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• W4210855973 abstract "Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Appendix 2 Data availability References Decision letter Author response Article and author information Metrics Abstract Nicotinic partial agonists provide an accepted aid for smoking cessation and thus contribute to decreasing tobacco-related disease. Improved drugs constitute a continued area of study. However, there remains no reductionist method to examine the cellular and subcellular pharmacokinetic properties of these compounds in living cells. Here, we developed new intensity-based drug-sensing fluorescent reporters (iDrugSnFRs) for the nicotinic partial agonists dianicline, cytisine, and two cytisine derivatives – 10-fluorocytisine and 9-bromo-10-ethylcytisine. We report the first atomic-scale structures of liganded periplasmic binding protein-based biosensors, accelerating development of iDrugSnFRs and also explaining the activation mechanism. The nicotinic iDrugSnFRs detect their drug partners in solution, as well as at the plasma membrane (PM) and in the endoplasmic reticulum (ER) of cell lines and mouse hippocampal neurons. At the PM, the speed of solution changes limits the growth and decay rates of the fluorescence response in almost all cases. In contrast, we found that rates of membrane crossing differ among these nicotinic drugs by >30-fold. The new nicotinic iDrugSnFRs provide insight into the real-time pharmacokinetic properties of nicotinic agonists and provide a methodology whereby iDrugSnFRs can inform both pharmaceutical neuroscience and addiction neuroscience. Editor's evaluation Nichols et al. developed and characterized the first fluorescent sensors for several nicotinic receptor partial agonists relevant to smoking cessation. It is potentially a major advance for the field. They leveraged crystallography to understand the mechanism by which the ligands enhance fluorescence, then characterized top sensors for sensitivity, selectivity, and kinetics, and their utility in plasma membrane and ER sensing in neurons and cell lines. The tools developed by this team will enable investigators to track nicotinic receptor partial agonists in different subcellular compartments with relatively fast time resolution. https://doi.org/10.7554/eLife.74648.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Smoking cessation is an important goal to help decrease the burden, both individual and societal, of tobacco-related disease. The addictive tobacco alkaloid nicotine itself, via transdermal patches and other devices, remains available for people trying to quit smoking; but nicotine replacement therapy has distressingly low rates of success. Therefore, various research projects are continuing with the aim of developing more effective ligands for nicotinic acetylcholine receptors (nAChRs). Prior work suggests that partial agonists with lower efficacy than nicotine could serve as effective smoking-cessation drugs (Rose et al., 1994), and efforts continue in that direction (Rollema and Hurst, 2018). Another plant alkaloid, (-)-cytisine (also called cytisinicline and Tabex), an α4β2 nAChR partial agonist, served as a basis for the synthesis of analogs that have not yet entered the clinic (Chellappan et al., 2006; Houllier et al., 2006; Imming et al., 2001; Kozikowski et al., 2007; Marcaurelle et al., 2009; Philipova et al., 2015; Rouden et al., 2002). Varenicline (Chantix) has four rings, two more than nicotine or cytisine, and is currently the only FDA-approved smoking-cessation drug, but the modest quit rate of ~18% at 12 months invites further investigation (Coe et al., 