A Global Map of G Protein Signaling Regulation by RGS Proteins

1Heterotrimeric G proteins transduce a vast variety of extracellular stimuli, including hormones, ions, organic molecules, and light into the regulation of intracellular “effectors” to generate cellular responses. Collectively, G protein systems play a role in nearly every physiological process and in numerous pathologies. G proteins are activated by the binding of GTP to the α subunits (Gα) that release them from inhibitory occlusion by the βγ dimer (Gβγ). Mammalian genomes encode a conserved set of 16 Gα subunits, each possessing unique signaling properties and the ability to selectively engage a distinct set of effectors, including adenylate cyclases, phospholipase C isozymes, Rho guanine nucleotide exchange factors (GEFs), and ion channels.

2The key determinant of G protein action in cells is their lifetime in an active state. Thus, the activation and deactivation of G proteins is tightly controlled and ought to occur with selectivity for individual G proteins to ensure the selectivity of downstream signaling. Deciphering molecular mechanisms of this selectivity is of paramount importance for understanding how the signals are routed in the cells. A number of G protein activators have been described and demonstrated to act as GEFs on the Gα subunits with clear subtype selectivity. Among them, the largest class is the G protein-coupled receptor (GPCR) family. GPCRs exhibit clear preferences for activating particular Gα species, and there has been tremendous progress in understanding the molecular mechanisms in establishing this selectivity.

3The opposing process of G protein deactivation occurs when G proteins hydrolyze guanosine triphosphate (GTP), a process assisted by the action of the GTPase-activating proteins (GAPs). The GAP action is essential for avoiding response saturation and for achieving temporal resolution dictated by individual physiological reactions. Most well-characterized GAPs for heterotrimeric G proteins belong to the regulator of G protein signaling (RGS) family, consisting of 20 canonical members in mammals. RGS proteins bind to active Gα proteins and facilitate their GTPase activity, thereby accelerating the termination of G protein signaling. It is now well established that this action of RGS proteins is crucial for achieving the physiologically relevant timing and extent of GPCR signaling. Accordingly, the loss of RGS-mediated control leads to a range of pathologies observed in mouse models and is increasingly associated with human diseases. Studies in several members of the RGS family indicate that they exert considerable selectivity in recognizing Gα. There has been significant progress documenting cases of selective RGS-Gα interactions, analyzing the structural basis for this selectivity, and mapping amino acid residues involved in specific recognition. Although these studies provide insights into the selectivity of RGS action for isolated cases, a comprehensive understanding of the complete landscape of Gα preferences of RGS proteins is still lacking.

4This study presents a map of Gα selectivity for all canonical RGS proteins. We monitored the temporal regulation of GPCR-mediated G protein signaling and quantitatively characterized the GAP activity of the RGS proteins, testing nearly all of the theoretically possible Gα-RGS pairings (300 combinations). Using the functional activity as a readout in the context of a physiologically relevant cellular environment allowed us ot document the preferences of RGS proteins for Gα substrates, revealing pairings and disallowed combinations. This information led to the identification of molecular determinants involved in the selectivity of Gα-RGS recognition. Applying computational algorithms, we also show how these determinants have evolved and can be used to create designer RGS proteins with novel selectivity profiles. Analysis of human genomic data further suggests that genetic variations in RGS selectivity determinants may contribute to non-disease traits, pathological dysregulation of GPCR signaling, and variable responsiveness to drug treatments.

5To test their possible RGS-Gα coupling systematically, we used a cell-based system that provides a cellular environment to study the action of RGS in the context of GPCR signaling. This assay monitors RGS-induced acceleration of G protein deactivation by real-time bioluminescence resonance energy transfer (BRET) strategy tracking the kinetics of heterotrimer re-association upon antagonizing GPCR, a reaction catalyzed by RGS proteins physiologically (Figure 1A). The key features of the assay include a “bystander” approach that allows the use of unmodified Gα subunits (Figure 1B) and full-length RGS proteins (Figure 1C).

6Using a set of GPCRs with varying Gα selectivity, we recorded the deactivation kinetics of 15 Gα subunits (omitting sensory Gαt1, Gαt2, and Gαgust, but including the two common splice variants of Gαs and Gαo) in the absence of exogenous RGS proteins. A combination of intrinsic differences in Gα properties and the action of endogenous RGS proteins in HEK293T/17 cells yielded characteristic baseline deactivation rates (Figures 1D and 1E). Using a previously established approach, we ensured that the deactivation kinetics were rate limited by the Gα GTPase activity. Disruption of RGS-Gα interactions by RGS-insensitive or GAP-deficient mutations substantially prolonged response recovery (Figure S1). These mutations interfere with the conserved interaction of RGS proteins with the switch I region of the Gα subunits. Further controls demonstrated that (1) the exogenous expression of RGS proteins does not alter the expression of signaling molecules and sensors (Figures S2A and S2B), (2) the different expression levels of GPCRs or the different amounts of active G proteins do not change the G protein deactivation rates (Figure S2C), and (3) deactivation rates are directly proportional to the amount of RGS (Figure S2D). These results confirm that RGS action dictates the kinetics of G protein deactivation. Analysis of the deactivation traces for a representative Gα (GαoA) shows the varying impact of different exogenous RGS proteins on the kinetics of Gα termination (Figures 1F and 1G).

7To quantify the activity of RGS proteins, the baseline deactivation rates (1/τ) of each Gα were subtracted from the deactivation rates in the presence of exogenous RGS proteins, yielding the kGAP parameter (Figure 1H), a widely used metric of RGS catalytic activity. Plotting kGAP values for each of the Gα substrates provides a profile of relative activity for a given RGS protein. Analysis of the representative members of the RGS subfamilies using this strategy revealed differences in Gα preferences in a fingerprint-like fashion (Figure 1I). These Gα selectivity fingerprints were not affected by differences in the RGS expression levels (Figure S3A–S3D).

8This strategy was applied to measure the activity of all 20 canonical RGS proteins on the deactivation of each of 15 Gα subunits in a total of 300 possible combinations. We optimized RGS expression levels, ensuring at least 3-fold acceleration of the deactivation rate for the preferred Gα substrate to reliably assess even minor coupling. Ιn particularly difficult cases (e.g., RGS13, RGS18), proteasomal blockade and codon optimization strategies were applied to augment RGS expression (Figures S3E–S3K). Given the differences in the expression levels of various RGS proteins, we did not attempt to compare their absolute activities and instead focused on elucidating the relative differences in G protein preferences. Collectively, our results provide a comprehensive Gα selectivity profile for the entire RGS family (Figures 1J and S4; Table S1).

