please write a separate annotated bibliography for each of these primary research papers( attached below). - Physiology
guidelines (in word file) and 3 primary articles (in pdf file) are attached.
GUIDELINES FOR ANNOTATED BIBLIOGRAPHY
Annotated Bibliography
Annotated bibliography is a list of references (journal articles in your case) along with a brief descriptive and evaluative paragraph for each. Annotations should include both descriptive and critical statements on the subject article, whereas an abstract is just a summary description of article. Annotations address the main points of the article and critiques, entailing rationale of study purpose and hypotheses, validity of investigation methods, or study, appropriateness of results and conclusion, and overall clarity of expression. Each annotation of the articles in your bibliography should include the followings with 300-500 words, excluding the word counts for the title and author information.
· Title & author information – Author(s) and their affiliation(s) of the article, year of publication, title of the article, Title, volume number and page number (or DOI) of the journal in which the article is published
· Introduction & hypothesis – brief background information on the topic investigated in the research paper, clear and concise purpose of the study, and hypotheses tested
· Methods – concise statement of the experimental design (description of subjects/tissues/cells/animal models, etc., utilized, inclusion and exclusion criteria, identity of controls, types of measurements/lab analyses, and concise statement of the methods of statistical analysis and levels of significance to be accepted
· Results & conclusions – report and analysis of the outcome variables (the data resulting from the study), statement of the relative significance of the study as it applies to the hypothesis tested and the study data presented
· Critique – Your personal comments on how this paper could have been made better (methods of data collection, statistical analysis, numbers of observations, their statement of conclusions and implications, etc.?
Gut Microbiota and Bipolar Disorder
Emiko Aizawa et al. Bifidobacterium and Lactobacillus counts in the gut microbiota of patients with bipolar disorder healthy controls. Frontiers in psychiatry. 2019.
Annotated Bibliography
Bipolar disorder is neuropsychological disorder in which patients have mood swings due to altered neurotransmitter action, however concise mechanism remains elusive. Recent studies on various animal models have shown a positive bi-directional relationship between gut microbiota and neuro psychological disorders and are indicated to be involved directly or indirectly that is by effecting neural action, immune system and neurotransmitter levels. some gut microbes are beneficial while other microbes are harmful especially if their count alter and compete with protective ones. Bifidobacterium and Lactobacillus are those substantiated for healthy gut-brain axis. To see their effect/involvement or possible relation in bipolar disorders, a study was conducted.
The study was conducted on 39 bipolar disorder patients and 58 healthy controls, recruited from outpatient clinic at National Center of Neurology and Psychiatry (NCNP). Both groups are random Japanese participants. Out of 39 patients, 13 patients were affected with bipolar disorder I and 26 with bipolar II. All enrolled patients and healthy controls were interview, screened and diagnosed by research psychiatrist using Japanese version of Mini-International Neuropsychiatric Interview. Healthy candidates were excluded with any history of psychiatric disorder or contact with psychiatric services. All participants were biological unrelated and were screened to exclude candidate with history other CNS disease, gastrointestinal disease, recent use of antibiotics, head injury etc. There was no significant difference between age, BMI, education between both groups; moreover, patients’ onset of disorder had no significant difference as well. Patients’ antipsychotic and antidepressant drugs were converted to chlorpromazine and imipramine equivalents, respectively. Out of 39 patients, 9 patients were on probiotic medication. For the analysis of bacterial count, approx. 1 gram of fecal samples were collected in tube containing RNA stabilization solution from both groups. The samples were then processed for bacterial RNA extraction. Reverse transcription-quantitative polymerase chain reaction was targeted on 16S or 23S rRNA, by using primers of Bifidobacterium and lactobacillus were used to determine the count of bacteria. The quantification cycle values in the linear range were applied to analytical curve to obtain the corresponding bacterial count. Models’ cortisol level was also determined from their fasting venous blood samples collected in morning. In statistical analysis, data was presented as mean ±SD (standard deviation) unless otherwise specified. ANCOVA was used to compare bacterial counts between two groups and controlling of age and sex within the group. In the results obtained, showed no significant difference in Bifidobacterium and Lactobacillus count was found between bipolar disorder patient and healthy control. However, there was a negative correlation between Bifidobacterium count and cortisol levels in the bipolar patients with sleeping disorder.