2005; Mills et al., 2009). Dianicline, another tetracyclic compound, was discontinued after unfavorable Phase III clinical trials (Cohen et al., 2003; Fagerstrom and Balfour, 2006). A nicotinic ligand for smoking cessation must satisfy at least three criteria (Rollema et al., 2010; Tashkin, 2015). (1) It must enter the brain, where the most nicotine-sensitive nAChRs (α4β2) occur. It must also (2) activate α4β2 nAChRs with an EC50 sufficient to reduce cravings and withdrawal (1–2 μM). Finally, it must (3) block nicotine binding to reduce the reward phase of smoking (2–30 min). Varenicline meets these criteria, while cytisine (low brain penetration) and dianicline (EC50 = 18 μM) each fail one of the criteria (Rollema et al., 2010). Membrane permeation is interesting for investigating and treating nicotine addiction in at least two ways. Firstly, note criterion #1 above. For uncharged molecules, the conventional metric for membrane permeability is logP, where P is the octanol-water partition coefficient. For weak bases including most orally available neural drugs, logP must be corrected to account for the fraction of uncharged (deprotonated) molecules at the pH of interest, usually pH 7.4; the resulting metric, termed logDpH7.4, is always less positive than logP. Enhancing the membrane permeability of cytisine analogs and probing nAChR subtype selectivity was addressed via direct functionalization of cytisine within the pyridone ring (Rego Campello et al., 2018). Two of the resulting derivatives, 10-fluorocytisine and 9-bromo-10-ethylcytisine, have cytisine-like EC50 for the α4β2 nAChRs, but more positive calculated logDpH7.4 values, suggesting greater membrane permeability at the nearly neutral pH of the blood, brain, and cytoplasm (Blom et al., 2019). Estimates of logDpH7.4 are inexact, extrapolated, or rely on algorithmic calculations whose results differ over 2 log units for individual molecules (Pieńko et al., 2016). These estimates have unknown applicability to biological membranes at the logDpH7.4 values < 0 that characterize varenicline, dianicline, and the cytisine analogs. Secondly, nicotine dependence involves one or more ‘inside-out’ mechanisms. Nicotine itself (logDpH7.4 0.99) enters the endoplasmic reticulum (ER), binds to nascent nAChRs, becomes a pharmacological chaperone for the nAChRs, and eventually causes selective upregulation of these receptors on the plasma membrane (PM) (Henderson and Lester, 2015). For this reason, it is especially important to understand permeation into the ER. These two neuroscience aspects of nicotinic ligands – pharmaceutical science and addiction science – call for direct measurements of drug movements in living cells (Video 1). We previously explored the subcellular pharmacokinetics of nicotine and varenicline in immortalized cell lines and cultured neurons using the iDrugSnFRs iNicSnFR3a and iNicSnFR3b to visualize that these nicotinic agonists enter the ER within seconds of drug application and exit equally rapidly from the ER upon extracellular washing (Shivange et al., 2019). That nicotine diffuses across cellular membranes in seconds has been suspected for decades: nicotine crosses six PMs to enter the brain within 20 s, providing a ‘buzz.’ That varenicline becomes trapped in acidic vesicles suggests appreciable membrane permeation but may also underlie unwanted effects (Govind et al., 2017; Le Houezec, 2003). Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Genetically encoded fluorescent biosensors show how drugs cross membranes in real time. We sought to generate and apply additional intensity-based drug-sensing fluorescent reporters (iDrugSnFRs) for candidate smoking-cessation drugs: dianicline, cytisine, 10-fluorocytisine, and 9-bromo-10-ethylcytisine. We hypothesized that a family of newly developed iDrugSnFRs would enable quantifiable fluorescence signals that compare the differences in permeation among these compounds. Results Generation of additional nicotinic iDrugSnFRs: Structural tactic To generate iDrugSnFRs for cytisine and dianicline, we followed two converging tactics. In the ‘structure-based’ tactic, we obtained the first structural data for OpuBC-based SnFRs bound by nicotinic ligands (nicotine and varenicline) (Figure 1, Supplementary file 1). Crystals of iNicSnFR3adt in the presence of 10 mM nicotine diffracted to 2.95 Å resolution (PDB 7S7U). Overall, the liganded periplasmic binding protein (PBP) domain of iNicSnFR3adt adopts a closed conformation (Figure 1A). In the binding pocket between the top and bottom lobes of the PBP, we observed an ‘avocado’-shaped electron density in the nicotine binding site, enclosed by several aromatic residues (Figure 1B). The combination of protonation/deprotonation and the rotatable bond of nicotine (Elmore and Dougherty, 2000) vitiate unambiguously localizing it within the binding pocket. Figure 1 with 2 supplements see all Download asset Open asset Apo and ligand-bound structures of iNicSnFR3adt (dt indicates that His6 and Myc tags have been removed to aid crystallization). To form an intensity-based drug-sensing fluorescent reporter (iDrugSnFR), a circularly permuted GFP molecule, flanked by two 4-residue linking sequences, is inserted into a PBP at a position (77–78, in our numbering system) that changes backbone Φ-Ψ angles between the apo and liganded PBP. (A) Overall conformation of iNicSnFR3adt crystallized with nicotine; an electron density appears at the nicotine binding site (PDB 7S7U). (B) iNicSnFR3adt binding site residues. (C) Overall conformation of iNicSnFR3adt with varenicline bound (PDB 7S7T). (D) iNicSnFR3adt binding site with varenicline present. (E) Aspects of the PBP-Linker1-cpGFP interface, emphasizing contacts that change upon ligand binding. The Phe76-Pro77-Glu78 cluster (in Linker 1) lies 11–16 Å from position 43, which defines the outer rim of the ligand site (B); therefore, the cluster makes no direct contact with the ligand site. (E1) In the apo conformation, Glu78 acts as a candle snuffer that prevents fluorescence by the chromophore (PDB 7S7V). (E2) In the liganded conformation (PDB 7S7T), the Phe76-Pro77-Glu78 cluster moves Glu78 at least 14 Å away from the fluorophore. Pro77 is flanked by Phe76 and Pro396 (in the top lobe of the PBP moiety). The presumably deprotonated Glu78 forms salt bridges with Lys97 and Arg99, both facing outward on the β6 strand of the original GFP (within the original Phe165-Lys-Ile-Arg-His sequence). We obtained an unambiguous ligand placement for iNicSnFR3adt in the presence of 10 mM varenicline in the same crystallization condition. Crystals of iNicSnFR3adt with varenicline bound were isomorphous to those of the nicotine-bound crystals and diffracted to 3.2 Å resolution (PDB 7S7T). While the protein structure (Figure 1D) is identical to that of the nicotine bound structure (Figure 1A), the rigidity and additional ring of varenicline allowed us to unambiguously localize it in the binding pocket. Varenicline is enclosed by the same aromatic residues as nicotine, forming cation-π interactions with Tyr65 and Tyr357, in addition to other interactions with the pocket residues (Figure 1E). The data confirm that similar ligand-induced conformational changes occur in the PBP for nicotine, varenicline, ACh (Borden et al., 2019), and choline (Fan, 2020; Figure 1—figure supplement 1). These changes resemble those for other OpuBC PBPs (Schiefner et al., 2004). In the full iDrugSnFR, in the apo state, the Glu78 in Linker 1 approaches within ~2.5 Å of the oxygen of the tyrosine fluorophore (Figure 1E1; PDB 7S7V). Figure 1E2 provides structural details confirming the hypothesis (Barnett et al., 2017; Nasu et al., 2021) that in the liganded state Glu78 has moved away, presumably allowing the fluorescent tyrosinate to form (Video 2). We term this mechanism the ‘candle snuffer’. Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Video morph of PDB 7S7V to 7S7T. PBP at the left; cpGFP at the right; key side chains in the linkers are shown as spheres. The ligand, varenicline, is shown as sticks. Generation of additional nicotinic iDrugSnFRs: Mutational tactic In the mutational tactic, we screened each drug shown in Figure 1—figure supplement 2 against a panel of biosensors that included iNicSnFR3a and iNicSnFR3b (Shivange et al., 2019) and iAChSnFR (Borden et al., 2019) as well as intermediate constructs from their development process. From this screen, we chose sensors with the lowest EC50 for each drug as our starting protein for iDrugSnFR evolution. Because the candle snuffer mechanism explains several details of the agonist- and pH sensitivity of both iNicSnFR3a and iSketSnFR (see ‘Discussion’), we presume that it represents a general mechanism for OpuBC-cpGFP SnFRs. We did not mutate residues that lie (in 3D space) between the binding site and linkers. For dianicline and cytisine separately, we incrementally applied site-saturation mutagenesis (SSM) to first- and second-shell amino acid positions within the binding pocket. We evaluated each biosensor and drug partner in lysate from Escherichia coli and carried forward the biosensor with the highest S-slope to the subsequent round. S-slope, ΔFF0[ligand] at the beginning of the dose–response relation, emphasizes the response to ligand concentrations in the pharmacologically relevant range (Bera et al., 2019). Table 1 and Figure 2 summarize dose–response relations for the optimized sensors. The dianicline sensor, iDianiSnFR, has EC50 6.7 ± 0.3 µM, ΔFmax/F0 7.4 ± 0.1, and S-slope 1.1. The cytisine sensor, iCytSnFR, has EC50 9.4 ± 0.8 µM, ΔFmax/F0 5.0 ± 0.2, and S-slope 0.5 (Table 1, Figure 2A and B). After generating iCytSnFR, we performed additional SSM to progress from iCytSnFR to SnFRs for 10-fluorocytisine and 9-bromo-10-ethylcytisine. This optimization gave us iCyt_F_SnFR (EC50 1.4 ± 0.04 µM, ΔFmax/F0 7.9 ± 0.1, S-slope 5.6) and iCyt_BrEt_SnFR (EC50 5.7 ± 0.1 µM, ΔFmax/F0 4.0 ± 0.03, and S-slope 0.7) (Table 1, Figure 2C and D). Figure 2 Download asset Open asset Nicotinic agonist intensity-based drug-sensing fluorescent reporter (iDrugSnFR) development. Dose–response relations on intermediate constructs using E. coli lysate were performed with respective drug partners to identify site-saturation mutagenesis (SSM) winners. (A–D) The progenitor biosensor is listed in black. Dashed lines indicate data that did not reach saturation at the concentrations tested; therefore, EC50 and ∆Fmax/F0 could not be determined. Development of (A) iDianiSnFR, (B) iCytSnFR, (C) iCyt_F_SnFR, and (D) iCyt_BrEt_SnFR. Table 1 Nicotinic agonist iDrugSnFR naming, dose-response relations, and residues mutated. Parent constructs in bold. Measurements in E. coli lysates (L) or with purified protein (P). ND, not determined. Data for iAChSnFR from Borden et al., 2019; data for iNicSnFR3b from Shivange et al., 2019. Informal nameDrug of interestΔFmax/F0EC50 (µM)S-slopeResidues mutated vs. parent constructs LPLPLP11434468324360391395 