Please prepare an extended outlines for Bioinformatic class project. . guidelines are attached below. - Biology
I am attaching here a guideline in a word file, this guideline in the word file is from my professors, which also talks about the presentation, please disregard the presentation part, this time I am only supposed to make a 2-page outline (not more than 2 pages). And the topic I have picked is Gut Microbiota and Bioinformatics. I have also attached 4 primary papers for your convenience but if you like you can pick another primary paper on the same topic. But make sure the papers you have picked be Primary research papers only and on same topic.
Also, eventhough these papers talk more about Gut microbiota, you have to make these project outline by keping bioinformatics and its tools in mind, since this project is for Bioinformatics class.
Note: This topic mainly focuses on bioinformatics tools and the biological topic/field will be Gut Microbiota.
Thanks
BI
OINFORMATICS
– Outline for the end of the semester project
Each individual will find 2 or 3 papers in the literature (Primary research papers only) that A) represent an area of biology that you are interested in; and B) represent work in the area of bioinformatics. Once you have honed in on these papers, read them with an eye toward understanding:
What is the biological problem that the authors are trying to address, and what was their motivation?
What previous work has been done in the area, and what tools were used in address this problem?
What are the specifics about the bioinformatics approaches (e.g., particular programs, methods, programming language, databases, etc.) used?
Did the authors evaluate whether the approaches used provided biologically meaningful results?
You will prepare a 15 (+/- 2) minute presentation that includes some discussion of the biology/problem/background, but that focus most of the attention on the bioinformatics aspects. You will discuss methods/approaches, software used, etc., and present some results that you have obtained using e.g., a bioinformatics approach outlined in the papers.
Remember that this will be due at the END of the semester; however, at this point I would like you to prepare a short outline that cites the papers you have chosen, briefly reports on the area of biology/problem/background related to the topic you have chosen. Next include a brief synopsis of the bioinformatics tools used, and a hint at some of the results obtained. Finally, include a list of names (if any) of individuals that you are considering working with as a group.
This outline is not a complete report, and should be one or two outlining pages and nothing longer. The point here is that I would like to A) check that you have selected and read some papers; B) to have an idea about the bioinformatics work you will be presenting on.
RESEARCH Open Access
Associations between gut microbiota and
Alzheimer’s disease, major depressive
disorder, and schizophrenia
Zhenhuang Zhuang1, Ruotong Yang1, Wenxiu Wang1, Lu Qi2,3* and Tao Huang1,4,5,6*
Abstract
Background: Growing evidence has shown that alterations in the gut microbiota composition were associated
with a variety of neuropsychiatric conditions. However, whether such associations reflect causality remains
unknown. We aimed to reveal the causal relationships among gut microbiota, metabolites, and neuropsychiatric
disorders including Alzheimer’s disease (AD), major depressive disorder (MDD), and schizophrenia (SCZ).
Methods: A two-sample bi-directional Mendelian randomization analysis was performed by using genetic variants from
genome-wide association studies as instrumental variables for gut microbiota, metabolites, AD, MDD, and SCZ, respectively.
Results: We found suggestive associations of host-genetic-driven increase in Blautia (OR, 0.88; 95\%CI, 0.79–0.99;
P = 0.028) and elevated γ-aminobutyric acid (GABA) (0.96; 0.92–1.00; P = 0.034), a downstream product of
Blautia-dependent arginine metabolism, with a lower risk of AD. Genetically increased Enterobacteriaceae family
and Enterobacteriales order were potentially associated with a higher risk of SCZ (1.09; 1.00–1.18; P = 0.048),
while Gammaproteobacteria class (0.90; 0.83–0.98; P = 0.011) was related to a lower risk for SCZ. Gut
production of serotonin was potentially associated with an increased risk of SCZ (1.07; 1.00–1.15; P = 0.047).
Furthermore, genetically increased Bacilli class was related to a higher risk of MDD (1.07; 1.02–1.12; P = 0.010).
In the other direction, neuropsychiatric disorders altered gut microbiota composition.
Conclusions: These data for the first time provide evidence of potential causal links between gut microbiome
and AD, MDD, and SCZ. GABA and serotonin may play an important role in gut microbiota-host crosstalk in
AD and SCZ, respectively. Further investigations in understanding the underlying mechanisms of associations
between gut microbiota and AD, MDD, and SCZ are required.
Keywords: Gut microbiota, Neuropsychiatric disorder, Mendelian randomization, Genetic association, Causality
Background
The human intestine comprises a very complex group of
gut microbiota, which influence the risk of neuropsychiatric
disorders [1, 2]. Accumulating evidence has suggested that
microbiota metabolites such as neurotransmitters and
short-chain fatty acids (SCFAs) may play a central role in
microbiota-host crosstalk that regulates the brain function
and behavior [3, 4]. Therefore, to understand the mechan-
ism of the gut-brain axis in neuropsychiatric disorders may
have clinical benefits.
Observational studies, most of case-control designs, have
shown differences in the composition of the gut microbiota
between healthy individuals and patients with neuropsychi-
atric disorders such as Alzheimer’s disease (AD), major
depression disorder (MDD), and schizophrenia (SCZ);
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The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the
data made available in this article, unless otherwise stated in a credit line to the data.
* Correspondence: [email protected]; [email protected]
2Department of Epidemiology, School of Public Health and Tropical
Medicine, Tulane University, New Orleans, LA, USA
1Department of Epidemiology & Biostatistics, School of Public Health, Peking
University, 38 Xueyuan Road, Beijing 100191, China
Full list of author information is available at the end of the article
Zhuang et al. Journal of Neuroinflammation (2020) 17:288
https://doi.org/10.1186/s12974-020-01961-8
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http://orcid.org/0000-0002-0328-1368
http://creativecommons.org/licenses/by/4.0/
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mailto:[email protected]
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however, such associations substantially differed across
studies [5–7]. Noteworthy, genome-based metabolic mod-
eling of the human gut microbiota revealed that several
genera have predictive capability to produce or consume
neurotransmitters (called microbial neurotransmitters) such
as γ-aminobutyric acid (GABA) and serotonin [8, 9], which
have been consistently shown to played a key role in the
regulation of brain function [10, 11]. A meta-analysis of 35
observational studies reported that increased GABA levels
were associated with a lower risk of AD [12]. In addition, a
previous study (n = 40) reported that plasma serotonin was
lower and platelet serotonin was higher in SCZ patients
compared with controls [13], while another study showed
that lower platelet serotonin concentrations were associated
with depressive symptoms of SCZ (n = 364) [14]. There is
no doubt that these small observational studies were sus-
ceptible to confounding bias and reverse causation. It is
crucial to elucidate whether such associations reflect causal
relations or spurious correlations due to bias.
Mendelian randomization (MR), which overcomes the
bias due to confounding and reverse causation above-
mentioned, has been widely used to assess causal rela-
tionships by exploiting genetic variants as instrumental
variables of the exposure [15]. Recent genetic studies
have demonstrated that the host genetic variants influ-
ence the gut microbiota composition [16–18]. Thus,
such findings allowed us to deploy an MR approach to
infer the mutually causal relations of gut microbiota and
metabolites with neuropsychiatric disorders.
Therefore, we for the first time applied a two-sample
bi-directional MR approach to detect causal relation-
ships among gut microbiota, metabolites, and diverse
forms of neuropsychiatric disorders including AD, SCZ,
and MDD.
Methods
Study design overview
We employed a two-sample bi-directional MR approach
to investigate the causal relationships among gut micro-
biota, metabolites, and AD, MDD, or SCZ using
summary-level data from large genome-wide association
studies (GWASs) for gut microbiota and AD, MDD, or
SCZ. Ethical approval for each study included in the MR
analysis can be found in the original articles [19–23].
Data sources and instruments
Gut microbiota
We leveraged summary statistics from a GWAS of gut
microbiota conducted among two independent but geo-
graphically matched cohorts of European ancestry (n =
1812) using 16S rRNA gene sequencing (Table 1) [19],
which yielded a total of 38 and 374 identified phyla and
genera respectively. The GWAS defined a “core measur-
able microbiota” after removing rare bacteria and
investigating associations between host genetic variants
and specific bacterial traits, including 40 operational
taxonomic units (OTUs) and 58 taxa ranging from the
genus to the phylum level. Accordingly, the GWAS fur-
ther identified 54 genome-wide significant associations
involving 40 loci and 22 bacterial traits (meta-analysis P
< 5 × 10−8). We selected single nucleotide polymor-
phisms (SNPs) at thresholds for genome-wide signifi-
cance (P < 5 × 10−8) from this GWASs as genetic
instruments (Table S1).
Gut microbial metabolites
Considering the important roles of gut microbiota-
derived metabolites in microbiota-host crosstalk in the
brain function and behavior, we further chose key me-
tabolites with available GWAS, including propionic acid,
β-hydroxybutyric acid (BHB), serotonin, GABA, tri-
methylamine N-oxide (TMAO), betaine, choline, and
carnitine. These gut microbial metabolites play crucial
roles in maintaining a healthy neuropsychiatric function,
and if dysregulated, potentially causally linked to neuro-
psychiatric disorders according to previous studies [3,
24, 25]. We searched PubMed for GWASs of the gut
metabolites and leveraged summary-level data from a re-
cent GWAS of the human metabolome conducted
among 2076 participants of the Framingham Heart
Study (Table 1) [20]. Since few loci identified by gut me-
tabolite GWAS have reached the level of genome-wide
significance, we only selected SNPs at thresholds for
suggestive genome-wide significance (P < 1 × 10−5) from
the GWAS for each metabolite (Table S2).
Neuropsychiatric disorders
We searched PubMed for GWASs of the neuropsychi-
atric disorders and identified SNPs with genome-wide
significant (P < 5 × 10−8) associations for AD [21], MDD
[22], and SCZ [23], respectively (Table 1, Table S3).
Summarized data for AD were obtained from the Inter-
national Genomics of Alzheimer’s Project (IGAP), in-
cluding 25,580 AD cases and 48,466 controls, and the
analysis was adjusted for age, sex, and principal compo-
nents when necessary [21]. Genetic associations for
MDD were obtained from Psychiatric Genomics Consor-
tium 29 (PGC29) including135,458 MDD cases and 344,
901 controls, using sex and age as covariates [22]. Gen-
etic associations for SCZ were obtained from a meta-
analysis of Sweden and PGC including 13,833 SCZ cases
and 18,310 controls [23]. Detailed information on diag-
nostic criteria for AD, MDD, and SCZ are provided in
Table S4. These GWASs identified 19 SNPs for AD, 44
SNPs for MDD, and 24 SNPs for SCZ (P < 5 × 10−8), re-
spectively (Table S3).
Zhuang et al. Journal of Neuroinflammation (2020) 17:288 Page 2 of 9
Statistical analysis
For instrumental variables, we only selected independent
genetic variants which are not in linkage disequilibrium
(LD) (defined as r2 < 0.1) with other genetic variants
based on European ancestry reference data from the
1000 Genomes Project. We chose the variant with the
lowest P value for association with the exposure when
genetic variants were in LD. Moreover, for SNPs that
were not available in GWASs of the outcome, we used
the LD proxy search on the online platform (https://
snipa.helmholtz-muenchen.de/snipa3/index.php/) to re-
place them with the proxy SNPs identified in high-LD
(r2 > 0.8) or discard them if the proxies were not avail-
able. Power calculations for the MR study were con-
ducted based on the website: mRnd (http://cnsgenomics.
com/shiny/mRnd/).
We combined MR estimates by using inverse variance
weighting (IVW) as primary method. Weighted mode,
weighted median, and MR-Egger methods were used as
sensitivity analyses. Detailed information about the MR
methods mentioned above has been explained previously
[26, 27]. The MR-Egger method examined for unknown
horizontal pleiotropy as indicated by a non-zero inter-
cept value. We also applied leave-one-SNP-out approach
assessing the effects of removing these SNPs from the
MR analysis to rule out potential pleiotropic effects. Ef-
fect estimates are reported in beta values for the con-
tinuous outcome and ORs (95\% CIs) for binary
outcome. Bonferroni correction was used to adjust for
multiple comparisons, giving a cutoff of P = 7.6 × 10−4
for the causal effect of gut microbiota on disorders and a
cutoff of P = 1.7 × 10−4 for reverse causation.
The MR analyses were conducted in the R version
3.5.1 computing environment (http://www.r-project.org)
using the TwoSampleMR package (R project for Statis-
tical Computing). This package harmonized effect of the
exposure and outcome data sets including combined in-
formation on SNPs, including phenotypes, effect alleles,
effect allele frequencies, effect sizes, and standard errors
for each SNP. In addition, we assumed that all alleles are
presented on the forward strand in harmonization. In
conclusion, the bi-directional MR results using the full
set of selected SNPs.
Results
Associations of gut microbiota and metabolites with
neuropsychiatric disorders
We found suggestive evidence of a protective effect of
the host-genetic-driven increase in Blautia on the risk of
AD (per relative abundance: OR, 0.88; 95\% CI, 0.79–
0.99; P = 0.028) (Fig. 1, Figure S1). Importantly, we fur-
ther observed suggestive evidence that genetically ele-
vated gut metabolite GABA was associated with a lower
risk of AD (per 10 units: 0.96; 0.92–1.00; P = 0.034)
(Figs. 1 and 2).
