Early preterm infant microbiome impacts adult learning

Interventions to mitigate long-term neurodevelopmental deficits such as memory and learning impairment in preterm infants are warranted. Manipulation of the gut microbiome affects host behaviors. In this study we determined whether early maturation of the infant microbiome is associated with neurodevelopment outcomes. Germ free mice colonized at birth with human preterm infant microbiomes from infants of advancing post menstrual age (PMA) demonstrated an increase in bacterial diversity and a shift in dominance of taxa mimicking the human preterm microbiome development pattern. These characteristics along with changes in a number of metabolites as the microbiome matured influenced associative learning and memory but not locomotor ability, anxiety-like behaviors, or social interaction in adult mice. As a regulator of learning and memory, brain glial cell-derived neurotrophic factor increased with advancing PMA and was also associated with better performance in associative learning and memory in adult mice. We conclude that maturation of the microbiome in early life of preterm infants primes adult associative memory and learning ability. Our findings suggest a critical window of early intervention to affect maturation of the preterm infant microbiome and ultimately improve neurodevelopmental outcomes.

www.nature.com/scientificreports/ have revealed that different from term infants, GA and postmenstrual age (PMA) are the dominant factors in preterm microbiome assembly; independent of confounders such as mode of delivery, breastfeeding duration and antibiotic exposure 19 , which all play important roles in determining the colonization and assembly of the microbiome in term infants. We therefore hypothesize that initial colonization and microbiota development of the preterm infant microbiome, has distinct effects on the risk of developing deficits later in life.
In this study, we transfaunated fecal samples from human preterm infants born at 27-34 weeks GA to pregnant C57/BL6J germ free (GF) dams in a gnotobiotic environment. This allowed us to investigate brain development under the "isolated" influence of the preterm microbiome at the earliest stages of colonization and assembly. Offspring neurodevelopment was evaluated by behavioral testing at 12 weeks of age (adult) after colonization with the respective microbiome representing different PMA. PMA was defined in this study as GA+ weeks of life of donor samples collected and actually used to colonize the mice. The composition of the respective microbiome and the metabolites in the fecal and serum were analyzed to identify the significant microbial markers influencing brain functions.

Results
Diversity and taxonomic analysis of microbiota. To first confirm the fidelity of the colonization model, fecal samples were collected from dams colonized with human fecal samples from preterm infants with varying PMA (see Table 1 for clinical characteristics) at E20-21 (within a day of delivery), and from pups at 2 and 4 weeks of age for 16S rRNA sequencing analysis. NMDS analysis of fecal samples revealed that there was no distinction in fecal samples based on the time of sampling (indicated by different shapes in Fig. 1a), but there was a differentiation in fecal samples based on the transfaunation group (gr) of the sample (indicated by different colors in Fig. 1a). Gr_1-7 corresponded to animals colonized with fecal samples of patient 1-7 in Table 1. Gr_5 was excluded in the analysis due to missing samples at 2 and 4 weeks of age.
PERMANOVA analysis showed that the dam and her respective 2 and 4 weeks old pups clustered together with no significant difference in beta-diversity within sampling time (dam, 2 weeks pups, 4 weeks pups) when controlling for transfaunation group (p = 0.071). However, PERMANOVA p-value for difference in beta-diversity by transfaunation group was statistically significant for the 2 weeks old pups (p = 0.001), 4 weeks old pups (p = 0.001) and dam (p = 0.001) samples, respectively, demonstrating that there were compositional differences among the transfaunation groups. In particular, PCA loading plot (Fig. 1b) demonstrated that transfaunation Groups_1, 2, 3, and 4 separate towards the leftmost direction and this separation was driven by the presence of Staphylococcus, Streptococcus, and Stenotrophomonas. Notably, groups_1,2.3, and 4 were from infants born less than 30 weeks of GA. Transfaunation Groups 6 and 7 (from infants born greater than 30 weeks of GA) separated in the rightmost direction from the other samples, and the features that largely contributed to the separation in that direction were Enterobacter_D, Bifidobacterium, Lactococcus, Phocaeicola, Parabacteroides, and Escherichia. These data largely agreed with the previously demonstrated succession order of microbiome colonization in preterm infant with class Bacilli (mostly genera Staphylococcus and Streptococcus in our study) dominating between 25-30 weeks of GA, followed by γ-Proteobacteria (genus Enterobacter_D in our study) and later Actinobacteria (genus Bifidobacterium in our study) between GA of 30-35 weeks 5,20 .
