Tumor collection/processing under physioxia uncovers highly relevant signaling networks and drug sensitivity

Tumor tissue collection and processing under physioxia allow highly relevant detection of signaling networks and drug sensitivity.

Signal Amplification in Highly Ordered Networks Is Driven by Geometry

Here, we hypothesize that, in biological systems such as cell surface receptors that relay external signals, clustering leads to substantial improvements in signaling efficiency. Representing cooperative signaling networks as planar graphs and applying Euler’s polyhedron formula, we can show that clustering may result in an up to a 200% boost in signaling amplitude dictated solely by the size and geometry of the network. This is a fundamental relationship that applies to all clustered systems regardless of its components. Nature has figured out a way to maximize the signaling amplitude in receptors that relay weak external signals. In addition, in cell-to-cell interactions, clustering both receptors and ligands may result in maximum efficiency and synchronization. The importance of clustering geometry in signaling efficiency goes beyond biological systems and can inform the design of amplifiers in nonbiological systems.

A Ratiometric Calcium Reporter CGf Reveals Calcium Dynamics Both in the Single Cell and Whole Plant Levels Under Heat Stress

Land plants evolved to quickly sense and adapt to temperature changes, such as hot days and cold nights. Given that calcium (Ca2+) signaling networks are implicated in most abiotic stress responses, heat-triggered changes in cytosolic Ca2+ were investigated in Arabidopsis leaves and pollen. Plants were engineered with a reporter called CGf, a ratiometric, genetically encoded Ca2+ reporter with an mCherry reference domain fused to an intensiometric Ca2+ reporter GCaMP6f. Relative changes in [Ca2+]cyt were estimated based on CGf’s apparent KD around 220 nM. The ratiometric output provided an opportunity to compare Ca2+ dynamics between different tissues, cell types, or subcellular locations. In leaves, CGf detected heat-triggered cytosolic Ca2+ signals, comprised of three different signatures showing similarly rapid rates of Ca2+ influx followed by differing rates of efflux (50% durations ranging from 5 to 19 min). These heat-triggered Ca2+ signals were approximately 1.5-fold greater in magnitude than blue light-triggered signals in the same leaves. In contrast, growing pollen tubes showed two different heat-triggered responses. Exposure to heat caused tip-focused steady growth [Ca2+]cyt oscillations to shift to a pattern characteristic of a growth arrest (22%), or an almost undetectable [Ca2+]cyt (78%). Together, these contrasting examples of heat-triggered Ca2+ responses in leaves and pollen highlight the diversity of Ca2+ signals in plants, inviting speculations about their differing kinetic features and biological functions.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems’ level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

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.

The Hippo signaling pathway in leukemia: function, interaction, and carcinogenesis

Cancer can be considered as a communication disease between and within cells; nevertheless, there is no effective therapy for the condition, and this disease is typically identified at its late stage. Chemotherapy, radiation, and molecular-targeted treatment are typically ineffective against cancer cells. A better grasp of the processes of carcinogenesis, aggressiveness, metastasis, treatment resistance, detection of the illness at an earlier stage, and obtaining a better therapeutic response will be made possible. Researchers have discovered that cancerous mutations mainly affect signaling pathways. The Hippo pathway, as one of the main signaling pathways of a cell, has a unique ability to cause cancer. In order to treat cancer, a complete understanding of the Hippo signaling system will be required. On the other hand, interaction with other pathways like Wnt, TGF-β, AMPK, Notch, JNK, mTOR, and Ras/MAP kinase pathways can contribute to carcinogenesis. Phosphorylation of oncogene YAP and TAZ could lead to leukemogenesis, which this process could be regulated via other signaling pathways. This review article aimed to shed light on how the Hippo pathway interacts with other cellular signaling networks and its functions in leukemia.

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.

HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks

Signaling networks mediate many aspects of cellular function. The conventional, mechanistically motivated approach to modeling such networks is through mass-action chemistry, which maps directly to biological entities and facilitates experimental tests and predictions. However such models are complex, need many parameters, and are computationally costly. Here we introduce the HillTau form for signaling models. HillTau retains the direct mapping to biological observables, but it uses far fewer parameters, and is 100 to over 1000 times faster than ODE-based methods. In the HillTau formalism, the steady-state concentration of signaling molecules is approximated by the Hill equation, and the dynamics by a time-course tau. We demonstrate its use in implementing several biochemical motifs, including association, inhibition, feedforward and feedback inhibition, bistability, oscillations, and a synaptic switch obeying the BCM rule. The major use-cases for HillTau are system abstraction, model reduction, scaffolds for data-driven optimization, and fast approximations to complex cellular signaling.