The saNeuroGut Initiative: Investigating the Gut Microbiome and Symptoms of Anxiety, Depression, and Posttraumatic Stress

Michaela A O’Hare; Patricia C Swart; Stefanie Malan-Müller; Leigh L van den Heuvel; Erine Bröcker; Soraya Seedat; Sian MJ Hemmings

doi:10.1159/000542696

. 2024 Nov 19;32(1):1–15. doi: 10.1159/000542696

The saNeuroGut Initiative: Investigating the Gut Microbiome and Symptoms of Anxiety, Depression, and Posttraumatic Stress

Michaela A O’Hare ^a,^b,^c, Patricia C Swart ^a,^b, Stefanie Malan-Müller ^d,^e, Leigh L van den Heuvel ^a,^b, Erine Bröcker ^a,^b, Soraya Seedat ^a,^b, Sian MJ Hemmings ^a,^b,^✉

PMCID: PMC11844704 PMID: 39561720

Abstract

Introduction

Common mental disorders, such as anxiety disorders, depression, and posttraumatic stress disorder (PTSD), present a substantial health and economic burden. The gut microbiome has been associated with these psychiatric disorders via the microbiome-gut-brain axis. However, previous studies have focused on the associations between the gut microbiome and common mental disorders in European, North American, and Asian populations. As part of the saNeuroGut Initiative, we assessed associations between gut microbial composition and self-reported symptoms of anxiety, depression, and posttraumatic stress (PTS) among South African adults.

Methods

Participants completed validated, online self-report questionnaires to evaluate symptoms of state anxiety, trait anxiety, depression, and PTSD. Eighty-six stool-derived microbial DNA samples underwent sequencing of the V4 region of the 16S rRNA gene to characterise gut bacterial taxa in the sample.

Results

No significant associations were observed between symptom severity scores and alpha (Shannon and Simpson indices) and beta (Aitchison distances) diversity metrics. Linear regression models revealed that the abundances of Catenibacterium, Collinsella, and Holdemanella were significantly positively associated with the severity of PTS symptoms.

Conclusion

Catenibacterium, Collinsella, and Holdemanella have each previously been associated with various psychiatric disorders, with Catenibacterium having been positively associated with symptoms of PTSD in another South African cohort. This study sheds light on the relationship between the human gut microbiome and symptoms of anxiety, depression, and PTS in a South African adult sample.

Keywords: Mental health, Psychiatry, Gut microbiota, Microbiome-gut-brain axis, Gut-brain interaction

Introduction

In 2019, nearly a billion individuals worldwide (13% of the global population) were affected by mental disorders [1]. Among these, anxiety and depressive disorders emerged as the most prevalent, each with a global prevalence of approximately 4% [1]. According to data from 2022, 25.7% of South Africans suffer from probable depression, while 17.8% have probable anxiety [2]. An earlier study, the South African Stress and Health survey, revealed that ∼74% of respondents had experienced at least one potentially traumatic event during their lifetime [3]. Posttraumatic stress disorder (PTSD) is a sequelae of such exposure and may manifest as intrusive reminders of the trauma, avoidance of trauma reminders, alterations in mood or cognition, and changes in arousal or reactivity [4]. The lifetime prevalence of PTSD in South Africa has been reported as 2.3%, although this figure is a likely underestimate of the true prevalence [3].

Anxiety, depression, and PTSD are associated with substantial disability due to their high prevalence, chronic symptoms, and comorbid occurrence [5]. In fact, these stress-related disorders have been shown to be linked to various health conditions and behaviours, including higher rates of irritable bowel syndrome, asthma, and cardiovascular disease [6], and poorer adherence to self-care regimens, such as diet, exercise, and cessation of smoking [7]. Underpinning these disorders is no single causal factor, but rather a combination of heterogeneous factors including, though not limited to, sex [8], alcohol [9] and/or tobacco product use, and genetics [10].

An emerging area of research linked to various psychiatric disorders is the gut microbiome – the diverse community of microorganisms residing in our gastrointestinal tract, which, along with their collective activity, influence host health [11, 12]. While the notion of the gut and the brain having a connection is not novel per se [13], recent advancements in tools such as DNA sequencing technologies and germ-free murine models have permitted rapid progress in research within this field [14]. The composition of the gut microbiome is influenced by various forms of stress, including psychological, social, environmental, and physical stress [15, 16]. However, the microbiota-gut-brain communication is bidirectional, as symptoms of psychiatric disorders have also been shown to be influenced by gut microbial composition. For instance, studies have shown that transferring gut microbial samples from depressed humans into rodent models led to the presentation of depressive-like behaviours [17]. Many studies have demonstrated links between the gut microbiome and psychiatric conditions, such as anxiety, depression, and PTSD [18–20]. Of note, a recent Mendelian Randomisation study reported a potentially causal role of the gut microbiota on the development of PTSD [20].

Mechanisms underlying the interaction between the gut microbiome and stress-related disorders include the vagus nerve [21], the host immune system and inflammation [14], and the endocrine system (including the hypothalamic-pituitary-adrenal axis) [22], as well as their combined interactions. Psychiatric treatments targeting the gut microbiome, referred to as psychobiotics, are being explored, with some showing promise [23]. For instance, probiotics containing species of Lactobacillus, Bacillus, and Bifidobacterium have demonstrated efficacy in ameliorating symptoms of depression [24–26].

