The results suggest that fast eating is associated with higher glycemic excursion in healthy women.Epidemiological studies have shown that self-reported fast eating increases the risk of diabetes and obesity. Our aim was to evaluate the acute effect of fast eating on glycemic parameters through conducting a randomized controlled cross-over study with young healthy women. Nineteen healthy women wore a flash glucose monitoring system for 6 days. Each participant consumed identical test meals with a different eating speed of fast eating (10 min) or slow eating (20 min) on the 4th or the 5th day. The daily glycemic parameters were compared between the 2 days. The mean amplitude of glycemic excursion (MAGE; fast eating 3.67 ± 0.31 vs. slow eating 2.67 ± 0.20 mmol/L,  It has been demonstrated that self-reported eating at a fast speed leads to weight gain ,2, increThe aim of this study was to evaluate the acute effect of different eating speeds on glycemic parameters in young healthy women with a flash glucose monitoring system . FGM is a new technology among continuous glucose monitoring systems that does not require regular capillary glucose sampling by finger prick  like traditional continuous glucose monitoring (CGM). The FGM sensor stores interstitial fluid glucose levels every 15 min for 14 days and was reported to be accurate and effectively replaced SMBG .University students were recruited from Kyoto Women’s University, Kyoto, Japan, after being informed the study requirements. Twenty-one participants were enrolled in the study and 2 participants were excluded because of discontinuation of the study protocol of eating speed. The study was conducted between December 2019 and February 2020. The volunteers had no history of any metabolic diseases. None of the volunteers were pregnant, smokers, had an eating disorder, weight loss, or followed any other special diet in the previous 6 months, and they refrained from taking any medications and supplements known to affect their metabolism. The purpose, design, and risks of this study were explained to each participant and written informed consent was obtained prior to the study.The study was designed as a randomized controlled two-treatment cross-over within-participant clinical study to avoid the characteristic differences of the two groups in different eating speed. The study protocol involving human subjects was approved by the Ethics Committee of Kyoto Women’s University (2019-8) according to the guidelines laid down in the Declaration of Helsinki and was registered at the UMIN Clinical Trials Registry (UMIN 0000038684). All participants consumed identical test meals for two days, which were consumed at two different eating speeds during the 6 day study period, as shown in Two weeks before the study, anthropometric measurements and blood samples of participants were collected in the morning after an overnight fast. Blood samples were examined in Rakuwakai Toji Minami Hospital. Fasting plasma glucose concentration (FPG) was measured by amperometric methods. Hemoglobin A1c (HbA1c) levels were determined by high-performance liquid chromatography (HPLC). The incremental area under the curves (IAUC) for glucose after breakfast, lunch, and dinner were calculated from the baseline by the trapezoidal method. The parameters to evaluate glycemic variability were the mean amplitude of glycemic excursions (MAGE)  and the p < 0.05 was considered statistically significant. The results are expressed as mean ± standard error of the mean (SEM) unless otherwise stated. All analyses were performed with SPSS Statistics ver. 22 software . The composition and macronutrient content of the menu of the test meals was shown in A sample size of 14 participants in the current study provided 95% power to detect 5% difference in postprandial glucose concentrations , referring to our previous study of consuming test meals in different sequences in healthy women . Twenty-2, HbA1c 34 ± 2 mmol/mol (5.4 ± 0.2%), FPG 4.86 ± 0.39 mmol/L: mean ± SD). The results were based on 19 women , increasing serum adiponectin concentration, and suppressing endotoxin and non-esterified fatty acid, as reported previously . It has The strength of our study is that this is the first randomized controlled cross-over interventional study to explore the association between eating speed and the glycemic responses in healthy Japanese women. Postprandial hyperglycemia and higher MAGE are associated with increased risk of type 2 diabetes and cardiovascular diseases in people with and without diabetes ,28,29. ISome limitations to the present study should be mentioned. First, the present study is an acute interventional study, therefore, it is unable to translate all these effects on glycemic responses to long-term benefits. Second, the participants of this study consisted only of young healthy Japanese women who experience a diet and lifestyle specific to Japan. Therefore, we should be cautious to apply our results to individuals with other gender, race, genetic backgrounds, and lifestyle, and individuals with diabetes. For the third, because how the metabolism is regulated by eating speed is not fully understood, the role of insulin, incretin hormones, cytokines, and endogenous glucose production on significance of eating speed is still unclear. Fourth, the eating protocol between fast eating  and slow eating (eating at once as a mixture) in this study was not exactly the same, leaving possibility that the difference in the eating protocol contributed to the results. As the fifth limitation, psychological conditions such as satiety may have influenced the glycemic responses, but we did not measure the extent of satiety between the study days of fast and slow eating. It has been reported that eating fast tended to increase the amount of food intake by suppressing satiety ,12,13. SThe disadvantage of eating fast shown in this study brings the possibility of raising the risk of type 2 diabetes and obesity in healthy young individuals. Eating slowly is potentially advantageous to public health, because the modification of eating speed could be cost effective for promoting management of body weight and glycemic control ,34. DietTo summarize, we have shown that eating fast is associated with higher daily glycemic excursions and postprandial glucose concentrations in a randomized controlled cross-over trial for the first time. Real-world approaches are needed to better understand the negative influence of eating fast on glycemic responses and to support approaches for slowing down eating speed in healthy individuals.


Among them, four out of the five most probable gene states exhibit gene expression patterns observed in single mouse embryonic cells at 32-cell and 64-cell stages. These gene states can be robustly predicted by the stemness GRN but not by randomized versions of the stemness GRN. Strikingly, we found a hierarchical structure of the GRN with the Oct4/Cdx2 motif functioning as the first decision-making module followed by Gata6/Nanog. We propose that stem cell populations, instead of being viewed as all having a specific cellular state, can be regarded as a heterogeneous mixture including cells in various states. Upon perturbations by external signals, stem cells lose the capacity to access certain cellular states, thereby becoming differentiated. The new gene states and key parameters regulating transitions among gene states proposed by RACIPE can be used to guide experimental strategies to better understand differentiation and design reprogramming. The findings demonstrate that the functions of the stemness GRN is mainly determined by its well-evolved network topology rather than by detailed kinetic parameters.Stem cells can precisely and robustly undergo cellular differentiation and lineage commitment, referred to as stemness. However, how the gene network underlying stemness regulation reliably specifies cell fates is not well understood. To address this question, we applied a recently developed computational method,  Due tA substantial amount of research has been conducted to identify key TFs and their roles in directing stem cell differentiation –11. Thesrandom circuit perturbation (RACIPE) [To address these issues, we here have applied a recently developed mathematical modelling algorithm, (RACIPE) ,23, to e(RACIPE) –24 (figu(RACIPE) –24.Figua). We find that applying RACIPE to the stemness GRN can recapitulate the gene expression patterns of mouse ESCs at 32-cell and 64-cell stages. These gene expression patterns are conserved robust features of the stemness GRN but disappear when the network topology is randomized. Furthermore, through performing in silico perturbation analysis, we show that (i) the presence of external signals can exclude the accessibility of some of the gene states enabled by the stemness GRN; (ii) the stemness GRN has a hidden hierarchical structure, which enables a two-step decision-making process with the Oct4/Cdx2 motif functioning as the first decision-making module and the Gata6/Nanog motif as the second one. In summary, we demonstrate that the robustness of stemness regulation is mainly determined by the topology of the stemness GRN, and RACIPE can be applied straightforwardly to elucidate the hierarchical decision-making of stem cell differentiation.Here, we use RACIPE to analyse a core stemness GRN which contains eight master regulatory TFs involved in stem cell differentiation a. We finBefore proceeding, it is important to clarify the connection between RACIPE model ensembles and actual cell populations. Ever since the seminal work of Elowitz and co-workers on ‘external’ noise, it has become clear that cell populations exhibit distributions of phenotypic properties due to differing single-cell values of the kinetic parameters governing the genetic circuitry. RACIPE, with one slight change from its baseline algorithm , also can be used to create a nominal cell population, making use of the null model choice of uniform uncorrelated parameter distributions over relevant physiological ranges. This will then need to be modified to account for correlations among the parameters. It is nonetheless useful to exhibit results for this uncorrelated ensemble, just to provide a guide as to what experimental data would indicate the need for a more biologically accurate formulation. Thus, we provide below initial results for distributions with the aforementioned goal.2.2.1.a, details illustrated in §3.1) was studied by RACIPE. RACIPE is a free open source software distributed under (Apache 2.0) license and can be downloaded from GitHub (https://github.com/simonhb1990/RACIPE-1.0). Specifically, the RACIPE procedure creates an ensemble of models in each of which the temporal dynamics of the eight TFs  and the protein complex (Oct4–Sox2) are simulated by a set of ordinary differential equations (ODEs) accounting for their production, degradation and regulatory interactions. The transcriptional regulation between these TFs is modelled by the shifted Hill function [In this study, the dynamical behaviour of the stemness GRN . The details of the method to compare RACIPE-generated data with experimental data can be found in electronic supplementary material, §S5 and S6.2.3.To generate a null model of a cell population replete with phenotypic heterogeneity, we modify the RACIPE method to properly count states found by the algorithm. In particular, when a given set of parameters gives rise to multiple steady states, each steady state is weighted by the proportion of the initial conditions leading to that particular state.3.3.1.a). Due to the complexity of the GRN, elucidating its dynamical behaviours can be difficult through traditional modelling approaches. Here, we use RACIPE to identify the robust gene states enabled by the stemness GRN. As all regulatory links in the stemness GRN are transcriptional except for the binding/unbinding process between the TFs Oct4 and Sox2, for the simplicity, we initially model the temporal dynamics of the Oct4–Sox2 