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Article

Integrative Genomic–Epigenomic Analysis of Clozapine-Treated Patients with Refractory Psychosis

by
Yerye Gibrán Mayén-Lobo
1,2,
José Jaime Martínez-Magaña
3,
Blanca Estela Pérez-Aldana
1,
Alberto Ortega-Vázquez
1,
Alma Delia Genis-Mendoza
3,
David José Dávila-Ortiz de Montellano
2,
Ernesto Soto-Reyes
4,
Humberto Nicolini
3,5,
Marisol López-López
1 and
Nancy Monroy-Jaramillo
2,*
1
Department of Biological Systems, Metropolitan Autonomous University-Xochimilco, Mexico City 04960, Mexico
2
Department of Genetics, National Institute of Neurology and Neurosurgery, “Manuel Velasco Suárez”, Mexico City 14269, Mexico
3
Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, Instituto Nacional de Medicina Genómica, SSA, Mexico City 14610, Mexico
4
Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa, Mexico City 05348, Mexico
5
Grupo de Estudios Médicos y Familiares Carracci, Mexico City 03740, Mexico
*
Author to whom correspondence should be addressed.
Pharmaceuticals 2021, 14(2), 118; https://doi.org/10.3390/ph14020118
Submission received: 9 December 2020 / Revised: 19 January 2021 / Accepted: 26 January 2021 / Published: 4 February 2021
(This article belongs to the Special Issue The Pharmacogenomics of Mood Stabilizers)
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Versions Notes

Abstract

:
Clozapine (CLZ) is the only antipsychotic drug that has been proven to be effective in patients with refractory psychosis, but it has also been proposed as an effective mood stabilizer; however, the complex mechanisms of action of CLZ are not yet fully known. To find predictors of CLZ-associated phenotypes (i.e., the metabolic ratio, dosage, and response), we explore the genomic and epigenomic characteristics of 44 patients with refractory psychosis who receive CLZ treatment based on the integration of polygenic risk score (PRS) analyses in simultaneous methylome profiles. Surprisingly, the PRS for bipolar disorder (BD-PRS) was associated with the CLZ metabolic ratio (pseudo-R2 = 0.2080, adjusted p-value = 0.0189). To better explain our findings in a biological context, we assess the protein–protein interactions between gene products with high impact variants in the top enriched pathways and those exhibiting differentially methylated sites. The GABAergic synapse pathway was found to be enriched in BD-PRS and was associated with the CLZ metabolic ratio. Such interplay supports the use of CLZ as a mood stabilizer and not just as an antipsychotic. Future studies with larger sample sizes should be pursued to confirm the findings of this study.

1. Introduction

Antipsychotic drugs are effective in treating symptoms of psychosis and preventing relapses [1,2,3]. Psychotic symptoms (hallucinations, delusions, and distorted behavior) can be observed in different psychiatric disorders, such as schizophrenia (SZ), schizoaffective disorder (SD), bipolar disorder (BD), and even in major depressive disorder (MDD) [1,2,3,4,5,6]. Among these patients, about 30% are considered refractory, and clozapine (CLZ), an atypical antipsychotic, remains the treatment of choice for the population who has failed to improve on two other previous antipsychotic treatments [7,8,9]. CLZ has also been proposed as an effective mood stabilizer, although its mechanism of action is still unclear [10].
It is noteworthy that the mechanisms of action for approximately 18% of approved therapeutic drugs at present, including CLZ, remain unknown [11,12,13]. CLZ is considered the last pharmacological option to treat refractory psychosis, thus knowledge of its mechanisms of action will help to improve patient treatment and drug repositioning [14,15]. Among the strategies for pharmacological repositioning, the omics approach of biological data has provided integrative data through computational and statistical methods [14,16,17,18].
The plasma concentrations (lower range of utility = 250–400 ng/mL) [7] and metabolic ratios of CLZ are broadly related to the prescribed dose, exhibiting a great variability between individuals. The metabolic ratio is calculated as the ratio of unmetabolized drug to its main metabolite, N-desmethylclozapine or norclozapine (NCLZ), in plasma samples [19] and is optimally defined as approximately two [20]. Other CLZ-associated phenotypes of interest that should be evaluated during its prescription are dosage and response. CLZ dosage is controversial in terms of clinical response, effectiveness, and the presence of side effects, and although several exploratory studies have been carried out in this regard the relationship still remains unclear. Despite the wide variation in CLZ dosage in clinical practice, there is a consensus that doses below 100 mg may be insufficient for patients to respond to, thus the standard dose is usually between 300 and 600 mg [21,22,23]. In this context, an integrative omics data analysis of patients with refractory psychosis would be of aid in identifying markers to improve or predict some of the CLZ-associated phenotypes (i.e., metabolic ratio, dosage, and response).
The high interindividual variability of CLZ-associated phenotypes is due to interactions between nongenetic, genetic, and epigenetic factors [8,24]. Genome-wide studies of psychosis have explored polygenic risk scores (PRS), showing that most disorders associated with psychosis share a genetic basis [25]. Moreover, when comparing individuals with a high PRS vs. individuals with a low PRS, a positive correlation between PRS and DNA methylation changes has been observed (the higher the PRS, the greater the methylation changes) [26].
Herein, we present an integration of clinical, genomic, and epigenomic data from CLZ-treated patients with refractory psychosis in order to identify genes related to the potential mechanisms of action of CLZ and its possible pharmacogenomics applications.

2. Results

2.1. Clinical and Demographic Characteristics of Patients

Table 1 shows the clinical and demographic characteristics of CLZ-treated patients. A total of 75% of our patients were taking concomitant medications.

2.2. Association Between Genetic Risk Scores and Clozapine-Associated Phenotypes

After the samples were genotyped using the Illumina Infinium PsychArray v1.2 BeadChip, we calculated the PRSs for schizophrenia (SZ-PRS), bipolar disorder (BD-PRS), and major depressive disorder (MDD-PRS). Two nominal associations were observed between PRS and CLZ-associated phenotypes—namely, MDD-PRS with the CLZ dose (pseudo-R2 = 0.386, p-value = 0.0035) and SZ-PRS with the response to CLZ (pseudo-R2 = 0.191, p-value = 0.0545); however, they did not remain significant after adjustment for multiple comparisons (adjusted p-values = 0.0759 and 0.2278, respectively) (Figure 1). The only PRS that showed a significant association with any CLZ-related phenotype was the BD-PRS. The BD-PRS was associated with the CLZ metabolic ratio (pseudo-R2 = 0.2080, p-value = 0.0008, adjusted p-value = 0.0189).

2.3. Functional Prediction of the SNPs Included in the BD-PRS Associated with CLZ Metabolic Ratio

The BD-PRS associated with the CLZ metabolic ratio was constituted by 2112 single-nucleotide polymorphisms (SNPs), of which 1288 were located in intronic regions, 223 in exonic regions, and 562 in intergenic regions. The SNPs that made up the BD-PRS were found in 1370 genes. These genes were the top enriched in four pathways: circadian rhythms (ADCY2, CACNA1C, CACNA1D, MAPK1), insulin secretion (ABCC8, ADCY9, ATP1B2, KCNMA1), GABAergic synapse (CACNA1B, GABRA1, KCNJ6, SLC12A5), and the thyroid hormone signaling pathway (AKT3, ATP1B3, RXRA, TP53) (Table 2). We found a total of 17 SNPs that could have a high impact on the protein structure in genes such as LRP8 and ADCY2, among others (Supplementary Tables S1–S3).

2.4. Differentially Methylated Sites Between Patients Grouped by BD-PRS and CLZ Metabolic Ratios

In order to explore whether BD-PRSs associated with the CLZ metabolic ratio could alter DNA methylation patterns, we evaluated the differential methylation using the Infinium MethylationEPIC array in subgroups of CLZ-treated patients according to their metabolic ratios (CLZ/NCLZ) and BD-PRS values. The cut-off point for the metabolic ratio was defined as 2.0 according to published recommendations [7], and the medians were 3.2639 and 2.1922 for the high and medium BD-PRS cut-off points, respectively. Thus, samples with a metabolic ratio < 2.0 or ≥ 2.0 were assigned a low or high metabolic ratio, respectively. Accordingly, the following three groups were obtained for the BD-PRSs (Figure 2): samples with a high metabolic ratio and a high BD-PRS (HH ≥ 3.2639), a medium BD-PRS (M) for values <3.2639 but > 2.1922, and a low BD-PRS (L) of ≤2.1922.
In the comparison between these subgroups (HH vs. M, HH vs. L, M vs. L) regarding the differential methylation analysis, the associations were not statistically significant at the genome-wide level (p-value < 5.0 × 10−8). We observed nominal associations after comparing the HH vs. M (in three CpG sites), and M vs. L groups (in three different CpG sites) (Table 3 and Table S2). No significance was found between the HH and L groups.
CpG sites with a nominal association (p-value < 5.0 × 10−5) between the H and M groups were located on the TESPA1 and APOB genes. The CpG site for TESPA1 (cg23612423) was hypomethylated in the H group, whereas the CpG site for APOB (cg16723488) was hypermethylated in the M group. CpG sites with a nominal association between the M and L groups were located on the APOB (cg05337441) and STAG1 (cg16760310) genes. Both genes were hypermethylated in the M group. In contrast, the CpG site at FUOM (cg05456948) was hypomethylated in the same group (Table 3).

