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Identification of a serotonin/glutamate receptor complex implicated in psychosis (2008)

J. Gonzalez-Maeso ; R. L. Ang ; T. Yuen ; [et al.]

Nature Publishing Group (NPG)

In: Nature. 452(7183): 93-7. doi: 10.1038/nature06612.

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Publication Date: 2008-02-26

Description: The psychosis associated with schizophrenia is characterized by alterations in sensory processing and perception. Some antipsychotic drugs were identified by their high affinity for serotonin 5-HT2A receptors (2AR). Drugs that interact with metabotropic glutamate receptors (mGluR) also have potential for the treatment of schizophrenia. The effects of hallucinogenic drugs, such as psilocybin and lysergic acid diethylamide, require the 2AR and resemble some of the core symptoms of schizophrenia. Here we show that the mGluR2 interacts through specific transmembrane helix domains with the 2AR, a member of an unrelated G-protein-coupled receptor family, to form functional complexes in brain cortex. The 2AR-mGluR2 complex triggers unique cellular responses when targeted by hallucinogenic drugs, and activation of mGluR2 abolishes hallucinogen-specific signalling and behavioural responses. In post-mortem human brain from untreated schizophrenic subjects, the 2AR is upregulated and the mGluR2 is downregulated, a pattern that could predispose to psychosis. These regulatory changes indicate that the 2AR-mGluR2 complex may be involved in the altered cortical processes of schizophrenia, and this complex is therefore a promising new target for the treatment of psychosis.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2743172/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉 〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2743172/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Gonzalez-Maeso, Javier -- Ang, Rosalind L -- Yuen, Tony -- Chan, Pokman -- Weisstaub, Noelia V -- Lopez-Gimenez, Juan F -- Zhou, Mingming -- Okawa, Yuuya -- Callado, Luis F -- Milligan, Graeme -- Gingrich, Jay A -- Filizola, Marta -- Meana, J Javier -- Sealfon, Stuart C -- G9811527/Medical Research Council/United Kingdom -- P01 DA012923/DA/NIDA NIH HHS/ -- P01 DA012923-06A10004/DA/NIDA NIH HHS/ -- T32 DA007135/DA/NIDA NIH HHS/ -- T32 DA007135-25S1/DA/NIDA NIH HHS/ -- T32 GM062754/GM/NIGMS NIH HHS/ -- England -- Nature. 2008 Mar 6;452(7183):93-7. doi: 10.1038/nature06612. Epub 2008 Feb 24.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Neurology, Mount Sinai School of Medicine, New York, New York 10029, USA. javier.maeso@mssm.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/18297054" target="_blank"〉PubMed〈/a〉

Keywords: Animals ; Brain/cytology/metabolism ; Cell Line ; Cells, Cultured ; Down-Regulation ; Hallucinogens/metabolism/pharmacology ; Humans ; Mice ; Models, Molecular ; Multiprotein Complexes/chemistry/genetics/metabolism ; Protein Binding ; Protein Structure, Tertiary ; Psychotic Disorders/drug therapy/genetics/*metabolism ; Receptor, Serotonin, 5-HT2A/analysis/deficiency/genetics/*metabolism ; Receptors, Metabotropic Glutamate/analysis/antagonists & ; inhibitors/genetics/*metabolism ; Schizophrenia/metabolism ; Signal Transduction/drug effects ; Up-Regulation

Print ISSN: 0028-0836

Electronic ISSN: 1476-4687

Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics

Published by Nature Publishing Group (NPG)

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Cortical 5-HT2A receptor signaling modulates anxiety-like behaviors in mice (2006)

N. V. Weisstaub ; M. Zhou ; A. Lira ; [et al.]

American Association for the Advancement of Science (AAAS)

In: Science. 313(5786): 536-40. doi: 10.1126/science.1123432.

