The aim of this phase 2 trial was to ascertain the feasibility and effect of community-based aerobic exercise training for people with 2 of the more common neuromuscular diseases: Charcot-Marie-Tooth disease type 1A (CMT) and inclusion body myositis (IBM).2 peak) during a maximal exercise test, with secondary measures of muscle strength, function, and patient-reported measures.A randomized single-blinded crossover trial design was used to compare a 12-week aerobic training program using recombinant exercise bicycles compared to a control period. The training occurred 3 times per week in community gyms local to the participants. Support was available from trained gym staff and a research physiotherapist. The 2 disease groups were analyzed separately. The primary outcome measure was peak oxygen uptake  and a strong effect size in the IBM group (Cohen d = 1.72). No major changes were observed in the secondary outcome measures. Qualitative interviews revealed that participants valued the support of gym instructors and the research physiotherapists in overcoming challenges to participation.Data from 23 people with CMT and 17 people with IBM were included in the analysis. Both disease groups had high levels of participation and demonstrated improvements in VOTwelve weeks of aerobic training in community gyms was feasible, safe, and improved aerobic capacity in people with CMT and IBM.This study provides Class II evidence that for patients with CMT type 1A and IBM, an aerobic training program increases aerobic capacity. This phase 2 feasibility trial aims to establish whether community-based aerobic training is safe, feasible, and can improve cardiopulmonary fitness in 2 more common neuromuscular diseases (NMDs): Charcot-Marie-Tooth disease type 1A (CMT) and sporadic inclusion body myositis (IBM).1–3 and are deconditioned, as measured by oxygen uptake during exercise.4 Similar reductions in aerobic capacity have been reported in people with idiopathic inflammatory myopathy.5Aerobic deconditioning and secondary disuse muscle atrophy are common in people with NMD and are a likely consequence of reduced general activity levels. Investigations of people with CMT found they are less active than the general population6–8 These preliminary studies indicate positive benefits of aerobic training for people with CMT and IBM but there is a need for larger trials with comparison to a control. These early studies were undertaken in hospital environments with supervision by health professionals. For lifelong behavior change, taking exercise away from the hospital and into community gym facilities may be a step to encouraging self-management, supporting the transition from exercise as a medical intervention to a leisure activity.9 The feasibility of moving exercise interventions into leisure facilities is untested in these conditions.Only 3 underpowered, uncontrolled studies have been conducted in these 2 conditions.There were 3 stated aims of this study: (1) calculate the effect of training on aerobic capacity; (2) ascertain whether supported aerobic training in community leisure facilities is feasible, safe, and acceptable; (3) explore secondary physical and nonphysical effects of exercise.10 These rare diseases are challenging to recruit, so strategies to retain participants, and reduce the effect of intersubject variation in smaller samples, were key factors in this choice of design. We have prior experience of conducting crossover exercise trials.11We used a single-blinded crossover design to explore the effect and feasibility of aerobic exercise training in the community. This design was used in response to observations in the field that participants are more likely to stay in a study if they receive treatment.12 older men detrain to baseline in 4 weeks,13 and people with mitochondrial disease return to baseline aerobic fitness after 8 weeks.14 From this evidence, 8 weeks was deemed sufficient for the detraining period.Participants were randomized to 2 groups. Group A underwent a 12-week training period (T1), an 8-week reversal period to detrain, then a 12-week control period (C2). Group B went through the control period first (C1) and the training period second (T2). We decided to also include an 8-week period between C1 and T2 for group B to maintain blinding of assessors. People with metabolic syndrome detrain within in 1–2 months,Four assessments were undertaken by blinded assessors to measure outcomes before and after the treatment and control periods. It was not possible to blind participants in an exercise trial where aerobic capacity testing was performed using a bicycle ergometer, similar to the training intervention.Participants from both disease groups were recruited and partook in the trial concurrently, but data were analyzed separately.8 and our own experience. We estimated an intrasubject coefficient of variation of 0.2, which translates into VO2 measurements outside the range 17–40 mL/min/kg being extremely unlikely. Using the approach based on 2 one-sided hypothesis tests for a 2 × 2 crossover trial, a total sample size of 20 participants gave at least 80% power to detect an effect of up to 1.06 in the ratio of mean VO2 under the treatment and the control regimes. To account for dropouts, the target sample size was set at 30 (15 in each treatment sequence) in 2 parallel studies (one for each condition) with 30 participants each.The primary purpose of this study was to explore effect on aerobic capacity and feasibility of training, but an estimated sample size was calculated combining evidence from similar studiesThis study achieved NHS NRES ethical approval (ref: 11/LO/0760) and consent for participation was obtained, recorded, and filed. This study was registered on the ISRCTN clinical trials registry (ID: 99826269)Potential participants were recruited from clinics and research databases of the National Hospital for Neurology and Neurosurgery, plus national clinics of colleagues from the British Myology Society for people with IBM.Participants were included in the study if they met the following criteria: clinical and genetic diagnosis of CMT type 1A, or a clinical diagnosis of IBM, supported by histologic confirmation as per the established Griggs criteria (only Griggs definite IBM cases were included); aged 18–80; able to walk for 30 meters with or without a walking aid or orthotic devices; ability to safely mount/dismount an exercise bike unaided or with minimal assistance; and signed informed participant consent.Exclusion criteria were presence of other significant neurologic disorders or major comorbidities; limb surgery during the 6 months prior to screening ; failure to pass the screening assessment for exercise testing; aged over 80 or under 18 years; concurrent involvement in another intervention trial; people already participating in moderate to vigorous aerobic exercise more than 3 times per week; and women of childbearing age if they were pregnant or planning to become pregnant during the study.16A block randomization method was used to allocate participants to groups. Block sizes of 4 were used based on random numbers generated in MATLAB . The random block sequences were stored on a password-protected spreadsheet. Allocation to the groups was input and spreadsheets held by an unblinded member of staff who were not involved in screening, recruitment, assessment, or training of the participants. This ensured allocation was concealed after enrollment and consent. Allocation was stratified according to baseline disease severity scores. For the Charcot-Marie-Tooth Neuropathy Score, categories were mild (0–10), moderate (11–20), and severe (over 20). For the IBM, the IBM Functional Rating Scale (IBMFRS) categories were mild (over 28), moderate (14–28), and severe (0–14).17 We also used previous protocols used in other neuromuscular exercise studies, some of which were developed by our team.14There are no specific guidelines for exercise training in neuromuscular diseases and therefore we adapted the aerobic training protocols recommended by the American College of Sports Medicine, considered gold standard for aerobic exercise to maintain health and fitness.Participants trained in their local gyms for convenience and to increase participation. Weekly sessions with professionally qualified fitness instructors were arranged for the first month, then every other week for the second and third months. Fitness instructors received face-to-face training in the intervention protocol by the research physiotherapist, and received a training manual. The research physiotherapist visited the gym at the beginning and midpoint of the training period, and conducted a telephone review every 2 weeks. Extra calls or visits were made where either the participant or the fitness instructor identified a need for more support.2 peak. The intensity was progressively increased to 70% after 4 weeks and 80% after 8 weeks. Target heart rates were calculated using the standard formula: 220 minus the participant's age. We found in some weaker participants that they struggled to meet the heart rate training levels. In these cases, we used the BORG perceived exertion ratings to ensure they were training at a sufficiently high intensity.Participants exercised on a bicycle ergometer 3 times per week for 12 weeks (36 sessions) working towards a duration of 30 minutes. All had heart rate monitors to set training targets. Initially the target heart rate corresponded to 60% of VO17 Exercise participation and symptoms such as fatigue, pain, and mood were recorded using an exercise diary. Diary completion was monitored by the research physiotherapist during fortnightly phone calls and a midway gym visit.Each exercise session began with a 5-minute warm-up on the bicycle ergometer and ended with a 5- to 10-minute cool-down period. Participants were encouraged to exercise on alternative days to allow time for recovery and reduce general orthopedic stress.For the control and detraining period, participants were asked to continue their normal, prestudy activity levels. They continued to complete the exercise diary and were encouraged to complete any additional exercise or activity they had done. Participants had a monthly telephone review during the control period where general activity, fatigue, pain, and mood were recorded.2 peak) during a symptom-limited progressive exercise test on a semi-recombinant bicycle ergometer. Oxygen uptake was measured using indirect calorimetry .The primary outcome measure was maximum aerobic capacity measured as peak oxygen uptake  and percentage body fat, measured using skin fold calipers; blood pressure and lung function (hand-held spirometry); fatigue severity using the Fatigue Severity Scale; pain, using a visual analogue scale; and isometric and isokinetic lower limb muscle function, using Cybex HUMAC dynamometer. Activity measures were maximum work rate during the exercise testing (W); 10-meter timed walk; 6-minute walk distance; and perceived walking function using the Walk-12 scale plus physical activity measured using Sensewear activity monitors for 1 week at the start of the trial and 1 week at the end of the training phase. Disease-specific measures were used to ascertain impairment and disability: CMT Examination Score (CMTES) for participants with CMT and the IBMFRS for participants with IBM. Other measures were self-efficacy for managing chronic diseases ; barriers to activity and exercise; Short Form–36 quality of life measure; Pittsburgh Sleep Quality Scale; Epworth Sleepiness Scale; and International Physical Activity Questionnaire.During the training and control periods, participants recorded pain and fatigue levels using