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. 2022 Aug 19:13:964910.
doi: 10.3389/fimmu.2022.964910. eCollection 2022.

Changes in fecal microbiota composition and the cytokine expression profile in school-aged children with depression: A case-control study

Zongxin Ling  1   2 , Yiwen Cheng  1 , Feng Chen  1 , Xiumei Yan  3 , Xia Liu  4 , Li Shao  5   6 , Guolin Jin  7 , Dajin Zhou  3 , Guizhen Jiang  3 , He Li  7 , Longyou Zhao  3 , Qinghai Song  7
Affiliations

Changes in fecal microbiota composition and the cytokine expression profile in school-aged children with depression: A case-control study

Zongxin Ling et al. Front Immunol. .

Abstract

Depression in childhood negatively affects the growth and development, school performance, and peer or family relationships of affected children, and may even lead to suicide. Despite this, its etiology and pathophysiology remain largely unknown. Increasing evidence supports that gut microbiota plays a vital role in the development of childhood depression. However, little is known about the underlying mechanisms, as most clinical studies investigating the link between gut microbiota and depression have been undertaken in adult cohorts. In present study, a total of 140 school-aged children (6-12 years) were enrolled, including 92 with depression (male/female: 42/50) and 48 healthy controls (male/female: 22/26) from Lishui, Zhejiang, China. Illumina sequencing of the V3-V4 region of the 16S rRNA gene was used to investigate gut microbiota profiles while Bio-Plex Pro Human Cytokine 27-plex Panel was employed to explore host immune response. We found that, compared with healthy controls, children with depression had greater bacterial richness and altered β-diversity. Pro-inflammatory genera such as Streptococcus were enriched in the depression group, whereas anti-inflammatory genera such as Faecalibacterium were reduced, as determined by linear discriminant analysis effect size. These changes corresponded to altered bacterial functions, especially the production of immunomodulatory metabolites. We also identified the presence of a complex inflammatory condition in children with depression, characterized by increased levels of pro-inflammatory cytokines such as IL-17 and decreased levels of anti-inflammatory cytokines such as IFN-γ. Correlation analysis demonstrated that the differential cytokine abundance was closely linked to changes in gut microbiota of children with depression. In summary, key functional genera, such as Streptococcus and Faecalibacterium, alone or in combination, could serve as novel and powerful non-invasive biomarkers to distinguish between children with depression from healthy ones. This study was the first to demonstrate that, in Chinese children with depression, gut microbiota homeostasis is disrupted, concomitant with the activation of a complex pro-inflammatory response. These findings suggest that gut microbiota might play an important role in the pathogenesis of depression in school-aged children, while key functional bacteria in gut may serve as novel targets for non-invasive diagnosis and patient-tailored early precise intervention in children with depression.