2.5. Protein–Protein Interactions Between Gene Products with High Impact Variants in the Top Enriched Pathways and Differentially Methylated Sites

A second pathway enrichment analysis was carried out, but this time the protein–protein interactions included genes products of: (i) BD-PRSs showing variants with a high functional impact, (ii) previous enriched pathways, and (iii) differentially methylated genes between the three BD-PRS groups (Figure 3).
This analysis revealed multiple interactions. For instance, APOB (a gene with differentially methylated sites) interacts strongly with LRP8 (a gene that contains the missense variant p.Arg952Gln), which, in turn, interacts with genes enriched in the circadian rhythm pathway (e.g., GRIN2B, GRIN2A, and GRIA4). Two of the aforementioned genes (GRIN2B and GRIN2A) also interact with genes involved in the GABAergic synapse (i.e., GABRR1, GABRR3, and GABRA1) and with DLGAP2 (a gene that shows the missense variant p.Pro464Gln). Interestingly, GRIA4 interacts with PRKCB and PRKCA, and both genes are included in the BD-PRS and are enriched in the top four observed canonical pathways—namely, circadian entrainment, insulin secretion, GABAergic synapse, and the thyroid hormone signaling pathway. Moreover, the PRKCA and PRKCB genes interrelate with ADCY2 (a gene that contains the missense variant p.Val147Met), which in turn interconnects with PLCG1 and links with TESPA1 (this gene shows differentially methylated sites) (Table 3 and Table S1).

3. Discussion

Overall, CLZ has been utilized as an antipsychotic drug due to its simultaneous affinity for both dopamine and serotonin receptors [28]. Nonetheless, its complex mechanisms of action are not yet fully known, involving the modulation of norepinephrine, the regulation of the endocrine system (including pregnenolone and cortisol), the intracellular system-dependent modulation of N-methyl-d-aspartate (NMDA) receptor expression, brain-derived neurotrophic factor up-regulation, and the regulation of the arachidonic acid cascade [29,30,31,32]. Herein, we performed an integration of the genomic and epigenomic data of CLZ-associated phenotypes to identify genes related to the potential mechanisms of action of CLZ and possible pharmacogenomic applications. First, we identified that the BD-PRSs were associated with the CLZ metabolic ratios. The CLZ/NCLZ ratio may be interpreted as the rate of hepatic metabolism of the antipsychotic administered orally (as is the case). Consequently, the higher the ratio the lower the metabolism in the liver [7]. This result might be related to the SNPs contained in the BD-PRS (Table 2), which were enriched in the insulin secretion pathway and the thyroid hormone signaling pathway (Table S1).
The thyroid hormone signaling pathway is activated by the consumption of glucose-rich foods [33], mainly through Ca2+ currents that are modulated by channels such as CACNA1C or CACNA1D (genes found in the BD-PRS) [34,35,36]. It is known that individuals with BD and psychosis have an increased risk of diabetes mellitus (i.e., high blood glucose levels) [37,38,39]. In fact, it has been reported that CLZ response and diabetes mellitus share genetic mechanisms [40,41,42], including recurrent genes such as CACNA1C in common pathways (e.g., insulin secretion). Additionally, it has been documented that hyperglycemia may reduce the response to the mood stabilizer treatments [43]. This reduction may be due to a long-term consequence of hyperglycemia disrupting the hepatic expression of genes involved in pharmacological metabolization [44,45,46]. Besides the effects that hyperglycemia could have in CLZ-treated patients with refractory psychosis, we also identified relevant gene enrichment in the thyroid hormone signaling pathway, including the RXRA/RXRG genes. The retinoid X receptors (RXRA and RXRG) are considered xenobiotic sensors that may induce the expression of the cytochrome P450 system [47,48,49]. In this sense, the induction of cytochrome P450 enzymes would promote an increase in the CLZ metabolism; however, if an increase in deleterious genetic variants affecting that pathway exists (as shown in this study), then it will diminish the induction of CLZ-DMEs, and its metabolic ratio will increase. An interesting finding that could be related to this effect of an increase in risk variants in RXRA/RXRG is the observed hypomethylation in APOB in patients of the HH group. PPARs, together with retinoid X receptors (RXRs), regulate the transcription of APOB and APOE, among others [50,51,52]. In this context, the hypomethylation of APOB could increase the gene expression in group HH patients as a mechanism of compensation for the pathway dysfunction due to the increase in risk variants in genes from the retinoic acid pathway [53,54,55].
We also found that the LRP8 variant p.Arg952Gln (rs5174), which was included in the BD-PRS, shows a high functional impact, and it has previously been associated with psychosis [56]. The encoded protein, LRP8, is a receptor of RELN (whose abnormal expression is associated with major neuropsychiatric disorders), but also functions as a receptor for the cholesterol transport protein APOE [57]. It is known that the hepatic APOE levels increase during CLZ treatment, as well as other genes participating in the transport of cholesterol; however, if this LRP8 variant promotes a decrease in the receptor function [58,59], one can hypothesize that the hypomethylation of APOB could also be a compensatory mechanism for this decrease [60,61].
The identified relationship between APOB and LRP8 points towards an association of CLZ with glutamatergic regulation. The receptor LRP8 interacts with NMDA receptor subtypes 2B and 2A (GRIN2B and GRIN2A), thereby mediating reelin signaling [61,62]. NMDA receptors are generally located next to glutamatergic and GABAergic vesicles [63]. Interestingly, the GABAergic synapse pathway was found to be enriched in BD-PRS and was associated with the CLZ/NCLZ ratio. This complex association of the GABAergic synapse with BD-PRS and the metabolic ratio poses the question of whether CLZ could be used as a mood stabilizer and not just as an antipsychotic. Indeed, GABAergic dysfunction has been considered as a hypothesis of mood disorders [64]. This hypothesis was proposed after treatment with valproate showed efficacy in BD patients, thus becoming the most widely used mood stabilizer. CLZ, although not an approved drug for the treatment of BD, has been used with some improvement in individuals with resistance to treatment or in severe cases of mania [65,66]. Considering that CLZ might have some effect in stabilizing mood, it should bind to GABAergic receptors. CLZ generally binds to dopamine and serotonergic receptors; however, its binding to GABAergic receptors is still being explored. Two studies in animal models have demonstrated that acute CLZ treatment induces epigenetic changes in the GABAergic gene promoters [57,67,68]. In a molecular docking study, the authors found that CLZ could bind to the receptor GABABR in the same manner as baclofen does (an agonist of GABABR) [69]. Herein, the GABAergic synapse was found to be enriched in BD-PRS and associated with the metabolic ratio, supporting the potential effect of CLZ in the GABAergic synapse.
Another point that may support the use of CLZ as a mood stabilizer is the fact that many BD-PRS variants are found in calcium-dependent genes (CACNA1C and CACNA1D). CACNA1C is one of the genes that has been associated with BD in both genome- and epigenome-wide association studies [70,71], modulating the cerebral cortex and hippocampus function [72,73]. CACNA1C is generally hypermethylated in BD patients [71], and these DNA methylation changes may depend on genetic variants close to the gene locus.
Finally, in the protein–protein interaction analysis, we identified that CACNA1C interacts with ITPR3, which, in turn, interacts with TESPA1. Their corresponding genes were included in BD-PRS and were found to be differentially methylated, respectively. ITPR3 is the inositol 1,4,5-trisphosphate receptor type 3, a second messenger that mediates the release of intracellular calcium with ubiquitous expression [74,75,76]. ITPR3 and TESPA1 interaction regulates calcium flux and modulates different immune system functions [77,78,79]. In this sense, we found that individuals with a high metabolic ratio and high BD-PRS presented hypermethylation in TESPA1, which may be promoted by an increase in Ca2+ signaling due to the accumulation of deleterious variants in the calcium pathway (such as those present in CACNA1C and ITPR3).
We included patients with psychosis that met the clinical criteria of SZ, BD, or SD, but we should consider that these disorders constitute a well-recognized clinical spectrum. In relation to this, there are clinical and epidemiological studies considering SZ and BD as a single major psychosis phenotype, demonstrating the shared genetic liability and overlapping polygenic component of the two illnesses [80,81]. SD has been less investigated but shows substantial familial overlap with both SZ and BD [82]. The pharmacogenomics of the antipsychotics response in this clinical spectrum has mainly focused on the study of genetic variants associated with pharmacokinetics, whereas pharmacodynamics has not been explored in detail. In this regard, the obligated phenotypes for evaluation are SZ-associated genes [83,84]. In the present study, we analyzed BD-PRS, SZ-PRS, and MDD-PRS, and no association with CLZ response was found; however, evaluating other associated genes, even when they are not within the strict diagnostic criteria, becomes of some importance.
The findings of this study feature some limitations. First, the small sample size and lack of patient biochemical data (e.g., lipid profiles) prevented us from further exploring our results regarding other clinical variables. Second, because we used peripheral tissue, some of our results might not be the same as those observed at the brain level (e.g., TESPA1, a gene with differentially methylated sites, is highly expressed in leukocytes but not in the brain). Third, we cannot rule out the possibility of other unidentified associations in these samples, since we only analyzed the genes included in the microarrays we used. Fourth, all the CLZ-treated patients included here had refractory psychosis, even though their clinical diagnoses were different (SZ, SD, or BD), which might have affected the estimation ability of our study (statistical power = 70%, calculated with the Graw algorithm for the relationship between DNA methylation and CLZ-associated phenotypes) [85]. Thus, future studies with larger sample sizes should consider the inclusion of these missing elements.
This study pioneers the exploration of genomics and methylomics simultaneously in Mexican patients with psychosis in the context of CLZ treatment. Our results suggest the use of CLZ as a mood stabilizer, primarily in the treatment of psychosis. Furthermore, we present methods integrating both omic technologies to better characterize the pharmacogenomics of clozapine.