visual analogue scales. This was monitored by the research team during telephone reviews and the measurement sessions. Blood creatine kinase levels were recorded at each of the measurement sessions to monitor any change that could be indicative of muscle damage.2 peak using both the Cohen d and Hedges g statistics, to account for any clustering within individuals. Data for participants who withdrew partway through the study were included for the time points prior to their exit.The main analysis was descriptive as this phase 2 exploratory study was not powered for efficacy, and the size of effect was calculated for the primary outcome only. Means and SDs were calculated for continuous and interval data, with medians and interquartile ranges used for categorical measures. Missing data postintervention were imputed using a missing at random assumption. Where preintervention primary outcome data were missing , the data for that individual were not included in the effect size analysis. We calculated the size of the effect of training on VOStudy participants were invited for a semi-structured telephone interview once they had completed the final assessment. The interviewer was not part of the main intervention team and was unknown to the participant (A.S.). Questions centered on attitudes to exercise, experience of being in the trial, and how the training intervention affected the participant. The interview was digitally recorded, transcribed, and coded using thematic analysis for emerging themes.All participants were telephoned by the research physiotherapist 3 months after the cessation of the study to see if they were continuing to exercise.Any data not published within the article will be shared in an anonymized format, by request from any qualified investigator.This intervention study provides Class II evidence for the primary research question: What is the effect of community-based aerobic exercise training  on aerobic capacity in people with CMT and IBM?For the secondary aims, this study provides Class II evidence for the second research question: Is the intervention safe and acceptable? It also provides Class III evidence for the third research question: Are there effects on physical function, lower limb strength, fatigue, sleep, physical and quality of life?The recruitment target for the CMT group was 30 people over a 26-month period. In total, 282 people with CMT were invited to participate and 254 were excluded, refused, or did not respond. The most common reasons for active exclusion were coexisting illness or recent limb surgery (60), already exercising over 3 days per week (32), or unable to meet time commitments (27). Thirty-one people underwent more detailed screening but 3 failed to meet the study criteria. A total of 28 people with CMT enrolled during the study period, giving a recruitment rate of 1.1 per month. Of these 28 people, 5 withdrew before starting  and 3 partway through the study . In total, 20 people fully completed the study protocol, but the data for the 23 people who started the study were used for analysis using an intention-to-treat approach .The recruitment target for the IBM group was 30 people over a 26-month period. In total, 122 people with IBM were invited to participate and 102 were unable to commit to the trial or did not meet the study criteria on initial screening. The most common reasons were as follows: too old for the age criteria (27); did not want to participate (19); coexisting illness (18). A total of 20 people with IBM enrolled during the study period, giving a recruitment rate of 0.9 per month. Of these 20 people, 3 withdrew before being randomized  and 1 withdrew part way through. In total, 16 people fully completed the study protocol, but the data for the 17 people who started the study were used for analysis using an intention-to-treat approach .The CMT group was significantly younger than the IBM group, and both groups were mildly overweight, according to the mean BMI . The proThe training diaries revealed that the CMT group participated in 76% of total training sessions, and the IBM group completed 91% of sessions.2 peak with training  compared to a 0.1% deterioration with the control period . The Cohen d and the Hedges g calculations for change in VO2 peak showed moderate treatment effect sizes.In the CMT group, the differences with training and control periods for groups A and B are detailed in 2 peak with training  compared to a 1.3% deterioration with the control period . The effect sizes for training vs no-training were strong  compared to a 1.5% improvement with the control period . Differences between the 2 groups are detailed in There were no major differences with training observed in the other secondary measures for the CMT and IBM participants  and 3. AActivity monitors administered for 7 days at the start and end of the trial showed no changes in physical activity duration over 3 metabolic equivalent tasks  or other physical activity variables.There were no increases in serum creatine kinase (CK) at group or individual level with exercise training in both conditions. There were also no changes in energy, mood, or fatigue as recorded via the visual analogue scales in the training diaries.All participants were contacted by telephone 3 months after the trial. Contact was successfully made with 16 of the CMT participants. Eight of the 16 were still exercising, but of those who had stopped, time and work commitments were the most common reason given. Twelve participants with IBM were successfully contacted. Only 5 of the 12 were still exercising. Of the 7 who had stopped, various reasons were given, such as gym expense and access, lost confidence, time pressures, and one case of an injurious fall.A total of 46 participants agreed to be interviewed following the trial, 32 with CMT and 14 with IBM. The experiences were common to both conditions and the following themes were identified from the data: reasons for participating in the trial; expectations; experience of participating in the trial; support; outcomes of exercise . Partici18 indicates that self-motivation alone may not be sufficient for people with NMDs to continue independently. The qualitative findings indicated that support of health or exercise professionals and free or low-cost gym membership could be key factors in longer term participation in exercise. This has also been suggested from exercise trials in other neurologic conditions.19–21 Safety of exercise has been questioned in NMDs previously, so the lack of change in serum CK levels, pain, or fatigue is reassuring.It was encouraging that delivering community-based aerobic training in leisure-based exercise facilities was achievable. The excellent participation rates indicate that the approach is feasible for people with CMT and IBM; however, the low rate of continuation following the study, compared to a review of follow-up participation in other disabilities,23 The effect sizes are compelling despite not meeting target recruitment for analysis in the IBM group. It is worth noting that a priori, there was an underestimation of the intrasubject coefficient of variation of 0.2, due to an assumption that VO2 measurements outside the range 17–40 mL/min/kg would be extremely unlikely. In the IBM group, however, 10 of the 17 participants had baseline VO2 measures below 17 mL/min/kg and the group mean was 14.5 mL/min/kg. This may have been due to their older age and potential greater predisposition to sedentary lifestyles. Although the CMT group presented as fitter at baseline, 3 participants were also below the 17 mL/min/kg threshold. This may have influenced the effect sizes in this study, especially with the small sample, and must be considered when estimating the sample size of a phase 3 trial. A key aim for future training trials in IBM will be to see if VO2 peak can be increased to greater than or equal to 17.9 mL/min/kg, the threshold for independent community living in older adults.24Both disease groups showed improvement with aerobic training, with a particularly strong effect of training in the IBM group. This group was more deconditioned at baseline and may have had greater potential for improvement, plus they showed better adherence to the study protocol. Both disease groups showed better effect sizes than training trials in other neurologic diseases.2 peak, but none of the other secondary measures showed consistent group changes with the training or control interventions. The sample size in this study was likely to be insufficient to show change in some of the functional measures, but it may also be because the exercise intervention did not specifically train leg strength or walking. Larger samples may also be required to ascertain the effect on some of the nonmotoric factors, such as mood, fatigue, self-efficacy, and sleep. There are no studies to date that relate these factors to aerobic capacity in CMT or IBM.The improvement in work rate showed similar magnitudes of change as VO26There were limitations and challenges encountered during this trial that must be considered in the design of a phase 3 study. Recruitment of these rare conditions was a factor, particularly in IBM, where potential recruits tended to have more comorbidity and did not pass the safety screening for maximal exercise testing. To overcome this problem, submaximal exercise tests could be used in a phase 3 trial to reduce risk to the patient and thus increase the recruitment pool and provide a more representative sample of patient populations. Gas exchange threshold and oxygen uptake kinetic variables are ideal candidates for future studies as they are derived from submaximal exercise tests, have been successfully utilized as a markers of fitness in many clinical populations, and are responsive to exercise training.2 peak did not fully return to baseline after the 8-week washout period. This will have influenced the group means and the effect sizes. If some of the recruitment challenges are addressed with a multicenter phase 3 trial and submaximal testing, a more traditional 2-arm design will be possible and would be preferable to a crossover trial.The crossover design was also a challenge in 2 ways. First, people were put off by the prospect of detraining if they were in the group that exercised first. This affected recruitment. In the CMT group, the VOTo date, this is the largest study of aerobic exercise training in people with CMT and IBM. The protocol was conducted in community leisure facilities and demonstrated that a standard aerobic training program using a bicycle ergometer is feasible, well-tolerated, and safe for people with CMT and IBM. The prescribed program had an effect on cardiopulmonary exercise capacity and efficacy will require investigation in larger trials. Consideration must also be given to the degree of motivational and financial support required for people with NMDs to exercise on a longer-term basis as part of a self-management strategy.