complex in the same manner as the other TFs. Later, we will generalize RACIPE to include binding/unbinding reactions and verify that none of the results change in any meaningful ways.We integrate the master gene regulators of stemness characterized by previous studies ,13,26, afigure 2b) and stable states  of the 10 000 models. Similarly, we show that 10 000 models are sufficient to capture the robust gene states of the stemness GRN, as the consistency of the top 14 clusters of gene expression profiles from three distinct 10 000 sets of parameters is statistically significant .In our approach, instead of finding a representative set of kinetic parameters, we randomly generate 10 000 sets of parameters (i.e. 10 000 RACIPE models) within their given biologically reasonable ranges. For each model, we numerically solve the governing ODEs with 1000 random initial conditions so as to thoroughly identify all possible stable steady-state solutions. These 1000 initial conditions give rise to 1000 solutions, based on which we identify the number of distinct stable states and their corresponding gene expression profiles. We show that 1000 initial conditions are sufficient, as increasing the number of initial conditions to 1500 or 2000 generates consistent probability distributions of the number of stable states b and stab). There are rare occasions (less than 1%) where RACIPE models generate more than six stable steady states. Since they are not statistically significant, we excluded these data for further analysis. The circuit also has approximately 2% chance of having oscillatory or chaotic dynamics (e.g. time-dependent dynamics), which are not the focus of this manuscript and therefore are excluded from further analysis. We collected the gene expression profiles from all 10 000 RACIPE models and constructed a data matrix, where each column represents a gene and each row represents a stable steady-state solution. These data resemble experimental gene expression data, thus inspiring us to apply similar statistical methods.For the majority of RACIPE models (approx. 98%), the stemness GRN allows one to six stable steady states b. There c). Regarding the first principal component (PC1), Oct4, Sox2 and Nanog contribute positively while Cdx2 and Gata6 contribute negatively (d), indicating an anti-correlation of the activity between these two sets of TFs, which is consistent with the experimental observations [figure 2c.Since the kinetic parameters of the circuit are randomized with large variations (up to one or two orders of magnitude), one might expect that the gene expression profiles from different models would be very different. Strikingly, we found that the gene expression profiles can be segregated into only a few clusters when projected onto two independent components by the commonly used principal component analysis (PCA) c. Regardgatively d, indicarvations . We have3.2.a,b). We found that most of the TFs exhibit bi-modal distributions, which indicates the up/downregulation of these TFs is associated with different phenotypes during stem cell differentiation . To evaluate how well RACIPE can recapitulate the characteristic gene expression of various cellular phenotypes during stem cell differentiation, we compared the RACIPE-generated gene expression profiles with those observed experimentally. We found that the most significant RACIPE-generated gene states, as determined by the reproducibility in different ensembles of RACIPE models, recapitulate the gene expression patterns measured by experiments . We compare the RACIPE-generated data with the gene expression data of single mouse embryo cells at various stages during development [figure 3a, electronic supplementary material, §S6, figures S6–S8). Interestingly, the RACIPE-generated gene states match those during the late stage of embryo development , where totipotent cells start to differentiate into trophectoderm (TE) and inner cell mass (ICM) [Hi, Gata6Hi/NanogHi/Oct4Hi/Sox2Hi, NanogHi/Oct4Hi/Sox2Hi and Gata6Hi/Oct4Hi/Sox2Hi—are among the most probable gene states identified by RACIPE (figure 3b). Notably, the matching between RACIPE data and experimental data is statistically significant .To identify the pattern of RACIPE-generated gene expression profiles, we applied hierarchical clustering analysis (HCA) to the RACIPE-generated gene expression data. We found that the RACIPE-generated data form several major clusters, representing different gene expression patterns a,b. We felopment  (figure ss (ICM) , but notState 1, the state numbers are indicated in figure 3c) [State 6) [States 1 and 12) [State 8) [State 5) [State 2) [State 3) [States 7, 9, 11 and 15) [Indeed, the RACIPE-generated gene expression patterns can characterize multiple developmental stages. For example, the first cell fate determination during embryonic development happens at the blastocyst stage when the ICM and TE are formed . Oct4 angure 3c) ,30. At tState 6) ,32, but  and 12) ,30. FurtState 8) . Upon GaState 2) ,34. OverState 3) . TE coul and 15) ,35,36. AThese results suggest that RACIPE can identify the gene expression patterns of various cellular phenotypes especially those of the late developmental stages and also characterize potential additional gene states during stem cell differentiation.3.3.To further investigate the role of the topology of the stemness GRN in maintaining the robust gene states, we compare the stemness GRN with two types of its randomized versions (Type I including 10 networks and Type II including 10 networks) with randomly generated connections among genes. Both Type I and Type II randomized networks preserve the total number of inward and outward links for each gene, and the binding of Oct4 and Sox2 . For the Type I randomized network, we also keep the same numbers of excitatory and inhibitory inward links for each gene. We then apply RACIPE to these two types of randomized networks and compare their dynamic behaviours with those of the actual stemness GRN.Neither Type I nor Type II randomized networks can generate the aforementioned robust gene states and recapitulate the experimentally observed gene expression features. Compared to the stemness GRN, both Type I and Type II randomized networks are much more likely to generate oscillatory or chaotic dynamics but not multi-stable states for each RACIPE model. Instead, both randomized networks are more likely to have only one stable state for each RACIPE model . When the gene states from all RACIPE models are combined, the histogram of each gene expression generated by the stemness GRN typically exhibits multi-modal distributions but those generated by the randomized networks mainly exhibit mono-modal distribution . In addition, we also find that it is difficult to cluster the gene expression data generated by the randomized networks , partly because the stemness GRN has much higher local density of the RACIPE-generated gene expression data relative to the randomized networks . Furthermore, the RACIPE-predicted gene expression profiles of the stemness GRN are significantly better than those of the randomized networks in recapitulating the experimental observation of the gene expression features of various stages during development, and especially the single-cell gene expression data of mouse embryo (3.4.a). We will discuss two examples of this feature in the following paragraphs.In addition to characterizing the gene expression profiles of different cell phenotypes, RACIPE enables us to uncover the key parameters representing physiological conditions that mediate the phenotypic transitions during stem cell differentiation. From the output of RACIPE simulations, we can identify the kinetic parameters that are significantly changed between gene states . The results indicate the transition from gene state 1 to gene state 2 is characterized by increased degradation of Sox2, decreased production of Sox2 and accumulation of Gata6. This indication is consistent with the experimental result that loss of Sox2 results in the differentiation of ESCs as characterized by the upregulation of trophoblast markers [To characterize the most differential kinetic processes between gene state 1 (representing the pluripotent epiblast stage) and gene state 2 (representing the trophoblast stage), we quantify the change of the mean values of each parameter in gene state 2 relative to gene state 1. We identify the parameters whose values increase the most (e.g. the degradation rate of Sox2 and the degradation rate of Oct4–Sox2), and the parameters whose values decrease the most  in gene state 2 relative to gene state 1 b. The re markers .a,c). The results indicate that transition from gene state 1 to gene state 6 requires the upregulation of Gata6 that can be accomplished by either increasing Gata6 production or decreasing the inhibition of Gata6 by Nanog. This simulation result is again consistent with the experimental observation that Gata6 is highly expressed at the early stage of ICM differentiation [figure 5a, can be found in electronic supplementary material, figure S14.To characterize the stimuli that can promote the transition from gene state 1 (representing the pluripotent epiblast stage) to gene state 6 (representing the early stage of ICM differentiation), we quantify the changes of each parameter in gene state 6 relative to gene state 1. We identify the parameters whose values increase most (the maximum production rate of Gata6) and the parameters whose values decrease most (the threshold for Gata6 self-activation and fold-change of the inhibition of Gata6 by Nanog) in gene state 6 relative to gene state 1 a,c. The ntiation . The fulIn summary, RACIPE can identify the parameters that differ the most between different gene states, such as the production/degradation rates and the threshold/fold-change of a regulatory link. Consequently, the biological process represented by these parameters can be considered as the regulatory target needed to drive cells to undergo certain phenotypic transitions.3.5.in silico perturbation analysis of the stemness GRN, including knocking out genes and removing links, by which we can understand the role of the knocked-out gene or the removed link in the dynamical behaviours of the stemness GRN. Here, we perform two types of perturbation analyses, knocking out a gene each time or removing a regulatory link each time . In both types of perturbation analyses, we quantify the change of the probability distribution of the multi-stability exhibited by the stemness GRN using the Kullback–Leibler (KL) divergence .RACIPE also enables a). Strikingly, removal of the regulatory links among these specific TFs also leads to the most significant changes in the probability distribution of the number of stable states (b). These TFs and the regulatory links between them indeed form a sub-network, representing the first decision-making module, referred to as the Oct4/Cdx2 module . The rest of the TFs and regulatory links form a sequential second sub-network, referred to as the Gata6/Nanog module (d). The RACIPE simulation results are consistent with experimental observations showing that the TFs Oct4 and Cdx2 govern the commitment of totipotent cells to either the ICM (Oct4high) or the TE (Cdx2high) lineages and the Gata6/Nanog module governing the commitment of ICM cells to either epiblast (Nanoghigh) or primitive endoderm (Gata6high) lineages [Through analysing the gene knockout results, we found that the knockout of TFs Cdx2, Oct4, Sox2 and the complex OCT4–SOX2 leads to the most significant changes in the probability distribution of the multi-stable behaviour of the GRN a. Strikie states b. These  figures c,d. The g module d. The RAlineages .Figure To compare the behaviours of these two sub-networks, we apply RACIPE to each of them and then compared the RACIPE-generated gene expression profiles with those generated by applying RACIPE to the full network. In other words, we want to evaluate how the dynamic behaviours of the two sub-networks change