Dietary prebiotics alter novel microbial dependent fecal metabolites that improve sleep

Dietary prebiotics produce favorable changes in the commensal gut microbiome and reduce host vulnerability to stress-induced disruptions in complex behaviors such as sleep. The mechanisms for how prebiotics modulate stress physiology remain unclear; however, emerging evidence suggests that gut microbes and their metabolites may play a role. This study tested if stress and/or dietary prebiotics (Test diet) alter the fecal metabolome; and explored if these changes were related to sleep and/or gut microbial alpha diversity. Male F344 rats on either Test or Control diet were instrumented for electroencephalography biotelemetry measures of sleep/wake. After 5 weeks on diet, rats were either stressed or remained in home cages. Based on untargeted mass spectrometry and 16S rRNA gene sequencing, both stress and Test diet altered the fecal metabolome/microbiome. In addition, Test diet prevented the stress-induced reduction in microbial alpha diversity based on PD_Whole_Tree, which has been previously published. Network propagation analysis revealed that stress increased members of the neuroactive steroidal pregnane molecular family; and that Test diet reduced this effect. We also discovered links between sleep, alpha diversity, and pyrimidine, secondary bile acid, and neuroactive glucocorticoid/pregnanolone-type steroidal metabolites. These results reveal novel microbial-dependent metabolites that may modulate stress physiology and sleep.

test diet and stress significantly alter specific ions and metabolites. Volcano plot analysis (graphic shown in Supplemental Fig. 1) of the fecal metabolome on PND 70 revealed that 21 features were significantly impacted by diet (statistical results shown in Supplemental Table 2). All features except one were significantly higher in the Test diet when compared to the control diet. We identified 10 of these significant metabolites using CSIfinger ID achieving metabolomics standards initiative (MSI) level 3 annotation (Fig. 4a-j; Table 1). The following metabolites were significantly increased by Test diet: Glycerol Glucoside Derivative (Fig. 4a), Ethanebis(thioate) Derivative (Fig. 4b), Disaccharide (Fig. 4d), Pyrimidine Nucleotide 1 (Fig. 4e), Aminoglycoside Analog (Fig. 4f ), Disaccharide Derivative (Fig. 4g), Aminonucleoside Analog (Fig. 4h),  Heat map of the top 50 features that cluster (unsupervised) by diet and stressor exposure measured in fecal samples collected on PND 91. The features within the control diet clustered mostly by whether they were exposed to stress or not, suggesting a potential effect of stress exposure (upper right half of heat map). This effect of clustering by stress was absent in the Test diet group (upper left half of heat map). Overall, the features mostly clustered by diet, rather than stress, which is consistent with the PC plot. Individual subjects are along the bottom and features [mass/charge (m/z) retention times (min)] are listed to the right of the heat map.

Relationships between identified metabolites, sleep architecture, and microbiome alpha diversity.
We previously reported that rats fed Test diet had an increase in NREM sleep bout durations, when compared to those fed control diet [ Fig. 4B,D from 7 ]. Potential relationships between the observed NREM sleep changes at PND 71,72 from our previous findings and the identified fecal metabolome at PND 70 ( Fig. 4) were explored using stepwise multiple regression analysis. We performed a regression analysis of all of the identified metabolites as well as bacterial phylum with NREM sleep to see if there were any relationships between NREM sleep and any of these variables at PND 70. This analysis revealed a significant linear relationship between Pyrimidine Nucleotide (C 16 H 25 N 2 O 14 P) depicted in Fig. 4i and NREM sleep (F (1, 28) = 8.939; p = 0.006; adj. r 2 = 0.249; y = 10.013x + 415.225; Fig. 7a).
We also previously published that rats eating Test diet had increased REM sleep rebound following acute stress exposure [ Fig. 5 from 7 ]. It may be possible that a relationship exists between the increased REM sleep rebound after stress exposure and the gut metabolome. Similarly, we performed a regression analysis of all of the identified metabolites as well as bacterial phylum with REM sleep to see if there were any relationships between REM sleep and any of these variables at PND 91. Indeed, stepwise multiple regression revealed that two identified features at PND 91 were related to REM sleep after stress at PND 87 (F (2, 27) = 5.736; p = 0.009; adj. r 2 = 0.260; y = 0.907x 1 + 0.467x 2 + 26.021; Fig. 7b). The first feature x 1 is the stress responsive Ketone Steroid (Fig. 5k) and the second x 2 is Ethanebis(thioate) Derivative (Fig. 5b).  7 ] and thus we examined potential relationships between the identified gut metabolites and alpha diversity. We performed a separate regression analysis between all of the identified metabolites as well as bacterial phylum with alpha diversity to examine any relationships between these variables at PND 91. There was a significant relationship between two identified metabolites at PND 91 and alpha diversity (F (2, 27) = 27.092; p < 0.001; adj. r 2 = 0.659; y = 12.895x 1 − 0.727x 2 + 36.027; Fig. 7c). The two metabolites are x 1 = Hyodeoxycholic acid and x 2 = Alloprenanolone Precursor (yellow in Fig. 6; Fig. 5j).

