Clarifying mechanisms underlying the ecological succession of gut microbiota is a central theme of gut ecology. Under experimental manipulations of zebrafish hatching and rearing environments, we test our core hypothesis that the host development will overwhelm environmental dispersal in governing fish gut microbial community succession due to host genetics, immunology, and gut nutrient niches. We find that zebrafish developmental stage substantially explains the gut microbial community succession, whereas the environmental effects do not significantly affect the gut microbiota succession from larvae to adult fish. The gut microbiotas of zebrafish are clearly separated according to fish developmental stages, and the degree of homogeneous selection governing gut microbiota succession is increasing with host development. This study advances our mechanistic understanding of the gut microbiota assembly and succession by integrating the host and environmental effects, which also provides new insights into the gut ecology of other aquatic animals. However, the mechanism underlying gut microbiota succession in aquatic animals remains elusive2, especially at a relatively long-term scale3. Understanding host-associated microbial succession needs to consider interactions with host genetics and ecology5, which undoubtedly increases the complexity to elucidate mechanisms governing the gut microbial community succession6. Although many studies indicated that both ecological and evolutionary forces could affect the succession of intestinal microbiota in terrestrial animals8, much less is known about that in aquatic fish gut ecosystem, which also colonizes with diverse microbial communities11. Fish encompass nearly one half of the vertebrate diversity12 and are considered as the most successful vertebrates evolved on Earth13. Their successful evolution may not be possible without the help of gut microbiota10. Of course, fish in turn provide gut microorganisms with appropriate habitats and necessary nutrients, and protect the gut microbiota from adverse disturbances 14. Such host-microbiota interactions are especially important for fish health, immunity, metabolism, development, reproduction, behavior, as well as defense of disease15.Ecological succession has been a central theme in ecology for more than 120 years14. As oviparous fish cannot get heritable microorganisms from their mother as that of viviparous mammals delivered vaginally16. Recent studies found that fish gut microbiota was closely correlated with host genetics17, immunology18, physiology19, and ecology20, suggesting that such host-associated features should be important in governing fish gut microbiota. However, some other studies indicated that environmental factors were crucial forces shaping the fish microbiome22. Theoretically, all microorganisms colonize in the fish gut ecosystem are expected to be derived from the surrounding environment. However, fish are also able to retain some low abundant microorganisms in the environment, meaning that the bacteria (even the poor colonizer) obtained from the environment could further evolve to be more prolific colonizers23. Moreover, bacteria increasingly better adapt to colonization of fish by repeatedly moving from host to host through the external water environment24. So, it would be better to consider gut and water microbiota as a metacommunity to get a full understanding of fish-associated microbiomes. However, the contribution of fish microbiota to the ecosystem diversity of metacommunity (γEcosystem) is rarely addressed25. But in fact, any community is governed both by processes that occur within the local community and all linked communities27.The mechanism governing aquatic fish gut microbiomes may be utterly different from those of terrestrial mammals13. Host selection due to fish genetics could be a primary deterministic process to colonize core gut microbiota in zebrafish, regardless of whether they were recently collected from natural habitats or reared for generations in different labs28. Moreover, the relative importance of non-neutral processes  governing zebrafish gut microbiota was found to be increased as host development29. Some other studies fund that ecological processes governing gut microbiota are variable across fish development30. So far, only a few fish microbiome studies considered the host developmental issue, but the fish microbial diversity and composition could be significantly affected by the fish development33. Also, some other uncontrollable field factors  may overestimate the discrepancy of fish gut microbiome among different studies. Fortunately, laboratory zebrafish allow us to dissect resultant effects from the environment and host under controllable conditions, and have been a powerful model for dissecting host-microbial interactions15. In addition, the succession of gut microbiota across zebrafish life cycle can be examined within a short period , which greatly shortens the experimental time and therefore can reduce unexpected biases from long-term experiments. Another advantage of using zebrafish model is we can perform whole gut ecosystem analysis due to its small size, so that the overall microbial diversity can be precisely estimated.So far, the mechanism underlying fish-microbiota-environment associations remains controversialThis study aimed to clarify how host development and environment dispersal affect the assembly and succession of fish gut microbiota using the manipulated zebrafish model. We hypothesized that the host would have much stronger effects on the fish gut microbiota succession than the environment due to host genetics, immunology, and gut nutrient niches. So, we first tested whether fish hatched in different environments could assemble similar microbiota. Second, we examined whether established gut microbiota could be disturbed after zebrafish husbandry environment was switched. Third, we quantified the relative effects of host development and environmental dispersal on the gut microbiota succession. We found that the gut microbiota assembly and succession were mainly governed by host development rather than environmental dispersal. This study not only clarifies the effects of the host development, hatching environment, and environmental transition on the fish gut microbiota succession, but also provides new insights into our mechanistic understanding of gut ecology of other aquatic animals.p < 0.05). Specifically, the environment A showed the highest concentrations of Chl-a and total organic carbon (TOC), whereas the soluble orthophosphate (SOP), NH4-N and NO2-N in the environment B were the highest, and the environment C had the highest NO3-N , whereas the Shannon diversity in the environment A was statistically higher than those of B and C . Then, we further explored whether water environments would influence the turnover of the established gut microbiota in zebrafish that consistently kept in original environments . The results showed that gut microbiotas were also different from water microbiotas across zebrafish development  and OTU_4 (Aeromonadaceae), which cumulatively accounted for >50.0% of the microbial abundance  became dominant (16.2–26.7%), whereas OTU_5 (Vibrio) decreased to only 5.0–5.8% of the total abundance. However, OTU_4 (Aeromonadaceae) was generally kept at a relatively high abundance (10.6–31.4%) from 12 to 42 dph  increased considerably and accounted for 40.0% of the total abundance, but OTU_4 (Aeromonadaceae) decreased to 8.2–11.0% , the dominant OTUs in zebrafish gut also showed similar succession patterns. Specifically, zebrafish at 12–20 dph colonized relatively high abundances of OTU_5  whether zebrafish hatched in the same environment but raised in different environments tend to show different succession patterns? (ii) do zebrafish hatched from different environments but raised in the same environment tend to have similar succession patterns? Our results indicated that the ecological succession of gut microbial communities was mainly associated with zebrafish developmental stages regardless of their hatching environment Fig.  or rearip < 0.05). The general linear model (GLM) analysis indicated that the developmental stage was the only significant predictor for shaping the alpha-diversity ; the environment only explained 3.4% of the richness significantly (p < 0.05), but the transition and food showed no significant explanation (p > 0.05). In addition, hierarchical partitioning variance of these alpha-diversity indices showed that the contribution of developmental stage (64.3–82.4%) was much stronger than those of environment (1.2–9.8%), transition (5.8–14.8%), or food , and partial Mantel tests estimate the correlations between diversity matrix and stage whilst controlling for the effects of environment/transition/food were kept consistent ; and the environment and food only showed a weak (|r| ≤ 0.2) correlation to the beta-diversity  to the gut microbiota succession. The multivariate regression tree (MRT) analysis explained 68.3% of the microbial diversity variance Fig. . SpecifiEcosystem) across all tanks or within each tank showed similar contributions from different parts of diversity. Specifically, the metacommunity was mainly contributed by the mean of diversity within water or gut habitats  and the sum of diversity between water and gut habitats (βinter-Habitats) represented 24.8–29.2% and 20.8–34.3% , showing relatively high deterministic ratios  of monitoring Poecilia sphenops, the fish gut microbiota found to be mainly driven by deterministic host effects independent of water microbiota35. Many other studies also showed that gut microbial communities in different fish species were considerably different due to the host genetics or phylogeny37, and the same fish species could colonize core gut microbiota despite radical differences in host provenance and domestication status28. Also, host sub-genomes (with hybrid fish lineages)38 and host genotypes (among-population differences)39 could affect microbial community in the fish gut ecosystem. However, those studies were always focused on a particular short developmental stage of fish. Interestingly, recent studies suggested that fish development could be one of the most important factors driving the succession of gut microbiota. For example, Burn et al.29 found that the relative importance of non-neutral processes for gut microbial community assembly in zebrafish increased over host development. This study agreed with such conclusion due to the deterministic process of homogenous selection increased with fish development for the assembly of gut microbiota. These studies along with some others33 started a new era to understand fish gut microbiota as the diversity and composition of microbial communities were found to be variable over zebrafish development. Our previous studies also indicated that aquaculture fish  could assemble a respective gut microbial community ~3 dph16, followed by stage-specific patterns across fish development31, which is consistent with the microbial community turnover across host development in other animals such as shrimp40, insect42, bird43, mouse44, and human46. Thus, valid comparisons across studies about the gut microbiota should at least strive to use consistent ages of the sampled hosts32.The host genetics is known as the most important force to provide a primary selection of gut microbiota47. This is coincident with the lumen formation of intestinal tract, and the whole gastrointestinal tube is opened at day 6 post-fertilization47. By this time, the yolk sac is almost consumed47, and the larvae start to get outside foods and simultaneously colonize diverse microbial species48. The colonized microbes in turn can further facilitate intestinal development in zebrafish49. That means the functions assisted by the colonized microbes in gut ecosystem may vary across zebrafish development, especially during the period of developing organs, and therefore showed stage-specific gut microbiota. Second, host immunity is also known as an important force to affect the fish gut microbiota. Although zebrafish innate immunity at early stages  only has a small effect on the diversity of gut microbiota15, their adaptive immunity increases the role of selective processes in gut microbiota assembly18. Other animals with high immune functions could support much smaller microbial populations in the intestine than those with immune-compromised hosts50. In this study, the gut microbiota succession in zebrafish showed a clear split between 12–20 and 27–42 dph. This could associate with the development of zebrafish adaptive immune system, which is only functional after 25 dph51. Third, host-associated microbes could also benefit from their host with a protected and nutrient-rich niche52, which may considerably differ within a host at different developmental stages. Thus, the assembly of gut microbiota suggested to be largely driven by the nutrient landscape created by host diet and secretions53. The availability of nutrients in gut ecosystems, as well as gut motility and digestive processes varied across zebrafish development may