post-translational modification (PTM), which is involved in many important cellular functions. Understanding protein phosphorylation at the molecular level is critical to deciphering its relevant biological processes and signaling networks. Mass spectrometry (MS) has become a powerful tool for the comprehensive profiling of protein phosphorylation. Yet the low ionization efficiency and low abundance of phosphopeptides among complex biological samples make its MS analysis challenging; an enrichment strategy with high efficiency and selectivity is always necessary prior to MS analysis. In this study, we developed a phosphorylated cotton-fiber-based Ti(IV)-IMAC material (termed as Cotton Ti-IMAC) that can serve as a novel platform for phosphopeptide enrichment. The cotton fiber can be effectively grafted with phosphate groups covalently in a single step, where the titanium ions can then be immobilized to enable capturing phosphopeptides. The material can be prepared using cost-effective reagents within only 4 h. Benefiting from the flexibility and filterability of cotton fibers, the material can be easily packed as a spin-tip and make the enrichment process convenient. Cotton Ti-IMAC successfully enriched phosphopeptides from protein standard digests and exhibited a high selectivity (BSA/β-casein = 1000:1) and excellent sensitivity (0.1 fmol/μL). Moreover, 2354 phosphopeptides were profiled in one LC-MS/MS injection after enriching from only 100 μg of HeLa cell digests with an enrichment specificity of up to 97.51%. Taken together, we believe that Cotton Ti-IMAC can serve as a widely applicable and robust platform for achieving large-scale phosphopeptide enrichment and expanding our knowledge of phosphoproteomics in complex biological systems.

Predictive modeling of signal transduction anchored by quantitative imaging.

Signal transduction is challenging to investigate because it depends upon both spatial organization of signaling and receptor molecules and alterations of gene expression spanning multiple time and space scales. We argue that mathematical modeling provides the tools to build upon the results from quantitative imaging to analyze and interpret cell signaling across these scales. Quantitative imaging tools such as fluorescence fluctuation spectroscopy methods provide many of the key parameters needed for predictive modeling, including measurements of: protein mobility, oligomerization, and concentrations, as functions of time and space. Comprehensive predictive models can integrate such multi-dimensional and multi-scale data into frameworks that can be used to expand our understanding of biological signaling networks.

The roles of eicosanoids in myocardial diseases.

Myocardial disease, the abnormalities of the cardiac muscle, is the leading cause of death in humans. Eicosanoids represent a large spectrum of lipid mediators with critical roles in physiological and pathophysiological conditions. Arachidonic acid (AA) is the major resource of eicosanoids and is metabolized via cyclooxygenases (COXs), lipoxygenases (LOXs), and cytochrome P450 (CYP) enzymes producing a diverse family of lipid mediators called eicosanoids, including prostanoids, leukotrienes (LTs), epoxyeicosatrienoic acids (EETs), dihydroxyeicosatetraenoic acid (diHETEs), eicosatetraenoic acids (ETEs), and lipoxins (LXs). Beyond the well-established roles of eicosanoids in inflammation and vascular biology, a growing body of evidence showed that eicosanoids, especially CYP450 derived eicosanoids EETs, are preventive and therapeutic targets for many of the myocardial diseases. EETs not only ameliorate the cardiac injury and remodeling in different pathological models, but also attenuate subsequent hemodynamic disturbances and cardiac dysfunction. EETs have direct and indirect protective properties in the myocardium, and thus relieve dietetic cardiomyopathy and inflammatory cardiomyopathy. Moreover, EETs are capable to attenuate the ischemic cardiomyopathy, including the myocardial infarction and cardiac ischemic reperfusion injury. Multiple biological events and signaling networks are targeted during the myocardial protection of EETs, these are including mitochondria hemostasis, angiogenesis, oxidative stress, inflammatory response, metabolic regulation, endoplasmic reticulum (ER) stress and cell death. Additionally, eicosanoids from COX and LOX also have important roles in some of the myocardial diseases, such as cardiac hypertrophy and ischemic heart disease. This chapter summarizes the physiological and pathophysiological significance, and the signal mechanisms of the eicosanoids, especially the EETs, in myocardial diseases.

Influence maximization in Boolean networks

The optimization problem aiming at the identification of minimal sets of nodes able to drive the dynamics of Boolean networks toward desired long-term behaviors is central for some applications, as for example the detection of key therapeutic targets to control pathways in models of biological signaling and regulatory networks. Here, we develop a method to solve such an optimization problem taking inspiration from the well-studied problem of influence maximization for spreading processes in social networks. We validate the method on small gene regulatory networks whose dynamical landscapes are known by means of brute-force analysis. We then systematically study a large collection of gene regulatory networks. We find that for about 65% of the analyzed networks, the minimal driver sets contain less than 20% of their nodes.