Furthermore, the host-genetic-driven increases in En-
terobacteriaceae family and Enterobacteriales order were
potentially related to a higher risk of SCZ (1.09; 1.00–
1.18; P = 0.048), while Gammaproteobacteria class was
related to a lower risk of SCZ (0.90; 0.83–0.98; P =
0.011) (Fig. 1, Figure S1). Interestingly, gut production of
serotonin was potentially associated with a higher risk of
SCZ (1.07; 1.00–1.15; P = 0.047) (Figs. 1 and 3). In
addition, we found suggestive association of the host-
genetic-driven increase in Bacilli class with a higher risk
of MDD (1.07; 1.02–1.12; P = 0.010) (Fig. 1, Figure S1).
Sensitivity analysis yielded similar results for the causal
effects of gut microbiota on neuropsychiatric disorders, and
no horizontal pleiotropy or outliers were observed (Tables
S5 and S6). No significant results were found for any of
other selected gut microbiota or metabolites with neuro-
psychiatric disorders (Table S7). MR power calculation
showed strong power to detect significant (P < 7.6 × 10−4)
causal effect (OR = 1.2) for most of gut microbiota with the
risk of AD, MDD, and SCZ, respectively (Table S8).
Associations of neuropsychiatric disorders with gut
microbiota
In the opposite direction, we applied the MR method to
investigate the causal relationship of neuropsychiatric
Table 1 Description of gut microbiota, metabolites, and neuropsychiatric disorders
Traits Consortium or study Sample size Populations Journal Year
Gut
Gut microbiota PopGen/FoCus 1812 individuals European Nat Genet. 2016
Gut metabolites FHS 2076 individuals European Cell Metab. 2013
Neuropsychiatric disorders
Alzheimer’s disease IGAPa 25,580 cases and 48,466 controls European Nat Genet. 2013
Major depression disorder PGC29/deCODE/GenScotland/GERA/iPSYCH/UK
Biobank/23andMeD
135,458 cases and 344,901 controls European Nat Genet. 2018
Schizophrenia Sweden/PGC 21,246 cases and 38,072 controls European Nat Genet. 2013
FoCus Food-Chain Plus, GERA Genetic Epidemiology Research on Adult Health and Aging, PGC Psychiatric Genomics Consortium
a IGAP includes the Alzheimer’s Disease Genetics Consortium (ADGC), the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE),
the European Alzheimer’s disease Initiative (EADI), and the Genetic and Environmental Risk in Alzheimer’s disease consortium (GERAD)
Zhuang et al. Journal of Neuroinflammation (2020) 17:288 Page 3 of 9
https://snipa.helmholtz-muenchen.de/snipa3/index.php
https://snipa.helmholtz-muenchen.de/snipa3/index.php
http://cnsgenomics.com/shiny/mRnd/
http://cnsgenomics.com/shiny/mRnd/
http://www.r-project.org
disorders with gut microbiota. We found a suggestive as-
sociation of AD with lower relative abundance of Erysi-
pelotrichaceae family, Erysipelotrichales order, and
Erysipelotrichia class (per 1-unit odds ratio: Beta±SE, −
0.274 ± 0.090; P = 0.003) and higher relative abundance
of unclassified Porphyromonadaceae (0.351 ± 0.170; P =
0.040) (Fig. 1, Table S9). Additionally, MDD was associ-
ated with higher relative abundance of unclassified Clos-
tridiales (0.577 ± 0.241; P = 0.017), OTU16802
Bacteroides (0.842 ± 0.386; P = 0.029), and unclassified
Prevotellaceae (0.978 ± 0.464; P = 0.035) (Fig. 1, Table
S9). We further identified that SCZ was nominally re-
lated to 2 genera, including higher relative abundance of
OTU10589 unclassified Enterobacteriaceae (0.457 ±
0.220; P = 0.037) and lower relative abundance of un-
classified Erysipelotrichaceae (− 0.248 ± -0.019; P =
0.045) (Fig. 1, Table S9).
Associations were almost consistent in sensitivity ana-
lyses using the weighted mode and weighted median
methods. The MR-Egger method showed directional
pleiotropy in the analysis of association between MDD
and OTU16802 Bacteroides (P = 0.022) but not in any
other potential significant associations (Table S9). How-
ever, we had limited power (all less than 50\%) to test sig-
nificant (P < 1.7 × 10−4) causal effect (Beta = 0.5) of the
risk of AD, MDD, and SCZ on specific gut microbiota
(data not shown), possibly due to small sample size of
the gut microbiota GWAS.
Discussion
In this two-sample bi-directional MR study, we found
suggestive evidence of causal relationships of Blautia
with AD, of Enterobacteriaceae family, Enterobacteriales
order, and Gammaproteobacteria class with SCZ, and of
Bacilli class with MDD. More importantly, several neu-
rotransmitters such as GABA and serotonin produced
by gut microbiota were also potentially linked to the
risks of neuropsychiatric disorders, implying their im-
portant roles in microbiota-host crosstalk in the brain
function and behavior. In the other direction, our results
suggested that neuropsychiatric disorders, including AD,
SCZ, and MDD might alter the composition of gut
microbiota.
Microbiota-gut-brain communication has been shown
to play a key role in cognitive function [2]. However,
animal studies regarding the effects of Blautia genus on
AD have yielded conflicting results, but extrapolating
these findings to human beings is challenging [28, 29]. A
cohort study (n = 108) reported that decreased propor-
tion of Blautia hansenii was associated with a higher
risk of AD [30], while two case-control studies observed
that Blautia were more abundant in AD patients [5, 31].
Fig. 1 Schematic representation of the present study, highlighting for each step of the study design and the significant results obtained. We
aimed to estimate causal relationships between gut microbiota (98 individual bacterial traits) and neuropsychiatric disorders (Alzheimer’s disease,
major depression disorder, and schizophrenia) using a bi-directional Mendelian randomization (MR) approach (step 1). Then, we performed a two-
sample MR analysis to identify which microbiota metabolites associated with these disorders (step 2). Finally, we identified 14 individual bacterial
traits and 2 gut metabolites to be associated with these disorders. GABA, γ-aminobutyric acid; SCFA, short-chain fatty acids
Zhuang et al. Journal of Neuroinflammation (2020) 17:288 Page 4 of 9
Although the direction of associations between Blautia
and the risk of AD substantially differed across studies,
one consistent finding was that gut microbial neuro-
transmitter GABA, a downstream product of Blautia-
dependent arginine metabolism, was related to a reduced
risk of AD. Notably, lower levels of gut product of
GABA were observed in patients with AD in several
case-control studies [32, 33]. In this bi-directional MR
study, our results for the first time provide evidence of a
causal relationship between relative abundance of Blau-
tia and AD. More importantly, we demonstrated that el-
evated GABA was potentially associated with a lower
risk of AD. Our findings supported previous meta-
analysis of 35 observational studies which suggested that
GABA level in AD were significantly lower than that of
controls [12]. Our findings suggest that GABA produced
by gut microbiota may play an important role in
microbiota-host crosstalk in the brain function and be-
havior. Although not significant, our findings show very
similar association directions for Blautia with MDD and
SCZ. Our findings are in line with recent studies which
indicated that decreased Blautia was associated with an
increased risk of autistic spectrum disorder (ASD), sug-
gesting a general change associated with psychiatric dis-
orders [34].
There are many potential pathways linking specific gut
microbiota to AD, among which metabolites produced
by gut microbiota may play an important role. It is
worth noting that GABA, as a primary inhibitory neuro-
transmitter in the human central nervous system (CNS),
has been shown to shape neurological processes and
cognition [35]. Recent evidence has demonstrated that
GABAergic functions could be an essential factor in the
whole stage of AD pathogenesis which seemed to be
more resistant to neurodegenerative changes in aged
brain [36, 37]. Our MR results that increased GABA
levels was potentially associated with a lower risk of AD
lent further support to the hypotheses. The biological
mechanisms of GABA production include degradation
of putrescine, decarboxylation of glutamate, or from ar-
ginine or ornithine [8]. Interestingly, the genus Blautia
has shown a strong correlation with arginine metabolism
[38], which may be involved in AD pathogenesis by
regulating its downstream products such as GABA, sup-
porting the potential pathway [39]. Since AD does not
break out suddenly but develops through a long pro-
dromal phase instead, it is plausible that our findings may
be potentially effective in early interventions of such dis-
order in the future by targeting the microbiota (e.g., gut
microbiota transplantation, psychobiotics, or antibiotics).
Fig. 2 Causal effect of GABA with the risk of AD. a Schematic representation of the MR analysis results: genetically determined higher GABA
plasma levels were potentially associated with a lower risk of AD. b The odds ratios (95\% confidence interval) for AD per 10 units increase in
GABA, as estimated in the inverse-variance weighted, weighted mode, weighted median, and MR-Egger MR analysis. The intercept of MR-Egger
can be interpreted as a test of overall unbalanced horizontal pleiotropy. c The scatter plot represents instruments association including AD
associations (y-axis) against instrument GABA associations (x-axis). The tunnel plot represents instrument precision (i.e., instrument AD regression
coefficients divided by the correspondent instrument GABA SEs) (y-axis) against individual instrument ratio estimates in log odds ratio of AD (x-
axis). βIV indicates odds ratio estimate per 1-ln 10 units increment in GABA levels. AD, Alzheimer’s disease; OR, odds ratio; CI, confidence interval;
SNP, single-nucleotide polymorphism; SE, standard error; IVW, inverse variance weighted
Zhuang et al. Journal of Neuroinflammation (2020) 17:288 Page 5 of 9
Recently, Enterobacteriales family and Gammaproteo-
bacteria class have been identified to be important bio-
markers of SCZ in recent cross-sectional studies,
consistent with our findings [6, 40]. Furthermore, a case-
control study (n = 364) identified a strong relationship
of lower platelet serotonin concentrations with depres-
sive symptoms of SCZ [14]. However, available evidence
is still largely inadequate since observational studies
mainly rely on self-reported information and are suscep-
tible to confounding (e.g., diet and health status) and re-
verse causation bias. Ertugrul et al. observed plasma
serotonin increased while platelet serotonin decreased in
SCZ patients after clinical treatments, which was incon-
sistent with our findings [13]. In addition, our results
support the finding that increased Bacilli is potentially
associated with a higher risk of MDD, possibly involving
dopamine metabolism which might play a role in the
major symptoms of MDD [41, 42]. A meta-analysis of
RCTs showed that probiotics, typically including Lacto-
bacillus and Bifidobacterium, had some benefit for
MDD, but we found no associations for these micro-
biota, possibly due to the synergistic effect of gut micro-
biome so that the influence of a particular taxon may be
different from multiple taxa [43]. Furthermore, these
clinical trials might draw biased conclusions because of
small sample sizes (ranging from 17 to 110) or short-
term effects (ranging from 3 to 24 weeks). Therefore, a
large and long-term RCT in a well-characterized popula-
tion using probiotic capsules containing specific micro-
biota might provide further evidence for the gut-brain
axis in these disorders. Importantly, epidemiological
study indicated that elevated Enterobacteriales was also
associated with a higher risk of ASD, suggesting that the
same changes in intestinal microbiota composition
might lead to different outcomes due to gene-gene inter-
actions and gene-environment interactions [44]. Al-
though our results showed no significant association for
Gammaproteobacteria and MDD, animal models found
increased levels of Gammaproteobacteria were also asso-
ciated with higher MDD risk and fluoxetine treatment
was effective, implying strong correlations between gut
microbiota and anxiety- and depression-like behaviors
[45].
The serotonin hypothesis of SCZ originated from earl-
ier studies of interactions between the hallucinogenic
drug D-lysergic acid diethylamide and serotonin in per-
ipheral systems. However, direct evidence of serotoner-
gic dysfunction in the pathogenesis of SCZ remains
unclear [46]. According to the principle of brain plasti-
city, glutamate signals are destroyed by serotonergic
overdrive, leading to neuronal hypometabolism, synaptic
atrophy, and gray matter loss in the end [47]. Our find-
ings that genetically increased serotonin levels was po-
tentially related to a high risk of SCZ using a MR
Fig. 3 Causal effect of serotonin with the risk of SCZ. a Schematic representation of the MR analysis results: genetically determined higher
serotonin plasma levels were potentially associated with a higher risk of SCZ. b The odds ratios (95\% confidence interval) for SCZ per 10 units
increase in serotonin, as estimated in the inverse-variance weighted, weighted mode, weighted median, and MR-Egger MR analysis. The intercept
of MR-Egger can be interpreted as a test of overall unbalanced horizontal pleiotropy. c The scatter plot represents instruments association
including SCZ associations (y-axis) against instrument serotonin associations (x-axis). The tunnel plot represents instrument precision (i.e.,
instrument SCZ regression coefficients divided by the correspondent instrument serotonin SEs) (y-axis) against individual instrument ratio
estimates in log odds ratio of SCZ (x-axis). βIV indicates odds ratio estimate per 1-ln 10 units increment in serotonin levels. SCZ, schizophrenia
Zhuang et al. Journal of Neuroinflammation (2020) 17:288 Page 6 of 9
approach supported such hypothesis. Importantly, En-
terobacteriaceae family and Enterobacteriales order can
produce SCFAs (e.g., acetic acid and formic acid) in
carbohydrate fermentation, thus inducing serotonin bio-
synthesis by enterochromaffin cells which are the major
producers of serotonin, and ultimately increasing the
risk of SCZ [48, 49]. Our novel findings highlighted the
potentially important role of gut microbiota-related neu-
rotransmitters in effective and benign therapies of psy-
chiatric disorders.