Therefore, the findings suggest that Bifidobacterium and Lactobacillus may not play any role in the pathophysiology of bipolar disorder, unlike their evident reduction in counts in major depressive disorders from previous studies. However, the negative relation between Bifidobacterium and cortisol level indicates its role in sleep and stress response in the patients.
CRITIQUES:
GUIDELINES FOR ANNOTATED BIBLIOGRAPHY
Annotated Bibliography
Annotated bibliography is a list of
references (journal articles in your case) along with a brief
descriptive and evaluative paragraph for each. Annotations should include both descriptive and
critical statements on the subject article, whereas an abstract is just a summary description of
a
rticle. Annotations address the main points of the article and critiques, entailing rationale of study
purpose and hypotheses, validity of investigation methods, or study, appropriateness of results and
conclusion, and overall clarity of expression. Each
annotation of the articles in your bibliography
should include the followings with 300
-
500 words, excluding the word counts for the title and
author information.
·
Title & author information
–
Author(s) and their affiliation(s) of the artic
le, year of
publication, title of the article, Title, volume number and page number (or DOI) of the
journal in which the article is published
·
Introduction
& hypothesis
–
brief background information on the topic investigated
in the research paper
,
clear an
d concise purpose of the study
, and hypotheses tested
·
Methods
–
concise statement of the experimental design (description of
subjects/tissu
es/cells/animal models, etc.,
utilized, inclusion and exclusion criteria,
identity of controls, types of measurements
/lab analyses
, and c
oncise statement of the
methods of statistical analysis and levels of significance to be accepted
·
Results
& conclusions
–
report and analysis of the outcome variables (the data
resulting from the study)
,
statement of the relative signif
icance of the study as it applies
to the hypothesis test
ed and the study data presented
·
Critique
–
Your personal comments on how this paper could have been made better
(methods of data collection, statistical analysis, numbers of observations, their
statem
ent of conclusions and implications, etc
.
?
GUIDELINES FOR ANNOTATED BIBLIOGRAPHY
Annotated Bibliography
Annotated bibliography is a list of references (journal articles in your case) along with a brief
descriptive and evaluative paragraph for each. Annotations should include both descriptive and
critical statements on the subject article, whereas an abstract is just a summary description of
article. Annotations address the main points of the article and critiques, entailing rationale of study
purpose and hypotheses, validity of investigation methods, or study, appropriateness of results and
conclusion, and overall clarity of expression. Each annotation of the articles in your bibliography
should include the followings with 300-500 words, excluding the word counts for the title and
author information.
Title & author information – Author(s) and their affiliation(s) of the article, year of
publication, title of the article, Title, volume number and page number (or DOI) of the
journal in which the article is published
Introduction & hypothesis – brief background information on the topic investigated
in the research paper, clear and concise purpose of the study, and hypotheses tested
Methods – concise statement of the experimental design (description of
subjects/tissues/cells/animal models, etc., utilized, inclusion and exclusion criteria,
identity of controls, types of measurements/lab analyses, and concise statement of the
methods of statistical analysis and levels of significance to be accepted
Results & conclusions – report and analysis of the outcome variables (the data
resulting from the study), statement of the relative significance of the study as it applies
to the hypothesis tested and the study data presented
Critique – Your personal comments on how this paper could have been made better
(methods of data collection, statistical analysis, numbers of observations, their
statement of conclusions and implications, etc.?
OPEN
ORIGINAL ARTICLE
Inflammasome signaling affects anxiety- and depressive-like
behavior and gut microbiome composition
M-L Wong1,2,9,10, A Inserra1,2,9, MD Lewis1,2, CA Mastronardi3, L Leong4,5, J Choo4,5, S Kentish6, P Xie7,10, M Morrison8, SL Wesselingh4,5,
GB Rogers4,5,10 and J Licinio1,2,10
The inflammasome is hypothesized to be a key mediator of the response to physiological and psychological stressors, and its
dysregulation may be implicated in major depressive disorder. Inflammasome activation causes the maturation of caspase-1 and
activation of interleukin (IL)-1β and IL-18, two proinflammatory cytokines involved in neuroimmunomodulation, neuroinflammation
and neurodegeneration. In this study, C57BL/6 mice with genetic deficiency or pharmacological inhibition of caspase-1 were
screened for anxiety- and depressive-like behaviors, and locomotion at baseline and after chronic stress. We found that genetic
deficiency of caspase-1 decreased depressive- and anxiety-like behaviors, and conversely increased locomotor activity and skills.