4. Materials and Methods

4.1. Patients

Forty-four unrelated patients with refractory psychosis (unresponsive to at least two previous antipsychotic treatments) were consecutively recruited from the outpatient service at the National Institute of Neurology and Neurosurgery “Manuel Velasco Suárez” (NINN) in Mexico City. The inclusion criteria were patients with at least the two previous generations having been born and brought up in Mexico and those with a Spanish surname. The clinical diagnosis of SZ, SD, or BD was carried out based on the DSM-5 criteria [86] and was performed by at least one psychiatrist specialized in psychotic disorders. All the patients experienced CLZ monotherapy as an antipsychotic treatment for more than 18 weeks. The exclusion criteria were neurologic disease, heavy drinkers and/or heavy smokers, substance abuse within the past 6 months, history of a head injury with a loss of consciousness greater than 5 min or with documented neurocognitive sequelae, intellectual disability, trauma in general, and medical illnesses that may be associated with significant neurocognitive impairment.
This study was carried out in accordance with the latest version of the Declaration of Helsinki and was approved by the local research and ethical committees (protocol NINN_104/17, amended in 2018). Written informed consent was obtained from all participants after the nature of the procedures had been fully explained.

4.2. Clozapine and Norclozapine Plasma Concentrations.

Blood samples were taken at steady state (i.e., at week 18 of treatment). The preparation of plasma samples was carried out as previously reported [27], and plasma concentrations of CLZ and its main metabolite (ng/mL), N-desmethylclozapine or nor-clozapine (NCLZ), were determined by HPLC, and the metabolic ratios of CLZ/NCLZ were also calculated.

4.3. Analysis and Quality Control of Microarrays

Blood DNA was isolated by standard procedures after 18 weeks of treatment under CLZ. The samples were genotyped using the Infinium PsychArray v1.2 BeadChip (Illumina, San Diego, CA, USA) and then imputed. The genome-wide DNA methylation levels were measured using the Infinium MethylationEPIC BeadChip (Illumina, San Diego, CA, USA). The Genome Reference Consortium Human Build 37 (GRCh37/hg19) was used for all the analyses.
DNA samples were hybridized with PsychArray according to the manufacturer’s instructions and scanned on an iScan Microarray Scanner (Illumina). The genotypes obtained with GenomeStudio (Illumina) were filtered for quality control following the PLINK v.6.21 program criteria [87]. Thus, we discarded genetic variants and samples with either a variant calling <95%, a minor allele frequency (MAF) < 0.05 (as reported in the 1000 Genomes Project), and variants that were not in Hardy–Weinberg equilibrium using a chi-square method with a value of p < 1 × 10−6. For the epigenomic analysis, DNA was bisulfite-converted (Zymo, Irvine, CA, USA) and hybridized to EPIC while following the manufacturer’s protocol. The fluorescence intensities were measured with the iScan instrument and transformed into idat files with the algorithm implemented in the GenomeStudio. Raw methylation data were filtered out using the following criteria in the ChAMP package [88]: detection of p-value > 0.01, probes with less than 3 beads in <5% of the samples, probes located on sites not-CpGs or associated with SNPs, sex chromosome probes, multihit probes, and probes with rates greater than 0.1 were removed. After performing the quality control, 741,030 probes remained, and a matrix of beta values was built including the 44 patients. The matrix was adjusted for the differences in cell proportions by a deconvolution method in the ChAMP package. Genotyping and microarray analyses were carried out by specialized staff in the Microarray Unit of the National Institute of Genomic Medicine Mexico City, Mexico (INMEGEN).

4.4. Analysis of Polygenic Risk Score

To calculate the polygenic risk score (PRS) for SZ, BD, and major depressive disorder (MDD), we used the latest available GWAS summary statistics from the Psychiatric Genomics Consortium (i.e., SZ-PRS was derived from PGC wave-2 group, BD-PRS was calculated using BIP2018 dataset, and MDD-PRS was generated from results of the PGC GWAS and 23 and Me) as a training set [89,90,91] and our genotyped sample as the target. Poisson correlations were used to test the associations between PRS and CLZ-associated phenotypes—namely, disease improvement (CLZ response and non-response), the dose of CLZ, the CLZ plasma concentrations, and the metabolic ratios (CLZ/NCLZ). Depending on the studied phenotype, logistic or linear regressions were performed with PRSice v.2.3.3 [92]. PRSice uses two steps to construct the PRS. First, there is the clumping process, where SNPs in linkage disequilibrium (LD) between the associated loci in the target sample and the discovery sample are unified. Second, PRSice calculates the individual PRS using different p-value thresholds for the associated variants in the discovery sample, thereby calculating the best-fit PRS for the target sample (starting with a p-value threshold of 0.5 from the GWAS with increments of 0.00005). The best-fit model for the PRS explains the greatest amount of variance in the phenotype by estimating Nagelkerke’s pseudo-R2 value. We considered an association between PRS and CLZ phenotypes after 1000 permutations to correct for multiple tests when the p-value was less than 0.05. Additionally, all the regressions were adjusted for age, gender, and the 10 main components of global ancestry. Global ancestry estimation was performed with the PC-AiR package [93] using the reference panel of the Human Genome Diversity Project.

4.5. Analysis of Differentially Methylated Regions (DMRs)

The methylation patterns among groups were evaluated by linear models implemented in the limma package [94], and the statistically significant p-value was <1 × 10−8.

4.6. Functional Annotation and Pathway Enrichment Analysis

The SNPs integrating the PRS were annotated using the Variant Effect Predictor (VEP, web version) using the human genome reference assembly hg19. The VEP is a toolset for the analysis, annotation, and prioritization of genomic variants that predicts the functional effect of variants in silico using different databases and prediction algorithms (CADD, SIFT, PolyPhen2, and LoF) [95]. We classified SNPs based on their coding positions in exonic and non-exonic variants. Exonic variants were filtered out if they were predicted as deleterious in SIFT, and possibly or probably damaging in Polyphen. A CADD value higher than 25 was used for non-exonic variants. The functional enrichment analysis of the PRS genes and differentially methylated genes was carried out by WebGestalt [96]. In addition, the protein interaction analysis was carried out using STRING [97].