also contribute to the stage-specific gut microbiota.This study aimed to further understand the assembly and succession of gut microbiota by integrating effects of host development, hatching environment, and environmental transition. We found that the effects of zebrafish development on the gut microbiome were much stronger than those from the environment. The observed stage-specific gut microbiota succession patterns could be attributed to three aspects. First, the colonization of intestinal microbes in zebrafish initially occurs when zebrafish mouth opens at approximately day 3 post-fertilization29. It also explained why microbial communities in larval zebrafish were more similar to environmental communities than those of adult fish32. Besides the selective pressures from host, the absence of significant relationships between gut microbiota and water microbiota may also attribute to different conditions in gut and water habitats, which could cause community composition to be dissimilar due to the process of heterogeneous selection. Generally, only the microbial species in the surrounding water environment with the tolerance to conditions in the gut ecosystem could colonize and thrive as part of the gut microbiota. Of course, we also should acknowledge that some other processes such as microbe-microbe interactions and active dispersal currently classified as undominated process may also contribute to the succession of fish gut microbiota.We found that the homogeneous selection was the major deterministic process governing gut microbiota, but the impact of maternal/chorion variation as a source of gut microbiota variation was not explored here. Moreover, the homogeneous selection increased with zebrafish development, resulting in similar microbial communities as gut environments within a stage tend to be similar. The homogenizing dispersal was only important at the early stage, as the newly formed gut ecosystem initially have no pioneer species to exclusive dispersal of other microbial species. This is consistent with previous study, which suggested that the relative importance of neutral process decreased over host development32 and those in grass carp16, showing that gut microbial communities were different from the surrounding environment. Some other aquatic animals also exhibited considerably different gut microbial communities from those in the surrounding water55 or sediments56. Rudi et al.57 identified stable core gut microbiota in Atlantic salmon during the freshwater-to-saltwater transition. Besides the selective pressures from the host as discussed above, the interhost microbial transmission through the water is also a significant force that help fish shaping similar gut microbiota in different individuals24. In this study, we found that the overall diversity of metacommunity was mainly attributed to the mean diversity within gut or water habitats, indicating gut microbiotas contribute significantly to the metacommunity of the rearing ecosystem. Generally, the host-associated microbiota is strongly shaped by the factors that influence microbial survival and persistence in the gut ecosystem. The host gut habitat is a key issue for selecting the species able to colonize and thrive as part of its microbiota59. The successful colonizers in the gut ecosystem should have selective advantages of traits to occupy space, obtain resources, and avoid removal via excretions24. Their success of colonization in fish gut with relatively high abundances in turn can affect the metacommunity of the rearing system due to ceaseless communication  with surrounding water.Compared with strong host effects on the succession of gut microbiota across zebrafish development, the environmental influences derived from hatching environment or environmental transition were much smaller. This is consistent with previous findings in zebrafishIn summary, this study sought to resolve the effects of host development, hatching environment and environmental transition on the gut microbiota succession from larvae to adult fish. Although the impact of maternal/chorion variation as a source of gut microbiota variation was not explored, we found that zebrafish developmental stage substantially explained the ecological succession of gut microbial communities and dominant OTUs. Moreover, the ecological succession of zebrafish gut microbiota was mainly governed by stage-dependent homogeneous selection due to host effects, and the environmental impacts of hatching environments and environmental transitions were much smaller. These findings expand our current understanding of the ecological succession of gut microbiota across fish development, and also provide new insights into gut ecology of other aquatic animals.Hydrocotyle vulgaris and Epipremnum aureum, respectively, and their roots were kept in water to exchange materials between plants and water, which were distinct from environment C with no plants. After plants grew for 30 days, water samples were collected from the left, center, and right of each tank as three replicates to evaluate the environmental differences among A, B, and C. The water microbiota and water chemical factors  were monitored as described previously60.We investigated the gut microbiota assembly and succession in wild-type zebrafish (AB strain) from larvae to adults. We first established three different water environments using independent glass tanks (130 × 30 × 40 cm). Specifically, environments A and B were planted with The three different water environments constructed were then used to manipulate zebrafish and test our hypothesis in three aspects. First, to test possible effects of different water environments on the assembly and turnover of gut microbiota, the same batch of fertilized zebrafish embryos were randomly assigned to three independent circular plates (~1000 embryos per plate) and hatched with waters from environments A, B, and C, respectively  as well as 14/10-h light/dark cycle was controlled. No additional food was provided before the yolk sac was completely consumed (4 dph). Then, zebrafish were fed with cultured Paramecium (5–8 dph), 20 µm mesh filtrated boiled egg yolk (9–11 dph), live brine shrimp (12–19 dph), and a standard dry fish food from 20 dph onward .Three fish were randomly sampled per tank per treatment per timepoint as replicates, and each fish was used as an individual specimen in subsequent experiment. The intestines of larval individuals were immediately removed aseptically under a dissecting microscope as described previously60. We totally obtained 189 zebrafish gut samples  and 54 water samples  for following microbial analyses. The intestines and filters were immediately stored at −80 °C until DNA extraction. Microbial community DNA was extracted using the PowerFecal® (gut samples) or PowerWater® (water samples) DNA Isolation Kit  following the manufacturer’s instructions. The concentration and quality of extracted DNA were determined using a NanoDrop One spectrophotometer , and all the DNA samples were diluted to the same concentration (10 ng/μL) for subsequent PCR amplification.At each sampling time (except 20 dph), water samples were also collected from the left, center, and right of each tank as three replicates within tanks. Then, 500 mL of each water sample was sequentially filtered through 1.2-mm  and 0.22-mm filters  to collect microbial cells for evaluating the water microbiotaThe V4-V5 regions of the 16S rRNA gene were amplified by using the primer set of 515 F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 907R (5′-CCGTCAATTCMTTTRAGTTT-3′). Each sample was amplified in a reaction volume of 50 μL containing 1× Premix Taq DNA polymerase , 0.2 mM of each primer, and 50 ng genomic DNA. The program for PCR amplification included DNA pre-denaturation for 5 min at 95 °C, then 30 cycles of 30 s at 95 °C, 30 s at 52 °C, and 30 s at 72 °C, followed by a final extension at 72 °C for 10 min. Negative controls were always performed to make sure there is no contamination. After all samples were successfully amplified, the PCR products were quantified and equally combined. The target band visualized by 2.0% agarose gel was excised and purified with a QIAquick Gel Extraction Kit . After re-quantifying the concentration of purified DNA, it was subjected to library construction. The constructed amplicon library was finally sequenced by the Illumina HiSeq 2500 platform  in Guangdong Magigene Biotechnology Co., Ltd. with a 2 × 250 bp kit.http://mem.rcees.ac.cn:8080/) as described previously61. In brief, the overlapped paired-end sequences were first assembled using QIIME 62, and poorly overlapped and low-quality sequences such as those with length <140 and moving-window (5 bp) quality score <20 were removed before downstream analysis. The table of zOTUs  generated by the UNOISE method according to the database of Greengenes 13_863. The singletons were removed from the original OTU table. All samples were resampled to a same sequencing depth  before subsequent community analysis and statistics.Quality filtering and processing of sequence reads were conducted on the publicly available Galaxy pipeline  was partitioned into the sum of inter-habitat compositional differences , the mean intra-habitat compositional differences  and the mean diversity of local communities .The role of water and gut habitats for shaping the metacommunity in each tank or across all tanks was separated into their contributions at smaller scales from habitats to local communities as described previously16. First, the representative sequences of each OTU were used to align with the 16 S GreenGene sequences using PyNAST64, and a maximum-likelihood tree was constructed using FastTree65. Then, phylogenetic diversity (PD) of each given pair of communities was quantified by the weighted beta nearest taxon index (βNTI)66. The βNTI in a combination of Bray-Curtis-based Raup-Crick (RCbray)68 was further used to quantify the ecological processes. In brief, the relative impact of community turnover determined by the heterogeneous selection and homogeneous selection can be indicated by the fraction of communities with βNTI > 2 and βNTI < −2, respectively69. These selection processes include environmental filtering, biotic interactions, nonrandom dispersal, and positive mutations70. If |βNTI| < 2, but with RCbray > 0.95 or <−0.95 suggested that the community turnover is governed by dispersal limitation or homogenizing dispersal, respectively. However, if |βNTI| < 2 and |RCbray| < 0.95, that means the community turnover is not governed by any process as mentioned above and therefore classified as undominated process, which includes some weak selection, weak dispersal, diversification, and drift70.To quantify the influence of ecological processes on fish-associated microbial communities, the relative contribution of key processes  governing the gut microbiota succession was determined as described previously71 was used to explore and predict relationships between the gut microbiota and expected influence factors . This method has been widely used for modeling species-environment relationships, and it is well suited for complex ecological datasets with high-order interactions. General linear model (GLM) analysis was used to ascertain what underlying factors shaped the alpha-diversity . Hierarchical partitioning was performed to identify important potential causal variables with independent effects on the alpha-diversity. Mantel and partial Mantel tests were performed to determine relationships between environmental factors and the microbial community structure based on both Bray–Curtis and Jaccard distances.Multivariate regression tree (MRT) analysis72; (iv) in order to determine whether the observed community similarities within each stage are indistinguishable from the null expectation, the null model analysis based on the method proposed by Chase et al.47 by holding α-diversity and γ-diversity across total dataset constant. We performed this analysis based on the Bray–Curtis distance without data transformation. It provides a quantitative estimation of the role of deterministic selection processes in shaping community composition and structure, such ratio is termed as selection strength. (v) canonical correspondence analysis (CCA) was used to explain the contributions of the top 20 dominant OTUs to the overall compositional variation of the gut microbiota;73 vi) significance tests were performed through an analysis of variance (ANOVA) with least-significant-difference (LSD) to examine whether differences among comparisons were significant or not. All statistical analyses were performed using the R software .In order to show a general pattern of the relatively dominant microbial members, the top OTUs in the gut or water were estimated by proportions based on averages per sample across all environments . More specifically, the generated OTU table was analyzed tank-by-tank or compared across all tanks using the following statistical methods: (i) alpha- and beta-diversity comparisons were conducted to reveal changes in gut microbiotas throughout host development, or between water microbiota and gut microbiota; (ii) detrended correspondence analysis (DCA) was conducted to illustrate overall similarities of microbial communities based on the Bray-Curtis distances; (iii) permutational multivariate analysis of variance (PERMANOVA) was performed to evaluate the significance of community dissimilarities based on both Bray-Curtis and Jaccard distancesFurther information on experimental design is available in the Supplementary informationReporting Summary