upon removal of the regulatory links connecting them. We find that the gene expression profiles determined by the first decision-making module Oct4/Cdx2 is conserved while those determined by the second module Gata6/Nanog are largely disrupted, upon the removal of the regulatory links connecting these two modules . The results here support the hierarchical structure of these two decision-making modules.3.6.e). Relative to the WT, upregulation of Cdx2 restricts most RACIPE models to acquire the stable states with Cdx2Hi (representing the TE stage) [hi (representing the ICM stage) [e). After cells reach the ICM stage, additional signals acting on Nanog and Gata6 can convert the GRN largely to either the NanogHi state (representing the epiblast stage) or the Gata6Hi state (representing the primitive endoderm stage) [e). These simulation results indicate that external signals often do not create new gene states but instead make a subset of gene states more accessible and the rest less accessible as also observed in As stem cell differentiation is a cascading event induced by signalling, we would like to analyse how the stemness GRN responds to various external signals  by RACIPE. The effects of external signals on a certain gene are simulated by scaling the production rate of that gene to 50-times larger  or smaller . We then calculated the probability distribution of gene states upon the imposition of external signals (excitatory or inhibitory) on each gene. As we showed before, without any external signals, the stemness GRN allows 15 robust gene states, referred to as the wild-type (WT) e. RelatiE stage) , while uM stage) ,39 . As one set of parameters can give rise to multiple stable state solutions, a weight factor proportional to the percentage of initial conditions leading to a stable state solution was multiplied to that solution. We systematically simulated the external signals acting on each TF by scaling the maximum production rate of that TF from 1/100 to 100 of the base level, representing inhibitory signals and excitatory signals respectively. For each TF, we apply RACIPE to the stemness GRN considering 20 different scenarios representing 10 inhibitory and 10 excitatory signals with varying strengths on that TF. We then applied the entropy-based index to quantify the heterogeneity of the ensemble stable states generated by RACIPE in each scenario for each TF . As we show that the WT stemness GRN exhibits the highest entropy, e.g. highest heterogeneity, and external signals that either upregulate or downregulate the maximum production rate of a TF usually decreases the entropy, leading to decreased heterogeneity of the nominal cell population. The decrease of heterogeneity partially results from the limited access to only a few gene states instead of all due to external signals.With this in mind, we wish to quantify the effects that various signals would have on the population heterogeneity as generated by the Altogether, our results suggest that RACIPE can explore the possible roles of external signals in the dynamic behaviours of the stemness GRN. These external signals may indeed restrict the gene states that can be accessible and may thereby lower population entropy. Indeed, we expect that it should be a generic property for cellular networks that the number of the stable states will decrease when strong perturbation signals are imposed.3.7.a, electronic supplementary material, figures S17 and S18). We observed consistent gene states by RACIPE-wb  relative to those acquired by RACIPE (c). We show that the RACIPE-wb generated gene expression profiles are quantitatively consistent with the RACIPE-generated ones (figure 7c). By RACIPE-wb, we performed perturbation analysis by knocking out genes and removing regulatory links one by one. Consistent with the result by RACIPE , we found that knocking out the TFs Oct4, Sox2, Cdx2 or Oct4–Sox2 or removing the regulatory links among these genes have the most pronounced effects on the multi-stable behaviours of the stemness GRN . Indeed, RACIPE-wb amplifies the differences observed under link removal relative to RACIPE . The effects of link removal for the rest of the regulatory interactions are in general smaller in RACIPE-wb than those in the original RACIPE. But the overall trends are similar for both modelling algorithms. Among these interactions, we found that removal of the inhibitory link from Cdx2 to Oct4 exhibits the highest KL divergence . The result by RACIPE-wb also indicates a similar hierarchical decision-making structure of the stemness GRN with the Oct4/Cdx2 module forming the first decision-making module and the rest forming the second decision-making module. In summary, when the detailed binding/unbinding processes are considered, the RACIPE-wb characterized dynamic behaviours of the stemness GRN remain consistent with RACIPE characterized ones.RACIPE provides a straightforward way to identify the robust dynamic behaviours of the stemness GRN. As we discussed before, RACIPE was originally developed for transcriptional regulation. In the stemness GRN, in addition to the majority of the links representing transcriptional regulation, there is one binding/unbinding process, between the TFs Oct4 and Sox2. We have, therefore, extended the RACIPE framework to explicitly model this binding/unbinding process to analyse how that may affect the network behaviour. We performed a parallel analysis of the stemness GRN using the updated RACIPE including binding/unbinding details (referred to as RACIPE-wb) , and 11 RACIPE-generated gene states are consistent with experimental results qualitatively . We also elucidate a hierarchical structure of the stemness GRN by RACIPE, which is consistent with experimental observations. The fact that our results reproduce known findings is a positive indicator of RACIPE's validity in analysing the dynamical behaviours of GRNs in general, including both transcriptional regulation and binding/unbinding processes.By applying RACIPE to the stemness GRN, we have identified a significant number of distinct gene states. The four most probable gene states generated by RACIPE match the gene expression patterns of single mouse embryonic cells at 32-cell and 64-cell stages quantitatively a, and 11There are two important insights generated by RACIPE. First, multiple RACIPE-generated gene states are associated with one experimentally observed stage. This is because the developmental stages are often characterized by expression of a few genes and these few genes can have similar expression patterns in various gene states. Our findings thus predict that future work using the expression of additional stemness genes to characterize developmental stages may help further delimit possible sub-developmental stages. Second, RACIPE can identify the specific parameters that differ the most between different gene states, such as the production/degradation rates of genes and the threshold/fold-change of regulatory links. Consequently, the biological process represented by these parameters can be considered as the regulatory targets needed to drive cells to undergo certain phenotypic transitions.highCDX2highNANOGlow proposed by RACIPE  while not those at early stages. This may be due to the incompleteness of the decision-making network of stemness. Although the TFs in the stemness GRN have been shown to be the master regulators governing stem cell differentiation by experimental studies, and the stemness GRN is largely consistent with stemness regulatory networks proposed by other studies ,13,47, iTo conclude, by applying RACIPE to a core stemness GRN, we showed that the network topology plays an essential role in cell fate decision-making during stem cell differentiation. This result is analogous to the findings from protein structure modelling, where conformational motions have been found to be determined by the overall molecular shape  and prot


Myelin destruction is followed by resident glia activation and mobilization of endogenous progenitors (OPC) which participate in myelin repair. Here we show that in response to demyelination, mature oligodendrocytes (OLG) bordering the lesion express Ndst1, a key enzyme for heparan sulfates (HS) synthesis. Ndst1+ OLG form a belt that demarcates lesioned from intact white matter. Mice with selective inactivation of Ndst1 in the OLG lineage display increased lesion size, sustained microglia and OPC reactivity. HS production around the lesion allows Sonic hedgehog (Shh) binding and favors the local enrichment of this morphogen involved in myelin regeneration. In MS patients, Ndst1 is also found overexpressed in oligodendroglia and the number of Ndst1-expressing oligodendroglia is inversely correlated with lesion size and positively correlated with remyelination potential. Our study suggests that mature OLG surrounding demyelinated lesions are not passive witnesses but contribute to protection and regeneration by producing HS. In multiple sclerosis (MS), OLG are the target of inflammatory and immune attacks and their death results in multiple focal demyelinated lesions in the CNS. Myelin loss remains in defined areas, rather than expanding to involve all of the white matter, and the mechanism by which demyelinated lesions stop expanding is not understood. Some of these lesions may stop expanding as inflammation is controlled and lesions are remyelinated. Remyelination involves OPC recruitment to the lesion, differentiation into myelin forming cells and remyelination of denuded axons, and the success of this depends on environmental context, including secreted factors from neighboring cells . This reMultiple factors are involved in this regenerative process including those produced by reactive astrocytes or microglia and macrophages . These cNdst1 KO mice , as there is now compelling evidence that HSPGs play a critical role in regulating spatiotemporal coordination of signals in the extracellular microenvironment of many tissues during brain development and in adulthood . HS chai KO mice . During  KO mice  and the  KO mice . Followi KO mice . Some HS KO mice  or negat KO mice  environm KO mice . TherefoNdst1 expression is induced in OLG around the lesion throughout the phases of demyelination and remyelination. Ndst1 expression and subsequent HS accumulation mostly accumulate at the margin of the lesion, delimiting the lesion from the intact corpus callosum during demyelination. To evaluate the relevance of Ndst1 induction for lesion formation and repair, we exposed genetically modified mice with selective deletion of Ndst1 in oligodendroglia to focal demyelination of the corpus callosum. Lack of Ndst1 in OLG resulted in an increased lesion size, and a sustained OPC and microglia/macrophage activation at the early stage of remyelination. HS enrichment correlates with and is necessary for the binding around the lesion site of the morphogen Shh, suggesting that Ndst1 expression and HS secretion by OLG enhances Shh signaling after demyelination, thus favoring remyelination . This up-regulation of Ndst1 was confirmed in vivo at 21 days in mice exposed to EAE by in situ hybridization combined with Olig2 labeling, a pan OLG marker. While Ndst1 was not detected in the corpus callosum of control brains    by the tplp-GFP) . The lesplp-GFP)  that strNdst1 expression levels by performing RT-qPCR analysis using the corpus callosum of healthy or demyelinated mice on the ipsi- and contralateral sides to the LPC injection, 8 days post injection (dpi). We quantified a mean 41% increase in the Ndst1 expression level in the demyelinated corpus callosum compared to healthy corpus callosum . This may indicate that not all stages of oligodendroglial maturation or not all oligodendrocytes respond equally to the lesion. We observed that, 8.5 ± 3.2% of PDGFRα+ cells and 48.9 ± 8.5% of CC1+ cells surrounding the lesion co-labeled with Ndst1. Our data indicate that the majority of Ndst1+ cells around the lesion are mature OLG which are more prone than OPC to activate Ndst1 