Background: Acute type two respiratory failure (AT2RF) is characterized by high carbon dioxide levels (PaCO2 >6kPa). Non-invasive ventilation (NIV), the current standard of care, has a high failure rate. High flow nasal therapy (HFNT) has potential additional benefits such as CO2 clearance, the ability to communicate and comfort. The primary aim of this systematic review is to determine whether HFNT in AT2RF improves 1) PaCO2, 2) clinical and patient-centred outcomes and 3) to assess potential harms.Methods: We searched EMBASE, MEDLINE and CENTRAL  (January 1999-January 2021). Randomised controlled trials (RCTs) and cohort studies comparing HFNT with low flow nasal oxygen (LFO) or NIV were included. Two authors independently assessed studies for eligibility, data extraction and risk of bias. We used Cochrane risk of bias tool for RCTs and Ottawa-Newcastle scale for cohort studies.Results: From 727 publications reviewed, four RCTs and one cohort study (n=425) were included. In three trials of HFNT vs NIV, comparing PaCO2 (kPa) at last follow-up time point, there was a significant reduction at four hours  and no significant difference at  24-hours or five days. Comparing HFNT with LFO, there was no significant difference at 30-minutes. There was no difference in intubation or mortality.Conclusions: This review identified a small number of studies with low to very low certainty of evidence. A reduction of PaCO2 at an early time point of four hours post-intervention was demonstrated in one small RCT. Significant limitations of the included studies were lack of adequately powered outcomes and clinically relevant time-points and small sample size. Accordingly, systematic review cannot recommend the use of HFNT as the initial management strategy for AT2RF and trials adequately powered to detect clinical and patient-relevant outcomes are urgently warranted. Acute exacerbations of COPD (AECOPD) account for 100,000 admissions annually in England. Of these, around 20% will present with or develop hypercapnia, an indicator of increased risk of death.3 Development of AT2RF in patients with COPD is associated with a significantly increased risk for requiring invasive ventilation and mortality rate,5 with mortality rates up to 15% in patients who require admission to the intensive care unit (ICU).Acute type two respiratory failure (AT2RF) is characterised by arterial hypercapnia  or invasive mechanical ventilation (IMV). Current guidelines recommend the use of NIV. In type I ARF with different aetiologies, HFNT has been demonstrated to lead to improved oxygenation, lower rates of endotracheal intubations and lower mortality.17High flow nasal oxygen or insufflation  in this manuscript) is novel respiratory support that integrates humidified air with a high flow rate of up to 60 L/minute. Reported benefits from HFNT include consistent fractional inspired oxygen delivery, dead space washout, reduced work of breath, comfort and tolerability, ability to communicate, mucous clearance and NIV-like effects, which makes it a more tolerable method for patients.18 Several observational studies have suggested potential benefits of HFNT for AT2RF as demonstrated by improved gas exchange and acidosis,20 and reductions in the respiratory rate and work of breathing. Individual studies have shown that HFNT improves blood gas levels in AT2RF patients and is associated with improved comfort.24In the last 10 years, evidence has emerged for its increasing use and a role for these modalities in clinical practice for the treatment of AT2RF.2 clearance is essential to prevent intubation and invasive ventilation. NIV, despite its frequent use, has limitations and a high failure rate. HFNT might overcome the limitations of NIV and could be used in AT2RF patients as an initial intervention or in patients who do not tolerate NIV. Despite the increase in current literature suggesting benefits from the use of HFNT in AT2RF, current evidence is limited. Other systematic reviews are exploring the use of HFNT for the management of AT2RF post-extubation and after initial stabilization of the patient using a respiratory optimisation method like NIV or LFO.28 However, there is no systematic review that focuses on the use of HFNT as an initial management strategy for AT2RF.Adequate respiratory support through controlled oxygen, reduced work of breathing and CO2 in comparison to LFO or NIV. Secondary objectives were to examine whether HFNT in patients with AT2RF improves other clinical or patient-centred outcomes and to assess any potential harms.The primary objective of this systematic review was to determine whether the use of HFNT for patients with AT2RF improves PaCOCRD42019148748, 05/09/2019) and published a priori. We conducted this systematic review according to the PRISMA guidelines (seeReporting guidelines).30The systematic review was registered in the PROSPERO database  patients with AT2RF (>6 kPa or >45 mmHg) managed as inpatients in an acute care setting . We excluded reports that described the use of HFNT in peri-operative settings, drug overdose, or ventilator weaning.2 post-intervention (measured at time points reported by authors). The secondary outcomes were: respiratory parameters including pH, the partial arterial pressure of oxygen (PaO2), dyspnoea score, tidal volume and minute volume; mucous clearance ; the level of consciousness; patient comfort; intubation rate; length of stay in hospital; mortality; post-discharge COPD exacerbation rate and readmission rate secondary to AECOPD.The primary outcome for this review was the change in PaCOExtended data).30 The systematic review software management systemCovidence was used to store citations, remove duplication and aid screening.We searched the electronic databases MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials from January 1999 to January 2021. The databases search was conducted on 15/01/2021. Language restrictions were not applied. In addition, we searched Google Scholar and references of all articles for any additional studies. With the assistance of a professional librarian, we developed a systematic search strategy using appropriate keywords and MeSH terms and these are detailed in the data availability section (seeTwo review authors (AAA and MS) independently screened the titles and abstracts of all citations. The full texts of all potentially eligible studies were independently reviewed for inclusion confirmation. Any disagreement was resolved through discussion within the review team.Data were independently extracted from included studies using a standardized data extraction form by two reviewers (AAA and MS). The information extracted included type and setting of the study, recruitment information, participant characteristics (age and underlying conditions), inclusion criteria, nature of interventions, in each group (e.g. flow rate and method of delivery), time-points of measurement and outcomes. Any disagreement was resolved through discussion with BB. Data that were unavailable or insufficient from publications were requested from study authors.32 Each potential source of bias was marked as high, low or unclear. We assessed the quality of the evidence associated with HFNT for AT2RF using GRADE to determine the strength of the evidence into one of four grades: high, moderate, low or very low.33 The quality of evidence is reported in the Summary of Findings (SOF) tables (Two reviewers (AAA and MS) independently assessed the quality of included studies using the Cochrane risk of bias tool for RCTs and the Newcastle-Ottawa scale for cohort studies. tables ,2,3.Measurement of effectRevMan software  was used for data analysis. Results are reported as odds ratios (ORs) with 95% confidence intervals (CIs) for binary variables and mean differences (MD) with 95% CIs for continuous variables. A meta-analysis was planned, but there were insufficient studies and results are presented narratively.Subgroup and sensitivity analysisThe planned subgroup analyses of patient conditions , and the planned sensitivity analysis excluding trials with a high risk of bias could not be undertaken due to the low number of trials.The search identified 727 records. Following the removal of duplicates and non-eligible studies, 39 full-text studies were screened and 34 studies were excluded. Five studies with 425 participants were included in this review .23,24, and one was a cohort study.23 Four studies compared HFNT with NIV and one RCT compared HFNT with LFO using simple nasal prongs.24 The disease state of interest was an acute-moderate hypercapnic respiratory failure (n = 88) in one study,23 and AECOPD (n = 337) in the remaining four studies.The characteristics of the included studies are summarized in Blinding of participants and personnel were not possible in the trials. One trial showed a high risk for selection bias due to unexplained randomization sequence and allocation concealment.35 The trials showed a high risk or unclear risk of detection bias due to no35 or unclear24 blinding of the outcomes assessor. One trial showed a high risk of attrition bias due to unreported incomplete data.34 The cohort study showed a low risk of bias in all domains and did not describe how the outcomes were assessed.23The risk of bias assessments for the four RCTs are described in2 after the intervention was reported in all five studies ). In the other studies comparing HFNT to NIV,36 there was no significant difference in PaCO2 at various time-points with a similar trend in PaCO2 ) or four hours ). Conget al.35 reported no significant difference in pH between HFNT and NIV at 12 hours  or five days . Leeet al.23 showed no significant difference in pH between HFNT and NIV at six hours  or 24 hours . The PaO2 level was reported in four trials. Doshiet al.34 reported no significant difference in PaO2 between HFNT and NIV at one hour ) or four hours ). Conget al.35 reported no significant difference in PaO2 between HFNT and NIV at 12 hours  or five days . Leeet al.23 reported no significant difference in PaO2 between HFNT and NIV at six hours  or 24 hours .pH level was reported in three studies.36 Patient comfort assessed using a self-designed survey in Conget al.35 a 10-point numerical rating scale in Cortegianiet al.36 and the Likert scale in Pilcheret al.24 showed that HFNT was more comfortable than LFO but louder than LFO . Cortegianiet al.36 reported no significant difference in intubation rate at two hours  or six hours . Leeet al.23 reported no significant difference at 30 days   .36 and there was no difference between HFNT and NIV groups  when compared to the NIV group (16.1%), which did not achieve statistical significance in this study that was not powered for this outcome.Studies have shown a reduction in intubation rate and mortality between NIV versus usual care46 increased mortality when compared to immediate IMV, and increased mortality when IMV is delayed.47 In this systematic review, Leeet al.23 and Cortegianiet al.36 taken together with lower intubation rate in the HFNT arm,34 suggests that HFNT is unlikely to be associated with harm through delayed initiation of IMV, but this hypothesis needs to be confirmed in a clinical trial.A key balancing outcome is an increase in adverse outcomes that have been highlighted in studies comparing NIV to usual care that include a delay in escalation to IMV,24 HFNT, when compared to NIV, was shown to be associated with improved comfort in Conget al.35 and Cortegianiet al.36 In this review, Doshiet al.34 and Cortegianiet al.36 did not detect any difference in dyspnoea between HFNT and NIV. The lack of demonstrable benefit is likely secondary to the earlier time points in the studies investigating the role of HFNT in the initial management of AT2RF.One of the putative benefits of HFNT is patient