Fecal microbiota in pediatric depression and its relation to bowel habits

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Journal of Psychiatric Research 150 (2022) 113–121

Available online 28 March 2022

Fecal microbiota in pediatric depression and its relation to bowel habits

Yuan-yue Zhou

^a^,^b^,¹

, Xue Zhang

^c^,¹

, Li-ya Pan

^d^,¹

, Wen-wu Zhang

^e

, Fang Chen

^e

, Sha-sha Hu

^e

, Hai-yin Jiang

^d^,^*

aDepartment of Medical Psychology, The First Affiliated Hospital, Hainan Medical University, Haikou, Hainan, China

bDepartment of Child and Adolescent Psychiatry, Hangzhou Seventh People’s Hospital, Hangzhou, Zhejiang, China

cDepartment of Clinical Infectious Disease, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou City, China

dState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Department of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China

eDepartment of Child and Adolescent Psychiatry, Ningbo Kangning Hospital, Ningbo, Zhejiang, China

A R T I C L E I N F O Keywords:

Children Adolescent Depressive Microbiome Gut

A B S T R A C T

Although gut microbiota dysbiosis has been observed in the fecal samples of depressive adult patients, the detailed structure and composition of microbiota in pediatric depression remain unclear. To enhance our understanding of gut microbiota structure in depressive children, as well as the relationship between gut microbiota and bowel habits, we performed 16S rRNA sequencing to evaluate the gut microbial population in a cohort of 171 children (101 depressive patients and 70 controls) aged 12–18 years. Further analysis consisting of 30 drug- naive patients and 23 controls was performed to validate the results. Compared to controls, we found markedly decreased microbial richness and diversity, a distinct metagenomic composition with reduced short-chain fatty acid-producing bacteria (associated with healthy status), and overgrowth of bacteria such as Escherichia–Shigella and Flavonifractor in pediatric depression. Further analyses limited to drug-naive patients found similar results.

Notably, we also observed that several taxa may be involved in the pathogenesis of disordered bowel habits in pediatric depression. Our findings suggest could inform future pediatric depression interventions specifically targeting the bacteria associated with bowel movements.

1. Introduction

Depression is the most common mental health disorder among children and adolescents. Among teens between 13 and 18, approxi- mately 5.6 percent suffer from depression, according to a recent meta- analysis (Jane Costello et al., 2006). Previous studies have demonstrated that a diagnosis of pediatric depression is associated with an increased risk of recurrence during adulthood, and that, in approxi- mately 57.2% cases, depression in childhood and adolescence continues into adulthood (Rutter et al., 2006). Furthermore, pediatric depression is associated with an increased risk of social problems, poor academic performance, substance misuse, suicide attempts, and other psychiatric comorbidities (Fergusson and Woodward, 2002). These facts underscore the need to explore the pathophysiological mechanism of pediatric depression and identify objective biomarkers to guide treatment management.

Over the past decade, increasing evidence from animal studies has indicated that the gut microbiota can exert a considerable influence on host behavior (Cowan et al., 2020), namely through inflammation, and the hypothalamic–pituitary–adrenal (HPA) axis, and by affecting neurotransmission. The subsequent proposal of a gut microbiota–brain axis has stimulated intense interest in the structure of the gut microbiota in psychiatric disorders (Rea et al., 2020), and in exploring the impli- cations of targeted therapies for depression (Bastiaanssen et al., 2020).

Decreased bacterial diversity and alterations in the abundance of certain bacterial groups have been observed in the fecal samples of depressive patients compared to samples from healthy subjects, in studies of un- related subjects (Huang et al., 2018; Jiang et al., 2020; Liu et al., 2020;

Rong et al., 2019), and monozygotic twins (Jiang et al., 2019)). How- ever, all reports have been based on samples obtained from adults; no study has addressed pediatric depression.

Depression is a risk factor for many health problems, including

* Corresponding author. .

E-mail address: [email protected] (H.-y. Jiang).

