A systems approach to investigate GPCR-mediated Ras signaling network in chemoattractant sensing

A GPCR-mediated signaling network enables a chemotactic cell to generate adaptative Ras signaling in response to a large range of concentrations of a chemoattractant. To explore potential regulatory mechanisms of GPCR-controlled Ras signaling in chemosensing, we applied a software package, Simmune, to construct detailed spatiotemporal models simulating responses of the cAR1-mediated Ras signaling network. We first determined dynamics of G-protein activation and Ras signaling in Dictyostelium cells in response to cAMP stimulations using live-cell imaging and then constructed computation models by incorporating potential mechanisms. Using simulations, we validated the dynamics of signaling events and predicted the dynamic profiles of those events in the cAR1-mediated Ras signaling networks with defective Ras inhibitory mechanisms, such as without RasGAP, with RasGAP overexpression, or RasGAP hyperactivation. We described a method of using Simmune to construct spatiotemporal models of a signaling network and run computational simulations without writing mathematical equations. This approach will help biologists to develop and analyze computational models that parallel live-cell experiments.

McSNAC: A software to approximate first-order signaling networks from mass cytometry data

Mass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful to dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. With a proper computational analysis, timestamped CyTOF data of signaling proteins could help develop predictive and mechanistic models for signaling kinetics. These models would be useful for predicting the effects of perturbations in cells, or comparing signaling networks across cell groups. We propose our Mass cytometry Signaling Network Analysis Code, or McSNAC, a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data.McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption breaks down often as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of the protein species that are involved in signaling. We carry out a series of in silico experiments here to show that 1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; 2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured. These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from timestamped CyTOF data.

DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification

The analysis and comparison of compounds' transcriptomic signatures can help elucidate a compound's Mechanism of Action (MoA) in a biological system. In order to take into account the complexity of the biological system, several computational methods have been developed that utilize prior knowledge of molecular interactions to create a signaling network representation that best explains the compound's effect. However, due to their complex structure, large scale datasets of compound-induced signaling networks and methods specifically tailored to their analysis and comparison are very limited. Our goal is to develop graph deep learning models that are optimized to transform compound-induced signaling networks into high-dimensional representations and investigate their relationship with their respective MoAs. We created a new dataset of compound-induced signaling networks by applying the CARNIVAL network creation pipeline on the gene expression profiles of the CMap dataset. Furthermore, we developed a novel unsupervised graph deep learning pipeline, called deepSNEM, to encode the information in the compound-induced signaling networks in fixed-length high-dimensional representations. The core of deepSNEM is a graph transformer network, trained to maximize the mutual information between whole-graph and sub-graph representations that belong to similar perturbations. By clustering the deepSNEM embeddings, using the k-means algorithm, we were able to identify distinct clusters that are significantly enriched for mTOR, topoisomerase, HDAC and protein synthesis inhibitors respectively. Additionally, we developed a subgraph importance pipeline and identified important nodes and subgraphs that were found to be directly related to the most prevalent MoA of the assigned cluster. As a use case, deepSNEM was applied on compounds' gene expression profiles from various experimental platforms (MicroArrays and RNA sequencing) and the results indicate that correct hypotheses can be generated regarding their MoA.

Integration of Light and Brassinosteroid Signaling during Seedling Establishment

Light and brassinosteroid (BR) are external stimuli and internal cue respectively, that both play critical roles in a wide range of developmental and physiological process. Seedlings grown in the light exhibit photomorphogenesis, while BR promotes seedling etiolation. Light and BR oppositely control the development switch from shotomorphogenesis in the dark to photomorphogenesis in the light. Recent progress report that substantial components have been identified as hubs to integrate light and BR signals. Photomorphogenic repressors including COP1, PIFs, and AGB1 have been reported to elevate BR response, while photomorphogenesis-promoting factors such as HY5, BZS1, and NF-YCs have been proven to repress BR signal. In addition, BR components also modulate light signal. Here, we review the current research on signaling network associated with light and brassinosteroids, with a focus on the integration of light and BR signals enabling plants to thrive in the changeable environment.

Direct regulation of cell cycle regulatory gene expression by NtrX to promote Sinorhizobium meliloti cell division

Cell division of the alfalfa symbiont, Sinorhizobium meliloti, is regulated by the CtrA signaling network. The gene expression of regulatory proteins in the network is affected by nutrient signaling. In this study, we found that NtrX, one of the regulators of nitrogen metabolic response, can directly regulate the expression of several regulatory genes from the CtrA signaling network. Three sets of S. meliloti ntrX mutants, including the plasmid insertion strain, the depletion strain and the substitution of the 53rd aspartate (ntrXD53E) from a plasmid in the wild-type strain (Sm1021), showed similar cell division defects, such as slow growth, abnormal morphology of partial cells and delayed DNA synthesis. Transcript quantitative evaluation indicated that the transcription of genes such as ctrA and gcrA was up-regulated, while the transcription of genes such as dnaA and ftsZ1 was down-regulated in the insertion mutant and the strain of Sm1021 expressing ntrXD53E. Correspondingly, inducible transcription of ntrX activates the expression of dnaA and ftsZ1, but represses ctrA and gcrA in the depletion strain. The expression levels of CtrA and GcrA were confirmed by western blotting, which were consistent with the transcription data. The transcriptional regulation of these genes requires phosphorylation of the conserved 53rd aspartate in the NtrX protein. The NtrX protein binds directly to the promoter regions of ctrA, gcrA, dnaA and ftsZ1 by recognizing the characteristic sequence CAAN2-5TTG. Our findings reveal that NtrX is a novel transcriptional regulator of the CtrA signaling pathway genes, and positively affects bacterial cell division, associated with nitrogen metabolism.

Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets

Identification of intercellular signaling changes across multiple single-cell RNA-sequencing (scRNA-seq) datasets as well as how intercellular communications affect intracellular transcription factors (TFs) to regulate target genes is crucial in understanding how distinct cell states respond to evolution, perturbations, and diseases. Here, we first generalized our previously developed tool CellChat, enabling flexible comparison analysis of cell–cell communication networks across any number of scRNA-seq datasets from interrelated biological conditions. This greatly facilitates the ready detection of signaling changes of cell–cell communication in response to any biological perturbations. We then investigated how intercellular communications affect intracellular signaling response by inferring a multiscale signaling network which bridges the intercellular communications at the population level and the cell state–specific intracellular signaling network at the molecular level. The latter is constructed by integrating receptor-TF interactions collected from public databases and TF-target gene regulations inferred from a network-regularized regression model. By applying our approaches to three scRNA-seq datasets from skin development, spinal cord injury, and COVID-19, we demonstrated the capability of our approaches in identifying the predominant signaling changes across conditions and the critical signaling mechanisms regulating target gene expression. Together, our work will facilitate the identification of both intercellular and intracellular dysregulated signaling mechanisms responsible for biological perturbations in diverse tissues.

The Stringent Response Inhibits 70S Ribosome Formation in Staphylococcus aureus by Impeding GTPase-Ribosome Interactions

The stringent response is a bacterial signaling network that utilizes the nucleotides pppGpp and ppGpp to reprogram cells in order to survive nutritional stresses. However, much about how these important nucleotides control cellular reprogramming is unknown.

Modeling and analysis of the macronutrient signaling network in budding yeast

A computational model of the underlying regulatory mechanisms is proposed to study nutrient signaling. The model’s predictions are consistent with literature-curated experimental measurements. Using this model, novel, testable predictions are made in genetic mutant strains undergoing complex nutrient shifts.