comfort due to the lack of a tight-fitting mask, prevention of skin breakdown, better communication and mucous clearance.42 NIV interruption,21 or physiological studies24 and even in studies that explored its efficacy in acute exacerbations, the place of intervention could lead to bias, for example after initial management in the emergency medicine department, thus introducing unintentional bias such as lead-time bias as well selection bias.35 The location of patients in a closely monitored environment, as opposed to a general ward,47 might mask any adverse outcomes due to deterioration through earlier intervention. Hence, it is essential to investigate its utility in the early management of AT2RF in the emergency medicine department.HFNT is increasingly emerging as a therapeutic option for AT2RF, but various studies have combined it with other clinical scenarios such as post-extubation,48 Depending on the type of respiratory failure, type 1 or 2, a specific nasal cannula design that alters flow pattern could have a differential effect. A small-bore nasal cannula as seen in high flow nasal insufflation might purge the anatomical dead space more efficiently, thereby providing minimal ventilator assistance.49High flow nasal cannula can flush anatomical dead space, provide mild positive distending pressure, improve mucociliary clearance as well as be better tolerated.50 using a comprehensive search strategy of all electronic databases. Despite this, the recommendations of the review are limited by the small number of trials, which highlights the need for further adequately powered trials.The strength of the systematic review is that it was conducted to a high standard following recommended methods for the conduct, quality assessment and reporting,51 The role of nasal cannula diameter in the efficiency of CO2 clearance should be tested to determine whether the type of device used has an impact on therapy efficacy.52 Studies should also encompass a robust health economic analysis, include outcome analysis of patients who fail therapy and identify any features to predict the outcome of the therapy to allow patient selection.We recommend that future research needs to address the following research gaps in the evidence base for the use of HFNT in AT2RF. Future trial designs should be randomized controlled trials, they should include sufficiently large patient numbers to ensure they are adequately powered for important clinical outcomes. Outcomes should be standardized with clear definitions including clinical outcomes, use validated scales and relevant time points.2 was seen between HFNT and NIV at various time-points,36 while a significantly higher PaCO2 clearance with HFNT, when compared to NIV, was demonstrated at an early time point in one study.34 Similarly, HFNT use was associated with better comfort in two studies,36 while a similar benefit was not shown in the other study.24 The evidence is also moderate in quality and the benefit demonstrated is limited to clinically irrelevant time points, with no studies powered to detect clinical outcomes. Therefore, a change in practice cannot be recommended until further high-quality clinical trials are conducted.In conclusion, this review found very few studies investigating the clinical efficacy of HFNT in AT2RF. A similar reduction in PaCOAll data underlying the results are available as part of the article and no additional source data are required.https://doi.org/10.17034/4080c4eb-38f0-4c02-91ee-37129ceb65a6.30Queen’s University Belfast institutional data repository (Pure system): High flow nasal therapy for acute type 2 respiratory failure: A systematic review.-Search strategy for Medline for research article High flow nasal therapy for acute type two respiratory failure. A systematic review.pdfThis project contains the following extended data:Creative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of thehttps://doi.org/10.17034/4080c4eb-38f0-4c02-91ee-37129ceb65a6.30Queen’s University Belfast institutional data repository (Pure system): PRISMA checklist for “High flow nasal therapy for acute type 2 respiratory failure: A systematic review”.Creative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of the No further comments on my side.Are the rationale for, and objectives of, the Systematic Review clearly stated?YesIs the statistical analysis and its interpretation appropriate?YesAre sufficient details of the methods and analysis provided to allow replication by others?YesAre the conclusions drawn adequately supported by the results presented in the review?YesReviewer Expertise:NAI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Please note that at least two articles should be included in the review for HFNC vs NIV for the primary outcome (Sklar 2018 and Papachatzakis 2020).I suggest to check for other articles in the review by Pisani et al (PMID: 31591056), that also merits to be cited in the manuscript.Please update findings and discussion according to the aforementioned points.2 due to the low quality of evidence, lack of standardization of time-points for PaCO2 measurement and the lack of adequately powered sample sizes." I would mitigate this message. In case of failure of NIV due to interface intolerance, with improving blood gases, I would  attempt to shift the treatment to HFNC, in order to avoid intubation. noteworthy, several studies have demonstrated that delay in intubation for hypercapnic respiratory failure does not impact on patients' outcome and survival."While this could suggest that HFNT is non-inferior to NIV, HFNT cannot be recommended as an alternative management strategy to reduce PaCOI have reviewed the manuscript and there are some criticism requiring discussion:Are the rationale for, and objectives of, the Systematic Review clearly stated?YesIs the statistical analysis and its interpretation appropriate?YesAre sufficient details of the methods and analysis provided to allow replication by others?YesAre the conclusions drawn adequately supported by the results presented in the review?YesReviewer Expertise:Respiratory failure, HFNC, NIV, invasive mechanical ventilationI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We would like to thank the reviewer for his time in reviewing this manuscript. The comments have been responded to individually as stated below. We feel that the manuscript has improved with his input and hope that he is satisfied by our response and additional changes.C1: Please note that at least two articles should be included in the review for HFNC vs NIV for the primary outcome (Sklar 2018 and Papachatzakis 2020).R1: Thank you for this comment, the studies cited have their merits in exploring the role of HFNT for AHRF. However, the studies cited don’t fit our inclusion criteria which have been described in the protocol published in the PROSPERO database (CRD42019148748) and the methodology section of the systematic review, therefore, cannot be included in our SR. Specifically, the systematic review focusses on studies that have utilised HFNO as the initial management strategy for acute hypercapnic respiratory failure (AHRF). This systematic review is unique in that respect as evidence synthesis in this emergency clinical scenario is lacking with studies that have utilised at later stages of management such as post initial NIV use, interspersed with NIV and indeed studies not limited to AHRF are included in previous systematic reviews.et al have conducted a systematic review to investigate the impact of HFNT for patients with immunocompromise which don’t meet most of the inclusion criteria which we established as our review include only randomized controlled trials, uncontrolled trials and cohort studies focusing on HFNT as initial treatment when compared to LFO and/or NIV for AHRF. Papachatzakis et al was excluded due to various reasons including the inclusion of a mixed population and did not utilise HFNT as an initial treatment plan for the patient. SklarC2: I suggest to check for other articles in the review by Pisani et al (PMID: 31591056), that also merits to be cited in the manuscript.R2: Thank you for the comment. We have cited the review by Pisaniet al whose group have done a lot of work in this area. The various papers included in that review were also captured through our search and included in our review if they conformed to our protocol published in the PROSPERO database.C3: Please update findings and discussion according to the aforementioned points.R3: The citations suggested are outside the scope of the systematic review inclusion criteria and hence not included for outcome analysis. To give a broader picture of the field and the scope of HFNO, we have amended the background section to include the articles suggested above.C4: While this could suggest that HFNT is non-inferior to NIV, HFNT cannot be recommended as an alternative management strategy to reduce PaCO2 due to the low quality of evidence, lack of standardization of time-points for PaCO2 measurement and the lack of adequately powered sample sizes." I would mitigate this message. In case of failure of NIV due to interface intolerance, with improving blood gases, I would  attempt to shift the treatment to HFNC, in order to avoid intubation. noteworthy, several studies have demonstrated that delay in intubation for hypercapnic respiratory failure does not impact on patients' outcome and survival.R4: Thank you for this comment. The current evidence base to suggest HFNO in patients’ failing NIV is limited to small studies with no definitive efficacy studies. The authors do agree that it is an area that requires immediate attention. The paragraph has been amended to reflect the reviewers’ comments. “While this could suggest that HFNT is non-inferior to NIV, HFNT cannot be recommended as an alternative management strategy to reduce PaCO2 due to the low quality of evidence, lack of standardization of time-points for PaCO2 measurement and the lack of adequately powered sample sizes. Similarly, in patients failing NIV due to compliance issues, HFNO may be a promising option to limit mechanical ventilation. This recommendation falls beyond the scope of this systematic review and is a clinical scenario that requires urgent attention". We once again thank the reviewer for the time and effort taken in reviewing the manuscript and providing the comments. This is a thorough systematic review which has appropriate methodology and identifies the paucity of evidence available comparing HFNO to NIV.  There are minimal differences detectable and no clinically important differences. The conclusions drawn are appropriate and importantly, mention is made of the benefits of NIV which provide a rationale for further study including a trial of NIV vs HFNO.Are the rationale for, and objectives of, the Systematic Review clearly stated?YesIs the statistical analysis and its interpretation appropriate?YesAre sufficient details of the methods and analysis provided to allow replication by others?YesAre the conclusions drawn adequately supported by the results presented in the review?YesReviewer Expertise:Acute respiratory supportI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. We would like to thank the reviewer for his time in reviewing this manuscript. The comments have been responded to individually as stated below. We feel that the manuscript has improved with his input and hope that he is satisfied by our response and additional changes.C1: This is a thorough systematic review which has appropriate methodology and identifies the paucity of evidence available comparing HFNO to NIV. There are minimal differences detectable and no clinically important differences.R1: Thank you for this comment, as you mentioned the paucity has been identified in HFNO vs NIV. This is an important point to mention as this is currently a signal that HFNO is non-inferior to NIV in managing mild to moderate acute type 2 respiratory failure but the evidence base is poor and important clinical outcomes need to be robustly investigated.C2: The conclusions drawn are appropriate and importantly, mention is made of the benefits of NIV which provide a rationale for further study including a trial of NIV vs HFNO.R2: We agree with the rational comment you had given. Further studies are required to thoroughly evaluate the clinical significance between the treatment groups. We once again thank the reviewer for the time and effort taken in reviewing the manuscript and providing the comments.