Furthermore, we also found that neuropsychiatric disor-
ders might alter the composition of gut microbiota. Our
findings were consistent with a small case-control study (n
= 50) suggesting that Erysipelotrichaceae family were all
less abundant in patients with AD [5]. An observational
study showed that Porphyromonadaceae were associated
with poor cognitive performance, partly consistent with our
results [50]. However, the results from animal studies are
conflicting. Although several animal studies suggested that
anti-AD microbes, such as Erysipelotrichiaceae, decreased
in mouse models with AD, and Porphyromonadaceae in-
creased in aged mice [28, 51], other animal studies showed
that the relative abundance of Erysipelotrichiaceae was
positively correlated with AD [52, 53]. Therefore, the asso-
ciation of neuropsychiatric disorders with specific gut
microbiota requires further study. It is universally accepted
that the CNS modulates gut microbiota compositions
mainly through hypothalamic-pituitary-adrenal (HPA) axis,
or classical neurotransmitters liberated by neuronal efferent
activation, which explains the microbiota-host crosstalk in
neuropsychiatric disorders from another direction [54].
Additionally, it is plausible that alterations in gut
microbiota and related metabolites would lead to a sys-
temic change in inflammation that may contribute to
the neuroinflammation in AD, MDD, and SCZ. Increas-
ing evidence suggests that bacteria populating the gut
microbiome may excrete large quantities of lipopolysac-
charides and amyloids, resulting in the pathogenesis of
AD during aging when the permeability of gastrointes-
tinal tract epithelium or blood-brain barrier increases
[55]. Recent research has indicated that gut inflamma-
tion can induce activation of microglia and the kynure-
nine pathway, which activate systemic inflammation-
inducing depressive or schizophrenic symptoms [56, 57].
Therefore, more studies are required to explore the
mechanisms underlying the relationships of inflamma-
tion with the gut microbiota-brain axis and its relations
with AD, MDD and SCZ.
Strengths of the present study …
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INTRODUCTION
Major depressive disorder (MDD) is viewed as a major
public health problem globally. MDD has a substantial
impact on society and individuals, such as increasing
economic burden and decreasing labor productivity
[1–3]. At a global level, more than 300 million people
are estimated to suffer from MDD, which is equivalent
to 4.4\% of the world’s population [4]. However, the
pathogenesis of MDD is still unclear. Some theories
have been developed to explain the biological
mechanisms of MDD, such as neurotrophic alterations
www.aging-us.com AGING 2020, Vol. 12, No. 3
Research Paper
Age-specific differential changes on gut microbiota composition in
patients with major depressive disorder
Jian-Jun Chen1,2,*,#, Sirong He3,*, Liang Fang2,4,*, Bin Wang1, Shun-Jie Bai5, Jing Xie6, Chan-Juan
Zhou7, Wei Wang8, Peng Xie4,7,8,#
1Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
2Chongqing Key Laboratory of Cerebral Vascular Disease Research, Chongqing Medical University, Chongqing
400016, China
3Department of Immunology, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
4Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing 402160, China
5Department of Laboratory, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
6Department of Endocrinology and Nephrology, Chongqing University Central Hospital, Chongqing Emergency
Medical Center, Chongqing 400014, China
7NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, Chongqing Medical University,
Chongqing 400016, China
8Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
*Equal contribution
#Co-senior authors
Correspondence to: Peng Xie, Jian-Jun Chen; email: [email protected], [email protected]
Keywords: major depressive disorder, gut microbiota, Firmicutes, Bacteroidetes, Actinobacteria
Received: November 21, 2019 Accepted: January 12, 2020 Published: February 10, 2020
Copyright: Chen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License
(CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and
source are credited.
ABSTRACT
Emerging evidence has shown the age-related changes in gut microbiota, but few studies were conducted to
explore the effects of age on the gut microbiota in patients with major depressive disorder (MDD). This study was
performed to identify the age-specific differential gut microbiota in MDD patients. In total, 70 MDD patients and 71
healthy controls (HCs) were recruited and divided into two groups: young group (age 18-29 years) and middle-aged
group (age 30-59 years). The 16S rRNA gene sequences were extracted from the collected fecal samples. Finally, we
found that the relative abundances of Firmicutes and Bacteroidetes were significantly decreased and increased,
respectively, in young MDD patients as compared with young HCs, and the relative abundances of Bacteroidetes
and Actinobacteria were significantly decreased and increased, respectively, in middle-aged MDD patients as
compared with middle-aged HCs. Meanwhile, six and 25 differentially abundant bacterial taxa responsible for the
differences between MDD patients (young and middle-aged, respectively) and their respective HCs were identified.
Our results demonstrated that there were age-specific differential changes on gut microbiota composition in
patients with MDD. Our findings would provide a novel perspective to uncover the pathogenesis underlying MDD.
mailto:[email protected]
mailto:[email protected]
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and neurotransmission deficiency [5, 6]. However, none
of these theories has been universally accepted.
Therefore, there is a pressing need to identify novel
pathophysiologic mechanisms underlying this disease.
In recent years, mounting evidence has shown that gut
microbiota could play a vital role in every aspect of
physiology [7]. It is the largest and most direct external
environment of humans. Previous studies found that the
disturbance of gut microbiota had a crucial role in the
pathogenesis of many diseases [8–10]. Recent studies
reported that gut microbiota could affect the host brain
function and host behaviors through microbiota-gut-
brain axis [11, 12]. Using germ-free mice, we found that
gut microbiota could influence the gene levels in the
hippocampus of mice and lipid metabolism in the
prefrontal cortex of mice [13, 14]. Our clinical studies
demonstrated that the disturbance of gut microbiota
might be a contributory factor in the development of
MDD [15, 16].
Nowadays, emerging evidence has shown the age-
related changes in gut microbiota composition. For
example, Firmicutes is the dominant taxa during the
neonatal period, but Actinobacteria and Proteobacteria
are about to increase in three to six months [17]. While
in adults, Vemuri et al. reported that Bacteroidetes and
Firmicutes were the dominant taxa [18]. Meanwhile,
compared to younger individuals, the abundance of
Bacteroidetes is significantly higher in frailer older
individuals [19]. These results showed that there was a
close relationship between age and gut microbiota
composition. Ignoring this relationship would affect the
robust of results when exploring the mechanism of
action of gut microbiota in diseases. Therefore, to study
the relationship between gut microbiota and MDD
patients in different age groups, we recruited 52 young
subjects aged from 18 to 29 years (27 healthy controls
(HCs) and 25 MDD patients) and 89 middle-aged
subjects aged from 30 to 59 years (44 HCs and 45 MDD
patients). The main purpose of this study was to identify
the age-specific differential changes on gut microbiota
composition in MDD patients. Our results would
display the different changes of gut microbiota
composition along with age between HCs and MDD
patients.
RESULTS
Differential gut microbiota composition
As shown in Figure 1, the results of abundance-based
coverage estimator (ACE) and Chao1 showed that there
was no significant difference in OTU richness between
MDD patients (young and middle-aged, respectively)
and their respective HCs. However, the OPLS-DA
model built with young HCs and young MDD patients
showed an obvious difference in microbial abundances
between these two groups (Figure 2A). The relative
abundances of Firmicutes and Bacteroidetes were
Figure 1. Comparison of alpha diversity between HCs and MDD patients. (A, B) ACE and Chao1 indexes showed no significant
differences between young HCs (n=27) and young MDD patients (n=25); (C, D) ACE and Chao1 indexes showed no significant differences
between middle-aged HCs (n=44) and middle-aged MDD patients (n=45).
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significantly decreased and increased, respectively, in
young MDD patients as compared with young HCs
(Figure 2B). Meanwhile, the OPLS-DA model built
with middle-aged HCs and middle-aged MDD patients
showed an obvious difference in microbial abundances
between these two groups (Figure 3A). The relative
abundances of Bacteroidetes and Actinobacteria were
significantly decreased and increased, respectively, in
middle-aged MDD patients as compared with middle-
aged HCs (Figure 3B).
Key discriminatory OTUs
In order to find out the gut microbiota primarily
responsible for the separation between MDD patients
(young and middle-aged, respectively) and their
respective HCs, the Random Forests classifier was used.
A total of 92 OTUs responsible for the separation
between young MDD patients and young HCs were
identified (Figure 4). These OTUs were mainly assigned
to the Families of Bacteroidaceae, Clostridiaceae_1,
Figure 2. 16S rRNA gene sequencing reveals changes to microbial abundances in young MDD patients. (A) OPLS-DA model
showed an obvious difference in microbial abundances between the two groups (HCs, n=27; MDD, (n=25); (B) the relative abundances of
Firmicutes and Bacteroidetes were significantly changed in young MDD patients (n=25) as compared with young HCs (n=27).
Figure 3. 16S rRNA gene sequencing reveals changes to microbial abundances in middle-aged MDD patients. (A) OPLS-DA
model showed an obvious difference in microbial abundances between the two groups (HCs, n=44; MDD, (n=45); (B) the relative abundances
of Bacteroidetes and Actinobacteria were significantly changed in middle-aged MDD patients (n=45) as compared with middle-aged HCs
(n=44).
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Coriobacteriaceae, Erysipelotrichaceae, Lachnospiraceae,
Peptostreptococcaceae and Ruminococcaceae.
Meanwhile, a total of 122 OTUs responsible for the
separation between middle-aged MDD patients and
middle-aged HCs were identified (Figure 5). These OTUs
were mainly assigned to the Families of Lachnospiraceae,
Coriobacteriaceae, Streptococcaceae, Prevotellaceae,
Bacteroidaceae, Eubacteriaceae, Actinomycetaceae,
Sutterellaceae, Acidaminococcaceae, Erysipelotrichaceae,
Ruminococcaceae, and Porphyromonadaceae.
Figure 4. Heatmap of discriminative OTUs abundances between young HCs (n=27) and young MDD patients (n=25).
Figure 5. Heatmap of discriminative OTUs abundances between middle-aged HCs (n=44) and middle-aged MDD patients
(n=45).
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Differentially abundant bacterial taxa
Differentially abundant bacterial taxa responsible for
the differences between MDD patients (young and
middle-aged, respectively) and their respective HCs
were identified by the metagenomic Linear
Discriminant Analysis (LDA) Effect Size (LEfSe)
approach (LDA score>2.0 and p-value<0.05). In total,
six bacterial taxa with statistically significant and
biologically consistent differences in young MDD
patients were identified (Figure 6). Meanwhile, fifteen
bacterial taxa with statistically significant and
biologically consistent differences in middle-aged MDD
patients were identified (Figure 7). In addition, using
Figure 6. Differentially abundant features identified by LEfSe that characterize significant differences between young HCs
(n=27) and young MDD patients (n=25).
Figure 7. Differentially abundant features identified by LEfSe that characterize significant differences between middle-aged
HCs (n=44) and middle-aged MDD patients (n=45).
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the receiver operating characteristic (ROC) curve
analysis, we found that Clostridium_sensu_stricto,
Clostridium_XI and Clostridium_XVIII showed good
diagnostic performance (area under the curve (AUC)
>0.7) in diagnosing young MDD patients (Figure 8A–
8C). We also found that Anaerostipes, Streptococcus,
Blautia, Faecalibacterium and Roseburia showed good
diagnostic performance (AUC>0.7) in diagnosing
middle-aged MDD patients (Figure 8D–8H).
Effects of age on microbial abundances
Using the LEfSe approach, we identified four
differentially abundant bacterial taxa (the Family
level) between young HCs and middle-aged HCs
(Streptococcaceae, Coriobacteriaceae, Carnobacteriaceae
and Clostridiales_Incertae_Sedis_XIII) (Figure 9A);
we also identified six differentially abundant bacterial
taxa (the Family level) between young MDD patients
and middle-aged MDD patients (Prevotellaceae,
Acidaminococcaceae, Veillonellaceae Peptostrep-
tococcaceae, Lachnospiraceae and Ruminococcaceae)
(Figure 9B). Meanwhile, using the LEfSe approach, we
identified five differentially abundant bacterial taxa (the
Genus level) between young HCs and middle-aged HCs
(Streptococcus, Veillonella, Granulicatella, Collinsella
and Megamonas) (Figure 10A). All these bacterial
taxa were significantly decreased in middle-aged
HCs; we also identified nine differentially abundant
bacterial taxa (the Genus level) between young MDD
patients and middle-aged MDD patients (Megamonas,
Prevotella, Phascolarctobacterium, Anaerostipes,
Clostridium_XVIII, Gordonibacter, Eggerthella,
Clostridium_XI and Turicibacter) (Figure 10B).
Effects of medication on microbial abundances
To determinate the homogeneity of gut microbiota
composition between medicated and non-medicated
MDD patients, we firstly used the middle-aged HCs and
non-medicated middle-aged MDD patients to built
OPLS-DA model (Figure 11A). The results showed that
41 of the 44 middle-aged HCs and 30 of the 31 non-
medicated middle-aged MDD patients were correctly
diagnosed by the OPLS-DA model. Then, we used the
built model to predict class membership of 14
medicated middle-aged MDD patients. The T-predicted
scatter plot showed that 11 of the 14 medicated middle-
aged MDD patients were correctly predicted (Figure
11B). These finding indicated that the gut microbiota
composition of non-medicated middle-aged MDD
patients were distinct from middle-aged HCs, but not
from medicated middle-aged MDD patients.
