Association of the ZNF804A gene polymorphism rs1344706 with white matter density changes in Chinese schizophrenia

Progress in Neuro-Psychopharmacology & Biological Psychiatry 36 (2012) 122–127

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Association of the ZNF804A gene polymorphism rs1344706 with white matter density changes in Chinese schizophrenia Qinling Wei a, b, Zhuang Kang c, Feici Diao b, Baoci Shan d, Leijun Li b, Liangrong Zheng b, Xiaofeng Guo a, Chunlei Liu e, f, Jinbei Zhang b,⁎⁎, Jingping Zhao a,⁎ a

Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, PR China Psychiatry Department, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, PR China Radiology Department, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, PR China d Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, PR China e Brain Imaging and Analysis Center, School of Medicine, Duke University, Durham, NC 27705, USA f Department of Radiology, School of Medicine, Duke University, Durham, NC 27705, USA b c

a r t i c l e

i n f o

Article history: Received 12 July 2011 Received in revised form 19 August 2011 Accepted 31 August 2011 Available online 3 September 2011 Keywords: Magnetic resonance imaging Schizophrenia Single nucleotide polymorphism White matter ZNF804A

a b s t r a c t Background: ZNF804A gene polymorphism rs1344706, the first genetic risk variant to achieve genome wide significance for schizophrenia, has been linked to neural functional connectivity. Dysconnectivity of WM may be the primary pathological mechanism of schizophrenia. Association of this variant with regional WM density has not been investigated in schizophrenic patients. Methods: 69 healthy controls and 80 patients with schizophrenia underwent genotyping of rs1344706 SNPs, and were examined for WM density (T1-weighted MRI). The association of rs1344706 with WM changes in schizophrenia patients and healthy controls was analyzed using a full-factorial 2 × 2 analysis of variance. Results: 1. There was an interaction on WM density in the left prefrontal lobe between the rs1344706 genotype and schizophrenic diagnosis, where the risk T allele carriers presented higher WM density in the schizophrenia patients and lower WM density in healthy controls in comparison with the non-risk allele carriers. 2. The risk allele was associated with an increased WM density of the bilateral hippocampus in both the patients and the healthy group. Limitation: The influence of antipsychotics to the white matter in schizophrenic patients was not fully eliminated. Conclusions: The ZNF804A variant may confer risk for schizophrenia by exerting its effects on the WM in the left prefrontal lobe together with other risk factors for schizophrenia. © 2011 Elsevier Inc. All rights reserved.

1. Introduction Schizophrenia is a major mental illness characterized by the splitting of different mental domains (Insel, 2010), and it is thought that dysconnectivity of white matter (WM), may be the primary pathological mechanism (Di et al., 2009; Konrad and Winterer, 2008; Pettersson-Yeo et al., 2011; Walterfang et al., 2006). Particularly, white matter density abnormality in the prefrontal subgyral and the hippocampus has been found in schizophrenic patients using T1-weighted magnetic resonance imaging (MRI) and other MRI techniques. Furthermore, these abnormalities are

Abbreviations: MRI, magnetic resonance imaging; WM, white matter; GM, gray matter; SPM, statistical parametric mapping; VBM, voxel-based morphometry; PANSS, positive and negative syndrome scale. ⁎ Correspondence to: J. Zhao, 139 Middle Renming Road, 2nd Xiangya Hospital of Central South University, Changsha, Hunan 410011, PR China. Tel./fax: +86 731 85360921. ⁎⁎ Correspondence to: J. Zhang, 600 Tianhe Road, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, PR China. Tel.: + 86 20 85253129; fax: + 86 20 85252479. E-mail addresses: [email protected] (J. Zhao), [email protected] (J. Zhang). 0278-5846/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.pnpbp.2011.08.021