Discussion
Ingestion of a prebiotic diet (Test diet) improves undisturbed non-rapid eye movement (NREM) sleep, promotes REM sleep rebound after stress exposure, and prevents stress-induced reductions in gut microbial alpha diversity 7 . The results from the current report demonstrate that Test diet also modulates the fecal metabolome community, increases specific metabolites (fatty acids, sugars, steroids, nucleotides) several of which are consistently elevated across time, and prebiotic-induced sleep improvements are related to several fecal metabolites. This report along with prior studies 7,9,19,20 suggests that fecal metabolites are an important effector arm of the microbiota-gut-brain axis and adds to emerging evidence linking metabolomics and sleep physiology 21 .
Stressor exposure affected the fecal metabolome differently in the control vs. the Test diet. Test diet attenuated the stress-induced increase in the Allopregnanolone Precursor, the Ketone Steroid, as well as two other unidentified metabolite features. These small fecal metabolites belong to a family of endogenous metabolites of corticosterone/progesterone. Network propagation analysis 18 revealed several other potential metabolites in the family. One such metabolite is 5.alpha.-Pregnane-3.alpha., 11.beta., 21-triol-20-one, which has been reported to block voltage-dependent calcium channels 22 and another is 5.alpha.-Pregnane-3.alpha., 21-diol-20-one, better known as allotetrahydrodeoxycorticosterone, which is also a neuroactive steroid that potentiates GABAergic inhibition 23 . This metabolite has also recently been linked with reduced sleep quality during pregnancy 24 and is involved in the acute stress response in rodents [25][26][27] . These data suggest that stress may affect neuroactive steroid signaling in the gut, which is attenuated by a prebiotic diet. It could be that the negative consequences of stress exposure, in part, are mitigated through gut microbial modulatory substrates such as dietary prebiotics. This idea is congruent with a role for dysregulated neuroactive steroid signaling in stress-related psychiatric disorders 28 . It is important to note that although fecal microbiome/metabolome likely reflects intestinal composition, there are examples in the literature that challenge this idea 29,30 .
We discovered several novel fecal metabolites that were related to measures of sleep suggesting a potential link between the fecal metabolome and sleep physiology. Specifically, improved NREM sleep was related to a Pyrimidine Nucleotide at PND 70. This relationship is consistent with prior literature examining a role for pyrimidine metabolism in sleep 31 . CSIfinger ID gives the molecular formula for this Pyrimidine Nucleotide as C 16 H 25 N 2 O 14 P, but no known reference standard is yet available for this byproduct of pyrimidine metabolism, however based on the proposed molecular formula it may be involved in uracil metabolism, a metabolite known to increase NREM sleep 32,33 . Similarly, the previously reported enhanced REM sleep rebound following stress 7 was significantly related to the stress-responsive Ketone Steroid (C 21 H 32 O 2 ) + Ethanebis(thioate) Derivative (C 10 H 16 N 2 O 4 S 2 ). Based on the molecular formula for the Ketone Steroid this molecule is a steroid derivative with a pregnane skeleton, however we verified using a standard that this molecule is not pregnenolone, per se, rather likely related to pregnenolone. Given that this class of molecules has been implicated in the regulation of sleep physiology 34 ; it is possible that the Ketone Steroid detected in this study is a novel sleep modulatory fecal metabolite. The second factor in the relationship with REM sleep was an Ethanebis(thioate) Derivative. Based on the molecular formula this gut metabolite is still unannotated and represents a novel potential molecule potentially www.nature.com/scientificreports www.nature.com/scientificreports/ involved in sleep. Although the mechanisms for how changes in fecal metabolites impact complex brain functions such as sleep remain unknown, these data reveal novel relationships between fecal pyrimidine metabolism, stress responsive fecal neuroactive steroids, and sleep physiology. Previous studies have linked both plasma/urine     www.nature.com/scientificreports www.nature.com/scientificreports/ metabolites with sleep physiology [35][36][37][38] , but to the best of our knowledge this is the first study to relate changes in fecal metabolome produced after ingestion of gut microbial modulatory substrates with sleep physiology.
