Microbiome and metabolome integrally reveal the anti-depression effects of Cistanche deserticola polysaccharides from the perspective of gut homeostasis

Ulcerative colitis (UC) and Crohn's disease (CD), collectively known as Inflammatory Bowel Diseases (IBD), are caused by a complex interplay between genetic, immunologic, microbial and environmental factors. Dysbiosis of the gut microbiome is increasingly considered to be causatively related to IBD and is strongly affected by components of a Western life style. Bacteria that ferment fibers and produce short chain fatty acids (SCFAs) are typically reduced in mucosa and feces of patients with IBD, as compared to healthy individuals. SCFAs, such as acetate, propionate and butyrate, are important metabolites in maintaining intestinal homeostasis. Several studies have indeed shown that fecal SCFAs levels are reduced in active IBD. SCFAs are an important fuel for intestinal epithelial cells and are known to strengthen the gut barrier function. Recent findings, however, show that SCFAs, and in particular butyrate, also have important immunomodulatory functions. Absorption of SCFAs is facilitated by substrate transporters like MCT1 and SMCT1 to promote cellular metabolism. Moreover, SCFAs may signal through cell surface G-protein coupled receptors (GPCRs), like GPR41, GPR43, and GPR109A, to activate signaling cascades that control immune functions. Transgenic mouse models support the key role of these GPCRs in controlling intestinal inflammation. Here, we present an overview of microbial SCFAs production and their effects on the intestinal mucosa with specific emphasis on their relevance for IBD. Moreover, we discuss the therapeutic potential of SCFAs for IBD, either applied directly or by stimulating SCFAs-producing bacteria through pre- or probiotic approaches.

ABSTRACT Blautia is a genus of anaerobic bacteria with probiotic characteristics that occur widely in the feces and intestines of mammals. Based on phenotypic and phylogenetic analyses, some species in the genera Clostridium and Ruminococcus have been reclassified as Blautia, so to date, there are 20 new species with valid published names in this genus. An extensive body of research has recently focused on the probiotic effects of this genus, such as biological transformation and its ability to regulate host health and alleviate metabolic syndrome. This article reviews the origin and biological characteristics of Blautia and the factors that affect its abundance and discusses its role in host health, thus laying a theoretical foundation for the development of new functional microorganisms with probiotic properties.

The gut microbiota affects many important host functions, including the immune response and the nervous system 1 . However, while substantial progress has been made in growing diverse microorganisms of the microbiota 2 , 23–65% of species residing in the human gut remain uncultured 3,4 , which is an obstacle for understanding their biological roles. A likely reason for this unculturability is the absence of key growth factors in artificial media, which are provided by neighboring bacteria in situ 5,6 . In the present study, we used co-culture to isolate KLE1738, which required the presence of Bacteroides fragilis to grow. Bio-assay driven purification of B. fragilis supernatant led to the isolation of the growth factor, which, surprisingly, is the major inhibitory neurotransmitter γ-aminobutyric acid (GABA). GABA was the only tested nutrient that supported growth of KLE1738, and genome analysis supported a GABA-dependent metabolism. Using growth of KLE1738 as an indicator, we isolated a variety of GABA-producing bacteria, and found that Bacteroides ssp. produced large quantities of GABA. Genome based metabolic modeling of the human gut microbiota revealed multiple genera with the predicted capability to produce or consume GABA. Transcriptome analysis of human stool from healthy individuals showed that GABA producing pathways are actively expressed by Bacteroides, Parabacteroides, and Escherichia species. By coupling 16S rRNA sequencing with fMRI imaging in patients with Major Depressive Disorder (MDD), a disease associated with an altered GABAergic response, we found that relative abundance levels of fecal Bacteroides are negatively correlated with brain signatures associated with depression. We previously identified quinones as growth factors for uncultured bacteria of the human microbiome 6 . In this study, we searched for previously undescribed growth factors. On a densely inoculated plate, some uncultured bacteria may be growing because they are in proximity to cultivable organisms producing growth