The effects of CACNA1C gene polymorphism on prefrontal cortex in both schizophrenia patients and healthy controls

The Potential of Single Nucleotide Polymorphisms (SNPs) as Biomarkers and Their Association with the Increased Risk of Coronary Heart Disease: A Systematic Review

Human genetic analyses and epidemiological studies showed a potential association between several types of gene polymorphism and the development of coronary heart disease (CHD). Many studies on this pertinent topic need to be investigated further to reach an evidence-based conclusion. Therefore, in this current review, we describe several types of gene polymorphisms that are potentially linked to CHD. A systematic review using the databases EBSCO, PubMed, and ScienceDirect databases was searched until October of 2022 to find relevant studies on the topic of gene polymorphisms on risk factors for CHD, especially for the factors associated with single nucleotide polymorphisms (SNPs). The risk of bias and quality assessment was evaluated by Joanna Briggs Institute (JBI) guidelines. From keyword search results, a total of 6243 articles were identified, which were subsequently narrowed to 14 articles using prespecified inclusion criteria. The results suggested that there were 33 single nucleotide polymorphisms (SNPs) that can potentially increase the risk factors and clinical symptoms of CHD. This study also indicated that gene polymorphisms had a potential role in increasing CHD risk factors that were causally associated with atherosclerosis, increased homocysteine, immune/inflammatory response, Low-Density Lipoprotein (LDL), arterial lesions, and reduction of therapeutic effectiveness. In conclusion, the findings of this study indicate that SNPs may increase risk factors for CHD and SNPs show different effects between individuals. This demonstrates that knowledge of SNPs on CHD risk factors can be used to develop biomarkers for diagnostics and therapeutic response prediction to decide successful therapy and become the basis for defining personalized medicine in future.

Neurodevelopmental disturbances in schizophrenia: evidence from genetic and environmental factors

Since more than 3 decades, schizophrenia (SZ) has been regarded as a neurodevelopmental disorder. The neurodevelopmental hypothesis proposes that SZ is associated with genetic and environmental risk factors, which influence connectivity in neuronal circuits during vulnerable developmental periods. We carried out a non-systematic review of genetic/environmental factors that increase SZ risk in light of its neurodevelopmental hypothesis. We also reviewed the potential impact of SZ-related environmental and genetic risk factors on grey and white matter pathology and brain function based on magnetic resonance imaging and post-mortem studies. Finally, we reviewed studies that have used patient-derived neuronal models to gain knowledge of the role of genetic and environmental factors in early developmental stages. Taken together, these studies indicate that a variety of environmental factors may interact with genetic risk factors during the pre- or postnatal period and/or during adolescence to induce symptoms of SZ in early adulthood. These risk factors induce disturbances of macro- and microconnectivity in brain regions involving the prefrontal, temporal and parietal cortices and the hippocampus. On the molecular and cellular level, a disturbed synaptic plasticity, loss of oligodendrocytes and impaired myelination have been shown in brain regions of SZ patients. These cellular/histological phenotypes are related to environmental risk factors such as obstetric complications, maternal infections and childhood trauma and genetic risk factors identified in recent genome-wide association studies. SZ-related genetic risk may contribute to active processes interfering with synaptic plasticity in the adult brain. Advances in stem cell technologies are providing promising mechanistic insights into how SZ risk factors impact the developing brain. Further research is needed to understand the timing of the different complex biological processes taking place as a result of the interplay between genetic and environmental factors.

A functional neuroimaging association study on the interplay between two schizophrenia genome-wide associated genes (CACNA1C and ZNF804A)

The CACNA1C and the ZNF804A genes are among the most relevant schizophrenia GWAS findings. Recent evidence shows that the interaction of these genes with the schizophrenia diagnosis modulates brain functional response to a verbal fluency task. To better understand how these genes might influence the risk for schizophrenia, we aimed to study the interplay between CACNA1C and ZNF804A on working memory brain functional correlates. The analyses included functional and behavioural N-back task data (obtained from an fMRI protocol) and CACNA1C-rs1006737 and ZNF804A-rs1344706 genotypes for 78 healthy subjects and 78 patients with schizophrenia (matched for age, sex and premorbid IQ). We tested the effects of the epistasis between these genes as well as of the three-way interaction (CACNA1C × ZNAF804A × diagnosis) on working memory-associated activity (N-back: 2-back vs 1-back). We detected a significant CACNA1C × ZNAF804A interaction on working memory functional response in regions comprising the ventral caudate medially and within the left hemisphere, the superior and inferior orbitofrontal gyrus, the superior temporal pole and the ventral-anterior insula. The individuals with the GWAS-identified risk genotypes (CACNA1C-AA/AG and ZNF804A-AA) displayed a reduced working memory modulation response. This genotypic combination was also associated with opposite brain activity patterns between patients and controls. While further research will help to comprehend the neurobiological mechanisms of this interaction, our data highlight the role of the epistasis between CACNA1C and ZNF804A in the functional mechanisms underlying the pathophysiology of schizophrenia.

