Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor

Signal execution modes emerge in biochemical reaction networks calibrated to experimental data

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells and provide previously unrecognized mechanistic explanation for the survival responses of CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses.

The CD6 interactome orchestrates ligand-independent T cell inhibitory signaling

BACKGROUND

T-cell membrane scaffold proteins are pivotal in T cell function, acting as versatile signaling hubs. While CD6 forms a large intracellular signalosome, it is distinguished from typical scaffolds like LAT or PAG by possessing a substantial ectodomain that binds CD166, a well-characterized ligand expressed on most antigen-presenting cells (APC), through the third domain (d3) of the extracellular region. Although the intact form of CD6 is the most abundant in T cells, an isoform lacking d3 (CD6∆d3) is transiently expressed on activated T cells. Still, the precise character of the signaling transduced by CD6, whether costimulatory or inhibitory, and the influence of its ectodomain on these activities are unclear.

METHODS

We expressed CD6 variants with extracellular deletions or cytosolic mutations in Jurkat cells containing eGFP reporters for NF-κB and NF-AT transcription factor activation. Cell activation was assessed by eGFP flow cytometry following Jurkat cell engagement with superantigen-presenting Raji cells. Using imaging flow cytometry, we evaluated the impact of the CD6-CD166 pair on cell adhesiveness during the antigen-dependent and -independent priming of T cells. We also examined the role of extracellular or cytosolic sequences on CD6 translocation to the immunological synapse, using immunofluorescence-based imaging.

RESULTS

Our investigation dissecting the functions of the extracellular and cytosolic regions of CD6 revealed that CD6 was trafficked to the immunological synapse and exerted tonic inhibition wholly dependent on its cytosolic tail. Surprisingly, however, translocation to the synapse occurred independently of the extracellular d3 and of engagement to CD166. On the other hand, CD6 binding to CD166 significantly increased T cell:APC adhesion. However, this activity was most evident in the absence of APC priming with superantigen, and thus, in the absence of TCR engagement.

CONCLUSIONS

Our study identifies CD6 as a novel 'on/off' scaffold-receptor capable of modulating responsiveness in two ways. Firstly, and independently of ligand binding, it establishes signaling thresholds through tonic inhibition, functioning as a membrane-bound scaffold. Secondly, CD6 has the capacity for alternative splicing-dependent variable ligand engagement, modulating its checkpoint-like activity.

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.

Long non-coding RNA DUXAP8 promotes tumorigenesis by regulating IGF1R via miR-9-3p in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide with a low 5-year survival rate. Long non-coding RNA (lncRNA) double homeobox A pseudogene 8 (DUXAP8) is an oncogene and a potential biomarker in various tumors, such as ovarian, colorectal and non-small-cell lung cancer. However, the function and molecular mechanism underlying DUXAP8 in HCC progression is not completely understood. The expression of DUXAP8, microRNA (miR)-9-3p and insulin-like growth factor 1 receptor (IGF1R) in HCC tissues and cells was detected via reverse transcription-quantitative PCR. The expression levels of IGF1R and epithelial-mesenchymal transition-associated proteins (Snail, Slug, E-cadherin, N-cadherin and vimentin) were assessed via western blotting. The effects of DUXAP8, miR-9-3p and IGF1R on proliferation, migration and invasion were examined by conducting Cell Counting Kit-8 and Transwell assays, respectively. The interaction between miR-9-3p and DUXAP8 or IGF1R was predicted using StarBase or TargetScan, and further assessed using dual luciferase reporter and RNA immunoprecipitation assays. DUXAP8 and IGF1R were upregulated and miR-9-3p was downregulated in HCC tissues and cells compared with adjacent healthy tissues and a normal liver cell line, respectively. miR-9-3p overexpression decreased the protein expression level of IGF1R, and miR-9-3p knockdown enhanced the protein expression level of IGF1R in HCC cells compared with the corresponding control groups. Moreover, compared with the corresponding control groups, DUXAP8 knockdown and miR-9-3p overexpression increased E-cadherin protein expression levels, and decreased Snail, Slug, N-cadherin and vimentin protein expression levels. However, miR-9-3p inhibitor and IGF1R overexpression reversed DUXAP8 knockdown- and miR-9-3p overexpression-induced effects, respectively. In addition, compared with the corresponding control groups, DUXAP8 knockdown and miR-9-3p overexpression suppressed proliferation, migration and invasion, which was reversed by miR-9-3p inhibitor and IGF1R overexpression, respectively. Moreover, miR-9-3p as the target of DUXAP8 and IGF1R as the target of miR-9-3p were verified in HCC cells. lncRNA DUXAP8 contributed to HCC tumorigenesis via the miR-9-3p/IGF1R axis, providing a novel therapeutic approach for HCC diagnosis and treatment.

