Band-pass processing in a GPCR signaling pathway selects for NFAT transcription factor activation

Frequency-preference response in covalent modification cycles under substrate sequestration conditions

Covalent modification cycles (CMCs) are basic units of signaling systems and their properties are well understood. However, their behavior has been mostly characterized in situations where the substrate is in excess over the modifying enzymes. Experimental data on protein abundance suggest that the enzymes and their target proteins are present in comparable concentrations, leading to substrate sequestration by the enzymes. In this enzyme-in-excess regime, CMCs have been shown to exhibit signal termination, the ability of the product to return to a stationary value lower than its peak in response to constant stimulation, while this stimulation is still active, with possible implications for the ability of systems to adapt to environmental inputs. We characterize the conditions leading to signal termination in CMCs in the enzyme-in-excess regime. We also demonstrate that this behavior leads to a preferred frequency response (band-pass filters) when the cycle is subjected to periodic stimulation, whereas the literature reports that CMCs investigated so far behave as low-pass filters. We characterize the relationship between signal termination and the preferred frequency response to periodic inputs and we explore the dynamic mechanism underlying these phenomena. Finally, we describe how the behavior of CMCs is reflected in similar types of responses in the cascades of which they are part. Evidence of protein abundance in vivo shows that enzymes and substrates are present in comparable concentrations, thus suggesting that signal termination and frequency-preference response to periodic inputs are also important dynamic features of cell signaling systems, which have been overlooked.

Selective Photoswitchable Allosteric Agonist of a G Protein-Coupled Receptor

G protein-coupled receptors (GPCRs) are the most common targets of drug discovery. However, the similarity between related GPCRs combined with the complex spatiotemporal dynamics of receptor activation in vivo has hindered drug development. Photopharmacology offers the possibility of using light to control the location and timing of drug action by incorporating a photoisomerizable azobenzene into a GPCR ligand, enabling rapid and reversible switching between an inactive and active configuration. Recent advances in this area include (i) photoagonists and photoantagonists that directly control receptor activity but are nonselective because they bind conserved sites, and (ii) photoallosteric modulators that bind selectively to nonconserved sites but indirectly control receptor activity by modulating the response to endogenous ligand. In this study, we designed a photoswitchable allosteric agonist that targets a nonconserved allosteric site for selectivity and activates the receptor on its own to provide direct control. This work culminated in the development of aBINA, a photoswitchable allosteric agonist that selectively activates the Gi/o-coupled metabotropic glutamate receptor 2 (mGluR2). aBINA is the first example of a new class of precision drugs for GPCRs and other clinically important signaling proteins.

Molecular and DNA Artificial Neural Networks via Fractional Coding

This article considers implementation of artificial neural networks (ANNs) using molecular computing and DNA based on fractional coding. Prior work had addressed molecular two-layer ANNs with binary inputs and arbitrary weights. In prior work using fractional coding, a simple molecular perceptron that computes sigmoid of scaled weighted sum of the inputs was presented where the inputs and the weights lie between [-1,1]. Even for computing the perceptron, the prior approach suffers from two major limitations. First, it cannot compute the sigmoid of the weighted sum, but only the sigmoid of the scaled weighted sum. Second, many machine learning applications require the coefficients to be arbitrarily positive and negative numbers that are not bounded between [-1,1]; such numbers cannot be handled by the prior perceptron using fractional coding. This paper makes four contributions. First molecular perceptrons that can handle arbitrary weights and can compute sigmoid of the weighted sums are presented. Thus, these molecular perceptrons are ideal for regression applications and multi-layer ANNs. A new molecular divider is introduced and is used to compute sigmoid(ax) where . Second, based on fractional coding, a molecular artificial neural network (ANN) with one hidden layer is presented. Third, a trained ANN classifier with one hidden layer from seizure prediction application from electroencephalogram is mapped to molecular reactions and DNA and their performances are presented. Fourth, molecular activation functions for rectified linear unit (ReLU) and softmax are also presented.