Evidence for the contribution of HCN1 gene polymorphism (rs1501357) to working memory at both behavioral and neural levels in schizophrenia patients and healthy controls

Gene HCN1 polymorphism (rs1501357) has been proposed to be one of the candidate risk genes for schizophrenia in the second report of the Psychiatric Genomics Consortium–Schizophrenia Workgroup. Although animal studies repeatedly showed a role of this gene in working memory, its contribution to working memory in human samples, especially in schizophrenia patients, is still unknown. To explore the association between rs1501357 and working memory at both behavioral (Study 1) and neural (Study 2) levels, the current study involved two independent samples. Study 1 included 876 schizophrenia patients and 842 healthy controls, all of whom were assessed on a 2-back task, a dot pattern expectancy task (DPX), and a digit span task. Study 2 included 56 schizophrenia patients and 155 healthy controls, all of whom performed a 2-back task during functional magnetic resonance imaging (fMRI) scanning. In both studies, we consistently found significant genotype-by-diagnosis interaction effects. For Study 1, the interaction effects were significant for the three tasks. Patients carrying the risk allele performed worse than noncarriers, while healthy controls showed the opposite pattern. For Study 2, the interaction effects were observed at the parietal cortex and the medial frontal cortex. Patients carrying the risk allele showed increased activation at right parietal cortex and increased deactivation at the medial frontal cortex, while healthy controls showed the opposite pattern. These results suggest that the contributions of rs1501357 to working memory capability vary in different populations (i.e., schizophrenia patients vs. healthy controls), which expands our understanding of the functional impact of the HCN1 gene. Future studies should examine its associations with other cognitive functions.

Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers

Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction.

Investigation of functional near-infrared spectroscopy signal quality and development of the hemodynamic phase correlation signal

Significance: There is a longstanding recommendation within the field of fNIRS to use oxygenated (HbO 2 ) and deoxygenated (HHb) hemoglobin when analyzing and interpreting results. Despite this, many fNIRS studies do focus onHbO 2 only. Previous work has shown thatHbO 2 on its own is susceptible to systemic interference and results may mostly reflect that rather than functional activation. Studies using bothHbO 2 and HHb to draw their conclusions do so with varying methods and can lead to discrepancies between studies. The combination ofHbO 2 and HHb has been recommended as a method to utilize both signals in analysis. Aim: We present the development of the hemodynamic phase correlation (HPC) signal to combineHbO 2 and HHb as recommended to utilize both signals in the analysis. We use synthetic and experimental data to evaluate how the HPC and current signals used for fNIRS analysis compare. Approach: About 18 synthetic datasets were formed using resting-state fNIRS data acquired from 16 channels over the frontal lobe. To simulate fNIRS data for a block-design task, we superimposed a synthetic task-related hemodynamic response to the resting state data. This data was used to develop an HPC-general linear model (GLM) framework. Experiments were conducted to investigate the performance of each signal at different SNR and to investigate the effect of false positives on the data. Performance was based on each signal's mean T -value across channels. Experimental data recorded from 128 participants across 134 channels during a finger-tapping task were used to investigate the performance of multiple signals [HbO 2 , HHb, HbT, HbD, correlation-based signal improvement (CBSI), and HPC] on real data. Signal performance was evaluated on its ability to localize activation to a specific region of interest. Results: Results from varying the SNR show that the HPC signal has the highest performance for high SNRs. The CBSI performed the best for medium-low SNR. The next analysis evaluated how false positives affect the signals. The analyses evaluating the effect of false positives showed that the HPC and CBSI signals reflect the effect of false positives onHbO 2 and HHb. The analysis of real experimental data revealed that the HPC and HHb signals provide localization to the primary motor cortex with the highest accuracy. Conclusions: We developed a new hemodynamic signal (HPC) with the potential to overcome the current limitations of usingHbO 2 and HHb separately. Our results suggest that the HPC signal provides comparable accuracy to HHb to localize functional activation while at the same time being more robust against false positives.

