Sample Collection Moayyeri et al., 2013 Moayyeri A.

Hammond C.J.

Valdes A.M.

Spector T.D. Cohort Profile: TwinsUK and healthy ageing twin study. All work involving human subjects was approved by the Cornell University IRB (Protocol ID 1108002388). Fecal samples were collected at home by participants in the United Kingdom Adult Twin Registry (TwinsUK); () in 15 ml conical tubes and refrigerated for 1-2 days prior to the participants’ annual clinical visits at King’s College London (KCL). Upon arrival at KCL, the samples were stored at −80°C and shipped by courier on dry ice to Cornell University, where they were stored at −80°C until processing.

DNA Extraction, Amplicon Generation, and Sequencing Caporaso et al., 2011 Caporaso J.G.

Lauber C.L.

Walters W.A.

Berg-Lyons D.

Lozupone C.A.

Turnbaugh P.J.

Fierer N.

Knight R. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Genomic DNA was isolated from an aliquot of ∼100 mg from each sample using the PowerSoil® - htp DNA isolation kit (MoBio Laboratories Ltd, Carlsbad, CA). 16S rRNA genes were amplified by PCR from each of the 1,081 samples (245 DZ twin pairs, 171 MZ twin pairs, 2 twin pairs with no zygosity status reported, 143 unrelated individuals, and 98 samples taken from individuals at a second, and for six individuals, a third time point) using the 515F and 806R primers for the V4 hypervariable region as previously described (). PCR reactions, carried out in duplicate, consisted of 2.5 U Easy-A high-fidelity enzyme, 1 × buffer (Stratagene, La Jolla, CA), 10-100 ng DNA template, and 0.05 μM of each primer. Reaction conditions consisted of initial denaturation at 94°C for 3 min followed by 25 cycles of denaturation at 94°C for 45 s, annealing at 50°C for 60 s, extension at 72°C for 90 s, and a final extension at 72°C for 10 min. The replicate PCR reactions were combined and purified using a magnetic bead system (Mag-Bind® EZPure, Omega Bio-Tek, Norcross, GA). PCR amplicons were quantified using the QuantiT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA). Aliquots of amplicons (at equal masses) were combined for a final concentration of approximately 15 ng/μl. DNA was sequenced using the Illumina MiSeq 2x250 bp platform at Cornell Biotechnology Resource Center Genomics Facility.

16S rRNA Gene Sequence Analysis Aronesty, 2011 Aronesty, E. (2011). ea-utils: “Command-line tools for processing biological sequencing data” (http://code.google.com/p/ea-utils). Caporaso et al., 2010 Caporaso J.G.

Kuczynski J.

Stombaugh J.

Bittinger K.

Bushman F.D.

Costello E.K.

Fierer N.

Peña A.G.

Goodrich J.K.

Gordon J.I.

et al. QIIME allows analysis of high-throughput community sequencing data. Faith, 1992 Faith D.P. Conservation evaluation and phylogenetic diversity. Chao, 1984 Chao A. Nonparametric estimation of the number of classes in a population. Lozupone et al., 2007 Lozupone C.A.

Hamady M.

Kelley S.T.

Knight R. Quantitative and qualitative beta diversity measures lead to different insights into factors that structure microbial communities. Bray and Curtis, 1957 Bray J.R.

