An operational view of intercellular signaling pathways

Molecular principles underlying aggressive cancers

Aggressive tumors pose ultra-challenges to drug resistance. Anti-cancer treatments are often unsuccessful, and single-cell technologies to rein drug resistance mechanisms are still fruitless. The National Cancer Institute defines aggressive cancers at the tissue level, describing them as those that spread rapidly, despite severe treatment. At the molecular, foundational level, the quantitative biophysics discipline defines aggressive cancers as harboring a large number of (overexpressed, or mutated) crucial signaling proteins in major proliferation pathways populating their active conformations, primed for their signal transduction roles. This comprehensive review explores highly aggressive cancers on the foundational and cell signaling levels, focusing on the differences between highly aggressive cancers and the more treatable ones. It showcases aggressive tumors as harboring massive, cancer-promoting, catalysis-primed oncogenic proteins, especially through certain overexpression scenarios, as predisposed aggressive tumor candidates. Our examples narrate strong activation of ERK1/2, and other oncogenic proteins, through malfunctioning chromatin and crosslinked signaling, and how they activate multiple proliferation pathways. They show the increased cancer heterogeneity, plasticity, and drug resistance. Our review formulates the principles underlying cancer aggressiveness on the molecular level, discusses scenarios, and describes drug regimen (single drugs and drug combinations) for PDAC, NSCLC, CRC, HCC, breast and prostate cancers, glioblastoma, neuroblastoma, and leukemia as examples. All show overexpression scenarios of master transcription factors, transcription factors with gene fusions, copy number alterations, dysregulation of the epigenetic codes and epithelial-to-mesenchymal transitions in aggressive tumors, as well as high mutation loads of vital upstream signaling regulators, such as EGFR, c-MET, and K-Ras, befitting these principles.

Photoreceptor-Like Signal Transduction Between Polymer-Based Protocells

Deciphering inter- and intracellular signaling pathways is pivotal for understanding the intricate communication networks that orchestrate life's dynamics. Communication models involving bottom-up construction of protocells are emerging but often lack specialized compartments sufficiently robust and hierarchically organized to perform spatiotemporally defined signaling. Here, the modular construction of communicating polymer-based protocells designed to mimic the transduction of information in retinal photoreceptors is presented. Microfluidics is used to generate polymeric protocells subcompartmentalized by specialized artificial organelles. In one protocell population, light triggers artificial organelles with membrane-embedded photoresponsive rotary molecular motors to set off a sequence of reactions starting with the release of encapsulated signaling molecules into the lumen. Intercellular communication is mediated by signal transfer across membranes to protocells containing catalytic artificial organelles as subcompartments, whose signal conversion can be modulated by environmental calcium. Signal propagation also requires selective permeability of the diverse compartments. By segregating artificial organelles in distinct protocells, a sequential chain of reactions mediating intercellular communication is created that is further modulated by adding extracellular messengers. This connective behavior offers the potential for a deeper understanding of signaling pathways and faster integration of proto- and living cells, with the unique advantage of controlling each step by bio-relevant signals.

Bone morphogenetic protein signaling: the pathway and its regulation

In the mid-1960s, bone morphogenetic proteins (BMPs) were first identified in the extracts of bone to have the remarkable ability to induce heterotopic bone. When the Drosophila gene decapentaplegic (dpp) was first identified to share sequence similarity with mammalian BMP2/BMP4 in the late-1980s, it became clear that secreted BMP ligands can mediate processes other than bone formation. Following this discovery, collaborative efforts between Drosophila geneticists and mammalian biochemists made use of the strengths of their respective model systems to identify BMP signaling components and delineate the pathway. The ability to conduct genetic modifier screens in Drosophila with relative ease was critical in identifying the intracellular signal transducers for BMP signaling and the related transforming growth factor-beta/activin signaling pathway. Such screens also revealed a host of genes that encode other core signaling components and regulators of the pathway. In this review, we provide a historical account of this exciting time of gene discovery and discuss how the field has advanced over the past 30 years. We have learned that while the core BMP pathway is quite simple, composed of 3 components (ligand, receptor, and signal transducer), behind the versatility of this pathway lies multiple layers of regulation that ensures precise tissue-specific signaling output. We provide a sampling of these discoveries and highlight many questions that remain to be answered to fully understand the complexity of BMP signaling.

A guide to ERK dynamics, part 2: downstream decoding

Signaling by the extracellular signal-regulated kinase (ERK) pathway controls many cellular processes, including cell division, death, and differentiation. In this second installment of a two-part review, we address the question of how the ERK pathway exerts distinct and context-specific effects on multiple processes. We discuss how the dynamics of ERK activity induce selective changes in gene expression programs, with insights from both experiments and computational models. With a focus on single-cell biosensor-based studies, we summarize four major functional modes for ERK signaling in tissues: adjusting the size of cell populations, gradient-based patterning, wave propagation of morphological changes, and diversification of cellular gene expression states. These modes of operation are disrupted in cancer and other related diseases and represent potential targets for therapeutic intervention. By understanding the dynamic mechanisms involved in ERK signaling, there is potential for pharmacological strategies that not only simply inhibit ERK, but also restore functional activity patterns and improve disease outcomes.