expression in response to demyelination.Of note, in the belt delimitated by Ndst1 in Olig2+ cells, by breeding Olig2-Cre+/- mice and Flox/FloxNdst1  mice  and mutant  adult mice, thus indicating that conditional deletion of Ndst1 in the Olig2+ cell population does not interfere with brain development and with subsequent myelin maturation.To test if Ndst1 activity in oligodendroglia controls demyelination and/or remyelination, we generated transgenic mice with a conditional deletion of lox mice . The eff control . As reve control ; p=0.05. control ; p=0.2,  control ; p=0.4 aWe first performed LPC-induced demyelination of the corpus callosum in control and mutant mice and measured the size of the lesion at 4, 8 and 14 dpi. As before, lesions were identified based on the high density of nuclei at the injection point . While nNdst1 inactivation.We examined how these changes in the local environment in these mutant mice affect OPC mobilization during remyelination by analyzing Olig2+ cells density , maturatNdst1 mutant mice compared to control mice at eight dpi  (We observed that the density of Ki67+ proliferating cells within the lesion area was two-fold increased in p=0.037) . Some ofp=0.037) . At fourp=0.037) . At eighNdst1 expression in the Olig2+ population has no effect on initial demyelination  but protects the lesion from enlarging and participates in the control of OPC mobilization.Together, these results suggest that Ndst1 mutant mice compared to control mice at eight dpi   . Upon CNp=0.026)  and CD68p=0.026)  immunostp=0.026) . While tp=0.026)  and obsep=0.026) . These rAs previously mentioned, HSPGs form a scaffold that shapes the distribution and activity of numerous growth factors and morphogens during development and provide environmental support for regenerative responses following CNS injury. Among several known HSPG-binding morphogens, Shh was previously identified as a positive regulator for myelin repair . In ordeNdst1 mutant mice compared to control mice, we quantified Ptch1 (the main SHH receptor) expression around LPC-induced demyelination lesions at eight dpi using RNAscope . However, NDST1+ cells were distributed evenly throughout the lesion, rather than forming a delimiting band (To examine the relevance of our findings for multiple sclerosis (MS) physiopathology, we examined Ndst1 expression in MS tissue. We first probed the snRNAseq data provided by Jakel et al work , and obsing band .We then performed double-labeling immunohistochemistry to characterize NDST1-positive cells in various types of lesions and normal appearing WM, using OLIG2 for oligodendroglia , GFAP foThe vast majority of OLIG2+ cells in the lesions expressed NDST1, with a gradual reduction in the proportion of OLIG2+NDST1+ cells as lesions become more chronic, and with significantly fewer in control tissue ; p=0.016Ndst1 is almost exclusively expressed by oligodendroglia present at the margin of the lesion delimiting the lesion from the intact corpus callosum. Second, we found that the conditional deletion of Ndst1 in the Olig2 population concomitantly triggers an enlargement of the LPC-induced demyelinated area, alters OPC mobilization and favors the pro-inflammatory (M1) phenotype in microglia. We propose that these effects could be mediated through HS dependent binding of the morphogen Shh which has been shown to be a positive regulator of myelin repair through increased oligodendrogenesis and microglial regulation  that may affect OPC since they express a battery of receptors . Recent The beneficial action of HS may be related to their ability to bind numerous growth factors and morphogens, as observed during development . HS can Flox/FloxOlig2-Cre; Ndst1  mice. This suggests that FGF is probably not the main ligand regulated by HS activity during myelin repair in this model.Concerning FGF, conflicting results have been published on its activity . A recenFlox/FloxOlig2-Cre; Ndst1  mice. However, these effects persist at later time points at the end of remyelination, and OPC maturation is also inhibited after Shh inactivation  . Animals were housed under standard conditions with enrichment and access to water and food ad libitum on a normal 12 hr light/dark cycle.All experimental and surgical protocols were performed following the guidelines established by the French Ministry of Agriculture . The architecture and functioning rules of our animal house, as well as our experimental procedures have been approved by the ‘‘Direction Départementale des Services Vétérinaires’’ and the ethic committee . Surgery and perfusions were performed under ketamine )/xylazine  anesthesia. C57BL/6 wild-type and transgenic mice were successively used to characterize post-lesional expression of kground)  and doublox mice  will be Postmortem unfixed frozen tissues were obtained from the UK Multiple Sclerosis Tissue Bank via a UK prospective donor scheme with full ethical approval (MREC/02/2/39). Luxol fast blue (LFB)  and exon 7 ; for Gapdh: exon1  and exon2 (reverse 5’-ctggcactgcacaagaagat-3’). Primers from EUROFINS GENOMICS, Ebersberg, GERMANY). For histological analysis, mice were anesthetized and perfused with ice-cold 4% paraformaldehyde  in PBS . Brains were post-fixed overnight in 4% paraformaldehyde in PBS and cut on a vibratome (Leica) in 4 series of coronal sections (50 µm thick) for immunofluorescence, or cryopreserved and cut with cryostat (20 µm thick) for in situ hybridization.Focal demyelination was performed by stereotactic injection of Lysolecithin (LPC)  as described previously . The corplp/dm20  1 hr at RT, frozen adjacent sections from healthy or demyelinated C57BL/6 mice were incubated with 5 nM of two AP tagged versions of Shh: AP-Shh recombinant proteins carrying the N-terminal CW sequence (AP-SHH), the main HS-binding site for Shh , or lackTissue was fixed in 4% paraformaldehyde in PBS for 30 min. Endogenous peroxidase and AP activity was blocked by 10 min incubation with Vector Bloxall . Slides were blocked with ready-to-use 2.5% normal horse serum from Vector secondary antibody kits for at least 20 min. Primary antibodies were incubated overnight in antibody diluent  at 4°C. Primary antibodies used: mouse anti-NDST1 , rabbit anti-NeuN , rabbit anti-IBA1 , rabbit anti-Olig2 . HS staining with the mouse IgM anti-N-sulfated motifs on HS chains  did not give any signal on human tissue. NDST1 intensity was evaluated after a short exposition (exactly 2 min). All other stainings were fully developed. To ensure antibody specificity, the NDST1 antibody was pre-absorbed with human NDST1 recombinant protein , and added to tissue sections, with no staining seen .Secondary antibodies were incubated at RT for 1 hr. Staining was developed with a DAB Peroxidase (HRP) Substrate Kit (with Nickel), 3,3’-diaminobenzidine  and a VECTOR Blue AP Substrate Kit  as per manufacturer’s guidelines. Secondary antibodies used: ImmPRESS-AP Anti-Rabbit IgG Polymer Detection Kit  and ImmPRESS HRP Anti-Mouse IgG Polymer Detection Kit, made in Horse . PBS washes were performed between each treatment.plpGFP mice . The demyelinated area and cell counts were evaluated using Zen software (Zeiss). Immunofluorescent or in situ hybridization positive cells were counted in every fourth section through the whole demyelinated lesion per mouse and averaged for each mouse. Cell counts are presented as the mean of at least three mice. For RNAscope ISH, each punctate dot signal was counted around lesion  and reported to total nuclei number. For Heparan sulfate labeling quantification, 10E4-mmunopositive area was analyses on five sections per mouse. A constant exposure time was applied to all sections and for image acquisition, and the area occupied by 10E4+ labeling was quantified using ImageJ software. Lesion size was quantified by measuring the area of high density of nuclei in every fourth section through the whole demyelinated lesion per mouse. In this analysis, high density of nuclei was correlated with myelin loss visualized by MBP staining or by loss of fluorescence in GFP mice .For the human post-mortem tissue analysis, slides were imaged using a ZEISS Axio Scan.Z1 slide scanner. One researcher marked out the lesion sites and normal appearing white matter (WM) as areas of interest, while another counted single positive (NDST1+ cells) and double positive cells (NDST+ cells and other brain cell markers combined as above) in these areas of interest (ensuring blinding of counting).Myelin content was evaluated by double blind scoring of images taken from Plp immunostaining on brain sections (three photos per section and three sections per brain). Score of 4 was attributed to maximum myelination down to 0 for absence of myelin. The mean score for the control group was considered as 100%.This protocol is fully described in All the presented values in mice are means ± S.E.M unless otherwise stated. Data were statistically processed with the non-parametric Mann-Whitney test (independent two group comparisons). p<0.05 was considered significant and p<0.01 highly significant. All measurements and subsequent evaluations were performed blind to the experimental group to which the animals belonged. For the human post-mortem tissue analysis, a d'Agostino and Pearson omnibus normality test was used to test whether the data fit a normal distribution and a parametric test were done only if all compared data sets passed the normality test. The NDST1+ cells in control versus multiple sclerosis WM was compared using a two-tailed Mann Whitney U test. Multiple sclerosis lesions and their surrounding WM were compared using a paired two-tailed t test. The absolute numbers of NDST1+ cells and double positive NDST1+ OLIG2+ cells in individual lesions/normal appearing WM were compared by Kruskal-Wallis test. As MS tissue blocks contained more than one lesion, and we had several blocks from the same patients, we gave each patient an overall remyelination ability score corresponding to how many lesions in the blocks from that patient were remyelinated, or likely to remyelinate if the patient had survived. Remyelinated lesions received an arbitrary three points, active lesions two points, chronic active lesions one point and chronic inactive lesions 0 points. This was divided by the number of lesions counted for each patient, to allow comparisons. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This manuscript explored the role of mature oligodendrocytes in regions surrounding demyelinating lesions in the production of heparin sulfates. These results have implications not only for limiting demyelination, but also in remyelination in MS and other demyelinating disorders.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: James Fawcett (Reviewer #3).Thank you for submitting your work entitled "Mature oligodendrocytes bordering lesions limit demyelination and favor myelin repair via heparan sulphate production" for consideration by eLife, at least not in its present scope. As you will see from the individual reports there was certainly interest in your findings of an oligodendroglial response to a demyelinating lesion. However, the reviewers felt that at present the experiments aimed at mechanistic insight resulted mostly in correlative data. Perhaps, using genetic mouse models would be a strategy to reach stronger conclusions on causation. These would be time-consuming additional experiments and we do not invite a revised manuscript at this point. Any new submission would be independently assessed, possibly with new reviewers.