Curtis J.T. An ordination of the upland forest communities of southern wisconsin. Matching paired-end sequences (mate-pairs) were merged using the fastq-join command in the ea-utils software package (), and merged sequences over 275 bp in length were filtered out of the data set. The remaining merged sequences were analyzed using the open-source software package QIIME 1.7.0 (Quantitative Insights Into Microbial Ecology;). Quality filters were used to remove sequences containing uncorrectable barcodes, ambiguous bases, or low quality reads (Phred quality scores ≤ 25). We performed closed-reference OTU picking at 97% identity against the May 2013 Greengenes database (97% OTU reference sequences), which excluded 6.2% of total sequences. The taxonomic assignment of the reference sequence was used as the taxonomy for each OTU. We calculated α-diversity (Faith’s phylogenetic diversity [], Chao 1 [], and Observed Species) and β-diversity (unweighted and weighted UniFrac; [] metrics and Bray-Curtis Dissimilarity []) using the Greengenes phylogenetic tree where necessary. β-diversity was calculated with a rarefied OTU table containing 10,000 sequences per sample, and Principal Coordinate Analysis (PCoA) on the distance matrices. β-diversity was also calculated separately on the three most abundant bacterial families containing the most OTUs: Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae. β-diversity between twin pairs was compared to unrelated individuals, where distances between unrelated individuals were only used if the samples were sent in the same shipment (the stool samples were shipped to the laboratory at Cornell in eight batches, twin pairs were sent in the same batch), and given the large number of unrelated pairs, we randomly sampled only 20% of the unrelated pairs. p values were calculated using the Student’s t test with 1,000 Monte Carlo simulations. For α-diversity measurements, means were calculated from 100 iterations using a rarefaction of 10,000 sequences per sample. We generated summaries of the taxonomic distributions of OTUs at six levels from genus to phylum from the non-rarefied OTU table.

Tests of Repeatability of Microbiota Measures Ninety-eight individuals supplied two fecal samples spaced 2 to 812 days apart (average = 467 ± 28 days), 44 of which are from DZ twin pairs and 16 from MZ pairs. To determine if the microbial communities of the repeat samples from the same individuals were more similar to each other than to samples from unrelated individuals, the β-diversity distances between pairs of repeat samples were compared to the β-diversity distances between unrelated individuals. P values were calculated using the Student’s t test with 1,000 Monte Carlo simulations. Microbiotas for repeat samples were more similar to each other than pairs of samples from unrelated individuals using unweighted UniFrac, weighted UniFrac and Bray-Curtis dissimilarity ( Table S1 ).

Covariates In the heritability analyses below, the gender of the participant, age at the time of collection, and the number of OTU counts per sample (after filtering the data as mentioned above) were used as covariates in analyses. The following technical covariates were also included in the models: identity of technician (of two), sequencing run (16 instrument runs) and shipment batch (8 shipments).

Sequence-Based Traits Used in Heritability Calculations The traits used for heritability estimates were the raw unrarefied counts for the OTUs (97% ID), and the abundances of taxa (genus, family, order, class, and phylum bins) generated by summing counts for OTUs with the same classification. The methods for estimating heritability assume normally distributed data, so we performed steps to filter and transform the traits to meet this assumption. OTUs or taxonomic groups shared by fewer than 50% of the individuals in the study (less than 50% of the counts are non-zero) were excluded from further analyses. A multiple linear regression was performed where the Box-Cox transformed trait abundances (using the PowerTransform command implemented in the R package ‘car’ and an offset of 1) were regressed on the covariates listed above. The residuals from this regression were then used for the heritability estimates. The total number of traits used in the heritability calculations was 909.

Use of the Microbial Phylogeny in Heritability Calculations Edgar, 2010 Edgar R.C. Search and clustering orders of magnitude faster than BLAST. We estimated heritability throughout the phylogeny. We obtained the phylogenetic tree from Greengenes ( http://greengenes.secondgenome.com/downloads ; May 2013). This phylogenetic tree was pruned to keep only the tips corresponding to OTUs found in our data set after identifying OTUs that matched Greengenes at 97% ID (using UCLUST to perform the reference-based OTU picking;). The abundances of the OTUs were propagated up the tree to generate abundances at each node, and these abundances were used in the heritability calculations. We used the same procedures for filtering (50% sharing), transformation, and covariate regression as those mentioned above for the heritability calculations on the OTU and collapsed taxonomy traits. Heritability values and their corresponding levels of significance were then displayed visually on the phylogeny using a color scale applied to the tree branches.

