Altered Expression of a Unique Set of Genes Reveals Complex Etiology of Schizophrenia

Abstract

Background: The etiology of schizophrenia is extensively debated, and multiple factors have been contended to be involved. A panoramic view of the contributing factors in a genome-wide study can be an effective strategy to provide a comprehensive understanding of its causality.

Materials and Methods: GSE53987 dataset downloaded from GEO-database, which comprised mRNA expression data of post-mortem brain tissue across three regions from control (C) and age-matched subjects (T) of schizophrenia (N = Hippocampus [HIP]: C-15, T-18, Prefrontal cortex [PFC]: C-15, T-19, Associative striatum [STR]: C-18, T-18). Bio-conductor—affy—package used to compute mRNA expression, and further t-test applied to investigate differential gene expression. The analysis of the derived genes performed using the PANTHER Classification System and NCBI database. Further, a protein interactome analysis of the derived gene set was performed using STRING v10 database (https://string-db.org/)

Results: A set of 40 genes showed significantly altered (p < 0.01) expression across all three brain regions. The analyses unraveled genes implicated in biological processes and events, and molecular pathways relating basic neuronal functions.

Conclusions: The aberrant expression of genes maintaining basic cell machinery explains compromised neuronal processing in SCZ.

Introduction

The etiology of schizophrenia (SCZ) is extensively debated (1, 2). Uncertainty of the etiology has greatly impeded the treatment of the disease, and neither of the therapeutic approaches (1) is proving much helpful in halting its progression.

Many candidate genes have been reported (3) for SCZ but none of them got validated in population-based studies for persistent association (4, 5). A disease signature derived from genome-wide expression patterns in affected brain regions was highly desirable that would help to reach the diagnosis and developing optimal therapeutic approaches for SCZ.

SCZ gives a life time risk of ∼1% and shows high heritability (∼69–81%) (6–8). The SCZ heritability is derived from CNVs, SNPs, de novo mutations, and structural modifications at gene promoter regions without involving gene sequences as have been revealed in the genome-wide studies (9). Expression derangement of the genes also evidenced to arise of the gene- environment interactions during fetal development and in the lifetime of the individuals (10, 11).

SCZ has been noted to cause significant architectural changes in many brain regions, the hippocampus, prefrontal cortex, and basal nuclei (more specifically associative or dorsal striatum) have been chief among them (12–14). Neuronal gene expressions in the affected brain regions are known to alter in SCZ (15, 16). A comprehensive study of the gene expression dynamics in SCZ patients, which is shared by all three brain regions (hippocampus, prefrontal cortex, and associative striatum) may plausibly give a glimpse of the disease etiology.

How the neural architectural changes are instructed by the changes in the neural genes has also been shown by some recent studies. Piskorowski et al. (17) have shown in the mouse model that deletion of 22q11 locus may involve the genes making synaptic proteins and that may produce SCZ like symptoms (17). Fromer and colleagues (18) have identified over 100 of genetic loci harboring SCZ associated variants which together involve scores of genes, and altering the expression or knock down of some of such genes in animal or human stem cell models has shown to compromise neural functions effectively (18). Plausibly, the dysregulation of the neuronal genes, especially which are involved in maintaining basic cell architecture and machinery, may compromise the information processing in neurons in affected brain regions in SCZ (19, 20).

In this study, we hypothesized that the contributory factors involved in the etiogenesis of SCZ may get reflected in the altered expression of neuronal genes; hence an ontological analysis of these genes from the affected brain regions may unravel the components of the complex etiology of SCZ.