1 Authors contributed equally to this work.

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journal homepage: www.elsevier.com/locate/jpsychires

https://doi.org/10.1016/j.jpsychires.2022.03.037

Received 9 August 2021; Received in revised form 18 February 2022; Accepted 21 March 2022

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gastrointestinal comorbidities (Haug et al., 2002). For example, the prevalence of gastrointestinal system complaints among depressive adults was estimated at 29.6%, and bowel-related symptoms were reportedly positively associated with the severity of depressive symptoms (Hillil¨a et al., 2008). In addition to their role in the brain-gut interaction, the gut microbiota are also involved in the gastrointestinal physiology through the modulation of the intestinal motility, the immune system, and the functions of the epithelial barrier. Thus, it is easy to hypothesize that altered bowel habits may be associated with gut microbiota dysbiosis. One previous study showed that stool consistency, as measured by the Bristol Stool Scale (BSS), was strongly associated with gut microbiota richness and composition in healthy women (Van- deputte et al., 2016). A recent study (Ahluwalia et al., 2021) demonstrated that patients with IBS have a distinct fecal microbiota profile linked to bowel habits. Number of research (Dan et al., 2020; Turriziani et al., 2022) also found autistic patients with gastrointestinal symptoms present microbial changes with plausible relation with autistic symptoms, as well as comorbid anxiety and hyperactivity. Although high rates of chronic constipation and diarrhea have been reported in patients with depression (Ballou et al., 2019), no intestinal microbiota studies considered bowel habits. Thus, comparison of the enteric microbiota of depressive patients with disordered bowel habits to that of those with normal bowel habits could enhance understanding of the currently un- known mechanisms by which the gut microbiota contributes to depression-associated gastrointestinal comorbidities.

Although the gut microbiota composition of adult patients with depression has been extensively studied, no study has included pediatric patients with depression. Here, for the first time, we characterized depression-associated gut bacterial microbiota in the 70 healthy control and 101 pediatric patients. Then, we analyzed the fecal microbiota in pediatric depression patients with different bowel habits. Furthermore, we explored the correlations between bacterial microbiota and clinical parameters in pediatric depression patients.

2. Methods 2.1. Subject selection

The study protocol was reviewed and approved by the Institutional Review Board of the First Affiliated Hospital of Zhejiang University (grant number: 2019–1034) before the start of subject recruitment. The study was registered in the Chinese Clinical Trial Registry (registration number: ChiCTR1900024396). Informed consent forms were signed by all participants or their parents. The patients were then interviewed regarding their psychoactive drug use (antidepressants, antipsychotics, or mood stabilizers) by specially trained psychiatrists using a structured questionnaire, within 4 weeks of admission. Participants’ demographic and clinical data were obtained from questionnaires and hospital elec- tronic medical records.

Adolescent patients (aged 12–18 years) were prospectively recruited from child and adolescent outpatient clinical centers at Seventh People’s Hospital of Hangzhou and Kangning Hospital of Ningbo, Zhejiang, China, from February 2019 to December 2020. Depression was diag- nosed based on the International Classification of Diseases (ICD)-10 by two experienced pediatric psychiatrists (Y.Y.Z., W.W.Z., F.C., and S.S.

H.). Age- and sex-matched control was recruited via advertisements and evaluated using a semi-structured clinical interview to exclude individuals with physical illnesses. Furthermore, psychiatric assessment of control was performed by an experienced pediatric psychiatrist using the Mini-International Neuropsychiatric Interview (Sheehan et al., 1998).

The exclusion criteria were as follows: use of antibiotic, probiotics, prebiotics, synbiotics, or anti-inflammatory drugs during the 4 weeks before collection of the fecal sample; BMI >24; bipolar depression, schizophrenia, neurodevelopmental disorder, congenital heart disease, epilepsy, asthma, diabetes mellitus, fatty liver disease, irritable bowel

syndrome (IBS), inflammatory bowel disease, or celiac disease; fever or known active bacterial, fungal, or viral infections; and missing clinical information.