shared by unaffected relatives in proportion to their proximity to an affected subject (Camchong et al., 2009; Konrad and Winterer, 2008; McIntosh et al., 2006; Pettersson-Yeo et al., 2011). Together, these findings suggest that abnormalities in white matter density are likely to be associated with genetic factors that are potentially related to the etiology of schizophrenia. However, no specific genetic variants have yet been found which can account for these findings. Recently, the first genetic risk variant, ZNF804A rs1344706, achieved genome wide significance for schizophrenia in multiple population samples (Donohoe et al., 2010; O'Donovan et al., 2008; Riley et al., 2010; Williams et al., 2010), including the Han Chinese population (Zhang et al., 2010; Xiao et al., in press. The study on zfp804a, the mouse homologue of ZNF804A, suggested that ZNF804A may be involved in the regulation of early neurodevelopment (Chung et al., 2010). Bioinformatic analyses of the conserved mammalian sequence around rs1344706 suggested that the presence of transcription factor binding sites is predicted to maintain binding sites for the brainexpressed transcription factors MYT1l and POU3F1/OCT-6 (Donohoe et al., 2010; Riley et al., 2010), both of which are involved in oligodendrocyte differentiation and proliferation (Collarini et al., 1992; Nielsen

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et al., 2004). Oligodendrocytes have been thought to be related to the dysconnectivity in schizophrenia (Takahashi et al., 2010). The studies by Esslinger et al. (2009, 2011) suggested that a state-independent influence of the genetic variant, rs1344706, on interhemispheric functional connectivity and white matter may be the main structural basis (Begre and Koenig, 2008; Konrad and Winterer, 2008; Pettersson-Yeo et al., 2011). Using T1-weighted MRI, the risk allele (T) homozygotes of rs1344706 have been found to be associated with larger total WM volumes in health subjects using T1-weighted MRI (Lencz et al., 2010). Considering the role of white matter in the neuropathological mechanism of schizophrenia and the possibility that the variant rs1344706, one of the strongest candidate genes variant for schizophrenia, may influence the white matter, we hypothesized that the ZNF804A variant may confer risk for schizophrenia by exerting its effects on the white matter. Schizophrenia belongs to a group of pathologies known as “complex genetic disorders” and many genes may be involved in the etiology of schizophrenia (Owen et al., 2010). These genes may interact to show dominance or epistasis (Hill et al., 2008), and the interactions at the level of variance are likely to generate more interaction than at the level of genes (Phillips, 1998). The zinc-finger protein family associated with ZNF804A has diverse interactions with many molecules including RNA and proteins (Gamsjaeger et al., 2007). We thus further hypothesized that the same variation in ZNF804A may have different effects on the white matter in schizophrenic patients and healthy subjects, just as the altered effect of the dopamine transporter 3'UTR VNTR genotype had on prefrontal and striatal function in schizophrenia (Prata et al., 2009). While Lencz et al. (2010) have found that risk allele (T) healthy homozygotes showed larger total WM volumes than healthy carriers of the other allele, there are no studies reported to investigate the effect of ZNF804A (rs1344706) on regional white matter density in schizophrenic patients. In the present study, to examine the effects of ZNF804A gene polymorphism rs1344706 on the WM density in schizophrenic patients and healthy subjects, a Chinese case–control sample, large enough to yield subgroups of sufficient size, underwent T1-weighted MRI. 2. Methods 2.1. Subjects All 149 subjects were Han Chinese, and gave written informed consent in accordance with protocols approved by the Clinical Research Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University. Patients who met DSM-IV criteria for schizophrenia (n= 58), or schizophreniform disorder (after follow-up, a diagnosis of schizophrenia was established; n = 22) were recruited from inpatient units of the Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, PR China. The DSM-IV diagnosis was made by an experienced psychiatrist using patient version of the Structured Clinical Interview for DSM-IV (SCID-I/P) (First MB, 1995). The inclusion criteria were the following: a) age being between 18 and 45 years; b) years of education being greater than 9 years; c) had to be right-handed. The exclusion criteria were: a) presented with chronic neurological disorders; b) had a history of alcohol or substance abuse; c) had a history of electroconvulsive therapy; d) had contraindications to MRI scanning. The Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987) was used to measure psychopathologic symptoms at the time of imaging. Family psychiatric history was obtained by interviewing the patients and their relatives when possible, who provided information on family history in details during the clinical interview. This study adopted the definition of family history as described by Xu et al. (2008). Patients with positive family history (PFH) were defined as having at least one relative with schizophrenia