Although there is a substantial number of reports on the association between the gut microbiome and psychiatric disorders, studies conducted in African populations are extremely sparse. In 2023, a list was published showing the top ten countries with the most published journal articles pertaining to the gut microbiome and mental health – none of which were African, highlighting the relative paucity of studies in African and South African populations [27]. Disparities were also evident in the number of studies on depression (n = 1,243) compared to those of anxiety disorders (n = 129) and PTSD, with PTSD not represented in these statistics at all. Indeed, studies exploring the relationship between the human gut microbiome and PTSD are notably scarce, with less than ten original research articles published, two of which originate from our research group [28, 29]. The findings from these two papers showed that, within a South African context, PTSD status was associated with the relative abundances of certain gut microbial taxa. These results suggest that mental health status may be linked to the gut microbiome in South African populations, and that further investigations could offer valuable insights. Therefore, given the prevalence and substantial health burdens associated with anxiety, depression, and posttraumatic stress (PTS) symptoms, coupled with the limited research conducted in African populations, the current study aimed to assess potential associations between gut microbial composition and self-reported symptom severity of anxiety, depression, and PTS among South African adults.

Methods

The saNeuroGut Initiative

The South African Microbiome Initiative in Neuroscience (saNeuroGut) is a population-based study that seeks to explore the role of the gut microbiome and host genetics in the context of neuropsychiatric disorders in South African populations (www.saneurogut.org). Participants were recruited via print, social media (mainly Facebook), and word-of-mouth. Ethics approval was obtained from the Health Research Ethics Committee at Stellenbosch University (N18/03/038), and written informed consent was obtained from all participants. Inclusion criteria included being over 18 years of age, able to read, write, and understand English, and being a South African resident. If participants had previously undergone a gastric bypass surgery or used antibiotics in the past month, they were excluded from stool sample collection. If participants had experienced diarrhoea in the past week, they were advised to postpone stool sample collection for approximately 3 weeks. Participants collected stool samples within 4 weeks of completing the questionnaires.

Online Questionnaires

Self-report online questionnaires were used to garner information regarding participants’ demographics, anthropometrics, general medical health, lifestyle habits, and stool sample features (with the Bristol stool scale [BSS] [30]). The Spielberger State-Trait Anxiety Inventory (STAI) was used to assess state and trait anxiety (STAI-S and STAI-T, respectively) [31]. Symptoms of depression and PTS were assessed using the Centre for Epidemiologic Studies – Depression (CES-D) scale and the PTSD Checklist for DSM-5 (PCL-5), respectively [32, 33]. Study data were collected and managed using REDCap electronic data capture tools hosted at Stellenbosch University [34, 35].

Stool Sample Collection

Sample collection kits consisted of an OMNIgene GUT OMR-200 (DNA Genotek) stool collection tube, along with the necessary personal protective equipment and instructions for sample collection. Between October 2020 and 2021, these kits were dispatched to participants and returned to the Neuropsychiatric Genetics Lab at Stellenbosch University through a local courier service. Once returned, samples were initially stored at room temperature for a few days before being aliquoted and subsequently stored at −80°C.

DNA Isolation and Sequencing

Stool samples were thawed and extracted using the QIAamp PowerFecal Pro DNA Kit (Qiagen, Hilden, Germany), according to the manufacturer’s protocol (“QIAamp PowerFecal Pro DNA Kit Handbook”). ZymoBIOMICS Microbial Community Standard (D6300; Zymo Research, Irvine, CA, USA) and OMNIgene GUT stabilisation buffer were used as positive and negative controls, respectively, for DNA extraction. DNA quality (absorbance ratios: 260/280 and 260/230) and quantity (ng/µL) were assessed using the NanoDrop 2000c Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA) and the Qubit 4 Fluorometer (Thermo Fisher Scientific, Wilmington, DE, USA), respectively. Where applicable, samples were normalised to 25 ng/µL before being sent to the Centre for Proteomic and Genomic Research (CPGR), Cape Town, South Africa, for sequencing of the fourth hypervariable region (V4) of the 16S rRNA gene.

Based on results from Liu et al. [36], the V4 region was chosen for amplification using 515 forward and 806 reverse primers from the Earth Microbiome project, with additional adaptor sequences (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGYCAGCMGCCGCGGTAA-3′ and 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACNVGGGTWTCTAAT-3′, respectively) [37]. Purification of the amplicons, including the removal of free primers and primer dimer species, and final sequencing libraries, was performed using Agencourt AMPure XP beads (Beckman Coulter Life Sciences, Brea CA, USA). Dual indices and sequencing adapters were attached using the Nextera XT Index Kit v2 (Illumina, San Diego, CA, USA). Sequencing of V4 libraries occurred on an Illumina MiSeq Sequencing Instrument with a MiSeq Reagent v2 Kit (500 cycles), producing 2 × 250 paired-end reads, where the expected V4 amplicon size was approximately 292 bp. For sequencing reactions, ZymoBIOMICS Microbial Community DNA standard (D6305; Zymo Research, Irvine, CA, USA) and elution buffer (Qiagen, Hilden, Germany) were used as positive and negative controls, respectively, as well as a 10% PhiX DNA library control. A total of 8.09 Gb of non-indexed sequence data were generated, resulting in FASTQ files for forward and reverse reads. Each sample contained approximately 140,000 reads (forward and reverse together), on average, before any data processing.