5. Conclusions

Our study is the first to show simultaneous genomic–epigenomic signatures in samples from patients with refractory psychosis and those under CLZ treatment, which raises several questions regarding the genetic/epigenetic determinants of BD-PRS for CLZ-associated phenotypes and opens up many avenues for future studies. We strongly believe that these are important results for this field.

Supplementary Materials

The following files are available online https://www.mdpi.com/1424-8247/14/2/118/s1: Table S1: Table summarizing the top enriched molecular pathways of the genes included in the bipolar disorder polygenic risk score and associated to clozapine metabolic ratios; Table S2: The contrast matrix; Table S3: The complete lists of SNPs for each CLZ-associated phenotype.

Author Contributions

Conceptualization, N.M.-J., M.L.-L., and H.N.; methodology, Y.G.M.-L., A.O.-V., D.J.D.-O.d.M., and B.E.P.-A.; software, Y.G.M.-L., J.J.M.-M., A.D.G.-M.; formal analysis, Y.G.M.-L. and J.J.M.-M.; investigation, E.S.-R., Y.G.M.-L., A.O.-V., D.J.D.-O.d.M., J.J.M.-M., A.D.G.-M., and B.E.P.-A.; resources, N.M.-J., M.L.-L., and H.N.; data curation, Y.G.M.-L. and J.J.M.-M.; writing—original draft preparation, Y.G.M.-L., J.J.M.-M., N.M.-J.; writing—review and editing, E.S.-R., H.N., M.L.-L., N.M.-J.; visualization, E.S.-R., Y.G.M.-L., A.O.-V., D.J.D.-O.d.M., J.J.M.-M., A.D.G.-M., B.E.P.-A., N.M.-J., M.L.-L., and H.N.; supervision, N.M.-J., M.L.-L., A.D.G.-M., and H.N.; project administration, N.M.-J., M.L.-L., and H.N.; funding acquisition, N.M.-J. and H.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by CONACyT, grant number 233695.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board and Ethics Committees of National Institute of Neurology and Neurosurgery “Manuel Velasco Suárez” (NINN) in Mexico City, Mexico (protocol NINN_104/17, date of approval: 8 March 2018).

Informed Consent Statement

Written informed consent has been obtained from the patients to publish this paper.

Data Availability Statement

The data presented in this study are available in Supplementary material. Additional data are available on request from the corresponding author due to privacy and ethical issues.