A Structural Characterisation of the Mitogen-Activated Protein Kinase Network in Cancer

Gene regulatory networks represent collections of regulators that interact with each other and with other molecules to govern gene expression. Biological signalling networks model how signals are transmitted and how activities are coordinated in the cell. The study of the structure of such networks in complex diseases such as cancer can provide insights into how they function, and consequently, suggest suitable treatment approaches. Here, we explored such topological characteristics in the example of a mitogen-activated protein kinase (MAPK) signalling network derived from published studies in cancer. We employed well-established techniques to conduct network analyses, and collected information on gene function as obtained from large-scale public databases. This allowed us to map topological and functional relationships, and build hypotheses on this network’s functional consequences. In particular, we find that the topology of this MAPK network is highly non-random, modular and robust. Moreover, analysis of the network’s structure indicates the presence of organisational features of cancer hallmarks, expressed in an asymmetrical manner across communities of the network. Finally, our results indicate that the organisation of this network renders it problematic to use treatment approaches that focus on a single target. Our analysis suggests that multi-target attacks in a well-orchestrated manner are required to alter how the network functions. Overall, we propose that complex network analyses combined with pharmacological insights will help inform on future treatment strategies, exploiting structural vulnerabilities of signalling and regulatory networks in cancer.

SNAREs Regulate Vesicle Trafficking During Root Growth and Development.

SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) proteins assemble to drive the final membrane fusion step of membrane trafficking. Thus, SNAREs are essential for membrane fusion and vesicular trafficking, which are fundamental mechanisms for maintaining cellular homeostasis. In plants, SNAREs have been demonstrated to be located in different subcellular compartments and involved in a variety of fundamental processes, such as cytokinesis, cytoskeleton organization, symbiosis, and biotic and abiotic stress responses. In addition, SNAREs can also contribute to the normal growth and development of Arabidopsis. Here, we review recent progress in understanding the biological functions and signaling network of SNAREs in vesicle trafficking and the regulation of root growth and development in Arabidopsis.

Graph neural networks for biochemistry that incorporate substructure

The success of neural networks has been considerable interest in applying neural networks to biological and chemical data. Often, this data is graph-like in nature: chemical structures, biological signalling networks, and cytoskeletons all take the form of graphs. Most attempts to apply neural networks to this data have used generic graph neural network architectures such as message-passing neural networks. However, these networks are strictly limited in their expressivity. Moreover, there is no obvious way to include our knowledge of important biochemical structures into the network.

Comprehensive transcriptome analysis of erythroid differentiation potential of olive leaf in haematopoietic stem cells.

Anaemia is one of the leading causes of disability in young adults and is associated with increased morbidity and mortality in elderly. With a global target to reduce the disease burden of anaemia, recent researches focus on novel compounds with the ability to induce erythropoiesis and regulate iron homeostasis. We aimed to explore the biological events and potential polypharmacological effects of water‐extracted olive leaf (WOL) on human bone marrow–derived haematopoietic stem cells (hHSCs) using a comprehensive gene expression analysis. HPLC analysis identifies six bioactive polyphenols in the WOL. Treatment with WOL for 12 days regulated gene expressions related to erythroid differentiation, oxygen homeostasis, iron homeostasis, haem metabolism and Hb biosynthesis in hHSCs. Functional clustering analysis reveals several major functions of WOL such as ribosomal biogenesis and mitochondrial translation machinery, glycolytic process, ATP biosynthesis and immune response. Additionally, the colonies of both primitive and mature erythroid progenitors, CFU‐E and BFU‐E, were significantly increased in WOL‐treated hHSCs. The expressions of erythroid markers, CD47, glycophorin A (GYPA), and transferrin receptor (TFRC) and adult Hb subunits‐HBA and HBB were also confirmed in immunofluorescent staining and flow cytometer analysis in WOL‐treated hHSCs. It is well known that induction of lineage‐specific differentiation, as well as the maturation of early haematopoietic precursors into fully mature erythrocytes, involves multiple simultaneous biological events and complex signalling networks. In this regard, our genome‐wide transcriptome profiling with microarray study on WOL‐treated hHSCs provides general insights into the multitarget prophylactic and/or therapeutic potential of WOL in anaemia and other haematological disorders.

Mixed volume of small reaction networks

An important invariant of a chemical reaction network is its maximum number of positive steady states. This number, however, is in general difficult to compute. Nonetheless, there is an upper bound on this number -- namely, a network's mixed volume -- that is easy to compute. Moreover, recent work has shown that, for certain biological signaling networks, the mixed volume does not greatly exceed the maximum number of positive steady states. Continuing this line of research, we further investigate this overcount and also compute the mixed volumes of small networks, those with only a few species or reactions.

E-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks.

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.

Brassinosteroids Regulate Circadian Oscillation via the BES1/TPL-CCA1/LHY Module in Arabidopsisthaliana.