Caspase-1 deficiency also prevented the exacerbation of depressive-like behaviors following chronic stress. Furthermore,
pharmacological caspase-1 antagonism with minocycline ameliorated stress-induced depressive-like behavior in wild-type mice.
Interestingly, chronic stress or pharmacological inhibition of caspase-1 per se altered the fecal microbiome in a very similar manner.
When stressed mice were treated with minocycline, the observed gut microbiota changes included increase in relative abundance
of Akkermansia spp. and Blautia spp., which are compatible with beneficial effects of attenuated inflammation and rebalance of gut
microbiota, respectively, and the increment in Lachnospiracea abundance was consistent with microbiota changes of caspase-1
deficiency. Our results suggest that the protective effect of caspase-1 inhibition involves the modulation of the relationship
between stress and gut microbiota composition, and establishes the basis for a gut microbiota–inflammasome–brain axis, whereby
the gut microbiota via inflammasome signaling modulate pathways that will alter brain function, and affect depressive- and
anxiety-like behaviors. Our data also suggest that further elucidation of the gut microbiota–inflammasome–brain axis may offer
novel therapeutic targets for psychiatric disorders.
Molecular Psychiatry (2016) 21, 797–805; doi:10.1038/mp.2016.46; published online 19 April 2016
INTRODUCTION
Increasing evidence suggests an involvement of neuroinflamma-
tory pathways in the etiopathophysiology of major depressive
disorder (MDD) and antidepressant response.1,2 Depressive
symptoms are underlined by increased levels of proinflammatory
cytokines (that is, interleukin (IL)-1β and IL-6), decreased levels of
anti-inflammatory cytokines (that is, IL-4 and IL-10) and are
associated with polymorphisms in inflammation-related genes.3–5
IL-1 receptor type-I and its ligands are expressed in brain areas
relevant to stress response,6–8 and IL-1β signaling is fundamental
in mediating the deleterious neurobehavioral and neuroendocrine
responses to stress and adaptation.9,10 Chronic stress or IL-1β
administration triggers depressive-like behavior.11
A variety of stressors activate the inflammasome through the
NLRP3 or P2X7 receptors, resulting in caspase-1 maturation that
processes and releases bioactive IL-1β and IL-18.12,13 Caspase-1 and
NLRP3 mRNA are increased in blood cells of depressed patients,14
suggesting that the inflammasome is a key mediator by which
physical and psychological stressors contribute to the development
of depression, leading to the ‘inflammasome hypothesis’ of depres-
sion.15 If that proves to be correct, caspase-1 inhibiting compounds
may have antidepressant effects. Minocycline is a semisynthetic
tetracycline antibiotic that inhibits caspase-1 and caspase-3 trans-
cription and has anti-apoptotic, anti-inflammatory and neuroprotec-
tive properties as well as acute antidepressant-like effects.16–22
Caspase-1 knockout (casp1− / −) mice are overtly normal, despite
having undetectable IL-1β and low IL-1α levels.23 They have
decreased systemic inflammatory response and increased survival
to lethal endotoxin doses when compared with wild-type (wt)
mice.23,24 This is underlined by reduced inflammation-induced
brain transcription, decreased inflammasome assembly and
consequently decreased circulating IL-1β and IL-18.23,24
1Mind and Brain Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia; 2Department of Psychiatry, Flinders Medical Centre, Adelaide, SA,
Australia; 3Genomics and Predictive Medicine, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia; 4Infection and Immunity
Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia; 5Department of Microbiology and Infectious Diseases, Flinders University School of
Medicine and Flinders Medical Centre, Adelaide, SA, Australia; 6Gastrointestinal Vagal Afferent Research Group, The University of Adelaide, Adelaide, SA, Australia; 7Department of
Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Neurobiology, and Institute of Neuroscience and the Collaborative
Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China and 8Translational Research Institute, The University of Queensland Diamantine Institute,
Wooloongabba, QLD, Australia. Correspondence: Professor M-L Wong or Professor J Licinio, Mind and Brain Theme, South Australian Health and Medical Research Institute, North
Terrace, PO Box 11060, Adelaide, SA 5001, Australia or Professor Peng Xie, Department of Neurology, The First Affiliated Hospital of Chongqing Medical Universtiy, 1 Youyi Road,
Yuzhong District, Chongping 400016, China.
E-mail: [email protected] or [email protected] or [email protected]
9These authors contributed equally to this work.
