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Rosati D, Palmieri M, Brunelli G, Morrione A, Iannelli F, Frullanti E, Giordano A. Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review. Comput Struct Biotechnol J 2024; 23:1154-1168. [PMID: 38510977 PMCID: PMC10951429 DOI: 10.1016/j.csbj.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/22/2024] Open
Abstract
In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.
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Affiliation(s)
- Diletta Rosati
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
- Cancer Genomics & Systems Biology Lab, Dept. of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Italy
| | - Maria Palmieri
- Cancer Genomics & Systems Biology Lab, Dept. of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Italy
| | - Giulia Brunelli
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Italy
| | - Andrea Morrione
- Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, Department of Biology, College of Science and Technology, Temple University, Philadelphia, PA 19122, USA
| | - Francesco Iannelli
- Laboratory of Molecular Microbiology and Biotechnology, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Elisa Frullanti
- Cancer Genomics & Systems Biology Lab, Dept. of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Italy
| | - Antonio Giordano
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
- Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, Department of Biology, College of Science and Technology, Temple University, Philadelphia, PA 19122, USA
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Snelleksz M, Dean B. Higher levels of AKT-interacting protein in the frontal pole from people with schizophrenia are limited to a sub-group who have a marked deficit in cortical muscarinic M1 receptors. Psychiatry Res 2024; 341:116156. [PMID: 39236366 DOI: 10.1016/j.psychres.2024.116156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 06/29/2024] [Accepted: 07/03/2024] [Indexed: 09/07/2024]
Abstract
We are studying the molecular pathology of a sub-group within schizophrenia (∼ 25 %: termed Muscarinic Receptor Deficit subgroup of Schizophrenia (MRDS)) who can be separated because they have very low levels of cortical muscarinic M1 receptors (CHRM1). Based on our transcriptomic data from Brodmann's area ((BA) 9, 10 and 33 (controls, schizophrenia and mood disorders) and the cortex of the CHRM1-/- mouse (a molecular model of aberrant CHRM1 signaling), we predicted levels of AKT interacting protein (AKTIP), but not tubulin alpha 1b (TUBA1B) or AKT serine/threonine kinase 1 (AKT1) and pyruvate dehydrogenase kinase 1 (PDK1) (two AKTIP-functionally associated proteins), would be changed in MRDS. Hence, we used Western blotting to measure AKTIP (BA 10: controls, schizophrenia and mood disorders; BA 9: controls and schizophrenia) plus TUBA1B, AKT1 and PDK1 (BA 10: controls and schizophrenia) proteins. The only significant change with diagnosis was higher levels of AKTIP protein in BA 10 (Cohen's d = 0.73; p = 0.02) in schizophrenia compared to controls due to higher levels of AKTIP only in people with MRDS (Cohen's d = 0.80; p = 0.03). As AKTIP is involved in AKT1 signaling, our data suggests that signaling pathway is particularly disturbed in BA 10 in MRDS.
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Affiliation(s)
- Megan Snelleksz
- The Molecular Psychiatry Laboratory, The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia.
| | - Brian Dean
- The Molecular Psychiatry Laboratory, The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
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3
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Xu Z, Wen C, Wang W. Role of MAPK and PI3K-Akt signaling pathways in cuprizone-induced demyelination and cognitive impairment in mice. Behav Brain Res 2024; 458:114755. [PMID: 37949321 DOI: 10.1016/j.bbr.2023.114755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/31/2023] [Accepted: 11/07/2023] [Indexed: 11/12/2023]
Abstract
This study aimed to find the genes and signaling pathways underlying cuprizone-induced demyelination and cognitive impairments in mice. We used the cuprizone-exposed mice as an animal model of schizophrenia and assessed cognitive function in mice. Total RNA was extracted from mouse brain tissues for RNA sequencing. The DESeq2 R package was utilized to analyze the differentially expressed genes (DEGs). Functional and pathway enrichment analyses were performed simultaneously. We also constructed a protein-protein interaction (PPI) network to screen potential hub genes, and quantitative real-time polymerase chain reaction (qRT-PCR) was employed to validate the screened genes. After 6 weeks of cuprizone treatment, the cognitive function of mice was impaired. Compared to the controls, the cuprizone-exposed mice contained 351 DEGs, including 167 upregulated and 184 downregulated genes. Enrichment analysis showed that the DEGs were enriched in some biological processes involved in demyelination, including the MAPK pathway. Functional pathway analysis revealed that the DEGs were significantly enriched in the PI3K-Akt signaling pathway, which may be associated with cognitive impairments. MBP, IGF1, GFAP, PTPRC, CD14, CD68, ITGB2, LYN, TLR2, TLR4, VAV1, and PLEK were considered as potential hub genes. Except for MBP, all genes were upregulated in the cuprizone models, as verified by qRT-PCR. We suggest that the MAPK and PI3K-Akt signaling pathways may be associated with demyelination and cognitive impairments, respectively. GFAP and IGF-1 expression levels increased in cuprizone-exposed mice, suggesting that astrocytes may play a role in protecting the myelin sheath following treatment with cuprizone.