Heritability Calculations Heritability was assessed first by using intraclass correlation coefficients (ICCs) calculated within the group of MZ twins and DZ twins for all traits. All ICC calculations were generated with the ‘icc’ command from the R package ‘irr’. We used the difference of ICC between MZ and DZ twin pairs as an indication of the amount of genetic influence on the variation of the abundances for the given trait or node. A Wilcoxon signed rank test was performed to assess the significance of the difference between the MZ and DZ OTU abundance ICC distributions. The phylogenetic relationship of the OTUs likely imposes some structure to the correlations among their abundances (more closely related OTUs are expected to have similar attributes). To address this concern we permuted the MZ/DZ twin pair labels 10,000 times and calculated the MZ/DZ ICCs for each OTU in the permuted data set. By permuting only the zygosity labels, the correlation structure of the OTUs (i.e., their phylogenetic relatedness) is maintained. Then, for each permuted data set we calculated the Wilcoxon signed rank test where the null hypothesis is that the difference between the MZ and DZ ICCs (MZ ICC - DZ ICC) is less than or equal to 0, and the alternative hypothesis is that this difference is greater than 0. The 10,000 test statistics provide an empirical distribution of test statistics that we can compare to our actual test statistic. We obtained a p value of 0.0006 by dividing the number of tests where the permuted test statistic was greater than the actual test statistic by 10,000. We also performed this test using 1,000 permutations on the Turnbaugh et al. and Yatsunenko et al. data sets, obtaining P values of < 0.001 and 0.047, respectively. The trees with bar plots in Figures 2 and S2 were created using the command plotTree.wBars in the phytools R package. Eaves et al., 1978 Eaves L.J.

Last K.A.

Young P.A.

Martin N.G. Model-fitting approaches to the analysis of human behaviour. We used the ACE model to estimate the heritability of the traits ( Table S2 ) and nodes throughout the phylogenetic tree (). The ACE model assumes that three sources of variance make up the total population phenotypic variance (V): genetic effects (A), common environment (C), and unique environment (E). The heritability is defined as the proportion of total variance that is due to genetic effects (A/V). Note that the term “heritability” is used in the twin-sense here: the A term is neither additive nor dominance variance, but instead is a confounded mixture of the two. Consequently, the heritability we refer to is neither strictly speaking the narrow-sense nor the broad-sense heritability. Boker et al., 2011 Boker S.

Neale M.

Maes H.

Wilde M.

Spiegel M.

Brick T.

Spies J.

Estabrook R.

Kenny S.

Bates T.

et al. OpenMx: An open source extended structural equation modeling framework. We used the structural equation modeling (SEM) software OpenMx () to calculate the full ACE model and 95% confidence intervals ( Table S2 ). A permutation test was performed to determine the significance of the SEM heritability estimates (A). The permutation p values were calculated by permuting the zygosity (MZ or DZ) labels for the twin pairs 10,000 times, and then the ACE model was used to calculate the heritability for each of the permuted data sets. To calculate a p value, the number of times a heritability estimate (A) met or exceeded the observed heritability estimate was divided by the total number of permutations performed (n = 10,000). To provide multiple testing correction in the heritability analysis, we used the Benjamini-Hochberg algorithm in R to correct for all 909 traits tested (OTUs and collapsed taxonomy bins). The traits with a q value < 0.1 are presented in Table S2 B. If all of the traits are included in the analysis, many of the traits with a q value below 0.1 are redundant because they represent the same taxa sampled at different levels in the phylogeny. To address this redundancy, we recalculated the q values while omitting the OTUs from the analysis and also when including only the families ( Tables S2 C–S2E).

Association of Traits with BMI We compared microbiotas of high-BMI (BMI > 30) to low-BMI (BMI < 25) individuals to determine which taxa were enriched or depleted in each group. For each of the traits (residuals after regression of covariates, described above) we performed a t test. p values were corrected for multiple testing using the Benjamini-Hochberg algorithm in R.

