Adenosine Kinase Expression in the Frontal Cortex in SchizophreniaMoody, Cassidy, L;Funk, Adam, J;Devine,, Emily;Devore Homan, Ryan, C;Boison,, Detlev;McCullumsmith, Robert, E;O’Donovan, Sinead, M
doi: 10.1093/schbul/sbz086pmid: 32275755
Abstract The adenosine hypothesis of schizophrenia posits that reduced availability of the neuromodulator adenosine contributes to dysregulation of dopamine and glutamate transmission and the symptoms associated with schizophrenia. It has been proposed that increased expression of the enzyme adenosine kinase (ADK) may drive hypofunction of the adenosine system. While animal models of ADK overexpression support such a role for altered ADK, the expression of ADK in schizophrenia has yet to be examined. In this study, we assayed ADK gene and protein expression in frontocortical tissue from schizophrenia subjects. In the dorsolateral prefrontal cortex (DLPFC), ADK-long and -short splice variant expression was not significantly altered in schizophrenia compared to controls. There was also no significant difference in ADK splice variant expression in the frontal cortex of rats treated chronically with haloperidol-decanoate, in a study to identify the effect of antipsychotics on ADK gene expression. ADK protein expression was not significantly altered in the DLPFC or anterior cingulate cortex (ACC). There was no significant effect of antipsychotic medication on ADK protein expression in the DLPFC or ACC. Overall, our results suggest that increased ADK expression does not contribute to hypofunction of the adenosine system in schizophrenia and that alternative mechanisms are involved in dysregulation of this system in schizophrenia. postmortem, neuropsychiatric, DLPFC, ACC Introduction Schizophrenia is a serious mental illness that affects approximately 1% of the world’s population and costs society billions of dollars each year.1,2 Schizophrenia is characterized by 3 main type of symptoms, positive (eg, hallucinations, delusions), negative (eg, apathy) and cognitive symptoms (eg, inattention, difficulty completing tasks, memory problems).3 Dysregulation of the dopamine and glutamate neurotransmitter systems are implicated in the onset of these symptoms in schizophrenia.4,5 The purinergic hypothesis of schizophrenia posits that altered adenosine activity could contribute to the pathophysiology of schizophrenia via its neuromodulatory actions on both the dopamine and glutamate transmitter systems.6,7 Extensive preclinical work implicates the adenosine system in schizophrenia.8,9 Knockout of astrocytic A2A receptor induces schizophrenia-related features including cognitive impairment and altered psychomotor response to MK-801.10 Receptor knockout is sufficient to disrupt glutamate homeostasis, which is thought to underlie several endophenotypes relevant to schizophrenia.10 Behavioral pharmacology studies suggest that adenosine receptor agonists have antipsychotic-like effects in models of dopamine hyperfunction and glutamate NMDA receptor hypofunction, while adenosine receptor antagonists may prove to be effective in treating cognitive deficits, see review.8 In genetic models, primarily adenosine receptor knockout studies, deficits in adenosine affects dopamine and glutamate transmission and results in severe cognitive impairment, suggesting that imbalance in adenosine may result in endophenotypes associated with schizophrenia.8 A number of postmortem and clinical studies also support a role for the adenosine system in schizophrenia. Adenosine receptor A2A density is significantly increased in the striatum in schizophrenia and may contribute to a hyperdopaminergic state.11 Polymorphisms in adenosine A1 receptor are implicated in schizophrenia in a Japanese population.12 Others have reported that a low activity variant of adenosine deaminase, which metabolizes extracellular adenosine to inosine, is found at lower frequencies in schizophrenia patients and likely results in reduced levels of extracellular adenosine.13 Furthermore, purine degradation inhibitors and nucleoside transporter blockers, like allopurinol and dipyridamole, have had moderate success in treating symptoms of schizophrenia when administered with antipsychotics.14–16 In addition to its role as a neuromodulator, adenosine, as the product of ATP hydrolysis, is directly related to energy consumption.17 Bioenergetic deficits are an established feature of schizophrenia18,19 but the role of the adenosine system in energy metabolism in schizophrenia has yet to be fully elucidated. The purinergic hypothesis was refined to suggest a role for hypofunction of the adenosine system in schizophrenia.8,20,21 Adenosine kinase, a highly conserved ribokinase,22 is an important regulator of extracellular adenosine levels. ADK has a low Km for adenosine and efficiently removes adenosine by converting it to AMP in a largely astrocyte-based cycle.23 Small changes in ADK activity can result in significant changes in adenosine levels.24 A mouse model of brain-wide ADK overexpression results in adenosine deficits and produces schizophrenia-relevant behavioral phenotypes, including working memory and attentional deficits that are improved by augmenting adenosine expression via ADK inhibition.20,21 We hypothesize that, in line with the adenosine hypofunction hypothesis of schizophrenia, ADK is overexpressed in the frontal cortex in schizophrenia. In this study, we assay ADK expression in frontocortical tissue from subjects with schizophrenia. ADK gene expression in rodents treated chronically with haloperidol-decanoate was assayed to determine the effects of antipsychotic medication on ADK expression. In addition, we apply an in silico analysis of ADK gene expression in postmortem RNAseq and microarray studies to determine the effects of antipsychotic medication on ADK in schizophrenia subjects. Materials and Methods Subjects Postmortem tissue from the dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC) of schizophrenia subjects and controls was obtained from 3 brain banks. DLPFC was obtained from the Maryland Brain Collection (MBC) and the NIH Human Brain Collection Core (HBCC). ACC was obtained from the Bronx-Mt. Sinai NIH Brain and Tissue Repository (NBTR). The tissue was acquired with consent from next of kin with IRB approval protocols. Subjects were matched for age, sex, postmortem interval (PMI), and pH (Table 1). Two independent psychiatrists established DSM-IV diagnoses based on available medical records, autopsy reports, and interviews with family members using the Structured Clinical Interview for DSM-IIIR and DSM-IV. Subjects were excluded if they had a history of substance abuse, death by suicide, or were comatose for more than 6 hours before death. If subjects were on medication during the last 1–6 months of life, then antipsychotic medication status was determined to be “on”. Brains were stored at −80°C until dissected. Tissue from the MBC and NBTR were obtained as blocks of tissue approximately 1 cm3. Tissue from the NIH was provided as 14 µm thick sections on glass slides. Table 1. Subject Demographics DLPFC ACC Tissue Bank MBC HBCC NBTR Diagnosis CTL SCZ CTL SCZ CTL SCZ N 20 20 46 26 12 12 Sex 3F/17M 3F/17M 17F/29M 13F/13M 6F/6M 6F/6M Age 43.9 ± 9.1 44.9 ±10.5 42.2 ± 14.8 58 .2± 16.8 74.7 ± 10.6 75.0 ± 11.7 PMI (h) 12.9 ± 4.1 13.6 ± 5.6 35.3 ± 15.6 42.7 ± 21.8 9.1 ± 6.2 10.7 ± 4.6 pH 6.6 ± 0.3 6.6 ± 0.3 6.5 ± 0.3 6.4 ± 0.3 6.4 ± 0.2 6.2 ± 0.3 RIN 5.5 ± 1 6.1 ± 1 - - - - Medication - 17/1/2 - 16/10/0 - 4/6/2 DLPFC ACC Tissue Bank MBC HBCC NBTR Diagnosis CTL SCZ CTL SCZ CTL SCZ N 20 20 46 26 12 12 Sex 3F/17M 3F/17M 17F/29M 13F/13M 6F/6M 6F/6M Age 43.9 ± 9.1 44.9 ±10.5 42.2 ± 14.8 58 .2± 16.8 74.7 ± 10.6 75.0 ± 11.7 PMI (h) 12.9 ± 4.1 13.6 ± 5.6 35.3 ± 15.6 42.7 ± 21.8 9.1 ± 6.2 10.7 ± 4.6 pH 6.6 ± 0.3 6.6 ± 0.3 6.5 ± 0.3 6.4 ± 0.3 6.4 ± 0.2 6.2 ± 0.3 RIN 5.5 ± 1 6.1 ± 1 - - - - Medication - 17/1/2 - 16/10/0 - 4/6/2 Note: CTL, control subjects; SCZ schizophrenia subjects; F, female; M, male; RIN RNA integrity number; DLPFC, dorsolateral prefrontal cortex; ACC, anterior cingulate cortex; MBC, Maryland Brain Collection; N, number of subjects; NBTR Bronx-Mt. Sinai NIH Brain and Tissue Repository, NIH Human Brain Collection Core (HBCC); PMI postmortem interval. Data presented as mean ± SD. Medication status is displayed as subjects on/off/unknown antipsychotics. Open in new tab Table 1. Subject Demographics DLPFC ACC Tissue Bank MBC HBCC NBTR Diagnosis CTL SCZ CTL SCZ CTL SCZ N 20 20 46 26 12 12 Sex 3F/17M 3F/17M 17F/29M 13F/13M 6F/6M 6F/6M Age 43.9 ± 9.1 44.9 ±10.5 42.2 ± 14.8 58 .2± 16.8 74.7 ± 10.6 75.0 ± 11.7 PMI (h) 12.9 ± 4.1 13.6 ± 5.6 35.3 ± 15.6 42.7 ± 21.8 9.1 ± 6.2 10.7 ± 4.6 pH 6.6 ± 0.3 6.6 ± 0.3 6.5 ± 0.3 6.4 ± 0.3 6.4 ± 0.2 6.2 ± 0.3 RIN 5.5 ± 1 6.1 ± 1 - - - - Medication - 17/1/2 - 16/10/0 - 4/6/2 DLPFC ACC Tissue Bank MBC HBCC NBTR Diagnosis CTL SCZ CTL SCZ CTL SCZ N 20 20 46 26 12 12 Sex 3F/17M 3F/17M 17F/29M 13F/13M 6F/6M 6F/6M Age 43.9 ± 9.1 44.9 ±10.5 42.2 ± 14.8 58 .2± 16.8 74.7 ± 10.6 75.0 ± 11.7 PMI (h) 12.9 ± 4.1 13.6 ± 5.6 35.3 ± 15.6 42.7 ± 21.8 9.1 ± 6.2 10.7 ± 4.6 pH 6.6 ± 0.3 6.6 ± 0.3 6.5 ± 0.3 6.4 ± 0.3 6.4 ± 0.2 6.2 ± 0.3 RIN 5.5 ± 1 6.1 ± 1 - - - - Medication - 17/1/2 - 16/10/0 - 4/6/2 Note: CTL, control subjects; SCZ schizophrenia subjects; F, female; M, male; RIN RNA integrity number; DLPFC, dorsolateral prefrontal cortex; ACC, anterior cingulate cortex; MBC, Maryland Brain Collection; N, number of subjects; NBTR Bronx-Mt. Sinai NIH Brain and Tissue Repository, NIH Human Brain Collection Core (HBCC); PMI postmortem interval. Data presented as mean ± SD. Medication status is displayed as subjects on/off/unknown antipsychotics. Open in new tab Animal Studies All rat studies were performed in accordance to the IACUC guidelines at the University of Alabama at Birmingham. Adult male Sprague-Dawley rats (250 g) were pair-housed and maintained on a 12-hour light/dark cycle with ad libitum access to food and water. Rats received intramuscular injections of 28.5 mg kg−1 haloperidol-decanoate or vehicle (sesame oil) once every 3 weeks for 9 months.25,26 Brains were removed and stored at −80°C. Sample Preparation DLPFC and ACC tissue blocks were cryo-sectioned as necessary and mounted on 1 × 3 inch superfrost plus glass slides (Superfrost Plus glass slides, Fisher Scientific). Tissue sections (14 µm) were scraped from glass slides and homogenized in 60 µl of mammalian protein extraction reagent (MPERS) (#78501, ThermoFisher Scientific) with protease and phosphatase inhibitor (#78440, ThermoFisher Scientific). Total protein concentration was determined for each sample using a bicinchoninic acid assay.27 Quantitative Polymerase Chain Reaction RNA was isolated using the RNeasy Minikit (Qiagen) according to the manufacturer’s instructions. Complementary DNA (cDNA) was made using a High-Capacity cDNA Reverse Transcription Kit (4368814, Applied Biosystems, ThermoFisher Scientific). SYBR-Green quantitative real-time PCR reactions were performed in duplicate using 96-well plates (MicroAmp Fast Optical 96-well reaction plate, Applied Biosystems, ThermoFisher Scientific) on a StepOne Plus machine (Applied Biosystems, ThermoFisher Scientific). For each reaction, 3 μl of cDNA (diluted 1:3) was placed in a 20 μl reaction containing 10 μl of SYBR-Green PowerUp Master Mix (Applied Biosystems, ThermoFisher Scientific) and 3pmol of each primer (Invitrogen, ThermoFisher Scientific). PCR reactions occurred at 95°C for 3 minutes (3 cycles), 95°C for 15 seconds (1 cycle), and 59°C for 60 seconds (50 cycles). cDNA was omitted in the non-template control samples. No RT controls contained cDNA made without reverse transcriptase. Primer specificity was tested by running PCR product on a 1% agarose gel and submitting for Sanger sequencing or as previously described.28 The primer sequences were as follows: Human: ADK Long (ADK-L) F: CCTTCCCTCCAATCAGCACG; R: AGCAGGTACCACAGCAACTG. ADK Short (ADK-S) F: TGGGCTGTA GAGCCAAAGTG; R: GCCCAAAAAGCTGAA GGTGG. β-actin F: AGTACTCCGTGTGGATCGGC; R: GCTGATCCACATCTGCTGGA. B2M F: GTGGGA TCGAGACATGTAAGC; R: AGCAAGCAAGCAGAA TTTGGAAT. Rat: ADK Long (ADK-L) F: CCAGAAGCGCTGAGTGAAAAT; R: GTCTTCGG CCAAGATCTGGT. ADK Short (ADK-S) F: ATGACGTCCACCAGTGAAAAT; R: GTCTTCGG CCAAGATCTGGT. CyclophilinA (PPIA) F: CTGCTTCGAGCTGTTTGCAG; R: GACCACAT GCTTGCCATCC. 