factors. Using this rationale, a fecal sample from a healthy human donor was plated on Fastidious Anaerobic Agar supplemented with yeast extract (FAAy), and newly formed colonies were noted daily for a week. Late forming colonies (appearing after 4–7 days – “candidate dependent”) were diluted and spread on FAAy, and a heavy inoculum of a neighboring, early forming colony (appearing after1–3 days – “candidate helper”) was spotted (Fig. 1A). Given that E. coli induced the growth of all quinone-dependent organisms in our previous study 6 , candidate dependents were counter-screened against E. coli (Fig. 1B). After screening approximately 200 colonies for the desired dependency phenotype, we identified a single isolate that failed to grow in the presence of E. coli, but grew on a plate with B. fragilis. This isolate, KLE1738, required the presence of Bacteroides fragilis KLE1758 (100% similar by 16S rRNA sequence to Bacteroides fragilis ATCC 25852), for growth (Fig. 1C). KLE1738 is a Gram-positive bacterium of the Ruminococcaceae family, and is 93.4% similar to Flavonifractor plautii VPI 0310 [S1] 7 as well as Intestinimonas butyriciproducens SRB-521–5-I(T) 8 by 16S rRNA gene sequence, with its closest relative by genome sequence similarity being the uncultured Firmicutes bacterium CAG:114 (Fig. 1D). Its dissimilarity by 16S rRNA gene sequence to existing type strains suggests KLE1738 is the first representative of an unreported genus of bacteria, using recent guidelines for assigning taxonomy 9 . KLE1738 is found on the NIH Most Wanted list 10 , indicating it has not been cultured, although it is fairly prevalent in the human gut microbiome, being detectable in 6,001 of 31,451 (19.08%) of human gut metagenomes available in the Integrated Microbial Next Generation Sequencing database (16S similarity threshold >99%, with a minimum sequence length of 200bp) 11 . However, KLE1738 appears to be a relatively minor constituent of the microbiome, detectable at a relative abundance >1% in only 60/31,451 human gut metagenomes (0.19%). The supernatant of a 48-hour culture of B. fragilis KLE1758 grown in rich medium induced growth of KLE1738 (Supplemental Information Fig. 1A, control in 1B), enabling bioassay-driven purification of the growth factor. The supernatant was solvent-partitioned with ethyl acetate, and the aqueous fraction induced growth of KLE1738. The aqueous fraction was then separated on an HP-20 column, and the most polar fraction induced growth of KLE1738 (Supplemental Information Fig. 1C, non-inducing fraction in 1D). This active fraction was then further fractionated by preparative HPLC, yielding a single active fraction. NMR analysis revealed that it contained 10 compounds including 6 common amino acids, 3 carboxylic acids and gamma-aminobutyric acid (GABA) (Supplemental Information Fig. 2). While all compounds identified in the active fraction were tested, only GABA induced growth of KLE1738 (Supplemental Information Fig. 1E, medium without GABA in 1F). The growth requirement of GABA a was surprising, since this suggested that KLE1738 was unable to grow on glucose or the numerous amino acids and other nutrients present in the rich medium. Precursors and breakdown products of GABA, and over 100 common carbon or nitrogen sources (including all standard amino acids) were tested for their ability to induce growth of KLE1738, but none were active (Supplemental Information Table 1). In addition to the rich FAAy medium, multiple commercially available and published growth media were tested -- including those shown to enable recovery of diverse gut bacteria, YCFAg 2 and GMM 12 -- but none were found to promote the growth of KLE1738 in the absence of exogenous GABA. Systematically excluding components of FAAy medium showed that KLE1738 grows on solid agar with only peptone and GABA (Supplemental Information Table 2). These results indicate that GABA is a required nutrient for KLE1738 in the tested experimental conditions. To gain insight into the unusual growth requirements of KLE1738, the genome was sequenced and annotated. The annotated draft genome of KLE1738 revealed an unusual profile – there were no apparent entry points into central metabolism for common sugars, amino acids, or other carbon sources. Bacteria use phosphotransferases to uptake a variety of sugars, such as glucose, fructose, and mannose. Uptake is coupled to phosphorylation by the membrane transporter Enzyme II, the last component of a phosphorylation pathway: phosphoenolpyruvate – Enzyme I – Hpr – Enzyme II 13 . KLE1738 appears to lack Enzyme I. The fact that only one component of the phosphorylation pathway is missing suggests a recent loss of