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in Reviewer #1:This paper is interesting because it provides to my knowledge the first report of a molecular response of intact oligodendrocytes close to a demyelinating lesion, the induction of an enzyme (Ndst1) that increases the expression of heparan sulfate and thus HS-modifed proteins. Based on the phenotype of OPC/OL-specific Ndst1 cKO mice the authors suggest that this oligodendroglial response limits the extent of demyelination and promotes remyelination, possibly by locally increasing sonic hedgehog signaling. The authors also show a similar upregulation of NDST1 within (not around) human MS lesions, where the expression level inversely correlates with lesion size.Major points:1) Since primary development and myelination are not perturbed in the brains of Ndst1 cKO mice, another possible explanation of the data is that enhanced activation of microglia  affects remyelination in a non-cell autonomous way.2) Thus, the Shh data are plausible but only correlations, and one should not leave the impression that reduced Shh binding was shown to cause the delay of remyelination.3) It is unclear how "remyelination" in the human MS autopsy samples was assessed . In fact, this may explain the different pattern of Ndst1 expression in mouse and human demyelinating lesions.4) Figure 3. It is unclear what the anti-PLP staining is actually labeling outside the lesion area , as PLP is still a myelin marker5) Figure 1C-H: In-situ hybrization of Ndst1 mRNA in demyelinaing lesions: these observations are critical. but lack quantification (required standard). The alignment of the "lesion" with the "belt" is not obvious when using only dashed lines. Why is Ndst1 expression not shown also as an overview type image ? Same is true for Figure 2, which also lacks quantitation. While the "estimated" level of upregulation is stronger than in Ndst1 in Figure 1B, the IHC images are (at present) only case reports.Reviewer #2:This paper examines the role of heparan sulphate in remyelination by examining the expression of a key enzyme NDST1 and the consequences of an NDST1 conditional knockout in a mouse model of remyelination. In addition, the authors examine tissue from patients with MS. They conclude that HS containing proteoglycans are involved in remyelination as the enzyme is expressed around demyelinating lesions and its loss delays remyelination as evidenced by an increase in lesion size. Moreover there is a correlation between the degree of expression and the degree of remyelination in MS tissue.While I think that the role of proteoglycans in remyelination is interesting, this paper is predominantly descriptive and correlative and doesn't provide sufficient mechanistic detail. The evidence for SHH binding in the region of putative HS expression is noteworthy but it is purely correlative. Equally the increase in microglial proliferation is interesting but not explored further.My view would be that experiments are required to define one or more mechanisms by which HS is involved in remyelination, with gain and/or loss of function experiments clearly proving that the proposed mechanism is both linked to HS expression and regulates oligodendrocyte behaviour during remyelination in vivo.Additionally I think there are three other areas that require attention.First, the authors argue that the distribution around the experimental lesions is significant and that they have uncovered a novel function for surviving oligodendrocytes around lesions. It's notable however that they do not see this distribution in the MS tissue and I wonder whether their results simply reflect the lack of any surviving oligodendrocytes following LPC injection. In other words the distribution they see is simply a consequence of the model rather than having any great biological significanceSecond, the characterization of the OPC response in the presence or absence of HS is very poor and studies of proliferation and differentiation, including the quantification of newly formed oligodendrocytes, is requiredThird, a better characterization of the microglial phenotype (e.g. M1/M2 or similar) is required if the significance of this response is to be understoodReviewer #3:This is a thorough and interesting account of the role of HSPGs in remyelination. Many mechanisms of remyelination are known, but the idea that a particular sulfation change in HSPGs could be important is new. There is also data from human MS pathology in the paper, which gives clinical relevance.1) The paper focuses on NDST which gives a particular form of sulfation to HS. There needs to be some information about the sulfation motif that is produced and any known information on relevant molecules that might bind to this form of HS. Shh is binding is shown, but it is unlikely to be the only effector of the local HS change.2) If there is an increase in NDST, do the authors have information about any HS core proteins that might also be upregulated?3) Why would a sulfation change in mature oligos increase myelination? Surely OPCs would be more relevant?4) The authors show that there is some increase in Shh binding around lesions, but not that it does anything. The statement in the Discussion needs to be modified. Also they do not show local upregulation. Any published information that might link Shh or Shh inhibitor to remyelination needs to be stated. This part of the paper needs attention.5) Do the authors believe that the changes they see in NDST etc. are specific to demyelination, or are similar changes seen with traumatic lesions and inflammation?6) Figure 4: Is the increase in cell proliferation in the knockouts entirely accounted for by increased division of microglia?7) Figure 6 shows some increase in Shh binding around the lesion, but also widespread binding elsewhere. Some comment is warranted.eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Gary Westbrook as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: James W Fawcett (Reviewer #2).Thank you for submitting your article "Mature oligodendrocytes bordering lesions limit demyelination and favor myelin repair via heparan sulphate production" for consideration by The Reviewing Editor has drafted this decision to help you prepare a revised submission.SummaryThe authors have submitted a comprehensive revision of the manuscript, with several pieces of new data. These changes address the main issues raised by the reviewers, and the paper is considerably improved. However, the authors have not addressed the mechanistic underpinning of the proposed mechanism – i.e. the belt of HS synthesis and deposition in response to Ndst1 upregulation in oligodendrocytes around the lesion, and the proposal that binding of Shh promotes both, OPC proliferation and microglia polarization. Three points require further revisions.1) We think a fairly simple IHC experiment with existing materials should provide direct mechanistic insight:a) by showing that HS production around the LPC lesions is lost or reduced in the Ndst1 KOb) by showing that after LPC lesion the Shh downstream targets in OPCs (and in other cells) are less activated in Ndst1 KO micec) by showing that M2 microglia are indeed increased in number.2) The authors are also encouraged to check for Ndst1 upregulation in the available databases on single nuclei gene expression in MS tissue.3) Finally, the role of microglia has been experimentally explored by additional experiments, but the possibility of non-cell autonomous effects on remyelination (by microglia) has not been adequately discussed, despite some reassurance in the rebuttal letter. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Reviewer #1:This paper is interesting because it provides to my knowledge the first report of a molecular response of intact oligodendrocytes close to a demyelinating lesion, the induction of an enzyme (Ndst1) that increases the expression of heparan sulfate and thus HS-modifed proteins. Based on the phenotype of OPC/OL-specific Ndst1 cKO mice the authors suggest that this oligodendroglial response limits the extent of demyelination and promotes remyelination, possibly by locally increasing sonic hedgehog signaling. The authors also show a similar upregulation of NDST1 within (not around) human MS lesions, where the expression level inversely correlates with lesion size.Major points:1) Since primary development and myelination are not perturbed in the brains of Ndst1 cKO mice, another possible explanation of the data is that enhanced activation of microglia  affects remyelination in a non-cell autonomous way.We fully agree with this interpretation of our results. As requested by reviewers, we have further clarified this point and better described microglia activation. We have performed new experiments to further characterize the proliferation and activation states of the macrophage/microglia participating in demyelination-remyelination in the acute demyelination model . We have shown:1) An increase in proliferation of CD68+ cells in mutant compared to control mice at 8 dpi .2) A switch of the microglia/macrophage polarization among the whole Iba1 and CD68 population in favor of the rhomboid phenotype in mutant mice compared to control at 8 dpi .3) A significant increase in Cox2 expression (a marker of pro-inflammatory (M1) microglia/macrophage) in mutant mice compared to control, indicating a delay in the pro-inflammatory (M1) to pro-regenerative (M2) switch in the absence of Ndst1 in oligodendroglia.Ndst1 deletion in the Olig2 population is sufficient to enhance microglia/macrophage proliferation and activation at the lesion site at the onset of remyelination. The implication of Microglia (in a non cell autonomous effect) is presented in the Discussion.These results demonstrate that 2) Thus, the Shh data are plausible but only correlations, and one should not leave the impression that reduced Shh binding was shown to cause the delay of remyelination.Previous reports from our lab and others have shown that Shh is a strong candidate for myelin repair acting both on oligodendrogenesis and regulation of microglia activation . Indeed, Shh is produced by OLG and OPC at the onset of demyelination in lysophosphatidyl choline (LPC)-induced lesions. In this context, blocking Shh activity induces an increase in lesion size and a block in OPC proliferation and differentiation, and conversely Shh overexpression leads to the attenuation of the lesion extent and promotes oligodendrogenesis. Microglia express Shh receptors  and Shh treatment reduces the number of activated microglia in LPC model. Thus, Shh production by OLG lineage cells could act both directly on OPC (cell autonomous) and on microglia activation (non cell autonomous). Since Shh is bound and enriched at the border of the lesion in a HS dependent manner, it is likely that this morphogen contributes to the role of Ndst1/HS in the regeneration process.We made the mistake of not presenting these results  in the Introduction of the paper but only in the Discussion. To better underline these key findings concerning Shh for the understanding of our work, we described these results and references in the Introduction of new version of the manuscript.FGFR1 and FGFR2 specifically in OLG revealed that FGF signaling is not required for myelin regeneration. Overall, in all the analyses modulating FGF signaling in acute model of demyelination the phenotypes observed never recapitulate the phenotype observed in the present report using Olig2-Cre; Ndst1 Flox/Flox mice. This suggests that FGF is probably not the main ligand regulated by HS activity during myelin repair in this model. These data from the literature were absent from the first version of the manuscript and are now added in the Discussion.A second possible candidate could be FGF since HS can also modulate its signaling and since Ndst1 global knock out mice display developmental defects that mainly resemble those found in embryos deficient for Shh or FGFs. Concerning FGF recent work using the simultaneous ablation of both 3) It is unclear how "remyelination" in the human MS autopsy samples was assessed . In fact, this may explain the different pattern of Ndst1 expression in mouse and human demyelinating lesions.