Inhibition of NFAT Signaling Restores Microvascular Endothelial Function in Diabetic Mice

Central to the development of diabetic macro- and microvascular disease is endothelial dysfunction, which appears well before any clinical sign but, importantly, is potentially reversible. We previously demonstrated that hyperglycemia activates nuclear factor of activated T cells (NFAT) in conduit and medium-sized resistance arteries and that NFAT blockade abolishes diabetes-driven aggravation of atherosclerosis. In this study, we test whether NFAT plays a role in the development of endothelial dysfunction in diabetes. NFAT-dependent transcriptional activity was elevated in skin microvessels of diabetic Akita (Ins2 ^+/- ) mice when compared with nondiabetic littermates. Treatment of diabetic mice with the NFAT blocker A-285222 reduced NFATc3 nuclear accumulation and NFAT-luciferase transcriptional activity in skin microvessels, resulting in improved microvascular function, as assessed by laser Doppler imaging and iontophoresis of acetylcholine and localized heating. This improvement was abolished by pretreatment with the nitric oxide (NO) synthase inhibitor l-N ^G-nitro-l-arginine methyl ester, while iontophoresis of the NO donor sodium nitroprusside eliminated the observed differences. A-285222 treatment enhanced dermis endothelial NO synthase expression and plasma NO levels of diabetic mice. It also prevented induction of inflammatory cytokines interleukin-6 and osteopontin, lowered plasma endothelin-1 and blood pressure, and improved mouse survival without affecting blood glucose. In vivo inhibition of NFAT may represent a novel therapeutic modality to preserve endothelial function in diabetes.

Genetically Targeted Optical Control of an Endogenous G Protein-Coupled Receptor

G protein-coupled receptors (GPCRs) are membrane proteins that play important roles in biology. However, our understanding of their function in complex living systems is limited because we lack tools that can target individual receptors with sufficient precision. State-of-the-art approaches, including DREADDs, optoXRs, and PORTL gated-receptors, control GPCR signaling with molecular, cell type, and temporal specificity. Nonetheless, these tools are based on engineered non-native proteins that may (i) express at nonphysiological levels, (ii) localize and turnover incorrectly, and/or (iii) fail to interact with endogenous partners. Alternatively, membrane-anchored ligands (t-toxins, DARTs) target endogenous receptors with molecular and cell type specificity but cannot be turned on and off. In this study, we used a combination of chemistry, biology, and light to control endogenous metabotropic glutamate receptor 2 (mGluR2), a Family C GPCR, in primary cortical neurons. mGluR2 was rapidly, reversibly, and selectively activated with photoswitchable glutamate tethered to a genetically targeted-plasma membrane anchor (membrane anchored Photoswitchable Orthogonal Remotely Tethered Ligand; maPORTL). Photoactivation was tuned by adjusting the length of the PORTL as well as the expression level and geometry of the membrane anchor. Our findings provide a template for controlling endogenous GPCRs with cell type specificity and high spatiotemporal precision.

A Two-Pulse Cellular Stimulation Test Elucidates Variability and Mechanisms in Signaling Pathways

Mammalian cells respond in a variable manner when provided with physiological pulses of ligand, such as low concentrations of acetylcholine present for just tens of seconds or TNFα for just tens of minutes. For a two-pulse stimulation, some cells respond to both pulses, some do not respond, and yet others respond to only one or the other pulse. Are these different response patterns the result of the small number of ligands being able to only stochastically activate the pathway at random times or an output pattern from a deterministic algorithm responding differently to different stimulation intervals? If the response is deterministic in nature, what parameters determine whether a response is generated or skipped? To answer these questions, we developed a two-pulse test that utilizes different rest periods between stimulation pulses. This "rest-period test" revealed that cells skip responses predictably as the rest period is shortened. By combining these experimental results with a mathematical model of the pathway, we further obtained mechanistic insight into potential sources of response variability. Our analysis indicates that in both intracellular calcium and NFκB signaling, response variability is consistent with extrinsic noise (cell-to-cell variability in protein levels), a short-term memory of stimulation, and high Hill coefficient processes. Furthermore, these results support recent works that have emphasized the role of deterministic processes for explaining apparently stochastic cellular response variability and indicate that even weak stimulations likely guide mammalian cells to appropriate fates rather than leaving outcomes to chance. We envision that the rest-period test can be applied to other signaling pathways to extract mechanistic insight.