Materials and Methods

Data Resources

The mRNA expression data were retrieved from the GEO (Genome Expression Omnibus, GSE53987) (http://www.ncbi.nlm.nih.gov/geo/), a public repository for high-throughput microarray. The RNA was originally isolated from post-mortem brain tissue across three specific regions (hippocampus [HIP], prefrontal cortex [PFC]: Brodmann Area 46 [dorsolateral part of PFC], and Associative [or dorsal] striatum [STR]) of control (N = 18 [HIP], 19 [PFC], 18[STR]) and age-matched subjects with schizophrenia (N = 15 [HIP], 15 [PFC], 18 [STR]). For the original data, designated tissue samples were acquired from the curated collection of brain samples from the University of Pittsburgh which was permitted by the institute ethics committee (21, 22). Equal numbers of male and female (except for odd number samples) diagnosed SCZ cases and controls of adult age were chosen for this purpose (Table S1). The controls were matched for the age and sex with cases, and were free of any neurological or psychiatric illness during their life course. Tissue was collected from same hemisphere of the brain using same anatomical landmarks in all individuals. The post-mortem interval (PMI) and pH of the brain tissue, storage timing (at −80 degree Celsius), and RNA integrity number (RIN) (to confirm quality of processed RNA) for the test and controls were maintained to the set standard as were declared in the published records related to the original data source (21, 22). Also, history of taking tobacco products, centrally acting drugs, and any other medications and manner of death for the tests as well control samples were noted from the records (Table S1) (21, 22).

Data Retrieval and Analysis

The RNA was isolated from HIP, PFC (Brodmann Area 46), and associative STR and hybridized to U133_Plus2 Affymetrix chips for m-RNA expression study (21, 22). Expression analysis of mRNA was done by using "affy" package (http://www.bioconductor.org/packages/release/bioc/html/affy.html), which was deposited at Bioconductor and developed in R statistical software program and scripting language. It used three steps to calculate the expression intensities: (i) background correction; (ii) normalization (data were normalized by RMA, subjected to pair wise comparison followed by Benjamini and Hochberg False Discovery rate correction [FDR]), and (iii) expression calculation. After calculation of mRNA expression intensity, a simple unpaired two tailed t-test (significance set at p ≤ 0.01) was applied to the data to filter out the set of genes expressed significantly in all three brain regions.

To categorize the derived significantly altered genes on the basis of their involvement in molecular functions, molecular pathways, and biological events, PANTHER (Protein ANalysis THrough Evolutionary Relationships) Classification System (http://www.pantherdb.org/) and NCBI gene database (http://www.ncbi.nlm.nih.gov/gene/) were exploited. To construct a protein interactome network of the derived gene set STRING v10 database was used (https://string-db.org/). The pathway enrichment analysis results were extracted using Reactome Pathways of the STRING utility.

Results

A set of 40 genes (protein coding-38; RNA-gene-2) was identified showing statistically significant (p ≤ 0.01) altered mRNA expression in schizophrenic patients in the all three brain regions studied (Table 1). Interestingly, it was observed that most of the genes were down-regulated in all three brain regions (32/40). Also, the same genes in all three brain regions have shown the similar direction of expression changes.

These genes were classified into six categories on the basis of their molecular functions (Figure 1). Further, the genes were classified into fourteen categories on the basis of involvement in biological processes and events (Table 2). However, some genes belong to more than one category. The protein interactome network analysis of the gene-set revealed weak to strong interaction between only limited genes (Figure S1). Two cluster hubs were identified where more than two genes were observed to be interacting with each other. The salient genes which showed cross-talk in the interactome were ATP5D, UQCRC1 and ANAPC5, PSMC3 which are mainly involved in the basic cellular functions like energy production mechanisms and cellular growth, cell cycle regulation, and enzyme activity (Table 2).

Figure 1

Furthermore, in pathway linkage analysis, the gene set was found to link with 36 molecular pathways (Figure 2) that broadly could be placed in seven categories based on their commonality (Table S2).

Figure 2

Discussion

The structural and functional brain abnormalities have been repeatedly reported in patients with SCZ (23). The brain regions chosen for this study (hippocampus, prefrontal cortex, and striatum) are known to be predominantly affected (12–14) and their dysfunctions (contributing to characteristic symptoms as altered cognition, loss of executive functions, and disorganized thought and behavior) are common in SCZ (24–26). Recent studies supported an altered gene expression in of these brain regions in SCZ (27, 28). A comprehensive study of the genome expression status in these brain regions was expected to unravel mysterious etiology of SCZ.

As the genes selected for the analysis were found significantly altered in all three studied brain regions, these expression changes may be reflecting the actual pathophysiological changes occurring in neuronal functions in SCZ. None of the genes revealed in this study had been reported earlier as a candidate gene for SCZ, hence the new set appealed for a fresh attention to understand the disease etiology.