9Analysis of the RGS-Gα interaction network provided several key insights. We found that RGS proteins vary markedly in the breadth of their selectivity, with some members (e.g., RGS1) regulating all Gi/o- and Gq-type proteins, whereas others (e.g., RGS11) regulated only one Gα type, Gαo (Figures 2A, 2B, and S5A–S5C). The R4 and RZ subfamilies regulated the broadest range of Gα substrates (Figures 2A, 2B, and S5A–S5C). Collectively, R4 and RZ members regulated all Gαq and Gαi/o types with a spectrum of biases (Figures 1J, 2A, and S4). For example, RGS3 and RGS4 preferred the Gαi/o over the Gαq, whereas RGS5 and RGS13 selected Gαq over Gαi/o. No RGS protein was shown to be specific for the Gαq subfamily. The narrowest selectivity was observed for the R7 subfamily, the members of which regulated Gαi/o proteins exclusively (but not Gαz) with prominent selectivity for Gαo.

10This analysis revealed that Gα subunits vary substantially in their sensitivity to RGS regulation (Figures 2C and S5E). For example, we found Gαo to be the most indiscriminate Gα in that it was regulated by all of the canonical RGS proteins, whereas Gαz could be deactivated only by a limited number of RGS proteins (Figures 2C, S5D, and S5F). We also noticed that a relatively slow rate (0.0021 ± 0.0003 s−1) of basal GTPase activity of Gαz possibly underestimated the selectivity of its regulation by RGS proteins when assessed by the kGAP parameter (Figures S5G–S5I). Accordingly, we calculated a discrimination index (kdis) defined by fold increase in the deactivation constant (1/τ) upon the addition of RGS (Figure S5J). Although considering that kdis did not change the overall picture of G protein selectivity for most RGS members, it was useful in showing the unique ability of RZ subfamily members to uniquely regulate Gαz (Figures S5K and S5L) amidst their significant activity on virtually all of the other Gαi/o and Gαq proteins based on the kGAP.

11These data also revealed high selectivity in the regulation of the poorly studied Gα15. This G protein is activated by a wide range of GPCRs and thus likely contributes to a variety of cellular responses. We found that it has a very slow intrinsic deactivation rate (0.0081 ± 0.0006 s−1), making RGS regulation paramount for the temporal control of its signaling. Interestingly, Gα15 can be deactivated by only a few RGS proteins (Figure S5D), mostly Gαq-type-preferring R4 members and an RZ subfamily member, RGS17 (Figures 2C and S5F).

12These studies further revealed that no canonical RGS proteins could regulate the deactivation of Gαs, Gαolf, Gα12, or Gα13 (Figure 1J). This outcome is perhaps not unexpected. Structural modeling shows that the switch I region of Gα12/13 contains Lys-204 instead of a Thr present in all of the other Gα subfamilies in the corresponding position, rendering it incompatible with RGS binding (Figures S5M and S5N). Furthermore, the structure of the αB–C loop in the α-helical domain is also fundamentally different in Gα12/13, contributing to the steric occlusion of canonical RGS protein binding. Similarly, the presence of Asp229 in Gαs, a position conserved as serine in all other Gα subfamilies, renders it incapable of RGS binding in Gαs family members due to collisions with the α5–α6 loop of RGS proteins (Figures S5O and S5P). The Gαs D229S mutation restores the ability of RGS4 and RGS16 to bind and the ability of RGS16 to accelerate GTP hydrolysis on Gαs.

13To study how global patterns of RGS-Gα selectivity affect the processing of GPCR signals endogenously, we used striatal medium spiny neurons (MSNs) as a model (Figure 3A). The MSNs were chosen because of their undisputed physiological importance and the critical role of several well-defined GPCRs in processing neuromodulatory inputs to these neurons (Figure 3B). More important, several RGS proteins in the MSNs have been implicated in controlling behavioral responses to GPCR stimulation. The best-documented examples of these are RGS4, a member of the R4 subfamily, and RGS9, a member of the R7 subfamily.

14We surveyed the expression landscape of RGS and Gα proteins by curating the available quantitative RNA sequencing (RNA-seq) data. This analysis revealed a significant expression of 12 RGS genes, with RGS4 and RGS9 being the most abundant. Three members of the R4 subfamily (RGS4, RGS2, and RGS8) and 3 members of the R7 subfamilies (RGS9, RGS11, and RGS7) were estimated to be more highly expressed by at least an order of magnitude than other striatal RGS proteins (Figure 3C). Interestingly, our dataset indicates that these RGS subfamilies have distinct patterns of Gα selectivity; the R7 RGS proteins are narrowly tuned for Gi/o, whereas the R4 RGS members are capable of regulating a broad spectrum of Gα, including both Gi/o and Gq members (Figures 2B and 2C). Accordingly, transcripts encoding the members (Gαo, Gαi1–3, Gαz, Gαq, and Gα11) of the Gαi/o and Gαq subfamilies were abundantly expressed by the MSNs (Figure 3C). Thus, we predicted that R4 RGS proteins would have a major influence on the processing of GPCR signals via both Gi/o and Gq pathways, whereas R7 RGS proteins would selectively affect only Gαi/o-mediated signals.

15To test this prediction, we used biosensors to monitor the dynamics of second messenger pathway engagement downstream of both Gi/o and Gq while inactivating RGS proteins by CRISPR-Cas9 editing in the primary cultures of MSNs (Figure 3D). The Gi/o activity was assessed by studying its inhibitory influence on cyclic AMP (cAMP) production in response to stimulation of the Gi/o-coupled dopamine receptor D2 (D2R) by dopamine, whereas Gq-type activity was monitored by Ca2+ transients induced in response to the activation of the muscarinic M1/M3 receptors (M1/3R) by acetylcholine (Figure 3B). Considering the intra-class similarity of RGS-Gα pairing and abundant expression of several members from each RGS class, we chose to simultaneously eliminate all MSN-expressed RGS proteins belonging to the same subfamily by CRISPR-Cas9 editing. The elimination of either the R4 or the R7 subfamily resulted in a significantly enhanced cAMP response, consistent with the role of these RGS members in the deactivation of the Gi/o pathway (Figures 3E and 3F). In contrast, the elimination of R4 members but not R7 proteins augmented the Ca2+ response, which is in line with their observed Gα selectivity profiles (Figures 3G and 3H).

16We next tested the effect of overexpressing individual RGS proteins. We chose to focus on RGS2, an abundantly expressed RGS protein, widely believed to be Gq selective based on biochemical measurements but able to regulate Gi/o proteins according to our data (Figures 1J and S4). The overexpression of RGS2 had an opposite effect from eliminating RGS proteins and dramatically suppressed the amplitudes of both cAMP and calcium responses (Figures 3I–3L). These observations indicate that the comprehensive RGS-Gα selectivity maps have predictive power in dissecting the logic of GPCR signal processing in an endogenous setting.