2.2. Questionnaire

The BSS is widely used in clinical studies to assess constipation and diarrhea in functional bowel disorders (Longstreth et al., 2006; Videlock et al., 2013). This scale classifies human feces into seven consistency-based categories, with the highest scores corresponding to loose stools and fast transit, and lower scores indicating harder stools and longer colon transit times. In our study, all participants were shown a card with colored pictures and descriptions of the seven BSS types (Types 1–7) and instructed as follows: “Look at this card and tell me the number that corresponds with your usual or most common stool type.” Bowel habits were categorized into four types: normal (BSS Type 3–5), constipation (BSS Types 1–2), diarrhea (BSS Types 6–7), and mixed (≥2 types of bowel habits). The severity of depressive and anxious symptoms was assessed using the Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS), respectively. The severity of gastrointestinal symptoms was assessed using the Gastrointestinal Symptom Rating Scale (GSRS). To confirm the accuracy of bowel habit reports, the BSS was compared to the GSRS, on which items 12 and 13 also reflect bowel habits.

2.3. Fecal sample collection and DNA extraction

After participants had completed the SDS and SAS, fresh fecal samples were collected and immediately transferred to the laboratory for storage at − 80 ^◦C. DNA extraction was conducted as described previously. Briefly, fecal bacterial and fungal DNA were extracted from 200 mg of feces using the FastDNA SPIN Kit for Feces (MP Biomedicals, Santa Ana, CA, USA) according to the manufacturer’s instructions, with glass- bead beating performed using the Mini-BeadBeater (FastPrep; Thermo Electron Corp., Waltham, MA, USA). The NanoDrop instrument (Thermo Scientific, Waltham, MA, USA) was used to measure DNA concentra- tions, and agarose gel electrophoresis was used to estimate molecular size.

2.4. Polymerase chain reaction (PCR) analysis and pyrosequencing A set of primers targeting the high-variant v3–v4 region (338F/

806R) of the 16S rRNA genes was used to amplify the extracted DNA samples by PCR. PCR products were detected on a 2% w/v agarose gel, and the band was extracted and purified using the AxyPrep DNA Gel (Axygen, Union City, CA, USA) and the PCR Clean-up System. The purified PCR products for each sample were pooled in equimolar concen- trations, and the final concentration of the library was determined using the Qubit 2.0 Fluorometer (Thermo Fisher). Sequencing was performed on the Illumina MiSeq platform by Shanghai MoBio Biomedical Tech- nology Co., Ltd. (Shanghai, China).

2.5. Bioinformatics and statistical analyses

The sequence dataset generated by the MiSeq run was processed using Quantitative Insights into Microbial Ecology (QIIME) software (version 1.9.0), with the default parameters. Preliminary quality control steps were performed as described previously, and chimeric sequences were removed using ChimeraSlayer. OTUs containing <0.005% of the total number of sequences were discarded, as recommended. The remaining (effective) sequences were binned into operational taxonomic units (OTUs), with a cutoff of 97% sequence similarity identity, to determine alpha diversity (index of observed species, abundance-based coverage estimator (ACE), Chao1 estimator, Shannon index, Simpson index). Beta diversity was examined by principal coordinate analysis (PCoA) and weighted UniFrac analysis, performed using QIIME.

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Journal of Psychiatric Research 150 (2022) 113–121

115 Bacterial genera with an average relative abundance ≥0.01 were considered major genera. The linear discriminant analysis effect size (LEfSe) method was used together with the Kruskal–Wallis rank-sum test to identify microorganismal biomarkers in the fecal samples of the depression and control groups; this approach emphasizes biological relevance (Segata et al., 2011). Bacterial groups with a linear discriminant analysis score of 2 were defined as significantly abundant. Hier- archical clustering was performed and a heatmap was generated using a customized script developed in the R statistical package, with Spear- man’s rank correlation coefficient as the distance measure. All statistical tests were performed with GraphPad Prism 6 (GraphPad Software, Inc., La Jolla, CA, USA) or R software (McMurdie and Holmes, 2013) (R Development Core Team, Vienna, Austria). P-values <0.05 were considered significant.