Peptide drugs have been used in the treatment of multiple pathologies. During peptide discovery, it is crucially important to be able to map the potential sites of cleavages of the proteases. This knowledge is used to later chemically modify the peptide drug to adapt it for the therapeutic use, making peptide stable against individual proteases or in complex medias. In some other cases it needed to make it specifically unstable for some proteases, as peptides could be used as a system to target delivery drugs on specific tissues or cells. The information about proteases, their sites of cleavages and substrates are widely spread across publications and collected in databases such as MEROPS. Therefore, it is possible to develop models to improve the understanding of the potential peptide drug proteolysis. We propose a new workflow to derive protease specificity rules and predict the potential scissile bonds in peptides for individual proteases. WebMetabase stores the information from experimental or external sources in a chemically aware database where each peptide and site of cleavage is represented as a sequence of structural blocks connected by amide bonds and characterized by its physicochemical properties described by Volsurf descriptors. Thus, this methodology could be applied in the case of non-standard amino acid. A frequency analysis can be performed in WebMetabase to discover the most frequent cleavage sites. These results were used to train several models using logistic regression, support vector machine and ensemble tree classifiers to map cleavage sites for several human proteases from four different families . Finally, we compared the predictive performance of the developed models with other available public tools PROSPERous and SitePrediction. Proteolytic enzymes play critical role in many processes including cell proliferation, immune response, cell death and others . ProteasNowadays it is known that the new peptide-based drugs must be designed considering the localization of potential protease SoCs to make the compound less susceptible to protease reaction. Firstly, efforts are spent on evaluating a large numbers of lead compounds and their derivatives to reveal motifs recognized by proteases. Secondly, peptide sequences of potent drug candidates are typically chemically modified to eliminate these motifs or to incorporate structural features preventing proteolysis . VariousThe information about proteases and their sites of cleavage is widely spread across publications and databases such as MEROPS , CutDB  and ProtDifferent bioinformatic tools were developed to for protease-specific substrates and their cleavage sites prediction. Efficient computational tools potentially can help to reduce the number of experiments and improve peptide drug structure before synthesis. These approaches use as an input data extracted from databases described above. Several reviews were published to compare developed models and their predictive performance , 13, 14.In accordance with comparison reported in the literature machine learning methods overperform the methods from the first and the second group. In this group approaches models are trained using different features: binary features, PSSMs, molecular descriptors, structural features such as the secondary structure of the cleavage sites, the solubility of the sequence around the cleavage site and others. The cleavage site specific molecular descriptors are used to explain physical, physicochemical, pharmacophoric properties of the residues around the cleavage site. One more feature that is considered during model training is the size of local window around the cleavage site, typically from two to sixteen residues. Different machine learning algorithm such as support vector machine (SVM) learning algorithm , support vector regression (SVR) , logistic regression (PROSPERous), neural networks  and otheThis article presents a new approach which aim is to derive cleavage site appearance rules for the specific peptide family or for specific experimental condition . The system considers the following steps: firstly, the information coming from liquid chromatography mass spectrometry (LC-MS) based experimental data or from external sources such as MEROPS database is stored in a chemically aware database (i.e. WebMetabase ). We repNevertheless, the proposed methodology could be applied in the case of non-natural amino acid and/or cyclic peptides. Moreover, since the system used to derive the cleavage site appearance rules  could be linked to the software assisted metabolite structure elucidation based on MS data, the database is automatically enriched with the new experiments. Rules can then be refined to tune the system for the experimental conditions and/or peptide families of interest. This knowledge can be applied during the design-make-test drug discovery cycle.In this study we exported peptide cleavage data from the MEROPS database (version 11 1/09/2017)  for all Two different strategies were followed depending if the source organism for the protease was indicated in the MEROPS database or it was not. The summary information extracted from the MEROPS database includes: substrate peptide sequence, parent sequence peptide range, substrate Uniprot number, cleavage site position in substrate, metabolite peptide sequence, protease name, protease source organism and references list related to the protease. All the information was collected and written in a xml file. The xml has four blocks of information:Properties: The Matrix (name of the protease), the MEROPS Peptidase Code, the MEROPS Peptide Uniprot_acc, the MEROPS Peptide Range, the MEROPS Organism and the MEROPS Peptidase Reference.Parent: the parent structure in sdf format, the InchiKey, the molecular formula and the m/z.Metabolized Parent: as the previous one without the InchiKey.Metabolites: There is a new block for each metabolite. It contains the metabolite name and the structure. Each structure has the structure ID inside the xml file, the sdf (V3000 or V2000 format), the formula, the m/z, the formula difference compared to the parent structure, the metabolic mechanism, the m/z difference compared to the parent, the collection of atoms in the parent that suffer the metabolic transformation and the list that contains the isomorphism between the number of the atom in the parent and the number of atom in the metabolite.After the xml files were imported into WebMetabase the same steps to annotate the information for each file that were described elsewhere for the case of experimentally derived approach were followed . Enablinhttp://webmetabase.com:8182/WebMetabaseBioinformatics/. To access this database following credentials should be used: login “User”, password “WebMetabase”.All files extracted from MEROPS database were uploaded into the web application “WebMetabase 3.2.12” . WebMetabase imports the data from external sources in a predefined xml format where the structure of the peptides introduced in MDL mol format (V3000 or V2000). This conversion to a structure format is necessary to feed the chemically aware system. A complete list of uploaded substrates for each protease grouped by organism can be found at Once the experimental results and/or data from external database were interpreted and approved in WebMetabase, the parent and metabolite structures were stored in the database. Each peptide structure was annotated by the Structural Blocks (SBs) defined as the structural fragment between amide bonds and their connectivity. SBs in the substrate sequence were numbered as …-P4-P3-P2-P1-/-P1’-P2’-P3’-P4’-…, with the cleaved bond located between the P1 and P1’ sites. , 33In this article we described a new approach implemented in WebMetabase to store each peptide described as a combination of physicochemical properties of each SB contained in the sequence. These physicochemical properties of the SBs were represented through molecular descriptors calculated by the cheminformatics library Volsurf.  ThereforThe annotation of the peptide information in this manner enables doing an exact substructure and similarity-based substructure search inside the database without being limited to any type of peptides and/or amino acid . This methodology can be used to perform a structural search on the exact sequences identifying the experiments where a certain bond of interest participated in a metabolic reaction. Also, the described algorithm implemented in WebMetabase includes a system to perform searches based on similarity of the molecular descriptors. As an output, searches give a list of experiments that fulfill the search criteria. We used the cheminformatics toolkit Volsurf , 34 to cThe algorithm can perform frequency analysis (FA) based on exact match of the sequence of interest  and/or on the similarity based on the molecular descriptors . A simple peptide frequency analysis of the cleaved amide bonds can be performed for the entire database or for the selected set of the approved experiments in WebMetabase. The algorithm collects information about all cleaved peptide bonds that were involved in the metabolic reactions of interest. Herein site of cleavage (SoC) considers the two SBs involved in the amide cleavage containing the C-terminal and N-terminal of the SBs. We define a potential SoC (pSoC) as the pair of structural blocks (referred as P1 and P1’) that may and may not be involved in the catalysis. The frequency analysis refers to the number of times that a pSoC is observed in the parent structure and how many times this is an actual SoC (aSoC).The output of the frequency analysis is done by protease and provides two tables. The first table contains the counts of a pSoC found and the counts that it was an aSoC. The second table contains the counts a pSoC found and if it was cleaved once, twice or more than twice. A frequency analysis of the aSoCs depending on the protease can be done to create a set of empirically derived rules that later can be used to predict the metabolic liability of different amide bonds in a new non-tested peptide. WebMetabase automatically generates an excel file in temporary folder to store FA results. Filename format starts with string “soc” and after some random number, e.g. soc7511073558888689580.xls. This file can be used for the following analysis if needed. Moreover, frequency analysis results can be persisted in the database for the further usage.The information registered in the database and FA results based on MEROPS dataset for eighteen proteases were selected to make some examples of the methodology. These proteases from four different families  from Homo sapiens were chosen to develop classifier models. They included granzyme A, granzyme B, granzyme B (rodent-type), granzyme M, thrombin, trypsin1, caspase-1, caspase-2, caspase-3, caspase-6, caspase-7, cathepsin E, cathepsin D, cathepsin L, MMP2, MMP3, MMP8, MMP9. Trypsin1, MMP2, caspase-6 and cathepsin L were selected because the number of the identified aSoCs was the highest for proteases imported in WebMetabase. Others were selected as a representative of the respective protease family. For each selected protease we split all extracted substrates in train and external dataset (ED). Train dataset contained experiments with 80% of extracted cleavages events and was used to develop models and to perform the cross-validation. ED was used only as a final assessment of model predictive performance quality for each protease and to perform comparison with publicly available PROSPERous and SitePrediction approaches. In accordance with the literature it was revealed that increasing of the investigated window’s size around cleavage site could improve the performance of the model . Due to To derive a training and external dataset for each of the selected proteases, we cut out all possible 2 SBs and 8 SBs sequences from substrate peptides. Thus, a substrate with 100 amino acids would lead to 93 and 99 sequences of length 8 and 2, respectively. Each sequence than was characterized as a combination of physicochemical properties described by Volsurf descriptors of the SBs in the sequence. These sequences were then classified as either cleaved (aSoC2 and aSoC8) or non-cleaved sequences (pSoC2 and pSoC8). Cleaved sequence for aSoC2 was the one involved in metabolic reaction. In cleaved sequence for aSoC8 the proteolytic event happened between fourth and fifth residue. Substrate