SummaryBrassinosteroids (BRs) regulate a variety of physiological processes in plants via extensive crosstalk with diverse biological signaling networks. Although BRs are known to reciprocally regulate circadian oscillation, the molecular mechanism underlying BR-mediated regulation of circadian clock remains unknown. Here, we demonstrate that the BR-activated transcription factor bri1-EMS-SUPPRESSOR 1 (BES1) integrates BR signaling into the circadian network in Arabidopsis. BES1 repressed expression of CIRCADIAN CLOCK-ASSOCIATED 1 (CCA1) and LATE ELONGATED HYPOCOTYL (LHY) at night by binding to their promoters, together with TOPLESS (TPL). The repression of CCA1 and LHY by BR treatment, which occurred during the night, was compromised in bes1-ko and tpl-8 mutants. Consistently, long-term treatment with BR shortened the circadian period, and BR-induced rhythmic shortening was impaired in bes1-ko and tpl-8 single mutants and in the cca1-1lhy-21 double mutant. Overall, BR signaling is conveyed to the circadian oscillator via the BES1/TPL-CCA1/LHY module, contributing to gating diurnal BR responses in plants.

Using activity time windows and logical representation to reduce the complexity of biological network models: G1/S checkpoint pathway with DNA damage

Biological systems are difficult to understand complex systems. Scientists continue to create models to simulate biological systems but these models are complex too; for this reason, new reduction methods to simplify complex biological models into simpler ones are increasingly needed. In this paper, we present a way of reducing complex quantitative (continuous) models into logical models based on time windows of system activity and logical (Boolean) models. Time windows were used to define slow and fast activity areas. We use the proposed approach to reduce a continuous ODE model into a logical model describing the G1/S checkpoint with and without DNA damage as a case study. We show that the temporal unfolding of this signalling system can be broken down into three time windows where only two display high level of activity and the other shows little or no activity. The two active windows represent a cell committing to cell cycle and making the G1/S transition, respectively, the two most important high level functions of cell cycle in the G1 phase. Therefore, we developed two models to represent these time windows to reduce time complexity and used Boolean approach to reduce interaction complexity in the ODE model in the respective time windows. The developed reduced models correctly produced the commitment to cell cycle and G1/S transfer through the expected behavior of signalling molecules involved in these processes. As most biological models have a large number of fast reactions and a relatively smaller number of slow reactions, we believe that the proposed approach could be suitable for representing many, if not all biological signalling networks. The approach presented in this study greatly helps in simplifying complex continuous models (ODE models) into simpler models. Moreover, it will also assist scientists build models concentrating on understanding and representing system behavior rather than setting values for a large number of kinetic parameters.

Estimating information in time-varying signals.

Across diverse biological systems—ranging from neural networks to intracellular signaling and genetic regulatory networks—the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data. Here we develop and evaluate decoding-based estimation methods to lower bound the mutual information about a finite set of inputs, encoded in single-cell high-dimensional time series data. For biological reaction networks governed by the chemical Master equation, we derive model-based information approximations and analytical upper bounds, against which we benchmark our proposed model-free decoding estimators. In contrast to the frequently-used k-nearest-neighbor estimator, decoding-based estimators robustly extract a large fraction of the available information from high-dimensional trajectories with a realistic number of data samples. We apply these estimators to previously published data on Erk and Ca2+ signaling in mammalian cells and to yeast stress-response, and find that substantial amount of information about environmental state can be encoded by non-trivial response statistics even in stationary signals. We argue that these single-cell, decoding-based information estimates, rather than the commonly-used tests for significant differences between selected population response statistics, provide a proper and unbiased measure for the performance of biological signaling networks.

Oscillations and bistability in a model of ERK regulation.

This work concerns the question of how two important dynamical properties, oscillations and bistability, emerge in an important biological signaling network. Specifically, we consider a model for dual-site phosphorylation and dephosphorylation of extracellular signal-regulated kinase (ERK). We prove that oscillations persist even as the model is greatly simplified (reactions are made irreversible and intermediates are removed). Bistability, however, is much less robust-this property is lost when intermediates are removed or even when all reactions are made irreversible. Moreover, bistability is characterized by the presence of two reversible, catalytic reactions: as other reactions are made irreversible, bistability persists as long as one or both of the specified reactions is preserved. Finally, we investigate the maximum number of steady states, aided by a network's "mixed volume" (a concept from convex geometry). Taken together, our results shed light on the question of how oscillations and bistability emerge from a limiting network of the ERK network-namely, the fully processive dual-site network-which is known to be globally stable and therefore lack both oscillations and bistability. Our proofs are enabled by a Hopf bifurcation criterion due to Yang, analyses of Newton polytopes arising from Hurwitz determinants, and recent characterizations of multistationarity for networks having a steady-state parametrization.