17The analysis presented in this study revealed a wide range of Gα preferences across RGS proteins, which also feature considerable structural diversity. This opens questions about the flexibility of recognition patterns across the family and the degree with which Gα selectivity is determined by the RGS domain shared by all RGS proteins. To address these questions in an unbiased way and gain insight into how the selectivity of mammalian RGS subfamilies may have evolved, we performed the reconstitution of ancestral RGS proteins (Figure 4A). We traced the RGS family tree to reconstitute common ancestral RGS domains at three branch points before the diversification into the current four subfamilies and generated a series of chimeric RGS proteins (Figure 4B).

18Examination of the Gα selectivity of the primal ancestral RGS protein (AncR4/Z/12/7) revealed that it regulated all Gα subunits that RGS proteins can regulate, except Gα15 (Figures 4C and 4D). We next reconstructed two ancestral RGS proteins at the roots of the subfamily divisions (AncR4/Z and AncR12/7). Interestingly, AncR4/Z showed equally strong GAP activity toward Gαi/o and Gαq subfamilies, but not toward Gαz (Figure 4D). Diversification of this precursor RGS subsequently generated various patterns of Gαi/o- and Gαq selectivity observed in current R4 and RZ subfamilies. The other ancestral RGS protein, AncR12/7, showed Gαi/o selectivity and was devoid of the ability to regulate the Gαq subfamily. This ancestral RGS gave rise to Gαi/o-selective R12 and R7 RGS proteins. These results suggest that Gα selectivity patterns of extant human RGS proteins resulted from a combination of specialization along the Gαi/o versus Gαq axis and de novo acquisition of Gαz and Gα15 selectivity. This supports a predominantly evolutionary divergence model in which the primordial RGS precursor with balanced activity on different Gα substrates acquired various biases that followed different routes—for example, by suppressing the GAP activity toward the Gαq subfamily in R7 and R12 RGS or re-gaining the activity on Gαi/o subfamily by the R12 RGS. We thus conclude that the sequence composition of the RGS domain has considerable bearing on dictating the evolving Gα preferences of the RGS proteins, strongly suggesting that the major determinants of Gα selectivity are contained within the RGS domain.

19Elucidation of a Gα-RGS coupling map and demonstration of the crucial role of the RGS domain in determining the pairings prompted the identification of molecular determinants that govern their differential preferences. We compared the sequences of all human RGS domains, aligning them with reference to 20 available high-resolution structures that show the same conserved fold and preservation of key elements, with 9 α-helices and 10 loops (Figure S6A; Data S1). RGS11, RGS13, RGS20, and RGS21 were not included in this analysis because their structures have not been reported. This analysis allowed us to develop a Common RGS Numbering (CRN) system for labeling amino acids relative to their structural position similar to what was previously done for Gα and GPCRs (Figures S6B and S6C). This system helps to identify the position of every residue with reference to the secondary structure. For instance, RGS4 Asn128, which directly binds to Gαi1, is denoted as L6.10, indicating that this residue is the 10th amino acid located in loop 6 of the RGS domain (Figure S6B). It should be noted that this nomenclature cannot be applied to the H6 region in the R12 subfamily because it is structurally distinct from other RGS subfamilies.

20We further analyzed eight currently available structures of RGS/Gα complexes and found that all RGS and Gα subunits interact in a very similar manner, with low root mean square deviation (RMSD) in the range of 0.46–1.42 Å. In the RGS domain, there are 11 residues directly contacting Gα that are almost 100% conserved in all structures (Figure S6B). In addition to these contacting positions, we found 20 residues on the RGS protein and 38 amino acids on Gα that contribute to the organization of binding interfaces based on their localization within the 5Å radius of any atom in the interface. On the RGS side, these residues are distributed across 3 structural elements, 2 loops (H3–H4 and L6–H6) and 1 helix (H7–L9) (Figures 5A and 5B). The surface on Gα is more distributed and involves both GTPase and α-helical domains.

21To determine which elements most strongly contribute to the selectivity of Gα recognition, we analyzed these 31 RGS residues at the Gα-binding interface across all 20 human RGS paralogs in comparison with their orthologs from 21–65 animal species (Figures 5C and 5D; Data S2). This analysis revealed 14 highly conserved positions across orthologs and paralogs, suggesting that they likely serve as invariable architectural pillars that organize Gα binding and/or GAP activity. These residues included all of the direct Gα-contacting positions found in the RGS4/Gαi1 complex (Figure S6B). A minor fraction of the scattered residues was ortholog variable and neutrally evolving (Figures 5C and 5E). The remaining fraction of ortholog-specific residues comprised 17 amino acids. Mapping them on the RGS domain structure showed that they are distributed at the periphery of the Gα-binding surface, surrounding the central positions of the conserved amino acids (Figure 5D), suggesting that they may contribute to Gα selectivity by modulating the interaction. We subsequently refer to these peripheral amino acid residues that are variable among paralogs but conserved within their respective orthologs as Gα selectivity bar codes for RGS proteins.

22To identify motifs in the RGS domain that contribute to establishing Gα selectivity, we reconstructed and analyzed the RGS-Gα interaction network at a single amino acid resolution (Figures 5F and 5G). This analysis confirmed that the vast majority of selectivity bar code residues are engaged in non-conserved contacts that vary between different structures of the RGS-Gα complexes (Figure 5H). In contrast, the contacts involving the conserved residues were also predominantly conserved across RGS-Gα structures (Figure 5H). The highest degree of conserved residue-residue contacts is observed for the H3–H4 region with G.H2 and switch I in Gα and for the L7–L9 region with switch I (Figure 5F), indicating its crucial role as a structural backbone for RGS/Gα binding. In contrast, the interaction of the H7–L9 region with the α-helical domain showed the highest number of non-conserved contacts (Figure 5G), suggesting that these domains could significantly contribute to the RGS/Gα selectivity.

23To better characterize the organization of the Gα-binding surface, we analyzed properties of the amino acids that form the Gα selectivity bar codes across different RGS subfamilies. This investigation revealed distinct patterns in accordance with the experimentally determined Gα selectivity patterns (Figure 5I). For example, R4 and RZ subfamilies that are dually selective for the Gi/o and Gq proteins showed a similar distribution of hydrophobic and positively charged residues in the H7–L9 region; hydrophobic and positively and negatively charged residues in L6–H6; and a nucleophilic residue in H3–H4. In contrast, the Gi/o-selective R12 family exhibited a different pattern featuring nucleophilic, aromatic, and amide residues in the H7–L9 region, and a unique positively charged patch in the L6–H6 lobe surrounded by the nucleophilic cluster. However, another pattern was observed in the narrowly tuned R7 proteins whose L6–H6 region is populated by small amino acids adjacent to the hydrophobic patch and a prominent positive charge in H7–L9. These findings reinforce the idea that the nature of amino acid properties at the selectivity bar code region on the Gα-binding interface of the RGS protein comprises major determinants of Gα recognition selectivity.