10These authors are co-senior authors.
Received 21 September 2015; revised 19 February 2016; accepted 22 February 2016; published online 19 April 2016
Molecular Psychiatry (2016) 21, 797–805
© 2016 Macmillan Publishers Limited All rights reserved 1359-4184/16
www.nature.com/mp
http://dx.doi.org/10.1038/mp.2016.46
mailto:[email protected]
mailto:[email protected]
mailto:[email protected]
http://www.nature.com/mp
The microbiota–gut–brain axis is a complex multiorgan bidirec-
tional signaling system between the microbiota and the brain that
plays a fundamental role in host physiology, homeostasis,
development and metabolism.25 Growing evidence shows repro-
ducible and consistent effects of microbial states on mouse
behavior, supporting a role for microbiota in modulating
behavior.26–28 Differences in anxiety-related behaviors are com-
monly reported in mice with altered gut microbiomes, implicating
the role of gut microbiota in stress and depression.29,30 Casp1− / −
mice display depressive-like behavior and anorexia after periph-
eral but not central LPS administration and differ in gut microbiota
composition compared with wt mice.31–33 Therefore, our primary
and secondary hypotheses were, respectively, (1) that decreased
caspase-1 activity would result in decreased depressive-like
behavior and (2) that caspase-1 inhibition using intraperitoneal
minocycline administration and chronic restraint stress would
result in changes in the gut microbiome. The null hypothesis was
that there would be no difference in these parameters between
casp1− / −, wt and minocycline-treated mice.
MATERIALS AND METHODS
Procedures were approved by the Animal Ethics Committees of the
Australian National University and the South Australian Health and Medical
Research and are in accordance with the Australian Code for the Care and
Use of Animals for Scientific Purposes (8th edition, 2013). Male mice
(C57BL/6J background, wt, n = 81; casp1− / −, n = 20) aged 60–90 days were
obtained from the Australian Phenomics (Canberra, ACT, Australia) or the
Bioresources Facilities (Adelaide, SA, Australia). Genetic caspase-1 defi-
ciency was confirmed by genotyping in experimental mice (Supplementary
Figure S1). Littermates were group housed (Green Line IVC Sealsafe PLUS
mouse, Tecniplast, Varese, Italy) in a temperature-specific (22C ± 1 °C) and
light-specific (12 h cycles, lights on at 0700 h) pathogen-free room
with water and standard regular chow ad libitum. Animals were assigned
and randomized as described in the Supplementary Materials and
Methods. The investigators were not blinded to group assignment.
Behavioral phenotyping was performed between 0900 h and
1600 h. Animals were given 30 min of habituation to the behavioral
testing room. Tests were performed from the least to the most invasive
to minimize the influence of prior test history (in order: rotarod, elevated
plus maze, marble burying test, open field test, sucrose preference test,
novelty suppressed feeding and forced swim test; see Supplementary
Materials and Methods for details).34 Following chronic restraint stress this
order was reversed for a bell-shaped stress exposure (Supplementary
Figure S2).
Chronic restraint stress
After baseline behavioral testing, animals were submitted to restraint stress
for 21 days. Every day, mice were placed in a horizontal resting position
inside a well-ventilated (12 holes, 0.5 mm diameter) 50 ml falcon tube at
1000 h and after 4–6 h they were unrestrained.
Pharmacological caspase-1 inhibition with minocycline
The wt animals were treated with either saline (0.2 ml, intraperitoneally,
n = 27) or minocycline (LKT Laboratories, St Paul, MN, USA; 5 mg kg− 1
per day in 10 ml kg− 1 saline, intraperitoneally, n = 27). Treatment lasted for
the same duration of the restraint procedure (21 days).
Respirometry
Minocycline- or saline-treated restrained animals were individually housed
in the Promethion Metabolic Monitoring System (Sable Systems Interna-
tional, Las Vegas, NV, USA) for 48 h to assess the effects of minocycline on
exploratory behavior, food intake, energy expenditure and volume of
oxygen inhaled and of carbon dioxide exhaled at baseline and after
chronic restraint.