Drosophila larvae are able to sense a wide range of chemicals. Although evidence for taste multimodality has been provided in single neurons, an overview of gustatory responses at the periphery is missing and hereby we explore whole-organ calcium imaging of the external taste center. We find that neurons can be activated by different combinations of taste modalities, including opposite hedonic valence and identify distinct temporal dynamics of response. Although sweet sensing has not been fully characterized so far in the external larval gustatory organ, we recorded responses elicited by sugar. Previous findings established that larval sugar sensing relies on the Gr43a pharyngeal receptor, but the question remains if external neurons contribute to this taste. Here, we postulate that external and internal gustation use distinct and complementary mechanisms in sugar sensing and we identify external sucrose sensing neurons.Despite the small number of gustatory sense neurons,  Drosophila larvae are able to sense a wide range of chemicals but the principles of how a small neuronal population encodes a large sensory space entail unresolved questions  family mediate sweet or bitter taste, namely cells expressing Gr5a, Gr61a, or Gr64a-f detect sugars and do not overlap with bitter-taste cells expressing Gr66a or Gr33a (Ionotropic receptors (IRs) or pickpocket receptors (Ppk)  activated by both sugar and bitter tastants  , but maitastants  and therChemosensory organs are located at the tip of larval head, and comprise bipolar sensory neurons that extend axonal projections into defined regions of the brain . The maiGr66a/Gr33a-expressing neurons, mediators of bitter perception in the adult fly, are also present in the larva and have been associated with roles in bitter taste avoidance , denatonium (bitter), NaCl 1 M , valine (amino acid), and citric acid (sour) or with sucrose (sweet), quinine (bitter), NaCl 100 mM , arginine (amino acid), and citric acid (sour) . We recoThe above fractions observed across pooled data recapitulate the responses recorded per each organ, as represented in To test if order of tastant presentation would generally alter neuronal responses within a series of stimulations, we looked into responses of C7 to sucrose 500 mM before or after the denatonium stimulation. C7 is a TOG neuron previously shown to respond to both sugar and bitter substances , but we The observations described above for grouped or individual series of tastant stimulations refer to canonical recorded calcium signals characterized by a rise in GCaMP fluorescence during stimulus application. However, at a closer look, tastants elicit different types of signals within the TOG organ, besides canonical ON response, neurons also showing OFF responses and late activation or even fluorescence intensity drop upon stimulation, as exemplified in In line with unmapped responses summarized in Tastant-evoked deactivation (fluorescence drop) was identified for different test substances  but mappLarval sugar taste has been associated with the pharyngeal and brain expression of Gr43a receptor . Yet, asTNTE    . Moreove(15 min) . Interes(15 min) . These o(15 min) , is moreGr43a brain silenced larvae  expression  see Mat.All in all, our observations subscribe to the hypothesis that C2 and possibly other TOG neurons complement internal taste in sugar detection and that larvae can use distinct sensing paths—pharyngeal neurons being employed for sugar taste during food ingestion, whereas external taste neurons are participating in sugar detection especially when eating is not accessible . Thus faDrosophila larval taste neurons using a whole-organ imaging approach. We designed an experimental framework for calcium imaging in all GRNs comprised by the larval primary taste-sensing organ, the TOG . It is possible that, at least in part, these neurons integrate sucrose response with other modalities for purposes of evaluating mixed dietary substrates for the purpose of avoiding dangerous food mixtures rather than having a direct role in sucrose sensing and output behavior, as previously indicated for C7 neuron .2 sensing  could be due to Gr43a expression in several internal tissues  and to its role as hemolymph fructose sensor. It has been shown that hemolymph carbohydrates influence larval feeding behavior through insulin signaling and SLC5A11  and caffeine in DP1; in the brain, hemolymph sugar is detected by Gr43a receptor neurons. Based on this summary, taste sensing in Drosophila larva would entail cell multimodality for food mixture processing, combinatorial taste through co-activation of neurons of distinct valence, and anatomical or molecular segregation of specific tastant detection. Evidently, despite its numerical simplicity, the study of taste in the fruit fly larva reveals numerous layers of complexity.The observations reported here in relation to taste sensing and cellular segregation of behavior can be paired with previous data for a depiction of sugar/bitter taste detection at the periphery : in the Drosophila larva. Our data illustrates the sensing capacity and complexity of responses at this level as revealed by multimodality of response in a fraction of neurons and by distinct temporal dynamics in activation. We also determine for the first time roles in sugar sensing behavior of larval external taste neurons, by segregating external and pharyngeal taste. We thus identify C2 as contributor to sucrose taste and open directions for further investigations on additional external sugar sense neurons and the query of their redundancy, role in computing substance mixtures, as well as specific molecular mechanisms and receptors involved in sugar detection.The present study offers a sample overview for physiological taste responses in the peripheral taste organ of the nSyb-Gal4 (gift from Shanhaz Lone), UAS-GCaMP6m (Bloomington #42748), UAS-H2B:RFP (gift from Boris Egger), GMR57BO4-Gal4 (Bl. #46355), Gr94a-gal4 (Bl. #57686), Gr21a-Gal4 (Bl. #23890), Gr22e-Gal4 (Bl. #57608), Gr66a-Gal4 (Bl. #57670), Gr33a-Gal4 (Bl. #57623), Gr59d-Gal4 (Bl.57653), Gr32a-Gal4 (Bl. #57622), Gr97a-Gal4 (Bl. #57687), Gr59e-Gal4 (Bl. #57655), UAS-myr:GFP (Bl. #32198), and UAS-TNTE (Bl. #28837). Gr43a lines: Gr43a-KI-Gal4 mutant, Gr43a-KI-GAL4; UAS-Gr43a and Gr43a-KI-GAL4; UAS-Cha7.4kb were kindly offered by Hubert Amrein.We used the following fly strains: Chemicals of highest purity available were chosen for chemicals: L-Alanine , L-Glutamine , L-Aspartic acid , L-Glutamic acid , L-Asparagine , L-Proline , L-Tyrosine , L-Threonine , L-Phenylalanine , L-Lysine , L-Arginine , L-Histidine , L-Serine , L-Valine , L-Cysteine , L-Methionine , L-Leucine , L-Isoleucine , L-Tryptophan , L-Glycine , D-Sucrose , D-Glucose , G-Fructose , D-Trehalose dihydrate , D-Arabinose , D-Maltose monohydrate , D-Mannose , D-Galactose , D-Cellobiose , Lactose monohydrate , Quinine hemisulfate salt monohydrate , Denatonium benzoate , Caffeine anhydrous , Coumarin , Sucrose octaacetate , Lobeline hydrochloride , Theophyline , Strychnine , Potassium chloride , Sodium chloride , Citric acid , Agarose standard , and Brilliant Blue FCF .Third instar larvae (4 days after egg laying) were washed and dissected in ice-cold PBS. Tissue containing chemosensory neurons was fixed in 3.7% formaldehyde for 20 min at room temperature. After fixation, incubation in primary antibodies was performed at 4°C overnight, followed by washing steps and incubation in secondary antibodies at 4°C overnight, and finally, in Vectashield antifade medium (Vector Laboratories) for at least 1 hr before mounting the samples on microscope slides. After each step described above, PBST (PBS 0.3% Triton X-100) was employed for three consecutive plus three 30-min washes at room temperature. Primary antibodies: chicken anti-GFP , rabbit anti-DsRed , and rat anti-ELAV . Secondary antibodies conjugated with Alexa Fluor fluorescent proteins  were used in dilution of 1:200 .Confocal images were acquired on a Leica TCS SPE-5 confocal, using the 40× oil immersion objective, at 0.8–1 µm slice thickness. Images were assembled using Fiji , Adobe IGCaMP6m  diameter was 55 µm. Laser power for both channels was set at 150 mW, and the exposure time for each Z-section was set at 80 ms. Imaging parameters: 25 slices/stack; 1.5 μm each slice for a total depth size of 37.5 μm; time acquisition speed 0.5 time points per second (7.5 fr/s) or 2 seconds per stack.Calcium imaging was performed using a Visitron VisiScope CSU-W1 and Nikon Ti-E inverted spinning disk confocal microscope. The images were captured using a Photometrics Evolve 512 Electron Multiplying Charge-Coupled Device (EM-CCD) of 16×16 µmFor each recording, an RFP stack (one time point) was acquired separately from the GCaMP time series consisting in 60 time points per taste stimulation. Recordings were processed in Fiji/ImageJ  and tranEach tastant was administered using a micropump  controlled in VisiView software through built-in macros. For each taste stimulation, macros were designed for 2-min length recording: 60-s pre-wash followed by 30-s stimulation and a final 30-s wash. Millipore water was used as solvent for taste solutions and as washing control.We compiled two groups of sugars, each containing five sugars at a concentration of 100 mM each: monosaccharides , and disaccharides . Amino acids were prepared as previously described by We used two series of tastants , each coRFP stacks and GCaMP recordings were treated for deconvolution using the third-party program Huygens (Scientific Volume Imaging). A Fiji/ImageJ plugin was written to duplicate the RFP signal onto all time points of the GCaMP recording . For cort−F0)/F0, where Ft is the intensity at time point t and F0 the baseline calculated as average intensity of 10 frames prior to stimulation. Response amplitude was calculated as the difference between the peak intensity after stimulation  and F0. All traces were manually validated to exclude false positive responses. Any of the following criteria was sufficient to exclude traces as possibly false positives: the baseline signal was unstable or the baseline variance was higher than the calculated response amplitude; the fluorescence rise upon stimulation could have been caused by an artifact; a delocalized fluorescence signal could have been captured by the cell spot, compromising the measured response. For further analysis, a response threshold was set at 20% DF/F0. To calculate taste integration percentages  with a brush, washed in tap water, and tested for taste preference in a behavior dark room to exclude additional stimulation of the animal.Petri dishes (94 mm) were prepared on the day of testing as follows: plain agarose at 2.5% concentration was poured in the dish; after hardening, a middle straight line was used to cut the substrate in two halves; one half was discarded and replaced with agarose of the same concentration containing a test substance. Agarose plates were left to reach ambient temperature for at least 1 hr before testing. For comparison with previously reported results for Gr43a sugar sensing role, agarose