function. Similarly, KLE1738 is predicted to have a limited set of ABC transporters (Supplemental Information Table 3). This agrees with the inability of KLE1738 to grow on the tested nutrients. The utilization of GABA for carbon and nitrogen by bacteria has been reported before, although not as a required nutrient. The GABA shunt serves as a pathway for its conversion into succinate, which then enters into the TCA cycle 14 . However, KLE1738 appears to lack succinate semialdehyde dehydrogenase, an essential enzyme of this pathway. An alternative ATP-generating pathway for GABA consumption was described for the environmental anaerobe Clostridium aminobutyricum 15 . KLE1738 has homologs of all enzymes of this pathway (Supplemental Information Fig. 3), with some enzymes present in multiple copies (Supplemental Information Table 4). Feeding KLE1738 with 13C, 15N labeled GABA allowed us to detect its metabolites, butyrate and acetate, as well as hexanoic acid, an intermediate of fatty-acid biosynthesis (Supplemental Information Fig. 4 and Fig. 5A-D, 4E-H). No incorporation of the 15N label was found. These results suggest that GABA is a carbon and energy source for KLE1738. For some species, including E. coli, Lactobacillus ssp., and Bifidobacterium ssp., GABA secretion has been reported to serve as an acid-resistance mechanism. Decarboxylation of glutamate is induced at a low pH and produces GABA, which is then exported from the cell in a protonated form, alkalinizing the cytoplasm 14 . While E. coli does not induce growth of KLE1738, a strain overexpressing glutamate decarboxylase in E. coli K12 does, comparably to B. fragilis KLE1758 (Supplemental Information Fig. 6A-D). Bacteroides fragilis, the helper of KLE1738, is a common gut bacterium, but we found that similarly to E. coli, GABA production by Bacteroides fragilis is only observed at a low pH (≤5.5) (Supplemental Information Fig. 7). Apparently, Bacteroides fragilis growing on a Petri dish acidifies the medium enough to induce GABA production. We therefore sought to find gut microorganisms capable of producing GABA at a physiologically relevant pH for the human large intestine (pH 5.7 – 7.4) 16 . The unique GABA-dependence of KLE1738 was exploited to identify additional GABA producers in a co-culture assay. Stool samples were mixed with molten FAAy agar, allowed to solidify, and KLE1738 was then spread on top of the medium. By looking for zones of KLE1738 growth, we were able to identify gut bacteria that produce GABA (Fig. 2A, B). In addition to Bacteroides species, representatives from the Parabacteroides, Eubacterium, and Bifidobacterium genera were identified as GABA producers in this assay (Fig. 2B). Of those, Bacteroides ssp. (and to a lesser extent Parabacteroides sp.) were found to produce GABA within the pH range of the human large intestines (Fig. 2B). We next sought to profile the GABA modulating potential of the human gut microbiome in silico. All available bacterial genes encoding for GABA consumption and production were obtained from RAST 17 and/or UniProt 18 . These sequences were then used as input for a bidirectional best hit analysis against genomes of known members of the gut microbiota, as evident by their presence in the fecal 16S rRNA data set of the American Gut Project – a crowd sourced microbiome sequencing project with over 10,000 participants 19 . Genomes were scored as “producer” if they had at least one complete metabolic route for GABA production or a “consumer” if they had the GABA shunt or GABA transaminase and at least 7/11 of the remaining enzymes of the KLE1738 pathway (the presence of 7/11 enzymes was chosen due to poor annotation of the remaining enzymes). Of 1,159 genomes analyzed (consisting of 919 species), we identified 105, 205, and 211 species harboring the genetic potential to only produce, only consume, or both produce and consume GABA, respectively (Fig. 2C; more detail in Supplemental Information Table 5). Due to its dissimilarly to type strains and unique growth requirements, we propose the name “Evtepia gabavorous” for KLE1738. It will be described in a future manuscript, using traditional description methods for bacteria 20 . To complement this approach, we applied a more rigorous in silico constraint-based modeling method to survey the potential GABA consumption and/or production of the gut microbiota. This was performed using KBase, a database that contains metabolic models computationally derived from genome sequences of many microorganisms 21 . Of the 919 species used in the above bi-directional analysis, 533 had models represented in KBase, and these were examined for their ability to produce GABA from known precursors – glutamate, arginine, putrescine, and ornithine – or consume GABA via the KLE1738 pathway or the GABA shunt. The analysis predicted that 97 organisms had the capability to produce GABA, mostly via glutamate decarboxylase (Supplemental Information Fig. 7). Of these, >25% belong either to the genera Bacteroides or Parabacteroides (Fig. 2D). For consumption, we identified 102 potential consumers of this neurotransmitter, with the majority belonging to the Pseudomonas, Acinetobacter, and Mycobacterium genera (Fig. 2D). The number of GABA consumers is likely an underestimate, since the KLE1738 GABA consumption is poorly annotated and thus not captured in KBase. To validate activity of the genes of interest in humans, we surveyed an existing human transcriptome stool dataset 22 for active expression of transcripts associated with the bottlenecks of bacterial GABA metabolism (glutamate decarboxylase, gamma-aminobutyrate:alpha-ketoglutarate aminotransferase and succinate semialdehyde dehydrogenase). The RNAseq data was assembled using the Trinity platform 23 , and retrieved coding sequences were subjected to BLASTN (90% cutoff at 1e−5). No transcripts were identified for enzymes involved in GABA consumption (perhaps attributable to the depth of sequencing), but multiple hits were found for glutamate decarboxylase (Supplemental Information Table 6). These transcripts were successfully mapped to the nearest type strains of some of our GABA producing panel, suggesting Bacteroides and Parabacteroides species produce GABA in humans (Supplemental Information Table 7). Surprisingly, a set of transcripts also mapped to Escherichia coli (Supplemental Information Table 6). Given the pH restrictions for GABA production by E. coli in vitro 14 , this may indicate the presence of acidic microenvironments in the large intestine. In germ-free mice, GABA levels have been reported to be significantly decreased both in the stool and the blood 24 (but an older report showed no difference in GABA blood levels in germ-free and specific pathogen free rats 25 ). Similarly, in specific pathogen free mice, fecal GABA levels can be modifiable by antibiotics 26 , suggesting that the microbiota may contribute to circulating levels of GABA. This is of interest, because multiple diseases are associated with an altered GABAergic profile, such as depression 27 . Accordingly, we sought to explore whether Bacteroides, perhaps the major bacterial producers of GABA in the human gut, were associated with clinically diagnosed Major Depressive Disorder (MDD). To do this, stool from 23 patients with MDD was collected and analyzed by 16S rRNA sequencing using the American Gut protocols. Resting state fMRI was acquired within 3 days of stool sample collection and scans were co-registered to MNI-space. To investigate the relationship between neural circuitry important in depression and fecal Bacteroides abundance we focused, a priori, on the left dorsolateral prefrontal cortex (DLPFC) and default mode network (DMN). Our choice of the left DLPFC was guided by the highly replicated finding that the left DLPFC is hypoactive in depression 28 . Similarly, the DMN is involved in self-referential processing 29 and negative rumination in depression 30 . Functional connectivity is elevated both within the DMN and with other networks in depression 31 and normalizes with treatment response 32 . We found an expansive region of the default mode network spanning the left anterior medial frontal cortex in which functional connectivity with the left DLPFC was inversely correlated with the relative abundance of fecal Bacteroides (Fig. 3A,B). This region of significance overlapped extensively with both the left medial prefrontal and left frontopolar cortex, regions highly interconnected to the limbic system and thought to be important in emotional reappraisal and reward processing 33,34 . This cluster was unique, and we found no clusters in which functional connectivity was positively correlated with the relative abundance of Bacteroides. We found no associations with KLE1738, perhaps due to its low abundance. Interestingly, a recent study of 40 healthy women found that levels of Bacteroides were associated with increased gray matter in the cerebellum, hippocampus, and frontal regions of the brain, and exhibited reduced levels of anxiety, distress, and irritability after looking at photos to evoke an emotional response 35 . Furthermore, a high fat diet has been shown to reduce GABA levels in the rat prefrontal cortex by about 40%, which was associated with reduced levels Bacteroides and depressive-like behavior 36 . Nonetheless, our pilot cohort has limitations – the sample size was