The availability of genomic data from extinct homini such as Neanderthals has caused a revolution in palaeontology allowing the identification of modern human-specific protein substitutions. Currently, little is known as to how these substitutions alter the proteins on a molecular level. Here, we investigate adenylosuccinate lyase, a conserved enzyme involved in purine metabolism for which several substitutions in the modern human protein (hADSL) have been described to affect intelligence and behaviour. During evolution, modern humans acquired a specific substitution  in ADSL distinguishing it from the ancestral variant present in Neanderthals (nADSL). We show here that despite this conservative substitution being solvent exposed and located distant from the active site, there is a difference in thermal stability, but not enzymology or ligand binding between nADSL and hADSL. Substitutions near residue 429 which do not profoundly affect enzymology were previously reported to cause neurological symptoms in humans. This study also reveals that ADSL undergoes conformational changes during catalysis which, together with the crystal structure of a hitherto undetermined product bound conformation, explains the molecular origin of disease for several modern human ADSL mutants. These comparisons allow the identification of genetic changes which are specific to modern humans, hence providing insights into the differences distinguishing modern humans from our closest relatives on the tree of Life. Currently, genetic changes in around 90 protein coding regions have been identified as resulting in a stable amino acid substitution in the corresponding protein3. Despite growing interest in the scientific community, it remains largely unknown which of these modern human specific substitutions are functionally significant and contribute to phenotypical differences between modern humans and Neanderthals. While cellular assays using cells carrying Neanderthal genes are currently being performed and expected to yield exciting results4, studies at the molecular level are missing. Such biophysical and biochemical evaluation could reveal how modern human-specific substitutions affect protein structure and function and would serve as a bridge between the ongoing genetic and cellular studies.Genomic comparisons between modern humans and extinct homini such as Neanderthals recently became possible due to improved methods to extract and sequence ancient DNA samples3. While the published genomes from three Neanderthals and one Denisovan all possess the ancestral Ala429 ADSL variant, all available genetic information from present-day humans exhibit exclusively a Val in this position7. Of the proteins reported to carry modern human-specific substitutions we selected ADSL because of its medical relevance and its association with intelligence and behaviour. Several missense mutations in the adsl gene cause ADSL deficiency, a disease characterised by serious neurological and physiological symptoms such as autism, increased aggression, seizures, microcephaly and muscle wasting9. ADSL is involved in the de novo purine biosynthesis and recycling pathways in which it catalyses the conversion of 5-aminoimidazole-(N-succinylocarboxamide) (SAICAR) to 5-aminoimidazole-4-carboxamide ribotide (AICAR) and the conversion of succinyladenosine monophosphate (SAMP) to adenosine monophosphate (AMP)10, respectively. Both reactions are β-eliminations in which fumarate is released. Despite extensive research, the molecular basis on how mutations of the protein cause ADSL deficiency is not always well understood.This work aims at providing a first step in filling this gap in knowledge by reporting a structural, biochemical and biophysical comparison of modern human and Neanderthal adenylosuccinate lyase (ADSL), an enzyme which acquired a single specific substitution  during modern human evolution11, hADSL has a decreased thermal stability compared to nADSL. This difference is not translated into a measurable alteration in the enzymology of the purified protein, but we propose that the Ala429Val substitution, similar to the Arg426His substitution, affects protein-protein interactions in purinosomes. In addition, this study shows that ADSL domain 3 is conformationally flexible and we present the high resolution molecular structure of a previously unknown product-bound conformation of ADSL. This conformational flexibility is associated with the catalytic cycle and results in the insertion of Arg396 into the active site, thus explaining how the Arg396His and Arg396Cys substitutions cause ADSL deficiency in present-day humans8.Here we describe the first crystal structure of a protein from an extinct hominin species and reveal that, similar to the effect of the ADSL deficiency-causing Arg426His substitution13. Although one would naturally expect such a substitution not to have large impact on the protein function, some ADSL deficiency-causing substitutions have been reported for solvent exposed residues distant from the ADSL active site. Interestingly, these disease-causing substitutions often affect the protein’s stability16. In light of this, a comparison of the melting temperatures of modern human and Neanderthal ADSL was carried out. This showed that hADSL has a reduced thermal stability compared to nADSL with melting temperatures of 58 °C and 61 °C for hADSL and nADSL, respectively  at resolutions of 1.7 and 2.3 Å, respectively Table  and comp17. A central domain 2 (residues 113–364) contains the tetramerisation surface and contributes the catalytic C3-loop (residues 280–300) to the active site12. The active site itself is formed by residues from all three domains with each domain originating from a different protomer of the tetramer. Domain 1 (residues 22–112) and domain 2 form the base and one side of the active site thus creating a binding cleft for the substrate, while domain 3 (residues 365–450) resembles a lid which sits on top of the active site  yielded a different crystal form than seen for the apo and AMP/fumarate bound crystals Table . In thisThe closed and semi-closed domain 3 conformations have not been previously reported for ADSL and therefore no structure of hADSL in these conformations (nor bound to AICAR/fumarate) is available for comparison. However, since domain 3 closure occurs through a rigid body movement involving two hinge points near its N- and C-terminus (Leu377 and Phe449), the central region of the domain (including residue 429) is structurally identical in all three conformations Figs  and 3. TTo evaluate whether this conformational change is specific for nADSL or also shared by hADSL, we collected Small Angle X-ray Scattering (SAXS) data from both nADSL and hADSL in their apo and product bound states Fig. . Here, t−1 and 4 nm−1; Fig. The SAXS curves from apo nADSL and hADSL are identical and fit well the theoretically calculated curves from the corresponding apo crystal structures . This is evident from the theoretically calculated SAXS curve from the nADSL tetramer EFGH structure which better fits  the experimental SAXS curve than calculated SAXS curves derived from either tetramer ABCD or apo nADSL structures  or is associated with product release only. To evaluate this, SAXS data were collected for the inactive nADSL mutant His159Asnsis Fig. .−1 q-range) in the AMP/fumarate (and AICAR/fumarate) bound states . Given that the crystal structures of nADSL and hADSL do not reveal any overall structural difference, we propose this difference to be attributed to a change in the dynamics of domain 3 closure.Comparison of the scattering curves of nADSL and hADSL reveals a small but reproducible difference  and a slightly different Michaelis constant  that, nonetheless, remains within the experimental error.To determine whether the Ala429Val substitution has an effect on the enzyme kinetics of ADSL, both the forward (breakdown of SAMP into AMP/fumarate) and reverse (production of SAMP from AMP/fumarate) catalytic reactions of nADSL and hADSL were analysed by monitoring the change in absorbance at λ = 282 nm caused by the conversion of SAMP into AMP. Both proteins display positive cooperativity for the forward reaction with similar kinetic parameters  for AMP of around 25 µM for both hADSL and nADSL . These experiments yielded dissociation constants  was performed. Of these substitutions, two (Asp422 and Arg426) are located on the same α-helix as Val429 and also have their side chains solvent exposed. The Arg396 residue on the other hand is located on the opposite side of domain 3 and protrudes into the active site upon domain 3 closure  binding in the form of a closure of domain 3 over the active site. Following catalysis and release of the product fumarate, ADSL returns to its open conformation allowing the dissociation of the products AMP or AICAR and thus freeing the active site for another round of catalysis , while the closed conformation is favoured in the product bound state. Product binding causes domain 1 to move closer towards the active site due to direct interactions with both AMP and fumarate. In this orientation, the enthalpy gain of interacting with domain 3 could overcome the entropy cost associated with locking domain 3 in the semi-closed or closed conformation. Correspondingly, dissociation of fumarate from the active site and consequential loss of its interactions with domain 1 would push the equilibrium back to the entropically preferred open domain 3 conformation.Human adenylosuccinate lyase is a well-studied protein for which several mutations have been described to cause ADSL deficiency, a disease characterised by profound psychomotor retardation and currently without an effective therapysis Fig. . This or14. However, in the AICAR/fumarate bound nADSL structure, Arg396 is inserted into the active site and forms part of a hydrogen bond network involving residues from domain 1 in the semi-closed conformation and the catalytic C3-loop (belonging to domain 2) in the fully closed conformation 26, suggesting that the former helix-turn-helix motif is the binding site for ATIC. A role for this helix-turn-helix motif as recognition element for binding partners25 and a direct interaction between ADSL and ATIC has already previously been proposed28. Thus, taking together with previous work, our data hints towards an effect of the Ala429Val substitution on purinosome assembly and suggests the substitution could affect the human phenotype. Additional studies, in particular cellular or mice assays, are required to validate this hypothesis.Adenylosuccinate lyase is one of few proteins known to have acquired a fixed substitution in modern humans during homini evolution29. These substitutions were suggested to entropically affect the dynamics of local unfolding of the domain they are located on and were shown to impact the enzymatic parameters. Therefore, glycine substitutions of surface exposed residues distant from the active site were suggested to be used to fine-tune the enzymatic parameters of protein homologues30. Interestingly, these substitutions are commonly found in regions or domains whose mobility is necessary for catalysis. In this, it reflects the ADSL Ala429Val substitution. An effect of the Ala429Val substitution on the dynamics of domain 3 would also be consistent with the observed difference in SAXS curve between the product (AMP/fumarate) bound proteins.Although the Arg426His and Ala429Val substitution both affect ADSL stability, the molecular origin of this effect is different. The side chain of Arg426His forms H-bonds with residues from the second helix of the helix-turn-helix motif, thus directly stabilising the motif. The molecular origin of the reduced thermal stability of the Ala429Val substitution is less obvious, but can be caused by differences in the hydration shell around domain 3 or by an altered entropy of unfolding. Recently, a difference in the thermal stability of adenylate kinase by glycine substitutions of surface exposed alanine and valine residues located distant from the active site was observedin vivo/in cellulo studies, to study the impact of these proteins on the human evolution. The current study shows that genetic data and