The association of polymorphisms in related circadian rhythm genes and clopidogrel resistance susceptibility

Previous studies have confirmed that a dynamic change in circadian rhythm will affect platelet activity, resulting in clopidogrel resistance (CR). We attempted to evaluate whether polymorphisms of related circadian rhythm genes are involved in CR in stable coronary artery disease (SCAD) patients. A sum of 204 SCAD patients met our requirements and were recruited, and 96 patients were considered to have CR. After clinical data collection and platelet function evaluation, genomic DNA was isolated from human peripheral blood, and 23 tagSNPs from related circadian rhythm genes were genotyped by GenomeLab SNPstream Genotyping System. After RNA isolation, relative expression of related gene mRNAs (CLOCK, CRY1, CACNA1C and PRKCG) was measured by real-time PCR. The results showed that polymorphisms in CRY1, CACNA1C and PRKCG changed the response to clopidogrel. And then, the rs1801260 polymorphism might lead to higher mRNA expression in CLOCK and potentially induce the occurrence of CR. Additionally, the TC genotype of rs3745406 might lower mRNA expression of PRKCG, resulting in CR. These findings support the hypothesized role of circadian rhythm genes in CR and indicate probable biomarkers for CR susceptibility, providing new insight into individualized medicine for coronary heart disease.

The genome-wide supported CACNA1C gene polymorphisms and the risk of schizophrenia: an updated meta-analysis

Background

The CACNA1C gene was defined as a risk gene for schizophrenia in a large genome-wide association study of European ancestry performed by the Psychiatric Genomics Consortium. Previous meta-analyses focused on the association between the CACNA1C gene rs1006737 and schizophrenia. The present study focused on whether there was an ancestral difference in the effect of the CACNA1C gene rs1006737 on schizophrenia. rs2007044 and rs4765905 were analyzed for their effect on the risk of schizophrenia.

Methods

Pooled, subgroup, sensitivity, and publication bias analysis were conducted.

Results

A total of 18 studies met the inclusion criteria, including fourteen rs1006737 studies (15,213 cases, 19,412 controls), three rs2007044 studies (6007 cases, 6518 controls), and two rs4765905 studies (2435 cases, 2639 controls). An allele model study also related rs2007044 and rs4765905 to schizophrenia. The overall meta-analysis for rs1006737, which included the allele contrast, dominant, recessive, codominance, and complete overdominance models, showed significant differences between rs1006737 and schizophrenia. However, the ancestral-based subgroup analysis for rs1006737 found that the genotypes GG and GG + GA were only protective factors for schizophrenia in Europeans. In contrast, the rs1006737 GA genotype only reduced the risk of schizophrenia in Asians.

Conclusions

Rs1006737, rs2007044, and rs4765905 of the CACNA1C gene were associated with susceptibility to schizophrenia. However, the influence model for rs1006737 on schizophrenia in Asians and Europeans demonstrated both similarities and differences between the two ancestors.

CACNA1C Gene rs11832738 Polymorphism Influences Depression Severity by Modulating Spontaneous Activity in the Right Middle Frontal Gyrus in Patients With Major Depressive Disorder

OBJECTIVES

This study aimed to examine whether the CACNA1C gene rs11832738 polymorphism and major depressive disorder (MDD) have an interactive effect on the untreated regional amplitude of low-frequency fluctuation (ALFF) and to determine whether regional ALFF mediates the association between CACNA1C rs11832738 and MDD.

METHODS

A total of 116 patients with MDD and 66 normal controls (NCs) were recruited. The MDD and NC groups were further divided into two groups according to genotype: carriers of the G allele (G-carrier group, GG/GA genotypes; MDD, n = 61; NC, n = 26) and AA homozygous group (MDD, n = 55; NC, n = 40). MDD was diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Depression severity was assessed using the Hamilton Depression Scale-24 (HAMD-24) at baseline and follow-up (after 2 and 8 weeks of treatment). All subjects underwent functional MRI (fMRI) scans at baseline, and the ALFF was calculated to reflect spontaneous brain activity. The interactions between MDD and CACNA1C single nucleotide polymorphism rs11832738 were determined using two-way factorial analysis of covariance, with age, sex, education, and head motion as covariates. We performed mediation analysis to further determine whether regional ALFF strength could mediate the associations between rs11832738 and depression severity, MDD treatment efficacy.

RESULTS

MDD had a main effect on regional ALFF distribution in three brain areas: the right medial frontal gyrus (MFG_R), the left anterior cingulate cortex (ACC_L), and the right cerebellum posterior lobe (CPL_R); CACNA1C showed a significant interactive effect with MDD on the ALFF of MFG_R. For CACNA1C G allele carriers, the ALFF of MFG_R had a significant positive correlation with the baseline HAMD-24 score. Exploratory mediation analysis revealed that the intrinsic ALFF in MFG_R significantly mediated the association between the CACNA1C rs11832738 polymorphism and baseline HAMD-24 score.

CONCLUSIONS

A genetic variant in CACNA1C rs11832738 may influence depression severity in MDD patients by moderating spontaneous MFG_R activity.