The effects of visual spatial attention on neuronal firing rates have been well characterized for neurons throughout the visual processing hierarchy. Interestingly, the mechanisms by which attention generates more or fewer spikes in response to a visual stimulus remain unknown. One possibility is that attention boosts the likelihood that synaptic inputs to a neuron result in spikes. We performed a novel analysis to measure local field potentials (LFPs) just prior to spikes, or reverse spike-triggered LFP “wavelets,” for neurons recorded in primary visual cortex (V1) of monkeys performing a contrast change detection task requiring covert shifts in visual spatial attention. We used dimensionality reduction to define LFP wavelet shapes with single numerical values, and we found that LFP wavelet shape changes correlated with changes in neuronal firing rate. We then tested whether a simple classifier could predict monkeys’ focus of attention from LFP wavelet shape. LFP wavelet shapes sampled in discrete windows were predictive of the locus of attention for some neuronal types. These findings suggest that LFP wavelets are a useful proxy for local network activity influencing spike generation, and changes in LFP wavelet shape are predictive of the focus of attention. The effects of attention on the activity of neurons in the visual cortex have been the subject of much study; however, the neuronal mechanisms that give rise to various attention effects are poorly understood. Perhaps the most straightforward effect of attention is the alteration of neuronal firing rates when subjects attend to visual stimuli overlapping recorded neuronal receptive fields . Both faAn important caveat to most studies of attentional modulation of neuronal activity is that neuronal responses are usually averaged over long timescales. For example, attentional modulation of neuronal firing rate is usually computed over hundreds of milliseconds to several seconds. Computing attentional modulation of neuronal activity over long timescales is inconsistent with evidence suggesting that effects of attention are not constant over these long timescales . BehavioSome insight into a possible mechanism for producing more spikes with attention comes from studies of attentional modulation of synaptic efficacy among connected pairs of neurons. In both geniculocortical circuits and local circuits within primary visual cortex (V1), attention significantly enhances the likelihood that a presynaptic input gives rise to a postsynaptic spike . AlthougIn this study, we sought to determine whether local field potentials (LFPs) occurring just prior to spikes and recorded on the same electrode contacts as neuronal spikes could provide information about the local network activity influencing neuronal spike generation. We asked whether the shape of reverse spike-triggered LFPs could predict whether and when attention modulated neuronal spiking. LFPs do not provide the resolution or specificity of intracellular recordings of synaptic inputs to a single neuron. However, because LFPs are thought to reflect both sub- and suprathreshold activities among neurons within hundreds of microns of the recording electrode , it is pThe data analyzed for this study were collected as a part of previous studies of attentional modulation of single neurons and local field potentials (LFPs) recorded across the cortical layers of primary visual cortex or V1 .Data were collected from 3 monkeys performing a contrast-change detection task requiring shifts in covert visual spatial attention . Data frSingle electrodes or multi-electrode arrays were used to record from V1 neurons spanning the cortical layers while monkeys performed the attention task. Single-unit spikes were sorted using the same methods and criteria described previously , or throP > 0.36), and the standard deviations in LFP wavelet shape were not different across monkeys for neurons in any laminar compartment (P > 0.08). Accordingly, data from all monkeys were combined. Example average LFP wavelets from attend-toward and attend-away trials for two V1 neurons from different monkeys are illustrated in A set of “LFP wavelets” was computed for each individual neuron as reverse spike-triggered LFPs from spikes recorded on the same contact/electrode. First, LFPs measured during each trial were detrended and then z-scored . Next, fPower spectra were computed for individual LFP wavelets using Welch’s method with a Hamming window of 106 msec and a sampling frequency of 1000 Hz. Power spectra were separated by attention condition and then averaged per attention condition per neuron. Power spectra per attention condition were then averaged across neurons of the same type and laminar compartment location.In order to compute a single value representing the shape of each spike-triggered LFP wavelet, we performed dimensionality reduction on LFP wavelets per neuron using a principal components analysis (PCA). We calculated the first principal component (PC1) and second principal component (PC2) scores for each LFP wavelet. These PC scores provided single numerical representations of the position of each LFP wavelet in the high-dimensional PC space representing LFP wavelet shape. We performed parallel analyses using PC1 and PC2, and we found that PC1 captured more variance in LFP wavelet shape per neuron. Changes in PC2 score over time were more consistent with luminance modulations in the visual stimulus than with attentional modulation A. For a 2, slope, and P values for linear regression fits. We performed this PC1 versus firing rate correlation analysis separately for spikes on attend-toward and attend-away trials per neuron. 2 values for neurons of each type and laminar compartment, as well as the number of neurons per type for which there was a significant correlation between PC1 score and firing rate in at least one attention condition.To test whether PC1 scores correlated with changes in neuronal firing rates, we compared the PC1 score per LFP wavelet with the firing rate in a 100-msec bin preceding each spike and we repeated this for all spikes per neuron. We then computed the correlation between PC1 score and firing rate for each neuron and extracted RTo examine whether LFP wavelets provide information about the locus of attention, we used a simple linear classifier to test whether variations in LFP wavelets for neurons of each type could predict the monkeys’ focus of attention. We performed two separate classification analyses, described below. We evaluated each classification using a receiver operator characteristic (ROC) approach, computing the area under the curve (AUC) for each ROC curve following the assumption that larger ROC AUCs represent better classification of the correct attention trial type (attend-toward or attend-away). For statistical comparisons, we compared ROC curves and AUCs from real data to those computed using shuffled data in which trial type (attend-toward or attend-away) was randomly shuffled.P values corrected for multiple comparisons was used to examine significant differences across real versus shuffled data and across neuron types.In the first classification analysis, we tested whether average LFP wavelet shapes on attend-toward and attend-away trials could predict the locus of attention. Average LFP wavelet shapes were defined by their mean LFP wavelet PC1 scores averaged over the full 1-s idealized single trial duration. Input to the classifier was average PC1 scores for all neurons of the same type on attend-toward and attend-away trials. Real and shuffled data were resampled 10 times to provide 10 repeats, and average ROC curves were computed from these repeats. Nonparametric analysis of variance tests with P values corrected for multiple comparisons was used to examine significant differences across real versus shuffled data for each window and neuron type. Separate significance tests were applied to neuronal populations in each laminar compartment.In the second classification analysis, we tested whether LFP wavelet shapes sampled in 300-msec windows on attend-toward and attend-away trials could predict the locus of attention. LFP wavelet PC1 scores were sampled every 50-msec within 300-msec windows . Input to the classifier for each window analysis was 6 PC1 scores per neuron for neurons of the same type on attend-toward and attend-away trials. Real and shuffled data were resampled 10 times to provide 10 repeats and average ROC curves were computed per window analysis. Nonparametric analysis of variance tests with For illustrative purposes, distributions of ROC AUC values from the second classification analysis (the windowing approach) were created for each neuron type to provide a secondary illustration of significant and nonsignificant differences between real and shuffled data for each neuronal type B. ROC AUAttention modulates the firing rates of visual cortical neurons; however, the mechanisms that give rise to attention-mediated changes in neuronal spiking probabilities are not known. In order to gain insight into mechanisms underlying neuronal spike rate modulation, we pursued two study objectives. First, we sought a measure of local network activity that could influence spike generation in individual neurons. Second, we asked whether attention alters this local network activity during certain time points within attention task trials in order to adjust neuronal spiking probabilities.We specifically tested whether reverse spike-triggered local field potentials (LFPs) might serve as a proxy for local network activity influencing neuronal spike generation. Because LFPs contain a mixture of sub- and supra-threshold activity from nearby neurons , LFPs oc, solid vs. dashed lines). While time-varying PC1 score curves displayed larger amplitude and lower frequency fluctuations over the idealized trial, PC2 score curves often appeared to fluctuate near the frequency of luminance modulations in the visual stimulus . For all neurons in the sample, PC1 captured more variance in LFP wavelet shape than PC2, so all subsequent analyses were performed using PC1 scores.LFP wavelets were not flat lines but instead displayed dynamic shapes that usually consisted of broad, that is, low-frequency, deflections . Indeed,Fig. 1We next asked whether time-varying PC1 score curves were similar across neurons of the same type and laminar compartment location. Smoothed time-varying PC1 score curves averaged for all neurons of the same type and laminar location revealed some similarities among neurons of the same type and some differences across neuronal types A. While In order to quantify the utility of LFP wavelet shape as a predictor of neuronal activity and attention, we first compared LFP wavelet PC1 scores with neuronal firing rates computed from the same 100-msec time bins. If LFP wavelet shape tracks with neuronal firing rate measured over the same time bin, LFP wavelet shape would be a useful proxy for neuronal network activity influencing spike generation. Another reason to study neuronal activity in such small time bins is the following. While attentional modulation of V1 neuronal firing rates averaged over long (~1 sec) analysis windows is weak , neuronaP = 0.0117, nonsignificant after multiple comparisons correction; average ROC AUC across all neurons: real = 0.55 ± 0.02, shuffled = 0.54 ± 0.01). Average ROC curves computed from average full trial PC1 scores for each neuronal type are illustrated with yellow curves in P = 0.0012; To further quantify possible attention-mediated differences in LFP wavelet shape over a trial, we used a simple linear classifier to test whether LFP wavelet shape was predictive of monkeys’ locus of attention. We performed 2 separate classifications of attention trial type using PC1 scores for neurons of the same type and evaluated each classification using an ROC approach. In the first classification analysis, we tested whether average LFP wavelet shape, computed as the mean LFP wavelet PC1 score over the full idealized single trial analysis window per neuron, could predict the locus of attention, that is, whether a given trial was an attend-toward or attend-away trial. We performed this classification for each neuronal type and compared ROC AUC values for real and shuffled data. There were no differences in ROC AUC values between real and shuffled data for any neuronal type for PC1 scores averaged over the full analysis window ; National Science Foundation (EPSCoR 1632738); the Whitehall Foundation; and the Hitchcock Foundation. V.L.M. was supported by a graduate fellowship from the Albert J. Ryan Foundation. A.S. was supported by a University Research Award from the University of Rochester.