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in their first-degree or second-degree relatives; otherwise, they were defined as patients with negative family history (NFH). The inclusion and exclusion criteria for healthy controls (n = 69) were the same as those of the patient group, except for the requirement that the healthy controls had no history of mental illnesses and did not have first-degree relatives with a psychotic disorder according to the non-patient version of SCID. 2.2. MRI acquisition and images preprocessing Images were acquired on a 1.5-T GE Signa Twinspeed MRI scanner (General Electric Medical System, Milwaukee, WI, USA) equipped with a quadrature birdcage head coil. The first sequence was a transverse spin-echo scan, which acquired both T2- and proton-densityweighted images of the brain. These images were subsequently clinically reported by a neuroradiology consultant. High-resolution whole brain volumetric T1-weighted images were acquired sagittally with an inversion-recovery prepared 3-D spoiled gradient echo (SPGR) pulse sequence (TI = 650 ms, TE= 9.6 ms, flip angle= 15°, field-of-view, FOV= 240 × 240 mm2, slice thickness= 1.8 mm, matrix = 256 × 256). Data were preprocessed with a VBM box in the SPM5 software package (http://www.fil.ion.ucl.ac.uk/spm/software/spm5/), running under the MATLAB version 7.1 (MathWorks Inc, Sherbon, MA, USA). The current version of SPM5 uses a new voxel-based morphometry (VBM) method, called unified segmentation (Ashburner and Friston, 2005), to segment the brain into WM, gray matter (GM), and cerebrospinal fluid, with unmodulated normalized parameters. Unified segmentation deploys a framework where tissue classification, bias correction, and image registration are integrated within the same model. The WM images were smoothed using a Gaussian kernel with a full width half maximum (FWHM) l of 12 mm. 2.3. Molecular analysis Blood samples were collected in EDTA-containing tubes, and leukocytes were isolated using red blood cell lysis buffer containing 150 mM NH4Cl, 10 mM KHCO3 and 0.1 mM EDTA. Genomic DNA was extracted from the leukocytes using SDS-proteinase K treatment, followed by phenol/chloroform extraction. Genomic DNA was amplified by polymerase chain reaction (PCR) to generate a 443 bp product spanning rs1344706. Primers were as follows: upper GAATCTAGA GTCATGCAGG, and lower CAAGTTATTC TCTAGAGTCC. The PCR products were subjected to direct sequencing, conducted by the Beijing Genomics Institute. Genomic DNA samples were randomly selected for replication. The correspondence between the repeated sequencing and the original sequencing was 100%. The data analyses were performed by a biotechnologist and kept blind to the radiologist. According to the genotype, the healthy controls and the patients were separated into two subgroups, respectively: non-risk allele carriers [homozygous for the allele (G)] and the risk allele (T) carriers [heterozygous and homozygous for the allele (T)]. 2.4. Statistical analysis All four groups underwent T1-weighted white matter imaging. Analysis of variance tests (for numeric variables) and χ 2 tests (for categorical variables) were conducted with the Statistical Package for Social Sciences, version 15.0 (SPSS Inc, Chicago, Illinois), to detect demographic differences in relation to diagnosis, genotype, and their interactions. The smoothed T1-weighted MRI images were entered into a fullfactorial 2 × 2 analysis of variance. The first factor was diagnosis, which was classified into two levels, healthy control and schizophrenia. The second factor was ZNF804A, which was classified into two levels, non-risk allele carriers and risk allele carriers. This analysis allowed us to investigate the main effects of the diagnostic group