iNicSnFR3bNicotineND10ND19ND0.5EENHSTFG iDianiSnFRDianicline7.4 ± 0.14.7 ± 0.26.7 ± 0.315 ± 11.10.3DR-SNG-N iAChSnFRAChND12ND1.3ND9.2IVNHATFG iCytSnFRCytisine5.0 ± 0.27.3 ± 0.49.4 ± 0.811 ± 10.50.7-Y----W- iCyt_F_SnFR10-Fluorocytisine7.9 ± 0.12.3 ± 0.11.4 ± 0.041.6 ± 0.35.61.4-NG---W- iCyt_BrEt_SnFR9-Bromo-10-ethylcytisine4.0 ± 0.033.6 ± 0.045.7 ± 0.14.2 ± 0.20.70.9-QG---W- Specificity and thermodynamics of nicotinic iDrugSnFRs We characterized the specificity of purified iDrugSnFRs for their drug partners versus a panel of related nicotinic agonists (Table 2, Figure 3). The newly developed iDrugSnFRs showed some sensitivity to related nicotinic agonists. iDianiSnFR had the greatest fidelity for its drug partner but also showed an increased EC50 (15 µM) as a purified protein versus its EC50 in lysate (6.7 µM), possibly indicating decreased stability in a purified form. iCytSnFR, iCyt_F_SnFR, and iCyt_BrEt_SnFR showed a greater level of promiscuity for the compounds comprising the nicotinic agonist panel. Of note, iCytSnFR, iCyt_F_SnFR, and iCyt_BrEt_SnFR have an exceptionally low (60‒90 nM) EC50 for varenicline. The newly developed iDrugSnFRs showed negligible binding to choline or the neurotransmitter acetylcholine, leading one to expect minimal endogenous interference during future in vivo experiments. Figure 3 with 1 supplement see all Download asset Open asset Dose–response relations of intensity-based drug-sensing fluorescent reporter (iDrugSnFR) protein versus a nicotinic agonist panel. (A–D) Relevant EC50 values for each iDrugSnFR are listed in Table 2. Dashed lines indicate dose–response relations that did not approach saturation for the concentration ranges tested; therefore, EC50 and ∆Fmax/F0 could not be determined. (A) iDianiSnFR shows preference for dianicline, with some promiscuity for other nicotinic agonists. (B) iCytSnFR, (C) iCyt_F_SnFR, and (D) iCyt_BrEt_SnFR bind their drug partner, but also respond to other nicotinic agonists. Ch, choline; ACh, acetylcholine; Cyt, cytisine; Diani, dianicline; Nic, nicotine; Var, varenicline; 10FC, 10-fluorocytisine; 9Br10EtC, 9-bromo-10-ethylcytisine. Table 2 Intensity-based drug-sensing fluorescent reporter (iDrugSnFR) dose–response relations versus a selected panel of nicotinic agonists. ND, not determined. *, ** EC50 and ∆Fmax/F0 could not be determined from the data (Figure 3). Therefore, the upper limit to the S-slope is estimated from the data at the foot of the dose–response relation. Drug nameiDianiSnFRiCytSnFRiCyt_F_SnFRiCyt_BrEt_SnFRΔFmax/F0EC50 (µM)S-slopeΔFmax/F0EC50 (µM)S-slopeΔFmax/F0EC50 (µM)S-slopeΔFmax/F0EC50 (µM)S-slopeCholine2.0 ± 0.184 ± 20< 0.15.8 ± 0.2240 ± 30< 0.12.6 ± 0.118 ± 10.12.6 ± 0.112 ± 10.2Acetylcholine7.4 ± 1.0660 ± 80< 0.12.9 ± 0.135 ± 3< 0.14.4 ± 0.3222 ± 50< 0.12.5 ± 0.273 ± 6<0.1Cytisine--<0.1*7.3 ± 0.411 ± 10.74.4 ± 0.12.6 ± 0.31.74.7 ± 0.13.5 ± 0.21.3Dianicline4.7 ± 0.215 ± 10.36.5 ± 0.434 ± 40.22.3 ± 0.343 ± 6< 0.14–6>100<0.1**Nicotine2.2 ± 0.1440 ± 100< 0.16.4 ± 0.214 ± 20.54.7 ± 0.13.8 ± 0.21.24.8 ± 0.15.5 ± 0.20.9Varenicline2.4 ± 2.01200 ± 500< 0.16.5 ± 0.10.06 ± 0.011107.1 ± 0.20.09 ± 0.02795.3 ± 0.10.06 ± 0.018810-FluorocytisineNDNDNDNDNDND2.3 ± 0.11.6 ± 0.31.43.0 ± 0.14.7 ± 0.30.69-Bromo-10-ethylcytisineNDNDNDNDNDND3.1 ± 0.131 ± 20.13.6 ± < 0.14.2 ± 0.20.9 We also performed dose–response experiments with iDianiSnFR, iCytSnFR, iCyt_F_SnFR, and iCyt_BrEt_SnFR against a panel of nine endogenous molecules, including neurotransmitters (Figure 3—figure supplement 1). iDianiSnFR showed no response to any of the nine selected compounds above background. iCytSnFR, iCyt_F_SnFR, and iCyt_BrEt_SnFR showed no response above