DISCUSSION
Individuals in the different phases of life cycle (named
children, young, middle-aged and elderly) present
different biological characteristics and disease risks
[20]. Understanding the different characteristics of
patients in particular age phases could be facilitated to
prevent and treat diseases. According to the World
Health Organization reported, the prevalence rates of
depression vary by age, peaking in older adulthood. It
also occurs in children, but at a lower level compared
with older age groups. Here, we conducted this work to
investigate how the gut microbiota composition
changed in different age phases of MDD patients, and
found some age-specific differential gut microbiota in
Figure 8. Differential taxa (at the genus level) with AUC>0.7 in diagnosing MDD patients from HCs. (A–C) the diagnostic
performances of three taxa in diagnosing young MDD patients (n=25) from young HCs (n=27); (D–H) the diagnostic performances of five taxa
in diagnosing middle-aged MDD patients (n=45) from middle-aged HCs (n=44).
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Figure 9. 16S rRNA gene sequencing reveals changes to microbial abundances at family level (Mean±SEM). (A) the abundances
of four taxonomic levels were significantly changed between young HCs (n=27) and middle-aged HCs (n=44); (B) the abundances of six
taxonomic levels were significantly changed between young MDD patients (n=25) and middle-aged MDD patients (n=45).
Figure 10. 16S rRNA gene sequencing reveals changes to microbial abundances at genus level (Mean±SEM). (A) the abundances
of five taxonomic levels were significantly changed between young HCs (n=27) and middle-aged HCs (n=44); (B) the abundances of nine
taxonomic levels were significantly changed between young MDD patients (n=25) and middle-aged MDD patients (n=45).
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MDD patients. Our results could provide a new
perspective on exploring the pathogenesis of MDD.
Many previous studies focused on the effects of gut
microbiota on brain functions [21, 22]. However, few
studies have taken the effects of age on gut microbiota
into consideration when exploring the pathogenesis of
MDD. Our previous study found that the relative
abundances of Bacteroidetes and Actinobacteria were
significantly decreased and increased, respectively, in
MDD patients as compared with HCs [15]. But, in this
study, we found that the relative abundances of
Firmicutes and Bacteroidetes were significantly
decreased and increased, respectively, in young MDD
patients as compared with young HCs, and the relative
abundances of Bacteroidetes and Actinobacteria were
significantly decreased and increased, respectively, in
middle-aged MDD patients as compared with middle-
aged HCs. This disparity might be caused by the
different age structures. Meanwhile, only 35 key
discriminatory OTUs were significantly changed in both
young (92 key discriminatory OTUs) and middle-aged
(127 key discriminatory OTUs) MDD patients.
Moreover, the differentially abundant bacterial taxa in
young and middle-aged MDD patients were totally
different at both Family level and Genus level. These
results demonstrated that it was necessary to identify the
age-specific differential gut microbiota in patients with
MDD.
As far as we known, gut microbiota composition and its
function could be easily influenced by many factor,
such as gender, age, life experiences, dietary habit and
genetics. Mariat et al reported that the
Firmicutes/Bacteroidetes ratio of the human microbiota
could change with age [23]. Interestingly, here we
found that the relative abundance of Firmicutes was
significantly decreased in young MDD patients, but not
in middle-aged MDD patients; the relative abundance of
Bacteroidetes was significantly increased and
decreased, respectively, in young and middle-aged
MDD patients. In our previous studies, we did not
analyze the potential effects of medication on gut
microbiota composition in MDD patients [15, 16]. Here,
due to the small samples of young group, we only used
the middle-aged group to analyze the effects of
Figure 11. Assessment of gut microbiota composition in non-medicated and medicated middle-aged MDD patients. (A)
middle-aged HCs (n=44) and non-medicated middle-aged MDD patients (n=31) were effectively separated by the built OPLS-DA model; (B) 14
medicated middle-aged MDD patients were correctly predicted by the model.
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medication on the gut microbiota composition. The
results showed that the medication seemed to have little
effects on gut microbiota composition in MDD patients.
However, our findings had to be cautiously interpreted
due to the relatively small samples using to analyze the
effects of medication on gut microbiota composition.
The relative abundance of genus Clostridium_XVIII
was not found to be significantly different between
MDD patients and HCs in our previous study [15].
However, in this study, we found that the relative
abundance of genus Clostridium_XVIII was
significantly decreased in young MDD patients
compared with young HCs, while increased in middle-
aged MDD patients compared with middle-aged HCs.
The reason of this disparity might be that age could
significantly affect the relative abundance of genus
Clostridium_XVIII in MDD patients, but not HCs: i)
compared to young MDD patients, the middle-aged
MDD patients had a significantly higher relative
abundance of genus Clostridium_XVIII; and ii) the
relative abundance of genus Clostridium_XVIII was
similar between young and middle-aged HCs.
Meanwhile, we found that the relative abundance of
genus Megamonas was significantly decreased in both
middle-aged HCs and middle-aged MDD patients
compared to their respective young populations. In
addition, most of differential bacterial taxa were
significantly decreased in middle-aged HCs compared
with young HCs, but only about half of differential
bacterial taxa were significantly decreased in middle-
aged MDD patients compared with young MDD
patients. Lozupone et al. reported that gut microbiota
could not only simply determine the certain host
characteristics, but also respond to signals from host via
multiple feedback loops [24]. Therefore, our results
suggested that age might have the different effects on
the gut microbiota composition of HCs and MDD
patients, and should always be considered in
investigating the relationship between MDD and gut
microbiota.
Limitations should be mentioned here. Firstly, the
number of HCs and MDD patients was relatively small,
and future works were still needed to further study and
support our results. Secondly, we only explored the age-
specific differential changes on gut microbiota
composition in patients with MDD; future studies
should further investigate the functions of these
identified differential gut microbiota using
metagenomic technology. Thirdly, all included subjects
were from the same site and ethnicity; thus, the
potential site- and ethnic-specific biases in microbial
phenotypes could not be ruled out, which might limit
the applicability of our results [25–28]. Fourthly, only
young and middle-aged groups were recruited, future
studies should recruit old-aged group and children
group to further identify the age-specific differential gut
microbiota in the different phases of life cycle. Fifthly,
we only investigated the differences in gut microbiota
between HCs and MDD patients on phylum level,
family level and genus level. Future studies were
needed to further explore the differences on other
levels, such as class level and species level. Sixthly, we
did not collect information on smoking, a factor which
could influence the gut microbiota composition. Future
studies were needed to analyze how the smoking
influenced the gut microbiota composition in the
different phases of life cycle of subjects. Finally, we
found that the medication status of subjects could not
significantly affect our results. However, limited by the
relatively small samples, this conclusion was needed
future studies to further validate.
In conclusion, in this study, we found that there were
age-specific differential changes on gut microbiota
composition in patients with MDD, and identified some
age-specific differentially abundant bacterial taxa in
MDD patients. Our findings would provide a novel
perspective to uncover the pathogenesis underlying
MDD, and potential gut-mediated therapies for MDD
patients. Limited by the small number of subjects, the
results of the present study were needed future studies
to validate and support.
MATERIALS AND METHODS
Subject recruitment
This study was approved by the Ethical Committee of
Chongqing Medical University and conformed to the
provisions of the Declaration of Helsinki. In total, there
were 27 young HCs (aged 18-29 years) and 25 young
MDD outpatients (aged 18-29 years) in the young
group; there were 44 middle-aged HCs (aged 30-59
years) and 45 middle-aged MDD outpatients (aged 30-
59 years) in the middle-aged group. Most of MDD
patients were first-episode drug-naïve depressed
subjects. There were only seven young MDD patients
and 14 middle-aged MDD patients receiving
medications. The detailed information of these included
subjects was described in Table 1. All HCs were
recruited from the Medical Examination Center of
Chongqing Medical University, and all MDD patients
were recruited from the psychiatric center of Chongqing
Medical University. MDD patients were screened in the
baseline interview by two experienced psychiatrists
using the DSM-IV (Diagnostic and Statistical Manual
of Mental Disorders, 4th Edition)-based Composite
International Diagnostic Interview (CIDI, version2.1).
The Hamilton Depression Rating Scale (HDRS) was
used to assess the depressive symptoms of each patient,
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Table 1. Demographic and clinical characteristics of MDD patients and HCsa.
Young group (18-29 years) Middle-aged group (30-59 years)
HC MDD p-value HC MDD p-value
Sample Size 27 25 – 44 45 –
Age (years)c 24.96±2.31 24.0±3.74 0.26 47.16±8.07 44.96±7.76 0.19
Sex (female/male) 19/8 18/7 0.89 34/10 31/14 0.37
BMI 21.53±2.37 22.13±2.24 0.35 23.23±2.33 22.64±2.64 0.26
Medication (Y/N) 0/27 7/18 – 0/44 14/31 –
HDRS scores 0.29±0.61 22.64±3.18 <0.00001 0.34±0.74 23.0±4.61 <0.00001
aAbbreviations: HDRS: Hamilton Depression Rating Scale; HCs: healthy controls; MDD: major depressive disorder; BMI: body
mass index.
and those patients with HDRS score >=17 were
included. Meanwhile, MDD patients were excluded if
they had other mental disorders, illicit drug use or
substance abuse, and were pregnant or menstrual
women. HCs were excluded if they were with mental
disorders, illicit drug use or systemic medical illness.
All the included subjects provided written informed
consent before sample collection.
16s rRNA gene sequencing
We used the standard PowerSoil kit protocol to extract
the bacterial genomic DNA from the fecal samples.
Briefly, we thawed the frozen fecal samples on ice and
pulverized the samples with a pestle and mortar in
liquid nitrogen. After adding MoBio lysis buffer into
the samples and mixing them, the suspensions were
centrifuged. The obtained supernatant was moved into
the MoBio Garnet bead tubes containing MoBio buffer.
Subsequently, we used the Roche 454 sequencing (454
Life Sciences Roche, Branford, PA, USA) to extract the
bacterial genomic DNA. The extracted V3-V5 regions
of 16S rRNA gene were polymerase chain reaction-
amplified with bar-coded universal primers containing
linker sequences for pyrosequencing [29].
The Mothur 1.31.2 (http://www.mothur.org/) was used
to quality-filtered the obtained raw sequences to
identify unique reads [30]. Raw sequences met any one
of the following criteria were excluded: i) less than
200bp or greater than 1000bp; ii) contained any
ambiguous bases, primer mismatches, or barcode
mismatches; and iii) homopolymer runs exceeding six
bases. The remaining sequences were assigned to
operational taxonomic units (OTUs) with 97\%
threshold, and then taxonomically classified according
to Ribosomal Database Project (RDP) reference
database [31]. We used these taxonomies to construct
the summaries of the taxonomic distributions of OTUs,
and then calculated the relative abundances of gut
microbiota at different levels. The abovementioned
procedure and most of data were from our previous
studies [15, 16].
Statistical analysis
Richness was one of the two most commonly used alpha
diversity measurements. Here, we used two different
parameters (Chao1 and ACE) to estimate the OTU
richness [32, 33]. The orthogonal partial least squares
discriminant analysis (OPLS-DA) was a multivariate
method, which was used to remove extraneous variance
(unrelated to the group) from the sequencing datasets. The
LEfSe was a new analytical method for discovering the
metagenomic biomarker by class comparison. The
bacterial taxa with LDA score>2.0 were viewed as the
differentially abundant bacterial taxa responsible for the
differences between different groups. Here, both OPLS-
DA [34, 35] and LEfSe were used to reduce the
dimensionality of datasets and identify the differentially
abundant bacterial taxa (the Family level and Genus level)
that could be used to characterize the significant
differences between HCs and MDD patients. Meanwhile,
we used the Random Forest algorithm to identify the
critical discriminatory OTUs. The ROC curve analysis
was used to assess the diagnostic performance of these
identified differential bacterial taxa. The AUC was the
evaluation index. Finally, we used the LEfSe to reveal the
changes of microbial abundances at Family level and
Genus level in HCs and MDD patients, respectively.
ACKNOWLEDGMENTS
Our sincere gratitude is extended to Professors Delan
Yang and Hua Hu from Psychiatric Center of the First
Affiliated Hospital of Chongqing Medical University
for their efforts in sample collection.
CONFLICTS OF INTEREST
The authors declare no financial or other conflicts of
interest.
http://www.mothur.org/
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FUNDING
This work was supported by the National Key R&D
Program of China (2017YFA0505700), the Non-profit
Central Research Institute Fund of Chinese Academy of
Medical Sciences (2019PT320002300), the Natural
Science Foundation Project of China (81820108015,
81701360, 81601208, 81601207), the Chongqing
Science and Technology Commission
(cstc2017jcyjAX0377), the Chongqing Yuzhong
District Science and Technology Commission
(20190115), and supported by the fund from the Joint
International Research Laboratory of Reproduction &
Development, Institute of Life Sciences, Chongqing
Medical University, Chongqing, China, and also
supported by the Scientific Research and Innovation
Experiment Project of Chongqing Medical University
(CXSY201862, CXSY201863).