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and the genotype as well as their interaction on the density of the WM. The extent and location of between-group differences were illustrated using statistical maps, threshold at a significance level of P b 0.001 and uncorrected with a cluster extent of 30 voxels. To verify the statistical significance and further analyze the interaction between the influences of diagnosis and genotype, and the main effects of genotype, we extracted the mean WM density values of these clusters, using a program written in-house (Institute of High Energy Physics) (Kong et al., 2011). These values were analyzed with a 2 × 2 analysis of variance using the Statistical Package for Social Sciences, version 15.0. 3. Results 3.1. Subjects The patient group (GG = 12, GT = 35, TT = 22, P = 0.17) and the healthy control group (GG = 19, GT = 46, TT = 15, P = 0.76) were both in Hardy–Weinberg equilibrium as calculated with GENEPOP. As shown in Table 1, there were no significant differences (P N 0.05) between genotype subgroups in age, sex, nor years of education within the total sample and within each diagnostic group. There were no significant differences (P N 0.05) between the patients and the healthy controls in all the above demographic variables. None of the aforementioned demographic variables showed significant genotype to diagnosis interactions. Within the patient group, the genotype subgroups did not differ significantly (P N 0.05) in total scores on the PANSS, positive scores, negatives scores, the subtypes of the disease, the duration of psychosis, age at onset of psychosis, family psychotic history, nor in the type (first or second generation), maximal total dose (chlorpromazine equivalents; Kane et al., 2003) of antipsychotic medication during the 2 months before the MRI scanning. 3.2. T1-weighted imaging: white matter density The results of the full-factorial 2 × 2 analysis of variance through the SPM5 showed that there were main effects of diagnosis on WM in the right prefrontal lobe (Fig. 1; MNI: 40, 6, 44; cluster= 35 voxels), and main effects of genotype on WM in the right hippocampus (Fig. 2; MNI: 26, −34, −2; cluster = 136 voxels) and left hippocampus (MAI: −28, −36, −4; cluster= 70 voxels). There was an interaction between

the ZNF804A genotype and the diagnosis of schizophrenia in the left prefrontal lobe (Fig. 3; −34, 28, 32; cluster= 51 voxels). As shown in Table 2, the extracted data of the mean density of the cluster in the left prefrontal lobe, where there was an interaction of ZNF804A genotype and schizophrenic diagnosis, verified the statistical significance of the result of VBM. The data also showed that the risk allele carriers had higher WM density (144.6 ± 18.8) in patients with schizophrenia, but had reduced WM density (136.4 ± 18.9) in healthy controls (Table 2), compared with non-risk allele carriers (127.6 ± 22.5 and 150.33 ± 13.1 respectively; Table 2). The extracted data of the mean density of the clusters in the bilateral hippocampus verified the main effects of ZNF804A genotype and showed that the risk allele carriers of ZNF804A rs1344706 (including patients and healthy controls) showed higher WM density in the two clusters compared with the non-risk carriers. 4. Discussion Our hypothesis that the ZNF804A gene polymorphism rs1344706 may be associated with the white matter (WM) structure in schizophrenic patients was confirmed in the bilateral hippocampus while the result was same as that in healthy controls. In both of these regions, the risk T allele was associated with increased WM density in both healthy and schizophrenic subjects. The hippocampus has been found to incur a dose-dependent increase in functional connectivity with other brain regions in the risk-allele healthy carriers (Esslinger et al., 2009). In addition, no change of GM volume has been found in the risk-allele healthy subjects (Donohoe et al., 2011). Together, these findings suggested that the ZNF804A gene polymorphism rs1344706 may affect the functional connectivity in hippocampus by influencing the related WM. However, the effects of this genetic variant on the WM in bilateral hippocampus in schizophrenia may not be associated tightly with the neuropathological mechanism of ZNF804A in this disease, because the effects were the same in both schizophrenic patients and healthy controls. Our further hypothesis that the ZNF804A gene polymorphism rs1344706 may have different effects on the white matter of schizophrenic patients compared to healthy subjects was confirmed in the left prefrontal lobe. The current study found that there was an interaction between the genotype of rs1344706 and the schizophrenic diagnosis in the left prefrontal lobe. Specially, the risk allele carriers showed higher WM density in patients with schizophrenia, but had

Table 1 Demographic and clinical characteristics for schizophrenia patients and healthy subjects. Means (SD) Main effect of diagnosis

Main effect of ZNF804A

Diagnosis × ZNF804A interaction Controls

Age, y Sex, NO. F/M Years of education Subtypes of SZ NO. Para/Dis/Un Positive scores of PANSS Negative scores of PANSS Total scores of PANSS Duration of psychosis (weeks) Age of onset Family psychotic history NO. PFH/NFH Antipsychotic type, none/1st/2nd generation Antipsychotic dose, CPZ equivalents

