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List of Supplemental Digital Content:

Supplemental Digital Content 1

Supplemental methods

Table S1. Descriptive statistics for each module identified using WGCNA.

Figure S1: Stacked bar charts indicating the relative abundances of the most abundant taxa

detected in the gut microbiomes of PTSD subjects and trauma-exposed (TE) controls.

Figure S2. Alpha diversity measures of gut microbial communities of PTSD subjects and

trauma-exposed (TE) controls.

Figure S3. Principal coordinate analysis (PCoA) plots of Bray-Curtis compositional dissimilarity

(on genus level) of microbiota communities among PTSD participants and trauma-exposed (TE)

controls.

Figure S4. Distance comparison plots with box plots illustrating weighted and unweighted

UniFrac distances within and between sample groupings using the β-diversity distance matrix.

Figure S5. Biplot illustrating genus-level gut microbial community analysis and composition

analysis using principal component analysis of centre log ratio-transformed and standardized data

from 18 PTSD participants and 12 trauma-exposed (TE) controls.

Figure S6. Dendrogram displaying module assignment within weighted gene co-expression

network analysis (WGCNA).

Figure S7. Bar chart illustrating predicted functional genomic content of the microbiota in PTSD

participants and trauma-exposed (TE) controls.

Figure S8. Partial plots of the random forests predicted values for the estimated probability of

PTSD from the random forest versus the relative abundance of the three phyla identified as

important for distinguishing PTSD status.

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Figure S9. Relationship between the random forests prediction model, relative abundance of

[Actinobacteria, Verrucomicrobia] and PTSD scores (CAPS Total Score). PTSD was negatively

associated with the relative abundance of Actinobacteria and Verrucomicrobia phyla

Figure S10. Relationship between the random forests prediction model, relative abundance of

[Actinobacteria,Verrucomicrobia] and childhood trauma scores (Childhood Trauma

Questionnaire (CTQ), Total Score).

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Supplemental Digital Content 1

Supplemental methods

Clinical and metabolic measures

Lifetime trauma exposure was assessed with the Life Events Checklist for DSM-5 (LEC-5) (64).

The LEC-5 assesses for exposure to 16 types of events that often meet criteria for a traumatic

stressor and includes one additional item for any other potentially traumatic events not covered in

the first 16 items. For each of the 17 items, the LEC-5 specifies whether it happened to the

individual personally, whether they witnessed it happen to someone else, whether they learned

about it happening to someone close to them or whether they were exposed to the event in the

course of their occupation. Participants identify the traumatic experience that is still affecting

them the most and this index trauma is used to assess for the symptoms of current posttraumatic

stress disorder (PTSD).

All participants were evaluated for current and lifetime psychiatric and substance use disorders

using the MINI International Neuropsychiatric Interview, version 6.0 (MINI) (63), which is a

clinician-administered short, semi-structured diagnostic interview that takes about 15 minutes to

administer.

Participants with metabolic syndrome (MetS) were excluded to eliminate additional confounding

factors that could influence gut microbiome results. We assessed for the presence of MetS based

on the harmonized Joint Interim Statement (JIS) criteria (110), with three or more out of five

criteria indicating the presence of MetS. The criteria used were (i) raised waist circumference

according to population- and country-specific definitions (men and women ≥ 90 cm) based on a

recent validation study in the population assessed (111); (ii) raised triglycerides (≥ 1.7 mmol/l);

(iii) low high-density lipoprotein cholesterol (HDL-C) (men <1.0 mmol/l, women <1.3 mmol/l);

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(iv) elevated blood pressure (≥130/85 mmHg) or on antihypertensive treatment; and (v) raised

fasting glucose (≥5.6 mmol/l) or on treatment for diabetes (97).