Processing Raw Reads

FASTQ files were processed using the Divisive Amplicon Denoising Algorithm (dada2) package v1.16 [38] in R v4.0.0 (R Core Team, 2020) on ilifu, a local cloud computing platform (www.ilifu.co.za). Reads were filtered according to default parameters in dada2 and truncated at base pair positions 240 and 200 for forward and reverse, respectively, after visualising read quality profiles.

Filtered reads underwent dereplication and denoising through the application of dada2 default parameters, which involved learning the error rates and employing high-resolution sample inference from the amplicon data to eliminate identified sequencing errors. The denoised forward and reverse reads were merged to generate amplicon sequence variants (ASVs), which were then used to construct a sample-by-sequence observation matrix. Subsequent removal of chimeric sequences resulted in the generation of paired-end read files for each sample. Taxonomy was assigned to ASVs by implementing the RDP Naïve Bayesian Classifier algorithm [39] using the SILVA rRNA gene database v138.1 as a reference dataset [40, 41]. A feature table was constructed with 15,301 unique ASVs from 86 samples, with an average read length of ∼292 bp, as expected. The vegan package v2.6-4 [42] in R was used to generate a rarefaction curve to infer whether the sampling depth was sufficient to capture diversity within samples. The integration of ASVs, taxonomic assignments, and participant metadata was performed using phyloseq v1.42 [43]. Taxa were agglomerated to and only analysed at genus level.

Statistical Analysis

Total scores were derived for STAI-S, STAI-T, CES-D, and PCL-5 and analysed as continuous variables. Continuous descriptive, clinical, and demographic characteristics were reported as mean ± standard deviation (SD) if parametric, and as median with interquartile range (IQR) if non-parametric. Categorical data were reported as counts with proportions (%). Associations between non-parametric continuous data were assessed using Spearman’s rank correlation test (no tests were performed for only normally distributed variables). Associations between parametric and non-parametric continuous and categorical data were assessed using Welch two-sample t test and Wilcoxon rank sum test with continuity correction, respectively.

Variables assessed for association with symptom scores were age, sex, BMI, nicotine or tobacco product usage, alcohol usage, and bowel disease status (inflammatory bowel disease, irritable bowel, or coeliac disease). Results for the last three variables were categorised as “ever” and “never,” reflecting whether participants had ever been diagnosed with or used such substances at any point in their lifetime. Variables demonstrating a significant association (p < 0.05) with symptom severity scores were included as covariables in relevant analyses.

For the assessment of variables associated with gut microbiome data, the R package, Microbiome Multivariable Associations with Linear Models (MaAsLin2) v1.12.0 [44], was employed. In addition to the previously mentioned variables, whether the participant was using psychiatric medication at the time of stool collection (yes/no), and the BSS was also assessed for association with taxonomic abundance. Variables demonstrating a significant association (adjusted p value <0.25 [default in MaAsLin2] based on Benjamini-Hochberg’s procedure [45]) were included as covariables in relevant analyses.

Alpha Diversity

Shannon and Simpson diversity estimates were generated using untransformed and unfiltered abundance data using the estimate_richness function within phyloseq [43]. Raw read counts were used rather than those having undergone rarefaction or other normalisation techniques in order to ease the comparability of results between papers with similar study designs [29, 46–48] and to avoid the introduction of bias with which rarefaction has been associated [49]. Associations between these alpha diversity indices and psychiatric symptom severity scores were assessed using linear regression models, with psychiatric symptom severity scores selected as the outcome variable. Covariables were included based on their significant association with symptom severity scores. Therefore, covariables were added to the models as follows: STAI-S – sex and alcohol usage, STAI-T – age and sex, CES-D – age and sex, and PCL-5 – sex, alcohol use, and bowel disease status. Significance was defined as p < 0.05 for associations between psychiatric symptom severity and alpha diversity estimates.

clr-Data Transformation

To evaluate microbial community variation, abundance data were filtered using the CoDsaSeq package v0.99.1 in R [50] according to default parameters. Zeroes were imputed using the minimal proportional abundance detected for each taxon. Filtered abundance data were then transformed using a centre log-ratio (clr) transformation using the compositions package v.0-6 in R [51].

Beta Diversity

Dissimilarity indices were computed by calculating the Euclidean distances between the clr-transformed abundance data (i.e., Aitchison distances [52]) using the vegdist function from vegan [42]. From these, a matrix was constructed containing pairwise dissimilarity values between samples. To examine the potential association between psychiatric symptom severity scores and differences in gut microbial profiles, adonis2 from vegan was used [42]. adonis2 is a permutational analysis of variance (PERMANOVA) that evaluates the dispersion of the centroids of microbiome profiles in comparison with symptom severity scores (number of permutations = 9,999, seed number = 3,003).