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ORIGINAL ARTICLE
Gut microbiome remodeling induces depressive-like behaviors
through a pathway mediated by the host’s metabolism
P Zheng1,2,3,8, B Zeng4,8, C Zhou1,2,3,8, M Liu1,2,3, Z Fang1,2,3, X Xu1,2,3, L Zeng1,2,3, J Chen1,2,3, S Fan1,2,3,
X Du1,2,3, X Zhang1,2,3, D Yang5, Y Yang1,2,3, H Meng6, W Li4, ND Melgiri1,2,3, J Licinio7,9, H Wei4,9 and P Xie1,2,3,9
Major depressive disorder (MDD) is the result of complex gene–environment interactions. According to the World Health
Organization, MDD is the leading cause of disability worldwide, and it is a major contributor to the overall global burden of disease.
However, the definitive environmental mechanisms underlying the pathophysiology of MDD remain elusive. The gut microbiome is
an increasingly recognized environmental factor that can shape the brain through the microbiota-gut-brain axis. We show here that
the absence of gut microbiota in germ-free (GF) mice resulted in decreased immobility time in the forced swimming test relative to
conventionally raised healthy control mice. Moreover, from clinical sampling, the gut microbiotic compositions of MDD patients
and healthy controls were significantly different with MDD patients characterized by significant changes in the relative abundance
of Firmicutes, Actinobacteria and Bacteroidetes. Fecal microbiota transplantation of GF mice with ‘depression microbiota’ derived
from MDD patients resulted in depression-like behaviors compared with colonization with ‘healthy microbiota’ derived from
healthy control individuals. Mice harboring ‘depression microbiota’ primarily exhibited disturbances of microbial genes and host
metabolites involved in carbohydrate and amino acid metabolism. This study demonstrates that dysbiosis of the gut microbiome
may have a causal role in the development of depressive-like behaviors, in a pathway that is mediated through the host’s
metabolism.
Molecular Psychiatry (2016) 21, 786–796; doi:10.1038/mp.2016.44; published online 12 April 2016
INTRODUCTION
Major depressive disorder (MDD) is a debilitating mental disorder
affecting up to 15\% of general population and accounting for
12.3\% of the global burden of disease.1 MDD increases health-
care expenditures and suicide rates.2 In recent decades, several
theories have attempted to explain the pathogenesis of MDD,
including neurotransmission deficiency,3 neurotrophic alterations,4
endocrine-immune system dysfunction5 and neuroanatomical
abnormalities.6 As none of these theories has been universally
accepted, a definitive pathogenesis of MDD remains largely
elusive, and there is a pressing need to identify novel pathophy-
siologic mechanisms underlying the disorder.
Previously, we found that MDD was associated with obvious
disturbances in peripheral and central metabolites.7–10 Interest-
ingly, several altered metabolites in MDD subjects (such as
hippurate, dimethylamine and dimethylglycine) are metabolic
byproducts of gut microbiota.9 Moreover, previous clinical studies
have reported disturbances in the gut microbiotic compositions in
limited samples of heterogeneous depressed subjects with similar
findings observed in animal models of depression.11–13 These
preliminary studies highlight the potential association of gut
microbiotic changes and the development of MDD. However,
further investigation in larger, well-characterized MDD popula-
tions is still required, as these previous studies have not addressed
whether disturbances in gut microbiota have a causative role in
the onset of MDD.
In recent years, mounting evidence has demonstrated that gut
microbiota can greatly influence all aspects of physiology.14
Variations in the composition of gut microbiota have been
reported to have crucial roles in the pathogenesis of several
enteric and metabolic diseases, such as irritable bowel syndrome,
diabetes and obesity.15–19 Emerging evidence also suggests that
gut microbiota can influence brain function and behavior through
the ‘microbiota-gut-brain axis’.20–24 Germ-free (GF) mice, which
are devoid of any bacterial contamination, have been widely used
to investigate such phenomena.24 Recent studies have demon-
strated that GF mice display reduced non-spatial memory, social
motivation and anxiety compared with their conventionally raised
specific pathogen-free (SPF) counterparts.21,23,25,26 Another recent
study has described that targeted modifications in gut microbiota
can correct autism spectrum disorder-related behavioral
abnormalities.27 On the basis of these findings, we hypothesized
that the dysbiosis of gut microbiota may be a contributory factor
to the development of depression.
1Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; 2Chongqing Key Laboratory of Neurobiology, Chongqing, China;
3Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China; 4Department of Laboratory Animal
Science, College of Basic Medical Sciences, Third Military Medical University, Chongqing, China; 5Department of Neurology, Yongchuan Hospital, Chongqing Medical University,
Chongqing, China; 6Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China and 7Mind & Brain Theme, South Australian Health
and Medical Research Institute and Department of Psychiatry, School of Medicine, Flinders University, Adelaide, SA, Australia. Correspondence: Professor H Wei, Department of
Laboratory Animal Science, College of Basic Medical Sciences, Third Military Medical University, Gaotanyan Street, Chongqing 400038, China or Professor P Xie, Department of
Neurology, The First Affiliated Hospital of Chongqing Medical University, 1 Youyi Road, Yuzhong District, Chongqing 400016, China or Professor Julio Licinio, Mind & Brain Theme,
South Australian Health and Medical Research Institute and Department of Psychiatry, School of Medicine, Flinders University, Adelaide, SA, Australia.
E-mail: [email protected] or [email protected] or [email protected]
8These authors contributed equally to this work.
9These authors are co-senior authors.
Received 8 June 2015; revised 15 February 2016; accepted 17 February 2016; published online 12 April 2016
Molecular Psychiatry (2016) 21, 786–796
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http://dx.doi.org/10.1038/mp.2016.44
mailto:[email protected]
mailto:[email protected]
mailto:[email protected]
http://www.nature.com/mp
In this study, we initially assessed how gut microbiota
physiologically influence the psychobehavioral characteristics of
GF and SPF mice. Then, using 16S rRNA gene sequencing, the gut
microbial communities of 58 MDD patients and 63 healthy
controls were compared to evaluate whether alterations in the gut
microbiome are associated with MDD status. Furthermore, to
assess whether alterations of gut microbiota have a causal role in
depression-like behavior, gut microbiome remodeling was accom-
plished through fecal microbiota transplantation (FMT) from either
MDD patients or healthy individuals on GF mice followed by
behavioral testing to assess depression-like behaviors. Finally,
metagenomic and metabolomic analyses of samples from the
mice harboring ‘depression microbiota’ were conducted to
examine how the gut microbiome influences host metabolism.
MATERIALS AND METHODS
Behavioral testing
The protocols of animal experimentation were reviewed and approved by
the Ethical Committee of Chongqing Medical University (ECCMU,
Chongqing, China) and the Third Military Medical University (Chongqing,
China). Male GF Kunming mice and SPF Kunming mice were bred in the
Experimental Animal Research Center at the Third Military Medical
University. GF mice were kept in flexible film gnotobiotic isolators until
the beginning of experiments. All animals were fed the same autoclaved
chow and water ad libitum under a 12-h light-dark cycle (lights on
at 0730) and a constant temperature of 21–22 °C and humidity of 55 ± 5\%.
For each test, the mice were transferred to the experimental room for
acclimation at least 1 h prior to behavioral testing. All tests described
below were carried out by observers blind to the animal genotypes
between 0800 and 1700. All behavioral tests were videotaped and
quantified by a video-computerized tracking system (SMART, Panlab,
Barcelona, Spain).28
Open-field test (OFT): All mice were individually tested in an open-field
apparatus29 consisting of a black square base (45 × 45 cm2) with black
walls (45 cm in height). A single mouse was gently placed in the corner of
the chamber, and after 1 min of adaptation, all spontaneous activities were
recorded for 5 min using the video-computerized tracking system. The
total motion distance was used as an index of locomotor activity, while the
proportion of distance spent in the center (inner 25\% of the surface area)
was construed as an index of anxiety-like behavior.
Y-maze: The Y-maze apparatus30 consisted of three dark gray arms (45 cm
in length × 10 cm in width × 29 cm in height). Each mouse was placed at the
end of one arm and allowed to freely explore the maze for 8 min. The
sequence and total number of arms entered was recorded. Entry into an arm
was considered valid only when all four paws of the mouse were inside that
arm. The percentage of alternation was the number of triads containing
entries into all three arms divided by the maximum possible number of
alternations (the total number of arms entered minus 2)× 100\%.
Tail suspension test (TST): Mice were individually suspended by their
tails31 using adhesive tape (distance from tip of tail was 2 cm). Test
sessions lasted for 6 min with the last 5 min scored for immobility. Mice
that climbed on their tails were removed from further testing. Animals
were considered to be immobile when they exhibited no body movement
and hung passively.
Forced swimming test (FST): The mice were placed individually in a
Plexiglas cylinder (30 cm in height × 15 cm in diameter) filled with 15 cm
water (24 ± 1 °C).32 Immobility was defined as the absence of all motion
with the exception of movements required to keep the mouse’s head
above water. Test sessions lasted for 6 min with the last 5 min scored for
immobility.
Subject recruitment and sample collection
The protocols of clinical experimentation were reviewed and approved by
ECCMU. Written informed consent was obtained from all recruited human
subjects. Recruitment of MDD and healthy subjects was performed as
previously described.7,9 Briefly, MDD diagnoses were carried out according
to the Structured Psychiatric Interview using DSM-IV-TR criteria,33 and the
17-item Hamilton Depression Rating Scale was used to quantify the
severity of MDD.34 MDD candidates were excluded on the basis of
substance abuse in addition to pregnancy, nursing or current menstruation
for female subjects. Healthy controls were excluded on the basis of a
history of systemic medical illness or mental disorders or family history of
any psychiatric disorder. A total of 58 MDD patients and 63 demographi-
cally matched healthy controls were recruited from the psychiatric center
and medical examination center of the First Affiliated Hospital at
Chongqing Medical University, respectively. The majority of MDD subjects
(n = 39) were drug-naive, while the remaining MDD subjects (n = 19) were
being treated with various anti-depressants. All MDD subjects and healthy
controls who were using antibiotics or prebiotics were excluded. The
detailed characteristics of these recruited subjects are shown in
Supplementary Table S1.
Fecal sample collection and 16S rRNA gene sequencing
Fecal samples were collected from the recruited subjects or FMT model,
frozen immediately following collection and stored at − 80 °C prior to
analyses. Fecal samples were pulverized with a mortar and pestle in liquid
nitrogen, and bacterial genomic DNA was extracted by the standard Power
Soil Kit protocol. Briefly, the fecal samples were thawed on ice. The MoBio
lysis buffer was added to these fecal samples, which were further vortex
mixed. Fecal suspensions were centrifuged and the supernatant placed
into the MoBio Garnet bead tubes containing MoBio buffer.
Roche 454 sequencing (454 Life Sciences Roche, Branford, PA, USA): The
V3-V5 regions of the 16S rRNA gene extracted from the fecal samples of
recruited subjects and cecum samples from recipient mice at 2 weeks post
FMT were PCR-amplified with barcoded universal primers containing linker
sequences for 454-pyrose-quencing.35
Illumina MiSeq sequencing (San Diego, CA, USA): The V4-V5 regions of
the 16S rRNA gene extracted cecum samples from recipient mice at 1 week
post FMT were PCR-amplified with primers containing linker sequences for
Illumina MiSeq sequencing.36
16S rRNA gene sequencing analysis
Raw sequences obtained from 454 sequencing were quality-filtered using
Mothur (Version 1.31.2, http://www.mothur.org/) to obtain unique reads.37
Sequences of less than 200 bp and greater than 1000 bp as well as
sequences containing any primer mismatches, barcode mismatches,
ambiguous bases and homopolymer runs exceeding six bases were
excluded. Raw sequences obtained from MiSeq sequencing were quality-
filtered using QIIME (version 1.17) with the following criteria: (i) 300-bp
reads were truncated at any site receiving an average quality score of less
than 20 over a 50-bp sliding window and discarding the truncated reads
that were shorter than 50 bp; (ii) exact barcode matching, two-nucleotide
mismatch in primer matching and reads containing ambiguous characters
were removed; and (iii) only sequences that overlapped longer than 10 bp
were assembled according to their overlap sequence.
All remaining sequences were assigned to operational taxonomic units
(OTUs) with a 97\% threshold of pairwise identity and then classified
taxonomically using the RDP reference database (http://www.mothur.org/
wiki/RDP_reference_files).38 These taxonomies were used to construct
summaries of the taxonomic distributions of OTUs, which can then be
applied to calculate the relative abundances of microbiota at different
levels. Alpha diversity was calculated by four different parameters: (i)
observed species; (ii) Shannon Index; (iii) phylogenetic diversity and (iv)
Simpson.39,40 Distance matrices (Beta diversity) between samples were
generated on the basis of weighted (Bray-Curtis similarity) and non-
weighted (unweighted UniFrac) algorithms and reported according to
principal coordinate analysis (PCoA).41 To perform the UniFrac analysis,
representative sequences for each OTU were aligned using PyNAST, and a
phylogenetic tree from this alignment was constructed with Fast Tree.42
Random Forest algorithm was carried out to identify the key discriminatory
OTUs,43 which assigns an importance score to each OTU by estimating the
increase in error caused by removing that OTU from the set of predictors.
FMT
Fecal samples from randomly chosen MDD patients (n = 5, male, age 27–61
years) and healthy controls (n = 5, male, age 29–62 years) were used to
colonize the guts of GF mice. The procedures of preparing the fecal
samples for microbiota transplantation were as described in a previous
study.16 Briefly, fecal samples were handled under anaerobic conditions.
