Disclosing common biological signatures and predicting new therapeutic targets in schizophrenia and obsessive–compulsive disorder by integrated bioinformatics analysis

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Abstract

Schizophrenia (SCZ) is a severe mental illness mainly characterized by a number of psychiatric symptoms. Obsessive–compulsive disorder (OCD) is a long-lasting and devastating mental disorder. SCZ has high co-occurrence with OCD resulting in the emergence of a concept entitled “schizo-obsessive disorder” as a new specific clinical entity with more severe psychiatric symptoms. Many studies have been done on SCZ and OCD, but the common pathogenesis between them is not clear yet. Therefore, this study aimed to identify shared genetic basis, potential biomarkers and therapeutic targets between these two disorders. Gene sets were extracted from the Geneweaver and Harmonizome databases for each disorder. Interestingly, the combination of both sets revealed 89 common genes between SCZ and OCD, the most important of which were BDNF, SLC6A4, GAD1, HTR2A, GRIN2B, DRD2, SLC6A3, COMT, TH and DLG4. Then, we conducted a comprehensive bioinformatics analysis of the common genes. Receptor activity as the molecular functions, neuron projection and synapse as the cellular components as well as serotonergic synapse, dopaminergic synapse and alcoholism as the pathways were the most significant commonalities in enrichment analyses. In addition, transcription factor (TFs) analysis predicted significant TFs such as HMGA1, MAPK14, HINFP and TEAD2. Hsa-miR-3121-3p and hsa-miR-495-3p were the most important microRNAs (miRNAs) associated with both disorders. Finally, our study predicted 19 existing drugs (importantly, Haloperidol, Fluoxetine and Melatonin) that may have a potential influence on this co-occurrence. To summarize, this study may help us to better understand and handle the co-occurrence of SCZ and OCD by identifying potential biomarkers and therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-023-04543-z.

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KEGG: kyoto encyclopedia of genes and genomes.

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KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).

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The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

Damian Szklarczyk, Annika Gable, Katerina C. Nastou … (2020)

Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.

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Gene Set Knowledge Discovery with Enrichr

Zhuorui Xie, Allison A. Bailey, Maxim Kuleshov … (2021)

Profiling samples from patients, tissues, and cells with genomics, transcriptomics, epigenomics, proteomics, and metabolomics ultimately produces lists of genes and proteins that need to be further analyzed and integrated in the context of known biology. Enrichr (Chen et al., 2013; Kuleshov et al., 2016) is a gene set search engine that enables the querying of hundreds of thousands of annotated gene sets. Enrichr uniquely integrates knowledge from many high-profile projects to provide synthesized information about mammalian genes and gene sets. The platform provides various methods to compute gene set enrichment, and the results are visualized in several interactive ways. This protocol provides a summary of the key features of Enrichr, which include using Enrichr programmatically and embedding an Enrichr button on any website. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Analyzing lists of differentially expressed genes from transcriptomics, proteomics and phosphoproteomics, GWAS studies, or other experimental studies Basic Protocol 2: Searching Enrichr by a single gene or key search term Basic Protocol 3: Preparing raw or processed RNA-seq data through BioJupies in preparation for Enrichr analysis Basic Protocol 4: Analyzing gene sets for model organisms using modEnrichr Basic Protocol 5: Using Enrichr in Geneshot Basic Protocol 6: Using Enrichr in ARCHS4 Basic Protocol 7: Using the enrichment analysis visualization Appyter to visualize Enrichr results Basic Protocol 8: Using the Enrichr API Basic Protocol 9: Adding an Enrichr button to a website.

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Author and article information

Contributors

Ali Shahbazi: shahbazi.a@iums.ac.ir

Journal

Journal ID (nlm-ta): BMC Psychiatry

Journal ID (iso-abbrev): BMC Psychiatry

Title: BMC Psychiatry

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-244X

Publication date (Electronic): 14 January 2023

Publication date PMC-release: 14 January 2023

Publication date Collection: 2023

Volume: 23

Electronic Location Identifier: 40

Affiliations

[1 ]GRID grid.411746.1, ISNI 0000 0004 4911 7066, Department of Neuroscience, Faculty of Advanced Technologies in Medicine, , Iran University of Medical Sciences, ; Tehran, Iran

[2 ]GRID grid.411583.a, ISNI 0000 0001 2198 6209, Neuroscience Research Center, Mashhad University of Medical Sciences, ; Mashhad, Iran

[3 ]GRID grid.411746.1, ISNI 0000 0004 4911 7066, Cellular and Molecular Research Center, , Iran University of Medical Sciences, ; Tehran, Iran

[4 ]GRID grid.37728.39, ISNI 0000 0001 0730 3862, Department of Biotechnology, , Bangalore University, ; Bangalore, Karnataka India

[5 ]GRID grid.411705.6, ISNI 0000 0001 0166 0922, Department of Audiology, School of Rehabilitation, , Tehran University of Medical Sciences, ; Tehran, Iran

Article

Publisher ID: 4543

DOI: 10.1186/s12888-023-04543-z

PMC ID: 9840830

PubMed ID: 36641432

SO-VID: d7afaa63-4ea8-474d-9d5b-bac2734e637d

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Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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.

History

Date received : 9 June 2022

Date accepted : 11 January 2023

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ScienceOpen disciplines: Clinical Psychology & Psychiatry

Keywords: schizophrenia,obsessive–compulsive disorder,microrna,common mechanisms,drug repurposing,bioinformatics approach

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ScienceOpen disciplines: Clinical Psychology & Psychiatry

Keywords: schizophrenia, obsessive–compulsive disorder, microrna, common mechanisms, drug repurposing, bioinformatics approach

Disclosing common biological signatures and predicting new therapeutic targets in schizophrenia and obsessive–compulsive disorder by integrated bioinformatics analysis

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Supplementary Information

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Drug Repurposing Research Collection

Most cited references 105

KEGG: kyoto encyclopedia of genes and genomes.

The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

Gene Set Knowledge Discovery with Enrichr

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