“Shadow” plaques where there are patches of pale blue staining on luxol fast blue, suggesting thinner myelin sheaths, have long been suggested to be remyelinated lesions. This assumption is based on their similarity to remyelinated lesions seen in animal models. These lesions are not surrounded by many myeloid cells, and these do not contain myelin debris, again suggesting that these are at least not recently demyelinated. Thus, we believe these are repaired lesions but will call these lesions shadow plaques, to cover all possibilities including remyelination or partial demyelination.4) Figure 3. It is unclear what the anti-PLP staining is actually labeling outside the lesion area , as PLP is still a myelin markerNdst1 expressing cells are immuno-positive for Olig2  and Plp+ using double in situ hybridization ”.There is a misunderstanding. In Figure 3 for plp labeling a double in situ hybridization staining was performed. We make it clearer in the text by writing “We found that 5) Figure 1 C-H: In-situ hybrization of Ndst1 mRNA in demyelinaing lesions: these observations are critical. but lack quantification (required standard). The alignment of the "lesion" with the "belt" is not obvious when using only dashed lines. Why is Ndst1 expression not shown also as an overview type image ? Same is true for Figure 2, which also lacks quantitation. While the "estimated" level of upregulation is stronger than in Ndst1 in Figure 1B, the IHC images are (at present) only case reports.Concerning quantitative analysis. At this stage of the work  we describe the expression pattern of ndst1 and HS in the lesioned brain. Both are virtually absent in the healthy corpus callosum as well as in the contralateral part after lesion using ISH and IF. We thus decided to perform quantitative analysis using RT-qPCR, to compare ndst1 expression in the corpus callosum of healthy or demyelinated mice using 5 mice in each condition. These quantitative data are presented in Figure 1. In a second step, we characterized the phenotype of cells expressing ndst1 and performed quantitative analysis showing that 100% of ndst1 positive cells are Olig2 positive.As mentioned in the manuscript Ndst1 expression is monitored by in situ hybridization. This technic is not compatible with good quality myelin immunostaining. Thus, in order to delimit precisely lesions in the LPC model, we use Hoechst staining since the lesion is also characterized by an increase in cell density due to glia and microglia proliferation. In order to validate this approach we have performed injection of LPC in a reporter mouse (plp-GFP) and show that the limit of the lesion visualized by loss of GFP fluorescence strictly corresponds with the area exhibiting strong increase in cell density. These data are presented in Figure 1—figure supplement 2. Thus the alignment of the lesion was based on cell density after in situ hybridization.Reviewer #2:This paper examines the role of heparan sulphate in remyelination by examining the expression of a key enzyme NDST1 and the consequences of an NDST1 conditional knockout in a mouse model of remyelination. In addition, the authors examine tissue from patients with MS. They conclude that HS containing proteoglycans are involved in remyelination as the enzyme is expressed around demyelinating lesions and its loss delays remyelination as evidenced by an increase in lesion size. Moreover there is a correlation between the degree of expression and the degree of remyelination in MS tissue.While I think that the role of proteoglycans in remyelination is interesting, this paper is predominantly descriptive and correlative and doesn't provide sufficient mechanistic detail. The evidence for SHH binding in the region of putative HS expression is noteworthy but it is purely correlative. Equally the increase in microglial proliferation is interesting but not explored further.My view would be that experiments are required to define one or more mechanisms by which HS is involved in remyelination, with gain and/or loss of function experiments clearly proving that the proposed mechanism is both linked to HS expression and regulates oligodendrocyte behaviour during remyelination in vivo.Additionally I think there are three other areas that require attention.First, the authors argue that the distribution around the experimental lesions is significant and that they have uncovered a novel function for surviving oligodendrocytes around lesions. It's notable however that they do not see this distribution in the MS tissue and I wonder whether their results simply reflect the lack of any surviving oligodendrocytes following LPC injection. In other words the distribution they see is simply a consequence of the model rather than having any great biological significanceNdst1 is up-regulated by the Olig2+ cell population in close proximity to inflammation sites in corpus callosum. Since we have observed this expression in both mouse models (LPC and EAE), it is probably relevant.First, as shown in Figure 1—figure supplement 1, we have observed the same pattern of Ndst1 expression in the EAE model were Concerning the distribution of Ndst1 expression in the LPC model: Even though OLG are massively killed within the lesion, remaining OLG in close proximity to the lesion do express ndst1 while OLG remote from the lesion in the CC do not. This shape “as a belt” is may be linked to the model but the presence of reactive mature OLG ndst1+ at the lesion site observed in all models including human is biologically relevant.This point has been discussed in the new version of the manuscript.Second, the characterization of the OPC response in the presence or absence of HS is very poor and studies of proliferation and differentiation, including the quantification of newly formed oligodendrocytes, is requiredNdst1 expression in the Olig2+ population has no effect on initial demyelination  but protects the lesion from enlarging and participates in the control of OPC mobilization. We have included these data in the new version of the manuscript.We have performed new experiments presented now in Figure 4 in the new version of the manuscript. We performed a quantification of proliferating of OPC (Ki67+/olig2+ cells) in lesion sites at 4 and 8 dpi. We show that the dynamic of OPC proliferation is altered in mutant mice. Overall, Our data show that Third, a better characterization of the microglial phenotype (e.g. M1/M2 or similar) is required if the significance of this response is to be understoodNdst1 deletion in the Olig2 population is sufficient to enhance microglia/macrophage proliferation and activation at the lesion site at the onset of remyelination. We demonstrate accentuated M1 phenotype in mutant mice compared to wild-type mice.See response to reviewer 1. We have performed new data showing that Reviewer #3:This is a thorough and interesting account of the role of HSPGs in remyelination. Many mechanisms of remyelination are known, but the idea that a particular sulfation change in HSPGs could be important is new. There is also data from human MS pathology in the paper, which gives clinical relevance.1) The paper focuses on NDST which gives a particular form of sulfation to HS. There needs to be some information about the sulfation motif that is produced and any known information on relevant molecules that might bind to this form of HS. Shh is binding is shown, but it is unlikely to be the only effector of the local HS change.We have now included in the new version of the manuscript a full discussion concerning which factors could bind to HS in this context.Concerning Shh binding, we have used an AP tagged version of Shh (AP-SHH) to directly assay its binding capacity in demyelinating context. We believe that an important piece of information is the data presented using the AP-SHH recombinant proteins deleted for the CW sequence (AP-SHH-CW deleted) since CW sequence serves as a major HS-binding site for Shh . This sequence is necessary for Shh binding to HS.Concerning other factors and in particular FGF signalling, please see response 2 to reviewer 1.2) If there is an increase in NDST, do the authors have information about any HS core proteins that might also be upregulated?We have preliminary data concerning this issue. We compared by qRT-PCR the expression level of transcripts of the main proteoglycan HS transcripts in mouse CC seven days after injection of the LPC and showed that only glypicans 4 and 5 are over-expressed after injury. Nevertheless, we would rather not include these data in the work since there are several carriers, Glypicans, Syndecans and perlecans, and since their activity is also depending on the fact that some are membrane bound others are soluble. Here focusing on Ndst1 we work on HS “as a whole” and would rather keep this level of complexity. However, these data can be provided and included in the manuscript as a supplementary figure if requested by the reviewer.3) Why would a sulfation change in mature oligos increase myelination? Surely OPCs would be more relevant?As discussed in the manuscript we believe that HS on OLG changes the amount/stability of soluble factors, and thus the recruitment and proliferation of surrounding cells (such as OPC or microglia). Thus, OLG trigger non cell autonomous effects by generating a matrix (environment) that in turn regulates the behavior of cells in the lesioned CC. This point is presented in the Discussion.4) The authors show that there is some increase in Shh binding around lesions, but not that it does anything. The statement in the Discussion needs to be modified. Also they do not show local upregulation. Any published information that might link Shh or Shh inhibitor to remyelination needs to be stated. This part of the paper needs attention.The data concerning pattern of expression and role of shh during demyelination and myelin repair are already published and were mentioned in the Discussion of the first version of the manuscript. To be more accurate and better present these data we have now included a sentence in the Introduction “Interestingly oligodendrocyte lineage cells also produce factors that can modulate remyelination like the morphogen Shh which is produced by OLG and OPC at the onset of demyelination in lysophosphatidyl choline (LPC)-induced lesions . In this context, blocking Shh activity induces an increase in lesion size and a block in OPC proliferation and differentiation, and conversely Shh overexpression leads to the attenuation of the lesion extent and promotes oligodendrogenesis” These data are also included in the Discussion. See also response 2 to reviewer 1.5) Do the authors believe that the changes they see in NDST etc. are specific to demyelination, or are similar changes seen with traumatic lesions and inflammation?As mentioned below and shown in Figure 1—figure supplement 1, we have observed the same pattern of Ndst1 expression in the EAE model. It would indeed be interesting to explore non-demyelinating models in the context of another study.6) Figure 4: Is the increase in cell proliferation in the knockouts entirely accounted for by increased division of microglia?We have performed co-immunolabelling of Olig2 and Ki67 showing a significant increase of OPC proliferation mutant mouse compared to control 8dpi. These results are now included in the new version of the manuscript.7) Figure 6 shows some increase in Shh binding around the lesion, but also widespread binding elsewhere. Some comment is warranted.Indeed as described in the literature Shh receptors are expressed in various cortical populations in the brain ; Shikawa et al., Dev Biol. 2011 Jan 15;349(2):147-59). As shown in Figure 6B of the presented manuscript AP-SHH probe binds on both size of the brain in the Cortex independently of lesion side.