The structure of the PERMANOVA equation differs from that of a linear regression model in that it necessitates dissimilarity values (Aitchison distances) as the outcome variable, with the primary predictor variable (psychiatric symptom severity scores) listed as the final covariable rather than the first. Consequently, the covariables employed in linear regression models with psychiatric symptom severity scores as the outcome are not applicable to these PERMANOVA analyses. Therefore, for beta diversity analyses, covariables were chosen based on their significant association (adjusted p value <0.25, according to default parameters) with taxonomic abundances, as determined by MaAsLin2 [44]. Consequently, BSS was the only covariable included in PERMANOVA analyses. Significance for PERMANOVA results was established as p < 0.05.

Differential Abundance

Taxa present in less than 10% of samples were excluded from analyses to mitigate the risk of spurious associations. To evaluate the associations between individual clr-transformed genus abundances and symptom severity scores, linear regression models were used, with symptom severity scores as the outcome variable. Models were adjusted for the relevant covariables identified through significant associations with symptom severity scores. Results underwent correction for multiple testing according to Benjamini-Hochberg’s procedure and were represented as q values [45]. Significance was defined as q < 0.1.

Results

Clinical and Demographic Characteristics of Participants

A total of 86 participants between the ages of 18 and 68 years were recruited from various regions in South Africa (Table 1), predominantly the Western Cape (49/86, 57%). The median total STAI-S score was 39 (IQR = 30.25–49.75), with 37/86 (43.02%) of participants having probable state anxiety (STAI-S score <16). The mean total STAI-T score was 44.58 (SD = 12.68), with 41/86 (47.67%) of participants having probable trait anxiety (STAI-T score <45). The median total CES-D score was 14 (IQR = 7–24.75), with 39/86 (45.34%) of participants having probable depression (CES-D score <16). The median total PCL-5 score was 16 (IQR = 6–33), with 23/86 (26.74%) of participants having probable PTSD (PCL-5 score <33). It is imperative to note that these figures are not nationally representative.

Table 1.

Clinical and demographic characteristics of saNeuroGut participants

Demographic and clinical variables	Total cohort	STAI-S		STAI-T		CES-D		PCL-5
Demographic and clinical variables	median (IQR), or N (%)	median (IQR)	p value	mean±SD	p value	median (IQR)	p value	median (IQR)	p value
Age (years)	33.5 (27–48.75)	NA	0.077	NA	0.004	NA	0.021	NA	0.299
Sex
Male	22 (26%)	31 (33.5–51)	0.009	39.14±10.8	0.013	9.5 (5–15)	0.016	9 (0–18.5)	0.045
Female	64 (74%)	40.5 (23.8–39)	0.009	46.45±12.9	0.013	16 (8.75–25.2)	0.016	18.5 (7.5–33.2)	0.045
BMI	25 (22–29.84)	NA	0.468	NA	0.476	NA	0.936	NA	0.177
Nicotine use
Ever	30 (35%)	39.5 (34.2–51)	0.200	46.5±10.4	0.269	16 (11.2–25)	0.214	15.5 (5.25–33)	0.931
Never	56 (65%)	38 (27–47.5)	0.200	43.6±13.9	0.269	13 (5–24)	0.214	16.5 (6–30)	0.931
Alcohol use
Ever	68 (79%)	37.5 (29.8–44)	0.026	43.3±11.8	0.129	13.5 (7–22.5)	0.140	14 (4–27.2)	0.001
Never	18 (21%)	48 (39–54)	0.026	49.4±15.2	0.129	21 (11–27.2)	0.140	31 (15.2–47.8)	0.001
Bowel disease
Ever	14 (16%)	50 (39–54)	0.069	50.1±13.7	0.114	18.5 (9–27)	0.290	33.5 (20–42)	0.010
Never	72 (84%)	38 (29.8–45.2)	0.069	43.5±12.4	0.114	14 (7–24.2)	0.290	14 (4.75–28.2)	0.010

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Significance is defined as p < 0.05 and represented by boldface type.

STAI-S, Spielberger’s State and Trait Anxiety Inventory – State; STAI-T, Spielberger’s State and Trait Anxiety Inventory – Trait; CES-D, Centre for Epidemiologic Studies – Depression scale; PCL-5, PTSD Checklist for DSM-5; BMI, body mass index; SD, standard deviation; IQR, inter-quartile range.

Processing Raw Reads

On average, 51,764 reads were generated for each sample, ranging from 30,587 to 125,425 (online suppl. Fig. 1; for all online suppl. material, see https://doi.org/10.1159/000542696). Most reads had a quality score >30, and in the case of forward reads, near 40 (online suppl. Fig. 2). Based on the rarefaction curves reaching a plateau, it is suggested that the sequencing depth was sufficient to capture the diversity within the samples (online suppl. Fig. 3). In total, 15,301 distinct ASVs were found, belonging to two kingdoms (Archaea and Bacteria), 14 phyla, 24 classes, 50 orders, 85 families, and 265 genera before filtering steps. Subsequent to the filtering steps, two kingdoms, seven phyla, ten classes, 20 orders, 33 families, and 80 genera were used for downstream analyses.

Alpha Diversity

No symptoms were associated with any alpha diversity metrics (Table 2).

Table 2.

Results of linear regression models of alpha diversity and psychiatric symptom severity scores

Psychiatric symptoms	Covariates in linear regression models	p value (Shannon diversity)	p value (Simpson diversity)
State anxiety	Sex + alcohol use	0.441	0.295
Trait anxiety	Age + sex	0.902	0.807
Depression^a	Age + sex	0.604	0.284
PTS^a	Sex + alcohol use + bowel disease	0.341	0.741

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Significance is defined as p < 0.05.