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Affiliation(s)
- Zhizhong Xu
- Xiamen Xianyue Hospital, Xianyue Hospital Affiliated with Xiamen Medical College, Fujian Psychiatric Center, Fujian Clinical Research Center for Mental Disorders, Xiamen, Fujian 361012, China.
| | - Chunyan Wen
- Xiamen Xianyue Hospital, Xianyue Hospital Affiliated with Xiamen Medical College, Fujian Psychiatric Center, Fujian Clinical Research Center for Mental Disorders, Xiamen, Fujian 361012, China
| | - Wenqiang Wang
- Xiamen Xianyue Hospital, Xianyue Hospital Affiliated with Xiamen Medical College, Fujian Psychiatric Center, Fujian Clinical Research Center for Mental Disorders, Xiamen, Fujian 361012, China
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Javan GT, Singh K, Finley SJ, Green RL, Sen CK. Complexity of human death: its physiological, transcriptomic, and microbiological implications. Front Microbiol 2024; 14:1345633. [PMID: 38282739 PMCID: PMC10822681 DOI: 10.3389/fmicb.2023.1345633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 12/28/2023] [Indexed: 01/30/2024] Open
Abstract
Human death is a complex, time-governed phenomenon that leads to the irreversible cessation of all bodily functions. Recent molecular and genetic studies have revealed remarkable experimental evidence of genetically programmed cellular death characterized by several physiological processes; however, the basic physiological function that occurs during the immediate postmortem period remains inadequately described. There is a paucity of knowledge connecting necrotic pathologies occurring in human organ tissues to complete functional loss of the human organism. Cells, tissues, organs, and organ systems show a range of differential resilience and endurance responses that occur during organismal death. Intriguingly, a persistent ambiguity in the study of postmortem physiological systems is the determination of the trajectory of a complex multicellular human body, far from life-sustaining homeostasis, following the gradual or sudden expiry of its regulatory systems. Recent groundbreaking investigations have resulted in a paradigm shift in understanding the cell biology and physiology of death. Two significant findings are that (i) most cells in the human body are microbial, and (ii) microbial cell abundance significantly increases after death. By addressing the physiological as well as the microbiological aspects of death, future investigations are poised to reveal innovative insights into the enigmatic biological activities associated with death and human decomposition. Understanding the elaborate crosstalk of abiotic and biotic factors in the context of death has implications for scientific discoveries important to informing translational knowledge regarding the transition from living to the non-living. There are important and practical needs for a transformative reestablishment of accepted models of biological death (i.e., artificial intelligence, AI) for more precise determinations of when the regulatory mechanisms for homeostasis of a living individual have ceased. In this review, we summarize mechanisms of physiological, genetic, and microbiological processes that define the biological changes and pathways associated with human organismal death and decomposition.
