Fecal microbiota transplantation improves spatial learning disability caused by developmental anesthetic neurotoxicity in neonatal rats

Fecal microbiota transplantation improves spatial learning disability caused by developmental anesthetic neurotoxicity in neonatal rats | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fecal microbiota transplantation improves spatial learning disability caused by developmental anesthetic neurotoxicity in neonatal rats Tomohiro Chaki, Yuri Horiguchi, Shunsuke Tachibana, Satoshi Sato, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3910445/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Anesthetic exposure induces neurodegeneration in children. Although this problem has been elucidated in decades, the prophylaxis for developmental anesthetic neurotoxicity (DAN) has not been established. It has been reported that gut microbiota produces various metabolites and influences brain function of host, which has been called as Gut microbiota-Brain axis. We report the effect of fecal microbiota transplantation (FMT) on spatial learning disability caused by DAN in neonatal rats. Methods: In experiment 1, neonatal rats were divided into C (Control) and S (Sevoflurane) groups to elucidate the effect of sevoflurane exposure on gut microbiota composition. In S group, rats were exposed by 2.1% sevoflurane for 2 hours in postnatal day (PND) 7-13. In experiment 2, neonatal rats were divided into S and SF groups. In SF group, neonatal rats were received FMT just after sevoflurane exposure in PND 7-13. The sample of FMT was obtained from non-anesthetized mother rat. Behavioral tests were performed to evaluate spatial learning ability from PND 26-39. Results: Sevoflurane exposure significantly altered the gut microbiota composition. Especially, the relative abundance of Bacteroidetes phylum was significantly increased and that of Firmicutes phylum was significantly decreased by sevoflurane exposure. The FMT improved spatial learning ability. The microbiota analysis revealed that the α-diversity of gut microbiota was increased by FMT. Particularly, FMT decreased the relative abundances of Bacteroidetes phylum, Bacteroidia class, Bacteroidales order, Bacteroidaceae family, Bacteroides genus. Meanwhile, the relative abundances of Firmicutes phylum, Clostridia order, Clostridiales class, Ruminococcaceae family, Ruminococcus genus, and butyric acid-producing bacteria increased by FMT. Moreover, the FMT increased the fecal concentration of butyrate, and exerted the histone acetylation and the mRNA expression of brain derived neurotrophic factor in hippocampus. Immunofluorescence staining with Iba-1 antibody revealed that microglia infiltration in hippocampus was significantly suppressed by FMT. The mRNA expressions of apoptosis-inducing proteins were significantly suppressed and those of anti-apoptotic proteins were significantly promoted by FMT. The TUNEL staining indicated that neuronal apoptosis in hippocampus was significantly suppressed by FMT. Conclusions: FMT improved spatial learning ability in rats with DAN. The modulation of gut microbiota might be an effective prophylaxis for DAN in children. Anesthesia-induced developmental neurotoxicity Butyrate Fecal microbiota transplantation Gut microbiota Gut-brain axis Histone acetylation Neuroinflammation Neuronal apoptosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Anesthetic exposure in childhood prevents neurodevelopment and causes cognitive dysfunctions, such as learning disabilities. An anesthetic mixture containing midazolam, nitrous oxide, and isoflurane has been reported to induce apoptotic neurodegeneration and spatial learning disabilities in infant rats [ 1 ]. The numerous animal and clinical studies on developmental anesthetic neurotoxicity (DAN) in neonates have been published. Recently, three major clinical studies (GAS, PANDA and MASK) have been performed, with each study reporting that a single exposure to anesthesia did not ameliorate cognitive function [ 2 – 4 ]. However, a meta-analysis of the three studies showed that even a single general exposure to anesthesia increased behavioral problems [ 5 ]. Moreover, it has been elucidated that the multiple exposure of general anesthesia has significantly caused DAN [ 4 ]. Because neurotoxicity is a major problem in pediatric anaesthesia, prophylaxis for DAN in children should be established. Although the mechanism of DAN has not been well clarified, various mechanisms might involve in the development of DAN. The major mechanism is neuronal cell death caused by neuronal apoptosis and neurodegeneration. Other possible mechanisms include neural cell damage, impaired synaptic plasticity or abnormal myelination, tau phosphorylation and disturbance of neuroendocrine system [ 6 ]. The bidirectional communication between the gut microbiota and the brain is referred to as the gut–brain axis [ 7 ]. The gut microbiota influences various organs and are associated with host health and various diseases. For example, attention-deficit/hyperactivity disorder (ADHD), a major neurodevelopmental disease in children, is associated with gut dysbiosis, including reduced α-diversity and increased abundances of Bacteroides and Bifidobacterium [ 8 – 10 ]. Moreover, microbiota transfer therapy from a healthy donor significantly improves gastrointestinal and autism-like behavioral symptoms in children with ADHD [ 11 ]. Further, Parkinson’s disease pathogenesis may be related to gut dysbiosis [ 12 , 13 ]. Various patterns of dysbiosis have been reported in human clinical studies on Parkinson’s disease. Fecal microbiota transplantation (FMT) therapy improves both motor and non-motor symptoms, such as wearing off and constipation [ 14 – 16 ]. The gut microbiota produces various short-chain fatty acids (SCFA), including lactate, acetate, and butyrate, by metabolizing dietary fiber. Butyrate, which plays a crucial role in the gut–brain axis, is generated by butyrate-producing bacteria, such as Ruminococcus [ 17 ]. It acts as a histone deacetylase inhibitor, promoting histone acetylation and brain-derived neurotrophic factor (BDNF) expression, which exert neuroprotective and anti-apoptotic effects in the hippocampus [ 18 , 19 ]. Anesthetic exposure can modulate gut microbiota. In rodents, a single anesthetic exposure significantly alters the composition of gut microbiota as well as SCFA production in feces [ 20 , 21 ]. Accordingly, we hypothesized that gut microbiota dysbiosis caused by inhalational anesthesia exposure is one of the possible pathogeneses of DAN in neonates and modulation of gut microbiota would improve DAN by increasing butyrate production and BDNF expression and suppressing hippocampal neuronal apoptosis. We used FMT as an intervention for gut microbiota and evaluated its effect on spatial learning ability. Methods The study was approved by the Ethics Committee (approval code: 20–066) for Animals of the Sapporo Medical University School of Medicine. This experiment complied with the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines and adhered to the guidelines for proper conduct of animal experiments issued by the Japanese Ministry of Education, Culture, Sports, Science and Technology and the Ministry of Health, Labor and Welfare, as well as those issued by the Science Council of Japan. Animals Experiments were conducted using Wistar rats. Pregnant rats were purchased (Sankyo Labo Service Corporation, Inc., Tokyo, Japan) and housed under controlled illumination (12-h light/12-h dark, lights on at 7AM), temperature (21–25°C) and humidity (40–60%) with free access to food and water. In experiment 1, neonatal rats were divided into Control (C) and Sevo (S) groups ( n = 7 in each group, Supplementary Figure S1 a). In experiment 2, they were divided into S and Sevo + FMT (SF) groups ( n = 20 in each group, Fig. 1 a). The pregnant rats were randomly allocated to each group in each experiment. Their neonatal rats were allocated to each group according to their mother’s allocation. Sevoflurane exposure The modified protocol of sevoflurane exposure by Xu et al and Ju et al was applied in this study [ 22 , 23 ]. Pups were divided into Sevo (S) or Sevo + FMT (SF) groups. In both groups, pups were received sevoflurane exposure on postnatal day (PND) 7–13. Pups were transferred to the anesthesia cage (15*15*15 cm) and exposed 2.1% sevoflurane for 2 hours under 100% oxygen 2 L/min flow. The concentration of sevoflurane and oxygen fraction in cage were measured using a gas sampling system (Datex Ohmeda, GE HealthCare, IL). The temperature in the cage was maintained 21–25°C using a heat lamp. After anesthetic exposure, inhalation of sevoflurane was discontinued and the pups in S group was re-transferred to the mother’s cage after confirmation of spontaneous movement. In SF groups, pups were re-transferred to the mother’s cage after fecal microbiota transplantation (FMT). Fecal microbiota transplantation The FMT was performed according to past reports [ 24 ]. In SF group, 5 g of fresh feces was obtained from the mother rat on PND 6. Feces were dissolved in 50 mL of saline solution and mixed fully. The fecal solution was passed through 2.0, 1.0, 0.5 and 0.25 mm stainless steel laboratory sieves (WS Tyler, Mentor, OH) to remove solids including undigested food and small particulate material. After filtration, the fecal solution was centrifuged at 5,000 r/min, 0°C for 15 min. The supernatant was collected and stored at 4°C until FMT [ 25 ]. After sevoflurane exposure on PND 7–13, 24-gauge cannula was inserted into the anus up to approximately 2.5 cm, and 0.3 mL of the fecal supernatant solution was injected and maintained for 3 min. After FMT, the pups were returned to the cage with their mother after confirming adequate respiration and spontaneous movement. Open field test Open field test was performed to evaluate exploratory, locomotor activity and anxiety-like behavior. The box of open field test was made by acrylic resin (90*90*30 cm, width*length*height) and the center zone was defined as the 54*54 cm zone in the center of the box. The box was placed in a quiet room and illuminated so as not to cast shadows. The rats were placed in the center of the open field box, and their behavior was traced and analyzed with SMART 3.0 video tracking system (Panlab Harvard Apparatus, Barcelona, Spain) during 5 min. The time in central zone, resting time, total moving distance and mean velocity were recorded [ 26 ]. The inside of the field was cleaned with 70% ethanol after each trial. Y-maze test Y-maze test was performed to evaluate the locomotor activity and spatial learning ability. The maze, made by black polyvinylidene, was consisted by center zone and three arms (10*51.5*25 cm, width*length*height), which the angle between each arm was 120 degrees. The Y-maze was placed in a quiet room and illuminated so as not to cast shadows. The rat was placed into the center of the maze and freely explored for 8 min. Their behavior was traced with SMART 3.0 video tracking system (Panlab Harvard Apparatus). Total moving distance, total arm entry and spontaneous alternation was analyzed. The maze was cleaned with 70% ethanol after each trial [ 27 – 29 ]. Morris water maze test and reversal Morris water maze test Morris water maze test was performed to evaluate the spatial learning and working memory ability. The maze was a circular pool (170 cm in diameter and 60 cm in height). The field of maze was divided into 4 quadrants and filled with water. The temperature of water was set at 25°C. In acquisition test, the 10 cm clear platform was placed under 1.0 cm into water in zone 1. The zone of the platform placement was defined as a target zone. The rats were trained 5 days to find a hidden platform under water. Each training day was consisted of four sequential trials. Each trial was started when the rats were placed by the wall gently and they freely explored the hidden platform during 2 min. If the rat reached to platform, the trial was stopped, and they allowed to stay on the platform for 15 sec. On the other hand, if the rat could not reach to platform during 2 min, they were rescued and placed onto the platform gently, and allowed to stay on the platform for 15 sec. The start position was in the order of zone 1, 2, 3 and 4 in each trial day. Twenty-four hours after the day 5 trial, a probe test was performed. In probe test, the platform was removed, and the rats explored freely for 30 sec. Next 24 hours after the probe test, reversal Morris water maze test was performed. In reversal acquisition test, the platform was place in zone 3, which was located on the opposite side of zone 1. After 5 days reversal acquisition trial, the reversal probe test was performed [ 30 , 31 ]. The reversal test was performed in the same manner as the acquisition and probe test. The latency to the platform was determined as the mean time of 4 trials at each trial day in acquisition and reversal acquisition test. In probe and reversal probe tests, the latency to target zone, the crossing number and time spent in target zone, and the time spent in zone 1 or 3 which the platform was placed at acquisition or reversal acquisition tests, respectively, were analyzed with SMART 3.0 video tracking system. Sample collection Samples were collected on PND 21 and 39. On PND 21, the abdomen was opened after induction of anesthesia with 5% sevoflurane. Fecal samples were collected from the left semicolon for microbiota and organic acid analyses. Following this, the hippocampus for real-time quantitative reverse transcription-polymerase chain reaction (RT-PCR) and quantitative evaluation of histone acetylation was collected. The bilateral hippocampi were stored at − 80°C. On PND 39, the rats were decapitated after anesthetic induction using 5% sevoflurane. Phosphate-buffered saline (PBS), followed by 4% paraformaldehyde, was perfused from the apex of the left ventricle and the brains were extracted and postfixed overnight for terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) and immunofluorescent staining. Fecal microbiota analysis The fecal sample from PND 21 were used for gut microbiota analysis. The 16S ribosomal RNA sequencing and analysis of fecal samples was used to evaluate the difference of fecal microbiota. The DNA extraction was performed using QIAamp Fast DNA Stool Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. The 16S ribosomal RNA gene amplicon libraries were prepared following the “16S Metagenomic Sequencing Library Preparation Protocol” (Illumina, San Diego, CA). Sequencing was performed using an Illumina MiSeq sequencer with 2 x 300 cycle MiSeq Reagent Kit v3 (Illumina). The adapter sequences and low-quality regions were trimmed by Cutadapt and Trimmomatic, respectively. Further data processing was performed using the open-source software pipeline QIIME2 version 2019.4.0. Reads were trimmed of primers and sequence quality control was performed using QIIME2’s script. Amplicon Sequence Variants (ASVs) were formed using DADA2 (denoise-paired). The phylogenetic assignment of representative sequences from each ASV was carried out with a 16S reference dataset obtained from Greengenes version 13_8. Summary of taxonomic assignments were plotted as bar charts, and alpha and beta diversities were calculated by QIIME2. Fecal organic acid analysis The fecal sample from PND 21 were used for organic acid analysis. The organic acid analysis was performed by high performance liquid chromatography (HPLC). A certain amount of fecal sample was precisely weighted into a bead tube, suspended in extraction solution, and heat-treated (85°C, 15 min). After disrupting with beads, the sample solution was centrifuged (18,400 g, 10 min), and the supernatant was filtered through a membrane filter with a pore size of 0.20 µm to obtain the sample solution. HPLC was performed using Shimadzu organic analysis system (Shimadzu, Kyoto, Japan), as follow conditions: column, Shim-pack Fast-OA, 100*7.8 mm ID; guard-column, Shim-pack Fast-OA, 10*4.0 mm ID; eluent solution, 5 