Involvement of the Gene Set in Molecular Functions

The functional analysis of the gene-set (Figure 1) elucidated the genes being involved in regulation of basic machinery and housekeeping functions of the neurons viz. receptor-ligand binding, catalysis, enzymatic regulation, nucleic acid binding transcription factor activity, structural molecule activity, and transport activities. It is well evident that dysregulation of these basic functions in neurons may manifest in compromised information processing in brain which has been a hallmark of the progressed SCZ (29). The molecular function analysis also showed hierarchy of the functions that may be compromised in SCZ (Figure 1) (the catalysis and receptor-ligand binding being most affected functions).

Involvement of the Gene Set in Biological Processes and Events

The comprehensive influence of the dysregulation of these genes in the pathogenesis of SCZ gets further clarified in the analysis for the involvement in the biological processes and cellular events (Table 2). The implication of genes involved in ubiquitination (Table 2-a), enzyme activity (Table 2-b), and energy production mechanisms (Table 2-c) may point towards a failure of the basic functions in neurons; as ubiquitination is known to regulate the diverse spectrum of cellular functions (30) and the same should be true for the genes encoding enzymes, especially those necessary for mitochondrial functions (ATP5D, PDK4) (31), regulating specific signaling pathways (MAPK9) (32) and involved in phosphorylation (ATP5D, PDK4) or dephosphorylation (PPM1E) (25, 33). The dysregulation of genes involved in energy production (Table 2-c) supports prevailed view in the literature that energy production mechanisms get compromised in SCZ (34, 35).

Furthermore, down-regulation of genes which function as regulator of the cell growth mechanisms (Table 2-d) provides a possible explanation for reduced neuronal cell sizes, synaptic connection and brain volume in specific brain regions noted in schizophrenia (36) The significant upregulation of genes involved in the programmed death (Table 2-e) may indicate pro-apoptotic mechanisms prevailing in particular brain regions in schizophrenia which gets support from some earlier studies (37, 38). Though, the pro-apoptotic mechanisms may not be a generalized feature in SCZ, as we also noted contrary evidence that an anti-apoptotic gene MCL-1 was found significantly upregulated in all three brain regions. [Contrastingly, sMCL-1 regulates cell cycle negatively hence limiting the mitosis (39)].

Also, the significantly altered expression of the genes involved in cytoplasmic vesicular transport and exocytosis (Table 2-f), dynamic regulation of actin and tubulin cytoskeleton (Table 2-g), and ion channel homeostasis (Table 2-h), lipid-binding (PITPNA) and synthesis (CADPS) (Table 2-i) hint of compromised neuronal information processing in SCZ.

Indications From Interactome Analysis of Gene Set

The protein-protein interaction analysis of the gene set revealed cross-talk between ATP5D (Table 2-b, c), UQCRC1 (Table 2-c), and ANAPC5 (Table 2-d, n), PSMC3 (Table 2-b) (Figure S1) which further strengthen the view that basic (neuronal) cell functionary of the selective brain regions could be the focus of the pathogenesis in SCZ. We detected two central hubs showing strong interaction between the genes involved in ubiquitination (ANAPC5), enzyme activity (ATP5D, PSMC3), energy production mechanism or mitochondrial functions (ATP5D), and cell cycle regulation (ANAPC5) (Figure S1). Existing literature suggests that a dysregulation of the noted biological processes and events could be at the core of SCZ pathogenesis (40–42).

Involvement of the Gene Set in Molecular Pathways

In pathway linkage analysis (Figure 2, Table S2), the category involving largest number of molecular pathways has been that of neurotransmitters/modulators and neurohormones (Table S2-a) which fits with clinical manifestations of the disease and also gets support from existing theories that the etiology of SCZ majorly may be based on dysregulation of this category of molecules (43).

Various neurotransmitters-based hypotheses have been proposed for the etiology of SCZ (44) but none of them are primarily explaining causality of the diseases. The result of this study (Figure 2, Table S2) indicates that disease etiology is not implicating any single transmitter but many of them together (45).

The linkage of the immune cell/chemokine mediated pathways (Table S2-b) is strongly supported by literature (46, 47). An immunogenic basis of SCZ etiogenesis had also been brought forward (48) although counter to this hypothesis has also been placed which limits the role of immune function related genes as a solo or major factor in SCZ etiology (49).