16S rRNA analysis
Please see Supplementary Materials and Methods for a detailed explanation
of the methods used for the 16S rRNA analysis. Briefly, fecal pellets were
collected with autoclaved toothpicks, placed in 1.5 ml tubes, snap-frozen on
dry ice and stored at − 80 °C. Following DNA extraction, fecal microbiota
profiling was performed by paired-end 16S rRNA gene amplicon
sequencing, based on the Illumina MiSeq platform (Australia and New
Zealand, VIC, Australia) to a depth of ∼ 40 000 reads per sample. Sequence
data processing was performed as previously described.35
Statistical analysis
Statistical analyses were performed using the Statistical Package for the
Social Sciences version 22.0 for Windows (SPSS, Chicago, IL, USA) using a
general linear model for repeated measures. The effects of genotype,
stress, treatment and their interaction were explored and the significance
set at Po0.05. Sphericity of the variances of the groups was assessed with
Mauchly’s sphericity test. If the assumption of sphericity was violated, the
Greenhouse–Geisser correction was generated. Effect size was reported as
partial eta-squared (η2p). Significant stress × genotype or stress × treatment
interaction was unpacked as described previously.36 Comparison of
microbiota composition between groups (β-diversity) was performed
using Bray–Curtis similarity matrices in PRIMER (v6, PRIMER-E, Plymouth,
UK). Matrices were generated from sample-normalized, square-root
transformed, relative operational taxonomic unit abundance.
Community-level changes were assessed for significance using one-way
permutational multivariate analyses of variance (PERMANOVA) tests with
9999 random permutations and at a significance threshold of Po0.01. The
contribution of individual taxa to between-group variation was assessed by
similarity percentage analysis, as previously reported.37 Where specific
bacterial taxa were identified as contributing to change in microbiota
composition, variation in their relative abundance was further assessed
through Mann–Whitney U-tests between groups. Differences of median
relative abundance between groups were assessed using Hodges–
Lehmann estimator.
RESULTS
Our primary outcome measure was the assessment of depressive-
like behavior in the forced swim test. Secondary outcome
measures included anxiety-like behavior, changes in the sucrose
preference test, locomotor activity, gut microbiome and respiro-
metry. Analyses and results of behavioral tests results are available
in Supplementary Tables S1–S3.
Caspase-1 deficiency decreases depressive and anxiety-like
behaviors
Our results showed that caspase-1 deficiency decreased depres-
sive- and anxiety-like behaviors. In the forced swim test, the
total floating time was lower in casp1− / − compared with wt
mice (F1, 45 = 117.04, Po0.0001, Figure 1a and Supplementary
Table S1). At the same time, swimming and climbing behaviors
were higher in casp1− / − mice compared with wt (respectively
F1, 45 = 117.10, Po0.0001, and F1, 45 = 38.69, Po0.0001). Anxiety-
like behaviors had a significant main effect of genotype in 4 tests:
(1) elevated plus maze, (2) novelty suppressed feeding, (3) marble
burying and (4) open field tests. We found a significant main
effect of genotype in the elevated plus maze open to closed arms
time ratio (F1, 45 = 4.16, P = 0.047, Figure 1b), indicating an
anxiolytic phenotype in casp1− / − mice. Accordingly, in the novelty
suppressed feeding, casp1− / − mice showed decreased latency to
eat in a novel environment following fasting (F1, 43 = 32.17,
Po0.0001, Figure 1c). In the marble burying test, which
is considered predictive of anxiolytic compounds,38 we observed
a decreased number of marbles buried by casp1− / − mice
(F1, 45 = 11.55, P = 0.001, Figure 1d). Moreover, casp1
− / − mice
displayed a decreased number of fecal boli during the open field
test (F1, 45 = 4.72, P = 0.035, Figure 1e), whereas no differences
were observed for the time spent in the center area of the arena,
another measure of anxiety-related behavior (F1, 45 = 0.05,
P = 0.826). In the sucrose preference test, casp1− / − mice displayed
an increased preference for a 1% sucrose solution (F1, 33 = 5.52,
P = 0.025, Supplementary Table S1), suggesting greater hedonic
behavior.
Inflammasome signaling affects behavior and gut microbiome composition
M-L Wong et al
798
Molecular Psychiatry (2016), 797 – 805 © 2016 Macmillan Publishers Limited
Caspase-1 deficiency affects chronic restraint stress response
Our results suggest that casp-1− /− mice had an attenuated response
to chronic stress. We found a significant (genotype × stress)
interaction for swimming and climbing time in the forced swim
test (respectively F1, 45 = 7.02, P = 0.011, and F1, 45 = 8.60, P = 0.005).