Each fecal sample (0.1 g) was suspended with 1.5 ml of reduced sterile
phosphate-buffered saline, and pools were made from equal volumes of
donor suspensions. Adult (6–8-week-old) male GF Kunming mice were
colonized with pooled samples derived from either MDD patients or
healthy controls. The ‘depression microbiota’ and the ‘healthy microbiota’
Altered gut microbiome induces depression
P Zheng et al
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© 2016 Macmillan Publishers Limited Molecular Psychiatry (2016), 786 – 796
http://www.mothur.org/
http://www.mothur.org/wiki/RDP_reference_files
http://www.mothur.org/wiki/RDP_reference_files
recipient mice were separately bred in different gnotobiotic isolators to
prevent normalization of gut microbiota. Within each individual gnotobiotic
isolator, either ‘depression microbiota’ or ‘healthy microbiota’ recipient
mice were bred in different cages (five mice per cage). The weights of the
mice were measured at the beginning of FMT experimentation and
immediately prior to killing of the mice. The behavioral tests (including OFT,
FST and TST) were performed on weeks 1 and 2 after microbiota
transplantation. Cecal samples were collected at the time the mice were
killed and immediately snap-frozen in liquid N2 and stored at − 80 °C.
Comparisons of metabolite profiles from the FMT model
On week 2 post FMT, the depressed and control mice were killed, and
cecum, serum and hippocampus samples were obtained. These samples
were subsequently extracted and analyzed by gas chromatography-mass
spectrometry (Agilent 7890A/5975C (Agilent Technologies, Santa Clara, CA,
USA)), liquid chromatography-mass spectrometry (Agilent 6538 UHD and
Accurate-Mass Q-TOF/MS) and nuclear magnetic resonance (Bruker
AVANCE II 600, Bruker Biospin, Rheinstetten, Germany), based metabolo-
mics platforms.
Gas chromatography-mass spectrometry metabolite profiles were
processed according to our previously published work.44 The resulting
three-dimensional matrix—including peak indices (RT-m/z pairs), sample
names (observations) and normalized peak area percentages—were
introduced into SIMCA-P 12.0 (Umetrics, Umeå, Sweden). Multivariate
statistical methods, such as partial least squares discriminant analysis (PLS-
DA), were used to identify differential metabolites between groups.45 The
differential metabolites were identified using a statistically significant
threshold of variable influence on projection values obtained from the PLS-
DA model and a two-tailed Student’s t-test. Metabolites with variable
influence on projection values of greater than 1.0 and P-values of less than
0.05 were deemed statistically significant.46 The Human Metabolome
Database was used to comprehensively analyze the differential metabo-
lites in terms of in vivo metabolic activity.
Nuclear magnetic resonance-based metabolomics analysis of cecum
samples: a 70-mg fecal sample was extracted with 700 μl of phosphate
buffer solution (phosphate-buffered saline, 0.1 M, pH 7.4). Then, 400 μl
samples of supernatant were mixed with 200 μl of phosphate-buffered
saline. After centrifugation at 12 000 r.p.m. for 10 min, 500 μl samples of
supernatant were transferred into 5-mm nuclear magnetic resonance
tubes. The proton spectra were collected on a 600 Spectrometer operating
at 599.925 MHz 1H frequency. A standard NOESYPR1D pulse sequence was
used (recycle delay-90o-t1-90o-tm-90o-acquire free induction decay). PLS-
DA was used to visualize discrimination between healthy controls and
MDD subjects.45 The coefficient loading plots of the model were used to
identify the spectral variables responsible for sample differentiation on the
scores plot. A correlation coefficient (|r|) of greater than 0.500 was used as
the cutoff value for statistical significance based on a P-value of 0.05.
Liquid chromatography-mass spectrometry-based metabolomics analy-
sis of cecum samples: an 80-μl aliquot of serum sample was mixed with
240 μl of methanol. Then, 0.01 g of hippocampal tissue was extracted with
1000 μl of buffer (chloroform/methanol = 2:1). The extracted supernatants
of serum and hippocampus were subjected to Agilent 6538 UHD and
Accurate-Mass Q-TOF/MS. Spectra were collected in both positive and
negative ESI mode. PLS-DA were used to identify differential metabolites in
GF mice relative to SPF mice.45 The differential metabolites were identified
with variable influence on projection values of greater than 1.0 and
P-values of less than 0.05.
Shotgun metagenomic analysis of cecum samples
Samples were sequenced by Illumina HiSeq2500. Raw datasets of PE read
files were run through Trimmomatic (v0.32) to remove low-quality base
pairs and sequence adapters using these parameters [SLIDINGWIN-
DOW:4:15 MINLEN:36].47 Trimmed reads were filtered using the Fastq
quality filter program (Fastx toolkit v0.0.13.2 ) with the parameters [-q 10 -p
10].48 MetaPhlAn v1.7.8 and Bowtie2 v2.2.1 were used for profiling the
taxonomic clades in the high-quality metagenomic datasets.49,50 The
paired-end and singleton reads were assembled using an IDBA-UD v1.1.1
assembler.51 The open reading frames of the assembled scaffold sequence
were annotated using Prodigal v2.60 gene finder with the parameter [-p
meta].52 Bowtie2 v2.2.1 aligner was used to map the reads to the
assembled scaffold. The method for estimating the abundance of each
predicted open reading frame from a sample has been previously
described.53 KEGG Orthology was assigned through BLAST (BLASTP
v2.2.29+) search against the KEGG GENES Database v58 and eggNOG
v3.0 database with an E-value cutoff of 1e-5. Bitscore (greater than or equal
to 60) and minimum alignment length (greater than or equal to 15 aa)
were then used to filter the blast hits.54 Linear discriminant analysis was
used to identify the differential KEGG pathway and KEGG enzyme
commission number (E.C.s) representation between fecal microbiomes of
the humanized depressed and healthy control mice.55
RESULTS
Absence of gut microbiota produces decreased immobility time in
the FST
We initially assessed how the absence of gut microbiota
physiologically influences psychobehavioral characteristics by
comparing GF mice with SPF mice. From the OFT, representative
motion tracks for a GF mouse and a SPF mouse are presented in
Figures 1a and b. We found no difference in total motion distance
between GF and SPF mice (Figure 1c). In contrast, the proportion
of central motion distance was significantly increased in GF mice
relative to SPF mice (Figure 1d), suggesting reduced anxiety-like
behavior in GF mice. In the Y-maze spontaneous alternation test,
the alternation percentage was significantly decreased in GF mice
relative to SPF mice, indicating that GF mice displayed better
memory performance compared with SPF mice (Figure 1e). In the
FST, immobility time is widely used as an index of depression-like
behavior. Here, we found that GF mice displayed decreased
depression-like behavior as evidenced by a significantly decreased
immobility time (Figure 1f). Expanding on previous studies that
have observed altered anxiety and memory states from changes in
gut microbiotic composition,56–58 we have for, we believe, the
first time found that the absence of gut microbiota decreases
immobility time in the FST, suggesting a potential link between
the ‘microbiota-gut-brain axis’ and depression-like behavior.
MDD patients exhibit significant alterations in their gut
microbiomes
Given clinical metabonomic observations of metabolic distur-
bances generated from changes in gut microbiota in addition to
murine studies showing that the absence of gut microbiota
influences depression-like behavior, we next sought to determine
whether MDD patients displayed disturbances in their gut
microbiomes. The stool samples, as well as demographic and
clinical data, from 58 MDD patients and 63 demographically
matched healthy controls were collected. The detailed character-
istics of these recruited subjects are shown in Supplementary
Table S1. There were no significant differences in their key
demographic characteristics (Supplementary Table S2).
A culture-independent, 16S ribosomal RNA gene-sequence-
based approach was used to compare the gut microbial commu-
nities of MDD patients and healthy controls. DNA was extracted
from their fecal samples. V3-V5 variable region of bacterial 16S rRNA
genes was PCR-amplified. Samples were multiplexed and pyrose-
quenced, followed by quality filtering and chimera checking. We
obtained a total of 854 639 high-quality 16S rRNA gene sequences
(7063± 2352 reads/fecal sample, Supplementary Table S3), which
were subsequently clustered into OTUs at a 97\% similarity level. The
majority of these OTUs belonged to only two phyla (Firmicutes and
Bacteroidetes; 83.1 ± 11.9\%).
Initially, the within-sample (α) phylogenetic diversity analysis
showed that there were no significant difference between the two
groups (Supplementary Figures S1a–d; Supplementary Table S4).
In addition, the unweighted UniFrac analysis—which focuses on
the degree of microbial phylogenetic similarity (β-diversity)—was
used to determine the degree by which the gut microbiota
within MDD subjects differed from those within healthy controls.
The three-dimensional plots of unweighted UniFrac analysis
showed an obvious difference in the gut microbial community
Altered gut microbiome induces depression
P Zheng et al
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Molecular Psychiatry (2016), 786 – 796 © 2016 Macmillan Publishers Limited
compositions between MDD patients and healthy controls
(Figure 2a). A similar discrimination between MDD and healthy
control group was also observed using weighted UniFrac
analysis (Supplementary Figure S2). These differences in the
gut microbiomes were not significantly related to any key
categorical variables (that is, sex, smoking status and antidepres-
sant use, Supplementary Figures S3a–e) nor to any key continuous
variables (that is, age and body mass index, Supplementary
Figures S4a and b).
To identify the gut microbiota primarily responsible for
discriminating MDD subjects from healthy controls, we applied a
Random Forests classifier, which assigns an importance score to
each OTU by estimating the increase in error caused by removing
that OTU from the set of predictors. A total of 54 OTUs whose
relative abundance reliably distinguished MDD and healthy
control samples were identified (Figure 2b, Supplementary Table
S5). Of these 54 differential OTUs, a total of 29 OTUs were
overrepresented in MDD subjects and assigned to the families of
Figure 1. Effect of gut microbiota on mood-related behavior. (a, b) Representative motion tracks for a germ-free (GF) mouse and a specific
pathogen-free (SPF) mouse. (c) Open-field test (OFT): the total motion distance was measured to assess locomotor activity. There was no
difference in total motion distance between GF and SPF mice (n = 22/GF group; n = 15/SPF group). (d) OFT: the proportion of central motion
distance was quantified to assess anxiety-like behavior. GF mice displayed an increased proportion of central motion distance relative to SPF
mice (n = 22/GF group; n = 15/SPF group). (e) Y-maze test: alternation rate of GF mice was significantly decreased compared with SPF mice
(n = 17/GF group; n = 20/SPF group). (f) Forced swimming test (FST): GF mice displayed decreased depression-like behavior as evidenced by a
significant decreased immobility time (n = 21/GF group; n = 15/SPF group). Data presented as means ± standard errors of the mean. **Po0.01,
***Po0.001 by t-test.
Altered gut microbiome induces depression
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© 2016 Macmillan Publishers Limited Molecular Psychiatry (2016), 786 – 796
Actinomycineae, Coriobacterineae, Lactobacillaceae, Streptococ-
caceae, Clostridiales incertae sedis XI (Parvimonas), Eubacteria-
ceae, Lachnospiraceae (Anaerostipes, Blautia, Dorea,
Lachnospiracea incertae sedis), Ruminococcaceae (Clostridium
IV) and Erysipelotrichaceae incertae sedis, while a total of 25
OTUs were overrepresented in healthy control subjects and
assigned to the families of Bacteroidaceae, Rikenellaceae
(Alistipes), Lachnospiraceae (Coprococcus, Clostridium XlVa,
Lachnospiracea incertae sedis, Roseburia and Faecalibacterium),
Acidaminococcaceae (Phascolarctobacterium), Veillonellaceae
(Megamonas) and Sutterellaceae. Those discriminative OTUs were
mainly assigned to the phyla Firmicutes (45/56, 76.7\%), Actino-
bacteria (5/56, 10.9\%) and Bacteroidetes (3/56, 5.3\%). Compared
with healthy controls, the relative abundances of Actinobacteria
were increased in MDD subjects, while those of Bacteroidetes
were decreased (Figure 2c, Supplementary Figures S5a and b).
Although there were no significant differences in the overall
relative abundances of Firmicutes between MDD patients and
healthy controls (Figure 2c, Supplementary Figure S5c), change of
Firmicutes was still one of hallmark in MDD. This is because some
members of the Firmicutes OTUs (24/56) were increased in MDD
patients, while others were decreased (Supplementary Table S5).
In sum, we observed that MDD was associated with a disturbance
in the gut microbiome characterized by alterations in specific
OTUs assigned to the phyla Firmicutes, Actinobacteria and
Bacteroidetes.
Transplantation of MDD patient microbiota induces depression-
like behaviors in GF recipient mice
To investigate whether changes in gut microbiome contribute to
the pathogenesis of MDD, FMT experiments were performed.59
This approach has been successfully used to determine the
causative role of gut microbiota in the onset of obesity, colitis and
Figure 2. 16S rRNA gene sequencing reveals changes to microbial diversity in MDD. (a) Three-dimensional principal coordinate analysis (PCoA)
of unweighted UniFrac distances showed an obvious difference in gut microbiotic composition between major depressive disorder (MDD)
patients and healthy controls (n = 58/MDD, red plots; n = 63/HC, blue plots). The percentage of variation explained by principal coordinates is
marked on the axes. (b) Heatmap of the 54 discriminative operational taxonomic units (OTUs) abundances between depressed subjects and
healthy …
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sex differences in gut microbiota in patients with
major depressive disorder
Jian-jun chen1–4
Peng Zheng2,3
Yi-yun liu2,3
Xiao-gang Zhong2,3
hai-yang Wang2,3
Yu-jie guo2,3
Peng Xie2,3
1institute of life sciences,
2Department of Neurology, First
affiliated hospital, 3institute of
Neuroscience, 4Joint international
research laboratory of reproduction
and Development, chongqing Medical
University, chongqing, china
Objective: Our previous studies found that disturbances in gut microbiota might have a causative
role in the onset of major depressive disorder (MDD). The aim of this study was to investigate
whether there were sex differences in gut microbiota in patients with MDD.