Elucidating the role of RBM5 in osteoclastogenesis: a novel potential therapeutic target for osteoporosis

Osteoporosis is a prevalent bone disease with multigene involved, and the molecular mechanisms of its pathogenesis are not entirely understood. This study aims to identify novel key genes involved in osteoporosis to discover potential pharmacological targets. We analyzed three microarray datasets and identified four differentially expressed genes. The LASSO model indicated that RNA-binding motif protein 5 (RBM5) is associated with osteoporosis and is a potential drug target. We conducted the Spearman correlation analysis and found 52 genes that were significantly related to RBM5. Enrichment analysis showed that these genes were primarily involved in RNA splicing and osteoclast differentiation pathways. By using lentivirus-based shRNA, we successfully knocked down RBM5 expression in RAW264.7 cell line, which showed that RBM5 knockdown significantly impaired their differentiation potential to mature osteoclasts and significantly inhibited bone-resorbing activity. RT-qPCR analyses revealed the expression of osteoclastogenesis marker genes was downregulated along with RBM5 expression. These findings suggest that RBM5 plays a crucial role in the pathogenesis of osteoporosis and provides a new potential pharmacological target.

Exosomes multifunctional roles in HIV-1: insight into the immune regulation, vaccine development and current progress in delivery system

Human Immunodeficiency Virus (HIV-1) is known to establish a persistent latent infection. The use of combination antiretroviral therapy (cART) can effectively reduce the viral load, but the treatment can be costly and may lead to the development of drug resistance and life-shortening side effects. It is important to develop an ideal and safer in vivo target therapy that will effectively block viral replication and expression in the body. Exosomes have recently emerged as a promising drug delivery vehicle due to their low immunogenicity, nanoscale size (30-150nm), high biocompatibility, and stability in the targeted area. Exosomes, which are genetically produced by different types of cells such as dendritic cells, neurons, T and B cells, epithelial cells, tumor cells, and mast cells, are designed for efficient delivery to targeted cells. In this article, we review and highlight recent developments in the strategy and application of exosome-based HIV-1 vaccines. We also discuss the use of exosome-based antigen delivery systems in vaccine development. HIV-1 antigen can be loaded into exosomes, and this modified cargo can be delivered to target cells or tissues through different loading approaches. This review also discusses the immunological prospects of exosomes and their role as biomarkers in disease progression. However, there are significant administrative and technological obstacles that need to be overcome to fully harness the potential of exosome drug delivery systems.

Exosome Liberation by Human Neutrophils under L-Amino Acid Oxidase of Calloselasma rhodostoma Venom Action

L-Amino acid oxidase (LAAO) is an enzyme found in snake venom that has multifaceted effects, including the generation of hydrogen peroxide (H2O2) during oxidative reactions, leading to various biological and pharmacological outcomes such as apoptosis, cytotoxicity, modulation of platelet aggregation, hemorrhage, and neutrophil activation. Human neutrophils respond to LAAO by enhancing chemotaxis, and phagocytosis, and releasing reactive oxygen species (ROS) and pro-inflammatory mediators. Exosomes cellular nanovesicles play vital roles in intercellular communication, including immune responses. This study investigates the impact of Calloselasma rhodostoma snake venom-derived LAAO (Cr-LAAO) on human neutrophil exosome release, including activation patterns, exosome formation, and content. Neutrophils isolated from healthy donors were stimulated with Cr-LAAO (100 μg/mL) for 3 h, followed by exosome isolation and analysis. Results show that Cr-LAAO induces the release of exosomes with distinct protein content compared to the negative control. Proteomic analysis reveals proteins related to the regulation of immune responses and blood coagulation. This study uncovers Cr-LAAO's ability to activate human neutrophils, leading to exosome release and facilitating intercellular communication, offering insights into potential therapeutic approaches for inflammatory and immunological disorders.

Functional and Clinical Proteomic Exploration of Pancreatic Cancer

Pancreatic cancer, most cases being pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal cancers with a median survival time of less than 6 months. Therapeutic options are very limited for PDAC patients, and surgery is still the most effective treatment, making improvements in early diagnosis critical. One typical characteristic of PDAC is the desmoplastic reaction of its stroma microenvironment, which actively interacts with cancer cells to orchestrate key components in tumorigenesis, metastasis, and chemoresistance. Global exploration of cancer-stroma crosstalk is essential to decipher PDAC biology and design intervention strategies. Over the past decade, the dramatic improvement of proteomics technologies has enabled profiling of proteins, post-translational modifications (PTMs), and their protein complexes at unprecedented sensitivity and dimensionality. Here, starting with our current understanding of PDAC characteristics, including precursor lesions, progression models, tumor microenvironment, and therapeutic advancements, we describe how proteomics contributes to the functional and clinical exploration of PDAC, providing insights into PDAC carcinogenesis, progression, and chemoresistance. We summarize recent achievements enabled by proteomics to systematically investigate PTMs-mediated intracellular signaling in PDAC, cancer-stroma interactions, and potential therapeutic targets revealed by these functional studies. We also highlight proteomic profiling of clinical tissue and plasma samples to discover and verify useful biomarkers that can aid early detection and molecular classification of patients. In addition, we introduce spatial proteomic technology and its applications in PDAC for deconvolving tumor heterogeneity. Finally, we discuss future prospects of applying new proteomic technologies in comprehensively understanding PDAC heterogeneity and intercellular signaling networks. Importantly, we expect advances in clinical functional proteomics for exploring mechanisms of cancer biology directly by high-sensitivity functional proteomic approaches starting from clinical samples.