predictions alone are not always sufficient to evaluate the effect of a substitution. Here, a conservative Ala/Val substitution of a solvent exposed residue distant from the active site was investigated. No large effect would be expected from such a substitution, yet our experimental results reveal an altered thermal stability. The hypothesis that this substitution, like the nearby disease-causing Arg426His substitution, can affect the human phenotype through its involvement in purinosome assembly can act as a starting point to design cellular studies. Hence, a multi-disciplinary approach can aid in evaluating the effect protein substitutions have on the human phenotype and hominin evolution.The biophysical and biochemical analysis presented here of an enzyme that differ between modern humans and extinct homini fits well in a synergetic approach, in combination with genetic data and 31. In short, the ADSL gene containing pET-14b vector was transformed in E. coli Rosetta2 (DE3) cells which were grown at 37 °C in Luria Broth media till an optical density at λ = 600 nm of 0.6 was reached. At this point the temperature was reduced to 20 °C and protein overexpression was induced by addition of 1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG). Following overnight overexpression, the cells were harvested by centrifugation and resuspended in buffer A  supplemented with protease inhibitors, DNase I and lysozyme. The bacterial cells were lysed by sonication and the lysate cleared by centrifugation. Subsequently, the histidine tagged ADSL protein was purified from the lysate by immobilised metal affinity chromatography using a 5 ml GE Healthcare HisTrap HP column. Following a wash step using buffer A supplemented with 50 mM imidazole, elution of the ADSL protein from the column was accomplished by running a gradient towards buffer A supplemented with 500 mM imidazole. Fractions containing pure ADSL (as determined by SDS-PAGE) were pooled and thrombin at 10 units/mg ADSL was added to the mixture in order to remove the N-terminal Histidine tag. The sample was dialysed overnight against 10 mM HEPES pH 7.5, 100 mM NaCl, 1 mM dithiothreitol (DTT) and 2 mM CaCl2. After overnight thrombine cleavage, the thrombin and uncleaved ADSL proteins were removed by Histrap HP and benzamidine-sepharose  affinity columns. The flow-through of these columns was concentrated and applied on a Superdex 200  size exclusion chromatography column run in 10 mM HEPES pH 7.5, 100 mM NaCl, 1 mM DTT. ADSL eluted as a single peak at an elution volume corresponding to the molecular weight of an ADSL tetramer. Fractions corresponding to this peak were pooled, concentrated and stored at −20 °C.All ADSL variants were expressed with an N-terminal thrombin cleavable histidine tag from a pET-14b vector. The expression and purification protocol was adapted from the protocol described in Lee & Colman, 2006Due to the appearance of precipitation during the thrombin cleavage of the hADSL mutant D422Y, the thermal shift assay and enzymatic assay Fig.  of the d32. In short, a two microliter protein solution at 5 mg/mL was mixed with 1 µL of 100x Sypro Orange (Molecular Probes) and 22 µL of buffer in a 96-well PCR plate. The samples were heated from 25 °C till 95 °C while monitoring the fluorescence changes (excitation wavelength at 490 nm and emission wavelength at 575 nm) on a charge-coupled device camera.The thermal shift assays were performed in triplicate according to standard protocols33. Small crystals were obtained in several conditions. Manual reproduction of these crystals by the hanging drop vapour diffusion method yielded suitable crystals for X-ray diffraction experiments after 1–2 days at 20 °C. The crystals used for data collection were obtained by mixing 1 µL of nADSL protein solution at 10 mg/mL with 1 µL of a 20% PEG6000, 0.1 M Tris pH 8 crystallisation solution. The obtained crystals were fished in a crystallisation solution supplemented with 25% glycerol prior to flash freezing them in liquid nitrogen.High throughput crystallisation screens were set up with nADSL at concentrations of 5, 10 and 20 mg/mL34. Initial nADSL crystals containing AICAR and fumarate were obtained in the Morpheus screen (Molecular Dimensions) after 3 days at 4 °C. The crystallisation condition consisted of 20% ethylene glycol, 10% PEG8000, 0.1 M imidazole/MES pH 6.5 and 0.12 M alcohols. Larger needle like crystals were obtained by reproducing the latter crystallisation condition in a hanging drop vapour diffusion experiment mixing 1 µL of protein sample with 1 µL of crystallisation solution. These crystals were harvested and flash frozen in liquid nitrogen.Crystals of product bound nADSL were obtained from high throughput crystallisation screens at 4 °C using a 10 mg/mL nADSL sample supplemented with 2 mM fumarate and either 2 mM AMP or 2 mM AICAR. Needle shaped nADSL crystals containing AMP and fumarate appeared after 3 days in a drop containing 25% PEG1000, 0.1 M MES pH 6.5 as crystallisation solution. These crystals were harvested and cryo-cooled using the EMBL CrystalDirect Harvester35 was used for the apo nADSL data set, while the datasets from the product (AMP/fumarate and AICAR/fumarate) containing nADSL crystals were collected at ID30A-336. Selection of the best diffracting crystal, determination of the optimal data collection strategy and initial data processing was automatically performed by EDNA/BEST in the mxCuBE pipeline40. A helical data collection strategy was applied for the needle shaped AICAR/fumarate containing nADSL crystal.All X-ray diffraction data were collected at 100 K at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. Beam line ID2941 with the data being truncated at high resolution when the <I/σ(I)> value fell below 1 or the CC1/2 value below 30%. Initial data quality was assessed by phenix.xtriage42. The published structure of tetrameric hADSL13 was used as search model to determine the structure of apo nADSL by molecular replacement using Phaser43 in the CCP4 software package44. This structure was subsequently used as search model for the AMP/fumarate and AICAR/fumarate containing nADSL data sets. Manual and automated model building and refinement cycles were alternated using respectively coot45 and Refmac546 and let to the final models in which all temperature factors were isotropically refined. All models were validated using the MolProbity server47. Data collection, processing and validation statistics are summarized in Supplementary Table 48.Data indexing, integration and scaling were performed using the XDS suite50. The X-ray beam energy was 12.5 keV and the beam size 700 × 700 μm2. Ten frames with an exposure time of each one second were collected, normalized to absolute values and averaged starting from a 50 µL sample at 5 mg/mL. Initial data quality assessment, data reduction and buffer subtraction was performed by BioSAXS EDNA and monitored using BsxCuBE and ISPyB52. In this set-up no signs of radiation damage were detected. Varying the ADSL concentration (between 10 mg/mL and 1 mg/mL) did not result in shifts in the low q-range indicating no interparticle interference or other concentration dependent effects occurred during data collection. For the product bound scatter curves of nADLS and hADSL, a final concentration of 1 mM product(s) was added to the sample. To confirm the difference seen between the scatter curves of AMP/fumarate bound nADSL and hADSL , SAXS data collection on these samples was repeated in triplicate, collecting each time 15 frames on a sample of 50 µL. The data of all collected SAXS curves was of sufficient quality for further data analysis as determined by Guinier analysis and the pairwise distribution function calculated using the PRIMUS and GNOME programs available within the ATSAS software package55. The calculation and fitting of the theoretical SAXS curve from the different crystal structures with the experimentally collected SAXS curves (between a q-range of 0.07 nm−1 and 4 nm−1) was performed by the CRYSOL program and included a constant subtraction to remove systematic errors56. The obtained Chi2 values were 9.4 for the fit of the calculated SAXS curve from the apo nADSL structure with the experimental SAXS curve of apo nADSL, and 3.2 for the fit of the calculated SAXS curve from tetramer EFGH of the AICAR/fumarate bound nADSL structure with its experimental SAXS curve. For comparison, the fit of the calculated SAXS curve from the apo nADSL structure with the experimental AICAR/fumarate nADSL curve yielded a Chi2 value of 44.9.All SAXS data were collected in batch at 20 °C on beam line BM29 of the European Synchrotron Radiation Facility (ESRF) using the sample changer robot and PILATUS 1 M detector (Dectris) at a distance of 2.864 met al.12. In short, the conversion of SAMP into AMP and fumarate was monitored at room temperature by following the decrease in absorbance at λ = 282 nm in a 500 µL quartz cuvette on a UV-2401PC UV-Vis spectrophotometer (Shimadzu). The experiment was repeated in triplicate for each ADSL variant. The final protein concentration in the cuvette was 0.01 mg/mL for all ADSL variants except for the hADSL R396C mutant where a concentration of 0.02 mg/mL was used. The SAMP concentrations were varied between 11.25 and 0.35 µM. Each reaction was followed for 60 seconds and the difference in extinction coefficient  between SAMP and AMP at λ = 282 nm was used to calculate the specific ADSL activity at each SAMP concentration. Kinetic parameters of the forward reaction were calculated by fitting the initial velocity data to a cooperative enzymatic model (v = Vmax* Sh/(K0.5h + Sh)) in which v is the initial catalytic velocity, Vmax is the maximal catalytic velocity, S is the substrate (SAMP) concentration, K0.5 is the substrate concentration yielding half the Vmax and h the Hill coefficient. The overall set up for the assay monitoring the reverse enzymatic reaction was identical as for the forward reaction with the exception that 1 mM AMP was added to the protein solution prior to performing the assay. The reverse enzymatic reaction was followed by varying the fumarate concentration between 375 and 6.6 µM and the kinetic parameters were calculated by fitting the initial velocity data to the Michaelis-Menten equation.The enzymatic assay was performed as described in Ray ITC experiments were performed in triplicate with a MicroCal ITC200 Instrument . The product AMP at a concentration of 1 mM  was titrated into the cell containing 90 µM hADSL or nADSL at 20 °C. For each experiment a preliminary 1 μL injection  was followed by 20 injections of 2 μL each at a stirring speed of 800 rpm and in intervals of 180 seconds. The data were fitted using MicroCal Origin version 7.0. A blank measurement in which AMP was titrated into the cell containing only buffer displayed a very low heat of dilution for AMP and no apparent binding signal Fig. .Supplementary Information


