Proton-gated coincidence detection is a common feature of GPCR signaling

The evolutionary expansion of G protein-coupled receptors (GPCRs) has produced a rich diversity of transmembrane sensors for many physical and chemical signals. In humans alone, over 800 GPCRs detect stimuli such as light, hormones, and metabolites to guide cellular decision-making primarily using intracellular G protein signaling networks. This diversity is further enriched by GPCRs that function as molecular sensors capable of discerning multiple inputs to transduce cues encoded in complex, context-dependent signals. Here, we show that many GPCRs are coincidence detectors that couple proton (H+) binding to GPCR signaling. Using a panel of 28 receptors covering 280 individual GPCR-Gα coupling combinations, we show that H+ gating both positively and negatively modulates GPCR signaling. Notably, these observations extend to all modes of GPCR pharmacology including ligand efficacy, potency, and cooperativity. Additionally, we show that GPCR antagonism and constitutive activity are regulated by H+ gating and report the discovery of an acid sensor, the adenosine A2a receptor, which can be activated solely by acidic pH. Together, these findings establish a paradigm for GPCR signaling, biology, and pharmacology applicable to acidified microenvironments such as endosomes, synapses, tumors, and ischemic vasculature.

Combining Mass Spectrometry-Based Phosphoproteomics with a Network-Based Approach to Reveal FLT3-Dependent Mechanisms of Chemoresistance

FLT3 mutations are the most frequently identified genetic alterations in acute myeloid leukemia (AML) and are associated with poor clinical outcome, relapse and chemotherapeutic resistance. Elucidating the molecular mechanisms underlying FLT3-dependent pathogenesis and drug resistance is a crucial goal of biomedical research. Given the complexity and intricacy of protein signaling networks, deciphering the molecular basis of FLT3-driven drug resistance requires a systems approach. Here we discuss how the recent advances in mass spectrometry (MS)-based (phospho) proteomics and multiparametric analysis accompanied by emerging computational approaches offer a platform to obtain and systematically analyze cell-specific signaling networks and to identify new potential therapeutic targets.

Proton-gated coincidence detection is a common feature of GPCR signaling

The evolutionary expansion of G protein-coupled receptors (GPCRs) has produced a rich diversity of transmembrane sensors for many physical and chemical signals. In humans alone, over 800 GPCRs detect stimuli such as light, hormones, and metabolites to guide cellular decision making primarily using intracellular G protein signaling networks. This diversity is further enriched by GPCRs that function as molecular logic gates capable of discerning multiple inputs to transduce cues encoded in complex, context-dependent signals. Here, we show that many GPCRs are switch-like Boolean-gated coincidence detectors that link proton (H+) binding to GPCR signaling. Using a panel of 28 receptors, covering 280 individual GPCR-Gα coupling combinations, we show that H+ gating both positively and negatively modulates and controls GPCR signaling. Notably, these observations extend to all modes of GPCR pharmacology including ligand efficacy, potency, and cooperativity. Additionally, we show that GPCR antagonism and constitutive activity are regulated by H+ gating and report the discovery of a new acid sensor, the adenosine A2a receptor (ADORA2A), which can be activated solely by acidic pH. Together, these findings establish a new paradigm for GPCR biology and pharmacology in acidified microenvironments such as endosomes, synapses, tumors, and ischemic vasculature.

In Silico Design of Self-Optimizing Integrated Metabolic and Gene Regulatory Networks

Biological organisms acclimatize to varying environmental conditions via active self-regulation of internal gene regulatory networks, metabolic networks, and protein signaling networks. While much work has been done to elucidate the topologies of individual networks in isolation, understanding of inter-network regulatory mechanisms remains limited. This shortcoming is of particular relevance to synthetic biology. Synthetic biological circuits tend to lose their engineered functionality over generational time, primarily due to the deleterious stress that they exert on their host organisms. To reduce this stress (and thus minimize loss of functionality) synthetic circuits must be sensitive to the health of the host organism. Development of integrated regulatory systems is therefore essential to robust synthetic biological systems. The aim of this study was to develop integrated gene-regulatory and metabolic networks which self-optimize in response to varying environmental conditions. We performed \emph{in silico} evolution to develop such networks using a two-step approach: (1) We optimized metabolic networks given a constrained amount of available enzyme. Here, we found that a proportional relationship between flux control coefficients and enzyme mass holds in all linear sub-networks of branched networks, except those sub-networks which contain allosteric regulators. Network optimization was performed by iteratively redistributing enzyme until flux through the network was maximized. Optimization was performed for a range of boundary metabolite conditions to develop a profile of optimal enzyme distributions as a function of environmental conditions. (2) We generated and evolved randomized gene regulatory networks to modulate the enzymes of a target metabolic pathway. The objective of the gene regulatory networks was to produce the optimal distribution of metabolic network enzymes given specific boundary metabolite conditions of the target network. Competitive evolutionary algorithms were applied to optimize the specific structures and kinetic parameters of the gene regulatory networks. With this method, we demonstrate the possibility of algorithmic development of integrated adaptive gene and metabolic regulatory networks which dynamically self-optimize in response to changing environmental conditions.