The implications of alternative pre-mRNA splicing in cell signal transduction

Cells produce multiple mRNAs through alternative splicing, which ensures proteome diversity. Because most human genes undergo alternative splicing, key components of signal transduction pathways are no exception. Cells regulate various signal transduction pathways, including those associated with cell proliferation, development, differentiation, migration, and apoptosis. Since proteins produced through alternative splicing can exhibit diverse biological functions, splicing regulatory mechanisms affect all signal transduction pathways. Studies have demonstrated that proteins generated by the selective combination of exons encoding important domains can enhance or attenuate signal transduction and can stably and precisely regulate various signal transduction pathways. However, aberrant splicing regulation via genetic mutation or abnormal expression of splicing factors negatively affects signal transduction pathways and is associated with the onset and progression of various diseases, including cancer. In this review, we describe the effects of alternative splicing regulation on major signal transduction pathways and highlight the significance of alternative splicing.

DNA-based customized functional modules for signal transformation

Information on the temporal and spatial scale of cellular molecules in biological systems is crucial for estimating life processes and may be conducive to an improved understanding of disease progression. This intracellular and extracellular information is often difficult to obtain at the same time due to the limitations of accessibility and sensing throughput. DNA is an excellent material for in vivo and in vitro applications, and can be used to build functional modules that can transform bio-information (input) into ATCG sequence information (output). Due to their small volume and highly amenable programming, DNA-based functional modules provide an opportunity to monitor a range of information, from transient molecular events to dynamic biological processes. Over the past two decades, with the advent of customized strategies, a series of functional modules based on DNA networks have been designed to gather different information about molecules, including the identity, concentration, order, duration, location, and potential interactions; the action of these modules are based on the principle of kinetics or thermodynamics. This paper summarizes the available DNA-based functional modules that can be used for biomolecular signal sensing and transformation, reviews the available designs and applications of these modules, and assesses current challenges and prospects.

Exosome-Based Carrier for RNA Delivery: Progress and Challenges

In the last few decades, RNA-based drugs have emerged as a promising candidate to specifically target and modulate disease-relevant genes to cure genetic defects. The key to applying RNA therapy in clinical trials is developing safe and effective delivery systems. Exosomes have been exploited as a promising vehicle for drug delivery due to their nanoscale size, high stability, high biocompatibility, and low immunogenicity. We reviewed and summarized the progress in the strategy and application of exosome-mediated RNA therapy. The challenges of exosomes as a carrier for RNA drug delivery are also elucidated in this article. RNA molecules can be loaded into exosomes and then delivered to targeted cells or tissues via various biochemical or physical approaches. So far, exosome-mediated RNA therapy has shown potential in the treatment of cancer, central nervous system disorders, COVID-19, and other diseases. To further exploit the potential of exosomes for RNA delivery, more efforts should be made to overcome both technological and logistic problems.

Input integration by the circadian clock exhibits nonadditivity and fold-change detection

Circadian clocks are synchronized by external timing cues to align with one another and the environment. Various signaling pathways have been shown to independently reset the phase of the clock. However, in the body, circadian clocks are exposed to a multitude of potential timing cues with complex temporal dynamics, raising the question of how clocks integrate information in response to multiple signals. To investigate different modes of signal integration by the circadian clock, we used Circa-SCOPE, a method we recently developed for high-throughput phase resetting analysis. We found that simultaneous exposure to different combinations of known pharmacological resetting agents elicits a diverse range of responses. Often, the response was nonadditive and could not be readily predicted by the response to the individual signals. For instance, we observed that dexamethasone is dominant over other tested inputs. In the case of signals administered sequentially, the background levels of a signal attenuated subsequent resetting by the same signal, but not by signals acting through a different pathway. This led us to examine whether the circadian clock is sensitive to relative rather than absolute levels of the signal. Importantly, our analysis revealed the involvement of a signal-specific fold-change detection mechanism in the clock response. This mechanism likely stems from properties of the signaling pathway that are upstream to the clock. Overall, our findings elucidate modes of input integration by the circadian clock, with potential relevance to clock resetting under both physiological and pathological conditions.

Reversible and bidirectional signaling of notch ligands

The Notch signaling pathway is a direct cell-cell communication system involved in a wide variety of biological processes, and its disruption is observed in several pathologies. The pathway is comprised of a ligand-expressing (sender) cell and a receptor-expressing (receiver) cell. The canonical ligands are members of the Delta/Serrate/Lag-1 (DSL) family of proteins. Their binding to a Notch receptor in a neighboring cell induces a conformational change in the receptor, which will undergo regulated intramembrane proteolysis (RIP), liberating the Notch intracellular domain (NICD). The NICD is translocated to the nucleus and promotes gene transcription. It has been demonstrated that the ligands can also undergo RIP and nuclear translocation, suggesting a function for the ligands in the sender cell and possible bidirectionality of the Notch pathway. Although the complete mechanism of ligand processing is not entirely understood, and its dependence on Notch receptors has not been ruled out. Also, ligands have autonomous functions beyond Notch activation. Here we review the concepts of reverse and bidirectional signalization of DSL proteins and discuss the characteristics that make them more than just ligands of the Notch pathway.