In a recent study (21), Lanz et al. who performed pathway analysis of transcriptional profiles in post-mortem samples of HPC, PFC, and associative STR from SCZ patients (using the same dataset which we used in our study) found enrichment of the transcripts involved in inflammatory pathways. Enrichment of the inflammatory pathways was also reported by Scarr et al., who examined transcriptional profiles from PFC of post-mortem SCZ patients (50).

Also, the involvement of growth, differentiation, and survival of neurons in the specific brain regions (Table S2-c) (51, 52) (also discussed in subsection Involvement of the Gene Set in Biological Processes and Events) and pathways related to apoptosis (Table S2-d) (37, 38), and related to protein synthesis (Table S2-e) and degradation (Table S2-f) (53) has been well documented in the literature (also discussed in subsection Involvement of the Gene Set in Biological Processes and Events). The linkage of FGF signaling pathway (Table S2-c) under neuronal growth, differentiation, and survival to SCZ etiology has been corroborated by a freshly published study by Narla et al. (54) who regarded it as a central pathway commanding all other pathways in developing brain strengthening the view that SCZ has a neurodevelopmental etiology (54). In contrast, the linking of the pathways involved in the pathogenesis of major neurodegenerative diseases (Table S2-g) such as Alzheimer, Parkinson, and Huntington’s disease indicates neurodegenerative nature of SCZ.

Non-Protein-Coding Genes: Unknown Functions

The neuronal functions associated with two non-coding genes (LOC100507534, LOC100507534) couldn’t be ascertained from the literature but it’s interesting to find significant alterations of these long non-coding RNAs in SCZ which have never been reported before. There are now strong indications that non-coding genes are implicated in SCZ pathology (55).

Limitations of This Study and Further Research

The confounding factors such as the history of addiction (including alcohol intake) or substance abuse, and drug intake might have some impact on the transcriptional data. A separate analysis of the impacts of these confounding factors (especially that for addiction and substance abuse) might have given additional insights to this study. Additionally, a separate analysis of the impact of the sex of the subject on the resultant data could have provided important insights regarding sex-specific pathogenesis in SCZ. We couldn’t analyze these factors separately either due to the lack of related data or limitation of the sample sizes.

Though, we have included only those genes for further analysis which showed significant change of expression in all three studied brain regions, the fold changes for the genes are not very large to derive strong conclusions. Validation of the analyzed data with more than one gene expression analysis methods could have been necessary, and could have further augmented the value of this study. The genes which were significantly altered in only one or two and not in all three brain regions selected for the study have not been included in analysis to keep the study design robust, but they might carry some value in disease etiology. A neural circuit specific analysis of the changes in gene expressions targeted to the individual neurocognitive domains may further enhance the etiological clarity on SCZ.

Testing validity of the proposed gene set as a SCZ genetic signature is a remaining task which needs to be studied further. A rigorous search of the SCZ gene expression databases linked to the noted brain regions will make this clear. Also, looking for the similar gene expression changes in the blood cells and/or skin fibroblasts (though there is limited evidence for this in literature for now) (56, 57), will be greatly informative for assigning any prognostic (or diagnostic) value to the gene set.

Conclusions

Molecular characterization of the gene set unraveled in this study gives a glimpse of the complex etiogenetic mechanisms involved in SCZ, an understanding of which may have useful implications in the therapeutic management of the disease. Most of the genes in the set participate in the maintenance of basic cell machinery which explains why their aberrant expression may cause compromised neuronal processing in SCZ.

References

• 1

  RubešaGGudeljLKubinskaN. Etiology of schizophrenia and therapeutic options. Psychiatr Danub (2011) 23:308–15.

