Evolving circuitries in plant signaling cascades.

Land plants are astounding processors of information; due to their sessile nature, they adjust the molecular programs that define their development and physiology in accordance with the environment in which they dwell. Transduction of the external input to the respective internal programs hinges to a large degree on molecular signaling cascades, many of which have deep evolutionary origins in the ancestors of land plants and its closest relatives, streptophyte algae. In this Review, we discuss the evolutionary history of the defining factors of streptophyte signaling cascades, circuitries that not only operate in extant land plants and streptophyte algae, but that also likely operated in their extinct algal ancestors hundreds of millions of years ago. We hope this Review offers a starting point for future studies on the evolutionary mechanisms contributing to the current diversity and complexity of plant signaling pathways, with an emphasis on recognizing potential biases.

Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets.

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.

Information content and optimization of self-organized developmental systems.

A key feature of many developmental systems is their ability to self-organize spatial patterns of functionally distinct cell fates. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack a principled framework to quantify the performance of such stochastic self-organizing systems. To that end, we introduce an information-theoretic measure for self-organized fate specification during embryonic development. We show that the proposed measure assesses the total information content of fate patterns and decomposes it into interpretable contributions corresponding to the positional and correlational information. By optimizing the proposed measure, our framework provides a normative theory for developmental circuits, which we demonstrate on lateral inhibition, cell type proportioning, and reaction-diffusion models of self-organization. This paves a way toward a classification of developmental systems based on a common information-theoretic language, thereby organizing the zoo of implicated chemical and mechanical signaling processes.

The ability to sense the environment is heterogeneously distributed in cell populations.

Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information-theoretic framework to quantify the distribution of sensing abilities from single-cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an 'average cell'. We verify these predictions using live-cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells' sensing abilities. This information-theoretic framework will significantly improve our understanding of how cells sense in their environment.

System-Level Analysis of the Effects of RPTPs on Cellular Signaling Networks.

Tyrosine phosphorylation regulates signaling network activity downstream of receptor tyrosine kinase (RTK) activation. Receptor protein tyrosine phosphatases (RPTPs) serve to dephosphorylate RTKs and their proximal adaptor proteins, thus serving to modulate RTK activity. While the general function of RPTPs is well understood, the direct and indirect substrates for each RPTP are poorly characterized. Here we describe a method, quantitative phosphotyrosine phosphoproteomics, that enables the identification of specific phosphorylation sites whose phosphorylation levels are altered by the expression and activity of a given RPTP. In a proof-of-concept application, we use this method to highlight several direct or indirect substrate phosphorylation sites for PTPRJ, also known as DEP1, and show their quantitative phosphorylation in the context of wild-type PTPRJ compared to a mutant form of PTPRJ with increased activity, in EGF-stimulated cells. This method is generally applicable to define the signaling network effects of each RPTP in cells or tissues under different physiological conditions.

Shortest Hyperpaths in Directed Hypergraphs for Reaction Pathway Inference.

Signaling and metabolic pathways, which consist of chains of reactions that produce target molecules from source compounds, are cornerstones of cellular biology. Properly modeling the reaction networks that represent such pathways requires directed hypergraphs, where each molecule or compound maps to a vertex, and each reaction maps to a hyperedge directed from its set of input reactants to its set of output products. Inferring the most likely series of reactions that produces a given set of targets from a given set of sources, where for each reaction its reactants are produced by prior reactions in the series, corresponds to finding a shortest hyperpath in a directed hypergraph, which is NP-complete. We give the first exact algorithm for general shortest hyperpaths that can find provably optimal solutions for large, real-world, reaction networks. In particular, we derive a novel graph-theoretic characterization of hyperpaths, which we leverage in a new integer linear programming formulation of shortest hyperpaths that for the first time handles cycles, and develop a cutting-plane algorithm that can solve this integer linear program to optimality in practice. Through comprehensive experiments over all of the thousands of instances from the standard Reactome and NCI-PID reaction databases, we demonstrate that our cutting-plane algorithm quickly finds an optimal hyperpath-inferring the most likely pathway-with a median running time of under 10 seconds, and a maximum time of less than 30 minutes, even on instances with thousands of reactions. We also explore for the first time how well hyperpaths infer true pathways, and show that shortest hyperpaths accurately recover known pathways, typically with very high precision and recall. Source code implementing our cutting-plane algorithm for shortest hyperpaths is available free for research use in a new tool called Mmunin.

Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding.

The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing.

The amount of Nck rather than N-WASP correlates with the rate of actin-based motility of Vaccinia virus.

IMPORTANCE: Vaccinia virus is a large double-stranded DNA virus and a close relative of Mpox and Variola virus, the causative agent of smallpox. During infection, Vaccinia hijacks its host's transport systems and promotes its spread into neighboring cells by recruiting a signaling network that stimulates actin polymerization. Over the years, Vaccinia has provided a powerful model to understand how signaling networks regulate actin polymerization. Nevertheless, we still lack important quantitative information about the system, including the precise number of viral and host molecules required to induce actin polymerization. Using quantitative fluorescence microscopy techniques, we have determined the number of viral and host signaling proteins accumulating on virions during their egress. Our analysis has uncovered two unexpected new aspects of this process: the number of viral proteins in the virion is not fixed and the velocity of virus movement depends on the level of a single adaptor within the signaling network.

A regulatory node involving Gαq, PLCß, and RGS proteins modulates platelet reactivity to critical agonists.

BACKGROUND: Most platelet agonists work through G protein-coupled receptors, activating pathways that involve members of the Gq, Gi, and G12/G13 families of heterotrimeric G proteins. Gq signaling has been shown to be critical for efficient platelet activation. Growing evidence suggests that regulatory mechanisms converge on G protein-coupled receptors and Gq to prevent overly robust platelet reactivity. OBJECTIVES: To identify and characterize mechanisms by which Gq signaling is regulated in platelets. METHODS: Based on our prior experience with a Gαi2 variant that escapes regulation by regulator of G protein signaling (RGS) proteins, a Gαq variant was designed with glycine 188 replaced with serine (G188S) and then incorporated into a mouse line so that its effects on platelet activation and thrombus formation could be studied in vitro and in vivo. RESULTS AND CONCLUSIONS: As predicted, the G188S substitution in Gαq disrupted its interaction with RGS18. Unexpectedly, it also uncoupled PLCß-3 from activation by platelet agonists as evidenced by a loss rather than a gain of platelet function in vitro and in vivo. Binding studies showed that in addition to preventing the binding of RGS18 to Gαq, the G188S substitution also prevented the binding of PLCß-3 to Gαq. Structural analysis revealed that G188 resides in the region that is also important for Gαq binding to PLCß-3 in platelets. We conclude that the Gαq signaling node is more complex than that has been previously understood, suggesting that there is cross-talk between RGS proteins and PLCß-3 in the context of Gαq signaling.

The advantages of crosstalk during the evolution of the BZR1-ARF6-PIF4 (BAP) module.

The BAP module, comprising BRASSINAZOLE RESISTANT 1 (BZR1), AUXIN RESPONSE FACTOR 6 (ARF6), and PHYTOCHROME-INTERACTING FACTOR 4 (PIF4), functions as a molecular hub to orchestrate plant growth and development. In Arabidopsis thaliana, components of the BAP module physically interact to form a complex system that integrates light, brassinosteroid (BR), and auxin signals. Little is known about the origin and evolution of the BAP module. Here, we conducted comparative genomic and transcriptomic analyses to investigate the evolution and functional diversification of the BAP module. Our results suggest that the BAP module originated in land plants and that the ζ, ε, and γ whole-genome duplication/triplication events contributed to the expansion of BAP module components in seed plants. Comparative transcriptomic analysis suggested that the prototype BAP module arose in Marchantia polymorpha, experienced stepwise evolution, and became established as a mature regulatory system in seed plants. We developed a formula to calculate the signal transduction productivity of the BAP module and demonstrate that more crosstalk among components enables higher signal transduction efficiency. Our results reveal the evolutionary history of the BAP module and provide insights into the evolution of plant signaling networks and the strategies employed by plants to integrate environmental and endogenous signals.

Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm.

A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.

Network and functional analyses of differentially expressed genes in gastric cancer provide new biomarkers associated with disease pathogenesis.