Allostery, and how to define and measure signal transduction

Here we ask: What is productive signaling? How to define it, how to measure it, and most of all, what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream node in a specific cell? These questions have either not been considered or not entirely resolved. The requirements for the signal to propagate downstream to activate (repress) transcription have not been considered either. Yet, the questions are pivotal to clarify, especially in diseases such as cancer where determination of signal propagation can point to cell proliferation and to emerging drug resistance, and to neurodevelopmental disorders, such as RASopathy, autism, attention-deficit/hyperactivity disorder (ADHD), and cerebral palsy. Here we propose a framework for signal transduction from an upstream to a downstream node addressing these questions. Defining cellular processes, experimentally measuring them, and devising powerful computational AI-powered algorithms that exploit the measurements, are essential for quantitative science.

Intermittent ERK oscillations downstream of FGF in mouse embryonic stem cells

Signal transduction networks generate characteristic dynamic activities to process extracellular signals and guide cell fate decisions such as to divide or differentiate. The differentiation of pluripotent cells is controlled by FGF/ERK signaling. However, only a few studies have addressed the dynamic activity of the FGF/ERK signaling network in pluripotent cells at high time resolution. Here, we use live cell sensors in wild-type and Fgf4-mutant mouse embryonic stem cells to measure dynamic ERK activity in single cells, for defined ligand concentrations and differentiation states. These sensors reveal pulses of ERK activity. Pulsing patterns are heterogeneous between individual cells. Consecutive pulse sequences occur more frequently than expected from simple stochastic models. Sequences become more prevalent with higher ligand concentration, but are rarer in more differentiated cells. Our results suggest that FGF/ERK signaling operates in the vicinity of a transition point between oscillatory and non-oscillatory dynamics in embryonic stem cells. The resulting heterogeneous dynamic signaling activities add a new dimension to cellular heterogeneity that may be linked to divergent fate decisions in stem cell cultures.

Towards a Systems Biology Approach to Understanding the Lichen Symbiosis: Opportunities and Challenges of Implementing Network Modelling

Lichen associations, a classic model for successful and sustainable interactions between micro-organisms, have been studied for many years. However, there are significant gaps in our understanding about how the lichen symbiosis operates at the molecular level. This review addresses opportunities for expanding current knowledge on signalling and metabolic interplays in the lichen symbiosis using the tools and approaches of systems biology, particularly network modelling. The largely unexplored nature of symbiont recognition and metabolic interdependency in lichens could benefit from applying a holistic approach to understand underlying molecular mechanisms and processes. Together with ‘omics’ approaches, the application of signalling and metabolic network modelling could provide predictive means to gain insights into lichen signalling and metabolic pathways. First, we review the major signalling and recognition modalities in the lichen symbioses studied to date, and then describe how modelling signalling networks could enhance our understanding of symbiont recognition, particularly leveraging omics techniques. Next, we highlight the current state of knowledge on lichen metabolism. We also discuss metabolic network modelling as a tool to simulate flux distribution in lichen metabolic pathways and to analyse the co-dependence between symbionts. This is especially important given the growing number of lichen genomes now available and improved computational tools for reconstructing such models. We highlight the benefits and possible bottlenecks for implementing different types of network models as applied to the study of lichens.

Modeling cell-specific dynamics and regulation of the common gamma chain cytokines

The γ-chain receptor dimerizes with complexes of the cytokines interleukin-2 (IL-2), IL-4, IL-7, IL-9, IL-15, and IL-21 and their corresponding "private" receptors. These cytokines have existing uses and future potential as immune therapies because of their ability to regulate the abundance and function of specific immune cell populations. Here, we build a binding reaction model for the ligand-receptor interactions of common γ-chain cytokines, which includes receptor trafficking dynamics, enabling quantitative predictions of cell-type-specific response to natural and engineered cytokines. We then show that tensor factorization is a powerful tool to visualize changes in the input-output behavior of the family across time, cell types, ligands, and concentrations. These results present a more accurate model of ligand response validated across a panel of immune cell types as well as a general approach for generating interpretable guidelines for manipulation of cell-type-specific targeting of engineered ligands.

CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data

Single-cell technology enables study of signal transduction in a complex tissue at unprecedented resolution. We describe CytoTalk for de novo construction of cell type-specific signaling networks using single-cell transcriptomic data. Using an integrated intracellular and intercellular gene network as the input, CytoTalk identifies candidate pathways using the prize-collecting Steiner forest algorithm. Using high-throughput spatial transcriptomic data and single-cell RNA sequencing data with receptor gene perturbation, we demonstrate that CytoTalk has substantial improvement over existing algorithms. To better understand plasticity of signaling networks across tissues and developmental stages, we perform a comparative analysis of signaling networks between macrophages and endothelial cells across human adult and fetal tissues. Our analysis reveals an overall increased plasticity of signaling networks across adult tissues and specific network nodes that contribute to increased plasticity. CytoTalk enables de novo construction of signal transduction pathways and facilitates comparative analysis of these pathways across tissues and conditions.

ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways

The SARS-CoV-2 pandemic of 2020 has mobilised scientists around the globe to research all aspects of the coronavirus virus and its infection. For fruitful and rapid investigation of viral pathomechanisms, a collaborative and interdisciplinary approach is required. Therefore, we have developed ViralLink: a systems biology workflow which reconstructs and analyses networks representing the effect of viruses on intracellular signalling. These networks trace the flow of signal from intracellular viral proteins through their human binding proteins and downstream signalling pathways, ending with transcription factors regulating genes differentially expressed upon viral exposure. In this way, the workflow provides a mechanistic insight from previously identified knowledge of virally infected cells. By default, the workflow is set up to analyse the intracellular effects of SARS-CoV-2, requiring only transcriptomics counts data as input from the user: thus, encouraging and enabling rapid multidisciplinary research. However, the wide-ranging applicability and modularity of the workflow facilitates customisation of viral context, a priori interactions and analysis methods. Through a case study of SARS-CoV-2 infected bronchial/tracheal epithelial cells, we evidence the functionality of the workflow and its ability to identify key pathways and proteins in the cellular response to infection. The application of ViralLink to different viral infections in a context specific manner using different available transcriptomics datasets will uncover key mechanisms in viral pathogenesis.

Tissue-autonomous immune response regulates stress signaling during hypertrophy

Postmitotic tissues are incapable of replacing damaged cells through proliferation, but need to rely on buffering mechanisms to prevent tissue disintegration. By constitutively activating the Ras/MAPK-pathway via Ras^V12-overexpression in the postmitotic salivary glands (SGs) of Drosophila larvae, we overrode the glands adaptability to growth signals and induced hypertrophy. The accompanied loss of tissue integrity, recognition by cellular immunity, and cell death are all buffered by blocking stress signaling through a genuine tissue-autonomous immune response. This novel, spatio-temporally tightly regulated mechanism relies on the inhibition of a feedback-loop in the JNK-pathway by the immune effector and antimicrobial peptide Drosomycin. While this interaction might allow growing SGs to cope with temporary stress, continuous Drosomycin expression in Ras^V12-glands favors unrestricted hypertrophy. These findings indicate the necessity to refine therapeutic approaches that stimulate immune responses by acknowledging their possible, detrimental effects in damaged or stressed tissues.

Tissues and organs work hard to maintain balance in everything from taking up nutrients to controlling their growth. Ageing, wounding, sickness, and changes in the genetic code can all alter this balance, and cause the tissue or organ to lose some of its cells. Many tissues restore this loss by dividing their remaining cells to fill in the gaps. But some – like the salivary glands of fruit fly larvae – have lost this ability.

Tissues like these rely on being able to sense and counteract problems as they arise so as to not lose their balance in the first place. The immune system and stress responses are crucial for this process. They trigger steps to correct the problem and interact with each other to find a common decision about the fate of the affected tissue. To better understand how the immune system and stress response work together, Krautz, Khalili and Theopold genetically manipulated cells in the salivary gland of fruit fly larvae. These modifications switched on signals that stimulate cells to keep growing, causing the salivary gland’s tissue to slowly lose its balance and trigger the stress and immune response.

The experiments showed that while the stress response instructed the cells in the gland to die, a peptide released by the immune system called Drosomycin blocked this response and prevented the tissue from collapsing. The cells in the part of the gland not producing this immune peptide were consequently killed by the stress response. When all the cells in the salivary gland were forced to produce Drosomycin, none of the cells died and the whole tissue survived. But it also allowed the cells in the gland to grow uncontrollably, like a tumor, threatening the health of the entire organism.

Mapping the interactions between immune and stress pathways could help to fine-tune treatments that can prevent tissue damage. Fruit flies share many genetic features and molecular pathways with humans. So, the next step towards these kinds of treatments would be to screen for similar mechanisms that block stress activation in damaged human tissues. But this research carries a warning: careless activation of the immune system to protect stressed tissues could lead to uncontrolled tissue growth, and might cause more harm than good.

Probing pluripotency gene regulatory networks with quantitative live cell imaging

Live cell imaging uniquely enables the measurement of dynamic events in single cells, but it has not been used often in the study of gene regulatory networks. Network components can be examined in relation to one another by quantitative live cell imaging of fluorescent protein reporter cell lines that simultaneously report on more than one network component. A series of dual-reporter cell lines would allow different combinations of network components to be examined in individual cells. Dynamical information about interacting network components in individual cells is critical to predictive modeling of gene regulatory networks, and such information is not accessible through omics and other end point techniques. Achieving this requires that gene-edited cell lines are appropriately designed and adequately characterized to assure the validity of the biological conclusions derived from the expression of the reporters. In this brief review we discuss what is known about the importance of dynamics to network modeling and review some recent advances in optical microscopy methods and image analysis approaches that are making the use of quantitative live cell imaging for network analysis possible. We also discuss how strategies for genetic engineering of reporter cell lines can influence the biological relevance of the data.

