Trace amine associated receptor 1: Predicted effects of single nucleotide variants on structure-function in geographically diverse populations

Trace amine associated receptor 1: Predicted effects of single nucleotide variants on structure-function in geographically diverse populations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trace amine associated receptor 1: Predicted effects of single nucleotide variants on structure-function in geographically diverse populations Britto Shajan, Shashikanth Marri, Tarun Bastiampillai, Karen J. Gregory, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4407652/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jun, 2024 Read the published version in Human Genomics → Version 1 posted You are reading this latest preprint version Abstract Trace Amine Associated Receptor 1 (TAAR1) is a novel pharmaceutical target under investigation for the treatment of several neuropsychiatric conditions. TAAR1 single nucleotide variants (SNV) have been found in patients with schizophrenia and metabolic disorders. However, the frequency of variants in geographically diverse populations and the functional effects of such variants are unknown. In this study, we aimed to characterise the distribution of TAAR1 SNVs in five different WHO regions using the Database of Genotypes and Phenotypes (dbGaP) and conducted a critical computational analysis using available TAAR1 structural data to identify SNVs affecting ligand binding and/or functional regions. Our analysis shows 19 orthosteric, 9 signalling and 16 micro-switch SNVs hypothesised to critically influence the agonist induced TAAR1 activation. These SNVs may non-proportionally influence populations from discrete regions and differentially influence the activity of TAAR1-targeting therapeutics in genetically and geographically diverse populations. Notably, our dataset presented with orthosteric SNVs D103 3.32 N (found only in the South-East Asian Region and Western Pacific Region) and T194 5.42 A (found only in South-East Asian Region), and 2 signalling SNVs (V125 3.54 A/T252 6.36 A, found in African Region and commonly, respectively), all of which have previously demonstrated to influence ligand induced functions of TAAR1. Furthermore, bioinformatics analysis using SIFT4G, MutationTaster 2, PROVEAN and MutationAssessor predicted all 16 micro-switch SNVs are damaging and may further influence the agonist activation of TAAR1, thereby possibly impacting upon clinical outcomes. Understanding the genetic basis of TAAR1 function and the impact of common mutations within clinical populations is important for the safe and effective utilisation of novel and existing pharmacotherapies. neuropsychiatric conditions single nucleotide variants trace amines trace amine associated receptor 1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The endogenous trace amines (TAs) are small molecules that selectively activate a group of G protein-coupled receptors (GPCRs) called trace amine associated receptors (TAARs) ( 1 , 2 ). Human TAARs are encoded by an intron-less cluster of Taar genes located on chromosome 6q23.2, with six genes encoding for functional TAARs (TAAR1, 2, 5, 6, 8 and 9) ( 3 ). TAARs are distantly related to monoamine neurotransmitter GPCRs and share close structural similarities with the broader class A GPCR family, featuring a membrane spanning hepta-helical transmembrane domain (TM) and three intracellular and extracellular loops (ICL1-3 and ECL1-3, respectively) ( 1 ). While most TAARs function primarily as olfactory receptors, with low CNS expression, TAAR1 is highly expressed in the CNS, particularly in monoaminergic nuclei ( 1 , 2 ). Furthermore, TAAR1 expression is also reported in peripheral systems including the gastrointestinal tract, heart, thyroid, and immune systems ( 4 ). When inactive, TAAR1 is proposed to be localised on the endoplasmic reticulum and mitochondrial membranes ( 4 ). However, TAAR1 activation via trace amine binding and/or heterodimerisation with other receptors triggers translocation to the plasma membrane and the production of cellular responses, primarily by altering intracellular cyclic-adenosine monophosphate (cAMP) levels ( 5 ). Through these actions, TAAR1 plays important roles in the modulation of monoaminergic and glutamatergic neurotransmission, hormone secretion and glucose metabolism, regulating vital processes involved with cognition, mood, and metabolism ( 2 , 3 ). As such, TAAR1 has arisen as the major focus of TAAR research due to its therapeutic potential in treating CNS and metabolic disorders ( 2 ). The critical role of TAAR1 in modulating monoaminergic neurotransmission is exemplified in TAAR1 knockout mice, which display hypersensitivity to amphetamine and elevated release of monoaminergic neurotransmitters ( 5 , 6 ). Further, receptor stimulation with partial TAAR1 agonists significantly improved psychological distress, cognitive functioning, sleep patterns, and produced anti-diabetic effects in preclinical models of schizophrenia and diabetes ( 6 – 9 ). Many structurally distinct TAAR1 full and partial agonists have since been developed, with several entering clinical trials for schizophrenia, anxiety, Parkinson's L-Dopa related psychosis, sleep disorder and diabetes ( 10 ). Of note is ulotaront (SEP-363856), a dual TAAR1/5-HT1A agonist, which received FDA Breakthrough Therapy status for schizophrenia treatment in 2019 ( 11 , 12 ). However, despite promising preclinical and early clinical results, ulotaront failed to hit primary endpoints in two recent Phase 3 trials for schizophrenia (ClinicalTrials.gov Identifiers: NCT04072354, NCT04092686) ( 13 , 14 ). Initial reports suggested a high placebo effect masking the therapeutic effects of ulotaront as the primary reason for the clinical failures ( 15 ). However, it remains unknown exactly what factors play a role in a lack of ulotaront efficacy in patient populations. As Phase 2 clinical trials for ulotaront are currently underway as a treatment for generalised anxiety disorder (ClinicalTrials.gov Identifier: NCT05729373) and as adjunctive therapy for major depressive disorder (ClinicalTrials.gov Identifier: NCT05593029), understanding the reasons for this failure is of critical importance for the future clinical success of both ulotaront and other TAAR1 clinical candidates ( 16 , 17 ). Genetic mutations are of particular interest in GPCR drug discovery due to their disease-causing effects and potential to interfere with drug activity by disrupting drug metabolism, protein-ligand interactions, and receptor function ( 18 – 21 ). A partial TAAR1 agonist, RO5263397, failed Phase 1 clinical trials due to splicing mutations in the drug metabolism enzyme uridine 5′-diphosphate-glucuronosyltransferases 2B10, which increased exposure to parent compound by more than 100-fold ( 22 ). The causative single nucleotide variant (SNV) mutation was particularly pervasive in African regions, indicating geographical heterogeneity in susceptibility to altered drug effects ( 22 ). The TAAR1 gene cluster has also been identified via linkage analysis as a locus of susceptibility for schizophrenia, with clinical and experimental studies showing correlation between TAAR1 SNVs and patients with impaired glucose homeostasis and schizophrenia in geographically distinct populations ( 19 , 23 , 24 ). Indeed, numerous SNVs identified in human TAAR1 alter expression and functional properties, with previous studies relying on mutagenesis and in vitro characterisation to test the functional effects of TAAR1 SNVs ( 19 , 23 , 25 , 26 ). Furthermore, multiple high resolution cryo-electron microscopy (cryo-EM) TAAR1 structures bound to both natural TAs and synthetic agonists have recently been solved ( 27 – 31 ). Such advances pave the way for structure-based approaches to studying TAAR1 ligand interactions and structure-function, which until recently have relied on homology models ( 32 ). Computational approaches using experimentally determined structures represent a powerful method for rapidly screening the effects of TAAR1 SNVs that potentially impact drug action in genetically and geographically diverse clinical populations. The purpose of this study was to leverage recently published experimental structures of TAAR1 to identify the putative effects of SNVs on TAAR1 structure-function using bioinformatic approaches. The Database of Genotypes and Phenotypes (dbGaP) was used to identify and characterise putative detrimental SNVs in populations belonging to five diverse geographical regions. With the aim of predicting the impact of these SNVs on TAAR1 function, we have identified 19 SNVs at the orthosteric binding sites, 9 at signalling domains and 16 at micro-switch domains. Additionally, in silico functional prediction algorithms predicted all 16 micro-switch SNVs are damaging to TAAR1 function. We found several crucial mutations affecting conserved regions that are geographically non-uniform in their distribution. The results of our study provide a basis for further in silico and in vitro studies that may provide deeper insights and experimental validation for TAAR1 SNV functional effects. Methods SNV data from NCBI dbGaP repository All SNV data were collected from the National Center for Biotechnology Information (NCBI) dbGaP database (2023) ( 33 ). The Reference SNP cluster ID (rsID) were compiled based on five of WHO classification of regions, African Region (AR), South-East Asian Region (SEAR), Region of the Americas (ROA), European Region (ER) and Western Pacific Region (WPR). Regions containing pan-ethnic groups, such as Region of the Americas, were classified based on their genetic architecture ( 34 ). Prediction of SNV functional effects using in silico tools. To analyse the effect of specific TAAR1 SNVs, in silico algorithms were employed to predict tolerability. The tools were accessed through dbNSFP (version 4.4a), a repository containing a range of functional prediction algorithms ( 35 ). To prevent ambiguity in relation to cut-off scores, tools yielding a binary output as damaging (D) or Tolerable (T) were selected. Furthermore, tools that utilised High, Medium, and Low scoring formats were considered. All High outputs were characterised as damaging, while Medium and Low outputs were characterised as tolerable. Four algorithms satisfied these inclusion criteria. 1) Sorting Intolerant from Tolerant (SIFT4G v2.4), which predicts the likelihood of a mutation to cause functional damage using sequence homology, assumes that evolutionarily conserved regions are essential for protein function and therefore any missense substitution is generally considered intolerable ( 36 ). 2) Protein Variation Effect Analyzer (PROVEAN v1.1) utilises sequence homology to estimate the tolerability of a mutation by computing the change in delta alignment score using the BLOSUM62 substitution matrix ( 37 ). 3) Mutation Assessor (v3.0) utilises sequences from homologous protein families and sub-families within and between species, to calculate an impact probability score ( 38 ). 