24The identification of selectivity bar code residues in RGS proteins raises a question about their necessity and sufficiency in setting the selectivity of Gα recognition. This question was addressed experimentally, by transplanting the entire distributed pattern of selectivity residues (Figure 6A). For these experiments, we chose RGS13 and RGS18, which belong to the same R4 subfamily but differ in G protein selectivity (Figure 6C). RGS13 prefers Gq members over the Gi/o subfamily, whereas RGS18 equally regulates both Gi/o and Gq proteins. A comparison of their Gα selectivity bar codes indicates that they differ by 12 amino acid residues (Figure S7A). All of the amino acid residues of RGS13 were replaced with the ones from RGS18, resulting in RGS13/18-F chimera (Figure 6B). In agreement with the prediction based on our selectivity bar code model, RGS13/18-F protein exhibited RGS18-like Gα selectivity (Figure 6C).

25These experiments were then extended to RGS8 and RGS14, a pair that belongs to different subfamilies and also have markedly different Gα selectivity and composition of Gα selectivity residues (Figures 6D and 6E). We identified 15 different amino acids within the Gα selectivity bar code different between these RGS proteins (Figures 6D and S7B) and transplanted all of these from RGS14 into corresponding positions of RGS8, generating a “full” chimera (RGS8/14-F) (Figure 6D). The RGS8/14-F chimera completely recapitulated the Gα fingerprint of RGS14 without gaining activity on G proteins not regulated by RGS8 or RGS14 (Figure 6E). We further probed whether the change in selectivity could be achieved by mutating fewer bar code residues (i.e., by replacing only nine amino acid residues) (Figures S7B). The resulting “partial” RGS8/14 chimera (RGS8/14-P) had the same Gαq over Gαi/o preference as parental RGS8 (Figure S7C). It thus failed to switch the Gα-selectivity fingerprint from the RGS8 to the RGS14 pattern, indicating that all of the bar code amino acids are required for establishing exact selectivity patterns of Gα-RGS recognition. Curiously, the RGS8/14-P mutant unexpectedly gained activity on Gαz (Figure S7D), indicating that individual residues within the bar code can have an impact on the Gα selectivity of RGS proteins. Overall, these results indicate that identified selectivity bar codes are sufficient in dictating Gα substrate preferences.

26To gain insight into how ongoing evolutionary diversification shapes Gα selectivity, we analyzed natural variation in RGS sequences. Prevalence analysis of missense variations (MVs) reported for 2,504 healthy individuals from the 1000 Genomes Project revealed that, on average, an individual harbors 5 MVs within the canonical RGS proteins. Examination of the database indicated that a de novo MV occurs at approximately every 260 newborns, suggesting that RGS proteins are undergoing active evolution. We further analyzed the data on MVs within all of the canonical RGS proteins in 141,456 individuals (Data S3) from the gnomAD database. We found 106,521 rare MVs (minor allele frequency < 2%), with 79,167 MVs on the outside of the RGS domain, 27,354 MVs in the RGS domain, 1,220 MVs in conserved residues, and 1,757 MVs in selectivity residues (Figure 7A). In this analysis, the same variant type is counted multiple times if it occurs in multiple people, illustrating the scale of ongoing evolution (Figures 7A–7D). On average, 13 MVs exist in each amino acid residue of RGS proteins (Figure 7A). This density of MVs (14.8) was the highest outside of the RGS domain. In contrast, functionally important regions exhibited lower densities. The conserved and selectivity residues in RGS11 were the most variable among all of the RGS proteins (Figures 7B and 7C). The ratio of the MV density between selectivity and conserved residues revealed the highest MV frequency in the selectivity residues over the conserved residues in RGS17 (Figure 7D), suggesting likely extensive natural variation of Gα selectivity in RGS17.

27To understand the functional implications of the observed variations, we investigated the impact of randomly chosen seven mutations across various positions in the selectivity bar code region of six RGS proteins by testing their activity on the panel of six Gα subunits (Figure 7E). We found that all of the evaluated amino acid changes affected Gα selectivity. Notably, changes at L7.13 in RGS19 (R190W) increased the GAP activity toward Gα15, but decreased the activity on Gαo, Gαi1, and Gαq without any influence on Gαz. Alterations in L6.8, H6.2, H7.6, and H7.9 selectively augmented the regulation of Gαi/o without diminishing the activity on other Gα. The balance between Gαi and Gαo regulation can also be affected by these mutations—for example, E98G (L6.8) in RGS13 preferentially increased activity toward Gαo over Gαi, while R351Q (H6.2) in RGS11 and N164S (H7.9) in RGS12 augmented Gαi regulation more than Gαo. Altering the H6.4 position in RGS9 M370K resulted in a net loss of activity across Gα regulated by this RGS.

28Interestingly, variants in RGS proteins are also increasingly viewed as possibly contributing to pathological conditions due to generally disruptive effects. However, the exact mechanisms of functional alterations and implications for Gα selectivity for a vast number of cases remain unexplored. For instance, RGS16 has been recently implicated in insomnia, and knockout of this gene in mice disrupts circadian regulation. The genetic variation (rs1144566) in human RGS16 reported in the genome-wide association study (GWAS) catalog affects selectivity bar code residue H6.4 (Figures 7F and 7G). We experimentally evaluated the functional implication of minor allele variations in H6.4 of RGS16 prevalently occupied by arginine. Our data showed that the R137P mutation nearly completely abrogated the GAP activity of RGS16 for both of its representative preferred substrates, Gαo and Gαq, indicating a strong loss of function (Figure 7H). Curiously, the R137L substitution selectively compromised the activity of RGS16 only on Gαq without significant effects on the regulation of Gαo. These results indicate that mutations in the selectivity bar code may lead to RGS dysfunction associated not only with the complete loss of function but also with a more subtle alteration in the Gα selectivity.

29In this study, we present a nearly complete map of Gα recognition selectivity for all 20 canonical human RGS proteins. The wealth of accumulated evidence in the past 2 decades since their discovery revealed that members of the RGS family exert two distinct effects on the G protein signaling. First, they accelerate G protein deactivation and thus control the duration of signaling. The slow intrinsic GTPase activity of Gα subunits rate limits the termination of the response and does not permit the rapid signaling cycles often demanded by the physiological processes (e.g., in neuronal communication and cardiac activity). By accelerating the Gα GTPase, RGS proteins speed up termination of the response and thereby increase the temporal fidelity of GPCR-initiated signaling. This function is best exemplified by studies on photoreceptors in which the loss of RGS protein in the visual cascade initiated by rhodopsin diminishes the temporal resolution of visual signals, preventing the detection of moving objects. Second, by deactivating G proteins and/or competing with the effector molecules, RGS proteins interfere with signal propagation, thus taming the extent of signaling and allowing adjustment of the signaling volume, depending on the physiological needs. The loss of this RGS function is well noted to sensitize responses causing cellular overreactivity. From this perspective, RGS proteins could be considered endogenous genetically encoded antagonists of GPCR signaling.