Protein aggregation is a complex process resulting in the formation of heterogeneous mixtures of aggregate populations that are closely linked to neurodegenerative conditions, such as Alzheimer’s disease. Here, we find that soluble aggregates formed at different stages of the aggregation process of amyloid beta (Aβ42) induce the disruption of lipid bilayers and an inflammatory response to different extents. Further, by using gradient ultracentrifugation assay, we show that the smaller aggregates are those most potent at inducing membrane permeability and most effectively inhibited by antibodies binding to the C-terminal region of Aβ42. By contrast, we find that the larger soluble aggregates are those most effective at causing an inflammatory response in microglia cells and more effectively inhibited by antibodies targeting the N-terminal region of Aβ42. These findings suggest that different toxic mechanisms driven by different soluble aggregated species of Aβ42 may contribute to the onset and progression of Alzheimer’s disease. Amyloid beta (Aβ42) peptides form heterogeneous mixtures of aggregates, which are closely linked to Alzheimer’s disease. This study shows how different types of Aβ42 aggregates are associated with distinct mechanisms of toxicity In AD, the normally soluble amyloid beta peptide (Aβ) forms insoluble deposits in the brains of affected individuals4. The molecular mechanism that governs the aggregation of Aβ involves the formation of a variety of soluble oligomeric intermediates of different sizes and structures6. It has been shown that these soluble oligomeric species play a critical role in the cascade of events that ultimately leads to AD via a wide range of neurotoxic events, including the permeabilization of cellular membranes, calcium dysregulation, mitochondrial damage, induction of inflammation, and oxidative stress11.Many human pathologies, including neurodegenerative disorders such as Alzheimer’s disease (AD), are associated with the aggregation of amyloidogenic proteins12. These differences could give rise to variations in both the mechanism and degree of cytotoxicity. It is therefore important to characterize the structural differences between different soluble aggregates formed over the course of the aggregation reaction and to determine how specific structural features are related to toxicity and could contribute to disease pathogenesis. These soluble aggregates populated during the aggregation of Aβ in particular have been shown to be low in abundance and heterogeneous in both size and conformation, making it challenging to characterize their structure and mechanism of toxicity13. In this context, a recent advance has been the design and generation of rationally designed antibodies that recognize specific epitopes of Aβ and inhibit its aggregation15. The binding of such antibodies to Aβ aggregates provides information about the accessibility of specific epitopes, and have been found to inhibit aggregate-induced toxicity11. Thus, the systematic targeting of soluble aggregates of Aβ with designed antibodies should allow us to determine the accessibility and significance of individual regions of the Aβ sequence in generating aggregate-induced toxicity.The soluble oligomeric aggregates formed during the process of amyloid formation are typically highly heterogeneous, and exist in a variety of sizes, shapes and structures, with different degrees of β-sheet content and surface hydrophobicity18. We study the aggregation of Aβ42, the 42-residue isoform of Aβ, and monitor the toxicity for aggregates of different sizes in the presence of the designed antibodies that target distinct epitopes. Two ultrasensitive methods able to measure well-defined effects of the aggregates are used. The first is a quantitative assay that determines the ability of the protein aggregates to induce the permeabilization of lipid bilayers and the second is an assay that measures the ability of aggregates to induce inflammation through the measurement of cytokine production by microglial cells. We observe a relatively decreased ability of the oligomers to disrupt lipid bilayers and a relatively increased capability to cause inflammation as the size of the Aβ42 aggregates increased. We then find that these effects correlate with changes in the surface structure of the aggregates as indicated by differences in the effects of the designed antibodies.Given the heterogeneous nature of these soluble aggregates, we use a previously described gradient ultracentrifugation method that allows oligomeric species formed in an aggregation reaction to be separated on the basis of their size and density19. We immobilized thousands of individual vesicles onto glass cover slides, which act as individual nano-reactors, each containing a reporter of the presence of calcium ions (Ca2+) that enter into the vesicle, the quantity of which can be measured using total internal reflection fluorescence (TIRF) microscopy22  in the aggregation reaction, when diluted to a concentration of 50 nM, induced twice as much Ca2+ influx compared to aggregates present in the aliquots taken at later stages (30 min) of the aggregation reaction  on microglia cells, resulting in the production of inflammatory cytokines that are thought to contribute to the neuronal damage associated with the progression of AD27. To study and quantify the inflammation induced in cells by the Aβ42 protein aggregates, we monitored the levels of secreted tumour necrosis factor alpha (TNF-ɑ), a pro-inflammatory cytokine, using an enzyme-linked immunosorbent assay (ELISA) assay28  y28 Fig. . We tooky28 Fig. . We also18. Therefore, we combined in equal proportions aggregates taken from three different time points during the aggregation reaction; (i) the end of the lag phase, (ii) the middle of the growth phase and (iii) in the plateau phase 30. Then, by means of statistical analysis of the cross-sectional dimensions of the species in the individual images31, is capable to distinguish monomers from the early oligomeric aggregated species, a capability that is at the limit of any existing biophysical single-molecule method. In Fig. 32; we therefore used an established approach to obtain de-convolved cross-sectional diameters of the aggregates34 (see Methods for details). The results showed a significant increase in the average height of the aggregates as a function of the sucrose fraction density  to acquire 3D images of the aggregates within each fraction with sub-nanometre resolutionity Fig. ; an incrity Fig. . The sma36. First, we generated co-aggregates from the 1:1 molar ratio of Aβ42 by aggregating monomers labelled with FRET donors and FRET acceptors  approachors Fig. . This apers Fig. 40,41. Thons Fig. , i.e. wi43. We used a solvatochromic dye, Nile Red, which transiently binds to protein aggregates and whose emission spectra is sensitive to the local hydrophobicity43. We compared the average of the mean emission maxima from individual aggregates and found that the 20% faction was more hydrophobic than the aggregates present at higher sucrose fractions  of antibody DesAb3–9 was grafted with a sequence (HETLTLR), which is designed to bind to the N-terminal region of Aβ42. The antibodies DesAb13–19 (CDR3: LSVIKEI) and DesAb18–24 (CDR3: VFVGTEA) were designed to bind to the central region of Aβ42, and the antibodies DesAb29–36 (CDR3: GSMYKATV) and DesAb36–42 (CDR3: LGIKAEL) were designed to target its C-terminal region of  that target five different linear epitopes on Aβ42 that effectively scan its whole sequence of Fig. . These D2+ influx into the tethered vesicles . The centre of the elution peak was collected, and the peptide concentration was determined from the absorbance of the integrated peak area using ε280 = 1490 L/molcm. Aliquots of monomeric Aβ42 were diluted with SSPE buffer to a concentration of 2 µM in low-binding Eppendorf tubes on ice. Individual samples were then pipetted into multiple wells of a 96-well half-area plate (Corning 3881) and the plate was placed into an incubator at 37 °C, under quiescent conditions. Aliquots for measurements were then taken at ~15 min (early aggregates) and ~30 min (late aggregates) after the plate was placed in the incubator.The Aβ42 peptide (DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA) (Abcam) was purified with slight modifications19. Briefly, 16:0–18:1 PC (10 mg/mL) and 18:1–12:0 Biotin PC (1 mg/mL) (Avanti Lipids) were mixed with 100:1 ratio. The lipid mixture was then hydrated in HEPES buffer  with 100 µM Cal-520 (Stratech). Freeze and thaw cycles were performed using a water bath and dry ice to get the unilamellarity. The lipid solution was extruded at least for 10 times through a membrane with a size cut off of 200 nm and size of the vesicles were measure using a Zetasizer (Zetasizer Nano ZSP). Free dyes were removed using size exclusion chromatography.Vesicles with average diameter of 200 nm were prepared by five freeze and thaw cycles combined with extrusion as previously described49. Glass cover slides  were cleaned by sonicating in 2% (v/v) Hellmanex III (Hellma GmbH & Co. KG) in milliQ water for 10 min followed by sonicating twice in milliQ water and in methanol for 10 min each. Then the cover slides were dried under a stream of nitrogen gas, and plasma-etched using an argon plasma cleaner  for at least 30 min to remove any fluorescent impurities. Each cover slide was fixed by frame-seal incubation chambers  and the surface was coated with 100:1 PLL-g-PEG and PLL-g-PEG biotin (SuSoS AG) (1 g/L) in 50 mM HEPES buffer. Then the cover slides were washed three times and 0.1 mg/mL neutravidin (Thermo Scientific) solution was added to the cover slide and incubated for 15 min and washed three times with reaction buffer. Then, a 50 µL aliquot of the solution of purified biotinylated vesicles was added to the cover slide and incubated for 30 min before washing carefully at least five times with reaction buffer. Single vesicles tethered to borosilicate glass cover slides via biotin neutravidin linkage were incubated with 50 µL Ca2+ containing buffer solution Leibovitz’s L-15 (phenol red free) and background image was recorded (Fbackground). Thereafter, 50 µL of sample was added and images were acquired (Fsample) and care was taken to avoid moving the glass cover slides during the addition of each sample. Next, 10 µL of a solution containing 1 mg/mL of ionomycin, an ionophore for Ca2+ ion, and subsequently images of Ca2+ saturated single vesicles in the same fields of view were acquired (Fionomycin). For each field of view 50 images were taken with an exposure time of 50 ms. The fields of view were chosen using an automated programme  to avoid any user bias, which can be available upon reasonable request. The relative influx of Ca2+ into an individual vesicle due to aggregates of Aβ42 was then determined asThe single vesicle based assay was used to measure the permeability of lipid bilayers in presence of toxic protein aggregates2+ influx into individual vesicles. Where representative data set is shown, all data sets show similar qualitative trends. The sucrose fractions were diluted 1:10 for all experiments in order to be in the linear regime of the measurement.The average degree of was calculated by averaging the Ca35. The expanded and collimated laser beams were focused onto the back-focal plane of the ×60, 1.49 NA oil immersion objective lens . The fluorescence signal emerged from the sample was collected by the same objective and was separated from the excitation beam by a dichroic (Di01-R405/488/561/635). The emitted light was passed through an appropriate set of filters  to remove any unwanted light. The fluorescence signal was then expanded 2.5 times and imaged onto a 512 × 512 pixel EMCCD camera . The open source software Micro Manager 1.4 was used to control the microscope hardware and image acquisition50. Images were acquired in an unbiased way using bean-shell based script.Imaging was performed using a homebuilt TIRFM microscope based on an inverted Nikon Ti microscope using a 488 nm laser for membrane permeabilization assayl-lysine solution and incubated for 30 min and then washed three times with filtered phosphate-buffered saline (PBS) buffer. Then aggregated Aβ42 present in sucrose fraction was added to the coverslips and incubated for 20 min before imaging using spectrally resolved PAINT42. The imaging was done using through objective  TIRF mode. A 532 nm solid state laser was used to excite the Nile red dye and the emitted fluorescence was passed through a dichroic mirror (Di02-R532-25 × 36) and a bandpass filter (F01-650/200-25). Nile red were dissolved into dimethyl sulfoxide to make a stock solution and then diluted using filtered PBS buffer to a final concentration of 1 nM for imaging. To obtain spectrally resolved images, a physical aperture  which acts as slit and a transmission diffraction grating  were mounted just before the emCCD camera. Spectral calibration was performed using Tetra spec beads (Life Technologies).Argon plasma cleaned glass coverslips were affixed to frame-seal slide chambers. They were then coated with poly-g in an SW 60 Ti swinging-bucket rotor (Beckmann) at room temperature. The fractions were collected immediately and stored on ice until required.Aliquots of the aggregation reaction mixture of Aβ42 carried out in the absence of agitation were sampled at three different time points: (i) after 15 min (the end of the lag phase), (ii) after 30 min (the midpoint of the