4) MutationTaster 2 (v2015) utilises machine learning approaches and is trained using > 390,000 disease variants and > 6 million tolerable polymorphisms. MutationTaster 2 uses Bayes classifier algorithm to calculate the probability of a mutation being damaging or tolerable ( 39 ). Identification of TAAR1 SNVs at ligand binding and other functional domains through structural mapping The snake plot of human TAAR1 (TA1 human) was acquired from GPCRdb ( 40 ). To identify SNVs at the ligand binding and other functional domains of TAAR1, current literature on human TAAR1 experimental structures were utilised. The complexes of TAAR1-ligand (8W8A, 8W89, 8W88, 8W87, 8JSO, 8JLN, 8JLO, 8JLP, 8JLQ, 8JLR, 8WC8, 8WCA and 8UHB) were accessed using the RCSB PDB database ( 27 – 30 ). Search term “TAAR1” was provided to make the initial query and the output was refined using “Homo sapiens” under ‘Scientific Name of Source Organism’. The resulting structures and primary literature were reviewed to identify residues involved in ligand activated mechanisms of TAAR1. Generating plots using bioinformatic tools and online databases. The position of SNVs within the TAAR1 structure were sorted based on the geographical distribution and a 5-way Venn diagram was generated using the Venn package through R studio version 2023.06. In addition, the geographical distribution of each SNV were segregated and illustrated using river plots, generated using ggalluvial package in RStudio version 2023.06. ChimeraX (version 1.7.1) and OpenEye Scientific VIDA (version 5.0.5.3) were utilised for visualisation and annotations of 3D structures (41–43). Results Allelic heterogeneity and geographical distribution of human TAAR1 SNVs. A list of rare missense TAAR1 SNVs was collated from the NCBI dbGaP database. To identify the geographical distribution of each SNV, the reference SNP cluster ID (rsID) were classified based on five WHO regions, African Region (AR), South-East Asian Region (SEAR), Region of the Americas (ROA), European Region (ER), and Western Pacific Region (WPR). A total of 290 individual TAAR1 SNVs were identified, all of which are segregated at varying capacity, covering all domains of TAAR1 including TM, ICL, ECL, N-terminal extension and C-terminal tail. To visualise the position of individual SNVs and identify allelic heterogeneity at each residue, SNVs were mapped onto a TAAR1 snake plot from GPCRdb (Fig. 1 ) ( 40 ). As highlighted, the current dataset shows 108 residues with a single SNV, which constitutes approximately 37% of this dataset. The remaining 63% are segregated as residues with double, triple, and quadruple allelic heterogeneity. Notably, the highest heterogeneity (four variants) was displayed at six positions; V94 3.23 (Superscript is position according to Ballesteros & Weinstein numbering system, which indicates helix position of residue relative to the most conserved amino acid within a TM ( 44 )), R121 3.50 and M135 ICL2 , G181 ECL2 , T271 6.55 and M302 7.51 . In addition, SNVs in two post translational modification sites (Predicted PTM site, identified using GPCRdb) were identified, T67 2.48 , K134 ICL2 ( 40 ). Overall, this dataset presents with SNVs covering approximately 55% (186 residues) of human TAAR1. To characterise the geographical distribution and consequently define shared and unshared SNVs, a Venn analysis was performed. As shown in Fig. 2 A,C, 20 SNVs are shared across all five geographical regions (referred to as “common SNVs” henceforth) and a total of 75 SNVs are unique. Notably, unique SNVs were most pervasive in SEAR and AR (31 unique SNVs in each) and no unique SNVs were identified in the ROA. To elucidate the prevalence of TAAR1 SNVs regionally, we defined the burden of TAAR1 SNVs by tallying all the unique and shared SNVs present in each region (Fig. 2 B). In this dataset, the total burden of TAAR1 mutations were the highest in AR (n = 212) and lowest in the WPR (n = 50). To best illustrate our overall data, a river plot was utilised (Supplementary Figs. 1–4). Briefly, all plots were composed of two complementary horizontal axes, top axis with the WHO region and bottom axis with the SNVs. Both axes are bridged by up to five groups of “streams”, each representing a WHO region. In this case, the stream can be seen to originate from the axis of WHO region and links to a single/multiple SNVs that is in association with the region. The stream also can be interpreted to originate from the axis of SNVs and linking to a WHO region/s to precisely locate the region/s in association with SNV. Thus, the relative axial area, i.e. the size of each box a given SNV, or a WHO region occupies, represents the sum of associations with its complementary variable. In addition, shared SNVs are presented using the grey shade and unique SNVs are presented with varying colour shades corresponding to the WHO region (e.g. V288 7.37 A in AR with sky blue). SNVs at the ligand binding regions of human TAAR1. The potential impact of identified SNVs on important aspects TAAR1 function, such as agonist binding and signalling remain unexplored. Hence, our analysis focused on studying the SNVs present in the TAAR1 agonist binding and signalling domains. With the emergence of various agonist bound TAAR1 cryo-EM structures, residues critical for differential agonist binding and signalling are beginning to unfold ( 27 – 30 ). Considering this, we performed identical structural mapping analysis using the available cryo-EM structures to map the distribution of SNVs across TAAR1 ligand binding pockets. This was conducted in conjunction with the geographical data to analyse and define the influence of SNVs present in each region. ‘Orthosteric SNVs’ are defined as SNVs at residues surrounding the binding site of various TAAR1 agonists, identified from experimentally determined TAAR1 structures (Fig. 3 ) including RCSB entries 8W8A, 8W89, 8W88, 8W87, 8JSO, 8JLN, 8JLO, 8JLP, 8JLQ, 8JLR, 8WC8, 8WCA and 8UHB. A total of 19 orthosteric SNVs, affecting 16 residues were identified (Fig. 3 A, supplementary table 1). Three common orthosteric SNVs were identified at two positions, I104 3.33 (variant; V104 3.33 ) and T197 5.45 (variants; I197 5.45 , S197 5.45 ), which are crucial for the binding of several exogenous and endogenous TAAR1 agonists (Fig. 3 B, C). Specifically, residue I104 3.33 forms interactions with 3-iodothyronamine (T1AM), amphetamine and other synthetic agonists, while residue T197 5.45 interacts with ralmitaront (Fig. 3 C) ( 27 – 30 ). As shown in Fig. 3 B, six unique orthosteric SNVs were found, including I104 3.33 S, S108 3.37 P, F112 3.41 L, V150 4.52 I, S183 ECL2 F and T194 5.42 A. All residues form key interactions with ralmitaront, with residue specific interactions with other agonists such as T1AM (I104 3.33 , S108 3.37 , S183 ECL2 , T194 5.42 ), ulotaront (I104 3.33 , and S183 ECL2 F), amphetamine (I104 3.33 and S183 ECL2 F), methamphetamine (I104 3.33 and T194 5.42 ), β-phenylethylamine (PEA) (I104 3.33 and T194 5.42 ) and Ro5256390 (I104 3.33 and T194 5.42 ) ( 27 – 30 ) as illustrated in Fig. 3 C. Apart from F112 3.41 L and S183 ECL2 F (both found in ER), all unique orthosteric SNVs belong to SEAR. Notably, shared orthosteric SNVs such as S80 2.61 G (WPR/SEAR), H99 3.28 R (ROA and others, Fig. 3 B) and V184 ECL2 L (SEAR/ER) may influence the binding of endogenous compounds PEA and T1AM ( 27 ). Furthermore, residues D103 3.32 and W264 6.48 interact with all TAAR1 agonists tested thus far (Fig. 3 C), with a single shared SNV at each residue: D103 3.32 N (WPR and SEAR) and W264 6.48 L (AR, SEAR, and ROA) as illustrated in Fig. 3 B ( 27 – 30 ). SNVs at the micro-switch domains of human TAAR1. SNVs found at the highly conserved micro-switch domains may influence TAAR1 functionality. Micro-switches are structural motifs conserved across the Class A GPCR superfamily that link ligand binding with conformational changes associated with G protein coupling and receptor activation ( 29 ). From the cryo-EM data, it was evident TAAR1 has the typical micro-switches for the classical class A GPCR helical rearrangement ( 27 – 30 ). These micro-switches include the DRY (D120 3.49 , R121 3.50 and Y122 3.51 ), PIF (P202 5.50 , I111 3.40 and F260 6.44 ), CWxP (C263 6.47 , W264 6.48 , C265 6.49 and P266 6.50 ), and NPxxY motifs (N300 7.49 , P301 7.50 , M302 7.51 , V303 7.52 and Y304 7.53 ). Using structural mapping analysis, we classified SNVs within the four micro-switch domains as ‘micro-switch SNVs’ (Fig. 4 A, Supplementary table 2). A total of 16 micro-switch SNVs were identified, located within the DRY (seven SNVs), CWxP (six SNVs), PIF (one SNV) and NPxxY (two SNVs) motifs (Fig. 4 B). When considering geographical distribution, three common micro-switch SNV were identified: R121 3.50 C/S affecting the DRY and N300 7.49 K affecting NPxxY motif. Furthermore, a total of seven unique micro-switch SNVs were identified; four in AR, two in SEAR and one in the ER as shown in Fig. 4 B. Shared variants in micro-switch regions were also identified, one of each affecting DRY, PIF, NPxxY and three affecting CWxP (Fig. 4 B). Although the importance of the micro-switch regions in class A receptor rearrangement and activation are well known, the functional implications of SNVs in these motifs on TAAR1 activity remains largely unknown. Notably, putative deleterious effects of R121 3.50 C/L, C263 6.47 G, W264 6.48 L, P266 6.50 S/A and N300 7.49 S/K were previously described by GPCRdb using SIFT and PolyPhen algorithms. Therefore, to characterise the putative effects of TAAR1 micro-switch SNVs, four different in silico algorithms were employed to predict the effect of variants as either tolerable or damaging; SIFT4G, PROVEAN, Mutation Assessor and MutationTaster 2. All micro-switch SNVs were predicted to be damaging by all four selected algorithms (Fig. 4 C). SNVs affecting the G protein-coupling residues of human TAAR1. Cryo-EM structures of TAAR1 in complex with structurally diverse ligands have revealed TAAR1 agonists engender divergent G protein coupling and downstream signalling outcomes, with mutagenesis revealing residues involved in this preferential G protein activation ( 27 – 30 ). As such, we characterised 9 SNVs at five positions within TAAR1 identified as critical for divergent G protein activation and signalling transduction (referred to as signalling SNVs ) (Fig. 5 A, B). Two common signalling SNVs were identified, one affecting a residue important for TAAR1-G αs subunit activity (H55 ICL1 R), and T252 6.36 A critical TAAR1-G αi activity. Two unique SNVs were present in the AR; one affecting residue critical for TAAR1-G αs activity (Q220 5.68 E), and one, V125 3.54 A, involving a residue implicated with creating selective bias for G αi over G αs coupling ( 29 ). Additional signalling SNVs were shared across multiple regions and may influence TAAR1-G α -protein interactions based on sites from previous experimental studies, as summarised in Fig. 5 B ( 29 ). Discussion A greater understanding of the impact of SNVs is warranted in light of emerging studies connecting TAAR1 SNVs with neuropsychiatric disorders ( 19 , 23 – 25 ). Here, demographic distribution and atomic insights of human TAAR1 experimental structures were utilised to define and predict effects of selected TAAR1 missense SNVs. Our findings showed a large degree of allelic heterogeneity, presence of multi-regional SNVs and implicated AR with highest overall count of SNVs. Our structural mapping analysis utilising cryo-EM structural insights isolated the primary data set consisting of over 40 SNVs. Within the dataset were 16 SNVs at four different micro-switch regions, with putative damaging effects predicted by four in silico algorithms. Here, AR had the highest number