plates were prepared identically at 1% concentration. For each test 30 larvae: after washing, larvae were placed on a middle line region of the plate and left to wander. After 5 min, animals were counted on each side of the plate and the preference index was calculated as follows:For experiments consisting in several time points, counting was repeated at 2 min, 5 min, and 15 min. At the time of counting, animals that were found on the middle region defined as a 1-cm width-line between the two halves were not considered on either choice side and therefore taken as neutral. For example, if among 30 larvae, 19 were found on the sucrose side, 7 on the empty half, and 4 on the middle stripe region, the index is calculated as PI=(19−7)/30=0.4.For each experiment, tests were repeated for N≥7  show more traces (as in 2D) for individual cells from individual animals across trials with different tastant stimulations.1. We would like to get a sense of the variability in responses to the tastants – both inter- and intra- individual variability. To illustrate this, we are asking the authors to (1) represent the data as 2. To irrefutably assign taste response profiles to each of the neurons, we're also suggesting (1) dose responses to each tastant, and (2) swapping the order of tastant presentation while recording responses.3. Central to better describing inter-individual variability will be the unambiguous identification of individual GRNs across individuals. This needs to be shown more definitively and we recommend a uniformed naming system to avoid confusion. One possible way they could do this is by labelling known GRNs in the whole organ imaging. If only a small number of GRNs can be so identified, perhaps the authors could focus on just these for their analyses.4. The methods and criteria used for image acquisition and processing needs to be described better.– BehaviourThe authors' central thesis – peripheral sweet neurons>hard food; internal sweet neurons>soft food – hinges on the result that on hard food, despite the absence of 43a from the pharyngeal neurons, larvae still show a preference for sugar. Presumably larvae are not eating during the 2-5 minute window (4B) and so the brain 43a receptors cannot driving this preference for sugar. They reason, it must be peripheral neurons that are responsible for this. We have three issues with this central finding and their interpretations:Sugar or Texture? There's a possibility that mechanosensation (response to texture of the food) might be playing a role in this observation. The authors could demonstrate the specificity of this response by performing a dose response curve with sucrose (we recommend sticking to sucrose as it shows more robust PIs). So, we recommend that they perform the experimental conditions in 4B(bottom) with sucrose at different concentrations.1. The 43a null: If the peripheral neurons were driving this behaviour, the 43a null mutants should show at least a residual sugar preference, which they don't on either soft or hard food. It's possible that the authors are missing this in the low PIs of 4B. The above experiment should uncover this.2. Peripheral neurons on soft food only? What keeps the peripheral neurons from driving sugar preference on soft food in the absence of 43a? If the authors are suggesting that the peripheral sugar neurons are gated by both sugar and texture, they would need to demonstrate this through functional imaging. And in that case, activating the peripheral-GRNs in the brain-only 43a animal should induce sugar feeding on soft food.3. In the individual reviews below, the authors will find more specific points that pertain to the broad points mentioned here. Could the authors please pay attention to them as they address these points?Reviewer #1:The authors use a very nice microfluidic set-up to monitor neuronal activity in an ex-vivo larval TOG while simultaneously bathing it in a variety of tastants. They show a surprisingly high level of multimodality in the GRNs – >30% respond to 2-4 tastants. On occasion, these overlaps were of opposing hedonic value and so the authors propose a high level of signal integration in the larval taste organ. They seem to suggest that this could occur via temporally distinct responses in the GRNs.Subsequently, the authors focus on sugar sensing in this peripheral organ. Interestingly in larvae, the 43a receptor that senses sugars is not expressed in the periphery but in the pharynx (where it can detect sugar in the ingested food) and in the brain (where it can detect haemolymph sugars). Here the authors demonstrate that many of the peripheral GRNs can respond to sugars. They silence 3 GRN classes to find that one of them is necessary/involved in sugar sensing.They next turn to addressing the issue of peripheral vs internal sensing of sugar. They perform behavioural experiments to calculate preference for sugars while silencing the peripheral GRN or mutationally inactivating GR43a. They perform these experiments in soft and hard food and suggest that in soft food  the internal sugar sensors are utilised. On hard food however, they suggest that the internal sensors are not as important, but that the peripheral GRNs involved in sugar sensing are necessary. This is an interesting idea.My primary concern with this manuscript is the section dealing with the internal vs peripheral sensing of sugars. It's possible that some of my concerns arise out of the manner in which these results are reported and discussed, nevertheless, I will list them out here:– The preference indices in figure 4B seem very low: 0.2. At such low PIs I wonder if the authors might mistakenly interpret a lower π for a 'no-preference'. In general I did not understand the reasons for using the various sugars at differing concentrations in this section.– The whole animal 43a mutant on both hard and soft food shows a complete inability to detect sugar. If this is true, then it suggests that the pheripheral neurons are NOT involved in sugar sensing. Do the authors agree with this interpretation?– The brain-only 43a mutant data makes the whole animal mutant data more difficult to interpret. If the difference between soft and hard food is a difference in time taken to ingest the food , then there should be no difference in the trends of sugar responsiveness except in the axis of time. I have not understood why the authors think that on soft food the peripheral GRNs don't compensate for the lack of pharynx-43a to drive sugar feeding. Can the authors please comment on this?Reviewer #2:Drosophila larvae using in vivo calcium imaging and behavior. Authors developed a volumetric imaging method to capture taste responses in all of the TOG neurons in larvae. The whole organ imaging method developed here will be a very useful technique for Drosophila larval chemosensory imaging. However, the imaging method and data analysis pipeline to identify cellular identity is not clearly explained in the manuscript. It is important to provide a metric for how effective and reliable cellular identity classifications are and the variations among individual larvae. The volumetric imaging results indicate that TOG neurons can respond to one modality or multiple modalities. The neural response dynamics are also variable. Some neurons show ON responses while some show OFF-responses or inhibition upon tastant stimulation. Furthermore, the authors test the function of TOG neurons in regulating sugar preference and found that the C2- TOG neuron is important for discriminating sugar when food is more viscous and harder to ingest. Overall, I think the volumetric imaging method is promising however, the imaging method and data analysis pipeline is not clearly explained and no metric has been provided to show how effective 3D cellular identity analysis is. The behavioral experiments regarding C2 neurons are not consistent with the previously published functional imaging analysis from the same group. Further functional imaging experiments are necessary to support the claims in the paper.Maier et al., investigates neural basis of sugar perception/coding in Drosophila larval chemosensory imaging. However, the details of image capture and processing is somewhat vaguely explained in the manuscript. Multiple details and metrics are missing about how imaging is done. What is the laser power used in these experiments? What is the frame rate for each volume scanned? What is the size of each volume, field of view etc. The movement and the drift correction for calcium imaging makes sense, however more detailed explanation about how they process imaging data is necessary. For example, in Figure Supplement 1, the authors are correcting GFP channels using the position information from the RFP channel and their method is able to handle the xy-plane. However, it is not clear how they handle z-drift. Did the post-processing also apply for the movement and drift correction in the Z-axis? What is the error rate for x-y or z correction? It would be good to know how accurate their methods are in identifying the same cell identities across animals. The authors need to expand the methods section and explain clearly how volumetric imaging and data processing was done in the paper. This is key for accurate interpretation of cell identification and the calcium imaging results.1. The whole organ imaging method developed here will be a very useful technique for 2. Is figure 2B and 2C showing the percent responsive cells recorded across multiple animals or the percent responsive cells within an animal? I think it is important to clarify this point. What is the variance between individuals? I would like to understand how reliable cell identification and functional imaging analysis is across animals. This can be achieved if authors show data per animal rather than lumping all the data across the animals together. Another reason to visualize individual animal responses is to investigate whether the order of tastant stimulation is a critical factor for larval taste perception: For a neuron that can respond to bitter and sugar, would the sequence of stimulation matter? Will the neuron change activity if it was stimulated first with a bitter compound then a sugar compound? What about the opposite? I strongly encourage authors to show more traces for individual animals across trials with different tastant stimulations as done in Figure 2D. This will help the reader understand what is the variation in taste responses across animals and it will also increase confidence for the consistency of authors' volumetric imaging method.3. In figure 3A, the authors show different calcium dynamics upon tastant stimulation in different TOG neurons. They have observed, off responses and inhibition of neural activity in certain neurons upon stimulation. I would like to see what is the percentage of OFF-responses and inhibition observed in these experiments. Are these events frequent or rare? Are they seen in all animals or some of the animals used in the calcium recordings? It is important to understand whether these are biologically relevant neural responses or artifacts seen in some of the animals used in the experiments.4. In Figure 3, authors replot the results from Figure2 this time by identifying cellular identity using anatomical position. This is something similar to what I was trying to get to in my previous comment (see comment 2). But even in plots shown in Figure 3C, it is hard to understand how successful authors' methods are in classifying each neuron type in each animal. For example, how many larvae in total have been tested in Figure 3C and in what percentage of the animals they were able to identify each neuron type and what is the variation in neural responses among each animal. I think this is a very critical point that needs to be