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Affiliation(s)
- Gulnaz T. Javan
- Department of Physical and Forensic Sciences, Alabama State University, Montgomery, AL, United States
| | - Kanhaiya Singh
- Department of Surgery, School of Medicine, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Sheree J. Finley
- Department of Physical and Forensic Sciences, Alabama State University, Montgomery, AL, United States
| | - Robert L. Green
- Department of Physical and Forensic Sciences, Alabama State University, Montgomery, AL, United States
| | - Chandan K. Sen
- Department of Surgery, School of Medicine, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States
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Arihisa W, Kondo T, Yamaguchi K, Matsumoto J, Nakanishi H, Kunii Y, Akatsu H, Hino M, Hashizume Y, Sato S, Sato S, Niwa S, Yabe H, Sasaki T, Shigenobu S, Setou M. Lipid-correlated alterations in the transcriptome are enriched in several specific pathways in the postmortem prefrontal cortex of Japanese patients with schizophrenia. Neuropsychopharmacol Rep 2023; 43:403-413. [PMID: 37498306 PMCID: PMC10496066 DOI: 10.1002/npr2.12368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/21/2023] [Accepted: 06/26/2023] [Indexed: 07/28/2023] Open
Abstract
AIMS Schizophrenia is a chronic relapsing psychiatric disorder that is characterized by many symptoms and has a high heritability. There were studies showing that the phospholipid abnormalities in subjects with schizophrenia (Front Biosci, S3, 2011, 153; Schizophr Bull, 48, 2022, 1125; Sci Rep, 7, 2017, 6; Anal Bioanal Chem, 400, 2011, 1933). Disturbances in prefrontal cortex phospholipid and fatty acid composition have been reported in subjects with schizophrenia (Sci Rep, 7, 2017, 6; Anal Bioanal Chem, 400, 2011, 1933; Schizophr Res, 215, 2020, 493; J Psychiatr Res, 47, 2013, 636; Int J Mol Sci, 22, 2021). For exploring the signaling pathways contributing to the lipid changes in previous study (Sci Rep, 7, 2017, 6), we performed two types of transcriptome analyses in subjects with schizophrenia: an unbiased transcriptome analysis solely based on RNA-seq data and a correlation analysis between levels of gene expression and lipids. METHODS RNA-Seq analysis was performed in the postmortem prefrontal cortex from 10 subjects with schizophrenia and 5 controls. Correlation analysis between the transcriptome and lipidome from 9 subjects, which are the same samples in the previous lipidomics study (Sci Rep, 7, 2017, 6). RESULTS Extraction of differentially expressed genes (DEGs) and further sequence and functional group analysis revealed changes in gene expression levels in phosphoinositide 3-kinase (PI3K)-Akt signaling and the complement system. In addition, a correlation analysis clarified alterations in ether lipid metabolism pathway, which is not found as DEGs in transcriptome analysis alone. CONCLUSIONS This study provided results of the integrated analysis of the schizophrenia-associated transcriptome and lipidome within the PFC and revealed that lipid-correlated alterations in the transcriptome are enriched in specific pathways including ether lipid metabolism pathway.
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Affiliation(s)
- Wataru Arihisa
- Department of Cellular and Molecular AnatomyHamamatsu University School of MedicineShizuokaJapan
| | - Takeshi Kondo
- Department of Cellular and Molecular AnatomyHamamatsu University School of MedicineShizuokaJapan
- International Mass Imaging CenterHamamatsu University School of MedicineShizuokaJapan
- Department of Biochemistry, Faculty of Medicine and Graduate School of MedicineHokkaido UniversitySapporoJapan
| | | | - Junya Matsumoto
- Department of Neuropsychiatry, School of MedicineFukushima Medical UniversityFukushimaJapan
| | | | - Yasuto Kunii
- Department of Neuropsychiatry, School of MedicineFukushima Medical UniversityFukushimaJapan
- Department of Disaster PsychiatryInternational Research Institute of Disaster Science, Tohoku UniversitySendaiJapan
| | - Hiroyasu Akatsu
- Choju Medical Institute, Fukushimura HospitalToyohashiJapan
- Department of Community‐based Medical Education/Department of Community‐based MedicineNagoya City University Graduate School of Medical ScienceNagoyaJapan
| | - Mizuki Hino
- Department of Neuropsychiatry, School of MedicineFukushima Medical UniversityFukushimaJapan
- Department of Disaster PsychiatryInternational Research Institute of Disaster Science, Tohoku UniversitySendaiJapan
| | | | - Shumpei Sato
- RIKEN Center for Biosystems Dynamics ResearchOsakaJapan
| | - Shinji Sato
- Business Development, Otsuka Pharmaceutical Co., Ltd. Shinagawa Grand Central TowerTokyoJapan
| | - Shin‐Ichi Niwa
- Department of Psychiatry, Aizu Medical CenterFukushima Medical UniversityFukushimaJapan
| | - Hirooki Yabe
- Department of Neuropsychiatry, School of MedicineFukushima Medical UniversityFukushimaJapan
| | - Takehiko Sasaki
- Department of Biochemical PathophysiologyMedical Research Institute, Tokyo Medical and Dental UniversityTokyoJapan
| | | | - Mitsutoshi Setou
- Department of Cellular and Molecular AnatomyHamamatsu University School of MedicineShizuokaJapan
- International Mass Imaging CenterHamamatsu University School of MedicineShizuokaJapan