Acknowledgments

The authors acknowledge Carlos L. Aviña-Cervantes for his collaboration in the recruitment of the patients.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Siskind, D.J.; Lee, M.; Ravindran, A.; Zhang, Q.; Ma, E.; Motamarri, B.; Kisely, S. Augmentation strategies for clozapine refractory schizophrenia: A systematic review and meta-analysis. Aust. N. Z. J. Psychiatry 2018, 52, 751–767. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Ifteni, P.; Teodorescu, A.; Dima, L.; Burtea, V. Rapid Titration of Clozapine in Schizophrenia and Bipolar Disorder. Am. J. Ther. 2019, 10. [Google Scholar] [CrossRef] [PubMed]
  3. Iglesias-García, C.; Iglesias-Alonso, A.; Bobes, J. Concentrations in plasma clozapine levels in schizophrenic and schizoaffective patients. Rev. Psiquiatr. Salud Ment. 2017, 10, 192–196. [Google Scholar] [CrossRef]
  4. Scheepers, F.E.; de Mul, J.; Boer, F.; Hoogendijk, W.J. Psychosis as an Evolutionary Adaptive Mechanism to Changing Environments. Front Psychiatry 2018, 9, 237. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Burton, C.Z.; Ryan, K.A.; Kamali, M.; Marshall, D.F.; Harrington, G.; McInnis, M.G.; Tso, I.F. Psychosis in bipolar disorder: Does it represent a more “severe” illness? Bipolar Disord. 2018, 20, 18–26. [Google Scholar] [CrossRef]
  6. Rothschild, A.J. Challenges in the Treatment of Major Depressive Disorder with Psychotic Features. Schizophr. Bull. 2013, 39, 787–796. [Google Scholar] [CrossRef] [Green Version]
  7. Costa-Dookhan, K.A.; Agarwal, S.M.; Chintoh, A.; Tran, V.N.; Stogios, N.; Ebdrup, B.H.; Sockalingam, S.; Rajji, T.K.; Remington, G.J.; Siskind, D.; et al. The clozapine to norclozapine ratio: A narrative review of the clinical utility to minimize metabolic risk and enhance clozapine efficacy. Expert Opin. Drug Saf. 2020, 19, 43–57. [Google Scholar] [CrossRef]
  8. Numata, S.; Umehara, H.; Ohmori, T.; Hashimoto, R. Clozapine Pharmacogenetic Studies in Schizophrenia: Efficacy and Agranulocytosis. Front. Pharmacol. 2018, 9, 1049. [Google Scholar] [CrossRef] [Green Version]
  9. Ameer, M.A.; Saadabadi, A. Neuroleptic Medications. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2020. Available online: http://www.ncbi.nlm.nih.gov/books/NBK459150/ (accessed on 12 November 2020).
  10. Fehr, B.S.; Ozcan, M.E.; Suppes, T. Low doses of clozapine may stabilize treatment-resistant bipolar patients. Eur. Arch. Psychiatry Clin. Neurosci. 2005, 255, 10–14. [Google Scholar] [CrossRef]
  11. Haidary, H.A.; Padhy, R.K. Clozapine. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2020. Available online: http://www.ncbi.nlm.nih.gov/books/NBK535399/ (accessed on 29 November 2020).
  12. Schenone, M.; Dančík, V.; Wagner, B.K.; Clemons, P.A. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 2013, 9, 232–240. [Google Scholar] [CrossRef] [Green Version]
  13. Gregori-Puigjané, E.; Setola, V.; Hert, J.; Crews, B.A.; Irwin, J.J.; Lounkine, E.; Marnett, L.; Roth, B.L.; Shoichet, B.K. Identifying mechanism-of-action targets for drugs and probes. Proc. Natl. Acad. Sci. USA 2012, 109, 11178–11183. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Nagaraj, A.B.; Wang, Q.Q.; Joseph, P.; Zheng, C.; Chen, Y.; Kovalenko, O.; Singh, S.; Armstrong, A.; Resnick, K.; Zanotti, K.; et al. Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment. Oncogene 2018, 37, 403–414. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Oprea, T.I.; Bauman, J.E.; Bologa, C.G.; Buranda, T.; Chigaev, A.; Edwards, B.S.; Jarvik, J.W.; Gresham, H.D.; Haynes, M.K.; Hjelle, B.; et al. Drug repurposing from an academic perspective. Drug Discov. Today Ther. Strategy 2011, 8, 61–69. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Shukla, R.; Henkel, N.D.; Alganem, K.; Hamoud, A.; Reigle, J.; Alnafisah, R.S.; Eby, H.M.; Imami, A.S.; Creeden, J.F.; Miruzzi, S.A.; et al. Signature-based approaches for informed drug repurposing: Targeting CNS disorders. Neuropsychopharmacology 2021, 46, 116–130. [Google Scholar] [CrossRef]
  17. Alaimo, S.; Pulvirenti, A. Network-Based Drug Repositioning: Approaches, Resources, and Research Directions. Methods Mol. Biol. 2019, 1903, 97–113. [Google Scholar] [CrossRef]
  18. Sahu, N.U.; Kharkar, P.S. Computational Drug Repositioning: A Lateral Approach to Traditional Drug Discovery? Curr. Top. Med. Chem. 2016, 16, 2069–2077. [Google Scholar] [CrossRef]
  19. González-Esquivel, D.F.; Castro, N.; Ramírez-Bermúdez, J.; Custodio, V.; Rojas-Tomé, S.; Castro-Román, R.; Jung-Cook, H. Plasma levels of clozapine and norclozapine in Mexican schizophrenia patients. Arzneimittelforschung 2011, 61, 335–339. [Google Scholar] [CrossRef]
  20. Couchman, L.; Morgan, P.E.; Spencer, E.P.; Flanagan, R.J. Plasma Clozapine, Norclozapine, and the Clozapine:Norclozapine Ratio in Relation to Prescribed Dose and Other Factors: Data from a Therapeutic Drug Monitoring Service, 1993–2007. Ther. Drug Monit. 2010, 32, 438–447. [Google Scholar] [CrossRef]
  21. Subramanian, S.; Völlm, B.A.; Huband, N. Clozapine dose for schizophrenia. Cochrane Database Syst. Rev. 2017, 6, CD009555. [Google Scholar] [CrossRef]
  22. Simpson, G.M.; Josiassen, R.C.; Stanilla, J.K.; de Leon, J.; Nair, C.; Abraham, G.; Odom-White, A.; Turner, R.M. Double-blind study of clozapine dose response in chronic schizophrenia. Am. J. Psychiatry 1999, 156, 1744–1750. [Google Scholar] [CrossRef]
  23. Suzuki, T.; Uchida, H.; Watanabe, K.; Kashima, H. Factors Associated with Response to Clozapine in Schizophrenia: A Review. Psychopharmacol. Bull. 2011, 44, 32–60. [Google Scholar]
  24. Alessandro, G.; Erbo, D.; Grayson, D.R. Epigenetic Basis of Clozapine Action. J. Drug Des. Res. 2017, 4, 1055. [Google Scholar] [PubMed]
  25. O’Donovan, M.C.; Owen, M.J. The implications of the shared genetics of psychiatric disorders. Nat. Med. 2016, 22, 1214–1219. [Google Scholar] [CrossRef] [PubMed]
  26. Hannon, E.; Dempster, E.; Viana, J.; Burrage, J.; Smith, A.R.; Macdonald, R.; St Clair, D.; Mustard, C.; Breen, G.; Therman, S.; et al. An integrated genetic-epigenetic analysis of schizophrenia: Evidence for co-localization of genetic associations and differential DNA methylation. Genome Biol. 2016, 17, 176. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Vaquero-Baez, M.; Díaz-Ruíz, A.; Tristán-López, L.; Aviña-Cervantes, C.; Torner, C.; Ramírez-Bermúdez, J.; Montes, S.; Ríos, C. Clozapine and desmethylclozapine: Correlation with neutrophils and leucocytes counting in Mexican patients with schizophrenia. BMC Psychiatry 2019, 19, 295. [Google Scholar] [CrossRef] [Green Version]
  28. Nucifora, F.C.; Mihaljevic, M.; Lee, B.J.; Sawa, A. Clozapine as a Model for Antipsychotic Development. Neurotherapeutics 2017, 14, 750–761. [Google Scholar] [CrossRef] [Green Version]
  29. Wilkowska, A.; Cubała, W.J. Clozapine as Transformative Treatment in Bipolar Patients. Neuropsychiatr. Dis. Treat. 2019, 15, 2901–2905. [Google Scholar] [CrossRef] [Green Version]
  30. Marx, C.E.; Shampine, L.J.; Duncan, G.E.; VanDoren, M.J.; Grobin, A.C.; Massing, M.W.; Madison, R.D.; Bradford, D.W.; Butterfield, M.I.; Lieberman, J.A.; et al. Clozapine markedly elevates pregnenolone in rat hippocampus, cerebral cortex, and serum: Candidate mechanism for superior efficacy? Pharmacol. Biochem. Behav. 2006, 84, 598–608. [Google Scholar] [CrossRef]
  31. Leveque, J.-C.; Macías, W.; Rajadhyaksha, A.; Carlson, R.R.; Barczak, A.; Kang, S.; Li, X.M.; Coyle, J.T.; Huganir, R.L.; Heckers, S.; et al. Intracellular Modulation of NMDA Receptor Function by Antipsychotic Drugs. J. Neurosci. 2000, 20, 4011–4020. [Google Scholar] [CrossRef] [Green Version]
  32. Spivak, B.; Shabash, E.; Sheitman, B.; Weizman, A.; Mester, R. The effects of clozapine versus haloperidol on measures of impulsive aggression and suicidality in chronic schizophrenia patients: An open, nonrandomized, 6-month study. J. Clin. Psychiatry 2003, 64, 755–760. [Google Scholar] [CrossRef]
  33. Klemen, M.S.; Dolenšek, J.; Rupnik, M.S.; Stožer, A. The triggering pathway to insulin secretion: Functional similarities and differences between the human and the mouse β cells and their translational relevance. Islets 2017, 9, 109–139. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Moon, A.L.; Haan, N.; Wilkinson, L.S.; Thomas, K.L.; Hall, J. CACNA1C: Association with Psychiatric Disorders, Behavior, and Neurogenesis. Schizophr. Bull. 2018, 44, 958–965. [Google Scholar] [CrossRef] [PubMed]
  35. Ross, J.; Gedvilaite, E.; Badner, J.A.; Erdman, C.; Baird, L.; Matsunami, N.; Leppert, M.; Xing, J.; Byerley, W. A Rare Variant in CACNA1D Segregates with 7 Bipolar I Disorder Cases in a Large Pedigree. Mol. Neuropsychiatry 2016, 2, 145–150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Fan, T.; Hu, Y.; Xin, J.; Zhao, M.; Wang, J. Analyzing the genes and pathways related to major depressive disorder via a systems biology approach. Brain Behav. 2020, 10, e01502. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Charles, E.F.; Lambert, C.G.; Kerner, B. Bipolar disorder and diabetes mellitus: Evidence for disease-modifying effects and treatment implications. Int. J. Bipolar Disord. 2016, 4, 13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Calkin, C.V.; Gardner, D.M.; Ransom, T.; Alda, M. The relationship between bipolar disorder and type 2 diabetes: More than just co-morbid disorders. Ann. Med. 2013, 45, 171–181. [Google Scholar] [CrossRef] [PubMed]
  39. Padmanabhan, J.L.; Nanda, P.; Tandon, N.; Mothi, S.S.; Bolo, N.; McCarroll, S.; Clementz, B.A.; Gershon, E.S.; Pearlson, G.D.; Sweeney, J.A.; et al. Polygenic risk for type 2 diabetes mellitus among individuals with psychosis and their relatives. J. Psychiatr. Res. 2016, 77, 52–58. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Hackinger, S.; Prins, B.; Mamakou, V.; Zengini, E.; Marouli, E.; Brčić, L.; Serafetinidis, I.; Lamnissou, K.; Kontaxakis, V.; Dedoussis, G.; et al. Evidence for genetic contribution to the increased risk of type 2 diabetes in schizophrenia. Transl. Psychiatry 2018, 8, 1–10. [Google Scholar] [CrossRef] [Green Version]
  41. Postolache, T.T.; Bosque-Plata L del Jabbour, S.; Vergare, M.; Wu, R.; Gragnoli, C. Co-shared genetics and possible risk gene pathway partially explain the comorbidity of schizophrenia, major depressive disorder, type 2 diabetes, and metabolic syndrome. Am. J. Med. Genet B Neuropsychiatr. Genet. 2019, 180, 186–203. [Google Scholar] [CrossRef]
  42. Reay, W.R.; Atkins, J.R.; Carr, V.J.; Green, M.J.; Cairns, M.J. Pharmacological enrichment of polygenic risk for precision medicine in complex disorders. Sci. Rep. 2020, 10, 879. [Google Scholar] [CrossRef] [Green Version]