clarified in the paper variation of taste responses among animals and a metric to show how successful cellular identification is across all the animals tested. If my understanding is correct, the total number of tested organs/animals is 15, however the data plotted in Figure 3C does not correspond to this number. Please explain what is the success rate to define a cell type across animals.5. The authors mention that the only known sugar receptor gene Gr43a is not expressed in TOG but TOG neurons can respond to sugars. This is a puzzling finding. Is the expression analysis of Gr43a receptor based on transgenic reporters or single cell sequencing? In other words, is there other evidence that TOG does not express any known sugar receptors in larvae?6. In figure 4, authors performed behavioral experiments to test the function TOG sugar sensing in larvae. Inhibiting one of the TOG neurons, C2 causes defects in sucrose preference when agarose ss 2.5 % but not 1%. They claim the external sugar sensing via TOG sensory neurons is critical when food is not soft enough to ingest. These are interesting hypothesis however, in their previous study, authors have shown C2 neurons respond weakly to 500mM sucrose but not to any other sugar. However, in this manuscript the stronger behavior phenotype is seen in 250mM fructose preference upon silencing of C2 neurons. It is not clearly to me what is the mechanism here. Please explain.7. In general, I think it will help to use one nomenclature for the paper or provide a table matching C-X identity to cellular identities in Figure 3C and list which compound(s) each neuron responds to and indicate the type of response; ON, OFF or inhibition.8. The manipulations for larval GRNs in the manuscript are done chronically by expressing TNT-E. This might impact the development and wiring of the larval chemosensory system and cause problems in sugar detection unrelated to GRN function. I think using a conditional inhibitor of neural activity such as shits or newly developed ontogenetic inhibitors, GtACRs, might be better to use here to show conditional inactivity of the neurons result in similar defects in sugar sensing.Reviewer #3:Drosophila larvae have been put forth as a good model to study taste because of the simplicity of the system (in terms of the number of organs and neurons) and efficient behavior assays. However, an understanding of taste coding in the larval system lags behind that described for the adult stage, in which most taste sensilla/neurons are accessible for recordings or imaging. Maier et al., seek to address this gap by developing a whole organ calcium imaging approach to assess responses of neurons in the larval terminal organ to sweet, bitter, salt, amino acid, and acid tastants. From imaging 15 organs, the authors describe responses to a set of sweet, bitter, salt, amino acid, and acid tastants, collectively represented in neurons that appear to be tuned to one, two or multiple categories of tastants.Although the new approach has promise, the current study makes a limited conceptual advance and the number of positive responses used to ascribe responses to neurons is small. Overlap of tastant sensitivities has now been described, and more rigorously so, in the adult stage and in the authors' own previous work focusing on genetically labeled subsets of taste neurons. The identification of responsiveness to sucrose, which may be somewhat unexpected given the absence of sweet Gustatory receptors (Grs) in larvae, has also been previously described for a few neurons, one of which is further studied in behavior assays.The imaging analysis is followed by investigation of the potential roles of sugar sensing neurons in the periphery. While this analysis provides further support for a previously identified peripheral neuron (C2), it does not convincingly parse contributions of internal and external neurons.Specific weaknesses are elaborated via points below and could be addressed either with additional experiments or textual modifications.Calcium imaging:– Neuron categories were determined by imaging responses to only a single concentration of each tastant; more careful dose response analysis may alter the conclusions. This possibility has not been considered.– There is no effort to reconcile the imaging results with predictions from previous studies. For example, the number of bitter-responsive neurons is 1-2 per larva. However, bitter-GAL4 lines have been mapped to many more neurons in the TO.– The correspondence between neurons named from previous GAL4 mapping experiments to those named by position in this study is not at all clear. A better comparison between the two could be presented.–The criteria that were used to manually exclude false positive responses in calcium imaging experiments (Line 475-476) have not been described.– A response was ascribed to an identified neuron if the position was the same in >3 organs. This threshold seems low, considering that 7-8 organs were tested with each stimulus. In the table for Figure 3, for instance, several tastants have n=3. Is this from a higher number of tastant applications? If so, what was observed in the other cases – that a response could not be ascribed to the identified neuron or that there was no response at all?Behavior:– There are no indications of statistically significant differences between the genotypes in Figure 4B; the legend only mentions that the preference of Gr43a mutants is not significantly different from zero. Finally, data in Figure 4C are compared with Mann-Whitney tests, but ANOVA should be used instead.– Whether sucrose response of internal neurons is affected in Gr43a mutants has not been tested .– In Figure 4B right, assuming that Gr43a mutant controls are not significantly different from zero, whereas Gr43a "brain only" are, I don't know how to interpret a potential role for Gr43a brain neurons in preference for 2.5% agarose food, at 2 minutes when the larvae are not consuming anything.– The calcium imaging analysis is at the cellular level, not looking into contributions of receptors. Manipulating cells rather than receptors for behavior experiments would allow for better comparison between the two aims of the study. If the Gr43a mutant is the only way to go, suggested experiments are:1. Test if the complementary brain ablated genotype  yields the expected phenotypes on 1% and 2.5% agarose.2. Test if peripheral sugar responses are affected in Gr43a mutants. Essential revisions:The methods used in this manuscript are useful and the findings extremely interesting. There were a few points that concerned us, and we list here experiments that will help support the claims made in manuscript better.– Functional Imaging1. We would like to get a sense of the variability in responses to the tastants – both inter- and intra- individual variability. To illustrate this, we are asking the authors to (1) represent the data as per neuron, per animal, and (2) show more traces (as in 2D) for individual cells from individual animals across trials with different tastant stimulations.Thank you for your observation, we indeed agree that showing more traces helps in better grasping the data and the potential entailed by imaging tools described here.We have therefore added more traces in Figure 2D per neuron to different tastants within the same organ in 2 separate representations. Also, in Figure 3 we are including traces per mapped neuron to a given tastant from three different organs. The previous data representation of Figure 3 was moved to Figure 3-figure supplement 1 with a more stringent threshold for plotted tastant responses per cell. All responses recorded as above 20% in amplitude included in the paper are available in the supporting information (source data excel tables).2. To irrefutably assign taste response profiles to each of the neurons, we're also suggesting (1) dose responses to each tastant, and (2) swapping the order of tastant presentation while recording responses.Drosophila of low vs. high salt concentrations that activate distinct taste cell populations for divergent output behaviors. Nevertheless, manually mapped neurons in response to a given tastant across separate organs are displayed in Figure 3 and Figure 3-Supplement1 as proof of concept and we believe that more ambitious attempts for neuronal mapping and to a range of tastant concentrations should be assisted by tissue registration techniques. We propose that irrefutably assigning response profiles to each neuron does not make the main object of this study, but we present a valuable whole organ analysis tool in assisting future efforts using complementary necessary techniques for neuron labeling towards this purpose. Instead, our data aims at providing a grasp of the variety of recorded responses to 5 tastants from distinct modalities in average in the larval external taste organ.1. This is an excellent suggestion, as proved by the well documented example in We hope that reviewers accept our reasoning, as a dose response analysis on a manually mapping approach of cell identities would be extremely challenging.2. The tastant order swapping is an important point and this is included in the protocol design of whole organ recordings, as the order of tastant presentation was randomized for each organ. Also, we have now additionally performed single neuron calcium imaging in C7 – neuron known to respond to both sugar and bitter tastants  – to exemplify the influence of order presentation on sucrose-elicited responses. We have found no significant difference between sucrose response before or after stimulation with denatonium in this neuron, as shown in Figure 2-Supplement 2.3. Central to better describing inter-individual variability will be the unambiguous identification of individual GRNs across individuals. This needs to be shown more definitively and we recommend a uniformed naming system to avoid confusion. One possible way they could do this is by labelling known GRNs in the whole organ imaging. If only a small number of GRNs can be so identified, perhaps the authors could focus on just these for their analyses.This valid point constitutes one main challenge for manual cell annotation in whole organ imaging, as touched upon under the previous reviewer comment. The system employed here for neuronal annotation as proof of concept relies on an estimated neuron location relative to the sagittal plane, anterior-posterior axis and ventral-dorsal axis . In Figure 3-Figure Suppl.1 we have plotted mapped neurons using more narrow constraints than previously in Figure 3, in line with the reviewer’s comment for more focused analyses. However, in order to retackle these identities for further characterization and for building a reference retraceable naming system, our tools constitute only one building block and should be complemented by:– A working model for cell stamping of known GRNs within a whole organ labeling in physiological recordings,– A tissue registration approach of whole organ imaging for automatic cell mapping circumventing manual cell annotation and– A higher number of Gal4 transgenic lines mapped alongside the 7 known identities of GR-expressing neurons, such as co-mapping GR lines with IR and Ppk Gal4 lines.Also, considering that individual physiological characterizations for the few known GRNs of the larval TOG organ have been tackled previously for 5 out of 7 mapped neurons, the main aim of our study was rather towards an overview of the range of response types per organ, attempted for the first time in larval peripheral taste sensing, to our knowledge. We thus could identify an average cell response multimodality ratio per organ, cell-specific deactivation events and sucrose-evoked responses in the larval TOG. This informed the second part of this manuscript for sugar sensing in the larva and could similarly direct further dissections of larval taste coding.4. The methods and criteria used for image acquisition and processing needs to be described better.We appreciate this feedback and have clarified the methods description, all textual changes marked with track change.