mmol/L p-toluene sulfonic acid; reaction solution, 5 mmol/L p-toluene sulfonic acid; 0.8 mL/min, 50°C. The concentrations of butyrate, lactate, acetate, succinate and propionate were detected using CDD-10Avp. Histone acetylation quantification The hippocampus on PND 21 was used to evaluate histone acetylation. Histones were extracted using a histone extraction kit (ab113476 Abcam, Cambridge, UK) according to the manufacturer’s protocol. Histone H3 and H4 total acetylation was performed using the Histone H3 Total Acetylation Detection Fast Kit (ab115124, abcam) and Histone H4 Total Acetylation Detection Fast Kit (ab115125, abcam), according to the manufacturer’s protocol. Absorbance was read on a microplate reader at 450 nm wavelength (Sunrise™ reader, Tecan Ltd., Männedorf, Switzerland) and histone H3 and H4 acetylation was calculated as follows: (Sample absorbance – blank absorbance) / (Protein*delta absorbance/ng), according to the manufacturer’s protocol. RT-PCR The hippocampal samples from PND 21 were used for rt-PCR. Total RNA was extracted from right hippocampus according to the following methods. The hippocampus was added into 1,000 µL TRIzol RNA isolation reagent (Thermo Fisher Scientific, Waltham, MA), and homogenized. After administration of 200 µL chloroform, samples were centrifuged 12,000 g, 4°C for 15 min. The supernatant was centrifuged 14,000 g, 4°C for 30 min, after administration of 500 µL isopropanol. The precipitation was mixed into 1,000 µL of 75% ethanol. The solution was centrifuged 12,000 g, 4°C for 10 min. After dehydration of the precipitation, 30 µL nuclease free water was added. The concentration of extracted total RNA was measured using Nano Drop Spectrophotometer (Thermo Fisher Scientific). An A260/A280 > 1.8 and A260/A230 > 1.8 were considered highly purified RNA. The highly purified RNA sample was reverse transcribed to complementary DNA by miScript II RT Kit (Qiagen) according to the manufacturer’s protocol. rt-PCR was conducted using QuantiTect SYBR Green PCR Kits (Qiagen) and the StepOnePlus RT-PCR system (Thermo Fisher Scientific) according to the manufacturer’s protocol. The primer of BDNF (Qiagen, QT00375998), Bad-1 (QT00190407, Qiagen), Bax (QT01081752, Qiagen), BCL2L1 (QT01081346, Qiagen), BCL2L11 (QT00193963, Qiagen), Mcl-1 (QT00375564, Qiagen) and GAPDH (QT00199633, Qiagen) were purchased from Qiagen. The delta-delta cycle threshold method was used to quantify the expression level of mRNA. Immunofluorescent staining The brain samples from PND 21 and 39 were used for immunofluorescent staining. The immunofluorescent staining was performed according to our laboratory procedure [ 32 ]. Extracted brains were postfixed with paraformaldehyde overnight and stored in 15% sucrose solution. Fixed brains were cut into 20 µm coronal sections by a cryostat. Each section which has CA1, CA3, and DG were incubated with primary antibody for ionized calcium-binding adapter molecule 1 (Iba-1, 1:500, FUJIFILM, Tokyo, Japan, 011-27991) overnight at 4°C. The sections were incubated with secondary antibodies; 1:500 of Alexa Fluor 594-conjugated secondary antibody (abcam; ab150132) for 2 hours. The counterstain of nuclei was performed with 4’,6-diamidino-2-phenyl-indole dihydrochloride solution (1:1000; DOJINDO; Kumamoto, Japan, 342–07431). The images of bilateral hippocampus were obtained with the BZ9000 fluorescence microscope (Keyence Corp., Osaka, Japan). The percentage of positive areas of Iba-1 in bilateral hippocampus were measured using Image J software (National Institutes of Health, MD) in blind manner. TUNEL staining The whole brain sample from PND 39 was used for TUNEL staining. The TUNEL staining was performed by In situ Apoptosis Detection Kit (Takara Bio Inc., Shiga, Japan) according to the manufacturer’s instruction. After deparaffinization of paraffin-embedded whole brain sample, 5 µm coronal sections of brain, which has cornu ammonis (CA)1, CA3, and dentate gyrus (DG), was washed with distilled water. The sections were treated with proteinase K (10–20 µg/mL, 15 min) followed by washing with phosphate-buffered saline. 3% H 2 O 2 aqueous solution for blocking endogenous peroxidase was applied for 5 min, and then the sections were incubated with TdT Enzyme for 90 min at 37°C. Images of bilateral hippocampus were obtained with a BZ9000 fluorescence microscope (Keyence Corp., Osaka, Japan), the percentage of TUNEL-positive cell in the bilateral hippocampus was measured using QuPath v0.4.3 in blind manner. Statistical analysis Sample size was determined using resource equation method in latency to target zone of Morris water maze probe test as a primary outcome of our study in Experiment 2 [ 33 ]. E = 10*2 (total number of animals) – 2 (total number of groups) = 18, which indicates that a total of 10 animals was considered as appropriate sample size for comparing between two groups. In experiment 2, sample collection was performed at PND 21 and 39. Therefore, we determined that a total of 20 animals in each group in experiment 2 was appropriate in our study. Statistical analyses were performed using GraphPad Prism10 (GraphPad Software, Boston, MA). In the Morris water maze test, crossing times of the target zone in the probe and reversal probe tests are expressed as median (interquartile range) and analyzed using the Mann–Whitney U test. Further, latency to the target in the acquisition and reversal acquisition phases is expressed as mean ± standard error of the mean and was analyzed with the two-way repeated analysis of variance (ANOVA) followed by Bonferroni multiple comparisons between the two groups. The other outcomes are expressed as mean ± standard error of the mean and were analyzed using an unpaired t -test. In particular, β-diversity was compared using permutational ANOVA. Normality of data distribution was tested using the Shapiro-Wilk test. Statistical significance was set at P < 0.05. Results Alternation of gut microbiota caused by sevoflurane exposure To elucidate the effect of sevoflurane on gut microbiota, gut microbiota analysis was performed between C and S groups in Experiment 1 (Supplementary Figure. S1a). A total of 3,858,776 high-quality valid sequences were obtained from 14 samples (C group, seven; S group, seven). In the C group, 241,589 ± 16,718 sequences were produced per sample. In the SF group, 240,759 ± 13,137 sequences were produced per sample. High-quality sequences were assigned to 4,720 operational taxonomic units. Most of the fecal microbiota diversity could be obtained from the current sequencing depth using rarefaction analysis (Supplementary Figure. S1b–f). In the α-diversity analysis, there were no significant differences between C and S groups (Supplementary Figure S1 g–j), However, the UniFrac principal coordinate analysis revealed that diversity of fecal microbiota significantly differed between C and S groups in the unweighted and weighted analyses of the β-diversity analysis (Supplementary Figure S1 k–n). These results indicate that sevoflurane exposure did not change the α-diversity of gut microbiota, but significantly altered the composition of gut microbiota in neonate rats. To clarify the detail alternation of gut microbiota caused by sevoflurane exposure in neonate rats, a differential abundance analysis between C and S groups were performed at phylum, class, order, family, genus and species. At the level of phylum, a total of nine bacteria phylum was detected in the feces of C and S groups: Bacteroidetes , Firmicutes , Proteobacteria , Verrucomicrobia , Lentisphaerae , Actinobacteria , Deferribacteres , Tenericutes and Cyanobacteria . Bacteroidetes and Firmicutes phyla were the major components of gut microbiota in C and S groups (approximately 87%; Supplementary Figure S2a). The relative abundance of Bacteroidetes was significantly higher in S group (Supplementary Figure. S2b). On the other hand, those of Firmicutes , Proteobacteria and Actinobacteria were significantly lower in S group (Supplementary Figure S2c–e). At the class level analysis, a total of 15 classes were detected. Bacteroidia and Clostridia were the majority of gut microbiota component (Supplementary Fig. 3a). The relative abundances of Clostridia , Gammaproteobacteria , Bacilli , Deltaproteobacteria , Actinobacteria and Coriobacteriia were significantly lower in S group. But that of Bacteroidia was only significantly higher in S group (Supplementary Figure S3b–h). At the level of order, a total of 18 orders were identified. Bacteroidales , Clostridiales and Enterobacteriales were the major components of gut microbiota (Supplementary Figure S4a). The relative abundances of Bacteroidales and SHA-98 were significantly higher in S group. However, those of Clostridiales , Enterobacteriales , Lactobacillales , Desulfovibrionales , Actinomycetales , Coriobacteriales and Bacillales were significantly lower in S group (Supplementary Figure S4b–j). At the family level, a total of 33 families were detected and the relative abundances of the top 20 families was compared (Supplementary Figure S5a). The relative abundances of Bacteroidaceae and Dehalobacteriaceae were significantly higher in S group. On the other hand, those of Ruminococcaceae , Enterobacteriaceae , Lactobacillaceae , Rikenellaceae , Odoribacteraceae , Desulfovibrionaceae , Mogibacteriaceae , and Micrococcaceae were significantly lower in S group (Supplementary Figure S5b–k). At the level of genus, a total of 43 genera were detected and the top 20 genera were analyzed in the relative abundance analysis (Supplementary Figure S6a). The relative abundances of Bacteroides , Roseburia Coprococcus , and Dehalobacterium were significantly lower in S group. But those of Oscillospira , Lactobacillus , Blautia , Odoribacter , Rothia and Dorea were significantly lower in S group (Supplementary Figure S6b–k). At the species level, a total of 21 species were identified and the relative abundances of the top 20 species were compared between C and S groups (Supplementary Figure S7a). The relative abundances of Ruminococcus callidus and Blautia producta were significantly higher in S group. On the other hand, those of Ruminococcus flavefaciens , Morganella morganii , Ruminococcus gauvreauii and Staphylococcus haemolyticus were significantly lower in S group (Supplementary Figure S7b–g). Effect of FMT on spatial learning ability There was no data exclusion in behavioral tests analysis. In open field test, total distance travelled, mean velocity, resting times, and time spent in central zone did not differ significantly between the two groups (Fig. 1 b-e). Thus, locomotor activity and emotional behaviors were equivalent in the S and SF groups. After open field test, we evaluated their spatial learning ability using Y-maze, Morris water maze, and reversal Morris water maze tests. The Y-maze test revealed no significant differences in total distance, total arm entries, or spontaneous alternations (Supplementary Figure S8a–c). In the Morris water maze test, latencies to target zone 1 on trial days 1–5 did not differ significantly between groups (Supplementary Figure S8d, e). In the probe test, spatial learning ability did not differ significantly between S and SF groups (Supplementary Figure S8f–i). However, in the reversal acquisition phase of the reversal Morris water maze test, the latencies to target zone on days 2 and 5 were significantly shorter in the SF group than in the S group (Fig. 1 f, g). In the reversal probe test, latency to the target zone was significantly shorter in the SF group than in the S group (Fig. 1 h); crossing time of the target zone and time spent in the target zone and zone 3 were significantly longer in the SF group than in the S group (Fig. 1 i–k). These results suggest that the SF group, which received FMT from non-anesthesia-exposed mother rats, had higher spatial learning ability, and that FMT improved spatial learning ability in rats with sevoflurane exposure. Fecal microbiota alterations by FMT A total of 2,762,218 high-quality valid sequences were obtained from 12 samples (n = 6 in each group). In the S group, 228,690 ± 7,188 sequences were produced per sample. In the SF group, 231,679 ± 6,764 sequences were produced per sample. High-quality sequences were assigned to 3,288 operational taxonomic units. Most of the fecal microbiota diversity could be obtained from the current sequencing depth using rarefaction analysis (Fig. 2 a–e). In the α-diversity analysis, although the Shannon index score did not differ significantly between the S and SF groups (Fig. 2 f), the Chao1, ACE, and Simpson index scores were significantly higher in the SF group than in the S group (Fig. 2 g–i). Moreover, the UniFrac principal coordinate analysis revealed that diversity of fecal microbiota significantly differed between groups in the unweighted or weighted analyses of the β-diversity analysis (Fig. 2 j–m). These results indicate that FMT increased diversity and significantly altered the composition of gut microbiota in sevoflurane-exposed rats. A differential abundance analysis for each category, including phylum, class, order, family, genus and species was performed to elucidate gut microbiota alterations caused by FMT. Eight bacterial phyla were identified in the feces of S and SF groups: Bacteroides , Firmicutes , Proteobacteria , Lentisphaerae , Deferribacteres, Verrucomicrobia , Tenericutes , and Actinobacteria . Among these, Bacteroides and Firmicutes constituted the majority of microbiota (approximately 97%; Fig. 3 a). The relative abundances of Bacteroides and Tenericutes were significantly lower, while those of Firmicutes and Lentisphaerae were significantly higher in the SF group than in the S group (Fig. 3 b–e). At the class level, a total of 13 classes was detected and Bacteroidia and Clostridia were the major components of the fecal microbiota in both groups (Fig. 3 f). The relative abundances of Bacteroidia, Deltaproteobacteria, Betaproteobacteria , Mollicutes , and Actinobacteria were significantly lower (Fig. 3 h, j–m), while that of Lentisphaeria was significantly higher in the SF group than in the S group (Fig. 3 i). Particularly, the relative abundance of Clostridia , a member of the Firmicutes phylum, was significantly higher in the SF group than in the S group (Fig. 3 g). At the order level, 16 orders were identified and Bacteroidales and Clostridiales accounted for the majority of fecal microbiota (Fig. 4 a). The relative abundances of Bacteroidales , Desulfovibrionales , Burkholderiales , RF39 , and Actinomycetales were significantly lower (Fig. 4 b,e–h), while that of Victivallales was significantly higher in the SF group than in the S group (Fig. 4 d). Particularly, the relative abundance of Clostridiales , a member of the Clostridia class, Firmicutes phylum, was significantly higher in the SF group than in the S group (Fig. 4 c). At the family level, a total of 31 families was detected (Fig. 4 i) and the relative abundances of Bacteroidaceae , S24-7 , Desulfovibrionaceae , Dehalobacteriaceae , and Alcaligenaceae were significantly lower (Fig. 4 j, l, n–p), while that of Victivallaceae was significantly higher in the SF group than in the S group (Fig. 4 m). Particularly, the relative abundance of Ruminococcaceae , a member of the Clostridiales order, Clostridia class, Firmicutes phylum, was significantly higher in the SF group than in the S group (Fig. 4 k). At the genus level, 38 genera were identified (Fig. 5 a) and the relative abundances of Bacteroides , Dehalobacterium , Sutterella , and Desulfovibrio were significantly lower in the SF group than in the S group (Fig. 5 b, d–f). On the other hand, the relative abundance of Ruminococcus , a member of Ruminococcaceae family, Clostridiales order, Clostridia class, Firmicutes phylum, was significantly higher in the SF group than in the S group (Fig. 5 c). In addition, the relative abundances of butyrate-producing bacteria were evaluated at the genus level. According to previous reports, genera of butyrate-producing bacteria include Alistipes , Odoribacter , Clostridium , Anaerostipes , Coprococcus , Roseburia , Butyricicoccus , and Ruminococcus [ 34 , 35 ]. The total abundance of butyrate-producing bacteria increased after FMT (Supplementary Figure S9a). The relative abundance of Clostridium was significantly higher in the S group than in the SF group (Supplementary Figure S9d). However, the relative abundances of other bacteria, except Ruminococcus , did not differ significantly between groups (Supplementary Figure S9b–c, e–h). A total of 17 species was identified, but classifiable species did not differ significantly between the two groups (Fig. 5 g). In summary, FMT decreased the relative abundances of Bacteroidetes phylum, Bacteroidia class, Bacteroidales order, Bacteroidaceae and S24-7 family, Bacteroides genus, and increased those of Firmicutes phylum, Clostridia class, Clostridiales order, Ruminococcaceae family, Ruminococcus genus, which are butyrate-producing bacteria [ 36 ]. These changes in relative abundances of phyla, classes, orders, and families were confirmed using heatmaps (Supplementary Figure S10). FMT increased butyrate production in feces As FMT increased the number of butyrate-producing bacteria in the gut microbiota, the concentrations of organic acids, including butyrate, lactate, acetate, succinate, and propionate, in feces were measured. Although lactate, acetate, succinate, or propionate concentrations did not differ significantly between the S and SF groups, the butyrate concentration in the feces was significantly higher in the SF group than in the S group (Fig. 6 a–e). FMT promoted histone acetylation and mRNA expression of BDNF As butyrate inhibits histone deacetylase, the amounts of histone H3 and H4 total acetylation in hippocampus were quantified, with both being significantly greater in the SF group (Fig. 6 f, g). These results indicate that a higher level of butyrate production following FMT induces histone acetylation in the hippocampus. Butyrate promotes BDNF transcription [ 19 , 37 , 38 ]; therefore, BDNF expression was significantly higher in the SF group than in the S group (Fig. 6 h). FMT suppressed neuroinflammation in hippocampus BDNF exerts anti-inflammatory effects in the hippocampus by activating PI3K/Akt and inhibiting MyD88/NF-κB signaling [ 18 ]. Expressions of IL-1β and caspase-1 mRNA in the hippocampus were evaluated. Although caspase-1 expression did not differ significantly between the S and SF groups, IL-1β expression was significantly lower in the SF group than in the S group at PND 21 (Fig. 7 a, b). Microglia can phagocytose apoptotic cells in the inflamed CNS [ 39 , 40 ]. Immunofluorescence staining was performed to elucidate microglial involvement in anti-inflammatory effects of FMT. In the hippocampus at PND 21, Iba-1 positive cell count, or positive area did not differ significantly between groups (Supplementary Fig. S11). However, the Iba-1-positive cell and areas in the hippocampus at PND 39 were significantly lower in the SF group than in the S group (Fig. 7 c–e). These results indicate that FMT attenuated the neuroinflammatory effects of sevoflurane in the hippocampus. FMT reduced neuronal apoptosis in hippocampus As BDNF exerts anti-apoptotic effects in addition to anti-oxidative effects in neurons [ 41 ], mRNA expression of apoptosis-related proteins was evaluated. Apoptosis-promoting proteins include Bad-1, Bax, and BCL2L11 [ 42 – 44 ]. RT-PCR revealed significantly lower mRNA expression levels of these proteins in the SF group compared with the S group (Fig. 8 a–c). Anti-apoptotic proteins include Mcl-1 and Bcl2L1 [ 45 , 46 ], whose expression levels were significantly higher in the SF group than in the S group (Fig. 8 d, e). These results suggest that FMT suppresses expression of apoptosis-inducing proteins and enhances expression of anti-apoptotic proteins in the hippocampus at PND 21. TUNEL staining was performed to determine histological level of apoptosis [ 47 ]. TUNEL-positive cells were counted in the bilateral CA1, CA3, and DG regions. The proportions of TUNEL-positive cells in the CA1, CA3, and DG were significantly lower in the SF group than in the S group at PND 39 (Fig. 8 f–k). These results indicated that FMT suppressed histological apoptosis in the hippocampus. Discussion In this animal study, we elucidated that FMT altered gut microbiota, particularly by increasing the Firmicutes phylum, Clostridia class, Clostridiales order, Ruminococcaceae family, Ruminococcus genus, which produce butyrate, and improved spatial learning memory in DAN rat model. FMT promoted mRNA expression of BDNF and suppressed expression of IL-1β and microglial infiltration in the hippocampus. Moreover, it decreased mRNA expression of apoptosis-inducing proteins and increased mRNA expression of anti-apoptotic proteins, resulting in suppression of neuronal apoptosis (Supplementary Fig. 12). Although the mechanism of DAN has not been well elucidated, oxidative stress appears to be a factor. Cheng et al. investigated the effect of vitamin C on DAN, reporting attenuation of both caspase-3 activation and cognitive impairment caused by isoflurane [ 48 ]. Additionally, an in vitro model showed that a water-soluble vitamin E analogue prevents ketamine-induced neuronal death [ 49 ]. Further, several antioxidants, such as carbon monoxide, apocynin, and ubiquinone, improve cognitive dysfunction caused by anesthetics, and oxidative stress is thought to cause neurotoxicity [ 50 ]. Another possible mechanism is anesthetic-induced neuroinflammation. In a rodent model, lidocaine with anti-inflammatory properties attenuated elevated IL-1β levels and cognitive dysfunction caused by isoflurane inhalation. Moreover, mice with IL-1β deficiency did not present isoflurane-induced learning impairment [ 51 ]. DAN involves FAS signaling, which plays a key role in cell apoptosis by binding to the FAS ligand. Song et al. reported that FAS/FAS ligand-knockout mice exposed to sevoflurane had a higher spatial learning ability [ 52 ]. In the present study, butyrate was increased by FMT. Since butyrate inhibits histone deacetylase and exhibits anti-inflammatory and neuroprotective effects [ 53 ], it seems to be a key factor in attenuating learning disabilities. Suberanilohydroxamic acid (SAHA) also inhibited histone deacetylation. In a DAN mice model, SAHA attenuated sevoflurane-induced learning and memory impairments, and showed potential to prevent DAN [ 54 ]. Complex mechanisms, including oxidative stress, neuroinflammation, and epigenetics, may be involved in DAN. Exposure to inhalational anesthetics significantly alters gut microbiota. Serbanescu et al. investigated alterations in gut microbiota caused by isoflurane inhalation. Old mice were exposed to 1.5% isoflurane for 4 h, resulting in reduced diversity of gut microbiota, decreased Bacteroidales and S24-7 family, and increased Ternericutes phylum [ 55 ]. 55 In juvenile rats, Firmicutes and Lachnospiraceae were decreased, but Bacteroidetes was not significantly altered by sevoflurane exposure [ 56 ]. Sevoflurane inhalation increases Bacteroides , Alloprevotella , and Akkermansia and decreases Lactobacillus 14 days after anesthesia [ 20 ]. Additionally, exposure to isoflurane for 4 h increased Proteobacteria and Lachnospiraceae and decreased Bacteroidetes and Actinobacteria , particularly in the Ruminococcus genus of the Ruminococcaceae family. Alterations in gut microbiota diversity 7 days after anesthesia were remarkable [ 21 ]. Changes in gut microbiota due to anesthetics vary across reports; however, these changes may exert various effects on the host. Mice with dysbiosis are highly susceptible to Listeria monocytogenes infection, as this infection is prevented by the gut bacteria Clostridiales [ 57 ]. Reduction in abundance of Firmicutes is associated with several pathologies, such as chemotherapy-caused gastrointestinal mucositis in non-Hodgkin’s lymphoma, major burn injury and Roux-en-Y gastric bypass [ 58 , 59 ]. To clarify the association between specific bacteria and various pathophysiologies, further gut microbiota analyses of various diseases may be necessary. Two mechanisms are primarily involved in gut microbiota changes induced by inhalational anesthetics. First, these anesthetics appear to exert antibacterial effects, which were investigated by Martínez-Serrano et al. in vitro . Sevoflurane and isoflurane showed antibacterial activities against resistant bacteria, such as Staphylococcus aureus , Escherichia coli , and Pseudomonas aeruginosa [ 60 ]. Another mechanism is the indirect effect of gut microbiota, referred to as the brain–gut–bacterial axis. The vagus nerve modulates the functional relationship between the brain and the gastrointestinal tract and transmits information affecting endocrine and gastrointestinal peristalsis from the CNS, resulting in alterations in gut microbiota [ 61 ]. Butyrate inhibits histone deacetylase, which promotes histone acetylation and plays a crucial role in neuronal development, differentiation, and survival by accelerating BDNF expression [ 19 ]. BDNF activates tyrosine kinase receptor B (TrkB) signaling and exerts anti-inflammatory and anti-apoptotic effects in the hippocampus by activating PI3K/Akt and inhibiting MyD88/NF-κB signaling [ 18 ]. Further, it exerts neuroprotective effects in various CNS disease models. In a diabetic neuroischemia model, BDNF treatment improved learning, memory, and hippocampal neurogenesis [ 62 ]. In contrast, sevoflurane inhalation in neonatal rodents increases histone deacetylase 3 and 8 levels and reduces acetylated histone H3 and H4, BDNF, and TrkB expression, resulting in spatial learning disability [ 63 ]. Moreover, butyrate facilitates conversion of microglia from the inflammatory (M1) to anti-inflammatory (M2) phenotype [ 64 ]. Transformation of microglia from M1 to M2 plays an important role in attenuating brain damage and underlies the neuroprotective effects of butyrate [ 65 ]. In the present study, the mechanism of spatial learning ability improvement by FMT was the increase in butyrate-producing bacteria due to FMT, and subsequent production of butyrate. Butyrate facilitated histone H3 and H4 acetylation and BDNF expression, resulting in reduction of hippocampal neuroinflammation and apoptosis. Several possible treatments have been reported for DAN in neonates. Jia et al. showed that intraperitoneal sodium butyrate normalizes sevoflurane-induced BDNF reduction and neurobehavioral abnormalities by facilitating histone acetylation [ 63 ]. Oral administration of the butyrate precursor tributyrin improves scopolamine-induced impairment of spatial memory [ 66 ]. Since inhalational anesthesia-induced spatial learning disability is also caused by hippocampal neuronal damage, tributyrin intervention might provide some protection. Dexmedetomidine, an α-2 adrenergic agonist, also exerts neuroprotective effects. In a traumatic brain injury model, administration of dexmedetomidine after brain injury suppressed the infiltration of monocyte-derived macrophages and improved postoperative neurocognitive disorder [ 32 ]. The effect of dexmedetomidine on DAN was elucidated, and its neuroprotective effect was demonstrated through the miR-330-3p/ULK1 axis regulating hippocampal cell apoptosis [ 67 ]. Although numerous reports of preventive interventions for DAN have been published, no clinically effective treatments have been established. FMT is an effective treatment for Clostridium difficile infections. The relationship between neurodegenerative diseases and dysbiosis has been probed, and FMT may be a potential treatment for several neurodegenerative diseases. Alzheimer’s disease (AD) is the most common neurodegenerative disease, characterized by extracellular deposition of amyloid-β [ 68 ]. In an AD mouse model, FMT from young donor mice significantly decreased amyloid plaques and improved cognitive ability [ 69 ]. Several clinical studies have confirmed the efficacy of FMT. In patients with mild cognitive impairment and AD, FMT significantly increases Prevotella and decreases Bacteroides . Further, it significantly alters serum metabolite levels, resulting in improved cognitive function [ 70 ]. Patients with Parkinson’s disease can be treated with FMT. Although deposition of Lewy bodies in CNS neurons may cause Parkinson’s disease, dysbiosis of gut microbiota contributes to disease progression [ 71 ]. Oral administration of lyophilized FMT improves motor and non-motor symptoms in patients with Parkinson’s disease. Microbiota analysis of these patients revealed that FMT increased Firmicutes phylum and reduced Proteobacteria phylum [ 72 ]. In children with autism, Bifidobacterium is significantly reduced in gut microbiota [ 73 ]. However, FMT from healthy individuals increased relative abundance of Bifidobacterium by four-folds, and gastrointestinal symptoms related to autism, such as constipation, diarrhea, and abdominal pain, improved for 8 weeks after FMT treatment [ 74 ]. Thus, FMT can be used to treat CNS diseases, and its therapeutic effect is expected to alleviate DAN in humans. Conclusions In conclusion, we investigated the effects of FMT on sevoflurane-induced learning disabilities in neonatal rats. FMT increased the abundances of Firmicutes phylum, Clostridia order, Clostridiales class, Ruminococcaceae family, Ruminococcus genus, and butyric acid-producing bacteria. Moreover, it induced an increase in butyrate level in feces, BDNF expression, and anti-apoptotic proteins, and a decrease in expression of IL-1β and apoptosis-inducing proteins. This resulted in suppression of neuronal inflammation, apoptosis in the hippocampus, and improvement in spatial learning ability. Modulation of gut microbiota might be an effective treatment for DAN in neonates; however, further studies should be performed to identify the effective bacterial species and clinical effectiveness of gut microbiota modulation by FMT or probiotics. Abbreviations ADHD Attention deficit hyperactivity disorder FMT Fecal microbiota transplantation DAN Developmental anesthetic neurotoxicity SCFA short-chain fatty acids BDNF Brain-derived neurotrophic factor PND Postnatal day TUNEL Terminal deoxynucleotidyl transferase dUTP nick end labeling HPLC high performance liquid chromatography CA Cornu ammonis DG Dentate gyrus CNS Central nervous system Declarations Ethics approval and consent to participate All experimental protocols were approved by the Ethics Committee (approval code: 20-066) for Animals of the Sapporo Medical University School of Medicine. Consent for publication Not applicable. Availability of data and material 16s rRNA sequencing raw data for fecal microbiota analysis in this study are available in https://www.ncbi.nlm.nih.gov/. The BioProject ID in Sequence Read Archive of NCBI is PRJNA1070816, and submission ID is SUB14180994. Competing interests The authors declare that they have no competing interests. Funding This study was supported by Yakult Bio-Science Foundation (No. 518) and Grant-in-Aid for Young Scientists from the Japan Society for the Promotion of Science (No. 20K17786). Authors’ contributions TC, ST, NN, NK designed the experiment protocol. TC, YH, SS, and TH obtained the experimental data. TC, YH, ST, NN, NK and YY analyzed and interpret data. TC and YY drafted the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Jevtovic-Todorovic V, Hartman RE, Izumi Y, Benshoff ND, Dikranian K, Zorumski CF, et al. Early exposure to common anesthetic agents causes widespread neurodegeneration in the developing rat brain and persistent learning deficits. J Neurosci. 