30The results of our systematic profiling of RGS substrate preferences prompt reconsideration of the mechanisms involved in cellular signaling diversification. Despite their large numbers, GPCRs can only signal through the same limited number of G proteins that they can activate. Previous studies indicated that signaling diversity is in part dictated by a combination of G proteins activated by individual GPCRs. The negative regulation of individual Gα by RGS proteins, if sufficiently selective, would greatly contribute to signaling diversification to allow much more refined signaling characteristics with cellular specificity depending on the available RGS and G proteins. Whereas recent large-scale efforts have provided tremendous system-level insights into the selectivity of G protein activation by GPCRs, the information about the selectivity of RGS has been missing. We fill this gap by establishing Gα selectivity profiles for the entire family of RGS proteins. Based on this information, we propose that RGSs and GPCRs work in synergy to generate diverse cell-type-specific signaling.

31Although the experiments presented in this study demonstrate the importance of the bar code residues on the Gα-interacting interface of RGS proteins in dictating Gα preferences, the sufficiency of this residue-residue contact network in dictating precise selectivity patterns across the entire RGS family remains to be tested. It appears quite likely that the secondary network of residues that make contact with the Gα-binding residues on the surface can further adjust and/or reinforce the stringency of Gα recognition. In support of this possibility, members of the R4 subfamily show more diverse functional properties than sequence similarity, suggesting contributions of additional residues within the RGS domain outside of the Gα-interacting surface in shaping Gα selectivity. This is consistent with the results of our ancestral reconstitution experiments, that shuffling wider group of the amino acid residues in the entire RGS domain can also modulate Gα selectivity. Furthermore, elements outside of the RGS domains may further contribute to the Gα recognition preferences of RGS proteins. Such a possibility is suggested by studies on complex multi-modular members of the R7 family, in which interaction partners (Gβ5 and R7BP) and domains (DEP, PGL) have been shown to regulate Gα recognition. Many RGS genes also produce multiple splice isoforms that alter the structure of RGS proteins by adding or eliminating functionally important motifs without changing the RGS domain and may further fine-tune Gα selectivity. Finally, several RGS proteins also interact with GPCRs, G protein effectors, and scaffold proteins, and this event may further alter Gα specificity. Although these possibilities were not addressed in this study, our experiments with shuffling determinants, mutagenesis, and ancestral reconstitutions all within the RGS domain indicate that these additional mechanisms may contribute to establishing the Gα selectivity but are unlikely to completely overwrite it.

32Previous biochemical studies used purified recombinant proteins to examine the preferences of RGS proteins on Gα substrates selected ad hoc yielding important information that has served as a reference for RGS-Gα pairing. For example, RGS4 was shown to regulate both Gαi/o and Gαq subfamilies, but not Gαs or Gα12. In contrast, RGS2 was found to have no appreciable GAP activity toward Gαi/o and to be selective for Gαq in both solution GTPase assays and pull-down experiments. R7 RGS family members were reported to be Gαo selective, with weaker GAP activity on Gαi. The selectivity of RGS7 for Gαo over Gαi was observed with the purified RGS domain, which is consistent with our conclusion that its RGS domain encodes a Gα selectivity bar code. Gαz selectivity of RZ subfamily members RGS17 (RGSZ2), RGS19 (GAIP), and RGS20 (RGSZ1) was also observed. Finally, an R12 RGS member, RGS10, has been shown to regulate Gαo, Gαi, and Gαz, but not Gαs. Our investigation confirms many of the previously noted Gα preferences of RGS proteins, while additionally refining them to include G proteins not previously studied. However, in some cases, our results contradict previously documented coupling. One of the notable examples of this is Gαq selectivity of RGS2. Although our investigation shows that RGS2 can indeed regulate several members of the Gαq subfamily, we also find that it exhibits strong activity on the Gαi/o proteins comparable to that on Gαq. We think that the discrepancy is largely related to the choice of the assay system. Most of the previous studies used purified RGS and Gα proteins and measured GTP hydrolysis rates using biochemical assays conducted in solution. This approach has limited sensitivity and is devoid of the membrane environment where GPCRs, RGS, and G proteins normally operate under physiological context. In fact, the activity of RGS proteins has been shown to be significantly modulated by the membranes and lipid modification on Gα subunits. Furthermore, the proteoliposome-based assay was found to yield ∼100-fold higher sensitivity as compared to the solution-based assay. RGS2, in particular, was noted to act on Gαi/o in the presence of lipid bilayer. Thus, the cellular BRET assay strategy that we chose provides physiologically relevant information on RGS-Gα coupling as it exploits the endogenous environment and appropriate context of RGS action.

33One of the key insights provided by this work is the delineation of the determinants involved in RGS-Gα recognition. Establishing principles involved in the selectivity of protein-protein interaction has been a major goal of many investigations. Interaction between RGS and Gα provides an excellent model for interrogation of the underlying principles with possible general implications. Both protein families are well represented by numerous members with clearly defined orthologs and paralogs, and conservation of the structural organization. Thus, the experimental definition of the Gα preferences of all of the RGS proteins naturally prompted dissection of the underlying selectivity determinants. This study was focused on examining the contribution of the Gα-binding surface of the RGS domain. A combination of gene orthology/paralogy analysis with structural mapping identified a set of 17 variable amino acids that surround the core critical for forming direct contacts with the Gα subunits. We found that mutations in these amino acids significantly change the Gα preferences of RGS proteins. Interestingly, transplanting sets of variable amino acids from one RGS protein to another completely overwrites the Gα selectivity of the recipient. These observations support the idea that the selectivity of Gα recognition is, at least in part, encoded by the property of the amino acids that form this bar code region on the surface.

34Previous studies explored the role of electrostatic interactions in specifying the selectivity of Gα recognition by several RGS proteins across all of the subfamilies. Collectively, these studies reported 12 amino acid residues in RGS proteins that influence their ability to recognize Gα. Mutation of these residues either alone or in combination (up to 7 simultaneously) was shown to either increase or decrease the GAP activity of RGS proteins on the Gα substrates of choice. These studies examined one Gα substrate at a time, thus making it unclear whether the manipulations resulted in switching relative Gα preferences for a given RGS as opposed to overall gain or loss of substrate recognition. Nevertheless, these studies convincingly demonstrate that changes in electrostatic properties of amino acids at the RGS-Gα interface can alter the efficiency of the Gα recognition. Interestingly, all but two (H4.4 and H5.14) of these residues mapped on the Gα selectivity bar code region identified in this study, supporting the idea that electrostatic interactions play an important role in shaping the selectivity of RGS-Gα recognition. Similarly, mutations in RGS2 at the interface with the α-helical domain of Gα subunit diminished GAP activity on Gαq. In agreement with a large number of contacts made by the α-helical domain with the RGS domain, our analysis shows that variants mapping to this domain in several RGS proteins (H7.6, H7.9, H8.3) affect their Gα selectivity. Taken together with our observations that even single amino acid substitutions within the selectivity bar code can change the Gα preferences of RGS proteins, these results point to critical determinants of RGS-Gα recognition. Curiously, we found that altering these determinants can generate RGS proteins with novel selectivity profiles not displayed by canonical members of the family (e.g., RGS8/14-P, AncR4/Z/12/7; see Figures 4 and 6). Thus, we believe that the Gα-selectivity determinants identified here may pave the way for the de novo creation of RGS proteins with rationally designed G protein selectivity.