BACKGROUND: Gastric cancer is a dominant source of cancer-related death around the globe and a serious threat to human health. However, there are very few practical diagnostic approaches and biomarkers for the treatment of this complex disease. METHODS: This study aimed to evaluate the association between differentially expressed genes (DEGs), which may function as potential biomarkers, and the diagnosis and treatment of gastric cancer (GC). We constructed a protein-protein interaction network from DEGs followed by network clustering. Members of the two most extensive modules went under the enrichment analysis. We introduced a number of hub genes and gene families playing essential roles in oncogenic pathways and the pathogenesis of gastric cancer. Enriched terms for Biological Process were obtained from the "GO" repository. RESULTS: A total of 307 DEGs were identified between GC and their corresponding normal adjacent tissue samples in GSE63089 datasets, including 261 upregulated and 261 downregulated genes. The top five hub genes in the PPI network were CDK1, CCNB1, CCNA2, CDC20, and PBK. They are involved in focal adhesion formation, extracellular matrix remodeling, cell migration, survival signals, and cell proliferation. No significant survival result was found for these hub genes. CONCLUSIONS: Using comprehensive analysis and bioinformatics methods, important key pathways and pivotal genes related to GC progression were identified, potentially informing further studies and new therapeutic targets for GC treatment.

Crosstalk between melatonin and reactive oxygen species in fruits and vegetables post-harvest preservation: An update.

Fruits and vegetables contain numerous nutrients, such as vitamins, minerals, phenolic compounds, and dietary fibers. They reduce the incidence of cardiovascular diseases and the risk of certain chronic diseases, and improve the antioxidant and anti-inflammatory capacity. Moreover, melatonin was found in various fruits and vegetables species. Melatonin acts as a multifunctional compound to participate in various physiological processes. In recent years, many advances have been found that melatonin is also appraised as a key modulator on the fruits and vegetables post-harvest preservation. Fruits and vegetables post-harvest usually elicit reactive oxygen species (ROS) generation and accumulation. Excess ROS stimulate cell damage, protein structure destruction, and tissue aging, and thereby reducing their quality. Numerous studies find that exogenous application of melatonin modulates ROS homeostasis by regulating the antioxidant enzymes and non-enzymatic antioxidants systems. Further evidences reveal that melatonin often interacts with hormones and other signaling molecules, such as ROS, nitric oxide (NO), hydrogen sulfide (H2S), and etc. Among these 'new' molecules, crosstalks of melatonin and ROS, especially the H2O2 produced by RBOHs, are provided in fruits and vegetables post-harvest preservation in this review. It will provide reference for complicated integration of both melatonin and ROS as signal molecules in future study.

The Game of Timing: Circadian Rhythms Intersect with Changing Environments.

Recurring patterns are an integral part of life on Earth. Through evolution or breeding, plants have acquired systems that coordinate with the cyclic patterns driven by Earth's movement through space. The biosystem responses to these physical rhythms result in biological cycles of daily and seasonal activity that feed back into the physical cycles. Signaling networks to coordinate growth and molecular activities with these persistent cycles have been integrated into plant biochemistry. The plant circadian clock is the coordinator of this complex, multiscale, temporal schedule. However, we have detailed knowledge of the circadian clock components and functions in only a few species under controlled conditions. We are just beginning to understand how the clock functions in real-world conditions. This review examines what we know about the circadian clock in diverse plant species, the challenges with extrapolating data from controlled environments, and the need to anticipate how plants will respond to climate change.

Crosstalk of miRNAs with signaling networks in bladder cancer progression: Therapeutic, diagnostic and prognostic functions.

Urological cancers are considered as life-threatening diseases around the world. Bladder cancer is one of the most malignant urological tumors with high mortality and morbidity. Bladder cancer is a heterogenous disease and genetic alterations have shown to be key players in regulating its progression. Although conventional therapies are somewhat beneficial in improving prognosis and survival, bladder cancer patients suffer from recurrence. MicroRNAs (miRNAs) are endogenous short RNA molecules that do not encode proteins and show dysregulated expression in human cancers. miRNAs are regulators of vital biological processes in cells such as proliferation, migration, differentiation and apoptosis. Dysregulation of miRNAs is observed in bladder cancer and they are used as biomarkers for diagnosis and prognosis of patients. LncRNAs and circRNAs are modulators of bladder cancer progression via miRNA expression regulation. Overexpression of onco-suppressor miRNAs impairs bladder cancer progression, while oncogenic miRNAs drive tumor progression. Glycolysis and EMT mechanisms are two important factors for proliferation and migration of bladder cancer that are modulated by miRNAs. Furthermore, miRNAs can affect STAT3 and Wnt/ß-catenin as instances of molecular factors in regulating bladder tumor progression. Bladder tumor response to drug therapy and radiotherapy is regulated by miRNAs. Hence, aim of current review is to provide function of miRNAs in bladder cancer based on their crosstalk with other molecular pathways and interaction with biological processes.