3. Results

3.1. Participant characteristics

The participant recruitment and sample collection processes are depicted in Fig. S1. Of the total of 721 respondents invited to participate in the study, 647 returned valid questionnaires, giving a response rate of 89.7%. After applying rigorous diagnostic procedures, 101 fecal samples were collected prospectively from 203 depressed patients recruited from two mental hospitals. Additionally, 70 fecal samples were collected from age- and sex-matched Control. 30 patients who provided fecal samples were identified as having drug-naive pediatric depression, and fecal samples from these cases were collected prior to the initiation of psychotropic drug treatment. The mean total SDS score at the time of hospital admission was 76.3, indicating that most of the patients with severe depressive symptoms required medical treatment. No patients had consumed alcohol or tobacco products. The demographic and clinical characteristics of the pediatric depression patients and Control who provided fecal samples are presented in Table 1.

3.2. Bacterial dysbiosis in pediatric depression

The structure of the gut microbiota was explored using high- throughput sequencing of the bacterial 16S rRNA gene. Rarefaction analysis showed that the OTU richness approached saturation in both groups as the number of samples increased (Figs. S2,3,4). As estimated by the Shannon index (Fig. 1a), the Chao index (Fig. 1b), and the Ace index (Fig. 1c), gut microbial diversity and the number of OTUs (Fig. 1d) were significantly decreased in pediatric depression patients compared to control. In terms of beta diversity, PCoA analyses based on weighted UniFrac distances (Fig. 1e) revealed a distinctive cluster between the two groups, indicating a significant decrease in beta diversity in the pediatric depression group compared to the control group. A Venn di- agram depicting the overlap between groups showed that the total number of OTUs was 1502, of which 1150 were shared by both groups.

Notably, 199 OTUs were unique to pediatric depression patients (Fig. S5).

We further analyzed the taxonomic composition and alterations of gut bacteria in pediatric depression patients. The composition and relative abundance of the bacterial community in each sample at the phylum and genus levels are presented in Figs. S6–S9. The average compositions and relative abundance of the bacterial community in both groups at the phylum level are presented in Fig. 1f. The analysis of phylotypes showed that Bacteroidetes, Firmicutes, Proteobacteria, Fusobacteria, and Actinobacteria were the dominant taxa in both the pediatric depression and HC groups. However, only Proteobacteria was significantly enriched in the pediatric depression patients compared to control (Fig. 1g). At the family level, 14 bacterial populations, including Bacteroidaceae, Enterobacteriaceae, and Coriobacteriaceae, were significantly enriched, whereas five bacterial populations including Prevotellaceae, Rikenellaceae, and the Eubacterium coprostanoligenes group, were significantly reduced in pediatric depression patients compared to control (Fig. S10). At the genus level, five bacterial populations including Bacteroides, Escherichia–Shigella, and unclassified f Enterobacteriaceae were significantly enriched, whereas 11 bacterial populations, including Prevotellaceae, Rikenellaceae, and the Eubacterium coprostanoligenes group, were significantly reduced in pediatric depression patients compared to control (Fig. 1i). We further applied LEfSe to explore the significance of changes in, and the relative richness of, bacterial communities in the pediatric patients and control (Figs. S11 and S12).

To rule out any influence of psychotropic drugs on the gut microbiota, we conducted a subgroup analysis between 30 treatment-naïve pediatric depression patients and 23 Control to validate the results.