The gut metabolome and gut microbiome are linked through gut microbial metabolism. Our findings help clarify this relationship, such that microbiome alpha diversity was related to a fecal hyodeoxycholic acid and the Allopregnanolone Precursor. Hyodeoxycholic acid is a secondary bile acid that is dependent on the gut microbiota and impacts health and disease [39][40][41] . It is possible, therefore, that some of the health benefits often associated with elevated gut microbial alpha diversity 7,42,43 are due to modulation of secondary bile acids. This idea is further supported by our data demonstrating that a Fatty Acid Derivative is modulated by both stress and Test diet. The second metabolite in the regression is the Allopregnanolone Precursor, which belongs to a molecular class of neuroactive steroids acting at GABA receptors 25 . The relationship reported here between alpha diversity, secondary bile acids, and a neuroactive gut metabolite strengthens the idea that the fecal metabolome is linked through gut microbial metabolism and taken together with other results suggests that these play a role in modulating stress and sleep physiology. This work represents an important step towards uncovering the potential mechanisms underlying health promoting gut microbial modulating substrates. There remain, however, many currently unidentifiable features that were modulated by prebiotic and/or stress. With continued improvements in available reference samples 44 , future work will expand the identification of potential gut derived biomolecular pathways and could facilitate the discovery of additional novel molecules capable of impacting physiology and complex behavior.

Materials and Methods
Animals. Adult male F344 rats (n = 52, Harlan Laboratories) were housed with controlled temperature and humidity and all procedures were approved by the University of Colorado Animal Care and Use as previously described in detail 7 . Briefly, animals weighed 40-50 g upon arrival at post-natal day (PND) 24 and were maintained on a 12:12 h light/dark cycle. All rats were housed in Nalgene Plexiglas cages and were placed on control or Test diet (ad libitum) upon arrival at PND 24. Rats were allowed to acclimate to housing conditions for 1 week prior to study initiation. As previously described 7 , upon arrival rats were double housed due to the young age. After biotelemetry implantation, rats were single housed in order to acquire accurate telemetry signals from each rat. All fecal samples were collected from individually housed rodents for fecal metabolomics analysis. No differences in body weight for food consumption were found and there were 14-15 animals per group (diet) at PND day 70 data and 7-8 animals per group (diet x stress) for the PND 91 data (Fig. 1a). experimental design. The experimental design is depicted in Fig. 1 and adapted from Thompson et al., 2017. Fecal samples used for gut metabolomics analysis were collected at the same time points as those used for gut microbiome analyses previously reported 7 . Animals consumed diet for 7 weeks prior to the first fecal sample collection at PND 70 and then for 11 weeks. Animals were then exposed to inescapable tail shock stress or not, and four days following inescapable tail shock stress another fecal sample was taken at PND 91 (Fig. 1a). . GOS is a non-absorbable complex carbohydrate derived from the enzymatic breakdown of lactose; and PDX is a processed polymer derived from glucose and classified as a soluble fiber by the US Food and Drug Administration. Both GOS and PDX are classified as prebiotic substrates because they are (1) not hydrolyzed or absorbed in the stomach or small intestine; (2) selective substrates for beneficial commensal bacteria in the colon, such as Lactobacillus spp.; and (3) induce beneficial luminal/systemic effects within the host [45][46][47][48][49] . LAC impacts the gut microbiota through microbiostatic and antimicrobial activity 50,51 , and MFGM alters antimicrobial activity 52 as well as the microbiota 53,54 . Animals were fed either control or Test diet and experimenters were blind to diet type. The diets were formulated by Mead Johnson Nutrition (MJN, Evansville IN, USA) based on AIN-93G specifications and were isocaloric with similar carbohydrate, protein, fat, vitamin, and mineral levels. fecal sample collection. Stool samples were collected as previously described 7 . Briefly, rats were placed into a sterile cage until defecation (<10-min) where samples were collected on ice and rats were immediately returned to the home cage. Samples were then transferred and stored in a −80 C freezer for untargeted metabolomics analysis.
Stress protocol. Unpredictable and inescapable tail shock is a well-characterized laboratory stressor that robustly and reproducibly produces depression/anxiety-like behavior, elevates corticosterone, and disrupts diurnal physiology, sleep and the microbiome 3,4,7,8,55,56 . In brief, rats were placed in Plexiglas restraining tubes (23.4 cm long and 7.0 cm in diameter) and exposed to 100, 5-s, 1.5 mA inescapable tail shocks (Stress). Shocks were delivered at random with an average interval of 60-s between shocks and occurred during the inactive (light) cycle between ~0800 and 1100 h. After exposure to inescapable tail shock rats were immediately returned to their home cages. Animals that were not exposed to the stressor (No Stress), remained undisturbed in their home cages.