Microbiome analyses

Microbial DNA was extracted from 1.4 ml stabilized stool (stool specimen homogenized in stool

DNA stabilizing buffer) using the PSP® Spin Stool DNA Plus Kit (STRATEC Molecular,

Birkenfeld, Germany) according to the manufacturer’s protocol 2 (“Isolation of total DNA from

1.4 ml stabilized stool homogenate with enrichment of bacterial DNA”). This protocol includes

an initial cell lysis at 95 °C for 10 minutes to increase efficiency of DNA extraction of gram-

positive bacteria. Quality and integrity of the DNA was evaluated on the NanoDrop2000c

(ThermoFisher Scientific, Wilmington, DE, USA) and the Quant-iT™ PicoGreen® dsDNA

Assay Kit with the Quantus™ Fluorometer (ThermoFisher Scientific). A minimum of 20 ng of

purified DNA was used for the library preparation. The 16S ribosomal RNA (rRNA) gene

amplicons were generated for the V3 and V4 regions of the 16S rRNA bacterial gene, which were

recommended by Klindworth et al. (69). Illumina adapter overhang nucleotide sequences were

added to the gene‐specific sequences. The full-length primer sequences targeting this region were

341 forward primer (5’-CCTACGGGNGGCWGCAG-3’) and

785 reverse primer (5’-GACTACHVGGGTATCTAATCC-3’)

Libraries were prepared using the 16S Metagenomic Sequencing Library Preparation Kit from

Illumina, according to the manufacturer’s instructions. After amplification of the V3 and V4

region and, using a limited cycle PCR, adding Illumina sequencing adapters and dual-index

barcodes to the amplicon target, amplicon sizes were measured using an Agilent Technologies

2100 Bioanalyzer trace (Agilent, Part # G2940CA). Library quantification, normalization, and

pooling were performed according to the manufacturer’s specifications. Libraries were sequenced

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using multiplexed Illumina HiSeq paired-end 100 base pair sequencing according to the

manufacturer’s instructions. Base calling was performed and FASTQ sequence reads generated

using Illumina Casava Pipeline 1.8.2. Initial quality assessment was based on data passing the

Illumina Chastity filter. Subsequently, reads containing adaptors and/or PhiX control signal were

removed. The second quality assessment was based on the remaining reads using the FASTQC

quality control tool version 0.10.0.

Taxonomy from DNA sequences

The operational taxonomic unit (OTU) table was prepared using QIIME v. 1.9 (70). Forward

reads were demultiplexed using default parameters with a minimum quality score threshold set to

25. Following this step, 1,738,164 of 1,959,124 HiSeq reads passed quality control. These reads

were assigned to OTUs using the closed-reference OTU picking method with Greengenes 97%

reference database (Aug 13) (71). 1,690,568 reads mapped to the Greengenes database,

identifying a total of 7,556 OTUs. The reference Greengenes taxonomy assignments and

phylogenetic tree were used for all analysis where a tree or taxonomy assignment was required.

The samples were rarefied at 30,000 sequences per person and then filtered to retain only OTUs

that were present in at least 20% of the participants. This resulted in a total of 1,565 OTUs that

were used in subsequent analyses of microbial diversity and taxonomic abundances.

Microbial diversity analyses

For analysis of α- and β-diversity, the rarefied and filtered data set, i.e., excluding OTUs that

were present in <20% of participants, generating a raw OTU table containing 1,565 OTUs, was

used. While rarefaction is a conservative approach that limits power for discovery of differences,

it is essential to correct for different library sizes for α- and β-diversity analysis (112). Beta

diversity was determined using QIIME for Bray-Curtis and UniFrac (unweighted and weighted)

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analyses (70, 113-115) and was visualized using EMPeror (116). The beta diversity

measurements were compared between 18 cases (diagnosed with PTSD) and 12 trauma-exposed

(TE) controls (not diagnosed with PTSD) using analysis of similarities (ANOSIM) using Bray

Curtis, weighted UniFrac, and unweighted UniFrac metrics. Alpha diversity metrics were

determined after rarefaction for Chao1, observed OTUs, phylogenetic diversity (PD)-whole tree,

and Shannon diversity index using QIIME (70, 115, 117-120).

Comparison of taxa abundances

Operational taxonomic units of the rarefied data set were collapsed by taxonomic assignment and

compared using QIIME (70). The abundances of each taxon within the cases and controls were

compared for all taxa levels shown. The significance of the difference between groups was

evaluated using the Kruskal-Wallis test, correcting for multiple testing using a Bonferroni

correction (121-123).