cleavage site prediction was formulated as a binary classification problem for two classes cleaved SoCs so-called positive examples or non-cleaved SoCs so-called negative examples. We calculated number of times each pattern was cleaved and number of times it was met. For the SoC2 it was done during sFA in WebMetabase, for the SoC8 additional script was applied. Since the size of positive dataset was significantly smaller than the number of non-cleaved motifs, we split all negative dataset in groups with number of objects equal to the number of cleaved patterns, than combined positive dataset with each of the received negative subsets, therefore obtained balanced datasets to derive models. In details, we decomposed the non-cleaved objects dataset into X partitions, where X depended on the number of cleaved sequences and combined all the metabolized SoCs with each partition of pSoCs to be an individual subset. Thus, we generated multiple training subsets with better class distribution and each non-cleaved object occurred in at least one training subset . MoreoveN is the number of times sequence pattern will be repeat in a final dataset. M is the number of times the sequence pattern was met in the initial exported dataset. Train dataset and external dataset for each of the selected proteases used for model training for local window of two residues and local window of eight residues are provided in the Supporting information The next step, for each protease we applied multiple learning algorithms to each training subset of sequences described by Volsurf descriptors to train prediction models independently. In this study, we used Logistic regression (LR) , 40, SupWhile models were generated, we performed 5-fold cross-validation (5CV). The predictive performance of each model was evaluated using the accuracy, sensitivity, specificity, area under the curve (AUC) for the receiver–operator characteristic curve (ROC), area under the curve for the precision-recall curve (PRC), Mathews correlation coefficient (MCC) using these measures based on 5-fold cross-validation. Finally, we combined all models measures for each learning algorithm by calculating the mean for each measure.To evaluate the model’s performance in a real scenario and to inspect the potential over-fitting effect on these algorithms, we also performed prediction on EDs, peptides that had not been included in the model building. Prediction was performed for each peptide from the external dataset independently by each model. To complete this goal each peptide from external dataset also was split in motifs and each motif was characterized as a combination of physiochemical properties of its SBs. To combine the results from all models for each learning method we calculated a mean of predicted probabilities for each bond in the peptide, using the predicting bond breaking probability we evaluated ranking performance for each classifier. The procedures are described in detail below.Finally, we performed independent tests and compared the predictive performance by ranking on ED between selected LR and RFC model classifiers and the other public available tools such as SitePrediction and PROSPERous.We performed comprehensively evaluation and comparison of the predictive performance between different classifiers  on the external dataset (ED) in each protease family combining the prediction for all proteases inside the family. Based on the intermediate results presented below we decided to proceed with classifiers using local window P4-P4’. To complete this task, we cut out each external peptide in subsequences of length 8, calculated Volsurf descriptors for each of the SB in this peptide and characterized each sequences of 8 SBs as a combination of physicochemical properties of SBs contained in the subsequence. We used these combinations of Volsurf descriptors to perform prediction on each of them. Since the final goal of the model was to select which of the bonds was more likely to be broken in each sequence, an analysis of the rank (sorted by the predicted SoC) was performed. Therefore, when the probability for each bond was computed by the model, we sorted each sequence by their value and registered a rank of each predicted cleavage site. Since peptides differ in a size ranking was normalized by the amount of possible cleavage sites. A recovery analysis of the rank value for the known SoC was done and finally we accumulated all ranking positions for each external peptide and each protease inside the family. Moreover, we calculated the best and the random ranking. The random ranking was done by assigning a random number for the probability to be broken for each SoC and continuing in the same way as in the model case. In the case of the best ranking positions, the known SoCs in a peptide were assigned to 1,2,3, etc. rank for each of the known SoCs. As a summary of the results, we plotted the cumulative ranking score for the recovered known SoCs for each family including the best and the random cumulative ranking. Python algorithm used to perform predictive models training and to perform prediction on external dataset for each model for each of the selected protease with ReadMe.txt was provided in the Supporting materials  Files.We performed comprehensively evaluation and comparison of the predictive performance between selected LR and RFC model classifiers and other public available tools such SitePrediction and PROSPERous. Comparison with the external methodologies was performed only for the proteases in each family where models were available. Models for trypsin1, granzyme M and caspase-2 were not available in both methods, also for granzyme A, granzyme B, granzyme B (rodent-type) and thrombin were not available in SitePrediction approach. To complete this task, we used as an input for SitePrediction and PROSPERous FASTA format sequences of the peptides in the external dataset and exported prediction results. In SitePrediction we selected models trained for each protease for Homo sapiens. In PROSPERous we selected logistic regression models and local window P4-P4’. After each potential cleavage site was sorted in accordance with the predicted scores from each method. For the SitePrediction case where a score was not predicted it was assigned to 0. After we followed the same rank strategy explained for the models developed in this article, registering the rank of each predicted cleavage site and normalized it by the amount of possible cleavage sites for each peptide. Finally, we plotted the cumulative ranking score for each family for our models and for each method including the best and the random cumulative ranking.In this section, we present the results of the application of our approach in the analysis of the MEROPS. All files extracted from MEROPS database were uploaded into the web application “WebMetabase 3.2.12” . WebMetabase imports the data from external sources in a predefined xml format. Metabolites structures extracted from MEROPS were directly uploaded into WebMetabase and automatically approved. After approval parent peptide and metabolites were persisted into the database and annotated as described before. Finally, the methodology was applied to perform a sFA on the stored information for selected proteases: granzyme A, granzyme B, granzyme B (rodent-type), granzyme M, thrombin, trypsin1, caspase-1, caspase-2, caspase-3, caspase-6, caspase-7, cathepsin E, cathepsin D, cathepsin L, MMP2, MMP3, MMP8, MMP9. The sequence pattern around the potential cleavage site was represented as a combination of Volsurf descriptors. Moreover, an additional script was applied on the database to perform sFA for the sites of cleavage with the local window P4-P4’. The results from both FA were used to build predictive models for the selected proteases enabling the cleavage site prioritization based on the physicochemical properties for the cleavage sites. The MEROPS/WebMetabase workflow used is shown in http://webmetabase.com:8182/WebMetabaseBioinformatics.In total we extracted 18760 xml files and they contained information for 18760 substrate peptides and 21804 metabolites. For each selected protease we split all extracted experiments in train and external dataset. Train dataset contained experiments with 80% randomly selected of the extracted identified cleavages events and it was used to develop models and perform cross-validation. External dataset was used to evaluate the model performance and compare it with other methods. In both datasets each SoC was represented as a combination of the Volsurf descriptors of the SBs in the sequence. We performed a simple frequency analysis of the metabolized chemical moieties for each protease in the train and external datasets. The summary on the amount of identified individual metabolized bonds by frequency analysis is presented in Using the prepared substrate training datasets, we analyzed the statistical distributions of cleavage sites for the eighteen proteases analyzed . The amiIn addition, for trypsin1 we identified arginine  in position P1 in 36.8% and lysine  in 63.2%. These results agreed with literature where it was described trypsin preferentially cleaved at Arg and Lys in position P1  and withTo evaluate the performance of the selected classifier models for cleavage site prediction of multiple proteases, we carried out a 5-fold cross validation (5CV) test on each of selected proteases under investigation in this study. Moreover, the predictive performance on several external datasets was explored. Firstly, we trained and compared the predictive performance of models with two learning methods LR and SVC using two sizes of local windows P1-P1’ and P4-P4’ For evaluation of predictive performance during 5CV test the following criteria were calculated: accuracy, ROC AUC, sensitivity and specificity. For evaluation of the predictive performance on external dataset we calculated the accuracy, sensitivity and specificity. For caspases-1, 3 and 7 all performance measures decreased for both classifiers when window was widened, on the other hand for caspases-2 and 6 they increased for LR and decreased for SVC. For example, for caspase-1 and 2 comparing LR classifier performance based on AUC ROC for the 5CV results for window P1-P1’ was 0.77 and 0.87 and for P4-P4’ was 0.73 and 0.93, respectively. Comparing results for LR and SVC specificity was higher for SVC classifier in both P1-P1’ and P4-P4’. Meanwhile, when we compared predictive performance on external dataset of the models trained using two local windows AUC ROC for P4-P4’ were higher for caspases-2, 3, 6, 7 and cathepsin L in both classifiers. Also, AUC PRC and MCC were higher for P4-P4’ were higher for caspases-2, 6 and cathepsin L in both classifiers. For caspase-1, -3 and -7 AUC ROC, AUC PRC and MCC were lower that also could be related with small amount of training data. It can be explained by the fact that models trained for window P4-P4’ are more complex and involve higher number of variables to be defined during the training process. We suppose that because the amount of data is not sufficient to properly train the model for window P4-P4’. For caspases-1, 3, and-7. This theory is also confirmed by the fact that all parameters increased when P4-P4’ dataset was used to train both learning methods for cathepsin L. Based on this analysis results we decided to continue using local window sequence P4-P4’.In addition, we compared the difference of amount of recovered known cleavage sites for all selected proteases for the P1-P1’ and P4-P4’ LR models for the 10 top positions. To complete this task, we calculated a difference and normalized it by total amount of known cleavage sites. In Different