[Editors’ note: what follows is the authors’ response to the second round of review.]The authors have submitted a comprehensive revision of the manuscript, with several pieces of new data. These changes address the main issues raised by the reviewers, and the paper is considerably improved. However, the authors have not addressed the mechanistic underpinning of the proposed mechanism – i.e. the belt of HS synthesis and deposition in response to Ndst1 upregulation in oligodendrocytes around the lesion, and the proposal that binding of Shh promotes both, OPC proliferation and microglia polarization. Three points require further revisions.1) We think a fairly simple IHC experiment with existing materials should provide direct mechanistic insight:a) by showing that HS production around the LPC lesions is lost or reduced in the Ndst1 KOAll the requested experiment could not be performed on existing materials since tissue preparation is different depending on the antibodies used; for example, heparan sulfate immunostaining had to be performed on fresh frozen samples while microglia staining requested strong fixative procedure. We had thus to produce new cohorts of mutant mice and perform LPC-induced demyelination to satisfy the reviewer’s requests, explaining the delay in our response.b) by showing that after LPC lesion the Shh downstream targets in OPCs (and in other cells) are less activated in Ndst1 KO miceDespite numerous tests, we have reached the conclusion that shh signaling pathway activation cannot be monitored by immunostaining since there are no good antibodies against shh downstream targets. We contacted various experts including Dr Traifford, Dr Zeller and Dr Dahmane who confirmed these points.Ptch1 expression is decreased around LPC-induced demyelination lesions at 8 dpi in mutant animals compared to controls.We thus used RNAscope technic to perform comparative analysis of patched1 expression in wild-type and mutant animals. We could show that These data are now included in a new version of Figure 6 and a paragraph has been added in the Results section.c) by showing that M2 microglia are indeed increased in number.We believe that the request is concerning M1 Microglia which is the population we have observed to be affected in mutant condition. We gained information on microglia phenotype using Cox2 immunostaining, Cox2 being a pro-inflammatory enzyme thus labeling M1 microglia. We show a significant increase of Cox2+ cells /mm2 in Ndst1 KO mice 8 days post lesion indicating that the number of M1 microglia is increased in mutant mice after demyelination.2) The authors are also encouraged to check for Ndst1 upregulation in the available databases on single nuclei gene expression in MS tissue.Interrogation of our Jaekel et al., 2019 single nuclei RNAseq dataset shows few nuclei that express NDST1 RNAs generally, which is likely due to the dropseq platform only identifying around 15% of the nuclear RNAs, and so favors RNAs with high expression. This is also very similar on interrogation of the Rowitch dataset from human brain.Therefore, there are too few nuclei here expressing NDST1 to be able to be confident. However, with this caveat, if the cells that express NDST1 are compared, there is a trend to increased expression in MS tissue, which also holds true if only oligodendroglia expressing NDST1 are compared, supporting our biological conclusions. We added part of these data to the manuscript as Figure 8—figure supplement 1.3) Finally, the role of microglia has been experimentally explored by additional experiments, but the possibility of non-cell autonomous effects on remyelination (by microglia) has not been adequately discussed, despite some reassurance in the rebuttal letter.A paragraph has been added in the Discussion.


Supplemental Digital Content is available in the text. 10) and <2.5 µm (PM2.5) concentrations on the estimation of health effects.Various spatiotemporal models have been proposed for predicting ambient particulate exposure for inclusion in epidemiological analyses. We investigated the effect of measurement error in the prediction of particulate matter with diameter <10 µm  models; dispersion models; models using satellite data and applying machine learning algorithms; and combinations of these methods through generalized additive models. Two health outcomes were simulated to assess whether the bias varies with the effect size. We applied multilevel Poisson regression to simultaneously model the effect of long- and short-term pollutant exposure. For each scenario, we ran 1,000 simulations to assess measurement error impact on health effect estimation.We sampled 1,000 small administrative areas in London, United Kingdom, and simulated the “true” underlying daily exposure surfaces for PM2.5 for which only LUR underestimated the effect. For short-term exposure, results were variable between exposure models and bias ranged from −11% (underestimate) to 20% (overestimate) for PM10 and of −20% to 17% for PM2.5. Integration of models performed best in almost all cases.For long-term exposure to particles, we observed bias toward the null, except for traffic PMNo single exposure model performed optimally across scenarios. In most cases, measurement error resulted in attenuation of the effect estimate. Epidemiological studies of the health effects of long- and short-term exposure to outdoor particulate air pollution that utilize modeling techniques to derive pollution exposures will generally underestimate the magnitude of the associations (with overestimates in some cases). These biases are not trivial and should therefore be considered when assessing the evidence from epidemiological studies in policy evaluation and health impact assessment exercises. This study also suggests no single air pollution modeling method is optimal and further work on the integration of models to maximize performance is advisable.1 measurement error in air pollution exposure estimates and the resulting impact on the estimation of health effects has attracted attention in recent years.2 Szpiro et al3 showed that better exposure prediction by land-use regression (LUR) models does not necessarily result in less bias in the health effect estimate following long-term exposure. A review4 concluded that measurement error mostly negatively biased the effect estimates and increased standard errors, especially when exposure concentration was modeled with low spatial and temporal resolution for a spatially variable pollutant.The difficulty in defining ambient particles, given that their chemical and physical properties vary by time period, location, sources and meteorology, makes the understanding of measurement error implications on health effects estimation even more important than gaseous pollutants unless we assume equitoxicity. As mentioned in our joint article,5 We previously6 evaluated the impact of several scenarios and indicated that measurement error in NO2 and PM10 resulted mostly in the attenuation of effect estimates for both short- and long-term exposure. In this article, we present the results of an extensive simulation study to address the impact of measurement error from spatiotemporal predictions of PM10 and PM2.5 concentrations from various exposure assessment models on the effect estimates of daily mortality and hospital admissions due to cardiovascular diseases (CVD).Within the framework of the “Comparative evaluation of Spatio-Temporal Exposure Assessment Methods for estimating the health effects of air pollution” (STEAM) project, we assessed the impact of measurement error in spatiotemporal exposure predictions developed for greater London for 2009–2013 on the health effect estimate in a mixed Poisson model that allows for the simultaneous estimation of effects following short- and long-term exposure.7 informed by correlation coefficients and variance ratios estimated from validation datasets for 2009–2013, which compare modeled pollutant data with measurements from the extensive London network of fixed-site monitors. We simulated from reported coefficients for two different outcomes: all-cause mortality and CVD hospital admissions, driven by the need to assess differential behavior depending on the prevalence and the variability of the outcome and the range of the effect estimate. Simulations were based on concentration-response functions (CRF) that varied in magnitude to allow for the assessment of a range of situations that have been reported in the literature. For each scenario, 1,000 simulations were run.We set up simulations for a sample of 1,000 lower super output areas  in the Greater London area10 and PM2.5) concentrations including all measurements from sites within the M25 orbital highway, during the years 2009–2013, obtained from the London Air Quality Network,8 Air Quality England,9 and the Automatic Urban and Rural Network.10 For PM10, we compiled data from 115 sites while PM2.5 data were available only from 33 sites. To inform LUR and machine learning models and the validation datasets used in the simulations, we needed a larger PM2.5 database, hence we enhanced the available data based on a modeling approach.We constructed a database of ambient particles  and a random forest approach,11 both of which incorporated seasonality trend, concurrent measurements of other pollutants, and meteorological variables. The predictions from each model were entered as spline functions in a new GAM. The 10-fold cross-validation adjusted R2 of the combined model was 98.9%.Briefly, at each PM10 and 32 for PM2.5, and from 65 roadside/kerbside for PM10 and 60 for PM2.5.The final data included information from 37 urban/suburban sites for PM1 For PM2.5, we further incorporated satellite measurements and applied three machine learning algorithms that were combined in a GAM to produce spatiotemporal concentrations at a 1 × 1 km grid.We developed spatiotemporal LUR and dispersion models to estimate the particles’ concentration at the postcode centroid level which were then averaged to produce concentrations at LSOA level. LUR models provide estimates at specific geographical point coordinates  while dispersion estimates provide exposure maps at a 20 × 20 m grid and we subsequently applied bilinear interpolation to estimate concentrations at a certain location.i on day t is modeled as a combination of smooth functions reflecting the nonlinear effects of several temporal covariates  and a spatial covariate (inverse distance of monitoring sites to the nearest major road).We included indicator variables for different years (reference category was 2009), daily mean relative humidity, daily mean wind speed, total traffic load in a buffer of 100 m around each monitoring site and total length of major roads in a buffer of 300 m around each monitoring site. A bivariate smooth function of geographical coordinates accounting for residual correlation between locations was included.We developed spatiotemporal semiparametric models where the measurement of the particles at location 13 combines emissions data with the Weather Research and Forecasting meteorological model14 and the Community Multiscale Air Quality (CMAQ) model (v5.0.2),15 which has been coupled to the Atmospheric Dispersion Modelling System roads model (v4).16 Driven by meteorological fields from the WRF model, the CMAQ-urban model outputs hourly air pollution concentrations at high spatial resolution and predicts air pollution concentrations at points spaced 20 m apart across the STEAM area. To provide a concentration at the fixed sites, we used bilinear interpolation of the nearest 20 m points.The Community Multiscale Air Quality urban (CMAQ-urban) model2.5, we applied three machine learning algorithms that incorporated all the available spatiotemporal covariates along with satellite measurements on aerosol optical depth (AOD) using the MAIAC algorithm for MODIS. Specifically we used measurements from both the Aqua and Terra Satellite, with data on population density, cloudiness, barometric pressure, wind direction, wind speed, dewpoint temperature, temperature, land use type, distance to water, distance to Heathrow, inverse of the height of the planetary boundary layer, normalized difference vegetation index, traffic counts, and day of the year (with a sine and cosine function). Machine learning algorithms are prediction algorithms that train on a subset of the data, predict on held out data, and choose training parameters that maximize predictive power in the held out, testing data. By design, they