Altered iron metabolism is one of the pathophysiological mechanisms occurring during hypoxic injuries in the central nervous system. Proper homeostasis of cellular iron is regulated by iron import, storage, and export proteins that prevent excess iron overload or iron starvation in cells. Therapeutic hypothermia is an approved treatment for hypoxic ischemia in newborns, but the underlying molecular mechanism is still unknown. We studied the effects of hypoxia, preceded with preconditioning, on the iron homeostasis of glial cells, known as a major actor in the inflammatory process during perinatal brain injury.Primary microglia and astrocytes in culture were exposed to 12 h of hypoxia with or without mild hypothermic preconditioning. The mRNA expression was assessed using qPCR. Iron accumulation was visualized via modified Perl’s histochemistry. Cytokine levels in cell cultures were measured using ELISA.Hypothermic preconditioning enhanced microglial viability, which previously was decreased in both cell types due to hypoxia. Hypoxia increased iron accumulation in the mixed glial cells and in ferritin expression in both microglia and astrocytes. Hypotermic preconditioning decreased the elevated ferritin-light chain expression significantly in microglia. Iron importer proteins, DMT1 and TfR1, both increased their mRNA expression after hypoxia, and hypothermic preconditioning continued to support the elevation of DMT1 in both glial cell types. Ferroportin expression increased as a survival factor of the glial cell following hypoxia. Hypothermic preconditioning supported this increase in both cell types and was especially significant in astrocytes. IL-10 levels were prominently increased in cell culture after hypothermic preconditioning.The data suggest that hypothermic preconditioning affects cellular iron homeostasis by regulating the storage and transfer proteins of iron. Regulation of the cellular iron traffic may prevent glial cells from experiencing the detrimental effects of hypoxia-related inflammation. Motor or cognitive neurological disorders originating from the perinatal period pose an increasingly serious health problem in developing countries [1]. Hypoxic ischemic (HI) conditions are the main cause of brain damage in newborns. Oxygen deprivation is seen in many childhood diseases such as obstructive sleep apnea, bronchopulmonary dysplasia, congenital heart diseases, asthma, epilepsy, and sickle cell anemia, as well as in nonprogressive but long-term cognitive and/or motor disorders [2].In addition to the impaired cerebral blood flow associated with hypoxia-reperfusion injury, several intracellular pathways triggered by excessive inflammation also play a role in the pathogenesis of brain white matter lesions in newborns [3]. Astrocytes and microglia are the main determinants of this inflammatory process [4]. The excessive gliosis that occurs in the hypoxic brain injury process can lead to the development of tertiary brain damage, and microglial signaling may induce the cytotoxicity of astrocytes. In contrast, a controlled regular microglial response can speed up the regeneration process [5]. Another feature of hypoxic injury is that the damage is progressive and moves forward to solid tissue within days/weeks. Although the mechanism of this spread is not fully explained, glial cells are known to play a role via ‘gap junctions’ [4].Iron is a key player in brain development in the fetal and early neonatal period. Excessive accumulation of iron causes iron-mediated free radical damage within the cell. On the other hand, iron is necessary for the activity of many metabolic enzymes and thus critical for maintaining cell viability [6]. Proper functioning of mechanisms controlling cellular iron levels through various transport and storage proteins is responsible for maintaining intracellular iron homeostasis. Studies in infants and neonatal rats have shown that hypoxic-ischemic damage increases free iron levels in serum and cerebrospinal fluid [7]. Accumulated iron, which is associated with hypoxia at the cellular level, especially in microglia, stimulates reactive oxygen products, cytokine production, and the resultant apoptosis of oligondendrocytes. Microglia prevents excessive iron accumulation in the environment by binding it following hypoxic damage [8]. It has been suggested that excessive iron retained in microglia may be a regulator for oligodendrocyte cytotoxicity seen in periventricular white matter following hypoxia [9]. Hypoxic damage in the brain enables activation of a transcription complex, hypoxia-inducible factor 1 subunit alpha (HIF-1a), resulting in the activation of many genes that allows cells to adapt to hypoxia and thus recover from cell death [10]. Transferrin receptor (TfR)1 and divalent methyl carrier (DMT)1, proteins involved in cellular iron transport are regulated with expression of HIF-1a in neurons, astrocytes, [11] and microglia [12].Containing heavy and light chain, ferritin is directly affected by hypoxia in terms of both cellular distribution and subcortical white matter myelinization pattern. Brain damage that develops in the neonatal rat brain mediated by HI results in increased ferritin-positive microglial cells in the environment. In the hypoxia and subsequent reoxygenation process, glial ferritin increases in the early period, while neuronal ferritin increases in the late stages of reoxygenation. Ferritin expression is indirectly regulated by other factors such as HIF-1a during hypoxia-reoxygenation. Changes in iron homeostasis in cortical and glial cells during an experimental ischemia process are considered to be part of the adaptive response of cells to hypoxia [13].Preconditioning processes can be protective in both experimental and human studies [14]. Studies have shown that HIF-1a and its target genes are involved in hypoxic preconditioning-induced neuroprotection. In addition, the adaptation of cells and tissues to hypoxia induce many gene transcriptions involved in iron metabolism. In multicenter controlled studies, exposure of low or moderate hypothermia in encephalopathic newborns has also been shown to significantly suppress the development of hypoxic damage . The molecular mechanisms underlying hypothermia-generated neuroprotective effects have not yet been fully clarified. It has been shown that hypothermia and subsequent reheating cause activation in microglia, increases phagocytosis, and changes the cytokine balance in favor of antiinflammatory cytokines [17]. Hypothermia may also be protective by controlling the inflammatory process via suppressing interferon (IFN) beta and nitric oxide (NO) [18].In this study, the mechanism of action of hypothermia on iron storage and transport proteins that regulate iron homeostasis were investigated in primary astrocyte and microglia cell culture in mice.Glial cell cultures were prepared as described previously [19]. Briefly, the neonatal mouse cerebral cortex was dissociated and plated in tissue-culture flasks. Ten days later the confluent cells were treated for mixed glial cell culture group. Another group of cells were treated for 5 min with mild trypsinization (% trypsin with 0.25 mM EDTA), which detached a layer of loosely adherent cells, most of which being mainly astrocytes. This left behind firmly adherent cells, which are >90% microglia. Afterward, the cells were cultured for 5 days in DMEM/F12 culture medium containing 10% FBS, 1% penicillin/streptomycin, and 1% vitamins mixed 1:1 with mixed glial cell-conditioned media before stimulation. For astrocytes, a 3rd group of confluent cells were left at 37 °C overnight in a horizontal shaker (200 rpm) with paraffine closed lids, and then the floating cells were discarded. After a mild trypsinization, cells were replated at 1/3 the density in DMEM culture medium containing 10% FBS, 1% penicillin/streptomycin, and 1% vitamins. Cells were ready to use after a 7-day mitotic inhibitor cytosine arabinoside (10 IM) treatment to remove proliferating meningeal cells. The purity of astrocyte and microglia cultures was assessed by immunofluorescence labeling for GFAP and CD11b, respectively. Purified microglia were plated at a density of 8 × 105 cells/well, and purified astrocytes were plated in fresh media at a density of 5 × 105 cells/well 2 days before the start of the experiment. Throughout the experiment, different and newly prepared—but always confluent—cell cultures were used at each time point and condition.Hypoxia induction was performed using a chamber  containing 94% N2, 5% CO2, and 1% O2 as previously described in the literature  . During hypoxia and ischemic conditions neither oxygen nor glucose can reach the tissues, so we applied oxygen-glucose deprivation as a closer in vitro model to in vivo. To reach that goal, we incubated the cells in PBS during the experimental procedure. After draining the chamber and filling it with the desired concentration of gas, it was locked and left in the 37 °C incubator with the normoxic control plates. Hypoxia induction was confirmed using the increased expression of HIF-1a, using qPCR as described below. Cell viability was tested with the colorimetric method based on the tetrazolium salt XTT at 2, 6, 12, and 24 h . Cytotoxicity was accepted as occurring when more than 50% of the cells in culture had died. HP was performed by incubating the cells at 33 °C for 20 min as previously indicated as mild hypothermia in the literature [21].Cellular iron accumulation was shown using modified Perl’s histochemistry as previously described in literature [22]. Briefly, cells were plated to PLL coated glass coverslips and left in hypoxic conditions as described above. Coverslips were then fixed with 4% paraformaldehyde for 15 min in the wells. After washing with PBS, the coverslips were first incubated in 4% potassiumferricyanide, and then in a mixture of 4% HCl and 4% potassiumferricyanide for 1 h. Cells stored in iron became visible with a serial incubation with diaminobenzidin (DAB)  and 30% H2O2. Stained coverslips were fixed before visualization with a light microscope.After hypoxic incubation with or without HP, cells were harvested using β-merkaptoetanol-added lysis solution and stored at –80 °C until RNA extraction . Total RNA was extracted using an RNeasy Mini Kit  following the manufacturer’s instructions. cDNA was reverse transcribed with an Omniscript Kit  and was amplified in an Abi OneStep Cycler  using specific primer pairs for ferritin heavy (-H) and light (-L) chains, ferroportin (Fpn), transferrin receptor (TfR)1, and divalent metal transporter 1 (DMT1). Peptidylprolyl isomerase A (PPIA) was used as an internal control gene. The results were quantified using the ΔΔCT method following standardization relative to the internal control gene [23]. Primers are listed in the Table.Cells were plated on poly-L-Lysine-coated round glass coverslips and allowed to attach for 24 h in 24-well plates. Each group was incubated in hypoxic conditions with or without HP. Primary antibodies  were used at 1:200, and the appropriate secondary antibody  conjugated with Alexa Fluor 488, which was used at 1:400 dilution. Cells were stained as