of micro-switch SNVs. In addition, 19 orthosteric SNVs, and 9 signalling SNVs were noted, with highest count was in SEAR and AR, respectively. Structure-based predictions and bioinformatics approaches have been used to predict the functional effects of SNVs on multiple pharmacological targets ( 19 , 45 – 48 ). With the emergence of various agonist bound human TAAR1 cryo-EM structures, the binding residues for several TAAR1 agonists have been characterised ( 27 – 30 ). The TAAR1 cryo-EM structures in complex with various agonists have identified D103 3.32 and W264 6.48 as critical residues for agonist binding through a combination of in vitro and in silico mutagenesis studies ( 27 – 30 ). In all cases, alanine substitution at both positions significantly affected TAAR1 activation in response to agonists for both mouse and human receptors ( 27 – 31 ). Notably, our dataset identified D103 3.32 N in WPR and SEAR, and W264 6.48 L in ROA and few others (Fig. 3 B). In previous experimental studies, the negatively charged D103 3.32 formed a critical salt bridge with the positively charged amine group of all complexed TAAR1 agonists ( 27 – 30 , 32 ). In comparison, the modified asparagine (D103 3.32 N) replaces the negative charge for a neutral charge, which was recently shown to abolish the binding PEA, tyramine, T1AM, and synthetic agonists including the atypical anti-psychotic asenapine ( 30 ). Thus, D103 3.32 N will likely diminish binding of other agonists and raises a major concern regarding the efficacy of TAAR1 therapeutics in WPR and SEAR. Likewise, W264 6.48 , part of the CWxP motif, forms hydrophobic contacts with all complexed TAAR1 agonists ( 27 – 30 ). Hence, the substitution of W264 6.48 L may influence the binding of selective TAAR1 agonists. Previous experimental studies show that W264 6.48 A completely inhibits TAAR1 activation by PEA and other agonists, with substitution to phenylalanine (W264 6.48 F) also reducing agonist potency ( 27 – 30 ). This is likely divorced from the role of W264 6.48 as a binding determinant and is more likely due to its role as part of the critical CWxP micro-switch ( 27 ) which is essential for helical rearrangements in response to ligand binding. In addition, alanine substitution of T194 5.42 (found in SEAR) were previously experimentally validated and demonstrated to influence ligand induced activity of TAAR1 ( 27 , 29 , 30 ). Hence it is plausible that populations belonging to these regions may see a varied response to TAAR1 therapeutics. In addition, these SNVs may contribute to symptomology associated with disease states such as attention deficit hyperactivity disorder (ADHD) due to their potential impact on endogenous ligand binding and trace amine activity ( 49 ). Notably, previous mutagenesis studies demonstrated the importance of selective residues for ligand induced signalling. Our dataset showed SNVs at all these positions summarised in Fig. 5 B. In this case, the alanine variants have been experimentally validated to affecting protein activity, including the common SNV T252 6.36 A, which affects G αi coupling ( 29 ). In addition, the G protein selectivity may be influenced by the variant V125 3.54 A (found in AR), to bias G-proteins towards a specific type (i.e giving preference for G αi over G αs ) in a ligand dependent manner( 29 ). Such changes in G protein selectivity may reverse TAAR1s putative dopamine modulatory functions, possibly contributing to neuropsychiatric pathologies or affecting the ability of agonists to alleviate existing symptomology in African regions. Indeed, recent work suggests preferential activation of Gα q pathways, or dual Gα s /Gα q activation, by TAAR1 agonists has added benefit in animal models of schizophrenia ( 28 ). While it remains to be seen if such observations translate to humans, such discoveries provide a framework for rational drug design but also highlight crucial evidence for how signalling SNVs may impact TAAR1 function and the clinical use of agonists for therapeutic benefit. Whilst the clinical impact of TAAR1 SNVs affecting micro-switch motifs is yet to be elucidated, SNVs have been linked to pathologies in other GPCRs ( 50 ). The DRY motif establishes intrahelical electrostatic interactions between D120 3.49 and R121 3.50 establishing a more critical interhelical ionic lock with TM6, both critical mechanisms for the receptor “packing”. These interactions may not be facilitated by residues with diverging electrostatic properties such as D120 3.49 G (ER), R121 3.50 S/C/L (variants S/C: common and variant L: AR) and Y122 3.51 C (SEAR/ER) ( 51 – 55 ). In comparison, the interactions may still be established if the modified residue mirrors those electrostatic properties of original residues such as D120 3.49 E (SEAR) and R121 3.50 H (AR) ( 56 , 57 ). On the contrary, the structural features and stability facilitated by certain residues may be indispensable. The SEAR SNV P266 6.50 L of CWxP and P202 5.50 S of PIF motif (ROA and AR) may compromise the helical rearrangement facilitated by the cyclic proline. The helical rearrangement is critical for G protein coupling, thereby reducing the binding and signal transduction efficiencies ( 58 ). Translationally, this implies regions harbouring TAAR1 SNVs may display varied response to agonists, warranting dose optimisation in certain populations belonging to certain geographical regions to achieve optimal pharmacological effects. Indeed, current literature shows a spectrum of receptor impact from micro-switch mutations. Some receptors are virtually unharmed, whilst others lose their functionality, warranting in vitro validation to assess the functional impacts of the SNVs predicted using in silico methods ( 51 , 56 , 57 , 59 ). Future studies may also consider the limitations surrounding in silico -based modalities and the absence of a population-specific categorisation. Datasets with such assortment can only be accomplished through targeted and controlled genomic studies, which may also mitigate population specific bias. Thus, further exploration of population specific SNVs is needed to aid in further development of personalised medicines. In this study, we used NCBI's dbGaP repository and the recent cryo-EM studies on human TAAR1 to identify, map and define the putative effect of SNVs on TAAR1 function. A total of 290 missense TAAR1 SNVs were found in our dataset. A structural mapping analysis of the primary data revealed over 40 SNVs that may influence TAAR1 activation by endogenous and exogenous ligands. Orthosteric SNV, D103 3.32 , found in certain regions (WPR, SEAR) and signalling SNVs (V125 3.54 A (AR) T252 6.36 A (common)) likely influence ligand recognition and alter signalling properties of TAAR1 ligands. From the current dataset, the highest number of TAAR1 SNVs were found in the AR, followed by SEAR, ROA, ER, and WPR, respectively, suggesting agents targeting TAAR1 may have differential therapeutic outcomes in geographically diverse populations, especially given most SNVs are predicted to reduce ligand binding. In essence, our study provides insights into the putative impact of missense SNVs on TAAR1 activation-signalling mechanisms and its implications with the use of next-generation therapeutics in population from various demographic regions. Functional validation and more rigorous in vitro and computational studies may help us better understand the clinical impact of SNVs. Future investigation of how TAAR1 SNVs affect expression and function, such as subcellular localisation, GPCR heterodimerisation, biased signalling, as well as analysis of SNVs outside of our primary dataset will build a molecular understanding of neuropsychiatric disorders and to the development of novel TAAR1 therapeutics. Declarations Acknowledgements PCN and TB acknowledge Flinders University and Southern Adelaide Local Health Network for Innovation Partnership Seed Funding. Competing Interests The authors have no conflicts of interest to declare. Funding Flinders University Innovation Partnership Seed Funding (ID:90037803) funded by Flinders University and the Southern Adelaide Local Health Network. Ethical Approval Genetic data were obtained from the publicly available National Center for Biotechnology Information (NCBI) database of Genotypes and Phenotypes (dbGaP). References Borowsky B, Adham N, Jones KA, Raddatz R, Artymyshyn R, Ogozalek KL, et al. Trace amines: identification of a family of mammalian G protein-coupled receptors. Proc Natl Acad Sci U S A. 2001;98(16):8966-71. Berry MD, Gainetdinov RR, Hoener MC, Shahid M. Pharmacology of human trace amine-associated receptors: Therapeutic opportunities and challenges. Pharmacol Ther. 2017;180:161-80. Raul RG, Marius CH, Mark DB. Trace Amines and Their Receptors. Pharmacological Reviews. 2018;70(3):549. Rutigliano G, Accorroni A, Zucchi R. The Case for TAAR1 as a Modulator of Central Nervous System Function. Frontiers in Pharmacology. 2018;8. Espinoza S, Salahpour A, Masri B, Sotnikova TD, Messa M, Barak LS, et al. Functional interaction between trace amine-associated receptor 1 and dopamine D2 receptor. 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Therapeutic Potential of TAAR1 Agonists in Schizophrenia: Evidence from Preclinical Models and Clinical Studies. Int J Mol Sci. 2021;22(24). Nair PC, Chalker JM, McKinnon RA, Langmead CJ, Gregory KJ, Bastiampillai T. Trace Amine-Associated Receptor 1 (TAAR1): Molecular and Clinical Insights for the Treatment of Schizophrenia and Related Comorbidities. ACS Pharmacol Transl Sci. 2022;5(3):183-8. Heffernan MLR, Herman LW, Brown S, Jones PG, Shao L, Hewitt MC, et al. Ulotaront: A TAAR1 Agonist for the Treatment of Schizophrenia. ACS Med Chem Lett. 2022;13(1):92-8. ClinicalTrials.gov. A Clinical Trial That Will Study the Efficacy and Safety of an Investigational Drug in Acutely Psychotic People With Schizophrenia. Sumitomo Pharma America, Inc.; 2023. Report No.: NCT04092686. ClinicalTrials.gov. A Clinical Trial to Study the Efficacy and Safety of an Investigational Drug in Acutely Psychotic People With Schizophrenia. Sumitomo Pharma America, Inc.; 2023. Report No.: NCT04072354. 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Possible role of rare variants in Trace amine associated receptor 1 in schizophrenia. Schizophr Res. 2017;189:190-5. Masellis M, Basile V, Meltzer HY, Lieberman JA, Sevy S, Macciardi FM, et al. Serotonin Subtype 2 Receptor Genes and Clinical Response to Clozapine in Schizophrenia Patients. Neuropsychopharmacology. 1998;19(2):123-32. Shastry BS. SNPs in disease gene mapping, medicinal drug development and evolution. Journal of Human Genetics. 2007;52(11):871-80. Fowler S, Kletzl H, Finel M, Manevski N, Schmid P, Tuerck D, et al. A UGT2B10 Splicing Polymorphism Common in African Populations May Greatly Increase Drug Exposure. Journal of Pharmacology and Experimental Therapeutics. 2015;352(2):358. Mühlhaus J, Dinter J, Jyrch S, Teumer A, Jacobi SF, Homuth G, et al. Investigation of Naturally Occurring Single-Nucleotide Variants in Human TAAR1. Frontiers in Pharmacology. 2017;8. Rutigliano G, Zucchi R. Molecular Variants in Human Trace Amine-Associated Receptors and Their Implications in Mental and Metabolic Disorders. Cell Mol Neurobiol. 