- Preeminent Medical Photonics Education & Research CenterHamamatsu University School of MedicineShizuokaJapan
- Department of AnatomyThe University of Hong KongHong KongChina
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Sorokin M, Buzdin AA, Guryanova A, Efimov V, Suntsova MV, Zolotovskaia MA, Koroleva EV, Sekacheva MI, Tkachev VS, Garazha A, Kremenchutckaya K, Drobyshev A, Seryakov A, Gudkov A, Alekseenko IV, Rakitina O, Kostina MB, Vladimirova U, Moisseev A, Bulgin D, Radomskaya E, Shestakov V, Baklaushev VP, Prassolov V, Shegay PV, Li X, Poddubskaya EV, Gaifullin N. Large-scale assessment of pros and cons of autopsy-derived or tumor-matched tissues as the norms for gene expression analysis in cancers. Comput Struct Biotechnol J 2023; 21:3964-3986. [PMID: 37635765 PMCID: PMC10448432 DOI: 10.1016/j.csbj.2023.07.040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 07/17/2023] [Accepted: 07/30/2023] [Indexed: 08/29/2023] Open
Abstract
Normal tissues are essential for studying disease-specific differential gene expression. However, healthy human controls are typically available only in postmortal/autopsy settings. In cancer research, fragments of pathologically normal tissue adjacent to tumor site are frequently used as the controls. However, it is largely underexplored how cancers can systematically influence gene expression of the neighboring tissues. Here we performed a comprehensive pan-cancer comparison of molecular profiles of solid tumor-adjacent and autopsy-derived "healthy" normal tissues. We found a number of systemic molecular differences related to activation of the immune cells, intracellular transport and autophagy, cellular respiration, telomerase activation, p38 signaling, cytoskeleton remodeling, and reorganization of the extracellular matrix. The tumor-adjacent tissues were deficient in apoptotic signaling and negative regulation of cell growth including G2/M cell cycle transition checkpoint. We also detected an extensive rearrangement of the chemical perception network. Molecular targets of 32 and 37 cancer drugs were over- or underexpressed, respectively, in the tumor-adjacent norms. These processes may be driven by molecular events that are correlated between the paired cancer and adjacent normal tissues, that mostly relate to inflammation and regulation of intracellular molecular pathways such as the p38, MAPK, Notch, and IGF1 signaling. However, using a model of macaque postmortal tissues we showed that for the 30 min - 24-hour time frame at 4ºC, an RNA degradation pattern in lung biosamples resulted in an artifact "differential" expression profile for 1140 genes, although no differences could be detected in liver. Thus, such concerns should be addressed in practice.
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Affiliation(s)
- Maksim Sorokin
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
- Omicsway Corp., Walnut, CA 91789, USA
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
| | - Anton A. Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
- World-Class Research Center "Digital biodesign and personalized healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium
| | - Anastasia Guryanova
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
| | - Victor Efimov
- World-Class Research Center "Digital biodesign and personalized healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Maria V. Suntsova
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Marianna A. Zolotovskaia
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
- Omicsway Corp., Walnut, CA 91789, USA
| | - Elena V. Koroleva
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
| | - Marina I. Sekacheva
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Victor S. Tkachev
- Omicsway Corp., Walnut, CA 91789, USA
- Oncobox Ltd., Moscow 121205, Russia
| | - Andrew Garazha
- Omicsway Corp., Walnut, CA 91789, USA
- Oncobox Ltd., Moscow 121205, Russia
| | | | - Aleksey Drobyshev
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | | | - Alexander Gudkov
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Irina V. Alekseenko
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
- Institute of Molecular Genetics of National Research Centre "Kurchatov Institute", 2, Kurchatov Square, Moscow 123182, Russian
- FSBI "National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov" Ministry of Healthcare of the Russian Federation, Moscow 117198, Russia
| | - Olga Rakitina
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
| | - Maria B. Kostina
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
| | - Uliana Vladimirova
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
- Oncobox Ltd., Moscow 121205, Russia
| | - Aleksey Moisseev
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Dmitry Bulgin
- Research Institute of Medical Primatology, 177 Mira str., Veseloye, Sochi 354376, Russia
| | - Elena Radomskaya
- Research Institute of Medical Primatology, 177 Mira str., Veseloye, Sochi 354376, Russia
| | - Viktor Shestakov
- Research Institute of Medical Primatology, 177 Mira str., Veseloye, Sochi 354376, Russia
| | | | - Vladimir Prassolov
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 32 Vavilova str., Moscow 119991, Russia