18S F: CGCCGCTAGA GGTGAAATTC; R: TTGGCAAATGCTTTCGCTC. Immunoblotting Twenty-five micrograms of total protein in sample buffer (6X solution: 4.5% SDS, 15% β-mercaptoethanol, 0.018% Bromophenol blue, and 36% glycerol in 170 mM Tris-HCl pH 6.8) were loaded into 10 well pre-cast 4%–12% Bis-Tris gel (NuPAGE Invitrogen, ThermoFisher Scientific) and run at 180V for 1 hour in 1X MES buffer (Invitrogen, ThermoFisher Scientific). Protein loading amount was selected based off the linear part of the standard curve when running test strips. Following semidry transfer (Bio-Rad) to polyvinylidene fluoride (PVDF) membrane (Bio-Rad) at 20V for 45 minutes, membranes were blocked for 1 hour at room temperature in 5% milk in 1X tris-buffered saline with tween 20 (TBS-T) or Licor blocking buffer in 1X phosphate-buffered saline (PBS). Membranes were incubated in primary antibody mouse anti-ADK (1:1000, sc-365470, Santa Cruz Biotechnology), rabbit anti-ADK (1:4000, kindly provided by D. Boison29), mouse anti-glial fibrillary acidic protein (GFAP) (1:1000, MAB360 EMD Millipore) or housekeeping controls mouse anti-valosin containing protein (VCP; 1:5000, ab11433, Abcam) or rabbit anti-beta-tubulin (1:500, ab18207, Abcam) overnight at 4°C. Membranes were then washed 3X in TBS-T and incubated with goat anti-mouse (1:5000, #68070, Licor) or donkey anti-rabbit (1 in 5000, #68073, Licor) secondary antibodies for 1 hour at room temperature. Membranes were then washed 3X in TBS-T and imaged on a Licor Odyssey laser based imaging system. Intensity values with all-segment median intra-lane background subtraction were measured for each band using Image Studio 4.0 software. Each band was normalized to intensity value of the calibrator sample (pool of all samples) present on each blot as an internal control. All samples were run in duplicate. In Silico Analysis “Lookup studies” were performed to examine ADK expression in publically available postmortem schizophrenia gene expression databases (figure 4A). The Stanley Medical Research Institute (SMRI) Online Genomics Database multi-study microarray repository was analyzed in silico30,31 for ADK gene expression. The fold change and p-value for each search criteria: schizophrenia lifetime antipsychotics (within SCZ), heavy alcohol use (within SCZ), heavy drug use (within SCZ) and male and female sex effect (all subjects), is reported in figure 4B. Microarray studies from the DLPFC and ACC from postmortem schizophrenia were searched.32 Gene expression of ADK in postmortem schizophrenia microarray meta-analysis and RNAseq replication studies from the frontal cortex were also searched.33 Fig. 1. Open in new tabDownload slide Relative mRNA expression of adenosine kinase (ADK) splice variants. There was no significant change in the expression of the (A) long (ADK-L) or the (B) short (ADK-S) variants of ADK in schizophrenia compared to controls. (C) There was also no significant difference in the ratio of ADK-S expression to ADK-L expression. (D) There was a significant decrease in GFAP mRNA expression in schizophrenia. Data presented as mean ± SEM. n = 14–16 per group control and schizophrenia. Fig. 1. Open in new tabDownload slide Relative mRNA expression of adenosine kinase (ADK) splice variants. There was no significant change in the expression of the (A) long (ADK-L) or the (B) short (ADK-S) variants of ADK in schizophrenia compared to controls. (C) There was also no significant difference in the ratio of ADK-S expression to ADK-L expression. (D) There was a significant decrease in GFAP mRNA expression in schizophrenia. Data presented as mean ± SEM. n = 14–16 per group control and schizophrenia. Fig. 2. Open in new tabDownload slide Relative ADK protein expression. (A) ADK protein expression was not significantly altered in schizophrenia in the DLPFC (2A-B; MBC; n = 17–18 per group and HBCC; n = 26–46 per group) or in the ACC (2C; NBTR; n = 12 per group). (D) GFAP protein expression was not significantly altered in schizophrenia in the DLPFC (MBC, n = 16 per group). (E) Representative ADK and (F) GFAP immunoblot images. Control and schizophrenia subjects were run in duplicate. ACC, anterior cingulate cortex; ADK, adenosine kinase; DLPFC, dorsolateral prefrontal cortex; HBCC, NIH Human Brain Collection Core; MBC, Maryland Brain Collection; NBTR, Bronx-Mt. Sinai NIH Brain and Tissue Repository. Fig. 2. Open in new tabDownload slide Relative ADK protein expression. (A) ADK protein expression was not significantly altered in schizophrenia in the DLPFC (2A-B; MBC; n = 17–18 per group and HBCC; n = 26–46 per group) or in the ACC (2C; NBTR; n = 12 per group). (D) GFAP protein expression was not significantly altered in schizophrenia in the DLPFC (MBC, n = 16 per group). (E) Representative ADK and (F) GFAP immunoblot images. Control and schizophrenia subjects were run in duplicate. ACC, anterior cingulate cortex; ADK, adenosine kinase; DLPFC, dorsolateral prefrontal cortex; HBCC, NIH Human Brain Collection Core; MBC, Maryland Brain Collection; NBTR, Bronx-Mt. Sinai NIH Brain and Tissue Repository. Fig. 3. Open in new tabDownload slide In rats treated chronically with haloperidol-decanoate, there was no significant difference in expression of (A) ADK-L or (B) ADK-S. There was no significant difference in (C) the ratio of ADK-S expression to ADK-L expression following chronic haloperidol administration. ADK protein expression was not significantly altered in schizophrenia subjects on antipsychotic medication compared to those off medication in the DLPFC (D) or ACC (E). Subjects whose medication status at time of death was unknown were excluded from analysis. Data presented as mean +/- SEM. N = 10–16 per group HBCC, n = 4–6 per group NBTR, n = 10 per group vehicle and haloperidol-decanoate. ACC anterior cingulate cortex, ADK adenosine kinase, DLPFC dorsolateral prefrontal cortex, HBCC Human Brain Collection Core, MBC Maryland Brain Collection. Fig. 3. Open in new tabDownload slide In rats treated chronically with haloperidol-decanoate, there was no significant difference in expression of (A) ADK-L or (B) ADK-S. There was no significant difference in (C) the ratio of ADK-S expression to ADK-L expression following chronic haloperidol administration. ADK protein expression was not significantly altered in schizophrenia subjects on antipsychotic medication compared to those off medication in the DLPFC (D) or ACC (E). Subjects whose medication status at time of death was unknown were excluded from analysis. Data presented as mean +/- SEM. N = 10–16 per group HBCC, n = 4–6 per group NBTR, n = 10 per group vehicle and haloperidol-decanoate. ACC anterior cingulate cortex, ADK adenosine kinase, DLPFC dorsolateral prefrontal cortex, HBCC Human Brain Collection Core, MBC Maryland Brain Collection. Fig. 4. Open in new tabDownload slide (A) In silico work flow of “lookup studies” of ADK expression in postmortem schizophrenia databases and bioinformatic analysis of ADK signatures generated in iLINCS. (B) “Lookup” studies of ADK gene expression in publically available postmortem schizophrenia datasets. The SMRI dataset reports ADK gene expression in subjects on and off antipsychotic medication. (C) Pathways associated with ADK KD and ADK OE gene signatures. ADK KD is associated with metabolism-related pathways. ADK OE is associated with immune-related pathways. (D) Heat map of ADK KD and ADK OE top 50 “lookup” study. The top 50 genes that compose the ADK KD and ADK OE gene signatures were searched and a heat map representing changes in gene expression in postmortem schizophrenia datasets was created in Kaleidoscope. ACC, Anterior cingulate cortex; ADK, adenosine kinase; CTL, control; DLPFC, dorsolateral prefrontal cortex; FC, fold change; KD, knockdown; OE, overexpression; SCZ, schizophrenia. Fig. 4. Open in new tabDownload slide (A) In silico work flow of “lookup studies” of ADK expression in postmortem schizophrenia databases and bioinformatic analysis of ADK signatures generated in iLINCS. (B) “Lookup” studies of ADK gene expression in publically available postmortem schizophrenia datasets. The SMRI dataset reports ADK gene expression in subjects on and off antipsychotic medication. (C) Pathways associated with ADK KD and ADK OE gene signatures. ADK KD is associated with metabolism-related pathways. ADK OE is associated with immune-related pathways. (D) Heat map of ADK KD and ADK OE top 50 “lookup” study. The top 50 genes that compose the ADK KD and ADK OE gene signatures were searched and a heat map representing changes in gene expression in postmortem schizophrenia datasets was created in Kaleidoscope. ACC, Anterior cingulate cortex; ADK, adenosine kinase; CTL, control; DLPFC, dorsolateral prefrontal cortex; FC, fold change; KD, knockdown; OE, overexpression; SCZ, schizophrenia. Using the integrative LINCS (iLINCS; http://ilincs.org) genomic data portal, we retrieved knockdown (ADK KD) and overexpression (ADK OE) signatures for ADK (figure 4A), as described previously.34 The signatures were obtained from HEPG2 cell lines. ADK consensus gene knockdown (signature ID: LINCSKD_14594) and ADK overexpression (signature ID: LINCSOE_11634, perturbagen ID: ccsbBroad304_05779) signatures are comprised of the transcriptional changes of 978 landmark genes (L1000 genes) when ADK is perturbed. Using the Enrichr (http://amp.pharm.mssm.edu/Enrichr/) platform, the top 50 most significantly altered genes (P < .05) from each signature were interrogated to assess the biological pathways (Reactome 2016) underlying the transcriptional changes observed following ADK KD and ADK OE (figure 4C). The top 50 genes from the ADK KD and ADK OE signatures were also examined in the “lookup studies” workflow using an RShiny package developed in-house, Kaleidoscope (https://kalganem.shinyapps.io/BrainDatabases/). A heat map of expression changes in these genes in postmortem schizophrenia datasets was generated (figure 4D). Data Analysis Data sets were tested for normal distribution using D’Agostino and Pearson omnibus normality test and homogeneity of variance using F-test. Outliers greater than 2 SDs from the mean were excluded. Data were log transformed (human protein studies). Regression analyses were performed to detect association between protein/transcript expression and age, pH and PMI (protein studies) or RIN value (gene expression analysis). Analysis of covariance (ANCOVA) was applied if a significant association was found. Student’s t-test (parametric) or Mann-Whitney test (nonparametric) were applied if no significant associations were found. Post hoc power analysis of our data (1-β) was calculated for RNA data in the MBC cohort (mean 0.16, range 0.08–0.23) and protein data in the DLPFC in the MBC cohort (0.06) and HBCC cohort (0.97) and in the ACC in the NBTR cohort (0.84). Alpha = 0.05 for all statistical tests. Data were analyzed using Statistica 13.0 (Statsoft) and Graphpad Prism 6, (GraphPad Software, www.graphpad.com). Results We measured gene and protein expression of ADK in frontocortical regions in schizophrenia and control subjects. There was no significant difference in gene expression of ADK-L (figure 1A, t(28) = 1.2, P = .24), ADK-S (figure 1B, t(30) = 0.185, P = .86) or in the ratio of ADK-L to ADK-S mRNA expression in the DLPFC (MBC cohort) in schizophrenia subjects compared to controls (figure 1C, t(27) = 1.8, P = .078). Gene expression of GFAP, a marker of reactive astrocytes and astrogliosis, was significantly reduced in schizophrenia (figure 1D, t(28) = 2.3, P = .028). We did not detect any significant associations between expression of ADK-L, ADK-S or GFAP and age, PMI or RIN but there was a significant interaction effect (P < .05) for each regression analysis. There was no significant difference in ADK-S protein expression in schizophrenia subjects compared to controls in the DLPFC from the same tissue set (MBC cohort) that gene expression was measured in (figure 2A, t(33) = 0.25, P = .81). In addition, we measured ADK-S expression in DLPFC tissue obtained from a different brain bank (HBCC cohort) and found no significant change in expression (figure 2B, MWU = 440, P = .08). ADK-S protein expression was also examined in the ACC (NBTR cohort) and no significant change in expression was found after controlling for pH (figure 2C, F(1,21) = 1.09, P = .31). The ADK-L isoform is not the primary brain isoform and it could not be reliably measured in all tissue sets. However, no change in ADK-L was detected in a subset of subjects in the MBC cohort (MWU = 125, P = 0.94, n = 15–17 per group; data not shown). Total GFAP protein levels (all multimer bands) were not significantly altered (figure 2D, MWU = 129, P = .61) in the DLPFC (MBC cohort). There were no significant associations between expression of ADK-S and age or PMI or GFAP and age, pH and PMI in any cohort. Representative immunoblots are shown in figure 2E and 2F and supplementary figure 1. In silico analysis of microarray and RNAseq databases of postmortem frontocortical expression of ADK in schizophrenia found that, in line with our results, ADK gene expression is largely unchanged in illness (figure 4A). The SMRI database reports the effects of antipsychotic medication on ADK gene expression in postmortem frontal cortex in schizophrenia and found ADK was significantly increased (1.08 FC, P < .001). Thus, to determine the effects of antipsychotic medication on ADK splice variant expression, we assayed changes in gene expression of ADK-S and ADK-L in the frontal cortex of rats treated chronically with haloperidol-decanoate compared to vehicle-treated controls. There was no significant difference in expression of ADK-L (figure 3A, Student’s t-test P = 0.9) ADK-S (figure 3B, Student’s t-test P = .8) or the ratio of ADK-S/ADK-L (figure 3C, Student’s t-test P = .9) in antipsychotic-treated animals compared to vehicle-treated animals. In addition, we compared ADK-S protein expression in schizophrenia subjects who were “on” medication to those who were “off” medication at time of death in the DLPFC (HBCC cohort) and ACC (NBTR cohort). There were insufficient subjects “off” medication in the DLFPC MBC cohort to conduct meaningful statistical analysis. ADK-S expression was not significantly altered in the DLPFC (figure 3D, HBCC cohort; P = .336, n = 10–16/group) or in the ACC (figure 3E, NBTR cohort; P = 0.316, n = 4–6/group). Gene signatures generated from knockdown and overexpression of ADK in the HEPG2 cell line were