background for seven of the compounds. However, they exhibited a ΔF/F0 of 0.25‒0.8 to dopamine at 316 µM/1 mM and a ΔF/F0 of 0.8–1.5 to serotonin (5-HT) at 316 µM/1 mM. In terms of S-slope, the relevant metric for most cellular or in vivo experiments, the SnFRs are at least 250-fold more sensitive to their eponymous partners than to other molecules we have tested. To examine the thermodynamics of the iDrugSnFR:drug interaction, we conducted isothermal titration calorimetry (ITC) binding experiments (Figure 4). The experimentally determined KD of each iDrugSnFR:drug pair using ITC was within a factor of 1.5 from the experimentally determined EC50 for fluorescence in E. coli lysate or purified protein (Table 3). We infer that the EC50 for fluorescence is dominated by the overall binding of the ligand for all the iDrugSnFRs. Figure 4 Download asset Open asset Isothermal titration calorimetry traces, fits, and thermodynamic data. Top row: exemplar heat traces of iCytSnFR, iCyt_F_SnFR, iCyt_BrEt_SnFR, and iDianiSnFR paired with their drug partners obtained by isothermal calorimetry. The heats for iCytSnFR, iCyt_F_SnFR, and iCyt_BrEt_SnFR were exothermic, while that for iDianiSnFR was endothermic. Middle row: the resulting fits for each iDrugSnFR:drug pair from the integrated heats comprising each series of injections. Bottom row: energy calculations. All iDrugSnFRs show exergonic reactions, but the relative enthalpic and entropic contributions vary among iDrugSnFRs. Data are from three separate runs, mean ± SEM. iDrugSnFR, intensity-based drug-sensing fluorescent reporter. Table 3 Affinity, occupancy number, and thermodynamic data calculated from isothermal titration calorimetry. Data are the mean ± SEM, three runs. BiosensorKD (μM)nΔH(kcal/mol)-TΔS(kcal/mol)ΔG(kcal/mol)iCytSnFR13.7 ± 1.10.84 ± 0.05–2.1 ± 0.1–4.6 ± 0.2–6.6 ± 0.1iCyt_F_SnFR1.8 ± 0.50.83 ± 0.02–5.5 ± 0.1–2.4 ± 0.2–7.9 ± 0.1iCyt_BrEt_SnFR5.4 ± 0.80.69 ± 0.09–1.12 ± 0.036.1 ± 0.1–7.2 ± 0.1iDianiSnFR7.6 ± 1.40.92 ± 0.023.2 ± 0.510.1 ± 0.4–7.0 ± 0.2 Kinetics of nicotinic agonist iDrugSnFRs: Stopped-flow In a stopped-flow apparatus, we measured the fluorescence changes of iDrugSnFRs with millisecond resolution during multiple 1 s trials and an independent 100 s trial. The stopped-flow data revealed that iDrugSnFRs do not have pseudo-first-order kinetic behaviors typical of two-state binding interactions. Time courses of iDianiSnFR (both over 1 s and 100 s) were best fitted by double exponential equations. Most of the fluorescence change occurs within the first 0.1 s of mixing (Figure 5A), with only minor additional increase by 100 s. Figure 5 Download asset Open asset Stopped-flow fluorescence kinetic data for (A) iDianiSnFR, (B) iCytSnFR, (C) iCyt_F_SnFR, and (D) iCyt_BrEt_SnFR over 1 s and 100 s. Fluorescence was activated by mixing with the agonists, producing the indicated final concentrations. Stopped-flow data show a departure from first-order kinetics for this set of iDrugSnFRs. iDianiSnFR and iCyt_F_SnFR are fitted to a double exponential; iCytSnFR and iCyt_BrEt_SnFR are fitted to a single exponential. (E–H) Plots of the observed apparent rate constant kobs against [agonist] for the 1 s data obtained in (A–D). Changes in fluorescence from iCytSnFR during the first 1 s of mixing fit well to a single exponential (Figure 5B) and have close to pseudo-first-order kinetics (i.e., the observed rate of fluorescence change is nearly linear with drug concentration). As with iDianiSnFR, most of the fluorescence change occurs within the first second, with additional fluorescent increase continuing over the next minute (Figure 5B, right panel). Like iDianiSnFR, iCyt_F_SnFR