described previously [19]. Briefly, after washing with HBSS, the cells were incubated with the primary antibody for 30 min, followed by washing and incubation with the appropriate secondary antibody for 30 min. For surface antigens such as CD11b, the cells were then fixed with 95% ethanol and 5% acetic acid for 15 min at 4 °C. Intracellular antigens  required cell permeabilization before incubation with the primary antibody [24]. After washing in PBS, the coverslips were mounted in antifade mounting medium containing DAPI  and analyzed using a fluorescence microscope.After hypoxic induction with or without HP, cell culture supernatants were collected and stored at –80 °C until the day of analysis. IL-6, TNF-alpha, and IL-10 levels were measured using commercial ELISA kits following the manufacturer’s instructions .Statistics were preformed using SPSS v.20. Analyses were performed with the Kruskal–Wallis test for multiple comparisons and with the Mann–Whitney U test for pairs. Differences were considered significant at P < 0.05. Data were presented as mean ± SEM.Mixed glial cell culture was incubated in hypoxic conditions for 2, 6, 12, and 24 h. After checking cell proliferation for mixed glial cells, 24 h of incubation was excluded because more than 50% of the cells had died. Hypoxia was confirmed by checking the expression of HIF-1a in mixed glial cells with qPCR. Compared to the control, HIF-1a expression did not change after 2 h of hypoxia (P > 0.05) but was significantly elevated both after 6 and 12 h of hypoxic conditions (P < 0.05). Based on this result, the 2-h incubation was removed from the time points as it did not provide a successful hypoxia in mixed glial cell culture.Iron accumulation due to hypoxia in mixed glial cell culture was shown using Perl’s histochemistry. Representative images showed iron accumulation within glial cells in culture after 12 h of hypoxia but, in contrast, very little staining was seen after 6 h of incubation .Ferritin-H and -L expression in hypoxic conditions were elevated for both 6 and 12 h in hypoxic conditions in mixed glial cell culture, but the increase was significant at 12 h (P < 0.05) .In mixed glial cell culture, regarding iron accumulation displayed by modified Perl’s histochemistry and ferritin expression detected by q-PCR, 12 h of hypoxia produced significant changes compared to 6 h (P < 0.05). Based on these results, subsequent single cell culture experiments were performed with 12 h of hypoxia application for both microglia and astrocyte.To study the different roles of both glial cells in hypoxic conditions with and without hypothermic conditioning, primary astrocyte and microglia were isolated from newborn pup brains (P2-4) and, following the cell-specific protocol, cultured for 2 extra days until the cells got confluent. An XTT test was performed after hypoxia, and both cell types showed a significant decrease in cell numbers after 12 h of hypoxia (P < 0.05). Microglia counts were observed as significantly increased after HP when compared to both control and hypoxic conditions (P < 0.05) .Cellular iron storage was shown by measuring ferritin mRNA expression as heavy (-H) and light chain (-L). In astrocytes, hypoxia increased both ferritin chain expressions, being significant for the light chain (P < 0.05). HP did not have any significant effect on ferritin expression in astrocytes. Hypoxia significantly increased ferritin-L and -H expression in microglia (P < 0.05). HP reduced the expression of both chains, which were significantly lower for ferritin-L (P < 0.05) ..The antibody used in ferritin immunofluorescence staining did not determine the different chains, so pure ferritin was stained in both microglia and in astrocytes after hypoxia. Cells stained more comprehensively; they were particularly weaker in microglia but still positive after HP followed by hypoxia Hypoxia definitely increased DMT1 mRNA expressions both in microglia and the astrocytes (P < 0.05). DMT1 mRNA expression was significantly higher, especially after HP (P < 0.01) in both cell types . TfR1 mRNA expression after hypoxia was significantly elevated in both microglia and in astrocytes with and without HP (P < 0.05). HP did not make any significant difference on TfR1 expression when compared to hypoxic conditions .Ferroportin mRNA expression increased in both cells incubated in hypoxic conditions (P < 0.01). Compared to the control group, HP increased ferroportin mRNA expression significantly in astrocytes (P < 0.01) and microglia (P < 0.05) but, compared to hypoxia, the difference was significant only for astrocytes (P < 0.05) .TNF-alpha levels in both glial cell types and IL-6 levels in astrocytes were under detection limits. When compared to the control group, IL-10 levels significantly increased in both cells after hypoxia (P < 0.05), and HP remained and sustained this increase in both cells types, being significant in microglia . Microglial IL-6 levels increased following hypoxia (P < 0.05); however, they were not significantly affected by HP .In this study, the response of glial cells, the key players in the inflammatory responses generating in the brain to HP before hypoxia, was investigated through iron-related cellular storage and transport proteins. We provide evidence that HP before hypoxia decreases the cytotoxicity in microglia and suppresses the expression of the light chain of ferritin specifically by altering the expression of transport proteins; this may be beneficial by suppressing the iron related detrimental effect following hypoxic injury.Although various methods are applied for treatment of HI encephalopathy, inflammatory cells and pathways stimulated by the mechanisms of these treatments have not been fully elucidated yet. In addition, since there is no definite treatment for HI damage, so it is of great importance to understand the molecular mechanisms of pathogenesis, to identify specific markers for diagnosis in order to minimize the level of degeneration, and to develop new treatment and rehabilitation options in addition to prevention.Therapeutic hypothermia is a neuroprotective strategy that improves neurological outcomes after several brain damage mechanisms, including hypoxic injury [25]. In addition to neurons, all glial cells are affected by hypothermic treatment with different mechanisms and at different rates. Short-term hypothermia applied immediately after injury reduced hypoxic damage to axons by supporting the proliferation of oligodendrocyte progenitors and increasing axonal myelination [26]. Hypothermia also prevents astrogliosis and suppresses aberrant angiogenesis, which protects the body from ischemic proliferative retinopathy, a common complication following hypoxic injury in newborns [27]. Hypothermic treatment has time-dependent effects on microglial functions that are regulated by several cellular mechanisms, including neuroinflammation . Secondary to in utero hypoxia, there are also several conditions that directly cause white matter damage during postnatal periods, including neonatal cardiac surgeries [29]. HP is an effective treatment for limiting ischemic injury, but the mechanism is poorly understood. A brief cycle of HP applied before an ischemia-reperfusion episode significantly decreased both damage and oxidant indicators while increasing antioxidant levels in isolated perfused rat liver [30]. Jiang et al. (2018) showed that the downregulation of COX-2 expression and the increase of Na+/K+ ATPase activity increase tolerance to hypoxic damage and may be associated with the effectiveness of mild HP at 33 °C [31]. These results show that HP might be a promising strategy in protecting the brain against hypoxic damage.Several studies have shown that chronic neonatal hypoxia reduced cerebral cortex volume and cell number and caused a significant suppression in the number of glial cells located in white matter [32]. In our study, hypoxia with glucose deprivation caused a significant decrease in the number of both microglia and astrocytes, especially after 12 h. However, a mild HP applied before hypoxia increased the number of microglial cells (P < 0.05) and provided a protective response against hypoxic effects in terms of cell viability. Under normal circumstances, there is a crosstalk between astrocytes and microglia in CNS, which are together responsible for astrogliosis in an injured brain. It has previously been shown that not only resident microglia but also peripherally derived macrophages travelling to the brain parenchyma after injury have different effects on neuroinflammation and regeneration [33]. How this protective effect of HP on microglia affects astrocytes in in vivo hypoxic conditions needs to be further investigated.Hypoxic damage in the brain results in the activation of a transcription complex, HIF-1a, resulting in the activation of many genes that will allow cells to adapt to hypoxia and thus recover from cell death [10]. HIF-1a regulates the expression of ferritin by binding to a HIF responsive element upstream of the iron responsive element [34]. We have shown that application of both 6 and 12 h of hypoxia in cell culture induced significant upregulation in HIF-1a expression in both microglia and astrocytes when compared to control.The proper homeostasis of cellular iron is important to prevent excess iron overload or iron starvation in cells. Although it is essential for the activity of various metabolic enzymes, excessive iron can be harmful via iron-mediated free radical damage at the molecular level. Impairment of these properties might lead to dysfunction of iron metabolism and neurodegeneration in CNS. Glial cells play a critical role in the regulation of iron homeostasis. A complex system of interactions involving several transport and storage proteins, locating both on the cell surface or intracellularly, could regulate the levels of cellular iron [35]. “Dysregulation of cellular iron homeostasis is considered to contribute to neural damage in several conditions including ischemic stroke.” [36] Ryan et al. showed that iron labeled cells are seen in the lesion along the lesion border at 72 h of medial carotid artery occlusion in mice. At 14 days of injury, there was a marked elevation in iron-labeled cell counts throughout the lesion [36]. Another study showed that in a period of 28 days following spinal cord injury iron-dextran, treated mice showed increased cellular iron accumulation at the epicenter of the injury when compared to vehicle (dextran) control [37]. Glial cells are the main cells maintaining iron in CNS . Zarruk et al. (2015) showed in experimental autoimmune encephalitis that iron accumulates in macrophage/microglia but not in astrocytes [35]. The differences in the expression of iron import and export proteins in these 2 cell types appear to underlie the differences in iron accumulation as indicated above. Increased intracellular iron also alters macrophage polarization to M1 in the injured spinal cord [37]. In our study, iron deposition in the cell culture was assessed histochemically using modified Perl’s histochemistry [22]. Strong staining of deposition was detected after 12 h of