35Our findings also have implications for pharmacogenomics and understanding disease mechanisms associated with the disruption in RGS-mediated G protein control. We uncovered a significant variation affecting nearly all of the RGS proteins. More importantly, many of these variants occurred in selectivity bar code domains and were found experimentally to affect the Gα selectivity of RGS proteins. These genetic alterations are expected to change the profiles of signaling pathways engaged by the GPCRs, creating a situation that the same drug targeting the same receptor would produce varying effects due to RGS heterogeneity. Such a situation may be cryptic in the population if one only profiles variation within GPCRs, but it may still lead to interindividual variability in drug response. Therefore, understanding the impact of RGS proteins and their genetic variability on GPCR signaling is expected to be important for individualizing drug prescriptions in the implementation of precision medicine.

36Further information and requests for reagents should be directed to and will be fulfilled by the Lead Contact, Kirill Martemyanov (kirill@scripps.edu).

37Plasmids generated in this study will be distributed upon request without restriction.

38The published article includes all datasets generated and analyzed during this study.

39All experimental work involving mice was approved by The Scripps Research Institute’s IACUC committee in accordance with NIH guidelines. Mice were housed under standard conditions in a pathogen-free facility on a 12:12 light:dark hour cycle with continuous access to food and water. Male and female CAMPER (Gt(ROSA)26Sortm1(CAG-ECFP∗/Rapgef3/Venus∗)Kama and wild-type C57/Bl6 mice of both sexes aged from postnatal day 0 to postnatal day 3 were utilized in these studies and were not subjected to any prior experiments.

40HEK293T/17 cells were obtained from ATTC (Manassas, VA) and grown in DMEM supplemented with 10% FBS, minimum Eagle’s medium non-essential amino acids, 1mM sodium pyruvate, and antibiotics (100 units/ml penicillin and 100 μg/ml streptomycin) at 37°C in a humidified incubator containing 5% CO2.

41Primary striatal neurons were cultured similar to previous work. The striatum from either wild-type or homozygous CAMPER pups were rapidly isolated at age P0 in ice-cold HBSS supplemented with 20% FBS, 4.2 mM NaHCO3, and 1 mM HEPES. Striatal tissue was washed in HBSS without FBS prior to digestion at 37°C for 15 minutes in a buffer (pH 7.2) containing 137 mM NaCl, 5 mM KCl, 7 mM Na2HPO4, 25 mM HEPES, and 0.3 mg/ml papain. Striatal tissue was washed three times with HBSS (20% FBS), three times with HBSS, and three times with growth media (Neurobasal-A containing 2 mM GlutaMAX, 2% B27 Supplement serum-free, and 1% Penicillin-Streptomycin). Striatal tissue was then dissociated through pipetting ∼15 times with a standard P1000 pipette in the presence of DNase I (0.05 U/μL) and plated on poly-D-lysine coated glass coverslips. The cells were maintained in a humidified incubator at 37°C and 5% CO2. Half of the growth media was replenished every three days. For Ca2+ imaging, neuronal cultures from wild-type mice were incubated for 14-18 days with lentiviral-containing supernatant and AAV9-syn-FLEX-jGCaMP7s-WPRE. For cAMP imaging, neuronal cultures from CAMPER mice were incubated for 14-18 days with lentiviral-containing supernatant. Lipofectamine 2000 was used to transfect RGS2 along with control pSECC (1 μg each/coverslip) in wild-type or CAMPER neurons as indicated in the text for overexpression experiments.

42M3 muscarinic acetylcholine receptor (AF498917), dopamine D1 receptor (GenBank: NM_000794 with one silent SNP (A1263G)), bradykinin B2 receptor (GenBank: AY275465), GαoB (GenBank: AH002708), Gαz (GenBank: J03260), Gα11 (GenBank: AF493900), Gα14 (GenBank: NM_004297), Gα15 (GenBank: AF493904), Gαs long isoform (GαsL) (GenBank: NM_000516), Gαolf (GenBank: AF493893), Gα12 (GenBank: NM_007353), Gα13 (GenBank: NM_006572), RGS2 (GenBank: AF493926), RGS3-2 (GenBank: NM_001282922), RGS4 (GenBank: AF493928), RGS5 (GenBank: AF493929), RGS6 (GenBank: NM_004296), RGS7 (GenBank: AY587875), RGS8 (GenBank: AF300649), RGS10 (GenBank: AF493934), RGS11 (GenBank: NM_003834), RGS12 (GenBank: NM_198227), RGS13 (GenBank: NM_002927), RGS14 (GenBank: NM_006480), RGS16 (GenBank: AF493937), RGS18 (GenBank: NM_130782), RGS19 (GenBank: NM_005873), RGS20 (GenBank: NM_003702), Gβ5S (GenBank: NM_006578) and Gβ5L (GenBank: NM_016194) in pcDNA3.1(+) were purchased from cDNA Resource Center (https://www.cdna.org). masGRK3ct-Nluc-myc, RGS1 (GenBank: NM_002922), RGS9-1 (GenBank: NM_001165933), codon-optimized RGS13, RGS17 (GenBank: NM_012419), codon-optimized RGS18, RGS21 (GenBank: NM_001039152), AncR4/Z/12/7, AncR4/Z, AncR12/7, RGS13/18-F, RGS13/18-P, RGS8/14-F, RGS9-1 M370K, RGS11 R351Q, RGS12 Q161R, RGS12 N164S, RGS13 E98G, RGS18 T190L, and RGS19 R190W proteins in pcDNA3.1(+) were synthesized by GenScript. Flag-tagged dopamine D2 receptors (GenBank: NM_000795) containing the hemagglutinin signal sequence (KTIIALSYIFCLVFA) at the N terminus was a gift from Dr. Abraham Kovoor. The pCMV5 plasmids encoding rat GαoA, rat Gαi1, rat Gαi2, rat Gαi3, human Gαq, and bovine Gαs short isoform (GαsS) were gifts from Dr. Hiroshi Itoh. Rat GαoA G184S was a gift from Dr. Osamu Saitoh. Venus 156-239-Gβ1 (amino acids 156-239 of Venus fused to a GGSGGG linker at the N terminus of Gβ1 without the first methionine (GenBank: NM_002074)) and Venus 1-155-Gγ2 (amino acids 1-155 of Venus fused to a GGSGGG linker at the N terminus of Gγ2 (GenBank: NM_053064)) were gifts from Dr. Nevin A. Lambert. Flag-tagged Ric-8A (GenBank: NM_053194) in pcDNA3.1 was a gift from Dr. Jean-Pierre Montmayeur. Flag-tagged Ric-8B (GenBank: NM_183172 with one missense mutation (A1586G)) in pcDNA3.1 was a gift from Dr. Bettina Malnic. The masGRK3ct-Nluc-HA constructs were constructed by introducing HA tag at the C terminus of masGRK3ct-Nluc reported previously. PTX-S1 constructs were reported previously. pSECC vector (#60820), pCMV-VSV-G (#8454), pMDLg/pRRE (#12251), and pRSV-Rev (#12253) were purchased from Addgene. Sequences of oligonucleotides used to construct vectors are provided in Table S2.