Using BMI as a Covariate in Heritability Analysis Since obesity has been shown to impact the composition of the microbiota, we reran the heritability analysis on the taxa including BMI as an additional covariate. We found a highly significant Pearson’s correlation coefficient of 0.93 between the estimates with and without BMI as a covariate. The most highly heritable traits (specifically the Christensenellaceae) maintained the high heritability with the addition of BMI as a covariate. This analysis indicates that host genotype impacts the composition of the gut microbiome over and above what can be attributed to host BMI. However, we note that host genetics may impact BMI through interactions with the microbiota.

Heritability Analysis Applied to Published Twin Microbiome 16S rRNA Gene Sequence Data Turnbaugh et al. (2009) Turnbaugh P.J.

Hamady M.

Yatsunenko T.

Cantarel B.L.

Duncan A.

Ley R.E.

Sogin M.L.

Jones W.J.

Roe B.A.

Affourtit J.P.

et al. A core gut microbiome in obese and lean twins. Yatsunenko et al. (2012) Yatsunenko T.

Rey F.E.

Manary M.J.

Trehan I.

Dominguez-Bello M.G.

Contreras M.

Magris M.

Hidalgo G.

Baldassano R.N.

Anokhin A.P.

et al. Human gut microbiome viewed across age and geography. Yatsunenko et al. (2012) Yatsunenko T.

Rey F.E.

Manary M.J.

Trehan I.

Dominguez-Bello M.G.

Contreras M.

Magris M.

Hidalgo G.

Baldassano R.N.

Anokhin A.P.

et al. Human gut microbiome viewed across age and geography. 16S rRNA gene sequence data for theandstudies were downloaded from the QIIME database ( http://www.microbio.me/qiime/index.psp ; study numbers 77 and 850 respectively). We also downloaded “mapping files” containing the metadata (covariates) for the samples and the respective OTU tables built from closed reference-based OTU picking against the GG database at 97% ID. For the Turnbaugh et al. data, if two samples were provided from the same individual, a single sample was chosen randomly to be included in the analysis. The final Turnbaugh set consisted of 23 DZ twin pairs and 31 MZ twin pairs, all of which were women. Ancestry and the number of sequences per sample were used as covariates. From thestudy we only included data from the twin pairs aged 13 or older (note that all of these were located in the USA), yielding 34 DZ twin pairs and 29 MZ twin pairs. Age and the total number of sequences per sample were used as covariates, and since there were both female and male participants in this data set, gender was also included as a covariate. We applied the ACE model to these data and calculated the ICCs for each of these data sets as described above ( Figures S2 and S3 Table S2 ). A p value for each was also generated by permutation test as described above.

Co-occurrence Network We used SparCC to calculate correlation coefficients between all bacterial and archaeal families (OTU sequence counts collapsed at family level). Since co-occurrence calculations are sensitive to differences in sequencing depth between samples, we first rarefied the OTU table that excluded repeat samples at 80,000 sequences per sample. We also eliminated features (families) that were found in fewer than 50% of samples. The feature elimination was done to control runtime and to reduce the potential for false discovery of network edges. Although the rarefaction depth excludes many samples (222 remained), we chose this depth because at lower rarefaction (i.e., < 80,000 sequences/sample), Christensenellaceae does not pass the filter of sharing by 50% of the samples. We ran SparCC using default settings, 500 bootstraps to assign p values, and divided the computation across 100 nodes on a large cluster. From the pairwise correlation matrix returned by SparCC we made a co-occurrence network, where each node in the network represents a family, and the edges between the nodes represent above-threshold the correlation coefficients between families. The network was filtered to include only correlations with a ‘two-tailed’ p value < 0.002, as assigned by SparCC. This value was selected because it was 1/500 bootstraps and our experience with SparCC suggests a p value threshold of 0.05 produces significant numbers of false edges. The network filtration and calculation were done using code available at http://www.github.com/wdwvt1/correlations/ Smoot et al., 2011 Smoot M.E.

Ono K.

Ruscheinski J.

Wang P.L.

Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Yatsunenko et al. (2012) Yatsunenko T.

Rey F.E.

Manary M.J.

Trehan I.

Dominguez-Bello M.G.

Contreras M.

Magris M.

Hidalgo G.

Baldassano R.N.