  • Google Scholar

• 2

  TandonRKeshavanMSNasrallahHA. Schizophrenia, “Just the Facts” What we know in 2008. 2. Epidemiology and etiology. Schizophr Res (2008) 102:1–18. doi: 10.1016/j.schres.2008.04.011

  • CrossRef
  • Google Scholar

• 3

  GogosJAGerberDJ. Schizophrenia susceptibility genes: emergence of positional candidates and future directions. Trends Pharmacol Sci (2006) 27:226–33. doi: 10.1016/j.tips.2006.02.005

  • CrossRef
  • Google Scholar

• 4

  CrowTJ. The emperors of the schizophrenia polygene have no clothes. Psychol Med (2008) 38:1681–5. doi: 10.1017/S0033291708003395

  • CrossRef
  • Google Scholar

• 5

  FarrellMSWergeTSklarPOwenMJOphoffRAO’DonovanMCet al. Evaluating historical candidate genes for schizophrenia. Mol Psychiatry (2015) 20(5):555. doi: 10.1038/mp.2015.16

  • CrossRef
  • Google Scholar

• 6

  LichtensteinPYipBHBjörkCPawitanYCannonTDSullivanPFet al. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet (2009) 373(9659):234–9. doi: 10.1016/S0140-6736(09)60072-6

  • CrossRef
  • Google Scholar

• 7

  SullivanPFKendlerKSNealeMC. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry (2003) 60:1187–2. doi: 10.1001/archpsyc.60.12.1187

  • CrossRef
  • Google Scholar

• 8

  WrayNRGottesmanII. Using summary data from the danish national registers to estimate heritabilities for schizophrenia, bipolar disorder, and major depressive disorder. Front Genet (2012) 3:118. doi: 10.3389/fgene.2012.00118

  • CrossRef
  • Google Scholar

• 9

  KavanaghDHTanseyKEO’DonovanMCOwenMJ. Schizophrenia genetics: emerging themes for a complex disorder. Mol Psychiatry (2015) 20(1):72. doi: 10.1038/mp.2014.148

  • CrossRef
  • Google Scholar

• 10

  CaspiAMoffittTE. Gene–environment interactions in psychiatry: joining forces with neuroscience. Nat Rev Neurosci (2006) 7:583–90. doi: 10.1038/nrn1925

  • CrossRef
  • Google Scholar

• 11

  ChampagneFA. Early environments, glucocorticoid receptors, and behavioral epigenetics. Behav Neurosci (2013) 127:628–36. doi: 10.1037/a0034186

  • CrossRef
  • Google Scholar

• 12

  KegelesLSAbi-DarghamAFrankleWGGilRCooperTBSlifsteinMet al. Increased synaptic dopamine function in associative regions of the striatum in schizophrenia. Arch Gen Psychiatry (2010) 67(3):231–9. doi: 10.1016/S0920-9964(02)00294-3

  • CrossRef
  • Google Scholar

• 13

  ManoachDS. Prefrontal cortex dysfunction during working memory performance in schizophrenia: reconciling discrepant findings. Schizophr Res (2003) 60:285–8. doi: 10.1176/appi.ajp.2010.09081187

  • CrossRef
  • Google Scholar

• 14

  TammingaCAStanADWagnerAD. The hippocampal formation in schizophrenia. Am J Psychiatry (2010) 167:1178–93. doi: 10.1038/tp.2016.173

  • CrossRef
  • Google Scholar

• 15

  DarbyMMYolkenRHSabunciyanS. Consistently altered expression of gene sets in postmortem brains of individuals with major psychiatric disorders. Transl Psychiatry (2016) 6:e890–0. doi: 10.1038/tp.2016.173

  • CrossRef
  • Google Scholar

• 16

  RoussosPKatselPDavisKLSieverLJHaroutunianV. A system-level transcriptomic analysis of schizophrenia using postmortem brain tissue samples. Arch Gen Psychiatry (2012) 69:1205. doi: 10.1001/archgenpsychiatry.2012.704

  • CrossRef
  • Google Scholar

• 17

  PiskorowskiRANasrallahKDiamantopoulouAMukaiJHassanSISiegelbaumSAet al. Age-dependent specific changes in area CA2 of the hippocampus and social memory deficit in a mouse model of the 22q11. 2 deletion syndrome. Neuron (2016) 89(1):163–76. doi: 10.1016/j.neuron.2015.11.036

  • CrossRef
  • Google Scholar

• 18

  FromerMRoussosPSiebertsSKJohnsonJSKavanaghDHPerumalTMet al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci (2016) 19(11):1442. doi: 10.1001/archpsyc.59.7.631

