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Abstract
Prototypic antidepressants, such as tricyclic/tetracyclic antidepressants (TCAs),
have multiple pharmacological properties and have been considered to be more effective
than newer antidepressants, such as selective serotonin reuptake inhibitors, in treating
severe depression. However, the clinical contribution of non-monoaminergic effects
of TCAs remains elusive. In this study, we discovered that amitriptyline, a typical
TCA, directly binds to the lysophosphatidic acid receptor 1 (LPAR1), a G protein-coupled
receptor, and activates downstream G protein signaling, while exerting a little effect
on β-arrestin recruitment. This suggests that amitriptyline acts as a G protein-biased
agonist of LPAR1. This biased agonism was specific to TCAs and was not observed with
other antidepressants. LPAR1 was found to be involved in the behavioral effects of
amitriptyline. Notably, long-term infusion of mouse hippocampus with the potent G
protein-biased LPAR agonist OMPT, but not the non-biased agonist LPA, induced antidepressant-like
behavior, indicating that G protein-biased agonism might be necessary for the antidepressant-like
effects. Furthermore, RNA-seq analysis revealed that LPA and OMPT have opposite patterns
of gene expression changes in the hippocampus. Pathway analysis indicated that long-term
treatment with OMPT activated LPAR1 downstream signaling (Rho and MAPK), whereas LPA
suppressed LPAR1 signaling. Our findings provide insights into the mechanisms underlying
the non-monoaminergic antidepressant effects of TCAs and identify the G protein-biased
agonism of LPAR1 as a promising target for the development of novel antidepressants.
Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets. Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets. Availability: The causal analytics tools ‘Upstream Regulator Analysis', ‘Mechanistic Networks', ‘Causal Network Analysis' and ‘Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). Supplementary information: Supplementary material is available at Bioinformatics online.
Summary Background Major depressive disorder is one of the most common, burdensome, and costly psychiatric disorders worldwide in adults. Pharmacological and non-pharmacological treatments are available; however, because of inadequate resources, antidepressants are used more frequently than psychological interventions. Prescription of these agents should be informed by the best available evidence. Therefore, we aimed to update and expand our previous work to compare and rank antidepressants for the acute treatment of adults with unipolar major depressive disorder. Methods We did a systematic review and network meta-analysis. We searched Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, PsycINFO, the websites of regulatory agencies, and international registers for published and unpublished, double-blind, randomised controlled trials from their inception to Jan 8, 2016. We included placebo-controlled and head-to-head trials of 21 antidepressants used for the acute treatment of adults (≥18 years old and of both sexes) with major depressive disorder diagnosed according to standard operationalised criteria. We excluded quasi-randomised trials and trials that were incomplete or included 20% or more of participants with bipolar disorder, psychotic depression, or treatment-resistant depression; or patients with a serious concomitant medical illness. We extracted data following a predefined hierarchy. In network meta-analysis, we used group-level data. We assessed the studies' risk of bias in accordance to the Cochrane Handbook for Systematic Reviews of Interventions, and certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework. Primary outcomes were efficacy (response rate) and acceptability (treatment discontinuations due to any cause). We estimated summary odds ratios (ORs) using pairwise and network meta-analysis with random effects. This study is registered with PROSPERO, number CRD42012002291. Findings We identified 28 552 citations and of these included 522 trials comprising 116 477 participants. In terms of efficacy, all antidepressants were more effective than