Sleep measures. Sleep was measured using in vivo biotelemetry, as previously described 4,8,57 . The complete sleep results from these rats have been previously reported in 7 . In brief, the F40-EET biotelemetry transmitters (Data Sciences International, St. Paul Minnesota) were implanted into animals and the electroencephalographic (EEG) insulated leads were passed subcutaneously to the base of the skull, where they were attached to stainless steel screws (Plastics One Inc.) and served as EEG recording electrodes. Biotelemetry recordings of EEG Scientific RepoRtS | (2020) 10:3848 | https://doi.org/10.1038/s41598-020-60679-y www.nature.com/scientificreports www.nature.com/scientificreports/ were acquired using Dataquest ART 4.3 Gold Acquisition/Analysis Software (Data Sciences International, St. Paul, MN) and sleep/wake cycles were scored using the automated Neuroscore 2.1.0 software (Data Sciences International, St. Paul, MN). After sleep recordings were autoscored, they were corrected for accuracy by an observer blind to the experimental treatment of each animal.
The current study used two measures of sleep, NREM bout duration and % REM. NREM and REM measures were derived from the trace EEG signal after fast Fourier Transformation (FFT), yielding spectra between 0.5 and 30 Hz in 0.5-Hz frequency bins. NREM sleep was identified by increased absolute EEG amplitude with integrated values for the delta frequency band (0.5-4.5 Hz) being greater than those for the theta frequency band (6.0-9.0 Hz). REM sleep was characterized by low amplitude EEG with integrated values for the delta frequency band less than those for the theta frequency band. Time spent in REM was calculated as a percentage (%) of time spent in a specific behavioral state per hour. Average bout durations per hour of NREM were also calculated. Bout durations were defined by any change in sleep/wake state for 10 seconds (i.e., an NREM bout was defined based on the appearance of 10-sec epoch or longer of NREM and the end of that epoch was defined as the appearance of any 10-sec epoch of either REM or WAKE).
16S rRNA gene sequencing and microbial alpha diversity analyses. Fecal samples were previously collected at PND 70 and PND 91 and sequenced as described 7 . These same fecal samples were used for metabolomics analysis. In brief, after purification and precipitation to remove polymerase chain reaction (PCR) artifacts, samples were exposed to multiple sequencing on an Illumina Genome Analyzer IIx. Operational taxonomic units (OTUs) were picked using a 'closed reference' approach 58 . GreenGenes May 2013 version was the reference database used 59 , and all sequence processing was done with QIIME v 1.8.0 60 using the UCLUST algorithm 61 . Taxonomy and phylogeny were taken from the GreenGenes reference collection. The current experiment generated 14,207,155 sequences, where 11,016,354 were discarded because of uncorrectable barcode errors, of which 6,481 were too short to read (based on default parameters set in QIIME script 'split_libraries_fastq.py') and the remaining 3,190,801 sequences were used. The resulting OTU table was rarefied at 7400 sequences/sample to correct for uneven sequencing depth due to amplification differences between samples. Alpha diversity for this manuscript was measured using species richness (PD_Whole_Tree). This measure captures phylogenetic diversity for a given sample 62 .

Metabolomics. Sample information -LC -MS/MS. A subset of frozen fecal samples was transferred via
dry ice to the University of California, San Diego for metabolomic analysis. Fecal samples were stored in 1.5 mL centrifuge tubes at −80 °C prior to extractions. Sample ID's were manually uploaded into an electronic spreadsheet and subsequently used to assign filenames during LC-MS/MS data acquisition. All solvents used for the metabolomic analysis were of LC-MS grade. LC-MS/MS parameters. Fecal extracts were analyzed using an ultra-high performance liquid chromatography system (Vanquish, Thermo) coupled to a hybrid quadrupole-Orbitrap mass spectrometer (Q-Exactive, Thermo) fitted with a HESI probe. Reverse phase chromatographic separation was achieved using a Kinetex C18 1.7 µm, 100 Å, 50 × 2.1 mm column (Phenomenex) held at 40 °C with a flow rate of 0.5 mL/min. 5.0 µL aliquots were injected per sample/QC. The mobile phase used was (A) 0.1% formic acid in water and (B) 0.1% formic acid in acetonitrile. The elution gradient was: 5% B for 1 min, increased to 100% B in the next 8 min, held at 100% B for two min, returned to 5.0% B in 0.5 min, equilibrated at 5.0% B for two min. Positive electrospray ionization parameters were: sheath gas flow rate of 52 (arb. units), aux gas flow rate of 14 (arb. units), sweep gas flow rate of 3 (arb. units), spray voltage of 3.5 kV, capillary temperature of 270 °C, S-Lens RF level of 50 (arb. units), and aux gas heater temperature of 435 °C. Negative electrospray ionization parameters were: sheath gas flow rate of 52 (arb. units), aux gas flow rate of 14 (arb. units), sweep gas flow rate of 3 (arb. units), spray voltage of 2.5 kV, capillary temperature of 270 °C, S-Lens RF level of 50 (arb. units), and aux gas heater temperature of 435 °C. MS data was acquired using a data dependent acquisition method with a resolution of 35,000 in MS 1 and 17,000 in MS 2 . An MS 1 scan from 100-1500 m/z was followed by an MS 2 scan, produced by collision induced disassociation, of the five most abundant ions from the prior MS 1 scan.