2003;23:876–82. McCann ME, de Graaff JC, Dorris L, Disma N, Withington D, Bell G, et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3910445","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270194558,"identity":"b194dfb2-c44b-4d24-9e3b-f64a501152bf","order_by":0,"name":"Tomohiro Chaki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYDACCQZmhgQwg7GBgaECJggEID5uLQkwLWeI1QK2BqSMsQ1JCy7AP7v5scHDHzbR/NLNbR8+zquTNxc7fPAGQ40dA/Ns7NZI3DlmnJCQkJY7c87B5pkztx023Dk7LdmC4VgyA+OcA1i1GEgkGB9ISDicu+FGYjMz77YDjBtu55hJMLAdYGCckYBDS/pnJC1z6uw33M7/JsHwD5+WHJDDYFoamBOBtrBJMLbh1iJxI6fYICEN6JcZic2MM44dTgb6xdgisS+ZB5df+Gekb5b8YWOT2y+R/pjhQ02d7Xbp5Ic3PnyzkzPEEWJYnAoigE7iMZxBpA6IFhCQxxuho2AUjIJRMIIAAJtsYz7Eab6UAAAAAElFTkSuQmCC","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":true,"prefix":"","firstName":"Tomohiro","middleName":"","lastName":"Chaki","suffix":""},{"id":270194559,"identity":"68480a15-2052-405a-a749-787760bbea22","order_by":1,"name":"Yuri Horiguchi","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuri","middleName":"","lastName":"Horiguchi","suffix":""},{"id":270194560,"identity":"ae32c4ec-8dd8-40d9-9f23-656bbeaebf9a","order_by":2,"name":"Shunsuke Tachibana","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shunsuke","middleName":"","lastName":"Tachibana","suffix":""},{"id":270194561,"identity":"fbb176fc-e700-43e8-aec3-93c82ac1ba2c","order_by":3,"name":"Satoshi Sato","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Satoshi","middleName":"","lastName":"Sato","suffix":""},{"id":270194562,"identity":"3dc25d08-28bf-4285-b14d-c232a9eb8f3a","order_by":4,"name":"Tomoki Hirahata","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tomoki","middleName":"","lastName":"Hirahata","suffix":""},{"id":270194563,"identity":"994f52ca-2bc9-4d98-b144-dc08a84fd6cc","order_by":5,"name":"Noriaki Nishihara","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Noriaki","middleName":"","lastName":"Nishihara","suffix":""},{"id":270194565,"identity":"d8fb5ab3-8f0c-4549-b478-c1ab5ab54cf3","order_by":6,"name":"Natsumi Kii","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Natsumi","middleName":"","lastName":"Kii","suffix":""},{"id":270194567,"identity":"f4f999be-1fd3-4c0d-a871-65c685e32d0e","order_by":7,"name":"Yusuke Yoshikawa","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Yoshikawa","suffix":""},{"id":270194568,"identity":"221693ea-2dc5-4e3c-94b2-4d4361e2f5d4","order_by":8,"name":"Kengo Hayamizu","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kengo","middleName":"","lastName":"Hayamizu","suffix":""},{"id":270194570,"identity":"76b60807-5151-40b2-8fe9-6a72ffbc5f7c","order_by":9,"name":"Michiaki Yamakage","email":"","orcid":"","institution":"Sapporo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Michiaki","middleName":"","lastName":"Yamakage","suffix":""}],"badges":[],"createdAt":"2024-01-30 11:46:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3910445/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3910445/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50573961,"identity":"024a3003-7e65-4e86-97b8-8a314734503a","added_by":"auto","created_at":"2024-02-02 16:57:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFecal microbiota transplantation improves spatial learning disability. a,\u003c/strong\u003e Schema of protocol in Experiment 2. Sevoflurane exposure was performed on postnatal days (PND) 7–13. In the SF group, fecal microbiota transplantation was performed immediately after sevoflurane exposure on PND 7-13. \u003cstrong\u003eb,\u003c/strong\u003e Evaluation of locomotor activity and emotional change with the open field test. Activity and emotion did not differ significantly between S and SF groups. \u003cstrong\u003ec,\u003c/strong\u003e Schema of reversal Morris water maze test. The platform was placed in zone 3, on the opposite side of the Morris water maze. \u003cstrong\u003ed,\u003c/strong\u003e Reversal acquisition test. Latency to the target was significantly shorter in the SF group compared with the S group on trial days 2 and 5. Two-way analysis of variance with Bonferroni multiple-comparison test was used for statistical analysis. *\u003cem\u003ep\u003c/em\u003e = 0.0275, †\u003cem\u003ep\u003c/em\u003e = 0.0295. \u003cstrong\u003ee–h,\u003c/strong\u003e Spatial learning ability evaluated by the reversal probe test. In the SF group, rats took a significantly shorter time to reach the target zone, and spent a longer time around it. Unpaired \u003cem\u003et\u003c/em\u003e-test was used for statistical analysis. (n = 10 in each group: female, 6; male, 4). In target zone crossing, data are expressed as median (interquartile range). For other outcomes, data are expressed as mean ± standard error of mean.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3910445/v1/9b0be72eefe5843c1ff26e14.png"},{"id":50574550,"identity":"be076187-fd0b-45ea-aaa7-6c8fa0a3728f","added_by":"auto","created_at":"2024-02-02 17:05:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":234744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eα- and β-diversity analyses. a–d, \u003c/strong\u003eRarefaction curve of operational taxonomic units, Shannon index, ACE index, Simpson index, and Chao1 index. \u003cstrong\u003ee,\u003c/strong\u003e Shannon index. \u003cstrong\u003ef,\u003c/strong\u003eACE index. \u003cstrong\u003eg,\u003c/strong\u003e Chao1 index. \u003cstrong\u003eh,\u003c/strong\u003e Simpson index. These four indices represent α-diversity of gut microbiota. The SF group had a richer diversity of gut microbiota compared with the S group. Unpaired \u003cem\u003et\u003c/em\u003e-test was used for statistical analysis. \u003cstrong\u003ei,j\u003c/strong\u003e Unweighted UniFrac principal coordinate analysis (PCoA). Gut microbiota of the S and SF groups were found to differ significantly in the unweighted analysis. Permutational analysis of variance (ANOVA) was performed to compare gut microbiota between groups (n = 6 in each group). \u003cstrong\u003ek–l,\u003c/strong\u003eWeighted UniFrac PCoA. Gut microbiota of the S and SF groups were found to differ significantly in the weighted analysis. Permutational ANOVA was performed to compare gut microbiota between groups (n = 6 in each group). In PCoA, data are expressed as median (interquartile range). For other outcomes, data are expressed as mean ± standard error of the mean. OTU, operational taxonomic unit.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3910445/v1/bf5a3040052bd9aaeead90f1.png"},{"id":50573963,"identity":"e72ff483-b312-4902-8c36-902e2514a114","added_by":"auto","created_at":"2024-02-02 16:57:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":310980,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundant analysis at phylum and class levels. a, \u003c/strong\u003eTaxonomy chart at the phylum level. \u003cstrong\u003eb,\u003c/strong\u003e Relative abundance of \u003cem\u003eBacteroidetes\u003c/em\u003e phylum. \u003cstrong\u003ec,\u003c/strong\u003e Relative abundance of \u003cem\u003eFirmicutes\u003c/em\u003e phylum. \u003cstrong\u003ed,\u003c/strong\u003e Relative abundance of \u003cem\u003eLentisphaerae\u003c/em\u003ephylum. \u003cstrong\u003ee,\u003c/strong\u003e Relative abundance of \u003cem\u003eTenericutes\u003c/em\u003e phylum. Fecal microbiota transplantation (FMT) decreased \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eTenericutes\u003c/em\u003ephyla, and increased \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eLentisphaerae\u003c/em\u003e phyla. \u003cstrong\u003ef,\u003c/strong\u003eTaxonomy chart at the class level. \u003cstrong\u003eg,\u003c/strong\u003e Relative abundance of \u003cem\u003eClostridia\u003c/em\u003eclass. \u003cstrong\u003eh,\u003c/strong\u003e Relative abundance of \u003cem\u003eBacteroidia\u003c/em\u003e class. \u003cstrong\u003ei,\u003c/strong\u003eRelative abundance of \u003cem\u003eLentisphaeria\u003c/em\u003e class. \u003cstrong\u003ej,\u003c/strong\u003e Relative abundance of \u003cem\u003eDeltaproteobacteria\u003c/em\u003e class. \u003cstrong\u003ek,\u003c/strong\u003e Relative abundance of \u003cem\u003eBetaproteobacteria\u003c/em\u003eclass. l, Relative abundance of \u003cem\u003eMollicutes\u003c/em\u003e class. \u003cstrong\u003em,\u003c/strong\u003e Relative abundance of \u003cem\u003eActinobacteria\u003c/em\u003e class. FMT decreased \u003cem\u003eBacteroidia\u003c/em\u003e, \u003cem\u003eDeltaproteobacteria\u003c/em\u003e, \u003cem\u003eBetaproteobacteria\u003c/em\u003e, \u003cem\u003eMollicutes\u003c/em\u003e, and \u003cem\u003eActinobacteria\u003c/em\u003e class, and increased \u003cem\u003eClostridia\u003c/em\u003e and \u003cem\u003eLentisphaeria\u003c/em\u003e class. Unpaired \u003cem\u003et\u003c/em\u003e-test was used for statistical analysis (n = 6 in each group). Data are expressed as mean ± standard error of mean.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3910445/v1/dfdf95cdc0d46decf2efa514.png"},{"id":50573965,"identity":"f0fc2d61-3480-4039-b5ff-f5f61daa96d8","added_by":"auto","created_at":"2024-02-02 16:57:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":351186,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundant analysis at order and family levels. a, \u003c/strong\u003eTaxonomy chart at the order level. \u003cstrong\u003eb,\u003c/strong\u003eRelative abundance of \u003cem\u003eBacteroidales\u003c/em\u003e order. \u003cstrong\u003ec,\u003c/strong\u003e Relative abundance of \u003cem\u003eClostridiales\u003c/em\u003e order. \u003cstrong\u003ed,\u003c/strong\u003e Relative abundance of \u003cem\u003eVictivallales\u003c/em\u003eorder. \u003cstrong\u003ee,\u003c/strong\u003e Relative abundance of \u003cem\u003eDesulfovibrionales\u003c/em\u003e order. \u003cstrong\u003ef,\u003c/strong\u003eRelative abundance of \u003cem\u003eBurkholderiales\u003c/em\u003e order. \u003cstrong\u003eg,\u003c/strong\u003e Relative abundance of \u003cem\u003eRF39\u003c/em\u003e order. \u003cstrong\u003eh,\u003c/strong\u003e Relative abundance of \u003cem\u003eActinomycetales\u003c/em\u003eorder. Fecal microbiota transplantation (FMT) decreased \u003cem\u003eBacteroidales\u003c/em\u003e, \u003cem\u003eDesulfovibrionales\u003c/em\u003e, \u003cem\u003eBurkholderiales\u003c/em\u003e, \u003cem\u003eRF39\u003c/em\u003e, and \u003cem\u003eActinomycetales\u003c/em\u003e order, and increased \u003cem\u003eClostridiales\u003c/em\u003e and \u003cem\u003eVictivallales\u003c/em\u003e order. \u003cstrong\u003ei,\u003c/strong\u003eTaxonomy chart at the family level. \u003cstrong\u003ej,\u003c/strong\u003e Relative abundance of \u003cem\u003eBacteroidaceae\u003c/em\u003efamily. \u003cstrong\u003ek,\u003c/strong\u003e Relative abundance of \u003cem\u003eRuminococcaceae\u003c/em\u003e family. \u003cstrong\u003el,\u003c/strong\u003eRelative abundance of \u003cem\u003eS24-7\u003c/em\u003e family. \u003cstrong\u003em,\u003c/strong\u003e Relative abundance of \u003cem\u003eVictivallaceae\u003c/em\u003efamily. \u003cstrong\u003en,\u003c/strong\u003e Relative abundance of \u003cem\u003eDesulfovibrionaceae\u003c/em\u003e family. \u003cstrong\u003eo,\u003c/strong\u003eRelative abundance of \u003cem\u003eDehalobacteriaceae\u003c/em\u003e family. \u003cstrong\u003ep,\u003c/strong\u003e Relative abundance of \u003cem\u003eAlcaligenaceae\u003c/em\u003e family. FMT decreased \u003cem\u003eBacteroidaceae\u003c/em\u003e, \u003cem\u003eS24-7\u003c/em\u003e, \u003cem\u003eDesulfovibrionaceae\u003c/em\u003e, \u003cem\u003eDehalobacteriaceae\u003c/em\u003e, and \u003cem\u003eAlcaligenaceae\u003c/em\u003efamily, and increased \u003cem\u003eRuminococcaceae\u003c/em\u003e and \u003cem\u003eVictivallaceae\u003c/em\u003e family. Only the 20 most abundant amplicon sequence variants are presented. Unpaired \u003cem\u003et\u003c/em\u003e-test was used for statistical analysis (n = 6 in each group). Data are expressed as mean ± standard error of mean.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3910445/v1/6c328fd03167876122b2adfb.png"},{"id":50573964,"identity":"2356536a-c3bd-4702-bf98-45b236c2211d","added_by":"auto","created_at":"2024-02-02 16:57:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":256410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundant analysis at genus and species levels. a,\u003c/strong\u003e Taxonomy chart at the genus level. \u003cstrong\u003eb,\u003c/strong\u003e Relative abundance of \u003cem\u003eBacteroides\u003c/em\u003e genus. \u003cstrong\u003ec,\u003c/strong\u003e Relative abundance of \u003cem\u003eRuminococcus\u003c/em\u003e genus. \u003cstrong\u003ed,\u003c/strong\u003e Relative abundance of \u003cem\u003eDehalobacterium\u003c/em\u003egenus. \u003cstrong\u003ee,\u003c/strong\u003e Relative abundance of \u003cem\u003eSutterella\u003c/em\u003e genus. \u003cstrong\u003ef,\u003c/strong\u003eRelative abundance of \u003cem\u003eDesulfovibrio\u003c/em\u003e genus. Fecal microbiota transplantation decreased \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eDehalobacterium\u003c/em\u003e, \u003cem\u003eSutterella\u003c/em\u003e, and \u003cem\u003eDesulfovibrio\u003c/em\u003e genus, and increased \u003cem\u003eRuminococcus\u003c/em\u003e genus. \u003cstrong\u003eg,\u003c/strong\u003eTaxonomy chart at the species level. Relative abundance of species did not differ significantly between the S and SF groups. Only the 20 most abundant amplicon sequence variants are presented. Unpaired \u003cem\u003et\u003c/em\u003e-test was used for statistical analysis (n = 6 in each group). Data are expressed as mean ± standard error of mean.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3910445/v1/5a12fc0a450e1c09be9f8947.png"},{"id":50573967,"identity":"d9e74e24-0d45-4ec4-a1ef-387867dbb42a","added_by":"auto","created_at":"2024-02-02 16:57:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":203997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOrganic acid production, histone acetylation, and mRNA expression of brain-derived neurotrophic factor. a,\u003c/strong\u003e Fecal concentration of butyric acid. Fecal microbiota transplantation (FMT) increased fecal concentration of butyric acid on postnatal day (PND) 21 (n = 6 in each group). \u003cstrong\u003eb,\u003c/strong\u003e Fecal concentration of acetic acid. \u003cstrong\u003ec,\u003c/strong\u003e Fecal concentration of lactic acid. \u003cstrong\u003ed,\u003c/strong\u003eFecal concentration of succinic acid. \u003cstrong\u003ee,\u003c/strong\u003e Fecal concentration of propionic acid. FMT did not significantly change fecal concentrations of acetic, lactic, succinic, or propionic acid on PND 39 (n = 6 in each group). \u003cstrong\u003ef,\u003c/strong\u003eHistone H3 total acetylation in the hippocampus. \u003cstrong\u003eg,\u003c/strong\u003e Histone H4 total acetylation in the hippocampus. FMT increased histone H3 and H4 total acetylation on PND 21 (n = 8 in each group). \u003cstrong\u003eh,\u003c/strong\u003e Expression of brain-derived neurotrophic factor (BDNF) mRNA. FMT enhanced the BDNF mRNA expression on PND 21 (n = 10 and 8 in S and SF groups). Unpaired \u003cem\u003et\u003c/em\u003e-test was used for statistical analysis. Data are expressed as mean ± standard error of mean.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-3910445/v1/e91513d9e3e2f22103ae192f.png"},{"id":50573968,"identity":"675fbf25-fea3-4bbc-90b7-cb1e3599ee5e","added_by":"auto","created_at":"2024-02-02 16:57:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":878605,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeuroinflammation and microglia infiltration in the hippocampus. a,\u003c/strong\u003emRNA expression of IL-1β. \u003cstrong\u003eb,\u003c/strong\u003e mRNA expression of caspase-1. Although fecal microbiota transplantation (FMT) did not change caspase-1 mRNA expression, it induced IL-1β mRNA expression on postnatal day (PND) 21 (n = 9 and 8 in S and SF groups). \u003cstrong\u003ec,\u003c/strong\u003e Representative image of microglia in the hippocampus. In the S group, numerous and large microglia (Iba-1 positive cell) were infiltrated in the hippocampus. In the SF group, FMT suppressed microglial infiltration in the hippocampus on PND 39 (n = 5 and 6 in S and SF groups). \u003cstrong\u003ed,\u003c/strong\u003eQuantitative evaluation of Iba-1-positive cell count in the hippocampus. \u003cstrong\u003ee,\u003c/strong\u003eQuantitative evaluation of Iba-1-positive area in the hippocampus. In the SF group, FMT suppressed Iba-1-positive cell count and area on PND 39 (n = 5 and 6 in S and SF groups). The scale bar represents 25 μm. Unpaired \u003cem\u003et\u003c/em\u003e-test was used for statistical analysis. Data are expressed as mean ± standard error of mean.