It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology

Thoughtful use of simplifying assumptions is crucial to make systems biology models tractable while still representative of the underlying biology. A useful simplification can elucidate the core dynamics of a system. A poorly chosen assumption can, however, either render a model too complicated for making conclusions or it can prevent an otherwise accurate model from describing experimentally observed dynamics. Here, we perform a computational investigation of sequential multi-step pathway models that contain fewer pathway steps than the system they are designed to emulate. We demonstrate when such models will fail to reproduce data and how detrimental truncation of a pathway leads to detectable signatures in model dynamics and its optimised parameters. An alternative assumption is suggested for simplifying such pathways. Rather than assuming a truncated number of pathway steps, we propose to use the assumption that the rates of information propagation along the pathway is homogeneous and, instead, letting the length of the pathway be a free parameter. We first focus on linear pathways that are sequential and have first-order kinetics, and we show how this assumption results in a three-parameter model that consistently outperforms its truncated rival and a delay differential equation alternative in recapitulating observed dynamics. We then show how the proposed assumption allows for similarly terse and effective models of non-linear pathways. Our results provide a foundation for well-informed decision making during model simplifications.

Potential frameworks to support evaluation of mechanistic data for developmental neurotoxicity outcomes: A symposium report

A key challenge in systematically incorporating mechanistic data into human health assessments is that, compared to studies of apical health endpoints, these data are both more abundant (mechanistic studies routinely outnumber other studies by several orders of magnitude) and more heterogeneous (e.g. different species, test system, tissue, cell type, exposure paradigm, or specific assays performed). A structured decision-making process for organizing, integrating, and weighing mechanistic DNT data for use in human health risk assessments will improve the consistency and efficiency of such evaluations. At the Developmental Neurotoxicology Society (DNTS) 2016 annual meeting, a symposium was held to address the application of existing organizing principles and frameworks for evaluation of mechanistic data relevant to interpreting neurotoxicology data. Speakers identified considerations with potential to advance the use of mechanistic DNT data in risk assessment, including considering the context of each exposure, since epigenetics, tissue type, sex, stress, nutrition and other factors can modify toxicity responses in organisms. It was also suggested that, because behavior is a manifestation of complex nervous system function, the presence and absence of behavioral change itself could be used to organize the interpretation of multiple complex simultaneous mechanistic changes. Several challenges were identified with frameworks and their implementation, and ongoing research to develop these approaches represents an early step toward full evaluation of mechanistic DNT data for assessments.

Information-theoretic analysis of multivariate single-cell signaling responses

Mathematical methods of information theory appear to provide a useful language to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm, SLEMI—statistical learning based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values. Our approach is efficient in terms of computational time as well as sample size needed for accurate estimation. Analysis of the NF-κB single—cell signaling responses to TNF-α reveals that NF-κB signaling dynamics improves discrimination of high concentrations of TNF-α with a relatively modest impact on discrimination of low concentrations. Provided R-package allows the approach to be used by computational biologists with only elementary knowledge of information theory.

In light of single-cell, live-imaging experiments understanding of how cells transmit information about identity and quantity of stimuli is incomplete. When exposed to the same stimulus individual cells exhibit substantial cell-to-cell heterogeneity. Besides, stimuli have been shown to regulate temporal profiles of signaling effectors. Therefore, it is, for instance, not entirely clear whether single-cell responses are binary or contain more information about the quantity of stimuli. The above questions resulted in a considerable interest to study cellular signaling within the framework of information theory. Unfortunately, the utilization of the information-theoretic perspective is handicapped in part by the lack of suitable methods that account for multivariate signaling data. Here, we propose a novel algorithm that breaks a considerable computational barrier by allowing the effective information-theoretic analysis of highly-dimensional single-cell measurements. Our approach is computationally efficient, robust and straightforward to use. Moreover, we provide a simple R-package implementation.

Do cells use passwords in cell-state transitions? Is cell signaling sometimes encrypted?

Organisms must maintain proper regulation including defense and healing. Life-threatening problems may be caused by pathogens or by a multicellular organism's own cells through cancer or autoimmune disorders. Life evolved solutions to these problems that can be conceptualized through the lens of information security, which is a well-developed field in computer science. Here I argue that taking an information security view of cells is not merely semantics, but useful to explain features of signaling, regulation, and defense. An information security perspective also offers a conduit for cross-fertilization of advanced ideas from computer science and the potential for biology to inform computer science. First, I consider whether cells use passwords, i.e., initiation sequences that are required for subsequent signals to have effects, by analyzing the concept of pioneer transcription factors in chromatin regulation and cellular reprogramming. Second, I consider whether cells may encrypt signal transduction cascades. Encryption could benefit cells by making it more difficult for pathogens or oncogenes to hijack cell networks. By using numerous molecules, cells may gain a security advantage in particular against viruses, whose genome sizes are typically under selection pressure. I provide a simple conceptual argument for how cells may perform encryption through posttranslational modifications, complex formation, and chromatin accessibility. I invoke information theory to provide a criterion of an entropy spike to assess whether a signaling cascade has encryption-like features. I discuss how the frequently invoked concept of context dependency may oversimplify more advanced features of cell signaling networks, such as encryption. Therefore, by considering that biochemical networks may be even more complex than commonly realized we may be better able to understand defenses against pathogens and pathologies.