Keywords: children; depression; dysbiosis; inflammation; microbiota-targeted diagnosis.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Altered overall structure of the fecal microbiota in patients with childhood major depressive disorders (MDD). The diversity indices of Shannon (A) and Simpson (B), and the richness indices of ACE (C), Chao1 (D), and the observed OTUs (E) were used to evaluate the overall structure of the fecal microbiota in childhood MDD patients and the healthy controls. The data are presented as mean ± standard deviation. Unpaired t tests (two tailed) were used to analyze the variation between the groups. *p < 0.05 and #p < 0.01 compared with the control group. Principal coordinate analysis (PCoA) plots of individual fecal microbiota based on Bray–Curtis (F), jaccard (G), and unweighted (H) and weighted (I) UniFrac distances in childhood MDD patients and the healthy controls. Each symbol represents a sample. The Venn diagram illustrates the overlap of OTUs in childhood MDD-associated microbiota and healthy controls (J).
Figure 2
Figure 2
Krona charts showing the taxonomic identification and relative abundance of the most abundant bacterial OTUs recorded in childhood MDD patients and healthy controls. These taxa represent the internal core microbiota at the individual level.
Figure 3
Figure 3
Differential bacterial taxa between childhood MDD patients and the healthy controls. Comparisons of the relative abundance of the abundant bacterial taxa at the level of bacterial phylum (A), family (B), and genus (C). The data are presented as the mean ± standard deviation. Mann–Whitney U-tests were used to analyze variation between childhood MDD patients and the healthy controls. *p < 0.05 and #p < 0.01 compared with the control group.
Figure 4
Figure 4
Taxonomic differences of the fecal microbiota between childhood MDD patients and the healthy controls. LEfSe identified the features of the fecal microbiota that are discriminative with respect to childhood depression using the LDA model results for the bacterial hierarchy (A), while LDA coupled with effect size measurements identified the most differentially abundant taxa between the two groups (B). Only the taxa meeting a significant LDA threshold value of > 3 are shown.
Figure 5
Figure 5
The differential genera as childhood MDD diagnostic markers. Receiver operating characteristic (ROC) curves for the differential genera such as Bacteroides, Phocaeicola, Faecalibacterium, Escherichia/Shigella, Parabacteroides, Gemmiger, Streptococcus, Klebsiella, Romboutsia, Dorea alone (A) or in combination including Bacteroides, Streptococcus, Faecalibacterium, Dorea, Romboutsia and Parabacteroides (B) used to discriminate childhood MDD patients from healthy controls. AUC, the area under the receiver operating characteristic curve.
Figure 6
Figure 6
PiCRUSt-based examination of the fecal microbiota of childhood MDD patients and the healthy controls. The different bacterial functions were evaluated between them based on two-sided Welch’s t-test. Comparisons between the groups for each KEGG functional category (levels 2 and 3) are shown by percentage. The Benjamini–Hochberg method was used for multiple testing correction based on the false discovery rate (FDR) through STAMP.
Figure 7
Figure 7
Mean (SEM) concentrations (pg/ml) of 27 pro- and anti-inflammatory cytokines and chemokines in childhood MDD patients and in healthy controls determined using Bio-Plex immunoassays. The concentrations of IL-1β, IL-4, IL-8, IL-17, IP-10, MCP-1, MIP-1α and TNF-α increased significantly in childhood MDD patients, while those of IFN-γ, MIP-1β and RANTES decreased significantly. *p < 0.05.
Figure 8
Figure 8
Correlation between fecal microbiota, and pro- and anti-inflammatory cytokines and chemokines in childhood depression. The heatmap shown Pearson’s correlation coefficients between differential genera and host immunity in childhood MDD patients. Pearson correlation (r) and probability (p) were used to evaluate statistical importance. *p < 0.05.
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References

    1. Hopkins K, Crosland P, Elliott N, Bewley S. Diagnosis and management of depression in children and young people: summary of updated NICE guidance. BMJ (2015) 350:h824. doi: 10.1136/bmj.h824 - DOI - PubMed
    1. Ryan ND. Treatment of depression in children and adolescents. Lancet (2005) 366(9489):933–40. doi: 10.1016/s0140-6736(05)67321-7 - DOI - PubMed
    1. Amitai M, Kaffman S, Kroizer E, Lebow M, Magen I, Benaroya-Milshtein N, et al. . Neutrophil to-lymphocyte and platelet-to-lymphocyte ratios as biomarkers for suicidal behavior in children and adolescents with depression or anxiety treated with selective serotonin reuptake inhibitors. Brain Behav Immun (2022) 104:31–8. doi: 10.1016/j.bbi.2022.04.018 - DOI - PubMed
    1. Saluja G, Iachan R, Scheidt PC, Overpeck MD, Sun W. And giedd, J.N. prevalence of and risk factors for depressive symptoms among young adolescents. Arch Pediatr Adolesc Med (2004) 158(8):760–5. doi: 10.1001/archpedi.158.8.760 - DOI - PubMed
    1. Dopheide JA. Recognizing and treating depression in children and adolescents. Am J Health Syst Pharm (2006) 63(3):233–43. doi: 10.2146/ajhp050264 - DOI - PubMed

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