Mixing Times and Structural Inference for Bernoulli Autoregressive Processes

We introduce a novel multivariate random process producing Bernoulli outputs per dimension, that can possibly formalize binary interactions in various graphical structures and can be used to model opinion dynamics, epidemics, financial and biological time series data, etc. We call this a Bernoulli Autoregressive Process (BAR). A BAR process models a discrete-time vector random sequence of $p$ scalar Bernoulli processes with autoregressive dynamics and corresponds to a particular Markov Chain. The benefit from the autoregressive dynamics is the description of a $2^p\times 2^p$ transition matrix by at most $pd$ effective parameters for some $d\ll p$ or by two sparse matrices of dimensions $p\times p^2$ and $p\times p$, respectively, parameterizing the transitions. Additionally, we show that the BAR process mixes rapidly, by proving that the mixing time is $O(\log p)$. The hidden constant in the previous mixing time bound depends explicitly on the values of the chain parameters and implicitly on the maximum allowed in-degree of a node in the corresponding graph. For a network with $p$ nodes, where each node has in-degree at most $d$ and corresponds to a scalar Bernoulli process generated by a BAR, we provide a greedy algorithm that can efficiently learn the structure of the underlying directed graph with a sample complexity proportional to the mixing time of the BAR process. The sample complexity of the proposed algorithm is nearly order-optimal as it is only a $\log p$ factor away from an information-theoretic lower bound. We present simulation results illustrating the performance of our algorithm in various setups, including a model for a biological signaling network.

Towards understanding pressure ulcer formation: Coupling an inflammation regulatory network to a tissue scale finite element model

Pressure ulcers (PUs) are a major clinical concern affecting more than 2.5 million adults annually in the US. Ischemia is recognized as one of the most important contributors to ulcer formation, triggering a sterile inflammatory cascade that culminates in necrosis of native skin cells and ulceration. While there is a general understanding of the biological elements involved in this process and their interdependence within the biological PU signaling network, this system’s spatio-temporal dynamics have not yet been studied in detail. Here we first present a 0D mathematical description of the PU regulatory network consisting of two cell types – keratinocytes and neutrophils- and six chemical species – TNFα, KC, ROS, DAMPs, O2 and XO. We extend this regulatory network from a set of ordinary differential equations to realistic spatial domains by coupling each species’ dynamic equations to reaction diffusion partial differential equations. Furthermore, we couple this model to mechanical deformation of the spatial domain by including a pressure-sensitive oxygen perfusion term. The total model provides solutions to the regulatory network dynamics at the tissue scale with spatio-temporal detail on the evolution of each species. Among other features, the model correctly predicts patterns of PU formation in response to moderate loads, as seen clinically and experimentally. In conclusion, this work extends our current PU modeling capabilities and improves our understanding of PUs by carefully analyzing the motifs of the regulatory network and by exploring the implications of coupling this system to a tissue scale model of skin mechanics and oxygen perfusion.

Real-Time Genetic Compensation Defines the Dynamic Demands of Feedback Control

Biological signaling networks use feedback control to dynamically adjust their operation in real time. Traditional static genetic methods such as gene knockouts or rescue experiments can often identify the existence of feedback interactions but are unable to determine what feedback dynamics are required. Here, we implement a new strategy, closed-loop optogenetic compensation (CLOC), to address this problem. Using a custom-built hardware and software infrastructure, CLOC monitors, in real time, the output of a pathway deleted for a feedback regulator. A minimal model uses these measurements to calculate and deliver-on the fly-an optogenetically enabled transcriptional input designed to compensate for the effects of the feedback deletion. Application of CLOC to the yeast pheromone response pathway revealed surprisingly distinct dynamic requirements for three well-studied feedback regulators. CLOC, a marriage of control theory and traditional genetics, presents a broadly applicable methodology for defining the dynamic function of biological feedback regulators.

Co-imaging extrinsic, intrinsic and effector caspase activity by fluorescence anisotropy microscopy

In order to overcome intercellular variability and thereby effectively assess signal propagation in biological networks it is imperative to simultaneously quantify multiple biological observables in single living cells. While fluorescent biosensors have been the tool of choice to monitor the dynamics of protein interaction and enzymatic activity, co-measuring more than two of them has proven challenging. In this work, we designed three spectrally separated anisotropy-based Förster Resonant Energy Transfer (FRET) biosensors to overcome this difficulty. We demonstrate this principle by monitoring the activation of extrinsic, intrinsic and effector caspases upon apoptotic stimulus. Together with modelling and simulations we show that time of maximum activity for each caspase can be derived from the anisotropy of the corresponding biosensor. Such measurements correlate relative activation times and refine existing models of biological signalling networks, providing valuable insight into signal propagation.