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-3910445/v1/726a2c4be4fc99faf1e00c33.png"},{"id":50573969,"identity":"1f77cf39-44b4-40fd-995e-09515d7437bd","added_by":"auto","created_at":"2024-02-02 16:57:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1239878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeuronal apoptosis in the hippocampus.\u003c/strong\u003e \u003cstrong\u003ea,\u003c/strong\u003e mRNA expression of Bad-1 in the hippocampus on postnatal day (PND) 21. \u003cstrong\u003eb,\u003c/strong\u003e mRNA expression of Bax in the hippocampus on PND 21. \u003cstrong\u003ec, \u003c/strong\u003emRNA expression of BCL2L11 in the hippocampus on PND 21. Fecal microbiota transplantation (FMT) suppressed expression of apoptosis-inducing proteins, including Bad-1, Bax, and BCL2L11 (n = 10 and 8 in S and SF groups). \u003cstrong\u003ed,\u003c/strong\u003e mRNA expression of Mcl-1 on PND 21.\u003cstrong\u003ee, \u003c/strong\u003emRNA expression of BCL2L1 in the hippocampus on PND 21. FMT enhanced expression of anti-apoptosis proteins, including Mcl-1 and BCL2L1 (n = 10 and 8 in S and SF groups). \u003cstrong\u003ef,\u003c/strong\u003e Representative image of terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) positive cells of the cornu ammonis (CA) 1 region of the hippocampus on PND 39. \u003cstrong\u003eg,\u003c/strong\u003eQuantitative evaluation of the percentage of TUNEL-positive cells in CA1 on PND 39. In the SF group, FMT decreased the proportion of TUNEL-positive cells in CA1 (n = 7 in each group). \u003cstrong\u003eh,\u003c/strong\u003e Representative image of TUNEL-positive cells in CA3 of the hippocampus on PND 39. \u003cstrong\u003ei,\u003c/strong\u003e Quantitative evaluation of the percentage of TUNEL-positive cells in CA3 on PND 39. FMT decreased the proportion of TUNEL-positive cells in CA3 (n = 7 in each group). \u003cstrong\u003ej,\u003c/strong\u003eRepresentative image of TUNEL-positive cells in the dentate gyrus (DG) of the hippocampus on PND 39. \u003cstrong\u003ek,\u003c/strong\u003e Quantitative evaluation of the percentage of TUNEL-positive cells in DG on PND 39. FMT decreased the proportion of TUNEL-positive cells in DG (n = 7 in each group). Further, it suppressed mRNA expression of apoptosis-inducing protein and increased the mRNA expression of anti-apoptotic protein in the hippocampus on PND 21, resulting in suppression of neuronal apoptosis on PND 39. The scale bar represents 25 μm. Unpaired \u003cem\u003et\u003c/em\u003e-test was used for statistical analysis. Data are expressed as mean ± standard error of mean.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-3910445/v1/4a72ef5e10b9ca95b25054f3.png"},{"id":50850910,"identity":"0e96a96d-c423-4779-8da3-24cf9051ab58","added_by":"auto","created_at":"2024-02-08 10:57:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3104664,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3910445/v1/decac51d-5d6e-4436-a8a4-08ab2dad13cf.pdf"},{"id":50574930,"identity":"b4013c62-e974-4e22-9458-a3da0587a45e","added_by":"auto","created_at":"2024-02-02 17:13:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4817468,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-3910445/v1/e2ed1c93b1228a7e2a9d573b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fecal microbiota transplantation improves spatial learning disability caused by developmental anesthetic neurotoxicity in neonatal rats","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnesthetic exposure in childhood prevents neurodevelopment and causes cognitive dysfunctions, such as learning disabilities. An anesthetic mixture containing midazolam, nitrous oxide, and isoflurane has been reported to induce apoptotic neurodegeneration and spatial learning disabilities in infant rats [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The numerous animal and clinical studies on developmental anesthetic neurotoxicity (DAN) in neonates have been published. Recently, three major clinical studies (GAS, PANDA and MASK) have been performed, with each study reporting that a single exposure to anesthesia did not ameliorate cognitive function [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, a meta-analysis of the three studies showed that even a single general exposure to anesthesia increased behavioral problems [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, it has been elucidated that the multiple exposure of general anesthesia has significantly caused DAN [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Because neurotoxicity is a major problem in pediatric anaesthesia, prophylaxis for DAN in children should be established. Although the mechanism of DAN has not been well clarified, various mechanisms might involve in the development of DAN. The major mechanism is neuronal cell death caused by neuronal apoptosis and neurodegeneration. Other possible mechanisms include neural cell damage, impaired synaptic plasticity or abnormal myelination, tau phosphorylation and disturbance of neuroendocrine system [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe bidirectional communication between the gut microbiota and the brain is referred to as the gut\u0026ndash;brain axis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The gut microbiota influences various organs and are associated with host health and various diseases. For example, attention-deficit/hyperactivity disorder (ADHD), a major neurodevelopmental disease in children, is associated with gut dysbiosis, including reduced α-diversity and increased abundances of \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, microbiota transfer therapy from a healthy donor significantly improves gastrointestinal and autism-like behavioral symptoms in children with ADHD [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Further, Parkinson\u0026rsquo;s disease pathogenesis may be related to gut dysbiosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Various patterns of dysbiosis have been reported in human clinical studies on Parkinson\u0026rsquo;s disease. Fecal microbiota transplantation (FMT) therapy improves both motor and non-motor symptoms, such as wearing off and constipation [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The gut microbiota produces various short-chain fatty acids (SCFA), including lactate, acetate, and butyrate, by metabolizing dietary fiber. Butyrate, which plays a crucial role in the gut\u0026ndash;brain axis, is generated by butyrate-producing bacteria, such as \u003cem\u003eRuminococcus\u003c/em\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It acts as a histone deacetylase inhibitor, promoting histone acetylation and brain-derived neurotrophic factor (BDNF) expression, which exert neuroprotective and anti-apoptotic effects in the hippocampus [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnesthetic exposure can modulate gut microbiota. In rodents, a single anesthetic exposure significantly alters the composition of gut microbiota as well as SCFA production in feces [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Accordingly, we hypothesized that gut microbiota dysbiosis caused by inhalational anesthesia exposure is one of the possible pathogeneses of DAN in neonates and modulation of gut microbiota would improve DAN by increasing butyrate production and BDNF expression and suppressing hippocampal neuronal apoptosis. We used FMT as an intervention for gut microbiota and evaluated its effect on spatial learning ability.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e The study was approved by the Ethics Committee (approval code: 20\u0026ndash;066) for Animals of the Sapporo Medical University School of Medicine. This experiment complied with the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines and adhered to the guidelines for proper conduct of animal experiments issued by the Japanese Ministry of Education, Culture, Sports, Science and Technology and the Ministry of Health, Labor and Welfare, as well as those issued by the Science Council of Japan.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003eExperiments were conducted using Wistar rats. Pregnant rats were purchased (Sankyo Labo Service Corporation, Inc., Tokyo, Japan) and housed under controlled illumination (12-h light/12-h dark, lights on at 7AM), temperature (21\u0026ndash;25\u0026deg;C) and humidity (40\u0026ndash;60%) with free access to food and water. In experiment 1, neonatal rats were divided into Control (C) and Sevo (S) groups (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7 in each group, Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). In experiment 2, they were divided into S and Sevo\u0026thinsp;+\u0026thinsp;FMT (SF) groups (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20 in each group, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The pregnant rats were randomly allocated to each group in each experiment. Their neonatal rats were allocated to each group according to their mother\u0026rsquo;s allocation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSevoflurane exposure\u003c/h2\u003e \u003cp\u003eThe modified protocol of sevoflurane exposure by Xu et al and Ju et al was applied in this study [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Pups were divided into Sevo (S) or Sevo\u0026thinsp;+\u0026thinsp;FMT (SF) groups. In both groups, pups were received sevoflurane exposure on postnatal day (PND) 7\u0026ndash;13. Pups were transferred to the anesthesia cage (15*15*15 cm) and exposed 2.1% sevoflurane for 2 hours under 100% oxygen 2 L/min flow. The concentration of sevoflurane and oxygen fraction in cage were measured using a gas sampling system (Datex Ohmeda, GE HealthCare, IL). The temperature in the cage was maintained 21\u0026ndash;25\u0026deg;C using a heat lamp. After anesthetic exposure, inhalation of sevoflurane was discontinued and the pups in S group was re-transferred to the mother\u0026rsquo;s cage after confirmation of spontaneous movement. In SF groups, pups were re-transferred to the mother\u0026rsquo;s cage after fecal microbiota transplantation (FMT).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFecal microbiota transplantation\u003c/h2\u003e \u003cp\u003eThe FMT was performed according to past reports [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In SF group, 5 g of fresh feces was obtained from the mother rat on PND 6. Feces were dissolved in 50 mL of saline solution and mixed fully. The fecal solution was passed through 2.0, 1.0, 0.5 and 0.25 mm stainless steel laboratory sieves (WS Tyler, Mentor, OH) to remove solids including undigested food and small particulate material. After filtration, the fecal solution was centrifuged at 5,000 r/min, 0\u0026deg;C for 15 min. The supernatant was collected and stored at 4\u0026deg;C until FMT [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. After sevoflurane exposure on PND 7\u0026ndash;13, 24-gauge cannula was inserted into the anus up to approximately 2.5 cm, and 0.3 mL of the fecal supernatant solution was injected and maintained for 3 min. After FMT, the pups were returned to the cage with their mother after confirming adequate respiration and spontaneous movement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOpen field test\u003c/h2\u003e \u003cp\u003eOpen field test was performed to evaluate exploratory, locomotor activity and anxiety-like behavior. The box of open field test was made by acrylic resin (90*90*30 cm, width*length*height) and the center zone was defined as the 54*54 cm zone in the center of the box. The box was placed in a quiet room and illuminated so as not to cast shadows. The rats were placed in the center of the open field box, and their behavior was traced and analyzed with SMART 3.0 video tracking system (Panlab Harvard Apparatus, Barcelona, Spain) during 5 min. The time in central zone, resting time, total moving distance and mean velocity were recorded [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The inside of the field was cleaned with 70% ethanol after each trial.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eY-maze test\u003c/h2\u003e \u003cp\u003eY-maze test was performed to evaluate the locomotor activity and spatial learning ability. The maze, made by black polyvinylidene, was consisted by center zone and three arms (10*51.5*25 cm, width*length*height), which the angle between each arm was 120 degrees. The Y-maze was placed in a quiet room and illuminated so as not to cast shadows. The rat was placed into the center of the maze and freely explored for 8 min. Their behavior was traced with SMART 3.0 video tracking system (Panlab Harvard Apparatus). Total moving distance, total arm entry and spontaneous alternation was analyzed. The maze was cleaned with 70% ethanol after each trial [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMorris water maze test and reversal Morris water maze test\u003c/h2\u003e \u003cp\u003eMorris water maze test was performed to evaluate the spatial learning and working memory ability. The maze was a circular pool (170 cm in diameter and 60 cm in height). The field of maze was divided into 4 quadrants and filled with water. The temperature of water was set at 25\u0026deg;C. In acquisition test, the 10 cm clear platform was placed under 1.0 cm into water in zone 1. The zone of the platform placement was defined as a target zone. The rats were trained 5 days to find a hidden platform under water. Each training day was consisted of four sequential trials. Each trial was started when the rats were placed by the wall gently and they freely explored the hidden platform during 2 min. If the rat reached to platform, the trial was stopped, and they allowed to stay on the platform for 15 sec. On the other hand, if the rat could not reach to platform during 2 min, they were rescued and placed onto the platform gently, and allowed to stay on the platform for 15 sec. The start position was in the order of zone 1, 2, 3 and 4 in each trial day. Twenty-four hours after the day 5 trial, a probe test was performed. In probe test, the platform was removed, and the rats explored freely for 30 sec. Next 24 hours after the probe test, reversal Morris water maze test was performed. In reversal acquisition test, the platform was place in zone 3, which was located on the opposite side of zone 1. After 5 days reversal acquisition trial, the reversal probe test was performed [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The reversal test was performed in the same manner as the acquisition and probe test. The latency to the platform was determined as the mean time of 4 trials at each trial day in acquisition and reversal acquisition test. In probe and reversal probe tests, the latency to target zone, the crossing number and time spent in target zone, and the time spent in zone 1 or 3 which the platform was placed at acquisition or reversal acquisition tests, respectively, were analyzed with SMART 3.0 video tracking system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eSamples were collected on PND 21 and 39. On PND 21, the abdomen was opened after induction of anesthesia with 5% sevoflurane. Fecal samples were collected from the left semicolon for microbiota and organic acid analyses. Following this, the hippocampus for real-time quantitative reverse transcription-polymerase chain reaction (RT-PCR) and quantitative evaluation of histone acetylation was collected. The bilateral hippocampi were stored at \u0026minus;\u0026thinsp;80\u0026deg;C. On PND 39, the rats were decapitated after anesthetic induction using 5% sevoflurane. Phosphate-buffered saline (PBS), followed by 4% paraformaldehyde, was perfused from the apex of the left ventricle and the brains were extracted and postfixed overnight for terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) and immunofluorescent staining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFecal microbiota analysis\u003c/h2\u003e \u003cp\u003eThe fecal sample from PND 21 were used for gut microbiota analysis. The 16S ribosomal RNA sequencing and analysis of fecal samples was used to evaluate the difference of fecal microbiota. The DNA extraction was performed using QIAamp Fast DNA Stool Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s protocol. The 16S ribosomal RNA gene amplicon libraries were prepared following the \u0026ldquo;16S Metagenomic Sequencing Library Preparation Protocol\u0026rdquo; (Illumina, San Diego, CA). Sequencing was performed using an Illumina MiSeq sequencer with 2 x 300 cycle MiSeq Reagent Kit v3 (Illumina). The adapter sequences and low-quality regions were trimmed by Cutadapt and Trimmomatic, respectively. Further data processing was performed using the open-source software pipeline QIIME2 version 2019.4.0. Reads were trimmed of primers and sequence quality control was performed using QIIME2\u0026rsquo;s script. Amplicon Sequence Variants (ASVs) were formed using DADA2 (denoise-paired). The phylogenetic assignment of representative sequences from each ASV was carried out with a 16S reference dataset obtained from Greengenes version 13_8. Summary of taxonomic assignments were plotted as bar charts, and alpha and beta diversities were calculated by QIIME2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFecal organic acid analysis\u003c/h2\u003e \u003cp\u003eThe fecal sample from PND 21 were used for organic acid analysis. The organic acid analysis was performed by high performance liquid chromatography (HPLC). A certain amount of fecal sample was precisely weighted into a bead tube, suspended in extraction solution, and heat-treated (85\u0026deg;C, 15 min). After disrupting with beads, the sample solution was centrifuged (18,400 g, 10 min), and the supernatant was filtered through a membrane filter with a pore size of 0.20 \u0026micro;m to obtain the sample solution. HPLC was performed using Shimadzu organic analysis system (Shimadzu, Kyoto, Japan), as follow conditions: column, Shim-pack Fast-OA, 100*7.8 mm ID; guard-column, Shim-pack Fast-OA, 10*4.0 mm ID; eluent solution, 5 mmol/L p-toluene sulfonic acid; reaction solution, 5 mmol/L p-toluene sulfonic acid; 0.8 mL/min, 50\u0026deg;C. The concentrations of butyrate, lactate, acetate, succinate and propionate were detected using CDD-10Avp.