learning approaches methods were used to train the models in other tools , 25, 28.Lastly, we evaluated predictive performance of LR, SVC, GBC and RFC models on external dataset to understand if trained models were able to predict known cleavage sites with the top-ranking positions for the protease of interest. To complete this task, we explored the ranking positions of the probable cleavage sites sorted by predicted probability for each learning approach. Since peptides differ in a size, ranking was normalized by the amount of possible cleavage sites. Therefore, we completed a recovery analysis of the rank value for the known SoCs and calculated the sum of all ranking positions for each external peptide for each protease inside the family. Moreover, we calculated the best and random ranking. The cumulative percentage ranking score for the recovered sites of cleavage for all proteases is demonstrated in We noted that the best ranking recovered 100% of known SoC reaching 20% of the normalized ranking positions. All classifiers were able to recover all known cleavage sites better than random but at lower percentage of ranking positions than the best. LR models were the best considering they recovered all known SoCs at 99% of the ranking positions, while the best recovered known sites of cleavage at 31%. RFC and GBC recovered all SoCs from external dataset for all proteases at 91% of the ranking positions, accordingly. Moreover, we compared percentage of recovered known SoCs at the percentage ranking positions when the best ranking collected 100% of known sites of cleavage for each protease family selected for this study, results are presented in Finally, we compared the predictive performance by ranking on external dataset between LR and RFC classifiers and other tools such as SitePrediction and PROSPERous. For following proteases predictive performance was compared with PROSPERous and SitePrediction: caspase-1, caspase-3, caspase-6, caspase-7, cathepsin E, cathepsin D, cathepsin L, MMP2, MMP3, MMP8, MMP9. Also, PROSPERous contained models trained for serine proteases: granzyme A, granzyme B, granzyme B (rodent-type), thrombin. Our LR and RFC models as well as PROSPERous correctly predicted 376 known cleavage sites, while 306 known cleavage sites were correctly predicted by both PROSPERous and SitePrediction. Our models were able to collect the same amount of known cleavage sites as PROSPERous and SitePrediction. SitePrediction is based on statistical scoring methods, while PROSPERous is based on SVM learning and LR approach. In the dataset of proteases compared to SitePrediction and PROSPERous LR and RFC models recovered higher percentage of known SoCs at the ranking percentage position equal to 50% but reaching 88% the percentage of recovered known sites of cleavage was the same for our models, PROSPERous and SitePrediction. In the dataset of proteases compared only to PROSPERous RFC model performed worse than PROSPERous and recovered less percentage of known cleavage sites. On the other hand, LR model recovered higher percentage of known SoCs at the ranking percentage position equals to 50% but reaching about 85% of ranking position percentage of recovered known sites of cleavage was the same for LR and PROSPERous. While we performed detailed analysis of ranking position percentage between 0 and 40% LR outperformed RFC and PROSPERous, while last ones performed at the same level. We can suppose that LR model works better for the peptides of the smaller size.Predicting possible sites of cleavage for individual proteases is an important task to be completed during drug-design process of peptide therapeutics to improve their stability and availably as a promising drug. In this study we presented a new approach in WebMetabase that helps to predict cleavage site for the specific peptide family or for specific experimental condition . One of the main advantages of this approach is that it generates a searchable database for the information coming from LC-MS based experimental data or from external sources such as MEROPS database. In this database each amino acid is described as a vector of physicochemical properties, Volsurf molecular descriptors. Thus, the sequence pattern around the potential cleavage was represented as combination of Volsurf descriptors. The proposed methodology can be applied in the case of non-natural amino acid. Comparing to MEROPS this database type can be enriched with new experimental or external data. This way to store the data can be utilized to perform frequency analysis to discover the most frequent scissile bonds within the generated database. The FA results can be used to derive a cleavage site appearance rules based on molecular properties of the cleavage sites. To demonstrate this, we trained several models using Logistic Regression (LR), Support Vector Machine (SVM) and Ensemble Trees (ET) classifier learning approaches for eighteen proteases from four protease families: serine, cysteine, aspartic and matrix metalloproteases. In the training dataset each sequence pattern around the potential cleavage site and actual site of cleavage was represented as a combination of Volsurf descriptors that characterized physicochemical properties of the SBs in the sequence. We compared predictive performance of the models trained with different learning approaches applying 5-fold cross validation test and external dataset validation test. Moreover, we examined the influence of the local window sequence size around the site of cleavage by comparing the models trained for P1-P1’ and P4-P4’ range. We revealed that LR and RFC models trained using window P4-P4’ outperformed other learning methods and the models trained using P1-P1’ window. We noted that training dataset size influenced on the predictive performance of the models analyzing data for caspases. Finally, we compared the predictive performance of LR and RFC models with other approaches such as PROSPERous and SitePrediction tools. LR model recovered higher percentage of the known cleavage site in the first 30% of the ranking positions comparing to the other tools. It can be explained by the fact that it performs better prediction on smaller peptides. Moreover, since the system can be linked to the software assisted metabolite structure elucidation based on MS data, the database is automatically enriched with the new experiments. Nevertheless, models can be re-trained with updated dataset and derived rules can be refined to tune the system for the experimental conditions and/or peptide families of interest. This knowledge can be applied during the design-make-test drug discovery cycle.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S9 Table(PDF)Click here for additional data file.S10 Table(PDF)Click here for additional data file.S1 FigSubstrate cleavage sites for P4-P4’ local window for cathepsins: a) cathepsin D; b) cathepsin E; c) cathepsin L. The cumulative amino acid occurrences in P4–P4’ were calculated and displayed in the form of a two-dimensional heat map. The scissile peptide bond was between sites P1 and P1’.(TIF)Click here for additional data file.S2 Figa) granzyme A; b) granzyme B; c) granzyme B (rodent-type); d) granzyme M. The cumulative amino acid occurrences in P4–P4’ were calculated and displayed in the form of a two-dimensional heat map. The scissile peptide bond was between sites P1 and P1’.(TIF)Click here for additional data file.S3 Figa) MMP2; b) MMP3; c) MMP8; d) MMP9. The cumulative amino acid occurrences in P4–P4’ were calculated and displayed in the form of a two-dimensional heat map. The scissile peptide bond was between sites P1 and P1’.(TIF)Click here for additional data file.S1 File(ZIP)Click here for additional data file.S2 File(ZIP)Click here for additional data file.S3 File(ZIP)Click here for additional data file.


G protein-coupled receptors (GPCRs) are a family of proteins containing seven transmembrane helices, with the N- and C-terminus of the protein located at the extracellular space and cytosol, respectively. Here, we report that ceramide or related sphingolipids might invert the topology of many GPCRs that contain a GXXXN motif in their first transmembrane helix. The functional significance of this topological regulation is illustrated by the CCR5 chemokine receptor. In the absence of lipopolysaccharide (LPS), CCR5 adopts a topology consistent with that of GPCR, allowing mouse peritoneal macrophages to migrate toward its ligand CCL5. LPS stimulation results in increased production of dihydroceramide, which inverts the topology of CCR5, preventing macrophages from migrating toward CCL5. These results suggest that GPCRs may not always adopt the same topology and can be regulated through topological inversion.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that major issues remain unresolved (resolved . G protein-coupled receptors (GPCRs) are a family of proteins containing seven transmembrane helices that are crucial for cell signaling . A commoWe have recently analyzed the topology of a transmembrane protein called TM4SF20 (transmembrane 4 L6 family member 20). Similar to GPCRs, the N-terminus of TM4SF20 is inserted into ER lumen in the absence of a functional signal peptide . We reveIn the current study, we show that the GXXXN motif is also present in many GPCRs. We further demonstrate that topology of CCR5 (CC chemokine receptor type 5), one of the GPCRs containing this motif, is inverted through RAT in lipopolysaccharides (LPS)-stimulated macrophages. These results suggest that functions of GPCRs may be regulated by topological inversion through RAT.6-ceramide, a cell permeable analogue of ceramide that is metabolically converted to natural ceramide inside cells , which does not contain this motif ), this extracellular region  containsIf the lower molecular weight of CCR5(B) is caused by the lack of glycosylation, then blocking glycosylation of CCR5(A) by mutating the four O-linked glycosylation sites should abolish the reduction in molecular weight observed above even if ceramide still induces RAT of CCR5. To test this hypothesis, we mutated all four glycosylation sites to alanine . These mutations did not block ceramide-induced RAT of CCR5 judging by the localization of C-terminus of the protein using the assay shown in 6-ceramide but not those in the presence of the lipid  but leaving those located at the extracellular side intact. The reporter protein expressed in cells cultured in the absence of ceramide should be biotinylated by a cell surface sulfhydryl reactive biotinylation reagent thereby precipitable by streptavidin beads, as CCR5(A) of the reporter protein contains extracellular cysteine residues . If ceraceramide . Wildtypceramide . These oWe then investigated the physiological function of RAT of CCR5. LPS was reported to stimulate production of ceramide in mouse macrophage-like RAW264.7 cells . Unlike Ccr5-/- mice showed no such signal  . This anh signal . We staih signal . As a coTo confirm that LPS stimulates RAT of CCR5 through increased production of dihydroceramide, we treated macrophages with fumonisin B1 (FB1), an inhibitor of ceramide synthase that catalyzes formation of the sphingolipid . Treatme-/- macrophages. Some CCR5 was labeled by a single gold particle, as their number in wild type macrophages was higher than that in CCR5-/- macrophages. However, the specificity of such labeling was difficult to determine, as these particles did exist in CCR5-/- macrophages. For this reason, we only analyzed CCR5 labeled by gold clusters, the number of which should be smaller than that of CCR5 molecules. In the absence of LPS, the gold particle clusterswere found on extracellular side of plasma