Camphor white oil induces tumor regression through cytotoxic T cell-dependent mechanisms

Bioactive derivatives from the camphor laurel tree, Cinnamomum camphora, are posited to exhibit chemopreventive properties but the efficacy and mechanism of these natural products are not fully understood. We tested an essential-oil derivative, camphor white oil (CWO), for anti-tumor activity in a mouse model of keratinocyte-derived skin cancer. Daily topical treatment with CWO induced dramatic regression of pre-malignant skin tumors and a two-fold reduction in cutaneous squamous cell carcinomas. We next investigated underlying cellular and molecular mechanisms. In cultured keratinocytes, CWO stimulated calcium signaling, resulting in calcineurin-dependent activation of nuclear factor of activated T cells (NFAT). In vivo, CWO induced transcriptional changes in immune-related genes identified by RNA-sequencing, resulting in cytotoxic T cell-dependent tumor regression. Finally, we identified chemical constituents of CWO that recapitulated effects of the admixture. Together, these studies identify T cell-mediated tumor regression as a mechanism through which a plant-derived essential oil diminishes established tumor burden.

Gene expression patterns in synchronized islet populations

In vivo levels of insulin are oscillatory with a period of ~5-10 minutes, indicating that the islets of Langerhans within the pancreas are synchronized. While the synchronizing factors are still under investigation, one result of this behavior is expected to be coordinated and oscillatory intracellular factors, such as intracellular Ca²⁺ levels, throughout the islet population. In other cell types, oscillatory intracellular signals, like intracellular Ca²⁺, have been shown to affect specific gene expression. To test how the gene expression landscape may differ between a synchronized islet population with its reproducible intracellular oscillations and an unsynchronized islet population with heterogeneous oscillations, gene set enrichment analysis (GSEA) was used to compare an islet population that had been synchronized using a glucose wave with a 5-min period, and an unsynchronized islet population. In the population exposed to the glucose wave, 58/62 islets showed synchronization as evidenced by coordinated intracellular Ca²⁺ oscillations with an average oscillation period of 5.1 min, while in the unsynchronized population 29/62 islets showed slow oscillations with an average period of 5.2 min. The synchronized islets also had a significantly smaller drift of their oscillation period during the experiment as compared to the unsynchronized population. GSEA indicated that the synchronized population had reduced expression of gene sets related to protein translation, protein turnover, energy expenditure, and insulin synthesis, while those that were related to maintenance of cell morphology were increased.

Computing Mathematical Functions using DNA via Fractional Coding

This paper discusses the implementation of mathematical functions such as exponentials, trigonometric functions, the sigmoid function and the perceptron function with molecular reactions in general, and DNA strand displacement reactions in particular. The molecular constructs for these functions are predicated on a novel representation for input and output values: a fractional encoding, in which values are represented by the relative concentrations of two molecular types, denoted as type-1 and type-0. This representation is inspired by a technique from digital electronic design, termed stochastic logic, in which values are represented by the probability of 1's in a stream of randomly generated 0's and 1's. Research in the electronic realm has shown that a variety of complex functions can be computed with remarkably simple circuitry with this stochastic approach. This paper demonstrates how stochastic electronic designs can be translated to molecular circuits. It presents molecular implementations of mathematical functions that are considerably more complex than any shown to date. All designs are validated using mass-action simulations of the chemical kinetics of DNA strand displacement reactions.