The wt mice showed a greater decrease in swimming time (70%,
F1, 45 = 45.48, Po0.0001) than casp1−/ − mice (14%, F1, 45 = 5.33,
P = 0.026) following stress. Accordingly, wt animals displayed a
greater reduction in climbing time (91%, F1, 45 = 33.33, Po0.0001)
compared with casp1− /− mice (64%, F1, 45 = 78.13, Po0.0001)
following restraint (Figure 1a). We found a significant (genotype×
stress) interaction for body weight changes (F1, 45 = 6.06, P = 0.018)
that decreased in wt mice following restraint (F1, 45 = 14.24,
Po0.0001, average Δ body weight = − 1.3 g) but remained
unchanged in casp1−/ − mice (F1, 45 = 0, P = 1, average Δ body
weight= 0 g). Furthermore, we found a significant (genotype×
stress) interaction in the number of defecations in the open field
test (F1, 45 = 30.93, Po0.0001, Figure 1d); casp1− /− mice did not
show an increase in this parameter following restraint (F1, 45 = 1.73,
P = 0.196) whereas wt mice did (F1, 45 = 48.98, Po0.0001).
Caspase-1 deficiency increases locomotion and locomotor skills
We found that caspase-1 deficiency increases locomotor activity in
the open field test (F1, 45 = 10.54, P = 0.002, Figure 2a). Moreover,
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Figure 1. Caspase-1 (casp1) deficiency decreases anxiety-like and depressive like behavior and affects chronic restraint stress response.
(a) Casp1 knockout (casp1− / −) mice displayed decreased floating time in the forced swim test in comparison with wild-type (wt) mice and
(b) displayed decreased anxiety-like behavior as measured by the open/closed arms time ratio in the elevated plus maze. (c) In the novelty
suppressed feeding test, casp1− / − mice showed significantly decreased latency to feed following 16 h of fasting but not water deprivation.
(d) Moreover, casp1 deficiency resulted in less marbles buried in the marble burying test. (e) In the open field test, we observed a decreased
number of fecal boli as a result of casp1 deficiency as well as a different response to chronic restraint stress. Data are presented as mean ±
s.e.m. Genotype effect: *Po0.05, **Po0.01, ****Po0.0001; stress effect: +Po0.05, ++Po0.01, ++++Po0.0001; genotype × stress effect:
####Po0.0001. BL, baseline; STR, after chronic restraint stress paradigm; wt, wild type.
Inflammasome signaling affects behavior and gut microbiome composition
M-L Wong et al
799
© 2016 Macmillan Publishers Limited Molecular Psychiatry (2016), 797 – 805
casp1− / − mice acquired skills more quickly than wt mice to
perform in the accelerating rotarod test (F1, 45 = 15.35, Po0.0001,
Figure 2b and Supplementary Table S2).
Chronic restraint stress increases anxiety-like and depressive-like
behaviors
Chronic restraint stress (4–6 h per day for 21 days) increased the
floating time in the forced swim test (F1, 45 =66.92, Po0.0001,
Figure 1a), whereas it decreased swimming (F1, 45 =37.80, Po0.0001)
and climbing behavior (F1, 45 =109.52, Po0.0001). It also increased
anxiety-like behavior in the elevated plus maze test, decreasing the
time spent in the open arms (F1, 45 = 5.65, P=0.022) and the open to
closed arms time ratio (F1, 45 =4.55, P=0.038, Figure 1b), as well as in
the open field test, increasing the number of defecations
(F1, 45 =12.74, P =0.001, Figure 1e). Furthermore, restraint decreased
body weight gain (F1, 45 =6.06, P =0.018) and food intake
(F1, 43 =5.75, P=0.021). Nevertheless, restrained mice showed an
increase in ratio quotient (F1, 28 =4.79, P =0.037). Following restraint,
no changes were observed in the sucrose preference test
(F1, 33 =0.05, P=0.817, Supplementary Table S1) or in locomotor
activity in the open field test (F1, 45 = 3.64, P= 0.063, Figure 2a).
Minocycline treatment affects stress response and metabolic
parameters
We found a significant (treatment × stress) interaction in the
floating time in the forced swim test (F1, 28 = 6.67, P = 0.015,
Figure 3a and Supplementary Table S3). Saline- and minocycline-
treated animals displayed similar floating times at baseline
(F1, 28 = 2.35, P = 0.137); however, minocycline-treated mice were
less immobile than saline-treated mice following restraint
(F1, 28 = 5.25, P = 0.030). No differences were observed between
restrained mice receiving saline or minocycline in locomotion,
food intake, energy expenditure, body mass and volume of
oxygen inhaled (not shown). We found a significant effect of
treatment and stress on the volume of carbon dioxide exhaled
(respectively F1, 28 = 5.64, P = 0.025 and F1, 28 = 8.13, P = 0.008,
Figure 3b).