hypoxia, while 6 h of incubation did not cause a visible iron accumulation in mixed glial cells. However, in cases when separately staining microglia and astrocytes, there was no significant difference between those cells in terms of iron accumulation (data not shown) at 12 h.Iron acquisition of the cell takes place in 2 ways and is defined by the iron transport substrate. In the nontransferrin bound uptake, ferrous iron (Fe+3) is reduced to ferric iron (Fe+2) by an endogenous ferrireductase and transported through the cell membrane by DMT1. Fe+3 is bound to transferrin in TBI, and this complex is internalized by endocytosis into the endosome, then released and reduced to ferric form followed by transportation into the cytosol . Ferritin is the major iron storage protein, and by accumulating iron in the cell, it makes it accessible in conditions such as myelin and enzyme synthesis; it also provides a protective effect in the development of free iron-mediated oxidative damage [36]. It has been previously shown that “iron-overload in neurons increase ferritin to sequester iron in nontoxic form, to downregulate iron importers and to increase iron export.” [40] Each ferritin molecule consists of heavy and light chain subunits that form a sphere in which iron is efficiently stored [41]. While ferritin-L is the major storage form of iron, ferritin-H has ferroxidase activity and could provide a cytoprotective mechanism in several conditions such as sickle cell anemia [42]. The ratio of ferritin-L and -H is tissue specific, and this differential expression may regulate heme-catalyzed oxidative damage [43]. In our study, mixed glial cells increased both ferritin -L and -R expression significantly at 12 h but not so at 6 h of hypoxia.Cellular distribution and the subcortical white matter myelinization pattern of ferritin are directly affected by hypoxia. Brain damage that develops in the neonatal rat brain mediated by hypoxia-ischemia results in increased ferritin-positive microglial cells in the environment [13]. Ferritin expression is also indirectly regulated by other factors such as HIF-1a during hypoxia-reoxygenation. Ryan et al. showed that there is a significant increase at the mRNA level of ferritin-L in the ipsilateral hemispheres in both wild type and ceruloplasmin (an iron exporter) null mice compared to the contralateral hemispheres after cerebral artery occlusion. In contrast, mRNA expression of ferritin-H showed no difference after ischemia in either genotype, except for a small but significant increase in the ipsilateral side in ceruloplasmin null mice at 72 h after ischemia [36]. In our study, hypoxia increased ferritin expression, both in microglia and astrocytes. However, HP prevented the accumulation of iron in the cell by significantly suppressing the expression of ferritin-L. Although ferritin-H expression also decreased after hypothermic preconditioning, the difference between hypoxic conditions was not statistically meaningful. Considering that mildly decreased temperature does not have any effect on the increased expression of both chains of ferritin in astrocytes, it is believed that HP performed before hypoxia might produce its favorable effects, especially by decreasing the main iron storage form of ferritin in microglia.DMT1 and TfR1are the key proteins that import iron into the cell. Both proteins play a role in transferrin-bound and nontransferrin-bound iron uptake and control the cell’s oxidative stress response by regulating the fenton reaction [39]. Various studies have evaluated the relationship of hypoxia and hypoxia reperfusion with the expression of iron transport proteins and HIF-1a in glial cells, and it has been found that hypoxia increases the expression of proteins that release iron from the cell, as well as the proteins that allow iron to enter the cell [12]. Based on these results, it has been suggested that hypoxia significantly elevates the iron transport rate, which in turn may contribute to the protective mechanism that develops after hypoxia with the increase in iron levels. Yang et al. suggested that DMT1 and TfR1 induced ferrous uptake under acute hypoxia [44]. DMT1 regulates microglia polarization, which plays an important role in determining the regeneration process after injury [38]. DMT1 facilitates iron export from the endosome to be utilized by the cell . Although McCarthy et al. have proposed a model in which uptake of iron is primarily mediated by inflammatory conditions [38], transcription of the proteins involved in cellular iron transport is regulated by HIF-1a in many cells, including astrocytes  and microglia [12]. In our study, hypoxia significantly increased the expression of DMT1 and TfR1 in both glial cell types. In contrast, HP specifically induces DMT1 expression in both cell types but more dramatically in microglia; it did not have any additional effect on increased expression of TfR1. The fact that hypoxic damage increases DMT1 rather than TfR1 suggests that DMT1 is much more coherently regulated by HIF-1a, as shown previously by several reports mentioned above. While HP continues to increase DMT1 in microglia, the suppression of the expression of both ferritin chains suggests that the protective effects of HP may be more related to iron traffic, independent of DMT1.Ferroportin is a transmembrane protein and the major iron exporter from the cell. “The discovery of increased ferroportin expression in response to cellular iron accumulation is a major break-through in the understanding of cell survival under conditions of different iron accumulation [40].” Since ferroportin expression is also affected by HIF-1a, it has been suggested that the gene encoding this protein may also be another hypoxia-induced gene . In our study, the increase of ferroportin levels, which exports iron from the cell, shows that ferroportin production serves as a protective mechanism for the removal of iron accumulated in the cell following hypoxia. In addition, HP continues to increase ferroportin expression in both microglia and astrocytes after hypoxia. This increase is significant in astrocytes. Ferroportin expression increased in both cell types but only suppressed ferritin-L in microglia, and this shows that this major exporter protein is mostly sensitive to the DMT1-related increase of stored iron as ferritin-L. Since astrocytes, contrary to microglia, do not generate any significant difference in ferritin levels to hypoxia, enhanced mobilization of iron may be one of astrocyte’s self-protective responses to high levels of iron. The fact that ferritin-H, which is responsible for rapid mobilization of iron in the cell, changes more clearly due to hypothermia is another point supporting this argument.Therapeutic hypothermia may produce its neuroprotective role by having regulatory effects on inflammation. In one study, hypothermia and subsequent reheating caused activation in microglial cells, resulted in an increase in phagocytosis, and changed cytokine balance in favor of antiinflammatory cytokines [17]. According to Lee et al., hypothermic treatment decreased the expression of M1 type reactive factors, including proinflammatory cytokine [46]. The regulatory effect of hypothermia on inflammation in hypoxic injury is dependent on duration and severity. Matsui et al. showed that mild hypothermia reduced the production of TNF-alpha at 6 h and IL-10 and NO at 48 h after hypoxic injury [47]. There is disagreement in the literature about whether ferritin has anti- or proinflammatory effects or whether it is simply a byproduct of release during inflammation [48]. When focusing on serum ferritin, the heavy chain has been found mostly to be responsive to the inflammatory process [49]. Ferritin-H may facilitate a tolerance mechanism to sepsis [50] and limit the labile iron from activating the proapoptotic c-Jun N-terminal kinase (JNK) [51]. In contrast, several studies show that ferritin can play an antiinflammatory role by either inhibiting the transcription of proinflammatory cytokines [52] or by binding to kininogen and reducing the bradykinin release [53].In our study, TNF-alpha levels were undetectable for glial cells in cell culture supernatant. On the other hand, glial cells increased IL-10 levels after hypoxia and caused the response developed to be pulled in an antiinflammatory direction. Hypotermic preconditioning further increased the hypoxia induced high levels of IL-10 in microglia, while there was no significant change in astrocytes. Microglial cells also increased IL-6 levels with hypoxia, and HP did not have any effect on this elevation. IL-6 levels in astrocytes were under detection limits. IL-6 is a pleiotropic cytokine with pro- but also antiinflammatory effects [54]. Many inflammatory factors, as well as neuropeptides and molecules, have been shown to affect IL-6 regulation in brain cells [55]. While it is still controversial whether increased iron has pro- or antiinflammatory effect in the cell, our study suggests that, in addition to other survival mechanisms, HP may stimulate an antiinflammatory response, especially in microglia via ferritin related mechanisms.Although gene expression is often interpreted in terms of protein levels, it is well known that change in mRNA and protein levels does not correlate that well. The reason for this discussion relies on mainly the regulation control at different levels. However, there are also subsets, where change in mRNA expression strongly correlates with change in protein levels. “The journey from gene to protein” is tightly controlled within each cell, and this complex correlation is very much dependent on the system. In our study, mRNA expression seems to be almost correlated with ferritin IFA staining. We are aware of the fact that our study has not any determined quantitative information regarding the proteins and that must be further investigated to support our data. Although our results are based on single cell culture experiments, it should definitely be kept in mind that glial cells behave by interacting with each other and under the effect of the surrounding milieu in CNS. Coculture and in vivo studies would lead to more beneficial insight in resolving the hesitation as to whether they contribute to homeostatic or pathologic processes in the brain.Our data suggest that HP, which is claimed to be preventive in hypoxia-induced brain damage, definitely has a favorable effect on cellular iron homeostasis via regulation of the storage and transfer proteins of iron. Ferritin-L, which is well known as the main storage protein of iron in the cell, plays a major role in this process, considering that changes in iron homeostasis in glial cells during ischemia are part of the adaptive response of cells to hypoxia. We conclude that the resultant iron homeostasis controlled by HP might present a protective approach during hypoxic injury. Therefore, further investigation of in vivo models in which glial cells function as a major actor of the immune system in CNS may provide a better understanding of this in the near future.This study was approved by the Gazi University Ethical Committee for Research Animals on 27.03.2014 .