growth phase) and (iii) after 60 min (in the plateau phase). The samples collected from the different time points were mixed together and 400 µL aliquots were injected into a sucrose step gradient (10–50% with 10% increment w/v in SSPE buffer). The sucrose density in a given solution was determined by measurement of its refractive index. The sucrose solutions were centrifuged for 15 h at 24.69651. The level of experimental noise  for each image was measured using SPIP software. It was well below 1 Å, leading to a signal to noise ratio ranging between 8 and 30. This allowed the cross-sectional height of individual objects within an AFM map as small as a monomer to be determined with high sensitivity and accuracy.AFM studies were performed on freshly cleaved mica substrates. Ten microlitres aliquots of each diluted sample were deposited on the substrate at room temperature, incubated for 10 min, rinsed with 1 mL water, and then dried under a gentle flow of nitrogen gas. AFM maps were generated by means of a NX10  and JPK nanowizard2 system  operating in tapping mode and equipped with a silicon tip with a nominal radius of <10 nm. Image flattening and single-aggregate statistical analysis was performed by (Scanning Probe Image Processor) SPIP software . While the geometry of the AFM tip does not strongly affect the measurement of the height of an observed object, it is the primary determinant of the lateral resolution. Indeed, a tip with an apical radius of the same size, or larger than the dimensions of the object under investigation, will affect the measurement of the diameter because of the so-called convolution effect, resulting in an enlargement of the lateral dimensions. In the present experiments, objects with typical lateral dimensions within 0.3–10 nm were measured by a tip radius of approximately 10 nm. For this reason, we quantified the deconvoluted diameter of the aggregates−1 cm−1 for HiLyte™ Fluor 647 and 70,000 M−1 cm−1 for HiLyte™ Fluor 488. Monomeric fractions were frozen immediately after size exclusion and kept at −80 °C until further use. For aggregation, aliquots of labelled monomeric Aβ42 were diluted to an equimolar concentration of 2 µM of HiLyte647 Aβ42 and HiLyte488 Aβ42 in low-binding Eppendorf tubes and incubated at 37 °C in SSPE buffer for aggregation under quiescent conditions.Monomeric Aβ42-labelled N-terminally with HiLyte™ Fluor 647 and HiLyte™ Fluor 488 were purchased from AnaSpec. Monomeric Aβ42 solutions were prepared by dissolving the labelled peptides in 10 mM NaOH at high concentration and then purified using a Bio-Sep 2000 HPLC column (Phenomenex) in SSPE buffer. Peak fractions were collected, and the peptide concentration was determined using the absorbance of the fluorescent label with an extinction coefficient of 250,000 M52. by use of the 488 nm laser (1.5 mW) only. In brief, a 488 nm laser beam was directed to the back aperture of an inverted microscope (Nikon Eclipse TE2000-U) through a high numerical aperture oil immersion objective . Fluorescence was directed onto a 50-μm pinhole, and then separated in two different channels by using a dichroic mirror  and sent to two avalanche photodiodes (APD) . For all of the single-molecule experiments, data were collected at 20 °C. A custom-programmed field-programmable gate array (Colexica) was used to count the signals from the APD and combine these into time bins. These were selected according to the expected residence time of molecules passing through the confocal probe volume. For each sample, data were collected for 10 min . Confocal FRET data analysis was done using custom written software in IgorPro (wavemetrics)53.Centrifuged samples were separated, and each aliquot were taken and diluted to nanomolar concentrations in buffer. Diluted protein samples were withdrawn through a single-channel microfluidic device , at a flow velocity of 50 µL/h to a syringe via polyethylene tubing. Single-molecule confocal FRET experiments were performed on the instrument described by Li et al.41. Glass coverslips were cleaned using an argon-based plasma cleaner and incubated with poly-(l)-lysine solution for 30 min for surface coating. Then Aβ42 aggregates only or Aβ42 aggregates and DesAbs were mixed with 10 µM ThT and added to the coverslips. The solution was incubated on the coverslip for 20 min to ensure attachment to the surface. For each sample nine different fields of view, which were selected in an unbiased way, were imaged. Each image was recorded at 100 ms exposure and 100 frames for each field of view. The number of ThT-active spots was calculated using ImageJ using constant noise tolerance for all the datasets.To check if the aggregates of the Aβ42 peptide disaggregate in the presence of DesAbs, we have performed ThT based single-aggregate imaging using TIRF microscopy15. Briefly, complementary peptides were selected using the cascade method14 to target linear epitopes within Aβ42 that scan its entire sequence. These complementary peptides were then grafted into the CDR3 of the DesAb scaffold by means of a mutagenic polymerase chain reaction with phosphorylated oligonucleotides (Supplementary Table 2+-NTA Superflow column (Qiagen), previously equilibrated using PBS containing 10 mM imidazole. The column was then washed with PBS containing 40 mM imidazole. Finally, His-tagged DesAbs were eluted with PBS containing 200 mM imidazole. Imidazole was then removed using size exclusion chromatography with a HiLoad 16/600 Superdex 75 pg column . Protein concentration was determined by absorbance measurement at 280 nm using theoretical extinction coefficients calculated with ExPASy ProtParam. All the DesAbs used in this study are available from the author upon reasonable request.Rationally designed antibodies (DesAbs) were generated using previously described procedurel-glutamine (Life Technologies) and incubated at 37 °C in a humidified atmosphere of 5% CO2 and 95% air, until a density was reached of approximately 1.6 × 106 cell/mL. All sucrose fractions were diluted 1:5 and added to the BV2 cells to perform the inflammatory assays. No significant cell death was observed over 24 h in any experiment.The BV2 cell line (RRID:CVCL_0182) was derived from immortalized murine neonatal microglia. They were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% foetal bovine serum and 1% 28. TNF-α was analysed using the Duoset® enzyme-linked immunosorbent assay (ELISA) development system . Three wells were used in each experiment to estimate error bars. RSLA was obtained from InvivoGen.To determine cumulative TNF-α production, samples of supernatant were obtained after incubation of BC2 cells with early and late soluble aggregates of Aβ42 over 24 h and stored at −80 °C until analysedt-test (unpaired) with three biological replicates  at the highest antibody concentration using nine different fields of view (for membrane permeability assay) or three different wells (for the inflammation assay).To assess the statistical significance of the difference in the early and late aggregate-induced membrane permeability and inflammation, we performed two-sample tes Fig.  using orSupplementary Information FilePeer Review FileSource Data File


To inform evidence-based design and implementation ofmedical school learning communities (LCs) by investigating which LC componentsmedical students at one school with a multi-component LC were most valued andwhich were associated with desirable outcomes.In this cross-sectional study, all Johns Hopkins School ofMedicine (JHSOM) students were surveyed in Spring 2016 regarding perceivedvalue of LC components  clinical skills course, quarterly reflective discussionsessions, social activities, and LC rooms) with learning environment (LE)perceptions, quality of life, burnout, and empathy assessed as outcomes.Multivariate logistic regressions analyzed associations between LC componentsand outcomes. Overall 368/480 (77%) students responded.  CFM was highly valued by 286 (80%) students,advisors by 277 (75%).  All LC componentswere significantly associated with favorable overall LE perceptions, butassociations with LE subdomains varied. CFM was the only LC component to have significant associations withgreater empathic concern  and perspective-taking , less emotional exhaustion  anddepersonalization , and good quality of life .  Every other LCcomponent, except LC rooms, was associated with greater empathy or enhancedwell-being. Components within an LC are valued differently andvary in their relationships with student outcomes.  Future LC research may isolate the effects ofand explore interactions among different LC components, leading to morepurposeful LC design and allocation of resources.  The study received expedited approval from the JHSOM Institutional Review Board.Based on our previous research and experience leading LCs, we developed seven items that were intended to isolate discrete LC components that may already be in place or could be implemented at other institutions.  Items asked about the “value to you” for each LC component, with response options along with a five-point Likert scale .  The 7 LC components were (1) interacting with peers in Molecules, (2) interacting with peers in Colleges, (3) interacting with faculty advisors, (4) participating in the Clinical Foundations of Medicine (CFM) course, (5) participating in reflective discussion sessions, (6) participating in social activities , and (7) having a College room.18–21Learning environment (LE) perceptions were measured using the Johns Hopkins Learning Environment Scale (JHLES).,24–26 This item asked individuals to rate their overall quality of life on a 1–5 scale, ranging from “as bad as it can be” to “as good as it can be.”Quality of life was assessed using a single-item linear analog self-assessment commonly used in other medical education studies27–29 asking respondents to report along with a seven-point Likert scale  how often they felt “burned out from my work”  or “callous toward people” .Burnout was assessed using two single-item questions validated in previous studies,Empathy was assessed using the Interpersonal Reactivity Index (IRI),Basic descriptive statistics were tabulated, with ANOVA and Chi-squared tests for significant differences across class years applied as appropriate.27–29 or not high. Empathy was dichotomized at item mean of 4, which approximated the sample median.  In logistic regression models, we analyzed the association of highly valuing each LC component with favorable JHLES total and domain ratings.  Based on previous work, we hypothesized that effects of LCs on wellness could be mediated through their LE perception.,,In bivariate and multivariate analyses, for ease of data interpretation, we dichotomized variables to create odds ratios in logistic regression models.  Sensitivity analyses showed that the strengths of associations were not affected when variables were dichotomized compared to if they were treated as ordinal or continuous variables.  LC component value was dichotomized by aggregating “exceptional” and “a lot” of value vs. other.  JHLES total and domain scores were dichotomized at item means of 3.5, which corresponded to more favorable than unfavorable ratings and approximated the median in most cases. Quality of life was dichotomized as good (aggregating “as good as it can be” and “somewhat good”) or not good. Burnout was dichotomized as high (weekly or more often)Overall 368/480 (77%) students responded to our survey with response rates across each class exceeding 70%. The average age was 26 years (SD 4.7), and 192 (53%) students were male . = 7.38, p=0.0001) and its domains of “Community of Peers”  = 5.36, p=0.0013), “Academic Climate”  = 7.97, p=<0.0001), “Meaningful Engagement”  = 12.26, p=<0.0001), and “Mentorship”  = 7.32, p=0.0001). Differences were also seen across classes for emotional exhaustion  = 18.7, p<0.001) and quality of life  = 11.8, p=0.008).  IRI measures of empathy were similar across all class years  = 22.9, p<0.001), interacting with peers in Colleges  = 26.4, p<0.001), and participating in social activities  = 19.1, p<0.001)  students placed high value on participation in the Year 1 CFM course, and this high value assessment was found even among 69 (78%) fourth year students.  