Integrative meta-modeling identifies endocytic vesicles, late endosome and the nucleus as the cellular compartments primarily directing RTK signaling

Recently, intracellular receptor signaling has been identified as a key component mediating cell responses for various receptor tyrosine kinases (RTKs). However, the extent each endocytic compartment (endocytic vesicle, early endosome, recycling endosome, late endosome, lysosome and nucleus) contributes to receptor signaling has not been quantified. Furthermore, our understanding of endocytosis and receptor signaling is complicated by cell- or receptor-specific endocytosis mechanisms. Therefore, towards understanding the differential endocytic compartment signaling roles, and identifying how to achieve signal transduction control for RTKs, we delineate how endocytosis regulates RTK signaling. We achieve this via a meta-analysis across eight RTKs, integrating computational modeling with experimentally derived cell (compartment volume, trafficking kinetics and pH) and ligand-receptor (ligand/receptor concentration and interaction kinetics) physiology. Our simulations predict the abundance of signaling from eight RTKs, identifying the following hierarchy in RTK signaling: PDGFRβ > IGFR1 > EGFR > PDGFRα > VEGFR1 > VEGFR2 > Tie2 > FGFR1. We find that endocytic vesicles are the primary cell signaling compartment; over 43% of total receptor signaling occurs within the endocytic vesicle compartment for these eight RTKs. Mechanistically, we found that high RTK signaling within endocytic vesicles may be attributed to their low volume (5.3 × 10^-19 L) which facilitates an enriched ligand concentration (3.2 μM per ligand molecule within the endocytic vesicle). Under the analyzed physiological conditions, we identified extracellular ligand concentration as the most sensitive parameter to change; hence the most significant one to modify when regulating absolute compartment signaling. We also found that the late endosome and nucleus compartments are important contributors to receptor signaling, where 26% and 18%, respectively, of average receptor signaling occurs across the eight RTKs. Conversely, we found very low membrane-based receptor signaling, exhibiting <1% of the total receptor signaling for these eight RTKs. Moreover, we found that nuclear translocation, mechanistically, requires late endosomal transport; when we blocked receptor trafficking from late endosomes to the nucleus we found a 57% reduction in nuclear translocation. In summary, our research has elucidated the significance of endocytic vesicles, late endosomes and the nucleus in RTK signal propagation.

Temporal Bias: Time-Encoded Dynamic GPCR Signaling

Evidence suggests that cells can time-encode signals for secure transport and perception of information, and it appears that this dynamic signaling is a common principle of nature to code information in time. G-protein-coupled receptor (GPCR) signaling networks are no exception as their composition and signal transduction appear temporally flexible. In this review, we discuss the potential mechanisms by which GPCRs code biological information in time to create 'temporal bias.' We highlight dynamic signaling patterns from the second messenger to the receptor-ligand level and shed light on the dynamics of G-protein cycles, the kinetics of ligand-receptor interaction, and the occurrence of distinct signaling waves within the cell. A dynamic feature such as temporal bias adds to the complexity of GPCR signaling bias and gives rise to the question whether this trait could be exploited to gain control over time-encoded cell physiology.

New insights into mammalian signaling pathways using microfluidic pulsatile inputs and mathematical modeling

Temporally modulated input mimics physiology. This chemical communication strategy filters the biochemical noise through entrainment and phase-locking. Under laboratory conditions, it also expands the observability space for downstream responses. A combined approach involving microfluidic pulsatile stimulation and mathematical modeling has led to deciphering of hidden/unknown temporal motifs in several mammalian signaling pathways and has provided mechanistic insights, including how these motifs combine to form distinct band-pass filters and govern fate regulation under dynamic microenvironment. This approach can be utilized to understand signaling circuit architectures and to gain mechanistic insights for several other signaling systems. Potential applications include synthetic biology and biotechnology, in developing pharmaceutical interventions, and in developing lab-on-chip models.