membranes  analysis to determine the localization of the epitope localized at the second extracellular loop of CCR5(A). Gold clusters presumably generated by binding of multiple gold-conjugated secondary antibodies to anti-CCR5 are considered as CCR5-specific  as such embranes . These pembranes . After cembranes .To rule out the possibility that the results shown above are artifacts of in vitro treatment with LPS, we injected LPS into mouse peritoneal cavity, and then isolated macrophages to determine the topology of CCR5. Since we did not treat the cells with LPS after their isolation, we analyzed the macrophages through immunofluorescent microscopy as soon as they stick to the culture plates. As a result, these cells appeared to be rounded, as they did not have time to spread out. Under this condition, LPS also induced RAT of CCR5 in macrophages, and this topological inversion was inhibited by co-injection of FB1 .Ccr5-/- macrophages  with the GPCR configuration in LPS or ceramide-treated cells may also be interpreted as indicating ER retention of the protein. This model may also be consistent with the observation that ceramide treatment blocked O-linked glycosylation of CCR5, as this post-translational modification takes place in the Golgi complex. However, this model cannot explain our observations that ceramide treatment exposed C-terminus of CCR5 to the extracellular space. This model is also inconsistent with the observation that significant amount of CCR5 in cells treated with ceramide or macrophages treated with LPS, which stimulated production of dihydroceramide, was localized on cell surface. Thus, taking all of our data together, we believe that topological inversion through RAT is the better interpretation of our results.A major implication of the current study is that membrane proteins can be regulated by topological inversion through RAT. We have previously reported that ceramide stimulates RAT of TM4SF20, and this reaction depends on the presence of a GXXXN motif in the first transmembrane helix of the protein . In the An effective approach to delineate the function of CCR5 with the inverted topology is to make a mutant CCR5 that is locked into CCR5(B) regardless of the presence of ceramide or dihydroceramide. We previously reported that mutating the glycine or asparagine residue to leucine in the GXXXN motif in the first transmembrane helix of TM4SF20 locked the topology of the protein into the B form, and the mutant protein performed a function similar to that of wild type TM4SF20 with the inverted topology (TM4SF20(B)) . HoweverWe obtained anti-human CCR5 45531 from R and D Systems , anti-actin from Sigma Aldrich , anti-Giantin 924302 from Biolegend , Alexa Fluor 488 FluoroNanogold goat anti-mouse IgG Fab from Nanoprobes.com , AffiniPure Donkey Anti-Rabbit IgG (H + L) from Jackson ImmunoResearch , Alexa Fluor 488 goat Anti-Mouse IgG (H + L) from Invitrogen , Anti-SNAP-tag Antibody  from New England Biolabs . Hybridoma cells expressing anti-Myc 9E10 were obtained from ATCC. Saponin (from quillaja bark), LPS (from Escherichia coli 0111:B4), glutaraldehyde, and fumonisin B1 (from Fusarium moniliforme) was purchased from Sigma Aldrich . Recombinant human CCL5 was obtained from R and D Systems .Ccr5-/- mice (Stock: 005427) were purchased from Jackson Laboratories .Male and female littermates of 6–8 week-old mice of C57Bl/6 background were used for all studies under APN# 2015–100860 approved by UTSW IACUC. Wildtype mice were ordered from UTSW Breeding Core. 2, respectively. To guard against potential genomic instability, an aliquot of each cell line is passaged for only 4 weeks before a fresh batch of cells is thawed and propagated for experimental use. All the cell lines have been confirmed to be free of mycoplasma infection using the MycoAlert Mycoplasma Detection Kit .HEK293  and SV589  cells were maintained in medium A  in monolayers at 37°C in 8% and 5% CO2. After 2 hr, non-macrophage cells were removed by multiple washes of medium B. Primary macrophages, which stick to the plates, were cultured medium B in monolayers at 37°C in 5% CO2.To obtain primary mouse macrophages, mice were intraperitoneally injected with 1 ml of 38.5 mg/ml thioglycolate. After 4 days, 3 ml phosphate buffer saline (PBS) was injected into abdomen of the mice euthanized through isoflurane overdosing. After brief massage, cells suspended in PBS were extracted and seeded in medium B  at 37°C in 5% COThe original cDNA clone for human CCR5 was obtained from UTSW Vector Core Laboratory (IOH27324). pCCR5-Myc encodes full length human CCR5 followed by five tandem repeats of the Myc epitope tag. pSNAP-CCR5 was purchased from CisBio and encodes full length human CCR5 preceded with an N-terminal SNAP-tag. pβ2AR-SNAP, pCCR1-SNAP, pCCR4-SNAP, pCCR5-SNAP, pCCR10-SNAP and pMAS1-SNAP encode indicated full length human GPCRs followed by a C-terminal SNAP-tag. CCR5 mutants were generated through site-directed mutagenesis with the QuikChange Multi Site-Directed Mutagenesis Kit  on plasmids encoding CCR5 described above. Desired mutations were confirmed by sequencing the entire open reading frame of the gene.5 cells per 60 mm dish. On day 1, cells were transfected with 0.15 µg/dish of Myc-tagged WT or mutant CCR5 plasmid. Following treatments described in the figure legends, cells were lysed in buffer A  containing cOmplete Protease Inhibitor cocktail . After brief centrifugation, buffer B  was added to clarify lysate at 1:1 ratio. Cell lysate was analyzed by SDS-PAGE followed by immunoblot analysis with the indicated antibodies . Bound antibodies were visualized with a peroxidase-conjugated secondary antibody using the SuperSignal ECL-HRP substrate system .On day 0, SV589 cells were seeded at 5 × 105 cells per 60 mm dish. On day 1, cells were transfected with 0.5 µg/dish of SNAP-tagged GPCRs. Following treatments described in the figure legends, for experiments shown in 2. Labeled cells were washed in Tag-Lite Buffer (CisBio) and resuspended to 7.5 × 105 cells/ml. Cell suspension (100 μl) were added in triplicate to a 96-well dish and fluorescence was measured using a Tecan plate reader with excitation and emission of 506 and 526 nm for SNAP-Surface 488 and SNAP-Cell 505 Star or an excitation and emission of 340 and 620 nm for Lumi4-Tb, respectively.On day 0, HEK293 cells were seeded at 5 × 105 cells per 60 mm dish. On day 1, cells were transfected with 0.15 µg/dish of Myc-tagged WT or mutant CCR5 plasmid. Following treatments described in the figure legends, cell surface cysteine residues were labeled by 0.25 mg/ml EZ-link maleimide-PEG2-biotin (Thermo Fisher Scientific) and immunoprecipitated with the Pierce Cell Surface Labeling Kit according to manufacturer’s direction. Resulting fractions were mixed with buffer B at 1:1 ratio and subjected to immunoblot analysis.On day 0, SV589 cells were seeded at 5 × 105 cells were seeded on each 35 mm Magtek plate on day 0. On day 1, cells were transfected with the indicated plasmids (0.1 µg/dish). After 2 hr, cells were treated with or without C6-ceramide. 16 hr later on day 2, cells were fixed in DMEM containing 4% PFA for 5 m, washed with PBS, and then incubated in the absence or presence of 0.25% saponin in PBS for 10 m as indicated. After a PBS wash and blocking with Mouse On Mouse  blocking buffer for 30 m, cells were incubated with 3 µg/ml 9E10 antibody or anti-Giantin (1:600 dilution) diluted in MOM Diluent buffer for 40 m. Following another wash, cells were stained with 4 µg/ml Alexa Fluor 488 goat anti-mouse IgG, 4 µg/ml Alexa Fluor 633 goat anti-rabbit IgG and 0.1 µg/ml DAPI diluted in MOM diluent buffer for 1 hr. The cells were then washed and subjected to confocal imaging analysis using the Zeiss LSM880 Airyscan microscope utilizing the Zen Digital Imaging Software.For immunofluorescent microscopy of transfected SV589 cells, 2 × 106 cells per 35 mm No. 1.5 coverslip dish, and treated as described in the figure legend. Plates containing mouse primary macrophages were washed with PBS, fixed in PBS containing 1% glutaraldehyde and 4% PFA for 20 min, washed with PBS, and incubated with 50 mM glycine dissolved in PBS for 15 min to block the remaining glutaraldehyde. For cells subjected to permeabilization, the plates were treated with 0.25% saponin in PBS for 30 min. Following this step the wash was performed with either PBS (without cell permeabilization) or PBS containing 0.2% saponin (with cell permeabilization). After a wash, plates were blocked with MOM blocking buffer for 1 hr. Plates were then incubated with 1 µg/ml 45531 antibody diluted in MOM Diluent buffer overnight at 4°C. Following another wash, plates were incubated with 4 µg/ml Alexa Fluor 488 goat anti-mouse IgG and 0.1 µg/ml DAPI diluted in MOM diluent buffer for 2 hr. The plates were then washed and subjected to confocal imaging analysis using the Zeiss LSM880 Airyscan microscope utilizing the Zen Digital Imaging Software.For immunofluorescent microscopy of Primary mouse macrophages, cells were set up at 1 × 10For immuno-gold EM analysis, the plates were treated the same way as that of immunofluorescent microscopy for permeabilized cells up to the step before addition of the secondary antibody. For this purpose, the plates were incubated with 10 µg/ml Alexa Fluor 488 FluoroNanogold goat anti-mouse IgG for 2 hr. The plates were then washed with PBS containing 50 mM glycine, and submitted to the UTSW EM Core for gold enhancement, sectioning, and sample preparation. Imaging was performed on a Tecnai Spirit electron microscopy utilizing the iTEM software and a Morada camera.5 cells per well, and treated as described in the figure legend. Migration assays were performed with the CytoSelect 96-Well Cell Migration Assay  using Corning HTS Transwell 96 well permeable supports  according manufacturer’s direction. Signals generated in the absence of CCL5 were subtracted to normalize for random migration.Primary mouse macrophages from mice were seeded into upper wells of transwell plates at 2 × 106 cells per 100 mm dish, and treated with or without 100 ng/ml LPS for 24 hr. Sphingolipids including dihydroceramide were quantitated by the UT Southwestern Lipidomics Core as previously described GXXXN[LVAWPMIF](5-7). Resulting proteins confirmed to have the GXXXN motif within the first transmembrane domain were included to run three additional rounds of Position-Specific Iterative BLAST. All proteins reported in Methods of quantification and statistical analysis are reported in the Figure Legends. In the interests of transparency, eLife includes the editorial decision letter, peer reviews, and accompanying author responses.