Chronic restraint stress affects the gut microbiome
Chronic restraint stress (4–6 h per day for 21 days) affected the gut
microbiome compared with nonstressed animals (PERMANOVA
P = 0.0027, t = 2.3492). Although the shallowest level of classifica-
tion (that is, phylum level) only revealed a nonsignificant trend
toward an increased ratio of Firmicutes to Bacteroidetes
(Figure 4a), the deeper analysis did identify clear differences
between the animal groups. In particular, the relative abundances
of the genera Allobaculum (difference in median relative
abundance − 7.8%, Po0.0001 Mann–Whitney U-test), Bifidobac-
terium (−4.6%, P = 0.0002), Turicibacter (−3.4%, Po0.0007),
Clostridium (−0.7%, Po0.0001) and the family S24-7 (−5.8%,
P = 0.0021) were all reduced in restrained animals, and the relative
abundance of the family Lachnospiraceae was increased (+0.3%,
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Figure 2. Caspase-1 (casp1) deficiency increases spontaneous locomotion and locomotory skills. (a) Casp1 knockout (casp1− / −) mice had
increased locomotor activity in the open field test when compared with wild-type (wt) mice and (b) acquired quicker the skills to perform the
rotarod test. Data are mean ± s.e.m. Genotype effect: Po0.05; **Po0.01; ****Po0.0001. BL, baseline; STR, after chronic restraint stress
paradigm.
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Figure 3. Caspase-1 antagonism affects chronic restraint stress response. (a) Minocycline treatment (mino) in wild-type (wt) animals during
chronic restraint stress (STR) prevented stress-induced increased floating time in the forced swim test. (b) Respirometry measurement for
volume of CO2 exhaled revealed a significant effect of stress as well as treatment. Data are mean ± s.e.m. Treatment effect: *Po0.05; stress
effect: ++Po0.01; treatment × stress effect: #Po0.05. BL, baseline.
Inflammasome signaling affects behavior and gut microbiome composition
M-L Wong et al
800
Molecular Psychiatry (2016), 797 – 805 © 2016 Macmillan Publishers Limited
P = 0.0244). Variance in the relative abundance of these taxa
accounted for 440% of intergroup variance.
Minocycline affects the gut microbiome
Minocycline treatment (5 mg kg− 1 per day for 21 days) also
affected microbiota composition compared with saline-treated
controls (PERMANOVA P = 0.0001, t = 3.0947, Figure 4b) and,
interestingly, in a manner very similar to that observed for
restrained animals. In particular, minocycline-treated animals
were also found to possess lower relative abundances of the
genera Allobaculum (difference in median relative abundance
− 7.8%, Po0.0001 Mann–Whitney U-test), Bifidobacterium (−5.8%,
Po0.001), Turicibacter (−4.2%, Po0.0001), Clostridium (−0.7%,
Po0.0001) and the family S24-7 (−7.4%, P = 0.003), and signifi-
cantly high relative abundances of the family Lachnospiraceae
(+25.3%, P = 0.005) and Ruminococcaceae incertae sedis (+2.4%,
P = 0.024). Variance in the relative abundance of these taxa
accounted for 467% of intergroup variance.
Effect of chronic restraint stress on the gut microbiome in
combination with minocycline
Combining chronic restraint with minocycline treatment resulted
in a microbiota composition that was different to that of
nonrestrained saline-treated controls (PERMANOVA P = 0.0002,
t = 3.4593, Figure 4b). There were also no significant differences
in the shallow, phylum-level profiles produced from mice receiv-
ing each treatment alone or in combination (using a PERMANOVA
threshold of Po0.01), and at deeper levels of analysis, the signifi-
cant reductions in the relative abundances of both Turicibacter
and Bifidobacterium spp. were also observed in mice receiving
Figure 4. Minocycline treatment and chronic restraint stress affect the gut microbiome and chronic restraint stress changes the gut
Firmicutes/Bacteroidetes (F/B) ratio. (a) Box and whiskers plot displayed the analysis of the differences of the main composition of the
microbiota (Firmicutes to Bacteroidetes). Upper and lower quartiles defined the box with median midline, and the whiskers were assessed
using Tukey’s method. (b) Microbiota distribution at species level of taxon contributing to 97.5% of sample variations. Heatmap shows square
root-transformed read counts for the 20 taxa determined by similarity percentage analysis. The dendrogram shows the clustering of genera
based on Ward’s hierarchical clustering method. Phyla are abbreviated as follows: Actinobacteria (A), Bacteroidetes (B), Firmicutes (F),
Proteobacteria (P) and Verrocomicrobia (V).