  43. Łojko, D.; Owecki, M.; Suwalska, A. Impaired Glucose Metabolism in Bipolar Patients: The Role of Psychiatrists in Its Detection and Management. Int. J. Environ. Res. Public Health 2019, 16, 1132. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Davidson, M.D.; Ballinger, K.R.; Khetani, S.R. Long-term exposure to abnormal glucose levels alters drug metabolism pathways and insulin sensitivity in primary human hepatocytes. Sci. Rep. 2016, 6, 28178. [Google Scholar] [CrossRef]
  45. Yau, L.S.; Strother, A.; Buchholz, J.; Abu-el-Haj, S. Glucose effect on drug action, metabolism, and pharmacokinetic parameters in mice. Drug Nutr. Interact. 1987, 5, 9–20. [Google Scholar] [PubMed]
  46. Imam, M.U.; Ismail, M. Effects of Brown Rice and White Rice on Expression of Xenobiotic Metabolism Genes in Type 2 Diabetic Rats. Int. J. Mol. Sci. 2012, 13, 8597–8608. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Chen, S.; Wang, K.; Wan, Y.-J.Y. Retinoids activate RXR/CAR-mediated pathway and induce CYP3A. Biochem. Pharmacol. 2010, 79, 270–276. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Cai, Y.; Konishi, T.; Han, G.; Campwala, K.H.; French, S.W.; Wan, Y.-J.Y. The role of hepatocyte RXRα in xenobiotic-sensing nuclear receptor-mediated pathways. Eur. J. Pharm. Sci. 2002, 15, 89–96. [Google Scholar] [CrossRef]
  49. Wan, Y.J.; Cai, Y.; Lungo, W.; Fu, P.; Locker, J.; French, S.; Sucov, H.M. Peroxisome proliferator-activated receptor alpha-mediated pathways are altered in hepatocyte-specific retinoid X receptor alpha-deficient mice. J. Biol. Chem. 2000, 275, 28285–28290. [Google Scholar] [CrossRef] [Green Version]
  50. Mak, P.A.; Laffitte, B.A.; Desrumaux, C.; Joseph, S.B.; Curtiss, L.K.; Mangelsdorf, D.J.; Tontonoz, P.; Edwards, P.A. Regulated expression of the apolipoprotein E/C-I/C-IV/C-II gene cluster in murine and human macrophages. A critical role for nuclear liver X receptors alpha and beta. J. Biol. Chem. 2002, 277, 31900–31908. [Google Scholar] [CrossRef] [Green Version]
  51. Zhang, R.; Wang, Y.; Li, R.; Chen, G. Transcriptional Factors Mediating Retinoic Acid Signals in the Control of Energy Metabolism. Int. J. Mol. Sci. 2015, 16, 14210–14244. [Google Scholar] [CrossRef] [Green Version]
  52. Vu-Dac, N.; Gervois, P.; Torra, I.P.; Fruchart, J.C.; Kosykh, V.; Kooistra, T.; Princen, H.M.; Dallongeville, J.; Staels, B. Retinoids increase human apo C-III expression at the transcriptional level via the retinoid X receptor. Contribution to the hypertriglyceridemic action of retinoids. J. Clin. Investig. 1998, 102, 625–632. [Google Scholar] [CrossRef] [Green Version]
  53. Dursun, S.M.; Szemis, A.; Andrews, H.; Reveley, M.A. The effects of clozapine on levels of total cholesterol and related lipids in serum of patients with schizophrenia: A prospective study. J. Psychiatry Neurosci. 1999, 24, 453–455. [Google Scholar] [PubMed]
  54. Kumar, M.; Sidana, A. Clozapine-induced Acute Hypertriglyceridemia. Indian J. Psychol. Med. 2017, 39, 682–684. [Google Scholar] [CrossRef] [PubMed]
  55. Procyshyn, R.M.; Wasan, K.M.; Thornton, A.E.; Barr, A.M.; Chen, E.Y.H.; Pomarol-Clotet, E.; Stip, E.; Williams, R.; Macewan, G.W.; Birmingham, C.L.; et al. Changes in serum lipids, independent of weight, are associated with changes in symptoms during long-term clozapine treatment. J. Psychiatry Neurosci. 2007, 32, 331–338. [Google Scholar] [PubMed]
  56. Li, M.; Huang, L.; Grigoroiu-Serbanescu, M.; Bergen, S.E.; Landén, M.; Hultman, C.M.; Forstner, A.J.; Strohmaier, J.; Hecker, J.; Schulze, T.G.; et al. Convergent Lines of Evidence Support LRP8 as a Susceptibility Gene for Psychosis. Mol. Neurobiol. 2016, 53, 6608–6619. [Google Scholar] [CrossRef] [Green Version]
  57. Guidotti, A.; Grayson, D.R.; Caruncho, H.J. Epigenetic RELN Dysfunction in Schizophrenia and Related Neuropsychiatric Disorders. Front. Cell. Neurosci. 2016, 10, 89. [Google Scholar] [CrossRef] [Green Version]
  58. Vik-Mo, A.O.; Fernø, J.; Skrede, S.; Steen, V.M. Psychotropic drugs up-regulate the expression of cholesterol transport proteins including ApoE in cultured human CNS- and liver cells. BMC Pharmacol. 2009, 9, 10. [Google Scholar] [CrossRef] [Green Version]
  59. Samanaite, R.; Gillespie, A.; Sendt, K.-V.; McQueen, G.; MacCabe, J.H.; Egerton, A. Biological Predictors of Clozapine Response: A Systematic Review. Front Psychiatry 2018, 9, 327. [Google Scholar] [CrossRef]
  60. Van Eck, M.; Van Dijk, K.W.; Herijgers, N.; Hofker, M.H.; Groot, P.H.E.; Van Berkel, T.J.C. Essential role for the (hepatic) LDL receptor in macrophage apolipoprotein E-induced reduction in serum cholesterol levels and atherosclerosis. Atherosclerosis 2001, 154, 103–112. [Google Scholar] [CrossRef]
  61. Peloso, G.M.; Nomura, A.; Khera, A.V.; Chaffin, M.; Won, H.-H.; Ardissino, D.; Danesh, J.; Schunkert, H.; Wilson, J.G.; Samani, N.; et al. Rare Protein-Truncating Variants in APOB, Lower Low-Density Lipoprotein Cholesterol, and Protection Against Coronary Heart Disease. Circ. Genom. Precis. Med. 2019, 12, e002376. [Google Scholar] [CrossRef] [Green Version]
  62. Lee, G.H.; D’Arcangelo, G. New Insights into Reelin-Mediated Signaling Pathways. Front. Cell. Neurosci. 2016, 10, 122. [Google Scholar] [CrossRef]
  63. Gill, I.; Droubi, S.; Giovedi, S.; Fedder, K.N.; Bury, L.A.D.; Bosco, F.; Sceniak, M.P.; Benfenati, F.; Sabo, S.L. Presynaptic NMDA receptors—Dynamics and distribution in developing axons in vitro and in vivo. J. Cell Sci. 2015, 128, 768–780. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Brambilla, P.; Perez, J.; Barale, F.; Schettini, G.; Soares, J.C. GABAergic dysfunction in mood disorders. Mol. Psychiatry 2003, 8, 721–737. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Ifteni, P.; Teodorescu, A.; Moga, M.A.; Pascu, A.M.; Miclaus, R.S. Switching bipolar disorder patients treated with clozapine to another antipsychotic medication: A mirror image study. Neuropsychiatr. Dis. Treat. 2017, 13, 201–204. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Li, X.-B.; Tang, Y.-L.; Wang, C.-Y.; de Leon, J. Clozapine for treatment-resistant bipolar disorder: A systematic review. Bipolar Disord. 2015, 17, 235–247. [Google Scholar] [CrossRef] [PubMed]
  67. Dong, E.; Tueting, P.; Matrisciano, F.; Grayson, D.R.; Guidotti, A. Behavioral and molecular neuroepigenetic alterations in prenatally stressed mice: Relevance for the study of chromatin remodeling properties of antipsychotic drugs. Transl. Psychiatry 2016, 6, e711. [Google Scholar] [CrossRef]
  68. Dong, E.; Nelson, M.; Grayson, D.R.; Costa, E.; Guidotti, A. Clozapine and sulpiride but not haloperidol or olanzapine activate brain DNA demethylation. Proc. Natl. Acad. Sci. USA 2008, 105, 13614–13619. [Google Scholar] [CrossRef] [Green Version]
  69. Nair, P.C.; McKinnon, R.A.; Miners, J.O.; Bastiampillai, T. Binding of clozapine to the GABA B receptor: Clinical and structural insights. Mol. Psychiatry 2020, 25, 1910–1919. [Google Scholar] [CrossRef]
  70. Guo, X.; Liu, D.; Wang, T.; Luo, X. Aetiology of bipolar disorder: Contribution of the L-type voltage-gated calcium channels. Gen. Psychiatry 2019, 32, e100009. [Google Scholar] [CrossRef] [Green Version]
  71. Starnawska, A.; Demontis, D.; Pen, A.; Hedemand, A.; Nielsen, A.L.; Staunstrup, N.H.; Grove, J.; Als, T.D.; Jarram, A.; O’Brien, N.L.; et al. CACNA1C hypermethylation is associated with bipolar disorder. Transl. Psychiatry 2016, 6, e831. [Google Scholar] [CrossRef] [Green Version]
  72. Bigos, K.L.; Mattay, V.S.; Callicott, J.H.; Straub, R.E.; Vakkalanka, R.; Kolachana, B.; Hyde, T.M.; Lipska, B.K.; Kleinman, J.E.; Weinberger, D.R. Genetic Variation in CACNA1C Affects Brain Circuitries Related to Mental Illness. Arch. Gen. Psychiatry 2010, 67, 939. [Google Scholar] [CrossRef] [Green Version]
  73. Smedler, E.; Abé, C.; Pålsson, E.; Ingvar, M.; Landén, M. CACNA1C polymorphism and brain cortical structure in bipolar disorder. J. Psychiatry Neurosci. 2020, 45, 182–187. [Google Scholar] [CrossRef] [PubMed]
  74. Garcia, M.I.; Boehning, D. Cardiac inositol 1,4,5-trisphosphate receptors. Biochim. Biophys. Acta BBA Mol. Cell Res. 2017, 1864, 907–914. [Google Scholar] [CrossRef] [PubMed]
  75. Ramos-Lopez, O.; Riezu-Boj, J.I.; Milagro, F.I.; Martinez, J.A. Dopamine gene methylation patterns are associated with obesity markers and carbohydrate intake. Brain Behav. 2018, 8, e01017. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Prole, D.L.; Taylor, C.W. Inositol 1,4,5-trisphosphate receptors and their protein partners as signalling hubs. J. Physiol. 2016, 594, 2849–2866. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  77. Matsuzaki, H.; Fujimoto, T.; Tanaka, M.; Shirasawa, S. Tespa1 is a novel component of mitochondria-associated endoplasmic reticulum membranes and affects mitochondrial calcium flux. Biochem. Biophys. Res. Commun. 2013, 433, 322–326. [Google Scholar] [CrossRef] [PubMed]
  78. Wang, D.; Zheng, M.; Qiu, Y.; Guo, C.; Ji, J.; Lei, L.; Zhang, X.; Liang, J.; Lou, J.; Huang, W.; et al. Tespa1 negatively regulates FcεRI-mediated signaling and the mast cell–mediated allergic response. J. Exp. Med. 2014, 211, 2635–2649. [Google Scholar] [CrossRef] [Green Version]
  79. Matsuzaki, H.; Fujimoto, T.; Ota, T.; Ogawa, M.; Tsunoda, T.; Doi, K.; Hamabashiri, M.; Tanaka, M.; Shirasawa, S. Tespa1 is a novel inositol 1,4,5-trisphosphate receptor binding protein in T and B lymphocytes. FEBS Open Biol. 2012, 2, 255–259. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Johnsen, E.; Kroken, R.A. Drug treatment developments in schizophrenia and bipolar mania: Latest evidence and clinical usefulness. Ther. Adv. Chronic Dis. 2012, 3, 287–300. [Google Scholar] [CrossRef] [Green Version]
  81. Calafato, M.S.; Thygesen, J.H.; Ranlund, S.; Zartaloudi, E.; Cahn, W.; Crespo-Facorro, B.; Díez-Revuelta, A.; Forti, M.D.; Hall, M.H.; Iyegbe, C.; et al. Use of schizophrenia and bipolar disorder polygenic risk scores to identify psychotic disorders. Br. J. Psychiatry 2018, 213, 535–541. [Google Scholar] [CrossRef] [Green Version]
  82. Cardno, A.G.; Owen, M.J. Genetic Relationships between Schizophrenia, Bipolar Disorder, and Schizoaffective Disorder. Schizophr. Bull. 2014, 40, 504–515. [Google Scholar] [CrossRef] [Green Version]
  83. Santoro, M.L.; Ota, V.; de Jong, S.; Noto, C.; Spindola, L.M.; Talarico, F.; Gouvea, E.; Lee, S.H.; Moretti, P.; Curtis, C.; et al. Polygenic risk score analyses of symptoms and treatment response in an antipsychotic-naive first episode of psychosis cohort. Transl. Psychiatry 2018, 8, 1–8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Zhang, J.-P.; Robinson, D.; Yu, J.; Gallego, J.; Fleischhacker, W.W.; Kahn, R.S.; Crespo-Facorro, B.; Vazquez-Bourgon, J.; Kane, J.M.; Malhotra, A.K.; et al. Schizophrenia Polygenic Risk Score as a Predictor of Antipsychotic Efficacy in First-Episode Psychosis. Am. J. Psychiatry 2018, 176, 21–28. [Google Scholar] [CrossRef] [PubMed]