  • CrossRef
  • Google Scholar

• 19

  HembySEGinsbergSDBrunkBArnoldSETrojanowskiJQEberwineJH. Gene expression profile for schizophrenia: discrete neuron transcription patterns in the entorhinal cortex. Arch Gen Psychiatry (2002) 59(7):631–40. doi: 10.1038/mp.2009.18

  • CrossRef
  • Google Scholar

• 20

  MaycoxPRKellyFTaylorABatesSReidJLogendraRet al. Analysis of gene expression in two large schizophrenia cohorts identifies multiple changes associated with nerve terminal function. Mol Psychiatry (2009) 14(12):1083. doi: 10.1038/s41398-019-0492-8

  • CrossRef
  • Google Scholar

• 21

  LanzTAReinhartVSheehanMJRizzoSJSBoveSEJamesLCet al. Postmortem transcriptional profiling reveals widespread increase in inflammation in schizophrenia: a comparison of prefrontal cortex, striatum, and hippocampus among matched tetrads of controls with subjects diagnosed with schizophrenia, bipolar or major depressive disorder. Transl Psychiatry (2019) 9:151. doi: 10.1038/s41398-019-0492-8

  • CrossRef
  • Google Scholar

• 22

  LanzTAJoshiJJReinhartVJohnsonKGranthamLEIIVolfsonD. STEP levels are unchanged in pre-frontal cortex and associative striatum in post-mortem human brain samples from subjects with schizophrenia, bipolar disorder and major depressive disorder. PloS One (2015) 10(3):e0121744. doi: 10.1016/j.schres.2003.12.002

  • CrossRef
  • Google Scholar

• 23

  AntonovaESharmaTMorrisRKumariV. The relationship between brain structure and neurocognition in schizophrenia: a selective review. Schizophr Res (2004) 70(2-3):117–45. doi: 10.3389/fpsyt.2013.00035

  • CrossRef
  • Google Scholar

• 24

  OrellanaGSlachevskyA. Executive functioning in schizophrenia. Front Psychiatry (2013) 4:35. doi: 10.3389/fpsyt.2013.00035

  • CrossRef
  • Google Scholar

• 25

  SimpsonEHKellendonkCKandelE. A possible role for the striatum in the pathogenesis of the cognitive symptoms of schizophrenia. Neuron (2010) 65:585–96. doi: 10.1016/j.neuron.2010.02.014

  • CrossRef
  • Google Scholar

• 26

  LiebermanJAGirgisRRBrucatoGMooreHProvenzanoFKegelesLet al. Hippocampal dysfunction in the pathophysiology of schizophrenia: a selective review and hypothesis for early detection and intervention. Mol Psychiatry (2018) 23:1764–2. doi: 10.1038/mp.2017.249

  • CrossRef
  • Google Scholar

• 27

  FaizMAcarinLCastellanoBGonzalezB. Proliferation dynamics of germinative zone cells in the intact and excitotoxically lesioned postnatal rat brain. BMC Neurosci (2005) 6:26. doi: 10.1186/1471-2202-6-26

  • CrossRef
  • Google Scholar

• 28

  MirnicsKMiddletonFAMarquezALewisDALevittP. Molecular characterization of schizophrenia viewed by microarray analysis of gene expression in prefrontal cortex. Neuron (2000) 28:53–7. doi: 10.1016/S0896-6273(00)00085-4

  • CrossRef
  • Google Scholar

• 29

  GierschAPonceletPECapaRLMartinBDuvalCZCurziettiMet al. Disruption of information processing in schizophrenia: the time perspective. Schizophr Res: Cogn (2015) 2(2):78–3. doi: 10.1146/annurev.biochem.67.1.425

  • CrossRef
  • Google Scholar

• 30

  HershkoACiechanoverA. The ubiquitin system. Annu Rev Biochem (1998) 67:425–9. doi: 10.1186/s40591-016-0047-9

  • CrossRef
  • Google Scholar

• 31

  Enriquez-BarretoLMoralesM. The PI3K signaling pathway as a pharmacological target in Autism related disorders and Schizophrenia. Mol Cell Ther (2016) 4:1. doi: 10.1385/JMN:24:2:315