Molecular Engineering of the TGF-β Signaling Pathway

Transforming growth factor beta (TGF-β) is an important growth factor that plays essential roles in regulating tissue development and homeostasis. Dysfunction of TGF-β signaling is a hallmark of many human diseases. Therefore, targeting TGF-β signaling presents broad therapeutic potential. Since the discovery of the TGF-β ligand, a collection of engineered signaling proteins have been developed to probe and manipulate TGF-β signaling responses. In this review, we highlight recent progress in the engineering of TGF-β signaling for different applications and discuss how molecular engineering approaches can advance our understanding of this important pathway. In addition, we provide a future outlook on the opportunities and challenges in the engineering of the TGF-β signaling pathway from a quantitative perspective.

Gene Expression in Patients With Abdominal Aortic Aneurysm – More Than Immunological Mechanisms Involved

Abdominal aortic aneurysm (AAA) is a serious condition of unclear pathogenesis and progression. Two samples were collected from 48 patients during AAA surgery. One sample was collected from the aneurysm, the other from the aneurysm proximal neck where the tissue did not exhibit any aneurysmal changes. Subsequently, gene expression profiles using microarrays (Illumina) were compared in RNA extracted from the samples. Overall, 2,185 genes were found to be upregulated and 2,100 downregulated; from which 158 genes had a different expression with FDR<0.05 (False Discovery Rate) and FC≥2 (Fold Change). Of this number, 115 genes were over-expressed and 43 under-expressed. The analysis of the gene list based on their biological pathways revealed that the regulation of inflammation was mediated by chemokine and cytokine signaling pathways, the integrin signaling pathway, and T and B cell activation. Moreover, a change was identified in the expression of genes involved in both intercellular and intracellular signaling systems.

Regulation of Gene Expression and Signaling Pathway Activity in Mammalian Cells by Automated Microfluidics Feedback Control

Gene networks and signaling pathways display complex topologies and, as a result, complex nonlinear behaviors. Accumulating evidence shows that both static (concentration) and dynamical (rate-of-change) features of transcription factors, ligands and environmental stimuli control downstream processes and ultimately cellular functions. Currently, however, methods to generate stimuli with the desired features to probe cell response are still lacking. Here, combining tools from Control Engineering and Synthetic Biology (cybergenetics), we propose a simple and cost-effective microfluidics-based platform to precisely regulate gene expression and signaling pathway activity in mammalian cells by means of real-time feedback control. We show that this platform allows (i) to automatically regulate gene expression from inducible promoters in different cell types, including mouse embryonic stem cells; (ii) to precisely regulate the activity of the mTOR signaling pathway in single cells; (iii) to build a biohybrid oscillator in single embryonic stem cells by interfacing biological parts with virtual in silico counterparts. Ultimately, this platform can be used to probe gene networks and signaling pathways to understand how they process static and dynamic features of specific stimuli, as well as for the rapid prototyping of synthetic circuits for biotechnology and biomedical purposes.

DNA-based memory devices for recording cellular events

Measuring biological data across time and space is critical for understanding complex biological processes and for various biosurveillance applications. However, such data are often inaccessible or difficult to directly obtain. Less invasive, more robust and higher-throughput biological recording tools are needed to profile cells and their environments. DNA-based cellular recording is an emerging and powerful framework for tracking intracellular and extracellular biological events over time across living cells and populations. Here, we review and assess DNA recorders that utilize CRISPR nucleases, integrases and base-editing strategies, as well as recombinase and polymerase-based methods. Quantitative characterization, modelling and evaluation of these DNA-recording modalities can guide their design and implementation for specific application areas.

Experimental and engineering approaches to intracellular communication

Communication between and within cells is essential for multicellular life. While intracellular signal transduction pathways are often specified in molecular terms, the information content they transmit remains poorly defined. Here, we review research efforts to merge biological experimentation with concepts of communication that emerge from the engineering disciplines of signal processing and control theory. We discuss the challenges of performing experiments that quantitate information transfer at the molecular level, and we highlight recent studies that have advanced toward a clearer definition of the information content carried by signaling molecules. Across these studies, we emphasize a theme of increasingly well-matched experimental and theoretical approaches to decode the data streams directing cellular behavior.

Programming self-organizing multicellular structures with synthetic cell-cell signaling

A common theme in the self-organization of multicellular tissues is the use of cell-cell signaling networks to induce morphological changes. We used the modular synNotch juxtacrine signaling platform to engineer artificial genetic programs in which specific cell-cell contacts induced changes in cadherin cell adhesion. Despite their simplicity, these minimal intercellular programs were sufficient to yield assemblies with hallmarks of natural developmental systems: robust self-organization into multidomain structures, well-choreographed sequential assembly, cell type divergence, symmetry breaking, and the capacity for regeneration upon injury. The ability of these networks to drive complex structure formation illustrates the power of interlinking cell signaling with cell sorting: Signal-induced spatial reorganization alters the local signals received by each cell, resulting in iterative cycles of cell fate branching. These results provide insights into the evolution of multicellularity and demonstrate the potential to engineer customized self-organizing tissues or materials.