2020;40(2):239-55. Loftis JM, Lasarev M, Shi X, Lapidus J, Janowsky A, Hoffman WF, Huckans M. Trace amine-associated receptor gene polymorphism increases drug craving in individuals with methamphetamine dependence. PLoS One. 2019;14(10):e0220270. Shi X, Walter NA, Harkness JH, Neve KA, Williams RW, Lu L, et al. Genetic Polymorphisms Affect Mouse and Human Trace Amine-Associated Receptor 1 Function. PLoS One. 2016;11(3):e0152581. Liu H, Zheng Y, Wang Y, Wang Y, He X, Xu P, et al. Recognition of methamphetamine and other amines by trace amine receptor TAAR1. Nature. 2023. Shang P, Rong N, Jiang JJ, Cheng J, Zhang MH, Kang D, et al. Structural and signaling mechanisms of TAAR1 enabled preferential agonist design. Cell. 2023. Xu Z, Guo L, Yu J, Shen S, Wu C, Zhang W, et al. Ligand recognition and G protein coupling of trace amine receptor TAAR1. Nature. 2023. Zilberg G, Parpounas AK, Warren AL, Yang S, Wacker D. Molecular basis of human trace amine-associated receptor 1 activation. Nature Communications. 2024;15(1):108. Nair PC, Shajan B, Bastiampillai T. Newly identified structures of trace-amine associated receptor-1 (TAAR1) will aid discovery of next generation neuropsychiatric drugs. Mol Psychiatry. 2024. Nair PC, Miners JO, McKinnon RA, Langmead CJ, Gregory KJ, Copolov D, et al. Binding of SEP-363856 within TAAR1 and the 5HT1A receptor: implications for the design of novel antipsychotic drugs. Molecular Psychiatry. 2022;27(1):88-94. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R, et al. The NCBI dbGaP database of genotypes and phenotypes. Nat Genet. 2007;39(10):1181-6. Zakharia F, Basu A, Absher D, Assimes TL, Go AS, Hlatky MA, et al. Characterizing the admixed African ancestry of African Americans. Genome Biol. 2009;10(12):R141. Liu X, Li C, Mou C, Dong Y, Tu Y. dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs. Genome Medicine. 2020;12(1):103. Sim N-L, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Research. 2012;40(W1):W452-W7. Choi Y, Chan AP. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics. 2015;31(16):2745-7. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Research. 2011;39(17):e118-e. Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nature Methods. 2014;11(4):361-2. Munk C, Isberg V, Mordalski S, Harpsøe K, Rataj K, Hauser AS, et al. GPCRdb: the G protein-coupled receptor database - an introduction. Br J Pharmacol. 2016;173(14):2195-207. Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, et al. RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Research. 2023;51(D1):D488-D508. Pettersen EF, Goddard TD, Huang CC, Meng EC, Couch GS, Croll TI, et al. UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Sci. 2021;30(1):70-82. VIDA 5.0.5.3. OpenEye CMS, Santa Fe, NM, 0, http://www.eyesopen.comVIDA. Ballesteros JA, Weinstein H. [19] Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. In: Sealfon SC, editor. Methods in Neurosciences. 25: Academic Press; 1995. p. 366-428. Singh RK, Mahalingam K. In silico approach to identify non-synonymous SNPs in human obesity related gene, MC3R (melanocortin-3-receptor). Computational Biology and Chemistry. 2017;67:122-30. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. 2013;Chapter 7:Unit7.20. Stoy H, Gurevich VV. How genetic errors in GPCRs affect their function: Possible therapeutic strategies. Genes Dis. 2015;2(2):108-32. Hauser AS, Chavali S, Masuho I, Jahn LJ, Martemyanov KA, Gloriam DE, Babu MM. Pharmacogenomics of GPCR Drug Targets. Cell. 2018;172(1):41-54.e19. Kusaga A, Yamashita Y, Koeda T, Hiratani M, Kaneko M, Yamada S, Matsuishi T. Increased urine phenylethylamine after methylphenidate treatment in children with ADHD. Ann Neurol. 2002;52(3):372-4. Torsten S, Ines L. Mutations in G Protein–Coupled Receptors: Mechanisms, Pathophysiology and Potential Therapeutic Approaches. Pharmacological Reviews. 2021;73(1):89. Valentin-Hansen L, Groenen M, Nygaard R, Frimurer TM, Holliday ND, Schwartz TW. The arginine of the DRY motif in transmembrane segment III functions as a balancing micro-switch in the activation of the β2-adrenergic receptor. J Biol Chem. 2012;287(38):31973-82. Hauser AS, Kooistra AJ, Munk C, Heydenreich FM, Veprintsev DB, Bouvier M, et al. GPCR activation mechanisms across classes and macro/microscales. Nature Structural & Molecular Biology. 2021;28(11):879-88. Alewijnse AE, Timmerman H, Jacobs EH, Smit MJ, Roovers E, Cotecchia S, Leurs R. The effect of mutations in the DRY motif on the constitutive activity and structural instability of the histamine H(2) receptor. Mol Pharmacol. 2000;57(5):890-8. Najafzadeh H, Safaeian L, Mirmohammad Sadeghi H, Rabbani M, Jafarian A. The effect of aspartate-lysine-isoleucine and aspartate-arginine-tyrosine mutations on the expression and activity of vasopressin V2 receptor gene. Iran Biomed J. 2010;14(1-2):17-22. Clayton CC, Bruchas MR, Lee ML, Chavkin C. Phosphorylation of the mu-opioid receptor at tyrosine 166 (Tyr3.51) in the DRY motif reduces agonist efficacy. Mol Pharmacol. 2010;77(3):339-47. Römpler H, Yu H-T, Arnold A, Orth A, Schöneberg T. Functional consequences of naturally occurring DRY motif variants in the mammalian chemoattractant receptor GPR33. Genomics. 2006;87(6):724-32. Huang H, Tao Y-X. Functions of the DRY motif and intracellular loop 2 of human melanocortin 3 receptor. Journal of Molecular Endocrinology. 2014;53(3):319-30. Nomiyama H, Yoshie O. Functional roles of evolutionary conserved motifs and residues in vertebrate chemokine receptors. Journal of Leukocyte Biology. 2015;97(1):39-47. Ragnarsson L, Andersson Å, Thomas WG, Lewis RJ. Mutations in the NPxxY motif stabilize pharmacologically distinct conformational states of the α1B- and β2-adrenoceptors. Science Signaling. 2019;12(572):eaas9485. Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4407652","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301484850,"identity":"6fe50451-25ea-4214-8118-4ad554369c31","order_by":0,"name":"Britto Shajan","email":"","orcid":"","institution":"Flinders University","correspondingAuthor":false,"prefix":"","firstName":"Britto","middleName":"","lastName":"Shajan","suffix":""},{"id":301484851,"identity":"67acde4f-a4fa-43ca-aa70-b414739a44ed","order_by":1,"name":"Shashikanth Marri","email":"","orcid":"","institution":"Flinders University","correspondingAuthor":false,"prefix":"","firstName":"Shashikanth","middleName":"","lastName":"Marri","suffix":""},{"id":301484852,"identity":"7043da88-ed50-47fb-b5c3-dfd0f4c2fde5","order_by":2,"name":"Tarun Bastiampillai","email":"","orcid":"","institution":"Flinders University","correspondingAuthor":false,"prefix":"","firstName":"Tarun","middleName":"","lastName":"Bastiampillai","suffix":""},{"id":301484853,"identity":"c96ec188-f172-477f-bc24-30c50207d2af","order_by":3,"name":"Karen J. Gregory","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"J.","lastName":"Gregory","suffix":""},{"id":301484854,"identity":"b201316a-6452-4a24-a742-5c28a5e445ad","order_by":4,"name":"Shane D. Hellyer","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Shane","middleName":"D.","lastName":"Hellyer","suffix":""},{"id":301484855,"identity":"baeb65d0-1a5d-41eb-bc8d-2c9f6ec61fe9","order_by":5,"name":"Pramod C. Nair","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie3RPUvEMBjA8ScE0qVn10fE7xAQqsf5YS4IvUWOwkHpINKpLnquuuhXqItzINAuQdfKDQpipxsyOYq5Hi7a+rI55A9NIfBLmhTA5fq3MftQIE9pjACynaK/IpRr/idiR9Qcfia7W+qlMAlMg5NBk0q+N92ozp4NpCORee0KXxrOo/Dh4g5mqLywlhxnm7oKEfREZP5hJ+EawsUgB5EpxhaGoyjqiAHJlcigj3ivLbm2JJYr8thQQ94sCZY9xF/vUlgCLakZIMkswe5dhqd+Ys+C4kYxiityqSOG43Kyk2MTd96Y793WJtkXV/clMTI9FvOqpMYcjbbPg4Oi88PWL/w0PYaPn9VLXC6Xy/VN7xd7YgR6vdKHAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8630-0121","institution":"Flinders University","correspondingAuthor":true,"prefix":"","firstName":"Pramod","middleName":"C.","lastName":"Nair","suffix":""}],"badges":[],"createdAt":"2024-05-12 07:42:32","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4407652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4407652/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40246-024-00620-w","type":"published","date":"2024-06-11T14:51:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56487337,"identity":"612aac57-4617-4c23-951a-2b4187eda839","added_by":"auto","created_at":"2024-05-14 20:57:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":798607,"visible":true,"origin":"","legend":"\u003cp\u003eSnake plot of human TAAR1 demonstrating allelic heterogeneity observed in this dataset. The data was accrued from NCBI dbGaP consisting of 290 TAAR1 SNVs. The allelic heterogeneity was characterised using the TAAR1 snake plot, accessed from GPCRdb (TA1 human structure). As shown, up to four variants were originated from six individual residues. Three post-translational modification sites (PTM sites) have been identified (T67\u003csup\u003e2.48\u003c/sup\u003e (phosphorylation), K134\u003csup\u003eICL2\u003c/sup\u003e (methylation) and K322\u003csup\u003eC-term\u003c/sup\u003e (acetylation)) using GPCRdb, with two residues affected by SNVs (T67\u003csup\u003e2.48\u003c/sup\u003e and K134\u003csup\u003eICL2\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4407652/v1/c33cf99c68d87892487f283e.png"},{"id":56487341,"identity":"b210ed65-e077-412b-94f4-5b172b2e7036","added_by":"auto","created_at":"2024-05-14 20:57:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1977306,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of TAAR1 SNVs across different demographic regions. The data was accrued from NCBI dbGaP and characterised into five WHO regions of classifications; WPR, AR, ROA, SEAR, and ER. (\u003cstrong\u003eA\u003c/strong\u003e) Venn analysis showing the distribution of SNVs across five regions. The analysis was conducted using the ggVenndiagram package in R studio. (\u003cstrong\u003eB\u003c/strong\u003e) Burden of TAAR1 SNV in each region. All SNVs, including unique and shared variants found in each demographic region were tallied. (\u003cstrong\u003eC\u003c/strong\u003e) World map showing each WHO regions of classification. Eastern Mediterranean Region and Antarctica (grey colour) were not considered in this study due to very low SNV prevalence. The bar graph and World map was generated in R studio using ggplot and maps packages, respectively.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4407652/v1/717cfdc8bb58bd37cbad75f2.png"},{"id":56487343,"identity":"c92c77ee-6b2a-4307-bae4-7c052b46a0ee","added_by":"auto","created_at":"2024-05-14 20:57:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10705201,"visible":true,"origin":"","legend":"\u003cp\u003eSNVs affecting residues involved in ligand binding, defined as orthosteric SNVs. Amino acid residues involved in ligand binding were curated using the human ligand-TAAR1 complexes deposited in RSCB, including 8W8A, 8W89, 8W88, 8W87, 8JSO, 8JLN, 8JLO, 8JLP, 8JLQ, 8JLR, 8JSO, 8WC8, 8WCA and 8UHB (ligand name is displayed in parenthesis). Primary SNV data was accrued from NCBI dbGaP and mapped on the ulotaront-TAAR1-Gas coupled cryo-EM complex (RCSB: 8JLO). (\u003cstrong\u003eA\u003c/strong\u003e) Ulotaront-TAAR1-Gas coupled cryo-EM complex (RCSB: 8JLO) (\u003cstrong\u003ei\u003c/strong\u003e) residues involved in synthetic and endogenous agonist binding affected by SNVs (shown in sticks), (\u003cstrong\u003eii\u003c/strong\u003e) complexed ulotaront at the binding site with WT residues affected by SNVs, including D103\u003csup\u003e3.32\u003c/sup\u003e which establishes a critical ionic bond required for ligand recognition. (\u003cstrong\u003eB\u003c/strong\u003e) River plot demonstrating the demographic distribution of orthosteric SNVs. Top panel displays the proportional burden of orthosteric SNVs in WPR, AR, ROA, SEAR and ER. Bottom panels show the list of orthosteric SNVs. The ribbons link each region with associated SNVs. Unique SNVs are colour matched with respective WHO region, and shared SNVs are coloured using light grey. (\u003cstrong\u003eC\u003c/strong\u003e) List of residues found to interact with complexed ligands. The river plot and scatter plot were generated using the ggalluvial package and ggplot of R studio, respectively. 