Biplot analyses

To explore correlations among the different phyla and genera present in the samples, and their

relationships to microbial community structure in PTSD participants and TE controls,

compositional biplot analyses were performed. The full set of reads mapped to Greengenes,

identifying a total of 7,556 OTUs, was used for this analysis. A multiplicative zero replacement

technique was used to determine pseudo counts for zeros (124). A centre log ratio transformation

(125) was conducted to transform the data and a singular value decomposition was performed to

obtain a 2 rank approximation of the phyla and genera and the samples.

Weighted gene co-expression network analysis (WGCNA)

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Network-based analysis was conducted using weighted gene co-expression network analysis

(WGCNA). The full set of reads mapped to Greengenes, identifying a total of 7,556 OTUs, was

used for this analysis. Modules were tested for association with variables of interest (age,

Childhood Trauma Questionnaire (CTQ) score, time since index trauma (in months), plasma C-

reactive protein (CRP) concentration (mg/L)) using general linear models. All statistical analyses

were performed in R via the R Studio platform and used the WGCNA statistical package (126,

127). Initial analyses to check for potential outliers and excessive missingness in our data set

were performed using the WGCNA package. In addition, hierarchical average linkage cluster

analysis was performed to identify outlying samples. Neither of these resulted in the removal of

any participants.

We used WGCNA to identify clusters, or modules, of OTUs that are highly co-expressed as well

as a hub OTU that is the most highly connected. The nodes were the relative abundance values of

OTUs, and we used this analysis to build modules of these nodes. To build the modules we used

a “soft threshold” for assigning module membership, as suggested and thoroughly reviewed by

Zhang and Horvath (128). A topological overlap matrix (TOM) was built and used to develop

modules of connected nodes. Average linkage hierarchical cluster analysis was used to develop a

dendrogram, and the “cutreeDynamic” function in the WGCNA package was used to assign

modules based on this dendrogram (127, 128). The minimum module size of thirty nodes was

assigned. Eigenvalues were generated for each of the modules. The eigenvalues were organized

by treatment group and Student’s t-tests with Bonferroni correction were used to analyze the

modules for differences between PTSD participants and TE controls.

In order to test the relationship of these modules to traits of the participants, ordinary least

squares (OLS) regression models were generated. Twenty-two models were constructed, in which

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the module eigenvalues were regressed against age, CTQ total score, time since index trauma (in

months), and plasma CRP concentration (mg/L) for each participant, using Bonferroni correction.

Phylogenetic investigation of communities by reconstruction of unobserved states

(PICRUSt)

The functional potential of microbial communities was assessed using PICRUSt (129). The full

set of reads mapped to Greengenes, identifying a total of 7,556 OTUs, was used for this analysis.

Kyoto Encyclopedia of Genes and Genomes (KEGG) has been organized into 4 levels of

hierarchies. Level one is the most general level of categories and level four is the most specific

level of categories, each with KEGG Orthology (KO) terms. The analyses based on PICRUSt

prediction were done on level 3 after grouping predicted KO terms into a higher level of

categorization. Pairwise distances between samples were calculated using the Bray-Curtis metric

on level 3 KO categories. The level 3 PICRUSt output was filtered to include only metabolism-

related KO categories, and a second set of principal coordinates was calculated.

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Table S1. Descriptive statistics for each module identified using WGCNAModule Cluster