Patients and methods: First-episode drug-naïve MDD patients and healthy controls were
included. 16S rRNA gene sequences extracted from the fecal samples of the included subjects
were analyzed. Principal-coordinate analysis and partial least squares-discriminant analysis
were used to assess whether there were sex-specific gut microbiota. A random forest algorithm
was used to identify the differential operational taxonomic units. Linear discriminant-analysis
effect size was further used to identify the dominant sex-specific phylotypes responsible for
the differences between MDD patients and healthy controls.
Results: In total, 57 and 74 differential operational taxonomic units responsible for separating
female and male MDD patients from their healthy counterparts were identified. Compared with
their healthy counterparts, increased Actinobacteria and decreased Bacteroidetes levels were
found in female and male MDD patients, respectively. The most differentially abundant bacte-
rial taxa in female and male MDD patients belonged to phyla Actinobacteria and Bacteroidia,
respectively. Meanwhile, female and male MDD patients had different dominant phylotypes.
Conclusion: These results demonstrated that there were sex differences in gut microbiota
in patients with MDD. The suitability of Actinobacteria and Bacteroidia as the sex-specific
biomarkers for diagnosing MDD should be further explored.
Keywords: major depressive disorder, MDD, gut microbiota, biomarker
Introduction
Major depressive disorder (MDD) is a mental disorder characterized by loss of interest
in normally enjoyable activities, low self-esteem, and low energy. This disease imposes
a huge economic burden on the whole society. The pathogenesis of MDD is still
unclear, and there are no objective diagnostic methods or 100\%-effective treatment
methods.1,2 Many factors, such as genetics, biochemical or neurophysiological changes,
and psychosocial variables, are associated with MDD.3,4 Recently, many researchers
have attempted to explain its pathogenesis using neuroanatomical abnormalities,
neurotransmission deficiency, and neurotrophic alterations.5 However, none of these
theories has been universally accepted. Therefore, there is an urgent need to identify
novel pathophysiologic mechanisms underlying MDD.
A previous study reported that gut microbiota could influence all aspects of physi-
ology.6 Many diseases have been found to be related to disturbed gut microbiota, such
as obesity and diabetes.7,8 Researchers have also found that gut microbiota had an
influence on brain function and behavior through the microbiota–gut–brain axis.9,10
Our previous study proved that gut microbiota could influence the expression levels of
correspondence: Peng Xie
Department of Neurology, First Affiliated
hospital, chongqing Medical University,
1 Yixueyuan road, Yuzhong, chongqing
400016, china
Tel +86 23 6848 5490
Fax +86 23 6848 5111
email [email protected]
Journal name: Neuropsychiatric Disease and Treatment
Article Designation: Original Research
Year: 2018
Volume: 14
Running head verso: Chen et al
Running head recto: Gut-microbiota sex differences in MDD
DOI: 159322
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genes in the hippocampi of mice.11 Meanwhile, our previous
metabolomic studies showed an interesting phenomenon
wherein several differential metabolites in MDD patients
were the metabolic byproducts of gut microbiota.12,13 More-
over, clinical studies have reported disturbed gut microbiota
in limited samples of depressed patients.14,15 Based on these
results, we conducted a further study, and found that the
dysbiosis of gut microbiota might be a contributory factor
in the development of MDD.16
Nowadays, the disproportionate prevalence of MDD in
women might be the most consistent finding among studies
on MDD. In our previous study, compared to healthy con-
trols (HCs), the relative abundance of Bacteroidetes and
Actinobacteria were found to be significantly changed in
patients with MDD.16 However, possible sex differences
in gut microbiota were not taken into consideration. Actu-
ally, our previous metabolomic study found that there were
divergent urinary metabolic phenotypes between males
and females with MDD.17 Yurkovetskiy et al reported that
hormonal regulation of microbe-controlling mechanisms
could result in differences in microbial composition between
males and females.18 Moreover, a recently published study
reported that there were sex-specific transcriptional signa-
tures in human depression.19 Therefore, we hypothesized
that there were sex differences in gut microbiota in patients
with MDD, and conducted this study to identify sex-specific
gut microbiota.
Patients and methods
subject recruitment
The protocol of this study was reviewed and approved by
the ethical committee of Chongqing Medical University
(Chongqing, China). The methods were carried out in
accordance with approved guidelines and regulations. The
17-item Hamilton Depression Rating Scale (HDRS-17)
was used to assess depression severity.20 Two experienced
psychiatrists studying and treating depression for several
years systematically used the HDRS-17 to diagnose each
participant. MDD patients who met the following criteria
were included: $18 years old, first-episode drug-naïve,
and without obesity, diabetes, substance abuse, preexisting
physical diseases, or other mental disorders. HCs did not
have any previous lifetime history of Diagnostic and Statis-
tical Manual of Mental Disorders IV axis I/II neurological
diseases or systemic medical illness. MDD patients receiving
nonpharmacologic treatments were also excluded. Subjects
using antibiotics or probiotics were excluded. Pregnant,
nursing, or currently menstruating candidates were excluded.
All subjects recruited provided written informed consent.
MDD patients and HCs were recruited from the psychiatric
center and medical examination center, respectively.
16s rrNa gene sequencing
Fecal samples were collected and stored at -80°C prior to
analysis. The standard PowerSoil kit protocol was used to
extract bacterial genomic DNA. Briefly, the frozen samples
were thawed on ice and then pulverized with a pestle and mor-
tar in liquid nitrogen, we added MoBio lysis buffer to these
fecal samples and then mixed them, and after centrifuging,
we placed the obtained supernatant into MoBio garnet-bead
tubes containing MoBio buffer. Extracted V3–V5 regions of
the 16S rRNA gene from these fecal samples were ampli-
fied by polymerase chain reaction with bar-coded universal
primers containing linker sequences for pyrosequencing.21
The 454 sequencing system (Hoffman-La Roche, Basel,
Switzerland) was used.
16s rrNa gene-sequencing analysis
In order to obtain unique reads, Mothur 1.31.2 was
used to quality-filter the raw sequences obtained from
454 sequencing.22 Sequences meeting any one of the follow-
ing criteria were excluded: ,200 bp or .1,000 bp, contained
any bar-code mismatches, primer mismatches, or ambiguous
bases, and contained homopolymer runs exceeding six bases.
Finally, the remaining sequences were assigned to operational
taxonomic units (OTUs) and then taxonomically classified
using RDP reference database.23 To calculate the relative
abundance of gut microbiota at different levels, summaries
of taxonomic distributions of OTUs were constructed using
these taxonomies. Four parameters (observed species, phylo-
genetic diversity, Shannon index, Simpson index) were used
to calculate α-diversity.24 β-Diversity was reported accord-
ing to principal-coordinate analysis (PCoA).25 Meanwhile,
both PCoA and partial least squares-discriminant analysis
(PLS-DA) were used to find out whether MDD patients
could be separated from HCs. A random forest algorithm
was used to identify the differential OTUs responsible for
sample differentiation. Cytoscape 3.2.1 software was used
to analyze the potential relationship between demographic
data and differential OTUs. Finally, the linear discriminant-
analysis effect size (LEfSe) was further used to identify the
dominant sex-specific phylotypes responsible for differences
between MDD patients and HCs.26
Results
Demographic data
A total of 24 first-episode drug-naïve female MDD patients
and 24 demographically matched female HCs were recruited.
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gut-microbiota sex differences in MDD
The average ages of these MDD patients and HCs were
41.5±11.53 and 43.95±12.11 years (P=0.475), respec-
tively. These MDD patients (22.01±2.17 kg/m2) and HCs
(22.63±2.43 kg/m2) had similar average body-mass index
(BMI; P=0.356). The average HDRS scores of these MDD
patients were 23.04±4.93. Meanwhile, 20 first-episode
drug-naïve male MDD patients and 20 demographically
matched male HCs were recruited. The average ages
of these MDD patients and HCs were 40.35±11.05 and
42.80±15.13 years (P=0.562), respectively. These MDD
patients (22.22±2.18 kg/m2) and HCs (22.50±2.25 kg/m2)
had similar average BMI (P=0.694). The average HDRS
scores of these MDD patients were 23.9±3.68. Three
female and two male MDD patients had coexisting
anxiety disorders.
Disturbed gut microbiota
The majority of the obtained OTUs belonged to four phyla
(female vs male): Firmicutes (73\% vs 75.2\%), Bacteroidetes
(10\% vs 9.7\%), Actinobacteria (6.5\% vs 6.8\%), and Pro-
teobacteria (4.8\% vs 5.3\%). The results of within-sample
(α) phylogenetic diversity analysis showed no significant
differences between MDD patients and HCs. The results of
PCoA showed that gut microbial community composition
was significantly different between female MDD patients
and HCs (Figure 1A). The PLS-DA model showed similar
results (Figure 1B). Meanwhile, the PCoA and PLS-DA
models also showed significant differences in gut microbial
community composition between male MDD patients and
HCs (Figure 2).
Differential gut microbiota
A random forest classifier was used to identify the key dis-
criminatory OTUs responsible for separating MDD patients
from HCs. In total, 57 OTUs whose relative abundance could
reliably distinguish female MDD patients from female HCs
were identified. Of these differential OTUs, the 29 increased
OTUs in female MDD patients were mainly assigned to
the families of Coriobacteriaceae, Lachnospiraceae, and
Ruminococcaceae, and the 28 decreased OTUs were mainly
assigned to the families of Lachnospiraceae and Ruminococ-
caceae (Figure 3). These differential OTUs were mainly
assigned to the phyla Firmicutes (45 of 57, 78.9\%), Acti-
nobacteria (six of 57, 10.5\%), and Bacteroidetes (three of
57, 5.3\%). Meanwhile, 74 OTUs whose relative abundance
could reliably distinguish male MDD patients from HCs
were identified. Of these differential OTUs, the 21 increased
OTUs in male MDD patients were mainly assigned to the
families of Lachnospiraceae and Erysipelotrichaceae, and the
decreased 53 OTUs were mainly assigned to the families of
Lachnospiraceae and Ruminococcaceae (Figure 4). The phyla
these were mainly assigned to were Firmicutes (62 of 74,
83.8\%), Actinobacteria (three of 74, 4\%), and Bacteroidetes
(six of 74, 8.1\%). Compared with female HCs, the relative
abundance of Actinobacteria was increased in female MDD
patients. Compared with male HCs, the relative abundance
of Bacteroidetes was decreased in male MDD patients.
Dominant phylotypes
Dominant phylotypes responsible for the differences between
MDD patients and HCs were identified by the metagenomic
Figure 1 Obvious differences in gut microbial composition between female MDD patients and hcs.
Notes: (A) Three-dimensional principal-coordinate analysis; (B) partial least squares-discriminant analysis.
Abbreviations: MDD, major depressive disorder; hcs, healthy controls.
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chen et al
Figure 2 Obvious differences in gut microbial composition between male MDD patients and hcs.
Notes: (A) Three-dimensional principal-coordinate analysis; (B) partial least squares-discriminant analysis.
Abbreviations: MDD, major depressive disorder; hcs, healthy controls.
Figure 3 heat map of differential operational taxonomic unit abundance between female MDD patients and hcs.
Notes: assignment of each operational taxonomic unit provided at right. green and red indicate increase and decrease, respectively.
Abbreviations: MDD, major depressive disorder; hcs, healthy controls.
LEfSe approach. LEfSe is a new method for metagenomic
biomarker discovery by way of class comparison. In total, 22
bacterial clades with statistically significant and biologically
consistent differences in female MDD patients were identi-
fied (Figure 5A). The most differentially abundant bacterial
taxa in female MDD patients belonged to Actinobacteria.
At the genus level, Actinomyces, Bifidobacterium, Asacchar-
obacter, Atopobium, Eggerthella, Gordonibacter, Olsenella,
Eubacterium, Anaerostipes, Blautia, Roseburia, Faecali-
bacterium, and Desulfovibrio, which were most abundant
in female MDD patients, and Howardella, Sutterella, and
Pyramidobacter, which were most abundant in female HCs,
were the key phylotypes that contributed to the different
gut microbiota between female MDD patients and HCs
(Figure 5B).
Meanwhile, six bacterial clades with statistically significant
and biologically consistent differences in male MDD patients
were identified (Figure 6A). The most differentially abundant
bacterial taxa in male MDD patients belonged to Bacteroidia
(Bacteroidetes). At the genus level, Bacteroides, Erysip-
elotrichaceae incertae sedis, Veillonella, and Atopobium,
which were most abundant in male MDD patients, and
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gut-microbiota sex differences in MDD
Anaerovorax, Gordonibacter, and Pyramidobacter, which
were most abundant in male HCs, were the key phylotypes
that contributed to the different gut microbiota between male
MDD patients and HCs (Figure 6B).
correlation analysis
CoNet 1.1 (Cytoscape application) was used to evaluate
correlations among the demographic data and relative
abundance of bacterial genera (Figure 7). For female MDD
patients, six genera (Asaccharobacter, Clostridium XIVa,
Erysipelotrichaceae incertae sedis, Streptococcus, Faeca-
libacterium, and Lachnospira incertae sedis) were nega-
tively correlated with age, three genera (Clostridium XIVa,
Erysipelotrichaceae incertae sedis, and Streptococcus)
were negatively correlated with HDRS score, and one
genus (Streptococcus) was negatively correlated with BMI.