Bacterial diversity was significantly lower in the treatment-naive pediatric patients than control, as indicated by the sobs indices (Fig. 2a, b, c and d). PCoA based on weighted UniFrac distances indicated a difference in bacterial community composition between treatment-naive patients and control (Fig. 2e). Bacterial communities were also compared at the genus level (Fig. 2f). Three bacterial populations, i.e., Escher- ichia–Shigella, Flavonifractor, and Turicibacter were significantly enriched, whereas 13 bacterial populations, including Faecalibacterium, Ruminococcus, and the Eubacterium eligene group, were significantly reduced in pediatric depression patients compared to control. We further applied LEfSe to explore the significance of changes in, and the relative richness of, bacterial communities in the drug naive pediatric patients and control (Figs. S13 and S14).

We next examined whether BMI in pediatric depression was associated with the gut microbiome. According the definition of overweight in Chinese children, we only included children with BMI <22 in our analysis. The differences in gut microbiota at phylum and genus levels were shown in Figs. S15 and S16. We then classified the patients according to age (<14 years old or ≥14 years old), which is indicative of different learning stages (primary school or middle school). However, the findings of relative abundance of bacteria at phylum and genus levels were consistent with the results of our overall analysis (Figs. S17, S18, S19, and S20).

3.3. Associations between bowel habits and gut microbiota in pediatric depression

A total of 647 subjects completed the Bowel Health Questionnaire, including 203 pediatric depression patients and 444 controls. The demographic and clinical characteristics of the pediatric depression patients and control who provided valid questionnaire are presented in Table 1. In our weighted sample, 34.5% of depressed subjects and 10.8%

of nondepressed subjects reported disordered bowel habits (P <0.001).

We found that diarrhea, constipation, and mixed conditions together were significantly more prevalent in depressed (Fig. 3a) or drug naive depressed (Fig. 3b) than nondepressed individuals.

Fecal samples from pediatric patients with depression were classified Table 1

Demographic characteristics of study subjects who providing fecal samples.

Parameters Pediatric depression (n

=70) Control (n =

101) P value

Proportion of Females,

No. (%) 56 (62.2%) 54 (53.4%) >0.05

Age (years; means ±SD) 13.7 ±2.6 13.5 ±3.1 >0.05

BMI (means ±SD) 19.6 ±4.5 19.9 ±5.1 >0.05

SDS (means ±SD) 76.3 ±8.5 33.7 ±6.2 <0.001

SAS (means ±SD) 63 ±7.2 31.1 ±5.8 <0.001

GSRS (mean, IQR) 9 (7.5) 4 (5) <0.001

Drug-naive, No. (%) 30 (43%) 101 (100%) <0.001

Smoking, No. (%) 2 (2.9%) 3 (3%) >0.05

BMI, body mass index; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale; GSRS, Gastrointestinal Symptom Rating Scale.

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Fig. 1.Comparison of the Shannon (a), Chao (b), ACE (c), and sobs (d) index between two groups; PCoA of weighted UniFrac distance matrix analysis demonstrated that the bacterial microbiome composition of pediatric depression clustered separately from control (e); Composition of fecal bacterial microbiome at the phylum level between the two groups (f); Compared with control, Proteobacteria were significantly enriched

Zhou et al.

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117 according to the Bowel Health Questionnaire into normal (n = 52), chronic constipation (n =13), chronic diarrhea (n =7), and mixed (n = 13) groups. No significant differences were found in the alpha diversity values calculated using a variety of indices (Figs. S21, 22, 23, 24). In terms of beta diversity, PCoA analyses based on weighted UniFrac distances indicated a significant difference in beta diversity between the mixed type and normal groups (Fig. 3c). A taxonomy-based comparison was performed to determine differences among the four groups. At the phylum level, Bacteroidetes were significantly more abundant in the fecal microbiota of the mixed type group compared to the normal group, whereas Firmicutes was significantly more abundant in the fecal microbiota of the mixed than normal group; the Firmicutes/Bacter- oidetes ratio was significantly lower in the mixed than normal group (Fig. S25). At the family level, Enterobacteriaceae were significantly more abundant in the fecal microbiota of the mixed than normal group

(Fig. S26). Compared to the normal group, the relative abundance of Peptostreptococcaceae was higher in the chronic constipation and diarrhea groups (Fig. S27); however, the relative abundance of Peptos- treptococcaceae in the mixed group was lower than in the constipation and diarrhea groups (Fig. S27). At the genus level, compared to the normal group, the relative abundance of Blautia was higher (Fig. 3d), but that of the Ruminococcus torques group was lower (Fig. 3e), in the mixed group, while the relative abundance of Escherichia− Shigella was higher in the chronic diarrhea group (Fig. 3f).