small and consequently we did not correct for medications (listed in Supplemental Information Table 8), and the sequencing data was not quantitative. As such, this cohort should be expanded, to further profile whether Bacteroides, or the GABA they may produce, are involved in affecting behaviors. Notably, it has been reported that treatment of mice with the probiotic bacterium Lactobacillus rhamnosus JB-1 reduces stress and depression-like behavior in a vagus nerve-dependent manner 37 . This was accompanied by changes in the expression of GABA receptors in several areas of the brain, including the amygdala and hippocampus 37 , and a later study showed elevated brain GABA in mice post-supplementation with JB-1 after four weeks 38 . While JB-1 was not explicitly tested for its ability to produce GABA, other L. rhamnosus strains have been shown to produce GABA around a pH of 3.6–5.2 39 . Some Bifidobacterium have been shown to produce GABA 40 , and introduction of a GABA producing Bifidobacterium strain resulted in an improved visceral sensitivity in a rat model of pain 41 . Importantly, a recent human study found that fecal microbiome transplant from lean to obese individuals resulted in increased levels of GABA in the plasma 42 , showing manipulating the microbiome may alter GABA levels. Our findings, combined with these reports, suggest microbial-derived GABA may influence the host, and are the first step in understanding the biology of this intriguing connection. Methods Human stool collection For cultivation experiments, stool samples from an adult healthy human donor were collected using a stool collection vessel (Medline Industries). Within five minutes of collection, 1 gram of stool was resuspended in 9 mL of sterile 20% glycerol in PBS and homogenized for 30 seconds using a vortex. 1 mL aliquots of this mixture were loaded in cryotubes and stored at −80 °C. Stool samples for cultivation experiments were collected with informed consent following an IRB approved protocol at Northeastern University (IRB# 08–11-16). For the Major Depressive Disorder cohort, study participants were provided with sterile plastic 4 oz specimen collection cups at their first visit. They were instructed to collect stool the day of or the night before their second visit depending on their ability to produce a sample to make sure no more than 24 hours pass between stool sample collection and processing. Study participants were instructed to keep their stool samples at room temperature until they bring it to their second visit. Once a sample was received by study personnel, it was processed within an hour. American Gut sample kits were shipped at room temperature the day of sample processing (the standard shipping protocol used for the American Gut). Two 1.5 ml screw top plastic tubes per sample were filled with stool and immediately frozen at −80C for future studies. Cultivation of helper-uncultured pairs from human stool samples All cultivation was performed in a Coy Anaerobic Vinyl chamber with an atmosphere of 5% hydrogen, 10% CO2, 85% nitrogen. Stool samples were thawed and serially diluted in PBS under anaerobic conditions and bead-spread (7–10 beads/plate) on 1X Fastidious Anaerobic Agar (Accumedia) plates with 2.5% yeast extract (FAAy). Plates were incubated at 37 °C anaerobically for one week, and each day appearance of colonies was tracked by spotting the outside of the plates with different color markers. At the end of the week, serial dilutions of late forming colonies (appearance after 4–7 days) were prepared in PBS and bead spread on FAAy plates. Nearby ( 24 hours) and bead spread on top of the agar. Inoculated plates were incubated for one week, and growth of KLE1738 around colonies embedded in the agar indicated candidate GABA producing colonies. These candidate GABA producers were then restreaked on fresh FAAy and identified by 16S rRNA gene sequencing. All strains were confirmed to produce GABA on their respective mediums by co-culture with KLE1738 in isolation. Co-culture of KLE1738 with E. coli overexpressing glutamate decarboxylase. 