can incorporate highly nonlinear and highly interactive models, without prespecifying which variables are nonlinear, what the nonlinearity looks like, and which variables interact.Only for the exposure assessment of PM11 neural network,17 gradient boosting18) to predict PM2.5 separately from Aqua AOD and Terra AOD, therefore six models in total. Training was based on a grid search of hyperparameters for each learner, using internal cross-validation (CV) and mean square error as the criteria for selection. The neural network included a Least Absolute Shrinkage and Selection Operator on the variables to reduce overfitting. We then combined the six individual predictions of the output of the methods in a GAM using unconstrained smooth functions and a smooth function for longitude and latitude.We trained three models . For the LUR, we used 10-fold cross-validated predictions.Hybrid 2: For PM2.5, the hybrid model 2 was extended to include a smooth function of the predictions from the combined machine learning methods.Hybrid 3: In the case of PM1 Briefly, we (1) sample 1,000 LSOAs with their coordinates from the study area; (2) For this sample of 1,000 LSOAs, we simulated “true” daily pollutant concentrations (X*) informed by either the urban/suburban or the kerbside/roadside fixed sites assuming that differential measurement error occurs by site type. Temporal correlation and the spatial variation, as estimated by a covariance model fitted to the empirical semivariogram, were incorporated in the “true” surface that was also adjusted for instrument error in the monitor measurements; (3) We simulated a daily health outcome (Y) over 2009−2013 from the “true” pollutant data using CRF from the literature  based on a simple multilevel Poisson regression model, with a random intercept per LSOA, where the effect of short-term exposure is estimated by the coefficient corresponding to the daily time-series and the effect of long-term exposure to the coefficient of the average over the period exposure; (4) We added to the “true” daily pollutant concentrations measurement error informed by the validation data at the fixed sites that provided estimates of the spatial and temporal correlations and variance ratios. A new pollution variable (Z) corresponding to each exposure method was created; (5) We analyzed the association between each health outcome (Y) and new pollutant (Z) and estimated the two coefficients denoting the effect following short- and long-term exposure and their standard errors; (6) We ran 1,000 simulations and assessed the results in terms of bias (mean difference between true and estimated effect estimate), statistical power  and coverage probability .The set-up of the simulations has been presented in the companion paper.http://www.R-project.org/, 2017) using the libraries mgcv, randomForest, Hmisc, lme4, MASS, and foreign. In GAM, the default generalized cross-validation criterion (GCV) was used for the choice of the smoothing parameter as defined in the mgcv library.All analyses were run in R version 3.4.3  bias for roadside/kerbside monitors ranging from −2% (hybrid 2) to −11% (hybrid 1) and variable bias for urban/suburban monitors: relatively small for the dispersion (−2%), the hybrid 1 (+2%), and the hybrid 2 (+6.4%) and larger for the LUR (+20%). Coverage probabilities ranged from 93% to 95% with power between 11% and 15%. Hospital admission analysis provided similar results but with higher statistical power. For both outcomes, the best-performing models were the hybrid 2 model for kerbside/roadside concentrations and the dispersion prediction when considering urban/suburban sites.2.5 and total mortality, while eAppendix, Table S3; http://links.lww.com/EE/A88, presents results for CVD hospital admissions. Results were more variable in the direction of bias compared with PM10 results. For the long-term results, considerable negative bias  was exhibited for all models under the urban/suburban characterization of the simulated “true” exposure, with the hybrid 3 model having the smallest bias . For the kerbside/roadside sites, positive bias  ranging from +7% to +73% was displayed for all except the LUR  predictions. The best-performing model in terms of the magnitude of the bias being hybrid 1 (incorporating dispersion estimates into LUR) for total mortality and the LUR model for the CVD admissions. For short-term results, biases, though variable in direction, were generally small ranging from −20% to +17% across outcomes and site-type. Coverage probabilities for PM2.5 were generally high, except for long-term exposure based on roadside/kerbside sites. Statistical power was highest for short-term exposure within roadside/kerbside scenarios (>74%) and lowest for long-term exposure within urban/suburban scenarios (<25%). Validation statistics  support better performance of the hybrid models.Table 2.5 that showed a bias away from the null for long-term exposure. For PM10 under most scenarios, the hybrid 2 model combining predictions from LUR and dispersion methods exhibited the smallest bias. The combination of methods under Hybrid model 3 performed best for urban/suburban PM2.5 for both outcomes, while for kerbside/roadside, the machine learning algorithms provided the most accurate estimate for short-term exposure but not for long-term exposure, where the best model appeared to be hybrid 1 for mortality and LUR for CVD admissions. Our approach simulates situations in which the spatial and temporal correlation coefficients and variance ratios relating the “pseudo” modeled and “true” data mirror those estimated from the validation datasets (including adjustment for instrument error in the measurements) and it is the correlation coefficients and variance ratio that we are testing out in our simulations.Our simulations indicated bias toward the null for most scenarios, except in the case of kerbside/roadside PM6 we find that as correlation gets smaller and the variance ratio larger the bias toward the null is increased, while bias away from the null was noted for high correlations and small variance ratios. The error in the “modeled” exposures is a combination of classical and Berkson that is not distinguishable, although larger Berkson-like error is expected in methods with smaller variance ratio. This scenario in most cases corresponds to Hybrid models but is not consistent across the temporal and spatial terms.In line with Butland et al,2.5 are more variable, combination of methods performed better than individual ones. Bias toward the null for long-term effects was observed for all methods for urban/suburban monitors, while bias away from the null was observed for kerbside/roadside PM2.5 for five out of six methods. However for short-term exposures, biases though varying in direction were relatively small.Although results from PM2.5 as it may be more flexible in capturing a variety of interactions between covariates and their shapes and hence better capture variability of levels near traffic. In all cases, the Hybrid models attributed more degrees of freedom to estimates derived from the dispersion models, then to machine-learning predictions and less to those from LUR.The optimal performance of combinations of methods under nearly every scenario may be attributed to potentially better capture of different characteristics of the particles’ distribution and composition. For example, the combination of several machine learning algorithms may perform better in traffic-related PM20 mainly focused on methods’ performance assessment in terms of estimating concentrations. Szpiro et al3 found in a simulation study that improving the predictions in spatial LUR models did not always improve the health effect estimate as this was dependent on the Berkson-type of error and its differential impact on the exposure and health association as a component of the complex combination between classical and Berkson type-error in exposure assessment. Lee et al21 in a subsequent simulation study found that the validity and reliability of the health effect estimate can be greatly affected by the sampling of the monitor locations used to inform spatial LUR models, while Wang et al22 reported that decreases in forced vital capacity in relation to air pollution exposure were larger for LUR models with larger predictive ability in terms of holdout validation and cross-holdout validation. A recent review2 indicated that application of measurement error correction methods mainly in cohort designs, that applied a variety of exposure methods including spatial and spatiotemporal LUR and kriging methods, resulted in increases in effect estimates and their standard errors, which is in accordance with our simulation findings for long-term effects on background PM.Previous air pollution exposure research23 We produced the validation data sets for our simulation on LUR and the machine learning algorithms using a 10% cross-validation to avoid including monitors incorporated in the methods in the setting of our simulation, although retrospectively that may be an overcorrection. Also our study was based in London where the number of fixed site monitors is large compared with other urban centers. The classification of our validation data and corresponding simulations by site type guards against driving the simulated “true” exposure at the centroid of the LSOA by this characteristic and further helps to identify if there is a weakness in terms of the ability of the model to predict for the one or the other of the site types.We recognize that the great majority of air pollution epidemiological studies follow either a time-series approach to investigate effects following short-term exposure or a cohort design for long-term exposure. As previous reports on measurement error investigated its effect under these designs, we aimed to expand the literature under a mixed modeling approach. In addition, the main objective of the STEAM project was the development of several exposure models for London and the optimal choice based on the best performance in terms of the effect estimation as assessed by simulations under this a-priori defined modeling approach. Hence we consider among the strengths of our study the comparison of several exposure assessment models in both the short- and long-term associations. Further the set up of the simulated surface incorporated both spatial and temporal complex covariances and correlations in contrast with most previous reports that focus on either aspect.1. More importantly, the amount of measurement error in each exposure method may differ in other locations; hence our results are not directly transferable to other settings. We expect differential measurement error due to varying covariates informing the methods by location, although Vlaanderen et al24 suggest that the impact is modest in LUR providing the models perform well.Limitations include the lack of confounders in our epidemiological model and the uncertainty in mean bias over the simulations, which seems to be larger in our results compared with the gaseous analysis2.5 and PM10 from various exposure assessment models on the health effect estimates support that the underestimation was larger when assessing long-term exposures. There were instances of nontrivial bias away from the null especially when roadside/kerbside monitoring sites were considered. Averaging of different exposure predictions performed best in almost all cases indicating that the integration of models to maximize performance is advisable.Our simulations investigating the impact of the measurement error for PMThe authors declare that they have no conflicts of interest with regard to the content of this report.We are grateful to the UK Met Office for provision of meteorological data, accessed through the Centre for Environmental Data Analysis (CEDA). We are also grateful to the UK Government and Local Authorities providing air pollution measurements used in this study, managed by King’s College London and Ricardo Energy and Environment.