fluorescence changes are best fit by a double exponential (Figure 5C), but the time course of fluorescence change is significantly slower. Fluorescence gradually increases throughout the recording period and beyond. This information was considered in later in vitro and ex vivo experiments. iCyt_BrEt_SnFR fits well to a single exponential (Figure 5D) for the first 1 s of data collection, but like the other sensors, continues to increase its fluorescence over longer periods. We plotted the kobs (s–1) obtained in the 1 s stopped-flow experiments versus concentration (Figure 5E–H) (see also Supplementary file 2). The aberrations from ideal first-order kinetics vitiate generation of definitive koff and kon values but we can approximate a Kmax and KD from our fitting procedures. Our stopped-flow experiments reinforced previous observations (Unger et al., 2020) that the kinetics of iDrugSnFR binding involve complexities beyond a simple first-order kinetic model governing two binding partners. Kinetics of nicotinic agonist iDrugSnFRs: Millisecond microperfusion We also studied iCytSnFR_PM expressed in HEK293T cells during fluorescence responses to ACh, cytisine, or varenicline in a microperfusion apparatus that exchanged solutions near the cell on a millisecond time scale (Materials and methods). This system directly measures the decay of the response when ligand is suddenly removed. The rank order of the iCytSnFR steady-state sensitivities is varenicline > cytisine > ACh. The time constant for decay decreased with increasing steady-state EC50 of the ligands, as though more tightly binding ligands dissociate more slowly (Figure 6A). Figure 6 Download asset Open asset Decay of the iCytSnFR_PM responses after removal of ACh, cytisine, or varenicline. (A) The red, blue, and black traces are mean ΔF/F0 values for the ACh (200 µM), cytisine (15 µM), and varenicline (2 µM) responses as a function of time (n = 4–10 areas per ligand). The ΔF/F0 was normalized to the peak response for each ligand. Sampling rate was 5 frames/s. Ligand was applied for 5 s, denoted by the black horizontal bar above the traces. (B–D) Examples of the decay phase of the response to ACh (200 µM), cytisine (15 µM), and varenicline (2 µM) in individual areas (black traces in each panel). Red lines are fits to the sum of one or two negative exponential terms and a constant (red lines in each panel) using nonlinear least-squares regression. (B) The decay of the ACh (200 µM) response (n = 1 area, 3 cells) was monophasic with a single time constant (τ0ff) of 0.61 ± 0.02 s (± SE, n = 86 frames, sampling rate of 9.8 frames/s). The red line is a fit to the sum of a negative exponential component (R2 of 0.98). (C) The decay of the cytisine (15 µM) response (n = 1 area, four cells) was biphasic with time constants (τfoff, τsoff) of 1.9 ± 0.2 and 6.6 ± 0.5 s (n = 149 frames, sampling rate of 5 frames/s). The red line is a fit to the sum of two negative exponential components and a constant (R2 of 0.996). It was significantly better than that of the sum of a single negative exponential term and a constant (F-test, p<0.05). The relative amplitude of the slower decay component (As/(As+ Af), where As is amplitude of the slower component of decay in units of ΔF/F0 and Af is amplitude of the faster component) was 61%. Inset: neither rate constant changed significantly over the [cytisine] range from 5 to 15 μM. Dashed lines give the average over this range. (D) The decay of the varenicline (2 µM) response (n = 1 area, three cells) was also biphasic with a τfoff and τsoff of 9 ± 1 s and 150 ± 10 s (n = 178 frames, sampling rate of 1 frame/s), respectively. The