Anokhin A.P.

et al. Human gut microbiome viewed across age and geography. The network was displayed using Cytoscape (), we used the edge-weighted (by correlation coefficient) Prefuse Force Directed layout to display the network and reveal network modules. Negative correlations are represented by gray edges and positive correlations by blue. The same procedure was performed on thedata set rarefied at 929,918 sequences per sample.

Identification of Network Modules To identify modules within the family level networks generated from the TwinsUK data set, we used the R packages flashClust and dynamicTreeCut. FlashClust is a fast implementation of hierarchical clustering. We clustered the taxa based on the correlation coefficients (cor_matrix) returned by SparCC, where the dissimilarity matrix passed to flashClust was 1 – cor_matrix. Then the function cutreeDynamic was used to identify modules in the data set ( Figure S5 A).

Analysis of Christensenellaceae in Published Data Sets Tabled 1 16S rRNA Gene Data Sets Used for Association of Christensenellaceae Abundance with Health and Diet Ref Title QIIME ID Comparison within Study Statistical Test Result Papa et al. (2012) Papa E.

Docktor M.

Smillie C.

Weber S.

Preheim S.P.

Gevers D.

Giannoukos G.

Ciulla D.

Tabbaa D.

Ingram J.

et al. Non-invasive mapping of the gastrointestinal microbiota identifies children with inflammatory bowel disease. Non-invasive mapping of the gut microbiota as a screening method for IBD in children and young adults 1458 Healthy versus IBD Wilcoxon rank sum Christensenellaceae enriched in healthy compared to pediatric and young adult IBD patient fecal samples (p = 0.0001) Turnbaugh et al. (2009) Turnbaugh P.J.

Hamady M.

Yatsunenko T.

Cantarel B.L.

Duncan A.

Ley R.E.

Sogin M.L.

Jones W.J.

Roe B.A.

Affourtit J.P.

et al. A core gut microbiome in obese and lean twins. A core gut microbiome in obese and lean twins 77 Compared obese versus lean (only time point 2 samples) Wilcoxon rank sum one sided (lean higher) Lean has more than obese, but not significant (p = 0.07135) Koenig et al. (2011) Koenig J.E.

Spor A.

Scalfone N.

Fricker A.D.

Stombaugh J.

Knight R.

Angenent L.T.

Ley R.E. Succession of microbial consortia in the developing infant gut microbiome. Succession of microbial consortia in the developing infant gut microbiome 101 NA NA Christensenellaceae present at 8.6% in mother, and 20% in the infant meconium (first stool), and found at less than 5% at all other time points in infant Muegge et al. (2011) Muegge B.D.

Kuczynski J.

Knights D.

Clemente J.C.

González A.

Fontana L.

Henrissat B.

Knight R.

Gordon J.I. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans 626 Compared diets Kruskal-Wallis rank sum Christensenellaceae is enriched in omnivores compared to herbivores and carnivores (p = 0.017) Wu et al. (2011) Wu G.D.

Chen J.

Hoffmann C.

Bittinger K.

Chen Y.Y.

Keilbaugh S.A.

Bewtra M.

Knights D.

Walters W.A.

Knight R.

et al. Linking long-term dietary patterns with gut microbial enterotypes. Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes 1011 Correlation with all continuous dietary info Spearman (Benjamini-Hochberg correction) Nothing significantly correlated with Christensenellaceae Martínez et al. (2010) Martínez I.

Kim J.

Duffy P.R.

Schlegel V.L.

Walter J. Resistant starches types 2 and 4 have differential effects on the composition of the fecal microbiota in human subjects. Resistant Starches Types 2 and 4 Have Differential Effects on the Composition of the Fecal Microbiota in Human Subjects 495 Compared dietary treatment among subjects ANOVA Resistant starch type did not affect Christensenellaceae levels within an individual Koren et al. (2012) Koren O.

Goodrich J.K.

Cullender T.C.

Spor A.

Laitinen K.

Bäckhed H.K.

Gonzalez A.

Werner J.J.