3D structure depictions were created using ChimeraX and OpenEye Scientific VIDA. *Indicates presence of mutation/s. PEA (β-phenethylamine), METH (methamphetamine), AMPH (amphetamine) and T1AM (3-Iodothyronamine).\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4407652/v1/8863c9708c8290be348d7b88.png"},{"id":56487340,"identity":"0b2078e5-6fe0-4175-a365-371cec6f5d64","added_by":"auto","created_at":"2024-05-14 20:57:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":9287410,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic distribution and predicted effects of class A micro-switch SNVs. Primary SNV data was accrued from NCBI dbGaP and mapped on the ulotaront-TAAR1-Gas cryo-EM complex (RCSB: 8JLO). (\u003cstrong\u003eA\u003c/strong\u003e) Micro-switch residues containing SNVs, shown in sticks; (\u003cstrong\u003ei\u003c/strong\u003e) residues of CWxP motif with SNVs, (\u003cstrong\u003eii\u003c/strong\u003e) residues of DRY motif with SNVs (\u003cstrong\u003eiii\u003c/strong\u003e) residues of NPxxY and PIF motif with SNVs (\u003cstrong\u003eB\u003c/strong\u003e) River plot displaying demographic distribution of microswitch SNVs. The bottom panel show the list of SNVs. The ribbons link each region with associated SNVs. Unique SNVs are colour matched with respective WHO region, and shared SNVs are coloured using light grey. (\u003cstrong\u003eC\u003c/strong\u003e) Prediction of putative effects of micro-switch SNVs using SIFT4G, MutationTaster 2, PROVEAN and MutationAssessor. The tools were accessed using dbNSFP database. Variants were called damaging upon meeting the following threshold scores (denoted by the red vertical line): \u0026lt;0.05 (SIFT4G), \u0026gt;0.5 (MutationTaster 2), \u0026lt; -2.5 (PROVEAN) and \u0026gt;3.5 (MutationAssesor).\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4407652/v1/664bd7d1bc600b691dbcc72d.png"},{"id":56487342,"identity":"42778b28-62d3-4236-988f-40d3add37d31","added_by":"auto","created_at":"2024-05-14 20:57:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5645294,"visible":true,"origin":"","legend":"\u003cp\u003eSNVs identified at the G-protein coupling interface of TAAR1, referred to as signalling SNVs. Primary SNV data was accrued from NCBI dbGaP and current cryo-EM literature was utilised to map signalling SNVs. (\u003cstrong\u003eA\u003c/strong\u003e) SNVs identified at residues demonstrated to be critical for different TAAR1 signalling pathways, shown in sticks. (\u003cstrong\u003eB\u003c/strong\u003e) River plot displaying demographic distribution of signalling SNVs. Unique SNVs are colour matched with respective WHO region, and shared SNVs are coloured using light grey. Alanine variants present in this subset has been functionally validated and shown to disrupt G-protein activity.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4407652/v1/76425e61edafc5bab0707acc.png"},{"id":58822207,"identity":"80162c83-61a9-4746-b408-dd5946ad072b","added_by":"auto","created_at":"2024-06-21 16:37:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":31266514,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4407652/v1/c80d4cb1-e235-4845-a3ce-cc0ef74e0e35.pdf"},{"id":56487338,"identity":"ab764a89-b1b3-471c-a25a-25a3517a6633","added_by":"auto","created_at":"2024-05-14 20:57:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3083077,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Files\u003c/p\u003e","description":"","filename":"SI.docx","url":"https://assets-eu.researchsquare.com/files/rs-4407652/v1/a1f1c1a25b52d94413694578.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTrace amine associated receptor 1: Predicted effects of single nucleotide variants on structure-function in geographically diverse populations\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe endogenous trace amines (TAs) are small molecules that selectively activate a group of G protein-coupled receptors (GPCRs) called trace amine associated receptors (TAARs) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Human TAARs are encoded by an intron-less cluster of \u003cem\u003eTaar\u003c/em\u003e genes located on chromosome 6q23.2, with six genes encoding for functional TAARs (TAAR1, 2, 5, 6, 8 and 9) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). TAARs are distantly related to monoamine neurotransmitter GPCRs and share close structural similarities with the broader class A GPCR family, featuring a membrane spanning hepta-helical transmembrane domain (TM) and three intracellular and extracellular loops (ICL1-3 and ECL1-3, respectively) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). While most TAARs function primarily as olfactory receptors, with low CNS expression, TAAR1 is highly expressed in the CNS, particularly in monoaminergic nuclei (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Furthermore, TAAR1 expression is also reported in peripheral systems including the gastrointestinal tract, heart, thyroid, and immune systems (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). When inactive, TAAR1 is proposed to be localised on the endoplasmic reticulum and mitochondrial membranes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, TAAR1 activation via trace amine binding and/or heterodimerisation with other receptors triggers translocation to the plasma membrane and the production of cellular responses, primarily by altering intracellular cyclic-adenosine monophosphate (cAMP) levels (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Through these actions, TAAR1 plays important roles in the modulation of monoaminergic and glutamatergic neurotransmission, hormone secretion and glucose metabolism, regulating vital processes involved with cognition, mood, and metabolism (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). As such, TAAR1 has arisen as the major focus of TAAR research due to its therapeutic potential in treating CNS and metabolic disorders (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe critical role of TAAR1 in modulating monoaminergic neurotransmission is exemplified in TAAR1 knockout mice, which display hypersensitivity to amphetamine and elevated release of monoaminergic neurotransmitters (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Further, receptor stimulation with partial TAAR1 agonists significantly improved psychological distress, cognitive functioning, sleep patterns, and produced anti-diabetic effects in preclinical models of schizophrenia and diabetes (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Many structurally distinct TAAR1 full and partial agonists have since been developed, with several entering clinical trials for schizophrenia, anxiety, Parkinson's L-Dopa related psychosis, sleep disorder and diabetes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Of note is ulotaront (SEP-363856), a dual TAAR1/5-HT1A agonist, which received FDA Breakthrough Therapy status for schizophrenia treatment in 2019 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, despite promising preclinical and early clinical results, ulotaront failed to hit primary endpoints in two recent Phase 3 trials for schizophrenia (ClinicalTrials.gov Identifiers: NCT04072354, NCT04092686) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Initial reports suggested a high placebo effect masking the therapeutic effects of ulotaront as the primary reason for the clinical failures (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, it remains unknown exactly what factors play a role in a lack of ulotaront efficacy in patient populations. As Phase 2 clinical trials for ulotaront are currently underway as a treatment for generalised anxiety disorder (ClinicalTrials.gov Identifier: NCT05729373) and as adjunctive therapy for major depressive disorder (ClinicalTrials.gov Identifier: NCT05593029), understanding the reasons for this failure is of critical importance for the future clinical success of both ulotaront and other TAAR1 clinical candidates (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenetic mutations are of particular interest in GPCR drug discovery due to their disease-causing effects and potential to interfere with drug activity by disrupting drug metabolism, protein-ligand interactions, and receptor function (\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A partial TAAR1 agonist, RO5263397, failed Phase 1 clinical trials due to splicing mutations in the drug metabolism enzyme uridine 5\u0026prime;-diphosphate-glucuronosyltransferases 2B10, which increased exposure to parent compound by more than 100-fold (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The causative single nucleotide variant (SNV) mutation was particularly pervasive in African regions, indicating geographical heterogeneity in susceptibility to altered drug effects (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The TAAR1 gene cluster has also been identified via linkage analysis as a locus of susceptibility for schizophrenia, with clinical and experimental studies showing correlation between TAAR1 SNVs and patients with impaired glucose homeostasis and schizophrenia in geographically distinct populations (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Indeed, numerous SNVs identified in human TAAR1 alter expression and functional properties, with previous studies relying on mutagenesis and \u003cem\u003ein vitro\u003c/em\u003e characterisation to test the functional effects of TAAR1 SNVs (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Furthermore, multiple high resolution cryo-electron microscopy (cryo-EM) TAAR1 structures bound to both natural TAs and synthetic agonists have recently been solved (\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Such advances pave the way for structure-based approaches to studying TAAR1 ligand interactions and structure-function, which until recently have relied on homology models (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Computational approaches using experimentally determined structures represent a powerful method for rapidly screening the effects of TAAR1 SNVs that potentially impact drug action in genetically and geographically diverse clinical populations.\u003c/p\u003e \u003cp\u003eThe purpose of this study was to leverage recently published experimental structures of TAAR1 to identify the putative effects of SNVs on TAAR1 structure-function using bioinformatic approaches. The Database of Genotypes and Phenotypes (dbGaP) was used to identify and characterise putative detrimental SNVs in populations belonging to five diverse geographical regions. With the aim of predicting the impact of these SNVs on TAAR1 function, we have identified 19 SNVs at the orthosteric binding sites, 9 at signalling domains and 16 at micro-switch domains. Additionally, in silico functional prediction algorithms predicted all 16 micro-switch SNVs are damaging to TAAR1 function. We found several crucial mutations affecting conserved regions that are geographically non-uniform in their distribution. The results of our study provide a basis for further in silico and in vitro studies that may provide deeper insights and experimental validation for TAAR1 SNV functional effects.