accessed and downloaded from iLINCS and interrogated in Enrichr using Reactome 2016 to identify the biological pathways associated with ADK perturbation. The ADK KD signature was associated primarily with metabolism-related changes including energy metabolism (figure 4C). The ADK OE signature was primarily associated with immune-related pathways (figure 4C). A heat map, generated in Kaleidoscope to visualize gene expression changes of the Top 50 genes in postmortem schizophrenia datasets, is shown in figure 4D. A subset of the Top 50 genes (19/50) that compose the ADK KD and ADK OE signatures were altered more than 1.15 fold in at least 2 postmortem schizophrenia datasets in the “lookup” study analysis, demonstrating that, while ADK expression is not significantly altered in postmortem schizophrenia, a subset of the genes associated with perturbation of ADK expression and the adenosine system are altered in disease. Discussion Adenosine plays a number of roles in key cellular processes. Adenosine modulates glutamate and dopamine, neurotransmitter systems that are strongly implicated in schizophrenia.5 Adenosine also acts as an energy modulator, as energy consumption and adenosine formation are directly linked,35 and bioenergetic deficits are commonly reported in schizophrenia.18 Thus, perturbation of the adenosine system, as described by both preclinical and clinical studies, may contribute to the symptoms of schizophrenia.8,9 The adenosine hypofunction hypothesis of schizophrenia proposes that overexpression of ADK reduces extracellular adenosine levels, leading to dysregulation of adenosine neuromodulatory targets and contributing to the pathophysiology of disease.8 In support of this hypothesis, a mouse model of global brain ADK overexpression results in reduced extracellular adenosine and produces schizophrenia-relevant behavioral phenotypes, including cognitive deficits that are improved by ADK inhibition.20,21 We tested this hypothesis by assaying ADK expression in the DLPFC and ACC from subjects with schizophrenia. However, our data suggests that there is no significant difference in ADK gene or protein expression in these brain regions in this disorder. Dysregulation of extracellular adenosine generating pathways, previously reported in postmortem tissue in schizophrenia,36 offer an alternative mechanism for abnormalities of the adenosinergic system reported in this disorder. ADK is expressed as a long (ADK-L) isoform with primarily nuclear expression or a short (ADK-S) variant which is expressed cytoplasmically.37 ADK-S has a truncated N-terminus with the first 21 amino acids of the canonical ADK-L isoform replaced with an alternate 4 amino acid sequence.38 Exon 1A of ADK-S is located in the intron between exon 1 and 2 of the canonical ADK-L sequence.39 In schizophrenia, ADK-L and ADK-S splice variant expression was not significantly altered in the DLPFC. There was also no significant difference in the ratio of ADK-S to ADK-L gene expression. We have previously measured pan-ADK gene expression at the region level and in enriched populations of astrocytes and pyramidal neurons in the DLPFC and found no significant difference in expression in schizophrenia,36 suggesting that measuring expression of this gene at the region level in tissue composed of a heterogeneous mix of cell types does not hamper detection of differences in ADK gene expression. In silico analysis of postmortem frontal cortex microarray and RNAseq datasets, described in figure 4B, identified no significant changes in ADK gene expression in schizophrenia nor was ADK mRNA expression altered in peripheral blood in schizophrenia subjects compared to controls.40 ADK protein expression was not significantly different in the DLPFC or ACC in schizophrenia compared to controls. ADK-S is the primary isoform expressed in the brain39 and is the main isoform measured in this study. An important consideration in postmortem studies is the potential impact of medication on the expression of ADK. In silico analysis of publically available postmortem schizophrenia microarray data suggested that antipsychotic administration may increase ADK gene expression.30,31 As only a single subject was “off” medication in the cohort of subjects we measured ADK gene expression in (MBC cohort), it was not possible to examine the effects of antipsychotics on ADK gene expression in this group. Thus, we examined ADK gene expression in rats treated for 9 months with haloperidol-decanoate, a typical antipsychotic. There was no significant difference in expression of either ADK variant or the ratio of ADK-S/ADK-L in these animals, suggesting that antipsychotic medication does not significantly alter ADK gene expression. We expanded our study to determine whether antipsychotic administration effects ADK protein expression. ADK expression was not significantly different in the DLPFC or ACC in schizophrenia subjects who were “on” antipsychotic medication at the time of death compared to those who were “off” medication. Overall, ADK gene or protein expression is not significantly altered in the DLPFC or ACC in schizophrenia nor does antipsychotic administration appear to effect ADK expression. Interestingly, while ADK overexpression is purported to result in schizophrenia-relevant symptoms, recent gene studies suggest an ADK loss of function mutation is associated with schizophrenia. A single schizophrenia patient with a deletion in the ADK region was identified in a copy number variant (CNV) study.41 In a follow-up case report, the loss of function variant of ADK resulted in low ADK mRNA and suggests that the CNV in the ADK region results in susceptibility to schizophrenia and other ADK-deficiency-related phenotypes in this patient.40 Gene signatures generated following knockdown or overexpression of ADK in vitro are associated with primarily metabolic and immune-related pathways, respectively. Schizophrenia is associated with deficits in energy metabolism19 and abnormalities in neuroimmune pathways, with reports of elevated inflammatory markers in a subset of schizophrenia subjects.42 A subset of the Top 50 genes that compose the ADK KD and ADK OE signatures were also altered in postmortem schizophrenia “lookup” analysis. The effects of dysregulation of the adenosine system on energy metabolism and neuroimmune pathways in schizophrenia has yet to be elucidated. Overall, our data suggest that the reduced extracellular adenosine levels that are posited to contribute to dysregulation of glutamate and dopamine transmission systems and the symptoms of schizophrenia,7,8 are not due to overexpression of ADK. Other adenosine metabolism pathways are altered in schizophrenia and may lead to reduced availability of extracellular adenosine.36 Ectonucleoside triphosphate diphosphohydrolase (ENTPD 1) and ENTPD 2, a primarily glial enzyme, convert ATP to ADP and AMP and have decreased gene expression in enriched populations of astrocytes in the DLPFC in schizophrenia. Altered ENTPD expression may result in reduced generation of AMP, the substrate for adenosine. Others have also reported reduced ENTPD enzyme activity in the striatum in schizophrenia.43 Additionally, we found increased gene expression levels of adenosine deaminase, an enzyme that catabolises adenosine to inosine, and reduced levels of the adenosine transporter, equilibrative nucleoside transporter 1 (ENT1), in enriched populations of pyramidal neurons in schizophrenia. Taken together, these data suggest that generation of extracellular adenosine may be reduced in schizophrenia, in line with the adenosine hypofunction hypothesis of schizophrenia, but driven by cell-specific, non-ADK dependent mechanisms. Limitations It should be noted that ADK localization shifts dramatically during development from neuronal expression of the ADK-L isoform in early development to astrocytic expression of the ADK-S isoform during postnatal development.24,39,44 Gestational knockout of ADK in mice results in significant social and contextual learning impairments that are not seen when astrocytic ADK is knocked out in early adulthood.45 This is in line with human data where patients with mutations that result in ADK deficiency have severe developmental delays and neurological impairment.46,47 As schizophrenia can be considered a neurodevelopmental disorder,48 aberrant ADK activity during development may contribute to the development of this disorder but may not be detected in postmortem studies of adult tissue. ADK may be rapidly degraded in postmortem tissue leading to overall depression of ADK levels. However, we found no significant association between PMI and ADK levels nor was the protein expression of an additional astrocyte marker associated with ADK, GFAP,49 significantly altered in control and schizophrenia subjects in this study. In summary, we examined ADK expression in 2 different brain regions, in tissue obtained from 3 brain bank collections, and found no significant change in gene or protein expression of ADK in schizophrenia. Thus, hypofunction of adenosine in schizophrenia does not appear to be driven by overexpression of ADK. Acknowledgments D.B. is a co-founder of PrevEp LLC and served as a consultant for Hoffman LaRoche AG. The other authors have no conflict of interest to declare. We wish to thank the NIH HBCC for providing tissue. Funding Lindsey Brinkmeyer Schizophrenia Research Fund (National Institute of Mental Health R01 MH094445). References 1. Bhugra D . The global prevalence of schizophrenia . PLoS Med. 2005 ; 2 ( 5 ): e151 ; quiz e175. 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A Network Analysis of Epigenetic and Transcriptional Regulation in a Neurodevelopmental Rat Model of Schizophrenia With Implications for Translational ResearchDu,, Yang;Li,, Xue-Song;Chen,, Lei;Chen,, Guang-Yang;Cheng,, Yong
doi: 10.1093/schbul/sbz114pmid: 31738422
Abstract Prenatal administration of mitotoxin methylazoxymethanol acetate (MAM) in rats produces behavioral, pharmacological, and anatomical abnormalities once offspring reach adulthood, thus establishing a widely used neurodevelopmental model of schizophrenia. However, the molecular aspects underlying this disease model are not well understood. Therefore, this study examines epigenetic and transcriptional dysregulation in the prefrontal cortex and hippocampus of MAM rats as these are brain regions closely associated with schizophrenia pathogenesis. Upon sequencing messenger and microRNA (mRNA and miRNA, respectively), differential expression was revealed in the prefrontal cortex and hippocampus between MAM- and saline-treated rats; sequencing data were validated by qualitative real-time polymerase chain reaction. Bioinformatic analyses demonstrated that the differentially expressed (DE) genes were strongly enriched in interactive pathways related to schizophrenia, including chemical synaptic transmission, cognition, and inflammatory responses; also, the potential target genes of the DE miRNAs were enriched in pathways related to synapses and inflammation. The blood of schizophrenia patients and healthy controls was further analyzed for several top DE mRNAs: DOPA decarboxylase, ret proto-oncogene, Fc receptor-like 2, interferon lambda receptor 1, and myxovirus (influenza virus) resistance 2. The results demonstrated that the expression of these genes was dysregulated in patients with schizophrenia; combining these mRNAs sufficiently differentiated schizophrenia patients from controls. Taken together, this study suggests that the MAM model has the potential to reproduce hippocampus and prefrontal cortex abnormalities, relevant to schizophrenia, at the epigenetic and transcriptional levels. These data also provide novel targets for schizophrenia diagnoses and treatments. methylazoxymethanol acetate, mRNA, miRNA, prefrontal cortex, hippocampus Introduction Schizophrenia is a severe mental disease that affects approximately 1% of the population worldwide.1,2 Multiple brain region abnormalities are thought to be involved in schizophrenia pathogenesis; the most implicated regions are the prefrontal cortex3 and hippocampus.4 Although the etiology of schizophrenia is still poorly understood, it is generally accepted that the disease starts early in life with neurodevelopmental abnormalities,5 and psychosis usually develops during late adolescence or early adulthood.1 Therefore, utilizing neurodevelopmental models of schizophrenia to investigate disease pathophysiology and facilitate novel drug discovery has attracted great interest over the last several decades. Exposing pregnant rats to methylazoxymethanol acetate (MAM) on gestational day 17 selectively disrupts the mitosis of neural precursor cells, leading to abnormal neurodevelopment in their offspring.6 Once the offspring reach adulthood, they show anatomical alterations in the brain resembling phenomena seen in postmortem brains with schizophrenia, including parvalbumin dysregulation in the hippocampus and frontal cortex.7 Adult MAM-treated rats also exhibit behavioral problems, including sensorimotor gating deficits, reduced social interaction, and cognitive dysfunction.7,8 In addition, these rats show increased dopamine neuron population activity and enhanced sensitivity to psychostimulants.9,10 Due to the characteristics of MAM-treated rats, the MAM-E17 rat model has been widely used as a neurodevelopmental of