Inflammasome signaling affects behavior and gut microbiome composition
M-L Wong et al
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© 2016 Macmillan Publishers Limited Molecular Psychiatry (2016), 797 – 805
both treatments (Figures 5a and b), as was the increase in
members of the Lachnospiraceae (Figure 5e). Additional changes
in the microbiome profiles not observed with either treatment
alone were found when restraint and minocycline were used
together. For example, the relative abundances of Akkermansia
spp. and Blautia spp. were increased (Figures 5d and e).
Minocycline also appears to have a stronger effect on the relative
abundances of Lactobacillus spp. and Anaerovorax spp., with
relatively greater abundances of these genera observed in the
restrained animals, but reductions in their relative abundances
when minocycline was also administered (Figures 5f and g).
DISCUSSION
Caspase-1 is a cysteine protease that cleaves pro-IL-1β and
pro-IL-18 into their mature isoforms in the NLRP3 inflammasome
in response to stressful stimuli such as psychosocial and microbial
stress, adenosine triphosphate, toxins and particulate matter.13,39
Because casp1− / − mice lack caspase-1 mRNA and its mature
protein product, they have decreased inflammasome bioactivity
and inflammasome-driven IL-1β and IL-18 production, and could
be helpful in identifying the role of caspase-1 in behavior, via
either innate or after stress-induced inflammasome activation.23
Our data highlight a role for caspase-1 in the modulation of innate
behavior as well as in the response to chronic stress, as caspase-1
modulation decreased baseline anxiety- and depressive-like
behaviors, as well as the exacerbation of depressive-like behaviors
following chronic restraint stress. Our results are in line with
studies reporting that modulation of the IL-1β axis is a potential
approach to attenuate the behavioral and molecular effects of
stress-induced inflammation.40,41 Our findings strengthen the
role of caspase-1 as a potential therapeutic target aiming at
modulating inflammasome-mediated pathways in psychiatric
disorders.
Minocycline exerts anti-inflammatory and neuroprotective
effects in animal models of neurodegenerative disorders, neuro-
toxicity and brain injury as well as presents acute antidepressant-
like effects in the forced swim test by increasing climbing,
potentially through interaction with glutamatergic and/or nora-
drenergic systems.17,19,42,43 Its antidepressant-like effects might be
related to the protection of serotonergic and dopaminergic
circuitries.44,45 Consistent with this literature, we found that
minocycline prevented the exacerbation of depressive-like beha-
vior in the forced swim test following chronic restraint stress.
Given this finding, we suggest that minocycline may be valuable
in the treatment of MDD and other psychiatric disorders. Indeed,
two clinical trials investigating minocycline as a stand-alone or
adjuvant treatment in psychotic depression and schizophrenia
yielded promising results.46–49 Two studies have planned to
investigate the efficacy of minocycline in MDD and bipolar
Figure 5. The effect of minocycline treatment, chronic restraint stress and their combination assessed at the level of individual taxa. Individual
minocycline effect on the (a) Turicibacter and (b) Bifidobacterium populations; synergistic effect of minocycline and chronic restraint stress on
the (c) Akkermansia, (d) Blautia and (e) Lachnospiraceae populations; and antagonistic effect of minocycline and chronic restraint stress on the
(f) Lactobacillus and (g) Anaerovorax populations. Significant difference between treatment groups: *Po0.05, **Po0.01, ***Po0.001.
Inflammasome signaling affects behavior and gut microbiome composition
M-L Wong et al
802
Molecular Psychiatry (2016), 797 – 805 © 2016 Macmillan Publishers Limited
disorder (Clinical Trials.gov identifier NCT01574742 and
NCT01403662); yet, no recruitment data have been available.
Our findings show that the gut microbiota composition of mice
subjected …
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
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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/
www.aging-us.com 2774 AGING
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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