ER Stress-Induced Sphingosine-1-Phosphate Lyase Phosphorylation Potentiates the Mitochondrial Unfolded Protein Response.

The unfolded protein response (UPR) is an elaborate signaling network that evolved to maintain proteostasis in the endoplasmic reticulum (ER) and mitochondria (mt). These organelles are functionally and physically associated, and consequently, their stress responses are often intertwined. It is unclear how these two adaptive stress responses are coordinated during ER stress. The inositol-requiring enzyme-1 (IRE1), a central ER stress sensor and proximal regulator of the UPRER, harbors dual kinase and endoribonuclease (RNase) activities. IRE1 RNase activity initiates the transcriptional layer of the UPRER, but IRE1's kinase substrate(s) and their functions are largely unknown. Here, we discovered that sphingosine 1-phosphate (S1P) lyase (SPL), the enzyme that degrades S1P, is a substrate for the mammalian IRE1 kinase. Our data show that IRE1-dependent SPL phosphorylation inhibits SPL's enzymatic activity, resulting in increased intracellular S1P levels. S1P has previously been shown to induce the activation of mitochondrial UPR (UPRmt) in nematodes. We determined that IRE1 kinase-dependent S1P induction during ER stress potentiates UPRmt signaling in mammalian cells. Phosphorylation of eukaryotic translation initiation factor 2α (eif2α) is recognized as a critical molecular event for UPRmt activation in mammalian cells. Our data further demonstrate that inhibition of the IRE1-SPL axis abrogates the activation of two eif2α kinases, namely double-stranded RNA-activated protein kinase (PKR) and PKR-like ER kinase upon ER stress. These findings show that the IRE1-SPL axis plays a central role in coordinating the adaptive responses of ER and mitochondria to ER stress in mammalian cells.

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model.

Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene-gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene-gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene-phenotype relations.

FAK-Mediated Signaling Controls Amyloid Beta Overload, Learning and Memory Deficits in a Mouse Model of Alzheimer's Disease.

The non-receptor focal adhesion kinase (FAK) is highly expressed in the central nervous system during development, where it regulates neurite outgrowth and axon guidance, but its role in the adult healthy and diseased brain, specifically in Alzheimer's disease (AD), is largely unknown. Using the 3xTg-AD mouse model, which carries three mutations associated with familial Alzheimer's disease (APP KM670/671NL Swedish, PSEN1 M146V, MAPT P301L) and develops age-related progressive neuropathology including amyloid plaques and Tau tangles, we describe here, for the first time, the in vivo role of FAK in AD pathology. Our data demonstrate that while site-specific knockdown in the hippocampi of 3xTg-AD mice has no effect on learning and memory, hippocampal overexpression of the protein leads to a significant decrease in learning and memory capabilities, which is accompanied by a significant increase in amyloid ß (Aß) load. Furthermore, neuronal morphology is altered following hippocampal overexpression of FAK in these mice. High-throughput proteomics analysis of total and phosphorylated proteins in the hippocampi of FAK overexpressing mice indicates that FAK controls AD-like phenotypes by inhibiting cytoskeletal remodeling in neurons which results in morphological changes, by increasing Tau hyperphosphorylation, and by blocking astrocyte differentiation. FAK activates cell cycle re-entry and consequent cell death while downregulating insulin signaling, thereby increasing insulin resistance and leading to oxidative stress. Our data provide an overview of the signaling networks by which FAK regulates AD pathology and identify FAK as a novel therapeutic target for treating AD.

Another Step Forward to the understanding of Biological Signaling Networks.

In this editorial I briefly review the new JCCS scientific directions that have emerged from the complicated situations created by the pandemics of COVID- 19, and by the internal audit of both Journal of Cell Communication and Signaling and International CCN Society, that were initiated since my proposal at the 2019 International Workshop on the CCN family of genes.I also welcome the distinguished members of our renewed JCCS Editorial and congratulate all those who have in many different ways participated to the consolidation of the 2021 JCCS Impact Factor attaining 5.908.