schizophrenia. Although the anatomical, pharmacological, and behavioral characteristics of MAM-treated rats have been well addressed, studies on the potential molecular mechanisms underlying the MAM-17 rat model are limited. Therefore, in this study, microRNA and messenger RNA sequencing (miRNA-seq and mRNA-seq, respectively) were performed in the prefrontal cortex and hippocampus of MAM- and saline-treated rats to analyze epigenetic and transcriptional regulation in the model. In addition, bioinformatics and gene network analyses were conducted to explore the functional involvement of epigenetic and transcriptional dysregulation in the disease model. Finally, representative molecular components of the disease model were assessed for their potential with translational research in schizophrenia patients. Methods Animals and Samples Pregnant female Sprague Dawley rats were purchased from Vital River. The MAM animal model of schizophrenia was obtained from Moore et al.11 Rats were intraperitoneally injected with a single dose of 20 mg/kg MAM (Wako) or saline on gestational day 17 accordingly. The animals were housed at 24 ± 1ºC and 50 ± 1% humidity under a 12-h light/dark cycle (lights on from 9 am to 9 pm) and provided ad libitum access to a standard diet and drinking water. Their offspring were sacrificed at 8 weeks of age; then, whole blood samples were collected and the hippocampus and prefrontal cortex of the MAM- and saline-treated rats were dissected. Samples were stored at −80ºC until use. All animal experiments were conducted in full compliance with the National Institutes of Health Laboratory Animal Care and Use Guidelines (NIH Publication No. 80-23) and were approved by the Animal Care and Use Committee of Minzu University of China. Human Participants and Sample Collection Schizophrenia patients diagnosed via the Structured Clinical Interview for DSM-IV and International Classification of Diseases 10 were recruited from the Third People’s Hospital of Foshan, Guangdong, China; schizophrenia patients with medical illnesses were excluded. In addition, healthy people were recruited as controls through advertisements; the demographic and clinical characteristics of the participants are shown in supplementary table S1. All schizophrenia patients and controls provided written, informed consent and the study protocol was approved by the ethics committee at The Third People’s Hospital of Foshan, Guangdong, China. Experiments were conducted in accordance with the Declaration of Helsinki. miRNA and mRNA Library Construction and Sequencing Total RNA samples were extracted from the prefrontal cortex and hippocampus of 6 male MAM-treated and 6 male saline-treated rats using the Trizol method according to the manufacturer’s protocol (Thermo Fisher Scientific). Library construction and sequencing for both miRNA and mRNA were performed as previously described.12,13 Differential Expression Analysis Transcripts per million miRNA values >1 and mRNA values for fragments per kilobase of transcript per million mapped reads >10 were selected across all samples for differential expression analysis between MAM- and saline-treated rats using edgeR14; outliers, defined as data points that did not fall within 2 SDs of the mean, were excluded from the analysis. Significantly differentially expressed (DE) miRNAs and mRNAs were reported at P < .05. Metascape Analysis for DE miRNA and mRNA Target Genes Metascape pathway enrichment analysis15 was used for DE mRNAs and miRNA target genes. The TargetScan database (version 6.2) was used to predict DE miRNA target genes and the threshold of TargetScan context+ scores was set as −0.20. Quantitative Real-time Polymerase Chain Reaction Total RNA was extracted from the whole blood, hippocampus, and prefrontal cortex of MAM- and saline-treated rats and the whole blood of schizophrenia patients and normal controls using the Trizol method according to the manufacturer’s protocol (Thermo Fisher Scientific). Next, quantitative real-time polymerase chain reaction (qRT-PCR) was performed as previously described.12 The cycling conditions were: 10 min preincubation at 95°C, 35 cycles of deoxyribonucleic acid synthesis at 95°C for 15 s, 59°C for 15 s, and 72°C for 30 s. Primer sequences are shown in supplementary table S2. The potential of mRNA levels to differentiate schizophrenia patients from normal controls was assessed by a receiver operating characteristic (ROC) curve. The area under curve (AUC) indicates the accuracy with which a particular mRNA can differentiate patients and controls. AUC values above 50% suggest a particular mRNA can possibly differentiate between patients and controls and an AUC value of 100% indicates it can perfectly differentiate between cases and controls. ROC curve and Pearson’s correlation analyses were achieved by SPSS 22.0 statistical analysis software. The Student’s t-test was used for statistical analyses to compare 2 groups, and statistical analyses for comparing multiple groups were achieved by 1-way analysis of variance, followed by a Tukey’s multiple comparison test. Statistical significance levels were set at *P < .05, **P < .01, and ***P < .001. Results Bioinformatics Analysis of DE mRNAs in the Hippocampus and Prefrontal Cortex The hippocampi and prefrontal cortices were analyzed from MAM- and saline-treated male rats by mRNA-seq, resulting in the identification of thousands of mRNAs. The analysis showed that 705 mRNAs in the hippocampus (figure 1A) and 1430 mRNAs in the prefrontal cortex (figure 1B) had significant expression changes between MAM- and saline-treated rats. Of the DE mRNAs in the hippocampus, 377 were upregulated and 328 were downregulated. In the prefrontal cortex, 728 mRNAs were upregulated and 702 were downregulated. Moreover, the transcriptional expression of 213 genes was significantly altered in both the hippocampus and prefrontal cortex of MAM-treated rats (supplementary figure S1A and supplementary table S3). Fig. 1. Open in new tabDownload slide Bioinformatics analysis of DE mRNAs. Heatmaps show cluster analysis data for 705 DE mRNAs in the hippocampus (A) and 1430 DE mRNAs in the prefrontal cortex (B). A Metascape bubble map for viewing the top 20 enrichment clusters in the hippocampus (C) and prefrontal cortex (D) is shown, and a unique square code represents a specific cluster. Metascape enrichment network analysis data depicting the intracluster and intercluster similarities of enriched terms for the hippocampus (E) and prefrontal cortex (F), up to 10 terms per cluster, are shown. DE, differentially expressed; mRNA, messenger RNA, MAM, methylazoxymethanol acetate. Fig. 1. Open in new tabDownload slide Bioinformatics analysis of DE mRNAs. Heatmaps show cluster analysis data for 705 DE mRNAs in the hippocampus (A) and 1430 DE mRNAs in the prefrontal cortex (B). A Metascape bubble map for viewing the top 20 enrichment clusters in the hippocampus (C) and prefrontal cortex (D) is shown, and a unique square code represents a specific cluster. Metascape enrichment network analysis data depicting the intracluster and intercluster similarities of enriched terms for the hippocampus (E) and prefrontal cortex (F), up to 10 terms per cluster, are shown. DE, differentially expressed; mRNA, messenger RNA, MAM, methylazoxymethanol acetate. To understand the molecular mechanisms underlying the disease model, a Metascape enrichment analysis was performed for the DE genes in the hippocampus and prefrontal cortex. The bioinformatic analyses showed that the top 5 Metascape enrichment pathways for the affected hippocampal genes include chemical synaptic transmission, cognition and antigen processing, and the presentation of peptide antigen via major histocompatibility complex class I (figure 1C). Additionally, the most significant Metascape enrichment cluster for the prefrontal cortex-affected genes is behavior; the other top 5 Metascape enrichment pathways include cognition and chemical synaptic transmission (figure 1D). Next, Metascape was used to form enrichment networks to analyze intracluster and intercluster relatedness for the top 20 enrichment clusters in the hippocampus (figure 1E) and prefrontal cortex (figure 1F). The analyses suggested that high intracluster similarities drove the formation of tight local complexes and a substantial proportion of clusters were bridged through subterms with similarities. Notably, Metascape also allows the submission of multiple gene lists to facilitate the understanding of shared pathways; findings based on the input of the 2 DE gene lists suggested that most of the top 20 Metascape enrichment clusters involved the DE genes in both the hippocampus and prefrontal cortex, whereas the rest only involved the DE genes in the prefrontal cortex (supplementary figure S1B). Bioinformatics Analysis of DE miRNAs in the Hippocampus and Prefrontal Cortex The analysis of the hippocampi and prefrontal cortices from MAM- and saline-treated male rats by miRNA-seq resulted in the identification of thousands of miRNAs. The expression of 10 miRNAs in the hippocampus (figure 2A and supplementary table S4) and 37 in the prefrontal cortex (figure 2B and supplementary table S4) were found to be significantly changed in MAM-treated rats compared with saline-treated rats. Of the 10 DE miRNAs in the hippocampus, 4 were downregulated and 6 were upregulated. Of the 37 miRNAs in the prefrontal cortex, 11 were downregulated and 26 were upregulated. Next, TargetScan was used to predict the mRNA targets of the MAM-associated miRNAs in the hippocampus and prefrontal cortex. Metascape bioinformatics analysis results indicated that the top enriched pathways for the predicted targets of various DE miRNAs were related to inflammation, synaptic transmission, and neuronal differentiation (figure 2C). Fig. 2. Open in new tabDownload slide Bioinformatics analysis of DE miRNAs. Heatmaps showing cluster analysis data of 10 DE miRNAs in the hippocampus (A) and 37 miRNAs in the prefrontal cortex (B). (C) The top 3 Metascape enrichment pathways for predicted target genes of DE miRNAs are shown. DE, differentially expressed; miRNA, microRNA; MAM, methylazoxymethanol acetate. Fig. 2. Open in new tabDownload slide Bioinformatics analysis of DE miRNAs. Heatmaps showing cluster analysis data of 10 DE miRNAs in the hippocampus (A) and 37 miRNAs in the prefrontal cortex (B). (C) The top 3 Metascape enrichment pathways for predicted target genes of DE miRNAs are shown. DE, differentially expressed; miRNA, microRNA; MAM, methylazoxymethanol acetate. Interactive Analysis of Epigenetic and Transcriptional Regulation in a MAM Rat Model To systematically understand the functional involvement of epigenetic and transcriptional dysregulation in the disease model, we bioinformatically predicted the gene targets of the DE miRNAs in MAM rats and selected genes that have been validated by mRNA-seq. This identified a number of genes that were negatively regulated by multiple miRNAs in both the hippocampus (supplementary figure S2A) and prefrontal cortex (supplementary figure S2B). A Metascape enrichment analysis was also performed for these validated target genes. The top 3 enriched pathways for the genes were cytokine signaling in the immune system; response to growth factor and interferon signaling in the hippocampus (figure 3A); and cellular response to organic cyclic compound, learning or memory, and regulation of lipid biosynthetic process (figure 3B). Fig. 3. Open in new tabDownload slide Interactive analysis of DE miRNAs and mRNAs. The top 20 Metascape enrichment pathways of validated DE miRNA target genes in the hippocampus (A) and prefrontal cortex (B) are shown. DE, differentially expressed; miRNA, microRNA; mRNA, messenger ribonucleic acid. Fig. 3. Open in new tabDownload slide Interactive analysis of DE miRNAs and mRNAs. The top 20 Metascape enrichment pathways of validated DE miRNA target genes in the hippocampus (A) and prefrontal cortex (B) are shown. DE, differentially expressed; miRNA, microRNA; mRNA, messenger ribonucleic acid. qRT-PCR Validation of DE mRNAs in the Hippocampus, Prefrontal Cortex, and Blood Next, qRT-PCR was performed to validate the mRNA-seq data and chose genes that were highly dysregulated in both the hippocampus and prefrontal cortex of MAM-treated rats (figure 4A). Metascape enrichment analysis suggests that the selected 11 genes were enriched in pathways related to an inflammatory response (supplementary figure S3A). The dysregulated transcriptional expression of these 11 genes in the hippocampus and prefrontal cortex of male MAM-treated rats was validated by qRT-PCR except that RT1 class Ib, locus S3 (RT1-S3), and RT1 class I, locus T24, gene 4 (RT1-T24-4) mRNA levels in the prefrontal cortex were not significantly different between male MAM- and saline-treated rats (figure 4B and supplementary fig. S3B–L). Fig. 4. Open in new tabDownload slide Validation of qRT-PCR data for DE mRNAs in the hippocampus, prefrontal cortex, and blood between MAM- and saline-treated rats. (A) The 11 DE mRNAs were selected for validation. Note that gene function descriptions were acquired from the National Center for Biotechnology Information’s Gene database.30 (B) Fcrl2, Rps2-ps2, Rpl3, RT1-N2, Ifnlr1, Ret, DDC, Rexo4, RT1-S3, RT1-T24-4, and Mx2 expression in the hippocampus and prefrontal cortex between MAM- and saline-treated rats is shown. (C) Blood