3.4. Associations between clinical variables and gut microbiota in pediatric depression

The depression-associated clinical indicators that we examined, including the SDS, SAS, and GSRS scores, were significantly higher in the Fig. 2.Comparison of the Shannon (a), Chao (b), ACE (c), and sobs (d) index between two groups; PCoA of weighted UniFrac distance matrix analysis demonstrated that the bacterial microbiome composition of drug naive pediatric depression clustered separately from control (e); Compared with control, three genera were significantly enriched, while 11 genera were significantly reduced in drug naive pediatric depression (f). *, p <0.05, **, p <0.01, ***, p <0.001.

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Fig. 3.Percentages of pediatric depression and control individuals with normal bowel habit, chronic constipation, chronic diarrhea, and mixed bowel habit (a); Percentages of drug-naive pediatric depression and control individuals with normal bowel habit, chronic constipation, chronic diarrhea, and mixed bowel habit (b); PCoA of weighted UniFrac distance matrix analysis demonstrated that the bacterial microbiome composition of pediatric depression with mixed bowel habit clustered separately from pediatric depression with normal bowel habit (c); Comparison of the relative abundance of Blautia(d), Ruminococcus torques group (e), and Escherichia-Shigella between four groups (f).; Heatmap showing the partial Spearman’s correlation coefficients among 25 OTUs and 3 clinical indicators of pediatric depression (g). *, p <0.05, **, p <0.01.

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119 pediatric depression group compared to the control group (p <0.01) (Table S1). Next, we investigated associations between the key differential bacteria and clinical parameters of pediatric depression using Spearman’s rank correlation in pediatric depression. We found that Escherichia− Shigella and unclassified_f_Enterobacteriaceae, which were enriched in pediatric depression patients, were positively correlated with SDS and SAS scores (Fig. 3g). However, Eubacterium_hallii_group and Subdoligranulum, which were prevalent in control, were negatively correlated with SDS and SAS scores (Fig. 3g). The expression of another depression-enriched bacterium, Acidaminococcus, was positively correlated with GSRS scores. Eubacterium_hallii_group and Subdoligranulum, both of which were enriched in control, were negatively correlated with the GSRS scores (Fig. 3g).

4. Discussion

Our study is the first to compare the composition of the distal gut microbiome between healthy children and children with depression.

Using high-throughput sequencing, we found that alterations in several key differential bacteria were associated with pediatric depression and showed clear correlations with the severity of depressive symptoms.

Intriguingly, we observed that disordered bowel habits in depression patients were associated with specific alterations in the gut microbiota.

Disturbances of the normal gut microbiome have been implicated in the pathogenesis of diverse psychiatric diseases, such as schizophrenia (Zhang et al., 2020), bipolar disorder (Jiang et al., 2019), autism (Liu et al., 2021), and anxiety (Jiang et al., 2018). In recent years, the role of the gut microbiome in adult depression has been gradually explored. As the microbiome can change throughout the lifespan, it is important to study the impact of disease at different stages of the lifecycle. In this study, we characterized gut microbiota dysbiosis in pediatric depression patients as a decline in α-diversity, which may reflect a reduction in the variety of microbial taxa. Although greater bacterial α-diversity is potentially beneficial to human health, the findings of previous studies on adult depression patients have been conflicting. Recently, Liu et al.