Plasmodium falciparum malaria. This study aimed to characterise the extent and spread of resistance across this entire region between 2007 and 2018.The Greater Mekong subregion is a recurrent source of antimalarial drug resistance in P falciparum isolates from Myanmar, Thailand, Laos, and Cambodia were obtained from clinical trials and epidemiological studies done between Jan 1, 2007, and Dec 31, 2018, and were genotyped for molecular markers  of antimalarial drug resistance. Genetic relatedness was assessed using microsatellite and single nucleotide polymorphism typing of flanking sequences around target genes.pfkelch Cys580Tyr haplotype (from −50 kb to +31·5 kb) conferring artemisinin resistance (PfPailin) now dominates across the eastern Greater Mekong subregion. Piperaquine resistance associated with pfplasmepsin2 gene amplification and mutations in pfcrt downstream of the Lys76Thr chloroquine resistance locus has also developed. On the Thailand–Myanmar border a different pfkelch Cys580Tyr lineage rose to high frequencies before it was eliminated. Elsewhere in Myanmar the Cys580Tyr allele remains widespread at low allele frequencies. Meanwhile a single artemisinin-resistant pfkelch Phe446Ile haplotype has spread across Myanmar. Despite intense use of dihydroartemisinin–piperaquine in Kayin state, eastern Myanmar, both in treatment and mass drug administrations, no selection of piperaquine resistance markers was observed. pfmdr1 amplification, a marker of resistance to mefloquine, remains at low prevalence across the entire region.10 632 isolates were genotyped. A single long P falciparum is now prevalent across the Greater Mekong subregion. In the eastern Greater Mekong subregion a multidrug resistant P falciparum lineage (PfPailin) dominates. In Myanmar a long pfkelch Phe446Ile haplotype has spread widely but, by contrast with the eastern Greater Mekong subregion, there is no indication of artemisinin combination therapy (ACT) partner drug resistance from genotyping known markers, and no evidence of spread of ACT resistant P falciparum from the east to the west. There is still a window of opportunity to prevent global spread of ACT resistance.Artemisinin resistance in Thailand Science Research and Innovation, Initiative 5%, Expertise France, Wellcome Trust. Plasmodium falciparum parasites in the Greater Mekong subregion in the past 13 years represents a serious potential threat to malaria control in the Indian subcontinent and Africa. Artemisinin resistance compromises artemisinin combination therapies (ACTs), the first-line treatment for falciparum malaria.pfkelch; PF3D7_1343700) on P falciparum chromosome 13 are strongly associated with artemisinin resistance,pfplasmepsin2-3 (PF3D7_1408000) amplification and mutations in the P falciparum chloroquine resistance transporter  downstream of the 4-aminoquinoline resistance locus (positions 72–76 with Lys76Thr) have both been associated with piperaquine resistance.pfmdr1 (PF3D7_0523000) amplification is a well established marker of mefloquine resistance,The emergence and uncontrolled spread of artemisinin-resistant PF3D7_140000 ampliEvidence before this studyPlasmodium falciparum. Several articles described the finding of multiple different pfkelch13 mutations conferring artemisinin resistance in P falciparum malaria parasites isolated across the Greater Mekong subregion, with reports by Imwong and colleagues and Amato and colleagues describing the emergence of a dominant parasite lineage in the eastern Greater Mekong subregion . pfplasmepsin2 gene amplification and pfcrt mutations linked with piperaquine resistance and high rates of treatment failure were associated with this same lineage. The possibility of resistance spreading from the eastern to the western Greater Mekong subregion (comprising western Thailand and Myanmar), the spread of resistant lineages within Myanmar, and the role of intensive dihydroartemisinin–piperaquine deployment, particularly in mass drug administrations, in the selection of antimalarial drug resistance has not been explored.We searched PubMed on Dec 18, 2019, using the terms “artemisinin resistance” or “piperaquine resistance”, and “genotype”, and either “Cambodia”, ”Thailand”, “Myanmar” or “Laos” without any date or language restrictions. This search identified 36 non-duplicate articles, of which 26 contained information on the molecular epidemiology of artemisinin-resistant or piperaquine-resistant Added value of this studypfkelch mutant (artemisinin resistant) parasite lineages have been followed by hard selective sweeps by presumably fitter parasites. These increasingly dominant lineages have emerged in Myanmar and the eastern Greater Mekong subregion, but they differ from one another, and there is no evidence of spread from the eastern to the western Greater Mekong subregion. There is also no evidence for piperaquine resistance in the western Greater Mekong subregion. Intense targeted elimination activities using dihydroartemisinin–piperaquine in the treatment of symptomatic malaria and mass treatments have been successful and did not select for resistance.This very large molecular epidemiology study done over 12 years describes the evolution and spread of antimalarial drug resistance across the entire Greater Mekong subregion. It shows that multiple soft selective sweeps with different Implications of all the available evidenceOutside the eastern Greater Mekong subregion there is still a window of opportunity to halt the spread of artemisinin resistance westward to India and Africa by using dihydroartemisinin–piperaquine in targeted malaria elimination activities.As part of studiesDNA was extracted from either dried blood spots, completed malaria rapid diagnostic test strips, or frozen whole blood samples, by standard methods at the Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand. DNA was purified with QIAamp DNA Mini kits .pfkelch gene was examined by nested PCR amplification covering the propeller region of the genepfkelch gene  using Bioedit software. pfcrt was amplified from the DNA template using nested PCR covering exons 1 and 2 (amino acids 1–120) and Sanger sequenced. A restriction fragment length polymorphism assay was developed to assess previously identified pfcrt mutations:Polymorphism in the pfplasmepsin2 and pfmdr1 copy numbers were quantified using a Taqman Relative quantitative real-time PCR on a Corbett Rotor-Gene Q  as described previously.−ddCt, where ddCt denotes the difference between the unknown sample dCt and the reference sample dCt. A copy number cutoff of 1·5 defined pfplasmepsin2 and pfmdr1 amplification. Reactions were repeated if either profile did not conform to exponential kinetics, ddCt spread was more than 1·5, or the Ct value was more than 35. Samples with an estimated copy number more than 1·3 were also retested once, the second result counting as final.pfkelch gene spaced from −56 kb to +225 kb were assessed.Nine microsatellite markers and five single nucleotide polymorphisms (SNPs) in the flanking regions each side of the pfplasmepsin2 were designed and developed covering 510 kb  was assessed using He=(n/[n–1])(1–Σpi2), with sampling variance defined as 2(n–1/n3[2(n–2)]Σpi3–Σpi2)2, where n is the sample size and pi the frequency of the allele at position i. Heterozygosity at each location was compared with wild-type sequences using Fisher's least significant difference and Mann-Whitney U tests. Proportions were compared with χ2 or Fisher's exact test.Genetic variation at each microsatellite locus or expected heterozygosity . For markers where two or more alleles were observed, phased haplotypes were not estimated but instead identity-by-state was called if at least one allele was shared in both isolates. Hierarchical agglomerative clustering was applied to the distance matrix, where the pairwise distance was defined as one minus the proportion identical-by-state. The output isolate ordering was used to plot the observed marker data using previously published software.The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.14 509 positive samples were obtained . 10 632 P falciparum isolates collected between Jan 1, 2007, and Dec 31, 2018, were sequenced for pfkelch mutations. Two genotypes, the Cys580Tyr mutation in the eastern Greater Mekong subregion and, to a lesser extent, the Phe446Ile mutation in Myanmar, have come to predominate with a corresponding decline in the proportions of other propeller mutations  in the western Greater Mekong subregion between 2007 and 2017 revealed a marked reduction in the heterozygosity of mutant infections over time  suggesting a selective sweep (e of 0·130 (SE 0·001) versus 0·696 (0·001) in contemporary wild-type infections (p<0·0001). Flanking sequence variation was diminished for about 50 kb either side of the Cys580Tyr pfkelch gene (e <0·1) was observed in a smaller segment of the genome surrounding Cys580Tyr in Myanmar in 2016–17 than in earlier years (2010–15), suggesting continued recombination after the initial selection event.Comparison of the Cys580Tyr flanking sequences (n=113) with contemporary ve sweep . In the lch gene . Comparilch gene . Reducedpfkelch Phe446Ile allele were assessed in 202 samples from Myanmar obtained between Jan 1, 2014, and Dec 31, 2017. In 2014–16, at least two pfkelch Phe446Ile haplotypes were observed in Kayin, Kachin, and Sagaing states (n=90), but by 2017, one predominated and had spread across more than 1000 km from the northwest to the east of Myanmar  with a mean He of 0·207 .Flanking sequences around the pfplasmepsin2 amplification was considerably more frequent in the eastern Greater Mekong subregion  than in Myanmar where it was found only in Kyain Seikgyi, Kayin state , characterised previously, was around 18 kb in length and carried three genes: pfplasmepsin1, 2, and 3.pfplasmepsin2 genes had the same breakpoint as the Cambodian isolates and the amplicons varied in length from 81 kb to more than 190 kb. These findings suggest independent origins for pfplasmepsin2 amplification in Myanmar and provide no evidence for selection or importation from the eastern Greater Mekong subregion.Between Jan 1, 2007, and Dec 31, 2018, pfplasmepsin2 gene in 51 multiple-copy isolates and 54 single-copy isolates from the eastern Greater Mekong subregion were compared , compared with high diversity in wild-type isolates  showed different haplotypes  and providing no evidence of selection by the intensive use of dihydroartemisinin–piperaquine in mass treatments.Flanking sequences around the compared . There wplotypes , suggestP falciparum isolates were assessed for pfmdr1 amplification. Amplified pfmdr1 was found across the region , and Stung Treng, northern Cambodia (seven [8%] of 89 in 2018), and in northeastern Thailand (Ubon Ratchathani and Srisaket) near the Cambodian border (one [7%] of 13 in 2016).Between Jan 1, 2007, and Dec 31, 2018, 6984 e region ; in MandP falciparum parasitaemias, the proportion of isolates with pfplasmepsin2 amplification was low or zero before (n=142) and after (n=38) mass treatment with dihydroartemisinin–piperaquine in the sites in Myanmar and Cambodia. There was also no evidence for selection of pfcrt mutations associated with piperaquine resistance; none were found in Kayin State, Myanmar, and pfcrt Gly353Val was found in seven isolates before and seven after mass treatment in Cambodia  propeller region.6Antimalarial drug resistance in pfkelch Cys580Tyr mutant parasite lineage (PfPailin) has now spread across the eastern Greater Mekong subregion and has also acquired piperaquine resistance (associated with pfplasmepsin2 amplification and pfcrt mutations).pfcrt,pfplasmepsin amplification, which is associated with reduced piperaquine susceptibility. These new pfcrt mutations are downstream of the 4-aminoquinoline resistance locus (positions 72 to 76), which is mutated as the CVIET haplotype in nearly all P falciparum parasites from the region.In terms of malaria epidemiology, the Greater Mekong subregion can be divided into two discrete transmission regions: the eastern region, comprising Cambodia and adjacent southern Vietnam, southern Laos, and north-eastern Thailand, and the western region, comprising Myanmar and adjacent western Thailand, with borders with Yunnan province, China, northeast India, and Bangladesh. The area between these two regions, comprising northern Laos and the main body of Thailand, is largely malaria free. In the eastern region, except for Laos, dihydroartemisinin–piperaquine has been used extensively for many years. A single long haplotype pfkelch Cys580Tyr