DDC, Ifnlr1, Ret, Fcrl2, and Mx2 expression between MAM- and saline-treated rats (4 males and 3 females for each group) is shown. MAM, methylazoxymethanol acetate; qRT-PCR, quantitative reverse transcription-polymerase chain reaction; DE, differentially expressed; mRNA, messenger RNA, DDC, DOPA decarboxylase; Ret, ret proto-oncogene; Fcrl2, Fc receptor-like 2; Ifnlr1, interferon lambda receptor 1; Rexo4, RNA exonuclease 4; Mx2, myxovirus (influenza virus) resistance 2; Rps2-ps2, ribosomal protein S2; Rpl3, ribosomal protein L3; NA, not applicable. Data are expressed as the mean ± SE. *P < .05, **P < .01, ***P < .001. Fig. 4. Open in new tabDownload slide Validation of qRT-PCR data for DE mRNAs in the hippocampus, prefrontal cortex, and blood between MAM- and saline-treated rats. (A) The 11 DE mRNAs were selected for validation. Note that gene function descriptions were acquired from the National Center for Biotechnology Information’s Gene database.30 (B) Fcrl2, Rps2-ps2, Rpl3, RT1-N2, Ifnlr1, Ret, DDC, Rexo4, RT1-S3, RT1-T24-4, and Mx2 expression in the hippocampus and prefrontal cortex between MAM- and saline-treated rats is shown. (C) Blood DDC, Ifnlr1, Ret, Fcrl2, and Mx2 expression between MAM- and saline-treated rats (4 males and 3 females for each group) is shown. MAM, methylazoxymethanol acetate; qRT-PCR, quantitative reverse transcription-polymerase chain reaction; DE, differentially expressed; mRNA, messenger RNA, DDC, DOPA decarboxylase; Ret, ret proto-oncogene; Fcrl2, Fc receptor-like 2; Ifnlr1, interferon lambda receptor 1; Rexo4, RNA exonuclease 4; Mx2, myxovirus (influenza virus) resistance 2; Rps2-ps2, ribosomal protein S2; Rpl3, ribosomal protein L3; NA, not applicable. Data are expressed as the mean ± SE. *P < .05, **P < .01, ***P < .001. To investigate whether there is a sex difference for DE mRNAs in the MAM model of schizophrenia, transcriptional expression of these 11 genes in the hippocampus and prefrontal cortex of female MAM- and saline-treated rats was evaluated. Data from qRT-PCR showed that female and male MAM-treated rats had similar dysregulation patterns for the 11 selected genes, although there were no statistically significant differences between female MAM- and saline-treated rats for interferon lambda receptor 1 (Ifnlr1) and RNA exonuclease 4 (Rexo4) mRNA levels in the hippocampus and for Rexo4 and myxovirus (influenza virus) resistance 2 (Mx2) mRNA levels in the prefrontal cortex (figure 4B and supplementary figure S3B–L). DE mRNA Levels in the Blood of MAM-Treated Rats Next, blood samples were collected from MAM- and saline-treated rats to analyze several molecular components found in disease model brains. Results from qRT-PCR showed that the mRNA expression of DOPA decarboxylase (DDC), ret proto-oncogene (Ret), and Fc receptor-like 2 (Fcrl2) were upregulated and mRNA expression of Mx2 was downregulated in the peripheral blood of MAM-treated rats (figure 4C); these changes are consistent with the data from the brains of MAM-treated rats. However, Ifnlr1 mRNA levels were significantly increased in the peripheral blood of MAM-treated rats when compared with saline-treated rats (figure 4C); this was inconsistent with the data from the brains of MAM-treated rats. mRNA-seq Implications in Translational Research To assess the utility of data from the neurodevelopmental model of schizophrenia for clinical research, blood samples were collected from schizophrenia patients and healthy controls and evaluated for the 5 mRNAs that were dysregulated in the blood of the disease model. The results showed that DDC mRNA levels were significantly increased in first-episode, drug-free schizophrenia patients but not in chronically medicated patients (figure 5A). In contrast, Ifnlr1 and Ret mRNA levels were significantly increased in both first-episode, drug-free and chronically medicated patients with schizophrenia when compared with controls (figure 5B, C). In addition, Fcrl2 and Mx2 mRNA levels were significantly decreased in both first-episode, drug-free and chronic medicated schizophrenia patients (figure 5D, E). Fig. 5. Open in new tabDownload slide Implications of MAM rat model data in translational research. Data from qRT-PCR analyses of DDC (A), Ifnlr1 (B), Ret (C), Fcrl2 (D), and Mx2 (E) blood mRNA levels between 68 FEDF schizophrenia patients, 72 CT schizophrenia patients, and 72 HCs are shown. (F) Pearson’s correlation coefficients (and P values in parentheses) between disease status, age, sex, disease severity, and the aforementioned 5 genes are shown. (G) ROC curves were utilized to evaluate the accuracy of the 5 mRNAs in the blood for potentially differentiating between FEDF patients and HCs. (H) ROC curves were used to assess the accuracy of the 5 mRNAs in the blood to potentially differentiate between total patients and HCs. qRT-PCR, quantitative reverse transcription-polymerase chain reaction; FEDF, first-episode, drug free; CT, chronically treated; DDC, DOPA decarboxylase; Ret, ret proto-oncogene; Fcrl2, Fc receptor-like 2; Ifnlr1, interferon lambda receptor 1; Mx2, myxovirus (influenza virus) resistance 2; HC, healthy control; ROC, receiver operating characteristic; PANSS, Positive and Negative Syndrome Scale; GADPH, glyceraldehyde 3-phosphate dehydrogenase; MAM, methylazoxymethanol acetate. Data are expressed as the mean ± SD. Fig. 5. Open in new tabDownload slide Implications of MAM rat model data in translational research. Data from qRT-PCR analyses of DDC (A), Ifnlr1 (B), Ret (C), Fcrl2 (D), and Mx2 (E) blood mRNA levels between 68 FEDF schizophrenia patients, 72 CT schizophrenia patients, and 72 HCs are shown. (F) Pearson’s correlation coefficients (and P values in parentheses) between disease status, age, sex, disease severity, and the aforementioned 5 genes are shown. (G) ROC curves were utilized to evaluate the accuracy of the 5 mRNAs in the blood for potentially differentiating between FEDF patients and HCs. (H) ROC curves were used to assess the accuracy of the 5 mRNAs in the blood to potentially differentiate between total patients and HCs. qRT-PCR, quantitative reverse transcription-polymerase chain reaction; FEDF, first-episode, drug free; CT, chronically treated; DDC, DOPA decarboxylase; Ret, ret proto-oncogene; Fcrl2, Fc receptor-like 2; Ifnlr1, interferon lambda receptor 1; Mx2, myxovirus (influenza virus) resistance 2; HC, healthy control; ROC, receiver operating characteristic; PANSS, Positive and Negative Syndrome Scale; GADPH, glyceraldehyde 3-phosphate dehydrogenase; MAM, methylazoxymethanol acetate. Data are expressed as the mean ± SD. Additional analyses were performed on blood DDC, Ifnlr1, Ret, Fcl2, and Mx2 mRNA levels in male and female schizophrenia patients. The analyses showed that male and female schizophrenia patients had comparable dysregulated expression patterns for DDC, Ifnlr1, Ret, Fcl2, and Mx2 in their blood (supplementary figure S4), suggesting that sex is unlikely to be a confounding factor. Pearson’s correlation analysis was utilized to investigate whether age, sex, or disease severity affected the transcriptional expression levels of these 5 genes in schizophrenia patients. There were significant correlations between Positive and Negative Syndrome Scale total scores, positive scores and DDC mRNA levels, and positive scores and Fcrl2 mRNA levels in patients (figure 5F). Given the significant differences between schizophrenia patients and controls for blood DDC, Ifnlr1, Ret, Fcrl2, and Mx2 mRNA levels, it was necessary to explore their potential as biomarkers for a schizophrenia diagnosis. The ROC-AUC analysis suggested that these mRNAs could moderately differentiate between first-episode, drug-free schizophrenia patients and controls; also, combining the 5 mRNAs increased diagnostic accuracy (figure 5G). Furthermore, Ifnlr1, Ret, Fcrl2, and Mx2, but not DDC, moderately differentiated between total schizophrenia patients and controls (figure 5H). These results indicate that the 5 mRNAs have the potential to be schizophrenia biomarkers. Additionally, an ROC-AUC analysis suggested that sex differences do not affect the accuracy of the 5 mRNAs in differentiating between schizophrenia patients and controls (supplementary figure S5). Discussion This genome-wide, integrative analysis of 2 molecular profiling platforms provides novel insights into the role of epigenetic and transcriptional dysregulation in the pathophysiology of MAM-E17 rats, a neurodevelopmental model of schizophrenia. In both the hippocampus and prefrontal cortex of MAM-E17 rats, DE miRNA and mRNA were present during late adolescence or early adulthood. Bioinformatic analyses of the DE mRNAs in MAM-treated rats revealed that the top enriched pathways for these genes include cognition, behavior, chemical synaptic transmission, synapse organization, and ion transport regulation; a number of implicated pathways for the target genes of the DE miRNAs are related to synapses. Many of the DE mRNAs and miRNAs in the prefrontal cortex and hippocampus appear to be components of functional interaction networks. Interestingly, the top 5 enrichment pathways for the DE mRNAs in the hippocampus and prefrontal cortex both include cognition and chemical synaptic transmission. These findings are consistent with previous studies suggesting that impaired cognition in schizophrenia patients and animal models are associated with dysregulated connectivity between the hippocampus and prefrontal cortex during working memory processing.16,17 Therefore, the neurodevelopmental model of schizophrenia has the potential to reproduce hippocampus and prefrontal cortex abnormalities relevant to schizophrenia pathogenesis at the epigenetic and transcriptional levels. Another set of molecular components that were strongly disrupted in the brains of MAM-treated rats is associated with the inflammatory response. Previously, studies have shown neuroinflammation in the brains of patients with schizophrenia,18,19 as well as dysregulated inflammatory cytokine expression in the cerebral cortex of schizophrenia patients.20 Although neuroinflammation marker findings in postmortem brain samples from patients with schizophrenia were inconsistent across studies, a systematic review suggested that microglia activity, serpin family A member 3, and interferon-induced transmembrane levels were consistently increased in postmortem brains of schizophrenia patients.21 The important role of the inflammatory response in pathogenesis was further supported by clinical trials showing the beneficial effects of anti-inflammatory drugs on schizophrenia patients.22–24 In addition, the antioxidant N-acetylcysteine, which also exhibits anti-inflammatory properties, has been shown to improve neurocognitive impairments in early psychosis schizophrenia patients.25 However, the direct evaluation of molecular components underlying neuroinflammatory responses in schizophrenia patients at the time of psychosis is inherently difficult. In this study, gene expression screening of a rat model of schizophrenia identified several novel inflammatory-related genes that were strongly dysregulated in the prefrontal cortex and hippocampus. These genes include Fcrl2, Ifnlr1, and Mx2; these have not been linked to neuropsychiatric diseases in the literature. Also, an analysis of DDC, Ret, Fcrl2, Ifnlr1, and Mx2 mRNA expression in the blood of MAM-treated rats revealed that DDC, Ret, Fcrl2, and Mx2 mRNA expression abnormalities in MAM-treated rats were consistent in the brains and peripheral blood, supporting the “peripheral as a window to the brain” hypothesis.26 However, the mechanism underlying the inconsistent results regarding Ifnlr1 mRNA expression between the central and peripheral blood of MAM-treated rats is unclear. Nevertheless, future studies are necessary to understand the potential roles of these genes in the onset and/or development of schizophrenia and may provide novel targets for schizophrenia treatment. In further research on the pathophysiology of the disease model at the “omics” level, Hradetzky et al used proteomics and metabolomics to investigate the potential underlying molecular pathways affected in MAM-E17 rats and found 38 DE proteins in the hippocampus.10 Bioinformatics analysis of 38 proteins indicated that the most affected canonical pathways were related to calcium signaling and synaptic and glutamatergic neurotransmission10; these findings are consistent with the present data. However, the proteomics and metabolomics study did not find DE proteins in the frontal cortex between MAM- and saline-treated rats; this is in direct contrast to the large number of DE mRNAs in the prefrontal cortex found in the present study. One explanation for the discrepancy is the subfield-specific gene expression in the model. Another possibility is that the methodology utilized by Hradetzky et al was not sensitive enough to detect protein changes in the frontal cortex of MAM-treated rats as suggested by the authors. In addition, Hradetzky et al used 3-month-old animals for their study, whereas the animals used in this study were 8-week old and were considered to be in late adolescence or early adulthood. The potential translational implications of the molecular changes identified in the prefrontal cortex and hippocampus of MAM-treated rats were analyzed by gene expression in the blood of schizophrenia patients. Consistent with