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSNV data from NCBI dbGaP repository\u003c/h2\u003e \u003cp\u003eAll SNV data were collected from the National Center for Biotechnology Information (NCBI) dbGaP database (2023) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The Reference SNP cluster ID (rsID) were compiled based on five of WHO classification of regions, African Region (AR), South-East Asian Region (SEAR), Region of the Americas (ROA), European Region (ER) and Western Pacific Region (WPR). Regions containing pan-ethnic groups, such as Region of the Americas, were classified based on their genetic architecture (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrediction of SNV functional effects using in silico tools.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo analyse the effect of specific TAAR1 SNVs, in silico algorithms were employed to predict tolerability. The tools were accessed through dbNSFP (version 4.4a), a repository containing a range of functional prediction algorithms (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). To prevent ambiguity in relation to cut-off scores, tools yielding a binary output as damaging (D) or Tolerable (T) were selected. Furthermore, tools that utilised High, Medium, and Low scoring formats were considered. All High outputs were characterised as damaging, while Medium and Low outputs were characterised as tolerable. Four algorithms satisfied these inclusion criteria. 1) Sorting Intolerant from Tolerant (SIFT4G v2.4), which predicts the likelihood of a mutation to cause functional damage using sequence homology, assumes that evolutionarily conserved regions are essential for protein function and therefore any missense substitution is generally considered intolerable (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). 2) Protein Variation Effect Analyzer (PROVEAN v1.1) utilises sequence homology to estimate the tolerability of a mutation by computing the change in delta alignment score using the BLOSUM62 substitution matrix (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). 3) Mutation Assessor (v3.0) utilises sequences from homologous protein families and sub-families within and between species, to calculate an impact probability score (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). 4) MutationTaster 2 (v2015) utilises machine learning approaches and is trained using\u0026thinsp;\u0026gt;\u0026thinsp;390,000 disease variants and \u0026gt;\u0026thinsp;6\u0026nbsp;million tolerable polymorphisms. MutationTaster 2 uses Bayes classifier algorithm to calculate the probability of a mutation being damaging or tolerable (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of TAAR1 SNVs at ligand binding and other functional domains through structural mapping\u003c/h2\u003e \u003cp\u003eThe snake plot of human TAAR1 (TA1 human) was acquired from GPCRdb (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). To identify SNVs at the ligand binding and other functional domains of TAAR1, current literature on human TAAR1 experimental structures were utilised. The complexes of TAAR1-ligand (8W8A, 8W89, 8W88, 8W87, 8JSO, 8JLN, 8JLO, 8JLP, 8JLQ, 8JLR, 8WC8, 8WCA and 8UHB) were accessed using the RCSB PDB database (\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Search term \u0026ldquo;TAAR1\u0026rdquo; was provided to make the initial query and the output was refined using \u0026ldquo;Homo sapiens\u0026rdquo; under \u0026lsquo;Scientific Name of Source Organism\u0026rsquo;. The resulting structures and primary literature were reviewed to identify residues involved in ligand activated mechanisms of TAAR1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenerating plots using bioinformatic tools and online databases.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe position of SNVs within the TAAR1 structure were sorted based on the geographical distribution and a 5-way Venn diagram was generated using the Venn package through R studio version 2023.06. In addition, the geographical distribution of each SNV were segregated and illustrated using river plots, generated using ggalluvial package in RStudio version 2023.06. ChimeraX (version 1.7.1) and OpenEye Scientific VIDA (version 5.0.5.3) were utilised for visualisation and annotations of 3D structures (41\u0026ndash;43).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAllelic heterogeneity and geographical distribution of human TAAR1 SNVs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA list of rare missense TAAR1 SNVs was collated from the NCBI dbGaP database. To identify the geographical distribution of each SNV, the reference SNP cluster ID (rsID) were classified based on five WHO regions, African Region (AR), South-East Asian Region (SEAR), Region of the Americas (ROA), European Region (ER), and Western Pacific Region (WPR). A total of 290 individual TAAR1 SNVs were identified, all of which are segregated at varying capacity, covering all domains of TAAR1 including TM, ICL, ECL, N-terminal extension and C-terminal tail. To visualise the position of individual SNVs and identify allelic heterogeneity at each residue, SNVs were mapped onto a TAAR1 snake plot from GPCRdb (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) (\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e). As highlighted, the current dataset shows 108 residues with a single SNV, which constitutes approximately 37% of this dataset. The remaining 63% are segregated as residues with double, triple, and quadruple allelic heterogeneity. Notably, the highest heterogeneity (four variants) was displayed at six positions; V94\u003csup\u003e3.23\u003c/sup\u003e (Superscript is position according to Ballesteros \u0026amp; Weinstein numbering system, which indicates helix position of residue relative to the most conserved amino acid within a TM (\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e)), R121\u003csup\u003e3.50\u003c/sup\u003e and M135\u003csup\u003eICL2\u003c/sup\u003e, G181\u003csup\u003eECL2\u003c/sup\u003e, T271\u003csup\u003e6.55\u003c/sup\u003e and M302\u003csup\u003e7.51\u003c/sup\u003e. In addition, SNVs in two post translational modification sites (Predicted PTM site, identified using GPCRdb) were identified, T67\u003csup\u003e2.48\u003c/sup\u003e, K134\u003csup\u003eICL2\u003c/sup\u003e (\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e). Overall, this dataset presents with SNVs covering approximately 55% (186 residues) of human TAAR1.\u003c/p\u003e\n\u003cp\u003eTo characterise the geographical distribution and consequently define shared and unshared SNVs, a Venn analysis was performed. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA,C, 20 SNVs are shared across all five geographical regions (referred to as \u0026ldquo;common SNVs\u0026rdquo; henceforth) and a total of 75 SNVs are unique. Notably, unique SNVs were most pervasive in SEAR and AR (31 unique SNVs in each) and no unique SNVs were identified in the ROA. To elucidate the prevalence of TAAR1 SNVs regionally, we defined the burden of TAAR1 SNVs by tallying all the unique and shared SNVs present in each region (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). In this dataset, the total burden of TAAR1 mutations were the highest in AR (n\u0026thinsp;=\u0026thinsp;212) and lowest in the WPR (n\u0026thinsp;=\u0026thinsp;50).\u003c/p\u003e\n\u003cp\u003eTo best illustrate our overall data, a river plot was utilised (Supplementary Figs.\u0026nbsp;1\u0026ndash;4). Briefly, all plots were composed of two complementary horizontal axes, top axis with the WHO region and bottom axis with the SNVs. Both axes are bridged by up to five groups of \u0026ldquo;streams\u0026rdquo;, each representing a WHO region. In this case, the stream can be seen to originate from the axis of WHO region and links to a single/multiple SNVs that is in association with the region. The stream also can be interpreted to originate from the axis of SNVs and linking to a WHO region/s to precisely locate the region/s in association with SNV. Thus, the relative axial area, i.e. the size of each box a given SNV, or a WHO region occupies, represents the sum of associations with its complementary variable. In addition, shared SNVs are presented using the grey shade and unique SNVs are presented with varying colour shades corresponding to the WHO region (e.g. V288\u003csup\u003e7.37\u003c/sup\u003eA in AR with sky blue).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNVs at the ligand binding regions of human TAAR1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe potential impact of identified SNVs on important aspects TAAR1 function, such as agonist binding and signalling remain unexplored. Hence, our analysis focused on studying the SNVs present in the TAAR1 agonist binding and signalling domains. With the emergence of various agonist bound TAAR1 cryo-EM structures, residues critical for differential agonist binding and signalling are beginning to unfold (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e). Considering this, we performed identical structural mapping analysis using the available cryo-EM structures to map the distribution of SNVs across TAAR1 ligand binding pockets. This was conducted in conjunction with the geographical data to analyse and define the influence of SNVs present in each region.\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;Orthosteric SNVs\u0026rsquo; are defined as SNVs at residues surrounding the binding site of various TAAR1 agonists, identified from experimentally determined TAAR1 structures (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) including RCSB entries 8W8A, 8W89, 8W88, 8W87, 8JSO, 8JLN, 8JLO, 8JLP, 8JLQ, 8JLR, 8WC8, 8WCA and 8UHB. A total of 19 orthosteric SNVs, affecting 16 residues were identified (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, supplementary table 1). Three common orthosteric SNVs were identified at two positions, I104\u003csup\u003e3.33\u003c/sup\u003e (variant; V104\u003csup\u003e3.33\u003c/sup\u003e) and T197\u003csup\u003e5.45\u003c/sup\u003e (variants; I197\u003csup\u003e5.45\u003c/sup\u003e, S197\u003csup\u003e5.45\u003c/sup\u003e), which are crucial for the binding of several exogenous and endogenous TAAR1 agonists (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, C). Specifically, residue I104\u003csup\u003e3.33\u003c/sup\u003e forms interactions with 3-iodothyronamine (T1AM), amphetamine and other synthetic agonists, while residue T197\u003csup\u003e5.45\u003c/sup\u003e interacts with ralmitaront (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC) (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, six unique orthosteric SNVs were found, including I104\u003csup\u003e3.33\u003c/sup\u003eS, S108\u003csup\u003e3.37\u003c/sup\u003eP, F112\u003csup\u003e3.41\u003c/sup\u003eL, V150\u003csup\u003e4.52\u003c/sup\u003eI, S183\u003csup\u003eECL2\u003c/sup\u003eF and T194\u003csup\u003e5.42\u003c/sup\u003eA. All residues form key interactions with ralmitaront, with residue specific interactions with other agonists such as T1AM (I104\u003csup\u003e3.33\u003c/sup\u003e, S108\u003csup\u003e3.37\u003c/sup\u003e, S183\u003csup\u003eECL2\u003c/sup\u003e, T194\u003csup\u003e5.42\u003c/sup\u003e), ulotaront (I104\u003csup\u003e3.33\u003c/sup\u003e, and S183\u003csup\u003eECL2\u003c/sup\u003eF), amphetamine (I104\u003csup\u003e3.33\u003c/sup\u003e and S183\u003csup\u003eECL2\u003c/sup\u003eF), methamphetamine (I104\u003csup\u003e3.33\u003c/sup\u003e and T194\u003csup\u003e5.42\u003c/sup\u003e), \u0026beta;-phenylethylamine (PEA) (I104\u003csup\u003e3.33\u003c/sup\u003e and T194\u003csup\u003e5.42\u003c/sup\u003e) and Ro5256390 (I104\u003csup\u003e3.33\u003c/sup\u003e and T194\u003csup\u003e5.42\u003c/sup\u003e) (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e) as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC. Apart from F112\u003csup\u003e3.41\u003c/sup\u003eL and S183\u003csup\u003eECL2\u003c/sup\u003eF (both found in ER), all unique orthosteric SNVs belong to SEAR. Notably, shared orthosteric SNVs such as S80\u003csup\u003e2.61\u003c/sup\u003eG (WPR/SEAR), H99\u003csup\u003e3.28\u003c/sup\u003eR (ROA and others, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB) and V184\u003csup\u003eECL2\u003c/sup\u003eL (SEAR/ER) may influence the binding of endogenous compounds PEA and T1AM (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e). Furthermore, residues D103\u003csup\u003e3.32\u003c/sup\u003e and W264\u003csup\u003e6.48\u003c/sup\u003e interact with all TAAR1 agonists tested thus far (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC), with a single shared SNV at each residue: D103\u003csup\u003e3.32\u003c/sup\u003eN (WPR and SEAR) and W264\u003csup\u003e6.48\u003c/sup\u003eL (AR, SEAR, and ROA) as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNVs at the micro-switch domains of human TAAR1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSNVs found at the highly conserved micro-switch domains may influence TAAR1 functionality. Micro-switches are structural motifs conserved across the Class A GPCR superfamily that link ligand binding with conformational changes associated with G protein coupling and receptor activation (\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e). From the cryo-EM data, it was evident TAAR1 has the typical micro-switches for the classical class A GPCR helical rearrangement (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e). These micro-switches include the DRY (D120\u003csup\u003e3.49\u003c/sup\u003e, R121\u003csup\u003e3.50\u003c/sup\u003e and Y122\u003csup\u003e3.51\u003c/sup\u003e), PIF (P202\u003csup\u003e5.50\u003c/sup\u003e, I111\u003csup\u003e3.40\u003c/sup\u003e and F260\u003csup\u003e6.44\u003c/sup\u003e), CWxP (C263\u003csup\u003e6.47\u003c/sup\u003e, W264\u003csup\u003e6.48\u003c/sup\u003e, C265\u003csup\u003e6.49\u003c/sup\u003e and P266\u003csup\u003e6.50\u003c/sup\u003e), and NPxxY motifs (N300\u003csup\u003e7.49\u003c/sup\u003e, P301\u003csup\u003e7.50\u003c/sup\u003e, M302\u003csup\u003e7.51\u003c/sup\u003e, V303\u003csup\u003e7.52\u003c/sup\u003e and Y304\u003csup\u003e7.53\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eUsing structural mapping analysis, we classified SNVs within the four micro-switch domains as \u0026lsquo;micro-switch SNVs\u0026rsquo; (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, Supplementary table 2). A total of 16 micro-switch SNVs were identified, located within the DRY (seven SNVs), CWxP (six SNVs), PIF (one SNV) and NPxxY (two SNVs) motifs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). When considering geographical distribution, three common micro-switch SNV were identified: R121\u003csup\u003e3.50\u003c/sup\u003eC/S affecting the DRY and N300\u003csup\u003e7.49\u003c/sup\u003eK affecting NPxxY motif. Furthermore, a total of seven unique micro-switch SNVs were identified; four in AR, two in SEAR and one in the ER as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB. Shared variants in micro-switch regions were also identified, one of each affecting DRY, PIF, NPxxY and three affecting CWxP (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eAlthough the importance of the micro-switch regions in class A receptor rearrangement and activation are well known, the functional implications of SNVs in these motifs on TAAR1 activity remains largely unknown. Notably, putative deleterious effects of R121\u003csup\u003e3.50\u003c/sup\u003eC/L, C263\u003csup\u003e6.47\u003c/sup\u003eG, W264\u003csup\u003e6.48\u003c/sup\u003eL, P266\u003csup\u003e6.50\u003c/sup\u003eS/A and N300\u003csup\u003e7.49\u003c/sup\u003eS/K were previously described by GPCRdb using SIFT and PolyPhen algorithms. Therefore, to characterise the putative effects of TAAR1 micro-switch SNVs, four different \u003cem\u003ein silico\u003c/em\u003e algorithms were employed to predict the effect of variants as either tolerable or damaging; SIFT4G, PROVEAN, Mutation Assessor and MutationTaster 2. All micro-switch SNVs were predicted to be damaging by all four selected algorithms (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNVs affecting the G protein-coupling residues of human TAAR1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCryo-EM structures of TAAR1 in complex with structurally diverse ligands have revealed TAAR1 agonists engender divergent G protein coupling and downstream signalling outcomes, with mutagenesis revealing residues involved in this preferential G protein activation (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e). As such, we characterised 9 SNVs at five positions within TAAR1 identified as critical for divergent G protein activation and signalling transduction (referred to as signalling SNVs\u003cstrong\u003e)\u003c/strong\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). Two common signalling SNVs were identified, one affecting a residue important for TAAR1-G\u003csub\u003e\u0026alpha;s\u003c/sub\u003e subunit activity (H55\u003csup\u003eICL1\u003c/sup\u003eR), and T252\u003csup\u003e6.36\u003c/sup\u003eA critical TAAR1-G\u003csub\u003e\u0026alpha;i\u003c/sub\u003e activity. Two unique SNVs were present in the AR; one affecting residue critical for TAAR1-G\u003csub\u003e\u0026alpha;s\u003c/sub\u003e activity (Q220\u003csup\u003e5.68\u003c/sup\u003eE), and one, V125\u003csup\u003e3.54\u003c/sup\u003eA, involving a residue implicated with creating selective bias for G\u003csub\u003e\u0026alpha;i\u003c/sub\u003e over G\u003csub\u003e\u0026alpha;s\u003c/sub\u003e coupling (\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e). Additional signalling SNVs were shared across multiple regions and may influence TAAR1-G\u003csub\u003e\u0026alpha;\u003c/sub\u003e-protein interactions based on sites from previous experimental studies, as summarised in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB (\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA greater understanding of the impact of SNVs is warranted in light of emerging studies connecting TAAR1 SNVs with neuropsychiatric disorders (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Here, demographic distribution and atomic insights of human TAAR1 experimental structures were utilised to define and predict effects of selected TAAR1 missense SNVs. Our findings showed a large degree of allelic heterogeneity, presence of multi-regional SNVs and implicated AR with highest overall count of SNVs. Our structural mapping analysis utilising cryo-EM structural insights isolated the primary data set consisting of over 40 SNVs. Within the dataset were 16 SNVs at four different micro-switch regions, with putative damaging effects predicted by four \u003cem\u003ein silico\u003c/em\u003e algorithms. Here, AR had the highest number of micro-switch SNVs. In addition, 19 orthosteric SNVs, and 9 signalling SNVs were noted, with highest count was in SEAR and AR, respectively.\u003c/p\u003e \u003cp\u003eStructure-based predictions and bioinformatics approaches have been used to predict the functional effects of SNVs on multiple pharmacological targets (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). With the emergence of various agonist bound human TAAR1 cryo-EM structures, the binding residues for several TAAR1 agonists have been characterised (\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The TAAR1 cryo-EM structures in complex with various agonists have identified D103\u003csup\u003e3.32\u003c/sup\u003e and W264\u003csup\u003e6.48\u003c/sup\u003e as critical residues for agonist binding through a combination of \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein silico\u003c/em\u003e mutagenesis studies (\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In all cases, alanine substitution at both positions significantly affected TAAR1 activation in response to agonists for both mouse and human receptors (\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Notably, our dataset identified D103\u003csup\u003e3.32\u003c/sup\u003eN in WPR and SEAR, and W264\u003csup\u003e6.48\u003c/sup\u003eL in ROA and few others (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In previous experimental studies, the negatively charged D103\u003csup\u003e3.32\u003c/sup\u003e formed a critical salt bridge with the positively charged amine group of all complexed TAAR1 agonists (\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In comparison, the modified asparagine (D103\u003csup\u003e3.32\u003c/sup\u003eN) replaces the negative charge for a neutral charge, which was recently shown to abolish the binding PEA, tyramine, T1AM, and synthetic agonists including the atypical anti-psychotic asenapine (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Thus, D103\u003csup\u003e3.32\u003c/sup\u003eN will likely diminish binding of other agonists and raises a major concern regarding the efficacy of TAAR1 therapeutics in WPR and SEAR. Likewise, W264\u003csup\u003e6.48\u003c/sup\u003e, part of the CWxP motif, forms hydrophobic contacts with all complexed TAAR1 agonists (\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Hence, the substitution of W264\u003csup\u003e6.48\u003c/sup\u003eL may influence the binding of selective TAAR1 agonists. Previous experimental studies show that W264\u003csup\u003e6.48\u003c/sup\u003eA completely inhibits TAAR1 activation by PEA and other agonists, with substitution to phenylalanine (W264\u003csup\u003e6.48\u003c/sup\u003eF) also reducing agonist potency (\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This is likely divorced from the role of W264\u003csup\u003e6.48\u003c/sup\u003e as a binding determinant and is more likely due to its role as part of the critical CWxP micro-switch (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) which is essential for helical rearrangements in response to ligand binding. In addition, alanine substitution of T194\u003csup\u003e5.42\u003c/sup\u003e (found in SEAR) were previously experimentally validated and demonstrated to influence ligand induced activity of TAAR1 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Hence it is plausible that populations belonging to these regions may see a varied response to TAAR1 therapeutics. In addition, these SNVs may contribute to symptomology associated with disease states such as attention deficit hyperactivity disorder (ADHD) due to their potential impact on endogenous ligand binding and trace amine activity (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, previous mutagenesis studies demonstrated the importance of selective residues for ligand induced signalling. Our dataset showed SNVs at all these positions summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. In this case, the alanine variants have been experimentally validated to affecting protein activity, including the common SNV T252\u003csup\u003e6.36\u003c/sup\u003eA, which affects G\u003csub\u003eαi\u003c/sub\u003e coupling (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In addition, the G protein selectivity may be influenced by the variant V125\u003csup\u003e3.54\u003c/sup\u003eA (found in AR), to bias G-proteins towards a specific type (i.e giving preference for G\u003csub\u003eαi\u003c/sub\u003e over G\u003csub\u003eαs\u003c/sub\u003e) in a ligand dependent manner(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Such changes in G protein selectivity may reverse TAAR1s putative dopamine modulatory functions, possibly contributing to neuropsychiatric pathologies or affecting the ability of agonists to alleviate existing symptomology in African regions. Indeed, recent work suggests preferential activation of Gα\u003csub\u003eq\u003c/sub\u003e pathways, or dual Gα\u003csub\u003es\u003c/sub\u003e/Gα\u003csub\u003eq\u003c/sub\u003e activation, by TAAR1 agonists has added benefit in animal models of schizophrenia (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). While it remains to be seen if such observations translate to humans, such discoveries provide a framework for rational drug design but also highlight crucial evidence for how signalling SNVs may impact TAAR1 function and the clinical use of agonists for therapeutic benefit.