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that major issues remain unresolved.] The authors have raised the issue by drawing in their interpretive drawing that this mechanism also allows phagocytes to locate microbes, but based on the sequence information, this doesn't seem to be the case and the LPS treated phagocytes will also be blinded to microbes after about 16 hours. This would give this mechanism a broader impact on leukocyte chemotaxis than the previously defined hierarchies. This would seem to have implications for challenges with clearing extracellular microbes in sepsis.We would like to thank the reviewer for his positive comments. We agree with the reviewer that our model shown in Figure 6 of the original manuscript could be misleading. We mean to show that after contacting LPS, macrophages stop migrating further toward chemokines so they can combat bacterial infection at the current location through mechanism such as secreting proinflammatory cytokines but not necessarily through enhanced phagocytosis of bacteria. In the revised manuscript, we redraw the figure to remove any implication of phagocytosis of bacteria by macrophages. We also clarify that point, and emphasize that RAT of the chemokine receptors may be responsible for impaired clearance of bacterial infection during sepsis when we discuss the figure in the first paragraph of the Discussion section.Reviewer #2:This manuscript arrives at the remarkable conclusion that CCR5 (and certain other GPCRs) can be made in a fully inverted topology in response to altered ceramide levels. The functional consequence of this inversion is the reduced biological activity of the receptor, which in the case of CCR5 involves macrophage migration toward its ligand, CCL5. Due to the striking nature of these claims, we read the manuscript with great interest. While there is no doubt that ceramide affects CCR5 in some manner, numerous conceptual and experimental flaws in the study's design and interpretation greatly weaken the argument that the basis of these effects is topology inversion. When the below points are considered together, it would seem that much of the data argue for ceramide affecting CCR5 trafficking, not topology. I would therefore strongly encourage the authors to reconsider publication of this study until the data for an effect on topogenesis is examined more carefully and thoroughly. Indeed, it is noteworthy (and rather surprising) that not a single experiment in the paper actually looks at CCR5 topogenesis at the ER, opting instead for relatively blunt end-point assays prone to alternative interpretations as outlined below.Major concerns:1) The assays in which the C-terminus of CCR5 is tagged and monitored by cell impermeable labelling are only sufficient to qualitatively demonstrate the topological location of the tagged terminus, but cannot accurately report on CCR5 topogenesis as a whole. It is easily conceivable that drastic changes in lipid composition within the cell result in misfolding or incomplete insertion of some TMDs such that the C-terminus winds up in the ER lumen at some low level. Given that these are all performed with an overexpressed protein, it is also conceivable that a misfolded population would "escape" the quality control machinery and successfully reach the PM. For these reasons, it is important to carefully quantify the amount of surface-exposed 'inverted' CCR5 relative to total cellular CCR5. One way to do this would be to use biotin-SNAP to label the surface exposed inverted CCR5, then use avidin pulldowns and blots to determine what proportion of total cellular CCR5 was surface labeled. Controls using pre-labeling with fluorescent SNAP label prior to biotin labeling could verify that labeling efficiencies are close to quantitative. Such an experiment would also verify that what is being detected is actually full length CCR5, not a fragment.An experiment very similar to that proposed by the reviewer was shown in Figure 2H of the original manuscript . The result shows that majority of full length CCR5(B) produced in ceramide-treated cells can be labeled by a thiol-reactive cell surface biotin labeling reaction thereby precipitable by streptavidin beads. These results support the hypothesis that CCR5 produced in ceramide-treated cells is also localized on plasma membranes.2) In several of the critical experiments aimed at demonstrating topology inversion, the authors use glycosylation as an assay for topology. While N-linked glycosylation is a reasonable proxy for topology, O-linked glycosyation is problematic. The reason is that this reaction occurs in the Golgi on the folded protein. Thus, the absence of O-linked glycosylation could be due to a failure of the protein to reach the Golgi and/or altered folding, and not necessarily indicative of topology. Furthermore, I did not see where the authors provided evidence that O-linked glycosylation is not impacted by 16 h of ceramide treatment. This would seem to be a critical control for any assay relying on an endogenous biochemical reaction.The data shown in Figure 2H of the original manuscript  clearly demonstrated that the unglycosylated CCR5 produced in ceramide-treated cells reached cell surface. This observation is inconsistent with the interpretation that the lack of the glycosylation is caused by retention of the protein in the ER.Unlike N-linked glycosylation, the carbohydrate attachment to proteins through O-linked glycosylation is different among individual protein. Even for the same protein, the O-linked glycosylation reaction could be different when the protein is expressed in a different cell. The extreme heterogeneity of this reaction makes it impossible to perform a control experiment to determine the effect of ceramide on O-linked glycosylation in general.3) G44L and N48L are used as mutants intended to confine topogenesis to the inverted form. However, these mutants are problematic for three reasons. First, inspection of the crystal structure of CCR5 reveals that they both face the interior of the folded protein, with N48 interacting with D76, and the absence of a side chain on G44 being important for avoiding clashes with L80. Thus, mutating either residue to Leucine will very likely disrupt the folding of the protein, and hence might cause it to be primarily retained in the ER. This would readily explain why it is not O-glycosylated by Golgi enzymes  and why none of it is labelled at the surface in Figure 2E without having to invoke an inverted topology. Second, the non-conservative mutations that substantially increase hydrophobicity is ill-advised as it may have effects on topology independently of the GXXXN motif. Instead, mutating this motif to residues of comparable hydrophobicity is preferable to more cleanly illustrate that N is crucial to ceramine-induced effects. Even this may well be confounded by effects on protein structure/stability. Third, the increased hydrophobicity of the G44L or N48L mutations is actually predicted to favour the correct orientation with the N-terminus facing the extracellular side (see PMID 9151664), not the inverted orientation as proposed by the authors. For all of these reasons, it is quite unlikely that mutants are acting in the way the authors propose.Unlike wild type CCR5, we did not perform experiments to carefully examine the localization of CCR5(N48L) in the original manuscript. Following the analogy with TM4SF20, we assumed that the mutant protein is locked into the inversed topology, as the apparent molecular weight of the mutant protein is identical to unglycosylated CCR5(B) regardless of ceramide treatment. After reading the comments of the reviewer, we performed a cell surface biotin labeling experiment similar to that shown in Figure 2G to determine the localization of the mutant protein. Unlike the results obtained from wild type CCR5, we were surprised to find out that the mutant protein barely reached cell surface regardless of ceramide treatment. This observation and results from immunofluorescent microscopy suggest that CCR5(N48L) is primarily localized in the ER. Thus, the reviewer is correct in that the data showing the lack of the O-linked glycosylation and the absence of the N-terminal SNAP labeling of the mutant protein may not be used to support our hypothesis. As a result, we remove all data using the mutant CCR5 from the original manuscript, and discuss our new finding regarding the mutant CCR5 in paragraph six of the Discussion in the revised manuscript. We thank the reviewer for pointing out this important problem in our original manuscript. However, we would like to emphasize that removing these data does not alter our conclusion regarding ceramide-induced topological inversion of wild type CCR5, because both CCR5(A) produced in the absence of ceramide and CCR5(B) generated in ceramide-treated cells reach the cell surface.4) The SNAP-CCR5 construct used in Figure 2E and Figure 2—figure supplement 2 would seem to directly disprove the authors' conclusions. As stated in the main text, the construct contains a SNAP tag (~20 kD) at the N-terminus, and according to the Materials and methods, this was obtained from CisBio. The sequence of the construct from CisBio indicates that the construct contains the N-terminal signal sequence from human CD8. This would explain how the SNAP tag domain actually gets translocated across the ER membrane. Thus, the topology of this construct is determined by the N-terminal signal sequence, not the first transmembrane domain as in the untagged construct. Rather remarkably, the SNAP-CCR5 also shows strong ceramide-induced exposure at the cell surface  and loss of O-linked glycosylation (supplement). Since this construct's topology is not determined by the first transmembrane domain, it would seem that both the surface exposure and glycosylation are affected by ceramide for other reasons. The simplest interpretation is retention of the protein in the ER after ceramide treatment, which would also explain why the N48L is constitutively not only the surface and why it is constitutively not glycosylated. The authors seems to have completely overlooked the fact that this construct has a signal sequence. If they have removed it, then it is not stated in the Materials and methods (and it would be very hard to explain how the N-terminus could be translocated across the ER membrane).The reviewer assumes that addition of an N-terminal signal peptide will override the RAT signal generated by the GXXXN motif present in the first transmembrane helix. We actually had the same hypothesis when we studied RAT of TM4SF20. Astonishingly, the translocation signal generated by the GXXXN motif in the first transmembrane domain prevails over that produced by the signal peptide, as fusing a prolactin signal peptide at the N-terminus of TM4SF20 did not affect ceramide-induced RAT of TM4SF20 . Thus, we are not surprised at the finding that the presence of a signal peptide at the N-terminus of SNAP-CCR5 does not affect RAT of the fusion protein. This is one of the reasons why we are unable to make a mutant CCR5 or TM4SF20 that is locked in the A form, resisting ceramide-induced topological inversion. We make this point clearer when we present the data shown in Figure 2D in paragraph five of the Results section of the revised manuscript. This point is also discussed in paragraph three of the Discussion section of the revised manuscript.5) The cysteine mutations intended to be a reporter of topology are problematic because several cysteines on the cytosolic side of CCR5 are palmitoylated. Thus, mutating these may well have consequences for trafficking of the receptor (see PMID 21819967 for a review), confounding any interpretations from this approach.The result shown in Figure 2G of the original manuscript  clearly demonstrates that CCR5(A) with all cysteines at the cytosolic side mutated still reached cell surface in the absence of ceramide treatment. This observation should address the concern of the reviewer.6) The authors use immunogold labelling to infer topology of CCR5 in macrophages . This is conceptually problematic and the observations do not seem to fit with their favoured interpretation. Given that their primary antibody recognizes an epitope close to the membrane and the secondary antibody appears to be a gold-conjugated Fab , the label can be up to ~21 nm away from the epitope . This distance increases to ~28-30 nm if the secondary antibody is a whole IgG. In either case, the label can easily be on either side of the membrane regardless of the topology because the membrane thickness is only ~5 nm. Hence, without extensive numbers and statistical power, one simply cannot get the 5 nm resolution needed to infer topology using