coefficient1Network density2

Network centrality3

Number of nodes4 P value5 Most connected

node by phylum6

Black 0.166 0.047 0.114 87 0.67 BacteroidetesBlue 0.182 0.05 0.137 123 0.55 FirmicutesBrown 0.188 0.046 0.089 104 0.93 BacteroidetesCyan 0.132 0.041 0.072 56 0.32 LentisphaeraeDark Green 0.228 0.07 0.109 35 0.60 FirmicutesDark Red 0.254 0.062 0.128 40 0.09 FirmicutesGreen 0.175 0.054 0.106 98 0.50 FirmicutesGreen Yellow 0.153 0.043 0.065 61 0.38 FirmicutesGrey 60 0.188 0.05 0.087 47 0.26 FirmicutesLight Cyan 0.295 0.064 0.112 49 0.67 ProteobacteriaLight Green 0.138 0.055 0.115 47 0.25 FirmicutesLight Yellow 0.175 0.06 0.064 45 0.95 FirmicutesMagenta 0.203 0.052 0.068 75 0.40 BacteroidetesMidnight Blue 0.175 0.049 0.129 54 0.30 BacteroidetesPink 0.201 0.065 0.144 82 0.36 BacteroidetesPurple 0.296 0.086 0.145 66 0.51 BacteroidetesRed 0.166 0.055 0.1 91 0.47 FirmicutesRoyal Blue 0.227 0.055 0.105 41 0.51 FirmicutesSalmon 0.238 0.073 0.122 57 0.65 BacteroidetesTan 0.241 0.65 0.087 58 0.35 BacteroidetesTurquoise 0.205 0.064 0.14 135 0.44 FirmicutesYellow 0.243 0.072 0.15 99 0.69 Firmicutes

1The cluster coefficient is a measure of localized network density or “cliquishness” within the modules. 2Network density is a measure of the total amount of possible connections that exist within a given module. 3Network centrality is a measure of how much one individual node dominates the module’s connectedness. 4The P value was generated from the Student’s t-test comparison between the eigenvalues of the posttraumatic stress disorder (PTSD) participants and trauma-exposed (TE) controls, using Bonferroni correction. 5The most connected node by phylum was determined by the scaled connectivity coefficient.

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Figure S1: Stacked bar charts indicating the relative abundances of the most abundant taxa

detected in the gut microbiomes of posttraumatic stress disorder (PTSD) participants and trauma-

exposed (TE) controls. A) Genus level. B) Family level. C) Class level. D) Order level. E)

Phylum level.

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Figure S2. Alpha diversity measures of gut microbial communities of PTSD subjects and

trauma-exposed (TE) controls. A) Chao1. B) Observed species. C) Phylogenetic diversity (PD)-

whole tree. D) Shannon diversity index. Analyses were performed on 16S rRNA V3/V4 amplicon

data (a single amplicon of approximately 460 base pairs), with different rarefaction depths,

represented by the x-axis. Values represent means ± SD.

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Figure S3. Principal coordinate analysis (PCoA) plots of Bray-Curtis compositional dissimilarity

(on genus level) of microbiota communities among posttraumatic stress disorder (PTSD)

participants (red circles) and trauma-exposed (TE) controls (blue circles). Red or blue circles that

are further apart have greater compositional dissimilarity. The proportion of variance explained

by each principal coordinate (PC) axis is denoted in the corresponding axis label; PC1 explains

30.96% of the variability, PC2 explains 9.90% of the variability, and PC3 explains 8.24% of the

variability; together, the axes explain 49.1% of the variability. Superimposed on the PCoA plot

are gray spheres indicating the ten most abundant bacterial genera. The sizes of the spheres

represent the mean relative abundance of the respective taxon and the locations of the spheres are

the weighted average locations based on the relative abundance in different participants. Subject

identification numbers are indicated in gray text for each sample. There were no differences in

Bray-Curtis β-diversity between PTSD and TE control groups based on analysis of similarities

(ANOSIM; test statistic = –0.033, P = .70).

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Figure S4. Distance comparison plots with box plots illustrating A) weighted and B) unweighted

UniFrac distances within and between sample groupings using the β-diversity distance matrix.

The bottoms and tops of boxes indicate the first and third quartiles; whiskers indicate 1.5

interquartile range (IQR) beyond the upper and lower quartiles. Values outside the whiskers are

indicated by “+”. Analysis of similarities (ANOSIM) revealed no differences between

posttraumatic stress disorder (PTSD) participants and TE controls, weighted UniFrac distance

comparisons, PTSD vs PTSD versus TE control vs TE control (test statistic = –0.016, P = .56);

unweighted UniFrac distance comparisons, PTSD vs PTSD versus TE control vs TE control (test

statistic = –0.013, P = .52).