A total of 277 (75%) placed high value on interacting with their advisors.  The strongest differences across classes were found for interacting with peers in Molecules (χp<0.001) .All LC components were significantly associated with overall JHLES score and multiple LE domains.  After adjusting for gender and class year, the largest magnitudes of associations between LC components and JHLES total were seen with participation in social activities , interacting with advisors , and participation in the CFM course  .  The CFAfter adjusting for effect of overall LE perception, the CFM course was the only LC component to have significant associations with greater empathic concern  and perspective-taking , less emotional exhaustion  and depersonalization , and a good quality of life .  Interactions with one’s advisor were significantly associated with empathic concern and less burnout, while reflective sessions were significantly associated with both empathic concern and perspective taking .  Interactions with peers in one’s Molecule and one’s College were significantly associated with quality of life and empathic concern.  Valuing the College room was not associated with any of these outcomes .In this study of 368 JHSOM medical students, we found variation in how students valued learning community (LC) components and in how valuing LC components related to learning environment (LE) perceptions, quality of life, burnout, and empathy.  The Clinical Foundations of Medicine (CFM) course was unique among LC components for being highly valued by large majorities of students across class years and for having positive associations with each outcome we measured.,LCs are meant to enhance students’ learning experiences, which should in turn improve LE perceptions.  Rosenbaum described improvements in students’ LE perceptions at the University of Iowa after LC implementation.20,36–38 and suggests that structures that foster relationships may have broader impacts than others.  The Colleges rooms, which give groups of students specific places to congregate, was associated with yet another pattern, not having associations with any of the outcomes, despite being highly valued by over two-thirds of students.  While the physical environment and actual spaces are believed to influence learning and morale in medical education,Just as there were distinctive patterns in how LC components related to LE perceptions, there were variations in how they related to students’ empathy and well-being.  Valuing reflective sessions had a strong association with a single outcome, empathy, but no association with burnout or quality of life.  We cannot determine causation, but it is possible that these sessions - which were intended to provide students the opportunity to process their feelings on difficult encounters with a group of trusted individuals and thereby enhance empathy - may have had their intended effect.  Interactions with peers and advisors exhibited a second distinct pattern of relationships with outcomes, showing positive associations with more than one type, as each was associated with affective empathy and aspects of well-being.  This is consistent with previous work,,-The CFM course was unique among LC components for being highly valued across all class years and having strong associations with favorable LE perceptions, greater empathy, and better well-being.  Clinical skills courses are common components of medical school LCs,Several limitations of this study should be considered.  First, we can only describe associations among variables in a cross-sectional study; a longitudinal study may be able to demonstrate whether LC components cause improvements in student well-being and empathy. Second, although JHSOM’s LC model of curricular and advising components are shared by other medical school LCs,In conclusion, our study showed that LC components are valued differently by students and likely impact students in variable ways, suggesting that future LC research should seek to disentangle the effects that each LC component has.  By describing the associations LC components have with desirable student outcomes, we contribute evidence that could lead to more efficient resource allocation for those seeking to develop or refine their own LCs.  A significant finding in this study was the importance of the first-year LC clinical skills course, which suggests that LC educational structures interweaving curricular learning of clinical content, advising, and longitudinal peer and faculty relationships may be particularly beneficial.  We hope that this work can pave the way for future studies to understand better the role that LCs can play in medical students’ well-being, skill development, and professional formation.The authors declare that they have no conflict of interest.


Drosophila is mediated by synaptic plasticity between the Kenyon cells of the mushroom body and their output neurons. Both Kenyon cells and their inputs from projection neurons are cholinergic, yet little is known about the physiological function of muscarinic acetylcholine receptors in learning in adult flies. Here, we show that aversive olfactory learning in adult flies requires type A muscarinic acetylcholine receptors (mAChR-A), particularly in the gamma subtype of Kenyon cells. mAChR-A inhibits odor responses and is localized in Kenyon cell dendrites. Moreover, mAChR-A knockdown impairs the learning-associated depression of odor responses in a mushroom body output neuron. Our results suggest that mAChR-A function in Kenyon cell dendrites is required for synaptic plasticity between Kenyon cells and their output neurons.Olfactory associative learning in  We can learn a surprising amount about how the brain forms memories by studying the humble fruit fly. These insects can learn to associate odors with positive or negative experiences, allowing them to then seek out ‘rewarded’ odors and avoid ‘punished’ ones.This association takes place in a brain region called the mushroom body, and it involves two types of neurons: Kenyon cells, which detect odors, and MBONs, which lead to approach or avoidance behaviors. When Kenyon cells detect an odor accompanying an unpleasant event, they weaken their connections with the MBONs that trigger approach behaviors. This prevents the fly from coming close to that odor in the future.Kenyon cells exchange signals with other neurons using a chemical called acetylcholine, which attaches onto the cells through two types of receptors: nicotinic and muscarinic. Studies in fruit fly larvae suggest that muscarinic receptors are required in Kenyon cells for the insects to learn how to associate odors with unpleasant experiences.Bielopolski et al. now show that this is also the case in adult flies. Surprisingly, while acetylcholine usually excites fly neurons, activating muscarinic receptors inhibits Kenyon cells rather than exciting them. Labeled muscarinic receptors revealed that the receptors act within the input region of Kenyon cells. Moreover, reducing the levels of muscarinic receptors inside the cells stops flies from associating an odor with a mild electric shock. This manipulation also prevents the learning experience from weakening connections from Kenyon cells onto an MBON that triggers approach behavior. This suggests that allowing these changes in connectivity might be why muscarinic receptors are important for memory.Understanding how memory works in flies can reveal basic principles that apply to many species, including humans. Such knowledge could ultimately help us improve the memory of patients with dementia, but also inspire better algorithms for artificial intelligence. Drosophila, where powerful genetic tools and well-defined anatomy have yielded a detailed understanding of the circuit and molecular mechanisms underlying associative memory . In mammals, where glutamate is the principal excitatory neurotransmitter, metabotropic glutamate receptors (mGluRs) have been widely implicated in synaptic plasticity and memory . Given te memory . The priFlies can learn to associate an odor  with a positive (sugar) or a negative (electric shock) unconditioned stimulus (US), so that they later approach ‘rewarded’ odors and avoid ‘punished’ odors. This association is thought to be formed in the presynaptic terminals of the ~2000 Kenyon cells (KCs) that make up the mushroom body (MB), the fly’s olfactory memory center . These KDrosophila , mAChR-A  is the most closely homologous to mammalian mAChRs (1-type’ (M1/M3/M5), which signal via Gq and are generally excitatory, and ‘M2-type’ (M2/M4), which signal via Gi/o and are generally inhibitory  cells, it signals via Gq protein  and output (KCs) are cholinergic , and KCsn mAChRs . Mammalihibitory . Drosoph protein  to activDrosophila larvae, as knocking down mAChR-A expression in KCs impairs learning , flies were grown at 23°C without transferring them to 31°C, thus blocking RNAi expression also in adults. When tested for learning at 10 days old, these flies showed normal learning . These different classes play different roles in olfactory learning . To unraβ′ lobes . No GFP la brain  confirm la brain . HoweverThe higher expression of mAChR-A in αβ and γ KCs compared to α′β′ KCs suggests that learning would be impaired by mAChR-A knockdown in αβ or γ, but not α′β′, KCs. To test this, we expressed mAChR-A RNAi in different KC classes. As expected, aversive olfactory learning was reduced by knocking down mAChR-A in αβ and γ KCs together using MB247-GAL4, but not by knockdown in α′β′ KCs using c305a-GAL4. To examine if αβ and γ KCs both participate in the reduced learning observed in mAChR-A knockdown flies, we sought to limit mAChR-A RNAi expression to either αβ or γ neurons. While strong driver lines exist for αβ neurons, the γ GAL4 drivers we tested were fairly weak , perhaps too weak to drive mAChR-A-RNAi enough to knock down mAChR-A efficiently. Therefore, we used MB247-GAL4, which was strong enough to affect behavior, and blocked GAL4 activity in either αβ or γ KCs by expressing the GAL80 repressor under the control of R44E04-LexA (αβ KCs) or R45H04-LexA (γ KCs) . These c2+ influx in the mushroom body calyx, where KC dendrites reside , and Drosophila mAChR-A may join these as another example of an inhibitory mAChR signaling through Gq.We next asked what effect mAChR-A knockdown has on the physiology of KCs, by expressing GCaMP6f and mAChR-A RNAi 2 together in KCs using OK107-GAL4 .RNAi-based knockdown of mAChR-A might induce homeostatic compensation that obscures or even reverses the primary effect of reduced mAChR-A expression. To test the acute role of mAChR-A in regulating KC activity, we took the complementary approach of pharmacologically activating mAChR-A. Initially, we bath-applied 10 µM muscarine, an mAChR-A agonist . Muscar2+ in the calyx most likely did not reflect increased excitability, as applying muscarine to the calyx did not increase the calyx odor response  lasted ~6–7 s and thus would have continued into the odor pulse in the muscarine +odor condition. If the odor response was unaffected by muscarine, the muscarine-evoked and odor-evoked increases in GCaMP6f signal should have summed. Instead, the peak calyx ∆F/F during the odor pulse was the same before and after locally applying muscarine, suggesting that the specifically odor-evoked increase in GCaMP6f was decreased by muscarine.To test mAChR-A function even more acutely, we locally applied muscarine to the MB calyx by pressure ejection . Red dyeresponse . If anyt2+ induced by muscarine alone (without odor) might reflect Ca2+ release from internal stores triggered by Gq signaling, which then inhibits KC excitability . Note that muscarine on the calyx is unlikely to reduce KC odor responses via presynaptic inhibition of PNs, because bath muscarine does not affect odor-evoked Ca2+ influx in PNs in the calyx  amount of mAChR-A is trafficked to and functions in axons, but due to a bottleneck in axonal transport, the excess tagged mAChR-A is trapped in KC dendrites. While our results do not rule out this possibility, a general bottleneck in axonal transport seems unlikely as many overexpressed proteins are localized