  85. Graw, S.; Henn, R.; Thompson, J.A.; Koestler, D.C. pwrEWAS: A user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS). BMC Bioinform. 2019, 20, 218. [Google Scholar] [CrossRef] [PubMed]
  86. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, DSM-5, 5th ed.; American Psychiatric Publishing: Washington, DC, USA, 2013; pp. 87–123. [Google Scholar]
  87. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.R.; Bender, D.; Maller, J.; Sklar, P.; Bakker, P.I.W.; Daly, M.J.; et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Tian, Y.; Morris, T.J.; Webster, A.P.; Yang, Z.; Beck, S.; Feber, A.; Teschendorff, A.E. ChAMP: Updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics 2017, 33, 3982–3984. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  89. Ripke, S.; Neale, B.M.; Corvin, A.; Walters, J.T.R.; Farh, K.-H.; Holmans, P.A.; Lee, P.; Bulik-Sullivan, B.; Collier, D.A.; Huang, H.; et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014, 511, 421–427. [Google Scholar] [CrossRef] [Green Version]
  90. Howard, D.M.; Adams, M.J.; Clarke, T.-K.; Hafferty, J.D.; Gibson, J.; Shirali, M.; Coleman, J.R.I.; Hagenaars, S.P.; Ward, J.; Wigmore, E.M.; et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 2019, 22, 343–352. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  91. Stahl, E.A.; Breen, G.; Forstner, A.J.; McQuillin, A.; Ripke, S.; Trubetskoy, V.; Mattheisen, M.; Wang, Y.; Coleman, J.; Gaspar, H.A.; et al. Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder. Nat. Genet. 2019, 51, 793–803. [Google Scholar] [CrossRef] [PubMed]
  92. Euesden, J.; Lewis, C.M.; O’Reilly, P.F. PRSice: Polygenic Risk Score software. Bioinformatics 2015, 31, 1466–1468. [Google Scholar] [CrossRef] [Green Version]
  93. Conomos, M.P.; Miller, M.B.; Thornton, T.A. Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness. Genet. Epidemiol. 2015, 39, 276–293. [Google Scholar] [CrossRef] [Green Version]
  94. Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015, 43, e47. [Google Scholar] [CrossRef] [PubMed]
  95. McLaren, W.; Gil, L.; Hunt, S.E.; Riat, H.S.; Ritchie, G.R.S.; Thormann, A.; Flicek, P.; Cunningham, F. The Ensembl Variant Effect Predictor. Genome Biol. 2016, 17, 122. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Wang, J.; Vasaikar, S.; Shi, Z.; Greer, M.; Zhang, B. WebGestalt 2017: A more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 2017, 45, W130–W137. [Google Scholar] [CrossRef]
  97. Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019, 47, D607–D613. [Google Scholar] [CrossRef] [PubMed] [Green Version]
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Figure 1. Results of polygenic risk score (PRS) analysis for major depressive disorder (A), bipolar disorder (B), and schizophrenia (C) and their associations with clozapine-associated phenotypes. The X-axis contains the clozapine-associated phenotypes and the Y-axis shows the proportion of variance explained by the PRS that was calculated by Nagelkerke’s pseudo-R2 value. The colors of the bars are in accordance with the associated p-values (e.g., the redder the bar, the more significant the p-value). Abbreviations: CLZ, clozapine; conc, concentration; MR, metabolic ratio; R, response; avg, average; std, standard.
Figure 1. Results of polygenic risk score (PRS) analysis for major depressive disorder (A), bipolar disorder (B), and schizophrenia (C) and their associations with clozapine-associated phenotypes. The X-axis contains the clozapine-associated phenotypes and the Y-axis shows the proportion of variance explained by the PRS that was calculated by Nagelkerke’s pseudo-R2 value. The colors of the bars are in accordance with the associated p-values (e.g., the redder the bar, the more significant the p-value). Abbreviations: CLZ, clozapine; conc, concentration; MR, metabolic ratio; R, response; avg, average; std, standard.
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Figure 2. Subgroups of clozapine-treated patients according to their metabolic ratios and bipolar disorder genetic risk score. Box plot showing the distribution of values and the cut-off points for the metabolic ratios and genetic risk scores.
Figure 2. Subgroups of clozapine-treated patients according to their metabolic ratios and bipolar disorder genetic risk score. Box plot showing the distribution of values and the cut-off points for the metabolic ratios and genetic risk scores.
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Figure 3. Protein–protein interaction network between gene products with high-impact variants contained in the top enriched pathways and those presenting differentially methylated sites in CLZ-treated patients. Proteins are grouped by colors according to the pathway they are involved in (e.g., proteins in red: circadian entrainment; blue: insulin secretion; yellow: thyroid hormone signaling pathway; green: GABAergic synapse). Clusters are based on STRING analysis. The center of the figure highlights the close interactions between nodes.
Figure 3. Protein–protein interaction network between gene products with high-impact variants contained in the top enriched pathways and those presenting differentially methylated sites in CLZ-treated patients. Proteins are grouped by colors according to the pathway they are involved in (e.g., proteins in red: circadian entrainment; blue: insulin secretion; yellow: thyroid hormone signaling pathway; green: GABAergic synapse). Clusters are based on STRING analysis. The center of the figure highlights the close interactions between nodes.
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Table
Table 1. Clinical and demographic characteristics of clozapine-treated patients (n = 44).
Table 1. Clinical and demographic characteristics of clozapine-treated patients (n = 44).
CharacteristicNumber (%) or
Mean ± Standard Deviation
Clinical diagnosis
Schizophrenia31 (70.45%)
Schizoaffective disorder9 (20.45%)
Bipolar disorder4 (9.09%)
Number of Male Patients (%)28 (63.60%)
Age (years)37.40 ± 11.30
Age at onset18.50 ± 9.80
School (Years)13.30 ± 2.90
Number of patients who are smokers (%)22 (50.00%)
Number of patients who are drinkers (%)13 (29.50%)
CLZ Dose (mg/day)202.60 ± 138.02
CLZ responders36 (81.80%)
CLZ and its metabolite determinations
* Plasma concentrations of CLZ (ng/mL)154.03 ± 191.97
CLZ: clozapine; NCLZ: norclozapine. * Determined by HPLC [27].
Table
Table 2. Functional single-nucleotide polymorphisms (SNPs) with a possible high impact on the polygenic risk score for bipolar disorder and clozapine metabolic ratios.
Table 2. Functional single-nucleotide polymorphisms (SNPs) with a possible high impact on the polygenic risk score for bipolar disorder and clozapine metabolic ratios.
Location †Gene SymbolGene NameGenetic Variant IDMinor Allele FrequencyProtein IDVariant Location in Coding Region
chr1:53712727LRP8LDL receptor-related protein 8rs5174T = 0.204NP_004622.2:p.Arg952GlnMissense variant
chr1:151374025PSMB4Proteasome 20S subunit beta 4rs4603C = 0.273NP_002787.2:p.Ile234AsnMissense variant
chr1:151733335MRPL9Mitochondrial ribosomal protein L9rs8480G = 0.443NP_113608.1:p.Glu210ValMissense variant
chr4:162307312FSTL5Follistatin-like 5rs3749598A = 0.216NP_064501.2:p.Asp711TyrMissense variant
chr5:7520768ADCY2Adenylate cyclase 2rs13166360T = 0.057NP_065433.2:p.Val147MetMissense variant
chr5:898209847LYSMD3LysM domain containing 3rs10069050C = 0.375NP_938014.1:p.Glu41AspMissense variant
chr6:142396790NMBRNeuromedin B receptorrs7453944T = 0.307NP_002502.2:p.Leu390MetMissense variant
chr7:64439701ZNF117Zinc finger protein 117rs3807069T = 0.307NP_056936.2:p.Cys83TyrMissense variant
chr7: 92733766SAMD9Sterile alpha motif domain-containing 9rs10279499A = 0.091NP_001180236.1:p.Val549Leu Missense variant
chr7:104717517KMT2ELysine methyltransferase 2E (inactive)rs2240455T = 0.216NP_061152.3:p.Tyr292Ter* Stop_gained
chr7:129663496ZC3HC1Zinc finger C3HC-type containing 1rs11556924T = 0.148NP_057562.3:p.Arg363HisMissense variant
chr8:1514009DLGAP2DLG associated protein 2rs2301963C = 0.284NP_001333739.1:p.Pro464GlnMissense variant
chr12:108618630WSCD2WSC domain containing 2rs3764002T = 0.125NP_055468.2:p.Thr266IleMissense variant
chr15:84639350ADAMTSL3ADAMTS-like 3rs2277849T = 0.189NP_997400.2:p.Leu869PheMissense variant
chr16:3639827SLX4SLX4 structure-specific endonuclease subunitrs3810813A = 0.079NP_115820.2:p.Ser1271PheMissense variant
chr17:35988672DDX52DExD-box helicase 52rs7224513C = 0.239NP_008941.3:p.Arg264SerMissense variant
chr17:73513677TSEN54tRNA splicing endonuclease subunit 54rs11559205C = 0.091NP_997229.2:p.Ile137LeuMissense variant
† Physical location of the gene (hg19). Genetic variant and protein identifiers (ID) according to the Single Nucleotide Polymorphism Database (dbSNP) and the protein database at the National Center for Biotechnology Information (NCBI). * Combined Annotation Dependent Depletion (CADD) prediction score = 35.
Table
Table 3. Differentially methylated regions in DNA samples according to their bipolar disorder-PRS and clozapine metabolic ratios.
Table 3. Differentially methylated regions in DNA samples according to their bipolar disorder-PRS and clozapine metabolic ratios.
Location †Gene SymbolCpG SiteFeatureLocation Relative to cgiLogFCAvg Methylationp-Value
High PRSMedium PRSLow PRSHigh-Medium PRSMedium-Low PRS
TESPA1cg236124233’UTROpen sea−0.143467610.56511550.421647890.523745749.06 × 10−74.01 × 10−2
chr2:21266669-21266961APOBcg16723488TSS200Island0.098157760.373687730.47184550.397975918.38 × 10−62.42 × 10−5
chr2:21266669-21266961APOBcg05337441BodyShore0.088636180.153379780.25556180.166925622.46 × 10−53.02 × 10−6
chr8:58055960-58056244-cg11062466IGRShore0.274641510.300182640.574824150.366962248.92 × 10−66.11 × 10−3
chr10:135170645-135171954C10orf125cg05456948TSS200Island−0.040007160.191071890.154665240.19467243.04 × 10−041.54 × 10−06
STAG1cg16760310BodyOpen sea0.029464890.9321803910.965747140.936282251.09 × 10−37.21 × 10−6
† Physical location of the gene (hg19). CGI, CpG island. FC, fold-change. Avg, average. PRS, bipolar disorder-polygenic risk score. Chr, chromosome. UTR, untranslated region. TSS, transcription start site. IGR, intergenic region.
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Mayén-Lobo, Y.G.; Martínez-Magaña, J.J.; Pérez-Aldana, B.E.; Ortega-Vázquez, A.; Genis-Mendoza, A.D.; Dávila-Ortiz de Montellano, D.J.; Soto-Reyes, E.; Nicolini, H.; López-López, M.; Monroy-Jaramillo, N. Integrative Genomic–Epigenomic Analysis of Clozapine-Treated Patients with Refractory Psychosis. Pharmaceuticals 2021, 14, 118. https://doi.org/10.3390/ph14020118