a labelling method with a precision of 20-30 nm.Did the reviewer suggest that immunogold EM is not suitable to determine whether an epitope is at the intracellular or extracellular side of plasma membranes? Using immunogold EM, a previous study  clearly showed that the N-terminal domain of CCR5 is exclusively localized at the extracellular side of plasma membranes in cells under resting state. They did not observe any intracellular staining. This technique has been frequently used to determine the topology of transmembrane proteins localized on plasma membranes .Remarkably however, they do see a difference. But inspection of the micrograph in Figure 3K shows that the gold particles are ~200 nm or more away from the plasma membrane. As should be obvious from the above considerations, this cannot be compatible with labelling of a cell surface protein. Many of the examples in the supplement are also too far away . Thus, the simplest explanation is that after LPS treatment, the CCR5 that is labelled is in intracellular compartments, perhaps the ER (which is often close to the plasma membrane). However, the membrane morphology is not retained very well in their EM images, so one cannot really evaluate this.The CCR5-specific gold cluster signal is caused by binding of multiple gold-conjugated secondary antibodies to anti-CCR5. As a result, the distance between these signals and plasma membranes are longer than that calculated by the reviewer. This pattern of CCR5 staining was also observed in an earlier study . Consistent with this explanation, we observed that the distance between extracellular CCR5 signal and plasma membranes in resting cells is not too much different from that between intracellular CCR5 signal and plasma membranes in LPS-treated cells.We also added the scale bar in images shown in Figure 3—figure supplement 3 of the revised manuscript. The scale bar was somehow lost when we converted our figure into PDF format during last submission.A second major issue is that they only observe an average of ~1.3 labels per cell. It is difficult to know what to make of this given how much signal is seen in the fluorescent images using the same antibody. For these reasons, the authors' interpretation of the immuno-EM cannot be supported from the data they present.-/- macrophages. Some CCR5 was labeled by a single gold particle, as their number in wild type macrophages was higher than that in CCR5-/- macrophages. However, the specificity of such labeling was difficult to determine, as these particles did exist in CCR5-/- macrophages. As a result, we only counted the gold cluster signal. This number should be smaller than that of CCR5 molecules, as it did not include CCR5 labeled by a single gold particle. We make this point clearer when we discuss these results in paragraph ten of the Results section of the revised manuscript.As stated in the manuscript, we observed both gold cluster and single gold particle-labeled signal in the EM images. Only the gold cluster signal is considered as CCR5-specific as such signal was never observed in CCR57) In the absence of the immuno-EM experiment, the macrophage data is very weak in establishing any role for topology inversion of CCR5 in LPS-stimulated macrophage migration. Figure 3A to 3I simply establishes that LPS reduces surface levels of CCR5 and does not speak at all to its topology. The same is true for Figure 4. All of these data are equally consistent with LPS causing intracellular retention (or endocytosis) of CCR5 in a dihydroceramide dependent manner. Thus, the effects on migration seen in Figure 5 simply reflects the fact that there is less CCR5 on the surface of these macrophages.As stated in our comments above, we believe that our immuno-EM data do provide a critical piece of evidence to support out hypothesis. In addition, our immunofluorescent microscopy results shown in Figures 3D and E suggest that CCR5 was localized on plasma membranes regardless of LPS treatment. Thus, our data about endogenous CCR5 expressed in macrophages are consistent with our model that LPS-induced dihydroceramide production triggers RAT of CCR5.Minor Comments:1) The fluorescence microscopy is poor resolution and often over-exposed (at least in the images I was provided) so it is very hard to judge localization (surface versus intracellular locations).The immunofluorescent signals generated in non-permeabilized cells were much weaker than that produced in permeabilized cells. Since the exposure was kept at the same level for all images, we had to overexpose the images of the permeabilized cells in order to see a clear signal of non-permeabilized cells. In the revised manuscript, we include immunofluorescent microscopy results of the permeabilized cells with normal intensity in Figure 3—figure supplement 2. These results show that CCR5 in macrophages is primarily localized on plasma membranes regardless of LPS treatment.2) Introduction – the authors state that "it remains unclear how GPCRs can adopt such a membrane topology without the signal peptide." This is not accurate, as the study of how an N-terminal transmembrane domain directs topology has been extensively studied. Such sequences are termed signal anchors because they act as both a signal sequence and transmembrane anchor. The features that determine their topology has been investigated by extensive mutagenesis many years ago , and quite a bit has also been done on the molecular basis of their insertion (see PMID 10943843).We rewrite this part of Introduction in the first paragraph of the revised manuscript to incorporate this information.3) Throughout the paper, the authors essentially assume that an inverted GPCR would traffic to the cell surface. This seems very unlikely because the extracellular domain facing the cytosol would not form disulphide bonds and therefore not fold correctly, while the C-terminus facing the outside would not be palmitoylated. A more nuanced discussion is merited.We do not ASSUME that GPCR with the inverted topology reaches cell surface. This conclusion is directly supported by the data shown in Figure 1 and Figure 2G of the revised manuscript. However, we agree with the reviewer that a better discussion is needed to clarify this point. We now show in paragraph four of the Discussion section that our data regarding inaccessibility of the antibodies against extracellular regions of CCR5(A) with the GPCR configuration in ceramide-treated cells may be interpreted by ER retention of the protein. This model may also be consistent with the observation that ceramide treatment blocked O-linked glycosylation of CCR5, as this post-translational modification takes place in the Golgi complex. However, this model cannot explain our observations that ceramide treatment exposed C-terminus of CCR5 to the extracellular space. Nor is this model consistent with the observation that significant amount of CCR5 in cells treated with ceramide or macrophages treated with LPS, which stimulated production of dihydroceramide, was localized on cell surface. Thus, taking all of our data together, we believe that topological inversion through RAT is the better interpretation of our results.4) The surface labelling kit cited in the Materials and methods labels amines, not sulfhydryls. Some clarification is needed here about how the experiment was actually done.We thank the reviewer for pointing out this important mistake we made in the original manuscript. We actually labeled cell surface cysteine residues with 0.25 mg/ml EZ-link maleimide-PEG2-biotin (Thermo Fisher Scientific) and then immunoprecipitated the labeled proteins with the Pierce Cell Surface Labeling Kit. We correct this mistake in the section of Materials and methods.Reviewer #3:This study proposes a very nice mechanism of chemokine receptor inversion to explain why cells may stop chemotaxing. While interesting, the biological situation chosen is a difficult one to understand i.e., LPS stimulation of peritoneal macrophages prevents migration to CCL5. I will ask a few questions that would need to be answered with additional experiments.There is much work to demonstrate that when high enough concentrations of a chemokine for sufficient time leads to downregulation of the receptor or the receptor becomes non-responsive. While to date inversion has not been proposed, it would be important to see whether inversion is important under these conditions to cause desensitization of the chemokine receptor. In other words is inversion responsible for desensitization. There is much data to suggest that a very different mechanism (β-arrestin) is responsible and this needs to be addressed. As this work may be very important for chemotaxis field.LPS can stimulate integrin activation leading to a stop signal for immune cells mediated through TLR4. While the authors argue that there is chemokine receptor inversion, there may be a modifying role for integrins and other effectors (Ca levels etc). As such the LPS experiments becomes very hard to interpret.There is a growing view that chemokines function in a hierarchical model with some being turned off while others become activated. It would be good to consider this potential reason for inversion.The current study suggests that LPS-induced topological inversion of CCR5 may be one of the mechanisms for LPS to inhibit chemotaxis mediated by the receptor. This conclusion is supported by our observations that FB1, which blocked LPS-induced topological inversion of CCR5 by inhibiting synthesis of dihydroceramide, restored the chemotaxis reaction of macrophages exposed to LPS. However, we agree with the reviewer that the evidence we provided is not sufficient to demonstrate that topological inversion of CCR5 is the primary mechanism for LPS to desensitize CCR5, as dihydroceramide may activate the arrestin and/or integrin pathway mentioned by the reviewer, or any unknown pathways that inactivate CCR5. The only way to address the question of the reviewer is to make a mutant CCR5 resisting dihydroceramide-induced RAT by locking its topology into the GPCR configuration (CCR5(A)). Unfortunately, fixing topology of CCR5 and TM4SF20, another protein subjected to RAT, into their A form appears to be challenging. Just adding a signal peptide at the N-terminus of these proteins does not block ceramide/dihydroceramide-induced topological inversion. Thus, understanding the molecular details of the regulatory mechanism behind RAT may be required before reagents can be developed to address the question raised by the reviewer. This point is now discussed in paragraph three of the Discussion section. We would like to reassure the reviewer that we are vigorously pursuing study to address this question, but owing to the lack of the key reagent, we are unable to include these studies into the current manuscript.While this is very exciting cell biology, I fail to understand how this contributes to a bigger picture of the immune response. The authors in the first paragraph of discussion suggest that this is critical for a good immune response, however an alternative interpretation could be that LPS subverts the immune response and does not allow immune cells to come to the nidus of infection. Without experiments to see whether the inversion is better or worse for infection outcome, the discussion is purely speculative.We agree with the reviewer that our model shown in Figure 6 is speculative at this time. We have revised our discussion in the first paragraph of the Discussion in the revised manuscript to demonstrate this point. As suggested by the reviewer, we also discuss in this paragraph the potential contribution of this mechanism to inhibit macrophage migration during sepsis.