to KC axons . We feel2+-dependent adenylyl cyclase rutabaga   , so therq ; in these cases, the experimental animals and all relevant controls were grown at 23˚C. To de-repress the expression of RNAi with GAL80ts, experimental and control animals were incubated at 31˚C for 7 days. Subsequent behavioral experiments were performed at 25˚C.Fly strains (see below) were raised on cornmeal agar under a 12 hr light/12 hr dark cycle and studied 1–10 days post-eclosion. Strains were cultivated at 25˚C unless they expressed temperature-sensitive gene products . Fragments were created by PCR using Phusion High-Fidelity DNA Polymerase (NEB). The full-length mAChR-A cDNA was purchased from GenScript (clone ID OFa11160). The vector was pTWF-attB, a gift from Prof. Oren Schuldiner . This vetgggaattatcgacaagtttgtacaaaaaagcaggctATGGAGCCGGTCATGAGTC and cactttgtacaagaaagctgggtaATTGTAGACGCCGCGTACFor mAChR-A: aaagctgggtaCTTGTACAAAGTGGTGAGCTCC and agcctgcttttttgtacAAACTTGTCGATAATTCCCFor pTWF-AttB: Transgenes were injected into the attP2 landing site using φC31 integration (by BestGene).1118 flies (for MIMiC experiments).Total RNA was extracted by EZ-RNA II Total RNA Isolation kit  from 30 adult heads for each biological replicate. cDNA was generated from 1 µg total RNA with the High-Capacity cDNA Reverse Transcription Kit with RNase Inhibitor (Applied Biosystems). Real-time quantitative PCR was carried with TaqMan Fast Advanced Master Mix (Applied Biosystems) and run in technical triplicates on a StepOne Plus Real-Time PCR System (Applied Biosystems). Taqman assays were Dm01820303_g1 for mAChR-A and Dm02151962_g1 for EF1 . The expression levels obtained for mAChR-A were normalized to those of the housekeeping gene EF1. The fold change for mAChR-A was subsequently calculated by comparing to the normalized value of either ELAV-gal4 parent (for RNAi experiments) or wBehavioral experiments were performed in a custom-built, fully automated apparatus . Single Air flow was controlled with mass flow controllers . A carrier flow (2.7 l/min) was combined with an odor stream (0.3 l/min) obtained by circulating the air flow through vials filled with a liquid odorant. Odors were prepared at 10 fold dilution in mineral oil. Therefore, liquid dilution and mixing carrier and odor stimulus stream resulted in a final 100 fold dilution of odors. Fresh odors were prepared daily.The 3 l/min total flow (carrier and odor stimulus) was split between 20 chambers resulting in a flow rate of 0.15 l/min per half chamber. Two identical odor delivery systems delivered odors independently to each half of the chamber. Air or odor streams from the two halves of the chamber converged at a central choice zone. The 20 chambers were stacked in two columns each containing 10 chambers and were backlit by 940 nm LEDs (Vishay TSAL6400). Images were obtained by a MAKO CMOS camera (Allied Vision Technologies) equipped with a Computar M0814-MP2 lens. The apparatus was operated in a temperature-controlled incubator (Panasonic MIR-154) maintained at 25˚C.A virtual instrument written in LabVIEW 7.1  extracted fly position data from video images and controlled the delivery of odors and electric shocks. Data were analyzed in MATLAB 2015b (The MathWorks) and Prism 6 (GraphPad).A fly’s preference was calculated as the percentage of time that it spent on one side of the chamber. Training and odor avoidance protocols were as depicted in 2, 5% CO2) containing 103 mM NaCl, 3 mM KCl, 5 mM trehalose, 10 mM glucose, 26 mM NaHCO3, 1 mM NaH2PO4, 3 mM CaCl2, 4 mM MgCl2, 5 mM N-Tris (TES), pH 7.3. Odors at 10−1 dilution were delivered by switching mass-flow controlled carrier and stimulus streams (Sensirion) via software controlled solenoid valves (The Lee Company). Flow rates at the exit port of the odor tube were 0.5 or 0.8 l/min.Brains were imaged by two-photon laser-scanning microscopy . Cuticle5C, and #2 for Fluorescence was excited by a Ti-Sapphire laser centered at 910 nm, attenuated by a Pockels cell (Conoptics) and coupled to a galvo-resonant scanner. Excitation light was focussed by a 20X, 1.0 NA objective (Olympus XLUMPLFLN20XW), and emitted photons were detected by GaAsP photomultiplier tubes , whose currents were amplified and transferred to the imaging computer. Two imaging systems were used, #1 for Movies were motion-corrected in X-Y using the moco ImageJ plugin , with prInter-odor correlations were calculated by first aligning the activity maps of each odor response by maximizing the inter-odor correlations of baseline fluorescence, and then converting image matrices of the activity maps of each odor response into linear vectors and calculating the Pearson correlation coefficients between each ‘odor vector’. A threshold for baseline fluorescence was applied as a mask to the activity map to exclude pixels with no baseline GCaMP6f signal. Population sparseness was calculated for activity maps using the following equation :SP=11-1N2PO4, 8.41 mM Na2HPO4, 175 mM NaCl) and fixed for 20 min at room temperature. Samples were washed for 3 × 20 min in PBS containing 0.3% (v/v) Triton-X-100 (PBT). The neuropil was counterstained with nc82 (DSHB) or monoclonal anti-FLAG M2 antibody  and goat anti-mouse Alexa 647 or Alexa 546. Primary antisera were applied for 1–2 days and secondary antisera for 1–2 days in PBT at 4˚C, followed by embedding in Vectashield. Images were collected on a Leica TCS SP5, SP8, or Nikon A1 confocal microscope and processed in ImageJ.Brain dissections, fixation, and immunostaining were performed as described . To visuAPL expression of tetanus toxin was scored by widefield imaging of mCherry. mCherry expression in APL was distinguished from 3XP3-driven dsRed from the GH146-FLP transgene by using separate filter cubes for dsRed  and mCherry .Statistical analyses were carried out in GraphPad Prism as described in figure legends and  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.Drosophila" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Mani Ramaswami as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor.Thank you for submitting your work entitled "Inhibitory muscarinic acetylcholine receptors enhance aversive olfactory conditioning in adult eLife. However, we do feel the work could be made acceptable but only after the essential changes noted below are handled experimentally.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in Drosophila mAChR mediates inhibition in the mushroom body as well that it is required in Kenyon cells for aversive associative memory. However, the mechanism by which it functions, in terms of the receptor's localisation at Kenyon cell presynaptic terminals and role in mediating signalling required for learning-associated depression of γ Kenyon cell – MBON synapses is not sufficiently well established. In addition to responding constructively to the various specific comments listed in the appended reviews, the reviewers felt that would be crucially important to: (a) demonstrate  the physical presence of mAChR in γ Kenyon cell presynapses; and (b) reanalyse data for the crucial Figure 7D in a way shows changes to MCH/OCT and IAA induced responses separately, in order to clarify that there is no depression to either the shock paired or shock unpaired odorant. .All the reviewers note the interest in the core observation that the Reviewer #1:Drosophila mushroom body as well as in their function in the physiology and plasticity of Kenyon cells that involved in encoding associative memory. This is interesting as the role of metabotropic ACh signalling has not been previously analysed at this level of resolution, in neurons and synapses that encode memory. The major conclusion of the paper is that mAchR is required in γ Kenyon cells to mediate synaptic depression that underlies reduced response to odor that has been associated with an aversive stimulus. This conclusion is well justified, based on several well-done experiments that point to the cell-autonomous function of mAChRs in Kenyon cells, their physiological role in reducing odor-evoked response, and their function both for physiological depression and associative memory formation. It is also broadly interesting mAChR function in Drosophila may prove to be anagolous to the role of mGluR in mammalian synapses. The paper is well written and clear.Bielopolsky and coauthors analyse the expression muscarinic AcRs in the Other, less clearly resolved but interesting aspects of the paper involve fundamental aspects of mAChR physiology in the calyx and in different classes of Kenyon cell axons, as revealed by RNAi based knockdown experiments and direct muscarine application. Muscarine application increases basal calcium-levels in calyx, but reduces odor evoked responses in axons may be explained as the authors proposed, but there could be alternative explanations.Essential revisions:1) Figure 7 shows normal levels of memory – which superficially contradicts Figure 2. Better and more clear labelling should be used to show that this is only true when there are much higher levels of aversive stimulation. This is also confusing because the imaging experiments shown in subsequent panels of the same figure use "normal" and lower levels of aversive stimulation.2) The observations Figure 6 (subsection “KC odor responses are decreased by an mAChR agonist”) seem to be overinterpreted. I think the data are fine to include, but the differences in OCT responses vs. MCH responses in the different classes of Kenyon cell axons etc. are not particularly compelling and may simply be at the level of experimental and biological noise. I suggest focusing on the main, robust and interpretable thread of these observations, and to present the other data observations that will take follow up work to understand completely.3) I also found the robust effect of mAChR in α′β′ cells to be confusing, given its apparent weak expression in this class of Kenyon cell. Can this be explained – perhaps the expression is not perfectly revealed by the MiMIC conversion line?4) In subsection “mAChR-A is required for olfactory learning in γ KCs, not αβ or α′β′ KCs”, "γ neurons project to the horizontal lobes only, while the axons of αβ and α′β′ neurons bifurcate to form the α an α′ portions of the vertical lobes and the β and β′ portions of the horizontal lobes" is a bit confusing.Reviewer #2:In this manuscript, the authors knock down a muscarinic acetylcholine receptor (mAChR-A) first, in all Kenyon cells (KCs) and then in subsets, while reading out flies' ability to form odor-shock associations as well as neuronal calcium responses. They conclude that mAChR-As mediate a spatially localized inhibition of odor responses in the dendritic as well as the axonal compartments of Kenyon cells and that this inhibition is needed in γ Kenyon cells, specifically in the horizontal lobe for learning.A major failing is the use of imprecise RNAi based methods to knockdown mAChR-A, the efficacy of which in blocking behavior depends on the drivers used . Another issue is the way the Calcium imaging data was analyzed. The authors use imprecise analyses and then report a negative result .I feel the manuscript has convincingly shown that mAChRs mediate cholinergic inhibition and that this may affect learning in some way. This is an interesting, initial result. The additional claims about how it affects learning and that it is necessary in γ KCs lobes are not justified by the data and analyses. Significant re-analysis and possibly some new experiments might be needed in order to draw any mechanistic conclusions. How does the mAChR-A mediated inhibition influence plasticity? This question has not been answered here.Essential revisions:1) γ KC specificity claim is not justified:The RNAi knock downs depended on the driver lines used . The authors use different RNAi driver in the behavior  and imaging experiments . In fact, the significant reduction in response for OCT not seen in Figure 3, appears in Figure 4E, reinforcing the notion that the differences being detected here fluctuate depending on the strength of the manipulation and might not reflect real physiological differences.The signal to noise of the Ca response trace would depend on the size of the ROI being averaged. More careful