We observed significant increases in number of observed species (Richness) as the PMA increased in fecal samples of dams ( Supplementary Fig. S1a, p = 0.02), 2 weeks ( Supplementary Fig. S1b, p = 0.03) and but not 4 weeks old pups ( Supplementary Fig. S1c, p = 0.06). Shannon diversity was positively correlated with PMA ( Supplementary Fig. S1d, p = 0.03) in the fecal samples from the dams, but not 2 weeks old pups (Supplementary Fig. S1e, p = 0.09) and 4 weeks old samples ( Supplementary Fig. S1f, p = 0.06). These data demonstrate that Maturation of early microbiota was associated with better learning and memory but did not affect locomotor activity, anxiety-like behaviors and social interaction. Given that we have previously shown that microbiota affects behaviors 10 , we conducted a series of behavioral testing to evaluate locomotor development, anxiety-like behaviors, social behaviors and associative fear learning and memory. We first tested whether the early microbiota from preterm infants with different PMA could potentially affect adult locomotor activity and anxiety-like behaviors using the open field and elevated-plus maze tests. We did not observe any correlation between the average speed, time in the border area and the center area and PMA (Supplementary Fig. S2a-c) at 12 weeks of age. We also did not detect any correlation between the time spent in closed arm or the time spent in the open arm and PMA in the elevated-plus maze test (Supplementary Fig. S2d and S2e, respectively). We further investigated whether the maturation of microbiota signified by PMA affects social interactions in the three-chamber social test. In the sociability test for social versus empty cage preference, there was no significant correlation between PMA and the ratio of the time pups spent in the chamber with a stranger vs an empty cage ( Supplementary Fig. S3a). In the test for social novelty, PMA also did not affect the ratio of the time pups spent in the chamber with a strange vs a familiar partner ( Supplementary Fig. S3b).
Fear conditioning test was then conducted to assess associative fear learning and memory. During the contextual test trial, we observed that immobile time in the same environment was not correlated with PMA at 12 weeks of age (Fig. 4a). However, in the second half of the cued test trial when the animals were placed in the new environment and the conditioned stimulus (CS: the auditory cue) was given without the unconditioned stimulus (US: the electric foot shock), PMA was significantly positively correlated with the immobile time at 12 weeks of age (Fig. 4b, p < 0.01, Pearson's r 2 = 0.87), indicating a difference in fear learning and memory. (a) Significant separation in the gut microbiome composition (beta-diversity) was observed among different transfaunation groups (gr) by PERMANOVA (color, p = 0.001) and no differences were detected among sample types (shape: 2wpup, 4wpup and dam) while controlling for group (PERMANOVA, p = 0.071). NMDS plot was generated using Aitchison distance metric at genus level. (b) Principal Component Analysis (PCA) biplot showed the variation among the fecal samples based on type and transfaunation groups and the relative abundance of microbial taxa (genera). Arrows represent the strength (through the length) of each taxa to the overall distribution. www.nature.com/scientificreports/ Discrete characteristics of the microbiota and cognitive function. We then attempted to identify the discrete characteristics of the PMA-dependent microbiota maturation that affect the above observed fear learning and memory. The increasing immobile time in the 2nd half of the cued test was significantly correlated with the increased richness of fecal samples from 2 ( Supplementary Fig. S4a) and 4 ( Supplementary Fig. S4b) weeks old pups. The immobile time also increased with the Shannon diversity of fecal samples from 4 weeks old pups ( Supplementary Fig. S4d), but not from 2 weeks old pups ( Supplementary Fig. S4c). Furthermore, at 2 weeks of age, the higher relative abundances of phylum Firmicute_A (Fig. 5a), family Peptostreptococcaceae ( Fig. 5b) and Clostridiaceae (Fig. 5c), genus Clostridioides (Fig. 5d) and Clostridium_P (Fig. 5e) were correlated with higher immobile time in the fear conditioning test. Two PMA-depended families Streptococcaceae (Fig. 6a) and Bifidobacteriaceae (Fig. 6b), and four genera Lactococcus (Fig. 6c), Bifidobacterium (Fig. 6d), Enterobacter_D (Fig. 6e) and Phocaeicola (Fig. 6f) at 4 weeks of age were also positively correlated with the immobile behavior. These data demonstrate that the maturation of early microbiome signified by PMA, alpha diversity, and taxonomic features improved associative fear learning and memory in adult mice.