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHistone acetylation quantification\u003c/h2\u003e \u003cp\u003eThe hippocampus on PND 21 was used to evaluate histone acetylation. Histones were extracted using a histone extraction kit (ab113476 Abcam, Cambridge, UK) according to the manufacturer\u0026rsquo;s protocol. Histone H3 and H4 total acetylation was performed using the Histone H3 Total Acetylation Detection Fast Kit (ab115124, abcam) and Histone H4 Total Acetylation Detection Fast Kit (ab115125, abcam), according to the manufacturer\u0026rsquo;s protocol. Absorbance was read on a microplate reader at 450 nm wavelength (Sunrise\u0026trade; reader, Tecan Ltd., M\u0026auml;nnedorf, Switzerland) and histone H3 and H4 acetylation was calculated as follows: (Sample absorbance \u0026ndash; blank absorbance) / (Protein*delta absorbance/ng), according to the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRT-PCR\u003c/h2\u003e \u003cp\u003eThe hippocampal samples from PND 21 were used for rt-PCR. Total RNA was extracted from right hippocampus according to the following methods. The hippocampus was added into 1,000 \u0026micro;L TRIzol RNA isolation reagent (Thermo Fisher Scientific, Waltham, MA), and homogenized. After administration of 200 \u0026micro;L chloroform, samples were centrifuged 12,000 g, 4\u0026deg;C for 15 min. The supernatant was centrifuged 14,000 g, 4\u0026deg;C for 30 min, after administration of 500 \u0026micro;L isopropanol. The precipitation was mixed into 1,000 \u0026micro;L of 75% ethanol. The solution was centrifuged 12,000 g, 4\u0026deg;C for 10 min. After dehydration of the precipitation, 30 \u0026micro;L nuclease free water was added. The concentration of extracted total RNA was measured using Nano Drop Spectrophotometer (Thermo Fisher Scientific). An A260/A280\u0026thinsp;\u0026gt;\u0026thinsp;1.8 and A260/A230\u0026thinsp;\u0026gt;\u0026thinsp;1.8 were considered highly purified RNA. The highly purified RNA sample was reverse transcribed to complementary DNA by miScript II RT Kit (Qiagen) according to the manufacturer\u0026rsquo;s protocol. rt-PCR was conducted using QuantiTect SYBR Green PCR Kits (Qiagen) and the StepOnePlus RT-PCR system (Thermo Fisher Scientific) according to the manufacturer\u0026rsquo;s protocol. The primer of BDNF (Qiagen, QT00375998), Bad-1 (QT00190407, Qiagen), Bax (QT01081752, Qiagen), BCL2L1 (QT01081346, Qiagen), BCL2L11 (QT00193963, Qiagen), Mcl-1 (QT00375564, Qiagen) and GAPDH (QT00199633, Qiagen) were purchased from Qiagen. The delta-delta cycle threshold method was used to quantify the expression level of mRNA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescent staining\u003c/h2\u003e \u003cp\u003eThe brain samples from PND 21 and 39 were used for immunofluorescent staining. The immunofluorescent staining was performed according to our laboratory procedure [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Extracted brains were postfixed with paraformaldehyde overnight and stored in 15% sucrose solution. Fixed brains were cut into 20 \u0026micro;m coronal sections by a cryostat. Each section which has CA1, CA3, and DG were incubated with primary antibody for ionized calcium-binding adapter molecule 1 (Iba-1, 1:500, FUJIFILM, Tokyo, Japan, 011-27991) overnight at 4\u0026deg;C. The sections were incubated with secondary antibodies; 1:500 of Alexa Fluor 594-conjugated secondary antibody (abcam; ab150132) for 2 hours. The counterstain of nuclei was performed with 4\u0026rsquo;,6-diamidino-2-phenyl-indole dihydrochloride solution (1:1000; DOJINDO; Kumamoto, Japan, 342\u0026ndash;07431). The images of bilateral hippocampus were obtained with the BZ9000 fluorescence microscope (Keyence Corp., Osaka, Japan). The percentage of positive areas of Iba-1 in bilateral hippocampus were measured using Image J software (National Institutes of Health, MD) in blind manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTUNEL staining\u003c/h2\u003e \u003cp\u003eThe whole brain sample from PND 39 was used for TUNEL staining. The TUNEL staining was performed by \u003cem\u003eIn situ\u003c/em\u003e Apoptosis Detection Kit (Takara Bio Inc., Shiga, Japan) according to the manufacturer\u0026rsquo;s instruction. After deparaffinization of paraffin-embedded whole brain sample, 5 \u0026micro;m coronal sections of brain, which has cornu ammonis (CA)1, CA3, and dentate gyrus (DG), was washed with distilled water. The sections were treated with proteinase K (10\u0026ndash;20 \u0026micro;g/mL, 15 min) followed by washing with phosphate-buffered saline. 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e aqueous solution for blocking endogenous peroxidase was applied for 5 min, and then the sections were incubated with TdT Enzyme for 90 min at 37\u0026deg;C. Images of bilateral hippocampus were obtained with a BZ9000 fluorescence microscope (Keyence Corp., Osaka, Japan), the percentage of TUNEL-positive cell in the bilateral hippocampus was measured using QuPath v0.4.3 in blind manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSample size was determined using resource equation method in latency to target zone of Morris water maze probe test as a primary outcome of our study in Experiment 2 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. E\u0026thinsp;=\u0026thinsp;10*2 (total number of animals) \u0026ndash; 2 (total number of groups)\u0026thinsp;=\u0026thinsp;18, which indicates that a total of 10 animals was considered as appropriate sample size for comparing between two groups. In experiment 2, sample collection was performed at PND 21 and 39. Therefore, we determined that a total of 20 animals in each group in experiment 2 was appropriate in our study.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using GraphPad Prism10 (GraphPad Software, Boston, MA). In the Morris water maze test, crossing times of the target zone in the probe and reversal probe tests are expressed as median (interquartile range) and analyzed using the Mann\u0026ndash;Whitney U test. Further, latency to the target in the acquisition and reversal acquisition phases is expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean and was analyzed with the two-way repeated analysis of variance (ANOVA) followed by Bonferroni multiple comparisons between the two groups. The other outcomes are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean and were analyzed using an unpaired \u003cem\u003et\u003c/em\u003e-test. In particular, β-diversity was compared using permutational ANOVA. Normality of data distribution was tested using the Shapiro-Wilk test. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAlternation of gut microbiota caused by sevoflurane exposure\u003c/h2\u003e \u003cp\u003eTo elucidate the effect of sevoflurane on gut microbiota, gut microbiota analysis was performed between C and S groups in Experiment 1 (Supplementary Figure. S1a). A total of 3,858,776 high-quality valid sequences were obtained from 14 samples (C group, seven; S group, seven). In the C group, 241,589\u0026thinsp;\u0026plusmn;\u0026thinsp;16,718 sequences were produced per sample. In the SF group, 240,759\u0026thinsp;\u0026plusmn;\u0026thinsp;13,137 sequences were produced per sample. High-quality sequences were assigned to 4,720 operational taxonomic units. Most of the fecal microbiota diversity could be obtained from the current sequencing depth using rarefaction analysis (Supplementary Figure. S1b\u0026ndash;f). In the α-diversity analysis, there were no significant differences between C and S groups (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eg\u0026ndash;j), However, the UniFrac principal coordinate analysis revealed that diversity of fecal microbiota significantly differed between C and S groups in the unweighted and weighted analyses of the β-diversity analysis (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ek\u0026ndash;n). These results indicate that sevoflurane exposure did not change the α-diversity of gut microbiota, but significantly altered the composition of gut microbiota in neonate rats.\u003c/p\u003e \u003cp\u003eTo clarify the detail alternation of gut microbiota caused by sevoflurane exposure in neonate rats, a differential abundance analysis between C and S groups were performed at phylum, class, order, family, genus and species. At the level of phylum, a total of nine bacteria phylum was detected in the feces of C and S groups: \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eVerrucomicrobia\u003c/em\u003e, \u003cem\u003eLentisphaerae\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eDeferribacteres\u003c/em\u003e, \u003cem\u003eTenericutes\u003c/em\u003e and \u003cem\u003eCyanobacteria\u003c/em\u003e. \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e phyla were the major components of gut microbiota in C and S groups (approximately 87%; Supplementary Figure S2a). The relative abundance of \u003cem\u003eBacteroidetes\u003c/em\u003e was significantly higher in S group (Supplementary Figure. S2b). On the other hand, those of \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e were significantly lower in S group (Supplementary Figure S2c\u0026ndash;e).\u003c/p\u003e \u003cp\u003eAt the class level analysis, a total of 15 classes were detected. \u003cem\u003eBacteroidia\u003c/em\u003e and \u003cem\u003eClostridia\u003c/em\u003e were the majority of gut microbiota component (Supplementary Fig.\u0026nbsp;3a). The relative abundances of \u003cem\u003eClostridia\u003c/em\u003e, \u003cem\u003eGammaproteobacteria\u003c/em\u003e, \u003cem\u003eBacilli\u003c/em\u003e, \u003cem\u003eDeltaproteobacteria\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003eCoriobacteriia\u003c/em\u003e were significantly lower in S group. But that of \u003cem\u003eBacteroidia\u003c/em\u003e was only significantly higher in S group (Supplementary Figure S3b\u0026ndash;h).\u003c/p\u003e \u003cp\u003eAt the level of order, a total of 18 orders were identified. \u003cem\u003eBacteroidales\u003c/em\u003e, \u003cem\u003eClostridiales\u003c/em\u003e and \u003cem\u003eEnterobacteriales\u003c/em\u003e were the major components of gut microbiota (Supplementary Figure S4a). The relative abundances of \u003cem\u003eBacteroidales\u003c/em\u003e and \u003cem\u003eSHA-98\u003c/em\u003e were significantly higher in S group. However, those of \u003cem\u003eClostridiales\u003c/em\u003e, \u003cem\u003eEnterobacteriales\u003c/em\u003e, \u003cem\u003eLactobacillales\u003c/em\u003e, \u003cem\u003eDesulfovibrionales\u003c/em\u003e, \u003cem\u003eActinomycetales\u003c/em\u003e, \u003cem\u003eCoriobacteriales\u003c/em\u003e and \u003cem\u003eBacillales\u003c/em\u003e were significantly lower in S group (Supplementary Figure S4b\u0026ndash;j).\u003c/p\u003e \u003cp\u003eAt the family level, a total of 33 families were detected and the relative abundances of the top 20 families was compared (Supplementary Figure S5a). The relative abundances of \u003cem\u003eBacteroidaceae\u003c/em\u003e and \u003cem\u003eDehalobacteriaceae\u003c/em\u003e were significantly higher in S group. On the other hand, those of \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, \u003cem\u003eLactobacillaceae\u003c/em\u003e, \u003cem\u003eRikenellaceae\u003c/em\u003e, \u003cem\u003eOdoribacteraceae\u003c/em\u003e, \u003cem\u003eDesulfovibrionaceae\u003c/em\u003e, \u003cem\u003eMogibacteriaceae\u003c/em\u003e, and \u003cem\u003eMicrococcaceae\u003c/em\u003e were significantly lower in S group (Supplementary Figure S5b\u0026ndash;k).\u003c/p\u003e \u003cp\u003e At the level of genus, a total of 43 genera were detected and the top 20 genera were analyzed in the relative abundance analysis (Supplementary Figure S6a). The relative abundances of \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eRoseburia Coprococcus\u003c/em\u003e, and \u003cem\u003eDehalobacterium\u003c/em\u003e were significantly lower in S group. But those of \u003cem\u003eOscillospira\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eOdoribacter\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e and \u003cem\u003eDorea\u003c/em\u003e were significantly lower in S group (Supplementary Figure S6b\u0026ndash;k).\u003c/p\u003e \u003cp\u003e At the species level, a total of 21 species were identified and the relative abundances of the top 20 species were compared between C and S groups (Supplementary Figure S7a). The relative abundances of \u003cem\u003eRuminococcus callidus\u003c/em\u003e and \u003cem\u003eBlautia producta\u003c/em\u003e were significantly higher in S group. On the other hand, those of \u003cem\u003eRuminococcus flavefaciens\u003c/em\u003e, \u003cem\u003eMorganella morganii\u003c/em\u003e, \u003cem\u003eRuminococcus gauvreauii\u003c/em\u003e and \u003cem\u003eStaphylococcus haemolyticus\u003c/em\u003e were significantly lower in S group (Supplementary Figure S7b\u0026ndash;g).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEffect of FMT on spatial learning ability\u003c/h2\u003e \u003cp\u003eThere was no data exclusion in behavioral tests analysis. In open field test, total distance travelled, mean velocity, resting times, and time spent in central zone did not differ significantly between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-e). Thus, locomotor activity and emotional behaviors were equivalent in the S and SF groups.