24 hour cultures of E. coli clones harboring native glutamate decarboxylases (gadA, gadB) or the GABA antiporter (gadC) in the pCA24N IPTG inducible high-copy number vector, were tested for GABA production via co-cultivation assay with KLE1738 on FAAy (described in Fig 1) with the addition of 1 mM IPTG. An E. coli clone harboring the empty vector and a 48 hour culture of B. fragilis KLE1758 growth in BHIYch served as a control. E. coli strains were taken from the ASKA library 46 . Metagenomic analysis of potential GABA producing or consuming bacteria To identify potential GABA-producing and -consuming bacteria, a bidirectional best hit (BBH) nucleotide BLAST was performed. Operon nucleotide fasta sequences were retrieved from KBase (ftp://ftp.kbase.us/assets/KBase_Reference_Data/blast/fasta/kbase.ffn) and filtered for members of the gut microbiota by comparing 16S rRNA sequences that shared >99% sequence similarity to the healthy fecal set of organisms from the American Gut Project 19 . In total, sequences for 913 different gut bacteria were found in the KBase operon file (some species had multiple strain representatives, bringing the total analyzed genomes to 1,159). To identify potential GABA consumers, genes involved in the KLE1738 GABA consumption pathway were downloaded from RAST 45 and UniProt 18 . To identify organisms utilizing the GABA shunt, the list of GABA consumption genes was completed by adding all bacterial succinate semialdehyde dehydrogenase sequences found in UniProt. To identify potential GABA-producing organisms, nucleotide sequences for genes involved in the different pathways associated with GABA production (decarboxylation of glutamate, degradation of putrescine, or from arginine or ornithine) were downloaded from UniProt. The compiled lists of genes involved in GABA production or consumption were used as input for the BBH analysis against the gut microbe operon nucleotide sequences from KBase. Metabolic modeling In March 2016, all of the metabolic models from KBase, which were automatically generated in 2014 or earlier from all publicly available prokaryotic genome sequences, were downloaded. By comparing 16S rRNA sequences that shared >99% sequence similarity to the healthy fecal set of organisms from the American Gut project, 533 models were identified as gut-related (not all 913 microbes identified as gut related in Kbase have associated models). The models were forced to produce GABA (cpd00281) intracellularly ([c]) by introducing the constraint “cpd00281[c] → “ and maximizing it, with all exchange reactions (except for GABA) allowed to be unbounded (−1000 to 1000). Reversing the GABA constraint revealed that no model can consume free GABA. To identify potential GABA consumers, the biochemical reactions involved in the KLE1738 GABA consumption pathway were defined, and models were examined for similar reactions. Specificity of cofactor molecules were not considered, only that GABA was being metabolized. Transcriptomic analysis We surveyed a published human stool transcriptome dataset 22 to explore the activity of three major genes involved in GABA metabolism i.e. glutamate decarboxylase (gad), gamma-aminobutyrate:alpha-ketoglutarate aminotransferase and succinate semialdehyde dehydrogenase in humans. The transcriptome data was assembled using Trinity 23 which was further annotated for CDSs using prodigal 47 . BLASTN was used at % identity cut-off of 75% and E-value of 1e-5 to retrieve active transcripts from the CDS-annotated transcriptome data using gene sequences. Five active transcripts were obtained for glutamate decarboxylase with lengths 519, 234, 231, 276 and 286 bp. This was additionally confirmed by mapping of raw transcriptome reads data on to the gene sequences which led to reconstruction of a 500 bp long gad transcript. Transcripts for gamma-aminobutyrate:alpha-ketoglutarate aminotransferase and succinate semialdehyde dehydrogenase could not be recovered from this data. Major Depressive Disorder Cohort -- Subjects 23 currently depressed subjects between the ages of 19 and 65 (15 female) participated in the study. Subjects were recruited through referral from the outpatient clinic in the Department of Psychiatry at Weill Cornell Medical College. Subjects were also self-referred by directly contacting our mood disorders research program or from the local community via flyers, outreach at local events, or direct contact. The recruitment procedure and all other aspects of our experimental protocol were approved by the Institutional Review Board of Weill Cornell Medical College, and all experiments were conducted in accordance with institutional guidelines and regulations. Patients provided written informed consent. All subjects participated in an initial screening interview to determine eligibility for enrollment in the study. Patients were eligible for inclusion if they met DSM-IV-Text Revision criteria for a major depressive episode with a diagnosis of major depressive disorder or bipolar II disorder and if they also met criteria for treatment resistance, including a failure to respond to at least two previous antidepressant trials at adequate doses for 8 weeks during the current episode. Diagnoses were determined by a Board-Certified psychiatrist (MJD) in an unstructured