Angenent L.T.

Knight R.

et al. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Host remodeling of the gut microbiome and metabolic changes during pregnancy 867 Correlation with all continuous dietary info Spearman (Benjamini-Hochberg correction) Nothing significantly correlated with Christensenellaceae Henao-Mejia et al. (2012) Henao-Mejia J.

Elinav E.

Jin C.

Hao L.

Mehal W.Z.

Strowig T.

Thaiss C.A.

Kau A.L.

Eisenbarth S.C.

Jurczak M.J.

et al. Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity 909 Compared mouse genotypes ANOVA No genotype was significantly associated with relative abundance of Christensenellaceae To assess (i) the prevalence of Christensenellaceae in published studies, and (ii) its association with health status or dietary factors, we selected a set of studies (listed in section below) that addressed diet and/or health-related questions and also had adequate sequence coverage to detect Christensenellaceae sequences. All had been performed prior to the incorporation of the name Christensenellaceae into the reference databases. For each data set, the mapping files and split-library sequence files were downloaded from the QIIME database ( http://www.microbio.me/qiime/index.psp ). We performed closed-reference OTU picking at 97% against all representative sequences from the 97% ID Greengenes OTUs that were classified as Christensenellaceae. For any given sample within a study, the number of sequences matching these OTUs was divided by the total number of sequences for that sample to yield a relative abundance of Christensenellaceae. The study titles, the QIIME database IDs of their data sets, the comparison that we made within the data sets, the test we used, and the outcome are listed in the following section.

PICRUSt Langille et al., 2013 Langille M.G.

Zaneveld J.

Caporaso J.G.

McDonald D.

Knights D.

Reyes J.A.

Clemente J.C.

Burkepile D.E.

Vega Thurber R.L.

Knight R.

et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. We used PICRUSt v1.0.0 to generate predicted metagenomes for each sample (). Counts from the rarefied OTU Table (10,000 sequences per sample) were normalized by the known/predicted 16S gene copy number abundance and functional prediction of Clusters of Orthologous Groups (COGs) summarized to general category letter associations was determined using precomputed files for the May 2013 Greengenes database. Relative abundances of the functional predictions were calculated and then transformed as described above. Since the data are relative abundances rather than counts, an offset of one during the transformation can skew the data, so an optimal offset was determined by minimizing the squared skewness of the transformed data using nlminb in R. The same covariates described above were regressed out, except the number of sequences per sample, and then the residuals were standardized using stdres from the R package MASS. The ICC and ACE models were used to determine the heritability of the COGs using the same methods described above. We also compared the high-BMI and low-BMI individuals to determine which functions were enriched or depleted in the obese group of individuals using a t test. P values were corrected for multiple testing using the Benjamini-Hochberg algorithm in R.

Animal Experiments All animal experimental procedures were reviewed and approved by the Institutional Animal Care and Usage Committee of Cornell University. Six-week old germ-free (GF) Swiss Webster mice were purchased from Taconic Farms Inc. (Hudson, NY). None of the Taconic mice used were siblings, and there is a low probability of any cousins used within a study.