  • CrossRef
  • Google Scholar

• 32

  BubberPTangJHaroutunianVXuHDavisKLBlassJPet al. Mitochondrial enzymes in schizophrenia. J Mol Neurosci (2004) 24(2):315–21. doi: 10.1523/JNEUROSCI.4650-03.2004

  • CrossRef
  • Google Scholar

• 33

  EmamianESKarayiorgouMGogosJA. Decreased phosphorylation of NMDA receptor type 1 at serine 897 in brains of patients with Schizophrenia. J Neurosci (2004) 24:1561–4. doi: 10.1038/sj.mp.4001511

  • CrossRef
  • Google Scholar

• 34

  PrabakaranSSwattonJERyanMMHuffakerSJHuangJJGriffinJLet al. Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress. Mol Psychiatry (2004) 9(7):684. doi: 10.1038/mp.2013.67

  • CrossRef
  • Google Scholar

• 35

  RobicsekOKarryRPetitISalman-KesnerNMüllerFJKleinEet al. Abnormal neuronal differentiation and mitochondrial dysfunction in hair follicle-derived induced pluripotent stem cells of schizophrenia patients. Mol Psychiatry (2013) 18(10):1067. doi: 10.1016/S0920-9964(96)00076-X

  • CrossRef
  • Google Scholar

• 36

  WardKEFriedmanLWiseASchulzSC. Meta-analysis of brain and cranial size in schizophrenia. Schizophr Res (1996) 22:197–3. doi: 10.1016/j.pnpbp.2005.03.010

  • CrossRef
  • Google Scholar

• 37

  JarskogLFGlantzLAGilmoreJHLiebermanJA. Apoptotic mechanisms in the pathophysiology of schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry (2005) 29(5):846–58. doi: 10.1176/appi.ajp.161.1.109

  • CrossRef
  • Google Scholar

• 38

  JarskogLFSelingerESLiebermanJAGilmoreJH. Apoptotic proteins in the temporal cortex in schizophrenia: high Bax/Bcl-2 ratio without caspase-3 activation. Am J Psychiatry (2004) 161(1):109–15. doi: 10.1074/jbc.M006626200

  • CrossRef
  • Google Scholar

• 39

  FujiseKZhangDLiuJLYehET. Regulation of apoptosis and cell cycle progression by MCL1 differential role of proliferating cell nuclear antigen. J Biol Chem (2000) 275(50):39458–65. doi: 10.1038/s41598-019-38490-1

  • CrossRef
  • Google Scholar

• 40

  BousmanCALuzaSMancusoSGKangDOpazoCMMostaidMSet al. Elevated biquitinated proteins in brain and blood of individuals with schizophrenia. Sci Rep (2019) 9(1):2307. doi: 10.1186/1471-2164-15-S9-S6

  • CrossRef
  • Google Scholar

• 41

  HuangKCYangKCLinHTsaoTTLeeSA. Transcriptome alterations of mitochondrial and coagulation function in schizophrenia by cortical sequencing analysis. BMC Genomics (2014) 15(9):S6. doi: 10.1073/pnas.0903066106

  • CrossRef
  • Google Scholar

• 42

  BenesFMLimBSubburajuS. Site-specific regulation of cell cycle and DNA repair in post-mitotic GABA cells in schizophrenic versus bipolars. Proc Natl Acad Sci (2009) 106(28):11731–6. doi: 10.1093/med/9780199378067.003.0010

  • CrossRef
  • Google Scholar

• 43

  GillKMGraceAA. The Role of Neurotransmitters in Schizophrenia. In: SchulzSCGreenMFNelsonKJ, editors. Schizophrenia and Psychotic Spectrum Disorders. (Oxford, United Kingdom: Oxford University Press) (2016). p. 153–4. doi: 10.1007/978-1-4613-4042-3_7

  • CrossRef
  • Google Scholar

• 44

  MatthysseSSugarmanJ. Neurotransmitter Theories of Schizophrenia. In: IversenLLIversenSDSnyderSH, editors. Handbook of Psychopharmacology. (Basel, Switzerland: Springer) US (1978). p. 221–2. doi: 10.1016/j.mehy.2012.01.035