Alterations in fecal and serum microbial metabolites were associated with microbial maturation-dependent learning and memory. To further identify the underlying mechanisms by which microbiota regulate the cognitive function we observed in the fear conditioning test, we analyzed fecal and serum metabolites from pups at 4 weeks of age colonized with fecal samples from four different PMAs (see Supplementary Table S1). Group_4 and 2 with corresponding 29.6 and 30.6 weeks PMA (see Table 1) were defined as the early group and Group_6 and 7 in Table 1 Table S2 and Table S3) (Fig. 7a). There were also 137 and 13 downregulated (> twofold) fecal and serum metabolites, respectively (Fig. 7b). 31 of the upregulated and four of the downregulated metabolites were present in both fecal and serum sample pools. In the contextual memory test, the immobile time was not associated with increased PMA (a). In the cued fear retention test, the immobile time was significantly increased with increased PMA (b) in the second part of the cued fear conditioning test (different chamber and sound cue presented) by Pearson's correlation coefficient analysis. The coefficient of determination (R 2 ) is also presented when significant correlation was reached at p < 0.05. Animal numbers used in each transfaunation groups were: gr_1 = 36, gr_2 = 8, gr_3 = 14, gr_4 = 6, gr_5 = 9, gr_6 = 11, and gr_7 = 7.  Table S4) demonstrated that out of the 31 upregulated metabolites, the relative abundance levels (measured by area under the curve and presented as ScaledIMP ± SEM) of fecal 5-aminovalerate, phenol sulfate, 2,3-dihydroxy-2-methylbutyrate, ferulate, and ursodeoxycholate and the PMA (weeks) were positively correlated. In addition, PMA (weeks) was also positively correlated with serum nicotinate ribonucleoside and beta-muricholate. Both fecal and serum glutarate (C5-DC), cholate, phenylacetylglycine, ferulic acid 4 sulfate, and 2-oxindole-3-acetate were positively correlated with PMA.
Microbiota-depended associative learning is associated with glial cell line-derived neurotrophic factor (GDNF) expression in the early postnatal brain. To further investigate the molecular mechanisms by which microbiota regulate associative learning and memory in the fear conditioning test, we www.nature.com/scientificreports/ examined the cerebral gene expression of GDNF and brain-derived neurotrophic factor (BDNF) in pups at 2 weeks of age by RT-PCR. The 2 weeks old time point was chosen because both neurotrophic factors play key roles in early development, differentiation, synaptogenesis, and survival of neurons in the neonatal brain as well as neuronal plasticity and cognitive function. We demonstrate here that GDNF (Fig. 8a), but not BDNF (Fig. 8b), mRNA expression levels were associated with the PMA of the fecal samples. Furthermore, the increased GDNF  www.nature.com/scientificreports/ levels were significantly correlated with the immobile time in the second half of the cued fear testing (Fig. 8c). BDNF levels were not associated with the immobile time (Fig. 8d).