haplotype predominated.pfplasmepsin2 amplification and no pfcrt mutations associated with piperaquine resistance were identified in this region and, although low prevalences of pfplasmepsin2 amplification were found elsewhere in Myanmar, there was no evidence for selection. There was also no evidence for the spread of drug-resistant P falciparum from the east to the west of the Greater Mekong subregion. There is therefore still a window of opportunity to continue the elimination of multi-drug resistant malaria in the western Greater Mekong subregion using dihydroartemisinin–piperaquine in targeted mass treatments, as proved successful in eastern Kayin state. In Myanmar, a P falciparum lineage bearing the pfkelch Phe446Ile mutation,pfkelch propeller mutants. This evolving pattern, in which a single parasite lineage dominates, is similar to that observed earlier in the eastern Greater Mekong subregion with the Cys580Tyr lineage (PfPailin).pfkelch Phe446Ile mutation confers a lesser degree of artemisinin resistance (in terms of slowing of parasite clearance) than Cys580Tyr,The majority of falciparum malaria in Thailand used to occur in areas adjacent to Myanmar, with the highest case numbers in Tak province.pfplasmepsin amplification was identified in some P falciparum isolates from Myanmar but the mechanism of amplification differed to that in eastern Greater Mekong, and the sequences flanking the amplified genes were unrelated. Whether these strains are piperaquine resistant in vivo is unknown, but the diversity of haplotypes does suggest that they are not being selected. Piperaquine has not been used in these areas. In Kayin state and adjacent Thailand, where dihydroartemisinin–piperaquine has been used extensively in mass treatments,P falciparum parasites in or around the elimination areas shows no evidence that mass treatments selected for artemisinin or piperaquine resistance. This finding supports the use of targeted mass treatment in a low transmission setting to reduce the burden of malaria in foci of high transmission, thereby reducing the risk of resistance emerging.The development and spread of piperaquine resistance in the eastern Greater Mekong subregion has raised concerns that resistance could spread further afield or emerge elsewhere. In this study, pfmdr1 amplification occurs readily within malaria infections and was largely responsible for the rapid reduction in mefloquine susceptibility on the eastern and western borders of Thailand in the early 1990s before ACTs were introduced.pfkelch mutations) and re-emerging mefloquine resistance (associated with pfmdr1 amplification).pfmdr1 amplification has declined again on both the eastern and western borders of Thailand and adjacent countries after artesunate-mefloquine was replaced as first-line treatment. This finding is somewhat reassuring for artemether-lumefantrine, the first-line treatment in MyanmarP falciparum positive rapid diagnostic test samples from village health workers would clarify the geographic distribution and monitor the spread of resistance. The marked reduction in the incidence of falciparum malaria in areas where targeted malaria elimination activities have been done means that few parasite isolates remain to provide accurate estimates of the prevalence of resistance markers after the interventions. But the low number is itself a testament to success. Nevertheless, the overall size, duration, and geographic extent of this investigation does allow characterisation of the emergence and spread of antimalarial drug resistance in the region, and it gives confidence in concluding that dihydroartemisinin–piperaquine mass treatments have not selected resistance further.The main limitation of this study is that sampling was usually associated with studied interventions, so it was focused in areas already known to have a higher incidence of malaria than elsewhere. Although the tested samples were obtained from multiple sites across the region this was not planned as a geographic survey and, as with many other observational molecular epidemiology studies, might therefore give a biased picture of genotype distributions. Surveillance by genotyping routine P falciparum malaria to be interrupted in all areas of multidrug resistance, including ACT resistance; by 2020 P falciparum malaria to be eliminated in Cambodia; and by 2025 P falciparum malaria to be eliminated in all Greater Mekong countries. These targets will clearly not be met. The selection and subsequent spread of fit multidrug-resistant parasites across the large landmasses of southeast Asia has been repeatedly highlighted as a major threat to global malaria control. The solution is to eliminate falciparum malaria in the Greater Mekong subregion before it spreads further. There is still time for radical action before drug resistance prevents it.The WHO strategy for malaria elimination in the Greater Mekong subregion (2015–30)Mahidol–Oxford Tropical Medicine Research Unit Data Access Committee for researchers and access follows the Mahidol–Oxford Tropical Medicine Research Unit data access policy. Queries and applications for datasets should be directed to Rita Chanviriyavuth .The data are available upon request to the


The neuromuscular junction (NMJ) is a highly developed synapse linking motor neuron activity with muscle contraction. A complex of molecular cascades together with the specialized NMJ architecture ensures that each action potential arriving at the motor nerve terminal is translated into an action potential in the muscle fiber. The muscle-type nicotinic acetylcholine receptor (AChR) is a key molecular component located at the postsynaptic muscle membrane responsible for the generation of the endplate potential (EPP), which usually exceeds the threshold potential necessary to activate voltage-gated sodium channels and triggers a muscle action potential. Two AChR isoforms are found in mammalian muscle. The fetal isoform is present in prenatal stages and is involved in the development of the neuromuscular system whereas the adult isoform prevails thereafter, except after denervation when the fetal form is re-expressed throughout the muscle. This review will summarize the structural and functional differences between the two isoforms and outline congenital and autoimmune myasthenic syndromes that involve the isoform specific AChR subunits. The nicotinic AChR in muscle forms a heteropentamer consisting of two α-, one β-, and one δ-subunit with one γ-subunit in the fetal AChR isoform that is replaced by an ε-subunit in the adult AChR isoform . All AChC4γ or αεC4γ), the exchange of the C4 group largely swapped fetal versus adult AChR affinities and open-channel lifetimes . Further analyses revealed that the C4 group changed the aromatic core into a more compact, organized and stable conformation, with W55 now significantly contributing to AChR affinity in the αδC4γ and αεC4γ constructs  contributed to a 20% decrease of the adult AChR conductance and a 22% increase of the fetal AChR conductance (Adult AChRs are characterized by higher conductance (60 pS) as compared to the fetal AChR (40 pS) . Using rlt AChRs . Furtherlt AChRs , which dlt AChRs . The sigductance . As anotductance . These fductance , thus reDesensitization corresponds to an accumulation of receptors in an agonist-bound but non-conducting conformational state , with a Efficient neuromuscular transmission strongly depends on the tuned coordination of structural and molecular interactions between motor neurons and muscle fibers during neuromuscular junction (NMJ) development. Rodent NMJs have been extensively studied and helped to gain insight into some of the fundamental processes involved in NMJ maturation. Soon after myoblasts fuse to form myotubes and before innervation, fetal AChRs are evenly distributed along muscle fibers  and, wheMuscle innervation undergoes a substantial change during NMJ development. The period between E13.5 and E18 in mice is dominated by co-innervation of NMJs by multiple axons, most of which are eliminated in the first two postnatal weeks . In a siMost of the data on neuromuscular development derives from experiments with rodents, and partly chicken , and migThe AChR isoform conversion is accomplished by complementary γ- and ε-subunit mRNA expression and integrates two different mechanisms that depend on motor neuron innervation. Electrical stimulation of the muscle fiber has a repressive impact on AChR gene transcription in muscle, whereas nerve-released signals increase AChR gene transcription in subsynaptic nuclei, thereby concentrating AChR surface expression to the postsynaptic membrane . The γ- 2-terminal kinase (JNK)   resultinse (JNK) . Phosphose (JNK)  and enhase (JNK) .Motor units in mammalian muscle differ in their contractile properties and pattern of activity. Slow-twitch muscle fibers are innervated by motor neurons that fire in long trains of lower frequencies ranging between 10 and 20 Hz. Fast-twitch muscle fibers, by contrast, are innervated by motor neurons firing at short bursts of higher frequencies around 100 Hz . ImmunohExtraocular muscles (EOMs) are characterized by specific functional and antigenic features , but as As compared to limb muscles, EOMs are characterized by higher motor neuron firing rates, and consist of a mixture of both multiply innervated slow-twitch or tonic fibers and singly innervated fast-twitch fibers, with singly innervated fibers exhibiting the highest discharge frequencies  followed by gene elimination expanded a set of 10 ancestral AChR subunit genes to 16 genes in mammals including humans . As a reε -subunit  instead of the γ-subunit, prepatterning was substantially altered with formation of functional neuromuscular synapses outside the central, narrow endplate band region in the diaphragm  could have additional physiological relevance in the regulation of neuromuscular synapse efficacy  is an autoimmune disorder of the neuromuscular junction and mediated by autoantibodies binding to molecular targets at the postsynaptic muscle membrane. MG is clinically characterized by muscle weakness and increased fatiguability of limb, bulbar and/or extraocular muscles . AutoantThe AChR-Abs appear to be polyclonal and heterogenous even in individual patients and were shown to target the extracellular domains of all five subunits including the γ- or ε-subunit , with a Higher motor neuron firing frequencies could further contribute to a lower safety factor by increasing the release of acetylcholine from the presynaptic terminal sufficiently to overcome the catalytic activity of the acetylcholine esterase, thus leading to prolongation of the lifetime of acetylcholine in the synaptic cleft and accumulation of AChRs in desensitized states . Additioin utero during the second trimester, the clinical picture is characterized by multiple joint contractures or skeletal deformities such as scoliosis and lung hypoplasia. This condition is termed arthrogryposis multiplex congenita (AMC) and often associated with the requirement of the neonates for assisted ventilation or with prenatal or neonatal death  due to placental transfer of AChR-IgG-Abs . In caseal death . AMC is al death . Mothersal death , suggestal death , whereasal death . Fetal Aal death . These aal death , with 80al death . When inal death , confirmMutations of the AChR γ-subunit gene CHRNG have been found to underlie Escobar type multiple pterygium syndrome , an autoMutations of the AChR ε-subunit gene CHRNE are the most frequent and depending on the ethnic population may account for over 30% of cases with congenital myasthenic syndromes . MutatioThe transition from fetal to adult AChR expression during NMJ development represents a characteristic feature in mammals with complex regulatory mechanisms mediated by the fine-tuned electrical and molecular communication between nerve and muscle. Intensive research of the past decades produced insight into functional differences between both AChR isoforms and has helped to understand better their physiological significance and their role in different myasthenic syndromes. However, some key aspects of fetal and adult AChR function remain to be answered. Especially the elucidation of the structural determinants of desensitization and recovery from desensitization in fetal and adult AChRs and their evaluation in animal models could further help to unravel the significance of desensitization in NMJ physiology and disease.HC drafted the manuscript and prepared the figures. DB, AV, and RW edited and reviewed the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.