  • CrossRef
  • Google Scholar

• 45

  BencherifMStachowiakMKKucinskiAJLippielloPM. Alpha7 nicotinic cholinergic neuromodulation may reconcile multiple neurotransmitter hypotheses of schizophrenia. Med Hypotheses (2012) 78(5):594–0. doi: 10.2174/157339510791823673

  • CrossRef
  • Google Scholar

• 46

  MüllerNSchwarzMJ. Immune system and Schizophrenia. Immunol Rev (2010) 6:213–20. doi: 10.1186/1471-2202-12-13

  • CrossRef
  • Google Scholar

• 47

  RealeMPatrunoADe LutiisMAPesceMFelacoMDi GiannantonioMet al. Dysregulation of chemo-cytokine production in schizophrenic patients versus healthy controls. BMC Neurosci (2011) 12(1):13. doi: 10.1038/s41537-017-0010-z

  • CrossRef
  • Google Scholar

• 48

  MalaviaTAChaparalaSWoodJChowdariKPrasadKMMcClainLet al. Generating testable hypotheses for schizophrenia and rheumatoid arthritis pathogenesis by integrating epidemiological, genomic, and protein interaction data. NPJ Schizophr (2017) 3(1):11. doi: 10.1093/schbul/sbw059

  • CrossRef
  • Google Scholar

• 49

  PougetJGGonçalvesVFSchizophrenia Working Group of the Psychiatric Genomics ConsortiumSpainSLFinucaneHKRaychaudhuriSet al. Genome-wide association studies suggest limited immune gene enrichment in schizophrenia compared to 5 autoimmune diseases. Schizophr Bull (2016) 42:1176–84. doi: 10.1093/schbul/sbw059

  • CrossRef
  • Google Scholar

• 50

  ScarrEUdawelaMDeanB. Changed frontal pole gene expression suggest altered interplay between neurotransmitter, developmental, and inflammatory pathways in schizophrenia. NPJ Schizophr (2018) 4:4. doi: 10.1038/s41537-018-0044-x

  • CrossRef
  • Google Scholar

• 51

  LeeASDe Jesús-CortésHKabirZDKnobbeWOrrMBurgdorfCet al. The neuropsychiatric disease-associated gene cacna1c mediates survival of young hippocampal neurons. Eneuro (2016) 3(2):0006-16. doi: 10.1186/s12920-015-0098-9

  • CrossRef
  • Google Scholar

• 52

  MaschiettoMTahiraACPugaRLimaLMarianiDda Silveira PaulsenBet al. Co-expression network of neural-differentiation genes shows specific pattern in schizophrenia. BMC Med Genomics (2015) 8(1):23. doi: 10.1038/npp.2013.84

  • CrossRef
  • Google Scholar

• 53

  RubioMDWoodKHaroutunianVMeador-WoodruffJH. Dysfunction of the ubiquitin proteasome and ubiquitin-like systems in schizophrenia. Neuropsychopharmacology (2013) 38: (10):1910. doi: 10.1016/j.schres.2016.12.012

  • CrossRef
  • Google Scholar

• 54

  NarlaSTLeeYWBensonCASarderPBrennandKJStachowiakEKet al. Common developmental genome deprogramming in schizophrenia—Role of Integrative Nuclear FGFR1 Signaling (INFS). Schizophr Res (2017) 185:17–2. doi: 10.1016/j.celrep.2014.10.015

  • CrossRef
  • Google Scholar

• 55

  RoussosPMitchellACVoloudakisGFullardJFPothulaVMTsangJet al. A role for noncoding variation in schizophrenia. Cell Rep (2014) 9(4):1417–29. doi: 10.3389/fncel.2013.00095

  • CrossRef
  • Google Scholar

• 56

  ChanaGBousmanCAMoneyTTGibbonsAGillettPDeanBet al. Biomarker investigations related to pathophysiological pathways in schizophrenia and psychosis. Front Cell Neurosci (2013) 7(0):95. doi: 10.1371/journal.pone.0116686

  • CrossRef
  • Google Scholar

• 57

  CattaneNMinelliAMilanesiEMajCBignottiSBortolomasiMet al. Altered gene expression in schizophrenia: findings from transcriptional signatures in fibroblasts and blood. PloS One (2015) 10(2):e0116686. doi: 10.1371/journal.pone.0116686

  • CrossRef
  • Google Scholar