For male MDD patients, one genus (Erysipelotrichaceae
incertae sedis) was negatively correlated with age, two
genera (Veillonella and Collinsella) were negatively and
positively, respectively, correlated with HDRS score, and
Figure 4 heat map of differential operational taxonomic unit abundance between male MDD patients and hcs.
Notes: assignment of each operational taxonomic unit provided at right. green and red indicate increase and decrease, respectively.
Abbreviations: MDD, major depressive disorder; hcs, healthy controls.
Figure 5 Taxonomic differences in gut microbiota in female subjects.
Notes: (A) Bacterial clades (25) with statistically significant and biologically consistent differences in female MDD patients and HCs (LDA score .2); (B) MDD-enriched taxa
indicated by positive lDa scores (green), and hc-enriched taxa indicated by negative scores (red).
Abbreviations: MDD, major depressive disorder; hcs, healthy controls; lDa, linear discriminant analysis.
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chen et al
two genera (Dorea and Lachnospira incertae sedis) were
positively correlated with BMI. These results showed that
only three key phylotypes (Erysipelotrichaceae incertae sedis,
Asaccharobacter, and Faecalibacterium) were negatively
correlated with age and one key phylotype (Veillonella) was
negatively correlated with HDRS, while other key phylotypes
showed no correlation with demographic data.
Discussion
In total, 57 female-specific and 74 male-specific differential
OTUs were identified. Among these OTUs, only 18 differential
OTUs (mainly assigned to the phyla Firmicutes, 14 of 18,
77.8\%) were present in both female and male MDD patients.
The relative abundance of Actinobacteria and Bacteroidetes
was increased and decreased in female and male MDD
patients compared to their healthy counterparts, respectively.
Moreover, the most differentially abundant bacterial taxa
belonged to Actinobacteria in female MDD patients and
Bacteroidia (phyla Bacteroidetes) in male MDD patients.
The dominant phylotypes in female and male MDD patients
were also different. These results demonstrated that there
were sex differences in gut microbiota in patients with MDD,
Figure 7 associations among demographic data (age, BMi, and hDrs) and gut microbiota.
Notes: (A) Female and (B) male MDD patients. red lines indicate positive relationships, green lines negative relationship, and circles: red, demographic data; green,
Asaccharobacter; blue, Clostridium XiVa; orange, erysipelotrichaceae incertae sedis; dark orchid, Faecalibacterium; yellow, lachnospiraceae incertae sedis; deep sky blue,
Streptococcus; crimson, Collinsella; light green, Dorea; pink, Veillonella; olive, others.
Abbreviations: BMi, body-mass index; hDrs, hamilton Depression rating scale; MDD, major depressive disorder.
Figure 6 Taxonomic differences in gut microbiota in male subjects.
Notes: (A) Twelve bacterial clades with statistically significant and biologically consistent differences in male MDD patients and HCs (LDA score .2); (B) MDD-enriched
taxa indicated by positive lDa scores (green), and hc-enriched taxa indicated by negative scores (red).
Abbreviations: MDD, major depressive disorder; hcs, healthy controls; lDa, linear discriminant analysis.
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gut-microbiota sex differences in MDD
and the suitability of Actinobacteria and Bacteroidia as the
sex-specific biomarkers for diagnosing MDD should be
further evaluated.
Our previous study found that the relative abundance of
Bacteroidetes and Actinobacteria was significantly decreased
and increased, respectively, in MDD patients.16 The purpose of
this previous study was to investigate whether the dysbiosis
of the gut microbiome played a causal role in the development
of depressive-like behaviors. Therefore, sex-based differ-
ences were not taken into consideration. However, sex-
based differences are prominent in affective disorders.27–29
Our previous metabolomic studies also found sex-specific
differential metabolites in MDD and bipolar disorder.17,30
Therefore, when separately analyzing the differential gut
microbiota in female and male MDD patients, we found that
the Bacteroidetes level was only significantly decreased in
male MDD patients and the Actinobacteria level only signifi-
cantly increased in female MDD patients. Moreover, in order
to rule out the potential influence of antidepressants on gut
microbiota, only drug-naïve MDD patients were recruited,
which might make our conclusion more robust in identifying
sex-specific gut microbiota.
A previous study reported a general underrepresentation
of Bacteroidetes related with depression.14 However, Jiang
et al found that Bacteroidetes levels were increased in MDD
patients.15 In this study, decreased Bacteroidetes levels were
found in male MDD patients. Meanwhile, Jiang et al also
reported the decreased phyla Firmicutes level and increased
phyla Actinobacteria level in MDD patients. However our
results showed that increased Actinobacteria levels were
only found in female MDD patients, and the overall rela-
tive abundance of Firmicutes was not significantly changed
in female or male MDD patients. This disparity might be
caused by the sex factor. Additionally, the differences in
demographic and clinical characteristics of the recruited
MDD patients, sample sizes, and/or statistical methods used
to identify MDD-associated gut microbiota might also have
a role in this disparity. However, these results consistently
showed that MDD was linked to distinct alterations in gut
microbial compositions.
In clinical practice, depression and metabolic-disease
comorbidity, such as diabetes and obesity, is common.31
Previous studies have shown that low Bacteroidetes levels
were associated with obesity.32,33 Stunkard et al reported a link
between depression and obesity through low-grade inflam-
mation.34 Meanwhile, Troseid et al established a correlation
between low-grade inflammation and bacteria.35 Therefore,
in order to rule out the potential influence of obesity and
diabetes, we excluded subjects with either. Finally, the
difference in BMI between MDD patients and HCs was
almost negligible. Moreover, the correlation analysis also
showed that there was no correlation between any key phy-
lotypes and BMI. It is unlikely that obesity or diabetes could
be a confounding factor in this study.
Although no significant differences in the overall abun-
dance of Firmicutes between HCs and MDD patients were
identified in this study, some Firmicutes OTUs in both female
and male MDD patients were increased, while others were
decreased. Therefore, the disturbed Firmicutes could still be a
hallmark in MDD.16 Moreover, the correlation analysis found
that the relative abundance of seven genera (Clostridium XIVa,
Erysipelotrichaceae incertae sedis, Streptococcus, Dorea,
Faecalibacterium, Lachnospira incertae sedis, and
Veillonella) belonging to Firmicutes showed correlations
with age, HDRS score, and BMI. A previous study reported
that there was a negative relationship between the sever-
ity of depressive symptoms and the relative abundance
of Faecalibacterium,15 but in this study only the negative
relationship between the severity of depressive symptoms
and the relative abundance of four other genera (Clostridium
XIVa, Streptococcus, Erysipelotrichaceae incertae sedis, and
Veillonella) was identified. These different results might also
be caused by the sex factor. In addition, a positive relation-
ship between the severity of depressive symptoms and the
relative abundance of Collinsella in male MDD patients
was found here. Schnorr and Bachner reported a reduction
in Actinobacteria levels after intervention, mainly from the
loss of Collinsella.36 As such, Collinsella might also be
a useful index for the clinical management and treatment
of MDD.
The brain can alter gut function, and is widely acknowl-
edged. However, it is less readily accepted that signals from
the gut can influence brain function. Actually, gut microbiota
could regulate the size and composition of bile-acid pool
size, and in turn be an important regulator of the blood–
brain barrier and hypothalamic–pituitary–adrenal axis.37
Meanwhile, metabolites from the gut microbiota have a
significant effect on regulating the gut–brain axis and host
immunity.38 Gut microbiota can also regulate brain func-
tion by influencing tryptophan metabolism39 and influence
the development and activity of brain tissue by regulating
microglia homoeostasis.40 In addition, Sobko et al found
that Lactobacillus could convert nitrate to nitric oxide,
which is a potent regulator of the immune and nervous
systems.41 Previous studies have reported that L. rhamnosus
and L. acidophilus might have an analgesic action on the
host by inhibiting the spinal neuron cellular memory of the
distension.42,43
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Limitations
Limitations should be noticed here. First, the number of
recruited samples was relatively small, and the results of
this exploratory research need future studies to verify and
support them. Second, an animal experiment was not per-
formed to determine whether Actinobacteria or Bacteroi-
detes could be potential targets for MDD treatment. Third,
a previous study reported that dietary habits could influence
gut microbiota,44 but the relationship between dietary intake
and gut microbial compositions could not be analyzed here,
because of missing detailed dietary information. However,
our findings were not likely to be significantly influenced
by this potential confounding factor, because of the similar
lifestyles and dietary habits of the recruited samples from
Chongqing. Fourth, all recruited subjects were from the same
place, and thus ethnic biases and site-specificity in microbial
phenotypes could not be ruled out. Fifth, we did not measure
estrogen levels in female patients, but Baker et al reported
that gut microbiota could regulate estrogen levels through
secretion of β-glucuronidase.45 Future studies should explore
the potential relationship between estrogen levels and gut
microbiota. Finally, we did not analyze depressive episode
duration, although previous a study found that there was
evidence for the interplay between immune and endocrine
systems in drug-naïve MDD patients with short-illness-
duration first affective episodes.46
Conclusion
This study firstly determined that there were sex differences
in gut microbiota in patients with MDD and identified gender-
specific gut microbiota. The most differentially abundant
bacterial taxa in female and male MDD patients belonged to
Actinobacteria and Bacteroidia, respectively. These findings
could provide new insights for uncovering the pathogenesis
of MDD and studying potential gut-mediated therapies for
MDD. However, due to the risk of overseeing effects in small
cohorts, these findings must be verified and supported in the
larger cohorts. Meanwhile, future studies are warranted to
evaluate the suitability of Actinobacteria and Bacteroidia as
sex-specific biomarkers for diagnosing MDD.
Acknowledgments
This work was supported by the Natural Science Founda-
tion Project of China (81701360, 81601208, 81771490),
Chongqing Science and Technology Commission (cstc2017j-
cyjA0207, cstc2014kjrc-qnrc10004), Science and Technol-
ogy Research Program of Chongqing Municipal Education
Commission (grant KJ1702037), Special Project on Natural
Chronic Noninfectious Diseases (2016YFC1307200),
National Key Research and Development Program of China
(2017YFA0505700), and National Basic Research Program
of …
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In my opinion
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The ability to view ourselves from an unbiased perspective allows us to critically assess our personal strengths and weaknesses. This is an important step in the process of finding the right resources for our personal learning style. Ego and pride can be
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While you must form your answers to the questions below from our assigned reading material
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5 The family dynamic is awkward at first since the most outgoing and straight forward person in the family in Linda
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The most important benefit of my statistical analysis would be the accuracy with which I interpret the data. The greatest obstacle
From a similar but larger point of view
4 In order to get the entire family to come back for another session I would suggest coming in on a day the restaurant is not open
When seeking to identify a patient’s health condition
After viewing the you tube videos on prayer
Your paper must be at least two pages in length (not counting the title and reference pages)
The word assimilate is negative to me. I believe everyone should learn about a country that they are going to live in. It doesnt mean that they have to believe that everything in America is better than where they came from. It means that they care enough
Data collection
Single Subject Chris is a social worker in a geriatric case management program located in a midsize Northeastern town. She has an MSW and is part of a team of case managers that likes to continuously improve on its practice. The team is currently using an
I would start off with Linda on repeating her options for the child and going over what she is feeling with each option. I would want to find out what she is afraid of. I would avoid asking her any “why” questions because I want her to be in the here an
Summarize the advantages and disadvantages of using an Internet site as means of collecting data for psychological research (Comp 2.1) 25.0\% Summarization of the advantages and disadvantages of using an Internet site as means of collecting data for psych
Identify the type of research used in a chosen study
Compose a 1
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effect relationship becomes more difficult—as the researcher cannot enact total control of another person even in an experimental environment. Social workers serve clients in highly complex real-world environments. Clients often implement recommended inte
I think knowing more about you will allow you to be able to choose the right resources
Be 4 pages in length
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One thing you will need to do in college is learn how to find and use references. References support your ideas. College-level work must be supported by research. You are expected to do that for this paper. You will research
Elaborate on any potential confounds or ethical concerns while participating in the psychological study 20.0\% Elaboration on any potential confounds or ethical concerns while participating in the psychological study is missing. Elaboration on any potenti
3 The first thing I would do in the family’s first session is develop a genogram of the family to get an idea of all the individuals who play a major role in Linda’s life. After establishing where each member is in relation to the family
A Health in All Policies approach
Note: The requirements outlined below correspond to the grading criteria in the scoring guide. At a minimum
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Read Connecting Communities and Complexity: A Case Study in Creating the Conditions for Transformational Change
Read Reflections on Cultural Humility
Read A Basic Guide to ABCD Community Organizing
Use the bolded black section and sub-section titles below to organize your paper. For each section
Losinski forwarded the article on a priority basis to Mary Scott
Losinksi wanted details on use of the ED at CGH. He asked the administrative resident