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Publication Date: 2006-07-29

Description: Serotonin [5-hydroxytryptamine (5-HT)] neurotransmission in the central nervous system modulates depression and anxiety-related behaviors in humans and rodents, but the responsible downstream receptors remain poorly understood. We demonstrate that global disruption of 5-HT2A receptor (5HT2AR) signaling in mice reduces inhibition in conflict anxiety paradigms without affecting fear-conditioned and depression-related behaviors. Selective restoration of 5HT2AR signaling to the cortex normalized conflict anxiety behaviors. These findings indicate a specific role for cortical 5HT2AR function in the modulation of conflict anxiety, consistent with models of cortical, "top-down" influences on risk assessment.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Weisstaub, Noelia V -- Zhou, Mingming -- Lira, Alena -- Lambe, Evelyn -- Gonzalez-Maeso, Javier -- Hornung, Jean-Pierre -- Sibille, Etienne -- Underwood, Mark -- Itohara, Shigeyoshi -- Dauer, William T -- Ansorge, Mark S -- Morelli, Emanuela -- Mann, J John -- Toth, Miklos -- Aghajanian, George -- Sealfon, Stuart C -- Hen, Rene -- Gingrich, Jay A -- KO8 MH01711/MH/NIMH NIH HHS/ -- P01 DA12923/DA/NIDA NIH HHS/ -- New York, N.Y. -- Science. 2006 Jul 28;313(5786):536-40.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Biology, Columbia University and the New York State Psychiatric Institute, New York, NY 10032, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/16873667" target="_blank"〉PubMed〈/a〉

Keywords: Animals ; Anxiety/*physiopathology ; Cerebral Cortex/*metabolism ; Conditioning (Psychology) ; Conflict (Psychology) ; Depression/physiopathology ; Exploratory Behavior ; Fear ; Limbic System/metabolism ; Mice ; Mice, Knockout ; Patch-Clamp Techniques ; Periaqueductal Gray/metabolism ; Prosencephalon/metabolism ; Receptor, Serotonin, 5-HT2A/genetics/*metabolism ; Receptor, Serotonin, 5-HT2C/metabolism ; Receptors, Neurotransmitter/metabolism ; Risk-Taking ; Serotonin/physiology ; *Signal Transduction ; Synaptic Transmission

Print ISSN: 0036-8075

Electronic ISSN: 1095-9203

Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics

Published by American Association for the Advancement of Science (AAAS)

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CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations (2015)

Chikina, M., Zaslavsky, E., Sealfon, S. C.

Oxford University Press

In: Bioinformatics

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Publication Date: 2015-05-12

Description: Motivation: Identifying alterations in gene expression associated with different clinical states is important for the study of human biology. However, clinical samples used in gene expression studies are often derived from heterogeneous mixtures with variable cell-type composition, complicating statistical analysis. Considerable effort has been devoted to modeling sample heterogeneity, and presently, there are many methods that can estimate cell proportions or pure cell-type expression from mixture data. However, there is no method that comprehensively addresses mixture analysis in the context of differential expression without relying on additional proportion information, which can be inaccurate and is frequently unavailable. Results: In this study, we consider a clinically relevant situation where neither accurate proportion estimates nor pure cell expression is of direct interest, but where we are rather interested in detecting and interpreting relevant differential expression in mixture samples. We develop a method, Cell-type COmputational Differential Estimation (CellCODE), that addresses the specific statistical question directly, without requiring a physical model for mixture components. Our approach is based on latent variable analysis and is computationally transparent; it requires no additional experimental data, yet outperforms existing methods that use independent proportion measurements. CellCODE has few parameters that are robust and easy to interpret. The method can be used to track changes in proportion, improve power to detect differential expression and assign the differentially expressed genes to the correct cell type. Availability and implementation: The CellCODE R package can be downloaded at http://www.pitt.edu/~mchikina/CellCODE/ or installed from the GitHub repository ‘mchikina/CellCODE’. Contact: mchikina@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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MOCCASIN: converting MATLAB ODE models to SBML (2016)

Gomez, H. F., Hucka, M., Keating, S. M., Nudelman, G., Iber, D., Sealfon, S. C.