^aTo ensure residuals of the models conformed to normality, depression and PTS symptom severity scores were square-root transformed.

Beta Diversity

Results from multivariate linear models used to assess associations of metadata variables with taxonomic abundances showed that BSS score was significantly associated with Akkermansia, Lachnospiraceae UCG 004, Parabacteroides, and Faecalibacterium (online suppl. Table 1). Therefore, BSS was the only covariable we used for beta diversity analyses.

PERMANOVA models explained between 8.61% (trait anxiety) and 8.99% (state anxiety) of the variations in the Aitchison distances. None of the psychiatric symptom severity scores were significantly associated with the dissimilarity between samples (Table 3; online suppl. Fig. 4).

Table 3.

Results of PERMANOVA of microbial community dissimilarity (as measured by Aitchison distances) and psychiatric symptom severity scores

Psychiatric symptoms	Homoscedastic (p value)	Adjusted R²	p value
State anxiety	Yes (0.377)	8.99	0.143
Trait anxiety	Yes (0.812)	8.61	0.490
Depression	Yes (0.468)	8.68	0.409
PTS	Yes (0.942)	8.67	0.440

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Significance is defined as p < 0.05.

Differential Abundance

Variables that were associated with symptom severity scores were used as covariates in the linear regression models (see Table 1). Several genera were associated with psychiatric symptom severity scores (p < 0.05) but did not survive correction for multiple testing (Table 4). None of the taxa were associated with STAI-T total score severity. Only the positive associations between three taxa, namely, Catenibacterium, Collinsella, and Holdemanella, and PTS survived correction for multiple testing (q = 0.082 for all three) (Fig. 1).

Table 4.

Results from linear regression models of clr-transformed taxonomic abundances with psychiatric symptom severity scores

Psychiatric symptoms	Taxon	Beta coefficient	Standard error	p value	q value
State anxiety	Acidaminococcus	5.620	2.440	0.024	0.381
	Coprobacter	1.712	0.726	0.021	0.381
	Coprococcus	−3.812	1.331	0.005	0.259
	Escherichia-Shigella	1.676	0.758	0.030	0.398
	Faecalibacterium	−4.595	1.715	0.009	0.259
	Lachnospiraceae ND3007 group	−4.008	1.514	0.010	0.259
	Methanosphaera	4.531	2.144	0.038	0.430
Depression	Paraprevotella	2.332	0.713	0.002	0.126
PTS	Alistipes	−3.460	1.739	0.050	0.266
	Bacteroides	−5.008	1.847	0.008	0.126
	Catenibacterium	8.513	2.594	0.002	0.082
	Collinsella	3.934	1.289	0.003	0.082
	Erysipelatoclostridium	−2.844	1.142	0.015	0.148
	Faecalibacterium	−6.584	2.407	0.008	0.126
	Holdemanella	4.753	1.535	0.003	0.082
	Methanobrevibacter	2.451	1.073	0.025	0.192
	Methanosphaera	6.780	2.997	0.026	0.192
	Odoribacter	−6.963	2.618	0.009	0.126
	Parasutterella	−2.220	1.113	0.049	0.266
	Prevotella	2.158	0.897	0.018	0.163
	Ruminococcaceae incertae sedis ^a	−3.346	1.601	0.040	0.244
	Senegalimassilia	5.869	2.266	0.011	0.130
	Eubacterium xylanophilum group	−4.454	2.083	0.035	0.237

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Results were corrected for multiple testing according to Benjamini-Hochberg’s procedure (significance is defined as q < 0.1 and represented by boldface type).

^a“Incertae sedis” is a term used to refer to taxa with unknown or undecided taxonomic relationships. As such, this genus was manually prepended with the family name.

Fig. 1. — Relationship between the clr-transformed abundances of *Catenibacterium*, *Collinsella*, and *Holdemanella* with PTS symptom severity, as assessed by PCL-5 total scores, from 86 saNeuroGut participants.

Discussion

Findings revealed no significant associations between symptom severity scores for anxiety, depression, or PTS and alpha or beta diversity metrics. However, the individual abundances of three genera, namely, Catenibacterium, Collinsella, and Holdemanella, exhibited significant positive associations with PTS symptoms. The current study is one of the first population-based investigations in South Africa and Africa to investigate the link between the human gut microbiome and symptoms of anxiety, depression, and PTS. Moreover, this study represents one of very few worldwide reports on the association between the human gut microbiome and the severity of PTS symptoms.

We found no significant associations between intra-sample (alpha) diversity and psychiatric symptom severity scores. This observation aligns with a common trend observed in studies of a similar nature that also assessed anxiety and depressive disorders [53] and PTSD [29]. Conversely, some studies do report associations between mental health symptoms and intra-sample diversity. However, the results of these studies are inconsistent, with higher intra-sample diversity associated with psychiatric symptoms, such as depression [54] and PTSD [47], while others report a lower intra-sample diversity associated with psychiatric symptoms, such as trait anxiety [47], depression [55], and PTSD [56].