(2020) and Chen et al. (2020) assessed the gut microbiota of young adults with depression and found no difference in α-diversity compared to Control, consistent with several previous studies of Chinese adults with depression. In contrast, our prior study (Jiang et al., 2015) reported increased α-diversity in the intestinal bacteriome of depression patients based on the Shannon index. We suggest three potential reasons for the lower α-diversity observed in the depression patients in this study. First, the gut microbiota in children is immature and fluctuant, rendering it more vulnerable to stress. Second, the microbiota profile may reflect the individual’s environmental exposure history, which could contribute to individual differences in the risk of illness. Thus, early onset depression may be associated with lower α-diversity. Third, the pediatric patients were all inpatients with severe depressive symptoms, which have been shown to be associated with the gut microbiota.

A loss of multiple short-chain fatty acid (SCFA)-producing bacteria in the fecal samples of our pediatric depression patients, including the genera Subdoligranulum, Dialister, Fusicatenibacter, Ruminococcus, and Dorea, is of particular interest and is in line with previous reports (Jiang et al., 2015, 2020; Li´skiewicz et al., 2021; Rong et al., 2019). Forms of SCFA such as acetate, butyrate, and propionate, which are produced via microbial fermentation of non-digestible carbohydrates, are a primary source of energy for colonic epithelial cells (Sitolo et al., 2020; Zim- merman et al., 2012). Thus, a lack of SCFA in the gut could undermine the integrity of the intestinal wall, allowing enteral bacterial translocation through the leaky gut (Rook and Lowry, 2008), which could in turn induce abnormal behavior in the host by activating the immune system and hypothalamic–pituitary–adrenal (HPA) axis (Maes et al., 2013). Furthermore, SCFA exerts immunomodulatory and anti-inflammatory effects, mainly by mediating the homeostasis of colonic regulatory T cell populations (Hao et al., 2021). Previous research demonstrated that increase of acetate and propionate

concentration in gut could promote resilience to stress-induced anxiety- and depressive-like behaviors in mice through modulating the T helper 17 to regulatory T cell ratio (Abildgaard et al., 2017; Burokas et al., 2017). Additionally, our treatment-naive pediatric depression patients had a lower abundance of Faecalibacterium compared to the Control, supporting previous work linking lower levels of this genus to depression and other psychiatric disorders. Faecalibacterium includes only a single named species, F. prausnitzii, which also produces butyrate and thus can block NF-κB activation and IL-8 production (Sokol et al., 2008).

Hence, a reduction in SCFA-producing species may favor a shift toward an inflammation-promoting microbiome, thereby enhancing host inflammation and exacerbating depression.

The expression of Bacteroides, a dominant genus in the gut, was increased in the pediatric depression group compared to the control group, contrary to our findings in adults with depression (Jiang et al., 2015). However, a recent study of young adults also observed Bacter- oides enrichment in depressive patients (Liu et al., 2020), suggesting that up-regulation of the lipopolysaccharide (LPS) biosynthesis pathways in depression is partially due to certain commensal Gram-negative Bac- teroides species. It should also be noted that the enrichment of Escher- ichia–Shigella seen in the depression patients was consistent with our previous study (Jiang et al., 2018), in which pyrosequencing of fecal samples showed that patients with generalized anxiety disorder exhibited significant fecal overgrowth of Escherichia–Shigella. A number of animal studies have shown that the presence of, or exposure to, pathogenic bacteria in the gut can increase depression-like behavior (Defaye et al., 2020; Gaykema et al., 2004; Giacomin et al., 2018). Due to their ability to produce exotoxins and promote conditions favoring inflammation, overgrowth of this genus may exacerbate depressive symptoms. Unexpectedly, dysbiosis of the gut microbiota in our pediatric depression patients was also characterized by a higher relative abundance of Flavonifractor, one of which (F. plautii) is present in all mammals and can alleviate mucosal damage by suppressing the overexpression of IL-17 (Mikami et al., 2020). It is reasonable to speculate that overexpression of F. plautii in the gut may relieve depressive symptoms. However, F. plautii was demonstrated in vitro to have epithelial invasive potential (Karpat et al., 2021) and has been linked with several autoimmune disorders (Li et al., 2021; Liu et al., 2021)).