Lysophosphatidic acid regulates the motility of MCF10CA1a breast cancer cell sheets via two opposing signaling pathways

Aberrant cell migration leads to the dispersal of malignant cells. The ubiquitous lipid mediator lysophosphatidic acid (LPA) modulates cell migration and is implicated in tumor progression. Yet, the signaling cascades that regulate LPA's effect on cell motility remain unclear. Using time-lapse imaging and quantitative analyses, we studied the role of signaling cascades that act downstream of LPA on the motility of MCF10CA1a breast cancer cells. We found that LPA alters cell motility via two major signaling pathways. The Rho/ROCK signaling cascade is the predominant pathway that increases E-Cadherin containing cell-cell adhesions and cortical arrangement of actomyosin to promote slow, directional, spatially coherent and temporally consistent movement. In contrast, Gαi/o- and Gαq/11-dependent signaling cascades lessen directionality and support the independent movement of cells. The net effect of LPA on breast cancer cell migration therefore results from the integrated signaling activity of the Rho/ROCK and Gαi/o- and Gαq/11-dependent pathways, thus allowing for a dynamic migratory response to changes in the cellular or microenvironmental context.

Morphogen gradient reconstitution reveals Hedgehog pathway design principles

In developing tissues, cells estimate their spatial position by sensing graded concentrations of diffusible signaling proteins called morphogens. Morphogen-sensing pathways exhibit diverse molecular architectures, whose roles in controlling patterning dynamics and precision have been unclear. In this work, combining cell-based in vitro gradient reconstitution, genetic rewiring, and mathematical modeling, we systematically analyzed the distinctive architectural features of the Sonic Hedgehog pathway. We found that the combination of double-negative regulatory logic and negative feedback through the PTCH receptor accelerates gradient formation and improves robustness to variation in the morphogen production rate compared with alternative designs. The ability to isolate morphogen patterning from concurrent developmental processes and to compare the patterning behaviors of alternative, rewired pathway architectures offers a powerful way to understand and engineer multicellular patterning.

There's (still) plenty of room at the bottom

Motifs, circuits, and networks are core conceptual elements in modern systems and synthetic biology. While there are still undoubtedly more fascinating computations to discover at network level, there are also rich computations that we are only beginning to uncover within the diverse molecules that constitute the networks. Here we explore some work, both new and old, that showcases the incredible computational capacity of seemingly simple molecular mechanisms. A more sophisticated understanding of computations at the molecular level will inspire the development of a more nuanced toolbox for future biological engineering.

Redundancy and cooperation in Notch intercellular signaling

During metazoan development, Notch signaling drives spatially coordinated differentiation by establishing communication between adjacent cells. This occurs through either lateral inhibition, in which adjacent cells acquire distinct fates, or lateral induction, in which all cells become equivalent. Notch signaling is commonly activated by several distinct ligands, each of which drives signaling with a different efficiency upon binding to the Notch receptor of adjacent cells. Moreover, these ligands can also be distinctly regulated by Notch signaling. Under such complex circumstances, the overall spatial coordination becomes elusive. Here, we address this issue through both mathematical and computational analyses. Our results show that when two ligands have distinct efficiencies and compete for the same Notch receptor, they cooperate to drive new signaling states, thereby conferring additional robustness and evolvability to Notch signaling. Counterintuitively, whereas antagonistically regulated ligands cooperate to drive and enhance the response that is expected from the more efficient ligand, equivalently regulated ligands coordinate emergent spatial responses that are dependent on both ligands. Our study highlights the importance of ligand efficiency in multi-ligand scenarios, and can explain previously reported complex phenotypes.

Hilbert-Schmidt and Sobol sensitivity indices for static and time series Wnt signaling measurements in colorectal cancer - part A

Background

Ever since the accidental discovery of Wingless [Sharma R.P., Drosophila information service, 1973, 50, p 134], research in the field of Wnt signaling pathway has taken significant strides in wet lab experiments and various cancer clinical trials, augmented by recent developments in advanced computational modeling of the pathway. Information rich gene expression profiles reveal various aspects of the signaling pathway and help in studying different issues simultaneously. Hitherto, not many computational studies exist which incorporate the simultaneous study of these issues.

Results

This manuscript ∙ explores the strength of contributing factors in the signaling pathway, ∙ analyzes the existing causal relations among the inter/extracellular factors effecting the pathway based on prior biological knowledge and ∙ investigates the deviations in fold changes in the recently found prevalence of psychophysical laws working in the pathway. To achieve this goal, local and global sensitivity analysis is conducted on the (non)linear responses between the factors obtained from static and time series expression profiles using the density (Hilbert-Schmidt Information Criterion) and variance (Sobol) based sensitivity indices.

Conclusion

The results show the advantage of using density based indices over variance based indices mainly due to the former’s employment of distance measures & the kernel trick via Reproducing kernel Hilbert space (RKHS) that capture nonlinear relations among various intra/extracellular factors of the pathway in a higher dimensional space. In time series data, using these indices it is now possible to observe where in time, which factors get influenced & contribute to the pathway, as changes in concentration of the other factors are made. This synergy of prior biological knowledge, sensitivity analysis & representations in higher dimensional spaces can facilitate in time based administration of target therapeutic drugs & reveal hidden biological information within colorectal cancer samples.