We found no significant associations between inter-sample (beta) diversity and symptom severity scores. Although this lack of significant association has previously been reported [47, 53], many studies have also reported significant differences associated with the overall gut microbial composition based on symptoms of depression [57] and various forms of anxiety [19, 58], including trait anxiety [59].

Inconsistencies in alpha and beta diversity associations may be due to a number of reasons. Firstly, it is imperative to note the plethora of various alpha and beta diversity measures, each employing distinct methodologies for computing intra- and inter-sample diversity. This may yield varying outcomes, although some may be highly correlated [60]. For instance, of the results previously listed that are inconsistent with ours, Ye et al. [54] used Chao1 indices, Liu et al. [57] used Jaccard dissimilarity indices, and Chen et al. [58] and Wang et al. [59] used UniFrac distances. Secondly, alpha and beta diversity metrics are influenced by factors that include age, sex [61], and BMI [62], which are commonly incorporated in analyses. However, additional factors, such as diet [63], physical or cardiorespiratory fitness [64], and socioeconomic elements, including income and area-level socioeconomic status [65], have also been associated with alpha diversity. Despite their significance, these, and other less common variables, are seldom considered as covariables in analyses, potentially contributing to the observed lack of consistency in results within this field. Regarding beta diversity, we corrected for variables that were associated with taxonomic abundances, which, in this case, was stool consistency as indicated by the BSS score. This finding aligns with that of Falony et al. [66], who identified BSS as a top feature correlating with faecal microbial composition among 503 investigated features. However, of the studies that assess beta diversity in this context, only Malan-Muller et al. [47] accounted for BSS score, allowing for the potential introduction of bias in interpretation of results. Finally, the inclusion of participants from diverse geographic locations in microbiome studies poses a challenge in direct comparability, as gut microbial diversity has been demonstrated to vary based on geography [67, 68]. This notion is strengthened by the fact that results of alpha and beta diversity analyses from this study are concordant with that of both previous studies conducted in South Africa [28, 29], compared to the other previously mentioned studies which were conducted in Asia [54, 57–59], Europe [19, 47, 55], and North America [56].

The relative abundances of Catenibacterium (q = 0.082), Collinsella (q = 0.082), and Holdemanella (q = 0.082) were positively associated with PTS symptom severity. To the best of our knowledge, the individual abundances of these three bacteria in the human gut microbiome have not previously been associated with symptoms of PTS. However, as part of a group of four bacteria (the others being Mitsuokella, Olsenella, and Odoribacter), Catenibacterium has previously been reported as positively associated with symptoms of PTSD (as assessed using a semi-structured interview, Clinician-Administered PTSD Scale for DSM-5 [CAPS-5]) [29]. The partial concordance in results is particularly interesting as both studies were conducted in South African populations. Additionally, the current study revealed that Odoribacter was also associated with symptoms of PTS (p = 0.009), but contrary to the positive association reported in Malan-Muller et al. [47], we found that Odoribacter was negatively associated with symptoms of PTS. However, the significance of this association did not survive correction for multiple testing (q = 0.126). In contrast to results of the current study, Catenibacterium has also been shown to be depleted in depressed individuals with anxiety [69] and anxious distress [53], compared to depressed individuals without symptoms of anxiety.

Although Collinsella has not previously been reported as differentially abundant in individuals with symptoms of PTS, Hemmings et al. [28] demonstrated that the Actinobacteria phylum, of which Collinsella represented a significant proportion (54.2%), was decreased in individuals with PTSD compared to trauma-exposed controls. However, this was evident only when Actinobacteria occurred within a consortium of three phyla (the other two being Lentisphaerae and Verrucomicrobia). Despite this study also being conducted within a South African population, one cannot speculate too much about disparities in results, as Hemmings et al. [28] only detected significant associations at the phylum level (i.e., the abundance of Collinsella in comparison with other genera was not directly assessed). Collinsella has also been reported to be depleted in individuals experiencing social anxiety during puberty [70] and adolescent depression [71]. In contrast, Collinsella has been demonstrated to be enriched in individuals with both moderate and severe MDD [72], and to have a positive correlation with depression symptom severity scores in males [73] and females [74]. Further, and in line with the results of the current study, this genus has been found to be increased in obese women with lower expression of the persistence temperamental trait, suggesting negative emotionality [75], which has been shown to predict PTSD symptoms [76].

Holdemanella has been reported as negatively associated with depression symptom severity in individuals with post-stroke depression [77]. However, the genus has also been reported as enriched in individuals with symptoms of dementia, such as Alzheimer’s disease [78], as well as children and adults with autism spectrum disorders [79, 80]. Holdemanella has also been reported as positively associated with externalising behaviour in children [70], which has been linked to PTS symptoms, childhood trauma [81], and emotional dysregulation as a result of early trauma exposure [82].

Catenibacterium, Collinsella, and Holdemanella are all gram-positive obligatory anaerobes, which produce various short-chain fatty acids (SCFAs), including acetic acid and lactic acid [83–85]. Accordingly, we may hypothesise that, in the current study, individuals with increased abundances of these bacteria, and thus more severe PTS symptoms, may potentially have higher concentrations of these SCFAs. However, this would have to be assessed in functional studies before any definitive conclusions may be drawn. Concentrations of acetic acid have been found to be depleted in individuals with PTSD compared to healthy controls [86], which does not align with the proposed theoretical relative SCFA concentrations from the current study. However, this does align with results of Hemmings et al. [28], who reported that, compared to trauma-exposed controls, individuals with PTSD had lower abundances of Actinobacteria and Verrucomicrobia, phyla that comprise many acetate-producing bacteria, such as Bifidobacterium and Akkermansia.