Multisite EGFR phosphorylation is regulated by adaptor protein abundances and dimer lifetimes

Differential epidermal growth factor receptor (EGFR) phosphorylation is thought to couple receptor activation to distinct signaling pathways. However, the molecular mechanisms responsible for biased signaling are unresolved due to a lack of insight into the phosphorylation patterns of full-length EGFR. We extended a single-molecule pull-down technique previously used to study protein-protein interactions to allow for robust measurement of receptor phosphorylation. We found that EGFR is predominantly phosphorylated at multiple sites, yet phosphorylation at specific tyrosines is variable and only a subset of receptors share phosphorylation at the same site, even with saturating ligand concentrations. We found distinct populations of receptors as soon as 1 min after ligand stimulation, indicating early diversification of function. To understand this heterogeneity, we developed a mathematical model. The model predicted that variations in phosphorylation are dependent on the abundances of signaling partners, while phosphorylation levels are dependent on dimer lifetimes. The predictions were confirmed in studies of cell lines with different expression levels of signaling partners, and in experiments comparing low- and high-affinity ligands and oncogenic EGFR mutants. These results reveal how ligand-regulated receptor dimerization dynamics and adaptor protein concentrations play critical roles in EGFR signaling.

Analysis of Fn14-NF-κB signaling response dynamics using a mechanistic model

Fn14 is a transmembrane receptor protein belonging to the tumor necrosis factor receptor (TNFR) superfamily. Many experimental reports have shown that crosslinking of the receptor by its extracellular ligand TWEAK induces prolonged activation of transcription factor NF-κB. This behavior is distinct from TNF-α receptor, which is a more well-characterized member of the TNFR family. TNF-α receptor, despite sharing many similar molecular interactions with Fn14, only transiently activates NF-κB in response to TNF-α stimulation. Here, we investigate molecular mechanisms that enable Fn14 to display such distinctive behavior. In particular, we focus on two specific features of the Fn14 pathway that potentially give rise to a positive feedback regulation and differentiate it from the TNF-α receptor signaling. By developing a mechanistic model, we analyze how these features may determine the dynamics of an Fn14-NF-κB response. Our analysis reveals that stimulation of Fn14 by TWEAK may generate highly non-linear dynamics, including stable limit cycles and bistable responses. The type of response depends both on the strength and duration of a TWEAK signal. Our predictions and analyses also show that the molecular interactions underlying the positive feedback explain the prolonged activation of NF-κB under certain parameter regimes. In light of the model predictions, we propose possible deregulations of Fn14 leading to its overexpression in solid tumors and tissue injuries.

PyBioNetFit and the Biological Property Specification Language

In systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modelers perform these steps, we developed the software PyBioNetFit, which in addition supports checking models against known system properties and solving design problems. PyBioNetFit introduces Biological Property Specification Language (BPSL) for the formal declaration of system properties. BPSL allows qualitative data to be used alone or in combination with quantitative data. PyBioNetFit performs parameterization with parallelized metaheuristic optimization algorithms that work directly with existing model definition standards: BioNetGen Language (BNGL) and Systems Biology Markup Language (SBML). We demonstrate PyBioNetFit's capabilities by solving various example problems, including the challenging problem of parameterizing a 153-parameter model of cell cycle control in yeast based on both quantitative and qualitative data. We demonstrate the model checking and design applications of PyBioNetFit and BPSL by analyzing a model of targeted drug interventions in autophagy signaling.