Discussion
From 23 to 40 weeks of gestation the human fetal brain gradually reaches critical developmental benchmarks 21 .
After birth, the maturation process continues until 2-3 years of age when the brain reaches 90-95% of adult brain weight and synaptogenesis reaches its peak rate 22 . Preterm infants, especially those born at less than 32 weeks of gestational age and therefore deprived of the in utero developmental period, are at increased risk for adverse neurological outcomes with later cognitive and behavioral deficits 1,23-25 . In meta-analysis studies, poor neurodevelopment exhibited in motor skills, behavior, reading, mathematics and spelling were observed in preterm infants at primary school age 25 . When following up preterm infants to young adulthood (age 19), the impairment of cognitive function of preterm individuals persisted 26 . While many intrinsic and extrinsic factors determine the trajectory of brain development, studies have suggested that targeting the microbiome early in life might alter the early programming of the brain 9,27-29 . These observations revealed to us that preterm infants might be at specific risk for neurodevelopment deficits due to an initial immature microbiome associated with preterm birth. In this study, we specifically targeted the earliest colonization period by transfaunating pregnant GF mice with preterm infant fecal samples of different PMA and then studied the offspring's adult behavior. To our knowledge the present study is the first to demonstrate that in GF mice transfaunated with a human preterm microbiome, and with early development of microbiota defined by PMA and with bacterial composition mimicking the preterm microbiota development, the microbiome primed a difference in associative learning in adult mice.
The strength of our model is to isolate the effect of microbiota at the earliest stage on neurodevelopment outcomes later in life. Our GF animals live in isolators in a gnotobiotic facility and are exposed to the same environment with one exception: colonization of different microbiota from human preterm infants at E16. The pups were reared naturally and stayed with the dams until weaning. The pups continued to live in the respective isolators in separated cages until the time points at which various experiments were assigned. We showed the fidelity of this colonization model by demonstrating that the microbiota of the pups at 2 and 4 weeks clusters together with the respective dams. Therefore, this model allowed us to investigate the direct impact of different microbiomes from preterm infants born at different GA and sampled at different PMA on adult behaviors.
PMA as the only significant and dominant factor in multifactorial analysis including delivery mode, feeding type, and antibiotic exposure has been previously shown to contribute significantly to the increase of species richness in preterm infants born between 24 and 30 weeks of GA 30 . Along this line of evidence, our current study demonstrated that colonizing mice at the earliest time point (around birth) with the fecal samples of human preterm infants at different PMA recapitulated the age-depended microbiota maturation phenotype in the mice. PMA of preterm donors was significantly positively correlated with the measurement of offspring fecal alphadiversity including richness at 2 weeks of age.
We further identified several key PMA-depended taxonomic characteristics. First, PCA analysis demonstrated that Staphylococcus, Streptococcus, and Stenotrophomonas in < 31 weeks PMA groups contributed the separation from the > 31 weeks of PMA groups which featured Enterobacter_D, Bifidobacterium, Lactococcus, Phocaeicola, Parabacteroides, and Escherichia. These data agree with the current literature that the microbial community in preterm infants changes from dominance of Staphylococcus between PMA 25 and 30 weeks, Enterococcus from 30 to 35 weeks, and Enterobacter at 35 weeks. Bifidobacterium dominance starts to develop gradually only after 30 weeks PMA 5,20,31,32 . Secondly, there was an overall microbial taxonomic diversity (beta-diversity) difference among the different PMA groups in fecal samples from both 2 and 4 weeks of age. The observed differences in diversity were due to the increased abundance of Clostridiaceae and Clostridium_P in the later GA and PMA transfaunation groups in the 2 weeks fecal samples in contrast to the Bacteroides, Phocaeicola, Bifidobacterium, Lactococcus, Enterobacter_D in 4 weeks old fecal samples.