data from the animal model of schizophrenia, qRT-PCR results showed that blood DDC mRNA levels were significantly increased in first-onset, drug-free schizophrenia patients when compared with controls whereas long-term treatment with antipsychotics reduced blood DDC mRNA levels in patients. These results were reasonable given that it is well known that antipsychotics target the dopamine system to alleviate schizophrenia symptoms.27,28 In fact, DDC activity was reported to be elevated in the brains of patients with schizophrenia at the time of psychosis.29 The present data also reveal that the expression of 3 inflammatory response-related genes, Fcrl2, Ifnlr1, and Mx2, were dysregulated in patients with schizophrenia; antipsychotics did not significantly affect the expression of these genes. Moreover, of the 5 genes analyzed in schizophrenia patients and the disease model, blood DDC, Ret, Ifnlr1, and Mx2 mRNA expression showed consistent dysregulation between the schizophrenia patients and the disease model, thus confirming the MAM model as a useful tool for translational schizophrenia research. Therefore, these genes may provide novel targets for schizophrenia treatment, though future studies are necessary to investigate the expression of these genes in the brains of patients with schizophrenia. Additionally, ROC-AUC curve analysis showed that combining these mRNAs could suitably differentiate between schizophrenia patients and controls, suggesting that the representative molecular components found in the neurodevelopment model of schizophrenia have the potential to guide schizophrenia diagnoses. In conclusion, the present mRNA-seq and miRNA-seq data provide a framework for evaluating the molecular mechanisms underlying the pathophysiology of the neurodevelopmental model of schizophrenia at the transcriptional and epigenetic levels and offer rich data sets of DE miRNAs and mRNAs in the brains of MAM rats for further study. Network analyses of miRNA target genes and mRNA-seq data elucidated evidence for the functional involvement of miRNA dysregulation in the disease model. Utilization of the model for translational research identified several genes that were dysregulated in the patients with schizophrenia; hence, these genes may serve as biomarkers for schizophrenia. Therefore, future investigations into the data sets are warranted to translate the findings into benefits for patients with schizophrenia. Funding This work was supported by the National Natural Science Foundation of China (81703492), Beijing Natural Science Foundation (7182092), the Minzu University Research Fund (2018CXTD03), High-Level Hospital Development Program for Foshan “Climbing” Project, and the Minzu University of China 111 project. Conflict of Interest The authors have declared that there are no conflicts of interest in relation to the subject of this study. Contributions of Authors Y.C. conceived the study; Y.D. and Y.C. designed the research; and Y.D., X.S.L., L.C., and G.Y.C. conducted the research. All the authors analyzed and interpreted the data. 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Are Randomized Controlled Trials on Pharmacotherapy and Psychotherapy for Positive Symptoms of Schizophrenia Comparable? A Systematic Review of Patient and Study CharacteristicsBighelli,, Irene;Leucht,, Claudia;Huhn,, Maximilian;Reitmeir,, Cornelia;Schwermann,, Felicitas;Wallis,, Sofia;Davis, John, M;Leucht,, Stefan
doi: 10.1093/schbul/sbz090pmid: 32275756
Abstract Background We examined patient and study characteristics of pharmacotherapy and psychotherapy trials to establish whether the effects of these 2 treatment strategies can be compared meaningfully. Methods We inspected all randomized controlled trials included in 2 recent meta-analyses on antipsychotics and psychotherapy in patients with positive symptoms of schizophrenia, searching EMBASE, MEDLINE, PsycINFO, Cochrane Library, and ClinicalTrials.gov. Differences between psychotherapy and pharmacotherapy trials were analyzed with Wilcoxon–Mann–Whitney and chi-square tests. Results Eighty studies with 18 271 participants on antipsychotic drugs and 53 studies with 4068 participants on psychotherapy were included. Psychotherapy studies included less severely ill patients (P < .0001), with a shorter duration of illness (P = .021), lasted for a longer period (P < .0001), administered the intervention as add-on to antipsychotics (P < .0001), had higher risk of bias in some domains including blinding of outcome assessment (P < .0001), and were funded publicly more frequently (P < .0001). Antipsychotic trials had larger sample sizes (P < .0001) and more study centers (P < .0001), included more males (P = .0001), inpatients (P < .0001), and slightly older patients (P = .031), more often used diagnostic operationalized criteria (P = .006), and were sponsored by pharmaceutical companies. They did not differ in conflict of interest (P = .24). Conclusions We found key differences between the 2 groups of studies that encompass higher risk of bias in psychotherapy studies and the inclusion of more severe patients in drug trials. These differences imply that study and patient characteristics should be carefully taken into account before considering a network meta-analysis. In the interest of patients, psychopharmacologists and psychotherapists should optimize their treatments rather than seeing them in competition. schizophrenia, antipsychotics, psychotherapy, systematic review, trial-methodology Introduction There is controversy and ongoing debate about the appropriateness and efficacy of different treatment options for schizophrenia. The use of pharmacotherapy or psychotherapy has been supported as well as discouraged, based on contradictory evidence, with conflicting arguments brought by advocates of each treatment modality. Pharmacological therapy with antipsychotics, considered the first-line treatment for schizophrenia, has been criticized for burdensome side-effects, high nonresponse and noncompliance rates.1,2 Furthermore, meta-analyses have suggested the efficacy of antipsychotics in terms of “clinically meaningful benefits” may have been overestimated, and adverse effects underestimated.3 Psychological treatments for schizophrenia are also being investigated by an increasing number of randomized controlled trials (RCTs), providing a more solid evidence base on the use of these interventions. They are addressing, among others, negative symptoms4, cognition,5 and social outcomes6, as well as positive symptoms, which are at the core of the disorder. Above all, cognitive behavioral therapy (CBT) has been found to be an effective intervention for positive symptoms when used in addition to pharmacological treatment with antipsychotics.7–9 Some authors went further, claiming that the use of CBT for patients with schizophrenia even without a parallel pharmacotherapy would be safe and acceptable, and found promising results when offering CBT alone.1,10 In a study by Morrison,10 CBT was compared with antipsychotics and with a combination of both; however, the trial was criticized for lacking an appropriate comparator, like a psychological placebo arm, to measure nonspecific effects of therapy.11 The administration of CBT without concomitant antipsychotic medication has been criticized and deemed to be unethical, suggesting that convincing evidence about its efficacy is lacking.12 Other authors claim that CBT is associated only with small effects that are not maintained in masked studies and argue on this basis that the efficacy of this treatment on positive symptoms of schizophrenia is not tenable.13 To the extreme, a recent editorial14 has stated that, due to poor trial methodology, the benefits of CBT might be inflated 5 or 6 times and argue that it should be offered no longer to people with schizophrenia. These arguments suffer from a major constraint: they are founded on indirect comparisons, via effect sizes compared mainly with placebo in studies on antipsychotics or with “treatment as usual” in studies on psychological interventions. The only attempt to compare a psychological intervention as monotherapy with antipsychotics is represented by a small pilot trial on CBT in first-episode patients.10 As a result, the debate on the use of pharmacological and psychotherapeutic treatments for schizophrenia thus far has been based on the assumption that findings of studies in these 2 domains are somehow comparable. However, a systematic comparison of study characteristics has never been conducted in the field of schizophrenia. In particular, quality of evidence can have an impact on funding allocation. We aimed to fill this gap by (1) investigating whether and how patients enrolled in antipsychotic trials are different from those in psychotherapy trials; (2) investigating to what extent and how trials examining antipsychotics and trials examining psychotherapies differ from methodological, study quality, and conflict of interest points of view; (3) establishing whether the effects of these 2 treatment strategies could be compared meaningfully by means of these trials. Methods Study Design and Participants We included randomized controlled trials from 2 recent systematic reviews on pharmacological15 and psychological16 treatments for schizophrenia. The systematic reviews were chosen because both were the most up-to-date and comprehensive in their respective fields, focused on acute treatment of positive symptoms and were conducted by the same team, assuring consistent methodology and the application of the same rules in data extraction and critical study appraisal. Both reviews followed PRISMA guidelines and were preceded by protocols registered on PROSPERO (registration numbers CRD42013003342 and CRD42017067795) and published.17 Both reviews included published and unpublished RCTs, but in the review on antipsychotics studies also had to be double blind. This was not an inclusion criterion in the review on psychotherapy studies since in this case only the outcome assessors but not the therapists can be blind. In both reviews, studies with a high risk of bias for randomization and allocation concealment were excluded. The 2 reviews had a similar focus: the one on antipsychotics included patients with acute exacerbations of schizophrenia, and the other on psychotherapy included studies recruiting participants who presented positive symptoms. Studies in first-episode patients were excluded in the psychotherapy review and were not excluded in the antipsychotic one, but no one was found. Trials conducted in patients with predominantly negative symptoms and with concomitant physical or psychiatric illnesses were excluded in both reviews. Risk of bias was independently assessed by 2 reviewers with the Cochrane Risk of Bias tool.18 Interventions and Comparators All antipsychotics licensed in at least one country, with the exception of clozapine and of intramuscular formulations, and psychological treatments aimed at treating positive symptoms were included as interventions. The comparators were placebo for antipsychotic studies and treatment as usual, psychological placebo, or other psychological interventions for psychotherapy studies, although the great majority compared the psychological intervention with treatment as usual (Supplementary File 7). Search Strategy and Selection Criteria The reviews searched multiple databases for relevant RCTs up to January 201816 and October 2016,15 without language restrictions (Supplementary File 1). Reference lists of previous reviews were also searched. As trial methodology has changed over the years,15 we only included pharmacotherapy studies published from the publication year of the first psychotherapy trial onward (1996) to make the datasets even more comparable. The process of study selection is presented in Supplementary File 2. Data Extracted At least 2 reviewers among I.B., C.R., F.S., S.W., M.H., and C.L. extracted the data independently. As patient characteristics, we extracted mean age, duration of illness in years, baseline severity measured in Positive and Negative Syndrome Scale (PANSS) equivalents, education, and marital status. As methodological characteristics, we extracted total sample, number of study centers involved, duration in weeks, administration of the intervention in monotherapy or in combination, ratio of male participants included, use of diagnostic operationalized criteria (such as Diagnostic and Statistical Manual of Mental Disorders [DSM] or International Classification of Diseases [ICD]), patients status at baseline by inclusion criteria (inpatients/outpatients), country, and comparator used. Study quality was assessed according to Cochrane Risk of Bias tool, in the following domains: randomization, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, and selective reporting. We extracted data about funding (public, pharmaceutical company, other) and conflict of interest, judging a potential conflict of interest for studies funded by a pharmaceutical company in the case of antipsychotic trials and for studies conducted by the same authors who developed the treatment in psychotherapy trials (researchers’ allegiance).19 In addition, we evaluated the 2 meta-analyses with AMSTAR 2, a tool for critical assessment of systematic reviews.20 Data Analysis Analyses were conducted in R (version 3.4.3).21 We compared the 2 samples