– BehaviourThe authors' central thesis – peripheral sweet neurons>hard food; internal sweet neurons>soft food – hinges on the result that on hard food, despite the absence of 43a from the pharyngeal neurons, larvae still show a preference for sugar. Presumably larvae are not eating during the 2-5 minute window (4B) and so the brain 43a receptors cannot driving this preference for sugar. They reason, it must be peripheral neurons that are responsible for this. We have three issues with this central finding and their interpretations:1. Sugar or Texture? There's a possibility that mechanosensation (response to texture of the food) might be playing a role in this observation. The authors could demonstrate the specificity of this response by performing a dose response curve with sucrose (we recommend sticking to sucrose as it shows more robust PIs). So, we recommend that they perform the experimental conditions in 4B(bottom) with sucrose at different concentrations.Gr43a mutant/rescue genotypes.Thank you for your suggestion. We have modified the figure 4 to include 3 different sucrose concentrations for C2 on 1% versus 2.5% agarose, and have included in Figure 4-Supplemental1 two sucrose and fructose concentrations for Overall, these results indicate that sugar sensing in TOG neurons, specifically in C2, can detect sucrose on a hard substrate when food ingestion is impaired or slowed down. We propose that pharyngeal Gr43a neurons come into play mainly on ingestible substrate, at lower concentration, as also schematized in Figure 5. This hypothesis is supported by the expression of Gr43a receptors, which are present at the level of pharyngeal neurons, brain and other peripheral tissues such as in the foregut, but absent from the TOG external neurons .2. The 43a null: If the peripheral neurons were driving this behaviour, the 43a null mutants should show at least a residual sugar preference, which they don't on either soft or hard food. It's possible that the authors are missing this in the low PIs of 4B. The above experiment should uncover this.Indeed, as the reviewer correctly suggested, the above experiment shows residual preference of the Gr43a mutant to sucrose/fructose at higher concentrations.3. Peripheral neurons on soft food only? What keeps the peripheral neurons from driving sugar preference on soft food in the absence of 43a? If the authors are suggesting that the peripheral sugar neurons are gated by both sugar and texture, they would need to demonstrate this through functional imaging. And in that case, activating the peripheral-GRNs in the brain-only 43a animal should induce sugar feeding on soft food.Gr43a mutant larvae do not show a complete defect on higher concentrations, including on 1% agarose. This indicates that sugar sensing via C2/TOG neurons contribute to output preference including during food ingestion but that it is overran by Gr43a-dependent pathways.We believe the above experiment equally addresses this point, as All in all, we do not believe C2/TOG neurons would be inactivated on soft food, but rather they play a role in a multicomponent system with different leverages for sugar sensing in the larva, as we are illustrating in Figure 5. Also worth noting is that the natural environment the fruit fly larva feeds on often implies the animal is immersed in a soft substrate, with a lesser need for clear taste dissociation between external and internal taste sensing.In the individual reviews below, the authors will find more specific points that pertain to the broad points mentioned here. Could the authors please pay attention to them as they address these points?Reviewer #1:[…]My primary concern with this manuscript is the section dealing with the internal vs peripheral sensing of sugars. It's possible that some of my concerns arise out of the manner in which these results are reported and discussed, nevertheless, I will list them out here:– The preference indices in figure 4B seem very low: 0.2. At such low PIs I wonder if the authors might mistakenly interpret a lower π for a 'no-preference'.Indeed, the 100mM Fructose concentration tests yield low preferences in the Gr43a mutant. We have therefore, at the reviewer’s suggestion, included higher tastant concentration in what is now Figure 4 – Figure Suppl.1.In general I did not understand the reasons for using the various sugars at differing concentrations in this section.To briefly describe our reasoning:1. We have initially focused on sucrose 500mM because this was previously employed in other larval taste studies and in the whole organ imaging in the first part of the manuscript, which informed the behavioral testing in the second part.2. Then, having formulated our hypothesis that larvae can sense sucrose despite not being able to readily ingest the food, we wanted to connect our observations with the previous reports for Gr43a sensing. As Mishra et al., 2013, have mainly described Gr43a sensing on fructose 100mM at 1% agarose concentration, we have added this sugar to our analysis to reproduce observations from this study and to compare between agarose concentrations.3. Other sucrose and fructose concentrations have been added at the reviewer’s suggestion showing a lesser role of Gr43a at higher concentrations and also, as expected from literature reports, lesser to sucrose and more specific to fructose.– The whole animal 43a mutant on both hard and soft food shows a complete inability to detect sugar. If this is true, then it suggests that the pheripheral neurons are NOT involved in sugar sensing. Do the authors agree with this interpretation?Gr43a mutant does not yield a complete defect for fructose 500mM preference, and neither is this the case for sucrose 100mM or 500mM.At the reviewer’s suggestion we added behavior data in Figure 4-Figure Suppl.1 which show Gr43a mutant in sugar sensing, be it on soft or hard substrate. Since Gr43a is the main internal fructose sensor in Drosophila, expressed not only in the DPS, but also in the brain and in the proventriculus of the gastrointestinal system, we propose that a null mutant presents with a more systemic defect, which may be due to metabolic signaling, as shown for hemolymph carbohydrates influencing larval feeding behavior though insulin signaling and SLC5A11  or through the inability to gauge the state of satiety. Sugar sensing in the adult also consists of a multicomponent sensing system, with brad sugar sensing receptors on the proboscis distinct from Gr43a expressed internally that acts both as internal nutrient sensor and as feeding suppresor in satiated animals, while other receptors also carry roles in limiting sugar intake .However, we were generally not taken aback by an inability of the Altogether, we propose a model whereby C2 / TOG neurons act to signal the presence of sugar, but Gr43a expressed elsewhere can outweigh the behavioral output, either directly triggered by food ingestion, but also potentially indirectly through internal signaling . This model for larval sugar-sensing mechanism aligns with, and complements, reports in the adult.– The brain-only 43a mutant data makes the whole animal mutant data more difficult to interpret. If the difference between soft and hard food is a difference in time taken to ingest the food , then there should be no difference in the trends of sugar responsiveness except in the axis of time. I have not understood why the authors think that on soft food the peripheral GRNs don't compensate for the lack of pharynx-43a to drive sugar feeding. Can the authors please comment on this?Thank you. These are very interesting points.Gr43a internally guided preference would overrun, as proposed and schematized in Figure 5 according to our current understanding.Firstly, does the difference between soft and hard food preference for sugar resume to only time axis? We believe this is not the case because behaviors driven by C2 or Gr43a-neurons seem to not be merely additive in output but rather Gr43a mutant is less impacting on the preference to sucrose (disaccharide of fructose and glucose), which is likely better compensated for in lack of Gr43a by external neurons . It’s worth mentioning that in the adult fly Gr43a receptor in the pharynx is also more specifically tuned for fructose sensing while external GR-expressing sweet sensing neurons are more broadly detecting sugars.If Gr43a-neuron sensing outweighs sugar detection via C2 as we believe to be the case, this could also explain why peripheral GRNs don’t compensate for the lack of Gr43a. Gr43a is also expressed in other peripheral tissues such as the gut and the brain, acting as an internal nutrient sensor in both the larva and the adult . Additionally, Gr43a seems to be more specifically tuned to fructose sensing, as Reviewer #2:Drosophila larvae using in vivo calcium imaging and behavior. Authors developed a volumetric imaging method to capture taste responses in all of the TOG neurons in larvae. The whole organ imaging method developed here will be a very useful technique for Drosophila larval chemosensory imaging. However, the imaging method and data analysis pipeline to identify cellular identity is not clearly explained in the manuscript. It is important to provide a metric for how effective and reliable cellular identity classifications are and the variations among individual larvae. The volumetric imaging results indicate that TOG neurons can respond to one modality or multiple modalities. The neural response dynamics are also variable. Some neurons show ON responses while some show OFF-responses or inhibition upon tastant stimulation. Furthermore, the authors test the function of TOG neurons in regulating sugar preference and found that the C2- TOG neuron is important for discriminating sugar when food is more viscous and harder to ingest. Overall, I think the volumetric imaging method is promising however, the imaging method and data analysis pipeline is not clearly explained and no metric has been provided to show how effective 3D cellular identity analysis is. The behavioral experiments regarding C2 neurons are not consistent with the previously published functional imaging analysis from the same group. Further functional imaging experiments are necessary to support the claims in the paper.Maier et al., investigates neural basis of sugar perception/coding in Drosophila larval chemosensory imaging. However, the details of image capture and processing is somewhat vaguely explained in the manuscript. Multiple details and metrics are missing about how imaging is done. What is the laser power used in these experiments? What is the frame rate for each volume scanned? What is the size of each volume, field of view etc. The movement and the drift correction for calcium imaging makes sense, however more detailed explanation about how they process imaging data is necessary. For example, in Figure Supplement 1, the authors are correcting GFP channels using the position information from the RFP channel and their method is able to handle the xy-plane. However, it is not clear how they handle z-drift. Did the post-processing also apply for the movement and drift correction in the Z-axis? What is the error rate for x-y or z correction? It would be good to know how accurate their methods are in identifying the same cell identities across animals. The authors need to expand the methods section and explain clearly how volumetric imaging and data processing was done in the paper. This is key for accurate interpretation