More importantly, we report here the first evidence that early maturation of microbiota, highlighted by bacterial diversity and dominance of the above mentioned key taxa led to improved performance in the cued fear conditioning test later in adult life. A previous study has shown that age (state of the maturation)-discriminatory taxa in healthy and stunted infants had direct impact on growth phenotypes 33 . We now show that increased PMA of fecal samples from preterm infants and PMA-depended diversity in early fecal samples of colonized mice projected better fear learning and memory in adult mice. We further demonstrate that not only the compositional differences, but also the abundance difference of early microbial taxa at the genus level, such as Clostridioides and Clostridium_P at 2 weeks of age and Phocaeicola, Bifidobacterium, Lactococcus, Enterobacter_D at 4 weeks of age, had long lasting effects in our study. The specific taxa identified in this study that were associated with fear retention could be the targets for future studies to investigate their contribution as single species or a consortia of species to improve neurological outcomes.
A few recent studies have suggested a role for the gut microbiota on fear retention and extinction using GF, antibiotic-treated specific pathogen free (SPF) and ex-GF in which a bacterial community was introduced to the GF mice at various ages 10,28,34 . GF mice exhibited reduced freezing during the cued memory retention test and the impaired function was related to a unique baseline transcriptome in the GF amygdala 34 . Our previous study demonstrated a reduced fear retention in GF mice when compared to SPF at 12 weeks of age 10 . Using several MRI methods, we documented that commensal microbiota regulate contextual memory and fear retention through white matter organization and myelination of several gray matter structures including the neocortex, hippocampus, brainstem and major white matter tracts including the corpus callosum, anterior commissure and internal capsule. One intriguing and perhaps more revealing study demonstrated that extinction learning deficits in fear conditioning tests were not reversible in GF mice after weaning when GF mice were colonized at either adult or weaning age 28  www.nature.com/scientificreports/ to SPF surrogate mothers, their fear extinction deficit was restored, indicating there is a critical developmental period before weaning during which microbiota can impact fear extinction learning and learning-related plasticity. With the previous studies comparing mice with or without commensal bacteria, our current study provides the first evidence that PMA-dependent early microbiome development has direct impact on adult fear behavior. The age-dependent shift in the gut microbiota profile also shaped the metabolome in the offspring serum and fecal samples. Changes in a number of metabolites as the gut microbiota matured were associated with differences in fear learning and memory in our study. We identified 5-aminovalerate, phenol sulfate, 2,3-dihydroxy-2-methylbutyrate, ferulate, and ursodeoxycholate in fecal samples; nicotinate ribonucleoside, cholate, beta-muricholate, ferulic acid 4 sulfate, 2-oxindole-3-acetate, and 4-methylcatechol sulfate in serum samples; and phenylacetylglycine and glutarate (C5-DC) in both pools were associated with the altered fear retention.
Several of the metabolites we identified have been shown to be associated with brain function. Compared to SPF with normal fetal thalamocortical axonogenesis, 5-aminovalerate was significantly decreased in E14.5 fetal brains from embryos of GF and antibiotic-treated SPF dams 29 . In a study where fear extinction deficit was restored when GF pups were fostered to SPF surrogate mothers, phenyl sulfate levels in serum and fecal samples were also restored in GF pups 28 . Ferulate (ferulic acid) significantly repaired the spatial cognitive and memory deficits induced by ischemia in rats by reduced neuronal apoptosis and oxidative stress 35 . Recently, a large number of metabolites including the 5-aminovalerate, phenol sulfate and phenylacetylglycine identified in our study were profiled in postnatal brains 36 , suggesting that these microbial-derived metabolites might cross the blood-brain barrier to have direct effects on postnatal brain development. The role of most of these metabolites in regulating fear learning and memory are largely unknown. Future studies are warranted to investigate the age-associated shifts of microbiota metabolites on specific pathways related to associative learning and memory.