AMA Style

Mayén-Lobo YG, Martínez-Magaña JJ, Pérez-Aldana BE, Ortega-Vázquez A, Genis-Mendoza AD, Dávila-Ortiz de Montellano DJ, Soto-Reyes E, Nicolini H, López-López M, Monroy-Jaramillo N. Integrative Genomic–Epigenomic Analysis of Clozapine-Treated Patients with Refractory Psychosis. Pharmaceuticals. 2021; 14(2):118. https://doi.org/10.3390/ph14020118

Chicago/Turabian Style

Mayén-Lobo, Yerye Gibrán, José Jaime Martínez-Magaña, Blanca Estela Pérez-Aldana, Alberto Ortega-Vázquez, Alma Delia Genis-Mendoza, David José Dávila-Ortiz de Montellano, Ernesto Soto-Reyes, Humberto Nicolini, Marisol López-López, and Nancy Monroy-Jaramillo. 2021. "Integrative Genomic–Epigenomic Analysis of Clozapine-Treated Patients with Refractory Psychosis" Pharmaceuticals 14, no. 2: 118. https://doi.org/10.3390/ph14020118

APA Style

Mayén-Lobo, Y. G., Martínez-Magaña, J. J., Pérez-Aldana, B. E., Ortega-Vázquez, A., Genis-Mendoza, A. D., Dávila-Ortiz de Montellano, D. J., Soto-Reyes, E., Nicolini, H., López-López, M., & Monroy-Jaramillo, N. (2021). Integrative Genomic–Epigenomic Analysis of Clozapine-Treated Patients with Refractory Psychosis. Pharmaceuticals, 14(2), 118. https://doi.org/10.3390/ph14020118

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