| | - Petr V. Shegay
- National Medical Research Radiological Center of the Ministry of Health of the Russian Federation, 249036 Obninsk, Russia
| | - Xinmin Li
- UCLA Technology Center for Genomics & Bioinformatics, Department of Pathology & Laboratory Medicine, 650 Charles E Young Dr., Los Angeles, CA 90095, USA
| | | | - Nurshat Gaifullin
- Department of Physiology and General Pathology, Faculty of Medicine, Lomonosov Moscow State University, Moscow 119991, Russia
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Torsvik A, Brattbakk HR, Trentani A, Holdhus R, Stansberg C, Bartz-Johannessen CA, Hughes T, Steen NE, Melle I, Djurovic S, Andreassen OA, Steen VM. Patients with schizophrenia and bipolar disorder display a similar global gene expression signature in whole blood that reflects elevated proportion of immature neutrophil cells with association to lipid changes. Transl Psychiatry 2023; 13:147. [PMID: 37147304 PMCID: PMC10163263 DOI: 10.1038/s41398-023-02442-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 04/20/2023] [Indexed: 05/07/2023] Open
Abstract
Schizophrenia (SCZ) and bipolar disorder (BD) share clinical characteristics, genetic susceptibility, and immune alterations. We aimed to identify differential transcriptional patterns in peripheral blood cells of patients with SCZ or BD versus healthy controls (HC). We analyzed microarray-based global gene expression data in whole blood from a cohort of SCZ (N = 329), BD (N = 203) and HC (N = 189). In total, 65 genes were significantly differentially expressed in SCZ and 125 in BD, as compared to HC, with similar ratio of up- and downregulated genes in both disorders. Among the top differentially expressed genes, we found an innate immunity signature that was shared between SCZ and BD, consisting of a cluster of upregulated genes (e.g., OLFM4, ELANE, BPI and MPO) that indicate an increased fraction of immature neutrophils. Several of these genes displayed sex differences in the expression pattern, and post-hoc analysis demonstrated a positive correlation with triglyceride and a negative correlation with HDL cholesterol. We found that many of the downregulated genes in SCZ and BD were associated with smoking. These findings of neutrophil granulocyte-associated transcriptome signatures in both SCZ and BD point at altered innate immunity pathways with association to lipid changes and potential for clinical translation.
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Affiliation(s)
- Anja Torsvik
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway.
| | - Hans-Richard Brattbakk
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Andrea Trentani
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Rita Holdhus
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Christine Stansberg
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | | | - Timothy Hughes
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Nils Eiel Steen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Melle
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Vidar M Steen
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
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8
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Santarriaga S, Gerlovin K, Layadi Y, Karmacharya R. Human stem cell-based models to study synaptic dysfunction and cognition in schizophrenia: A narrative review. Schizophr Res 2023:S0920-9964(23)00084-1. [PMID: 36925354 PMCID: PMC10500041 DOI: 10.1016/j.schres.2023.02.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 03/18/2023]
Abstract
Cognitive impairment is the strongest predictor of functional outcomes in schizophrenia and is hypothesized to result from synaptic dysfunction. However, targeting synaptic plasticity and cognitive deficits in patients remains a significant clinical challenge. A comprehensive understanding of synaptic plasticity and the molecular basis of learning and memory in a disease context can provide specific targets for the development of novel therapeutics targeting cognitive impairments in schizophrenia. Here, we describe the role of synaptic plasticity in cognition, summarize evidence for synaptic dysfunction in schizophrenia and demonstrate the use of patient derived induced-pluripotent stem cells for studying synaptic plasticity in vitro. Lastly, we discuss current advances and future technologies for bridging basic science research of synaptic dysfunction with clinical and translational research that can be used to predict treatment response and develop novel therapeutics.
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Affiliation(s)
- Stephanie Santarriaga
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Chemical Biology and Therapeutic Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Kaia Gerlovin
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Chemical Biology and Therapeutic Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yasmine Layadi
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Chimie ParisTech, Université Paris Sciences et Lettres, Paris, France
| | - Rakesh Karmacharya
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Chemical Biology and Therapeutic Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA.