At the molecular level, both GDNF and BDNF play important roles in regulating learning and memory [37][38][39][40] . A reduction in endogenous GDNF levels in GDNF heterozygous mice has been associated with impaired spatial memory and learning 39 . Intracerebroventricular injection of GDNF attenuated anesthesia-surgery-induced fear learning and memory impairment in rats 40 . We observed that increased transcriptional levels of GDNF were associated with the PMA of the fecal microbiota and with better performance on the fear conditioning test, suggesting a potential early signaling interaction between microbiota maturation and GDNF in the brain. GDNF specifically promotes the development and survival of midbrain dopaminergic neurons 41,42 , motor neurons 43 , central noradrenergic neurons 44 , and cerebellar Purkinje neurons 45 via its canonical GDNF family receptor α1(GFRα1)/RET receptor complex 46 , or an alternative neural adhesion molecule NCAM signaling pathways 47 . It would be of significant interest to investigate whether microbiota-mediated GDNF could specifically affect certain population of neurons and delineate the signaling pathways involved in future studies.
One limitation of our study is the common challenge in gut microbiome and brain development research of translating animal study results to humans. We isolated the mice in a gnotobiotic environment while the development of human infant microbiome will be shaped by many factors. However, our results demonstrated that the "degree" of early maturation of microbiome, simply signified by PMA, can predict behavioral outcome. This strengthens the notion that early microbiome development is critical for the neurodevelopment. However, the key taxa and metabolites identified that were related to improved associative learning ability will need to be tested directly in colonization or treatment experiments to establish causation. Transfaunating each experimental group with one human fecal sample and n ≥ 3 transfaunated animals in each PMA group are other limitations of this study. Although the number of donors is limited in this study, the study focus was directed at isolating the microbiome effect on long tern neurodevelopment based on their PMA and highlighting the importance of early intervention in the preterm population.
In conclusion, we have demonstrated that PMA-dependent maturation of the early gut microbiota affects associative learning and memory. Preterm infants born with gut microbiota immaturity might be at specific risk for impaired associative learning ability. The microbial characteristics including taxa and microbial metabolites identified in this study provide potential interventional targets to improve maturation of the microbiota in preterm infants and ultimately improve long term neurological outcomes.

Methods
Clinical study design. Patients were enrolled from The University of Chicago Comer Children's Hospital, a level IV NICU in Chicago, Illinois, from 2015-2018. The study was approved by the Institutional Review Board. All methods were performed in accordance with the relevant guidelines and regulations. Informed consent for the study was obtained from infant parent(s). All infants enrolled were delivered between 27 and 34 + 1/7 weeks gestation and had no congenital anomalies (see Table 1). Patients with seizures were not included in the study shown at the last line in Table 1.

Preparation of human fecal samples for transfaunation to GF mice. Transfaunation protocols
were carried out as previously described 48 . Aliquots of frozen fecal samples from patients (100 mg per patient) in each PMA group prior to 2 weeks of life were resuspended under anaerobic conditions in 5 mL phosphatebuffered solution (PBS). Homogenates were clarified by 100 μM pore-diameter nylon filters (BD Falcon) before being stored in PBS containing glycerol (final concentration 15% v/v) at − 80 °C in 600 μL aliquots. To initiate microbial colonization, timed-pregnant GF eight to 9-week old mice (estimated between E15-17) were gavaged with 0.25 mL aliquots of fecal supernatant from each PMA group (n > 3 dams per group). Pups were delivered spontaneously and litters remained with the mother until weaning.
Animals. All animal studies were approved by the Institutional Animal Care and Use Committee of the University of Chicago and Northshore University HealthSystem (NorthShore). GF C57/BL6J mice were main-Scientific Reports | (2022) 12:3310 | https://doi.org/10.1038/s41598-022-07245-w www.nature.com/scientificreports/ tained in the gnotobiotic facility of the Digestive Disease Research Core Center at the University of Chicago. All groups of mice were allowed ad-libitum access to NIH-31 GF chow and water. Mice were tested for behaviors at 12 weeks of age. Animals were transported to NorthShore in sterile containers 3-5 days before testing to allow an acclimation period. Upon arrival to NorthShore mice were housed in individually ventilated cages with HEPA filter until the behavioral testing began. Separate cohorts of pups were sacrificed at 2 weeks of age for the experiments listed below. All methods were performed in accordance with the relevant guidelines and regulations. The reporting in the manuscript follows the recommendations in the ARRIVE guidelines.