Oxford University Press

In: Bioinformatics

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Publication Date: 2016-06-16

Description: : MATLAB is popular in biological research for creating and simulating models that use ordinary differential equations (ODEs). However, sharing or using these models outside of MATLAB is often problematic. A community standard such as Systems Biology Markup Language (SBML) can serve as a neutral exchange format, but translating models from MATLAB to SBML can be challenging—especially for legacy models not written with translation in mind. We developed MOCCASIN ( Model ODE Converter for Creating Automated SBML INteroperability ) to help. MOCCASIN can convert ODE-based MATLAB models of biochemical reaction networks into the SBML format. Availability and implementation: MOCCASIN is available under the terms of the LGPL 2.1 license ( http://www.gnu.org/licenses/lgpl-2.1.html ). Source code, binaries and test cases can be freely obtained from https://github.com/sbmlteam/moccasin . Contact : mhucka@caltech.edu Supplementary information: More information is available at https://github.com/sbmlteam/moccasin .

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding (2012)

Ge, Y., Sealfon, S. C.

Oxford University Press

In: Bioinformatics

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Publication Date: 2012-07-21

Description: Motivation: For flow cytometry data, there are two common approaches to the unsupervised clustering problem: one is based on the finite mixture model and the other on spatial exploration of the histograms. The former is computationally slow and has difficulty to identify clusters of irregular shapes. The latter approach cannot be applied directly to high-dimensional data as the computational time and memory become unmanageable and the estimated histogram is unreliable. An algorithm without these two problems would be very useful. Results: In this article, we combine ideas from the finite mixture model and histogram spatial exploration. This new algorithm, which we call flowPeaks, can be applied directly to high-dimensional data and identify irregular shape clusters. The algorithm first uses K -means algorithm with a large K to partition the cell population into many small clusters. These partitioned data allow the generation of a smoothed density function using the finite mixture model. All local peaks are exhaustively searched by exploring the density function and the cells are clustered by the associated local peak. The algorithm flowPeaks is automatic, fast and reliable and robust to cluster shape and outliers. This algorithm has been applied to flow cytometry data and it has been compared with state of the art algorithms, including Misty Mountain, FLOCK, flowMeans, flowMerge and FLAME. Availability: The R package flowPeaks is available at https://github.com/yongchao/flowPeaks . Contact: yongchao.ge@mssm.edu Supplementary information: Supplementary data are available at Bioinformatics online

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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Hybrid Bayesian-rank integration approach improves the predictive power of genomic dataset aggregation (2015)

Badgeley, M. A., Sealfon, S. C., Chikina, M. D.

Oxford University Press

In: Bioinformatics

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Publication Date: 2015-01-10

Description: Motivation : Modern molecular technologies allow the collection of large amounts of high-throughput data on the functional attributes of genes. Often multiple technologies and study designs are used to address the same biological question such as which genes are overexpressed in a specific disease state. Consequently, there is considerable interest in methods that can integrate across datasets to present a unified set of predictions. Results : An important aspect of data integration is being able to account for the fact that datasets may differ in how accurately they capture the biological signal of interest. While many methods to address this problem exist, they always rely either on dataset internal statistics, which reflect data structure and not necessarily biological relevance, or external gold standards, which may not always be available. We present a new rank aggregation method for data integration that requires neither external standards nor internal statistics but relies on Bayesian reasoning to assess dataset relevance. We demonstrate that our method outperforms established techniques and significantly improves the predictive power of rank-based aggregations. We show that our method, which does not require an external gold standard, provides reliable estimates of dataset relevance and allows the same set of data to be integrated differently depending on the specific signal of interest. Availability : The method is implemented in R and is freely available at http://www.pitt.edu/~mchikina/BIRRA/ Contact : mchikina@pitt.edu Supplementary information : Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine

Published by Oxford University Press

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