placebo, with ORs ranging between 2·13 (95% credible interval [CrI] 1·89–2·41) for amitriptyline and 1·37 (1·16–1·63) for reboxetine. For acceptability, only agomelatine (OR 0·84, 95% CrI 0·72–0·97) and fluoxetine (0·88, 0·80–0·96) were associated with fewer dropouts than placebo, whereas clomipramine was worse than placebo (1·30, 1·01–1·68). When all trials were considered, differences in ORs between antidepressants ranged from 1·15 to 1·55 for efficacy and from 0·64 to 0·83 for acceptability, with wide CrIs on most of the comparative analyses. In head-to-head studies, agomelatine, amitriptyline, escitalopram, mirtazapine, paroxetine, venlafaxine, and vortioxetine were more effective than other antidepressants (range of ORs 1·19–1·96), whereas fluoxetine, fluvoxamine, reboxetine, and trazodone were the least efficacious drugs (0·51–0·84). For acceptability, agomelatine, citalopram, escitalopram, fluoxetine, sertraline, and vortioxetine were more tolerable than other antidepressants (range of ORs 0·43–0·77), whereas amitriptyline, clomipramine, duloxetine, fluvoxamine, reboxetine, trazodone, and venlafaxine had the highest dropout rates (1·30–2·32). 46 (9%) of 522 trials were rated as high risk of bias, 380 (73%) trials as moderate, and 96 (18%) as low; and the certainty of evidence was moderate to very low. Interpretation All antidepressants were more efficacious than placebo in adults with major depressive disorder. Smaller differences between active drugs were found when placebo-controlled trials were included in the analysis, whereas there was more variability in efficacy and acceptability in head-to-head trials. These results should serve evidence-based practice and inform patients, physicians, guideline developers, and policy makers on the relative merits of the different antidepressants. Funding National Institute for Health Research Oxford Health Biomedical Research Centre and the Japan Society for the Promotion of Science.
Publisher:
Springer International Publishing
(Cham
)
ISSN
(Print):
0893-133X
ISSN
(Electronic):
1740-634X
Publication date
(Electronic):
6
September
2023
Publication date PMC-release: 6
September
2023
Publication date
(Print):
February
2024
Volume: 49
Issue: 3
Pages: 561-572
Affiliations
[1
]Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, (
https://ror.org/02cgss904)
Kumamoto, 860-8556 Japan
[2
]Center for Metabolic Regulation of Healthy Aging, Faculty of Life Sciences, Kumamoto
University, (
https://ror.org/02cgss904)
Kumamoto, 860-8556 Japan
[3
]Division of Psychiatry and Neuroscience, Institute for Clinical Research, National
Hospital Organization Kure Medical Center and Chugoku Cancer Center, (
https://ror.org/05te51965)
Kure, 737-0023 Japan
[4
]Laboratory of Molecular and Cellular Biochemistry, Graduate School of Pharmaceutical
Sciences, Tohoku University, (
https://ror.org/01dq60k83)
Sendai, 980-8578 Japan
[5
]Department of Pain Control Research, The Jikei University School of Medicine, (
https://ror.org/039ygjf22)
Tokyo, 105-8461 Japan
[6
]GRID grid.272242.3, ISNI 0000 0001 2168 5385, Division of Cancer Pathophysiology, , National Cancer Center Research Institute, ; Tokyo, 104-0045 Japan
[7
]Department of Pharmaceutical Microbiology, Faculty of Life Sciences, Kumamoto University,
(
https://ror.org/02cgss904)
Kumamoto, 862-0973 Japan
[8
]Department of Molecular Brain Science, Graduate School of Medical Sciences, Kumamoto
University, (
https://ror.org/02cgss904)
Kumamoto, 860-8556 Japan
[9
]Department of Health Chemistry, Graduate School of Pharmaceutical Sciences, The University
of Tokyo, (
https://ror.org/057zh3y96)
Tokyo, 113-0033 Japan
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History
Date
received
: 9
March
2023
Date
revision received
: 1
August
2023
Date
accepted
: 24
August
2023
Funding
Funded by: FundRef https://doi.org/10.13039/501100001691, MEXT | Japan Society for the Promotion of Science (JSPS);
Award ID: 18H02756
Award ID: 21K07501
Award ID: 21H04791 and 21H051130
Award Recipient
:
Naoto KajitaniAsuka InoueMinoru Takebayashi
Funded by: FundRef https://doi.org/10.13039/501100008667, SENSHIN Medical Research Foundation;
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