Sample collection. Mice were euthanized under isoflurane followed by cardiac puncture for blood collection. Blood samples were allowed to clot for 15 min and spun down at 2000×g for 10 min. Supernatants were collected as sera and stored at − 80 °C for metabolomics analysis. Fecal samples of the pups were collected from the colons and stored at − 80 °C for sequencing analysis. Fecal samples from the dams were collected at the gnotobiotic facility. Brains of the pups were removed from the skulls and cerebrums were snap frozen on dry ice and stored at − 80 °C for molecular analysis.
Microbial data analysis. Numbers of the fecal samples used in the following analysis were presented in Supplementary  Supplementary Fig. S8). A subset of the samples was sent to both facilities to ensure even study group distribution and control for differences in background contamination; microbial genera that were only present in one of the sequencing runs through comparison of these samples and were not present at a threshold of ≥ 0.1% composition in any of the other samples in the sequencing run were removed. This procedure was found to be satisfactory as equivalent samples within this subset clustered together irrespective of sequencing run. Sequencing data has been submitted to NCBI SRA: BioProject ID PRJNA767601. Data processing was conducted using R statistical software version 3.6.2 and the R package DADA2 version 1.14.1 pipeline 49 . The GTDB database version 95 was used for sequence classification. After merging and decontaminating the sequencing runs as above, α-diversity metrics of richness and Shannon diversity were calculated using R package iNEXT version 2.0.20, and for β-diversity analysis, data were center-log ratio (CLR) transformed via R package ALDEx2 version 1.18.0 while taking the median of the Monte-Carlo instances as the value, which allows standard statistical testing to be applied to the inherently compositional data 50 . Principle component analysis (PCA) was completed using R package ropls version 1.18.8 at the genus level. Non-metric multidimensional scaling (NMDS) and the PERMANOVA statistical calculation were applied to the Aitchison distance matrix (Euclidean distance matrix of CLR transformed data) computed from the genus level data by R package vegan version 2.5.7. Statistically significant differences by gestational age and postmenstrual age between alpha diversity metrics and individual taxon abundances at the phylum, family and genus levels were determined from constructing linear mixed models with transfaunation group as a random effect by R package lme4 version 1.1.26 and p values derived from Satterthwaite's method by R package lmerTest version 3.1.3. To correct for multiple testing, the Benjamini-Hochberg method was utilized to evaluate which tests passed a false-positive threshold of < 1%. The effect size reported was the marginal coefficient of determination for generalized mixedeffect models (variance explained by fixed effects) calculated from R package MuMIn version 1.43.17. All plots were generated by R package ggplot2 version 3.3.3.
Behavioral studies. Behavioral testing was conducted as previously described 10 . Animal movements were recorded and processed with ANY-maze software (Stoelting Co., Wood Dale, IL).  Social interaction test. Two social behaviors (social interaction and social memory/ novelty recognition) were quantified using a rectangular 3-chamber test that included a 20 × 45 × 30 cm middle chamber made of acrylic plastic, with two 10 × 10 cm openings leading to two separate (left and right) chambers of the same size, each containing a steel cage enclosure. Each mouse (experimental subject) was placed in the middle chamber and allowed to explore for 10 min, with the right chamber empty but an unfamiliar congener (Stranger I) (nonlittermate control SPF mouse of the same gender, housed in a separate container) held in the steel cage enclosure in the left chamber. Social interaction was determined by measuring the time spent by the experimental subject around the cage holding the unfamiliar congener versus the right empty steel cage. To measure social memory www.nature.com/scientificreports/ (or novelty recognition), a new novel stimulus mouse (Stranger II) was subsequently placed in the previously empty right steel cage. The tested mouse was allowed to explore and interact for 10 min. The same parameters as above were measured to determine the preference of the experimental subject for Stranger I (Familiar) or Stranger II. The social chamber was wiped with 70% alcohol after each test.