with Wilcoxon–Mann–Whitney test for continuous variables and with chi-square test for dichotomous variables. Because all analyses were considered exploratory rather than confirmatory, adjustments for multiple testing were not made. Results The process of study selection (Supplementary File 2) brought to the inclusion of 80 studies involving 18 271 participants on antipsychotics and 53 studies involving 4068 participants on psychological treatments (cognitive behavioral therapy, metacognitive training, mindfulness, acceptance and commitment therapy, experience-focused counseling, hallucination-focused integrative treatment, AVATAR therapy) (Supplementary File 8). Patient and study characteristics of drug studies and psychotherapy studies are presented in Supplementary File 7. The complete references of included studies are presented in Supplementary File 3. The reviews had a positive score on 15 and 14 out of 16 items of the AMSTAR 2 tool, respectively (Supplementary File 4). Patient Characteristics Patients enrolled in drug studies were significantly more severely ill at baseline (93.97 vs 71.72 PANSS equivalents, P < .0001), had a longer history of illness (14 vs 12.37 years, P = .021), and were older than in psychotherapy studies (38.95 vs 37.42 years, P = .031) (table 1; figure 1). Data on marital status, living conditions, and education were collected only in the studies investigating psychotherapies; this information was not reported usually in drug trials and, therefore, could not be compared among the 2 groups of studies. Table 1. Results of Comparison of Continuous Variables (Mann–Whitney Wilcoxon Test) Results of Mann–Whitney Wilcoxon Test Variable Drug Studies, Mean (SD) Psychotherapy Studies, Mean (SD) W P Agea 38.95 (3.78) 37.42 (4.79) 13 316 .031 Duration of illnessa 14.01 (3.82) 12.37 (4.79) 2989.5 .021 Baseline severityb 93.97 (7.56) 71.72 (12.6) 1222.5 <.0001 Total sample 313.53 (185.07) 76.35 (53.42) 400.5 <.0001 Number of study centers 36.69 (21.65) 3.33 (2.03) 155 <.0001 Study durationc 6.05 (2.61) 19.17 (12.35) 3861.5 <.0001 Ratio of male participants 69.96 (11.4) 61.21 (16.5) 1046 .0001 Results of Mann–Whitney Wilcoxon Test Variable Drug Studies, Mean (SD) Psychotherapy Studies, Mean (SD) W P Agea 38.95 (3.78) 37.42 (4.79) 13 316 .031 Duration of illnessa 14.01 (3.82) 12.37 (4.79) 2989.5 .021 Baseline severityb 93.97 (7.56) 71.72 (12.6) 1222.5 <.0001 Total sample 313.53 (185.07) 76.35 (53.42) 400.5 <.0001 Number of study centers 36.69 (21.65) 3.33 (2.03) 155 <.0001 Study durationc 6.05 (2.61) 19.17 (12.35) 3861.5 <.0001 Ratio of male participants 69.96 (11.4) 61.21 (16.5) 1046 .0001 Note:aYears. bPANSS equivalents. cWeeks. Open in new tab Table 1. Results of Comparison of Continuous Variables (Mann–Whitney Wilcoxon Test) Results of Mann–Whitney Wilcoxon Test Variable Drug Studies, Mean (SD) Psychotherapy Studies, Mean (SD) W P Agea 38.95 (3.78) 37.42 (4.79) 13 316 .031 Duration of illnessa 14.01 (3.82) 12.37 (4.79) 2989.5 .021 Baseline severityb 93.97 (7.56) 71.72 (12.6) 1222.5 <.0001 Total sample 313.53 (185.07) 76.35 (53.42) 400.5 <.0001 Number of study centers 36.69 (21.65) 3.33 (2.03) 155 <.0001 Study durationc 6.05 (2.61) 19.17 (12.35) 3861.5 <.0001 Ratio of male participants 69.96 (11.4) 61.21 (16.5) 1046 .0001 Results of Mann–Whitney Wilcoxon Test Variable Drug Studies, Mean (SD) Psychotherapy Studies, Mean (SD) W P Agea 38.95 (3.78) 37.42 (4.79) 13 316 .031 Duration of illnessa 14.01 (3.82) 12.37 (4.79) 2989.5 .021 Baseline severityb 93.97 (7.56) 71.72 (12.6) 1222.5 <.0001 Total sample 313.53 (185.07) 76.35 (53.42) 400.5 <.0001 Number of study centers 36.69 (21.65) 3.33 (2.03) 155 <.0001 Study durationc 6.05 (2.61) 19.17 (12.35) 3861.5 <.0001 Ratio of male participants 69.96 (11.4) 61.21 (16.5) 1046 .0001 Note:aYears. bPANSS equivalents. cWeeks. Open in new tab Fig. 1. Open in new tabDownload slide Patients’ and studies’ characteristics. Wilcoxon rank sum test and chi-square test. (a) P = .031; (b) P = .021; (c) P < .0001. PT, psychotherapy; AP, antipsychotics. Fig. 1. Open in new tabDownload slide Patients’ and studies’ characteristics. Wilcoxon rank sum test and chi-square test. (a) P = .031; (b) P = .021; (c) P < .0001. PT, psychotherapy; AP, antipsychotics. Study Characteristics Eighty-six percent of drug studies included only inpatients at study start, whereas this was the case only for 25.64% of psychotherapy studies (P < .0001) (table 2). Studies on antipsychotics included, on average, larger samples than psychotherapy studies (313.53 vs 76.35 patients, P < .0001), involved more study centers (36.69 vs 3.33, P < .0001), and often located in different countries. Psychotherapy studies, on the contrary, were conducted mainly in only one country, and about half were conducted in the UK. Table 2. Results of Comparison of Dichotomous Variables (Chi-Square Test) Variable Drug Studies, n/Na (%) Psychotherapy Studies, n/Na (%) χ 2 P Use of operationalized criteria for diagnosis 76/80 (95) 42/54 (77.8) 7.53 .006 Only inpatients included 64/74 (86.49) 10/39 (25.64) 39.19 <.0001 Intervention administered in monotherapy 80/80 (100) 1/52 (1.89) 124.79 <.0001 High risk of bias for blinding of participants and personnel 0/80 (0)b 54/54 (100) 129.88 <.0001 High risk of bias for blinding of outcome assessment 0/80 (0)b 14/54 (25.92) 20.47 <.0001 High risk of bias for incomplete outcome data 5/80 (6.25) 35/54 (64.81) 50.05 <.0001 High risk of bias for selective reporting 10/80 (12.5) 19/54 (35.19) 8.49 .0035 Public funded 1/61 (1.6) 33/40 (82.5) 67.16 <.0001 Conflict of interest 60/78 (76.92) 35/53 (66.04) 1.36 .2418 Variable Drug Studies, n/Na (%) Psychotherapy Studies, n/Na (%) χ 2 P Use of operationalized criteria for diagnosis 76/80 (95) 42/54 (77.8) 7.53 .006 Only inpatients included 64/74 (86.49) 10/39 (25.64) 39.19 <.0001 Intervention administered in monotherapy 80/80 (100) 1/52 (1.89) 124.79 <.0001 High risk of bias for blinding of participants and personnel 0/80 (0)b 54/54 (100) 129.88 <.0001 High risk of bias for blinding of outcome assessment 0/80 (0)b 14/54 (25.92) 20.47 <.0001 High risk of bias for incomplete outcome data 5/80 (6.25) 35/54 (64.81) 50.05 <.0001 High risk of bias for selective reporting 10/80 (12.5) 19/54 (35.19) 8.49 .0035 Public funded 1/61 (1.6) 33/40 (82.5) 67.16 <.0001 Conflict of interest 60/78 (76.92) 35/53 (66.04) 1.36 .2418 Note:aDenominator is not always equal to the total number of studies because only studies reporting the corresponding information were used for each analysis. bThe studies in this review were double blinded by inclusion criteria. Open in new tab Table 2. Results of Comparison of Dichotomous Variables (Chi-Square Test) Variable Drug Studies, n/Na (%) Psychotherapy Studies, n/Na (%) χ 2 P Use of operationalized criteria for diagnosis 76/80 (95) 42/54 (77.8) 7.53 .006 Only inpatients included 64/74 (86.49) 10/39 (25.64) 39.19 <.0001 Intervention administered in monotherapy 80/80 (100) 1/52 (1.89) 124.79 <.0001 High risk of bias for blinding of participants and personnel 0/80 (0)b 54/54 (100) 129.88 <.0001 High risk of bias for blinding of outcome assessment 0/80 (0)b 14/54 (25.92) 20.47 <.0001 High risk of bias for incomplete outcome data 5/80 (6.25) 35/54 (64.81) 50.05 <.0001 High risk of bias for selective reporting 10/80 (12.5) 19/54 (35.19) 8.49 .0035 Public funded 1/61 (1.6) 33/40 (82.5) 67.16 <.0001 Conflict of interest 60/78 (76.92) 35/53 (66.04) 1.36 .2418 Variable Drug Studies, n/Na (%) Psychotherapy Studies, n/Na (%) χ 2 P Use of operationalized criteria for diagnosis 76/80 (95) 42/54 (77.8) 7.53 .006 Only inpatients included 64/74 (86.49) 10/39 (25.64) 39.19 <.0001 Intervention administered in monotherapy 80/80 (100) 1/52 (1.89) 124.79 <.0001 High risk of bias for blinding of participants and personnel 0/80 (0)b 54/54 (100) 129.88 <.0001 High risk of bias for blinding of outcome assessment 0/80 (0)b 14/54 (25.92) 20.47 <.0001 High risk of bias for incomplete outcome data 5/80 (6.25) 35/54 (64.81) 50.05 <.0001 High risk of bias for selective reporting 10/80 (12.5) 19/54 (35.19) 8.49 .0035 Public funded 1/61 (1.6) 33/40 (82.5) 67.16 <.0001 Conflict of interest 60/78 (76.92) 35/53 (66.04) 1.36 .2418 Note:aDenominator is not always equal to the total number of studies because only studies reporting the corresponding information were used for each analysis. bThe studies in this review were double blinded by inclusion criteria. Open in new tab In all drug studies, the antipsychotic was given in monotherapy, whereas in 52 of 53 psychotherapy studies (98%), the psychological intervention was offered as add-on to the treatment as usual, which could include the administration of antipsychotics. Studies on antipsychotics were on average also shorter (6 vs 19.17 weeks, P < .0001), included a higher ratio of male participants (69.96% vs 61.21%, P = .0001) and referred more frequently to operationalized diagnostic criteria, rather than clinical judgement alone (95% vs 77.8%, P = .06) (tables 1 and 2; figures 1 and 2). All antipsychotic trials used pill placebo as control group, whereas the comparators used in psychotherapy were multiple: other psychological interventions not focused on the treatment of positive symptoms (20.8% of the studies), inactive controls, defined as interventions intended to control for nonspecific aspects of the therapy (17%), treatment as usual (54.7%), and waiting list (13.2%). Fig. 2. Open in new tabDownload slide Percentages of individual studies presenting the given characteristics. Chi-square test. (a) P = .006; (b) P < .0001; (c) P = .0035; (d) P = .24. Fig. 2. Open in new tabDownload slide Percentages of individual studies presenting the given characteristics. Chi-square test. (a) P = .006; (b) P < .0001; (c) P = .0035; (d) P = .24. Study Quality When looking at the evaluation with the Cochrane Risk of Bias tool, the first evident difference is that none of the 8 evaluated risks is greater than 25% for the drug studies, whereas 5 of the bias domains are greater for the psychotherapy studies. In all the drug studies, patients and clinicians were blind (due to inclusion criteria of the review), where this was the case in none of the psychotherapy studies, due to the nature of the intervention. Blind assessors were employed in all drug studies, resulting in none having high risk of bias in this domain, whereas 25.92% of psychotherapy studies did (P < .0001). Sixty-four percent of psychotherapy studies were rated as having a high risk of bias concerning incomplete outcome data, not having applied intention-to-treat analysis, or other strategies to account for missing data, whereas this was the case in 6.25% of the studies on antipsychotics (P < .0001). Reporting of results also differed in the 2 groups, with psychotherapy studies being more frequently judged at high risk of bias for selective reporting (35.19% vs 12.5%, P = .0035) (table 2). No studies were at high risk of bias for randomization and allocation concealment, by inclusion criteria of the original reviews. Detailed risk of bias judgments and criteria used are reported in Supplementary Files 5 and 6. Conflict of Interest and Funding Eighty-two percent of the trials on psychotherapy were funded publicly, in comparison with 1.6% of the drug studies (P < .0001). When looking at potential conflict of interest, identified as pharmaceutical company sponsorship for drug trials and researchers’ allegiance for psychotherapy trials, we did not observe significant differences (76.92% vs 66.04%, P = .24) (table 2). Discussion Main Findings We found that studies conducted to examine the efficacy of antipsychotics and psychotherapies for schizophrenia differed in many patient and study characteristics. On average, drug studies enrolled more severely ill patients, with a longer duration of illness, and included more frequently only inpatients in comparison with psychotherapy studies. Patients were older and more likely to be males. Also, drug studies used operationalized criteria more often to make the initial diagnosis. Psychotherapy studies had overall a higher risk of bias across all domains. They were longer, had smaller samples, involved less study centers, and they delivered the intervention primarily as add-on to pharmacological treatment. The great majority of psychotherapy studies were funded publicly, in contrast with drug studies that were funded by the manufacturers of the drugs examined. However, the 2 groups did not differ for potential conflict of interest because many of the psychotherapy studies presented potential researchers’ allegiance. Interpretation of Findings The severity of illness of the patients enrolled by the 2 kinds of studies is drastically different. With a baseline PANSS of 94, patients in drug studies can be considered markedly ill (score of 5) on the Clinical Global Impression Scale (CGI), whereas patients in the psychotherapy studies are on average not even moderately ill (a score of 71 on the PANSS corresponds to less than 4 on the CGI).22 More acutely and severely ill patients seemed, in general, not to have been enrolled in psychotherapy studies. This reflects real-world practice, where psychotherapies are probably not as feasible with patients in severe acute states, who might not have the minimum ability and readiness to collaborate. We also found that psychotherapy was almost always given in addition to usual care, that