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9
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Webster MJ. Infections, Inflammation, and Psychiatric Illness: Review of Postmortem Evidence. Curr Top Behav Neurosci 2023; 61:35-48. [PMID: 35505055 DOI: 10.1007/7854_2022_362] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
While there is an abundance of epidemiological evidence implicating infectious agents in the etiology of severe mental illnesses, postmortem studies have not yet detected an increased incidence of microbial nucleic acid or proteins in the brains of people with mental illness. Nevertheless, abnormally expressed immune and inflammatory markers have consistently been found in the postmortem brain of patients with schizophrenia and mood disorders. Some of these abnormalities may be the result of an infection in utero or early in life that not only impacted the developing immune system but also the developing neurons of the brain. Some of the immune markers that are consistently found to be upregulated in schizophrenia implicate a possible viral infection and the blood brain barrier in the etiology and neuropathology of the disorder.
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Mohd Asyraf AJ, Nour El Huda AR, Hanisah MN, Noorul Amilin H, Norlelawati AT. DNA methylation and copy number variation of the complement C4A gene in schizophrenia. GENE REPORTS 2022. [DOI: 10.1016/j.genrep.2022.101702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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11
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Shi W, Fan L, Wang H, Liu B, Li W, Li J, Cheng L, Chu C, Song M, Sui J, Luo N, Cui Y, Dong Z, Lu Y, Ma Y, Ma L, Li K, Chen J, Chen Y, Guo H, Li P, Lu L, Lv L, Wan P, Wang H, Wang H, Yan H, Yan J, Yang Y, Zhang H, Zhang D, Jiang T. Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement. Cereb Cortex 2022; 33:3683-3700. [PMID: 36005854 DOI: 10.1093/cercor/bhac301] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 11/12/2022] Open
Abstract
Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.
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Affiliation(s)
- Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yunchun Chen
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China.,Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Ping Wan
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
| | - Huiling Wang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China.,Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Hao Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Jun Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China.,Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Hongxing Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China.,Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China.,Department of Psychology, Xinxiang Medical University, Xinxiang 453002, China
| | - Dai Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.,Key Laboratory of Mental Health, Ministry of Health, National Clinical Research Center for Mental Disorders, Peking University, Beijing 100191, China.,Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100191, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China.,Innovation Academy for Artificial Intelligence, Chinese Academy of Sciences, Beijing 100190, China
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Childers E, Bowen EFW, Rhodes CH, Granger R. Immune-Related Genomic Schizophrenic Subtyping Identified in DLPFC Transcriptome. Genes (Basel) 2022; 13:1200. [PMID: 35885983 PMCID: PMC9319783 DOI: 10.3390/genes13071200] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/26/2022] [Accepted: 06/29/2022] [Indexed: 02/04/2023] Open
Abstract
Well-documented evidence of the physiologic, genetic, and behavioral heterogeneity of schizophrenia suggests that diagnostic subtyping may clarify the underlying pathobiology of the disorder. Recent studies have demonstrated that increased inflammation may be a prominent feature of a subset of schizophrenics. However, these findings are inconsistent, possibly due to evaluating schizophrenics as a single group. In this study, we segregated schizophrenic patients into two groups ("Type 1", "Type 2") by their gene expression in the dorsolateral prefrontal cortex and explored biological differences between the subgroups. The study included post-mortem tissue samples that were sequenced in multiple, publicly available gene datasets using different sequencing methods. To evaluate the role of inflammation, the expression of genes in multiple components of neuroinflammation were examined: complement cascade activation, glial cell activation, pro-inflammatory mediator secretion, blood-brain barrier (BBB) breakdown, chemokine production and peripheral immune cell infiltration. The Type 2 schizophrenics showed widespread abnormal gene expression across all the neuroinflammation components that was not observed in Type 1 schizophrenics. Our results demonstrate the importance of separating schizophrenic patients into their molecularly defined subgroups and provide supporting evidence for the involvement of the immune-related pathways in a schizophrenic subset.
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Affiliation(s)
- Eva Childers
- Dartmouth College, Hanover, NH 03755, USA; (E.C.); (E.F.W.B.)
| | | | | | - Richard Granger
- Dartmouth College, Hanover, NH 03755, USA; (E.C.); (E.F.W.B.)
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