Data processing and analysis. The orbitrap files (.raw) were exported to mzXML files using MSConvert 64 . Feature detection of the MS 1 data was performed using MZmine2 65 , parameters can be found in Supplemental Table 1, which generated a data matrix of MS 1 features (m/z and retention time) and peak area. Each feature in Scientific RepoRtS | (2020) 10:3848 | https://doi.org/10.1038/s41598-020-60679-y www.nature.com/scientificreports www.nature.com/scientificreports/ a given sample was normalized against the spiked internal standard, to remove any spray variation across runs, followed by normalization of each sample by the row sum of its features. The twice-normalized data matrix was used for all subsequent statistical analysis. The SIRIUS export module found within MZmine was used to create. mgf files for molecular formula annotation and molecular structure prediction in the SIRIUS desktop software.
Global natural products social molecular networking (gnps) job parameters. Molecular networking was ran using networking parameters that yielded a false-discovery rate (FDR) of annotation, using Passatuto, for spectral matches against reference libraries of 1% (Supplemental Fig. 4). A molecular network was created using the online workflow at GNPS 66 . The data was then clustered with MS-Cluster with a parent mass tolerance of 0.05 Da and a MS/MS fragment ion tolerance of 0.05 Da to create consensus spectra. Further, consensus spectra that contained less than 2 spectra were discarded. A network was then created where edges were filtered to have a cosine score above 0.6 and more than 6 matched peaks. Further edges between two nodes were kept in the network if and only if each of the nodes appeared in each other's respective top 10 most similar nodes. The spectra in the network were then searched against GNPS' spectral libraries. All matches kept between network spectra and library spectra were required to have a score above 0.52 and at least 6 matched peaks 67 . The molecular networking job can be accessed using the following GNPS positive mode link: https://gnps.ucsd.edu/ProteoSAFe/status. jsp?task=92166ef840924b078fed960323cbd558.
Standards run for bile acids. Primary, secondary, conjugated and unconjugated bile acids were purchased and used for level 1 identification of some of our unknown molecules. Standards were solubilized to a final concentration of 10uM in 50% MeOH prior to LC-MS/MS injection. Statistical analysis. Metabolomics data were visualized and analyzed using metaboanalyst open source www.metaboanalyst.ca built on R statistical software [70][71][72][73][74] and all other analyses were performed in SPSS version 25. All features were log transformed prior to further analysis. Principal components analysis was used to reduce the high dimensionality of the untargeted fecal metabolomics dataset. Unsupervised heat maps were generated using the Euclidean distance matrix with Ward clustering algorithm. For clarity and ease of interpretation only the top 50 unidentified features are displayed on the heat maps. For PND 70, a volcano plot (two group data) was used to identify significant differences between control and Test diet groups (p < 0.05; FDR p < 0.05). For PND 91, two-way ANOVA was used (p < 0.05; FDR p < 0.05). When appropriate, post hoc analysis was performed using Fisher's PLSD with alpha set to p < 0.05. In a final step, we examined potential relationships between host physiology, the gut microbiome (phylum level), and the identified gut metabolites using stepwise multiple regression analyses. These analyses were run on the normalized microbiome/metabolomics data and examined relationships between sleep (NREM and REM) and alpha diversity data that have been previously published 7 . Differences were considered significant when p < 0.05, unless otherwise noted. ethical approval. This manuscript has not been submitted to more than one journal for simultaneous consideration. All procedures were approved by the University of Colorado Animal Care and Use and all applicable guidelines for care and use of animals were followed.

Data availability
All mass spectrometry data (.raw,.mzXML, and mgf files), mzMine, and Sirius files can be found in the online mass spectrometry repository Massive (http://massive.ucsd.edu) using the following accession numbers: MSV000079329 and MSV000079339.