would typically include antipsychotics; only one psychotherapy study attempted to deliver cognitive behavioral therapy to patients who were not receiving concomitant antipsychotic medication.1 This finding is also consistent with what happens in clinical practice, where psychotherapy is offered usually as an add-on to antipsychotics. However, the possibility that CBT could be delivered without concomitant medication in people with schizophrenia is under scrutiny, and there have been attempts to provide CBT as monotherapy1 and to compare directly CBT as monotherapy with antipsychotics.10 The fact that participants enrolled in studies on antipsychotics were more often inpatients can be seen as a severity marker and, in this way, it is consistent with the higher severity of illness found in these patients. However, trials on antipsychotics might have been conducted more frequently in inpatient settings to better monitor for the onset of side effects and to control closely patients on placebo. Patients in drug studies were also older and had a longer history of illness; the difference was significant but not large, and in this case would favor psychotherapy trials because response rates are usually lower with more chronic patients.23 Drug studies usually involved many centers and were able to reach large samples, unlike psychotherapy trials, which had smaller samples; this could lead to greater effect sizes in psychotherapy trials,24 since larger trials have usually smaller effect sizes.25 However, in our reviews the effect size for overall symptoms was 0.47 for antipsychotics vs placebo (all double-blind studies)15 and 0.36 for CBT vs treatment as usual (11 of 13 studies rater-blind).16 Big samples are important also for better generalizability of results; however, to involve a large number of patients, participants in multicenter drug trials are often recruited by advertisements, attracting the so-called “professional patients,” and this might represent a problem in terms of external validity of the results.26 The higher ratio of male participants in drug studies may reflect the greater willingness of women to initiate psychotherapy27 or of men to enroll in a drug trial; it might have been easier for psychotherapy studies to enroll women instead of men. Because the prevalence of schizophrenia is approximately equal in men and women, psychotherapy studies appear to be more representative of this population from the gender point of view. The choice of an appropriate control group is of crucial importance and is complicated especially for psychotherapy studies. The most frequent situation in the included studies was that the psychological treatment was given in addition to usual care, whereas the control group just continued to be treated as usual. Because usual care can be different in different settings, ranging from a comprehensive package of interventions to almost no care at the other extreme, special attention must be paid such that this condition is comparable in the different studies. About 13% of the studies compared the experimental treatment with a waiting list; this might be problematic because this control has been associated with overestimation of the experimental condition.24,28 This has been defined “a nocebo effect” because the patients in the waiting list group receive the subtle message that only once they receive the treatment, in the future, will an improvement be expected from them. Seventeen percent of the studies adopted a psychological placebo as comparator, in which patients receive attention for the same amount of hours, and thus the change can be attributed to the specific therapeutic components of the therapy. The use of placebo as control can be problematic also because the side effects associated with antipsychotic treatment might result in unblinding; regrettably, this is rarely tested in clinical trials.29 A possible alternative would be the use of an active placebo, which produces side effects similar to the ones of the experimental compound. An ongoing review is investigating the role of barbiturates and benzodiazepines as an active placebo in comparison with antipsychotics.30 From a methodological point of view in studies on psychological interventions, the blinding of clinicians and patients is not possible. This is a general limitation of psychotherapy studies that cannot be overcome. What would be possible is a blind outcome assessor, but still 25% of the studies did not employ one, resulting in an evaluation of high risk of bias in this domain. Few psychotherapy studies applied an intention-to-treat analysis or other approaches to deal with missing data, which is relevant given that completer analysis can lead to overestimation of effect sizes.31 On average, psychotherapy studies showed a lower quality also in other domains. In addition, on average 2 of 10 studies on psychotherapy did not use operationalized criteria for the initial diagnosis, on which basis the patients were enrolled in the studies; this could represent a problem in the validity of results. These results are in line with a previous overview of reviews by Huhn et al,24 that analyzed methodological characteristics of pharmacological and psychotherapy trials across different psychiatric conditions. We argue that there is still considerable room for methodological improvement in studies about psychological treatments; specific guidelines for this kind of studies have been suggested and discussed.32 Improving the quality of psychotherapy studies is crucial to have a higher confidence in their results also because sources of bias have been found to influence dramatically the effect sizes.33,34 On the other side, drug trials suffer from many limitations, as well. In addition to those discussed above, dropout rates (37.2%)15 are higher compared with the ones found in CBT studies (around 15.4%).16 The majority of drug studies were funded by pharmaceutical companies. We have found that trials comparing 2 antipsychotics are prone to “industry bias.” 35 But, placebo-controlled trials conducted by pharmaceutical companies had, on average, smaller effect sizes.24 In contrast, the majority of psychotherapy studies were funded by public grants or institutions. However, more than the half were conducted by authors with a potential conflict of interest, which has been defined as researchers’ allegiance to the experimental intervention.19 If on one side it is reasonable that authors who develop a new psychological treatment want to test it in a trial, their potential vested interest must be taken into account when considering the results. Findings of (underpowered) sensitivity analyses controlling for this variable have conflicting results.16,36 Psychotherapy studies were longer because usually more weeks are needed for a psychological treatment to show an effect (eg, according to NICE guidelines CBT should be provided for around 16 sessions to patients with schizophrenia).37 However, the optimal duration of psychotherapy is still not clear. There are no studies that compare long and short term CBT (as assessed by a Cochrane Review,38 which found 0 studies). An ongoing meta-analysis is investigating the duration of different psychotherapies across different psychiatric conditions.39 On the contrary, it has been shown that the largest symptoms’ reduction is seen typically with antipsychotics within the first weeks of treatments.40,41 On the other side, there is the hope that psychotherapy induces lasting cognitive and behavioral changes and evaluating to which extent this happens is meaningful.33 Forty-three of 53 studies about psychotherapies (81.13%) included a follow-up after the end of the treatment (ranging from some weeks to 5 years) (Supplementary File 7). Such a follow-up would be difficult in drug studies because patients in the placebo-groups would receive medication likely in the meantime and because it is already known that many patients will relapse once antipsychotics have been stopped.42 Still, it could be interesting to observe these patients in time with a naturalistic follow-up. Limitations and Strengths We acknowledge that this work suffers from some limitations. The last search conducted for the 2 sets of studies was not exactly the same (2016 for antipsychotics and 2018 for psychotherapies), but we argue that this is a minor issue because we are aware of only one antipsychotic study that was published afterwards.43 Moreover, the 2 reviews had very similar but not identical inclusion criteria; for example, in the review on psychotherapy studies, patients with first-episode were excluded, whereas in the review on drugs, they were not excluded by inclusion criteria. However, no such drug studies were found. From the interventions’ point of view, the drug review focused on the comparison with placebo, excluding head-to-head comparisons between active treatments, whereas these were not excluded in the psychotherapy review; however, no active comparisons between psychotherapies were found because the majority of the studies used standard treatment as control. Therefore, we conclude in the end the 2 sets of studies are comparable. This work also presents important strengths: it is based on 2 reviews that were conducted applying the highest standards in terms of systematic search, study selection, and data extraction, by the same team, and were based on a priori registered protocols. This rigorous methodology ensures the quality of the subsequent analyses and results. Implications for Research and Practice These findings have substantial implications for research. We argue that an eventual comparison of their results should consider carefully the significant differences identified in studies investigating pharmacotherapy and psychotherapy for schizophrenia. For example, one could be tempted to conduct a network meta-analysis that allows simultaneous comparison of multiple treatments, including both these kinds of interventions, as has been done for other conditions in psychiatry.44,45 In such case, the authors should thoroughly take into account the specific characteristics of the 2 kinds of trials; above all, because the patients enrolled in the studies are noticeably different regarding variables that would have a clear impact on the results (most importantly, severity of illness), there is the risk that the transitivity principle could be violated.46 It must be noted that the meaning of such a comparison would be debatable from a clinical point of view: pharmacotherapy and psychotherapy in schizophrenia are not intended as alternative treatments but are offered to different patients or at a different phase of the illness, as made clear by our results. A possible solution could be the conduction of studies with a 2 × 2 factorial design, in which patients are simultaneously randomized to different pharmacological and psychological interventions.47 Future studies on psychological interventions should aim for a more rigorous methodological quality. Enhancing the precision and credibility of the evidence is crucial for a meaningful allocation of resources. Also, it has been claimed that psychotherapy should focus on specific symptoms, rather than be evaluated as a substitute for antipsychotics.48,49 We go further and argue that, in the interest of patients, antipsychotic and psychological treatments should not be seen in competition, but rather, should be intended to be used jointly. Future studies should investigate the effect of combinations of antipsychotic and psychological treatments, to establish which patients can best profit from which synergistic association of these treatment options. Funding The original reviews whose data were reanalysed for this work were funded by the German Federal Ministry for Education and Research (Bundesministerium für Bildung und Forschung, BMBF), grant FKZ 01KG1115 (Leucht et al. 2017) and by European Union’s Horizon 2020 Research and Innovation Programme, Marie Skłodowska-Curie grant agreement no. 701717 (Bighelli et al. 2018). The funder had no role in study design, data collection, analysis or interpretation, writing of the report, or decision to submit the paper for publication. Acknowledgments We thank colleagues who helped with data extraction, Patricia Kratochwill for full-text acquisition and proof-reading, and Yikang Zhu for screening and data extraction (Chinese studies). In the last 3 years, S.L. has received honoraria as a consultant/advisor and/or for lectures from LB Pharma, Otsuka, Lundbeck, Boehringer Ingelheim, LTS Lohmann, Janssen, Johnson and Johnson, TEVA, MSD, Sandoz, SanofiAventis, Angelini, Sunovion, Recordati, and Gedeon Richter. C.L. is the spouse of S.L.; therefore, his conflicts of interest are also relevant for her. M.H. has received speaker’s honoraria from Janssen and Lundbeck. All other authors declare no competing interests. References 1. Morrison AP , Dunn G , Turkington D , et al. Cognitive therapy for patients with schizophrenia – authors’ reply . Lancet . 2014 ; 384 ( 9941 ): 401 – 402 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Lieberman JA , Stroup TS , McEvoy JP , et al. . ; Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Investigators . Effectiveness of antipsychotic drugs in patients with chronic schizophrenia . N Engl J Med . 2005 ; 353 ( 12 ): 1209 – 1223 . 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