Contextual and cued fear conditioning test. The contextual and cued fear conditioning tests the ability of mice to learn and remember an association between environmental cues and aversive experiences. In this test, mice were placed into a conditioning chamber and were given parings of a CS (an auditory cue) and an aversive US (an electric foot shock). The conditioning chamber consisted of opaque acrylic plastic 30 × 30 × 21 cm walls and a shocking grid on the floor. During the conditioning stage at day 1, mice were allowed to freely explore the chamber for 120 s. Thereafter, a white 55 dB noise auditory cue was presented as a CS for 30 s, and a 0.8 mA foot shock was given to the mice as an US continuously during the last 2 s of the sound. The presentation of CS-US was repeated three times per session (120, 240, and 360 s after the beginning of the conditioning). Following the final foot shock, the mice were left undisturbed in the chambers for 90 s. After the conditioning session had been completed, the mice were returned to the same conditioning chamber 24 h later and scored for freezing behavior to measure contextually conditioned fear (context test). The mice were placed in the conditioning chamber and were allowed to freely explore the chamber for 300 s without CS and US presentations. The cued test was conducted on the same day 2 h after the context test. In this test, the shocking grid was removed and the walls of the chamber were covered with checkerboard pattern wallpaper, providing a novel context that was unrelated to the conditioning chamber. Mice were placed into the testing chamber for 3 min. At the end of the first 3 min, the CS auditory cue that had been presented at the time of conditioning was given to mice for 3 min. The fear conditioning chamber was wiped with 70% alcohol after each test. Fear memory was assessed based on freezing behavior to the conditioned cued or the contexts to which mice were previously exposed. The outcome variables were immobile time in the context test and during the first and last 30 s of the cued test.

Metabolomics analysis. All samples were prepared and analyzed using an Ultrahigh Performance Liquid
Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS) platform (Metabolon, Durham, NC). This platform utilized a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution.
Raw data was extracted, peak-identified and QC processed using Metabolon's hardware and software. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. Metabolon maintains a library based on authenticated standards that contains the retention time/index (RI), mass to charge ratio (m/z), and chromatographic data (including MS/MS spectral data) on all molecules present in the library. Furthermore, biochemical identifications are based on three criteria: retention index within a narrow RI window of the proposed identification, accurate mass match to the library ± 10 ppm, and the MS/MS forward and reverse scores between the experimental data and authentic standards. The MS/MS scores are based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum. Data were presented as scaled imputed log-transformed (ScaledIMP) ± S.E.M and used for statistical analysis.

RNA isolation and real-time PCR.
Total RNA from snap frozen brains were isolated using the RNeasy ® Plus Mini Kit (QIAGEN GmbH, Hilden, Germany). 500 ng of isolated total RNA was used to synthesize cDNA using RT 2 First Strand Kit from QIAGEN. TaqMan probes and primers (Thermo Scientific) were used for gene of interests and the housekeeping gene Gapdh. Gene expression was normalized to the housekeeping gene and expressed as relative expression of experimental controls. Statistical analyses. Linear regression was used to generate fit curve in the offspring with values presented as the mean ± SEM. Benjamini-Hochberg procedure was used to correct for multiple comparisons in metabolite data. GraphPad Prism 6 software (GraphPad Software, Inc., La Jolla, CA, USA) was used and a p value ≤ 0.05 was considered significant.
Ethics approval and consent to participate. Institutional Review Board approval (#16-1431) at the University of Chicago was obtained for the study and all participants were provided written informed consent. All animal procedures were approved by the Institutional Animal Care and Use Committees of the University of Chicago (No. 71703) and Northshore University HealthSystem (No. EH16-264) and performed strictly in accordance with approved Animal Care and Use Protocols (ACUPs) by the U.S. National Institutes of Health.