\u003c/p\u003e \u003cp\u003eAfter open field test, we evaluated their spatial learning ability using Y-maze, Morris water maze, and reversal Morris water maze tests. The Y-maze test revealed no significant differences in total distance, total arm entries, or spontaneous alternations (Supplementary Figure S8a\u0026ndash;c). In the Morris water maze test, latencies to target zone 1 on trial days 1\u0026ndash;5 did not differ significantly between groups (Supplementary Figure S8d, e). In the probe test, spatial learning ability did not differ significantly between S and SF groups (Supplementary Figure S8f\u0026ndash;i). However, in the reversal acquisition phase of the reversal Morris water maze test, the latencies to target zone on days 2 and 5 were significantly shorter in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, g). In the reversal probe test, latency to the target zone was significantly shorter in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh); crossing time of the target zone and time spent in the target zone and zone 3 were significantly longer in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei\u0026ndash;k). These results suggest that the SF group, which received FMT from non-anesthesia-exposed mother rats, had higher spatial learning ability, and that FMT improved spatial learning ability in rats with sevoflurane exposure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFecal microbiota alterations by FMT\u003c/h2\u003e \u003cp\u003eA total of 2,762,218 high-quality valid sequences were obtained from 12 samples (n\u0026thinsp;=\u0026thinsp;6 in each group). In the S group, 228,690\u0026thinsp;\u0026plusmn;\u0026thinsp;7,188 sequences were produced per sample. In the SF group, 231,679\u0026thinsp;\u0026plusmn;\u0026thinsp;6,764 sequences were produced per sample. High-quality sequences were assigned to 3,288 operational taxonomic units. Most of the fecal microbiota diversity could be obtained from the current sequencing depth using rarefaction analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u0026ndash;e). In the α-diversity analysis, although the Shannon index score did not differ significantly between the S and SF groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef), the Chao1, ACE, and Simpson index scores were significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg\u0026ndash;i). Moreover, the UniFrac principal coordinate analysis revealed that diversity of fecal microbiota significantly differed between groups in the unweighted or weighted analyses of the β-diversity analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ej\u0026ndash;m). These results indicate that FMT increased diversity and significantly altered the composition of gut microbiota in sevoflurane-exposed rats.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA differential abundance analysis for each category, including phylum, class, order, family, genus and species was performed to elucidate gut microbiota alterations caused by FMT. Eight bacterial phyla were identified in the feces of S and SF groups: \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eLentisphaerae\u003c/em\u003e, \u003cem\u003eDeferribacteres, Verrucomicrobia\u003c/em\u003e, \u003cem\u003eTenericutes\u003c/em\u003e, and \u003cem\u003eActinobacteria\u003c/em\u003e. Among these, \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e constituted the majority of microbiota (approximately 97%; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The relative abundances of \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eTenericutes\u003c/em\u003e were significantly lower, while those of \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eLentisphaerae\u003c/em\u003e were significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb\u0026ndash;e).\u003c/p\u003e \u003cp\u003eAt the class level, a total of 13 classes was detected and \u003cem\u003eBacteroidia\u003c/em\u003e and \u003cem\u003eClostridia\u003c/em\u003e were the major components of the fecal microbiota in both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). The relative abundances of \u003cem\u003eBacteroidia, Deltaproteobacteria, Betaproteobacteria\u003c/em\u003e, \u003cem\u003eMollicutes\u003c/em\u003e, and \u003cem\u003eActinobacteria\u003c/em\u003e were significantly lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh, j\u0026ndash;m), while that of \u003cem\u003eLentisphaeria\u003c/em\u003e was significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei). Particularly, the relative abundance of \u003cem\u003eClostridia\u003c/em\u003e, a member of the \u003cem\u003eFirmicutes\u003c/em\u003e phylum, was significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the order level, 16 orders were identified and \u003cem\u003eBacteroidales\u003c/em\u003e and \u003cem\u003eClostridiales\u003c/em\u003e accounted for the majority of fecal microbiota (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The relative abundances of \u003cem\u003eBacteroidales\u003c/em\u003e, \u003cem\u003eDesulfovibrionales\u003c/em\u003e, \u003cem\u003eBurkholderiales\u003c/em\u003e, \u003cem\u003eRF39\u003c/em\u003e, and \u003cem\u003eActinomycetales\u003c/em\u003e were significantly lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb,e\u0026ndash;h), while that of \u003cem\u003eVictivallales\u003c/em\u003e was significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Particularly, the relative abundance of \u003cem\u003eClostridiales\u003c/em\u003e, a member of the \u003cem\u003eClostridia\u003c/em\u003e class, \u003cem\u003eFirmicutes\u003c/em\u003e phylum, was significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eAt the family level, a total of 31 families was detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei) and the relative abundances of \u003cem\u003eBacteroidaceae\u003c/em\u003e, \u003cem\u003eS24-7\u003c/em\u003e, \u003cem\u003eDesulfovibrionaceae\u003c/em\u003e, \u003cem\u003eDehalobacteriaceae\u003c/em\u003e, and \u003cem\u003eAlcaligenaceae\u003c/em\u003e were significantly lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej, l, n\u0026ndash;p), while that of \u003cem\u003eVictivallaceae\u003c/em\u003e was significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003em). Particularly, the relative abundance of \u003cem\u003eRuminococcaceae\u003c/em\u003e, a member of the \u003cem\u003eClostridiales\u003c/em\u003e order, \u003cem\u003eClostridia\u003c/em\u003e class, \u003cem\u003eFirmicutes\u003c/em\u003e phylum, was significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ek).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the genus level, 38 genera were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) and the relative abundances of \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eDehalobacterium\u003c/em\u003e, \u003cem\u003eSutterella\u003c/em\u003e, and \u003cem\u003eDesulfovibrio\u003c/em\u003e were significantly lower in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, d\u0026ndash;f). On the other hand, the relative abundance of \u003cem\u003eRuminococcus\u003c/em\u003e, a member of \u003cem\u003eRuminococcaceae\u003c/em\u003e family, \u003cem\u003eClostridiales\u003c/em\u003e order, \u003cem\u003eClostridia\u003c/em\u003e class, \u003cem\u003eFirmicutes\u003c/em\u003e phylum, was significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e In addition, the relative abundances of butyrate-producing bacteria were evaluated at the genus level. According to previous reports, genera of butyrate-producing bacteria include \u003cem\u003eAlistipes\u003c/em\u003e, \u003cem\u003eOdoribacter\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e, \u003cem\u003eAnaerostipes\u003c/em\u003e, \u003cem\u003eCoprococcus\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The total abundance of butyrate-producing bacteria increased after FMT (Supplementary Figure S9a). The relative abundance of \u003cem\u003eClostridium\u003c/em\u003e was significantly higher in the S group than in the SF group (Supplementary Figure S9d). However, the relative abundances of other bacteria, except \u003cem\u003eRuminococcus\u003c/em\u003e, did not differ significantly between groups (Supplementary Figure S9b\u0026ndash;c, e\u0026ndash;h).\u003c/p\u003e \u003cp\u003eA total of 17 species was identified, but classifiable species did not differ significantly between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg). In summary, FMT decreased the relative abundances of \u003cem\u003eBacteroidetes\u003c/em\u003e phylum, \u003cem\u003eBacteroidia\u003c/em\u003e class, \u003cem\u003eBacteroidales\u003c/em\u003e order, \u003cem\u003eBacteroidaceae\u003c/em\u003e and \u003cem\u003eS24-7\u003c/em\u003e family, \u003cem\u003eBacteroides\u003c/em\u003e genus, and increased those of \u003cem\u003eFirmicutes\u003c/em\u003e phylum, \u003cem\u003eClostridia\u003c/em\u003e class, \u003cem\u003eClostridiales\u003c/em\u003e order, \u003cem\u003eRuminococcaceae\u003c/em\u003e family, \u003cem\u003eRuminococcus\u003c/em\u003e genus, which are butyrate-producing bacteria [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These changes in relative abundances of phyla, classes, orders, and families were confirmed using heatmaps (Supplementary Figure S10).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFMT increased butyrate production in feces\u003c/h2\u003e \u003cp\u003eAs FMT increased the number of butyrate-producing bacteria in the gut microbiota, the concentrations of organic acids, including butyrate, lactate, acetate, succinate, and propionate, in feces were measured. Although lactate, acetate, succinate, or propionate concentrations did not differ significantly between the S and SF groups, the butyrate concentration in the feces was significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u0026ndash;e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFMT promoted histone acetylation and mRNA expression of BDNF\u003c/h2\u003e \u003cp\u003eAs butyrate inhibits histone deacetylase, the amounts of histone H3 and H4 total acetylation in hippocampus were quantified, with both being significantly greater in the SF group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef, g). These results indicate that a higher level of butyrate production following FMT induces histone acetylation in the hippocampus. Butyrate promotes BDNF transcription [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]; therefore, BDNF expression was significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eh).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eFMT suppressed neuroinflammation in hippocampus\u003c/h2\u003e \u003cp\u003eBDNF exerts anti-inflammatory effects in the hippocampus by activating PI3K/Akt and inhibiting MyD88/NF-κB signaling [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Expressions of IL-1β and caspase-1 mRNA in the hippocampus were evaluated. Although caspase-1 expression did not differ significantly between the S and SF groups, IL-1β expression was significantly lower in the SF group than in the S group at PND 21 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, b). Microglia can phagocytose apoptotic cells in the inflamed CNS [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Immunofluorescence staining was performed to elucidate microglial involvement in anti-inflammatory effects of FMT. In the hippocampus at PND 21, Iba-1 positive cell count, or positive area did not differ significantly between groups (Supplementary Fig. S11). However, the Iba-1-positive cell and areas in the hippocampus at PND 39 were significantly lower in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec\u0026ndash;e). These results indicate that FMT attenuated the neuroinflammatory effects of sevoflurane in the hippocampus.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eFMT reduced neuronal apoptosis in hippocampus\u003c/h2\u003e \u003cp\u003eAs BDNF exerts anti-apoptotic effects in addition to anti-oxidative effects in neurons [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], mRNA expression of apoptosis-related proteins was evaluated. Apoptosis-promoting proteins include Bad-1, Bax, and BCL2L11 [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. RT-PCR revealed significantly lower mRNA expression levels of these proteins in the SF group compared with the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea\u0026ndash;c). Anti-apoptotic proteins include Mcl-1 and Bcl2L1 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], whose expression levels were significantly higher in the SF group than in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed, e). These results suggest that FMT suppresses expression of apoptosis-inducing proteins and enhances expression of anti-apoptotic proteins in the hippocampus at PND 21.\u003c/p\u003e \u003cp\u003eTUNEL staining was performed to determine histological level of apoptosis [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. TUNEL-positive cells were counted in the bilateral CA1, CA3, and DG regions. The proportions of TUNEL-positive cells in the CA1, CA3, and DG were significantly lower in the SF group than in the S group at PND 39 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ef\u0026ndash;k). These results indicated that FMT suppressed histological apoptosis in the hippocampus.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this animal study, we elucidated that FMT altered gut microbiota, particularly by increasing the \u003cem\u003eFirmicutes\u003c/em\u003e phylum, \u003cem\u003eClostridia\u003c/em\u003e class, \u003cem\u003eClostridiales\u003c/em\u003e order, \u003cem\u003eRuminococcaceae\u003c/em\u003e family, \u003cem\u003eRuminococcus\u003c/em\u003e genus, which produce butyrate, and improved spatial learning memory in DAN rat model. FMT promoted mRNA expression of BDNF and suppressed expression of IL-1β and microglial infiltration in the hippocampus. Moreover, it decreased mRNA expression of apoptosis-inducing proteins and increased mRNA expression of anti-apoptotic proteins, resulting in suppression of neuronal apoptosis (Supplementary Fig.\u0026nbsp;12).\u003c/p\u003e \u003cp\u003eAlthough the mechanism of DAN has not been well elucidated, oxidative stress appears to be a factor. Cheng et al. investigated the effect of vitamin C on DAN, reporting attenuation of both caspase-3 activation and cognitive impairment caused by isoflurane [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Additionally, an in vitro model showed that a water-soluble vitamin E analogue prevents ketamine-induced neuronal death [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Further, several antioxidants, such as carbon monoxide, apocynin, and ubiquinone, improve cognitive dysfunction caused by anesthetics, and oxidative stress is thought to cause neurotoxicity [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Another possible mechanism is anesthetic-induced neuroinflammation. In a rodent model, lidocaine with anti-inflammatory properties attenuated elevated IL-1β levels and cognitive dysfunction caused by isoflurane inhalation. Moreover, mice with IL-1β deficiency did not present isoflurane-induced learning impairment [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. DAN involves FAS signaling, which plays a key role in cell apoptosis by binding to the FAS ligand. Song et al. reported that FAS/FAS ligand-knockout mice exposed to sevoflurane had a higher spatial learning ability [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In the present study, butyrate was increased by FMT. Since butyrate inhibits histone deacetylase and exhibits anti-inflammatory and neuroprotective effects [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], it seems to be a key factor in attenuating learning disabilities. Suberanilohydroxamic acid (SAHA) also inhibited histone deacetylation. In a DAN mice model, SAHA attenuated sevoflurane-induced learning and memory impairments, and showed potential to prevent DAN [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Complex mechanisms, including oxidative stress, neuroinflammation, and epigenetics, may be involved in DAN.