43Anti-GAPDH antibody (MAB374), anti-HA tag antibody (clone 3F10) (11867423001), anti-GFP antibody (clones 7.1 and 13.1) (11814460001), Anti-GFP, N-terminal antibody (G1544), anti-c-myc antibody (clone 9E10) (11667149001), and anti-muscarinic acetylcholine receptor m3 antibody (AB9018) were purchased from MilliporeSigma. Anti-Gαo antibody (551) was purchased from MBL life science. Anti-Gαq antibody (sc-392) and anti-D2R antibody (sc-9113) were purchased from Santa Cruz Biotechnology. Anti-RGS13 antibody (H00006003-B01) and anti-RGS18 antibody (NBP1-92329) were purchased from Novus Biologicals. HRP-conjugated anti-rabbit antibody (211-032-171), HRP-conjugated anti-mouse antibody (115-035-174), and HRP-conjugated anti-rat antibody (112-035-175) were purchased from Jackson ImmunoResearch.

44For transfection, cells were seeded into 3.5-cm dishes at a density of 2 × 106 cells/dish. After 2 h, expression constructs (total 5 μg/dish) were transfected into the cells using PLUS (5 μl/dish) and Lipofectamine LTX (6 μl/dish) reagents. The GPCR (dopamine D2 receptor (D2R) (1) for Gi/o, M3 muscarinic acetylcholine receptor (M3R) (1) for Gq, dopamine D1 receptor (D1R) (1) for Gs, and bradykinin B2 receptor (BDKRB2) (1) for G12/13), Gα (GαoA (2), GαoB (1), Gαi1 (1), Gαi2 (2), Gαi3 (1.5), Gαz (1.5), Gαq (2), Gα11 (2), Gα14 (4), Gα15 (2), Gαs short (6), Gαs long (4), Gαolf (6), Gα12 (3), or Gα13 (4)), Venus 156-239-Gβ1 (1), Venus 1-155-Gγ2 (1), masGRK3ct-Nluc-HA (1) were transfected with different amounts of RGS construct (the number in parentheses indicates the ratio of transfected DNA (ratio 1 = 0.21 μg)). RGS1 (12), RGS2 (12), RGS3-2 (6), RGS4 (12), RGS5 (12), RGS6/Gβ5S (1), RGS7/Gβ5S (2), RGS8 (6), RGS9-1/Gβ5L (2), RGS10 (6), RG11/Gβ5S (6), RGS12 (6), RGS13 (6), RGS14 (6), RGS16 (6), RGS17 (6), RGS18 (12), RGS19 (6), RGS20 (6), and RGS21 (12) were transfected to examine comprehensive G protein selectivity. Gα14/15 and Gαolf were transfected with Ric-8A (1) and Ric-8B (1), respectively. A construct carrying catalytic subunit of pertussis toxin PTX-S1 were transfected with Gαz, M3R, D1R, or BDKRB2 to inhibit the possible coupling of endogenous Gi/o to GPCRs. An empty vector (pcDNA3.1(+)) was used to normalize the amount of transfected DNA.

45Cellular measurements of BRET between Venus-Gβ1γ2 and masGRK3ct-Nluc-HA were performed to examine GAP activity of RGS protein in living cells (described in detail in, ). Sixteen to twenty-four hr post-transfection, HEK293T/17 cells were washed once with BRET buffer (Dulbecco’s Phosphate-Buffered Saline (PBS) containing 0.5mM MgCl2 and 0.1% glucose) and detached by gentle pipetting over the monolayer. Cells were harvested by centrifugation at 500 g for 5 min and resuspended in BRET buffer. Approximately 50,000 to 100,000 cells per well were distributed in 96-well flatbottomed white microplates (Greiner Bio-One). The NanoLuc (Nluc) substrate, furimazine, were purchased from Promega and used according to the manufacturer’s instruction. BRET measurements were made using a microplate reader (POLARstar Omega; BMG Labtech) equipped with two emission photomultiplier tubes, allowing us to detect two emissions simultaneously with the highest possible resolution of 20 ms per data point. All measurements were performed at room temperature. To activate and then deactivate, the final concentration of 100 μM ligands were used. Specifically, dopamine and haloperidol for D2R, dopamine and SCH39166 for D1R, acetylcholine and atropine for M3R, and bradykinin and B-9430 for BDKRB2 were applied on the transfected cells to control the activity of those GPCRs. The BRET signal is determined by calculating the ratio of the light emitted by the Venus- Gβ1γ2 (535 nm with a 30 nm band path width) over the light emitted by the masGRK3ct-Nluc-HA (475 nm with a 30 nm band path width). The average baseline value (basal BRET ratio) recorded prior to agonist stimulation was subtracted from the experimental BRET signal values and the resulting difference (ΔBRET ratio) was normalized against the maximal ΔBRET value recorded upon agonist stimulation. The rate constants (1/τ) of the deactivation phases were obtained by fitting a single exponential curve to the traces with Clampfit 10.3. kGAP rate constants were determined by subtracting the basal deactivation rate (kapp) from the deactivation rate measured in the presence of exogenous RGS protein. Obtained kGAP rate constants were used to quantify GAP activity.

46For each 3.5-cm dish, transfected cells were lysed in 1 mL of sample buffer (62.5 mM tris-HCl, pH 6.8, 2 M urea, 2% SDS, 5% 2-mercaptoethanol, 10% glycerol, bromophenol blue (0.08 mg/ml)). Western blotting analysis of proteins was performed after samples were resolved by SDS–polyacrylamide gel electrophoresis and transferred onto PVDF membranes. Blots were blocked with 5% skim milk in PBS containing 0.1% Tween 20 (PBST) for 30min at room temperature, which was followed by 90 min incubation with specific antibodies diluted in PBST containing 1% skim milk (anti-D2R antibody (1:1,000), anti-M3R antibody (1,1,000), anti-Gαo antibody (1:1,000), anti-Gαq antibody (1:1,000), anti-GFP antibody (1:1,000), anti-HA antibody (1:1,000), anti-c-myc antibody (1:1,000), anti-RGS13 antibody (1:1,000), anti-RGS18 antibody (1:5,000), and anti-GAPDH antibody (1:10,000)). Blots were washed in PBST and incubated for 45 min with a 1:10,000 dilution of secondary antibodies conjugated with horseradish peroxidase (HRP) in PBST containing 1% skim milk. Western blotting was performed with BlotCycler automated western blot processor (Precision Biosystems). Proteins were visualized with Kwik Quant imager (Kindle Biosciences).