\u003c/p\u003e \u003cp\u003eWhilst the clinical impact of TAAR1 SNVs affecting micro-switch motifs is yet to be elucidated, SNVs have been linked to pathologies in other GPCRs (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The DRY motif establishes intrahelical electrostatic interactions between D120\u003csup\u003e3.49\u003c/sup\u003e and R121\u003csup\u003e3.50\u003c/sup\u003e establishing a more critical interhelical ionic lock with TM6, both critical mechanisms for the receptor \u0026ldquo;packing\u0026rdquo;. These interactions may not be facilitated by residues with diverging electrostatic properties such as D120\u003csup\u003e3.49\u003c/sup\u003eG (ER), R121\u003csup\u003e3.50\u003c/sup\u003eS/C/L (variants S/C: common and variant L: AR) and Y122\u003csup\u003e3.51\u003c/sup\u003eC (SEAR/ER) (\u003cspan additionalcitationids=\"CR52 CR53 CR54\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). In comparison, the interactions may still be established if the modified residue mirrors those electrostatic properties of original residues such as D120\u003csup\u003e3.49\u003c/sup\u003eE (SEAR) and R121\u003csup\u003e3.50\u003c/sup\u003eH (AR) (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). On the contrary, the structural features and stability facilitated by certain residues may be indispensable. The SEAR SNV P266\u003csup\u003e6.50\u003c/sup\u003eL of CWxP and P202\u003csup\u003e5.50\u003c/sup\u003eS of PIF motif (ROA and AR) may compromise the helical rearrangement facilitated by the cyclic proline. The helical rearrangement is critical for G protein coupling, thereby reducing the binding and signal transduction efficiencies (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Translationally, this implies regions harbouring TAAR1 SNVs may display varied response to agonists, warranting dose optimisation in certain populations belonging to certain geographical regions to achieve optimal pharmacological effects. Indeed, current literature shows a spectrum of receptor impact from micro-switch mutations. Some receptors are virtually unharmed, whilst others lose their functionality, warranting \u003cem\u003ein vitro\u003c/em\u003e validation to assess the functional impacts of the SNVs predicted using \u003cem\u003ein silico\u003c/em\u003e methods (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Future studies may also consider the limitations surrounding \u003cem\u003ein silico\u003c/em\u003e-based modalities and the absence of a population-specific categorisation. Datasets with such assortment can only be accomplished through targeted and controlled genomic studies, which may also mitigate population specific bias. Thus, further exploration of population specific SNVs is needed to aid in further development of personalised medicines.\u003c/p\u003e \u003cp\u003eIn this study, we used NCBI's dbGaP repository and the recent cryo-EM studies on human TAAR1 to identify, map and define the putative effect of SNVs on TAAR1 function. A total of 290 missense TAAR1 SNVs were found in our dataset. A structural mapping analysis of the primary data revealed over 40 SNVs that may influence TAAR1 activation by endogenous and exogenous ligands. Orthosteric SNV, D103\u003csup\u003e3.32\u003c/sup\u003e, found in certain regions (WPR, SEAR) and signalling SNVs (V125\u003csup\u003e3.54\u003c/sup\u003eA (AR) T252\u003csup\u003e6.36\u003c/sup\u003eA (common)) likely influence ligand recognition and alter signalling properties of TAAR1 ligands. From the current dataset, the highest number of TAAR1 SNVs were found in the AR, followed by SEAR, ROA, ER, and WPR, respectively, suggesting agents targeting TAAR1 may have differential therapeutic outcomes in geographically diverse populations, especially given most SNVs are predicted to reduce ligand binding. In essence, our study provides insights into the putative impact of missense SNVs on TAAR1 activation-signalling mechanisms and its implications with the use of next-generation therapeutics in population from various demographic regions. Functional validation and more rigorous \u003cem\u003ein vitro\u003c/em\u003e and computational studies may help us better understand the clinical impact of SNVs. Future investigation of how TAAR1 SNVs affect expression and function, such as subcellular localisation, GPCR heterodimerisation, biased signalling, as well as analysis of SNVs outside of our primary dataset will build a molecular understanding of neuropsychiatric disorders and to the development of novel TAAR1 therapeutics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003ePCN and TB acknowledge Flinders University and Southern Adelaide Local Health Network for Innovation Partnership Seed Funding.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eFlinders University Innovation Partnership Seed Funding (ID:90037803) funded by Flinders University and the Southern Adelaide Local Health Network.\u003c/p\u003e\n\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eGenetic data were obtained from the publicly available National Center for Biotechnology Information (NCBI) database of Genotypes and Phenotypes (dbGaP).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBorowsky B, Adham N, Jones KA, Raddatz R, Artymyshyn R, Ogozalek KL, et al. Trace amines: identification of a family of mammalian G protein-coupled receptors. Proc Natl Acad Sci U S A. 2001;98(16):8966-71.\u003c/li\u003e\n\u003cli\u003eBerry MD, Gainetdinov RR, Hoener MC, Shahid M. Pharmacology of human trace amine-associated receptors: Therapeutic opportunities and challenges. Pharmacol Ther. 2017;180:161-80.\u003c/li\u003e\n\u003cli\u003eRaul RG, Marius CH, Mark DB. Trace Amines and Their Receptors. Pharmacological Reviews. 2018;70(3):549.\u003c/li\u003e\n\u003cli\u003eRutigliano G, Accorroni A, Zucchi R. The Case for TAAR1 as a Modulator of Central Nervous System Function. Frontiers in Pharmacology. 2018;8.\u003c/li\u003e\n\u003cli\u003eEspinoza S, Salahpour A, Masri B, Sotnikova TD, Messa M, Barak LS, et al. Functional interaction between trace amine-associated receptor 1 and dopamine D2 receptor. Mol Pharmacol. 2011;80(3):416-25.\u003c/li\u003e\n\u003cli\u003eRevel FG, Moreau JL, Gainetdinov RR, Bradaia A, Sotnikova TD, Mory R, et al. 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Science Signaling. 2019;12(572):eaas9485.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Flinders University","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"neuropsychiatric conditions, single nucleotide variants, trace amines, trace amine associated receptor 1","lastPublishedDoi":"10.21203/rs.3.rs-4407652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4407652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTrace Amine Associated Receptor 1 (TAAR1) is a novel pharmaceutical target under investigation for the treatment of several neuropsychiatric conditions. TAAR1 single nucleotide variants (SNV) have been found in patients with schizophrenia and metabolic disorders. However, the frequency of variants in geographically diverse populations and the functional effects of such variants are unknown. In this study, we aimed to characterise the distribution of TAAR1 SNVs in five different WHO regions using the Database of Genotypes and Phenotypes (dbGaP) and conducted a critical computational analysis using available TAAR1 structural data to identify SNVs affecting ligand binding and/or functional regions. Our analysis shows 19 orthosteric, 9 signalling and 16 micro-switch SNVs hypothesised to critically influence the agonist induced TAAR1 activation. These SNVs may non-proportionally influence populations from discrete regions and differentially influence the activity of TAAR1-targeting therapeutics in genetically and geographically diverse populations. Notably, our dataset presented with orthosteric SNVs D103\u003csup\u003e3.32\u003c/sup\u003eN (found only in the South-East Asian Region and Western Pacific Region) and T194\u003csup\u003e5.42\u003c/sup\u003eA (found only in South-East Asian Region), and 2 signalling SNVs (V125\u003csup\u003e3.54\u003c/sup\u003eA/T252\u003csup\u003e6.36\u003c/sup\u003eA, found in African Region and commonly, respectively), all of which have previously demonstrated to influence ligand induced functions of TAAR1. Furthermore, bioinformatics analysis using SIFT4G, MutationTaster 2, PROVEAN and MutationAssessor predicted all 16 micro-switch SNVs are damaging and may further influence the agonist activation of TAAR1, thereby possibly impacting upon clinical outcomes. Understanding the genetic basis of TAAR1 function and the impact of common mutations within clinical populations is important for the safe and effective utilisation of novel and existing pharmacotherapies.\u003c/p\u003e","manuscriptTitle":"Trace amine associated receptor 1: Predicted effects of single nucleotide variants on structure-function in geographically diverse populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-14 20:57:53","doi":"10.21203/rs.3.rs-4407652/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8c377be2-e16b-438c-bc6a-d4775c486179","owner":[],"postedDate":"May 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T14:51:38+00:00","versionOfRecord":{"articleIdentity":"rs-4407652","link":"https://doi.org/10.1186/s40246-024-00620-w","journal":{"identity":"human-genomics","isVorOnly":false,"title":"Human Genomics"},"publishedOn":"2024-06-11 14:51:38","publishedOnDateReadable":"June 11th, 2024"},"versionCreatedAt":"2024-05-14 20:57:53","video":"","vorDoi":"10.1186/s40246-024-00620-w","vorDoiUrl":"https://doi.org/10.1186/s40246-024-00620-w","workflowStages":[]},"version":"v1","identity":"rs-4407652","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4407652","identity":"rs-4407652","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}