\u003c/p\u003e \u003cp\u003eExposure to inhalational anesthetics significantly alters gut microbiota. Serbanescu et al. investigated alterations in gut microbiota caused by isoflurane inhalation. Old mice were exposed to 1.5% isoflurane for 4 h, resulting in reduced diversity of gut microbiota, decreased \u003cem\u003eBacteroidales\u003c/em\u003e and \u003cem\u003eS24-7\u003c/em\u003e family, and increased \u003cem\u003eTernericutes\u003c/em\u003e phylum [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003csup\u003e55\u003c/sup\u003e In juvenile rats, \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eLachnospiraceae\u003c/em\u003e were decreased, but \u003cem\u003eBacteroidetes\u003c/em\u003e was not significantly altered by sevoflurane exposure [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Sevoflurane inhalation increases \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eAlloprevotella\u003c/em\u003e, and \u003cem\u003eAkkermansia\u003c/em\u003e and decreases \u003cem\u003eLactobacillus\u003c/em\u003e 14 days after anesthesia [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, exposure to isoflurane for 4 h increased \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eLachnospiraceae\u003c/em\u003e and decreased \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e, particularly in the \u003cem\u003eRuminococcus\u003c/em\u003e genus of the \u003cem\u003eRuminococcaceae\u003c/em\u003e family. Alterations in gut microbiota diversity 7 days after anesthesia were remarkable [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Changes in gut microbiota due to anesthetics vary across reports; however, these changes may exert various effects on the host. Mice with dysbiosis are highly susceptible to \u003cem\u003eListeria monocytogenes\u003c/em\u003e infection, as this infection is prevented by the gut bacteria \u003cem\u003eClostridiales\u003c/em\u003e [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Reduction in abundance of \u003cem\u003eFirmicutes\u003c/em\u003e is associated with several pathologies, such as chemotherapy-caused gastrointestinal mucositis in non-Hodgkin\u0026rsquo;s lymphoma, major burn injury and Roux-en-Y gastric bypass [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. To clarify the association between specific bacteria and various pathophysiologies, further gut microbiota analyses of various diseases may be necessary.\u003c/p\u003e \u003cp\u003eTwo mechanisms are primarily involved in gut microbiota changes induced by inhalational anesthetics. First, these anesthetics appear to exert antibacterial effects, which were investigated by Mart\u0026iacute;nez-Serrano et al. \u003cem\u003ein vitro\u003c/em\u003e. Sevoflurane and isoflurane showed antibacterial activities against resistant bacteria, such as \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Another mechanism is the indirect effect of gut microbiota, referred to as the brain\u0026ndash;gut\u0026ndash;bacterial axis. The vagus nerve modulates the functional relationship between the brain and the gastrointestinal tract and transmits information affecting endocrine and gastrointestinal peristalsis from the CNS, resulting in alterations in gut microbiota [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eButyrate inhibits histone deacetylase, which promotes histone acetylation and plays a crucial role in neuronal development, differentiation, and survival by accelerating BDNF expression [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. BDNF activates tyrosine kinase receptor B (TrkB) signaling and exerts anti-inflammatory and anti-apoptotic effects in the hippocampus by activating PI3K/Akt and inhibiting MyD88/NF-κB signaling [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Further, it exerts neuroprotective effects in various CNS disease models. In a diabetic neuroischemia model, BDNF treatment improved learning, memory, and hippocampal neurogenesis [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In contrast, sevoflurane inhalation in neonatal rodents increases histone deacetylase 3 and 8 levels and reduces acetylated histone H3 and H4, BDNF, and TrkB expression, resulting in spatial learning disability [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Moreover, butyrate facilitates conversion of microglia from the inflammatory (M1) to anti-inflammatory (M2) phenotype [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Transformation of microglia from M1 to M2 plays an important role in attenuating brain damage and underlies the neuroprotective effects of butyrate [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In the present study, the mechanism of spatial learning ability improvement by FMT was the increase in butyrate-producing bacteria due to FMT, and subsequent production of butyrate. Butyrate facilitated histone H3 and H4 acetylation and BDNF expression, resulting in reduction of hippocampal neuroinflammation and apoptosis.\u003c/p\u003e \u003cp\u003eSeveral possible treatments have been reported for DAN in neonates. Jia et al. showed that intraperitoneal sodium butyrate normalizes sevoflurane-induced BDNF reduction and neurobehavioral abnormalities by facilitating histone acetylation [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Oral administration of the butyrate precursor tributyrin improves scopolamine-induced impairment of spatial memory [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Since inhalational anesthesia-induced spatial learning disability is also caused by hippocampal neuronal damage, tributyrin intervention might provide some protection. Dexmedetomidine, an α-2 adrenergic agonist, also exerts neuroprotective effects. In a traumatic brain injury model, administration of dexmedetomidine after brain injury suppressed the infiltration of monocyte-derived macrophages and improved postoperative neurocognitive disorder [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The effect of dexmedetomidine on DAN was elucidated, and its neuroprotective effect was demonstrated through the miR-330-3p/ULK1 axis regulating hippocampal cell apoptosis [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Although numerous reports of preventive interventions for DAN have been published, no clinically effective treatments have been established.\u003c/p\u003e \u003cp\u003eFMT is an effective treatment for \u003cem\u003eClostridium difficile\u003c/em\u003e infections. The relationship between neurodegenerative diseases and dysbiosis has been probed, and FMT may be a potential treatment for several neurodegenerative diseases. Alzheimer\u0026rsquo;s disease (AD) is the most common neurodegenerative disease, characterized by extracellular deposition of amyloid-β [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. In an AD mouse model, FMT from young donor mice significantly decreased amyloid plaques and improved cognitive ability [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Several clinical studies have confirmed the efficacy of FMT. In patients with mild cognitive impairment and AD, FMT significantly increases \u003cem\u003ePrevotella\u003c/em\u003e and decreases \u003cem\u003eBacteroides\u003c/em\u003e. Further, it significantly alters serum metabolite levels, resulting in improved cognitive function [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Patients with Parkinson\u0026rsquo;s disease can be treated with FMT. Although deposition of Lewy bodies in CNS neurons may cause Parkinson\u0026rsquo;s disease, dysbiosis of gut microbiota contributes to disease progression [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Oral administration of lyophilized FMT improves motor and non-motor symptoms in patients with Parkinson\u0026rsquo;s disease. Microbiota analysis of these patients revealed that FMT increased \u003cem\u003eFirmicutes\u003c/em\u003e phylum and reduced \u003cem\u003eProteobacteria\u003c/em\u003e phylum [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. In children with autism, \u003cem\u003eBifidobacterium\u003c/em\u003e is significantly reduced in gut microbiota [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. However, FMT from healthy individuals increased relative abundance of \u003cem\u003eBifidobacterium\u003c/em\u003e by four-folds, and gastrointestinal symptoms related to autism, such as constipation, diarrhea, and abdominal pain, improved for 8 weeks after FMT treatment [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Thus, FMT can be used to treat CNS diseases, and its therapeutic effect is expected to alleviate DAN in humans.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, we investigated the effects of FMT on sevoflurane-induced learning disabilities in neonatal rats. FMT increased the abundances of \u003cem\u003eFirmicutes\u003c/em\u003e phylum, \u003cem\u003eClostridia\u003c/em\u003e order, \u003cem\u003eClostridiales\u003c/em\u003e class, \u003cem\u003eRuminococcaceae\u003c/em\u003e family, \u003cem\u003eRuminococcus\u003c/em\u003e genus, and butyric acid-producing bacteria. Moreover, it induced an increase in butyrate level in feces, BDNF expression, and anti-apoptotic proteins, and a decrease in expression of IL-1β and apoptosis-inducing proteins. This resulted in suppression of neuronal inflammation, apoptosis in the hippocampus, and improvement in spatial learning ability. Modulation of gut microbiota might be an effective treatment for DAN in neonates; however, further studies should be performed to identify the effective bacterial species and clinical effectiveness of gut microbiota modulation by FMT or probiotics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAttention deficit hyperactivity disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFecal microbiota transplantation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDevelopmental anesthetic neurotoxicity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eshort-chain fatty acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBDNF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrain-derived neurotrophic factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePND\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePostnatal day\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTUNEL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTerminal deoxynucleotidyl transferase dUTP nick end labeling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHPLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh performance liquid chromatography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCornu ammonis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDentate gyrus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentral nervous system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll experimental protocols\u0026nbsp;were approved by the Ethics Committee (approval code: 20-066) for Animals of the Sapporo Medical University School of Medicine.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003e16s rRNA sequencing raw data for fecal microbiota analysis in this study are available in https://www.ncbi.nlm.nih.gov/. The BioProject ID in Sequence Read Archive of NCBI is PRJNA1070816, and submission ID is SUB14180994.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by Yakult Bio-Science Foundation (No. 518) and Grant-in-Aid for Young Scientists from the Japan Society for the Promotion of Science (No. 20K17786).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eTC, ST, NN, NK designed the experiment protocol. TC, YH, SS, and TH obtained the experimental data. TC, YH, ST, NN, NK and YY analyzed and interpret data. TC and YY drafted the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJevtovic-Todorovic V, Hartman RE, Izumi Y, Benshoff ND, Dikranian K, Zorumski CF, et al. Early exposure to common anesthetic agents causes widespread neurodegeneration in the developing rat brain and persistent learning deficits. J Neurosci. 2003;23:876\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCann ME, de Graaff JC, Dorris L, Disma N, Withington D, Bell G, et al. Neurodevelopmental outcome at 5 years of age after general anaesthesia or awake-regional anaesthesia in infancy (GAS): an international, multicentre, randomised, controlled equivalence trial. 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Microbiome. 2017;5:10.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anesthesia-induced developmental neurotoxicity, Butyrate, Fecal microbiota transplantation, Gut microbiota, Gut-brain axis, Histone acetylation, Neuroinflammation, Neuronal apoptosis","lastPublishedDoi":"10.21203/rs.3.rs-3910445/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3910445/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Anesthetic exposure induces neurodegeneration in children. Although this problem has been elucidated in decades, the prophylaxis for developmental anesthetic neurotoxicity (DAN) has not been established. It has been reported that gut microbiota produces various metabolites and influences brain function of host, which has been called as Gut microbiota-Brain axis. We report the effect of fecal microbiota transplantation (FMT) on spatial learning disability caused by DAN in neonatal rats.\u003c/p\u003e\n\u003cp\u003eMethods: In experiment 1, neonatal rats were divided into C (Control) and S (Sevoflurane) groups to elucidate the effect of sevoflurane exposure on gut microbiota composition. In S group, rats were exposed by 2.1% sevoflurane for 2 hours in postnatal day (PND) 7-13. In experiment 2, neonatal rats were divided into S and SF groups. In SF group, neonatal rats were received FMT just after sevoflurane exposure in PND 7-13. The sample of FMT was obtained from non-anesthetized mother rat. Behavioral tests were performed to evaluate spatial learning ability from PND 26-39.\u003c/p\u003e\n\u003cp\u003eResults: Sevoflurane exposure significantly altered the gut microbiota composition. Especially, the relative abundance of \u003cem\u003eBacteroidetes\u003c/em\u003ephylum was significantly increased and that of \u003cem\u003eFirmicutes\u003c/em\u003e phylum was significantly decreased by sevoflurane exposure. The FMT improved spatial learning ability. The microbiota analysis revealed that the α-diversity of gut microbiota was increased by FMT. Particularly, FMT decreased the relative abundances of \u003cem\u003eBacteroidetes \u003c/em\u003ephylum, \u003cem\u003eBacteroidia \u003c/em\u003eclass, \u003cem\u003eBacteroidales \u003c/em\u003eorder, \u003cem\u003eBacteroidaceae \u003c/em\u003efamily, \u003cem\u003eBacteroides \u003c/em\u003egenus. Meanwhile, the relative abundances of \u003cem\u003eFirmicutes\u003c/em\u003e phylum, \u003cem\u003eClostridia\u003c/em\u003e order, \u003cem\u003eClostridiales\u003c/em\u003e class, \u003cem\u003eRuminococcaceae \u003c/em\u003efamily,\u003cem\u003e Ruminococcus \u003c/em\u003egenus, and butyric acid-producing bacteria increased by FMT. Moreover, the FMT increased the fecal concentration of butyrate, and exerted the histone acetylation and the mRNA expression of brain derived neurotrophic factor in hippocampus. Immunofluorescence staining with Iba-1 antibody revealed that microglia infiltration in hippocampus was significantly suppressed by FMT. The mRNA expressions of apoptosis-inducing proteins were significantly suppressed and those of anti-apoptotic proteins were significantly promoted by FMT. The TUNEL staining indicated that neuronal apoptosis in hippocampus was significantly suppressed by FMT.\u003c/p\u003e\n\u003cp\u003eConclusions: FMT improved spatial learning ability in rats with DAN. The modulation of gut microbiota might be an effective prophylaxis for DAN in children.\u003c/p\u003e","manuscriptTitle":"Fecal microbiota transplantation improves spatial learning disability caused by developmental anesthetic neurotoxicity in neonatal rats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-02 16:57:12","doi":"10.21203/rs.3.rs-3910445/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6357217-cd9f-4325-ab9c-48f9ff16c8f5","owner":[],"postedDate":"February 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-08T10:49:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-02 16:57:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3910445","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3910445","identity":"rs-3910445","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}