of cell identification and the calcium imaging results.1. The whole organ imaging method developed here will be a very useful technique for Microscopy settings, lines 619-698.Thank you for your observation, we now added to the description in the Methods section .3D drift correction explanations and corresponding reference for the drift correction plugin employed are included in Methods and we added additional specifications under Data processing and analysis of whole-organ physiological recordings (from line 744)2. Is figure 2B and 2C showing the percent responsive cells recorded across multiple animals or the percent responsive cells within an animal? I think it is important to clarify this point.Thank you for pointing this out, we recognize the importance for this elucidation and have attempted to describe the data more clearly. Figure 2 shows data pooled across multiple recordings, responding neurons were pooled together and the multimodality percentage was thus calculated over the entire dataset. Figure 2 B and C represent all responsive cells from organs stimulated with mixtures of tastants, while Figure 2 D and D’ represent all responsive cells from organs stimulated with single tastants.In Figure 2-figure supplement 1 we have now also illustrated data per animal, as alternative representation of Figure 2 D’. This newly added figure shows the unimodality vs multimodality percentage calculated within each of the 15-recorded organs, giving a total average of 67% unimodality vs 33% multimodality. These numbers are very closely mirroring the 68%/32% fraction from Fig2D’ for all pooled responses.What is the variance between individuals? I would like to understand how reliable cell identification and functional imaging analysis is across animals. This can be achieved if authors show data per animal rather than lumping all the data across the animals together. Another reason to visualize individual animal responses is to investigate whether the order of tastant stimulation is a critical factor for larval taste perception: For a neuron that can respond to bitter and sugar, would the sequence of stimulation matter? Will the neuron change activity if it was stimulated first with a bitter compound then a sugar compound? What about the opposite? I strongly encourage authors to show more traces for individual animals across trials with different tastant stimulations as done in Figure 2D. This will help the reader understand what is the variation in taste responses across animals and it will also increase confidence for the consistency of authors' volumetric imaging method.Thank you, we agree with your observations and with the validity of the raised points.We have included more traces in Figure 2 D for 2 separate representations of traces per organ. We have also added in Figure 2-Figure Suppl1 an alternative representation per each recorded organ of the multimodality computations done on pooled data in Figure 2D’ as mentioned under the previous reviewer comment. This will give a feel of how variable the captured neuronal responses were across organs, without taking into account cell identity mapping.Regarding order of tastants: this aspect was incorporated in the experimental design through a randomized order of stimulation. We considered through this approach an eventual effect caused by the order of application would be averaged out as noise. To exemplify this, we have now added as Figure 2-Supplement 2 single neuron calcium imaging recordings in C7 . The order of presentation of two substances, sucrose and denatonium, yields no significant difference in sucrose responses recorded in this neuron.We also replaced Figure 3 to add illustrative traces for some of the mapped neurons. To your comment we would like to add that ease in identifying cell identities equally depends on the nature of recorded tissue. For TOG cells we have observed relatively high variability of cell localization in live imaging as well as in immunostainings due to a supple architecture of the organ. On the other hand, we have found the DOG organ, which does not make the subject of this work, to be more compact with fewer variables in cell position. Similarly, the adult proboscis has a fairly rigid conformation and hair bristles that aid for referencing in cell identification. All in all, consistency of our imaging tools in the TOG can be improved through future efforts by using complementary methods for cell labeling and/or tissue registration.3. In figure 3A, the authors show different calcium dynamics upon tastant stimulation in different TOG neurons. They have observed, off responses and inhibition of neural activity in certain neurons upon stimulation. I would like to see what is the percentage of OFF-responses and inhibition observed in these experiments. Are these events frequent or rare? Are they seen in all animals or some of the animals used in the calcium recordings? It is important to understand whether these are biologically relevant neural responses or artifacts seen in some of the animals used in the experiments.OFF-responses have been assigned when a neuron showed activation after the stimulation, and a deactivation response was considered when a neuron responded with a decrease in florescence during stimulation. It remains to be determined if the 2 types of events are related to distinct cellular mechanisms or if they are coupled, but it is often the case that a neuron responds with both deactivation and a subsequent OFF-response to a tastant.In particular, such events have been systematically assigned to the CDL2 neuron as presented in Figure 3-figure supplemental 1 B. Citric acid, employed for stimulation in all 15 organs subjected to individual tastants, yielded a deactivation in an approximated cell location named by us CDL2 in 9 out of 15 recorded organs . An OFF-response to citric acid in the same neuron was actually captured in 11 out of 15 organs. The two numbers don’t coincide because the amplitude for deactivation or OFF-response didn’t always simultaneously meet the criteria of 20% threshold, but all in all we consider a deactivation/OFF-response to citric acid in CDL2 neuron to be more than merely an artifact.Other tastants than citric acid have elicited OFF-responses but less robustly, especially in CDL2 but also in other neuronal locations. Generally, we believe such events are neuron and tastant/concentration specific and that a larger range of tested tastants and concentrations would reveal further such neuron-deactivation pairings. However, CDL2 associated most deactivation signals in our dataset (included in source data supplementary files), indicating this neuron could comport distinctive tuning properties.4. In Figure 3, authors replot the results from Figure2 this time by identifying cellular identity using anatomical position. This is something similar to what I was trying to get to in my previous comment (see comment 2). But even in plots shown in Figure 3C, it is hard to understand how successful authors' methods are in classifying each neuron type in each animal. For example, how many larvae in total have been tested in Figure 3C and in what percentage of the animals they were able to identify each neuron type and what is the variation in neural responses among each animal. I think this is a very critical point that needs to be clarified in the paper variation of taste responses among animals and a metric to show how successful cellular identification is across all the animals tested. If my understanding is correct, the total number of tested organs/animals is 15, however the data plotted in Figure 3C does not correspond to this number. Please explain what is the success rate to define a cell type across animals.We believe the whole-organ approach described here can serve for future characterizations with more precise neuronal annotation results by using complementary tissue registration or double neuronal labeling techniques. Within the purpose for this study we have manually mapped cells for which a tastant was found to yield a response in at least 3 different instances. Therefore, for some data plotted in the Figure 3-Figure Suppl1  the N was 3, while a given tastant was presented in at least 7 recordings, except for sucrose and citric acid that were used for stimulation in all 15 organs. We have now restricted the threshold to N>=4, being at least half of stimulated organs with a given tastant, except for bitter stimulations for which N>=3 as these responses were more fade and more difficult to map. This threshold does not necessarily mean a given neuron only responded in 3-4 organs out of 7-8 stimulated with a given tastant but it can also mean one of the following:1. The response was not picked up above 20% DF/F0 amplitude or2. the fluorescent trace was filtered out due to unstable baseline or high variance of signal before stimulation or3. The response could not be unambiguously assigned to the given cell location or4. the experimenter was not confident about the cell identity even if a response to the given tastant was picked up.Therefore we would like to propose Figure 3-Figure Suppl.1 should be interpreted as illustration of cell tuning types that can be picked up in the TOG, rather than a complete cell mapping of the organ.Nevertheless, the reproducible mono/multimodality percentages  and the captured deactivation/OFF-responses add to our understanding of the taste sensing range in the larval external organ.5. The authors mention that the only known sugar receptor gene Gr43a is not expressed in TOG but TOG neurons can respond to sugars. This is a puzzling finding. Is the expression analysis of Gr43a receptor based on transgenic reporters or single cell sequencing? In other words, is there other evidence that TOG does not express any known sugar receptors in larvae?Yes, indeed, thus far a sugar receptor expression in the larval TOG has not been reported in previous efforts for transgenic lines mapping. Except for a newly identified appetitive taste sensing to ribonucleosides via Gr28a  and to certain amino acids via IRs , not much has been described for attractive behaviors attributed to neurons/receptors in the larval TOG.Known sugar receptors  rely on description in the adult fly, but these have not been detected in the larval TOG. Conversely, certain Gr receptors are larval specific and incompletely characterized and therefore might cover sugar sensing at this developmental stage.According to studies using transgenic reporters, larval Gr43a – similar to adult Gr43a – is expressed in internal tissues  but not in external sensing neurons.We are presenting an enticing opportunity to investigate novel sugar receptors based on these results.6. In figure 4, authors performed behavioral experiments to test the function TOG sugar sensing in larvae. Inhibiting one of the TOG neurons, C2 causes defects in sucrose preference when agarose ss 2.5 % but not 1%. They claim the external sugar sensing via TOG sensory neurons is critical when food is not soft enough to ingest. These are interesting hypothesis however, in their previous study, authors have shown C2 neurons respond weakly to 500mM sucrose but not to any other sugar. However, in this manuscript the stronger behavior phenotype is seen in 250mM fructose preference upon silencing of C2 neurons. It is not clearly to me what is the mechanism here. Please explain.Indeed, we found C2 to respond physiologically to fructose 100mM with higher amplitudes than to fructose 500mM (data not shown), therefore this neuron’s role in fructose detection might depend to a large extent on concentration. Given that sucrose is a disaccharide constituted by fructose and glucose, we would expect C2 to also respond at least partially to fructose. On the other hand, it seems the Gr43a role in sugar