Studies investigating lactic acid in PTSD have yielded varying results, with one study reporting lactic acid concentration to be decreased in male subjects with PTSD compared to healthy controls [86], while another study demonstrated the opposite effect [87]. In line with the latter study and the proposed relative SCFA concentrations from the current study, lactate (the ionised form of lactic acid) has been found to be enriched in the urine of individuals with more severe symptoms of MDD [88]. Additionally, it has been shown that sodium lactate can induce flashbacks and panic attacks in individuals with PTSD, although these studies are outdated and were conducted using limited sample sizes, and effects of sodium lactate may differ to those of lactic acid produced by gut microbiota [89, 90]. The mechanisms behind the association between lactate levels and PTSD have yet to be entirely elucidated, although a few have been proposed: (1) lactate activates GPR81, a cell-surface receptor found in the neurons of the cerebral cortex, hippocampus, and cerebellum [91], and enriched at the blood-brain barrier, suggesting it may play a role in signalling in the brain [87]; (2) lactate increases the firing rate of hippocampal neurons, which may partially explain symptoms of panic associated with PTSD [92]; (3) lactate influences the neuronal expression of genes associated with synaptic plasticity, which has been linked to symptoms of PTSD [93, 94]; (4) lactate concentration affects clock genes, which may contribute to sleep disturbances and glucocorticoid sensitivity associated with PTSD [92, 95].

However, functional studies are needed to validate whether SCFA concentrations do indeed vary based on PTS symptom severity in this cohort, as hypothesised. Although the bacteria associated with PTS symptom severity in the current study produce acetic and lactic acid, due to the complexity and interkingdom dynamics of the gut metagenome, concentrations of microbial byproducts may not be directly proportional to bacterial abundances. For instance, the products of one type of bacteria may affect the production of metabolites in another type of bacteria. Further, within each genus are many species and strains, each of which may produce varying concentrations of SCFAs, if at all any.

Interestingly, the abundance of Holdemanella has been reported as being negatively associated with the size of the right amygdala and right hippocampus [78]. The former is a region of the brain involved in fear conditioning [96], memory formation [97], automatic emotional and unconscious threat processing [98, 99], and harm avoidance [100], while the latter is involved in explicit memory processes and in the encoding of context during fear conditioning [101]. These brain regions have been shown to be reduced in volume in individuals with PTSD [102, 103] and represent two of the three most clearly altered regions within the limbic system affected by PTSD [104]. A reduction in these central nervous system tissues has been suggested to be a consequence of at least four potential mechanisms: (1) inflammation, (2) apoptosis, (3) microglial activation, and (4) decreased neurogenesis [105]. Holdemanella has also previously been positively associated with three central inflammatory markers – TNF-α, IL-1β, and IL-6 in rats [106], the first two of which have been linked to inflammation and apoptosis [107]. All three of these markers have also been reported to play a role in synaptic plasticity and the process of memory and learning – both of which are factors associated with PTSD [108]. To this end, reduced brain volume and pro-inflammatory processes may be additional factors in the development of PTSD. However, it is imperative to note that these associations are speculative and need to be interpreted with extreme caution in this cohort.

This exploratory study is not without limitations. First, taxonomic identification was restricted to genus level given that only 16S rRNA gene V4 sequencing data were available. Data generated from hypervariable regions lack sufficient genetic diversity to accurately resolve taxa to species and strain levels. Second, the self-report nature of the questionnaires may have introduced self-report bias, regardless of whether they were administered online or on paper. However, the online format might have skewed the sample towards individuals with internet access. Additionally, participants could not ask a clinician questions in-person, but they were encouraged to contact us via email or phone at any time. Third, although we accounted for sex disparities in statistical models, males were underrepresented in this study. Finally, a larger sample would increase the power of the study and allow us to investigate and account for the comorbidity among these psychiatric symptoms. Additionally, a larger sample would allow for the inclusion of additional and more complex factors known to contribute to gut microbial composition, for instance, diet and host genetics.

This study also presents notable strengths. The utilisation of well-established tools, such as the OMNIgene Gut OMR-200 stool collection tube (DNA Genotek), and platforms, such as Illumina MiSeq, resulted in the generation of high-quality data. Despite some limitations, online administration of the questionnaires allowed us to reach a larger sample, and participants may have felt more comfortable and therefore provided more honest answers without a clinician’s presence. This study provides insights into multiple understudied spheres: (1) psychiatric symptoms and the gut microbiome in a South African population, (2) symptoms of anxiety and the gut microbiome, and (3) symptoms of PTS and the gut microbiome.