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Figure S5. Biplot illustrating genus-level gut microbial community analysis (closed symbols)

and composition analysis (vectors) using principal component analysis of centre log ratio-

transformed and standardized data from 18 PTSD participants (red symbols) and 12 trauma-

exposed (TE) controls (blue symbols). The distance between symbols approximates the

dissimilarity of their microbial communities, as measured by Euclidean distance. PCA axes 1 and

2 explain 15.7% and 10.5% the variation, respectively. Vectors point in the direction of the

greatest increase of values for the corresponding genus across all PTSD and TE control subjects;

677 different genera were detected. The angle between arrows indicates approximated correlation

(>90° indicates a negative correlation). Gray text indicates subject identification number and

plasma C-reactive protein (CRP) concentrations for individual participants as measured using

high-sensitivity CRP (HS-CRP; CRP ≤ 3.0 mg/l) or sensitive (S-CRP; CRP > 3.0 mg/l) assays.

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Figure S6. Dendrogram displaying module assignment within weighted gene co-expression

network analysis (WGCNA), represented by arbitrary colors. The dendrogram was generated by

the hierarchical average linkage cluster analysis. Branch length represents relatedness of the

bacterial operational taxonomic units (OTUs). The WGCNA generated twenty-two modules,

each arbitrarily assigned a unique color.

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Figure S7. Bar chart illustrating predicted functional genomic content of the microbiota in PTSD

participants and trauma-exposed (TE) controls.

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Figure S8. Partial plots of the random forests predicted values for the estimated probability of

posttraumatic stress disorder (PTSD) from the random forest versus the relative abundance of the

three phyla identified as important for distinguishing PTSD status. Red points indicate partial

values, black lines are loess smoothed lines, and dashed red lines indicate a smoothed error bar of

+/- two standard errors. Actinobacteria detected in subjects from this study was represented, at

the genus level, by [g__Collinsella] (54.2%; expressed as mean percent of Actinobacteria among

those subjects with detectable Actinobacteria), [g__Bifidobacterium] (33.4%),

[f__Coriobacteriaceae; g__] (8.61%), [f__Bifidobacteriaceae; g__] (2.27%), [g__Slackia]

(0.78%), [g__Eggerthella] (0.43%), and [g__Nesterenkonia] (0.25%). Lentisphaerae detected in

subjects from this study was represented, at the genus level, by [f__Victivallaceae; g__] (90.3%),

and [g__Victivallis] (9.7%). Verrucomicrobia detected in subjects from this study was

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represented, at the genus level, by [g__Akkermansia] (61.9%), [c__Opitutae; o__; f__; g__]

(24.5%), and [f__Puniceicoccaceae; g__] (13.7%).

Figure S9. Relationship between the random forests prediction model, relative abundance of

[Actinobacteria, Verrucomicrobia] and posttraumatic stress disorder (PTSD) scores (Clinician

Administered Posttraumatic Stress Disorder Scale for DSM-5 (CAPS) Total Score). PTSD was

negatively correlated with the relative abundance of Actinobacteria and Verrucomicrobia phyla.

In other words, PTSD diagnosis was associated with decreased abundance of these phyla

(Pearson’s r = –0.364; P = .048). Percentages in parentheses indicate the percent relative

abundance of Akkermansia; Akkermansia was below the threshold of detection for all other

subjects. Sample sizes, PTSD subjects, n = 18; TE controls, n = 12). *P < .05, Student’s t-test.

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Figure S10. Relationship between the random forests prediction model, relative abundance of

[Actinobacteria,Verrucomicrobia] and childhood trauma scores (Childhood Trauma

Questionnaire (CTQ), Total Score). Childhood Trauma Questionnaire scores were negatively

correlated with the relative abundance of Actinobacteria and Verrucomicrobia phyla. In other

words, high childhood trauma scores were associated with decreased abundance of these phyla

(Pearson’s r = –0.375; P = .041). Percentages in parentheses indicate the percent relative

abundance of Akkermansia; Akkermansia was below the threshold of detection for all other

subjects. Sample sizes, PTSD subjects, n = 18; TE controls, n = 12). *P < .05, Student’s t-test.