analysis and/or experiments would be needed to conclude that γ KCs have larger increases in response sizes than other cell types. The authors should either image and analyze single KC somata or at least, draw ROIs that average a fixed number of pixels from a single plane in the volume .2) Claims of lobe specific effects of localized muscarine application are not justified:In Figure 6, the authors claim to measure Ca-responses to either muscarine alone or muscarine plus odor in the various lobes and compare the effect of local muscarine application. When muscarine was applied in the horizontal lobes , the spread of muscarine into the various lobes could be non-uniform. The regions of interest over which ∆F/F values were calculated  would probably include different ratios of muscarine-applied and muscarine un-applied neuropil. Ca-fluorescence should only be extracted from regions of the lobes over-lapping with muscarine (red dye) spread. This should be straightforward to re-analyze. The lack of a clear and significant difference in most if not all the lobes in Figure 6G might also be a result of this effect.Reviewer #3:Drosophila melanogaster. They use an RNAi approach to knockdown a metabotropic acetylcholine receptor in the mushroom body, and this affects aversive associative olfactory learning. Downregulation of this receptor in γ-type Kenyon cells, but not other Kenyon cell types, is sufficient to impair learning. Odor-evoked calcium activity increases mainly in the Kenyon cell input region of the mushroom body , but also to some degree in axonal regions and not for both odorants used. Application of the receptor agonist muscarine also slightly decreased odor-evoked calcium activity in Kenyon cells when applied to the axons and slightly increased baseline calcium. However, application of muscarine to the calyx increased baseline calcium more pronounced and decreased odor-evoked calcium influx. Local injections of muscarine into lobes cause a slight decrease of calcium in γ lobes which leads them to conclude that mAChR-A might be present in axons and contribute to learning-induced synaptic plasticity. The data I do not find convincing because the restriction of the injected drug is only confirmed by dye application. Injection of the drug into the calyx causes effects in the α lobe. It might be possible that injection into the in γ lobe also affects calycal input. The argument that increasing the strength of the US compensates for the learning deficit induced by the RNAi construct I do not find convincing because one could conclude that a modified CS (changed through the RNAi expression) requires a stronger US to be learned. Also, the conclusion that "odor coding" is not affected by the mAchR-A knockdown I find difficult given that odor-evoked calcium is significantly increased. The plasticity observed in the MB-MVP2 neuron 3 hours after training and the absence of this plasticity when the receptor is downregulated is a very indirect hint that the receptor might be present on axons. Overall, the finding that a muscarinic acetylcholine receptor is required in γ-type Kenyon cells for learning but not odor avoidance is very nice, and I would like to congratulate the authors for that interesting result. The conclusion that this receptor is also localized to axons and has a role in presynaptic plasticity is not convincing. Localization studies of the receptor would be required.Bielopolsky et al. report learning experiments in [Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]Drosophila" for further consideration at eLife. Your revised article has been favorably evaluated by K VijayRaghavan (Senior Editor), Mani Ramaswami as Reviewing Editor, and two reviewers.Thank you for resubmitting your work entitled "Inhibitory muscarinic acetylcholine receptors enhance aversive olfactory learning in adult The manuscript has been greatly improved by careful and constructive revisions but there is one remaining issue that needs to be addressed before acceptance.We would like you to please consider the comments below (reproduced verbatim) and respond appropriately with revisions if you agree, or with a brief rebuttal if you do not, and to submit a final revised manuscript that will then be acceptable for publication.1) The authors emphasize differential effects on subsets of KCs when the data do not justify these claims. The claim of differential effects on KC subtypes may detract from the clear communication of the central observations.a) The authors claim in Figure 2 with the driver MB247 Gal4 that only γ KC based learning involves AChR-A, by receptor knockdown. The behavioral readout here is aversive, differential odor conditioning with a specific CS-US pairing protocol. Had the authors used appetitive reinforcement or spaced training protocols with more repeats, they might have seen behavioral effects with other KC subtypes as well. Furthermore, they use a different driver, OK107 Gal4 for knockdown in Figure 3 to claim that odor responses are significantly increased by receptor knockdown only in γ KCs. The use of different drivers to draw a parallel between behavior and neuronal activity is not strictly correct.b) The main claim made in Figure 6 is that muscarine application in the calyx differentially affects KC subtypes. This claim is based on the observation that muscarine application in the calyx depressed odor responses in all lobes except the α lobe. It is hard to explain how a manipulation in the calyx differentially affects only the α lobe axonal branch of α/β KCs. The odor-response traces for this same α lobe in Figure 6D rise when muscarine is applied, before odor delivery. It is unclear if the experiment is clean enough to support the claim being made. Note also that the γ KCs, which are described as the main targets of the effect of mAChR-A in Figure 2 and Figure 3 behave similarly to other KC subtypes in this figure.c) Figure 8 shows that mAChR-A knockdown prevents learning-related plasticity in a γ KC compartment. This does not show that other KCs and KC compartments are not similarly affected by mAChR-A.d) I would instead argue that Figure 5 indicates that odor responses in all KC subtypes are affected by mAChRs in a similar, important, surprising manner; consistent with the behavior and MBON imaging observations. [Editors’ note: the author responses to the first round of peer review follow.]In addition to responding constructively to the various specific comments listed in the appended reviews, the reviewers felt that would be crucially important to: (a) demonstrate  the physical presence of mAChR in γ Kenyon cell presynapses;We tried to use CRISPR to generate an mAChR-A-FRT-stop-FRT-V5 allele that would allow endogenous mAChR-A to be conditionally tagged with V5 in cells expressing FLP. However, due to various problems, we have not managed to obtain the correct flies.In parallel, we took an alternative approach: we overexpressed mAChR-A-FLAG in KCs and observed anti-FLAG immunostaining in the calyx, not the lobes. Thissuggests that mAChR-A is localized to KC dendrites. This overexpressed construct can also rescue learning in a mAChR-A hypomorph background, showing that dendritically localized mAChR-A functions in learning . See subsection “mAChR-A localized to the MB calyx can rescue learning in a mAChR-A hypomorphic mutant” and new Figure 7. Even though this doesn’t use the suggested genomically-tagged mAChR-A, we feel this is strong evidence that mAChR-A functions in KC dendrites.Thus, based on new evidence, we changed our original conclusion. Although wecannot rule out the possibility that a low, undetectable level of mAChR-A functions in KC axons (see seventh paragraph of the Discussion section) we feel the most parsimonious explanation is that mAChR-A indeed functions in KC dendrites.… and (b) reanalyse data for the crucial Figure 7D in a way shows changes to MCH/OCT and IAA induced responses separately, in order to clarify that there is no depression to either the shock paired or shock unpaired odorant. .We reanalyzed the data used for Figure 7 in a new Figure 8—figure supplement 1  and demonstrate that there is no general depression in odor responses.Reviewer #1:[…]Essential revisions:1) Figure 7 shows normal levels of memory – which superficially contradicts figure 2. Better and more clear labelling should be used to show that this is only true when there are much higher levels of aversive stimulation. This is also confusing because the imaging experiments shown in subsequent panels of the same figure use "normal" and lower levels of aversive stimulation.We have moved the confusing panel Figure 7A to Figure 1—figure supplement 1A (with added labeling to note “90 V shock”) .2) The observations Figure 6 (subsection “KC odor responses are decreased by an mAChR agonist”) seem to be overinterpreted. I think the data are fine to include, but the differences in OCT responses vs. MCH responses in the different classes of Kenyon cell axons etc. are not particularly compelling and may simply be at the level of experimental and biological noise. I suggest focusing on the main, robust and interpretable thread of these observations, and to present the other data observations that will take follow up work to understand completely.In light of all three reviewers’ concerns about the localized application of muscarine to the lobe, we removed these results. We kept the localized application of muscarine to the calyx, but we focused our interpretation on the temporal rather than spatial specificity of application . See subsection “KC odor responses are decreased by an mAChR agonist”.3) I also found the robust effect of mAChR in α′β′ cells to be confusing, given its apparent weak expression in this class of Kenyon cell. Can this be explained – perhaps the expression is not perfectly revealed by the MiMIC conversion line?We added this sentence to the Results following the discussion of the MiMIC conversion line: “However, mAChR-A is still clearly present in α′β′ KCs’ transcriptomes, suggesting that mAChR-A-MiMIC-GAL4 may not reveal all neurons that express mAChR-A.” See subsection “KC odor responses are decreased by an mAChR agonist”.We also added this sentence to the Results discussing muscarine application: “The effect of muscarine on α′β′ KCs is consistent with single-cell transcripome analyses showing that α′β′ KCs express mAChR-A, albeit at a lower level than αβ or γ KCs .” See subsection “KC odor responses are decreased by an mAChR agonist”.4) Subsection “mAChR-A is required for olfactory learning in γ KCs, not αβ or α′β′ KCs”: "γ neurons project to the horizontal lobes only, while the axons of αβ and α′β′ neurons bifurcate to form the α an α′ portions of the vertical lobes and the β and β′ portions of the horizontal lobes" is a bit confusing.We now write: “Kenyon cells are subdivided into three main classes according to their innervation of the horizontal and vertical lobes of the MB: γ neurons send axons only to the γ lobe of the horizontal lobes, while the axons of αβ and α′β′ neurons bifurcate and go to both the vertical and horizontal lobes .” See subsection “mAChR-A is required for olfactory learning in γ KCs, not αβ or α′β′ KCs”.Reviewer #2:[…]Essential revisions:1) γ KC specificity claim is not justified:The RNAi knock downs depended on the driver lines used . The authors use different RNAi driver in the behavior  and imaging experiments .This comment may be due to a misunderstanding. Actually, we used the same RNAi lines and GAL4 drivers in both behavior and imaging. In particular, we used both RNAi 1 and RNAi 2 for behavior  and for imaging . We used the same GAL4 drivers for both behavior and imaging: OK107 to label all KCs , and mb247-GAL4, R44E04-LexA>lexAop-GAL80 . It’s true that in Figure 2 we also tested some other drivers on behavior, but as they either caused no behavior defect or were less specific than the γ-specific driver, there was no need to test them on imaging. We apologize for our text being unclear, and we have added the following text to stress this point: “” (subsection “mAChR-A suppresses odor responses in γ KCs”) and “the same driver and RNAi combination used in the behavioral experiments in Figure 2B” (subsection “mAChR-A suppresses odor responses in γ KCs”).In fact, the significant reduction in response for OCT not seen in Figure 3, appears in Figure 4E, reinforcing the notion that the differences being detected here fluctuate depending on the strength of the manipulation and might not reflect real physiological differences.Unfortunately, we don’t understand this comment. First, we found that mAChR-ARNAi causes an increase in KC odor responses, not a reduction. Second, comparing Figure 3 and Figure 4E, the response to OCT is increased in both the calyx and γ lobe in both figures.The signal to noise of the Ca response trace would depend on the size of the ROI being averaged. More careful analysis and/or experiments would be needed to conclude that γ KCs have larger increases in response sizes than other cell types. The authors should either image and analyze single KC somata or at least, draw ROIs that average a fixed number of pixels from a single plane in the volume .It is true that the signal-to-noise ratio (SNR) of the measured GCaMP signal differed between the various regions of the mushroom body . However, the SNR was high enough that these differences do not affect the statistical power of our comparisons. We measured the SNR as the reciprocal of the coefficient of variation  of GCaMP signal over time during the pre-stimulus period (2 s before the odor). Because ∆F/F = (F-F0)/F0 = F/F0 – 1 (so F/F0 = ∆F/F + 1), and F0 = mean signal during the pre-stimulus period, the CoV of fluorescence during the pre-stimulus interval equals the standard deviation of ∆F/F during the pre-stimulus interval.d = 1.3 for OCT, 1.8 for MCH). We simulated 2 groups of 20 random samples  where the effect size of the difference between the 2 groups was 1.3. Each sample had a ‘ground truth’ value, from which we sampled 3 ‘time points’ that were subject to noise with SNR from 1–50 . The maximum of these 3 time points was taken as the measured ‘peak odor response’. We ran 1000 simulations, ran t-tests on the simulated data, and counted how many gave a p-value < 0.0125 (a Holm-Bonferroni correction for the 4 mushroom body regions that did not consistently show significant differences between control and mAChR-A-RNAi flies) – this fraction is the statistical power for detecting a difference in the non-γ lobes with effect size 1.3. The power was constant at ~0.91 for SNR above 12, whereas our SNRs were in the range 20–50. Thus, in our data, the SNR was high enough that differences in SNR between lobes would not affect the statistical power to detect an effect as large as what we observed in the γ lobe. This analysis now appears in new Figure 3—figure supplement1, referred to in subsection “mAChR-A suppresses odor responses in γ KCs”.The CoV was in general very low . Differences in SNR in this range would not substantially affect the chances of detecting an effect size as large as what we observed in the γ lobe  Claims of lobe specific effects of localized muscarine application are not justified:In Figure 6, the authors claim to measure Ca-responses to either muscarine alone or muscarine plus odor in the various lobes and compare the effect of local muscarine application. When muscarine was applied in the horizontal lobes , the spread of muscarine into the various lobes could be non-uniform. The regions of interest over which ∆F/F values were calculated  would probably include different ratios of muscarine-applied and muscarine un-applied neuropil. Ca-fluorescence should only be extracted from regions of the lobes over-lapping with muscarine (red dye) spread. This should be straightforward to re-analyze. The lack of a clear and significant difference in most if not all the lobes in Figure 6G might also be a result of this effect.In light of all three reviewers’ concerns about the localized application of muscarine to the lobe, we removed these results. We kept the localized application of muscarine to the calyx, but we focused our interpretation on the temporal rather than spatial specificity of application . See subsection “KC odor responses are decreased by an mAChR agonist”.Reviewer #3:Bielopolsky et al. report learning experiments in Drosophila melanogaster. They use an RNAi approach to knockdown a metabotropic acetylcholine receptor in the mushroom body, and this affects aversive associative olfactory learning. Downregulation of this receptor in γ-type Kenyon cells, but not other Kenyon cell types, is sufficient to impair learning. Odor-evoked calcium activity increases mainly in the Kenyon cell input region of the mushroom body , but also to some degree in axonal regions and not for both odorants used. Application of the receptor agonist muscarine also slightly decreased odor-evoked calcium activity in Kenyon cells when applied to the axons and slightly increased baseline calcium. However, application of muscarine to the calyx increased baseline calcium more pronounced and decreased odor-evoked calcium influx. Local injections of muscarine into lobes cause a slight decrease of calcium in γ lobes which leads them to conclude that mAChR-A might be present in axons and contribute to learning-induced synaptic plasticity.The data I do not find convincing because the restriction of the injected drug is only confirmed by dye application. Injection of the drug into the calyx causes effects in the α lobe. It might be possible that injection into the in γ lobe also affects calycal input.In light of all three reviewers’ concerns about the localized application of muscarine to the lobe, we removed these results. We kept the localized application of muscarine to the calyx, but we focused our interpretation on the temporal rather than spatial specificity of application . (Subsection “KC odor responses are decreased by an mAChR agonist”).But we would like to clarify that the effect of calyx-applied muscarine on the α lobe does not necessarily mean that the drug spread farther than the dye indicator; rather, it could mean that an effect on KC dendrites altered KC spiking, which altered GCaMP signals in KC axons.The argument that increasing the strength of the US compensates for the learning deficit induced by the RNAi construct I do not find convincing because one could conclude that a modified CS (changed through the RNAi expression) requires a stronger US to be learned.We thank the reviewer for this comment. We now place this panel in the supplemental material  and present it as a side note, without drawing any conclusions as to whether the RNAi affects the CS, US, or CS-US integration. We also discuss the concept raised by the reviewer in more depth in the Discussion section.Also, the conclusion that "odor coding" is not affected by the mAchR-A knockdown I find difficult given that odor-evoked calcium is significantly increased.As part of removing the previous point about the weaker US, we have also removed the reference to “odor coding”.The plasticity observed in the MB-MVP2 neuron 3 hours after training and the absence of this plasticity when the receptor is downregulated is a very indirect hint that the receptor might be present on axons. Overall, the finding that a muscarinic acetylcholine receptor is required in γ-type Kenyon cells for learning but not odor avoidance is very nice, and I would like to congratulate the authors for that interesting result. The conclusion that this receptor is also localized to axons and has a role in presynaptic plasticity is not convincing. Localization studies of the receptor would be required.See discussion of localization studies in response to the summary comments.[Editors' note: the author responses to the re-review follow.]We would like you to please consider the comments below (reproduced verbatim) and respond appropriately with revisions if you agree, or with a brief rebuttal if you do not, and to submit a final revised manuscript that will then be acceptable for publication.1) The authors emphasize differential effects on subsets of KCs when the data do not justify these claims. The claim of differential effects on KC subtypes may detract from the clear communication of the central observations.a) The authors claim in Figure 2 with the driver MB247 Gal4 that only γ KC based learning involves AChR-A, by receptor knockdown. The behavioral readout here is aversive, differential odor conditioning with a specific CS-US pairing protocol. Had the authors used appetitive reinforcement or spaced training protocols with more repeats, they might have seen behavioral effects with other KC subtypes as well.We agree, and have added a sentence in the Discussion section: “It may be that mAChR-A is required in non-γ KC types for other forms of memory besides short-term aversive memory, e.g., appetitive conditioning or other phases of memory like long-term memory.”Furthermore, they use a different driver, OK107 Gal4 for knockdown in Figure 3 to claim that odor responses are significantly increased by receptor knockdown only in γ KCs. The use of different drivers to draw a parallel between behavior and neuronal activity is not strictly correct.We have added this caveat to the Results section: “However, note that we do not exclude the possibility that αβ- or α′β′-specific (as opposed to pan-KC) knockdown of mAChR-A might significantly increase αβ or α′β′ KC odor responses.” See subsection “mAChR-A suppresses odor responses in γ KCs”.1B) The main claim made in Figure 6 is that muscarine application in the calyx differentially affects KC subtypes.We agree that the figure title was too strongly stated. We have changed the title of Figure 6 and its supplement to “Local muscarine application to the calyx inhibits KC odor responses”.This claim is based on the observation that muscarine application in the calyx depressed odor responses in all lobes except the α lobe. It is hard to explain how a manipulation in the calyx differentially affects only the α lobe axonal branch of α/β KCs. The odor-response traces for this same α lobe in Figure 6D rise when muscarine is applied, before odor delivery. It is unclear if the experiment is clean enough to support the claim being made.We added the sentence: “Although muscarine did not significantly affect peak ∆F/F during the odor in the α lobe, muscarine most likely did decrease α lobe odor responses, by the same logic as for calyx odor responses (see above).” See subsection “KC odor responses are decreased by an mAChR agonist”.We also note that the β lobe also shows a small bump in response to muscarine alone in Figure 6B, so at least the α and β branches show qualitatively similar responses . We added the phrase “with small increases in the β and γ lobe that were not statistically significant” (subsection “KC odor responses are decreased by an mAChR agonist”).Note also that the γ KCs, which are described as the main targets of the effect of mAChR-A in Figure 2 and Figure 3 behave similarly to other KC subtypes in this figure.We agree and we feel that we already address this point by discussing the different results for mAChR-A RNAi knockdown vs. muscarine application in the Discussion section, starting with “while mAChR-A knockdown mainly affects γ KCs, with other subtypes inconsistently affected, muscarine reduces responses in all KC subtypes.”.c) Figure 8 shows that mAChR-A knockdown prevents learning-related plasticity in a γ KC compartment. This does not show that other KCs and KC compartments are not similarly affected by mAChR-A.We agree, and added two sentences to the Discussion section: “Our finding that mAChR-A is required in γ KCs for aversive short-term memory is consistent with our finding that mAChR-A knockdown in KCs disrupts training-induced depression of odor responses in MB-MVP2, an MBON postsynaptic to γ KCs required for aversive short-term memory . However, the latter finding does not rule out the possibility that other MBONs postsynaptic to non-γ KCs may also be affected by mAChR-A knockdown in KCs.”.d) I would instead argue that Figure 5 indicates that odor responses in all KC subtypes are affected by mAChRs in a similar, important, surprising manner; consistent with the behavior and MBON imaging observations.We agree and think that this point is conveyed by the above changes together with existing discussion.