47As previously described, sgRNA sequences targeting RGS proteins were designed with CHOPCHOP (https://chopchop.cbu.uib.no/). According to the design, oligo DNAs were synthesized by Integrated DNA Technologies. The oligo DNAs were treated by T4 polynucleotide kinase and annealed in a thermal cycler. Finally, the oligo DNAs were ligated into the BsmBI site of the pSECC vector with T4 DNA Ligase. Three sgRNA constructs were made for each target gene. The plasmids were purified from Stbl3 E. coli. Lentiviruses were generated by Lipofectamine LTX-mediated transfection of HEK293T/17 cells with the packaging vectors, pSECC, pCMV-VSV-G, pMDLg/pRRE, and pRSV-Rev. The supernatant containing the lentiviral particles was collected at 48 hours post-transfection.

48Primary neuronal cultures were imaged under a Leica TCS SP8 confocal microscope through a 25x objective lens. Changes in cAMP were recorded from CAMPER neurons, as previously described. Briefly, excitation of mTurquoise FRET donor with a 442 nm diode laser was paired with simultaneous acquisition of XYZ image stacks at 10 s intervals collected through two HyD detectors tuned to 465-505 nm (mTurquoise FRET donor) and 525–600 nm (Venus FRET acceptor). Quantification of fluorescence intensity was performed on neuronal cell bodies using ImageJ to calculate FRET from the donor/acceptor ratio. The FRET ratio was converted to the concentration of cAMP using a dose-response curve to cAMP standards in permeabilized neurons. Segregated dopamine receptor subtype expression in striatal neurons enabled the identification of D2R-expressing neurons according to the directionality of cAMP response to dopamine. Dopamine was added in phasic puffs during continuous perfusion (2 mL/minute) of a pH 7.2 buffer consisting of 1.3 mM CaCl2, 0.5 mM MgCl2, 0.4 mM MgSO4, 0.4 mM KH2PO4, 4.2 mM NaHCO3, 138 mM NaCl, 0.3 mM Na2HPO4, 5.6 mM D-Glucose, and 20 mM HEPES. Changes in intracellular calcium concentration were recorded from wild-type neurons expressing jGCaMP7s. Excitation was performed with a 488 nm laser, and the acquisition of XYZ image stacks at 1 s intervals was collected through a HyD detector tuned to 494–593 nm. Quantification of fluorescence intensity was performed on neuronal cell bodies using ImageJ. Acetylcholine was added in phasic puffs during continuous perfusion (2 mL/minute) of a pH 7.3 buffer consisting of 2.2 mM CaCl2, 1 mM MgCl2, 138 mM NaCl, 11 mM D-Glucose, 10 mM HEPES, 50 μM picrotoxin, 300 nM CGP55845, and 10 μM DNQX.

49Whole protein sequences of human RGS proteins were downloaded from the UniProt database (https://www.uniprot.org/). The core RGS domain in each of these human RGS proteins was assigned based on HMMER searches conducted on pfam database domain profiles using human RGS proteins. Then the core RGS domains assigned in all of the human RGS paralogs were aligned using MSAProbs and this alignment was termed as human RGS domain alignment (HRDA). Animal orthologs of RGS proteins were obtained from the OMA database (https://omabrowser.org/oma/home/) and equivalent regions to the core RGS domain of human RGS were only considered for further investigations. We aligned the core RGS domain regions in the animal orthologs with human ones. For each human RGS, i.e., RGS1 to RGS21, we constructed multiple sequence alignments of the given RGS with its corresponding animal orthologs.

50We developed a common RGS numbering scheme (CRN), by integrating consensus secondary structure information of available crystal structures of the RGS domain on to HRDA sequence alignment. This allowed us to uniquely assign an alignment position to a combination three types of information: 1) Secondary structural element i.e. “H” for helix, “S” for strand and “L” for loop, along with the index of the secondary structural element i.e. “H1” stands for helix number 1 and “L2” stands for loop number 2, etc. 2) Residue number of the alignment position within the index of the given structural element i.e. “H1.12” denotes 12th position in helix number 1 or helix H1 or L3.2 denotes 2nd position in loop number 3 or loop L3.

51For any given alignment position n in the alignment, amino acid residues at this position for across organisms Ri, where i = 1 to m, where m is the total number of sequences in the alignment.Where = BLOSUM score (Ri → Rj)/Maximum [BLOSUM score (Ri → Ri) OR BLOSUM score (Rj → Rj)] and “ → “ refers to amino-acid residue substitution

52Evaluate mean of all over all the “l” positions in the alignment:

53To identify the ortholog specific conserved residues and commonly conserved residues between paralogs of human RGS in the core RGS domain. We developed a strategy, by comparing assigning the CRN to each of the RGS alignments and we then categorized the residue at a given CRN position is: (a) Ortholog-specifically conserved if the normalized BLOSUM score for this CRN is 1.5 times higher in a given RGS alignment than in the equivalent CRN of HRDA alignment position and the given CRN position also displays above average normalized BLOSUM score within the RGS alignment. (b) Paralog-specifically conserved if the normalized BLOSUM score for this CRN in the HRDA alignment is 1.5 times higher than in the equivalent CRN of RGS alignment and the given position displays above average normalized BLOSUM score within the HRDA alignment. (c) Conserved in both if CRN in RGS alignment and the HRDA display comparable normalized BLOSUM scores, i.e., within 1.5 times normalized BLOSUM score of either of them. The given position displays above average normalized BLOSUM score within the HRDA and RGS alignments. (d) Neutrally evolving if the above three conditions were not met. The alignment of RGS domain from orthologs is provided as Data S1 and S2. In the datasets, the residue numbers following the accession OMA database ID and UniProt ID or Ensembl database ID are presented.

54The reconstitution of ancestral RGS proteins based on the computational algorithm using FastML was performed on different groups of RGS alignments i.e., for e.g., R4, RZ, R12, and all RGS proteins. Ancestral reconstruction methods identify most likely sequences, including indels, in a specific ancestral node in a phylogenetic tree for given multiple sequence alignment.

55Multiple t tests with correction for multiple comparison using the Holm–Sidak method was conducted to determine the effect of RGS on the deactivation rates of Gα subunits with GraphPad Prism Ver. 6. Only statistically significant values are plotted. Values represent means ± SEM from three independent experiments each performed with three replicates.