Supplemental material
Metabolite Derivatization, gas chromatography and mas spectrometry
(GC/MS)
The GC/MS procedure was run in batches, as the lifetime of derivatized samples
is only about 48 hours at room temperature. Sample extraction was also done in a batch
mode. We have established a protocol in which batches had almost no deviation between
runs (less than 0.8%). Dried extracts were derivatized with 80 μl methoxyamine
hydrochloride (Aldrich, USA) (40 mg ml-1 in pyridine) for 60 min at 500C and then with
100 μl MSTFA+1% TMCS (Thermo Sci., USA) at 700C for 120 min and following 2hour incubation at room temperature. Five microliters (5 μL) of the internal standard
(hentriacontanoic acid (10 mg ml-1); Sigma, USA) were added to each sample prior to
derivatization. Samples were analyzed on a GC/MS system (Agilent Inc, Palo Alto, CA,
USA) consisting of an Agilent 7890 gas chromatograph, an Agilent 5975 mass selective
detector, and a HP 7683B autosampler.
Gas chromatography was performed on a ZB-5MS (60m×0.32mm I.D. and
0.25m film thickness) capillary column (Phenomenex, CA, USA). The inlet and MS
interface temperatures were 2500C, and the ion source temperature was adjusted to
2300C. An aliquot of 1 L was injected with the split ratio of 10:1. The helium carrier gas
was kept at a constant flow rate of 2 ml min-1. The temperature program was: 5-min
isothermal heating at 700C, followed by an oven temperature increase of 50C min-1 to
3100C and a final 10 min at 3100C. The mass spectrometer was operated in positive
electron impact mode (EI) at 69.9 eV ionization energy in m/z 30-800 scan range.
Supplemental figure legends
Figure S1. Gut bacterial community composition of western lowland gorillas at the
phylum (a) and genus (b) levels.
Figure S2. Gut bacterial community composition in 4 gorilla groups. (a) Average
pair-wise Bray-Curtis dissimilarity within (left side) and between (right side) groups.
Different letters denote significant differences (Tukey’s adjusted pair-wise comparisons,
P<0.05). (b) Principal coordinate analyses based on Jaccard dissimilarity matrices and
showing community composition differences at species level (97% 16S rRNA sequence
similarity) between the 4 groups. H1 (red circles), H2 (cyan squares), U1 (blue diamonds)
and U2 (gray triangles). Number of reads in each sample is rarefied to 3,756. (c) Statistics
for the partition around medoids (PAM) clustering analysis showing that microbiome
composition is best described in two or three clusters. Average silhouette width for two
clusters=0.077, three clusters=0.081, four clusters=0.055 and 5 clusters =0.061.
Figure S3. Correlations between the relative abundance of indicator taxa and PCoA
scores from SCFA ordination patterns. The PCoA was done based on a Bray-Curtis
dissimilarity matrix. For this analysis, only samples with matching SCFA and
microbiome data were used. For this reason the number of samples in each plot is fewer
for H2, U1 and U2 (see methods) and the percent variation explained by coordinate one is
now 37.2%.
Figure S4. Network view of linear relationships between microbiome and
metabolome markers in fecal samples of 4 western lowland gorilla groups. Each
letter represents sub-networks obtained based on the Fruchterman-Rheingold layout as
applied in cytoscape. This force directed algorithm, in which nodes have stronger
repulsive force from distant nodes, allows for detection of connected subnetworks. Edges
represent Spearman correlation coefficient, rho≥ 0.5 (FDR-q <0.05). Edge thickness
shows correlation strength (0.5<rho<0.97). Node size is correlated to degree of
connectivity and color nodes correspond to either metabolites (yellow) or bacterial taxa
(green).
Figure S5. Hierarchically-clustered heatmap showing Spearman correlation values
between metabolites and bacterial taxa in fecal samples of 4 western lowland gorilla
groups. Clustering was performed based using the heatmap.2 function in gplots (R). The
Spearman Correlation matrix and FDR-q values can be seen in table S4.
Table S1. Significant indicator values and relative abundances of taxa within
groups. +P<0.1; *P<0.05, **P<0.01, denote significant differences according to
Kruskal-Wallis pairwise tests adjusted for multiple comparisons. Taxa are compared to
the group displaying highest abundance of indicator taxa (colored and in bold).
Table S2 Discriminant metabolites characterizing the fecal metabolomes H2 and U2.
Z-scores denote discriminant metabolites as identified by the EBAM procedure (6.98-0.8
=FDR< 0.057).
Table S3. Network statistics for subnetworks characterizing H1-U1 and H2-U2.
Table S4. Spearman correlation matrix and FDR-q values of metabolitemicrobiome relationships.