Patients

Controls (n = 69)

Patients (n = 80)

G homozygous (n = 31)

T carriers (n = 118)

G homozygous (n = 12)

T carriers (n = 57)

G homozygous (n = 19)

T carriers (n = 61)

25.4(5.7) 30/39 12.9(2.9) NA

26.6(6.6) 35/45 12.5(2.8) NA

27.1(5.4) 12/19 13.4(2.6) NA

25.8(6.4) 53/65 12.5(2.9) NA

27.3(4.4) 5/7 13.7(2.9) NA

25.0(5.9) 25/32 12.7(2.9) NA

27.0(6.1) 7/12 13.2(2.5) 10/1/8

26.4(6.8) 28/33 12.3(2.9) 37/6/18

NA NA NA NA NA NA

NA NA NA NA NA NA

NA NA NA NA NA NA

NA NA NA NA NA NA

NA NA NA NA NA NA

NA NA NA NA NA NA

22.3(6.7) 17.5(6.9) 76.7(13.5) 126.5(159.1) 24.3(5.3) 2/17

24.5(4.7) 15.0(5.6) 76.7(11.1) 139.6(183.7) 23.9(5.9) 10/51

NA

NA

NA

NA

NA

NA

14/1/4

44/2/15

NA

NA

NA

NA

NA

NA

420.0(152.5)

352.1(191.9)

Abbreviations: CPZ, chlorpromazine hydrochloride; Dis, disorganized type; NFH, negative family history; PFH, positive family history; Para, paranoid type; PANSS, Positive and Negative Syndrome Scale; Un, undifferentiated type.

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Fig.1. Brain regions of decreased white matter density in the right frontal lobe in schizophrenic patients compared with that in healthy controls (the main effect of diagnosis of schizophrenia). The color bar signifies the F value of the analysis of variance.

Fig. 3. Brain regions where the ZNF804A rs1344706 genotype and the diagnosis had an interaction on the white matter density in the left prefrontal lobe. The color bar signifies the F value of the analysis of variance.

reduced WM density in healthy controls in comparison with the nonrisk-allele carriers. Esslinger et al. (2009)also reported that the connectivity was reduced in risk-allele healthy carriers within both the dorsolateral prefrontal cortex (DLPFC) (same side) and the contralateral DLPFC. The dysconnectivity within and between the prefrontal lobe and other brain regions is important in the neural pathological mechanism of schizophrenia (Eisenberg and Berman, 2010;

Minzenberg et al., 2009; Pettersson-Yeo et al., 2011). The abnormality is heritable (Di et al., 2009; Konrad and Winterer, 2008; McIntosh et al., 2006) and has been reported to be associated with several genes (Eisenberg and Berman, 2010; Walterfang et al., 2006). For example, schizophrenic patients Pro/Arg heterozygous (Pro72Arg polymorphism) showed a WM deficit in the frontal lobe (Molina et al., 2011). These findings raised the possibility that ZNF804A variant may confer risk for schizophrenia by exerting its effects on the WM in the left prefrontal lobe together with other factors contributing to the perturbation of WM in the disorder. Our data also points to the potential involvement of an increased connectivity in schizophrenia. Reductions in density and integrity are often thought to be abnormalities of WM in schizophrenia and are related to the risk allele of schizophrenia (Di et al., 2009; Konrad and Winterer, 2008; Walterfang et al., 2006). However, our data showed that the risk T allele of the ZNF804A variant rs1344706 is associated with an increased WM density in the left frontal lobe and the bilateral hippocampus in the schizophrenic patients. A study by Esslinger et al. also showed that the risk T allele of the ZNF804A variant rs1344706 was not only associated with a decreased connectivity in the frontal lobe, but also with an increased connectivity in the hippocampus (Esslinger et al., 2009). The risk T allele was also reported to be associated with an increased total WM volume in the healthy controls (Lencz et al., 2010), and an increased hippocampal GM volume in schizophrenia patients (Donohoe et al., 2011). These results suggest that increased connectivity may also be a risk phenotype for schizophrenia, especially in the case of a dysfunctional prefrontal cortex (Pettersson-Yeo et al., 2011; Whitfield-Gabrieli et al., 2009).