As/(As+ Af) was 83%. The red line is a fit to the sum of two negative exponential terms and a constant (R2 of 0.994), and it was significantly better than that to the sum of a single negative exponential term and a constant (F-test, p<0.05). We measured the decay waveforms after drug pulses at concentrations ≥ the EC50 of the steady-state response to maximize the ΔF/F0 signal/noise ratio (Figure 6A–D). Because the decay phases are measured in zero [ligand], one expects that the decay rate constant(s) (koff) for an iDrugSnFR do not depend on the pulsed ligand concentration. Decay of the ACh response followed a single exponential time course (Figure 6B). The values of the koff for 30, 100, and 200 µM ACh did not differ significantly (ANOVA, p=0.62, degrees of freedom [df] = 2 (model), 20 [error]). We pooled them to obtain a mean koff of 1.9 ± 0.1 s–1 (mean ± SEM, n = 23 areas [50 cells]). The corresponding time constant τ0ff was 530 ± 30 ms. Hence, the temporal resolution of the CytSnFR_PM sensor for changes in the ACh concentration was in the subsecond range. The decay of the cytisine and varenicline response was biphasic (Figure 6C and D): two exponential decay terms with an additional constant component fitted the cytisine decay significantly better than a single exponential term (F-test, p<0.05). As expected, neither the faster decay rate constants (kfoff) (ANOVA, p=0.30, df = 3,32) nor the slower decay rate constants (ksoff) (ANOVA, p=0.54, df = 3,31) differed among the tested cytisine concentrations (5–15 µM). The kfoff and ksoff for 5–15 µM cytisine were 0.61 ± 0.04 s–1 (n = 36 areas, 105 cells) and 0.146 ± 0.006 s–1 (n = 35 areas, n = 103 cells), respectively. The corresponding decay time constants (τf0ff, τs0ff) were 1.8 ± 0.1 s and 6.9 ± 0.2 s. Therefore, the temporal resolution of CytSnFR_PM sensor for cytisine was <10 s, adequate for the temporal resolution of the live-cell experiments presented below. Interestingly, the decay waveform of the varenicline response was much slower than that for cytisine or ACh (Figure 6A and D). We pulsed 2 µM varenicline, >> the EC50 of the steady-state response of the isolated protein (60 ± 10 nM) (Figure 6D). The values of the kfoff and ksoff were 0.9 ± 0.2 s–1 and 0.0065 ± 0.0002 s–1, respectively (n = 4 areas [nine cells]). The slower component dominated the decay phase, with a fractional amplitude of 85% ± 1%. Thus, the temporal resolution of the iCytSnFR_PM sensor for varenicline was in the minute range. In the live-cell experiments described below, it would not be possible to resolve the differences between varenicline at the PM and in the ER. The relatively high affinity of iCytisineSnFR for varenicline, which presumably arises in part from the increased lifetime of the varenicline-iDrugSnFR complex, has drawbacks. The temporal resolution of iNicSnFR3a and iNicSnFR3b, which bind varenicline ~100-fold less tightly, is appropriate for subcellular experiments (Shivange et al., 2019). The previous experiments showing ER entry of varenicline used iNicSnFR3a and iNicSnFR3b (Shivange et al., 2019). For additional microperfusion data and analyses, see Appendix 2—figures 1–3. Characterization of nicotinic iDrugSnFRS in HeLa cells and primary mouse hippocampal culture We examined the subcellular pharmacokinetics of the nicotinic agonists in mammalian cell lines and primary mouse hippocampal neurons. The nicotinic iDrugSnFRs were targeted to the PM (iDrugSnFR_PM) or the ER (iDrugSnFR_ER) as previously described (Bera et al., 2019; Shivange et al., 2019). We then performed a dose–response experiment" @default.
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