detection is more specific for fructose than for sucrose, as also reported by others  and as we show in Figure 4-Figure Suppl.1 B.Since the phenotype yielded by C2 in sucrose preference is more pronounced, we chose to focus on this sugar and to not include fructose preference indexes or physiological activation responses in C2, for simplicity and overall readability of the data.7. In general, I think it will help to use one nomenclature for the paper or provide a table matching C-X identity to cellular identities in Figure 3C and list which compound(s) each neuron responds to and indicate the type of response; ON, OFF or inhibition.Thank you for your suggestion, a table is included in the supplementary information (corresponding source data excel table).8. The manipulations for larval GRNs in the manuscript are done chronically by expressing TNT-E. This might impact the development and wiring of the larval chemosensory system and cause problems in sugar detection unrelated to GRN function. I think using a conditional inhibitor of neural activity such as shits or newly developed ontogenetic inhibitors, GtACRs, might be better to use here to show conditional inactivity of the neurons result in similar defects in sugar sensing.This is an excellent suggestion and we now incorporated in Figure 4 D new data for C2 neuron behavior for temporally controlled silencing with GtACR1. This experiment reproduces a defect to sucrose 500mM on 2.5% substrate, as observed with TNTE silencing.Reviewer #3:Drosophila larvae have been put forth as a good model to study taste because of the simplicity of the system (in terms of the number of organs and neurons) and efficient behavior assays. However, an understanding of taste coding in the larval system lags behind that described for the adult stage, in which most taste sensilla/neurons are accessible for recordings or imaging. Maier et al., seek to address this gap by developing a whole organ calcium imaging approach to assess responses of neurons in the larval terminal organ to sweet, bitter, salt, amino acid, and acid tastants. From imaging 15 organs, the authors describe responses to a set of sweet, bitter, salt, amino acid, and acid tastants, collectively represented in neurons that appear to be tuned to one, two or multiple categories of tastants.Although the new approach has promise, the current study makes a limited conceptual advance and the number of positive responses used to ascribe responses to neurons is small. Overlap of tastant sensitivities has now been described, and more rigorously so, in the adult stage and in the authors' own previous work focusing on genetically labeled subsets of taste neurons. The identification of responsiveness to sucrose, which may be somewhat unexpected given the absence of sweet Gustatory receptors (Grs) in larvae, has also been previously described for a few neurons, one of which is further studied in behavior assays.The imaging analysis is followed by investigation of the potential roles of sugar sensing neurons in the periphery. While this analysis provides further support for a previously identified peripheral neuron (C2), it does not convincingly parse contributions of internal and external neurons.Specific weaknesses are elaborated via points below and could be addressed either with additional experiments or textual modifications.Calcium imaging:– Neuron categories were determined by imaging responses to only a single concentration of each tastant; more careful dose response analysis may alter the conclusions. This possibility has not been considered.Discussion section.Indeed, this is a valid observation, and we have in fact noticed an important difference in responses to different concentrations of the same tastants, in fact as also exemplified by distinct responding cells and divergent behaviors elicited by low and high salt concentrations. This possibility is also discussed in the Given the high level of complexity we faced in our analysis, particularly as the cell identity mapping was approached manually in this study, dose response would exponentially add to the complexity rendering manual response annotations impossible, and therefore we have focused on unique concentrations. We judge our tools valuable for future efforts in elucidating this complexity in tuning using various tastant concentrations with complementary methodology for tissue registration or double labeling of neurons that would make room for computerized cell response annotations.– There is no effort to reconcile the imaging results with predictions from previous studies. For example, the number of bitter-responsive neurons is 1-2 per larva. However, bitter-GAL4 lines have been mapped to many more neurons in the TO.In effect, our approach comports limitations in cell identification, as mentioned above. However, physiological characterization of bitter neurons in the TOG is relatively limited. Namely, neurons described to comport aversive behavior in the TOG are more or less 3: C1 to a diversity of bitter tastants , C3 to quinine  and C7 to denatonium and quinine (van Giesen 2016a) and these are also summarized in Figure 5A. Any other contributions to bitter taste in the larva is inferred based on transgenic expression of GRs characterized as bitter sensing in the adult but not physiologically associated to bitter sensing in the corresponding larval neurons. All considering, bitter responding neurons per organ identified in our study is conform in number to previous physiological characterizations.– The correspondence between neurons named from previous GAL4 mapping experiments to those named by position in this study is not at all clear. A better comparison between the two could be presented.As restated in several other places above, the manual cell identification based on position did not allow for a high confidence association with previously Gal4-mapped neurons. We believe the tools presented by our manuscript can serve future studies in bridging the gap by using double labeling of neurons.–The criteria that were used to manually exclude false positive responses in calcium imaging experiments (Line 475-476) have not been described.Data processing and analysis of whole-organ physiological recordings, from line 744.Thank you for raising this valid point, a description is added under the corresponding Methods section – A response was ascribed to an identified neuron if the position was the same in >3 organs. This threshold seems low, considering that 7-8 organs were tested with each stimulus. In the table for Figure 3, for instance, several tastants have n=3. Is this from a higher number of tastant applications? If so, what was observed in the other cases – that a response could not be ascribed to the identified neuron or that there was no response at all?In other cases (instances where a response could not be ascribed to a given cell) the response was either not picked up / not above the set threshold of 20% amplitude, or the responding cell could not be confidently associated to a given identity.Therefore we would like to propose Figure 3-Suppl.1 should be interpreted as illustration of cell tuning types that can be picked up in the TOG, rather than a complete cell map of the organ. The TOG tissue is flexible and shows a fluid structure, much more than its DOG counterpart that is more compactly organized. Therefore we anticipate the above question will be addressed by other studies by using a computerized tissue registration.Nevertheless, the reproducible mono/multimodality percentages , the sucrose responses in the TOG, as well as the captured deactivation/OFF-responses, all add to our understanding of the taste sensing range in the larval external organ.Behavior:– There are no indications of statistically significant differences between the genotypes in Figure 4B; the legend only mentions that the preference of Gr43a mutants is not significantly different from zero. Finally, data in Figure 4C are compared with Mann-Whitney tests, but ANOVA should be used instead.Thank you for your very useful comment; we have now replaced Mann-Whitney by ANOVA tests, except for specific comparisons where 2 genotypes instead of 3 were measured in Figure 4-Supplement1. All statistical comparisons are indicated in the Supporting information (source data excel files).– Whether sucrose response of internal neurons is affected in Gr43a mutants has not been tested .Gr43a mutant shows a mild defect in sucrose preference , which is confirmed from observations in the adult system where Gr43a has been described as an internal nutrient sensor  more specifically tuned to fructose than to other sugars, including sucrose (disaccharide of fructose and glucose).Thank you for raising this point – we have tested and now included this experiment in Figure 4-Figure Suppl.1 B. In our hands the – In Figure 4B right, assuming that Gr43a mutant controls are not significantly different from zero, whereas Gr43a "brain only" are, I don't know how to interpret a potential role for Gr43a brain neurons in preference for 2.5% agarose food, at 2 minutes when the larvae are not consuming anything.We have now included more data points and proposed a supporting model in Figure 5. Indeed, we suggest that even for immediate preference at 2 min on 2.5% in absence of food ingestion a Gr43a mutant can still weigh on the output behavior. This could be due to Gr43a expression in several internal tissues  and to its role as hemolymph fructose sensor. It has been shown that hemolymph carbohydrates influence larval feeding behavior though insulin signaling and SLC5A11 , but it remains to be determined if and how does a Gr43a mutant correlate with metabolic pathways and possibly interfere with sugar preference output behavior.Furthermore, the data added for Gr43a mutant behavior in Figure 4-Figure Suppl.1 shows a lesser defect at higher concentrations and lesser in sucrose preference than fructose, implying a possible systemic effect of Gr43a more specifically to fructose, as mentioned under reviewer’s previous comment.– The calcium imaging analysis is at the cellular level, not looking into contributions of receptors. Manipulating cells rather than receptors for behavior experiments would allow for better comparison between the two aims of the study. If the Gr43a mutant is the only way to go, suggested experiments are:1. Test if the complementary brain ablated genotype  yields the expected phenotypes on 1% and 2.5% agarose.UAS-TNT (Gr43a brain receptor silenced) on 1% and 2.5% . In the reference study for this test, the brain ablated Gr43a larvae don’t show a defect compared to control but on our hands a deficient preference was calculated for immediate preference on 1% and no defect on 2.5% percent. This difference with the reference paper might be due to laboratory dissimilarities in experimental manipulation, in fly food differences linked to subliminal metabolic state variations between animals or perhaps due to distinct potency of the UAS-TNTE line employed. A lack of defect of brain Gr43a silencing suggests that internal expression from pharynx and gut tissues is sufficient for internal sugar sensing.Thank you very much for your suggestions. We have now added the complementary brain silencing genotype by reproducing the corresponding crosses described in the original paper by Mishra et al., 2013. We tested therefore Gr43a-Gal4-ki; Cha-Gal80 x 2. Test if peripheral sugar responses are affected in Gr43a mutants.Gr43a mutants associate some defect in sucrose preference but lesser than to fructose on both 1% and 2.5% . This confirms adult fly reports describing Gr43a to have the highest specificity for fructose among sugars , including compared to sucrose (disaccharide of fructose and glucose).Thank you, we followed your suggestion and found that