Fecal Transplants from Lean and Obese TwinsUK Donors Stool samples that were termed methanogen-positive contained approximately 0.2%–10% of sequence reads that corresponded to methanogenic archaea. Stool samples that had no methanogen sequences were considered methanogen-negative. Under anaerobic conditions in an anoxic glove box (Coy Lab Products, Grass Lake, MI), approximately 1 g of stool was resuspended in 15 ml of anaerobic PBS that contained 2 mM DTT as a reducing agent. Each stool sample was vortexed for 5 min, removed from the anaerobic chamber, and then immediately used. In the initial experiment, we randomly assigned 21 (14 male, 7 female) 6-week-old Swiss Webster germ-free mice (Taconic Farms) to one donor each such that initial mouse mean weights were equivalent between treatment groups. Immediately prior to inoculation, the stool suspension was inverted 3 times and 500 μl were drawn up into a syringe fitted with a 20G gavage needle; 300 μl were stored for subsequent DNA extraction and analysis, whereas the remaining 200 μl was immediately inoculated into the recipient mouse via oral gavage. Fecal material from each donor was orally administered by gavage to 6-week old germ-free Swiss Webster mice in a 1:1 donor:mouse ratio. Mice were single-housed, kept under a 12 hr light/dark cycle, and fed an autoclaved 7017 NIH-31 mouse diet produced by Harlan Teklad (Madison, WI) ad libitum. Body weight and chow consumption were monitored weekly, where chow was measured before and after cage changes. Chow consumption rates were not different between treatment groups. A single mouse that had no remaining food in the cage at day 19 lost weight and was removed from any analysis at day 19. Stool samples were harvested weekly and immediately placed on dry ice. We replicated the experiment using stool samples from a set of 21 new donors, chosen similarly (by BMI and methanogen carriage). Again, 21 mice (female 6-week-old Swiss Webster germ-free mice) were each assigned to a unique donor. Over the duration of the replication 3 mice died and were excluded from the data set, leaving 5 L+, 4 L-, 3 O+, and 5 O- recipient mice. Sample collection and weight measurement were performed 20 hr, 5 days, and 10 days after inoculation as described above.

Fecal Transplants of C. minuta Amended Microbiome This experiment was similar to the obese/lean transfer described above, except for the following differences: (i) all mice were female (n = 24) and housed 4 per cage, with 3 cages per treatment; (ii) a single obese subject was selected as the donor based on a lack of OTUs mapping to Christensenella (i.e., none out of 478,633 sequences obtained for that sample when the inoculum used in the transplant was sequenced). C. minuta (DSM 22607) was grown in brain heart infusion broth supplemented with yeast (5 g/l), menadione (1 mg/l), hemin (10 mg/l), and L-cysteine-HCL (0.5 g/l) at 37°C under anaerobic conditions. Stool suspensions were prepared as above, with the exception that the mice receiving C. minuta were given an inoculum containing an addition of approximately 1 × 108 C. minuta cells, and the donor stool lacking C. minuta was amended with the same volume of PBS as a vehicle control. The second C. minuta addition experiment was similar to the first, but had 21 mice that were divided into 3 treatments: “minus C. minuta,” “plus C. minuta,” and “plus heat-killed C. minuta.” The minus and plus C. minuta samples were prepared as described in the first experiment. To prepare the heat-killed C. minuta inoculum, the culture was autoclaved for 20 min, and the donor stool was amended to contain approximately 1 × 108 C. minuta heat-killed cells. There were 7 mice per treatment group and mice were divided into 2 cages per treatment, one containing 3 mice and the other cage containing 4. The third C. minuta addition experiment also contained 21 mice, with 10 mice receiving an inoculum of donor stool amended with heat-killed C. minuta that was prepared as described above, and 11 mice receiving donor stool amended with live C. minuta, prepared as above. Mice were housed 2 per cage (within the same treatment group), with the exception that one of the plus C. minuta cages contained 3 mice.

Percent Body Fat Directly after euthanasia, mice were scanned by DEXA (Lunar PIXImus Mouse, GE Medical Systems, Waukesha,WI).

Total Energy and Free Short-Chain Fatty Acid Measurements Gross energy content of mouse stool samples was measured by bomb calorimetry using an IKA C2000 calorimeter (Dairy One, Ithaca, NY). Wet cecal contents were weighed and resuspended in 2% (v/v) formic acid by vortexing. The sample was centrifuged at 15,000 rpm for 5 min and the resulting supernatant was syringe filtered using a 0.22 μm filter to remove solids. One μl was injected into the gas chromatograph (HP series 6890) with a flame ionization detector. The temperatures of the injector and detector were 200°C and 275°C, respectively. The column temperature was increased from 70°C to 200°C at a rate of 12°C /min. SCFAs were separated using a Nukol capillary column (fused silica, 15 m x 0.53 mm x 0.5 μm, Supelco), using helium as the carrier gas at 21.4 ml/min.