5. Study limitation

Fig. 2. Brain regions of increased white matter density in the bilateral hippocampus in the risk allele (T) carriers compared with the non-risk allele carriers (the main effect of genotype of ZNF804A). The color bar signifies the F value of the analysis of variance.

Part of the patients in the present study received antipsychotics before taking part in the study. Recent studies suggested that antipsychotics may impact later-myelinating intracortical circuitry and brain tissues (Bartzokis et al., 2009; Ho et al., 2011). The effects of medication thus should not be ignored in interpreting the findings. However,

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Table 2 Mean density values of white matter in brain regions which were associated with the ZNF804A gene polymorphism rs1344706. Brain regions (MNI coordinates)

Left prefrontal lobe (− 34, 28, 32) Right hippocampus (26, − 34, − 2) Left hippocampus (− 28, − 36, − 4) a b c

Voxels

51 136 70

Mean white matter density values(mean ± SD) Main effect of diagnosis

Main effect of ZNF804A

Diagnosis × ZNF804A interaction

Controls (n = 69)

Patients (n = 80)

G homozygous (n = 31)

T carriers (n = 118)

Controls G homozygous (n = 12)

T carriers (n = 57)

G homozygous (n = 19)

T carriers (n = 61)

138.9 (18.7) 127.1 (9.7) 112.0 (6.6)

140.6 (20.9) 128.4 (9.2) 113.1 (7.5)

136.4 (22.2) 122.3 (8.5) 108.7 (7.9)

140.7 (19.2) 129.2 (9.2)b 113.7 (6.5)c

150.33 (13.1) 120.6 (9.2) 106.4 (6.4)

136.4 (18.9) 128.5 (9.3) 113.2 (6.1)

127.6 (22.5) 123.4 (8.1) 110.2 (8.5)

144.6 (18.8)a 130.0 (9.0) 114.1 (7.0)

Patients

Significant interaction (genotype × diagnosis) on mean values of cluster in left frontal lobe white matter: F = 15.55, P = 0.000. Significant main effect of genotype on mean values of cluster in right hippocampus: F = 14.58, P = 0.000. Significant main effect of genotype on mean values of cluster in left hippocampus: F = 15.15, P = 0.000.

within the patient group, the genotype subgroups did not differ significantly in the type (first or second generation) and the maximal total dose (chlorpromazine equivalents; Kane et al., 2003) of antipsychotic medication during the 2 months before the MRI scanning. Therefore, our findings were likely of the consequence of the genotype rather than the medication, although we could not completely eliminate the effects of medication. 6. Conclusion The ZNF804A gene polymorphism rs1344706 was associated with white matter density changes in the bilateral hippocampus and the left prefrontal lobe. While the effect of the genetic variant on the WM in the bilateral hippocampus of the schizophrenic patients was the same as that in the healthy controls, the effect in the left prefrontal lobe of the schizophrenic patients was opposite to that in the healthy controls. We thus concluded that the ZNF804A variant may confer risk for schizophrenia by exerting its effects on the WM in the left prefrontal lobe together with other risk factors for schizophrenia. Contributors Dr. Zhao J designed the study along with Drs. Zhang J and Wei Q. Drs. Wei Q, Kang Z, Diao F, Li L and Zheng L collected the original imaging data. Dr Shan B wrote the program for extracting the mean WM density values of the clusters. Drs. Wei Q, Shan B, Guo X and Kang Z managed and analyzed the imaging data. Dr. Wei Q wrote the first draft of the manuscript. Drs. Wei Q, Zhao J and Liu C revised the paper. All authors contributed to and have approved the final manuscript. Conflict of interest No conflict of interest declared. Acknowledgments The study was supported by grants from the R&D Special Fund for Health Profession (grant no. 201002003) and the Natural Science Foundation of China (grant nos. 81071093 and 30900485). References Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005;26:839–51. Bartzokis G, Lu PH, Stewart SB, Oluwadara B, Lucas AJ, Pantages J, et al. In vivo evidence of differential impact of typical and atypical antipsychotics on intracortical myelin in adults with schizophrenia. Schizophr Res 2009;113:322–31.

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