clinical interview and through consultation with family members and the current treating psychiatrist. Potential subjects were excluded from the study if they presented with a history of claustrophobia, seizure disorder or other neurological disorder, head injury resulting in loss of consciousness, metal implants, pacemakers, intrauterine contraceptive devices, or braces, or if they were currently pregnant or lactating. Potential subjects were also excluded if they had bipolar I disorder or a psychotic disorder, were actively suicidal with plan or intent, had been in their current episode for longer than 3 years, had a history of clinically significant personality disorder as established in the diagnostic interview, or had substance abuse disorder or substance dependence within the past 3 years. 16S rRNA sequencing of the Major Depressive Disorder stool samples via the American Gut Sequence data for the MDD patients in the American Gut dataset were produced in accordance with the standard Earth Microbiome Project 16S rRNA V4 amplicon sequencing protocol 49 . The MDD sequences analyzed corresponded to the set of samples represented by the April 26th 2017 processing. The American Gut sequences were processed using Deblur v1.0.2 50 , and sequences corresponding to blooms were removed as previously reported 51 . Relative abundances of Bacteroides post-bloom filtering were used for the functional connectivity analysis. Magnetic resonance imaging data acquisition and preprocessing Magnetic resonance imaging data was collected on a 3.0 Tesla Siemens Trio MRI Scanner that was upgraded to a 3.0 Tesla Prisma MRI Scanner (Siemens, Munich, Germany) after the acquisition of imaging data from the first 10 subjects. Data were obtained from subjects in one session that occurred on or prior to the day of stool sample collection (mean=1.6d; SD=3.6d). Each imaging session included a resting-state fMRI (rsfMRI) sequence (Trio Scanner: repetition time 2.25sec, 161 volumes; Prisma Scanner: repetition time 0.77sec, 425 volumes) and a T1-weighted (MP-RAGE) anatomical scan. Preprocessing of rs-fMRI data was conducted with the AFNI (http://afni.nimh.nih.gov/afni/) and FSL (http://www.fmrib.ox.ac.uk/fsl/) software packages and included motion correction (AFNI), spatial smoothing (6-mm full-width half-maximum Gaussian kernel; FSL), temporal band-pass filtering (0.005–0.1 Hz; AFNI), linear and quadratic detrending (AFNI), and removal of nuisance signals by regression on six motion parameters (roll, pitch, yaw, and translation in three dimensions) and signal time courses for white matter and cerebrospinal fluid (CSF) regions-of-interest (ROIs) determined on an individual basis using an automated segmentation algorithm (FSL). We did not use global signal regression 52 . Regions of interest The region of interest comprising the DMN was defined a priori based on previously published reports 53 . The left DLPFC seed was an ROI within BA46 (9 mm seed centered on MNI coordinates: –44, 40, 29) 32 . Functional connectivity analysis We first quantified functional connectivity between the left DLPFC and DMN areas by testing for correlations between the BOLD signal time series seeded from the left DLPFC with voxels in the DMN (see above). Next, to identify cortical clusters in the DMN in which left-DLPFC:DMN functional connectivity correlated with Bacteroides relative abundance, we performed an analysis of covariance (ANCOVA; implemented using AFNI’s 3dttest++ function), with age, gender, head motion, scanner (pre vs. post upgrade) and Bacteroides relative abundance as covariates. This generated a statistical map identifying areas of the DMN in which functional connectivity with the left DLPFC varied with Bacteroides relative abundance, controlling for the remaining covariates. Importantly, this analysis showed that the effects of relative Bacteroides abundance on left DLPFC:DMN functional connectivity were not driven by Bacteroides effects on head motion. This analysis also showed that effects of relative Bacteroides abundance on left DLPFC:DMN functional connectivity were indistinguishable in the groups of subjects scanned before and after the scanner upgrade. Next, a Monte Carlo simulation was performed for this map using AFNI’s 3dClustSim function to determine statistical thresholds for voxel cluster size needed to achieve a family-wise α < 0.01 at voxel-wise p < 0.05. This yielded a threshold voxel cluster size of 213 voxels. In addition, the spatial mean connectivity value (Z) of each significant cluster was extracted for each subject and a linear regression was performed for each cluster with Bacteroides relative abundance. Supplementary Material 1 2