This study investigated the association between the gut microbiome and self-reported symptoms of anxiety, depression, and PTS in a South African adult sample. Although alpha and beta diversities were not associated with any psychiatric symptom severity scores, the abundances of three bacteria, Catenibacterium, Collinsella, and Holdemanella, were significantly positively associated with symptoms of PTS. Each of these bacteria has previously been associated with psychiatric disorders, to some extent. However, Catenibacterium is the only genus which has previously been linked to PTSD symptom severity. A commonality of these bacteria is their production of lactic acid, which has a few postulated pathways of interaction with PTSD. Future recommendations are to validate these findings in a larger cohort and expand the analysis to include case-control comparisons. The use of more comprehensive sequencing strategies may aid in a deeper understanding of the current results; for instance, shotgun metagenomic sequencing could be used to resolve taxonomic identification to strain level and to infer functionality and thus mechanisms of action. Furthermore, functional studies should be used to determine whether associations found in previous literature regarding SCFA concentrations and brain volumes hold true in this cohort. This study does, however, contribute to the currently limited knowledge based on psychiatric disorders and the gut microbiome, and may thus act as a foundation for future studies in the context of the human gut microbiome and symptoms of anxiety, depression, and PTS in South African and African populations.

Acknowledgments

We acknowledge the use of the ilifu cloud computing facility – www.ilifu.ac.za, a partnership between the University of Cape Town, the University of the Western Cape, Stellenbosch University, Sol Plaatje University, the Cape Peninsula University of Technology, and the South African Radio Astronomy Observatory. The ilifu facility is supported by contributions from the Inter-University Institute for Data Intensive Astronomy (IDIA – a partnership between the University of Cape Town, the University of Pretoria, and the University of the Western Cape), the Computational Biology Division at UCT, and the Data Intensive Research Initiative of South Africa (DIRISA). The authors would also like to thank Dr. Yolandi Espach for her vital role in participant recruitment, administrative logistics, and sample collection. Additionally, we would like to thank Kim Stanley for her assistance in REDCap administration.

Statement of Ethics

This study protocol was reviewed and approved by the Health Research Ethics Committee at Stellenbosch University, Approval No. N18/03/038. Written informed consent was obtained from all participants.

Conflict of Interest Statement

The authors have no conflicts of interest to declare.

Funding Sources

Research reported in this publication was supported by the South African Medical Research Council (SAMRC) for the “Shared Roots” Flagship Project (Grant No. MRC‐RFA‐IFSP01‐2013/SHARED ROOTS). This work was supported by the South African Medical Research Council (SAMRC)/Stellenbosch University Genomics of Brain Disorders (GBD) Extramural Research Unit, the SA Society for Biology Psychiatry (NPC 2021/925075/08), and the Harry Crossley Foundation Research Grant. MO was supported by the Stellenbosch University Postgraduate Scholarship Programme and the Prof HW Truter bursary. PS was supported by the Stellenbosch University Subcommittee C Grant, the National Research Foundation of South Africa, and the SAMRC/Stellenbosch University GBD Unit. SMM is supported by an Una4Career grant (European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 847635), a Knowledge Generation Grant from the Ministry of Science and Innovation (Spain) (PID2021-126468OA-I00). The work by Leigh van den Heuvel was supported in part by the National Research Foundation of South Africa (Grant No. 138430) and by the SAMRC under a Self-Initiated Research Grant. The content hereof is the sole responsibility of the authors and does not necessarily represent the official views of the SAMRC or the funders. The funders had no role in the design, data collection, data analysis, and reporting of this study.

Author Contributions

M.A.O.: formal analysis, data curation, visualisation, and writing – original draft preparation, review, and editing. P.C.S.: data curation, funding acquisition, project administration, supervision, and writing – review and editing. S.M.-M.: conceptualisation, funding acquisition, investigation, project administration, and writing – review and editing. L.L.vdH. and E.B.: conceptualisation and writing – review and editing. S.S.: conceptualisation, funding acquisition, and writing – review and editing. S.M.J.H.: conceptualisation, funding acquisition, investigation, project administration, supervision, and writing – review and editing.

Funding Statement

Data Availability Statement

The datasets presented in this article are not readily available due to ethical and legal restrictions. Requests to access the datasets should be directed to S.M.J.H. (smjh@sun.ac.za). The authors are open to collaborating and sharing data within the limits of ethical review restrictions and data transfer policies of Stellenbosch University.

Supplementary Material.

Supplementary_material-Suppl.1-s1.docx^{(1MB, docx)}

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Data Availability Statement

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PERMALINK

The saNeuroGut Initiative: Investigating the Gut Microbiome and Symptoms of Anxiety, Depression, and Posttraumatic Stress

Michaela A O’Hare

Patricia C Swart

Stefanie Malan-Müller

Leigh L van den Heuvel

Erine Bröcker

Soraya Seedat

Sian MJ Hemmings

Abstract

Introduction

Methods

Results

Conclusion

Introduction

Methods

The saNeuroGut Initiative

Online Questionnaires

Stool Sample Collection

DNA Isolation and Sequencing

Processing Raw Reads

Statistical Analysis

Alpha Diversity

clr-Data Transformation

Beta Diversity

Differential Abundance

Results

Clinical and Demographic Characteristics of Participants

Table 1.

Processing Raw Reads

Alpha Diversity

Table 2.

Beta Diversity

Table 3.

Differential Abundance

Table 4.

Fig. 1.

Discussion

Acknowledgments

Statement of Ethics

Conflict of Interest Statement

Funding Sources

Author Contributions

Funding Statement

Data Availability Statement

Supplementary Material.

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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