Structural Elements Directing G Proteins and ß-Arrestin Interactions with the Human Melatonin Type 2 Receptor Revealed by Natural Variants.

G protein-coupled receptors (GPCRs) can engage distinct subsets of signaling pathways, but the structural determinants of this functional selectivity remain elusive. The naturally occurring genetic variants of GPCRs, selectively affecting different pathways, offer an opportunity to explore this phenomenon. We previously identified 40 coding variants of the MTNR1B gene encoding the melatonin MT2 receptor (MT2). These mutations differently impact the ß-arrestin 2 recruitment, ERK activation, cAMP production, and Gαi1 and Gαz activation. In this study, we combined functional clustering and structural modeling to delineate the molecular features controlling the MT2 functional selectivity. Using non-negative matrix factorization, we analyzed the signaling signatures of the 40 MT2 variants yielding eight clusters defined by unique signaling features and localized in distinct domains of MT2. Using computational homology modeling, we describe how specific mutations can selectively affect the subsets of signaling pathways and offer a proof of principle that natural variants can be used to explore and understand the GPCR functional selectivity.

Recurrent high-impact mutations at cognate structural positions in class A G protein-coupled receptors expressed in tumors.

G protein-coupled receptors (GPCRs) are the largest family of human proteins. They have a common structure and, signaling through a much smaller set of G proteins, arrestins, and effectors, activate downstream pathways that often modulate hallmark mechanisms of cancer. Because there are many more GPCRs than effectors, mutations in different receptors could perturb signaling similarly so as to favor a tumor. We hypothesized that somatic mutations in tumor samples may not be enriched within a single gene but rather that cognate mutations with similar effects on GPCR function are distributed across many receptors. To test this possibility, we systematically aggregated somatic cancer mutations across class A GPCRs and found a nonrandom distribution of positions with variant amino acid residues. Individual cancer types were enriched for highly impactful, recurrent mutations at selected cognate positions of known functional motifs. We also discovered that no single receptor drives this pattern, but rather multiple receptors contain amino acid substitutions at a few cognate positions. Phenotypic characterization suggests these mutations induce perturbation of G protein activation and/or ß-arrestin recruitment. These data suggest that recurrent impactful oncogenic mutations perturb different GPCRs to subvert signaling and promote tumor growth or survival. The possibility that multiple different GPCRs could moonlight as drivers or enablers of a given cancer through mutations located at cognate positions across GPCR paralogs opens a window into cancer mechanisms and potential approaches to therapeutics.

Author Correction: Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response.

Signaling diversity of G protein-coupled (GPCR) ligands provides novel opportunities to develop more effective, better-tolerated therapeutics. Taking advantage of these opportunities requires identifying which effectors should be specifically activated or avoided so as to promote desired clinical responses and avoid side effects. However, identifying signaling profiles that support desired clinical outcomes remains challenging. This study describes signaling diversity of mu opioid receptor (MOR) ligands in terms of logistic and operational parameters for ten different in vitro readouts. It then uses unsupervised clustering of curve parameters to: classify MOR ligands according to similarities in type and magnitude of response, associate resulting ligand categories with frequency of undesired events reported to the pharmacovigilance program of the Food and Drug Administration and associate signals to side effects. The ability of the classification method to associate specific in vitro signaling profiles to clinically relevant responses was corroborated using ß2-adrenergic receptor ligands.

Type 2 diabetes-associated variants of the MT2 melatonin receptor affect distinct modes of signaling.

Melatonin is produced during the night and regulates sleep and circadian rhythms. Loss-of-function variants in MTNR1B, which encodes the melatonin receptor MT2, a G protein-coupled receptor (GPCR), are associated with an increased risk of type 2 diabetes (T2D). To identify specific T2D-associated signaling pathway(s), we profiled the signaling output of 40 MT2 variants by monitoring spontaneous (ligand-independent) and melatonin-induced activation of multiple signaling effectors. Genetic association analysis showed that defects in the melatonin-induced activation of Gαi1 and Gαz proteins and in spontaneous ß-arrestin2 recruitment to MT2 were the most statistically significantly associated with an increased T2D risk. Computational variant impact prediction by in silico evolutionary lineage analysis strongly correlated with the measured phenotypic effect of each variant, providing a predictive tool for future studies on GPCR variants. Together, this large-scale functional study provides an operational framework for the postgenomic analysis of the multiple GPCR variants present in the human population. The association of T2D risk with signaling pathway-specific defects opens avenues for pathway-specific personalized therapeutic intervention and reveals the potential relevance of MT2 function during the day, when melatonin is undetectable, but spontaneous activity of the receptor occurs.

Evolutionary action and structural basis of the allosteric switch controlling ß2AR functional selectivity.

Functional selectivity of G-protein-coupled receptors is believed to originate from ligand-specific conformations that activate only subsets of signaling effectors. In this study, to identify molecular motifs playing important roles in transducing ligand binding into distinct signaling responses, we combined in silico evolutionary lineage analysis and structure-guided site-directed mutagenesis with large-scale functional signaling characterization and non-negative matrix factorization clustering of signaling profiles. Clustering based on the signaling profiles of 28 variants of the ß2-adrenergic receptor reveals three clearly distinct phenotypical clusters, showing selective impairments of either the Gi or ßarrestin/endocytosis pathways with no effect on Gs activation. Robustness of the results is confirmed using simulation-based error propagation. The structural changes resulting from functionally biasing mutations centered around the DRY, NPxxY, and PIF motifs, selectively linking these micro-switches to unique signaling profiles. Our data identify different receptor regions that are important for the stabilization of distinct conformations underlying functional selectivity.

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling.

Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype-phenotype relationship.

HUMAN KINASES DISPLAY MUTATIONAL HOTSPOTS AT COGNATE POSITIONS WITHIN CANCER.

The discovery of driver genes is a major pursuit of cancer genomics, usually based on observing the same mutation in different patients. But the heterogeneity of cancer pathways plus the high background mutational frequency of tumor cells often cloud the distinction between less frequent drivers and innocent passenger mutations. Here, to overcome these disadvantages, we grouped together mutations from close kinase paralogs under the hypothesis that cognate mutations may functionally favor cancer cells in similar ways. Indeed, we find that kinase paralogs often bear mutations to the same substituted amino acid at the same aligned positions and with a large predicted Evolutionary Action. Functionally, these high Evolutionary Action, non-random mutations affect known kinase motifs, but strikingly, they do so differently among different kinase types and cancers, consistent with differences in selective pressures. Taken together, these results suggest that cancer pathways may flexibly distribute a dependence on a given functional mutation among multiple close kinase paralogs. The recognition of this "mutational delocalization" of cancer drivers among groups of paralogs is a new phenomena that may help better identify relevant mechanisms and therefore eventually guide personalized therapy.