Therefore, we considered that Flavonifractor may be associated with depression despite its anti-inflammatory effects. Further studies are needed to clarify this issue.

We also observed that the frequency of disordered bowel habits in the depression group differed from that in the control group. A population-based study from the USA also reported high incidences of chronic diarrhea and constipation in adult depression patients (Ballou et al., 2019), suggesting that hyperactivity of corticotropin-releasing factor-mediated neuronal pathways in the brain may render depressed patients more susceptible to bowel habit disorders (Tach´e et al., 2001).

Consistent with a previous study of healthy women (Vandeputte et al., 2016), our data showed that chronic diarrhea and “mixed” bowel habits were associated with the gut microbiota composition in pediatric depression patients. Previous work demonstrated that the genus Blautia was associated with IBS, in which bowel habits are always irregular; in particular, one study found that the genus Blautia was overexpressed in participants with high intraepithelial lymphocyte counts in the terminal ileum (Talley et al., 2020). Finally, a large population-based cohort study found that bowel symptoms and IBS were associated with increased abundance of Blautia (Brunkwall et al., 2021). Escherichia− - Shigella, a common resident pathogen in the gut, was significantly increased in pediatric depression patients with chronic diarrhea (Liu et al., 2020). Furthermore, patients with IBS and diarrhea showed a higher relative abundance of Escherichia–Shigella compared to healthy subjects.

The main limitation of the present study was its cross-sectional design; therefore, further large-scale longitudinal studies involving repeated (pre- and post-interventional) stool measurements are Y.-y. Zhou et al.

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necessary to characterize specific changes in the gut microbiome during relapse and remission. Additionally, we utilized 16S rRNA sequencing, which can generally only identify taxa down to the genus level, so may have missed important species- or strain-level differences between communities. Metagenomic sequencing might improve taxonomic assignment. Finally, our research is lack of information on diet, which may lead to attenuated association results. The changes of diet habit induced by depressive symptoms may place them in a dramatically different nutrient environment, which could also contribute the dysbiosis in gut microbiota. Research with repeated measured fecal sam- pling before the depression onset, including dietary approaches, are necessary to overcome this limitation.

In conclusion, dysbiosis was identified in the fecal microbiota of pediatric depression patients for the first time, and was characterized by reduced biodiversity, an increase in opportunistic bacteria, and a decrease in SCFA-producing bacteria. Our results indicated a potential contribution of the microbial composition to disordered bowel habits in depression patients. These findings suggested possible future modalities for pediatric depression intervention through targeting the specific bacteria associated with altered bowel habits.

Funding

This study was supported by Natural Science Foundation of Zhejiang Province (Grant No. LY20H090012).

Authors’ contributions

X.Z. and H.Y.J. conceived the study and revised the manuscript critically for important intellectual content. Y.Y.Z., X.Z., and L.Y.P.

made substantial contributions to its design, acquisition, analysis and interpretation of data. W.W.Z, F.C., S.S.H., and C.M.J. participated in the design, acquisition, analysis and interpretation of data. All authors read and approved the final manuscript.

Author statement

Y.Y.Z. and H.Y.J. conceived the study and revised the manuscript critically for important intellectual content. Y.Y.Z., X.Z., and L.Y.P.

made substantial contributions to its design, acquisition, analysis and interpretation of data. W.W.Z, F.C., and S.S.H. participated in the design, acquisition, analysis and interpretation of data. All authors read and approved the final manuscript.

Data availability

The raw Illumina read data for all samples in this study were deposited in the GenBank Sequence Read Archive under accession number SRP316309.

Declaration of competing interest

The authors declare that they have no competing interest. The fun- ders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Appendix ASupplementary data

Supplementary data to this article can be found online at https://doi.

org/10.1016/j.jpsychires.2022.03.037.

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